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Stanford-BIS/syde556
SYDE 556 Lecture 8 Memory.ipynb
1
465716
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SYDE 556/750: Simulating Neurobiological Systems\n", "\n", "##Administration\n", "\n", "- 2-slide presentations on project topics\n", "- Mar 31st: Keplinger, Filipowicz, Morcos, Lynett, Fawcett, Mathur, Allana, Baksh, Dedakia, Ling\n", "- Apr 4th: Shein, Duggins, Prodan, Kahn, Mendelsohn, Skikos, Bains, Chandran, Jeong, Tzoganakis\n", "\n", "## Memory\n", "\n", "Readings: [Serial Working Memory](http://compneuro.uwaterloo.ca/files/publications/choo.2010a.pdf); [Associative Memory](http://compneuro.uwaterloo.ca/files/publications/stewart.2011.pdf)\n", "\n", "- We've seen how to represent symbol-like structures using vectors\n", "- Typically high dimensional vectors\n", "- How can we store those over short periods of time (working memory)\n", "- Long periods of time?\n", "- Recall Jackendoff's 4th challenge (same repn in long and working memory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- An attractor in dynamical systems theory is a system state (or states) towards which other states tend over time\n", "- The standard analogy is to imagine the state space as a ‘hill-like’ topology which a ball travels through (tending downhill).\n", "\n", "<img src=\"lecture_memory/point_attractor.png\">\n", "\n", "- In neural network research, attractor networks (networks with dynamical attractors) have long been thought relevant for various behaviours \n", " - e.g., memory, integration, off-line updating of representations, repetitive pattern generation, noise reduction, etc.\n", " \n", "- The neural integrator is an example of an attractor network\n", "\n", "- The neural integrator can be thought of as a line attractor as in a) \n", " - Actually an approximate one as in b)\n", "\n", "<img src=\"lecture_memory/line_attractor.png\">\n", "\n", "- Attractor networks were extensively examined in the ANN community (e.g. hopfield nets). Amit suggested that persistent activity could be associated with recurrent biological networks \n", "- Persistent activity is found in motor, premotor, parietal, prefrontal, frontal, hippocampal, and inferotemporal cortex; and basal ganglia, midbrain, superior colliculus, and brainstem\n", "- Focus to date is often on simple attractors:\n", " - The NEF lets us easily generalize." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Generalizing dynamics\n", "\n", "- A cyclic attractor (i.e. a set of attractive points that the system moves through)\n", "\n", "<img src=\"lecture_memory/cyclic_attractor.png\">\n", "\n", "- Note: The hill and ball analogy doesn't work anymore.\n", "\n", "- This is an oscillator. Technically a nonlinear one, since it should be stable (the Simple Harmonic Oscillator is linear). Let's build a stable oscillator." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: pylab import has clobbered these variables: ['seed']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] }, { "data": { "image/png": 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Snw1K0rFdjfxs0eT9RR7uvnPVz741QnrN8Rbfnn5bnlBxuWrosQFctX9PpiGI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\nzq3PXUZak5jJEulCfad0R/ymKDwOkomBrG4eJlcc4++yxmy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"text/plain": [ "<matplotlib.figure.Figure at 0x10e12a210>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zNcKttCLsQOB+4ECwzwOvQLMKUduSqnA1tXxjUxQ2R7zbEZsi3/kfIp/LO4CP\ngLfkOPtXsDuWfZRVjgru5ElacM8ieUHXA5hJgE/FkVPCy5ZoKflOeOnqFR6GvI6JwCYExYhuZpP8\n+7MKicK7UsvEQ2YDLxH/vTmF5MpZxmUn4FmMnUenhaVKRlWUSnBpeHke8HvEJzsSeDTtmKsQYfNa\nAg25FMWHMMIdYT4CdkesJlciTXIuAnMuUkFqBvA62CFlH2kVo4I7eZIW3KWoxd0DqbDhwwLgEmAy\nLU9wRxYZ1/KJ/ZEFx7dIwf/Ni/jds5FOXUni+xmqQyqaTEai93Eolbfeh17AHGoa5kjipKK0BmwH\n4FxgEpjbwfwbzAfIvHVseNBHSCL7+UhDEkUpE3YwYm/6X9P7zWuI9SmqlvVvsPcgHVBHAueDSWu6\npDW7C6GCO3l64y9m81EKwd0T/zEORBrLfEYqohyHUni4I6H9jePx6T77KYg3LS6lqCLjUXHFGlIl\nBDsiLXzj0FIE93zM6rl0WmBKVH9eUcrNTeHlfhn3n0MqqtgT+BGwA165NYpSDLYj8DfgNjBZFnpm\nPpLn9EPks3oq8l2xOzAc7DVg/w52CvBVaJ1ScqCCO1FsHSIakmy121Ii3AORbaRPgEEEsRu+JO/h\nltJzFxE08aDlI11wT0deW1yWIAI5SXwi3J2BFWDWAK8CxEycLMVC6CtgD4/jewHzMMynbvYySlN/\nXlHKiD0bESsNSN5IdH9X4EKkFNsrwLZgRgGbIF5vRSkHNyO21etyH2IsmIfBjKTpLvJ5iN3kcWBP\n4AHgH2GpSyULKriTZTgwORQ/STGP5IVHD/xrhUvrdKk48Q3xbSWliKS6N4kR0gXtDKpbcNcSvfaA\nUcCX+CcrQmnely7A5x7H90Q+7wvoMms5KriVqsbuDVyNdPJ7McOX/VukDNuLwD5gZsrdpl7abytK\nqbGnAHsDJ7lrFrMCsVFuCpwMPBSK8S+Qz/lMpISgkgUV3MnSjWTtJNDyItxQXOLkAuL7jHPRHfeE\nSWga4Z5BcZaSZAV3Kjrt+noi/3bEDOLVbS/FzkNXfJoRRZYSmE+X+lWo4FaqBtsh4/b6wF+Q7fg+\nwDtpj12K2EduliQ045O8rihFYjuDvQK4ATgSjK8WGAX8GNERC1N3mzVIMvA+YHdIZqytCxXcyZKK\nNibHfKB7wm23ZeveGds+PGd2eEcxiZOPA4cllmAhAvWHFCe4W1KEe0NgFgGuVQr6I1uCEXEj1eF5\nib0v7ZH6QnLMAAAgAElEQVS/i2NtdNsJ2V5fAsym64w1SMRbUVo41gD1YF8A+z2wGwIPItv0byDl\n1d4B2w7sH8P77wTz88qNWWl7WAP2cOBTpNfDtmA+jfFEtwPHI6UrjwF7ecpGYhYDf0DsJkoGlRbc\nByBJeBORChiZ7IFERN8Pf64o28jiUUckMALOJWAEAUUKGLMaEUNxPdPZGIqfz3wAMDtsAQ7FCe7J\ngCW5yh614aVjdNaOQERqlGDZ0jzc7+FnCXkXyRiPWESsFu1mFeIzTSpR8deAT7fM0E5iLDCLLvUG\njXC3RFrbnJ0AxiL/h1OBPyJ/nx0Qf+xyZNv+SeT/6xzgcTBnV2asStvEbgQ8i3QwPQvMd8FMivdc\n5lvgX0gZ131pTPS1e4M9A9gGEeIZGsGatl7JpJKCux1wKzKBb4JsvW2c5bj/IquxLYFflW108UiP\ncP8I8TIl4eeehkQ+k2IYfo0VBiHCNGIKsRusGAtFN89JJxKI/fIelWIQ8EW4EoeWZimR5jN/9jxn\nYcb1uF7sJBMnfbfJ03ddZlE3O9pVUVoOrXHOTopnkEY26XWJM73YFvF0F9PdVlE8sN3B3gi8BjwH\njATzQgJPfA3SgfIV5PtzIyQf4S4kgg7wDdjfg42Ca9cCU8A+JMLcrt/WBHglBfd2SNb2JOTL+VHg\n8CzHVdMbIhFuiWqPQFr1QhAn4tiEF4Fdi3yOdIYiotmVwcC3abdnA8cQxBZEi0lOpEYZ0Q84Hp9u\nJwER3CMIYovur5ESSUmxGHjY4/jPaNokp5jkxznAejHPzfZcv/M4fhBQH16fRe28jrgvopTy0Brn\n7KR4BhHSp4W3ZyG7Nk/LTWPA1IC5RpMildJja8CeiHw/9AY2BXNTcvkCZgKYA5AOz1tnPLgP8Pfw\n+s+BWWAtojfrgHWB3ZCF+WSwcXKOqpJKCu4hNBV9U2kaHQCJCOyEtBd9BomqtGSiBLbNSFkdIF7V\niHRmkWy0L+q06EpmhHsekmGfp5RQXpIW3J8ROL+e7qQL7qAxqe+3MX//y8gEkhTd8Uto7U7TCHcx\ngvspkls8dMbPV78FqZrEs+i4qBax/igth9Y4ZxeJbQf2ACRy3Q1pXPNP4BUwy5GyaZ9UcIBKm8Nu\ng0S0zwGOAHNqqgqO1/P0kvbtNotd03YL8xH+htTm/lv4wC1I3sLRWZ5wN0Rzbg88j+SnjaZpDlKr\npn0Ff7dLUth7iDhciqykngA2yHFskHZ9TPhTbmqRsWbWgx4LsWojR8yjeNEuBHRBxNBcj7MG0TTC\nHW39x602spjkKmKEdaid6UZTgQribfsy5u9fCnSSL17j6lfOh28FmUzBPQm4h4DbCBqTXF2ZTXIL\nu+h/wZW1SL0H82m3qgMdlgzyNqZUB3vgV5+8pdAa5+wisKchW+vTSO2wvQ88Ipe2HVIWc11JPG/M\ngVGUEmD7IUGwQ4DLgfv9SxTb9REb2DbIDuOXwHpg30R8288g/8+3Izvvm4KZC/be8AnOzXjCqYgI\nv1HspLY9cCZwLxLhPh/YFhHjLZk9SGDOrqTgnkZTH282X3H61v+zyJvcm+xiMUhycDGpo2ZlelRv\nPOJxrCXAeFSeyCTJWtx98KuCASK407ufRTW8DySgF4FPxRMgWcHdCb9IatMIt/AcsaPUxoKNfNyZ\nQj4OmQI6D7YdspBLr9k9GrgH2WUZ4/m75wNre56TC99FXR+iz1iA5YoO8+lSP9i7Wnx1MIam783V\nlRmGN61xzi6G7YB7wFwlN+3XwMVgbgBbD2wJZizYb5H5xacmvaI4YtsjZfquRPJ/Ng47RMbhViRg\nGAATJIhkuyGJvwcDFyGL6ZPB/Cf8/bnyy6I8jp5pNeh3RBLq24XPCbI4aOmMIYE5u5KWkrHA+ojX\nuSNwDJLJnc4AUn7A7cLrPl/i5aYfvb5ejAjkGgI2QVabIMkEcZmLCJIk8BB0jfRGPLkRs0ltk24b\nYwxJJhrGiXBnCu45FPf3XY4kkiWBT4Q7rHOdFsUIWIwI55dj/O55JFeKz7cZUR+afMbMbOpmtxlv\nX5XQGufsYniZphWCxgBbgY36JkS5O1HgRVESxu6J7KocBuwO5vz4YtvuhwRcLgMzPrVjaxaBeQLM\nGchO+wYpsQ3I9+kfkKo86bs47yOR7F+k3dddnr/x+3IUmFfjjbf6qKTgjkokPYdktf4VmZjOCn9A\nfEAfIZGvm4Fjyz9MLwYxYNwSYE5jBDngX4iAOp0gdgObr5AvuiSII7ijDoBCQAOy5QTyxevLYpIT\ndpfT9J+8ENksJVMpLlmwH/L5LQ6pXd0Z92Yxud7Ly/D7m0TMJ7mdlKH47Tw0FdxmzQxq5yZVOlJJ\nhtY4ZxfDl0hPgaFgByMLkcXI32UOqUXvp7R6L7tSXuxaYEcD9yHR1n1j1tSOnq8GaYRzaf7ESmMz\nOqYiZQLNeYh1JN01cQPwEE12s82/wNxKamfsLHkdNsk8qBZLpetwP4uUu1sPqQ8J0sVoVHj9NmRr\nfAskEefNcg/Qk0EMencFzSM6kSj6Tszn/QroGfqriiVbhLcQUQfAFAHLke2rOIuI15FM5iTYh9we\n0Wxks5S8CaxHUHFfrQhod7tPLsH9Aqk64z4UWyJRCBiJRFx8djH6QprnvGbVTCkNaIvJfVCSp7XN\n2Z7YQ8C+DXYC8HZ45xTEbvM88v+zD1Kb+Do5nhlohFtJBFsL9kokV+ITxD7yWHMR7PWcBllILwMe\ni/885mvgjLQ7liK1+rPs2JpPEQsMwPdIJV22aiotuFsbfRj8XidSLdAjIo9tZka/I2Y1krCQRLvU\n4iPcKeJaXcYTv3FOiqDx8+tThqx5hFsWD3dQ+UQ23/cm1/ELidcoaSqwmUTsiiKKVvgk7GREuKmn\n+9TFaKUSpWWxFpJAfijwMVJh4S4kUTK9/OvBSF3u85DEyqT6DihtEmvAHoGI7JHANmD+D0wRna1t\nR7AnIZ7t04GzixPugOQPRU2dAqRs7iCw2QI5v0m7PgJsTH1UPajgTpYe9JnQl+YVL+4OLy8giGXB\nAFktJtEF8DCSiHALU4hXqWQ2yXSajGpw+2Q4Z4twg0QMtoo5jqSqDxRboSRiAdAjRpfTaGfmBM/z\nMqlFqlPc4Xa4bY8shNI/Y9/Sc9IKxBOsKC2FbxAb3WTE73o5UnWhHtgvPObHYJ4HczWYfZCAxaGV\nGKzSGrAbAf9GGsecCeZovy6Rth3YLpJbYAeLfcNehOycnwBciDTE+SD/87hgLPAnUh1mI73zEdgL\nwrk+OnYVsH94oxeyMG3VqOBOlu50m7Y/It5SBNyETMZbIZNzHJbRtLZ3XDZDVqGOWIMIwWyC+xNg\n0xhjmA/Uge1U8Mj8dAbmE/CVxzm5LDXvA1sT8L0Yi6LQn2mL/X/qS9Pk1EL0IJvgDlgBrMa7Eoyx\niDWg2NogUqEkcI5whwu6JiWsptLr69VohFtpWUxGoty7Ipap/wMmINUYnkKihBkLTdMgiWeK4kNj\nl8hXESvXFmBeLHDOQWCngJ0DdjHYBqRJ1SykadW7wEvI5/UwWRCa5xKIbEe//6dIadpDkRwOEJvK\nCuBGpEJJdOwOSDS8HvkfOjWB3dUWjQruxLA1QDdqGtZG/jmaEvACEvH7I0GshMHlJBPhHo58uF3p\nBizLUUP2E0TAe2IsEuUutvKKb3MVEM95NnvMJKSj5mi8/ZZmIrIgKvb9GYx4QV3pT6o7YyYvIVUk\nfFlCcTXjQRaGPu9LU/+2MJXuk2uIbcNSlJIwGQkyPBfe/hr5/L4KrA/mzkoNTGkt2BqwJyNdInsi\nta5vLtwl0nZHAiY/QvIsBiJzcTswdWB6gxkEZgSYH4B5L+dTxacj8v26PWKnAjgK+W4DuADs9mB7\nIg1y/gesDWbDsBtrZpnRVoUK7uToilm9FEN3ckcII79VnFbvxQtuidx2o7m4yUcu/zaI57cuZvWV\n2RTfujuO4M7eZVMSFf8c3moXYyxLKV6oDqFpg6FCDABydRC7DriegC09x7CM4l9HkSUBAZhCtxmd\nScLrryiJYTItX+8Am4C5oojax4oSYrdBigqcDRwO5nQwuYIqmVwP/DusBDIbzGIR6UlFr524m9xl\neiPLyJuIplgErALj0yCtqnEV3HXIiknJTTc6LVgCLCEgV8fBqD16ZQR3ZA3xa3qTy78didS4tpJZ\nFO/j9hTctguy4s++4Ag4EVl1x7HuJCG4fSPcA2meoCsEvIFUjPiB5xiWUrx1yTfCnU1wT6PTgm6Y\nBhXcSktlBymHFqdttqKkY/uD/RNiSxoF7AjmHY/zdwKOBC4uyfCcMQuREqGnAzdlPLgrUk54IySh\nvjvw+9ZuI0nHRXAfhvhboy20LWne7ECBWrrOXEHuaDCIp2k68QV3sUKoJ35JedE5+V7T/5CSQr5U\nIsLdR35v3hV/XOGclOD2jXBnF9zCe/gvhioR4W7uXQ9Yhm23hG7Ti6mPrigJY9MSkc1blRuH0jqw\nHcD+DLF5LgQ2AnO/X0t22xuplHMeGN+uzwljj0EaZP0J+HnaA0cCTwOPI9baQ5Dd5qeBcWDvBDui\nvGMtPy6CO0D8ONEb+T6wTqkGVMXUcs4ma5Gvaoc0jJmGtDf1JQmPcK7kx3zkjnALlwNHEHgvBuop\nv+DuTuEFR1zBWQnBPZDclhKAifg39EnidSQR4QZb8y3dpsWpgqMopaLYCj6KEmL3QvTUj5Hv5pE4\n9bWwncHuDfZ6sO8g+Uev0DJqWT8L/ATxb19IykbyOFIeeV0kYPsU8CDSofJXSGBoAth7E+o30iJp\nX/gQVtFccPnU120ruArObYBtCJhNwAMez5+UpSTZCHfAcgLmIsLcJ6pZT/El3+o8f6dLnetKRrjj\neLjzRbinkUpWcSWJajjHkmqK4kK485BBzaqv6DZ9fykllTVpV1HKhN0ZqTB1Ytp964H5olIjUqoV\nOxyp2LENYvs7B4kGdwTeCRvbjEpFuW0N0khqH2BfpB/HR8CLwAXAm2BWlvc15MIsBG5vep+tRRpC\npTfIAtgTqaT1BdJLox44GThJkkbNn2lluES4PwGOR8T5+sAfEZ+r0hRXkbJ5eHm85/MvxbvMWzPW\nJdVS1ZVCEW6Q+s2+iZMzKV5wD8Pv9WQvo9eUdUjVTfehOMEtTXwGkfL5u9AP8cLnYhHQjsCr4+Mi\n4lme0hmEfBm4kj3CXbPma/p8vhj/RYOiJM1kmndOnShl1+wBTa0mipINWwv2KqQ030eIzeJnwHVh\nFZIbEJ/zScCLYM8O27fXAw8DQxH9NRTMTmCuAvNKyxHb2bB9gYuAH+Y44GHgR2B2ATMUCSquj/Rx\naHW4CO6fIuH+FUg3rYWkyr0oKSLBfXneowI+QrZcfEQQyD9dsTWJt8W/1XKpBPcsin8964BXDW4X\nS8m2QJe0LpauFOt97gMsCrteOmDbIxGR3BnektQ6A7GeuBLtVsRDKuE0ICWtXMlVf3wCA8YtB0bE\nHo+iJIKZIg1HmuUwtUO20XepyLCUKsAasEcCnwLfAbZGalP/C7gCzF2pY814YGfE27xjeMwWYDYG\ncy6YJ7NUymmB2H5gH6FpQCyzIMCxwE7AJLA3S0MesxLMV621br2LqFgCXEZkhRBB6VuKrS1QR0PH\nNaSKvefjefyjdjMoPiLcg1Q3QVfylZ6LiCO451N8JHUdpA6uKy4R7ijJcITnWIq1lBSyh2TSDVjs\nUPJpIn51xefh/16mIws0v0o4A8heT/xz+n9igQ2KGI+iJIStRf6Xsln73i/zYJSqwG6MfN//EjgN\nzPeQ+fVF4EIwWWylZjWY34M5UR6vytrUHZAg7ZekgkKZPRVeAnMM4l1vT2bDwFaIi+B+OcvPf0o5\nqKqk/bI62q0yuHmK4zQXmYlfpDIbXZHEBR9cbA5z8G9ik4R1YW38I9z5BXfAp0gbXd+GPsUK7mzN\nX/KRq2NmJu+CVy3ueRQT4ZZzfRd1w5At+0wm0HNSHd6NiBQlKawR/7a9C4nQnQTcj/yvp3XN40Ow\nJ6m1RBFsD7C/Q5IZnwK2BPMfsNsi3y8/AfNIRYdYUsy3YE5GOrFGZZJXkxLfp0itcJDdIyYg0fxW\njUvS5EVp1zsD30W2jJV0us7oRUOnBq5d7hLZi5OYNh/oBradrIBj0RU3kZaOi+COE+FeiIjGYvCN\ncLtYSkAa4wzyHMtS/G1C6cQR3IWi9SBR83U9njfOe5lOf7xeh+0QnpMtWXQKHRfX0mn+ZjlbKShK\nSbBdEevkScj33f3Ad8BMA9sLycH5RXjw9kiDptGIwPKZk5RWha1BEmuvA55BukTWhwu33ZFKIqeB\neaqSoywjtcBYMHvJTRvpo7TIvjVIQnKc8sJVhYvgHptx+zWku5aSTpeZA1ndaYWj2yaG4DZrwEZR\n4bi1NkMbgheDKFw5I45IKy7CHVCHRFN9qnr0wO3LMM7rKTYyXCgBMhPXCPdsYDuP510KtJPtc+NT\nASZiOPCNx/GDgfqsVUgCVnNFh2/oM3Ezr3dZUYrnEqQaxPHI910tcCjY45DqCtGisg6Z9C8FbgSj\nYrvNYrdFkhoBjkDm0qPB7ookQ9YAJ4B5LscTtEZGA78La2xH30MjM6yQOyL5SP8t89jKjoulpHfa\nT1/gAIq3ArQ+ek5aixXdXGtcr0SqR7gseNJph7/VIR1PS4mtQaKPhTzcc/Cvqb0QGBBGkuKwM/AF\ngVeJSpeygCCC29ciM4vi6or7RrhdmxjNwaujp7EUlzi5N34Rvlx2EqHdyg/pN75v2CVUUcqA7Qtc\ngSzQr0cqdS0BHgUOR6p0Rb0oHkeCUOsCV5Z9qEoLwA4A+yDwNvIdOwtJpH0CScJ/HtgDGNLGxDZh\n0OYhpPPkX8M7P8446EzgrjK3oK8ILoL7PcQH+i7SVfAC4LRSDqoq6TZ9CKu6ZEv8ao4klMVpZNMV\n2bL0J2Ao0gTFRXBG9AMWOJQdGodkXvuwJLw8xfO8iJ0Qb5wPPiI1TtWVfSU7Oxa+gjt7Kb3mzMZL\ncAOSxHiM5zkRO+L3vgxDLDzZMXYsw19ZgH/HTEWJgf0lqZ2mbYG9aJ6sfkB4eQ3SuONqOc6o8anN\nYI20U7fzENveCUig4WXgz8BmYNYDcwqY+6Ree+sXlDkYRap626fAm2DXlpu2J7KI9elJUrW4RFhH\nlHoQrYLuUweysstEjzMiW4mvxSMuWwFjCbwqYbgmwI3Hu/uosVJnlG38zmukFq/Fg61FtvkCh4Pj\nWEqmIfVDLwXO9zwXRBS/7XG8q+COk9AK8Hvgphjndabwjkg6a5FPcMNYhr2xGtmO9Pn7KEocvpd2\nfTxSFndPRDDsiTR9s0ggYlz5h6dUFrseYjMK0u68ELgHjG8X57ZCemGDbZEFyQFIi/fjgefA+Ngp\nq5Z8gvu7kLe012MJj6W66TF5AA21//A4I07i5OZIIkYchtHcj1+ILrgtCBYBXQmo8bR4LCR+M59a\n/ITdweHleIdj44jU18NLH/+yENAOidj/yeOsPrgthuJEuG9B/KtxqMWv++cwJEM9F+/R+4ue1Kza\ngTXcGnNMiuLKhuHlGMQGMA7Z7bkYzJjKDEmpPPY44FyazotHAv9sw5FrV65HKrPMQ+xXS+THGuAs\n2lBfl3yC+1BUcLtTO6cbHRd95HFGHME9k/jt3Qfjl2AIrp7vgNUEjVU6fKqgLCV+G3FfYVcH/Nmx\nK1eMCLdZE5aB6uB3HiBe0f7Il7wr6+FWnnMx0JGAzu5NdXiI+I08fN+XAeSzSQXM4wozi/4f7+q1\nN6Mo8ZiIVJjYGRHcFwBH4W+ZU1oNdh/gVuSzsQD4HZIgGyepvI1h90N2jUYigvtcpFdJ+/CnFr/v\nvaomn+A+uVyDqH5sezpt14H+n/hENycgftfPPc6JI9IjanGzIKTThZTXuhALEeHoI7iLfT0+E55P\nwmhcG0bc0oDdgDmezWJ2RGqc5ifAEjRGuV0bKBSTAOr7vvSgkK++3arnGTHmB8yw/aXElqKUjH8g\n+QI/QYTCJcBHYPx3rpRWgO0HvBDemAx8H0zuJG8lkxOQlvTjEC0RNb/ZIfw5nfyB3VaFa/vqQ4CL\ngavSfpQUdXSeb6lZ7dNy9UGa+gVdCAVqrOYKnfHvEOojUhfiX72mmHbovsLOpySiRLgDfP/OcRoa\ngXuJv3T64t6Zsh4/AT0PSTD1I6ADYIBVHmcVFtzGvsCmf1tAKllNUUrFfcAPEREQ5Zd8p3LDUSqH\n3ZRUB9y9wKjY9udUZEdgCNIxOLPZz5+A5WAng30b7AnlHmA5cRHco4DvI1sBJrw+vJSDqkJEcEtz\nGlc+xTsh1TQgSTtxbAulFtxz8Y+KltNS4t70J2AFUrrRN1pdTITbp1xjB0TYu4r0esSy4soioIs0\nWfJC3hO/SH1hwQ0vMWhsD9ovO9RzPIriiZmAJHkdQJMdIRtnx0upSmwPsDeRKl/XBczLlRxR9WJW\ngbkQEdx/QRazIAG668AYJLhzBNJRuBrb2DvjIrh3QjonzUW2sHcglViiCF3otKAGt5JzEXEbpcS1\nYcQR3Bvhvoj4CPFp+dBSLSUQrxZ3uSLcvYB5Hsk6noLbrCFeYyLf9wRcBHfAbKj5iPX+fSDYTp7P\nryi+3I9YKg8klSfhU7JTqUpsDdhTEevIieGdG4BZmuckxQnzLVICOMpB647sJhF64S8A7m3tCxsX\nwR19gS5FVikNwMCSjagaGfZaX2yNxU/Qxm2hXU7BvRfuybFfIiXefFhK+SwlPfGrQR63e2YPz3PA\nJ/ou9MWtQkmEb4QbZKHlayupQ95TR6xBFg+FF3XtV9zDtrcvQSIhilJKRiMNnI5AcmyOlbvtUZUb\nklI6rAF7ErAauAcRg+OAg8H4lPpV8pNu0bwZeFiu2qORcoGXln9I5cVFcD+NfCn+Fml+M4nmPpy2\nzVqvD2dJv5VeW+lBKBYDb/FcjOD2bcwwBPeugUvwt1MsAoaD3dzzPPBvFLMOTeuBFsKzQyMgkRHf\nRQf4R7jXQxY4rsQR3LPwX1j3xi8xtyfQ4BhBGs3wV7rQ8+ufeo5JUTwxC5DvvRlIU45Hwwc+qdiQ\nlISx3cAeDvYxxKZ5PxKQ+i7QA8yeYOKW4FWyMzi8nABsCWwDdiOkAsxJbWEnwUVwX4PYH/6BeI43\nQlvYNqX7lKEs7e8bPYZ4UdSlxElo845w246I4HStdR1DcJvZwPvAz/zOA6ScnOPYrEFsUF94PL9v\noiHI4mSE5zngL7g3xq2eeEQ9zbvlFeITYDPPc/riJ7g/x/UzEzAfY+9npxs3lw5vilJS7gP2Q6xy\nAD8G41NRSmlxWAP2dLD/QQIKTyC1tJ9FRHYtmMfA+OyEKs6Yr4EbEAvhN4j+vBhYBuZ/lRxZuXAR\n3OOAy4B1EcGm3ZQy6bSoPyu6x6nJGcfH/RJSNcYXX0vJZsDnYaKmC3Ei3CAluE71OkN2BTrh7plf\nD6mc4ZOQETcq3AWsbzOfXvj5y30F90z8o9XjkEZLPrh2v4zwW9C0W3U9W91jGPj+rTESOhXFh5eB\nLYD9w9vngvXNaVBaDLYjUhHjJ0gyZD3wHLAxmINUZJeNXyAFI/ohlYBOAUbIjkPrx0VwH4Z4m0Yj\nnQovJN62eTYOAD5DCspfkuOYW8LHP0S2IVoeNQ29WVUbZztkHv4R7k+Q7ny+dMJPcH8H+Zu7Eldw\nT5IL61qiEkQI13tYeDYF3vfsCBZDcBuLvJ4RfudxDdDR4/j1yd+dMZPJ+H9m4gjufsiiw5UPgH2c\njw6YRk3D5Rx1wnp0nqulSStD65iz82IN8j8Z8TyymDyzMuNRisP2QqLY2yHz+iHAT4EDwXxWyZG1\nPYwFzkD83Aa4PXzgrIoNqYy4iJxJwG+QTlvHIV/Crr7efLRDvDsHAJuEz71xxjEHIdHJ9ZHJ7o4E\nfm/ymNW9Wd3RtUFMOnPxj3DXA2eAdRe3AV0QEejTabI/MN3j+LiCe7fw0ifa6WEnAeQz5OPfhng2\nDJDIs7vICBpLPPpEz/qRqg/rwmRguGdd8VBwe9V8H4Ffa/sB+DV+gprVt9J9yot8/+iL6PH1L2LW\npFfi0Xrm7KzYAWAfQDy9l6c9sB8iwP9QkWEpRWDXQWyLeyENWF4FNgGjLdkrhlmFzA+A+QkwDfht\nW0hKdo0qjkCiGY8iHu6LE/jd2yGe2knIdv+jSIJKOocBD4TX30K8y3FEUGlp19CT1R3jbEnFKT33\nTnjpYxFYB5hB4CWgfUVtTMFtZiLibnChI9PwHdtg/Ot7zsTfUgLwX6QLpCtRVRMfX75fcmLAAiSa\n4GF1MfVIku1Qj3GNoHHHwomeyC6POwFr6LzwWAZ8/HdO2e3/2PrOt+g8VxuTNGUwkvAc/fww/+HO\ntJ45Ozt1pMrBfUwTy5q5NRQKStVgd0SSy4cjO/Sbg/kVmDj5Vkpi2EMQi/Ij4e5DVArwb2BPrtiw\nykC+1u4RbyHb3aORzoi+kcJcDAGmpN2eCmzvcMxQ/MRW6alZ2Z1V3X220iMm49/8ZgrYD/ArP9cL\nvzJyIGLzo4JHpfgKWJeArgRefmSQyPtgJBLhOjafz0Bv/F4LxPNwR+f5JPVF7+OFbofbGuIIVano\n0ge/5MzIVjKl0IEh/XHufmk7IfOPvxUrYCXMOpGLe/+d3a69jT2DD/j62LksGTCWpX3eYlXdOOaP\n+JxlvScxac+lbTCStS1wEilL2IbAQwk8b+uZs7Nivg67TB+IiO2nU4/ZDiq4qwkbAFeHN/YEM6Zy\nY1EEuwdwGqkAwHGIvSfybz+IXy5T1eEiuE9CPHtJ4/olmLllnP28n41YWdRoiqH2lQ58+t17Y5w5\nEZncfVmAn+Dujb/g9osiB8wLI+jD8EvoA9lS8vEYD8DPUhHn9ccV3L6VZ3oAHxA427R2Adp5JLNG\nzEaqiEzyOOcLYG2P43vgnlTdW44tQgzfMPdJmPskF/XdmK4zz6bH5F3ptOAcOi/oSu3cjnRYZljT\nHuEuAiMAACAASURBVFb1gIZOFowVB0rjZVi4oCYcQ3g765hKqNn/MCmpZ1oHsYH9E3iT1P9vnM9x\nNpKds1smdyG7uK+QmmOXqtiuJuzdwOlIkuTZMeZKpTSsRsouguwEr43Yz2YCm4XNcVo1LoK7VEkF\nmSJrGM23/TOPGRre15z7Vvy18XqvIR+ywZ7jEhmlK6tq/xvjrHr8LSUgosY3wu0bEZXERD/i+rhf\nRcoz3el4/AD8hGNLFtw98etQugVSzsqXOfiXOZyHn9XF57VcHGM82fnt7PHw8rnN7g8wTN+qE/PW\n7smaDj1Y3L8zqzt1BGowqw01q9sh4rCGmoYaxGInYtEag60JhWNNsj7xCS9vzrxp6V1Zk7J8XAD8\nDRgDbBD+vIr//3Eukp2zIUi7Pib8qTRvIDWZ65Fo3DlIveBfAVe2wd2SauRS4I9gyqsBlAKYV8Hu\nC7wG7I7ohd2BiVUgtvcIf4rCRXCXirGIcX4EYik4BtliSOdJZMJ7FGkpP59cUdeF009Iuw7fjE16\nvKUgrkBdgF+y5VCct/obiSO4F+PlE25kAn6e9AGI1ckB2wdJ7PK1Qi0BTAyLzGxga0nWMS6/s3Br\n8+bHx2nAMQ//LpjzgEFOR0pi7hDcI9x1wEWe4/EjwMJby+GtGfh//kvJ8xm3kxLcbyNRo29ILWKT\nJNk5u6ngbgHYjUklRh6PiO8/IBWb7gMGgr1FhVxLx8zGrymaUjbM62CPQ5ondkFyQP5Z2TE5MYam\nAYGrsx+WH59SbEnTgEzMzyF1Gf+KWBHOIlUi5hlEKH0BjAJ+XP5hlpQlxBOovtHXrYD33A+3GyKe\nal/fZdwFhK9QXwf3ahjbAR+AyRVly46UHIwT5Z4cXj6d96gUPjYMkC05H4EesQi/Sijg1979J+Gl\n6+JkGL4VSpRCDEMSXc9HEpG2Tvj5W/ucvYLUl+oLwFXAW2A+BvZEIt4+pVIVRWnO39Kun0N1CO5E\ncIlwd0Em8LWQ+onrI0k4roIiH8+GP+mMyrh9TgK/p6WylHgC9Vv8ki3Xwq+UY2gj8vYtxo1wuwt1\nKW23MfKF78KWuCdjZhJVKvGIjps1YN8FXnc8wSPCbXsAI4G/u4+nkYWkklNc8WnMtBj42K02ujVI\nYyWtgZssXyEdgf+C+PVLUWarFc/Z5ivgCLBDEKtMWGfcDkIsWV8iiwxFUeJzaHg5BrFpHA/28rZg\n13KJcN8HrCRVeeFb4NqSjahtETciPA3pfObqLfVtSDIZ2U71JW7E3uc8EY2Bsye9GMEd18f9CJIg\n4oKPpWTd8LLWe0QiuH0j3FNwT5rshkQ3XeiPfO6/8ByPkp+/Ik2eQN63ainH18Iw05DmPQD3AL9D\nGqVMBK6o1KgUpfqxBrgSaXizB/BnpM79bZ7N76oSlxe4LtL4JqoCEqfBi5KduIL7qfCyc8EjJSLs\nK7iXEa/z02L8o6jRea5/h774+fOKFdxxRMsKpLOnCz6CeygSZbs+xpgW4f/efAps5DgRdkdEvQuD\ngGltIaJRZlaTso69A/yygmOpdp4MLy9FdgpuBH6mn1lFKYoDkTLT54W3xyBNibZEnBStGpcv0hU0\njaitG96nFI8Ibr8OgIBZhlTDcBGpXQBL4LpQsr0R/3Ycf23ciPAyoDPYdg7HeiwebB2SyBfXKxz3\n9ZRKcHcD3gYTp1bpQryTJs1ipKSbS0TdR3D7Ni5SlHLzn/DyFGR+Ar8uqoqiNOfHwO+RwBnAIDAL\nET3T0iuVFI2L4A6AfyPRtb8gE9ElJRxT2yFgJSK24pRHc/VL+0a3twA+jFl39ltcq1o0waxByrFN\ncDjYJ8IdVloxrvaOTMohuHvinjTZg/iNAaI63L647j70wC/C3ZKqhihKBo2R7N8gOwdjkLKLiqLE\n5xvke6hjeDsAuzWwK6md+1aLi+B+HqmMcAoiuLch1YpTKZ6vkKobvrj6nn0F94b4N66JmI5ElONw\nD80bZmQjhuCOTTkE93DcI9y3Ea9uO8hnoJQLO5/3ZV2S61irKKXiuvCyD3A3cKZH3oyiKM15AdgX\n6bnxNJI4PxboBsanC3JV4iK4X0K+SJ8Of2aF9ynJMB5pn+2La+TRV3BvRPzqEe8D2xDEKjf5AG5b\nSj6vx/e1ZxJVKfFlOW7++j2RBayD4G6029TFGA/EXzy4Lux8FjfrIQloitKS+U/a9bOQqJxP51VF\nURqxfZDeAPsCByBt3TcLH7yjUqMqJ/mEUS2ysu+HdM6LfkYQP4qpNOctRHT50gFp31yIzfCLJm5E\nXM+ztHYHv6Y8Ea5VNMoZ4Z5DPBuGa4R747TjCxFFtl0j55nEtZS0w62Gez+c/taNJQG1QonS0kmf\nX3dD5qi9KzQWRal2DE2Di68j/1cdwFRTvf7Y5BPcZyGh/g2Bd9N+ngRuLf3Q2gzTiRd5HC4XBbc4\nd6ZppKYQw/Brm55JnPJzPuf1xT1qfS/FJfguwL87I0hFn/3AFqpw0g/pyOfSTCOygyyNMR6I6nD7\n7z5sjIjuQvRFFiiFOBoR3BrhVlo4JqoiFS0kNyTebqSiKNIBdMe0O3YG1gXTUKEBlZ18X743I9tn\nF4WX0c/mqOBOkjnE8+VGtp5CFSSG4Zxdb7dHBFYxUeFSC+5+OEW4bYfwSpy64BFxBXf0OkYUOK4O\n+MCtWQz9kffxhBjjgYAGRKz7/j3ulQubO7Ie0B6x0LhUwhmIdP50EeeKUmHMUqRqU8QWbaFesKKU\niL5IkHGv8PZ9YDMbZ7VaXCaOW5CI1PeBE9N+lGSIK7hPQMpV9S5w3FCka5oLkY9qbozxRMQV3Avk\nvIKlAV0tJZFQLn7x4F22kWeQ5kHDCxxXh3vEuj9SEtC14U82FuDeqj3EnIZ8RvMtPLoBixwXDkOB\n0X5jUJSKkj4n7YK0fFcUxZ91gK/AvExKu5wJdmWec1oNrmUB/4hEtfcEbgAOK+GY2hpzKSyas2CW\nI01Qcp8r9oHeuFswQvEXu4wexBbcpgFpJV5o8eFqKemFlNv7hf9YQgJWIQmQnlFhsxJpsZ2k4C42\nARTiR+wLnSeC240BaElApbq4BFlER6LgFLDfq+B4FKVaCQU3hMGjGsRW2QGsbe27Ry4v7mhgH2Qb\n4BRgJN5RMiUPcSPCIFU9huZ5vDuwmMC5zfhK5L0uhvnES5qEQp0dxbowBBqTM/PRE/gCTLFNmmbl\nHVNuvgHWKnBMHe6dWw8HpsUYRzoxF3cFI+M+TW98Gv0oSktgY2B7Up/b44DbwW5ZuSEpSlXSBwms\nhRgL5jhSwaRzKzCmsuEiuJchhf8bkC/LesQXrCTDEqBTKCZ9mQisn+dxn6Yq4BepzMUX5B9TPgqV\nrtsEmELgJO6iCHexfA5sEOO8SUi96ezI+30s7hHurYGHYowjnRnEakzEl+R/T4fj3pBHBbdSZZgf\nAFshu7sg1RVuB54AeybYP4F9DmycfgqK0pYYT6o6V4itIVUU4N4yj6esuAjusYh4uTu8/j7wRikH\n1aYQ36trc5FMCkVRe9FkNVmQrhQvuLP8QzlTqO71QNz96PcCa2KOI50viVd7921g+zxVZDye03ZC\n3ptiI9zTkUoLvowDvpPn8YOA/zk+lwpupQoxk8HcCI27hRchdblHAacB+yGRb0VRmmBrwY4AOxQJ\nqm0u32n2QLB3AFPCA18P27y3WlwE99mIaLsTmVROQqwlSnIsQqLLvswlv30jToQ7buvwiM+IL7jz\nW0r8FhBDkAYrxTKLWPWrzXSkckeu5kTR++aSaBi2Qi/KWw+yQIuT8DWH/JaSwcArhZ/G7oZEClVw\nK9XKLeFlLRIAiPgxqc6UiqKkuBbp5TAeeAf5jp8DXI74uaOKJdtXZHRlxLXTZMTXSM1g7TSZLMUI\n7nyeXA+Bag9ERGqxEe6JxBe6hSwlvXGqoGKjBL/lMceRzmzitUQHWezkEqr9kK3ppx2eZyjFR7cB\n/kK8xEup4Z2bcEFQkPPDSxcPvqK0RF5ARMJbafftDOYO8aMqipJBF+BSxCWxBDgdWAfMLmB+C+Zz\nRFPGsdVWFdppsmUwl3i++EKC2yfCPTK8LDbCvQRoR+DQ2rw5hSwlrguI6PMZtytjOnE7NEJhwf2V\nYym9IbhbafIxD+gVo8xhocTe/rgJ+WnAT8EU+xlTlErxMlJpIT0a59PJV1HaGu2RHMCrEHvizsDu\nYC8CezvYZ2gjHVy102TL4FniVQeZRdNtzUx64m7BsMDoors+iYDMJzTzUchS4ur/jewah8cYQyaz\nKE2E26dj5veAT2OOIUXAcuR99l0MFRLcXXGrUtLl/9s773A3iqv/f8a9994oBmxsegATCHAhdAi9\nJXT4JRAIBAIvNYQNgbxAIISWvJRQUiCEAKE3Bxx6x2B6McY2NgZsMG7YGM/vjzNr7dVVmd2rlVa6\n5/M8eiTtzkozd69mz54553vwV2VRlAxivkaugVG2qkVPFKVO6Ah84xwtuyAhyf9EbMy3kfofB9IG\n5GK10mQ2mEmy8u7vAkPAFovjjqPUcSRy91kJviSZ3nO5kBJfGb3ewINg3kjQh3xa4+H+guIrEHF0\ntUchOsCVQLzc8fiK0ufT15COozuuKFnlHuDJyPu4K0aK0pbowMrfiPmSnANrNJIX+BLiOLynBn2r\nKqUM7k2Q2MwwSeQw5M7+cpJp+SrFKWdoFsEsRyoaFgvx6Y9fzHM7RPruqfh9KEhSD/fHwKolxO+7\n42ewVVIJozUe7lJyenEM7t7461yXYyblS87nM4fm5a1zSHGlrvidlzi644qSVe5FEsPDiqln1LAv\nipJ1HgAuAruLvDWfI9eTj4CtgcmIg3dijfpXNUoZ3NcAYdGQrYALgJuQC/81KferrZFW2MJIxCAv\nxzBEBWNSwj7k8ymJ9J7NdEQiaK8iDeJ4uCtlcIuHO6BcyflCvAWMLbJvBP7Jg5Ucz6vIKlUcZgGD\nwBY6rhuwhMBLgtH3hklRMoz5BKm2HKorrO+q5F0P9iSw24FNsmKpKA2I+SuwD3Ad2B3dttlIEn8P\ncsIBJ4Jt6KKKpQzuduS8owcgeqO3A78keWETpTAJPdxAaYN7FfwM7lHI3WaleIPSus2l+BfF5YF8\nDbYBVKboDQQsRQznzRIcPY9C50aSFjdC7ux9qLTHPmZIibGInNOeBXZuhH+irXq4lQbBPE1Od/sR\n9zwZWSY/G3gH7BywE8WzZ40Y4XYTsJWQK1WUOsI8iaxgL5H31pJbGQplpvuQ3PFYF5QyuNsjwe4g\nCX2PRfY1vHxLlfkMGJRAPQIkNmrfFlsDTkaM3nc9PmMA4smtFB+RrKIhlPb2+xpsW1HZ4kxvUb5M\neyGKyT32ALoSMKv8R9jOyO+tUp7hxcjfMS5/IT9BN2Ao8F8kA70M1iBe/SSyhIqSRT5F/vfDVazL\nEAWG7yDGwyBEfeE+5Kb0TeAWpKaForQhbD9EgOPZvB3zgTuR1aLxYN6rds+qSSmD+xbkYno3cpF+\nwm1fk0p5DxUhYDHwDcmqTTYh8fX5rAt84T67HP0RIfpKkdSog9IGt6+HewQSP10pkuqkL0Dkjzrl\nbR+EXKx9GA18WEGN36TnZjYtFXHCoj6F47ubs6p7/jDBdytKFhkOPIrc4IerwRvQvNjVp8A6wGAw\nAxCveNykZUWpd7ZFcsR2APsguSrQC8HsDeaxtqBjX8rgPh84GbgB+B65P5ABjk+5X22RpAVWtqCw\nEfMFcJ7nZ4zAK7nSm8ob3AG9kTtkH098HDlEH1pjcHeh5Q1RHIN7PeD1BN9djMVIkmNcCoU9hYbF\nEo/jVwPebwuTqtJm6AJsCdyGGN63F2gzCPn9buPezyeZgpOi1DPbIVETdyHJxt2RUJLhYA+tZceq\nSblKk88g7v7oMv67SJlOpbIsIpmR+jGFNZLjaB6fS+sL3kRZRPGS5uUo5uHeHfgvAT5LTnFL2pcj\nqcEd/k3zEy4H4h/Csw2y0lQplpDs/6xQtcnuiHbqBh7Hr0Jl8wQUpdb8GymCczzilNoHkVbdANgQ\nmIaEm1xNLrRvPqU17RWlEdkekZieCKwhevbmRiTM6iaw42vZuWrhU9pdqQ5JvcJfAH0KSOnFMbhn\nANcl+O5ipBFSMg5J3CuD7YzkHlRSDSOpwR162fOP7YX/DcEQ/BJffVkM9KpQtcnuwBsEXnkCw6hM\neXpFyQjGimKJeRZZJfwTsiI1GXgFmQM/BnOMK18N6uFW2hS2I9jVkXDZ14BDgENzaiVs6Z5fb3SF\nElCDO0sk9Aqb5YgnNX8SjyPB1hMxKitFUm89iGHXCWx+NcRR+BmeLpykoqEL5Qq/FMHMA06jZShG\nL/x1tSt9bhYjsovnxDyumMHtuzIyGNHzVpQGxHwL5lhgc0TrHiQvZwbYa8CGq0BLAVNgflOURmQZ\ncAww0d2gfgochHi1h7tr5K9d27+WqMHRENRqcP0QKaV3gYcpLms3DbkregV4vio9qx1dEfnFJMxF\nEh+jeHq4rUGMukqGlCwGNiVIouxhLIXj2Yfj5yGtdDgJwFQgqZRXIY9WLQ3u8H8i7nicwW2jnvE4\nqyhDaAOlexscnbfLYp4BfurefAHsh/xObow0WoB6uZWGZ6XxvCcyXwC2P3AK4oCZ6eQBzwF+h+T5\n/Krq3awitTK4T0cm7rWA/7j3hbCICseGwKZV6VntmAAcnfDYebSs/ulrDHUGvgWzLOF3FyLUjE6q\nxV0orKQffomdfalswiRInNnaCY8t5BmOY0RX2uAOY+Bj3pSYbxCPXTThsg/+Nw7q4a5/dN72Y1vE\ngLgUSRD7EXA+2G5I8bjpVE5XX1GyygD3PA+4HeyViDNtt7x2dyMVJwcDvwQ7uHpdrC61Mrh3RyYe\n3HOhghohSbSp65dkWtyFPNyD8VPCqLRBBwEzkPNarOR8OQoZ3L6GdBoe7s+BfgnPzXxgszzPcC09\n3OEqQRJVmvybhzjl6dXDXf/ovO3HncDhSJLwtW7bmcBzyDV3C0kaU5SGJpSLfQK5hh2HKPr0pbkW\n/a1gJoAZCHQH07COmVoZ3FFv1xz3vhAWyWp9EfhxFfqVBZJksE9Dii0IAe0QY3eGx7E9qGw4Sch0\n/PSZC5EtgztgGVJ8xqd8eT4LEYWOqLd/AuI196GyBndAuISXhNYY3Orhrn903vbCPIEoltwDnApc\njCiXTAQOAVPJhG5FySrDgAeREBKQ6/e5yLyxH5Lb9Bzwd6nCCmCWVr2XVSTNipGP0LJQBsBZee+t\nexRiC6TgxkD3eW+TK8DTaHwHeAlZhom73DgRODDyfjgwn8BLH7nyHm7hS2BkwmObG9wBnRHNW59+\nphFS0hpCT3ZUGnAcXrGttjuiuFLp5eclFDeWStEV2JGcxNlo4PHyh9ku7tgsnRelMDpvVwTzBNhH\ngV3JGRwnAjuDfR6Jb5/nHvdL0qWiNBTDgFlIUuQ5yLV5itv3GzCfIau/xyDJlQ1Pmgb39iX2zSG3\nxDyU4qEPs93zZ8gy3aYUn7iDyOtJ7lE/BLxMwBwkcfK3MY/Ol+HbAv/S5mkZ3POBSwj4XwLvIi8h\n+R7uvYGXnXe2HGmElLQCMxnsc4TnJ6ATEjfv8zd3iaIVLxazGNg4wXEjgMuBK9wqyg74Jfq68CYt\neuNoco8sUs15O4i8nkS9zdnluQYxuKOMcY9D3PvPEB3vW6vYL0VJGTsI2AgxuP+DGNxfkku0Phvs\nq8AdYP6v8GdkiiYqMGfXKqTkbnLV9w5DCgjk042cfnF35OI+pUC7kCDymNT6LtaEwUiFz7jkG9wj\n8S9tnlZISRijmCREppDBfYPnseka3MniuBeSOz99gS89bx6GkTNeKskQYGuCFgV54tANWEzg5X0f\njMZvR5lE8/mqXqj0vB1Q/3N2EewY4DLgDOD7wE+AP7qdC4CHEGP8LErHwitKPXIMotazgFz9jHxV\no38B94NNqgBWTSZRgTm7Vgb3BYgn5V0ko/sCt30YcJ97PQTxikxG4nzuZaW0TMMyMeFx+QZ3nNja\ntDzcoaZ4Ej3uOYhEUEhXctq25UgrpCQMz0lSQTOqsd4X/4TFHvgnV8Yh/FsOKNmqJU9GXseVBNT4\n7fpH520v7ASkOuyvgQvdxp3Jhf3tA2YnMPcjcd47uYJditIonOeef4Voz4ccg8wJoUNwJ+A9sD+p\nYt9qRpohJaWYB2xXYPsscktwU/ErGd1InEFLtREf8st1DwDeKdI2n7QM7nAJOYmB+gjwT7kImaWI\nwe0Tjw7pebjXQSaJXsRfEYjeEPXD/4agG5WtmCkEXE3A8cRPZDwauN29jtM39XA3Bjpvl8WOAB5F\nVuSkEit8C1wJHAo8RrObaPMJ2DeBbZAEM0VpAMwKsF2RHJ9TgLOB3yChUxcCqyP25xzgCuCtGnW0\nqjR0VZ86JGnZ39Z4uHuRhsEd8BYSu9Uj/sFmEWKUhtriXcmFqJQjHYM7YCri2UtS4j2phzsdg1v4\nhMLJcaWIlrhXD7eitGQWkhx5HGJkDwE2BXM1mIUU/t3diVR/VZQGwnyNqPX8gpXhJOZL5MbyAKk6\naQyYE5yyT8OjBne2aI3BHS1IMhDRjvZhJH7ygUlYSCKDG2hezCeOhztNlZIFJItJX0zu7+BbwAfE\n4PY1auNSStatGAvJGdzq4VaUFpgVYK4FOiFe7r7AYrCfgl2Nwr+7u4A9Gr2stdLWsN2Ag2mmtGWb\nEA3/w2vUqZqiP/BsMQ/oSUCXmMctBoaADZckB+Dv4V4d+DDm9/myiOQGd7SYTxdqH1IChatG+vAZ\n8HuwHYh3Q5C2hzuuwb0A6OGK+MTxcKsGt9LGMN+AOVI8ePwYcYJMBY4Cjshr+x7iIJlQ5U4qSoWx\n7eQ6Z3+MVDXeEPguOQnR37vtY8Emrd5ct6jBnSUCliOekTdiHhmGhOzonuN4uIciy6Bp8DqwR8Jj\nk3q4+5Ceh/srkoWUhEZzP0SDe67ncWkb3DELE5nliF5qN+RGSkNKFKUs5jpneG/mNmwG1rrHJLDX\nAeOB+xq5rLXS6NhOSL7CN4gk5ldIgv4jSPw2iAH+nnvkq5Y0PGpwZ4/J+HunHSZXATFgPcQQ8vXy\nxokpjssNwLYJpfSWIbGN4G1w23bIj7jShWJCvgLuTLACEf7OuiHqDveVaBslzZCSKcCuBLG93N8i\nlTPjhMZoSImiYJ5DlEqeAm50G7dGvN4gc/FLYDdreayiZAXbC+xWLikygllGzsG2GBgLnIRcL36L\nXDN+BDwLbALmmWr1OCuowZ09fkMy4+Qp9xgHPOGp8wxpxjwHfAIsR7zocYl6X3093IOAL9wPPw3C\nG5vVSrZqyVXuuTtyQ/CR53F9SEcWEGTSWwvxPsQhVF4YgP8qiit8oyhtnk+QPJ2jXcKYQZbZQapP\nLsN/flCUWjABUdv5DOzjYM8Fu63EbJu7kZywUMThLqA9mLMQJ+A0YD1ElafNoQZ39pgPbE8QW7Lx\nUsSo6Y23h9ca4snUJeF9IImwvXjFpax7V/yUVFYBpif4Ll9CPdHR8Q4zi4EXafdNaHD7euDTi30O\nVq6A9CvZrtQn+K06GNLTE1eUeuNZZE6cCHaAi2P9hds3EPg+mDSKXSlKpXgK8WCPQfS2OwAXA4vA\nTkdyrkKn4R7ANWAvQnLFbkRuMF+rcp8zgRrc2WM+cncY9w5wKVIyvBf+Bl0POc4sLdsyOROBfRMc\n9zIAX/cS72jAitLNATGEpyb4Ll+udc9x5fQAFtHnwwHAcgJ8/95pJxveCTyQ8NgeiDeuHN2Br8F8\nm/B7FKWBMEsRqbSnkPjWNyM755KsboGiVBGzGCmC9l0wD4M5E0kMBlE9G0jza+RRSNLk7sBYMGeD\n8V0dbSjU4M4eYcyub0hIyNfInWUMDzdDST+29hVgVILjfg7Ash5r4F/efF0kUTMdAl5E7uSTeIUX\n0v/9QXjH1tsOyMqAb4XNJNwHscu7/5D2y0BChc71aJ9WYSVFqVPMCjBnIA6SkIsRnf8nwd4BdqPa\n9E1RvLgf2CXyfjbiHGoHbJ7XdgXQAcxrYOLaNQ2FGtxZQwrGTCJ+Bu9S2i/tAmyB//L9MPyN2aR8\nTvwS4jhFjC/p/NX+yN/Dh+GkH//4BRL3HpeF9P5oKP6xzOOBeWDSHE9+wSQf7qTr3G+AeZ55Ampw\nK0phLo28PgVZCbweqc53D9h7mydQ2h5gN9akSiUDPADsHNGO/xSR8e0A/A8Sq/0esuLeBzi/Bn3M\nHGpwZ5OpxDfqlrLOrYOAJvwL2fwA+WGkSUKDG4CvMStWRZZffYjj3U9KVK4wDnPZ7djfAut7tu9L\n+lJ6SQzuZXT7rB3WW6GkJ1IwR1GU5jwJPBd5bxBVhwHAYcgK5DM5CUEWIM6H31a5n4qSh3kfcext\n4N4vR4zukxB54n7AD8EscI+0lNDqCjW4s0m0rLkvX7O8c1iU5UnPY8YB/4z5PXGZAwwjSPS/NoS5\na20HfOzZPk78elKmAlslOC5u6E6aeuIhCQxuY1n7zuUs7/Kq5wFpyjQqSh1j7gCzGVJ7YUPgP27H\nWYh6UKGwku7AadXpn6KU5D5WhpXYTsiK+YXINeVsMC/VrGcZRQ3ubPIWsE7MY5bS4etBvHoIBN46\n3gNIW64tYB7iFU6iVAI9Z7fHP465F+mrYTwCrElAx5jHhQb3np7t+5JexcyQxchY4umk9/q4M1O3\n8y3esy7NE8MURWmG+QbMZOBIJCTuI+B5xAAf76QDx7nGj4F5oTb9VJRm3A/8HOyt0EwIYCMwl9eo\nT5lGDe5s8giwM0GsMuKz6PoFLIkVidIffy3l1vACUt41Hu2XQdd54B9akb43VeKW48dxd/niIb7t\nAJdO8w33SU8fPcfLyN9sTKyjun0Grx52rGfrtZAbSEVRSmKmIxr/uyArjxZ4Duyj5G5a1butZAB7\nPnAb4rTb3218EKnRMLlWvco6anBnkYDpyAQ7wf8gs4B1b/4bX8fKteyPf5nx1nAX8JvYXuHeX3D+\nLQAAIABJREFUH73JwsEQ4CEpZ9cCVqc6BVbix3Gf3q8rX/dZzPxVxpVvDEis97txOxaLgCXAi8Bb\nBDHUSrp9DosG+Mr8pb+KoigNg7Fg3gRzCZjtkTjuWyINngd7M9gk0qSK4oG9EexjYlTbXcH2L9Do\nv7QUdngKCXnaIO0e1itqcGeX1xClCn+Gvfg1C4fYSOZwCWxHJNYq/fjagL8hRVLieVKP2fAIFg3C\nFU8px2rAw2CqUWBlHnAIQQv5o1JsxJerzUB0Sn3YkOYJVWkR/r0KTaotCejByGfm8+Vq0zw/fyDV\nWUVRlAbELESK5UT5ITAb7Kdg3wX7HNgHnSG+Yw06qTQWpyHG80+Be4HPwb4N9gb3+AB4KO+YE8Cc\nB/wVOLS63a0f1ODOLnOBS12lRT+MHcmC4UvAKxSlP1IGvVq6mDOJW+K906KeLO25Aqk0WY4+pB/z\nHPI0cCZSZcuXXqxoP5fmJetLMRSYFbdjCTjKPQ/ybL82K9pPZ/4qPucE4pWAVxSlBWYKovwwJbKx\nM5IfsTtwInA58ChwI9gf53+Covhj5oD5JVI/4wTEFhkDHO4eq7uGSwkL1MHlYO8G9gO2rmZv6wk1\nuLNLJ/ccJ9lwOF8NW0rh7PZ8qhW/HTIbWC2mWklvlvVcjl/1tWoa3Pe45zjlaXtg2y3A62bIdkLG\n45v8mhxJan0c8UT7sBqYD5FKk2WwXaR9qtU/FaUtMBExsEPWcIbR22CeAXM/mOsQBaXTwPoUpVKU\nItjOSP7NeHK2CEjBs5DONLc1foA41nZIvXt1ihrc2eUCxMO5VoxjerNkQF9y8lKlqFb8dsh04Grk\njtmX3izrvhT4jkfbaqh6hDwO/AVixaT3wLb7CjgUbDkpvuHA7CqWQ5+Nv+d9GO2+mQ708Aj12RmY\nLjqsiqIkx6xwLx5Cymjf7Iyi/HbvIRrex7uwQUVJwl3AS8A2wMHAdohN8rXbfz2wj3sOkEI4C4A1\ngVWr29X6QQ3urBIwH/g3MCLGUT1Z6i1sMpzqhCyEhBUTv4lxzBA6L3gH2NKj7cZUwyMMELACSRqJ\no2HdA0yoOlJOGnAsUqWrWkwFRnu27U67FQuQuaOczuodwNqt6ZiiKLYd2FAJ4mLgz0jBst8UOeB4\n4CKRG1SUuNgNgPXcm7UQ43sicDq5lc3dEKfTmsgK9LXAGDCrgnmxip2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bAAAA\ndUlEQVQkMEVRWk97YG3EqC7ED4C5SKiJoiiKki7TEcnXZ2vcD0VRFEVRFEVRFEVRFEVRFEVRFEVR\nFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVR\nlPj8f3R2lSRAwYDEAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f66cf10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "import nengo\n", "from nengo.utils.ensemble import response_curves\n", "from nengo.dists import Uniform\n", "\n", "model = nengo.Network('Oscillator')\n", "\n", "freq = .25\n", "scale = 1.1\n", "N=300\n", "\n", "with model:\n", " stim = nengo.Node(lambda t: [.5,.5] if .1<t<.12 else [0,0])\n", " inhib = nengo.Node(lambda t: [-1]*N if .8<t<.82 else [0]*N)\n", " \n", " osc = nengo.Ensemble(N, dimensions=2, intercepts=Uniform(.3,1))\n", " \n", " def feedback(x):\n", " return scale*x[0]+freq*x[1], -freq*x[0]+scale*x[1]\n", " \n", " nengo.Connection(osc, osc, function=feedback)\n", " nengo.Connection(inhib, osc.neurons)\n", " nengo.Connection(stim, osc)\n", " \n", " osc_p = nengo.Probe(osc, synapse=.01)\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(1)\n", "\n", "x, A = response_curves(osc, sim)\n", "figure(figsize=(4,2))\n", "plot(x, A)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)')\n", "\n", "figure(figsize=(12,4))\n", "subplot(1,2,1)\n", "plot(sim.trange(), sim.data[osc_p]);\n", "xlabel('Time (s)')\n", "ylabel('State value')\n", " \n", "subplot(1,2,2)\n", "plot(sim.data[osc_p][:,0],sim.data[osc_p][:,1])\n", "xlabel('$x_0$')\n", "ylabel('$x_1$');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- This also generalizes to chaotic attractors and many other fun dynamic networks (see Lecture 5).\n", "- Can also generalize representation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Generalizing representation\n", "\n", "- Generalizing a line attractor gives something much more interesting\n", " - A plane attractor\n", " \n", "<img src=\"lecture_memory/plane_attractor.png\">\n", "\n", "- The attractor space is much more complicated, so you need a lot more neurons to do as good a job ($N^D$)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e0ff210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#A 1D integrator with a few hundred neurons works great\n", "import nengo\n", "from nengo.utils.functions import piecewise\n", "\n", "model = nengo.Network(label='1D Line Attractor', seed=5)\n", "\n", "N = 300\n", "tau = 0.01\n", "\n", "with model:\n", " stim = nengo.Node(piecewise({.3:[1], .5:[0] }))\n", " neurons = nengo.Ensemble(N, dimensions=1)\n", "\n", " nengo.Connection(stim, neurons, transform=tau, synapse=tau)\n", " nengo.Connection(neurons, neurons, synapse=tau)\n", "\n", " stim_p = nengo.Probe(stim)\n", " neurons_p = nengo.Probe(neurons, synapse=.01)\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(4)\n", "\n", "t=sim.trange()\n", "\n", "plot(t, sim.data[stim_p], label = \"stim\")\n", "plot(t, sim.data[neurons_p], label = \"position\")\n", "legend(loc=\"best\");" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10ddc6bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Need lots of neurons to get reasonable performance in higher D\n", "import nengo\n", "from nengo.utils.functions import piecewise\n", "\n", "model = nengo.Network(label='2D Plane Attractor', seed=4)\n", "\n", "N = 2000\n", "tau = 0.01\n", "\n", "with model:\n", " stim = nengo.Node(piecewise({.3:[1, -1], .5:[0, 0] }))\n", " neurons = nengo.Ensemble(N, dimensions=2)\n", "\n", " nengo.Connection(stim, neurons, transform=tau, synapse=tau)\n", " nengo.Connection(neurons, neurons, synapse=tau)\n", "\n", " stim_p = nengo.Probe(stim)\n", " neurons_p = nengo.Probe(neurons, synapse=.01)\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(4)\n", "\n", "t=sim.trange()\n", "\n", "plot(t, sim.data[stim_p], label = \"stim\")\n", "plot(t, sim.data[neurons_p], label = \"position\")\n", "legend(loc=\"best\");" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10f30c590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Note that the representation saturates at the radius\n", "with model:\n", " stim.output = piecewise({.2:[1, -1], 1.2:[0, 0] })\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(4)\n", "\n", "t=sim.trange()\n", "\n", "plot(t, sim.data[stim_p], label = \"stim\")\n", "plot(t, sim.data[neurons_p], label = \"position\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- So for higher dimensional memories, you may want the dimensions to be independent\n", " - To make this easy, Nengo has 'ensemble arrays'" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e2bc910>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = nengo.Network(label='Ensemble Array', seed=123)\n", "\n", "N = 300\n", "tau = 0.01\n", "\n", "with model:\n", " stim = nengo.Node(piecewise({.3:[1, -1], .5:[0, 0] }))\n", "\n", " neurons = nengo.networks.EnsembleArray(N, n_ensembles=2)\n", " \n", " nengo.Connection(stim, neurons.input, transform=tau, synapse=tau)\n", " nengo.Connection(neurons.output, neurons.input, synapse=tau)\n", "\n", " stim_p = nengo.Probe(stim)\n", " neurons_p = nengo.Probe(neurons.output, synapse=.01)\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(4)\n", "\n", "t=sim.trange()\n", "\n", "plot(t, sim.data[stim_p], label = \"stim\")\n", "plot(t, sim.data[neurons_p], label = \"position\");" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/celiasmi/Documents/nengo/nengo_gui/nengo_gui/ipython.py:58: ConfigReuseWarning: Reusing config. Only the most recent visualization will update the config.\n", " \"Reusing config. Only the most recent visualization will \"\n" ] }, { "data": { "text/html": [ "\n", " <div id=\"14d39301-9d90-4c21-9225-25bb00a30e2f\">\n", " <iframe\n", " src=\"http://localhost:61105\"\n", " width=\"100%\"\n", " height=\"600\"\n", " frameborder=\"0\"\n", " class=\"cell\"\n", " style=\"border: 1px solid #eee;\"\n", " allowfullscreen></iframe>\n", " </div>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from nengo_gui.ipython import IPythonViz\n", "IPythonViz(model, \"ensemble_array.py.cfg\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- And saturation effects are quite different" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10ee5d2d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Note that the representation saturates at the radius\n", "with model:\n", " stim.output = piecewise({.2:[1, -1], 1.2:[0, 0] })\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(4)\n", "\n", "t=sim.trange()\n", "\n", "plot(t, sim.data[stim_p], label = \"stim\")\n", "plot(t, sim.data[neurons_p], label = \"position\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Working memory\n", "\n", "- We can build high dimensional working memories using plane attractors\n", "- In general we want some saturation, and some independence between dimensions\n", " - Gives both a 'soft normalization' \n", " - And good temporal stability for fewer neurons\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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V6BV0ITJYWAK2+ahKVMQ3UQnYwpJh+wIYCsYJuiASSWG5MJkBdAy6EBksLAHb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"text/plain": [ "<matplotlib.figure.Figure at 0x10e2bc610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import nengo\n", "from nengo import spa\n", "\n", "def color_input(t):\n", " if t < 0.15:\n", " return 'BLUE'\n", " elif 1.0 < t < 1.15:\n", " return 'GREEN'\n", " elif 1.7 < t < 1.85:\n", " return 'RED'\n", " else:\n", " return '0'\n", "\n", "model = spa.SPA(label=\"HighD Working Memory\", seed=5)\n", "\n", "dimensions = 32\n", "\n", "with model:\n", " model.color_in = spa.Buffer(dimensions=dimensions)\n", "\n", " model.mem = spa.Memory(dimensions=dimensions, subdimensions=4, \n", " synapse=0.1, neurons_per_dimension=50)\n", "\n", " # Connect the buffers\n", " cortical_actions = spa.Actions(\n", " 'mem = color_in'\n", " )\n", " \n", " model.cortical = spa.Cortical(cortical_actions) \n", "\n", " model.inp = spa.Input(color_in=color_input)\n", " \n", " model.config[nengo.Probe].synapse = nengo.Lowpass(0.03)\n", " color_in = nengo.Probe(model.color_in.state.output)\n", " mem = nengo.Probe(model.mem.state.output)\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(3.)\n", "\n", "plt.figure(figsize=(10, 10))\n", "vocab = model.get_default_vocab(dimensions)\n", "\n", "plt.subplot(2, 1, 1)\n", "plt.plot(sim.trange(), model.similarity(sim.data, color_in))\n", "plt.legend(model.get_output_vocab('color_in').keys, fontsize='x-small')\n", "plt.ylabel(\"color\")\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(sim.trange(), model.similarity(sim.data, mem))\n", "plt.legend(fontsize='x-small')\n", "plt.legend(model.get_output_vocab('color_in').keys, fontsize='x-small')\n", "plt.ylabel(\"memory\")\n", "plt.xlabel(\"time [s]\");" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <div id=\"202b3354-091f-4eb1-9f9a-7e1870bce629\">\n", " <iframe\n", " src=\"http://localhost:61240\"\n", " width=\"100%\"\n", " height=\"600\"\n", " frameborder=\"0\"\n", " class=\"cell\"\n", " style=\"border: 1px solid #eee;\"\n", " allowfullscreen></iframe>\n", " </div>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from nengo_gui.ipython import IPythonViz\n", "IPythonViz(model, \"simple_spa_wm.py.cfg\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.all_ensembles\n", "\n", "model.all_ensembles[2].n_neurons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- You can see interference effects here\n", "- If you recalled things from this memory, you'd probably have a 'recency effect'\n", " - i.e. The things most recently put in memory would be best recalled\n", "\n", "- This is seen in human memory, but so is primacy\n", " - How could we get primacy? " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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V6BV0ITJYWAK2+ahKVMQ3UQnYwpJh+wIYCsYJuiASSWG5MJkBdAy6EBksLAHb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"text/plain": [ "<matplotlib.figure.Figure at 0x1120ed2d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import nengo\n", "from nengo import spa\n", "\n", "def color_input(t):\n", " if t < 0.15:\n", " return 'BLUE'\n", " elif 1.0 < t < 1.15:\n", " return 'GREEN'\n", " elif 1.7 < t < 1.85:\n", " return 'RED'\n", " else:\n", " return '0'\n", "\n", "model = spa.SPA(label=\"HighD Working Memory\", seed=5)\n", "\n", "dimensions = 32\n", "\n", "with model:\n", " model.color_in = spa.Buffer(dimensions=dimensions)\n", "\n", " model.mem = spa.Memory(dimensions=dimensions, subdimensions=4, \n", " synapse=0.1, neurons_per_dimension=50, tau=-.2)\n", "\n", " # Connect the buffers\n", " cortical_actions = spa.Actions(\n", " 'mem = color_in'\n", " )\n", " \n", " model.cortical = spa.Cortical(cortical_actions) \n", "\n", " model.inp = spa.Input(color_in=color_input)\n", " \n", " model.config[nengo.Probe].synapse = nengo.Lowpass(0.03)\n", " color_in = nengo.Probe(model.color_in.state.output)\n", " mem = nengo.Probe(model.mem.state.output)\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(3.)\n", "\n", "plt.figure(figsize=(10, 10))\n", "vocab = model.get_default_vocab(dimensions)\n", "\n", "plt.subplot(2, 1, 1)\n", "plt.plot(sim.trange(), model.similarity(sim.data, color_in))\n", "plt.legend(model.get_output_vocab('color_in').keys, fontsize='x-small')\n", "plt.ylabel(\"color\")\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(sim.trange(), model.similarity(sim.data, mem))\n", "plt.legend(fontsize='x-small')\n", "plt.legend(model.get_output_vocab('color_in').keys, fontsize='x-small')\n", "plt.ylabel(\"memory\")\n", "plt.xlabel(\"time [s]\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- By making tau negative, we forced the recurrent connection to be positive\n", " - So, whatever is in memory 'grows' over time\n", "- This gives a 'primacy' effect\n", " - i.e. Whatever you ran into first is what you remember\n", " - You could also get this by changing connection weights \n", "- Together, primacy and recency capture the most salient properties of human working memory\n", "- You see these effects in both 'free recall' and 'serial recall'\n", "\n", "<img src=\"lecture_memory/human_wm.png\" width=400>\n", "\n", "- So far, the networks we've seen would work well for free recall presumably.\n", " - How to include order information?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Serial Working Memory\n", "\n", "- An ordered list is a very simple kind of structure\n", "- To represent structures, we can use what we learned last time: vector binding\n", " - Specifically, Semantic Pointers\n", " \n", "- In this case, we can do something like:\n", "\n", "$Pos_0\\circledast Item_0 + Pos_1\\circledast Item_1 + ...$\n", "\n", "- The $Item$ can just whatever vector we want to remember\n", "- How should we generate $Pos$? What features does it need?\n", " - It would be good if we could generate them on the fly, forever\n", " - It would be good if $Pos_0$ \"came before\" $Pos_1$ in some sense\n", "- There is a kind of vector that's ideal for this called a 'unitary vector'\n", " - A unitary vector does not change length when convolved\n", " - So, you can convolve forever\n", " - The convolution of a unitary vector with itself is 'unlike' the original vector\n", " - So, you can go fwd/backward with convolution/correlation\n", "- This gives a way of 'counting' positions:\n", " - $Pos_0 \\circledast PlusOne = Pos_1$\n", " - $Pos_1 \\circledast PlusOne = Pos_2$\n", " - $Pos_2 \\circledast PlusOne = Pos_3$\n", " - etc.\n", " - And, $Pos_3 \\circledast PlusOne' = Pos_2$\n", " \n", "- We can put this representation together with primacy and recency, and we get something like this\n", "\n", "<img src=\"lecture_memory/wm_model.png\">\n", "\n", "- To do 'recall' from this representation, we want to remember what we've already recalled, so we can add a circuit like this\n", "\n", "<img src=\"lecture_memory/wm_recall.png\">\n", "\n", "- This does a pretty good job of capturing lots of working memory data\n", "\n", "<img src=\"lecture_memory/model_data1.png\">\n", "\n", "- Order effects\n", "\n", "<img src=\"lecture_memory/model_data2.png\">\n", "\n", "- Error probability of items in the list (closer items are more likely to be incorrect, unless you change similarity between items)\n", "\n", "<img src=\"lecture_memory/model_data3.png\">\n", "\n", "- Item similarity effects\n", "\n", "<img src=\"lecture_memory/model_data4.png\">\n", "\n", "- Arbitrary list lengths\n", "\n", "- This model can actually do lots more as well\n", " - backwards recall, etc.\n", "- A slight variation does free recall\n", " - you have to include non-bound item representations as well\n", "- You'll notice that it relies on a 'clean-up' \n", " - This is a kind of long term memory\n", " - We saw this show up before when talking about symbols\n", " - How can we build such a thing in spiking neurons?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Long term memory\n", "\n", "\n", "- In order for memories to be stable over the long term, they must be encoded in connection weights\n", "- These kind of memories are often called 'associative memories'\n", " - Auto-associative: recall the same thing you're shown\n", " - e.g. for noise reduction\n", " - pattern completion\n", " - Hetero-associative: recall some arbitrary association\n", " - e.g. for capturing domain relationships\n", " - reasoning\n", " \n", "- Typical solutions in ANNs are \n", " - Hopfield networks: A many-point attractor network\n", " - Linear associators: Do SVD on the input/output to get a weight matrix\n", " - Multi-layer Perceptron (MLP): Use backprop to train a network to compute the feedfwd mapping\n", "\n", "<img src=\"lecture_memory/hopfield.png\">\n", "\n", "\n", "- Often worried about how to learn these\n", " - We'll talk about learning in a later lecture\n", " - Often the learning is so un-'biologically plausible' that it's best thought of as an optimization (like least squares in the NEF).\n", " \n", "- How to compute these kinds of mappings with the NEF/SPA?\n", " - We want to 'recognize' specific vectors (in a vocabulary)\n", " - We want only some neurons to fire when a given vector is present (sparse)\n", " - We want to activate a given output vector if the input is recognized\n", " \n", "- Can do it in one layer with a feedforward approach\n", " - Set the encoding vectors of some small set of neurons to be a vocabulary item\n", " - That will pick out specific vectors\n", " - Set the intercepts of neurons to be high in the positive direction\n", " - So they will only fire if there is 'enough' of that vector in the input\n", " - Set the linear transformation on the output of those neurons to be the desired output vector\n", " - Since these are in the weights, the output will perfectly represent the desired output\n", " \n", "- We've seen several techniques that will make this easy\n", " - Ensemble arrays: For collecting lots of small populations together\n", " - SPA module: For defining vocabs, etc." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0iYiISBEsAp7DLkH1OPBrYKJ/7WzsYvJrM7fb/GuzsIn612be+2fgdW0ptfSh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\nynpsq7De1EUJm5RL79SNyNtpziO/XvlSQVpx0mY9gc3xMb2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qI7yYdlGzo5JK1ZX85pB9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"text/plain": [ "<matplotlib.figure.Figure at 0x10e527dd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import nengo\n", "from nengo import spa\n", "\n", "seed=1\n", "np.random.seed(seed)\n", "model = spa.SPA(\"Associative Memory\", seed=seed)\n", "\n", "D = 32\n", "vocab = spa.Vocabulary(D)\n", "vocab.parse('BLUE+GREEN+RED')\n", "\n", "noise_RED = vocab.parse(\"RED\").v + .2*np.random.randn(D)\n", "noise_RED = noise_RED/np.linalg.norm(noise_RED)\n", "\n", "noise_GREEN = vocab.parse(\"GREEN\").v + .2*np.random.randn(D)\n", "noise_GREEN = noise_GREEN/np.linalg.norm(noise_GREEN)\n", "\n", "def memory_input(t):\n", " if t < 0.2:\n", " return vocab.parse(\"BLUE\").v\n", " elif .2 < t < .5:\n", " return noise_RED\n", " elif .5 < t < .8:\n", " return vocab.parse(\"RED\").v\n", " elif .8 < t < 1:\n", " return noise_GREEN\n", " else:\n", " return vocab.parse(\"0\").v\n", "\n", "with model:\n", " stim = nengo.Node(output=memory_input, label='input')\n", " model.am = spa.AssociativeMemory(vocab)\n", " nengo.Connection(stim, model.am.input)\n", "\n", " in_p = nengo.Probe(stim)\n", " out_p = nengo.Probe(model.am.output, synapse=0.03)\n", "\n", "sim = nengo.Simulator(model)\n", "sim.run(1)\n", "t = sim.trange()\n", "\n", "figure(figsize=(10,10))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(t, spa.similarity(sim.data[in_p], vocab))\n", "plt.ylabel(\"Input\")\n", "plt.ylim(top=1.1)\n", "plt.legend(vocab.keys, loc='best')\n", "plt.subplot(2, 1, 2)\n", "plt.plot(t, nengo.spa.similarity(sim.data[out_p], vocab))\n", "plt.ylabel(\"Output\")\n", "plt.legend(vocab.keys, loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- This implementation has several nice features\n", " - Extremely fast (1 synapse feedforward ~ 5ms)\n", " - Fully spiking, integrates with SPA models\n", " - Scales very well\n", " \n", "<img src=\"lecture_memory/cleanup.png\" width=500>\n", "\n", "- Scaling properties of a neural clean-up memory (Eliasmith, 2013). A semantic pointer is formed by binding $k$ pairs of random lexical semantic pointers together and then adding the bound pairs. The clean-up memory must recover one of the lexical semantic pointers that formed the input by convolving the input with a random probe from that set. This figure plots the minimum number of dimensions required to recover a lexical item from the input 99% of the time. Data were collected using average results from 200 simulations for each combination of $k$, $M$, and $D$ values. The vertical dashed line indicates the approximate size of an adult lexicon. The horizontal dashed lines show the performance of a non-neural clean-up directly implementing the three-step algorithm. The neural model performs well compared to this purely computational implementation of clean-up." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##SPA Example\n", "\n", "- This is the same example as for the symbols lecture, but with a working memory\n", " - The WM is just a single attractor network\n", " - The model is supposed to memorize the bound inputs and then answer questions about what is bound to what" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pylab inline\n", "import nengo\n", "from nengo import spa\n", "\n", "def color_input(t):\n", " if t < 0.25:\n", " return 'RED'\n", " elif t < 0.5:\n", " return 'BLUE'\n", " else:\n", " return '0'\n", "\n", "def shape_input(t):\n", " if t < 0.25:\n", " return 'CIRCLE'\n", " elif t < 0.5:\n", " return 'SQUARE'\n", " else:\n", " return '0'\n", "\n", "def cue_input(t):\n", " if t < 0.5:\n", " return '0'\n", " sequence = ['0', 'CIRCLE', 'RED', '0', 'SQUARE', 'BLUE']\n", " idx = int(((t - 0.5) // (1. / len(sequence))) % len(sequence))\n", " return sequence[idx]\n", "\n", "seed=1\n", "model = spa.SPA(label=\"Simple question answering\", seed=seed)\n", "\n", "dimensions = 32\n", "vocab = model.get_default_vocab(dimensions)\n", "vocab.parse('BLUE+RED+CIRCLE+SQUARE')\n", "\n", "with model: \n", " model.color_in = spa.Buffer(dimensions=dimensions)\n", " model.shape_in = spa.Buffer(dimensions=dimensions)\n", " model.conv = spa.Memory(dimensions=dimensions, subdimensions=4, synapse=0.4)\n", " model.cue = spa.Buffer(dimensions=dimensions)\n", " model.out = spa.Buffer(dimensions=dimensions)\n", " model.am = spa.AssociativeMemory(vocab, threshold=0.1)\n", "\n", " model.inp = spa.Input(color_in=color_input, shape_in=shape_input, cue=cue_input)\n", " \n", " # Connect the buffers\n", " cortical_actions = spa.Actions(\n", " 'conv = color_in * shape_in',\n", " 'out = conv * ~cue',\n", " 'am = out'\n", " )\n", " model.cortical = spa.Cortical(cortical_actions) \n", " \n", " model.config[nengo.Probe].synapse = nengo.Lowpass(0.03)\n", " color_in = nengo.Probe(model.color_in.state.output)\n", " shape_in = nengo.Probe(model.shape_in.state.output)\n", " cue = nengo.Probe(model.cue.state.output)\n", " conv = nengo.Probe(model.conv.state.output)\n", " out = nengo.Probe(model.out.state.output)\n", " clean = nengo.Probe(model.am.output)\n", " \n", "sim = nengo.Simulator(model)\n", "sim.run(3.)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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V1VAZRc2aIWzYncYZ5dEYY06w73gcuVMOw7RDHB11gvg755N4OFE+v7ML5Ly4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889oA/8MfA8FrGWvBuDSI96iUxCJQFKPOmfkAHm+hi0qTH+BOwBNdmFY3vL4Bb52hysxlULDCmK\ncxXwkXK/IM5nwFFYxLqQynBggcdKuo8inN+TaV6NTBQecMLSXMKYC+Qiscixpj+iR+0NJA9oMCEc\nKI5m+BHAHcrdhNMGCcfg/lXQI6BWEolymuGUdg/mEuQGotHEgoFIlTsvlXQfThghLgZUAYchS6Rf\nKUkccQeLlxEN/VirDHmxQuih1C68UD+muRFxcNyJbd8YavPoYVQg+tIx9dY6SVteM7jDnewCvAW8\nDjyppIqoO1jsRmKT78aKaTv6I6vTXmIstYuFNcYNyKTpm+g1Jyx+B0x2IY8iEgZ3LpKU2PKHRTUW\n7bHYGWpbA+YakGxIRERkjl/zyG3hdxKWwe1HXPQXIzFqTyJe7pbSlNmvieh/1xfnrdFEA68N+NCE\nQd+Q/vUwkii0S7kT1hHgNeBGrJga/gMIHb4RQ5QPGcjCa5Nprkbut3dh227GBM8FrgZ1aAyPmYEY\n3TtjeMxQhH0/MKDKgN8iq7OlqnbCVqy52nm8EsNj9sNLHlJhAOGuVprmD4jNMQnbXoxtJ0SzYY0w\nG1kV+32sDuhMdiNhcHdA9tXyh8VRWMxpymcUzHKs5BlKCkFFoi0dWvidhGVwj6S2Zb8b8dS0lHBK\nuweO/wxS0r2xGYYV9MiKQPs0BzaZeM/gbjBpqz4MKS7VFzGapil4U0nhjlgzFQlzmR4TmUBJ2god\nsxlbegI5YDQYf78Ppvkskmn/MLb9tHNeMcaYAfwVeEQcSDFhKLDcaJpTJtoMouk5AUORuhWvK1ir\noGPkmxUCi+3I2HlYDL3cHvNwq6YbkaY5DdFoHgF8g233bPwD0cDYjUQVPAVqeIwO2gHpd7tjdLxw\nqF/OsREMqRz5CpIDWKHkb0vIoraN2SzCMbjrWvYdEK93SwmntHsvRJ7qfEIPnlbQY1oE2qc5sPGW\nh1sM1b40cSAzJEdiPGI0nQ1sV/CxisBsPWwsSpDBKw04PQZH7AKUYpp5MThWuDRvAmCajyCDx2Rg\nJrZ9ErYdu2snPAK0J3Yxyd7qe0I4Ca+1MERH/A5EOagfkK3gDSUKOrHkEyQu+coYHc9rHu7OQIUU\nlWkCpvl/SGgawAps+wJsO8aKS8ZXiNzrQzE64ABgrccmuyHjt+vDkJCc0Yhzd7qClxQMbKZk7jRi\nZHA/gCxt3I7cPGYj1SdbSnBp92VIZbpAafeA6PstyI3+SSQe84cIHFejCYeheEpHlvZAJVbTNfAd\nybIHEWnPCiQfI0fB2wquVBB9vVcLBfwHeBKLw6N8tIF4K9kVxLGwvlmfNM1ZiJfnG8R4ysG2r8K2\n+0SobSEwqpDwludBRSJ/JxReNLgH0Ewj0pCQKh8SlnkOsEWBUnCxgkRnyTt6SN+7FLjfKf0ePZ1w\nUdbphbf078PKfakX01yEhOP9HZGcW4ttv49tx7Io1G+QWO4/x+BY3kqYFAbSzPBAA340RGzjaMQA\nXwVUKXhaQYKKjPO4Ke0Ji2FIgxVSutZLhghIu9ysaKfZj3A6YQHQ2ZDCT+5jcTDwElbLteiVeKDO\noPbEeRaS0Pcx8v5PSAzqqoh6Oyz+hOj4H4oVpaRG274YMDHNC6Ky/2ah/g1Ug2G1aDe23R5Zrfin\n88oCJLExGfgJ04yibrz6HEk+OxKMqBnESiobP2x4JUneIh4pA52MRVVLdqXE+BuNGMDBsp1zgBwk\nSdtCDNbCCPe9O4F/AH/F4sGI7TcYmQR+h2m6EILREOoaYCAYLfPw23YKcCMSJhRgFeIMfBVRqMkD\nxmOa37foWPug/orI3g0Fo0nhFU06ipxbsiHVGL2BjBOXYrVMdcpRHrsGkV4dFPTWj4jjdxWwAZhu\nQLUTz57kqJ+gwAjqj82yOcPVeV1KE2JHNZpWTm8g2zPGttAL2BiJHRkSlnK/Eq93V0R16LeIF++r\nutsrKEZ0WRWieLLJ+VwhMLlJ5bctnsQiBfgRiwHABiwqW3ZG++BVD7fd4r2YZi62fTOi/HIQItX6\nBuKV6oNtB+J0FwNbEedIEeJlzQGORZQzVgPzm1gY6CT2rkiqJDCipeDjNQ93N2BHS41tAEM8rUuA\nF5WMv2cjnu9g6cW93lMl12m38zjBeflDZDL8lgGfhn1wi5uw2AhOH7SiIjnXD0/FbwPS99a1eC+m\nWQzcgm0/iuSxfYoYboOQlQuQiVIfbHsWEoaQAryNhPQcitwrfwRyMc3q8A9uPAAqGVgOKguM6S0+\nn/rpD3wXpX03l57UVrRrFgaUI06m+5ywkv5I3zsZmYhW4tjESq5XGdA7eMarxPhe3Vzv7v7iFdYe\nbk3EcFQFrjbg+LA+YNGVGtmgwBJVErIfhUxWVyJLk2uQjn4wcDgyGFyNJAyPRzr0SKTDVyPG4y5k\nZj4Ti9+09Pwaw5ExS0KMuEeddiY553EaEoedilSAPR5Z8brHEK9keEg8+qPUxJT6sWjC4BMC234L\neA/TfCNi+9wH5Ueu73cSG6riwHAmDqotUFzzHEBNB24D49uIN8W2DUxTYdtDgGOQ8L/NSKhQKE/j\nr4FvMM38ENsFoWYBhwB9wYho+WclYU/bgTYGTfhN1CTjGsAEZPI4E/Fq3YEk3ivk99sRievdjoTq\nFCC/397ISsEDwK1IX+6AJB0Ox4re8rNjfPdAJkmDkUnS80j/2omEVeawb+Lle8C1RlMMEotzkQnY\nQmCyIx8YGWz7KmA4pnlZyG3DRvmdsKa6ryeDUeL0xS5gbHVeN4B4qdiokpAJystgRF6pRRKZOyAT\noEOQAn7XOe/uNeAa4DVM87zwD6Z8iOTkMGAKGBua0eLGjyDG9r8MaJpBX9P/OiJ9uAPiqMlEirGN\nQb4LHzJ5zEDuT32QVbpM4HPk/rUd6aeBMfAuIj1G1EGBz/Fqd0dW8dKdv2Ocx3qgrXNegZNtss3p\ntpE6BZEu8wPPIlW66vIoMrMvRpJ26luG1ga3JmIoKfhxsFGTSyDIgJuKeC2GIbPibUhSb3OooOH4\nTRuRplqDGA8g0nr19RFXUBJXfj3yPXwLvGSEW1pXbtA3IcbQS4hH9pnmxKjvg23/AFyNac4JvbFK\nQjyYW5Eb6hRk0NxAzbLjD4hnuAPiJRmMJHifEmLnaxCDdyUyieoPRuy8f7btQ7LrdyKTwU5Itv8x\nzv8DqFkeH+fIoYWB6o4kvXdDQlsebLBkdhNRMsF72RB1iBqk0qMfGcyPRWJqP0a8U3lIyGNL+AXx\nzpYjRnoZdTW0Y6GwEwJnmbsH0s4TkLyIdsjY+YBBw1Voa2ExBTFw1iNJedc5Osctw7b/D1iJaT4a\n3gdUIrK69h6iJNEZub6TnPYZyLj/PpJw/SRiyF7l7KAUcQjg7KMXUoOg7ntHgRF7z61tB4zP7siE\n6UTEkPsOCa0DOAbTDHMirpKA/wLnIfLMdzdJ+SjU3mU8G2PUpxhn0Q3J/0lG1FPKCFqNiSoe6HsA\nCjIMcYA1y+Z08yT8yEA0GckinYcMdMFLiSciXrATgXFItnx9CVfa4NZEDAW3ARji5QKL9ohCyHuI\nF6wuvyAGyNlI7PP9wBC6/WoGSil2fLEZVZmIzPSzSeh0MUNu/ID2o0uZbuYjVdBWYVGMhX/v0rVF\nBha7nBjSSif5yXM4MeH/QGLj5gM3GfWEptSLRQdkubet88rdiBdnTaiqfg1i2xuBiZhmPR4g1R7x\nHI4HnqZ2PGYoNiDXfzMSX3weMgGbhcTjPoYYAnOc9/ulUnhKEWlPOp952imZ7h1seySyWgFSnW0k\ncIFT8bIRVBIySWmPeKEmg9HikBlndelKIxA+IQNtJuJ4qWtUBzy+84G3wTgU1FLE+PwrMlGaiEwG\nl9LukI7kLWwPbMFiMxZ+4tr2p7q0mIlfbGXhVT0o2dKNCe/9hGmWcW/vXlQWFNP99GGoyhlc/KLn\n+p+SyX+wwXWbEa6KgsgELkCcByD94UVgdrPvNbb9NvA2pvlWnZYaSBjaREQg4RPEqA5Vujwf8Ta+\niYRtPIhMeI9AnG+5iMf0MESdqBhZQbwWODmdvDX5tJsNvCTyzB7CtkdTUw33KsSOeSX0apMykLEm\nsII3DowWC0oouZdlA6l7V5dk7LkHuVYNyUG/htwLspFVpT8CD5DYuQ1VxZuoLOyAOC9s/MnV3FyS\ng0UKbTIPJqXndnZ8UQ1sxfAfTKeJi8m8NQ3IwzQrHacBTQu/iQmtzuAejxg0U5zngYpqwUV1/ot4\n+t50nq9ALnzdZUxtcGsihpLf3WLDYh7iwT3Leesn0kd8R7eTs2gz9FFSeqxCbu4XAcWo6nhgG4Zv\nLTIxDCckZTUSNvIJ4j34GVlu6xr0fltkMBkR3aS4lqFktSpQpKUAGRBmA2uMUHGB1l4vczA3I/rd\nZcA2rDBi6mWJtxRID8QnK/D1YNM72+l62sH8ZCxiFD3YzFHMQGGo2Yy3HubaDfFUZI9mwYQlDO89\njKX+TuQsQb73OGRgH69kIEo25B6UgqzM/VZBpiGe1k2IMVCOnPP5iBPBAt72mNyWIEb3BMR7CBKb\n/xhi0M5Hwi6mYZr1SHOpYH3qp5DBd1b9IQChUTJYjzUsLnPacyn+FDD8UFkA7Q55gcE3jsaffCHx\nbU5FvtsrEK/n0cj3fTTivQ/W3t2NeBo/QfpsGqHDbUCM2VRgK6bZWCVk13DCUcYj/a07cq7tkUnv\njYazKqwg0aivcq6s3O1yPgMyAe6L5HbMBQqxwvSc2/Z04FZHxxrHg309cHs3tuKjmmP4hjUM4B/c\n9XMC5Y9WEN9pFkcMn8zX2zNZVtSZnZevZuB7ndm5oz152cCFiLf7ZJy4WmoS27sCjyMrHl8jk+Jl\nyO85G1ml/Bo4y+mf3sO2f4NUJl2PhFh8gFyz/wK7Mc0GclxUH+SajwNmALeD8XVzm6FkVelNAzKx\n6Ag8DMb5+BIgtc9UijftYfDf4+gw7i18iVMwjF3Iatki5zECcR4NQcIoxwXtPpea31dd9iD30rrh\nN98j1xFM02v2XaszuM9EDJJLnefnIxfoqqBtPkbidwIZv18j1SbrlkdW2HZ7ppupwDYGXBXHQWdU\nYJrK8ZB0OWUF8R+9wSYs4t56i+pnDyXpywGUMP69nqx7xqDfn3Z3tS+p2HbXLu6dlO6/wcwvx6KC\n/5vkY9f0scACLCoUpE4dwJATrjpsJ7nzNyPGkHi9LHbz+euJicsezCgrmLcl/05G/tiNJebFtB+Q\nQ1nC4FuLlmXf5gN8TLIrAB/TzeRDtpG08CmysfAxyVZAF2Wau66Zgv/RqZQrUcuQ5T6LdKDrrTar\nbjPxccQnPeKqDV/lD2flUVWcCiRhscY5b4OMrLSRVZP+2HfWbV99OCxxh7qlbMeYJ5/stn3Vn3b/\ncQHpN89gp2ERP+1/GFkXw+Gb6Orve0XCp3e+kJ1eXBxnwC4sDFJ6x6m/b6hEfjPqsXH+0Vff/fUC\n1r9Aj8WvTHhw7sE/X3j0kpSS9INKlz9aUXnPoZsqXjiEAcjNuxQpz9oVqEg57IO2xanpG5l5Yv9b\nvy5ZfduxaT1/v+GIwss//aJizFbKDShT4B9/CX3zj/zfhg7fXXLSrdOql08dgP/BCexAOqiUerXt\nNNa/UNR25Yvttt9PckolW7Htg4HNyjRzDPkeBimLVW+M7hJ/+R9OSMhPUoWXffx5n3nH377px22X\nVQFpyoI13bv3uuuwrYX3fcnzdx9pFN13/u8nUrI5m+6nVZHcPQF/ciq++E6EltOch3gDGor5fMY5\nh7bIzD8Z8Yp2c37bgUnnE8iAMQe5ia3BNKMtqdcilJxLD+T8/4WoMgRTgfTjN5AB/RVkGXPNhadx\n6rjN/HTDsRQaihWDcmBBd+hcBGVx7OpQTEZpPEu2pdFr+C7/a0s6V92DGBpbblzQu3Ra5w3r5xzz\nt2S6nrhQHX10vwUc+qs82lnH8G3/ZpzKYsTgNOu8XoKEHBQh1y942XonYgBOQjzaPep89vgmxbrH\nGtsei6zQnIesAtRlPrLKswAZYG8AnmVe+zH8c8RYKnxXEFcNPgXxahm9ix5kWRe54+UAACAASURB\nVHoBx25fxk0r4jGzVmMZxWRMYhxnJ8z585/7GbBsWm8SszZQhkXq8sd44H+TMqfce+LwbRj+w+n3\nx1Ct3oqE7hyFGI1LkGsWSJpti/SfscjE4AfEMChHfn8ByhEDbhbym+2HjDtFyArWZUB/TNNrCYG1\nUKKAciwyboJMRAIlsQ9Czu8ZxFBdjEyulizsSmq3Ao6+1aR0S1su29SWoxc7mSnpZawuSGDV8Wto\n91V/kir9lBPX9iUq9xwDzDp7Ca+/NZzunYooyJny9ef3P/nf2857Z0aHnXS+fRmZbTuRTSlJnBSe\n6EzAMKsb//wOYjM0xgqgipo44WAVmKsNmUR6E9tOwzQLse0sxMlYzb7jzAfUjA0lyHXtwW2ZvZie\ncR8+1Yn0iiJ2J2YA95Jc+Snjc9pw7eoiTjly/XB+Lj/p4rtLr3/jteqOJXQD1nf7K4dvb8NMJtmd\nV/3mjKN3JZbcfsQ1fXLodspY2o8uJqlzSoiWb6fGOfQoko8EsloS6LzTqal0HMgtKXDOZyJyPy1E\nxr9uSFJpNdInFXIdJ2KaM8P5KmNEqzO4f414t0MZ3HcjNwmQgfrvyAUJRmHbGNVVJJaXUpqUStvC\nQsYsW8Q3Y4+gx47tbO4iv4kRa9dSFh9PTno6Gbm7WdGnb60djVqzhkUDBtBjx1biKkpZ36NG575r\nTg7bO3Zk2Lp1LO3bF19VFf7qairi9w3DPWrRImaMGsX5Uz/nlSkn4K+qosrvZ/TKlfzSNYPcdKld\ncdD2jXTNL6Hnto302F3A68ccQ056Oh3y89mdng7AsHXryE9NJa6imLTSctoVlTJi3Tqmjh7Juh61\n29+mqJD2BYUM2bSJL8eMYejqRSwfOIo+27axvlu3Wtt2yssjrqqK1NJS1h50ED127mRz5857308v\nLKT/1q1kt0lhY7ceJJeWklpaSna78GSbz/n2WzLy8tjRoQMLBg6k/9atbEmPZ9kgqSXgr6ri1Fmz\neO8ocUSd9t13dMnNpcQoYXXfYSjDYM6wYSSVlZG5YQMj164lOz2dTybIpPfm55/i9t/XhFn327KF\nvJR4drfvXOtaHrZ8OfOHDq23jV1272ZHh5o6Iu0KCrj2nXewLr44eLNliLdoD+KBuA65MQwDPsA0\ny7DtxH2W4W27E5DjJLTFY5oVYX1xtp2MaZbUec2PGBeHY5otz7iPEU7M6SikcEMPauLRa1Hl89H/\n1Ve596mn6LN9O11yc/nwiCPY3KlT0a527VIzN2zgtWOOYfyyZbx/5JF7++HGzp0ZtHEdC4YO55JP\nP+W5k06iW3Y2W886q77DbEZijqsRz3svxHBsiwwcWcAXwV5oJYmi04F0oxFtYQV+g9oqFgpSDFni\nRomX7iJjXwPeu9j2cMQLPAkJ7WtUvzmtIDe/sE379Fqv5RdQmN541MBf33yT9V27MndQXyYuWcHr\nxx5Xd5MXkb53LhI7ugQJfVm01/tn23FB/0sSabjYtm+fJeu6+7DtN4FVmGZTQpBcIyBhpqTvHY4Y\nr/ciE4iMutsXJyZS5fNhH3IIXXJzmTl8ONMOPpjMDRvotXMn340YQXlcHD2ys5k2ahQ/9+/PxB9/\nYFn/weSkp3Pt22+zs317Xps8mexTT6XjHknFWMKwuf34hWJSViVT8n4qxasQg3qbIdU3kxHD8nBg\nhiET8sA5JAHlwcmzzv2kC7Aj0E+VSLc1mEOgZMXjTWCsEQm1kmhj26nIuY1D+tzt1BTgCcmo2XNK\nt/QclJTdo2ZcG7F8Hj8PHbP3+ZnTpvHRhAmUJzRStV6pZRjG+8hqUA9q5PPSkWv4njO2pQGdMM31\neycOkcC245Hf7XVAH0wz5Ow7hrQ6g/twZJk1EFLyD6RjBSeF/ReR1gnEKjUcUnLhhQC0KSigYOJE\nenTtwkE7tpKTnkKckUJRUhKnz5zJE6ecQredO9h0UI3zacgv64ivLKbLuhy2dy5lySHHMWjpyopV\nwwbHA4xbsoS5w2scde1ycsnr2J6EslLKE5M46u2PmH3y8VQkSY7NkKVLq/rtyvHtyTeMLYf0YFe7\nDhS2TafP+nV0KSpi7rCaffVesYpHX36R7zMzeX/iUWxr2552RdlsOqjPXsM+uTAPfHGk5RcwduUq\n5gzPpM2u3awfOhiAybOmqa+PyDIAjpr5NQVJGShl8NMYkWw+/Mcf2ZKShs8XrzYM6mv4Kytpv30P\ngzasrSpJN/wLhx+2tz29tq5n5M9z+eR4UTk6av4CKnILyzcPzEjY1Cez3gs5Yv4yxm5axnOnn0nm\nvMVsHNhTdS0sMvpt20a73Fx2J8HwVb+oxy6+1Kjy+0kpKuDX383k5SknYM77HnvMBFJKShj944qy\ngtQ4f6eS/Lg2Gxfy/jlXMWDzZsYvXMwH5kR6ZOdw0MZN+PwlbGyfyIpMmTQPXLmE8rjU6viCJb41\nB59Ml5072eFMHMYsW86SPn0oTUokIy+PnUHGdeedO8ht34GK+HiGbNhAAWWkl8KywYPwl1Y+V5UU\n9y2wBNNcXO+Jxxrbfg2Z6XtI47ZpKEhc2rt39dyhQ4/701/+cnqFP+6UDgV7fs5JT29y0lvKnlJ6\nb96lEnZXGrndEtnYXwr4dd8wn1E3ljFr+5Vn7yH9E1EycBclybHliMZrlsvNaTIKMpZ3ImNTetwl\nR6+r/su8Qf2rctp19KeWljJx8WJenDKF7amwpvcQhm7eyrKe3djRsQvddu9mR0Jh9bbO3X0Vqd0o\nSPQxeu1GfNXVvJOVRcb2LQzankPvraspKU3h/TNOIGV15ezigXF/JCBF2xTjOVrY9u+Q5F6fJ9rT\nDBTEvzVpUlWbkpKzL7zxxtS8tDanV1bHxxlxVWa139eI5bUvmd8uLVt29LDE5NJSSpKSiKuoZMys\neQWzs8a3aXfHa+R98/RjwJ1gbI/S6TQJJcozfwHaGRIX3mpQ0HFjenzaio4VQwcXdPhoVr928aT1\n5dj58ylKSuLVyZMZ+csvLOrfn48mTCDzl1WM+3keuZ368H1mJp9OmMCYxXNY1Wcwh6xdT+aGDRQl\nJbEtqZjCToMoTkxk9KpVPHfSSfg3lu2s6pV4TXQVnpqIbQeM/aMwzahpkIcgi9r37VtpZQZ3HLLE\ndwziufuBxpMmD0diROtPmnxqXjwdy+M5c8LI1AuW5Ba9NDwBmYUkpFI4uAr/oFKSC5EkzS+HjZt2\n2NL5R+ai/OVUGzMRZYpjkZhMKV9vqKkobnWSLW4D/ou/+iOqfL1IrEqm0qikf5HBqjbfYqghKHrQ\ntqIHexLuc9pZicSUKfzV/anyzQH6k1jVlTJ/d+f8k5AZ+BxkOU2RXr6eTuXHsTZtIW0qfqEwLgtl\n9HPaNRXx8BwC6lcYvIsy1nFQ8QhyEk+i1P8NkojyFlKh6lDEgzAbGG9QvU7he9Z5fSWQ5aMqoxr/\nOuKrx+Pbk0dZu7uc9q9GPCTxwCpQx4LRBtiKT/UirnoB5X6FrFasQsIjPgUS2pLfuYA2cxS+y5AE\nFoALEI/iYCCH+OqtVNA5KT6veEjF2oKfGJPqnOMPQE+SqrLjS6uOriSut8L3MqKZ2ZGMpa+TM8jA\n8JdjMIhK3wZEdSERmaCdRHzVIir8O5AlrmRk+XQH/uqlVBlXYfA4yjjWuT7xznc2Dkl4/Jt8zvDW\nEqR4zHcBXTHNiMqxRR3bHsmuBIuM8tql3XPj4fVeMKAQ+hTB9x3hjV6FKBLoUbKNXYm9KYqDaqME\n+AVf9TCqjZ1gbEFkuL5FvC/9SKn8ns5lR3H49fMZ8Nwu4sp/FU0pqaaiJB5xFvA3QwwAT6Mg5cPB\n9G1TxhVHr+eKuu9vTWPPN/1Y5K9m1ceDeeGzgZTsSSIho4jFu1IpxkIp6GRNIuc2k2SghC/vSVbf\n31BuiHMyAQm7ugTpt92R0I3hwGFNLsUdTSQ3YCdwHaYZeXm5aPPWrJfIKP9drde2J8KsTtCtFKZn\nQNuKCj7sHk/vYhhYCDM7QYVvAaX+0Rjqa5QxmYQqqDZWUOnbRLvyY+PyjF2JvpKMovjUWyjzp9Dv\nSzj/hCvxVXfBktUdL+DoLm8EntubDO9xPhxMxsjt/NAnnz71vf/KCGZ0LCHlq348eOpKirIuZtP1\nM9l139eiMOJIbGJ0XuRn58hACGPBa5ybAkz5La/PBI4ENvup7FiVXn0c+QmXAxPBmBuTkwwX234Y\nKMc0/+52UxxanYcbJBM9IAv4HBJ3FogReMr5+zjiBS9CDK664SQQsaRJZTSeydyQHmhrQSWGLlYR\n6jto9LNJkZIHa2D/3YEEMNZHaf/OuauRSPjSf8B4JDrHaia2/SwykcrYJ+zEa3w2fSB+NYRtyf+h\nd3GNzNvaVLhr6Criqreysu0tyMBQhYR1OHrChqMNrHqAsa9EVYMoHzcn9MNfsRqYhcWRkTmZyKAk\nNvpi4EijJlTOc7w7hNFnrBAFhW1p0K0QrCzWpZfyxK9W8fbA3WyMTgKoMpDchdMRGcWISZ61GNF3\nPwtJyG25fGU0kQlCJ0p9z7I7YTLdSyUWd357eL1nPruSfmZz8hsoowQJmduGGKTxiGGWDvzi3A9T\na66DGg6sBKOiwbHC4ivEeXSOl5SVlMTxvw3cErZ8qQucdC7jn/6YB7sXinPx7UzKi+P5pk0ZFUds\n4twuRZREL/la/RaJdR/VtPtulLHtTOR+OdEjwgEHtFCHZzq1Zn9BDZUQSPUrt1tSC9s2sO1p2PY+\nHkdvoUbx33ml2LbildmKv65QTNl6DqieoAY6hlX0sJiChcLaJ2nTVZRYKLOU/LgaktlyDQVx21N5\nQYFa2w710DjmnHkWZ2HRJcYt+R5UOai02B43BLb9Bra9JqiipwdRPl6dPRfbVti24v6FZXw2vRhU\nJ0c1JLpYHOL0vUtDbxxbFIxWsEV50Mut4KCtaXzk3BvURaeiTj6XC7HoFOOW3AhqCahQCZOxxbYv\nx7aXYtvJbjeFA9zmPKBPXhMt1HnOve8fbrekFrZ9uDOYhiq84gIqgyH5M3livuLdmdU8vmA9w/I+\nRaqkxRYL2xn4G6v2FnMUxCt4SEGuqtFAdh0Fl5f7KFjQFfXxQBaOuJwLXGxNX1CFoCpBTY76BC1c\nbLs3tr0c274y9MYukFJxFcdtE0P7tp9XcOuS50B1dJKuY4fF+U7faxt649ii4ELHqJ0SeuvYoOCw\ngKF91RQUVr31HmLZohdBFYEa7W47ghBn02fYthfC8Q5om/OAPnlNtFAJoNY598HnQfUL/ZkYYduf\nOEb3mNAbxwo1jOQKxRfTZMD/Ylo4OuTRxWI+Fhd4pVJZMAqucgbZt1TjJaCjzqoOnK5APXUoqv0N\nXO5mW2pQaaC2Of3vqNDbx4iaCe8DTuiGB1DJGNWv8vCP0vc+m+5+RVqLT7B42e1m1IeCY5y+N01J\nHQQ32zJ+Q1u25iegel/LXCw84MFVBqhbnL6X5XZr9mLbnZy+96HLfe+AtjkP6JPXRBs1CFSpc/O5\nxRVvbV1sOx3bXolt/+RkcbuMGke/Ahnsv7bXS76DB7C42fG0tbxsdRRQ0M8Z+FcpuFJJImGs2zCk\n0qD6zyegsLjae5MTdafT95ZLqJcHsO0/OwP/CY58mYuoISRXvMsLcxWfzijEtveR/XMFi25O33vZ\nKa7jKZQUslrs9L+PlcStx7oN3RWorWlUJ/+Tb7HoFes2NIwyQP3N6Xu5oE5yu0VAYJVph1MwyC1a\nnc3ZAdE2XoUUg6hP3LknIgK/FFHmuLqebaAVnrymtaEcWVulHK/39a4vc9u2D9v+J7a9BdseEfoD\n0UKlkVCpuO8nxQffzce2O4T+TIywMLA40hn478VikNtNqouCFAXPKNgR5PFeomCQinJijoJzFaib\njkZhhSyz7RIqHtRYULbT/z4DNcID/e96x+h215ucVr6N+35SfDpjecxDR0JhMcbpe9Ox6O52c+qi\nIE7Bv1XQzV3BpUoKjUX72GnlPrJn9KIi/mZv5ZrUoAxQx4N6EFSe8xU9FpNcgMaw7UudvrfYJU93\nq7M570WK2IBULLu7nm26UiP4nobI2NXn4Wh1J69pjahEUO1Bbap9f1bvgOoOyh2Dxbbfcm4+t7jj\nbasu5O1Z1U4bvGm0WVzmDPwKa2/VM8+hpEx8YdCPq0LBQgV3KxioIElBhoIE1XCp5HCPlVRpkPfS\nSBQWt0XqHKKLOhvUiqC+NxPUh6CuATUF1GGgkkLvJwLI8nap87u/F9s+LPSHIo26hj+truRbuxLb\n9pB3NAiLrlisxWIdFp6sIaAgUUFm0KRXKdikYLmCTxRco6CzglRn2xaFfSgwSvw89/5gyrH2yuZ6\nHPXXOuNePqi/gzrXCf9KAhW70DhJolTY9hJsO+oTpDo0y+Z000MQXMSmK6LDGupL+wCRrPmmzuuK\nA1iiReMGKgEpOXscojMePMgvRlZl3kR+2z0Rve9RyGrOAkSLXAEzgRRqKqWVO1JcnZDJ5c9g5DnH\n7I7ocLcFCsAoB8C2OyJ61COdfZyIaX4egXMMQyJS3cR5G27g9+vK8XEIpukdKam6WHRGqkUmI6Wf\nj8WqV2bUVZRoBgekUn9X5+0yRG8+mG8RLf8VwOtI9d7PEB39WUiVy0WIw+Ic5F6bATxS7oMON3JH\n0Z20igqKgjIQhZeHkNLQdclG5GOHAX9Gykh3QsrW90KqDZ4KfIiUbe+BVPTLBZLA2OAcxy/HMeaB\nagsYYOTX7hfK4Pl5x9G3aKpz7H8CL2CaW8M8l4S9/bjJqFPoWPY0b8/OwGCYi0VBQiMhJWtgr6Z0\ndyy2udeg+lGy8t4T+b3cCIyABhV6Atfte+Q3VkDN7+gLpMBOf8Rp+CVwMlK1NhsYnZfIX8b/gTkr\n/o9jonM2kUYZyH0pEXGQNpQ4PBOJSOiEyD4PR6IZ2iBa9j2R8J0tyD3Jj8jCXgC8DEal9DdjT9Bx\nDTD2ralg20cC3znPnkOqcmZjmtGWE22WzemmkZpLjYfGQAbAxjw2fZAf6zCgbulQbXBrXEZ1Q3Re\nhwPHI4P7VRHa+WygL2K8B6iCWnGRGzh6x5fcvFykuPLjKimOq+Sag6eRnbgaZVyF9Jt3gY7OZ/6D\n6N4fi1RzLUBWkrojVbW6I5PbF4DRSNnlNCS7/1GgL+nlF/DB9+2AyzHNgHa+d7E4CDnngIF5G3Af\nFt7Rew5i9B9Jnv80lUgRrVErO5I+OIdhO1IZtyOV3SN2co0RdO9TkGvU3EerEeO9QY65AL7tR5pX\nzz88lB/ojRjfA5Cx4vwW7PBnxNAKUN8kZz5iTMl3PajgO87fMJKJ2RIHPKvjch4Z+C3pFd1Z06Y3\nMkGYA7wH3ARcgyTr/Qu4E7lOqcgE4FfI/aMI6fODkNL25cjEfDUQj0/9i6kziohXqZhm6xj/LP6F\nGEUg53oScj7VXtLsBnDUjaqUeOXzgQ5lfkYlVnEa8DFyXe4EqDTYEKdqlEUU5AAFRs0EYx+ePZSy\nS09hGBZro3ka0UX9ATGuyxCHz1+cv3Oov0hhQ+RRf1jxSsQ5BeKcLUbGQimOB4X0LF5Ju/JdPLDo\nN8SrZLYlFfLvzBxWtrkdZdyCXIutzqMMcYbtQCIsNiHOiDxkInQKMj6WO393IffepUiRxjeAXmBc\njwcN7q+obSQE+CciPB9sYO9GZpf1kYZ4Ze5AvNx1UVBrSXSa89BoXEQlIwNkG2RA9SGDdAoycVwH\nZCKGcC5yI9mM9JnRSNjVEUBn4CCkQMVkYC5SgGOYs/1uZECWIi9D8+GIHDhvY01THhkAW5KhMB52\nJ0CZD/LiIU4todLXBfF4NkQRMsBUIEZNn73v/N+C1WQWfIppXtesr8gtLDoA9wB/CHr1G6R891fA\nTiyiW+TKoh1i/H+DXLtTkcGhIzLh+QQxvgIEBp9gB0NlYgVxZXUDiZwtEiqhPA7alkJBAvgVdCmE\nXalQ7gcMxmIxL1qn6C5Kir/AHxFnTQHSBzshK1I+xNgbiPS/MxDP5LmIB24JYhT2RQbeNKRvbkfG\nor8iFYrnOZ+B7iVw7Ha4aENNM77ospXF6d0p8cOcjpBStYycxMx6GrwT6esBliOrXLuor3+aO+GW\nZUuA0ZhmM73kLmExClmFCJ4QXox4R9djURnl43dA+ly88zgZuZf+HZjhbBVQxtmITLjiqbFRKoAt\npy2n++yebNuRRu/ueyAviYriBKoJTNAUpJdKXysJ6qO98iE/ib/k381D0TzN2BPIq9i7AjQBGeeq\nkHuXHxkD1yH9aimSKD4U+c7aIsaus6rLYcjqQIbzuXzg38gKXjniVNgBdKNj2UkckR3Pdavlk1VA\nbgKsbLOLhOoq7hraldyEYuQ6D3L2FZwom4f0tbqqNdPBngjTfVCUDamdHHPTcwZ3Y6xABpXtQDck\nObK+kJJ4ZOD5HFmeqA/t4dZoUPFApROSYmBPi0cMgeepz9NZzR58jk5uufE8RXEzKfeV0qlsFbkJ\nQ+hQPpzXe33DeRsTEY/e0cAvfJtRxOG7t5BSdSMyQHXGNHfF6CQjh6hxtEW8j9cgBm8w6xCj6zjk\nBv8MMuH4HjGyOiATIxup1JeOTK6capkkIE4FH+IlyQBMZIm5G3BiPa1agdwHdyIGVxwysMxDDMXn\ngHHI6kMKcD/ieSlDvD8HIwZCd+czCjHif4v8DrogpdRfB6ZhkR/+F6apoaFwKxWPDPUG9rSJyG+j\nfor8s0ip+olKw0+8SkMmgMMoN+YSry7E4H/I5Gs1plmIbR+L/BZmsLBdV0blLcfH6Zhmw8fwOpJP\n8RTSH+pqdk9FvKXPOf//HumTjyKOiOHAq4hnshrpe0VYVGDRHujgxI4nISsfhwHnIash/WAf5ZQF\nSH9+GbFLzkP60PtIv3oDcXKMRibJBUiYTD/n2OOQPluGXKcqJFzpDMQTno84SmzgS8959PcXpk7v\nSpE/nq3JtzF8z8X1blNNNj4+Q7GcEr+PlKrPqWYkPmZRxVgqfAtJqq5EnEvZVNMTqMDHbKZl7OG2\n4dLHm4ibRuq9yEBwDxIr1c75G4yBeMJzgMY8aNrg1mgaoyaT+y7EU/cdMoAEE/D2NY3WspwdCouu\niKezF2IYD0BWH6KRUPgEcm+bhxjYu4ByLPY4yip6MN5fsO1uiOFcjYRnLULGtD9QE94FYYQAUTuU\nbDem2bGxjVsVFqnIhLYfYny3A06jJqSgpaxB+vSbwENYzMViGJCLRZgx95pWhVSlHIasAB+NTITO\nBMYjuTyJiIOkacm8pgmtzODuALyFDG7rgbMRl353xJN0ErLkMwNJQgsMQP9AZrvBaINbo2kqtt0J\nuQEVAEmYZonzemBZOwNZVv0eifPOR4yFrkhfNYHPMM39NCQhCIvOe7W8LYYik5ZU5P50NiJZ+gCy\nEnAR4onbini35iCekveQsBCwqIhh6zVexbYNTFNh2z5kHItHflslSILrWmTV4wjECJ2OTND8QB6m\n+b0r7Y4lFj0Qg6kAWUUajHirv0U84+8gK3mbEY/4r5EJzUbELrgdWU2aqieymr2IE6ozprkD2z4U\nca7kIffqJciKZ2fkd7QNGf8SgTGY5tMcwDan7kQajUaj0Wg0mmjTLJvT/Yp5Go1Go9FoNBrNfow2\nuDUajUaj0Wg0mijilsEdTln3AH5gISJLptFoNBqNRqPRtCrcMrhvRAzuQYi8Tl11kmCuQfSHdZz2\n/kmW2w3QtIgstxugaRFZbjdA02yy3G6ApkVkud0ATWxxy+A+BZHEwvl7WgPb9UCyi5/lAM4I3c/J\ncrsBmhaR5XYDNC0iy+0GaJpNltsN0LSILLcboIktbhncXZDqQDh/uzSw3UPA9Yg+qUaj0Wg0Go1G\n0+qIi+K+GyvrHoyi/nCRXyHV1haiZ4IajUaj0Wg0mlaKW2Ea4ZR1vxP4HVISOQkR/X8XuKCe/a0B\n+keprRqNRqPRaDQaDUhBqgFuNyJc7gVucP6/Ebg7xPaT0ColGo1Go9FoNBpN2HQAvmZfWcDuwKf1\nbD8J+Cg2TdNoNBqNRqPRaDQajUaj0Wg0Go0mCkxB4r9XUxOSUpdHnfcXAYfEqF2a0IS6dllAPpIk\nuxD4V8xapgnF84ia0M+NbKP7nXcJdf2y0H3Pq/REcpyWAkuAqxvYTvc/bxLO9ctC9z8vkgTMBX5C\nasHc1cB2+2Xf8yPJkX2AeORLGFpnmxOBz5z/xwFzYtU4TaOEc+2y0GFDXmUiciNpyGDT/c7bhLp+\nWei+51W6Agc7/6cBK9HjXmsinOuXhe5/XiXF+RuH9Ksj67zfpL7nlg53cxiLGG3rgQrgDeDUOtsE\nF9SZi8SGN6TxrYkd4Vw70MWNvMp3QG4j7+t+521CXT/Qfc+rbEccFACFwHIk1ykY3f+8SzjXD3T/\n8yrFzt8ExHG4u877Tep7rcngPgjYFPR8s/NaqG16RLldmtCEc+0UMAFZlvkMyIxN0zQRQPe71o3u\ne62DPshKxdw6r+v+1zroQ/3XT/c/7+JDJkw7kNCgZXXeb1Lfi2bhm0hTX3Gc+qg7Uwz3c5roEc41\n+BGJdysGTgA+AAZFs1GaiKL7XetF9z3vkwa8A1yDeErrovuft2ns+un+512qkZCgdOALJPxnWp1t\nwu57rcnDvQX5UQboicwmGtumh/Oaxl3CuXYF1CzffI7EeneIftM0EUD3u9aN7nveJh4p+vYKYozV\nRfc/bxPq+un+533yEcnqw+q8vt/2vTikuk8fJJ4mVNLk4ejkEa8QzrXrQs1McSwS763xDn0IL2lS\n9ztv0oeGr5/ue97FAF4CHmpkG93/vEs410/3P2/SiZoaMcnADOCYOtu0qr7XVLmxK5Es3zXAP5z3\nL3MeAR533l8EHBrh9mqazwk0fu3+jMgm/QR8j/x4Nd7gdWArUI7Eq/0e3e9aE6Gun+573uVIZFn7\nJ2pk405A97/WQjjXT/c/bzICCff5CVgMXO+83mr7npYb02g0Go1Go9Fo8A3hiwAAIABJREFUokwf\nGja4/wucE/R8BVruSKPRaDQajUbTivB60qSWO9JoNBqNRqPRtGq8bnCDljvSaDQajUaj0bRivK7D\nHa7kyhqgf0xapNFoNBqNRqM5UFkLDHC7Ec2hDy2XG9Ne79aL5XYDNC3CcrsBmhZhud0ATbOx3G6A\npkVYbjdA02yaZXO67eF+HZiE6B1uAm5FRN8BnkKM7RMRD3YRcLELbdRoNBqNRqPRaJqN2wb3uWFs\nc2XUW6HRaDQajUazf7MbaO92I1opuegKoIAOKWnNZLndAE2LyHK7AZoWkeV2AzTNJsvtBmhaRJYL\nx9S2UvNRDfx/wHFAn7xGo9FoNBpNCLSt1HxabHC7HVKiCQuVDJSCoTuLRqPRaDQaL3ER8GsgB1gF\n3Am8AFQAlYAN7ABuQ0qlpwOPIqXuAVKRcI1jgLnA8qB9Xw0MA6qcz5YB2UBGPcc8BzgOKEUU7WYB\nw4H/C2rnWcBG5/07InL2YaINbs+hArrjA4AbgEOAQ533jge+2m8MbwsDGAK0A0qw+MnlFmk0Go1G\no2kaCqkM/inwWtBr1yKCFyACGW8jxm8CIprxa+e9IuAPwO+AH4P2OxzoBlzmPI8HftvAMTsAJyBG\ndWDbCfW08wnnMzHH7cI3U5By7asR47IunYCpyKxmCTVf5H6IMkCdD2wAqpEZ28HA+4gW+V3AF8Bp\nrjWxpVj0xuI4LB7EYi1ynsuAL4GFWGx3t4EajUaj0WiawaWIR/nroNceBp5EvM7BlCOe6gBpiB34\nP2rXXhkKzA96XtHAMb9BarH83Mi2Aa5w2hRzQQ43Pdx+4HFgMuLanwd8RO2lhCuRJYd/IMb3SuAV\nZImilaMMYDAihWggSyEFyEzvaGA5GMEG6E2gFgH/AfURGFWxbnGTsYgH/gIcAaQgy0UB3gTuAaZi\nsRGLUcBPWPwZa+/yj0aj0Wg0mqihmrBibtSt/B3M00joyFvA885rdT3cARKdR4BCRAa6CxJ6EmAZ\n4tF+13me0Mgx30eMaRrYNoBrHm43De6xiL72euf5G8Cp1Da4twEjnf/bIrE6rdzYVoOAC5CllCHO\ni7cjYSOLwKhu5MNvAdcBZyIGq/ew6IPEUrUBfgXsAbY7r10FrMWivJ7PLcJiLPARFs9i1Zr9ajQa\njUajiTiNGtFNpQRZiT/def4wYrPNBdYh8dMDkRju2+v5/I46z5cCO5HQkUpgEdSyH4ygY05CVsuf\nRWK4NwOzEWfmcCAfsS+vQGyTXOCm5p5oc4jkF91UzgSOR5YEAM4HxiFGWQAf8C0wCDHgzgY+r2df\nCnfPpRFUHGJMT0S8vd2RsqD/AuaBsbaJ+zsR8QyPCmGcxwZrb8dpi6xWHOS88zfgTSw2N3F/84BV\nWJwXyWZqNBqNRnOA42FbyfMEf3fN+h7d9HCHs4xxExK/nYXE53wFjEJCLzyO6o1MJv6JePFnIcsi\nb4DxfQt2/DlwN2LcftnCRjYPi2SkaNFzQa8+hMwc5wE7sWhuyMvZwGIsOmCxu2UN1Wg0Go1Go3Ef\nNw3uLdQOju8J+3hDJwD/cf5fiyxJDKZ2EH0AK+j/ac4jxqhEwAReRmLO8xBD9AYwGgrgbyKGAvU/\n4EJibXBbHIl46U9Cln52IkkNeVhExttusQ6LT4FLgPsisk+NRqPRaDSa5pHl/LVcbEOLiEOM6D5I\ncPtPiPEWzINIUiFIMP1m6i+t6bJMnjJAnSrJB0qBmgtqYhSP1wVUAaiO0TuGg4WBxUQslPN4BGtv\nXH20jjkGi/VYrqvoaDQajUazv7B/SAq7Q6sufFOJqJB8gSiWPIcEtAf0Fp9CEu3+hwTK+4C/g5fC\nDJQPuBGRK6xAJgd3ghHlxE5jB6ivgPMQ8fjIY9EGOBl4FQnhmQtcHiOt7PlIsuVRuLJSodFoNBqN\nRhM7OoR4eIUYz9qUAerfjjd7CahzggrWxKoNk0AtDL1dExGP9nlY7MQiD4srsUiK+HFCt+PPWLwd\n8+NqNBqNRrN/Ei1b6SLgY6SozcPOa/9DKkiCRDIEh4i+7vxdi2hiP8m++tsXAT0Qje4A1zrbPo7I\nDScAjyGhu/8DxjjbBdsOr9f5/8agNi1EnLuvIEmQFyGSgU8iwhbBRN3D/SM12Zi9EBkVgPZIgZa+\nzTlo60adS00lpauBZ8AodaEhs4CekpxpbIjIHi36Ij+8bog3+72I7Ld5vAnciUU6FvkutkOj0Wg0\nGk3DBFd9fLmB9+vjR+BP9byegERAtEEM6gCTkeJ/gSiCK4BPqImUeJeGiwN2Q3LPDg567WvgekSb\nuw1RrkQZyuDu4/x9BhEV/8x5fgI1OosHCKoNcAdiZD8PXAVGsXvtMSpBvY+ohdzdol1ZewvvPIf8\n2G7FwsVzAyyysfgCCZt5wtW2aDQajUazP2I1wVtrNSqFdyli7AaH/Yba9yGINxkkZDigQNcD8Viv\nRQzhwOv/RsJok4AHgGHU1CSpQjS5/Q0c60IkRHY8Im7xi/P3baQ4zx7EuRzQ6V6KeNJjzpIwX3OL\nKIeUqLGg1oNaAapPdI/VFNQEUCtbFM5ikYDFz1hswcKMYONajsUZWNhuN0Oj0Wg0mv2AaNlKFyLq\nZSAhGyOpHVKS7jwHqTD5qvN/Y2GjXUK89zxiHAfKxschYS0gDuIA7zh/5yLG/YvAC0BvasJcXkZq\niASfR11aHFISrqH2JTCDmjiX3yIJbcc356BRIIpi7upopMLjfcD93iqprgwk0fQSMGY1+eMWHZHZ\n4RDgUCx2RrZ9LcQiEdgITMRildvN0Wg0Go2mFRMtW+lCpJjhOiS/7zLEO1zpPKYi3uyOSJG8J4E5\nSLXxr5x9PAysDHGcexDvdjvEHp2OeLqrEcloy9nvFcAIIB4xwncjMtP3OPt5BbgfWUG/3tn2EiSm\n+2zE7qhbibLFhW/CpSPixl/oPB7hgEiaVBeD2gHq/OjsPxKovzu63E3DIhmLz7D4GouUKDQsMljc\njcUDbjdDo9FoNJpWzv4sC/gEEi4SLVrs4W4qqaE3aRJTgBXAauCGBrbJQoz8JTQsEReFk1dngtoC\nanDk9x1JVGdQeaC6hf0RCx8WL2HxpVM10rtY9MNil+fbqdFoNBqNt9mfDe5oEzODewKwDNjkPB9F\nyxPZ/MhyQh/E7V9f4Zt2SOB6D+d5pwb2FeGTVweDKgLllZCZEKj3muSFt3gYi/lYtI9ioyKHxedY\n/M7tZmg0Go1G04rRBnfziZnB/QMiCxis+7y0hfscj8T1BLiRGn3EAFcgWamhiODJqx6gtoG6LPS2\nXkGdBWp+WMmTFhc7FSPbxaBhkcHiVCzmhsiQ1mg0Go1G0zC7EXtJP5r+aIr6Sr00pXT2xjrPW1pN\n8SBqPOYgZdsPqrPNQCRW3EaqD0bZy6lSEU3Hx8F4KrrHiijvIqsBYxvdyiITuBcYi0VeDNoVKT5B\ntN8nut0QjUaj0WhaKR2QZD/9aPqjxXmL4ZZ234hU9QERJL8aUcdoCeHMEOKBQ4FjgBRgNpKBurqe\nba2g/6fRvJLg1yKTgLua8VkXMapBPYlcl/Pq3US8wy8A/8ZiXuzaFgEsqrC4H8kmnuF2czQajUaj\n0RwwZDmPmJCBVFfcCexCNBQ7tnCfh1M7pOQf7Js4eQO1DelnEemZukQgpEQdBWo7qP4t35cbqLag\ndjXYfovrsViJ1aAovLexSMJiOxbD3G6KRqPRaDSaA5aoxnBnRGGfcUgVoT6I17y+pMkhSOlNP+Lh\n/hnIrGdfLTx5FQ/qR1AXtGw/bqNuA/XMPi9bdHCUPga50KjIYfFPLD7QsdwajUaj0WhcIqox3N8j\nxW8ugYgpW1QCVwJfIAoobyJhKpc5DxDJwKnAYqRK0DPOtpHmT87fxqoetQYeBX4Nqled1+8B3tkP\nisc8ggjUtxL1GI1Go9FoNJqmMQ54CKk//wlRT2BsEi3wcKu2oPJBnRG55riJug9UTVlTi95YZGPR\n1sVGRQ6Lk7EowCLJ7aZoNBqNRqM54IiZvGInpO58dawOGAYtMbgfAvVq5JriNioNVA6o3wBg8TgW\n+4aZtGZEl/tet5uh0Wg0Go3mgCOqISXpwEXA54hSyDZgTHMO6C1UJ+S8/upyQyKIUQj8Bribs8/o\nC1wKPOxumyLO74Azsfan66bRaDQajeZAZx1itI0HTyasNdPDra4F9XJkm+IV1Kuc/rv53MrTbrck\nKlgMwWInlo7n1mg0Go1GEzOiGlLiRSM7mGacvEoCtQbUsZFvjgcY+9hwbmhXRb8vbwirAmVrxCLL\nqZp5sttN0Wg0Go1Gc0AQFYP740YeH0Vg/1MQJZLV7KvBHcwYRNWkocTG5hjcF4KaC6p16lKHwuJ3\n3NDeBqVA3eF2c6KGxYOO0X2R203RaDQajUaz39MsgztUpckHIn3AIPzA48BkYAswDzHi61aw9COy\ndlOJrKf9fOCB/2fvPMPjqK4G/M6uercsF7k3jBs1BEy1JjQbCIQWWgiEhAABAiShEzIEEkICoScQ\nPjokhJ6EDsnYNFPdcMHYxr3KkizJVtfe78e5q11JK2kl7e6sxH2fZ6Xdndk7Z8qdOffcU8BqjmGb\nycRlZFbcBFwKLBJrvvWYxzLFg18CW4BHcTgOOBUncRHEBoPBYDAYDLEkHcmBPA0pud5bDqR1pclr\n9KstlwM/Ax4FTu6grW4qWGo4qHJQmd37XR/BYRwOW0NVJdXeoKpAXQmqq0FW38RhKg5l2tp9oymO\nYzAYDAaDIQ7ExcIdpAR4HFirP48CzgHm9GSjmuHA+rDPG5Bc323XOQH4DuJWEivL5RnAS2DVxqi9\nZONE4F84aOu9tQDU/sCLwA9B/RKstzyUL/Y4LMFhPOAANwEzcHCRYjl1ODR6KZ7BYDCEUBYtM7ZW\nByl2la/jZS3r+DuepVXpiCumD6zG0HatsOeoygKrRr/P0VmuiLxuy/fZut0soBIphlePGOUqgWb5\nnUpFdIwGpJq0AjKRlMJ+YCeQoZfngLVdH5egQU8BBUCjtEkAyAZrG6ghQJ6Wo1S3Uah/tw1JX+zT\n8uyFuK4O1etvAMYCNcA6vQ0L2A1YqGVuAgbq7Q7RMn2h961Jy5mm2yzVv6nTMu2QddodN59ez6e3\nWar/79THcCuSES5HH6dV+n+tPl7N+pgU6v/1er8z9fJJQAVSK6VJ71O6nF9l6WNyiMjHu0gF8Wot\n92BgAvC+lqlOH3dL73+dfj8Q2KR3yAdU6W3n6n0r1csy9DEbrZcvBYYhOuQALYuFzE436G0FgGKw\nNurjVaT3vVm3VQeMQDwiiuWztTXs+FqIbhpAzrFfy1Svr8cMLVcaUC7HMLzvqFRC11SVyGcFQOUT\nuo5rI5/b6IjWCjgPUVKX688TgWeAfXu6YcRaPRNJWwfi4nEA4gIR5DngdqTK5GOI7/gLEdpSdMvd\nRC0ELgNrdvdE7gOIZXcB8Esc3mm9UKUhA6W/AS8BV4O1ItEixh2HHOBY4F7kpgJSLfWfyA31PZyk\nyiPvDQ5pODR0stzSMwZWKzcdBx8OARzygSq9jryXm2oWsB15APuRB0MlciNMQwZACociQOFQFtZ2\nHnLTrMNRq7h4SgoFq4tIrSsHGlrkcNhbt5UHlCEPncEEfPX4AgORe8YEYDulkxcwaFk24CPg2w9f\noBwZ7MvDuDGjntS6k6kc8SmN2RXkrW8grWY89TlL2brnEEZ9uBEoYNuUvRm4opClp1jUDvCTv24R\n9XkFjJmzC2U1k1mxhqoROWSWDSS9Oo2yiYXkrx3BjrEVVA2fw+h3h1E9rIHcTSdQNnEj2/bYSvG8\nGvz1g6gbMI3m1AyUfzb5a3dn876b2LaHxbcezKVy9G7sGOOjfJzF4CWVZFZsYdO+Qxj9Xhmb9ykm\nkDqf9OoifE3lZJR/Qc7WFDZMn0r2VmjMyaMpvYkBX6eTXjWYbVOb2Vn8MQO/KmDAqhPYWdxAxdj3\nKFo+nMbMMWzc/3XyNo0kvXI72yeNI7P8MKpG1FD05Qc0ZR5ESm09tYW1pO38mJzNu1O2eyXZpTup\nGJtB3oZc1h0yCFQTeRuGk11qEfCvJ3fLILJK61gzYw5Fy32M/d8hLPrBlzTkZOKvX8+6QweTs2U0\n494ZRkNOGdmlg6kYt45dRVUM/Go3fM01bN2jjLKJUxj1vo+GvAb8DZVUDd/ArsF5jHFTWX/wcLZP\nSmPUB8MY/e520qtW8PXhm6krGEj2Vh9Vo+pRVLHXk0MpH1/GgK/3pWzSeprTUqkYW42/cQoVYz+m\naFkBWWUD2bZHBo2ZWxm0dChY+Wyd9iU7xm4kb30N9fmTqRwVYPScSjJ3TCbgryFtZxGZ5fU0ZheS\nvzaVqpEpfH04DFy+jZwt1WSVWQxYlcOiH2xHpeQy8KuRbN0TynaDtJ3QmLmEgV8NZPukdEbOXUtK\n7QBWHz6agjUKX+NKir4spWpEOo3ZU2lOz2DDdBj+UQMpDWkUrqjB37CFylHVlE1U1Azam5zNDWCl\nsWMMjHFh29QljH5vNzbtl0blyC0E0oZSuBIaM6FwlciwaxDUFSynangKjVmDGP5ZHpnlDaw9tJGi\nZRbrDoEBa7Koz4WmTEivhJS6OurzMmjIhaztor+k1kDZRAikQGZZE8rfxNRnM/jiLMgqhdqBkL0N\nUmqhthAydkD5BEitaWT0e6msKZFllgJ/PWBBQw5klkPFWPDrW5YVkFdzumxL+SCjPEBdwXawBgOy\nbmYF1BWIfM2p8r45HbK3yvvUGvnvb4SUOlmnKUM+p1XLd7uGSPvh46QgVkDr0m0IthVc5mtqQPnT\nAFC+AKG0zI2IQtlWj2nW988mOjSOKr39boei1coMvyXnLPT7KmhbJK+b6lVogNVdgvvbHbaDfo60\nFrIaGQSAKO2VyOAonODgK4/QYC2cAPJsSUUGIWFYLX+6Q7Q/WATsGcV33WE6Yo2cqT9fi+zgbWHr\nfE1IxiJkRHo+7QM2FWLVDDJbvyKg9kQqZY7p2nrQB3GYDLwFjOrYl1kdhCjePwVuQQYyn0a2ZvRx\nHNJpTr2YquHjKVhzHpauUKloIpCykUBKM77mNCrGfkh6dR7ZW0dSV7CU0qnvkb9OsX330RTPTyW7\ntIim9PE0ZayhOWUXX5xZSkbVMkbPzmfbtBE05qRSOWobOZsb8TcWMeGN7xJImUfp5ByUbxeN2esJ\n+HcxaNkx1OetI+DzkVU+jIwdWZRN2M6uIXVUjC9lj6dTqSnaixXHZJNVlkLhyhrSq+rIXzeUirG1\nZJZnUbCumKb0enaM2UbhymE0pwbYOXQz1cOaGPD1QHK35FM9ZDPlu60nq2wYg5aNYP30FewcWkP2\ntqGk1Dcy7PMRADSnNuNr8lE6ZR3++gyyS3NozGrCak4nZ1sGAX8AX7M8GKqGVVEzKMDQha1vPpUj\nGsjfkEZDdgNpu7q+0dblNZFendJyudUWQOaOGJ70DmjIlgc5FjRkN5NR1b2b+/rpcsvIqJSHdEot\nlO8GA1ZBzjYI+MGnDSa1A6A5TZGz1Yq4f9t3h51DYUwXk4TB35ZOgkFfhr6vHAmpu8DXBFUjYfCS\n0LKaQsgqh4bsAOsPqiF/XQ5Fy4PKRoDs0tbaQVM6pNTL+52DG9k1uI76/FxytjRTuMpPfS6U7VaO\n8lk0p6VQOzCblDoY/IWP3C2wY1QNaTszySrv+nmyYxQUrIP1B1Yzcm4u5eMhb0No+xVjYMCa9r/b\nNaiG7NIskTctQEqD7EN1cT25m9PZMWor/oYB5G4JXX/h56Mur5JAajpZZRls3rucnC35wC6a0yCz\nPJP0nalhv1M0pwZIrfPTmLEVS+UAFin1sv3qoTvJLs2iLr+cqpFN5GyxyNk6pMN9bk6pp2rENnK2\nDCa1Lr3D9epzq0jdlUPtwHKyS4tkv4vKSNuVhr8+C1/Ar/e/Dn9jGpbW5BozS0FlkVqX3WHbAV8j\nvkDnrqCNmeWk1orioawmrE7cDxVNWFHPkMcXZdVhqQyUtR5LjZTvqAf8PZZRWVuwVFsFTWhO2Yzy\n+fA3bEf5fPgCk2lKK8UKNAP1BPwDwLeF1NqJHbbfnPohysrA1zQCyMEK+MHaQSBlG6ixBFK+xNc0\nAl/TV2BNw1KFBPybacxqwN9QhrIC+JqG4W8apmVaDFYFTRk78ddn4m/MwFLTW7bXlL6SlPoJADRk\nPQzWAaTtmtayvD73P6TtPBTlq8BqriWQWkEgpZ7U2u8AUJf3L5R/d1J3FeBrKkD5S6kuXkHexumg\n1qL8zUA25eNXkFHZTFr1BBpyykmpW4EVqMRSh+CvfxIrcAyW2k59biYZO5ppThuL8teirFTq894A\nK526vDFYymLA6lJqBjbha95EVul4AinF+JqbqM/dRFPGEKqHb6Ngtc26QxaRt6GU+rxm8te9ivKN\npymjiR1jmylcMZCUuslUjaggf30xyredlLpmrEANjVnl1Bam0JB1GMq/gEHLppFRMZZdQxbir/eR\nUr+DVUcso/Bri883FrAiZSJpucWo7CxKFxxAHBXuR5HRx1P6N2cho7PzurvBMFIQi/nhyBTFJ4gV\nvW3QZLgM/0HcItrSjSGYugVIA+uq7onbR3C4GlG2L+56ZXUwMqPwfeT4LQX+h0y7zAceAasubrJG\nhRqADMQORqa9fMgUl4NMf9Uio9BCZLppb/3DMmS03Fo59DeIdWXQUkipbaRoeQMZO7JJqYWCtfUM\nXZDOhumwxz9Cv6nPhfRqeb9tCuRugopxkLsZGrMUuwYHGPGRn+27N2I1N1O0IlR2ft1BkLYLhi4U\n60hTeoAvzgxgBXyMmePD1wT562Hn4Dp2jLEY8Uk6zSmwfXIzvsZGAikWO8buoC6/kaYMi8FL8mjI\nbqApvZCUhlIGfpXHtinQnFZL6dQcBqxeD6qAquENNKevA3IZ/mkNpZMLyahoYt1hu5G3fi7F81Jo\nzMpmx+j1DJs3kJ1DMti87zrq8/00ZQwipbaIbdPKSKvewaAvR9GYuZpdgxsY+eFKSqceSn1eOpNf\nrGPVkZ/hbxhN6dSvySivw9ccIL1qT4qWb6ApvZ41M/YlbecCRn6YwvITtlE+IY1vPVTF2kP3o2rE\nEka9t4umrMGM/KCYlNpVfPzzUurzyxn8RTZj3Y0MWpbBilnlbN1rGKgMRs4dT8HXuTRlVDJg9Rre\nvSGD6uIVpNZkUjXKYtCSJirGNuNvGEBmeQGVo5aRu3kamRVZFC27kw3TB1Jd/G0CaZuZ+kw2K2fl\nUjx/GzWFAUqn+Mmo/Jys7cMp2z2V1J1baMwZhq8xG3/DLhqzhyL3rdnI4H8CYiGZBGwno8LHpJe3\ncMylufx+p1yDhStSKd8tG1/DKAYty2DrXquY8lwWS0+tRqZ2ByDWmCYcC6qHZvDw3ABHXtnIjtFZ\nvH17I3J/awRLceopPqY+r3AUbVwDLEoc2HDAEFbO2sm1eamkV++QQbdK51sP+vj8QnGhkxmgGiAH\nhyo9K+YHAqGZn47cCVr1zdYuDw5piFVJpmODsyfS/iBgR6sZFQd/i9ubrGN1uP2gjA5NYZ+zcQhz\ngQAcUpCZlVp93MStIvi7jnCYiBh4msNmUYLHROGQikyn1+hjFpI91Eb4QCYLh51antBxdRgClIXt\nRxZiaUtrtS8y05OCQ3kH8oprQuvjGZx5Cro7BF0jgnJaODTiMAqZhVqlP4c/O/fD4dNW+y+unenI\nlP4ApPDdDhya9Tlv1u3tJOQukap/t4aQhXA9Ys09EJmFHYO4TeQCh+p21iJ6wS5gPDASqQNSpfdn\nKNLnspHnwEqkH5br7zYB47SMk/Xyci2DX29f6WWbEdeSoFvIJqQvZiAzdUOQZ8weWv40xGVjHTLL\n34TM3mUjs3JBd4kCxCC5ULc9AHHlUHq/9iQ0A/ellmWWXvdWxLVljt6PiVr+lYirzEHA4rDjWQdM\n0cdyuT4HA/UxPRN4Tf8+OCBcpM/l9cgz/hktv6W3tRY4Xu9bJXKf8OvzJH1YXHw3IbOKGfr9KH1s\n1yPuv6P0tj7Vx+tCxBVkPy3HUr2sGokLVIhl/HMt/1h9DqbpNpfr45Sjj0UuYt0epfejAfGaWKaP\nd6X+XS3wXeSevZ/+/Uv6GGfrYzFYyzaaEFX6eNci10a+PublwC4cSoijwp0OXIIoPQDvAX9BLrbe\nMAspqOMHHkYutgv0sgfbrBsrhXsRcCFYH3Zf3D6AwwfAb3F4M/ofKQuYgXTw3ZGBVJFeuBi56b6D\n+MItRizomUgnXo90znL9XSZyc1uO+GeNQzrpOmSgUxv5Ya7y9e+OQ2YxViIDu7ZWyFeRm/CewJvI\njWkQcmNoQK7RLchNuhS5wQV91oaIDCwCcsGq0vN9BciDYgfSqddR/FkKO4cWUj1iq97/UQz8ch1l\nk/KQG3Xw4aoVhIi+jjn62ARwrEYc5dPzd2H7rywcC60YWcj01s5+nD3HYDAYDIbEED5wjh3d9rOh\nGz84CVF0eqtgx4sod15NB54FxvZLhcZhMPAVMASnt+dKZSEjWR+iaOciJeNXIZaI/ZBRXyTKiewT\nFc5aRFFWyGiyOHzjiL/1+4Rcg75GrHw9DlgwGAwGg8Fg6CVxVbgfQ5SvOYgi9AZ0MUWXWKJVuG9D\nIlt/E2+BPMHhBmAcTq9cfXqIykIieEOWW7FOB6OGLUQJn4ZEY29DXECq9PJ5QJP3LiwGg8FgMBgM\nHdIjhbs7pCEp+p5G3AMejufGukmUUwVqAagD4yuKhzi8j0P/LFVvMBgMBoPB4D1xzcMN4h/7OmKN\nzAK+B/y4Jxv1BjUeccL/1GtJ4oJDNhIw+IHXohgMBoPBYDAYQkRIHhmRYxC3khXAKcBDhKJe+won\nAy/0Yx/g44APcKjxWhCDwWAwGAwGQ4hoFe6zgZeRDBbnIKlmYqG4zkTS4qwAro6w/Cwktc4ixHLb\nm7zfxyD5t/srxyDpbgwGg8FgMBgMBkAC6lYi+SNTkbyck9uscyDmqOYSAAAgAElEQVShTBgzgY86\naKurfLEDQFXpwL7+h1QC3IzDOK9FMRgMBoPBYOjH9MiHO1oL98mIFboKSVRerd/3hv0RhXsNkjD9\nGSQoM5y5SAJzkETxI3q4rZnAbLD6q7vFHkgy9q+9FsRgMBgMBoPB0JpoFe4/ItWH8pB8zLn6fW8Y\njhRNCbJBf9cRP0ZcWXrC8UjRnP7KacC/vBbCYDAYDAaDwdCeaBXuLXRccr2ndMckbyPVDyP5eXe1\nmTTgaKRwT39lFvC810IYDAaDwWAwGNoTbVrAz5CCNy8j6QFBFOZIZdajZSNSojvISMTK3ZY9kawo\nM4GKTtpzwt7PJlSh8HBgGVibeihncuOQBkxCgksNBoPBYDAYDLGjRL8SwmP69WibV29IQcqEj0GK\n6kQKmhyF+HlP76KtTqzl6j5QV/VUyKTH4Vgc5nkthsFgMBgMBsM3gLgWvjm3J413QRNwCfAmkrHk\nYcRt5QK9/EHgRmAA8Ff9XSMSbNkdjgJO7a2wSczxwJNeC2EwGAwGg8Fg6B0jkRzPpfr1Aj3PGBIP\nOhhtqLGgtoCK1le9byHpANfitJsZMBgMBoPBYDDEnrimBXwU+DdSGn0YkvGjty4lieAo4G2wAl4L\nEifGIrMDX3otiMFgMBgMBoMhMtEq3IMQBbtRvx4DBsdJplhyEv07O8n+wCc4PRttGQwGg8FgMBji\nT7QKdxlS3t2P+H3/ANgeL6FigxoCHIBY5vsrxwHveS2EwWAwGAwGg6H3jEbcSII+3P9CMogkCxEs\nvOpSUP03mNAhE4ddOBR4LYrBYDAYDAbDN4S4ehU8jmQLCVIIPBLPDXaTSAr3B6COTbwoCcLhYBw+\n9VoMg8FgMBgMhm8QcQ2a3IvWRWfKgX17ssE2zEQC/lbQcRXJe/TyhcA+0TWr9kcyq7zVawmTlyOB\n970WwmAwGAwGg8EQGxYiVu0ghcAXvWzTjxS1GQOkErnwzTHAa/r9AcBHHbQVNtpQ2aAUqMt6KV/y\n4pCDwxacaAcgBoPBYDAYDIYYENfCN3cAc4FnAQspJPO7nmwwjP0RhXuN/vwMcAJS/CbI8Yg7C8DH\nQAEwBNjaSbuX6Xb/0kv5kpnvAEtxmO+1IAaDwWAwGAyGzolW4X4C+BxR9BRwIrC0l9seDqwP+7wB\nsWJ3tc4IIinc9vXLGbC6iHV/zWTKcz9mnAvgw6E/5uA+GnjdayEMBoPBYDAYDF0TrcINsES/YkW0\nJnkrqt8FfjeRMiD772Dxd+DvADhUIQGedcB/EaV9BaD6pDLuMBw4D/GrNxgMBoPBYDDEjxL96hXd\nUbhjzUYksDHISEQZ7mydEfq79swJU8xtMhHf8O8AByMVGUuAa1r9xuFVRIEvR1IdDkCCOOuAxTjU\nd2N/EsUpwBs4fOW1IAaDwWAwGAz9nNn6FeQ3PWmkrfU4kaQAy4HDgU3AJ8AZtPbhPga4RP+fDtyl\n/7dFEc2+OBQCecAkoAgYirixfBvJ2LIaOKzNr7brdUsRP/YsIAA0I8r/VkRB9+t1FgG1ep8ykKJB\nOThswiFd/35Hj6pDOhwAvA3MwuGDbv/eYDAYDAaDwdAbotM52+Clwg0wC1Gi/cDDwK3ABXrZg/r/\nfUj6wF3Aj4B5Edrp0c5HxCFbt3cIUIwo2YcBVYgFfByQiQwQZiABpIuQkvff6aDVOqAJyGkj6xL9\nORcpLrQOUfqnIAOPaiT94lt6m98HTsfhnzHZV4PBYDAYDAZDd+iTCnesiJ3C3Vsc/IgFPB2pxrkN\nqEeU992QAcO3kdSKdUhKxPFApV5/JXAxYhlvQqzwW5E0jC/jMCeBe2MwGAwGg8FgCJE8OqcHxLXM\npsFgMBgMBoPBQJwrTRoMBoPBYDAYDIYeYBRug8FgMBgMBoMhjhiF22AwGAwGg8FgiCNeKtyFSIq7\nr5AsHAUR1hkJuEg2j8XAzxMmnSFRlHgtgKFXlHgtgKFXlHgtgKHHlHgtgKFXlHgtgCGxeKlwX4Mo\n3BORCpDXRFinEbgCmIrk374YmJwoAQ0JocRrAQy9osRrAQy9osRrAQw9psRrAQy9osRrAQyJxUuF\n+3jgcf3+ceB7EdbZAizQ73ciuamHxV80g8FgMBgMBoMhNnipcA9B8kuj/w/pYv0xwD7Ax3GUyWAw\nGAwGg8FgiCnxTtz9NlK4pS3XI1btAWHflSN+3ZHIQerY3wK8HGH5SqR4jMFgMBgMBoPBEC9WARO8\nFqI7fElIGS/WnyORCrwJXJ4IoQwGg8FgMBgMhlji93Dbo5CAyQ+AS4A1wDtt1rGAR4F1wE2JFM5g\nMBgMBoPBYOjrFCIKdtu0gMOAV/X7Q4AAEjg5X79mJlZMg8FgMBgMBoPBYDAYDAaDwWAwGGLATMTX\newVwdQfr3KOXL0SymhiSg67OXQlQSWgm44aESWboikeQTEJfdLKO6XfJS1fnrwTT95KVaIu/mf6X\nnERz/kow/S8ZyUCy4i0AlgK3drBev+x7fiQbyRgkkHIB7YvgHAO8pt8fAHyUKOEMnRLNuSsB/p1Q\nqQzRcihyI+lIYTP9Lrnp6vyVYPpesjIU2Fu/zwGWY557fYlozl8Jpv8lK1n6fwrSrw5ps7xbfc/L\nPNzdZX9EaVuDVKB8BjihzTrhxXQ+RvzCu8rvbYg/0Zw7iH+aSkPPeA+o6GS56XfJTVfnD0zfS1ai\nKf5m+l/yEm3xPtP/kpMa/T8NMRyWt1nerb7XlxTu4cD6sM8b9HddrTMiznIZuiaac6eAg5BpmdeA\nKYkRzRADTL/r25i+1zcYQ+Tib6b/9Q3GEPn8mf6XvPiQAdNWxDVoaZvl3ep7KbGWLo6oKNdrO1KM\n9neG+BHNOZiH+LvVALOQAkcT4ymUIaaYftd3MX0v+ckBngcuQyylbTH9L7np7PyZ/pe8BBCXoHyk\nHkwJUoQxnKj7Xl+ycG9ELsogI5HRRGfrjNDfGbwlmnNXTWj65nXE17ujyqOG5ML0u76N6XvJTSrw\nAvAUkSstm/6X3HR1/kz/S34qkXTV+7X5vt/2vRSknOYYxJ+mq6DJ6ZjgkWQhmnM3hNBIcX/E39uQ\nPIwhuqBJ0++SkzF0fP5M30teLOAJ4M5O1jH9L3mJ5vyZ/pecFBGqD5MJvAsc3madPtX3upsq7nEk\nynclcK1e5wL9CnKfXr4Q2DceQht6xCw6P3cXI2mTFgAfIhevITn4B7AJaED81c7D9Lu+RFfnz/S9\n5CVS8bdZmP7XV4jm/Jn+l5zsgbj7LAAWAVfq7/tk3zOp4gwGg8FgMBgM/R4vfbhNqjiDwWAwGAwG\nQ7/HS4XbpIozGAwGg8FgMPR7vEwLGMtUcSuB8bETzWAwGAwGg8FgaMcqYILXQnSH6cAbYZ+vJXLg\nZDiriZwux+Qc7bs4Xgtg6BWO1wIYeoXjtQCGHuN4LYChVzheC2DoMT3SOb10KfkM2I1QqrjTaB8g\n2TZdjkX70poGg8FgMBgMBkPS4qVLSRNwCVK9xw88DCwjlG7lQeAU4CK9bg1weuLFNBgMBoPBYDAY\nDMalpO9S4rUAhl5R4rUAhl5R4rUAhh5T4rUAhl5R4rUAcaQc0cv60yvcu+IbrXN+o3feYDAYDAaD\nIUnojzqZ6uB91Hjpww1dV5oM8m3EreSkRAhlMBgMBoPBYDD0B6KpNBlc73/AK8DJHbTVH0dTBoOh\nX6DyQGXql99raQz9AyXPTYMhGYmXTnYu8B/gfuBu4FEgWy8bA/wpbN1/6P+rgL/q18hebLtPW7ij\nrTR5KfA8UJowyQwGQysUDFAwLuzzYAX5Cq5SkOOlbN6jskAdCep6UINA/QrUU6BeBPUkUIkEfdcA\nT3smputauG4arpva6jtDUqIgU8Fh+n2WggMUfEvB60oSDDQoeFnBFUb57ghlgSoGlS/vDX0cBTwA\nXIykiI5GCZ6HJN+4iNbFFhOOl1lKIlWaPCDCOicA30HcSowl22BIAAoGIoriSUj/u0B/D/AhUgE2\nyG1KPn9kfeP6qEoDLgN+r7+4JcJKZcjxBDgN1Idg3RN30Vw3G6jBthWuOxVYrJeU47qFwA6gANf9\nEDgE2/6GnbvkRMEwJCPXHfpzZ5ygX7sDF8ZZtCRE+YECRAk7EDl2DwAnAkMjrH8xWH9JoICGFlQ3\n7i9WZ4Oj84HvARVALl0/c/ZBrNsAVwHV0Uqh4HLgU2CpJZ+PB/J6OnJL9kqTdwHX6HUtQjm5I+GE\nvZ+tXwaDIQqU5MIvRBTslUi6zo4IV7aDffND4HfADfGSMXlQfwB+QXur4v8Q17i5wLvIDOJ25Nj+\nBLgX+C9wN6gPwPo85qK57qHIg8hGlP/VuO5ebdYKFg/LBRqQ8xnAdffGthfGXCZDpyhxm5wEHA1c\nSURFkVVAFjIbPAq5nt4HDgH+D/ixgmssGUT1c9TVwMGIon1ohBUuivDdHxBd4n5QfwOrKY4Ctkdm\nkgYBn4gMvIwYFAuAT7HtjQmVxxM6VaK7w0PAq0ixxBGE9MIdQJF+nw4E9Pv5RL4m2qGkrZ8jFc1f\nBe6EkDLpwL96I7iXUyzTESV5pv58LXKAbgtb52tCMhYhU7Ln075AjsLbfTEY+hxKbkqNiHL2Ttii\nVUhe/P0Q5awBufG8hSiPU4AtiO/cZv2b8YiSeZAFSxIhf+JQOch+DwamIi5uQW5GLGvHA81gNUTR\n3u+BerBuipmIrpuLPHA6chN8FNiG3G9nYNuVYb89FomRcbHt78RMpv6EQxrQiBO7GRwl5+os4Ejg\n7LBFZyH+p2OBjRbUd9HOEcDbwK0WXBcr+ZILNR2py/HLCAuXAD8F60NQPuAeYBfwG7Dq5DsrAOpk\npO9+F6xX4iqu6+6GzHrNR/yGu5p9GIxt9ye32eh1MocJet2VOCgcBiD32xG6nWzEkKFwOBm5DlYj\ns4ZpyH2vCalcvo/+Pg+xan+EGJDe1lu7C1jegcATgR8C14d93Yw8DydqC/cnwLGWuDh3W+f0UklN\nQXb8cGATsiNnIL5pkXgUcZZ/McIyo3AbDN1AQTHS7yDoWgCLgO8DKy250XS3zT8DVwC+/uVaot5B\n7lNB7qZlRs3qgUVRnQ08AeSDVdVb6XDdmcDrbb69CHEzuArIaqVgR24jH1gLjMe2y3otU1/EYSTi\nkrAZiVfYHfG5nwy8hFiYi3E6V4CjQYkSti7sq7cQQ9JWq/WALtr29kGUi90tiYvqR6jLEEUpnI2I\nlXtj96zV6kXE3WQgWLGrWi39ZwdwO/CrLta+CzFYjER8kWuBTKAY294SM5m8JTqdzCED2f9o2AiM\nj0X/C6KV7B8B30WMKSDPsDuR+IlPLKhXYp4PENqnHumcXiups5CLL1hp8lZaV5oMxyjchv6Lgw+n\nZQqs7TI/0tl3Q/w1b++NpU1Pm4Vv6wrg3p4o2W3a9SOWhtuBq/qH0q32QAYiIOlLHwNuj86S3WGb\ng4GtwPfBeq7HzbhuGmJhvwpRDO9FrNgnA3d02yfbdR8B6rHtqKZf+zQOFnAfUswiH5np+UWUv56D\n07OiJbrvnUYog8JVwO2x6CsK5gCzLfhNb9tKDtTuwEJkJm4XsAdYq3vZpgU8C7wP1t29lRAA1z0a\nUdbuCPv2IeReuAzRXWoQt6EqbHtDhDaeB/6DbT8eE5m8J7JOJoPaFMR7oTtUIv30LzhcHAPhTkUU\n6kv0V7OBfwJ/syCgJGbwszb9Mnyf+qTCHSuMwm3ouzjMAl4DZiDWtGmI8qqAn0X4xWfARTh81t1N\n6WwGjyOzSbcCN1h0oOj3ACVT408Ae1khRbWPoqYQco8pBiuG1id1PeJjndojf1LXnYAYJb6DuPxk\n9Dro0XULkEHFdGx7Va/aSkZEyc4D/gj8tIO1XkOy7jyFXMe3IEr5EiSwP+h3fxwOr3ZXBCXT1cHA\n2hGWWO1ignYtuRvYs7eDZ+9RKYgP7QzgArBiqIiqE4HLwCrpVTOS7ecOJJNakLnATGy7ezNXrvtD\n4CbgiH7S91rrZA5jkL5TGLZOcGb178CZiMHgXsSFowAZoGxHZmLTkOBzgANw+KQXgp1OaMD7PLDQ\nihzsHuGnRuEGo3Ab+hLis3Yl8tD/BzABGVF3RDA4qpqWwB8AxuEQtcVHyRT5l/rjmVbophNTlFiB\n51rtZ6n6EOpK4EbEunY0WG6M2w9OpU4Dq3s+7xIY+a7+tC+wENuOzaDJdf8EKGz7qpi0lwzItPV1\nyOzp4LAlbyAKsIO4Y/wJh8Yo2rsSUdoH40SfrlZPX3+KKA97Wx34kvYUbT2fC/zBkqC8Poq6BjEG\nABSCVRHj9tMQv/jzwHq02z+XAMjHEH9fkLizScCN2HbPjAwhl5Ql2Pa0HrWRXCgcCpHrcDwSIAoy\nILwMOAmHl1rWdrC6nLWVfnwVEjA8DYduu+MpuV8GB82XWyJPN37etxXumYRcSv6P1gGTINPnv0Us\ncAFESflfhHaMwm1IfsQ1xCVyZD1I5z8P8eW8ALlRLcFpY60Sa8ECxCJyPw5dujfoNH/bkeDIYyyi\nUCx6iJKBxMEWnBOvbcQXdQxiXdsJVm4ct/NP4D9gPRX1T1z3NKRmwefALdh2bBUr1z0QuB/b3jem\n7XqFw1G0zrhzPjLV/z4OK3vY5iDEdecuHK6I5idKfHRr9Ed/LGeV2mznUiRoMC2efTx+qHDr41Cw\ntsZpO+cDx4EVqfZHx0iqzcuQjEw7gdOx7W7PdHTQ9sXAudh2Z8aXvoLC4VpCA6flwGQdFNm1ct0Z\nDv8BPsHh5u4JxAHIwPoO4KEeDHh7rXB7STSVJrPD3u+h149EP/AVNfRbHI7B4RQcyvQNR+HwUxxd\nBMUhD6clT3Nw6rurNs/U7TwRjQgKmpQkQo37TULBaAWlCvaO97Zij8oGNQdUPajRcd7WtaDu6Ho9\njev+BNdVuG7UeWS7jevm4LpbtGLft3E4J6y/tQ26623bJ+h2J3S1qoIpuu8pJVPlcUMXy1EqZH3t\nY6hH9KGKc7YcNVhvp8vz1wrXPV/3wRNjLpL0PYXr/jnmbScSh+HQ0u+ux2H3GLZ+LpnMYU8ayeRm\nxD/+ISQjyfcRF6TZiCH3USSYGAWHhvXBAT3cdjRFdjol2StN7gp7n4NY6AyGvoHDJBwuR6ylzyH+\na9OBH+PwNxzO0utV4VAW9ruuO7PD3xH3kOGdraakQuSHyAC3JBGBjJZku7gHKcPbh1AW4ipQi2QQ\nWRvnDb4KnAUqv8s1ZRr7IeReWdTF2j3HtnciWRb6rsLtMAqH3yLT/vcjrh+Xx3grb+n/0RQwOgjx\nAR8Z7zzZlly7ZyNBYX0MNQPJGHEuca+jYW1D4hVmdrUmIJZt13WBvwFXYNsvdfWTbiN9D4hu1iQp\ncchEYh1AssH8ESemrlOKWv7ISbxKDocjz7PLkaxMz+p1ntPfXQDcoMQdM+iCd4QldQo8IdkrTYJU\nFLoVSWN2VALkMhh6j7iPfIIUF3kDCdSo0sr0xzHayhnAmzgcjMMHHaxzo/6/nxXyXUsELwDvKbiy\nD01tn4Wcp2mSvzfeWItAfYo89P/Z4WqibAfTpaYnoCLkbOAhXHcwtr0tztuKLQ6pSFDxIOA0nJaH\ncKy3U4vDJcB9OBTiEDHFnBJ/04cQf9H22Sniw2vAPUpyB3+VoG32EnUIct09AzwpebPjzj1IgHo0\nHAktmWn+ExdphGnAbFw3G9ve1eXayYTDXoinQvDzy22WR3/f6nyW93xuZwrTaeIdLMSa3YSk7gxP\nGdigP5+iP+dYrY24CcdLhTvag/+yfh0KPAkdTk84Ye9nYypNGrzCwYekGFsDzMKJXSaCNttZoC15\ntyPFV1qhJO3R5cBoq3XO37hjwVIlGRimA+8lcts9Q/mR+8scsDZ3tXYMWU4o/2tH3Ibc905MSPl1\n296A6z6L5AjuOynmHA5CgiMHASntYh9izwPIMZoM7Qe8StKYPYQMehNWTtyCcgVfINdWH/AzVT5C\n94izE6RsgxyjM7pcy3WLEWXudmz7yrhKZNtLcN2PkdSeUbkLeo4Mco8nlD9+P4iQQSsaV8no+BuH\n8AmrKGUw29nGLEKK9Iyw9dL3EZeSScjgszfKdon+7/SiDU9dSjYiid+DjKRzC8B7yABhYAfLnbDX\n7N4KZzD0gncR14Sn46Zsh3gKmNLKBzzEHIBEK9thPAO8pLy9z0TLcfr/6Qne7lvAr3UatPa4bgYS\nSLtvzAMkO+dvSJ2EvsRvgWMBEqBsB7cxHxnYRuIy/f9OD2Z5LgCC1WSTnWBlv60JLrm+GDgIVFdB\nj8HgvDgFcLbjv4jS2lc4kJCyfTZO3GdSLd5gOxOASg5FLNx/JeTCeKr+7oFHRNm+2RL3od4wW/93\n6IXS7eWD8DOkkMcYJE3SabQv2T6e0Ag9GDX/zayCZugbODyMVEC7Gadd1p14bK8ScUcIzwWLkilQ\nkMp5nmBJaeM6YJRXMkSH2guZRTsjtrm2o8F6C6lsOLSDFU4CPse25ydOJgCWArvjuh0ZOJILh2FI\nNdD1iM9movgn8Guc1rPFSsqy3wT8ypJsFgnFkviOtxFLaRKjUpCB0lwkj3gCscqQ1KUdb9d1jwB+\njJzLhxIjFx8BJ+O6SX7fbGFM2Pun47ytx0Hnvz+Al7kWcLgA8eF+DDEylQCXK/h6bzHi/i7OMkWN\nlwp3E1Ll503k5v5PxE/xAkLVJk9Gpn3mIynTEm19Mhiix+FQxBr5M5wW3+lE8AohBTvIW8DTlihz\nXvIJ9KwqXwL5AzLdGKmKbSJYh5QSb434bv8ESZmaWGy7AvF/jE01vvgTLG0/qZN4htjj8G+kwFPb\nZ9OlSJD/nQmTpT1zAEdXgE1WgtljZoG12IPtXw/U6YDp1kj/ux14Hdt2sO3KhEhk23ORGBgnIdvr\nDeJO8iNkRtffq3R/3d/2icAWIhiVlHhMXAEcbRG7UvC9xeup3tcR38QJhPI1PkioYMYfkSCCfRAf\n7k8TLaDBEBUOYxFXkuU4/DXBW18OHIQjmQlUKMr9JwmWIxLaZSJZUenIjMTo3pVr7xWfEDlg/APA\nBv6VWHFaiBgbkHQ4/BDYE5iI05LrOpHcS1hfC3vY3xKvfNtR8jEyizzdQxm6QuecthKjzLbDKkMU\nsuIIC48G9kISNySau4AzcF2vdbSuuBExqNyN48m1vpFQefZw9gfes8SYmzREczIjTc8dHGtBDIY+\ni6RCuhspp9xVAFw8tr8csaidedMM9gH+DPzVEncOr3kCGKd0PtQk5ChggX7wesXbwLk6cFMQ69q3\ngVOxba8GAg8BRbrke3IiRWge1+9766fZU54Hvo1Dnv78S2B7N6vYxRxLilw9QMgdM8lQf0AUo/Ee\nC1IL3BnByv1T4ENP+p9tf40ok5MSvu1occhHrvUf47DJIynuBS4PD8jUA97naV3HJSmIRuG+N8J3\n98Vo+zMRX7MVwNURlp8FLESm7D5ArBgGQ7JxCvBd4N6EBGtFZjbwvU25vAa4FvzMIzlaYUl1vReB\neYkoutMDDgTe91iGV5Bp/73CvgsgQeIveCIRBN1KXEIBpcmFPGSDbkBZHspRj1znVyrxZ70MydqQ\nDLyPNxbaLlB+Qs/81V5KApyJuOSFZgJc9wIkj3TsC9xEz2okf3uycgfwbxweSeA20xCd9F7gQRwc\n6qnkHt5CBpcv/FoHK+8r7pT3IwGVJyMZl8INYo/RumhO3OksLeCBSML+QUiKs+DDMpfYuKL4EcX9\nCGQk9ykSNLksbJ2vkQjwSkQ5/xvJPT1m+GbyG6SIU6KCatrjsHjGuTzywCucV5vCtSQy1r9rzkKK\nDQzFe5/yMNTpwLXAMd7KYSlQXyC+5Edp6zbACwlJA9g5byL33qc8liMSByMzsGfhUOuxLM8ANzRb\nNPnljH3prTgtPI/k5B6RwDzg0TA29N/y+Bq3PgD1D0Tnmau/fADA4zz09wJH4LpDse0EB3NHxREk\nPpPR+YiB4k39+WmaWEkxl1PO9wZA/nqYews8OF9ygj+g10ulfc71YNGchOXm7kxxTkOUa7/+n6Nf\nVYQSifeGaCpNzkWUbRB/tBEx2K7BEDsc3kWmRC/C8dZfzH2MQQCjrohv+ejuol1b3ib5LG3nAeVI\nYSKveQA4EtRApCJpDbYdi/tsb3keOAvXPdtrQSJwOvBbXXXVay4D+Hg45wAXelnNLhwdMPYySVXq\nXfkQxckFa43HwgT5GNFJgmyg45oficG2/42kCDzIUzki4XA2kEc3BpZhpdW7fHXSzBRa5/hu4Hnu\nIIUsUrgzAJ/9ArIegFJax/x1lJYzmFIwaYoqjo5Tu6fQ2iL4AyK7rwT5FWLhjoTXViDDNxUHldDI\n7E5Q8OLc4fwOhx045HotTzgKDlWwPHlycqt0UDWgCr2WJIR6B9SxuK6D6y7yWpoWXPduXDeZrKPi\nTuJQidMqJZmn/P4QztmYQ7OixZc7KVCwr4JK5aXbTSvULK1XXdD1uolCjQe1C9RgXLcE112VFAGL\nrns2rptchcMcUnFowOGGTtaK1zPxZ7RWjp8GspnCdq7g+2/BczPEDe4YxAc/SBrtXUoepXt+3qqD\n91ETzQX1GLID4a//9WRjbeiOwDZijYrk5x3ECXuV9FAmgyF6nJZ0RAnx/+oMBcOBkukbuQPx+/uL\nrniZLLyPTN0li6X0z8BSsCKW5PaID8lvmIE8GD7xWpgwrgMG4LqZXgsSxnVAHg5rvBYkyDXv87N3\nxrHTcpIrSNGCecCHJE+2oGlIEZkkqqRorQLexKeOQnScf2DbXmaYCfIsMBnXTaZsQYci/uW3drVi\nHHiIYLyUVG+V+gU+0snkn0fCqNVizZ6HGIvv169gHMqvEYv2efpz26I5HVGi/zvEOV3jfmGvQ5C8\non+KQbvTaT2Vey2RFeo9EdeTCZ20lRQWRsM3DLFuJ4VvnYInlQSxgMPdWra2ubk9RcGJesrQY0ub\nsrSFzYu8v52gZnHItvm47vthftzJgevOx3XP63rFBCHX9+Mu/awAACAASURBVH+9FiOIghQFyj6H\nK3BYi5NcGRIUTNZ97yyvZQH1EqgfeC1Fe9RfGLFL4boK1y3yWpoWXPc6XPfPXosBgEO67nsPdrFm\nYnUyh0GHnYuq9VOrxJodDxJi4f4s7PU+kl+0pCcbi9BuV5UmRyFR6D9AlG6DITlwWoJ+EhmhHREl\n/eQYQiWIL0cCnd/CSap0fMHS5N5l3hCCFdwSWZwoGj5irx1TCfB1EgRLtqUeeNhrIQBwOBbx0Tza\na1HC+BOAO5Z7kevrY2/FaY0VSkbwR08FQR2NxHIkrjhR9FzFyBposP6HbW/3WpgwyoErcN0MrwVB\nkljsQKoIJw3KYfucxyCjmQwLvEqj2iXRKNyFYa8iJGI9Fj5q0VSavBEYgJj855Nc06yGbzYnAQ/h\ncJ3XgiBBNbMtuRGifcrv18vmheco9RJLrAI3ADNV6xR4iUYXk7G8qizZAVYF+1U08+zIpLKOamYA\n5bjuGE+lkGv5HuBOnKTKxTMYSQvaBBwLTMVJuiD/IcAwJcYtrzgeuAIsr1MBtsedncrvF8PnhWu9\nFqUNjyHVaH/usRwghcz+h0OyHaOJVWnUFF4FOAzxWpiOiEbhngd8rl9zCSY6jw1dVZr8CTAQKZqx\nD62jiA0Gb5CH/ql4VwGwBSX95x/IgDSEQwPiurGO5PHdBPG7A7hFgQcWGzUYCZbpKADbO1w3i+K6\ndB4dc5LXorTDtuuR6/2KrlaNMycB44DXPJajBR0IfAByXYHDa8ixOtVDsdphQTDF3TNKDFleMB5Y\n5dG2u2J/NmZs4LppPwKV77UwLdh2HXLdX4LrdpbKOb6EZnVTPZOhYw7Na+CViiweBK73WpiOiEbh\nHoPkzByLuIAcifeFIgwGLzkSyEcquXnNL/X/N9stkdzElwI34fCtRArVETpd2lFIEIsXAZQ/QmbU\nkiEVYFt+Q2pgEw1+kit7SgvXAD/EdQ/3UIbrgNtwWOihDG2ZAexE8v4GeRz4cxJauacCy/Gk+qRK\nRWK3ksrdJozRDKt7E6xlJFuRPdv+HKmgWOOhFPshBpyfeCjDVCQzyd3AlcAZwN/OhBuPkmDJe3iZ\nSzmHiXr9C5D+CTJo+TysrdeR4Mt/EaqEvArxqPgrcrxjSjQKdybyUH8J8ae+gthZprqqNDkJsarX\nEVIsDAavuQn4na4w5xk6OOR84L9W65yj4QQV8c9wGIjDzMRI1ynvIJk4rvPAteRbwM1gJUMGgrac\njY9rkOOTDOepNVIExAHewXUT70rlkIIoip7PLLXhZ8BiKzyQyuElZGC33iuhImGJTB8C76jE5x4+\nBvgSLC+LyXTGj7BYAXyE5HtONh4EUnHdoR5tfx/gERy8PH9HAk8iee8fBo5ScMPfIe99WM3N5AAw\nTM82teZ7iLJ+qP68EzFI/QFJCALi0XGRfsW870ajcD+BXHz3IJUhpyI73FuClSZn6vbPACa3WacM\nOSC3x2B7BkPvcRiJXKf/9FoUYCKwnc4KUcmgwIekl9oOvO61T7dWTO7Q8ixQJNT6Ppykqnapcd1s\noAC5rp4BngaVVPmcNR/p/1d5sO1g+s2ksZAqeY6dghik2iJBnQ6TEilTFPwWuc7OSdwmVSYSNL0p\ncdvsBtL/pgOvImkBH5Bc/UnFRYhFdjOum9jiZg5ZSCa5BV2tGmceRpTj/0Ms7YsRC/Z7tfABzUyj\nWrssOa0s1MORGKfHCRWBykZ00EcQt0yQQUXQwh3zWhbRKNxTEZ/tYP7tn9A6eXhPiabSZCmSzaSj\nKkEGQ6K5FHgKx9trUsnN4k3gHy3Bkh0hQZS/IOQC8wscUnAYGF8pO8aSnNy/0h+/r0jEIEAdiuQA\nTpay2+HsAyzHthtpffNPLmz7YyQmIB/X9Sd46xcBN+CQTLMTRwCrrEiZdxw2IDMCr4Xl7PccS565\n1wFnKrhfda/4R085Xv+/NmYtOnTuZ+2QgRN1tchvAx9j24vBChoUP+vsBwlHMhcFZ/oTOFgCQvfM\nnqfilHSL0b06phoJvP8JEvy+BxI/8RHi8rKMDbzFTooRfXUwkuXlXKRS+S3IwCoPeQZdgrimBJXw\n+YQs3NU93tcOiMYBfx5wIOLaASLs5x2vHjXDaW2y34AcOIMhORHL8PEkR5nkmcAw5AbSNQ4bgSNx\nuA2ZMQrOGnlm7bZgjpL7yUdAnoKLLeKqTN0EXAjW1jhuo6f8EpnuBKwaUHchFt05HsrUEfcgKSi/\nSyjVY3xxyEMsW88nZHvRcy1wWyfLb0bioK5DHu7JwhokDe/PENfNePvl/xy4HazlPW7BwQ8tr5OB\nJ3H4HaIsr0KUpO8jbrAl+lc/CitTchvwXxzejtD6kbROVTgNWAxqJljJE+9h24247g3AXbjus9h2\n/GfrxJVrJPAwDjt73I5tx+JZcwIyc9QELNoLNl4Ad78lBuEPgLnUU84DFLEvpzKYfdnGzYjbyLG6\nje8i+eiDiv0ryMzGXwhZuEGK4rS+Xl3X15uCSNEcgC+Rqev1WsBRWogm/bmnwQUnI0rD+frzDxCF\n+9II6/4G8be5o4O2FPIwDTJbvwyG2OFwMDL9NMnLcu5Ksvp8hCio3XdtcTiaUNDgY8hM13k4NMdK\nxu6g5D7wOrAF6eN3WzGf1VKTkLSjo8FaF9u2e4lUcCwHirDtXfKlGoNUc/sxWJ7nem+H6x6LuClN\nw7bjn+LN4XLkQXucV9dpW7Q7yU5goNVZMJvMJG0H/oPTYulNCpT4tb4EzLUkvWg8tjIcmc3eE6wV\nUf1ElOurkVm5TYgVMpgWbzGiEPeUgTiEKsy67jCkOu+B2HbY7JdqAFLBSoq0qi1IppIXEOPP3th2\nfAOIZZbgjbAsJdGgSIAxR8GFwH1DYNY2UaRDqRMdHkUs25k41HXZmBQbux7J/LUcUbiPBC7i73+f\nx0MPXc0554BSzTzxhJ8e7F80PxgdYb3wg7mmuxvVTEem24LBQdci1q1I1oJoFO7k6hSG/oVk+fgM\nuBGnpcAM2hXidETxtZDr+QbkAfaLNuvthgQIZ+hXFjKz8x3kIVKBWPHm6OV7IQPdPSytICux3tQg\nG+v5Ne8wg9aD0gAS1LQQseD/OZF5jpUcg+B05WnAy7EtYKB+jlgZi8FKrqIyrvs94HZsu001XfUF\nMB+sZJhRaY/rzkXu4ynYdvyUYId0pN+cjNNhcHDCUaLw/NEiCh9tKUA1T38qwKEynrJ1hzCleycw\nwCLW/V5dChwAVsfVJWX28DDgXSRG5joiV8V8CTgRUYYeB55D7oPjkRiIYYiSlIUYJuYh+cd/CnyF\nJGD4FjBSZ3EC170a2B3bblNJVeUh8R6Tk3CQPg34Qn8agW1vjNu2HF4B8nFagg2joZs6mUoDmpHn\nXgNYbQwuygJS2n6v4CnkWXs/bXEoRgZrK3B01hLXTUdSYh6OGHv/hWQvOaTd79ti2+C6ofdxUrif\npH36rkjfdZcUZBRxOHJQPkECJ5dFWNdB/GmMwm3wBoffI4PCKcrhSyTCfxYSLQ2iEA+htd/ta4hy\nvDFsvWhYAOwd4fv5iFVtT2C41dsAJHnIDdKfXgQObrPG28BDiDWlkNCgYS7yYNukv0/D6X0gopLY\nkPBy63sAWyyxDvamZT+iRJwN1lO9aysOuO7LQAW2/aPWC9SxyHTnUWBFmgb3FtctRNwLq4Fzse15\nXfyiZzicC5yeJBl2WlBiFX3E6vi51BqHUqR43DvI1HitlzNl4SgxEgQNCb+2onVV67plH/A18EOw\n3m23WAwZa4jcxzci95g/IYp4BTLzNRCnzfpScrzrrFEOGaAVbQdLWzUDwDXYdgRjnwqen0ywuraS\nJhLXvRGZ2V+K3CtVzCvUSrDkVuBAnFb35k5QqWA10EonUz5gKPLcPAHxrz4ROe9lyODqEcTzYRES\nHPooEl+wltCMxqfA38G6S+eSXw1M6PAZ4TALeQ6PZZ/7t5A3pbYL4d9GBmS/Q/r1ZOT62KBnHy1c\ndyy2/TVxUrjn01qJSEEOSCzS5sxCRqp+JPr0VkJVJh9ETtCniIN7ALmxT4F2fkRG4TbEF4eHhuxk\n45bbKUb8AydGWOtypCjHIiSKGv0+6HZVjiioLwDpyI1gIRIAMg6x6M5BHnZ/RB4wwYJQm4Fi/f5A\nK5QtIjZIANJpSLvXIlb77nIQUAV82dNpfyX+rr8CLg77+kVkMN7YKvVa9K3+EvFZHwSWdyWbZYBz\nCfBAS9CtPPDXAodj2xGm29U9yARJdwZsicN1j0AGm2XYdnwqvDncB3yFwz1xab8HKCnIthUYGvWA\n0GEoYqGdHvZdz55brjsa2KSDbNEBrMOx7R5bYpXMbP0eia+6CrlvLbJ02foetJiO9Lv9wWofnxWa\nNQynGZnW/xOg4jIgcfgNYsQbzgw3E3F3uQ7bvrX9yuogxDe4CazkKvjiuqmI+0PQ0n0ztn1jTLfh\ncCYSZL9f6wXKCs0UqmxEP8tAnnFPg3UWqJeQQWkjrV1+e8tasMYoOA+YZXVVYOq+wx5n/MU/pKly\nGzm7DdbffoYYMxYiVu4jgMWtfOJdNw3bDp9lDdcze6RzdvaD65AHbybBEaHQiFRpu6a7G4sjSa1w\nKwk42GHpqFclislnsZ0y9w4lVptiK9Tx+xUfD+OEtQU8c+pSMhAXkI8Rq8z3kBmaHKDKCgsC1q4f\n9RYEFPiCwYB6VF5r0bFPWZv1/Yjl9wtLzC0TLZkajR+iBExFLEz5SCDSeGRadit0WTr3X8jgYHpP\nH5g6P3fbFFS7CGVUGITc3DOBms4VcfUG8CJYvasu6VCEDPZHIpa3gUgGpxQkhdSlyPXwbWQGYD4y\nrb0cUV4yEIV7Gg5LAHDd6ci06G6RrVPqYGTafF+wtvRK/njhuj5kQLgCGTA+hW1HmqnsPg7BwdIM\nHNpbSD1CiUxFlviIRo/DYCTQ9ED9TRViUILR5/gYc64lzXMCMtV9i/48EZlNGIf0w2CWiqA/82dI\nloY1yED+ZmA7tt1t9xAl1/SV0JLh403gSgu+UHJdD0cU8Xq9/jALNikZfIRdo+p0JOPORWA9oPd/\nFA7rcNiXUPKFm5F7xsKEubEF70sz3KOQIip7t1GuwlCHAO8hVuSlXeXwD8+4FLwv6fu4D7lXnI+k\no/s5cl991pL227UTfl/T7VrAaAtWtyx33XOQWBwQ3ezCmFi6HQYA5TSn3snNDb+S/VbZSKXJ6xFr\n9BokW0gbChF7Ub+iAtkxiIPCHeQPJJdyHYmeKdyuuw9gRTsVqi/4YRZsDPPdnYf4jqUhN5uzkfQz\nlyEXZXhBn+cQJeZyZBroSUIWzNssuEaJZeFgxD9won4VIjfWBXqbtYjldD/EUroGmXL7LRJ8OhG5\niV+EKAYViJI0Eun07+vtfx/pLOXAg8FOr2R/RgHrLNihpGPlIor1S0gaul9ZcO388eOPnbpmzS9T\nm5ttoNCKopeF3TjGWWJdaLvcD4ywxPLXSgHVn4uQ2Y8T9TG+SL//GAnkOlzv36XIKPY4xK9vIqIw\n3oC0/SNEsVyvf7sEmf6cjQyGfqHbmhUm3retZEsXlSicFr/zQTgsx8GHXKcOoiA/gzyEg8rAUsTS\nvwYZZP4Fh6gD7JRcs/sj574ZCazsiDeQ63sRYrXIteAGUIchSuDQTrOTOGQixoRhWiG4DLkGLkTi\nSu5EUkwFCSlK3WcJ8AMcFuC6jwBLsO0O3BKUhUytrgYrllai2OK6pyHnH+BPZ7399nNP/f73Y4Bl\nVpibkLYMj7Qk/3pqMDg22Mf1QDXdgh04ZFmKUmWxDnHlmgR8GXlwpYoR38+y6ARWaXrau7N1MoCx\nuvJg6yXi+niG1ZO8xA4T8Gc+xsBDmph83Qx2LISCvaByMeT3JhawHXOAn2Lb0Q/QgxZ3B6X730bk\nftyWSuRZdw7yfLgZSRdZAhTWk7Ywg/pTEf3hGLBex2E64o72KHLvBfgxDokPCnZYA4zm0LeewJe6\nFdvuIq+8egaZAbwcrLtbvpVny0qkIOBPkfvFA2E//BIxRHVmhS1DZvrPQAbphciz+OfI8Qo+534X\n9ptJuu2zgcWW66ah89NbgUBgWFnZgorc3Fu3nXjiQdl1dfsgBaOGIvfnbCTW6CXd7i2W3F9RUhRm\nGVDnv97/s4DP92v+vC6FXUM/RYwN4c/CBkTveU7vXyMyS7IYyRoTdNe5HNF1jgVqw9IudoDKBauD\nlHwqA6gdzob/bmDk4cAPrJbsThqZNTyK8GrC8y4+guqlbyLX8m5avtDAq/vETeGeQWTrUSysDTMJ\nuZT8H5EDJu9BTnINYk2YH2GdjnfedXOAOmy7CdctRi6mY5CbyYt6renpDQ2bPr3wwoI9H3nkjqM+\n/fTqN6+6Kh/YXpuWNjWzoWEGokgeh1iq2rrZ9GkaUlLYUlhIYVUVOXXtDK+7GlJSslObmtod4Nq0\ntFez3nzz2Lvuu4/LXgiloV05bBgTNm2iOjOTSy67jPNfeeWTvVf+P3vnHR5FtTbw36QnECDSewdB\nQEAEEZCMohSx93IR2+e1XHuvY7kqVhQV5VpQ5FqwchXFwkRpIgIivSO9QyCQkHa+P97ZbMluskm2\nBc7vefJsm51zsjPnnPe8dfXCmnl5Tx1KSuqdlp8/CRn4yVP69Lmr36JF/3cwNfWnJrt3N0Su4wXO\nqSYjgr5ZZBjL45X6AScKudgwKIyPJ6kwOIXInvR06uTksKFBA5rs3h309wBuGgZje1Mbi/1Bf+lo\nRczEVyPjtQdiqvM+Qv42IELsj4i28F1EiN2BWAwKkLiOZxF/v54L3+Dbbjs4EdEYjwL6bq3JrMY5\ngbMrvBh3C6OL72UTLQLPdVaJNn0H3kK1LwWIdgdkYWmKbJhXI5rs1xHLxdmIS1BHROhpjtvv/VuP\n87VgoP0DcAmm+VfgZtUJyMaxIxgRvQeVaOVb487DayDz9cXIPGohC2zn7RkZ9zedNOnqovj4eIDv\n772XDhs3UhQXd2m7LVs6IvEA9wB3bKvBzEYH6ZcXz6K/GvJV7y08osRKVMtpd/fONIoXN6B+w32p\nX3bclz8sgaJk4IHbGH1MY7bOuJ9RSYZsSDY3ZNvM7TRahmhn1zp9vhmZ45ci12U8ci894fwvSci9\nej6ioT8fufcmIhtzV554kPvNAP5cxrGrj2XFj0h2kuBcp0QgGuz8XlMJlI96z1xIcQxIaS0gZ9U+\naravw74/IanuLnb8PI6WI7pixD0ErKDgQDKJ6UVALrtnt6Bu32aIBrUTsll1cQGm+YVvc6WwGA0M\nxpIidN+2o+7JG2lQ5zCtEW110JvMYXzLEL5/I6PbmNEjzsdGxguIZnu84yoUHSwSMRLzOfkrSEir\nj2mW4xakjHP46tf/cnnfIuInp5PTnsCZUiYhv30DRChdhrgRFSIa7u2IleN3RHC+J8B59iHKr3JR\nwIL27Zl93HHccpvb+ywtN5dDqalkn3kmucnJGErRYF/ZZRs8+SO5LU8dfoGvOQcPEet7RLHlKMuM\n6RJgGqm5SV06gvc/el+MS50Nz7g/yeDiGVh5FTAN09zkWCh3enw2r7SrTPCdIEwC9ze4Be4U5Eaa\nh2QVqArxyMI5CNlFz6V00OQwxAQ7DEkZ+Aqe/m9uSv/zEo3aAFnYQSbbkcF07JwZMxi4cCF33ixu\npNd++y1PvfMONXNzGTd8+LbLpk3bsbh1626d16+fk1xQkJian9+zhltQfQ6ZtH9BJv4GyEI+DBlc\nzzl9aoX8jkmIdqATslCfgGSG6YloYkE00FOd36cdMvn3QHzL/gFcPIErf72Mj25OoKgHsjGggIS3\nbuXVS0zsl9uxek02tXuczKyDn3F+9jpa88Md3yfMbxw3qnnGiKLl7brFA4wd9eS+G+97pGSQt9i6\nuXhD46ZxDbdvLm6Ykxf3V9u2jH/2Weyux27suGVn8wevl6yOj40fT/uNGw9d+cgjaQBd16xhUdu2\nfn/fhnv20HrrVvITE5nfwe0KffukSfzWuTPHrV/PO2eeSfrBgxyoUYMW27ZR69AhclJTOXXBAnKT\nk1ncJGPX4g496iRtin+ooFlh6+m33frPWjkH88/Z/V3S3Pw+n61s2/TCfqPG/v3w+A8fH75k+ru9\n33yT+9749K9RN13c7Y2XX2b7b51+NLrt+K/10Ij35l97zejx8e2zRu7bcNm7l57+xmljNvyS3nMj\nx69ezYjh+/muA89gKQuoDYYzYFVcjJYHjy0sxiMLTCgqE85CNOXPAvFxxQwpjqNrfBG0zIaMXJkE\nhq+Ev2vD61Mg1dlX7U0hp/kd1Dzorh3nWvw8+QKZX1xFSmoiWp1Pkew0/nMIi1awORbl+89anOGc\n9zmSG+TT5+M2GEatwOZscLTc/0OsMSdVNI+4Y1Gqj2PWdix05yHa5M+BfMddyUDm9xMQofMAsim6\nFdHClVso6UBqKrWmTPF6r+3mzWyqX59DQ4YQp8pXKH3fKokh6yvnbfc552Njsoiu9GU2i+nCIdL4\nm5bUYj9/VlFPEk8hf9OSLzlvyb94vXx1tLjb1EQsjZ4FZrYh9+C9yDXoRUH2Ocw693hE+B9Jcn04\nvBO5LH5/N1dw9QXIWrMA2XCMB1rS+bHbSG1xGzXbyNEF+/NY9fIKVPHx5G76hYJ9D5O/px8ynmYB\nj+IqjlW37+cUFk4he+6tTuPvphys0W3cm72ve/uMZWNP25jXd8D2fXXNv2m+3ai775FhexJ/Pzwo\n7qP501JvOr7P311nn97y1Ty3Qeaas+GHtrC5Nk2xYqTa5Khmiq7PwrAr/cpBStx+uiL3/YXImuzJ\nAURT+j6Sw/lYxGpbKje9M7ZqAXkuVxyfzwcgOdFHOOe5GXE1a4jknl+GCI+/e35vIpc/fgmf3J5A\nUUkRoEV0ye1mj0kN9G/f/uJk9i1sU3Derm8S3/1HEU98NY3iPe1oWrSLAhJoonZw0UXwxg+1qZ8t\nyXQOkTr/CiZe/xXn1QbDDnTuSPEJF323gRZD7uGFNDByHa32LcgGujNibamBaVpeXxSr7ATgcued\nOxHL6VqsCrn4hk3g9qU5IvieX4nvetIXSffnijx3ua0863HMm0hCc1eu4eWIxt13wVHc/cbU1lsS\nPjiW7bd/d9ppKbQ53NW3wRq5uVsu+HF66tw2xy3/v+++XtpvzV/X/tGxIycuX87zl17Kp6YfVySg\n4Z49OduPOaamn49KNGJGcfHvKi6uNxCHaSps+5/IhWyEyyXENDf6OYfvv5IMxEvxi3KPTVQYKeIb\nrnIQd5FUg2IUcQsxio6n/RQobDufm5f25MByaNAJmmyB2qV+njKaKQYjjlrrv2V/qzO9P8vdBqmN\nyj9HwX7O+u0PfurRldya9X0+OwSJacH3J0IYh7aithTAqPP+ZEtqdw7FA0YWskCOQfzlEhAXnfES\nRKKa46TXAvaAUYY6QSUjZnB/JvL+wPzg7oMoIsFaiZhm4Ah+207iFzMR0Xo3A4roNzmHudecAPyD\n/F1T5UAjFVRdUhpfQt62dqAySs6xf/A91Jr6PLW7wqGth6AojV5vw+KH4a+HNzBgcgv2L4KctbCl\nA6RvJS51J4lFkOdhiO11PSxoDMXuGrsW4g5zcYn/qEUfIB2rpDJn6LHoS/u7ZpHeYSZn31B+Siop\nje3cC5IX2MMN42lE8z4fuR/3I0JYU0R71hPRLrv4FHeZdBdZyJxckXLWjRDB8ddC4qc8x71nzKbP\nqb8w4NO8KyYdLriuo99MVnH5OTT46SLMNXn81jKNXSn5tN8bx7w38ymMiyPpuY40W7aPnI9nMC+9\nH1du+4wBTOdjLuVvWlKPXfyTN9c2YcveQfzUsD2rmwXb4W00nPMgT/dIIW+eid16Ay1efJVb182g\nf8bL3JGdR0oPE/tAJlmNruTDGl9zTscZ9L/oBe7usp9ajacw7P71tOrcgwWAcQPwtt+NtwjaTfAu\n7gaiDPkuqOIZFt8im6xzkLn9TsRlsCxmIcoeDw16HHS8BxqcBnF+4v4KsmH7TxCXAE3OgVWjof3t\nsGsGrHsHMnrClq+h4WBofgnsmg7r3wMjkSvnH2Zl82b8Xneju62EGlCzHR12Fu5Y8e9FvhajKcDw\nsmMuIsRnz7wJ6gYWP+gVH6BE4O2FCGYungGa38ez76yhrf0l51FM/IRIpOx04n6KDci+mI9/SiX3\ntEOkLc4kK+5m3ugMUIe9NGcj22nIDhruxSjOYPg2OHU7zEiBIQshLS6fJg2SymysKJf4ZS9RdMzd\n0CSZAfYy65ZfP7UuzsoCUfxNAUZE8/o5VrfcK5nARK48E4wp2PaLyPjYCfTGNNcHPIGFK9j0Z7wt\nmnIfWHRFAv8LPL5TA4uD3t2IjMBtICa6TpX4ricXIia2sgrf/A+50Wc5r39CfKJ9K12qkvyIQJvN\nm8muWZNXXnuN3bVqcesXX5T56zxkPM42Gmx/r2nvu9SmHh8mdt9Owf6aAF+RXriW9gfacvMaKTt/\nKH4daUWty/nfXAEsvryF7FrXAts5q19n/jezAReftIymudk891djzhg4hnqHj6Xfri/4uulCZKK9\n3fl9diHajwREw106J+1x2XDML5C2AjKfh24viGnSH4veha4+qUf/3AUchgkpMPxySEqCcVOg0WIY\ndhNgwNT/wVBnv6UU5NwLv7aAoTfCti7Qei+kNIK9TveMBFAuF444qNkG0jvBN23hhNHO+y637mKI\n7ws1sqHd3ZDeGpY8CnEpULcPFOXD+nfgmD6wbwEk1oGc1XDKD3KaxQ9DzbbQaCgsfQLytkLGiZDa\nGJpdDEYcxHts/nM3Q+EhSG8PK1+Eg+uhxxhY9zbUOB9q1lekFcmt81FzGNeGAHfSImRj1dTjPWdC\nVsmIZWIf4pK0EdHUvoXsyg8j9/kkZBPaFXdO6l3IJHcTos26FlgPxhOEEtEQJCDazUXIP3kIyY17\nLjLpv+X8j88hQdWrkPvyfmQ8z8A0t2PbNZz3NiNaBBNHdgAAIABJREFUvRcQ7dtXSDBlXUr7NLq0\n1+7AyCIKiCeRQwWQVoEEAduSIVtBx3z483aMtNZTV9/31Ult9kkp6Fd7M7PXVq45eSNbjTCU7w2K\n1zNr0e5f2Sy6D3JWxwXjS3gMu2bup9bJc+jTqCcL8pD76XW8M7oEywYkpdZAP589j/wuS5H5Zwty\nXRa6NOGuBbcpG4/fTLNx1Fnfm2a/wYWXuc/SwCle2OlhGYtNLlLU6eQePAUF+0lMLHFRMJ5sv0wN\n2NuJTMe6n5OgqFkox2cnvAS8wZj2yfzcaKlvh5fQ6dZGbCs0UB9msO8aRDPfF1GKXIRoCq/EnQaz\n0uyi7s312XUHMqZbgFFakWLb03AHk1mINXJ1mYKAL5J/PLnElU1eD0C0nLnIJqshcn1aIn6rY/yc\n6WPgUpqcdzVtb3yPuEQ4sDKf9A7ewlfR4UPEJ1dO87E3IZuMwlKl1jM/HflYfEqLx396uVRMoGWE\nNnNFxbHtrSx9/DN2ZnUAzlaSWWkx7mDRzxAh8yXDq+Kg6ob4Iz+NxGP8hQS3LvOvPKksqi7islSE\nWLffCnDgbqc/F2E+MpmBTz2IyFalqdsPkhvIepjSbD7J9XqS2mQZh3c0oEbrgBasTn//bT8wcaKp\nDIPcpKSJN3zzzaM47hmRnkOVzFnfJJP3fj7JK7Gz8nD/NmmYZnmp/wSJ22mLd7KHzxFlxR/I9Z2G\naM1fxpUjXObqsAncngM4Dln011H+Trs8gqk0+T9E4+0qufoTYoLzDXJUXHUVg/74g2M3bOCCAwdK\n6roCFBnwyMB4ns4qYkVd+OB4mNwRNtWC7GRxDijh4y8gJRuaz4TDteEHpwJ2fD4kxEtisvY5cNYW\n+LAlpBfCrmRonQPZSXDGpjyO21XA1lrpDNpDhTmkIM25LN+mQr/9cLgm/FAXzDVQWBfGr4dWXWB2\nM9iZDPGH4fab9pGTP53BN51V5vl/u/gYap3VnJ8erccPyb9Rc9sNbO++lCm/3s2WlF1c1/sWZCJM\nJm3HZs6+Hj7+ehKygchCFrAuJOReQ71lf3LRJX0Ys+pHYDE1dpxPXh2LoqTJZD56OR2+vYUm85/F\nUkPB+J7kvXXIT7+BbhPWkr7lb2Y8mMp5/2hMk7kp1Nqyj4nfjGBHl48564bfqbVpEO/MHMSZM78k\nq30Ot3ZYQvKBxrhzRYtmFV7EIpfJYwezcVFjdox9hdyMpiTkDiMp9yUKk+8k4fBaIIFt3fL58rMd\n3NxlILCWx7N38EjqanIatKTmjk3I/ZgPzOTJ3P5cuXUaI/+uheTjlCCfDanjeKrzv1mVbjnvzUOE\n7CDU/CFlGvCko3GvOCJgN0GyrrRDYiWaI8K+i9m4sykEyxbcLhll4ZqwFvm06WZbMmQnFtMxxz1C\nV9T8iY45gzgct5/tybVokes62w4MR1tRxFqUsZEEVSJMph88+EednJw6U++9d3qnDRtcAVtTgfON\nsqoEhgvb7oVSH/DrqS7FRZkxAkoW3bhAnzuMQ/yTH0Gu6xfIRu5SZBOT5pzjb08NlZL3khE3txaG\n/C5loJogloo5ADSeBzf46Be2Hf8kE75fz4WXNmJHt/F0+1Axau9WbLsGi2s1osOBS0hSfVDFp2DE\nBeWn6rAGsXx+ViHhFa/sETWQDeSVyKIKsg7dgFtR0hC4C7cr1J3AKkNcLAF1DhJ3cDkY8ntJmraH\nkE0zyDr1CKbp7WMTTixGIMLCRuT/zEUEooFY2E5V0wRM84DT5xQgicEDjqUgbg71tkF6OnTLhjgF\np+z8nry8CfQ57BmY5msh2YpsopOACRQX1iMuwTO4jl7Ll9eae+ONvRDruGu8r0UUCEsMb9/a8GPb\ndYE1/HLahQ0PFP84aRIrBmwoEbRnIQqPd42Ahf2UgSjCXvJ4c7bzvc+RbCZVyEKm2hM4G9UsZJzP\nB2OnIzjGIa6mvpvvVxB3lIeQ+R1EabkJ8clv7gSJ9wfjBgZOux4Z27uQ+6ej8/eG64Sttm5l3eWX\nezRBM0OUKxFBiYx4nIFaS/sD/2bcPJdrYFJJmsyKIG4mIxGl1gl+j1mH953wCxAmgftG3L6O+5BB\nMjPw4UETTKXJNxEhzxX9HtCl5AQxFd9WrBKm/e+TwrikQvYNHkHHAaO+SJzePmc3G/onp9RZvC7v\nQMeLuKGnQfzhW9je7SY2njyF3q9txvDSTPpnzi1FrB30Jedc05e0PU1ZP+BmtvQ6l4x1p5O2Cwpq\nbKbdVPd5jESIc5QI/b+B3bMgfw+kNoM6Tl2TrTOhQS/Y8RMU5UKzC4P/Bcsif88cigveJaVhFn9/\nUIeWI3YCG8JaEQ7AKz8nYr6xQl2mO4RYNMUKYrKw7VbgkWEjL+4pruwzmt0pHlkRVCIypgqQMXMp\nIsh0RhakH5EF+xskCHcOMqm9htzT5yLDegWysbgN0bJMR8bHSJ9eHQKjBhXBtq+GkqwA/vyYfZmK\nTKbXIAJET2TBbYSMz/8ik7eNxBNMRCwvLtPFXCTA+iCQjWm+hG0PBpZjmn87AkARZuZ5wMf03g0L\n60DLg7Aq/QmU8RKTZxTzR8aFrKuxnwmt3dG5pf+3dOBgibnetpsiGRFOR7LStASILyoqGrBo0Vpr\n/Pj2vZctIzU/fwpwiVE6v3/4sO2XgRx+Mbcgi9kFWHgFtSkxd76GhzUgm1obPmCEATSfQf9X9lFn\n7FSGbARqGV4p2cKBqoNYW2QT1nkSNJt9Lye//JzHQdcBn2IFqfWSjd9nyNi6CxGCpyJaO5C4ltaI\nhsmT9xBN7w9OEYpKo+S32+/xOskIKmWrug9RCNXGzroRidM5xeOA+KBcR6KOuhuxaryN+PVPdv4a\nI9Y0mc/lWiVhmoc9XicHdCez7TrI3OXydb/zx7vumjho/vzmyD3kqczbhQis/wGywq41te2TUWr0\noktO/aLLzpJMYSCBgB0q5jKhBiFur8f4fLAL+R3fAMOxyquuyG88F5l720kWHOUy8RqIFeO/uOsu\nuJQYdYECrwweYvUYj3fdhCuQdaQxFp87xzVAtLmLgx6bnti2ZxpHhsyZM/vDp5/um3r4MIcTE/My\ncnKWArcZ4l4ZNpSkqd0H3G/Uy/sPr8/fxZaU3XTPHohplrJ8VRgpX38YEbxzkI32cI8jNgNNsYAQ\nC9yJSBqaa3AHHrZAJroHocpCVALlV5r0DJo8CVnE/QdNWqQjlbsqKFCqVDBysbgWESSmIkV4TkIW\njyBPwxqgIQZuX+/i+N3EFbnNNIVJo4kvqE1C2tVknLaX7Bm1KdgTx95Ws8hYvxqMEcQlgyoAVbSN\nRmc2QqktJKQ2Yfds6DMR9i4opFaneA7vMEpcRQpz9rPnj5dI7zAEiicz/6bXePhAdEzlRzISAf0e\n3tadpphmhIKAVDegIRg/Oj7eXyMmzcFg+Dej2bYJfIBon3zT2m3DXcDHlVbxWGQTkIVMOr8iY7WJ\nIyDXRIRa1yKcCfxWatG17YTycwArl1b1AdypBP+FFE4IXRJX226ACHCtETeYEtJyc5lx6610X70a\nQ7RDnxulN/ShRQSVJcDVmOYcLF4CDCzucLSwZyD+o57uDy8CB9xmePURssh+CVwQWlO2LyrF6dPX\nzhujSN73KQ9keLr2PQpMcNKthR6JFeiCaJxd6jWXleQcIAvTjHAWIdWaxrlrOWXnY/xzrcs94n7g\n+WoiaHdHBNxWiAb0HTBCr5CxbVc+bhDL3JfAFGWa65AsVA/inT4XRFC9BtnYjzBKf14uTvrYPYa7\npkEyItnmG9OmTW2ya1ftTRdf3Adg2BUcWF6PLuteCSLw2X9rvRDtdmVShX6Hd7o9Fw8CzzqxQfFe\n18aiCyLkuwoQ/gg8i8W0SrQfHDIGb8GtTCGuuLjomP3745dfdRV19+8H2SzMQzTrnxhu18iQ4KRh\nnDDsmWcyvzvpJEnJOXhANvnxx4PxdyjbKkGqbaYDNbBYi0W8E+8TUoF7NBJdfQfu3abLvH6IipWq\nDkR5lSZBNDxDEA3Z1ZR2J4HK5uEuD4vaWGQ7u8Ni5GZqhmjxkp0+DQNmYLHJ43uvIplHDiCaytlA\nGq5ytBa9gfn4JvmXPKXZWD7l7S3qAIUMtN2Czqim9ej1bg3ikrpjml+jiRy2XR9xrXFxGmCHvKxu\nuah1yGJ5DfC+V/CWbV+LCNlv+PnidOAix9/6VKAxpjnRz3FhRCXjXfznfcRFZk1Ym5VgtrqIYHSn\n6+2TFy8moaiIE1auRIG1p1atpe+OGpWRMG3a+0BDJZuqoioHC0l6uC8Qn8samOYhLDrWymP525P5\n6aKldEJclLYiloP7gVlGqUJJKgmxProWvx7AwtAL3uoiRBEB8AsYmXiWxxYt3iAsFoa23QDIZgXE\nmlIXb5M+SJBhIvBX2MejbY9A7lsXGZhm8DnXooZKQTZzLuGyJRiVrlAZFJKn/Qa8i6T0RUpmb1Ky\nqR+ByAH+2ItYV/IQLepFyP/wifPZbmRea4dk2rkeiZd5AHHH6o5oLq/enZ4+v/2HH/b869prabZr\n11xgkmHRHtEIl+2SGRTqNGRzOAmRD8YhVs1A596LyBS3IP//6873/xtwA+Qd83Eh8EUwcSAhQSwX\nXfAp1jN81qytkx96qLGPILYK0ej/jLjBjAKu8LUgOYqGloFdeEqOe/mHE05IGPzCC7c4b12KmXkO\nMjcF8nEPByH34V6NaL98d+qudH7tKtpYGAmPwK3RlIVMPJ6a2DGY5q2R64BKQ4J17gDuws5yuW+I\n2ctNIaLBng/UwjSX+54p8qgbkc1AEWIxmBQW7VpZ2PbpiDb/AJSdkeTt559nf1oa+QkJWSctW/ZB\nal7eiHkdO25b3Lr1N2+ce+5OZPE5jGQpKsS2m33y+OO7LsnKcgvKtl0P0ca+DfwL03xNyXx6P3Id\nXawEugbp1nAbbqF7GXKNrxH/UWUA/wDjgyB+DX/nHooIOSAFPd7BMgxEkDgH+BKrytmqKo/4TJ+E\n/5oQtyGWnRqYZuj9S8UVyu37/1abD/i45VWBvxBLqO2ItWsdcAoYm8r5QmiQzZKBaLs9fcAHIr7z\nr2CaRUpcv3ohJv2rEHcKf8G9FabYMMb0+M9//i8/IaFw2ciRIw1xZwKLO5DNWxsqUJyrYqhmSBpk\nA3dWq65IqfLgrDNWSTEZkCJDv0ZM0PbFtq9ErkszHNfgevv25e6qUyf1/okTF560bNnxZ8ydizIM\n9qel0Wjv3vVAq121agHk19u/fw9iwfCsf/ArIl9ORxS8fyNz8y1A3T/btr2xx9tvu4Jzj8U0V4Aa\ngmxUWoIRmUqlYRC4V+I2N1fks2igBW5NdBBzaQvE1J/ivDsC0U4eRkzxb2Ka4fGvvW2FRc3Cxxi0\nw/eTcxGtzlhMs9SH0UWZiGl5OnALGGUUfYkQtn18Un5+9/ykpPFttmz55M5Jk1rdctttfY5bt44l\nrctLSiTU37v3150ZGaf0Xbx44uwuXa7ovmoV+9PSfl/btGnvuOJiiuMk5vHZceP23PfRRwbi0lOS\n7andv2Bjbch/qqJzmeqAuP/5BoCcj2jT/w/RgK8AxiJzZaB0lOIyJNr/FxDz8E1g/I5FEp45hK0Y\nmXMl8C8NGYdjAN80i18gQcGtkIC2XMQUvxbTPIht3+C8JwUy3OeNxzSLsG3DS1tu265S38KYdnfz\nRbMLwAhYgCl2UG2QwNMs4PQICije2PaTuOtM+HIf8KJnvJFHCsxEZKP+IiKo9Ua03R2Q6/4tci88\njCga2iH/b1OgfeKPPx4oTEh4HTgR03RXDJZ7eQZwGKvKNUbCg/gX/4y4xlWlaEtoEYvvK4hLcEBu\n+uorfuvcuaT2xhlz59J3yRKu/v57znviCU5dsIB+ixdTGB/PwIULqb9vH4dSUkgsLGR7RgbLW7Tg\njBdecJ3O+/qhFPAeGNf4thsmQi5wf41MVO/7vP8P5AY/u6KNeXAMYgpqiZgQLkYc4X15FzG97iBQ\nJgNBC9ya6GPbXyCaZH88gJSUfQjJEzq5ZEH3f66mSOT+acjimAi0xzTnYdvnI+bg/ngGk83NgBP3\nnoWk54tR07ZqhLhL3AqGvxRm0cM3CMy2kzHNw71ff332hoYNW/ZfvLhl/0WLhk7r0ePrrO7dD1yU\nlbUxsbCw85vnnBN0E/X27WPJ1VfTYN++kiBO4ERgm2HxMyI4BJUm0BsV53z3SkTre1oQXzoLyQy1\nBxFUv8GdTmwPcBsYH5YcLXEubyMayjc9cxfHFLbdFxG6XkNiEsrCV3k0CXFFaIGYwkFijN5xznkX\nUtZ8CfAbsIpvGr/Pix0PATeAMS5U/0boUa6As4lgVDXLWNUQ164UZP4qQLTYnnNnvvP+SkyzJ7b9\nHnCfX+WBjNteyjQXDnjllfQZ3bodQII7c7DtOI8g6tORe2IGpnltqfNYuCw63bC8UsVFH4sE3HFz\ng7BC6xsdEmy7IRIYn4dtD0XWp7sR18Yc8Ihvqzw/A2djmj6ZpdTDiBtvctWywwRNyAXuZojAnYs7\nOvUEZNI5D6iKGeo5xPfvOWQ3m4G78I0nA5AL9QFa4NZUB8RtwECC/7IRn8Uz/RzpyjftErwOIj6N\nmcAluNMf+rIQKdTiyV7uPP4B/qr9JkVxDcGIMY22C1UTSQO4DBjut2BINUBBquH4MCsxiaYA+9Y1\natTwvCefjKudkzPnr7ZtZ8QXF3/zry+/PLvmoUOfDJ89e3bjPXv61Dp0aCmQY8Ayx2+xuyE5ygUR\ntG/H4pUq9jIVsSLURTZ64E7BOBcR8n3+La85tDUY6z361QvRHl4Us4K2PyT9W18k2Oos3CkrixB3\nHn+4svHcjfx2l+EdDNcH0/zd+yslvu71wSinTHi0UFmIC8DtYFTx/goDtn0m4nLxNpKR6W6fI3Yj\nm5+NiG/0A4gV5wdEkbEc7w3Wb8jm80PnO65iQEMxze/99sHiE2AnFrf4/TxaWMxE6hR8jFW2Jjmm\nELevNsjGSWHbnZG5ph2SXK8vktlnK2KFWIPkNb8DuDShsPDzwoSECxAN+reY5o+BG3OVsTUiIQuG\nJQ+3gZRwP85pYCmhiTr1TO/XCNHgBdJEtELycWuBW1M9se1kJMdnFyTDwgQkqGc1Yu3xTSflD997\nfCmSLvPfSPqxAlCuSTkuvFkrKoM6DikqAdCICpYnP2qQlF01gf7OIltFlAEkgOGTVUqlIFrbB5DM\nHzcjJdxrAm+WyhLj1rhXQvseI4gmtC0ifOUjmu3WSGaqBohP9mFMs7SGTHy2zcA5tVU8lATBx8fe\nZlKNQ+acBmBENud1ZbHtFkie7rsQ5cR8xPIyyOOoNcg1DZZOwIqAAbUWpyHW/RZYVKKQRhiw+AuR\nf4Zi4X+jcKQhgnoappkd/JfUKGQui4TSKWKVJkPBXkSr7erDHo/XvrRCC9yaIw1PdxK3IHAKUrTi\nMOJONRhxcdjtHPciksnnLEzzvdInLRG4P0fM27tLHxMtXNoH1oMRnFP00YhFPFI45SzgBKxSQeth\nQKUCKQFTMVr0RYpt1KpUDt+jBvUMYqkdA0YEg6fLQ92EZL64DoxAWUCqD7bdC6iD5GT+AolNWIa4\nRH2EaFQXIW5BfRDfbNngB1OHwuJjJINa66gL3RYuRcVBrJC4ZBzhqK2IEjeptJIhtA0RYzLnj8hN\n7/t3Nt6ZHYAyb+pWUK4/lUKK6Lj+MivWVY3mSEDdIIKtUqBmRbs3blS606dLncwqmrKwSMVCYVWg\nDkD4+tLH6Uso0sAe4ahUUI+Aimy2nTJR9Z2x5xuLpQmEFENTWNwY7a5g8RJWSaEyTbmooc79/lz5\nx1aITLxlzGpl5VuOuxR2Y+d1IFoRnMCt0WhQjZ0J54DjThDt/jRy+hP9TCTVCYsRWBzCCqICbvj6\n0NwRPMbETEaSmEcZzv3ur0BbpPsSByoP1Opo96TaYTHcuff7RrEPDzp9KC/wV+OF+reHRTVsjVTm\nS3Gh7kWQTMZdWe4q4Kso9UOjOcIwtgINEV/cSuZfDimucV7hSnFHOZ8hWTLmYHF6lAReVzGUz6qt\n33bEKYmdmA0q2q5TtyAF2tKj3I/qyLfO4ywsfolSH/4NLMMqUyGpKc2T8qBuim43YodjkGTmK5EI\n4zrO+01w3+gg/lhbEJ/WjUilSX/oxUCj8UKtdjRtUdSOqN5OH86IXh+qMRYJjoYr8po2CwOLYq1d\nqwyqjnPfh6/Mdvl9aObhXhZLNTOqDxZJJeMvsu0OwmKn07Z2wasUJfd+uCxNR7XMeVT/8xpNabwW\n3Hsi7zutDFBrnPZTyj9e4xeLf3oI3Y0j2O7fWqtdFVSqx/iLwv2v7nDa7h75to8gLJ52xt4mrJL0\nmuFu80unzYci0t4RiZriMf4ahqOBynwpWi4lGo0mrBiboERAew44CKpzBDtwKpItoDUYeeUdrAmA\nxZtIXlqALVgswOKSMLd5HZLhIZBFUVMuRi6SThdgJaiECHegPXAvGH9GuN0jC4sHkboITYHZWJSR\nBzpkHI9UhH2hvAM1gTCGIUXAQCr6xgTRFLiPQTKZ+LqVeNIcsJGqXouRPLEajSYojG3IwjvZeWMJ\nqAbhb1dlIC5j73kVT9FUlkygO/Cd8/gxFo2w6BjyliwuBf4D9MNifMjPf1RhLEXWueZAQWSsTMoA\ndS9wI1L4SFNVLD4FbkCKSA1yqj6Gq62/kdzw92BxOGztHBUYE5E6A/mgJoJKjnqPoth2MNUmGzl/\nfyJBYPOAc5Gcm57EXE5EjSa2UD1xV4y9GxgLxqEyvlDZdgwkD/h5RK7M7tGDVMAb4/HO88B3WNgh\nOr9yHvV8GjJUB2TT2xGpoFgMxoQwtWUigvYE4KrYK4BVTZH8+NciVp+ViAVoMhYvh7iNQuAwFtoN\nL2SoJsBm50VNMA6G4qRUo8I3ULFqky6+QhYb32qXWuDWaMpFdUNKw4OU1W0IXAHG/BCdPw0YJ+fk\nFDCmh+a8mhJEEB6MVMy7D7mGIGbvAcj82AeLigt0Fh8BlwItsUoylGhCgnoOuMfjjQwgP/SbXvUC\n0AGMs0N7Xg0AFr2BOR7vPAe8j8XSEJxbAfuBjMgUvDqa8FI4tQZ2VHHsVTuBuyLVJkHycf+C+MXl\n+HymBW6NJihUbWScrfN48zOkYtsqYDUY+yp5bldFuyzgdDAKyz5eUyXEtN0a0bj58h6ijfsP4o63\nGKli2gHIBa4DfsHiKyxmQUkWlJewuCvcXT/6ULWQmIYFPh90BWNxiNpwafK+BWN4aM6pKYVFSySj\n2kPAmR6fvA1MRawZJyEW/OVBCc8WjwOPAk2x2BLqLmtACq/xBOJmuQtxaf4ZcX2s6AYnJgXuH3EX\nuPHkIeB9vAXsPYi/mz9qIov4U/jP2a2Axz1eZzl/Go3GLyoDeAPRaPryDfA0snhvRzSnWd4CtGqG\nlKOfDiQgG+LvEX/VZmBsD1vXNd5YxAG9kYWksrnXvwLexvJKy6oJOSoOcbk613ljPWKVmAV0AePt\nSp63GaKQKgI6aleSCCGb07KCG68CTkSu9/FAcywWOmO2BZLy+BTgY2ACFiPC3OOjHJWB+OM/4/NB\nOnAQiWtMAGqA4VkBPRPvCuaPEYMCd1ksR/6BbUg2BRv/LiWJiADwHTA6wLm0hlujqTTqSuAs4Dfg\npTIOnA3l5oMeCsb3oeqZpoJI6sBtyPVcB7yJaOPGIZsoT1xacO2zHVFUAlDgvPgESmWdWQt0Aw7L\nJlclAqlg7A/ifKeBoYMlI42Mn9qITPNlEN9Yh1inXHziBCxrIoJqA3QG/ufnw1yk6Nge4J/Iung8\nGN94noBqJnA/B+wGRiHBknUoHTRpIJrw3cAdZZxLC9waTUhQdZGxeBkS+NgTeBfRoHYp58sdwfDn\n3qCJBSxqIQtJAyAbKSbWGOiKxdRodu3oRKXhdu8Zg1SF9GWt8zcIcVU4G3E9qA28hWhJf3KOvR2M\nV8LcaU15WNQA8oHVSDrBhogrrG/Fyk8QueVqLMIQwK4pH5WMCNWBlLmejELiDk9yxM1qJXMGU22y\nP1CMZClZ4PwN8XMubT7TaMKCinceDVCXgPoIVBKoDaAGOp+dqovbaDRVRR3njLP+oP7Po3CHArXD\n57W/v2olAByVWNTA4hgsxkW7KxpPVEtZw1QyqIfF6qseDDzWjm6Z86j+5zUajUZzpKGuA1XDLUir\nmqC6Oot+PKjTxd1EJTlBmRqNJqSoBFDvSrVK1d0Zj/U5ymXOo/qf12g0Go1Go9FEhGpV2j2YKpMp\nSL7LP4GllI4q1RwZZEa7A5oqkRntDmiqRGa0O6CpNJnR7oCmSmRGuwOayBItgft+RODugORB9A2W\nBMgDTKSUcTfnef9IdVATMTKj3QFNlciMdgc0VSIz2h3QVJrMaHdAUyUyo90BTWSJlsB9NpJ9BOfx\n3ADHuSJ3k4B4JE2LRqPRaDQajUZTbYiWwN0QKaiB89gwwHFxiEvJdiRPd9XLp2o0Go1Go9FoNBEk\nnGmEQlVlEiTn6FTE9STLz+erkap3Go1Go9FoNBpNuFgDtIt2J4JlOW5hvLHzujweAe4OW480Go1G\no9FoNJowEC2XksnAVc7zq4Cv/BxTD3f2klTgdKTwjUaj0Wg0Go1GoymHYKpMdgPmIz7cfwH3RLiP\nGo1Go9FoNBqNRqPRaDQajUaj0YSOIYi/9yrgvgDHvOp8vhDoEaF+acqnvGuXCWQjbkMLgIcj1jNN\nebyLZApaVMYxetzFLuVdv0z02ItVmiMZupYAi4FbAxynx19sEsz1y0SPv1gk2OKLR+TYi0eykbQC\nEpEfoZPPMcOAKc7zPsBvkeqcpkyCuXaZiG+/JvYYgEwkgQQ2Pe5im/KuXyZ67MUqjZDibwA1gRXo\nda86Ecz1y0SPv1glzXlMQMaVb/HFCo29aAVNVobeiNC2HigAPgbO8TnGs6DOHMQ3PFCOb03kCOba\nQXjTVGoqz3Rgbxmf63EX25R3/UCPvVhlG6JGIR9AAAAgAElEQVSgAMgBliGxTp7o8Re7BHP9QI+/\nWKW84osVGnvVSeBuCmz0eL3Jea+8Y5qFuV+a8gnm2ingZMQsMwXoHJmuaUKAHnfVGz32qgetEEvF\nHJ/39firHrTC//XT4y92Ka/4YoXGXkIQDaYAeUG8F25UkMf57hSD/Z4mfARzDeYj/m6HgKFIqsgO\n4eyUJqTocVd90WMv9qkJfAbchmhKfdHjL7Yp6/rp8Re7FCMuQa7ii5mULr4Y9NgLRsM9K8j3ws1m\n5KZ00RzZTZR1TDPnPU10CebaHcBtvvkO8fUuq/qoJnbQ4656o8debJMIfA58iP+aFXr8xTblXT89\n/mKfbCRldS+f90M29hoDJyCZJXo6z3siEn4wlSGDobzo+SsQM8tfwExEdd8K8acpL2jyJHTwSKyQ\ngJRCbUXga9cQ906xN+LvrYkdWhFc0KQed7FJKwJfPz32YhcD+AB4uYxj9PiLXYK5fnr8xSa+xRd/\nBU7zOSZkY+8qxGflgPPo+psMnF/Zk/pQXvR8X0SVD5JWbhkS5bsaeMB5/wbnz8VrzucLkQ2CJjYY\nStnX7mYkbdKfiAXlpEh3UBOQj4AtQD6y6b0GPe6qE+VdPz32Ypf+iFn7T9xp44aix191IZjrp8df\nbNIV/8UXwzr2LqjqCcqhFWXn93WRQWk3BI1Go9FoNBqNJqYJJmiyC3AcYvLwdAZ/Iiw9Csy1uFX3\nGo1Go9FoNBpNtSAYgfsgbkE7FRhO6dQo4cZEzKD9ItyuRqPRaDQajUZTJSqTbD0Z+AEYGKI+tAL+\nh/jL+KMb8AXiw706wDGrgbYh6o9Go9FoNBqNRuOPNUC7SDR0DIEF38rQisA+3C2ctsoLItA5R6sv\nVrQ7oKkSVrQ7oKkSVrQ7oKk0VrQ7oKkSVrQ7oKk0lZI5g3Ep8RSG44AGhM5/+yNEU14PiZ5/DMlB\nCfAW8CgSLDnWea8ASZuj0Wg0Go1Go9FUC4IRuM9yHhVQCOxABN9QcFk5n1/n/Gk0Go1Go9FoNNWS\nYATu9UjRG1c+yZlIbkKNJhRkRbsDmiqRFe0OaKpEVrQ7oKk0WdHugKZKZEW7A2FkD+KdcCSxlwhU\nAH0UcSt5HHElWQg8EqJzl1dpEuBVYJXTbo8Ax2gfbo1Gc7RQG7jR43UTYFIY2rGQ2gdWGce0QQpD\nHAhD+xqNpnpyJMpkKsDzkLISSPF4neq8FwrKqzTpWTazD4HLZh6JF1ej0RwxqDhQ8SE6WSuCKxZW\nVR4D7gzyWC1wa2IIlQrq7Gj34ijmSJTJqixwxwVxzGZEyHaRQugqPk5H1PSBOBt433k+B6lr3zBE\nbWs0Gk0IUWtAtfZ4HQ8qAVRz4EvgV1DtQWWASgx0liB4FkmDugAYBbTELYCPBL5CUreuA24B7kbc\nAGfjNvO2Bb4D/pB+0TFAW56pYwfiLk89H6hZhf9Bowkn5wFfg/pFxp+qEe0OaULCSCSN9OvAK8B7\ngOvatgKe9zj2I+dxDZJ4YyzQPBKdDEQwAvd+YAkw3vlbDGQDYxB3j3DSFMle4mIT0CzMbWo0sY/F\n21j0jGSTCi5Wzs5egaGgjoIUBasUpDvvXalkY3wUUSI8twGGguoL6gMkyLwA2ABkAicj1sE9wBug\nGoCqTC2E+5BFpIfz3PccxyECx4nAv5E5vCcicI9wjhnHRx+twrYf4P77J3HOOZ9j2+UtRncBNznt\n9gdyK9H36odFHSx+dJ7PwaJpNLrhjK84n/fSFZys4EQF30SjX9FHDQfVElR3UG+Bmgrc7nx4CjL+\nckBtBvUOqMtBfS7fiQFsezu2/QC23SjaXYk5LAws2jjPT6QWaRi8CdyM+FMHo3Wej7jg3Yi3PBlx\nggma/ALRzoD8Q1nOo2+p93Dhu5gciaYKjaZsLCYCVyFpMxOAa4HDhDGAWUFrxML1K/AwTkpOR+ie\nBpwK5ANJSDXYrz2+W9cQwfJoIN9jWno9wDG1fF67MjANRH7filCekG4jFYIP0qTJASZOfAv4iM8+\nS6ZduxvYtu0zDh4cSKNGpwL/YvBgGDwYYAO2vRfoh2ku83PemcDLwERkXdhcwX5XHyzqIr/hP4AL\ngUFYJCBj4D0sUoBnsPguVE0qaGzAVuf5fMSN8higO5CO+OlvU3A5ogRLAu4F/gWMA85U0AVYYhzx\n66RyWWoKEI3nCgJbaVw0QSpWX+O8nozbgh5abHsmMv+9gGn+gm1fhly/MUhMWn3geyT9cQPgacRS\n9c+w9CcmUMHfk5YxEFEqDALGY5EI/E5v5rORnuTyB9uYTz5JlH+v98CdWvpeKuj+pmSt/c0ReBUw\npDJaEghO4M4ARvu8d7uf98LBZrxNAM0IPMlbHs+zOLIjgDVHEhatkCJPOxFt5J3IQjIImaTnIovs\nFsQ9wEU3LDoCcVj4E5CCRol7wHkGTHBeNwHWehzyo89XTnUek5zHh3w+f0rBbUboUojGCGoQkIMs\nlj8D51fxhKHXshnGYaZNuwyow6OPuub4/Vx4oeuIslwCM4Cl2HYfTNP3s1GIFvVMRPgejAg61ReL\nzshGtjmyiXgZWTuu9HO0617ujQSuDsDiPiyeq2o3FHRGhGhDiXWiB7LQj/U5tBGy2fXl/5zHRYhl\n44+q9in2UOlgHHBiIXw384GE7S3At8D1SOKF4z0+G0qoBW7brotp7kasWQDDse05yObpvz5H3+/z\nehi2PQfT7BPSPsUMRmk51WIUIgT78ovPaxl7NenJCUAHevEha1hNEm4FxD6kpgtIRfRi5/kCvIPM\ny0RBPGIV+Qo4HWj/FcyT7gIy91eKYAT1BZTODvInsusOBa0IXNp9GOKDOAypNjka/1UnXRp3jab6\nYVFEcO5dZVEPi92V/bKSxWcK4vu7AtGaVZblwLHO89qGbCKOAFQPgrcotEOq5LbBvXG5H9k82chG\n5VEgD4wnK9iRusgC0AqAGjVaY5q/cNdd05kxIx7DOJZ+/USw2L27iLp1KxKsuQzoBIBpWsjm4kXn\ns7aI1glkIzgB0RKCaI3SK/h/RBeLesgmtzJ4rovzsTihMidRot1cjGxifkd+e9fmeT2ua+yfbYgA\n7o+LDPisMn2KTZSJbDSmI8kWgmU7GI1A1UcsFgXI5qQ2ojhsKHucEGDbBiLk9aTilsfLcQvkCZhm\nUUj6FD38y2QWxyJj7jzgPxU851j+5EaWIk6Lm/iTbayjmN2I+973yJisi1gUxyKJNlbjVhiNxkdJ\noMQHfCSQhqxVz+NnLvN06TC8HoKnrC9chtwEA5Cb3EU6UAScVtHG/OBZaXI7pStNArwGDEEGy9X4\nv5G1wK2pHlg0ArZjoRyzdXfgpwqcoSuBMlRYFR8DSsbfYOABPx/7M9MORoSECc7rZcA5iAB5gdO/\nixEtYTfgbMN7/qimqHhkUvfHeES4SUQE0Hpg7ADVEIztoFoB+8Hw0cqp64F+znd/Ee1d0ExEft8p\nfPDBfpo3fyrgkXl5E0lJuQKAZ575ggceqMH69TajRp3J2LEDeOKJDTz4YBMSEhKQa/cpAEOHPkle\nXjZugftVxHWoGBESR+LW+lYPgdviRSQJQE3EXcQfVwIfIhqup4E3kEW5EyKkxSO/fztghvOdFCwO\nB9MFJWvVMAO+VVLj4g/kt721gv9NCpCHWL1eQK5TEu7NctyR4VqiTkcUAb78hYwBcF+zDOAMREPZ\nHPgbjN8DnHcZMvaeBqNq8Qi2fQpureyPiGa0LHoj7mcnOq9PxW25uBD4HtM8WKU+RRe3TCZWpLOQ\nNSFQpjkXe5FrOAH3+PweiT951fn8CsT6ezWwA6viiTQUJLqsryrIMeII3NOBAeEQuFsiPpzP4h2Y\ncwAxzQRafKKBFrg11QMLhWTfmYZoD/3RD1kw4pCsPHmIubkOcq/vQRb9wbjTZgLUx2JXMN1QMqk9\njX+fwQ3AQwZ86PiS9kBcRl7z1FYrETgOGwECURS8A/xuuDfP1RSVQGnXmP6IgDkbjOxKnrcDbm3L\nLWAE8v8OjG2nIgJbZz+f5kn/uBJxxVsA9Mc0D3l8/3PgDkxzA7b9IXAHotxYyN13f8+8eetxC9xl\nEdsCt8X5iAYxF/9Bvc8h69pEJ1ArESh0xitO4FZtLBZ4nPMYKLEqnYIV3MbSGXt7kM321/h3KxqD\n+GV3Qdx4hgGfIFaST4H1hjjFDgemGG7zuWvcXQO0MSRTTTXG70Z3BfK7r0Q2fXUqNwbVeMSdaDgY\n31aqe7Y9GJmvb8f//T8G2QCdiVj+4oBETHMZth2HbJq6IWMzD9nA9Xe+G49pFpc+ZbXAU+B+Hm9X\nSF+eQmKE2mKxFouWiPWmANk0bcSi2FEouSwBp+HWWsdjUaHfyRGyWxN4fDyGKBU+QNyOXjDEUumS\nMyslcx4pQqoWuDWxi8VqZNP6Nf59mu9GBOt7EL/N90oW+vLPXQdZYM8HzijJplAGSsxt/haolUAH\noJbhBJYoWbj7GdKvCqHEF72lAbdV9LuxhWpC6diRemBU2oXH49wPIBuf+8ComC+wbddDFp6PPd49\nEXFb+Rq4H9Nc7hw7BJgetNbMtqfw7rtbmTChP2KJtAIc2Qbxfa4BtK9Q/yOBLNInIhvTuj6fzkS0\nZeuBy7C8fsdgz+8ap7dgBQyY9cLDX9sfHwDXGxKM7Pu9BgbsCLKNH4GZRtlFi6oBqjHih+3ifOAP\nMDZKrm3qg7Ghkue+Gim+B5BWYS23bTdDEkr0CnDEemRs34VpHhvgGN9zngFMdV6dgGlW16rengL3\nw4Cn25wr0P404CosrsKiERbbgj67RXNEMXQA6IwVfKpqJW0HskYlAw0DKJE85cxKyZzBBE16mjmT\nELNpDqWj7ivDEMSnJh54GwnK8aQeYiZqhPT1BcR8q9FUJ9oipnpfn8pVWHQAwCIVGOcI58FjsQ+4\nAIuXEd/BgAK3ck8Q/X0+ugTR7m1GBOSSMW/IgvQulWMpotmppqhURCDz9DU8AVgNRoj80o1nQO2D\nCqZ4tO1uyDXzZTWyaBRgmu5Nm2lWNNCnmGuuuYYJE8pbVNYSuniecNACqeHgybVYHve0xX34d1kI\nhj6IltmfhaEUSlxyfI89AfgcuNQo3dcSghW2HX4FnlDwlSExV9UQdQuiIfbA+NLjeS4idFWW8YiG\n+Q1Eyxzwty/BthMRC0UhpYWyich9dCIwHtOch20fR5D3hoPL5eInJA6mugrcYDES0VA/4fNJE+Ag\nFnm43GgqImzL8RuxqIHUEuhAkLVhlPhpn+Lno6cNd+B/2FIHVlRCj0PM4SdROsK2osQjpqFByEI/\nF/Eb98y2YCGLxwOI8L0C0QT6mpi0hlsTu/jXVu8HvsHiihC18Q/gTCwuDXSIkkC7psgYWoT41B1v\niB93yFEyZtcgKQJjyQUtSJQrkNTFajDCoMVVpwIWGP4WAm/EDB2H+IoP9fjkIeANTHNfSLpk2y7N\nYg0vF5TqhoWFmIddTAcyK2qCLqeNXoiWv2VZlikPVxIX+cBIw12gI2QoUVAtAHYYoYm3ihAqDsla\ncRXuwGuXm0U/MGaFuL26wC5gJBjlZyyxbZdFykUu4nK3y8lOUnXEGtUPKMY0Hyvv8BhF+bGtvO8I\n4VVhJBIrJC5FbbiKArLZyDwkGH078DiyyayN+H2XuIEpCaT0daO0DXfWrbKosoa7opkRipFAkiEV\nbcgPvRFtzHrEzP4xEnzlyVbcmvRaUBKNqtFUD0oHMp6KbDYbQZUnH08WEEBLquA2JZq+nrgrtT5v\nwK3hErYBDFnItiGuMtUI1RzUNETYXoUE75xK+ASXYHIIuxiNzJcnIfPwg8CpmObTIRO2AUxzK7JB\n85cVKvax6ItFf7yFbYCxIRW2hXmIMO0v0xYASgL5PIMiPzcgORzCNoCzwT0XKYoTCmt0pKgNPINb\n2K5HSQxBqIVtcNzCHkKKRQWDZ4XYh4FWmOaKkAnb4LJGVWROiC2sUjEJuxArbyhcCxXwJrJ2diGb\nDVxBMZL271PnmEmIT/0NyDXyxFPYPs2QXIXBCNshIRiXkgs8nsch5q9QVBjzV0XSN//kfxCTwxYk\nIOHiELSr0UQGi254+/6mOmY0CH2VvuVAeyw+wCqpJuhiNN5581sZ8HeI2w/EnYhQ+EyE2gsFfRHT\nP8BWMD4Mc3tbgDQp5mHsLedYlwCcAczGNKeWdXAV+QHRLPrL+xzrTEPcBUDcEhYCs7FYGvKWJONQ\nTaeNQFovz+t0PxVPiVZhDFijxJLVk+pTlyLF43m+CMRqKlIkKlwsQYSzYPAcn1MwzYq4+VSExcDj\n2Lbh5RoW61jEUboCeU/HBcSf1jvQecrSHl+PrCnvko3J9wzE4E0UX+Dtm53veq0kveZg5/3NSAaf\niM9rwQjcZ+FOm1KIaKR9NdGVIZib6EHENJCJ7JB+RBLXV6hSkEYTcSwa4+1j28FD2A5He4XOZPYP\nLK7BCmgJSjMiW5J7FtBFSbvVxTXBc9KOwMbEUKAWIL6fgX2J3e4kK4FHMc1w5zdfgrgQVi8kkHg9\nLi2pVeF0e5XhVkoLGgAoWbtctDPcucwjwVxkzcyKYJtVIdXjuVNUy8hFAsPDxRKC0XDbdl0kePxP\nYDSmuaCcb1SFhchv0YLIKUdCwbm454yFwPNYjmK1EmlrAzAOcR+ZRCG7GMpmzuUlLFbibbFNdv4A\nXnH69TziVhKVtSgYgXtkmNr2rSLZnNKO7ycD/3aer0FSuHTEfxUty+N5FtVngtEcmXTyeB50ur4q\nshmxHLXHiYVQ3oUxukVY2MaAvUoExO6I8B3jqPqI2xzAODCC1XxVlZmI32Zpgdu2XeWL/41YGC/D\nND+JQJ+W4D8/e6zjqYWM1MI6Fin1XhurVAYgVylxIixsgwg9fSPcZlVIcx4nIQXxIsE6IB1UIzD8\nB+/Z9klIik2AhzDN8JSEd2GaCtt2VcasTgK3p8vNtVhSoTHEGMg69j3wb75gD0nMII77KGYtcBGy\nBta24H+PyXx+NjDCcNePqCiZzqNVlY6X58M9DIl23u38/ULosg78gfworZCd7CW4q5a5WI4EVYL4\nnnbEu9y0J5bHX1aI+qjRVByLpoiv2Bqga4SEbXCPzXFQkpVkq/NebSNQwZzws4gy/FtjjGHOYzal\n/f/CiUvg9scsxDzq8oWP1P20AjgO2w5UzTD2kNzYLjN/c6B+hNotRAqxeFVlVqKJuxeJBzjRzzfD\njW858xhG9Ufmil1gXAxGZYWjCmIUIbJNWb68N3s8Hxve/pTgWdgn9rG4Fs8UpeERtt8HXDnTXwNq\ncxmTuID6PMo85DpmIj7cVz8G7+H2yKiKa2CW82hRBaG7LIH7eiSdi4XkWm2DRH8+RvD+TmVRiJRt\nn4qkD/sE0crd4HH+p5EclwuRNDn34h3lrdHEFmI224Tssl/FYnEE216ILOz9nX60cH0U5fLq1Wnh\nGO88zgejsmW/K8MsoLdTZMcXV9nwLxHT988R6ZFp5iJufNXDrcSiPaIYagCchMUmrIiajufhEbis\nZH3NRCzJluHfMhtuFgPHKm/NY6ziKhxULwpt/0rpdKmCbQ/FXclyIKZZXpxFqKg+86ZFCpLaORq4\nrBJeCgvlHU8xORaqrpblUnIncgN6Rt9OQ1JRzSQ01eO+c/488TzvLsSHXKOpLtR2Hr+G4AphhJjh\niFa7OVJeeBPR02y7WAS8rOA+I3B1zRhAefqProxs28YeUJuQBdY39+4+pDriaEwz9EF/ZfM/Kpoj\nPHq0cR5XYAWRUzn0zMO7pLfLZfIg0RG2MeCgEn/244jpfNyqgceL8sqih4NZSCpCf7hSg96EaUYy\nfmwh8EgE26sKntmM+iOpHCPFW4jv9sl4Wx88K8oGTJcbScpzKfGX6mY3MbBT0GhiFJf24wosiiLe\nuuQB/g2ZAB8F/s9wu0lEi2mIf3ksF0gBUTKsBGoiaaYiTWm3Eind7lKMRNT/3mE+1UfgdmlGoxVU\nPx+3NQLEJRIDakZZuzYP737FIs86j0PB+CkK7S8A2oPyTqEogcoAp0dY2AZx6WqBbaeVe2T0qY8o\nd3phMTOiLVscRDL/nOzzicsb4rRIxy4FoiyBez/+F8hQZgkZgkxKq5DS1/7IRAbDYrRvtqa6IJNA\ntPjtgiWchwhqFa0wGHIcYeNrApdAjgGUATwFdADjoGQOiTj+/LjPRzZQnYmOlnQh0MWpsBe7iAvV\nIMTHc2g5R4eLpUBTLOooyTCRgmx6o02Mb5pUDWSTC1Grs2HkI79T75K3bPt4cJQmphn5TYBpFuCK\no4hlLNKRHNiFYfLbDoYVQAYWpeJNopH+LxBluZTchSyS7yE7ZAPZJY9E/JmqSjzi9O5ZaXIy3pUm\n6yBm+cHI7ikavl0aTXBYJeasaJsBZ1+wjNuB0bHgt+bwB7Fd8a6D8xgocDESzASeLHkl1R4/BJ7C\nNJcF+lJYMc0cbHsDknXnr6j0ITiORVKSdYlgkLI3kprzD8S65PK9DW82i+CYhyQliFVeRWJegJAX\nJaoIMxEtqUu4dmlMo5nOdCESiDs3in0oD1es0O0RaCuJkkJIJCEFpbpg8TqdOMA2vgQeVzDrYeBZ\nKZIzFrmmXYDPkOxLIPE6Bcgmz8ZdOCdslCVwz0AK0dyMOzXgUue9itW9949npUlwV5r0XFguBz7H\nnS4wOhOpRhMcUsXK4qko9+OPzjupv60G06OqZ/fmD8TsF6s41ywc1eyCZjWQDKoFGBtwZ5doHMU+\ngVtDGpsCt2jYlgLTsLwKTUWDWUgavtaI1s031W00WAB0VZBgxGalZleF1IcRwSdazMI7G4mTBzwk\nFRIryxxkAzcuin0IjEUyOIkBLL6OQIvXA9/gLiQ10XlUDOc9DtKQNzj9X3BSI1hR5HYNTEQEbk8U\nskmI2CpZXh7ubYRPWxdMpcn2yA9lI5UmX6HyeRQ1mvBhlUzOd0W1H4Cy6J2bAG1vwyjRBUSfZUBT\nJekJffMURxn1IHAh0QnW8sBQoFxuJRsQ7c0sJFtTNHH5Jo+Pcj8CMcR5/GeZR0WGWcAdiDVnpBFd\njS0ABhxQstZ2IvoB1D6odkjsxGjgmSi5crmYDUwAFe+kCmzmvB/pQGXfPv0riu2Xx0nlHyKoClhb\njcAVWzvjmXpQ0qUK79KdIs5IgF4tYElb7/mqIMD5RiOb0C8pq+hYiCgvaDKcBPPjJyKalWGIW8kj\niBCu0cQa7YE1WLwUzU4oMe9N25PK3q3pJeXJo46jWVtIbAZvuYprRSOzhS+eftytgZcwzUC1ByLF\nb5QOSIolzgXexWJVtDsC/Pr8VPoXSzaeKeUeHTn+oLRCKxZwzQdxYER5c2LsBLbj9pluC1yOaUbT\n6rUYaI5tZ0SxD2XRFnHXKHetMcAI9q+M0yzBew1JKnnWjeu4FJLjuP044Ha8UvIm4Z/bES142IVt\nCK7SZLgIptLkRsSNJNf5+xUxs/qbWC2P51noAEtNZLmH2AjO6Ajw3648AVyBp09w9Pn/9s47zK3i\n3MPvkbbb627cC7YxGLBpAUxJWIVA6HC5hIRAKAlwkwCBBFJIwuXk5oaWXEgooRNaAgkEHEJvQw/V\nYBuwsbFNM8W9rHe9RZr7x+/I2l1rtdpdSeeYzPs8erQrHemMdDQz33zzfb/vRbTdHoXvqS2LgS3B\nC0vdoi3PA8diTBnwJeQtDZtXgK0xpjYEpYZ8+CYwKexGAFif+laPiphKSpdKrzkfngW+SHhayZ2R\nlgMcm/Oo0qHCKca8g4zIMNSKMiQSrRiTXiyFngCfhfHA8/gls7euBy5FctFx2lZSNjSwD7OPSHHK\nzsCHMIiMNG+6dsF5qF+mY+LTHu6XyL2DVxfc+739APnSD4V1FIoyVIlvPFp9vEH7ctigRJjH0Rdb\ng7bDts3yXlFJDHP8O+IzFp8G/PBl7ywcaeEefCrwWYXPsLDblMbCYVaFVCKEfRqsBRtGFcAs2Eqw\n9Vz3yu4YMy/s1mzEmGcwZv+wm7EJPmvxsYFKSehYqExC8rEtS1jwKg8sTLFaWEYIOzLoexbst8Nu\njbDHgJ2BMftjTCm1pDvHmAsxxg+7GVlR3/tBlmdCscmqf8ElwQ/qP4rw9raTv/Mmn5CSXZGhOwe5\n6GdRGHmvfCpNzkOrutloBXI94cZTORzZuBzJgEUhqawWWIdPMzJuw9bgbsuzwPSIVb37UnC/OudR\nJcNrAl7iw+oTIDSJrWykPaRRQ04gPzJOl9pkjPr9TohccuI8oK9tv6scNukF3PfAuynUlmR4EtiH\npti2RGM8B+UFZK+CGSb+xvHg1lDb0YYD3uW1ZTW0eorJjhz5GNw3Ad8HxgW304LHCsFDaAt8EnBh\n8Ni1tK82+TsUUzUVGTYOR9QYCnwdP/wEKWSApKs53o8qT0YCT1t57xItPe70VmNEDG7gzn+N5svL\nvodCcKJC9AzujFf7xDCb0YG+ZSlWA6PxIxMmkdbCf4ZoXcORwX22Ansh4X3G/p8uoTJ1GarYGwWe\nAXYPimBFiWcA8KMzdt48g7dXVgP+JtESkSAfg7sVDbZpniOa0kIOR+mRLNKeRCD730q3/goyCSIP\nAfsGbYwKipGMDhY4JkiYigbDmqQJ3uK9HnJL2vICsEvEJv2xwMf4kdC6TrNlsLC8Fzg67MZ04BlU\nAjsqTEM5C490dWBJ+f67g4O/Hgq1HWkSiTVofomOlzuz2J2W87gS06+Jfp5lBRFyNLUlH4P7aeRx\nrgtuVweP7Uzvq1flU2kSFNbSiqquORxRYntgPj7hFCZpT3qL9p8A+CxDIVh1IbUnG4bIFMCxV6HC\nWx92dWSJuY97Rq1g/32i49hIJNYiecC6kFvSlp+iYmmRwOp3/SQSBLgTJXNGiceBA2xuFYgSYQei\nYjz/A97asFvTjprkUtaWWX46NSohJaDwwJBlS9tRF9y/leugIrAd0t7+AxIqOAZplF/hqc7DrT9d\nQzVNHB4c/19kFplH0j5M7yHkoPoHKhVrcE4AACAASURBVC4Eyiu8OrgVPPwqH4N7R1SF7fzgtk3w\n2P9Br1R+05UmD0CJkMewadJk+riLUSx3BAYKh6MdRwAzwm5EwN7Aj732RsgdwLdCak82ngB2s0rC\nDpsjgvuVobZiU/qwpPqfwPFhN6QDDwIHh92INmxFtGI1ByMH0jFIJWs4PtuE2qL2vI10waNQKjxd\n8r4551FhUG77ctaOs3l5cJTCb6JmcE8AbgkhjHI/VIvlTOBG5GQ6FThjKkx4BSZ4SZrxmJIlpOsI\nZKynr2s90ji/iMzuwUykTPM9iuCIyUcWsK7QJw3Ip9Ik6Au5G3m5HY6osSe9W3gWkmFo678tdwC/\nxqc/fvgFZzyot9pG/ipwV8jNWQjcDl4UdifaMoAdV/+Be0b/CewPg0TKKPAgcB/GnEEiEW6SogpN\nfQU4N9R2tKcP8KIHa/EBnzvRYvcXobYqwJN6QzqvI2wVlXQZ8DdCbUV2BtMcuwn4Npkcj7B5CRiP\nMSNIJKIQW/5rupssaUz+Y0Yi0Zlz9Ua0s3UUMJ82v+NvQOphePkReJckDSi347Pg6VEoT+cWZGA/\ni/rrlWhnKm2E74S82wA/AQoqg5rL4E5XzLNt7pejGO5CyAvlU2lyFDLCv4wM7qhkojscaaJUvW0A\nHZP/fJbj8wSKJ70+jEZl4T7gMMI3uMcDJ4TchmwM4IvL5yKVhP+gfWW1MHkTzRnbsKljpNSkPcdR\nCkfoS/sy0TcAT+Dzq0A1KArcj7SILwq5HcuAXcCLTMIdQKB/35eW2FXAIrBjwAs/5CyRaMGYB1BY\nxFVdHV5UFL89gkzSa350bkR3h3XAL4O/HyJjUPMGTDwU3m6AVczicaZzDR43YHkBGd+jgf9F1TH7\nob56OlqAHo/0vV+niNrruUJKatEAUhvc+iGj92G0ZdZb8jGef4/iciwKJ3EhJY7o4FOGBp7lYTfF\nwkC0G5VtAruZaCk53A8cFK48oJ2IYvTCn0w3Jb1wugpNCNFAXu0ZyLsUNr8K7qNQXRKrhfflZBSC\nwOdtFGJyRCcvC4Onge1s24IhJcdWI3tiSXht6JRBwGqWVqe9odk0psPiLqLR99IqU5eFcO7DgT+i\nvjYbVYi8tj/MGAO7HAfvANfyMN9kBuVswReQQ2xPtFj5HvBzVBQubYPej7zcVWQ83FcTFJELm0Fo\nFdBbptO+ctK5bJo4uQh50xejlc1nyDPWEQvBJp5udQVon8ORG5/bo6L/m64eERje7fEpx2cJ/sbE\nkNCx8IINNR7YztLXFTGM+Q3GWIwpB1sG9v3oFOUBjNkDY+ZhTLjODxXciMz1s3B80P/aGyE+Xyth\nFb68sHCzzYR0hNGCXcC+G975c2DMyRizVP/YiWCXqh9GAGOqMWY1xoRbzMznT3n0vZL2zcvhyjPU\n/3608UGfY/AD6cLeU0d7O7NohW86UqgEo1dR0st4JGP2dTbNOJ8AbBnc7kark86y0v02t6cK1EaH\nIxfHEgEPm8304228bOWkfVrQFvJ/b/JceNxKuOEcHxG9pESQ90VbyHitqA7BeaG2qD0vop2JKCze\nvhZ2A9rQmab0DGASfqS0528Hjgvx/Iciz2S0MGYkCru7Qg94C9H4Xogd/d6TSDQCDxD+734XpBIS\nGc6AQUGRlj5tHr4LGIXPXgU4xVPBvU8vyrv3xOBOkG1S7z75VJp0OKKJv9GT/P1Q2yFqgXpP22md\ncSOwJ/4meRJh8Vdg/6we+dIwFCXdRAdj0vrp09s8ej2wE9ho6CcrrOQvhKl845PWSY5QwRSGIXmy\n37Z7VIvdC1CSWVQwwEibXRWsyFgPqZ2FGNLSKTsCj5BItL1WvwTOB1sTUps6chNwasg7TFNRpEOU\nGI8WchdvfMSnFbiEiCQtQ26De06W20foA5xWoPPnU2kyzUnAPQU6r8PRW7YDZuLzeNgNQUZr7p0n\nnwYUsnVpm6IFoRF44h8hFE+bHY5218JO/OvIRGAJicRLmYe8DSiP5fyQ2pSNm4BvYUyfLo8sDici\nhZmnQjp/NrYAnvUgm6LMDcDW+HyjxG3KigdJFJ/83RBOv0Vwf0kI5+6KI2ATmbungFlkRCTCxgDV\ntF+Ulw6fYSjZ8KRQzt85fYAPvU1lJm8GpuBzUOmbtCm5DO5DO9wOQZnhuxK9icrhKDW7I8WeKDCC\nNtnaObgNJUKHvSWZ5nLgLCut/VJSB5jIFdzQGPuPLI/fBWwF9j9K3J7sJBKLkaxWWF7uY4AZUYrh\nRh7u7H1QCiUnABfjE5VKnVcB37LQv8TnHQHMBu/FEp83H05hE8eFZ4FzgLPAjg6hTe1JJFJoJ6Vo\nShpdsDvwND7JLo5bheKcS3LzYJonicn2z/lswGc8Pg8U4Dy9juzIZXC/1+H2Pm0zsAtDV5Umj0Wr\ny9lIuzdSZUQd/9YMQtJWUWAr8pHq1CB5BvJyD+7q8BLwArAUNlYFKxU7Er4OcTYORTGaHfCakZF5\nJdhSG0idcTlwJsb0JCyxtzSQrqYaAaykErdFv+Xs+DyLtJR/2+kxJcTTbvXDwMklPvVw4NMSn7Nr\n9DtuJuv34S1Gcd3XBCExYXMDcDDGjA/h3GOAD/I4bhAZZbmi3ywsDZR3sh/j8w98LunleXodRhPG\nYJkmn0qTi4AvIUP712hl53BEgYEUJpehEByBwjO6RlnbdwJ34lNZzEZ1hSevwW+BX9jSjkV7Inm0\nqDGOTncPveeQfNWVJWxPLp4C1kAoYRL9gnNHhW1QmEZXevynAUdFZXsb9b2zrXa9SkU0DW4YAqwj\nkdjQyfMXIPGGY0vXpE5IJFYB16BQs1ITOSnVQF52C9pr4HfkZOBYfPYtTauyE6bB3bbSZAuZSpNt\n+ReZgfUlJFzucESBLckvjKOoWGmH7kv2UITO+BmwAfh9BOK5ZyBj5eslPOcWQBSqtWVQElRXxshZ\nwG5gw/eSKnnyXOB/2iR7Fh+fUcAOFLgCXC/ZDnjZ2zT+tz0+y5AW8C347FiKhuXCk7zv05RWInA4\nERg3szAKef07wWtG+SZXgY2CasllwNcwZmKJzzuWnN9TKOyNhDc6N7h9lqPKoX/BZ+cStWsTwjS4\ns1WaHJXj+O+g0sIOR7j4VKFSsI+F3RSkGjTb645ig7K3T0Kf4eIwje7ASDkHuMBCCZQA7ECU0R4t\ng1vblY2B9FcneI0oWepIsCeWplk5SCQMcpqcUcKzpj1U+Wxrl4rtyTdEyecF4MfAs/hsW8xG5ckv\nUR5F96oG9pwvEc0csNF0WYjHex3Fed8sLfEQSSSWI0WOy0usWLI/CgWMEtsAr3pdaWP7PIpi3+/H\n31iptqTkMrjrkRch260QyUbdSXhJoNVJtjhvh6PU7A68jR+Jbe29gCe7/Sqt+A9HnuUb8Smdl7ID\nHjyDdrN+U4LT7Q08B15UwoHSDCevRYC3CiVXXiBPtw3TaQKSdj23hJ624cDvArm9qDAVeCvvo31u\nRt/bY2HLdHpSe7kKuMYWvZKz3Qo4ECUBR41R5FX50vsbSoB9HOyhRW5TV/we1So5siRn8+mPdlQX\nluR8+XMI+YdU3oN2B+biU/Lrl2uwTpd1/wMydEcFt59QGNHzJSgeKM0Ysm9VTENatIeRO2bWb3Or\nK0D7HI7O+CrRiQEeSk+9tT4LUf8aDLwScnzbGcDRVovrYrIj8EqRz9ETdkbbonngzUVhDHsAfwOb\na2ewuCQS76KiSreUKLQkX0WekhAYqQnoZkU7n1tQvYkHg4q1Yeo8/waFyJ1Y5PMEDjOv0OILheDH\n5B2m5N2JlJ7+CPb3YPsVsV2dk0g0I0fkHzFmTFeHF4A9gFlRUgcK4rd3R6pJ+eHzW+Sp/ws+5wU7\n1l1RR3sbs0fk4x05DNWuXxvcrqYwqgL5VJoci7S3j0Nbl7nw29yeKkD7HI7OOADFHkeBIcDyHr9a\nXvr/RJnvt+NzTxAnW1KCkJgTgTusxoRiMRkpI0WNHejWQsBbBXwFGZ/zwF4JdmxxmtYllyJnyP+V\n4FwTiZaHrRaIez1JBPS5H1Xt648WvOeHEd4VaBd/HbjESva3WMQpvSpKvkygW3aD9ziqtjoQmA32\nS6HsNiUS/0Ke7r9hTLElJ7dFdluUGAes87qbyOnzGDLUvwjMw+dn+DnlaZ+iRAb3emTwxoPbsRRG\nHjCfSpP/jX7QV6MEj5cLcF6Ho7eMJB8ZvtLQ+yQkn1Z8rkDqHQ3AHHz+gs/R+KVTMPAUE38h8KAt\nXiW6IURHzrEt41ACeTfwNoB3GvBl5IFZDPZPYEtbRlzawN8C9sWYYld125rcFVVLTef62/ng8x5y\nav0aTeRv4uPjc2QpjW9Pc/ApwD8sRYst34quHWelRzszLXQ7R8xbDt4JyDt+K/Ah2F+ALbXk6sVI\n0e0OjCkv4nlGEL3cl2H0VPXG523kPDsdFRb7AJ9f4bNlsfpePm+6JQoh2TP4/3ngTLo9ORQVS9Hj\nzxwOCKT01gJ9guTDULHwMbCbV8jMcZ8tkCfqQNTvlyAv5mzkDZsZVK4sChbOQwv7/b2CJ8fZl4HT\nwYvW4t2Yx4Dfkkg82vM3sdNQPOcZyJnyMNKrXg08Bl5x456NGYFCrf4C/CpQMikcPuVo278/ftaK\njiXHKofit15mfuw5/sbwlG+j3//bZKo8P4H6wkp8OpOu6zVWzrWLgUMCFZNCvvt7wJfBW1TY9+0l\nxgwF5pJIDOn5m1gP9b1voZDDD9HO463A7UADeLlVbHqDFg1/R07Rr5FI5JLI6xk+DwPX4nNvwd+7\nh1h958d7ksbtOT4x5Lj4BnAUUsd7DlVDX4CSohvahNP0yOb8vBipzuB2lAafacDfSp3lbKGPF8ge\nBXGjI9D/S4GaoFxz4fHZCnneLFJj2CF45jM0qSxBxt3jqLxuPfKSv4XCDOb3JObPSqrsZ8AJXr4J\nMfm98yJgP/AKH5ZgzCBk3MbQxFeGrksfVPK7HDYm+zUBycA7DMa8DJzRvqx7T7GVKDZ/L2A/tCMz\nGe0qvoOu2ZKgrauQRGQTmlQqdO+t3PR980BG971oAXhKoBlcGHwmAw/hU9AEzUAD3vMgGVQ9TVeW\nKwsOqQier7fSAE+i76wKeciO86CwVUBlAOyFDOCtUY2KdFn014H5qA/G0HjwbvD3+8Fz7+H3TDrR\nKsTsagLPbZfqD/m/81JgKniRicEHwJhJwCMkEgX6XdmxKDm7Dskfp8fMxchB8iYaJ+ei73YL1F9e\nAlaD17MdOHm3r0VhSseQSOSZE5IHPmWoCueEIOG+IARzWdorX4XGqCbU91pQ3+uHxqmhaMwagAzi\nBhQNsbMHpxaqTUHf2w/lyRyErl+/oJ0LgOX47EmRDO6tUQz38KAB09AW2P9292RZOADFH8VRDOnF\nWY65HHnaGlCMZ7ZVtzO4HaXB51RgL3xOyHVYMHFPA6pRBcFmFM4wFg0otSgxOV3Bams0CNegiXMt\nmck9iQaeFcHzbWP1kl7GMCgNPoPQ59gBfaZxQfviaJxoAiahAXIBGj/u665nzmrCug0wwE96FCe7\n6buuBcaA17nCjDGVyGM5GC1sypDBMxIljsfRtUkFf6cfA00Sbbd1uxqbHkcLl5OAKSQSRYovt1uh\n61GBEkfHoN/Z7shr3AcZB3PQOL8GGc5/BG92t06l7+93yHA7B7hz48KiN/gcApyGz4G5DrOakCeh\nzzgRXZ8J6Le5Dn0HSdT/tmVTOcokdBrP2URmQdWMjIQrPPhBDz5R9/AZi67TMLT4LUOfq4KMpG4V\nGncGoGv5CnBFsH2eN1bKK39BC7MzPPXjXmLrgeE5kyYlcXcAMkJrg9t4FC9t0UKnEsn4jUILnUp0\nPSYGj++ErtN0NGYmUWhqU/B/S3DcG2gXYRCwN4nEiN5/xo7YWPAZKtHv8QvBfQqN+X1Rv/wQGXce\nGucWo9okfwo0wPND39/JqFDPlcDvCuLt9tkVuAmfqZ0d0kblJn0NtkPzw1AyRQ2HIyfREDS2NqPf\ndFckkYOpEn131ejabYt2mH7SzU/UPXwGojZPAibj83uKZHA/g1a616Av0UMrtO26e7IOxJG35Suo\nU7+Cqk221eg8CMXXHIQmhj+gTtQRZ3A7SoPPb4AN+Pw6/VAw0OyAFoZ7o62pKrSl+AFanX+IvBhr\n0KRhg+ca0UDSgCb+FWgVX4lW+ykyq/4BZFb4a9Fg3erlKikdJvKKHI2MyZ2RHNgtwIv5er2DKnj/\njSaRO4CrvLzVPDZ5t0pkcFWqGjBp43A8Gl/2QeNLOoZ1NvIYfoImhiVofOqPQupiwXMtZK7VemQA\nrUGTQqPOR1lwTGNwXAUyzOvQJHQ0cASJRAQKulgPTZBHAt9Hn/ty4G/g5R/KYcwewetqkAF+d68+\nn8/ZwBj8TKEWq0XRFKTvvD2aQ0De30+RoVgT3NciI+tT1M/Wo+s0Es1Fzei6rEJ9OtXmsbSBXo+u\nZaMH1sqbuNiT9y86+NSi+XofNIe+jObPJ/G7KNATEChA/BAZM4+ia/lSzzze1kPfYTl4md04Y/oj\nL+VOyNAeEDzzDjI630b9ZhwwCyXtNat5/BTZBhuQSsUy9FttRde4Ho25C8mMnemF8hrUz7dHffAj\nEonCeUl7hI2hsWI6MkJPRwufq4Dru+X5Vtn3C9Bnuwa4nkSi5/HXPj8CJuHz/XYt1s7Z8SgUqm1Y\n1TwUklGLbDtLZkdmGRqH61GfWhTcf4j6XRUaJ+Oo761O/+aC+aAxeHw3tFia55Ve271oISWvolXZ\n66hTgAat3lbK2gMFqh8Q/J8uU3pRm2OuQd6tvwb/z0MDSMctKWdwO4qPDMjPgHOtz/XIu3QiKsrU\ngEIfPkC/2Vke0Ygz7THG1JLx6pahFf4gNBmUocmrKrglg/9jyAAtQwPqfGAeH9y5nMXX7Qs2vTNw\nP/LuPo/fta5/kER5BtLAXYEUjQzwL08DcB7YkcBM8IZjzHYoVu8MZLQ9i5KmnkOTfSOJRBTlyzol\nCI0Yiya5gcgTWo48ThvQdUyhsXIAGQ9tPzIGZjUyRFcD8xupenM6Lw6ZzQ7fQRPcw8ADqPjFoo0L\nl86Qx20/9D1/ERlujyPt+IXdivP2eQm4Jeh7+6Ncon3QoughFMb0ArDIo3g5BkXHmBiZXaPhwf0g\n1P9q0QKilswirg+6ztXB/2vR518BfEbjknm8esoOpBq/G7zHP9BY9SJ+1wmfQRjNd1FseRlSaHoS\neM7LuyaHrULhElWBMXgiWtBNRUbxqyg/bAawnERidZdvqd9WVe5iUaUjWKCMQUbzJNTHatA1GUMQ\nmkRmrKwKHusfHLNGb8Mn6L3mnMHlq67itK9ZYv+JvqP70Fg1J6+cDGO2B05DC9E3UE7H08DsQFYw\nP3yuA17H52orz/wP0fhZg3YhFwbvvQxY2WXV1SijReBo5JnvQ2anpe/G+0TixxTJ4H4IDZZ3IYP7\nKGRg5NzWy4OjUHLBKcH/xyEvU9uqZf9EqgXpykaPo1Xtax3eyxncjuLjsz0w5/4/c/zBCzgZedYe\nAf7Xi5ZyQv5o0toGGVO7ICWB0WjCTy8w0t7dz5D3rwVN6M3BfQPqg2uD11g0gfRDHpApyJNUjbVz\naV6+kjVzKljz5hgaP9qS1nXLaVkzh5Z1c0muXwz2o+B8K5Hht4ag0ElgVH4RjR11aEx6Dxlb88jE\nJy9BBsdaYK0HrWCnMnX1DC5/YzHyuN+MdvAeicqknQ/BjsoUFOM7FU2A44LbCjRxrwxuTcig6YsM\nU4u+w9VklKfS3tlKtCtThQy8KWjnZgdgeSNVi15jl+QzfGnIW2w35mNGlq1i4NwVDJ6/mgFz66ld\njCbcpcH9ynZGgZLTDkHVIhNoMpuDdkzfD9r5EfqdrSEdp5lI2CChMPXIrZy+/6KNzpm/Apd6iovd\n/FDfm4J2edPhBiPQjkvaYF6JvIFpj3w9+l5WoT5WEzzeiPqjh4yCarSQHIl+6+OBebSs+ZQ1c6pY\n8+YWNH6wJS1r19Kybh4tq+eRXD8Pm1xC5votQ30vCRt/d7uguT+B5utP0fWbh67dkuA+/Ttc40EL\n2CFst2YhV75+GzLc/xHcngSWFTzBtohYjXHTkNNwJ3QNR6Nr+DG6dunf8GpkVH+ArmMzev1atAhO\nG73rUZ/z0DVrRk7NvYDyVuJz32bbdc+x96A32HHUIiYMXsHgd9bT592VDJq7ioHzU8SXkrl2K/T+\nG3fyqlG/OxR5oieg67YAxf8vCtq8PHj9cuR00Ot9nvjBi9z5h4c5JPjcBvgtMHOzNa6NGYt2E6ah\nnektkEhIK/ouPiZTALL9fSJxEUUyuCcC16GLtApt8xxL71VK/hN5t7syuC9CK1+Qwf0TYGaH97J7\n/+Z/olR5zPF5xEvGdp8/3/7m9rver2xtvQj4y2bpSdNEvy/yMB0SPPoCWsh+iLbn3gfWFHQilMG1\nNTLCJwNjsHYLUs2jgeHEyvqCFyfVkiS1AZKNkGz0SLXEsakU2CTWWlLWYq3F2lQsmaLvhhT9NyRj\ntc2pWFVLyqtpTcWqW5JeZTLllSdTVKRSXgpI4dlHvrAb9f36ng7cSCKx2exABDkB+yBHxX+Q2Uaf\ng67Xx8B7xQhtCM49AV27rdGcMGwDlWOTxEfFSfYvp6U6RYxGqpP19LX19I1toCreQjkpYskk8aTF\nppLxVLI1nrIt8ZRdOajKWzhhZOz9ccNiy4YOjC0fPCC2YlB/b3X/fl5DTZXXUF3jJeMxqjdssJVN\nTYxd+pH36E/OWzSkft3PgLsLl8xXQpTYdgAyOndFi9dn0E7Q+2gneVHBF4DG9EMhFFuhvjcea4eS\nah4DDMOL98eLl2FbkiSbUiQbPZINMVItMWzSgk1hbQpLipRNYa31Uinbpynp9W9Mxmqbk7HqFuvV\ntKRi1a1Jr7I1RUUy5ZWnUh4WWmOefWj6nl5jdZUP3Ewi8X5BP1+RCfICjgQORv1vIbpuryHD9WO0\ns1JQ4zNY5GyBHBbboR2sUc2Uj2mhfKyH7V9Ga02cZHw9fZLr6WMbqY41URlrotJrpSzVSlmylXgy\nGU8mU/FWa+Ot1Pcp492JI2MfjRoS+3jEkNinwwd7q/vXxtbW9vXW1vb11var9ZKxGJVNTVQ2N9sh\na5Z5j/74F6u2XLbsd8D/bZa7t5r3dkH267FoXHsWLRZfQYuPRcD6POa9oquU9EGekULFGE5H6gfp\nkJJz0Y+1beLkNUhw/M7g/05DSsbtuPPGzNnBo8Y0DBk3fvMzhByRprGypuKF/fedEHt02Astl03d\nK+z2dBvFKx8DnI0GjDuR9vVrBUlq6zXWY+iGCVSm9qIstQsVdiJlqXGk7GgqGmupqV9PVdM6qhsb\nqGpqompDisrmJJVNlvLmJGXJVmKpFLEUxFKWWNISs+ClqLZNscEta6tuffDWXWpWz586fQlvhv1p\n8yGIWfwOiuesR17dvxcmia1wBIZBxcwRDLh5R3Za2odd1lQypdnzxqZi3qhULDUinqK8qiW+vqop\n3ljdXNZY3hpviifLWuPJWDKWKkvFW+PJeLIsGUvGbSwVtzHr2ZRXRlN1bby5siZ23+Hfnbq8Yasl\nqZ/uMinsz9ttZPCegkIn56OExMeABZHx7g7eMJrK1J6Up3alPLUV5XYcNjWaeMtAqhsaqalfR9/1\nDVQ3NFHR0kpFS4qKZJLyliRlrUnirUnirRBLQizpEUtZPGurbEusX2p9+Z8fvm23cR+8td3klT3N\nwSg9VmFYP0MOwcfRjua9njzIkcFCRX05tVfvyjYvj+IL6yqY0hpjQqsXGwd2WNza2uqWWENlS1l9\neUu8MZ6Mt5Ql463xZFlrvLWspay1rDWejFsvFSeWitl4ysPGymiu6OO1VtTEnt7na1svGDS9JfXn\nLbfixaFdh/tECS1yj0PzXg2SapyBwmrynffqaF/B/HyKZHBXIW/0eLQi8NBk/T/dPVkHytA2/L5o\ndfgyuZMmpyNFE5c06QiPC2ftzXZrn+FX2/6a1wb5XcawRgVjEmgB+wlSGHoiGhO9rUD9+9Dg3qKx\n4HUyHofFwPJ2yVY95LLpvHPsbAYNbWBqYVRPioeVGtR1KK78DyhmNgLXrAM+A1Cp64NRqMHHyPP3\nBtqufg95b1f3RCJyI99bMIL9PvuQm8f/hvtGn9/bZpcEedWORUmjzwMXkEh0DIkMCZvWHj4cXTtV\nvNS1m0+m730KXq9rDly4FwvP/hfjP+3L0WPX8vfevl8xCULXvgf8ChloF3u9LTBWDFQXYn/kdT8Q\n2UH/Qgmm6XCR95GUXc+v4YmLqtl95SIW9l3F77bevqia4oXEmANRlEQ98Evg6QI5l4rm4X4ExSG9\nRnut30KU8T2QjCzgjSheO11l8trg/krkBV+P1A46hpOAM7gdpeTmly6iKnk2l2zzC2YOuiTs5uRE\nXu2r0aB8GnBfRAztwSjx5jtocr8HuL8o+thtqPwlEy98nDe/M5P6fs0c7SkWMVJYxeBegBYh3/Qy\nOSzRQlr05yKD7THkNXoUv4iVPC99/Qom1J/OOTt8jXf73V208xQCJR3fgbykp5BIRKQstu2DjMnv\no7n9bygR9s2iOhB8Rh4zm4duu5epKY+vV6S4q2jn6gVWIVM3I6P75BAUMLpGxclOR/rT77Bx/GRR\nrxa1ufj9zGlsVT+TP2x1LY+OOK0o5ygUxpShaIlvIMnOewo87xXN5twctl4jYEA4/q247cV/cOEs\nSzx5WyB5FT2MqcGYxzDmXozpqDUcErYM7I/BLgN7DdgpXb+mwPhM+0WC9VaybjfY/HRgS4KFGgvP\nWrjTKokqevgMxOcGfJbh83N8elGhr5sYU8ZDT6/j2lcs1S3Xdv2CkDBmOMbMwphrg8k/AlgP7Ilg\nl4D9K9hdSz52+Qz9/kHYVo+UhVutlCAig4XtLSyx8KPAyx0tfMrxOQef5fhcXeoCbNz4ss/fnrcM\na1wQKM9ED2MqMebvwdw3uEhnu9X9TQAAFBhJREFUKZrNeR3K4owyzuB2lBZjRmCM5YFnLLHURWA7\nK5QRDsZUBQPO7RgTkbbZ8WBfAPsY2MmhNsVnj4O/SSowuq1VSeZQsVBp4QULf7KdF14JF586fJbg\ncyU+/UNpgzHjMMZijKU8eWrkFrzGDMCYORhzfhBSEgHsILD3gX1dhnaI+Az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"text/plain": [ "<matplotlib.figure.Figure at 0x10e3ff8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 10))\n", "\n", "plt.subplot(6, 1, 1)\n", "plt.plot(sim.trange(), model.similarity(sim.data, color_in))\n", "plt.legend(model.get_output_vocab('color_in').keys, fontsize='x-small')\n", "plt.ylabel(\"color\")\n", "\n", "plt.subplot(6, 1, 2)\n", "plt.plot(sim.trange(), model.similarity(sim.data, shape_in))\n", "plt.legend(model.get_output_vocab('shape_in').keys, fontsize='x-small')\n", "plt.ylabel(\"shape\")\n", "\n", "plt.subplot(6, 1, 3)\n", "plt.plot(sim.trange(), model.similarity(sim.data, cue))\n", "plt.legend(model.get_output_vocab('cue').keys, fontsize='x-small')\n", "plt.ylabel(\"cue\")\n", "\n", "plt.subplot(6, 1, 4)\n", "for pointer in ['RED * CIRCLE', 'BLUE * SQUARE']:\n", " plt.plot(sim.trange(), vocab.parse(pointer).dot(sim.data[conv].T), label=pointer)\n", "plt.legend(fontsize='x-small')\n", "plt.ylabel(\"convolved\")\n", "\n", "plt.subplot(6, 1, 5)\n", "plt.plot(sim.trange(), spa.similarity(sim.data[out], vocab))\n", "plt.legend(model.get_output_vocab('out').keys, fontsize='x-small')\n", "plt.ylabel(\"Output\")\n", "plt.xlabel(\"time [s]\");\n", "\n", "plt.subplot(6, 1, 6)\n", "plt.plot(sim.trange(), spa.similarity(sim.data[clean], vocab))\n", "plt.legend(model.get_output_vocab('am').keys, fontsize='x-small')\n", "plt.ylabel(\"Cleaned Up Output\")\n", "plt.xlabel(\"time [s]\");" ] }, { "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
benbovy/cosmogenic_dating
Models.ipynb
1
49658
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A mathemactical model for calculating profiles of cosmogenic nuclides concentration vs. depth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we show the equations of the model that we'll use to calculate profiles of nuclides concentration vs. depth. A more detailled description of the theoretical background behind this model can be found in, e.g., [Braucher et al., 2003][1], [Siame et al., 2004][2] and [Rixhon et al., 2011][3]. \n", "\n", "The contribution of a specific particule type $p$ to the concentration of a particular nuclide can be written as:\n", "\n", "$$P_p,\\Lambda_p \\rightarrow c_p(z,\\epsilon,t,\\rho) = \\frac{P_p} {\\frac{\\rho \\, \\epsilon}{\\Lambda_p} + \\lambda} \\cdot \\exp\\left[{-\\frac{\\rho \\, z}{\\Lambda_p}}\\right] \\left[1 - \\exp\\left\\{-t \\left(\\frac{\\rho \\, \\epsilon}{\\Lambda_p} + \\lambda \\right)\\right\\}\\right]$$\n", "\n", "where $z$ (cm) is the depth below the surface, $\\epsilon$ (cm yr$^{-1}$) is the erosion rate, $t$ (yr) is the exposure time, $\\rho$ (g cm$^{-3}$) is the soil density, $\\lambda$ (yr$^{-1}$) is the radioactive decay constant, $P_p$ (atom g$^{-1}$yr$^{-1}$) is the production rate of the nuclide at the surface due to the particule, and $\\Lambda_p$ (g cm$^{-2}$) is the effective apparent attenuation length for that particule type.\n", "\n", "The nuclide concentration is the sum of individual contributions plus inheritance (i.e., a function of the concentration of the nuclide at the initiation of the exposure scenario). For example, for $^{10}Be$, we have:\n", "\n", "\n", "$$\n", "\\begin{align}\n", "C^{\\mathrm{10Be}}(z,\\epsilon,t,\\rho) = & P_n,\\Lambda_n & \\rightarrow & c_n(z,\\epsilon,t,\\rho) & + \\\\\n", "& P_{\\mu \\mathrm{Slow}},\\Lambda_{\\mu \\mathrm{Slow}} & \\rightarrow & c_{\\mu \\mathrm{Slow}}(z,\\epsilon,t,\\rho) & + \\\\\n", "& P_{\\mu \\mathrm{Fast}},\\Lambda_{\\mu \\mathrm{Fast}} & \\rightarrow & c_{\\mu \\mathrm{Fast}}(z,\\epsilon,t,\\rho) & + \\\\\n", "& C^{\\mathrm{10Be}}_{t0} \\cdot e^{-\\lambda t} & &\n", "\\end{align}\n", "$$\n", "\n", "where $C^{\\mathrm{10Be}}_{t0}$ (atoms g$^{-1}$) is the $^{10}Be$ concentration at the initiation of the exposure scenario, and $c_n(z,\\epsilon,t,\\rho)$, $c_{\\mu \\mathrm{Slow}}(z,\\epsilon,t,\\rho)$ and $c_{\\mu \\mathrm{Fast}}(z,\\epsilon,t,\\rho)$ are the contributions of neutrons, slow muons and fast muons, respectively. Inheritance is assumed to be equal for all sediment particles taken along the depth profile, which is a simplistic assumption though reasonable if the depth profile formed during a single geomorphic event that involved particle mixing. \n", "\n", "Here below is a Python/Numpy vectorized implementation of this model. The code is saved in the file `models.py` so that is can be re-used in other notebooks. \n", "\n", "[1]: http://dx.doi.org/10.1016/S0012-821X(03)00205-X\n", "[2]: http://dx.doi.org/10.1016/S0012-821X(04)00061-5\n", "[3]: http://dx.doi.org/10.1016/j.quageo.2010.11.001" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting models.py\n" ] } ], "source": [ "%%writefile models.py\n", "\n", "\"\"\"\n", "Models for calculating profiles of cosmogenic nuclide\n", "concentration vs. depth\n", "\n", "Author: B. Bovy\n", "\"\"\"\n", "\n", "import math\n", "\n", "import numpy as np\n", "\n", "\n", "def _particle_contrib(depth, erosion, exposure, density,\n", " nuclide, particle):\n", " \"\"\"\n", " Contribution of a specific particle type\n", " to the nuclide concentration.\n", " \"\"\"\n", " return (\n", " particle['prod_rate'] / ((density * erosion / \n", " particle['damping_depth']) \n", " + nuclide['rdecay']) *\n", " np.exp(-1. * density * depth / particle['damping_depth']) *\n", " (1. - np.exp(-1. * exposure * ((erosion * density / \n", " particle['damping_depth'])\n", " + nuclide['rdecay'])))\n", " )\n", "\n", "\n", "def C_nuclide(depth, erosion, exposure, density,\n", " inheritance, nuclide, particles):\n", " \"\"\"\n", " Calculate the concentration(s) of a nuclide\n", " at given depth(s) (generic function).\n", " \n", " Parameters\n", " ----------\n", " depth : float or array_like\n", " depth(s) below the surface [cm]\n", " erosion : float or array_like\n", " erosion rate(s) [cm yr-1]\n", " exposure : float or array_like\n", " exposure time [yr]\n", " density : float or array_like\n", " soil density [g cm-3]\n", " inheritance : float or array_like\n", " concentration of the nuclide at the\n", " initiation of the exposure scenario\n", " [atoms g-1]\n", " nuclide : dict\n", " nuclide parameters\n", " particles : [dict, dict, ...]\n", " parameters related to each particule type\n", " that contribute to the nuclide production\n", " \n", " Returns\n", " -------\n", " float or array-like (broadcasted)\n", " the nuclide concentration(s) [atoms g-1]\n", " \n", " Notes\n", " -----\n", " if arrays are given for several arguments, they must\n", " have the same shape or must be at least be broadcastable.\n", " \n", " `nucleide` must have the following key(s):\n", " 'rdecay': radioactive decay [yr-1]\n", " \n", " each item in `particles` must have the following keys:\n", " 'prod_rate': surface production rate [atoms g-1 yr-1]\n", " 'damping_depth': effective apparent attenuation depth\n", " [g cm-2]\n", " \n", " \"\"\"\n", " return (\n", " np.sum((_particle_contrib(depth, erosion, \n", " exposure, density,\n", " nuclide, p)\n", " for p in particles),\n", " axis=0) +\n", " inheritance * np.exp(-1. * nuclide['rdecay'] * exposure)\n", " )\n", "\n", "\n", "def C_10Be(depth, erosion, exposure, density, inheritance,\n", " P_0=5.):\n", " \"\"\"\n", " A model for profiles of 10Be concentration vs. depth.\n", " \n", " Notes\n", " -----\n", " The following parameters are used\n", " (Braucher et al. 2003)\n", " \n", " 10Be radioactive decay: log(2) / 1.36e6\n", " \n", " Contribution of\n", " - neutrons\n", " - production rate: 0.9785 * P_0\n", " - damping depth: 160\n", " - slow muons\n", " - production rate: 0.015 * P_0\n", " - damping depth: 1500\n", " - fast muons\n", " - production rate: 0.0065 * P_0\n", " - damping depth: 5300\n", " \n", " See Also\n", " --------\n", " C_nuclide\n", " \n", " \"\"\"\n", " # nuclide parameters\n", " berillium10 = {'rdecay': math.log(2.) / 1.36e6}\n", " \n", " # particles parameters\n", " neutrons = {'prod_rate': 0.9785 * P_0,\n", " 'damping_depth': 160.}\n", " slow_muons = {'prod_rate': 0.015 * P_0,\n", " 'damping_depth': 1500.}\n", " fast_muons = {'prod_rate': 0.0065 * P_0,\n", " 'damping_depth': 5300.}\n", " \n", " return C_nuclide(depth, erosion, exposure,\n", " density, inheritance,\n", " berillium10,\n", " [neutrons, slow_muons, fast_muons])\n", "\n", "\n", "def C_26Al(depth, erosion, exposure, density, inheritance,\n", " P_0=35.):\n", " \"\"\"\n", " A model for profiles of 26Al concentration vs. depth.\n", " \n", " Notes\n", " -----\n", " The following parameters are used\n", " (Braucher et al. 2003)\n", " \n", " 26Al radioactive decay: log(2) / 0.72e6\n", " \n", " Contribution of\n", " - neutrons\n", " - production rate: 0.9785 * P_0\n", " - damping depth: 160\n", " - slow muons\n", " - production rate: 0.015 * P_0\n", " - damping depth: 1500\n", " - fast muons\n", " - production rate: 0.0065 * P_0\n", " - damping depth: 5300\n", " \n", " See Also\n", " --------\n", " C_nuclide\n", " \n", " \"\"\"\n", " # nuclide parameters\n", " aluminium26 = {'rdecay': math.log(2.) / 0.72e6}\n", " \n", " # particles parameters\n", " neutrons = {'prod_rate': 0.9785 * P_0,\n", " 'damping_depth': 160.}\n", " slow_muons = {'prod_rate': 0.015 * P_0,\n", " 'damping_depth': 1500.}\n", " fast_muons = {'prod_rate': 0.0065 * P_0,\n", " 'damping_depth': 5300.}\n", " \n", " return C_nuclide(depth, erosion, exposure,\n", " density, inheritance,\n", " aluminium26,\n", " [neutrons, slow_muons, fast_muons])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model behavior" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interactive plotting of the concentration profile interactively to show the model sensitivity (IPython widget)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from IPython.html import widgets\n", "\n", "import models\n", "\n", "sns.set_context('notebook')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "depths = np.linspace(20, 500, 100)\n", "\n", "def plot_profile_model(**kwargs):\n", " \n", " C10Be = models.C_10Be(depths,\n", " kwargs['erosion_rate'],\n", " kwargs['exposure_time'],\n", " kwargs['soil_density'],\n", " kwargs['inheritance'])\n", " \n", " fig, ax = plt.subplots()\n", " \n", " ax.plot(C10Be, -depths)\n", " plt.setp(ax,\n", " xlim=[0, 4e5], ylim=[-500, 0],\n", " xlabel='10Be concentration [atoms g-1]',\n", " ylabel='-1 * depth [cm]')\n", " \n", " return fig\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAtMAAAH/CAYAAACVclHhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "we = widgets.FloatSliderWidget(min=0., max=3e-3, step=5e-4, value=1e-3,\n", " description=\"erosion rate [cm yr-1]\")\n", "wt = widgets.FloatSliderWidget(min=0., max=3e5, step=5e4, value=1e5,\n", " description=\"exposure time [yr]\")\n", "wd = widgets.FloatSliderWidget(min=1.8, max=2.3, step=0.1, value=2.,\n", " description=\"soil density [g cm-3]\")\n", "wi = widgets.FloatSliderWidget(min=0., max=1e5, step=2.5e4, value=0.5e5,\n", " description=\"inheritance [atoms g-1]\")\n", "\n", "w = widgets.interact(plot_profile_model,\n", " erosion_rate=we,\n", " exposure_time=wt,\n", " soil_density=wd,\n", " inheritance=wi)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
apetrin/EM-algorithm
EM-Submit.ipynb
1
1238288
null
gpl-2.0
tensorflow/docs-l10n
site/zh-cn/agents/tutorials/9_c51_tutorial.ipynb
1
22151
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "klGNgWREsvQv" }, "source": [ "##### Copyright 2021 The TF-Agents Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "nQnmcm0oI1Q-" }, "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": "oMaGpi7TciQs" }, "source": [ "# DQN C51/Rainbow\n", "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td> <a target=\"_blank\" href=\"https://tensorflow.google.cn/agents/tutorials/9_c51_tutorial\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a>\n", "</td>\n", " <td> <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/agents/tutorials/9_c51_tutorial.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 运行</a>\n", "</td>\n", " <td> <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/agents/tutorials/9_c51_tutorial.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 Github 上查看源代码</a>\n", "</td>\n", " <td> <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/agents/tutorials/9_c51_tutorial.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a> </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "ZOUOQOrFs3zn" }, "source": [ "## 简介" ] }, { "cell_type": "markdown", "metadata": { "id": "cKOCZlhUgXVK" }, "source": [ "本示例说明了如何使用 TF-Agents 库在 Cartpole 环境中训练[分类 DQN (C51)](https://arxiv.org/pdf/1707.06887.pdf) 代理。\n", "\n", "![Cartpole environment](https://github.com/tensorflow/agents/blob/master/docs/tutorials/images/cartpole.png?raw=1)\n", "\n", "确保您已事先阅读 [DQN 教程](https://github.com/tensorflow/agents/blob/master/docs/tutorials/1_dqn_tutorial.ipynb)。本教程假定您熟悉 DQN 教程,并主要关注 DQN 与 C51 之间的差异。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "lsaQlK8fFQqH" }, "source": [ "## 设置\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-NzBsZzPcyBm" }, "source": [ "如果尚未安装 TF-Agents,请运行以下命令:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KEHR2Ui-lo8O" }, "outputs": [], "source": [ "!sudo apt-get update\n", "!sudo apt-get install -y xvfb ffmpeg freeglut3-dev\n", "!pip install 'imageio==2.4.0'\n", "!pip install pyvirtualdisplay\n", "!pip install tf-agents\n", "!pip install pyglet" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sMitx5qSgJk1" }, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import base64\n", "import imageio\n", "import IPython\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import PIL.Image\n", "import pyvirtualdisplay\n", "\n", "import tensorflow as tf\n", "\n", "from tf_agents.agents.categorical_dqn import categorical_dqn_agent\n", "from tf_agents.drivers import dynamic_step_driver\n", "from tf_agents.environments import suite_gym\n", "from tf_agents.environments import tf_py_environment\n", "from tf_agents.eval import metric_utils\n", "from tf_agents.metrics import tf_metrics\n", "from tf_agents.networks import categorical_q_network\n", "from tf_agents.policies import random_tf_policy\n", "from tf_agents.replay_buffers import tf_uniform_replay_buffer\n", "from tf_agents.trajectories import trajectory\n", "from tf_agents.utils import common\n", "\n", "# Set up a virtual display for rendering OpenAI gym environments.\n", "display = pyvirtualdisplay.Display(visible=0, size=(1400, 900)).start()" ] }, { "cell_type": "markdown", "metadata": { "id": "LmC0NDhdLIKY" }, "source": [ "## 超参数" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HC1kNrOsLSIZ" }, "outputs": [], "source": [ "env_name = \"CartPole-v1\" # @param {type:\"string\"}\n", "num_iterations = 15000 # @param {type:\"integer\"}\n", "\n", "initial_collect_steps = 1000 # @param {type:\"integer\"} \n", "collect_steps_per_iteration = 1 # @param {type:\"integer\"}\n", "replay_buffer_capacity = 100000 # @param {type:\"integer\"}\n", "\n", "fc_layer_params = (100,)\n", "\n", "batch_size = 64 # @param {type:\"integer\"}\n", "learning_rate = 1e-3 # @param {type:\"number\"}\n", "gamma = 0.99\n", "log_interval = 200 # @param {type:\"integer\"}\n", "\n", "num_atoms = 51 # @param {type:\"integer\"}\n", "min_q_value = -20 # @param {type:\"integer\"}\n", "max_q_value = 20 # @param {type:\"integer\"}\n", "n_step_update = 2 # @param {type:\"integer\"}\n", "\n", "num_eval_episodes = 10 # @param {type:\"integer\"}\n", "eval_interval = 1000 # @param {type:\"integer\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "VMsJC3DEgI0x" }, "source": [ "## 环境\n", "\n", "像以前一样加载环境,其中一个用于训练,另一个用于评估。在这里,我们使用 CartPole-v1(DQN 教程中则为 CartPole-v0),它的最大奖励是 500,而不是 200。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Xp-Y4mD6eDhF" }, "outputs": [], "source": [ "train_py_env = suite_gym.load(env_name)\n", "eval_py_env = suite_gym.load(env_name)\n", "\n", "train_env = tf_py_environment.TFPyEnvironment(train_py_env)\n", "eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)" ] }, { "cell_type": "markdown", "metadata": { "id": "E9lW_OZYFR8A" }, "source": [ "## 代理\n", "\n", "C51 是一种基于 DQN 的 Q-learning 算法。与 DQN 一样,它可以在具有离散操作空间的任何环境中使用。\n", "\n", "C51 与 DQN 之间的主要区别在于,C51 不仅可以简单地预测每个状态-操作对的 Q 值,还能预测表示 Q 值概率分布的直方图模型:\n", "\n", "![Example C51 Distribution](images/c51_distribution.png)\n", "\n", "通过学习分布而不是简单的期望值,此算法能够在训练过程中保持更稳定的状态,从而提高最终性能。这种算法尤其适用于具有双峰甚至多峰值分布的情况,此时单个平均值无法提供准确的概览。\n", "\n", "为了基于概率分布而不是值来训练,C51 必须执行一些复杂的分布计算才能计算其损失函数。但不用担心,我们已在 TF-Agents 中为您处理好一切!\n", "\n", "要创建 C51 代理,我们首先需要创建一个 `CategoricalQNetwork`。除了有一个附加参数 `num_atoms` 外,`CategoricalQNetwork` 的 API 与 `QNetwork` 的 API 相同。这表示我们的概率分布估算中的支撑点数。(上面的图像包括 10 个支撑点,每个支撑点都由垂直的蓝色条表示。)您可以从名称中看出,默认原子数为 51。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TgkdEPg_muzV" }, "outputs": [], "source": [ "categorical_q_net = categorical_q_network.CategoricalQNetwork(\n", " train_env.observation_spec(),\n", " train_env.action_spec(),\n", " num_atoms=num_atoms,\n", " fc_layer_params=fc_layer_params)" ] }, { "cell_type": "markdown", "metadata": { "id": "z62u55hSmviJ" }, "source": [ "我们还需要一个 `optimizer` 来训练刚刚创建的网络,以及一个 `train_step_counter` 变量来跟踪网络更新的次数。\n", "\n", "请注意,与普通 `DqnAgent` 的另一个重要区别在于,我们现在需要指定 `min_q_value` 和 `max_q_value` 作为参数。这两个参数指定了支撑点的最极端值(换句话说,任何一侧有全部 51 个原子)。确保为您的特定环境适当地选择这些值。在这里,我们使用 -20 和 20。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jbY4yrjTEyc9" }, "outputs": [], "source": [ "optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n", "\n", "train_step_counter = tf.Variable(0)\n", "\n", "agent = categorical_dqn_agent.CategoricalDqnAgent(\n", " train_env.time_step_spec(),\n", " train_env.action_spec(),\n", " categorical_q_network=categorical_q_net,\n", " optimizer=optimizer,\n", " min_q_value=min_q_value,\n", " max_q_value=max_q_value,\n", " n_step_update=n_step_update,\n", " td_errors_loss_fn=common.element_wise_squared_loss,\n", " gamma=gamma,\n", " train_step_counter=train_step_counter)\n", "agent.initialize()" ] }, { "cell_type": "markdown", "metadata": { "id": "L7O7F_HqiQ1G" }, "source": [ "最后要注意的一点是,我们还添加了一个参数来使用 $n$ = 2 的 n 步更新。在单步 Q-learning ($n$ = 1) 中,我们仅使用单步回报(基于贝尔曼最优性方程)计算当前时间步骤和下一时间步骤的 Q 值之间的误差。单步回报定义为:\n", "\n", "$G_t = R_{t + 1} + \\gamma V(s_{t + 1})$\n", "\n", "其中,我们定义 $V(s) = \\max_a{Q(s, a)}$。\n", "\n", "N 步更新涉及将标准单步回报函数扩展 $n$ 倍:\n", "\n", "$G_t^n = R_{t + 1} + \\gamma R_{t + 2} + \\gamma^2 R_{t + 3} + \\dots + \\gamma^n V(s_{t + n})$\n", "\n", "N 步更新使代理可以在将来进一步自助抽样,而在 $n$ 值正确的情况下,这通常可以加快学习速度。\n", "\n", "尽管 C51 和 n 步更新通常与优先回放相结合构成 [Rainbow 代理](https://arxiv.org/pdf/1710.02298.pdf)的核心,但我们发现,实现优先回放并未带来可衡量的改进。此外,我们还发现,仅将 C51 代理与 n 步更新结合使用时,在我们测试过的 Atari 环境样本中,我们的代理在性能上与其他 Rainbow 代理一样出色。" ] }, { "cell_type": "markdown", "metadata": { "id": "94rCXQtbUbXv" }, "source": [ "## 指标和评估\n", "\n", "用于评估策略的最常用指标是平均回报。回报是针对某个片段在环境中运行策略时获得的奖励总和,我们通常会评估多个片段的平均值。计算平均回报指标的代码如下。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bitzHo5_UbXy" }, "outputs": [], "source": [ "#@test {\"skip\": true}\n", "def compute_avg_return(environment, policy, num_episodes=10):\n", "\n", " total_return = 0.0\n", " for _ in range(num_episodes):\n", "\n", " time_step = environment.reset()\n", " episode_return = 0.0\n", "\n", " while not time_step.is_last():\n", " action_step = policy.action(time_step)\n", " time_step = environment.step(action_step.action)\n", " episode_return += time_step.reward\n", " total_return += episode_return\n", "\n", " avg_return = total_return / num_episodes\n", " return avg_return.numpy()[0]\n", "\n", "\n", "random_policy = random_tf_policy.RandomTFPolicy(train_env.time_step_spec(),\n", " train_env.action_spec())\n", "\n", "compute_avg_return(eval_env, random_policy, num_eval_episodes)\n", "\n", "# Please also see the metrics module for standard implementations of different\n", "# metrics." ] }, { "cell_type": "markdown", "metadata": { "id": "NLva6g2jdWgr" }, "source": [ "## 数据收集\n", "\n", "与 DQN 教程中一样,使用随机策略设置回放缓冲区和初始数据收集。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wr1KSAEGG4h9" }, "outputs": [], "source": [ "#@test {\"skip\": true}\n", "replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(\n", " data_spec=agent.collect_data_spec,\n", " batch_size=train_env.batch_size,\n", " max_length=replay_buffer_capacity)\n", "\n", "def collect_step(environment, policy):\n", " time_step = environment.current_time_step()\n", " action_step = policy.action(time_step)\n", " next_time_step = environment.step(action_step.action)\n", " traj = trajectory.from_transition(time_step, action_step, next_time_step)\n", "\n", " # Add trajectory to the replay buffer\n", " replay_buffer.add_batch(traj)\n", "\n", "for _ in range(initial_collect_steps):\n", " collect_step(train_env, random_policy)\n", "\n", "# This loop is so common in RL, that we provide standard implementations of\n", "# these. For more details see the drivers module.\n", "\n", "# Dataset generates trajectories with shape [BxTx...] where\n", "# T = n_step_update + 1.\n", "dataset = replay_buffer.as_dataset(\n", " num_parallel_calls=3, sample_batch_size=batch_size,\n", " num_steps=n_step_update + 1).prefetch(3)\n", "\n", "iterator = iter(dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "hBc9lj9VWWtZ" }, "source": [ "## 训练代理\n", "\n", "训练循环包括从环境收集数据和优化代理的网络。在训练过程中,我们偶尔会评估代理的策略来了解效果。\n", "\n", "运行以下代码需要约 7 分钟。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0pTbJ3PeyF-u" }, "outputs": [], "source": [ "#@test {\"skip\": true}\n", "try:\n", " %%time\n", "except:\n", " pass\n", "\n", "# (Optional) Optimize by wrapping some of the code in a graph using TF function.\n", "agent.train = common.function(agent.train)\n", "\n", "# Reset the train step\n", "agent.train_step_counter.assign(0)\n", "\n", "# Evaluate the agent's policy once before training.\n", "avg_return = compute_avg_return(eval_env, agent.policy, num_eval_episodes)\n", "returns = [avg_return]\n", "\n", "for _ in range(num_iterations):\n", "\n", " # Collect a few steps using collect_policy and save to the replay buffer.\n", " for _ in range(collect_steps_per_iteration):\n", " collect_step(train_env, agent.collect_policy)\n", "\n", " # Sample a batch of data from the buffer and update the agent's network.\n", " experience, unused_info = next(iterator)\n", " train_loss = agent.train(experience)\n", "\n", " step = agent.train_step_counter.numpy()\n", "\n", " if step % log_interval == 0:\n", " print('step = {0}: loss = {1}'.format(step, train_loss.loss))\n", "\n", " if step % eval_interval == 0:\n", " avg_return = compute_avg_return(eval_env, agent.policy, num_eval_episodes)\n", " print('step = {0}: Average Return = {1:.2f}'.format(step, avg_return))\n", " returns.append(avg_return)" ] }, { "cell_type": "markdown", "metadata": { "id": "68jNcA_TiJDq" }, "source": [ "## 可视化\n" ] }, { "cell_type": "markdown", "metadata": { "id": "aO-LWCdbbOIC" }, "source": [ "### 绘图\n", "\n", "我们可以通过绘制回报与全局步骤之间关系的图形来了解代理的性能。在 `Cartpole-v1` 中,长杆每直立一个时间步骤,环境就会提供 +1 的奖励,由于最大步骤数为 500,因此可以获得的最大回报也是 500。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NxtL1mbOYCVO" }, "outputs": [], "source": [ "#@test {\"skip\": true}\n", "\n", "steps = range(0, num_iterations + 1, eval_interval)\n", "plt.plot(steps, returns)\n", "plt.ylabel('Average Return')\n", "plt.xlabel('Step')\n", "plt.ylim(top=550)" ] }, { "cell_type": "markdown", "metadata": { "id": "M7-XpPP99Cy7" }, "source": [ "### 视频" ] }, { "cell_type": "markdown", "metadata": { "id": "9pGfGxSH32gn" }, "source": [ "在每个步骤都渲染环境有助于可视化代理的性能。在此之前,我们先创建一个函数,以便在此 Colab 中嵌入视频。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ULaGr8pvOKbl" }, "outputs": [], "source": [ "def embed_mp4(filename):\n", " \"\"\"Embeds an mp4 file in the notebook.\"\"\"\n", " video = open(filename,'rb').read()\n", " b64 = base64.b64encode(video)\n", " tag = '''\n", " <video width=\"640\" height=\"480\" controls>\n", " <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\">\n", " Your browser does not support the video tag.\n", " </video>'''.format(b64.decode())\n", "\n", " return IPython.display.HTML(tag)" ] }, { "cell_type": "markdown", "metadata": { "id": "9c_PH-pX4Pr5" }, "source": [ "以下代码可将代理策略可视化多个片段:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "owOVWB158NlF" }, "outputs": [], "source": [ "num_episodes = 3\n", "video_filename = 'imageio.mp4'\n", "with imageio.get_writer(video_filename, fps=60) as video:\n", " for _ in range(num_episodes):\n", " time_step = eval_env.reset()\n", " video.append_data(eval_py_env.render())\n", " while not time_step.is_last():\n", " action_step = agent.policy.action(time_step)\n", " time_step = eval_env.step(action_step.action)\n", " video.append_data(eval_py_env.render())\n", "\n", "embed_mp4(video_filename)" ] }, { "cell_type": "markdown", "metadata": { "id": "exziB27hY8ia" }, "source": [ "C51 在性能上往往略微优于基于 CartPole-v1 的 DQN,但是,在越来越复杂的环境中,两种代理之间的差异变得越来越明显。例如,在完整的 Atari 2600 基准测试中,针对随机代理进行归一化之后,C51 的平均得分相比 DQN 提高 126%。通过包含 n 步更新,可以进一步提高性能。\n", "\n", "要深入了解 C51 算法,请参阅 [A Distributional Perspective on Reinforcement Learning (2017)](https://arxiv.org/pdf/1707.06887.pdf)。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "9_c51_tutorial.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
dnc1994/MachineLearning-UW
ml-regression/blank/week-2-multiple-regression-assignment-2-blank.ipynb
1
22449
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression Week 2: Multiple Regression (gradient descent)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the first notebook we explored multiple regression using graphlab create. Now we will use graphlab along with numpy to solve for the regression weights with gradient descent.\n", "\n", "In this notebook we will cover estimating multiple regression weights via gradient descent. You will:\n", "* Add a constant column of 1's to a graphlab SFrame to account for the intercept\n", "* Convert an SFrame into a Numpy array\n", "* Write a predict_output() function using Numpy\n", "* Write a numpy function to compute the derivative of the regression weights with respect to a single feature\n", "* Write gradient descent function to compute the regression weights given an initial weight vector, step size and tolerance.\n", "* Use the gradient descent function to estimate regression weights for multiple features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fire up graphlab create" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure you have the latest version of graphlab (>= 1.7)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "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 }, "outputs": [], "source": [ "sales = graphlab.SFrame('kc_house_data.gl/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to do any \"feature engineering\" like creating new features or adjusting existing ones we should do this directly using the SFrames as seen in the other Week 2 notebook. For this notebook, however, we will work with the existing features." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Convert to Numpy Array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although SFrames offer a number of benefits to users (especially when using Big Data and built-in graphlab functions) in order to understand the details of the implementation of algorithms it's important to work with a library that allows for direct (and optimized) matrix operations. Numpy is a Python solution to work with matrices (or any multi-dimensional \"array\").\n", "\n", "Recall that the predicted value given the weights and the features is just the dot product between the feature and weight vector. Similarly, if we put all of the features row-by-row in a matrix then the predicted value for *all* the observations can be computed by right multiplying the \"feature matrix\" by the \"weight vector\". \n", "\n", "First we need to take the SFrame of our data and convert it into a 2D numpy array (also called a matrix). To do this we use graphlab's built in .to_dataframe() which converts the SFrame into a Pandas (another python library) dataframe. We can then use Panda's .as_matrix() to convert the dataframe into a numpy matrix." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np # note this allows us to refer to numpy as np instead " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will write a function that will accept an SFrame, a list of feature names (e.g. ['sqft_living', 'bedrooms']) and an target feature e.g. ('price') and will return two things:\n", "* A numpy matrix whose columns are the desired features plus a constant column (this is how we create an 'intercept')\n", "* A numpy array containing the values of the output\n", "\n", "With this in mind, complete the following function (where there's an empty line you should write a line of code that does what the comment above indicates)\n", "\n", "**Please note you will need GraphLab Create version at least 1.7.1 in order for .to_numpy() to work!**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_numpy_data(data_sframe, features, output):\n", " data_sframe['constant'] = 1 # this is how you add a constant column to an SFrame\n", " # add the column 'constant' to the front of the features list so that we can extract it along with the others:\n", " features = ['constant'] + features # this is how you combine two lists\n", " # select the columns of data_SFrame given by the features list into the SFrame features_sframe (now including constant):\n", "\n", " # the following line will convert the features_SFrame into a numpy matrix:\n", " feature_matrix = features_sframe.to_numpy()\n", " # assign the column of data_sframe associated with the output to the SArray output_sarray\n", "\n", " # the following will convert the SArray into a numpy array by first converting it to a list\n", " output_array = output_sarray.to_numpy()\n", " return(feature_matrix, output_array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For testing let's use the 'sqft_living' feature and a constant as our features and price as our output:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(example_features, example_output) = get_numpy_data(sales, ['sqft_living'], 'price') # the [] around 'sqft_living' makes it a list\n", "print example_features[0,:] # this accesses the first row of the data the ':' indicates 'all columns'\n", "print example_output[0] # and the corresponding output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting output given regression weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we had the weights [1.0, 1.0] and the features [1.0, 1180.0] and we wanted to compute the predicted output 1.0\\*1.0 + 1.0\\*1180.0 = 1181.0 this is the dot product between these two arrays. If they're numpy arrayws we can use np.dot() to compute this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_weights = np.array([1., 1.]) # the example weights\n", "my_features = example_features[0,] # we'll use the first data point\n", "predicted_value = np.dot(my_features, my_weights)\n", "print predicted_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "np.dot() also works when dealing with a matrix and a vector. Recall that the predictions from all the observations is just the RIGHT (as in weights on the right) dot product between the features *matrix* and the weights *vector*. With this in mind finish the following predict_output function to compute the predictions for an entire matrix of features given the matrix and the weights:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_output(feature_matrix, weights):\n", " # assume feature_matrix is a numpy matrix containing the features as columns and weights is a corresponding numpy array\n", " # create the predictions vector by using np.dot()\n", "\n", " return(predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to test your code run the following cell:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_predictions = predict_output(example_features, my_weights)\n", "print test_predictions[0] # should be 1181.0\n", "print test_predictions[1] # should be 2571.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Computing the Derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now going to move to computing the derivative of the regression cost function. Recall that the cost function is the sum over the data points of the squared difference between an observed output and a predicted output.\n", "\n", "Since the derivative of a sum is the sum of the derivatives we can compute the derivative for a single data point and then sum over data points. We can write the squared difference between the observed output and predicted output for a single point as follows:\n", "\n", "(w[0]\\*[CONSTANT] + w[1]\\*[feature_1] + ... + w[i] \\*[feature_i] + ... + w[1]\\*[feature_k] - output)^2\n", "\n", "Where we have k features and a constant. So the derivative with respect to weight w[i] by the chain rule is:\n", "\n", "2\\*(w[0]\\*[CONSTANT] + w[1]\\*[feature_1] + ... + w[i] \\*[feature_i] + ... + w[1]\\*[feature_k] - output)\\* [feature_i]\n", "\n", "The term inside the paranethesis is just the error (difference between prediction and output). So we can re-write this as:\n", "\n", "2\\*error\\*[feature_i]\n", "\n", "That is, the derivative for the weight for feature i is the sum (over data points) of 2 times the product of the error and the feature itself. In the case of the constant then this is just twice the sum of the errors!\n", "\n", "Recall that twice the sum of the product of two vectors is just twice the dot product of the two vectors. Therefore the derivative for the weight for feature_i is just two times the dot product between the values of feature_i and the current errors. \n", "\n", "With this in mind complete the following derivative function which computes the derivative of the weight given the value of the feature (over all data points) and the errors (over all data points)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def feature_derivative(errors, feature):\n", " # Assume that errors and feature are both numpy arrays of the same length (number of data points)\n", " # compute twice the dot product of these vectors as 'derivative' and return the value\n", "\n", " return(derivative)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test your feature derivartive run the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(example_features, example_output) = get_numpy_data(sales, ['sqft_living'], 'price') \n", "my_weights = np.array([0., 0.]) # this makes all the predictions 0\n", "test_predictions = predict_output(example_features, my_weights) \n", "# just like SFrames 2 numpy arrays can be elementwise subtracted with '-': \n", "errors = test_predictions - example_output # prediction errors in this case is just the -example_output\n", "feature = example_features[:,0] # let's compute the derivative with respect to 'constant', the \":\" indicates \"all rows\"\n", "derivative = feature_derivative(errors, feature)\n", "print derivative\n", "print -np.sum(example_output)*2 # should be the same as derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Descent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will write a function that performs a gradient descent. The basic premise is simple. Given a starting point we update the current weights by moving in the negative gradient direction. Recall that the gradient is the direction of *increase* and therefore the negative gradient is the direction of *decrease* and we're trying to *minimize* a cost function. \n", "\n", "The amount by which we move in the negative gradient *direction* is called the 'step size'. We stop when we are 'sufficiently close' to the optimum. We define this by requiring that the magnitude (length) of the gradient vector to be smaller than a fixed 'tolerance'.\n", "\n", "With this in mind, complete the following gradient descent function below using your derivative function above. For each step in the gradient descent we update the weight for each feature befofe computing our stopping criteria" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import sqrt # recall that the magnitude/length of a vector [g[0], g[1], g[2]] is sqrt(g[0]^2 + g[1]^2 + g[2]^2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def regression_gradient_descent(feature_matrix, output, initial_weights, step_size, tolerance):\n", " converged = False \n", " weights = np.array(initial_weights) # make sure it's a numpy array\n", " while not converged:\n", " # compute the predictions based on feature_matrix and weights using your predict_output() function\n", "\n", " # compute the errors as predictions - output\n", "\n", " gradient_sum_squares = 0 # initialize the gradient sum of squares\n", " # while we haven't reached the tolerance yet, update each feature's weight\n", " for i in range(len(weights)): # loop over each weight\n", " # Recall that feature_matrix[:, i] is the feature column associated with weights[i]\n", " # compute the derivative for weight[i]:\n", "\n", " # add the squared value of the derivative to the gradient magnitude (for assessing convergence)\n", "\n", " # subtract the step size times the derivative from the current weight\n", " \n", " # compute the square-root of the gradient sum of squares to get the gradient matnigude:\n", " gradient_magnitude = sqrt(gradient_sum_squares)\n", " if gradient_magnitude < tolerance:\n", " converged = True\n", " return(weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A few things to note before we run the gradient descent. Since the gradient is a sum over all the data points and involves a product of an error and a feature the gradient itself will be very large since the features are large (squarefeet) and the output is large (prices). So while you might expect \"tolerance\" to be small, small is only relative to the size of the features. \n", "\n", "For similar reasons the step size will be much smaller than you might expect but this is because the gradient has such large values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running the Gradient Descent as Simple Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's split the data into training and test data." ] }, { "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": [ "Although the gradient descent is designed for multiple regression since the constant is now a feature we can use the gradient descent function to estimat the parameters in the simple regression on squarefeet. The folowing cell sets up the feature_matrix, output, initial weights and step size for the first model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# let's test out the gradient descent\n", "simple_features = ['sqft_living']\n", "my_output = 'price'\n", "(simple_feature_matrix, output) = get_numpy_data(train_data, simple_features, my_output)\n", "initial_weights = np.array([-47000., 1.])\n", "step_size = 7e-12\n", "tolerance = 2.5e7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next run your gradient descent with the above parameters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do your weights compare to those achieved in week 1 (don't expect them to be exactly the same)? \n", "\n", "**Quiz Question: What is the value of the weight for sqft_living -- the second element of ‘simple_weights’ (rounded to 1 decimal place)?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use your newly estimated weights and your predict_output() function to compute the predictions on all the TEST data (you will need to create a numpy array of the test feature_matrix and test output first:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(test_simple_feature_matrix, test_output) = get_numpy_data(test_data, simple_features, my_output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now compute your predictions using test_simple_feature_matrix and your weights from above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question: What is the predicted price for the 1st house in the TEST data set for model 1 (round to nearest dollar)?**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you have the predictions on test data, compute the RSS on the test data set. Save this value for comparison later. Recall that RSS is the sum of the squared errors (difference between prediction and output)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running a multiple regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will use more than one actual feature. Use the following code to produce the weights for a second model with the following parameters:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model_features = ['sqft_living', 'sqft_living15'] # sqft_living15 is the average squarefeet for the nearest 15 neighbors. \n", "my_output = 'price'\n", "(feature_matrix, output) = get_numpy_data(train_data, model_features, my_output)\n", "initial_weights = np.array([-100000., 1., 1.])\n", "step_size = 4e-12\n", "tolerance = 1e9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the above parameters to estimate the model weights. Record these values for your quiz." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use your newly estimated weights and the predict_output function to compute the predictions on the TEST data. Don't forget to create a numpy array for these features from the test set first!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question: What is the predicted price for the 1st house in the TEST data set for model 2 (round to nearest dollar)?**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the actual price for the 1st house in the test data set?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question: Which estimate was closer to the true price for the 1st house on the Test data set, model 1 or model 2?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use your predictions and the output to compute the RSS for model 2 on TEST data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question: Which model (1 or 2) has lowest RSS on all of the TEST data? **" ] }, { "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
letsgoexploring/teaching
winter2017/econ129/python/Econ129_Class_09_Complete.ipynb
1
478172
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Class 9: The Solow growth model\n", "\n", "The Solow growth model is at the core of modern theories of growth and business cycles. The Solow model is a model of *exogenous growth*: long-run growth arises in the model as a consequence of exogenous growth in the labor supply and total factor productivity. The Solow model, like many other macroeconomic models, is a *time series* model.\n", "\n", "## The Solow model without exogenous growth\n", "\n", "For the moment, let's disregard population and total factor productivity growth and assume that equilibrium in a closed economy is described by the following four equations:\n", "\n", "\\begin{align}\n", "Y_t & = A K_t^{\\alpha} \\tag{1}\\\\\n", "C_t & = (1-s)Y_t \\tag{2}\\\\\n", "Y_t & = C_t + I_t \\tag{3}\\\\\n", "K_{t+1} & = I_t + ( 1- \\delta)K_t \\tag{4}\\\\\n", "\\end{align}\n", "\n", "Equation (1) is the production function. Equation (2) is the consumption function where $s$ denotes the exogenously given saving rate. Equation (3) is the aggregate market clearing condition. Finally, Equation (4) is the capital evolution equation specifying that capital in yeat $t+1$ is the sum of newly created capital $I_t$ and the capital stock from year $t$ that has not depreciated $(1-\\delta)K_t$.\n", "\n", "Combine Equations (1) through (4) to eliminate $C_t$, $I_t$, and $Y_t$ and obtain a single-variable recurrence relation for $K_{t+1}$:\n", "\\begin{align}\n", "K_{t+1} & = sAK_t^{\\alpha} + ( 1- \\delta)K_t \\tag{5}\n", "\\end{align}\n", "\n", "Given an initial value for capital $K_0 >0$, iterate on Equation (5) to compute the value of the capital stock at some future date $T$. Furthermore, the values of consumption, output, and investment at date $T$ can also be computed using Equations (1) through (3).\n", "\n", "### Simulation\n", "\n", "Simulate the Solow growth model for $t=0\\ldots 100$. For the simulation, assume the following values of the parameters:\n", "\n", "\\begin{align}\n", "A & = 10\\\\\n", "\\alpha & = 0.35\\\\\n", "s & = 0.15\\\\\n", "\\delta & = 0.1\n", "\\end{align}\n", "\n", "Furthermore, suppose that the initial value of capital is:\n", "\n", "\\begin{align}\n", "K_0 & = 20\n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "capital(t=0): 20.0\n", "capital(t=T): 64.4074036084\n" ] } ], "source": [ "# Initialize parameters for the simulation (A, s, T, delta, alpha, K0)\n", "K0 = 20\n", "T= 100\n", "A= 10\n", "alpha = 0.35\n", "delta = 0.1\n", "s = 0.15\n", "\n", "\n", "# Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to K0\n", "capital = np.zeros(T+1)\n", "capital[0] = K0\n", "\n", "\n", "# Compute all capital values by iterating over t from 0 through T\n", "for t in np.arange(T):\n", " capital[t+1] = s*A*capital[t]**alpha + (1-delta)*capital[t]\n", " \n", "\n", "# Print the value of capital at dates 0 and T\n", "print('capital(t=0):',capital[0])\n", "print('capital(t=T):',capital[-1])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " capital\n", "0 20.000000\n", "1 22.280079\n", "2 24.496972\n", "3 26.642223\n", "4 28.709959\n" ] } ], "source": [ "# Store the simulated capital data in a pandas DataFrame called data\n", "data = pd.DataFrame({'capital':capital})\n", "\n", "# Print the first five rows of the DataFrame\n", "print(data.head())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "capital 20.000000\n", "output 2.853386\n", "consumption 2.425378\n", "investment 0.428008\n", "Name: 0, dtype: float64\n" ] } ], "source": [ "# Create columns in the DataFrame to store computed values of the other endogenous variables\n", "data['output'] = data['capital']**alpha\n", "data['consumption'] = (1-s)*data['output']\n", "data['investment'] = data['output'] - data['consumption']\n", "\n", "# Print the first row of the DataFrame\n", "print(data.iloc[0])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "capital 64.407404\n", "output 4.296626\n", "consumption 3.652132\n", "investment 0.644494\n", "Name: 100, dtype: float64\n" ] } ], "source": [ "# Print the last row of the DataFrame\n", "print(data.iloc[-1])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1151e02b0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TAwYM4JFHQsdpyTp3hsceC8m3NM+AAQPy3YSSonjGp5iKlJYpU0L/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UtERKScPfoo/OIX8battmp7ZZqIiJQ2Ta5WIi68MLp/O1vfvlBXFw26J+jG7uCUaVjK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\nq8lkMv997b13hhUrqoDZAPTrB7vtBnPnz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"text/plain": [ "<matplotlib.figure.Figure at 0x112baf0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a 2x2 grid of plots of capital, output, consumption, and investment\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(data['capital'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital')\n", "\n", "ax = fig.add_subplot(2,2,2)\n", "ax.plot(data['output'],lw=3)\n", "ax.grid()\n", "ax.set_title('Output')\n", "\n", "ax = fig.add_subplot(2,2,3)\n", "ax.plot(data['consumption'],lw=3)\n", "ax.grid()\n", "ax.set_title('Consumption')\n", "\n", "ax = fig.add_subplot(2,2,4)\n", "ax.plot(data['investment'],lw=3)\n", "ax.grid()\n", "ax.set_title('Investment')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Solow model with exogenous population growth\n", "\n", "\n", "Now, let's suppose that production is a function of the supply of labor $L_t$:\n", "\n", "\\begin{align}\n", "Y_t & = AK_t^{\\alpha} L_t^{1-\\alpha}\\tag{6}\n", "\\end{align}\n", "\n", "The supply of labor grows at an exogenously determined rate $n$ and so it's value is determined recursively by a first-order difference equation:\n", "\n", "\\begin{align}\n", "L_{t+1} & = (1+n) L_t \\tag{7}\n", "\\end{align}\n", "\n", "The rest of the economy is characterized by the same equations as before:\n", "\n", "\\begin{align}\n", "C_t & = (1-s)Y_t \\tag{8}\\\\\n", "Y_t & = C_t + I_t \\tag{9}\\\\\n", "K_{t+1} & = I_t + ( 1- \\delta)K_t \\tag{10}\\\\\n", "\\end{align}\n", "\n", "Combine Equations (6), (8), (9), and (10) to eliminate $C_t$, $I_t$, and $Y_t$ and obtain a recurrence relation specifying $K_{t+1}$ as a funtion of $K_t$ and $L_t$:\n", "\\begin{align}\n", "K_{t+1} & = sAK_t^{\\alpha}L_t^{1-\\alpha} + ( 1- \\delta)K_t \\tag{11}\n", "\\end{align}\n", "\n", "Given an initial values for capital and labor, Equations (7) and (11) can be iterated on to compute the values of the capital stock and labor supply at some future date $T$. Furthermore, the values of consumption, output, and investment at date $T$ can also be computed using Equations (6), (8), (9), and (10).\n", "\n", "### Simulation\n", "\n", "Simulate the Solow growth model with exogenous labor growth for $t=0\\ldots 100$. For the simulation, assume the following values of the parameters:\n", "\n", "\\begin{align}\n", "A & = 10\\\\\n", "\\alpha & = 0.35\\\\\n", "s & = 0.15\\\\\n", "\\delta & = 0.1\\\\\n", "n & = 0.01\n", "\\end{align}\n", "\n", "Furthermore, suppose that the initial values of capital and labor are:\n", "\n", "\\begin{align}\n", "K_0 & = 20\\\\\n", "L_0 & = 1\n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x115fad240>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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D2WfDo49mP7/ddvDnP8Nee4WtSySbQm4jrdsTIiJ1WLkSbrrJT2LM1jC0bQtX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\n3HSTXw3x/fd1j+vY0T9pctg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"text/plain": [ "<matplotlib.figure.Figure at 0x1151d8400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize parameters for the simulation (A, s, T, delta, alpha, n, K0, L0)\n", "K0 = 20\n", "L0 = 1\n", "T= 100\n", "A= 10\n", "alpha = 0.35\n", "delta = 0.1\n", "s = 0.15\n", "n = 0.01\n", "\n", "\n", "# Initialize a variable called labor as a (T+1)x1 array of zeros and set first value to L0\n", "labor = np.zeros(T+1)\n", "labor[0] = L0\n", "\n", "# Compute all labor values by iterating over t from 0 through T\n", "for t in np.arange(T):\n", " labor[t+1] = (1+n)*labor[t]\n", " \n", " \n", "# Plot the simulated labor series\n", "plt.plot(labor,lw=3)\n", "plt.grid()\n", "plt.title('Labor')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1160042e8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3nl/GOXZs9j6bbgqXXgoVFdCiRfZ+IuUqpzW1mW0OtAEm1F1zzs0H3gC6JC/t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\nOmx8IiJxpkJCSpZz8PjjcNF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"text/plain": [ "<matplotlib.figure.Figure at 0x115fa5828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to K0\n", "capital = np.zeros(T+1)\n", "capital[0] = K0\n", "\n", "# Compute all capital values by iterating over t from 0 through T\n", "for t in np.arange(T):\n", " capital[t+1] = s*A*capital[t]**alpha*labor[t]**(1-alpha) + (1-delta)*capital[t]\n", " \n", "\n", "# Plot the simulated capital series\n", "plt.plot(capital,lw=3)\n", "plt.grid()\n", "plt.title('Capital')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " capital labor\n", "0 20.000000 1.000000\n", "1 22.280079 1.010000\n", "2 24.525814 1.020100\n", "3 26.729921 1.030301\n", "4 28.887152 1.040604\n" ] } ], "source": [ "# Store the simulated capital data in a pandas DataFrame called data_labor\n", "data_labor = pd.DataFrame({'capital':capital,'labor':labor})\n", "\n", "# Print the first five rows of the data_labor\n", "print(data_labor.head())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "capital 20.000000\n", "labor 1.000000\n", "output 2.853386\n", "consumption 2.425378\n", "investment 0.428008\n", "Name: 0, dtype: float64\n" ] } ], "source": [ "# Create columns in the DataFrame to store computed values of the other endogenous variables\n", "data_labor['output'] = data_labor['capital']**alpha*data_labor['labor']**(1-alpha)\n", "data_labor['consumption'] = (1-s)*data_labor['output']\n", "data_labor['investment'] = data_labor['output'] - data_labor['consumption']\n", "\n", "# Print the first five rows of data_labor\n", "print(data_labor.iloc[0])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " capital labor output consumption investment capital_pw \\\n", "0 20.000000 1.000000 2.853386 2.425378 0.428008 20.000000 \n", "1 22.280079 1.010000 2.982495 2.535121 0.447374 22.059484 \n", "2 24.525814 1.020100 3.104459 2.638790 0.465669 24.042558 \n", "3 26.729921 1.030301 3.220148 2.737126 0.483022 25.943798 \n", "4 28.887152 1.040604 3.330291 2.830748 0.499544 27.759985 \n", "\n", " output_pw consumption_pw investment_pw \n", "0 2.853386 2.425378 0.428008 \n", "1 2.952965 2.510021 0.442945 \n", "2 3.043289 2.586796 0.456493 \n", "3 3.125444 2.656628 0.468817 \n", "4 3.200344 2.720293 0.480052 \n" ] } ], "source": [ "# Create columns in the DataFrame to store capital per worker, output per worker, consumption per worker, and investment per worker\n", "data_labor['capital_pw'] = data_labor['capital']/data_labor['labor']\n", "data_labor['output_pw'] = data_labor['output']/data_labor['labor']\n", "data_labor['consumption_pw'] = data_labor['consumption']/data_labor['labor']\n", "data_labor['investment_pw'] = data_labor['investment']/data_labor['labor']\n", "\n", "\n", "# Print the first five rows of data_labor\n", "print(data_labor.head())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1162d0518>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sk74Q3203vzTJP/9ZuIW43qMSJTPbwMxGm9lcMys3s4/N7OJsxxU3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\nLbaILj6ROFMDtxibNWtWtkMoKJnOZ2U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1zTWLV1PSMy01yjM8ZZocGk1dSsqXX8Kf/uRH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Au87T9CEZHmsRb++lc3VdnChenH7Lqrm860qCjY2CT/qRgXkby1dCmMHw/l5e6/0/nl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\nPXt22CHkFeXTP+XUL+VTJFjz58Mxx8BRR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U2NXijRilCjSAgsM6PMddElA9ukUJPUXduS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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11514d710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a 2x2 grid of plots of capital, output, consumption, and investment\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(data_labor['capital'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital')\n", "\n", "ax = fig.add_subplot(2,2,2)\n", "ax.plot(data_labor['output'],lw=3)\n", "ax.grid()\n", "ax.set_title('Output')\n", "\n", "ax = fig.add_subplot(2,2,3)\n", "ax.plot(data_labor['consumption'],lw=3)\n", "ax.grid()\n", "ax.set_title('Consumption')\n", "\n", "ax = fig.add_subplot(2,2,4)\n", "ax.plot(data_labor['investment'],lw=3)\n", "ax.grid()\n", "ax.set_title('Investment')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x116813128>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dRfhucl+WZ7hTfQCcbmYbEtr4Iwi31h+f7LAM4Haahxn7ISE57ko4qX0YsBfNSXpnTEtu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WuNyC0iIiLSf9xh2bIwkG16WrKk62tvpqVLO1+X9/OyZZ0/p6d0efnnZcuW/3NPp/I29/bnckM1\n/fOK3stOK3q//Dvp6fvp+fLPjahXDW8zOwE4EdgiKZoO/NDdb+zBZ3cH7gAedXfdzNXPxo0bx09/\n+tO8w2hor7wCU6fCnXf+f/buPL6K6v7/+OvIokQEd9AWKm5V6oKJC7Tu1lijXOuKuIe6VWhdKlbb\nr4K0LtDWDbVuVFFL3Cq4UIW6/RQVl9y6VMWlWrEqaoQiEpAln98fk5g7N+tNzs3cmft+Ph7zCHMy\n997PvGGYnMzMOTBnTnDLeF1da68YC7Sc6RprBJ3pwYPhBz8Ilm23DTrZa6/tu/r4079R/5SpSHHR\nMe+fMvVv7NixTJr0B1auhGXLgoFnly8P/rxsWeOfM79+803wNfPP33zT+OcVKxrXV6xoXDLXV65s\n+ufMr4XaWWtb6z+PSuHI9Yr3R8CvgXcBB5wEPOCcG2Jmb7X0IudcX2Aq8BjQr2OlSmcMHDgw6hIK\nzmefwRNPBMvTTwcjjOemMdN114WddoIdd4QddgiWwYODOaWlffRv1D9lKlJcdMz7V+yZNtx6+9VX\nwbJkSXBH4JIljX/OXpYuDS+1tY1fa2vhf/8byJVXBldpxYfi/jcaJ50e1dw59yVwrpnd2so2VcA7\nQB1wSFtXvDXSqeRDbS38v/8Hs2bB44/Dv/7VsfdZbz3YdVcoKwuW0lL43vf07LVIkmlUc/90rhfp\nGsuXB+PRLFwYTF26aFHw5//9r+myeHF4+eqr4GqwSPFIAwU2qrlzbg3gKKAEeL6V7SqBQcCxwIUd\n/TyRjnj3XZg5Ex59NOh0L1+e2+u7dw861sOGwdChsMsuwYBn6mSLiIhIV6urCzrOn30WjDvz+efB\n8sUXUFMTXr78Muhg19ZGXXWy9OgRLN26BV+7dw+WhvVu3RrXs79m/7mlZY01wl+b+3P2Ng1tDevO\nBX92Lvz9zPXsbTLXW/rasGS+X3Pfb6mtvQvk9v3M9Zb+nLme/bXhz2+8AT/9aX7+7eTc8XbObUfQ\n0V4LWAIcambzWth2K+BSYHczq3PqrUierV4Nzz8PDz4YLG+/ndvr11kHdt8d9tgj+LrzzrpdXERE\nRPJr1apgrJmPP4ZPPgmWTz8NlgULGpfPPw+2TaqePYOpU3v1Cr42LL16wZprBn/O/Jq99OzZ9GvP\nnkGHOHu94WtbS/fujV+7dYs6Icm3r77K33t35Ir3PGBHoC9wBHC7c27P7M53/RXxvwLjzOzfDc2d\nKVY6bt68eWyzzTZRl5EXK1fCU0/BfffB9Om5jTzeuzfstRfsuy/svXfwbHb3dh4VSc40CsrTP2Uq\nUlx0zPvnI1Oz4GeTDz8Mlvnz4aOPGpf//jfoVLc+oGthWGON4CJFw9K7d+PX3r2DwWNbWkpKguWL\nL+ax7bbbUFISdKgblrXWUse2I3Tcx4iZdWoB/gH8uZn2vgTPdK8AVtYvqzPa9m7lPUsB69evnw0f\nPjy0DB061KZPn26ZZs2aZcOHD7dsZ5xxht1yyy2hturqahs+fLh98cUXofaLLrrILr/88lDbhx9+\naMOHD7e33nor1H7NNdfYueeeG2pbunSpDR8+3J555plQ+7Rp0+ykk05qUttRRx3VpfvRr1+/ROxH\nw9/HqlVmjz1mNmqUWUnJNQbnZk00sNRguMEzoXbnptnGG59kF19s9uyzZitWdHw/Mr9XrP+ufO5H\nZo1x3o9MUe/H8OHDE7EfZl379zFt2jTbbLPNbMcdd/z23LPnnnsaYECpdfK8qSV8rq+urm7ydyUd\n09wxKZ3T3kxra83+9S+zGTPMrrjCbMwYs4oKs223NevVq7kJkaJZ+vY122wzs512MttnH7PDDjP7\n2c/MfvUrswkTzK6+2uy228ymTzd74gmzl182e+cdswULzJYuNaur67pMpX2Up1/V1dV5O9/7GFzt\nceBDMxuV1e6AbbM2Hw3sAxwO/MfMlrXwnhpwxbP58+fHfmROM3j5ZZg2De66K/jtcHv07w8VFXDg\ngbDffsHgaD4kIdNCojz9U6b+aHA1/3Su90/HvH+Zma5eDR98EDzG1rC88w68915w1bqr9e0LG20E\nG28cfN1oI9hww2DZYIPGrxtsAOuvH8zA0t67+vJJ/079Up5+5fN8n+s83pcCjwDzgXUIBkzbCyiv\n//5lwKZmdqIFPfo3s17/ObDcWpl6TPIjzgfkggVwxx1w663wVjv/5Wy/PaRSwbLzzsGtUb7FOdNC\npDz9U6YixUXHvB+rV8P778Prr8MbbwzkzTfhzTeDjvY33+T3s3v2hE02gU03Db42LP37B0u/fsGy\n8cbBM8txpH+nfinP+Mj1914bE8zHvQmwGHgNKDezJ+q/3x8Y4K88KVarVwejkd9yC/z97+2b63GX\nXeCII+Dww2GLLfJfo4hIXDnnRgPnEpy3XwV+YWYvtbJ9T2AcwS/c+wOfABPM7LaMbY4EJgCbEUwh\ner6ZPZKnXRDx4uuv4dVX4Z//hFdegddeC0Y1zsdI4N26wYABwRSkAwaEl+9+F77zneDqdD4uFohI\n9HLqeJvZyW18v7KN718MXJzLZ0px+fRTmDIFbropGHCkLaWlMHIkHHlkcCITEZHWOedGAH8CTgVe\nBM4GZjnntjazmhZedi+wEVAJ/JvgF/Dfdg+ccz8EpgG/BmYSdNBnOOd2MrM3m76dSNdbuhTS6eCx\ntZdegurqYNrRTj51GbLJJsG0o1tsAYMGBX8eNAg22yy4iq3Bw0SKl36nViQmTpwYdQmtmjsXRoyA\ngQPhwgtb73RvvnmwzVtvBSfNc8+NptNd6JnGjfL0T5lKC84GbjSz2y2YkeR0oBYY1dzGzrmfAHsA\nFWb2pJnNN7MXzOz5jM1+CTxiZleY2dtmdhGQBsbkd1ckk475RnV1MG9e8JjaaacFs5b06QN77gnn\nnANVVcHz2W13uptmut56MGwYnHQSXHYZ/O1vwVXzJUuCacDmzIGpU2H8eDjhhGCK0gED1OluoH+n\nfinP+CiAIRakK9Tm456pTlq1Cu6/H668Muh4t6akBI46CiorgxNYIUwJX4iZxpny9E+ZSjbnXA+g\nDLi0oc3MzDn3GDCshZcNB14Gfu2cOx5YCjwIXGhmy+u3GUZwFT3TLOAQj+VLG4r5mF+5Mvhl/DPP\nBMuzz8LChZ17zw03hN69azn4YBg8OFi23TYYxKwQfg6Jq2L+d5oPyjM+Oj2qeT5opNNkW7YsuJ38\nj38M5rNszc47w+mnB53uddbpmvpERLIlZVRz59wmwMfAMDN7IaN9IrCnmTXpfDvnHgH2Jpg+dAKw\nIfBn4Akz+1n9Nt8AJ5jZ3Rmv+zlwkZlt0kItOtdLh61eHdw2/sQTwTJnTsefy+7WLehQ77QTDBkS\nDNC6/fbBIGbqYIsUl4IZ1VykMxYvhuuvh6uugs8/b3m7Xr3gmGOCDvfOO3ddfSIi0qw1gDrgGDP7\nGsA5dw5wr3PuDDPL8zjPIoEPPoDZs2HWrKCzvXhx7u/RrRtst13w88Uuu0BZWbC+1lr+6xURyaRn\nvCXvFi+Giy8OnsP+zW9a7nQPGACTJsHHHwejmavTLSLiXQ2wGuiX1d4PWNDCaz4FPm7odNd7C3DA\nd+vXF+T4nt+qqKgglUqFlmHDhjFjxozQdrNnzyaVSjV5/ejRo5kyZUqoLZ1Ok0qlqKkJjxU3bty4\nJs9Dzp8/n1Qqxbx580LtkydPZuzYsaG22tpaUqkUc+bMCbVXVVVRWdl0fNkRI0ZoPzqxHytWwD/+\nAWeeCRtvPJnNNx/L6afD9OkNne5aIAWE9wOqCMYBDAY0O/zw4C67ffcdwV//OoNXXgl+zjjtNFi4\ncDZHHaW/D+2H9qMY96O8vJwhQ4aEzj8nn9zqWOKdolvNi0RNTQ0bbrhhl37m11/D5Mnwhz/AokUt\nb7fbbsFAJ4cdBt1jdA9GFJkmmfL0T5n6k5RbzQGcc3OBF8zszPp1B8wHrjGzPzSz/SnAlcDGZlZb\n33YIcB/Q28y+cc7dBfQys0MyXvcs8KqZndFCHTrXe5aEY37RInjoIXjwweDq9pIlub1+8OBgLJg9\n9oDddw8Gbe3M7eJJyLTQKFO/lKdf+Tzf64p3kRg1qtnBavNixQq45ppg9PHf/KblTvcBB8DTTwcD\nqx11VLw63dC1mRYD5emfMpUWXAGc4pw7wTm3DXADUALcBuCcu8w5NzVj+2nAl8CtzrltnXN7ApOA\nKRm3mV8N/MQ5d45z7vvOufEEg7hd2yV7JEB8j/kFC+DGG6G8HDbeGE48MRgpvD2d7m23hTPOgPvu\ngy++CObgvuEGOPbY4E67zj6jHddMC5ky9Ut5xkfMujrSUePHj8/7Z5gFo5Sffz68917z2zgXXNm+\n4ILguao464pMi4ny9E+ZSnPM7B7n3IYEA6X1A14BDjCzL+o36Q8MyNh+qXNuf2Ay8BJBJ/xu4MKM\nbZ53zh0DXFK/vAscojm8u1acjvmFC4POdVUVPPVU++fS3mgj2H//4Jf3P/5xcCt5PsUp07hQpn4p\nz/jQrebixQsvBLeLP/dcy9scdljwrPd223VdXSIiPiTpVvNCoXN98Vm+HB54AO64IxggbdWqtl/j\nHOy6Kxx8MFRUBKOOr6H7NUUkTzSquRSszz8PrnDfemvL2xx0EEyYAPq5SkREpLiYBb+cnzoV7roL\n/ve/tl+z1lrBbeeHHBL8DNEve9g+EZEYUsdbOmTVquAZqgsvbPkkuttuwSiiu+/etbWJiIhItBYt\ngttvh5tugjfb8cBB797BVe3DDoMDDwzWRUSSRDfrFInsIf874+WXg7kvf/GL5jvdm28O99wDzz+f\n7E63z0xFeeaDMhUpLlEf82bBgKknnRQ8e33WWa13utdcM5jq6957gzvoqqrgyCMLq9MddaZJpEz9\nUp7xoY53kUinO/+IQm0tnHtucCX7lVeafr9372DqsDffDE6cnR1JtND5yFQaKU//lKlIcYnqmF+x\nAqZNC34+GDYsuK18+fKWt993X7jttqCzfd99cMQR0KtXl5WbE/0/6p8y9Ut5xocGV5N2efxxOPVU\neP/95r9/7LFBp3uTTbq2LhGRrqDB1fzTuT7+vvwyeOzsuuvg009b33aLLYJpwk44IZjmS0SkEGlw\nNYnM0qXBVe4bbmj++9ttF5xw99yza+sSERGRaPz3v3DFFcHz20uXtrxdjx5w6KFw2mmwzz7JvxNO\nRKQ16nhLi154AY47rvk5uddcEy66CMaODU6sIiIikmzvvguXXQZ33gkrV7a83cCB8POfQ2WlRiQX\nEWmgjrc0sXIl/P73cMklsHp10+/vsQfcfDN8//tdX5uIiIh0rffeg9/9Luhw19W1vN0ee8CZZwbT\ngHXXT5giIiEaXK1IpFKpdm334YfBbeMTJjTtdK+9Nlx/PTz1lDrd0P5MpX2Up3/KVKS4+D7mP/gA\nRo2CbbYJpgZrrtPdrRsccwyk0/D008Eo5UnqdOv/Uf+UqV/KMz4S9F+jtGbMmDFtbvPQQ8HAJ4sW\nNf3e0KFwxx2w5ZZ5KC6m2pOptJ/y9E+ZihQXX8f8l18Gd75dd13Lt5SvtRb87Gfwq1/BoEFePrYg\n6f9R/5SpX8ozPjSqubBiBVxwQTBQSrbu3WHcODj//GT9BltEJBca1dw/nesLz7JlcPXVcPnlsHhx\n89usvTaMGQPnnAMbb9y19YmI5JtGNZe8+eSTYP7M559v+r0ttoC77oKdd+76ukRERKRrmMG99waz\nmHz0UfPblJTA6NHBoKobbdS19YmIJIE63kXshReCaT6am3vzqKOCAdT69On6ukRERKRrvP46/PKX\nwfgtzenRA844I7gzTiOUi4h0nAZXKxIzZswIrd92WzCIWnanu2fPYAC1u+5Sp7st2ZlK5yhP/5Sp\nSHHJ5ZhfvDgYgXynnVrudI8cCW+/DVddVbydbv0/6p8y9Ut5xoc63kWiqqoKgFWr4Kyzgrk1V6wI\nbzNoEMydG8y96VwERcZMQ6bih/L0T5mKFJf2HvPTp8PgwXDNNc1PG7rPPvDyy2FwphkAACAASURB\nVDBtWrIHTmsP/T/qnzL1S3nGhwZXKyJLl8LRR8PDDzf93r77wj33wAYbdH1dIiKFToOr+adzfdf7\n5JNgYLTp05v//sCBwUCrhx2mX8CLSHHK5/leV7yLxIIFsNdezXe6zzoLZs1Sp1tERCSJzOCWW2Db\nbZvvdK+1VjCDyVtvBfNwq9MtIuKfBlcrAm+9BRUV8J//hNt79oQbb4STToqiKhEREcm3Tz+Fk0+G\nv/+9+e//5CfB2C7Ffku5iEi+qeOdcM8+C8OHw6JF4fb114cHHoDdd4+mLhEREcmve+4Jxm1ZuLDp\n9zbaKBg0beRIXeEWEekKutU8wf7xD9h//4ZOd+W37YMGwXPPqdPdWZWVlW1vJO2mPP1TpiLFpeGY\n/+orOO44GDGi+U73iScGd8Mdc4w63W3R/6P+KVO/lGd86Ip3Qk2fHgyk1jhyeTkAu+wCDz1UvNOC\n+FReXh51CYmiPP1TpiLFpby8nHQ66HC/917T7/frB1OmwEEHdX1tcaX/R/1Tpn4pz/jQFe8EuvNO\nOPLI7OnCRnLwwfDkk+p0+zJy5MioS0gU5emfMhUpHmZQUzOSYcOa73QfcQT861/qdOdK/4/6p0z9\nUp7xoSveCXPTTXD66cEJONPIkTB1KvToEU1dIiIikh+LF0NlZfMjlvftGwyepme5RUSipY53gtxy\nC5x2WtP2U06BP/8ZunXr+ppEREQkf+bNg5/+FN5+u+n3dtsNqqo0YrmISCHQreYJMXUqnHpq0/Zz\nzgmmDHv++TldX1TCzZmjTH1Snv4pU5Fke/BB2HXXzE534zE/diw884w63Z2l/0f9U6Z+Kc/4UMc7\nAaZNC24xy769/MIL4Y9/DG4tmzRpUjTFJZgy9Ut5+qdMRZKprg4mTIBDDoElSzK/M4kNNoCZM2HS\nJD1e5oP+H/VPmfqlPONDt5rH3L33wvHHN+10//a3cPHFjc9z3XXXXV1fXMIpU7+Up3/KVCR5li8P\npgO7556m39txx7t44AH43ve6vq6k0v+j/ilTv5RnfOR0xds5d7pz7lXn3OL65Tnn3E9a2f5Q59xs\n59znGdtrzHtPZs0K5uCsqwu3n3ce/O534UFUSkpKura4IqBM/VKe/ilTkWSpqYH99mu+033ssfDc\ncyXqdHum/0f9U6Z+Kc/4yPVW84+AXwOlQBnwBPCAc27bFrbfE5gNHFj/mieBh5xzO3asXGnw0ktw\n+OGwalW4/ayz4PLLNXKpiIhIkrz7LgwbBs89F25fYw3405/gjjtAP3+LiBSunG41N7OZWU3/55z7\nOTAUeKuZ7c/Oavqtc+4QYDjwai6fLY3eeQcqKmDp0nD76NFwxRXqdIuIiCTJs89CKgULF4bb11kH\n7rsPynUvoYhIwevw4GrOuTWcc0cDJcDz7XyNA9YBFra1rTTvk0+CE2xNTbj9mGPgmmta7nSPHTs2\n/8UVGWXql/L0T5mKxN+jj8L++zftdA8YEHTIMzvdOub9U6b+KVO/lGd85Dy4mnNuO4KO9lrAEuBQ\nM5vXzpePBdYGmnk6SdqyeDEceCB8+GG4ff/94dZbg9vNWjJw4MD8FleElKlfytM/ZSoSb/feGzy7\nvXJluH2nneDhh2HTTcPtOub9U6b+KVO/lGd8OMseDrutFzjXHRgI9AWOAE4B9myr8+2cOwa4EUiZ\n2ZNtbFsKVFdXV1NaWppTfUm1ahUMHx785jvTzjvDE08Et5uJiEh+pNNpysrKAMrMLB11PUmgc33r\nbrkFTjut6QCqFRVw993Qu3c0dYmIJFk+z/c532puZqvM7H0z+6eZ/ZbgWe0zW3tN/S3pNwFHttXp\nzlRRUUEqlQotw4YNY8aMGaHtZs+eTSqVavL60aNHM2XKlFBbOp0mlUpRk3Wv9rhx45g4cWKobf78\n+aRSKebNC/9OYfLkyU1u66itrSWVSjWZxL6qqorKysomtY0YMSKn/TjwwClZne40JSUpbr+9JtTp\nLvT9SMrfh/ZD+6H9SO5+VFVVMWjQIIYMGfLtuefss7OHLBHJnyuugFNOadrpPvZYmDFDnW4RkTjK\n+Yp3kzdw7nHgQzMb1cL3RwK3ACPM7OF2vqd+C57hppuC33pn2mgjmDsXNt88mppERIqJrnj7p3N9\n8664An71q6btZ5wBkye3/liZiIh0TsFc8XbOXeqc28M59z3n3HbOucuAvYA7679/mXNuasb2xwBT\ngV8BLznn+tUvfTzuQ6I9+WQwWnmmnj1h+vTcOt3ZV4+k85SpX8rTP2UqEi9XXdV8p/s3v4Frr227\n061j3j9l6p8y9Ut5xkeuvzfdmKAjPQ94jGAu73Ize6L++/2BARnbnwJ0A64DPslYrupEzUXjvfea\nn6v75pvhRz/K7b3OO+88f4UJoEx9U57+KVOR+Jg8GZp7omHiRLjkkvZNFapj3j9l6p8y9Ut5xkeu\n83if3Mb3K7PW9+lIURLM0X3oobBoUbj9/PPhhBNyf79rr73WT2HyLWXql/L0T5mKxMP118Mvf9m0\n/Q9/gHPPbf/76Jj3T5n6p0z9Up7xoSeFCpAZnH46/Otf4faf/jT4rXdHaKoB/5SpX8rTP2UqUvhu\nv73pI2UAl1+eW6cbdMzngzL1T5n6pTzjQx3vAnTjjXDnneG27baDO+7QoCoiIiJJMXMmjGpmaNpL\nLoFf/7rr6xERkfxRN67AvPginJk1OVufPnD//Zo+REREJCmeew6OPBJWrw63T5gQDKYmIiLJoo53\nAampCU7CK1aE22+7DbbaqnPvnT1XrnSeMvVLefqnTEUK0xtvwMEHw7Jl4fZzz4ULL+z4++qY90+Z\n+qdM/VKe8aGOd4GoqwsGTZs/P9w+dmwwyFpn1dbWdv5NJESZ+qU8/VOmIoVn/nw44ICmg6eecEIw\ngnln6Jj3T5n6p0z9Up7x4cws6hqacM6VAtXV1dWUlpZGXU6XuOaapreY77knPP44dM9p7HkREfEt\nnU5TVlYGUGZm6ajrSYJiPNcvWRJMB/r66+H2gw6C6dOhR49o6hIRkUA+z/e64l0AXn8dsqfg698f\n7r5bnW4REZEkWL0aRo5s2un+4Q/hnnvU6RYRSTp1vCO2bFlwIv7mm3D77bcHnW8RERGJv7Fjg1HM\nM22zDTz0EJSURFOTiIh0HXW8I3beecEgK5l+9SvYf3+/n1NTU+P3DUWZeqY8/VOm0hLn3Gjn3AfO\nuWXOubnOuV1a2XYv51xd1rLaObdxxjYnZrQ3bKMHD+vdeCNceWW4bYMNgo74+uv7+xwd8/4pU/+U\nqV/KMz7U8Y7QzJlw7bXhtiFDgvk7fRvV3ESh0inK1C/l6Z8yleY450YAfwLGATsBrwKznHMbtvIy\nA7YC+tcvm5jZ51nbLM74fn/ge55Lj6XHH4fRo8NtPXoEz3Rvvrnfz9Ix758y9U+Z+qU840Md74h8\n8QVUVobbevWCadNgzTX9f9748eP9v2mRU6Z+KU//lKm04GzgRjO73czmAacDtUBbP719YWafNyzN\nfN/MLHObL3wXHjcffghHHdV0ru6bb4Y99vD/eTrm/VOm/ilTv5RnfKjjHZFf/jLofGe64grYdtv8\nfF6xjBjblZSpX8rTP2Uq2ZxzPYAy4PGGNgumN3kMGNbaS4FXnHOfOOdmO+d+2Mw2vZ1z/3HOzXfO\nzXDODfZafMwsXw5HHAELF4bbL7gATjwxP5+pY94/ZeqfMvVLecaHOt4RmDED7ror3DZ8OJx2WjT1\niIhI0dgQ6AZ8ltX+GcHt4c35FDgNOBw4DPgIeMo5NyRjm7cJrpingGMJfr54zjm3qb/S4+XMM+Hl\nl8NthxwCv/99NPWIiEi01PHuYgsXws9/Hm5bb71g4BXnoqlJRESkJWb2jpndbGb/NLO5ZvYz4DmC\nW9YbtplrZnea2Wtm9gxBB/0Lgg57qyoqKkilUqFl2LBhzJgxI7Td7NmzSaVSTV4/evRopkyZEmpL\np9OkUqkmgw6NGzeOiRMnhtrmz59PKpVi3rx5ofbJkyczduzYUFttbS2pVIo5c+aE2quqqqjMeH7s\nttvgppsARgDBfmy1FUydCo89Fp/9aDBixIhY/31oP7Qf2g/tR3P7UV5ezpAhQ0Lnn5NPPrnJdt6Y\nWcEtQClg1dXVljQnnGAG4WXq1Px/7i233JL/DykyytQv5emfMvWnurraCAYYK7UCOE92dAF6ACuB\nVFb7bcD0HN5nEvBsG9vcA/y1le8n8lyfTputtVb4PF9SYvbaa/n/bB3z/ilT/5SpX8rTr3ye73XF\nuwvNnBnMz52pogKOPz7/n51Op/P/IUVGmfqlPP1TppLNzFYC1cB+DW3OOVe//lwObzWE4Bb0Zjnn\n1gC2b22bJPrqq+C57uXLw+033wzbb5//z9cx758y9U+Z+qU848NZ8FvnguKcKwWqq6urEzNgwOLF\n8IMfwMcfN7b16RPM4f3d70ZXl4iItC2dTlNWVgZQZmax/inHOXcUwRXu04EXCW4ZPwLYxsy+cM5d\nBmxqZifWb38m8AHwBrAWcAowGtjfzJ6q3+ZCYC7wHrAucB7B895lFoyc3lwdiTvXH3883HlnuG3M\nGJg8OZp6REQkN/k833f3+WbSsgsvDHe6IRjFXJ1uERHpSmZ2T/2c3ROAfsArwAHWOP1Xf2BAxkt6\nEsz7vSnBtGOvAfuZ2dMZ26wH3FT/2kUEV9WHtdTpTqI772za6R46FP70p2jqERGRwqKOdxdIp+G6\n68Jt5eWg+e5FRCQKZnY9cH0L36vMWv8D8Ic23u8c4BxvBcbM++/DGWeE2/r2DWYw6dkzmppERKSw\n6BnvPKurC07GdXWNbWutBTfcoFHMRURE4m7lSjjmGFiyJNx+ww3wve9FU5OIiBQedbzzbMoUeOGF\ncNtvfwuDBnVtHc0NoS+do0z9Up7+KVOR/Jswoel5/qST4Oiju74WHfP+KVP/lKlfyjM+1PHOo5oa\nOP/8cNtWW0HW9HRdYsyYMV3/oQmnTP1Snv4pU5H8ev55uPTScNuWW8I110RTj455/5Spf8rUL+UZ\nH+p459H558PCheG2666DNdfs+lrKy8u7/kMTTpn6pTz9U6Yi+bNsGVRWhh8l694dpk2DddaJpiYd\n8/4pU/+UqV/KMz7U8c6T554LbjPPNGIE7L9/NPWIiIiIP+PGwdtvh9smTIBddommHhERKWzqeOdB\nXR2cdVa4rXdvTSkiIiKSBC+80PScvuuu0TxKJiIi8aCOdx5UVcFLL4XbLr4YvvOdaOoBmDFjRnQf\nnlDK1C/l6Z8yFfFv+fKmt5j37Am33hrcah4lHfP+KVP/lKlfyjM+1PH2bNkyuOCCcNv3vw+/+EU0\n9TSoqqqKtoAEUqZ+KU//lKmIfxMmwFtvhdvGj4fBgyMpJ0THvH/K1D9l6pfyjA9nZlHX0IRzrhSo\nrq6uprS0NOpycnLppcF0YZkefBCGD4+mHhER6bx0Ok1ZWRlAmZmlo64nCeJ4rq+uht12g9WrG9vK\nymDu3OivdouISOfl83yvK94eLVgAl10Wbtt3Xzj44GjqERERET9Wr4bTTgt3unv0KIxbzEVEpPCp\n4+3RuHHw9deN684Fg684F11NIiIi0nk33hhc8c504YWw/fbR1CMiIvGijrcnr78Ot9wSbjvpJBgy\nJJJyRERExJPPPoPf/CbcNngw/PrX0dQjIiLxo463J7/+dXiE05IS+P3vo6snW2VlZdQlJI4y9Ut5\n+qdMRfw491xYvDjcdv31wWjmhUTHvH/K1D9l6pfyjA91vD2YMwceeSTcdt55sOmm0dTTnPLy8qhL\nSBxl6pfy9E+ZinTeU0/BnXeG244/HvbaK5JyWqVj3j9l6p8y9Ut5xodGNe8kM9h7b3j66ca2fv3g\n3/+GtdeOrCwREfFIo5r7F4dz/YoVwSNjmdOH9e0Lb78dnOtFRCRZNKp5AXv88XCnG4LpxNTpFhER\nibcrr2w6Z/ell6rTLSIiuVPHuxPMms7ZPWAAnHpqNPWIiIiIH5991nSslrKyYEoxERGRXOXU8XbO\nne6ce9U5t7h+ec4595M2XrO3c67aObfcOfeOc+7EzpVcOB5+GF58Mdx20UWw5prR1NOaOXPmRF1C\n4ihTv5Snf8pUpOMuuqjpFKF//jN06xZdTW3RMe+fMvVPmfqlPOMj1yveHwG/BkqBMuAJ4AHn3LbN\nbeyc2wx4GHgc2BG4GrjFObd/B+stGHV1wfydmbbYAk4s0F8rTJo0KeoSEkeZ+qU8/VOmIh3T0hSh\nu+wSSTntpmPeP2XqnzL1S3nGR6cHV3POfQmca2a3NvO9icCBZrZDRlsV0NfMKlp5z4IfcOWee2DE\niHDbnXfCscdGU09bamtrKSkpibqMRFGmfilP/5SpPxpczb9CPdebwQEHwD/+0dhWUgLvvltYs5U0\nR8e8f8rUP2Xql/L0qyAHV3POreGcOxooAZ5vYbOhwGNZbbOAYR393EKwejWMGxduGzwYjj46mnra\nQwekf8rUL+XpnzIVyd2jj4Y73VB4U4S2RMe8f8rUP2Xql/KMj+65vsA5tx1BR3stYAlwqJnNa2Hz\n/sBnWW2fAX2cc2ua2Te5fn4huP9+mJe1xxMmFPZzXyIiItK6VavgV78Kt226KZx7bjT1iIhIcnTk\nivc8gue1dwX+DNzunNvGa1UFzCyYSiTTjjvCoYdGU4+IiIj4ccstzU8fpilCRUSks3LueJvZKjN7\n38z+aWa/BV4Fzmxh8wVA9myX/YCv2nO1u6KiglQqFVqGDRvGjBkzQtvNnj2bVCrV5PWjR49mypQp\nobZ0Ok0qlaKmpibUPm7cOCZOnBhqmz9/PqlUinkZl7cffRReeWUyMPbbtt/+FpYvryWVSjUZWbCq\nqorKysomtY0YMaJL92PLLbcM7QfA5MmTGTt2bKittraw9yP77yPK/cj8zDjvR6Yo9yPzNXHej0xR\n78fYsWMTsR/QtX8fVVVVDBo0iCFDhnx77jn77LObvJ8ky9dfByOZZyotheOPj6aejsg+RqTzlKl/\nytQv5RkjZtaphWDE8r+08L3LgVez2qYBf2/jPUsBq66utkKzxx5mwXXvYNl6a7NVq6Kuqm3XXHNN\n1CUkjjL1S3n6p0z9qa6uNsCAUuvkeVNLYZ7rL7kkfH4HsyefjLqq3OiY90+Z+qdM/VKefuXzfJ/T\nqObOuUuBR4D5wDrAsQSXfsvN7Ann3GXApmZ2Yv32mwGvA9cDfwH2A64CKswse9C1zM8pyJFOn3kG\n9twz3PaXv0AzF1ZERCRBNKq5f4V0rv/f/2DQoOBrg4MPhoceiq4mERHpevk83+c6uNrGwFRgE2Ax\n8Br1ne767/cHBjRsbGb/cc4dBFwJ/BL4L/Cz1jrdheyyy8LrAwYU7vRhIiIi0j5XXBHudAP87nfR\n1CIiIsmUU8fbzE5u4/tNrv2a2dNAWY51FZx//hMeeSTcdu650LNnNPWIiIhI59XUwJVXhtuOPBKG\nDImmHhERSaYOz+NdbC6/PLy+4YZwcqu/higs2QMVSecpU7+Up3/KVKRtkyYFA6s1WGMNuPji6Orp\nDB3z/ilT/5SpX8ozPtTxbod334V77w23nX02xGm++vPOOy/qEhJHmfqlPP1TpiKt+/RTuPbacNux\nx8K220ZTT2fpmPdPmfqnTP1SnvGhjnc7XH11ML5pgz594IwzoqunI67N/slCOk2Z+qU8/VOmIq27\n9FJYtqxxvXt3GDcuuno6S8e8f8rUP2Xql/KMD3W827BoEdx6a7jt9NNh3XWjqaejBg4cGHUJiaNM\n/VKe/ilTkZZ99BHcdFO4bdQo2GKLaOrxQce8f8rUP2Xql/KMD3W82zBlCtTWNq536wZjxkRXj4iI\niHTeH/8IK1Y0rvfsCf/3f9HVIyIiyaaOdytWrYLJk8NtRxwRTCMmIiIi8fTFF3DzzeG2U0/V+V1E\nRPJHHe9WzJgB8+eH2846K5paOmvixIlRl5A4ytQv5emfMhVp3tVXN322e+zY6OrxRce8f8rUP2Xq\nl/KMD3W8W3HVVeH13XaDoUOjqaWzajPvlxcvlKlfytM/ZSrS1FdfNR3J/LjjIAmPSeqY90+Z+qdM\n/VKe8eEsc7juAuGcKwWqq6urKS0tjaSGl16CXXcNt1VVwdFHR1KOiIhEKJ1OU1ZWBlBmZumo60mC\nqM71EyfC+edn1gFvvgnbbNNlJYiISIHK5/leV7xbcPXV4fXvfAcOPzyaWkRERKTzli2DK64Itx12\nmDrdIiKSf+p4N+OTT+Duu8NtY8ZAjx7R1CMiIiKdd+ut8Pnn4bYLLoimFhERKS7qeDfjhhuCEc0b\n9OoFp5wSXT0+1NTURF1C4ihTv5Snf8pUpNHKlTBpUrht//0huKMwGXTM+6dM/VOmfinP+FDHO8vK\nlXDLLeG2446DDTaIph5fRo0aFXUJiaNM/VKe/ilTkUZ33w0ffhhu+81voqklX3TM+6dM/VOmfinP\n+FDHO8vDD8Onn4bbxoyJphafxo8fH3UJiaNM/VKe/ilTkYAZXHlluG3oUNhrr2jqyRcd8/4pU/+U\nqV/KMz7U8c5y003h9aFDYYcdoqnFp6hGh08yZeqX8vRPmYoEnn0W0llj0553XjCieZLomPdPmfqn\nTP1SnvGhjneG//wHZs0Kt512WiSliIiIiCfZM5UMGgSpVDS1iIhIcVLHO8PNNwe3ozXo2xeOOiq6\nekRERKRzPvwQ7r8/3PaLX0C3btHUIyIixUkd73orV8Jf/hJuO+EEKCmJph7fpkyZEnUJiaNM/VKe\n/ilTaYlzbrRz7gPn3DLn3Fzn3C6tbLuXc64ua1ntnNs4a7sjnXNv1b/nq865A/O/J2277jqoq2tc\n790bkjoWkY55/5Spf8rUL+UZH+p413vwQViwINyWpNvM09kPt0mnKVO/lKd/ylSa45wbAfwJGAfs\nBLwKzHLObdjKywzYCuhfv2xiZt/OiO2c+yEwDbgZGAI8AMxwzg3Oy06009dfB3ezZaqsDO5oSyId\n8/4pU/+UqV/KMz6cZd5bXSCcc6VAdXV1dZcNGFBeDv/4R+P6j34Ec+Z0yUeLiEiBS6fTlAUTPpeZ\nWax/ynHOzQVeMLMz69cd8BFwjZlNamb7vYAngPXM7KsW3vMuoMTMUhltzwP/NLMzWnhN3s/1118P\no0dnfia8/TZstVVePk5ERGIun+d7XfEG/v3vcKcbknW1W0REBMA51wMoAx5vaLPgN/CPAcNaeynw\ninPuE+fc7Por3JmG1b9HplltvGde1dXBNdeE2w4+WJ1uERGJhjreNL0Nbb314IgjoqlFREQkjzYE\nugGfZbV/RnALeXM+BU4DDgcOI7g6/pRzbkjGNv1zfM+8mzUruLqd6cwzo6lFRESke9QFRG3VKpg6\nNdx2wgnQq1c09YiIiBQSM3sHeCejaa5zbgvgbODEaKpq2+TJ4fXttoN9942mFhERkaK/4v3YY00H\nVTvllGhqyaeUJiz1Tpn6pTz9U6bSjBpgNdAvq70fsKDp5i16EdgyY31BR9+zoqKCVCoVWoYNG8aM\nGTNC282ePbvZf9OjR49uMqrvzJlpHnkkRbC7gTPPhPHjxzFx4sTQtvPnzyeVSjFv3rxQ++TJkxk7\ndmyorba2llQqxZysQWCqqqqorKxsUtuIESM6tR/pdJpUKkVNTU2ofdy45vejf//+idiPQvr7yPxe\nnPcjU9T70VBn3PejQdT7kUqlErEf0PV/H+Xl5QwZMiR0/jn55JObbOdL0Q+udswxUFXVuL7zzvDS\nS3n9yEjMnj2b8vLyqMtIFGXql/L0T5n6UwSDq80nGFztD+18j9nAV2Z2RP36XUAvMzskY5tngVej\nGFztoovgd79rXO/bFz75JDlThLZEx7x/ytQ/ZeqX8vQrn+f7or7VfPFimD493HbCCdHUkm86IP1T\npn4pT/+UqbTgCuA251w1wZXrs4ES4DYA59xlwKZmdmL9+pnAB8AbwFrAKcA+wP4Z73k1wXPf5wAz\ngZEEg7h1+T1kq1ZB9rS2xx2X/E436JjPB2XqnzL1S3nGR1F3vO+7D5Yvb1zv3h1GjoyuHhERkXwz\ns3vq5+yeQHA7+CvAAWb2Rf0m/YEBGS/pSTDv96ZALfAasJ+ZPZ3xns87544BLqlf3gUOMbM3870/\n2f7+9+DqdqZTT+3qKkRERMKKuuOdPajaQQfBhhtGU4uIiEhXMbPrgetb+F5l1vofgDZvQTezvwF/\n81JgJ9x4Y3h96FDYYYdoahEREWlQtIOrvf8+PPNMuO3Egh2btfOyBxmQzlOmfilP/5SpFJv58+GR\nR8JtxXS1W8e8f8rUP2Xql/KMj6LteN9xR3h9/fWhoiKaWrpCVeYIcuKFMvVLefqnTKXYTJkCmWPG\n9ukDRx0VXT1dTce8f8rUP2Xql/KMj6Ic1dwMttwyuOrdYPRouPZa7x8lIiIJkKRRzQuF73P9qlWw\n2Wbw8ceNbWecAddd1+m3FhGRIpHP831RXvF+9tlwpxuSO5q5iIhIMXjkkXCnG4rrNnMRESlsRdnx\nvv328Po228Auu0RTi4iIiHRe9qBqu+4KO+4YTS0iIiLZiq7jvXw53HNPuO2EE8C5aOoRERGRzvn0\n06aDqp12WjS1iIiINKfoOt6zZ8PixY3rzsFxx0VXT1eprKxseyPJiTL1S3n6p0ylWFRVQV1d43rv\n3sU1qFoDHfP+KVP/lKlfyjM+iq7jfffd4fXdd4cBA6KppSuVl5dHXULiKFO/lKd/ylSKRfYjZIcf\nHnS+i42Oef+UqX/K1C/lGR85dbydcxc45150zn3lnPvMOTfdObd1O153rHPuFefcUufcJ865Kc65\n9TtedscsWwYPPhhuGzGiq6uIxsiRI6MuIXGUqV/K0z9lKsXgtdfg1VfDbcU6YKqOef+UqX/K1C/l\nGR+5XvHeA5gM7Ab8GOgBzHbO9WrpBc65HwFTgZuBwcARwK7ATR0puDMeR62OmAAAHsFJREFUeQS+\n/rpxfY01gt+Ki4iISDzdcUd4/bvfhb33jqQUERGRFnXPZWMzq8hcd86dBHwOlAFzWnjZUOADM2uY\nSfND59yNwHm5ldp52beZ77UX9O/f1VWIiIiID6tXw1//Gm477rjgF+siIiKFpLOnpnUBAxa2ss3z\nwADn3IEAzrl+wJHAzE5+dk6WLoWHHw63FdPAK3PmtPR7EekoZeqX8vRPmUrSPf54MKJ5puOPj6aW\nQqBj3j9l6p8y9Ut5xkeHO97OOQdcBcwxszdb2s7MngOOA+52zq0APgUWAWM6+tkdMXMm1NY2rhfb\nbeaTJk2KuoTEUaZ+KU//lKkkXfagamVlMHhwNLUUAh3z/ilT/5SpX8ozPjpzxft6gme2j25tI+fc\nYOBqYDxQChwADAJu7MRn5yx77u5994WNNurKCqJ11113RV1C4ihTv5Snf8pUkmzJEpg+PdxWrIOq\nNdAx758y9U+Z+qU846NDHW/n3LVABbC3mX3axubnA8+a2RVm9i8z+wdwBjCq/rbzFlVUVJBKpULL\nsGHDmDFjRmi72bNnk0qlmrx+9OjRTJkyhSVLgivegTSQoqKiJrTtuHHjmDhxYqht/vz5pFIp5s2b\nF2qfPHkyY8eODbXV1taSSqWa3O5RVVXV7Px6I0aMyHk/MqXTaVKpFDU17duPo48+OhH7UUh/HyUl\nJYnYj0xR7kdmnnHej0xR70dJSUki9gO69u+jqqqKQYMGMWTIkG/PPWeffXaT95No3X9/+E62bt3g\n6FYvBSRf5v+j4ocy9U+Z+qU848OZWW4vCDrdhwB7mdn77dj+PmCFmR2T0TaMYDC275jZgmZeUwpU\nV1dXU1pamlN9zamqgmOOaVzv3h0WLIANNuj0W4uISBFIp9OUlZUBlJlZOup6kqCz5/r99oMnnmhc\nP/hgeOghf/WJiEjxyef5Ptd5vK8HjgWOAZY65/rVL2tlbHOpc25qxsseAg53zp3unBtUP73Y1cAL\nzXW68yF7NPMf/1idbhERkbj673/hySfDbcU8qJqIiBS+XG81Px3oAzwFfJKxZI4PvgkwoGHFzKYC\n5wCjgdeBu4G3gC4Z2uyrr4L5uzONGNEVn1xYsm/RlM5Tpn4pT/+UqSTVffdB5g17ffrA8OHR1VMo\ndMz7p0z9U6Z+Kc/4yHUe7zY76mbW5MG6+jm8r2tm87ybORNWrGhc79EDDjkkikqiNXDgwKhLSBxl\n6pfy9E+ZSlLde294/dBDoVevaGopJDrm/VOm/ilTv5RnfOT8jHdX8PmM99FHh281P/BA+PvfO1ef\niIgUFz3j7V9Hz/UffQTZP2fOnAkVFX7rExGR4lMwz3jHzYoVTW8z/+lPo6lFREREOu9vfwuvr7tu\nMHaLiIhIIUt0x/upp4JnvDPpGTAREZH4uuee8Pohh0DPntHUIiIi0l6J7nhnTdfKbrvBJptEU0vU\nsufElc5Tpn4pT/+UqSTNRx/B88+H2446qvlti5GOef+UqX/K1C/lGR+J7XibwYMPhtuK+Tbz8847\nL+oSEkeZ+qU8/VOmkjT33Rde123mYTrm/VOm/ilTv5RnfCS2411dDR9/HG4rxtHMG1x77bVRl5A4\nytQv5emfMpWkyR7N/Kc/1W3mmXTM+6dM/VOmfinP+Ehsx/uBB8LrW20F22wTTS2FQFMN+KdM/VKe\n/ilTSZLmbjM/8shoailUOub9U6b+KVO/lGd8FE3H+5BDwLloahEREZHO0W3mIiISZ4nseL//Prz+\neritmJ/vFhERibvs0cx1m7mIiMRJIjve2Ve7N9oIhg6NppZCMXHixKhLSBxl6pfy9E+ZSlJ89BHM\nnRtu023mTemY90+Z+qdM/VKe8VEUHe/hw6Fbt2hqKRS1tbVRl5A4ytQv5emfMpWkuP/+8LpuM2+e\njnn/lKl/ytQv5RkfzsyirqEJ51wpUF1dXU1paWlOr/3yS9h4Y6ira2x74AFIpfzWKCIixSOdTlNW\nVgZQZmbpqOtJglzO9fvtB0880bh+wgkwdWp+6xMRkeKTz/N94q54z5wZ7nSXlMD++0dXj4iIiHTc\nokXw//5fuE3jtoiISNwkruP9yCPh9fJy6NUrmlpERESkcx59FFavblxfc039Ql1EROInUR3v1ath\n9uxw20EHRVNLoampqYm6hMRRpn4pT/+UqSTBgw+G1/fbD3r3jqaWQqdj3j9l6p8y9Ut5xkeiOt4v\nvQQLF4bbDjggmloKzahRo6IuIXGUqV/K0z9lKnG3YkXTO9k0ZkvLdMz7p0z9U6Z+Kc/4SFTHO/vk\n/IMfwIAB0dRSaMaPHx91CYmjTP1Snv4pU4m7Z56BxYvDbQcfHE0tcaBj3j9l6p8y9Ut5xkeiOt6P\nPhpeP/DAaOooRLmODi9tU6Z+KU//lKnE3UMPhdfLyuA734mmljjQMe+fMvVPmfqlPOMjMR3vmprg\nVvNMP/lJNLWIiIhI55g1fb5bt5mLiEhcJ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"text/plain": [ "<matplotlib.figure.Figure at 0x1163640b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a 2x2 grid of plots of capital per worker, outputper worker, consumption per worker, and investment per worker\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(data_labor['capital_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital per worker')\n", "\n", "ax = fig.add_subplot(2,2,2)\n", "ax.plot(data_labor['output_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Output per worker')\n", "\n", "ax = fig.add_subplot(2,2,3)\n", "ax.plot(data_labor['consumption_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Consumption per worker')\n", "\n", "ax = fig.add_subplot(2,2,4)\n", "ax.plot(data_labor['investment_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Investment per worker')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### An alternative approach\n", "\n", "Suppose that we wanted to simulate the Solow model with different parameter values so that we could compare the simulations. Since we'd be doing the same basic steps multiple times using different numbers, it would make sense to define a function so that we could avoid repetition.\n", "\n", "The code below defines a function called `solow_example()` that simulates the Solow model with exogenous labor growth. `solow_example()` takes as arguments the parameters of the Solow model $A$, $\\alpha$, $\\delta$, $s$, and $n$; the initial values $K_0$ and $L_0$; and the number of simulation periods $T$. `solow_example()` returns a Pandas DataFrame with computed values for aggregate and per worker quantities." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def solow_example(A,alpha,delta,s,n,K0,L0,T):\n", " '''Returns DataFrame with simulated values for a Solow model with labor growth and constant TFP'''\n", " \n", " # Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to K0\n", " capital = np.zeros(T+1)\n", " capital[0] = K0\n", " \n", " # Initialize a variable called labor as a (T+1)x1 array of zeros and set first value to L0\n", " labor = np.zeros(T+1)\n", " labor[0] = L0\n", "\n", "\n", " # Compute all capital and labor values by iterating over t from 0 through T\n", " for t in np.arange(T):\n", " labor[t+1] = (1+n)*labor[t]\n", " capital[t+1] = s*A*capital[t]**alpha*labor[t]**(1-alpha) + (1-delta)*capital[t]\n", " \n", " # Store the simulated capital df in a pandas DataFrame called data\n", " df = pd.DataFrame({'capital':capital,'labor':labor})\n", " \n", " # Create columns in the DataFrame to store computed values of the other endogenous variables\n", " df['output'] = df['capital']**alpha*df['labor']**(1-alpha)\n", " df['consumption'] = (1-s)*df['output']\n", " df['investment'] = df['output'] - df['consumption']\n", " \n", " # Create columns in the DataFrame to store capital per worker, output per worker, consumption per worker, and investment per worker\n", " df['capital_pw'] = df['capital']/df['labor']\n", " df['output_pw'] = df['output']/df['labor']\n", " df['consumption_pw'] = df['consumption']/df['labor']\n", " df['investment_pw'] = df['investment']/df['labor']\n", " \n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `solow_example()` defined, we can redo the previous exercise quickly:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x116d31c50>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BDxkyxKdNm5ZWPmnSJD/22GNb1O3www8v6H7svPPOFbEfpfT7SF13qezHQQcd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WuNyC0iIiLSf9xh2bIwkG16WrKk62tvpqVLO1+X9/OyZZ0/p6d0efnnZcuW/3NPp/I29/bnckM1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\nIJfqpDUbbbRR1CUkjjL1S3n6p0ylJc650c65D5xzy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"text/plain": [ "<matplotlib.figure.Figure at 0x1168a35c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the DataFrame with simulated values\n", "df = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=20,L0=1,T=100)\n", "\n", "# Create a 2x2 grid of plots of the capital per worker, outputper worker, consumption per worker, and investment per worker\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(df['capital_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital per worker')\n", "\n", "ax = fig.add_subplot(2,2,2)\n", "ax.plot(df['output_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Output per worker')\n", "\n", "ax = fig.add_subplot(2,2,3)\n", "ax.plot(df['consumption_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Consumption per worker')\n", "\n", "ax = fig.add_subplot(2,2,4)\n", "ax.plot(df['investment_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Investment per worker')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`solow_example()` can be used to perform multiple simulations. For example, suppose we want to see the effect of having two different initial values of capital: $k_0 = 20$ and $k_0'=10$." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df1 = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=20,L0=1,T=100)\n", "df2 = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=10,L0=1,T=100)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1172a9eb8>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9738wdy7s3l3JylJ2Qqs50Op/eFrPQVrPwZe2scpt9IqXDvU70LlRZzo16ETn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2AAAg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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116884ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a 2x2 grid of plots of the capital per worker, outputper worker, consumption per worker, and investment per worker\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(df1['capital_pw'],lw=3)\n", "ax.plot(df2['capital_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital per worker')\n", "\n", "ax = fig.add_subplot(2,2,2)\n", "ax.plot(df1['output_pw'],lw=3)\n", "ax.plot(df2['output_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Output per worker')\n", "\n", "ax = fig.add_subplot(2,2,3)\n", "ax.plot(df1['consumption_pw'],lw=3)\n", "ax.plot(df2['consumption_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Consumption per worker')\n", "\n", "ax = fig.add_subplot(2,2,4)\n", "ax.plot(df1['investment_pw'],lw=3,label='$k_0=20$')\n", "ax.plot(df2['investment_pw'],lw=3,label='$k_0=10$')\n", "ax.grid()\n", "ax.set_title('Investment per worker')\n", "ax.legend(loc='lower right')" ] } ], "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
mne-tools/mne-tools.github.io
0.18/_downloads/4365eab31ed2fa347de7f294ac9500c3/plot_label_from_stc.ipynb
1
5496
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Generate a functional label from source estimates\n\n\nThreshold source estimates and produce a functional label. The label\nis typically the region of interest that contains high values.\nHere we compare the average time course in the anatomical label obtained\nby FreeSurfer segmentation and the average time course from the\nfunctional label. As expected the time course in the functional\nlabel yields higher values.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Luke Bloy <[email protected]>\n# Alex Gramfort <[email protected]>\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.minimum_norm import read_inverse_operator, apply_inverse\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'\nsubjects_dir = data_path + '/subjects'\nsubject = 'sample'\n\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = \"dSPM\" # use dSPM method (could also be MNE or sLORETA)\n\n# Compute a label/ROI based on the peak power between 80 and 120 ms.\n# The label bankssts-lh is used for the comparison.\naparc_label_name = 'bankssts-lh'\ntmin, tmax = 0.080, 0.120\n\n# Load data\nevoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))\ninverse_operator = read_inverse_operator(fname_inv)\nsrc = inverse_operator['src'] # get the source space\n\n# Compute inverse solution\nstc = apply_inverse(evoked, inverse_operator, lambda2, method,\n pick_ori='normal')\n\n# Make an STC in the time interval of interest and take the mean\nstc_mean = stc.copy().crop(tmin, tmax).mean()\n\n# use the stc_mean to generate a functional label\n# region growing is halted at 60% of the peak value within the\n# anatomical label / ROI specified by aparc_label_name\nlabel = mne.read_labels_from_annot(subject, parc='aparc',\n subjects_dir=subjects_dir,\n regexp=aparc_label_name)[0]\nstc_mean_label = stc_mean.in_label(label)\ndata = np.abs(stc_mean_label.data)\nstc_mean_label.data[data < 0.6 * np.max(data)] = 0.\n\n# 8.5% of original source space vertices were omitted during forward\n# calculation, suppress the warning here with verbose='error'\nfunc_labels, _ = mne.stc_to_label(stc_mean_label, src=src, smooth=True,\n subjects_dir=subjects_dir, connected=True,\n verbose='error')\n\n# take first as func_labels are ordered based on maximum values in stc\nfunc_label = func_labels[0]\n\n# load the anatomical ROI for comparison\nanat_label = mne.read_labels_from_annot(subject, parc='aparc',\n subjects_dir=subjects_dir,\n regexp=aparc_label_name)[0]\n\n# extract the anatomical time course for each label\nstc_anat_label = stc.in_label(anat_label)\npca_anat = stc.extract_label_time_course(anat_label, src, mode='pca_flip')[0]\n\nstc_func_label = stc.in_label(func_label)\npca_func = stc.extract_label_time_course(func_label, src, mode='pca_flip')[0]\n\n# flip the pca so that the max power between tmin and tmax is positive\npca_anat *= np.sign(pca_anat[np.argmax(np.abs(pca_anat))])\npca_func *= np.sign(pca_func[np.argmax(np.abs(pca_anat))])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot the time courses....\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure()\nplt.plot(1e3 * stc_anat_label.times, pca_anat, 'k',\n label='Anatomical %s' % aparc_label_name)\nplt.plot(1e3 * stc_func_label.times, pca_func, 'b',\n label='Functional %s' % aparc_label_name)\nplt.legend()\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot brain in 3D with PySurfer if available\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "brain = stc_mean.plot(hemi='lh', subjects_dir=subjects_dir)\nbrain.show_view('lateral')\n\n# show both labels\nbrain.add_label(anat_label, borders=True, color='k')\nbrain.add_label(func_label, borders=True, color='b')" ] } ], "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
Pittsburgh-NEH-Institute/Institute-Materials-2017
schedule/week_2/collation/2_collate-plain-text.ipynb
1
5280
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Collating for real with Collatex (2)\n", "Here we can repeat the same steps done in the previous exercise, with a new and slightly more complicated text case. You can create a new notebook for this exercise and follow the instructions below.\n", "\n", "We will be using different editions of Virginia Woolf's \"To the lighthouse\":\n", "- USA = New York: Harcourt, Brace & Company, 1927 (1st USA edition)\n", "- UK = Londond: R & R Clark Limited, 1827 (1st UK edition)\n", "- EM (EVERYMAN) = London: J. M. Dent & Sons LTD, 1938 (reprint 1952)\n", "\n", "The facsimiles and trascriptions of the editions are available at http://woolfonline.com/ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First exercise\n", "Try to reproduce what you have done with the Darwin text." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the *collatex* Python library" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from collatex import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a collation object" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "collation = Collation()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now open the texts in \"../fixtures/Woolf/Lighthouse-1\", read them, and add them to the collation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open( \"../fixtures/Woolf/Lighthouse-1/Lighthouse-1-USA.txt\", encoding='utf-8' ) as witness_USA, \\\n", " open( \"../fixtures/Woolf/Lighthouse-1/Lighthouse-1-UK.txt\", encoding='utf-8' ) as witness_UK, \\\n", " open( \"../fixtures/Woolf/Lighthouse-1/Lighthouse-1-EM.txt\", encoding='utf-8' ) as witness_EM:\n", " collation.add_plain_witness( \"USA\", witness_USA.read() )\n", " collation.add_plain_witness( \"UK\", witness_UK.read() )\n", " collation.add_plain_witness( \"EM\", witness_EM.read() )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Align, using the HTML output option" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alignment_table = collate(collation, layout='vertical', output='html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to know more about this text:\n", "\n", "Look at the sources\n", "- USA, p. 14\n", "http://woolfonline.com/?node=content/text/transcriptions&project=1&parent=2&taxa=19&content=2817&pos=15\n", "- UK, pp. 16-17\n", "http://woolfonline.com/?node=content/text/transcriptions&project=1&parent=2&taxa=20&content=3139&pos=19\n", "- EVERYMAN, p. 7\n", "http://woolfonline.com/?node=content/text/transcriptions&project=1&parent=2&taxa=22&content=3804&pos=24\n", "\n", "Start thinking about how to handle situation like the following (proof with corrections in the margin)\n", "- http://woolfonline.com/?node=content/text/transcriptions&project=1&parent=2&taxa=18&content=4172&pos=14\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Second exercise\n", "In the second exercise, repeat the previous steps, now using the texts at \"../fixtures/Woolf/Lighthouse-2\" and visualizing the output with the more sophisticated HTML option (`html2`)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "collation = Collation()\n", "witness_USA = open( \"../fixtures/Woolf/Lighthouse-2/Lighthouse-2-USA.txt\", encoding='utf-8' ).read()\n", "witness_UK = open( \"../fixtures/Woolf/Lighthouse-2/Lighthouse-2-UK.txt\", encoding='utf-8' ).read()\n", "witness_EM = open( \"../fixtures/Woolf/Lighthouse-2/Lighthouse-2-EM.txt\", encoding='utf-8' ).read()\n", "collation.add_plain_witness( \"USA\", witness_USA )\n", "collation.add_plain_witness( \"UK\", witness_UK )\n", "collation.add_plain_witness( \"EM\", witness_EM )\n", "alignment_table = collate(collation, output='html2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don’t like the colors, you can use the `html` output option:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alignment_table = collate(collation, output='html', layout='vertical')" ] } ], "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
davisincubator/seal_the_deal
notebooks/amp_6.0_fill_untested_data_with_averages.ipynb
1
12271
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "my_dir = \"/Volumes/dax/seals/Kaggle-NOAA-SeaLions/\"\n", "train = 'Train/train.csv'\n", "so_far = '2017-06-24_submission_stripped.csv'" ] }, { "cell_type": "code", "execution_count": 3, "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>train_id</th>\n", " <th>adult_males</th>\n", " <th>subadult_males</th>\n", " <th>adult_females</th>\n", " <th>juveniles</th>\n", " <th>pups</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>62</td>\n", " <td>12</td>\n", " <td>486</td>\n", " <td>42</td>\n", " <td>344</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>20</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>38</td>\n", " <td>20</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>8</td>\n", " <td>5</td>\n", " <td>41</td>\n", " <td>7</td>\n", " <td>38</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " train_id adult_males subadult_males adult_females juveniles pups\n", "0 0 62 12 486 42 344\n", "1 1 2 20 0 12 0\n", "2 2 2 0 38 20 0\n", "3 3 8 5 41 7 38\n", "4 4 6 9 2 0 0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_d = pd.read_csv(my_dir + train)\n", "t_d.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "t_m = t_d.mean()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "train_id 473.500000\n", "adult_males 5.687764\n", "subadult_males 4.583333\n", "adult_females 39.595992\n", "juveniles 21.221519\n", "pups 17.178270\n", "dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_m" ] }, { "cell_type": "code", "execution_count": 6, "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>adult_males</th>\n", " <th>subadult_males</th>\n", " <th>adult_females</th>\n", " <th>juveniles</th>\n", " <th>pups</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>62</td>\n", " <td>28</td>\n", " <td>117</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>38</td>\n", " <td>10</td>\n", " <td>199</td>\n", " <td>95</td>\n", " <td>534</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>324</td>\n", " <td>27</td>\n", " <td>821</td>\n", " <td>401</td>\n", " <td>543</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>69</td>\n", " <td>8</td>\n", " <td>201</td>\n", " <td>157</td>\n", " <td>926</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>215</td>\n", " <td>36</td>\n", " <td>441</td>\n", " <td>235</td>\n", " <td>711</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " adult_males subadult_males adult_females juveniles pups\n", "0 6 3 62 28 117\n", "1 38 10 199 95 534\n", "2 324 27 821 401 543\n", "3 69 8 201 157 926\n", "4 215 36 441 235 711" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sf_d = pd.read_csv(my_dir + so_far)\n", "\n", "del sf_d['test_id']\n", "\n", "sf_d.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sums = sf_d.sum(axis = 1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "adult_males 38\n", "subadult_males 10\n", "adult_females 199\n", "juveniles 95\n", "pups 534\n", "Name: 1, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sf_d.iloc[1]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "18631 0\n", "18632 0\n", "18633 0\n", "18634 0\n", "18635 0\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sums[-5:]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "counter = 0\n", "for i in sums:\n", " if i == 0:\n", " sf_d.set_value(index = counter, col = 'adult_males', value = t_m['adult_males'])\n", " sf_d.set_value(index = counter, col = 'subadult_males', value = t_m['subadult_males'])\n", " sf_d.set_value(index = counter, col = 'adult_females', value = t_m['adult_females'])\n", " sf_d.set_value(index = counter, col = 'juveniles', value = t_m['juveniles'])\n", " sf_d.set_value(index = counter, col = 'pups', value = t_m['pups'])\n", " counter += 1" ] }, { "cell_type": "code", "execution_count": 11, "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>adult_males</th>\n", " <th>subadult_males</th>\n", " <th>adult_females</th>\n", " <th>juveniles</th>\n", " <th>pups</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>18631</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>39</td>\n", " <td>21</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>18632</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>39</td>\n", " <td>21</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>18633</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>39</td>\n", " <td>21</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>18634</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>39</td>\n", " <td>21</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>18635</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>39</td>\n", " <td>21</td>\n", " <td>17</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " adult_males subadult_males adult_females juveniles pups\n", "18631 5 4 39 21 17\n", "18632 5 4 39 21 17\n", "18633 5 4 39 21 17\n", "18634 5 4 39 21 17\n", "18635 5 4 39 21 17" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sf_d.tail()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sf_d.to_csv(my_dir + 'hail_mary.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ajmaradiaga/tf-examples
cnn/Tensorflow - Padding.ipynb
1
2487
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "input = tf.placeholder(tf.float32, (None, 32, 32, 3))\n", "filter_weights = tf.Variable(tf.truncated_normal((8, 8, 3, 20))) # (height, width, input_depth, output_depth)\n", "filter_bias = tf.Variable(tf.zeros(20))\n", "strides = [1, 2, 2, 1] # (batch, height, width, depth)\n", "padding = 'SAME'\n", "conv = tf.nn.conv2d(input, filter_weights, strides, padding) + filter_bias" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor 'add_1:0' shape=(?, 16, 16, 20) dtype=float32>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The padding algorithm Tensorflow uses is not exactly the same as normal padding. [More info](https://www.tensorflow.org/api_guides/python/nn#Convolution) of how Tensorflow does padding\n", "\n", "TensorFlow uses the following equation for 'SAME' vs 'PADDING'\n", "\n", "**SAME Padding**, the output height and width are computed as:\n", "\n", "out_height = ceil(float(in_height) / float(strides1))\n", "\n", "out_width = ceil(float(in_width) / float(strides[2]))\n", "\n", "**VALID Padding**, the output height and width are computed as:\n", "\n", "out_height = ceil(float(in_height - filter_height + 1) / float(strides1))\n", "\n", "out_width = ceil(float(in_width - filter_width + 1) / float(strides[2]))" ] }, { "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 }
apache-2.0
jdhp-docs/python-notebooks
python_scipy_optimize_global_optimization_en.ipynb
3
20543
{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [ "meta", "toc_en", "draft_en" ] }, "source": [ "# Unconstrained global optimization with Scipy" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "hide" ] }, "source": [ "**TODO**:\n", "* Plots:\n", " 0. error w.t. ... => add an option to plot the current solution or the best current solution \n", " 4. error w.t. number of function evaluations + error w.t. *total* number of function evaluations (i.e. including the number of gradient and hessian evaluations)\n", " 6. (benchmark session ! distinguish the derivative-free to the non-derivative free case) average version of 3., 4., 5. over several runs with random initial state (+ error bar or box plot)\n", " 7. (benchmark session) err w.t. algorithms parameters (plot the iteration or evaluation number or execution time to reach in average an error lower than N% with e.g. N=99%)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import required modules" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide" ] }, "outputs": [], "source": [ "# Init matplotlib\n", "\n", "%matplotlib inline\n", "\n", "import matplotlib\n", "matplotlib.rcParams['figure.figsize'] = (8, 8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide" ] }, "outputs": [], "source": [ "# Setup PyAI\n", "import sys\n", "sys.path.insert(0, '/Users/jdecock/git/pub/jdhp/pyai')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import time\n", "import warnings\n", "\n", "from scipy import optimize" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot functions\n", "from pyai.optimize.utils import plot_contour_2d_solution_space\n", "from pyai.optimize.utils import plot_2d_solution_space\n", "\n", "from pyai.optimize.utils import array_list_to_array\n", "from pyai.optimize.utils import plot_fx_wt_iteration_number\n", "from pyai.optimize.utils import plot_err_wt_iteration_number\n", "from pyai.optimize.utils import plot_err_wt_execution_time\n", "from pyai.optimize.utils import plot_err_wt_num_feval" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the objective function" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide" ] }, "outputs": [], "source": [ "## Objective function: Rosenbrock function (Scipy's implementation)\n", "#func = scipy.optimize.rosen" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set the objective function\n", "#from pyai.optimize.functions import sphere as func\n", "from pyai.optimize.functions import sphere2d as func\n", "#from pyai.optimize.functions import additive_gaussian_noise as noise\n", "from pyai.optimize.functions import multiplicative_gaussian_noise as noise\n", "#from pyai.optimize.functions import additive_poisson_noise as noise\n", "\n", "func.noise = noise # Comment this line to use a deterministic objective function\n", "\n", "xmin = func.bounds[0]\n", "xmax = func.bounds[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide" ] }, "outputs": [], "source": [ "print(func)\n", "print(xmin)\n", "print(xmax)\n", "print(func.ndim)\n", "print(func.arg_min)\n", "print(func(func.arg_min))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The \"basin-hopping\" algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basin-hopping is a **stochastic** algorithm which attempts to find the **global** minimum of a function.\n", "\n", "Official documentation:\n", "* https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.basinhopping.html#scipy.optimize.basinhopping\n", "* More information about the algorithm: http://www-wales.ch.cam.ac.uk/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic usage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy import optimize\n", "\n", "x0 = np.random.uniform(-10., 10., size=2)\n", "\n", "res = optimize.basinhopping(optimize.rosen,\n", " x0, # The initial point\n", " niter=100) # The number of basin hopping iterations\n", "\n", "print(\"x* =\", res.x)\n", "print(\"f(x*) =\", res.fun)\n", "print(\"Cause of the termination:\", \";\".join(res.message))\n", "print(\"Number of evaluations of the objective functions:\", res.nfev)\n", "print(\"Number of evaluations of the jacobian:\", res.njev)\n", "print(\"Number of iterations performed by the optimizer:\", res.nit)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performances analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "it_x_list = []\n", "it_fx_list = []\n", "it_time_list = []\n", "it_num_eval_list = []\n", "\n", "def callback(x, f, accept):\n", " it_x_list.append(x)\n", " it_fx_list.append(f)\n", " it_time_list.append(time.time() - init_time)\n", " if hasattr(func, 'num_eval'):\n", " it_num_eval_list.append(func.num_eval)\n", " print(len(it_x_list), x, f, accept, it_num_eval_list[-1])\n", "\n", "x_init = np.random.random(func.ndim) # draw samples in [0.0, 1.0)\n", "min_bounds = func.bounds[0]\n", "max_bounds = func.bounds[1]\n", "x_init *= (max_bounds - min_bounds)\n", "x_init += min_bounds\n", "\n", "func.do_eval_logs = True\n", "func.reset_eval_counters()\n", "func.reset_eval_logs()\n", "\n", "init_time = time.time()\n", "\n", "with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " res = optimize.basinhopping(func,\n", " x_init, # The initial point\n", " niter=100, # The number of basin hopping iterations\n", " callback=callback,\n", " disp=False) # Print status messages\n", "\n", "func.do_eval_logs = False\n", "\n", "eval_x_array = np.array(func.eval_logs_dict['x']).T\n", "eval_error_array = np.array(func.eval_logs_dict['fx']) - func(func.arg_min)\n", "\n", "it_x_array = np.array(it_x_list).T\n", "it_error_array = np.array(it_fx_list) - func(func.arg_min)\n", "\n", "it_time_array = np.array(it_time_list)\n", "it_num_eval_array = np.array(it_num_eval_list)\n", "\n", "print(\"x* =\", res.x)\n", "print(\"f(x*) =\", res.fun)\n", "print(\"Cause of the termination:\", \";\".join(res.message))\n", "print(\"Number of evaluations of the objective functions:\", res.nfev)\n", "print(\"Number of evaluations of the jacobian:\", res.njev)\n", "print(\"Number of iterations performed by the optimizer:\", res.nit)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_contour_2d_solution_space(func,\n", " xmin=xmin,\n", " xmax=xmax,\n", " xstar=res.x,\n", " xvisited=it_x_array,\n", " title=\"Basin-Hopping\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_contour_2d_solution_space(func,\n", " xmin=xmin,\n", " xmax=xmax,\n", " xstar=res.x,\n", " xvisited=eval_x_array,\n", " title=\"Basin-Hopping\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide" ] }, "outputs": [], "source": [ "print(eval_x_array.shape)\n", "print(eval_error_array.shape)\n", "print(it_x_array.shape)\n", "print(it_error_array.shape)\n", "print(it_time_array.shape)\n", "print(it_num_eval_array.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=3, squeeze=True, figsize=(15, 5))\n", "\n", "ax = ax.ravel()\n", "\n", "plot_err_wt_iteration_number(it_error_array, ax=ax[0], x_log=True, y_log=True)\n", "plot_err_wt_execution_time(it_error_array, it_time_array, ax=ax[1], x_log=True, y_log=True)\n", "plot_err_wt_num_feval(it_error_array, it_num_eval_array, ax=ax[2], x_log=True, y_log=True)\n", "\n", "plt.tight_layout(); # Fix plot margins errors" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_err_wt_num_feval(eval_error_array, x_log=True, y_log=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Benchmark" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "eval_error_array_list = []\n", "\n", "NUM_RUNS = 100\n", "\n", "for run_index in range(NUM_RUNS):\n", " x_init = np.random.random(func.ndim) # draw samples in [0.0, 1.0)\n", " min_bounds = func.bounds[0]\n", " max_bounds = func.bounds[1]\n", " x_init *= (max_bounds - min_bounds)\n", " x_init += min_bounds\n", "\n", " func.do_eval_logs = True\n", " func.reset_eval_counters()\n", " func.reset_eval_logs()\n", "\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " res = optimize.basinhopping(func,\n", " x_init, # The initial point\n", " niter=100, # The number of basin hopping iterations\n", " disp=False) # Print status messages\n", "\n", " func.do_eval_logs = False\n", "\n", " eval_error_array = np.array(func.eval_logs_dict['fx']) - func(func.arg_min)\n", "\n", " print(\"x* =\", res.x)\n", " print(\"f(x*) =\", res.fun)\n", " #print(\"Cause of the termination:\", \";\".join(res.message))\n", " #print(\"Number of evaluations of the objective functions:\", res.nfev)\n", " #print(\"Number of evaluations of the jacobian:\", res.njev)\n", " #print(\"Number of iterations performed by the optimizer:\", res.nit)\n", " \n", " eval_error_array_list.append(eval_error_array);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_err_wt_num_feval(array_list_to_array(eval_error_array_list), x_log=True, y_log=True, plot_option=\"mean\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The \"Differential Evolution\" (DE) algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Differential Evolution is a **stochastic** algorithm which attempts to find the **global** minimum of a function.\n", "\n", "Official documentation:\n", "* https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html#scipy.optimize.differential_evolution\n", "\n", "More information:\n", "* [Practical advice](http://www1.icsi.berkeley.edu/~storn/code.html#prac)\n", "* [Wikipedia article](https://en.wikipedia.org/wiki/Differential_evolution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic usage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy import optimize\n", "\n", "bounds = [[-10, 10], [-10, 10]]\n", "\n", "res = optimize.differential_evolution(optimize.rosen,\n", " bounds, # The initial point\n", " maxiter=100, # The number of DE iterations\n", " polish=True)\n", "\n", "print(\"x* =\", res.x)\n", "print(\"f(x*) =\", res.fun)\n", "print(\"Cause of the termination:\", res.message)\n", "print(\"Number of evaluations of the objective functions:\", res.nfev)\n", "print(\"Number of iterations performed by the optimizer:\", res.nit)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performances analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "bounds = func.bounds.T.tolist()\n", "\n", "it_x_list = []\n", "it_fx_list = []\n", "it_time_list = []\n", "it_num_eval_list = []\n", "\n", "def callback(xk, convergence):\n", " it_x_list.append(xk)\n", " it_fx_list.append(func(xk))\n", " it_time_list.append(time.time() - init_time)\n", " if hasattr(func, 'num_eval'):\n", " it_num_eval_list.append(func.num_eval)\n", " print(len(it_x_list), xk, it_fx_list[-1], convergence, it_num_eval_list[-1])\n", "\n", "func.do_eval_logs = True\n", "func.reset_eval_counters()\n", "func.reset_eval_logs()\n", "\n", "init_time = time.time()\n", "\n", "with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " res = optimize.differential_evolution(func,\n", " bounds, # The initial point\n", " maxiter=100, # The number of DE iterations\n", " callback=callback,\n", " polish=False,\n", " disp=False) # Print status messages\n", "\n", "func.do_eval_logs = False\n", "\n", "eval_x_array = np.array(func.eval_logs_dict['x']).T\n", "eval_error_array = np.array(func.eval_logs_dict['fx']) - func(func.arg_min)\n", "\n", "it_x_array = np.array(it_x_list).T\n", "it_error_array = np.array(it_fx_list) - func(func.arg_min)\n", "\n", "it_time_array = np.array(it_time_list)\n", "it_num_eval_array = np.array(it_num_eval_list)\n", "\n", "print(\"x* =\", res.x)\n", "print(\"f(x*) =\", res.fun)\n", "print(\"Cause of the termination:\", res.message)\n", "print(\"Number of evaluations of the objective functions:\", res.nfev)\n", "print(\"Number of iterations performed by the optimizer:\", res.nit)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_contour_2d_solution_space(func,\n", " xmin=xmin,\n", " xmax=xmax,\n", " xstar=res.x,\n", " xvisited=it_x_array,\n", " title=\"Differential Evolution\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_contour_2d_solution_space(func,\n", " xmin=xmin,\n", " xmax=xmax,\n", " xstar=res.x,\n", " xvisited=eval_x_array,\n", " title=\"Differential Evolution\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=3, squeeze=True, figsize=(15, 5))\n", "\n", "ax = ax.ravel()\n", "\n", "plot_err_wt_iteration_number(it_error_array, ax=ax[0], x_log=True, y_log=True)\n", "plot_err_wt_execution_time(it_error_array, it_time_array, ax=ax[1], x_log=True, y_log=True)\n", "plot_err_wt_num_feval(it_error_array, it_num_eval_array, ax=ax[2], x_log=True, y_log=True)\n", "\n", "plt.tight_layout(); # Fix plot margins errors" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_err_wt_num_feval(eval_error_array, x_log=True, y_log=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Benchmark" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "eval_error_array_list = []\n", "\n", "NUM_RUNS = 100\n", "\n", "for run_index in range(NUM_RUNS):\n", " bounds = func.bounds.T.tolist()\n", "\n", " func.do_eval_logs = True\n", " func.reset_eval_counters()\n", " func.reset_eval_logs()\n", "\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " res = optimize.differential_evolution(func,\n", " bounds, # The initial point\n", " maxiter=100, # The number of DE iterations\n", " polish=False,\n", " disp=False) # Print status messages\n", "\n", " func.do_eval_logs = False\n", "\n", " eval_error_array = np.array(func.eval_logs_dict['fx']) - func(func.arg_min)\n", "\n", " print(\"x* =\", res.x)\n", " print(\"f(x*) =\", res.fun)\n", " #print(\"Cause of the termination:\", \";\".join(res.message))\n", " #print(\"Number of evaluations of the objective functions:\", res.nfev)\n", " #print(\"Number of evaluations of the jacobian:\", res.njev)\n", " #print(\"Number of iterations performed by the optimizer:\", res.nit)\n", " \n", " eval_error_array_list.append(eval_error_array);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_err_wt_num_feval(array_list_to_array(eval_error_array_list), x_log=True, y_log=True, plot_option=\"mean\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The \"simulated annealing\" algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This algorithm has been replaced by the \"basin-hopping\" algorithm since Scipy 0.15.\n", "\n", "See the official documentation for more details: https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.optimize.anneal.html." ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Tags", "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
arnoldlu/lisa
ipynb/examples/energy_meter/EnergyMeter_HWMON.ipynb
6
13163
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Energy Meter Examples\n", "\n", "## Linux Kernel HWMon\n", "\n", "More details can be found at https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#linux-hwmon." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2016-12-08 12:40:25,090 INFO : root : Using LISA logging configuration:\n", "2016-12-08 12:40:25,091 INFO : root : /home/vagrant/lisa/logging.conf\n" ] } ], "source": [ "import logging\n", "from conf import LisaLogging\n", "LisaLogging.setup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import required modules" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Generate plots inline\n", "%matplotlib inline\n", "\n", "import os\n", "\n", "# Support to access the remote target\n", "import devlib\n", "from env import TestEnv\n", "\n", "# RTApp configurator for generation of PERIODIC tasks\n", "from wlgen import RTA, Ramp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Target Configuration\n", "The target configuration is used to describe and configure your test environment.\n", "You can find more details in **examples/utils/testenv_example.ipynb**." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Setup target configuration\n", "my_conf = {\n", "\n", " # Target platform and board\n", " \"platform\" : 'linux',\n", " \"board\" : 'juno',\n", " \"host\" : '192.168.0.1',\n", "\n", " # Devlib modules to load\n", " \"modules\" : [\"cpufreq\"], # Required by rt-app calibration\n", " \n", " # Folder where all the results will be collected\n", " \"results_dir\" : \"EnergyMeter_HWMON\",\n", "\n", " # Energy Meters Configuration for BayLibre's ACME Cape\n", " \"emeter\" : {\n", " \"instrument\" : \"hwmon\",\n", " \"conf\" : {\n", " # Prefixes of the HWMon labels\n", " 'sites' : ['a53', 'a57'],\n", " # Type of hardware monitor to be used\n", " 'kinds' : ['energy']\n", " },\n", " 'channel_map' : {\n", " 'LITTLE' : 'a53',\n", " 'big' : 'a57',\n", " }\n", " },\n", " \n", " # Tools required by the experiments\n", " \"tools\" : [ 'trace-cmd', 'rt-app' ],\n", " \n", " # Comment this line to calibrate RTApp in your own platform\n", " # \"rtapp-calib\" : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353},\n", "}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03:59:42 INFO : Target - Using base path: /data/lisa\n", "03:59:42 INFO : Target - Loading custom (inline) target configuration\n", "03:59:42 INFO : Target - Devlib modules to load: ['bl', 'hwmon', 'cpufreq']\n", "03:59:42 INFO : Target - Connecting linux target:\n", "03:59:42 INFO : Target - username : root\n", "03:59:42 INFO : Target - host : 192.168.0.1\n", "03:59:42 INFO : Target - password : \n", "03:59:42 INFO : Target - Connection settings:\n", "03:59:42 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", "04:00:23 INFO : Target - Initializing target workdir:\n", "04:00:23 INFO : Target - /root/devlib-target\n", "04:00:28 INFO : Target - Topology:\n", "04:00:28 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", "04:00:30 INFO : Platform - Loading default EM:\n", "04:00:30 INFO : Platform - /data/lisa/libs/utils/platforms/juno.json\n", "04:00:30 WARNING : Target - Using configuration provided RTApp calibration\n", "04:00:30 INFO : Target - Using RT-App calibration values:\n", "04:00:30 INFO : Target - {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n", "04:00:30 INFO : HWMon - Scanning for HWMON channels, may take some time...\n", "04:00:30 INFO : HWMon - Channels selected for energy sampling:\n", "04:00:30 INFO : HWMon - a57_energy\n", "04:00:30 INFO : HWMon - a53_energy\n", "04:00:30 INFO : TestEnv - Set results folder to:\n", "04:00:30 INFO : TestEnv - /data/lisa/results/EnergyMeter_HWMON\n", "04:00:30 INFO : TestEnv - Experiment results available also in:\n", "04:00:30 INFO : TestEnv - /data/lisa/results_latest\n" ] } ], "source": [ "# Initialize a test environment using:\n", "te = TestEnv(my_conf, wipe=False, force_new=True)\n", "target = te.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Workload Execution and Power Consumptions Samping\n", "\n", "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**.\n", "\n", "Each **EnergyMeter** derived class has two main methods: **reset** and **report**.\n", " - The **reset** method will reset the energy meter and start sampling from channels specified in the target configuration. <br>\n", " - The **report** method will stop capture and will retrieve the energy consumption data. This returns an EnergyReport composed of the measured channels energy and the report file. Each of the samples can also be obtained, as you can see below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "04:00:30 INFO : WlGen - Setup new workload ramp\n", "04:00:30 INFO : RTApp - Workload duration defined by longest task\n", "04:00:30 INFO : RTApp - Default policy: SCHED_OTHER\n", "04:00:30 INFO : RTApp - ------------------------\n", "04:00:30 INFO : RTApp - task [ramp], sched: using default policy\n", "04:00:30 INFO : RTApp - | calibration CPU: 1\n", "04:00:30 INFO : RTApp - | loops count: 1\n", "04:00:30 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n", "04:00:30 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n", "04:00:30 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n", "04:00:30 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n", "04:00:30 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n", "04:00:30 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n", "04:00:30 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", "04:00:30 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", "04:00:30 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n", "04:00:30 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n", "04:00:30 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n", "04:00:30 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n", "04:00:30 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n", "04:00:30 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n", "04:00:30 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", "04:00:30 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", "04:00:30 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n", "04:00:30 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", "04:00:30 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", "04:00:35 INFO : WlGen - Workload execution START:\n", "04:00:35 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" ] } ], "source": [ "# Create and RTApp RAMP task\n", "rtapp = RTA(te.target, 'ramp', calibration=te.calibration())\n", "rtapp.conf(kind='profile',\n", " params={\n", " 'ramp' : Ramp(\n", " start_pct = 60,\n", " end_pct = 20,\n", " delta_pct = 5,\n", " time_s = 0.5).get()\n", " })\n", "\n", "# EnergyMeter Start\n", "te.emeter.reset()\n", "\n", "rtapp.run(out_dir=te.res_dir)\n", "\n", "# EnergyMeter Stop and samples collection\n", "nrg_report = te.emeter.report(te.res_dir)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "04:00:41 INFO : Collected data:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[01;34m/data/lisa/results/EnergyMeter_HWMON\u001b[00m\r\n", "├── energy.json\r\n", "├── output.log\r\n", "├── ramp_00.json\r\n", "├── rt-app-ramp-0.log\r\n", "├── trace.dat\r\n", "├── trace.raw.txt\r\n", "└── trace.txt\r\n", "\r\n", "0 directories, 7 files\r\n" ] } ], "source": [ "logging.info(\"Collected data:\")\n", "!tree $te.res_dir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Power Measurements Data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "04:00:41 INFO : Measured channels energy:\n", "04:00:41 INFO : {'big': 14.410717999999179, 'LITTLE': 1.1058980000016163}\n" ] } ], "source": [ "logging.info(\"Measured channels energy:\")\n", "logging.info(\"%s\", nrg_report.channels)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "04:00:41 INFO : Generated energy file:\n", "04:00:41 INFO : /data/lisa/results/EnergyMeter_HWMON/energy.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{\r\n", " \"LITTLE\": 1.1058980000016163, \r\n", " \"big\": 14.410717999999179\r\n", "}" ] } ], "source": [ "logging.info(\"Generated energy file:\")\n", "logging.info(\" %s\", nrg_report.report_file)\n", "!cat $nrg_report.report_file" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" }, "toc": { "toc_cell": false, "toc_number_sections": true, "toc_threshold": 6, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
utds/workshops
workshop_1/predictive_models.ipynb
1
16904
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#First let's import the necessary modules\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import os\n", "from IPython.display import display, HTML\n", "\n", "pd.set_option('display.max_columns', 500)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Specifying the Data Path\n", "\n", "cwd = os.getcwd()\n", "file_path = os.path.join(cwd, 'cleaned_speed_dating.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df=pd.read_csv(file_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split the dataset into training and test sets" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "train, test = train_test_split(df, test_size = 0.2, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split the dataset by gender" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "female_df = df.loc[df['gender'] == 0]\n", "male_df = df.loc[df['gender'] == 1]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "female_train, female_test = train_test_split(female_df, test_size = 0.2, random_state=42)\n", "male_train, male_test = train_test_split(male_df, test_size = 0.2, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Logistic Regression" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import linear_model\n", "lr = linear_model.LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training set performance is 0.727696032781\n", "test set performance is 0.729709605361\n" ] } ], "source": [ "# Do logistic regression using only one variable\n", "predictors = ['attr_partner']\n", "lr_model = lr.fit(train[predictors].values, train['dec'].values)\n", "print('training set performance is {}'.format(lr_model.score(train[predictors].values, train['dec'].values)))\n", "print('test set performance is {}'.format(lr_model.score(test[predictors].values, test['dec'].values)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, if you only use one variable, that is no different than just doing a cut off based on one of the previous graphs and make naive predictions based on that. Let's see what are the other variables that we can use." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['gender',\n", " 'age',\n", " 'date',\n", " 'sports',\n", " 'tvsports',\n", " 'exercise',\n", " 'dining',\n", " 'museums',\n", " 'art',\n", " 'hiking',\n", " 'gaming',\n", " 'clubbing',\n", " 'reading',\n", " 'tv',\n", " 'theater',\n", " 'movies',\n", " 'concerts',\n", " 'music',\n", " 'shopping',\n", " 'yoga',\n", " 'attr_want',\n", " 'sinc_want',\n", " 'intel_want',\n", " 'fun_want',\n", " 'amb_want',\n", " 'shar_want',\n", " 'attr_self',\n", " 'sinc_self',\n", " 'fun_self',\n", " 'intel_self',\n", " 'amb_self',\n", " 'pid',\n", " 'age_partner',\n", " 'int_corr',\n", " 'samerace',\n", " 'attr_partner',\n", " 'sinc_partner',\n", " 'intel_partner',\n", " 'fun_partner',\n", " 'amb_partner',\n", " 'shar_partner',\n", " 'prob']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(df.columns[1:-1])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training set performance is 0.713540696592\n", "test set performance is 0.707371556217\n" ] } ], "source": [ "predictors = ['age','date','int_corr',\n", " 'samerace',\n", " 'sinc_partner',\n", " 'intel_partner',\n", " 'fun_partner',\n", " 'amb_partner',\n", " 'shar_partner','prob']\n", "lr_model = lr.fit(train[predictors].values, train['dec'].values)\n", "print('training set performance is {}'.format(lr_model.score(train[predictors].values, train['dec'].values)))\n", "print('test set performance is {}'.format(lr_model.score(test[predictors].values, test['dec'].values)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.01254919 -0.01858074 0.03288736 0.06893665 -0.07068834 0.04977025\n", " 0.42423327 -0.15730692 0.258164 0.15273089]]\n" ] } ], "source": [ "# You can also see how important each of those factors is (sort of)\n", "print(lr_model.coef_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But it does seems like attractiveness is more indicative than anything else. Next you can try to combine them and repeat the same procedure. Is there an improvement to the performance? What can you infer from the coefficients this time? " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try to include also those variables that you think are important and repeat the same step again. Observe what happen to the performances when you add more and more predictors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the graphs on EDA, it seems that male and female make their decisions quite differently. Try to repeat the above with female_df and male_df and see if the results improve." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Benchmark" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to know how good our prediction performance is, we should at least compare it to the performances of some naive algorithms. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Proportion of rejection by female in training set is 0\n", "Proportion of rejection by female in test set is 0\n" ] } ], "source": [ "# what if I just look at the training set and guess the most popular decisions?\n", "no_female_train = female_train.query('dec == 0')\n", "print('Proportion of rejection by female in training set is {}'\\\n", " .format(no_female_train.shape[0]/female_train.shape[0]))\n", "\n", "no_female_test = female_test.query('dec == 0')\n", "print('Proportion of rejection by female in test set is {}'\\\n", " .format(no_female_test.shape[0]/female_test.shape[0]))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Proportion of rejection by male in training set is 0\n", "Proportion of rejection by male in test set is 0\n" ] } ], "source": [ "# what if I just look at the training set and guess the most popular decisions?\n", "no_male_train = male_train.query('dec == 0')\n", "print('Proportion of rejection by male in training set is {}'\\\n", " .format(no_male_train.shape[0]/male_train.shape[0]))\n", "\n", "no_male_test = male_test.query('dec == 0')\n", "print('Proportion of rejection by male in test set is {}'\\\n", " .format(no_male_test.shape[0]/male_test.shape[0]))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "male test set performance is 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/paul/anaconda2/lib/python2.7/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" ] } ], "source": [ "# what if I simply do a cut off at attr_partner and base my decision on that? (refer to graphs plotted in EDA)\n", "male_test['attr_cut_predict'] = (male_test['attr_partner']>=7)\n", "print('male test set performance is {}'\\\n", " .format((male_test['attr_cut_predict'] == male_test['dec']).sum()/male_test.shape[0]))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "female test set performance is 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/paul/anaconda2/lib/python2.7/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" ] } ], "source": [ "female_test['attr_cut_predict'] = (female_test['attr_partner']>=8)\n", "print('female test set performance is {}'\\\n", " .format((female_test['attr_cut_predict'] == female_test['dec']).sum()/female_test.shape[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tree model allows combination of factors (as opposed to logistic regression model)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "dt = DecisionTreeClassifier(min_impurity_split=0.3)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training set performance is 0.879307133544\n", "test set performance is 0.731198808637\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/paul/anaconda2/lib/python2.7/site-packages/sklearn/tree/tree.py:282: DeprecationWarning: The min_impurity_split parameter is deprecated and will be removed in version 0.21. Use the min_impurity_decrease parameter instead.\n", " DeprecationWarning)\n" ] } ], "source": [ "predictors = ['age','date','int_corr',\n", " 'samerace',\n", " 'sinc_partner',\n", " 'intel_partner',\n", " 'fun_partner',\n", " 'amb_partner',\n", " 'shar_partner','prob', 'attr_partner','attr_want',\n", " 'sinc_want',\n", " 'intel_want',\n", " 'fun_want',\n", " 'amb_want',\n", " 'shar_want']\n", "dt_model = dt.fit(train[predictors].values, train['dec'].values)\n", "print('training set performance is {}'.format(dt_model.score(train[predictors].values, train['dec'].values)))\n", "print('test set performance is {}'.format(dt_model.score(test[predictors].values, test['dec'].values)))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.02580046, 0.01579462, 0.04281306, 0.00661296, 0.01798519,\n", " 0.01638643, 0.03296896, 0.0242345 , 0.10993459, 0.04845516,\n", " 0.42419231, 0.05331946, 0.04078902, 0.05114997, 0.03566275,\n", " 0.02324129, 0.03065928])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_model.feature_importances_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ensemble Methods" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "rf = RandomForestClassifier()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training set performance is 0.991246042093\n", "test set performance is 0.751303052867\n" ] } ], "source": [ "predictors = ['age','date','int_corr',\n", " 'samerace',\n", " 'sinc_partner',\n", " 'intel_partner',\n", " 'fun_partner',\n", " 'amb_partner',\n", " 'shar_partner','prob', 'attr_partner','attr_want',\n", " 'sinc_want',\n", " 'intel_want',\n", " 'fun_want',\n", " 'amb_want',\n", " 'shar_want']\n", "rf_model = rf.fit(train[predictors].values, train['dec'].values)\n", "print('training set performance is {}'.format(rf_model.score(train[predictors].values, train['dec'].values)))\n", "print('test set performance is {}'.format(rf_model.score(test[predictors].values, test['dec'].values)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "gb = GradientBoostingClassifier(max_depth=7)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training set performance is 0.982305829763\n", "test set performance is 0.793000744602\n" ] } ], "source": [ "predictors = ['age','date','int_corr',\n", " 'samerace',\n", " 'sinc_partner',\n", " 'intel_partner',\n", " 'fun_partner',\n", " 'amb_partner',\n", " 'shar_partner','prob', 'attr_partner','attr_want',\n", " 'sinc_want',\n", " 'intel_want',\n", " 'fun_want',\n", " 'amb_want',\n", " 'shar_want']\n", "gb_model = gb.fit(train[predictors].values, train['dec'].values)\n", "print('training set performance is {}'.format(gb_model.score(train[predictors].values, train['dec'].values)))\n", "print('test set performance is {}'.format(gb_model.score(test[predictors].values, test['dec'].values)))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
azane/for-learnings-sake
notebooks/BayesLinearParametricRegression.ipynb
2
239805
{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", "import numpy as np\n", "\n", "import sys as sys\n", "sys.path.append('..')\n", "\n", "from bayes.regression.linearparametric import BayesLinear as Model\n", "from bayes.regression.linearparametric.bases import gaussianType as phi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Gaussian Basis Function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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PqNuxfei+A5ET3P8MlQqtfX5oQAqgG3FeVs/VwDbIuTPJdCCQOzxt9FGMZQIBx7YOp62PsCb54xRwY8/rh7qWWf9fg9le+/WmgDGl9tcc831rTSEPHz55ZffDtXFUGXIR/TU/e9//yvcNq4JKYhenUjl9gDGYoToKdxnn30Kw2sxJ23GmHzZlMaNG1eQy/jSjeui5yVkonkqJYDRGxkSFWL53//+99th2viCjt6fSEVpOOmkkzj44IO/LfKuu+4qyGyIafRoFQWyeEFRqKa2CCR65ULOorcvelljODOGdkM6Ysi8OaOQz+jhK6bo3Qzxi57VbbbZptDzGUOn0fPZPEVPVAy7tyVWUd9dd92VEM1IH3/8MTEkGtIfvXgHHXTQt8WW82zjOcQ9I1/0WkaPV8th3JY9gNGe6MWLHrNoa/x/W6maAhjHOcYUhpYp5sPGfMCQ7UjRc7zZZpsV+AfrGO6N5xniHL2HxRQxEj3Hf/vb33jvvfdKNq2tzw8FUAFs651o6+cKYFuE/LkEmhG4//5swUeMosXQ79RSWx/gzfN+9BE0G0WqOPMJE2C2eNsTUmtDwPHv0bsRKebmxdyoogCGwMR8qBCkEIEQxegFCcGYY445vh02LVcA4x7RS3bhhRcWhpBjiC2++GNO3HzzzVeoQ4jWn//858J/4ws4pCz+vzik11IAoycnenZCHEM4Yog1yo15cDGMGoIVqSiA0b5oSzG1nDcWw7TRnkghurHtSwxdTk0Ao9crVsFGb2LIbQjsxIkTC1JZTEVGzz33XGGYu5hiWD0kI9oYQ9khWSFmpVLUJ+oSdWottTYHMIaPI29xyLPcZxvtCHkMqQ1Bj+ce8+7i34ry3lIAY/5j9DjGLwghSDH3MtoWWwy1lqo5BBw9stEL2DIVn0nESMRf9D7HuxBzSSOF0MdzbdmjWiwn2h1ll0ptfX4ogApgwsd5IasCmErQ/LkhMHw4bLEFxIjcVL4/v+XR1gd4c3D10AMY9W1rEUhRrooCGD0fsWgjWDTv9Ymhs5hLVZw3F3PLovcqvjib9/bsv//+heHOUnv2xTBv9L5FT0p8yTaXpahr1CHmmkXPS3wph1TE3K2WAhhCF/IQIhiCWkzR8/TGG2+UJYDN541Fnua9OjHcGlIZAhjXtbYKOKQo6hnD2LG4oKWoFWWjtR7AmB8Z+/TFn8cff7wgXMUFJ81jLdgHh/YKYAx1hgTGfMLll1++0KtVzrNtfp/oSYtFJLGgInoEi/Myp7YPYPQcx4KReE4xJzN6jkulYq9plN/WVjXFXuvYGiYWxxRTDMduueWW35sDGPFX7OVrfu+Y21mcFhHTE+LvwaU4VB7yO9tssxW4hXSXeh4xN1AB7NjXiNvAdIxbMZcCmMbP3Dkh8NBDIT/R+wTl7oXbHgGsF4whbNHDFGIWvW4tU/TaHHfccd/2AIacRW9dLCQoCmCUEUNnIVhFAQypiOHc+AKOIbRI8aUbvXIxlDm1TZtDJOKLPIYqS6XisFwsmIh94VoKYGxoHdvavPvuu98OF7/00kuFe8c8vHJ6AFsuHChVj2Iboz4bxRyCFqn48+ipHD16dGEuW/PFDMU5gNELFotDiil60oJ79FbGIpa4LlYNRxnFVbbtia/WegDjvtH7Gr14IZHlPttS947nEHPlgkWktjaCjmcbCy1iqLz5EHTzsptvAxNDsC3nOcbPY+FFcRuY6H0M8T/ttNO+LSZ6doNfy0UgrQlgZIwh9+iNjh7K4B5Dxc03+o65nZGizPaktj4/7AG0B7A98VTqWgUwlaD5G55AjG7GlKs//xma9sYtq81tfYCXVUgNXhRfwrEQJAQueq1ieC6GWkOaoqcmhr0+/PDDwrBXcX5c9O7F0Gj08B1//PGFYeDo6SsKYHzBxny7SLE9RmxvEpITCxuiR60ogPGFHcOdK6+8cmFrkCjv8MMPLwzvRs9PDEFHT018yYe8xXy+KC96fEIko9yWAhhz/JZZZplC71zIRdQ/hCrqGAsbyhHA+JJvvnVIqccW8x+jh2iTTTYp3CfqEj19zRerhBjFNjsh2MUFMsWySq0CjpXDMXcwViRHD1mkGFJcb731Cu2N+8SWKzFPLcQkhOuWW26Z6irZkLF4VsWNoEO+Ql5i7mMM5cd8vkjlPtsQ+379+hWG2aOtwSliINoXPWMtBTDENTZxjoUUESPx7GN1b/xyEJLc2qrZKCfiJX5BiZ62KKO42jqYhrzG0Hlx25ZYKBN/j/iI5xCreqNt0VPcHgEMqQwusdVO9GpHT2/zFEPla665ZmEqREx9iF7m6CGOOZkRXzE/tlRq6/NDAVQAU78eFMBUguZvaAIx7zu26jrggNiWo31NbesDvH2l1dbVMdwaw38xxBqS1fIouObHbcUQbvQWRg9bbEkSw34hACGIzVfTxpd3//79CwsxYogy5uPFMG/zY9tihWvIQEhb/Cx6kWK4L4Qt5CIEIebCxZdrLFqJcmKfvuiVjJW2kUIA4/+bz70KeYwtU4p778WwcQw3xpd5cwGML/gQiZZzAMvpAYx7R96oX1Fqm4tG/DzYxNzDlvP84mfFIeCoU+z7F/Ic29yEXIeUh1AWUwhfiE1sRxJb48Q2MsXj0GJ4srW99CJ/yxXcsRI6JD+2mgmpat4rWc6zjV8WQhaDYzAPhiGX8ayLqbjKOLhHPMUegBFjMSwf9wt5jHpHz3FbKUQ7+BRlrrjKOiQ0xLrYMxjxE/8fzzlELObvhZzHyuOWAhg/byl2xXrEz2IKQsRb/AISktcyxS8qEXfxzOK+sTAphn6jVzWmVSiAbT3V0j93CLhj3Iq5FMA0fuZuYAJvvw2xi0bM+4tFH23sn/s9Eo0sgA382Lu0abFtSyxAKSUbRQGMnr2QKFNjE2jr88MeQHsAU98ABTCVoPkbkkDsVhIHAcRxsRde2H75CyhtfYA3JDgb1W4C0VsXPZaxOCR6MqPnqtQcQQWw3WjrOkNbnx8KoAKYGuAKYCpB8zccgU8/hRhp+tnP4LrroMwTz+wBbLhI6JwGFbeXie1RYli0+Rm8zWugAHbO86iVuyiAbT8Jh4DbZjS1KxTANH7mbjACceTslltC7M0a277MMEPHG9jWB3jHSzanBCTQ6ATa+vywB9AewNR3QAFMJWj+hiIQJ11dcw08+ijMOWda09r6AE8r3dwSkEAjE2jr80MBVABT418BTCVo/oYhcPnlsP/+EIcvNO1IktS2tj7Akwo3swQk0NAE2vr8UAAVwNQXQAFMJWj+hiAQ5/uuvz7cfDOst15lmtTWB3hl7mIpEpBAIxJo6/NDAVQAU+NeAUwlaP66J/Daa9lq36OPhn32qVxz2voAr9ydLEkCEmg0Am19fiiACmBqzCuAqQTNX9cEPvoIVlkF4rSmM8+sbFPa+gCv7N0sTQISaCQCbX1+KIAKYGq8K4CpBM1ftwRixW8cOztpEtx+e8e3e2kNQPED+s033ywcCG+SgAQkUC6B+PyI4wzj5JBSnx8KoAJYbiy1dp0CmErQ/HVL4E9/gjg+ddQomGOOyjcjzseNI7TiCDSTBCQggfYSiLOj4yi/5sfvFctQABXA9sZTy+sVwFSC5q9LAvfeC5tuCiNGQJ8+1WtCSGCc9GCqPIFnnoE4RvXqq2HNNStfviVKoKsJxFnPpeQv6qUAKoCp8akAphI0f90ReOutTPqiB7DEue111548Vzh6cA8/HJ56Cn7+8zyTsO15I6AAKoCpMa8AphI0f10RiM64tdaCRRfNzvg11TeByZMziR87Fh54AKabrr7bY+0lUC4BBVABLDdWWrtOAUwlaP66InDggfDPf8K//gUzzlhXVbeyrRD47DNYeeVsJfdf/iImCeSDgAKoAKZGugKYStD8dUPg2mthzz2zRR8LLVQ31baiZRB45RX45S/h/POzs5xNEmh0AgpgfgTwGGA3IITtCaA/8HwrAd4bGAosT8bnFuAA4JMS1yuAjf4pYfsKBF56KROEK66Afv2E0ogEbrkFtt8+E/yePRuxhbZJAt8RUADzIYCDgH2BDYFxwFHADkB8xE1s8ULM2iSGlwIhjbG5xXXA+8AWCqAfH3kkEPP+YrPnWCl66ql5JJCfNg8cCA8+mA3x9+iRn3bb0vwRUADzIYCvNvXondUU4tMAbwMDgStbhP0GwPXALM3+fR1gODAf8FaL6+0BzN/nRu5afOihcNdd8MgjMP30uWt+rhr8xRew4orZ9jAnnpirptvYnBFQABtfAEPQPgRWBh5tFt8hdKOBg1vEfPQSRo9f9ARObvrZ+sCdQAx83aEA5uxTIufNLe73F8OCvWNyhKnhCbzwAiy/PNx6K6y9dsM31wbmlIAC2PgCOA/wBrAoMLZZnF8d+0ACe7SI/dmBF4FLmoaA5wT+BqwGbAdcpQDm9NMih81+7z1Yemk46ijYo+WbkkMeeWryuefCscfCs8/CnPEpaJJAgxFQABtfANvbAxghvjRwctN/JwCnAH8F+gJ3K4AN9ilgc0oSiP3hNt8cuneHG26AbrEcypQbAsXnH8/9xht9/rl58DlqqALY+AIY4VxqDuA7wIAScwBLhf+mwBXA3MDHpQSwf//+xJEzkfr27Vv4Y5JAPROwB6ien15l6m4PcGU4WkrtEBg+fDjxJ1IcMTls2LD4a4z8xYhg7lIefq+PeX6xCnijJhkc0jSc26vEKuAIgGWbhoG/AFYBYkVwLCCJrWFaJheB5O6VafwGOwes8Z9xuS0szgF9/PHs9BeTBBqFgD2A+egBjHg9GtizaXHHqGb7AM4LjAFi9e/IpsCOXwm2BmYAxjeJ38WtBL0C2CifBrajQODLL7NVoOutB3/+s1AkAIccAiGCsQrco+KMiEYhoADmRwCrFbMKYLXIWm6XEIiJ/9ddl20G7JYvXfIIau6msTXMcsvB1lvDkUfWXPWskAQ6REABVAA7FDjNMimAqQTNXzMEnn462/D5oYdg2ZgIYZJAE4EnnoDVV882iF5mGbFIoP4JKIAKYGoUK4CpBM1fEwTitI/Y+22zzeCYOAPHJIEWBIYMyfYGfOwxTwkxOOqfgAKoAKZGsQKYStD8NUHAL/eaeAw1XQl/Sajpx2Pl2klAAVQA2xky37tcAUwlaP4uJ1Ac3nv44WzjZ5MEWiPgNAFjo1EIKIAKYGosK4CpBM3fpQSc4N+l+Ovy5i4UqsvHZqVbEFAAFcDUl0IBTCVo/i4lcMQR8I9/uMVHlz6EOrt5caugX/8ajj++zipvdSXQREABVABTXwYFMJWg+buMQGz1ssYa8OijsOSSXVYNb1yHBEaPzvaLHDEi2yLGJIF6I6AAKoCpMasAphI0f5cQiF6cFVbIVv0edVSXVMGb1jmBo4/+blXwtNPWeWOsfu4IKIAKYGrQK4CpBM3fJQROPhkuvhieesoNn7vkATTATWP+aOwJuOuucHAcuGmSQB0RUAAVwNRwVQBTCZq/0wm8+iostVQ292+11Tr99t6wgQjEpuEbbADPPgsLLthADbMpDU9AAVQAU4NcAUwlaP5OJTB5MvTtCwstBOec06m39mYNSmCvvWD8+OwXim7dGrSRNqvhCCiACmBqUCuAqQTN36kELr8cDj0UxoyBH/ygU2/tzRqUwIcfwqKLQkwr2G67Bm2kzWo4AgqgApga1ApgKkHzdxqB996D3r3h3HNhiy067bbeKAcErr8e9t4bXngBfvSjHDTYJtY9AQVQAUwNYgUwlaD5O43AjjvChAlw000O1XUa9JzcKKYWxIryOeaASy7JSaNtZl0TUAAVwNQAVgBTCZq/UwjcfXfW6xdDv/PM0ym39CY5I/Dmm7D44nDjjbDuujlrvM2tOwIKoAKYGrQKYCpB81edwOefwxJLwIEHwr77Vv123iDHBM48E844A2Kj6BlmyDEIm17zBBRABTA1SBXAVILmrzqB446Dm2+Gxx6Daaap+u28QY4JfP01LL88/OY3MHhwjkHY9JonoAAqgKlBqgCmEjR/VfdeY4UAACAASURBVAnE9hzR+3fffdnRXSYJVJvAI4/AOuvAc8/BAgtU+26WL4GOEVAAFcCORc53uRTAVILmryqBfv3gZz+D886r6m0sXAJTENh9d/jPf+CWWwQjgdokoAAqgKmRqQCmEjR/1QjcdhvstBOMHevWHFWDbMElCcSWQ716waWXwsYbC0kCtUdAAVQAU6NSAUwlaP6qEPjsM1hsMTj8cIjeGJMEOptA9DqfeCI8/zzMOGNn3937SWDqBBRABTD1HVEAUwmavyoEhgyB4cPh4Yehe/eq3MJCJTBVArEgZOWVYcMN4ZhjhCWB2iKgACqAqRGpAKYSNH/FCbzyCiy1FIwYAcstV/HiLVACZRMYNQrWWAOefRYWXrjsbF4ogaoTUAAVwNQgUwBTCZq/ogTiRIZf/zpbfXn22RUt2sIk0CECcUTc66/DHXd4Ak2HAJqpKgQUQAUwNbAUwFSC5q8ogVh1udtu8NJL2bFcJgl0NYH//S9bEHLBBbDppl1dG+8vgYyAAqgApr4LCmAqQfNXjMAXX2QLPw47zIUfFYNqQRUhEAtCTjopWxAy/fQVKdJCJJBEQAFUAJMCCFAAUwmav2IE/vxnuPpqiHlXnvhRMawWVAECsSAk5qNusw0cckgFCrQICSQSUAAVwMQQUgBTAZq/MgTeeQd69szmWcWke5MEao3AP/8Jm2ySTU+IzclNEuhKAgqgApgaf/YAphI0f0UI7LwzTJwI11xTkeIsRAJVIbDVVjDLLHDRRVUp3kIlUDYBBVABLDtYWrlQAUwlaP5kAo8/DmutBWPGwHzzJRdnARKoGoHXXoPFF4cHHoDll6/abSxYAm0SUAAVwDaDpI0LFMBUguZPIhDbvqyyCqy3Hhx7bFJRZpZApxA48ki4914YOdJtYToFuDcpSUABVABTXw0FMJWg+ZMIXHklHHpodt7vzDMnFWVmCXQKgU8/zbaFiVXBsSjEJIGuIKAAKoCpcacAphI0f4cJfPJJ9kV68sl+kXYYohm7hIC/uHQJdm/ajIACqACmvhAKYCpB83eYQAyl3XcfPPSQQ2kdhmjGLiEQUxdWXRXWXdepC13yALypG0GjAKa+BgpgKkHzd4jAm29C795Opu8QPDPVBIHHHoNf/SqbvjDPPDVRJSuRIwL2ACqAqeGuAKYSNH+HCOy4I3z1FcRQmkkC9Uog5gD26AGXXFKvLbDe9UpAAVQAU2NXAUwlaP52E3jySVhtNXjhBbd9aTc8M9QUgdgWJo4vjBXBffrUVNWsTIMTUAAVwNQQVwBTCZq/XQRi7tTaa8OKK8KJJ7YrqxdLoCYJxCr22Msytobp1q0mq2ilGpCAAqgApoa1AphK0PztInDbbbDLLvDKKzD77O3K6sUSqEkCEybAQgtlw8Abb1yTVbRSDUhAAcyPAB4D7AaFs3ufAPoDz7cS00sCpwDLAd8AI4ABwBslrlcAG/CDoVab9OWXsOSSsN9+0D8i2CSBBiFw1lkwbBiMHg3TTtsgjbIZNU1AAcyHAA4C9gU2BMYBRwE7AD2BiSUiNETveuAQYHogTq2MNWqrKoA1/T43fOXOOQdOPz37kpxuuoZvrg3MEYH45WaJJWDAANhrrxw13KZ2GQEFMB8C+CowFDirKdKmAd4GBgIt11DOCfwXWBoY3XT9RsC1QKlzFuwB7LLXN183/ugjWHhhuOAC6NcvX223tfkgcMstsPvu2fSG2eKT1SSBKhJQABtfAONj5ENgZeDRZrE0vEnwDi4RX/8EngEObeoBPB/4rKnXsOXlCmAVX1CL/o7A4YfDww9nGz87Ud7IaEQCscAp9gWMDaL/+MdGbKFtqiUCCmDjC2AM3caQ7qLA2GbBdzXwEbBHiYBcGPg7sEDTRtlPNw0fR8+gAlhLb3BO6vLGG9mmzyNGwHIxM9UkgQYlMGoUrLFGtjn0vPM2aCNtVk0QUAAbXwDb2wP406aewSOb5v7FdOTDgG2BWBwSPYHNU6EHsH///vSI3UyBvn37Fv6YJFApAjvvDJMmuelzpXhaTm0T2HZbmH56uChmX5skUEECw4cPJ/5EmjRpEsNi5RHEfgrRIZS7lIddl0rNAXynaWVvyzmAWwAXAHM0i4RZQ/KAFYHHSwnghAkTmM1JK7l7eTqjwbHgY4UVYMwYWCD6pE0SaHACr74Kiy+e7Q0YC0NMEqgGAXsAG78HMOIm5vnFKuBYzBEyOATYDuhVYhVwDP/G9jCxycbFQKy1jLmAsWDkF00i2DwWnQNYjTfTMr8lsMkm2R5pp50mFAnkh8ABB8D48XDrrflpsy3tXAIKYD4EMKLqaGBPIHrzRjXbBzBmmYwBNgBGNoXfJkAMAYcMTm4aEh4MPFQiPBXAzn1nc3W3Bx/MNsYdNw5+/ONcNd3G5pzAf/+b/eJzxx2w+uo5h2Hzq0JAAcyPAFYlgJo2lp7gEHC18Oa33FgRucoqsNFGMDh+/TBJIGcEjjsO7rwzOyfYle85e/id0FwFUAFMDTN7AFMJmr8kgZtugr33znr/Zi61A6XcJNDgBD75JNv78q9/hc02a/DG2rxOJ6AAKoCpQacAphI0//cIfPVVNvn9wAM9FcHwyDeBOP3mjDM8Ii7fUVCd1iuACmBqZCmAqQTN/z0C558PJ58Mzz/vkW+GR74JxBFxiy0Ghx4Ku8Vp7iYJVIiAAqgApoaSAphK0PxTEJg4MRv2ijN/t9xSOBKQwLXXZmcEv/wyzDSTPCRQGQIKoAKYGkkKYCpB809B4MQT4cYb4dFHnfhuaEggCHzzDay4ImyxBRwW2/KbJFABAgqgApgaRgpgKkHzf0vggw+yzZ5DANdeWzASkECRwL33wm9/C7FJ9BzNt+kXkQQ6SEABVAA7GDrfZlMAUwma/1sChx+enX5w991CkYAEWhJYd92sJ/CPf5SNBNIJKIAKYGoUKYCpBM1fIPDuu9ncv/vuy45+M0lAAlMSiGkR66yTbY300zi13SSBBAIKoAKYED6FrApgKkHzFwjsvz+8+SbE/n8mCUigNIHYD3C++bJFUiYJpBBQABXAlPhRAFPpmb9A4PXXoXfvbPg39v8zSUACpQmMHp31kL/4YiaCJgl0lIACqAB2NHaK+ewBTCVofnbZBWK/s8svF4YEJNAWge22g+mnhwsvbOtKfy6B1gkogApg6vuhAKYSzHn+6MlYZpls0+eFFso5DJsvgTIIvPJK1lP+zDPQq1cZGbxEAiUIKIAKYOqLoQCmEsx5/q22gjnnhDjyyiQBCZRHYK+9ILZNuuaa8q73Kgm0JKAAKoCpb4UCmEowx/mffBJWXz074WDuuXMMwqZLoJ0E3noLFlkERo6EPn3amdnLJQAogApg6ougAKYSzHH+X/86G8o66aQcQ7DpEugggUGDYMwYuOOODhZgtlwTUAAVwNQXQAFMJZjT/A89BCGA48dnQ8AmCUigfQTeew8WXBD+/ndYbbX25fVqCSiACmDqW6AAphLMYf7Jk+FXv4K11oKjj84hAJssgQoROOooePBBuP/+ChVoMbkhoAAqgKnBrgCmEsxh/jjXdMsts96/2WfPIQCbLIEKEfjww+z87Btu8PzsCiHNTTEKoAKYGuwKYCrBnOWP3r9VV4WNN4Y4+9ckAQmkEYizgWMYOKZVdOuWVpa580NAAVQAU6NdAUwlmLP8d94JO+yQ9f7NMkvOGm9zJVAFAh9/nPUCXnEFbLBBFW5gkQ1JQAFUAFMDWwFMJZij/NH7F8dYbb01HHxwjhpuUyVQZQInnwzXXguPPWYvYJVRN0zxCqACmBrMCmAqwRzlv+UW2HNPePVVmGmmHDXcpkqgygQmTsxWBJ93HvTrV+WbWXxDEFAAFcDUQFYAUwnmJP8332Qb1sa5vwcckJNG20wJdCKB006DSy6B2GC9e/dOvLG3qksCCqACmBq4CmAqwZzkv+46GDAA4hzTGWbISaNtpgQ6kcDnn2fnaZ9+Ovz2t514Y29VlwQUQAUwNXAVwFSCOcj/9dew5JKw336w9945aLBNlEAXETj7bBg2DJ59FqaZposq4W3rgoACqACmBqoCmEowB/mvvBKOOAJeegl69MhBg22iBLqIwBdfQM+e8Kc/wTbbdFElvG1dEFAAFcDUQFUAUwk2eP6vvoLFFoNDD4Vdd23wxto8CdQAgQsuyM7XjnOCp522BipkFWqSgAKoAKYGpgKYSrDB8192GRxzDLz4Ikw3XYM31uZJoAYIfPkl9OqVvXfbb18DFbIKNUlAAVQAUwNTAUwl2MD5o/dv0UWzEz923rmBG2rTJFBjBC66CE480V7AGnssNVUdBVABTA1IBTCVYAPnv/RSOO64rPfPoagGftA2reYIRC9g795w1FHZyTsmCbQkoAAqgKlvhQKYSrBB80fvX3wBDR4MO+3UoI20WRKoYQIXXwwnnAAvvOAvYDX8mLqsagqgApgafApgKsEGzR8b0h5/vL1/Dfp4bVYdECj+EnbkkbDjjnVQYavYqQQUQAUwNeAUwFSCDZjfL54GfKg2qS4J+ItYXT62Tqm0AqgApgaaAphKsAHzO/TUgA/VJtUlAadi1OVj65RKK4AKYGqgKYCpBBssv5PPG+yB2py6J+BirLp/hFVpgAKoAKYGlgKYSrDB8kfvX5xC4Ca0DfZgbU7dEihuxxSn8bggq24fY8UrrgAqgKlBpQCmEmyg/G5A20AP06Y0FAE3ZG+ox1mRxiiACmBqICmAqQQbKL+bzzbQw7QpDUWgeCTjH/7gpuwN9WATGqMA5kcAjwF2A0LYngD6A8+XiJ15gTHA5GY/6wFMA/wU+F+LPApgwgvYSFnjCyaOnzr6aI+faqTnalsah0D0Ah57rFszNc4TTWuJApgPARwE7AtsCIwDjgJib/iewMQyQugGII4U37TEtQpgGQDzcElMNI99/9x0Ng9P2zbWI4HiiuAhQzwdpB6fX6XrrADmQwBfBYYCZzUFUPTmvQ0MBK5sI6h+DowHNgbuUgAr/Qo2RnlOMm+M52grGp+Ai7Qa/xmX20IFsPEFMHroPgRWBh5tFhjDgdHAwW0Ey7HA74FFWrnOHsBy37YGvu6KKyB6FcaOhemma+CG2jQJ1DmB4kKtOKN7223rvDFWP4mAAtj4AjgP8AawKDC2WbRcDXwE7DGVCIph39eBU5t6EEtdqgAmvYL1n/nrr2HxxWHQINh11/pvjy2QQKMTuOACOOUUeP55mCbGg0y5JKAANr4ApvQAbgVcDIREfjC1HsD+/fvTo0esFYG+ffsW/pjyQeCqqyBWFr78sr1/+XjitrLeCUyaBD17woknwu9+V++tsf7tITB8+HDiT6RJkyYxbNiw+OvsTR1C7SmqIa7t1hCtmHojSs0BfAcY0MYcwPub5v/tMpXi7QHMQQC11sTo/VtySTjwQNhjan3JOWZk0yVQiwTOPRfOOANGj4bu3Wuxhtap2gTsAWz8HsCIoZjnF6uANwJCBocA2wG9prIKeDHgOWAFYJQCWO1XsT7Lv/ZaOPhgeOUVaOoArs+GWGsJ5IzAF1/AIovAqafCllvmrPE2t0BAAcyHAMazPhrYE5i1SeiK+wAW9/3bABjZ7L04A1ipSQCn9rrYA5jTD5NvvoGlloL+/WHvvXMKwWZLoI4JnH02nHMOPPOMvYB1/Bg7XHUFMD8C2OEgaSOjAlgtsjVe7g03ZEO/0fs3/fQ1XlmrJwEJfI9A9AIutFA2FPyb3wgobwQUQAUwNeYVwFSCdZg/ev/69IHdd4d9Y3KBSQISqEsCZ50FsSr4qaegWx5mxNflU6pOpRVABTA1shTAVIJ1mP+WW7Jh31dfhRlmqMMGWGUJSKBA4LPPYMEFIRaF9OsnlDwRUAAVwNR4VwBTCdZZ/smTYfnls01kB8Q6cpMEJFDXBIYOhdjO6bHH7AWs6wfZzsorgApgO0Pme5crgKkE6yz/nXfCTjvB+PEw00x1VnmrKwEJfI/Ap5/CAgvAZZfBBrEc0JQLAgqgApga6ApgKsE6yh+9f6usAptvDoccUkcVt6oSkMBUCfz5zxBTO0aOtBcwL6GiACqAqbGuAKYSrKP899wDW28Nr70Gs8aGQiYJSKAhCHz8Mcw/P8Tenuus0xBNshFtEFAAFcDUl0QBTCVYR/nXXBPWWw8GD66jSltVCUigLALHHQf33gsPPFDW5V5U5wQUQAUwNYQVwFSCdZL/wQezVYKvvw6zx8mRJglIoKEIfPhh1gt4222w+uoN1TQbU4KAAqgApr4YCmAqwTrJHz1/K68Mxx5bJxW2mhKQQLsJHHkkPPoo3HVXu7Oaoc4IKIAKYGrIKoCpBOsg/yOPZEO/0fv3wx/WQYWtogQk0CEC77+f9QLGfN8VV+xQEWaqEwIKoAKYGqoKYCrBOsi/0UbZub9/+lMdVNYqSkACSQQOOwyeew5uvz2pGDPXOAEFUAFMDVEFMJVgjed/4glYY41s5e+Pf1zjlbV6EpBAMoH//CfbF3DECFh22eTiLKBGCSiACmBqaCqAqQRrPH8cEh9DQnFagEkCEsgHgYEDsykfN9yQj/bmsZUKoAKYGvcKYCrBGs4fw0Bx7Nu4cTD33DVcUasmAQlUlMDbb8NCC8GoUbD44hUt2sJqhIACqACmhqICmEqwhvNvsw3MMQcMG1bDlbRqEpBAVQjssw9MmABXXlmV4i20iwkogApgaggqgKkEazT/Sy9lCz/GjoX55qvRSlotCUigagRi3m/v3jB6NCyySNVuY8FdREABVABTQ08BTCVYo/l33hm6d4cLL6zRClotCUig6gR22SW7xUUXVf1W3qCTCSiACmBqyCmAqQRrML+/+dfgQ7FKEugCAsWRgBdfzBaDmRqHgAKoAKZGswKYSrAG8++9N3z0kXN/avDRWCUJdDqBmAv8gx/A2Wd3+q29YRUJKIAKYGp4KYCpBGss/1tvwcILu/qvxh6L1ZFAlxFwN4AuQ1/VGyuACmBqgCmAqQRrLP+AAfDGG+7/VWOPxepIoEsJuB9ol+Kvys0VQAUwNbAUwFSCNZTfEwBq6GFYFQnUEAFPBKqhh1GhqiiACmBqKCmAqQRrKP8f/gDPPgt33FFDlbIqEpBATRD49a9h6aU9E7wmHkYFKqEAKoCpYaQAphKskfz/+1+2yu8f/4BVVqmRSlkNCUigZgj861+wwQbZEXGxQbypvgkogApgagQrgKkEayT/McfAgw/CvffWSIWshgQkUHME1l4b1loLhgypuapZoXYSUAAVwHaGzPcuVwBTCdZA/o8/zk77uP56iA94kwQkIIFSBOIXxC23zHoBZ51VRvVMQAFUAFPjVwFMJVgD+U86CW6+GUaOhG7daqBCVkECEqhJApMnZ1NEYlXwoEE1WUUrVSYBBVABLDNUWr1MAUwl2MX5P/ssm/sXRz1ttFEXV8bbS0ACNU/g9ttht91g/HiYccaar64VbIWAAqgApr4cCmAqwS7Of+aZmfw9+aS9f138KLy9BOqCQPQC9umTSeC++9ZFla1kCQIKoAKY+mIogKkEuzD/F19kp36cdhpssUUXVsRbS0ACdUUg5gvHpvHjxkGPHnVVdSvbREABVABTXwYFMJVgF+Y//3z4y18gjnrq3r0LK+KtJSCBuiLwzTew+OJw0EFZT6Cp/ggogApgatQqgKkEuyj/V19Br14Q279st10XVcLbSkACdUvg8suzz48XX4Rpp63bZuS24gqgApga/ApgKsEuyn/FFXDUUTB2rB/eXfQIvK0E6ppA/BLZsyccdxxsu21dNyWXlVcAFcDUwFcAUwl2Qf4YvlliiWwOz+67d0EFvKUEJNAQBM47D04/HUaPdhpJvT1QBVABTI1ZBTCVYBfkv+EGOOCAbAL39NN3QQW8pQQk0BAEYiHZQgvBGWdkewOa6oeAAqgApkarAphKsJPzxxYOyy0HO+0E++/fyTf3dhKQQMMRiB7Ayy6DUaPcSqqeHq4CqACmxqsCmEqwk/P//e+Z/L32Gsw0Uyff3NtJQAINR2DixGwz+UsvhQ03bLjmNWyDFMD8COAxQCzWD2F7AugPPD+VyN4JGAgsCHwCXA0cWOJ6BbCOPh6i92+11aBfPzj00DqquFWVgARqmsCJJ0KcEDJihL2ANf2gmlVOAcyHAMaJjbFfe/xuNg44CtgB6AlMLBGsBwH7ANsDjwAxS6wX8LQCWC+vdul6PvAAbL55dpD7bKHuJglIQAIVIPDRRzDffNmZ4muuWYECLaLqBBTAfAjgq8BQ4KymiJoGeLuph+/KFlE2a9PPtgb+XkYE2gNYBqRauWTddbMewKOPrpUaWQ8JSKBRCMS2Uv/6F9x9d6O0qLHboQA2vgCGoH0IrAw82iychwOjgYNbhHjfJvGLXsO9moaMnwJiwPBZewDr9wPh0UchBDB6/374w/pthzWXgARqk8D772dzAe+9F1ZYoTbraK2+I6AANr4AzgO8ASwKjG0W/DGn7yNgjxYvRGzneTkwAohewA+AmD8YQ8YxDPxxi+vtAayTT5SY99e7N5x0Up1U2GpKQAJ1R2DQIHjpJbjllrqreu4qrAA2vgC2twdwEyBe3Q2Au5reiDglNsQvdnmKnsPmSQGsg4+NZ56BlVeGV1+Fn/2sDipsFSUggbok8O67sOCC8MgjsNRSddmE3FRaAWx8AYxgLjUH8B1gANByDmCxx7C5AMacwegtbFUA+/fvT48ePQovTt++fQt/TLVDYOut4Sc/gTPPrJ06WRMJSKAxCey7L7z3Hlwd40ymmiIwfPhw4k+kSZMmMWzYsPjr7E3f8TVV186oTLfOuEkX3yPm+cUq4I2aZHAIsF3TkG6pVcA3AHMCWwETmlYNx4rgGEaOLWGaJ3sAu/jhtnX7OKh9mWWyYZlf/KKtq/25BCQggTQCb7yRnREcIw+9YuKQqSYJ2AOYjx7ACL5Y97knEKt8RzXbB3BeYEzTkO/IpiidBTitqcfva+DxpsUicV3LpADW5Kv9XaVi0+dpp4ULLqjxilo9CUigYQjsuivEmeMXX9wwTWq4hiiA+RHAagWvAlgtshUoN077iIUfzz0HCy9cgQItQgISkEAZBF5+GZZcEmIEIlYGm2qPgAKoAKZGpQKYSrCK+ffeG2KD1itbzvSs4j0tWgISkEAQ2GYb+MEP4Oyz5VGLBBRABTA1LhXAVIJVyv/WW1mvXxzQvvjiVbqJxUpAAhJohUCMPCy/PIwbB3PPLaZaI6AAKoCpMakAphKsUv6BAyGGgG+8sUo3sFgJSEACbRCIoydjW5hTTxVVrRFQABXA1JhUAFMJViH/f/+bzbt58EFYbrkq3MAiJSABCZRBIEYg4mzgOIHoRz8qI4OXdBoBBVABTA02BTCVYBXyH3EEPPkk3HlnFQq3SAlIQALtILDBBvDLX8Lxx7cjk5dWnYACqACmBpkCmEqwwvk/+ADmmy+Tv1VXrXDhFicBCUignQQeegg22ijrBYxFIabaIKAAKoCpkagAphKscP7jjoP77oP7769wwRYnAQlIoIME1loL1l0XBg/uYAFmqzgBBVABTA0qBTCVYAXzf/JJ1vt3zTXZh61JAhKQQC0QuPtu+P3vs4Vps8RRA6YuJ6AAKoCpQagAphKsYP5TToHrr4eHH4ZueTjksILsLEoCEqgegcmTYaWVYKut4KCDqncfSy6fgAKoAJYfLaWvVABTCVYo/2efwQILwPnnwyabVKhQi5GABCRQIQK33gp77gnjx8MMM1SoUIvpMAEFUAHscPA0ZVQAUwlWKP+wYXDeefD00/b+VQipxUhAAhUkEL2AyywDe+wB/ftXsGCL6hABBVAB7FDgNMukAKYSrED+SZOyUz9is9Utt6xAgRYhAQlIoAoErr0WBg2COCu4R48q3MAiyyagACqAZQdLKxcqgKkEK5D/ggsy+Yujl6aZpgIFWoQEJCCBKhD4+uvsaMqQwF13rcINLLJsAgqgAlh2sCiAqaiqk/+rr6BXLzj6aNh+++rcw1IlIAEJVIrAZZfBscfCiy/CtNNWqlTLaS8BBVABbG/MtLzeHsBUgon5r7gCjjoKxo71wzQRpdklIIFOIBC/tPbsCbFn6bbbdsINvUVJAgqgApj6aiiAqQQT8n/zTTacEtsq7LZbQkFmlYAEJNCJBGK3gr/8JZu20r17J97YW31LQAFUAFNfBwUwlWBC/tjzb8AAGDfOCdUJGM0qAQl0MoEvvsgWrp12GmyxRSff3NsVCCiACmDqq6AAphLsYP7YUqFPn6znb999O1iI2SQgAQl0EYEzz4SLLoInn3Trqq54BAqgApgadwpgKsEO5r/tNth992xT1Rln7GAhZpOABCTQRQRi8/r554cLL4SNN+6iSuT4tgqgApga/gpgKsEO5C8eqxR7/h18cAcKMIsEJCCBGiBw8slwww0eX9kVj0IBVABT404BTCXYgfwerN4BaGaRgARqjsAnn8B888E118C669Zc9Rq6QgqgApga4ApgKsEO5F9zTVh/fTjiiA5kNosEJCCBGiJw/PEQv9T+8581VKkcVEUBVABTw1wBTCXYzvwPPgj9+sFrr8EPftDOzF4uAQlIoMYIfPhhNhcw5jWvvnqNVa6Bq6MAKoCp4a0AphJsZ/711oOVV8520jdJQAISaAQCRx4Jjz4Kd93VCK2pjzYogApgaqQqgKkE25H/kUcgBPD11+GHP2xHRi+VgAQkUMME3n8/6wW85x5YccUarmgDVU0BVABTw1kBTCXYjvwbbQRLLgknntiOTF4qAQlIoA4IHHooPP883H57HVS2AaqoACqAqWGsAKYSLDP/qFEQiz9i37+f/KTMTF4mAQlIoE4I/Oc/sMACEPOcl1uuTipdx9VUABXA1PBVAFMJlpl/s81gwQVh6NAyM3iZBCQggTojEEdbxgK3m26qs4rXYXUVQAUwNWwVwFSCZeR/9tlsXsyrr8Jcc5WRoR+SAQAAIABJREFUwUskIAEJ1CGBt9+GhRbKFoQstVQdNqCOqqwAKoCp4aoAphIsI/9WW2XDvmedVcbFXiIBCUigjgn07w/vvZdtDm2qHgEFUAFMjS4FMJVgG/lfeAH69IGXX4Z5563yzSxeAhKQQBcTeOMN6NkTnnoKFl20iyvTwLdXABXA1PBWAFMJtpF/u+1g5pnh3HOrfCOLl4AEJFAjBPbYAz77DC6/vEYq1IDVUAAVwNSwVgBTCU4l/yuvwBJLQPQCxuo4kwQkIIE8EIj5zostBs89BwsvnIcWd34bFUAFMDXqFMBUglPJv8suMHkyXHxxFW9i0RKQgARqkMBOO8E008CFF9Zg5RqgSgqgApgaxgpgKsFW8hd/Ax49GhZZpEo3sVgJSEACNUog5j3HxveOgFTnASmACmBqZCmAqQRbyb/77vDFF3DZZVW6gcVKQAISqHEC228PM84I551X4xWtw+opgApgatgqgKkES+SPjVB794ZnnoFevapwA4uUgAQkUAcEXnwRllkGxo6F+eargwrXURUVQAUwNVwVwFSCJfLvtRd8/DFceWUVCrdICUhAAnVEYJttYPbZ4Zxz6qjSdVBVBVABTA1TBTCVYIv8b76Zzfl78slsFZxJAhKQQJ4JPP88/PKX8NJL7oVayThQAPMjgMcAuwEhbE8A/YHnWwmmB4CVgS/I+EwGDgH+WuJ6BbCSb2Q8GHfBrzBRi5OABOqdgKchVf4JKoD5EMBBwL7AhsA44ChgB6AnMLFEWN0PPNh0XVtRpwC2RagdP3/rrWzPq8cfz/b/M0lAAhKQAMRuCCusALE36s9/LpFKEFAA8yGArwJDgeJJstMAbwMDgVKzzEIARwBDyggyBbAMSOVesv/+EIehX399uTm8TgISkEA+CGyxBcwzD5x+ej7aW+1WKoCNL4AhaB82Dek+2iyghgOjgYNb6QGM/qfuwP8BtwDHA5+WuFYBrNBb+s47sNBC8PDDsPTSFSrUYiQgAQk0CIGnn4ZVVoFx42CuuRqkUV3YDAWw8QVwHuANII7UHtss1q4GPgL2KBF/KwEvNonjksClTXl/rwBW720dMABi+5ebbqrePSxZAhKQQD0T2GwzWHBBGBpjWqYkAgpg4wtgR3oAWwbVGsA9wKxNC0Oa/7zQA9i/f3969OhR+Pe+ffsW/pjKJ/Duu1nv34gRsOyy5efzSglIQAJ5IvDEE7D66jB+PPz0p3lqeWXaOnz4cOJPpEmTJjFs2LD46+xNHUKVuUkdlRKrXBs9lZoD+A4woJU5gK0JYMje5y1+6BBwBaLnoIOyyc23xGC7SQISkIAEWiXQrx/07AmnnCKkFAL2ADZ+D2DER8zzi1XAGwEhg7G4YzsgzphouQr4J0CfpkUg8bPFgUuA14AtSwSbApjyBgL2/iUCNLsEJJArAtELuMYaEOel2wvY8UevAOZDACNCjgb2bBrGHdVsH8B5gTHABsBI4BfAdU1bxMRq4XeBG1wE0vGXrK2c9v61RcifS0ACEpiSgL2A6RGhAOZHANOjpXQJ9gAmkLX3LwGeWSUggdwSsBcw/dErgApgahQpgAkE7f1LgGdWCUgg1wTsBUx7/AqgApgWQdnRchMmTJjAbLPFX03lErD3r1xSXicBCUjg+wTsBUyLCgVQAUyLIAWww/zs/eswOjNKQAISKBCwF7DjgaAAKoAdj54spz2AHSBo718HoJlFAhKQQAsC9gJ2PCQUQAWw49GjAHaYnb1/HUZnRglIQAJTELAXsGMBoQAqgB2LnO9y2QPYToL2/rUTmJdLQAISmAoBewE7Fh4KoALYschRADvMbeDA7NSPW2/tcBFmlIAEJCCBZgSiF3DhhT0juD1BoQAqgO2Jl1LX2gPYDoJvv519SI0cCX3ivBWTBCQgAQkkE3jqKVh1VRg3DuaaK7m4XBSgACqAqYGuALaD4P77w1tvwQ1xtopJAhKQgAQqRuA3v4F554XTT69YkQ1dkAKoAKYGuAJYJsF//xsWWQQeewyWXLLMTF4mAQlIQAJlEXj2WVhxRXj5ZZhnnrKy5PoiBVABTH0BFMAyCe6zD7z/PlxzTZkZvEwCEpCABNpFYKut4Mc/hmHD2pUtlxcrgApgauArgGUQfP116N0bYrXaYouVkcFLJCABCUig3QSefx5++UsYOxZ+8Yt2Z89VBgVQAUwNeAWwDIJ77AGffgpXXlnGxV4iAQlIQAIdJrDNNjDrrHDuuR0uIhcZFUAFMDXQFcA2CL76atbr98wz0KtXKm7zS0ACEpDA1Ai8+CIsswy88AIssICsWiOgACqAqW+HAtgGwV12ga+/hksvTUVtfglIQAISKIfADjvAdNPBhReWc3U+r1EAFcDUyFcAp0IwVqPFit/nnsv2/zNJQAISkED1CfjZ2zZjBVABbDtKpn6FAjgVPttvDz16+FtoapCZXwISkEB7CcToy5dfwuWXtzdnPq5XABXA1EhXAFshOGYMLLus81BSA8z8EpCABDpCYPx4WHRRiFNC4r+mKQkogApg6juhALZCcMsts/2ozj47FbH5JSABCUigIwT23jvbf/XaazuSu7HzKIAKYGqEK4AlCBbPpYx5KD//eSpi80tAAhKQQEcIFE9gevjhbGWw6TsCCqACmPo+KIAlCG6ySXbs29ChqXjNLwEJSEACKQQGDIBx4+DWW1NKaby8CqACmBrVCmALgo88AuuuC7H/309+korX/BKQgAQkkELgP/+BBReEe+6BlVZKKamx8iqACmBqRCuALQiG/MWB5H/8Yypa80tAAhKQQCUIHH44PP443H13JUprjDIUQAUwNZIVwGYE778fNt8cYvXZHHOkojW/BCQgAQlUgsAHH2Sngtx8M6y1ViVKrP8yFEAFMDWKFcAmgpMnw2qrwYYbwuDBqVjNLwEJSEAClSRw3HEwfDiMGAHdulWy5PosSwFUAFMjVwFsIvj3v8OOO2Zz/+IgcpMEJCABCdQOgY8/znoBY2Po+EU970kBVABT3wEFEIjev+WWg223hYMOSkVqfglIQAISqAaBU06Bq66CUaPsBVQAFcDUd0wBBG64AfbbL9tqYMYZU5GaXwISkIAEqkFg4sTsXPazzoLf/KYad6ifMhVABTA1WnMvgF99BUsuCfvvD7HrvEkCEpCABGqXQJzOFAI4ejRMM03t1rPaNVMAFcDUGMu9AF58MRx/fHbmb48eqTjNLwEJSEAC1SQwaRL07g1DhsBOO1XzTrVdtgKoAKZGaK4F8IsvoGdPOOGEbP6fSQISkIAEap/AFVfAEUfASy/B9NPXfn2rUUMFUAFMjatcC+Dpp8OFF8LTT0P37qkozS8BCUhAAp1B4Ouvs7OBd989m76Tx6QAKoCpcZ9bAYwtBRZaCC66CDbeOBWj+SUgAQlIoDMJ3HYb7LprtnXXLLN05p1r414KoAKYGom5FcDYVPQf/4CHHnI7gdQgMr8EJCCBziYQ23etuir8+tf53LxfAVQAU9+5XArg++9nh4vHb5BrrJGK0PwSkIAEJNAVBP75T+jXL+sFnHPOrqhB191TAVQAU6MvlwI4aBA89xzceWcqPvNLQAISkEBXEthgA1hqKTjppK6sReffWwFUAFOjLncC+O9/Zyt/R46EPn1S8ZlfAhKQgAS6ksCTT2bnuL/8Mvz8511Zk869twKoAKZGXO4EcI89YMIEuOaaVHTml4AEJCCBWiCw1VYwxxxw7rm1UJvOqYMCmB8BPAbYDQhhewLoDzzfRpjNCowG5gWmA74pcX2uBHDs2GzrgGeeyXoBTRKQgAQkUP8E8vjZrgDmQwAHAfsCGwLjgKOAHYBQmIlTeXUvBOYG1lcAM0q//S38+Mdwzjn1/4FnCyQgAQlI4DsCe+0FscDvuuvyQUUBzIcAvgoMBc5qCus4/fBtYCBwZSuhvgkwGPgDcLcCCI88Auuum80TmWuufHxA2EoJSEACeSHwzjuw8MJw332w4oqN32oFsPEFMIZoPwRWBh5tFtLDm4Z3Dy4R5rEYPoaJo8fwJ8B9eRfA2C9qzTWzP7H/n0kCEpCABBqPwODBMGIEPPBA4+/vqgA2vgDOA7wBLAqMbfa6Xg18BOxR4hWO5Q1PA38C1lQA4Y47skPDx42D2UKpTRKQgAQk0HAEPvoo2+P1ssuyDaIbOSmAjS+A7e0B/B1wEBAd4LHoYy3gXqAH8HWJl6GwCKR///706BGXQN++fQt/GiV5ZmSjPEnbIQEJSKBtAnHG+wUXZGe8TxMTphooDR8+nPgTadKkSQwbNiz+OntTh1ADtbS8pnQr77K6vqrUHMB3gAEl5gBeHGsdgM+bWhyrf0Py3msSw8tbkGj4VcCXXALHHgsvvADTT1/XcWDlJSABCUigDQJffAG9e8PRR8OOOzYuLnsAG78HMKI35vnFKuCNgJDBIcB2QK8Sq4DjN4GZm4X8KkAMCc8HvA98licB/OyzbLuX2CH+979v3A8CWyYBCUhAAt8R+Nvf4LDD4KWXYIYZGpOMApgPAYzoPRrYE4i9/UY12wcw9vgbA2wAjCwR5rmeA3jyyXDVVTBqFHTv3pgfArZKAhKQgASmJPDNN7DccrDttnBwqaWSDQBMAcyPAFYrXBt2CPiDD7LJwNdeC+utVy18lisBCUhAArVI4K674He/yxb/xSkhjZYUQAUwNaYbVgAPOQSeegrujl0QTRKQgAQkkDsC8ct/nPke04AaLSmACmBqTDekAL72Giy2GPzrX9nRbyYJSEACEsgfgVgJvMoqMGYMzD9/Y7VfAVQAUyO6IQVwm22yFb8Xx5pokwQkIAEJ5JZA7AH75ZdwZWvnZtUpGQVQAUwN3YYTwMceg1/9CuJw8HliG22TBCQgAQnklsC//w29emWngyy/fONgUAAVwNRobigBLB75tsYacPzxqWjMLwEJSEACjUDgiCPgoYca64g4BVABTH03G0oAb74Z9twTXnkFZo0Nc0wSkIAEJJB7AnFE3MILw/nnw6abNgYOBVABTI3khhHAmOOxxBIwYADstVcqFvNLQAISkEAjETjnHIhj4kaPhunijKw6TwqgApgawg0jgHEk4llnZS/3tNOmYjG/BCQgAQk0EoHoJFhqKdhvP9hnn/pvmQKoAKZGcUMI4IQJWfd+rPrdeONUJOaXgAQkIIFGJHDbbbDLLtk0odnj4NQ6TgqgApgavg0hgH/4AzzyCNx3H3TrlorE/BKQgAQk0IgEYqFg7BIRewOecEJ9t1ABVABTI7juBfD112HRRWHEiOzsR5MEJCABCUigNQJxNnzsFPHCCzDffPXLSQFUAFOjt+4FMM56nGEGuOSSVBTml4AEJCCBPBDYcUeYNAmuuqp+W6sAKoCp0VvXAjhyJPTtCy+9BHPPnYrC/BKQgAQkkAcCb72VbQ59113ZcHA9JgVQAUyN27oVwG++gRVXzPZ0Gjw4FYP5JSABCUggTwSOOw5iUUjMH+/evf5argAqgKlRW7cCeNllmfjFkW8zzpiKwfwSkIAEJJAnAhMnQu/e8Mc/wvbb11/LFUAFMDVq61IAP/0UevaEU0+FmANokoAEJCABCbSXQMwBPPjgbBrRzDO3N3fXXq8AKoCpEViXAnjUUXD33RBzAN32JTUEzC8BCUggnwRiW5iYAxhzyY8+ur4YKIAKYGrE1p0AvvFGtu3L/ffDCiukNt/8EpCABCSQZwKPPgprrw0vvgjzzls/JBRABTA1WutOALfdFqaZBmIOoEkCEpCABCSQSiDmAEZv4BVXpJbUefkVQAUwNdrqSgAffhjWWy/7TW2eeVKbbn4JSEACEpAA/Pvf2YKQe+6BlVaqDyIKoAKYGql1I4DFbV822QSGDElttvklIAEJSEAC3xE49li4/fb62RZGAVQAU9/fuhHAiy6C2LdpzBi3fUl96OaXgAQkIIEpCXz2WTa/PBYZ7rxz7dNRABXA1CitCwH88MNs25dzz4XNN09tsvklIAEJSEAC3ydw442w997ZtjCzz17bhBRABTA1QutCAAcMgOeey47tcduX1EdufglIQAISKEUgFoLEPPOlloKhQ2ubkQKoAKZGaM0LYAz5LrccPPlk1j1vkoAEJCABCVSLQPE754knYLHFqnWX9HIVQAUwNYpqWgDr6bex1AdhfglIQAISqA0C9TDqpAAqgKlvS00LYD3Nx0h9EOaXgAQkIIHaIFAP884VQAUw9W2pWQGstxVZqQ/C/BKQgAQkUDsEan3nCQVQAUx9W2pWAI85Bv7+d4jNn7t3T22m+SUgAQlIQALlE4i9Z2NT6I03rs29ZxVABbD8aC59ZU0K4PjxsMQScN99sOKKqU00vwQkIAEJSKD9BIrnBD//PMw/f/vzVzOHAqgApsZXTQpgv37w05/C+eenNs/8EpCABCQggY4T2G03+O9/4ZZbOl5GNXIqgApgalzVnADeeivssguMHQtzzpnaPPNLQAISkIAEOk7gvfegVy+45BKIo0hrJSmACmBqLNaUAE6cmO27NHgwxG9dJglIQAISkEBXE4jRqBNOgBgKnmmmrq5Ndn8FUAFMjcSaEsAQv3vvhZEjXfiR+mDNLwEJSEAClSEQC0JWWSU7JSTOpK+FpAAqgKlxWDMCGEO+ffpk8hf/NUlAAhKQgARqhUCcRrXaavD009nZ9F2dFEAFMDUGa0IA48SP9dfPjno744zUJplfAhKQgAQkUHkC++2XzU8fPrzrz6VXABXA1AivCQG89lrYf//sxZp99tQmmV8CEpCABCRQeQJxQkjv3nDmmbDllpUvvz0lKoAKYHvipdS1XS6AH3+cvVB//jNst11qc8wvAQlIQAISqB6Byy+HP/wBXngBZp21evdpq2QFMD8CeAwQ62JD2J4A+gPPtxIgsVvRckCE5mfAQ8AhwKslru9yARw4EGJuxf33d32XelsvnD+XgAQkIIF8E4gpS2utBcstB0OHdh0LBTAfAjgI2BfYEBgHHAXsAMQ01Iklwm9J4CXgCyAGVP8K/AJYtdYE8IknYPXVIf4b8/9MEpCABCQggVonEL1/IYAPPQTLLts1tVUA8yGA0XMXv2ec1RRm0wBvAwOBK9sIvTma8kWe39WSAH71VXbMW5yzGOf+miQgAQlIQAL1QmDIkOy8+kcegWmn7fxaK4CNL4AxRPshsDLwaLMQGw6MBg5uJexOaOo1nBl4ENiold7CLhsCPu00OOcceOYZmGGGzn95vKMEJCABCUigowQ+/xyWXhr22QcOOKCjpXQ8nwLY+AI4D/AGEAOkY5uFytWxETiwRxvhsyBwEfA+sEWt9AC+8QYsvjjcdls2l8IkAQlIQAISqDcCMXc9zq4fMwbmnbdza68ANr4AdrQHsHkkLt/Uexgn637QIkQLPYD9+/enR48ehR/17du38KdaKSbQbrppds7vxRdX6y6WKwEJSEACEqg+gZ12gg8+gJtvrv5CxuHDhxN/Ik2aNIlhw4bFX2Ouf3QI5S51y0GLS80BfAcYUMYcwMATiz8eaFoV/HkpAZwwYQKzzRYuWP10ww2w117w4ouZBJokIAEJSEAC9UrgvfeyrczOOw9+85vOa4U9gI3fAxjRFPP8YhVwzOMLGRwCxI55vUrM61sEWAK4B/i46ZoYAn6zFhaBTJiQrfaNPf+2377zXhTvJAEJSEACEqgWgcsu+25vwE7qS0EBzIcARsweDezZ1Is3qtk+gDHrYAywATCyaWuYC4HFgVj5+y5wPRCLQj4tEfydughk332znr+7765+V3m1XnTLlYAEJCABCTQnEFOb1l0XFlssOyWkM5ICmB8BrFY8dZoA/utfsN562UHai0Q/pUkCEpCABCTQIARefhmWWSbr4Fhlleo3SgFUAFOjrFMEMJbL9+kDO+8Mh8SZJCYJSEACEpBAgxGI6U2XXAJPPVX97c0UQAUw9fXpFAE84giIhUtdtWFmKiTzS0ACEpCABNoiUDzgYMMN4fjj27o67ecKoAKYFkHZ2cITqrkKOH4TWnVVePjhbNNMkwQkIAEJSKBRCcThBiuvDDHtKYaEq5UUQAUwNbaqKoBffgkrrACbbALHHptaVfNLQAISkIAEap/AkUfCHXfAY49V75g4BVABTH0TqiqAf/oTXHEFPPkkTD99alXNLwEJSEACEqh9Al98kc1732EHOOyw6tRXAVQAUyOragIY270suyzcdx+stFJqNc0vAQlIQAISqB8CMe0ptoaJDpBesWtvhZMCqACmhlRVBPCbb2D11WHFFWHo0NQqml8CEpCABCRQfwQGDIDHH4cHH4Tu3StbfwVQAUyNqKoI4BlnwOmnw7PPwswzp1bR/BKQgAQkIIH6I/Dpp7DkkhAiuN9+la2/AqgApkZUxQUwNsOMuQ+33w5rrZVaPfNLQAISkIAE6pfA/fdnCyFjR4xKHoKgACqAqW9FRQXw66+zod/ll896AE0SkIAEJCCBvBPYf3944olsKHiaOKS1AkkBVABTw6iiAnjyyXD++dlxbzPNlFo180tAAhKQgATqn8DEidmegHvsAQcfXJn2KIAKYGokVUwAx4zJev7uuSfbBNMkAQlIQAISkEBGIDaGXm+9bFHIYoulU1EAFcDUKKqIAMaGz3H49dprQ5yFaJKABCQgAQlIYEoChxwCMScwtoiZdto0OgqgApgWQRU6Cu644+Caa2DUqOofgJ3aYPNLQAISkIAEuoLA55/DcsvB738Pgwen1UABVADTIqgCAhjz/aL3b8SILLBNEpCABCQgAQmUJhCLQWKxZPQCLr10xykpgApgx6Mny5k0BBzH3cRZv5ttBscck1oV80tAAhKQgAQan8CQIXDLLdlZwR09JlUBVABT35QkARw0KDvqLX6T6dEjtSrml4AEJCABCTQ+gUmTssWS66wDJ53UsfYqgApgxyLnu1wdFsAQv379snl/vXunVsP8EpCABCQggfwQePFF+OUv4bbb4Fe/an+7FUAFsP1RM2WODgng//4HSy2VTWLda6/UKphfAhKQgAQkkD8C55wDJ5yQHZs6xxzta78CqAC2L2K+f3W7BXDyZNh6a/jsM7j1VujWLbUK5peABCQgAQnkj0B8n8YxcTPPDFdf3b7vUwVQAUx9Y9otgJddBjH3b/Ro+MlPUm9vfglIQAISkEB+Cfzf/8GSS8Kpp8L225fPQQFUAMuPltJXtksAx4/Plq1fdRVstFHqrc0vAQlIQAISkMDtt8O222bHqC6wQHk8FEAFsLxIaf2qsgXwq69gzTUzATz77NTbml8CEpCABCQggSKBvffORtYeeKC8U0IUQAUw9e0pWwCPPRb+9jd48kmYaabU25pfAhKQgAQkIIEigYkTYdlls57AI49sm4sCqAC2HSVTv6IsAYxTPjbYAB56CPr0Sb2l+SUgAQlIQAISaEkgOlhWWw2GD89OC5laUgAVwNQ3qE0BfP99WGYZiEOs99sv9Xbml4AEJCABCUigNQJnnAEnn5zNB5xzztY5KYAKYOpbNFUBjCXqm24K00wDN97YviXqqRUzvwQkIAEJSCBvBOJ7N45XjXTzza1/7yqACmDquzFVATz99Gxpevwm8sMfpt7K/BKQgAQkIAEJtEWgOPIWW67tv3/pqxVABbCtOGrr560K4BNPwBprwF13waqrtlWMP5eABCQgAQlIoFIEYs59374Qc/BjcUjLpAAqgKmxVlIAP/ooC7hddoHDD0+9hfklIAEJSEACEmgvgT/+ES65JNt9Y9ZZp8ytACqA7Y2nltd/TwBj/sF220HsTh4rkWL+n0kCEpCABCQggc4l8PXXsP76MNdccPnlU84HVAAVwNRo/J4AnnsuDBkCzzwDP/tZavHml4AEJCABCUigowTeeQe22AJuu23KVcEKoALY0Zgq5ptCAEeNyk77uOMOWGut1KLNLwEJSEACEpBAKoEYmevWbcpSFEAFMDWuvhXAr76arTDvL46jOfTQ1GLNLwEJSEACEpBAtQgogApgamwVBPCDDyaw7bazMe20U993KPVm5peABCQgAQlIIJ2AAqgApkZRQQAHD57A3/42G7H1yw9+kFqk+SUgAQlIQAISqCYBBVABTI2vggDONNMERo6crXDkm0kCEpCABCQggdomoAAqgKkRWhDAs86aQP/+8VeTBCQgAQlIQAK1TkABzI8AHgPsBoSlPQH0B54vEaA/Bk4G1gB+BLwHXA0cDUwqcf1Uj4Kr9RfA+klAAhKQgATySEABzIcADgL2BTYExgFHATsAPYGJLQJ/AWBr4BpgPLAgcBNwLzBQAazux8Tw4cPpG2f3mJIIyDEJ3xSZZSnLyhGoTEnGZGU4KoD5EMBXgaHAWU1hE2dzvN0kdFeWEUoHADsBfRTAMmglXDJw4ECGDo1HZUohIMcUelPmlaUsK0egMiUZk5XhqAA2vgDGEO2HwMrAo83CZjgwGji4jFC6A3gX2FUBLINWwiV+sCXAa5ZVjpXhGKXIUpaVI1CZkozJynBUABtfAOcB3gAWBcY2C5uY1/cRsEcboXTk/7d3L6HWjXEcx38pl1xeMiB0mCHXCVFIbkXuYSDKSExfA+WQATqRKUXJLTEQEWVAiJgQBpKBog7JQHIrd/q957+dZVtrnedZe7+nvdf/u0rpbe+11/NZv732/6z1XCTdLOnUuGs4/fJdfQDX19e1YweDQGb9Wq6urmptbW3W3aR/P47ziwCWWM5PYD57IpPzcXQBuLKy4p0dGPXAfHa8RHuZWhxliY687FBnuQN4d/QVPE/SZx0fd4SkL8sOhVchgAACCCCAwIIJ+EbRVwt2TNtyOGMvAI3Y1gfwa0k7JXX1AXxQ0gWSXPyt95wJ+x0u6cdtOVt8CAIIIIAAAgjMS+CAeLr397x2uEz7yVAAup+fRwFfHMXgnZKul3RMyyhgDxB5UtJJks6X9M0ynUyOFQEEEEAAAQQQKBHIUADawfNkcj8TAAAGZklEQVT43STJ1f77jXkA3QHgE0kXSnon5v97Q9Kvkv4IQBv5rwM6+ZUkitcggAACCCCAwMILZCkAF/5EcIAIIIAAAggggMB2CVAA1kn70fC9MSfgofGY+PUtduGJpz2a2JNOT+4mviTpurqPHt2rh1gaoXRVl9GB9TToakketHSkpC8k3RETmHe9hUxuytTk6SBJ7h/s7iR/SfIUUe5e8n2msPW0tcbyzZiey09bJtfFWyU9lNzSCxF4paqTJe0vac/IWhcLmewOTK1lukxSANZdbY6VdIakDyW9FwNFSgpADybx8nJsmwJDLGtWdclifZokX7iuleQ/LC6X9JSkMyV90IHgApBMSrV5csHnH2T/sPja6RWDfpZ0RZaw9bSz1tJdbd6KlZng2xTw4MODJe0r6ZGCApBMdqen1jJdJikAh196fAfAA0UoAIcbTt5Zajnrqi6zH+ni7eHRmMfqqsahPS/pW0k3UgD2nrCaPE3urvrO9cexV///R3HnNft0UDWW5vOP7duSPCiP7f8CZ8dvS98dQDJZlpwSy5SZpAAsC1Dbq0qLFt9t8UhkPwL2f+9Kuj0e1Q3/9HG9s8Ryljkdx6X139b4Lp/vRN3X+OfbJLkgPKWnAMyeydo8XSbJE8j7zkxz+0WSH8G/POaQbdG2WsvJj+0JkvaI2RZelHRP3FFNTPlv00uKFjJZlpQSy5SZpADcCNBjkm6I0b5tJn7Edu5U1kqKFr/luJgn0PMJHibpfkmnx1QzLgjHtu0uy1lXdVk251JHT1LuTD3caKBXr7lF0tEdjc6WyTaG2jx56ig7+zvc3LxMpK2fXraAzfF4ay390b4GfhpLdZ4o6YlYrcldGdikkqKFTJYlpcQyZSYpADcC5L/q9+nJ0u8tkz2XFoDTu90rOo1fKum1svwu1at2l+WQuwxLBTd1sKWOQ+4AZstkWw5q88Tdlu5vU61l257cR9rXQ0/V5YEh2beSooVMlqWkxDJlJikAywLU9qpZC0B/eV8d/vGjemep5ZBVXUYF1dIY9wH0D7AfQ062rfoAdhWA2TJZkyf3t/o8RmdO+gB6pKYL8KNYEnLQikvNHE4KQGfZj9WzbyVFC5ksS0mJZV8BONpMUgCWBaj5qr1jBKAf314UIzA9afSfHbu6JjrzulO+p47xYySP0PRjD48gzLzVWtas6pLF1aOA3aHej848ItCjgL2azVk9o4DJ5EY6avPkUdbulO8pnHztfEbST5KuzBK2nnbWWB4SU2l5EIivo8dLejz6RTubmTf3iXTGXLS8EndE/dvyW3RRmrYhk91pqbFMmUkKwLpLjf/S912A6XUDPf/VXbEr3x3wNByeL9CbOze7v8t+kr6LqQ88L6DvPmTehljaq2tVl8yWHvDhDvQ29TyAq5JeaICQye50lK4S5D14zrUHJF0S1wD/+HoewB8yh6/R9lJL37l6NvqoevlN96N8jkEguyTdF939fye/MZM5Es+J357mylVksv+LV2OZMpMUgFy5EUAAAQQQQACBZAIUgMlOOM1FAAEEEEAAAQQoAMkAAggggAACCCCQTIACMNkJp7kIIIAAAggggAAFIBlAAAEEEEAAAQSSCVAAJjvhNBcBBBBAAAEEEKAAJAMIIIAAAggggEAyAQrAZCec5iKAAAIIIIAAAhSAZAABBBBAAAEEEEgmQAGY7ITTXAQQQAABBBBAgAKQDCCAAAIIIIAAAskEKACTnXCaiwACCCCAAAIIUACSAQQQQAABBBBAIJkABWCyE05zEUAAAQQQQAABCkAygAACCCCAAAIIJBOgAEx2wmkuAggggAACCCBAAUgGEEAAAQQQQACBZAIUgMlOOM1FAAEEEEAAAQQoAMkAAggggAACCCCQTIACMNkJp7kIIIAAAggggAAFIBlAAAEEEEAAAQSSCVAAJjvhNBcBBBBAAAEEEKAAJAMIIIAAAggggEAyAQrAZCec5iKAAAIIIIAAAhSAZAABBBBAAAEEEEgmQAGY7ITTXAQQQAABBBBAgAKQDCCAAAIIIIAAAskEKACTnXCaiwACCCCAAAIIUACSAQQQQAABBBBAIJkABWCyE05zEUAAAQQQQAABCkAygAACCCCAAAIIJBOgAEx2wmkuAggggAACCCBAAUgGEEAAAQQQQACBZAIUgMlOOM1FAAEEEEAAAQT+AfYNLgy3K/O0AAAAAElFTkSuQmCC\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x10d23eed0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phiV = 1.2\n", "xGB = np.arange(90.)/30. - 1.5\n", "plt.plot(xGB, phi(np.expand_dims(xGB, axis=1), 0, phiV), label='Gaussian-type Basis Curve')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Test Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x10d26b210>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "noiseStdDev = 0.2\n", "pointRange = (0.,10.)\n", "numPoints = 200.\n", "def testFunc(x):\n", " return np.sin(x/2)\n", "\n", "x = np.arange(pointRange[0], pointRange[1], (pointRange[1]-pointRange[0])/numPoints)\n", "t = testFunc(x) + np.random.normal(0, noiseStdDev, size=x.size)\n", "plt.scatter(x, t, label='Test Data', c='g')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Fit!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fit = Model(phi, [{'u':np.array([i]), 'v':np.array(phiV)} \\\n", " for i in list(np.arange(pointRange[0],pointRange[1]+phiV,phiV))], noiseStdDev=noiseStdDev,\n", " priorStdDev=0.1, priorMean=0.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fit.train(np.expand_dims(x, axis=1),t)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x10d7d2910>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictiveMean = plt.plot(x, fit.predictive(np.expand_dims(x, axis=1))[0], label='Predictive Mean')\n", "testMean = plt.plot(x, testFunc(x), label='Test Data Mean')\n", "predictiveSampling = plt.scatter(x, fit.sample(np.expand_dims(x, axis=1)), label='Predictive Sample')\n", "plt.scatter(x, t, label='Test Data', c='g')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### \\#TODO\n", "1. Look into the chronic underestimation of extrema.\n", "2. Create animation demonstrating sequential training." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
AllenDowney/PythonCounterPmf
PythonCounterPmf.ipynb
1
16902
{ "metadata": { "name": "PythonCounterPmf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Using Counters\n", "--------------\n", "\n", "This notebook demonstrates the use of the Counter class to implement Multisets, PMFs, and suites of Bayesian hypotheses." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import Counter" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A counter is a map from values to their frequencies. If you initialize a counter with a string, you get a map from each letter to the number of times it appears. If two words are anagrams, they yield equal Counters, so you can use Counters to test anagrams in linear time." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def is_anagram(word1, word2):\n", " \"\"\"Checks whether the words are anagrams.\n", "\n", " word1: string\n", " word2: string\n", "\n", " returns: boolean\n", " \"\"\"\n", " return Counter(word1) == Counter(word2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "is_anagram('tachymetric', 'mccarthyite')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 3, "text": [ "True" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "is_anagram('banana', 'peach')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 4, "text": [ "False" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Multisets**\n", "\n", "A Counter is a natural representation of a multiset, which is a set where the elements can appear more than once. You can extend Counter with set operations like is_subset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Multiset(Counter):\n", " \"\"\"A multiset is a set where elements can appear more than once.\"\"\"\n", "\n", " def is_subset(self, other):\n", " \"\"\"Checks whether self is a subset of other.\n", "\n", " other: Multiset\n", "\n", " returns: boolean\n", " \"\"\"\n", " for char, count in self.items():\n", " if other[char] < count:\n", " return False\n", " return True\n", " \n", " # map the <= operator to is_subset\n", " __le__ = is_subset" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You could use <tt>is_subset</tt> in a game like Scrabble to see if a given set of tiles can be used to spell a given word." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def can_spell(word, tiles):\n", " \"\"\"Checks whether a set of tiles can spell a word.\n", "\n", " word: string\n", " tiles: string\n", "\n", " returns: boolean\n", " \"\"\"\n", " return Multiset(word) <= Multiset(tiles)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "can_spell('SYZYGY', 'AGSYYYZ')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 35, "text": [ "True" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Probability Mass Functions\n", "--------------------------\n", "\n", "You can also extend Counter to represent a probability mass function (PMF).\n", "\n", "`normalize` computes the total of the frequencies and divides through, yielding probabilities that add to 1.\n", "\n", "`__add__` enumerates all pairs of value and returns a new Pmf that represents the distribution of the sum.\n", "\n", "`__hash__` and `__id__` make Pmfs hashable; this is not the best way to do it, because they are mutable. So this implementation comes with a warning that if you use a Pmf as a key, you should not modify it. A better alternative would be to define a frozen Pmf.\n", "\n", "`render` returns the values and probabilities in a form ready for plotting" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Pmf(Counter):\n", " \"\"\"A Counter with probabilities.\"\"\"\n", "\n", " def normalize(self):\n", " \"\"\"Normalizes the PMF so the probabilities add to 1.\"\"\"\n", " total = float(sum(self.values()))\n", " for key in self:\n", " self[key] /= total\n", "\n", " def __add__(self, other):\n", " \"\"\"Adds two distributions.\n", "\n", " The result is the distribution of sums of values from the\n", " two distributions.\n", "\n", " other: Pmf\n", "\n", " returns: new Pmf\n", " \"\"\"\n", " pmf = Pmf()\n", " for key1, prob1 in self.items():\n", " for key2, prob2 in other.items():\n", " pmf[key1 + key2] += prob1 * prob2\n", " return pmf\n", "\n", " def __hash__(self):\n", " \"\"\"Returns an integer hash value.\"\"\"\n", " return id(self)\n", " \n", " def __eq__(self, other):\n", " return self is other\n", "\n", " def render(self):\n", " \"\"\"Returns values and their probabilities, suitable for plotting.\"\"\"\n", " return zip(*sorted(self.items()))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example, we can make a Pmf object that represents a 6-sided die." ] }, { "cell_type": "code", "collapsed": false, "input": [ "d6 = Pmf([1,2,3,4,5,6])\n", "d6.normalize()\n", "d6.name = 'one die'\n", "print(d6)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Pmf({1: 0.16666666666666666, 2: 0.16666666666666666, 3: 0.16666666666666666, 4: 0.16666666666666666, 5: 0.16666666666666666, 6: 0.16666666666666666})\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the add operator, we can compute the distribution for the sum of two dice." ] }, { "cell_type": "code", "collapsed": false, "input": [ "d6_twice = d6 + d6\n", "d6_twice.name = 'two dice'\n", "\n", "for key, prob in d6_twice.items():\n", " print(key, prob)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(2, 0.027777777777777776)\n", "(3, 0.05555555555555555)\n", "(4, 0.08333333333333333)\n", "(5, 0.1111111111111111)\n", "(6, 0.1388888888888889)\n", "(7, 0.16666666666666669)\n", "(8, 0.1388888888888889)\n", "(9, 0.1111111111111111)\n", "(10, 0.08333333333333333)\n", "(11, 0.05555555555555555)\n", "(12, 0.027777777777777776)\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using numpy.sum, we can compute the distribution for the sum of three dice." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# if we use the built-in sum we have to provide a Pmf additive identity value\n", "# pmf_ident = Pmf([0])\n", "# d6_thrice = sum([d6]*3, pmf_ident)\n", "\n", "# with --numpy inline we get numpy.sum, which does not require an identity\n", "d6_thrice = sum([d6]*3)\n", "d6_thrice.name = 'three dice'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then plot the results (using Pmf.render)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as pyplot" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "for die in [d6, d6_twice, d6_thrice]:\n", " xs, ys = die.render()\n", " pyplot.plot(xs, ys, label=die.name, linewidth=3, alpha=0.5)\n", " \n", "pyplot.xlabel('Total')\n", "pyplot.ylabel('Probability')\n", "pyplot.legend()\n", "pyplot.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bayesian statistics\n", "-------------------\n", "\n", "A Suite is a Pmf that represents a set of hypotheses and their probabilities; it provides `bayesian_update`, which updates the probability of the hypotheses based on new data.\n", "\n", "Suite is an abstract parent class; child classes should provide a likelihood method that evaluates the likelihood of the data under a given hypothesis. `update_bayesian` loops through the hypothesis, evaluates the likelihood of the data under each hypothesis, and updates the probabilities accordingly. Then it re-normalizes the PMF." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Suite(Pmf):\n", " \"\"\"Map from hypothesis to probability.\"\"\"\n", "\n", " def bayesian_update(self, data):\n", " \"\"\"Performs a Bayesian update.\n", " \n", " Note: called bayesian_update to avoid overriding dict.update\n", "\n", " data: result of a die roll\n", " \"\"\"\n", " for hypo in self:\n", " like = self.likelihood(data, hypo)\n", " self[hypo] *= like\n", "\n", " self.normalize()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example, I'll use Suite to solve the \"Dice Problem,\" from Chapter 3 of <i>Think Bayes</i>.\n", "\n", "\"Suppose I have a box of dice that contains a 4-sided die, a 6-sided die, an 8-sided die, a 12-sided die, and a 20-sided die. If you have ever played Dungeons & Dragons, you know what I am talking about. Suppose I select a die from the box at random, roll it, and get a 6. What is the probability that I rolled each die?\"\n", "\n", "I'll start by making a list of Pmfs to represent the dice:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def make_die(num_sides):\n", " die = Pmf(range(1, num_sides+1))\n", " die.name = 'd%d' % num_sides\n", " die.normalize()\n", " return die\n", "\n", "\n", "dice = [make_die(x) for x in [4, 6, 8, 12, 20]]\n", "print(dice)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[Pmf({1: 0.25, 2: 0.25, 3: 0.25, 4: 0.25}), Pmf({1: 0.16666666666666666, 2: 0.16666666666666666, 3: 0.16666666666666666, 4: 0.16666666666666666, 5: 0.16666666666666666, 6: 0.16666666666666666}), Pmf({1: 0.125, 2: 0.125, 3: 0.125, 4: 0.125, 5: 0.125, 6: 0.125, 7: 0.125, 8: 0.125}), Pmf({1: 0.08333333333333333, 2: 0.08333333333333333, 3: 0.08333333333333333, 4: 0.08333333333333333, 5: 0.08333333333333333, 6: 0.08333333333333333, 7: 0.08333333333333333, 8: 0.08333333333333333, 9: 0.08333333333333333, 10: 0.08333333333333333, 11: 0.08333333333333333, 12: 0.08333333333333333}), Pmf({1: 0.05, 2: 0.05, 3: 0.05, 4: 0.05, 5: 0.05, 6: 0.05, 7: 0.05, 8: 0.05, 9: 0.05, 10: 0.05, 11: 0.05, 12: 0.05, 13: 0.05, 14: 0.05, 15: 0.05, 16: 0.05, 17: 0.05, 18: 0.05, 19: 0.05, 20: 0.05})]\n" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next I'll define DiceSuite, which inherits `bayesian_update` from Suite and provides `likelihood`.\n", "\n", "`data` is the observed die roll, 6 in the example.\n", "\n", "`hypo` is the hypothetical die I might have rolled; to get the likelihood of the data, I select, from the given die, the probability of the given value. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "class DiceSuite(Suite):\n", " \n", " def likelihood(self, data, hypo):\n", " \"\"\"Computes the likelihood of the data under the hypothesis.\n", "\n", " data: result of a die roll\n", " hypo: Die object\n", " \"\"\"\n", " return hypo[data]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, I use the list of dice to instantiate a Suite that maps from each die to its prior probability. By default, all dice have the same prior.\n", "\n", "Then I update the distribution with the given value and print the results:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dice_suite = DiceSuite(dice)\n", "\n", "dice_suite.bayesian_update(6)\n", "\n", "for die, prob in sorted(dice_suite.items()):\n", " print die.name, prob" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "d4 0.0\n", "d6 0.392156862745\n", "d8 0.294117647059\n", "d12 0.196078431373\n", "d20 0.117647058824\n" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the 4-sided die has been eliminated; it now has 0 probability. The 6-sided die is the most likely, but the 8-sided die is still quite possible.\n", "\n", "Now suppose I roll the die again and get an 8. We can update the Suite again with the new data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dice_suite.bayesian_update(8)\n", "\n", "for die, prob in sorted(dice_suite.items()):\n", " print die.name, prob" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "d4 0.0\n", "d6 0.0\n", "d8 0.623268698061\n", "d12 0.277008310249\n", "d20 0.0997229916898\n" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the 6-sided die has been eliminated, the 8-sided die is most likely, and there is less than a 10% chance that I am rolling a 20-sided die.\n", "\n", "These examples demonstrate the versatility of the Counter class, one of Python's underused data structures." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
PyLCARS/PythonUberHDL
myHDL_DigitalSignalandSystems/FloatingNumAndRounding/Untitled.ipynb
1
8026
{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from myhdl import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "Q=(4,4)" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "class Gen():\n", " def __init__(self, Q, N):\n", " self.Q=Q; self.N=N\n", " self.Qlen=self.Q[0]+self.Q[1]\n", " self.Qmin=0; self.Qmax=2**self.Qlen\n", " self.Qscale=2**self.Q[1]\n", " \n", " #storage\n", " self.aTV=np.zeros(0); self.aTVQ=np.zeros(0)\n", " self.bTV=np.zeros(0); self.bTVQ=np.zeros(0)\n", " self.cK=np.zeros(0); self.cKQ=np.zeros(0)\n", " \n", " def Genrator(self):\n", " np.random.seed(np.random.randint(self.Qlen))\n", " self.V1=np.array((1/np.random.ranf())).round(decimals=self.Q[1])\n", " self.V1Q=(self.V1*self.Qscale).astype(int)\n", " \n", " np.random.seed(np.random.randint(self.Qlen))\n", " self.V2=np.array((1/np.random.ranf())).round(decimals=self.Q[1])\n", " self.V2Q=(self.V2*self.Qscale).astype(int)\n", " \n", " def GenratorCheckAndAdd(self):\n", " if self.V1Q<self.Qmax and self.V2Q<self.Qmax:\n", " self.check=self.V1Q+self.V2Q-1\n", " self.V1pV2=(self.V1+self.V2).round(decimals=self.Q[1])\n", " self.V1pV2Q=(self.V1pV2*self.Qscale).astype(int)\n", " if self.check<self.Qmax:\n", " self.aTV=np.append(self.aTV, self.V1); self.aTVQ=np.append(self.aTVQ, self.V1Q).astype(int)\n", " self.bTV=np.append(self.bTV, self.V2); self.bTVQ=np.append(self.bTVQ, self.V1Q).astype(int)\n", " self.cK=np.append(self.cK, self.V1pV2); self.cKQ=np.append(self.cKQ, self.V1pV2Q).astype(int)\n", " \n", " def MakeTVs(self):\n", " while len(self.aTV)<=self.N:\n", " self.Genrator()\n", " self.GenratorCheckAndAdd()\n", " \n", " \n", " \n", " \n", " \n" ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(256, None)" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T=Gen(Q, 10)\n", "T.Q, T.N\n", "T.Qlen\n", "T.Qmin, T.Qmax\n", "T.Qscale\n", "T.Genrator()\n", "T.V1, T.V1Q\n", "T.V2, T.V2Q\n", "T.Qmax, T.GenratorCheckAndAdd()" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 38, 209, 16, 38, 209, 16, 38, 209, 16, 38, 209]),\n", " array([ 38, 209, 16, 38, 209, 16, 38, 209, 16, 38, 209]),\n", " array([ 54, 248, 226, 54, 248, 226, 54, 248, 226, 54, 248]))" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T.MakeTVs()\n", "T.aTVQ, T.bTVQ, T.cKQ" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_folding": [ 4, 20 ] }, "outputs": [], "source": [ "class AddPosTVGen():\n", " \"\"\"\n", " Class to generate postive random numbers to be Qed for testing \n", " \"\"\"\n", " def __init__(self, Q, N):\n", " \"\"\"\n", " Take in arguments and create output holds\n", " Args:\n", " Q(tuple): Q notation tuple where Q[0] is int bit len and Q[1] is\n", " dec bit len\n", " N(int): number of values to generate\n", " \"\"\"\n", " self.Q=Q; self.N=N\n", " self.Qlen=self.Q[0]+self.Q[1]; self.Qmax=2**self.Qlen\n", " self.Qscale=2**self.Q[1]\n", " \n", " self.aTV=np.zeros(0); self.aTVQ=np.zeros(0)\n", " self.bTV=np.zeros(0); self.bTVQ=np.zeros(0)\n", " self.cK=np.zeros(0); self.cKQ=np.zeros(0)\n", "\n", " def Genrator(self):\n", " \"\"\"\n", " Random Number genrator in floating point and supsequent Qed version\n", " \"\"\"\n", " self.V1=np.array((1/np.random.ranf())).round(decimals=self.Q[1])\n", " \n", " #needed to force np.random to generate a differint random num\n", " np.random.seed(np.random.randint(self.Qmax))\n", " \n", " self.V2=np.array((1/np.random.ranf())).round(decimals=self.Q[1])\n", " \n", " self.V1Q=(self.V1*self.Qscale).astype(int)\n", " self.V2Q=(self.V2*self.Qscale).astype(int)\n", " \n", " def GenratorCheckAndAdd(self):\n", " \"\"\"\n", " Cheacks if the sum of the two randome numbers generated are going to break the Qmax\n", " if they do dont append to retrun holds\n", " \"\"\"\n", " self.V1pV2=(self.V1+self.V2).round(decimals=self.Q[1])\n", " self.V1pV2Q=(self.V1pV2*self.Qscale).astype(int)\n", " if (self.V1Q+self.V2Q)<self.Qmax:\n", " self.aTV=np.append(self.aTV, self.V1); self.aTVQ=np.append(self.aTVQ, self.V1Q).astype(int)\n", " self.bTV=np.append(self.bTV, self.V1); self.bTVQ=np.append(self.bTVQ, self.V1Q).astype(int)\n", " self.cK=np.append(self.cK, self.V1pV2); self.cKQ=np.append(self.cKQ, self.V1pV2Q).astype(int)\n", " \n", " def MakeTVs(self):\n", " \"\"\"\n", " Automates the generating, testing and appending to make the TVs\n", " \n", " Returns:\n", " self.aTV(np.array): floating point numbers for a\n", " self.aTVQ(np.array): fixed point Qed from self.aTV\n", " self.bTV(np.array): floating point numbers for b\n", " self.bTVQ(np.array): fixed point Qed from self.bTV\n", " self.cK(np.array): known floating point rounded sum of self.aTV, self.bTV\n", " self.cKQ(np.array): known fixed point Qed from self.cK\n", "\n", " \"\"\"\n", " while len(self.aTV)<=self.N:\n", " self.Genrator()\n", " self.GenratorCheckAndAdd()\n", " #print('Done')" ] } ], "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" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "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 }
bsd-3-clause
anandha2017/udacity
nd101 Deep Learning Nanodegree Foundation/DockerImages/14_intro_to_deep_neural_networks/notebooks/02_save_and_restore_tensorflow_models/03 Save a Trained Model.ipynb
1
7186
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Remove previous Tensors and Operations\n", "tf.reset_default_graph()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "learning_rate = 0.001\n", "n_input = 784 # MNIST data input (img shape: 28*28)\n", "n_classes = 10 # MNIST total classes (0-9 digits)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting ./train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting ./train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting ./t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting ./t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import MNIST data\n", "mnist = input_data.read_data_sets('.', one_hot=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Features and Labels\n", "features = tf.placeholder(tf.float32, [None, n_input])\n", "labels = tf.placeholder(tf.float32, [None, n_classes])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Weights & bias\n", "weights = tf.Variable(tf.random_normal([n_input, n_classes]))\n", "bias = tf.Variable(tf.random_normal([n_classes]))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Logits - xW + b\n", "logits = tf.add(tf.matmul(features, weights), bias)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define loss and optimizer\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Calculate accuracy\n", "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "save_file = './train_model.ckpt'\n", "batch_size = 128\n", "n_epochs = 100" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "saver = tf.train.Saver()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 - Validation Accuracy: 0.09860000014305115\n", "Epoch 10 - Validation Accuracy: 0.287200003862381\n", "Epoch 20 - Validation Accuracy: 0.43140000104904175\n", "Epoch 30 - Validation Accuracy: 0.5249999761581421\n", "Epoch 40 - Validation Accuracy: 0.5924000144004822\n", "Epoch 50 - Validation Accuracy: 0.6353999972343445\n", "Epoch 60 - Validation Accuracy: 0.6668000221252441\n", "Epoch 70 - Validation Accuracy: 0.6895999908447266\n", "Epoch 80 - Validation Accuracy: 0.7092000246047974\n", "Epoch 90 - Validation Accuracy: 0.7214000225067139\n", "Trained Model Saved.\n" ] } ], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " # Training cycle\n", " for epoch in range(n_epochs):\n", " total_batch = math.ceil(mnist.train.num_examples / batch_size)\n", "\n", " # Loop over all batches\n", " for i in range(total_batch):\n", " batch_features, batch_labels = mnist.train.next_batch(batch_size)\n", " sess.run(\n", " optimizer,\n", " feed_dict={features: batch_features, labels: batch_labels})\n", "\n", " # Print status for every 10 epochs\n", " if epoch % 10 == 0:\n", " valid_accuracy = sess.run(\n", " accuracy,\n", " feed_dict={\n", " features: mnist.validation.images,\n", " labels: mnist.validation.labels})\n", " print('Epoch {:<3} - Validation Accuracy: {}'.format(\n", " epoch,\n", " valid_accuracy))\n", "\n", " # Save the model\n", " saver.save(sess, save_file)\n", " print('Trained Model Saved.')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from ./train_model.ckpt\n", "Test Accuracy: 0.7353000044822693\n" ] } ], "source": [ "saver = tf.train.Saver()\n", "\n", "# Launch the graph\n", "with tf.Session() as sess:\n", " saver.restore(sess, save_file)\n", "\n", " test_accuracy = sess.run(\n", " accuracy,\n", " feed_dict={features: mnist.test.images, labels: mnist.test.labels})\n", "\n", "print('Test Accuracy: {}'.format(test_accuracy))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
healthactuary/python-for-actuaries
Chapter_3_Data_Analysis_in_Pandas/Lesson_2_Combining_Data_Frames.ipynb
1
1971
{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 3.2 Combining Data Frames" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 3.2.1: Concatenation\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " a b\n", "0 1 4\n", "1 2 5\n", "2 3 6\n", "0 7 10\n", "1 8 11\n", "2 9 12\n", " a b\n", "0 1 4\n", "1 2 5\n", "2 3 6\n", "3 7 10\n", "4 8 11\n", "5 9 12\n" ] } ], "source": [ "# 3.2.1.1: Row concatenation (bottom-to-top)\n", "x = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})\n", "y = pd.DataFrame({'a': [7, 8, 9], 'b': [10, 11, 12]})\n", "df = pd.concat([x, y], axis=0)\n", "# If axis is 0 (default), then glue on top of one another. Data frames should have the same columns. (similar to UNION in SQL or rbind in R)\n", "# If axis is 1 (columns), then glue side-by-side.\n", "print(df)\n", "print(df.reset_index(drop=True)) # old indices are kept unless you reset index" ] }, { "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
DeDop/dedop
docs/notebooks/inspect-L1B_CS_LTA__SIR1SAR_FR_20150331T034023_20150331T034235_C001.DBL_default.nc-1.ipynb
2
231112
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DeDop L1B Inspection\n", "\n", "Use the following plot methods for displaying scaled waveform data:\n", "* `insp.plot.waveform_3d_surf()` - to draw a waveform 3D surface plot\n", "* `insp.plot.waveform_3d_line()` - to draw a waveform 3D line plot\n", "* `insp.plot.waveform_3d_poly()` - to draw a waveform 3D polygon plot\n", "* `insp.plot.waveform_im()` - to draw a waveform image\n", "* `insp.plot.waveform_line()` - to draw a waveform 2D line plot\n", "* `insp.plot.waveform_hist()` to draw a waveform histogram\n", "* `insp.plot.locations()` - to draw the footprints of the L1B product on a map\n", "\n", "Use the following generic methods for displaying any other variables:\n", "* `insp.plot.line(x, y)` - to draw two 1D variables (line plots)\n", "* `insp.plot.im_line(z)` - to draw a 2D variable (images + and line plots)\n", "* `insp.plot.im(z)` - to draw a 2D variable (images)\n", "\n", "Usage hints:\n", "* In menu **Cell** select **Run All** to run all notebook *cells*\n", "* Place cursor over a any field or function in a cell and press **SHIFT** + **TAB** to display help on\n", " that element.\n", "* Type `comp.` or `comp.plot.` then press **TAB** key to get a list of available\n", " elements of the `comp` and `comp.plot` objects for auto-completion.\n", "* Some functions have a `color` argument. Please refer to the \n", " [Matplotlib Colors API](http://matplotlib.org/api/colors_api.html) to learn how colors can be specified.\n", "* Some functions have a `cmap` argument, which names a colour map. Please refer to the\n", " [Matplotlib Colormaps Reference](http://matplotlib.org/examples/color/colormaps_reference.html) \n", " for possible names.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from dedop.ui.inspect import inspect_l1b_product\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" } ], "source": [ "insp = inspect_l1b_product('C:\\\\Users\\\\dedop-user\\\\.dedop\\\\workspaces\\\\default\\\\configs\\\\default\\\\outputs\\\\L1B_CS_LTA__SIR1SAR_FR_20150331T034023_20150331T034235_C001.DBL_default.nc')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(302.60141999999996, 303.43766699999998)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.lon_range" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-4.9084079999999997, 3.1390119999999997)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.lat_range" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<div class=\"bk-root\">\n", " <div class=\"bk-plotdiv\" id=\"fe135a25-d8d9-480f-8769-55dbd237a991\"></div>\n", "</div>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": {}, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "7011c7d7-93d4-4ec7-91b8-1330432e8473" } }, "output_type": "display_data" } ], "source": [ "insp.plot.locations()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20.688600000000001, 604393663.9586401)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.waveform_range" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 865.15088, 846.46111, 746.59211, ..., 143.24068,\n", " 123.98023, 152.94224],\n", " [ 1150.77622, 1107.54721, 979.42955, ..., 215.4317 ,\n", " 182.6176 , 228.12933],\n", " [ 964.16386, 1005.39549, 904.38513, ..., 594.9339 ,\n", " 452.54924, 340.83863],\n", " ..., \n", " [ 663.31932, 678.30072, 861.0738 , ..., 279.36744,\n", " 217.44432, 236.56344],\n", " [ 765.4782 , 912.4386 , 1021.30344, ..., 726.66924,\n", " 319.46052, 476.97924],\n", " [ 1085.0814 , 1193.66088, 1196.22912, ..., 471.41472,\n", " 356.98536, 476.26584]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.waveform" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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3j7JeSZIkadjGMRN+JXA5cNXcjiRrgHcALwVmgANJdgNrgEsXvP68qrqn//GrgJ8YdcGSJEnSMDUewqvqhiTrF+x+IXB7VX0aIMku4OyquhR42WLnSfIsYLaq7h9huZIkSdLQpar59aj7Ifzaqjq9v30OcFZV/UR/+7XAi6rqgiXOcQmwt6o+cpjPbwO2Aaxbt27jrl27hvoeBnX3vXcz8+DMEb9u4wkbR1CNRuXQoUOsXbt23GVohBzjyeA4TwbHeTKMa5w3b958sKqmlzuuLTdmPvZpNbDkTwdVddEyn98B7ACYnp6uTZs2HXVxK7F953YuvO3CI35dbfVhPavJvn37GNffMTXDMZ4MjvNkcJwnQ9vHuS1LFM4AJ83bPhG4c6UnTbIlyY7Z2dmVnkqSJEkamraE8APAKUlOTnIscC6we6Unrao9VbVtampqxQVKkiRJwzKOJQp3AvuB05LMJDm/qh4CLgD2ArcCV1fVLU3XJkmSJDVhHKujbD3M/uuA64Z5rSRbgC0bNmwY5mklSZKkFWlLO8pI2I4iSZKkNmrL6ihaIJc8dsGYusgVUyRJkrqg0zPhro4iSZKkNup0CLcdRZIkSW3U6RAuSZIktZEhXJIkSWpYp0O4PeGSJElqo06HcHvCJUmS1EadDuGSJElSGxnCJUmSpIYd8cN6kjwFOKmqPj6CerQEH+AjSZLUDQPNhCfZl+RJSZ4K/DXwB0neOtrSVs4bMyVJktRGg7ajTFXV/cArgD+oqo3A946urOHwxkxJkiS10aAh/JgkJwCvBK4dYT2SJElS5w0awi8B9gK3V9WBJN8MfGp0ZUmSJEndNeiNmXdV1fPmNqrq06uhJ1ySJElqo0Fnwt8+4L5W8cZMSZIktdGSM+FJzgDOBJ6e5E3zPvUkYM0oCxuGqtoD7Jmenn7DuGuRJEmS5izXjnIssLZ/3HHz9t8PnDOqojS4hWuHu264JElS+y0Zwqvqg8AHk1xZVZ9rqCZJkiSp0wa9MfPxSXYA6+e/pqq+ZxRFSZIkSV02aAh/P/Au4PeAr42uHEmSJKn7Bg3hD1XVO0daiSRJkjQhBl2icE+Sn05yQpKnzv0ZaWVD4BKFkiRJaqNBQ/iPA78AfAQ42P9z06iKGpaq2lNV26ampsZdiiRJkvSwgdpRqurkURei4Vi4ZCG4bKEkSVLbDBTCk7xusf1VddVwy5EkSZK6b9AbM//VvI+fALwE+D+AIVySJEk6QoO2o/zM/O0kU8B7RlKRJEmS1HGD3pi50JeBU4ZZiCRJkjQpBu0J3wPM3d23Bvg24OpRFSVJkiR12aA94ZfN+/gh4HNVNTOCejQCrpgiSZLULgO1o1TVB4G/BY4DngL80yiLkiRJkrpsoBCe5JXA/wZ+FHgl8JdJzhllYcPgEzMlSZLURoPemPnLwL+qqh+vqtcBLwT+w+jKGg6fmClJkqQ2GjSEP66q7pm3/YUjeK0kSZKkeQa9MfNPk+wFdva3fwy4bjQlqQnerClJkjQ+S4bwJBuAdVX1C0leAbwYCLAfeG8D9UmSJEmds1xLyW8BDwBU1Qeq6k1V9UZ6s+C/NeriJEmSpC5arh1lfVV9fOHOqropyfqRVKSxWdiiYnuKJEnSaCw3E/6EJT73jcMsRJIkSZoUy4XwA0nesHBnkvOBg6MpSZIkSeq25dpRfg744ySv5pHQPQ0cC/zwKAuTJEmSumrJEF5VdwNnJtkMnN7f/T+q6i9GXpkkSZLUUQOtE15V1wPXj7gWtYxriUuSJI2GT72UJEmSGmYIlyRJkho26GPrWyXJs4DLgc8Dt1XVr4+5JEmSJGlgjc+EJ7kiyT1Jbl6w/6wkn0xye5I3L3OaU+ndIHoe8JyRFStJkiSNwDhmwq+kN4t91dyOJGuAdwAvBWborU++G1gDXLrg9ecBHwN+OcmPAe9poGb1LXaz5mK8gVOSJOnwUtV8WOo/8v7aqjq9v30GcHFVfX9/+y0AVbUwgM+9/kLgf1fVDUmuqapzFjlmG7ANYN26dRt37do1ireyrLvvvZuZB2fGcu1x2njCxnGX0KhDhw6xdu3acZehEXKMJ4PjPBkc58kwrnHevHnzwaqaXu64tvSEPxO4Y972DPCiJY7/U+DiJK8CPrvYAVW1A9gBMD09XZs2bRpKoUdq+87tXHjbhWO59jjV1smaCd+3bx/j+jumZjjGk8FxngyO82Ro+zi3JYQv1uNw2BRXVTcDj5n9liRJklaDtoTwGeCkedsnAneu9KRJtgBbNmzYsNJTaYV88I8kSdIj2hLCDwCnJDkZ+HvgXOBVKz1pVe0B9kxPT79hpefSkRn0Bk5JkqRJNI4lCncC+4HTkswkOb+qHgIuAPYCtwJXV9UtTdcmSZIkNaHxmfCq2nqY/dcB1w3zWrajSJIkqY06/dj6qtpTVdumpqbGXYokSZL0sLb0hGsCebOmJEmaVJ2eCU+yJcmO2dnZcZciSZIkPazTIdx2FEmSJLVRp0O4JEmS1Eb2hKtV7BOXJEmToNMz4faES5IkqY06PRPuEzO7YeHsuDPjkiRptet0CFc32bIiSZJWu063o0iSJElt1OmZcB9bPzmcHZckSatJp2fCXSdckiRJbdTpEC5JkiS1UafbUTTZFmtRWciWFUmSNA7OhEuSJEkNM4RLkiRJDet0O4qro2g5rqoiSZLGodMh3Cdm6mgM0ksOhnVJknT0bEeRJEmSGmYIlyRJkhpmCJckSZIaZgiXJEmSGtbpGzOlUZp/A+dlp17GJjYtecwcb+iUJEmdnglPsiXJjtnZ2XGXIkmSJD2s0zPhLlGoJg26tKEkSVKnZ8IlSZKkNur0TLjURvaJS5IkZ8IlSZKkhhnCJUmSpIbZjiK1wMIWFdtTJEnqNkO41EL2jUuS1G22o0iSJEkNM4RLkiRJDet0O0qSLcCWDRs2jLsUaWxsbZEkqX06PRNeVXuqatvU1NS4S5EkSZIe1umZcKlLhjmj7ey4JEnj1emZcEmSJKmNDOGSJElSw2xHkVaxUbeVtLVtpa11SZI0KEO4JGDxYDvMcxmSJUl6hCFc6phhhmlJkjQa9oRLkiRJDXMmXFIjbFGRJOkRhnBJR2RhmF5JkB7muSRJWk0M4ZJWxB50SZKOnCFcUifZ/iJJarNVeWNmkuckuTrJO5OcM+56JEmSpCPR+Ex4kiuAlwH3VNXp8/afBbwNWAP8XlX9+hKn+QHg7VV1Y5LdwDWjrFlSN7R1drytdUmSRmcc7ShXApcDV83tSLIGeAfwUmAGONAP12uASxe8/jzgPcBFSV4OPK2BmiWNiT3nkqQuajyEV9UNSdYv2P1C4Paq+jRAkl3A2VV1Kb1Z88X8u354/8CoapUkSZJGIVXN/8qzH8KvnWtH6fd1n1VVP9Hffi3woqq6YInX/xLwROCdVfWhRY7ZBmwDWLdu3cZdu3YN/X0M4u5772bmwZmxXFvNOfHxJzrOY7bxhI2P2j5418Gjet3hHDp0iLVr1x5xXYNYrNZB69JwjXKc1R6O82QY1zhv3rz5YFVNL3dcW1ZHWez3zYf96aCqPks/YC9xzA5gB8D09HRt2rRpBeUdve07t3PhbReO5dpqzmWnXuY4j9ttR/ey2vrYbzWLrV++b98+5n8fGWYf9+ZLNg9Ul0Zv4TirmxznydD2cW5LCJ8BTpq3fSJw50pPmmQLsGXDhg0rPZWkjhpmz/kgwdwed0kStCeEHwBOSXIy8PfAucCrVnrSqtoD7Jmenn7DSs8lSUfjaEO3K6ZIUreNY4nCncAm4PgkM8BFVfX7SS4A9tJbEeWKqrql6dokaTUxqEvS6jWO1VG2Hmb/dcB1w7yW7SiShiGXhMtOvWzR3m1Jko5GW9pRRsJ2FEld0tZ+8sVuZJUkLW1VPrZekiRJWs06PRNuO4ok2TsuSW3U6ZnwqtpTVdumpqbGXYokSZL0sE7PhEuSuseZfUldYAiXJBlsJalhnQ7h9oRL0uJGudKKgV6SltfpEO4ShZImTVuXMZQkPVqnQ7gkScPgWuiShs0QLkla1CS0lSx8j5edehmb2DSeYiRNFEO4JKm1VtJeM6mz15P6vqXVptMh3BszJam9JmGmfVAGZ2ny+LAeSZIkqWGdDuGSJElSG3W6HUWSpDmrfflG23ekbjGES5IGdrRBdrUH4IUMxI/wayEdnU6HcG/MlCSN09EG1K790CLpsTodwn1ipiTpSBmAJTWh0yFckrS6TEK7S9O12i4itZMhXJIkDWQ1/bAjtZ1LFEqSJEkNcyZckqQhWE2zxIO2qKym9yStNs6ES5IkSQ3r9Ey4SxRKkrS6eWOpuqrTIdwlCiVJaqcuhuuF72m1vx+Nlu0okiRJUsM6PRMuSZImUxdn2kfJr1fznAmXJEmSGuZMuCRJGrlhLnc4zN5r+7jbrcsz9IZwSZI0EWuCz73Hy069jM2XbB5zNZp0tqNIkiRJDXMmXJKkVaqts9dHW9dqej9H2xLRtfaKYT59dTV/HY6GM+GSJElSwzodwpNsSbJjdnZ23KVIkiRJD+t0CK+qPVW1bWpqatylSJIkSQ+zJ1ySJK1qbe0lH6ZR96WP+pqjtlit13/39WOoZHCdngmXJEmS2sgQLkmSJDXMdhRJkqQxWk1tH6O0kjaZ1ciZcEmSJKlhzoRLkiT1tWWWdZh1tOU96dGcCZckSZIaZgiXJEmSGmY7iiRJkobG9pfBOBMuSZIkNcwQLkmSJDXMdhRJkqQjNEjLxajbMmz7WN1aPxOe5JuT/H6Sa+bte2KSdyf53SSvHmd9kiRJ0pEaaQhPckWSe5LcvGD/WUk+meT2JG9e6hxV9emqOn/B7lcA11TVG4CXD7lsSZIkaaRG3Y5yJXA5cNXcjiRrgHcALwVmgANJdgNrgEsXvP68qrpnkfOeCHyi//HXhlyzJEmSNFKpqtFeIFkPXFtVp/e3zwAurqrv72+/BaCqFgbwhee5pqrO6X/8WuCLVXVtkl1Vde4ix28DtgGsW7du465du4b3po7A3ffezcyDM2O5tppz4uNPdJw7zjGeDI7zZHCcj97GEzY+avvgXQfHVMnyTjvuNNauXdv4dTdv3nywqqaXO24cN2Y+E7hj3vYM8KLDHZzkacB/Bl6Q5C39sP4B4PIk/xrYs9jrqmoHsANgenq6Nm3aNJzqj9D2ndu58LYLx3JtNeeyUy9znDvOMZ4MjvNkcJyPXm199OTt5ks2j6mS5V3/3dczrvw3iHGE8MVu5T3sdHxVfQH4qQX7vgT8myHXJUmSJDViHKujzAAnzds+EbhzFBdKsiXJjtnZ2VGcXpIkSToq4wjhB4BTkpyc5FjgXGD3KC5UVXuqatvU1NQoTi9JkiQdlVEvUbgT2A+clmQmyflV9RBwAbAXuBW4uqpuGWUdkiRJUpuMtCe8qrYeZv91wHWjvDb02lGALRs2bBj1pSRJkqSBtf6JmSthO4okSZLaqNMhXJIkSWqjTodwV0eRJElSG41jnfDGVNUeYM/09PQbxl2LJEnSapdLFnvci45Gp2fCJUmSpDYyhEuSJEkN63QItydckiRJbdTpEO4ShZIkSWqjTodwSZIkqY0M4ZIkSVLDOh3C7QmXJElSG3U6hNsTLkmSpDbqdAiXJEmS2sgQLkmSJDXMEC5JkiQ1zBAuSZIkNazTIdzVUSRJktRGnQ7hro4iSZKkNkpVjbuGkUvyD8DnxnT544HPj+naao7j3H2O8WRwnCeD4zwZxjXOz66qpy930ESE8HFKclNVTY+7Do2W49x9jvFkcJwng+M8Gdo+zp1uR5EkSZLayBAuSZIkNcwQPno7xl2AGuE4d59jPBkc58ngOE+GVo+zPeGSJElSw5wJlyRJkhpmCB+SJGcl+WSS25O8eZHPPz7JH/U//5dJ1jdfpVZigDF+U5K/SfLxJH+e5NnjqFMrs9w4zzvunCSVpLV33uvwBhnnJK/s/z99S5L3NV2jVm6A79vPSnJ9ko/1v3f/4Djq1NFLckWSe5LcfJjPJ8l/6/8d+HiSf9l0jYfr21KWAAAG7klEQVRjCB+CJGuAdwA/ADwH2JrkOQsOOx/4YlVtAH4T+I1mq9RKDDjGHwOmq+p5wDXAf2m2Sq3UgONMkuOAnwX+stkKNQyDjHOSU4C3AN9RVc8Ffq7xQrUiA/7//CvA1VX1AuBc4LebrVJDcCVw1hKf/wHglP6fbcA7G6hpIIbw4XghcHtVfbqq/gnYBZy94JizgXf3P74GeEmSNFijVmbZMa6q66vqy/3NjwInNlyjVm6Q/5cBfpXeD1n/2GRxGppBxvkNwDuq6osAVXVPwzVq5QYZ5wKe1P94Crizwfo0BFV1A3DvEoecDVxVPR8FnpzkhGaqW5ohfDieCdwxb3umv2/RY6rqIWAWeFoj1WkYBhnj+c4H/udIK9IoLDvOSV4AnFRV1zZZmIZqkP+fTwVOTfLhJB9NstRMm9ppkHG+GHhNkhngOuBnmilNDTrSf78bc8y4C+iIxWa0Fy47M8gxaq+Bxy/Ja4Bp4LtHWpFGYclxTvI4eu1kr2+qII3EIP8/H0Pv19eb6P1W68Ykp1fVfSOuTcMzyDhvBa6squ1JzgDe0x/nr4++PDWktfnLmfDhmAFOmrd9Io/9ldbDxyQ5ht6vvZb69YnaZZAxJsn3Ar8MvLyqHmyoNg3PcuN8HHA6sC/JZ4FvB3Z7c+aqM+j37P9eVV+tqs8An6QXyrV6DDLO5wNXA1TVfuAJwPGNVKemDPTv9zgYwofjAHBKkpOTHEvv5o7dC47ZDfx4/+NzgL8oF2lfTZYd436bwu/QC+D2j65OS45zVc1W1fFVtb6q1tPr/X95Vd00nnJ1lAb5nv0nwGaAJMfTa0/5dKNVaqUGGee/A14CkOTb6IXwf2i0So3abuB1/VVSvh2Yraq7xl0U2I4yFFX1UJILgL3AGuCKqrolyX8Cbqqq3cDv0/s11+30ZsDPHV/FOlIDjvF/BdYC7+/fc/t3VfXysRWtIzbgOGuVG3Cc9wLfl+RvgK8Bv1BVXxhf1TpSA47zzwO/m+SN9FoUXu8E2eqSZCe9trHj+739FwHfAFBV76LX6/+DwO3Al4F/M55KH8snZkqSJEkNsx1FkiRJapghXJIkSWqYIVySJElqmCFckiRJapghXJIkSRMvyRVJ7kly8wDH/maSv+r/uS3JET/IyxAuSatQ/x+An5u3vTfJ783b3p7kTUO83s8muTXJe4d1TklqmSuBswY5sKreWFXPr6rnA28HPnCkFzOES9Lq9BHgTIAkj6P3lL/nzvv8mcCHh3i9nwZ+sKpePcjB/ScDS9KqUVU3sOBp5km+JcmfJjmY5MYk37rIS7cCO4/0en6TlKTV6cPAb/Y/fi5wM3BCkqfQeyDFtwG3Jvlz4Cn0Hl7xK1X135P8BvC5qvptgCQXAw9U1fYkvwC8Eng88MdVdVGSdwHfDOxOcgXwbuCK/r4vA9uq6uP98zwDWA98PsmfAT9E70EppwPbgWOB1wIP0gv1j/oHT5JaZgfwU1X1qSQvAn4b+J65TyZ5NnAy8BdHemJDuCStQlV1Z5KHkjyL3qz3fuCZwBnALPBxegH5h6vq/v6j1z+aZDewC/gtev+YQC90n5Xk+4BTgBcCoRe6v6uqfirJWcDmqvp8krcDH6uqH0ryPcBVwPP759oIvLiqvpLk9fTC9wvoPQ78duAXq+oFSX4TeF2/DklqnSRr6X1/nXsSNvQmKOY7F7imqr52pOc3hEvS6vVhev9AnAm8lV4IP5NeCP8IvSD9a0m+C/h6//PrqupjSb4pyTOApwNfrKq/S/KzwPcBH+uffy29UH7Dguu+GPgRgKr6iyRPSzLV/9zuqvrKvGOvr6oHgAeSzAJ7+vs/ATxvKF8FSRqNxwH39fu+D+dc4N8dzckN4ZK0es31hf8Leu0odwA/D9xPr13k1fRC9saq+mqSz9KbkQa4BjgH+Of0ZsahF9ovrarfWea6WWRf9f/7pQX7H5z38dfnbX8d/w2S1GL93yJ+JsmPVtX705sOf15V/TVAktPotfvtP5rze2OmJK1eHwZeBtxbVV/r91c/mV5Lyn5gCrinH8A3A8+e99pd9GZwzqEXyAH2Auf1fwVLkmcm+aZFrnsDvYBPkk3A56vq/mG/OUlqUpKd9L53npZkJsn59L7XnZ/kr4FbgLPnvWQrsKuq6rFnW56zEJK0en2C3qoo71uwb22/d/u9wJ4kNwF/Bfzt3EFVdUuS44C/r6q7+vv+LMm3Afv7/Y+HgNcA9yy47sXAHySZ6zv/8VG8OUlqUlVtPcynFl22sKouXsn1cpThXZIkSdJRsh1FkiRJapghXJIkSWqYIVySJElqmCFckiRJapghXJIkSWqYIVySJElqmCFckiRJapghXJIkSWrY/wcEexMsjm/9mwAAAABJRU5ErkJggg==\n", 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\n", 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If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "interactive(children=(IntSlider(value=1499, description='ind', max=2998), Output()), _dom_classes=('widget-interact',))" ] }, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.plot.waveform_line()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n", "<p>\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "interactive(children=(IntSlider(value=1499, description='ind', max=2998), Output()), _dom_classes=('widget-interact',))" ] }, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.plot.waveform_line(ref_ind=520)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.dim_names" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['GPS_time_l1b_echo_sar_ku',\n", " 'SAR_mode_l1b_echo_sar_ku',\n", " 'UTC_day_l1b_echo_sar_ku',\n", " 'UTC_sec_l1b_echo_sar_ku',\n", " 'acq_stat_l1b_echo_sar_ku',\n", " 'agc_cor_ku_l1b_echo_sar_ku',\n", " 'agc_ku_l1b_echo_sar_ku',\n", " 'agccode_ku_l1b_echo_sar_ku',\n", " 'alt_l1b_echo_sar_ku',\n", " 'beam_ang_l1b_echo_sar_ku',\n", " 'beam_form_l1b_echo_sar_ku',\n", " 'cl_gain_l1b_echo_sar_ku',\n", " 'cor2_applied_l1b_echo_sar_ku',\n", " 'cor2_nav_dem_l1b_echo_sar_ku',\n", " 'dem_eeprom_l1b_echo_sar_ku',\n", " 'dh0_l1b_echo_sar_ku',\n", " 'flag_gnss_status_l1b_echo_sar_ku',\n", " 'flag_man_plane_l1b_echo_sar_ku',\n", " 'flag_man_pres_l1b_echo_sar_ku',\n", " 'flag_man_thrust_l1b_echo_sar_ku',\n", " 'flag_time_status_l1b_echo_sar_ku',\n", " 'h0_applied_l1b_echo_sar_ku',\n", " 'h0_nav_dem_l1b_echo_sar_ku',\n", " 'i2q2_meas_ku_l1b_echo_sar_ku',\n", " 'int_path_cor_ku_l1b_echo_sar_ku',\n", " 'isp_coarse_time_l1b_echo_sar_ku',\n", " 'isp_fine_time_l1b_echo_sar_ku',\n", " 'isp_time_status_echo_sar_ku',\n", " 'kurt_stack_l1b_echo_sar_ku',\n", " 'lat_l1b_echo_sar_ku',\n", " 'lon_l1b_echo_sar_ku',\n", " 'loss_track_l1b_echo_sar_ku',\n", " 'max_stack_l1b_echo_sar_ku',\n", " 'nav_bul_coarse_time_l1b_echo_sar_ku',\n", " 'nav_bul_fine_time_l1b_echo_sar_ku',\n", " 'nav_bul_source_l1b_echo_sar_ku',\n", " 'nav_bul_status_l1b_echo_sar_ku',\n", " 'nb_stack_l1b_echo_sar_ku',\n", " 'oper_instr_l1b_echo_sar_ku',\n", " 'orb_alt_rate_l1b_echo_sar_ku',\n", " 'range_ku_l1b_echo_sar_ku',\n", " 'range_rate_l1b_echo_sar_ku',\n", " 'scale_factor_ku_l1b_echo_sar_ku',\n", " 'seq_count_l1b_echo_sar_ku',\n", " 'sig0_cal_ku_l1b_echo_sar_ku',\n", " 'skew_stack_l1b_echo_sar_ku',\n", " 'sral_fine_time_l1b_echo_sar_ku',\n", " 'stdev_stack_l1b_echo_sar_ku',\n", " 'surf_type_l1b_echo_sar_ku',\n", " 'time_l1b_echo_sar_ku',\n", " 'time_time_corr_val_l1b_echo_sar_ku',\n", " 'uso_cor_l1b_echo_sar_ku',\n", " 'weighting_l1b_echo_sar_ku',\n", " 'x_pos_l1b_echo_sar_ku',\n", " 'x_vel_l1b_echo_sar_ku',\n", " 'y_pos_l1b_echo_sar_ku',\n", " 'y_vel_l1b_echo_sar_ku',\n", " 'z_pos_l1b_echo_sar_ku',\n", " 'z_vel_l1b_echo_sar_ku']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.var_names" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'echo_sample_ind': 256,\n", " 'max_multi_stack_ind': 240,\n", " 'time_l1b_echo_sar_ku': 2999}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.dim_name_to_size" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{('time_l1b_echo_sar_ku',): {'GPS_time_l1b_echo_sar_ku',\n", " 'SAR_mode_l1b_echo_sar_ku',\n", " 'UTC_day_l1b_echo_sar_ku',\n", " 'UTC_sec_l1b_echo_sar_ku',\n", " 'acq_stat_l1b_echo_sar_ku',\n", " 'agc_cor_ku_l1b_echo_sar_ku',\n", " 'agc_ku_l1b_echo_sar_ku',\n", " 'agccode_ku_l1b_echo_sar_ku',\n", " 'alt_l1b_echo_sar_ku',\n", " 'beam_form_l1b_echo_sar_ku',\n", " 'cl_gain_l1b_echo_sar_ku',\n", " 'cor2_applied_l1b_echo_sar_ku',\n", " 'cor2_nav_dem_l1b_echo_sar_ku',\n", " 'dem_eeprom_l1b_echo_sar_ku',\n", " 'dh0_l1b_echo_sar_ku',\n", " 'flag_gnss_status_l1b_echo_sar_ku',\n", " 'flag_man_plane_l1b_echo_sar_ku',\n", " 'flag_man_pres_l1b_echo_sar_ku',\n", " 'flag_man_thrust_l1b_echo_sar_ku',\n", " 'flag_time_status_l1b_echo_sar_ku',\n", " 'h0_applied_l1b_echo_sar_ku',\n", " 'h0_nav_dem_l1b_echo_sar_ku',\n", " 'int_path_cor_ku_l1b_echo_sar_ku',\n", " 'isp_coarse_time_l1b_echo_sar_ku',\n", " 'isp_fine_time_l1b_echo_sar_ku',\n", " 'isp_time_status_echo_sar_ku',\n", " 'kurt_stack_l1b_echo_sar_ku',\n", " 'lat_l1b_echo_sar_ku',\n", " 'lon_l1b_echo_sar_ku',\n", " 'loss_track_l1b_echo_sar_ku',\n", " 'max_stack_l1b_echo_sar_ku',\n", " 'nav_bul_coarse_time_l1b_echo_sar_ku',\n", " 'nav_bul_fine_time_l1b_echo_sar_ku',\n", " 'nav_bul_source_l1b_echo_sar_ku',\n", " 'nav_bul_status_l1b_echo_sar_ku',\n", " 'nb_stack_l1b_echo_sar_ku',\n", " 'oper_instr_l1b_echo_sar_ku',\n", " 'orb_alt_rate_l1b_echo_sar_ku',\n", " 'range_ku_l1b_echo_sar_ku',\n", " 'range_rate_l1b_echo_sar_ku',\n", " 'scale_factor_ku_l1b_echo_sar_ku',\n", " 'seq_count_l1b_echo_sar_ku',\n", " 'sig0_cal_ku_l1b_echo_sar_ku',\n", " 'skew_stack_l1b_echo_sar_ku',\n", " 'sral_fine_time_l1b_echo_sar_ku',\n", " 'stdev_stack_l1b_echo_sar_ku',\n", " 'surf_type_l1b_echo_sar_ku',\n", " 'time_l1b_echo_sar_ku',\n", " 'time_time_corr_val_l1b_echo_sar_ku',\n", " 'uso_cor_l1b_echo_sar_ku',\n", " 'weighting_l1b_echo_sar_ku',\n", " 'x_pos_l1b_echo_sar_ku',\n", " 'x_vel_l1b_echo_sar_ku',\n", " 'y_pos_l1b_echo_sar_ku',\n", " 'y_vel_l1b_echo_sar_ku',\n", " 'z_pos_l1b_echo_sar_ku',\n", " 'z_vel_l1b_echo_sar_ku'},\n", " ('time_l1b_echo_sar_ku', 'echo_sample_ind'): {'i2q2_meas_ku_l1b_echo_sar_ku'},\n", " ('time_l1b_echo_sar_ku', 'max_multi_stack_ind'): {'beam_ang_l1b_echo_sar_ku'}}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.dim_names_to_var_names" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "var = insp.dataset['i2q2_meas_ku_l1b_echo_sar_ku']" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('i2q2_meas_ku_l1b_echo_sar_ku',\n", " dtype('uint32'),\n", " ('time_l1b_echo_sar_ku', 'echo_sample_ind'),\n", " (2999, 256))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "var.name, var.dtype, var.dimensions, var.shape" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<class 'netCDF4._netCDF4.Dataset'>\n", "root group (NETCDF4 data model, file format HDF5):\n", " software_name: dedop\n", " software_version: 1.4.0.dev1\n", " dimensions(sizes): time_l1b_echo_sar_ku(2999), echo_sample_ind(256), max_multi_stack_ind(240)\n", " variables(dimensions): float64 \u001b[4mtime_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int16 \u001b[4mUTC_day_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4mUTC_sec_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4mGPS_time_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4misp_coarse_time_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4misp_fine_time_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4msral_fine_time_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mlat_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mlon_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4malt_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int16 \u001b[4morb_alt_rate_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mflag_time_status_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mtime_time_corr_val_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mflag_man_pres_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mflag_man_thrust_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mflag_man_plane_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mflag_gnss_status_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4mx_pos_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4my_pos_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4mz_pos_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4mx_vel_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4my_vel_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), float64 \u001b[4mz_vel_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mnav_bul_status_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mnav_bul_source_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4mnav_bul_coarse_time_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4mnav_bul_fine_time_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint16 \u001b[4mseq_count_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4misp_time_status_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4moper_instr_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mSAR_mode_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mcl_gain_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4macq_stat_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mdem_eeprom_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mweighting_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4mloss_track_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4mh0_nav_dem_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4mh0_applied_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int16 \u001b[4mcor2_nav_dem_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int16 \u001b[4mcor2_applied_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mdh0_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int16 \u001b[4magccode_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int8 \u001b[4msurf_type_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mrange_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4muso_cor_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mint_path_cor_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mrange_rate_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4magc_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mscale_factor_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4magc_cor_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4msig0_cal_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint16 \u001b[4mnb_stack_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4mmax_stack_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4mstdev_stack_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mskew_stack_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int32 \u001b[4mkurt_stack_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), int16 \u001b[4mbeam_ang_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k,max_multi_stack_ind), uint16 \u001b[4mbeam_form_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k), uint32 \u001b[4mi2q2_meas_ku_l1b_echo_sar_ku\u001b[0m(time_l1b_echo_sar_k,echo_sample_ind)\n", " groups: " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "insp.dataset" ] }, { "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": 1 }
gpl-3.0
Zhenxingzhang/AnalyticsVidhya
Articles/Getting_Started_with_BigMart_Sales(AV_Datahacks)/exploration_and_feature_engineering.ipynb
1
28126
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Exploration & Feature Engineering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Data Exploration" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading data:\n", "The files can be downloaded from: http://datahack.analyticsvidhya.com/contest/practice-problem-bigmart-sales-prediction" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Read files:\n", "train = pd.read_csv(\"train.csv\")\n", "test = pd.read_csv(\"test.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(8523, 13) (5681, 12) (14204, 13)\n" ] } ], "source": [ "#Combine test and train into one file\n", "train['source']='train'\n", "test['source']='test'\n", "data = pd.concat([train, test],ignore_index=True)\n", "print train.shape, test.shape, data.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Item_Fat_Content 0\n", "Item_Identifier 0\n", "Item_MRP 0\n", "Item_Outlet_Sales 5681\n", "Item_Type 0\n", "Item_Visibility 0\n", "Item_Weight 2439\n", "Outlet_Establishment_Year 0\n", "Outlet_Identifier 0\n", "Outlet_Location_Type 0\n", "Outlet_Size 4016\n", "Outlet_Type 0\n", "source 0\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Check missing values:\n", "data.apply(lambda x: sum(x.isnull()))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Item_MRP</th>\n", " <th>Item_Outlet_Sales</th>\n", " <th>Item_Visibility</th>\n", " <th>Item_Weight</th>\n", " <th>Outlet_Establishment_Year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>14204.000000</td>\n", " <td>8523.000000</td>\n", " <td>14204.000000</td>\n", " <td>11765.000000</td>\n", " <td>14204.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>141.004977</td>\n", " <td>2181.288914</td>\n", " <td>0.065953</td>\n", " <td>12.792854</td>\n", " <td>1997.830681</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>62.086938</td>\n", " <td>1706.499616</td>\n", " <td>0.051459</td>\n", " <td>4.652502</td>\n", " <td>8.371664</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>31.290000</td>\n", " <td>33.290000</td>\n", " <td>0.000000</td>\n", " <td>4.555000</td>\n", " <td>1985.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>94.012000</td>\n", " <td>834.247400</td>\n", " <td>0.027036</td>\n", " <td>8.710000</td>\n", " <td>1987.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>142.247000</td>\n", " <td>1794.331000</td>\n", " <td>0.054021</td>\n", " <td>12.600000</td>\n", " <td>1999.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>185.855600</td>\n", " <td>3101.296400</td>\n", " <td>0.094037</td>\n", " <td>16.750000</td>\n", " <td>2004.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>266.888400</td>\n", " <td>13086.964800</td>\n", " <td>0.328391</td>\n", " <td>21.350000</td>\n", " <td>2009.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Item_MRP Item_Outlet_Sales Item_Visibility Item_Weight \\\n", "count 14204.000000 8523.000000 14204.000000 11765.000000 \n", "mean 141.004977 2181.288914 0.065953 12.792854 \n", "std 62.086938 1706.499616 0.051459 4.652502 \n", "min 31.290000 33.290000 0.000000 4.555000 \n", "25% 94.012000 834.247400 0.027036 8.710000 \n", "50% 142.247000 1794.331000 0.054021 12.600000 \n", "75% 185.855600 3101.296400 0.094037 16.750000 \n", "max 266.888400 13086.964800 0.328391 21.350000 \n", "\n", " Outlet_Establishment_Year \n", "count 14204.000000 \n", "mean 1997.830681 \n", "std 8.371664 \n", "min 1985.000000 \n", "25% 1987.000000 \n", "50% 1999.000000 \n", "75% 2004.000000 \n", "max 2009.000000 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Numerical data summary:\n", "data.describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Item_Fat_Content 5\n", "Item_Identifier 1559\n", "Item_MRP 8052\n", "Item_Outlet_Sales 3494\n", "Item_Type 16\n", "Item_Visibility 13006\n", "Item_Weight 416\n", "Outlet_Establishment_Year 9\n", "Outlet_Identifier 10\n", "Outlet_Location_Type 3\n", "Outlet_Size 4\n", "Outlet_Type 4\n", "source 2\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Number of unique values in each:\n", "data.apply(lambda x: len(x.unique()))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Frequency of Categories for varible Item_Fat_Content\n", "Low Fat 8485\n", "Regular 4824\n", "LF 522\n", "reg 195\n", "low fat 178\n", "Name: Item_Fat_Content, dtype: int64\n", "\n", "Frequency of Categories for varible Item_Type\n", "Fruits and Vegetables 2013\n", "Snack Foods 1989\n", "Household 1548\n", "Frozen Foods 1426\n", "Dairy 1136\n", "Baking Goods 1086\n", "Canned 1084\n", "Health and Hygiene 858\n", "Meat 736\n", "Soft Drinks 726\n", "Breads 416\n", "Hard Drinks 362\n", "Others 280\n", "Starchy Foods 269\n", "Breakfast 186\n", "Seafood 89\n", "Name: Item_Type, dtype: int64\n", "\n", "Frequency of Categories for varible Outlet_Location_Type\n", "Tier 3 5583\n", "Tier 2 4641\n", "Tier 1 3980\n", "Name: Outlet_Location_Type, dtype: int64\n", "\n", "Frequency of Categories for varible Outlet_Size\n", "Medium 4655\n", "Small 3980\n", "High 1553\n", "Name: Outlet_Size, dtype: int64\n", "\n", "Frequency of Categories for varible Outlet_Type\n", "Supermarket Type1 9294\n", "Grocery Store 1805\n", "Supermarket Type3 1559\n", "Supermarket Type2 1546\n", "Name: Outlet_Type, dtype: int64\n" ] } ], "source": [ "#Filter categorical variables\n", "categorical_columns = [x for x in data.dtypes.index if data.dtypes[x]=='object']\n", "#Exclude ID cols and source:\n", "categorical_columns = [x for x in categorical_columns if x not in ['Item_Identifier','Outlet_Identifier','source']]\n", "#Print frequency of categories\n", "for col in categorical_columns:\n", " print '\\nFrequency of Categories for varible %s'%col\n", " print data[col].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Data Cleaning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imputation" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Orignal #missing: 2439\n", "Final #missing: 0\n" ] } ], "source": [ "#Determine the average weight per item:\n", "item_avg_weight = data.pivot_table(values='Item_Weight', index='Item_Identifier')\n", "\n", "#Get a boolean variable specifying missing Item_Weight values\n", "miss_bool = data['Item_Weight'].isnull() \n", "\n", "#Impute data and check #missing values before and after imputation to confirm\n", "print 'Orignal #missing: %d'% sum(miss_bool)\n", "data.loc[miss_bool,'Item_Weight'] = data.loc[miss_bool,'Item_Identifier'].apply(lambda x: item_avg_weight[x])\n", "print 'Final #missing: %d'% sum(data['Item_Weight'].isnull())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mode for each Outlet_Type:\n", "Outlet_Type\n", "Grocery Store Small\n", "Supermarket Type1 Small\n", "Supermarket Type2 Medium\n", "Supermarket Type3 Medium\n", "Name: Outlet_Size, dtype: object\n", "\n", "Orignal #missing: 4016\n", "0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/aarshay/anaconda/lib/python2.7/site-packages/numpy/lib/arraysetops.py:200: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n", " flag = np.concatenate(([True], aux[1:] != aux[:-1]))\n" ] } ], "source": [ "#Import mode function:\n", "from scipy.stats import mode\n", "\n", "#Determing the mode for each\n", "outlet_size_mode = data.pivot_table(values='Outlet_Size', columns='Outlet_Type',aggfunc=(lambda x:mode(x).mode[0]) )\n", "print 'Mode for each Outlet_Type:'\n", "print outlet_size_mode\n", "\n", "#Get a boolean variable specifying missing Item_Weight values\n", "miss_bool = data['Outlet_Size'].isnull() \n", "\n", "#Impute data and check #missing values before and after imputation to confirm\n", "print '\\nOrignal #missing: %d'% sum(miss_bool)\n", "data.loc[miss_bool,'Outlet_Size'] = data.loc[miss_bool,'Outlet_Type'].apply(lambda x: outlet_size_mode[x])\n", "print sum(data['Outlet_Size'].isnull())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Feature Engineering:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step1: Consider combining categories in Outlet_Type" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Outlet_Type\n", "Grocery Store 339.828500\n", "Supermarket Type1 2316.181148\n", "Supermarket Type2 1995.498739\n", "Supermarket Type3 3694.038558\n", "Name: Item_Outlet_Sales, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Check the mean sales by type:\n", "data.pivot_table(values='Item_Outlet_Sales',index='Outlet_Type')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step2: Modify Item_Visibility" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of 0 values initially: 879\n", "Number of 0 values after modification: 0\n" ] } ], "source": [ "#Determine average visibility of a product\n", "visibility_avg = data.pivot_table(values='Item_Visibility', index='Item_Identifier')\n", "\n", "#Impute 0 values with mean visibility of that product:\n", "miss_bool = (data['Item_Visibility'] == 0)\n", "\n", "print 'Number of 0 values initially: %d'%sum(miss_bool)\n", "data.loc[miss_bool,'Item_Visibility'] = data.loc[miss_bool,'Item_Identifier'].apply(lambda x: visibility_avg[x])\n", "print 'Number of 0 values after modification: %d'%sum(data['Item_Visibility'] == 0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 14204.000000\n", "mean 1.061884\n", "std 0.235907\n", "min 0.844563\n", "25% 0.925131\n", "50% 0.999070\n", "75% 1.042007\n", "max 3.010094\n", "Name: Item_Visibility_MeanRatio, dtype: float64\n" ] } ], "source": [ "#Determine another variable with means ratio\n", "data['Item_Visibility_MeanRatio'] = data.apply(lambda x: x['Item_Visibility']/visibility_avg[x['Item_Identifier']], axis=1)\n", "print data['Item_Visibility_MeanRatio'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Create a broad category of Type of Item" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Food 10201\n", "Non-Consumable 2686\n", "Drinks 1317\n", "Name: Item_Type_Combined, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Item type combine:\n", "data['Item_Identifier'].value_counts()\n", "data['Item_Type_Combined'] = data['Item_Identifier'].apply(lambda x: x[0:2])\n", "data['Item_Type_Combined'] = data['Item_Type_Combined'].map({'FD':'Food',\n", " 'NC':'Non-Consumable',\n", " 'DR':'Drinks'})\n", "data['Item_Type_Combined'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: Determine the years of operation of a store" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 14204.000000\n", "mean 15.169319\n", "std 8.371664\n", "min 4.000000\n", "25% 9.000000\n", "50% 14.000000\n", "75% 26.000000\n", "max 28.000000\n", "Name: Outlet_Years, dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Years:\n", "data['Outlet_Years'] = 2013 - data['Outlet_Establishment_Year']\n", "data['Outlet_Years'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5: Modify categories of Item_Fat_Content" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Categories:\n", "Low Fat 8485\n", "Regular 4824\n", "LF 522\n", "reg 195\n", "low fat 178\n", "Name: Item_Fat_Content, dtype: int64\n", "\n", "Modified Categories:\n", "Low Fat 9185\n", "Regular 5019\n", "Name: Item_Fat_Content, dtype: int64\n" ] } ], "source": [ "#Change categories of low fat:\n", "print 'Original Categories:'\n", "print data['Item_Fat_Content'].value_counts()\n", "\n", "print '\\nModified Categories:'\n", "data['Item_Fat_Content'] = data['Item_Fat_Content'].replace({'LF':'Low Fat',\n", " 'reg':'Regular',\n", " 'low fat':'Low Fat'})\n", "print data['Item_Fat_Content'].value_counts()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Low Fat 6499\n", "Regular 5019\n", "Non-Edible 2686\n", "Name: Item_Fat_Content, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mark non-consumables as separate category in low_fat:\n", "data.loc[data['Item_Type_Combined']==\"Non-Consumable\",'Item_Fat_Content'] = \"Non-Edible\"\n", "data['Item_Fat_Content'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Numerical and One-Hot Coding of Categorical variables" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Import library:\n", "from sklearn.preprocessing import LabelEncoder\n", "le = LabelEncoder()\n", "#New variable for outlet\n", "data['Outlet'] = le.fit_transform(data['Outlet_Identifier'])\n", "var_mod = ['Item_Fat_Content','Outlet_Location_Type','Outlet_Size','Item_Type_Combined','Outlet_Type','Outlet']\n", "le = LabelEncoder()\n", "for i in var_mod:\n", " data[i] = le.fit_transform(data[i])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#One Hot Coding:\n", "data = pd.get_dummies(data, columns=['Item_Fat_Content','Outlet_Location_Type','Outlet_Size','Outlet_Type',\n", " 'Item_Type_Combined','Outlet'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Item_Identifier object\n", "Item_MRP float64\n", "Item_Outlet_Sales float64\n", "Item_Type object\n", "Item_Visibility float64\n", "Item_Weight float64\n", "Outlet_Establishment_Year int64\n", "Outlet_Identifier object\n", "source object\n", "Item_Visibility_MeanRatio float64\n", "Outlet_Years int64\n", "Item_Fat_Content_0 float64\n", "Item_Fat_Content_1 float64\n", "Item_Fat_Content_2 float64\n", "Outlet_Location_Type_0 float64\n", "Outlet_Location_Type_1 float64\n", "Outlet_Location_Type_2 float64\n", "Outlet_Size_0 float64\n", "Outlet_Size_1 float64\n", "Outlet_Size_2 float64\n", "Outlet_Type_0 float64\n", "Outlet_Type_1 float64\n", "Outlet_Type_2 float64\n", "Outlet_Type_3 float64\n", "Item_Type_Combined_0 float64\n", "Item_Type_Combined_1 float64\n", "Item_Type_Combined_2 float64\n", "Outlet_0 float64\n", "Outlet_1 float64\n", "Outlet_2 float64\n", "Outlet_3 float64\n", "Outlet_4 float64\n", "Outlet_5 float64\n", "Outlet_6 float64\n", "Outlet_7 float64\n", "Outlet_8 float64\n", "Outlet_9 float64\n", "dtype: object" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "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>Item_Fat_Content_0</th>\n", " <th>Item_Fat_Content_1</th>\n", " <th>Item_Fat_Content_2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Item_Fat_Content_0 Item_Fat_Content_1 Item_Fat_Content_2\n", "0 1 0 0\n", "1 0 0 1\n", "2 1 0 0\n", "3 0 0 1\n", "4 0 1 0\n", "5 0 0 1\n", "6 0 0 1\n", "7 1 0 0\n", "8 0 0 1\n", "9 0 0 1" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[['Item_Fat_Content_0','Item_Fat_Content_1','Item_Fat_Content_2']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step7: Exporting Data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aarshay/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:9: 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", "/Users/aarshay/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:10: 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" ] } ], "source": [ "#Drop the columns which have been converted to different types:\n", "data.drop(['Item_Type','Outlet_Establishment_Year'],axis=1,inplace=True)\n", "\n", "#Divide into test and train:\n", "train = data.loc[data['source']==\"train\"]\n", "test = data.loc[data['source']==\"test\"]\n", "\n", "#Drop unnecessary columns:\n", "test.drop(['Item_Outlet_Sales','source'],axis=1,inplace=True)\n", "train.drop(['source'],axis=1,inplace=True)\n", "\n", "#Export files as modified versions:\n", "train.to_csv(\"train_modified.csv\",index=False)\n", "test.to_csv(\"test_modified.csv\",index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
kootsoop/DSP.SE
Python/82432-my-summed-sin-waves-seems-to-only-work-if-my-wavelength-is-divisible-with-the-ti.ipynb
1
62548
{ "cells": [ { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/kootsoop/opt/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n", " return array(a, dtype, copy=False, order=order)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from numpy import pi, sin, arange, mean, abs as absolute, where, nanmin, nanmax, angle, arctan2, sqrt, array, random, append, transpose\n", "from matplotlib import pyplot as plt\n", "from scipy import fftpack\n", "import pandas as pd\n", "\n", "\n", "class GenericSignals():\n", " \n", " def __init__(self, time_vec):\n", " self.time_vec = time_vec\n", " \n", " def createSingleSignal(self, w=.2, a=10, ph=0):\n", " # x is x axis time vector, w is wavelength, a is amplitude, ph is phase\n", " x = self.time_vec\n", " return a * sin((2 * pi / w * x) + ph)\n", " \n", " def combineNSignals(self, *args):\n", " return sum(args)\n", " \n", " def getFrequencies(self, signal, dt):\n", " sig_fft = fftpack.fft(signal)\n", " sample_freq = fftpack.fftfreq(len(signal), d=dt)\n", " power = absolute(sig_fft) * dt\n", " return power, sample_freq\n", " \n", " def findPeakFrequencies(self, sample_freq, power, n=1):\n", " #n is number of frequencies to get\n", " pos_mask = where(sample_freq > 0)\n", " freqs = sample_freq[pos_mask]\n", " pos_power = power[pos_mask]\n", " pos_power_sorted = sorted(pos_power)\n", " dominent = pos_power_sorted[-n:]\n", " # need to reorganize dominent to have proper sorting now\n", " dominant_freq_indices = []\n", " peak_freqs = []\n", " for i in range(len(dominent)):\n", " dominant_freq_indices.append(where(pos_power == dominent[i])[0][0])\n", " peak_freqs.append(freqs[dominant_freq_indices[i]])\n", " return peak_freqs\n", " \n", " def createIndividualSignalsForEachFreq(self, signal, sample_freq):\n", " filtered_signals = []\n", " for i in range(len(sample_freq)):\n", " high_freq_fft = fftpack.fft(signal)\n", " high_freq_fft[absolute(sample_freq) < nanmin(sample_freq[i])] = 0\n", " high_freq_fft[absolute(sample_freq) > nanmax(sample_freq[i])] = 0\n", " filtered_sig = fftpack.ifft(high_freq_fft)\n", " filtered_sig -= mean(filtered_sig)\n", " filtered_signals.append(filtered_sig)\n", " return filtered_signals\n", " \n", " def getPhaseAmplitudeWavelength(self, signal, freq, sample_freq):\n", " # if statement resolves a divide by 0 runtime warning\n", " if freq == 0:\n", " fixed_freq = .0000000000001\n", " wavelength = 1 / fixed_freq \n", " else:\n", " wavelength = 1 / freq \n", " sig_size = len(signal)\n", " sig_fft = fftpack.fft(signal)\n", " sample_index = where(sample_freq==freq)\n", " phase = (arctan2(sig_fft[sample_index].imag, sig_fft[sample_index].real))[0]\n", " ph = phase + pi/2\n", " amp = (sqrt((sig_fft[sample_index].real * sig_fft[sample_index].real) + (sig_fft[sample_index].imag * sig_fft[sample_index].imag)) / (sig_size / 2))[0]\n", " return ph, amp, wavelength\n", " \n", " def getAllPhaseAmplitudeWavelengths(self, all_signals, sample_freq):\n", " wavelengths = []\n", " phases = []\n", " amplitudes = []\n", " i = 0\n", " for individual_signal in all_signals:\n", " phase, amplitude, wavelength = self.getPhaseAmplitudeWavelength(individual_signal, sample_freq[i], sample_freq)\n", " i += 1\n", " wavelengths.append(wavelength)\n", " phases.append(phase)\n", " amplitudes.append(amplitude)\n", " return wavelengths, phases, amplitudes\n", "\n", " def eqn(self, signal, wavelength, time_vec, phase, amp):\n", " signals_mean = absolute(mean(signal))\n", " return (amp * sin((2 * pi / wavelength * time_vec) + phase)) + signals_mean\n", " \n", " def getEquations(self, wavelength, time_vec, ph, amp):\n", " equations = []\n", " for i in range(len(wavelength)):\n", " equation = (amp[i] * sin((2 * pi / wavelength[i] * time_vec) + ph[i]))\n", " equations.append(equation)\n", " return equations\n", " \n", " def predictFuture(self, new_time_vec, equations, wavelength, ph, amp):\n", " # addidtional_step = new_time_vec[-1] + 1\n", " # new_time_vec = append(new_time_vec, addidtional_step)[1:]\n", " pred = []\n", " for i in range(len(equations)):\n", " pred.append(self.eqn(equations[i], wavelength[i], new_time_vec, ph[i], amp[i]))\n", " return pred\n", " \n", "class PreProcessData():\n", " \n", " def __init__(self, data):\n", " self.data = data\n", " \n", " # separate data into test and validate sets for x and y and for each interval\n", " def createTestValidateSets(self, data, interval_to_predict):\n", " # interval to predict must be less than 1/3 size od dataset\n", " try:\n", " data_x = data[0]\n", " data_y = data[1]\n", " if interval_to_predict * 3 >= len(data_x):\n", " interval_to_predict = 1\n", " if interval_to_predict == 0:\n", " test_x = data_x\n", " val_x = []\n", " test_y = data_y\n", " val_y = []\n", " else:\n", " test_x = data_x[:-interval_to_predict]\n", " val_x = data_x[-interval_to_predict:]\n", " test_y = data_y[:-interval_to_predict]\n", " val_y = data_y[-interval_to_predict:]\n", " return test_x, test_y, val_x, val_y\n", " except Exception as e:\n", " return 'PreProcessData.createTestValidateSets failed: ' + e\n", " \n", "if __name__ == \"__main__\":\n", " \n", " dt = 1\n", " time_vec = arange(0, 100, dt)\n", " genSig = GenericSignals(time_vec)\n", " testing_signal = genSig.createSingleSignal(w=20, a=15, ph=0)\n", " testing_signal2 = genSig.createSingleSignal(w=10, a=15, ph=0)\n", " test_signal = testing_signal\n", " predict_interval = 10\n", " preProcessedData = PreProcessData(test_signal)\n", " test_x, test_y, val_x, val_y = preProcessedData.createTestValidateSets([time_vec, test_signal], predict_interval)\n", " power, sample_freq = genSig.getFrequencies(test_y, dt)\n", " individual_signals = genSig.createIndividualSignalsForEachFreq(test_y, sample_freq)\n", " test_index = 5\n", " fft = fftpack.fft(individual_signals[test_index])\n", " fft_phase = arctan2(fft[test_index].imag, fft[test_index].real)\n", "\n", " wavelengths, phases, amplitudes = genSig.getAllPhaseAmplitudeWavelengths(individual_signals, sample_freq)\n", " equations = genSig.getEquations(wavelengths, test_x, phases, amplitudes)\n", " index2 = int(len(test_x) / 2 - 1)\n", " all_equations = sum(equations[1:index2])\n", " y_shift = mean(test_y) - mean(all_equations)\n", " all_equations += y_shift\n", " predicted_curves = genSig.predictFuture(val_x, equations, wavelengths, phases, amplitudes)\n", " total_pred = sum(predicted_curves)\n", " total_pred += y_shift\n", " fig, axs = plt.subplots(1, 1, figsize=(15,5))\n", " axs.plot(test_x, individual_signals[test_index], 'r--', label='single exact curve')\n", " axs.plot(test_x, equations[test_index], 'ro', label='single equation curve')\n", " axs.plot(val_x, predicted_curves[test_index], 'r', label='single predcted curve')\n", " # axs.plot(time_vec, test_signal, 'b--', label='test set')\n", " # axs.plot(test_x, all_equations, 'bo', label='ALL equations set')\n", " # axs.plot(val_x, val_y, 'g--', label='val set')\n", " # for i in range(1, 8):\n", " # axs.plot(val_x, predicted_curves[i], label=f'pred set: {i}')\n", " # axs.plot(val_x, sum(predicted_curves[1:3]), label='pred curves')\n", " axs.plot(val_x, total_pred, 'go', label='predicted set')\n", " axs.plot(val_x, val_y, 'bo', label='val_y predicted set')\n", " axs.plot(test_x, test_y, 'bx', label='val_y test set')\n", " axs.legend()\n", " fig.tight_layout()\n", " plt.show() " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10000000000000.0, 90.0, 45.0, 30.0, 22.5, 18.0, 15.0, 12.857142857142858, 11.25, 10.0, 9.0, 8.181818181818182, 7.5, 6.9230769230769225, 6.428571428571429, 5.999999999999999, 5.625, 5.294117647058823, 5.0, 4.7368421052631575, 4.5, 4.285714285714286, 4.090909090909091, 3.913043478260869, 3.75, 3.5999999999999996, 3.4615384615384612, 3.3333333333333335, 3.2142857142857144, 3.103448275862069, 2.9999999999999996, 2.903225806451613, 2.8125, 2.727272727272727, 2.6470588235294117, 2.571428571428571, 2.5, 2.432432432432432, 2.3684210526315788, 2.3076923076923075, 2.25, 2.1951219512195124, 2.142857142857143, 2.093023255813953, 2.0454545454545454, -2.0, -2.0454545454545454, -2.093023255813953, -2.142857142857143, -2.1951219512195124, -2.25, -2.3076923076923075, -2.3684210526315788, -2.432432432432432, -2.5, -2.571428571428571, -2.6470588235294117, -2.727272727272727, -2.8125, -2.903225806451613, -2.9999999999999996, -3.103448275862069, -3.2142857142857144, -3.3333333333333335, -3.4615384615384612, -3.5999999999999996, -3.75, -3.913043478260869, -4.090909090909091, -4.285714285714286, -4.5, -4.7368421052631575, -5.0, -5.294117647058823, -5.625, -5.999999999999999, -6.428571428571429, -6.9230769230769225, -7.5, -8.181818181818182, -9.0, -10.0, -11.25, -12.857142857142858, -15.0, -18.0, -22.5, -30.0, -45.0, -90.0]\n" ] } ], "source": [ "print(wavelengths)" ] }, { "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": 4 }
mit
bbglab/adventofcode
2020/ferran/11/11.ipynb
1
8058
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 11" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "! cat README.md" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from itertools import product\n", "\n", "with open('input.txt', 'rt') as f:\n", " grid = np.array(list(map(list, f.read().splitlines())))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_adjacent(state):\n", " \n", " n, m = state.shape\n", " adjacent = [['' for _ in range(m)] for _ in range(n)]\n", " for (i, j), x in np.ndenumerate(state):\n", " for a, b in [(i + k, j + l) for k, l in product([1,0,-1], repeat=2) if ((k != 0) or (l != 0))]:\n", " if (0 <= a <= n-1) and (0 <= b <= m-1):\n", " adjacent[a][b] += x\n", " return adjacent" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def step(state):\n", " \n", " adjacent = get_adjacent(state)\n", " new_grid = np.empty_like(state)\n", " for (i, j), x in np.ndenumerate(state):\n", " if (x == 'L') and (adjacent[i][j].count('#') == 0):\n", " new_grid[i, j] = '#'\n", " elif (x == '#') and (adjacent[i][j].count('#') >= 4):\n", " new_grid[i, j] = 'L'\n", " else:\n", " new_grid[i,j] = x\n", " return new_grid" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def stable_sum(state):\n", " \n", " t = state\n", " while True:\n", " if np.all(t == step(t)):\n", " break\n", " t = step(t)\n", " return t, np.sum(t == '#')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# test\n", "\n", "with open('input_test.txt', 'rt') as f:\n", " test_grid = np.array(list(map(list, f.read().splitlines())))\n", "stable_sum(test_grid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# solution\n", "\n", "stable_sum(grid)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "--- Part Two ---\n", "As soon as people start to arrive, you realize your mistake. People don't just care about adjacent seats - they care about the first seat they can see in each of those eight directions!\n", "\n", "Now, instead of considering just the eight immediately adjacent seats, consider the first seat in each of those eight directions. For example, the empty seat below would see eight occupied seats:\n", "\n", ".......#.\n", "...#.....\n", ".#.......\n", ".........\n", "..#L....#\n", "....#....\n", ".........\n", "#........\n", "...#.....\n", "The leftmost empty seat below would only see one empty seat, but cannot see any of the occupied ones:\n", "\n", ".............\n", ".L.L.#.#.#.#.\n", ".............\n", "The empty seat below would see no occupied seats:\n", "\n", ".##.##.\n", "#.#.#.#\n", "##...##\n", "...L...\n", "##...##\n", "#.#.#.#\n", ".##.##.\n", "Also, people seem to be more tolerant than you expected: it now takes five or more visible occupied seats for an occupied seat to become empty (rather than four or more from the previous rules). The other rules still apply: empty seats that see no occupied seats become occupied, seats matching no rule don't change, and floor never changes.\n", "\n", "Given the same starting layout as above, these new rules cause the seating area to shift around as follows:\n", "\n", "L.LL.LL.LL\n", "LLLLLLL.LL\n", "L.L.L..L..\n", "LLLL.LL.LL\n", "L.LL.LL.LL\n", "L.LLLLL.LL\n", "..L.L.....\n", "LLLLLLLLLL\n", "L.LLLLLL.L\n", "L.LLLLL.LL\n", "#.##.##.##\n", "#######.##\n", "#.#.#..#..\n", "####.##.##\n", "#.##.##.##\n", "#.#####.##\n", "..#.#.....\n", "##########\n", "#.######.#\n", "#.#####.##\n", "#.LL.LL.L#\n", "#LLLLLL.LL\n", "L.L.L..L..\n", "LLLL.LL.LL\n", "L.LL.LL.LL\n", "L.LLLLL.LL\n", "..L.L.....\n", "LLLLLLLLL#\n", "#.LLLLLL.L\n", "#.LLLLL.L#\n", "#.L#.##.L#\n", "#L#####.LL\n", "L.#.#..#..\n", "##L#.##.##\n", "#.##.#L.##\n", "#.#####.#L\n", "..#.#.....\n", "LLL####LL#\n", "#.L#####.L\n", "#.L####.L#\n", "#.L#.L#.L#\n", "#LLLLLL.LL\n", "L.L.L..#..\n", "##LL.LL.L#\n", "L.LL.LL.L#\n", "#.LLLLL.LL\n", "..L.L.....\n", "LLLLLLLLL#\n", "#.LLLLL#.L\n", "#.L#LL#.L#\n", "#.L#.L#.L#\n", "#LLLLLL.LL\n", "L.L.L..#..\n", "##L#.#L.L#\n", "L.L#.#L.L#\n", "#.L####.LL\n", "..#.#.....\n", "LLL###LLL#\n", "#.LLLLL#.L\n", "#.L#LL#.L#\n", "#.L#.L#.L#\n", "#LLLLLL.LL\n", "L.L.L..#..\n", "##L#.#L.L#\n", "L.L#.LL.L#\n", "#.LLLL#.LL\n", "..#.L.....\n", "LLL###LLL#\n", "#.LLLLL#.L\n", "#.L#LL#.L#\n", "Again, at this point, people stop shifting around and the seating area reaches equilibrium. Once this occurs, you count 26 occupied seats.\n", "\n", "Given the new visibility method and the rule change for occupied seats becoming empty, once equilibrium is reached, how many seats end up occupied?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_adjacent(state):\n", " \n", " n, m = state.shape\n", " adjacent = [['' for _ in range(m)] for _ in range(n)]\n", " for (i, j), x in np.ndenumerate(state):\n", " for k, l in product([1,0,-1], repeat=2):\n", " if (k != 0) or (l != 0):\n", " d = 1\n", " a, b = i+k, j+l\n", " while (0 <= a <= n-1) and (0 <= b <= m-1) and (state[a,b] == '.'):\n", " d += 1\n", " a, b = i+d*k, j+d*l\n", " if (0 <= a <= n-1) and (0 <= b <= m-1):\n", " adjacent[a][b] += x\n", " return adjacent" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def step(state):\n", " \n", " adjacent = get_adjacent(state)\n", " new_grid = np.empty_like(state)\n", " for (i, j), x in np.ndenumerate(state):\n", " if (x == 'L') and (adjacent[i][j].count('#') == 0):\n", " new_grid[i, j] = '#'\n", " elif (x == '#') and (adjacent[i][j].count('#') >= 5):\n", " new_grid[i, j] = 'L'\n", " else:\n", " new_grid[i,j] = x\n", " return new_grid" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# test\n", "\n", "stable_sum(test_grid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# solution\n", "\n", "stable_sum(grid)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:stats_env]", "language": "python", "name": "conda-env-stats_env-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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
vtesin/sklearn_tutorial
doc/notebooks/12_exercise03.ipynb
3
18845
{ "metadata": { "name": "12_exercise03" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Exercise 3: Dimensionality Reduction of Spectra" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, we explore manifold learning techniques to visualize 4000\n", "SDSS spectral data. This is a much more exploratory exercise than the previous\n", "two. The goal is to determine how to best visualize this high-dimensional\n", "space. You will implement PCA, LLE, Modified LLE, and Isomap, for various\n", "data normalizations. The goal is to find the best visualization of the\n", "data, where \"best\" in this case is a qualitative measure of how well the\n", "different classes of points are separated in the projected space.\n", "\n", "Because we're going to be plotting things below, we'll first make sure we're in\n", "ipython's pylab mode:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import pylab as pl" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Loading Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial assumes the notebook is within the tutorial directory structure,\n", "and that the fetch_data.py script has been run to download the data locally.\n", "If the data is in a different location, you can change the DATA_HOME variable below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "DATA_HOME = os.path.abspath('../data/sdss_spectra')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "data = np.load(os.path.join(DATA_HOME, 'spec4000_corrected.npz'))\n", "\n", "X = data['X']\n", "y = data['y']\n", "labels = data['labels']\n", "\n", "# shuffle the data\n", "i = np.arange(y.shape[0], dtype=int)\n", "np.random.shuffle(i)\n", "X = X[i]\n", "y = y[i]\n", "\n", "print X.shape" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we see that the dataset consists of 4000 points in 1000 dimensions.\n", "Let's plot a few of the spectra so we can see what they look like:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_spectral_types(data):\n", " X = data['X']\n", " y = data['y']\n", " wavelengths = data['wavelengths']\n", " labels = data['labels']\n", "\n", " for i_class in (2, 3, 4, 5, 6):\n", " i = np.where(y == i_class)[0][0]\n", " l = pl.plot(wavelengths, X[i] + 20 * i_class)\n", " c = l[0].get_color()\n", " pl.text(6800, 2 + 20 * i_class, labels[i_class], color=c)\n", " \n", " pl.subplots_adjust(hspace=0)\n", " pl.xlabel('wavelength (Angstroms)')\n", " pl.ylabel('flux + offset')\n", " pl.title('Sample of Spectra')\n", " \n", "plot_spectral_types(data)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see what the 1000 dimensions mean: for each object, there is a measurement\n", "in each of 1000 wavelength bins. In other words, these objects exist in a\n", "1000 dimensional parameter space. If we could draw a graph in 1000 dimensions,\n", "each spectrum could be represented by a single point in this 1000 dimensional space.\n", "\n", "Unfortunately, it is very difficult for us to visualize even four or five dimensions,\n", "let alone 1000. This is why it is often useful to think about finding an optimal\n", "lower-dimensional projection of the dataset, where optimal is defined in some\n", "quantitative way.\n", "\n", "In this exercise we will be visualizing several three-dimensional projections\n", "of high dimensional data. To streamline this, we'll first define a function\n", "which lets us scatter-plot three dimensions of data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def three_component_plot(c1, c2, c3, color, labels,\n", " trim_outliers=True, sigma_cutoff=3):\n", " if trim_outliers:\n", " mask = np.zeros(c1.shape, dtype=bool)\n", " for c in [c1, c2, c3]:\n", " c = np.asarray(c)\n", " mu = np.mean(c)\n", " std = np.std(c)\n", " mask |= (c > mu + sigma_cutoff * std)\n", " mask |= (c < mu - sigma_cutoff * std)\n", " \n", " print \"removing %i outliers\" % mask.sum()\n", " \n", " c1 = c1[~mask]\n", " c2 = c2[~mask]\n", " c3 = c3[~mask]\n", " color = color[~mask]\n", " \n", " fig = pl.figure(figsize=(8,8))\n", " fig.subplots_adjust(hspace=0.05, wspace=0.05)\n", " \n", " kwargs = dict(s=8, lw=0, c=color, vmin=2, vmax=6)\n", " ax1 = fig.add_subplot(221)\n", " pl.scatter(c1, c2, **kwargs)\n", " ax1.set_ylabel('component 2')\n", "\n", " ax2 = fig.add_subplot(223, sharex=ax1)\n", " pl.scatter(c1, c3, **kwargs)\n", " ax2.set_xlabel('component 1')\n", " ax2.set_ylabel('component 3')\n", "\n", " ax3 = fig.add_subplot(224, sharey=ax2)\n", " pl.scatter(c2, c3, **kwargs)\n", " ax3.set_xlabel('component 2')\n", "\n", " for ax in (ax1, ax2, ax3):\n", " ax.xaxis.set_major_formatter(pl.NullFormatter())\n", " ax.yaxis.set_major_formatter(pl.NullFormatter())\n", " \n", " ax.axis('tight')\n", "\n", " color_format = pl.FuncFormatter(lambda i, *args: labels[i])\n", " pl.colorbar(ticks = range(2, 7), format=color_format,\n", " cax = pl.axes((0.52, 0.51, 0.02, 0.39)))\n", " pl.clim(1.5, 6.5)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try this out right now: we'll use three randomly-drawn\n", "dimensions for the points within the dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "three_component_plot(X[:, 100], X[:, 200], X[:, 300], y, labels, trim_outliers=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that there are some strong correlations between the different\n", "dimensions. In particular, component 2 and 3 seem to measure nearly\n", "the same information.\n", "\n", "As we saw in the earlier exercise, one possible projection to use is\n", "based on Principal Component Analysis. This looks for the linear\n", "combination of data attributes which show the largest variance, and\n", "thus are in some sense the most \"important\" combination of features.\n", "\n", "We'll want to experiment with different numbers of samples, and\n", "different normalizations, so we'll create a quick convenience\n", "function to streamline this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import preprocessing\n", "\n", "def preprocess(data, shuffle=True, n_samples=1000, normalization=None):\n", " \"\"\"Preprocess the data\n", "\n", " Parameters\n", " ----------\n", " shuffle: True or False\n", " n_samples: integer between 1 and 4000\n", " normalization: None or 'L1' or 'L2'\n", " \"\"\"\n", " X = data['X']\n", " y = data['y']\n", " \n", " # shuffle the data\n", " if shuffle:\n", " i = np.arange(y.shape[0], dtype=int)\n", " np.random.shuffle(i)\n", " X = X[i]\n", " y = y[i]\n", " \n", " # truncate the data\n", " X = X[:n_samples]\n", " y = y[:n_samples]\n", " \n", " # normalize the data\n", " if not normalization:\n", " pass\n", " elif normalization.lower() == 'l2':\n", " X = preprocessing.normalize(X, 'l2')\n", " elif normalization.lower() == 'l1':\n", " X = preprocessing.normalize(X, 'l1')\n", " else:\n", " raise ValueError(\"Unrecognized normalization: '%s'\" % normalization)\n", " \n", " return X, y" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Principal Component Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll perform the randomized PCA projection" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Principal Component Analysis\n", "from sklearn.decomposition import RandomizedPCA\n", "\n", "X, y = preprocess(data, shuffle=False, n_samples=1000, normalization='L2')\n", "rpca = RandomizedPCA(n_components=3, random_state=0)\n", "X_proj = rpca.fit_transform(X)\n", "\n", "three_component_plot(X_proj[:, 0], X_proj[:, 1], X_proj[:, 2], y, labels, trim_outliers=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The PCA projection allows us to visualize the data in a meaningful way.\n", "In particular, it shows that the absorption galaxies (blue points) do\n", "occupy a different region of parameter space than the emission galaxies\n", "(green points), and that quasars (orange and red points) are relatively\n", "rare in the dataset.\n", "This sort of projection has been successfully used, for example,\n", "as a projection method to understand the relationship between galaxies\n", "and quasars, and also a step toward automated classification based on\n", "spectra (see Yip *et al.* 2004).\n", "\n", "But the weakness of PCA is that it is a *linear* projection, and features\n", "that distinguish quasars (red and orange points) from emission galaxies\n", "(green points) are non-linear: for this reason, nonlinear projections can\n", "be a better choice (see Vanderplas *et al.* 2009). In this exercise we\n", "will explore several of these projection methods. For a description of\n", "the available methods, see http://scikit-learn.org/stable/modules/manifold.html." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Locally Linear Embedding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's try *Locally Linear Embedding* (available in ``sklearn.manifold.LLE``)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# here create an object from sklearn.manifold.LLE with n_components=3 and method='standard'\n", "# you will have to select n_neighbors. 15 is a good first guess.\n", "# initialize the object, fit on the dataset X, and compute the projection X_proj\n", "# The syntax is similar to RandomizedPCA, above\n", "\n", "# on older versions of scikit-learn, out_dim is used rather than n_components\n", "\n", "#X, y = preprocess(...\n", "\n", "# perform LLE fit here\n", "\n", "#three_component_plot(...\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the notebook is within the tutorial directory structure,\n", "the following command will load the solution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%loadpy soln/03-01.py" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do you notice about this projection? What are the effects of normalizing\n", "or not normalizing? How does the number of neighbors affect the results?" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Modified Locally Linear Embedding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we'll try out modified LLE (MLLE). MLLE essentially uses multiple\n", "coefficients in each neighborhood to better preserve the local geometry\n", "of the data in the projection. You can used modified LLE by copying the\n", "above LLE code, but with the keyword `method = 'modified'`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# train and plot the MLLE projection of the data here\n", "# use the LLE code from above as a template.\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the notebook is within the tutorial directory structure,\n", "the following command will load the solution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%loadpy soln/03-02.py" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How does this projection compare to that of standard LLE? Does this\n", "projection lead to a more intuitive representation of the relationship\n", "between the points? Experiment with a few choices of n_neighbors, and\n", "normalization vs. no normalization. " ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Isomap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final technique we'll explore is *Isomap*, short for \"Isometric Mapping\".\n", "It's an approach to the same problem that is based on graph theory. The\n", "scikit-learn implementation is in ``sklearn.manifold.Isomap``. As above,\n", "you should ``preprocess`` the data, compute the Isomap projection, and then\n", "plot the results with our ``three_component_plot`` function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# compute and plot the isomap projection here:\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the notebook is within the tutorial directory structure,\n", "the following command will load the solution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%loadpy soln/03-03.py" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#03-03.py\n", "X, y = preprocess(data, shuffle=False, n_samples=1000, normalization=None)\n", "\n", "from sklearn.manifold import Isomap\n", "iso = Isomap(n_neighbors=15, n_components=3)\n", "X_proj = iso.fit_transform(X)\n", "\n", "three_component_plot(X_proj[:, 0], X_proj[:, 1], X_proj[:, 2], y, labels, trim_outliers=True)\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, explore the different options for the projection. How does the\n", "normalization, the number of neighbors, etc. affect the resulting projection?\n", "Between PCA, LLE, MLLE, and Isomap, which method leads to the most useful\n", "visualization of the data (i.e. which could be used to construct and intuitive\n", "model for a simple classification of the different objects?)" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This has been a fairly qualitative exploration of several projection\n", "and visualization methods available in scikit-learn. These have been\n", "explored in more detail in several papers, a selection of which can be\n", "found below:\n", "\n", "- [Yip *et al.* 2004a](http://adsabs.harvard.edu/abs/2004AJ....128.2603Y)\n", " explores PCA of SDSS quasar spectra.\n", "- [Yip *et al.* 2004b](http://adsabs.harvard.edu/abs/2004AJ....128..585Y)\n", " explores PCA of SDSS galaxy spectra.\n", "- [Vanderplas *et al.* 2009](http://adsabs.harvard.edu/abs/2009AJ....138.1365V)\n", " explores LLE of SDSS galaxy and quasar spectra\n", "- [Daniel *et al.* 2011](http://adsabs.harvard.edu/abs/2011AJ....142..203D)\n", " explores LLE of SDSS stellar spectra\n", "- [Gal *et al.* 2012a](http://adsabs.harvard.edu/abs/2012AJ....143..123M)\n", " explores LLE as a preprocessing step for Kepler light curves (i.e. time-domain\n", " data)\n", "- [Gal *et al.* 2012b](http://adsabs.harvard.edu/abs/2012ApJS..200...14M)\n", " explores LLE as a preprocessing step for classification of RAVE spectra.\n", "\n", "There are still many unanswered questions in the use of manifold learning methods,\n", "both in general and in Astronomical applications." ] } ], "metadata": {} } ] }
bsd-3-clause
arsenovic/galgebra
examples/ipython/simple_ga_test.ipynb
1
13155
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from galgebra.printer import Format\n", "from galgebra.ga import Ga\n", "from sympy import symbols" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "Format()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "coords = (x,y,z) = symbols('x,y,z',real=True)\n", "(o3d,ex,ey,ez) = Ga.build('e_x e_y e_z',g=[1,1,1],coords=coords)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "v = o3d.mv('v','vector')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} v = v^{x} \\boldsymbol{e}_{x} + v^{y} \\boldsymbol{e}_{y} + v^{z} \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "v^{x} \\boldsymbol{e}_{x} + v^{y} \\boldsymbol{e}_{y} + v^{z} \\boldsymbol{e}_{z}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ " \\begin{align*} v =& v^{x} \\boldsymbol{e}_{x} \\\\ & + v^{y} \\boldsymbol{e}_{y} \\\\ & + v^{z} \\boldsymbol{e}_{z} \\end{align*} \n" ], "text/plain": [ " \\begin{align*} & v^{x} \\boldsymbol{e}_{x} \\\\ & + v^{y} \\boldsymbol{e}_{y} \\\\ & + v^{z} \\boldsymbol{e}_{z} \\end{align*} " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v.Fmt(3,'v')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "V = o3d.mv('V','vector',f=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} V = V^{x} \\boldsymbol{e}_{x} + V^{y} \\boldsymbol{e}_{y} + V^{z} \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "V^{x} \\boldsymbol{e}_{x} + V^{y} \\boldsymbol{e}_{y} + V^{z} \\boldsymbol{e}_{z}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "V.Fmt()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} V = V^{x} \\boldsymbol{e}_{x} + V^{y} \\boldsymbol{e}_{y} + V^{z} \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "V^{x} \\boldsymbol{e}_{x} + V^{y} \\boldsymbol{e}_{y} + V^{z} \\boldsymbol{e}_{z}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "V" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "gradV = o3d.grad*V" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( \\partial_{x} V^{x} + \\partial_{y} V^{y} + \\partial_{z} V^{z} \\right ) + \\left ( - \\partial_{y} V^{x} + \\partial_{x} V^{y} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - \\partial_{z} V^{x} + \\partial_{x} V^{z} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - \\partial_{z} V^{y} + \\partial_{y} V^{z} \\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( \\partial_{x} V^{x} + \\partial_{y} V^{y} + \\partial_{z} V^{z} \\right ) + \\left ( - \\partial_{y} V^{x} + \\partial_{x} V^{y} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - \\partial_{z} V^{x} + \\partial_{x} V^{z} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - \\partial_{z} V^{y} + \\partial_{y} V^{z} \\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gradV" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/latex": [ " \\begin{align*} \\nabla V =& \\left ( \\partial_{x} V^{x} + \\partial_{y} V^{y} + \\partial_{z} V^{z} \\right ) \\\\ & + \\left ( - \\partial_{y} V^{x} + \\partial_{x} V^{y} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} \\\\ & + \\left ( - \\partial_{z} V^{x} + \\partial_{x} V^{z} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} \\\\ & + \\left ( - \\partial_{z} V^{y} + \\partial_{y} V^{z} \\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{align*} \n" ], "text/plain": [ " \\begin{align*} & \\left ( \\partial_{x} V^{x} + \\partial_{y} V^{y} + \\partial_{z} V^{z} \\right ) \\\\ & + \\left ( - \\partial_{y} V^{x} + \\partial_{x} V^{y} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} \\\\ & + \\left ( - \\partial_{z} V^{x} + \\partial_{x} V^{z} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} \\\\ & + \\left ( - \\partial_{z} V^{y} + \\partial_{y} V^{z} \\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{align*} " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gradV.Fmt(3,r'\\nabla V')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/latex": [ " \\begin{align*} \\nabla V =& \\left ( \\partial_{x} V^{x} + \\partial_{y} V^{y} + \\partial_{z} V^{z} \\right ) \\\\ & + \\left ( - \\partial_{y} V^{x} + \\partial_{x} V^{y} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - \\partial_{z} V^{x} + \\partial_{x} V^{z} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - \\partial_{z} V^{y} + \\partial_{y} V^{z} \\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{align*} \n" ], "text/plain": [ " \\begin{align*} & \\left ( \\partial_{x} V^{x} + \\partial_{y} V^{y} + \\partial_{z} V^{z} \\right ) \\\\ & + \\left ( - \\partial_{y} V^{x} + \\partial_{x} V^{y} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - \\partial_{z} V^{x} + \\partial_{x} V^{z} \\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - \\partial_{z} V^{y} + \\partial_{y} V^{z} \\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{align*} " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gradV.Fmt(2,r'\\nabla V')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "lap = o3d.grad|o3d.grad" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\frac{\\partial^{2}}{\\partial x^{2}} + \\frac{\\partial^{2}}{\\partial y^{2}} + \\frac{\\partial^{2}}{\\partial z^{2}} \\end{equation*}" ], "text/plain": [ "\\frac{\\partial^{2}}{\\partial x^{2}} + \\frac{\\partial^{2}}{\\partial y^{2}} + \\frac{\\partial^{2}}{\\partial z^{2}}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lap" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\nabla^{2} = \\frac{\\partial^{2}}{\\partial x^{2}} + \\frac{\\partial^{2}}{\\partial y^{2}} + \\frac{\\partial^{2}}{\\partial z^{2}} \\end{equation*}" ], "text/plain": [ "\\frac{\\partial^{2}}{\\partial x^{2}} + \\frac{\\partial^{2}}{\\partial y^{2}} + \\frac{\\partial^{2}}{\\partial z^{2}}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lap.Fmt(1,'\\\\nabla^{2}')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [], "source": [ "A = o3d.lt('A')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left \\{ \\begin{array}{ll} L \\left ( \\boldsymbol{e}_{x}\\right ) =& A_{xx} \\boldsymbol{e}_{x} + A_{yx} \\boldsymbol{e}_{y} + A_{zx} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{y}\\right ) =& A_{xy} \\boldsymbol{e}_{x} + A_{yy} \\boldsymbol{e}_{y} + A_{zy} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{z}\\right ) =& A_{xz} \\boldsymbol{e}_{x} + A_{yz} \\boldsymbol{e}_{y} + A_{zz} \\boldsymbol{e}_{z} \\end{array} \\right \\} \n", " \\end{equation*}" ], "text/plain": [ "\\left \\{ \\begin{array}{ll} L \\left ( \\boldsymbol{e}_{x}\\right ) =& A_{xx} \\boldsymbol{e}_{x} + A_{yx} \\boldsymbol{e}_{y} + A_{zx} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{y}\\right ) =& A_{xy} \\boldsymbol{e}_{x} + A_{yy} \\boldsymbol{e}_{y} + A_{zy} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{z}\\right ) =& A_{xz} \\boldsymbol{e}_{x} + A_{yz} \\boldsymbol{e}_{y} + A_{zz} \\boldsymbol{e}_{z} \\end{array} \\right \\} " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left \\{ \\begin{array}{ll} L \\left ( \\boldsymbol{e}_{x}\\right ) =& A_{xx} \\boldsymbol{e}_{x} + A_{yx} \\boldsymbol{e}_{y} + A_{zx} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{y}\\right ) =& A_{xy} \\boldsymbol{e}_{x} + A_{yy} \\boldsymbol{e}_{y} + A_{zy} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{z}\\right ) =& A_{xz} \\boldsymbol{e}_{x} + A_{yz} \\boldsymbol{e}_{y} + A_{zz} \\boldsymbol{e}_{z} \\end{array} \\right \\} \n", " \\end{equation*}" ], "text/plain": [ "\\left \\{ \\begin{array}{ll} L \\left ( \\boldsymbol{e}_{x}\\right ) =& A_{xx} \\boldsymbol{e}_{x} + A_{yx} \\boldsymbol{e}_{y} + A_{zx} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{y}\\right ) =& A_{xy} \\boldsymbol{e}_{x} + A_{yy} \\boldsymbol{e}_{y} + A_{zy} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{z}\\right ) =& A_{xz} \\boldsymbol{e}_{x} + A_{yz} \\boldsymbol{e}_{y} + A_{zz} \\boldsymbol{e}_{z} \\end{array} \\right \\} " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.Fmt(1,'A')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left \\{ \\begin{array}{ll} L \\left ( \\boldsymbol{e}_{x}\\right ) =& A_{xx} \\boldsymbol{e}_{x} + A_{yx} \\boldsymbol{e}_{y} + A_{zx} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{y}\\right ) =& A_{xy} \\boldsymbol{e}_{x} + A_{yy} \\boldsymbol{e}_{y} + A_{zy} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{z}\\right ) =& A_{xz} \\boldsymbol{e}_{x} + A_{yz} \\boldsymbol{e}_{y} + A_{zz} \\boldsymbol{e}_{z} \\end{array} \\right \\} \n", " \\end{equation*}" ], "text/plain": [ "\\left \\{ \\begin{array}{ll} L \\left ( \\boldsymbol{e}_{x}\\right ) =& A_{xx} \\boldsymbol{e}_{x} + A_{yx} \\boldsymbol{e}_{y} + A_{zx} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{y}\\right ) =& A_{xy} \\boldsymbol{e}_{x} + A_{yy} \\boldsymbol{e}_{y} + A_{zy} \\boldsymbol{e}_{z} \\\\ L \\left ( \\boldsymbol{e}_{z}\\right ) =& A_{xz} \\boldsymbol{e}_{x} + A_{yz} \\boldsymbol{e}_{y} + A_{zz} \\boldsymbol{e}_{z} \\end{array} \\right \\} " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.Fmt(2,'A')" ] }, { "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": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
swails/mdtraj
examples/native-contact.ipynb
12
4954
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing native contacts with MDTraj\n", "\n", "Using the definition from Best, Hummer, and Eaton, \"Native contacts determine protein folding mechanisms in atomistic simulations\" PNAS (2013) [10.1073/pnas.1311599110](http://dx.doi.org/10.1073/pnas.1311599110)\n", "\n", "Eq. (1) of the SI defines the expression for the fraction of native contacts, $Q(X)$:\n", "\n", "$$\n", "Q(X) = \\frac{1}{|S|} \\sum_{(i,j) \\in S} \\frac{1}{1 + \\exp[\\beta(r_{ij}(X) - \\lambda r_{ij}^0)]},\n", "$$\n", "\n", "where\n", " - $X$ is a conformation,\n", " - $r_{ij}(X)$ is the distance between atoms $i$ and $j$ in conformation $X$,\n", " - $r^0_{ij}$ is the distance from heavy atom i to j in the native state conformation,\n", " - $S$ is the set of all pairs of heavy atoms $(i,j)$ belonging to residues $\\theta_i$ and $\\theta_j$ such that $|\\theta_i - \\theta_j| > 3$ and $r^0_{i,} < 4.5 \\unicode{x212B}$,\n", " - $\\beta=5 \\unicode{x212B}^{-1}$,\n", " - $\\lambda=1.8$ for all-atom simulations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import mdtraj as md\n", "from itertools import combinations\n", "\n", "def best_hummer_q(traj, native):\n", " \"\"\"Compute the fraction of native contacts according the definition from\n", " Best, Hummer and Eaton [1]\n", " \n", " Parameters\n", " ----------\n", " traj : md.Trajectory\n", " The trajectory to do the computation for\n", " native : md.Trajectory\n", " The 'native state'. This can be an entire trajecory, or just a single frame.\n", " Only the first conformation is used\n", " \n", " Returns\n", " -------\n", " q : np.array, shape=(len(traj),)\n", " The fraction of native contacts in each frame of `traj`\n", " \n", " References\n", " ----------\n", " ..[1] Best, Hummer, and Eaton, \"Native contacts determine protein folding\n", " mechanisms in atomistic simulations\" PNAS (2013)\n", " \"\"\"\n", " \n", " BETA_CONST = 50 # 1/nm\n", " LAMBDA_CONST = 1.8\n", " NATIVE_CUTOFF = 0.45 # nanometers\n", " \n", " # get the indices of all of the heavy atoms\n", " heavy = native.topology.select_atom_indices('heavy')\n", " # get the pairs of heavy atoms which are farther than 3\n", " # residues apart\n", " heavy_pairs = np.array(\n", " [(i,j) for (i,j) in combinations(heavy, 2)\n", " if abs(native.topology.atom(i).residue.index - \\\n", " native.topology.atom(j).residue.index) > 3])\n", " \n", " # compute the distances between these pairs in the native state\n", " heavy_pairs_distances = md.compute_distances(native[0], heavy_pairs)[0]\n", " # and get the pairs s.t. the distance is less than NATIVE_CUTOFF\n", " native_contacts = heavy_pairs[heavy_pairs_distances < NATIVE_CUTOFF]\n", " print(\"Number of native contacts\", len(native_contacts))\n", " \n", " # now compute these distances for the whole trajectory\n", " r = md.compute_distances(traj, native_contacts)\n", " # and recompute them for just the native state\n", " r0 = md.compute_distances(native[0], native_contacts)\n", " \n", " q = np.mean(1.0 / (1 + np.exp(BETA_CONST * (r - LAMBDA_CONST * r0))), axis=1)\n", " return q " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# pull a random protein from the PDB\n", "# (The unitcell info happens to be wrong)\n", "traj = md.load_pdb('http://www.rcsb.org/pdb/files/2MI7.pdb')\n", "\n", "# just for example, use the first frame as the 'native' conformation\n", "q = best_hummer_q(traj, traj[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.plot(q)\n", "plt.xlabel('Frame', fontsize=14)\n", "plt.ylabel('Q(X)', fontsize=14)\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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-2.1
MATH497project/MATH497-DiabeticRetinopathy
Bug_Report.ipynb
1
25021
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "from pandas import DataFrame\n", "import datetime\n", "import numpy as np\n", "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "table_names = ['all_encounter_data', 'demographics', 'encounters', \n", " 'family_hist_for_Enc','family_hist_list', \n", " 'ICD_for_Enc', \n", " 'macula_findings_for_Enc','SL_Lens_for_Enc', \n", " 'SNOMED_problem_list', 'systemic_disease_for_Enc', 'systemic_disease_list']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "path = 'ICO_data/'\n", "dfs = [pd.read_pickle(path + name + '.pickle') if name != 'ICD_list' else None \n", " for name in table_names]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# 1. Duplicated-case bug in all_encounter_data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Duplicated encounter amount:', 1320)\n", "('Most frequency: ', 6)\n", "('Top 10 frequent encounter: \\n', 'Enc_Nbr\\tduplicated Person_Nbr list')\n", "[(3323961, [642646, 642646, 642646, 642646, 642646, 642646]),\n", " (13193361, [938790, 938790, 938790, 938790, 938790, 938790]),\n", " (10023437, [600601, 600601, 600601, 600601, 600601, 600601]),\n", " (83734, [416597, 416597, 416597, 416597, 416597, 416597]),\n", " (1859931, [555680, 555680, 555680, 555680]),\n", " (3348565, [150929, 150929, 150929, 150929]),\n", " (7155803, [814160, 814160, 814160, 814160]),\n", " (6969441, [416558, 416558, 416558, 416558]),\n", " (3711105, [539050, 539050, 539050, 539050]),\n", " (5511351, [884266, 884266, 884266, 884266])]\n", "Example of the encounter with most frequent duplicated occurence.\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Enc_ID</th>\n", " <th>Enc_Nbr</th>\n", " <th>Enc_Date</th>\n", " <th>Person_ID</th>\n", " <th>Person_Nbr</th>\n", " <th>Primary_Payer</th>\n", " <th>Smoking_Status</th>\n", " <th>BMI</th>\n", " <th>BP</th>\n", " <th>Glucose</th>\n", " <th>...</th>\n", " <th>CYCLO_OD_SPH</th>\n", " <th>CYCLO_OD_CYL</th>\n", " <th>CYCLO_OD_AXIS</th>\n", " <th>CYCLO_OD_DVA</th>\n", " <th>CYCLO_OD_NVA</th>\n", " <th>CYCLO_OS_SPH</th>\n", " <th>CYCLO_OS_CYL</th>\n", " <th>CYCLO_OS_AXIS</th>\n", " <th>CYCLO_OS_DVA</th>\n", " <th>CYCLO_OS_NVA</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>70045</th>\n", " <td>6f1f8e83-4d29-15cb-e38b-2371c66bed8b</td>\n", " <td>3323961</td>\n", " <td>2015-02-14 14:30:00</td>\n", " <td>2d903e5a-3701-141e-7e6e-78dd4a00cd65</td>\n", " <td>642646</td>\n", " <td>Davis CCN Vision Plan</td>\n", " <td>Former smoker</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>168</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>70046</th>\n", " <td>6f1f8e83-4d29-15cb-e38b-2371c66bed8b</td>\n", " <td>3323961</td>\n", " <td>2015-02-14 14:30:00</td>\n", " <td>2d903e5a-3701-141e-7e6e-78dd4a00cd65</td>\n", " <td>642646</td>\n", " <td>Davis CCN Vision Plan</td>\n", " <td>Former smoker</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>190</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>70047</th>\n", " <td>6f1f8e83-4d29-15cb-e38b-2371c66bed8b</td>\n", " <td>3323961</td>\n", " <td>2015-02-14 14:30:00</td>\n", " <td>2d903e5a-3701-141e-7e6e-78dd4a00cd65</td>\n", " <td>642646</td>\n", " <td>Davis CCN Vision Plan</td>\n", " <td>Former smoker</td>\n", " <td>NaN</td>\n", " <td>84 / 47</td>\n", " <td>168</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>70070</th>\n", " <td>6f1f8e83-4d29-15cb-e38b-2371c66bed8b</td>\n", " <td>3323961</td>\n", " <td>2015-02-14 14:30:00</td>\n", " <td>2d903e5a-3701-141e-7e6e-78dd4a00cd65</td>\n", " <td>642646</td>\n", " <td>Davis CCN Vision Plan</td>\n", " <td>Former smoker</td>\n", " <td>NaN</td>\n", " <td>84 / 47</td>\n", " <td>190</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>70071</th>\n", " <td>6f1f8e83-4d29-15cb-e38b-2371c66bed8b</td>\n", " <td>3323961</td>\n", " <td>2015-02-14 14:30:00</td>\n", " <td>2d903e5a-3701-141e-7e6e-78dd4a00cd65</td>\n", " <td>642646</td>\n", " <td>Davis CCN Vision Plan</td>\n", " <td>Former smoker</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>168</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 41 columns</p>\n", "</div>" ], "text/plain": [ " Enc_ID Enc_Nbr Enc_Date \\\n", "70045 6f1f8e83-4d29-15cb-e38b-2371c66bed8b 3323961 2015-02-14 14:30:00 \n", "70046 6f1f8e83-4d29-15cb-e38b-2371c66bed8b 3323961 2015-02-14 14:30:00 \n", "70047 6f1f8e83-4d29-15cb-e38b-2371c66bed8b 3323961 2015-02-14 14:30:00 \n", "70070 6f1f8e83-4d29-15cb-e38b-2371c66bed8b 3323961 2015-02-14 14:30:00 \n", "70071 6f1f8e83-4d29-15cb-e38b-2371c66bed8b 3323961 2015-02-14 14:30:00 \n", "\n", " Person_ID Person_Nbr \\\n", "70045 2d903e5a-3701-141e-7e6e-78dd4a00cd65 642646 \n", "70046 2d903e5a-3701-141e-7e6e-78dd4a00cd65 642646 \n", "70047 2d903e5a-3701-141e-7e6e-78dd4a00cd65 642646 \n", "70070 2d903e5a-3701-141e-7e6e-78dd4a00cd65 642646 \n", "70071 2d903e5a-3701-141e-7e6e-78dd4a00cd65 642646 \n", "\n", " Primary_Payer Smoking_Status BMI BP Glucose \\\n", "70045 Davis CCN Vision Plan Former smoker NaN NaN 168 \n", "70046 Davis CCN Vision Plan Former smoker NaN NaN 190 \n", "70047 Davis CCN Vision Plan Former smoker NaN 84 / 47 168 \n", "70070 Davis CCN Vision Plan Former smoker NaN 84 / 47 190 \n", "70071 Davis CCN Vision Plan Former smoker NaN NaN 168 \n", "\n", " ... CYCLO_OD_SPH CYCLO_OD_CYL CYCLO_OD_AXIS CYCLO_OD_DVA \\\n", "70045 ... NaN NaN NaN NaN \n", "70046 ... NaN NaN NaN NaN \n", "70047 ... NaN NaN NaN NaN \n", "70070 ... NaN NaN NaN NaN \n", "70071 ... NaN NaN NaN NaN \n", "\n", " CYCLO_OD_NVA CYCLO_OS_SPH CYCLO_OS_CYL CYCLO_OS_AXIS CYCLO_OS_DVA \\\n", "70045 NaN NaN NaN NaN NaN \n", "70046 NaN NaN NaN NaN NaN \n", "70047 NaN NaN NaN NaN NaN \n", "70070 NaN NaN NaN NaN NaN \n", "70071 NaN NaN NaN NaN NaN \n", "\n", " CYCLO_OS_NVA \n", "70045 NaN \n", "70046 NaN \n", "70047 NaN \n", "70070 NaN \n", "70071 NaN \n", "\n", "[5 rows x 41 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "duplicated_enc=sorted({k:list(v) for k,v in dfs[0].groupby('Enc_Nbr')['Person_Nbr'] if len(v)>1}.items(), \n", " key=lambda x:len(x[1]), reverse=True)\n", "print('Duplicated encounter amount:', len(duplicated_enc))\n", "print('Most frequency: ', len(duplicated_enc[0][1]))\n", "print('Top 10 frequent encounter: \\n', 'Enc_Nbr\\tduplicated Person_Nbr list')\n", "import pprint\n", "pprint.pprint(duplicated_enc[0:10])\n", "print('Example of the encounter with most frequent duplicated occurence.')\n", "dfs[0][dfs[0].Enc_Nbr==duplicated_enc[0][0]].head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# 2. Unicode bug in Person_id of encounters and demographics" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Person_ID column name of', 'demographics', ': ', '\\xef\\xbb\\xbfPerson_ID')\n", "('Person_ID column name of', 'encounters', ': ', '\\xef\\xbb\\xbfPerson_ID')\n", "Look up Person_ID in all_encounter_data and demographics, and compare its related Person_Nbr to see if equal\n", "result is:\n", "Counter({False: 16087})\n", "Look up Person_ID in all_encounter_data and encounters, and compare its related Person_Nbr to see if equal\n", "result is:\n", "Counter({False: 16087})\n", "It indicates that the whole column of person id in two tables are wrong\n" ] } ], "source": [ "print('Person_ID column name of', table_names[1], ': ',dfs[1].columns.values[0])\n", "print('Person_ID column name of', table_names[2], ': ',dfs[2].columns.values[0])\n", "person_dict_correct={k:list(v) for k,v in \n", " dfs[0][['Person_ID','Person_Nbr']].drop_duplicates().groupby('Person_Nbr')['Person_ID']}\n", "person_dict_demographics={k:list(v) for k,v in \n", " dfs[1].ix[:,0:2].drop_duplicates().groupby('Person_Nbr')[dfs[1].columns.values[0]]}\n", "compare_demographics={k:v==person_dict_demographics[k] for k,v in \n", " person_dict_correct.items() if k in person_dict_demographics.keys()}\n", "print('Look up Person_ID in all_encounter_data and demographics, and compare its related Person_Nbr to see if equal')\n", "from collections import Counter\n", "print('result is:')\n", "print(Counter(compare_demographics.values()))\n", "\n", "person_dict_encounters={k:list(v) for k,v in \n", " dfs[2].ix[:,0:2].drop_duplicates().groupby('Person_Nbr')[dfs[2].columns.values[0]]}\n", "compare_encounters={k:v==person_dict_encounters[k] for k,v in \n", " person_dict_correct.items() if k in person_dict_encounters.keys()}\n", "print('Look up Person_ID in all_encounter_data and encounters, and compare its related Person_Nbr to see if equal')\n", "print('result is:')\n", "print(Counter(compare_encounters.values()))\n", "\n", "print('It indicates that the whole column of person id in two tables are wrong')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# 3. Multiple Values of A1C, BP, BMI, and Glucose per Enc_ID" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'A1C': 136, 'BMI': 280, 'BP': 991, 'Glucose': 260}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check all of the encounter ID's that show up multiple times in the table for where values are unique.\n", "\n", "d_enc = dfs[0]\n", "unique_key_count = {}\n", "\n", "for key in d_enc[d_enc.duplicated(subset=\"Enc_ID\")][\"Enc_ID\"].unique():\n", " table=d_enc[d_enc[\"Enc_ID\"]==key]\n", " for column in list(d_enc):\n", " if table[column].unique().size != 1:\n", " unique_key_count[column] = unique_key_count.setdefault(column,0)+1\n", " #print d_enc[d_enc[\"Enc_ID\"]==key].loc[:,[\"Enc_ID\",column]]\n", "unique_key_count" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# 4. Glucose Contains BP Measurements" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Enc_ID</th>\n", " <th>Enc_Nbr</th>\n", " <th>Enc_Date</th>\n", " <th>Person_ID</th>\n", " <th>Person_Nbr</th>\n", " <th>Primary_Payer</th>\n", " <th>Smoking_Status</th>\n", " <th>BMI</th>\n", " <th>BP</th>\n", " <th>Glucose</th>\n", " <th>...</th>\n", " <th>CYCLO_OD_SPH</th>\n", " <th>CYCLO_OD_CYL</th>\n", " <th>CYCLO_OD_AXIS</th>\n", " <th>CYCLO_OD_DVA</th>\n", " <th>CYCLO_OD_NVA</th>\n", " <th>CYCLO_OS_SPH</th>\n", " <th>CYCLO_OS_CYL</th>\n", " <th>CYCLO_OS_AXIS</th>\n", " <th>CYCLO_OS_DVA</th>\n", " <th>CYCLO_OS_NVA</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>11230</th>\n", " <td>8e40e970-f373-6331-80cb-beed755c62a8</td>\n", " <td>2682098</td>\n", " <td>2014-04-20 02:15:00</td>\n", " <td>31e96813-e876-c8d1-bfce-99b23e93943e</td>\n", " <td>12980</td>\n", " <td>Adv Cigna Health Spring HMO</td>\n", " <td>Smoker</td>\n", " <td>current status unknown</td>\n", " <td>NaN</td>\n", " <td>160 / 100</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>11231</th>\n", " <td>7a3a342f-b558-f7d9-b7a7-b7542f1c2790</td>\n", " <td>1320272</td>\n", " <td>2015-07-06 21:30:00</td>\n", " <td>31e96813-e876-c8d1-bfce-99b23e93943e</td>\n", " <td>12980</td>\n", " <td>Adv Cigna Health Spring HMO</td>\n", " <td>Smoker</td>\n", " <td>current status unknown</td>\n", " <td>NaN</td>\n", " <td>135/ 80</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>46729</th>\n", " <td>8fbd4328-a243-1d87-90ed-6c88a4ec9bde</td>\n", " <td>14365451</td>\n", " <td>2012-03-14 15:45:00</td>\n", " <td>c0fc026c-3aa5-7d9d-39cf-e282a562b797</td>\n", " <td>148299</td>\n", " <td>Adv AHC Wellcare</td>\n", " <td>Smoker</td>\n", " <td>current status unknown</td>\n", " <td>33.14</td>\n", " <td>140 / 85</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>46735</th>\n", " <td>9c59ee1a-23c1-97e4-dcc8-a8905582677a</td>\n", " <td>7335450</td>\n", " <td>2013-09-16 15:00:00</td>\n", " <td>c0fc026c-3aa5-7d9d-39cf-e282a562b797</td>\n", " <td>148299</td>\n", " <td>Adv AHC Wellcare</td>\n", " <td>Smoker</td>\n", " <td>current status unknown</td>\n", " <td>32.08</td>\n", " <td>170 / 72</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>80145</th>\n", " <td>9b77cea6-6901-81a5-4559-878d46e52308</td>\n", " <td>9938118</td>\n", " <td>2015-12-18 08:15:00</td>\n", " <td>99e74f8c-b02d-753a-2986-de1e0ff7c453</td>\n", " <td>154404</td>\n", " <td>Opticare IlliniCare ICP</td>\n", " <td>Smoker</td>\n", " <td>current status unknown</td>\n", " <td>NaN</td>\n", " <td>93/ 63</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>UTT</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>+3.50</td>\n", " <td>-4.50</td>\n", " <td>123</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 41 columns</p>\n", "</div>" ], "text/plain": [ " Enc_ID Enc_Nbr Enc_Date \\\n", "11230 8e40e970-f373-6331-80cb-beed755c62a8 2682098 2014-04-20 02:15:00 \n", "11231 7a3a342f-b558-f7d9-b7a7-b7542f1c2790 1320272 2015-07-06 21:30:00 \n", "46729 8fbd4328-a243-1d87-90ed-6c88a4ec9bde 14365451 2012-03-14 15:45:00 \n", "46735 9c59ee1a-23c1-97e4-dcc8-a8905582677a 7335450 2013-09-16 15:00:00 \n", "80145 9b77cea6-6901-81a5-4559-878d46e52308 9938118 2015-12-18 08:15:00 \n", "\n", " Person_ID Person_Nbr \\\n", "11230 31e96813-e876-c8d1-bfce-99b23e93943e 12980 \n", "11231 31e96813-e876-c8d1-bfce-99b23e93943e 12980 \n", "46729 c0fc026c-3aa5-7d9d-39cf-e282a562b797 148299 \n", "46735 c0fc026c-3aa5-7d9d-39cf-e282a562b797 148299 \n", "80145 99e74f8c-b02d-753a-2986-de1e0ff7c453 154404 \n", "\n", " Primary_Payer Smoking_Status BMI \\\n", "11230 Adv Cigna Health Spring HMO Smoker current status unknown \n", "11231 Adv Cigna Health Spring HMO Smoker current status unknown \n", "46729 Adv AHC Wellcare Smoker current status unknown \n", "46735 Adv AHC Wellcare Smoker current status unknown \n", "80145 Opticare IlliniCare ICP Smoker current status unknown \n", "\n", " BP Glucose ... CYCLO_OD_SPH CYCLO_OD_CYL CYCLO_OD_AXIS \\\n", "11230 NaN 160 / 100 ... NaN NaN NaN \n", "11231 NaN 135/ 80 ... NaN NaN NaN \n", "46729 33.14 140 / 85 ... NaN NaN NaN \n", "46735 32.08 170 / 72 ... NaN NaN NaN \n", "80145 NaN 93/ 63 ... NaN UTT NaN \n", "\n", " CYCLO_OD_DVA CYCLO_OD_NVA CYCLO_OS_SPH CYCLO_OS_CYL CYCLO_OS_AXIS \\\n", "11230 NaN NaN NaN NaN NaN \n", "11231 NaN NaN NaN NaN NaN \n", "46729 NaN NaN NaN NaN NaN \n", "46735 NaN NaN NaN NaN NaN \n", "80145 NaN NaN NaN +3.50 -4.50 \n", "\n", " CYCLO_OS_DVA CYCLO_OS_NVA \n", "11230 NaN NaN \n", "11231 NaN NaN \n", "46729 NaN NaN \n", "46735 NaN NaN \n", "80145 123 NaN \n", "\n", "[5 rows x 41 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "\n", "pattern = re.compile(\"\\d+\\s*\\/\\s*\\d+\")\n", "dfs[0][dfs[0]['Glucose'].str.contains(pattern, na=False)].head()" ] } ], "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
yanikou19/pymatgen
examples/Explanation of Corrections.ipynb
1
4133
{ "metadata": { "name": "", "signature": "sha256:1b0762dea41bd3b1b6224430bfd0661f6a71ee0df8e3f375cec94e2e5dacbca3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook illustrates how to obtain an explaination of the different corrections being applied in the Materials Project." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import re\n", "from pymatgen.entries.computed_entries import ComputedEntry\n", "from pymatgen.entries.compatibility import MaterialsProjectCompatibility" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from pymatgen import MPRester\n", "\n", "#To do our testing, let's use the MPRester to get a sample computed entry from the Materials Project.\n", "m = MPRester()\n", "entries = m.get_entries(\"LiFePO4\")\n", "entry = entries[0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "compat = MaterialsProjectCompatibility()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "compat.explain(entry)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The uncorrected value of the energy of P4 Fe4 O16 Li4 is -191.338121 eV\n", "The following corrections / screening are applied for MaterialsProjectCompatibility:\n", "\n", "MP Potcar Correction correction: Checks that POTCARs are valid within a pre-defined input set. This\n", " ensures that calculations performed using different InputSets are not\n", " compared against each other.\n", "\n", " Entry.parameters must contain a \"potcar_symbols\" key that is a list of\n", " all POTCARs used in the run. Again, using the example of an Fe2O3 run\n", " using Materials Project parameters, this would look like\n", " entry.parameters[\"potcar_symbols\"] = ['PAW_PBE Fe_pv 06Sep2000',\n", " 'PAW_PBE O 08Apr2002'].\n", "\n", "This correction does not make any changes to the energy.\n", "------------------------------\n", "MP Gas Correction correction: Correct gas energies to obtain the right formation energies. Note that\n", " this depends on calculations being run within the same input set.\n", "\n", "For the entry, this correction has the value -11.236640 eV.\n", "------------------------------\n", "MP Advanced Correction correction: This class implements the GGA/GGA+U mixing scheme, which allows mixing of\n", " entries. Entry.parameters must contain a \"hubbards\" key which is a dict\n", " of all non-zero Hubbard U values used in the calculation. For example,\n", " if you ran a Fe2O3 calculation with Materials Project parameters,\n", " this would look like entry.parameters[\"hubbards\"] = {\"Fe\": 5.3}\n", " If the \"hubbards\" key is missing, a GGA run is assumed.\n", "\n", " It should be noted that ComputedEntries assimilated using the\n", " pymatgen.apps.borg package and obtained via the MaterialsProject REST\n", " interface using the pymatgen.matproj.rest package will automatically have\n", " these fields populated.\n", "\n", "For the entry, this correction has the value -10.932000 eV.\n", "------------------------------\n", "The final energy after corrections is -213.506761\n" ] } ], "prompt_number": 4 } ], "metadata": {} } ] }
mit
ClaudioESSilva/SQLServer-PowerShell
Presentations/SQLSaturday#926 - Lisboa 2019/05-Refresh-Database.ipynb
1
10413
{ "metadata": { "kernelspec": { "name": "powershell", "display_name": "PowerShell" }, "language_info": { "name": "powershell", "codemirror_mode": "shell", "mimetype": "text/x-sh", "file_extension": ".ps1" } }, "nbformat_minor": 2, "nbformat": 4, "cells": [ { "cell_type": "markdown", "source": [ "<pre>\r\n", "██████╗ ██████╗ █████╗ ████████╗ ██████╗ ██████╗ ██╗ ███████╗ \r\n", "██╔══██╗██╔══██╗██╔══██╗╚══██╔══╝██╔═══██╗██╔═══██╗██║ ██╔════╝ \r\n", "██║ ██║██████╔╝███████║ ██║ ██║ ██║██║ ██║██║ ███████╗ \r\n", "██║ ██║██╔══██╗██╔══██║ ██║ ██║ ██║██║ ██║██║ ╚════██║ \r\n", "██████╔╝██████╔╝██║ ██║ ██║ ╚██████╔╝╚██████╔╝███████╗███████║ \r\n", "╚═════╝ ╚═════╝ ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═════╝ ╚══════╝╚══════╝ \r\n", " \r\n", "██████╗ ███████╗ ██████╗██╗██████╗ ███████╗ ██╗ ██╗ ██████╗ ███████╗\r\n", "██╔══██╗██╔════╝██╔════╝██║██╔══██╗██╔════╝ ████████╗██╔═████╗██╔════╝\r\n", "██████╔╝█████╗ ██║ ██║██████╔╝█████╗ ╚██╔═██╔╝██║██╔██║███████╗\r\n", "██╔══██╗██╔══╝ ██║ ██║██╔═══╝ ██╔══╝ ████████╗████╔╝██║╚════██║\r\n", "██║ ██║███████╗╚██████╗██║██║ ███████╗ ╚██╔═██╔╝╚██████╔╝███████║\r\n", "╚═╝ ╚═╝╚══════╝ ╚═════╝╚═╝╚═╝ ╚══════╝ ╚═╝ ╚═╝ ╚═════╝ ╚══════╝\r\n", "</pre>\r\n", "# Recipe #05 - Let's cook!\r\n", "## Another main course: \r\n", "### - Database refresh\r\n", "1. Export users on destination\r\n", "2. Backup source database and restore it on destination\r\n", "3. Run data masking (If used) \r\n", "4. Run exported permissions on step 1\r\n", "5. Fix/remove orphan users  \r\n", "<hr>" ], "metadata": { "azdata_cell_guid": "e3e5c233-5e97-4d5c-b33d-890404d779b3" } }, { "cell_type": "markdown", "source": [ "Set variables" ], "metadata": { "azdata_cell_guid": "d20de8e0-4ee2-4038-89fd-ec49d25e5bac" } }, { "cell_type": "code", "source": [ "$dbatools1 = \"localhost,1433\"\r\n", "$dbatools2 = \"localhost,14333\"\r\n", "$secureString = ConvertTo-SecureString \"dbatools.IO\" -AsPlainText -Force\r\n", "$cred = New-Object -TypeName System.Management.Automation.PSCredential -ArgumentList \"sqladmin\", $secureString\r\n", "$databaseToRefresh = \"dbatools\"" ], "metadata": { "azdata_cell_guid": "eccbf5f5-65da-4fcb-b69e-29b1dcd0b083" }, "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "source": [ "### 1 - Export users on destination" ], "metadata": { "azdata_cell_guid": "ed5c593c-a593-4bbf-ba66-078c30530b50" } }, { "cell_type": "code", "source": [ "# Export the user from the specific database and its permissions at database-roles and object level\n", "$usersExport = Export-DbaUser -SqlInstance $dbatools2 -SqlCredential $cred -Database $databaseToRefresh -Passthru\n", "\n", "$usersExport" ], "metadata": { "azdata_cell_guid": "151b11f8-086d-4b09-a20a-0e39c0e603f3" }, "outputs": [], "execution_count": 2 }, { "cell_type": "markdown", "source": [ "### 2 - Backup source database and restore it on destination" ], "metadata": { "azdata_cell_guid": "a90644b3-7105-4824-b3a7-aa0808f0787b" } }, { "cell_type": "code", "source": [ "$copyDatabaseSplat = @{\r\n", " Source = $dbatools1\r\n", " SourceSqlCredential = $cred\r\n", " Destination = $dbatools2\r\n", " DestinationSqlCredential = $cred\r\n", " Database = $databaseToRefresh\r\n", " BackupRestore = $true\r\n", " SharedPath = \"/tmp\"\r\n", " WithReplace = $true\r\n", "}\r\n", "Copy-DbaDatabase @copyDatabaseSplat" ], "metadata": { "azdata_cell_guid": "1e319582-02bc-4360-814a-4552b8e8331a" }, "outputs": [], "execution_count": 3 }, { "cell_type": "markdown", "source": [ "### Verify the orphan users" ], "metadata": { "azdata_cell_guid": "9c79e5e9-5d3a-4597-92b5-2d2da184d20e" } }, { "cell_type": "code", "source": [ "# Verify orphan users\r\n", "Get-DbaDbOrphanUser -SqlInstance $dbatools2 -SqlCredential $cred -Database $databaseToRefresh" ], "metadata": { "azdata_cell_guid": "6cae3fca-95d8-462d-811c-179b8bedb473" }, "outputs": [], "execution_count": 4 }, { "cell_type": "code", "source": [ "# Repair Orphan users and remove the none existing\r\n", "Repair-DbaDbOrphanUser -SqlInstance $dbatools2 -SqlCredential $cred -Database $databaseToRefresh" ], "metadata": { "azdata_cell_guid": "23c67982-537a-42d3-8c87-f8991ed9705d" }, "outputs": [], "execution_count": 5 }, { "cell_type": "code", "source": [ "# Remove Orphan Users\r\n", "Remove-DbaDbOrphanUser -SqlInstance $dbatools2 -SqlCredential $cred -Database $databaseToRefresh -Verbose" ], "metadata": { "azdata_cell_guid": "7c415402-a76c-4967-a453-77df7e76a7d1" }, "outputs": [], "execution_count": 6 }, { "cell_type": "markdown", "source": [ "### Recreate users and grant permissions from the exported command" ], "metadata": { "azdata_cell_guid": "346dd2ea-500e-4af9-b884-8440595603d0" } }, { "cell_type": "code", "source": [ "# Run the exported script\n", "Invoke-DbaQuery -SqlInstance $dbatools2 -SqlCredential $cred -Database $databaseToRefresh -Query $($usersExport -replace '\\bGO\\b', ' ') -Verbose" ], "metadata": { "azdata_cell_guid": "ee2c45b8-1953-4337-be9c-9089098ab80a" }, "outputs": [], "execution_count": 7 }, { "cell_type": "code", "source": [ "# Confirm that we don't have orphan users\n", "Get-DbaDbOrphanUser -SqlInstance $dbatools2 -SqlCredential $cred -Database $databaseToRefresh -Verbose" ], "metadata": { "azdata_cell_guid": "45daea6a-a65d-4fde-a0e6-c2f7863bd689" }, "outputs": [], "execution_count": 8 }, { "cell_type": "markdown", "source": [ "### Connect as `dbatools_dev` and try to select some data" ], "metadata": { "azdata_cell_guid": "6bd76228-0ba0-41c8-ac90-f04a989e548b" } }, { "cell_type": "code", "source": [ "# Test connect as dbatools_dev and select table where it does not have permissions\n", "$cred_dev = New-Object -TypeName System.Management.Automation.PSCredential -ArgumentList \"dbatools_dev\", $secureString\n", "Invoke-DbaQuery -SqlInstance $dbatools2 -SqlCredential $cred_dev -Database $databaseToRefresh -Query \"SELECT SUSER_NAME()\"" ], "metadata": { "azdata_cell_guid": "079c5bd5-0c14-449d-ba55-54b52875536e", "tags": [] }, "outputs": [], "execution_count": 10 } ] }
gpl-3.0
jstoxrocky/statsmodels
examples/notebooks/statespace_sarimax_internet.ipynb
6
9366
{ "metadata": { "name": "", "signature": "sha256:11278e6b7bba23662e5cb47cf1d0051bdac0d5f368e5de5e5ae814d866cf0c8b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "SARIMAX: Model selection, missing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import norm\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import requests\n", "from StringIO import StringIO\n", "from zipfile import ZipFile\n", "\n", "# Download the dataset\n", "dk = requests.get('http://www.ssfpack.com/files/DK-data.zip').content\n", "zipped = ZipFile(StringIO(dk))\n", "df = pd.read_table(\n", " StringIO(zipped.read('internet.dat')),\n", " skiprows=1, header=None, sep='\\s+', engine='python',\n", " names=['internet','dinternet']\n", ")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Selection\n", "\n", "As in Durbin and Koopman, we force a number of the values to be missing." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Get the basic series\n", "dta_full = df.dinternet[1:].values\n", "dta_miss = dta_full.copy()\n", "\n", "# Remove datapoints\n", "missing = np.r_[6,16,26,36,46,56,66,72,73,74,75,76,86,96]-1\n", "dta_miss[missing] = np.nan" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can consider model selection using the Akaike information criteria (AIC), but running the model for each variant and selecting the model with the lowest AIC value.\n", "\n", "There are a couple of things to note here:\n", "\n", "- When running such a large batch of models, particularly when the autoregressive and moving average orders become large, there is the possibility of poor maximum likelihood convergence. Below we ignore the warnings since this example is illustrative.\n", "- We use the option `enforce_invertibility=False`, which allows the moving average polynomial to be non-invertible, so that more of the models are estimable.\n", "- Several of the models do not produce good results, and their AIC value is set to NaN. This is not surprising, as Durbin and Koopman note numerical problems with the high order models." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import warnings\n", "\n", "aic_full = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "aic_miss = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "\n", "warnings.simplefilter('ignore')\n", "\n", "# Iterate over all ARMA(p,q) models with p,q in [0,6]\n", "for p in range(6):\n", " for q in range(6):\n", " if p == 0 and q == 0:\n", " continue\n", " \n", " # Estimate the model with no missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_full, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_full.iloc[p,q] = res.aic\n", " except:\n", " aic_full.iloc[p,q] = np.nan\n", " \n", " # Estimate the model with missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_miss.iloc[p,q] = res.aic\n", " except:\n", " aic_miss.iloc[p,q] = np.nan" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the models estimated over the full (non-missing) dataset, the AIC chooses ARMA(1,1) or ARMA(3,0). Durbin and Koopman suggest the ARMA(1,1) specification is better due to parsimony.\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.1} ~~ \\text{AIC for different ARMA models.}\\\\\n", "\\newcommand{\\r}[1]{{\\color{red}{#1}}}\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 549.81 & 519.87 & 520.27 & 519.38 & 518.86 \\\\\n", "1 & 529.24 & \\r{514.30} & 516.25 & 514.58 & 515.10 & 516.28 \\\\\n", "2 & 522.18 & 516.29 & 517.16 & 515.77 & 513.24 & 514.73 \\\\\n", "3 & \\r{511.99} & 513.94 & 515.92 & 512.06 & 513.72 & 514.50 \\\\\n", "4 & 513.93 & 512.89 & nan & nan & 514.81 & 516.08 \\\\\n", "5 & 515.86 & 517.64 & nan & nan & nan & nan \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "For the models estimated over missing dataset, the AIC chooses ARMA(1,1)\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.2} ~~ \\text{AIC for different ARMA models with missing observations.}\\\\\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 488.93 & 464.01 & 463.86 & 462.63 & 463.62 \\\\\n", "1 & 468.01 & \\r{457.54} & 459.35 & 458.66 & 459.15 & 461.01 \\\\\n", "2 & 469.68 & nan & 460.48 & 459.43 & 459.23 & 460.47 \\\\\n", "3 & 467.10 & 458.44 & 459.64 & 456.66 & 459.54 & 460.05 \\\\\n", "4 & 469.00 & 459.52 & nan & 463.04 & 459.35 & 460.96 \\\\\n", "5 & 471.32 & 461.26 & nan & nan & 461.00 & 462.97 \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "**Note**: the AIC values are calculated differently than in Durbin and Koopman, but show overall similar trends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Postestimation\n", "\n", "Using the ARMA(1,1) specification selected above, we perform in-sample prediction and out-of-sample forecasting." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Statespace\n", "mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(1,0,1))\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# In-sample one-step-ahead predictions\n", "predict_res = res.predict(full_results=True)\n", "\n", "predict = predict_res.forecasts\n", "cov = predict_res.forecasts_error_cov\n", "predict_idx = np.arange(len(predict[0]))\n", "\n", "# 95% confidence intervals\n", "critical_value = norm.ppf(1 - 0.05 / 2.)\n", "std_errors = np.sqrt(cov.diagonal().T)\n", "ci = np.c_[\n", " (predict - critical_value*std_errors)[:, :, None],\n", " (predict + critical_value*std_errors)[:, :, None],\n", "][0].T" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Out-of-sample forecasts and confidence intervals\n", "nforecast = 20\n", "forecast = res.forecast(nforecast)\n", "forcast_idx = len(dta_full) + np.arange(nforecast)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Graph\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "ax.xaxis.grid()\n", "ax.plot(predict_idx, dta_miss, 'k.')\n", "\n", "# Plot\n", "ax.plot(predict_idx, predict[0], 'gray');\n", "ax.fill_between(predict_idx, ci[0], ci[1], alpha=0.1)\n", "\n", "ax.plot(forcast_idx[-20:], forecast[0], 'k--', linestyle='--', linewidth=2)\n", "\n", "ax.set(title='Figure 8.9 - Internet series');" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
cggh/biipy
test.ipynb
1
315452
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v1.6.0\r\n" ] } ], "source": [ "!cat /biipy/version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check numpy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n", " 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n", " 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n", " 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "a = np.arange(100)\n", "a" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lapack_opt_info:\n", " define_macros = [('HAVE_CBLAS', None)]\n", " libraries = ['openblas']\n", " language = c\n", " library_dirs = ['/usr/lib']\n", "blas_opt_info:\n", " define_macros = [('HAVE_CBLAS', None)]\n", " libraries = ['openblas']\n", " language = c\n", " library_dirs = ['/usr/lib']\n", "blas_mkl_info:\n", " NOT AVAILABLE\n", "openblas_info:\n", " define_macros = [('HAVE_CBLAS', None)]\n", " libraries = ['openblas']\n", " language = c\n", " library_dirs = ['/usr/lib']\n", "openblas_lapack_info:\n", " define_macros = [('HAVE_CBLAS', None)]\n", " libraries = ['openblas']\n", " language = c\n", " library_dirs = ['/usr/lib']\n" ] } ], "source": [ "np.__config__.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check matplotlib" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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K0l9amfPss9a9JA1jokvfM2JK0mhNbOl7RkxJGr2JK33rXpLWz7qVfpIPJvlFkl8mme3n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\nTcO0QjMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1b0c045780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.plot(a);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check numba" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def f(x):\n", " y = np.empty_like(x)\n", " for i in range(x.size):\n", " y[i] = x[i] * 2\n", " return y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.arange(1000000)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 297 ms per loop\n" ] } ], "source": [ "%timeit f(x)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numba" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fjit = numba.jit(f)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 57.15 times longer than the fastest. This could mean that an intermediate result is being cached \n", "1000 loops, best of 3: 1.43 ms per loop\n" ] } ], "source": [ "%timeit fjit(x)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array_equal(f(x), fjit(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check bokeh" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.0.min.js'];\n", " \n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.css\");\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", " </script>\n", " <div>\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span>BokehJS successfully loaded.</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.plotting import figure, show\n", "from bokeh.io import output_notebook\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = 4000\n", "x = np.random.random(size=n) * 100\n", "y = np.random.random(size=n) * 100\n", "radii = np.random.random(size=n) * 1.5\n", "colors = [\"#%02x%02x%02x\" % (int(r), int(g), 150) for r, g in zip(np.floor(50+2*x), np.floor(30+2*y))]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<bokeh.models.renderers.GlyphRenderer at 0x7f1b29431358>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = figure()\n", "p.circle(x, y, radius=radii, fill_color=colors, fill_alpha=.9, line_color=None)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"plotdiv\" id=\"cee16cf8-bb3a-47f2-abe8-51ce7b9331bf\"></div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " 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mit
emk/statistiques_en_francais
Distribution binomiale.ipynb
1
2462
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Distribution binomiale\n", "\n", "Il y a beaucoup de vid\u00e9os sur le [site de l'Acad\u00e9mie Khan](https://fr.khanacademy.org/) qui concernent la distribution binomiale. Je n'ai pas envie de r\u00e9viser autant de combinatoire de base, mais il y en a qui m'int\u00e9ressent.\n", "\n", "## L'esp\u00e8rance d'une distribution binomiale\n", "\n", "J'avais du mal avec ce sujet quand j'\u00e9tudias la distribution Poisson." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo(\"sSLhCvlZN1w\")" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=300\"\n", " src=\"http://www.youtube.com/embed/sSLhCvlZN1w\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.lib.display.YouTubeVideo at 0x36cfb90>" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "La loi binomiale. Oui, elle est \u00e9vidente avec un peu de combinatoire.\n", "\n", "$$P(X=k) = {n \\choose k}p^k(1 - p)^{n-k}$$\n", "\n", "Et l'esp\u00e8rance, ou la valeur pr\u00e9vue :\n", "\n", "$$\\begin{aligned}\n", "E(X) &= \\sum_{k=0}^{n} k{n \\choose k} p^k (1-p)^{n-k} \\\\\n", " &= \\sum_{k=1}^{n} k{n \\choose k} p^k (1-p)^{n-k} \\quad &\\text{pas de terme quand }k=0 \\\\\n", " &= \\sum_{k=1}^{n} k\\frac{n!}{(n-k)!k!} p^k (1-p)^{n-k} \\\\\n", " &= np \\sum_{k=1}^{n} \\frac{(n-1)!}{(n-k)!(k-1)!} p^{k-1} (1-p)^{n-k} \\\\\n", " &= np \\sum_{a=0}^{b} \\frac{b!}{(b-a)!a!} p^a (1-p)^{b-a} \\quad &\\text{soit }a=k-1\\text{ et }b=n-1 \\\\\n", " &= np \\sum_{a=0}^{b} {b \\choose a} p^a (1-p)^{b-a} \\\\\n", " &= np\n", "\\end{aligned}$$" ] } ], "metadata": {} } ] }
unlicense
tedunderwood/character
post22hathi/charactergraph.ipynb
1
123899
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Character graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some changes I made to the original plan: \n", "\n", "I think it will be too complicated, and visually distracting, to attempt multiple words. Let's just do one word at a time.\n", "\n", "And given that choice, I think it's also not necessary to allow people to choose *not* to break the word by gender. They'll always be breaking it either by author gender or character gender.\n", "\n", "This, in turn, frees us up to add one additional choice, which is basically the choice of whether to look at words used to *describe* characters, or the words that the characters themselves speak in dialogue." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "fulldata = pd.read_csv('5kwordsbygender.csv')\n", "# Filter, because we're not going to use data about dialogue\n", "# In production, we'll just leave out that data.\n", "fulldata = fulldata[(fulldata['date'] > 1922) & (fulldata['date'] < 2010)]\n", "\n", "# Those date endpoints are created because the current dataset gets sparse\n", "# at the ends. In the final production version we will be using a larger\n", "# dataset stretching from 1800 through 2009. So twice to three times\n", "# this size. Let me know if scale is likely to pose a problem." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This function does the work of extracting word frequencies\n", "# for a given gender, dividing either on the author column\n", "# or the character column. Frequencies need to be\n", "# expressed **relative** to the total number of words\n", "# contained in that gender category. To achieve this \n", "# we divide the word counts by the total counts\n", "# for the category.\n", "\n", "def extract_relative_freqs(divide_column, gender2get, rows4word, totalcounts):\n", " \n", " words4gender = rows4word.loc[rows4word[divide_column] == gender2get]\n", " totals4gender = totalcounts.loc[totalcounts[divide_column] == gender2get]\n", " \n", " wordsbydate = words4gender.groupby(['date'])\n", " summedbydate = wordsbydate.aggregate(np.sum)\n", " countsofword4gender = summedbydate['count']\n", " \n", " totalsbydate = totals4gender.groupby(['date'])\n", " totsummedbydate = totalsbydate.aggregate(np.sum)\n", " totals4gender = totsummedbydate['count']\n", " \n", " proportions = countsofword4gender / totals4gender\n", " \n", " # NaNs are probably places where the word is simply absent\n", " proportions = proportions.fillna(value = 0)\n", " \n", " return proportions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Actual visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is expressed procedurally, with the user answering questions at the console.\n", "But on the web it would make more sense to have only one text field (for the word).\n", "The questions about data type, and about dividing by author/character, would more\n", "logically be expressed through check boxes or radio buttons." ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Choose either 1) dialogue or 2) description: 1\n", "word? crap\n", "divide by (author / character / nothing: char\n" ] }, { "data": { "image/png": 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EsGHDBqpWrcratWtxcnIyas+8efP4+OOPsbOzY+PGjezcuZNq1aqxYMECgz/U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BZyDQJ5Dr02/kvV1vU9dc69S59lXv5a285RTXFXH9V1fRaGl0fJCLbIqNFQe+4IK0i/t0KYTG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\ntAg9evTAl19+CR6PB01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y2MyZM5mlpSWzsbFh06ZNY2lpaRLnkNf85OXlMX9/f2Ztbc3s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\nYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b25e9b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We start by selecting a data type (either dialogue or description)\n", "# and dividing the data accordingly.\n", "\n", "datatype = 'invalid'\n", "validtypes = {'dialogue', 'description'}\n", "\n", "while datatype not in validtypes:\n", " dtype = input('Choose either 1) dialogue or 2) description: ')\n", " if dtype == '1':\n", " datatype = 'dialogue'\n", " elif dtype == '2':\n", " datatype = 'description'\n", "\n", "# Now, to subset the data.\n", "data = fulldata[fulldata['role'] == datatype]\n", "\n", "# Create a vocabulary of available words.\n", "vocabulary = set(data['word'].unique())\n", "word = 'nonsense_word'\n", "\n", "# Ask the user to select one.\n", "while word not in vocabulary:\n", " word = input('word? ')\n", " \n", "divideby = input('divide by (author / character / nothing: ')\n", " \n", "\n", "rows4word = data.loc[data['word'] == word]\n", "totalcounts = data.loc[data['word'] == '#totalforcategory']\n", "\n", "if divideby.startswith('a'):\n", " dividecolname = 'authgender'\n", " thetitle = 'Frequency of \\\"' + word + '\\\" in ' + datatype + ', by author gender\\n'\n", "else:\n", " dividecolname = 'chargender'\n", " thetitle = 'Frequency of \\\"' + word + '\\\" in ' + datatype + ', by character gender\\n'\n", "\n", "maleseries = extract_relative_freqs(dividecolname, 'm', rows4word, totalcounts)\n", "femaleseries = extract_relative_freqs(dividecolname, 'f', rows4word, totalcounts)\n", "\n", "plotframe = pd.concat([maleseries, femaleseries], axis=1)\n", "plotframe.columns = ['masculine', 'feminine']\n", "\n", "matplotlib.rcParams.update(matplotlib.rcParamsDefault)\n", "sns.set_style(\"darkgrid\")\n", "plt.rcParams.update({'axes.titlesize': 'large'})\n", "plt.rcParams.update({'axes.labelsize': 'medium'})\n", "\n", "plotframe.plot(title = thetitle, figsize = (8,6))\n", "plt.show()\n", " \n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 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 }
mit
Naereen/notebooks
agreg/Un exercice d'algorithmique - mise en page de paragraphe, résolutions gourmande et dynamique.ipynb
1
39456
{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table de matières<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Un-exercice-d'algorithmique---mise-en-page-de-paragraphe,-résolutions-gourmande-et-dynamique\" data-toc-modified-id=\"Un-exercice-d'algorithmique---mise-en-page-de-paragraphe,-résolutions-gourmande-et-dynamique-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Un exercice d'algorithmique - mise en page de paragraphe, résolutions gourmande et dynamique</a></span><ul class=\"toc-item\"><li><span><a href=\"#Question-1.\" data-toc-modified-id=\"Question-1.-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Question 1.</a></span><ul class=\"toc-item\"><li><span><a href=\"#Réponse-:-non-!\" data-toc-modified-id=\"Réponse-:-non-!-1.1.1\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>Réponse : non !</a></span></li><li><span><a href=\"#Contre-exemple-de-taille-fixée\" data-toc-modified-id=\"Contre-exemple-de-taille-fixée-1.1.2\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>Contre exemple de taille fixée</a></span></li><li><span><a href=\"#Faire-croître-la-différence-entre-les-deux-coûts-vers-l'infini\" data-toc-modified-id=\"Faire-croître-la-différence-entre-les-deux-coûts-vers-l'infini-1.1.3\"><span class=\"toc-item-num\">1.1.3&nbsp;&nbsp;</span>Faire croître la différence entre les deux coûts vers l'infini</a></span></li><li><span><a href=\"#Bonus-:-faire-croître-le-rapport-vers-l'infini-?\" data-toc-modified-id=\"Bonus-:-faire-croître-le-rapport-vers-l'infini-?-1.1.4\"><span class=\"toc-item-num\">1.1.4&nbsp;&nbsp;</span>Bonus : faire croître le <em>rapport</em> vers l'infini ?</a></span></li><li><span><a href=\"#Code-Python-pour-la-méthode-gloutonne\" data-toc-modified-id=\"Code-Python-pour-la-méthode-gloutonne-1.1.5\"><span class=\"toc-item-num\">1.1.5&nbsp;&nbsp;</span>Code Python pour la méthode gloutonne</a></span></li><li><span><a href=\"#Exemples\" data-toc-modified-id=\"Exemples-1.1.6\"><span class=\"toc-item-num\">1.1.6&nbsp;&nbsp;</span>Exemples</a></span></li></ul></li><li><span><a href=\"#Question-2.\" data-toc-modified-id=\"Question-2.-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Question 2.</a></span><ul class=\"toc-item\"><li><span><a href=\"#Problème-d'optimisation-à-résoudre\" data-toc-modified-id=\"Problème-d'optimisation-à-résoudre-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Problème d'optimisation à résoudre</a></span></li><li><span><a href=\"#Relation-de-récurrence\" data-toc-modified-id=\"Relation-de-récurrence-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Relation de récurrence</a></span></li><li><span><a href=\"#Implémentation-naïve-par-mémoïsation\" data-toc-modified-id=\"Implémentation-naïve-par-mémoïsation-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>Implémentation naïve par mémoïsation</a></span></li><li><span><a href=\"#Exemples\" data-toc-modified-id=\"Exemples-1.2.4\"><span class=\"toc-item-num\">1.2.4&nbsp;&nbsp;</span>Exemples</a></span></li></ul></li><li><span><a href=\"#Question-3.\" data-toc-modified-id=\"Question-3.-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Question 3.</a></span></li><li><span><a href=\"#Question-4.-Pourquoi-un-coût-cubique-et-pas-linéaire-?\" data-toc-modified-id=\"Question-4.-Pourquoi-un-coût-cubique-et-pas-linéaire-?-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Question 4. Pourquoi un coût cubique et pas linéaire ?</a></span></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></li></ul></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Un exercice d'algorithmique - mise en page de paragraphe, résolutions gourmande et dynamique\n", "\n", "> Source : http://lacl.fr/~lpellissier/Algo1/TD3.pdf, auteur : [Luc Pélissier](http://lacl.fr/~lpellissier/) (2020-21).\n", "\n", "Le problème étudié est l’impression équilibrée d’un paragraphe sur une imprimante.\n", "Le texte d’entrée est modélisé comme une séquence de $n$ mots de longueurs $l_1,l_2, \\dots, l_n$ (mesurées en caractères, que l'on suppose tous de même largeur - c'est le cas par exemple avec une police dite [*à chasse fixe*](https://fr.wikipedia.org/wiki/Police_d'%C3%A9criture_%C3%A0_chasse_fixe)).\n", "\n", "On souhaite imprimer ce paragraphe de manière équilibrée sur un certain nombre de lignes qui contiennent un maximum de $M\\geq1$ caractères chacune.\n", "Le critère d’équilibre est le suivant :\n", "Si une ligne donnée contient les mots $i$ à $j$ (avec $i \\leq j$) et qu’on laisse exactement [une espace](https://fr.wikipedia.org/wiki/Espace_(typographie)) entre deux mots, le nombre de caractères d’espacements supplémentaires à la fin de la ligne est $f(M - j+i - \\sum\\limits_{k=i}^j l_k)$, qui doit être positif ou nul pour que les mots tiennent sur la ligne.\n", "L’objectif est de minimiser la somme, sur toutes les lignes *hormis la dernière*, des cubes des nombres de caractères d’espacement présents à la fin de chaque ligne : cela correspond à $f(s) = s^3$.\n", "\n", "- Auteur: [Lilian Besson](https://github.com/Naereen/)\n", "- Date : jeudi 04/02/2021\n", "- Licence : [MIT](https://lbesson.mit-license.org/)\n", "- Lien : https://github.com/Naereen/notebooks/tree/master/agreg/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Remarque :* pour bien visualiser ces espaces en fin de fichier, je termine chaque ligne par `;`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Question 1.\n", "\n", "**1. Est-ce que l’algorithme glouton consistant à remplir les lignes une à une en mettant à chaque fois le maximum de mots possibles sur la ligne en cours, fournit l’optimum ?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Réponse : non !" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Contre exemple de taille fixée\n", "\n", "Comme le coût est la somme des cubes d'espaces en fin de ligne, on peut penser à un contre-exemple qui va exploiter le fait que $(2x)^3 >> 2 x^3$, et produire un texte qui aura deux lignes identiques (avec $k$ espaces en fin de lignes) lorsqu'on le met en page optimalement, et une ligne quasi complète mais une deuxième ligne quasi vide :" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T08:47:13.879894Z", "start_time": "2021-02-04T08:47:13.827954Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AA AA AA AA AA AA B ;\n", "AA AA AA AA AA AA B ;\n", "EOF > /tmp/test_nongreedy_optimal.txt\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "bash: ligne 4: avertissement : « here-document » à la ligne 1 délimité par la fin du fichier (au lieu de « EOF »)\n" ] } ], "source": [ "%%bash\n", "cat << EOF\n", "AA AA AA AA AA AA B ;\n", "AA AA AA AA AA AA B ;\n", "EOF > /tmp/test_nongreedy_optimal.txt" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T08:47:26.685859Z", "start_time": "2021-02-04T08:47:26.547394Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AA AA AA AA AA AA B ;\r\n", "AA AA AA AA AA AA B ;\r\n" ] } ], "source": [ "cat /tmp/test_nongreedy_optimal.txt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T08:47:14.686342Z", "start_time": "2021-02-04T08:47:14.643709Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AA AA AA AA AA AA B AA AA AA AA AA AA ;\n", "B ;\n", "EOF > test_greedy_suboptimal.txt\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "bash: ligne 4: avertissement : « here-document » à la ligne 1 délimité par la fin du fichier (au lieu de « EOF »)\n" ] } ], "source": [ "%%bash\n", "cat << EOF\n", "AA AA AA AA AA AA B AA AA AA AA AA AA ;\n", "B ;\n", "EOF > test_greedy_suboptimal.txt" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T08:47:33.339316Z", "start_time": "2021-02-04T08:47:33.196519Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AA AA AA AA AA AA AA AA AA AA AA AA AA ;\r\n", "B ;\r\n" ] } ], "source": [ "cat /tmp/test_greedy_suboptimal.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour l'instant, j'ai codé ça vite fait en Bash pour calculer le coût des deux fichiers :" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T08:47:36.720331Z", "start_time": "2021-02-04T08:47:36.566050Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[H\u001b[2J/tmp/test_greedy_suboptimal.txt\n", "################################################################################\n", "AA AA AA AA AA AA AA AA AA AA AA AA AA ;\n", "B ;\n", "################################################################################\n", "0\n", "X\n", "n = 0, i = 1\n", "=> n = 1, i = 1\n", "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n", "n = 1, i = 38\n", "=> n = 54873, i = 38\n", "/tmp/test_nongreedy_optimal.txt\n", "################################################################################\n", "AA AA AA AA AA AA B ;\n", "AA AA AA AA AA AA B ;\n", "################################################################################\n", "0\n", "XXXXXXXXXXXXXXXXXXXX\n", "n = 0, i = 20\n", "=> n = 8000, i = 20\n", "XXXXXXXXXXXXXXXXXXXX\n", "n = 8000, i = 20\n", "=> n = 16000, i = 20\n" ] } ], "source": [ "%%bash\n", "clear\n", "for file in /tmp/test_*txt; do\n", " echo $file\n", " hr\n", " cat $file\n", " hr\n", " n=0\n", " echo $n\n", " for line in $(cat $file | grep -o ' *;' | sed s/';'/''/g | tr ' ' 'X'); do\n", " echo $line; i=$(echo $line | wc -c)\n", " i=$((i-1))\n", " echo \"n = $n, i = $i\"; n=$((n + i*i*i))\n", " echo \"=> n = $n, i = $i\"\n", " done\n", "done" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On voit que la solution gourmande a un coût de 54873 alors que la solution non gourmande (optimale) a un coût de 16000." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Faire croître la différence entre les deux coûts vers l'infini\n", "\n", "On peut juste produire $n$ fois ces deux lignes, et le coût de la solution gourmande sera $54873 n$ et le coût optimal sera $16000 n$.\n", "Cela montre que la **différence** entre les deux coûts n'est pas bornée." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bonus : faire croître le *rapport* vers l'infini ?\n", "On devrait pouvoir aussi faire croître le rapport des deux coûts vers l'infini : plutôt que de générer ces $n$ lignes identiques, on a juste à augmenter la longueur de ces lignes (et n'en avoir que deux, mais très longues).\n", "Comme le coût est cubique en le nombre d'espaces, on aura bien un rapport non borné entre le coût gourmand (sous optimal) et le coût optimal.\n", "\n", "**Corollaire :** cela montre que la solution gourmande n'est pas un k-approximation du problème étudié.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code Python pour la méthode gloutonne\n", "\n", "Même si elle n'est pas efficace, on va commencer par écrire cette méthode gloutonne :" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:13:47.884832Z", "start_time": "2021-02-04T12:13:47.878866Z" } }, "outputs": [], "source": [ "from typing import Tuple, List" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:23:32.059174Z", "start_time": "2021-02-04T12:23:32.049018Z" } }, "outputs": [], "source": [ "def longueur_ligne(ligne: List[str]) -> int:\n", " return sum(len(mot) for mot in ligne)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:25:44.665265Z", "start_time": "2021-02-04T12:25:44.634102Z" } }, "outputs": [], "source": [ "def mise_en_page_paragraphe_gloutonne(longueur_max:int, mots: List[str]) -> List[List[str]]:\n", " print(f\"Longueur maximum de la ligne = {longueur_max}\")\n", " print(f\"Longueur des mots = {longueurs_mots}\")\n", " \n", " assert all(\n", " 1 <= len(mot) <= longueur_max\n", " for mot in mots\n", " )\n", " \n", " mots = list(mots)[::-1] # on les lit de la fin\n", "\n", " paragraphes = []\n", " ligne_actuelle = []\n", " longueur_ligne_actuelle = 0\n", "\n", " while mots:\n", " # print(f\"mots = {mots}\")\n", " mot_a_placer = mots.pop()\n", " # print(f\" mot_a_placer = {mot_a_placer}\")\n", " # print(f\" ligne_actuelle = {ligne_actuelle}\")\n", " \n", " if longueur_ligne(ligne_actuelle) + len(mot_a_placer) <= longueur_max:\n", " ligne_actuelle += [mot_a_placer]\n", " longueur_ligne_actuelle += len(mot_a_placer)\n", " if longueur_ligne_actuelle < longueur_max:\n", " ligne_actuelle += [\" \"]\n", " longueur_ligne_actuelle += 1\n", "\n", " # 1 + car on ajoute l'espace\n", " if longueur_ligne_actuelle + 1 >= longueur_max:\n", " paragraphes.append(ligne_actuelle)\n", " ligne_actuelle = []\n", " longueur_ligne_actuelle = 0\n", "\n", " # print(f\" ligne_actuelle = {ligne_actuelle}\")\n", " # print(f\" paragraphes = {paragraphes}\")\n", " \n", " # dernière ligne si pas encore ajoutée\n", " if ligne_actuelle:\n", " paragraphes.append(ligne_actuelle)\n", "\n", " # puis on complète avec des espaces en fin de lignes\n", " for ligne in paragraphes:\n", " espaces_fin_paragraphe = longueur_max - longueur_ligne(ligne)\n", " ligne += [\" \"] * espaces_fin_paragraphe\n", " \n", " assert all(\n", " longueur_ligne(ligne) == longueur_max\n", " for ligne in paragraphes\n", " )\n", " return paragraphes" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:25:44.993465Z", "start_time": "2021-02-04T12:25:44.979573Z" } }, "outputs": [], "source": [ "def print_paragraphes(paragraphes: List[List[str]]):\n", " print(f\"\\n# Mise en page finale d'un texte de {len(paragraphes)} lignes \")\n", " for ligne in paragraphes:\n", " print(\"\".join(ligne) + \";\")" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:35:54.354501Z", "start_time": "2021-02-04T12:35:54.345021Z" } }, "outputs": [], "source": [ "from typing import Callable" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:36:00.204485Z", "start_time": "2021-02-04T12:36:00.189314Z" } }, "outputs": [], "source": [ "def cout_paragraphes(paragraphes: List[List[str]], cout: Callable[[int], int]) -> int:\n", " lignes = [ \"\".join(ligne) for ligne in paragraphes ]\n", " espaces_de_fin = [\n", " len(ligne) - len(ligne.rstrip())\n", " for ligne in lignes\n", " ]\n", " return sum(cout(es) for es in espaces_de_fin)" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:01.870656Z", "start_time": "2021-02-04T12:38:01.857190Z" } }, "outputs": [], "source": [ "def print_couts(paragraphes):\n", " print(\"- Nombre d'espaces en fin de lignes =\", cout_paragraphes(paragraphes, cout= lambda i: i))\n", " print(\"- Somme des carrés des nombres d'espaces en fin de lignes =\", cout_paragraphes(paragraphes, cout= lambda i: i**2))\n", " print(\"- Somme des cubes des nombres d'espaces en fin de lignes =\", cout_paragraphes(paragraphes, cout= lambda i: i**3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exemples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier exemple simple :" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:37.248579Z", "start_time": "2021-02-04T12:38:37.239752Z" } }, "outputs": [], "source": [ "longueur_max = len(\"AA AA \") # sans le ;\n", "mots = [\"AA\", \"AA\", \"AA\", \"B\"]" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:37.544404Z", "start_time": "2021-02-04T12:38:37.538720Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Longueur maximum de la ligne = 6\n", "Longueur des mots = [2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1]\n" ] } ], "source": [ "paragraphes = mise_en_page_paragraphe_gloutonne(longueur_max, mots)" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:37.958606Z", "start_time": "2021-02-04T12:38:37.943294Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Mise en page finale d'un texte de 2 lignes \n", "AA AA ;\n", "AA B ;\n", "- Nombre d'espaces en fin de lignes = 3\n", "- Somme des carrés des nombres d'espaces en fin de lignes = 5\n", "- Somme des cubes des nombres d'espaces en fin de lignes = 9\n" ] } ], "source": [ "print_paragraphes(paragraphes)\n", "print_couts(paragraphes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Peut-on retrouver la solution suivante, qui avait été calculée à la main ?" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:40.585595Z", "start_time": "2021-02-04T12:38:40.467709Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AA AA AA AA AA AA AA AA AA AA AA AA AA ;\r\n", "B ;\r\n" ] } ], "source": [ "cat /tmp/test_greedy_suboptimal.txt" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:40.857877Z", "start_time": "2021-02-04T12:38:40.853713Z" } }, "outputs": [], "source": [ "longueur_max = len(\"AA AA AA AA AA AA AA AA AA AA AA AA AA \") # sans le ;\n", "mots = [\"AA\"]*13 + [\"B\"]*1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérifions cela :" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:41.591637Z", "start_time": "2021-02-04T12:38:41.581389Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Longueur maximum de la ligne = 39\n", "Longueur des mots = [2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1]\n" ] } ], "source": [ "paragraphes = mise_en_page_paragraphe_gloutonne(longueur_max, mots)" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T12:38:41.975305Z", "start_time": "2021-02-04T12:38:41.959171Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Mise en page finale d'un texte de 2 lignes \n", "AA AA AA AA AA AA AA AA AA AA AA AA AA ;\n", "B ;\n", "- Nombre d'espaces en fin de lignes = 39\n", "- Somme des carrés des nombres d'espaces en fin de lignes = 1445\n", "- Somme des cubes des nombres d'espaces en fin de lignes = 54873\n" ] } ], "source": [ "print_paragraphes(paragraphes)\n", "print_couts(paragraphes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Question 2.\n", "\n", "**2. Donner un algorithme de programmation dynamique résolvant le problème. Analyser sa complexité en temps et en espace. Et implémenter le dans le langage de votre choix. Vérifier qu'il donne la réponse optimale sur l'exemple trouvé en question 1. (ou en tous cas, une meilleure réponse).**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On va déjà écrire le problème d'optimisation à résoudre, puis une relation de récurrence.\n", "En écrivant un algorithme récursif naïf mais avec mémoïsation, on obtiendra un algorithme de programmation dynamique." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problème d'optimisation à résoudre\n", "\n", "On se donne $M\\in\\mathbb{N}^*$ la taille de ligne, et un nombre $N\\in\\mathbb{N}^*$ objets, de longueurs $l_k \\in [1,\\dots,M]$.\n", "On souhaite minimiser le coût suivant, qui dépend de :\n", "\n", "- $L$ nombre de ligne,\n", "- $\\forall x \\in{1,\\dots,L-1}, \\ell_x$ indique l'indice de fin des mots présents en ligne $x$. Avec $\\ell_0 = 0$ pour indiquer une ligne 0 vide.\n", "\n", "$$\n", " \\min_{\n", " L\\in\\{1,\\dots,M\\}, \\\\\n", " \\ell_1,\\dots,\\ell_{L-1}\\in\\{1,\\dots,N\\},\\\\\n", " \\forall x\\in\\{1,\\dots,L-1\\}, \\ell_{x+1} \\geq \\ell_x + 1,\n", " }\n", " \\sum_{x=1}^{L-1}\n", " (M - \\ell_{x+1} + \\ell_x - \\sum_{k=\\ell_x}^{\\ell_{x+1}} l_k)^3\n", "$$\n", "\n", "- On ne compte pas les espaces de la dernière ligne, d'où le L-1 dans la somme." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relation de récurrence\n", "\n", "**Initialisation :**\n", "S'il n'y a qu'un seul mot, la solution est triviale : on le place sur la première ligne, et on a terminé.\n", "\n", "**Hérédité :**\n", "On considère le premier mot $l_1$ et le deuxième mot $l_2$.\n", "Le coût de la solution optimale est le minimum des coûts des deux solutions optimales aux sous-problèmes suivants (de taille strictement plus petite) :\n", "\n", "1. on place les deux premiers mots ensemble, et on remplace donc $l_1,l_2$ par $l_1' := l_1 + l_2 + 1$, et la suite des mots est juste décalée : $l_k' := l_{k+1}$. Ce cas a $N-1$ mots ;\n", "2. on place le premier mot sur sa propre ligne (cas de base), et on résound avec les mots restants : $l_k' := l_{k+1}$. Ce cas a aussi $1$ et $N-1$ mots sur les deux sous-problèmes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implémentation naïve par mémoïsation" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T11:51:11.619820Z", "start_time": "2021-02-04T11:51:11.612586Z" } }, "outputs": [], "source": [ "from typing import List, Tuple" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T11:50:36.188539Z", "start_time": "2021-02-04T11:50:36.181404Z" } }, "outputs": [], "source": [ "from functools import lru_cache as memoize" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:24:14.875085Z", "start_time": "2021-02-04T13:24:14.865125Z" } }, "outputs": [], "source": [ "couts = {\n", " \"lineaire\": lambda i: i,\n", " \"quadratique\": lambda i: i**2,\n", " \"cubique\": lambda i: i**3,\n", "}" ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:37:35.928579Z", "start_time": "2021-02-04T13:37:35.904777Z" } }, "outputs": [], "source": [ "@memoize(maxsize=None)\n", "def mise_en_page_paragraphe(\n", " longueur_max:int,\n", " mots: Tuple[str],\n", " choix_cout: str=\"cubique\",\n", " ) -> List[List[str]]:\n", " print(f\"Longueur maximum de la ligne = {longueur_max}\")\n", " mots = list(mots)\n", " print(f\"Longueur des mots = {mots}\")\n", " \n", " assert len(mots) > 0\n", " if len(mots) == 1:\n", " return [ [mots[0]] ]\n", " \n", " else:\n", " cout = couts[choix_cout]\n", " \n", " # première possibilité, on regroupe les deux premiers mots ensemble\n", " mots1 = [mots[0] + \" \" + mots[1]] + mots[2:]\n", " cout1 = float('+inf')\n", " if len(mots1[0]) <= longueur_max:\n", " solution1 = mise_en_page_paragraphe(longueur_max, tuple(mots1))\n", " cout1 = cout_paragraphes(solution1, cout)\n", " \n", " # deuxième possibilité, on place mots[0] tout seul, et on résoud les autres mots\n", " sous_solution2 = mise_en_page_paragraphe(longueur_max, tuple(mots[1:]))\n", " morceau_gauche2 = [ mots[0] ] + [\" \"] * (longueur_max - len(mots[0]))\n", " solution2 = [ morceau_gauche2 ] + sous_solution2\n", " cout2 = cout_paragraphes(solution2, cout)\n", "\n", " if cout1 < cout2:\n", " recombinaison_1 = []\n", " for ligne in solution1:\n", " mots_ici = \"\".join(ligne).split(\" \")\n", " ligne_ici = [ mots_ici[0] ]\n", " for mot in mots_ici[1:]:\n", " if mot:\n", " ligne_ici += [\" \", mot] \n", " ligne_ici += [\" \"] * (longueur_max - longueur_ligne(ligne_ici))\n", " recombinaison_1.append(ligne_ici)\n", " return recombinaison_1\n", " else:\n", " recombinaison_2 = solution2\n", " return recombinaison_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exemples" ] }, { "cell_type": "code", "execution_count": 263, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:39:24.355620Z", "start_time": "2021-02-04T13:39:24.349805Z" } }, "outputs": [], "source": [ "longueur_max = len(\"AA AA AA\")\n", "\n", "mots = [\"AA\", \"AA\", \"AA\", \"B\"]\n", "mots = tuple(mots) # pour le rendre Hashable pour le @memoize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérifions cela :" ] }, { "cell_type": "code", "execution_count": 264, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:39:25.009903Z", "start_time": "2021-02-04T13:39:24.997780Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA', 'AA', 'AA', 'B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA AA', 'AA', 'B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA AA AA', 'B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA', 'B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA', 'AA', 'B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA AA', 'B']\n", "Longueur maximum de la ligne = 8\n", "Longueur des mots = ['AA AA B']\n" ] } ], "source": [ "paragraphes = mise_en_page_paragraphe(longueur_max, mots)" ] }, { "cell_type": "code", "execution_count": 266, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:39:39.058032Z", "start_time": "2021-02-04T13:39:39.049872Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Mise en page finale d'un texte de 2 lignes \n", "AA ;\n", "AA AA B ;\n", "- Nombre d'espaces en fin de lignes = 7\n", "- Somme des carrés des nombres d'espaces en fin de lignes = 37\n", "- Somme des cubes des nombres d'espaces en fin de lignes = 217\n" ] } ], "source": [ "print_paragraphes(paragraphes)\n", "print_couts(paragraphes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Peut-on retrouver la solution suivante, qui avait été calculée à la main ?" ] }, { "cell_type": "code", "execution_count": 280, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:42:13.318117Z", "start_time": "2021-02-04T13:42:13.197826Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AA AA AA AA AA AA B ;\r\n", "AA AA AA AA AA AA B ;\r\n" ] } ], "source": [ "cat /tmp/test_nongreedy_optimal.txt" ] }, { "cell_type": "code", "execution_count": 281, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:42:17.810459Z", "start_time": "2021-02-04T13:42:17.803151Z" } }, "outputs": [], "source": [ "longueur_max = len(\"AA AA AA AA AA AA B \")\n", "\n", "mots = ([\"AA\"]*6 + [\"B\"]*1) * 2\n", "mots = tuple(mots) # pour le rendre Hashable pour le @memoize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérifions cela :" ] }, { "cell_type": "code", "execution_count": 284, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:42:27.350655Z", "start_time": "2021-02-04T13:42:27.341729Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Longueur maximum de la ligne = 38\n", "Longueur des mots = (2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1)\n" ] } ], "source": [ "paragraphes = mise_en_page_paragraphe_gloutonne(longueur_max, mots)" ] }, { "cell_type": "code", "execution_count": 285, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:42:28.691726Z", "start_time": "2021-02-04T13:42:28.683281Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Mise en page finale d'un texte de 2 lignes \n", "AA AA AA AA AA AA B AA AA AA AA AA AA ;\n", "B ;\n", "- Nombre d'espaces en fin de lignes = 38\n", "- Somme des carrés des nombres d'espaces en fin de lignes = 1370\n", "- Somme des cubes des nombres d'espaces en fin de lignes = 50654\n" ] } ], "source": [ "print_paragraphes(paragraphes)\n", "print_couts(paragraphes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et pour la solution dynamique :" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:42:30.867982Z", "start_time": "2021-02-04T13:42:30.861229Z" } }, "outputs": [], "source": [ "paragraphes = mise_en_page_paragraphe(longueur_max, mots)" ] }, { "cell_type": "code", "execution_count": 287, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:42:31.235309Z", "start_time": "2021-02-04T13:42:31.229793Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Mise en page finale d'un texte de 2 lignes \n", "AA AA AA AA AA AA B ;\n", "AA AA AA AA AA AA B ;\n", "- Nombre d'espaces en fin de lignes = 38\n", "- Somme des carrés des nombres d'espaces en fin de lignes = 722\n", "- Somme des cubes des nombres d'espaces en fin de lignes = 13718\n" ] } ], "source": [ "print_paragraphes(paragraphes)\n", "print_couts(paragraphes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Question 3.\n", "\n", "**3. Supposons que pour la fonction de coût à minimiser, on ait simplement choisi la somme des nombres de caractères d’espacement présents à la fin de chaque ligne. Est-ce que l’on peut faire mieux en complexité que pour la question ?**\n", "\n", "Oui la solution gourmande, qui est donc au plus linéaire en temps et demande une mémoire de travail supplémentaire constante (ou bornée par la taille du plus long mot, selon de savoir si `len(mot)` est en $O(1)$ ou en $O(|\\text{mot}|)$), sera optimale." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Question 4. Pourquoi un coût cubique et pas linéaire ?\n", "\n", "**4. *(Plus informel)* Qu’est-ce qui à votre avis peut justifier le choix de prendre les cubes plutôt quesimplement les nombres de caractères d’espacement en fin de ligne ?**\n", "\n", "Si le coût est linéaire, alors la solution gourmande sera optimale (ou en tous cas une approximation à facteur constant).\n", "Mais c'est aussi que l'affichage ne fera pas de différence entre les deux exemples ci dessous, alors que l'on est clairement plus satisfait du rendu visuel du deuxième, qui équilibre mieux les deux lignes.\n", "\n", "(*je ne suis pas trop sûr de tout ça*)\n", "\n", "TODO mieux expliquer !" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut vérifier la solution trouvée avec un coût carré et pas cubique :" ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:44:44.265260Z", "start_time": "2021-02-04T13:44:44.261064Z" } }, "outputs": [], "source": [ "paragraphes = mise_en_page_paragraphe(longueur_max, mots, choix_cout=\"quadratique\")" ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "ExecuteTime": { "end_time": "2021-02-04T13:44:44.594181Z", "start_time": "2021-02-04T13:44:44.589626Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Mise en page finale d'un texte de 2 lignes \n", "AA AA AA AA AA AA B ;\n", "AA AA AA AA AA AA B ;\n", "- Nombre d'espaces en fin de lignes = 38\n", "- Somme des carrés des nombres d'espaces en fin de lignes = 722\n", "- Somme des cubes des nombres d'espaces en fin de lignes = 13718\n" ] } ], "source": [ "print_paragraphes(paragraphes)\n", "print_couts(paragraphes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sur cet exemple, on obtient exactement la même solution.\n", "\n", "Mais je pense qu'on peut trouver des exemples où la solution avec un coût cubique est différente de celle avec un coût quadratique." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Et si vous cherchiez à résoudre ça dans votre langage de programmation favori ?\n", "En Python ou en OCaml, cela ne devrait pas être trop difficile." ] } ], "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": "3.6.9" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table de matières", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": true }, "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": 4 }
mit
chappers/Data-Science
Think-Stats/2 Descriptive Statistics.ipynb
1
89426
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This HTML version of is provided for convenience, but it is not the best\n", "format for the book. In particular, some of the symbols are not rendered\n", "correctly.\n", "\n", "You might prefer to read the [PDF\n", "version](http://thinkstats.com/thinkstats.pdf), or you can buy a hardcopy\n", "[here](http://www.lulu.com/product/paperback/think-stats/12443331).\n", "\n", "# Chapter\u00a02\u00a0\u00a0Descriptive statistics\n", "\n", "## 2.1\u00a0\u00a0Means and averages\n", "\n", "In the previous chapter, I mentioned three summary statistics\u2014mean, variance\n", "and median\u2014without explaining what they are. So before we go any farther,\n", "let\u2019s take care of that.\n", "\n", "If you have a sample of \\\\(n\\\\) values, \\\\(x_i\\\\), the mean, \\\\(\\mu\\\\), is the sum of the\n", "values divided by the number of values; in other words\n", "\n", "$$\\mu = \\frac{1}{n} \\sum_{i} x_i$$\n", "\n", "The words \u201cmean\u201d and \u201caverage\u201d are sometimes used interchangeably, but I will\n", "maintain this distinction:\n", "\n", " * The \u201cmean\u201d of a sample is the summary statistic computed with the previous formula.\n", " * An \u201caverage\u201d is one of many summary statistics you might choose to describe the typical value or the **central tendency** of a sample. \n", "\n", "Sometimes the mean is a good description of a set of values. For example,\n", "apples are all pretty much the same size (at least the ones sold in\n", "supermarkets). So if I buy 6 apples and the total weight is 3 pounds, it would\n", "be a reasonable summary to say they are about a half pound each.\n", "\n", "But pumpkins are more diverse. Suppose I grow several varieties in my garden,\n", "and one day I harvest three decorative pumpkins that are 1 pound each, two pie\n", "pumpkins that are 3 pounds each, and one Atlantic Giant\u00ae\u00a0pumpkin that weighs\n", "591 pounds. The mean of this sample is 100 pounds, but if I told you \u201cThe\n", "average pumpkin in my garden is 100 pounds,\u201d that would be wrong, or at least\n", "misleading.\n", "\n", "In this example, there is no meaningful average because there is no typical\n", "pumpkin.\n", "\n", "## 2.2\u00a0\u00a0Variance\n", "\n", "If there is no single number that summarizes pumpkin weights, we can do a\n", "little better with two numbers: mean and **variance**.\n", "\n", "In the same way that the mean is intended to describe the central tendency,\n", "variance is intended to describe the **spread**. The variance of a set of\n", "values is\n", "\n", "$$\\sigma^2 = \\frac{1}{n} \\sum_{i} (x_i - \\mu)^2$$\n", "\n", "The term \\(x_i-\\mu\\)\u00a0is called the \u201cdeviation from the mean,\u201d so variance is the\n", "mean squared deviation, which is why it is denoted \u03c32. The square root of\n", "variance, \u03c3, is called the **standard deviation**.\n", "\n", "By itself, variance is hard to interpret. One problem is that the units are\n", "strange; in this case the measurements are in pounds, so the variance is in\n", "pounds squared. Standard deviation is more meaningful; in this case the units\n", "are pounds.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**Exercise\u00a01**\u00a0\u00a0_ For the exercises in this chapter you should download http://thinkstats.com/thinkstats.py, which contains general-purpose functions we will use throughout the book. You can read documentation of these functions in http://thinkstats.com/thinkstats.html. _\n", "\n", "_Write a function called `Pumpkin` that uses functions from `thinkstats.py` to\n", "compute the mean, variance and standard deviation of the pumpkins weights in\n", "the previous section. _\n", "\n", "**Exercise\u00a02**\u00a0\u00a0_ Reusing code from `survey.py` and `first.py`, compute the standard deviation of gestation time for first babies and others. Does it look like the spread is the same for the two groups? _\n", "\n", "_How big is the difference in the means compared to these standard deviations?\n", "What does this comparison suggest about the statistical significance of the\n", "difference? _\n", "\n", "If you have prior experience, you might have seen a formula for variance with\n", "_n_\u00a0\u2212\u00a01 in the denominator, rather than _n_. This statistic is called the\n", "\u201csample variance,\u201d and it is used to estimate the variance in a population\n", "using a sample. We will come back to this in Chapter\n", "[8](thinkstats009.html#estimation).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.3\u00a0\u00a0Distributions\n", "\n", "Summary statistics are concise, but dangerous, because they obscure the data.\n", "An alternative is to look at the **distribution** of the data, which describes\n", "how often each value appears.\n", "\n", "The most common representation of a distribution is a **histogram**, which is\n", "a graph that shows the frequency or probability of each value.\n", "\n", "In this context, **frequency** means the number of times a value appears in a\n", "dataset\u2014it has nothing to do with the pitch of a sound or tuning of a radio\n", "signal. A **probability** is a frequency expressed as a fraction of the sample\n", "size, \\\\(n\\\\).\n", "\n", "In Python, an efficient way to compute frequencies is with a dictionary. Given\n", "a sequence of values, `t`:\n", "\n", " \n", " \n", " h = {}\n", " for x in t:\n", " h[x] = h.get(x, 0) + 1\n", " \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# an example, using the book\n", "t = [1,1,1,2,2,3,3,3,3,4,4]\n", "hist = {}\n", "for x in t:\n", " hist[x] = hist.get(x,0) + 1\n", "\n", "hist" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "{1: 3, 2: 2, 3: 4, 4: 2}" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import pylab as pl\n", "import numpy as np\n", "\n", "pl.bar(hist.keys(), hist.values())\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x6f7bb30>" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "# plotting using pyplot\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.hist(t)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x8e59f30>" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "The result is a dictionary that maps from values to frequencies. To get from\n", "frequencies to probabilities, we divide through by _n_, which is called\n", "**normalization**:\n", " \n", " \n", " n = float(len(t))\n", " pmf = {}\n", " for x, freq in hist.items():\n", " pmf[x] = freq / n\n", " \n", "\n", "The normalized histogram is called a **PMF**, which stands for \u201cprobability\n", "mass function\u201d; that is, it\u2019s a function that maps from values to\n", "probabilities (I\u2019ll explain \u201cmass\u201d in Section\n", "[6.3](thinkstats007.html#density)).\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "n = float(len(t))\n", "pmf = {}\n", "for x, freq in hist.items():\n", " pmf[x] = freq / n\n", "\n", "pmf" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "{1: 0.2727272727272727,\n", " 2: 0.18181818181818182,\n", " 3: 0.36363636363636365,\n", " 4: 0.18181818181818182}" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "pl.bar(pmf.keys(), pmf.values())\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x71ef970>" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.hist(t, normed = True) # not the \"probability norm\"\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xc8294b0>" ] } ], "prompt_number": 69 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "It might be confusing to call a Python dictionary a function. In mathematics,\n", "a function is a map from one set of values to another. In Python, we _usually_\n", "represent mathematical functions with function objects, but in this case we\n", "are using a dictionary (dictionaries are also called \u201cmaps,\u201d if that helps).\n", "\n", "## 2.4\u00a0\u00a0Representing histograms\n", "\n", "I wrote a Python module called `Pmf.py` that contains class definitions for\n", "Hist objects, which represent histograms, and Pmf objects, which represent\n", "PMFs. You can read the documentation at `thinkstats.com/Pmf.html` and download\n", "the code from `thinkstats.com/Pmf.py`.\n", "\n", "The function `MakeHistFromList` takes a list of values and returns a new Hist\n", "object. You can test it in Python\u2019s interactive mode:\n", " \n", " \n", " >>> import Pmf\n", " >>> hist = Pmf.MakeHistFromList([1, 2, 2, 3, 5])\n", " >>> print hist\n", " <Pmf.Hist object at 0xb76cf68c>\n", " \n", "\n", "`Pmf.Hist` means that this object is a member of the Hist class, which is\n", "defined in the Pmf module. In general, I use upper case letters for the names\n", "of classes and functions, and lower case letters for variables.\n", "\n", "Hist objects provide methods to look up values and their probabilities. `Freq`\n", "takes a value and returns its frequency:\n", "\n", " \n", " \n", " >>> hist.Freq(2)\n", " 2\n", " \n", "\n", "If you look up a value that has never appeared, the frequency is 0.\n", "\n", " \n", " \n", " >>> hist.Freq(4)\n", " 0\n", " \n", "\n", "`Values` returns an unsorted list of the values in the Hist:\n", "\n", " \n", " \n", " >>> hist.Values()\n", " [1, 5, 3, 2]\n", " \n", "\n", "To loop through the values in order, you can use the built-in function\n", "`sorted`:\n", "\n", " \n", " \n", " for val in sorted(hist.Values()):\n", " print val, hist.Freq(val)\n", " \n", "\n", "If you are planning to look up all of the frequencies, it is more efficient to\n", "use `Items`, which returns an unsorted list of value-frequency pairs:\n", "\n", " \n", " \n", " for val, freq in hist.Items():\n", " print val, freq\n", " \n", "\n", "**Exercise\u00a03**\u00a0\u00a0The mode of a distribution is the most frequent value (see `http://wikipedia.org/wiki/Mode_(statistics)`). Write a function called `Mode` that takes a Hist object and returns the most frequent value.\n", "\n", "As a more challenging version, write a function called `AllModes` that takes\n", "a Hist object and returns a list of value-frequency pairs in descending order\n", "of frequency. Hint: the `operator` module provides a function called\n", "`itemgetter` which you can pass as a key to `sorted`.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.5\u00a0\u00a0Plotting histograms\n", "\n", "There are a number of Python packages for making figures and graphs. The one I\n", "will demonstrate is `pyplot`, which is part of the `matplotlib` package at\n", "`http://matplotlib.sourceforge.net`.\n", "\n", "This package is included in many Python installations. To see whether you have\n", "it, launch the Python interpreter and run:\n", "\n", " \n", " \n", " import matplotlib.pyplot as pyplot\n", " pyplot.pie([1,2,3])\n", " pyplot.show()\n", " \n", "\n", "If you have `matplotlib` you should see a simple pie chart; otherwise you will\n", "have to install it.\n", "\n", "Histograms and PMFs are most often plotted as bar charts. The `pyplot`\n", "function to draw a bar chart is `bar`. Hist objects provide a method called\n", "`Render` that returns a sorted list of values and a list of the corresponding\n", "frequencies, which is the format `bar` expects:\n", "\n", " \n", " \n", " >>> vals, freqs = hist.Render()\n", " >>> rectangles = pyplot.bar(vals, freqs)\n", " >>> pyplot.show()\n", " \n", "\n", "I wrote a module called `myplot.py` that provides functions for plotting\n", "histograms, PMFs and other objects we will see soon. You can read the\n", "documentation at `thinkstats.com/myplot.html` and download the code from\n", "`thinkstats.com/myplot.py`. Or you can use `pyplot` directly, if you prefer.\n", "Either way, you can find the documentation for `pyplot` on the web.\n", "\n", "Figure\u00a02.1 shows histograms of pregnancy lengths for first babies and others.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Histograms are useful because they make the following features immediately\n", "apparent:\n", "\n", "**Mode:**\n", " The most common value in a distribution is called the **mode**. In Figure\u00a02.1 there is a clear mode at 39 weeks. In this case, the mode is the summary statistic that does the best job of describing the typical value. \n", "**Shape:**\n", " Around the mode, the distribution is asymmetric; it drops off quickly to the right and more slowly to the left. From a medical point of view, this makes sense. Babies are often born early, but seldom later than 42 weeks. Also, the right side of the distribution is truncated because doctors often intervene after 42 weeks. \n", "**Outliers:**\n", " Values far from the mode are called **outliers**. Some of these are just unusual cases, like babies born at 30 weeks. But many of them are probably due to errors, either in the reporting or recording of data. \n", "\n", "Although histograms make some features apparent, they are usually not useful\n", "for comparing two distributions. In this example, there are fewer \u201cfirst\n", "babies\u201d than \u201cothers,\u201d so some of the apparent differences in the histograms\n", "are due to sample sizes. We can address this problem using PMFs.\n", "\n", "## 2.6\u00a0\u00a0Representing PMFs\n", "\n", "`Pmf.py` provides a class called `Pmf` that represents PMFs. The notation can\n", "be confusing, but here it is: `Pmf` is the name of the module and also the\n", "class, so the full name of the class is `Pmf.Pmf`. I often use `pmf` as a\n", "variable name. Finally, in the text, I use PMF to refer to the general concept\n", "of a probability mass function, independent of my implementation.\n", "\n", "To create a Pmf object, use `MakePmfFromList`, which takes a list of values:\n", "\n", " \n", " \n", " >>> import Pmf\n", " >>> pmf = Pmf.MakePmfFromList([1, 2, 2, 3, 5])\n", " >>> print pmf\n", " <Pmf.Pmf object at 0xb76cf68c>\n", " \n", "\n", "Pmf and Hist objects are similar in many ways. The methods `Values` and\n", "`Items` work the same way for both types. The biggest difference is that a\n", "Hist maps from values to integer counters; a Pmf maps from values to floating-\n", "point probabilities.\n", "\n", "To look up the probability associated with a value, use `Prob`:\n", "\n", " \n", " \n", " >>> pmf.Prob(2)\n", " 0.4\n", " \n", "\n", "You can modify an existing Pmf by incrementing the probability associated with\n", "a value:\n", "\n", " \n", " \n", " >>> pmf.Incr(2, 0.2)\n", " >>> pmf.Prob(2)\n", " 0.6\n", " \n", "\n", "Or you can multiply a probability by a factor:\n", "\n", " \n", " \n", " >>> pmf.Mult(2, 0.5)\n", " >>> pmf.Prob(2)\n", " 0.3\n", " \n", "\n", "If you modify a Pmf, the result may not be normalized; that is, the\n", "probabilities may no longer add up to 1. To check, you can call `Total`, which\n", "returns the sum of the probabilities:\n", "\n", " \n", " \n", " >>> pmf.Total()\n", " 0.9\n", " \n", "\n", "To renormalize, call `Normalize`:\n", "\n", " \n", " \n", " >>> pmf.Normalize()\n", " >>> pmf.Total()\n", " 1.0\n", " \n", "\n", "Pmf objects provide a `Copy` method so you can make and modify a copy without\n", "affecting the original.\n", "\n", "**Exercise\u00a04**\u00a0\u00a0 According to Wikipedia, \"Survival analysis is a branch of statistics which deals with death in biological organisms and failure in mechanical systems;\" see `http://wikipedia.org/wiki/Survival_analysis`. \n", "\n", "As part of survival analysis, it is often useful to compute the remaining\n", "lifetime of, for example, a mechanical component. If we know the distribution\n", "of lifetimes and the age of the component, we can compute the distribution of\n", "remaining lifetimes.\n", "\n", "Write a function called `RemainingLifetime` that takes a Pmf of lifetimes and\n", "an age, and returns a new Pmf that represents the distribution of remaining\n", "lifetimes.\n", "\n", "**Exercise\u00a05**\u00a0\u00a0In Section\u00a02.1 we computed the mean of a sample by adding up the elements and dividing by \\\\(n\\\\). If you are given a PMF, you can still compute the mean, but the process is slightly different: \n", "\n", "$$\\mu = \\sum_i p_i x_i$$\n", "\n", "where the \\\\(x_i\\\\)\u00a0are the unique values in the PMF and _pi_=PMF(_xi_).\n", "Similarly, you can compute variance like this: \n", "\n", "$$\\sigma^2 = \\sum_i p_i(x_i - \\mu)^2$$\n", "\n", "Write functions called `PmfMean` and `PmfVar` that take a Pmf object and\n", "compute the mean and variance. To test these methods, check that they are\n", "consistent with the methods `Mean` and `Var` in `Pmf.py`. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 2.7\u00a0\u00a0Plotting PMFs\n", "\n", "There are two common ways to plot Pmfs:\n", "\n", " * To plot a Pmf as a bar graph, you can use `pyplot.bar` or `myplot.Hist`. Bar graphs are most useful if the number of values in the Pmf is small. \n", " * To plot a Pmf as a line, you can use `pyplot.plot` or `myplot.Pmf`. Line plots are most useful if there are a large number of values and the Pmf is smooth. \n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "import gzip\n", "\n", "def makeRecord(line, fields):\n", " obs = {}\n", " for (field, start, end, cast) in fields:\n", " try:\n", " s = line[start-1:end]\n", " val = cast(s)\n", " except ValueError:\n", " val = np.nan #make use of numpy's nan\n", " obs[field]=val\n", " return obs\n", "\n", "fresp = gzip.open('./data/2002FemResp.dat.gz')\n", "resp_fields = [\n", " ('caseid', 1, 12, int),\n", " ]\n", "\n", "fpreg = gzip.open('./data/2002FemPreg.dat.gz')\n", "preg_fields = [ ('caseid', 1, 12, int),\n", " ('nbrnaliv', 22, 22, int),\n", " ('babysex', 56, 56, int),\n", " ('birthwgt_lb', 57, 58, int),\n", " ('birthwgt_oz', 59, 60, int),\n", " ('prglength', 275, 276, int),\n", " ('outcome', 277, 277, int),\n", " ('birthord', 278, 279, int),\n", " ('agepreg', 284, 287, int),\n", " ('finalwgt', 423, 440, float)]\n", "\n", "respondents = pd.DataFrame([makeRecord(line, resp_fields) for line in fresp])\n", "pregnancies = pd.DataFrame([makeRecord(line, preg_fields) for line in fpreg])\n", "\n", "#recode\n", "pregnancies['agepreg'] = pregnancies.agepreg/100.0\n", "pregnancies['totalwgt_oz'] = pregnancies.birthwgt_lb * 16 + pregnancies.birthwgt_oz\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "# add column if they are first born\n", "pregnancies['first_born'] = pregnancies.birthord == 1\n", "\n", "# display this histogram: not the same as what is in thinkstats, but the easiest solution to get \n", "# something similiar\n", "pregnancies.groupby('first_born')['prglength'].hist()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "first_born\n", "False Axes(0.125,0.125;0.775x0.775)\n", "True Axes(0.125,0.125;0.775x0.775)\n", "dtype: object" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xc470a30>" ] } ], "prompt_number": 61 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Figure\u00a02.2 shows the PMF of pregnancy lengths as a bar graph. Using the PMF,\n", "we can see more clearly where the distributions differ. First babies seem to\n", "be less likely to arrive on time (week 39) and more likely to be a late (weeks\n", "41 and 42).\n", "\n", "The code that generates the figures in this chapters is available from\n", "`http://thinkstats.com/descriptive.py`. To run it, you will need the modules\n", "it imports and the data from the NSFG (see Section\n", "[1.3](thinkstats002.html#nsfg)).\n", "\n", "Note: `pyplot` provides a function called `hist` that takes a sequence of\n", "values, computes the histogram and plots it. Since I use `Hist` objects, I\n", "usually don\u2019t use `pyplot.hist`.\n", "\n", "## 2.8\u00a0\u00a0Outliers\n", "\n", "Outliers are values that are far from the central tendency. Outliers might be\n", "caused by errors in collecting or processing the data, or they might be\n", "correct but unusual measurements. It is always a good idea to check for\n", "outliers, and sometimes it is useful and appropriate to discard them.\n", "\n", "In the list of pregnancy lengths for live births, the 10 lowest values are {0,\n", "4, 9, 13, 17, 17, 18, 19, 20, 21}. Values below 20 weeks are certainly errors,\n", "and values higher than 30 weeks are probably legitimate. But values in between\n", "are hard to interpret.\n", "\n", "On the other end, the highest values are:\n", "\n", " \n", " \n", " weeks count\n", " 43 148\n", " 44 46\n", " 45 10\n", " 46 1\n", " 47 1\n", " 48 7\n", " 50 2\n", " \n", "\n", "Again, some values are almost certainly errors, but it is hard to know for\n", "sure. One option is to **trim** the data by discarding some fraction of the\n", "highest and lowest values (see `http://wikipedia.org/wiki/Truncated_mean`).\n", "\n", "## 2.9\u00a0\u00a0Other visualizations\n", "\n", "Histograms and PMFs are useful for exploratory data analysis; once you have an\n", "idea what is going on, it is often useful to design a visualization that\n", "focuses on the apparent effect.\n", "\n", "In the NSFG data, the biggest differences in the distributions are near the\n", "mode. So it makes sense to zoom in on that part of the graph, and to transform\n", "the data to emphasize differences.\n", "\n", "Figure\u00a02.3 shows the difference between the PMFs for weeks 35\u201345. I multiplied\n", "by 100 to express the differences in percentage points.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pregnancies.groupby('first_born')['prglength'].hist(normed=True) # not the canonical norm!" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "first_born\n", "False Axes(0.125,0.125;0.775x0.775)\n", "True Axes(0.125,0.125;0.775x0.775)\n", "dtype: object" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xc2d5cb0>" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "This figure makes the pattern clearer: first babies are less likely to be born\n", "in week 39, and somewhat more likely to be born in weeks 41 and 42.\n", "\n", "## 2.10\u00a0\u00a0Relative risk\n", "\n", "We started with the question, \u201cDo first babies arrive late?\u201d To make that more\n", "precise, let\u2019s say that a baby is early if it is born during Week 37 or\n", "earlier, on time if it is born during Week 38, 39 or 40, and late if it is\n", "born during Week 41 or later. Ranges like these that are used to group data\n", "are called **bins**.\n", "\n", "**Exercise\u00a06**\u00a0\u00a0Create a file named `risk.py`. Write functions named `ProbEarly`, `ProbOnTime` and `ProbLate` that take a PMF and compute the fraction of births that fall into each bin. Hint: write a generalized function that these functions call.\n", "\n", "Make three PMFs, one for first babies, one for others, and one for all live\n", "births. For each PMF, compute the probability of being born early, on time, or\n", "late.\n", "\n", "One way to summarize data like this is with **relative risk**, which is a\n", "ratio of two probabilities. For example, the probability that a first baby is\n", "born early is 18.2%. For other babies it is 16.8%, so the relative risk is\n", "1.08. That means that first babies are about 8% more likely to be early.\n", "\n", "Write code to confirm that result, then compute the relative risks of being\n", "born on time and being late. You can download a solution from\n", "`http://thinkstats.com/risk.py`.\n", "\n", "## 2.11\u00a0\u00a0Conditional probability\n", "\n", "Imagine that someone you know is pregnant, and it is the beginning of Week 39.\n", "What is the chance that the baby will be born in the next week? How much does\n", "the answer change if it\u2019s a first baby?\n", "\n", "We can answer these questions by computing a **conditional probability**,\n", "which is (ahem!) a probability that depends on a condition. In this case, the\n", "condition is that we know the baby didn\u2019t arrive during Weeks 0\u201338.\n", "\n", "Here\u2019s one way to do it:\n", "\n", "1. Given a PMF, generate a fake cohort of 1000 pregnancies. For each number of weeks, _x_, the number of pregnancies with duration _x_\u00a0is 1000 PMF(_x_). \n", "2. Remove from the cohort all pregnancies with length less than 39. \n", "3. Compute the PMF of the remaining durations; the result is the conditional PMF.\n", "4. Evaluate the conditional PMF at _x_\u00a0=\u00a039 weeks.\n", "\n", "This algorithm is conceptually clear, but not very efficient. A simple\n", "alternative is to remove from the distribution the values less than 39 and\n", "then renormalize.\n", "\n", "**Exercise\u00a07**\u00a0\u00a0 Write a function that implements either of these algorithms and computes the probability that a baby will be born during Week 39, given that it was not born prior to Week 39.\n", "\n", "Generalize the function to compute the probability that a baby will be born\n", "during Week \\\\(x\\\\), given that it was not born prior to Week \\\\(x\\\\), for all \\\\(x\\\\).\n", "Plot this value as a function of \\\\(x\\\\)\u00a0for first babies and others.\n", "\n", "You can download a solution to this problem from\n", "`http://thinkstats.com/conditional.py`. \n", "\n", "## 2.12\u00a0\u00a0Reporting results\n", "\n", "At this point we have explored the data and seen several apparent effects. For\n", "now, let\u2019s assume that these effects are real (but let\u2019s remember that it\u2019s an\n", "assumption). How should we report these results?\n", "\n", "The answer might depend on who is asking the question. For example, a\n", "scientist might be interested in any (real) effect, no matter how small. A\n", "doctor might only care about effects that are **clinically significant**; that\n", "is, differences that affect treatment decisions. A pregnant woman might be\n", "interested in results that are relevant to her, like the conditional\n", "probabilities in the previous section.\n", "\n", "How you report results also depends on your goals. If you are trying to\n", "demonstrate the significance of an effect, you might choose summary\n", "statistics, like relative risk, that emphasize differences. If you are trying\n", "to reassure a patient, you might choose statistics that put the differences in\n", "context.\n", "\n", "**Exercise\u00a08**\u00a0\u00a0 Based on the results from the previous exercises, suppose you were asked to summarize what you learned about whether first babies arrive late.\n", "\n", "Which summary statistics would you use if you wanted to get a story on the\n", "evening news? Which ones would you use if you wanted to reassure an anxious\n", "patient? \n", "\n", "Finally, imagine that you are Cecil Adams, author of [The Straight Dope](http://straightdope.com), \n", "and your job is to answer the question, \"Do first\n", "babies arrive late?\" Write a paragraph that uses the results in this chapter\n", "to answer the question clearly, precisely, and accurately.\n", "\n", "## 2.13\u00a0\u00a0Glossary\n", "\n", "**central tendency:** \n", " A characteristic of a sample or population; intuitively, it is the most average value. \n", "**spread:** \n", " A characteristic of a sample or population; intuitively, it describes how much variability there is. \n", "**variance:** \n", " A summary statistic often used to quantify spread. \n", "**standard deviation:** \n", " The square root of variance, also used as a measure of spread. \n", "**frequency:** \n", " The number of times a value appears in a sample. \n", "**histogram:** \n", " A mapping from values to frequencies, or a graph that shows this mapping. \n", "**probability:** \n", " A frequency expressed as a fraction of the sample size. \n", "**normalization:** \n", " The process of dividing a frequency by a sample size to get a probability. \n", "**distribution:** \n", " A summary of the values that appear in a sample and the frequency, or probability, of each. \n", "**PMF:** \n", " Probability mass function: a representation of a distribution as a function that maps from values to probabilities. \n", "**mode:** \n", " The most frequent value in a sample. \n", "**outlier:** \n", " A value far from the central tendency. \n", "**trim:** \n", " To remove outliers from a dataset. \n", "**bin:** \n", " A range used to group nearby values. \n", "**relative risk:** \n", " A ratio of two probabilities, often used to measure a difference between distributions. \n", "**conditional probability:** \n", " A probability computed under the assumption that some condition holds. \n", "**clinically significant:** \n", " A result, like a difference between groups, that is relevant in practice. \n" ] } ], "metadata": {} } ] }
mit
davidbrough1/pymks
notebooks/localization_cahn_hilliard_2D.ipynb
1
205187
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cahn-Hilliard Example\n", "\n", "This example demonstrates how to use PyMKS to solve the Cahn-Hilliard equation. The first section provides some background information about the Cahn-Hilliard equation as well as details about calibrating and validating the MKS model. The example demonstrates how to generate sample data, calibrate the influence coefficients and then pick an appropriate number of local states when state space is continuous. The MKS model and a spectral solution of the Cahn-Hilliard equation are compared on a larger test microstructure over multiple time steps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cahn-Hilliard Equation\n", "\n", "The Cahn-Hilliard equation is used to simulate microstructure evolution during spinodial decomposition and has the following form,\n", "\n", "$$ \\dot{\\phi} = \\nabla^2 \\left( \\phi^3 - \\phi \\right) - \\gamma \\nabla^4 \\phi $$\n", "\n", "where $\\phi$ is a conserved ordered parameter and $\\sqrt{\\gamma}$ represents the width of the interface. In this example, the Cahn-Hilliard equation is solved using a semi-implicit spectral scheme with periodic boundary conditions, see [Chang and Rutenberg](http://dx.doi.org/10.1103/PhysRevE.72.055701) for more details." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pymks\n", "\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling with MKS\n", "\n", "In this example the MKS equation will be used to predict microstructure at the next time step using \n", "\n", "$$p[s, 1] = \\sum_{r=0}^{S-1} \\alpha[l, r, 1] \\sum_{l=0}^{L-1} m[l, s - r, 0] + ...$$\n", "\n", "where $p[s, n + 1]$ is the concentration field at location $s$ and at time $n + 1$, $r$ is the convolution dummy variable and $l$ indicates the local states varable. $\\alpha[l, r, n]$ are the influence coefficients and $m[l, r, 0]$ the microstructure function given to the model. $S$ is the total discretized volume and $L$ is the total number of local states `n_states` choosen to use.\n", "\n", "The model will march forward in time by recursively replacing discretizing $p[s, n]$ and substituing it back for $m[l, s - r, n]$.\n", "\n", "### Calibration Datasets\n", "\n", "Unlike the elastostatic examples, the microstructure (concentration field) for this simulation doesn't have discrete phases. The microstructure is a continuous field that can have a range of values which can change over time, therefore the first order influence coefficients cannot be calibrated with delta microstructures. Instead, a large number of simulations with random initial conditions are used to calibrate the first order influence coefficients using linear regression.\n", "\n", "The function `make_cahn_hilliard` from `pymks.datasets` provides an interface to generate calibration datasets for the influence coefficients. To use `make_cahn_hilliard`, we need to set the number of samples we want to use to calibrate the influence coefficients using `n_samples`, the size of the simulation domain using `size` and the time step using `dt`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pymks\n", "from pymks.datasets import make_cahn_hilliard\n", "\n", "n = 41\n", "n_samples = 400\n", "dt = 1e-2\n", "np.random.seed(99)\n", "X, y = make_cahn_hilliard(n_samples=n_samples, size=(n, n), dt=dt)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `make_cahnHilliard` generates `n_samples` number of random microstructures, `X`, and the associated updated microstructures, `y`, after one time step `y`. The following cell plots one of these microstructures along with its update." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HR6Nfv36IiooqkeUotBGcPeVDUc/9Q37ge3iHWuq0OrfsIOphBg/GXjjlE1H3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\nrsQsnSYk5xrgqWHXt2iKJL49/BDQlHgoyK+zgN++FhIJ3kZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f280bfa3c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_concentrations\n", "\n", "draw_concentrations((X[0], y[0]), labels=('Input Concentration', 'Output Concentration'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calibrate Influence Coefficients\n", "\n", "As mentioned above, the microstructures (concentration fields) does not have discrete phases. This leaves the number of local states in local state space as a free hyperparameter. In previous work it has been shown that, as you increase the number of local states, the accuracy of MKS model increases (see [Fast et al.](http://dx.doi.org/10.1016/j.actamat.2010.10.008)), but, as the number of local states increases, the difference in accuracy decreases. Some work needs to be done in order to find the practical number of local states that we will use. \n", "\n", "### Optimizing the Number of Local States\n", "\n", "Let's split the calibrate dataset into test and training datasets. The function `train_test_split` for the machine learning Python module [sklearn](http://scikit-learn.org/stable/) provides a convenient interface to do this. 80% of the dataset will be used for training and the remaining 20% will be used for testing by setting `test_size` equal to 0.2. The state of the random number generator used to make the split can be set using `random_state`. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sklearn\n", "from sklearn.cross_validation import train_test_split\n", "\n", "split_shape = (X.shape[0],) + (X[0].size,)\n", "X_train, X_test, y_train, y_test = train_test_split(X.reshape(split_shape), y.reshape(split_shape),\n", " test_size=0.5, random_state=3)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now going to calibrate the influence coefficients while varying the number of local states from 2 up to 20. Each of these models will then predict the evolution of the concentration fields. Mean square error will be used to compare the results with the testing dataset to evaluate how the MKS model's performance changes as we change the number of local states. \n", "\n", "First we need to import the class `MKSLocalizationModel` from `pymks`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymks import MKSLocalizationModel\n", "from pymks.bases import PrimitiveBasis\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will calibrate the influence coefficients while varying the number of local states and compute the mean squared error. The following demonstrates how to use scikit-learn's `GridSearchCV` to optimize `n_states` as a hyperparameter. Of course, the best fit is always with a larger value of `n_states`. Increasing this parameter does not overfit the data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise',\n", " estimator=MKSLocalizationModel(basis=<pymks.bases.primitive.PrimitiveBasis object at 0x7f2809547990>,\n", " lstsq_rcond=2.2204460492503131e-12, n_jobs=None,\n", " n_states=array([0, 1])),\n", " fit_params={'size': (41, 41)}, iid=True, n_jobs=1,\n", " param_grid={'n_states': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10])},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.grid_search import GridSearchCV\n", "\n", "parameters_to_tune = {'n_states': np.arange(2, 11)}\n", "p_basis = PrimitiveBasis(2, [-1, 1])\n", "model = MKSLocalizationModel(p_basis, n_jobs=4)\n", "gs = GridSearchCV(model, parameters_to_tune, cv=5, fit_params={'size': (n, n)})\n", "gs.fit(X_train, y_train)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MKSLocalizationModel(basis=<pymks.bases.primitive.PrimitiveBasis object at 0x7f280cbca850>,\n", " lstsq_rcond=2.2204460492503131e-12, n_jobs=None, n_states=10)\n", "0.99999908222\n" ] } ], "source": [ "print(gs.best_estimator_)\n", "print(gs.score(X_test, y_test))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ry2g0UlZWRmxsLCUlJZSXl7N06VIURcFsNmMwGHjggQd47rnnWow8bO3Dra2t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\n+eGHH9BoNIwZM4a7774bLy/3+t3/1Kl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"text/plain": [ "<matplotlib.figure.Figure at 0x7f28093af390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_gridscores\n", "\n", "draw_gridscores(gs.grid_scores_, 'n_states',\n", " score_label='R-squared', param_label='L-Number of Local States')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the accuracy of the MKS model monotonically increases, as we increase `n_states`, but accuracy doesn't improve significantly as `n_states` gets larger than signal digits. \n", "\n", "In order to save on computation costs, let's set (calibrate) the influence coefficients with `n_states` equal to 6, but realize that, if we need slightly more accuracy, the value can be increased." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = MKSLocalizationModel(basis=PrimitiveBasis(6, [-1, 1]))\n", "model.fit(X, y)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the first 4 influence coefficients. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f28383b2c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_coeff\n", "\n", "draw_coeff(model.coef_[...,:4])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predict Microstructure Evolution\n", "\n", "With the calibrated influence coefficients, we are ready to predict the evolution of a concentration field. In order to do this, we need to have the Cahn-Hilliard simulation and the MKS model start with the same initial concentration `phi0` and evolve in time. In order to do the Cahn-Hilliard simulation, we need an instance of the class `CahnHilliardSimulation`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymks.datasets.cahn_hilliard_simulation import CahnHilliardSimulation\n", "np.random.seed(191)\n", "\n", "phi0 = np.random.normal(0, 1e-9, (1, n, n))\n", "ch_sim = CahnHilliardSimulation(dt=dt)\n", "phi_sim = phi0.copy()\n", "phi_pred = phi0.copy()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to move forward in time, we need to feed the concentration back into the Cahn-Hilliard simulation and the MKS model." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "time_steps = 10\n", "\n", "for ii in range(time_steps):\n", " ch_sim.run(phi_sim)\n", " phi_sim = ch_sim.response\n", " phi_pred = model.predict(phi_pred)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the concentration fields." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hpZdeQiKRwGWXXYYvfvGLJdkPDQBFREQkdAbqAHD16tVIJBJ48MEHsWvXLnz7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\n6JU5QYQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f28083798d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_concentrations_compare\n", "\n", "draw_concentrations((phi_sim[0], phi_pred[0]), labels=('Simulation', 'MKS'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MKS model was able to capture the microstructure evolution with 6 local states. \n", "\n", "## Resizing the Coefficients to use on Larger Systems \n", "\n", "Now let's try and predict a larger simulation by resizing the coefficients and provide a larger initial concentratio field." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m = 3 * n\n", "model.resize_coeff((m, m))\n", "\n", "phi0 = np.random.normal(0, 1e-9, (1, m, m))\n", "phi_sim = phi0.copy()\n", "phi_pred = phi0.copy()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again we are going to march forward in time by feeding the concentration fields back into the Cahn-Hilliard simulation and the MKS model. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for ii in range(1000):\n", " ch_sim.run(phi_sim)\n", " phi_sim = ch_sim.response\n", " phi_pred = model.predict(phi_pred)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the results." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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vwgxheF+Yj+mXQt52kvjLQtvW3LthGRDxSGvWyTSmOQpGQ/NByFnn9R2yHFTT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gtZTcTIzJA6ALyGs+WbKL2WHbAFnI3aozaUr63qKY6r0CsPxI5cb2ZNgDSSvZrpz0IrgRpQU8bp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\nB6wMolFqQ67HhQCZQIY4C6hz6Hp+dmzFI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"text/plain": [ "<matplotlib.figure.Figure at 0x7f28083ca8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_concentrations_compare\n", "\n", "draw_concentrations_compare((phi_sim[0], phi_pred[0]), labels=('Simulation', 'MKS'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MKS model with resized influence coefficients was able to reasonably predict the structure evolution for a larger concentration field. " ] } ], "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
konradcybulski/GameTheory1041
Python_Resources/SimulationResultVerification.ipynb
1
210134
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Santos, Santos, Pacheco Results #\n", "The results achieved in the simulations by SSP (Santos Santos Pachecho) in their paper *Social Norms of Cooperation in Small-Scale Societies* can be seen in the following graph.\n", "\n", "\n", "<img src=\"http://journals.plos.org/ploscompbiol/article/figure/image?size=large&id=info:doi/10.1371/journal.pcbi.1004709.g001\"/>\n", "\n", "One can see an almost constant cooperation index for population sizes greater than 50 for Stern Judging, Shunning, and Image Score. While Simple Standing increases steadily over the range 50-300.\n", "\n", "Below we will be performing the same simulation and plotting the results in order to verify and compare the simulations results and those of the paper.\n", "\n", "First however let us define the social norms we will use as well as a function to pass varying population sizes and social norms for retrieving the results." ] }, { "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\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import SimulationInstance\n", "import SimulationMain\n", "import SimulationInstanceVectorized\n", "\n", "SternJudging = [[1, 0 ],\n", " [0, 1]]\n", "SimpleStanding = [[1, 1],\n", " [0, 1]]\n", "Shunning = [[1, 0],\n", " [0, 1]]\n", "ImageScore = [[1, 1],\n", " [0, 0]]\n", "\n", "def SSP(population_size, socialnorm):\n", " runs = 2\n", " generations = 3*np.power(10, 5)\n", " mutation_rate = np.power(10*population_size, -1)\n", "\n", " execution_error = 0.08\n", " reputation_assignment_error = 0.01\n", " private_assessment_error = 0.01\n", " reputation_update_probability = 0.2\n", " randomseed = np.random.randint(999999)\n", " cost = 1\n", " benefit = 5\n", " coop_index = SimulationInstanceVectorized.run_instance(runs, generations, population_size,\n", " mutation_rate, execution_error, reputation_assignment_error, private_assessment_error,\n", " reputation_update_probability, randomseed, socialnorm,\n", " cost, benefit)\n", " return coop_index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stern Judging#\n", "Now let us retrieve the results for populations in the ranges 10-150, with simulations at every multiple of 10, and then simulations at multiples of 25 from 150-300. \n", "\n", "Let's begin with the social norm of Stern Judging." ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": true }, "outputs": [], "source": [ "population_sizes = [5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stern_judging_results = [0.508516473,0.775682093,0.826424982,0.760320876,0.697826156,0.643724905,0.561129066,0.594354744,0.652819772,0.598121451,0.527160869,0.531084403,0.608492096]" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T+lyhe/iN6otmR/pef/31LesQGjVq5E899ZT/9ttviY/z8v/+5eXl+Zw5c/zS\nSy/1GjVqOOC1a9f2AQMGeE5OThCNiZCXl+fz58/36667zhs0aOCAV61a1Tt37uzPP/+8r127NqV9\nUahvx+Tl5fmlGRn+Onj/Y47Zrp+NxSlucJLs0wm2Apa4+5funguMAjoX2uccYKy7L4+PQL5LclOx\nNmzYwIwZM+jUqZPODSAV3kknncSsWbOYMmUKe++9N1lZWRx55JG88MIL5OXlpTovCEuXLuX222/n\nkEMO4dhjj+XJJ5+kXbt2vPLKKyxbtox//vOfNG3aNNWZCWNm/OEPf+Duu+/miy++YM6cOVx66aXM\nmzePnj17su+++9KjRw/Gjx/PL7/8suVx7rq+U0W0ZMkSTj/5ZNrm5GDAHz/4oGwuPrmtUUsibsQu\nBfFYge3zgIcK7XM/8DAwE3gXOH8bz5WQkVpJJk+e7IC/8sorZfJ6IqHIy8vzsWPH+hFHHOGAH330\n0f7yyy8n7F9JIcvLy/O7Bw7c8mtds2aNjxgxwk888cQtn27JzMz0J554wn/66acU16bGb7/95tnZ\n2d6vXz+vWbOmA77HHnv4+eef75MnT/YJzz+f8MP+kjoLFy70nj17upn5sWaeB+7geQk8ekIxR05C\nuGJUZaAZcDKwGzDHzOa4+2eFd8zKyqJhw4YApKenk5GRQWZmJpB/Wt2o25MmTWK33XYjLS2N7Ozs\nhD+/trUd8vaZZ55J586dGTRoEE8++SSdO3emVatWnHXWWTRv3pyTTjopqN5EbQ/+29+Y99BD3JGW\nxsLPP2fcuHHk5uZyyCGH8I9//IMDDzyQ/fbbL5jeVG63adOGbt26MX/+fD755BPGjRvHyJEjOTIt\njQ/y8rjs9tupWqMGZhZEr7a3b/u9996jf//+zJo1i913352enTpx9LRpvPHrr2QSu3zC/jk5DP7b\n37hu0KDtev7NXy9dupQSbWvU4vlHLJoAw4HpwOubbyU9Lv7Y1sDUAtvXU2hRLDCQAudNAUYAXYt4\nrsijtJLk5eV5/fr1/Ywzztjux4Y6V1hQ6I3qiybRfRs3bvQRI0b4AQcc4ICfeOKJ/uabb+7w84X2\n/q1Zs8ZnzJjhd9xxh3fYe29/HfwY8Bo1avjll1/u8+bNC+6oUWjvoXtscfDfBg70lypV8pngL4K3\nPeYYnz17tt6/7ZTKvtmzZ3uHDh0c8PT0dL/lllv8+++/93v69/dbTjzRB7Vp4xc0beqD2rTxW048\n0e/p3z8zN9HsAAAgAElEQVTyaxJlQSzwPnApsfUjzTffSnpc/LGVyF8QW5XYgtjDCu1zKLGLClYC\nqgMLgcOLeK7Ib0RJFixY4IA//vjj2/3Y0P+ndw+/UX3RJKvvl19+8Ycfftj3228/B7xdu3Y+b968\n7X6eVL5/69ev97ffftsffPBBP++887xJkyZbpmuqg48185ngE3fZxSeOGpWyzpKE+P9gXl5e7DA/\n+Mz4Yf/jK1VywFu1auWjRo3y3NzcVGe6e5jvX0Fl3ZeXl+evvfaan3TSSQ54zZo1/Y477tjmp7PK\n8iRspRlgvFfSPiU8vj3wKbAEuD5+X1+gT4F9riH2iZ0PgCu28TwJfVOK8ve//90BX7lyZdJfS6S8\nWbdunQ8ePNj32WcfB7xz587+/vvvpzrrd3Jzcz0nJ8eHDx/uffr08YyMDK9cufKWwcj+++/vnTt3\n9ttvv92nTp3qlzdvnpT59J3FKy++6FOrV4/9OInfplSv7lf17u2NGzd2wOvXr++DBw/2H3/8MdW5\n4rFByeTJk/3YY4/d8mfivvvuK/aTWMlQ3OCkNNfWuRX4FhjP1tfW+aHYByZYWZyE7ZhjjgFg7ty5\nSX0dkfLs559/5oEHHuDee+/l559/pkePHtx6660ccsghZd7i7nz22We8++67W27z589nw4YNQGxt\nWsuWLbe61a1bd8vji7pw4tTq1bGnn6Zd165l/uspj7Z1fafdmzXjL//8J5MnT+a+++4jOzub3Xff\nnYsuuoirrrqKAw88MIXVO6e8vDxeeuklbr/9dhYsWECDBg0YOHAgF154YUpOxBj12jpfFHH7vKTH\nJfpGko+crFy50gH/+9//vkOPD/1woXv4jeqLpqz7vv/+e7/hhhu8evXqnpaW5hdeeKF/8cUX29w/\nEX3Lli3z8ePH+4033uinnHKKp6enbzkiUq1aNT/++OO9f//+/uyzz/rixYtLPAKSzPn0ZCjP/w++\n9957ft5553nlypU9LS3NzzzzTH/rrbfK9ChVeX7/osjNzfVnn33WDz/8cAe8cePG/sQTT/jGjRtT\n2keUT+u4e6MEDJCCN3nyZAA6duyY4hKR8qFGjRrccccd9O/fn7vuuoshQ4bwzDPP0Lt3b2666aat\njlDsiB9++IH//Oc/Wx0VWbFiBQCVKlXiqKOOonv37luOiBxxxBFUrrx9H0C89v77t3ydXeDTeZJ4\nzZo1Y+TIkdx111088sgjDBs2jHHjxtGyZUsGDBhA165dqVKlSqozK5SNGzfyzDPPcOedd/LZZ59x\nxBFH8Nxzz9G9e3cqVaqU6rzibWvU4vlHLKoAVwJj4rfLgSolPS7RN5J85KRLly5er149zTWL7KBl\ny5Z5v379vHLlyr7rrrv6gAEDtlzVu/B5RApbt26dv/XWW37fffd5z549t6xV2Hxr0qSJn3vuuf7g\ngw/622+/7evXry/LX5okwdq1a33IkCFbTpdfr149v+eee7QuJQE2bNjgjzzyyJZP2jVr1szHjRvn\nmzZtSnXaVoi45mREfIDy7/hd5wOb3L13ogdKJXR4Sa076pdffqFmzZqcf/75DB06NCmvIbKz+OKL\nL7jtttsYOXIk1apV46qrruIPTZow+4oraP/kk5x8+uksXLhwqyMiixYt2nJG2nr16tGyZUtatWpF\ny5Ytad68Oenp6Sn+VUmy5OXlMWXKFO677z5mzpzJbrvtxsUXX8yVV17JQQcdlOq8cmXdunU8+uij\nDB48mJUrV3Lcccfx17/+lfbt2wd5xvOoa07eL819yb6RxCMnr7zyigM+efLkHX6O0Ocy3cNvVF80\nofV9/PHH3qNHDyf+0dLXwf+4226+yy67bDkiUqNGDW/Xrp3ffPPNPmHCBP/mm29S1hva+1eU0Buj\n9i1YsMB79erlVapUcTPzLl26+JtvvpmwI9oV9f1bs2aN33777Vs+SXfyySf766+/nvCZgKDWnACb\nzOwgd/9vfKRzILAp4oApKJMmTaJ69eqcfPLJqU4RqTAOPfRQRo0aRWbLlux33XUYcOmGDdQ89VTO\nOO88WrZsSaNGjYL8F52kRkZGBv/+97+58847GTJkCEOHDmX8+PG0aNGCAQMG0K1bN61LKeD777/n\nwQcf5KGHHuKnn37i1FNP5aabbuK4445LdVpkpZnWaQs8CXxO7My1DYAL3X1m8vO26vCSWneEu9Ow\nYUMyMjJ4+eWXE/78Ijszd2fAscdy39y5GLHDJQOOOYb75szRoERKtH79ep5++mkeeOABPv30U+rV\nq8cVV1zBJZdcwt57753qvJRZuXIl9913H0OGDGHdunV07dqVG2+8kWbNmqU6bbsUN61T4lWJ3f01\n4GBii2KvAA4p64FJMi1atIivvvqKTp06pTpFpMKZNnYs7RcuZPPfPga0W7iwbK5qKuVe9erV6dev\nHx999BGTJk2iSZMmDBw4kPr163PFFVfw2We/uwRbhfb1119z5ZVX0qhRI/75z39yxhlnsGjRIsaM\nGVPuBiYl2ebgxMxOjv/3TOA0oHH8dlr8vgph4sSJAJx22mmRnqfghY1CFXqj+qIJsW/h7Nm83aIF\nt7ZpQ1bTptzapg1zWrTgg1mzUp32OyG+f4WF3pisvrS0NE477TRee+01cnJy6NatG48++ihNmjSh\nS5cuvPXWW5TmyHp5ff/++9//0qdPHw466CCGDh3Kueeey6effsozzzzDEUcckfK+ZChuzUkbYhf5\nK+qQggMV4p8+kyZNokWLFuy///6pThGpcHQeEUm0pk2b8tRTT221LuWll16iefPmDBgwgLPOOqvC\nrEv5+OOPufPOO3nuueeoXLkyffr04dprr6VBgwapTku+ba2U9fxPyTQqzX3JvpGET+usWrXKzcxv\nu+22hD+3iIgk37p163zYsGF+6KGHOuB169b1u+66y3/44YdUp22XgucCWrBggZ911lluZl69enX/\ny1/+4itWrEh1YsJRzKd1SlxzAowt4r4xiRocpdKUKVNwd50VVkSknKpevTp9+/blww8/ZPLkyRx2\n2GFcf/311KtXj8svv5wlS5YAsX+I33P99aWa/kmFaWPHsuxf/yKzZUv+8Ic/MG3aNG688Ua+/PJL\n7r333p3u6P42P61jZocCRwD3ANcW+NaewLXuXnYTXSTn0zrdunVjzpw5LFu2LPInB8rDIevQG9UX\njfqiCb0Pwm8Mpe+DDz7g/vvv57nnniM3N5fTTz+d/2vWjDl33cXFTz/NSR07snHjRjZu3Mivv/66\n5evC29v6OtH7/frrrxzyww/8PS+PmytVot2gQVxxxRXBnXww0b+/xX1ap7g1J4cAHYF0tl538jNw\nScLqUuTXX39l2rRpnHPOOfpIo4hIBXL00Ufz5JNPblmX8sgjj7Dy5Ze5E7jhrLM4NcGvV7lyZapW\nrcouu+xC1apVt/l1tWrV2GuvvX73vR++/pqs11/H8vK4aZddSDv88OAGJmWtNOc5Odbd55RRT3Ed\nCT1y8uqrr/KnP/2JiRMnalpHRKQCe/m556h04YV03LiRCVWqMOmsszikWbMSBxMlfW+XXXahSpUq\npKWVZoVE0XwnPhdQcUdOSjM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ISJnTmhMREREpNzQ4SZDQ5woh/Eb1RaO+aELvg/Ab1ReN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\nYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1dd6e98f2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stern_judging_results = np.array(stern_judging_results)\n", "# results_temp_x2\n", "#x = stern_judging_results[:,0]\n", "#y = stern_judging_results[:,1]\n", "plt.plot(population_sizes, stern_judging_results, 'k', population_sizes, stern_judging_results, 'r^', linewidth=1.5)\n", "plt.ylim((-0.01, 1.1))\n", "plt.xlim((-0.01, 125))\n", "plt.xticks([10*i for i in range(13)])\n", "plt.yticks([0.1*i for i in range(11)])\n", "plt.ylabel('Cooperation Index')\n", "plt.xlabel('Population Size')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The results of the simulation as shown above do not seem to correlate with those of the paper by santos santos pacheco. However what seems to be the case is that the results of a number of runs seem to be either zero or a value close to 0.8. This means that some proportion of the runs seem to be fixated by defection, unable to recover to a stable cooperative population. While we cannot simply remove the zero values, if we take into account both the cooperation index average with zeros taken into account and the average without, we see the following.\n", "\n", "We also look at the proportion of runs which have result in a cooperation index of zero against the total number of runs:" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QfYiIHD58WCIiIuTVV1/NtP6OO+6QgQMHyqlTp2Tfvn3SsmVLmTx5sojYILt4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VAAAA\nLElEQVSIiMSYimwRERERkRhTkS0iIiIiEmMqskVEREREYkxFtoiIiIhIjP0/tIHU+eqU4L0AAAAA\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1dd6fb78f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coop_index = [0.508516473,0.775682093,0.826424982,0.760320876,0.697826156,0.643724905,0.561129066,0.594354744,0.652819772,0.598121451,0.527160869,0.531084403,0.608492096]\n", "population_sizes_temp = [5,10,20,30,40,50,60,70,80,90,100,110,120]\n", "zero_proportion = [0.472993542,0.135284757,0.039967674,0.127199045,0.183304844,0.212918715,0.317619392,0.277463455,0.217788767,0.277047708,0.345624097,0.345643889,0.257104097]\n", "coop_index_wo_zeros = [0.964804944,0.896984755,0.860765941,0.869928644,0.853073938,0.814867559,0.814542487,0.786577321,0.770007229,0.814391772,0.795729616,0.797411836,0.811981669]\n", "line_standard, = plt.plot(population_sizes_temp, coop_index, linewidth=1.5, label='Coop Index')\n", "line_zero_prop, = plt.plot(population_sizes_temp, zero_proportion, label='Zero Proportion', marker='^')\n", "line_wo_zeros, = plt.plot(population_sizes_temp, coop_index_wo_zeros, label='Coop Index w/o zeros', marker='o')\n", "plt.ylim((-0.01, 1.05))\n", "plt.xlim((-0.1, 125))\n", "plt.legend(handles=[line_standard, line_wo_zeros, line_zero_prop], loc='upper left', bbox_to_anchor=(1,1))\n", "plt.grid(b=True, which='both')\n", "plt.rcParams[\"figure.figsize\"][0] = 9\n", "plt.rcParams[\"figure.figsize\"][1] = 5\n", "plt.xticks([10*i for i in range(13)])\n", "plt.yticks([0.1*i for i in range(11)])\n", "plt.ylabel('Cooperation Index/Proportion')\n", "plt.xlabel('Population Size')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While initially one may come to tje conclusion that the zero proportion, which takes the form above of 1 minus the cooperation index, to be merely the defection index, however the way this is calculated is not by looking at the defection index, but moreso the number of runs which have a cooperation index of zero (calculated as less than 10^-5). This leads one to believe that the distribution of the cooperation index is zero inflated. WHere we see that for all cooperation indexes, it is either a value distributed around ~0.8 or it is equal to zero with certain probability." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stern judging with high mutation and low execution error #\n", "The following data comes from a series of runs where the mutation rate is set to Z^-1 where Z is the population size, and where the execution error (epsilon) is 0.01 compared to the 0.08 value used in the previous simulations." ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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P+/BhOxZ+s2Yk1KjBtGnTiP3993PjeH/7LQ+//z4Rl16a9QJP/prS0rJesTXT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"text/plain": [ "<matplotlib.figure.Figure at 0x1dd6eaa5630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coop_index = [0.55671953,0.717854758,0.764312844,0.748989805,0.707472638,0.834710665,0.838990698,0.767615598,0.708101936,0.709383435,0.671574256,0.730423794,0.606060629]\n", "population_sizes_temp = [5,10,20,30,40,50,60,70,80,90,100,110,120]\n", "zero_proportion = [0.286346651,0.172696458,0.145584712,0.158310288,0.186614808,0.074261587,0.076428718,0.129578862,0.171840962,0.171456635,0.185223429,0.147639872,0.235549487]\n", "coop_index_wo_zeros = [0.780095131,0.86768886,0.89447719,0.889273361,0.869099708,0.900894275,0.907260109,0.878697814,0.849517584,0.854871055,0.820141081,0.852480787,0.781452704]\n", "line_standard, = plt.plot(population_sizes_temp, coop_index, linewidth=1.5, label='Coop Index')\n", "line_zero_prop, = plt.plot(population_sizes_temp, zero_proportion, label='Zero Proportion', marker='^')\n", "line_wo_zeros, = plt.plot(population_sizes_temp, coop_index_wo_zeros, label='Coop Index w/o zeros', marker='o')\n", "plt.ylim((-0.01, 1.05))\n", "plt.xlim((-0.1, 125))\n", "plt.legend(handles=[line_standard, line_wo_zeros, line_zero_prop], loc='upper left', bbox_to_anchor=(1,1))\n", "plt.grid(b=True, which='both')\n", "plt.rcParams[\"figure.figsize\"][0] = 9\n", "plt.rcParams[\"figure.figsize\"][1] = 5\n", "plt.xticks([10*i for i in range(13)])\n", "plt.yticks([0.1*i for i in range(11)])\n", "plt.ylabel('Cooperation Index/Proportion')\n", "plt.xlabel('Population Size')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Standing #" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+DNJ8jtF8jjF7PhHzZzRbPqvVKntO7ZHhG4ZLvib5hL6I27/cpH1Qexm8drBM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"text/plain": [ "<matplotlib.figure.Figure at 0x1dd6e6ee5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coop_index = [0.270753407,0.33029135,0.365173906,0.255667253,0.215493488,0.15236222,0.150375082,0.123795391,0.145404647,0.098041625,0.079193873,0.064620633,0.09713768]\n", "population_sizes_temp = [5,10,20,30,40,50,60,70,80,90,100,110,120]\n", "zero_proportion = [0.68759684,0.601973681,0.549900174,0.677613316,0.717450052,0.779782604,0.783594149,0.813146215,0.788442622,0.842165365,0.860564097,0.87703309,0.839349549]\n", "coop_index_wo_zeros = [0.866315918,0.827693345,0.806530326,0.736667922,0.66959725,0.50391085,0.386810394,0.337837888,0.419821748,0.354295807,0.292116211,0.234500168,0.241363372]\n", "line_standard, = plt.plot(population_sizes_temp, coop_index, linewidth=1.5, label='Coop Index')\n", "line_zero_prop, = plt.plot(population_sizes_temp, zero_proportion, label='Zero Proportion', marker='^')\n", "line_wo_zeros, = plt.plot(population_sizes_temp, coop_index_wo_zeros, label='Coop Index w/o zeros', marker='o')\n", "plt.ylim((-0.01, 1.05))\n", "plt.xlim((-0.1, 125))\n", "plt.legend(handles=[line_standard, line_wo_zeros, line_zero_prop], loc='upper left', bbox_to_anchor=(1,1))\n", "plt.grid(b=True, which='both')\n", "plt.rcParams[\"figure.figsize\"][0] = 9\n", "plt.rcParams[\"figure.figsize\"][1] = 5\n", "plt.xticks([10*i for i in range(13)])\n", "plt.yticks([0.1*i for i in range(11)])\n", "plt.ylabel('Cooperation Index/Proportion')\n", "plt.xlabel('Population Size')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Complete rerun of results with 1...Z loop in each generation #\n", "For a simulation with parameters:\n", "\n", "R = 8, Z = 20, G = 3x10^3, 48 seconds\n", "\n", "R = 8, Z = 20, G = 3x10^4, 432 seconds\n", "\n", "R = 8, Z = 40, G = 3x10^3, 92 seconds\n", "\n", "# Estimated #\n", "# Z = 20 #\n", "Estimated R = 8, G = 3x10^5, ~4320 seconds (1.2 hours)\n", "\n", "Estimated R = 32 (96//3), G = 3x10^5, ~5 hours\n", "\n", "# Z = 40 #\n", "Estimated R = 8, G = 3x10^5, ~9200 seconds (2.5 hours)\n", "\n", "Estimated R = 32 (96//3), G = 3x10^5, ~10 hours\n", "\n", "# Z = 80 #\n", "Estimated R = 32 (96//3), G = 3x10^5, ~20 hours\n", "\n", "\n", "# Z = 120 #\n", "Estimated R = 32 (96//3), G = 3x10^5, ~30 hours" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Social Norm Simple Standing" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "population_sizes = [5, 12, 25, 35, 50, 70]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Simple Standing\n", "coop_index_SS_5 = [0.30300328890107,0.3122170267297286, 0.3694477572139156, 0.32269225071203966,0.34660439483109196,0.35753302790010333,0.34472536935321985,0.33506662987640695,]\n", "coop_index_SS_12 = [ 0.389835217625291, 0.503741053593408, 0.414803410665924, 0.373144341121016, 0.404481667833337, 0.409381477004441, 0.411456629366117, 0.417190360092897]\n", "coop_index_SS_25 = [ 0.575895970725817, 0.593555191685895, 0.346345244241645, 0.586595733317530, 0.435658189104404, 0.510932066415060, 0.522842995714799, 0.464933493087011]\n", "coop_index_SS_35 = [ 0.6024419644486968, 0.5611443005344464, 0.6177017545265285, 0.5946591774980875, 0.5203268341659013, 0.5146393535395558, 0.5037492489579137, 0.5102253043114068]\n", "coop_index_SS_50 = [ 0.625971801377042, 0.728209162811714, 0.526364328756773, 0.663999642961640, 0.428279452701676, 0.557728084918833, 0.696717869325442, 0.541151875932873]\n", "coop_index_SS_70 = [ 0.6142549683828524]\n", "coop_index_SS = [np.average(coop_index_SS_5), np.average(coop_index_SS_12), np.average(coop_index_SS_25), np.average(coop_index_SS_35), np.average(coop_index_SS_50), np.average(coop_index_SS_70)]\n", "\n", "# Stern Judging\n", "coop_index_SJ_5 = [0.6415401538524217,0.6446142578305846,0.6410012259304664,0.6680960299852963,0.6918668402475565,0.6640123852310564,0.6769155811688191,0.6492853677235841,]\n", "coop_index_SJ_12 = [0.7954687062112575,0.8120924624823713,0.8133768743896582,0.8168074297908923,0.8086529789359302,0.8090324331448141,0.8007545571928044,0.8254361654528761,]\n", "coop_index_SJ_25 = [0.828478139548665, 0.8283964836906803,0.8284225578613255,0.8284125859725406,0.8284379690295149,0.8284406979285108,0.8284578476216579,0.82846780617706,]\n", "coop_index_SJ_35 = [0.8286760075679797,0.8286752539748635,0.8286820007874557,0.8286810253492246,0.8286692010423924,0.8286773564013956,0.8286702717190714,0.8286818004843707,]\n", "coop_index_SJ_50 = [0.8289147343138308,0.8289050916855247,0.8289118700439632,0.8289093383679199,0.8289100281852896,0.8289160067259508,0.8289107137433842,0.8289103609642998,]\n", "coop_index_SJ_70 = [0.8340885668850333,0.834185941885406, 0.8340846263499717,0.8341202176521099,0.8341425765400396,0.8341473538307335,0.8340992111946139]\n", "coop_index_SJ = [np.average(coop_index_SJ_5), np.average(coop_index_SJ_12), np.average(coop_index_SJ_25), np.average(coop_index_SJ_35), np.average(coop_index_SJ_50), np.average(coop_index_SJ_70)]\n", "\n", "# Zero Norm\n", "coop_index_ZERO_5 = [0.019335418939590454,0.02019631862640381,0.02082008123397827,0.02134975790977478,0.022004514932632446,0.02099481225013733,0.020933866500854492,0.020770877599716187,]\n", "coop_index_ZERO_12 = [0.010089797529085499,0.010102098243367979,0.00979763145213295,0.010382693418411838,0.009933781647750958,0.010198327380636389,0.010313705251377474,0.01053330804517383,]\n", "coop_index_ZERO_25 = [0.008694934425683058,0.008677666954322052,0.00968594105904096, 0.008913428568253957,0.008520019703776681,0.008646592024414906,0.007169612480046222,0.008183021418597846,]\n", "coop_index_ZERO_35 = [0.00755965195940635, 0.008507915108680297,0.00806827062322453, 0.007357088850895971,0.007357534808707909,0.006010654914746094,0.007110214164814367,0.005497417186201219,]\n", "coop_index_ZERO_50 = [0.006506138356067986,0.004333089752184267,0.005001832784565869,0.005762029303812237,0.005873097678139549,0.005450347408694107,0.005294959610961522,0.005296161652450565,]\n", "coop_index_ZERO_70 = [0.0009228635878207811,0.0006507234238636115,0.0006376077663370713,0.0004388977710386438,0.0007317914197910949,0.00239054263795588,0.0007066109140949628,0.0007344184431828324,]\n", "coop_index_ZERO = [np.average(coop_index_ZERO_5), np.average(coop_index_ZERO_12), np.average(coop_index_ZERO_25), np.average(coop_index_ZERO_35), np.average(coop_index_ZERO_50), np.average(coop_index_ZERO_70)]\n", "\n", "# Image Scoring\n", "coop_index_IS_5 =[0.13157886266708374,0.12797850370407104,0.12999996542930603,0.1389734148979187, 0.1363530158996582, 0.12969133257865906,0.139630526304245, 0.1444832682609558, ]\n", "coop_index_IS_12 =[0.07933607058626967,0.06608765153348305,0.07403314135216962,0.07385864546411901,0.07386893035303423,0.0728666632664483, 0.07001568498607608,0.07498191672829241,]\n", "coop_index_IS_25 =[0.051464263200974816,0.05746115207834103,0.05665677206211875,0.055269822480824, 0.049308401882969935,0.0571610102259638, 0.060479542516824136,0.05478295697391472,]\n", "coop_index_IS_35 =[0.04855178866497691,0.04499552001364177,0.04594197854549877,0.045567311247033344,0.04700417219988192,0.04882126547558642,0.04925220849268452,0.05192522821891026,]\n", "coop_index_IS_50 =[0.049498805921503415,0.045692120526372317,0.04503519772295453,0.04016098504068533,0.03694476127689362,0.04080260482446231,0.04433218178585847,0.04906098281845488,]\n", "coop_index_IS_70 =[0.0728822786061512,0.08704757792335796,0.06320217416341833,0.000601568897288247,]\n", "coop_index_IS = [np.average(coop_index_IS_5), np.average(coop_index_IS_12), np.average(coop_index_IS_25), np.average(coop_index_IS_35), np.average(coop_index_IS_50), np.average(coop_index_IS_70)]\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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G1zExTJ0+HYfLRXrm2Vh/i9SimuPJJ59k8uTJdOrUieTkZJo0acKiRYu49NJL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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x980515f470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "line_SS, = plt.plot(population_sizes, coop_index_SS, label='Simple Standing', marker='o', ms=8, linewidth=2)\n", "line_SJ, = plt.plot(population_sizes, coop_index_SJ, label='Stern Judging', marker='o', ms=8, linewidth=2)\n", "line_ZERO, = plt.plot(population_sizes, coop_index_ZERO, label='Uniform Zero', marker='o', ms=8, linewidth=2)\n", "line_IS, = plt.plot(population_sizes, coop_index_IS, 'b--', label='Image Scoring', marker='s', ms=8, linewidth=2)\n", "plt.ylim((-0.01, 1.05))\n", "plt.xlim((-0.1, 125))\n", "plt.legend(handles=[line_SS, line_SJ, line_ZERO, line_IS], loc='upper left', bbox_to_anchor=(1,1))\n", "plt.grid(b=True, which='both')\n", "plt.rcParams[\"figure.figsize\"][0] = 9\n", "plt.rcParams[\"figure.figsize\"][1] = 5\n", "plt.xticks([10*i for i in range(13)])\n", "plt.yticks([0.1*i for i in range(11)])\n", "plt.ylabel('Cooperation Index')\n", "plt.xlabel('Population size')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
mfem/PyMFEM
examples/jupyter/plot_DOFs_and_qpts.ipynb
2
11787
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DOF and quadrature point plotter\n", "\n", "This notebook helps visualize the locations of MFEM's DOFs and quadrature points for various element types and quadrature rules.\n", "\n", "To use this, your python environment must have:\n", "* PyMFEM (https://github.com/mfem/PyMFEM)\n", "* Matplotlib (https://matplotlib.org)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preamble: import any necessary libs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import mfem.ser as mfem\n", "\n", "import numpy as np\n", "import os\n", "import math\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "%config InlineBackend.figure_formats = ['svg']\n", "\n", "\n", "# Create some output directories for images\n", "for d in [\"figs\", \"figs/dofs\", \"figs/qpts\", \n", " \"figs/dofs/svg\", \"figs/dofs/pdf\", \"figs/dofs/png\",\n", " \"figs/qpts/svg\", \"figs/qpts/pdf\", \"figs/qpts/png\"]:\n", " if not os.path.exists(d):\n", " os.makedirs(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup mesh and options\n", "\n", "Create an mfem mesh and define some parameters\n", "\n", "We currently support a simple Cartesian 2D mesh.\n", "This can be easily extended to load a mesh from file." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Options for Cartesian mesh constructor are: \n", "# 0D: POINT\n", "# 1D: SEGMENT \n", "# 2D: TRIANGLE, QUADRILATERAL\n", "# 3D: TETRAHEDRON, HEXAHEDRON, WEDGE\n", "\n", "mesh = mfem.Mesh(1,1,\"QUADRILATERAL\")\n", "mesh.EnsureNodes() " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define some parameters for the generated figures\n", "\n", "dim = mesh.Dimension()\n", "max_order = 6\n", "\n", "# Polynomial order of the FECollection\n", "orders = [i for i in range(max_order)]\n", "\n", "# Basis types of the FiniteElementCollections\n", "b_types = [ mfem.BasisType.GaussLobatto,\n", " mfem.BasisType.GaussLegendre,\n", " mfem.BasisType.Positive ]\n", "\n", "# Quadrature types for the integration rules\n", "q_types = [{'q': mfem.Quadrature1D.GaussLegendre, 'name': 'Gauss-Legendre'},\n", " {'q': mfem.Quadrature1D.GaussLobatto, 'name': 'Gauss-Lobatto'},\n", " {'q': mfem.Quadrature1D.OpenUniform, 'name': 'Open-Uniform'},\n", " {'q': mfem.Quadrature1D.OpenHalfUniform, 'name': 'Open-Half-Uniform'},\n", " {'q': mfem.Quadrature1D.ClosedGL, 'name': 'Closed-Gauss-Legendre'},\n", " {'q': mfem.Quadrature1D.ClosedUniform, 'name': 'Closed-Uniform'}]\n", "\n", "# Finite Element collection types\n", "fec_types = [ \"H1\", \"L2\" ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions to extract integration points and map them to physical space" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def getQptPositions(mesh, eid, ir):\n", " \"\"\"Returns list of dictionaries of points in physical space for an element and integration rule.\n", " \n", " The dictionaries have entries for physical space position ('x', 'y')\n", " and reference space position and weight ('ix', 'iy', 'w').\n", " \"\"\"\n", " \n", " t = mesh.GetElementTransformation(eid)\n", "\n", " pts = []\n", " mfem.Vector(3)\n", " for i in range(ir.GetNPoints()):\n", " ip = ir.IntPoint(i)\n", " v = t.Transform(ip)\n", "\n", " d = {'x' : v[0], \n", " 'y' : v[1], \n", " 'ix': ip.x, \n", " 'iy': ip.y,\n", " 'w' : ip.weight}\n", " #print(d)\n", " pts.append(d)\n", "\n", " return pts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def getDofPositions(fespace, eid):\n", " \"\"\"Returns a list of dictionaries containing the DOF positions \n", " in physical and reference space\"\"\"\n", " \n", " mesh = fespace.GetMesh()\n", " fe = fespace.GetFE(eid)\n", " ir = fe.GetNodes()\n", " \n", " return getQptPositions(mesh, eid, ir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions to generate plots for DOFs and quadrature points" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plotDofPositions(name, pts, wscale = .1):\n", " \"\"\"Creates a matplotlib plot for the DOFs in pts\n", " \n", " Note: Currently assumes all points are in a unit square.\n", " TODO: Extend this to draw curved elements of the mesh.\n", " \"\"\"\n", " \n", "\n", " plt.clf()\n", "\n", " fig, ax = plt.subplots(figsize=(6,6))\n", " ax.set_xlim((-.1, 1.1))\n", " ax.set_ylim((-.1, 1.1))\n", "\n", " rect = plt.Rectangle((0, 0), 1, 1, linewidth=3, edgecolor='k', facecolor='none')\n", " ax.add_patch(rect)\n", "\n", " for p in pts:\n", " circle=plt.Circle((p['x'],p['y']), wscale, facecolor='#c0504d', edgecolor='#366092')\n", " ax.add_patch(circle)\n", "\n", " plt.axis('off')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " print(F\"Saving DOFs for '{name}'\")\n", " fig.savefig(F'figs/dofs/svg/{name}.svg') # bbox_inches=extent ?\n", " fig.savefig(F'figs/dofs/png/{name}.png')\n", " fig.savefig(F'figs/dofs/pdf/{name}.pdf')\n", "\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plotQptPositions(name, pts, wscale = .1, use_weights = True, min_size = None):\n", " \"\"\"Creates a matplotlib plot for the quadrature points in pts\n", " \n", " Note: Currently assumes all points are in a unit square.\n", " TODO: Extend this to draw curved elements of the mesh.\n", " \n", " Parameters:\n", " name The name for the output figures\n", " pts A collection of dictionaries of point data\n", " wscale Used to scale the quadrature weights\n", " use_weights When true, use the quadrature weights to scale the quadrature points\n", " min_size Optionally sets a lower bound on the size of the quadrature points\n", " \"\"\"\n", "\n", " plt.clf()\n", "\n", " fig, ax = plt.subplots(figsize=(6,6))\n", " ax.set_xlim((-.1, 1.1))\n", " ax.set_ylim((-.1, 1.1))\n", "\n", " rect = plt.Rectangle((0, 0), 1, 1, linewidth=1.5, edgecolor='#969696', facecolor='none')\n", " ax.add_patch(rect)\n", "\n", " for p in pts:\n", " # Color depends on sign\n", " facecolor = '#0871b7C0' if (p['w'] >= 0) else '#B74E08C0'\n", " # Scale w/ weights proportional to area\n", " sc = wscale * math.sqrt(abs(p['w'])) if use_weights else wscale\n", " # Apply threshold to size, if applicable \n", " sc = min_size if (min_size and sc < min_size) else sc\n", " \n", " circle=plt.Circle((p['x'],p['y']), sc, facecolor=facecolor, edgecolor='#231f20')\n", " ax.add_patch(circle)\n", "\n", " plt.axis('off')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " print(F\"Saving qpts for '{name}'\")\n", " fig.savefig(F'figs/qpts/svg/{name}.svg') # bbox_inches=extent ?\n", " fig.savefig(F'figs/qpts/png/{name}.png')\n", " fig.savefig(F'figs/qpts/pdf/{name}.pdf')\n", "\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the DOFs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Create a grid function for each FE type, order and basis type and plot figures\n", "# Skip the invalid combinations\n", "\n", "for f in fec_types:\n", " for b in b_types:\n", " for p in orders:\n", " try:\n", " if \"H1\" in f:\n", " fec = mfem.H1_FECollection(p, dim, b)\n", " elif \"L2\" in f:\n", " fec = mfem.L2_FECollection(p, dim, b)\n", " fespace = mfem.FiniteElementSpace(mesh, fec) \n", " \n", " bname = mfem.BasisType.Name(b).split(\" \")[0]\n", " print(F\"Working on {fec.Name()} -- dim {dim} -- basis type {bname} -- fec type {f}\" )\n", "\n", " pts = getDofPositions(fespace, 0)\n", " plotDofPositions(F'{fec.Name()}_{bname}', pts, 0.05)\n", " except:\n", " #print(F\"\\tFEC {fec.Name()} did not work: dim {dim} -- basis type {b} -- fec type {f}\" )\n", " pass\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the quadrature points" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Create a chart for each quadrature type and order defined above\n", "# Skip the invalid combinations\n", "# Note: Rules for 2*order and 2*order+1 are the same, so only plot the even ones\n", "\n", "# Currently hard-coded for squares\n", "g_type = mfem.Geometry.SQUARE\n", "g_name = \"square\"\n", "\n", "wscale=.125\n", "\n", "for q in q_types:\n", " for o in orders:\n", " try:\n", " intrules = mfem.IntegrationRules(0, q['q'])\n", " ir = intrules.Get(g_type, 2*o)\n", " pts = getQptPositions(mesh, 0, ir)\n", " pts = sorted(pts, key = lambda p: (p['x']-.5)**2 + (p['y']-.5)**2, reverse=True)\n", " \n", " name = F\"qpts_{g_name}_{q['name']}_{dim}D_P{2*o}\"\n", "\n", " #for p in pts:\n", " # print(F\"{name}: P{2*o} {p['w']}\")\n", " \n", " plotQptPositions(name, pts, wscale, True, min_size=None)\n", " \n", " except:\n", " pass\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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-3-clause
quole/gensim
docs/notebooks/gensim_news_classification.ipynb
12
340822
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "e0085648-0300-4087-9b12-ee7d2392ce4f" } }, "source": [ "# News classification with topic models in gensim\n", "News article classification is a task which is performed on a huge scale by news agencies all over the world. We will be looking into how topic modeling can be used to accurately classify news articles into different categories such as sports, technology, politics etc.\n", "\n", "Our aim in this tutorial is to come up with some topic model which can come up with topics that can easily be interpreted by us. Such a topic model can be used to discover hidden structure in the corpus and can also be used to determine the membership of a news article into one of the topics.\n", "\n", "For this tutorial, we will be using the Lee corpus which is a shortened version of the [Lee Background Corpus](http://www.socsci.uci.edu/~mdlee/lee_pincombe_welsh_document.PDF). The shortened version consists of 300 documents selected from the Australian Broadcasting Corporation's news mail service. It consists of texts of headline stories from around the year 2000-2001.\n", "\n", "Accompanying slides can be found [here](https://speakerdeck.com/dsquareindia/pycon-delhi-lightening).\n", "\n", "### Requirements\n", "In this tutorial we look at how different topic models can be easily created using [gensim](https://radimrehurek.com/gensim/).\n", "Following are the dependencies for this tutorial:\n", " - Gensim Version >=0.13.1 would be preferred since we will be using topic coherence metrics extensively here.\n", " - matplotlib\n", " - nltk.stopwords and nltk.wordnet\n", " - pyLDAVis\n", "We will be playing around with 4 different topic models here:\n", " - LSI (Latent Semantic Indexing)\n", " - HDP (Hierarchical Dirichlet Process)\n", " - LDA (Latent Dirichlet Allocation)\n", " - LDA (tweaked with topic coherence to find optimal number of topics) and\n", " - LDA as LSI with the help of topic coherence metrics\n", "First we'll fit those topic models on our existing data then we'll compare each against the other and see how they rank in terms of human interpretability.\n", "\n", "All can be found in gensim and can be easily used in a plug-and-play fashion. We will tinker with the LDA model using the newly added topic coherence metrics in gensim based on [this](http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf) paper by Roeder et al and see how the resulting topic model compares with the exsisting ones." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "nbpresent": { "id": "25997dab-04e3-4abc-b22f-b36944b208c2" } }, "outputs": [], "source": [ "import os\n", "import re\n", "import operator\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "import gensim\n", "import numpy as np\n", "warnings.filterwarnings('ignore') # Let's not pay heed to them right now\n", "\n", "import nltk\n", "nltk.download('stopwords') # Let's make sure the 'stopword' package is downloaded & updated\n", "nltk.download('wordnet') # Let's also download wordnet, which will be used for lemmatization\n", "\n", "from gensim.models import CoherenceModel, LdaModel, LsiModel, HdpModel\n", "from gensim.models.wrappers import LdaMallet\n", "from gensim.corpora import Dictionary\n", "from pprint import pprint\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "nbpresent": { "id": "8778b874-a6d1-4f2f-ba02-35dc0fa10f0c" } }, "outputs": [], "source": [ "test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])\n", "lee_train_file = test_data_dir + os.sep + 'lee_background.cor'" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "bcbc3313-3a57-4923-b330-69691eaf7535" } }, "source": [ "Analysing our corpus.\n", " - The first document talks about a bushfire that had occured in New South Wales.\n", " - The second talks about conflict between India and Pakistan in Kashmir.\n", " - The third talks about road accidents in the New South Wales area.\n", " - The fourth one talks about Argentina's economic and political crisis during that time.\n", " - The last one talks about the use of drugs by midwives in a Sydney hospital.\n", "Our final topic model should be giving us keywords which we can easily interpret and make a small summary out of. Without this the topic model cannot be of much practical use." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "nbpresent": { "id": "d447e236-b1b9-4cf7-bc39-8bfb499d4730" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Hundreds of people have been forced to vacate their homes in the Southern Highlands of New South Wales as strong winds today pushed a huge bushfire towards the town of Hill Top. A new blaze near Goulburn, south-west of Sydney, has forced the closure of the Hume Highway. At about 4:00pm AEDT, a marked deterioration in the weather as a storm cell moved east across the Blue Mountains forced authorities to make a decision to evacuate people from homes in outlying streets at Hill Top in the New South Wales southern highlands. An estimated 500 residents have left their homes for nearby Mittagong. The New South Wales Rural Fire Service says the weather conditions which caused the fire to burn in a finger formation have now eased and about 60 fire units in and around Hill Top are optimistic of defending all properties. As more than 100 blazes burn on New Year\\'s Eve in New South Wales, fire crews have been called to new fire at Gunning, south of Goulburn. While few details are available at this stage, fire authorities says it has closed the Hume Highway in both directions. Meanwhile, a new fire in Sydney\\'s west is no longer threatening properties in the Cranebrook area. Rain has fallen in some parts of the Illawarra, Sydney, the Hunter Valley and the north coast. But the Bureau of Meteorology\\'s Claire Richards says the rain has done little to ease any of the hundred fires still burning across the state. \"The falls have been quite isolated in those areas and generally the falls have been less than about five millimetres,\" she said. \"In some places really not significant at all, less than a millimetre, so there hasn\\'t been much relief as far as rain is concerned. \"In fact, they\\'ve probably hampered the efforts of the firefighters more because of the wind gusts that are associated with those thunderstorms.\" \\n']\n", "[\"Indian security forces have shot dead eight suspected militants in a night-long encounter in southern Kashmir. The shootout took place at Dora village some 50 kilometers south of the Kashmiri summer capital Srinagar. The deaths came as Pakistani police arrested more than two dozen militants from extremist groups accused of staging an attack on India's parliament. India has accused Pakistan-based Lashkar-e-Taiba and Jaish-e-Mohammad of carrying out the attack on December 13 at the behest of Pakistani military intelligence. Military tensions have soared since the raid, with both sides massing troops along their border and trading tit-for-tat diplomatic sanctions. Yesterday, Pakistan announced it had arrested Lashkar-e-Taiba chief Hafiz Mohammed Saeed. Police in Karachi say it is likely more raids will be launched against the two groups as well as other militant organisations accused of targetting India. Military tensions between India and Pakistan have escalated to a level not seen since their 1971 war. \\n\"]\n", "['The national road toll for the Christmas-New Year holiday period stands at 45, eight fewer than for the same time last year. 20 people have died on New South Wales roads, with eight fatalities in both Queensland and Victoria. Western Australia, the Northern Territory and South Australia have each recorded three deaths, while the ACT and Tasmania remain fatality free. \\n']\n", "[\"Argentina's political and economic crisis has deepened with the resignation of its interim President who took office just a week ago. Aldolfo Rodregiuez Saa told a stunned nation that he could not rescue Argentina because key fellow Peronists would not support his default on massive foreign debt repayment or his plan for a new currency. It was only a week ago that he was promising a million new jobs to end four years of recession, days after his predecessor resigned following a series of failed rescue packages. After announcing that the senate leader, Ramon Puerta, would assume the presidency until congress appoints a new caretaker president, the government said he too had quit and another senior lawmaker would act in the role. Fresh elections are not scheduled until March leaving whoever assumes the presidency with the daunting task of tackling Argentina's worst crisis in 12 years, but this time, isolated by international lending agencies. \\n\"]\n", "['Six midwives have been suspended at Wollongong Hospital, south of Sydney, for inappropriate use of nitrous oxide during work hours, on some occasions while women were in labour. The Illawarra Area Health Service says that following an investigation of unprofessional conduct, a further four midwives have been relocated to other areas within the hospital. The service\\'s chief executive officer, Tony Sherbon, says no one was put at risk, because other staff not involved in the use of nitrous oxide were able to take over caring for women in labour. \"Well we\\'re very concerned and the body of midwives to the hospital - there are over 70 midwives that work in our service - are very annoyed and angry at the inappropriate behaviour of these very senior people who should know better,\" he said. \"And that\\'s why we\\'ve take the action of suspending them and we\\'ll consider further action next week.\" \\n']\n" ] } ], "source": [ "with open(lee_train_file) as f:\n", " for n, l in enumerate(f):\n", " if n < 5:\n", " print([l])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "nbpresent": { "id": "f4d505e5-5e90-4770-aaae-04ae05d697b5" } }, "outputs": [], "source": [ "def build_texts(fname):\n", " \"\"\"\n", " Function to build tokenized texts from file\n", " \n", " Parameters:\n", " ----------\n", " fname: File to be read\n", " \n", " Returns:\n", " -------\n", " yields preprocessed line\n", " \"\"\"\n", " with open(fname) as f:\n", " for line in f:\n", " yield gensim.utils.simple_preprocess(line, deacc=True, min_len=3)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "nbpresent": { "id": "4dbde9d2-3a9d-4677-8dd4-90066c0cb7c4" } }, "outputs": [], "source": [ "train_texts = list(build_texts(lee_train_file))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "nbpresent": { "id": "e7995c4c-f483-4cb5-9f53-3b8c5fef39c3" } }, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(train_texts)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "a6d40ad7-2abd-44e5-8839-62731fb5eab7" } }, "source": [ "### Preprocessing our data. Remember: Garbage In Garbage Out\n", " \"NLP is 80% preprocessing.\"\n", " -Lev Konstantinovskiy\n", "This is the single most important step in setting up a good topic modeling system. If the preprocessing is not good, the algorithm can't do much since we would be feeding it a lot of noise. In this tutorial, we will be filtering out the noise using the following steps in this order for each line:\n", "1. Stopword removal using NLTK's english stopwords dataset.\n", "2. Bigram collocation detection (frequently co-occuring tokens) using gensim's [Phrases](https://radimrehurek.com/gensim/models/phrases.html). This is our first attempt to find some hidden structure in the corpus. You can even try trigram collocation detection.\n", "3. Lemmatization (using gensim's [`lemmatize`](https://radimrehurek.com/gensim/utils.html#gensim.utils.lemmatize)) to only keep the nouns. Lemmatization is generally better than stemming in the case of topic modeling since the words after lemmatization still remain understable. However, generally stemming might be preferred if the data is being fed into a vectorizer and isn't intended to be viewed." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "nbpresent": { "id": "3ca3d45b-a28b-41c7-b5de-4c124c50d13d" } }, "outputs": [], "source": [ "bigram = gensim.models.Phrases(train_texts) # for bigram collocation detection" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "nbpresent": { "id": "862c087b-b918-47b9-b0cf-42b71996e061" } }, "outputs": [ { "data": { "text/plain": [ "[u'new_york', u'example']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bigram[['new', 'york', 'example']]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "nbpresent": { "id": "1181e3a8-6803-4f41-9d55-397f3d700c28" } }, "outputs": [], "source": [ "from gensim.utils import lemmatize\n", "from nltk.corpus import stopwords" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "nbpresent": { "id": "dbeeeabe-b6dd-477c-a798-fb7b39302ba9" } }, "outputs": [], "source": [ "stops = set(stopwords.words('english')) # nltk stopwords list" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "nbpresent": { "id": "3d784001-6875-4be5-b8e8-e6c490f5b7b4" } }, "outputs": [], "source": [ "def process_texts(texts):\n", " \"\"\"\n", " Function to process texts. Following are the steps we take:\n", " \n", " 1. Stopword Removal.\n", " 2. Collocation detection.\n", " 3. Lemmatization (not stem since stemming can reduce the interpretability).\n", " \n", " Parameters:\n", " ----------\n", " texts: Tokenized texts.\n", " \n", " Returns:\n", " -------\n", " texts: Pre-processed tokenized texts.\n", " \"\"\"\n", " texts = [[word for word in line if word not in stops] for line in texts]\n", " texts = [bigram[line] for line in texts]\n", " \n", " from nltk.stem import WordNetLemmatizer\n", " lemmatizer = WordNetLemmatizer()\n", "\n", " texts = [[word for word in lemmatizer.lemmatize(' '.join(line), pos='v').split()] for line in texts]\n", " return texts" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "nbpresent": { "id": "d8cfc39f-aa3b-469f-ae34-1ef99ef51a25" } }, "outputs": [ { "data": { "text/plain": [ "[['afghani',\n", " 'asylum_seeker',\n", " 'australia',\n", " 'return',\n", " 'home',\n", " 'environment',\n", " 'government',\n", " 'application',\n", " 'kabul',\n", " 'foreign_affair',\n", " 'downer',\n", " 'process',\n", " 'threat',\n", " 'person',\n", " 'asylum',\n", " 'afghan',\n", " 'australia',\n", " 'matter',\n", " 'britain',\n", " 'country',\n", " 'europe',\n", " 'taliban',\n", " 'power',\n", " 'afghanistan',\n", " 'taliban',\n", " 'airlift',\n", " 'detainee',\n", " 'christmas',\n", " 'island',\n", " 'island',\n", " 'nauru',\n", " 'total',\n", " 'person',\n", " 'island',\n", " 'operation',\n", " 'aircraft',\n", " 'airlift',\n", " 'today',\n", " 'asylum_seeker',\n", " 'claim',\n", " 'visa',\n", " 'department',\n", " 'immigration',\n", " 'detainee',\n", " 'christmas',\n", " 'island',\n", " 'spokesman',\n", " 'decision']]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_texts = process_texts(train_texts)\n", "train_texts[5:6]" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "0e5ca1a8-9c78-412a-9ab4-a4d0be5afd34" } }, "source": [ "Finalising our dictionary and corpus" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "nbpresent": { "id": "161e8770-8bc2-41ae-98f4-08d1c9311e82" } }, "outputs": [], "source": [ "dictionary = Dictionary(train_texts)\n", "corpus = [dictionary.doc2bow(text) for text in train_texts]" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "fe809373-e88b-44eb-9cb9-08be3dc5949a" } }, "source": [ "### Topic modeling with LSI\n", "This is a useful topic modeling algorithm in that it can rank topics by itself. Thus it outputs topics in a ranked order. However it does require a `num_topics` parameter (set to 200 by default) to determine the number of latent dimensions after the SVD." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "nbpresent": { "id": "58e7dda6-0dd2-4e4f-b81a-0b530c66b20b" } }, "outputs": [], "source": [ "lsimodel = LsiModel(corpus=corpus, num_topics=10, id2word=dictionary)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "nbpresent": { "id": "6b5572c0-2ab0-4b13-a08f-3db21b4c4f21" }, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[(0,\n", " u'-0.241*\"person\" + -0.202*\"australia\" + -0.201*\"government\" + -0.193*\"afghanistan\" + -0.182*\"day\" + -0.174*\"attack\" + -0.156*\"force\" + -0.155*\"area\" + -0.154*\"man\" + -0.147*\"security\"'),\n", " (1,\n", " u'0.524*\"fire\" + 0.274*\"sydney\" + 0.269*\"area\" + 0.219*\"firefighter\" + 0.180*\"wale\" + 0.163*\"wind\" + -0.139*\"israel\" + -0.138*\"attack\" + 0.136*\"line\" + 0.126*\"today\"'),\n", " (2,\n", " u'-0.333*\"australia\" + 0.320*\"israel\" + 0.243*\"palestinian\" + -0.205*\"afghanistan\" + 0.204*\"fire\" + 0.177*\"attack\" + 0.174*\"sharon\" + 0.128*\"yasser_arafat\" + -0.122*\"company\" + 0.119*\"office\"'),\n", " (3,\n", " u'0.353*\"afghanistan\" + -0.301*\"australia\" + 0.236*\"pakistan\" + 0.221*\"force\" + 0.153*\"afghan\" + -0.152*\"test\" + -0.150*\"company\" + 0.146*\"area\" + -0.132*\"union\" + 0.114*\"tora_bora\"'),\n", " (4,\n", " u'-0.331*\"union\" + -0.327*\"company\" + 0.197*\"test\" + 0.193*\"australia\" + -0.190*\"worker\" + 0.189*\"day\" + -0.169*\"qanta\" + -0.150*\"pakistan\" + 0.136*\"wicket\" + -0.130*\"commission\"')]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lsimodel.show_topics(num_topics=5) # Showing only the top 5 topics" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true, "nbpresent": { "id": "b1a8c7b4-dc46-4bfe-b17b-d604f212b389" } }, "outputs": [], "source": [ "lsitopics = lsimodel.show_topics(formatted=False)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "943a5fcd-7c2e-4c16-9879-34882a7a74d4" } }, "source": [ "### Topic modeling with [HDP](http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf)\n", "An HDP model is fully unsupervised. It can also determine the ideal number of topics it needs through posterior inference." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "nbpresent": { "id": "b492de13-0053-416c-b314-1bbae21ca828" } }, "outputs": [], "source": [ "hdpmodel = HdpModel(corpus=corpus, id2word=dictionary)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "nbpresent": { "id": "92dbb672-adca-4535-8089-de23712828d8" } }, "outputs": [ { "data": { "text/plain": [ "[u'topic 0: 0.004*collapse + 0.004*afghanistan + 0.004*troop + 0.003*force + 0.003*government + 0.002*benefit + 0.002*operation + 0.002*taliban + 0.002*time + 0.002*today + 0.002*ypre + 0.002*tourism + 0.002*person + 0.002*help + 0.002*wayne + 0.002*fire + 0.002*peru + 0.002*day + 0.002*united_state + 0.002*hih',\n", " u'topic 1: 0.003*group + 0.003*government + 0.002*target + 0.002*palestinian + 0.002*end + 0.002*terrorism + 0.002*cease + 0.002*memorandum + 0.002*radio + 0.002*call + 0.002*official + 0.002*path + 0.002*security + 0.002*wayne + 0.002*attack + 0.002*human_right + 0.001*four + 0.001*gunman + 0.001*sharon + 0.001*subsidiary',\n", " u'topic 2: 0.003*rafter + 0.003*double + 0.003*team + 0.002*reality + 0.002*manager + 0.002*cup + 0.002*australia + 0.002*abc + 0.002*nomination + 0.002*user + 0.002*freeman + 0.002*herberton + 0.002*lung + 0.002*believe + 0.002*injury + 0.002*steve_waugh + 0.002*fact + 0.002*statement + 0.002*mouth + 0.002*alejandro',\n", " u'topic 3: 0.003*india + 0.003*sector + 0.002*anthony + 0.002*interview + 0.002*suicide_bomber + 0.002*union + 0.002*marconi + 0.002*imprisonment + 0.002*document + 0.002*mood + 0.002*remember + 0.002*repair + 0.002*vicki + 0.001*training + 0.001*dressing + 0.001*government + 0.001*indian + 0.001*law + 0.001*convention + 0.001*pair',\n", " u'topic 4: 0.003*airport + 0.003*commission + 0.002*marathon + 0.002*tonne + 0.002*citizen + 0.002*dickie + 0.002*arrest + 0.002*taliban + 0.002*opposition + 0.002*agha + 0.002*pitch + 0.002*tune + 0.002*regulation + 0.002*monday + 0.002*chile + 0.002*night + 0.002*foreign_affair + 0.002*charge + 0.002*county + 0.002*signature',\n", " u'topic 5: 0.005*company + 0.002*share + 0.002*version + 0.002*entitlement + 0.002*staff + 0.002*value + 0.002*tanzim + 0.002*bay + 0.002*beaumont + 0.002*cent + 0.002*world + 0.002*hass + 0.002*broker + 0.002*line + 0.002*tie + 0.002*plane + 0.002*flare + 0.001*creditor + 0.001*pay + 0.001*administrator',\n", " u'topic 6: 0.002*hiv + 0.002*aids + 0.002*margin + 0.002*worker + 0.002*horror + 0.002*claire + 0.002*nation + 0.002*person + 0.002*battleground + 0.002*christmas + 0.002*quarters + 0.002*day + 0.002*underdog + 0.002*festival + 0.002*devaluation + 0.002*immunity + 0.001*quirindi + 0.001*auditor + 0.001*europe + 0.001*board',\n", " u'topic 7: 0.002*david + 0.002*victim + 0.002*navy + 0.002*promise + 0.002*symbol + 0.002*site + 0.002*agenda + 0.002*endeavour + 0.002*hamas + 0.002*installation + 0.002*bulli + 0.002*quarrel + 0.002*israeli + 0.002*leaf + 0.002*space + 0.002*sharon + 0.002*spa + 0.002*dispute + 0.002*council + 0.002*tit',\n", " u'topic 8: 0.005*storm + 0.004*tree + 0.002*roger + 0.002*aedt + 0.002*minister + 0.002*service + 0.002*sydney + 0.002*electricity + 0.002*power + 0.002*split + 0.002*impact + 0.002*australia + 0.002*area + 0.002*quirindi + 0.002*expansion + 0.002*hornsby + 0.002*standing + 0.002*judgment + 0.002*search + 0.002*thank',\n", " u'topic 9: 0.003*australia + 0.003*economy + 0.002*ward + 0.002*game + 0.002*brought + 0.002*johnston + 0.002*supporter + 0.002*recession + 0.002*stray + 0.002*boat_people + 0.002*ritual + 0.002*thousand + 0.001*police + 0.001*box + 0.001*britain + 0.001*year + 0.001*thing + 0.001*kill + 0.001*tour + 0.001*junction',\n", " u'topic 10: 0.003*match + 0.003*crowd + 0.002*team + 0.002*rafter + 0.002*scrapping + 0.002*decision + 0.002*guarantee + 0.002*masood + 0.002*tennis + 0.002*forestry + 0.002*world + 0.002*france + 0.002*member + 0.002*career + 0.002*australia + 0.002*single + 0.002*rubber + 0.002*road + 0.002*tower + 0.002*attack',\n", " u'topic 11: 0.002*cycle + 0.002*communication + 0.002*spend + 0.002*airline + 0.002*flight + 0.002*amendment + 0.002*swift + 0.002*morning + 0.002*ansett + 0.002*mark + 0.002*platform + 0.002*administrator + 0.002*screen + 0.002*launceston + 0.002*airplane + 0.002*alarming + 0.002*worker + 0.001*tent + 0.001*severance + 0.001*wilton',\n", " u'topic 12: 0.003*summit + 0.003*indonesia + 0.002*john + 0.002*pitwater + 0.002*president + 0.002*week + 0.002*howard + 0.002*issue + 0.002*baptist + 0.002*city + 0.002*model + 0.002*mile + 0.002*talk + 0.002*australia + 0.002*network + 0.002*head + 0.002*passage + 0.002*quinlan + 0.002*start + 0.002*match',\n", " u'topic 13: 0.002*sorrow + 0.002*australia + 0.002*israelis + 0.002*middle_east + 0.002*deck + 0.002*sydney + 0.002*variety + 0.002*zimbabwean + 0.002*general + 0.002*calculation + 0.002*instrument + 0.002*piece + 0.002*treatment + 0.002*truce + 0.002*wicket + 0.002*submission + 0.002*line + 0.002*december + 0.002*showing + 0.001*father',\n", " u'topic 14: 0.002*game + 0.002*giuliani + 0.002*care + 0.002*java + 0.002*mystery + 0.002*session + 0.002*seeker + 0.002*distance + 0.002*tennessee + 0.002*transmission + 0.002*hamid + 0.002*cabinet + 0.002*day + 0.002*regret + 0.002*australia + 0.002*lifestyle + 0.002*afghanistan + 0.002*preview + 0.002*test + 0.002*hit',\n", " u'topic 15: 0.003*president + 0.002*rabbani + 0.002*maxi + 0.002*penalty + 0.002*show + 0.002*sibling + 0.002*adjournment + 0.002*new_delhi + 0.002*permission + 0.002*jackie + 0.002*arrest + 0.002*motive + 0.002*outcome + 0.002*shift + 0.002*spy + 0.002*beech + 0.002*beset + 0.002*need + 0.002*personnel + 0.002*mitchell',\n", " u'topic 16: 0.002*today + 0.002*matter + 0.002*work + 0.002*debate + 0.002*agreement + 0.002*mastermind + 0.002*member + 0.002*downer + 0.002*intercept + 0.002*bedside + 0.002*felix + 0.002*assembly + 0.002*afghan + 0.002*saudi + 0.002*burn + 0.002*franc + 0.002*modification + 0.002*spelt + 0.002*declared + 0.002*resist',\n", " u'topic 17: 0.002*margaret + 0.002*government + 0.002*disruption + 0.002*hingis + 0.002*section + 0.002*security + 0.002*corps + 0.002*pakistan + 0.002*front + 0.002*insurance + 0.002*maintenance + 0.002*order + 0.002*plume + 0.002*amendment + 0.002*demand + 0.001*hawke + 0.001*coal + 0.001*discontent + 0.001*modification + 0.001*distress',\n", " u'topic 18: 0.002*speaker + 0.002*love + 0.002*safety + 0.002*chaman + 0.002*coastguard + 0.002*salfit + 0.002*soccer + 0.002*payment + 0.002*complexity + 0.002*personnel + 0.002*flood + 0.002*employment + 0.002*morrow + 0.002*community + 0.002*darren + 0.002*context + 0.001*tunnel + 0.001*negotiation + 0.001*friendship + 0.001*sutherland',\n", " u'topic 19: 0.003*brain + 0.003*team + 0.003*olympic + 0.002*cell + 0.002*embryo + 0.002*suburb + 0.002*speaking + 0.002*macfarlane + 0.002*sheet + 0.002*overtime + 0.002*man + 0.002*finding + 0.002*canyon + 0.002*research + 0.002*manhattan + 0.002*brutality + 0.002*spot + 0.002*backdrop + 0.001*pervez + 0.001*sector']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdpmodel.show_topics()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": true, "nbpresent": { "id": "85e46481-0245-448c-b4e2-e0c6e175357c" } }, "outputs": [], "source": [ "hdptopics = hdpmodel.show_topics(formatted=False)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "380ef8b7-6de1-4822-ae30-7120e12b5955" } }, "source": [ "### Topic modeling using [LDA](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf)\n", "This is one the most popular topic modeling algorithms today. It is a generative model in that it assumes each document is a mixture of topics and in turn, each topic is a mixture of words. To understand it better you can watch [this](https://www.youtube.com/watch?v=DDq3OVp9dNA) lecture by David Blei. Let's choose 10 topics to initialize this." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "nbpresent": { "id": "a02b72fb-0049-4ec3-825f-179e396f3904" } }, "outputs": [], "source": [ "ldamodel = LdaModel(corpus=corpus, num_topics=10, id2word=dictionary)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "672c009d-3dbc-4a1f-a789-2a0fe78729b9" } }, "source": [ "pyLDAvis is a great way to visualize an LDA model. To summarize in short, the area of the circles represent the prevelance of the topic. The length of the bars on the right represent the membership of a term in a particular topic. pyLDAvis is based on [this](http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf) paper." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true, "nbpresent": { "id": "f7724653-52ef-41e8-aa22-6232be216b08" } }, "outputs": [], "source": [ "import pyLDAvis.gensim" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true, "nbpresent": { "id": "2c5b03e0-ce0f-4999-8fe1-820a9fe06873" } }, "outputs": [], "source": [ "pyLDAvis.enable_notebook()" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false, "nbpresent": { "id": "7da56259-bbf2-4f63-93f6-033833ae4494" }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n", "\n", "\n", "<div 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LDAvis(\"#\" + \"ldavis_el62341396934228628008573380221\", ldavis_el62341396934228628008573380221_data);\n", " })\n", " });\n", "}\n", "</script>" ], "text/plain": [ "PreparedData(topic_coordinates= Freq cluster topics x y\n", "topic \n", "5 15.818395 1 1 0.020284 -0.003740\n", "6 12.159659 1 2 -0.022615 0.092623\n", "9 11.567771 1 3 0.095137 -0.056635\n", "7 10.030019 1 4 -0.041509 -0.014137\n", "0 9.723180 1 5 -0.093849 -0.047330\n", "8 9.499227 1 6 0.003548 0.063112\n", "2 9.403690 1 7 -0.033738 -0.046082\n", "3 8.338908 1 8 0.011367 -0.027304\n", "1 6.964024 1 9 0.038242 0.009804\n", "4 6.495127 1 10 0.023133 0.029689, topic_info= Category Freq Term Total loglift logprob\n", "term \n", "965 Default 69.000000 afghanistan 69.000000 30.0000 30.0000\n", "1918 Default 46.000000 israel 46.000000 29.0000 29.0000\n", "2464 Default 112.000000 australia 112.000000 28.0000 28.0000\n", "3280 Default 33.000000 afghan 33.000000 27.0000 27.0000\n", "2354 Default 67.000000 fire 67.000000 26.0000 26.0000\n", "1458 Default 38.000000 pakistan 38.000000 25.0000 25.0000\n", "757 Default 38.000000 palestinian 38.000000 24.0000 24.0000\n", "851 Default 37.000000 test 37.000000 23.0000 23.0000\n", "574 Default 51.000000 union 51.000000 22.0000 22.0000\n", "1922 Default 59.000000 company 59.000000 21.0000 21.0000\n", "1853 Default 18.000000 qanta 18.000000 20.0000 20.0000\n", "3333 Default 99.000000 government 99.000000 19.0000 19.0000\n", "593 Default 44.000000 child 44.000000 18.0000 18.0000\n", "157 Default 29.000000 india 29.000000 17.0000 17.0000\n", "1201 Default 13.000000 space 13.000000 16.0000 16.0000\n", "545 Default 20.000000 south_africa 20.000000 15.0000 15.0000\n", "1462 Default 12.000000 cancer 12.000000 14.0000 14.0000\n", "2195 Default 8.000000 virus 8.000000 13.0000 13.0000\n", "2060 Default 15.000000 event 15.000000 12.0000 12.0000\n", "589 Default 27.000000 worker 27.000000 11.0000 11.0000\n", "2735 Default 24.000000 metre 24.000000 10.0000 10.0000\n", "3430 Default 30.000000 commission 30.000000 9.0000 9.0000\n", "1553 Default 26.000000 director 26.000000 8.0000 8.0000\n", "985 Default 23.000000 agreement 23.000000 7.0000 7.0000\n", "2549 Default 20.000000 wicket 20.000000 6.0000 6.0000\n", "1844 Default 20.000000 rate 20.000000 5.0000 5.0000\n", "1377 Default 12.000000 dispute 12.000000 4.0000 4.0000\n", "2605 Default 28.000000 peace 28.000000 3.0000 3.0000\n", "2989 Default 67.000000 attack 67.000000 2.0000 2.0000\n", "1149 Default 6.000000 farmer 6.000000 1.0000 1.0000\n", "... ... ... ... ... ... ...\n", "1214 Topic10 1.961223 possibility 5.635690 1.6786 -6.3127\n", "2269 Topic10 2.441073 harrison 7.646867 1.5923 -6.0938\n", "1553 Topic10 5.589432 director 26.667974 1.1715 -5.2654\n", "991 Topic10 4.603794 change 21.175656 1.2081 -5.4594\n", "1303 Topic10 1.659603 premier 4.774002 1.6775 -6.4797\n", "3289 Topic10 3.672805 weapon 15.957912 1.2651 -5.6853\n", "2840 Topic10 2.639075 tension 10.503602 1.3528 -6.0159\n", "2263 Topic10 3.590576 river 18.683428 1.0848 -5.7080\n", "1140 Topic10 6.490434 group 58.260255 0.5395 -5.1160\n", "2319 Topic10 3.892480 community 24.092294 0.9113 -5.6272\n", "1922 Topic10 6.169639 company 59.473519 0.4682 -5.1666\n", "1584 Topic10 4.137423 death 27.575597 0.8373 -5.5662\n", "157 Topic10 4.256485 india 29.854661 0.7862 -5.5378\n", "2422 Topic10 8.052329 person 125.100187 -0.0090 -4.9003\n", "949 Topic10 4.045679 team 31.873346 0.6700 -5.5886\n", "1893 Topic10 3.870061 action 32.053391 0.6200 -5.6330\n", "2344 Topic10 4.239250 meeting 42.681139 0.4247 -5.5419\n", "2605 Topic10 3.525193 peace 28.066990 0.6595 -5.7264\n", "2464 Topic10 5.517708 australia 112.829012 -0.2838 -5.2783\n", "3165 Topic10 3.623557 police 34.081096 0.4928 -5.6988\n", "1608 Topic10 3.922546 world 45.568210 0.2816 -5.6195\n", "194 Topic10 3.747631 president 44.732037 0.2546 -5.6652\n", "3333 Topic10 4.302260 government 99.282266 -0.4047 -5.5271\n", "546 Topic10 3.148621 country 30.045861 0.4784 -5.8393\n", "2307 Topic10 3.397572 united_state 47.946180 0.0871 -5.7632\n", "1931 Topic10 3.315484 hour 47.117280 0.0801 -5.7877\n", "2989 Topic10 3.477635 attack 67.204296 -0.2273 -5.7399\n", "1110 Topic10 3.375391 man 70.001434 -0.2979 -5.7698\n", "2904 Topic10 3.383324 day 91.814892 -0.5668 -5.7674\n", "1692 Topic10 3.248350 area 67.475462 -0.2995 -5.8081\n", "\n", "[756 rows x 6 columns], token_table= Topic Freq Term\n", "term \n", "311 4 0.821215 abbott\n", "1059 4 0.619885 abortion\n", "2850 1 0.071487 abuse\n", "2850 2 0.285947 abuse\n", "2850 4 0.357434 abuse\n", "2850 5 0.071487 abuse\n", "2850 7 0.071487 abuse\n", "2850 8 0.071487 abuse\n", "2850 10 0.142973 abuse\n", "1893 1 0.062396 action\n", "1893 2 0.093594 action\n", "1893 3 0.093594 action\n", "1893 4 0.187188 action\n", "1893 5 0.062396 action\n", "1893 6 0.062396 action\n", "1893 7 0.062396 action\n", "1893 8 0.155990 action\n", "1893 9 0.093594 action\n", "1893 10 0.124792 action\n", "844 5 0.212931 actor\n", "844 7 0.425862 actor\n", "1719 3 0.804982 advantage\n", "1173 6 0.614371 advent\n", "3280 1 0.270599 afghan\n", "3280 2 0.090200 afghan\n", "3280 3 0.030067 afghan\n", "3280 4 0.030067 afghan\n", "3280 5 0.450999 afghan\n", "3280 7 0.030067 afghan\n", "3280 9 0.030067 afghan\n", "... ... ... ...\n", "1192 4 0.031473 year\n", "1192 5 0.157364 year\n", "1192 6 0.110155 year\n", "1192 7 0.141628 year\n", "1192 8 0.078682 year\n", "1192 9 0.078682 year\n", "1192 10 0.047209 year\n", "1166 1 0.234873 year_old\n", "1166 2 0.039145 year_old\n", "1166 3 0.039145 year_old\n", "1166 5 0.156582 year_old\n", "1166 6 0.195727 year_old\n", "1166 7 0.078291 year_old\n", "1166 8 0.156582 year_old\n", "1166 9 0.039145 year_old\n", "1166 10 0.039145 year_old\n", "2408 1 0.165536 yesterday\n", "2408 2 0.094592 yesterday\n", "2408 3 0.165536 yesterday\n", "2408 4 0.094592 yesterday\n", "2408 5 0.118240 yesterday\n", "2408 6 0.070944 yesterday\n", "2408 7 0.118240 yesterday\n", "2408 8 0.094592 yesterday\n", "2408 9 0.047296 yesterday\n", "2408 10 0.047296 yesterday\n", "238 9 0.421611 zaccarias\n", "3339 1 0.261213 zimbabwe\n", "3339 4 0.261213 zimbabwe\n", "3339 10 0.522426 zimbabwe\n", "\n", "[2006 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 7, 10, 8, 1, 9, 3, 4, 2, 5])" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pyLDAvis.gensim.prepare(ldamodel, corpus, dictionary)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": true, "nbpresent": { "id": "13f462fd-b0c3-4a29-90f3-050e2317b72c" } }, "outputs": [], "source": [ "ldatopics = ldamodel.show_topics(formatted=False)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "68d38063-fb92-4d36-b3fb-f2d3cbff9eb5" } }, "source": [ "### Finding out the optimal number of topics\n", "__Introduction to topic coherence__:\n", "<img src=\"https://rare-technologies.com/wp-content/uploads/2016/06/pipeline.png\">\n", "Topic coherence in essence measures the human interpretability of a topic model. Traditionally [perplexity has been used](http://qpleple.com/perplexity-to-evaluate-topic-models/) to evaluate topic models however this does not correlate with human annotations at times. Topic coherence is another way to evaluate topic models with a much higher guarantee on human interpretability. Thus this can be used to compare different topic models among many other use-cases. Here's a short blog I wrote explaining topic coherence:\n", "[What is topic coherence?](https://rare-technologies.com/what-is-topic-coherence/)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "nbpresent": { "id": "45b1a641-1152-4364-ad25-62d1f8187317" } }, "outputs": [], "source": [ "def evaluate_graph(dictionary, corpus, texts, limit):\n", " \"\"\"\n", " Function to display num_topics - LDA graph using c_v coherence\n", " \n", " Parameters:\n", " ----------\n", " dictionary : Gensim dictionary\n", " corpus : Gensim corpus\n", " limit : topic limit\n", " \n", " Returns:\n", " -------\n", " lm_list : List of LDA topic models\n", " c_v : Coherence values corresponding to the LDA model with respective number of topics\n", " \"\"\"\n", " c_v = []\n", " lm_list = []\n", " for num_topics in range(1, limit):\n", " lm = LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary)\n", " lm_list.append(lm)\n", " cm = CoherenceModel(model=lm, texts=texts, dictionary=dictionary, coherence='c_v')\n", " c_v.append(cm.get_coherence())\n", " \n", " # Show graph\n", " x = range(1, limit)\n", " plt.plot(x, c_v)\n", " plt.xlabel(\"num_topics\")\n", " plt.ylabel(\"Coherence score\")\n", " plt.legend((\"c_v\"), loc='best')\n", " plt.show()\n", " \n", " return lm_list, c_v" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "nbpresent": { "id": "e8936716-d06c-4cef-ae87-5c5da9a25a85" } }, "outputs": [ { "data": { "image/png": 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OIWwGbAQ0DiG0DiEMSzWIEMKjeHLTF5gM7AwcGH6f5LsZqxauNsQTiqnAWOAo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\nmJNOgi5doo5GJP8oERGRrIjHvU5kxYq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-5.4417999999999997, -5.9029999999999996, -5.4353999999999996, -5.2289000000000003, -5.2069000000000001, -6.3699000000000003, -6.3322000000000003, -6.0232000000000001, -5.9645000000000001, -5.7991999999999999, -5.9888000000000003, -6.1228999999999996, -5.6279000000000003, -5.7961, -5.7550999999999997, -5.7869000000000002, -5.7152000000000003, -5.7980999999999998, -5.7134999999999998, -5.9303999999999997, -5.6002999999999998, -5.9085999999999999]}};\n", "\n", "function LDAvis_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(LDAvis) !== \"undefined\"){\n", " // already loaded: just create the visualization\n", " !function(LDAvis){\n", " new LDAvis(\"#\" + \"ldavis_el6234139693600051664982684879\", ldavis_el6234139693600051664982684879_data);\n", " }(LDAvis);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/LDAvis\n", " require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n", " new LDAvis(\"#\" + \"ldavis_el6234139693600051664982684879\", ldavis_el6234139693600051664982684879_data);\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & LDAvis\n", " LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n", " LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n", " new LDAvis(\"#\" + \"ldavis_el6234139693600051664982684879\", ldavis_el6234139693600051664982684879_data);\n", " })\n", " });\n", "}\n", "</script>" ], "text/plain": [ "PreparedData(topic_coordinates= Freq cluster topics x y\n", "topic \n", "1 43.675712 1 1 0.030435 -0.011894\n", "0 28.636818 1 2 -0.026509 -0.018098\n", "2 27.687471 1 3 -0.003926 0.029991, topic_info= Category Freq Term Total loglift logprob\n", "term \n", "851 Default 37.000000 test 37.000000 30.0000 30.0000\n", "3437 Default 25.000000 airline 25.000000 29.0000 29.0000\n", "2422 Default 129.000000 person 129.000000 28.0000 28.0000\n", "1140 Default 56.000000 group 56.000000 27.0000 27.0000\n", "593 Default 43.000000 child 43.000000 26.0000 26.0000\n", "1853 Default 18.000000 qanta 18.000000 25.0000 25.0000\n", "757 Default 35.000000 palestinian 35.000000 24.0000 24.0000\n", "3183 Default 33.000000 wale 33.000000 23.0000 23.0000\n", "3044 Default 36.000000 centre 36.000000 22.0000 22.0000\n", "2351 Default 23.000000 flight 23.000000 21.0000 21.0000\n", "1931 Default 46.000000 hour 46.000000 20.0000 20.0000\n", "2464 Default 116.000000 australia 116.000000 19.0000 19.0000\n", "661 Default 17.000000 detainee 17.000000 18.0000 18.0000\n", "2457 Default 48.000000 sydney 48.000000 17.0000 17.0000\n", "2127 Default 25.000000 militant 25.000000 16.0000 16.0000\n", "325 Default 9.000000 refugee 9.000000 15.0000 15.0000\n", "2873 Default 11.000000 gunman 11.000000 14.0000 14.0000\n", "3143 Default 41.000000 state 41.000000 13.0000 13.0000\n", "2549 Default 19.000000 wicket 19.000000 12.0000 12.0000\n", "949 Default 32.000000 team 32.000000 11.0000 11.0000\n", "102 Default 16.000000 hamas 16.000000 10.0000 10.0000\n", "2111 Default 34.000000 number 34.000000 9.0000 9.0000\n", "2195 Default 9.000000 virus 9.000000 8.0000 8.0000\n", "1547 Default 36.000000 week 36.000000 7.0000 7.0000\n", "3254 Default 7.000000 woomera 7.000000 6.0000 6.0000\n", "2989 Default 68.000000 attack 68.000000 5.0000 5.0000\n", "965 Default 68.000000 afghanistan 68.000000 4.0000 4.0000\n", "1458 Default 40.000000 pakistan 40.000000 3.0000 3.0000\n", "109 Default 16.000000 army 16.000000 2.0000 2.0000\n", "1132 Default 25.000000 polouse 25.000000 1.0000 1.0000\n", "... ... ... ... ... ... ...\n", "791 Topic3 13.599766 way 33.653033 0.3781 -5.8262\n", "457 Topic3 9.772432 radio 21.961201 0.4745 -6.1567\n", "3469 Topic3 6.851469 problem 13.763973 0.5866 -6.5118\n", "1694 Topic3 7.397502 start 15.259331 0.5601 -6.4351\n", "142 Topic3 10.293808 work 23.795675 0.4462 -6.1047\n", "1038 Topic3 21.297940 today 63.918948 0.1852 -5.3776\n", "1931 Topic3 16.519764 hour 46.137796 0.2571 -5.6317\n", "1692 Topic3 21.211328 area 65.779869 0.1524 -5.3817\n", "884 Topic3 19.973123 time 61.517598 0.1593 -5.4418\n", "949 Topic3 12.593494 team 32.284527 0.3428 -5.9030\n", "2354 Topic3 20.102475 fire 63.934883 0.1272 -5.4354\n", "2904 Topic3 24.711735 day 91.674760 -0.0268 -5.2289\n", "3333 Topic3 25.261699 government 98.056135 -0.0721 -5.2069\n", "2169 Topic3 7.895483 new_south 17.051104 0.5143 -6.3699\n", "1853 Topic3 8.198638 qanta 18.098547 0.4923 -6.3322\n", "1346 Topic3 11.167314 per_cent 29.136717 0.3252 -6.0232\n", "3087 Topic3 11.842532 staff 32.048978 0.2886 -5.9645\n", "2408 Topic3 13.971238 yesterday 41.805680 0.1882 -5.7992\n", "1494 Topic3 11.557994 minister 31.327486 0.2871 -5.9888\n", "3437 Topic3 10.107526 airline 25.268230 0.3679 -6.1229\n", "713 Topic3 16.582083 security 56.237078 0.0629 -5.6279\n", "2344 Topic3 14.015395 meeting 43.123292 0.1603 -5.7961\n", "1608 Topic3 14.601073 world 46.283007 0.1305 -5.7551\n", "2307 Topic3 14.144147 united_state 47.424821 0.0743 -5.7869\n", "1922 Topic3 15.195149 company 61.156859 -0.1083 -5.7152\n", "41 Topic3 13.987025 force 53.790616 -0.0628 -5.7981\n", "1110 Topic3 15.221356 man 70.658805 -0.2510 -5.7135\n", "3382 Topic3 12.253432 leader 42.187497 0.0479 -5.9304\n", "2422 Topic3 17.046831 person 129.542161 -0.7439 -5.6003\n", "574 Topic3 12.523667 union 52.557544 -0.1501 -5.9086\n", "\n", "[259 rows x 6 columns], token_table= Topic Freq Term\n", "term \n", "1059 2 0.631342 abortion\n", "1059 3 0.315671 abortion\n", "1893 1 0.536429 action\n", "1893 2 0.238413 action\n", "1893 3 0.208611 action\n", "3280 1 0.622198 afghan\n", "3280 2 0.217769 afghan\n", "3280 3 0.186660 afghan\n", "965 1 0.700378 afghanistan\n", "965 2 0.145912 afghanistan\n", "965 3 0.145912 afghanistan\n", "985 1 0.612333 agreement\n", "985 2 0.174952 agreement\n", "985 3 0.218690 agreement\n", "3437 1 0.118726 airline\n", "3437 2 0.514480 airline\n", "3437 3 0.395754 airline\n", "2063 1 0.312281 alcohol\n", "2063 2 0.624561 alcohol\n", "207 1 0.249899 ambulance\n", "207 2 0.749696 ambulance\n", "207 3 0.249899 ambulance\n", "1957 2 0.838777 amendment\n", "2358 3 0.628736 anthony\n", "1841 3 0.836923 antibiotic\n", "1692 1 0.410460 area\n", "1692 2 0.258438 area\n", "1692 3 0.319247 area\n", "109 1 0.307961 army\n", "109 2 0.554330 army\n", "... ... ... ...\n", "3183 3 0.481597 wale\n", "794 1 0.433821 war\n", "794 2 0.371847 war\n", "794 3 0.185923 war\n", "791 1 0.416010 way\n", "791 2 0.178290 way\n", "791 3 0.416010 way\n", "1547 1 0.388289 week\n", "1547 2 0.194145 week\n", "1547 3 0.416024 week\n", "2549 1 0.200089 wicket\n", "2549 2 0.300133 wicket\n", "2549 3 0.500222 wicket\n", "3254 1 0.139154 woomera\n", "3254 2 0.695771 woomera\n", "3254 3 0.139154 woomera\n", "142 1 0.420244 work\n", "142 2 0.168098 work\n", "142 3 0.420244 work\n", "1608 1 0.453730 world\n", "1608 2 0.237668 world\n", "1608 3 0.324093 world\n", "2073 1 0.691764 world_heritage\n", "144 1 0.860906 worm\n", "1192 1 0.440823 year\n", "1192 2 0.204668 year\n", "1192 3 0.346361 year\n", "2408 1 0.382723 yesterday\n", "2408 2 0.287042 yesterday\n", "2408 3 0.334883 yesterday\n", "\n", "[434 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 3])" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pyLDAvis.gensim.prepare(lmlist[2], corpus, dictionary)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false, "nbpresent": { "id": "699e2ddb-add6-4134-a723-1bb37029f013" } }, "outputs": [], "source": [ "lmtopics = lmlist[5].show_topics(formatted=False)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "caa35df4-b625-4246-bc1f-42c561f31486" } }, "source": [ "### LDA as LSI" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "d2aa5aac-acc7-41c9-96d4-5410c0d8a14b" } }, "source": [ "One of the problem with LDA is that if we train it on a large number of topics, the topics get \"lost\" among the numbers. Let us see if we can dig out the best topics from the best LDA model we can produce. The function below can be used to control the quality of the LDA model we produce." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false, "nbpresent": { "id": "c39406c8-1e69-4249-91ac-894338d4053b" } }, "outputs": [], "source": [ "def ret_top_model():\n", " \"\"\"\n", " Since LDAmodel is a probabilistic model, it comes up different topics each time we run it. To control the\n", " quality of the topic model we produce, we can see what the interpretability of the best topic is and keep\n", " evaluating the topic model until this threshold is crossed. \n", " \n", " Returns:\n", " -------\n", " lm: Final evaluated topic model\n", " top_topics: ranked topics in decreasing order. List of tuples\n", " \"\"\"\n", " top_topics = [(0, 0)]\n", " while top_topics[0][1] < 0.97:\n", " lm = LdaModel(corpus=corpus, id2word=dictionary)\n", " coherence_values = {}\n", " for n, topic in lm.show_topics(num_topics=-1, formatted=False):\n", " topic = [word for word, _ in topic]\n", " cm = CoherenceModel(topics=[topic], texts=train_texts, dictionary=dictionary, window_size=10)\n", " coherence_values[n] = cm.get_coherence()\n", " top_topics = sorted(coherence_values.items(), key=operator.itemgetter(1), reverse=True)\n", " return lm, top_topics" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true, "nbpresent": { "id": "6b8eef4a-a87d-42bd-84dd-586610828698" } }, "outputs": [], "source": [ "lm, top_topics = ret_top_model()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "nbpresent": { "id": "43870992-c2ff-47cf-8b6b-f62855673b42" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(91, 0.99286550077029223), (42, 0.96031455145699274), (54, 0.87011963575683104), (2, 0.84575428129030361), (10, 0.83238343784453017)]\n" ] } ], "source": [ "print(top_topics[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inference\n", "We can clearly see below that the first topic is about __cinema__, second is about __email malware__, third is about the land which was given back to the __Larrakia aboriginal community of Australia__ in 2000. Then there's one about __Australian cricket__. LDA as LSI has worked wonderfully in finding out the best topics from within LDA." ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false, "nbpresent": { "id": "281d1434-ce22-46c6-ba53-71a24a476568" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[(u'actor', 0.034688196735986693),\n", " (u'picture', 0.023163878883499418),\n", " (u'award', 0.023163878883499418),\n", " (u'comedy', 0.023163878883499418),\n", " (u'globe', 0.023163878883499418),\n", " (u'nomination', 0.023163878883499418),\n", " (u'actress', 0.023163878883499418),\n", " (u'film', 0.023163878883499418),\n", " (u'drama', 0.011639561031012149),\n", " (u'winner', 0.011639561031012149)],\n", " [(u'virus', 0.064292949289013482),\n", " (u'user', 0.048074573973209883),\n", " (u'computer', 0.040350900997751814),\n", " (u'company', 0.028173623478117912),\n", " (u'email', 0.022580226976870982),\n", " (u'worm', 0.020928236506996975),\n", " (u'attachment', 0.014534311779706417),\n", " (u'outlook', 0.01260706654637953),\n", " (u'software', 0.011909411409069969),\n", " (u'list', 0.0088116041533348403)],\n", " [(u'claim', 0.0096511365969504694),\n", " (u'agreement', 0.0082836950379963047),\n", " (u'hectare', 0.0077564979304569235),\n", " (u'larrakia', 0.0065928813973845394),\n", " (u'rosebury', 0.006086042494624749),\n", " (u'term', 0.004880655853124416),\n", " (u'region', 0.004786636929111303),\n", " (u'title', 0.0045026307214029735),\n", " (u'palmerston', 0.0043726827115423677),\n", " (u'developer', 0.0040102561358092521)],\n", " [(u'government', 0.046880132726190141),\n", " (u'razor', 0.035772624674521684),\n", " (u'gang', 0.034958865711441162),\n", " (u'minister', 0.023615858300345904),\n", " (u'interest', 0.023531518290467797),\n", " (u'taxpayer', 0.023484887279677492),\n", " (u'nelson', 0.023408331025582648),\n", " (u'spending', 0.023363131530296326),\n", " (u'program', 0.022809499664362586),\n", " (u'colleague', 0.012039863390851384)],\n", " [(u'australia', 0.019022701887671096),\n", " (u'outlook', 0.012806577991883974),\n", " (u'price', 0.012017645637892888),\n", " (u'growth', 0.011021360611214826),\n", " (u'world', 0.010586500333515535),\n", " (u'imf', 0.0074848683800558145),\n", " (u'half', 0.0073080219523406773),\n", " (u'release', 0.0073069514968024446),\n", " (u'oil', 0.0071307771829650724),\n", " (u'weakening', 0.0067585126681211785)],\n", " [(u'role', 0.036823234375415084),\n", " (u'heart', 0.018676496748175567),\n", " (u'mcreddie', 0.018520830095514161),\n", " (u'sir', 0.018430691138823303),\n", " (u'actor', 0.018423768093119148),\n", " (u'attack', 0.018421603513127272),\n", " (u'minister', 0.018330977218667187),\n", " (u'cancer', 0.018246768643902407),\n", " (u'servant', 0.018246520413261125),\n", " (u'friend', 0.018230140539399531)],\n", " [(u'australia', 0.038230610979973961),\n", " (u'test', 0.03039802044037989),\n", " (u'day', 0.026478028361575149),\n", " (u'adam', 0.023237227270639361),\n", " (u'wicket', 0.018060239149805601),\n", " (u'match', 0.015652900511647725),\n", " (u'gilchrist', 0.015206348827236857),\n", " (u'steve_waugh', 0.01496754571623464),\n", " (u'south_africa', 0.013902623982144873),\n", " (u'selector', 0.012332915474867073)],\n", " [(u'product', 0.067729999063555119),\n", " (u'food', 0.033921347284742248),\n", " (u'consumer', 0.033921347284742241),\n", " (u'company', 0.033921347284742241),\n", " (u'hooke', 0.022651796691804622),\n", " (u'law', 0.022651796691804622),\n", " (u'grocery', 0.022651796691804622),\n", " (u'technology', 0.022651796691804622),\n", " (u'sultan', 0.014079780537934588),\n", " (u'stage', 0.013736597864617922)],\n", " [(u'credit', 0.020223411999648302),\n", " (u'way', 0.017706515460000523),\n", " (u'bank', 0.017459639386736926),\n", " (u'card', 0.016308335204832106),\n", " (u'consumer', 0.014565787979687885),\n", " (u'reserve_bank', 0.014365008462949415),\n", " (u'association', 0.011448453247788988),\n", " (u'rate', 0.010363334709658676),\n", " (u'movement', 0.010204675471073506),\n", " (u'inquiry', 0.0093452022355641085)],\n", " [(u'fire', 0.045611922604745642),\n", " (u'area', 0.021994719721821848),\n", " (u'firefighter', 0.018748173264525044),\n", " (u'sydney', 0.016599279291396325),\n", " (u'wind', 0.014270025525472343),\n", " (u'property', 0.0098028785236429564),\n", " (u'hour', 0.0097079779464512347),\n", " (u'today', 0.0093953004964965076),\n", " (u'year', 0.0089216764257795157),\n", " (u'state', 0.0086116373269496185)]]\n" ] } ], "source": [ "pprint([lm.show_topic(topicid) for topicid, c_v in top_topics[:10]])" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false, "nbpresent": { "id": "ada914a2-bacb-4300-8ce3-f1332843d24c" } }, "outputs": [], "source": [ "lda_lsi_topics = [[word for word, prob in lm.show_topic(topicid)] for topicid, c_v in top_topics]" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "4f313370-b5f0-4754-9744-157da0447fc4" } }, "source": [ "### Evaluating all the topic models\n", "Any topic model which can come up with topic terms can be plugged into the coherence pipeline. You can even plug in an [NMF topic model](http://derekgreene.com/nmf-topic/) created with scikit-learn." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "nbpresent": { "id": "4329af31-21b4-4570-903a-6df3715244c7" } }, "outputs": [], "source": [ "lsitopics = [[word for word, prob in topic] for topicid, topic in lsitopics]\n", "\n", "hdptopics = [[word for word, prob in topic] for topicid, topic in hdptopics]\n", "\n", "ldatopics = [[word for word, prob in topic] for topicid, topic in ldatopics]\n", "\n", "lmtopics = [[word for word, prob in topic] for topicid, topic in lmtopics]" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true, "nbpresent": { "id": "0924faf0-a957-4d12-b35c-b50ebb30f370" } }, "outputs": [], "source": [ "lsi_coherence = CoherenceModel(topics=lsitopics[:10], texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n", "\n", "hdp_coherence = CoherenceModel(topics=hdptopics[:10], texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n", "\n", "lda_coherence = CoherenceModel(topics=ldatopics, texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n", "\n", "lm_coherence = CoherenceModel(topics=lmtopics, texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n", "\n", "lda_lsi_coherence = CoherenceModel(topics=lda_lsi_topics[:10], texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true, "nbpresent": { "id": "59cc671f-041a-4d6f-a609-f1b9855aa05c" } }, "outputs": [], "source": [ "def evaluate_bar_graph(coherences, indices):\n", " \"\"\"\n", " Function to plot bar graph.\n", " \n", " coherences: list of coherence values\n", " indices: Indices to be used to mark bars. Length of this and coherences should be equal.\n", " \"\"\"\n", " assert len(coherences) == len(indices)\n", " n = len(coherences)\n", " x = np.arange(n)\n", " plt.bar(x, coherences, width=0.2, tick_label=indices, align='center')\n", " plt.xlabel('Models')\n", " plt.ylabel('Coherence Value')" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false, "nbpresent": { "id": "b86ba0c1-c7c4-43a5-a9f5-8dfe7967be6c" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0ce8179250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluate_bar_graph([lsi_coherence, hdp_coherence, lda_coherence, lm_coherence, lda_lsi_coherence],\n", " ['LSI', 'HDP', 'LDA', 'LDA_Mod', 'LDA_LSI'])" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "df6cc31a-0b01-4700-b210-836dd510007a" } }, "source": [ "### Customizing the topic coherence measure\n", "Till now we only used the `c_v` coherence measure. There are others such as `u_mass`, `c_uci`, `c_npmi`. All of these calculate coherence in a different way. `c_v` is found to be most in line with human ratings but can be much slower than `u_mass` since it uses a sliding window over the texts." ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "664fa23d-ba6e-4a30-9287-e4fef1cc093e" } }, "source": [ "### Making your own coherence measure\n", "Let's modify `c_uci` to use `s_one_pre` instead of `s_one_one` segmentation" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true, "nbpresent": { "id": "b3652f43-da03-4027-9170-83e1679dfa2b" } }, "outputs": [], "source": [ "from gensim.topic_coherence import (segmentation, probability_estimation,\n", " direct_confirmation_measure, indirect_confirmation_measure,\n", " aggregation)\n", "from gensim.matutils import argsort\n", "from collections import namedtuple" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "nbpresent": { "id": "8ce2b802-f0c1-4ffc-9ce5-c2792856b5b4" } }, "outputs": [], "source": [ "make_pipeline = namedtuple('Coherence_Measure', 'seg, prob, conf, aggr')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true, "nbpresent": { "id": "9994e7f0-f366-40da-9c14-76e8142ee46e" } }, "outputs": [], "source": [ "measure = make_pipeline(segmentation.s_one_one,\n", " probability_estimation.p_boolean_sliding_window,\n", " direct_confirmation_measure.log_ratio_measure,\n", " aggregation.arithmetic_mean)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "416f5820-1538-4483-a77f-5211c8179891" } }, "source": [ "To get topics out of the topic model:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true, "nbpresent": { "id": "aa9d35ae-c458-49d2-bdbe-d93d5cb3ba9c" } }, "outputs": [], "source": [ "topics = []\n", "for topic in lm.state.get_lambda():\n", " bestn = argsort(topic, topn=10, reverse=True)\n", "topics.append(bestn)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "13f246a0-918e-44c1-ae19-7af118d83585" } }, "source": [ "__Step 1__: Segmentation" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true, "nbpresent": { "id": "821ab68b-c377-40b1-bdf3-b92d66414383" } }, "outputs": [], "source": [ "# Perform segmentation\n", "segmented_topics = measure.seg(topics)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "e4b50714-b644-4213-b3cb-bdac9fe22476" } }, "source": [ "__Step 2__: Probability estimation" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "nbpresent": { "id": "d8a8bcc6-8b37-4d4f-ad06-903804776078" } }, "outputs": [], "source": [ "# Since this is a window-based coherence measure we will perform window based prob estimation\n", "per_topic_postings, num_windows = measure.prob(texts=train_texts, segmented_topics=segmented_topics,\n", " dictionary=dictionary, window_size=2)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "6f302dcb-787b-42b0-98f0-1df0a163a818" } }, "source": [ "__Step 3__: Confirmation Measure" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true, "nbpresent": { "id": "ffb5aaa9-5fa1-4b28-ba2f-e23525caa392" } }, "outputs": [], "source": [ "confirmed_measures = measure.conf(segmented_topics, per_topic_postings, num_windows, normalize=False)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "3544f061-6870-4630-bd54-79f75decf8b6" } }, "source": [ "__Step 4__: Aggregation" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "nbpresent": { "id": "31c64f41-a499-4f80-8ccd-1364940f7383" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-11.2873225334\n" ] } ], "source": [ "print(measure.aggr(confirmed_measures))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How this topic model can be used further\n", "The best topic model here can be used as a standalone for news article classification. However a topic model can also be used as a dimensionality reduction algorithm to feed into a classifier. A good topic model should be able to extract the signal from the noise efficiently, hence improving the performance of the classifier." ] } ], "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" }, "nbpresent": { "slides": { "04abaf09-1754-419e-9cf8-3c4f0accfc5f": { "id": "04abaf09-1754-419e-9cf8-3c4f0accfc5f", "prev": "67c9421a-0e61-4346-873f-bf3769d00c97", "regions": { "48b5cd39-b89b-4e2f-be2a-47a0fccafd3b": { "attrs": { "height": 0.8, "width": 0.8, "x": 0.1, "y": 0.1 }, "content": { "cell": "862c087b-b918-47b9-b0cf-42b71996e061", "part": "whole" }, "id": "48b5cd39-b89b-4e2f-be2a-47a0fccafd3b" } } }, "0527f44a-39b6-4cf8-9802-d16b8cd34754": { "id": "0527f44a-39b6-4cf8-9802-d16b8cd34754", 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lgpl-2.1
googlecolab/colabtools
tests/simple.ipynb
1
680
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Simple", "version": "0.3.2", "views": {}, "default_view": {}, "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "metadata": { "id": "NwK2eKbS4X96", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "1+1" ], "execution_count": 0, "outputs": [] } ] }
apache-2.0
lit-mod-viz/middlemarch-critical-histories
old/e1/e1a-analysis.ipynb
2
133335
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiment 1-A\n", "\n", "This experiment used the full corpus of 6K+ texts scraped from JSTOR." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "from ast import literal_eval\n", "import numpy as np\n", "import re\n", "import json\n", "from nltk.corpus import names\n", "from collections import Counter\n", "from matplotlib import pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = [16, 6]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('../txt/e1a.json') as f: \n", " rawData = f.read()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_json(rawData)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['Decade'] = df['year'] - (df['year'] % 10)" ] }, { "cell_type": "code", "execution_count": 6, "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>author</th>\n", " <th>coverdate</th>\n", " <th>disc_name</th>\n", " <th>doi</th>\n", " <th>id</th>\n", " <th>jcode</th>\n", " <th>journal</th>\n", " <th>la</th>\n", " <th>matches</th>\n", " <th>no</th>\n", " <th>...</th>\n", " <th>pages</th>\n", " <th>publisher_name</th>\n", " <th>sp</th>\n", " <th>srcHtml</th>\n", " <th>title</th>\n", " <th>topics</th>\n", " <th>ty</th>\n", " <th>vo</th>\n", " <th>year</th>\n", " <th>Decade</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>[Harriet Farwell Adams]</td>\n", " <td>[19840601]</td>\n", " <td>[Language &amp; Literature, Humanities]</td>\n", " <td>10.2307/3044822</td>\n", " <td>c6e6ce20-79c4-3c59-af91-b06c3208b37b</td>\n", " <td>[ninecentfict]</td>\n", " <td>Nineteenth-Century Fiction</td>\n", " <td>[eng]</td>\n", " <td>[23, [[5809, 6218], [8751, 8851], [8890, 9046]...</td>\n", " <td>[1]</td>\n", " <td>...</td>\n", " <td>69-90</td>\n", " <td>[University of California Press]</td>\n", " <td>69</td>\n", " <td>&lt;cite&gt;Nineteenth-Century Fiction&lt;/cite&gt;, Vol. ...</td>\n", " <td>[Dorothea and \"Miss Brooke\" in Middlemarch]</td>\n", " <td>[Sentiment, Fear, Martyrdom, Envy, Vocation, G...</td>\n", " <td>fla</td>\n", " <td>[39]</td>\n", " <td>1984</td>\n", " <td>1980</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>[HUGH WITEMEYER]</td>\n", " <td>[19910901]</td>\n", " <td>[Language &amp; Literature, Humanities]</td>\n", " <td>10.2307/43470798</td>\n", " <td>0d7eb58a-e4c1-326b-a195-012da1a4eb11</td>\n", " <td>[georelioghlnews]</td>\n", " <td>The George Eliot, George Henry Lewes Newsletter</td>\n", " <td>[eng]</td>\n", " <td>[0, [], []]</td>\n", " <td>[18/19]</td>\n", " <td>...</td>\n", " <td>73-78</td>\n", " <td>[Penn State University Press]</td>\n", " <td>73</td>\n", " <td>&lt;cite&gt;The George Eliot, George Henry Lewes New...</td>\n", " <td>NaN</td>\n", " <td>[Lecture methods, Feminism, Pedagogy, Novelist...</td>\n", " <td>brv</td>\n", " <td>NaN</td>\n", " <td>1991</td>\n", " <td>1990</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>[Alison Cree, Louis J. 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[2] ... \n", "\n", " pages publisher_name sp \\\n", "0 69-90 [University of California Press] 69 \n", "1 73-78 [Penn State University Press] 73 \n", "2 163-173 [Society for the Study of Amphibians and Repti... 163 \n", "3 70-84 [Hudson Review, Inc] 70 \n", "4 188-203 [University of Texas Press] 188 \n", "\n", " srcHtml \\\n", "0 <cite>Nineteenth-Century Fiction</cite>, Vol. ... \n", "1 <cite>The George Eliot, George Henry Lewes New... \n", "2 <cite>Journal of Herpetology</cite>, Vol. 29, ... \n", "3 <cite>The Hudson Review</cite>, Vol. 22, No. 1... \n", "4 <cite>Texas Studies in Literature and Language... \n", "\n", " title \\\n", "0 [Dorothea and \"Miss Brooke\" in Middlemarch] \n", "1 NaN \n", "2 [Biennial Reproduction with a Fourteen-Month P... \n", "3 [Middlemarch: Touching Down] \n", "4 [The Reader as Part of the Fiction: Middlemarch] \n", "\n", " topics ty vo year Decade \n", "0 [Sentiment, Fear, Martyrdom, Envy, Vocation, G... fla [39] 1984 1980 \n", "1 [Lecture methods, 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jtyV5Q/z+jqtFkrWMseN6+u/9rPXej3wb7hWTAAAAAEA5i0kAAAAAoNwtYw8A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\nr7sLGhFmTaWYNZVi1lSOWVMpZk2lmDV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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbcee445208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['year'].hist()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "textALength = 1793449" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['Locations in A'] = df['matches'].apply(lambda x: x[1])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def diachronicAnalysis(df, decades=(1950, 2020)): \n", " decades = np.arange(decades[0], decades[1], 10)\n", " # Make a dictionary of decades. \n", " # Values are a list of locations. \n", " decadeDict = {}\n", " for i, row in df.iterrows():\n", " decade = row['Decade']\n", " locations = row['Locations in A']\n", " if decade not in decadeDict: \n", " decadeDict[decade] = locations\n", " else: \n", " decadeDict[decade] += locations \n", " # Grab the beginnings of quotes. \n", " decadeStarts = {decade: [item[0] for item in loc] for decade, loc in decadeDict.items()}\n", " decadesBinned = {decade: \n", " np.histogram(locations, bins=50, range=(0, textALength))[0]\n", " for decade, locations in decadeStarts.items() if decade in decades}\n", " decadesDF = pd.DataFrame(decadesBinned).T\n", " #Normalize\n", " decadesDF = decadesDF.div(decadesDF.max(axis=1), axis=0)\n", " return decadesDF\n", "\n", "def plotDiachronicAnalysis(decadesDF): \n", " ylabels = [str(int(decade)) for decade in decadesDF.index] + ['2020']\n", " plt.pcolor(decadesDF, cmap='gnuplot')\n", " plt.yticks(np.arange(len(decadesDF.index)+1), ylabels)\n", " plt.gca().invert_yaxis()\n", " plt.ylabel('Decade')\n", " plt.xlabel('Novel Segment')\n", "# plt.title(\"Frequency of Quotations from George Eliot's Middlemarch in Criticism, By Decade\")\n", " plt.colorbar(ticks=[])\n", " plt.show()\n", " \n", "def plotSynchronicAnalysis(decadesDF): \n", " ax = decadesDF.sum().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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r0r+zwIZM2jm03J6lR2bTx9WfmuTwZWYCAAAAsJUttyx1+4yGTy92Y5Jdl387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\nAAAAAAxG5xAAAADAgHQOAQA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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc5de0f470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "decadesDF = diachronicAnalysis(df)\n", "plotDiachronicAnalysis(decadesDF)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# By (Guessed) Gender of Author" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "maleNames, femaleNames = names.words('male.txt'), names.words('female.txt')\n", "maleNames = [name.lower() for name in maleNames]\n", "femaleNames = [name.lower() for name in femaleNames]" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def guessGender(name): \n", " name = name.split()[0].lower() # Grab the first name. \n", " if name in maleNames and name in femaleNames: \n", " return 'A' #Ambiguous\n", " elif name in maleNames: \n", " return 'M'\n", " elif name in femaleNames: \n", " return 'F'\n", " else: \n", " return 'U'\n", "\n", "def averageGender(names): \n", " if type(names) != list: \n", " return 'U'\n", " genderGuesses = [guessGender(name) for name in names]\n", " stats = Counter(genderGuesses).most_common()\n", " if len(stats) == 1: \n", " # Only one author. We can just use that's author's gender guess. \n", " return stats[0][0]\n", " elif stats[0][1] == stats[1][1]: # There's a tie. \n", " return 'A' # Ambiguous. \n", " else: \n", " return stats[0][0] # Return the most common gender. \n", " " ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df['gender'] = df['author'].apply(averageGender)\n", "dfF = df.loc[df['gender'] == 'F']\n", "dfM = df.loc[df['gender'] == 'M']" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "decadesDFM, decadesDFF = diachronicAnalysis(dfM), diachronicAnalysis(dfF)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Shy7pQp/PZ1OwkOThST46Y41H91/KP5V+62GGDRP/PMmTtgz/+0nW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\nhBBCiI74H8xim5UM9XASAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd346f12550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Differences in citations between genders. \n", "decadesGenderDiff = decadesDFM - decadesDFF\n", "plotSynchronicAnalysis(decadesGenderDiff)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# By (Guessed) Country of Publication" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def getFirst(row): \n", " if type(row) == list: \n", " return row[0]\n", " else: \n", " return row\n", "\n", "topPublishers = df['publisher_name'].apply(getFirst).value_counts()" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [], "source": [ "publishers = topPublishers[:80].index" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [], "source": [ "publishers = publishers.tolist()" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def getCountry(publisher): \n", " brits = ['Oxford University Press', 'Cambridge University Press', 'Modern Humanities Research Association', \\\n", " 'BMJ', 'Taylor & Francis, Ltd.', 'Edinburgh University Press', \\\n", " 'Royal Society for the Encouragement of Arts, Manufactures and Commerce']\n", " canadians = ['Victorian Studies Association of Western Canada'] \n", " if type(publisher) != list: \n", " return 'Unknown'\n", " publisher = publisher[0]\n", " if publisher in brits: \n", " return 'Britain' \n", " elif publisher in canadians or 'Canada' in publisher: \n", " return 'Canada' \n", " elif 'GmbH' in publisher: \n", " return 'Germany'\n", " elif 'estudios' in publisher: \n", " return 'Spain'\n", " elif 'France' in publisher: \n", " return 'France' \n", " elif 'Ireland' in publisher: \n", " return 'Ireland'\n", " else: \n", " return 'US'" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df['country'] = df['publisher_name'].apply(getCountry)" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "US 3901\n", "Unknown 1247\n", "Britain 825\n", "Canada 59\n", "Germany 15\n", "Ireland 8\n", "Spain 8\n", "France 6\n", "Name: country, dtype: int64" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['country'].value_counts()" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfBrits = df.loc[df['country'] == 'Britain']\n", "dfYanks = df.loc[df['country'] == 'US']\n", "dfCanadians = df.loc[df['country'] == 'Canada']" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false }, "outputs": [], "source": [ "decadesDFBrits, decadesDFYanks = diachronicAnalysis(dfBrits), diachronicAnalysis(dfYanks)" ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd3a1b8f278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotSynchronicAnalysis(decadesDFYanks-decadesDFBrits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# By Journal" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Victorian Studies 424\n", "George Eliot - George Henry Lewes Studies 206\n", "Nineteenth-Century Fiction 192\n", "The Modern Language Review 188\n", "The Review of English Studies 185\n", "NOVEL: A Forum on Fiction 126\n", "Nineteenth-Century Literature 126\n", "Studies in the Novel 120\n", "Studies in English Literature, 1500-1900 85\n", "ELH 77\n", "Name: journal, dtype: int64" ] }, "execution_count": 213, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at the top journals. \n", "df['journal'].value_counts()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the specialist journal, \"George Eliot - George Henry Lewes Studies,\" with all other journals. " ] }, { "cell_type": "code", "execution_count": 211, "metadata": { "collapsed": false }, "outputs": [], "source": [ "geJournals = df.loc[df['journal'] == 'George Eliot - George Henry Lewes Studies']\n", "otherJournals = df.loc[df['journal'] != 'George Eliot - George Henry Lewes Studies']" ] }, { "cell_type": "code", "execution_count": 228, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HpW271zHdHr1bp1Bnpm/UdBsoj910v6Tbi3RbkodnOhsrx9KHLulC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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd346dcada0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plotSynchronicAnalysis(diachronicAnalysis(geJournals) - diachronicAnalysis(otherJournals))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
edeno/Jadhav-2016-Data-Analysis
notebooks/2016_09_15_Sharp Wave Ripple Exploration 1.ipynb
1
2272086
null
gpl-3.0
ianan/demreg
python/example_demregpy_aiapxl.ipynb
1
494323
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example using DEMReg and SDO/AIA data\n", "Here using a single pixel of SDO/AIA data taken from the event featured in [Hannah & Kontar 2013 A&A](https://doi.org/10.1051/0004-6361/201219727). \n", "\n", "* 26-Oct-2020 IGH\n", "* 04-Nov-2020 - Updated to use v10 aia deg_cor\n", "* 03-Feb-2021 - Updated using new dn2dem_pos_selfnorm function\n", "* 16-Feb-2021 - Updated error calc info\n", "* 16-Jun-2021 - Updated to work with fixed dn2dem_pos, which can do selfnorm, gloci or user wght\n", "* 16-Jun-2021 - Changed dn2dem_pos to now interp tresp in log-space\n", "* 02-Nov-2021 - Checked error from aiapy.calibrate.estimate_error => so now needs aiapy >0.6\n", "* 09-Nov-2021 - Reordered pixel selection to avoid confusion and added vso example\n", "* 23-Nov-2021 - Added in commented out code to save out prepd submaps (to speed code up)\n", "* 23-Nov-2021 - See example_aia_getprep.ipynb to efficiently prep many AIA fits\n", "* 26-Apr-2022 - Checked worked with updated code\n", "* 02-May-2022 - Tweaked import setup and examples" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import some of the stuff we will need\n", "import numpy as np\n", "import math\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import scipy.io as io\n", "import glob\n", "\n", "# So can have single copy of demreg on system, and don't need copy in working directory\n", "from sys import path as sys_path\n", "# Change to your local copy's location...\n", "sys_path.append('/Users/iain/github/demreg/python')\n", "from dn2dem_pos import dn2dem_pos\n", "\n", "import astropy.time as atime\n", "from astropy.coordinates import SkyCoord\n", "from astropy import units as u\n", "import sunpy.map\n", "\n", "from aiapy.calibrate import degradation\n", "from aiapy.calibrate.util import get_correction_table\n", "from aiapy.calibrate import register, update_pointing\n", "\n", "import warnings\n", "warnings.simplefilter('ignore')\n", "matplotlib.rcParams['font.size'] = 16" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# # Can either get the example data via fido/vso or adapt the example to your own AIA data set\n", "# from sunpy.net import Fido, attrs as a\n", "\n", "# # Only want the 6 coronal channels - this might also download 304A\n", "# wvsrch=a.Wavelength(94*u.angstrom, 335*u.angstrom)\n", "\n", "# result = Fido.search(a.Time('2010-11-03T12:15:09', '2010-11-03T12:15:19'), a.Instrument(\"aia\"), wvsrch)\n", "# files = Fido.fetch(result,path='/Users/iain/Desktop/')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['/Users/iain/Downloads/aia.lev1.131A_2010-11-03T12_15_09.62Z.image_lev1.fits', '/Users/iain/Downloads/aia.lev1.171A_2010-11-03T12_15_12.34Z.image_lev1.fits', '/Users/iain/Downloads/aia.lev1.193A_2010-11-03T12_15_19.84Z.image_lev1.fits', '/Users/iain/Downloads/aia.lev1.211A_2010-11-03T12_15_12.62Z.image_lev1.fits', '/Users/iain/Downloads/aia.lev1.335A_2010-11-03T12_15_15.62Z.image_lev1.fits', '/Users/iain/Downloads/aia.lev1.94A_2010-11-03T12_15_14.12Z.image_lev1.fits']\n" ] } ], "source": [ "# Get your AIA data from somewhere like vso or through sunpy and fido (see previous cell)\n", "# Now load in the fits files\n", "fdir='/Users/iain/Downloads/' \n", "ff=sorted(glob.glob(fdir+'aia*lev1*.fits'))\n", "print(ff)\n", "\n", "# This data is from the event in Hannah & Kontar 2013 A&A \n", "# https://doi.org/10.1051/0004-6361/201219727" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# # Load in the data, will worry about the order later\n", "# amaps=sunpy.map.Map(ff)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# # Get the wavelengths of the maps and get index of sort for this list of maps \n", "# wvn0 = [m.meta['wavelnth'] for m in amaps]\n", "# print(wvn0)\n", "# srt_id = sorted(range(len(wvn0)), key=wvn0.__getitem__)\n", "# print(srt_id)\n", "\n", "# # And now can reorder them\n", "# # OK to do it without creating new list as finding order and reordering in same cell\n", "# amaps = [amaps[i] for i in srt_id]\n", "# print([m.meta['wavelnth'] for m in amaps])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# # aiaprep the images, may take a while to run\n", "# # depending on what you are doing could skip this step\n", "# aprep=[]\n", "# for m in amaps:\n", "# m_temp = update_pointing(m)\n", "# aprep.append(register(m_temp))\n", "# print([m.meta['wavelnth'] for m in aprep])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# # As the above steps are the slowest it might be useful to save up the prepd maps, \n", "# # so only need to run once, and them can load them in for the DEM analysis\n", "# # \n", "# # Also we don't need the full map so can just save out the bits we need\n", "# blo=[-1150*u.arcsec,-500*u.arcsec]\n", "# tro=[-850*u.arcsec,-200*u.arcsec]\n", "\n", "# suba_maps=[]\n", "# for a in aprep:\n", "# bottom_left = SkyCoord(blo[0],blo[1], frame=a.coordinate_frame)\n", "# top_right = SkyCoord(tro[0],tro[1], frame=a.coordinate_frame)\n", "# suba_maps.append(a.submap(bottom_left=bottom_left, top_right=top_right))\n", " \n", "# # Now save out the maps - seems that have to be to individual files\n", "# # And quickest way to do this is to convert our list to a sunpy sequence first \n", "# # (better way to do this? - mapsequence by default saves sorted by data, which we don't want)\n", "# seq=sunpy.map.Map(suba_maps[0],suba_maps[1],suba_maps[2],\\\n", "# suba_maps[3],suba_maps[4],suba_maps[5],sequence=True,sortby=None) \n", "# seq.save(fdir+'aia_smd_{index:03}.fits',overwrite='True')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[94, 131, 171, 193, 211, 335]\n" ] } ], "source": [ "# If you have run the above cell once then can just load the submaps back in \n", "# and don't have to get and prep the data\n", "ffin=sorted(glob.glob(fdir+'aia_smd_*.fits'))\n", "# print(ffin)\n", "aprep=sunpy.map.Map(ffin)\n", "# Print out the order just to check they are stil sorted correctly\n", "print([m.meta['wavelnth'] for m in aprep])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.901069 2.901394 2.000186 2.000083 2.90081 2.900859]\n", "[ 94 131 171 193 211 335]\n" ] } ], "source": [ "# Get the durations for the DN/px/s normalisation and\n", "# wavenlength to check the order - should already be sorted above\n", "wvn = [m.meta['wavelnth'] for m in aprep]\n", "worder=np.argsort(wvn)\n", "\n", "durs = [m.meta['exptime'] for m in aprep]\n", "\n", "# Convert to numpy arrays as make things easier later\n", "durs=np.array(durs)\n", "wvn=np.array(wvn)\n", "print(durs)\n", "print(wvn)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 410.12400137 2417.61314603 2177.36951355 5662.02710411 2939.08467329\n", " 230.5958612 ]\n" ] } ], "source": [ "# Plot where the feature is so can pick an interesting location to calc the DEM\n", "bottom_left = SkyCoord(-1100*u.arcsec,-450*u.arcsec, frame=aprep[0].coordinate_frame)\n", "top_right = SkyCoord(-900*u.arcsec,-250*u.arcsec, frame=aprep[0].coordinate_frame)\n", "mm = aprep[0].submap(bottom_left=bottom_left, top_right=top_right)\n", "fig = plt.figure(figsize=(8,8))\n", "ax = plt.subplot(projection=mm)\n", "mm.plot()\n", "\n", "# I pick this bright lower coronal position, of hot occulted loop top\n", "# Which is roughly pixel 6 in fig 2 from https://doi.org/10.1051/0004-6361/201219727\n", "px=-915*u.arcsec\n", "py=-341*u.arcsec\n", "\n", "ax.plot_coord(SkyCoord(px,py, frame=mm.coordinate_frame), 'kx', fillstyle='none', markersize=15)\n", "plt.show()\n", "\n", "# Now use Sunpy to help work out pixel location in terms of data array indices from sun positions\n", "# Loop over each map to extra the DN value out at the chosen pixel location\n", "data=[]\n", "for m in aprep:\n", " px_loc=m.world_to_pixel(SkyCoord(px,py, frame=m.coordinate_frame))\n", "# print(px_loc)\n", "# Remember python array is first dimension row (y), second dimension x (column) - opposite of idl\n", " data.append(m.data[int(px_loc[1].value),int(px_loc[0].value)])\n", "data=np.array(data)\n", "# Only doing 1 pixel so immediately in units of DN/px\n", "print(data)\n", "\n", "# If know the array location already can just specify that, i.e. m.data[100,150]\n", "# Or if you want to average over a region, do a submap of the region and average over submap.data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5]\n" ] } ], "source": [ "# Just check things are sorted in the correct order of [94,131,171,193,211,335]\n", "worder=np.argsort(wvn)\n", "print(worder)\n", "# As sorted after loading the maps in don't need the following lines now\n", "# durs=durs[worder]\n", "# data=data[worder]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.14278863 0.91401251 0.99550979 0.98652938 0.97036462 0.83094366]\n" ] } ], "source": [ "# Let's get the degradation correction factors\n", "channels = [94,131,171,193,211,335] * u.angstrom\n", "time=atime.Time('2010-11-03T12:15:00', scale='utc')\n", "# print(channels)\n", "# print(time.isot)\n", "\n", "# nc=len(channels)\n", "# degs=np.empty(nc)\n", "# for i in np.arange(nc):\n", "# degs[i]=degradation(channels[i],time,calibration_version=10)\n", "# print(degs)\n", "# # v9 just for checking things, should use the latest one which as of 2021 is v10\n", "# # v9 values\n", "# # degs=np.array([1.14705432, 0.91535957, 0.97148718, 0.98810095, 0.97238522, 0.83229072])\n", "# # v10 values\n", "# #\n", "# # for speed just save in here and manually define\n", "degs=np.array([1.14278863, 0.91401251, 0.99550979, 0.98652938, 0.97036462, 0.83094366])\n", "print(degs)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# correct for the degradation\n", "cor_data=data/degs" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Now load in the response factors\n", "# Load in the SSWIDL generated response functions\n", "# Was produced by make_aiaresp_forpy.pro (can't escape sswidl that easily....)\n", "trin=io.readsav('aia_tresp_en.dat')\n", "\n", "# Get the temperature response functions in the correct form for demreg\n", "tresp_logt=np.array(trin['logt'])\n", "nt=len(tresp_logt)\n", "nf=len(trin['tr'][:])\n", "trmatrix=np.zeros((nt,nf))\n", "for i in range(0,nf):\n", " trmatrix[:,i]=trin['tr'][i]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dn_in: [ 123.70612888 911.6496299 1093.49353411 2869.55069688 1044.13790133\n", " 95.66505152]\n", "edn_in: [ 8.59303349 22.5092616 24.8330273 37.40480367 18.06952156 4.79912741]\n" ] } ], "source": [ "# Get the data in the correct format for the DEM code, i.e.\n", "# array of data and uncertainty in DN/px/s\n", "dn_in=cor_data/durs\n", "print('dn_in: ',dn_in)\n", "\n", "# Work out the uncertainty \n", "# And the associated uncertainty\n", "# If using AIA see Boerner et al. 2012 or see the sswidl aia_bp_estimate_error.pro\n", "# i.e. https://hesperia.gsfc.nasa.gov/ssw/sdo/aia/idl/response/aia_bp_estimate_error.pro\n", "# Values specifically for AIA\n", "gains=np.array([18.3,17.6,17.7,18.3,18.3,17.6])\n", "dn2ph=gains*np.array([94,131,171,193,211,335])/3397.\n", "rdnse=np.array([1.14,1.18,1.15,1.20,1.20,1.18])\n", "# Just the sqrt of the total photons detected, so going DN/px -> ph -> DN/px (deg corrected DN)\n", "num_pix=1\n", "shotnoise=(dn2ph*data*num_pix)**0.5/dn2ph/num_pix/degs\n", "# Combine errors and put into DN/px/s\n", "edn_in=(rdnse**2+shotnoise**2)**0.5/durs\n", "print('edn_in: ',edn_in)\n", "# You might also want to include a systematic uncertainty ~20% ... left as an excercise for the reader...." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[8.59828676] ct / pix -- 8.593033494793954\n", "[22.53934658] ct / pix -- 22.50926160380543\n", "[24.88963043] ct / pix -- 24.833027295219654\n", "[37.52380868] ct / pix -- 37.404803667542986\n", "[18.13891908] ct / pix -- 18.069521556783403\n", "[4.85209169] ct / pix -- 4.79912740870984\n" ] } ], "source": [ "# If have aiapy >0.6 can estimate error like in sswidl\n", "from aiapy.calibrate import estimate_error\n", "\n", "# If have aiapy >v0.6 installed can get errors from it instead\n", "# Here just try for one channel for comparison \n", "# - in future version just remove above cell and use this approach instead\n", "num_pix=1\n", "# Will download error table first time using\n", "# Also need to give data and channel in proper units\n", "for i in range(len(channels)):\n", " aerr_temp=estimate_error(data[i]*(u.ct/u.pix),channels[i],num_pix)\n", " print(aerr_temp/degs[i]/durs[i],\" -- \",edn_in[i])\n", "\n", "# It seems that my rough calculation is fine" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# What temperature binning do we want for the output DEM?\n", "# These are the bin edges\n", "# Need to tweak the range based on what you are looking at\n", "temps=np.logspace(5.7,7.6,num=42)\n", "# Temperature bin mid-points for DEM plotting\n", "mlogt=([np.mean([(np.log10(temps[i])),np.log10((temps[i+1]))]) \\\n", " for i in np.arange(0,len(temps)-1)])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x324 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x324 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x324 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Now work out the DEM - try 2 of 3 standard ways of running\n", "# 1. Default - reg runs twice, 1st time to work out weight for constraint matrix, then regs with that\n", "# Best option if don't know what doing, hence its the default \n", "# Probably best option of AIA data as well.\n", "dem0,edem0,elogt0,chisq0,dn_reg0=dn2dem_pos(dn_in,edn_in,trmatrix,tresp_logt,temps) #gloci=0 is default behaviour\n", "# 2. EMloci - reg runs once, works out weight for constraint matrix as min of EM Loci, then regs with that\n", "# If some of your filters have a sharper T response (lines or X-ray obs) might be useful to try\n", "dem1,edem1,elogt1,chisq1,dn_reg1=dn2dem_pos(dn_in,edn_in,trmatrix,tresp_logt,temps,gloci=1)\n", "# 3. EMloci - reg runs once, works out weight for constraint matrix as min of EM Loci, then regs with that\n", "# If some of your filters have a sharper T response (lines or X-ray obs) might be useful to try\n", "# Also does the calculation internally in EMD not DEM space, but returns DEM (as default emd_ret=False)\n", "dem2,edem2,elogt2,chisq2,dn_reg2=dn2dem_pos(dn_in,edn_in,trmatrix,tresp_logt,temps,gloci=1,emd_int=True)\n", "\n", "# Plot it all\n", "fig = plt.figure(figsize=(8, 4.5))\n", "plt.errorbar(mlogt,dem0,xerr=elogt0,yerr=edem0,fmt='or',ecolor='lightcoral', \\\n", " elinewidth=3, capsize=0,label='Def Self LWght, $\\chi^2 =$ {:0.2f}'.format(chisq0))\n", "plt.xlabel('$\\mathrm{\\log_{10}T\\;[K]}$')\n", "plt.ylabel('$\\mathrm{DEM\\;[cm^{-5}\\;K^{-1}]}$')\n", "plt.ylim([1e20,4e22])\n", "plt.xlim([5.7,7.6])\n", "plt.rcParams.update({'font.size': 16})\n", "plt.yscale('log')\n", "plt.legend()\n", "# plt.savefig('demregpy_aiapxl_slw.png',bbox_inches='tight')\n", "plt.show()\n", "\n", "# Plot it all\n", "fig = plt.figure(figsize=(8, 4.5))\n", "plt.errorbar(mlogt,dem1,xerr=elogt1,yerr=edem1,fmt='xb', ecolor='lightskyblue', \\\n", " elinewidth=3, capsize=0,label='Gloci LWght, $\\chi^2 =$ {:0.2f}'.format(chisq1))\n", "plt.xlabel('$\\mathrm{\\log_{10}T\\;[K]}$')\n", "plt.ylabel('$\\mathrm{DEM\\;[cm^{-5}\\;K^{-1}]}$')\n", "plt.ylim([1e20,4e22])\n", "plt.xlim([5.7,7.6])\n", "plt.rcParams.update({'font.size': 16})\n", "plt.yscale('log')\n", "plt.legend()\n", "# plt.savefig('demregpy_aiapxl_glw.png',bbox_inches='tight')\n", "plt.show()\n", "\n", "# Plot it all\n", "fig = plt.figure(figsize=(8, 4.5))\n", "plt.errorbar(mlogt,dem2,xerr=elogt2,yerr=edem2,fmt='^g',ecolor='lightgreen', \\\n", " elinewidth=3, capsize=0,label='Gloci LWght + EMD, $\\chi^2 =$ {:0.2f}'.format(chisq2))\n", "plt.xlabel('$\\mathrm{\\log_{10}T\\;[K]}$')\n", "plt.ylabel('$\\mathrm{DEM\\;[cm^{-5}\\;K^{-1}]}$')\n", "plt.ylim([1e20,4e22])\n", "plt.xlim([5.7,7.6])\n", "plt.rcParams.update({'font.size': 16})\n", "plt.yscale('log')\n", "plt.legend()\n", "# plt.savefig('demregpy_aiapxl_glw.png',bbox_inches='tight')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Def Self, chisq: 1.0236745458281165\n", "Gloci, chisq: 1.4688503180170327\n", "Gloci+EMD, chisq: 3.468115341676618\n", "Def Self: [0.8761303 0.97190634 0.99559927 0.99926815 0.99187762 0.94050588]\n", "Gloci: [0.87597568 0.94905615 0.99625512 1.00255071 0.98078251 1.01322041]\n", "Gloci+EMD: [0.82034628 0.93171304 0.97281742 1.00388789 0.96402143 1.03971336]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# How well did they actually do?\n", "print('Def Self, chisq: ',chisq0) # Not bad....\n", "print('Gloci, chisq: ',chisq1) # Worse but expected as just working with AIA filters here\n", "print('Gloci+EMD, chisq: ',chisq2) # Worse but expected as just working with AIA filters here\n", "\n", "print('Def Self: ',dn_reg0/dn_in)\n", "print('Gloci: ',dn_reg1/dn_in)\n", "print('Gloci+EMD: ',dn_reg2/dn_in)\n", "\n", "clrs=['darkgreen','darkcyan','gold','sienna','indianred','darkslateblue']\n", "fig,ax = plt.subplots(figsize=(8, 6))\n", "plt.scatter(dn_in,dn_reg0,color='red',marker='o',s=50,lw=2,label='DefSelf')\n", "plt.scatter(dn_in,dn_reg1,color='blue',marker='x',s=50,lw=2,label='Gloci')\n", "plt.scatter(dn_in,dn_reg2,color='green',marker='^',s=50,lw=2,label='Gloci+EMD')\n", "for i, lab in enumerate(trin['channels']):\n", " ax.annotate(lab.decode(\"utf-8\"), (dn_in[i], 0.6*dn_reg0[i]),color=clrs[i],fontsize=12)\n", "plt.xlabel('DN_in')\n", "plt.ylabel('DN_reg')\n", "xyrang=[1e1,1e4]\n", "plt.plot(xyrang,xyrang,color='grey',ls='dashed')\n", "plt.ylim(xyrang)\n", "plt.xlim(xyrang)\n", "plt.rcParams.update({'font.size': 16})\n", "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.legend()\n", "plt.show()\n", "\n", "fig,ax = plt.subplots(figsize=(8, 6))\n", "plt.scatter(np.arange(6),dn_reg0/dn_in,marker='o',color='red',label='DefSelf')\n", "plt.scatter(np.arange(6),dn_reg1/dn_in,marker='x',color='blue',label='Gloci')\n", "plt.scatter(np.arange(6),dn_reg2/dn_in,marker='^',color='green',label='Gloci+EMD')\n", "plt.plot([-1,12],[1,1],'--',color='grey')\n", "plt.ylim([0.75,1.1])\n", "plt.xlim([-0.5,5.5])\n", "chlab=[]\n", "for cc in channels:\n", " chlab.append(int(cc.value))\n", "plt.xticks(np.arange(6),chlab)\n", "plt.xlabel('AIA Channel [$\\mathrm{\\AA}$]')\n", "plt.ylabel('DN$_\\mathrm{reg}$/DN$_\\mathrm{in}$')\n", "plt.locator_params(axis='y', nbins=5)\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Final summary plot for the default run\n", "fig= plt.figure(figsize=(15, 5))\n", "plt.rcParams.update({'font.size': 16})\n", "\n", "ax1 = fig.add_subplot(1, 2, 1)\n", "plt.errorbar(mlogt,dem0,xerr=elogt0,yerr=edem0,fmt='or',\\\n", " ecolor='lightcoral', elinewidth=3, capsize=0)\n", "plt.xlabel('$\\mathrm{\\log_{10}T\\;[K]}$')\n", "plt.ylabel('$\\mathrm{DEM\\;[cm^{-5}\\;K^{-1}]}$')\n", "plt.ylim([1e20,4e22])\n", "plt.xlim([5.7,7.6])\n", "plt.yscale('log')\n", "\n", "ax2 = fig.add_subplot(1, 2, 2)\n", "plt.scatter(np.arange(6),(dn_in-dn_reg0)/edn_in,marker='o',color='red',\\\n", " s=50,lw=2,label='$\\chi^2 =$ {:0.2f}'.format(chisq0))\n", "plt.plot([-1,12],[0,0],'--',color='grey')\n", "plt.ylim([-2,2])\n", "plt.xlim([-0.5,5.5])\n", "chlab=[]\n", "for cc in channels:\n", " chlab.append(int(cc.value))\n", "plt.xticks(np.arange(6),chlab)\n", "plt.xlabel('AIA Channel [$\\mathrm{\\AA}$]')\n", "plt.ylabel('(DN$_\\mathrm{in}$ - DN$_\\mathrm{reg}$)/$\\sigma_\\mathrm{DN_{in}}$')\n", "plt.locator_params(axis='y', nbins=5)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": 4 }
gpl-2.0
google/trimmed_match
trimmed_match/notebook/post_analysis_colab_for_trimmed_match.ipynb
1
16257
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "xxwdh1o5rJDz" }, "outputs": [], "source": [ "\n", "#@markdown * Connect to the hosted runtime and run each cell after updating the necessary inputs\n", "#@markdown * Download the file \"example_data_for_post_analysis.csv\" from the folder \"example_datasets\" in github.\n", "#@markdown * Upload the csv file to your Google Drive and open it with Google Sheets\n", "#@markdown * In the cell below, copy and paste the url of the sheet." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "8VaZ1kJ_XA2y" }, "outputs": [], "source": [ "#@markdown ### Load the required packages, e.g. trimmed_match.\n", "\n", "BAZEL_VERSION = '3.0.0'\n", "!wget https://github.com/bazelbuild/bazel/releases/download/{BAZEL_VERSION}/bazel-{BAZEL_VERSION}-installer-linux-x86_64.sh\n", "!chmod +x bazel-{BAZEL_VERSION}-installer-linux-x86_64.sh\n", "!./bazel-{BAZEL_VERSION}-installer-linux-x86_64.sh\n", "!sudo apt-get install python3-dev python3-setuptools git\n", "!git clone https://github.com/google/trimmed_match\n", "!python3 -m pip install ./trimmed_match\n", "\n", "\"\"\"Loading the necessary python modules.\"\"\"\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import re\n", "import seaborn as sns\n", "\n", "from IPython.display import display\n", "from IPython.core.interactiveshell import InteractiveShell\n", "from pandas.plotting import register_matplotlib_converters\n", "\n", "import gspread\n", "import warnings\n", "from google import auth as google_auth\n", "from google.colab import auth\n", "from google.colab import data_table\n", "from google.colab import drive\n", "from trimmed_match.design import plot_utilities\n", "from trimmed_match.design import util\n", "from trimmed_match.post_analysis import trimmed_match_post_analysis\n", "\n", "warnings.filterwarnings('ignore')\n", "register_matplotlib_converters()\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "eSnK4_3zaCCW" }, "outputs": [], "source": [ "#@markdown ### Enter the trix id for the sheet file containing the Data: \n", "#@markdown The spreadsheet should contain the mandatory columns:\n", "#@markdown * date: date in the format YYYY-MM-DD\n", "#@markdown * geo: the number which identifies the geo\n", "#@markdown * pair: the number which identifies the geo pair\n", "#@markdown * assignment: geo assignment (1=Treatment, 2=Control)\n", "#@markdown * response: variable on which you want to measure incrementality\n", "#@markdown (e.g. sales, transactions)\n", "#@markdown * cost: variable on ad spend\n", "\n", "#@markdown ---\n", "\n", "## load the trix in input\n", "#@markdown Spreadsheet URL\n", "\n", "\n", "experiment_table = \"add your url here, which should look like https://docs.google.com/spreadsheets/d/???/edit#gid=???\" #@param {type:\"string\"}\n", "auth.authenticate_user()\n", "creds, _ = google_auth.default()\n", "gc = gspread.authorize(creds)\n", "wks = gc.open_by_url(experiment_table).sheet1\n", "data = wks.get_all_values()\n", "headers = data.pop(0)\n", "data = pd.DataFrame(data, columns=headers)\n", "\n", "data[\"date\"] = pd.to_datetime(data[\"date\"])\n", "for colname in [\"geo\", \"pair\", \"assignment\", \"response\", \"cost\"]:\n", " data[colname] = pd.to_numeric(data[colname])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "fR2v9cJdcn1G" }, "outputs": [], "source": [ "#@title Summary of the data for the design, test, and test+cooldown period \n", "\n", "test_start_date = \"2020-11-04\" #@param {type:\"date\"}\n", "test_end_date = \"2020-12-01\" #@param {type:\"date\"}\n", "cooldown_end_date = \"2020-12-16\" #@param {type:\"date\"}\n", "design_eval_start_date = \"2020-09-03\" #@param {type:\"date\"}\n", "design_eval_end_date = \"2020-10-01\" #@param {type:\"date\"}\n", "\n", "#@markdown Use an average order value of 1 if the experiment is based on sales/revenue or an actual average order value (e.g. 80$) for an experiment based on transactions/footfall/contracts.\n", "average_order_value = 1#@param{type: \"number\"}\n", "\n", "test_start_date = pd.to_datetime(test_start_date)\n", "test_end_date = pd.to_datetime(test_end_date)\n", "cooldown_end_date = pd.to_datetime(cooldown_end_date)\n", "design_eval_start_date = pd.to_datetime(design_eval_start_date)\n", "design_eval_end_date = pd.to_datetime(design_eval_end_date)\n", "\n", "#@markdown (OPTIONAL) List the pairs of geos you need to exclude separated by a comma e.g. 1,2. Leave empty to select all pairs.\n", "pairs_exclude = \"\" #@param {type: \"string\"}\n", "pairs_exclude = [] if pairs_exclude == \"\" else [\n", " int(re.sub(r\"\\W+\", \"\", x)) for x in pairs_exclude.split(\",\")\n", "]\n", "\n", "# these are numerical identifier used in the table in input to identify the two\n", "# groups\n", "group_treatment = 1\n", "group_control = 2\n", "\n", "geox_data = trimmed_match_post_analysis.check_input_data(\n", " data.copy(),\n", " group_control=group_control,\n", " group_treatment=group_treatment)\n", "geox_data = geox_data[~geox_data[\"pair\"].isin(pairs_exclude)]\n", "\n", "geox_data[\"period\"] = geox_data[\"date\"].apply(\n", " lambda row: 0 if row in pd.Interval(\n", " design_eval_start_date, design_eval_end_date, closed=\"both\") else\n", " (1 if row in pd.Interval(test_start_date, test_end_date, closed=\"both\") else\n", " (2 if row in pd.Interval(test_end_date, cooldown_end_date, closed=\"right\")\n", " else -1)))\n", "geox_data = geox_data[[\"date\", \"geo\", \"pair\", \"assignment\", \"response\", \"cost\",\n", " \"period\"]]\n", "pairs = geox_data[\"pair\"].sort_values().drop_duplicates().to_list()\n", "\n", "total_cost = geox_data.loc[geox_data[\"period\"]==1, \"cost\"].sum()\n", "print(\"Total cost: {}\".format(util.human_readable_number(total_cost)))\n", "\n", "print(\"Total response and cost by period and group\")\n", "output_table = geox_data.loc[\n", " geox_data[\"period\"].isin([0, 1]),\n", " [\"period\", \"assignment\", \"response\", \"cost\"]].groupby(\n", " [\"period\", \"assignment\"], as_index=False).sum()\n", "output_table.assignment = output_table.assignment.map(\n", " {group_control: \"Control\", group_treatment: \"Treatment\"})\n", "output_table.period = output_table.period.map({0: \"Pretest\", 1: \"Test\"})\n", "\n", "data_table.DataTable(output_table, include_index=False)\n", "\n", "tmp = geox_data[geox_data[\"period\"].isin([0, 1])].groupby(\n", " [\"period\", \"assignment\", \"pair\"])[\"response\"].sum()**0.5\n", "tmp = tmp.reset_index()\n", "\n", "pretreatment = (tmp[\"period\"]==0) \u0026 (tmp[\"assignment\"]==group_treatment)\n", "precontrol = (tmp[\"period\"]==0) \u0026 (tmp[\"assignment\"]==group_control)\n", "posttreatment = (tmp[\"period\"]==1) \u0026 (tmp[\"assignment\"]==group_treatment)\n", "postcontrol = (tmp[\"period\"]==1) \u0026 (tmp[\"assignment\"]==group_control)\n", "\n", "comp = pd.DataFrame({\"pretreatment\": tmp[pretreatment][\"response\"].to_list(),\n", " \"precontrol\": tmp[precontrol][\"response\"].to_list(),\n", " \"posttreatment\": tmp[posttreatment][\"response\"].to_list(),\n", " \"postcontrol\": tmp[postcontrol][\"response\"].to_list()})\n", "\n", "\n", "fig, ax = plt.subplots(4, 4, figsize=(15, 15))\n", "label = [\"pretreatment\", \"precontrol\", \"posttreatment\", \"postcontrol\"]\n", "min_ax = min(comp.min())\n", "max_ax = max(comp.max())\n", "for col_ind in range(4):\n", " for row_ind in range(4):\n", " if col_ind \u003e row_ind:\n", " useless = ax[row_ind, col_ind].scatter(comp[label[col_ind]],\n", " comp[label[row_ind]])\n", " useless = ax[row_ind, col_ind].plot([min_ax*0.97, max_ax*1.03],\n", " [min_ax*0.97, max_ax*1.03], 'r')\n", " useless = ax[row_ind, col_ind].set_xlim([min_ax*0.97, max_ax*1.03])\n", " useless = ax[row_ind, col_ind].set_ylim([min_ax*0.97, max_ax*1.03])\n", " elif col_ind == row_ind:\n", " useless = ax[row_ind, col_ind].annotate(label[col_ind],\n", " size=20,\n", " xy=(0.15, 0.5),\n", " xycoords=\"axes fraction\")\n", " useless = ax[row_ind, col_ind].set_xlim([min_ax*0.97, max_ax*1.03])\n", " useless = ax[row_ind, col_ind].set_ylim([min_ax*0.97, max_ax*1.03])\n", " else:\n", " useless = ax[row_ind, col_ind].axis(\"off\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "mit6ZMs5nMSO" }, "outputs": [], "source": [ "#@title Visualization of experiment data. \n", "\n", "geox_data = geox_data.sort_values(by=\"date\")\n", "\n", "def plot_ts_comparison(geox_data, metric):\n", " f, axes = plt.subplots(1,1, figsize=(15,7.5))\n", " treatment_time_series = geox_data[geox_data[\"assignment\"] ==\n", " group_treatment].groupby(\n", " [\"date\"], as_index=False)[metric].sum()\n", " control_time_series = geox_data[geox_data[\"assignment\"] ==\n", " group_control].groupby(\n", " [\"date\"], as_index=False)[metric].sum()\n", " axes.plot(treatment_time_series[\"date\"], treatment_time_series[metric],\n", " label=\"treatment\")\n", " axes.plot(control_time_series[\"date\"], control_time_series[metric],\n", " label=\"control\")\n", " axes.set_ylabel(metric)\n", " axes.set_xlabel(\"date\")\n", " axes.axvline(x=test_end_date, color=\"black\", ls=\"-\",\n", " label='Experiment period')\n", " axes.axvline(x=design_eval_start_date, color=\"red\", ls=\"--\",\n", " label='Design evaluation period')\n", " axes.axvline(x=cooldown_end_date, color=\"black\", ls=\"--\",\n", " label='End of cooldown period')\n", " axes.axvline(x=test_start_date, color=\"black\", ls=\"-\")\n", " axes.axvline(x=design_eval_end_date, color=\"red\", ls=\"--\")\n", " axes.legend(bbox_to_anchor=(0.5,1.1), loc='center')\n", "\n", "plot_ts_comparison(geox_data, \"response\")\n", "\n", "plot_ts_comparison(geox_data, \"cost\")\n", "\n", "def ts_plot(x,y, **kwargs):\n", " ax=plt.gca()\n", " data=kwargs.pop(\"data\")\n", " data.plot(x=x, y=y, ax=ax, grid=False, **kwargs)\n", "\n", "g = sns.FacetGrid(geox_data, col=\"pair\", hue=\"assignment\", col_wrap=3,\n", " sharey=False,sharex=False, legend_out=False, height=5,\n", " aspect=2)\n", "g = (g.map_dataframe(ts_plot, \"date\", \"response\").add_legend())\n", "for ind in range(len(g.axes)):\n", " cont=geox_data[(geox_data[\"pair\"]==pairs[ind]) \u0026\n", " (geox_data[\"assignment\"]==group_control)][\"geo\"].values[0]\n", " treat=geox_data[(geox_data[\"pair\"]==pairs[ind]) \u0026\n", " (geox_data[\"assignment\"]==group_treatment)][\"geo\"].values[0]\n", " useless = g.axes[ind].axvline(x=test_end_date, color=\"black\", ls=\"-\")\n", " useless = g.axes[ind].axvline(x=design_eval_start_date, color=\"red\", ls=\"--\")\n", " useless = g.axes[ind].axvline(x=cooldown_end_date, color=\"black\", ls=\"--\")\n", " useless = g.axes[ind].axvline(x=test_start_date, color=\"black\", ls=\"-\")\n", " useless = g.axes[ind].axvline(x=design_eval_end_date, color=\"red\", ls=\"--\")\n", " useless = g.axes[ind].legend([\"treatment\"+\" (geo {})\".format(treat),\n", " \"control\"+\" (geo {})\".format(cont),\n", " \"Experiment period\", \"Design evaluation period\",\n", " \"End of cooldown period\"], loc=\"best\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "aAWhBsrbpzLm" }, "outputs": [], "source": [ "#@title Exclude the cooling down period. \n", "\n", "geo_data = trimmed_match_post_analysis.prepare_data_for_post_analysis(\n", " geox_data=geox_data,\n", " exclude_cooldown=True,\n", " group_control=group_control,\n", " group_treatment=group_treatment\n", ")\n", "\n", "results = trimmed_match_post_analysis.calculate_experiment_results(geo_data)\n", "trimmed_match_post_analysis.report_experiment_results(results, average_order_value)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "BrDaNzpYtYuP" }, "outputs": [], "source": [ "#@title Include the cooling down period \n", "\n", "geo_data_including_cooldown = trimmed_match_post_analysis.prepare_data_for_post_analysis(\n", " geox_data=geox_data,\n", " exclude_cooldown=False,\n", " group_control=group_control,\n", " group_treatment=group_treatment\n", ")\n", "\n", "results_with_cd = trimmed_match_post_analysis.calculate_experiment_results(\n", " geo_data_including_cooldown)\n", "trimmed_match_post_analysis.report_experiment_results(results_with_cd, average_order_value)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "last_runtime": { "build_target": "//research/colab/notebook:notebook_backend_py3", "kind": "private" }, "name": "Trimmed Match PostAnalysis Colab.ipynb", "private_outputs": true, "provenance": [ { "timestamp": 1615400005483 }, { "timestamp": 1587119226109 }, { "timestamp": 1587024035829 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
JIC-CSB/python-intro-ipython-notebook
PythonBasics.ipynb
1
20961
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic training in Python\n", "\n", "Python is a flexible programming language that is becoming increasingly popular for scientific computing. This session will give you a basic foundation in how to work effectively with Python.\n", "\n", "During the session you will learn:\n", "\n", "- How to work interactively with Python\n", "- How to make use of Python’s built-in data structures\n", "\n", "During this part of the course we will illustrate how to work interactively with Python using IPython notebook.\n", "\n", "Let us start by reproducing the famous \"hello world\" program.\n", "\n", "As you work through this notebook you will need to press Shift-Enter to run the code." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"hello world\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike Bash, Perl and PHP single and double quotes are equivalent. I.e. the previous “hello world” example could just as well have been written as:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('hello world')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data types\n", "\n", "Python has a number of built in data types. These are used to represent things such as integers, floating point numbers and text strings. To find out what the type of something is one can use the ``type`` keyword." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type(7)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type(7.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is worth noting that, in Python 2, dividing two integers with each other results in integer division, i.e. division with the fractional part discarded." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "3 / 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If either the numerator or denominator is a float the result of the division will be returned as a floating point number." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "3 / 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Python 3 dividing two integers will return a floating point number. If you really want integer division and want to future proof your code you can use the integer division operator //." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "3 // 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Text is represented as a string (``str``). This is true both for individual characters as well as longer pieces of text." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type('h')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type('hello world')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Python 3 the default text representation is [Unicode](https://en.wikipedia.org/wiki/Unicode).\n", "\n", "To get a Unicode string in Python 2 one can prefix it with a ``u``." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type(u'hello world')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variables\n", "\n", "It is easy to assign a variable in Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = 100\n", "b = 10\n", "c = a * b\n", "print(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variables are not statically typed in Python. This means that it is possible to change a variable to a different type." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = 'hello world'\n", "type(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data structures\n", "\n", "Python has got several built-in data structures. Two of the most useful ones are lists and dictionaries.\n", "\n", "### Lists\n", "\n", "Let us have a look at lists first." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_list = ['b', 'a', 'a', 1.0]\n", "example_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example above illustrates a several points:\n", "\n", "- a list can be created by placing a number of comma separated objects in square brackets\n", "- the elements of a list do not need to be unique (there are two instances of the string ‘a’)\n", "- the elements of a list can have differing type (the list contains both strings and a floating point number)\n", "\n", "Lists have order. This means that we can access elements from the list using indices. The indices in Python are zero based, i.e. the first element in the list has got index zero." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_list[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we can change the elements of a list." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_list[0] = 'hello'\n", "example_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to get the last element of the list using the index -1." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_list[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another common task is to find out the length of a list. This can be achieved using the ``len()`` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "len(example_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tuples\n", "\n", "A tuple is an ordered collection of elements. **It is different from a list in that it cannot be modified once created.**\n", "\n", "Let us illustrate this with an example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_tuple = ('b','a', 'a', 1.0)\n", "example_tuple" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_tuple[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_tuple[0] = 'hello'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is easy to inadvertently create tuples as they are created by default when one separates objects by commas." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "1, 2, 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "4," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dictionaries\n", "\n", "A Python dictionary is a set of key value pairs. In other languages these are often referred to as maps or hashes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_dict = {'ccc': 1, 'b': 'hello world'}\n", "example_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_dict['b']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example above illustrates some key points:\n", "\n", "- one can create a dictionary by providing a set of comma separated key:value pairs in {curly braces}\n", "- a dictionary does not have any inherent order\n", "- one can access a value by its key\n", "\n", "The keys in a dictionary are unique, i.e. if one tries to add another element to the dictionary with a key that already exists the value of the existing element for that key will be replaced with the new one." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_dict['b'] = 2\n", "example_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Whitespace matters!\n", "\n", "In Python whitespace characters have meaning. Code which is indented to the same level represents a code block. Functions, if statements and loops are all examples of code blocks. In programming languages such as C and Perl code blocks are denoted using {curly braces}." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if True:\n", " print('hello')\n", " print('world')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If your code is not correctly aligned you will see IndentationError messages telling you that everything is not as it should be. You will also run into IndentationError messages if you mix white spaces and tabs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if True:\n", " print('hello')\n", " print('world')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The Python standard is to use four white spaces to indent code.**\n", "\n", "This may sound really annoying. However there is good reason for it in that it improves the readability of the code." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions\n", "\n", "Functions can be used to create re-usable pieces of code and are useful for breaking up code into smaller more manageable pieces.\n", "\n", "To create a function in Python we use the ``def`` keyword. The code snipped below defines a function named ``hello``." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def hello():\n", " print('hello world')\n", "\n", "hello" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To call the hello function we need a parenthesis at the end." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hello()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us improve the hello function to take a name as an input argument." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def hello(name):\n", " print('hello ' + name)\n", " \n", "hello('pat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally let us document the function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def hello(name):\n", " \"Returns a personal greeting as a string.\"\n", " print('hello ' + name)\n", " \n", "hello('jess')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hello.__doc__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditional statements and boolean operators\n", "\n", "The conditional statement keywords in Python are ``if``, ``elif`` and ``else``. Below is a simple, albeit boring, example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if False:\n", " print(\"you won't see this printed\")\n", "if True:\n", " print(\"hello\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us write a function to illustrate a slightly more realistic usage." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def net_salary(gross_salary):\n", " \"Returns the net salary by deducting tax from the gross.\"\n", " if gross_salary < 10000:\n", " return gross_salary * 1.0\n", " elif gross_salary < 32000:\n", " return gross_salary * 0.8\n", " else:\n", " return gross_salary * 0.6" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "net_salary(9000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "net_salary(31000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "net_salary(33000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In python the keyword for the “else if” conditional is ``elif``, this is often a source of confusion for people who are used to other languages.\n", "\n", "In the example above we make use of the ``<`` (less than) boolean operator. Other useful boolean operators include:\n", "\n", "Operator | Description\n", "--- | ---\n", "``==`` | Equal\n", "``!=`` | Not equal\n", "``>`` |\tGreater than\n", "``<`` | Less than\n", "``>=`` | Greater than or equal\n", "``<=`` | Less than or equal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Looping\n", "\n", "In scripting and programming one often needs to loop over a series of elements. These could be a series of numbers, list items or lines from a file.\n", "\n", "### The for loop construct\n", "\n", "The most commonly used loop structure in Python is the for loop. Let us illustrate this by iterating over the items in a list." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for item in ['hello', 'world']:\n", " print(item)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The range function\n", "\n", "To loop over the values one to ten one can make use of Python’s built-in range function. By default this generates zero-based arithmetic progressions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "range(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, we can tell it to start at one." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "range(1, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we extend the range to include the value 10." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "range(1, 11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is worth noting that we can alter the step size so it is, for example, possible to generate a list counting down in steps of 2 from 10 to 0." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "range(10, 0, -2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose that we wanted to know what the sum of these values were. We could achieve that using a ``for`` loop." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total = 0\n", "for value in range(10, 0, -2):\n", " total = total + value\n", "\n", "total" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, we could have used Python’s built-in ``sum()`` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sum( range(10, 0, -2) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The enumerate function\n", "\n", "One often wants to know how far through a loop one is and it is quite common to see code along the lines of the below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "count = 0\n", "for word in ['hello', 'my', 'name', 'is', 'pat']:\n", " print(\"{} {}\".format(count, word))\n", " count = count + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, a there is a neater solution to this problem. One can make use of the ``enumerate()``function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for count, word in enumerate(['hello', 'my', 'name', 'is', 'pat']):\n", " print(\"{} {}\".format(count, word))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's get some practice\n", "\n", "This introduction has gone through the first half of the material in the\n", "[Introduction to Python Course](http://training.scicomp.jic.ac.uk/introduction_to_python.html).\n", "\n", "Each section of the course has a set of exercises associated with it.\n", "\n", "If you are completely new to Python, go to the\n", "[course book](http://docs.scicomp.jic.ac.uk/python_intro_course_book/index.html)\n", "and complete these exercises.\n", "\n", "If you were already familiar with the material outlined in this introduction\n", "skip to the section on\n", "[working with files](http://docs.scicomp.jic.ac.uk/python_intro_course_book/working_with_files.html).\n", "\n", "Enjoy!" ] } ], "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 }
mit
SheffieldML/GPyOpt
manual/GPyOpt_entropy_search.ipynb
1
8665
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Entropy Search\n", "### Written by Simon Bartels, Max Planck Institute for Intelligent Systems, and Andrei Paleyes, Amazon.com\n", "This notebook demonstrates how to use Entropy Search (ES) in GPyOpt and compares it to Expected Improvement (EI). For details on ES have a look at the original paper:\n", "\n", "Hennig and C. J. Schuler. Entropy search for information-efficient global optimization. Journal of Machine Learning Research, 13, 2012" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import GPy\n", "import GPyOpt\n", "from GPyOpt.models.gpmodel import GPModel\n", "from GPyOpt.core.task.space import Design_space, bounds_to_space\n", "from GPyOpt.util.mcmc_sampler import AffineInvariantEnsembleSampler\n", "from GPyOpt.acquisitions.ES import AcquisitionEntropySearch\n", "from GPyOpt.acquisitions.EI import AcquisitionEI\n", "\n", "import matplotlib as mpl\n", "mpl.use('Agg')\n", "\n", "#configure plotting\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib;matplotlib.rcParams['figure.figsize'] = (8,5)\n", "import matplotlib;matplotlib.rcParams['text.usetex'] = True\n", "import matplotlib;matplotlib.rcParams['font.size'] = 16\n", "import matplotlib;matplotlib.rcParams['font.family'] = 'serif'\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Toy Problem\n", "The following toy problem demonstrates the possible advantage Entropy Search can have over Expected Improvement. The observations are chosen in a way such that EI will evaluate at the minimum whose location is pretty clear. Entropy Search on the other hand exhibits a more explorative behavior. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "X = np.array([[-1], [1], [2]])\n", "y = 2 * -np.array([[.1], [.5], [.5]])\n", "bounds = [(-5, 5)]\n", "input_dim = X.shape[1]\n", "\n", "kern = GPy.kern.RBF(input_dim, variance=1., lengthscale=1.)\n", "model = GPModel(kern, noise_var=1e-3, max_iters=0, optimize_restarts=0)\n", "\n", "model.updateModel(X, y, None, None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot of Data-Set, Model and Acquisition Functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Xs = np.arange(bounds[0][0], bounds[0][1], 0.01).reshape(-1, 1)\n", "ys, vs = model.predict(Xs)\n", "\n", "plt.fill_between(np.ndarray.flatten(Xs), \n", " np.ndarray.flatten(ys+np.sqrt(vs)), \n", " np.ndarray.flatten(ys-np.sqrt(vs)), alpha=0.1)\n", "plt.plot(Xs, ys, color='b')\n", "plt.plot(X, y, 'x')\n", "\n", "space = Design_space(bounds_to_space(bounds))\n", "def normalize(vs):\n", " return (vs - min(vs))/(max(vs - min(vs)))\n", "sampler = AffineInvariantEnsembleSampler(space)\n", "\n", "ei = AcquisitionEI(model, space)\n", "vei = normalize(ei.acquisition_function(Xs))\n", "\n", "es = AcquisitionEntropySearch(model, space, sampler)\n", "ves = normalize(es.acquisition_function(Xs))\n", "\n", "# plot Expected Improvement again\n", "plt.plot(Xs, ves, color='r')\n", "# plot Entropy Search values\n", "plt.plot(Xs, vei, color='g')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Improvement (green line) suggests to evaluate in the location of the minimum (around 1.9). In contrast, Entropy Search (red line) is more explorative, preferring points near 4 and -4. Evaluating the minimum location would not bring much insight.\n", "\n", "## Comparison on the Branin function" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# --- Function to optimize\n", "func = GPyOpt.objective_examples.experiments2d.branin()\n", "func.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's define the necessary objects for `ModularBayesianOptimization`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "objective = GPyOpt.core.task.SingleObjective(func.f)\n", "space = GPyOpt.Design_space(space =[{'name': 'var_1', 'type': 'continuous', 'domain': (-5,10)},\n", " {'name': 'var_2', 'type': 'continuous', 'domain': (1,15)}])\n", "acquisition_optimizer = GPyOpt.optimization.AcquisitionOptimizer(space)\n", "initial_design = GPyOpt.experiment_design.initial_design('random', space, 5)\n", "max_iter = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First run Expected Improvement." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ei_model = GPyOpt.models.GPModel(optimize_restarts=5,verbose=False)\n", "ei = AcquisitionEI(ei_model, space, optimizer=acquisition_optimizer)\n", "ei_evaluator = GPyOpt.core.evaluators.Sequential(ei)\n", "bo_ei = GPyOpt.methods.ModularBayesianOptimization(ei_model, space, objective, ei, ei_evaluator, initial_design)\n", "bo_ei.run_optimization(max_iter = max_iter)\n", "bo_ei.plot_acquisition()\n", "bo_ei.plot_convergence()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now run Entropy Search." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "es_model = GPyOpt.models.GPModel(optimize_restarts=5,verbose=False)\n", "ei = AcquisitionEI(es_model, space, optimizer=acquisition_optimizer)\n", "proposal_function = lambda x : np.clip(np.log(ei._compute_acq(x)), 0., np.PINF)\n", "sampler = AffineInvariantEnsembleSampler(space)\n", "es = AcquisitionEntropySearch(es_model, space, sampler, optimizer=acquisition_optimizer, num_representer_points=10, \n", " burn_in_steps=10, num_samples=100, proposal_function = proposal_function)\n", "es_evaluator = GPyOpt.core.evaluators.Sequential(es)\n", "bo_es = GPyOpt.methods.ModularBayesianOptimization(es_model, space, objective, es, es_evaluator, initial_design)\n", "bo_es.run_optimization(max_iter = max_iter)\n", "bo_es.plot_acquisition()\n", "bo_es.plot_convergence()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Let's plot the locations where Entropy Search (circles) and Expected Improvement (crosses) evaluated." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bounds = func.bounds\n", "x1 = np.linspace(bounds[0][0], bounds[0][1], 100)\n", "x2 = np.linspace(bounds[1][0], bounds[1][1], 100)\n", "X1, X2 = np.meshgrid(x1, x2)\n", "X = np.hstack((X1.reshape(100*100,1),X2.reshape(100*100,1)))\n", "Y = func.f(X)\n", "\n", "plt.figure() \n", "plt.contourf(X1, X2, Y.reshape((100,100)),100)\n", "plt.plot(np.array(func.min)[:,0], np.array(func.min)[:,1], 'w.', markersize=20, label=u'Observations')\n", "plt.colorbar()\n", "plt.plot(ei_model.model.X[:, 0],ei_model.model.X[:, 1], 'o')\n", "plt.plot(es_model.model.X[:, 0],es_model.model.X[:, 1], 'x')\n", "plt.xlabel('X1')\n", "plt.ylabel('X2')\n", "plt.title(func.name)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
xMyrst/BigData
python/ejercicios/ucm_numpy_ej.ipynb
1
32607
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ejercicios 7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1 Ejercicio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Crear un array de tamaño 20 donde todos los elementos estén inicializados a cero. Por favor llámalo __a__." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sol:\n", "import numpy as np\n", "a = np.zeros(20, dtype=int)\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Escribe una expresión Python para cambiar el valor de los primeros 5 elementos; el nuevo valor es 10." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([10, 10, 10, 10, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sol:\n", "a[:5] = 10\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Modifica los siguientes 10 valores. Los nuevos valores serán el resultado de la secuencia de números pares comenzando por el 12. Utiliza la función __arange__." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([10, 10, 10, 10, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 0, 0,\n", " 0, 0, 0])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sol:\n", "a[5:15] = np.arange(12,32,2)\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Modifica el valor de los últimos 5 elementos. Su nuevo valor es 30." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([10, 10, 10, 10, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 30, 30,\n", " 30, 30, 30])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sol:\n", "a[-5:] = 30\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Ahora ejecuta las siguientes líneas de código. Vamos a dibujar el array __a__ que has creado ..." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5, 35)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1f4eb425828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "x = np.arange(20) # eje x\n", "y = a # eje y\n", "plt.plot(x,y)\n", "plt.ylim(5,35) # rango del eje y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Ahora vamos a añadir una línea adicional desde el punto (4,0) al punto (4,40)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f4eb3fd8d0>]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1f4eb3fd1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.arange(20) # eje x\n", "y = a # eje y\n", "plt.plot(x,y)\n", "plt.ylim(5,35) # rango dele eje y\n", "plt.plot([4,4],[0,40], 'k--')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2 Ejercicio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El archivo de texto [holland_temperature.dat](./datos/holland_temperature.dat) recoge datos de las diferentes temperaturas registradas en Holanda en los últimos 12 meses.\n", "\n", "---\n", "* Calcula la media de las temperaturas.\n", " * Descarga el fichero [holland_temperature.dat](./datos/holland_temperature.dat) en tu directorio de trabajo.\n", " * Carga los datos en un array llamado __temperatura__ mediante la función __loadtxt__.\n", " \n", "__Nota__: La media es 10.125." ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10.125" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sol:\n", "a = np.loadtxt('../datos/holland_temperature.dat', dtype=str('float'))\n", "media = np.sum(a)/len(a)\n", "media" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------\n", "* Nos interesa conocer los meses del año donde la temperatura ha sido superior a la media.\n", " * Crear un array de meses [1,...,12]. Usa la función __arange__. \n", " * Escribir la expresión que devuelve los meses donde la temperatura ha sido superior a la media usando como máscara un array de booleanos.\n", " \n", "__Nota__: La solución es: [5, 6, 7, 8, 9, 10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Sol:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------\n", "* Queremos saber qué mes ha estado más cerca de la media. Para ello calcula el array de diferencias con respecto a la media. Luego utiliza la función __argmin__ de Numpy para calcular el índice del array que contiene el mínimo valor.\n", "\n", "__Nota__: la solución es: 'El mes más cercano a los 10.12 grados es el mes 9'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Sol:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Ahora ejecuta las siguientes líneas de código." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "media = temperatura.mean()\n", "\n", "x = np.arange(12)\n", "y = temperatura\n", "plt.plot(x , y) # curva de temperaturas\n", "plt.plot(x , np.ones(12)* media, 'k--') # recta par la media\n", "\n", "plt.ylim(0,temperatura.max() + 2)\n", "\n", "plt.xticks(np.arange(12),['E','F','M','A','M','J','J','A','S','O','N','D'])\n", "plt.xlim(-0.5,11.5)\n", "\n", "plt.legend(['Temperatura','Media'],loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3 Ejercicio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El archivo [WordPhones](./datos/WorldPhones.txt) \n", "recoge información acerca del número de teléfonos en varias regiones del mundo (en miles).\n", "\n", "Se trata de un array multidimensional de tamaño $7$ x $7$.\n", "- Cada columna se corresponde con una región del mundo. Las regiones son:\n", "[\"N.Amer\",\"Europe\",\"Asia\",\"S.Amer\",\"Oceania\",\"Africa\",\"Mid.Amer\"]\n", "\n", "- Cada fila se corresponde con un año. Los años son: \n", "[1951, 1956, 1957, 1958, 1959, 1960, 1961]\n", "\n", "-----\n", "\n", "* Escribir la expresión Python que recoja los datos del año 1960\n", " * Primero descarga el archivo [WordPhones](./datos/WorldPhones.txt) en tu directorio de trabajo.\n", " * Carga los datos en una variable llamada __datos__ mediante la función __loadtxt__.\n", " * Crea un array de tamaño 7 con los distintos años llamado __years__\n", " * Escribe la expresión Python para seleccionar la fila que se corresponde con el año 1960 (Utiliza como filtro un array de booleanos)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Sol: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------\n", "* Escribir la expresión Python que recoja los datos de la región 'Europe'\n", " * Primero crea un array de tamaño 7 con las distintas regiones llamado __regiones__.\n", " * Calcula la transpuesta de la matriz donde se encuentran almacenados los datos.\n", " * Escribe la expresión Python para almacenar en la variable __datos_europa__ la fila que se corresponde con el la región \"Europe\". " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Sol:\n", "regiones = np.array([\"N.Amer\",\"Europe\",\"Asia\",\"S.Amer\",\"Oceania\",\"Africa\",\"Mid.Amer\"])\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "* Ahora representa en una gráfica la evolución que ha tenido \"Europe\" entre los años 1951 y 1961. Ejecuta el siguiente bloque de código." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "plt.plot(years,datos_europa[0])\n", "plt.xlabel('Meses')\n", "plt.ylabel('Cantidad de telefonos')\n", "plt.title('Datos Europe')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "* Nos interesa conocer la región con menos cantidad de móviles y el año.\n", " * Calcular el mínimo por filas en el array __minimo_filas__\n", " * Calcular el mínimo por columnas en el array __minimo_columnas__\n", " * Calcular la posición __f__del mínimo valor en el array __minimo_filas__. Utiliza la función __argmin__.\n", " * Calcular la posición __c__del mínimo valor en el array __minimo_columnas__. Utiliza la función __argmin__.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Sol: \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Ahora representa en una gráfica la evolución que ha tenido \"Europe\" y \"Africa\" entre los años 1956 y 1960. Ejecuta el siguiente bloque de código." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.arange(1,8)\n", "europa = datos.T[regiones == \"Europe\" ]\n", "africa = datos.T[regiones == \"Africa\" ]\n", "plt.plot( x, europa[0] )\n", "plt.plot( x, africa[0] )\n", "plt.xticks(np.arange(1,8), years)\n", "plt.xlabel('Years')\n", "plt.ylabel('Cantidad de telefonos')\n", "plt.xlim(0.5,7.5)\n", "plt.title('Comparativa Europa - Africa')\n", "\n", "plt.legend(['Europa','Africa'],loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Podemos representar todos los países. Ejecuta el siguiente bloque de código." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "x = np.arange(1,8)\n", "for i in regiones:\n", " reg = datos.T[regiones == i ]\n", " plt.plot( x, reg[0] )\n", " \n", "plt.xticks(np.arange(1,8), years) \n", "\n", "plt.xlabel('Years')\n", "plt.ylabel('Cantidad de telefonos')\n", "plt.xlim(0.5,7.5)\n", "plt.ylim(datos.min() - 1000, datos.max() + 1000)\n", "plt.title('Comparativa todas las regiones')\n", "\n", "plt.legend(regiones,loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Exploratory computing with python](http://nbviewer.ipython.org/github/mbakker7/exploratory_computing_with_python/tree/master/)\n", "\n", "------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"><img alt=\"Licencia Creative Commons\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /></a><br />" ] } ], "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 }
gpl-3.0
ledeprogram/algorithms
class3/homework/Devulapalli_Harsha_3_1.ipynb
1
14674
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Assignment 1\n", "\n", " Implement the sorting algorithm you came up with in pseudocode with Python\n", " Test the sorting algorithm with a list of 10, 100, 1000 random numbers and compare the result using the %time to time your code and submit your results in code comments\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Sorting Algorithm\n", "\n", "def sortingalgo(numberslist):\n", " print(\"The unsorted list is\",numberslist)\n", " import time\n", " start_time = time.time()\n", " sorted_list = []\n", " while numberslist:\n", " minimum = numberslist[0] \n", " for x in numberslist: \n", " if x < minimum:\n", " minimum = x\n", " sorted_list.append(minimum)\n", " numberslist.remove(minimum) \n", " print(\"\")\n", " print(\"The sorted list is\",sorted_list)\n", " print(\"\")\n", " print(\"--- %s seconds ---\" % (time.time() - start_time))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The unsorted list is [3, 10, 5, 2]\n", "\n", "The sorted list is [2, 3, 5, 10]\n", "\n", "--- 0.0 seconds ---\n" ] } ], "source": [ "sortingalgo([3,10,5,2])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[88, 47, 475, 69, 981, 174, 190, 985, 496, 638]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "random.sample(range(1000), 10) # We will now use this code to be fed into our algorithm. As we can see here, it generates random numbers." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The unsorted list is [653, 601, 183, 89, 598, 520, 380, 420, 143, 0]\n", "\n", "The sorted list is [0, 89, 143, 183, 380, 420, 520, 598, 601, 653]\n", "\n", "--- 0.0 seconds ---\n" ] } ], "source": [ "sortingalgo(random.sample(range(1000), 10))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The unsorted list is [412, 601, 96, 584, 615, 337, 612, 977, 976, 701, 199, 333, 432, 815, 839, 537, 74, 65, 404, 698, 776, 132, 945, 164, 201, 200, 702, 870, 658, 203, 424, 377, 979, 770, 76, 457, 420, 88, 379, 324, 918, 607, 980, 990, 593, 292, 801, 441, 690, 438, 538, 284, 162, 632, 155, 304, 45, 68, 957, 103, 71, 388, 779, 150, 533, 375, 386, 735, 427, 879, 161, 194, 211, 832, 446, 415, 520, 844, 182, 523, 189, 418, 429, 997, 273, 973, 485, 563, 46, 950, 867, 518, 895, 693, 210, 672, 561, 627, 824, 270]\n", "\n", "The sorted list is [45, 46, 65, 68, 71, 74, 76, 88, 96, 103, 132, 150, 155, 161, 162, 164, 182, 189, 194, 199, 200, 201, 203, 210, 211, 270, 273, 284, 292, 304, 324, 333, 337, 375, 377, 379, 386, 388, 404, 412, 415, 418, 420, 424, 427, 429, 432, 438, 441, 446, 457, 485, 518, 520, 523, 533, 537, 538, 561, 563, 584, 593, 601, 607, 612, 615, 627, 632, 658, 672, 690, 693, 698, 701, 702, 735, 770, 776, 779, 801, 815, 824, 832, 839, 844, 867, 870, 879, 895, 918, 945, 950, 957, 973, 976, 977, 979, 980, 990, 997]\n", "\n", "--- 0.0010001659393310547 seconds ---\n" ] } ], "source": [ "sortingalgo(random.sample(range(1000), 100))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The unsorted list is [751, 431, 928, 71, 89, 649, 778, 995, 80, 623, 270, 773, 429, 406, 557, 299, 625, 634, 102, 101, 861, 237, 737, 314, 761, 148, 543, 632, 620, 887, 160, 525, 687, 614, 135, 5, 241, 422, 815, 512, 659, 457, 53, 218, 553, 513, 598, 138, 227, 816, 725, 791, 976, 146, 600, 268, 983, 759, 296, 958, 275, 391, 963, 951, 857, 940, 650, 93, 432, 949, 910, 301, 830, 467, 532, 363, 139, 219, 775, 50, 554, 886, 840, 32, 114, 486, 25, 947, 594, 964, 436, 825, 576, 144, 183, 822, 781, 437, 494, 442, 456, 592, 333, 140, 866, 980, 379, 24, 647, 98, 112, 711, 924, 551, 872, 819, 797, 845, 232, 347, 689, 521, 801, 875, 404, 246, 26, 221, 472, 149, 358, 172, 401, 731, 807, 234, 393, 889, 29, 293, 217, 621, 445, 859, 489, 143, 629, 483, 190, 205, 438, 736, 789, 35, 970, 162, 799, 13, 834, 78, 648, 779, 981, 398, 12, 36, 515, 932, 719, 609, 353, 477, 214, 133, 550, 545, 987, 881, 714, 933, 882, 969, 453, 777, 216, 894, 452, 306, 103, 669, 691, 900, 168, 424, 960, 323, 423, 533, 667, 776, 208, 844, 382, 235, 396, 81, 723, 385, 802, 705, 303, 835, 249, 397, 410, 877, 612, 867, 279, 462, 6, 316, 565, 73, 250, 345, 154, 703, 707, 676, 55, 717, 505, 904, 660, 417, 493, 559, 222, 988, 977, 54, 310, 400, 480, 17, 939, 918, 700, 764, 326, 556, 750, 91, 923, 30, 300, 798, 662, 967, 850, 61, 942, 726, 488, 96, 188, 315, 684, 831, 578, 734, 913, 863, 66, 993, 307, 454, 185, 944, 673, 468, 577, 9, 332, 516, 132, 690, 251, 744, 290, 399, 828, 373, 122, 908, 343, 941, 263, 563, 841, 43, 44, 271, 542, 564, 770, 685, 531, 811, 597, 898, 231, 116, 319, 599, 754, 479, 76, 286, 523, 668, 63, 972, 742, 870, 244, 727, 931, 790, 482, 520, 433, 602, 62, 853, 108, 420, 927, 936, 85, 946, 712, 67, 111, 33, 856, 341, 82, 220, 613, 582, 349, 448, 917, 540, 72, 283, 153, 464, 86, 95, 134, 145, 402, 852, 903, 915, 496, 536, 765, 538, 998, 388, 858, 259, 748, 158, 228, 627, 441, 771, 137, 289, 793, 741, 362, 439, 839, 504, 788, 463, 510, 999, 344, 215, 41, 199, 601, 921, 813, 537, 174, 862, 383, 22, 128, 435, 986, 371, 331, 198, 64, 978, 370, 645, 916, 743, 579, 720, 204, 753, 989, 407, 308, 52, 458, 434, 997, 937, 769, 330, 631, 679, 264, 851, 430, 605, 377, 42, 608, 14, 658, 69, 201, 644, 808, 675, 375, 105, 880, 896, 200, 48, 386, 864, 768, 233, 548, 567, 4, 394, 783, 291, 821, 177, 389, 94, 878, 88, 635, 236, 210, 473, 651, 2, 3, 127, 954, 562, 461, 677, 478, 366, 38, 785, 84, 302, 180, 178, 984, 637, 925, 47, 570, 661, 683, 528, 230, 342, 847, 888, 596, 922, 527, 879, 971, 549, 181, 83, 729, 552, 758, 99, 182, 738, 367, 304, 65, 10, 329, 511, 607, 211, 317, 873, 8, 961, 506, 672, 611, 617, 487, 558, 522, 636, 547, 911, 284, 49, 955, 125, 170, 715, 470, 364, 639, 572, 912, 225, 346, 846, 21, 169, 883, 956, 451, 243, 646, 427, 70, 378, 959, 419, 670, 16, 642, 31, 207, 459, 171, 664, 23, 384, 449, 962, 674, 109, 704, 335, 610, 584, 953, 823, 640, 59, 499, 337, 79, 495, 574, 106, 583, 324, 992, 968, 328, 833, 795, 804, 258, 157, 194, 618, 87, 591, 465, 560, 701, 930, 561, 508, 403, 837, 810, 868, 950, 809, 762, 466, 814, 192, 757, 257, 11, 212, 120, 292, 909, 312, 280, 123, 680, 760, 616, 643, 832, 113, 657, 589, 568, 692, 630, 826, 656, 392, 733, 604, 272, 990, 619, 920, 267, 186, 444, 530, 360, 415, 566, 509, 979, 255, 334, 695, 526, 57, 529, 994, 339, 409, 130, 628, 730, 763, 142, 374, 450, 355, 152, 368, 376, 247, 678, 239, 121, 836, 784, 792, 780, 51, 914, 586, 901, 281, 728, 996, 966, 408, 524, 126, 338, 974, 60, 119, 746, 240, 37, 226, 682, 161, 615, 356, 803, 492, 484, 829, 176, 735, 945, 580, 395, 518, 238, 297, 606, 365, 164, 151, 350, 56, 390, 229, 298, 485, 75, 519, 455, 796, 248, 555, 622, 514, 278, 426, 361, 20, 74, 716, 747, 165, 129, 254, 686, 265, 935, 593, 884, 497, 885, 266, 713, 213, 77, 421, 973, 242, 897, 666, 892, 569, 491, 351, 838, 273, 45, 772, 708, 262, 184, 652, 740, 18, 848, 681, 710, 294, 460, 322, 654, 595, 28, 348, 0, 19, 39, 282, 253, 893, 626, 855, 926, 321, 313, 147, 428, 245, 288, 380, 929, 260, 517, 500, 544, 688, 905, 476, 156, 15, 849, 694, 416, 261, 440, 193, 571, 638, 535, 320, 413, 774, 534, 575, 766, 115, 800, 663, 223, 107, 481, 141, 196, 824, 874, 782, 817, 985, 842, 787, 739, 502, 752, 919, 124, 641, 311, 414, 159, 899, 671, 498, 943, 588, 721, 369, 471, 501, 975, 724, 405, 503, 895, 191, 305, 665, 718, 541, 443, 269, 590, 697, 820, 699, 209, 818, 812, 906, 938, 860, 34, 653, 934, 357, 167, 806, 58, 827, 104, 352, 189, 991, 722, 702, 295, 871, 197, 92, 655, 805, 469, 336, 865, 187, 633, 446, 965, 155, 359, 195, 869, 447, 381, 767, 202, 581, 309, 285, 706, 166, 412, 732, 952, 68, 287, 603, 40, 854, 749, 387, 372, 274, 539, 902, 206, 907, 755, 891, 100, 624, 587, 325, 318, 890, 173, 490, 411, 474, 203, 7, 418, 327, 117, 546, 786, 90, 756, 425, 507, 179, 948, 982, 709, 256, 843, 136, 110, 150, 745, 693, 340, 97, 252, 698, 46, 277, 224, 696, 573, 354, 794, 1, 163, 276, 876, 118, 131, 475, 957, 585, 175, 27]\n", "\n", "The sorted list is [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999]\n", "\n", "--- 0.03702545166015625 seconds ---\n" ] } ], "source": [ "sortingalgo(random.sample(range(1000), 1000))" ] } ], "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
lockywolf/sdsj-2016-contest
sdsj_notebook.ipynb
1
3230
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<pre>\n", "This is the first notebook file for the *Sberbank Data Science Journey*.\n", "</pre>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "from sklearn.ensemble import GradientBoostingClassifier" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "transactions = pd.read_csv('transactions.csv')\n", "customers_gender = pd.read_csv('customers_gender_train.csv')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "X = transactions.groupby('customer_id') \\\n", " .apply(lambda x: x[['mcc_code']].unstack().value_counts()) \\\n", " .unstack() \\\n", " .fillna(0)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "customers_gender = customers_gender.set_index('customer_id')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Y_train = customers_gender.loc[X.index].gender\n", "Y_train = Y_train.reset_index()\n", "del Y_train['customer_id']\n", "Y_train = Y_train.dropna(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train = X.reset_index()\n", "X_train = X_train.loc[Y_train.index].set_index('customer_id')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "clf = GradientBoostingClassifier(random_state=13)\n", "clf.fit(X_train, Y_train.values[:, 0]);" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "X_test = X.drop(customers_gender.index)\n", "result = pd.DataFrame(X_test.index, columns=['customer_id'])\n", "result['gender'] = clf.predict_proba(X_test)[:, 1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "result.to_csv('baseline_a.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
nkmk/python-snippets
notebook/pandas_index_select_query.ipynb
1
2080
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " age state point\n", "name \n", "Alice 24 NY 64\n", "Bob 42 CA 92\n", "Charlie 18 CA 70\n", "Dave 68 TX 70\n", "Ellen 24 CA 88\n", "Frank 30 NY 57\n" ] } ], "source": [ "df = pd.read_csv('data/src/sample_pandas_normal.csv', index_col=0)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " age state point\n", "name \n", "Alice 24 NY 64\n", "Charlie 18 CA 70\n" ] } ], "source": [ "print(df.query('index.str.contains(\"li\")', engine='python'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " age state point\n", "name \n", "Alice 24 NY 64\n", "Charlie 18 CA 70\n", "Dave 68 TX 70\n" ] } ], "source": [ "print(df.query('name.str.endswith(\"e\")', engine='python'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ledeprogram/algorithms
class1/homework/bennion_kate_1.4.ipynb
1
4533
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The writer of this code wants to count the mean and median article length for recent articles on gay marriage in the New York Times. This code has several issues, including errors. When they checked their custom functions against the numpy functions, they noticed some discrepancies. Fix the code so it executes properly, retrieves the articles, and outputs the correct result from the custom functions, compared to the numpy functions." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests # a better package than urllib2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def my_mean(input_list):\n", " list_sum = 0\n", " list_count = 0\n", " for el in input_list:\n", " list_sum += el\n", " list_count += 1\n", " return list_sum / list_count" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def my_median(input_list):\n", " input_list = sorted(input_list)\n", " list_length = len(input_list)\n", " if list_length%2==0:\n", " return (input_list[int(list_length/2)] + input_list[int((list_length/2)-1)])/2\n", " else:\n", " return input_list[int((list_length-1)/2)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "api_key = \"ffaf60d7d82258e112dd4fb2b5e4e2d6:3:72421680\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = \"http://api.nytimes.com/svc/search/v2/articlesearch.json?q=gay+marriage&api-key=%s\" % api_key" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r = requests.get(url)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wc_list = []\n", "for article in r.json()['response']['docs']:\n", " wc_list.append(article['word_count'])\n", "wc_list = [i for i in wc_list if i is not None]\n", "wc_list = [int(x) for x in wc_list]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "720.7777777777778" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_mean(wc_list)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "720.77777777777783" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(wc_list)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "684" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_median(wc_list)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "684.0" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(wc_list)" ] } ], "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
liupengyuan/python_tutorial
chapter4/python爬虫入门.ipynb
1
22517
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### By liupengyuan[at]pku.edu.cn\n", "### Project: https://github.com/liupengyuan/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. 什么是爬虫\n", "简而言之,爬虫就是一段能够获取互联网信息(数据)的程序/工具。\n", "\n", "一般需要通过抓取网页来获取互联网的信息与数据。\n", "\n", "网页本身就是一个本文文件,只不过这个文本文件是由特定规则和符号标记的(HTML,超文本标记语言),称为超文本文件,也可称为网页源代码。\n", "\n", "这段文本经过浏览器的解析(各类图片视频等在此过程中从网页外部加载),就成为我们日常浏览的网页展示给我们的样子了。\n", "\n", "因此,爬虫首先就是要获得网页这个文本文件,并进行解析。\n", "\n", "如果所需数据就直接在网页文本文件中,则可直接得到;如果所需数据在网页外部,则通过解析的结果得到数据所在地址,将该数据下载。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2. 最简爬虫**\n", "\n", "这里我们以优秀的python第三方库requests为例,该库是“唯一一个非转基因的Python HTTP库,人类可以安全享用”。\n", "\n", "其中HTTP(HyperText Transfer Protocol)是互联网上应用最为广泛的一种网络协议,超文本文件传输时,必须遵守这个协议。\n", "\n", "非转基因是作者幽默的说法,表明该库是原汁原味符合python设计理念,易用易于理解。\n", "\n", "输入以下代码,并观察执行结果。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "import re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "导入必要的模块" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "\n", "\n", "<!DOCTYPE html>\n", "<html lang=\"en\">\n", " <head>\n", " <meta charset=\"utf-8\">\n", " <link rel=\"dns-prefetch\" href=\"https://assets-cdn.github.com\">\n", " <link rel=\"dns-prefetch\" href=\"https://avatars0.githubusercontent.com\">\n", " <link rel=\"dns-prefetch\" href=\"https://avatars1.githubusercontent.com\">\n", " <link rel=\"dns-prefetch\" href=\"https://avatars2.githubusercontent.com\">\n", " <link rel=\"dns-prefetch\" href=\"https://avatars3.githubusercontent.com\">\n", " <link rel=\"dns-prefetch\" href=\"https://github-cloud.s3.amazonaws.com\">\n", " <link rel=\"dns-prefetch\" href=\"https://user-images.githubusercontent.com/\">\n", "\n", "\n", "\n", " <link crossorigin=\"anonymous\" href=\"https://assets-cdn.github.com/assets/frameworks-c9193575f18b28be82c0a963e144ff6fa7a809dd8ae003a1d1e5979bed3f7f00.css\" media=\"all\" rel=\"stylesheet\" />\n", " <link crossorigin=\"anonymous\" href=\"https://assets-cdn.github.com/assets/github-1214f71e8309b7d4dab489c2765c4de1523517be4c68b861a78d93c8e840648e.css\" media=\"all\" rel=\"stylesheet\" />\n", " \n", " \n", " <link crossorigin=\"anonymous\" \n" ] } ], "source": [ "r = requests.get('https://github.com/liupengyuan/python_tutorial/blob/master/chapter1/0.md')\n", "print(r.text[:1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- import requests,首先导入requests库\n", "- r = requests.get('https://github.com/liupengyuan/python_tutorial/blob/master/chapter1/0.md'),调用requests的get方法,向网址https://github.com/liupengyuan/python_tutorial/blob/master/chapter1/0.md 发送获取(get)请求,该方法返回一个应答(Response)对象r,其中包含该网页的所有信息。\n", "- print(r.text[:1000]),利用对象r的text属性字符串,打印网页内容,因为内容较多,暂取前1000个字符。\n", "- 打开https://github.com/liupengyuan/python_tutorial/blob/master/chapter1/0.md网页,在网页正文区域点击右键,选择查看源代码,会发现代码示例D-1确实已经获取/抓取这个网页的文本文件。\n", "- 至此,一个最简爬虫已经完成。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3. 静态定向爬虫基础**\n", "\n", "- 本小节中的爬虫是开始就指定了要抓取网页的地址(定向),要抓取的内容直接存在在网页源代码中(静态),因此称为静态定向爬虫。\n", "\n", "- 由于需要用到Chrome浏览器中的**检查**功能,因此需要读者下载安装Chrome浏览器。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.1 抓取网易首页(www.163.com)上的全部新闻**\n", "\n", "- 我们打开网易首页,然后用鼠标右键点击页面,用chrome浏览器可选择检查,进入开发者模式。\n", "- 发者模式中,右侧为开发者工具面板,右上部的Elements标签页面中显示一片代码,是该网页html标记的文本(经过整理对齐的)。\n", "- 推荐教程:http://www.w3school.com.cn/html/index.asp ,快速浏览,可了解一些html基础知识。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " <!DOCTYPE HTML>\n", "<!--[if IE 6 ]> <html class=\"ne_ua_ie6 ne_ua_ielte8\" id=\"ne_wrap\"> <![endif]-->\n", "<!--[if IE 7 ]> <html class=\"ne_ua_ie7 ne_ua_ielte8\" id=\"ne_wrap\"> <![endif]-->\n", "<!--[if IE 8 ]> <html class=\"ne_ua_ie8 ne_ua_ielte8\" id=\"ne_wrap\"> <![endif]-->\n", "<!--[if IE 9 ]> <html class=\"ne_ua_ie9\" id=\"ne_wrap\"> <![endif]-->\n", "<!--[if (gte IE 10)|!(IE)]><!--> <html phone=\"1\" id=\"ne_wrap\"> <!--<![endif]-->\n", "<head>\n", "<meta http-equiv=\"Content-Type\" content=\"text/html; charset=gbk\">\n", "<meta name=\"google-site-verification\" content=\"PXunD38D6Oui1T44OkAPSLyQtFUloFi5plez040mUOc\" />\n", "<meta name=\"baidu-site-verification\" content=\"oiT8OEfzes\" />\n", "<meta name=\"360-site-verification\" content=\"527ad00f66a93c31134d6a20b2246950\" />\n", "<meta name=\"shenma-site-verification\" content=\"12c2d7067c72735f0bd75c8dcd26b0d8_1509937417\"/>\n", "<meta name=\"sogou_site_verification\" content=\"tCLG1xJc76\"/>\n", "<meta name=\"model_url\" content=\"http://www.163.com/special/0077rt/index.html\" />\n", "<title>网易</title>\n", "<base target=\"_blank\" />\n", "<meta na\n" ] } ], "source": [ "r = requests.get('http://www.163.com/')\n", "print(r.text[:1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 用鼠标右键在新闻标题上点击,并选择`检查`,可在Elements标签页面找到该新闻标题以及对应的链接在html代码中的位置。\n", "- 代码大致如下形式:\n", "```\n", "<li class=\"cm_fb\">\n", "<a href=\"http://news.163.com/xxxxxx.html\">yyyyyyyyy</a>\n", "::after\n", "</li>\n", "```\n", "- 其中xxxxx及yyyyyyy随新闻标题不同而不同。前者是新闻链接,后者新闻是标题,因此这两项是我们感兴趣并希望抓取的内容\n", "- 在Elements标签页面中继续查看各个新闻标题,可以发现各个新闻其实都在标签对`<li>`及`</li>`之间,且没有其间没有换行符。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<a href=\"http://you.163.com/item/recommend?from=web_fc_menhu_xinrukou_4\"><span>999+人气好评品</span></a>\n", "<a href=\"http://news.163.com/17/1204/20/D4RAVK1N000189FH.html\">互联网大会蓝皮书发布 中国数字经济规模全球第二</a>\n", "<a href=\"http://news.163.com/17/1204/09/D4Q8CECJ000189FH.html\">习近平的这几句话 引发外国政党领导人的强烈共鸣</a>\n", "<a href=\"http://news.cri.cn/special/2017hlwdhfpm.html\">互联网精准扶贫</a> <a target=\"_blank\" href=\"http://www.chinanews.com/m/shipin/spfts/20171202/1277.shtml\">大连接时代:创新 智能 变革</a>\n", "<a href=\"http://news.163.com/17/1204/11/D4QD03RS000189FH.html\">上海:对标全球最高 开放之风劲吹</a> <a target=\"_blank\" href=\"http://gov.163.com/special/S1509677294216/\">领航新征程</a>\n" ] } ], "source": [ "p = r'<li>(.+)?</li>'\n", "contents = re.findall(p, r.text)\n", "print('\\n'.join(contents[:5]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `p = r'<li>(.+)?</li>'` 构造了正则表达式,可提取`<li>...</li>`之间的内容(不包括之间的换行符)\n", "- `re.findall(p, r.text)` 将r.text中所有的符合上正则的提取出来放入列表\n", "- 最终打印前50行,可以发现后面有一些并非新闻标题与对应链接,并非所有在`<li>...</li>`之间的内容都是目标内容\n", "\n", "- 有关正则表达式,可参考教程:https://github.com/liupengyuan/python_tutorial/blob/master/chapter3 中的正则表达式快速教程。可以通过构造一些正则表达式来完成网页解析将目标内容提取。\n", "- 这里我们将介绍一个优秀的第三方网页解析包:Beautifulsoap。该包已经随Anaconda安装,名称为bs4。可以用这个包(必要时联合正则表达式)来进行网页解析。\n", "- 该包已经通过`from bs4 import BeautifulSoup`在开始导入" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type is: <class 'bs4.element.ResultSet'>\n" ] }, { "data": { "text/plain": [ "[<ul class=\"cm_ul_round\">\n", " <li class=\"cm_fb\"><a href=\"http://news.163.com/17/1204/20/D4RAQ8GD000189FH.html\">关于互联网,习近平给出的20条重要论断</a></li>\n", " <li><a href=\"http://news.163.com/17/1204/20/D4RAVK1N000189FH.html\">互联网大会蓝皮书发布 中国数字经济规模全球第二</a></li>\n", " <li><a href=\"http://news.163.com/17/1204/09/D4Q8CECJ000189FH.html\">习近平的这几句话 引发外国政党领导人的强烈共鸣</a></li>\n", " <li><a href=\"http://news.cri.cn/special/2017hlwdhfpm.html\">互联网精准扶贫</a> <a href=\"http://www.chinanews.com/m/shipin/spfts/20171202/1277.shtml\" target=\"_blank\">大连接时代:创新 智能 变革</a></li>\n", " <li><a href=\"http://news.163.com/17/1204/11/D4QD03RS000189FH.html\">上海:对标全球最高 开放之风劲吹</a> <a href=\"http://gov.163.com/special/S1509677294216/\" target=\"_blank\">领航新征程</a></li>\n", " </ul>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "soup = BeautifulSoup(r.text, 'html.parser')\n", "contents = soup.find_all('ul', attrs='cm_ul_round')\n", "print('type is:', type(contents))\n", "contents[:1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 函数BeautifulSoup(r.text, 'html.parser'),返回一个BeautifulSoup对象。第一个参数为要解析的网页文本,第二个参数是解析器的选择,暂选择python内置的'html.parser'作为解析器\n", "- soup.find_all('ul', attrs='cm_ul_round'),BeautifulSoup对象的方法,可返回指定标签之间的所有内容的ResultSet对象。其中第一个参数为标签,第二个参数为标签的属性。根据新闻标题,在Elements标签页面稍加分析可知:新闻标题及链接均在标签对`<ul class=\"cm_ul_round\">`及`</ul>`之间,且其他非新闻标题的内容,均不在这种规定了class的`<ul>`标签之间" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'bs4.element.Tag'> http://news.163.com/17/1204/20/D4RAQ8GD000189FH.html 关于互联网,习近平给出的20条重要论断\n", "<class 'bs4.element.Tag'> http://news.163.com/17/1204/20/D4RAVK1N000189FH.html 互联网大会蓝皮书发布 中国数字经济规模全球第二\n", "<class 'bs4.element.Tag'> http://news.163.com/17/1204/09/D4Q8CECJ000189FH.html 习近平的这几句话 引发外国政党领导人的强烈共鸣\n", "<class 'bs4.element.Tag'> http://news.cri.cn/special/2017hlwdhfpm.html 互联网精准扶贫\n", "<class 'bs4.element.Tag'> http://www.chinanews.com/m/shipin/spfts/20171202/1277.shtml 大连接时代:创新 智能 变革\n", "<class 'bs4.element.Tag'> http://news.163.com/17/1204/11/D4QD03RS000189FH.html 上海:对标全球最高 开放之风劲吹\n", "<class 'bs4.element.Tag'> http://gov.163.com/special/S1509677294216/ 领航新征程\n", "b\n" ] } ], "source": [ "for line in contents:\n", " url_titles = line.find_all('a')\n", " for url_title in url_titles:\n", " url = url_title.get('href')\n", " title = url_title.string\n", " print(type(url_title), url, title)\n", " if input()=='b':\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 可对ResultSet进行遍历,利用`line.find_all('a')`取得其中在`<a>`及`</a>`标签对(该标签对表示超链接)中间的内容,url_titles的内容仍然是一个ResultSet对象,含有所有的url及title\n", "- 对url_titles进行遍历,每一个对象均为`Tag`对象,利用`Tag`对象的`get('href')`方法,该方法可以取得标签的属性值,本例中参数为属性`href`,其值为超链接的URL\n", "- 利用标签的`string`属性,取得该超链接的文本" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url_title_dict = {}\n", "for line in contents:\n", " url_titles = line.find_all('a')\n", " for url_title in url_titles:\n", " url = url_title.get('href')\n", " title = url_title.string\n", " if url and url.endswith(r'.html'):\n", " try:\n", " url_title_dict[url] = title\n", " except KeyError:\n", " continue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 这里暂只需要得到首页的标题及对应的新闻页面链接,因此只抽取链接结尾为`.html`的条目\n", "- 所有结果存入到词典url_title_dict中\n", "\n", "我们还要获取各链接下的新闻文本" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "title_text_dict = {}\n", "for url, title in url_title_dict.items():\n", " r = requests.get(url)\n", " soup = BeautifulSoup(r.text, 'html.parser')\n", " html = soup.find('div', attrs = 'post_text')\n", " if html:\n", " passages = html.find_all('p')\n", " text = ''\n", " for passage in passages:\n", " if passage.string:\n", " text += passage.string\n", "\n", " title_text_dict[url] = title, text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 通过点击新闻页面,可以发现,所有正文在`<div class = 'post_text>...</div>`中\n", "- 通过soup对象的find()方法,可以得到上述标签中的内容,存放在html变量中\n", "- 内容是由`<p>...</p>`标签对之间(p即passage),因此需要利用find_all()方法提取之中所有的段落,放入passages中\n", "- 遍历passages,其中每一个passage均是Tag对象,利用Tag对象的string方法,得到该标签对应的文本\n", "\n", "至此,形成了一个词典,key为新闻的url,value为对应的标题与正文,可将上述程序整合,并将抓取内容保存到文件中" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "import re\n", "\n", "def get_163_home_news(filename):\n", " try:\n", " r = requests.get(r'http://www.163.com')\n", " except:\n", " print('Can not get the page.\\n')\n", " return False\n", " \n", "\n", " soup = BeautifulSoup(r.text, 'html.parser')\n", " contents = soup.find_all('ul', attrs='cm_ul_round')\n", " \n", " error_url = []\n", " f_err = open('error_urls', 'w', encoding = 'utf-8')\n", " fh = open(filename, 'w', encoding = 'utf-8')\n", " for line in contents:\n", " url_titles = line.find_all('a')\n", " for url_title in url_titles:\n", " url = url_title.get('href')\n", " title = url_title.string\n", " if url and url.endswith(r'.html'):\n", " try:\n", " r = requests.get(url)\n", " except:\n", " print('Error in getting:', url)\n", " error_url.append(url)\n", " continue\n", " soup = BeautifulSoup(r.text, 'html.parser')\n", " html = soup.find('div', attrs = 'post_text')\n", " if html:\n", " passages = html.find_all('p')\n", " text = ''\n", " for passage in passages:\n", " if passage.string:\n", " text += passage.string\n", " fh.write('{}\\n{}\\n{}\\n'.format(url, title, text))\n", " \n", " f_err.write('\\n'.join(error_url))\n", " fh.close()\n", " f_err.close()\n", " return True" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = r'news_163_home.txt'\n", "get_163_home_news(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.2 抓取有道词典查询词的双语例句**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "分析:\n", "\n", "- 通过在有道词典(dict.youdao.com)进行单词查询,发现对任意单词xxxxx,给出该单词翻译信息的页面URL为:http://dict.youdao.com/w/xxxxx\n", "\n", "- 由于我要抓取词xxxxx中查询结果的所有双语例句,则需要在页面下部的显示最后一个例句后,点击更多双语例句,这会更新当前页面的URL为:http://dict.youdao.com/example/blng/eng/xxxxx/#keyfrom=dict.main.moreblng 。所有双语例句均在其中,因此,我们只对这个URL进行爬取分析即可\n", "- 输入一个中文词汇进行查询,选中第一个双语例句,右键点击并选择检查,进入chrome开发者模式,发现所有例句均在`<ul class='ol'>...</ul>`标签对之间。\n", "- 每个例句均在`<p>...</p>`标签对之间" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "\n", "def get_word_sents(word):\n", " url = r'http://dict.youdao.com/example/blng/eng/{}/#keyfrom=dict.main.moreblng'.format(word)\n", " try:\n", " r = requests.get(url)\n", " except:\n", " print('Can not get the page.\\n')\n", " return False\n", " \n", " word_sents = []\n", " soup = BeautifulSoup(r.text, 'html.parser')\n", " contents = soup.find('ul', attrs = 'ol')\n", " sents = contents.find_all('p')\n", " for sent in sents:\n", " word_sents.append(sent.text.replace('\\n','')+'\\n')\n", " \n", " return word_sents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 整个过程与网易新闻抓取的方法类似\n", "- `word_sents.append(sent.text.replace('\\n','')+'\\n')`,是为了写入文件时候较为整齐" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "test_word = '爬虫'\n", "sents = get_word_sents(test_word)\n", "with open(test_word+'.txt', 'w', encoding = 'utf-8') as f:\n", " f.writelines(sents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4. 动态页面的抓取(以后)**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**5. Scrapy爬虫框架(以后)**" ] } ], "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
nwfpug/meetings
2017-05-08/widgets_list.ipynb
1
99863
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Widget List (verbatim from the github page of ipywidgets)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import ipywidgets as widgets" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Numeric widgets " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 8 widgets distributed with IPython that are designed to display numeric values. Widgets exist for displaying integers and floats, both bounded and unbounded. The integer widgets share a similar naming scheme to their floating point counterparts. By replacing `Float` with `Int` in the widget name, you can find the Integer equivalent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IntSlider" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7f3cf7f4fbbe4d2db736978a7a417395" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.IntSlider(\n", " value=7,\n", " min=0,\n", " max=10,\n", " step=1,\n", " description='Test:',\n", " disabled=False,\n", " continuous_update=False,\n", " orientation='horizontal',\n", " readout=True,\n", " readout_format='i',\n", " slider_color='white'\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### FloatSlider" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0a0fcd3a971643d298e077e1797185fc" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.FloatSlider(\n", " value=7.5,\n", " min=0,\n", " max=10.0,\n", " step=0.1,\n", " description='Test:',\n", " disabled=False,\n", " continuous_update=False,\n", " orientation='horizontal',\n", " readout=True,\n", " readout_format='.1f',\n", " slider_color='white'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sliders can also be **displayed vertically**." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2d7f96e818ce401b8d348fe1e6d32dd7" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.FloatSlider(\n", " value=7.5,\n", " min=0,\n", " max=10.0,\n", " step=0.1,\n", " description='Test:',\n", " disabled=False,\n", " continuous_update=False,\n", " orientation='vertical',\n", " readout=True,\n", " readout_format='.1f',\n", " slider_color='white'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IntRangeSlider" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd4ef7218ad2485b80562b72d8a88619" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.IntRangeSlider(\n", " value=[5, 7],\n", " min=0,\n", " max=10,\n", " step=1,\n", " description='Test:',\n", " disabled=False,\n", " continuous_update=False,\n", " orientation='horizontal',\n", " readout=True,\n", " readout_format='i',\n", " slider_color='white',\n", " color='black'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FloatRangeSlider" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8d6b02ee3e3d46f4b8f1aeece0aa6c0b" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.FloatRangeSlider(\n", " value=[5, 7.5],\n", " min=0,\n", " max=10.0,\n", " step=0.1,\n", " description='Test:',\n", " disabled=False,\n", " continuous_update=False,\n", " orientation='horizontal',\n", " readout=True,\n", " readout_format='i',\n", " slider_color='white',\n", " color='black'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IntProgress" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0ccec36f9395487ead1a16fa86e0bd06" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.IntProgress(\n", " value=7,\n", " min=0,\n", " max=10,\n", " step=1,\n", " description='Loading:',\n", " bar_style='', # 'success', 'info', 'warning', 'danger' or ''\n", " orientation='horizontal'\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### FloatProgress" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9a9684b454e14aada9017c0d0b0bf9d9" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.FloatProgress(\n", " value=7.5,\n", " min=0,\n", " max=10.0,\n", " step=0.1,\n", " description='Loading:',\n", " bar_style='info',\n", " orientation='horizontal'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BoundedIntText" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "41f39b73aad745e583a75c87545fd1b5" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.BoundedIntText(\n", " value=7,\n", " min=0,\n", " max=10,\n", " step=1,\n", " description='Text:',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### BoundedFloatText" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4d9e1741da6f4354941e19d83e3d4779" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.BoundedFloatText(\n", " value=7.5,\n", " min=0,\n", " max=10.0,\n", " step=0.1,\n", " description='Text:',\n", " disabled=False,\n", " color='black'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IntText" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "142f0c0a8abf4799b3550bb1d12bb556" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.IntText(\n", " value=7,\n", " description='Any:',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### FloatText" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "75c7377d10d5497cb8d626468f7cc705" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.FloatText(\n", " value=7.5,\n", " description='Any:',\n", " disabled=False,\n", " color='black'\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Boolean widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are three widgets that are designed to display a boolean value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ToggleButton" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3483d180e3314429a9776113eb534549" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.ToggleButton(\n", " value=False,\n", " description='Click me',\n", " disabled=False,\n", " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", " tooltip='Description',\n", " icon='check'\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Checkbox" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ad91915b7b7f46788bf285ce98a53fe7" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Checkbox(\n", " value=False,\n", " description='Check me',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Valid\n", "\n", "The valid widget provides a read-only indicator." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "86b68cd2c33f44e7b5836a9613eba0b2" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Valid(\n", " value=False,\n", " description='Valid!',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Selection widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are four widgets that can be used to display single selection lists, and one that can be used to display multiple selection lists. All inherit from the same base class. You can specify the **enumeration of selectable options by passing a list**. You can **also specify the enumeration as a dictionary**, in which case the **keys will be used as the item displayed** in the list and the corresponding **value will be returned** when an item is selected." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Dropdown" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f92ad595c58843a79da11fe8dd1afe43" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Dropdown(\n", " options=['1', '2', '3'],\n", " value='2',\n", " description='Number:',\n", " disabled=False,\n", " button_style='' # 'success', 'info', 'warning', 'danger' or ''\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following is also valid:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c09ba4a0592e47aa9ae4138e0db89dfa" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Dropdown(\n", " options={'One': 1, 'Two': 2, 'Three': 3},\n", " value=2,\n", " description='Number:',\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### RadioButtons" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "82c1b8e4c83143c88e24ad88cdd98364" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.RadioButtons(\n", " options=['pepperoni', 'pineapple', 'anchovies'],\n", "# value='pineapple',\n", " description='Pizza topping:',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Select" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bcffa07a56bc4b36ae9c5c0c1e25ecfd" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Select(\n", " options=['Linux', 'Windows', 'OSX'],\n", " value='OSX',\n", " # rows=10,\n", " description='OS:',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SelectionSlider" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b030477ca3ce46489a30f90e52b144db" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.SelectionSlider(\n", " options=['scrambled', 'sunny side up', 'poached', 'over easy'],\n", " value='sunny side up',\n", " description='I like my eggs ...',\n", " disabled=False,\n", " continuous_update=False,\n", " orientation='horizontal',\n", " readout=True,\n", "# readout_format='i',\n", "# slider_color='black'\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### ToggleButtons" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d0f3504066204322845975840d674ec3" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.ToggleButtons(\n", " options=['Slow', 'Regular', 'Fast'],\n", " description='Speed:',\n", " disabled=False,\n", " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", " tooltip='Description',\n", "# icon='check' \n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SelectMultiple\n", "Multiple values can be selected with <kbd>shift</kbd> and/or <kbd>ctrl</kbd> (or <kbd>command</kbd>) pressed and mouse clicks or arrow keys." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2b66f51aeeca44cfa59c4a2fa4181bac" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.SelectMultiple(\n", " options=['Apples', 'Oranges', 'Pears'],\n", " value=['Oranges'],\n", " #rows=10,\n", " description='Fruits',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## String widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 4 widgets that can be used to display a string value. Of those, the `Text` and `Textarea` widgets accept input. The `Label` and `HTML` widgets display the string as either Label or HTML respectively, but do not accept input." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Text" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4e9b1ccf51264ff2ae373f5800ce83ac" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Text(\n", " value='Hello World',\n", " placeholder='Type something',\n", " description='String:',\n", " disabled=False \n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Textarea" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8bd5cc351f7149ce834ec17744bea516" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Textarea(\n", " value='Hello World',\n", " placeholder='Type something',\n", " description='String:',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Label" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e9d4be9887a54fb78ef0a8b928b5b4ae" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Label(\n", " value=\"$$\\\\frac{n!}{k!(n-k)!} = \\\\binom{n}{k}$$\",\n", " placeholder='Some LaTeX',\n", " description='Some LaTeX',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HTML" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "350700dc83294d9da48a70327c1500b0" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.HTML(\n", " value=\"Hello <b>World</b>\",\n", " placeholder='Some HTML',\n", " description='Some HTML',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HTML Math" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0f1244f48e734d6f8358b2d2702602de" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.HTMLMath(\n", " value=r\"Some math and <i>HTML</i>: $x^2$ and $$\\frac{x+1}{x-1}$$\",\n", " placeholder='Some HTML',\n", " description='Some HTML',\n", " disabled=False\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Image" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "32d66722afb74234acca3f21b422306f" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "file = open(\"images/messi5.jpg\", \"rb\")\n", "image = file.read()\n", "widgets.Image(\n", " value=image,\n", " format='jpg',\n", " width=600,\n", " height=400\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Button" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "74347eb15b8645c2a10b565225ec063d" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Button(\n", " description='Click me',\n", " disabled=False,\n", " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", " tooltip='Click me',\n", " icon='check'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Play (Animation) widget" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Play` widget is useful to perform animations by iterating on a sequence of integers with a certain speed." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a5d0a500fb674770895040e70ce4ee77" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "play = widgets.Play(\n", "# interval=10,\n", " value=50,\n", " min=0,\n", " max=100,\n", " step=1,\n", " description=\"Press play\",\n", " disabled=False\n", ")\n", "slider = widgets.IntSlider()\n", "widgets.jslink((play, 'value'), (slider, 'value'))\n", "widgets.HBox([play, slider])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Date picker\n", "\n", "The date picker widget works in Chrome and IE Edge, but does not currently work in Firefox or Safari because they do not support the HTML date input field." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e56754e0829a434f923dcb276163b66c" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.DatePicker(\n", " description='Pick a Date'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Color picker" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d2fd5977dae94274b0aa8c44acff4e71" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.ColorPicker(\n", " concise=False,\n", " description='Pick a color',\n", " value='blue'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Controller" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0a624e67ae1e401b9132f11297d5fda4" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widgets.Controller(\n", " index=0,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Layout widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Box" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HBox" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0abc7b0672234b5db5eb7867441d64b3" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "items = [widgets.Label(str(i)) for i in range(4)]\n", "widgets.HBox(items)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### VBox" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c678d738046949e6a81a87e0f83ddd66" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "items = [widgets.Label(str(i)) for i in range(4)]\n", "widgets.HBox([widgets.VBox([items[0], items[1]]), widgets.VBox([items[2], items[3]])])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Accordion" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d7f2c96bbf914ec9a19443c09beabb10" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "accordion = widgets.Accordion(children=[widgets.IntSlider(), widgets.Text()])\n", "accordion.set_title(0, 'Slider')\n", "accordion.set_title(1, 'Text')\n", "accordion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tabs" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "873c97abab1c4740a9d5df37d9240778" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "list = ['P0', 'P1', 'P2', 'P3', 'P4']\n", "children = [widgets.Text(description=name) for name in list]\n", "tab = widgets.Tab(children=children)\n", "tab" ] } ], "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" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "00a98e976b894518bc9427d0f84063b0": { "model_module": "jupyter-js-widgets", "model_module_version": "~2.0.30", "model_name": "LayoutModel", "state": { "_model_module_version": "~2.0.30", "_view_module_version": "~2.0.30" } }, "045c7adbc8864ed2a2fc5df0e39b9709": { "model_module": "jupyter-js-widgets", "model_module_version": "~2.0.30", "model_name": "LayoutModel", "state": { "_model_module_version": "~2.0.30", "_view_module_version": "~2.0.30" } }, "05884679cab24894bf3385aae860b4f7": { "model_module": "jupyter-js-widgets", "model_module_version": "~2.0.30", "model_name": "FloatSliderModel", "state": { "_model_module_version": "~2.0.30", "_view_module_version": "~2.0.30", "continuous_update": false, "description": "Test:", "layout": 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gpl-3.0
zipeiyang/liupengyuan.github.io
chapter2/homework/computer/4-5/201611680595_ex1.ipynb
27
1603
{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "please enter an integer,end with a tab: 5\n", "please enter an integer,end with a tab: 10\n", "the number of the integer,end with a tab: 2\n", "4.795831523312719\n" ] } ], "source": [ "#练习 1:写函数,求n个随机整数均值的平方根,整数范围在m与k之间。\n", "#math.sqrt(x)\n", "#Return the square root of x.\n", "\n", "import random ,math\n", "\n", "\n", "m = int(input('please enter an integer,end with a tab: '))\n", "k = int(input('please enter an integer,end with a tab: '))\n", "\n", "if m>=k:\n", " temp = m\n", " m = k\n", " k = temp\n", "\n", "i = 0\n", "a = 0\n", "n = int(input('the number of the integer,end with a tab: '))\n", "while i <= n:\n", " i+=1\n", " a = random.randint(m,k) + a\n", "\n", " \n", "r = math.sqrt(a)\n", "print(r)\n", "\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
bearing/radwatch-analysis
PotteryComparison.ipynb
1
13044
{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SpeFile: Reading file /Users/jackiegasca/Documents/spectras/Pottery_15h_B_potteryatlid.Spe\n", "Unknown line: $PRESETS:\n", "Unknown line: None\n", "Unknown line: 0\n", "Unknown line: 0\n", "SpeFile: Reading file /Users/jackiegasca/Documents/2017.5.1_long_background.Spe\n", "Unknown line: $PRESETS:\n", "Unknown line: None\n", "Unknown line: 0\n", "Unknown line: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/jackiegasca/anaconda3/envs/py34/lib/python3.4/site-packages/ipykernel/__main__.py:96: RuntimeWarning: invalid value encountered in double_scalars\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1.1826785791133839e-06 +/- 1.4093223728557953e-08 g of Sb-121\n", "4.820075944320234e-06 +/- 5.707769859023024e-08 g of As-75\n", "3.511397448665832e-05 +/- 6.509775766057162e-08 g of Ba-130\n", "4.1271834075279435e-06 +/- 4.8872701028510736e-08 g of Br-81\n", "5.860272886917646e-06 +/- 5.9459320891142015e-08 g of Ce-140\n", "740472633.2896758 +/- 50218014.70524973 g of Dy-164\n", "0.00010717569559193055 +/- 1.2490381507808173e-06 g of Cs-133\n", "0.00037883205811526315 +/- 1.238895301506406e-06 g of Co-59\n", "9.388196121855049e-08 +/- 1.0708038425628731e-09 g of Au-197\n", "2.6284626784315826e-06 +/- 5.005835449203062e-08 g of Fe-58\n", "3.953489181216081e-05 +/- 1.2301823551786084e-05 g of Cu-63\n", "1.0850992408612959e-05 +/- 2.9835171322954745e-07 g of Ga-71\n", "6.382551023813956e-06 +/- 4.2516037381761234e-08 g of La-139\n", "1.8476645768278707e-06 +/- 9.699174932998465e-08 g of Rb-85\n", "6.514593635935671e-06 +/- 2.5374989119871398e-08 g of Sc-45\n", "9.755303846360073e-05 +/- 7.675047762061239e-07 g of Na-23\n", "7.907338243272127e-07 +/- 2.1878001174071196e-08 g of Sr-84\n", "3.035518697594427e-07 +/- 7.4059478961744735e-09 g of Hf-180\n" ] } ], "source": [ "import numpy as np\n", "from becquerel.tools.isotope import Isotope\n", "from becquerel.tools.isotope_qty import IsotopeQuantity, NeutronIrradiation\n", "import datetime\n", "from becquerel import Spectrum\n", "import efficiencies as ef\n", "from bs4 import BeautifulSoup\n", "import urllib.request\n", "import math\n", "import NAA_Isotopes as na\n", "from uncertainties import ufloat\n", "\n", "\n", "\n", "#load a spectra for testing\n", "spec_S1 = Spectrum.from_file('/Users/jackiegasca/Documents/spectras/Pottery_15h_B_potteryatlid.Spe')\n", "\n", "back_spec = Spectrum.from_file('/Users/jackiegasca/Documents/2017.5.1_long_background.Spe')\n", "\n", "\n", "#For now, just use an existing dictionary/class of isotopes\n", "spec_S1_ener_spec = spec_S1.energies_kev[0:len(spec_S1)]\n", "back_ener_spec = back_spec.energies_kev[0:len(back_spec)]\n", "\n", "#Isotopes not included b/c they're not in the NAA_Isotopes script: Al-28, Carbon\n", "#Cl-38, hydrogen, Lu-177, Mg-27, Mn-56, Ni-58?\n", "#Sm-153, Ta-182\n", "#Can't implement b/c initially radioactive: Ca-27, Cr-51, Eu_152, Zn-65\n", "isotope_list = [na.Sb_122, na.As_76, na.Ba_131, na.Br_82, na.Ce_141, na.Dy_165,\n", " na.Cs_134, na.Co_60, na.Au_198, na.Fe_59, na.Cu_64, na.Ga_72,\n", " na.La_140, na.K_42, na.Rb_86, na.Sc_46, na.Na_24, na.Sr_85,\n", " na.Hf_181]\n", "#Info regarding the irradiation:\n", "irr_start = '2017-04-27 14:02:00'\n", "irr_stop = '2017-04-27 14:12:00'\n", "flux = 3.1e11\n", "N_0 = 6.02e23\n", "\n", "def IsotopeActivity():\n", " iso_name = []\n", " iso_energy = []\n", " iso_cps = []\n", " iso_br = []\n", " for iso in isotope_list:\n", "\n", " E = iso.energies['energy'][0]\n", " FWHM = ((2.355 * (0.09 * 0.00296 * E) ** 0.5) ** 2\n", " + (1.3) ** 2) ** 0.5 #keV\n", " start = E - 1 * FWHM\n", " end = E + 1 * FWHM\n", " bkgd_start = E - 2 * FWHM\n", " bkgd_end = E + 2 * FWHM\n", "\n", " en = (np.abs(spec_S1_ener_spec - E)).argmin()\n", " val1 = (np.abs(spec_S1_ener_spec - start)).argmin()\n", " val2 = (np.abs(spec_S1_ener_spec - end)).argmin()\n", " val3 = (np.abs(spec_S1_ener_spec - bkgd_start)).argmin()\n", " val4 = (np.abs(spec_S1_ener_spec - bkgd_end)).argmin()\n", "\n", " cps_values = spec_S1.cps_vals[val1:val2]\n", " max_cps_index = np.argmax(cps_values)\n", " ex_val = max_cps_index - (en - val1)\n", " val1 = val1 + ex_val\n", " val2 = val2 + ex_val\n", " val3 = val3 + ex_val\n", " val4 = val4 + ex_val\n", "\n", " peak_vals = spec_S1.cps_vals[val1:val2]\n", " back_vals = back_spec.cps_vals[val1:val2]\n", " peak_vals_sub = [a - b for a, b in zip(peak_vals, back_vals)]\n", "\n", " bkgd_vals1 = spec_S1.cps_vals[val3:val1 - 1]\n", " back_bkgd_vals1 = back_spec.cps_vals[val3:val1 - 1]\n", "\n", " bkgd_vals2 = spec_S1.cps_vals[val2 + 1:val4]\n", " back_bkgd_vals2 = back_spec.cps_vals[val2+1:val4]\n", "\n", " back_sub1 = [a - b for a, b in zip(bkgd_vals1, back_bkgd_vals1)]\n", " back_sub2 = [a - b for a, b in zip(bkgd_vals2, back_bkgd_vals2)]\n", "\n", " bkgd_cps = (sum(back_sub1) + sum(back_sub2)) / (len(back_sub1)\n", " + len(back_sub2))\n", " peak_vals[:] = [x - bkgd_cps for x in peak_vals_sub]\n", " peak_cps = sum(peak_vals)\n", "\n", " name = '{0}_{1}'.format(iso.Symbol, iso.A + 1)\n", " iso_name.append(name)\n", " iso_energy.append(E)\n", " iso_cps.append(peak_cps)\n", " iso_br.append(iso.energies['branching_ratio'][0])\n", " #return(iso_name, iso_energy, iso_cps, iso_br)\n", " isotope_activities = []\n", " stat_uncertainties = []\n", " for j in range(len(iso_name)):\n", " ef_en = (np.abs(ef.x - iso_energy[j])).argmin()\n", " stat_unc = (((iso_cps[j]) ** 0.5) / (spec_S1.livetime ** 0.5)) / (ef.low[ef_en] * iso_br[j])\n", " activ = iso_cps[j] / (ef.low[ef_en] * iso_br[j])\n", " isotope_activities.append(activ)\n", " stat_uncertainties.append(stat_unc)\n", " return(iso_name, iso_energy, iso_cps, iso_br, isotope_activities, stat_uncertainties)\n", " for n in range(len(isotope_activities)):\n", " #statement = 'The activity of ' + '{0}'.format(iso_name[n]) + ' at ' + '{0}'.format(spec_S1.start_time) + ' is ' + '{0}'.format(iso_cps[n])\n", " statement = 'The activity of {0} at {1} is {2} bq'.format(iso_name[n], spec_S1.start_time, iso_cps[n])\n", " print(statement)\n", "\n", "\n", "lists = IsotopeActivity()\n", "iso_cps = lists[2]\n", "iso_name = lists[0]\n", "iso_energy = lists[1]\n", "iso_br = lists[3]\n", "isotope_activities = lists[4]\n", "stat_uncertainties = lists[5]\n", "\n", "#Remove any negative activity from the queue\n", "def Remover():\n", " for i in iso_cps:\n", " if i <= 0:\n", " n = iso_cps.index(i)\n", " iso_name.remove(iso_name[n])\n", " iso_energy.remove(iso_energy[n])\n", " iso_br.remove(iso_br[n])\n", " isotope_activities.remove(isotope_activities[n])\n", " iso_cps.remove(iso_cps[n])\n", " stat_uncertainties.remove(stat_uncertainties[n])\n", " else:\n", " pass\n", " return(iso_name, iso_cps, iso_energy, iso_br, isotope_activities, stat_uncertainties)\n", "\n", "lists = Remover()\n", "lists = Remover()\n", "lists = Remover()\n", "\n", "def Concentration():\n", " conc = []\n", " for i in range(len(iso_name)):\n", " c = iso_name[i].split('_')\n", " abb = c[0]\n", " A = int(c[1])\n", " A_0 = A - 1\n", " iso_2 = '{0}-{1}'.format(abb, A_0)\n", " iso_1 = '{0}-{1}'.format(abb, A)\n", " nuclide = Isotope(iso_1)\n", " def urlcreator(abb, A_0):\n", " A_num = str(A_0)\n", " if len(A_num) == 1:\n", " A_num = '00' + A_num\n", " elif len(A_num) == 2:\n", " A_num = '0' + A_num\n", " else:\n", " A_num = A_num\n", " url = 'http://wwwndc.jaea.go.jp/cgi-bin/Tab80WWW.cgi?/data' \\\n", " + '/JENDL/JENDL-4-prc/intern/' + abb + A_num + '.intern'\n", " html = urllib.request.urlopen(url)\n", " bslink = BeautifulSoup(html, 'lxml')\n", "\n", " return(bslink)\n", "\n", " bslink = urlcreator(abb, A_0)\n", " def tabledata(bslink):\n", " '''extracts data from the jaea website'''\n", "\n", " table = bslink.table\n", " table_rows = table.find_all('tr')\n", " for tr in table_rows:\n", " td = tr.find_all('td')\n", " row = [i.text for i in td]\n", "\n", " if len(row) == 7:\n", " if row[0] == 'total ':\n", " x_sec = row[1]\n", " x_sec_s = x_sec.split(' ')\n", " x_val = float(x_sec_s[0])\n", " barn = x_sec_s[1]\n", " if barn[1] == 'k':\n", " x_val = 10**(3) * x_val\n", " return(x_val)\n", " elif barn[1] == 'm':\n", " x_val = 10**(-3) * x_val\n", " return(x_val)\n", "\n", " elif barn[1] == '&':\n", " x_val = 10**(-6) * x_val\n", " return(x_val)\n", " else:\n", " x_val = x_val\n", " return(x_val)\n", "\n", " else:\n", " pass\n", "\n", " else:\n", " pass\n", " return(x_val)\n", "\n", " x_val = tabledata(bslink)\n", " #print(x_val)\n", "\n", " quantity = IsotopeQuantity(nuclide, date=spec_S1.start_time, bq=isotope_activities[i])\n", " unc_quantity = IsotopeQuantity(nuclide, date=spec_S1.start_time, bq=stat_uncertainties[i])\n", " irrad_quan = quantity.bq_at(irr_stop)\n", " unc_irrad_quan = unc_quantity.bq_at(irr_stop)\n", " irrad_act = IsotopeQuantity(nuclide, date=irr_stop, bq=irrad_quan)\n", " unc_irrad_act = IsotopeQuantity(nuclide, date=irr_stop, bq=unc_irrad_quan)\n", " ni = NeutronIrradiation(irr_start, irr_stop, n_cm2_s=flux)\n", " init_comp = ni.activate(x_val, initial=Isotope(iso_2), activated=irrad_act)\n", " unc_init_comp = ni.activate(x_val, initial=Isotope(iso_2), activated=unc_irrad_act)\n", " init_comp.is_stable = True\n", " unc_init_comp.is_stable = True\n", " sinit_comp = str(init_comp)\n", " sunc_init_comp = str(unc_init_comp)\n", " #print(sinit_comp, sunc_init_comp)\n", " s2 = sinit_comp.split(' ')\n", " s0 = s2[0]\n", " sunc2 = sunc_init_comp.split(' ')\n", " sunc0 = sunc2[0]\n", " isotope_type = ' ' + s2[1] + ' ' + s2[2] + ' ' + s2[3]\n", " print(s0+' +/- '+sunc0 +isotope_type)\n", "\n", " #conc.extend[str(init_comp)]\n", " #return(conc)\n", "\n", "Concentration()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
paultheastronomer/OAD-Data-Science-Toolkit
Teaching Materials/Machine Learning/Supervised Learning/Courses/Astrophysical Machine Learning/Part 1/Exercise 1.ipynb
2
6436
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 1\n", "\n", "The purpose of this exercise is to familiarize yourself with the Python programing language.\n", "\n", "1: INSTALL – The first step is to install Python on your computer. If you’re using a mac, it’s already on there. You can just open a terminal (go to Applications/Utilities to find it) and type python and it should open an interactive python session. If you’re not using a mac, you’ll need to go here and follow the instructions for downloading. I’m using the 2.7 version of python, which should be good for our needs.\n", "\n", "2: WARM UP – If you’re new to python (or programing), then I suggest you start here to get some background on what python is capable of. The official python page has its own tutorial as well, but it’s pretty long with lots of text. Don’t worry about learning everything now, just explore.\n", "\n", "3: NUMPY – This is where you start to learn the core of python’s mathematical routines. Numpy is the main package for scientific computing, and is the building block of just about everything we’ll do in this course. Go through a bit of the quick-start tutorial to get used to the indexing of the arrays (vectors, matrices, tensors). Here is a short reference guide to linear algebra, which will help because much of machine-learning (and most of scientific mathematics!) is linear algebra. Let me know if you need me to help out.\n", "\n", "4: ASSIGNMENT Part 1 – Write a python script and save it in a text file called firstname_lastname_softmax.py (substituting your names of course). The script should contain a function at the top called $softmax()$ that takes as an input a 1-dimensional array (vector) of any length and computes the function\n", "\n", "$$softmax(x^i)= \\frac{e^{x^i}}{\\Sigma_{j} e^{x^j}}$$\n", "\n", "where $x^i$ is the value of the i’th component of the array. This turns the elements of an array into a kind of probability, where the values of the elements sum to 1. Right after your function in the script, check that it works by trying it out on an array. The script should look something like:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is only psudo code.\n", "You will have to write this function yourself!\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Define your function\n", "def softmax(x):\n", " # This is where you write your code!\n", " vector = \"This is only psudo code.\\nYou will have to write this function yourself!\"\n", " return vector # Replace this with the new array\n", "\n", "# Test it out on an array\n", "test=[1,3,2]\n", "print(softmax(test))\n", "# The result should be [0.09003057 0.66524096 0.24472847]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5: ASSIGNMENT Part 2 – Now that you’ve got that down, modify the same script so that your function can take any two dimensional matrix and compute the $softmax()$ of every column (each column is its own unique independent array that your taking the $softmax()$ of). The solution is simple in numpy, and it has to do with how you take the sum in the denominator. Test it out on a sample array. It should look like:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2. 2. 2. 2.]\n", " [ 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1.]]\n", "This is only psudo code.\n", "You will have to write this function yourself!\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Define your function\n", "def softmax(x):\n", " # This is where you write your code!\n", " vector = \"This is only psudo code.\\nYou will have to write this function yourself!\"\n", " return vector # Replace this with the new array\n", "\n", "# Test it out on an array\n", "test=np.ones((3,4))\n", "test[0,:]=2.\n", "print(test)\n", "print(softmax(test))\n", "# The result should be [[ 2. 2. 2. 2.]\n", "# [ 1. 1. 1. 1.]\n", "# [ 1. 1. 1. 1.]]\n", "# [[ 0.57611688 0.57611688 0.57611688 0.57611688]\n", "# [ 0.21194156 0.21194156 0.21194156 0.21194156]\n", "# [ 0.21194156 0.21194156 0.21194156 0.21194156]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6: ASSIGNMENT Part 3 – Now you are going to write another function called linearClassifier(). This function will take three inputs, $x$, $W$, and $b$. The input $x$ is a one-dimensional array which we will call our feature vector, $W$ is a two-dimensional array of weights, and $b$ is a one-dimensional bias array. These names may sound strange now, but they’ll come back later. The function should take these values and return the value of $softmax(Wx+b)$. It’s important that $b$ is the same size as the number of rows in $W$, and $x$ must be the same size as the number of columns in $W$. The $softmax()$ will then turn the result into an array of probabilities the same size as $b$.\n", "\n", "After you write this function, make up your own values for $x$, $W$ and $b$ and test out the function. To get full points, $W$ can’t be a square matrix (e.g., try a 3×4 matrix).\n", "\n", "ADVANCED (optional): If this is all too easy and you’re bored, write a class for the linearClassifier(), where the above code is one of the methods (like .evaluate() or something), and where the weights $W$ and bias $b$ are stored as attributes that can be called and updated by appropriate methods." ] } ], "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": 2 }
gpl-3.0
albahnsen/PracticalMachineLearningClass
notebooks/04-logistic_regression.ipynb
1
225269
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 04 - Logistic Regression\n", "\n", "by [Alejandro Correa Bahnsen](albahnsen.com/) and [Jesus Solano](https://github.com/jesugome)\n", "\n", "version 1.5, January 2019\n", "\n", "## Part of the class [Practical Machine Learning](https://github.com/albahnsen/PracticalMachineLearningClass)\n", "\n", "\n", "\n", "This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Rick Muller](http://www.cs.sandia.gov/~rmuller/), Sandia National Laboratories(https://github.com/justmarkham)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Review: Predicting a Continuous Response" ] }, { "cell_type": "code", "execution_count": 1, "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>ri</th>\n", " <th>na</th>\n", " <th>mg</th>\n", " <th>al</th>\n", " <th>si</th>\n", " <th>k</th>\n", " <th>ca</th>\n", " <th>ba</th>\n", " <th>fe</th>\n", " <th>glass_type</th>\n", " </tr>\n", " <tr>\n", " <th>id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>22</th>\n", " <td>1.51966</td>\n", " <td>14.77</td>\n", " <td>3.75</td>\n", " <td>0.29</td>\n", " <td>72.02</td>\n", " <td>0.03</td>\n", " <td>9.00</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>185</th>\n", " <td>1.51115</td>\n", " <td>17.38</td>\n", " <td>0.00</td>\n", " <td>0.34</td>\n", " <td>75.41</td>\n", " <td>0.00</td>\n", " <td>6.65</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>1.52320</td>\n", " <td>13.72</td>\n", " <td>3.72</td>\n", " <td>0.51</td>\n", " <td>71.75</td>\n", " <td>0.09</td>\n", " <td>10.06</td>\n", " <td>0.0</td>\n", " <td>0.16</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ri na mg al si k ca ba fe glass_type\n", "id \n", "22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1\n", "185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6\n", "40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1\n", "39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1\n", "51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# glass identification dataset\n", "import pandas as pd\n", "import numpy as np\n", "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data'\n", "col_names = ['id','ri','na','mg','al','si','k','ca','ba','fe','glass_type']\n", "glass = pd.read_csv(url, names=col_names, index_col='id')\n", "glass.sort_values('al', inplace=True)\n", "glass.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question:** Pretend that we want to predict **ri**, and our only feature is **al**. How could we do it using machine learning?\n", "\n", "**Answer:** We could frame it as a regression problem, and use a linear regression model with **al** as the only feature and **ri** as the response.\n", "\n", "**Question:** How would we **visualize** this model?\n", "\n", "**Answer:** Create a scatter plot with **al** on the x-axis and **ri** on the y-axis, and draw the line of best fit." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('bmh')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b9b97abe0>" ] }, "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": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# scatter plot using Pandas\n", "glass.plot(kind='scatter', x='al', y='ri')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'ri')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# equivalent scatter plot using Matplotlib\n", "plt.scatter(glass.al, glass.ri)\n", "plt.xlabel('al')\n", "plt.ylabel('ri')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fit a linear regression model\n", "from sklearn.linear_model import LinearRegression\n", "linreg = LinearRegression()\n", "feature_cols = ['al']\n", "X = glass[feature_cols]\n", "y = glass.ri\n", "linreg.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 6, "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>ri</th>\n", " <th>na</th>\n", " <th>mg</th>\n", " <th>al</th>\n", " <th>si</th>\n", " <th>k</th>\n", " <th>ca</th>\n", " <th>ba</th>\n", " <th>fe</th>\n", " <th>glass_type</th>\n", " <th>ri_pred</th>\n", " </tr>\n", " <tr>\n", " <th>id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>22</th>\n", " <td>1.51966</td>\n", " <td>14.77</td>\n", " <td>3.75</td>\n", " <td>0.29</td>\n", " <td>72.02</td>\n", " <td>0.03</td>\n", " <td>9.00</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.521227</td>\n", " </tr>\n", " <tr>\n", " <th>185</th>\n", " <td>1.51115</td>\n", " <td>17.38</td>\n", " <td>0.00</td>\n", " <td>0.34</td>\n", " <td>75.41</td>\n", " <td>0.00</td>\n", " <td>6.65</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>6</td>\n", " <td>1.521103</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.520781</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.520781</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>1.52320</td>\n", " <td>13.72</td>\n", " <td>3.72</td>\n", " <td>0.51</td>\n", " <td>71.75</td>\n", " <td>0.09</td>\n", " <td>10.06</td>\n", " <td>0.0</td>\n", " <td>0.16</td>\n", " <td>1</td>\n", " <td>1.520682</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ri na mg al si k ca ba fe glass_type \\\n", "id \n", "22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1 \n", "185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6 \n", "40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \n", "39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \n", "51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1 \n", "\n", " ri_pred \n", "id \n", "22 1.521227 \n", "185 1.521103 \n", "40 1.520781 \n", "39 1.520781 \n", "51 1.520682 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# make predictions for all values of X\n", "glass['ri_pred'] = linreg.predict(X)\n", "glass.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'ri')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# put the plots together\n", "plt.scatter(glass.al, glass.ri)\n", "plt.plot(glass.al, glass.ri_pred, color='red')\n", "plt.xlabel('al')\n", "plt.ylabel('ri')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Refresher: interpreting linear regression coefficients" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Linear regression equation: $y = \\beta_0 + \\beta_1x$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.51699012])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute prediction for al=2 using the equation\n", "linreg.intercept_ + linreg.coef_ * 2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.51699012])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute prediction for al=2 using the predict method\n", "test = np.array(2)\n", "test = test.reshape(-1,1)\n", "linreg.predict(test)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['al'] [-0.00247761]\n" ] } ], "source": [ "# examine coefficient for al\n", "print(feature_cols, linreg.coef_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Interpretation:** A 1 unit increase in 'al' is associated with a 0.0025 unit decrease in 'ri'." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.5145125136125304" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# increasing al by 1 (so that al=3) decreases ri by 0.0025\n", "1.51699012 - 0.0024776063874696243" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.51451251])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute prediction for al=3 using the predict method\n", "test = np.array(3)\n", "test = test.reshape(-1,1)\n", "linreg.predict(test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting a Categorical Response" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 70\n", "2 76\n", "3 17\n", "5 13\n", "6 9\n", "7 29\n", "Name: glass_type, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# examine glass_type\n", "glass.glass_type.value_counts().sort_index()" ] }, { "cell_type": "code", "execution_count": 14, "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>ri</th>\n", " <th>na</th>\n", " <th>mg</th>\n", " <th>al</th>\n", " <th>si</th>\n", " <th>k</th>\n", " <th>ca</th>\n", " <th>ba</th>\n", " <th>fe</th>\n", " <th>glass_type</th>\n", " <th>ri_pred</th>\n", " <th>household</th>\n", " </tr>\n", " <tr>\n", " <th>id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>22</th>\n", " <td>1.51966</td>\n", " <td>14.77</td>\n", " <td>3.75</td>\n", " <td>0.29</td>\n", " <td>72.02</td>\n", " <td>0.03</td>\n", " <td>9.00</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.521227</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>185</th>\n", " <td>1.51115</td>\n", " <td>17.38</td>\n", " <td>0.00</td>\n", " <td>0.34</td>\n", " <td>75.41</td>\n", " <td>0.00</td>\n", " <td>6.65</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>6</td>\n", " <td>1.521103</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.520781</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.520781</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>1.52320</td>\n", " <td>13.72</td>\n", " <td>3.72</td>\n", " <td>0.51</td>\n", " <td>71.75</td>\n", " <td>0.09</td>\n", " <td>10.06</td>\n", " <td>0.0</td>\n", " <td>0.16</td>\n", " <td>1</td>\n", " <td>1.520682</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ri na mg al si k ca ba fe glass_type \\\n", "id \n", "22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1 \n", "185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6 \n", "40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \n", "39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \n", "51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1 \n", "\n", " ri_pred household \n", "id \n", "22 1.521227 0 \n", "185 1.521103 1 \n", "40 1.520781 0 \n", "39 1.520781 0 \n", "51 1.520682 0 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# types 1, 2, 3 are window glass\n", "# types 5, 6, 7 are household glass\n", "glass['household'] = glass.glass_type.map({1:0, 2:0, 3:0, 5:1, 6:1, 7:1})\n", "glass.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's change our task, so that we're predicting **household** using **al**. Let's visualize the relationship to figure out how to do this:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'household')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(glass.al, glass.household)\n", "plt.xlabel('al')\n", "plt.ylabel('household')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's draw a **regression line**, like we did before:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# fit a linear regression model and store the predictions\n", "feature_cols = ['al']\n", "X = glass[feature_cols]\n", "y = glass.household\n", "linreg.fit(X, y)\n", "glass['household_pred'] = linreg.predict(X)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'household')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# scatter plot that includes the regression line\n", "plt.scatter(glass.al, glass.household)\n", "plt.plot(glass.al, glass.household_pred, color='red')\n", "plt.xlabel('al')\n", "plt.ylabel('household')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If **al=3**, what class do we predict for household? **1**\n", "\n", "If **al=1.5**, what class do we predict for household? **0**\n", "\n", "We predict the 0 class for **lower** values of al, and the 1 class for **higher** values of al. What's our cutoff value? Around **al=2**, because that's where the linear regression line crosses the midpoint between predicting class 0 and class 1.\n", "\n", "Therefore, we'll say that if **household_pred >= 0.5**, we predict a class of **1**, else we predict a class of **0**.\n", "\n", "## $$h_\\beta(x) = \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + ... + \\beta_nx_n$$\n", "\n", "- $h_\\beta(x)$ is the response\n", "- $\\beta_0$ is the intercept\n", "- $\\beta_1$ is the coefficient for $x_1$ (the first feature)\n", "- $\\beta_n$ is the coefficient for $x_n$ (the nth feature)\n", "\n", "### if $h_\\beta(x)\\le 0.5$ then $\\hat y = 0$ \n", "\n", "### if $h_\\beta(x)> 0.5$ then $\\hat y = 1$ " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['small', 'big', 'small'], dtype='<U5')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# understanding np.where\n", "import numpy as np\n", "nums = np.array([5, 15, 8])\n", "\n", "# np.where returns the first value if the condition is True, and the second value if the condition is False\n", "np.where(nums > 10, 'big', 'small')" ] }, { "cell_type": "code", "execution_count": 19, "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>ri</th>\n", " <th>na</th>\n", " <th>mg</th>\n", " <th>al</th>\n", " <th>si</th>\n", " <th>k</th>\n", " <th>ca</th>\n", " <th>ba</th>\n", " <th>fe</th>\n", " <th>glass_type</th>\n", " <th>ri_pred</th>\n", " <th>household</th>\n", " <th>household_pred</th>\n", " <th>household_pred_class</th>\n", " </tr>\n", " <tr>\n", " <th>id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>22</th>\n", " <td>1.51966</td>\n", " <td>14.77</td>\n", " <td>3.75</td>\n", " <td>0.29</td>\n", " <td>72.02</td>\n", " <td>0.03</td>\n", " <td>9.00</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.521227</td>\n", " <td>0</td>\n", " <td>-0.340495</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>185</th>\n", " <td>1.51115</td>\n", " <td>17.38</td>\n", " <td>0.00</td>\n", " <td>0.34</td>\n", " <td>75.41</td>\n", " <td>0.00</td>\n", " <td>6.65</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>6</td>\n", " <td>1.521103</td>\n", " <td>1</td>\n", " <td>-0.315436</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.520781</td>\n", " <td>0</td>\n", " <td>-0.250283</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>1.52213</td>\n", " <td>14.21</td>\n", " <td>3.82</td>\n", " <td>0.47</td>\n", " <td>71.77</td>\n", " <td>0.11</td>\n", " <td>9.57</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>1.520781</td>\n", " <td>0</td>\n", " <td>-0.250283</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>1.52320</td>\n", " <td>13.72</td>\n", " <td>3.72</td>\n", " <td>0.51</td>\n", " <td>71.75</td>\n", " <td>0.09</td>\n", " <td>10.06</td>\n", " <td>0.0</td>\n", " <td>0.16</td>\n", " <td>1</td>\n", " <td>1.520682</td>\n", " <td>0</td>\n", " <td>-0.230236</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ri na mg al si k ca ba fe glass_type \\\n", "id \n", "22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1 \n", "185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6 \n", "40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \n", "39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \n", "51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1 \n", "\n", " ri_pred household household_pred household_pred_class \n", "id \n", "22 1.521227 0 -0.340495 0 \n", "185 1.521103 1 -0.315436 0 \n", "40 1.520781 0 -0.250283 0 \n", "39 1.520781 0 -0.250283 0 \n", "51 1.520682 0 -0.230236 0 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# transform household_pred to 1 or 0\n", "glass['household_pred_class'] = np.where(glass.household_pred >= 0.5, 1, 0)\n", "glass.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'household')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the class predictions\n", "plt.scatter(glass.al, glass.household)\n", "plt.plot(glass.al, glass.household_pred_class, color='red')\n", "plt.xlabel('al')\n", "plt.ylabel('household')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$h_\\beta(x)$ can be lower 0 or higher than 1, which is countra intuitive\n", "\n", "## Using Logistic Regression Instead\n", "\n", "Logistic regression can do what we just did:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# fit a logistic regression model and store the class predictions\n", "from sklearn.linear_model import LogisticRegression\n", "logreg = LogisticRegression(solver='liblinear',C=1e9)\n", "feature_cols = ['al']\n", "X = glass[feature_cols]\n", "y = glass.household\n", "logreg.fit(X, y)\n", "glass['household_pred_class'] = logreg.predict(X)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'household')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the class predictions\n", "plt.scatter(glass.al, glass.household)\n", "plt.plot(glass.al, glass.household_pred_class, color='red')\n", "plt.xlabel('al')\n", "plt.ylabel('household')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What if we wanted the **predicted probabilities** instead of just the **class predictions**, to understand how confident we are in a given prediction?" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# store the predicted probabilites of class 1\n", "glass['household_pred_prob'] = logreg.predict_proba(X)[:, 1]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'household')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the predicted probabilities\n", "plt.scatter(glass.al, glass.household)\n", "plt.plot(glass.al, glass.household_pred_prob, color='red')\n", "plt.xlabel('al')\n", "plt.ylabel('household')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.97161726 0.02838274]]\n", "[[0.34361555 0.65638445]]\n", "[[0.00794192 0.99205808]]\n" ] } ], "source": [ "# examine some example predictions\n", "print(logreg.predict_proba([[1]]))\n", "print(logreg.predict_proba([[2]]))\n", "print(logreg.predict_proba([[3]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first column indicates the predicted probability of **class 0**, and the second column indicates the predicted probability of **class 1**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probability, odds, e, log, log-odds\n", "\n", "$$probability = \\frac {one\\ outcome} {all\\ outcomes}$$\n", "\n", "$$odds = \\frac {one\\ outcome} {all\\ other\\ outcomes}$$\n", "\n", "Examples:\n", "\n", "- Dice roll of 1: probability = 1/6, odds = 1/5\n", "- Even dice roll: probability = 3/6, odds = 3/3 = 1\n", "- Dice roll less than 5: probability = 4/6, odds = 4/2 = 2\n", "\n", "$$odds = \\frac {probability} {1 - probability}$$\n", "\n", "$$probability = \\frac {odds} {1 + odds}$$" ] }, { "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>probability</th>\n", " <th>odds</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.10</td>\n", " <td>0.111111</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.20</td>\n", " <td>0.250000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.25</td>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.50</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.60</td>\n", " <td>1.500000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.80</td>\n", " <td>4.000000</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0.90</td>\n", " <td>9.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " probability odds\n", "0 0.10 0.111111\n", "1 0.20 0.250000\n", "2 0.25 0.333333\n", "3 0.50 1.000000\n", "4 0.60 1.500000\n", "5 0.80 4.000000\n", "6 0.90 9.000000" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create a table of probability versus odds\n", "table = pd.DataFrame({'probability':[0.1, 0.2, 0.25, 0.5, 0.6, 0.8, 0.9]})\n", "table['odds'] = table.probability/(1 - table.probability)\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is **e**? It is the base rate of growth shared by all continually growing processes:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.718281828459045" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# exponential function: e^1\n", "np.exp(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is a **(natural) log**? It gives you the time needed to reach a certain level of growth:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.999896315728952" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# time needed to grow 1 unit to 2.718 units\n", "np.log(2.718)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also the **inverse** of the exponential function:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.log(np.exp(5))" ] }, { "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>probability</th>\n", " <th>odds</th>\n", " <th>logodds</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.10</td>\n", " <td>0.111111</td>\n", " <td>-2.197225</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.20</td>\n", " <td>0.250000</td>\n", " <td>-1.386294</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.25</td>\n", " <td>0.333333</td>\n", " <td>-1.098612</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.50</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.60</td>\n", " <td>1.500000</td>\n", " <td>0.405465</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.80</td>\n", " <td>4.000000</td>\n", " <td>1.386294</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0.90</td>\n", " <td>9.000000</td>\n", " <td>2.197225</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " probability odds logodds\n", "0 0.10 0.111111 -2.197225\n", "1 0.20 0.250000 -1.386294\n", "2 0.25 0.333333 -1.098612\n", "3 0.50 1.000000 0.000000\n", "4 0.60 1.500000 0.405465\n", "5 0.80 4.000000 1.386294\n", "6 0.90 9.000000 2.197225" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add log-odds to the table\n", "table['logodds'] = np.log(table.odds)\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is Logistic Regression?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Linear regression:** continuous response is modeled as a linear combination of the features:\n", "\n", "$$y = \\beta_0 + \\beta_1x$$\n", "\n", "**Logistic regression:** log-odds of a categorical response being \"true\" (1) is modeled as a linear combination of the features:\n", "\n", "$$\\log \\left({p\\over 1-p}\\right) = \\beta_0 + \\beta_1x$$\n", "\n", "This is called the **logit function**.\n", "\n", "Probability is sometimes written as pi:\n", "\n", "$$\\log \\left({\\pi\\over 1-\\pi}\\right) = \\beta_0 + \\beta_1x$$\n", "\n", "The equation can be rearranged into the **logistic function**:\n", "\n", "$$\\pi = \\frac{e^{\\beta_0 + \\beta_1x}} {1 + e^{\\beta_0 + \\beta_1x}}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In other words:\n", "\n", "- Logistic regression outputs the **probabilities of a specific class**\n", "- Those probabilities can be converted into **class predictions**\n", "\n", "The **logistic function** has some nice properties:\n", "\n", "- Takes on an \"s\" shape\n", "- Output is bounded by 0 and 1\n", "\n", "We have covered how this works for **binary classification problems** (two response classes). But what about **multi-class classification problems** (more than two response classes)?\n", "\n", "- Most common solution for classification models is **\"one-vs-all\"** (also known as **\"one-vs-rest\"**): decompose the problem into multiple binary classification problems\n", "- **Multinomial logistic regression** can solve this as a single problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 6: Interpreting Logistic Regression Coefficients" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'household')" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the predicted probabilities again\n", "plt.scatter(glass.al, glass.household)\n", "plt.plot(glass.al, glass.household_pred_prob, color='red')\n", "plt.xlabel('al')\n", "plt.ylabel('household')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.64722323])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute predicted log-odds for al=2 using the equation\n", "logodds = logreg.intercept_ + logreg.coef_[0] * 2\n", "logodds" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.91022919])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert log-odds to odds\n", "odds = np.exp(logodds)\n", "odds" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.65638445])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert odds to probability\n", "prob = odds/(1 + odds)\n", "prob" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.65638445])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute predicted probability for al=2 using the predict_proba method\n", "logreg.predict_proba([[2]])[:, 1]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(['al'], array([4.18040386]))" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# examine the coefficient for al\n", "feature_cols, logreg.coef_[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Interpretation:** A 1 unit increase in 'al' is associated with a 4.18 unit increase in the log-odds of 'household'." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9920580839167457" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# increasing al by 1 (so that al=3) increases the log-odds by 4.18\n", "logodds = 0.64722323 + 4.1804038614510901\n", "odds = np.exp(logodds)\n", "prob = odds/(1 + odds)\n", "prob" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.99205808])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compute predicted probability for al=3 using the predict_proba method\n", "logreg.predict_proba([[3]])[:, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Bottom line:** Positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability)." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-7.71358449])" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# examine the intercept\n", "logreg.intercept_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Interpretation:** For an 'al' value of 0, the log-odds of 'household' is -7.71." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.00044652])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert log-odds to probability\n", "logodds = logreg.intercept_\n", "odds = np.exp(logodds)\n", "prob = odds/(1 + odds)\n", "prob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That makes sense from the plot above, because the probability of household=1 should be very low for such a low 'al' value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Logistic regression beta values](https://raw.githubusercontent.com/justmarkham/DAT8/master/notebooks/images/logistic_betas.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the $\\beta_0$ value shifts the curve **horizontally**, whereas changing the $\\beta_1$ value changes the **slope** of the curve." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing Logistic Regression with Other Models\n", "\n", "Advantages of logistic regression:\n", "\n", "- Highly interpretable (if you remember how)\n", "- Model training and prediction are fast\n", "- No tuning is required (excluding regularization)\n", "- Features don't need scaling\n", "- Can perform well with a small number of observations\n", "- Outputs well-calibrated predicted probabilities\n", "\n", "Disadvantages of logistic regression:\n", "\n", "- Presumes a linear relationship between the features and the log-odds of the response\n", "- Performance is (generally) not competitive with the best supervised learning methods\n", "- Can't automatically learn feature interactions" ] } ], "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" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
Trax-air/ddexreader
example/DDEX reading.ipynb
1
18124
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parsing XML file in /srv/git/ddexreader/fixtures/ern36.xml\n" ] } ], "source": [ "try:\n", " from ddexreader import open_ddex, ddex_to_dict\n", "except ImportError:\n", " # If ddexreader has not been installed, add the parent directory\n", " # to the system path\n", " import sys\n", " sys.path.append('..')\n", " from ddexreader import open_ddex, ddex_to_dict\n", "\n", "import os\n", "\n", "xml_path = os.path.abspath(os.path.join(os.getcwdu(), '..', 'fixtures/ern36.xml'))\n", "\n", "print u'Parsing XML file in {0}'.format(xml_path)\n", "\n", "ddex = open_ddex(xml_path)\n", "ddex_dict = ddex_to_dict(ddex)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<ern36.binding.CTD_ANON object at 0x7f51fc80f3d0>\n", "['BusinessProfileVersionId', 'CatalogTransfer', 'CollectionList', 'CueSheetList', 'DealList', 'Factory', 'IsBackfill', 'LanguageAndScriptCode', 'MessageHeader', 'MessageSchemaVersionId', 'ReleaseList', 'ReleaseProfileVersionId', 'ResourceList', 'UpdateIndicator', 'WorkList', '_Abstract', '_AddElement', '_AlternativeConstructor', '_AttributeMap', '_AttributeWildcard', '_Automaton', '_CTD_ANON__BusinessProfileVersionId', '_CTD_ANON__CatalogTransfer', '_CTD_ANON__CollectionList', '_CTD_ANON__CueSheetList', '_CTD_ANON__DealList', '_CTD_ANON__IsBackfill', '_CTD_ANON__LanguageAndScriptCode', '_CTD_ANON__MessageHeader', '_CTD_ANON__MessageSchemaVersionId', '_CTD_ANON__ReleaseList', '_CTD_ANON__ReleaseProfileVersionId', '_CTD_ANON__ResourceList', '_CTD_ANON__UpdateIndicator', '_CTD_ANON__WorkList', '_CT_ELEMENT_ONLY', '_CT_EMPTY', '_CT_MIXED', '_CT_SIMPLE', '_CompatibleValue', '_ContentTypeTag', '_DynamicCreate', '_DynamicCreate_mixin__AlternativeConstructorAttribute', '_DynamicCreate_mixin__SupersedingClassAttribute', '_ElementBindingDeclForName', '_ElementMap', '_ExpandedName', '_GetValidationConfig', '_HasWildcardElement', '_IsMixed', '_IsSimpleTypeContent', '_IsUrType', '_Locatable_mixin__location', '_Name', '_PerformValidation', '_PreFactory_vx', '_PyXBFactoryKeywords', '_RequireXSIType', '_ReservedSymbols', '_SetAlternativeConstructor', '_SetSupersedingClass', '_SetValidationConfig', '_SupersedingClass', '_TypeBinding_mixin__AttributesFromDOM', '_TypeBinding_mixin__WarnedUnassociatedElement', '_TypeBinding_mixin__checkNilCtor', '_TypeBinding_mixin__constructedWithValue', '_TypeBinding_mixin__element', '_TypeBinding_mixin__getValidationConfig', '_TypeBinding_mixin__namespaceContext', '_TypeBinding_mixin__xsiNil', '_TypeDefinition', '_UseForTag', '_XSDLocation', '__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__httpddex_netxmlern36_CTD_ANON_BusinessProfileVersionId', '__httpddex_netxmlern36_CTD_ANON_CatalogTransfer', '__httpddex_netxmlern36_CTD_ANON_CollectionList', '__httpddex_netxmlern36_CTD_ANON_CueSheetList', '__httpddex_netxmlern36_CTD_ANON_DealList', '__httpddex_netxmlern36_CTD_ANON_IsBackfill', '__httpddex_netxmlern36_CTD_ANON_LanguageAndScriptCode', '__httpddex_netxmlern36_CTD_ANON_MessageHeader', '__httpddex_netxmlern36_CTD_ANON_MessageSchemaVersionId', '__httpddex_netxmlern36_CTD_ANON_ReleaseList', '__httpddex_netxmlern36_CTD_ANON_ReleaseProfileVersionId', '__httpddex_netxmlern36_CTD_ANON_ResourceList', '__httpddex_netxmlern36_CTD_ANON_UpdateIndicator', '__httpddex_netxmlern36_CTD_ANON_WorkList', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_addContent', '_appendWildcardElement', '_automatonConfiguration', '_complexTypeDefinition__NeedWarnOnContent', '_complexTypeDefinition__WarnOnContent', '_complexTypeDefinition__automatonConfiguration', '_complexTypeDefinition__childrenForDOM', '_complexTypeDefinition__content', '_complexTypeDefinition__setContent', '_complexTypeDefinition__wildcardAttributeMap', '_complexTypeDefinition__wildcardElements', '_constructedWithValue', '_description', '_diagnosticName', '_element', '_finalizeContentModel', '_isNil', '_location', '_namespaceContext', '_performValidation', '_postDOMValidate', '_postFactory_vx', '_resetAutomaton', '_resetContent', '_setAttribute', '_setAttributesFromKeywordsAndDOM', '_setDOMFromAttributes', '_setElement', '_setIsNil', '_setLocation', '_setNamespaceContext', '_setValidationConfig', '_setValue', '_substitutesFor', '_symbolSet', '_toDOM_csc', '_validateAttributes', '_validateBinding_vx', '_validatedChildren', '_validationConfig', '_validationConfig_', 'append', 'content', 'extend', 'orderedContent', 'reset', 'toDOM', 'toxml', 'validateBinding', 'value', 'wildcardAttributeMap', 'wildcardElements', 'xsdConstraintsOK']\n" ] } ], "source": [ "# Underlying ddex data structure. Complex, right?\n", "\n", "print ddex\n", "print dir(ddex)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['CollectionList',\n", " 'CatalogTransfer',\n", " 'ReleaseList',\n", " 'UpdateIndicator',\n", " 'ReleaseProfileVersionId',\n", " 'BusinessProfileVersionId',\n", " 'MessageSchemaVersionId',\n", " 'LanguageAndScriptCode',\n", " 'MessageHeader',\n", " 'ResourceList',\n", " 'CueSheetList',\n", " 'WorkList',\n", " 'IsBackfill',\n", " 'DealList']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Level 1 keys\n", "\n", "ddex_dict.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Message version" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'ern/36'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddex_dict['MessageSchemaVersionId']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Message header" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Comment': None,\n", " 'LanguageAndScriptCode': None,\n", " 'MessageAuditTrail': None,\n", " 'MessageControlType': u'TestMessage',\n", " 'MessageCreatedDateTime': dateTime(1, 1, 1, 0, 0),\n", " 'MessageFileName': u'VideoSingle.ERN33.xml',\n", " 'MessageId': u'CBCDCC44-EE59-482b-B3CA-02986E6870EC',\n", " 'MessageRecipient': {'LanguageAndScriptCode': None,\n", " 'PartyId': [{'IsDPID': None, 'IsISNI': None, 'Namespace_': None}],\n", " 'PartyName': None,\n", " 'TradingName': u'Lamson Digital Distribution'},\n", " 'MessageSender': {'LanguageAndScriptCode': None,\n", " 'PartyId': [{'IsDPID': None, 'IsISNI': None, 'Namespace_': None}],\n", " 'PartyName': None,\n", " 'TradingName': u'Iron Crown Music'},\n", " 'MessageThreadId': u'CBCDCC44-EE59-482b-B3CA-02986E6870EC',\n", " 'SentOnBehalfOf': None}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddex_dict['MessageHeader']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Update indicator" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'OriginalMessage'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddex_dict['UpdateIndicator']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Resource list" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Image': [{'CreationDate': None,\n", " 'ImageDetailsByTerritory': [{'CLine': [],\n", " 'CourtesyLine': None,\n", " 'Description': None,\n", " 'ExcludedTerritoryCode': [],\n", " 'FulfillmentDate': None,\n", " 'Genre': [],\n", " 'IndirectResourceContributor': [],\n", " 'Keywords': [],\n", " 'LanguageAndScriptCode': None,\n", " 'OriginalResourceReleaseDate': None,\n", " 'ParentalWarningType': [{'Namespace_': None, 'UserDefinedValue': None}],\n", " 'ResourceContributor': [],\n", " 'Synopsis': None,\n", " 'TechnicalImageDetails': [{'AspectRatio': None,\n", " 'ColorDepth': None,\n", " 'ConsumerFulfillmentDate': None,\n", " 'ContainerFormat': None,\n", " 'DrmPlatformType': None,\n", " 'File': [{'FileName': u'A1UCASE0000000007X_01_01.jpeg',\n", " 'FilePath': None,\n", " 'HashSum': None,\n", " 'URL': None}],\n", " 'FileAvailabilityDescription': [],\n", " 'Fingerprint': [],\n", " 'FulfillmentDate': None,\n", " 'ImageCodecType': None,\n", " 'ImageHeight': None,\n", " 'ImageResolution': None,\n", " 'ImageWidth': None,\n", " 'IsPreview': None,\n", " 'LanguageAndScriptCode': None,\n", " 'PreviewDetails': None,\n", " 'TechnicalResourceDetailsReference': u'T2'}],\n", " 'TerritoryCode': [u'Worldwide']}],\n", " 'ImageId': [{'IsReplaced': None,\n", " 'ProprietaryId': [{'Namespace_': u'DPID:PADPIDA0000000001A'}]}],\n", " 'ImageType': u'VideoScreenCapture',\n", " 'IsArtistRelated': None,\n", " 'IsUpdated': None,\n", " 'LanguageAndScriptCode': None,\n", " 'ResourceReference': u'A2',\n", " 'Title': []}],\n", " 'LanguageAndScriptCode': None,\n", " 'MIDI': [],\n", " 'SheetMusic': [],\n", " 'Software': [],\n", " 'SoundRecording': [],\n", " 'Text': [],\n", " 'UserDefinedResource': [],\n", " 'Video': [{'CreationDate': None,\n", " 'Duration': duration(0, 1201),\n", " 'HasPreOrderFulfillment': None,\n", " 'IndirectVideoId': [],\n", " 'InstrumentationDescription': None,\n", " 'IsArtistRelated': None,\n", " 'IsBackground': None,\n", " 'IsBonusResource': None,\n", " 'IsHiddenResource': None,\n", " 'IsInstrumental': None,\n", " 'IsMedley': None,\n", " 'IsPotpourri': None,\n", " 'IsRemastered': None,\n", " 'IsUpdated': None,\n", " 'LanguageAndScriptCode': None,\n", " 'LanguageOfDubbing': [],\n", " 'LanguageOfPerformance': [],\n", " 'MasteredDate': None,\n", " 'NoSilenceAfter': None,\n", " 'NoSilenceBefore': None,\n", " 'NumberOfContractedArtists': None,\n", " 'NumberOfFeaturedArtists': None,\n", " 'NumberOfNonContractedArtists': None,\n", " 'NumberOfNonFeaturedArtists': None,\n", " 'PerformerInformationRequired': None,\n", " 'ReasonForCueSheetAbsence': None,\n", " 'ReferenceTitle': {'LanguageAndScriptCode': None,\n", " 'SubTitle': u'Live at Budokan',\n", " 'TitleText': u'Can you feel ...the Monkey Claw!'},\n", " 'ResourceContainedResourceReferenceList': None,\n", " 'ResourceMusicalWorkReferenceList': None,\n", " 'ResourceReference': u'A1',\n", " 'RightsAgreementId': None,\n", " 'SubTitleLanguage': [],\n", " 'TerritoryOfCommissioning': None,\n", " 'Title': [],\n", " 'VideoCollectionReferenceList': None,\n", " 'VideoCueSheetReference': [],\n", " 'VideoDetailsByTerritory': [{'AvRating': [],\n", " 'CLine': [],\n", " 'Character': [],\n", " 'CourtesyLine': None,\n", " 'DisplayArtist': [{'ArtistRole': [{'Namespace_': None,\n", " 'UserDefinedValue': None}],\n", " 'PartyId': [],\n", " 'PartyName': [{'AbbreviatedName': None,\n", " 'FullName': u'Monkey Claw',\n", " 'FullNameAsciiTranscribed': None,\n", " 'FullNameIndexed': None,\n", " 'KeyName': None,\n", " 'LanguageAndScriptCode': None,\n", " 'NamesAfterKeyName': None,\n", " 'NamesBeforeKeyName': None}],\n", " 'SequenceNumber': 1L}],\n", " 'DisplayConductor': [],\n", " 'ExcludedTerritoryCode': [],\n", " 'FulfillmentDate': None,\n", " 'Genre': [{'GenreText': u'Metal',\n", " 'LanguageAndScriptCode': None,\n", " 'SubGenre': u'Progressive Metal'}],\n", " 'HostSoundCarrier': [],\n", " 'IndirectResourceContributor': [],\n", " 'Keywords': [],\n", " 'LabelName': [],\n", " 'LanguageAndScriptCode': None,\n", " 'MarketingComment': None,\n", " 'OriginalResourceReleaseDate': None,\n", " 'PLine': [{'LanguageAndScriptCode': None,\n", " 'PLineCompany': None,\n", " 'PLineText': u'(P) 2010 Iron Crown Music',\n", " 'PLineType': None,\n", " 'Year': gYear(2010, 1, 1, 0, 0)}],\n", " 'ParentalWarningType': [{'Namespace_': None, 'UserDefinedValue': None}],\n", " 'RemasteredDate': None,\n", " 'ResourceContributor': [],\n", " 'RightsAgreementId': None,\n", " 'RightsController': [],\n", " 'SequenceNumber': None,\n", " 'Synopsis': None,\n", " 'TechnicalVideoDetails': [{'AspectRatio': None,\n", " 'AudioBitRate': None,\n", " 'AudioBitsPerSample': None,\n", " 'AudioCodecType': None,\n", " 'AudioSamplingRate': None,\n", " 'ColorDepth': None,\n", " 'ConsumerFulfillmentDate': None,\n", " 'ContainerFormat': None,\n", " 'DrmPlatformType': None,\n", " 'Duration': None,\n", " 'File': [{'FileName': u'A1UCASE0000000007X_01_01.mpeg',\n", " 'FilePath': None,\n", " 'HashSum': None,\n", " 'URL': None}],\n", " 'FileAvailabilityDescription': [],\n", " 'Fingerprint': [],\n", " 'FrameRate': None,\n", " 'FulfillmentDate': None,\n", " 'ImageHeight': None,\n", " 'ImageWidth': None,\n", " 'IsPreview': None,\n", " 'LanguageAndScriptCode': None,\n", " 'NumberOfAudioChannels': None,\n", " 'OverallBitRate': None,\n", " 'PreviewDetails': None,\n", " 'ResourceProcessingRequired': None,\n", " 'TechnicalResourceDetailsReference': u'T1',\n", " 'UsableResourceDuration': None,\n", " 'VideoBitRate': None,\n", " 'VideoCodecType': None,\n", " 'VideoDefinitionType': None}],\n", " 'TerritoryCode': [u'Worldwide'],\n", " 'Title': [{'LanguageAndScriptCode': None,\n", " 'SubTitle': [{'LanguageAndScriptCode': None, 'SubTitleType': None}],\n", " 'TitleText': u'Can you feel ...the Monkey Claw!',\n", " 'TitleType': u'FormalTitle'},\n", " {'LanguageAndScriptCode': None,\n", " 'SubTitle': [],\n", " 'TitleText': u'Can you feel ...the Monkey Claw! (Live at Budokan)',\n", " 'TitleType': u'DisplayTitle'},\n", " {'LanguageAndScriptCode': u'ja',\n", " 'SubTitle': [{'LanguageAndScriptCode': None, 'SubTitleType': None}],\n", " 'TitleText': u'\\u30ad\\u30e3\\u30f3\\u30fb\\u30e6\\u30fc\\u30fb\\u30d5\\u30a3\\u30fc\\u30eb\\uff0e\\uff0e\\uff0e\\u30b6\\u30fb\\u30e2\\u30f3\\u30ad\\u30fc\\u30fb\\u30af\\u30ed\\u30fc\\uff01',\n", " 'TitleType': u'TranslatedTitle'},\n", " {'LanguageAndScriptCode': u'ja-Kana',\n", " 'SubTitle': [{'LanguageAndScriptCode': None, 'SubTitleType': None}],\n", " 'TitleText': u'\\u30ad\\u30e3\\u30f3\\u30fb\\u30e6\\u30fc\\u30fb\\u30d5\\u30a3\\u30fc\\u30eb\\u2026\\u30b6\\u30fb\\u30e2\\u30f3\\u30ad\\u30fc\\u30fb\\u30af\\u30ed\\u30fc! ',\n", " 'TitleType': u'TranslatedTitle'}]}],\n", " 'VideoId': [{'CatalogNumber': None,\n", " 'ISAN': None,\n", " 'ISRC': u'CASE00000007',\n", " 'IsReplaced': None,\n", " 'ProprietaryId': [],\n", " 'VISAN': None}],\n", " 'VideoType': u'ShortFormMusicalWorkVideo'}]}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddex_dict['ResourceList']" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
tesfayeBris/sc-python
01-analysing-data.ipynb
1
226380
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysing tabular data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we are going to use a LIBRARY called numpy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 1., ..., 3., 0., 0.],\n", " [ 0., 1., 2., ..., 1., 0., 1.],\n", " [ 0., 1., 1., ..., 2., 1., 1.],\n", " ..., \n", " [ 0., 1., 1., ..., 1., 1., 1.],\n", " [ 0., 0., 0., ..., 0., 2., 0.],\n", " [ 0., 0., 1., ..., 1., 1., 0.]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variables" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Weight_kg = 55" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "55\n" ] } ], "source": [ "print (Weight_kg)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weight in pounds: 121.00000000000001\n" ] } ], "source": [ "print('Weight in pounds:', Weight_kg * 2.2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Weight_kg = 57.5" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New weight: 126.50000000000001\n" ] } ], "source": [ "print ('New weight: ', Weight_kg * 2.2)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable Type Data/Info\n", "-------------------------------\n", "Weight_kg float 57.5\n", "Wight_kg int 55\n", "numpy module <module 'numpy' from '//a<...>kages/numpy/__init__.py'>\n" ] } ], "source": [ "%whos\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 1. ..., 3. 0. 0.]\n", " [ 0. 1. 2. ..., 1. 0. 1.]\n", " [ 0. 1. 1. ..., 2. 1. 1.]\n", " ..., \n", " [ 0. 1. 1. ..., 1. 1. 1.]\n", " [ 0. 0. 0. ..., 0. 2. 0.]\n", " [ 0. 0. 1. ..., 1. 1. 0.]]\n" ] } ], "source": [ "print (data)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'numpy.ndarray'>\n" ] } ], "source": [ "print (type(data))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable Type Data/Info\n", "--------------------------------\n", "Weight_kg float 57.5\n", "Wight_kg int 55\n", "data ndarray 60x40: 2400 elems, type `float64`, 19200 bytes\n", "numpy module <module 'numpy' from '//a<...>kages/numpy/__init__.py'>\n" ] } ], "source": [ "%whos\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "float64\n" ] } ], "source": [ "# Finding out the data type\n", "print (data.dtype)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(60, 40)\n" ] } ], "source": [ "# Find out the shape\n", "print (data.shape)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This is 60 rows * 40 columns" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First value in data: 0.0\n" ] } ], "source": [ "# Getting a single number out of the array\n", "print (\"First value in data: \", data [0, 0])" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "print ('A middle value: ', data[30, 30])\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A middle value: 13.0\n" ] } ], "source": [ "print ('A middle value: ', data[30, 20])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 1. 3. 1. 2. 4. 7. 8. 3.]\n", " [ 0. 1. 2. 1. 2. 1. 3. 2. 2. 6.]\n", " [ 0. 1. 1. 3. 3. 2. 6. 2. 5. 9.]\n", " [ 0. 0. 2. 0. 4. 2. 2. 1. 6. 7.]]\n" ] } ], "source": [ "# Lets get the first 10 columns for the first 4 rows\n", "print (data[0:4, 0:10])\n", "# Start at index 0 and go up to BUT NOT INCLUDING index 4" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 6. 4. 7. 6. 6. 9. 9.]\n", " [ 5. 5. 8. 6. 5. 11. 9. 4.]\n", " [ 3. 5. 3. 7. 8. 8. 5. 10.]\n", " [ 5. 5. 8. 2. 4. 11. 12. 10.]\n", " [ 3. 5. 8. 6. 8. 12. 5. 13.]]\n" ] } ], "source": [ "# We don't need to start slicing at 0\n", "print (data [5:10, 7:15])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2. 3. 0. 0.]\n", " [ 1. 1. 0. 1.]\n", " [ 2. 2. 1. 1.]]\n" ] } ], "source": [ "# We don't even need to include the UPPER and LOWER bounds\n", "smallchunk = data [:3, 36:]\n", "print (smallchunk)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Arithmetic on arrays\n", "doublesmallchunk = smallchunk * 2.0" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 4. 6. 0. 0.]\n", " [ 2. 2. 0. 2.]\n", " [ 4. 4. 2. 2.]]\n" ] } ], "source": [ "print (doublesmallchunk)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "triplesmallchunk = smallchunk + doublesmallchunk " ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 6. 9. 0. 0.]\n", " [ 3. 3. 0. 3.]\n", " [ 6. 6. 3. 3.]]\n" ] } ], "source": [ "print (triplesmallchunk)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.14875\n" ] } ], "source": [ "print (numpy.mean(data))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 1. 1. ..., 1. 0. 0.]\n", " [ 1. 2. 1. ..., 1. 0. 1.]\n", " ..., \n", " [ 3. 1. 2. ..., 1. 0. 1.]\n", " [ 0. 0. 1. ..., 1. 2. 1.]\n", " [ 0. 1. 1. ..., 1. 0. 0.]]\n" ] } ], "source": [ "print (numpy.transpose(data))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20.0\n" ] } ], "source": [ "print (numpy.max(data))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "print (numpy.min(data))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get a set of data for the first station\n", "station_0 = data [0, :]\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18.0\n" ] } ], "source": [ "print (numpy.max(station_0))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We don't need to create 'temporary' array slices\n", "# We can refer to what we call array axes" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.45 1.11666667 1.75 2.43333333 3.15\n", " 3.8 3.88333333 5.23333333 5.51666667 5.95 5.9\n", " 8.35 7.73333333 8.36666667 9.5 9.58333333\n", " 10.63333333 11.56666667 12.35 13.25 11.96666667\n", " 11.03333333 10.16666667 10. 8.66666667 9.15 7.25\n", " 7.33333333 6.58333333 6.06666667 5.95 5.11666667 3.6\n", " 3.3 3.56666667 2.48333333 1.5 1.13333333\n", " 0.56666667]\n" ] } ], "source": [ "# axis = 0 gets the mean DOWN each column, so the mean temperature for each recording period\n", "print (numpy.mean(data, axis = 0))\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5.45 5.425 6.1 5.9 5.55 6.225 5.975 6.65 6.625 6.525\n", " 6.775 5.8 6.225 5.75 5.225 6.3 6.55 5.7 5.85 6.55\n", " 5.775 5.825 6.175 6.1 5.8 6.425 6.05 6.025 6.175 6.55\n", " 6.175 6.35 6.725 6.125 7.075 5.725 5.925 6.15 6.075 5.75\n", " 5.975 5.725 6.3 5.9 6.75 5.925 7.225 6.15 5.95 6.275 5.7\n", " 6.1 6.825 5.975 6.725 5.7 6.25 6.4 7.05 5.9 ]\n" ] } ], "source": [ "# axis = 1 gets the mean ACROSS each row, so the mean temperature for each recording period\n", "print (numpy.mean(data, axis = 1))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# do some simple visualisations" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "//anaconda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n", "//anaconda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "import matplotlib.pyplot" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SD6zUmkI0SOFjUjQBigDzmCB1o6LVOdt+QbIeoImW/e6LSx7vzqg3BWOrwDlM0pEte7Ll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vyGP7v9K/7tL/9PNuM147Pl4/SOh/0df/n8z3jgDdOVZNIKV8p4Ji/SGnDi7K53F9wGQ2Q6uoCq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\n1W2a0HCxUCdmxYGiOtHVGe0Qq9jeZbTvcobSnBVs5iYINO4JIL/z8J2Db11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iPyP4DF8B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DkqnJZDPa734RDw9FjrcZDkSoZb52yhlYdCtgG4YRcBZGDy3dT5jJA2OfEhN2fq8YXHLCXt\nIkbMLXYB7ULRRgltnNHECSbR+Fgh9gXSOrS0RGlHYluyqIKxIDoy5LpiPFrSLhJk7TC14rQ85Pxs\nH2kc0ZsdumkDGGHeEsmWhZxyIfaYsyPJTUexKdlfXPDg4hlvXTwmWnQsl1OW9ZQFU5bZDB8J3tr7\ngLf3HsKeI5uVTMYgFsC8P1a78F3UPuXc7/PQvs0H9h0emrepbYzogRKEd+BDKanGoIRFY0MTBZXw\nVL/Js/iIRTKhSRPooE2DJF/owORJH78LhbKCHvfmksm3r11fyuEwaBySlhhjdWDy0xnzx3vMH++x\nfDqjWSY0i4RmmWAXKhxgQ8b0rid9qGkYegyuh/WLsMeKXpPbTdEYhiVUmV0y+bCvZRBE0vd5Gxrc\nMPGOd91vwC4JwX8XgB9f0pt+nV5BJt9lbMPVtD5/9XMi63GyYtBpOCkHvcn3w3lwEZgoMLlQl6nu\nl0y+K8krtmXr15l8QJvJ6SU5MPWw76Dw2E2Q5PYE2oea6mGCjSNsEWPyCNNDQosGlPTo1BKPOxLb\nkKcb9NiQqcDg3UFMvchYPZ2wfDphdTZh9XRCeT4ibmpi0RDnDfF+eN2IlI3IqMioRIZDEhlDUW3Y\nm8+5f3LMtzx7CCvoupSTLkjy99N3qVVKtZfCgSc7LNk7OMVP2Jp9HQGOCqgIkvyhe5sv28/yRfvt\n1CZBuxbtOrRvUbShZlz0mHMi4M51MmKuD7iIDlgmU5o0xpsdSa4npLJC9cAS8rJxUugQN9SjhzYM\nFoe5rE63KFpiXN//bbGeMT/dY/5wj/lX9li+P7sSIze13gKBXG+ysdsVZ9EPR2DcwXbPFJddUC6l\nfX+fkQiVjTFXJbnshYyOwn51Q1bTTmjMleGzxoXkGlF+0oS3V5XJd2OBg0S/RoMtPgDh6QJEGv7O\nu1D6Z/uj2alQ1C9UyA8eTurBhhps8kGNH1KGb5LkAyz8oK7PfGh5PPKYZxJbR7THCvHLMeLfeXwm\nYaLxE4WfKJjIYGWkHj2xRAfmUpJn4xpfCHzfaWWzyHnovoWLsz3OykMePnyHp++/QSorkrwiPaxI\nmg2prPBy6IgqLyVfZExQ1xdzHpwc887jDzDrmBN1n0YlHKv7fDH7HPNsCnue9LDk4OiU6ijDzfq8\njo6grvde58pnnPt9Hrm3+JL9Nv6t+Q+pbUJiKxJXkfiKhArde4mF8JeP0SpFpQuqqGCTjEJuu+1t\n8ihjqSYoaXAEB99uAY+kuQyr0UtzoJfgCaZX7xtSNi5nUc5YnM1YfLDH4kv7LL84xQ+Zbv33iycc\n2kN+ybDlBiYv2fYqp98TIxGcc/v9XhiamfSaNQ1b5+VQSHZpk0c9o/eZlX6Xe/vXdhU2pmtBlmBf\nEOZ4CXoFmXzwcu16u65X5OxI68H2toRdedmBdNAAevUo7k/XAUs96Q+D2sHcQeS2aY7IYHcJ2Ttm\nxNYed4RTe+3Dv2b7RJnUwTPwCwGNwgvZe6xl6NoSy2CHSYF1mrpOWS0nXJztk6Ubqk32HNpM5TIu\n5B6LaMYqHVOORmwmBTaWGBdghNuziPZRQqJqUtFQiJJEhNdvLJ5wuDll5NbouAu9wCKFUoZCluzJ\n81DaGa84io/ZY07RlcTrLuzLHiZLDCrtBGLZUuiSmZ5zpI95Wz+kTPPghIpcAHwQCmE9cduStO3l\n1deC5XrKcu3wa0W3TmirhEh2ZLpmEq3Zjy+4F5+SqJpENMSyCVfRbhnZp5cM3biExic0roezcgnV\nKmdVTkJIcpPRbiJsrW6IQg2HNNsx639vrmXAIYL/ZXC2jvvvxNM7atlmxQ1/twsU2vhg03c+3Nv6\nPq6+u+8JAslFATDCD045w9Xo0suVn76CTD7Etna9XcOT2QmhDdA6zocvjB4gwrrw3vmrtfcD5O8g\nhfP+iZT9U9mY4HVXUahUKzRMoq3kN2xj6MMofYB2Tn1Q3U5FqDl2BLvsAYHBM7nt3JJDpyI2XcF8\ntUd03OGsJB+X22X2o+kSTs0R59E+5XhEexSiB24qMUojqxhxQmiMqBtyWbEnL9gTF+zJCw7bU+5V\np+SixI9hpQtsp5HeMvEL3uYhwnsamfCuep+3u0fsr87JugqxDJqiGNJbU2APUlmzr855Wz3EKE2k\nOuajCVWaUsUJlU6oRIoyhlFZMl0tmayWTJZL1NpzXN/nuGqhljRVRtXlJL5lIlYcyDPe1E95K/qA\nRDehv7pq0TpU0JUUzP2MymdsfMGFn7GxBZ2NaU1MZ2M6E9MsEzbrEXWV03YxzikugVTGvi9k7J2m\nY8L78c4o2VGRe+bz9P6X/jMDrNhNLaxvot22SW0fc78pWub9NubuB9XRcTVj6+VCyK8wk+96unaR\nE3fi5J4+lTA4exB2K9kHJoeruN5TwsMSPZNvatj0hlWhYJrANA7tQaYhr5zShyy4NdsG9Ov+3xCe\nS7jdhpABZXuV7oHo8dv7EQtIwGhNaQrUyuCspFpnJEWzxaXsrx0RSzNlGU1ZT0Z0R6Hlss8VVmu6\nCtyJwJYKoRYUquJQhQSVN9VjJmpBLisyVeFHsJ4WOK+QxjI1c5RxTM0S5yRHPOOeOeZgdU6+roMu\n1ZuMguD+IIZMVuzLc4zUxKplKhecFvucZ3ucJXuc6T2MCHOMyzWHZ2ccnRxz/+SEaN6RmQbRCeou\nZ9HNED7ANI/FinvqjDf1Yz4Tv08Ut6GNUWRQPjjuztin9ineSUpXcOYPWZgptoswrb68dsuYpgyF\nPW0bY23P5Gn/7I98qDI88lvVenekO+FTCHvA9ntnOBCG5pdD6H/o7jIcENc18arX+Jq+Eq1zL2Zy\nJ8BpcAn4Afd7d/+/FpJ8MI53m4EPek+vynu2UDne7TD7bozcPy/JZ4QYZ21h0zN5uYHNBvZ0sOUn\nPjRjuK+Dqn/Wz1f1knxFOInb/jQeMLfjHWYuJOzJXhkROwOMDJLcrRT1OmMpp+jMbEvlJ4SCulhR\nm5RaZ1TjjE7EUIBzEqzGVxJbKoy1oAW5qjnU57yjH/Gr9ZfJigo3EbixwI0l63GBVwLZGKbNgmmz\nhCbE5EfNinGzYlyvyJoa0fV2eBQCGQNYSSZDKmskOyZiyX3xjKfFfR5mb6Ljlk4rlmKMtJbROjD5\n248f8y0Pv0Z62oCX1D5j7vdIfAsSEtkwUSsO4jPeTJ7ymfR9dNoFNBvvEMKGPHUhuHD7eCvZuJxT\nd8h5dxDKQxuFa0KpqF1qTKkxVRRq5C8lea+eP/Dwtod3/POY/EN31CsY/wQmv5Ib0f98EKy73W0H\n2hW4VyS5C0xubvCoeRe0R697lX3AcRv2/mvB5ANX7ma87bq62V59r6qLa846f8Ptdpn8ELhwsGmh\nrOF0AyerUGgw8cE2LzTcj0OowxMKEeY+SPI1/fDbUftweBwCh716fiCDY3A3xC9CY8MALpEh2wmy\nccjYB2fOPpfZjb4QONtXd40VLpfQgVtK/FJgFx6xiGDpEUqQRxUH0TnvRI/4nP4y6rBjpUcspyNW\noxHrBwUknvGmZLRZMt6UjMuSvKxQxqI7g1ob9MIiS/roAVuH4ySo6xrDVCwwfXBsP3ubKGvoYsVS\njYjEUUg3LUsOzs946/EjPvuVr1A8q2hUzkLu8VS9QaIaRORJVcNYrzlMzngrfcK3Zu+jvMF737tX\ngqOqIiOxNd4JNrbgzB5y3B5BHeq/GerAFxK/7lsrtyFuvpXkHu57eNfDZ922O8uuGygRXAqIYRMN\noCK7Y0ATGph8gPve3XsDXZHkLlSl3STJBxvTD4Iuu7bvd/ngo9EryuRX6j95cZ5hH27zQwYLXDY7\nFLJPLxTBwaJ6R50xgWG7BpwJcUtNyDGOVZDc9A63tv8yBwfJ8L0nhJPZi50umzJ4Xp0I82cCpiKo\neoP6NigeXmCk3B7Iu7HZDZdamejcNqdctcRRaGxweZ71Dh7fwBvqCfejZxzqU/aic6Z6jksEVZLg\nU0GTJyyLESKBSBhyEYAbM7Gh8GtYe7z1UHq6C087B+c1LtHYicLFGjfRCNl3hrlMRLHEUU2uS2Z+\nwWF7xmozRpSe/faciVuRqRqVGMgdUhki3ZLohlRV5HFJMqqJ8haZWnwUvPBe+iDQdnyprQ9Z7LVP\nqV1K5TJqmwWIr0aEevCyvw7R15SgXpvQjkocODhwiAMbXgvwTl5GNLwTV0OmOdsY+OAtH5xtu4Cf\nuwVlQ7x9dztfmnb9A3e9Bnq5MYbXu/E82JqvH59eQSa/Ti8KEnqudloBhOljkDqAROiecWMbYpKV\nhYu+N5XpQogtFXCYwFjCLIJxGkJxZQTPelTNpQgSXPUeVgdbeKl+UzW+b80kwz0HBFh4Hu11yGYc\nDo28X8KQZtsAC1CNZZysmCYLZkmARhrrVahsA9Dgc2AG78r3eUd/jUN1TKHXCG2xs5h2P6Iap6zT\nogd3EGhtieOOxDVk1Ehj8MrircPVFr9wuDNPm8a005zW57RRTlvkSOkuY9dDzfdSTEKGXbfhyJwg\nNh6x8RzaU7J0gzlQzN0Mue9YqjGNjBDKkqmSqb4gmVX4maeeJcxnE56OjpCJxce9Wdr7P0/9IXM3\noxQFjUhwyKtpqEMTjAFGe+g664ARiAcOdWAD/npukJHBeYlzCmsVrguIOzQyPFt6E2t4LrBl5KGI\nbEmww4e8C7i5/KJjm4NxaQkMe9juXIdFDEkwL9r/H52+CZj8RarJrjjr3wsTcoOTJHi1Ex2uUS/t\nqy5gXJft1reXShgnECWhf/koDsbowORKbMMpmsDkivCztmfwteix23ecbIMTZzcVfziwd4uKbnqG\nvXTQG8OoWHM0OuYN9YQHyVOOomN8HjY++Ta9+Uge84Z8wqE6IZdrpLKYkaSdBiYvk4KlmuClIFYd\nSdRcAjGorsVqg7MmZFMuPfbMs5kkbJoxlZ+xiWZUxRQp3XNIs8ZqbCfJ2oojc8KkW0IDqalJ0xpz\noLjIp5hOs5A9k0tLJkumak5SVPiRpxolXBRTnhT3kVGQ6j4K5ikKTt0hczmldEVooCA+hMlh62jr\nkcJkz+R6GjIFddxhO03nI4QVmFbiGoGve+cpYpvGDFcjK32CEAu2DP8iJs/63w/O8UvFdNjDg6rQ\nXXv98s0Nb6JvAiZ/Ee3quP1TFl0IZcUyMGwuAhNbH1TzqoZ1BbaCaRQk9yQO11kcNIAhbFfqoIKr\n/jSPCJ7ywRfY0jO475vh+b7dcc/kqQgn+LD5dqyLSx/ibqRwcNwMm7QBhWXsVhypY741eY9fxVf4\nluhrYeMPaizh9USsmMoFE7mgECuEdNhU0uYxVb6V5E5IEtWSxDWZqsh1ie4ajJIh+lh77MJiTgWr\nw4RlPWblD1hFRyyLI7QyxD2Qw9DPLK47ItOFYphySVx2IZNNabpU0xURF3LKRuUsxJhGRAjRM7m8\nIIkrfOKpkoSLZIpKutDxtP+efO9rOuGQuZixFgUN1yT5bjurXUkeEQ5mAfLIoQ4N0bQjylviuKFz\nEQJojcS1KpheTX+wQ3j2Qzx8OKyHgrEVVyX5kI19kySv2Tr4npPkwz++K8GHk+vfC0n+9dT1nYIW\n0avoSRRa2IwIDrCNh9JA1QQverkCkQcVPZVwkMBbRRAbaxHGqpfQQlwFiO03DFXvgFuIwOSX4fyd\ngyBjK8F3G8IM6vruMASpMGAHLEEZy0itOUqOeXf0Pr+GX+Lb9Rcu21m7Pl/Cx6BFF5hPhNJVIWyo\niosi6iiljIIkt0KR6oCykuuSUZyh2xqjBcZ6TOVCDv6ZZ75KuGhGnPsD5tEbXORvEeldOOmQlTb1\nS/Y3c6bdir3ygv2LOXjB+WTGeTGjmqTMJ1Pm+ZQVgckllkxsAE+iapCeSiVcyClGyZAp19vig6/p\njF6Si4JWJNiByYctsHtIDjb14DFPgk2u9oMkj/OGJKqRrcd7hbUaM5QZd3IrQIdn5ejbILHttLNk\nC8+8K8mHg3vw3+T939woya838hwkwq6t/snoFWTyYWHDF/Bhp9k1tzUepAFlQ/ZVQmDiAaZZ+G0C\njSBI/EwFab6fQqe36a4VcBH+RkQeRj5UtI6D1PaboZe4wNeAF4h9h5g55NQhJh45CkkPocsKoMJr\nrwU+FbhU4jOBTwcvMAFMsQwuLRxI60IxizVENhSzSGGQsUUWBpmHq/C+DzL4y9cORSsiKpexbkch\npiw1qazJZFDVC1Ui4o42Smh0Rys7WtHR4LkQR8zVERfqHhfRPS6SIzJVk/dFoCHQFALpUdeSbUqm\n8wWHx6fBJecEtY5ZZQUIcJFEy5CfP7QgblUcGg9i8EJQiTTE2Xt7X+3kr1sncUaFvuGNgFogSpC1\nRZrQIUYqEIlHZD5kOWcekYb3yaQmnlQkRU2S1sSqplE2FIhJgRcKKzRu6Liyyx0iPGcfCZDD8xFX\n054H23045Hcb7w5IZlcCRdcl+QBgMDiexc4fuGtj+NuvT68gk++eboM9flNccPC+744bSBBa1ORx\nwN5KfPCCH6YwTUMDu1iHEsJdM7+PeQrlkdaGNjqJRY4sIve4TgWnjZLYVOL3JeqoI7pv0Pc7okOD\nnnWoziFzh2xsCJW1DqcUXawxSUSXaEyssa0ODiAncV7hvMI2ilUx5iS6x/vuXXRtqFcpuViT65I8\nKcn8mlyUKGtRnUO1FtmGNj/eSYyIacgoxYglMzodkSQNadyQJDVpUmMlbGLJphBsZpLqULBZa9YH\nh5STA9b5PdbRHjUZMR3aW3I2TP2cPebstxdMNgvyRUl02sBTBwaisqFYlcwWMf5Ckk4a2jSmS6Lg\n1EtjuiQOpoJSoSOpUtQqDbnrPjgGU1+RUSFbz2Y9Yrmekaw65Bpk5UlMS2xakqQl3m+IvUEmfcuj\nZBgWXXSoLGDQa2VQoqOSLToyqLTvbCrAmufZwsfiuefjldgCbQz56w1B6xuxTfH40L0+bLiByYcU\n1uFkGPpuD7a62Xn9ArTNa/QKMvnu6QZbT9V1klzNYHiRg04EJpdxyK8eiZD0f5jALIEiCR54cW3q\nIbEh8n1ZaBdgmKnDYRUAABQaSURBVEcdcuowXmOUxqQaP1bYJej9jni/Id2vSfYb0mmNtibEnzuD\n7kIsulOaRqfUUWgdXEcJXRNjXITwuvepCmylWKcjjqMjtDe0Tcx8PWNfn7IXn7Fvztj3pwhpiX1H\n1FrYCERlQksjKzE+piZj48cs/YwmTkiLhnTUkPqaTFcYIVnFCcsiZTlLWR2mrOqM9mBEOx3R5CPa\naERDyog1GkPmK2Z+wZE/ZtYtGG1W5POS6LSFJw5RQ7RqyBcl7kKgZ4bRpKQba8wowow1Zhzs9XVc\nUEY5ZVSwFjmNSsJhgiX3GyZ+ydQvEK1gWc44m5fE5y3q3CNbRxI3FHFJkZQU4zVZXKMig9Y2XCOL\nikP/ctK+2aEKEjFWHSp2iF67czrUFlwnF8vnno/XcsvgA7MPBSoFgVc/NAv1pg1nuVqvsXtSDPCx\nw2dvRu69Tq8gkw+SHLan3E3f0lAtAh8aSxwkeRL3ABM6eM/3dHC+FVFgcimuOnEG/HEbWv5q1REl\nLdGoRe5ZpIoQaYwfe9y+QGwEatyRjGvycUk+KSnGJZHriG1LZDui/tqKmI0qKFVBqXKkKqiboXra\n44VECIUtFSs55lge0bqYRT3lqXnAm/FD3sge0ZgI4S2Z3OCdhLZDVB69dIiVxbeKzsfULmftxizc\nHnWakM4aEt+Qqpo0q2il5iKeclFMOJ9Nubg3YW7G+EONn0b4XOMjjRca61WQ5G7DzM858sdM2iVJ\n2ZAuaqLTBv/EIkpBvGgoxoJobMjHNd00xu4p7L7EHYSwVYfmzO5z5vaxQlGqnCZKyf0m4Oa5ipmf\nc8+f4FvJWXnI6GJDctwin3mU9SR7LcVszSyZM9u7YDRaEaluZxi07nCRxEUSGwmcCtV6kbKIuLf/\ntcDEEuuf30s21s89HyICgw+MPvDdrq/lI0nyXXXds63xHcojI7Zq/G6J3DetJN/F0xlclTfR0ANt\n1yh6AcWq93qr0EEldsHpNu4z0+I+R/0mdd37oApLQ5S0JKMKOQtSwY/BVgJTq5AemnXEaUWWrRln\nK8bp8lLtjF1L4gNz1aQsxQQtg33tRUhogdDd1EqNkR6bKNbdiM5ELLopT+sH5L5kmY1oRhHSWDJf\nsifPEN4jO9Abh19ZxDn4RmJsTGNDr7SlnbHJM1Jfk6iKNK1IzYYmijlJ7nFaHHI8PeLk8JBzv48+\nsOiJQecGHYX8NodCeUPuN8zcgiN/wqhdIzcGtbCoUwNPHCwhylt0YcmLGldo/FTi74fOJN4JvBR0\nkSbypmfwAhHRe84Vylsyv2Hm5hz5E2yreVYuKOYbkpMO+cgjhSdRDaPxmllywb29Z8wO5sQ7sFOx\naAOQpIroZEQrIzqp6YiQ0uEjcEpgY0mXaYx/fi+ZRD/3fOjDmJeMPjD5rgX5oRmouy773eqWvL8O\nknzIvnptmPzDvIri2jVim0TQjytgEQTnCLIvN+VqIcJuUc/A4Lu4Xh0IHYAKlDKhn1jWokYGFwlM\nJJGJQtQaOolMDFHakiQ1WVoySlakriZ2DYlrSVxDbBsklo6AhdYQE9GhRYeLFD5TOKvwCEwU4SpJ\nXaXUPkEYT2Sm5G7NyC+Z+XP2xZSVHAfHnxXozhHXJqjtfbzXWYmzGmt0MDNGEaYNLY06F6FEwiYa\nsSwOuJg94KR5kxN9RHJQkY5rkqQikRWJrTFOI40nti2FKZnZOfm6xK0FrgxthUwtoRKhY7DrWwG3\nBmFBJh6RO+TYIWuPbSV1lLIyI85cjfI2hMagR5cJ2OuJb0hsG1pDlxa58IgLERrkzCxx15HKmjzd\nMCpWJK65PFQT3xD5ltYlNCIOHWRIaLA0IiGWKbFo0bJDO3NZ9nAlS1GAUhYZB3w9kff767pbSF+r\nnXCEEG7nAhCE7dGKEFtH8BUnM1zVUAdzdCidHiT/kGb39ekVZPKbaJDWu862IaYtuIyfeBlSpEwG\nrQkwuMr3Zo/vmxcS0lxHfWhsqBqDbYhSsK1c2wWK3c1TLglhlLkPY+1DXvSM7TWGrolwpaItU8rS\nITeO2masGV2ODQWdj/uKrCD5SQRCg05DdpZuLLoxxLblwf5jJpMFMndsooJjjmhlilFLRCSIEkuW\n1mhlyCnZ44IjnvK236POE+7tPeNo9Iyj5Bn35VMiWroooc5y1tMRSzthFY/R0w6ROZyQdE2MXwia\nJqHdxJhKYzcSV4E9lXQLjdGa7p7GuL6vWKJwicYn4bUoBOlhTXpYkxw2pJMaldlwOEYtma4YyRVT\nFsQEdNe5mCKFpZYpx+oBj6M3OYsPKNMCkwXnZ0PKuh0Rr/aQp5bGJMR9HXvUNuFqOsxIXxl2pFmZ\nMat2wrodsWkL6jbDdNHVngcdmFbRNgmmjXBN8PBfCt/LVkr91dqQPm1tyFG3JmD3r0TAGHQiCB2l\n+2yfvhDFD2isA1satnG7IYb+8gkyryCT/zzw6679bGDu4VTbjUXAFRR8m0LXhv5UtevTRHsGVz6c\ntNrD/CfhO39kJ7bZh9l20WJugnwfwAFK4NTDU+CpD3XlD/rhCAfJFN778Z/hze/5buyZxp0p7Jmi\naZMA00TeX7PQ4rdoifOWqGjD66gl6VqSriFpG+KuJbU1o8mK0WSFyB2bKOfv/0TJD/zOA4QWRJEl\nSxp8JonijlyVzNQF99VTV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"text/plain": [ "<matplotlib.figure.Figure at 0x10ad808d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image = matplotlib.pyplot.imshow(data)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let's look at the average temprature over time\n", "avg_temperature = numpy.mean(data, axis = 0)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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m35eRYXMJli3TRkgiicR7G024/nqYMQPmzoU//Sl0VVJaQUEBWTZLO8t7X1Dd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\nifWxorBokU0oPP54uPXW0NW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"text/plain": [ "<matplotlib.figure.Figure at 0x10ae00048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_plot = matplotlib.pyplot.plot(avg_temperature)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Tasks\n", " * Produce maximum and minimum plots of this data\n", " * What do you think?" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10dcc84a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max_temprature = numpy.max(data, axis = 0)\n", "min_temprature = numpy.min(data, axis = 0)\n", "max_plot = matplotlib.pyplot.plot(max_temprature)\n", "min_plot = matplotlib.pyplot.plot(min_temprature)\n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10dbab9b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "min_p = numpy.min(data, axis = 0)\n", "min_plot = matplotlib.pyplot.plot(min_p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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
bashtage/statsmodels
examples/notebooks/predict.ipynb
2
4411
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction (out of sample)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm\n", "\n", "plt.rc(\"figure\", figsize=(16, 8))\n", "plt.rc(\"font\", size=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Artificial data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nsample = 50\n", "sig = 0.25\n", "x1 = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x1, np.sin(x1), (x1 - 5) ** 2))\n", "X = sm.add_constant(X)\n", "beta = [5.0, 0.5, 0.5, -0.02]\n", "y_true = np.dot(X, beta)\n", "y = y_true + sig * np.random.normal(size=nsample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "olsmod = sm.OLS(y, X)\n", "olsres = olsmod.fit()\n", "print(olsres.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In-sample prediction" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ypred = olsres.predict(X)\n", "print(ypred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a new sample of explanatory variables Xnew, predict and plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1n = np.linspace(20.5, 25, 10)\n", "Xnew = np.column_stack((x1n, np.sin(x1n), (x1n - 5) ** 2))\n", "Xnew = sm.add_constant(Xnew)\n", "ynewpred = olsres.predict(Xnew) # predict out of sample\n", "print(ynewpred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot comparison" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x1, y, \"o\", label=\"Data\")\n", "ax.plot(x1, y_true, \"b-\", label=\"True\")\n", "ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), \"r\", label=\"OLS prediction\")\n", "ax.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting with Formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using formulas can make both estimation and prediction a lot easier" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from statsmodels.formula.api import ols\n", "\n", "data = {\"x1\": x1, \"y\": y}\n", "\n", "res = ols(\"y ~ x1 + np.sin(x1) + I((x1-5)**2)\", data=data).fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the `I` to indicate use of the Identity transform. Ie., we do not want any expansion magic from using `**2`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res.params" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we only have to pass the single variable and we get the transformed right-hand side variables automatically" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res.predict(exog=dict(x1=x1n))" ] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-3-clause
LTMana/code
ML_PY/.ipynb_checkpoints/text-checkpoint.ipynb
1
1639
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello world\n" ] } ], "source": [ "print 'hello world'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 你好事件" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def add2(x):\n", " y = x + 2\n", " return y" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "i = 5" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "add2(i)" ] }, { "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
telescopeuser/uat_shl
rnd01/SH_L_Bidding_v005.ipynb
1
638596
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "By: 顾 瞻 GU Zhan (Sam)\n", "\n", "July 2017" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# [2] Data pre-porcessing\n", "Explore and visualize data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Automatically created module for IPython interactive environment\n" ] } ], "source": [ "# from __future__ import print_function, division\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns; sns.set()\n", "import pandas as pd\n", "import operator\n", "from scipy import interp\n", "from itertools import cycle\n", "from sklearn import svm\n", "from sklearn.utils.validation import check_random_state\n", "from sklearn.model_selection import StratifiedKFold, cross_val_score\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.ensemble import AdaBoostRegressor\n", "from sklearn.ensemble import ExtraTreesRegressor\n", "from sklearn.ensemble import BaggingRegressor\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.ensemble import ExtraTreesClassifier\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "from sklearn.metrics import roc_curve, auc\n", "from statsmodels.graphics.mosaicplot import mosaic\n", "print(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read raw data" ] }, { "cell_type": "code", "execution_count": 2, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1886</th>\n", " <td>2017-07</td>\n", " <td>11:29:56</td>\n", " <td>92100</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>2017-07</td>\n", " <td>11:29:57</td>\n", " <td>92100</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>2017-07</td>\n", " <td>11:29:58</td>\n", " <td>92100</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>2017-07</td>\n", " <td>11:29:59</td>\n", " <td>92200</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>2017-07</td>\n", " <td>11:30:00</td>\n", " <td>92200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price\n", "1886 2017-07 11:29:56 92100\n", "1887 2017-07 11:29:57 92100\n", "1888 2017-07 11:29:58 92100\n", "1889 2017-07 11:29:59 92200\n", "1890 2017-07 11:30:00 92200" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts = pd.read_csv('data/history_ts.csv') \n", "df_history_ts_process = df_history_ts.copy()\n", "df_history_ts_process.tail()" ] }, { "cell_type": "code", "execution_count": 3, "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>ccyy-mm</th>\n", " <th>volume-plate</th>\n", " <th>deal-price-low</th>\n", " <th>deal-price-avg</th>\n", " <th>deal-early-second</th>\n", " <th>volume-bidder</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>26</th>\n", " <td>2017-03</td>\n", " <td>10356</td>\n", " <td>87800</td>\n", " <td>87916</td>\n", " <td>55</td>\n", " <td>262010</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2017-04</td>\n", " <td>12196</td>\n", " <td>89800</td>\n", " <td>89850</td>\n", " <td>59</td>\n", " <td>252273</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2017-05</td>\n", " <td>10316</td>\n", " <td>90100</td>\n", " <td>90209</td>\n", " <td>55</td>\n", " <td>270197</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2017-06</td>\n", " <td>10312</td>\n", " <td>89400</td>\n", " <td>89532</td>\n", " <td>45</td>\n", " <td>244349</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2017-07</td>\n", " <td>10325</td>\n", " <td>92200</td>\n", " <td>92250</td>\n", " <td>57</td>\n", " <td>269189</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm volume-plate deal-price-low deal-price-avg deal-early-second \\\n", "26 2017-03 10356 87800 87916 55 \n", "27 2017-04 12196 89800 89850 59 \n", "28 2017-05 10316 90100 90209 55 \n", "29 2017-06 10312 89400 89532 45 \n", "30 2017-07 10325 92200 92250 57 \n", "\n", " volume-bidder \n", "26 262010 \n", "27 252273 \n", "28 270197 \n", "29 244349 \n", "30 269189 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_table = pd.read_csv('data/history_table.csv') \n", "df_history_table_process = df_history_table.copy()\n", "df_history_table_process.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "parm_ts_cycle : 61 seconds\n", "parm_ts_month : 31 months\n", "parm_ts_valid_cycle : 39 seconds\n", "parm_ts_valid_month : 27 months\n", "parm_record_cut_ccyy : 2015-04-01 00:00:00\n", "parm_record_cut_month_head : 4 months\n", "parm_record_cut_row_head : 15 seconds\n", "parm_record_cut_row_tail : 7 seconds\n", " : \n", " : \n", " : \n" ] } ], "source": [ "parm_ts_cycle = 61 # seconds/records per month\n", "print('parm_ts_cycle : %d seconds' % parm_ts_cycle)\n", "parm_ts_month = int(len(df_history_ts) / parm_ts_cycle)\n", "print('parm_ts_month : %d months' % parm_ts_month)\n", "\n", "parm_calculate_base_price_second = 15 # Use the current month's bid-price as base-price at this seconds. Later to derive increment-price\n", "parm_calculate_prev_bp = 15 # Number of previous price/increment to include, i.e. previous 2sec, 3sec, 4sec, 5sec ... 15sec\n", "parm_calculate_mv = 15 # Number of previous price/increment Moving Average to calculate, i.e. previous 2sec, 3sec, 4sec, 5sec ... 15sec\n", "parm_calculate_target_second = 7 # How many seconds in future to predict: target variable\n", "parm_calculate_prev_month = 3 # Number of previous month to include (need to remove earliest x month from training data)\n", "\n", "parm_record_cut_row_head = max(parm_calculate_base_price_second, parm_calculate_prev_bp, parm_calculate_mv)\n", "parm_record_cut_row_tail = parm_calculate_target_second\n", "parm_record_cut_month_head = parm_calculate_prev_month + 1\n", "\n", "parm_ts_valid_cycle = parm_ts_cycle - parm_record_cut_row_head - parm_record_cut_row_tail\n", "print('parm_ts_valid_cycle : %d seconds' % parm_ts_valid_cycle)\n", "parm_ts_valid_month = parm_ts_month - parm_record_cut_month_head\n", "print('parm_ts_valid_month : %d months' % parm_ts_valid_month)\n", "\n", "if parm_record_cut_month_head < 10:\n", " parm_record_cut_ccyy = pd.to_datetime('2015-0'+str(parm_record_cut_month_head))\n", "else:\n", " parm_record_cut_ccyy = pd.to_datetime('2015-'+str(parm_record_cut_month_head))\n", "\n", "print('parm_record_cut_ccyy : %s' % parm_record_cut_ccyy)\n", "\n", "print('parm_record_cut_month_head : %d months' % parm_record_cut_month_head)\n", "print('parm_record_cut_row_head : %d seconds' % parm_record_cut_row_head)\n", "print('parm_record_cut_row_tail : %d seconds' % parm_record_cut_row_tail)\n", "print(' : ' )\n", "print(' : ' )\n", "print(' : ' )\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015-01</td>\n", " <td>11:29:00</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015-01</td>\n", " <td>11:29:01</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015-01</td>\n", " <td>11:29:02</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2015-01</td>\n", " <td>11:29:03</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2015-01</td>\n", " <td>11:29:04</td>\n", " <td>74000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price\n", "0 2015-01 11:29:00 74000\n", "1 2015-01 11:29:01 74000\n", "2 2015-01 11:29:02 74000\n", "3 2015-01 11:29:03 74000\n", "4 2015-01 11:29:04 74000" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare derived features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Process: df_history_ts_process" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# date of current month\n", "df_history_ts_process['date-curr'] = df_history_ts_process.apply(lambda row: pd.to_datetime(row['ccyy-mm']), axis=1)\n", "\n", "# date of previous month\n", "df_history_ts_process['date-prev'] = df_history_ts_process.apply(lambda row: row['date-curr'] - pd.offsets.MonthBegin(1), axis=1)\n", "\n", "\n", "# Year\n", "df_history_ts_process['year'] = df_history_ts_process.apply(lambda row: row['ccyy-mm'][0:4], axis=1)\n", "\n", "# Month\n", "df_history_ts_process['month'] = df_history_ts_process.apply(lambda row: row['ccyy-mm'][5:7], axis=1)\n", "\n", "# Hour\n", "df_history_ts_process['hour'] = df_history_ts_process.apply(lambda row: row['time'][0:2], axis=1)\n", "\n", "# Minute\n", "df_history_ts_process['minute'] = df_history_ts_process.apply(lambda row: row['time'][3:5], axis=1)\n", "\n", "# Second\n", "df_history_ts_process['second'] = df_history_ts_process.apply(lambda row: row['time'][6:8], axis=1)\n", "\n", "\n", "# datetime of current month\n", "df_history_ts_process['datetime-curr'] = df_history_ts_process.apply(lambda row: str(row['date-curr']) + ' ' + row['time'], axis=1)\n", "\n", "# datetime of previous month\n", "df_history_ts_process['datetime-prev'] = df_history_ts_process.apply(lambda row: str(row['date-prev']) + ' ' + row['time'], axis=1)\n" ] }, { "cell_type": "code", "execution_count": 7, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>datetime-curr</th>\n", " <th>datetime-prev</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1886</th>\n", " <td>2017-07</td>\n", " <td>11:29:56</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>56</td>\n", " <td>2017-07-01 00:00:00 11:29:56</td>\n", " <td>2017-06-01 00:00:00 11:29:56</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>2017-07</td>\n", " <td>11:29:57</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>57</td>\n", " <td>2017-07-01 00:00:00 11:29:57</td>\n", " <td>2017-06-01 00:00:00 11:29:57</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>2017-07</td>\n", " <td>11:29:58</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>58</td>\n", " <td>2017-07-01 00:00:00 11:29:58</td>\n", " <td>2017-06-01 00:00:00 11:29:58</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>2017-07</td>\n", " <td>11:29:59</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>59</td>\n", " <td>2017-07-01 00:00:00 11:29:59</td>\n", " <td>2017-06-01 00:00:00 11:29:59</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>2017-07</td>\n", " <td>11:30:00</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>30</td>\n", " <td>00</td>\n", " <td>2017-07-01 00:00:00 11:30:00</td>\n", " <td>2017-06-01 00:00:00 11:30:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour \\\n", "1886 2017-07 11:29:56 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1887 2017-07 11:29:57 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1888 2017-07 11:29:58 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1889 2017-07 11:29:59 92200 2017-07-01 2017-06-01 2017 07 11 \n", "1890 2017-07 11:30:00 92200 2017-07-01 2017-06-01 2017 07 11 \n", "\n", " minute second datetime-curr datetime-prev \n", "1886 29 56 2017-07-01 00:00:00 11:29:56 2017-06-01 00:00:00 11:29:56 \n", "1887 29 57 2017-07-01 00:00:00 11:29:57 2017-06-01 00:00:00 11:29:57 \n", "1888 29 58 2017-07-01 00:00:00 11:29:58 2017-06-01 00:00:00 11:29:58 \n", "1889 29 59 2017-07-01 00:00:00 11:29:59 2017-06-01 00:00:00 11:29:59 \n", "1890 30 00 2017-07-01 00:00:00 11:30:00 2017-06-01 00:00:00 11:30:00 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process.tail()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# df_history_ts_process\n", "# df_history_ts_process[1768:]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating : base-price15sec\n", "Total records processed : 1891\n" ] } ], "source": [ "# new ['base-price']\n", "gap = 1 # only one new feature/column\n", "\n", "for gap in range(1, gap+1):\n", " col_name = 'base-price'+str(parm_calculate_base_price_second)+'sec'\n", " col_name_base_price = col_name\n", " col_data = pd.DataFrame(columns=[col_name])\n", " print('Creating : ', col_name) \n", "\n", " for month in range(0, parm_ts_month):\n", " for i in range(0, parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = df_history_ts_process['bid-price'][month*parm_ts_cycle+parm_calculate_base_price_second]\n", " \n", " df_history_ts_process[col_name] = col_data\n", "\n", "print('Total records processed : ', len(col_data)) " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# df_history_ts_process\n", "# df_history_ts_process[1768:]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# new ['increment-price'] = ['bid-price'] - ['base-price']\n", "\n", "df_history_ts_process['increment-price'] = df_history_ts_process.apply(lambda row: row['bid-price'] - row[col_name_base_price], axis=1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# df_history_ts_process\n", "# df_history_ts_process[1768:]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/env_py3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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kJAOg6P1T+utwywA427xa13XUBvPOz5WfE3oTGDF8SDAXQsScop2nARg9IrlD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\nemvErH00sThWt+K2D/POzk4yMzOtxxkZGeMGYqxqb2/no48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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2179ef9c88>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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J+/TQDJnrPs2cGBqfc+cxqSzmSVXXLK2TVYzTDoU0KW9PDEyT8qhLtLmg5nD4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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2179ea1518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df_history_ts_process['bid-price'])\n", "plt.plot(df_history_ts_process[col_name_base_price])\n", "plt.plot()\n", "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price'])\n", "plt.plot()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### ['increment-price-target']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating : increment-price-target\n", "Total records processed : 1891\n" ] } ], "source": [ "# previous N sec ['increment-price-target']\n", "\n", "for gap in range(1, 2):\n", " col_name = 'increment-price-target'\n", " col_data = pd.DataFrame(columns=[col_name])\n", " print('Creating : ', col_name) \n", "\n", " for month in range(0, parm_ts_month):\n", " # print('month : ', month)\n", " for i in range(0, (parm_ts_cycle - parm_calculate_target_second)):\n", " col_data.loc[month*parm_ts_cycle+i] = df_history_ts_process['increment-price'][month*parm_ts_cycle+i+parm_calculate_target_second]\n", " for i in range((parm_ts_cycle - parm_calculate_target_second), parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = 0\n", " \n", " df_history_ts_process[col_name] = col_data\n", "\n", "print('Total records processed : ', len(col_data)) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/env_py3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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iYgITExM4fPiw9/nU1BSuueaa1H4WFtYbGWYkxsYGMDOjr320M/KaU6VaQ4fR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oygCS0jssH+5oVSNVug1eZBJU7JJvJ/dnOkBV9qc8l7Wo704GchvRj6hxhYzLJhAxE0oWWEL7X5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\n8gxceHNwcHBwcOQZ/j/k+x7/6HYMogAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2179eeb470>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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he/u5ZsngzcybiKiwJEUSnsAg1pTVo8qh/5h074gfEKmQx8zbpBRZghDguDcRUYEYmz6D\nmfgMuqr1XyIGAH0jAdiVVMhTS6SamSWDtyynxj3YdU5EVBi0euYGjHf7Q7M444+gqqwEQCrLNzsG\nbyIiyjsjJ6v1jgQAANUVDN6mpsxVguO4NxFRYfAEBmCX7WitaNa97d6R1GS1mopSAAzepqVl3hzz\nJiLKu0g8gpGQFx3Va6HI+mxAda7e4QAkADXMvM1NkZl5ExEViv7AEASEIZPVEskkPKMBtDZUwDG3\nhXUSDN6mxDFvIqLCoY53dxow3j00FkY0nkR3i1PbPIuZt0kpDN5ERAVDm2luRHGWufHuda3VUKRU\nl3wxDJlaMnjL7DYnIioIQgh4AgOoK62Fs6Ra9/Z7h1Mzzde1OCGDmbepMfMmIioM4zMTCMXChmTd\nQCrzLi+xoam+XNupjMHbpGR5rsoOgzcRUV5pO4k59Z+sFpyOYmxyBt0t1ZAlicHb7NR13sy8iYjy\ny+2fm6xmQObdN1ecpbsl1R2vBm+WRzUpjnkTERUGd6AfNknB2qoW3duen6zmBDAfvAWDtzlxnTcR\nUf5FE1EMh0bRVrUWdtmme/vqZDVm3kWC67yJiPJvIDiMpEgaUs88mRRwjwbQXF+OilI7AGbepjff\nbW7+PyARkVnN7ySm/2S1kTNhRKIJrGtxaseYeZscl4oREeWftpOYgcVZulvn144z8zY5LfMugio7\nRERmJISA298Pp6MaNSXOlb8hQ+cWZ1HJ6o6SDN7mxMybiCi/zkamEIgG0eXs0GqO66l3xI8Sh4LW\nNRXaMVktjwrz3/stHbw525yIKD/cAXW8W/8u83AkhtGJaXQ3V2s9rQDOKY+a0P1n5polgzdnmxMR\n5ZfHr4536z9ZzT1XnGVd68Ja6YpWYc38935LBm9m3kRE+eUODECWZLRVteredq9WWW3hWLrE8qjm\nxsybiCh/YokYBoPDaKtshUOx695+7/BcZbWWpTJvBm9TUmubM/MmIsq9wdAIEiKBTiOKswiBvpEA\nGmvLUFXuWPA1Zt4mx8ybiCh/tOIsBqzv9k5MY3o2fkHWDVg48z548CC2bt2K66+/Xjs2NTWFAwcO\n4JprrsGBAwfg96e6K4QQ+PKXv4xdu3Zh7969eOedd7TveeaZZ3DNNdfgmmuuwTPPPKPTr5I+heu8\niYjyxm3gNqDnb0ZyLstm3jfccAMef/zxBccOHTqErVu34vnnn8fWrVtx6NAhAMCxY8fg8Xjw/PPP\n46GHHsIDDzwAIBXsv/vd7+JHP/oRfvzjH+O73/2uFvBzhZk3EVH+uP39qLJXor60Vve2FyvOorLs\nbPMrrrgCTufCC3L06FHs378fALB//3688MILC45LkoQtW7YgEAhgbGwMr776KrZt24aamho4nU5s\n27YNr7zyik6/Tno425yIKD8mI1OYmvUbVpylb8QPh03G2saKC76mVljjOm8AExMTaGxsBAA0NDRg\nYmICAODz+dDU1KS9rqmpCT6f74LjLpcLPp8v29PICDNvIqL88AQGARgz3j0zG8fweBidzdVQ5AvD\nWzFVWNN1A1VJkgx5kgKA2tpy2GyKLm3VnZkGAJSVOdDQUKVLm0Yo5HPLJV6HebwWKbwO88x2LbzD\nowCALR0f0PXcGxqq8Lv3xiEAbF6/ZtG2z0qpbLy0zGa663a+rIN3fX09xsbG0NjYiLGxMdTV1QFI\nZdRer1d7ndfrhcvlgsvlwvHjx7XjPp8PH/7wh1f8OZOT09meqiYYnAEABIIRjI8HdWtXTw0NVQV7\nbrnE6zCP1yKF12GeGa/F772nIUGCM1mv27mr1+GNd1Mxp7m2bNG2A4EIACAYnjHFdVvuASPrbvOd\nO3fi8OHDAIDDhw/j6quvXnBcCIG33noLVVVVaGxsxJVXXolXX30Vfr8ffr8fr776Kq688spsTyMj\n6jpvdpsTEeVOPBnHYHAIrZXNKFEcK39DhvqWKM6ikqFuCWr+e39Gmfe9996L48ePY3JyEtu3b8dn\nP/tZ/NVf/RXuuecePPXUU2hpacE3v/lNAMCOHTvw8ssvY9euXSgrK8NXvvIVAEBNTQ3uvPNO3HTT\nTQCAu+66CzU1NTr/WsuTOWGNiCjnhkOjiCXjhiwRE0KgdySANc5SOCtLFn2Nup93MWwJmlHw/sY3\nvrHo8SeeeOKCY5Ik4f7771/09TfddJMWvPNBnchQDMsFiIjMwq1tRqL/ZLWxqRmEZmK4pHPp5Wdq\n8BZFELwtXWGNmTcRUe6o24AaURZ1vp75heu7VcWUeVsyeCtcKkZElHNu/wAqbOVoLFuje9u92jag\nKwdvZt4mxcybiCi3AtEgJiJn0elsN6Y4y3AANkVGu6tyydcw8zY5FmkhIsotI8e7I7NxDI6F0NFU\nCZuydFizbHnUYsHyqEREueUxcDOS00NTSAqx7Hg3AC3jZ3lUk2LmTUSUW25/PyRI6Kheq3vbf+if\nBLD8eDcAKEVUHtWSwVst0sLMm4jIeIlkAv2BQTRVNKLMVqZ7+6f6zwJYujiLaj7z5pi3KWmZdxGM\nexARFbqRsA/RZAxd1cYUZ/lD/yRqq0pQV1267GsVq+7nXSw45k1ElDueufXdXQas754IRDAZnEX3\nClk3AEhg8DY1jnlTsZiMTOGfTj6JQFT/TRaSSYEn/+09vOM+q3vbZC3aTHMDJqv1Ds+t715hshrA\nzNv0WKSFisWvRl/DG2O/w+8n/qB72/2+II6+MYRXTozo3jZZizvQj1KlFK7yBt3b7h1JVVZLJ/OW\nGbzNjUVaqFioGU0iqf/Sl9Nz5SYTCX5OaPVCsTDGps+gs7pNC5566h0OQJEldDatvD83J6yZ3Hzm\nbf4/IFlXUiS1tbMxEde9/b65cpN8yKVseAzsMo/FExjwBdHd6oTDrqz4elmSIUFi8DYrZt5UDMam\nz2A6PgPAmMxb3eghzodcysJ8cRb9J6v1+0JIJAUu7lh6J7HzyZJcFCuNrBm8JQkSOOZN5uaeuykC\n+gdvf2gWZ/yRVNvsNqcsqEM7nQaURVUfMD/QUZf298gSM29Tk2UJiSJ4+iLr8vj7tf/Xu9tc7TIH\ngETC/Dc6yg91aMdV3oAKe7nu7as7iWWeebM8qmkpssTMm0zNyMz79NwM3lTb/JzQ6njDY4gkZg3J\nuoFU5l1dboerLv0HA1mSWR7VzGRZYncgmVYkHsFIyIsSxQEAiOudeQ8HICH1kBtn8KZVcmvFWfSf\nrHZ2rjjLulZnRluMypLMLUHNTGG3OZlYf2AIAgLdzk4A+mbeiWQSbm8ArQ0VcNgVPuTSqnkM3AZU\nHdpJZ333uWRJhmDwNi+Z3eZkYmqX+YaabgBAPKlf5j00FkY0lkR3izP1kMvZ5rRKfYEBOBQHWiqb\ndG9bLc6yfoWdxM4ng5m3qcmyxLE8Mi21VvS6mi4AQFzHCTh9czfFdS3VsCn8nNDqTMdm4A370Fll\nUHGWkQAkCehsYuZtKZywRmYlhIDbP4DakhrUl6Zm2erZbX5arRXd6oQiy+w2p1XpDwwCADoNWN8d\nTyThGQ2iraESJY6Vi7Oci2PeJidLzCjInM7MnEUoFka3swM22QZA/8y7rMSGpvpyKIrEIi20Kupk\ntW4DJqsNjoUQTySxLsMucyC1OQkzbxNj5k1mpd4UO53tsMmprEOvMe/gdBS+yRl0t1RDlqTUmDcz\nb1oFI4uzqHX3M52sBgBSkWTetnyfQL4oioyEjpN8iPQUiAZx5PTPMJuMasdKSmyYnY1jNOQFAHRV\nd0CRUh/hTLrNhRB4+lgffJMzF3wtPBMDkBrvBpDqNudDLqVhataPn/b+HNFk6j102u/GmrJ6VDkq\ns247KQR+8lIvxueq/g14U1vgZjpZDVAzb/O/py0bvGWJmTcVruPe3+LX3teX/HpNiRNrq1q0/Ykz\n6TYfORNGz6/6l/y6LEnYvK4eAKAonG1O6fn16Ov4jfeNBccuXXOJLm0P+kL42W8GFhxrri9HY21Z\nxm1JksTM28y4zpsKmdrl+Pkr/jtqSlLZRX19BSYmwgCAUlsp7HPj3bIkZ9RtrpaU/MSfrsefbLpw\nCY/dJqOsJNW2jd3mlCb1PfvFD9+rZduV9gpd2laXhf2/H9uAD3/QBQAoL7VlVJxFpUgykmDwNi2u\n86ZC5gkMoNpRhbWVLdoNyllahajjwpuVIikZdZurS8Eu6axFdYVj2dcqc0sqhRCrulGSNQgh4AkM\noK601pg13XMrIC7prFvxPbsSSZK5MYmZccIaFarJyBSmZv3oqm5PK2DaZCWj8qi9wwGU2BW0Nqyc\nFSlK6hZRDFsoknHGZyYQioUNqaQGpDLv8rkVENlSiiR4Z5159/X14XOf+5z278HBQdx9990IBoP4\n0Y9+hLq61FZt9957L3bs2AEAeOyxx/DUU09BlmX8j//xP3DVVVdlexoZk5lRUIFya/sfp7fEJpPM\nezoSx8iZMC5ur4Eir/zsriipz0Y8IaBY9lGfVuLJ8D2bieB0FGOTM9jUVQdZh3u1BAZvAEB3dzeO\nHDkCAEgkEti+fTt27dqFp59+Grfddhtuv/32Ba8/ffo0enp60NPTA5/PhwMHDuAXv/gFFCWzhfbZ\nUuTUm0AIgLGbCol7bqvPdJfY2GRb2mPe7tEABIDulvRm6drmAnwiIQB7Wt9CFpTpezYTvausYb4U\ndZJnUiQNqfyWK7qe+a9+9Su0tbWhtbV1ydccPXoUe/bsgcPhQFtbGzo6OnDixAk9TyMt8lzw5jIY\nKjRu/wBkSUZH9dq0Xm+TlLRnm6sTf9a1pncjVLTPifkzFTKOOzAAm2xDW1WL7m33rbKG+VLkc4K3\nmekavHt6enD99ddr/37yySexd+9eHDx4EH5/6g/g8/nQ1DQ/ocHlcsHn8+l5GmlRb0oc96ZCEkvG\nMRgcQmtlMxxKehNzFNmWdre5OvEn3cxb7TbnQy4tJZqIYjg0irbKVq3in57U92yXTpl3sQRv3a50\nNBrFv//7v+Ov//qvAQCf+tSncOedd0KSJHzrW9/CI488gocffnjV7dfWlsNm069rvaw01QdYW1eB\nirLC7A9saKjK9ykUBCtdh/cn3IiLBD7oWrfo773YsVKHA6FYcMXrJISAezQAV1051nfWp3U+5eWp\nBwhnTTkaarOfLKQXK70nVpLva/H7sfeRFElsbFqv+7kkkgIebwBtrkp0ttUt+9p0f3ZpSep+X1df\ngTJ7adbnmC+6Be9jx45h48aNWLNmDQBo/wWAm2++GZ/5zGcApDJtr9erfc3n88Hlcq3Y/uTktF6n\nCgCIx1KZyth4EJUFGLwbGqowPh7M92nkndWuw5uDpwAAzY6WC37vJa9FQkI0EV/xOnnPTiM0E8Om\nrrq0r2k8mvqcjI8HIcX1q5+eDau9J5ZTCNfirYHUe9blaNb9XIbGQpiZTaCjcfnfM5PrEI+lepHG\nxv0ot8d0OU+jLPdAolu3eU9PD/bs2aP9e2xsTPv/F154ARs2bAAA7Ny5Ez09PYhGoxgcHITH48Gl\nl16q12mkjWPeVIhWM/FHkRUk0hjz7l1FPWh2m9NK1PesEcvETs+Nd3enOUcjHeqMdbMvf9Ql856e\nnsYvf/lLPPjgg9qxr3/96zh1KvVE1traqn1tw4YNuPbaa3HddddBURTcd999OZ9pDnDMmwqTOzCA\nSnsFGsrS69YGUhPWkiK54uxZddZuJjsxLZhtTnQeIQTcgQHUlDhRW1qje/t9c+Pd69Oco5EO9TNi\n9hKpugTv8vJy/OY3v1lw7Otf//qSr7/jjjtwxx136PGjV03mLFoqMP7ZAM5GJrGp/oMZ1R7QtgVN\nJuBYZjF277AfdpuMtsb0N4rQ1nnzc0KLOBuZRCAaxJaGzYa03zviR6lDQcsafcqsAvPBW5i8RKp5\nF7llSWbmTQVmvjhLZt2Pyty2oIllqqxFonEMjYfQ0VQFWwbVVrSlYsy8aRGrfc+mIxyJYXRiGl3N\n1dr9Wg9a5m3yB1LL1jZXOOZNOTIQHMLx0d9CYPn3Wn9gCEBqq890hSMxeM+ktkn80YvvwY7Fd1kK\nzsQgRObdjxzzpuV45jYjyeQ9my63Nsyj33g3UDyZt2WDNzNvypUjp3+GU5Pvp/XaUqU07eIsAPDq\niVGMnplJjCQyAAAgAElEQVSBbQ3w8olhILb80pdLOmvTbhuAVkI1kTD3jY6M0RfohyzJaKtaujDX\nap3WJljqN94NADI45m1qisSMgoyXFEl4AgNoKKvHpzfduuLrnSXVKLWlv/a0d9gPiNTN6J5PbEKN\nY+ngXOpQ0JjhWm32UNFSYokYhoIjaKtshUPRf7ltn5p561ScRSXPPZAKBm9z0jJvky8XoMI2GvYh\nkpjFlprNWGtA6cjekQAcLhuSABrrStFUoW+RjPkJa/yc0EKDoWEkRAKdBox3J4VA70gAjbVlqCrP\nbgvQ8xVL5m3ZCWvMKCgX5scE9b/BnQ1EMBmchbM8lamns9Y7UwqXitES3HPv7W4D3tveiWnMzMax\nTucuc+DcjUnM/Z62bPDmmDflQl9groCFAVslquu2aypTk9TS3VksEzaFSyppcepM805D3tuZbaCT\nCUkr0lIYFQNXy7LBm0VaKBc8/gGUKA40V6xcAjhTasW0+qrUOHY8zc1JMsGlYrQUt78fVY5K1Jdm\nNgkyHepmJMZk3qmllcy8TYrlUclo07EZeKfH0FHdbsi+wX0jAciShPqqVOa93Drv1VK7zVmkhc41\nGZnC1KwfXdUdGRUUSlffiB8Ou4y1jfoVZ1HNZ97mfk9bNngz8yajeQLGjQnGE0l4vEG0NVaixJaa\n6WtI5s113rQII4uzzMzGMTweRldTtfbwqCelSLYEtWzwZuZNRpsfE9T/BjfgCyGeSKK7tVqrsGbE\nmDe7zWkxRk7EdI8GIKDvZiTnkhi8zU1d583Mm4yymh3C0qWOd69rqZ6vbW7kbHN+Tugc7sAAZElG\ne3Wb7m1rG+gYMN4NnJN5m7zCmmWDNzNvMlKqOMsgGsrqUeVIfyOQdM3PxnXCNjcBJ2Fot7m5b3Sk\nn3gyjoHgEFormlCi6LsGG1j4YGoEdZ03M2+TUlikhQw0Nj2OmfgMOg2o+QykZuNWltnRWFMGxcDM\nW1sqxm5zmjMcGkU8GTdkiZgQAn0jAaxxlsJZWaJ7+8B8hTUGb5Ni5k1G0gpYGDDePRWaxUQggnUt\n1ZAkCXbJyDHvudnmrG1Oc/rmhoOMGO8em5xBaCaW0Z7zmZLB2eamxiItZCQjJ6upNZ+7525wauZt\nSLc5H3LpPB4DZ5qrw0HdBnWZA4CsDjOZPHhbtrY5b0rWlkgm8MvR1zATnzGk/d/53oUMG06cjOKk\n1K9LmxUVJQiHZ/Fu/ySA+TFBmzrb3Ih13lwqRudx+wdQYS9HQ9ka3ds2erIaAMhzk5W5MYlJsdvc\n2t6eeBc//MPThv6MhL8eTx/3GNK2wy6jqzkVvBUDJ6zZWNuczhGIBjEROYuN9R8wpDhL77AfNkVG\nu0v/SZ4qtWASM2+TUsfy2G1uTX1THgDAjeuvh6uiUde2B3xB/OTlPvxR63pceXOnbu06nWXw+1M9\nBQ01pSgrSX18taViRq7z5mxzwvxcji4DJmLORhMYGguju6UaNsW4EV2121yYfLKyZYO3zP28Lc0d\nGIAECdtaP6r7cpe+P3iQ9Afx0avbcem6et3abWiowvh48ILj893mrLBGxlJrFxgx3u3xBpAUwpDN\nSM6l3ftNnnlbdsIay6NaVzwZx2BwCK2VzYasU+0zeJ3q+YzsNmeFNTqXZ+6ht8OExVlUare52ce8\nLRu8ZXYHWtZwaBSxZNyQbTqFEOg1eJ3q+eYrrBmxJahaYY2fE6tLJBPoDwyiucKFMlup7u2rxVmM\nnGkOFM+Yt2WDNzNv63IbWJd5bCq1TtXoG9C5bNo6b+My7zgzb8sbCXsRTcYM6TJXH3prq0pQV63/\ng8G5ZJZHNTfONrcud2Cu5rgRa7AN3Id4KYau81ZY25xS1IdeI6oGTvgjCISjORlqkrkxibmxPKp1\nuf0DqLCVo9GAdaqnz6k5niuGrvPWxrzNfaOj7BlbnGWu8FAOHnq5JajJMfO2JnWdaqez3ZB1qn3D\nAcPXqZ5PyUG3OT8n5Pb3o8xWCld5g+5tq+Pd63Pw0CuxPKq5cczbmjxGrlONJTA4FkJnU5Wh61TP\np05YS7DCGhkkFAtjbOYMOqvbtW5nPfWOBKDIUk4eetWHXbP3ulo+ePOmZC1uA7v+PKOpdaq5nKwG\nGDthTVsTy25zS/No4936f25i8QQGfEG0uyrhsCu6t38+tcctaUBdhFyybPDmxiTW5Pb3G7ZOVd0w\nJJfj3QCgyMat85YkCYos8SHX4uYfevXvser3hpBIipxN8pwf8zb3e1q3Cms7d+5ERUUFZFmGoih4\n+umnMTU1hc997nMYHh5Ga2srvvnNb8LpdEIIgb/7u7/Dyy+/jNLSUjzyyCPYuHGjXqeSFnabW08i\nmUB/cMi4dapakYncZt6yJEOWZEMmrAGptd5cKmZt85m3EcVZ5tZ3G1xZTSVxwtqFnnjiCRw5cgRP\nP53a8OHQoUPYunUrnn/+eWzduhWHDh0CABw7dgwejwfPP/88HnroITzwwAN6nkZaOGHNekbCPkQT\nUUO6/oQQ6B3252Sd6mJskmJItzmAuczb3Dc6Wr2kSMITGICrvAEV9nLd2+/VKhLmOvM293va0Nrm\nR48exfe//30AwP79+3Hrrbfib/7mb3D06FHs378fkiRhy5YtCAQCGBsbQ2OjvhtELEeRmHlbwXBo\nFEPBEQBAr98DQL+uv0A4inc8ZyGEwMxsAv5wFJdfrP9M3HQosg0Jg8bwFIXd5lYzNevHe5O9EEIg\nGAshkpjFFp0mecbiSfzu9BlE46n36/tDflRXOLDGmZuH3mJZ561r8L799tshSRI++clP4pOf/CQm\nJia0gNzQ0ICJiQkAgM/nQ1NTk/Z9TU1N8Pl8OQ3eWuZt8nEPWlpSJPGt3z6GcHx6wfF1OgXv//vC\nezj+7tiCYxvW1ujSdqZSmbcx3eaKLLG2ucX84NTTODnx7oJj62o6dWn7lRMj+D/Pv7fg2B9f3GDI\n0s3FMHif5wc/+AFcLhcmJiZw4MABdHd3L/i6JElZ/XFqa8ths+k3E9FWYgcA2O02NDRU6daungr1\nvHJttddhYGoY4fg0NjZehKs6PgwAqCurxabmdbqcV+9IANUVDvzlnksAAA67gq2bm1Fi4IzZpa6F\nw26HkJKGvGccdgVimZ+dD4V0Lvmm97VIiiTcAQ/qy2px86Y9AIASmwMfaf0QbEr2IWNgLAwA+C97\nN6KizA5ZAv7oA66sh5vSvQ7T9tRytJLSwr33p0O34O1yuQAA9fX12LVrF06cOIH6+nqtO3xsbAx1\ndXXaa71er/a9Xq9X+/6lTE5OL/v1TAWnowCA6Znootss5ttS2z9aTTbX4bfDqczh0tpN2Fx1qXZc\nj+t6NhDBhD+CD21Ygw9112nHA1P6vk/Ptdy1kISMaCJu2HsmGksUzPuRn415RlwLb9iHcGwGG+s/\nuOBzM3l2Rpf23+mbQGWZHdsuadQSusRsDOPjsVW3mcl18IcjAIDQTKTg30fLPVzoMmFtenoaoVBI\n+///+I//wIYNG7Bz504cPnwYAHD48GFcffXVAKAdF0LgrbfeQlVVVU67zAHONreCvoC697D+y1vU\nmeW5qAiVDnabk16M3LhnKjSLiUAE61udOesmP588V2HN7FuC6pJ5T0xM4K677gIAJBIJXH/99di+\nfTs2b96Me+65B0899RRaWlrwzW9+EwCwY8cOvPzyy9i1axfKysrwla98RY/TyAhnmxc/j38AJYoD\nzRXL9+qsRq62L0yXIhs521zm58RCjFzT3afVMM/f50aeK2pk9i1BdQnebW1t+OlPf3rB8draWjzx\nxBMXHJckCffff78eP3rVmHkXt+nYDLzTY7iodr1B5Rz9kCUJnU2FEbxtss2Q8qgAYFMkVlizELe/\nH3bZjpaKppVfnKH5ZWH5DN6p+4Ew+WRly1dYY0ZRnNQdkLoNKeeYRL83hLbGSpQ4jC/nmA5D13lz\nqZhlROIRjIZ96Kheq1Xu01PvSACSBHQ25zN4qyuNWB7VlGSu8y5qatefEXt2D4wFEU8kc1YRKh2K\nbIOAMGT5i9ptbvZMhVbmCQxCQBiycU88kYRnNIDWNZUoKzG0xMiy1G5zs7+fLRu8JUmCLElc512k\njNw9rG94brJajipCpWN+cxID9/Tmg27RM3LP7uHxMKLxJNbn+aF3PvM291CQZYM3kOo6Z+ZdfFLr\nVAfQUFaPSkeF7u3nuhZzOtRtQQ3Z05vbglqGW6thbsQKDXWSZ34fehVtzJvB27S4W1JxGps+g5n4\njCE3IADoHQ6gssyOxpoyQ9pfDW1nMQPG8Wxy6jbB5WLFTQgBT2AA9aW1cJboX7xEm6yW54dedWMS\nZt4mxsy7OKnj3d0GdP2p61TXtVTnbZ3qYnLTbW7umx0tb3xmAqFY2JCNe4DUZLWKUhtcdfpvbpIJ\nbWMSmPv9bOngrTB4FyW3P1WcxYjJar1z493dBVKcRaVm3kZ2m3Nb0OKmfm6MWN8dmI5ibHIGXS3V\n2phzvmhbgpr8YdTSwVtmt3lR8gQGYJftaK1o1r3tvrlxu/UFUpxFpY55G7HWW1G7zU1+s6PlGTlZ\nTS3OUgiTPOczb3Pf+/M3X78AMPMuDtFEDJ5AP5JCICESGAl5sa6mU5d1qkIIeLxBTM+mguLvPZN5\nX6e6mPluc05Ys6JIfBb9c8u8Vuu9yV7YZBvWVrYsOJ4UAn0jAczGVv/eeuNUave9QpjkKc2VR02a\nfJ23pYO3LDHzLgbP9v0c/z74yoJj3c5OXdr+ff8k/tcP31pwrK0xv+tUF6N1mxuQedvUMW92mxes\nH793BL/2vp51O93OTq0XR/X6qTH8f0feybptSQK6C+ChN7VMWEbS5MuEC+sOlGOKLCHGso+mp2YM\n/7kjtfGNTVbw0ebLdWn7VP8kAGDHlhbUVZUAAC5dt0aXtvVkkwxcKqZ1m5v7ZlfM3pvqRbmtDDvb\ntq+6DUkCLl2z8YLj6mfgY5evRVWZfdXtr22oRHnp6r9fTzIk7udtZrIsIRHjDcnMZhNRDIdG0eXs\nwLVdV+vefu+wHxKAm//TepSXFu7HxaYuFTO029zcN7ti5Z8N4GxkEpvXfNCYz8BIAHabjE/86XrY\nlOKYJpXKvM39fi6Ov8Qqcczb/Aa0co76T7JJJgXco0E0r6ko6MANnJN5GzCOp7DbvKBppYANqGsQ\nicYxNB5CZ1NV0QRugMHb9Djb3Py0ilAGzJAdGg9hNpYomG0/lzO/VMyA2eacsFbQPAbuv+0eDUII\nYF0BzBLXE4O3ybFIi/nNF2Qxbu/h9QW2pnsx80vFjBvzjnN+SEHq8/dDgoSO6rX6tz1SGFXR9Mbg\nbXIsj2puQgi4/f2oKXGipkT/AKuWczRD5m1khTUbM++CFU8mMBAcQktlE0ptpbq3rxUlYuZdcCwd\nvJl5m9tEZBLBWMiQilBAaqJOWYmCljX6b26iN0MrrLG2ecEamBpGLBkzpMtcCIHeET/qq0tQO7fS\nolgweJucIklICu5TbFYetZyjATeu0EwM3rPT6GrOfznHdKiZtzEV1jjbvFC9N9EHAOg04AF23B9B\ncDpWdFk3kAre3JjExGTuU2xqfTko52iWGxe3BLWm9yfcAIx5gNV2ATPBsFGmZEnOqhpdIbB08FYz\nCnadm5PHPwBFUtBW2ap721oNc5NM1DFyS1AuFStc7024UW4rQ2O5/oWD+ubGu9eZYMJmpmRJMuSz\nkkuWDt7MvM0rmohhMDSMtVUtsCv6V22an6xmjhvXfIU14zYmibPbvKAEoyH4QuPorG6HLOl/Kz89\n4odNkdDu0n9v73yTJcX0w6WWDt5a5m3yP6IVDQaHkRRJdBtQmCIpBPpGA3DVlaMyi3KQuZSLLUH5\nkFtYjNwFbDaWwNBYCB2uKthtxRcmZEgc8zYzZt7m5Q4Yt2f36MQ0ZmYTphrrM3Kdt1pZi93mhcWt\nFWfR/wG23xtEIilM0/OUKUWSIUwevAu75qPBOOa9OoFoEOHYtCFtR2MJTIai2r+dgVL4A5ELXnfC\n9wcAQFmiAcNnwrqew1vvjwMw10QdI9d5z495m/tmV2zcgQFIkNDpbNO97d4iLc6ikopgtrmlg7fM\n4J2xiZlJfOnXXyuIyR4iWoL/9S+nABizlMtME3WMXefNHqpCkxRJ9AcG0FrdhDJbme7ta5PVmHkX\nLEsHb0XiTSlT7031IiESuLh2PRrLG3RtOxpL4D/eHkVZiU3bflOxyUjEF/+Q1chtqP2Q/iUhAWCN\nsxRtjZWGtG0EQ9d5z415x/k5KRijYR9mE1FsqO/SvW0hBE6P+OGsdKCuuriKs6iYeZscM+/MqYVR\n9q+7Du0611J+871xvNT/Nq6/qgt7t6VuSg0NVRgfD+r6c4qRoeu8tQpr5r7ZFRP33OfwIgOC99nA\nLPyhKP74ogZIJihQtBrK3DpvIYRpf0dLT1hT1Ik4DN5pcwcGYJftaK1s1r3t03PjbN0m6q4uFDlZ\n583PScFQJ6sZkXn3ap/D4hzvBlKZNwBTl0i1dvCWmHlnIhKPYCTkRXvVWi1Y6KlvOAAJQHdz8d40\njGLoOm8uFSs47sAASpUSrK3W/yG6t8jHu4FU5g0ASRNXWcs6eI+OjuLWW2/Fddddhz179uCJJ54A\nAHznO9/BVVddhX379mHfvn14+eWXte957LHHsGvXLuzevRuvvPJKtqewalwqlpmB4BAEhCHrShPJ\nJNzeAFoaKlBWYunRnFWxGThhzcYtQQvKdGwavumxVHEWWf/8q2/ED0WW0NFUfMVZVGpXuZkz76zv\nkoqi4POf/zw2btyIUCiEG2+8Edu2bQMA3Hbbbbj99tsXvP706dPo6elBT08PfD4fDhw4gF/84hdQ\nFP0zuRXPnUVaMtKnrSvVP3gPjYURjSVNtTyrkChG7ufNzLuguAODAIypcRCLJ9HvC2JtYyVK7Lm/\nJ+eKwm5zoLGxERs3bgQAVFZWoru7Gz6fb8nXHz16FHv27IHD4UBbWxs6Ojpw4sSJbE9jVZh5Z8Zj\nYGEUtZZ4MXfVGUm9GRm7zpufk0Jg5G56A74g4glR9A/RMswfvHXtnxwaGsK7776Lyy67DL/97W/x\n5JNP4vDhw9i0aRM+//nPw+l0wufz4bLLLtO+x+VyLRvsVbW15bDZ9H0SrKpMLYOori5DQ0PhdREV\n0jkJIeAJDmJNeR02rNV/edbQRKroy+Wbmi/4vQvpOuTbctdCkWRIiv7XKzHXNWt3KAXztyiU88iH\n4d8PAwAu774EgL7X4pfvjgEAPvQBl+mucSbnW1aauvfX1pXBWWqu31OlW/AOh8O4++678YUvfAGV\nlZX41Kc+hTvvvBOSJOFb3/oWHnnkETz88MOrbn9yUv+KXpFIDAAwcTaM8SqH7u1no9CWSI1PTyA4\nG8JFjZcZcl6/75tAWYkNJTIWtF9o1yGfVroWimxDJDqr+/VSK9yFp6MF8bew8nsiKZJ474wbjWVr\nEAkIVDVA12tx4r1U8G6ocpjqGmf6nohFU8NL42eCiJYU7lKx5R5IdJntEIvFcPfdd2Pv3r245ppr\nAABr1qyBoiiQZRk333wz3n77bQCpTNvr9Wrf6/P54HK59DiNjHGdd/qMrCUenI7CNzmD7pZqyCZd\nc1kIbJJibIU1dpvnnW96HDPxiCGfQyC1m15lmR0NNfpXbSskMse8U92pX/ziF9Hd3Y0DBw5ox8fG\nxrT/f+GFF7BhwwYAwM6dO9HT04NoNIrBwUF4PB5ceuml2Z7GqnD9avrcBk5W6xtRl6YU9zib0RRZ\nQdyQCmush1AojNyMZDI4i4nALNa3Ok1buCRdxRC8s+42f+ONN3DkyBFcdNFF2LdvHwDg3nvvxXPP\nPYdTp04BAFpbW/Hggw8CADZs2IBrr70W1113HRRFwX333ZeXmeYAtCyPmffKPIF+2CQFa6tadW+7\nVw3eLM6SFZtkQ8LQzNu8N7pioU4aNWK5pjpptNsCD9Fq8DZzidSsg/fll1+OP/zhDxcc37Fjx5Lf\nc8cdd+COO+7I9kdnjZl3eqKJKIZCo+ioWgu7rP8a7N5h69w0jGSTFUQTMd3bVT8nrG2ef27/AByy\nHS0VTbq33WuhHjA1eJt5cxJLV8OQLbjOWwiBmfhMRt/jDgwgKZLocq7cVTczG8/oegoBuEcDaK4v\nR0WpPaPzooUU2YZE/MLtU7M1v5+3eW90ZjYTn4EQArOJKEbDPqyv6dKtwuFsLKEV3zk95IckAZ0W\nqHDIzNvk5jNv8/4BM/WPJ/8P3hx/e1Xf27nCePex343gez87taq2mXVnLzVhTf8xb1mWIIE9VPnQ\n4/43/Kv73xYcW+lzmK73h6bwtf/75oK/69qGSktUONQybxOXRy3+v9IyrDbbPJ6M4+TEu6i0V2Cd\nszOj7y23l2PTmg8u+5o33xsHAGxZvwaZzHdRFBm7Lm/L6HzoQqkJa8bss64oEoN3HpwYfwc2ScHG\n+g8ASO0et63lI/q03TuBRFLgA+01KCu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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2177bd1e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price'])\n", "plt.plot(df_history_ts_process['increment-price-target'])\n", "plt.plot()\n", "\n", "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-target'][1768:])\n", "plt.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating : increment-price-prev1sec\n", "Creating : increment-price-prev2sec\n", "Creating : increment-price-prev3sec\n", "Creating : increment-price-prev4sec\n", "Creating : increment-price-prev5sec\n", "Creating : increment-price-prev6sec\n", "Creating : increment-price-prev7sec\n", "Creating : increment-price-prev8sec\n", "Creating : increment-price-prev9sec\n", "Creating : increment-price-prev10sec\n", "Creating : increment-price-prev11sec\n", "Creating : increment-price-prev12sec\n", "Creating : increment-price-prev13sec\n", "Creating : increment-price-prev14sec\n", "Creating : increment-price-prev15sec\n", "Total records processed : 1891\n" ] } ], "source": [ "# previous 'parm_calculate_prev_bp' sec ['increment-price']\n", "gap = parm_calculate_prev_bp\n", "\n", "for gap in range(1, gap+1):\n", " col_name = 'increment-price-prev'+str(gap)+'sec'\n", " col_data = pd.DataFrame(columns=[col_name])\n", "# col_data_zeros = pd.DataFrame({col_name: np.zeros(gap)})\n", " print('Creating : ', col_name) \n", "\n", " for month in range(0, parm_ts_month):\n", " # print('month : ', month)\n", "# col_data.append(col_data_zeros)\n", " for i in range(0, gap):\n", " col_data.loc[month*parm_ts_cycle+i] = 0\n", " for i in range(gap, parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = df_history_ts_process['increment-price'][month*parm_ts_cycle+i-gap]\n", " \n", " df_history_ts_process[col_name] = col_data\n", "\n", "print('Total records processed : ', len(col_data)) " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating : increment-price-mv2sec\n", "Creating : increment-price-mv3sec\n", "Creating : increment-price-mv4sec\n", "Creating : increment-price-mv5sec\n", "Creating : increment-price-mv6sec\n", "Creating : increment-price-mv7sec\n", "Creating : increment-price-mv8sec\n", "Creating : increment-price-mv9sec\n", "Creating : increment-price-mv10sec\n", "Creating : increment-price-mv11sec\n", "Creating : increment-price-mv12sec\n", "Creating : increment-price-mv13sec\n", "Creating : increment-price-mv14sec\n", "Creating : increment-price-mv15sec\n", "Total records processed : 1891\n" ] } ], "source": [ "# previous 'parm_calculate_mv' sec Moving Average ['increment-price']\n", "\n", "gap = parm_calculate_mv\n", "\n", "for gap in range(2, gap+1): # MV starts from 2 seconds, till parm_calculate_mv\n", " col_name = 'increment-price-mv'+str(gap)+'sec'\n", " col_data = pd.DataFrame(columns=[col_name])\n", " print('Creating : ', col_name) \n", "\n", " for month in range(0, parm_ts_month):\n", " # print('month : ', month)\n", " for i in range(0, gap):\n", " col_data.loc[month*parm_ts_cycle+i] = 0\n", " for i in range(gap, parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = \\\n", " np.mean(df_history_ts_process['increment-price'][month*parm_ts_cycle+i-gap:month*parm_ts_cycle+i])\n", " \n", " df_history_ts_process[col_name] = col_data\n", "\n", "print('Total records processed : ', len(col_data)) " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# df_history_ts_process[1768:]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/env_py3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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2a5hvYwOSxOQkGIajSmqWZRGYDdHU4qW+yPz7bitr8D516hR33nln/u/f/OY3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\nRLLCkMFbIpFIJJIVhgzeEolEIpGsMP4/JgVRbC9tVfEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2177b70d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-prev3sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-prev7sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-prev11sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-prev15sec'][1768:])\n", "plt.plot()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/env_py3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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1Hz7fvTjWdyfkZPDWVAVT0VBMCd5CzCax0UNUHnLFItXjmEQ3TNY3pQbciB7h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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2179d7cba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-mv3sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-mv7sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-mv11sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-mv15sec'][1768:])\n", "plt.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Process: df_history_table_process" ] }, { "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>ccyy-mm</th>\n", " <th>volume-plate</th>\n", " <th>deal-price-low</th>\n", " <th>deal-price-avg</th>\n", " <th>deal-early-second</th>\n", " <th>volume-bidder</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>26</th>\n", " <td>2017-03</td>\n", " <td>10356</td>\n", " <td>87800</td>\n", " <td>87916</td>\n", " <td>55</td>\n", " <td>262010</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2017-04</td>\n", " <td>12196</td>\n", " <td>89800</td>\n", " <td>89850</td>\n", " <td>59</td>\n", " <td>252273</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2017-05</td>\n", " <td>10316</td>\n", " <td>90100</td>\n", " <td>90209</td>\n", " <td>55</td>\n", " <td>270197</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2017-06</td>\n", " <td>10312</td>\n", " <td>89400</td>\n", " <td>89532</td>\n", " <td>45</td>\n", " <td>244349</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2017-07</td>\n", " <td>10325</td>\n", " <td>92200</td>\n", " <td>92250</td>\n", " <td>57</td>\n", " <td>269189</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm volume-plate deal-price-low deal-price-avg deal-early-second \\\n", "26 2017-03 10356 87800 87916 55 \n", "27 2017-04 12196 89800 89850 59 \n", "28 2017-05 10316 90100 90209 55 \n", "29 2017-06 10312 89400 89532 45 \n", "30 2017-07 10325 92200 92250 57 \n", "\n", " volume-bidder \n", "26 262010 \n", "27 252273 \n", "28 270197 \n", "29 244349 \n", "30 269189 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_table_process.tail()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# date of current month\n", "df_history_table_process['date-curr'] = df_history_table_process.apply(lambda row: pd.to_datetime(row['ccyy-mm']), axis=1)\n", "df_history_table_process['d-avg-low-price'] = df_history_table_process.apply(lambda row: row['deal-price-avg'] - row['deal-price-low'], axis=1)\n", "df_history_table_process['ratio-bid'] = df_history_table_process.apply(lambda row: row['volume-plate'] / row['volume-bidder'], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Merge dataframe" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_history_ts_process_tmp2 = df_history_ts_process.copy()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_history_ts_process = df_history_ts_process_tmp2.copy()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_history_ts_process = pd.merge(df_history_ts_process, df_history_table_process[['date-curr', 'volume-plate', 'ratio-bid']], how = 'left', left_on = 'date-curr', right_on = 'date-curr', suffixes=['', '_table'])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ccyy-mm', 'time', 'bid-price', 'date-curr', 'date-prev', 'year',\n", " 'month', 'hour', 'minute', 'second', 'datetime-curr', 'datetime-prev',\n", " 'base-price15sec', 'increment-price', 'increment-price-target',\n", " 'increment-price-prev1sec', 'increment-price-prev2sec',\n", " 'increment-price-prev3sec', 'increment-price-prev4sec',\n", " 'increment-price-prev5sec', 'increment-price-prev6sec',\n", " 'increment-price-prev7sec', 'increment-price-prev8sec',\n", " 'increment-price-prev9sec', 'increment-price-prev10sec',\n", " 'increment-price-prev11sec', 'increment-price-prev12sec',\n", " 'increment-price-prev13sec', 'increment-price-prev14sec',\n", " 'increment-price-prev15sec', 'increment-price-mv2sec',\n", " 'increment-price-mv3sec', 'increment-price-mv4sec',\n", " 'increment-price-mv5sec', 'increment-price-mv6sec',\n", " 'increment-price-mv7sec', 'increment-price-mv8sec',\n", " 'increment-price-mv9sec', 'increment-price-mv10sec',\n", " 'increment-price-mv11sec', 'increment-price-mv12sec',\n", " 'increment-price-mv13sec', 'increment-price-mv14sec',\n", " 'increment-price-mv15sec', 'volume-plate', 'ratio-bid'],\n", " dtype='object')" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process.columns" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_history_ts_process = pd.merge(df_history_ts_process, df_history_table_process[['date-curr', 'volume-plate', 'ratio-bid', 'deal-early-second', 'deal-price-avg']], how = 'left', left_on = 'date-prev', right_on = 'date-curr', suffixes=['', '_m0'])" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ccyy-mm', 'time', 'bid-price', 'date-curr', 'date-prev', 'year',\n", " 'month', 'hour', 'minute', 'second', 'datetime-curr', 'datetime-prev',\n", " 'base-price15sec', 'increment-price', 'increment-price-target',\n", " 'increment-price-prev1sec', 'increment-price-prev2sec',\n", " 'increment-price-prev3sec', 'increment-price-prev4sec',\n", " 'increment-price-prev5sec', 'increment-price-prev6sec',\n", " 'increment-price-prev7sec', 'increment-price-prev8sec',\n", " 'increment-price-prev9sec', 'increment-price-prev10sec',\n", " 'increment-price-prev11sec', 'increment-price-prev12sec',\n", " 'increment-price-prev13sec', 'increment-price-prev14sec',\n", " 'increment-price-prev15sec', 'increment-price-mv2sec',\n", " 'increment-price-mv3sec', 'increment-price-mv4sec',\n", " 'increment-price-mv5sec', 'increment-price-mv6sec',\n", " 'increment-price-mv7sec', 'increment-price-mv8sec',\n", " 'increment-price-mv9sec', 'increment-price-mv10sec',\n", " 'increment-price-mv11sec', 'increment-price-mv12sec',\n", " 'increment-price-mv13sec', 'increment-price-mv14sec',\n", " 'increment-price-mv15sec', 'volume-plate', 'ratio-bid', 'date-curr_m0',\n", " 'volume-plate_m0', 'ratio-bid_m0', 'deal-early-second',\n", " 'deal-price-avg'],\n", " dtype='object')" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Shift to copy previous 'parm_calculate_prev_month' month's data into current row" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# df_history_ts_process = df_history_ts_process_lookup.copy()" ] }, { "cell_type": "code", "execution_count": 82, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>...</th>\n", " <th>increment-price-mv13sec</th>\n", " <th>increment-price-mv14sec</th>\n", " <th>increment-price-mv15sec</th>\n", " <th>volume-plate</th>\n", " <th>ratio-bid</th>\n", " <th>date-curr_m0</th>\n", " <th>volume-plate_m0</th>\n", " <th>ratio-bid_m0</th>\n", " <th>deal-early-second</th>\n", " <th>deal-price-avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1886</th>\n", " <td>2017-07</td>\n", " <td>11:29:56</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>56</td>\n", " <td>...</td>\n", " <td>861.538</td>\n", " <td>828.571</td>\n", " <td>800</td>\n", " <td>10325</td>\n", " <td>0.038356</td>\n", " <td>2017-06-01</td>\n", " <td>10312.0</td>\n", " <td>0.042202</td>\n", " <td>45.0</td>\n", " <td>89532.0</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>2017-07</td>\n", " <td>11:29:57</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>57</td>\n", " <td>...</td>\n", " <td>946.154</td>\n", " <td>907.143</td>\n", " <td>873.333</td>\n", " <td>10325</td>\n", " <td>0.038356</td>\n", " <td>2017-06-01</td>\n", " <td>10312.0</td>\n", " <td>0.042202</td>\n", " <td>45.0</td>\n", " <td>89532.0</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>2017-07</td>\n", " <td>11:29:58</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>58</td>\n", " <td>...</td>\n", " <td>1023.08</td>\n", " <td>985.714</td>\n", " <td>946.667</td>\n", " <td>10325</td>\n", " <td>0.038356</td>\n", " <td>2017-06-01</td>\n", " <td>10312.0</td>\n", " <td>0.042202</td>\n", " <td>45.0</td>\n", " <td>89532.0</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>2017-07</td>\n", " <td>11:29:59</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>59</td>\n", " <td>...</td>\n", " <td>1100</td>\n", " <td>1057.14</td>\n", " <td>1020</td>\n", " <td>10325</td>\n", " <td>0.038356</td>\n", " <td>2017-06-01</td>\n", " <td>10312.0</td>\n", " <td>0.042202</td>\n", " <td>45.0</td>\n", " <td>89532.0</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>2017-07</td>\n", " <td>11:30:00</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>30</td>\n", " <td>00</td>\n", " <td>...</td>\n", " <td>1176.92</td>\n", " <td>1135.71</td>\n", " <td>1093.33</td>\n", " <td>10325</td>\n", " <td>0.038356</td>\n", " <td>2017-06-01</td>\n", " <td>10312.0</td>\n", " <td>0.042202</td>\n", " <td>45.0</td>\n", " <td>89532.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 51 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour \\\n", "1886 2017-07 11:29:56 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1887 2017-07 11:29:57 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1888 2017-07 11:29:58 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1889 2017-07 11:29:59 92200 2017-07-01 2017-06-01 2017 07 11 \n", "1890 2017-07 11:30:00 92200 2017-07-01 2017-06-01 2017 07 11 \n", "\n", " minute second ... increment-price-mv13sec \\\n", "1886 29 56 ... 861.538 \n", "1887 29 57 ... 946.154 \n", "1888 29 58 ... 1023.08 \n", "1889 29 59 ... 1100 \n", "1890 30 00 ... 1176.92 \n", "\n", " increment-price-mv14sec increment-price-mv15sec volume-plate ratio-bid \\\n", "1886 828.571 800 10325 0.038356 \n", "1887 907.143 873.333 10325 0.038356 \n", "1888 985.714 946.667 10325 0.038356 \n", "1889 1057.14 1020 10325 0.038356 \n", "1890 1135.71 1093.33 10325 0.038356 \n", "\n", " date-curr_m0 volume-plate_m0 ratio-bid_m0 deal-early-second \\\n", "1886 2017-06-01 10312.0 0.042202 45.0 \n", "1887 2017-06-01 10312.0 0.042202 45.0 \n", "1888 2017-06-01 10312.0 0.042202 45.0 \n", "1889 2017-06-01 10312.0 0.042202 45.0 \n", "1890 2017-06-01 10312.0 0.042202 45.0 \n", "\n", " deal-price-avg \n", "1886 89532.0 \n", "1887 89532.0 \n", "1888 89532.0 \n", "1889 89532.0 \n", "1890 89532.0 \n", "\n", "[5 rows x 51 columns]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process_lookup = df_history_ts_process.copy()\n", "df_history_ts_process_lookup.tail()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>...</th>\n", " <th>increment-price-mv10sec_m1</th>\n", " <th>increment-price-mv11sec_m1</th>\n", " <th>increment-price-mv12sec_m1</th>\n", " <th>increment-price-mv13sec_m1</th>\n", " <th>increment-price-mv14sec_m1</th>\n", " <th>increment-price-mv15sec_m1</th>\n", " <th>volume-plate_m0_m1</th>\n", " <th>ratio-bid_m0_m1</th>\n", " <th>deal-early-second_m1</th>\n", " <th>deal-price-avg_m1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1886</th>\n", " <td>2017-07</td>\n", " <td>11:29:56</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>56</td>\n", " <td>...</td>\n", " <td>630</td>\n", " <td>627.273</td>\n", " <td>616.667</td>\n", " <td>607.692</td>\n", " <td>592.857</td>\n", " <td>580</td>\n", " <td>10316.0</td>\n", " <td>0.03818</td>\n", " <td>55.0</td>\n", " <td>90209.0</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>2017-07</td>\n", " <td>11:29:57</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>57</td>\n", " <td>...</td>\n", " <td>640</td>\n", " <td>636.364</td>\n", " <td>633.333</td>\n", " <td>623.077</td>\n", " <td>614.286</td>\n", " <td>600</td>\n", " <td>10316.0</td>\n", " <td>0.03818</td>\n", " <td>55.0</td>\n", " <td>90209.0</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>2017-07</td>\n", " <td>11:29:58</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>58</td>\n", " <td>...</td>\n", " <td>660</td>\n", " <td>654.545</td>\n", " <td>650</td>\n", " <td>646.154</td>\n", " <td>635.714</td>\n", " <td>626.667</td>\n", " <td>10316.0</td>\n", " <td>0.03818</td>\n", " <td>55.0</td>\n", " <td>90209.0</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>2017-07</td>\n", " <td>11:29:59</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>59</td>\n", " <td>...</td>\n", " <td>680</td>\n", " <td>672.727</td>\n", " <td>666.667</td>\n", " <td>661.538</td>\n", " <td>657.143</td>\n", " <td>646.667</td>\n", " <td>10316.0</td>\n", " <td>0.03818</td>\n", " <td>55.0</td>\n", " <td>90209.0</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>2017-07</td>\n", " <td>11:30:00</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>30</td>\n", " <td>00</td>\n", " <td>...</td>\n", " <td>700</td>\n", " <td>690.909</td>\n", " <td>683.333</td>\n", " <td>676.923</td>\n", " <td>671.429</td>\n", " <td>666.667</td>\n", " <td>10316.0</td>\n", " <td>0.03818</td>\n", " <td>55.0</td>\n", " <td>90209.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 89 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour \\\n", "1886 2017-07 11:29:56 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1887 2017-07 11:29:57 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1888 2017-07 11:29:58 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1889 2017-07 11:29:59 92200 2017-07-01 2017-06-01 2017 07 11 \n", "1890 2017-07 11:30:00 92200 2017-07-01 2017-06-01 2017 07 11 \n", "\n", " minute second ... increment-price-mv10sec_m1 \\\n", "1886 29 56 ... 630 \n", "1887 29 57 ... 640 \n", "1888 29 58 ... 660 \n", "1889 29 59 ... 680 \n", "1890 30 00 ... 700 \n", "\n", " increment-price-mv11sec_m1 increment-price-mv12sec_m1 \\\n", "1886 627.273 616.667 \n", "1887 636.364 633.333 \n", "1888 654.545 650 \n", "1889 672.727 666.667 \n", "1890 690.909 683.333 \n", "\n", " increment-price-mv13sec_m1 increment-price-mv14sec_m1 \\\n", "1886 607.692 592.857 \n", "1887 623.077 614.286 \n", "1888 646.154 635.714 \n", "1889 661.538 657.143 \n", "1890 676.923 671.429 \n", "\n", " increment-price-mv15sec_m1 volume-plate_m0_m1 ratio-bid_m0_m1 \\\n", "1886 580 10316.0 0.03818 \n", "1887 600 10316.0 0.03818 \n", "1888 626.667 10316.0 0.03818 \n", "1889 646.667 10316.0 0.03818 \n", "1890 666.667 10316.0 0.03818 \n", "\n", " deal-early-second_m1 deal-price-avg_m1 \n", "1886 55.0 90209.0 \n", "1887 55.0 90209.0 \n", "1888 55.0 90209.0 \n", "1889 55.0 90209.0 \n", "1890 55.0 90209.0 \n", "\n", "[5 rows x 89 columns]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# _m1\n", "df_history_ts_process = pd.merge(df_history_ts_process, df_history_ts_process_lookup[[ \\\n", " 'datetime-curr', 'datetime-prev', \n", " 'base-price15sec', 'increment-price', 'increment-price-target',\n", " 'increment-price-prev1sec', 'increment-price-prev2sec',\n", " 'increment-price-prev3sec', 'increment-price-prev4sec',\n", " 'increment-price-prev5sec', 'increment-price-prev6sec',\n", " 'increment-price-prev7sec', 'increment-price-prev8sec',\n", " 'increment-price-prev9sec', 'increment-price-prev10sec',\n", " 'increment-price-prev11sec', 'increment-price-prev12sec',\n", " 'increment-price-prev13sec', 'increment-price-prev14sec',\n", " 'increment-price-prev15sec', \n", " 'increment-price-mv2sec',\n", " 'increment-price-mv3sec', 'increment-price-mv4sec',\n", " 'increment-price-mv5sec', 'increment-price-mv6sec',\n", " 'increment-price-mv7sec', 'increment-price-mv8sec',\n", " 'increment-price-mv9sec', 'increment-price-mv10sec',\n", " 'increment-price-mv11sec', 'increment-price-mv12sec',\n", " 'increment-price-mv13sec', 'increment-price-mv14sec',\n", " 'increment-price-mv15sec', \n", " 'volume-plate_m0', \n", " 'ratio-bid_m0', \n", " 'deal-early-second',\n", " 'deal-price-avg' \n", " ]], how = 'left', left_on = 'datetime-prev', right_on = 'datetime-curr', suffixes=['', '_m1'])\n", "df_history_ts_process.tail()" ] }, { "cell_type": "code", "execution_count": 84, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>...</th>\n", " <th>increment-price-mv10sec_m2</th>\n", " <th>increment-price-mv11sec_m2</th>\n", " <th>increment-price-mv12sec_m2</th>\n", " <th>increment-price-mv13sec_m2</th>\n", " <th>increment-price-mv14sec_m2</th>\n", " <th>increment-price-mv15sec_m2</th>\n", " <th>volume-plate_m0_m2</th>\n", " <th>ratio-bid_m0_m2</th>\n", " <th>deal-early-second_m2</th>\n", " <th>deal-price-avg_m2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1886</th>\n", " <td>2017-07</td>\n", " <td>11:29:56</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>56</td>\n", " <td>...</td>\n", " <td>710</td>\n", " <td>681.818</td>\n", " <td>658.333</td>\n", " <td>630.769</td>\n", " <td>607.143</td>\n", " <td>586.667</td>\n", " <td>12196.0</td>\n", " <td>0.048344</td>\n", " <td>59.0</td>\n", " <td>89850.0</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>2017-07</td>\n", " <td>11:29:57</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>57</td>\n", " <td>...</td>\n", " <td>770</td>\n", " <td>745.455</td>\n", " <td>716.667</td>\n", " <td>692.308</td>\n", " <td>664.286</td>\n", " <td>640</td>\n", " <td>12196.0</td>\n", " <td>0.048344</td>\n", " <td>59.0</td>\n", " <td>89850.0</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>2017-07</td>\n", " <td>11:29:58</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>58</td>\n", " <td>...</td>\n", " <td>840</td>\n", " <td>809.091</td>\n", " <td>783.333</td>\n", " <td>753.846</td>\n", " <td>728.571</td>\n", " <td>700</td>\n", " <td>12196.0</td>\n", " <td>0.048344</td>\n", " <td>59.0</td>\n", " <td>89850.0</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>2017-07</td>\n", " <td>11:29:59</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>59</td>\n", " <td>...</td>\n", " <td>910</td>\n", " <td>881.818</td>\n", " <td>850</td>\n", " <td>823.077</td>\n", " <td>792.857</td>\n", " <td>766.667</td>\n", " <td>12196.0</td>\n", " <td>0.048344</td>\n", " <td>59.0</td>\n", " <td>89850.0</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>2017-07</td>\n", " <td>11:30:00</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>30</td>\n", " <td>00</td>\n", " <td>...</td>\n", " <td>980</td>\n", " <td>945.455</td>\n", " <td>916.667</td>\n", " <td>884.615</td>\n", " <td>857.143</td>\n", " <td>826.667</td>\n", " <td>12196.0</td>\n", " <td>0.048344</td>\n", " <td>59.0</td>\n", " <td>89850.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 127 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour \\\n", "1886 2017-07 11:29:56 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1887 2017-07 11:29:57 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1888 2017-07 11:29:58 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1889 2017-07 11:29:59 92200 2017-07-01 2017-06-01 2017 07 11 \n", "1890 2017-07 11:30:00 92200 2017-07-01 2017-06-01 2017 07 11 \n", "\n", " minute second ... increment-price-mv10sec_m2 \\\n", "1886 29 56 ... 710 \n", "1887 29 57 ... 770 \n", "1888 29 58 ... 840 \n", "1889 29 59 ... 910 \n", "1890 30 00 ... 980 \n", "\n", " increment-price-mv11sec_m2 increment-price-mv12sec_m2 \\\n", "1886 681.818 658.333 \n", "1887 745.455 716.667 \n", "1888 809.091 783.333 \n", "1889 881.818 850 \n", "1890 945.455 916.667 \n", "\n", " increment-price-mv13sec_m2 increment-price-mv14sec_m2 \\\n", "1886 630.769 607.143 \n", "1887 692.308 664.286 \n", "1888 753.846 728.571 \n", "1889 823.077 792.857 \n", "1890 884.615 857.143 \n", "\n", " increment-price-mv15sec_m2 volume-plate_m0_m2 ratio-bid_m0_m2 \\\n", "1886 586.667 12196.0 0.048344 \n", "1887 640 12196.0 0.048344 \n", "1888 700 12196.0 0.048344 \n", "1889 766.667 12196.0 0.048344 \n", "1890 826.667 12196.0 0.048344 \n", "\n", " deal-early-second_m2 deal-price-avg_m2 \n", "1886 59.0 89850.0 \n", "1887 59.0 89850.0 \n", "1888 59.0 89850.0 \n", "1889 59.0 89850.0 \n", "1890 59.0 89850.0 \n", "\n", "[5 rows x 127 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# _m2\n", "df_history_ts_process = pd.merge(df_history_ts_process, df_history_ts_process_lookup[[ \\\n", " 'datetime-curr', 'datetime-prev', \n", " 'base-price15sec', 'increment-price', 'increment-price-target',\n", " 'increment-price-prev1sec', 'increment-price-prev2sec',\n", " 'increment-price-prev3sec', 'increment-price-prev4sec',\n", " 'increment-price-prev5sec', 'increment-price-prev6sec',\n", " 'increment-price-prev7sec', 'increment-price-prev8sec',\n", " 'increment-price-prev9sec', 'increment-price-prev10sec',\n", " 'increment-price-prev11sec', 'increment-price-prev12sec',\n", " 'increment-price-prev13sec', 'increment-price-prev14sec',\n", " 'increment-price-prev15sec', \n", " 'increment-price-mv2sec',\n", " 'increment-price-mv3sec', 'increment-price-mv4sec',\n", " 'increment-price-mv5sec', 'increment-price-mv6sec',\n", " 'increment-price-mv7sec', 'increment-price-mv8sec',\n", " 'increment-price-mv9sec', 'increment-price-mv10sec',\n", " 'increment-price-mv11sec', 'increment-price-mv12sec',\n", " 'increment-price-mv13sec', 'increment-price-mv14sec',\n", " 'increment-price-mv15sec', \n", " 'volume-plate_m0', \n", " 'ratio-bid_m0', \n", " 'deal-early-second',\n", " 'deal-price-avg' \n", " ]], how = 'left', left_on = 'datetime-prev_m1', right_on = 'datetime-curr', suffixes=['', '_m2'])\n", "df_history_ts_process.tail()" ] }, { "cell_type": "code", "execution_count": 85, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>...</th>\n", " <th>increment-price-mv10sec_m3</th>\n", " <th>increment-price-mv11sec_m3</th>\n", " <th>increment-price-mv12sec_m3</th>\n", " <th>increment-price-mv13sec_m3</th>\n", " <th>increment-price-mv14sec_m3</th>\n", " <th>increment-price-mv15sec_m3</th>\n", " <th>volume-plate_m0_m3</th>\n", " <th>ratio-bid_m0_m3</th>\n", " <th>deal-early-second_m3</th>\n", " <th>deal-price-avg_m3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1886</th>\n", " <td>2017-07</td>\n", " <td>11:29:56</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>56</td>\n", " <td>...</td>\n", " <td>720</td>\n", " <td>709.091</td>\n", " <td>691.667</td>\n", " <td>669.231</td>\n", " <td>650</td>\n", " <td>626.667</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>2017-07</td>\n", " <td>11:29:57</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>57</td>\n", " <td>...</td>\n", " <td>750</td>\n", " <td>736.364</td>\n", " <td>725</td>\n", " <td>707.692</td>\n", " <td>685.714</td>\n", " <td>666.667</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>2017-07</td>\n", " <td>11:29:58</td>\n", " <td>92100</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>58</td>\n", " <td>...</td>\n", " <td>790</td>\n", " <td>772.727</td>\n", " <td>758.333</td>\n", " <td>746.154</td>\n", " <td>728.571</td>\n", " <td>706.667</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>2017-07</td>\n", " <td>11:29:59</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>59</td>\n", " <td>...</td>\n", " <td>820</td>\n", " <td>809.091</td>\n", " <td>791.667</td>\n", " <td>776.923</td>\n", " <td>764.286</td>\n", " <td>746.667</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>2017-07</td>\n", " <td>11:30:00</td>\n", " <td>92200</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>30</td>\n", " <td>00</td>\n", " <td>...</td>\n", " <td>860</td>\n", " <td>845.455</td>\n", " <td>833.333</td>\n", " <td>815.385</td>\n", " <td>800</td>\n", " <td>786.667</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 165 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour \\\n", "1886 2017-07 11:29:56 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1887 2017-07 11:29:57 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1888 2017-07 11:29:58 92100 2017-07-01 2017-06-01 2017 07 11 \n", "1889 2017-07 11:29:59 92200 2017-07-01 2017-06-01 2017 07 11 \n", "1890 2017-07 11:30:00 92200 2017-07-01 2017-06-01 2017 07 11 \n", "\n", " minute second ... increment-price-mv10sec_m3 \\\n", "1886 29 56 ... 720 \n", "1887 29 57 ... 750 \n", "1888 29 58 ... 790 \n", "1889 29 59 ... 820 \n", "1890 30 00 ... 860 \n", "\n", " increment-price-mv11sec_m3 increment-price-mv12sec_m3 \\\n", "1886 709.091 691.667 \n", "1887 736.364 725 \n", "1888 772.727 758.333 \n", "1889 809.091 791.667 \n", "1890 845.455 833.333 \n", "\n", " increment-price-mv13sec_m3 increment-price-mv14sec_m3 \\\n", "1886 669.231 650 \n", "1887 707.692 685.714 \n", "1888 746.154 728.571 \n", "1889 776.923 764.286 \n", "1890 815.385 800 \n", "\n", " increment-price-mv15sec_m3 volume-plate_m0_m3 ratio-bid_m0_m3 \\\n", "1886 626.667 10356.0 0.039525 \n", "1887 666.667 10356.0 0.039525 \n", "1888 706.667 10356.0 0.039525 \n", "1889 746.667 10356.0 0.039525 \n", "1890 786.667 10356.0 0.039525 \n", "\n", " deal-early-second_m3 deal-price-avg_m3 \n", "1886 55.0 87916.0 \n", "1887 55.0 87916.0 \n", "1888 55.0 87916.0 \n", "1889 55.0 87916.0 \n", "1890 55.0 87916.0 \n", "\n", "[5 rows x 165 columns]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# _m3\n", "df_history_ts_process = pd.merge(df_history_ts_process, df_history_ts_process_lookup[[ \\\n", " 'datetime-curr', 'datetime-prev', \n", " 'base-price15sec', 'increment-price', 'increment-price-target',\n", " 'increment-price-prev1sec', 'increment-price-prev2sec',\n", " 'increment-price-prev3sec', 'increment-price-prev4sec',\n", " 'increment-price-prev5sec', 'increment-price-prev6sec',\n", " 'increment-price-prev7sec', 'increment-price-prev8sec',\n", " 'increment-price-prev9sec', 'increment-price-prev10sec',\n", " 'increment-price-prev11sec', 'increment-price-prev12sec',\n", " 'increment-price-prev13sec', 'increment-price-prev14sec',\n", " 'increment-price-prev15sec', \n", " 'increment-price-mv2sec',\n", " 'increment-price-mv3sec', 'increment-price-mv4sec',\n", " 'increment-price-mv5sec', 'increment-price-mv6sec',\n", " 'increment-price-mv7sec', 'increment-price-mv8sec',\n", " 'increment-price-mv9sec', 'increment-price-mv10sec',\n", " 'increment-price-mv11sec', 'increment-price-mv12sec',\n", " 'increment-price-mv13sec', 'increment-price-mv14sec',\n", " 'increment-price-mv15sec', \n", " 'volume-plate_m0', \n", " 'ratio-bid_m0', \n", " 'deal-early-second',\n", " 'deal-price-avg' \n", " ]], how = 'left', left_on = 'datetime-prev_m2', right_on = 'datetime-curr', suffixes=['', '_m3'])\n", "df_history_ts_process.tail()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/env_py3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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dtF1yuwF7s4vcRSlExYTia2jAdaeIsLm5mGP6zsgobCrixev/gVkx8cdLf59Z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\nW95CCD47tZ7PC3fTPszAE/0farjpkCRvCYmsG3Frq6r26n43f1vEmWI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"text/plain": [ "<matplotlib.figure.Figure at 0x7f217745fe10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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jD7eiXSbWFO+QZ3mLeAv+cTpX5mOf+4dtz1PiJaJv+E7r/1/y1HXM1cf4/nPH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\nAY05CsOJHouhYVMpV/1eCulC51ndGzEyGdA0QjO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"text/plain": [ "<matplotlib.figure.Figure at 0x7f217744f6a0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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0jDI9vyfEdoyngrdjkNkzTnTVQmutQtPx39zW8425fOjJD7jSB0CUI90wGJ/x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\nG5ndbk/9l1mCxRyH06dPR77fsWPHIsdBi+cEY8Hvfdddd7H77ruPvfP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2177583fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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10V7Ryo8GLgMO2k8cpub0r21rf/2uBdrifzaLIPGEKD6jbj+RqJlVStSVlfVs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\nxdkAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f21776785f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price-mv10sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-mv10sec_m1'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-mv10sec_m2'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-mv10sec_m3'][1768:])\n", "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price-prev10sec'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-prev10sec_m1'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-prev10sec_m2'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-prev10sec_m3'][1768:])\n", "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price'][1768:])\n", "plt.plot(df_history_ts_process['increment-price_m1'][1768:])\n", "plt.plot(df_history_ts_process['increment-price_m2'][1768:])\n", "plt.plot(df_history_ts_process['increment-price_m3'][1768:])\n", "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price-target'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-target_m1'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-target_m2'][1768:])\n", "plt.plot(df_history_ts_process['increment-price-target_m3'][1768:])\n", "\n", "plt.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Housekeeping to remove some invald data during pre-processing" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ccyy-mm\n", "time\n", "bid-price\n", "date-curr\n", "date-prev\n", "year\n", "month\n", "hour\n", "minute\n", "second\n", "datetime-curr\n", "datetime-prev\n", "base-price15sec\n", "increment-price\n", "increment-price-target\n", "increment-price-prev1sec\n", "increment-price-prev2sec\n", "increment-price-prev3sec\n", "increment-price-prev4sec\n", "increment-price-prev5sec\n", "increment-price-prev6sec\n", "increment-price-prev7sec\n", "increment-price-prev8sec\n", "increment-price-prev9sec\n", "increment-price-prev10sec\n", "increment-price-prev11sec\n", "increment-price-prev12sec\n", "increment-price-prev13sec\n", "increment-price-prev14sec\n", "increment-price-prev15sec\n", "increment-price-mv2sec\n", "increment-price-mv3sec\n", "increment-price-mv4sec\n", "increment-price-mv5sec\n", "increment-price-mv6sec\n", "increment-price-mv7sec\n", "increment-price-mv8sec\n", "increment-price-mv9sec\n", "increment-price-mv10sec\n", "increment-price-mv11sec\n", "increment-price-mv12sec\n", "increment-price-mv13sec\n", "increment-price-mv14sec\n", "increment-price-mv15sec\n", "volume-plate\n", "ratio-bid\n", "date-curr_m0\n", "volume-plate_m0\n", "ratio-bid_m0\n", "deal-early-second\n", "deal-price-avg\n", "datetime-curr_m1\n", "datetime-prev_m1\n", "base-price15sec_m1\n", "increment-price_m1\n", "increment-price-target_m1\n", "increment-price-prev1sec_m1\n", "increment-price-prev2sec_m1\n", "increment-price-prev3sec_m1\n", "increment-price-prev4sec_m1\n", "increment-price-prev5sec_m1\n", "increment-price-prev6sec_m1\n", "increment-price-prev7sec_m1\n", "increment-price-prev8sec_m1\n", "increment-price-prev9sec_m1\n", "increment-price-prev10sec_m1\n", "increment-price-prev11sec_m1\n", "increment-price-prev12sec_m1\n", "increment-price-prev13sec_m1\n", "increment-price-prev14sec_m1\n", "increment-price-prev15sec_m1\n", "increment-price-mv2sec_m1\n", "increment-price-mv3sec_m1\n", "increment-price-mv4sec_m1\n", "increment-price-mv5sec_m1\n", "increment-price-mv6sec_m1\n", "increment-price-mv7sec_m1\n", "increment-price-mv8sec_m1\n", "increment-price-mv9sec_m1\n", "increment-price-mv10sec_m1\n", "increment-price-mv11sec_m1\n", "increment-price-mv12sec_m1\n", "increment-price-mv13sec_m1\n", "increment-price-mv14sec_m1\n", "increment-price-mv15sec_m1\n", "volume-plate_m0_m1\n", "ratio-bid_m0_m1\n", "deal-early-second_m1\n", "deal-price-avg_m1\n", "datetime-curr_m2\n", "datetime-prev_m2\n", "base-price15sec_m2\n", "increment-price_m2\n", "increment-price-target_m2\n", "increment-price-prev1sec_m2\n", "increment-price-prev2sec_m2\n", "increment-price-prev3sec_m2\n", "increment-price-prev4sec_m2\n", "increment-price-prev5sec_m2\n", "increment-price-prev6sec_m2\n", "increment-price-prev7sec_m2\n", "increment-price-prev8sec_m2\n", "increment-price-prev9sec_m2\n", "increment-price-prev10sec_m2\n", "increment-price-prev11sec_m2\n", "increment-price-prev12sec_m2\n", "increment-price-prev13sec_m2\n", "increment-price-prev14sec_m2\n", "increment-price-prev15sec_m2\n", "increment-price-mv2sec_m2\n", "increment-price-mv3sec_m2\n", "increment-price-mv4sec_m2\n", "increment-price-mv5sec_m2\n", "increment-price-mv6sec_m2\n", "increment-price-mv7sec_m2\n", "increment-price-mv8sec_m2\n", "increment-price-mv9sec_m2\n", "increment-price-mv10sec_m2\n", "increment-price-mv11sec_m2\n", "increment-price-mv12sec_m2\n", "increment-price-mv13sec_m2\n", "increment-price-mv14sec_m2\n", "increment-price-mv15sec_m2\n", "volume-plate_m0_m2\n", "ratio-bid_m0_m2\n", "deal-early-second_m2\n", "deal-price-avg_m2\n", "datetime-curr_m3\n", "datetime-prev_m3\n", "base-price15sec_m3\n", "increment-price_m3\n", "increment-price-target_m3\n", "increment-price-prev1sec_m3\n", "increment-price-prev2sec_m3\n", "increment-price-prev3sec_m3\n", "increment-price-prev4sec_m3\n", "increment-price-prev5sec_m3\n", "increment-price-prev6sec_m3\n", "increment-price-prev7sec_m3\n", "increment-price-prev8sec_m3\n", "increment-price-prev9sec_m3\n", "increment-price-prev10sec_m3\n", "increment-price-prev11sec_m3\n", "increment-price-prev12sec_m3\n", "increment-price-prev13sec_m3\n", "increment-price-prev14sec_m3\n", "increment-price-prev15sec_m3\n", "increment-price-mv2sec_m3\n", "increment-price-mv3sec_m3\n", "increment-price-mv4sec_m3\n", "increment-price-mv5sec_m3\n", "increment-price-mv6sec_m3\n", "increment-price-mv7sec_m3\n", "increment-price-mv8sec_m3\n", "increment-price-mv9sec_m3\n", "increment-price-mv10sec_m3\n", "increment-price-mv11sec_m3\n", "increment-price-mv12sec_m3\n", "increment-price-mv13sec_m3\n", "increment-price-mv14sec_m3\n", "increment-price-mv15sec_m3\n", "volume-plate_m0_m3\n", "ratio-bid_m0_m3\n", "deal-early-second_m3\n", "deal-price-avg_m3\n" ] } ], "source": [ "for i in range(0, len(df_history_ts_process.columns)): print(df_history_ts_process.columns[i])" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# housekeeping: delete some columns\n", "# df_history_ts_process.drop('date-curr_y', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2015-04-01 00:00:00')" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parm_record_cut_ccyy" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# remove first 'parm_record_cut_ccyy' months from dataset\n", "df_history_ts_process = df_history_ts_process[df_history_ts_process['date-curr'] > parm_record_cut_ccyy]" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# total 61 seconds/rows per month:\n", "# remove first 'parm_record_cut_row_head' reconds\n", "# remove last 'parm_record_cut_row_tail' reconds\n", "df_history_ts_process = df_history_ts_process[df_history_ts_process['second'] >= str(parm_record_cut_row_head) ]\n", "df_history_ts_process = df_history_ts_process[df_history_ts_process['second'] <= str(60 - parm_record_cut_row_tail) ]\n", "# df_history_ts_process = df_history_ts_process[df_history_ts_process['second'] > parm_record_cut_row_head ]" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Reset index after housekeeping\n", "df_history_ts_process = df_history_ts_process.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 93, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>...</th>\n", " <th>increment-price-mv10sec_m3</th>\n", " <th>increment-price-mv11sec_m3</th>\n", " <th>increment-price-mv12sec_m3</th>\n", " <th>increment-price-mv13sec_m3</th>\n", " <th>increment-price-mv14sec_m3</th>\n", " <th>increment-price-mv15sec_m3</th>\n", " <th>volume-plate_m0_m3</th>\n", " <th>ratio-bid_m0_m3</th>\n", " <th>deal-early-second_m3</th>\n", " <th>deal-price-avg_m3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015-05</td>\n", " <td>11:29:15</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>15</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>-6.66667</td>\n", " <td>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015-05</td>\n", " <td>11:29:16</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>16</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015-05</td>\n", " <td>11:29:17</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>17</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2015-05</td>\n", " <td>11:29:18</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>18</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2015-05</td>\n", " <td>11:29:19</td>\n", " <td>78500</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>19</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 165 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour minute \\\n", "0 2015-05 11:29:15 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "1 2015-05 11:29:16 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "2 2015-05 11:29:17 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "3 2015-05 11:29:18 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "4 2015-05 11:29:19 78500 2015-05-01 2015-04-01 2015 05 11 29 \n", "\n", " second ... increment-price-mv10sec_m3 \\\n", "0 15 ... 0 \n", "1 16 ... 0 \n", "2 17 ... 0 \n", "3 18 ... 0 \n", "4 19 ... 0 \n", "\n", " increment-price-mv11sec_m3 increment-price-mv12sec_m3 \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " increment-price-mv13sec_m3 increment-price-mv14sec_m3 \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " increment-price-mv15sec_m3 volume-plate_m0_m3 ratio-bid_m0_m3 \\\n", "0 -6.66667 7990.0 0.081362 \n", "1 0 7990.0 0.081362 \n", "2 0 7990.0 0.081362 \n", "3 0 7990.0 0.081362 \n", "4 0 7990.0 0.081362 \n", "\n", " deal-early-second_m3 deal-price-avg_m3 \n", "0 48.0 74216.0 \n", "1 48.0 74216.0 \n", "2 48.0 74216.0 \n", "3 48.0 74216.0 \n", "4 48.0 74216.0 \n", "\n", "[5 rows x 165 columns]" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process.head()" ] }, { "cell_type": "code", "execution_count": 94, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>...</th>\n", " <th>increment-price-mv10sec_m3</th>\n", " <th>increment-price-mv11sec_m3</th>\n", " <th>increment-price-mv12sec_m3</th>\n", " <th>increment-price-mv13sec_m3</th>\n", " <th>increment-price-mv14sec_m3</th>\n", " <th>increment-price-mv15sec_m3</th>\n", " <th>volume-plate_m0_m3</th>\n", " <th>ratio-bid_m0_m3</th>\n", " <th>deal-early-second_m3</th>\n", " <th>deal-price-avg_m3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1048</th>\n", " <td>2017-07</td>\n", " <td>11:29:49</td>\n", " <td>91400</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>49</td>\n", " <td>...</td>\n", " <td>470</td>\n", " <td>454.545</td>\n", " <td>441.667</td>\n", " <td>430.769</td>\n", " <td>421.429</td>\n", " <td>413.333</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1049</th>\n", " <td>2017-07</td>\n", " <td>11:29:50</td>\n", " <td>91500</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>50</td>\n", " <td>...</td>\n", " <td>510</td>\n", " <td>490.909</td>\n", " <td>475</td>\n", " <td>461.538</td>\n", " <td>450</td>\n", " <td>440</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1050</th>\n", " <td>2017-07</td>\n", " <td>11:29:51</td>\n", " <td>91600</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>51</td>\n", " <td>...</td>\n", " <td>550</td>\n", " <td>527.273</td>\n", " <td>508.333</td>\n", " <td>492.308</td>\n", " <td>478.571</td>\n", " <td>466.667</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1051</th>\n", " <td>2017-07</td>\n", " <td>11:29:52</td>\n", " <td>91700</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>52</td>\n", " <td>...</td>\n", " <td>590</td>\n", " <td>563.636</td>\n", " <td>541.667</td>\n", " <td>523.077</td>\n", " <td>507.143</td>\n", " <td>493.333</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " <tr>\n", " <th>1052</th>\n", " <td>2017-07</td>\n", " <td>11:29:53</td>\n", " <td>91800</td>\n", " <td>2017-07-01</td>\n", " <td>2017-06-01</td>\n", " <td>2017</td>\n", " <td>07</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>53</td>\n", " <td>...</td>\n", " <td>620</td>\n", " <td>600</td>\n", " <td>575</td>\n", " <td>553.846</td>\n", " <td>535.714</td>\n", " <td>520</td>\n", " <td>10356.0</td>\n", " <td>0.039525</td>\n", " <td>55.0</td>\n", " <td>87916.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 165 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour \\\n", "1048 2017-07 11:29:49 91400 2017-07-01 2017-06-01 2017 07 11 \n", "1049 2017-07 11:29:50 91500 2017-07-01 2017-06-01 2017 07 11 \n", "1050 2017-07 11:29:51 91600 2017-07-01 2017-06-01 2017 07 11 \n", "1051 2017-07 11:29:52 91700 2017-07-01 2017-06-01 2017 07 11 \n", "1052 2017-07 11:29:53 91800 2017-07-01 2017-06-01 2017 07 11 \n", "\n", " minute second ... increment-price-mv10sec_m3 \\\n", "1048 29 49 ... 470 \n", "1049 29 50 ... 510 \n", "1050 29 51 ... 550 \n", "1051 29 52 ... 590 \n", "1052 29 53 ... 620 \n", "\n", " increment-price-mv11sec_m3 increment-price-mv12sec_m3 \\\n", "1048 454.545 441.667 \n", "1049 490.909 475 \n", "1050 527.273 508.333 \n", "1051 563.636 541.667 \n", "1052 600 575 \n", "\n", " increment-price-mv13sec_m3 increment-price-mv14sec_m3 \\\n", "1048 430.769 421.429 \n", "1049 461.538 450 \n", "1050 492.308 478.571 \n", "1051 523.077 507.143 \n", "1052 553.846 535.714 \n", "\n", " increment-price-mv15sec_m3 volume-plate_m0_m3 ratio-bid_m0_m3 \\\n", "1048 413.333 10356.0 0.039525 \n", "1049 440 10356.0 0.039525 \n", "1050 466.667 10356.0 0.039525 \n", "1051 493.333 10356.0 0.039525 \n", "1052 520 10356.0 0.039525 \n", "\n", " deal-early-second_m3 deal-price-avg_m3 \n", "1048 55.0 87916.0 \n", "1049 55.0 87916.0 \n", "1050 55.0 87916.0 \n", "1051 55.0 87916.0 \n", "1052 55.0 87916.0 \n", "\n", "[5 rows x 165 columns]" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process.tail()" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/env_py3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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p9HbunbUxrvdd3B/bsvDt3U3b1lcxOjpQk5PJ3vwHzL11PYp+/k/CuVNH6P4f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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21775c2c18>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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n+RgNOl7akEXH39/CUV5G2MJFRCy7v9/n1zSNQ5XHkJBYkDTntu9XVY29H+fT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21775c27b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price'][974:])\n", "plt.plot(df_history_ts_process['increment-price-mv3sec'][974:])\n", "plt.plot(df_history_ts_process['increment-price-mv7sec'][974:])\n", "plt.plot(df_history_ts_process['increment-price-mv11sec'][974:])\n", "plt.plot(df_history_ts_process['increment-price-mv15sec'][974:])\n", "plt.figure()\n", "plt.plot(df_history_ts_process['increment-price-mv15sec'][974:])\n", "plt.plot(df_history_ts_process['increment-price-mv15sec_m1'][974:])\n", "plt.plot(df_history_ts_process['increment-price-mv15sec_m2'][974:])\n", "plt.plot(df_history_ts_process['increment-price-mv15sec_m3'][974:])\n", "plt.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# [3] Modeling Part 2: Python scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Models to use:\n", "\n", "* GradientBoostingClassifier\n", "* RandomForestClassifier\n", "* AdaBoostClassifier\n", "* ExtraTreesClassifier\n", "* BaggingClassifier\n", "* LogisticRegression\n", "* SVM kernal RBF\n", "* SVM kernal Linear\n", "* KNeighborsClassifier\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import pre-processed data" ] }, { "cell_type": "code", "execution_count": 97, "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>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>date-curr</th>\n", " <th>date-prev</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>second</th>\n", " <th>...</th>\n", " <th>increment-price-mv10sec_m3</th>\n", " <th>increment-price-mv11sec_m3</th>\n", " <th>increment-price-mv12sec_m3</th>\n", " <th>increment-price-mv13sec_m3</th>\n", " <th>increment-price-mv14sec_m3</th>\n", " <th>increment-price-mv15sec_m3</th>\n", " <th>volume-plate_m0_m3</th>\n", " <th>ratio-bid_m0_m3</th>\n", " <th>deal-early-second_m3</th>\n", " <th>deal-price-avg_m3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015-05</td>\n", " <td>11:29:15</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>15</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>-6.66667</td>\n", " <td>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015-05</td>\n", " <td>11:29:16</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>16</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015-05</td>\n", " <td>11:29:17</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>17</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2015-05</td>\n", " <td>11:29:18</td>\n", " <td>78400</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>18</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2015-05</td>\n", " <td>11:29:19</td>\n", " <td>78500</td>\n", " <td>2015-05-01</td>\n", " <td>2015-04-01</td>\n", " <td>2015</td>\n", " <td>05</td>\n", " <td>11</td>\n", " <td>29</td>\n", " <td>19</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>7990.0</td>\n", " <td>0.081362</td>\n", " <td>48.0</td>\n", " <td>74216.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 165 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price date-curr date-prev year month hour minute \\\n", "0 2015-05 11:29:15 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "1 2015-05 11:29:16 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "2 2015-05 11:29:17 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "3 2015-05 11:29:18 78400 2015-05-01 2015-04-01 2015 05 11 29 \n", "4 2015-05 11:29:19 78500 2015-05-01 2015-04-01 2015 05 11 29 \n", "\n", " second ... increment-price-mv10sec_m3 \\\n", "0 15 ... 0 \n", "1 16 ... 0 \n", "2 17 ... 0 \n", "3 18 ... 0 \n", "4 19 ... 0 \n", "\n", " increment-price-mv11sec_m3 increment-price-mv12sec_m3 \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " increment-price-mv13sec_m3 increment-price-mv14sec_m3 \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " increment-price-mv15sec_m3 volume-plate_m0_m3 ratio-bid_m0_m3 \\\n", "0 -6.66667 7990.0 0.081362 \n", "1 0 7990.0 0.081362 \n", "2 0 7990.0 0.081362 \n", "3 0 7990.0 0.081362 \n", "4 0 7990.0 0.081362 \n", "\n", " deal-early-second_m3 deal-price-avg_m3 \n", "0 48.0 74216.0 \n", "1 48.0 74216.0 \n", "2 48.0 74216.0 \n", "3 48.0 74216.0 \n", "4 48.0 74216.0 \n", "\n", "[5 rows x 165 columns]" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_history_ts_process.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Include relevant features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = df_history_ts_process[[\n", "# 'ccyy-mm', 'time', 'bid-price', 'date-curr_x', 'date-prev', 'year',\n", " 'month', \n", "# 'hour', 'minute', \n", " 'second', 'base-price15sec',\n", " 'increment-price', \n", "# 'increment-price-target', \n", " 'increment-price-prev1sec',\n", " 'increment-price-prev2sec', 'increment-price-prev3sec',\n", " 'increment-price-prev4sec', 'increment-price-prev5sec',\n", " 'increment-price-prev6sec', 'increment-price-prev7sec',\n", " 'increment-price-prev8sec', 'increment-price-prev9sec',\n", " 'increment-price-prev10sec', 'increment-price-prev11sec',\n", " 'increment-price-prev12sec', 'increment-price-prev13sec',\n", " 'increment-price-prev14sec', 'increment-price-prev15sec',\n", " 'increment-price-mv2sec', 'increment-price-mv3sec',\n", " 'increment-price-mv4sec', 'increment-price-mv5sec',\n", " 'increment-price-mv6sec', 'increment-price-mv7sec',\n", " 'increment-price-mv8sec', 'increment-price-mv9sec',\n", " 'increment-price-mv10sec', 'increment-price-mv11sec',\n", " 'increment-price-mv12sec', 'increment-price-mv13sec',\n", " 'increment-price-mv14sec', 'increment-price-mv15sec', 'volume-plate_x',\n", " 'ratio-bid_x', 'volume-plate_y', 'ratio-bid_y', 'deal-early-second',\n", " 'deal-price-avg', 'deal-price-avg' \n", " ]]\n", "\n", "X_col = X.columns # get the column list\n", "\n", "# X = StandardScaler().fit_transform(X.as_matrix())\n", "X = X.as_matrix()\n", "\n", "# y = StandardScaler().fit_transform(df_wnv_raw[['increment-price-target']].as_matrix()).reshape(len(df_wnv_raw),)\n", "y = df_history_ts_process[['increment-price-target']].as_matrix().reshape(len(df_history_ts_process),)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "X_col" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure()\n", "plt.plot(X)\n", "plt.figure()\n", "plt.plot(y)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# [4] Evaluation\n", "### K-fold Cross-Validation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = check_random_state(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# GB\n", "classifier_GB = GradientBoostingRegressor(n_estimators=1500, # score: 0.94608 (AUC 0.81419), learning_rate=0.001, max_features=8 <<< Best\n", "# loss='deviance',\n", "# subsample=1,\n", "# max_depth=5,\n", "# min_samples_split=20,\n", " learning_rate=0.002,\n", "# max_features=10,\n", " random_state=rng)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# AB\n", "classifier_AB = AdaBoostRegressor(n_estimators=1500, # score: 0.93948 (AUC 0.88339), learning_rate=0.004 <<< Best\n", " learning_rate=0.002,\n", " random_state=rng)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# RF\n", "classifier_RF = RandomForestRegressor(n_estimators=1500, # score: 0.94207 (AUC 0.81870), max_depth=3, min_samples_split=20, <<< Best\n", "# max_features=10,\n", "# max_depth=3,\n", "# min_samples_split=20,\n", " random_state=rng)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ET\n", "classifier_ET = ExtraTreesRegressor(n_estimators=1000, # score: 0.94655 (AUC 0.84364), max_depth=3, min_samples_split=20, max_features=10 <<< Best\n", "# max_depth=3,\n", "# min_samples_split=20,\n", "# max_features=10,\n", " random_state=rng)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# BG\n", "classifier_BG = BaggingRegressor(n_estimators=500, # score: 0.70725 (AUC 0.63729) <<< Best\n", "# max_features=10,\n", " random_state=rng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "classifier_LR = LinearRegression() # score: 0.90199 (AUC 0.80569)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SVM Linear" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# classifier_SVCL = svm.SVC(kernel='linear', probability=True, random_state=rng) # score: 0.89976 (AUC 0.70524)\n", "classifier_SVRL = svm.SVR() # score: 0.89976 (AUC 0.70524)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SVM" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "classifier_SVRR = svm.SVR(kernel='rbf') # score: 0.80188 (AUC 0.50050)\n", "# classifier_SVRR = svm.SVR(kernel='poly') # score: 0.80188 (AUC 0.50050)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### KNN" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "classifier_KNN = KNeighborsRegressor(n_neighbors=2) # score: 0.94018 (AUC 0.72792)\n", "cv = cross_val_score(classifier_KNN,\n", " X,\n", " y,\n", " cv=StratifiedKFold(parm_ts_valid_month))\n", "print('KNN CV score: {0:.5f}'.format(cv.mean()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# classifier = classifier_GB # 324.632308296\n", "classifier = classifier_AB # 429.646733221\n", "# classifier = classifier_RF # 175.504322802\n", "# classifier = classifier_ET # 172.097916817, 0.0724812030075\n", "# classifier = classifier_BG # 175.451381872\n", "# classifier = classifier_LR # 128.465059749, 0.11\n", "# classifier = classifier_SVRL # 3789.82169312\n", "# classifier = classifier_SVRR # 3789.82169312, 0.10754224349" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_splits = parm_ts_valid_cycle\n", "print(n_splits)\n", "# n_splits=54 # 19 seconds/records for each bidding month\n", "# n_splits=19 # 19 seconds/records for each bidding month\n", "n_fold = parm_ts_valid_month\n", "print(n_fold)\n", "\n", "\n", "# X_train_1 = X[0:(len(X)-batch*n_splits)]\n", "# y_train_1 = y[0:(len(X)-batch*n_splits)]\n", "\n", "# X_test_1 = X[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]\n", "# y_test_1 = y[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CV" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_fold=5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "y_pred = {}\n", "y_test = {}\n", "\n", "y_pred_org = {}\n", "y_test_org = {}\n", "\n", "i = 0\n", "for batch in range(1, n_fold):\n", " X_train_1 = X[0:(len(X)-batch*n_splits)]\n", " y_train_1 = y[0:(len(X)-batch*n_splits)]\n", " X_test_1 = X[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]\n", " y_test_1 = y[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]\n", " print(len(X_train_1))\n", " \n", " # ReScale\n", " ScalerX = StandardScaler()\n", " ScalerX.fit(X_train_1)\n", " X_train_1 = ScalerX.transform(X_train_1)\n", " X_test_1 = ScalerX.transform(X_test_1)\n", " \n", " ScalerY = StandardScaler()\n", " ScalerY.fit(y_train_1.reshape(-1, 1))\n", " y_train_1 = ScalerY.transform(y_train_1.reshape(-1, 1))\n", " y_test_1 = ScalerY.transform(y_test_1.reshape(-1, 1))\n", " \n", " y_pred[i] = classifier.fit(X_train_1, y_train_1).predict(X_test_1)\n", " y_test[i] = y_test_1 \n", "\n", " y_pred_org[i] = ScalerY.inverse_transform(y_pred[i])\n", " y_test_org[i] = ScalerY.inverse_transform(y_test[i])\n", " \n", " plt.figure()\n", " plt.plot(y_train_1)\n", " plt.plot()\n", " plt.figure()\n", " plt.plot(y_test[i])\n", " plt.plot(y_pred[i])\n", " plt.plot()\n", " i += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### no inverse-scale" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "k = []\n", "for i in range(0, len(y_test)):\n", " k.append(np.mean(np.sqrt(np.square(y_test[i] - y_pred[i]))))\n", "\n", "k_mean = np.mean(k)\n", "\n", "print(k_mean)\n", "print()\n", "print(k)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "k = []\n", "for i in range(0, len(y_test)):\n", " k.append(np.mean(np.sqrt(np.square(y_test[i][35:37] - y_pred[i][35:37]))))\n", "\n", "k_mean = np.mean(k)\n", "\n", "print(k_mean)\n", "print()\n", "print(k)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### inverse-scale" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "k = []\n", "for i in range(0, len(y_test)):\n", " k.append(np.mean(np.sqrt(np.square(y_test_org[i] - y_pred_org[i]))))\n", "\n", "k_mean = np.mean(k)\n", "\n", "print(k_mean)\n", "print()\n", "print(k)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "k = []\n", "for i in range(0, len(y_test)):\n", " k.append(np.mean(np.sqrt(np.square(y_test_org[i][35:37] - y_pred_org[i][35:37]))))\n", "\n", "k_mean = np.mean(k)\n", "\n", "print(k_mean)\n", "print()\n", "print(k)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 50 second predicts 57 second\n", "k = []\n", "for i in range(0, len(y_test)):\n", " k.append(np.mean(np.sqrt(np.square(y_test_org[i][35:36] - y_pred_org[i][35:36]))))\n", "\n", "k_mean = np.mean(k)\n", "\n", "print(k_mean)\n", "print()\n", "print(k)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(y_test_org[0])\n", "plt.plot(y_pred_org[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(k)" ] }, { "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": [] }, { "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": [] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_test[1][13:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "y_pred[1][13:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.mean(np.sqrt(np.square(y_test[4] - y_pred[4])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.mean(np.sqrt(np.square(y_test[4][13:16] - y_pred[4][13:16])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred_df = pd.DataFrame.from_dict(y_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred_df.columns=['month 7','month 6','month 5','month 4','month 3','month 2','month 1']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred_df.to_csv('bid_results_v001.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# previous N sec ['bid-price']\n", "gap = parm_calculate_prev_bp\n", "\n", "for gap in range(1, gap+1):\n", " col_name = 'bid-price-prev'+str(gap)+'sec'\n", " col_data = pd.DataFrame(columns=[col_name])\n", " col_data_zeros = pd.DataFrame({col_name: np.zeros(gap)})\n", " print('Creating : ', col_name) \n", "\n", " for month in range(0, parm_ts_month):\n", " # print('month : ', month)\n", " col_data.append(col_data_zeros)\n", " for i in range(0, gap):\n", " col_data.loc[month*parm_ts_cycle+i] = 0\n", " for i in range(gap, parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = df_history_ts_process['bid-price'][month*parm_ts_cycle+i-gap]\n", " \n", " df_history_ts_process[col_name] = col_data\n", "\n", "print('Total records processed : ', len(col_data)) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# previous 2 sec Moving Average ['bid-price']\n", "\n", "gap = parm_calculate_mv\n", "\n", "for gap in range(2, gap+1): # MV starts from 2 seconds, till parm_calculate_mv\n", " col_name = 'bid-price-mv'+str(gap)+'sec'\n", " col_data = pd.DataFrame(columns=[col_name])\n", " col_data_zeros = pd.DataFrame({col_name: np.zeros(gap)})\n", " print('Creating : ', col_name) \n", "\n", " for month in range(0, parm_ts_month):\n", " # print('month : ', month)\n", " col_data.append(col_data_zeros)\n", " for i in range(0, gap):\n", " col_data.loc[month*parm_ts_cycle+i] = 0\n", " for i in range(gap, parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = \\\n", " np.mean(df_history_ts_process['bid-price'][month*parm_ts_cycle+i-gap:month*parm_ts_cycle+i])\n", " \n", " df_history_ts_process[col_name] = col_data\n", "\n", "print('Total records processed : ', len(col_data)) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df_history_ts_process[1768:]" ] }, { "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, "scrolled": true }, "outputs": [], "source": [ "# previous 2 sec Moving Average ['bid-price']\n", "\n", "gap = parm_calculate_mv\n", "\n", "for gap in range(1, gap+1):\n", " col_name = 'bid-price-mv'+str(gap)+'sec'\n", " col_data = pd.DataFrame(columns=[col_name])\n", " print('Creating : ', col_name) \n", "\n", " for month in range(0, parm_ts_month):\n", " # print('month : ', month)\n", " col_data.append(col_data_zeros)\n", " for i in range(0, gap):\n", " col_data.loc[month*parm_ts_cycle+i] = 0\n", " for i in range(gap, parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = df_history_ts_process['bid-price'][month*parm_ts_cycle+i-gap]\n", " \n", " df_history_ts_process[col_name] = col_data\n", "\n", "print('len : ', len(col_data)) " ] }, { "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, "scrolled": true }, "outputs": [], "source": [ "# previous N sec\n", "gap = 1\n", "gap = 2\n", "gap = 3\n", "gap = 4\n", "gap = 5\n", "gap = 6\n", "gap = 7\n", "gap = 8\n", "gap = 9\n", "gap = 10\n", "\n", "col_name = 'bid-price-prev'+str(gap)+'sec'\n", "col_data = pd.DataFrame(columns=[col_name])\n", "\n", "for month in range(0, parm_ts_month):\n", "# print('month : ', month)\n", " col_data.append(col_data_zeros)\n", " for i in range(0, gap):\n", " col_data.loc[month*parm_ts_cycle+i] = 0\n", " for i in range(gap, parm_ts_cycle):\n", " col_data.loc[month*parm_ts_cycle+i] = df_history_ts_process['bid-price'][month*parm_ts_cycle+i]\n", " \n", "print('len : ', len(col_data)) \n", "df_history_ts_process[col_name] = col_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(col_data)" ] }, { "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": [] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "# previous 1 sec\n", "gap = 10\n", "\n", "col_data = pd.DataFrame({'bid-price-prev'+str(gap)+'sec': np.zeros(gap)})\n", "\n", "# for i in range(gap, len(df_history_ts)-1768):\n", "for i in range(gap, parm_ts_cycle):\n", "# print(df_history_ts['bid-price'][i])\n", " col_data.loc[i] = df_history_ts['bid-price'][i]\n", "\n", "print(len(col_data))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_history_ts_process = df_history_ts.copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_history_table_process['tmp'] = col_data['bid-price-prev'+str(gap)+'sec']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_history_table_process.tail()" ] }, { "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, "scrolled": true }, "outputs": [], "source": [ "col_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The End" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
LapoFrati/DataScience
cuisine/CuisineAggregator.ipynb
1
1934223
null
apache-2.0
SuLab/scheduled-bots
scheduled_bots/disease_ontology/disease_ontology/maintenance/remove_instance_of_P31_disease/prototype_notebook.ipynb
1
3071
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from wikidataintegrator import wdi_core, wdi_login" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "WDUSER = \"\"\n", "WDPASS = \"\"\n", "\n", "login = wdi_login.WDLogin(WDUSER, WDPASS)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def remove_disease(qid):\n", " item = wdi_core.WDItemEngine(wd_item_id=qid)\n", " json = item.get_wd_json_representation()\n", "\n", " if \"P31\" in json[\"claims\"].keys():\n", " for claim in json[\"claims\"][\"P31\"]: \n", " print(claim[\"id\"])\n", " for reference in claim[\"references\"]:\n", " for snakP248 in reference[\"snaks\"][\"P248\"]:\n", " if snakP248[\"datavalue\"][\"value\"][\"id\"] == \"Q5282129\":\n", " wdi_core.WDItemEngine.delete_statement(statement_id=claim[\"id\"], revision=item.lastrevid, login=login)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "results = wdi_core.WDItemEngine.execute_sparql_query(\"SELECT * WHERE { ?disease p:P31 [ps:P31 wd:Q12136 ; prov:wasDerivedFrom [ pr:P248 wd:Q5282129 ; ]]}\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q1339466$A16D450D-B5DD-4C98-9611-A7F8A9A6F21E\n", "{'pageinfo': {'lastrevid': 1310822289}, 'success': 1, 'claims': ['Q1339466$A16D450D-B5DD-4C98-9611-A7F8A9A6F21E']}\n", "Q1339466$856319FD-E1F4-4E3B-807A-03673B3293EF\n" ] } ], "source": [ "for result in results[\"results\"][\"bindings\"]:\n", " remove_disease(result[\"disease\"][\"value\"].replace(\"http://www.wikidata.org/entity/\", \"\"))\n", " break" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q1340774$66A7912C-7C22-4AD2-8A44-E46F775F7565\n", "{'pageinfo': {'lastrevid': 1310821009}, 'success': 1, 'claims': ['Q1340774$66A7912C-7C22-4AD2-8A44-E46F775F7565']}\n" ] } ], "source": [ "remove_disease(\"Q1340774\")\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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
YAtOff/python0-reloaded
week6/For-Loop.ipynb
1
3391
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## *for* цикли" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Списък" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "numbers = [1, 2, 3, 4, 5]\n", "students = ['Богдан', 'Борис', 'Боян']" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5]\n", "['Богдан', 'Борис', 'Боян']\n" ] } ], "source": [ "print(numbers)\n", "print(students)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Обхождане" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "numbers = [1, 2, 3, 4, 5]\n", "for n in numbers:\n", " print(n)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Богдан\n", "Борис\n", "Боян\n", "Веселин\n" ] } ], "source": [ "students = ['Богдан', 'Борис', 'Боян', 'Веселин']\n", "for student in students:\n", " print(student)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Фунцкия `range`\n", "\n", "`range(start, end, step)`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "for n in range(1, 6):\n", " print(n)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "3\n", "5\n", "7\n", "9\n" ] } ], "source": [ "for n in range(1, 11, 2):\n", " print(n)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "4\n", "3\n", "2\n", "1\n" ] } ], "source": [ "for n in range(5, 0, -1):\n", " print(n)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
nishant-jain-94/Autofill
notebooks/load_squad_wiki_data.ipynb
1
7903
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import json\n", "import requests\n", "from load_from_wiki import load_data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def processing_squad_data(dataset):\n", " raw_data = []\n", " for data in dataset['data']:\n", " paragraphs = data['paragraphs'] \n", " for paragraph in paragraphs:\n", " para_ques_dict = {}\n", " para_ques_dict['Passages'] = paragraph['context'].lower()\n", " ques_list = []\n", " for questions in paragraph['qas']:\n", " ques_list.append(questions['question'])\n", " para_ques_dict['Question'] = list(set(ques_list)) \n", " raw_data.append(para_ques_dict)\n", " return raw_data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def combine_squad_dev_train(): \n", " with open('../data/dev-v1.1.json') as data_file:\n", " dataset = json.load(data_file)\n", " dev_set = processing_squad_data(dataset)\n", " with open('../data/train-v1.1.json') as data_file:\n", " dataset = json.load(data_file)\n", " train_set = processing_squad_data(dataset)\n", " dev_set.extend(train_set)\n", " with open('../data/squad_data.json', 'w') as outfile:\n", " json.dump(dev_set , outfile) " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def combine_squad_data():\n", " \"\"\"Merges two files squad_data and wiki_data and generates an merged file squad_wiki_data.json \"\"\"\n", " with open('../data/dev-v1.1.json') as data_file:\n", " dataset = json.load(data_file)\n", " with open('../data/train-v1.1.json') as data_file:\n", " dataset1 = json.load(data_file)\n", " dataset = processing_squad_data(dataset)\n", " dataset1 = processing_squad_data(dataset1)\n", " final_dict = {}\n", " final_para = []\n", " final_question = []\n", " for data in dataset:\n", " final_para.append(data['Passages'])\n", " final_question.extend(data['Question'])\n", " for data in dataset1:\n", " final_para.append(data['Passages'])\n", " final_question.extend(data['Question'])\n", " final_dict['Paragraph'] = ''.join(final_para)\n", " final_dict['Question'] = final_question\n", " final_data = []\n", " final_data.append(final_dict)\n", " with open('../data/combined_squad_data.json','w') as outfile:\n", " json.dump(final_data , outfile)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def merge_file():\n", " \"\"\"Merges two files squad_data and wiki_data and generates an merged file squad_wiki_data.json \"\"\"\n", " with open('../data/squad_data.json') as data_file:\n", " dataset1 = json.load(data_file)\n", " with open('../data/wiki_data.json') as data_file:\n", " dataset2 = json.load(data_file)\n", " final_dict = {}\n", " final_para = []\n", " final_question = []\n", " for data in dataset1:\n", " final_para.append(data['Passages'])\n", " final_question.extend(data['Question'])\n", " for data in dataset2:\n", " final_para.append(data['Passage'])\n", " final_question.extend(data['Question'])\n", " final_dict['Paragraph'] = ''.join(final_para)\n", " final_dict['Question'] = final_question\n", " final_data = []\n", " final_data.append(final_dict)\n", " with open('../data/squad_wiki_data.json','w') as outfile:\n", " json.dump(final_data , outfile)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def load_squad_wiki_data():\n", " if not os.path.isfile(\"../data/squad_wiki_data.json\"):\n", " # Check if the train-v1.1.json exists\n", " if not os.path.isfile(\"../data/train-v1.1.json\"):\n", " print(\"Loading Squad Training Data\")\n", " response = requests.get(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\")\n", " with open(\"../data/train-v1.1.json\", \"wb\") as outfile:\n", " for data in response.iter_content():\n", " outfile.write(data)\n", "\n", " # Check if the dev-v1.1.json exists\n", " if not os.path.isfile(\"../data/dev-v1.1.json\"):\n", " print(\"Loading Squad Dev Data\")\n", " response = requests.get(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json\")\n", " with open(\"../data/dev-v1.1.json\", \"wb\") as outfile:\n", " for data in response.iter_content():\n", " outfile.write(data)\n", "\n", " # Check if the squad_data exists if not generate squad_data.json\n", " if not os.path.isfile(\"../data/squad_data.json\"):\n", " print(\"Combining Squad Data\")\n", " combine_squad_dev_train()\n", "\n", " # Check if the wiki_data exists else call the respective script to load it\n", " if not os.path.isfile(\"../data/wiki_data.json\"):\n", " print(\"Loading Wiki Data\")\n", " load_data()\n", "\n", " merge_file() " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_squad_wiki_data():\n", " print(\"Loading Squad Data\")\n", " load_squad_wiki_data()\n", " with open(\"../data/squad_wiki_data.json\", \"r\") as dataset:\n", " squad_wiki_data = json.load(dataset)\n", " return squad_wiki_data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_squad_data():\n", " print(\"Combining Squad Data\")\n", " combine_squad_data()\n", " with open(\"../data/combined_squad_data.json\", \"r\") as dataset:\n", " squad_data = json.load(dataset)\n", " return squad_data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# data = get_squad_wiki_data()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# type(data[0][\"Question\"])" ] }, { "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
giacomov/3ML
examples/Plotting_showcase.ipynb
2
816252
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Configuration read from /Users/jburgess/.threeML/threeML_config.yml\n" ] } ], "source": [ "%matplotlib inline\n", "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "#np.seterr(all='ignore')\n", "from threeML import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING UserWarning: Field 6 has a repeat count of 0 in its format code, indicating an empty field.\n", "\n", "\n", "WARNING RuntimeWarning: Maximum MC energy is smaller than maximum EBOUNDS energy\n", "\n", "\n", "WARNING RuntimeWarning: Minimum MC energy is larger than minimum EBOUNDS energy\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Auto-probed noise models:\n", "- observation: poisson\n", "- background: gaussian\n", "Range 10.0-30.0 translates to channels 4-20\n", "Range 40.0-950.0 translates to channels 26-125\n", "Now using 117 channels out of 128\n", "Auto-probed noise models:\n", "- observation: poisson\n", "- background: gaussian\n", "Range 10.0-30.0 translates to channels 6-21\n", "Range 40.0-950.0 translates to channels 27-125\n", "Now using 115 channels out of 128\n", "Auto-probed noise models:\n", "- observation: poisson\n", "- background: gaussian\n", "Range 200-10000 translates to channels 2-89\n", "Now using 88 channels out of 128\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: Maximum MC energy is smaller than maximum EBOUNDS energy\n", "\n", "\n", "WARNING RuntimeWarning: Maximum MC energy is smaller than maximum EBOUNDS energy\n", "\n", "\n", "WARNING RuntimeWarning: Minimum MC energy is larger than minimum EBOUNDS energy\n", "\n" ] } ], "source": [ "triggerName = 'bn090217206'\n", "ra = 204.9\n", "dec = -8.4\n", "\n", "#Data are in the current directory\n", "\n", "datadir = os.path.abspath('.')\n", "\n", "\n", "#The .pha, .bak and .rsp files have been prepared with the Fermi\n", "#official software. In the future it will be possible to create\n", "#them directly from the plugin\n", "\n", "#Create an instance of the GBM plugin for each detector\n", "#Data files\n", "obsSpectrum = os.path.join( datadir, \"bn090217206_n6_srcspectra.pha{1}\" )\n", "bakSpectrum = os.path.join( datadir, \"bn090217206_n6_bkgspectra.bak{1}\" )\n", "rspFile = os.path.join( datadir, \"bn090217206_n6_weightedrsp.rsp{1}\" )\n", "\n", "#Plugin instance\n", "NaI6 = OGIPLike( \"NaI6\", obsSpectrum, bakSpectrum, rspFile )\n", "\n", "#Choose energies to use (in this case, I exclude the energy\n", "#range from 30 to 40 keV to avoid the k-edge, as well as anything above\n", "#950 keV, where the calibration is uncertain)\n", "NaI6.set_active_measurements( \"10.0-30.0\", \"40.0-950.0\" )\n", "\n", "#Now repeat for the other GBM detectors\n", "\n", "obsSpectrum = os.path.join( datadir, \"bn090217206_n9_srcspectra.pha{1}\" )\n", "bakSpectrum = os.path.join( datadir, \"bn090217206_n9_bkgspectra.bak{1}\" )\n", "rspFile = os.path.join( datadir, \"bn090217206_n9_weightedrsp.rsp{1}\" )\n", "#Plugin instance\n", "NaI9 = OGIPLike( \"NaI9\", obsSpectrum, bakSpectrum, rspFile )\n", "#Choose chanels to use\n", "NaI9.set_active_measurements( \"10.0-30.0\", \"40.0-950.0\" )\n", "\n", "\n", "obsSpectrum = os.path.join( datadir, \"bn090217206_b1_srcspectra.pha{1}\" )\n", "bakSpectrum = os.path.join( datadir, \"bn090217206_b1_bkgspectra.bak{1}\" )\n", "rspFile = os.path.join( datadir, \"bn090217206_b1_weightedrsp.rsp{1}\" )\n", "#Plugin instance\n", "BGO1 = OGIPLike( \"BGO1\", obsSpectrum, bakSpectrum, rspFile )\n", "#Choose chanels to use (in this case, from 200 keV to 10 MeV)\n", "BGO1.set_active_measurements( \"200-10000\" )\n", "\n", "\n", "data_list = DataList( NaI6, NaI9, BGO1 )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Let's use a Band model, a phenomenological model typically used for GRB\n", "\n", "bb= Blackbody()\n", "\n", "pl = Powerlaw()\n", "\n", "comp_model = bb+pl\n", "\n", "\n", "band = Band()\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: divide by zero encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: divide by zero encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: divide by zero encountered in log\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best fit values:\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Value</th>\n", " <th>Unit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>bn090217206.spectrum.main.composite.K_1</th>\n", " <td>(1.61 +/- 0.18) x 10^-6</td>\n", " <td>1 / (cm2 keV3 s)</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206.spectrum.main.composite.kT_1</th>\n", " <td>(7.94 +/- 0.28) x 10</td>\n", " <td>keV</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206.spectrum.main.composite.K_2</th>\n", " <td>6.3 +/- 0.5</td>\n", " <td>1 / (cm2 keV s)</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206...index_2</th>\n", " <td>-1.488 +/- 0.017</td>\n", " <td></td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Value \\\n", "bn090217206.spectrum.main.composite.K_1 (1.61 +/- 0.18) x 10^-6 \n", "bn090217206.spectrum.main.composite.kT_1 (7.94 +/- 0.28) x 10 \n", "bn090217206.spectrum.main.composite.K_2 6.3 +/- 0.5 \n", "bn090217206...index_2 -1.488 +/- 0.017 \n", "\n", " Unit \n", "bn090217206.spectrum.main.composite.K_1 1 / (cm2 keV3 s) \n", "bn090217206.spectrum.main.composite.kT_1 keV \n", "bn090217206.spectrum.main.composite.K_2 1 / (cm2 keV s) \n", "bn090217206...index_2 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Correlation matrix:\n", "\n" ] }, { "data": { "text/html": [ "<table id=\"table4806979408\">\n", "<tr><td>1.00</td><td>-0.95</td><td>-0.41</td><td>0.28</td></tr>\n", "<tr><td>-0.95</td><td>1.00</td><td>0.50</td><td>-0.46</td></tr>\n", "<tr><td>-0.41</td><td>0.50</td><td>1.00</td><td>-0.92</td></tr>\n", "<tr><td>0.28</td><td>-0.46</td><td>-0.92</td><td>1.00</td></tr>\n", "</table>" ], "text/plain": [ " 1.00 -0.95 -0.41 0.28\n", "-0.95 1.00 0.50 -0.46\n", "-0.41 0.50 1.00 -0.92\n", " 0.28 -0.46 -0.92 1.00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Values of -log(likelihood) at the minimum:\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>-log(likelihood)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>BGO1</th>\n", " <td>623.929841</td>\n", " </tr>\n", " <tr>\n", " <th>NaI6</th>\n", " <td>854.570183</td>\n", " </tr>\n", " <tr>\n", " <th>NaI9</th>\n", " <td>767.391170</td>\n", " </tr>\n", " <tr>\n", " <th>total</th>\n", " <td>2245.891193</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " -log(likelihood)\n", "BGO1 623.929841\n", "NaI6 854.570183\n", "NaI9 767.391170\n", "total 2245.891193" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: divide by zero encountered in log\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best fit values:\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING UserWarning: 51.46 percent of samples have been thrown away because they failed the constraints on the parameters. This results might not be suitable for error propagation. Enlarge the boundaries until you loose less than 1 percent of the samples.\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Value</th>\n", " <th>Unit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>bn090217206_band.spectrum.main.Band.K</th>\n", " <td>(1.80 +/- 0.05) x 10^-2</td>\n", " <td>1 / (cm2 keV s)</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206_band...alpha</th>\n", " <td>(-8.02 +/- 0.27) x 10^-1</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206_band.spectrum.main.Band.xp</th>\n", " <td>(6.01 +/- 0.31) x 10^2</td>\n", " <td>keV</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206_band.spectrum.main.Band.beta</th>\n", " <td>-5.00 +/- 0.20</td>\n", " <td></td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Value \\\n", "bn090217206_band.spectrum.main.Band.K (1.80 +/- 0.05) x 10^-2 \n", "bn090217206_band...alpha (-8.02 +/- 0.27) x 10^-1 \n", "bn090217206_band.spectrum.main.Band.xp (6.01 +/- 0.31) x 10^2 \n", "bn090217206_band.spectrum.main.Band.beta -5.00 +/- 0.20 \n", "\n", " Unit \n", "bn090217206_band.spectrum.main.Band.K 1 / (cm2 keV s) \n", "bn090217206_band...alpha \n", "bn090217206_band.spectrum.main.Band.xp keV \n", "bn090217206_band.spectrum.main.Band.beta " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Correlation matrix:\n", "\n" ] }, { "data": { "text/html": [ "<table id=\"table4808798096\">\n", "<tr><td>1.00</td><td>0.69</td><td>-0.92</td><td>0.02</td></tr>\n", "<tr><td>0.69</td><td>1.00</td><td>-0.78</td><td>0.01</td></tr>\n", "<tr><td>-0.92</td><td>-0.78</td><td>1.00</td><td>-0.04</td></tr>\n", "<tr><td>0.02</td><td>0.01</td><td>-0.04</td><td>1.00</td></tr>\n", "</table>" ], "text/plain": [ " 1.00 0.69 -0.92 0.02\n", " 0.69 1.00 -0.78 0.01\n", "-0.92 -0.78 1.00 -0.04\n", " 0.02 0.01 -0.04 1.00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Values of -log(likelihood) at the minimum:\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>-log(likelihood)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>BGO1</th>\n", " <td>579.088399</td>\n", " </tr>\n", " <tr>\n", " <th>NaI6</th>\n", " <td>824.220440</td>\n", " </tr>\n", " <tr>\n", " <th>NaI9</th>\n", " <td>732.669751</td>\n", " </tr>\n", " <tr>\n", " <th>total</th>\n", " <td>2135.978590</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " -log(likelihood)\n", "BGO1 579.088399\n", "NaI6 824.220440\n", "NaI9 732.669751\n", "total 2135.978590" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "GRB = PointSource( triggerName, ra, dec, spectral_shape=comp_model )\n", "\n", "model = Model( GRB )\n", "\n", "jl = JointLikelihood( model, data_list )\n", "\n", "res = jl.fit()\n", "\n", "\n", "GRB2 = PointSource( triggerName+'_band', ra, dec, spectral_shape=band )\n", "\n", "model2 = Model( GRB2 )\n", "\n", "jl2 = JointLikelihood( model2, data_list )\n", "\n", "res2 = jl2.fit()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = 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this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " 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button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#spec_plot_mle = SpectralPlotter(jl)\n", "\n", "plot_point_source_spectra(jl.results,\n", " flux_unit='erg2/(cm2 s keV)')\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_point_source_spectra(jl.results,\n", " use_components=True,\n", " equal_tailed=False,\n", " best_fit='median',\n", " flux_unit='erg2/(cm2 s keV)')\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_point_source_spectra(jl.results,\n", " energy_unit='MeV',\n", " ene_min=1e-1,\n", " ene_max=1e4,\n", " fit_cmap='jet',\n", " contour_cmap='rainbow_r',\n", " confidence_level=.99,\n", " flux_unit='erg/(cm2 s MeV)',\n", " legend_kwargs={'loc':'upper right'},\n", " plot_style_kwargs={'linestyle':'--'},\n", " contour_style_kwargs={'lw':0}\n", " )\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "plot_point_source_spectra(jl.results,\n", " jl2.results,\n", " confidence_level=.90,\n", " equal_tailed=False,\n", " flux_unit='erg2/(cm2 s keV)')\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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ZumnVM/nKt29Xyb/+ndSs/ZNJGGckg584R+46Fw5eOECy3jFMsJKFng9eagOP3OqxXYbGZ1dwtFZxfbd/bxjt4x7Y4P9sTVqtEdqJ6dNstyPw40AB2/WHWMQGAHXNH/UVL231eKRm3dIVgcyOx/9f8b0wbcuRI2BGfMOBhU0XAyMafFbm49aS5ykanYNv/THr3tban/1qozcUidMoDh0/jzZe9mFcnjhHOnNNue/Eg1soFFjbI2jMxq18p0cqe2VCb9/e1A3LaNHwpBNksYpE8A1FjN+FyympjGwQdg8tUOPAfU6atM2GKwtQoOV3dZfELgRiT+7r7xYjs6dKZWekjq8MOb8rwvfD/00DRDgRNG9rSdlfQrT4vOQfDH1hd/IVBiuvFOYlIoEjA333Cp7llwknYgp+YW6igrkKFyeZJuym1stY1axY4+Ugzlqcxq19tLigSQRJIrAuHG01gpDp+QNBGiwynbuk6pNW61kolEfbLemaVC6pgljZO/lF8JgzZJjc6ZLR2nJoNCVJwZ3Pb+jMTDPq0gF5HycepTfebfx0GDh12SjUoI09xk/fwluwtcRL+qVQ+fNkx03LJGj82ah6qw51boTjRPnqpXBhcovRsbVuC3Zd1Ayu/unA9CdSkNmJYkMxNbaMWdNKfkI2C7BUe9vt0ZZToPFaRYcXR2dO0OOnT1DOhD7DEWsRr906VIjbIOOwEJpUc97AoHjKP+0Be7Cna2p+VnIkcasJ5+X8cgm7EL8aNeyS2G4lkrLuNGewMNtITih+vjs6RbbsmR0dVmJIZYLkqM1uCFn/uxXkoEfHiS6H0/MqpXj5LOnSxPmqqXzjwAbt3i3TLooR/yyCi7BUe9jusbmOslG0g7JGmFddoFltIYzWPHK4eb9vjVgGgNz87WKv+01a1+V0fNnWdXiU1H1gJOMz1r5rIx9+z3pxHysD26/wTJcfnITxq+V4E/ozc62Rl0cee26WqxU7Blnz7RcjpXbdkslYoaMuTBbk8RRGr9Uj2E0wEzNUxNg0DxYUSR4b907y4zSSrynNFYjMcriPpMuSJy8vncJXIJzhh9hudeDxLfsWwOWeKj0ialAgFU0uCLymw0HZGxjcdKb5C/Y2f/9jIx96z3hPKn3/uxjUnfd5VrJIk7kmf144qxpFsvNV/ZXIzl6wvrytUYMyHzjKJfEidxHzjlLDp8zG9vZ0onYmpJITnOLZfxHvb9DRiJ+xREuXbV9MPZMptm17BI5NpsGa3raYmaEnzOWl1nngcWCmnv3sP4cq2hswkRkLjCZbCrB3Kiz/+tpfIm+a40Itt2yTA1XskEPeH4BSmlVo4IIq4hUbdgiuS2t1pczq4YcXnC2xScwAbwPGZ++J7z/hYePWRmuI+EKrESGYBnisKSerEyMcCfDWE2zRq50xzLpJllkUgxMDViy3gJ9bsQINGExvx2sFn/qSNKXnSisP2qNuOjO6kLJpu03XyXbb7pCR1wRays5F7KSCEfDlkFb/75Ubt1lxdA6EXNj4gynKtSfO0faUCLLD5QJ1x8TYCq31iGdHQYLbtZ8zCkksc8nMHfvOAwW442cZN6Tm7plYkwyYCb8tJkMnX4JzDzPT4BJi8FfB38Afhz8GngIaQxsCByePGBa/D6szbUe2YUsuuqkRM8lykEK/FlPPGelxPehziBHXFs/frWvUuGd+Lm5H4vu+jIzBuembUb8kfPSWJqLZbmqYdBsdyNjPdboDJVOjuHL3UuTxkNhzmQLZrVWwAXIJKEKGGpnNZRmVGs5smB2f6IL3K6a5BIKyTPPm2DAdkPse8BPOcTn/FUup5sLPuA4r7sGIJDKtHhmyE17do2VWciCuHuWLpIP7rxJ2kaiBqCSZxGgi+wgq5uAWc2/ZP8hqX57k4wB1z73isx4+mXpxqiERuwYEheYIk7Xo9vuRhre0r0HkMCCqQacZgCmscpE1QsSE4Q4otr6sWUD5bumSntF6JR2zyrII4Kl0oX4E/T5OvAR8FxH/5dh/7tgTq7hNQ+CgxENmD0Csz9nobnvgP/UPmFvNQZmI+GtLeNcdBUmfVVkfOmxcsbcR34mRYi11MMFtfFTfyKnUKFdyWwEmI3HahIcmY3esFlKUbyYxGkPjShW3DCtv/wVy2Fx+gNH3IkkujvzTjZK0cEjUnLwsLXyAA0sl8kpwNpuNjEpqHHyBMuwcqI35WpFcWevZ1yqC9HW4NDtIzj8PvhRx2karR+Al4D5Fr4Ffga8FXwX+Bzwt8D14GDGthHnU+ccRmNKsSHAONfOlgb54FTyq8VzHtL8/3hcRiG20AiDtfYbf2tlt8Umud7lNQSY4Xj4vLkWU7ZcxI6sybtIL6eLjlVTslD6i9SDEl9NNePk1MSxVuV9LgbaXlZiLQzajecwtsRpABzljWDVfmyzubBoK/j0acltwoKj1tI1XL6mEeu6nbC2znXcOBLkmmxH5820Vhs4hXluLLcVqtSWJZj+SQgCWQl5SmQPWYfLJgZc+hEc7wDvHTj/BLY3gmnAHhtgRm0fAs8HfxHMEdrN4KvAHHvTAJ5BGgM7AxJXTthxrncb67EUe/+XSiSCxBJHycOv37n/+XOZ9MofrC+pt/7qzy2XIeMrSqlDIBbdxSNdBwwS11Yjk1hCiUvKsGJI6W6487CIKEdsXPXarhgSTXs0UDRGXAWbLksmkpweWYGyYlUwXGP6DVUaV2eJBstEX5tKAxZM9nE4ud/xwQHs06g5qQEH9ztPYP/pAQ44rYdeQSCVqyKz0C5jIjOfegGLP/YgvnC1bL7tOs0s9MrLkGI56DJsguuOPIQwuspBqj4NWRYWB+UkYLojuRAo51bRx9M3IgPvTZ50IROwqzAfNQKLrX2vu/2G9DONDtw2YMHcgkzQiJu4oOVnP/tZqampsZ5VWloqc+bMsRa45AlWOyfZi13qcWLwuPCii6z5XE/9+gU52tkqtQvnWTjzVzkp0mNe6/wlH/R+fCEt7uqReT9+Ut7bd0B+hbTjrOWfs9bgCnp9FO3r/dHpy4kXdew8tnXJbaT6T+b9XBpm8Pnn07HT/65xO0S+piapRfFm0uD1Mb7PXr6fsr357GqrnxXory5oaUER9A9diM+B7SSOC7D/VTATOUjLwTRgoRI5eE1EpEkcEcGU0ItOoG7h9pbjFvdZakzo44c8jGnJ5/xopYxGqjWXgthw7+1DKqkPuVgPFAFFIGIENIkjNFQccTlHXUzaqAXTsDFR4zbw7eC4adWqVbJ69WprhGWPsuJ+qD4gKAKnutplV2uDvI8EjUTVLeSvQvvXsLNRpinPXvlLpFL/xnLtrL/vTtl5zaUJzzRztqn70SEQSnfRPUWvTjUC1BvZpBFYVgpBWom2LgVXgveBvwJ+BPx5MMevjLQzjX4LOG6qra2Vu+++O+7n6ANCI9Da3YmJyI2yoenMicih74rtE6YuT1qzTuYgSSMXk5J3Xr1Y3r/rFp2IHBucepcicAYC/MFI5gjMFHKOhkyROSI51YUYEUwxXdTe3SUHsT7XBlTQaO7pX74hpgdFeBPrwp3z8EqpwErBx86qlXcx6uJ8HyVFQBFIPAKmuhAXAIq/A+8G/wuYGYKcOMx944hp9OpCTKzaulDu6VBbM5Y4qU/JwpL5x5EWj4nIrFvIiuV//Pt7Zf/i8+GE9u3vrsQqTJ+mCESBgOkuxGvR18+AWargATDnVy0GG2nAILcsX86cEKV4EejGEieH25sxCfmI1Hc0x/u4Ye9n+af27/5Ubn59A1ZD7pHNt14nWz5xrXACq5L3EdAYmPd1FExCE12IzhjY++jULPCbYMahrgeXgZXSFIGevl4rJX7LqaOyr41FT5JMSIsf+8YGKy1+0559cuTSj8rGv7jVSotPcsv6eEVAETAQAacB2wT5abRowEhMd3d+bp006c+KFSs0CzEGhfXCkBxDzcJtzcdk9+mGJCfE9wtYsu8gyj89gfp2H8gpVDfo+t//KH/AmlBK5iEQLHvUvF6kn8QmuhB9G0zQJI7o/wNyUcljmHy8o/m41LWegOHqi/4hUd6R3dwqsx9/xkqL787PtSrF1117metVxaPshl6uCPgGAVOTOJwKYOxrrfOEaftaCzFyjdmGaycWldyBici9KTBcrFc39cXfyuz/+iUKp56W3VdeIu9/8maU7imxBNc4SuT689qVqjuvacS/8oRyEXKulpLPEaDhOo4RFw0XK2ikwnARUi4hT3dhKappHJ0zU9699zZpQvVuJUVAEVAEokEglAGL5hmevVZjYMFV48aIi5IUYe2keT9+Ssa9uUFaRo+U3//jZ+XghecGTYvXOEpw3ZlwVnVngpbOlNHEGJhvDdj8+fNlwQJObVOyEWByxnHUK6xrRYwLo65UjbhymlvkrMefldrnX5WenGxrYckdNyw1Yjl4GzvdKgJ+R8D0NHqnfoxP7tAY2IfqtLMKabR2IjkjVYYrA8tUTH3hNzBez1mLA+6+4mKUf0KcC4sKDkcaRxkOIe9+rrrzrm78JlmoEZjRCRx+U1Ks/enu7bXS4XfAcO0+zazCFBFGemNff1fm/fQpKT50VA6fM1s23nOrNGF1ZCVFQBFQBBKFQCgDdjxEA/fh/MMhPvPc6XSNgXExyaMD87hSMgHZofmyur0y/8dPSBVWwG2aMEZe++pfy+GFc4LGuRy3nbGrcZQzIDHmhOrOGFUNEdTEGFg4V+Gfo3d/BbarcfDaUeAisOcpHeeBdfR0WyWftrRg1IPST6mkgqPHUSn+F1bdwnasYvvBnTfJrmWX6DInqVSCtqUIJAABP8wDIwxV4I+CneXGv8YPTKB0ioG19XRKPYrsftB8RE50nk6pejgRedZTL8i0Z9dIX8YI2fIn18hWcFdhQVxyaBwlLvhcvVl15yr8adV4KBciQTgEdhovnnuef5S8gUBzV4ccwrIm7zcdTsmyJs5eZ3Z0IqvwFZn15AtI0GiTPZdfaCVotI2qcF6m+4qAIqAIJA2BcAZsE1p9ArwN3AOmC/Fq8AVgzxPT6P1KDRhlHWxrsqrDt2OJk1QSK2hM/M0f5GxU0CjAcif1iG+992cfS/hEZI2jpFKriW1LdZdYPPVpoREIZ8D+Brf9AdzkuN257zitu8lGwJrD1dkie1sbZVvLMelGpfiU0kBmIVdELt1fLyemT5Y3/uYeOTaPCxgoKQKKgCKQegTCGbAXIQ5HYE7a6Dzw8v6qVat8saClnVHIOVx7Tp9MSYHdQL2O2rhF5sJwVW7bhUrx1WEraATeG+uxxlFiRc79+1R37usgFgmoN3LvNHO8V+EM2FGAQJfhVjBdiCQueHm/tefxP7W1tXL33Xd7XMrQ4jEx43Bbi2xtPipHMPJygypgsM5+9BdSvWGznB5ZLm/91Z/LnqUXaWahG8rQNhWBJCNA1y+ZWYimUDgD9iQ6wYUtbePFPs0EG2HATI2BNXW19WcUYvXj5p7AHBqqIPlUunu/FeMah8nITIl/99O3yc5rLktp6SeNoyRfz8lqQXWXLGT1uYEIhDNgjIE9FnDDrQHHepgABBjfYvr7/tON1oirs8/5myEBDUT4iOJ9h2T2ymek5ndvSWdhvmxC2SfWLOwuyI/wCXqZIqAIKAKpQyCcAcuFGP8OvndAnOuw3TOw7/mNCfPAOjHx+CiK67I+4V7Et1JVozBQeawSP3vls1Kz9g3pzsuRzbddJ9tuukq6igsDL03ZscZRUgZ1whtS3SUcUn1gCASGM2APOe7jHLBPgt9wnNPdGBDg/K3DHc3WaCvVE4+d4hYdOiJnPfGc1Lz6R+nNzpZttyyTbR+7anBRSee1uq8IKAKKgNcQCGfA6Dd6N0DgqQHHnj30WgyMbkLO3zqA+VtbkJjRkeL5W05FFdYftSrET7QMV5bsuPFK2frxq6WjrH81ZOe1bu1rHMUt5ONvV3UXP4ZuPKEkK0+mFFZI74nDbjQfU5vhDFgrnvgCeB2Yk5gvBz8DNoLoQly9erUsWrTIYreEZn1CFtalm3AfYlxuuQnZf8twPfm8THzlD9KblSl11y+B4bpG2iuGX97ELfy0XUVAEUgOAvxSH5lTKJNhtCpzCmTzm+vlpT88L1VVrCJoBoUzYHQfshrHLWDmVf4L+FWwMbR8+XLXZGU24eH2Ftly6qg0dre5JgcbLjoIV+GTdBW+3m+4rrvcqlfYXmHXaXZVvKCNaxwlKCxGnFTdeVdNGRiLjM4rlokF5TBeBVKWnS9ZGf1p86MvWSyXgdevX+/dDgRIFs6A8VKOvshKESDQjWVMjg9kE25vOS5dLmUT2qKW7DlgFdqd8Ls3rRgXMwq3fWwZRlzeNVy27LpVBBSBxCCQCaM1Jq9UJhaWSYVltPIkc4Q5c73CoTCcAQt3r6c/S2UMrLW7U45gtLW99VjKlzEJpoTyHbutIrvj/7heuvJzZfvNV8k2cCQrIQd7nhvnNI7iBuqJaVN1lxgc43lKFgzU2LwSqSkos9yDpRhpZYyg09Bf5FsDlmw19aAWYX9R3VNWNmGqi+qe0T8kiYzCIpJc2qT63Q8wj6tAPrjjBmseV2exEUu4ndElPaEIKAKRI1CQmW25Bqtzi6QMI62SrFwZ4UOj5URkOAM2Gxd/4LzBlP1kzQNjiacj7a2yC0kZ+5FR2Id/rlJvr4x9c6PM/NmvZOTWndKGTMKNn/oTVM641OgJyBpHcfWtiqtx1V1c8EV8M8dTFUjCmIR4FpMwynPyJR9GLJ3IacD+AR139p74nAu+IZ0ACdZXOwW+HqscszZhK4yY2zSiu1tqXntTZq56UUr3HpTWqkpZf/+dsvuKi6UnN8dt8bR9RUARSAIC2SMyZXx+qeUerIDBKs3OQxJGZhJaMuORTgPGdb/eCRDbnSqyAULEcpiIGBhHW6yUsau1wSrz5GYKvI1BVlu7TH75NZn+y9VSeKxBGieOk9f/7tOy/+LzpC/LqU77DjO3GkcxU2+UWnWXWN2VYX6WlTWYW2hlDRZns0iSEhFwfuO9guPA9b5+lG4wMbZ1shMFdbHS8dbmY54YbVEHeQ1N1grIU194VXJbWuXo2dNl/Wf/VOrPm4dZev4Lzqbbe6f9VQRsBHIwomICBplZg6UwWNkZzq9q+0rdOlEJNF5Ex9gRWLQxMGYScsLxrpYGOdDugdjWwLvJArsznn4ZqyD/UTKwGvLBC86xqmY0zDSmKEpM/8s0jhITbJ64SXUXvRo4yrImFOdiblZWvhTpKCsiEJ0GzHnDYhysdZ4wbb+urk5WrFgRthJHNxIgrExCjLa2I7bV5mJ5pyH4MqPwva2W4Rr71nvSjZjW7qsulu03XSktY0cPuVQPFAFFwDwEODeLVTAmIM19FFyD5Uhzz8kM9XWcmv6tW7dOyCZV4gjle2L1jV+kBrbktLJmzZq+BQsWBH34qa52a7RVh0zCI0jMcDmPcFBGJmZMWPe2TP/Fy1Kxc6+1FlfddUuk7trLpBPrcikpAoqAmQjwi5a1BhnLYvIFK2AwluXFCcWsxLF06dJQtsFTCnDX5KcQCi5dcqyzVQ4i9Z2GqxNVM7xC2c2tMgWJGdOeXSMFJ07KqfFjrNWP91720ZQuIukVPFQORcB0BPjtXwyDNS6/xCrZxInEzBjMTuOMwWTo1LcGjDGw+eecIydRk/AoRlks7cR9LxHX4Zr+zK9l0prfS1YH5pfNmyVvf/7P5PC5Z6P6pD9KvcSKt8ZRYkXO/fvSVXecODwhH5Uv4BIsxX6JJl8k/WUMZcCMGD4Oh87a47vkACrA93jGSQiJEd8avWGzTIPhYnyrB6nv+y49HxUzrpDGqTXDdUk/VwQUAQ8gwC/IIhip8agxOBKJFxxh0YC5HcfyADQpFSGUAVubUimS0Bjngb2HVY69Qpy/xUzC2ufWSOn+emlHxYz377xRdl59qVE1ClOFp84lShXSiW/Hj7rLxFSVcTBW1ajkXpSVI4UwVkWZOZLrcuJF4rVn1hNDGbDjAd1g6ls1eGPAeT0cBgGuelz7/G/gJlwnOa1t0lA7Ud7423usicdcBVlJEQiHAL84WX2BiUasCNOLeYqe8iiEE97wz4h7dS6WHkEVd2YMMobl99qCpqkslAFjP54e4J3Y/jv4e+Al4O+APU+MgWUsmeuOnEjPr37nfctwjXlnk/QhnrV/0UJhRuGJWZi/pROPh9VLOsRRijJzpYop1JisykKsDPBzvSYaLTJLBLGquLVeEyyYZcBgytp6uvpXP2g5Ji0eKGsWqExTdZeHycJcK4vFcOkSLOYoC6MtNVqBGvbOcTgD9ijEpBH7GXgF+DHwnWClEAjkNLfIpF+vk6m/+q0U12NeWXmpbL79estNqGtwhQAtzU4zdjIur0ymF1fKaPy6j8UFVYKRAL9opxWPlOModXao7ZTsPO2tzFqvq5V6oIFitYtKjK6YcMHjAhgsJXMQCGfAKtGNceBLwPeB6e8yZjKSFQOTXoicfCrfsUdqX/iNTFj7hmR1dsmx2dPk/U/eIgc/ugALSYaDOPmymdqC3+Io/HKcWlhpxVDojrJXwY1HP6w8zomw5LNKRmMx1VbZi7jvfpcTl2Z/ZAFGlDmYnIuqEjl5wtJIJBoNUhc8FB2YxsKi2KdRAYcjyk4s/toHF2kuRkHEigYlH8/IgfeC99OFyqkv7ZgOw6o5jcgo5v18Du/jKIl5uxkYsfaPYPu3PfiM5eG4pUwshMuq7cwSZDuadEGNmEvhvl3fQLeWg28H54O/AO4AKwGBzPYO4UrHHG1Vbt8tXXm5snfJRVKHZUyapmg2ob4k+MWHGMqs4iqshlts1bSLZbQVKY6cFEvm0hqcLnIMI7N9MGTN3e3Wl3wnqswkY8K+bZjpBs2FoSHnwAjlIbkh0km6NDA0TlyaKGdEVkTGnUaL93RY/YIBwz+6X2nI2G4WDRq2jBmy4g6fTtnSuXJ7pO+SSdeFM2Cb0JHPOzrzRcd+Kncno7EvgUvAnxhomD/mvj5w7i1s6d4cQsmKgXHuFo0W526xqG7ThDGy/jN3wHhdKF1YRFIpMQiYGkdh7wvxS382RkScxMpYSiqJX+AsAEueUTwKX978ku/Bl323NHd1yImu07KvtVEau9tiNmiM3U0vGmmVQGI7gYaZ5YgWLVoUcbdpcPIzo5v3yH6y3cC2AxvNwI8INVqBqPjnOJwB80ovd0OQe8BPOQS6Eft0b54AH3CcT8ouSzyNe30DDNerMnrjFunNzJQDFy6QnddeLsdQFV6TMpICu3EPrcopkpkwGoxPFXoklmIlgmDkUShw6cHY1Ei5zCmphguu3XLDHWlvkUOoBdqNkQpHNVwyyBrNWCMYJJLAuNCtx/6wFNJoJJ1wom66LZxo3MuYJgKn0oD9BJheBz4CdqYHLsPxd8H8CcZrHgQPRzNwwR/A/wFmksmr4CGUiBhY4eFjVomnSat/J/mNp6R1VKVsQmyLi0a2V5QOaU8PEouASTEwplrPKR1jZRSaUCqIRm0kjBC5FiOpDsSV6MajC68Xfka6N6yYEowYDVi0fYpm9JXYt0aflm4IDGfAZgOQDxIEyiN4zvfBzG60iUbrB+Al4ENgugOfAW8F3wU+B/wtcD3YjgFjV/aD7WWRu3kiUZTR1S1j3sRo66W1MvrdzdKHVusXzpNdV1+CEk9zpS9KV0ei5NLneA8BxlzOKRsn05CckZdl7py+4dxw3kNeJVIE+hFwGrB/wCnn/0IajHPBN/RfGvffdXjCxICnfATHO8B7B84/gS3dgzRgjGuRK8APgeeDGYfjCI3p/TSGF4NfA59B0cbAig4e6R9tvfJ7ycNo6/TIcisFfteVl0jbKIqglEoEvB4DYzXx8ysmIEGDoVklJwLRxsCc9+q+IhANAk4Dtg03vhNwc7IXtGQci6Mpmw5gh0bNSQ04uN95Avtt4HsCzg05XLt2rdSvxbIkA+tn5RcXybgZUweXO+cXZAZS3i9t75Ipq18THp+Au6Ro8fmy66rF8ntmRWG0VTtgvPg5yXZt6XFy8Ti4badn8Z5VVCWnNu2UnVknZcxAsgK/tEm2+0yPFQ9T3ge+qytXrrTe35qaGmPXA2NQp8nqxYd/irCbSCPGEdhzYDsG9nHsXwm+F0z6U/B54Ad4EA9xPbD3KoPPAyur22sZrZrfvoHyTqeleUyV7L7yYtmz9CLEtsriaVbv9TECOchou6CiRmqQqp6IeVw+hkq7ZjACpq4H5jRei4H/WnAijVcwlXLE5Zw0NR7HjIUlnFglgwZrMhIyynftkx5MMD5w0ULLcB2dg5yQNF++JOGA++yBNfnlMq+02srA81nXtDuKgLEIZIWQvDLE+XhPM65GtolJG7VgjsyYqHEbmBOn46ZVq1bJ7p5mWVheJtfuq5dxf1wvmUjQOIklS9bfd6fsvfQC6SoujLsdfUByEKCL1nbXJqeFyJ7KiuMLy8djTldp1Nl4kbXgv6s0BmamTqk3clVVlTEdCGXAktEBOlkvBdM47gN/BfwImJOlV4OZkcg0+i3guKm2tlb+x2OPSuGxBukoKpRdyxYj/X0R1tyirVRSBIZHYEbRKGvOVBEqXCgpAn5HgPFbMl2IplAqDdgdIUB5EefJCSXOAzu1do1s/ItPyKHzz5HeHGeCZUKb0oclAQG3R19nF1fLXLgMtVZe9Mq1E1miv1PvUASiQyCUAXO6+aJ7okeuZhr96rEjpTY/V2rVeHlEK2aIMQ+Tks9GtYpoJ/Ca0TuVUhEIjoCfXIhM4DCelt33SeP7kK4dcCMGxl9t55aNt8pBaf282N88jYHFjp2bd/rJhRi4IrObuGrbikDSEeCSHRdWTLRS5DNQQklJEVAEvI9AoAuRCRWscOELeunhR61MNrfjKb4AM8WdSKXOKrFu1Ucra1AbkNMeleJFQGNg8SLozv0muhADf2qy6sXfgleBm92BMTGthpvInJgW9Cl+QGAmqmpw6ROupaWkCCgCYmUhLl26NNA2eBIapq476Xs4eAHM+Vg0ZDeAA0dpOOV9YhKHkrkI2KW6ktWD/IxsuXTkVDmvYrwarwSDzF/ySopAKhAINE7/PNAolykhTQXfB2YO+tvg34GVFAGjEajJL5MFqCJfhqXllRQBRcBcBAINWGBPWFH1VTBrFHJk9i54MdgI0hiYEWoKKmSyYmC1WPqEmYb5Bi9/EhQwD53UGJiHlBGFKH6IgS1Bf18BTwCzpNOdYNYq/Dn4cfBvwH1gz5PGwDyvopQLOB2VNRaUjjV67a6Ug6YNph0CJhXzDYyBcWXk34K5tMr54K+Bq8H3gGnYjDBekFM0BkYUzKVEx8C4BMq5cBuavPCkKdrUGJgpmjJfzkAXItcSoRG7Cdxofve0B4qAoIo8K2uMRmWNwNdd0VEEFAGTEQj8H80R109M7pBTdo2BOdEwaz8RMTDmAS8snyAsyqvrd6VO/xoDSx3WiWzJDzEwJx4X4eA7YGYffg7M1Po94NfBnieNgXleRUkVMAOr9nwUlTWmFlWKVtZIKtT6cJ8hYHIMzKmKq3FwOZhLnZCeAJ9t7RnwR2NgBigpjIjxxMBovC4eOVlq1XiFQTh5H2kMLHnY6pOHIhDoQnR+uhsHrWBn4gazE5UUAc8ikAnjtQjGa3JhhWdlVMEUAUUgMQiEM2AFaOIH4BwwR16XgFeBjSCuB/ae9Bohqwp5JgKxxMAyUYT34srJMkmN15mApvCMxsBSCHaaNxXOgLGo72XgG8EjwUzw2ApWUgQ8h0D2iEy5BCOvCQVMpFVSBBSBdEAgnAH7JgD4R/CrA0BkYPsz8McGjj29WbVqlezqa9Fq9J7WUmjholkPrDAzxzJeo/OKQz9QP0kZAoyB6SgsZXAnrCETsxDDGbC/AjKPgbeAzwPTnVgLNoJqa2tl+pK5RsiqQsaOwKicQiyFMlEqcujxVlIEFIFYEeCPDjKzEE2hcAaMGYisxvEF8Djwn4FPg40gjYEZoaaQQkYSAxuTWyIXwnjpUighYXTlAx19uQJ7WjbqNGB0EY53oHAY+0fA08CsTt8A/icwF71UUgRcRWBMbrEaL1c1oI0rAu4j4DRgjH5vBG8IItZHcY6FDWaCjTBgnAeWoS7EIKo041S4GFi1Zbwm6cjLo6rUGJhHFeNDsZwG7CT695fg/w7Tz1vDfKYfKQJJR4Ajr49WqvFKOtDagCJgAAIcVfmStJSU/9TKmoYszFuQxamJSoqAIpAMBEwqJeUcgSUDC9eeSRfi6h0bNI3eNQ0kruER8F4vLB8nXM8rOyMzcQ/WJykCisAgAn5Lox/smKk7y+77pKmip73cdgwsc0SGLILLcFJBuYxApQ0l7yOgMTDv6yiYhCam0TPz0KYF2FkJ/hcwJ9Uw+/BLYCVFwBUEsmC8FldOseoaqvFyRQXaqCLgaQScBuxaSPoZ8H+BHwAzjX4x2EjiPDAlcxGYed45snjkFKkp1NJQpmlR54GZpjFz5XXGwN5HN2aB3wSz+sb1YP32AAhKqUUgFysnXwy34Xita5ha4LU1RcAwBJwjsE2QnYtY2vQcdv6XfWDaVtcDM01jWPYABXnPLRsv11bPlD3r+XtKyUQEGANTUgRSgYBzBFaHBv+Po9EfYp+uRCfl4+BvwKVgFvttBCspAnEhwAUoZ5dUy1Qsg1KWw1dMSRFQBBSB4RFwjsACr96BE98GvwC2DRkTPPaAvwNmvMyzpDEwz6pmiGBVmJh85ejpsqBs7BDjpXGUITAZdaC6M0pdRgvrHIEFdmQqTrwGpiuRGYl/AWZVei6x0g4+BFZSBGJCgEugzMWkZKbH52aGew1jerzepAgoAmmAQLhvjs3oP9f/Ir0CvgdM1yGNF6mvf+PNv7oemDf1MjqnSGYUV0l1fpEUwIiFIp1LFAoZ759X3XlfR8EkpN7IVVVVwT725LlwBqwLEr8D5hIqJWAaMVakvwb8Nngs2LOk64F5SzUT8spkZvEoqcor0moa3lKNSqMIWAiYOJF5uNIGo9CziWCOxmjISHeC54KZxNEE9iRpLUT31cKXa1JBhUwrqhTGurIywoVc3ZdXJVAEFAGxFrRcunTpcLbBE1CFG4FxMcudYI62nPTfOCArKQJBEWD5p+mFI2USsgpH5hYIj5UUAUVAEUg0AuG+Wb6IxmoDGpwUcOzZQ50HlnrV0FCdjXT4azCP6/zKGhkNd2Gsxou+eCUzEVDdmak3E6UONwJbiQ6NA3MRS9t9yBWZmY2opAgMIpCJeVwzEN+aUliJEVfh4HndUQQUAUUgmQiEM2D/hoa3gnscAtCYGUGcB/ae9Bohq4lCZqIy/Ni8UqlBuafKnAIpz85PaLV4nUtk4lvRL7PqzlzdmSZ5OAP2t+jMYwEd0hWZAwBJt8PSrHyZhdFWdV6xlGbnJdRopRuW2l9FQBGID4FwMbBdePQbYJaUIt0G3mvtGfBHY2CJURKXNBmZUyhzSsbIklG1iG/NkJklVVbVjGQucaJxlMToz42nqO7cQD092ww3ArsakFwOXjoAzRPY3gN+feBYNz5EgAZrdG6RjMsvlTK4BYuzcqQAHGsyhg8h0i4pAoqARxAIZ8B2Q8ZWsLPixgSPyD2sGBoDGxaiwQs44WNMXgmK6VbKKCRhFGfluu4a1DjKoHqM21HdGacyYwUOZ8C4KvMPwKz3czb4EvAqcKppMhr8EpjVQD4x0PgibDmhmvLPAvNYKUoEuO7WLJR1GptfYiVi6CgrSgD1ckVAEXAVgXAxsO9Dsp+DmUI/Evw18I/BqSaOBOm6dNI6HNwPfh78n84P7H2NgdlIDN3SRTgKMa1FWDDyuupZMh9V4KvgMvSa8dI4ylC9mXSkujNJW2bLGm4Exp69OsCJ6OVP8JDrwEfALEVl0zLsfBdMY8prHgRHSnfgQp2XFgatkqw8qckvk3Kss1XIeFZmtlVEV8s6hQFNP1IEFAEjEBjOgCWyE4/gYRzVPep4KI0W3ZRLwIfAb4GfAXP+2V3gc8DfAteDGapxEuNxXFCzxXnS3k/XGNgIwDQGKe6TUcaJc7NKs3NRPDeVarY1EN9W4yjx4efm3ao7N9FPr7ZT+c22DtBODID3IzjeAd47cJ6ZjjeCacA4B41cAX4IPB/M8lb2CI0jLxpFJSDA+Vkzi0ciCaPIGm15zSWoSlIEFAFFINEIcATkJrFU1X6HAAewz3NO4hIujHdxUU3bePHzr4JDpvSnQwyMJZymFFQOzs+aVTLaKuXkB+OlcRS+4maS6s5MvZkodSpHYMHwCXQL8pq+YBdGe27t2rVSv/ZlqRg72ro1vxhzm2ZMldqF86zjurc3WlvTjqcvnI8STiVy4r3tVrr7xZcusFLe7S8N231j+vGmTZss/filP6brQ+WnAwnpzov6E579hAf7snIlS9+K1NTUGLWgZTADYnUkSX/oQnwObCdxXID9r4KZyEFaDqYBc460eD5q8tN6YFRSFVYynoJ1tThPixOMM1CLUEkRUAQUgUQjsH79evHDemCJxoXP47eu85uXSRu1YBo2JmrcBr4dnPZEkEbBaE0uLLfcguXZBbogZNq/FQqAIqAIOBFIpQuRY9RLwZXgfeCvgB8Bfx68Gsx4HNPot4DjplWrVsmuvhbLZWi7CeN+aAoewLR3FstlMgZHWuma7k63hu2uSQHs2kQCEVDdJRDMFD6KeiNXVVWlsNX4mkqlAeOcrWD0Ik6SE0q1tbUyfYntqUzooxP+sAwMSsej9uBUuAhHY9SVl5Wd8Db0gYqAIqAIhEOAPxjJdCGaQqk0YCnFxOvzwGi0qrBi8ZSCCstFqHGtoa+Hjr6G4mHSkerOJG2ZLatvDRjT6Ffv2OApF2IlSjixKkYZ1tEqwQTjEmz9kPJu9n8BlV4RUASIgLoQPfYeLLvvk65LVJiZI9OLRloLQFZg5eLsjEzXZTJBAI2jmKCl4DKq7oLj4vWz6kL0uoZSIF82iuVyaRIyR1plqEGYj/qDSoqAIqAIKAKJRcC3LkTC9NLDj6bEhcgK7xPzy2V8Qam1LAldg0rxIaBxlPjwc/Nu1Z2b6MfetokuROecrNh77sE7kzmRmSWcRiLNfTzW0eovmJsnxWq0PPgWqEiKgCIQLQImTWR2uxZitNhGfH2iayFyGZKzsPjj4pFT5PoxZ8lVo6fLnNIxGHWVqfGKWCuRX8hfg0pmIqC6M1NvJkrtaxdivAopycqVKYX95ZuYgKGxrHgR1fsVAUVAEUgcAr42YLHEwMqzClBzsMKqOUj3YG6mryFK3JuU4CdpHCXBgKbwcaq7FIKdwKY0BpZAMON9VKQxMM7Dqs4tlhq4AiuQMcjMQRMXgIwXL71fEVAEFAEioDEwD7wH4WJguRlZMr1wpBXPuqF6liytqpUZA/UH1Xh5QHkQQeMo3tBDLFKo7mJBTe+JBYG08I+xbBOXIZmAKhjl1igrXwqzcmLBS+9RBBQBRUAR8AgCvjZgv//xk3L5xYvlqksvl1K4BrVsk0feugjE0DhKBCB59BLVnUcVM4xYGgMbBqBUfswY2Lxz5qvRSiXo2pYioAgYj4DGwDygQsbAdMTlAUXEKILGUWIEzgO3qe48oIQ0EcG3E5nTRH/aTUVAEVAE0hYB3xowrgemZC4CGkdR3ZmLgEqeKgR8m8RhrQe2erW1wqh+GabqddJ2FAFFwFQETEzi8O0IjC/R8uXLLQNm6guVznJrHMVc7avuzNQdf+jzO9Mk75WvDZiZr5FKrQgoAoqAIhAJAr41YCb9iohEUel2jbp9zdW46s5c3ZkmuW8NmGmKUHkVAUVAEVAEokPA10kcCxYsCIrGiRMnpKOjI+hnetIdBHJzc6WysnKwccZR9Jf8IBxG7ajujFKX0cL61oCF0kpLS4v10dixY0NdouddQIA/KqiboqIiF1rXJhUBRcBEBHztQlyxYsUZVc2bmpqkoqLCRF35WmbqZMeOHXLq1Cmrnzr6MlfdqjszdceRM78zw63k4bWe+XYExiSOYC7EESNGCFnJWwjYennxxRflxhtvlLy8PG8JqNIoAj5HgD88yKyFaAr5dgRm0q8IU16WZMtJI8bYJN2JOpco2Wgn7/mqu+Rhq08eioBvDdjQbuoREfjc5z4n3/zmNz0NRkZGhibYeFpDKpwi4B0EfGvATJwHRplfe+21iN+OaK+P+MG48PHHH7eyAv/pn/5pyG0vvPCCdf4v//IvrfP79++3jnt7e4dcx4MHH3xQRo8eLTU1NYM8ZcqUM64LdkLjKMFQMeOc6s4MPflBSt8aMD8ox+0+TJ48WZ5++mlxGqcnn3xSamtrh4gWLqZ4yy23yL59+wZ5165dQ+7VA0VAEVAEYkXAtwbMtBjY/fffLwcOHJA77rjDGq18//vft3TKpIYLL7xQOHJhcgMz9Uihrv/Upz4ls2bNEhqf66+/XrZu3WpdH8ufqqoqOeuss+SVV16xbm9sbJS33npLrr766lgeF9U9GkeJCi5PXay685Q6fC2Mbw2YaVp76KGHZPz48ZbrjiOWz3/+81JXVyf33nuvldpKw7VkyRK5/fbbpbu7W4Jdzz5fccUV8s4778j27dtl7ty58pnPfCYkFDRyb7zxRsjPObK69dZb5YknnrCu+cUvfiHXXHONZGdnh7xHP1AEFAFFIFUI+DqNPloQO1c8KL1bt0V72xnXZ8ycITnLv3jG+UhO9PX1DV72y1/+Uq688kq55JJLrHM0aj/60Y/kzTfftEZlPOm8nsccwdn0hS98QR5++GFpbm6W4uJi+/Tgdvfu3YP7oXauvfZa+fKXv2zNz6Ih+8Y3viG//vWvQ11+xnm6IF9++eXB8zSq7NdwpHGU4RDy7ueqO+/qxm+S+daA+UFRhw8flgkTJgx2hSOicePGSX19/eA55w5jVV//+tfl2WeftVLReT25oaEhqAFz3htqn/OxOKr79re/LSdPnpSPfOQjURmwm2++2Rothnq+nlcEFAFFIFYEfGvAVq1aJbs5WEQAAAknSURBVKujXNAy1lFTrOAH3kdj46Tq6mrZsmWL85QcPHhQ7DJYgdezzy+99JI888wzljuSVS3oJgwcpQ15YAQHdCPedNNN8sUvxjaqjKCJMy5hHEV/yZ8BixEnVHdGqOkMIak3MmPfppBvY2DMlDNtQUu+OHv27Bl8d2g06K773e9+Z8W9mNjBEdF5551nXRN4PWsJsihuaWmptLa2yte+9rWEVB256KKLhPGvT3/604OyOXdoINvb2635W5yITLaNpr11Xq/7ioAi4D0E+INRF7T0iF5MnAf213/91/Kv//qvVsbhD3/4QytdnTEsxrKmTZtmGbOVK1dKVlb/wDnw+ttuu80aec2ePVtodOjuC0ecn/X666+Hu2Tws4svvtgyjIMnHDscCfJZdG9ydMgtjS6J8S7nPDDus9LGcKSjr+EQ8u7nqjvv6sZvkg31Wfmod2vWrOkLVgvx0KFDgy44H3XXF13ZuHGjbNq0aXDagC86pZ1QBAxDgLUQly5daoRt8K0L0bR5YIa940kXl754JTMRUN2ZqTcTpfatATNRGSqzIqAIKAKKQOQI+NaAmRgDi1xt/r9S4yjm6lh1Z67uTJPctwbMNEWovIqAIqAIKALRIeBbA6YxsOheBK9drXEUr2kkcnlUd5FjpVfGh4BvDVh8sOjdbiCgc8bcQF3bVATMRcC3BixUDCwzM9Oa5GuuyvwpOSdhc/I1jRgXtdQ4irl6Vt2ZqzvTJDehlNRkgPolcAn4EwMAs0Dg/wVzRizXF3kQHBGxegXngm3btk1ozJTcR4BGi8aLNR65X1ZW5r5QKoEioAh4HgETDBhLpt8DfsqB5hzs/wy8Evy44/zgLmNgwSYys2oEK0WwQjvXturp6UlIuaXBhnUnJgRouPiDgiNnGjDGUfSXfExQun6T6s51FaSNAKk0YD8BqteBj4DnOhBehv3vgunO5DWRjKZY/2gV+G7wY+AziGtphaOZM2daJY74y1/JGwgUFBRIYWGhJQwrcqgB84ZeopVCdRctYt663qQEuFQasEegJi4z/KhDXTRaPwAvAR8CvwV+BsxlhO8CnwP+FpjrhzhLm3wKx/8TvA7Mkdh/godQJIaJX5hkJe8h0NTU5D2hVKKIEFDdRQSTZy9iSTdTiAYkVURjczKgMVabZQxrL7gLzKV/bwSTOLL6G3AH+CHwfLC9nsdL2H9g4PzwqzLiQrcokSnFsT4rmvsiuTbcNdF+Fu56t3Rmt5to2WJ9XjT3DXdtrJ+Hui/UeRtDN7eJlC3WZ0VzXyTXDndNqM+jPe+m3qJpO5UGLJhc43Byv+ODA9jnOSc14OB+8DSw7V78APt/MnD+C9ieQVwM0gsU6sWJRbZYnxXNfZFcG+6aaD8Ldf2+fftigSih94SSLdZGYn1eNPcNd22sn4e6L9h5L+iOOgomm8m6i6RPofoc7flYcUr1fal0IQbrm9MtaH/eZ+/Es506dao88AAHaf00b948K0HAPk7VllmPrO6cCIr1WdHcF8m14a6J9rNQ1y9cuDBhuMWKfSjZUv28aOQY7tpYPw91X7DzXtAddRRMNpN1F0mfQvU53Pmf/vSn4nQb2nHoWLHy830T0bn3HB28APt0B9q0HDu2m9A+p1tFQBFQBBQBRcB1BCZBgk0OKTKxz3RBGrYc8AbwLLCSIqAIKAKKgCLgGQQ4Z4uZhkzKYICDmYSkq8HbwEzm4AhMSRFQBBQBRUARUAQUAUVAEVAEFAFFwHQEJqMDPwY7K3qY3qd0kp/TK/4dzMorV6RTx33S15noB6fD8P/ffT7pUzp1gxNm3wZfk06d9mJf1YB5USuRy8RCif8R+eV6pccQYObxox6TScUZHoF/xiV/D/aUAXN7HtjwsIW+gmWnjoCdWY28ehl4K3g7WDMaAYJHKVb9fRn9+aFH+5ROYsWiv+sB0PPgX6UTUB7sa7S6Y6WkzeCjYP4AUUoAAovwDFbncBowGmQ7qzEb+8xqpOvCSSw9peQ+ArHobwXEvtx90VUCIBCL/mzgaMSU3EMgWt19A6J+B/wy+Gn3xD6zZbcnMp8pUeRn1uFSpt87yVmaiuefADN2whFZBfhfwDR6HJnZVT2wq+QCAtHq7/OQkb8ES8C1YMbDlNxDIFr9LYaot4BzwS+4J7a2DASi1d2XB1D7JLbHvYRglpeESYAswUpT0aiR7JJU/Uf614sIhNMfC0GTlbyLQDj9rYXYZCVvIhBOd7bEnotdmhwDs0F1boP5Z/ucF+i+pxFQ/XlaPcMKp/obFiLPXmCk7vxmwA7g9ahxvCLjsc/J00pmIKD6M0NPoaRU/YVCxvvnVXcu6GgS2tTSVC4An6AmVX8JAtKlx6j+XAI+Ac2q7hIAYjyP0NJU8aDn/r2qP/d1EI8Eqr940HP3XtWdu/hr64qAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopAeiDAgqhctsdvNUbTQ3vaS0VAEVAE0hiBO9H3k2BnQelQcHwaH+wHs5Yn1zmz6SvY4dI/XP9MSRFQBBQBRUARSBkCr6KlSAwYBfoL8G+546Aq7H/Kcay7ioAioAgoAopAShCIxoCVQaLT4AkOyTgyK3Yc664ioAiEQEB99SGA0dOKQJwIcCXwFvA3B55DlyBHXN8AXzRwrhHbF8G3DxxzUwJudhzrriKgCCgCioAikBIEOAKbCP5z8Cww6TLwD6w9kUJs3xjY5+Zj4I0Dx1OxvWFgXzeKgCIwDAI6AhsGIP1YEYgBgXtxz2LwloF7b8Q2B0zjtBT8Ntim57HDmNls8NXgX4GVFAFFIAIE1IBFAJJeoghEgUAfrn0BPA583sB9vdjuAT8Lfgb8ObBNHdj5OfhPwdngbrCSIqAIKAKKgCKQcgToQmQm4UwwXYX8kXgh+CWwTbfZOwPbJdieAl8ccF4PFQFFQBFQBBSBlCBwK1qpB38fTLfgCTBHV5zcfA/4nwe2F2DrJE6AXus8ofuKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCHgHgf8PIHQ0BLzrUvEAAAAASUVORK5CYII=\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "plot_point_source_spectra(jl.results,\n", " jl2.results,\n", " confidence_level=0.68,\n", " sum_sources=True,\n", " flux_unit='erg2/(cm2 s keV)')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Mean acceptance fraction: 0.5794\n", "\n", "Maximum a posteriori probability (MAP) point:\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Value</th>\n", " <th>Unit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>bn090217206.spectrum.main.composite.K_1</th>\n", " <td>(1.61 -0.16 +0.21) x 10^-6</td>\n", " <td>1 / (cm2 keV3 s)</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206.spectrum.main.composite.kT_1</th>\n", " <td>(7.94 -0.29 +0.26) x 10</td>\n", " <td>keV</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206.spectrum.main.composite.K_2</th>\n", " <td>6.30 -0.5 +0.30</td>\n", " <td>1 / (cm2 keV s)</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206...index_2</th>\n", " <td>-1.490 -0.013 +0.021</td>\n", " <td></td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Value \\\n", "bn090217206.spectrum.main.composite.K_1 (1.61 -0.16 +0.21) x 10^-6 \n", "bn090217206.spectrum.main.composite.kT_1 (7.94 -0.29 +0.26) x 10 \n", "bn090217206.spectrum.main.composite.K_2 6.30 -0.5 +0.30 \n", "bn090217206...index_2 -1.490 -0.013 +0.021 \n", "\n", " Unit \n", "bn090217206.spectrum.main.composite.K_1 1 / (cm2 keV3 s) \n", "bn090217206.spectrum.main.composite.kT_1 keV \n", "bn090217206.spectrum.main.composite.K_2 1 / (cm2 keV s) \n", "bn090217206...index_2 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Values of -log(posterior) at the minimum:\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>-log(posterior)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>BGO1</th>\n", " <td>-620.587144</td>\n", " </tr>\n", " <tr>\n", " <th>NaI6</th>\n", " <td>-851.538653</td>\n", " </tr>\n", " <tr>\n", " <th>NaI9</th>\n", " <td>-764.490675</td>\n", " </tr>\n", " <tr>\n", " <th>total</th>\n", " <td>-2236.616472</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " -log(posterior)\n", "BGO1 -620.587144\n", "NaI6 -851.538653\n", "NaI9 -764.490675\n", "total -2236.616472" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Mean acceptance fraction: 0.5524\n", "\n", "Maximum a posteriori probability (MAP) point:\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Value</th>\n", " <th>Unit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>bn090217206_band.spectrum.main.Band.K</th>\n", " <td>(1.80 -0.04 +0.05) x 10^-2</td>\n", " <td>1 / (cm2 keV s)</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206_band...alpha</th>\n", " <td>(-8.03 -0.24 +0.29) x 10^-1</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206_band.spectrum.main.Band.xp</th>\n", " <td>(6.04 -0.35 +0.25) x 10^2</td>\n", " <td>keV</td>\n", " </tr>\n", " <tr>\n", " <th>bn090217206_band.spectrum.main.Band.beta</th>\n", " <td>-4.64 -0.13 +1.3</td>\n", " <td></td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Value \\\n", "bn090217206_band.spectrum.main.Band.K (1.80 -0.04 +0.05) x 10^-2 \n", "bn090217206_band...alpha (-8.03 -0.24 +0.29) x 10^-1 \n", "bn090217206_band.spectrum.main.Band.xp (6.04 -0.35 +0.25) x 10^2 \n", "bn090217206_band.spectrum.main.Band.beta -4.64 -0.13 +1.3 \n", "\n", " Unit \n", "bn090217206_band.spectrum.main.Band.K 1 / (cm2 keV s) \n", "bn090217206_band...alpha \n", "bn090217206_band.spectrum.main.Band.xp keV \n", "bn090217206_band.spectrum.main.Band.beta " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Values of -log(posterior) at the minimum:\n", "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>-log(posterior)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>BGO1</th>\n", " <td>-579.894174</td>\n", " </tr>\n", " <tr>\n", " <th>NaI6</th>\n", " <td>-825.481027</td>\n", " </tr>\n", " <tr>\n", " <th>NaI9</th>\n", " <td>-733.740692</td>\n", " </tr>\n", " <tr>\n", " <th>total</th>\n", " <td>-2139.115893</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " -log(posterior)\n", "BGO1 -579.894174\n", "NaI6 -825.481027\n", "NaI9 -733.740692\n", "total -2139.115893" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comp_model.K_1.prior = Log_uniform_prior(lower_bound = 1E-7, upper_bound = 1E-5)\n", "comp_model.K_2.prior = Log_uniform_prior(lower_bound =1E-1,upper_bound = 1E2)\n", "comp_model.index_2.set_uninformative_prior(Uniform_prior)\n", "comp_model.kT_1.prior = Log_uniform_prior(lower_bound =1E1,upper_bound = 1E4)\n", "\n", "bayes = BayesianAnalysis(model, data_list)\n", "\n", "res= bayes.sample(20,100,500)\n", "\n", "\n", "\n", "band.K.prior = Log_uniform_prior(lower_bound = 1E-3, upper_bound = 1)\n", "band.xp.prior =Log_uniform_prior(lower_bound = 10, upper_bound = 700) \n", "band.alpha.set_uninformative_prior(Uniform_prior)\n", "band.beta.set_uninformative_prior(Uniform_prior)\n", "\n", "\n", "bayes2 = BayesianAnalysis(model2, data_list)\n", "\n", "res2= bayes2.sample(20,100,500)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " 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{\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 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$(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_point_source_spectra(bayes.results,\n", " fit_cmap='plasma',\n", " contour_cmap='winter',\n", " confidence_level=0.9,\n", " flux_unit='erg/(cm2 s MeV)',\n", " legend_kwargs={'loc':'lower left'},\n", " plot_style_kwargs={'linestyle':'--'},\n", " contour_style_kwargs={'lw':0}\n", " )" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_point_source_spectra(bayes.results,bayes2.results,\n", " fit_cmap='plasma',\n", " equal_tailed=False,\n", " contour_cmap='winter',\n", " confidence_level=.95,\n", " flux_unit='erg2/(cm2 s MeV)',\n", " legend_kwargs={'loc':'lower left'},\n", " plot_style_kwargs={'linestyle':'--'},\n", " contour_style_kwargs={'lw':0}\n", " )" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_point_source_spectra(bayes.results,bayes2.results,\n", " fit_cmap='plasma',\n", " contour_cmap='winter',\n", " flux_unit='erg2/(cm2 s MeV)',\n", " legend_kwargs={'loc':'lower left'},\n", " plot_style_kwargs={'linestyle':'--'},\n", " contour_style_kwargs={'lw':0},\n", " sum_sources=True\n", " )" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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DDgmXU49LQtjMVkyYEC6xMZ6MpsgGHcRftfU8Yjw69NQvyN95jzrISeaEFsiwG4EHPeArWgcVGnag0NKgA0dOlS2bLlZDhwolWXLcuSruTvlh/l7ZOOGfPli9naZ8/kOqaqqlqroKjkGAs3S1AYNai/HHddeWrduCTRAQUkRUAQUAQciQOcqMk2ITqGW2NN+g5t7Hlxqu8lHsf6wbTvoq02NgRUWlsvy5bmyYMEeWbduP9b3mmVFRZVpdxw0svS2raV3nzZywokdZdToLjJ8eEdJTU0I+n1pAxQBRUAR8CcCThoDa4kAuw7g/csFQE4w/sFlX1A3n3nmmerc3FzzReGJ23ZJSYWsXr3PCLZvv9ktC3/YI7m5RRIFx32Om1VVV0tqm3g58aSOMmRIJuaUtTfLzMzEoN6nXlwRUAQUAV8gYDchjh8/viWywRfN8KqOljRyCK7wW/A6cCWYddBbcCQ4ZKgpDczTRlIjW7t2n8yfny3fzNslmzcXyOYtBXL4MCNb1VDHjknSu1cb6dEjTUaf2U1OhMbWrVuqdViXLgjoeIQLILrpdwT0mfMOYidpYC0Z6LkPcHwLPmCDxb5u2+3sVTp3cDyMfMstNfOrOW62aVOB0dSWLdtrlksX75UlC3Plw/c2Gk0tNi5aOmcly0knd4IG2MWMr/Xu3QZOJC35XnA2htp6RUARUAT8hUBL3qicUMzAunYaio2QigrvCw3MfoPNrW/bdgCDn3QW2SFLFufK9m0H5eDhMiPQeG5ycpwMHJhhhBmdRo4/viM8INtJXFxMc1XrcUVAEVAEAoZAuGtguUCSJsO1YJoQST8H32HWIvRf9+5pQr700n51COzefbiepkaNjebI1nFxtdpYtaRntJZ+/dvKyad2llNOyTJja4mJcXV16IoioAgoAoqAewRaooHtRVVMbmkJL9Y8ANyJK6FCgdbAPL3vvLwiWbo0V+Z9XePWvxnmyEMHy41AKyorM8s+fdrCQaS9EWY17v2ZkpYWHh6QOh7h6ZOi5XyFgD5z3iEZ7hoYx8D+6QLJVS7butkIAhkZiTJmTA/DVhFrrho1tBUr8jBvLVemTVsv775LPxkxGltSSpz07JVqTI9njOoiw4Z1kA4dkqwqdKkIKAKKQMQh0BIN7FagRLf522rRugBLamXza7cDseiJi/wfmO5+49xd0Fs3end1BHMf56qtWLHXaGtfzN4m69bul7y8EhMUkm79lXAmSW0XV6el0a2f2hq9ITVcVjB7Tq+tCDgTAWqqZAbzDWc3+rvQPfRCXGLrphuw/rZtO1Cr7+JCbgVYqJoQjwaY0tKauWocR/th/m7ZvuMQ5q7lSUnJEWtuSnK8dO6ULAOPy5AzzuiCididpD/G2GJiNFzW0WCv5yoCkYJAuJsQW6Mj7cKL/dr7KDv3dZxPTS4HXOOvXlPhuVjYo9E/VbM7Mv8nJMQa0yHNh7ffTsdPEc5VYzQRmh3JjC6yYW2+7Nh2SGZ9stV4QUZBdnWCW/9pp2cZLY3TAugRGYxwWToeEZnPbjDvWp+5YKLv32u3ZB5YIZr0MXgemCbIs8Afgo+GmDrlBbBdi6PK8CL4bPBu8AIwr0PvR4taYgK1zg2LJeeqURiRr732WHNPnKu2fv1+WbIkR77+aqcsW7pXdu48JG++ubLunmNioqRv37bQztrJscdkyMmndDbCTcNl1UGkK4qAIhDiCLRUAJyG+2J2ZgoZCpUvwUdL3VGBa0bmR7DvvNqKJ2BZDaYW1g78BHgMeBK4gWYWjiZE3GeLqRqhsLZsOWC0NMZ+pLbGMbYD+WXSKi7WAFtVVSUpqfHSG4JtJCZhjxxZI9TUWaTFsOuJioDjEAh3EyI7hNoX2Z+Uhcp32C6wE+t0HiHtB99h1vSfRwjQsaMXQl6R7XPVqJktXZqDUFmI/7ggG+GyDsjSRTlm/dlnF5q6O3RIrIv9aHcW8ejCWkgRUAQUAT8hEOunen1RrTvtkBqYRzRt2jSZNGkS4hJ2M+XT0tLwEh5kgvtyB+3iJCvQbyRvd+mSIm3a7JHzL2COtKtkz55CmTLlE9m4sQDpaDoZT8jZs+fI7NkiCbF9ayZhR2+VzPat5ZRTT5XTz8hCapqt0qVLMhxHzjC4NoanhXljx7U/aNzQ59OXz4dVl/XsKb71ny/iM3nyZMJi3pf0QnQKuRMSwWq7OxPiRDSGjhwkuwmxZk8T/9WE2AQ4LTjECdicp0bvx2+/3S0b1ufLwQOYeA3NjlxUVi6tWseY3GpWtH669R97bLrQ+cQi/lgsIWXt06Ui4E8E9JnzDl0nmRBbKsAGApJV3sHSbOkeKMExsEG1JWOw5ExeOnFkg38AXwNmFJBmyenzwJq9wRAoUFBQglBZe43n4+pVebJi5V5jgqQTiUUcX0tvGyu9usTJkL7xMrB7tPTuUC2tosqkEpFHDJeXSxW4uqJCKsFcVlVWSjXYWlZjfK6OMJ5nCIIzCmm27RyNMF0x8fGGo7GMTUiQ2NatJbZVK4kBcz0uMVHik5MljpyUJPEpKYZ1/lwdwroSgQhQ0JPDbR7Yb9GX9uB8FHrHgy/yYR9Tfx0NTgfngB8BvwE+D2x3o38S2x6RamAewVRXqLywUEoKCqT0wIF6XMbtgwel7NChGub64cOS0CZfYloXYtJ0MQRIiUTFlkpMHOapTS+XDTva44ujk+EdMX2kKCpVunUvkf37WkvbdiXSu1++THjoO2mbViyv3JoiB+fnm9LJQgfXGrr0447SZVQricZnTJQtiv+0sdmyc06JVaxuedH7HaTzyQlSWVotFWAuK0uqZfYdeZKzqKyunLVyzHXJ0rp9tJQdqJayg1VSWsDzWktxXgqEXhvcXxtpnZ4uiRmYbtAeGbqxTOrQQZI6dpTkTp3MPhV4Fpq6DCcEwk0Do7fhIpcOugLbf3bZF1KbkaqBUVOhICrOy5PiffukBMxl8f79EEB7sT8ffEBKsF2Sn2+EFcsPuDpeMocnSEJaNF7e4NSa5Zf37pNd8xoKjNHPpUvGcfFSXlgl5YfBhdVmfdnLhyR/fU2+NEsDqm4FTWd4eznYup3M2xQlhysGShmsisVlcbJze5IUF9eYGNMSyiQzqUr6pBfKMR0LpWd6mXRIwiRtTmQzMzZqH7FaDYz3amlo0bEQUlG8bplUV5eaJZOGF2wsk+K9RVIGAV0OwVtZymMix/40WdKPjYfmhftMi5JW7WLA0TL79jzJXdJQ4B1/X5rEA5PC3RVyeHelFO4RaI3toNl1kpQu3SSla1dJBad06SJpPXtKavfuEOj27z5zWf0XBATUhOgZ6OGqgaXh9g+4QJCM7cMu+0JqM5w0MAqYImSXplAq2kshlAfBswcaQ7Yc2nFQDu2EwOJ+CiwIJZreTvx1mnQb01papcdIa7yY+XKmDPjoqlzZ+mlxg77qe1miJHaKk6rSBKmqaI0JC0kQBCkQSu3wIsbLG2a2hNRUs+Q6TW80xZkl18Gx3AYbMx22o2NoBa5P1sukvLwSyUJrJmBbbv2rVubBfIgxtVqNi6bI2PhoEwPytNNr8qrRC7Jfv5ZHFqmCeZLapKumSYFOIW8Efu2yEJgX5eQYzPtcnCDtBsRLclYMOFaSO8dIUudYef/cbMlbWSOw7Xea0g1mzIQsSevWS9r07i1t+/WTdv37S/qAAUazs5fVdf8iYD1z/r1K+NQebhqYvWdGYWOufUeoroe6AKNZjkKJXMiXZG4OTHM7pTB7H4TSfvPS5H4KK44PnTA+TfpckiSJMHu1bh8jUZANxXur5PNf5DUQSDEY9+l6ZjpMXikQQm1g5msr0XHpEp+UIa3atjXmMS7NOrwzaS5LwJKCKZhmMWsC9vLlufLdd9myBO78W7YcFIbQKqs4Ei6LEUSYU+04hMsaPDhThg7NNM4irVodcRbx5XNJTY/9cHjPHjm8a5ccAh/eudMsD27fLge2bDHH7dccN6eTZA6LR19Wyv51ZbJvVbnkrSiTTdOLgHO6pB9zDDTY46T94MGSCaaAcyfw7XXquiIQCATCWYDRnPh+IEA82msEQ4DRnEVNiILH8J5sCCKs79lb8yVPYQVmmX7jooxASuoQI4mZ0JIglMoOVcmX9+yT9dOOjAURBzobdD4lA2MvUIajMiQ6tj2cEDrgSx7jMxyjadfOfNW3wpgN1ymIwom2bq0/AZsaW15escTAgaM1nESqYFLkX/vMRMSATDdRRRhui9paoCKLUKsrgCCjMMvfsEH2r10rBZvXYnrBNknrUW3MrRmD4uXTn+bCcaVh78QmtpaMYwdKxxNPlE5gLlOyOBVSSREILAIqwAKLt9ur+XIMjFEsaJ4rzM6GMMJXeO2S5iVrvU3fXOl0chm0nigM9MfCvAQTU8cY+eZ3+bL0xYMN2thldDpMS21hqkvHEE8mxlI6Q/h0gIYFhwEwl2YdAooedOFCvjLncAI2I4n88AMDG2fL+nX5UngYedXgmchI/aUwFWJVGJ2fgszKr0YX//btEwMGJz9qDmzeLHmrVsnelSthblwpe1esMM+S1YiEttFy8/qukruoVHZ9U2I4+/tSadWmk2Sdcop0Of106TpqlKTWzmm0ztOlZwj46pnz7GrOLUWcyOHmhWjvkbDTwCpKSmqEEISSEUa7d5slJ+a2St+NcYwCaDTF0JJEkjrFyrp3DsuK1w7ZMTHrXc9qJZlDE6WiKEkqy9vi5ZmJsaPOMM11liRMDEyEB1silvRko4Cie3ckkj9fJnvhrGEFNaaWRt627cjHQxwi8lNrS0qOkz5928iJIzrJCDCFW9euqQHtjiKYJHMWLZI9CxcaLti8RNIHlkvWqa2kMzhzaLxs/rhIZl7PTEU1RMeQrpgo3u2ss6TbmWeaMUnrmC4bR8Cfz1zjV3XukXDWwC5Ht7znhK6hCXFgnz5mzOIwhRK4eP92qSjZinGhPTDD5cHZ4KBk/3BANs8oanBL/a5Ikj6XJuJLuRICjV52cGqA11lFcSec11WS4U5Nl2rDcKumYGoF810wx5Aa3ITuEM5V4wRsCrbvv9stK4z5sURioJ5RW6NDYwm0tdS0eGhqGbXaWqZZMtix5VDibyg5zrZvzRpoX9/ITvCexd/CIeaAFGyoaHBpjn9GRcVKxxEjpMePfiQ9wenH1gRyblBYdygCXiIQzgIsA1jkeYlHUIpTgM0bM6bu2sfdkiJn/KkdBBrcoHdBKO2scYfe9W2VHNjU3sztSYIgSu7c2axzrg/XuY+akw6w10Hp+JVDh8qM+ZHR+inU1m/IxwTsAqmshDSzUVLrOGhmKTJ0WKacNJKaWgc4j7SrF1nEVtynqzRbcxxtx9y5svWzz2TXt9/WTQEYdk+qDPpZimyGQ8gmfHxlzy+VNr16S+8LL5S+F10E7a0m1Y5PG6SVRQwC4SzAXDuxA3Z0BC9zPRDsbQqwHy67rEYgUShlYb5O5yxsZxmhlGL2ZRnvu2C3NZKuH6rmnOLiClmJSCKWSz81tg3rCiQKMi2a0T7QSRxb444evVIh1DqY9DMcU6M3ZDISifqTyouKZOfXX8tWBKTcMnMmTNJ50vuiROl1YY1n6sYPi2Txcwcw3gZtEqbGvpdcIgPGjTPejv5slxPqDtVnLlSxC3cB9gGAJ28Cvwp+Hsx5YX8BhwwFwwsxZG4+hBvipJdJWVklMl7vM+ZHZsGmW/8OzLsrKqlv1qOzSO/ebaQ3Iv2fhBQ0FGqMA5mejvl0fiBqZxw/2/TRR7Lp449hBt0m/S5Pko3QxvLX1p+T1n7IEDnmqquk3xVXmKgifmhOyFfppGcuFMAMdwF2KUCmAPsPeDr4n+CfgP8NDhnypRdiyNyUNiToCNgzYFva2irEgTwMD8jE+Dgzrka3fmprzK024BjmVjsi1PzhLJK3erWsf+89w5yX5kqcxF52EBPCzz1XBt18s/Fo1LFaV5R0m4KeHM5eiOzlW8EzwYvBHDmmm9ct4FfAgSL6Qb8EZlwgTqyuyQWAFYtUA7OQ0KW/EeAE7I0b8435kTnVfvhhj2xCKprysiqYHqOkGBPRLWrbtlWdswi1NE7E9qWzSPb8+bKOwuyDD0xUkaROMXL9kizZObdEVv7jkGydVSxt+/SV4268UY699lo1oVsdo8s6BMJdAxuEO70NTC1sPfhuMAXJw+BA0XW4UD74Y/BU8NXgeqQCrB4cIbMRKeYcmvlqJmBzXC3XeEJyuW9fTVxJekAyUj+1tZjYKAQ7TpUTTuggx5/Q0ZggGWkkPh7uhi0kRvnfOGOGrHjjDUQA+U76XZksg29DCLDkaFn60kFZ/fYhqa5qbYTYsLvukjSMm4UrRcoz56v+C3cB5iuc7PW8jo0LwIxEP9h24Fys26PRP1V7bAKWn4CXg2m6pAmzHqkAqwdHyGxE+suEE7ApyBYvzpX5yK22ds1+OXwQedXgKELX/goGYy6vkLi4aOnfv53R0Go0tfYtdhbZv26drHzzTVk9ZQoigpTK0LtSTYT+RX85YJ6LKMSs7HvxxTL8nntMWKuQeVh81JBIf+a8hVEFmLeIITEyTmFw4LfBlgBjCHJqeGeDd4MXgKlprQVTYFEDoxCj+fBacD1SAVYPDt0IYQRycgqN+ZFu/YsX74HjyH6hoLMThVssJmK375BoBNmIkzrWCbeMDM8iizDc1cq335alr7xi5kfa67fWu44eLSf95jfSeeRIa5cuIwwBFWAt63DaMGaALQHGX9Aj4PPApAlg+DELtTD+Yl8EM6z6PPAUcD1SAVYPDt1wGAL79xfXmR05EXvxoj2Sk11knETsbv3lCFXVvkNr4/XI8TRqa/SC7NKl8XiYlRiTW//++7L4hRdkHxxA6gjelBkD40x0fQqykb/9rYnLWHdcVyICgUgQYP7IyOwqwBj1YyyY420kjnuNAN/Djebo9ttvry5AGpJutfHj0hBtfdCgQXXp7GlWIFnp7XU7MHhYmCve3uNdWFiO+WZ9jWCb+cnncBwpwJhaR6moZLbqLYQW1NP8T07aZYTYWWePkmGYiF1WtkmysjCZ/4zTzXEL/07IijD/ySdlAWI0JnWIlt9+myW5i0vlzfv3ycHtlTL67LNl5IMPygbkUyM58fdi3atT2892W/fgD/xZ9+TJNGSJeV+Gmxfib3Ff9sx8+E7zeUZmYucqwK7AvnPAdgF2IrbvBTdLqoE1C1FQCvDHYv0Ig9KAMLtoUVE5oork1TmKcAL2unX7Md86Shj7kc4illt/bFyU9Ovf1jiKWAGOBwxIN+NtG6dPl/lPPYVo+utk8O0pcsL9bWTbrCL5/rECI8j6Yx7ZKQ8/7MgI+frMeffQh5sGdhluf5ELBBQuf3bZd7SbrgKMJsSJYDpykOwmxJo9TfzXeWBNgKOHwhqBEky05tw0zlNbtHCPLMIE7O0IalwNRY151SjQLKKzyIAB7WTgQJoeM6Rt/hrJn/wXqT6wRYbfmyZD7kyV6ZfnSPZ3pSZh6XB4LB5/770mcalVhy7DAwEKenK4aWBp6J4ad6Uj/eSPjMw9UD3HwAbVXoY+xOvAdOLIBv8Avga8BtwsqQbWLERaIIIQcM2AvXRprhFyDKFF4iRsEoUbuW1SlaQXbpD+bTdJSn6OZFbkINxOjRmR8UFP+/3vhVqZUvgh4CQNLNYD+F2FF0857MF53hShAXY0OB28HfwI+A3w3eBZ4GgwXe09El4opxSiCKg5JzgdExcXYyLs03RoUSXGztYhjxrd+pcsyRVOwt6wPh8TsKvlYHGsFMYcK6v39rGKSwpiFnTCt2TH7D2y8La/yPDX35crX3pC2vSsGXerKxhiK/rMhViH+LA5HM/yhkah8FxvTghWWTUhBgv5pq+rL5Om8Qn2UU7A3rSpwDiKLFuWY8bXli7OlgOHajQ1q32dMquksCga+e8qpCeSt55xwRAZNrxTwNPQWO1paqnPXFPoHDlGnMjhZkI8cociHA97374jVNfVhBiqPaPtciICW7cWyEd/nSZfTp0j20vaycirRTKzKuSDd3vL3twkE63fmoSdlBSHMbUMjKm1NwKNrv2MLEItUCn0EXCSCdFbDUwFWOg/f9pCRcBvCDCT9Dx4I66dOlVST24nP341TTavTJSH7z5O1u3rLoeqG06q5ksmAWGxuvdIgwdkBxk6lAlDa9LQUNgphRYCKsBCoD9UAwuBTnDTBDXnuAHFgbu2z5kjs3/xC2Q5z5aRD7WRY29Ikdl37JUVH0dLYbeRknDODbJxT7TxhNy545DEx8bUTMK2ufVXwS2yR680M/maY3PWJGwGPPYl6TPnHZpOEmCeOHHY795bjc1+bkDXly5dKrNmzTJzjnTeUUCh14tFAALdEKnjJ8gSPXfCBPnmoamy4f1Cqa5kYsBiSd7+uUS/9ZX84te/lhPfvE8KDpRhTC23zq2fHpB7slEe3vwbkA2b/N57jBpXQ507J0NDy8AE7JqkoRRunISt5F8E7GNg/r2S72r3ViBl4NJ5vru8/2pSDcx/2GrNioAdASbW/OK++6QY5kVX6nLGGXLuq69KYmZmvUMHD5bCQWRvvXBZTElTWVmNCdgxkgCNjQ4llWCmq0lOwbgahNrw4R3qxtWYRFTzmtWD1ScbTtLAPBVgdGd/wSfoBKgSFWABAlovowgAgaK9e41JcetnnzXAI7FDBzl30iTpcuqpDY7ZdzCqyMqVeUZbo1s/4z9uwwRswQRsxn8sxyRsOopYlJxc4yxC06NlgmRkkaNJQ2PVHcnLcBRg+9Gh94OngQ85oXNVgIVmL+l4RGj2iy9aRY2JAYK/e/xxqaqocbs/7pYUY14sOxhlggOf8KtfeaU1lZYyqsg+M1eNQY05Z2316n1SWgp7ZS0xrxq/xKmtQc6ZcTVqagxqTMF26NA6+dGPzrSK67IZBJwkwDwdA3se9/wxGM6zkgreAGYqk/qTQ7BDSRFQBCITAZrzjkdOMaZi+fTWW+XQzp3Stl+cXPNtZ/loXK4RbMwYPfa11yQhla+R5ikhIdaYDSmQLKqoqJK1a/fVmR8XLtgj6zEBuxITsBkDcseWw7ITPGXKWmOGjIraKr177zDCzBJq1NrS01tbVerSoQh4akJ0vb3e2ME0J/SBXQj+GhxSpBpYSHWHNibCEChBJgh6KW7+5BPpPy5JRv0lXeYiwv26dwqlbd++ciGin7fpzdeIb4jjZOvX7zeOInQYYfzHNWv2Qfsqa3ABekSyfHpGaziKwPsRmpol2JpKQ9OgojDd4SQNrKUCjF3HlCrXge8CLwEzSkcgqCcu8n9gfsKNa+yCGomjMWR0vyIQOAQW/PnP8t0f/oA8Y7FywbsdZMsnRfLVA/slPjlNzvvHP6Tbmf4z7dGkuWXLgToPSAo28uGDFRITHdXArb8UZs/09FZIGFqTU41aGjlSnEXsXojjx48/GtkQsAfM00aejRZ9Du4KZkDdn4C7gd8DM5nkF2A4xQaU3sXVGhVgqoEFtC88vpiOgXkMVdgU3PK//8n/brtNomIK5dTH2sLtPl9KD1RhO0ZOf+wxGXr77X69V9dnbseOg8b8SGHGLNjLMbZWkF9az0HE3qBkTLYegEgiNGNaQq1//3ZhG1nESRqYp2Ngz6FD94FHgGeCHwV/BC4Ft5Rex4kXgHPAg22VMH0Kr2cF8GUGZiVFQBFwKAI9x46VcfBO/Oi66+SLuzfW3UU1skl/hWSZ+9evl9FPPy3REGiBoK5dU4V8wQVHTJjZ2YfrzI9MQ0PhtmvXYTicwAmyXGTtiv2yZvk+ebvWrT8ab04KNTqJWCGzqLm1bu3pKzUQdxr+1/BUA9sBKCaCqXEVgH1Bp6ESRrV/G2wJMAotzmikxrcbvABMx5G14OvBw8BPg5le5T/gK8FuSTUwt7DoTkUgaAiUIvvz/372M3Hnat/r/PPlXDh3xLbybRSOo7nZffuK68yP1NSWwrWfE7CZJJTmJuZWs1NMTJQxN9aEyjoSWSQ1NcFeLOTXnaSBeSrAfgbUX/MD8t1R5wywJcBGYv0RMB1ESBPAfFbsWlg7bD8BHgOe5HIMmzWkAsxCQpeKQOggUAWt68v775dVb/O7tT51Pvlk49yRkMYUhKFJnIBtaWhcMqoIo/fTKYTELNjxsbHYrqqZhA2NrVOnJBk67IijCB1GMjMbxowMlTt2kgDzVN/1h/By119Z2Eltz6KdWKHZ0k77sXGHfYeuOwcB1/EI57RcW+oLBGgmPPu55yQZSTHnP1XzXXr6k+0kb2WZrPnXdzLtxz+Wi6dNM8d9cT3W4ctnjtrUaad1MWy1r7Cw3EQVqRFoOZiAnSNbt2ICNigOE9P27y2V/32yVWbM2GSdIh07JjWIAdmtW2rdcV3xDAFPBZi9tlOx8Rcw3efpgUgT31bw9+CjJXcaYc2njZc1T8OPYBJm/3frRl8TkTR81Q0aNMjERuQ2H2qSFSdRtwODhwEd/xTvwOAdqs93OaJytL3rLil45RVZ+eYh6YSAwDEHK2Xf9DXyn3PPlY4YG0vJynLE75MR9SsqNsuxx4rcdts55hH/4ou5EGLMBdzDOIzMm/e17EBQ44qK7ub4nj0rZc8ekS8/72NMkuVVm8z42fBhI4xbf6tWO6UXAh1fddV5mJwd5dffC3+LkzGtgcT3JfOBOYXcCYzm2v44CvwRPAb8YW3hW7GkOc9bYm+6mhAnYh8dOUjuTIg1R5r5rybEZgDSw4pACCBAD8WZt9yCWIllctnMjrLqH4dkwdMHJKljR7n0v/+Vdv36hUArfdOEsrJKMzeNDiKWGXI1ooxUlFc3cOsvKa+QKpgfKRyPPTa91vsx0yyZW82f4bLC0YRo78Et2CgE2zWjrvYCXqxTgNqFKJ02+oAp2OioQe2Obvtek0aj9xoyPUERCDgC9FC8BNaSD8eNk/+clS2XfdJR4tOi4Wq/R96/+GK59IMPJH3AgIC3yx8XpNDh+BfZosrKKlm3Lh8CLdeMp9FRZPXqPKlCVBESzZMLEGmETIqFSdI4i/Rtg7xqR9z6jzsuQ5KT402Zlv6jJkYOdw2MgX37g4nWVvAZYMZI9FYDo846GpwOzgE/An4DfB7Y7kb/JLa9JtXAvIYsICfwB2KZtQJyQb2IIxDI/uEH+fDKKyU6vlBOe7ydzLkPmklxtbRu314uoxCjfa6F5LRnjg4hdAyhULPiP1JjKygohYNIjBFixhMSMq4SOdVYvgJLmhzpys+5apZ7f0aG984iTtLA7NpPU4/HmTj4pa0Aty8G04+UrvUMKTUXHDKkAixkuqJeQ5z2MqnXeN3wKwI5ixfLfy+/HJOcOXZ0hFpnZBhNLGPgwCM7vVgLl2eOY2qW+bFmAnauHDxQbsyPpS4u/RY8HTok1jM/Urh17960l2c4CjBOXn7BAsXN8k7s46TkkCENJRUyXaENUQQ8RiB32TL572WXSUl+fr1zWrVrJ5fPmCHpxxxTb3+kb+zcecgINbumtmcPR3hqKCmhxqxILc3S1hIxrjZ4SAYEW82YGrW1fv3aynfffVtnQgy3UFK5gGOVBYqbJT+Njhh23RQI9C7VwAKNuF5PEfANAntXrpQPLrlESvZzxswRSoLr/ZUzZ0pqrWfxkSO6ZkeAAsxufly8OEdyc4olBhOw6dHIv+LycvspxgOSziHU0K67ro2MGTPGU+tcvXoCveFpIzlOdRKYkTK2gleC7fQwNh617wj2ugqwYPeA++uHiznH/d3pXl8hkLNkiXHiKD+McE4x8Oo6p7VsnVksaT17yhWIcJ+EJJmekj5ziANYG1WEJkhqa1yaZKE2ECngEpBbbfonJzlGgMXa2t/UKuMgflJbgBOrLgJjCNEIsi1YhpTwQnvg0bNUZs2aZRwG1GmAiCgpAs5BoMOwYSYqB70TE1LL5Mxn02Vh1gFZMWmLfHjFFXL5Rx9JKEfsCDWkmfvsrLO6G7baVlBQYtz5GU2ETiLff/+NZO9ehXdnglUk5JeeamCN3cjpOHAH+CvwK40VCsZ+1cCCgbpeUxHwLQKbP/1UPrnhBknBZ/OVn3eSL+7ZJ5tnFEmnk06SS957T+ISvfey820Lw6s25k/bsGGlYzQwmgS9JT4xnJvFCcj/AnN8jFE5lBQBRUAR8CkCvRCVY8zf/iYHtlTKjCtyZMzLGdJxRIIws/MnN94ojK2o5DsEUlKObi6Z71riWU2eCjCaGi8ETwFvAI8BPw/uCf4leDdYSRFoFgGORygpAt4gMADzw0YhbmLOojKZdeteufA/mZLWO1a2zZ4tcx94oNmq9JlrFiLHFqBg8oQ4zjUfzMnHN4Jd84D9Eft+ClZSBBQBRcDnCAy59VY5vGuXLHr+efns53lSlFOjea1AVuc2vXrJsDvv9Pk1tcLQR8AbAUaTYQr4KpfbohY3xGWfbioCbhFQhxq3sOhODxA45eGH5cDWrbLxQysEa81J87A/DUKM5kZ3pM+cO1TCY5+nAuxp3C4FWGOU19iBYO1XL8RgIa/XVQT8g0AU3LzPefllo4ntWXhk2L0aubeYKPPyjz+WzMGD/XPxCKiVplZyuMdCDIWuZBir88HUCP8B/gxcj9QLsR4cIbPBH4h+EYdMdziyIUV798q755wjB7dtq9d+TnS+CuNizDVmJ33m7Gg0v+6kUFKeOnE0f9eBLUEbwm1guvCPC+yl9WqKgCIQTAQSEeD3oqlT6+aBxbSKkg7Hx0thdrZ8dN11UlHqOkQfzNbqtf2JQLAF2Ou4uRzwcpebpDF7LXg9uCk3o4dw/G9gJYcgoNqXQzoqxJvZrn9/+fGbbyJKR4y06x8nF3/YUdoOiJNcRPBw9UzUZy7EO/MomhdsAfYG2j7Wpf1s04u1+xljkXPOBoBJ14OZDbozmGlWGB1kKVhJEVAEIgyBrqNGyamPPCJ7l5XJvAf3y0XTOkhCm2hZ9fbbsvrf/44wNCLzdoMtwDgpqH7YaZER2Me5ZjRwM+LkVDDHvEj/BN8Hvhx8NvgKME2JSg5BgOMRSoqArxAY/otfSN9LL5XVbx+Wrf8rkvP+2V6i8Fb78te/ltzlNYYdfeZ8hXbo1RNsAeYOkSzs3GE7sBPr3GenF7BxIpiTP161H9B1RUARiCwExvz1rybh5dcP7JdYjIeNmNBGKktK5JOf/rRBWpbIQib879ZTN/pAIuEuPmO1tw2YhjTlkyZNkm61qRfS0tKQpXRQnQec9VVm2cd1u0YzUjxOM4+aPg/OeR7ikpIk/Z57ZPn998vMG/bKiQ+0EUZeQLh1afvznxuHD+3PxvuT2EyezBgVYt6X6kZvoPD4X3eU5BwzawLHSKxPBNORgzQBTAH2FDc8JXWj9xQpLacIhAcCW5B9YsY1GDKvrv+9O/LBB2XE+PHhcZMBuAt1o/cOZGpcdq1rAbb7gCnY4sFXg6eDvSJOZH7yySfNxDyvTtTCfkXA+hL260W08ohEoCfmho3A2JcrvYP3QPYPP7ju1m0XBPjb5DuT706nUGyQG0q9dTQ4Hbwd/Aj4DfDd4FlgjtHR1X4N2CsaOnSoDB8+3KtztLAioAg4G4GTfvMb2fXNN4atO6lipA6YEq+ZOxe5xVKt3bp0QYDDB2RqYE4hu+bjlDZ71M5nnnmmOjc3VxNaeoSWFlIEwgeBQwj6O+WMMxo4cPS7/HI597XXwudGfXwn1MDIHAMbP368I2SDIxrZkn7SMbCWoKbnKALhgcDmmTPlo5/8RGIwCHEh5od9euNeKdlfJT9CbrFjOE6m1CgCOgbWKDR6INIR0DGwSH8CAnP/vc47TwYjBUtlmcgXHxfJWX/lKIXIHOQPK9i8OTCN0Kv4HYFQnAfm95vWCygCikD4I3Dao49KxsCBsvIfhyT9uHjpPy5Jyg8flk8Rub6ynDESlJyOQIzTb6Cx9g8ePHji//73P3PYmgvWWFndHzgEtC8Ch3WkXyk6Nlay4JSw61+TZfe8w3Lum5my9p3DUrBhN6J1REsXHFM6ggCtI5wPdujQIZk1a9bvjxwJ3TUdAwvdvtGWKQKKgA8QWP766zIH7vUnPdhGOp+SIB9ckCPRcXEm9Up7BDdQqo+AjoHVx0O3FIE6BHQMrA4KXQkQAgf69ZMup58uC/5UIMtfO2SuWgUT4md33aWmxAD1gb8uo2Ng/kJW61UEFIGQQICZnM9GvMSYhCTZ9GFRXZvyVq6UhX9hcgslpyKgAsypPefQdluxFh3afG22AxHgM5fWvbucOnFig9YvgADbC0Gm5EwEVIA5s9+01YqAIuAlAoNuvtmYEu2nWabEqooK+25ddwgCThRgTG75Mvhd8O2N4ayxEBtDJrj7dQwsuPhH4tWtZ84yJTJ6vZ3yVqwQamKRTsTJabEQneyFyLa/Bb7B3YPXVCSOffv2SWlpqbvTdJ+fEThw4IAwtY1SaCCQkZEh8fGMmR2+xBez3XRteSV2HJEgg25Nkc9uyzNeidd+9ZW0698/fIHw8M6c5IUYzGC+DNJ7ATgHPNiGLdOoPAemdsgy7tKoXIj91L6YodkrOoyJjKTOnTt7dZ4W9g0CirtvcPRFLQxyuwtxAzt06BDWQswuvIgbTYnr339fchZ/L51OTpBeFybK5hlFMgeBgC/78ENfQKt1BAiBYJoQGXV+rMt9sj0v1u4fiCWDltFkSLoeTD2/E5j5w84HXwf2iqgBtGvXzqtztLAiEI4IRGMyb1ZWluTl5YXj7TV6TzQlnvn001JdGSOf37lPznw2XeJTomTn11/L2v/8p9Hz9EDoIRBMATYPcOS7QDIC2xvA28CM9TIVfDGYRG3rPnA/8PPgV8Afg70iPrxkJUVAEYCZA0Is3MkaA7PfZ/qxx8rQ22+XXV+XyLbPiuWUx2o+auf97ndSevCgvaiuhzACofb0ZgGrHTa8dmKd++w0Fxv3gmlCpDOHkiKgCCgCXiNwEgL7JmMo4esH90ufixOFY2JFSMH03eOPe12XnhAcBIIdC7ENbvtasCWIaDbsDaaJkMSxMQ5WfcoNbygxMXHie++9JyvgYcQvMC7puNG2bVtJSUnxpqqAlWUSzmPxZdgdc1aUgovAN0iK+OMf/1juvPPOo2pIU33a1DFvL3rRRRdJZWWlIAaot6ea2HfLli2T7du3ixWrkr+ZcNnmPbm7n1179sjAU0+VtVM+kHlfF8uunEpJPFgtuchInIucWHsxXh6OePABsePBdXoffvzxx+Y9qbEQPf8J8U1NYWX96kZifSL4XDBpArga7M6Rg8cbpca8EHfv3h2yDhx8of0VEQPOQDK+ltDEiRPlX//6lzGR/gS5kLht0aeffiqPPfaY7NixQwYiQvdzzz0n/W0eVy+99JK88MILUlJSIhdeeKH8+c9/ljjEi+P4yG9/+1v59ttvpaioSI455hhTz/HHH2+qzsnJkfvuu8+kId+DFwJfhF26dLEuK6eccors3Lmzbpv1/+hHP5J///vfsmnTJnnkkUfkB6R7p0PBsGHD5I9//KP06dOnrnxj7bIKvPLKK/L3v//dtJPXZb29evWyDje6fOqpp+QvcJ1OSEgwZTgW9OCDD5p75w4KsNthYuKHz9FQU33a1DFvr0kBNm7cOLnuOq+HhSWUfxPe4tCS8tOvukq2fvZZvVMz8SxehX0M+htp5CQvxGD3Dgej7ANSC7DNtxcFG317rwZPB3tNkTYP7M0335SZSOLHr6mvMRjNSPzcR6Kg+DlSqj/77LOydetWGTt2rFDAUWiQPv/8cyO8PoQHFgUQy/CLjFRYWCjDhw+XOXPmyGbkUboKP/arr77aCDMe5xjKmDFj5K233nI7tkjBxy95iylkLrnkEp4qdKg5D3mbFixYIOvWrTMCjO2yqKl2sczbb79tome/++67RjBPnTpV0tPTrdObXV522WV17XriiSeMwIo0h4ZmQQqDAvxNNEWj8DET06pVvSK5S5bIijfeqLcv3DcsTYzvTqdQMAXYZID0LbgfeDv4JnAl+G7wLPAq8FTwGrDXxK/bCRMm1Jv/4XUlQTiBXz8nn3yy9O7dW+6++24pKyszraBGcNxxx8nfkFGWmhO1KKY+sIgv77sQnLRjx46GuT5lyhRz+MsvvzSa0IgRI4zAuffeeyU7O9toGSzwzjvvmC/3fgh6mpqaynTidXXTnHnHHXdI+/btjYD66U9/atq0ceNGUzf333TTTUb4VFdTWW6ceA8UEBdcwNkTYgQjBRbnhcXExBhzHestKCgwx5tqF6/1NDzJKHj69u1ryrOtLZ1jdtZZZ0lycrJs2bLF1OX67/nnnxdqnTQpUaukucVOFOAjR46sO+5Oc1u/fr3B6YMPPqg7tbH+ZgHWecIJJxiNlJoVNVyL2KcnnXSS9OzZUx7AWI6FPZ8XPjtr1hz52RBzapj79++3TtelDYG0Hj3kxF/9yranZvX7P/xBSmqfxQYHw3AHpxvwncl3p1MomAKMY18c36INpxvY+tyZifX+YL6VatQArEQKTZs2Td7HHBW+2Pgyf+aZZ+puPRcDzJzHtnr1amMC/A3mrRys9Zhau3atEXBWYQo77iPx5Wa94LhNzYvb1kuO5SgQLeK5e/furRMk1n4u+WKuQNgdvji9JQpZmrpat27t9lQKOArgNm3amONNtYvzl2j6IhaDkBKDWqKlNfJkmi1pSmQ5Twj5j6QcEcrtZlX7ebxfarjUJIk7zYvsD9J///tfI0xpyuRxflhwrNVO1GyvuOIK+dOf/iSXXnpp3aHG+vsrTKp9HM4E1KLZT9Rcb0WGYRIn4t94443yO3jM8RnpgRfw/PnzzTFOSr788svlPzZ3cI4Fjx49OmKnj7jOAzNAufwbfs89kuoy9lySny8/4CNJKXQRCKYA8ysqTjUh/gzZYjt16mQ0CY4tUZhZxJfTr5HXiNoKx5GSEBJnw4YN5jBNfdSeLOI695H48qIpj8yXNMd+uCwuLjbH3Z1LAWdN+jaF8I/CktoYv/i9dYThtaZPn25Ml1Z99rgUdHMAABjuSURBVCUFDQUDX9oWNdUuCi/SnDlzzH3R/Ems/vnPf5r9fOHT5EnNozGiJkQhx7LUcH6Fr3A7hvbzKHgzMbBPogmU5/Ejg8Rxx3vwAhwyZIjZpkBhnRYRd2qaHK9jv9mpsf6mYGOb+DHBsUgKq4ULFxrBjPFdGTBggNFk+SywT6y2sW6aee0CjCZWjo8pNY5AbEJCXbDf+NQoOfHXaaYwo3YUNKKVN16bM4+oCTGE+q2lJsSouL9LdNLxDZj73ZG78o2VdXe+6z57pIquXbvWMxvxq94+b4eajCWkKMzoPWQR17mPRBMbTY8UfvRyzMeXJc2F1rXcncu5cjSpWUTnC76EaYbky9pbmjFjhtEAaB51JZq4qJ3wZW7XTppql6XF0RxKYUqsaN78zGUw3vVa9m1ei0KO2hqFETVEmu3cEY+NGjXKaJ7UxqgdUhMiUfg2pZGyTpr7aHp0JasPuN/e3zQXctsiYsH+p+DmMVfBbN+mqZPlqdHyA4dmUY41Rio1NwZm4dL34oulM57PiqJq6X9NsvS+KFGqYJL9Bo5GkUBqQgyDXq4u/7lUFS5qwNzvjtyVb6ysu/Nd99lNXvQYpEnNE+IX+UpbWgia+rjPInoWWi802rlZN81uJHfn8oveMuVxXIXaAF+S1N5aQhQA1AxciY4cFF7nn3++/PKXv6x3uKl20VPRlzH8qDHRGYXOL65EAUftjGNuFAZkts0yyxKXxsbOWBc9OlnH//3f/7lWXc/Eae9v9ju3LeKHCsewKPAY+on12cn+3HD/NddcY8Y2qX1Re/QlVvbrhtv66bAAVFVGydz798kZT7eDc0eUbProI9n13XfhdqthcT9qQgyxbnwdJgt+ZVNLoqu7XSNpqqn0DKTLOZ0zyFy/9loOM9YQx2A49kVthy9jznHiYD+JgoXu5/QEpAMFhZR1Lse7qNlgXp2ps6a2+v85v44aGolL10DJfLnyK5gvVTtRS+R4DZ0fHnroIfshs95Uu6iB0YuQ0w5o6uQ16JV47rnnNqinsR2WAOJxnk+vR04TcCUKD2q+9HAkhsTKGj9k2euvv15efPFF48HJbQozu4ChJkuT3nd4CT766KMsUkeN9TeFOsfSVq1aZfDkFAg6dFDQnnPOOaav6EjCuV80TXLM0k5XXnmlcTThdflsRDJ5MgZm4dMB7vMD8HvY8WWJ7F1WJkPvrDHLf43n0/68WOXDaakmxBDqzZaaEIN5CzTb8cXFlzrNQDRL3X///Y02yR4Si4P6fHnzx3o60qdznYLHIs7l4tgMhQVNUXSpt+jss882Ho8Xw4RC3Ohpx3EuEudo0SxHrzeez2Pk77//3jrdaAX0AGR7aCqzm7NYiC9R7nedoM0XMMcq+aK26uXS0iaaahfrpdMGBSvNojSRcZzHErwUIPa6WN6V6HxhXZdCgeZNmlldiY4dnNDMMtS8aD4kjhYRN45X3nbbbaY+CjTLk9LqI46tcYyOQpJz3UhN9TfnArLPbrjhBuNgQ+eQSZMmmfMYy/MNuHj//ve/Nx6KnPZAfO1ETY2TmnkNe1vtZXTdPQKnQFjF4rn65nf5cvx9aZLQJlroVr/O5hjj/kxn73WiCTHK2ZA33nonTmRu/G70iCLgPQKchkGHIE7Qboqo8dvH4poq68Rj1Cy80cJ4j/MxN4w8+rl02T67WDZ/VCTJMBXfgA+62Ea8aJ2Ijbs2czwY5nRHyIawNSG66xjdpwhECgLU2KjhUhtU8h6B4RD+jJM455f7jPBiDYdhZl7y8sveV6Zn+A0BFWB+g1YrVgSCg8AfMAGXGge9Re2ejMFpTfCv6q32xRbHwYR4MqYuuNJihFsrheORUmggoAIsNPpBW6EI+AwBmgypgbl6dfrsAhFS0QA4wrSzefLytim8FkGIKYUGAmErwJw6kTk0HgtthSIQPgh4Og/M9Y4ZyHckHGlcaRkirhS5eH26lnHithO9EGOdCDTanAj+Cvww+BNwA6I3nTXPqcFB3aEIKAKKgAcI9MH8SUampxciKTYxSsoxrWIhvHjPgKk2nIimVrIVYcYJ9+ZUDYw+3u84AWBtoyKgCAQXgZaMgdlbfHKtF2fXM1vJ5TNrAgswUv0hOHUoBReBYAqw13HrOeDlLhBwJupa8HpwzWSk+gXOxuZqMCOpOsLVs37zdUsRUASchEB3zJPsjDBgO+aUSFxKtPQY21oqMXl/gS3QtpPuJ5zaGkwBxujzY13AZHterN3P8OgM3WDFQ6I/MGffMrwEZ21yWROeGytKioAioAi4Q6ClY2D2uji5mal15z9RICMfqsmWsBoRWSIl0K8di1BaD6YAmwcg8l3AGIFthlffBi4HTwVfDCb9E8ykPbeA7wP/G/waOGyI43ZMo6EUmQho/4duv3dG5BVqYhveL5S45Gjpfk5rqUKYtfm1iV9Dt+Xh3bJgCjB3yGZh5w7bAUYs5T539DZ2unXgcFc4EvZNnDjRhBZi9Hmu2+nTTz+VU0891YQ6Ytglxj20E2MnMg4gw1dx/hDTrZAYO5FR4pkvjMcYQ3HRokV1p+bk5Jgo9TzOWIH2GIAsxAjsVrgmLhkk2Mq6zEzRDBLMyPgMzsv4fVaiTOsCjbXLOs44gMMwyM75TgwFxejyntBTiLLAoLhsE1OjWJmhPTlXyzgLgaMdA7Pu9mQTrzNKvn/8iBa2HrnW9tXm3bPK6TJwCISaAHM3pgXF3XtiPiXGr2O8PPLLmEHvC1OC9y0JzBlMfMiEi7zHr7/+2kRV5z4SBcXPf/5zE/+QcfPGjh1rhAgD05IYn+8FzG1hTi0G/WUZKzkkA9nSm5N5tygcGGCXwWGLiorMuQxyyyjuTBlixf0zB2r/MRcW5yRZzGC0zKdFYiR6S3BQoFIQWcKNx5tqF48zeC/jKDLiOiO3M+I9hainxGDAbBdTjjCWoD12pKd1+KIcA/IGm/jc2H8fut0Qj/UIPt0b2cSphU37ywHZhrdnNX5DCxH82sl4se18V1rvS05Bcgq5ExiBbHt3XGwGeHDtRRkhdSKYjhykCWAKsKe44Q05MRYiTUgMyvvOO++YbL/UdpiKg6kwmAqFQojJCxmBPTY21qTnsILXMngv1xn8lcQki0zuyPQgDAJLYTBlyhRzjFG1KUj4wmfgXwahZaBdK90HzZi8lj3iujmx9h/LMr8Xg8VaxJcwtSsKQNbtjngPjEhPYWXl87KXYwBcRsinwGUql6baxXvg9amh8R68JWpgFNT8sCExQC+/1CnMeG3iTvwYWZ9BhSnQmXfsrrvuMtoof+yM+s+Ek8yafdNNNxkBz4SVbD+J2DMqBoUkgwCzTgYeJrGvb775ZhPomOUpgBnAmX3LQL50ZWYw3/Xr15uAxRfgxfnEE0+YfmdbiBWXVnZsZmt+BHmrmA2AGiXvp7HknKYBtn8aC9EGRjOrecgOMBn9g9D0dSWjYmLkesRIbAMLRTiQxkL0vBcpQO1CdAG2+4Ap2OLBV4Ong70mp05kbizFPAFgCnumDlm9erVJtcIMxsySTOILiy9Ti7jOfSS+7O2pIKh5cdsSUCxHE6BFPJfpOayI6tZ+LplnzHpp2vd7sk6BydxU7oQXz6eAYx4sKw9ZU+1ixHq+eInFoEGDjJZoaY2si6ZMvsityPbc1xhRSFGTY0BbRupnuhR+RHyEPFD8MTPtixWdnyZRtpNE7ZJmVWub6VKspJUU5DTFMiUONVd+mPADwzLN8nxGp6f2yPQrzKxsJ25T+PFcCkJ+VDD1CommYOuabB8/HKxtZg+gCdlT4WW/pq43j0AGTemwYNipGh9vi9DPTidqYvwNOUkDC6YJcTI6/FtwP/B28E1g2lLuBs8CrwLTiWMN2GviFy4TN/rK/u11A1p4QmMp5lkdNTGm++DLjV/6zLpLjYFEU5/9pcV17iONHj3avGz5wuULlPm+uCwuLjbH3Z1LAUdhaScKS2qAfJlTG/GGeK3p06fXMxHaz6egoUB+HAkFLWqqXRRepDlz5ph7o/mTAoFaE4laIF/+rqldzMHafx988IERckOGDDGCmYKL9B7GNahhcVyN6Voefvhhs4+Cn8KDgopEPBnxff78+XXblgBjO26E0KJZlKZVml4TEhJk4cKFpiz/UctltHjudyW2iRoZz+W90LxpCakTTzzR3Bs/MNgWjiNSG6RZl22y2uBaZ6Ru+/odcKKbFEdr8MFzyCXJqNPwJk58Z/Ld6RQKpgCjG3xnMH+93cBvgEkzwf3BfcFPgltELdXAnn1moXTv/PcGzP3uyF35xsq6O991nz2tBV+gTB9vEbUDjjlZRE3GElIUZtQULOI695H4Rf63v/3NCD+asJgsk44T1rXcncsXJxMxWkTTFMenRowYYTQLa7+nS5ocmceKjhauREcR5kGj8LYn8GyqXZYWd++99xphSqz4kmfuMk+J16KQo6ZHYUZNjkRhYDeDsm5qndSAe/ToYXBdvny5ER4cT6TWSOcTChgKOBJNgjRvUgskU1Oj0GXdFln4W9v2Jc2KNLfSsYbXpPmQGZlJrVq1Mi8ZfjFTYPGa7BfmaLO27XXpum8R6IgPi674KLRTFbKWL0ZSUyeTEzWwWCcD3lTb+RXRklBSvxp/gpA9JW/LN1ev3eTFlyBfjp4Qx1hWrlxpvvhZnqY+7rPoQoTEIZOoSXGMzMLHOpeJGUk8l2Ypy5RXhh8nv/KpzVB7awnRfEgtxJXoyEHhdf755zcIPttUu6i1UCP1B1ErsntTsh/i4uIMJrweBQa1SQo19g81HpoceS+WECRWTHLJ7NeNkTunF6vs+PHjzRgfzYbUAultyY8Ai3hNOuuwz9mP3P7iiy9kCUIeqQZmoVSz5IvZ11rYCPTPDmj/pKzTW8mur0tkFbRuameJ7dub/U77R4zINEs7hY58zjulxWHezsZSzDd32/QM5Bc/v/DJXLccPHgux2RoAqO2w5cqHUToMEGiYKH5jM4VNEtRSFnn8iVNzYYvUdbpjjiGRA2NxCW37UShzJcINQo7UUtk9mlmDH7IuCjbjzbdLmpg9CKk0wNNnbwGvRLpzHK0xHrp3EHnC9ZNsyb3WdovtcjXXnutTpvkj/7VV18192EJJTrTMGuyNeWAmjK1Q0tjbq6NxIZmWuJORw7WZScKKX4UUJOmQw+FKs2WnBZATVfJvwhkAf/OeA6i8Ab90d8zEKkjQSpgJl8CS4dS4BBQARY4rJu9El9+1Eb4Uuf4B81O97uxt1sVWS9LbnO8hS9vvkzplcd1u1s4PdpoiqKwoCnyWQQjtYhedhzLoQZGzZUvQctpgU4BfPF++eWX5nweI9NcZRFNYfRMZHvoju467vQfpGLnfpaxExMu0tRLBwqrXi4tLbSpdrEeDjjzBU+zKN3xx40bVyd4qUHZ67Jft7l1apusi1oh+4HXsDuIUFhQEHFJ4r1RcNs1H+JIBw7iSBMiTXyWFyjPsfcdt0n2fY899pjxUOQ9UJOzm1ZZlvXxQ8FqA7VVCnVrm2WUahDgb8IfRG2rGjNRFj17QE4Y38ZcYvk//iEl+AhUCgwCUYG5TOCvAtfmao5Z8OG1P8Dh7jIceKT1ik5HQH8TLe/BdzAHMm/1Erl5XRd579w9sn9NuZyEjxay04hWEjKHD2DCdoRsCFsNjF/ATvRCdNpDr+1VBEIdAb6U/UXUwipLqmXpSwfl+F+lmcssRb4wplxxGvFDX70QndZr2l5FQBFQBFqIQE+Y6jk3bPmriNJxYaIkZ8VIKUyIDPSr5H8EwlYD8z90egVFQBFwAgL2IQRft5fjlkMxN7I0v0o+vCRHivMwKAaiFsYwU0r+RUAFmH/x1doVAUUgzBHoD8erRASGzp5fijxh1eZuDyC6yiY4KSn5FwEVYP7FV2tXBBSBICPgzzEw3loM5iMOvuWWBne5pJFpJw0K6o4WI+BEATYKd/sV+GXwGY3deUsjcTRWn+5XBBQBRaAxBAYhMHMspjHYKRshxvbYQofZj4XiOgW902IhOjESB3V0xkxiCKqdjT0IjUXiYBxBxozj3B4lRSCSEWC8S4ancheLMZxw8ecYmIVTa0weH4CAACvffNPaZZbUws7D3DAnEHEiOykSRzAFGENrXwDOAR/Jy1GTSoWhnakdsoxrKhVqX+RMMOMaXQf2mDjHgfPD3EVa97gSLagIhAECFGBpaWn1Yl6GwW0F7RaGwZljpcmLVy19LkWg7fcKZSPCfx1EKLJUxNNU8j0CwRRgb+B2XgC/bbstCi1GxDwbzHDjTK/yIZh5Qa4HDwM/DWZEVE539zoYHr2GmIlXKTgI0EwRiC/i4NydXjUUEQjUM9cWQbN7IEvE1lmzZOTv2sAjsVJ2zi2RpYhjeQaCMSv5HgEKjGDRPFw43+XiI7DN/CDbwOXgqeCaCLMi/8T6feCR4FfAb4GdHf4ZNxBpxEDBSopAIBEI5DM3DGl4SMtePihD70w166sROLu0Nm+f2aH/fIZAMAWYu5vIws4dtgMc4+I+O32AjdvBjAz7lf2Aroc+AozYrqQIBBKBQD5zXZGtOQNpedb867CJUp/SLVbKEJh5FQJNK/kegWCaEN3djbv4WzUTK9yVbmIfMxtPmjTJBHRlMdr6merCMl9ZrrWebFtlWY8n5VnOOsddeetYS+qzzrHqcFe/6/Wtsta5rsetbX6pMmGltc2la/3WPqvOlhxvqn7X+kJp2xN8fNleX1/vaOpjdH5vfj/elLeeJevZcvd8NFdfc8eta/iyfxprL8fCXoUmNuXRfBl8W4p881C+vPfCC1I4eLCcDgHn7v6aap91rLHreVufa3nWz4DaJAaPtrK8mx0h/s+dwAhkk7vjYkxyZDlx0Dw4EWzlxJiAdQowV0cO7GqaZs+eXW3lu2q6ZPNH6VrKGGG+oqOpryXnenqOJ+WaK9PccWY6biwti6/w9Vc9zd2br6/r6+sdTX3enutNeU/KNlemqeOBfuYqke38DQiruOR9ctXczvJ6nx0mXuKFU6ZITyRA9Zaaujdv6/KkPJPEIjVPsGWDJ02VYGtgBMkOFJ02+oAp2OiocTW4fhIp7PCUfOUOSs9FX9XFth9NfS0519NzPCnXXJnmjp9wwgk+xdLTZ8EX5Zq7N19cw16Hr693NPV5e6435T0p21yZpo4H45kbXhsLcfVakZM/qulVDvjntyBZZFP3Zn9efLU+ZMgQk1vOV/WFaz3UWelpyOyH28E3gUnngdeB6czhO7UHlSkpAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAhEBAIxEXGXepOhjAAnqt8PvhZ8ELwZrKQI+BuBAbjAY+CfgtPBC8FKioAioAi0CIE2OOu1Fp2pJykCLUeAXtA6y7jl+OmZikBYIcAAzDng5S53xbl9cCqW9eAHXI5x8xnwUDf7dZci4AkCLXnuLkTFzDrJ6TpKioAioAjIacCAgsguwKKxvRHM+X1x4KVgmnAsehIrZ1kbulQEWoBAS5476zK1M7WsTV06BYFgT2R2Ck7aTs8RmIeiFFR2sgdp5v6pYI59USO7G3w2mJFPOYn9VbCSIuAtAt4+d6NwgcvAzCtILUzJgQioAHNgpzmwye6CNFOokV6oZbOh/xQBHyLQ1HM3F9chKzkYAZp2lBQBfyPAgXJXqnbdoduKgI8R0OfOx4CGWnUqwEKtR8KzPTtxW91st9YF6wwjpqQI+BMBfe78ia7WrQiEKQI9cF8rbPfG+YaWE0c81unEcYztuK4qAr5AoAcq0efOF0hqHYpAhCKgQZojtOODfNv63AW5A/TyioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAjUQ4DBY9uBNcZoPVh0QxFQBBQBRSDUEfgJGpgPtgcubqzNP8OBHWDG7mM+NIsewcp+MPOkKSkCioAioAgoAgFD4EtcyRMBxgbdAp7D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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_point_source_spectra(bayes.results,\n", " fit_cmap='plasma_r',\n", " contour_cmap='jet_r',\n", " use_components=True,\n", " flux_unit='1/(cm2 s MeV)',\n", " legend_kwargs={'loc':'lower left'},\n", " plot_style_kwargs={'linestyle':'--'},\n", " contour_style_kwargs={'lw':0},\n", " )" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_point_source_spectra(bayes.results,jl.results,\n", " confidence_level=.95,\n", " equal_tailed=False,\n", " flux_unit='erg2/(cm2 s MeV)',\n", " plot_style_kwargs={'linestyle':'--'},\n", " contour_style_kwargs={'lw':1,'linestyle':':','alpha':0.4},\n", " fraction_of_samples=.01\n", " )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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bsd-3-clause
mhallett/MeDaReDa
demos/demo1/01_CreateSchemas.ipynb
1
6327
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 00 Create Schemas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import medareda_lib\n", "\n", "def get_conn():\n", " return medareda_lib.get_conn()\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_iPrice():\n", " conn = get_conn()\n", " cur = conn.cursor()\n", " cur.execute('''DROP TABLE IF EXISTS iPrice ''')\n", " cur.execute('''CREATE TABLE IF NOT EXISTS iPrice ( \\\n", " iPriceId SERIAL PRIMARY KEY, \\\n", " iDate timestamp,\n", " status text,\n", " date timestamp, symbol text, bid real, rate real, ask real)''')\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_vPrice():\n", " conn = get_conn()\n", " cur = conn.cursor()\n", " cur.execute('''DROP TABLE IF EXISTS vPrice ''')\n", " cur.execute('''CREATE TABLE IF NOT EXISTS vPrice \n", " ( vPriceId serial PRIMARY KEY, \n", " date timestamp, \n", " symbol text, \n", " price real,\n", " combinedRate real)''')\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_pPrice():\n", " conn = get_conn()\n", " c = conn.cursor()\n", " c.execute('''DROP TABLE IF EXISTS pPrice ''')\n", "\n", " c.execute('''CREATE TABLE IF NOT EXISTS pPrice ( \\\n", " pPriceId SERIAL PRIMARY KEY, \\\n", " iPriceID integer, \\\n", " iDate timestamp, \\\n", " pStartDate timestamp, \\\n", " pEndDate timestamp, \\\n", " worker text, \\\n", " error text, \\\n", " date timestamp, symbol text, bid real, rate real, ask real) ''')\n", " # no audit or market or type\n", " conn.commit()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_dPrice():\n", " conn = get_conn()\n", " c = conn.cursor()\n", " c.execute(\"DROP TABLE IF EXISTS dPrice \")\n", " c.execute('''CREATE TABLE IF NOT EXISTS dPrice (\\\n", " dPriceId SERIAL PRIMARY KEY, \\\n", " iPriceId integer, \\\n", " iDate timestamp, \\\n", " pStartDate timestamp, \\\n", " pEndDate timestamp, \\\n", " date timestamp, symbol text, bid real, roll_ave real, ask real)''')\n", " # no audit or market or type\n", "\n", " conn.commit()\n", " conn.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def register_price():\n", " print 'register_price'\n", " conn = get_conn()\n", " c = conn.cursor()\n", " viewpoint = [('dPrice', 0),]\n", " c.execute(\"INSERT INTO vViewpoint (dtable, row) VALUES ('dPrice',0)\" )\n", " conn.commit()\n", "\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_vViewpoint():\n", " conn = get_conn()\n", " c = conn.cursor()\n", " c.execute('''DROP TABLE IF EXISTS vViewpoint ''')\n", " c.execute('''CREATE TABLE IF NOT EXISTS vViewpoint ( vViewpointId SERIAL PRIMARY KEY, dtable text, row int)''')\n", " # id should be int\n", "\n", " conn.commit()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "create_iPrice()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "create_dPrice()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "create_pPrice()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "create_vViewpoint()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "register_price\n" ] } ], "source": [ "register_price()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "create_vPrice()" ] }, { "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
tschinz/iPython_Workspace
02_WP/General/CoE_Register_Extract.ipynb
1
29656
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coe Register Extract" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Register Extract Function" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import binascii\n", "\n", "def register_extract(hex_string='abcd0123', definition=([ 0, 'RegisterName', 'Comment'],) ):\n", " num_of_bits = 32\n", " bin_string = bin(int(hex_string, 16))[2:].zfill(num_of_bits)\n", " \n", " print(\"^ Hexadezimal Value: 0x{0} {1} => Binary Value: 0b{2} {3} {4} {5} {6} {7} {8} {9} ^^^\".format(hex_string[0:4],hex_string[4:8], bin_string[0:4], bin_string[4:8], bin_string[8:12], bin_string[12:16], bin_string[16:20], bin_string[20:24], bin_string[24:28], bin_string[28:32]))\n", " print(\"^ {0:>4} ^ {1:>15} ^ {2:50} ^\".format(\"Bit\", \"Registername\", \"Comment\"))\n", " \n", " for i in range(num_of_bits):\n", " for entry in definition:\n", " bit = bin_string[num_of_bits-1-i]\n", " if bit == \"1\" and entry[0] == i:\n", " print(\"| {0:4} | {1:>16} | {2:50} |\".format(i, entry[1], entry[2]))\n", " print(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CoE 0x8000 - Print Mode Setup Register (PrintControl)\n", "[WpWiki CoE Registers](http://wpwiki/doku.php?id=internal:cpp:products:coeregisters)\n", "\n", "Link table doc [PrintMode-PrintControl](PrintMode%20-%20PrintControl.ipynb)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "printModeSetup_def = ([ 4, '', 'Drop Multiplication'],)\n", "printModeSetup_def += ([ 8, '', 'Start Absolut'],)\n", "printModeSetup_def += ([ 9, '', 'Start Relativ'],)\n", "printModeSetup_def += ([10, '', 'Activate Img Offset'],)\n", "printModeSetup_def += ([11, '', 'Activate Img Shift'],)\n", "printModeSetup_def += ([12, '', 'ImageRotation'],)\n", "printModeSetup_def += ([13, '', 'ImageMapping'],)\n", "printModeSetup_def += ([16, '', 'Flip-X'],)\n", "printModeSetup_def += ([17, '', 'Flip-Y'],)\n", "printModeSetup_def += ([18, '', 'Activate Masking'],)\n", "printModeSetup_def += ([24, '', 'Emulation Encoder'],)\n", "printModeSetup_def += ([25, '', 'Use HW Encoder'],)\n", "printModeSetup_def += ([26, '', 'Reverse HW Encoder'],)\n", "printModeSetup_def += ([27, '', 'Encoder Single Phase Mode'],)\n", "printModeSetup_def += ([28, '', 'RS422 option [0] 0=HW_Enc, 1=Dropwatcher, 2=Cam_Sync, 3=NoOutput'],)\n", "printModeSetup_def += ([29, '', 'RS422 option [1] 0=HW_Enc, 1=Dropwatcher, 2=Cam_Sync, 3=NoOutput'],)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Digiround Settings" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x0004 0C00 => Binary Value: 0b0000 0000 0000 0100 0000 1100 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 18 | | Activate Masking |\n", "\n", "^ Hexadezimal Value: 0x0004 2C00 => Binary Value: 0b0000 0000 0000 0100 0010 1100 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 13 | | ImageMapping |\n", "| 18 | | Activate Masking |\n", "\n" ] } ], "source": [ "register_extract(\"00040C00\", printModeSetup_def) # Digiround Settings original\n", "register_extract(\"00042C00\", printModeSetup_def) # Digiround Settings with image mapping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Arcolor Settings" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x1002 0C10 => Binary Value: 0b0001 0000 0000 0010 0000 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 17 | | Flip-Y |\n", "| 28 | | RS422 option [0] 0=HW_Enc, 1=Dropwatcher, 2=Cam_Sync, 3=NoOutput |\n", "\n" ] } ], "source": [ "# Old < FPGA v1.3.0\n", "#register_extract(\"00021C10\", printModeSetup_def) # Flip Y <- Used @ Arcolor 20170608\n", "# New >= FPGA v1.3.0\n", "#register_extract(\"10010C10\", printModeSetup_def) # Flip X\n", "#register_extract(\"10011C10\", printModeSetup_def) # Flip X\n", "#register_extract(\"10021C10\", printModeSetup_def) # Flip Y <- Used @ Arcolor 20170608\n", "#register_extract(\"10031C10\", printModeSetup_def) # Flip X + Flip Y\n", "\n", "#register_extract(\"10001C10\", printModeSetup_def) # Flip X + Flip Y\n", "\n", "\n", "register_extract(\"10020C10\", printModeSetup_def) # Flip Y <- Used @ Arcolor 20170608" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Krones Settings" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x0004 0C00 => Binary Value: 0b0000 0000 0000 0100 0000 1100 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 18 | | Activate Masking |\n", "\n", "^ Hexadezimal Value: 0x0007 0C00 => Binary Value: 0b0000 0000 0000 0111 0000 1100 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 16 | | Flip-X |\n", "| 17 | | Flip-Y |\n", "| 18 | | Activate Masking |\n", "\n" ] } ], "source": [ "register_extract(\"00040C00\", printModeSetup_def) # Technikum Machine\n", "register_extract(\"00070C00\", printModeSetup_def) # DecoType + Flip X + Flip Y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Steinemann DMAX CPI-Books Settings\n", "DMAX CPI-Books default settings without Camera" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x0005 1C10 => Binary Value: 0b0000 0000 0000 0101 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 16 | | Flip-X |\n", "| 18 | | Activate Masking |\n", "\n", "^ Hexadezimal Value: 0x1005 1C10 => Binary Value: 0b0001 0000 0000 0101 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 16 | | Flip-X |\n", "| 18 | | Activate Masking |\n", "| 28 | | RS422 option [0] 0=HW_Enc, 1=Dropwatcher, 2=Cam_Sync, 3=NoOutput |\n", "\n" ] } ], "source": [ "register_extract(\"00051C10\", printModeSetup_def) # Dmax (Printlack, Mainfranken)\n", "register_extract(\"10051C10\", printModeSetup_def) # Dmax (Printlack, Mainfranken)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DMAX CPI-Books settings with Camera Sync Output" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x2005 1C10 => Binary Value: 0b0010 0000 0000 0101 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 16 | | Flip-X |\n", "| 18 | | Activate Masking |\n", "| 29 | | RS422 option [1] 0=HW_Enc, 1=Dropwatcher, 2=Cam_Sync, 3=NoOutput |\n", "\n", "^ Hexadezimal Value: 0x0006 1C10 => Binary Value: 0b0000 0000 0000 0110 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 17 | | Flip-Y |\n", "| 18 | | Activate Masking |\n", "\n", "^ Hexadezimal Value: 0x0007 1C10 => Binary Value: 0b0000 0000 0000 0111 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 16 | | Flip-X |\n", "| 17 | | Flip-Y |\n", "| 18 | | Activate Masking |\n", "\n" ] } ], "source": [ "register_extract(\"20051C10\", printModeSetup_def) # Dmax @ Steinemann + Camera sync\n", "register_extract(\"00061C10\", printModeSetup_def)\n", "register_extract(\"00071C10\", printModeSetup_def)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Steinemann DMAX WinTaiWoo Settings" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x0005 1C10 => Binary Value: 0b0000 0000 0000 0101 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 16 | | Flip-X |\n", "| 18 | | Activate Masking |\n", "\n", "^ Hexadezimal Value: 0x1005 1C10 => Binary Value: 0b0001 0000 0000 0101 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 16 | | Flip-X |\n", "| 18 | | Activate Masking |\n", "| 28 | | RS422 option [0] 0=HW_Enc, 1=Dropwatcher, 2=Cam_Sync, 3=NoOutput |\n", "\n", "^ Hexadezimal Value: 0x1005 1C20 => Binary Value: 0b0001 0000 0000 0101 0001 1100 0010 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 16 | | Flip-X |\n", "| 18 | | Activate Masking |\n", "| 28 | | RS422 option [0] 0=HW_Enc, 1=Dropwatcher, 2=Cam_Sync, 3=NoOutput |\n", "\n", "^ Hexadezimal Value: 0x0004 1C10 => Binary Value: 0b0000 0000 0000 0100 0001 1100 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | | Drop Multiplication |\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 12 | | ImageRotation |\n", "| 18 | | Activate Masking |\n", "\n" ] } ], "source": [ "register_extract(\"00051C10\", printModeSetup_def) # Dmax @ Steinemann\n", "register_extract(\"10051C10\", printModeSetup_def) # Dmax @ Steinemann + Dropwatcher output\n", "register_extract(\"10051C20\", printModeSetup_def) # Dmax @ Steinemann + Dropwatcher output\n", "register_extract(\"00041C10\", printModeSetup_def) # DEM @ Gallus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## XPrinter Settings" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x0007 0C00 => Binary Value: 0b0000 0000 0000 0111 0000 1100 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 16 | | Flip-X |\n", "| 17 | | Flip-Y |\n", "| 18 | | Activate Masking |\n", "\n" ] } ], "source": [ "register_extract(\"00070C00\", printModeSetup_def) # DecoType + Flip X + Flip Y" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0x0006 0C00 => Binary Value: 0b0000 0000 0000 0110 0000 1100 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | | Activate Img Offset |\n", "| 11 | | Activate Img Shift |\n", "| 17 | | Flip-Y |\n", "| 18 | | Activate Masking |\n", "\n" ] } ], "source": [ "register_extract(\"00060C00\", printModeSetup_def)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CoE 0xA010:06 - Print Mode Register (aka Job Settings)\n", "[WpWiki CoE Registers](http://wpwiki/doku.php?id=internal:cpp:products:coeregisters)\n", "\n", "Link table doc [PrintMode-Job](PrintMode%20-%20Job.ipynb)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0xffff ffff => Binary Value: 0b1111 1111 1111 1111 1111 1111 1111 1111 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 8 | SRC_NOTHING | - |\n", "| 9 | SRC_PATTERN | Fix pattern |\n", "| 10 | SRC_GIGABIT | Data taken from Ethernet Gigabit interface |\n", "| 11 | SRC_FOE | Data taken from File over EtherCAT interface |\n", "| 13 | ENDLESS | wrap mode is enabled in the JetMapping part |\n", "| 16 | INFINITE | - |\n", "| 17 | PREPOSTBLANK | Added PrePost Blank on the image |\n", "| 18 | SEGMENTED | - |\n", "| 22 | BPP_0 | - |\n", "| 23 | BPP_1 | - |\n", "| 31 | TEST | - |\n", "\n", "^ Hexadezimal Value: 0x0082 0800 => Binary Value: 0b0000 0000 1000 0010 0000 1000 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 11 | SRC_FOE | Data taken from File over EtherCAT interface |\n", "| 17 | PREPOSTBLANK | Added PrePost Blank on the image |\n", "| 23 | BPP_1 | - |\n", "\n", "^ Hexadezimal Value: 0x0086 0400 => Binary Value: 0b0000 0000 1000 0110 0000 0100 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 10 | SRC_GIGABIT | Data taken from Ethernet Gigabit interface |\n", "| 17 | PREPOSTBLANK | Added PrePost Blank on the image |\n", "| 18 | SEGMENTED | - |\n", "| 23 | BPP_1 | - |\n", "\n", "^ Hexadezimal Value: 0x0086 0800 => Binary Value: 0b0000 0000 1000 0110 0000 1000 0000 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 11 | SRC_FOE | Data taken from File over EtherCAT interface |\n", "| 17 | PREPOSTBLANK | Added PrePost Blank on the image |\n", "| 18 | SEGMENTED | - |\n", "| 23 | BPP_1 | - |\n", "\n" ] } ], "source": [ "printMode_def = ([ 8, 'SRC_NOTHING' , '-'],)\n", "printMode_def += ([ 9, 'SRC_PATTERN' , 'Fix pattern'],)\n", "printMode_def += ([10, 'SRC_GIGABIT' , 'Data taken from Ethernet Gigabit interface'],)\n", "printMode_def += ([11, 'SRC_FOE' , 'Data taken from File over EtherCAT interface'],)\n", "printMode_def += ([13, 'ENDLESS' , 'wrap mode is enabled in the JetMapping part'],)\n", "printMode_def += ([16, 'INFINITE' , '-'],)\n", "printMode_def += ([17, 'PREPOSTBLANK', 'Added PrePost Blank on the image'],)\n", "printMode_def += ([18, 'SEGMENTED' , '-'],)\n", "printMode_def += ([22, 'BPP_0' , '-'],)\n", "printMode_def += ([23, 'BPP_1' , '-'],)\n", "printMode_def += ([31, 'TEST' , '-'],)\n", "\n", "register_extract(\"ffffffff\", printMode_def)\n", "register_extract(\"00820800\", printMode_def)\n", "register_extract(\"00860400\", printMode_def)\n", "\n", "register_extract(\"00860800\", printMode_def)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CoE 0x8041:02 - Test pattern config\n", "[WpWiki CoE Registers](http://wpwiki/doku.php?id=internal:cpp:products:coeregisters)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0xffff ffff => Binary Value: 0b1111 1111 1111 1111 1111 1111 1111 1111 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 0 | VOID | clear memory |\n", "| 1 | FULL | fill memory |\n", "| 2 | LINEX | lines in x |\n", "| 3 | LINEY | lines in y |\n", "| 4 | CHECK | check board |\n", "| 5 | NOZZLE | ? |\n", "| 6 | DIAG | ? |\n", "| 7 | GRAY | ? |\n", "| 8 | EVEN | ? |\n", "| 9 | ODD | ? |\n", "| 10 | ROWTEST | ? |\n", "\n" ] } ], "source": [ "pattern_def = ([ 0, 'VOID' , 'clear memory'],)\n", "pattern_def += ([ 1, 'FULL' , 'fill memory'],)\n", "pattern_def += ([ 2, 'LINEX' , 'lines in x'],)\n", "pattern_def += ([ 3, 'LINEY' , 'lines in y'],)\n", "pattern_def += ([ 4, 'CHECK' , 'check board'],)\n", "pattern_def += ([ 5, 'NOZZLE' , '?'],)\n", "pattern_def += ([ 6, 'DIAG' , '?'],)\n", "pattern_def += ([ 7, 'GRAY' , '?'],)\n", "pattern_def += ([ 8, 'EVEN' , '?'],)\n", "pattern_def += ([ 9, 'ODD' , '?'],)\n", "pattern_def += ([ 10,'ROWTEST' , '?'],)\n", "\n", "register_extract(\"ffffffff\", pattern_def)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CoE 0xA011:03 - Job status\n", "[WpWiki CoE Registers](http://wpwiki/doku.php?id=internal:cpp:products:coeregisters)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^ Hexadezimal Value: 0xffff ffff => Binary Value: 0b1111 1111 1111 1111 1111 1111 1111 1111 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 0 | ERROR | Error exists in job preparing |\n", "| 1 | READY | Current job is ready for printing |\n", "| 4 | TRANSFER_ACTIVE | Data is being downloaded at the moment |\n", "| 5 | TRANSFER_25 | Transfer progress, if both flags are active this means the transfer is at 50% |\n", "| 6 | TRANSFER_50 | Transfer progress, transfer is at 50% |\n", "| 7 | TRANSFER_DONE | Active when job has been transferred completely |\n", "| 16 | TRANSFER_R0 | file requested for channel 0 |\n", "| 17 | TRANSFER_R1 | file requested for channel 1 |\n", "| 18 | TRANSFER_R2 | file requested for channel 2 |\n", "| 19 | TRANSFER_R3 | file requested for channel 3 |\n", "| 20 | TRANSFER_READY | Transfer ready for download |\n", "| 28 | LOCKED | Current job is locked |\n", "\n", "^ Hexadezimal Value: 0x1010 0010 => Binary Value: 0b0001 0000 0001 0000 0000 0000 0001 0000 ^^^\n", "^ Bit ^ Registername ^ Comment ^\n", "| 4 | TRANSFER_ACTIVE | Data is being downloaded at the moment |\n", "| 20 | TRANSFER_READY | Transfer ready for download |\n", "| 28 | LOCKED | Current job is locked |\n", "\n" ] } ], "source": [ "job_status = ([ 0, 'ERROR' , 'Error exists in job preparing'],)\n", "job_status += ([ 1, 'READY' , 'Current job is ready for printing'],)\n", "job_status += ([ 4, 'TRANSFER_ACTIVE' , 'Data is being downloaded at the moment'],)\n", "job_status += ([ 5, 'TRANSFER_25' , 'Transfer progress, if both flags are active this means the transfer is at 50%'],)\n", "job_status += ([ 6, 'TRANSFER_50' , 'Transfer progress, transfer is at 50%'],)\n", "job_status += ([ 7, 'TRANSFER_DONE' , 'Active when job has been transferred completely'],)\n", "job_status += ([ 16, 'TRANSFER_R0' , 'file requested for channel 0'],)\n", "job_status += ([ 17, 'TRANSFER_R1' , 'file requested for channel 1'],)\n", "job_status += ([ 18, 'TRANSFER_R2' , 'file requested for channel 2'],)\n", "job_status += ([ 19, 'TRANSFER_R3' , 'file requested for channel 3'],)\n", "job_status += ([ 20, 'TRANSFER_READY' , 'Transfer ready for download'],)\n", "job_status += ([ 28, 'LOCKED' , 'Current job is locked'],)\n", "\n", "register_extract(\"ffffffff\", job_status)\n", "register_extract(\"10100010\", job_status)" ] } ], "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
adam2392/paremap
.ipynb_checkpoints/Robust Spectrotemporal Decomposition-checkpoint.ipynb
1
268733
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Robust Spectrotemporal Decomposition by Iteratively Reweighted Least Squares\n", "\n", "Python notebook written by: Armen Gharibans\n", "\n", "Reference: Ba, D., Babadi, B., Purdon, P. L., & Brown, E. N. (2014). Robust spectrotemporal decomposition by iteratively reweighted least squares. Proceedings of the National Academy of Sciences, 111(50), E5336-E5345.\t" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.mlab as mlab\n", "import scipy as sp\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Toy Example\n", "\n", "This example points out the limitations of classical techniques in analyzing time series data. We simulated noisy observations from the linear combination of two amplitude-modulated signals using the following equation:\n", "\n", "$y_{t}=10 \\cos^{8}\\left(2\\pi f_{o}t\\right) \\sin\\left(2\\pi f_{1}t\\right)+10\\exp\\left(4\\frac{t-T}{T}\\right)\\cos\\left(2\\pi f_{2}t\\right)+v_{t}, \\quad$ for $\\enspace 0\\leq t \\leq T$\n", "\n", "where $f_{0}=0.04$ Hz, $f_{1}=10$ Hz, $f_{2}=11$ Hz, $T=600$ s, and $\\left(v_{t}\\right)_{t=1}^{T}$ is independent, identically distributed, zero-mean Gaussian noise with variance set to acheive a signal-to-noise ratio (SNR) of 5 dB.\n", "\n", "The simulated data consists of a 10 Hz oscillation whos amplitude is modulated by slow 0.04 Hz oscillation, and an exponentially gorwing 11 Hz oscillation.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0lZn9wnv/K6QLK04cs8q7x7p9NyQ2fSDS9qG285S8MNL29x3HHMMs+d0MoXk7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xMIBOYMhlDUqkpNNKRsU/vfeCTHowse4XoLcPzgyraVHMVQn5xJ5irFGqcsEAgsEILk\nBAKB2eJGp6yX5BR4TsY2xLwPv1YXiLBJhK9N2G8Shs74PqqzUZi8cPG1s6xPIBCYBcJcFQgEZgsv\n4sojNTU6Sc6A5IMHTlg/CfcBLnfKJyk56ki8mmEIkhMILCJCyQkEArOFZ04aQnK8uaE8NcQ7/kcH\nHL8TIlwo0soarehN6CcyciQeSl6C5AQCi4hFITkppSemlC5OKe1KKd2vWvfylNKPUkqXpJQethj1\nCwQCU8Fz+K2VGI+oaJnNdTOU5Hj5Z+YCo3qXDNFdGNp2hk9OILCIWCwl50LgJOCrtjCldE/y7Mn3\nBB4BvDOlFGpTILB0cS5whlM+RMlZB2PRT56ZyiM5nh/QXOAkp8xrgzzH51q12UEk/gsEFhWL4pMj\nIpcApPEYy8cCHxGRHcBlKaVLgWOA/1zYGgYCgSEQ4diuVdX/QyOkhk4E+rdkgjXXWOeUDSU5j67+\nvy0d2agDgcDCYKmpJHegPbneFcChi1SXQCAwcxw5h8caI0gibBbhi3N4DoWXOdoLS5/YdopwU0nU\nGAgEFgnzpuSklL4AHOyseoWIfHqKQw2RvQOBwNLC+smbuPC+9wVJqyfSeZ5/dso8Jefv5rA6gUBg\nDjBvJEdEHjqD3a4EDjf/H1bKxpBSOtX8e7aInD2D8wUCgYVBF4GoSc2ikZweeP4/Xln43wQCs0RK\n6QTghLk63lLIk2MbsDOBD6eU3kw2Ux1Jh91dRE6d/6oFAoF5xhACs9gma8/k5A2+FpuMBQK3ehTB\n4mz9P6X0mtkcb7FCyE9KKf0ceCDwmZTSZwFE5PvAx4DvA58FnisiYa4KBG59OGeG+y217/1U4DNV\n2enAe5xt3bmzAoHA4iHdGjlESklEJEZNgcASREoI8C4RnmvKtgFrrN9L2Y6q7BrgwKrsX4FH9fjM\nLDpS4mjgYpGIpgoE5hKz7e+XgrkqEAgsP9RKzk6GJcbzpmt4A/CzWddoHiHC+Ytdh0AgMI5QcgKB\nwLwjJW4C9hmg5IyVBQKBPRez7e+XWp6cQCCwPOEl2evCklZtAoHArQdBcgKBwEJg6KzdEFmCA4HA\nHCFITiAQWAhMM6FmkJxAIDAnCMfjQCAw7xBh34GbPhK4aj7rEggE9hyE43EgEGBEIGAAAAedSURB\nVFgUpMTLgKeLcI/FrksgEFiaCMfjWzlKCuvAEkM8l/mHCH89DcGJZ7I0Ec9laSKeS0aQnMXHCYtd\ngYCLExa7AoExnLDYFQi4OGGxKxBwccJiV2ApIEhOIBAIBAKBZYkgOYFAIBAIBJYlbrWOx4tdh0Ag\nEAgEAvOP2Tge3ypJTiAQCAQCgcAkhLkqEAgEAoHAskSQnEAgEAgEAssSQXLmESmlw1NKX0kpXZxS\nuiil9PxSflRK6Zsppe+llM5MKe1j9rlPWXdRWb928a5geWLa55JSWpdS+kgp/35K6WWLewXLE+U+\nn5NSuqDc59eX8gNSSl9IKf0wpfT5lNL+Zp+Xp5R+lFK6JKX0sMWr/fLEtM8kpfTQlNJ55Vs5L6V0\n4uJewfLETL6Vsv6OKaVNKaUXLU7NFx7hkzOPSCkdDBwsIheklDYA3wYeB/wj8EIR+VpK6RnAESLy\n6pTSqrLN00TkwpTSbYAbRWT3ol3EMsQMnsspwMNF5CkppfXA94EHi0jMlj3HSCntJSJbyrfwdeBP\ngccA14jIaSmllwK3EZGXpZTuCXwYeABwKPBF4G7xvcwtpnwm9wV+KSK/TCndCzhLRA5bxOovW0zz\nXMw+nwB2AeeKyJsWpeILjFBy5hEi8ksRuaD83gT8F7kxPlJEvlY2+yJwcvn9MOB7InJh2ef6aLDn\nHjN4LlcBe6eUVgJ7kyeQvGlha71nQES2lJ9rgJXA9eSG+wOl/ANkQgrwWOAjIrJDRC4DLgWOWbja\n7hmY5pmIyAUi8stS/n1gfUppmhnoAwMx5bdCSulxwE/Iz2WPQZCcBUJK6U7A0cA5wMUppceWVU8E\nDi+/7wZISulzKaVvp5RevOAV3cMw5LmIyFlkUnMVcBnwRhG5YaHruicgpbQipXQBcDXwFRG5GDhI\nRK4um1wNHFR+3wG4wux+BZmsBuYQUz4Ti5OBb4vIjgWq6h6FaZ5LUaxfApy6GHVdTATJWQCUF+wT\nwJ+IyM3AHwLPTSmdB2wgKwOQZ4U/DnhqWZ6UUnrIIlR5j8DQ55JSehqwHjgEOAL405TSEYtT6+UN\nEdktIvcFDgOOr306JNvX+2zsYX+fY8zkmRRT1V8Dz1mwiu5hmPK5nAq8pag/e9Tk1qsWuwLLHUWq\n/STwQRE5A0BEfgA8vKy/G/CosvnPga+KyHVl3b8B9wO+vND1Xu4Y+FweWTb/TeBfRGQXsDGl9A3g\n/sBPF7ziewhE5MaU0meA3wCuTikdXPw8DgF+VTa7kkYFhdzYX7nAVd1jMPCZkFI6DPh/wO+LSHwj\n84yBz+UY4OSU0mnA/sDulNItIvLORar2giGUnHlESikBpwPfF5G3mvLbleUK4M+Ad5VVZwH3Timt\nL85kDwYuXthaL39M8VzeXVZdAjykrNsbeCDZjycwh0gp3dZE6awHHgqcD5wJPL1s9nTgjPL7TODJ\nKaU1RVk7Ejh3YWu9vDHtMynbfgZ4qYh8c+FrvGdg2uciIseLyBEicgTwVuB1ewLBgVBy5hv/DXga\n8L2U0vml7BXAkSmlPy7/f1JE3g8gIjeklN4MfIssM35GRD67wHXeEzDVcwHeA5yeUrqQPDB4n4hc\ntJAV3kNwCPCBQjJXAP8kIl8qz+hjKaVnkn2ingQgIt9PKX2M7Ei5E3iuRLjoXGOqZwI8D7gL8JqU\n0mtK2UNF5JoFrvdyx7TPZY9FhJAHAoFAIBBYlghzVSAQCAQCgWWJIDmBQCAQCASWJYLkBAKBQCAQ\nWJYIkhMIBAKBQGBZIkhOIBAIBAKBZYkgOYFAIBAIBJYlguQEAoF5Q0rpwJTS+eXvqpTSFeX3zSml\nd8zTOZ9XZo7vWv+YlNKr5uPcgUBgaSHy5AQCgQVBSQ53s4i8eR7PkYDvAA8QkZ0925xftonJIwOB\nZYxQcgKBwEIiAaSUTkgpfbr8PjWl9IGU0ldTSpellB6fUvqblNL3UkqfLVOckFL6jZTS2Sml81JK\nn0spHewc/78BlyjBSSk9P6V0cUrpuymlj8Bo4sJvAg9biAsOBAKLhyA5gUBgKeAI4ETgMcAHgS+I\nyH2AW4BHlQlV3w6cLCL3B/4BeJ1znOOA88z/LwXuKyJH0Z4R+1zg+Dm/ikAgsKQQc1cFAoHFhgCf\nFZFdKaWLgBUiclZZdyFwJ+BuwL2AL2ZrEyuBXzjHuiPwdfP/94APp5TOoJnYk7LvI+byIgKBwNJD\nkJxAILAUsB1ARHanlKyfzG5yO5WAi0XkNwccK5nfjyIrNr8LvDKl9OsispusYodDYiCwzBHmqkAg\nsNhIkzfhB8DtUkoPBEgprU4p3dPZ7nLg4LJNAu4oImcDLwP2AzaU7Q4p2wYCgWWMIDmBQGAhIWbp\n/YZxhUVKFNQTgDeklC4gR0c9yDn+14H7l9+rgH9KKX2PHHH1tyJyU1l3DPDV2VxIIBBY+ogQ8kAg\nsGxgQsiPFZHtHdusKNvcvyvMPBAILA+EkhMIBJYNSnj4e4H/0bPZo4FPBMEJBJY/QskJBAKBQCCw\nLBFKTiAQCAQCgWWJIDmBQCAQCASWJYLkBAKBQCAQWJYIkhMIBAKBQGBZIkhOIBAIBAKBZYkgOYFA\nIBAIBJYl/j9ogQwGmOfAjQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108beb2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "T = 600 #[s]\n", "fs = 500 #[Hz]\n", "f0 = 0.04 #[Hz]\n", "f1 = 10 #[Hz]\n", "f2 = 11 #[Hz]\n", "\n", "t = np.linspace(0,T,fs*T)\n", "signal = 10*(np.cos(2*np.pi*f0*t))**8*np.sin(2*np.pi*f1*t) + \\\n", " 10*np.exp(4*(t-T)/T)*np.cos(2*np.pi*f2*t)\n", "noise = np.random.normal(0,0.3,T*fs)\n", "\n", "signal = signal + noise\n", "\n", "#PLOT\n", "fig, ax = plt.subplots(nrows=2,ncols=1,figsize=(8,6))\n", "\n", "ax[0].plot(t,signal)\n", "ax[0].set_ylabel('Amplitude')\n", "\n", "ax[1].plot(t,signal)\n", "ax[1].set_ylabel('Amplitude')\n", "ax[1].set_xlabel('Time (s)')\n", "ax[1].set_xlim([295,305])\n", "ax[1].set_ylim([-12,12])\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following is a spectrogram of the simulated signal that highlights the limitations of classical frequency analysis. The analysis is unable to resolve the closely spaced signals of 10 and 11 Hz in the frequency domain." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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zj/TVwMBxMN5pcOYuJKAQKHLIu+XNGqP5HBIwuxyq6rStu4B1wCYtn1Siuwl8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"text/plain": [ "<matplotlib.figure.Figure at 0x10e54fd68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(11,4))\n", "\n", "im1 = ax[0].specgram(signal,NFFT=1000,Fs=500,noverlap=500,interpolation='none')\n", "ax[0].set_ylim([0,20])\n", "ax[0].set_ylabel('Frequency (Hz)')\n", "ax[0].set_xlabel('Time (s)')\n", "im1[3].set_clim(-40,10)\n", "\n", "im2 = ax[1].specgram(signal,NFFT=1000,Fs=500,noverlap=500,interpolation='none')\n", "ax[1].set_xlim([250,350])\n", "ax[1].set_ylim([7,13])\n", "ax[1].set_xlabel('Time (s)')\n", "fig.tight_layout()\n", "im2[3].set_clim(-40,10)\n", "cb = fig.colorbar(im2[3])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Robust Spectral Decomposition\n", "\n", "### The State-Space Model\n", "\n", "In this analysis, we will consider a signal $y_{t}$ that is obtained by sampling a noisy, continuous-time signal at rate $f_{s}$ (above the Nyquist rate).\n", "\n", "* $y_{t}\\rightarrow$ discrete-time signal\n", "* $t = 1,2,...,T\\rightarrow$ samples\n", "* $f_{s}\\rightarrow$ sampling rate\n", "* $W\\rightarrow$ arbitrary window length\n", "* $N \\triangleq \\frac{T}{W}\\rightarrow$ number of windows\n", "* $y_{n} \\triangleq \\left(y_{\\left(n-1\\right)W+1},y_{\\left(n-1\\right)W+2},...,y_{nW}\\right)'$ for $n=1,2,...,N$\n", "\n", "Consider the following spectrotemporal representation of $y_{n}$ as:\n", "\n", "$$y_{n} = \\tilde{F}_{n}\\tilde{x}_{n} + v_{n}$$\n", "where,\n", "* $\\left ( \\tilde{F}_{n} \\right )_{l,k} \\triangleq \\exp \\left( j2\\pi \\left( \\left(n-1 \\right) W+l \\right) \\frac{\\left( k-1 \\right)}{K} \\right)$\n", "for $l=1,2,...,W$ and $k=1,2,...K$\n", "* $\\tilde{x}_{n} \\triangleq \\left(\\tilde{x}_{n,1},\\tilde{x}_{n,2},...,\\tilde{x}_{n,K} \\right)' $\n", "* $v_{n}\\rightarrow$ independent, identically distributed, additive zero-mean Gaussian noise\n", "\n", "Equivalently, we can define the linear observation model over a real vector space as follows:\n", "\n", "$$y_{n} = F_{n}x_{n}+v_{n}$$\n", "where,\n", "* $\\left ( F_{n} \\right )_{l,k} \\triangleq \\cos \\left( 2\\pi \\left( \\left(n-1 \\right) W+l \\right) \\frac{\\left( k-1 \\right)}{K} \\right)$ for $l = 1,2,...,W$ and $k=1,2,...\\frac{K}{2}$\n", "* $\\left ( F_{n} \\right )_{l,k+K/2} \\triangleq \\sin \\left( 2\\pi \\left( \\left(n-1 \\right) W+l \\right) \\frac{\\left( k-1 \\right)}{K} \\right)$ for $l = 1,2,...,W$ and $k=1,2,...\\frac{K}{2}$\n", "* $x_{n} \\triangleq \\left(x_{n,1},x_{n,2},...,x_{n,K} \\right)' $" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Define Matrix F\n", "\n", "numSamples = fs*T #num samples\n", "W = 1000 #window size\n", "K = W #frequency bands\n", "N = numSamples//W #number of windows\n", "\n", "F = np.zeros([W,K])\n", "k = np.array(range(1,K//2+1))\n", "l = np.array(range(1,W+1))\n", "\n", "for jj in range(0,np.size(k)):\n", " for ii in range(0,np.size(l)): \n", " F[ii,jj] = np.cos(2*np.pi*l[ii]*(k[jj]-1)/K)\n", " F[ii,jj+K//2] = np.sin(2*np.pi*l[ii]*(k[jj]-1)/K)\n", "\n", "#plt.imshow(F)\n", "#print(np.shape(F))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective is to compute an estimate $\\hat{x}$ of $x$ given the data $y$. The component-wise magnitude-squared of $\\hat{x}$ gives an estimate of the magnitude spectrum of $y$. By treating $\\left(x_{n}\\right)_{n=1}^{N}$ as a sequence of random variables and carefully selecting a prior distribution, a stochastic continuity constraint can be established across time. By imposing a model on the components $\\left(x_{n,k}\\right)_{k=1}^{K}$ for each $n = 1,2,..,N$, sparsity is enforced in the frequency domain. The stochastic continuity constraint can be expressed in the form of the first-order difference equation:\n", "\n", "$$x_{n} = x_{n-1} + w_{n}$$\n", "where $w = \\left( w_{1}',w_{2}',...,w_{N}'\\right)'$ is a random vector. The following joint prior probability density function is used to enforce sparsity in the frequency domain and smoothness in time:\n", "$$\\log p_{1}\\left(w_{1},w_{2},...,w_{N}\\right) = -\\alpha \\sum_{k=1}^{K} \\left( \\sum_{n=1}^{N} w_{n,k}^{2} + \\epsilon^{2} \\right)^\\frac{1}{2}+c_{1}$$\n", "where $\\alpha > 0$ is a constant and $\\epsilon > 0$ is a small constant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Inverse Solution\n", "\n", "Bayesian estimation is used to compute the robust spectral decomposition of $y$, where the posterior density of $x$ given $y$ fully characterizes the space of inverse solutions. This is computed by solving the following MAP estimation problem:\n", "\n", "$$\\max_{x_{1},...x_{N}} -\\sum_{n=1}^{N} \\frac{1}{2\\sigma^{2}} \\left \\| y_{n} - F_{n}x_{n} \\right \\|^{2}_{2} + f\\left(x_{1},x_{2},...,x_{N}\\right)$$\n", "\n", "where $f\\left(x_{1},x_{2},...,x_{N}\\right) \\triangleq \\log p_{i} \\left(x_{1}-x_{0},x_{2}-x_{1},...,x_{N}-x_{N-1}\\right)$. This is a strictly concave optimization problem that can be solved using standard techniques. However, these techniques do not scale well with $N$ becuase of the batch nature of the problem." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Kalman Filter\n", "The Kalman filter solves the least-squares estimation problem recursively, and in a computationally efficient manner. The algorithm works in a two-step process. In the prediciton step, the Kalman filter produces estimates of the current state variables, along with their uncertainties. Once the outcome of the next measurement (corrupted with some amount of error, including random noise) is observed, these estimates are updated with more weight being given to estimates with higher certainty.\n", "\n", "<img src=\"http://i.imgur.com/s1YU6Qy.png\">\n", "\n", "**Algorithm:**\n", "\n", "Initial Conditions: \n", "\n", "* $x_{0\\mid 0} = \\left(0,...,0\\right)' \\in \\mathbb{R}^{K}$\n", "* $\\Sigma_{0\\mid 0} = I_{k} \\in \\mathbb{R}^{KK}$\n", "\n", "Filter at time $n=1,2,...,N$:\n", "* $x_{n\\mid n-1}=x_{n-1 \\mid n-1}$\n", "* $\\Sigma_{n\\mid n-1}=\\Sigma_{n-1\\mid n-1}+Q^{\\left(l\\right)}$\n", "* $K_{n}=\\Sigma_{n\\mid n-1}F_{n}^{H}\\left(F_{n}\\Sigma_{n\\mid n-1}F_{n}^{H}+\\sigma^{2}I\\right)^{-1}$\n", "* $x_{n\\mid n}=x_{n\\mid n-1}+K_{n}\\left(y_{n}-F_{n}x_{n\\mid n-1}\\right)$\n", "* $\\Sigma_{n\\mid n}=\\Sigma_{n\\mid n-1}-K_{n}F_{n}\\Sigma_{n\\mid n-1}$" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#initialize\n", "Q = np.eye(K)*0.001\n", "xKalman = np.zeros([K,N+1])\n", "xPredict = np.zeros([K,N+1])\n", "sigKalman = np.zeros([K,K,N+1])\n", "sigPredict = np.zeros([K,K,N+1])\n", "sigKalman[:,:,0] = np.eye(K)\n", "\n", "#Kalman Filter\n", "for n in range(0,N):\n", " y = signal[n*W:(n+1)*W]\n", " xPredict[:,n+1] = xKalman[:,n]\n", " sigPredict[:,:,n+1] = sigKalman[:,:,n] + Q\n", " gainK = np.dot(sigPredict[:,:,n+1],F.T).dot(np.linalg.inv(np.dot(F,sigPredict[:,:,n+1]).dot(F.T)+np.eye(K)))\n", " xKalman[:,n+1] = xPredict[:,n+1] + np.dot(gainK,y-np.dot(F,xPredict[:,n+1]))\n", " sigKalman[:,:,n+1] = sigPredict[:,:,n+1] - np.dot(gainK,F).dot(sigPredict[:,:,n+1])\n", "\n", "#remove initial conditions\n", "xKalman = xKalman[:,1:N+1]\n", "xPredict = xPredict[:,1:N+1]\n", "sigKalman = sigKalman[:,:,1:N+1]\n", "sigPredict = sigPredict[:,:,1:N+1]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MRN8031ob6bLgrCUnyiVFUU4TUgvGklowtvNzVfHL3Ve5bvKlKIqinO4YY55C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\nD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"text/plain": [ "<matplotlib.figure.Figure at 0x10c234240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xEst = xKalman[0:K//2,:]-xKalman[K//2:W,:]*1j\n", "xPSD = 10*np.log10(np.abs(xEst)**2)\n", "\n", "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(11,4))\n", "\n", "im1 = ax[0].imshow(xPSD,origin='lower',extent=[0,N*W//fs,0,fs//2-5],aspect='auto',interpolation='none')\n", "ax[0].set_ylim([0,20])\n", "ax[0].set_ylabel('Frequency (Hz)')\n", "ax[0].set_xlabel('Time (s)')\n", "im1.set_clim(-40,10)\n", "\n", "im2 = ax[1].imshow(xPSD,origin='lower',extent=[0,N*W//fs,0,fs//2-5],aspect='auto',interpolation='none')\n", "ax[1].set_xlim([250,350])\n", "ax[1].set_ylim([7,13])\n", "ax[1].set_xlabel('Time (s)')\n", "fig.tight_layout()\n", "im2.set_clim(-40,10)\n", "cb = fig.colorbar(im2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kalman Smoother\n", "\n", "The Kalman filter is designed for real-time applications. It estimates the properties of a system at a given time using measurments of the system up to that time. However, when a real-time estimate is not needed, the Kalman filter effectively throws away half of the measurement data. The Kalman smoother is an extension of the Kalman filter that uses measurement information from after the time at which state estimates are required as well as before that time.\n", "\n", "**Algorithm**\n", "\n", "Smoother at time $n=N-1,N-2,...,1:$\n", "\n", "* $B_{n}=\\Sigma_{n\\mid n}\\Sigma_{n+1\\mid n}^{-1}$\n", "* $x_{n\\mid N}=x_{n\\mid n}+B_{n}\\left(x_{n+1\\mid N}-x_{n+1\\mid n}\\right)$\n", "* $\\Sigma_{n\\mid N}=\\Sigma_{n\\mid n}+B_{n}\\left(\\Sigma_{n+1\\mid N}-\\Sigma_{n+1\\mid n}\\right)B_{n}^{H}$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xSmooth = xKalman\n", "sigSmooth = sigKalman\n", "\n", "for n in range(N-2,-1,-1):\n", " B = np.dot(sigKalman[:,:,n],np.linalg.inv(sigPredict[:,:,n+1]))\n", " xSmooth[:,n] = xKalman[:,n] + np.dot(B,(xSmooth[:,n+1]-xPredict[:,n+1]))\n", " sigSmooth[:,:,n] = sigKalman[:,:,n] + np.dot(B,(sigSmooth[:,:,n+1]-sigPredict[:,:,n+1])).dot(B.T)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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IMRA9HfBeT5+DKOAAXAH0wlcwctQVw6KiQJ2ygKnAAkTFKgsyR7t2gObvkTfj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"text/plain": [ "<matplotlib.figure.Figure at 0x10cc00240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xEst = xSmooth[0:K//2,:]-xSmooth[K//2:W,:]*1j\n", "xPSD = 10*np.log10(np.abs(xEst)**2)\n", "\n", "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(11,4))\n", "\n", "im1 = ax[0].imshow(xPSD,origin='lower',extent=[0,N*W//fs,0,fs//2-5],aspect='auto',interpolation='none')\n", "ax[0].set_ylim([0,20])\n", "ax[0].set_ylabel('Frequency (Hz)')\n", "ax[0].set_xlabel('Time (s)')\n", "im1.set_clim(-40,10)\n", "\n", "im2 = ax[1].imshow(xPSD,origin='lower',extent=[0,N*W//fs,0,fs//2-5],aspect='auto',interpolation='none')\n", "ax[1].set_xlim([250,350])\n", "ax[1].set_ylim([7,13])\n", "ax[1].set_xlabel('Time (s)')\n", "fig.tight_layout()\n", "im2.set_clim(-40,10)\n", "cb = fig.colorbar(im2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IRLS Algorithm for Spectrotemporal Pursuit\n", "\n", "The solution to the optimization problem can be obtained as the limit of a sequence $\\left(\\hat{x}^\\left(l\\right)\\right)_{l=0}^{\\infty}$ whose $l^{th}$ element is the solution to the Guassian MAP estimation problem (constrained least-squares program) of the form:\n", "\n", "$$ \\max_{x_{1},...,x{N}} -\\sum_{n=1}^{N} \\frac{1}{2\\sigma^{2}} \\left \\| y_{n} - F_{n}x_{n} \\right \\|^{2}_{2} - \\sum_{k=1}^{K} \\sum_{n=1}^{N} \\frac{\\left(x_{n,k}-x_{n-1,k}\\right)^{2}}{2\\left(Q^{\\left(l\\right)}\\right)_{k,k}} $$\n", "\n", "where,\n", "$$ \\left(Q^{\\left(l\\right)}\\right)_{k,k} = \\frac{\\left(\\sum_{n=1}^{N}\\left(\\hat{x}_{n,k}^{\\left(l-1\\right)}-\\hat{x}_{n-1,k}^{\\left(l-1\\right)} \\right)+\\epsilon^{2}\\right)^{\\frac{1}{2}}}{\\alpha} \\rightarrow K\\times K \\textrm{ diagonal matrix}$$\n", "\n", "This is a quadratic program with strictly concave objective function and block-tridiagonal Hessian. It can be solved iteratively using the following steps:\n", "\n", "** Input: **\n", "* $y \\rightarrow$ observations\n", "* $\\hat{x}^{\\left(0\\right)} \\in \\mathbb{R}^{KN}\\rightarrow$ initial guess\n", "* $Q^{\\left(0\\right)} \\rightarrow$ initial state-noise covariance\n", "* $x_{0\\mid0}, \\Sigma_{0\\mid0} \\rightarrow$ intial conditions\n", "* $tol \\in \\left(0,0.01\\right) \\rightarrow$ tolerance\n", "* $L_{max} \\in \\mathbb{N}^{+} \\rightarrow$ maximum number of iterations\n", "\n", "**Step 0.** Initialize iteration number $l$ to 1\n", "\n", "**Step 1.** Kalman Filter at time $n=1,2,...,N$:\n", "\n", "* $x_{n\\mid n-1}=x_{n-1 \\mid n-1}$\n", "* $\\Sigma_{n\\mid n-1}=\\Sigma_{n-1\\mid n-1}+Q^{\\left(l\\right)}$\n", "* $K_{n}=\\Sigma_{n\\mid n-1}F_{n}^{H}\\left(F_{n}\\Sigma_{n\\mid n-1}F_{n}^{H}+\\sigma^{2}I\\right)^{-1}$\n", "* $x_{n\\mid n}=x_{n\\mid n-1}+K_{n}\\left(y_{n}-F_{n}x_{n\\mid n-1}\\right)$\n", "* $\\Sigma_{n\\mid n}=\\Sigma_{n\\mid n-1}-K_{n}F_{n}\\Sigma_{n\\mid n-1}$\n", "\n", "**Step 2.** Smoother at time $n=N-1,N-2,...,1$:\n", "\n", "* $B_{n}=\\Sigma_{n\\mid n}\\Sigma_{n+1\\mid n}^{-1}$\n", "* $x_{n\\mid N}=x_{n\\mid n}+B_{n}\\left(x_{n+1\\mid N}-x_{n+1\\mid n}\\right)$\n", "* $\\Sigma_{n\\mid N}=\\Sigma_{n\\mid n}+B_{n}\\left(\\Sigma_{n+1\\mid N}-\\Sigma_{n+1\\mid n}\\right)B_{n}^{H}$\n", "\n", "**Step3.** Let $\\hat{x}_{n}^{\\left(l\\right)}=x_{n\\mid N},n=1,...,N$ and $\\hat{x}^{\\left(l\\right)}=\\left(\\hat{x}_{1}^{\\left(l\\right)'},...,\\hat{x}_{N}^{\\left(l\\right)'}\\right)'$\n", "\n", "**Step4.** Stop if $\\frac{\\left \\| \\hat{x}^{\\left(l \\right )}-\\hat{x}^{\\left(l-1 \\right) } \\right \\|_{2}}{\\left \\| \\hat{x}^{\\left(l-1 \\right) }\\right \\|_{2}}<tol$ or $l=L_{max}$\n", "\n", "**Step5.** Let $l=l+1$, and update the state covariance $Q_n^{\\left(l\\right)}$\n", "\n", "**Step6.** Go back to Step 1\n", "\n", "** Output:** $\\hat{x}^{\\left(L\\right)}$, where $L\\leq L_{max}$ is the number of the last iteration of the algorithm\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] } ], "source": [ "#Parameters\n", "alpha = 21000\n", "tol = 0.005\n", "maxIter = 10\n", "\n", "Q = np.eye(K)*0.001\n", "iter = 1\n", "\n", "while (iter <= maxIter): \n", " #Step 1:\n", " #initialize\n", " xKalman = np.zeros([K,N+1])\n", " xPredict = np.zeros([K,N+1])\n", " sigKalman = np.zeros([K,K,N+1])\n", " sigPredict = np.zeros([K,K,N+1])\n", " sigKalman[:,:,0] = np.eye(K)\n", "\n", " #Kalman Filter\n", " for n in range(0,N):\n", " y = signal[n*W:(n+1)*W]\n", " xPredict[:,n+1] = xKalman[:,n]\n", " sigPredict[:,:,n+1] = sigKalman[:,:,n] + Q\n", " gainK = np.dot(sigPredict[:,:,n+1],F.T).dot(np.linalg.inv(np.dot(F,sigPredict[:,:,n+1]).dot(F.T)+np.eye(K)))\n", " xKalman[:,n+1] = xPredict[:,n+1] + np.dot(gainK,y-np.dot(F,xPredict[:,n+1]))\n", " sigKalman[:,:,n+1] = sigPredict[:,:,n+1] - np.dot(gainK,F).dot(sigPredict[:,:,n+1])\n", "\n", " #remove initial conditions\n", " xKalman = xKalman[:,1:N+1]\n", " xPredict = xPredict[:,1:N+1]\n", " sigKalman = sigKalman[:,:,1:N+1]\n", " sigPredict = sigPredict[:,:,1:N+1]\n", " \n", " #Step 2:\n", " #initialize\n", " xSmooth = xKalman\n", " sigSmooth = sigKalman\n", "\n", " #Kalman Smoother\n", " for n in range(N-2,-1,-1):\n", " B = np.dot(sigKalman[:,:,n],np.linalg.inv(sigPredict[:,:,n+1]))\n", " xSmooth[:,n] = xKalman[:,n] + np.dot(B,(xSmooth[:,n+1]-xPredict[:,n+1]))\n", " sigSmooth[:,:,n] = sigKalman[:,:,n] + np.dot(B,(sigSmooth[:,:,n+1]-sigPredict[:,:,n+1])).dot(B.T)\n", " \n", " #Step 4:\n", " if iter > 1 and np.linalg.norm(xSmooth-xPrev,'fro')/np.linalg.norm(xPrev,'fro') < tol:\n", " break\n", " \n", " #Step 5: Update Q\n", " Q = np.zeros([K,K])\n", " for k in range(0,K):\n", " qTemp = 0\n", " for n in range(1,N):\n", " qTemp += (xSmooth[k,n]-xSmooth[k,n-1])**2\n", " Q[k,k] = (qTemp + np.finfo(float).eps**2)**(1/2)/alpha\n", " \n", " xPrev = xSmooth\n", " iter += 1\n", " \n", "print(iter-1)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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WIcnLk1yW5NIkH0tyRNe2U5J8JcmVSR6/r7pM/iVJkqRR2T3ksj6vqaoHVdUx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\nJC0Ik39JkiRpQZj8S5IkSQvi/wclSO/WKvnBlwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108c5fda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xEst = xSmooth[0:K//2,:]-xSmooth[K//2:W,:]*1j\n", "xPSD = 10*np.log10(np.abs(xEst)**2)\n", "\n", "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(11,4))\n", "\n", "im1 = ax[0].imshow(xPSD,origin='lower',extent=[0,N*W//fs,0,fs//2-5],aspect='auto',interpolation='none')\n", "ax[0].set_ylim([0,20])\n", "ax[0].set_ylabel('Frequency (Hz)')\n", "ax[0].set_xlabel('Time (s)')\n", "im1.set_clim(-40,10)\n", "\n", "im2 = ax[1].imshow(xPSD,origin='lower',extent=[0,N*W//fs,0,fs//2-5],aspect='auto',interpolation='none')\n", "ax[1].set_xlim([250,350])\n", "ax[1].set_ylim([7,13])\n", "ax[1].set_xlabel('Time (s)')\n", "fig.tight_layout()\n", "im2.set_clim(-40,10)\n", "cb = fig.colorbar(im2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spectrotemporal pursuit gives the sparse, more compact representation that better characterizes the equation simulated in this toy example. We are able to recover the two frequencies (10 and 11 Hz) as well their temporal modulation. The estimate is also significantly denoised relative to the STFT spectrogram." ] } ], "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
maasg/spark-notebooks
languageclassification/language-detection-letter-freq.ipynb
1
253382
{ "metadata" : { "name" : "language-detection-letter-freq", "user_save_timestamp" : "1970-01-01T01:00:00.000Z", "auto_save_timestamp" : "1970-01-01T01:00:00.000Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mode" : "text/x-scala" }, "trusted" : true, "customLocalRepo" : null, "customRepos" : null, "customDeps" : null, "customImports" : null, "customArgs" : null, "customSparkConf" : null }, "cells" : [ { "metadata" : { "id" : "6D985D5B5E8740F384AA83EDE40A95D2" }, "cell_type" : "markdown", "source" : "#Language Classification\n###A naive approach to language classification.\nThis notebook explores the language classification problem by looking at the frequency distribution of the characters used in sample text.\nUsing the letter frequency we build simple models that will allow us to classify the language of new sentences.\n\nFor our exploration, we will use a dataset comprised of treaties and other official documents of the European Commission. Those are available in each language spoken in the European Union and hence ideal to have a datasets of equivalent quality for each language." }, { "metadata" : { "id" : "5A3E909A40B049FE9CB7DCB3383384CE" }, "cell_type" : "markdown", "source" : "## First, we will load some sample data and explore the character distribution in order to build up some intuition." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "BFF3A95A5486431F944D5D66AD38CD76" }, "cell_type" : "code", "source" : "val notebooksFolder = sys.env(\"NOTEBOOKS_DIR\")\nval baseFolder = s\"$notebooksFolder/languageclassfication/data\"", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "notebooksFolder: String = /home/maasg/playground/sparkfun/spark-notebooks\nbaseFolder: String = /home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 25, "time" : "Took: 391 milliseconds, at 2017-3-6 21:19" } ] }, { "metadata" : { "id" : "C9DB899751B54B4886E418E70D116C0F" }, "cell_type" : "markdown", "source" : "### We load the english dataset to explore the data" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "2A1476EDDD3344B98FEFF9988DB6786C" }, "cell_type" : "code", "source" : "val en = sparkSession.sparkContext.textFile(baseFolder + \"/en\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "en: org.apache.spark.rdd.RDD[String] = /home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data/en MapPartitionsRDD[20] at textFile at <console>:70\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 26, "time" : "Took: 506 milliseconds, at 2017-3-6 21:19" } ] }, { "metadata" : { "id" : "2FE75BFDD8AC4136827BDCDFFA4AC5E0" }, "cell_type" : "markdown", "source" : "### And we clean up the text from characters other than letters" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "41A6E9AA80E24EA2868949ECC6878BFD" }, "cell_type" : "code", "source" : "val cleanedLetters = en.flatMap(str => str).filter(java.lang.Character.isAlphabetic(_)).map(java.lang.Character.toLowerCase(_))", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "cleanedLetters: org.apache.spark.rdd.RDD[Char] = MapPartitionsRDD[23] at map at <console>:72\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 27, "time" : "Took: 434 milliseconds, at 2017-3-6 21:19" } ] }, { "metadata" : { "id" : "DD074C27F4C24EE88BF7D0849B0AB947" }, "cell_type" : "markdown", "source" : "#### We can use cells to interactively test our filter method to be sure we are getting the results that we expect\nThe API parity between Scala and Spark lets us easily tests on local collections. Like a string in this case." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "CEBFFF79769A4E77A11BF8DB068454E4" }, "cell_type" : "code", "source" : "val sample = \"Erwägung protección jurídica ci-après à l’aide Gemäß 987...\"\nsample.filter(java.lang.Character.isAlphabetic(_)).map(java.lang.Character.toLowerCase(_))\n", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "sample: String = Erwägung protección jurídica ci-après à l’aide Gemäß 987...\nres31: String = erwägungprotecciónjurídicaciaprèsàlaidegemäß\n" }, { "metadata" : { }, "data" : { "text/html" : "erwägungprotecciónjurídicaciaprèsàlaidegemäß" }, "output_type" : "execute_result", "execution_count" : 28, "time" : "Took: 557 milliseconds, at 2017-3-6 21:19" } ] }, { "metadata" : { "id" : "36CCEBEC50724BC79349061B101C76D2" }, "cell_type" : "markdown", "source" : "### We count the total of characters in our dataset\nWe will need this later to obtain relative frequency values" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "DD4F41176D2B4EEFAE850DB5AF815579" }, "cell_type" : "code", "source" : "val total = cleanedLetters.count()", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "total: Long = 1670805\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 29, "time" : "Took: 863 milliseconds, at 2017-3-6 21:19" } ] }, { "metadata" : { "id" : "68E13860F02440D29757330CC1EF7AD2" }, "cell_type" : "markdown", "source" : "### ...and the frequency of each alphabetic character in the dataset\nNote that the frequency is relative to the total count - this will enable us to compare different languages later on" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "6F6D9275B5E54FFAB770F4418BDF2554" }, "cell_type" : "code", "source" : "val freq = cleanedLetters.keyBy(char => char.toString.toLowerCase).countByKey.map{case (k,v) => (k.toString, v.toDouble/total)}", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "freq: scala.collection.Map[String,Double] = Map(e -> 0.12519294591529234, л -> 2.3940555600444098E-6, ς -> 5.386625010099922E-6, έ -> 1.1970277800222049E-6, s -> 0.05571326396557348, б -> 1.1970277800222049E-6, x -> 0.0023743046016740433, č -> 4.189597230077717E-6, α -> 6.583652790122127E-6, ā -> 2.9925694500555123E-6, n -> 0.07904632796765632, ε -> 6.583652790122127E-6, п -> 2.3940555600444098E-6, ω -> 1.1970277800222049E-6, ä -> 4.189597230077717E-6, ļ -> 5.985138900111024E-7, j -> 0.002664583838329428, y -> 0.011944541702951571, š -> 2.3940555600444098E-6, φ -> 1.1970277800222049E-6, μ -> 1.7955416700333072E-6, а -> 6.583652790122127E-6, t -> 0.10015710989612792, ó -> 2.9925694500555123E-6, в -> 5.985138900111024E-7, é -> 2.1546500040399688E-5, u -> 0.02913805022130051, ή -> 5.985138..." }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 31, "time" : "Took: 1 second 10 milliseconds, at 2017-3-6 21:22" } ] }, { "metadata" : { "id" : "48C9B6C691AC4DCE8DB57DC606463CAB" }, "cell_type" : "markdown", "source" : "#### We are interested in the frequency of each letter, alphabetically ordered" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "E31D0FA9B28249F08FA7C96F64EB2429" }, "cell_type" : "code", "source" : "val freqOrdered = freq.toList.sortBy{case (k,v)=> k}", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "freqOrdered: List[(String, Double)] = List((a,0.07892123856464399), (b,0.013841232220396755), (c,0.04509203647343646), (d,0.030138166931509062), (e,0.12519294591529234), (f,0.026006625548762423), (g,0.014792270791624396), (h,0.044525243819595946), (i,0.08238364141835822), (j,0.002664583838329428), (k,0.002872866672053292), (l,0.040070504936243305), (m,0.024439716184713356), (n,0.07904632796765632), (o,0.08121653933283657), (p,0.025622379631375296), (q,0.0011192209743207616), (r,0.06635484092997088), (s,0.05571326396557348), (t,0.10015710989612792), (u,0.02913805022130051), (v,0.00852882293265821), (w,0.0074197766944676365), (x,0.0023743046016740433), (y,0.011944541702951571), (z,2.1187391706393026E-4), (à,4.189597230077717E-6), (á,1.0174736130188742E-5), (ã,2.3940555600444098E-6), (ä,4...." }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 32, "time" : "Took: 544 milliseconds, at 2017-3-6 21:22" } ] }, { "metadata" : { "id" : "6E4B1184392945DAA606D18EF97AC806" }, "cell_type" : "markdown", "source" : "### We can now visualize the how the frequency distribution looks like" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab171562487-1\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 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(v,0.00852882293265821), (w,0.0074197766944676365), (x,0.0023743046016740433), (y,0.011944541702951571), (z,2.1187391706393026E-4), (à,4.189597230077717E-6), (á,1.0174736130188742E-5), (ã,2.3940555600444098E-6), (ä,4.189597..." }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon6a29b89179b5cc8e318b352c17a89f0c&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;171562487&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul171562487\"><li>\n <a href=\"#tab171562487-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab171562487-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab171562487-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab171562487-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab171562487\"><div class=\"tab-pane\" id=\"tab171562487-0\">\n <div>\n <script 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type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon7399abd20970ddc3c5d94fe7dda36214&quot;,&quot;initialValue&quot;:&quot;98&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> 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type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon38a5c2b4f5533c46f51e3cfcd7dab691&quot;,&quot;initialValue&quot;:&quot;98&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon7bbc65a909ef83260eda73670e8c3757&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab171562487-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonaf0fd1052fb490caffc3277234b2fa35&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.07892123856464399},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.013841232220396755},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.04509203647343646},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.030138166931509062},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12519294591529234},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.026006625548762423},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.014792270791624396},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.044525243819595946},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.08238364141835822},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.002664583838329428},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.002872866672053292},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.040070504936243305},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.024439716184713356},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07904632796765632},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08121653933283657},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.025622379631375296},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.0011192209743207616},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06635484092997088},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.05571326396557348},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.10015710989612792},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.02913805022130051},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.00852882293265821},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.0074197766944676365},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0023743046016740433},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.011944541702951571},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.00021187391706393026},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.000010174736130188742},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ä&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;å&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ç&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;é&quot;,&quot;_2&quot;:0.000021546500040399688},{&quot;_1&quot;:&quot;ì&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;í&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ñ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ó&quot;,&quot;_2&quot;:0.0000029925694500555123},{&quot;_1&quot;:&quot;ö&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ú&quot;,&quot;_2&quot;:0.0000047881111200888195},{&quot;_1&quot;:&quot;ü&quot;,&quot;_2&quot;:0.000005985138900111025},{&quot;_1&quot;:&quot;ā&quot;,&quot;_2&quot;:0.0000029925694500555123},{&quot;_1&quot;:&quot;ă&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;č&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;ē&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ė&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ģ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ļ&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ł&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ş&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;š&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ţ&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ū&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ů&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ų&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ά&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;έ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ή&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ί&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;α&quot;,&quot;_2&quot;:0.000006583652790122127},{&quot;_1&quot;:&quot;β&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;γ&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;ε&quot;,&quot;_2&quot;:0.000006583652790122127},{&quot;_1&quot;:&quot;η&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;θ&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ι&quot;,&quot;_2&quot;:0.0000047881111200888195},{&quot;_1&quot;:&quot;κ&quot;,&quot;_2&quot;:0.0000047881111200888195},{&quot;_1&quot;:&quot;λ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;μ&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ν&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;ξ&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ο&quot;,&quot;_2&quot;:0.000005985138900111025},{&quot;_1&quot;: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type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anona149bee4cbb15a1999d6d69e5d6d1c8b&quot;,&quot;initialValue&quot;:&quot;98&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anone2bc4f19b134160f86b284e6115dd1de&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab171562487-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon46c936b3abe0342d2494476eca3a5ddd&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.07892123856464399},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.013841232220396755},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.04509203647343646},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.030138166931509062},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12519294591529234},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.026006625548762423},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.014792270791624396},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.044525243819595946},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.08238364141835822},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.002664583838329428},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.002872866672053292},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.040070504936243305},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.024439716184713356},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07904632796765632},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08121653933283657},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.025622379631375296},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.0011192209743207616},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06635484092997088},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.05571326396557348},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.10015710989612792},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.02913805022130051},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.00852882293265821},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.0074197766944676365},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0023743046016740433},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.011944541702951571},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.00021187391706393026},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.000010174736130188742},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ä&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;å&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ç&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;é&quot;,&quot;_2&quot;:0.000021546500040399688},{&quot;_1&quot;:&quot;ì&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;í&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ñ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ó&quot;,&quot;_2&quot;:0.0000029925694500555123},{&quot;_1&quot;:&quot;ö&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ú&quot;,&quot;_2&quot;:0.0000047881111200888195},{&quot;_1&quot;:&quot;ü&quot;,&quot;_2&quot;:0.000005985138900111025},{&quot;_1&quot;:&quot;ā&quot;,&quot;_2&quot;:0.0000029925694500555123},{&quot;_1&quot;:&quot;ă&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;č&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;ē&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ė&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ģ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ļ&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ł&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ş&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;š&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ţ&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ū&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ů&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ų&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ά&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;έ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ή&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ί&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;α&quot;,&quot;_2&quot;:0.000006583652790122127},{&quot;_1&quot;:&quot;β&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;γ&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;ε&quot;,&quot;_2&quot;:0.000006583652790122127},{&quot;_1&quot;:&quot;η&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;θ&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ι&quot;,&quot;_2&quot;:0.0000047881111200888195},{&quot;_1&quot;:&quot;κ&quot;,&quot;_2&quot;:0.0000047881111200888195},{&quot;_1&quot;:&quot;λ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;μ&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;ν&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;ξ&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ο&quot;,&quot;_2&quot;:0.000005985138900111025},{&quot;_1&quot;:&quot;π&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;ρ&quot;,&quot;_2&quot;:0.000006583652790122127},{&quot;_1&quot;:&quot;ς&quot;,&quot;_2&quot;:0.000005386625010099922},{&quot;_1&quot;:&quot;σ&quot;,&quot;_2&quot;:0.0000029925694500555123},{&quot;_1&quot;:&quot;τ&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;υ&quot;,&quot;_2&quot;:0.000005386625010099922},{&quot;_1&quot;:&quot;φ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ω&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;а&quot;,&quot;_2&quot;:0.000006583652790122127},{&quot;_1&quot;:&quot;б&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;в&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;г&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;е&quot;,&quot;_2&quot;:0.0000035910833400666144},{&quot;_1&quot;:&quot;з&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;и&quot;,&quot;_2&quot;:0.0000029925694500555123},{&quot;_1&quot;:&quot;к&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;л&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;н&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;о&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;п&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;р&quot;,&quot;_2&quot;:0.0000017955416700333072},{&quot;_1&quot;:&quot;с&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;т&quot;,&quot;_2&quot;:0.0000029925694500555123},{&quot;_1&quot;:&quot;х&quot;,&quot;_2&quot;:5.985138900111024E-7},{&quot;_1&quot;:&quot;ц&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;ъ&quot;,&quot;_2&quot;:0.0000011970277800222049},{&quot;_1&quot;:&quot;я&quot;,&quot;_2&quot;:0.0000011970277800222049}],&quot;genId&quot;:&quot;1796136266&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonc3dd8735ea974c814805476e91919ede&quot;,&quot;initialValue&quot;:&quot;98&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonb1d3cf29f7dbb1d9731f090d30d620d2&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 33, "time" : "Took: 1 second 283 milliseconds, at 2017-3-6 21:23" } ] }, { "metadata" : { "id" : "90FDBDE535424CC0897C20A6989845C3" }, "cell_type" : "markdown", "source" : "### Probably better to take the relevant part" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab841865662-1\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "A780D4906EC84FCD8339A250ADF50EF0" }, "cell_type" : "code", "source" : "freqOrdered.take(30)\n", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res39: List[(String, Double)] = List((a,0.07892123856464399), (b,0.013841232220396755), (c,0.04509203647343646), (d,0.030138166931509062), (e,0.12519294591529234), (f,0.026006625548762423), (g,0.014792270791624396), (h,0.044525243819595946), (i,0.08238364141835822), (j,0.002664583838329428), (k,0.002872866672053292), (l,0.040070504936243305), (m,0.024439716184713356), (n,0.07904632796765632), (o,0.08121653933283657), (p,0.025622379631375296), (q,0.0011192209743207616), (r,0.06635484092997088), (s,0.05571326396557348), (t,0.10015710989612792), (u,0.02913805022130051), (v,0.00852882293265821), (w,0.0074197766944676365), (x,0.0023743046016740433), (y,0.011944541702951571), (z,2.1187391706393026E-4), (à,4.189597230077717E-6), (á,1.0174736130188742E-5), (ã,2.3940555600444098E-6), (ä,4.189597..." }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon27b59751243c1c738c1ff904d94d210e&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;841865662&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul841865662\"><li>\n <a href=\"#tab841865662-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab841865662-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab841865662-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab841865662-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab841865662\"><div class=\"tab-pane\" id=\"tab841865662-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon89a0047a550d63e36113500c4bc5dbef&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.07892123856464399},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.013841232220396755},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.04509203647343646},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.030138166931509062},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12519294591529234},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.026006625548762423},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.014792270791624396},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.044525243819595946},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.08238364141835822},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.002664583838329428},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.002872866672053292},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.040070504936243305},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.024439716184713356},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07904632796765632},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08121653933283657},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.025622379631375296},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.0011192209743207616},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06635484092997088},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.05571326396557348},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.10015710989612792},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.02913805022130051},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.00852882293265821},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.0074197766944676365},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0023743046016740433},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.011944541702951571},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.00021187391706393026},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.000010174736130188742},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ä&quot;,&quot;_2&quot;:0.000004189597230077717}],&quot;genId&quot;:&quot;583687893&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonafd195c9e58a3f1071b2dbb79a64b070&quot;,&quot;initialValue&quot;:&quot;30&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon5266ab4a2157236e605216525e88ffd4&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab841865662-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon1ecb55782dc18e31ec51673f9afe6c9a&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.07892123856464399},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.013841232220396755},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.04509203647343646},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.030138166931509062},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12519294591529234},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.026006625548762423},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.014792270791624396},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.044525243819595946},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.08238364141835822},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.002664583838329428},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.002872866672053292},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.040070504936243305},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.024439716184713356},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07904632796765632},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08121653933283657},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.025622379631375296},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.0011192209743207616},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06635484092997088},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.05571326396557348},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.10015710989612792},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.02913805022130051},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.00852882293265821},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.0074197766944676365},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0023743046016740433},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.011944541702951571},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.00021187391706393026},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.000010174736130188742},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ä&quot;,&quot;_2&quot;:0.000004189597230077717}],&quot;genId&quot;:&quot;364012928&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon48bcff2eff010d52746de8fbcbebc82d&quot;,&quot;initialValue&quot;:&quot;30&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonb1acd61aa1129eaa4724938dd01e1981&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab841865662-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonc0d2fc9f09fcc0c1e8cebfdf4eadca26&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.07892123856464399},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.013841232220396755},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.04509203647343646},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.030138166931509062},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12519294591529234},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.026006625548762423},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.014792270791624396},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.044525243819595946},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.08238364141835822},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.002664583838329428},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.002872866672053292},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.040070504936243305},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.024439716184713356},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07904632796765632},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08121653933283657},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.025622379631375296},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.0011192209743207616},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06635484092997088},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.05571326396557348},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.10015710989612792},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.02913805022130051},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.00852882293265821},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.0074197766944676365},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0023743046016740433},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.011944541702951571},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.00021187391706393026},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.000010174736130188742},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ä&quot;,&quot;_2&quot;:0.000004189597230077717}],&quot;genId&quot;:&quot;95881910&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon8693da42e103902696d009ca5f9b8285&quot;,&quot;initialValue&quot;:&quot;30&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon78b2352310507973a18d9ba530b79ba6&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab841865662-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon5c4c5069aa56444fa923358c20b76137&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.07892123856464399},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.013841232220396755},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.04509203647343646},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.030138166931509062},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12519294591529234},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.026006625548762423},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.014792270791624396},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.044525243819595946},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.08238364141835822},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.002664583838329428},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.002872866672053292},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.040070504936243305},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.024439716184713356},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07904632796765632},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08121653933283657},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.025622379631375296},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.0011192209743207616},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06635484092997088},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.05571326396557348},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.10015710989612792},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.02913805022130051},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.00852882293265821},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.0074197766944676365},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0023743046016740433},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.011944541702951571},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.00021187391706393026},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.000010174736130188742},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ä&quot;,&quot;_2&quot;:0.000004189597230077717}],&quot;genId&quot;:&quot;1723565630&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonf42fd4d26d65db5a6c8f420415444a20&quot;,&quot;initialValue&quot;:&quot;30&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonf9f54c11070e839ffcf5b99a7006ff27&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 34, "time" : "Took: 690 milliseconds, at 2017-3-6 21:24" } ] }, { "metadata" : { "id" : "1F8D760BD9094A1C8956F0D9EF2AEE8D" }, "cell_type" : "markdown", "source" : "## Let's put together these initial steps to process other languages and compare the distributions" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "26429C6A81D24253AF14041AEB781E0F" }, "cell_type" : "code", "source" : "import java.lang.Character\nimport org.apache.spark.rdd.RDD\n \ndef letterFreq(rdd: RDD[String]): Seq[(String, Double)] = {\n val cleanedChars = rdd.flatMap(str => str).collect{case char if Character.isAlphabetic(char) => Character.toLowerCase(char)}\n val total = cleanedChars.count\n val freq = cleanedChars.keyBy(identity).countByKey()\n // here we transform characters to String to help us with limited support for 'char' in Spark Datasets\n val ordered = freq.map{case (k,v) => (k.toString, v.toDouble/total)}.toList.sortBy{case (k,v)=> k}\n ordered\n}\n\n", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "import java.lang.Character\nimport org.apache.spark.rdd.RDD\nletterFreq: (rdd: org.apache.spark.rdd.RDD[String])Seq[(String, Double)]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 38, "time" : "Took: 667 milliseconds, at 2017-3-6 21:31" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "74CE994500F4495B9B123B94D6AB99F5" }, "cell_type" : "code", "source" : "val es = sparkSession.sparkContext.textFile(baseFolder + \"/es\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "es: org.apache.spark.rdd.RDD[String] = /home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data/es MapPartitionsRDD[39] at textFile at <console>:74\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 43, "time" : "Took: 462 milliseconds, at 2017-3-6 21:32" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "E21E00DDADFB43649D682F2E8807DAE4" }, "cell_type" : "code", "source" : "val esLetterFreq = letterFreq(es).take(30)", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "esLetterFreq: Seq[(String, Double)] = List((a,0.10811093644933893), (b,0.011199629499334257), (c,0.055396417899813415), (d,0.056432327964339314), (e,0.12497161127711401), (f,0.007402220332114659), (g,0.008447036662644004), (h,0.002932388081634834), (i,0.07469295647953117), (j,0.004328445277675108), (k,4.1748121891156525E-5), (l,0.05847687265375555), (m,0.023087268047434772), (n,0.07069237311910795), (o,0.08934988711307841), (p,0.028785051723139814), (q,0.004136403916975788), (r,0.06678196570196962), (s,0.0724157355907749), (t,0.05165856938649187), (u,0.03707344552259742), (v,0.006658547120826858), (w,1.5585965506031768E-5), (x,0.0015324343942180521), (y,0.006487101500260509), (z,0.0020684802793005017), (º,9.462907628662146E-6), (à,5.566416252154203E-7), (á,0.0061186047443679), (ã,2.2265..." }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 44, "time" : "Took: 881 milliseconds, at 2017-3-6 21:33" } ] }, { "metadata" : { "id" : "91F563460CC145CF931BAF324B1BF902" }, "cell_type" : "markdown", "source" : "### Let's compare the frequency distribution of English vs Spanish" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "337B9750FD2C4E6880194D2B835B2D8A" }, "cell_type" : "code", "source" : "BarChart(esLetterFreq)", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res53: notebook.front.widgets.charts.BarChart[Seq[(String, Double)]] = <BarChart widget>\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon282802a81a0aa478f935e3ad5cfbd85d&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.10811093644933893},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.011199629499334257},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.055396417899813415},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.056432327964339314},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12497161127711401},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.007402220332114659},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.008447036662644004},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.002932388081634834},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.07469295647953117},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.004328445277675108},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.000041748121891156525},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.05847687265375555},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.023087268047434772},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07069237311910795},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08934988711307841},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.028785051723139814},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.004136403916975788},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06678196570196962},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.0724157355907749},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.05165856938649187},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.03707344552259742},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.006658547120826858},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.000015585965506031768},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0015324343942180521},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.006487101500260509},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.0020684802793005017},{&quot;_1&quot;:&quot;º&quot;,&quot;_2&quot;:0.000009462907628662146},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:5.566416252154203E-7},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.0061186047443679},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.000002226566500861681}],&quot;genId&quot;:&quot;87745245&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon3ce7cb6e0774d3bb8a3d85bfffbfd278&quot;,&quot;initialValue&quot;:&quot;30&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonb0edb899ecd306f7f0e508e088dd756a&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 45, "time" : "Took: 666 milliseconds, at 2017-3-6 21:33" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "D8A1475EE43E4B4992A2AC970C408964" }, "cell_type" : "code", "source" : "BarChart(freqOrdered.take(30))", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res55: notebook.front.widgets.charts.BarChart[List[(String, Double)]] = <BarChart widget>\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon1e1e095854542a03509741e3af2c253a&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.07892123856464399},{&quot;_1&quot;:&quot;b&quot;,&quot;_2&quot;:0.013841232220396755},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.04509203647343646},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.030138166931509062},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.12519294591529234},{&quot;_1&quot;:&quot;f&quot;,&quot;_2&quot;:0.026006625548762423},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.014792270791624396},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.044525243819595946},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.08238364141835822},{&quot;_1&quot;:&quot;j&quot;,&quot;_2&quot;:0.002664583838329428},{&quot;_1&quot;:&quot;k&quot;,&quot;_2&quot;:0.002872866672053292},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.040070504936243305},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.024439716184713356},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.07904632796765632},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.08121653933283657},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.025622379631375296},{&quot;_1&quot;:&quot;q&quot;,&quot;_2&quot;:0.0011192209743207616},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.06635484092997088},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.05571326396557348},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.10015710989612792},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.02913805022130051},{&quot;_1&quot;:&quot;v&quot;,&quot;_2&quot;:0.00852882293265821},{&quot;_1&quot;:&quot;w&quot;,&quot;_2&quot;:0.0074197766944676365},{&quot;_1&quot;:&quot;x&quot;,&quot;_2&quot;:0.0023743046016740433},{&quot;_1&quot;:&quot;y&quot;,&quot;_2&quot;:0.011944541702951571},{&quot;_1&quot;:&quot;z&quot;,&quot;_2&quot;:0.00021187391706393026},{&quot;_1&quot;:&quot;à&quot;,&quot;_2&quot;:0.000004189597230077717},{&quot;_1&quot;:&quot;á&quot;,&quot;_2&quot;:0.000010174736130188742},{&quot;_1&quot;:&quot;ã&quot;,&quot;_2&quot;:0.0000023940555600444098},{&quot;_1&quot;:&quot;ä&quot;,&quot;_2&quot;:0.000004189597230077717}],&quot;genId&quot;:&quot;984514945&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonea265a9e3042917a56a3d21a88bde60e&quot;,&quot;initialValue&quot;:&quot;30&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anona4c885fb7ed069c306713b8174ec432e&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 46, "time" : "Took: 624 milliseconds, at 2017-3-6 21:33" } ] }, { "metadata" : { "id" : "3507E4C4387C462EB08775FD2419E945" }, "cell_type" : "markdown", "source" : "### Let's create a classifier for 6 common european languages" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "C0AA0B4BBA274409867FBF255D234787" }, "cell_type" : "code", "source" : "val languages = Seq(\"en\", \"es\", \"it\", \"de\", \"fr\", \"nl\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "languages: Seq[String] = List(en, es, it, de, fr, nl)\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 51, "time" : "Took: 554 milliseconds, at 2017-3-6 21:38" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "393F496631B549A583C26FE8AE7F850B" }, "cell_type" : "code", "source" : "val langDataset = languages.map{lang => (lang, sparkContext.textFile(baseFolder + s\"/$lang\"))}", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "langDataset: Seq[(String, org.apache.spark.rdd.RDD[String])] = List((en,/home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data/en MapPartitionsRDD[95] at textFile at <console>:76), (es,/home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data/es MapPartitionsRDD[97] at textFile at <console>:76), (it,/home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data/it MapPartitionsRDD[99] at textFile at <console>:76), (de,/home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data/de MapPartitionsRDD[101] at textFile at <console>:76), (fr,/home/maasg/playground/sparkfun/spark-notebooks/languageclassfication/data/fr MapPartitionsRDD[103] at textFile at <console>:76), (nl,/home/maasg/playground/sparkfun/spark-notebooks/languagecl..." }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 52, "time" : "Took: 563 milliseconds, at 2017-3-6 21:38" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "770E09E738CD472A8047D1F6CBAA32F1" }, "cell_type" : "code", "source" : "val langLetterFreq = langDataset.map{case (lang, rdd) => (lang, letterFreq(rdd))}\n", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "langLetterFreq: Seq[(String, Seq[(String, Double)])] = List((en,List((a,0.07892123856464399), (b,0.013841232220396755), (c,0.04509203647343646), (d,0.030138166931509062), (e,0.12519294591529234), (f,0.026006625548762423), (g,0.014792270791624396), (h,0.044525243819595946), (i,0.08238364141835822), (j,0.002664583838329428), (k,0.002872866672053292), (l,0.040070504936243305), (m,0.024439716184713356), (n,0.07904632796765632), (o,0.08121653933283657), (p,0.025622379631375296), (q,0.0011192209743207616), (r,0.06635484092997088), (s,0.05571326396557348), (t,0.10015710989612792), (u,0.02913805022130051), (v,0.00852882293265821), (w,0.0074197766944676365), (x,0.0023743046016740433), (y,0.011944541702951571), (z,2.1187391706393026E-4), (à,4.189597230077717E-6), (á,1.0174736130188742E-5), (ã,2.3..." }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 53, "time" : "Took: 2 seconds 527 milliseconds, at 2017-3-6 21:38" } ] }, { "metadata" : { "id" : "988317526ABC47D3828CC9D10180B436" }, "cell_type" : "markdown", "source" : "#### This case class helps to provide a schema to the dataset to help us plotting it." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "944D0547A7CD437F97DC6248A324D550" }, "cell_type" : "code", "source" : "case class LanguageLetterFrequency(lang: String, letter: String, freq: Double)", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "defined class LanguageLetterFrequency\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 54, "time" : "Took: 556 milliseconds, at 2017-3-6 21:39" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "CAB89D26080541CA9731BADBC4876794" }, "cell_type" : "code", "source" : "val langByLetterFreq = langLetterFreq.flatMap{case (lang, freqList) => freqList.take(30).map{case (letter, freq) => LanguageLetterFrequency(lang, letter, freq)}}", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "langByLetterFreq: Seq[LanguageLetterFrequency] = List(LanguageLetterFrequency(en,a,0.07892123856464399), LanguageLetterFrequency(en,b,0.013841232220396755), LanguageLetterFrequency(en,c,0.04509203647343646), LanguageLetterFrequency(en,d,0.030138166931509062), LanguageLetterFrequency(en,e,0.12519294591529234), LanguageLetterFrequency(en,f,0.026006625548762423), LanguageLetterFrequency(en,g,0.014792270791624396), LanguageLetterFrequency(en,h,0.044525243819595946), LanguageLetterFrequency(en,i,0.08238364141835822), LanguageLetterFrequency(en,j,0.002664583838329428), LanguageLetterFrequency(en,k,0.002872866672053292), LanguageLetterFrequency(en,l,0.040070504936243305), LanguageLetterFrequency(en,m,0.024439716184713356), LanguageLetterFrequency(en,n,0.07904632796765632), LanguageLetterFreque..." }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 55, "time" : "Took: 549 milliseconds, at 2017-3-6 21:40" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "A22112C532A34B759772E92B8770615A" }, "cell_type" : "code", "source" : "CustomPlotlyChart(langByLetterFreq,\n layout=\"{title: 'Language Frequency Distribution Comparison'}\",\n dataOptions=\"\"\"{type: 'bar', splitBy: 'lang'}\"\"\",\n dataSources=\"{x: 'letter', y: 'freq'}\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res68: notebook.front.widgets.charts.CustomPlotlyChart[Seq[LanguageLetterFrequency]] = <CustomPlotlyChart widget>\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script 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008857500416544541},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;w&quot;,&quot;freq&quot;:0.000011553790190467271},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;x&quot;,&quot;freq&quot;:0.004864753764407272},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;y&quot;,&quot;freq&quot;:0.0013408477563147544},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;z&quot;,&quot;freq&quot;:0.00008999794464153454},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;º&quot;,&quot;freq&quot;:0.000007905224867161817},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;à&quot;,&quot;freq&quot;:0.00475590489892866},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;á&quot;,&quot;freq&quot;:0.000010337601749365454},{&quot;lang&quot;:&quot;fr&quot;,&quot;letter&quot;:&quot;â&quot;,&quot;freq&quot;:0.000049255631864623634},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;a&quot;,&quot;freq&quot;:0.07266998140289496},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;b&quot;,&quot;freq&quot;:0.01743989051437275},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;c&quot;,&quot;freq&quot;:0.014131040610117487},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;d&quot;,&quot;freq&quot;:0.06408275435817914},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;e&quot;,&quot;freq&quot;:0.1976199016615989},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;f&quot;,&quot;freq&quot;:0.009007179475859634},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;g&quot;,&quot;freq&quot;:0.034438858568188464},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;h&quot;,&quot;freq&quot;:0.020866834794797225},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;i&quot;,&quot;freq&quot;:0.07223292184249301},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;j&quot;,&quot;freq&quot;:0.00905794902075481},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;k&quot;,&quot;freq&quot;:0.020905463796347903},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;l&quot;,&quot;freq&quot;:0.038258714978671275},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;m&quot;,&quot;freq&quot;:0.018432655854225186},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;n&quot;,&quot;freq&quot;:0.10600405052673403},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;o&quot;,&quot;freq&quot;:0.05133628753221383},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;p&quot;,&quot;freq&quot;:0.016104982589357157},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;q&quot;,&quot;freq&quot;:0.00010650567570401356},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;r&quot;,&quot;freq&quot;:0.06304970448813814},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;s&quot;,&quot;freq&quot;:0.03548680819597044},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;t&quot;,&quot;freq&quot;:0.06611188062534835},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;u&quot;,&quot;freq&quot;:0.01867822593551164},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;v&quot;,&quot;freq&quot;:0.03466456230582029},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;w&quot;,&quot;freq&quot;:0.009461346165519753},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;x&quot;,&quot;freq&quot;:0.0003068246408882463},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;y&quot;,&quot;freq&quot;:0.00017934893577100728},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;z&quot;,&quot;freq&quot;:0.008051939451799283},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;à&quot;,&quot;freq&quot;:5.51842879295407E-7},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;á&quot;,&quot;freq&quot;:0.000009381328948021919},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;ã&quot;,&quot;freq&quot;:0.000002207371517181628},{&quot;lang&quot;:&quot;nl&quot;,&quot;letter&quot;:&quot;ä&quot;,&quot;freq&quot;:0.000003862900155067849}],&quot;genId&quot;:&quot;926213187&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/customPlotlyChart'], \n function(playground, _magiccustomPlotlyChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magiccustomPlotlyChart,\n \"o\": {\"js\":\"var layout = {title: 'Language Frequency Distribution Comparison'}; var dataSources={x: 'letter', y: 'freq'}; var dataOptions = {type: 'bar', splitBy: 'lang'}\",\"headers\":[\"lang\",\"letter\",\"freq\"],\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anone873f3bdad15fccdb5286964ef3c9357&quot;,&quot;initialValue&quot;:&quot;180&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon43840ecddc97e9e4627feaf06e974c31&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 57, "time" : "Took: 704 milliseconds, at 2017-3-6 21:40" } ] }, { "metadata" : { "id" : "D0AFDA1AE6BB40E993942FC46C59FDF1" }, "cell_type" : "markdown", "source" : "#Naive Language Prediction" }, { "metadata" : { "id" : "4580665C68B2443F8C3386112625FA93" }, "cell_type" : "markdown", "source" : "## We first need to apply the same process to the text to evaluate\nAgain, API parity between Spark and Scala to the rescue" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "9657B6F72C124AB1A7A37F65B64A1E08" }, "cell_type" : "code", "source" : "def sentenceFreq(str: String):List[(String, Double)] = {\n val cleaned = str.collect{case char if Character.isAlphabetic(char) => Character.toLowerCase(char)}\n val total = cleaned.size\n val freq = cleaned.groupBy(identity).map{case (letter, group) => (letter, group.size)}\n val ordered = freq.map{case (k,v) => (k.toString, v.toDouble/total)}.toList.sortBy{case (k,v)=> k}\n ordered\n}\n\n", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "sentenceFreq: (str: String)List[(String, Double)]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 58, "time" : "Took: 516 milliseconds, at 2017-3-6 21:43" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab1556246145-1\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "CD5EF3BF238040108CBE4116F3F1ACBB" }, "cell_type" : "code", "source" : "sentenceFreq(\"estamos haciendo un modelo para prediccion de lenguage\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res244: List[(String, Double)] = List((a,0.10638297872340426), (c,0.06382978723404255), (d,0.0851063829787234), (e,0.14893617021276595), (g,0.0425531914893617), (h,0.02127659574468085), (i,0.06382978723404255), (l,0.0425531914893617), (m,0.0425531914893617), (n,0.0851063829787234), (o,0.10638297872340426), (p,0.0425531914893617), (r,0.0425531914893617), (s,0.0425531914893617), (t,0.02127659574468085), (u,0.0425531914893617))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon90846ca7b2a27ce14410b576305cabf3&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;1556246145&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul1556246145\"><li>\n <a 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type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anondf24eb6e3b14bf16662b34fb465dcc0e&quot;,&quot;initialValue&quot;:&quot;16&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries 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type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon6cb3f3b1f4dfbb8fa2ec3d90cc585ca3&quot;,&quot;initialValue&quot;:&quot;16&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon55deec4aab5abc4ad34b85905be1f928&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1556246145-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon9e80c797918621fac0da6de60f479419&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;a&quot;,&quot;_2&quot;:0.10638297872340426},{&quot;_1&quot;:&quot;c&quot;,&quot;_2&quot;:0.06382978723404255},{&quot;_1&quot;:&quot;d&quot;,&quot;_2&quot;:0.0851063829787234},{&quot;_1&quot;:&quot;e&quot;,&quot;_2&quot;:0.14893617021276595},{&quot;_1&quot;:&quot;g&quot;,&quot;_2&quot;:0.0425531914893617},{&quot;_1&quot;:&quot;h&quot;,&quot;_2&quot;:0.02127659574468085},{&quot;_1&quot;:&quot;i&quot;,&quot;_2&quot;:0.06382978723404255},{&quot;_1&quot;:&quot;l&quot;,&quot;_2&quot;:0.0425531914893617},{&quot;_1&quot;:&quot;m&quot;,&quot;_2&quot;:0.0425531914893617},{&quot;_1&quot;:&quot;n&quot;,&quot;_2&quot;:0.0851063829787234},{&quot;_1&quot;:&quot;o&quot;,&quot;_2&quot;:0.10638297872340426},{&quot;_1&quot;:&quot;p&quot;,&quot;_2&quot;:0.0425531914893617},{&quot;_1&quot;:&quot;r&quot;,&quot;_2&quot;:0.0425531914893617},{&quot;_1&quot;:&quot;s&quot;,&quot;_2&quot;:0.0425531914893617},{&quot;_1&quot;:&quot;t&quot;,&quot;_2&quot;:0.02127659574468085},{&quot;_1&quot;:&quot;u&quot;,&quot;_2&quot;:0.0425531914893617}],&quot;genId&quot;:&quot;1615295750&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon73aac1d9e7d796041dc2c7ab6deae8e1&quot;,&quot;initialValue&quot;:&quot;16&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon9891720128470c0bbb672d88b0fd9a19&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 187, "time" : "Took: 717 milliseconds, at 2017-3-5 21:34" } ] }, { "metadata" : { "id" : "B7E80377289A454E8265A0FF3B9CBA5B" }, "cell_type" : "markdown", "source" : "## Our classifier will use Euclidean distance between the sample and each of our pre-calculated models\nThe closer we are from a model, the higher likehood that we have a match " }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "0B2E891AEBDF45B586683CB38E20464A" }, "cell_type" : "code", "source" : "def classify(str:String): Seq[(String, Double)] = {\n val strFreq = sentenceFreq(str).toMap\n langLetterFreq.map{case (lang, refFreq) => \n val freqMap = refFreq.toMap\n val score = strFreq.map{case (letter, freq) => \n val modelFreq = freqMap(letter)\n (freq - modelFreq)*(freq-modelFreq)\n }.sum\n (lang, Math.sqrt(score))\n }.sortBy(_._2)\n}\n ", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "classify: (str: String)Seq[(String, Double)]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 59, "time" : "Took: 585 milliseconds, at 2017-3-6 21:56" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab1668356079-1\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "CBCB400234D543D286DFFBEF8094D4F5" }, "cell_type" : "code", "source" : "classify(\"estamos haciendo un modelo para clasificar el lenguage usado en un documento\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res72: Seq[(String, Double)] = List((es,0.08238430605362929), (fr,0.11604577757832807), (it,0.12226442259056455), (en,0.1267000540758025), (nl,0.14883738620927084), (de,0.1525148741851856))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon62a398ff40fc07d611318517d3cd5e95&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;1668356079&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul1668356079\"><li>\n <a href=\"#tab1668356079-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab1668356079-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab1668356079-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab1668356079-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab1668356079\"><div class=\"tab-pane\" id=\"tab1668356079-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonc3462bf7612098e5a00094b612c3f8f6&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.08238430605362929},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11604577757832807},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.12226442259056455},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.1267000540758025},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.14883738620927084},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.1525148741851856}],&quot;genId&quot;:&quot;983397085&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonfc2d20799ea89528ef5d5b50427eaac8&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon143a5cafecc455a360ee86482f56915b&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1668356079-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonec6145b4d9b9632a78de827c339acfa7&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.08238430605362929},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11604577757832807},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.12226442259056455},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.1267000540758025},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.14883738620927084},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.1525148741851856}],&quot;genId&quot;:&quot;550840147&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anond06c194e410f7e7a138508cc5156fbbf&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon7b9ee0ab5794f861f609b0b1c511b062&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1668356079-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonea7e65c8bba8686a07661c9d66d3e6f2&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.08238430605362929},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11604577757832807},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.12226442259056455},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.1267000540758025},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.14883738620927084},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.1525148741851856}],&quot;genId&quot;:&quot;1426437916&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon01551abfc16b4816156997eff3ba97af&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon8a5b6da409e987fb4901e951f60f2a49&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1668356079-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon7e24ced25d741cb1383ae5549769cd6a&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.08238430605362929},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11604577757832807},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.12226442259056455},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.1267000540758025},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.14883738620927084},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.1525148741851856}],&quot;genId&quot;:&quot;1243750179&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon852eadd45d8bbdc8593881b887fb6035&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anone263428305433343471551604a5bece5&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 60, "time" : "Took: 650 milliseconds, at 2017-3-6 21:56" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab1410281581-1\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "D897ABDAB4BB40CD81B9ABE9C3D19C97" }, "cell_type" : "code", "source" : "classify(\"wij maken een model om de taal te bepalen die in een document gebruikt wordt\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res74: Seq[(String, Double)] = List((nl,0.07504696135635662), (de,0.10206072502408552), (fr,0.11896964686997333), (en,0.13026324061404027), (es,0.13912996402195157), (it,0.1538555940616276))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonb182b356ca20b50829f9035f00a0230f&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;1410281581&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul1410281581\"><li>\n <a href=\"#tab1410281581-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab1410281581-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab1410281581-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab1410281581-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab1410281581\"><div class=\"tab-pane\" id=\"tab1410281581-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon781dcdcdd0d659914c9675f5f89ecb42&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.07504696135635662},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.10206072502408552},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11896964686997333},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.13026324061404027},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.13912996402195157},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.1538555940616276}],&quot;genId&quot;:&quot;699745996&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon43c7c54ffdad387cb43aaaabeba76807&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon5f92ef4f9d0099eba198148e974c9717&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1410281581-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon6bfdbd78733a03bd51e322d6b19125c1&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.07504696135635662},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.10206072502408552},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11896964686997333},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.13026324061404027},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.13912996402195157},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.1538555940616276}],&quot;genId&quot;:&quot;527840136&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anond913c0e83c9c55d287d1cdebdf006b8d&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon93a3dbe0a74bb4c82493c6baea38238f&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1410281581-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon7dfb36865134a0d463ed8971295280a7&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.07504696135635662},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.10206072502408552},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11896964686997333},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.13026324061404027},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.13912996402195157},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.1538555940616276}],&quot;genId&quot;:&quot;1076247851&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon7ccf4ced3eada9b1ff097e648923e48a&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon2d66622dddf5499c736fbf75a2188d83&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1410281581-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonb92e683e995f9a5de0c81510d92589fb&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.07504696135635662},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.10206072502408552},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.11896964686997333},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.13026324061404027},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.13912996402195157},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.1538555940616276}],&quot;genId&quot;:&quot;1786529973&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anona2729097a2cc86e3106a38464613a89b&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon47e1cecba4485014e22fd0f6c3df6948&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 61, "time" : "Took: 682 milliseconds, at 2017-3-6 21:56" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab1084104420-0\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "27DE7E84356D49AB9D0E4BF9B30E4777" }, "cell_type" : "code", "source" : "classify(\"language\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res76: Seq[(String, Double)] = List((it,0.3036339326924917), (es,0.30591839606321103), (nl,0.3199615720241069), (de,0.3202349895470698), (en,0.32109956126123407), (fr,0.32133700290867645))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon1ca70edf5c278918c54377f891890fa4&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;1084104420&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul1084104420\"><li>\n <a href=\"#tab1084104420-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab1084104420-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab1084104420-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab1084104420-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab1084104420\"><div class=\"tab-pane\" id=\"tab1084104420-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon8eeed658e04e5ba3f648ed8dc1e5970a&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.3036339326924917},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.30591839606321103},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.3199615720241069},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.3202349895470698},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.32109956126123407},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.32133700290867645}],&quot;genId&quot;:&quot;2056517328&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anondf28567de42cd295c565ea1ed9b273a1&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonead9fa1d0930184e2558d42e80462b55&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1084104420-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon4ba6d74952116348bb0f48d8a2e2e52f&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.3036339326924917},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.30591839606321103},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.3199615720241069},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.3202349895470698},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.32109956126123407},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.32133700290867645}],&quot;genId&quot;:&quot;886372120&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon04bc68e6c1c05a7e8d2f2412fad5922c&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon0c380ce6d5a37082094910ef971b93b4&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1084104420-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon585befb80abf86c01a7ff80c5fa568f3&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.3036339326924917},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.30591839606321103},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.3199615720241069},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.3202349895470698},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.32109956126123407},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.32133700290867645}],&quot;genId&quot;:&quot;59829172&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon2fe50b5740b8388b8bb4ccfdce9b2ee5&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon85f2cbe8e4bd26dd505f282b09d9ba36&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1084104420-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonac69a4b969c7a28b60b92722780b3f64&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.3036339326924917},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.30591839606321103},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.3199615720241069},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.3202349895470698},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.32109956126123407},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.32133700290867645}],&quot;genId&quot;:&quot;799402432&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonf92461289be0cd8ea5b9f4d0582375bd&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon5b47d2399cd0f83b6d4720f9dcc13305&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 62, "time" : "Took: 656 milliseconds, at 2017-3-6 21:57" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab2029337499-0\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "A7746E9B9DB24A3CB21E9BBA0A4CCDA3" }, "cell_type" : "code", "source" : "classify(\"these little sentences\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res78: Seq[(String, Double)] = List((nl,0.2190859315831615), (de,0.22205888191068252), (fr,0.22548803266578976), (en,0.23355825718020554), (es,0.25317339037675696), (it,0.2637927804402644))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon6f755cc610cc60dbe448a54b01f7e616&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;2029337499&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul2029337499\"><li>\n <a href=\"#tab2029337499-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab2029337499-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab2029337499-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab2029337499-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab2029337499\"><div class=\"tab-pane\" id=\"tab2029337499-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon0530ed9715280dff4ba1eba0e2867290&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2190859315831615},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.22205888191068252},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22548803266578976},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.23355825718020554},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.25317339037675696},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2637927804402644}],&quot;genId&quot;:&quot;197349236&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon51f21dc32d8f964f7fdd3b4ff6326a18&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon3b453e5af82057b41c8417fe134215e6&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab2029337499-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon90dbef741ddebb1ecab966ad20373399&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2190859315831615},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.22205888191068252},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22548803266578976},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.23355825718020554},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.25317339037675696},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2637927804402644}],&quot;genId&quot;:&quot;70108984&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon830d24b8aabfe096243b7977ae9797ac&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anona9872e627e6b3512bd12b2789a74862c&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab2029337499-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon181b2982126ccee9059105072719aa59&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2190859315831615},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.22205888191068252},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22548803266578976},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.23355825718020554},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.25317339037675696},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2637927804402644}],&quot;genId&quot;:&quot;1330218261&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon625ef3a92c4498e809a3d0fb19f353ee&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonae9d79e5456e25f7ddbe11bf172cdb26&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab2029337499-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anone73365a61cf0a8451018e89cb14086fa&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2190859315831615},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.22205888191068252},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22548803266578976},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.23355825718020554},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.25317339037675696},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2637927804402644}],&quot;genId&quot;:&quot;1037041534&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonc66302d6a4b54768a1c5e857a9d09db2&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon3c289c6bc81d5b61408c2705d129e5eb&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 63, "time" : "Took: 561 milliseconds, at 2017-3-6 21:57" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab433869420-0\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "E1F0E3038747454B8FC476C9F20AEBB8" }, "cell_type" : "code", "source" : "classify(\"are too small\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res80: Seq[(String, Double)] = List((it,0.18531944139887466), (es,0.1934450899958764), (en,0.21982178935422475), (fr,0.22420064355561053), (nl,0.2658023366151163), (de,0.2732915571268681))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon8e2f9900e02353114dd910c8694779b7&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;433869420&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul433869420\"><li>\n <a href=\"#tab433869420-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab433869420-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab433869420-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab433869420-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab433869420\"><div class=\"tab-pane\" id=\"tab433869420-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonfcd392434e32fd52eb224a43f675e2ba&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.18531944139887466},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.1934450899958764},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.21982178935422475},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22420064355561053},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2658023366151163},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.2732915571268681}],&quot;genId&quot;:&quot;1990952752&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonc243fe82396420f4e77c83bcbc6761b1&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonc873f0edb74ab4776859b86ff186a4f8&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab433869420-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon8f74c041a66e3e5c8c4b6e3772da6b9e&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.18531944139887466},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.1934450899958764},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.21982178935422475},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22420064355561053},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2658023366151163},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.2732915571268681}],&quot;genId&quot;:&quot;1607795996&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonbd0ca7829ddb38e4e48a15a37139d955&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon24261166e95d6d7209ba6a45bd879705&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab433869420-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon1af03dda7691d78dea0b685d7dac46c6&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.18531944139887466},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.1934450899958764},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.21982178935422475},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22420064355561053},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2658023366151163},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.2732915571268681}],&quot;genId&quot;:&quot;107673991&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon7f127f3ef255e39e5a4b7775aee6872d&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon25bafc53fbd94f3ea41b1c9e8ba77aba&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab433869420-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonb012f13dc315640ad8ce4c4be06b8892&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.18531944139887466},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.1934450899958764},{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.21982178935422475},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.22420064355561053},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.2658023366151163},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.2732915571268681}],&quot;genId&quot;:&quot;1212414218&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon5aa6de65fb21682ee7a8cb1760703ad2&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anone5c70d31a2aa1fc8f03a24b6e6b1f47f&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 64, "time" : "Took: 537 milliseconds, at 2017-3-6 21:57" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab1584325800-0\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "110C5EE4214344AF8F378BEA1908A8EC" }, "cell_type" : "code", "source" : "classify(\"thus too little\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res90: Seq[(String, Double)] = List((en,0.2596796104106738), (it,0.2768858204608613), (fr,0.28314036779773477), (es,0.2969931139658706), (de,0.31659343865450057), (nl,0.32414700650827355))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon2cfbee092f8b588036708b0e7ec266ca&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;1584325800&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul1584325800\"><li>\n <a href=\"#tab1584325800-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab1584325800-1\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab1584325800-2\"><i class=\"fa fa-pie-chart\"/></a>\n </li><li>\n <a href=\"#tab1584325800-3\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab1584325800\"><div class=\"tab-pane\" id=\"tab1584325800-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anondd8aa3091f2dbc2050b4b3ade59caf35&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.2596796104106738},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2768858204608613},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.28314036779773477},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.2969931139658706},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.31659343865450057},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.32414700650827355}],&quot;genId&quot;:&quot;1934031265&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"_1\",\"_2\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonc14f046803be7c721cff27b794f479fe&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anond637cf90b37e988fbb29c1b099319cef&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1584325800-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon2f13bfef0d3d07cb99c948ff1c7e5aa4&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.2596796104106738},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2768858204608613},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.28314036779773477},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.2969931139658706},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.31659343865450057},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.32414700650827355}],&quot;genId&quot;:&quot;520775561&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"_1\",\"y\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonfb535eb6b72785e68728b607d21c451f&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonb87c5f9c2ad9e5d07b8eefa5884f49a8&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1584325800-2\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonb5cc9c682a673c991dfd4c4ee9afcb84&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.2596796104106738},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2768858204608613},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.28314036779773477},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.2969931139658706},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.31659343865450057},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.32414700650827355}],&quot;genId&quot;:&quot;1499956121&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pieChart'], \n function(playground, _magicpieChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpieChart,\n \"o\": {\"series\":\"_1\",\"p\":\"_2\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon6f940dd6afbbba197d92fa8449c9051c&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon4764619fcc01627a786e2641efe3ee75&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1584325800-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon1e6f4eb6f481dcc1ca1dae4b6511239c&quot;,&quot;dataInit&quot;:[{&quot;_1&quot;:&quot;en&quot;,&quot;_2&quot;:0.2596796104106738},{&quot;_1&quot;:&quot;it&quot;,&quot;_2&quot;:0.2768858204608613},{&quot;_1&quot;:&quot;fr&quot;,&quot;_2&quot;:0.28314036779773477},{&quot;_1&quot;:&quot;es&quot;,&quot;_2&quot;:0.2969931139658706},{&quot;_1&quot;:&quot;de&quot;,&quot;_2&quot;:0.31659343865450057},{&quot;_1&quot;:&quot;nl&quot;,&quot;_2&quot;:0.32414700650827355}],&quot;genId&quot;:&quot;1436322992&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anona2a9a36cc48d86b416a3e3d3ad51b799&quot;,&quot;initialValue&quot;:&quot;6&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonbfd707eb09695430e933b5a5994c4b40&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 69, "time" : "Took: 553 milliseconds, at 2017-3-6 21:59" } ] }, { "metadata" : { "id" : "A30259B131B241D39A18EFABF8F1E9A3" }, "cell_type" : "markdown", "source" : "#Let's evaluate our classification model" }, { "metadata" : { "id" : "96C943D6FBBB4757A995A642EF5B2C8F" }, "cell_type" : "markdown", "source" : "## We need a test dataset" }, { "metadata" : { "id" : "DDD38FD5736C4E7080527B4C611DE253" }, "cell_type" : "markdown", "source" : "We want to see how our model performs with sentences of different lengths. We'll randomly sample the original text in order to create a labeled sample. \n\nNote that we are keeping only those samples where the sentence size matches the desired length." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "3C2764EC04334DB2880BFB48B85C638D" }, "cell_type" : "code", "source" : "val sampleDataByLanguage = langDataset.map{case (lang, rdd) => \n val sample = rdd.sample(withReplacement = false, fraction = 0.7)\n .map(str => str.collect{case char if Character.isAlphabetic(char) || Character.isWhitespace(char) => Character.toLowerCase(char)})\n sample.flatMap{str => \n val sentenceLength = scala.util.Random.nextInt(30)+1\n val sampledWords = str.split(\"\\\\W\").take(sentenceLength)\n if (sampledWords.size == sentenceLength) {\n Some((lang, sentenceLength, sampledWords.mkString(\" \")))\n } else {\n None\n }\n } \n }", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "sampleDataByLanguage: Seq[org.apache.spark.rdd.RDD[(String, Int, String)]] = List(MapPartitionsRDD[176] at flatMap at <console>:83, MapPartitionsRDD[179] at flatMap at <console>:83, MapPartitionsRDD[182] at flatMap at <console>:83, MapPartitionsRDD[185] at flatMap at <console>:83, MapPartitionsRDD[188] at flatMap at <console>:83, MapPartitionsRDD[191] at flatMap at <console>:83)\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 75, "time" : "Took: 641 milliseconds, at 2017-3-6 22:4" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "63DA0724BCB445BB88F6E4ED9870D1EC" }, "cell_type" : "code", "source" : "val testDataset = sparkContext.union(sampleDataByLanguage).coalesce(8).toDF(\"language\", \"sentenceSize\", \"sentence\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "testDataset: org.apache.spark.sql.DataFrame = [language: string, sentenceSize: int ... 1 more field]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 76, "time" : "Took: 520 milliseconds, at 2017-3-6 22:4" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "6900F4E571094472A152E0575EC155C3" }, "cell_type" : "code", "source" : "<h3>Our test dataset contains {testDataset.count()} records</h3>", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res101: scala.xml.Elem = <h3>Our test dataset contains 48844 records</h3>\n" }, { "metadata" : { }, "data" : { "text/html" : "<h3>Our test dataset contains 48844 records</h3>" }, "output_type" : "execute_result", "execution_count" : 77, "time" : "Took: 882 milliseconds, at 2017-3-6 22:4" } ] }, { "metadata" : { "id" : "C16E50D6CF32498F80F0E80A34D76D8B" }, "cell_type" : "markdown", "source" : "#### We can easily use shell functions\nIn this case, we clean up the target save dir to avoid issues when writing our dataset" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "06E7C327B8EB46D58FFFF77ACD2F7E7C" }, "cell_type" : "code", "source" : ":sh rm -rf /tmp/testDataset", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "\nimport sys.process._\n" }, { "metadata" : { }, "data" : { "text/plain" : "" }, "output_type" : "execute_result", "execution_count" : 80, "time" : "Took: 717 milliseconds, at 2017-3-6 22:6" } ] }, { "metadata" : { "id" : "D21391A771A84B74AEBD0F35174C98A9" }, "cell_type" : "markdown", "source" : "### We save our test dataset for potential use later on..." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "17DE60116B3441F5A810DBE5C311C7A1" }, "cell_type" : "code", "source" : "testDataset.write.parquet(\"/tmp/testDataset\")", "outputs" : [ { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 81, "time" : "Took: 1 second 365 milliseconds, at 2017-3-6 22:6" } ] }, { "metadata" : { "id" : "4B2DEFD0B4B04C2E89C82A3A6E566568" }, "cell_type" : "markdown", "source" : "### Lets do some sanity checks on out test data" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab1431351083-3\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "E2F623D45F914253BA2017D52E5948DB" }, "cell_type" : "code", "source" : "testDataset.select($\"language\", $\"sentenceSize\").groupBy($\"sentenceSize\").count().orderBy($\"sentenceSize\").collect()", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res117: Array[org.apache.spark.sql.Row] = Array([1,4530], [2,4052], [3,3894], [4,3615], [5,3456], [6,3240], [7,3106], [8,2997], [9,2930], [10,2722], [11,2541], [12,2299], [13,2158], [14,1897], [15,1557], [16,1232], [17,898], [18,668], [19,434], [20,263], [21,139], [22,101], [23,49], [24,28], [25,13], [26,6], [27,5], [28,1], [29,2])\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon7f186dd20e886913deb6310477830154&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;1431351083&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul1431351083\"><li>\n <a href=\"#tab1431351083-0\"><i class=\"fa fa-table\"/></a>\n </li><li>\n <a href=\"#tab1431351083-1\"><i class=\"fa fa-dot-circle-o\"/></a>\n </li><li>\n <a href=\"#tab1431351083-2\"><i class=\"fa fa-line-chart\"/></a>\n </li><li>\n <a href=\"#tab1431351083-3\"><i class=\"fa fa-bar-chart\"/></a>\n </li><li>\n <a href=\"#tab1431351083-4\"><i class=\"fa fa-cubes\"/></a>\n </li></ul>\n\n <div class=\"tab-content\" id=\"tab1431351083\"><div class=\"tab-pane\" id=\"tab1431351083-0\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anonad165c841a511f576b9ffedddd0b1655&quot;,&quot;dataInit&quot;:[{&quot;sentenceSize&quot;:1,&quot;count&quot;:4530},{&quot;sentenceSize&quot;:2,&quot;count&quot;:4052},{&quot;sentenceSize&quot;:3,&quot;count&quot;:3894},{&quot;sentenceSize&quot;:4,&quot;count&quot;:3615},{&quot;sentenceSize&quot;:5,&quot;count&quot;:3456},{&quot;sentenceSize&quot;:6,&quot;count&quot;:3240},{&quot;sentenceSize&quot;:7,&quot;count&quot;:3106},{&quot;sentenceSize&quot;:8,&quot;count&quot;:2997},{&quot;sentenceSize&quot;:9,&quot;count&quot;:2930},{&quot;sentenceSize&quot;:10,&quot;count&quot;:2722},{&quot;sentenceSize&quot;:11,&quot;count&quot;:2541},{&quot;sentenceSize&quot;:12,&quot;count&quot;:2299},{&quot;sentenceSize&quot;:13,&quot;count&quot;:2158},{&quot;sentenceSize&quot;:14,&quot;count&quot;:1897},{&quot;sentenceSize&quot;:15,&quot;count&quot;:1557},{&quot;sentenceSize&quot;:16,&quot;count&quot;:1232},{&quot;sentenceSize&quot;:17,&quot;count&quot;:898},{&quot;sentenceSize&quot;:18,&quot;count&quot;:668},{&quot;sentenceSize&quot;:19,&quot;count&quot;:434},{&quot;sentenceSize&quot;:20,&quot;count&quot;:263},{&quot;sentenceSize&quot;:21,&quot;count&quot;:139},{&quot;sentenceSize&quot;:22,&quot;count&quot;:101},{&quot;sentenceSize&quot;:23,&quot;count&quot;:49},{&quot;sentenceSize&quot;:24,&quot;count&quot;:28},{&quot;sentenceSize&quot;:25,&quot;count&quot;:13},{&quot;sentenceSize&quot;:26,&quot;count&quot;:6},{&quot;sentenceSize&quot;:27,&quot;count&quot;:5},{&quot;sentenceSize&quot;:28,&quot;count&quot;:1},{&quot;sentenceSize&quot;:29,&quot;count&quot;:2}],&quot;genId&quot;:&quot;699956666&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tableChart'], \n function(playground, _magictableChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictableChart,\n \"o\": {\"headers\":[\"sentenceSize\",\"count\"],\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anona61835e82bda17f668f070a06dea61a1&quot;,&quot;initialValue&quot;:&quot;29&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon69768729bef4249038c9a7a0564623e8&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1431351083-1\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anoncfcadaa11e2d43bfb01a63cd40c5d64b&quot;,&quot;dataInit&quot;:[{&quot;sentenceSize&quot;:1,&quot;count&quot;:4530},{&quot;sentenceSize&quot;:2,&quot;count&quot;:4052},{&quot;sentenceSize&quot;:3,&quot;count&quot;:3894},{&quot;sentenceSize&quot;:4,&quot;count&quot;:3615},{&quot;sentenceSize&quot;:5,&quot;count&quot;:3456},{&quot;sentenceSize&quot;:6,&quot;count&quot;:3240},{&quot;sentenceSize&quot;:7,&quot;count&quot;:3106},{&quot;sentenceSize&quot;:8,&quot;count&quot;:2997},{&quot;sentenceSize&quot;:9,&quot;count&quot;:2930},{&quot;sentenceSize&quot;:10,&quot;count&quot;:2722},{&quot;sentenceSize&quot;:11,&quot;count&quot;:2541},{&quot;sentenceSize&quot;:12,&quot;count&quot;:2299},{&quot;sentenceSize&quot;:13,&quot;count&quot;:2158},{&quot;sentenceSize&quot;:14,&quot;count&quot;:1897},{&quot;sentenceSize&quot;:15,&quot;count&quot;:1557},{&quot;sentenceSize&quot;:16,&quot;count&quot;:1232},{&quot;sentenceSize&quot;:17,&quot;count&quot;:898},{&quot;sentenceSize&quot;:18,&quot;count&quot;:668},{&quot;sentenceSize&quot;:19,&quot;count&quot;:434},{&quot;sentenceSize&quot;:20,&quot;count&quot;:263},{&quot;sentenceSize&quot;:21,&quot;count&quot;:139},{&quot;sentenceSize&quot;:22,&quot;count&quot;:101},{&quot;sentenceSize&quot;:23,&quot;count&quot;:49},{&quot;sentenceSize&quot;:24,&quot;count&quot;:28},{&quot;sentenceSize&quot;:25,&quot;count&quot;:13},{&quot;sentenceSize&quot;:26,&quot;count&quot;:6},{&quot;sentenceSize&quot;:27,&quot;count&quot;:5},{&quot;sentenceSize&quot;:28,&quot;count&quot;:1},{&quot;sentenceSize&quot;:29,&quot;count&quot;:2}],&quot;genId&quot;:&quot;956271253&quot;}\" 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/*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anona6fcd89e644ebf1b65cdf54fe07c812a&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1431351083-2\">\n <div>\n <script 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type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/lineChart'], \n function(playground, _magiclineChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magiclineChart,\n \"o\": {\"x\":\"sentenceSize\",\"y\":\"count\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon047d9b2d2e102f37cc93e205527fccbf&quot;,&quot;initialValue&quot;:&quot;29&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon544691394d7fd7e336d4abbd26b5e553&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1431351083-3\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anond7d638e4db53cf5a1669e0a209aac932&quot;,&quot;dataInit&quot;:[{&quot;sentenceSize&quot;:1,&quot;count&quot;:4530},{&quot;sentenceSize&quot;:2,&quot;count&quot;:4052},{&quot;sentenceSize&quot;:3,&quot;count&quot;:3894},{&quot;sentenceSize&quot;:4,&quot;count&quot;:3615},{&quot;sentenceSize&quot;:5,&quot;count&quot;:3456},{&quot;sentenceSize&quot;:6,&quot;count&quot;:3240},{&quot;sentenceSize&quot;:7,&quot;count&quot;:3106},{&quot;sentenceSize&quot;:8,&quot;count&quot;:2997},{&quot;sentenceSize&quot;:9,&quot;count&quot;:2930},{&quot;sentenceSize&quot;:10,&quot;count&quot;:2722},{&quot;sentenceSize&quot;:11,&quot;count&quot;:2541},{&quot;sentenceSize&quot;:12,&quot;count&quot;:2299},{&quot;sentenceSize&quot;:13,&quot;count&quot;:2158},{&quot;sentenceSize&quot;:14,&quot;count&quot;:1897},{&quot;sentenceSize&quot;:15,&quot;count&quot;:1557},{&quot;sentenceSize&quot;:16,&quot;count&quot;:1232},{&quot;sentenceSize&quot;:17,&quot;count&quot;:898},{&quot;sentenceSize&quot;:18,&quot;count&quot;:668},{&quot;sentenceSize&quot;:19,&quot;count&quot;:434},{&quot;sentenceSize&quot;:20,&quot;count&quot;:263},{&quot;sentenceSize&quot;:21,&quot;count&quot;:139},{&quot;sentenceSize&quot;:22,&quot;count&quot;:101},{&quot;sentenceSize&quot;:23,&quot;count&quot;:49},{&quot;sentenceSize&quot;:24,&quot;count&quot;:28},{&quot;sentenceSize&quot;:25,&quot;count&quot;:13},{&quot;sentenceSize&quot;:26,&quot;count&quot;:6},{&quot;sentenceSize&quot;:27,&quot;count&quot;:5},{&quot;sentenceSize&quot;:28,&quot;count&quot;:1},{&quot;sentenceSize&quot;:29,&quot;count&quot;:2}],&quot;genId&quot;:&quot;1970969594&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/barChart'], \n function(playground, _magicbarChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicbarChart,\n \"o\": {\"x\":\"sentenceSize\",\"y\":\"count\",\"width\":600,\"height\":400}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon39ecc5683ce8ce437f123d24f7f3f2b7&quot;,&quot;initialValue&quot;:&quot;29&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anon567e29d0a53382b08e7bfdccabd73cc7&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div><div class=\"tab-pane\" id=\"tab1431351083-4\">\n <div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anon4eb0d1f299190ee9bc301c1e7b97be87&quot;,&quot;dataInit&quot;:[{&quot;sentenceSize&quot;:1,&quot;count&quot;:4530},{&quot;sentenceSize&quot;:2,&quot;count&quot;:4052},{&quot;sentenceSize&quot;:3,&quot;count&quot;:3894},{&quot;sentenceSize&quot;:4,&quot;count&quot;:3615},{&quot;sentenceSize&quot;:5,&quot;count&quot;:3456},{&quot;sentenceSize&quot;:6,&quot;count&quot;:3240},{&quot;sentenceSize&quot;:7,&quot;count&quot;:3106},{&quot;sentenceSize&quot;:8,&quot;count&quot;:2997},{&quot;sentenceSize&quot;:9,&quot;count&quot;:2930},{&quot;sentenceSize&quot;:10,&quot;count&quot;:2722},{&quot;sentenceSize&quot;:11,&quot;count&quot;:2541},{&quot;sentenceSize&quot;:12,&quot;count&quot;:2299},{&quot;sentenceSize&quot;:13,&quot;count&quot;:2158},{&quot;sentenceSize&quot;:14,&quot;count&quot;:1897},{&quot;sentenceSize&quot;:15,&quot;count&quot;:1557},{&quot;sentenceSize&quot;:16,&quot;count&quot;:1232},{&quot;sentenceSize&quot;:17,&quot;count&quot;:898},{&quot;sentenceSize&quot;:18,&quot;count&quot;:668},{&quot;sentenceSize&quot;:19,&quot;count&quot;:434},{&quot;sentenceSize&quot;:20,&quot;count&quot;:263},{&quot;sentenceSize&quot;:21,&quot;count&quot;:139},{&quot;sentenceSize&quot;:22,&quot;count&quot;:101},{&quot;sentenceSize&quot;:23,&quot;count&quot;:49},{&quot;sentenceSize&quot;:24,&quot;count&quot;:28},{&quot;sentenceSize&quot;:25,&quot;count&quot;:13},{&quot;sentenceSize&quot;:26,&quot;count&quot;:6},{&quot;sentenceSize&quot;:27,&quot;count&quot;:5},{&quot;sentenceSize&quot;:28,&quot;count&quot;:1},{&quot;sentenceSize&quot;:29,&quot;count&quot;:2}],&quot;genId&quot;:&quot;412650475&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anonc5e77111671af2b27cb4b7372cfdd340&quot;,&quot;initialValue&quot;:&quot;29&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anond754636e3e087fcffb0914b75a6f7741&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 85, "time" : "Took: 1 second 776 milliseconds, at 2017-3-6 22:8" } ] }, { "metadata" : { "id" : "BF18914EE07242649E6A6D534663D7EE" }, "cell_type" : "markdown", "source" : "### Use an UDF to apply the predict function to our dataset\n" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "B01DFDE685CC448A80F700BE22EED85E" }, "cell_type" : "code", "source" : "val classifyUDF = {\n val func: String => String = str => classify(str).head._1\n udf(func)\n}", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "classifyUDF: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,StringType,Some(List(StringType)))\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 87, "time" : "Took: 607 milliseconds, at 2017-3-6 22:10" } ] }, { "metadata" : { "id" : "3C577ACBAE1847CBB9CD1ADE845F14BD" }, "cell_type" : "markdown", "source" : "### And we apply the classifier to the test dataset" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "D50DD2D023FF46E0AFB93BC42C9D4528" }, "cell_type" : "code", "source" : "val hitAndMiss = testDataset.withColumn(\"lang_classification\", classifyUDF($\"sentence\"))", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "hitAndMiss: org.apache.spark.sql.DataFrame = [language: string, sentenceSize: int ... 2 more fields]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 88, "time" : "Took: 472 milliseconds, at 2017-3-6 22:11" } ] }, { "metadata" : { "id" : "E2C948615EF84F278F5DC32F115AEFD7" }, "cell_type" : "markdown", "source" : "#### Let's see some of the results" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "76D1FED1200B4DF18E0C96301B4A02E1" }, "cell_type" : "code", "source" : "hitAndMiss.sample(false, 0.001)", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res122: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [language: string, sentenceSize: int ... 2 more fields]\n" }, { "metadata" : { }, "data" : { "text/html" : "<div class=\"df-canvas\">\n <script data-this=\"{&quot;dataId&quot;:&quot;anon834ac5c5e875cd6e039b2daa5e3cf000&quot;,&quot;partitionIndexId&quot;:&quot;anon8581db3c446774dce02f38d35f9fb643&quot;,&quot;numPartitions&quot;:2,&quot;dfSchema&quot;:{&quot;type&quot;:&quot;struct&quot;,&quot;fields&quot;:[{&quot;name&quot;:&quot;language&quot;,&quot;type&quot;:&quot;string&quot;,&quot;nullable&quot;:true,&quot;metadata&quot;:{}},{&quot;name&quot;:&quot;sentenceSize&quot;,&quot;type&quot;:&quot;integer&quot;,&quot;nullable&quot;:true,&quot;metadata&quot;:{}},{&quot;name&quot;:&quot;sentence&quot;,&quot;type&quot;:&quot;string&quot;,&quot;nullable&quot;:true,&quot;metadata&quot;:{}},{&quot;name&quot;:&quot;lang_classification&quot;,&quot;type&quot;:&quot;string&quot;,&quot;nullable&quot;:true,&quot;metadata&quot;:{}}]}}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/dataframe','../javascripts/notebook/consoleDir'], \n function(dataframe, extension) {\n dataframe.call(data, this, extension);\n }\n );/*]]>*/</script>\n <link rel=\"stylesheet\" href=\"/assets/stylesheets/ipython/css/dataframe.css\" type=\"text/css\"/>\n </div>" }, "output_type" : "execute_result", "execution_count" : 89, "time" : "Took: 1 second 728 milliseconds, at 2017-3-6 22:12" } ] }, { "metadata" : { "id" : "FB51DCBC56E0403FAFE43F1D263178C1" }, "cell_type" : "markdown", "source" : "## Let's evaluate the overall hit ratio\nAKA Model Accuracy" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "AC749C8104F8439D9375F975288F412C" }, "cell_type" : "code", "source" : "val hitCol = when($\"language\" === $\"lang_classification\", 1L).otherwise(0L)\nval hits = hitAndMiss.withColumn(\"hit\", hitCol).withColumn(\"counter\", lit(1))", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "hitCol: org.apache.spark.sql.Column = CASE WHEN (language = lang_classification) THEN 1 ELSE 0 END\nhits: org.apache.spark.sql.DataFrame = [language: string, sentenceSize: int ... 4 more fields]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 91, "time" : "Took: 476 milliseconds, at 2017-3-6 22:16" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "305662E192844B5BBB621DED26100CF4" }, "cell_type" : "code", "source" : "val hitRatio = hits.select(sum($\"hit\")/sum($\"counter\")).head.getDouble(0)", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "hitRatio: Double = 0.5528181242466956\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 94, "time" : "Took: 2 seconds 270 milliseconds, at 2017-3-6 22:17" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "17FBE2383F5549F396EEF0A97170E11A" }, "cell_type" : "markdown", "source" : "## We learnt that our method performs bad on small texts\n### How is the performance in relation to text length (measured in words, not characters)" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "5A8E8364FBE644CEAF55AE4FEC3CA126" }, "cell_type" : "code", "source" : "val hitRatioPerLength = hits.groupBy($\"sentenceSize\").agg(sum($\"hit\")/sum($\"counter\")).orderBy($\"sentenceSize\")", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "hitRatioPerLength: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [sentenceSize: int, (sum(hit) / sum(counter)): double]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 96, "time" : "Took: 510 milliseconds, at 2017-3-6 22:24" } ] }, { "metadata" : { "id" : "0B9C8D2BC336462BB30072272231EB1E" }, "cell_type" : "markdown", "source" : "#Model accuracy vs sentence lenght" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab187936961-0\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "9F7DA76D76C74FF4BE8D6C3E02B99776" }, "cell_type" : "code", "source" : "val ds = hitRatioPerLength.as[(Long, Double)]", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "ds: org.apache.spark.sql.Dataset[(Long, Double)] = [sentenceSize: int, (sum(hit) / sum(counter)): double]\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 97, "time" : "Took: 474 milliseconds, at 2017-3-6 22:24" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "presentation" : { "tabs_state" : "{\n \"tab_id\": \"#tab224983149-2\"\n}", "pivot_chart_state" : "{\n \"hiddenAttributes\": [],\n \"menuLimit\": 200,\n \"cols\": [],\n \"rows\": [],\n \"vals\": [],\n \"exclusions\": {},\n \"inclusions\": {},\n \"unusedAttrsVertical\": 85,\n \"autoSortUnusedAttrs\": false,\n \"inclusionsInfo\": {},\n \"aggregatorName\": \"Count\",\n \"rendererName\": \"Table\"\n}" }, "id" : "6E3C23EFF56F46E78C21B7AE89C6ED70" }, "cell_type" : "code", "source" : "ds.collect", "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "res134: Array[(Long, Double)] = Array((1,0.28963077603360604), (2,0.3569254185692542), (3,0.39979418574736303), (4,0.4559386973180077), (5,0.4881656804733728), (6,0.5452586206896551), (7,0.5520734409623298), (8,0.6035404141616566), (9,0.6347177848775293), (10,0.6615384615384615), (11,0.7100660707345511), (12,0.7177759056444819), (13,0.7181571815718157), (14,0.7434386716657739), (15,0.7503201024327785), (16,0.7451612903225806), (17,0.7407407407407407), (18,0.7348484848484849), (19,0.7562814070351759), (20,0.749034749034749), (21,0.7364864864864865), (22,0.6962025316455697), (23,0.6428571428571429), (24,0.5769230769230769), (25,0.5), (26,0.8), (27,0.75), (28,0.5), (29,1.0))\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{&quot;dataId&quot;:&quot;anona660d93d03ade6aac46d1bc22d005f08&quot;,&quot;dataInit&quot;:[],&quot;genId&quot;:&quot;224983149&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/tabs'], \n function(playground, _magictabs) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magictabs,\n \"o\": {}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <div>\n <ul class=\"nav nav-tabs\" id=\"ul224983149\"><li>\n <a href=\"#tab224983149-0\"><i class=\"fa 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type=\"text/x-scoped-javascript\">/*<![CDATA[*/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/pivotChart'], \n function(playground, _magicpivotChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magicpivotChart,\n \"o\": {\"width\":600,\"height\":400,\"derivedAttributes\":{},\"extraOptions\":{}}\n }\n \n \n \n );\n }\n );/*]]>*/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anoncaf11e3966bd6bd850b03aa840479066&quot;,&quot;initialValue&quot;:&quot;29&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p> entries total</span>\n <span class=\"chart-sampling-warning\"><p data-bind=\"text: value\"><script data-this=\"{&quot;valueId&quot;:&quot;anond345dfbf8e93322e7ea267879ba83d38&quot;,&quot;initialValue&quot;:&quot;&quot;}\" type=\"text/x-scoped-javascript\">/*<![CDATA[*/\nreq(\n['observable', 'knockout'],\nfunction (O, ko) {\n ko.applyBindings({\n value: O.makeObservable(valueId, initialValue)\n },\n this\n );\n});\n /*]]>*/</script></p></span>\n <div>\n </div>\n </div></div>\n </div></div>\n </div>\n </div></div>" }, "output_type" : "execute_result", "execution_count" : 98, "time" : "Took: 3 seconds 581 milliseconds, at 2017-3-6 22:24" } ] }, { "metadata" : { "id" : "2140F8A946F243E7A0A77C2DE9D0E541" }, "cell_type" : "markdown", "source" : "#=o=" } ], "nbformat" : 4 }
apache-2.0
Leguark/pynoddy
docs/notebooks/Stochastic-Events.ipynb
6
1874
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Stochastic events" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The parameters defining the geological events are, by their very nature, highly uncertain. In addition to these uncertainties, the kinematic approach by itself is only a limiting approximation. The picture that we obtain from the kinematic forword model is therefore a very overconfident repsresentation of reality - an aspect that (hopefully) everyone using Noddy is aware of...\n", "\n", "In order to respect the vast nature of these uncertainties, we introduce here an adapted version of the standard geological events defined in Noddy: the definition of a stochastic event:\n", "\n", "A stochastic event is geological event in the Noddy history that has an uncertainty associated to it in such a way that, recomputing the geolgoical history will result in a different result each time (note: the definition is borrowed from the notion of stochastic events in probabilistic programming, see for example pymc)." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of stochastic events\n", "-------------------------------\n", "\n", "We start, as before, with a pre-defined geological noddy history for simplicity:" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
quinngroup/fergus-ssl
Extras/notebooks/.ipynb_checkpoints/1000_size_Results-checkpoint.ipynb
1
323246
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "These are labels of known points: [0 1]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/madhura/.local/lib/python2.7/site-packages/IPython/kernel/__main__.py:46: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" ] } ], "source": [ "import numpy as np\n", "from sklearn import datasets\n", "import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt1\n", "import timeit\n", "import sys\n", "import os\n", "from sklearn.cross_validation import KFold\n", "from collections import OrderedDict\n", "import operator\n", "import random\n", "from sklearn.cluster import KMeans\n", "import numpy as np\n", "import scipy.linalg as LA\n", "import scipy.sparse\n", "import sklearn.utils.arpack as SLA\n", "from sklearn.base import ClassifierMixin\n", "from sklearn.base import BaseEstimator\n", "from sklearn.manifold import spectral_embedding\n", "from pyspark.mllib.clustering import GaussianMixture, GaussianMixtureModel\n", "import sklearn.metrics.pairwise as pairwise\n", "from sklearn import decomposition as pca\n", "from scipy import interpolate as ip\n", "import sklearn.mixture as mixture\n", "import sys\n", "from sklearn.metrics.pairwise import chi2_kernel\n", "from sklearn.neighbors import DistanceMetric\n", "from pyspark.sql import SQLContext\n", "from pyspark.sql.types import *\n", "%matplotlib inline\n", "\n", "dataX,dataY=datasets.make_blobs(n_samples=1000, n_features=50, centers=2, cluster_std=3.5, center_box=(-10.0, 10.0), shuffle=True, random_state=None)\n", "\n", "def labelremover(X,y):\n", " newX1 = np.around(X,decimals=2)\n", " newY1=np.copy(y)\n", " dim = X.shape[1]\n", " points = np.array(np.empty(len(np.unique(y))))\n", " knownX = np.empty((len(points),dim))\n", " knownY = np.empty(len(points))\n", " for i in np.unique(y):\n", " points[i] = np.where(y==(i))[0][0]\n", " for j in np.arange(0,len(newY1)):\n", " newY1[j]=-1\n", " for k in np.unique(y):\n", " newY1[points[k]] = y[points[k]]\n", " knownX = X[[i for i in points]]\n", " knownY = y[[i for i in points]]\n", " print \"These are labels of known points: \"+ str(knownY)\n", " return (newY1, knownX, knownY)\n", "\n", "trainX = dataX[0:800,:]\n", "trainY = dataY[0:800]\n", "testX = dataX[800:1000,:]\n", "testY = dataY[800:1000]\n", "\n", "\n", "newtrainY, knownX, knownY = labelremover(trainX,trainY)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#standalone code\n", "with open('/home/madhura/Computational_Olfaction/fergus-ssl/src/fergus_propagation.py') as source_file:\n", " exec(source_file.read())\n", "\n", "fp = FergusPropagation()\n", "fp.fit(trainX,newtrainY)\n", "predicted_labels = fp.predict(testX)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#distributed code\n", "%run LabelPropagationDistributed.ipynb\n", "lpd = LabelPropagationDistributed()\n", "dX = sc.parallelize(trainX)\n", "dy = sc.parallelize(newtrainY)\n", "lpd.fit(dX,dy)\n", "\n", "plabels_ = lpd.predict(sc.parallelize(testX))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f585807ba50>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TnZ0NpVJpXfEpk8ng4eFRIEfj5SuXYSpyP1GZ0dOIi/EXCyRH89bNseLQCpiiTDB7meHs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\nSdATAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5859ae4dd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(trainX[:, 0], trainX[:, 1], marker='o', c=trainY, cmap = ('ocean'))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f5853f5ffd0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TnZ0NpVJpXfEpk8ng4eFRIEfj5SuXYSpyP1GZ0dOIi/EXCyRH89bNseLQCpiiTDB7meHs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\nSdATAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f58581185d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(trainX[:,0], trainX[:,1], c=np.array(lpd.labels_.collect()), cmap = (('ocean')))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f5853e95510>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cqQ3NRv+3kpKSIjExMfLoo4/K4MGDJTg4WNatW5fv9kePHpVy5cpJhQoVJCoq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\nU1MhFAphaGiotm6Ql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"text/plain": [ "<matplotlib.figure.Figure at 0x7f58581184d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(trainX[:, 0], trainX[:, 1], marker='o', c=fp.labels_, cmap = ('ocean'))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f5853dc7210>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LlItXDhYVlyVLl2Dn6Z3QjNIAAiA+Mh7DRw/Hn2v+NFmGXC7HT3NKL8SzYZOG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\no4yj8rSMjAxycnKic+fOkUKhoKVLl2rcZJSbm0vr16+nHTt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kwe3cuRMrVqxAu3bt0K9f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\nzq7JjZq5t8QAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5859db34d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(testX[:, 0], testX[:, 1], marker='o', c=testY, cmap = ('ocean'))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f5853d71790>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LlItXDhYVlyVLl2Dn6Z3QjNIAAiA+Mh7DRw/Hn2v+NFmGXC7HT3NKL8SzYZOG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\nzq7JjZq5t8QAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5858095490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(testX[:,0], testX[:,1], c=np.array(plabels_.collect()), cmap = (('ocean')))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f5853c90b10>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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beUD/VQjD5jL4+oyWubm5ZaQ5ozLADD2DUQ4cPHoQKi8VIAYgANTeahw+drjIfu7u7si7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\n2bIl2djY0OjRoxtlHJWnZWZmkouLC509e5aUSiUtWbJE6ya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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5858095a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(testX[:, 0], testX[:, 1], marker='o', c=predicted_labels, cmap = ('ocean'))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(trainY!=fp.labels_)[0].shape[0]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(trainY!=np.array(lpd.labels_.collect()))[0].shape[0]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(testY!=np.array(plabels_.collect()))[0].shape[0]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(testY!=predicted_labels)[0].shape[0]" ] } ], "metadata": { "kernelspec": { "display_name": "pySpark (Spark 2.0.0)", "language": "python", "name": "pyspark" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
jjehl/poppy_education
poppy-torso/poppy-torso_find_motors.ipynb
1
16804
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Activité - Faire danser PoppyTorso" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Première partie : en utilisant, le simulateur V-REP :" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "#####Compétences visées par cette activité :\n", "*Savoir utiliser des **modules** en y récupérant des **classes**. Instancier un **objet** à partir d'une **classe**. Utiliser une **méthode** et un **attribut** liée à un **objet**.*\n", "Faire le lien entre **rotation des moteurs** et **position du robot dans l'espace**.\n", "Faire preuve de **créativité** en developpant une chorégraphie.\n", "\n", "------\n", "#####Lien avec les programmes scolaires, voir : \n", "Pour ICN en classe de seconde : http://www.poppy-prof.fr/?page_id=4&id=67<br>\n", "Pour les mathématiques en classe de seconde : http://www.poppy-prof.fr/?page_id=4&id=37\n", "\n", "------\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour faire fonctionner notre robot, il faut utiliser Python mais pas seulement. Nous allons aussi avoir besoin de ce que l'on appelle une librairie. La librairie qui permet d'utiliser notre robot s'appelle Pypot et elle est entièrement écrite avec le language Python.\n", "\n", "Cette librairie a été construite par des chercheurs très compétants et nous allons simplement apprendre à l'utiliser. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La première chose à faire est d'aller chercher dans la librairie Pypot, les bons \"livres\", ceux dont nous allons avoir besoin. Ces \"livres\" se nomment des modules en vocabulaire Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toutes les instructions seront passées au robot via l'interface sur laquelle vous êtes en train de lire ces lignes. Cette interface se nomme **Jupyter** ou **Notebook**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour éxécuter les instructions écrites dans une case de Jupyter, il faut :<br>\n", " _**Sélectionner** la case en cliquant dessus.<br>\n", " _Cliquez sur la case **lecture** située dans la barre de menu : <img src=\"images/play.jpg\" alt=\"play\" /><br>\n", " _Ou appuyez simultanément sur **shitf+entrée**.<br>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from poppy.creatures import PoppyTorso" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ensuite, vous allez créer un objet s'appellant poppy et étant un robot de type PoppyTorso. Vous pouvez donner le nom que vous souhaitez à votre robot. Il vous suffit d'écrire : \n", "\n", "><span style=\"color:green\">nom_du_robot</span><span style=\"color:red\"> = PoppyTorso(simulator='vrep')</span>\n", "\n", "La syntaxe ci-dessus donne une instruction qu'il faut adapter selon vos envies. Le texte écrit en rouge ne peut pas être modifié, il s'agit d'instruction du language Python que vous ne pouvez pas changer.\n", "\n", "Par contre, le texte en vert, représente un nom de variable et vous pouvez mettre le nom qui vous plait **sans toutefois\n", "utliser des caractères spéciaux (' \" / - ).**\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "poppy = PoppyTorso(simulator='vrep')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme toute chose en language Python, notre robot poppy est un objet qui contient d'autres objets qui sont ses moteurs.\n", "\n", "Ca y est, si vous arrivez à accéder aux moteurs de Poppy, vous pourrez le faire bouger...\n", "\n", "Vous devez donc accéder aux moteurs de poppy (qui se nomment \"motors\") et qui se trouve à l'intérieur de Poppy pour cela tapez :\n", " \n", "><span style=\"color:green\">nom_du_robot</span><span style=\"color:red\">.motors</span>" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<DxlMotor name=r_arm_z id=53 pos=0.0>,\n", " <DxlMotor name=r_elbow_y id=54 pos=0.0>,\n", " <DxlMotor name=l_arm_z id=43 pos=0.0>,\n", " <DxlMotor name=l_elbow_y id=44 pos=0.0>,\n", " <DxlMotor name=abs_z id=33 pos=0.0>,\n", " <DxlMotor name=r_shoulder_x id=52 pos=0.0>,\n", " <DxlMotor name=r_shoulder_y id=51 pos=1.5>,\n", " <DxlMotor name=head_y id=37 pos=-3.3>,\n", " <DxlMotor name=head_z id=36 pos=0.0>,\n", " <DxlMotor name=bust_y id=34 pos=0.3>,\n", " <DxlMotor name=bust_x id=35 pos=0.0>,\n", " <DxlMotor name=l_shoulder_x id=42 pos=-2.5>,\n", " <DxlMotor name=l_shoulder_y id=41 pos=0.0>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "poppy.motors\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Tous les mouvements sont basés sur des rotations des moteurs situés aux articulations. Il suffit de fixer l'angle que l'on désire pour un moteur. Pour cela, nous pouvons utiliser la méthode : \n", "> <span style=\"color:red\">goto_position(</span><span style=\"color:green\">angle_en_degrées</span><span style=\"color:red\">,</span><span style=\"color:green\">temps</span><span style=\"color:red\">)</span>\n", "\n", "Dans la syntaxe ci-dessus, angle_en_degrées doit être remplacé par une valeur entre 0 et 180. Le temps doit être remplacé par une durée en seconde que vous désirez donner au mouvement. Une durée longue (5) entraine un mouvement lent une durée courte (0.5) entraine un mouvement rapide.\n", " \n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "poppy.head_z.goto_position(90,1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "A présent choisissez un moteur au hasard dans la liste de moteurs obtenues précédemment et faîtes le bouger pour le localiser sur le robot.\n", "Vous devez remplir le tableau suivant avec les noms des 10 moteurs :" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"./images/moteur_torso2.jpg\" alt=\"poppy-torso\" style=\"height: 500px;\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le tableau suivant doit être rempli par les élèves.\n", "La correction est donnée à titre indicatif :\n", "\n", "Nom du moteur 1 : ........... <br>\n", "Nom du moteur 2 : ........... <br>\n", "Nom du moteur 3 : ........... <br>\n", "Nom du moteur 4 : ........... <br>\n", "Nom du moteur 5 : ........... <br>\n", "Nom du moteur 6 : ........... <br>\n", "Nom du moteur 7 : ........... <br>\n", "Nom du moteur 8 : ........... <br>\n", "Nom du moteur 9 : ........... <br>\n", "Nom du moteur 10 : ........... <br>\n", "Nom du moteur 11 : ........... <br>\n", "Nom du moteur 12 : ........... <br>\n", "Nom du moteur 13 : ........... <br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si lors de vos essais, vous faîtes tomber votre robot, il est important de connaitre l'instruction qui permet de remettre la simulation à zéro :\n", " > <span style=\"color:green\">nom_du_robot<span style=\"color:red\">.reset_simulation()\n", " " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "# pour remettre la simulation à zéro :\n", "poppy.reset_simulation()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si votre robot ne répond plus et que vous ne comprenez pas pourquoi, le programme de contrôle du robot ou l'interface Jupiter est peut être hors service, dans ce cas vous pouvez recharger les programmes en choissisant Kernel puis Restart dans le menu de Jupyter. Il faut ensuite tout recommencer au début de ce guide. \n", "\n", "Maintenant, à vous de mettre les bras de votre robot à l'horizontale." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "# pour mettre les bras à l'horizontale\n", "\n", "poppy.r_shoulder_x.goto_position(-100,1)\n", "poppy.l_shoulder_x.goto_position(100,1)\n", "\n", "poppy.r_elbow_y.goto_position(100,1)\n", "poppy.l_elbow_y.goto_position(100,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vous avez sans doute remarqué que les mouvements de tous les moteurs s'éxécutent en même temps, en simultané.\n", "\n", "Il peut être utile de décomposer les mouvements. Par exemple, pour mettre les bras à l'horizontale : bouger d'abord les épaules puis ensuite les coudes. Pour faire cela, il faut rajouter à la méthode goto_position() un argument wait='True' :\n", " ><span style=\"color:green\">nom_du_robot.nom_du_moteur<span style=\"color:red\">.goto_position(<span style=\"color:green\">angle_en_degrées,temps<span style=\"color:red\">,wait=True)\n", " \n", "A présent, mettez les bras à l'horizontale en bougeant d'abord les épaules, puis ensuite les coudes :" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "# pour mettre les bras à l'horizontale\n", "\n", "poppy.r_shoulder_x.goto_position(-100,1)\n", "poppy.l_shoulder_x.goto_position(100,1,wait=True)\n", "\n", "poppy.r_elbow_y.goto_position(100,1)\n", "poppy.l_elbow_y.goto_position(100,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les bras sont à l'horizontale, remettez les dans leur position de départ, c'est à dire avec les angles des moteurs à 0 degrés." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "# pour remettre les bras dans leur position de départ :\n", "\n", "\n", "poppy.r_elbow_y.goto_position(0,1)\n", "poppy.l_elbow_y.goto_position(0,1,wait=True)\n", "\n", "poppy.r_shoulder_x.goto_position(0,1)\n", "poppy.l_shoulder_x.goto_position(0,1,wait=True)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A présent que vous savez, faire bouger votre robot, soyez créatif et inventez une danse pour lui !" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "poppy.head_z.goto_position(40,1,wait=True)\n", "poppy.head_z.goto_position(-40,1,wait=True)\n", "poppy.head_z.goto_position(40,1,wait=True)\n", "poppy.head_z.goto_position(-40,1,wait=True)\n", "poppy.head_z.goto_position(0,1,wait=True)\n", "poppy.r_shoulder_x.goto_position(-90,2)\n", "poppy.l_shoulder_x.goto_position(90,2)\n", "poppy.l_arm_z.goto_position(90,2)\n", "poppy.r_arm_z.goto_position(50,2,wait=True)\n", "poppy.r_shoulder_x.goto_position(0,2)\n", "poppy.l_shoulder_x.goto_position(0,2)\n", "poppy.l_arm_z.goto_position(0,2)\n", "poppy.r_arm_z.goto_position(0,2,wait=True)\n", "poppy.r_shoulder_x.goto_position(-90,2)\n", "poppy.l_shoulder_x.goto_position(90,2)\n", "poppy.l_arm_z.goto_position(-50,2)\n", "poppy.r_arm_z.goto_position(-90,2,wait=True)\n", "poppy.r_shoulder_x.goto_position(0,2)\n", "poppy.l_shoulder_x.goto_position(0,2)\n", "poppy.l_arm_z.goto_position(0,2)\n", "poppy.r_arm_z.goto_position(0,2,wait=True)\n", "\n", "poppy.l_arm_z.goto_position(90,3)\n", "poppy.r_arm_z.goto_position(-90,3,wait=True)\n", "poppy.r_arm_z.goto_position(0,3)\n", "poppy.l_arm_z.goto_position(0,3,wait=True)\n", "poppy.l_arm_z.goto_position(90,3)\n", "poppy.r_arm_z.goto_position(-90,3,wait=True')\n", "poppy.r_arm_z.goto_position(0,3)\n", "poppy.l_arm_z.goto_position(0,3,wait=True)\n", "poppy.r_shoulder_x.goto_position(-90,3)\n", "poppy.l_shoulder_x.goto_position(90,3,wait=True')\n", "poppy.r_shoulder_y.goto_position(30,3)\n", "poppy.l_shoulder_y.goto_position(-30,3,wait=True)\n", "poppy.r_shoulder_y.goto_position(-30,3)\n", "poppy.l_shoulder_y.goto_position(30,3,wait=True)\n", "for m in poppy.motors : \n", " m.goto_position(0,1)\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour terminer la simulation, il faut arréter le robot :\n", " > <span style=\"color:green\">nom_du_robot<span style=\"color:red\">.close()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "poppy.close()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Deuxième partie : en utilisant un véritable robot :" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tout le code développé à l'aide du simulateur doit normalement être valide sur un véritable robot. \n", "\n", "Il suffit d'instancier la class robot sans l'argument du simulateur :\n", "\n", " > <span style=\"color:green\">nom_du_robot<span style=\"color:red\"> = PoppyTorso()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Attention dans le cas du controle d'un véritable PoppyTorso, le code doit être éxécuté dans une interface Jupyter qui pointe sur le nom réseau du robot et non pas sur localhost comme pour le simulateur." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ecrivez votre code ci-dessous et éxecutez le. \n", "\n", "# Une correction est donnée à titre indicatif :\n", "poppy = PoppyTorso()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recopier à présent le code de votre chorégraphie pour l'éxécuter sur le véritable robot :" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
lbenet/MetodosNumericosAvanzados
clases/03-02 Redondeo.ipynb
1
6124
{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:8fea13392ce8115d0b9ae0b5a24a889e47f27a0382836e9ba4cf798107e4e0c4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\u00bfQue hace lo siguiente:?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = 0.1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "0.1" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Guarda en `x` el valor ya redondeado (convertido a un flotante cercano), segun el modo actual de redondeo." ] }, { "cell_type": "code", "collapsed": false, "input": [ "get_rounding(Float64)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "Base.Rounding.RoundingMode{:Nearest}()" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "bits(x)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "\"0011111110111001100110011001100110011001100110011001100110011010\"" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "set_rounding(Float64, RoundDown)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "0" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "0.1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "0.09999999999999999" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "float(\"0.1\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "0.09999999999999999" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "set_rounding(Float64, RoundNearest)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "0" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "0.1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "0.1" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "big(0.1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "1.000000000000000055511151231257827021181583404541015625e-01 with 256 bits of precision" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "bits(0.1*1.25)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "\"0011111111000000000000000000000000000000000000000000000000000000\"" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "set_rounding(Float64)\n", "0.1*1.25" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "0.12499999999999999" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "bits(ans)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "\"0011111110111111111111111111111111111111111111111111111111111111\"" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "versioninfo()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Julia Version 0.4.0-dev+3472\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Commit 8c87a32* (2015-02-20 05:10 UTC)\n", "Platform Info:\n", " System: Darwin (x86_64-apple-darwin13.4.0)\n", " CPU: Intel(R) Core(TM) i7-4750HQ CPU @ 2.00GHz\n", " WORD_SIZE: 64\n", " BLAS: libopenblas (USE64BITINT NO_AFFINITY NEHALEM)\n", " LAPACK: libopenblas\n", " LIBM: libopenlibm\n", " LLVM: libLLVM-3.3\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
chingizp/FieldOpt
tools/ipython_notebooks/python3/AD-GPRS Reservoir Visualization.ipynb
2
4276
{ "metadata": { "name": "", "signature": "sha256:75ee4e52c4e35c71adfa8db1cb3c182c339f7d2c7b0b156244dcd16a584a935e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Paths and Grid Information" ] }, { "cell_type": "code", "collapsed": false, "input": [ "h5_path = \"/home/einar/Documents/GitHub/PCG/fieldopt_output/adgprs7/5SPOT.SIM.H5\"\n", "nx = 60\n", "ny = 60\n", "nz = 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Read Grid Data From HDF5 File" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import h5py\n", "import numpy as np\n", "\n", "# Read the HDF5 file\n", "h5_file = h5py.File(h5_path, 'r')\n", "\n", "# Get the RESTART and FLOW_TRANSPORT groups from the file\n", "h5_restart_group = h5_file['RESTART']\n", "h5_flow_transport_group = h5_file['FLOW_TRANSPORT']\n", "\n", "# Get the GRIDPROPTIME table from the FLOW_TRANSPORT group\n", "# It is a three dimensional table [Block, Prop, Time]\n", "# For a dead oil run:\n", "# Rows: Nx * Ny * Nz\n", "# Columns: 3 (status, pressure, S_w)\n", "# Layers: 1 pr. time step\n", "# \n", "# For a black oil run:\n", "# Rows: Nx * Ny * Nz\n", "# Columns: 6 (status, pressure, y_oil, x_oil, S_g, S_o)\n", "# Layers: 1 pr. time step\n", "h5_gridproptime_table = h5_flow_transport_group['GRIDPROPTIME']\n", "\n", "# Get the TIMES table from the RESTART group\n", "h5_times_table = h5_restart_group['TIMES']\n", "\n", "# Extract the 1D time step vector from the TIMES table\n", "vec_time_steps = h5_times_table[:] # 1D vector\n", "\n", "# Use the shape of the gridproptime table to determine whether this is a black or dead oil run\n", "if h5_gridproptime_table.shape[1] == 3:\n", " dead_oil = True\n", " black_oil = False\n", "else:\n", " dead_oil = False\n", " black_oil = True\n", "\n", "# Initialize arrays that will hold the block data\n", "pressure = np.zeros([len(vec_time_steps), nx, ny, nz])\n", "sat_oil = np.zeros([len(vec_time_steps), nx, ny, nz])\n", "sat_wat = np.zeros([len(vec_time_steps), nx, ny, nz])\n", "sat_gas = np.zeros([len(vec_time_steps), nx, ny, nz])\n", "\n", "for t in range(len(vec_time_steps)):\n", " row = 0\n", " for z in range(nz):\n", " for y in range(ny):\n", " for x in range(nx):\n", " pressure[t, x, y, z] = h5_gridproptime_table[row, 1, t]\n", " sat_wat[t, x, y, z] = h5_gridproptime_table[row, 2, t]\n", " sat_oil[t, x, y, z] = 1 - sat_wat[t, x, y, z]\n", " row += 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Plot Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import pyplot as plt\n", "x = np.arange(0, nx)\n", "y = np.arange(0, ny)\n", "z = sat_oil[0, :, :, 0]\n", "\n", "for t in range(len(vec_time_steps)):\n", " if t == 0:\n", " p = plt.imshow(z)\n", " fig = plt.gcf()\n", " plt.clim()\n", " plt.title('Oil saturation')\n", " else:\n", " z = sat_oil[t, :, :, 0]\n", " p.set_data(z)\n", " plt.pause(1.0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
almarklein/bokeh
examples/charts/notebook/donut.ipynb
1
2344
{ "metadata": { "name": "", "signature": "sha256:8066454deadd50d456d835a9eccecb9ca218da9c682c38baa9cdba1e35449a11" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import bokeh\n", "bokeh.load_notebook()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "from collections import OrderedDict\n", "\n", "from bokeh.sampledata.olympics2014 import data\n", "from bokeh.charts import Donut\n", "\n", "# we throw the data into a pandas df\n", "df = pd.io.json.json_normalize(data['data'])\n", "# filter by countries with at least one medal and sort\n", "df = df[df['medals.total'] > 8]\n", "df = df.sort(\"medals.total\", ascending=False)\n", "\n", "# then, we get the countries and we group the data by medal type\n", "countries = df.abbr.values.tolist()\n", "gold = df['medals.gold'].astype(float).values\n", "silver = df['medals.silver'].astype(float).values\n", "bronze = df['medals.bronze'].astype(float).values\n", "\n", "# later, we build a dict containing the grouped data\n", "medals = OrderedDict(bronze=bronze, silver=silver, gold=gold)\n", "\n", "donut = Donut(medals, countries, notebook=True, title='Medals Count', \n", " x_label='countries', y_label='medals')\n", "donut.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "from bokeh.plotting import output_notebook, show\n", "output_notebook()\n", "df = pd.DataFrame(medals)\n", "donut = Donut(df, countries, notebook=True, title='Medals Count', \n", " x_label='countries', y_label='medals', legend=True)\n", "show(donut)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
csaladenes/blog
hodlon/binance-may.ipynb
4
1205004
null
mit
alimanfoo/agam-vgsc-report
notebooks/artwork_hierarchical_cluster_vgsc.ipynb
2
121115
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate hierarchical clustering artwork and haplotype groups\n", "- using whole genome phasing \n", "- based upon Ag1000g phase 1 paper" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%run setup.ipynb\n", "%matplotlib inline\n", "import hapclust\n", "\n", "from scipy.cluster.hierarchy import _convert_to_double\n", "from scipy.spatial import distance\n", "from scipy.cluster.hierarchy import _hierarchy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# define the gene region\n", "region = 'PARA'\n", "region_vgsc = '2L', 2358158, 2431617" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def load_data(chrom, start=None, stop=None, n_variants=None):\n", " \n", " # load data\n", " callset_haps = np.load('../data/haps_phase1.npz')\n", " haps = allel.HaplotypeArray(callset_haps['haplotypes'])\n", " pos = allel.SortedIndex(callset_haps['POS'])\n", " ann = callset_haps['ANN']\n", "\n", " \n", " # locate the region of interest\n", " if start and stop:\n", " loc = pos.locate_range(start, stop)\n", " elif start and n_variants:\n", " start_idx = bisect.bisect_left(pos, start)\n", " stop_idx = start_idx + n_variants\n", " loc = slice(start_idx, stop_idx)\n", " elif stop and n_variants:\n", " stop_idx = bisect.bisect_right(pos, stop)\n", " start_idx = stop_idx - n_variants\n", " loc = slice(start_idx, stop_idx)\n", " else:\n", " raise ValueError('bad args')\n", " \n", " # obtain haplotypes for the region of interest\n", " pos = pos[loc]\n", " h = haps[loc]\n", " \n", " #setup missense\n", " tbl_variants_selected = etl.frompickle('../data/tbl_variants_missense_selected.pkl')\n", "\n", " tbl_selected_redux = (\n", " tbl_variants_selected\n", " .cut('POS', 'REF', 'ALT', 'AGAP004707-RA')\n", " .mergeduplicates(key=('POS'))\n", " .convert('ALT', lambda v: ','.join(v) if len(v) > 1 else v)\n", " .addfield('label', lambda rec: '%s:%s>%s %s' % (rec.POS, rec.REF, rec.ALT.ljust(3), rec['AGAP004707-RA'].rjust(6)))\n", " .sort('POS')\n", " )\n", " \n", " # extract positions for the missense variants\n", " pos_missense = allel.SortedIndex(tbl_selected_redux['POS'])\n", " \n", " # extract haplotypes for the missense variants\n", " missense_bool = np.in1d(pos, pos_missense)\n", " h_missense = h.compress(missense_bool)\n", " \n", " missense_mutations = list(tbl_selected_redux['AGAP004707-RA'])\n", " \n", " return pos, h, h_missense, missense_mutations" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "pos, h, h_missense, missense_mutations = load_data(*region_vgsc)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1713, 1530)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['R254K',\n", " 'V402L',\n", " 'D466H',\n", " 'M490I',\n", " 'T791M',\n", " 'L995S',\n", " 'L995F',\n", " 'A1125V',\n", " 'V1254I',\n", " 'I1527T',\n", " 'N1570Y',\n", " 'E1597G',\n", " 'K1603T',\n", " 'A1746S',\n", " 'V1853I',\n", " 'I1868T',\n", " 'P1874S',\n", " 'P1874L',\n", " 'F1920S',\n", " 'A1934V',\n", " 'I1940T']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "missense_mutations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot missense mutations" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def plot_missense_haplotypes(ax, h, mut_labels):\n", " h = h.copy()\n", " # colours for colormap\n", " mycol = ['r', 'w', 'k'] \n", " # alter rows with kdr mutations for color map to pick up\n", "# known_muts = ['L995S (2984T>C)', 'L995F (2985A>T)', 'N1570Y (4708A>T)']\n", " known_muts = ['L995S', 'L995F', 'N1570Y']\n", " for mut in known_muts:\n", " if mut in mut_labels:\n", " h[mut_labels.index(mut)] *= -1\n", "\n", " # make colormap\n", " cake = mpl.colors.ListedColormap(mycol, name='mymap', N=3)\n", " # plot\n", " ax.pcolormesh(np.asarray(h[::-1]), cmap=cake, vmin=-1, vmax=1, zorder=-10)\n", " \n", " ax.set_yticks(np.arange(h.shape[0])+.5)\n", " lbl = [l for l in mut_labels[::-1]]\n", "# lbl = ['%s' % l for l in mut_labels[::-1]]\n", " ax.set_yticklabels(lbl, family='monospace', fontsize=6)\n", " ax.set_ylabel('Non-synonymous SNPs')\n", " \n", " for ytick in ax.get_yticklabels():\n", " if ytick.get_text() in known_muts:\n", " ytick.set_color('r')\n", " \n", " ax.hlines(np.arange(h.shape[0]+1), 0, h.shape[1], color='k', lw=.5)\n", " ax.set_xlim(0, h.shape[1])\n", " ax.set_ylim(0, h.shape[0])\n", "# ax.set_xticks([])\n", " ax.yaxis.tick_left()\n", " ax.set_xticks([])\n", " # rasterize to avoid SVG antialiasing issues and reduce file size\n", " ax.set_rasterization_zorder(-5)\n", "# ax.set_xticks(list(range(0, h.shape[1], 200)) + [h.shape[1]])\n", "# ax.xaxis.tick_bottom()\n", "# ax.spines['top'].set_visible(False)\n", "# ax.spines['bottom'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plot_missense_haplotypes(ax, h_missense, missense_mutations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cluster haplotypes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_dendrogram(h, ax, method='complete', color_threshold=0, above_threshold_color='k'):\n", " \n", " # compute distance matrix\n", " dist = allel.stats.pairwise_distance(h, 'hamming') * h.shape[0]\n", "\n", " # HACKING SCIPY TO GET TO OLD CLUSTERING METHOD\n", " # https://github.com/scipy/scipy/blob/v0.18.1/scipy/cluster/hierarchy.py#L470-L667\n", " # 1. fiddle with format\n", " y = _convert_to_double(np.asarray(dist, order='c'))\n", " # 2. get n\n", " n = int(distance.num_obs_y(dist))\n", " # 3. do clustering\n", " method = dict(single=0, complete=1)[method]\n", " z = _hierarchy.linkage(y, n, method) \n", "\n", " # plot dendrogram\n", " sns.despine(ax=ax, offset=5, bottom=True, top=False)\n", " r = scipy.cluster.hierarchy.dendrogram(z, no_labels=True, count_sort=True, \n", " color_threshold=color_threshold, \n", " above_threshold_color=above_threshold_color,\n", " ax=ax)\n", " xmin, xmax = ax.xaxis.get_data_interval()\n", " xticklabels = np.array(list(range(0, h.shape[1], 200)) + [h.shape[1]])\n", " xticks = xticklabels / h.shape[1]\n", " xticks = (xticks * (xmax - xmin)) + xmin\n", " ax.set_xticks(xticks)\n", " ax.set_xticklabels(xticklabels)\n", " ax.set_xlabel('Haplotypes')\n", " ax.xaxis.set_label_position('top')\n", " ax.set_ylim(bottom=-10)\n", "# ax.set_xlim(left=-10)\n", " ax.set_ylabel('Distance (no. SNPs)')\n", " ax.autoscale(axis='x', tight=True)\n", " return z, r\n", "\n", "fig, ax = plt.subplots(figsize=(10, 5))\n", "plot_dendrogram(h, ax);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "populations = phase1_ar3.pop_ids\n", "pop_colours = phase1_ar3.pop_colors\n", "pop_labels = phase1_ar3.pop_labels" ] }, { "cell_type": "code", "execution_count": 11, "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>label</th>\n", " <th>ox_code</th>\n", " <th>population</th>\n", " <th>label_aug</th>\n", " <th>country</th>\n", " <th>region</th>\n", " <th>sex</th>\n", " <th>m_s</th>\n", " <th>kt_2la</th>\n", " <th>kt_2rb</th>\n", " </tr>\n", " <tr>\n", " <th>index</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>AB0085-Ca</td>\n", " <td>AB0085-C</td>\n", " <td>BFS</td>\n", " <td>AB0085-Ca [Burkina Faso, Pala, S, F]</td>\n", " <td>Burkina Faso</td>\n", " <td>Pala</td>\n", " <td>F</td>\n", " <td>S</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>AB0085-Cb</td>\n", " <td>AB0085-C</td>\n", " <td>BFS</td>\n", " <td>AB0085-Cb [Burkina Faso, Pala, S, F]</td>\n", " <td>Burkina Faso</td>\n", " <td>Pala</td>\n", " <td>F</td>\n", " <td>S</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>AB0087-Ca</td>\n", " <td>AB0087-C</td>\n", " <td>BFM</td>\n", " <td>AB0087-Ca [Burkina Faso, Bana, M, F]</td>\n", " <td>Burkina Faso</td>\n", " <td>Bana</td>\n", " <td>F</td>\n", " <td>M</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>AB0087-Cb</td>\n", " <td>AB0087-C</td>\n", " <td>BFM</td>\n", " <td>AB0087-Cb [Burkina Faso, Bana, M, F]</td>\n", " <td>Burkina Faso</td>\n", " <td>Bana</td>\n", " <td>F</td>\n", " <td>M</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>AB0088-Ca</td>\n", " <td>AB0088-C</td>\n", " <td>BFM</td>\n", " <td>AB0088-Ca [Burkina Faso, Bana, M, F]</td>\n", " <td>Burkina Faso</td>\n", " <td>Bana</td>\n", " <td>F</td>\n", " <td>M</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " label ox_code population label_aug \\\n", "index \n", "0 AB0085-Ca AB0085-C BFS AB0085-Ca [Burkina Faso, Pala, S, F] \n", "1 AB0085-Cb AB0085-C BFS AB0085-Cb [Burkina Faso, Pala, S, F] \n", "2 AB0087-Ca AB0087-C BFM AB0087-Ca [Burkina Faso, Bana, M, F] \n", "3 AB0087-Cb AB0087-C BFM AB0087-Cb [Burkina Faso, Bana, M, F] \n", "4 AB0088-Ca AB0088-C BFM AB0088-Ca [Burkina Faso, Bana, M, F] \n", "\n", " country region sex m_s kt_2la kt_2rb \n", "index \n", "0 Burkina Faso Pala F S 2.0 2.0 \n", "1 Burkina Faso Pala F S 2.0 2.0 \n", "2 Burkina Faso Bana F M 2.0 1.0 \n", "3 Burkina Faso Bana F M 2.0 1.0 \n", "4 Burkina Faso Bana F M 2.0 0.0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_haplotypes = phase1_ar31.df_haplotypes.query('population != \"colony\"')\n", "df_haplotypes.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "assert len(df_haplotypes) == h.shape[1]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def fig_hap_structure(h, h_display=None, mutations=None, vspans=[[]], cluster_labels=[], figsize=(10, 8), \n", " fn=None, dpi=150, height_ratios=(2.5, .2, 2.8, .2), hap_pops=None, legend=True):\n", " \n", " # create the figure\n", " fig = plt.figure(figsize=figsize)\n", " \n", " # define subplot layout\n", " gs_nrows = 4\n", " gs_ncols = 1\n", " gs = mpl.gridspec.GridSpec(gs_nrows, gs_ncols, hspace=0.04, wspace=0.04,\n", " height_ratios=height_ratios)\n", " \n", " # dendrogram\n", " ax_dend = fig.add_subplot(gs[0, 0])\n", " z, r = plot_dendrogram(h, ax_dend, color_threshold=0)\n", " ax_dend.set_ylim(bottom=-5)\n", "# for xmin, xmax in vspans:\n", "# ax.add_patch(plt.Rectangle((xmin, 0), (xmax-xmin), 1,\n", "# facecolor='k', edgecolor='k', alpha=.1,\n", "# transform=ax.transAxes, zorder=20))\n", "# ax.vlines(vspans.flat, 0, 1, transform=ax.transAxes, linestyle=':')\n", "# if legend:\n", "# handles = [mpl.patches.Patch(color=pop_colours[pop], label=pop_labels[pop]) for pop in populations]\n", "# ax_dend.legend(handles=handles, loc='upper right', bbox_to_anchor=(1, 1), ncol=3)\n", " ax_dend.set_yticklabels(ax_dend.get_yticks().astype(int))\n", " ax_dend.xaxis.set_tick_params(length=3, pad=2)\n", " ax_dend.yaxis.set_tick_params(length=3, pad=2)\n", "\n", " # population colours\n", " ax_pops = fig.add_subplot(gs[1, 0])\n", " if hap_pops is None:\n", " hap_pops = df_haplotypes.population.values\n", " x = hap_pops.take(r['leaves'])\n", " hap_clrs = [pop_colours[p] for p in x]\n", " ax_pops.broken_barh(xranges=[(i, 1) for i in range(h.shape[1])], yrange=(0, 1), color=hap_clrs);\n", " sns.despine(ax=ax_pops, offset=5, left=True, bottom=True)\n", " ax_pops.set_xticks([])\n", " ax_pops.set_yticks([])\n", " ax_pops.set_xlim(0, h.shape[1])\n", " ax_pops.yaxis.set_label_position('left')\n", " ax_pops.set_ylabel('Population', rotation=0, ha='right', va='center')\n", "\n", " # missense mutations\n", " if h_display is not None:\n", " ax_mut = fig.add_subplot(gs[2, 0])\n", " plot_missense_haplotypes(ax_mut, h_display.take(r['leaves'], axis=1), mutations)\n", " ax_mut.set_xticks([])\n", " ax_mut.yaxis.set_tick_params(length=3, pad=2)\n", "\n", " # KDR haplotype clusters\n", " ax_clu = fig.add_subplot(gs[3, 0])\n", " sns.despine(ax=ax_clu, bottom=True, left=True)\n", " ax_clu.set_xlim(0, h.shape[1])\n", " ax_clu.set_ylim(0, 1)\n", " for lbl, (xmin, xmax) in zip(cluster_labels, vspans):\n", " if lbl:\n", " # hack to get the \"fraction\" right, which controls length of bracket arms\n", " fraction = -20 / (xmax - xmin)\n", " ax_clu.annotate(\"\", ha='left', va='center',\n", " xy=(xmin, 1), xycoords='data',\n", " xytext=(xmax, 1), textcoords='data',\n", " arrowprops=dict(arrowstyle=\"-\",\n", " connectionstyle=\"bar,fraction=%.4f\" % fraction,\n", " ),\n", " )\n", " ax_clu.text((xmax + xmin)/2, 0.2, lbl, va='top', ha='center', fontsize=6)\n", " ax_pops.vlines([xmin, xmax], 0, 1, linestyle=':')\n", " ax_mut.add_patch(plt.Rectangle((xmin, 0), (xmax-xmin), h_display.shape[0],\n", " facecolor='k', edgecolor='k', alpha=.1,\n", " zorder=20))\n", " ax_mut.vlines([xmin, xmax], 0, h_display.shape[0], linestyle=':')\n", " ax_clu.set_xticks([])\n", " ax_clu.set_yticks([])\n", " ax_clu.set_xlabel('$kdr$ haplotype clusters')\n", " \n", " if fn:\n", " fig.savefig(fn, jpeg_quality=100, dpi=dpi, bbox_inches='tight')\n", " \n", " return z, r" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "z, r = fig_hap_structure(h, h_missense, missense_mutations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cut the dendrogram" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# gives the haplotype indices of the haplotypes in the n largest clusters\n", "def find_clusters(z, n, threshold=12):\n", " \n", " # find clusters\n", " f = scipy.cluster.hierarchy.fcluster(z, threshold, criterion='distance')\n", " \n", " # compute cluster sizes\n", " fsz = np.bincount(f)\n", " \n", " # sort largest first\n", " fsort = np.argsort(fsz)[::-1]\n", " \n", " # take largest n\n", " fsort = fsort[:n]\n", " \n", " # get haplotype indices for each cluster\n", " clusters = [set(np.nonzero(f == i)[0]) for i in fsort]\n", " \n", " return clusters" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def truspan(cluster, r):\n", " # get the index of the cluster haps in the dendrogram list of all haps\n", " cluster_leaves = sorted([r['leaves'].index(i) for i in cluster])\n", " # are these indices monotonic - they should be!\n", " x = np.asarray(cluster_leaves)\n", " dx = np.diff(x)\n", " mon = np.all(dx == 1)\n", " assert mon\n", " return min(cluster_leaves), max(cluster_leaves)\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 10., 5.])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig.get_size_inches()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clustard = find_clusters(z, n=15, threshold=10)\n", "vspans = [truspan(cluster, r) for cluster in clustard]\n", "cluster_labels = ['F1', 'F5', 'S3', 'S1', 'S2', 'F4', 'F3', 'S4', 'S5', '', '', '', '', '', 'F2']\n", "fig_hap_structure(h, h_missense, mutations=missense_mutations, vspans=np.array(vspans), cluster_labels=cluster_labels);\n", "fig = plt.gcf()\n", "ax_dend = fig.axes[0]\n", "# ax_dend.text(-0.08, 1.2, 'a', transform=ax_dend.transAxes, ha='left', va='top', fontsize=10, fontweight='bold')\n", "fig.savefig('../artwork/vgsc_haplotypes_hierarchical_clustering.pdf', jpeg_quality=100, dpi=600, bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate cluster file" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "hap_sample_labels = df_haplotypes.ox_code" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "cluster_samples = dict()\n", "for l, c in zip(cluster_labels, clustard):\n", " cluster_samples[l] = set(hap_sample_labels.take(list(c)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def save_cluster_membership(fn, h, clusters, cluster_labels):\n", "\n", " # save cluster membership\n", " cluster_membership = np.empty(h.shape[1], dtype='S2')\n", " cluster_membership[:] = b''\n", " for cluster, lbl in zip(clusters, cluster_labels):\n", " hidx = sorted(cluster)\n", " cluster_membership[hidx] = lbl.encode('ascii')[:2]\n", "\n", " np.save(fn, cluster_membership)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "save_cluster_membership('../data/hierarchical_cluster_membership.npy', h, clustard, cluster_labels)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
Islast/BrainNetworksInPython
scona/bits_and_bobs/NSPN_BehaviouralGraphs.ipynb
1
447597
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Graphs from the NSPN 2K cohort questionaire data\n", "\n", "These graph analyses are created by Kirstie, started on 11th May 2016, based on correlation matrices from Jan." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: uncompiled fa2util module. Compile with cython for a 10-100x speed boost.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "import community\n", "import forceatlas2\n", "import matplotlib.pylab as plt\n", "%matplotlib inline\n", "import matplotlib.gridspec as gridspec\n", "import matplotlib.patches as patches\n", "from matplotlib import rc\n", "import matplotlib as mpl\n", "import networkx as nx\n", "import numpy as np\n", "import os\n", "import pandas as pd\n", "import pickle\n", "import sys\n", "import seaborn as sns\n", "sns.set_style('white')\n", "sns.set_context('notebook', font_scale=1)\n", "\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sys.path.append('C:\\Users\\Kirstie\\Dropbox\\GitHub\\NSPN_CorticalMyelination\\SCRIPTS')\n", "from make_graphs import *\n", "from make_corr_matrices import *\n", "from make_figures import *" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set some variables so you can find the data\n", "corr_mat_dir = 'C:\\Users\\Kirstie\\Dropbox\\KW_NSPN\\NCAAPS\\PSYCHOPATHOLOGY\\MODULES'\n", "results_dict_file = os.path.join(corr_mat_dir, 'results_dict.p')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def show_network(corr_mat_file, cost=15, n_rand=10, plotting_dict=None, sep=',', header=True):\n", " \n", " name = os.path.basename(corr_mat_file).split('_')[1].replace('.csv', '').capitalize()\n", " \n", " corr_mat_df = pd.read_csv(corr_mat_file, sep=sep, header=header)\n", " M = corr_mat_df.values\n", " M_abs = np.abs(M)\n", " G = graph_at_cost(M_abs, cost)\n", " M_cost = nx.to_numpy_matrix(G)\n", " \n", " # Create your plotting dictionary\n", " # These are all the values you need to create the summary figure\n", " if not plotting_dict:\n", " print 'Creating the plotting dict - may take a little while'\n", " plotting_dict = create_plotting_dict(G, n_rand=n_rand)\n", " \n", " rich_edges, rich_nodes = rich_edges_nodes(G)\n", " plotting_dict['rich_edges'] = rich_edges\n", " plotting_dict['rich_nodes'] = rich_nodes\n", "\n", " # Re-order the matrix according to the modules\n", " corr_mat_reorder_df = corr_mat_df.iloc[plotting_dict['module_order'], plotting_dict['module_order']]\n", " M_reorder = corr_mat_reorder_df.values\n", " M_cost = nx.to_numpy_matrix(G)\n", " M_cost_reorder = M_cost[plotting_dict['module_order'], :]\n", " M_cost_reorder = M_cost_reorder[:, plotting_dict['module_order']]\n", " \n", " # Make the figure\n", " \n", " plotting_dict['figure_name'] = corr_mat_file.replace('.csv', '_SummaryFig_Cost_{}.png'.format(cost))\n", " plotting_dict['title'] = '{} {:2.0f}%'.format(name, cost)\n", " make_summary_fig(G, M, M_reorder, M_cost_reorder, plotting_dict)\n", " \n", " # Report which items are in each module\n", " plotting_dict['module_items_name'] = corr_mat_file.replace('.csv', '_ModuleItems_Cost_{}.csv'.format(cost))\n", " report_module_items(corr_mat_df, plotting_dict)\n", " \n", " return plotting_dict" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_plotting_dict(G, n_rand=10, R_list=None, R_nodal_partition_list=None):\n", " # Create your plotting dictionary\n", " # These are all the values you need to create the summary figure\n", " plotting_dict = {}\n", "\n", " # Split up the graph into modules\n", " nodal_partition = calc_nodal_partition(G)\n", " module_partition, module_order, module_colors, nodal_colors = color_by_module(nodal_partition)\n", "\n", " plotting_dict['module_partition'] = module_partition\n", " plotting_dict['module_order'] = module_order\n", " plotting_dict['module_colors'] = module_colors\n", " plotting_dict['nodal_colors'] = nodal_colors\n", " \n", " # Show the spring layout and the layout that emphasises modules\n", " # a little more clearly\n", " plotting_dict['pos_spring'] = nx.spring_layout(G)\n", " plotting_dict['pos_forceatlas2'] = forceatlas2.forceatlas2_networkx_layout(G, gravity=1)\n", " \n", " # Create a bunch of random networks\n", " R_list, R_nodal_partition_list = make_random_list(G, n_rand=n_rand)\n", " \n", " # Calculate the global network measures\n", " plotting_dict['global_measures'] = calculate_global_measures(G,\n", " R_list=R_list,\n", " nodal_partition=nodal_partition,\n", " R_nodal_partition_list=R_nodal_partition_list)\n", " \n", " # Calculate the rich club curve\n", " deg, rc, rc_rand = rich_club(G, R_list=R_list)\n", " \n", " plotting_dict['rc'] = rc\n", " plotting_dict['rc_rand'] = rc_rand\n", " \n", " rich_edges, rich_nodes = rich_edges_nodes(G)\n", " plotting_dict['rich_edges'] = rich_edges\n", " plotting_dict['rich_nodes'] = rich_nodes\n", "\n", " return plotting_dict" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def color_by_module(nodal_partition):\n", " \n", " # The nodal partition has nodes as keys and modules\n", " # as values. Here we make a module partition that has\n", " # modules as keys and nodes as values\n", " module_partition = {}\n", " for m,n in zip(nodal_partition.values(),nodal_partition.keys()):\n", " try:\n", " module_partition[m].append(n)\n", " except KeyError:\n", " module_partition[m] = [n]\n", " \n", " # Create a list of the nodes in module order\n", " module_order = []\n", " for key in module_partition.keys():\n", " module_order += module_partition[key]\n", " \n", " # Now we need to assign a color to each of the modules\n", " module_colors = sns.color_palette('husl', n_colors=len(module_partition.keys()))\n", " # And then map the appropriate color to the nodes\n", " nodal_colors = [ module_colors[i] for i in nodal_partition.values() ]\n", "\n", " return module_partition, module_order, module_colors, nodal_colors" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_summary_fig(G, M_orig, M_reorder, M_cost_reorder, plotting_dict):\n", " \n", " sns.set_context('poster', font_scale=1)\n", " \n", " fig, big_ax = plt.subplots(figsize=(18,10))\n", " \n", " # Top row\n", " grid = gridspec.GridSpec(1, 3)\n", " grid.update(bottom=0.4, left=0.1, right=0.94, wspace=0, hspace=0)\n", " ax0 = plt.subplot(grid[0,0])\n", " ax1 = plt.subplot(grid[0,1])\n", " ax2 = plt.subplot(grid[0,2])\n", " \n", " plot_reordered_mat(M_reorder, \n", " M_cost_reorder,\n", " plotting_dict['module_partition'], \n", " plotting_dict['module_colors'],\n", " ax=ax0)\n", " ax0.axis('off')\n", " x_min, x_max = ax0.get_xlim()\n", " y_min, y_max = ax0.get_ylim()\n", " ax0.axhline(y_min, linewidth=2, color = 'k')\n", " ax0.axvline(x_min, linewidth=2, color = 'k')\n", " \n", " nx.draw_networkx(G,\n", " pos=plotting_dict['pos_spring'], \n", " node_color=plotting_dict['nodal_colors'],\n", " with_labels=False,\n", " ax=ax1)\n", " ax1.axis('off')\n", "\n", " nx.draw_networkx(G,\n", " pos=plotting_dict['pos_forceatlas2'], \n", " node_color=plotting_dict['nodal_colors'],\n", " with_labels=False,\n", " ax=ax2)\n", " ax2.axis('off')\n", "\n", " plt.figtext(0.24, 0.93, 'Reordered Matrix', verticalalignment='top', horizontalalignment='center', size=20)\n", " plt.figtext(0.52, 0.93, 'Spring Layout', verticalalignment='top', horizontalalignment='center', size=20)\n", " plt.figtext(0.8, 0.93, 'ForceAtlas2 Layout', verticalalignment='top', horizontalalignment='center', size=20)\n", "\n", " # Bottom row\n", " grid = gridspec.GridSpec(1, 4, wspace=0.35, left=0.1, right=0.95)\n", " grid.update(top=0.4)\n", " ax0 = plt.subplot(grid[0,0])\n", " ax1 = plt.subplot(grid[0,1])\n", " ax2 = plt.subplot(grid[0,2])\n", " ax3 = plt.subplot(grid[0,3])\n", " \n", " # Correlation distribution\n", " plot_corr_dist(np.abs(M_orig), color=sns.color_palette()[1], label='Abs', ax=ax0)\n", " plot_corr_dist(M_orig, label='Orig', ax=ax0)\n", " \n", " # Degree distribution\n", " plot_degree_dist(G, ax=ax1, ER=False, x_max=None)\n", " \n", " # Rich club curve\n", " plot_rich_club(plotting_dict['rc'],\n", " plotting_dict['rc_rand'],\n", " ax=ax2,\n", " x_max=None)\n", "\n", " # Network measures\n", " plot_network_measures(plotting_dict['global_measures'], ax=ax3)\n", " \n", " plt.figtext(0.02, 0.5,\n", " plotting_dict['title'],\n", " rotation=90, \n", " verticalalignment='center',\n", " horizontalalignment='left',\n", " size=30)\n", " \n", " fig.savefig(plotting_dict['figure_name'], dpi=50, bbox_inches=0)\n", " \n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def report_module_items(corr_mat_df, plotting_dict):\n", " \n", " module_partition = plotting_dict['module_partition']\n", " module_items_name = plotting_dict['module_items_name']\n", " module_colors = plotting_dict['module_colors']\n", " title = plotting_dict['title']\n", " rich_nodes = plotting_dict['rich_nodes']\n", " \n", " # Turn on latex usage\n", " rc('text', usetex=True)\n", " mpl.rcParams['font.family'] = 'sans-serif'\n", " mpl.rcParams['font.sans-serif'] = 'Bitstream Vera Sans'\n", "\n", " # First just write them out into a csv file\n", " # The formatting is annoying because they're rows when it\n", " # would make more sense to be columns, but as they're\n", " # different lengths I'm just not going to worry about it\n", " # If you're reading them in excel you can just transpose them anyway!\n", " with open(module_items_name, 'w') as f:\n", " for i, module in enumerate(module_partition.keys()):\n", " item_list = ['{}'.format(module)]\n", " for item in module_partition[module]:\n", " item_list += [corr_mat_df.columns[item]]\n", " f.write(','.join(item_list))\n", " f.write('\\n')\n", "\n", " # Then, we're going to put each into a figure which will hopefully look\n", " # super cool and nice and easy to read\n", " \n", " # Start by figuring out the right shape for the grid\n", " n_cols = 6\n", " n_rows = 2\n", " if 'All' in title:\n", " n_rows = 3\n", " \n", " fig, ax_list = plt.subplots(n_rows, n_cols, figsize=(n_cols*3, n_rows*6))\n", "\n", " plt.subplots_adjust(hspace=0, wspace=0, left=0.07, right=0.98, top=0.98, bottom=0.02)\n", "\n", " ax_list = ax_list.reshape(-1)\n", " ax_id = 0\n", "\n", " # Loop through the modules\n", " for i, module in enumerate(module_partition.keys()):\n", " item_list = ['{}'.format(module)]\n", " for item in module_partition[module]:\n", " \n", " item_name = '{}'.format(corr_mat_df.columns[item])\n", " item_name = item_name.replace('_', '\\_')\n", " if item in rich_nodes:\n", " item_name = '\\\\textbf{{{}}}'.format(item_name)\n", " item_list += [item_name]\n", " # Figure out how many columns you're going to need\n", " n_cols = len(item_list)/30 + 1\n", "\n", " for col in range(n_cols):\n", " ax = ax_list[ax_id]\n", " items = '\\n'.join(item_list[30*col:30*(col+1)])\n", " ax.text(0.1, 0.95, \n", " r'{}'.format(items), \n", " horizontalalignment='left',\n", " verticalalignment='top',\n", " fontsize=12)\n", " ax_id += 1\n", "\n", " ax.add_patch(patches.Rectangle((0, 0), 1, 1, facecolor=module_colors[i], edgecolor='none', alpha=0.6))\n", "\n", " # Turn off all the axes\n", " for ax in ax_list:\n", " ax.axis('off')\n", " \n", " # Add a main title\n", " plt.figtext(0.02, 0.5,\n", " plotting_dict['title'],\n", " rotation=90, \n", " verticalalignment='center',\n", " horizontalalignment='left',\n", " size=30)\n", "\n", " # Save the figure\n", " fig.savefig(module_items_name.replace('.csv', '.png'), bbox_inches=0, dpi=150)\n", " \n", " plt.show()\n", " \n", " # Turn off latex usage\n", " rc('text', usetex=False)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_reordered_mat(M_reorder, M_cost_reorder, module_partition, module_colors, ax=None):\n", " \n", " # If you don't have an axis yet, then make one\n", " if ax is None:\n", " fig, ax = plt.subplots()\n", "\n", " # Put the whole re-ordered matrix in the background\n", " M_reorder = np.triu(M_reorder)\n", " M_mask=np.ma.array(M_reorder,mask=False)\n", " cax = ax.imshow(M_mask, cmap='RdBu_r', interpolation='none', vmin=-1, vmax=1)\n", "\n", " # Add colorbar, make sure to specify tick locations to match desired ticklabels\n", " cbar = plt.colorbar(cax, ax=ax, ticks=[-1, 0, 1], shrink=0.75, fraction=0.05, pad=0.1)\n", " cbar.ax.set_yticklabels(['-1', '0', '1'])\n", " \n", " # Plot the binarised matrix in the lower left corner\n", " M_cost_reorder = np.tril(M_cost_reorder)\n", " M_mask=np.ma.masked_where(M_cost_reorder==0, M_cost_reorder)\n", " ax.imshow(M_mask, cmap='Greys_r', interpolation='none', vmin=0, vmax=1)\n", " \n", " # Plot each of the other modules on top\n", " max_i = 0\n", " for module in module_partition.keys():\n", " module_color = module_colors[module]\n", " min_i = max_i\n", " max_i = min_i + len(module_partition[module])\n", "\n", " # Add the different modules on top\n", " M_mask=np.ma.array(M_reorder,mask=True)\n", " M_mask.mask[min_i:max_i, min_i:max_i]=False\n", " pal = sns.light_palette(module_color, as_cmap=True)\n", " ax.imshow(M_mask, cmap=pal, interpolation='none', vmin=-1, vmax=1, alpha=0.8) \n", " \n", " return ax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if os.path.isfile(results_dict_file):\n", " with open(results_dict_file, 'rb') as f:\n", " results_dict = pickle.load(f)\n", "\n", "else:\n", " results_dict = {}\n", " \n", "#for cost in [5, 10, 15, 20]:\n", "for cost in [ 10 ]: \n", " # Pathology\n", " #plotting_dict = results_dict['pathology_cost_{}'.format(cost)]\n", " plotting_dict = None\n", " plotting_dict = show_network('Corr_pathology.csv',\n", " n_rand=10,\n", " cost=cost,\n", " plotting_dict = plotting_dict)\n", " \n", " results_dict['pathology_cost_{}'.format(cost)] = plotting_dict\n", " \n", " '''\n", " # Personality\n", " plotting_dict = results_dict['personality_cost_{}'.format(cost)]\n", " plotting_dict = show_network('Corr_personality.csv',\n", " n_rand=10,\n", " cost=cost,\n", " plotting_dict = plotting_dict)\n", " results_dict['personality_cost_{}'.format(cost)] = plotting_dict\n", " \n", " # All\n", " plotting_dict = results_dict['all_cost_{}'.format(cost)]\n", " plotting_dict = show_network('Corr_all.csv',\n", " n_rand=10,\n", " cost=cost,\n", " plotting_dict = plotting_dict)\n", " results_dict['all_cost_{}'.format(cost)] = plotting_dict\n", " \n", " '''\n", "with open(results_dict_file, 'wb') as f:\n", " pickle.dump(results_dict, f)\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating the plotting dict - may take a little while\n", " Creating 2 random graphs - may take a little while\n" ] }, { "ename": "ValueError", "evalue": "Format \"txt\" is not supported.\nSupported formats: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-16-4b5129ddf149>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mplotting_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplotting_dict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'\\t'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m header=None)\n\u001b[0m", "\u001b[1;32m<ipython-input-15-2fa2fdd148f7>\u001b[0m in \u001b[0;36mshow_network\u001b[1;34m(corr_mat_file, cost, n_rand, plotting_dict, sep, header)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[0mplotting_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'figure_name'\u001b[0m\u001b[1;33m]\u001b[0m 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'.join(formats)))\n\u001b[0m\u001b[0;32m 2062\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2063\u001b[0m def print_figure(self, filename, dpi=None, facecolor='w', edgecolor='w',\n", "\u001b[1;31mValueError\u001b[0m: Format \"txt\" is not supported.\nSupported formats: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff." ] }, { "data": { "image/png": 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Ne++9x1tvvcW3vvWt4oiBn/3sZyxevJhf//rXvPjiiwwdOpTNmzczd+5c/H4/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MM+YfiwK0F+WPv7R+9n5/53uz9FkQoxkATLTr/HbGPJqc7yX6d51dm2UP3pU2djmxkCZTJj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\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qCvQw/zX/nB7nVh6J8fmZi3A8lx+8/CzNmRTnjOw9ofdEEEWB2xdewM/Xv8C92zYyLUvK\n7c8kqZk3m8u/80WumTJlUH1D8L3EYrEue44hvDUx5Pb7FkdPxEhOwZIjTnJpfr0h93lPxFhfBI1p\nGEe1yRExva3fGyHXnVx6s6AnL7BjX7u39X0fRVV7Xb/7Z71B1bSjvi9N148ap3u7E813oE8mvJ4k\nzFmiNdffJy67iBe27sdxbGRZwXIcvKy/jSRJwb8FgWg0gmXaiGKQIKnpKqlUClVVECUR1/XwfbKp\njDa2Y6OqgarL98EwzGxiZLAfIpEIvg+WbeE4No7tYJomtm2SSHSgqlpX/LCeVZP5vo8oSpimQTgc\nRdV0Uuk00UgYQQxOXrIsIUsykqQgihKGkcaxLTRdR8r6G8iygiTJ6KEwkUgEXQ+jKCohXUdRdBQl\nUFQFxKOPqoWyHmIyiqIgSRKiJGE5DrKsEA6HEbNJlXJFDYgSvmmA5yKXVSNE48gVIxGy5UNifjGI\nYkCYuS6IEoIoYdUdDIzsM6mAQMuVILY1BctiBZg7N+Nm/cMEWQlKLaP5+I6FFC9CLq7Adyx8I42Y\nVwSOjTZlXkDKxYuCsk1FwTfS+OF40H/DQaSSSqSCUoRwDLlyNL5tdSVFyvmFCHoEMRTBy6QCYiz3\nc8qWUSqVoxEkCfvwXqSCUuTK0SjV4wb0e+0L73//+/nqV7/Kww8/zA033EAymeTOO++kqur46O6+\n4A/wbwhvfyy94Hwe76zvs91nps9ne2sjv9v0EpkTJEF6vsc/dm7m4gfvYltLIz9c+yye37P58oSi\nMi4eM5kbVyynMZ084fidpsEP1jzDtNJKvjLnTFYf3s/Du1/rc969zXEwKZSO53Jf+0vMWDqwdLdQ\nWGLJ2SWsej4I+Zg+O86q51oGPL5lehzYOowp486hqXU/H373T08YoqEqYSw7M+BxusPzvKN8dnzf\n5+Gnf8yi2R/k9OlXMLbmNOZOfw/3L/8uz635A4aZOq6PdKaDx1f8inuX3YTruXzo0h/1SZzlMHnc\nmSipGfz8Z789qe0YwhD6C8uy+OR172HCzN2MGHXkOPH0Y01MPjWP0eP6NhwHGDEqzCVXlrD8oYZB\nzWPchCivb+4c0Dqm4RHLO/I47tknmrjw8vIBEVj5hSqzTi/AcXymzogDARGz8Mxilv2jfkClhnte\nyzDy4Cjmlh/90OL/XlvLZ2cs5Fu9EGE5RBSVj009jYVVo/jhy8/2a0xdVqiMxUnZFpbr8NdtG7lp\nxXJOLR3GJWMmk6fq1CY6+r0NOWxraaAoFOlSZN21eQ3jCkq4Yvypfd6nyKLEt+Yu5WCinRcP7Rnw\n2I3pJAV6mIpoHm1mhi/NXszXT3sXXz/tXVSNquHmX/w3k0+CCBvC2wdDyrC3OI49mJxImTXY/v5V\n4w+mtO8/hd6UYccSjr2pwfqjFOtp372RKrBjx/K6lc9x7HvoKiHMveZSJu98aBlXvOt0JFFEVTTS\nJALjelHqIsdKSktxHAdZlslkUoTDUTKZNPFwBMe2syWEWuDfZZv4fs7U30XXLWKxAmwngm2ZqKqO\n7ViB8bym09rSRDxeSFtrI3l5Uays6ioUCmPbNoqiIIgiouPg2EfKS+PxfCAg4SRJIpP2kEQJNSvP\nVjW96yZG0ZWuMk0I0rkUVcOxLSzLRI9EUZSAFPU8l/z8IjzfI5NOokZjCIDj2OihCOlUgnhefrYc\nU8P1HCRRBrsRpXocXqItILEcCz+dBFHEbWtErZgdqLBCEXzTQCooCdRhskKooAShpAY66gOCqqg8\nUH/Fj3iIaeOn4RvBTZZUPiJIrWypD0owE+3Iw8cGRNjwsbgNBwNiS1K7vMqkglK8jpYgCdLJktqi\n2NW/GMnr8i9TYvmBB9r+bShjT8VPJxFDEZzGQ0hFFYGSzHWxD+4AUUQePhavowW5tCpQh2UVaG8k\nrrnmGq655pqT7sfzg7/+th3C2x8FBQWkS/OxXReljxLfj0+dy/bWRn627nlEQeD8URMYFo0jCyKt\nRprHWw+xxUywufEw3/jpD/npT3/Kuu07uO6pv/OrpZf3eM6YUlLBsGge92zdQIuRYumIWxK//gAA\nIABJREFUccwuH44qyTiey+stDTy8awuvNdfzo8UXMTwWHPuKw1EK9BDNmVSvKWS9YbBeYw/ue5WR\nCwa3bkmZxsrngrLRqhFhDtcabFrfzqkz8/u1vm17PHKvzqULfs3eQxsZXT0LWTqxBrUov5Kd+1YP\nar45NLbsoaggKO3ZdWAtL214gIWz3s+IyiPJaBUlY7nq/P+ivnk3j7/4v3ieiyCKGGYK00pRkFdB\nQ8teYpFCrjjrpgFfH40dsZBn1v6YdDrd9bBoCEP4V+Gm73yeiTMPHUUqNdYb6CGR4TUD+/1VVIao\nqzTYtztFzeiBHadGj4vw8P11TD41PqD1cqg9mGF4TQhZHvgxq3pkmGceb8S2XRQlOC+UD9OZPivO\nQ/fVcc5FZYTCvZ8vPM9n84oUw/eP5IaJRwedPLpnKxWRvH75X+Uwo6yKjGPzpy3r+NCkWX22/8TU\nufy/zS+zp6OVa6bMPioA5ouzzuBbKx7lW3OXnpCI647aRAc3rVzOPRd+CIDXm+vxfVhcPabf2wDB\nOfTGFY8xo6xqQA9k/rL1Fd4/YQYQ+JDNzpZbJiwTrXrYiVYdwjsMQ8qwtyH+06TSQEoD32xqsBx6\nUnIdS/D1VgraG/rt43XM+97ItdzrYPah7/uI3frucW6CEJSH5V6z7T52yQXc//RqwuEIbW2NCILI\nodpabMskkQyIMcuyMIw0pmmg62Ecx0bTdCzTCLzGXBfHsUilEpiGQTqVAPygpFGSMcwMRiZIrsxk\nkiSzT6RsyyQajeG4NpoewraDlEZRlEgmO/A8N1u2amAYGUQpkOkHJvgOgU1aEBwgiCJ19QfxPJf2\n9jaMTIrDtQcxzQxNTfW0tzVjmQaJzjZs26SzoxXf93FsG9Mw6GhvobOjlWSig0Sijc6Otqz3nkEm\nE2x7KpnAsW18P9i2RGcb6WQCxzbxjRTGphWYOzcHRJhtI4Sj2Pu2oU2YhbXtlYAsajiI03AA++BO\nDv3lboRQFD+Twq/bib1vG26iDbelHrejBaduL16yHfvgDrzOFrxUArfhIAgiXrIdubQSsThIsXSb\navHNDPbuzYFCzUghGMmgBNOxcNsag9JHywBRDpIhR0wAz8UfPgn7wA4EUULQAsN+L9mOl+rET3bg\nJdpwGg/hdbbiGyk8M4NTtxc/k8JLtmPtehWpfATWnqxKRX6jiiTfePh+8P+lf3//6dkO4d+FSz9+\nDf84tKNfbccXlvLNuUv53IyF7Glv5WsrHuUL+9ax8N5fcNfuV4mdMpqxE07h6quvZuPGjfzoJz/m\nifp9fObZB0nbPT/oKApF+OyMBXzrtKU8e2AX1z5+H7e/9BQ/W/cCu9qa+eqcJbx73BTajHTXOiFZ\n4crxp3Lr6icHbIps95By2R883bmF6jEDTXA9gnETouzcFijg5swrxDI9nn2iCcvqWTmXQ2O9yT//\nlMdFc/9EPFbKK1seYebkvj1h8qIldCQaT8o0+vm1fyJp7OPnf343TS37ee+FtxxFhHVHefFoLln6\nVS47+xtcuvRrvPeC71JdMZmpp5zF+y+8Fcex0NTBkVmTRlzKH//fvYPejiEMoT/o7OzkcOM6CgqP\n1lese6mdOfMG5zN16sx8Nm8YmMILsmVrcZn6+v5fF3e//N2wtp3ps/tHtveEGacV8KffHsC2jxyf\nqkaEWXJOCS883czf7qyjdv/RytNUymHNI0k23AWXdZzBFycenxL50uH9XDxm0oDnM79yJHs6WnCP\nedh9MNHOXZvX8OO1z/GTtc/xxy1rydN0nju4m1sWnMvk4qPDBDRZ5uZ55/C9l55ma0vfqr319Yf4\n3w0ryFN1pKwS9x87N/PBiTMHvA0AV0+cxd8GEEqzqnYfYVmhNGtlUBg6cgz9a+0OPvC56wY1jyG8\nPTGkDBvCfxT/aeKuN/w7UiHfCJyUMqzbxb6fJYeOvQEQe9h+P0uI3fnQMt539iI6O9soKS4KygAF\nAcexkSSJSCSoobcsE1XVMDJpJFnBdYMyS0GUkGXIZNJomo5tWXieRzLZSTy/EBAQxYCw07QQjmMT\nicZoqK8jGovT2dlOOBzNmk37RKNxTDMosZQ9FVE0u1Ijcwou35exbRvHtkil01RWjSCTTlJYVIzn\nupSUlBIKRZAVBce2EEWJWF4hrmOj62FEKTD2D8g1CVlRsEwDRVFRlMBnTJIkJEnCtn1A6FLhCYKA\nHo6QyaTwPR9BVlGGjURQdbx0Z1BGKIho46fjNNehTpqL21aPMuIU/EgB3uFdVFTU4HU0I0byECN5\nQQqjYwdeYIrSZZavVIxEjBfh1O1FGTEBr7Ue3zS6yhbFrEG+NGw0QsthxIJS/EQbXrwMSWrGbWtE\nGTYS3zQQQhE8LYYYFzFWL0OftghaDiCNmdpFlomhCL5tgawEKi9ZQcwqT3zHRtQjgapNlBAjeSjh\nGH6qE3XUZDwz0+/yn/8E/AEow4bIsHcOZs6ZzWM1JWxtbWJCQUm/1okoKpUFhezZbaClE5wyfRqL\nFy/mD3/4A5lMhrvvvpurrrqK6667jtLSUp697Wfc8coKfHwuHzeVsd3GSVom923fyIHOdhZVjeLb\npx+fXHjpmMn8z/oXmVoyjJZMipc6d7HjwD4icx2+ueEhbpt+cb8UX82ZFHvaB16imLIt3MIMMHg/\nlVMmx1j+UEOXCfXseYU01Bk8tawRQYTps/MpKdUQJTBNj+1bkmxc4zKp+pO8d+nH0dRw9vgr9qkK\ny2HCmEW8vut5Jo1dPOD5WnaGjJGgKfU0C2d9memnXDrgPhbN/hD/ePJ2SgprWDr/EwNeP4ey4jE8\ns/oePjV03zeEfyHuvOsOTpmSBo48QLbM4FpE1QanuZAkgVBYJJlwiMYGdquaJsJfl4t84kr3hEqs\nHAQhmK+qBanfijJ4ncjkU/PYvT3Jow/Wc8kVR9RH8XyFcy4q47Xfy4x4qZp1j+9jT6YJy3MQXZHR\nkVJOKxzJ3LLj/RzX1x9iRtnALR5yOG/kBB7bu40LRk3gmQM7eeHQHiqjcS4aPYniUATP96lPJ7hn\n2wZUSeLVpjrmDhtxXD8xVeN7C8/n3m0buGfrBmaVD+f8Uad0lUCajsNft21gXcMh5g2r4daF53Hd\nkw+Qsi0830eVZDR5cLTD2MISfr95Tb/SOZ87sItXGmv5wsxFABiO3XUvYzoOByPyoJLFh/D2xRAZ\nNoQh9IJ/NxE2GGKrt1CCXOlmf9b3s+qv7kRYbnlvxuFCN0Ls3NmnkEpmKCouRpIkYnkFWGYGN+tD\nFgpH8T2XaF4c13GQZRVBDBxVEqkE8fwimpvqKCwswfd9YrF4oOoSRUwjk/UPA1XVSHZ2UFlZjes6\nxOMFqKqGZZkYRgbbtlBVNasKS+N5HgUFxaRSCURJJRSKBImJkoSmh4jlFbBt2+tUVQ6jtbWFWCyG\naRrYudLKrAG/ny1fcW0LTQohCiKSLCM4Nr7nompBm1QyQSyvANNMB95hrkckGiOZ6CAciWIagSpO\nUTQc10bIhk8IkRgYKZKvbSJiGbgdLYSmn0Fm9aPo0xZi7dkCwL4HllN9zlz04WeD52Dt3BSUHFaM\nDOao6gHZlPXr8jpaUIaPA89FLCxHLRqGe2hnlwrMS3XitRzGty38bPKkmGrBFyWkkkq8RDuekUJw\nLLzDT4AooY6bjrltPdr0xaSffxBt9KRs0mWgTpPLqrOkl4jX1hR4lhWUYLfUIegRvEQ7vmUEZNuo\nybgtdQGJBrxZ6bCBeIENcWHvLHzzR9/nps/cwHkt9czMhl+cCC82HuTl8jAvvrqBH/7wh9x6662M\nHz+ea6+9lh07dvC73/2OG2+8ka985SvMnz8fY+RY3lMzkYxj88D2V/n7js1d8fOKKHH5uKnUnCDh\nS5VkPN9nW2sDtx14mHd9Jtx1Y1k/ooNPLvsLnxm1mGnFPd9oOZ7LY3u38cLB3YyMF3Kgs43qvIIe\n2/aEViNNKH5y51FRFOCYLsoqdM6/rBzTcNm0voNN6ztwXR9NExk5NkJFwRyWzLq+q33G7CQa7r9C\nZdKYxfz54a8xftT8fhNoOTy9+k7OXXgdL6wPMapy/oDWzUGSZEJ6Hg3NuzljzocG1UcXnBimaaL1\n4DM6hCGcDDzPo7GxkRdXPsrMBWBZHqoaEEm7dyYZP7F/PmG9YfK0ONu3JJg5t//HHN/3OZAsIHz1\nR7nzL7/kQxfaxPNP/H94xpx8Vr/YwumLitBDJ1cwJYoCekiisEilrjZDReXRqtiGVII17MYZluKc\nxXEi0dxtuM/+/Vv49IrNVJqlXDdmMeXZB8qP7d3K1087k8FidvlwvrPqcV6uO8Di4aP5zrzjjetH\n5BVw/YyFuNPm8/DuLfzo5Wf50uwzEAWR2mQHrzQcotM00CSZ8YWlfHDiTNY3HOIna5/vKs0fk19M\n0rIIyQora/fyct0B4prOZ5/6OwsrR3Lp2MmD3gaAycXlfOPFR/ncjAVURo8vg11ff4hH97zO6Pxi\nvjjrjK7lD+x4lUvGTMbzPW7b8wrX//q/T2oeQ3j7YYgMe4tDUdVeyZOBkiuDVRm9k/Cv3keDIeBO\nVEbZ57r0fAN/rEJMOIYsyy372CUX8PuHlnHm1NG0d7ShKgp+RxuCIOA6dkAiZZIIgohhmBQWFZPo\nbEPPSpZFUaKjvQXHcbOkVhrbtlAUBdt2ukg6y8oginJgRm+bqFm/rhwJ5mcVUYlER6BI8wPyLJHo\nQJZljEwaz3OzSZgCrm0jCALDKspJJpPIsowgiGiajiwreJ6L7dqBwX/Whyww5M/g+z6SK4EgZEsg\nLcLhCKIUlGoCKKqOputk0oFnVzC+h22bKIqG57pY+7YiF2VN7E2D8MhRgRJL1TG2rEHQw5g7NuBb\nBnJZNSMuWAiihNdaj5fqxO1oQQzH8FKdOPUHUKrH4acT2C31yGXDg7JFIxWY5jccwDfSeGYGKV4U\n9FleHRBlsXz8dCIonWw4iKCFEMMxnLp9iKEIUkVNtlRSAs8NiK6mg0jRWNcYckklTsPB4HPPxbeM\nwIi/pBIxXhT8YDwPKV6E01IXEGa+h5cJ9k/O22wIQ3grQRRFbvnlz/ndT/6bZSvXcF60jNkllUe1\n8X2flY2HeDrTzISlZ/Ct6z4FwDe/+U06Ojr45S9/yejRo3nf+97H3XffzapVq7j99tu58cYb+eFp\nZwNBeeMHJw2uvKTNSHHb4YdYck3sKEPo8mqN4ms9/vjScn61SWOOPprJeZWEZZV2M8Nzh3bRmEri\nuC7jC0sp1MP832svc/O8c/o9tsAbo5bs7XSm6RJz5h9Ncrmuz+61wfklpyJwHAtZ7r/XjCAInLfo\nc/xt+S1cce5NXYEqfWHVhvsoLqimvGQMZ8+7npc23s85C/uWZSVSzSzf+P+o8w7jij4CAr5n01q7\nnWl71/D67ueCZdmzdUiLMW/GVcQiRX32ratxOjo6KC0t7dc2DGEIfaGpqYmf/PY3vLR3N4m8KMnK\nKWzblYHWBuKZQyyY5pJOuhSOGHjgRndEYzLJpDOgdf7+QAvCgk8gRyKIV9/AHx++m3IO8665IoXF\nR8/H83xe29jJnp0pUikHx/EQB5FWeyx8PyDYnljWwAWXHSHDnn+qidBUieln6Iji8URh5YgQlSMg\nk+7kq/ffyxfKz2N6cRWyKHaprwYDQRBoMzJ8Zc6SLv/I3iCJIpeNncJrTXXc8PSDFOghKmP5zK+s\nIU/VMRyH3e3NfGfV41RG43xq2ukkLJNvvLCMolCYz85YcFzKcm2yg68//wjvnzhj0NsAMKagmEI9\nzCO7X6chlUDN+nUKCKQdixllVXxj7ruO21c72pq4fNxUbtq5lo/e/l+DClIawtsbQ2TYWxzHqoK6\nkzU5hZCm60eRN8cSOrn3b4WywH81uu+LY/eRpuuB91MP+/TY9f9dhGJP4/VnDjnPsByOJbty73Ov\nvZFjkijyicsu4rf/+CdnTh2NZQXeYOBjWiaSJKGqOpZlENJ1jEwaBLJlix6JRCfxeD6Ok0AQBAzD\nJC9PD0zxfXA9F89zSKcNdF1DEiXSqQRiTMKyTGRZxvNcDNPEcRwc18V1XVw3uIASxCC9ModcSIAg\nCJhmmlQyQ148j6amZnRNI5FIEs/PJ51OkU5nCIcdNFXF9z08zw1UbYKAbVt4rouq6Ti2hWGIJJNJ\n8vPz6ejoQJIkOjs7CYdDQZ/xOKZpoMgK6XQSRZLBscns2Y5WUYXTXI+o68gVNSQ2rCM6fjyJrVvJ\nmz4Tp+EgblsjO+99Cr0wQs0HyvE6gpKlzu07iVkGjmHh792GlJeP1dIcfMdWYHrvmwaCpgfEk+ci\nxgqw9rwWlE12tCAVlILnIhWVI5dW4dsWbkt9oOByLKjdjW/beOlgTKspMP5PH6ojXKMgAta+rSCK\npDavJzR6HF6qE6utDbGzA3a/jjp8VDBnOVvK6dh47dl5Gim8VGJAv/t/J4YM9IdwIgiCwCe+/AWc\nzzs88re/c+ujj6OaDjgugiJjaDILrryY2y668LgUw4suuohnnnmG5uZm7rjjDi688ELmzZvHP//5\nTz77gatpqT25YAnbdXjF38NVHyjtMRlNlkWmLojCAmis38mDzVtwMgJqFGoVg1n1U/nM1IVd1wXf\ne+kp2ox0vw2UC/UwmcMn95/C83x6CdY8Dpblsew+CyGp8dBTP+xablpphAEGABQXDGfJaR/h3mU3\ncu6iz1CU3/sNlGmleXr1nZQW1TAr60uWn1dGOnPi9LXm9oPc/8r/0FbooF00HSU+pkshm1q5kQJr\nFIdbdnDh4i8cRea1dzbw4tq7SRntzJ9xFcNKx/c6huua6FkV8hCGcDLwPI8v3/JdVjTW4cyahjJh\nJDLQnV5xXZdl617CfnkZH6g+ufF836ehrvdE+2PbPvJgEztbCynLlsCJmkr0imvoSKf584tPEW7Z\nRUiyEfGx0wZGSztnLi3gkisr2PZagm2vJTAy/TzYnHAyoKgioihgGC66LvHi081UDNMZN7HvkvFQ\nWGLJ1VH+555H+bp4ycnPBygKhfskwrpjckkFV50yjT3tLVw9efZRn40rLOG8URM40NnGbaufosM0\nuPPcq1B7eWhQGY0ztWQYyknaYaiizFP7dzKhqJQpJRUsHTGOqKKe8L71kV1baJXgp1YdX/ztHQwb\nNmScP4TjMUSGvcXRE+nR/cAgimKPZXTHphYem4w4WPSXiHmzKtB6SovMLc8lO/a0T3tafzBjD3T/\n91Ym2Z/kSs/zECWpK0Wj+y1LjvzqzUss9z7XR44QmzeuinDIQZRkxKzSyrZNwuEoVtYcX5ICM3tV\n0wmHw0ECo6pgWSaKImPbBpKkBB4vggr46LqPLMkoqoooSiiKipZNf1TVQM3lui6S56DpIQQhuIkK\nR6LZREgh6wEmIkoSjm0TCgWJkJ7nUVZWimPb5MXz8DwXTVNRVZlorADbMlCz5v+yEigxc6WfnucR\njgYqp2g0GvimhcOIokhRUTGGkSEvFlz8hMNRJEkiJAhIkoSYKSJUEXymSBJCOIZUVE50wgTctiZi\nkyaB5yFG8xFj+Yy5cgk49hEll6wQnzolKF8k8AHDcwkVlYNjB99PqhN5WE3wpTk2QjiGGM1HKihF\nKihFCEWQSypx2xq7vMEEuwVp2MiATJMVBFkJ/MlUHWvPFkKjxiOoOuGqikA5VlDadQMnl1UjlVbh\nJ9oQRAmpqByvsxUpXoQYjnV5jMmVoyFejtRRj9tU+6ZOcumJDD5R2yG8MyHLMpe+90oufe+V/V5n\n4sSJ7NixgyVLllBVVcXixYu59dZbkVIGlwr5PJE+dFJz+smG55i2OIIk931OKS3XKC0/Uko3enyE\nDXfVHnUe+ez0BXx39RPcuuA89H6EXoQVFbFF75fPS2/YsqmT8ZNOXG7l+z4vPC5itk3lzCnXU1V2\nfOLa3Q9/fcBjlxWP4rKzv8HK9ffS0LybyePOZOKYM1AVHdd1qG/excuvPogoiMyd9h7Kikcdtb4g\n9n5k21v/Kvdsv4Pw1ecQVY5civueR+q+Zziv+FKmn7u0x3Xz88o4f/H1uK7Dsuf/h1HDDzN57JIe\n2xpOG7HY4D3bhjAECK71rv789bw+uhplyiJ6+98vSBKx0+bThkJ726OUDxs8EdvZ4VDX6POnezp4\n1yKNYZXH9+X7Pjt3Grzwiohx2ocwX3yJ9IFDhKuPkNdSOEzsnIsB6H7XYW15jQf/+RfG7xVYMj/M\nutVtdLTbg54vQEOdQUFRsHeqa8I0HDazgVxCv4iwHARBYNF7Y3zv948wkZqTmhOAPsByb4AFVaP4\nw5Z12J7LNVPmHOcvWZ1XwA8XX8QvN6xk9eH9nDF8dK995Wk67WaGogGmGHdHi5HigtETmFZaSX2q\nk19vXIXlOlw2dgoTi4+3KXixdi8PWy3c8dg/iMcHly46hHcGhsiwtzi6kx45kilHqJyIdOoptfCN\nIql6u+jt3v+bWYXW2754I1Mj38h1B1Mm6fv+EZWCIPReyyIIPZZSdvcay73PEWIXzi3IqqhkRFHE\n90FWVEwzg2Nb+PiIooSqaKS8DkLhCMlkAlXTMY1sOaSYM6G3gjJJ2w7ILlnGcRzC4SiyrCKKAolE\nB4aRyZY6BpJpyzQRRBHHsTEyaVRVw7btLt8U13WxLRNRknBdG1BAAEEQsW2DzkSSaCSM73s4jgOY\neF6QFOl5Hq5j43luYPzuewFh4mVffQ/P83EcA8e2sB0XXQ9KI53s/gqFIoihCG5LfWB031yHoLUh\nl1XjJdqRK2qCpMZQBPvw3iB5sqGZ1m37qcorCgz3ZRXfNHBam5CLy4O+ovnYddsQVQVBjyDICva+\nbcgllfhuMF/fSGWXb0WM5OHU7QPASbSjiBK+rOBkUx69pkbEWD5SUQVO7W7EWD5uWyNy5Wh8y8BL\nJfDt4OJRDEVwGg7gNtXiJdshlzSZaOsixcRoPl4qgZdoR8wrDQz9RamrjzcjvOxff9sOYQjHorW1\nlZ/fdSd76uuwPQ9FkhhWUMgXPvZxwuEwjY2N3HDDDXzuc5/jyiuvZLaaxwcXXsT+5ga2tTRwSlHZ\noMZ9tmMrV00vHtS6oijglCepS3ZSEc0DghuaL89ewrdXPMbXT3sXxX3c2DSlk7TWO+zfYVMzfnCJ\niLt3pLjkyopeP/c8n2V/VZg37kfUTJvVa7uaqukcrNvC8IqBJbKF9TzOmv8JHnj8NkRR4smVv8Fx\nTARRorhgOBcsvgFVGVhaZmPrPv6y/RdEP3z+cefq1APP8t7qjzOqYlqf/UiSzMVnfonlL/4vuhpl\nzIijFRyWbVBcrrxpr7WGcPLYsmULu/fsIJXspKi4nOnTZlBS0r9Aj4HgC/91M6+PGYFS1T9ljbz9\nZXaQ4JRJgydiX17VjnD2+7CmzeBvK59HX/0qlXlp4lFwfWjrgLp0Hs6ERUSunkVEEgmfMpGDf/4r\nFRefj5LfOwHiWRbNazZQdcONHDZM7nphObqZR3vjDvbsTDFq7OBIm/UvtbP0gqAkORSWSKcddm5N\ncuHlvR/DeoMoCoyYL3BwWTue7/Ur7KQ3GO7grrHed8p0DibauXHFcm46/eweDfCvmz6fH6x5hpp4\nISN68ZQ8o2oUT+zbzvsmDL5Ucn39QW6YuYiwolIcijC5uALTcbh32waePbib66bNC8riPZf7D2wn\nMX0sf/j2L4eOf0PoE0Nk2Fsc/VV89bevnsi1gai8TqRu6t7/W+Hg1J85/itVbr31/YaN2cf2dSe8\nuqvHjlXKeN0imz+e9RA7e/o4zOz8LMsmmWgjkUih6xqKItPRkcRzXaKxApqbG4hF88ikkkiSjO97\nRCJR2tqaCYUiJBIdaKqKKIhdJZCmmcHK9i+KIqqqIsuBusx1baJ5cQREEom2QL0mivi+h2FkMIwM\nqqIiiCKxWD6JRDuyLKNpedmkS5lIJIbj2KRTCWzbxvc8DNNE04KSyUzGxveDfZEXLySVbCMUimLb\nQeqk67pZVZyVVYpJgN8VCGAYaZyGAyjDx+F2tCBoOuqYqfh6NPDZiuQhyEqXt5dcUgniFoafMjEw\nygfESAxBj+BbBlK8CEHTEaP5CJqe9faSAsWXqoOs4BupwDsHcDtacJMJpKJyBFnF7WhBqR6HEI3j\ntTcjFZQGiq1IHmJeEW5BZVCS2tmKXFaNb6RQx03vMr/3OlsQ9DDIClJROVJJZVAKme5EKijFN43A\na8xIISgKfjqB37gPL9GOoOlI/Uzj+08g+J7733YIQ8hh02ub+cHvfsduM401axrqqCO+X68lEjx9\ny80oVcM4cPgw+fn5jB8/nus/fi3T1+0F4PJxU/nhy89y07yzBzz2+vqDFFWLPZZH9heTF+v88W+r\n+dqUIz5h5ZEY35l3Dr/ZtJqMY3PR6EmcWnr0DfLGhloe2fM6IVnh92e+l8+t+DM1vVfy9Yr6wwbF\npScug3niHxKLJt5BVdmJzZnnTL2Uh5/+8YDJMIC00Yksq0wdv5Sp43tWa/UE3+uZHr/nlZ8Quebs\n47YrvXk7i0NL+kWEdcc5C67j3mU3UlM17SjD/007HuTaL10xoL6G8OZHJpPhj3/6LavXPEoor5aC\nIgdFFclsd/nL32Lo8ijef9WnWbhw8RtyrV1XV8eqliaUU0/pV3uzoZHxRW2Qksik+5foeCxs22Nf\no0L+R+YCEFtyFnAWh5MpDiSTCKKIHIuih44mogVRpPKKy2hY9jjD3tN7iWHjE89QfvH5SKEQUiiE\nesl7ASh2XV74w22MGjvgKWMaLgh0hQhYlofj+IQj8qCPw6Mmhti+vI0n9+3gnJH92//HYnNTHRMG\n+UBlcfVoblv9FJ+eNo9bX3qS784/t8ff1GdnLOAXr6zga70Y/Z9SVMa92zYOag4QJBMLgkBYOdr3\nTZNlPjx5NmsO7+eW1U8SLy8lWRLn4q9+itlz5w56vCG8s/Bmrk4ZwiDQk+JrsOvMT4NUAAAgAElE\nQVTnyKu+2h/b5l+lmvp3ontZZH/a/qvm0NP+78/30p++Pc878tfDXXxXKaTrBsRZViXWV7+fuOwi\nntiwA01VSWcyKLJEOm0QDoUIuDURXddwPRd8D1VRMM0MCAKSHBjVJxIdSKIUnPzCUSRRQlYUPM8h\nGosjywqSJGWnJaKqOpqmo6pa4B1m2xhGmnA4SjQWR5JklOxJNByOgkBg0u9YqKqGIIiYZhrLMgmF\nItnURxVZDsoxFVUjHs/Pvg+hKDqiKBKJxLAtE8/10PUwECjuHMfG8zxkWckq1gRcxyEciQWlm66L\nMiK4sBE0PVBuZVL4SihQYsWLEFQdoWwkSuXoLpIJAgWWFC8KTOg9NyiPFMUuY3ypoBQxHJBp+uTT\nUarHIYYiKDUTUIaN7OpfnzwnINM8F7m0Mkik9H0QRQRZCfy9ADGWD4KIr4QRCocFyrJYAYgiYiQv\n6znmISgqSsVIpKKKYP6hCHJZdVBmGS9CLgsMRAQ1619WOR4pXoQyfFzQx5sUvn/EN6yvvyEybAg5\n3POPv/PxX97BrgWz4OwlqIVHPzFXYjHEMxdS/rXPI86fw8NPPwXAumVPMDF746LLCtPLKnlgx6sD\nGrvVSPPbV1dTNuzkEgQjUZl2L33c8qiq8aXZi/nanDPZ1d7M9156iptXLuezT/2dLz37MD9/5UVE\nBGzP5Wfrn6fjoMDLjw3MFzCVdHjkb83MXdh7CuThgybloff1SYQBKLJGRclYNm19YkDz8DyXB5/8\nAYvnfHhA67V3NqD0oBirb95NsjqE2IPCQlp3gNMnXjagcSA4786d9h42vP5o1zLLzpBwXmfSpIkD\n7m8Ib168/PIqPvyxJdR3/o65ZzZx6iyV6pFhKip1Ro2NMHeRx9S5O3ng0ev52LWXkUqdfDjNj377\nG7zZ0/vd3npxOWecrjFnXgGrXmgZ1Jgrn2tFWPLu45bL0Qh6eRlaaQlSqGdFphTS8TwXr7dwMdfD\nSSTQio8PoBAkieSkJaxaPbDjlef5PPL3euYvPtJnc6PJof1p5szvfxrmcfMRBPLHePxl64ZB9/HQ\nrte4dMzgkhxFQUSRJKpi+Vw2dgr39DKPiKLi+h4Zp3cF2tSSCjY21g5qHn/bvokrxp/a6+enDRvB\n7Koays5ayC2/+9UQETaEAWFIGfYORG8G+oNVIP2nCa5/lTqrp+0aiBJuoOirNLO3Nv3pp6c23Q30\nc4qv7sgpw441fe7+WQ6iIHT10b1k8pyZ47FsG01VSabSCIKAoshYlk08HieVTOB5HpZlEwoJ2fRG\nh3A4TGt7O+FQYIzvez6arqLrYRzbRhRFbNtGkgLiKZVKBWWVlk1BYSGOE7RJJjuRs6SOaQbkpoCA\nlS3Lc10PyzLwPZ9QOILj2LS0NCBJEplM1vTfNAmFQkfWFwRc10OSRCyrHU3TkSSZRKID2zbp6AiU\nbJ7nYFkOqVQKVVEQRAGjswNJFNF1HXv/psC3S9VJbd1MePRY5LZa7MP7cNiHl+5EDOfhpTvBsTHb\nE2iF+YjhWJAkGYrgZo30vURgtC2XDcdLtAUljJnAmF4qKMFNtuOlgsRIMRLDPrQbQZKCPosLs55e\neXiy2tWXn0nhdrTgZVIo2TRJL5MKyiUjeWT270XNiwRplZ6LaBrYtbtxWuqQiypw2xqDBEoITPt3\nZW/oPRcxrwh/32bctkbs2t0IepjwhEW9/l7/k/AZgGdYj9msQ3in4cFly/jJi88iLT2jz7aCKFJx\n+SU8tH4ja6+4gor6JujGX1w0ehJ/ef0V7tm6gfdN6PuG9FCine+veZoCLUxaajuZzQDAPUHxryJJ\nXD5uatf7m1cu5zvzzjnuvHX7S08xNl3IQ39fzZmXFfZ5vmxpsnjuoQqWTr+J5ffeScnwWmacLqKo\nR5+LVj8V4QNnXdPvbZk340qeXPkbNm19glMn9K22cxyLB564jcWnfZh4bGCE/VNrfoKmHX+zvnzL\nHwhfdXypkNXSyhh93KCvJUZWTWPd5oeYPeUSHNfmybW38bNfDtwnbQhvXqxc+Ty/vvMLLDnfRxB6\nT2oURYEJUxSSiT18/JOXcOdvHyYcHlyZsuM4rDu4H3lK/6RSvu+Tb9aih1T0kEQoJLH99QTjB+CX\ntW93inVrExTdMrvvxr2gcO5sWle/TNGi+aR278VvawLHwY/GcRIp4tN7J1Yipy1k3fJWxBVrmbug\nbyLLcTweeaCeeWcUEc8/oszcvSUNrkBefOB+Xd0RKvUZXVjGCwd3s+gEvlw9YWNDLcWhSK/m9gPB\njLIqHtr1Wq+fXz5uKg/u3NxrKeTFYybxjRceZWS8kHgPx8besKO1ibpUJ6PzT1zyf171OG5+bgX+\ndZ/6j9+XDuGthSEy7C0ORVUHTAINpJzyX22m/0bgZEzre0NffmvHpk4emz45WOT61rqlP/m+fxT5\n1tO2Hpsi2t/90VVCmHulm3n+MSb5x453LLorybqb6l84dwrNzS3E86IYpoXveaiqgm2baFoY27EJ\nh3VUNYTve4CBoqjkxaKEQhEymRT4PrKsUF9fT2VlJUYmgx4KYxoZBEGgsLAI1w0M7Ts7OygrH4bn\nuTiOgyQFXmKJhE00EkFWVEKAYaRRFI28vHwymTSSJOF5CuFwFM/zEBAwLZOQrncp1HLkoJFJoYci\nuK6DrodwHIdQKEwyCaqq4zhWVkHmAkEppyiKXWMmOztQRk4KTOodm/DosUgllQihKFK8CLm8Gqfx\nEFLZcNymw/hGCsSDiNF8ECXk0kq8RDtSQQliVqXlHN6X+1LxbbtLtSUUVyG0HkbQ27uUY25HC3JJ\nJXJZOiCyovlI5TXgWohGCi/ViaCoCIoalG8WDYe2WsRQBDFehBjNJySKCLKKVDY8KIlMtCOGY6jj\npuO2NQZKNlHCbapFUIOkTN8yEEQpULqVVeOnE6hjpuIZJ//0+l+FIc+wIQwE7e3t3H7/vUgXnzug\n9ZSZ09i4cTPtosOF214goyq4AqieT6ltMMNXufHFxzht2AjOG3kK0jEPKfa0t3D/9k00phMUaGEu\nHzeVu4z6k94eye9/iVNJKErCMsnTjja6FgSBy0dM54Xn9rLmzgzaKINJC8Jd5UQ51B002PG8zaGm\nIj5+xX3IssqUsedR27iVZx/4Ob5Ujy9YCEiYGYWQPwlFHpj67az5n2TVhvv4+xPfY+akCxlROfW4\nNo5jsXbzQ7z86kO894LvHmeM3xcsO0OHuZZYyKe+eSflxUeIhGapDTV0vBG4+eJmzp5604DGORaF\n8UoO1G3h1b1/5ns//gJlZYMrjRrCmw+HDx/mF7/5CovO7n8YRTQmM31eA1/88kf49S/vG9S4Bw8e\nJFEYp3fq7Wh4lkVeyIHsGvPOKOK5J5owDY+pM/o2Md+2JcH+3Sn0opMzPFeLCmm561dwYBXzRxmU\nFglIskAy6bJ2m0lLaCSZsE6ouufIy9i5l7DyjtdpOHyYU2fGqRl9vIeYbXtsWNvOof0ZFp9VQmHx\nkb10YFeGT5e9i3Wd+4CT80TVQxJLa8bxzIGdRFWNGWW9J9t2x662Jm5etZwHL/3oSY3fvSZkemkV\n6+sPMbP8+DmMLyzlwZ29k2WyKPHt05fy3VVP8vXTzqQkfOJgFIDXm+u5Z9sGvjPvnD7bAswWI7y0\nciWnL1jQr/ZDGAIMkWFveXQ/KfZX8XXsej297y/6GsM0DDRd/7cQYm90fycilLqXKuY80E6WkOuu\nMju2RLMv8/7u4/ebhBSEQPXV7VXwj2hbTqQMO3bsXJmlmC2nBBA44iG2dNoYUukMqqpimMG8VEXF\n8zwcx0VAoD3dhq5rpFJpFEXFth1EMY1hmOiaiuM6lJWVYlsmkVicdCqQsVumiW2ZSLIMPoR0nVQy\ngSRJyLKMbVkoqkYkEsZxHRAEPM9BVfWseqwD34d4fhF2RxuO4+C6LoIoIkkSjmOj6WGMTBqhm++D\nbZlYtoUkySSTieA7sAxkWcHLmuk7toWsKHR2dmSTMh1M0yIUCuE27cVLtuO7LoISmOGreUFJkLXr\nVcT8Epz92wMCq6g88NXKGuA7LfWBt1gogvHayygVwxE0HaepFilWgO9YSAUlOPUHELK+YU7dPuSK\nGtyWeqSCUuza3cgllUjxIty2RsSOJrxUZ0CmZZMnJVHEbapFKhiGIKugh5FFKWiXhdsS3HALshIY\n5jfVgqx0jSMoKr6RQooX4TQcwGlrDJRjtbvxHavLwP/NiiHPsCEMBD+/8/dY82f3++YRwEmlqH/4\nUaSSItLvuQQ7ntf1mQkc8Dx2rd9E4frXyE938MpLT6FmH1AICOztaKEmXsjCqlGsPryfL806A8/3\nSW89OSeMA7syzMzrv9lXvhai3cwcT4YRnCNLtBg3Tr+c7a2N/N+fVpLUUriii+ALiJbMTP0UZocL\nWTPnCmT5yB6sLJ3A5aW/OqrPzdufJqQPzpx73vQrcRyLJ1f9lkefv4Pigip0LXgI0tZ5mGi4iDlT\nL+UDF53GM2vu4opzb+73ud3zXO564DOc8/4UBUUqD9/9Kc6Z+RvKisYA4Mh+j78NKWMTCQ++nAog\nL1rModRf+fVdtwylp73N8Itf3s6chSaCMDD/rby4Asp2tmx5jUmTBl4q197ejqv3n3D2LAtNPfr/\nyuKzS9i0voMH/3qYmtFhpkyPI0lH2niez+uvdrLplQ4816eoRMPqTCNs30Zo3PgBX1en16+hYNsT\nXH9tjPxCCTiayBo7Poptt7P65T/wysph5F15DYJ0/LEyfPlHYPXvaG+zeei+w6iaiKaLeK6PkfFA\ngOmz85kz7+hSbs/z2fGMyzenT+PVTbX4vnVS9wZWAvJ1nS/PXswdG1bwenMDV55yaq9qL9fzeGTP\n66w6tJewPJAzUQ9juw6O53a9v3D0BH689rkeyTDoWx0f10J8d8G5/Hjts2iSzKenzetRJXYo0c69\n2zaiiCL/Nf+cfocHnFcxiu//4e4hMmwIA8IQGfY2wska6PfUX18lgH2NIYrimzo58kTojXTKEVU5\nRdiJ2g8Ex5JYA1GcDVYZhhAkSZ5I/ZVTj+Vejxvb8xBEEd/3A1LM94O+BAFJFLsUYmdMqsH3fXRd\nw/d8DDNQEAiCgKppqJqG7/tEoxE0TcfPqsEERCzbIhyO4tgWoiQFr6KUVdGFuoivoGQySSQaCxIj\nbRtF1bpM/qXsNoZCUUQxMOQPhaLZ8kwTIeslFpRCuriuSywWJ5NJEY3lYZoGmqaTTicRBAFdD6Eo\nCrIsoagqIT8cGPRniTZZURBFkVgsL/iOVB9BgEw61WU07yXbA4+tcHBjJxaUIFWMAMvEGz4B0Urh\nizLeoYC8EjQdpWpMkEAZjqGNnxYormQlSKNMdQYkVks92oRZgcm9KHWZ8rsdLeDYXR5evpkJjPSj\ncSRVx9r1Kkp1YOyPKAWeZACqjtfeiCBK+JaBGM5DLCgBz8NLdSKGIuB5iPEifMdCqZmA03AAuaIG\nCivx63cjFVUgV4zEN1KIxZX4e7d0Kc3erPByv+t+th3COxe+7/Pc1i2o5/ffZN1qa6PuH49QedW7\nkSM9J5gJoog2ezqp2dP/P3vnHR5HeW//z9TtK626rGLZMnLvvYMxmF5sINQUSCE3BO4vyU2D3BBS\nSULKTUJIcgOEEppNDdV0jAFjbDDuttxkWXW1q+27035/zEqWLFmWZMil6DwPj9jZd955Z3Y8M++Z\n8z2HR557hcsbPFxdfVjRZFom33/1KR7Z9T6/Wnw2giAgCQIjzWHEoq14fYN71Nv/hsX3x/RUTh0N\nKUPDJfcsCdKykynNNAmlEozOK+IXeb17Yz1SuwOP+9jXg3gyRGF+Vb/HdiRkWWXB9ItpadtHe6yV\npfO+jNPh5WDjVqLxIJXDbOJgzuQLeOiZH7H8lO93I+h6Q0ZL8tDTN9Ga3E44YpJfCOdcluaJ+64k\nIJ/JyXP+4+grfwCXDqfLw2VXXjxEhH3CkMlkqKvfQOXYgRvRA4yfIvD3O37Db359+4DXdTqdiJpx\n7IZZSC4XiWTPk3ny9BwmT89hX22cpx9tRMySYZpm0tKUxuWWOOnUAopKnEiSwJKUyaZN9/H20xpt\nVh5yzSQKT1qAdIxyz/gbrzIh/ionLu+7naKILJrvpqaxmQfuvQ3f5VcjHPF8K/v97Gr1U+gPcu5F\nw7AsmwSTZKGHqrUDlmWx5qEY36k6E1mUWJB/Aq9veYPqCYMrUwWI7heprAkgCALXTlvIo7ve59J/\n3cOM4grOGTWeEq8fAWhNxnlqzzaa4lGaEzEWlo9gXtkI3ji0n3llVYPa9qO7N3P2qMOhI7IocWz3\n4L7hUVR+OG8ZSx68jdWhQxRkLEYECvDKCkldQxElhnn9fGnS7AGVUwJIooiS1o9rfEP49GGIDPuY\n40iy6oNSfA1k/WO1+TgSYUdDV9LpeE3sj8SRaZsdqrr+EFu9EaED2DBCluw6ss9OZViHeqy31bNE\nWAe6tcua7ncQYnNrykgkklimhc/vJZNOZQ32E53KKTuR0UVbW4hAbg7xhG3gHIm043CoyIqaNc+3\nQwDSqSSalkYURDRdw+OxUx0Nw0DX0xiGiChKaJqGbhi4HE6CwVacTgculwdNy9gllVkvskwmY5vi\nWyaCIHRuNxRuwzRMMpk0mYyGKIkYmoEoShiGQTKRIBaL4nI5icWiKLJERtORZQlZlgiFIkiShMOh\nIokCRrARwWVPfvXGA7axfX4pZjyC2bAP0emBUDN6JoXoCyCoTrSGvbbZfjplq7k8fjvJUXViRqII\nLjtd0sqksEwDI2p7BlnplL09RUGQVZs8AyzTQMovsdvH2hGcHqTCsk5yzYyGMeMR5EwMLBMz1GKn\nRuoaWss+hKYDdrqknsFIRO3yS1HEStm/mRmPoKVTSMm4XUoZC4OsIJdWYbY12gozUcSMhpBGz+//\nOftvxhDFNYT+4O3162kpKaS/0x4jmaLh4SeouOJiRLV/b/ClUxdzz/OvUli3kxUVNYBtcjwiJ4/T\nR47tdu3/YvUC/vvF+5lz7rHLUY5ELKozwihFFvs/AW9JxAg4u09e3m44gGGafPeVf7FZ8/C3bZv5\n9tRZR+3DI4tktGOH14iChGl2n6THk2FqD6wnkQwjSQoBfykjK2Yc9d5lmgY5niJcLh+BnFIARg2f\nxUPP/Ihp488AoHLYBFxOP7f+80rGjJzPvGmfwevurgRpjzazduODxBMh3HmFKMsv47H9+4j96XXE\ntkPEmsKkU1tYv+UJUlW9nx+GZGEYdln/YJHWQgQCgzPKHsJHFw8+dDdVo9thQHrTw1BUkWBoK4lE\nYsDeYSUlJUihcL/bi7JMKNWzDLgDVdWezpLDd94KEW7TOP2cElRH93+jTpfErNl+Zs2GpsY0K59Z\nx6FH2hBkhfwFc3GVDevRd7J2FyNbX+XEpf0nUEpKVC6Y187DTzyA99xLAEjX1aG/9iTD1FYuPEti\n906Ll1e3sOCk/D6TMRNxnbdWJrm2eBkT8+3xnVoxmpXvvEn1IP9ZxuM6pekCmpMx1tbvY3OrnQx5\n75mXkzZ0Vu/fyZr6vRiWRZ7TzYkVo7h32zv8/uTzbVN70+Qnb64eNBn2Sl0tF43unm57NPXX7lAr\n5f18sdkQiyC5HLy5YxuIAr62Q2QyGc6uPIE7TrtkUGPthD5Ehg1hYBgiwz7mOBbp0eE/1dV36oNE\nXyV5/y7PsH8XuvqEQU9l2AeBrr/nQFV1gyIdj1CFdSW1BLIkQEe/WRVZzy6OWHYURV0HIXbihBGk\nUmlSqTQej4dYPI7b7SSTsY3ztUwa07RwuhyIWdJLEAVURcEyLWRZxjAM0qkksqJ2/g5OlxsxkwYE\nZFnunADpehpZdqBlBJwO1S6ZBBTVgcPhQtc1nE43qsNJKmmTch1efKlkHGf24VGRdNJGxjbx16Oo\nskzKMHF7vERjESRJxO12o2sZFNlWxnX4jVmWRSDgJ5lMI0kisigiDxvRWVJoZQkvq2gEcrQZLZNC\nzMlH8PjR9mxGcLoxY2Hk0io73dE0kT3VCG6vbWCva6QO7selOrH0jO3R5c9HyhrXS4VlCJKE6M8H\nXbPLGEPNKMNGkNm9CXXUJDBNUO2HWMFpP6wKkoSUk4/pzEHIEmeC04MgSd0SLs1QS6cirEMdJnpz\nESNBLC2DVDgMK5XATMVRyqrRW+qRK8dg7t6EPGwEHKMU9/8SHUmR/W07hE8vavfvh4Kjpx8eiZbn\nX2LYinP7TYR1QFq6iFv/di/nmtXIWYXsoXiESn/3Mrsit5fgOwoH9qeoHH70CeqRME2Ltx5K8rua\n/odaZAwdw7J6kGd/ee9NvjhpNs81BTllxjd57Nkv0vDuXkRJQLIkhisFfLZ6Dj7VLsWq8uXyQtNm\nqitn9Lk9v7eQtvZ6hhXVsL9+E+s3P45D9TBm5AKK8qrQjQzBcD2rnv0xOb6iXkmstvYGDjRu5muX\n3dm5TBAEZNnRGQYDEGqvZ9HMKxheNplX1t1NJpNAEEUEBAxTx+3KYf60zxBLhblz52/xVpyKq6KM\nvAXziW/aQuivd7B32wbee+89fvC732BqGqLSXUEnTRrB2zufYs7Yc/p9zI9EOLGDkSMHlno5hI8+\nNr3/NiMmHJ8Juzc3Tl1dHaNH97/sGeChlXfRvuVt8k45sd/PmInisbQ0b6Kw6OjllevfDCGKcPLp\nxw6mKC5x8OXLTO58aDfGOVfTtvYt0o3N5E4/gqR54zlOO7//17kOlJWplG7cQziRIL1tE2X7n+fs\ns51Isk2qzZijUl+X5JnHm1BVkZlzAwTyD1+zD+5JsW+tSaVWzC+rz6XYc7h8WxRExgoVhEMN5AYG\n/hu++Vw7ziaVF5SdTC0q75ao6JDlbgEmkXSKm954jp8tPKOzhFISRUbnFfHi/l0sGd6/EIQOPLjj\nXXJUJ6/U1bK4i3H/0QKFVu58j2unLexX33957w3uWHwe/6rYxl+2rGNnaxMAuXk9Ez4HCksenIJy\nCJ9eDJFhn3B0Lev7MBRaxzLf/7iWSPaGI/enNyLsgyIAB9rPoLfbRfHVsV/9Tc47GkzTPFwq2W1T\nhwmx+aMrUFUFUbTLG03TJuVkWSaeiONTVUzDRFUdKEoKURAQxcPeZGa2NFPXMrjcHkzTIhIJoyoq\nstNll1gqKrquYRomlmX7KJBV3dkElYlp6nZ5p2lg6BqyomDoeraEUkeWFTKZFF6vn2QybqdEGrba\nC0HA6/FgmiaqogBCZxmlnWYpYJgGYtZnLJPRcbtcdplnJt3pu2Ul4zZ55PZBrAUznewsRUQUbYWY\naSLll9hqMJcHwe2zlV6qE9Hjx9I1nOXD7fbZ8kW9vhYtFbeVXICgOpFy8kF1oNftQgoUYcYjOCYt\ngEwKTAPTYT/ECW6vXcqYW2gTY1oSIdGOHmq21Wt6BkwDKxG1SyRz8juN8gWHE0TR9j3z5drLLAvB\n4UIKFNmG/b5cBD2NY8x0zGQc0ePno4ohz7Ah9BeJVApB7t9jlWWY6LEYSu7gStrii+fw0Fs7uGTE\nON44tJ/5ZSN6tLlp21vULz2d3evf4nyxjcqKY3v/GLrFK/+M8f2Ks8l39V622Rse2bWZc0d1lz8c\niIRIGxoZMrwU2cDIpi9z4dfB5bawX7WYBFv28Y2XdpEbCfCVEYuoySsk+vYbMPPzfW7vhKo5rHz2\nx2yrfY2KkvGcu/Q7yFL3yWZ5yTgmjzmFUKSR1Wv+QlX5FKaOO73z+zXr/8mVF/yhh3JMEhWS6Shu\np13e/vb7j3PJWT9BFCXOPPG6XsdT17yduzf/Gs/nz+i23DNpPCOu+yo1M2cwa/QYxlVU8Pbat/At\n7u5p4xpTzbrXnx80GdYebaJmfPExPT6H8PGDYRyf5xSAoupEIpFjN+yCX91yIw1tj3LagjQvv78J\nz6SjJzB2hXvhUl58/D0+c5RTub4uSTyqs/iUwn6PRVFEPneBg7+u+gelV36T5udfov29zeRMtq85\nmVCI4b4QojiwsroOLJkrcsdDdzOpqIVTTu/ZR1mFi7IKF8mEwfo3Q8SiOpYF7a06Y9NV/Hnm+ShS\n7yTMl0Yt4GsP3cOJV8pIcv9/x8a6NM66XL4wYVa/lF13bVnPt2ae1MNL7OIxU/ncU/fhkOVe7xO9\n4cnarSQ1jZ8tOpMbXnuaheUjEAWR9Y11jC8o7dE+odlzD7dy7Bc7KV0jpqVZ27Cfq6fMY2H5CC75\n1z00aCnq2o8vATlj6JiewZ0DQ/j0Yuiu+SlAx+T/w+y/N3QQNJ8k9NfI/oPYzkD6Oa7tdviGGcbR\nnQCy3/cHYl8llVlC7PUddbZCSlFIp9PIkoxpmpimhZUlxnTdQJIVTNPCME0kScHhdJNOp2w/LlkB\nQSCTyaBrGfz+XNSsB5mUVYbJsoLT5UFV1ay/l8rhojcBRXGgKGp2ua3G6yjBVLNKBUmUbMJLVTEt\n0ya6BAHLtGwDfVFEFO19drs9yLKCqjpxu31YpoXD4cLhcOPz+bAwO8tgzGi4089LbzwAqgPTW2gn\nNBaW2SN0+w8rx5wem/jSMrYSy+NHECWMYCNmexCpsAy5uBLB4cRKRBFUp632EiWsZBwjGkLbvx0z\n3AqiBLqGlUqg792CEWwA1YEYbUGQJMxIG2Cb4xvBBkxPPqa/CLlqvE1u5ZcjyCqiN9cuu0zGkUdO\nRPQFEPNLEWQVpWoMoi/XJvWwSyatdBJt33bMkG3Wr7fU2/vZi8/QRwUm1oD+G8KnFyX5+Rix/iWj\nhje8S860KcdueBQ4aqpZGWkA7CTJsfndkwPv2reVZ8dXosyciu/SL/PolioeezpBNNJ7splpWry3\noZ3Hbg3yo/LljM0r6fdYkrrGu831TCw8PEkKp5J8+5UnaKGde13P89n/52HhMqFHmVF+oYP5F3mp\n+Vyanwcf5fn6Hczxy9Q3HD2VDOx7SUPLLhbP/CxzpqzoQYR1RcBfwvmnfry6oukAACAASURBVI9k\nKsq6TY8CkM4k8Hry8bh6kpGalkQS7ev0ymduwuXwHbV0Mxxp4v41v+CeA3/G8/kzejXi9p5QTeUl\nF/C9G25g8aJFNL74KlYvtgTRCgeNrbv73O+jYeOuB/nS1VcMat0hfLQhisevW9A1Ca+373JpXde5\n7/67+OJXzua8C8fx0qt/oXZXM3u2h0k98U+0cHu/tiW5nDT7xlC7t/dn0o3rwiw4qWDA+6CqIrNP\nSJDYtZOipScR3bodPXu9Tb/6LCfNG/xzRF6+Sl54O6ec2DeR4nJLLFxSwOnnlnDGeSVc8sVyEuVt\n1MeOXkrqdzi5adT5PH17CC3Tv8zpxgNpGp5ycUXlXAzr2OuYlklrMk6Jp2eoiG4ajMzJY0trI3/Y\n8BqhrI1Fb2iIRfjluhdJ6BqfmzATgGUjRvPiAfu69NSebZwxckyP9W59dy2fGX3s+5lpmfxo7XN8\nc8aJ7A61AjC+oJS7z7qCn/7XdznvK1fxdmPdMfs5Gh6vr+W8L35+0OsP4dOJIWXYED40fNKUYV3R\nVYl1LN+2wWKg/QykfYff1hELu33sqsLCsmxfsX5IXzoN9w3jsPF+lzdmXRViyxfloigKkizjyJYK\nebIm0g6HrcJSFDm7nkgyGaewsDRLgqVQVAeSJGGZFul0ErDQNM0mxbDJLVEUs8mQclaxJSFKMg6H\ng0zGJtYsy0KSFFKpBJ7sw0Q6nUSUZKysd5gkKagqyLKCYRhZ9ZuMojjIZHRycp2k0ykkSehmxi9J\nMoIokkomsCz7MIuihKAomMkMcn6JrdjSdcR4EMvlRzB1JEnCVNx2WSQ2mSQXltlKqqwSS3T77P8P\nNmClk5A14bcMwybO0ikEjw9BVlEDhbY3WNpWgQlON2JOPlJOPpZhgK7b3xsGourEkkGpGoPg9mMl\n2zEdHogFsdy5IIi2AszhQiqrxgw3Y7bU2X0HyBrs+xD8eUi+gF1maRhI/nzEshMw63dBXhliNIyg\nqDY59xHFkDJsCP3F3Nmz8Ty2CmpGHbNtYu9+hl143nFtryngJa1rvNdSz+ldJiiWZXFfvAl53inZ\nBSBgkUiYvPp8K7phUVjkwO2W0DSTUJtGNKJh6BZJy6I+EWZETv/KVTTD4Ka1z3Hd9MMllfvb2/jD\nxjXMGVlO66T9jJpy7Df1iiKy8CIfKx97jRXuBax6+VdccfGdR72vPfny7/nM6T+iaAAm+vOmXcTz\na//G3oPvUnvgbeZOWdFru3QmQX3Tdt7b/hwTRy8l11/Cs6/9iXCsGX9RGW5nDrF0iKZEPeFCC8d5\n0/D4ek4Qu8Izewaf/9Y3GCYp/M/1N/A/q5+HZUu6tzlpJnf9/Rdce9LvcTr6r8rbW7+OmokeCgoG\nTjAM4aOP6pHjaG58naKSwb9cjoZdlJWVHfX7v/7v73n19YeoHBVixiIH4AQOk9vtYY0Xnvg5B5VR\neM7/LKKjbwWQ5/TlPPaXW1hhxRk+8vC5nIjrqA5xQAqprhhTo/DKI09gicvJmTqJ1pdfo+Ss03Ck\nw3i8xzelLSqUME2rU83fX0w71cPf73udH08+uqpzRE4+Ur2X3/+slnknBZi9IL9bomYHIu0aW15O\nM6ytlN9OP53acJD1jQePOYbV+3ZySlVNr989tWc7Z1aPY0ZJBYdiEe7Y/DaxTJpZpZUUuDyYlkVT\nIsr6xjqK3D6umji7myp4QdkIblz7LFOKyvA7nD1K4X+3/lUmFpQwIrfve0ZK17hp7Wo+P2EmpV4/\nUpeEyMkFJdz26GPcuXEt542awH1nD47Y3yxrXD59+qDWHcKnF0Nk2BA+VHwSiTDoaXb/cYNlWYhd\nxm1ll3UQWR2fO5H1vToahK7fC9lyxq5/jyTegC+eeyZ/f+xJzp43hWAwSFFREeH2ELIkoWVs3622\nUBAAVZbJZFK4XG4SiSheb06WbLNIplPE41GkrBLMn5NHW7AJWVaQZAVFUTqTJ0VR6lR+ZTIZ0ukk\nkihhYdleYFqGcKgVUZKIRCIEAnlIkoJpGlkvGQVVdZBOp1EUBdM0CYeD+P05NDUeIjc3QCjUhtvl\nIp1KApBMxlEUB6ZhoOs6yWQCvz8A7a0IstqpwjJjYeSxs8m88zxqzRS0ul3I4+fb6Y7lo2h6bjWB\nEypQh59Aum4ncmEZiXUvILrdiE4PWnstWn0tgupErRqL0R7EjIYR9QxSoMhWX6lORLcfKxXHTMYx\n24OIbh96wz7bDyynENFfYJNdoQZbgWaaCG4v5rY3kEdOQN+7CbmiBq25HjEatv2+RInEtk24R49H\n27MZKVBE6t1XkUurbGLMn31IMm2STszJx6zbhuDx2WP4CKdJWgPwDBsiwz6d0DSNl19+mZUrV7Lr\nxZcZtWQh4rHKJcXjv3ckPC6uX/M0XkUllkl3TmCerd9DaNZkHICp68Tu+TMXLYpRWuoFvJimRXtI\nI5EwUFWRiVNlnC57ghNsSfPde1ZydWwJl42a0ecYG+NRbnn7Za6ZOp9it5c36vfx3P4d5Ds9XDpu\nKn8Xn2HmlJ5Khb4w+xwPN9/2ElPG3MjjL/6ac5Z8q8cY4okQgiBSWjQwDxyAJXOu4h+P/D+qyqZQ\nUtiTtNT1DIapc+8T3+MLy39HeclYAM45+b+45aWvETy3CCOVQnaXIrpG4u2vp6co4ps4nkd/9ktK\nS0u5/8GHeP/Of1L82Ys7U+xEVUW6bDF/uuf/cfXim/G4A8foFXbXvY7mXM/13/neAI7CED5OuOLy\nL3H11++nqGRw9huGYdHSKB1VsX/jTd8ipq1mwVIJ6J1wy8lVWH5BIe3hVu668zc4L78W2dO7Gb9l\nmkReeYnRw5Ls3BZj7+4EM+cH8Hhk3nkzzMy5xz6vu/VnWezYGmPHlihen8yiGgVHy13EEiI7DsSJ\nrmrBmUkOqM/e4PbIpFNmnyb5vcHpkmhwtpLWdRyyTErXeHjn+9SGW1ElGQsLwzIJJuMcak7z0gPt\nOHcUksyNIgZ0ZAdYSYm6PUkWuEfzk5HzKSy3VXyjcgu4e8s7xxzDxuZ6vjNrSa/fbW5t4JxsIuQw\nr59vzFiMZhi813KIUCqBKAhU+HI5c+RYRKHnOSIIAg5J4ua3XuCGuad0Lm+MR/nt+pepDbWytz3I\n3vY2Lhs3vdP/sQNtqQT3bdtISyLGNdMWMMxr22IcacR/7cQ5PLXrfbxjR/F6w37mlw4/5n53xaP1\nu1l0Ue8pxUMYQl8YIsM+4fiwDfQ/zehrkvBxCQ/oUG0JZA3zOwisrBKsgzTrIMeEo5BiVtbD60iS\nrS9YgsBV557J/z72JKfPHENd3UEKCgIcrG9mVHUVbcEgLpeDVDoDMmiajqKYyLKt4DIMg3g8isPh\nwuPxoWsZJFkmnU6SEyhAEiVSqQSZTAZVdWKaBqZpkE7b4xNFkdxAAYl4FEmSMU0Djy8H0zSwTJNA\nQCaVSiBLMg6ni0w6gWHoGLpOOpNGELyYpoHXl0NjQz2FRSUEW5vxeDzoWgaP10802o4iSqSScdwe\nH8l0CqfThaFrCKKE4PFh6XbZklJWjenwoE6zH2jkcXMxcofhqJmKmVdOyRln2OmTpomSNbF3jptu\nq7FkBQlssskXwIyGEGQF0eVBLq7ETETRG/Yh+vOQ8ksRnB6MAztt37BYGDEnH/LKQE9hBQ9B6QlY\nybhd4mga6AWjkFUvpKPIlWOwZCeO0VMxFReilsQyDHz5JbZ5vsuDmU6iDB+DVDIcs70V0Z+H0XII\nQVERPX701gakQBF60wF72+rATW//XRhShg2hN6TTaVavXs2qVat44oknqK6uZsWKFfxt2TJ+sn4t\njhlT+1z/eOPpARwIfG/2yTTEI7zVeIDhObZB/F3B/ahTbCPj2AO3c8lJMYqKDis5RFEgkK8S6OVF\nfn6hgwu/MIw7HnyJl97fzASpki9UzyM3mxJpWRavNtSyqmk97zc0MtNdzY/fWE2VP8D0kgpumHMK\nkijyXxtXMeVz/Vc3dUAQBE4600PrljATa07m/id/wDknfwuP6zBh/vqGB5g/7TMD7tved5G8nDKm\njjuj1+/Xb36csSMXUFE6gZfeuoPTF32dgkAFTcE9ZEbm4vV6kL0D3y8A16J53PHgAxiRKIf27eOe\nP/6B7/7ut9S5HRQvW4roUFH8PozPn8gfHvweY9SxnDLlc932vQP1TdvYVvcY02ZX8vX//P6gxjOE\njwc8Hg+FeePIZDagqgN3t9nybppUMoeqqiquvPJKrrnmGiorKwH47e9+StJazagx/SOAcnIVrlqh\n89ff/zfKRVfjHnWYUNZjcWIvPg37tyOEGik7MUD58FxkReT1l4Kk0yaRsMbCk/uvYGxpTvPycy1M\nnJLDOReW9njunj/XTTLRwIN3HwQGRp4ciUzK6JFq2V9UzIHLH70DXTcpsgL855RFXDpuWrc235+9\nlGf37eC+bRtY7BvLGcPH0pqMUxcO8+KBXUjxKN+ZtazbOoIgMDI3j9pwK9W5fR+3o81JensmVySJ\nGSUVnZ8N0+TZvTt4r+VQ57KA080FNZPId3kQEDi7egLhdIoNTQd5pW4PhW4vEwpKOaliFE5ZYVe4\nlR+seQpVktEME0kQyHE48aoOLhk7rUcJ55GVJmPzi7nilDNIlOTxnaef44+LzmZKfv/K9Z9v3Efb\n1BO4eMUQGTaEgWOIDPuYoyP17mg4HgP9jwuh81FC12P2kS8R7ertZVndSTC6K8OsrGIM6PYup/Mm\nKwidZZadZZFdlGY9/ma3JYoiX8wSYmfOGk8kGqWwIJe2YBALi0QyhWGYneb6mpbB6XTZBvNWBkVR\nyWRStLWF8Xo92dTGPJqbGlAVBafLhWWZtLY2oioqhq4jSQqWZWJZFq0tjUiiSCqdIRDIIxppQ5Yd\n6HqaRCKF06FiWRapVOKw35cgoCg2eQYQamvB5/PT2tqEx+0hHA7jcbtoaWlGlmXSqQiKqhAOh7As\ni2g0SiA3F72lHim/BNHpIbXrfbT6fcgFJWjNh5DzCmzSq7waPRnH2LmR1KFDOAryEEQ7yTHT0ozs\nVG1frkS0MwFSjLQhdEmVNGNhjFAzgtNt+4slonaZpKKiBxuQfAGMaAgO7kYuLLPJtANbMIKNWKk4\nyArClpcwUgmsQKGtVCurJl23C8HpxkolEFQnRiSM5PUhFZZhpeKkDuzFlUlhRsNI+XHMRNROzwSk\nwjKkkioQJTLb7beenhEfTWn7QLzAhjzDPtlIJBI888wzrFq1iqeeeooJEyZwwQUXcNNNN1FRcXhi\n8fBLL7ItGETNP3rZiGWZWIbZq8dUf6FEYuQEnOS7PDy04z3IBsWFPLZ3YrK2llllTRQV9a7gOBpy\nchWmz81FknQoruc7r92DlZIQLAFDMCisEZi4zMWu38l4VQc3zl/WzTg5kk7RnhNCUfr2KDoayipV\nnn7iVqTwQprS+2n/13dxiC4WzryM6orptEebCOT0NHHuL5bMvYo179zH6Yuu6bbcMHT21G1kxoSz\n0PUM08adwTOv3Uoy1U5JwQmYswa3Px2Qc/y8+OQq9r27iTVr1nDXXXex5bnnKSouYnJRKfsi7aSz\nL5YCZS5yXC3saLmNthYDwXQiS040M4GkpJi7cBx/vul6nM6P7ouEIXxw+OpXvsuPb76EeScN7B6T\nThnEQ1W8+MJT7Nmzhz/+8Y9MmTKFpUuX8oUvfIGNmx9h3kkDU0K5PTIXrMhhzct/wXonD82QCLem\n0JMJZk0UGHWGF1UtI5k02LE1RmtzmpEneBgz3sdjKw8dewNZNNSnWPd6G8svKeu1pLADLrdESamD\nVMrA6Ry85UI6bfa5nb6QX6jinRRi4ZICdm+Kccu7zzCxeThfqVmAM+uJKggCp40Yw2kjxvDs3u38\nfdNbXDNtARW+XOaVVfHD15/plmLbgQtHT+FX617ixvnLetv0MdHXi5dQKsE9W9+hLZXg1KrRfGfW\nks7tN8aj3L31HdrTSVpTGW49pJPYvYNMtI5cGeKaRjAZpdTj56eLzuTEyu5K24PRML9751Wum74I\nv6P7dWpHWzPD/T0VgpGDDdS2t/D8xrf5682/Zv2W97h0+NijGvOHUgnuqttO8bLF/Mc1Xx3ooRnC\nEIAhMuxjj/4QLoMlZD7Jnl8fFj6O5ZMd3l5At3LIDhVYt5LHI9ftuqxrmWRHn0eQZR3LRGxlWAfJ\n1kGInT13Mo2NjVRUVHCwvp7C/DySqRRut22c7/fnImdN9W3PL5uoLSkpRtPStq+Xw0lhYRGiKKNp\naURRtMsr4zEsi05STVVVXG4PmXQKj1dElCS83hJ0Q8c03Xg8BslEHE3TcHu8JBMxDFNHlmXbMwwQ\nBQl/Th6trU0U5BfSHg5RUFBAOBwmEAgQj8fx5+WQSiVQFBft7e243S67XDNQiOj0IDicKAXFOMbO\nQCsejSseREy2Y6oeLFcuUrQJsWYm0s63bSIMEHPyUSqzZFd7EEvX7KTG1GHzbiPUjCCrtmosv9RO\nmvT4symW9sOZKIqIOfkIbh9SSRWWpCIkwki+PMTSkZh1O2xvsMoJiJkkQiaGY+45WIKAwxdA8OZi\nNB1AzJZnWbqGlJOPGY/g9mf9yAJFkFsMdTuQx83CMg17XMkogijhmDQf0fHRTf+xieH+tx3CJwuR\nSIQnn3ySVatWsXr1ambOnMmKFSu45ZZbKCnp/a31nbf8hhVf/Qr1s6agFPb+Nt8/YRzt724id/rg\nTPQt06S8PdHp3zI2v5jNrQ1MKCglk72cm2+tZtbZg/u3NWa8jydWNjB6nI+55/WciKx/Lcy1I5ew\nLdjSY6Kyct8GTjj5+EIxRk0W2DZxHMUlJxFes5HgUy/RIHrR7v4Ro/P7Vt0dCz5PPulM96ADy7J4\nePXPWDrvS6x+62/UVsaRiguhLAdiDg689zbxdSkcxUU4igbvzbVhw0ZWP7SSL3/5ywSDQYYPH85N\nN93Eueee2+d6uq6TSqXweDwfm2eLIXxwOOGEGs4741s8+/IvmTq7f79/JmPy+vNebv3DXQCMHDmS\n3/zmN9x4443ceeed/MfXPsfV3yym43lgICivdONSwkybrLPu9RZOO7uAopKe6ZBlFXa6997aBP/4\n20HaM8dOGwSIRXXWvhLk/IuH9cvDa9rsAOvfCA3KmB8gGtGOy3NMVgR03fYbq5nipmYKBFsOcPXK\ne/n1xAspOCKZd9mIMTjlWu54fx1fmDgLgIXlI1lTv5eF5SO7tfUoKgvKR3D3lvVcMX5Gr9tXRImE\nlumVNDKyL3+PvG7sCrXw9/ff4hszTqTI3ZPoL/H4+M/pi9BNgz+/+yYNTVu5cvR4zq6ahZSdM6R1\nnfu2b+CHrz/D+IISLqiZhCiI7A61si8SZlrpCK5evYrfnXQuRZ7D23h45yb+c8binvshCDz55JM4\nnU6+/fOfsGL5cv50/x9ZPnM+J/tLKHC6MSyTplSc1zJhcseO4oobfsOwYcN6PS5DGEJ/MESGfczR\n20PRB6noGlKTDRz/1w+qA/oNekl+7FoW2bN5zzLJ3rzHjjTjzw6sUxHW8bmTHOtCiC0eX0Uo1EZh\nfh6twTb8fi+ZdJpkMoWiRBFFiUCgkHA4hiRJWfIRFNWRLYU0aWsLIokiTpcLl8tLOp3A4XRBlqSU\nZQVd14nFIsiyQjpbvpiIRe19kiTi8Tg+nx+X22Mr0xQVl6JiGAaKcjh5MhGLEMjNIxxuw+Px0R4J\n4/P5iUajOJ0q0WgEh0MhGo2iqmrn+WGlUxiahiBJIEqEnn2YnAVLMYKN4MtFbzyAXF6NdrAW0eMn\nvX8XkteLIEpIesZOozQNMi3NOIpLsRJREEXMaBhkBdHhQg824MgvIb1zI5IvYBNfsgqiiBFqsVVl\nyTh6S71NaIkSFmC21GOm4qBrGHXNOPOKESwLvbUBOIRUOIzMvm1IWQm7EWoms38XSnEZVjJujyPS\nBrqGGQsjtAeRC8sw2oOga+Dy2B5msoLZHkQIFB37XB3CEP5NCIVCPP7446xatYqXX36ZBQsWsGLF\nCm677bZ+mZSrqsrDt/2Vr91wPRs3bcWYNRXF171ExDumhrp7Hhg0GRZZv5ErC0d0fj5/1ES+8+q/\n+OXis5BNSMXjlKqtSPLgyDBRFPDnKoRDGrmB7pNly7KIbVfZJ4a5ZIxNTO0Jt3LH/rXE5ASbmhu4\ntKD/aZS9oTTfZFMwhLOkGPeCqYQaD6COq0IdX43jkebj6vtIZLQUj6z+OfOmXkhebhkHfSFyzz61\nWxvf7CnkJZMEX1uLHotTet5ZnV5f/YWZSrNo/nwuvPBCzj//fL71rW9x1VVXcdZZZx1zXVmWj5kG\nOIRPNpYvvwRRklj5yM1Mnafh8Rx9CtdQl2HH+4X84Xf397hm+f1+rrnmGtZtWElObmzQ4xlW7mTt\ny0Eu+mx5n8+9giAwcpSH8goXf7ytlf37Ugyv6lvR+OZrQZadXdxvM/vCYgevvxzslfTpD9a+EmTu\nov6FhvSGRNzA3UtS7pwvGHzjzge4dfJleI/w0lpcUc2GpoM0J2IUub0sqRzFTWtX9yDDAJYOr+GJ\n2i3c9u5avjJ5bo99PO+ECazc+R6fHT+zx7qzSyt57eAeFlVUdy7bHwlx15b1/HTBGZ3E1tEgixJf\nnzafsft3cjAa6tbeIct8foJN5r3dcIAvPPMQzrwacisWkFd2OoriYvzwMN/e+RwF6R1cXFnKCbkB\nREHE1UuKeGFpaafadd++fTz73HPMmTOH61fezdo1a9je2IQoSRQOG8aNC+ajKB/dJPIhfHwwRIZ9\nAtFVnXQkMfLvIKs6J/ufAGJssMfvw9z3/vQ9oIeBbNuut8O+PMCsztW6KMeO0q/Y299sH1aXbQuC\ngCSKnSmTy6bV0BJsQ1VkMhkNj9tNNJogx+9HEER0PYNpGmTSSQzTRBQFDENDQETLpHG5XDidLqKR\nMIIgIggCsWgE3TCwLBMsMEwDj8eHpml4vT5M00JVHWia7eGlqgqZTIpUMoHb48EyTVKpBKZhYpgm\nLpfLNuCXZLuN20t7ezhbmpzE7XYTi8VQFJm2tnZyc3yk0hl0XceVVUJJ+SXoTQcwo2G84yfZBvbZ\nREdl5HgETy5SOoVUOAwzGbdVWoCVSthElGnaCZCZlK36SsaRy+wHHjPcgujxg+rIqrWimPEIUqGd\nKGVG7HACuagMRBFL1xC9HqxUHDFQCO0iWn2t7SvmCiC07LPPE18uZrjVNuXPvoW0UgnkghK7pNLh\nxEzGsbQMiKKtOgsUoTfssz+rTqx0Ej0aynqSmQgOFx/VPEmzr3O8l7ZD+HiipaWFRx99lFWrVrF2\n7VqWLFnCRRddxF133UVu7sADHhRF4a83/5LGxkZ+/be/8va+N2lIJfF6/YipFI7WECMbmogfOIij\nsnxAfVuWRfNTz1Iy+TBh45BlFEHkhjVP48r30rxjB6eM6RlcMhCMn+Rj1/ZYD8PrNx+Pc3X5Ul6o\n3cWuaDM/3vUEUmWS8Ze6cDol6h8VGCBP1AOKIoB++B5XfPpSWla/RPGZy0gp2vF1jl061Bzcxxvv\nPoRp6Jwy/yvk5Qzj1fcfgAWje11HcrkoOvVkknX1HLxvJeWXXDAgQqzlhZfZ/eRT3P73v7N8+XIu\nvPBCrrvuOiTpo3r1G8JHDeedexGzZy3ktr/+inf3v01ZVRsFRRKKIpBMmuyvFUjHyli04Hyu/8bn\nUdXelVi7d+/GnxfmeKaBU2bmEmzN9Pt5U3WIfOWqACsfaOFzVx1dyWMYFsmEidc3sLGNnejjnbfC\nzJgzMIP+UDDNwf1J/DmDJ1a2vR+lZmxPstrplJh1mcoP73+CW6Zd0OP7y8dN577tG7l22kJEQWR0\nXiFrDu5hQS+E2NnV49nQdJAb1z5LqcfPxWOmdno5VucWcOfmt3slA0/KkmwdZJhlWfxxwxp+tvDY\nRFhXLB1ewz82v82GpoNMK+55z5pZWslPfTn8YGsdM6ZfgdgldXJM9UIMQ+exzY/wxvN/467FJ/a6\njYzz8G/w85//nJycHK677jrcbjdLTz2113WGMITjxRAZ9gmF0IVk6Frq+GGXPnZ4lHWUCX7cyywH\ne/w+zH0/Vt9df4O++jBNs1Od1WMS30UBdrQJfl/pksDhBMkuSrBeOunuJyZJhwmx6aPRNB23242W\nSZOXl4OsqKgOJ4Ig4vXmYJg6mXSaRCKGgF3qqKpOEvEosiwTyC/C0DU0LYPX5yeTSdtpkKkkiqLa\nZYKiiK7rnURYx1gAvF4/kqTgcDhpa2vG78sjEbcVaZIkoet20qTqctPeHiIQyKO1xfYQMwydnJxc\ndC2Dz2cfU0mScDocOF1elJHjMYKNOGqmotXtRJBVrGgblpZBcHkwWuqRdM02sK/fY/8somQbzosi\n+iE7AdI5YS7a/u0IqhOS2dIfXUOpmYpgGliibCdMVpyA0VQHWa8zZcR4W6UFdollOgluH4I3BzJp\nBEVFChShjBgHqXbw5tipnJkUotuHFmxA0DPIVeOxOspiTRPRm4vg9tkKNVFCUCW0AzuRh1Vhhlrs\ncyMWRh47F9rqO0myjypME4x+cgq9hKYO4SOMQ4cO8fDDD7Nq1So2btzIsmXLuPLKK1m5cuUHpsIp\nKSnh1z/4b0zT5PozV3CVuxCvx4E/z4F1Alyy6lkOXnkBSk5Ov/vc/7//ILRtJ19pbufZC76CV3UQ\nTiUp8fq5eOxUrnz1cYxhHjyTju8Rz+2RScT1zs+WZfHW43HOtuYwp6iKP216lSZ1PzOv8iAIh4+X\nwymSSg48la0ronELoeBwn7LHg5FKgyAQ0toG3S9AIhXhQMNm/N5Cli34Kk6HvZ2WcB1r2l7AO7J3\nc/0OuCrKyF8wl6Ynn6Xk7NP7vd32de9w3bXXctppp7F3715efPFFbr/99uPalyF8+lBaWsqPfvgb\ndF3nqaceY1ftNtpbIuTlF3L155cyefKxy4hDoRCqM8PxTAMVRcQ0aYyS0gAAIABJREFU7QTat98I\ndd7/BAF03aKyysXEqTnd1F0er4IiWSQTRo/rQweRs/nddiZO9Q94PKPH+XjtxVa2vR9h7MT+rR+N\naKx+qoX8IgeGbiHJA39mtyyLlqY080/sXVnm9cnEi8K0JGIUHlGOWOj2EkolMEwTSRS5ZOw0frnu\nRdyK2ivhNK24nGnF5RyMhrl98zo0wyCSThFMxbEsixf272JpVU23dURBpNjjY197G1U5ebxev5dT\nq2pQBkHCXzZuGj9784VexwZQ7s3h26M0/vLab1m4+FvdvpMkmSmTL2TEyEX899Pf5A+zZ3RTh+0O\nt1I9fzYABw4c4P777ycnJ4czzuj7ejyEIRwvhsiwTwGOJEU+bHKqa/8fZyKsA4M9fh/mvh+vT1yn\n4iv7n0BPcqtD+dXxt6/yyd7W61CUdazTa8pkFx8xQRRtr7EjCLGWllYcqorDaZcYWqZBJmPYqjTT\nxDB0LAvSmQyqKpNMxpBlBcuCWLS9k/CKRSMYpolpGuiajupwoOsahqFjmka2fFLOErk2mZeIx0gk\nk+TmBogn4miZNKIk0t6eQpIkZElCUVUi7WEcDiehUBtuj4tgsBWv10NbqJW8QA66bqCqKu3tUTKZ\nDIosozXsRcrJR6uvxYxHbTIrtwShQy0l28mLiJLt82UaWHoGI9QMHd5ckSBWKt6p+Op4yyc4nGi7\n38PSNJTKGptca6qzSzCzhJagOjFCzTjGzrCTLV0ecHogZadImsm4XcrZVIdUkk1pEkW79DEVx8oq\n6IxDtRjBRgSXx/YwS0TtMQLoGnqwAaWyxi6fBFtt5gtApBkzGsaMRxDag32eU/+XGFKGfbKwf/9+\nVq1axapVq9i6dStnnXUW1113HcuWLcPl+vC86xKJBPmqi3JfF5WZAHdOWcJnb1/JoYvPRCntu7TQ\nMk0a/vYPwmvfxOdysaGpnnMfvZ2V53yOB7a/y2fGTOHxhr3IZaVEdtZijD++89HQLWRZxNAttq5L\nENumcFXpUmYVV/HQvg14Z8WZdlJPFUbNGC/bNkeYNmtgCo2u2LJXwT21+4Qrd9pkIpu2IAcMovFW\nfJ7B+QO9seFBLjnrpxQEDoceNLXt5fb1P8F9Zf/ILffwCsIb3kWPx5E9x06XjO3ajSc/n3v37OCp\nb15H+GA9lVMmc+BgHePHjhvUfgzh0w1ZljnnnBWDWleSJDCP/xm1bl8Cn1/mxFMKcboOkyuWZbGv\nNsGTDzfi9cssXlrQSYqdfk4hD97fzOWfK2brpih7dsVRVLHTXaNuX4IvX9dTGdUfLFxSwBuvBnnu\nyWZOXFpw1HRIy7LYvjXGlnfbOffCUpoa0rz7Tpjpswd+zdq7O8HIE/q+Bow70cHfH32d707oaYI/\nrbicLcFGJhXaarn/mnkSf9i4hk0th/jMmKm9lhOW+3L56pR53L9tI25F4ScLT0cQBH7+5vOU+3IY\nk1/crf1VE2dx/WtPc/2cpTy/fxf/Pe+UAe8n2CWTfoeTYDJOvqv3fR6bV4Cw+x1M0+imDutAjq+Y\nuafdwvXPf5vfzJ7VuXxVsI7/uupGAH7xi18wfPhwLr300iHl7BA+dAyRYUMYwicMAynR7FBldSW4\nOsisjmXHUoh1Xadr+27997a+Zdmpk4IAltUtsVIQhE5C7OTJo3CoKocamnE67VQ0vz+PcNhWGVnZ\nN2oOp4tMJoVhGBimgWSZqKoDy7LQdR1FUVCyT1tujwfDMNE0zSYOLXusmpbpJM9EQcC0TPLy8kkm\nY3g9PizLRJYVRFG2fce0DIah20b8Gbs0MpFMkJubi65rFObnkUqnsSyLTCaD2+3CNE0kWUF0eTBj\nYURvLmY8gt5U13HQQFbANDDTSYxQM6LHb5dDmobt/ZVvm9RbaTspVgoU2Uqy/BLMaBgt2IBSUWOX\nNEbDoGtYesYubfT4MNuDGKEWlGEj7H4yKQTTsEkxp+0dBiAoCmakDdEXwAg2ZP82IhVXYOkZRF/u\n4bGlEli6Bh6/TfId2guAXFhmk3uKihlpQy6utI3+TRMxJx8pW+L5UYVhgdFPkssY4sL6jba2Nu75\n0220bN2BotsqVVOWID+Xz3ztasaOG/uBbWvXrl2sWrWKlStXsm/fPs477zxuuOEGTj755KOWEX1Q\niMVi1NbWsnXrVsKRSI/v3YrK/dNP4abH1/CGrBFdMBPH6O6pXHo0hvD8qwyvD6Lt2IW/cjh79uxh\n4cKFbNmyhc+//gTFhshaPcauMxejnjAS/vcOWloPUHIcvsJtrWka3jfZulvh8rJlTJ1ik1PhVJJH\nkm9y6kl5va5XUeVmw7rwoMmwdMogKJfidXT/bdTCAhIH6pCnVPHkC3/m4qU/GHDflmURbD/YSYTF\nEm08+94/2MluXFedjij3/7G4YNE8gq+tpfi0vieWWjRKcM2bVF7zJdsjEsgDzHSGK/5+G2Xtcf56\n008oLR18QuYQhjAQFBcXk4g5jt2wDyTiOqNGe1i4pCcpLQgCI0Z5GDHKQ1NDikfuP8Q5F5SiqCKB\nPJVkyuSeOw4xf2EO51xY2u0F7lOPNh7XuOYuyuelF0L86WEnJa4IC6ZZ5OUrSJJAIm6wfpPOjoMq\nw71Jll9ShigKVI5ws3F9mBGjPOTl9/+eEI/rbHgrxPJLy/ps589R2Cr0vl/5Tg9tyUTnZ0EQuHba\nQna0NfO79a8gCiJLh9dQ6LbJp+ZEjBf278K0TFbUTKYm73BwwbdnLeEXb73A4opRLCg/7CmpSjI3\nzF3KD9Y8Q6Xf9uwaLC4ZM5WHdrzH1VPmHbXNRRXFPLPlcSZNPL/X73P9xUjDFrAzVE9NoJDt4Vb8\nk8fgdrs5ePAg//znP7Esi6uuumrQ4xzCEPqLITLsYw5FVfskPY7Hu+qT4Pn1acSASjR7UYYdmSZ5\n5N/e0BuZdqQ5fw9lWNfPXRRinaWTXRRiJ00aSSDgxzR0HE4nGS2FLCsYhoEkK2i6hqFrZDQNRVGR\nJPvSZpoGmqbhdLqIx9NYponD6cY0TQRBwOFwdiZDyoqaJc40DF1HN0ws3cA0IsiKTCqVyA5VRMvY\nPhm6rqGoDhKJGE6nG01LI0sSyWQS0zIxNIPcQC7JZAIBgXgyjixLZNIp9GBDJ/FlRsOIvlykQJFt\nTJ+TldvnlEBLPaLbR6q5Bc/YibZxvp7pVJMZoRYEWbGTHPNLMKIhu/xR19DrdiGoTsRAIWZTHaYR\ntT3JTMNWm4kicnElmAZycaVNqDk9GMGGTkLMCDWjjJ6OEA0hZMlIKxnHCLfZBFgmhZVOIXp8WKkE\nZjSEEWqx/cBEESPYiDzs8EOZEWpGrRqLlUmh7duGmJOPmeye7PZRQp++eL20HULfCIfD3PKd6/E0\ntfGZompKS8d3+z6pazz8g5u5UzW5+BtfZ+rMnobAx4JlWWzdupWVK1eyatUqWlpaOP/887n55ptZ\nvHgx8gAIj/5sKxgMUltby+7du6mtre32/5FIhBEjRjBq1ChKU72f57IocdP4ueimwcp1O3jixbdI\nqwq6AA7TYoQu8J8jJlE8eRKz3t/Erl27KCoq4p133sEwDF7cuomyESPJu+xy1Gx6ZcXnLueNP9/A\nxEmDL/c8uE5g1ayv4jjieN3wzhPMurzvfkuGOWmoT1JaNnC13WtvpZEX9FRoiYpCurGZTEsre50+\nWsMHKcgdmN/ai2/eji4JPPbSLRxs3EpbpYTrzHl4AgNXSqj5+WSCoT5NuzNtIRoe/Rfll1zYSYR1\n7o9DRZw3m8Z0hhXf/iZ3/ejHjBpZ3Ws/QxjCB4mKigq0VAkw+DCKt98I9YvwLi51svT0Iv71cAPn\nXjSMNS8FmTXDfdR1P4iiCofHgeO0C4jn5/HIhnVYta2gawgeP+qM6VjFB5jseqpbCeeZ55fw6AOH\nOPGUQgqK+iYKY1GdNS+1Ul+XpKTUyTOPNwGgaSZjxvuoGevtcU0wVL23rrDovXJidF4R35uzlLiW\n4ZW6Wv7y3lp002R+2QiumbYATy/JkZIo8v05S1m1cxM3vv4sEwtLOad6PIokkeNw8aWJc3i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/HnZbDkLn+fVc567Pvn/Lzx0gEmTg4wpqSrbbK41EfdCzrG9Kl9juGbt5idG1PE/vQBl1yg9nut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+9/L2axWkukyMm+zCkWSsVcGAxu7tHqorQ+i6zowrMjGZRURRwJki4/FE0DQdUezZ+q7rOlUV\nIaZccuZB5elKFbmomLDbTWjDakxhLyIqoiDTkDaYV45YcWw+zMBMqD7lxZ4kow6dflbnKHXCWELV\nNVStWkO4rp7kkb0Tb0SrpUsT7NYIooi9T9siTjUff4Kia/zvM0/z6tPPxNzPZrNx9xWz+M17a8ma\nfUXcc4s0NDDwi+Pc/eB/t2wr6tOHlU//iaf+9jfWvvchNbnZSEMGIlos6KpKpLIa+76DjErL4MFH\nHuN/f/UrLrvsMh544JvsOfgy33o4B0nSgLY30WazyJiJABVs2PoL9u7bzv/86Nf/dtdFCb5cdF1n\nR8lJ5JHFcbUPnK7As20naiBIsKISyWpBiyjUbdlO2uSOadKeHbvInX91r+dnTk/D3qcwrkjMlDEj\nkZOdVK5YSe61VwEQqfdg79+3TTtd02jYs4+mw0cR5DNRT4IoIjuTyLh4KvpFU6j98GPqNm8jecQw\nkke29VHTwpGYEWP+klLcGzcbxTlmX4H+0qeMGTSzx8ctiiJzpt3PsdIdRCJGYa2Gpuo2JvtdoShh\nVq5fwg1zf4ZJ7j56WdM03l2/hDuv/wMZru4L5VktDhbP+Smvr/4pl06+kwxXIZqmkWRz8cGmZ5l5\n0b1xzTNKOBKgxredCRNu7tF+CRJ0R6crx/bt2wF48MEH2whhANnZ2Tz44IN861vfYtu2bV0OMGzY\nMJ577jm2bdvG7373u3Mw5QRfJl+li6yv0lz+ZRCEbqtotEkd7LSbjgJUm2qS0cgtzghvLSmQUaGq\ndUpkc/t4Q71FUWwrTrWfjyCcGSfOY2nfR+t9WhuWtk/7bC2IzZ04nDqPB6fDQZO/iezsdAKBEKqi\noJSeRM7ta0SEAVqTBwkMc3xFQUxKRRdltPpqBJsD/7Ej2PsNMFIQfY1ovkajCqUSQfN7W/pR66sN\nEaqm3DDPT04Gbw2CyYyYlErk6B7k3H5ooQBKxUlMhYPQgj6QTUj2JMTUDHR/E7rfi5iUiuatRw40\noHvr0EJB9PIjyFkFhI5/jpzXDyklnUjZMbQmD2qDG8uQ8YYXmdVhpFGmpBt9JaejVJagNxcLIDmD\nyPHPDbP9YEdT2q8KqgZivGmSiWKSbRg27gL27HuL6bl9e93H56FGFgzueXrd+ULXdZ5//nkefvhh\nHn74YR566KGWiE+AIYOHc/jAR8gmkaJ+RoTY0BHJDB2RTJ07zI7N9QQCRoSz2SIycowRvdXkVRg6\n0ogm9PkU7HaZKRenseqtSubOz+nR59+ad6uZMLVt6nHpqQD6wdfokxNk+iXmlsqSAA31dXy0WaFc\nSmdf7mWEa3eRW3YSaXjO2ZwqLBkZ+E+UUHDjdVSt/hA1EOhVGmLGJVNbzPd7Q6i8gn733sWWPz6H\nz+fD4YgdrXXf1+/guz/4AWkpKchTu6/iGKmqIX/HHl54cmmb9wAYWRIPf+tb/Jeus/6TjazesJGG\npkasZgtD+vfn1ie+1WIC/fzzz/O1r9/CP175Hlddlw1xmEwPHiFScmINv/yViR98/xfdn4QECXqJ\nz+cjYDF3ea2o6zqebTtpOnoca24OWbMubyk8oYWMNMWGz/ZSt2U72XOuwDnojLAmCGKXhvnx0Fn0\nVSwc/fsSqq6hcf8BkkcMw19SRtpFU4zj0DRqPlhHuLaOlDEjyV+8oM3aqwYCVK9dR+myV0ibPLGl\nuqX384OUv/YmoiQTfWwbqu4oSjXuO0DT0WMU3HgdgigSKDvNmJQLen3cY4fNIT9rCCfLzxQRcXvK\n8PpqcTq6jnTbtu8tpk24LS4hDOCTnS8yeczCuISwKKIosuCKH7Dsne+TndEfb1MtIwdfzsdb/87e\nwx8yavBlcfWjKGHWbv85v1v6vbjHTpAgXjpdfcLN3kTWTqroRLcrihLXQBMnTuTll1/uxRQTJEjQ\nml57hHVGc3phx81tRaF4IsNat+kgSOl6S6RXS0SaIJxJOWyfStmqfTxorfaN6RnWSliLx/8smp7Z\nrWdYq3GjUW6tBbErxw0mGAxhsZipqalHkkRsVjO6qqJUlYCmoXrrkbP7oMtW1JpyxEIHaoMbyWRu\n9vjSMDvtRKrKUZuFJgCtwY3qrTeqUabnGN5hKemEDmwzKjkG/ajuSswDR6FUl6HUlGPqM8gYV4mg\neT1oTR60BjcoEUSbw0iX9Naj+b2ES49jyshGl63oQR+CyUiR0AUBwWQ2RLkGtyGmaSpydiFKxQk0\nr4dQrRuz32t4hKWko7orkfP6Ein5AgBTUiqiMxX/oT2YM7Mwjetwer8SqLoevxiW8Axrw/QrLudH\nz/6N6b3cX9d1TieZvzJ+YdXV1dxzzz2cOHGCDz/8kFGjRnVoM3XKLByHd3PkUAOnjvuZONWF1Was\nYWnpZqbPPBMF2dgQYdPHbjKyLEyddiaKKxTUkGSBrBwrw0Yl895bVcyal91thJiu66xdWU2/AXby\nC89EJGz62E16KlxzLQhCx0iFFJeJ+XNMqKqPd1a/S0m/S9i15TjW0Wd3gyrIMnqkuYLlqOE07NnX\nYqjfE8zp6UTq6lG8TcjOnhUV8Ozeg6N4AKJJxnX9PJb+9a/897e/HXu+gsDggkJuLBrI2vc+ojIz\nDdOYER1u1ENl5Tj2HuTCwiJ+/cyfkLupfDfj4kuYcfElnbYJh8MEI8eZu7Bn/np9+kns2fEumzbP\nYeqU3lePS5CgK7xeL5rF0qkYpkUilL+8nNQJF8T0/RItZjKmX0TG9IvwHj5C9fsfUL9lB5LdhmSz\nEaqtPftJigK6qiFI8T1AdU0cR9lLr2PJzMCSlYkgCGgRhfJXlpN+8VTsM2MLPpLNRu41c4zo2/fW\nEq6rxzXhAiMybMSZVG4tonDsibZZU77jJ/CdOEleqyg4bdNBZoz5dS8O2EAQBAYWTWgjhjU11bHq\n4ydZPOfRLvctrTjA1LHxZWvpuk5FzVEumdDzgh6ybKZP7gh04PrZPwGg5PReKmuO4vaUcvH4m5Fj\nmO1HcXvK2HJwKb/83UPk5JzdA5oECWLR6Sd4cXEx+/fv5+mnn2bcuHFYLGeU43A4zLPPPgvA0KFf\nPR+PBAnONedcgDpLzlmUXFRwilV9MZapfBfzad8+Zuplc4RWFE3TzhjVt5qH3txWEEU0Ve1eEGsV\nRRYV2VqLXVEhqyVard0xtxfwxGaBsHU0WQfPsqjo1j7ds9m3TBJF7pl/Nc+++Q6XDC0ioig4nQ5E\nUcRssiBozWmFYJjXayq4S40Ki/XVqO5K9KDPELQa3CjBMLLVjJyZb0SDNbjRLVZMfQaBEiFy+oSR\nYimbkDPz0bwexNRMpMw8lJrTaH4vgiihnD6JHjZC6jVfI3ooAKJR9dE4Nyp6OGj0k5yCYLGCuxQ9\nFERtcCNIEpq3HrXBjRgJIzpTER3JqPXV+A7uwz6gGMFsxVbUD83XiCCbDB8yr8eoQKmqCLIZpfwY\noVNHMGdmtcznq0giTbL3iKJI3gWjOH2qkbyknnuofVJVwvQbF5yHmfWcFStWcO+99/K1r32NV155\npc01UWsWLriBFXc8xWWzLdRWh1i/tgZF0Rk4OIkkp4yu6TQ0RDj+hQ+HU+bC6ek4k8/cCJw85qOo\n/5l0wP4DHdjtEm+9cpqCIhtjJ6RiMrVdr1VFZ8snbspOBZg6PZ3CojP7b/2kjuRUmRGjO6+UFkWS\nBK6dY2PNug1sdeQj+3w9PU1tUJp8SM2eWdbcHMqXryCreZ3tKTnXzuXE0j/R/9v3dFoxsj1NR48R\nOFXakg5lzc1hzcoPeLiLOQwcOJBsl4v3//QcW7dv48lly6hVwoQUBVkUsQsiV46bwJ1Ln+70PdBT\nlr34FwaP8iIIPS8yNfICiWUv/iEhhiU4bzidTsROiljoqkbZi6+Rc9WVmNO7rhgJ4BxcjCUrg8oV\nq8idNxctHKHy3ffPfpKaHrcQBka6oznNReXK1RTceD26rlP+6nKyZl6GJat77zBBEMiZM5Pqteta\nIsyi6LrOiaefI1znabPNvXEzhbfd2KYfWRExm87ODD47o7nSriTi6j+AlOGjOFZ/HE1TO1SkjFJa\n8TmFufEX4jhWsp3iou6jZTvjovE3sXTZHYwafDkZrkICQS9XX/pflFbsZ8UH/4vFnMSUsdeTlpJn\nCJOayudHP+bA0Y8JqCd4452/4XQ6ez1+ggRd0akYduedd/Kd73yHbdu2MX36dMaMGUNKSgpNTU3s\n3r0bt9uNIAjce2/Pcn4TJPi/yD/LsywqwoVDISxWa0tlye72ibbtVrzrJhWxtagUT2RYLMEs2kdn\nYprYKjIsGgkmttqXqCDWXcpkqwi31pFnLWmagAhtfL/az6nlOKLziEZ6dTzgNiKiHk3DbPY3azkm\naCOIqYpKU9CHy5UGIcPYVQv40DUV0WxFdCQbQpevETmnD8gmcCQb1SZ9jWeqSAKi3QmaRqTkC6SU\ndEx5hvgkOs9UqERTUU4dRrA70QO+llRJwWJF83pArje2OZyI9mRD4IpWqdRUdMWI6tAz+iBZHShV\nJch9h6LWVyNn5hupla4s1Jpy5Ow+OFxZIIqIyemoNeWGOGe2GuKaxWq0T26+YJZN2EZMIFJ2DOkr\nXE1S13T0OEWueNv9O3HzN7/Bz2+5k58NmtSj9TOgRHgnUMOSuXPO4+y6p6GhgQcffJCNGzfy+uuv\nc+GFF3ba9ujRozzxxBOs/3A/YyYXkZFlYdY1OUQiGqeO+3HXhBFFcCbLXLUwN2ak155dDVy1oG30\nVE6elQU35lNeGmD121UgQDCo4nDIaKrO0S98fHHAS06+lTnzzzw5r64MEvCrTLqoZxXgZs6wUf5i\nFeW7dpNUPKBH+7amcc8+UsaNafnd3q8vTV8cxTk4Pu+h1gRKykibMoETTz1H3uL52As6jxbUVY26\nzVuJeBrImTe3zWu1+dms/2Rjp5FaxcXFHDlyBIBJEyYyaULvbwDjZeOmFUya3rtq66Io4Asdpba2\nloyM7m/iEyToKQ6HA3soQqxHVlWrPyDz8ulxCWFRzC4XWbMup+q9teReMwfRbEINBOIWuWOh9eJB\ndfolUyl78XUkm5W6rdtJvWBMXEJYa7KumEHpCy9jzsqkftNW1GAQ1R9AlCWs2Vmc/PPzpI4bi2g2\nG55i7T4DhXNwyWC1JOEoKCTrmlmkT7sQUZYJlZ7mxQ9/zi3T/ifmPqdO72Vwv6lxj7Hvi4+Yd9nD\nvZ6jSbZQkD2U9Vuf57pZPyIU8SOKIkX5oyjKH0V9YyWvrXoEQRTJTCtCFCQGFk1g0exH2PjZHwkE\nAgkxLMF5o9O7y9mzZ/PTn/4Um81GfX0969at46233uKDDz7A7XZjs9l47LHHuOSSzkO/EyT4VyKe\n8shmiwWTuXcXtZ2NGRXhQsFgm5+72ifaNs5BOn2pgxF9J4JWl+b+7dq0bxuNDmsfrRWtHCr0wDus\nPS3iVqsqpLqmdUh5bD+31h5kmqa1iWZrbngmEqy58mSbObYrfHDP/KvZcPAUoiSi6xDwNSGIEnro\nzN9RC/rQZQt6JIwgm9C8HsMcP+BDDwVRaspB05rTGD1ETh0C2dRifh/Yvx3R6UKpOGH01+RBsDkQ\nU9IRTGYkVxZSZr4hSgF6ONgiQkkuIz1HzsxHtDuRCwaih4OY8gcY/UcC6Eq4RYgzDRjVPB8VPehD\nC/jwH9qDUlOO5vOiNbrRNSMNVErPMcS1ZuFMtDkQnalGdcnKEuN8J/fsZv3LRNP0Hn0laIvL5eLm\nR77Pr47sQNPjM1ULKhG+s/NDvv/UE73+3z8XrFu3jtGjR2OxWPjss89iCmG6rrN+/XrmzZvHlClT\nSE5O5sVlb/OXpSVEIs3FJEwiAwcnMXpcCiPHptB3gCOmELZtUx39BzqQpNjraX6hjasW5jJ3fg52\nu8Ssa7KZuyCX+77Tn+xcKxVlIZb84iinywMA7NjiaZN+2ROuutxMpLISLU4rjPbouk6o1o0l48z4\nObMup+Ktd1G7+PyKhRoIUvfpFlyTJ2HOTMd/9Dily16lbst2tOY0TF3XiXgaqHh7FeWvLsecmUHO\nVbM6fN7IQ4pZvWFDp2MVFxdz9OjRHs3vbDh16hQma8VZ9TFkZJAX/vHsOZpRggRtEQSBCUV9iXi9\nbbZrioLi9WLrRWU/a042WjCIFomQfuEUajdsinvfQNlpaj5YT8Xbq6hatYbKVWswZ/e8CI/scGBy\nGRGzvqMncA7tuTel0uRDDYao27SVzMtnUHDDdRTdcSv97ruLAQ/cR9GdtyGazdSu34AeYy1VBLXL\nAlTx0OCrpf/D9xuVK5tTti2FeZSPtfDqp7+O2X8g2IjNGr+4ZFzjxmdZ0hkOeyqyZGL/kfX0KxiL\nrutUu0/y9of/j/Vb/spVl36Huxc9xbWXf5drLvsvhg2cZqSB5l/OmvfXndXYCRJ0RZeGEIsWLeLK\nK6/kgw8+4NChQ/j9fpKTkxk8eDCXXnpponR8ggStaC1SncsIslgiUnf9n4vxOzOSj0Xr1ztrH51T\n60iz5hfaikvN2zqISz24YNDaz6ftRNq0iZXi2VkkWbsDautr1k21z2iE2JTifMNjJgKKuwJTwUAi\nNeVGqmHlSbRGN6bCQUZKotmKIJtafMM0XyNKxUmjUmTSGSNq5fQJTBnZaN56ECUiFSeMdMpQ0PD5\n0lQEm4NI2VEjgszrQfXWG1FbShg0zUhl9BsXu3qDGzm3H6ED24x+BBE0DTl/AHrQh1pVYlS51DRD\nvJMkzOkZLf1F0yyxO1EqToJsQg/6jHRMJYwgioaxfjiIHg62jPtVpDMPus7aJujI2AkTEB/9Pj/8\nn8e4K3sgA1I7f/q+q6aCv1R9wbuH93LbwYPk5vbcY+psCQQC/PCHP+SVV17hT3/6E3PmdIxOC4fD\nvPzyyyxZsgS/38+DDz7ISy+9hN1u5+abb2bmZbfzyZpdTLnU2+IZ1hVbP6lDEGHk2O7TGY8caqLR\nE8FTF8GVbsZsESkemkRFeZD83JEc31/Amy9+RH4fK2ZL78TEzCwLycEy6jZtJeOSzqPhOsO7/yDJ\nI9raaAiSRMqoEZQ8/yJ9brsJyRbbk7Y1qt9P2cvLyVs0H9+RoySPGEbqBaMBaDp6nKqVq9EVFTUc\nIlRZRdGdt2FK6fwcihYLjU1Nnb5eXFzMH/7whziP8uypqKjA7gwBva+4mpxq4tSBsnM3qQQJ2vFf\n93yDjx97BC69uGWbZ/suXBN6b/7umjyRus3bsObl0Ljvc7JmXtrptauuqtRv34Xv2HGsebmkXDAa\n2eFAUyIEK6vw7PiM08tXkH7RFCzZPfPeC5SdxpzuIlB+GjQdKcmBKTWl2+vocF09FStWUnjz9Uj2\n2P+/giCQPHwIycOHULd5GzUffkzmZdNaXtcG5XDg5CcM73dxzP3j4VTkJNb8jkb0ttGDOWk/yZI3\n7mDW2DsZ0u/ClmOymB2EwgGSer/s9BhJlBk34mpefPcH9C8Yz+mqw6Qm53LFhd/oUphzOtKoqXZ/\neRNN8G9Ht+6oKSkpLFy48MuYS4IE/+f5MtIou/Mv61GaZAyi4lCX3l+d7BMdv9N2rfqKphl2EJN6\naJzfZp9mAS2mkNe6TdQcvzkircW0v+OBIeh658cfFe3imHNrQWx2SlKL2KW6KxCsDrSAD/PAUYQb\n3URKDZN5KTMPweZAkM0EKmuxF+QaQpgzFbXBjequRJAkBJsDxV2B6EhuEck0rwdJNqMFfZgKBqJU\nGzdLaoNxUaH5/ZicLuTsPigVJ410RVdmi0il+r0IVjuKuwIpuxClqgRBNiE6XYhmK0rFSSSrnUjp\nEbRgENFuN4Q1MDzB6moQrVY0UUIwmdEjYRAlI0XTZxjrC3YnSkUpshx/JagvG+M9khDDzpbR4y7g\nsVdf4MU//4W/f7KFC7AxLMmFw2ShMRRgh7eWoxaYcNUs/rjo19z8yScsXryYjz76iBEjRnxp89y5\ncye33XYbw4cPZ+/evaS3/hVZPwAAIABJREFUS/+pra3lmWeeYenSpQwfPpzHHnuMWbNmtaw9n3zy\nCRs2bODQoUOEw2F++OP7CCpfMHysgiOp7SWXpukc+tzLkYNNFA9JYtio+B4w7tvdwNwFueza6sFT\nH2HwcCez5+VwcF+ARYsW8fvf/57773+YRnXZWZ2LiybbeHfvfpKGDMKaFX/kRaShEc/O3R38cQAy\nL5vGqb+8QNnLrzcLW2Niev3oqopn52d4Dxwif9ECBJNM3aat9Pn6GQPnpIH9SRrYH8Xnp/yV5fT/\n1jcQLV1HZuuqisXc+XoTTZOMfn6cb8LhMJJ0duuGKApoWu8i+BIkiIecnBxG253sqnFjyjTWRP+p\nUtKmxJdGrIXChOs9aKEQks2GyZWKvU8B1Ws+RGn0knP1bGo+WE/WFTM67Ks0+Sh/9Q3SL7mQtMkT\n2rwmWswkDehP0oD+qIEg1Ws/wpyeRvqFk7udk65phGtqOb38LRwD+hM4VQqiiNLoJVRTizU7i7SL\nJrdUxWyNGghQ8da7FN6yGDHObJC0KROp27Kdui3bW47DccEwPv7Lm70Ww6pqj9HU14azk7VKTHOS\nm1lMMNjEq+89itORTmpyNm5PGaWn95GeGl9xmnNxbRMINpKfPYT0lAKC4UZmXfLf3Va8BFBVFdl0\ndsVcEiToisS7K0GCs+BcGuu37qu1V1is/ttfpLfeN54INaGdkX3r7e0/9Loyzm89fnui1R3bGPQ3\n+29Fo620Zj+vWGmIHbbpeqeiVet+Wrdp0zbqS9ZcqbL19g7H3MrYP3r8sc5XdF5Rga3TNq24a95c\n/vzmOzxwQQZybj8QRaOyY8BIk5Qy8yHaTziEHvAhuhzY8rIMDy6rHcFsxVQwAMHuRGtwI9gcWLIL\nERyp6D4PgiShN1ecFMxWwxxf0xAdyUiuLAJ7t2ApKjYM9DP6IDS4sRSPhnDISGlsno8gm7GMmAzh\nEHJmPoLVgVpfDaKIlJKOnNsP0ZFstDVb0RrdmPuPQK2vxmy2oja4jXTMlHT0UBDRlYnu9xo/5xej\nHtuDtXikccwJ/uVxOBzc/cD96P/xbXbt2MmBAwfwehpIzUjn4nHjuGvImTSVGTNmsGTJEubOncvm\nzZvJy+t5Kk5PUBSFX/7ylzz55JMsWbKEG2+8sc36efDgQZYsWcKrr77KggULWL16NSNHjmzTh6qq\n3H///fzmN7/B4XDgcDh46g8vU1FRwR+f+TV/W/EXcvOcmC0qgYCK2SwydKSTa67PjVt4aWyIUF8X\nwZlsYtoVmei6zt5djWze4Gb4yHyeffZZNm7cyLIXnyU98+xE5ux0gYzJF1D93lqyZl6KNbf7Sl4h\ndx0Vb7xN4a03GJWC2yGazeQtmEfle2uQHHbKX12O5HBgy89FtNrQggEC5RWoPj+p48dSePtNaKEQ\nZS++Rt518zr0GTxdSdX7a8lfvKBbIQwgXFlNcd9+nb4eFT/dbveX4sHlcrkIBc/u7xQOa9jtiUyN\nBOeXpx77OfO/cQ8VF43H5HJ1qLIaC//JEuq2bkeUZcyZGUgWC2ogSKi6BkGWUP1+cq6aBUC41k3t\nxk1kXHzGy0oNBCh/ZTkFN17XafRVFMlmJfeaOdRt3d6hn1jU79iNfUB/smdeGvP1YGUVle+8hyUn\nu0NfNR9+TN6Ca+IWwqKkTZ5A2UuvkzpuDKLJhCCKeDJ0PI1VpCZn96gvgPf2P4/9hs5LcUc+2sPV\nE36Kw5bK2GGz8Aca8PrcFOWNZsP2fzBm2Ky4xslw9eF09WHysnqeShol6hOWmdaHKy/+Jq+9/1Ou\nu/LH3aZrerwVDBnZ83OTIEG89EoMCwaDVFVV0djYSLj55ttisZCSkkJWVhbmc+iZlKBrTGZzTCEm\nGh3UfltXok17YSceoacnbToTdrriXM0hnnFaR1PF2+e5NNZv3Vfrn9v3HxW72m9r3S4ef7MbBgzu\ncHyapmGxWjt9T8XqtysRLzqnrtIme0I8T+ujY7X/+7VEuvVg3HijA3oaRbDw/vv56w/3MXpKDsHG\nIJIsYrKbkSotRHzJCKKAFlExJyfhq8nC5nKgRfIRa2V0TUNTNURJRFM11Eg+Eb+CNcWCKEtoSnKz\nN5lOQ7mXlHwn2lEVVc1EEECslJBsw4iUREAHy1Y7mjIVThsCnGg2AUWowRC6DvIeC1o4gijLhn+Q\nWGC835qCmOtsKIFsYz6igKbpmOpMqOF0EEVk82DUiIJUJxvVMO1Wwk0BTFYTcp0VLTwDJRRGPC0z\ntffXWOcVvQdeYAkD/fgQBIFxE8YzbsL4LtvddNNNlJSUMGfOHDZs2NCtNUN9fT1r1n1ERW0tJkmm\nb34+V1x6abfXJIcPH+a2224jJSWFXbt2UVBQABj/12vXruXxxx9n9+7d3HfffRw+fJisrNhpOM89\n9xxOp5PFixe32Z6bm8sPv/8rfvnzPzBm7GAuvExh1VuVjJ/sIisn/oqEmqazdmU1mdln9hEEgdHj\nUsjMNvPaCxVs2HCInJwcQuEQTvnsPpdkk4jgVym8ZTEVb69CkCTSL56K2ZXaoW3E68W9YRORxkYQ\nRPzHT5I0dHDMddGSlUHmjIspf+0tsmdfga2wgHBNLWogiMnlImnIIGSHw1jDdu+ldt0GCm+/sSX9\nUVc1PLs+o+mLI1iysyi89UbEOCMHHPsOcvuSJzt9XRCEluiwL0MM69u3LyXHBEaM6b5tZ3zxucbt\nixedu0klSBADs9nM8j8+zW3feZAjmS70LjwgFW8Tp998B8eAfuQtmBfz/1MNBKj+YL2RBr3wGtIv\nnEz9jt2UvbyczMumYcnM4PQbb5O/aEG3Qlhr0iZNoHrtOnzHTuAY0Lnw7TtyjIKbru/0dWtONvnX\nz8ez8zOqV39I1pVGKqKuaiheL6bU7lPaY+EY0I+Tz/4NS7PPmR5RePrd+3lo0QuY5Pg/D3Ydep+K\n/AiOTtLNNUUh3WvDYTuzXtttKdhtxrwzXIW4PeVxRYdNGj2fleufYP4V34t7fq05UbabvvlnFjmr\nJYn5V3yPd9c/zvWzYpv8RzleuZb/nPvdXo2bIEE8xHX1oOs6GzZs4P3332fLli1UVlZ22lYQBPLz\n8xk/fjyXX345M2bM+Kca4P6rEzVJjyU2tTEj17SYok37aoXticebqjMxqHXf7QWeeIlnn3MhSMWK\npoq3v/PhDxb9uTNRLtaYPT2voWCwg/AlimKn57K151f7ObU+Z61FPOhcsI2X9lFv8RwbEFNEjNVn\nPH3FQ0+OUxBFpl4/hs2v7WXIKBeyRUJvFrgksxnZbiHc6AMBrMl2RFlCECyGGCUIoBvCiyAImB02\nNMVPqCmCxQFKSEVVIpgsEqmFKeiajjU9hUiTH13TsSQnEfEHQVewpNrRwhFkmxU1FEGUJUSzjOIP\nGemXomiUIHcmoYYjSJKErmmYkmyAgChJSGYzEhqCKBEJhFCCCpqqY7KLCJKEqOvouoBssyKZTVhS\njD41RUMwyZgkCU1Ruz1n/yx0zfiKt22Cc8t3v/tdTp06xXXXXcfKlSsxmTpG0Wzdvo3fL/sHR0MB\nfAP7ITuTIBJA3bWFXy1/lbG5+fz3Pd9oEbmiaJrGU089xaOPPsqjjz7KfffdhyAIBAIBli1bxpIl\nSxBFkQcffJA333wTa4zPyCj19fX8+Mc/ZvXq1THXjWAwiM1mA8F4r8+6Jpu3XjnNRTMy4hLEVEXn\nneUVXDQjnZ1bPR1ezyuwMfvaLFauWs6dd3yL9LRM/D6VlNTeX3/5fCo4UhEkibz5V6N4m6jduImI\nx4OsgyPNRUNlNbooYnKltAhluqbRsGcf5S+9jjkjneRRI5AddjQlQqi6lobP9iJZLPS57UYCJaVU\nvr0KS2YGtj4FCKJIoKQM/8kSIvUe7AP7c8XwkWSW1uA7WsbxklMc9nlxTZ1EwU2LerRGa6EwI1LT\ncDgcXbaLimFTpkzp9bnrCl3X2bJlC88//zyvvfYaBX1S8PuSsDt6l6zRVJ/DhC+h6mWCBFarlVee\n+iPrP9nIXTt3x2wTaWjg9BvvUHDDwi4rREo2G7lXzybsrqP0H69QePMiXOPH4hw6GPfGTfhPlWAv\n6mOs5z0k89JplL+6vFMxLFRdgyUzPa71I3XcGOq37WxJb2z4bA8pY0f3eE6Nnx+k4bN92PJz6XvX\n7W0iWUNVtTz5j3v55tw/YLV0vT4B7Dm4lndKluG6t3MbI9/+w1zZ95pOX596wSLeXfc4i2Y/0u15\nMJtsiIJIMNSE1dLzv8fO/e9y7RXfQ9d1FNUoepJkTyMlKZP6hgpcKbG9QYMhHxl5kvHZmSDBeaLb\nT96tW7fy2GOPtZSa7g5d1yktLaW0tJQ333yToqIiHnnkkfN2UfHvTvsb/9bCk9/nw+5woGkawUAg\nZsRP632CgQDWVgtOrAikruYQa3vr6ofRebUmHv+rcy1anM8+zjXnMvIsVt+xBLeeCE7Rn1u/T1p/\nPxe+K709B+dbQD2b/nRdRxAlpi4ezaZX9jBqSg6CKCDIkhEVFlaMtFBBQFMUJLOM2hydBSBIOpLF\njBaJoCkKgiBgTTYT9oWxOC2oERVd0wk1hpBMhtikKRqiLKMqCoIsIpklBEFAtlpRI4ohrskSajCM\nZDER8SmgqYhWCzo6iAKaoiKZTeiajq6pCLIZPRgyql4qCmhgdhpriKYYfYqyjCA3e6lF82N1HZPD\niq5oKMEQ5uQv0cW1hxhZtfF6hp3nyfwbIggCTz75JPPnz+cb3/gGzz33XMv/maIo3PPdh9llljBf\nOB5BkmhzG5GVSXhIMZ/6/Mz7+aPcdsEEHrj7HgBKS0u544478Hq9fPrppwwaNIjKykqeeuopnnnm\nGcaPH8+SJUu47LLL4vq//slPfsKCBQsYMyZ2iE8wGDTENF0GIoiiwLWL81j9ThV2u8TEC9Ow2Tt6\nDmqazqH9Xj7f28ils7JIz+g8ym3Q0CQ2fPQWd3z9m/TvN4xlr/uYdkXHKK542XNUxn5135bfZWcS\nOXNmAuA9cpSy3/4BPRAk58rLSLvmTJEBQRRJHTua1LGjCVXXcPqNt7FkZ2FyOjGlu8hbOO9M1bPM\nDFLHjSVUU0uosoqQ14tkteGaNB5zmlHttnzVB/z9x/+D2WxGURSmf+1WwsUDerx+a59s5uH/eKjb\ndlEx7FxTUlLCCy+8wN///ncAbr/9dnbv3o0kSfz4Z1czsRe2QRXlYaZMvuoczzRBgs4RBIEZF1/C\n1Dff4JCitEmX1CIKp5e/TcHNi5As8UU5mdPTyL12LuWvvEHhrTcgO+xkz7qc8tfeJGNG77y0BElE\ndjqJNDR0KKihhcKUvfgqSUOGxN2f6/+zd95xUpT3H39P3Xb9qEdHegcBKYogIKIgShMliMYeC/qT\nGE1iSKLGmmLvBVADopRYUFERFKnSRFRApbeD61un/f6Y2+XK7t3u3hGJ7uf1OvaYeeYps3Mzz3ye\nz/fz7X86+/+9gOz+p1O260fS2p/Gof+8hxkKISoKzrymZPXpiRDDN/bYis/BsmgxNbqC09G4Ado1\n5/DYgtvp6u7J8N6XRyXF9h3axvqv/oNlWchDutbYZ6HQS4PM5jH3e1xZ9O02lqUrHmf02TfXej8d\nesZ0Fn74N6ZccG9CIpcN296mZV53ZEnh2x9W0a7lCd+3wX2m8Om6OVwwdEbUY9dvf43bZ02Ju60U\nUkgGNZJhixcv5o9//CN6eTpYURTp3LkzLVu2pEmTJrhcrshKaSAQwO/3c+jQIfbu3cu3336LaZrs\n2bOHq6++mnvuuYfx48ef/BH9wqCWP2wsy6oUmlZR5SWKIg6ns1LIWsWwtmjhlOFtyZqwh4+tGjIX\nTVFUG4kgSRKCw1GpL9HGUJe+husM1xNWNcU7xvrwDIuFRCf88YaWOpzOyLWRSH1Vya9oYbAVj6lK\nqNYUjhttX23XYjKhtCfje6tJyRe9PFiWwMBJPVi9YCs9BzfFCGnogZBNjEkihqZjaiZGSMc0TLA0\nBElEECWMYPlYTAvFraD5NFSPSqgshJpmvzBbhoWS7kT3BbAMC9PSkJ2q7ZNmWGhlfqTI6qRptyuK\naGU+jJCJ5teQ3U4E0yboLNNAC2moGR5CZaFIWKB9nIwR1CNEmWVaIFro/hCOdA+az39i7KKAIIgg\ngKmbtlLtFIVpWQhxhj/G46+XQuKQZZl58+YxdOhQ/vrXvzJr1ixM02TqLTezs3vHSLhJzOM9bhg5\nlDnbtuN7/DFa5uRy2223MWPGDH73u9/x9ddfc+WVV7J48WKmTJnCihUr6JTAi9JXX33FvHnz2L59\ne8wyYWWYJGYCxwDb+Hz0uCYUHA+x8uNjhEImTZo6cLklQiGLgmNBvF6DLt0zmDi1mb1oFTCI4jcf\ngTtjL507d+DYsUKycoNJk2GaZnLEakxajBCc9PbtaNO/L5+9Pp9b/vJnvtY0xCiqPbVhA5SMDPIu\nHltje46GDXA0jB6WWNq1E7Pnz+eaadOQZZkn7riLax/7B9boEXE/H/WNW7nmjMF06lh7PHb79u15\n++2346q3NpSVlbFw4UJmz57N5s2bmTx5MrNnz+aMM86o1PeWTYdxcN8y8lrErw4L+A12ftWU379w\nXb30NYUUEsGMX13ONQteQ62QUbJwzToaDh8aNxEWhpqdjaddW7w//IinbRvb59SyEq6nInLPGsTx\nz1fT5IJRkW2Gz8f++QtpMX0qvt17yP9kJQ3PGRJXfRk9urPnxTn2AqWrK43PG4GoqpihEL49+zjw\nxiIkt4vcIYMrhZIXrFmPqKq1JhtQMtJRrjqPrw8dYdO7N5FT5iIrOw9VdBAsKkQWVVo27cqF58xk\n76FtfBt4r8b69MISxKya7yenteyLZZm8+cE9nHfWb2o0tDctizJfIS+9dQtXXPwPZLl2S6QN296m\n1HucYWdcAcBX333EhFF3R/Z73Nn4AyVRj92xdwVd+3po3759re2kkEJdEPOv5IcffmDWrFnouk5m\nZiY333wzF198ca3y8jDKyspYtGgRTzzxBMXFxcyaNYsePXrQrl27eut8CkTUXGHyJqz2CYf8VUS0\nULYwgoEAlmXhdLkiKrGwqqwm1PTyXzEUL5b/VcV+RYMgCPh9vhrHEC3EMRnEOm+1HXOylFt1gSRJ\nUEPYXrSxhsmcqsRitJDKqsRPNGVixd+rthMthLFqWG20fsYK36x4TKwxRKs3lj9asoj3ehAEAdOw\nzf4RiBBiXXrlABayx4HuDSJ7RGSXjKHZShLJ5UArDSC7JEzdKieVbHJJTVMRRAlnjooZ0mySSxSR\nVQVDEFDcEprPb/t8CAKiIqJ4XFimWR52KWDpJpKqYOoGsltCdoUIFfsQROxjVBlL1zE1/UT2T+xV\n2FCZH8khI8gilm4hYCIgoLidhPNxCqJgq8UUBVGW0MvPvRnS6u07qG/Y5F7KM+ynhsfj4Z133mHg\nwIG0bNmSbw4eYGfn01BqIcIqQu7WhZc/XkFw2XLee+89Dh8+zKhRo/j222+56aab2LVrV7XskbXB\nsixmzJjBrFmzqnlM+Xw+Zs95li1frcDnK6Jh0wDH8o/y7qKjDD23AZ7ysLicXJVRYxtj6BYFBSF8\nXttcv1PX6qFzG1YXcvqA7Jj96d7Hzaa1Xr7/vhiv1+T7HV5O6xDfvK0i1qwPIA0cVWMZsUsndu/d\nw+2//jXTn3sa8ezqhtV6aRlqbk7C7VeEo1VzVq/ZxDVMA6BXjx48eu1vuO3Jx9BGDEGuYV5q6jrm\n52u4qnd/rr98elzt1VUZZpomK1asYPbs2SxZsoTBgwdz/fXXM3bs2JihtrP+9AhTLh2Nz/cD7TrW\nHobkLdNZvyKHp56Yj6MOhEEKKSSLvn360OTZpymosM23dz+5tRjXx0JWvz4cfHMJnrZt0MvKULKS\nV7UCKJkZmH57/mj4AxxftZrg4aM0m3QxcpoHNTuLox9+jG/vPtwtW9RaX3qXjhRt2Ejra66otF1U\nVdLan0Za+9PQS8s4/M77ZPbuQXqnDuheL/69+2k2+eK4++1s2hjn1eM4smoNx9ME5JXbuHXEk7ic\nJ+4LuVnNEXYXQJ/Y9VjfHcDbuLBWT7B2rfrTMKc1n385jzJfAT07nUvLpt1xqG6CIS/f793A9l0r\nyExvxGVj7+OVt25n/tJZtGnWm349xkX1Oftx/ya+3PYOLfO6R4iwQ0d3oCruaqoyRXZUiyL5ate7\n5LQ6ws233hzHGUshhbohJhk2Z84cgsEg6enp/Pvf/6Zt27YJVZyWlsa0adMYNGgQU6ZMobS0lJde\neom//e1vde50CicgimIl5U2scMSqqEoKVCSVBEFAFMUICVWXcLOqHlTJkEY1hWHWVqYubcUTIlqf\nbdcXKhKItX130bZFIxajeYlFI7zi/U5ihVkmSpbWVF9t/anJH60uiLs+gRMGtAKRkMn2HdNRM91o\nQQNB1JBdKqJkoWbYL3yaGMCRnUbgeDGCLKN6XOjBEJZh2n+7sh3+iAWOdA+I5eGqkogoyxElmCCK\n9qREkRFEkVCJF0EWERwyMg67PlHCkemk4Id8XFlOBFFAlMWIKg1AlGWUNBeWYWJqdiinQHnSBAEE\n7OtD8bgwDQNJlhFVxf5xKJhBDT148pSVdUWKDIsPlmWxdetW9uz5Hn/AR4PcJvTt24/MzORMhqOh\ncePGvPfeewwZMoSm556Dc3zNaqNoyB5+NqE9+5k6dSrp6encdtttTJo0KenEPwsXLuTYsWNcd90J\ndU5paSn33DeTw0e30q5rKb0G2XUPGmG/lBQV5vLFp8cJ+E3OOieXrBx7vyQLNGwUm9iwLIvj+SEa\n1FBGFAUKiw8jyzJz587ln4/9kabN9IT8qI4eDbHxaFMyhtf8gmi0yOPLLVu5cupUJrfvxILt3yF3\nqay8MrxeJE/dw6BDRuWFuYH9+7O45SPc//STbDywn9IuHXC0bnmi/PECHF9upZPbw/9ddT29e8bv\n8RMmwxIN89+5cydz5sxh7ty5ZGZmMn36dB544AGaNKk9C+f27dv55OMvubTxWL745Gvad/PSsFH1\na9LvM9i2UUKVuvD8s8+Tnl5zJrYUUjiZuOGi8dyz/EPEAX3x7dmHu1XtpFIsiLKM5HSge70Y/iCi\ns+4kr//QYfb/+01EVSFn8ECcI4ZV2t9g2BAOLvxPXGSYIAi13svk9DSaTZnAkXfeB0HA98NuGg4/\nO6m+5wzox96HnuZPF7xWiQgDyExvRMZREyPGPcq/9wCntx7Bth2f0DKvW61tZaY3YvSQmzBNg63f\nfcTytS8TCvk4cOQ7+nYfy4RRd9vZ1U2TjPQGCIg0a9yJtz/5O5Zl4XKkI8sK/kApQc1Hm+a9uWjk\nnciSrRYuLj3KR6tfxOWofr+SJAXdsOeAW3YsoUz/jjEXDWHcxamkICn8dxBzdrRq1SoArrvuuoSJ\nsIo47bTTuP7663n44YdZs2ZN0vWkEB1VTbvrSjZVPb4+/LpONbIoHvw3wh9PJhI559HGmohpf13P\nVaIG+bGOrximGU899Rk6mWjGVPslS6yQ4VJEqECIdc9y4iu0s0yGvGVIDhnNV4SapmKZFqFSH8GS\nIAg2maf7AoiKhGVayE4Hpm5gmQZGKBTx8zD9JmDaRJYFpm6gmwayy4lpGJi6jh4QUGUZrcwmE01d\nR5BEHGkq3gI/Wc0z8B7z4cpyYmgGoiyAZREq9hIs8aO4ZMyQhqmb6MEQiseJ5g0gu1Qs0zYO17wB\nZIeK7FQxDQPdH8TQTmEDfcuKO/wxXm+xnxPKysp48aUn+XLzh6RnHyYz20CSBfzbTOb8O4MMT0eu\nvOJW+vSuOXNkvOjUqROTp09nZavkU62X5jVm1pTL+NXUqXV6Pvl8Pm6//XZefvll5PK/s0OHDnHr\n7ZfS/+wC2naXgeqERla2wsgLGqNpJu+8dYgzzswhr3ntBsFrPiuga8+aM2oCKIrIkiVLmD17NsUF\nMs/+ax9X39yc9IzqYYxVcfBQiAUr00ifdmWtZSWXi+PFxQDcedPNaP/6J4vWbUTu1ztyXu0woror\nP+UosaFNmjTh0b/cg6ZpvLpgAQ8+8jiN85rSsV07WjVpyo0PPExWEuqS7OxsVFXl6NGjNG5c83VW\nVFTEG2+8wezZs9m1axeXXXYZixcvjukdFw3Hjh3jwgsv5B//+AfTpk3D6/Xy0itPsWb5hxhmIarD\nRNMELMNFy2a9+fPv76BFi+RJhxRSqC9cOHo0PxzYz6sbNlNWcJzsM+p2n/e0b4dv915cLZqjFRbX\nuX+S20XzSyfG3C8qCqKiYPh8CWWsrAmCINB4zHnse+0NsKyklbGCJJHToQuiGP1V/cyWY1i6dQOe\nntVD+q3PtnNuv7/zn48fxjB0JCm+xRBRlOjVeRS9Oo/CMHQWLbuf/j0uiuz/asfH9O9xETmZeXy4\n6lnGj7wLtyuTkOZH10M4HenVlF8Hj+5g+ZqXmXTen/hw1TP4AiW4nSeeYwVF+/hg3SyatWjAlTPG\n06NH7c+eFFKoT8T86zhy5AiCINC3b90nsL179wYgPz+/znWlUBknM0TvVA0B/G/glzT2aCGEiYy9\nrueqPo+vS7/r0o/k+iAAJ8gTszxcMUyIde2TixbUcKSrKGmuiDGrIGsobidmlhGpRnQoiKJoK5ME\nkJ0qoiqj+QIRlZioCpg6iIqt3BIkEcuUkN1OdH8QnODISEcPBFDSbHN7y5LLM1A6cGa58RV4cWXZ\nIaiiJCC71HLTfDeSQ0H3BxAVGVEVEFW7v5JLQXKqmJqOqWkoHieiJCHIErJDwTTMSga8pxpSyrDY\nWPbRUl58ZRbdTvcxaLgKVF7Jb9fRQNe38eKrv+bFl7rzz7+/nLQCqyJ2lRTiyoufbKiK7GFD+GzL\nV0yr4/394Ycfpn///gwbZqsNSktLufX2SzlzZBGKWvs1rSgi4ybn8Z8FhxgyQiInN/a52by+CFEU\n4gqh69ypO/fccw9nA9AMAAAgAElEQVRNmjThgQce4K677uKf9+3izGHNGDjEE9Wov7hI45MvdPbR\nivRpUxFqMiYrh+H3k519IgvY3bfexunLlvHcorfYp0oI/XojZ6SjFRbWWleN7fh85Hpij1tRFKZP\nmcJvb7mFZfPfqBeiqF27duzcuTMqGabrOh9++CFz5szh/fffZ+TIkdx5552cd955UbOd1oRQKMSE\nCROYNGkS06bZYaAej4ebb/wt8Ft0Xae0tBS3250Kh0zhlMStV19D9vz53P3EY0jDklNBhSF73ASP\n5lO2Yyeh48frVFfw6DH0khIChw7jbBpbnZkz6AwK1m6g4bDavcME4p9fZnbvgl7mjbu/0aCe1Z0v\nVi5m1OlXVdvXs91wPvxgHmaXdohK5edNbjAdVXHSu8toNm5/l37dxyXc9rqti+jdZXSlbTt+/IKJ\n5/0JQRAYN/y3fPTF82h6kP49LqJF0xOG/qZp8vWuT9m+ayUNsppz6Zh7EUWJQb0ns3rTAoYPPDGe\ng/nf8dxrs2nVqlXCfUwhhfpAzNmaoiiEQiFKS0vr3Ehx+cqhu55Y9xROoK5kTU1qmP91dVRd8FOM\n/ac638kqpBJVQ9WEul7HVT3H4kmoEE+Ch2T7EK+BPoiVfxeopBBr3zmD0iNe1NIQaroaacPSTXR/\nkEBZiMzm2WhlQdQ0J6auI6oywRIvoipjBDUUl8POBBnSMDQDWVUxdD2S7RHKCR/DJFRahuRQ0XwB\nzGAIBAFJVfAXBRAlAWeGg2BZEMWlECwLITtlBEFA8wUIlfltkk20VSDB0gCqR0EP6nbYpGnYbRT7\nUTwOLNO0STFBwNBO3XuMZSVAhv2ClGFvv/MWi9+7j6GjIZr6KQxZFuneR6Sw4CuuvWEizz/zVsKE\nQVWU1PE8iw6V40F/7QVrwJ49e3jsscfYuHFjZNu9f7uDfkMK4iLCIn0RBcZMaMI7bx3mokvyqu0v\nKdZY9elxGjd1cMaZtSsMfF6dz1ZuokGDXAzy+b+ZV1BUVEbB8RDvvHWQb7Y0pGlzBxlZFpZq4dNl\nCrwqJdntcJ07KqGwO+nAYfoMGVlp2/kjR3L+yJH8+OOP/PPllzju81J46CiWYcTMtFYb8pcu44a/\n1GyxsWnTJho2bFhviqlwqOSZZ54Z2fbVV18xe/ZsXnvtNVq1asX06dN56qmnyMlJTvlhWRY33ngj\n2dnZMS1EZFkmOzu2R1wKKZwKmH7JJWzfsYOPtRAoyS9umZqGqKqUfL0dNTcHvbQMOb32BYBoOLbi\nM1pdfQVH3l5K88smRbZbloVeXIJe5kWQRKS0NLTCovj6V55QLl7ESgoSL5TsLAp80f0LBUHg1wNm\n8eycv+CZfl5kUdEyTZyirTRu2+J0Nn79Lq2b9aJhTvxkU37BHtZuWciM6a9X7o/ijMzX0z25XDh8\nJpoeZP3WJWzY9nZkn2kadGo7mMmjZ1Wa3zfIbkFJ2QlhzO79mzhwZDstW7YkhRR+KsS8Y7Vv357N\nmzezcOFCzjorudS2Yfz73/8GSJnnn4KoScnyS1JHVcVPMfaf6nwnq5BKVpF1slCxH/EkVIjW/7qO\nIyED/QrJKSwLW70liZH9EYVY31xCXg1HZhqWadokkizb5vmyiChJKGkOZLcDIygiyTJCutv2CJMk\n5HJySxAlRNFEcjsQQiKSWW6U73JimTa5JnvsBBqCKCAqip2e3OXAmR5CdNjhTs4MJ2X5XtIbp2Fo\nBorHgajIuHIzMAIhJKeKgICoSLYKTQ6hprnQ/UFMzUCQrEiIpKjKmKaJEKeE/6eAaVoQbzbJX4gy\n7Ntvt7Ng8f0MGlZ72TCyc2Tad9/NXX/4DY889HzSbRuGgWaZxJ/YPTo0o26huTNnzmTGjBmR1Wy/\n38/BI5tp0y3xa1mWRdIzVDauK6JZCyeGYVFUqLHz2zLS0mXOHJZba4ijaVpsXFfE1i+LOHtkFmcO\ny0ZRRKAlhmGxeX0Jaz4v4ppf38j1191KMBjk7KmXIl46HlFVyUjivtekoJjePaMr9Nq0acNjf70H\ngDXr1nLDkjdRT09czWdZFsL3u3n99df505/+FLPcsmXLGDlyZMz9iSJMhuXn5/P6668ze/Zs8vPz\nmTZtGsuXL08o22gsPProo6xbt45Vq1ZVCy1KIYX/NfTo1In39+5EahM/6RIqLOL4ylWYwSCCKKF7\nvVimieHzkdmnF8c++4Im55+bcF9MTSNw5CiH//MeWmERgcNHUTLTOf7ZaoL5x3A0bICcnoZlGIQK\nCvHt2UfRxi1k9upuJzaKAq2oGDkt/mQklmmCWMd5sSBgmrGfVQ2yWnB1zz/w5JN3kH7JCBxNGoN1\nQsEmCAIXn/t73njvzwwbcAVNG9aemfFQ/k6Wr3mFlnndK81ldUNDEqs/hxTZwaA+8ft7Vaxz3dYl\ndO0wjC9WrWXwmQPiriOFFOoTMWdtY8eOZfPmzSxdupS8vDxmzJiRcHhDKBTiwQcfZOXKlQCMGTOm\nbr1NoVYkoy4K35jq00Opvvp2qqilfo5t1uTVlUioYfjzVFASJpKoIdr4T6bSsmq5sGeYIIiAhYVl\nhwxGIcQ6ds0kWFiGIAlIThWtzGcTVqJAqMxHyBvC0g0MzcAMv+SbECoLoLhDSKpCoMiHI8OFGQih\nBYJgAiJYZT47g6RmYPiD9uQrZAC271fYfF/3BxElkWBJEHe2i0BJEFmVsAAjEMIyDfSgjlo++TMC\nISTDJtns/liRVVUtEERxOtDKytU5p/B7oGXFr/j6pQjDnnrmfvqfpZPoF5fbUGbXNxs5fPhwXKbi\n0SBJEoogUleXOSVJlRLA8uXLWb9+PXPmzIlsmzP3Odp1KaUmlVxNGDw0m/vv/g6XIweHp5QLJzZl\n7ISmiHG8TIW9x3r1zeTXN7aptl+SBE4fkMnpAzI5cmgOV13zKXfe8TA/bviSViOG4Ghec7axaAju\nP8CYMwbGVXZA/zNo/PRTHO+hISaoCvSv+5K/3DKDmTNuZfz48XTr1g1d13nnnUV8t+MryrylZGfl\nsnDhYu6+e1bC44iGUChEYWEhb7zxBk899RRjx47loYceYtiwYXam5nrA0qVLeeihh1i9ejVpackp\nX1JI4VTCxAsv5OmbbkCLgwzTioo58t6HyFmZNDhnCEoVRaoZDHH8izWUbPuarNN74WzcKKG+HH7n\nfZqNH4czrwn+g4fZO/s1XC2a0WjEMByNqmcgtiyLsu92sm/uPHLPGoinbfX76LGVq2gwNH5xiJye\nhlZct+gqvbSMffu3sm3ncrq2G1ppjmoYOpu+Wcr3e7/kglaXcmzlUb4p+xCtex5p5oks7LKkMOWC\nv7J05RMYhsag3pNpkFNdiXWsYC9fbHoDqbz8ko8frrRfFCRMMzFlXDQcL9rPko8eorgsn1DIT+um\np/PU3/+DIAoMGnRGnetPIYVEEZMMmzx5MosWLWLbtm28+OKLLFmyhPPOO4/+/fvTqlUr8vLy8Hg8\nkdUsy7Lw+XwcOnSIvXv3smHDBt59912OHDkCQLdu3Zg8OZUZ4mSjvnyP6lpXffXtVFFL/RzbPJnf\n73+TGEu2rZNxvhNRhoUJFsuyvcIEUSzPMEl5CKFYiRDreVYeRsAm7iRViRBLkqrgECVEVUJ2lhNR\nIR1RlXBkuCLkmZqmYpV7lAkIWKIVWQEVKtzHJUUGTCzTsl/ETSuiKpNdDoygjhEykBSJoDdkE2ym\nYZeRTJv0Mk0E2SbKpLCfGbYSzggaKLIMou1bZmoGujdYb99BfcMy7Z94y/7cUVJSQnHZd8hycgxm\n194azzz3CH/+0yNJ9yFDEKiLC5UZDJHrqN2wPhp0XeeWW27h73//Oy7XiTo2b/2UngOT90NzuiQ6\ndGpEwdF09h3Ip7TUiIsIM02L/yw4xPDzGkayUtaExk1V0jP2c8ml53DLNdfz8dpNhJo2jcsjLAzL\nMElfu4mrXnw57mNeuOc+Jt11B8aF58UdLhncsYt9ry+gxaix3HvvvUybNo0xF57N7n1f0qx1AY2a\nSDRuLBLwm3TpU8wbb/2Lfft3ccX06xK25bAsiw0bNjB79mzmz59Py5YtkWWZffv21XvGxu3btzN9\n+nQWL16c8slJ4WcDh8NBz0ZNWOv3I7li318Dh49w9IOPaTZlAlIMHzzRodJw2BByBg/gh389RYsr\nf4UzzpDDI+8vw9O2Nc48e8GleNMWGp83ksyesbMqCoJAeqcOpHVsz+G3l2L4A2R07RzZr5WUYgYC\nKBnx3wvcrVpxYMEispNQxIYRXP0104f/laKSw7z1wb0osgMLCwEB3dDo1fk8Ljn/RFbl8y2L5Rvm\nsGH325imGXlHF0WJC4bOwBcoYfWmBRQUH0CRHciSA90IoulBcjKbMWLwtbidGRw+9j05mZXD9kVR\nRDfqlgzFsizczizGjbgjsq2kLJ9VG+cz8+b7ueehWxk+fGid2kghhURRo2fY888/z80338yGDRs4\nduwYr776Kq+++mqlF73wKplRIeSg6ip63759eeKJJ+ptRS2FE6iaTRIqK73i8U4K42Qrwyr27WQf\nkyj+G2NPtA8n89iK10ayXlm1eW7V9r3F49kV7zVcGwEXbV+09h1OZ6X9dTkn4boq1lNxfyVlWPmp\nskyzGikmiCIDJ/Vg9YKttO+cgeJ22h5gfgNDMxEk3VZ1BTXbdkwQbPP80gCCZIc8KmluBCBY4sUs\n95UwghqiJNoG+oEgkkNGdjuxdANRVSLqMESbfAtngxQVCUeGB//xEtIbZ0S8x0wthIXtPyZKEkYg\niGlaWCYIHhEjEEJJcwEBmwgTRRAELCyc2fX7slmfsCwr7vDHX4Jn2IsvP0HHHj6SVUClpctsWbu2\n0kQ9UZzTvRevHz6Ko0liaoEwjC83c9vVN9RezjAIhUJomhb5fP7550lLS6NTp05s2bIlsr2g8GhS\nfamIBg0yePyfr9KvXz9WfhgiPS3EaR1rPs8rPzrG4KG5cRFhYbg9Mr+6thEF+w7x3N2zuPL+e7HG\njIqLELMME+GdD3h+1l9wVrhf1obmzZvzyp/+wlV/mYVv+FkombGzYlqmifblZs7PyOXcua8yefJk\nbp95M1kNj5KV9wmDO8hUTNaQli4y4KxcoJCjh1/kyqsX8dD9c2jVqnWt/Tpw4ACvvvoqs2fPJhQK\ncfnll7Nu3TpycnJo3rx5vau2jh07xtixY3nkkUcYNGhQvdadQgo/Ne649jom/O2vMOqcqPu14hKO\nvv8RLaZdGtf9RlJVTrvtJr7/15PkDOhH9oD+1cziwwgcPkr+R8vJ6tOT9C52GPOxz77Amde0RiKs\nIgRBoOmF53PwzSUo2Vm48ppi+P0cfHNxJd+xeBDup+71InviD68Mw7Isjn2zhTltnoGQjtMh0CWj\nF8O6X4ZDjU72C4JAYcF+LjjrZr767iN6dq4cYup2ZkTM6y3LQjdCyJJabb6+ZvObjB5yc7X6ZUkh\nEPTidCQ+HoDtu1bQvWPlayMjrSGjh9yEad7A3KefZu/uA1x51dSk6k8hhWRQo7lFdnY2c+bMYdGi\nRbzyyivs3Gmb+FWc8Os1mAl269aNX/3qV1x00UUxy6RQN1RVoVQN/YrHO6kiKpY7Vbyg/huINtZo\n57U+zOJr60eyxyWiyhIEgYDfj7N89S7WNRQNlQkd+zhJkioRSeFrLx6FVE2eXVX7Gc9YYpWJdi1X\nVEGG+2xZFm6PB9M0EyYZK7YT8Psj9UTbH1GGlRNelmVSLVFRuaE+onhCIZZjT0JERcHUQzb5pEh2\nevBgCMsob08ELFsLJooiIZ8fUZWRVMUmuLBJK0EUQbIwgjqy60RfRUXCCOiIMnZIZjmJJpSrVSRV\nspVmomgrWFTBDqVUJCzDtNVjqoRlWgiAKItgWbbXWfmP7FCRVAXNd0LSf6rBSsAz7JeQTXLfvu/o\n0LtuGSElpYRNmzbh8XiqkU3hz2jbIp+lpQS2fInj0olJtX/o08+45MPltbYNoKoqqqqiKAqyLHPs\n2DGaNWvGxIkTI9tVVQVpL5AcOReGbmg89+I9dO6WgSgF2bjGYsPqQkaOdZOTW11BYZoWx48FaZJX\nPeynNmRlK3y7eQvN8/J45fd3c809f6bsjNNxNGsa85jg/kOkr9vIc3fPolOHDgm32bF9e95+/Eke\neOpJ1ny+luIObVHbtY3ch/UyL8K6jbS2BK6dOIlzh9kvTTNn3sKq9U9zyfTqmR2rolETlZxRJfz2\nrin846E3ad68ebUyPp+PxYsXM3v2bNavX8+ECRN47rnnGDx4cKVnhMvl4vDhwzRtGvucJIJQKMTE\niROZNGkSl19+eb3UmUIKpxJatWrFb4YO52/LV5AbJTtjWBGWiBJVVGTa3X4zPz71Av6DhxAVlbRO\n7ZHTPJghjVD+Mbw//IizcWPyJo5DKp+LmrpOYP9BmifxnGh68RgOLFhM7tmDObp0Gc0mXRypNxGo\nWZnkf7ySpheOrr1wFZRu/5bscwaT1rtnZNtXBw+z6dM76CS256L+t8ScY3c5bQjz3/1TNTKsIgRB\nQJGrP1cCwTJ7IToK4Tag10RWfflvhg+6OuHxgE2GTTwvuvejKEqMHHgTW9b/h1eVN/jV5aloshT+\nO6jV6VUURSZMmMCECRPYvXs3Gzdu5Pvvv+fIkSMUFxcTCoWQJAm3243H46FZs2a0a9eOnj170qxZ\n4j4UKSSGaGqmqqGO8aIqgVEf6qhTwUcqXiRqtn4y2o+HQKrp+PBnPH0URTHSXvgzrMSqrZ2KRvWC\nIOD3+aL2o7Z6wsfXdIwoigT8fhxOZ8zrKDyWcB3RrrtoRFjV8Yd/wuNJNrQ33KeK9cTqB0IFRVgU\nVCTTwgqx7v0bEPSGUN0qoixj6jqhMj+Ky4GJju4PgWkhe5yIkkigqDRCYmm+QIS0MfQQkiJjWpbt\n+RXxDNMQVdtAP+QNIJT7hUkO1Q6VDGkYIRNFEfAVeHFlOdHKNGSHhKHpGEENoTyMzjRMO8ulLEeI\nurC5qxHSQBBsr7JTFCkyrDJ0o+738kCwkIsuugi3212JUErkM9frp2zffpwtqpMdNba9ej0zp1/J\niCFDam2jqpr9+uuvx+Fw8Oijj1ar9/obLwL21uW0YJjF9BjwIz0GnAidO7APlrxxGNNw0atvJi53\nEC1kcvSIxvc7TEaNyU26vQ7dvbz40uPcdusfWPb8Szz/6lz+9sA/aDTsbIRWzZFcTgx/AGHfQZoc\nL+L8fv255vkXK4WHJors7Gwe/MMfMQyDBUuW8MnaNQRNA1kQaZiZyc133V1p7rh37162bF/E6Itq\nJ8LCkGWRs84N8Ns7L+f1uR9Hngmff/45s2fPZuHChfTv358rrriCxYsXxxxP2ES/PsiwcObIzMzM\nmJkjU0jh5wCHLKOXlHLkvQ9peO45kYU0w+9HkKWkSCVBFMno2Q13yxZ4f9xD4MBBlIwMBEXB2bQJ\n2QP6VZtbFa3fSPYZpyc1BkGSsAyDH598jo53/y5mOGdtCBUWoWRlUrZjF2kd4k8iFyoopHjjFppP\nrUwIOfKa4LisCTt37ubFT+7iqnPurzRun7+YAu9hBEGgd9fRfPzFCwkRV6Zp8NYH9zH2nP+Luv/g\n0R1888PnnNVvKqqS2HPgwJFvyclsVut8umf7C1m+9DGGDN2byjKZwn8FCaU9at26Na1btz5JXUkh\nGcTy+KoPs/r6In7qQq79lKbyVYmp+jJZrwn16d+VSLmKn7HUhDUZzteVwKsNoijGvBZjEV/xendF\n60eN5FUCiHWsHR4ZftG2zfOtKoZT4f9XVJEhEFGI9TorD1M3MTWbUJIdKpo/aJNgLoVQWQgjpCHK\nEpJTxQiEkF125kfLMNC8AQRRwNB1O5OlLJeHbpqIqgIWSA4VSVWR3c6IR5llmIiKjAJIsowzXSXk\n1VDTHWCaSKqCIIqYIQ1BllDLDbNN07T9yHSbJAuV+W3yURSQPYlPjv9bMC0LIW4D/Z8/GSZJiRmg\nR0NaWgM2bfqQBg2STztvmiZTbryBXQ4HShRD5GjQt33D5Oatuf3m6uEftWHTpk0sWrSIb7/9Nur+\n09r0pvD4LrJzk1PNaZqJKFW/XzRr4eDKG1pRUqzx75cK2LLxEGlpadx55500zP6S1u1+SKo9gJwG\nKutXrAT+gMvl4tpplzPv5VfoWerHvT+fV+bM4Zabb+GCSZfRt0+fpNuJBkmSmDJ+PFPGj6+x3FPP\nPEDfwTqQmM2Goojktc5nztyX+fGHvcyZMweXy8X06dPZtm0beXl5tdYRJsOGDKmucEkUjz76KGvX\nrk1ljkzhZ49VmzbR6MLz8e/bz8E3lyA6VBoMGUTRl1vIPTO+pBvRkDOgHwcX/gcMM66QRe8Pu8kZ\n2D/p9hqffy6+PXvZN3ceanYW6d26kNahXdxzQt/uvbhbtSBn8AAOLXobU9PJ6Fp7FtrA4aMcfX8Z\nzS+bFHOR1Nm+NQWSxLzP/8alZ/0Bw9BZ993brDzwHtk59gJJxzaDKPMV8tEXzzNi0DW1thvSAryy\n8DbO7n85GWkNK2z3s2bzQg7l76Rjm4H86sIHeeuD+5g8+s9IcWYCLyw5zIp1c5lywV/jKn9Gtyv4\n58NP8s/H742rfAop1AWnbj77FOJCTWFfoaCttoiXTKpKINQHEVVVdXOqG+gnc97+VxHr+411rk/W\ndxFvWGZN34UkSVDun5eoV14y7SV6bDSiORIeaYaN7K1ykkyo9BkmWE6QYkJEIda5ZzZqhgtL1zE1\nDdlhq69M3X6xll0O28xeFFDT3OjlBvyREE3DtM30sUC0+2RhgWXvMzUDUVEIFpVhmYZNmMkigmkS\nLPKhuG1iRE134D1SiivLiaiYGIEQetBAFHVAQHLYL7KWJGGZBqYu4MhOR/cGCJV6ER11J1hOFlLK\nsMpokNOMstLNpKUnP30I+p1kZWXVqR+iKPL6409y9W9nsnnvAdTe3WMasxs+H9YX6/lVr9O57drr\nEm7LsixuueUW7rnnHrKzs6OWuebqGcz47bsMHJpctq3N64vo3S/2OcnIVLjyNw157MFS/v7QXMaN\nG8e1N4yu8/3YsPzs2rWLZ59/iMNHv6LXwGOkZXyCocPwYQI/7vyETbluunXpkpBHWH0gGAxy4PBm\nWndNzm+2XSeZe+65jfNHTWfBggX06dMnofMVJsPqiqVLl/Lggw+yZs2aejfjTyGFUw2aYSAIAu6W\nLXC3bIHu9VKwag2l3+2k0bnRvcTigagoCIKIoMb37BEddQvnV7OzSOvQjmYTL8IyTIo2bWH/6wtw\nNGpIZq/umKEQoqIiZ6ZXU46Zmkb+JytsbzRBIG/8hRx+532Or/ychiOGkta+ukoscPgoBZ+vRpAl\nWkybUmuiEWfbFuzY9jEvf3gn+c4SzIHtcJ1/Pv7ZKyNlTu96ATt+XM389/5Mu5Z96dX5vGoEli9Q\nwuqNb3CsaD9jht7K17s+5dvvP8PC4ljhPhTZwcjB1zKk3wkfr3MG/Jp5797NxSPvxO3KrLGf+w59\nzWcbXmfieX9EFOO7l7udGXyz5TjPPPUy1//myriOSSGFZJEiw37GCE/6EiEwqipi6lMZVvX3ZI7/\nb6AiGfFzRl1CACsiWfIoWlhvMv2sGtYYj4dYbf2vSK4lgpq8yaoSzUJ4UlDuC2ZZAmCTXRC+/ir+\n7XDi/xUUYt3PkAmV2VkdQ2UBFLeCKNmm98FiH4rbAbKIiYFl6AjyCQWXqWl2RseghuxUQRQQHQpm\nUCNU4q0Q2mhnhxRVGSMQRFQVREW0lWVBE0kycWU5I9khJVVBD+jILhVTr+BDJkkYESLOQHKqKJZ5\napNIlhV//34ByrCrr7qVu/70If2HJDfWYMCgacPuyHLdpx+yLPPKP//FF2vW8MS/X+d7LYCvfVvk\n9DRb/XisgOLlKxnb7wzuuvOPSYdczJs3D6/Xy1VXXRWzjGVZbN9aQN9BaShqYsofy7LYv9dPv0E5\nNZZTHSLX3dqKNes+ZNy4cZhW3dLcm6bF+rVbuOPuYZw9IofTekjACbKmfScPkM++/U8zeuy/6Nhu\nGJ06dsPlcuF2uyM/Ff9fdZ+iJE90L3jzNdp0KCHZZA2CIHDWsNP44x//SKNGifu5tW/fnvnz5yfV\ndhjhzJGLFi1KZY5M4RcBWRQrzaFlj4dG5w5HKymte+WCEMmKXRNMTY+EZ9YHBEkku29vsvv2xn/w\nMPvmziOzRzcEWSZUUIAZCOJsnofkcWN4/RSu20DehHERE33/wUNohUW0vHIaxVu2su+1NyIEWqi4\nGNntQm3YgMYXjEJyxb/okDFiIN++/T7NLjmhsC3NUzl0bBdNG9iEW4c2A2nfegA/7PuSRcvuL1d3\n2/PMkBagoGg/k0bPokF2CwDyGnes1MayVc9y8OgO8hqd2N64QVsuHD6Tj1a/gKYF6N9jHC2ankhS\nYJoGm755n1171tMotzVTLrgnYUXsoN6Xsvy9ZWja89w8o3ZlWwopJIsUGfY/jmjZJKuiJqKhIhEQ\nj89Sovhf8gwLIx7frJ8LagqFTKSOWGq6muqr6j2W6PE1jaOiH1q8/a+oqozm9RUvaiLtqiKaV1g4\nPNI0TbCImOtbZvnv5Uovy7ROmOoPboplWjiy3Lb/lmUb3puGiWUaWEEdU7dQM1xIqmJ7dWFP8Ow2\nhBMKKNnCCGnILodtiC/LOLIzMPwhLNNEdjkRFdluRxSRHCDIMuUJvBFVCTNk4MpNwzItLDOEIyMN\nyzTtDIKKgiAKmCHdVr6ZVkJmuin8tGjcuDGq1BbT3GUnTkgQX28WuGPGb+u1T4MGDGDQgAEcP36c\nDz75hIP5+ThUJ616tWPOpq8p+OHHqEbq8aCsrIw77riDefPmxcyI/fnnnzN16lRGjBjOZ8u2M3R0\nMKFzs+zdo/QbGF1xVhXpGQpfHd5EIBBAkpLzsQGbCPvPgkNM/FUzmjar+XmX19zJ5OkOPn73Uz5f\nlU9OdmN8PgrCcZsAACAASURBVB8+nw+/3x/19/APEJMoq4lEc7lcfL5qKSMvrptqNDPbz+7du5Mm\nw+qiDKuYOXLw4MFJ15NCCv9L6NymLasPHcGR16TSdqFahqDEYJkmwfxjmMEAB99cgoVlK9tNg+z+\np+NufYJsFiQRyzDq1B5E77Mrrwltbria/a+/Qd7EcRRv/gr/vgNYhoGanYXYuDHOJo0o/eY7Cr5Y\ni6f9aZRu/5aW5Rk0c87oR84Z/QDw7z+Ad9ePNBh6ZlL9k9xuLMPA1DTE8oUH99l9eP/12Vw59J4T\n4xAEWuX1YF/RTjYf+wKf28RySAiaiZcilm59mfN7/prcrOrPyZGDr2PzNx/wxtI/07xxF/r1GIci\nO0j35HLhObfj9Rex8IO/UVx2hKz0JqSnNeBY4V46tBrA5NGzkn6PbJjTEs+BhuzepvDBB58wKkaG\n0hRSqCtSZNj/OIKBQMRYvCqxVZXQCYeNVfV+imW4X19hkmGiJFroWjwhcnUJeYsXVdtJRFn0c0KY\nmKrJrL4iqp63aNkga1N1VbwGoh0fDARwulxRr4Garo+6Kt5ONokrlGeJtFVh1T3ChCoroIIoVCbP\nhMohk1365KAV6aguBVM3ERQTWZXLjfMlrFAIf4EXxRnAssDUDGSPAz0QQJBl9EAASVXRynxYpoXm\nD4EIlqmhef2IkoSpG1i6jhC0FWWSLBPy+jHKQihuBcu0wzVN0yJY4seRZf8NBUvK0AM6zmyPPWlz\nqHa2TtPECIZOqOROQZgJhEliWvwSaL0rLr+Vl169iT4J2r+UlepIRifatYvfSDgR5Obmctmkyl4y\n5w4fzrnnnsvvfvc7Hn744YTrvP/++zn77LOjkhm6rnPvvffyzDPP8MILLzBmzBi2bdvKvfdfy+CR\nARSl5qvBsiyWvXuUVm3dNG9VPXNXLLTtVMKrr71ARloewcA+HM7E/34+ef8o/QZl1UqEhSEIAiPG\nZLDqkwPcfvsDdOzYOa7jNE2LSpTVRKL5/X6Kioo4djw/bk+aWFBUg9LSkqSObdeuHd9//71N4ieo\naghnjpw4cWIqc2QKvyhcNXUq8267BaMKGWaZ0ckpyzQx/AHAQnK5oiwQWhz7eAXBo/nknjmQjO5d\nKs3ZTE2ncN0Gjq9aQ0a3LmT27B7xLa0LrPLFxGiQXE6aTR7PD089T/NLJ9Hg7OpkVlqHdlimSdHG\nLWiFRRyYv9D2YwWkNA+5gwdQsvVrcofUjSh3t2/Ld/c9QnqXjva8UdMp1WSOF+2PkFsfbn6FjWXr\nEM7shKvNcNIq9hPI9/p49tN/kbXBZPrgP+FxVQ7Z79V5FL06j2Lfoa95Z/k/7UXjQBll3uOYlsnY\nc26rpBxbu2UhLZp2q5OgQpYd6HqQ3h0n8ta8e1NkWAonDSky7H8cYQVMmLyoSiYE/H7cHg+maUb2\nRTPaDwWDUdVQdbmRxUMwxRuqV7VMVRItEeIulgKuYv+SGff/mgouGpEU3hZv+GQ85602D7qK10A0\npVq47ljhh/VNXMYikuP5buP1DAsj2imO+HlVC7kTygkwTqjDLLNSyGS3vrn4CvyoHpVQSQA1zYll\nGshuBcvQkQTBDnGUJfzHShFFEUsUUdxOtDK/3YooAma5l5lQ/iOi+wORnigZafiPFyOIBpigptnh\nkIHiAJIqIasykiJh6UYki6XslDE0HcnpiHiVmeE25FOXQrKs8D/xFD6pXTllMGDAYLZt+zVfbX2R\nzj3i++78PoP1K3N58bmXTnLvKsPpdLJ48WIGDRpEq1atuOmmm6KWq+jNF8b333/PM888w9atW6uV\n37NnD1OnTsXlcrFx48aIIXu3bj145ME3+fM9N2FJe+jW28TlrkxWaZrJpnVFHNwfoO+ArISIMIAm\neSpfrv6Y2299mIf+NTFhUtLr1TFNaNYisXYBzhii8+TTf+Oxf82Nq7yiKGRmZpKZWbOvTDTcPes2\nDP1TJDn5eUgoKJOVFZ/qrirS09NJT0/n4MGDCSkLLcvipptuIiMjI5U5MoVfHDweD50zs9kaDFXy\n7XI0aoj/wEFczfKwLAvf19sQNq8gRyoiw2MH7pX64LiWgdFtMJ6evcGCA/PfIqv/6TQcMTRqe6Ii\nkzt4ALmDB3D8sy/I/2QFDc85G9HpQPd6kT2epMZRtHETmb16xNwvp6eR0b0rclrs+gXRDq9M79yB\nA/PeoulFE5HcbkIFheR//Knto3beiKT6F4aamUnDYWfh+3EPzadegiCKFKz/kife/g0zJ8zm7U3P\nsaebhbvXubHH4nGTdsGZBLw+Hp97OzcM+huZ6dUz+LZo2pUWTbuy+8AWNn79LmPPuY/Vm96oRIQB\nuF1Z+PxFdRqXz1+M25WFIAg4ac2OHTvo0KFDnepMIYVoiEmGzZqVvLSxJvz5z3+u9zp/yVBUNaqy\nq6JxvWmaNWZGDB9f36RCxfC3aEQH1E4yVBxH1e2xMmnG069oZSuG1sVqN5l6T3Ukex7DiDcksaa6\na1KOxcpumWj78SJam/Gel3g9wwRBwDRMxCjhgZWUYRVImLBnWMUwSZsQs8uGCbFOPbJxZLqwdNP2\nCJNkBFFEcjmwdNsrTJQl1Azb40tSZMyQ7T2kZLgxAxqGrqN47BAsUa78Ii+qCqZuICm2h5gS6YuF\np2E6iAKYFnowhCjLtvJLEhFlGVGR7HGX+48JsogRwD7mFEUiBvpxl/sZ4Oqrb+aV2TLLP32O3gNM\nnDWok/b9GGL3juY8+9Q80tLSYpY7WcjJyWHp0qWceeaZtGjRgnHjxgGwfv06Zs/9FyXevZhWENul\nz0FmemuumD6De/56HzNnzqRZs2aV6luwYAE33ngjM2fOZObMmdVUQ82bN+eFZxezd+9enn72QQ4f\n/QaLEJZlIggq332zixFj0uk/uGaPsJpgWkHatWuHEWqBZe1P6P63flVh0m3Lskhx2XcUFxcnRXAl\ngv59z+LzL5fRpn3yRtjFx9PqpEQMh0omQoY99thjrFmzhlWrVsUMrU0hhZ8z7rjmWqb+82EYOTSy\nLWfgGRx6+z1yenZB/nwRQ7qG6HaxC1Gs/N5hWSG++WYpX8xdxkF/Ng3GjsXVrPbsrwC5Zw2icN2X\nFHyxFnerFuR/vIKmF56f1BiKvtxM6+t+XWOZBkMGk//RcppeNKbGcrLHQ7NLJnBgwWJaTr8MNSeb\npheNwXxrSVS7jERg6Tpqg1zSu3Ti4IJFNL9sErkD+mH07Mas+y+iwYTRuHvWnsXS7qcb8cpRPPvi\n75kx/HEcauUFE5+/mFUb56NpAS4eeReWZVFcerRaPW2a9+LTtXNo1yr5bJ7bdnxC59POAqBXhwm8\n9NyzPPDIH5OuL4UUYiEmGVZX09BoEAQhRYbVM6oSN9GUXqIoVlIARSOgwqRa1boDfn+1kLnafMYq\nlgu3W5PyrCaSIZEwuLqExdUUVpoI/teIMKjbeYTKRvi1qaeSPT91zTaZCGJdc/GGEMfjGVbJQD9K\n+5U/TxjQC4IAolAeYilECLLwvhOEGIiygOyQMXUdMySieYNIDglT18G0MIIalIcpiqqChYXuC2KZ\nJrrPJgONoGH7jMkyOFQ7yyQWkioTKjERQrrtSVZOzBkhDcuwCHlDqB4VPRhCwCbQjEDIzkQJmLqB\nLAgYmm6HM5gnxniqwbQSIMN+AQb6FXHF9BsYctYonn3+IQ4e3kLL9iXkNJBQZBGfz+DHHSKavwXn\njriEv9x5ab2Y5ieLNm3asGTJEs4//3xKSgr58OPXyGxwhM6nS1WURz50fRvPzZ7Ozh+O8oc//j6y\nx+v1csstt7By5Ureffdd+vXrV2ObLVu25P77nqy2/fJfD6RZi2CUI+JH+G/m6ivv4IW5t9JvcJwZ\nTy2L4mKNrOzkvbg69vDxwkuPc/ttJ/fF5IILLmLBwkdp0z45421Dt0hzt68TaRcmw4YNGxZX+aVL\nl/LAAw+wevXqVObIFH6x6NihA7eecy7/XL0KeaB9nxQdKhw/TLOtX3HhJS4EIboyVRAEunRx0aUL\nLP3gIN8f3A1xkmEA2f1P58CCRXbopSiie33InsRUsGU7v8fw1b7IKqd5ykM8a4ec5iGtY3vKdv1A\nWru2AKjZ2WgFhTgaNUyofxUROl6Au01rHA0b4GrVAt/uvbhbt8QyLaQebeImwsIQHSrCxIE8/9rt\nDO9+GZKk4PUV8sO+L9l/+FsuGnEHrZrZijlBEFBkB4FgGU7HiYWuNHcO/mAJuqEhS4k/ayzL4mjB\njww9YzoADtWN33vqzhNT+N9GTCp6xowZdcoCFA3Vw35SqA+EX8yhuil5MBDA7/MRDAQiZFdFH6+q\nGf3CipjwvnDIXEVUPL7i71URJtMqEnYV669YLhYq1pEsLMtCUWteWa7Yv4DfjyiK/3ViK55+1ifC\nY65ru1Wvh/pE+PoMBgIx+1jTNVgX1FRnXdsUBFvhBZX9wqqWqQjTNDGNcnP9Cp/hYpZFxFT/262F\nCNghkZZlIalK+d1exNJNAsVlGEGj3ODesr01yj28wkSYIEtIDgkt/H9JAgEs3cQIaNWIH1FVykMq\nQVIlmzRTJFvSZlp2Py1swsw00P1BDM1WpFmnMBlmk33x//zS0LZtWx68/xmef3o5Pdv/HqP4YooO\nnEuaeDm3/WYeLz3/DlMumfaTEmFh9O3bl9tuu5GX5v6OQSOO0a23HDUET5ZFevd3cuNvW/DoU79h\n+afL2LhxI3369MEwDDZu3FgrEVYTRLHu93lJtJ/5Aweexahht7JxTXzHhYImDrVuaqWcXJX9+7+r\nUx3xQBRFOnYYTElxct4/a1cV0qn9GXXqQyIm+t988w3Tp0/nzTffpHXr1nVqN4UU/tfxq0mTuH3g\nWZgffILh8xHY/SNdcvIZd7477rni6FEZtD38Cf7vvkmo7YbnDEErKCTv4jEcmP8WZjD+xdLAwcMU\nbdhI9qB+lO36odbyoqrEPYfJ7teHovUbT/y//+kUrF4Xd9+iwbd3P85mTcvr60vB2vUAHP/si6he\nZvHA0agBJZ4gIKBpAbIz8xg3/A6um/IsKze8is9fHCk7sPckVm9+s1odp3cdw8av302q/V171tG+\nVeV7t6GnOIQUTg5izk5vuOEGBg8ezDXXXENxcTGSJPHII4/QvXv3/2b/UqgFYUVYVZP6sEonrMiq\n6gtVUdUSJquqKnxi+TiFj4n2e1WIohhRl8WjHKqKMCFXlzC4eELcqiYV8Hm9lUJGE1E/1QVV/bWi\ntVebGi8Z77R4QgDj8cOqz3BFOPHdiaJYqxF/Mn2PVVe8YZ/JoiZlWMUyldsTbduwcuN9e2PlrJP2\nfqFSlknZ5UAQRTs7pDeI7FKRHTKWYiHKtsG+hS21l1QF3Rewsz0GNQRRQFKkcsWYgSBK5eUtEGw/\nsVCZhuyQ0L0Bgl4NZ7qK7LDL6UEdURLtMEkU24RfFJBUFdnlQNQkTNNEUn56oiQmzNgmulUhnMKk\n3smG0+lk8qSpP3U3asTWrZvZvuttLpnerPbCgCgKnDnc5O+P3sjqlfk8/viTXHbZZXXuR7qnBbq+\nDTlJr7ziIo2WzU+ksZ88aRqZGdm8PPc+WncspHXb6CqII4dCbFmnkJFZd8WSbtT/AkQ03HDdTG64\n+RPOPi+xDJ3eMp3CI834xz/+xYoVn/GPf/yDFi1aJNx++/btee2112otd/z4ccaOHcvDDz+cyhyZ\nQgrluGz8BM7s24+Hnn+Wzz+dz8XXJX7vGT3cyXNvLoU4k3YAqLm5SGkeJLebvPEXsu+1+TS58Hwc\nDXJjHmNZFqXbvqFk+zc0mzwew+fn+KrVERVXLAiSZCcWimNRWZAkJLcr4mUmp3kwfH5MXUdMYsEo\ncPgIziaNI/NRUZERVRWtzEvoeAGOhg0SrjMMs09rAsd99O5wwmtMllXGn/t73vrwPob2n06Lpl1p\nlNuG5Wtn4/UV4nGf8Gds26IP679awmkt+5GbFd8zF8DrK2TVxvlcflHlhDd18Y5MIYWaUONMrEeP\nHsydO5e0tDQMw+D+++8nMzOT5s2bJ/2TQv2iogdR+CU+TBpUVK5U/L2qkqeiWitcTzSlWDTEoyoK\nExkV+5vI+OrDhyter6dY5Sv242SokMJtVPSqitVebWq8RPpX8fqJp2xN7cZbTzJQVLVOdSd7Xk42\nwsqumgjnE58njoFyE31BtH/CmSYF2yPQsiwGTurBllWH0P1BAoUl6P4gkkuxfcJUFUG0QyYlVUEQ\nBGS3CzOko2Z4UNLcCJKIIMkobheK2xmZqFmmhShLdr8lEdkpgyAgKhLuHA+iQ0VUJCwLFI8z4mtm\nmRaGrkfGZ4Q0TMOwVWN18Ms42Ugpw34+ePSJuxk4NPHv6PzxHkaP6V8vRBjAFdNn8M3W5InTb7Y4\nuO7a/6u0bdSoMTRvMpiP33aw7tMmrPsMvtqosW1TiPWrLNYub0jj9Kt56rGlyFLixvlVsf3r73jh\nhRf4/PPPyc/PP2nK/5ycHO767VN8tkyyM7vGAa9XZ/3KXF5/9X2+/vprunbtSu/evXnwwQcJJbiQ\nFY8yLBQKMWHCBMaPH8/06dMTqj+FFH7uaNmyJbf/+mr6dk9Lam4lCAJdm3vx/7g7oePSO3fEv28/\nSlYmzadOpmjDRva9Np/iLdsqKbmMYJD8T1bw/+zdd3hUZfYH8O8t0zLpDRJDD006SFWUImVRxAKi\nIosFcFdBdH9rWyyoq1jXAgrKWlexogIiIiKiiI0mAoamYAIEQnqm3/L74+ZOy5Q7Jf18nodnZm55\n73vvTIaZM+c9b9HK9yHabDjrystrg1YJEK22sMeRBRFsBKMrTO3y4Cj21NlKGzoIZVs1pvb6OfPV\nN0g/b5jPsqTuXVG9Zy+MbbKjatPdzsDeWLv9Raze9CSKzxyBVPv5Rscb0LXDEGz86UmsXH8Ldvy2\nChPPvwWrvngUNrvvkPbLx/8L679ZgjNlf2o6ZlVNCd5cfQeGD7gSrNcPxg6nFSZz0/2cSJq3sGHo\nbt264YknnsDNN9+M06dP49FHH8XixYsbom8kCoEytoIVoPe/750RozW4EUlh8WB9CaehAhP+1yTU\npAMN1Ydgxws3tFTrsdTgWyRZU8Haqa/r0lAB0YamZngpwyTrfsnzLFPGF7rrajFwzyKpFl51F9N3\nB5ZkyJDdGWIDRreDo7wGrJkDy3KQRBEMz4LjdXBZHeD0vPLhUJIBUWmXM+ohC0r7DM+CTzBCcgmQ\nJQksx4HVKZlefIIRsiiBMyh1wSRBAsPzYKEE2iApwzQZPZTgF29Q2tfxyvaSBK6pB8NEjQEUCoY1\nWYcPHwanL6xT7F4LlmVgMJ/GsWPH0KFDh5j7MmjgYDy/NAeyfDri9yVBkJBo7IGMDN8Mh9WrV+OD\nDz7Ajh07kJmZidLSUpw5cwaSJCE9PR1t2igzgykzSxsBaKtzE4jNKsJoTMF3332HV155BQUFBWBZ\nFj169ED37t19bjt37hxzyY3+/Qdi4Z2v4qFH/o7ufStxVvvAXzwlScaBfQIqT3fEiuUr3TW7Fi1a\nhJkzZ2LBggV47bXXsHTpUlx4YfgZ3ARBwK97d6Ks4g/c9o+ZYFkeqSlZuPGGBe5JFbxnjqTPxoQE\n9tKKJ9F7YPQB82GDjdj16RdAp7ma99GlpUKoUoIznMGANhPH1WZ/7ceJj9YAABgwYHgOaUPOQdaY\nC3z2l5xOTUEu0RHZeymXYPIJspk7d0J1wSFUHziEpO5dNbdTsmkLknr1BFdbJsfdvtkMy9Fj0GdE\nP0ELoIw2yD2rF8YP/Ru2712Ln/Z8AlkSUV5dhKuvPx/P3/A+GIbBju078ena99G9bwpeWDkTV078\nN9rnKpnLOt6Aqy56GJ9u/g9MhiScO+gqJCbU7ZfNXo3vd3+A0vJCDO8/DYzf1Ny7D67CvHuujOl8\nCAlGU07mmDFjcO211+Ktt97C6tWrMWPGDPTu3Tv8jqRJiKWwfFNov7E0p75GSz3HWINN9X2tWuJz\n4a4VJsOdPRVwO3X2yNr16pBm5QHct7IsewJlbgyGT+uL7z/Yg67dk6BPkiFDUoYtsiwkpwu8SQ/O\nqIer2gpWr1OWy6idiVIZ4ijaXWB5DqJLgN5sguhw1YbbAEelBYakBHeReV2CQclOYxgwgBIQk5Vt\n1fphapYZw7FgILt/cWyK5AiGSaIVD5Ns6l5a8QR6DYj+y1jvARKWv/Q4Fj/6Ylz6c/NN92PZK7di\n2AXa+yTLMrZuNODxRx71WX7kyBHMmTMHa9asQWamMiwmIyOjTsAMUDK1z2rbF3b79yFnAA1l324W\nLy79nzswKMsySkpKUFBQgIKCAhw4cABbtmzBgQMHUFRUhI4dO/oEyNT76enav6z17t0Xb72xGe+9\n/yY2f/0RdKYipGQ4YDCwsFkllJ5KglHXGVdf+XeMHDmqzv8ZXbp0waeffoq1a9dizpw5GDJkCJ5+\n+umAIxbKysrw/NJHcOT3n5CXX477HusGhtkHQMk6u//fX4JDR0yfdhP2/lpAM0cSEkZ55TF0NEX/\n98HzLFKYKkTySYFh2To/ZDEMg+Q+vZDcp1fY/e3Fp6APMawSgFKsvkP7CHoFiDY7+CSzz7I2fxmH\n4rXrIVTXIO2cASH3lyUJp9ZvhCE7Eyn96n4Xl1wusDo9IMb+eYQFC5MxCSPPUbKiy6uKsf/ky7jh\nRk8G7KBzBmLQOQMBAHf9cxGKTu7HD798iLw2Z6NvjwuRYEzFpAsW4PfCHXjto9uRYEjGWW16INGc\nDrujBpXVp6HjDRg+YBqy0jtgx751MBo8w2llWYZN/gPdunWL+XwICUTzAOX58+dj7dq1qKysxNKl\nS7F8+fL67BeJQaB6U1rqTNV3PaxQfQ01U2Cw2STrsy/xnqGwIWnpe6C6caSuhngdeIYIBv5S7B8o\n864rpzwGlBHvslKLjPG+D4BlPTXEMhM9w/lYFgwYCA4nZEmCJElgRBGCICgzP7IMJJcIWZSgT0qA\nJIqQJQlOS+2kGGAgWG1ga2uIMTwHV2UNRKdLqYfGApBliE4XeKMBkiBCEoTaLDI9JJ6F5Gz6Qwsj\nGv7YxM+lNSuvPIouUQZ/AMCUwOFM+R9x68/QoefidMm/8NHaxRgyUgz7HiwKMr79Uoc7bl+CDh06\nupfbbDZMnToV9913H4YNGxa8AS9zZ9+Bx5+ZioHDI++3LMtgxA4+GXIMwyA7OxvZ2dk4//zzfba3\n2+04fPgwDhw4gIKCAmzevBnLli1DQUEBjEZjnQBZjx490LFjx4ATLuj1esy8djZmXjsbhw8fxpEj\nh1FdXYH09Cz06dMXWVnhZ2SbPHkyLrzwQjz22GPo378/7rrrLixYsAD62gyQAwd+w/0P3ojB59fg\n3C48AN/MELOZx+DzAFn+A++vvh3rPjqBb7/ZRTNHEhKCIESfiariIEQUDHOWlcOU2zbq41X8vBM5\nl10ccpvyn7Yj57JLImrXfuIkMs7zffNlGAY5l0xC+fZdOPLsi0gZ0Bfpw4f4ZKa5qqtRuuU7CFXV\nSBs+BOZOgbOUnadLYMo7C5Yj4Yv/hyLa7TCxntrJ5ZUn8fORJXjplceD7tN/YG8c3ZGLEQOvROHJ\nvfh+1wew2qvAc3qkJedgzrQXoNclwGavhM1eDYPBDLMp1WdI5MnTB9Gzy0j3410HVuGK6eMDHY6Q\nuNAcDEtJScGyZctw9OhRsCxb+0WLvkA3NnUWSbVovho4ctjt7gLwakDJ+74aXPKerc+7YLy6bSyB\nAO8gm/fx/Qv5O+x2GIzGoEEolmU9Q8TCHCuavnpfH5ZlAb0eTodD+ZVXr2+UgJjW8wm0nZZhhfEa\netjchbvO9XmdlCaZ2mGHrHLfJ+jlCWqpfZFl2WdYJcMwkERlf1lmarf3ZJt5/92oGWL9zs0Bq9fB\ndqYG+kQ9WL0OokOA6HBBZ1TeT0Q44bIqM7jxCSwc1RZAksCwSqFYSZQgywDLsxCcEiTBBtHuhKPK\nAUOyAYwkQ3KJ0JmVvyvRKUByucBwLCRJhvqxhzfqIYSpS9jYKBjWMohxKPguSfGtFzn54iuQmZGN\nZS8/hOTMYvTsw4PjfN9rnA4J+3bLcFry8Mii59ClS77P+gULFqBbt26YN2+e5uN26dIFjJQPi+UA\nzObIijbv3SXjyqk3ad7eaDSid+/edUYTyLKM4uJidyZZQUEBNm7ciAMHDqC4uBidO3euM+yye/fu\nSE1NBQDk5+cjPz8/0CHDMplMePDBBzFz5kzceuut7qGT+fldcP9D1+GCvzjBsqGvC8MwGDDEiNT0\nPKx852Us/BcNkSQkGOXzTGzk0GWu66j4eQcMF/8lqmOJNhsYXikHEUzV3v3QZ2WCjWACIFmSIFRW\nQZeSHHB96oC+sP1ZiISO7XHkuWUw5uYo+1RUQnK5YMjKApdgDJmFbvnjKPKGD0HFzt0xfVe3bdmF\n0T3nwu6wYNfBVeDMx/Hyq0/A4Dcs09sVUy/BTWsfQvvcfmiX0xvtcgKPIjMnpPkU21cJogsOpwUJ\nRuX67P/9C3Q4246Jfxkb1TkQokVEn4IGDhyIgQMH1ldfSBT8i9+rj9VZHI0mU53C7N73vWfr816u\n3sYSCAhWiN+/ffXYakDM+5iBZnYMdyx/WgIe/hMFMAwDm9Xq09+GpPV8gm2ntSB+c1Cf2VlaA4f1\nwR3UUmeErLNe8nss11nuGRrptz0Dn8CaukwNiHXvnQJjagLAKu8BnEkPVs/BWW2BKEhgwMCYaoYM\nGZIggtfrwRl1cFnt0JsSAEYJcDEsA84oQnIqQyoTsjiwPAeXxQ5jehJEhwucXgdWx8MlCj5F+HV6\nPezlVWB1uiadHSbLEQyTlGmYZFMVjy9j8WjD3/DhIzF8+Eb8+OMP+N/bS1BtOQZJcuCPo8fQvl0X\n5LbthX/c8k90796jzr5vvPEGvvnmG/z8888Rv089+dgrmPO3yRg+tkLzcMnfD4rofNYVGHdhdF8w\nvTEMsDt1JwAAIABJREFUg5ycHOTk5GD06NE+66xWKw4dOuQOkm3YsAHPPfccDhw4gKSkpIDZZO3b\nt494mGJ+fj7WrVuH1atX4/rrr0d2LjDrb2kR1ZXrlK/Hvl2fYeOX58fluhDSEnFsAoDSmNpwSLzm\ncJirsgp8WhpKv/kO5k4dIp6k5/SGTcg4b0TQ9VX7CmA5/DtyLg2dOeavYucvEO32oPXIqvb+BlP7\ndji9+VskdsuHUGOBuXNHpA05xx10kwQBFT/vRNkPP8F0Vi4yRo4AU/veZy8+BWMbZXbJxO5dUXPg\nEJJ6RD68UJZlSAXHsP+sVcjM5XHbvVdr+vFBr9fjrI5mWGwVMJtSIz7uzr2fYmCvi2G1VeLHfW9i\nyAU5mPs37T++EBKNJjyfPdEiVBBEDTJ5F8RXBRoiF2mh9kj6F6wd7/XBtlUDe2qwLNyxAi2PNODR\nGMMkAw1d1JL51RT67i+efdAalI32mNEEUGOhti3Lrri3rfKpK+azAu4hk32HG+CocEBv1sEpSOBN\nevAmI2SrJzAs2hzKMkmqnUWSh7PaCk6vgygIkBxOcCaDMi04eMiCCEbHQ3QqQSFJEABZBqtXZrFU\nhl5K4AxK1hrDsRBd9Xcd4oEyw1oGjjUCqIypDQbBfxGP1dChwzB0qDLMUZZl9OrVC8uXrAhan3XP\nnj345z//ia+//jqqIXrJycl4cclHmH/bVeja5yRyzgpeKFoQJOz5GejV/SosmH9PxMeKVEJCAvr1\n64d+/fr5LJdlGcePH/fJJlu3bh0OHDiAM2fOID8/P2A2WajrwzAMLr30UqSkJOLd1TeD5yMPeJ7d\nn8UHq16mYBghQQwaeCFOnnwJbXK0z7rorbzUiZq0ngicT1XXydXrkDtlElzllTj50VrkXHGJ5u8z\nJZu/gTGnLQzZmXXWOcvLUfLVN7AVFiH/tlsiOAOlIH/Vnr1oM/kvKHzrPeROuxQ6v/em0m+/A5eY\nCGfxaWSNHAFzl0512mF5HunDhyB9+BBY/yzEn2++g7yrpoIzGVHy5dfInToFAJA6oB8K//cOErt3\njfi7nH3PPtx8zcWYM2sWjLUjZ7S6ef4s3H3bCxg/5J6IjmuxVWDPgS9hMBjRPj8BDzxxO9q1axfR\nsQmJBgXDWijvoX/ey/yziULVyYqm9lS4PkUaYAh0HoHaC1cTTUvdsUDDOhtSoKBPNJlfWoJHkdZi\ni3TYZrQZhbG+BuM5pDHc9YsmWOY9NJhh/NPv5cABLK/+eIZH+g+hBNRhlgzDQu26f3tqG2qGWO9z\nMuCyi2B5Bo5KG/RmPWRRBmfQQbA7IAoSZIsNhpREuLyCZDJkiHYnOKMe9nIreD0Ll8MBh8UJnUmA\n6BDgqFCyzFieAWqsEF0ieACSKIFxQaktxnPgWCbkUITGptRT0xbkYqmAfpPVpfNgVJR/jNS06F5r\npWec6Nk9iiJbUWAYBqmpqaisDBy8q6qqwtSpU/HMM8+gV6/whaCDycjIwOuvrMNbb7+CrV+vgSnp\nOPJ7MjCaWEgiUFbqxKH9ZqQl98Qtc25Dv36hCzvXN4ZhkJeXh7y8vDqzQVosFhw8eNBdxH/t2rV4\n8skncejQIaSlpQXMJsvLy3NngX340X/Rb4g50GE19UtkjqKwsJC+vBESwF+vnYM5N69Em5zohpp/\n9YOEhL+M07StY/8B6AQBupQU6FJSIAkCjr/zIdpOmQTeHPxvXHK5cOrzLyFU18B5ugTOsjLoMzIA\nloWztBTO02egz0hHm/FjYT16DKc+/xJtJoaflVZtu+idD5Fz2WTo01KRd/U0FL3zAfJmXOmeDdJZ\nWgpTh/ZwlZah403XQ58WPrMqoX075F4xBUXvfghDdiZSB/UHV/v9heFYZIwcgeI1nyFnykWa+gkA\nzuMnMcIhYv5N0WVk5ebmYs68i/Dm8qW4YOA8TZ/JrfYqvPHR/8GYwGDB3TPwl0nanmtC4oGRQ337\nIk2a91A+f2qwI9Dwv0Dbehfl9t5Gy3jzSMakh9o22DpJkmA0mdxZblr77r2Nei3C9VOSJPe2RpMJ\nTkd8a8Q0Jd51p7RuryWLSm3XYDRGdf3U/b0DnVoCew67PeKJIIIFtfzbDBZkDZeVF6h99fwO/cJ7\n1fdi3cMc1YCVf3YXwyjb+Ae76ryFq+35zULp3wYgY9t7v6DP0Cy4bAL0Zp2SwcUwEB1OMCwHSRDA\nGw1gWBaSoAyFdFbZoEsywl5uhd6sBBZEhwhWp3yp5BOMcFZawRl0kFwuOG0uGFMTIFgc4E0GCDYn\nGJ6B3mwCGMBVYwOr02HE9MhmZKpvRUVFGDt2LCo6T4ek15Z5wzqrkfr7e9i0aVPAWepI4ykvL8dt\nd47H8FHRZe9t+4rH0me+RHKy1tyE6OzctR1vr1yGrd99hY4d26FNdi7yzuqOObNvQ1paGmRZxrRp\n05CVlYVly5bF/diff/4xKivLoNMZ0K5dJ1x91fX1fs71SZIkFBYW+mSTqbcVFRXo1q0b8vPzYRd3\n4aIroj9Pm1VEVfF4PHDfk3HsPSEtxz33zkNK2y0R/yBhqRHw30/NSL4+fF1EYe9+pPx6ABV/GQNd\naop7ubO8AqVbtkK0O5A2ZBASOnVwf35znC5B6dbvIQsi0s8dCtNZuaja9xtEiwWGNm0g1NSg/Kcd\nECwWcEYTMs4fgaRu+ajaux9V+35Dm4njgtYAAwBb4XGc3vgVcqZMUoJrtVyVVSjZuBm5U6dAEgQU\n/u9d6FKSkT5iKIxt20R0jewlZ1C8eh06zp7ls1yWZVS/+S4MqSnQXzzBPZwyGNehIxhYUomXHn8i\n5tlxt237EUuffh+Du9+A7IyOAbeRZRm/HfkWm358CQPP6Ybb75iLrl2jqwVJSLQoM6yFCzbUzn+b\nQPcDPQ63fyzbege1vAMhar2wcMM4Q7XNsqymDCM1gNNc6mnFItJz1DoM1f0BQ0PwMdRx1DbDtaHW\nfPPuh9bnMFTmnX+bwfoZqr1g2X6AV40vJkAdMK9bz3LJfRvqJwx3bYwAdcM8ATdlA/eQyXPbQhZE\nQAZEQQDDcu5gliSI0CcZwBl0cNns4E16cDwPY1oCmNoZKRlegC7BCHtpJSSnAM6ggy7BAGeNiISM\nJLA8B1kQIYsCeBMPgAVYBqLdAc6gB9im+/cmy9qHScoyDZNsqtLS0pBi7gGbdQ9MCZF9yLdYBKSn\n9KvXoNDbK1/Fl5veR0LqCfTsy6Jr/zQANQAOoqJsL2678zMkmboiJakDjh49irfeeivufRg44BwM\nHHBO3NttTCzLokMHZfbLCRMm+KyrqqrCwYMHsXXrVuzYuzum45gSOBRWlsTUBiEt2QP3PoUb5lyC\nweef0vwe7HRI+GlLGib0H4mfP/sSln5nw3BWrs82sizDWXAQab//iatGjUWBxGOrXwaYPi0VOZde\n7K63VbFT+XtnwECXloo2fxkPzuQZEcKbE1BdcBCWw3+ASzTD3LkjWKMBotWO0m+3ofjT9TBkZ0EW\nJfyx/BUktM9DSr8+MJ6VA9ZggGi1oabgICx/HIMpLxftrp1ep0aYLiVZ+UGwuhonP/4U2ePHonTr\ntogDYQBgzMqELiUZosPhzjSTRQnM55vw4h13o01mJv697EUccdrgPKcf9Gme4vWyKMK1+1dkny7F\nJSPOw8133heX70AjRgxF37698Op/38amn99AZuIgZKbkw6BLgNVehX1HNuF4yW707JWHDZvfQEpK\nSvhGCakHlBnWjIXKDAO0ZU3FW7xqLfn3HYitfpnWNhrjmpHoNYX6aNE6sKvxXlvq0CBZVoYBfv/B\nHvQZkgnebPLUB2NZuCw26JPMgDJRpVJEv8YGXVICnJUWJYglyZAl2R0w0iUmwFVj9SqWL4IzGtyB\nL0eVFZyOgzEjBbJLhLPGAs5owNApOY11OQJSM8PK2l8OSZeoaR/WVYP0Pz+izLAmqry8HDfdPBnn\nT6zRXBvK5ZTw7cZk/Hf5p/USDJMkCf9aOA+ifiu6dAv9+6QoyHjvzeO46cYnMOOa6+Lel9bq4MGD\neO6lqegzMLbfhwt2nI1n/xP/ICUhLUVFRQVuuXU6zh50HBmZoeuHVVa4sPv7LDz3n3eRnZ0Nu92O\nV95+G5/v+AkWWYZLFKHneaQyLGZdfAkunjABLMti4WOLsSEvE1xCQtT9rDl4GNX7C9B2ykUBvwe4\nqqpR+s13sPxxDLlXTIGxTTaqft0Hx+nTSi3VhAQkdO4Ic6cOIY9jLz6FP99YiY6zZ6G64CCMuTlh\n9wnGduIkagoOInXQAHDbd6Mzq8OD829Fj26eAvqlpaV45r8v49CpU3CKIniWhZnjMXfqNIwYNiyq\n42ohyzJ+/nk7jhw+hpoaC9IzUjF48AC0b9+0RgSQ1okyw1qoQMXYA62LdzAhkqyccO0Akdf70pL9\npvW4zTXI0prE6/XW0NTaXoF+iwha/F7j+lDUfSVR8hpKyXoV1W8LhmUgOJzgdBxkQKmXJSs1LwS7\nXQl8VVshuVwAw0CwCwAATs8p92ULRKcE0SGA1XFKxI11QbAp2zEsA4bnILtESKIIhuUg2pvucORI\nCug35VkxiZId9vQTK/F/d83A0FGVMJtDfwSqqRbw8zdpePapd+otK+ze+xfAkLoVOXnhP45xPINr\nbsjDF5uXIL9LVwwdem699Km1SUlJgdMe25AgAOD5hq81SkhzkpqaildXrMEzzz2C7778Bm3bn0Hn\nrjqfz97Hfnfh+B/p6NplOP770v0w12Z5GY1G3HLjjbjlxhtDHqNbx45Ye+IYTB2jD4bZT5cgZWD/\noJ8tdclJaHvxRBx7/W2YctsCAFL694n4OMa2bZDQoT30GemwFR5HxrnRB6RMuTmoXrMe43La4/8e\neBjZ2dl1tsnIyMC/76r/SVD8MQyDIUMGY8iQwQ1+bELCoWBYCxVueJf3ELR4BxPi3ZZa9yzWovGR\nHrc5Bllao+b4HIUKaIULdMWSzCtLslJHDLVteA3RVIvqD7ggDzz04PQ6sDoRLM8rwxwNSp0PWZDA\nJRjgrFIyzHijAWCUwvh8ggG80QBnjZKxytbWnGB1PFiWheh0geVYSE4BTDIHR3kVOJ0OstR0E5Rp\nmGTL0q5de6xY9ikWP34PdhfvRuceVXVmUjxR5MQfB5KRlzMQ/31pcVSzNWrx+YZPYXF9jZ55kdXQ\nGXaBhP88dydW/u+bmOu6ECArKwt2azKA6qjbKPrTgf79RsSvU4S0UAaDAXff+RAkScL6z9fiiy8/\nxJ49O5CYmIROnbpi1PmTMWXh1Kjf266ccilevvUWCB2jn8zCdqxQU2BKl6gtazwUNYtevY1F5/x8\nPL7w3pjbIaQ1oWBYC6alPle47ZoCNXCg0+tDZmrF+zwa+rpQNhqJF//ZJj0r1Nd1gNc24wmI9R6c\nqQTARAkyK4Ix6cEyPFw1NvAmAySnC5xep8w6abWDYVglM8zqgMSLgATwRj0kQQBn1CvBLpaBLsEE\nVq+0I0sSDElmMCwDSaAgEmk4KSkpeOzRF2G32/HW2//Frh+3QJQcylBgVo9BA8di4W03wFBbe6W+\nrPr4FQw8L/KPYQzDoHPPSnz8yXuYesU19dCz1oVlWfTsdh4qK9YgJTW62UaPHUzFA/+cGeeeEdJy\nsSyLiyZNwUWTpmDu3LkYNGgQbopyBkNvJpMJvTOysMNud8+sGAn7iWIYc9pq+g7g/mExBmob8WhL\noBmtCYkYBcNaEDWY4nQ4NA0t9N830n2i7V+wmfnCrfPP1Ao23DMeQaWGuB7+KBut9Ypl6GPdttR6\nYHLA5Srvov3uPkD2GjKZDcHmAm82gLE4IEOG5BTgdCrDHSVBgGBlwOn1EF0uMDwLhmUgOl21Qwsl\nsDwPZ2UN+AQjRLsTMs/DXlkDTsdBdLrAgIFgdypDLpsopYaa1mGS9EG0OTEajZh94zwA4Wcpi7c/\n//wTEnM06vf79p10WL/hHQqGxclNc/+B/7vnCwy7IPLAvNUioEPeIOh00QXSCGntRFGMa5brHbPn\nYPp/ngAuvCCi/WRZRsmmr5F75WVx60tD0rPaamESQjzor6aZUwM/utpZStShj3abzWcmSZ3XLCZq\nlpX/fZZlfbKwvNfFS6gZCR12e8C+6g0G9zrv5QajERzHQW8wwGA01pnBL9D5Rsq7nVjbCkc9Vy3b\nhOqDuk247eKlPq9JY6nvc/KenEEJUkU3U6p3Gz6BYkmqs0y9K0lS7XrfNr1nsRw+rS/2fH8aDosT\nvEGvZG85BTC1RcfVbC9JkCA6nZAlEZLTBeuZGkhOJbBlr7AowyJ1OuV8eeWDLm/gwep14I0GsLXL\ndOboa3vUu9rrpeUfKBhGNHr9jaXoPTD61wvDMJDZ4ygpoRkM4yEjIwNd2o/GicLIgmGSJOPHLWYs\nuPW+euoZIS2fKIrg4zBMUJWfn4/ZQ0ZA2Lknov1Ord+I1MED3TMyaiE6oq95KjmdgFomQpJj+mFQ\nFkVk8hSQJyRSEQfDPv74Y3zyySeorKzUvM+pU6fw7LPP4tFHH430cEQDNYilkmUZRpMp6HrA98s1\nx3Ew+KUSq8u0zL4YLGgQaUCBZVl3EM+/L0aTqU5wwG6zufdj/X4N8S/Ar+U8AgWPgtVcqy+BAhT+\nfQr0fPpTr4d/MC9c29Fs5x98jKTtWHk/b8GOGWh5sICw+lh9zUT6+g7Vbl1qIMtvqXtB3WBZndeH\nJNcGvXzbY9jA2WHuY9S+hpR92bqBs9qi+kcOWSA6XEq2F2RIataXKEJySZBFGQzPQxIkyKIEVseC\nYTm4bAJ4Iw9ZEiE4lMxK0eaC6HAqBfgFQcksczohOl1wWW0hrlPsampqMHr0aGzYsCHifdUC+lr/\nEaJFVXUZTAmxZUIkJDlx+vTpOPWI3LvwcVSeGqg5ICaKMrZs4HHv3cuRlZVVz70jpOUSBCHu9Q//\nft11uK5TV4jffg9ZDP3Dg+R04sSq1UhodxaSenQLua239BFDUfbdj1H3sfS7H5DQqQOsv/yKtk4X\nqrd+H3Vbrt17Me/qGVHvT0hrFXEw7J577sE999yDkydPat7n1KlTWL58OdasWRPp4UgYDMPAZrXC\nYbe7s6vUW/W+ut57H++i9Op673bU+977BTt+sICLehzvrLVw7bEsW6evVosFAOrsy7IsbFar+1+g\ntr2vRbjzcNjtcDoc7mBIqO3qQ6C2/a9voOczUDvez2Wo7QIFsfyDgqGeY+9twrUdTrjgUrDjqs+b\n9zG99wvUF+9lgdarwcRwr+9I2/XeTh2m6J+p5cnQkty37vuS7HutmbpD8yRR8tnfvVzybkeCLHva\nVv55Amfq4+HT+uKX704qhV0lScnyEpV1nJ4Fb9JDsDnA8ix4swmGJBMACTojp8wayXJgWQaSUwTD\nMeATjJBcSjvOKgsYhgFvMoAz1l/gtKamBjfffDNOnjwZVSBbliXtwTCZMsOINvF4rbCMDCfVmIwb\nhmHw7H9eBS9MwA9bWFRVBs7SkCQZB/a7sPWLNDyyaCX69OnXwD0lpGWJ9zBJ1fwbZ+O5q65Fp29+\nRPWa9XBVVvmstxefQtG7H+Lwf5Yi/dxhSO7TK6L2Tbk5sJ88GVWZC1mWkVZ4Evf1H4JlEybjpw8/\nRmdH9D+oZZ0+gxHDop+NkpDWqkFqhqmBM6vV2hCHa3UCDacKN8QqkvWRHN8fy7I+dbC0tOu/jRog\ni7afWs/Fe7tQM1g2ZGZYoGX1dc7q40DnHs05R7pPoJppWuqoBXv9h3vdBTs/NZio3td67HDthuRT\n2F6uHUYZoAi+OwPMd5nkHRBjNHzR1rJN7XZqUf38bolIyFCGM0pOEfZqJ4zJDDgdB32iGS67A7Ik\nQpdggmB3gmEZZRgkyyjdZnmwHAfepAer46FLqm3LrmSH1YeffvoJDzzwAMrKyqJuQ5JESIy2D6gM\nZYYRjXjOAEmSwbLR/3/idPBIS0uLY68IwzC4/94ncObMGby04j/47uetMCVWQW8UIQoMrNV6JBg6\nYfq0v+GCC8bU6+cBQloLQRDiOkzS27lDh+HcocNQVFSE82fOgCunjXsdn5qMnCkXo3jd5zC2bROi\nleDShw/BqXUb0PbiiZHtuHkrnrnzbpw/wjML7W3XzMDCNR+BPS+yoJb4w3b8/dIr6P2IkCgEfec5\nePAgXnvttYDrZFnG888/j5SUlLAHsFgs2Lp1KwAgOzs7ym6S5iweb86N8QbfWmd2bMz/TOMZfNQ6\nNDbQc9xY14BhAFn2BLtkSa4d8lh3Zkh1GGf9dwruovp9hppQfdqCpDaJSr0whwDOwEMSRLiqbWB4\nVqkTpufgqLDCkGyCy2KHzmQAAwYuqw0MywCyDFeNFRyv7CsJQr10fd68eTj33HNx/fXX48orr4yq\nDVmUIENjAf0wQzEIUU2YcDnWbvwW3XtFnxVpqUpF+/bt49grosrMzMTCex6FJEkoLi5GRUUFjEYj\nsrOzkZiY2NjdI6RFqa/MMG95eXm4YsxYbMpKhi7bd1gzq9dDsFjBR1G/1Ny5E1wVVTi+ajVyL79E\n0+dHecs23DZ6nE8gDADGjxqNo0VFWPH9z2CHD9Z0fGn7blzb7WxcdtFFEfedEBIiGNa1a1ccPnwY\nv/76a8D1X331VcQHu4j+UFu1eMzy2JA4jgP0+mbT34bU3J7LQLyzx5rC+UiiBIZllSGTbG0dsAC1\nvzzDJxsgEFaLYRivWSbbwlFlA6dnwRl4ZSgkz0FnNkJ0KBlhkiBCl6CD6FAK8MuQAQkQXSL0Zs8Q\nZFbHw1ljVYZh1oOVK1ciPz8fRUVFUbchSyJkjZlhVDOMaDV61IV47c1sdO9VEdX+leUunN3j/Dr1\nMkl8sSyL3Nxc5ObmNnZXCGmx4l1AP5hH774Hl900BydGDoUuJdm9PH3EUJR+uw1tJl4YVbvJfXsh\na28B+DUbUNG1I/Q9uwesAez87SBSDx/Fv667AeNHjQrY1txrZyItJQXPrvoQNf3PhrF9u4DbOY+f\nhGn3XsybMAkzo/yxjxASIhjGMAweeOABTJs2LaYvXXq9HtnZ2Rg3bhzmzWv46ctbOl0zCtZoGfIW\nTLhgRaj10QY6JEmKur8tXVMLJEVD7bfT4XDPWGowGuFyOhv8nBiGrR0qCaA2ECbLSm0vlmPhKe9Y\nN0MstuN6ssuCZZopwzUBgHEPmTx7YDrsFQ6wrAuGZB5C7XViWMY9gFN0ieANekiCAJbXQRJcYFgG\n9kobjCnKBB/Oais4gx6Sq36CSPn5+TG3IcvaC+PLMgXDiDYMw2BA37E4c3olMrO1z1ym2v+LAc88\nfns99IwQQhpWfRTQD0Sn0+G9JS9gxoJbcbR3Nxja5wEADJkZcJaW+cz0HQnhl71YuvBeDB50Dj77\n4gu8uuYTFDkdkBKMABiwNhvydAbMuvgSTP7XorA/YkybfAmmTJiI1999B6s3bEGxnoOYmACAAWe1\nItvuwqTBQzHnheUwBqlxTAjRJmQYvnfv3vjtt998lvXo0QMMw+Djjz9Gjx496rVzJLzmFqyJZchb\nqHMNtT6aIJz3ZAQksGD1uRpLpAEs7347HQ6f+nYNfU6ewvaoDX55AmP+28TCP+AV7L5/39z9qp1l\nctt7v6BHn1SIThGsjgPDsuASE+CoqAZfWwyf0+vA8hxEJ6sM9ZRkMKwI3mQAbzTAUVkNQ1oyBKsd\nYgzTiat9F0VPIIphmPh9sJa0D5OERMMkiXZ/u+l23DD7S4y4sBx6g/YMr2NHRPTqPhnp6en12DtC\nCGkYDTFMUmU2m7HqpZfx/H9XYP36TSjJzoSuf2+k9O+D8h9+RvrwIRG1J7lcaHPiNIYOVva7eOJE\nXDxxIkRRREWFkvmbkpISceabXq/H3L/Owty/zkJJSQlKS0shyzIyMjKo7BAhcRRxfn1ubi5ycnKg\n0+nqoz8kQgajMeRse+GEmq0v3Ex+9SHUMYMFJtQgiN4Q/Nf1SIMaapsNqTGud6Djau2Hup16rbz3\ni+RcYn0N+s8cGWhGzGD7qf1Wbw1Go/u1FM3zH8m18y20zyr//H4tVDdhGK9t3EFI/8f+y9kAr3v/\nYwabRID1/cd6AnTqkMmCX8shOEW4amyQXALsJeVwVNrgrKyBs7IG1pIqOMqrYTtTA8kpQJZEOGuc\ncFRY4aiqgT4lEYLdAdHpgmCLrWbY0qVL0bt3b/e/8ePHx9SeN1a0gRMsmv6xoi1uxyUtX0JCAp57\n5l1s3ZgEm1VbwPXoYRFwjsQd/7eofjtHCCENpKGGSao4jsPtN/0NX6x4FS9MvhwDduzD4CobEvf+\nBsu+38I3UEsWRejWbcQr/3404DEyMjKQkZER87llZWWhR48e6NmzJwXCCImziP86o6kVRupPqFkP\ntdCScdWQYsniUu/Hsy8NqbEyrALNvKilH/6ZVVpmcNRyfK3rAm2jZvNpeT0E6rfDbocsy0gwm31n\naNQokmvnnYnlvbnv8EivZQEyw7yXqe1pySBTju+/nXfhfuX4gfqi7j9ien+lhlhuMmQADMeD00tg\ndTrIkgy9WTkpnYkHb9QrhfU5FyRRBsvzkCXZPSmmGOMwyenTp2PMmDHux/o4BpVzHYHrZhISD23a\ntMHLy9bi7n/NgcAcRq/+EkwJdTMkik84ceS3JAwecAlunX9PI/SUEELqR0MNkwxkxLBhGDFMmb1R\nlmXc/+QTWPfjDvCDB9T5gdKbq6IS5s3f4dVFD+Kss85qqO4SQuIs7mF4p9OJnTt3YujQoY0+bKo1\nCJctpWXImNqG/z6NNVTQ+3iRnEe8ajypGUKNofFmMWRCPg63X6DbSF474YJW0fYjmv0YhoHNao2o\nnUj7q2SGse5bZRncxfPBBA+Aqdsrh2G82pD9AmyeZZ66X7XHl2Slvlew+mOMUjMPslIHzF3cX+2P\nDDAs664h1mtgBmRJgiTKkHgXZEmG6PIE0Zw1VkCWYUgxw2XxzDrJ6XWQRBGmNFPYaxZKdnZ23H+T\nSK3JAAAgAElEQVQtbdu2LTZt2hT1voRolZqaiuUvfoBjx45h+ctPoPDEL2AYG3idDJeLA8+mY8Sw\ni3H3i3OoPgwhpMVp6MywYBiGwcN33oXRW7Zg2Qfv4Q9IkAb3hy4pCUBtEfwDh5F6+CjGdu2OO5e8\ngNTU1EbuNSEkFpreeURRxLvvvouVK1di8eLF6Nu3b9Btd+/ejeuuuw5ZWVmYPXs2ZsyY0STe4Fob\nNRihFgSPZL9Qj6MRazHyeARUwvEPgLEsG1VmUHNS30XiGyuwpz6XsizX+2suUt5F+9VMLFmW4I5J\nMd6zRfq+/tRsMjVg5v/69BTC9wTLZNlzDDWABcAzW6XXfv7Uov7KA9m3P2o/GbhriPU6JwM6AwuG\nZZRAl44FwymPBZsAWZbBmxlIkgzJKYLlWUhOFySHC7y56X3B53keeXl5jd0N0op06NABix95AQDg\ncrlgs9mQmJhIM0YSQlq0xswMC2TMBRdgzAUXoLi4GEtefw2n9/8OQZZh4DhcOGw4Lr/7fnpfJqSF\nCBulOn78OObNm+cupL9jx46QwbAffvgBAFBSUoLFixdj3bp1WLJkCdq0aROnLhOtvAuCa+GfzROv\nYEksw/+0ZhjFmsXmPbROHSbXkBprRsZA16uh+uI/m6N6G4/jxhrMjPQa+J+L/4yU3veVQJiHGuQK\nduu1JYDajC41g8yd+QUoJSA9gS94Z3Kpu6v9lSQwHOtpk6n7oY7xy06rO7TT0081IHZ2/3RwehYM\nz8JhcUFn4MDyLPRJSrBLEkRwOuUDL280QBQEcAZ9yKEIhLRGOp2OarMSQlqFhiygH4m2bdvikbtp\nWDohLVnIbyAlJSWYOXOmz4ySx44dC9lgp06d0L9/f/fjPXv2YNasWSgvL4+xqyQY/0Lm3v+8C4IH\nKm4eaB91mcFojDlDxrv9aArEByrO7r3c/zz89430eAajEQajEVaLRXP/4lX0nuM4zUXfQ22ndZtg\nQ0HV4KXW9rxff5EU0A9WcyxY+6HOxfs1zjCMz/Ontf+B+ud/bqG2VfvuP1RU7VPd9Z4i9oFu1eiV\np5A9PMsZzzBJn3ORlMBWoOCSd8DLtyC+J0ssGP/+qefhyUJTAmL7d5fBaVGGSfJ6DpIoQ5ZQWyTf\nAcHiUOqD1XZFLawv13MWZl5eHgoKCuJaWJ8QQgghkRNFEas+ehdz/jYF1885H8bEQjy7dB5uvGkS\nli1/BjYbTUZDCGkYjBy0aAwwd+5cfPPNNwCAvn374q677sKgQYM0Nbx371488MAD2LdvHwBg4sSJ\nePbZZ+PQZaLyrm0UOJMk8D7e26v3/bfxzwCJhWf4FuNz3GDb+mfkSJIEg9FYJ7vMu13/c/LeRmv/\n1QCR3WaD0aTUMAoUnAm0X7yGkwLhr7eW7eK1jdZt/TOavJfFg5bXjSrUazxY1mOo106wW619Dbb9\ngZ2Mbx2uGLAs687iUlP3/WuOeW+jDpn0rwHmvb1S0yx43xiGdWeoeQruy9j23m507pwIY7IBrE4Z\nJsnwHESHC/pEE2RRyUpjOQ6iS4DkcoHV6zDsMipASwghhLRkL614Flu3fYS8zmVo30lX5/PR6WIn\nDu1LRJeO5+G+hY83yYwxQkjLETQzbMeOHe5A2EUXXYR33nlHcyAMAHr37o2VK1dixIgRAIANGzb4\nZJiR+PIvAA54spz0BoPPcu/tvffx3sb/cay8M87CnUegYWSB+hLqnEItC8Q7U4phmIhqAcTrGmm9\n3lq2i9c2WrdVg0Tez288625pPRf/YJf/eu/H3q+zQIEwdf9ARfZDZatpqbsny7JP9lYk18q/P1pJ\nouTJBGPVTLPatvxe796ZaCGPU5uh5sk0U2aZ/P13i1IrzCFCdAiABOWfrGSJiS6lhpjkUrLIJCG2\n2SQJIYQQ0nTJsox771+AI0Wv4dwLa9Chsz7g54vstnqcO9YJLvELzP3bVDgbuHwIIaR1CfqNf/Xq\n1QCA3NxcPPLII1FF5g0GA5566ikkJSVBlmWsWrUq+p6SoAJ9MVeHaDns9pDZTaG+1MdrCGC4YXCB\ntvd/rNbzCibWvnpfL/9hdi2RlqGUkbTlPxQwkv20PHdaXqf+w4XD9cU7gOc/BDfcEGEtQzoD9dl7\nmW/zdYO6dW9Z1B1O6b2f31BIxu9vyasGmCxJtfeVjDn/YYrqY++hmHX741us37v/I6b3w76dpdAZ\nObgcIgSbEwzHQHQ6IdgFZTuOBZ9gAsNykJxCnWtJCCGEkJbhyacWQdJ9jc7dtH2fzGrDI7/PYdx6\n+6ywo14IISRaQYNhu3btAgBMmzYtpqm809PTcfnllwNQss1I/AX6Yq5+yQ+U0eP7hTx4ACPS4Ea4\nPnrfauUdaAnXfqx9VY+hNxjcwyRbKjX415jPbyRBUi2vU+9bh90eNgtR3df/OjAMA7uGehXBMhG9\na4Op9/3/5hiGgSRJCDRzpDtA5XfL1GZhyTLqBLMkUfJZ7n7s9wHSXQyf9cv88j8VRhme7NNHqe5Q\n7GBPtxoQ+/WnMzAm6cGbawOEojKckmVZsDwHyMqEALwp/HNFCCGEkObn+PHj+O3wGrTvHFliRVqG\nDolpv+Kz9WvqqWeEkNYuaDDs5MmTYBjGpxh+tIYOHQpAeTMk9SNQNpXWIJf30LJAReobUyRBrngM\ny1ODI1qCIfEWz2L8WsRzGGwsffC+1bKtlnbUmVS19kHL0EatAg3d9Q/U+dcRC7ZP0L6owxPVbDE1\nuOV/69Oud/F7T1OBfnH1zUZTh1Iy7oCYNzX4FqgNNSAGSRmiyRr0cFqcEOxOyJIMVsdDhgyXNXTm\nJyGEEEKap2XLH0efQdGVQ+jSQ4dP1rwe3w4RQkitoMEwdSaP5OTkmA+SmZkJALDWFnsnDUNL8MB/\nmX+QLF6ZQ7Fo6IBNYwWJmsr1jpSWYaz1KdQMj9Gqj3PyDm75Z1gFuu9/65+p5d6+dkijGpBSt1Fu\nvduV6maS+WSkBcpOk3z7wfj1R61DxjLuNtXlamba8Gl9sef707UzTOqQkJkIzqh3XwcGDIxpSdFd\nVEIIIYQ0WS6XC8eKdiDBzEe1P8MwkJijKCwsjHPPCCEkRDDMbDYDAKqrq2M+iL32S2Uswy1J5KLJ\nNIpnhky8xKumlJbjxKuOVrSay/X215j9jncQUevQ3FiPESxrK5ig27iL2DPK7I4h2vIeFumTWea3\nT6DMsLrrvIdX+rXpPQyTZTFiej/s3V6qBOgkEbIgQLA74LTYILlcEB2uMFeMEEIIIc3N7t27kd6m\nLKY2uvUS8NHHb8epR4QQ4hE0GNahQwfIsoyCgoKYD6K20aZNm5jbItrFGiSI97C9YO2FC0KpWTqh\ngl3hCqZzHBf2XPzrR7EsG7fzj8e11FJwXr0W6j+tBeq9NXSGWqzXpj6G82p5vWgRKmutbj0vuc42\nyi1bu71vppd3lpg7C4xBne087bE+x1ECU56aZd5DIANlqqlBM++C+nVrn3kK9Hv2l9wZYrs2F0Kw\nucDyPGRRguR0geF42Mtb9oQVhBBCSGt0+vRJmBLqllKIRGISjzNniuPUI0II8QgaDBs0aBAA4LPP\nPov5IOvWrQMA9OjRI+a2SGQCZYiECkoFmpUynn0JFGRRC5aH6muooYve9ZiCrbdZrRHNZGkwGiFJ\nUlwLzMejwL9aGD5UPTi7zQaWVf60gxWoDxeACjXpghbRBOCipeXaRtIfhlFmE1UDVrEGxYK9rn0z\nsQLVCGN81tXN0GID7BP8mIx35pa71phXNhnrHehSt/GuKSjVmXVS6ZenPfdylvU6JuM+nRHT+2H/\n7nJYyyxgdTwgy3BZHZAlmimKEEIIaWlYlkOsk0FKsgyGjaz4PiGEaBE0GDZp0iQAwK+//oq1a9dG\nfYANGzbgl19+AQCMGTMm6nZI/IQKSvnXDKuvukn+WJYNeiw1EBCqL1qCKVqzh9RjOez2uAcDYyHL\nMowmU9iaZizLwmqx+BwzkgkWAqnP7ePxOgs/xDB0dqE/9fUYaxAz2Ln5Z4Z51/fyrtelrvOv/6Wu\n99/Hs67uJ0/vmmNybUF7dXgjgDqBrkB1x9TMM2W9JzvN+5/3vkodM89zI0syRkzvh4P7KpWAIMdC\ncIrQm3WBLh8hhBBCmrG2bXNhrYmuXpiqqsKFnLZ5ceoRIYR4BA2G9e3bF8OGDYMsy1i4cCE2b94c\nceM7duzAwoULAQC5ubkYP3589D0lcRUqi0rLdvUhWFF/NZhQ39lDgfrSmPXD/HkH6cIJFVz0bi/S\n49fX9g3xOotkhkkgeCAxUk2hDlwdDPyGN0p+gS5PYCzY36V/If9gx/HZjvEU1d+1uRAMyyGxTTI4\nY+POWksIIYSQ+OvXrx8qzmTG1Mah/SZMmzozTj0ihBCPoMEwALj//vthNBrhdDoxf/58LFy4UNNs\nHkVFRVi8eDFmzZqFmpoaMAyDBx98EDod/fpPIhevYEI0wZymNrtjJDNdNskgTCNr6GsS77p78RC6\nwH7gIZ1alkV0bAYYPq0vfvnuJASbA6LDGVV7hBBCCGm6WJbF2T3OR2V5dBPlSJIMk64LMjNjC6gR\nQkggjBzmW83mzZsxf/58CIKg7MAwyM/Px6BBg5Cbm4u0tDQ4nU6Ul5fj1KlT+Omnn/Dnn3+6vyyx\nLItFixbhyiuvrP+zaWXUWlgkPLXIustZ90t3sHWh9mnK6qPf9XkttLQdzfPnv43BaIQsy3A6HO7t\ntZ6X9/7qtoH29V7mPbGDd2F6ddmhX7gghe7rFtcPxHs7Lfu4t5GVml6yLIXdz3+97zFZALK7/lmw\n7XyWSTJYjoUse/bb9t4v6NozGWNvopqShBBCSEtTVlaGW/8xDueNi/yHtF93unD15S/gvHPPr4ee\nEUJau7DBMADYuXMn7rzzThQVFUXUeF5eHv79739j2LBhUXeQBEeZP4SQliLabDNCCCGENG1vvPkS\ndu1fhh59Qg5K8nGySIBQMxqPPPxcPfaMENKaaQqGAYDD4cAHH3yA9957D4cOHQq6HcdxGDhwIC6/\n/HJMnjwZPB9b0URCCCGEEEIIIc3Xi8uewr5DK9FnUPhtjx0R4aoeiiceX+6eIZ0QQuJNczDM28mT\nJ7F//34cP34cFosFHMchJSUFHTp0QJ8+fWA2m+ujr4QQQgghhBBCmqH161fj/VXLYUw6jrP7sdDp\nPIEuSZJx+DcBJSezMHLEFMyds6ARe0oIaQ2iCoYRQgghhBBCCCGR2rNnN15741nY7KUQRQc4Tg+O\nNePyy27EmNHjqBQMIaRBUDCMEEIIIYQQQgghhLQaNAibEEIIIYQQQgghhLQaFAwjhBBCCCGEEEII\nIa0GBcMIIYQQQgghhBBCSKtBwTBCCCGEEEIIIYQQ0mpQMIwQQgghhBBCCCGEtBoUDCOEEEIIIYQQ\nQgghrQYFwwghhBBCCCGEEEJIq0HBMEIIIYQQQgghhBDSalAwjBBCCCGEEEIIIYS0GhQMI4QQQggh\nhBBCCCGtBgXDCCGEEEIIIYQQQkirQcEwQgghhBBCCCGEENJqUDCMEEIIIYQQQgghhLQaFAwjhBBC\nCCGEEEIIIa0GBcMIIaSZef/99zF+/Hj069cPV111FXbv3q1pv5qaGowePRobNmyos2779u2YNm0a\n+vfvjwkTJmDVqlXx7jYhhBBCCCGENAkUDCOEkGbk448/xqJFizBlyhQsWbIESUlJuPHGG1FUVBRy\nv5qaGtx88804efIkGIbxWXfkyBHMnj0b7du3x9KlSzFq1CgsXLgwYNCMEEIIIYQQQpo7vrE7QAgh\nRBtZlrFkyRJMnz4dt9xyCwBgxIgRmDhxIl5//XXce++9Aff76aef8MADD6CsrCzg+pdffhnt2rXD\n008/DQA477zzUF5ejhdeeAETJkyon5MhhBBCCCGEkEZCmWGEENJMHDt2DCdOnMCYMWPcy3iex6hR\no/Dtt98G3W/evHno0aMHVqxYEXD9tm3bMGrUKJ9lY8eOxcGDB1FSUhKXvhNCCCGEEEJIU0GZYYQQ\n0kwcPXoUANChQwef5Xl5eSgsLIQsy3WGQALAypUrkZ+fH3AopdVqRUlJCdq3b++zvF27du5jZmVl\nxekMCCGEEEIIIaTxUWYYIYQ0EzU1NQAAs9nss9xsNkOSJFit1oD75efnR9Wm93pCCCGEEEIIaSko\nGEYIIc2ELMsAEDD7CwBYNvK39PpokxBCCCGEEEKaMvqW00wJgoCioiIIgtDYXSGENJCkpCQAgMVi\n8VlusVjAcRxMJlPEbSYmJgZt03u9FvS+RAhpiui9iRDS1ND7EiGNj4JhzVRxcTHGjh2L4uLixu4K\nIaSBqLXCCgsLfZYXFhaiU6dOUbVpNpuRlZUVsE0AEbVL70uEkKaI3psIIU0NvS8R0vgoGEYIIc1E\nx44dkZOTg40bN7qXuVwufP311xg2bFjU7Q4fPhxfffUVJElyL/vyyy/RrVs3pKenx9RnQgghhBBC\nCGlqaDZJQghpJhiGwZw5c/Dwww8jOTkZAwcOxFtvvYXKykpcd911AIA///wTZWVl6N+/v+Z2b7jh\nBkydOhULFizA1KlTsW3bNqxduxbPP/98PZ0JIYQQQgghhDQeCoYRQkgzcs0118DhcODNN9/EG2+8\ngZ49e+KVV15BXl4eAODFF1/E6tWr8dtvv2lus0ePHli+fDmeeuopzJ8/H7m5uXjssccwfvz4+joN\nQgghhBBCCGk0FAwjhJBm5vrrr8f1118fcN1jjz2Gxx57LOC6vLw8FBQUBFx33nnn4bzzzotbHwkh\nhBBCCCGkqaKaYYQQQgghhBBCCCGk1aBgGCGEEEIIIYQQQghpNWiYJCGEEEIIIYQQQkgzVVlZiT17\n9gRd37dvX6SkpDRgj5o+CoYRQgghhBBCCCGENFN79uzB3x/4H5KzOtZZV1VyFMsenImRI0c2fMea\nMAqGEUIIIYQQQgghhDRjyVkdkZHXq7G70WxQzTBCCCGEEEIIIYQQ0mpQMIwQQgghhBBCCCGEtBo0\nTJKQFs4lCnh983cQXcCMC4YiKcHY2F0ihBBCCCGEEEIaDQXDCGnBJEnCTc9/iJIiEwDgyx9WY8Ud\nFyMt0dzIPSOEEEIIIYQQQhoHBcNaGafTibKyskbtQ3p6OvR6fcztXHrppSgoKMD777+Pvn37upd/\n9NFH+Ne//oWCgoKYj9Hcrdu1yx0IAwBHjREvrt+ChdMmNWKvCCGEEEIIIYSQxkPBsFamrKwMn+76\nAslpyY1y/KryKlw8YDzatm0bUzsHDx7EgQMH0LVrV3z44Yc+wTDi8dn3vwPwDTzu3FMDTGuc/hBC\nCCGEEEIIIY2NgmGtUHJaMtKzMhq7GzH5+OOP0bNnT1xyySVYsmQJ7rnnHphMpvA7tjInT8jKHUYE\nb3JCsJrgtBpw7EwJOmRmNW7nCCGEEEIIIYSQRkCzSZJmRxRFfPrppxg5ciQmTZoEm82Gzz77rM52\nGzZswIUXXoh+/fph1qxZPsMmRVHEE088gVGjRqFPnz646KKL8O677zbkadS7wjMlEB0GAIA5xYUO\neZ4MsZ8O/tFY3SKEEEIIIYQQQhoVBcNIs7Nt2zaUlJRg8uTJyM7OxvDhw/HBBx/U2W7RokWYO3cu\nnnnmGVgsFsyaNQtnzpwBALz00ktYtWoVbr/9drz66qsYOXIkFi1ahK1btzb06dSbHw8ddd9vk6VD\n57NS3I/3/1nSCD0ihBBCCCGEEEIaHw2TJM3OJ598grPPPhv5+fkAgClTpuDOO+/EkSNH0KVLF/d2\nDz/8MC688EIAQP/+/TFmzBi8++67mDdvHnbs2IHevXtjypQpAIDBgwfDZDK1qKGW+46ddt/vkpeC\nfp1zsXHzQQBAYbGlsbpFCCGEEEIIIYQ0KsoMI81KTU0NNm3ahHHjxqGqqgpVVVUYOnQoTCaTT3YY\nz/PuQBigzGDZv39//PzzzwCU4Nd3332Hv/71r3jzzTdRWFiIBQsWYNCgQQ1+TvXlZIkn4NWrQw76\nd+zgflxVJTZGlwghhBBCCCGEkEZHwTDSrGzYsAF2ux3PPfcchgwZgiFDhuCCCy6AzWbD6tWr4XK5\nACjBL3/p6emorq4GAMydOxd33303ysvL8eijj2LcuHGYMWMGCgsLG/R86lNFpeS+36tdLlISEsDq\nlOtjt1JSKCGEEEIIIYSQ1om+EZNm5ZNPPkHfvn1xxx13+Cw/ePAgHn74YXz55ZcA4A56eSspKUFa\nWhoAgGVZXHfddbjuuutQXFyMjRs3YsmSJXjooYewYsWK+j+RBmCzKLFuhnOhbWoqAMCQIMJWqYPs\n0qPSakFKgrkxu0gIIYQQQgghhDQ4ygwjzcaJEyewfft2TJkyBYMHD/b5d/XVVyMzMxMffvghGIaB\nzWbD9u3b3fueOnUKu3fvxtChQwEAM2fOxOLFiwEAbdu2xcyZMzF27FgUFxc3yrnFW6XVCsmpzCRp\nNAvu5UmJjPv+gRMnG7xfhBBCCCGEEEJIY6NgGGk2Vq9eDYZhMGHChDrrWJbFpEmT8P333+P48ePQ\n6XS46667sH79emzcuBGzZ89GRkYGrrrqKgDAkCFDsHLlSrzyyiv48ccfsXLlSnz++ecYN25cQ59W\nvfit6IT7flKy5888PUXvvv/HqdIG7RMhhBBCCCGEENIU0DDJVqiqvKpxj90xun3XrFmDQYMGITMz\nM+D6yZMn480338SqVauQkZGB2267DU8++SRKS0sxbNgw3HfffUhOTgYA3HLLLZAkCStXrsTp06eR\nlZWFG2+8ETfffHOUZ9a0/HHqjPt+ZponANYmIwEFsAEAikoqG7xfhBBCCABs2rQJd9xxB3bu3Bly\nu4MHD+KRRx7Bnj17kJqaimuuuQZz5sxpoF4SQkhgkiThjTfewPvvv4/i4mLk5ubimmuuwYwZMxq7\na4QQjSgYFsby5ctx0UUXoV27do3dlbhIT0/HxQPGN14HOgYubq/F+vXrQ67v06cPCgoKfJZNnjw5\n4LYsy2LBggVYsGBBVH1p6o6XegKe2WkJ7vt5mSlAbTDsVLm1obvV6rS09w9CCImHnTt31qn9GUhp\naSmuv/56dO/eHc899xz27duHZ599FhzH4YYbbmiAnhJCWjKn0wmGYaDT6SLe94UXXsCKFStwyy23\noF+/fti+fTseffRR2Gw2zJ49ux56S0hsampq8O233wZc17dvX6SkpDRwjxofBcPCWLJkCZ599ln0\n7t0bkyZNwqRJk9C2bdvG7lbU9Hp9s+4/0eZ0uRWAUh8sN8Pzxta5TSYApS5aaYWjEXrWurS09w9C\nCImF0+nEG2+8geeffx4JCQnuGaCDefvttyFJEpYtWwaDwYDzzz8fTqcTL730Ev7617+C5+ljLCFE\nm88++wxHjhzB/PnzAQAPPfQQ3nvvPTAMg6lTp+K+++4Dx3Ga2hJFEa+//jpmz56Nm266CQAwbNgw\nlJWV4dVXX6VgGGmSjhw5gs8++6zOd5Hi4mLcc889GDlyZCP1rPFQzbAwtm7digcffBCJiYl4+umn\nMXr0aFx99dV46623cObMmfANENIIKqqc7vvtM9Pc97vmet78aqrlBu1Ta0TvH4QQ4vHNN99gxYoV\nuOuuu3DttddClkP/P7Rt2zYMHz4cBoPBvWzs2LGorKzE3r1767u7hJAW4sMPP8Q//vEPbNmyBQDw\n9ddfY+XKlRgwYAAmT56M999/P6LZ5C0WCy677DKMH+872qZjx44oKyuD3W6Pa/8JiZe2bduiQ4cO\nPv9a8w/1FAwLIy0tDdOnT8frr7+Ob7/9Fvfffz/+n707j4uqbP8H/pmdHdmXwLVUZFFcHrdyLUPU\nSNNwTS2XJ8XHrW+pPy2tVDK13DDBFbXczSyXVERNtCw1JAF3ARFU9nXW8/tjmhkGBmaAmTnAXO/X\nqxdnzrnPmQuDmzPXue/rFgqFiIyMRJ8+fTBx4kQcOHAABQVUf4k0HEXFCvV2G0939baTrS04fOWT\n+PIyw55+kbqj/oMQQjQCAwMRFxeH8ePHG9T+8ePHaN68udY+1bTzR48eGTs8QkgTtWfPHvTo0QP7\n9u0DoKxDLBAIEBUVhZUrVyI8PBxHjx41+HoODg5YvHgx2rdvr7X//Pnz8PLygpWVlVHjJ4SYBiXD\nasHZ2RljxozBrl271CsP/v777/j000/Ru3dvzJo1Czdu3GA7TEJQXqqcIgmuHO7/LhqgIrKRAQAU\nEiGK6MmV2VD/QQixdB4eHrCzszO4fXFxMWxtbbX2qV4XFxcbNTZCSNP18OFDDBkyBHw+HzKZDL/9\n9hu6deumXlirQ4cOyMzM1HOVmh08eBBXrlyhKZKENCJUbKEWsrOzcfr0aZw+fRo3b96EQqFAcHAw\nhg4dCoZhcOTIEYwdOxYLFy7Ee++9x3a4xELJ5HLIxMoVJAVWUnC52jlvOzsOyv+tr3/3aRY6t2pp\n3gAtFPUfhBBSOwzDgMPh6DxW3X5Cmopz19JwMuERZAqF/sa1JBKnQSh9AcC4JTO+WDLfqNczFjs7\nO3UC/dq1aygsLESfPn3UxzMyMuq8wBegHGm2dOlShISE0GqShDQilAzT4+nTpzh9+jROnTqFv//+\nGwzDoH379pgzZw6GDBkCb29vddvRo0dj1KhR2LRpk8EfZiUSCcLCwtCpUyesXLnSVN8GsSAZubkA\no0yA2dhUvclxchDgxb8Pvx5lv6BkmAmZuv8ghJCmzN7eHiUlJVr7VK/t7e3ZCIkQsygtl2Ljwb8h\nkxs/ESbkiBFk/9Do1wUAqVRap5UZTS0oKAh79+6Fj48PtmzZAh6Ph5CQEEilUpw/fx7ff/89BgwY\nUKdr79ixA6tWrcLAgQOxevVqI0dOCDElSobp0b9/fwBAixYtMGPGDAwZMgStW7fW2ZbP58PX1xdi\nseGr9G3cuBEPHz5Ep06djBIvIQ+yn6u37e2r1gVzc7LBXSh/RtOf55stLktk6v6DEEKasrO4dwcA\nACAASURBVBYtWiAtLU1rX3p6OgCgVatWbIREiFk8zCw0SSIMAARcmUmuC6BBJsIAYPHixZg6dSpm\nzZoFDoeDefPmwdPTE7///jv+97//oXXr1pgzZ06tr7t27VpER0dj+PDhWL58eZXZGISQho2SYXpM\nnjwZQ4cOhb+/v0HtV69eDaFQaFDb27dvY/fu3XByctLfmBADpT3PVW+7OIqqHPdxdQCgTJhl55VU\nOU6Mx5T9ByGENHU9e/bE/v37UVZWBmtrawDA2bNn4eTkBD8/P5ajI8R0HmZqFtaZGhaAt/q0Mdq1\n79+/jz177gIAevfujddff91o126oXnrpJRw7dgzJycnw8PCAh4cHAMDf3x/r169H3759tVatNcSu\nXbsQHR2NiRMnYuHChaYImxBiYpS+1iM/Px8yWfVPUK5evYrp06erXxv6QVYmk2HRokWYMmWKukMm\nxBie5hapt92dbKscb+Huot7OKaBRSKZkqv6DEEKaorS0NNy8eVP9euzYsZBKpZg2bRrOnz+PzZs3\nIyYmBtOmTQOfT89zSdP14IkmGdb6JUejXlsikai3Lem+QyAQIDAwEEVFRbh06RJyc3PB4/HQp0+f\nWifCnj17htWrV6Nt27YIDQ3FzZs3tf6Ty+Um+i4IIcZEdxKViMVidYFFhmFw9OhRBAYGwsfHp0pb\nuVyOc+fOISEhodbvExMTA7lcjmnTpuHXX3+td9yEqLzIL4cqz+1qZ4XExESt47KSUvV2Xr5E63jr\n1q1rtdIX0Wau/oMQQho7DodTpQh+VFSUevQGALi5uWHHjh1Yvnw5Zs+eDVdXV8ydOxeTJ09mI2RC\nzOZBhZFhrbwpGWYM8fHx+PLLL5GRkQEOh4Pt27dDKpVi3rx5mDt3bq0K3//222+QSqW4e/cuwsPD\ntY5xOBxcuXIFzZo1M/a3QAgxMkqGVVJQUIDQ0FCtJbs///xzfP7559We85///KdW73H//n1s2bIF\nu3btMvvceolEgtzcXP0NTcjZ2blef3wvXryIXbt2ISkpCWKxGD4+PggJCcHEiRNrLKibkZGB119/\nHevXr8egQYPq/P4NXUGhFIDyCRdfVo5NRxPQzM1VfVzBMACnGcDwUF7Gw97f/gIA5D9/gZnD30RQ\nUBAbYTcJ5ug/CCGkKYiIiEBERITWvsjISERGRmrtCwgIwA8//GDO0AhhlUyuwOOnylH+ni42sLU2\n7meFirVJLSUZduXKFcycOROBgYF49913sXbtWgDK6ZNt2rTBF198AUdHRwwdOtSg640YMQIjRoww\nZciEEDOgZFgl7u7uWLNmjXq0zKZNm/DGG2+gbdu2VdpyuVy4uLggNDTU4OsrFAr8v//3/zBy5Eh0\n7NgRgHmXB8/NzcWxc4mwd2SnTllRQR7CBgbB09OzTuevWbMGMTExGDx4MJYvXw57e3skJiZi165d\nOH78OLZu3QpfX1+d57q7u+PAgQNo0aJFfb6FBq+4RFNw1cveDs3cXOFZaWTSncwXUJTbgJGI4O7t\nTAU/jcTU/QchhBBCmraMZ8Xq4vnGHhUGWObIsPXr16NDhw7Ys2cPCgsL1cmwNm3aYO/evZg8eTJ2\n7NhhcDKMENI0UDJMh759+6Jv374AgCdPnmD06NFGW+1x9+7dyMrKQkxMjLqWEMMwYBgGcrkcPF7V\n1f+Mzd7RCc4ubiZ/H2M7efIkYmJisGjRIrz33nvq/d27d8fQoUMxevRozJ8/H/v27dOZ3BEKhRYx\n6klc9u/3zpXBwcpKZxu+SAZJOQCGhzKpBLYi3e1I7Zmy/yCEEEJI02bKemGAZSbDkpOTMW/ePJ21\nBvl8PgYPHoyvv/6ahcgIIWyi4SB6REZGGvWD7NmzZ5GVlYVu3bohICAAAQEBSE1NxY8//gh/f39k\nZmYa7b2ami1btqBdu3ZaiTAVLy8vzJkzB4mJibh8+TI2bNiAESNGYMWKFejcuTNGjBiBJ0+eoH37\n9lo12k6ePImhQ4eiY8eOGDVqFM6ePYv27dvj2rVr5vzWjIZhGMgkyj/0fCtZtaMOBUJNYc/iciqi\nbyrG7j8IIYQQ0rRRMsz4RCKR1vTQynJzcy3m34IQokEjwyoJCgpCZGSkeupSUFAQOBwOGIap0la1\nn8Ph4O+//zbo+p9//jlKSzUFzBmGwUcffYRWrVohIiICbm6Nb8SWOeTm5iIlJQVTp06tts3rr7+O\nRYsW4cKFC2jWrBnu3LkDBwcHREVFQSwWV/l/ePHiRcybNw/Dhw/HwoUL8ccff2D+/PlmnbZqbC8K\niwGFcnShlVXVn1kVkRVQ8u92SbkEMP69lkUydf9BCCGEkKbtYYXi+a1pmqRR9OrVC/v27cOoUaOq\n3Oc/evQIu3fvRvfu3VmKjhDCFkqGVRIaGgpvb2+t1/rUJnnSqlWrKvtEIhGaNWsGf39/g69jaTIy\nMgAoC11Wx97eHo6Ojnjy5AmaNWsGmUyGBQsWoH379lrXUImKikK3bt2wYsUKAEDv3r1RUlKCPXv2\nmOi7ML3Hz5+rt21tq/+5tBJpBoWWldPyz8Zi6v6DEEIIIU0XwzDqkWH2NkK4OBq/jIVUKlVvW0oy\nbN68eQgPD8fQoUPVSa8jR47g0KFDOHPmDEQiEebMmcNylIQQc6NkWCWVVzGq/NoU6MOw4fTVVOPx\neFr/ni1bttTZTiwWIzExEQsWLNDa/+abbzbqZFh6Tr5628Gu+tWHbKw0/47l4upHkJHaYaP/IIQQ\nQkjT8Dy/DMVlymRV65ccTPIZwRJHhvn6+uLw4cNYu3Yt4uLiAADHjx+HlZUV+vTpg/nz5+scsEAI\nadooGWYAhmGQnZ2tXgHx8ePHOHLkCPh8Pt5+++1qVy801I8//miMMJs01Wibp0+fVtumtLQU+fn5\n8PLyAgBYW1vDqpoC8gUFBVAoFHB2dtba7+LiYqSI2ZGVW6jednYQVdvORqS5+ZFKqm1GjMDU/Qch\nhBBCmob07CL1dgsvB5O8hyUmw86fP4/g4GB8/fXXUCgUyMvLg1wuh7Ozs86i+oQQy0AF9PXIysrC\n0KFD8eGHHwIAnj9/jlGjRmHLli3YtGkT3n77bSQnJ7McZdPn6uqKwMBAnDlzpto258+fh1wuR9++\nfXXWaKrIxcUFfD4fubm5Wvsrv25snheUqLfdm9lV267i6pFSCXUDpkL9ByGEEEIMVTEZ1tzD3iTv\nYYnJsI8//hg7duwAAHC5XLi4uMDd3Z0SYYRYOPoUrMeaNWuQlZWFsWPHAgAOHjyIwsJCrFu3DnFx\ncfD09MS3337LcpSWYcaMGbh79y6io6OrHHv+/DnWrFkDf39/9O7dW++1eDwegoODce7cOa39lV83\nNrmFmpVyvJyrf6Io4gkBjrJWmFxKNwKmQv0HIYQQQgyVllUxGWbakWF8Ph9crmV8FORyuXBycmI7\nDEJIA0OfgvW4fPkyJk2ahFGjRgEAzp49C29vb7z55psAgFGjRmHjxo1shmgx+vfvj4iICKxduxa3\nb9/GsGHDYG9vj9u3b2Pbtm2wsrLCN998o7eumMqMGTPw/vvvY8mSJXjzzTdx8+ZN7N27F0DjreNW\nWCwDoPz+fV2dgMJCne24XC64QikUYh4UEoHekXSkbqj/IIQQQoih0iqMDPP1NM3IMLFY+eDUUkaF\nAcDixYsRGRkJgUCArl27wtnZWWcisLGXSyGkoTgafw9lYhmsRXwM7/cy2+FUi5JhepSUlKhrUGVl\nZeH27dsYPXq0+rhQKIRCoWArvDopKshj+b3rXiMpIiICXbp0wa5du/DZZ5+huLgYvr6+CA8Px6RJ\nk2Bnp5wayOFw9Ca0evbsiVWrVmHTpk348ccf4e/vj/nz52PlypWwtbWtc4xsKi3R/Cy2cHNFejXJ\nMADgCmRQiAEo+JDIpdW2I3XXFPsPQgghhNSfVKb8+y/gK5MyDMOop0k6O1jBzrr6hZDqQzUyzJKS\nYZ9//jnKysrwxRdfVNuGw+FQ6QpC6qigoACJiYnq1/vP5KGknIGtFQeuvKcICgqCo6MjixHqRskw\nPXx8fHDjxg2MHDlSXeh+4MCBAACFQoEzZ85Uu2JhQ+Ts7IywgUEsRuBbpWh9bfXs2RM9e/assU1E\nRAQiIiK09vn4+CAlJUX9+uzZs2jfvj1OnTql3rd//35wudxGW9S8vFx5Q8XhSeFka4f0GtryBXLI\n/t0uE1MyzBSaWv9BCCGEkPrLKyrHnLUXUC6RYdWs19DC0wE5BeUoLVfemZmqXhjDMBaZDHvvvff0\ntmmss0IIaQgSExPx4We74eDWEgDg5NUWXJ4ARSUSfPjZbmxeNgGvvfYau0HqQMkwPcaMGYMvv/wS\nt27dwv3799GmTRv07t0bd+/exccff4zk5GSsXLmS7TANJhQK1avaWbr4+Hj89ttvmD9/Pjw9PXH/\n/n188803CAsLU48wa0wUCgXkYuVTRL5Irrc9X6gZkVQmkYBuAYyvqfUfhBBCCKm/SzeeILewHADw\nY/x9zB4dbJYpknK5XF0aw5KSYbNmzWI7BEKaPAe3lnDx8dfax+UJ1AmyhoiSYXqMHz8e9vb2+Pnn\nn9GpUyfMmDFDWW+Jy4VMJsPKlSsxfPhwtsMkdbBo0SKsWbMGa9asQU5ODjw8PDB27FjMnDmT7dDq\nJKugAGCUI8OsrPXXABMKNW3KJXJYmywyy0X9ByGEEEIqS3msKVmScCsTH74TRCtJmlBOTo5B7ahm\nGCGWhZJhBggLC0NYWJjWvjZt2uD48eMsRUSMwcbGBkuWLMGSJUvYDsUoHj/X/KG3s9W/OpBAoGkj\nliooGWYi1H8QQgghpKKUx7nq7dJyGa4lZ2utJOlLyTCjqmmleQ6HA4ZhqGYYIRaIkmEGUCgUuH79\nOnJyciCX655+FhoaauaoCNH2JCdfve1gr7/oqpCvSYZJZXLqDUyE+g9CCCGEqOQUlOF5XpnWvgvX\nM5BfJFa/bm6iaZKWmgzTNetDLpcjNzcXFy9ehEgkwuzZs1mIjBDCJvr4q0dKSgqmT5+O7Ozsattw\nOBz6MEtYl5WreaLo4mClt71QyFNvS6Wg3sAEqP8ghBBCSEUVp0iqXLudDR5PWb3VyV4Ee5v6JaqS\nkpKQkJAAX19fpKenq786OTmp21hSMqymmmGlpaUIDw/HgwcPzBgRIaQhoI+/eqxcuRKFhYWYP38+\n2rdvb1F/OEjj8qKgVL3t7qR/AQCRQPPrL5UyoHmSxkf9ByGEEEIqSnmkmSLp5mSN53llkMkVkP07\neNwYUyTj4+ORk5OD7OxsKBQK9desrCx1G7onUbKxscG7776LmJiYKivRE0KaNkqG6XHz5k1Mnz4d\nU6dOZTsUQmqkXJVI+Svt5eSgt70VX3MTJJfRWpKmQP0HIYQQYrlyCsrw3ZFEeLnaYcJgPwj4XKRW\nGBk2NSwAK3Ze0zqnQ6v6F3EXi5VTLlUrR1b+ClAyrKKSkhIUFBSwHQYhxMwoGaaHnZ0dHBz0JxYI\nYVtRsQyqX2lfV6eaGwOwFgoAyAAAchkXgO56VqTuqP8ghBBCLFf0j7dwNUk5Gis9uwgfjeuCexnK\nGq9uTtboGeiNBRO7Ien+C+W+ZtYY3KuVWWKzpGRYYmKizv0SiQTJycmIiYlBx44dzRwVIYRtlAzT\nY9iwYThy5AjCw8MhEOgvSk4IW0pLNU/7Wri56m0v4PIBjgRguJBLeQCkJozOMlH/QQghhFimrJwS\nXL31VP36z+RszFh1DlKZAgDQvoUzAKB3kDd6B3mbPT5LSoa9++67NR53dXXFwoULzRQNIaShoGSY\nHkFBQTh9+jSGDh2K/v37w9nZGRxO1SllNA2KsE1crlwdksOXwsHaRm97LpcLDl8KRioCI6OuwBSo\n/yCEEEIs07GL96FgtPflFmpWjGzfQv8oflOypGTYihUrdO7ncrlwc3ND9+7dwefTvTAhloZ+6/WY\nN2+eenvnzp3VtqMPs4RNcrkCcoly5JFAJDP4PC5fDrkUYOR8KBhG/wmkVqj/IIQQQpoeuYJB5vNi\nyOQKncclUjnO/pEGABDwuZg/rgv2nExGxrNiAEAbH0cM6Oprtnh1saRk2IgRI9gOgRDSAFEyTI+z\nZ8+yHQIhemXm5wOMcmSYVS1WheTy5cpKYQwXMui+oSN1R/0HIYQQ0rQoFAw+33oV11OfGdR+QFdf\n1qZC1sSSkmEAkJGRgTt37mDAgAEAgBMnTiA2NhYCgQBjxoxBaGgoyxESQsyNkmF6+Pj4aL2WSCTg\n8Xjg8XgsRURIVWnPX6i37Wy5Bp/H4yvUlcJkCkqGGRv1H4SQxkwqleLHH39EfHw8MjMzweVy0bx5\nc7z++usYMmQI2+ERwoqz19IMToRxOUBYnzZ1ep+kpCQkJCTA19cX6enp6NWrFwBo7avuq0ymf5aA\nJSXD/vrrL7z//vvw9vbGgAEDkJKSgo8++ggODg5wcHDAvHnzwOFwMHjwYLZDJYSYESXDDPD06VOs\nW7cO8fHxKCgowPbt2yEQCLBp0ybMmzcPgYGBbIdILNyTnHz1tqO94Tc3fL5maqSUoWSYKVD/QQhp\njHJzczF58mSkpqbC0dER3t7ekMvluHTpEk6ePImDBw/iu+++g5WVFduhEmI2BcVi7Pz5H/Xr1zq9\nBJFA9wMuDgfoEeAFXw/7Or1XfHw8cnJykJ2dDYVCgfj4eADQ2lfdV131SSuzpGTYxo0b4eHhgY0b\nNwIADh06BIVCge+//x4tW7bEf//7X2zfvp2SYYRYGEqG6ZGeno7w8HBIJBJ07dpV/YeIYRgkJiZi\nwoQJiI2NRVBQELuBEov2NK9Ive3iaPgHE36FBQ5llau8knqj/oMQ0litXr0a9+/fR2RkJN566y1w\nucpRx1KpFAcPHsTy5cuxdu1aLFq0iOVICTGfXb/cRlGpckx9z0AvfDyhq8neSyxWFttn/q3pqnpd\ncV91Xw1hScmwxMRE/O9//0ObNspRenFxcejQoQNat24NABgwYABWrlzJZoiEEBYYPp/KQn399dfg\n8Xg4ceKE1kok3bp1w4kTJ+Di4oL169ezGCEhwIv8UvW2h5OdwecJBJouQEYDw4yO+g9CSGN17tw5\nTJgwAW+//bY6EQYAAoEAY8eOxZgxY/DLL7+wGCEh5pVTUIaz15RF8a2EPEwNa9wjuy0pGcbhcNSj\nWFNSUpCZmYk+ffqoj5eVlcHauhZFdwkhTQIlw/S4evUqxowZA3d39yrHPDw8MG7cONy6dYuFyAjR\nyC/SPC30cnI0+DwhXzOMXibXP6Se1A71H4SQxkoqlcLLy6va4y+//DJKS0urPU5IU3Ph+hOoBl4N\n6d0Kbk6NO3liScmwl19+GT///DMKCgqwbds2AMCgQYMAAM+ePcO+ffvQoUMHNkMkhLCAkmF6SKVS\nODpWn1zgcDiQSCRmjIiQqgqL5eptXzdng88TVhgZJldQMszYqP8ghDRWgwYNwuHDh1FeXl7lmFwu\nx88//4x+/fqZPzBCWHLheoZ6u39XXxYjMQ5LWsxn9uzZSEpKQvfu3XH8+HG88cYb6NChA/766y8M\nHDgQz549Q0REBNthEkLMjGqG6eHv74+TJ09i3LhxVY6JxWIcOXIEfn5+LERGiEZZqaZGREtXF4PP\nEwo0XYBcQblxY6P+gxDSWBw9elSr6HaHDh1w+vRpDBs2DGPHjlXX1snIyMCRI0fw5MkTnX0bIU3R\n46xCPMgsAAC09nZEC08HliMitdGzZ08cPnwY586dg5eXF0JCQgAoV/0ODw/HmDFj1PXECCGWg5Jh\nesyaNQvvv/8+PvjgAwwYMAAAcPv2baSlpSE2NhYPHjzAli1bWI6SWDpxufLpHocvhY3I8AL6ogrJ\nMIWckmHGRv0HIaSxWLhwoc796enp+Oqrr3QemzdvHq2+RixCxVFhfTv7mPS9kpKSkJCQAJlMZrL3\nMGS1yaamdevW6qS+ioeHBxYvXsxSRIQQtlEyTI/u3bsjKioKy5YtwxdffAFAWRQbAFxcXPD1119r\nFWAkxNykcjkUEuWykEKr2t04WQkEAJSV8+Vyyxkuby7UfxBCGouzZ8+yHQIhDZJCwaiTYRwO0Lfz\nSyZ9v/j4eOTk5Jg0YWWJybCHDx/i6tWrKC0thUKhWTVKLpejuLgY165dw/79+1mMkBBibpQMM0Df\nvn1x5swZJCcnIy0tDQqFAl5eXggKCoJAIGA7PGLhnuTkAlDe1FjZ1O5cZTLs36W7ZZQMMwXqPwgh\njYGPj2lHuxDSWMX9mYZneWUAgMA2rnBxNG3hfLFYrL8RqZX4+HjMnDkTcrlc53GBQEAF9AmxQJQM\nMxCPx0NAQAACAgLYDoUQLY9f5Kq37W1rl9Di83gAVw4oeFDIqTswFeo/CCGNTXR0tEGjR6ZOnWqG\naAhhR2m5FLEnktWv3+n/itHfQzUt0tfXF+np6SadHqliaSPDNm/eDCcnJ3z11VcQi8WYMWMGDhw4\nAIVCgb179yIxMRExMTFsh0kIMTP69FuN0tJSHD58GL/99htSUlKQn58PDocDZ2dntGvXDgMGDMBb\nb70FkUjEdqjEwmXm5Km3He1rv0w2hy8DI+GBkVF3YCzUfxBCGru1a9fWeFwgEIDP51MyjDRph+Lu\nIq9IOVKrq58HOrd3N9q1VUmwoqIiFBcXIzs7GwqFwuISVeZw584dTJkyBb1794ZcLodIJMKTJ08w\nePBgdOzYEePHj8fGjRuxaNEitkMlhJgRffrV4erVq5g7dy7y8vIgFArh6+sLb29vyGQy5Ofn4/z5\n8zh//jzWr1+PtWvXolu3bmyHTCxEcXExHjx4oLUv+UE6AGUSjM+IkZiYqD52584dyKU1P2Hk8mWQ\nS0SAXACZQvfwcWI46j8IIU2BrhpiCoUCOTk5OHXqFM6cOYPY2FgWIiPEPHIKyvDjhfsAAB6Xgw/e\n8jfq9SvXBmMYRs8ZxmNpCTeFQgFPT08AytH6zZs3R0pKCgYPHgwOh4PBgwcjJiaGkmGEWBhKhlWS\nmpqKadOmwd7eHqtWrUJISAiEQu3RNsXFxTh16hTWr1+PadOm4dChQ7QcLzGLBw8eYNPR02jm5qre\nl1YoBeABAMjMy8Xe3/5SH3t4OxnOnt41XpPHV0CZAuMgv6zc+EFbEOo/CCFNRXU1xJo3b47g4GDk\n5+fjyy+/xHfffWfmyAgxj+OXHkAqUxZaD+3dCj7u9ka9vilrg6kSa5W/WiofHx/cu3dP/bp169ZI\nTtZMf+VwOMjPz2cjNEIIi7hsB9DQREdHw9raGocPH8Zbb71V5YMsANjZ2WHkyJE4dOgQRCIRtm7d\nykKkxFI1c3OFp4+P+j9wNFPtXF2dtY45ODvpvR6Pr1lRJ7+szCQxWwrqPwghlqJLly74/fff2Q6D\nEJMoE8tw6upjAACXy8Hwvi+zHJFxWdrIsMGDB2PPnj347rvvIBaL8eqrryIhIQEnTpxASkoKfvjh\nB7Rs2ZLtMAkhZkbJsEquXbuGkSNHqofS1sTd3R1vv/023QwSVkklmhsaW2Hta1Dx+JqnhQXlEqPE\nZKmo/yCEWIqrV6/CysqK7TAIMYm4a2koKZMCAF7t6A03J9OuIGksho4As7TVrKdNm4Y33ngD69at\ng1wuR1hYGF555RXMmzcPb7/9Nh49eoRZs2axHSYhxMxommQleXl5aNGihcHtW7dujWfPnpkwIkJq\nJpOofo0Z2Ahr/8GEX+F+qLCMlvOuD+o/CCFNxWeffaZz9IhEIkFKSgpu376N8ePHsxAZIaYllcnx\n0yVNfdawPqYtZWDKKY0cDgcMw1T5yuVa1ngIoVCItWvXYsGCBbCxsQEA/PDDDzh58iTy8/PRq1cv\ntGvXjuUoCSHmRsmwSqRSKaytDX/6IxKJzLIEMiHVUUiVv8YcgQR8nm2tzxcINB92isrpZ7k+qP8g\nhDQV+/fv17mfy+XC1dUVkydPxuzZs80cFSGmkVdUjqcvSlBaLsOuX24j80UJAKBDK2e0ba6/5IQp\nWHqdL1Nwd3dHSUkJsrOz4enpiWHDhoHPp4/DhFgq+u0npBGTy+VgpMq6VFyBtE7XEPI1TwdLxJSY\nIYQQAqSkpLAdAiFmcT8jH/PXXYRcoZ184nE5GD/Yz2TvK5Uadt+mSopVfHhGBfJr759//kFkZCT+\n+usvMAyD7du3AwCWLl2KTz75BAMGDGA5QkKIuVEyTIe8vDxkZmYa1JZWHiFsKpGKAShHdvGF8jpd\nQyjQJMNKxXW7BtGg/oMQQghpPE5ffVwlEebrYYe5YzrjFV/TjAp79uxZrVeTLC+nFb/rSjWt29nZ\nGeHh4fjhhx8AKBc1kslkmDVrFrZs2YJXX32V5UgJIeZEyTAdVqxYgRUrVrAdBiF6lYolAJQjwwTC\nuj0VFFYYHl4uUdTQkhiC+g9CSGNUXY0wfZYuXWr8YAgxE4ZhcC05G4ByJNjgni3h7WaHQT1aQCTg\nmex9U1NTq+xT1fMixrdmzRp4eHjgyJEjKC8vVyfDAgMDcezYMYwbNw6bN2+mZBghFoaSYZXMnDmz\n1udY2vLEpOEoq5AMEwrrdo2KN3u0mGT9UP9BCGmsqqsRpg8lw0hj9uhpIV7klwEA/Fu7YPqIILO8\nb0ZGRrXHqkuKqVaANGR6JSXWtF2/fh0zZ86EjY1NlRF2dnZ2GDlyJNatW8dSdIQQtlAyrBJaVpc0\nJmUVpjWKRHVLqlgLBQCUN0wSSobVC/UfhJDGimqEEUt07Xa2ertbBw+zvCfDMDUmw1QqrwApEokA\nKJNh1a0SSUkw3bhcbo2F8svKyujfjRALRMkwQhoxsUTzh9tKWLfh/CK+EICyboVUallLbRNCCDFc\nSUkJbG1rv2oxIQ3Vn8kVk2GeBp+XlJSEhIQE9OrVCwCQkJAAX19fpKenV/naq1cvBAQEqM9xc3ND\naWmp+lqUwDK9Ll264OjRoxg3blyVY3l5edi3bx+Cg4NZiIwQwiZKhhHSiEkq1F61jQPFAgAAIABJ\nREFUEdVtniSfxwO4MkDBh4ySYYQQYtGOHTuGAwcOIDY2Fjye9kOWZcuW4caNG5g+fTpGjhzJUoSE\nGEdBsRgpj3MBAN6utnjJzc7gc+Pj45GTk4P4+HgAQE5ODrKzs6FQKKp8PX36NBISElBUVITi4mJk\nZWWZ4tshNZg3bx7GjBmDESNGoE+fPgCAixcv4sqVKzh48CCKi4vx7bffshwlIcTc6JMvIY2YTKL5\nFbatYzIMADh85XLdCinlxwkhxFJ99tln+OSTT5CSkoInT55UOe7q6orCwkIsXrwYixYtYiFCQoxD\nrmBw8NxdqAZk1WZUGAD1SpBisVi9rRrdVflrSUkJnj59ipKSEq39lVENUdNp37499u7dC3t7e2zb\ntg0AsGPHDkRHR8PT0xPbt29HUJB56sURQhoO+uRLSCMmk6qe2jOwEVrV+TpcvhxyCcDIBZDIZFor\nTBJCCGn6Dh8+jP3792P06NH4+OOPYWNjU6XNxx9/jIiICHz66ac4cuQIevTogbfeeouFaAmpPYlU\njgvXM5CdW4p/HuYg6X6O+livIC+DrqGa6iiTKR8iVkxsVZcM00coFMLFxUU9rTI3N7dKkXdSfx06\ndMD333+P3NxcZGRkQC6Xw9vbGx4e5qkVRwhpeOgTLyGNmEKiXFmII5CAy617HRceXw5VKf7nRUV4\nycnJCNERQghpLPbv349u3brpXR3SxsYGX331Fe7du4cffviBkmGk0dhx/B/8fPmh1j4OBxgf4ocO\nrVwMuoZqeqSKarRXTfQlxQQCAaZOnap+vWbNGoNiIXXj7OwMZ2dntsMghDQAlAzT480330RoaCiG\nDBmCl19+me1wCFGTyuRgZMqpkTyh/mW2a8ITKNTbz/ILKBlmJNR/EEIai7t372LOnDkGteXxeBg8\neDC+++47E0dFiHHkFZbj1NXHWvscbIWYP7YLOrd3N/g69RmxVV1STLVKJDGd3NxcrFq1CpcvX8aL\nFy90/r/gcDhITk5mITpCCFsoGabHyy+/jO3bt2Pz5s145ZVXEBoaitDQULRo0YLt0IiFKxFrqufz\nRbJ6XavirMgXhcX1uhbRMFX/ceDAAWzduhXZ2dnw8/PDggUL0KlTp2rb37lzB8uXL0diYiKaNWuG\nsWPHaj2FBoBhw4bh7t27WvucnJxw5cqVesVKCGkc+Hw++LWYIm9vbw8ul0rPksbhp0sPIJMrH/z1\n6+KD0J6t0NrHESKBYStxq6ZHVk6GqX4HFAqFrtNqxOfz4e7url6RkpjO0qVLcebMGfTo0QN9+/bV\n2XcZo2bbuXPn8H//93+4fv16va9FCDE9SobpsWnTJhQXF+PcuXM4efIkNm/ejHXr1qFDhw7qD7be\n3t5sh0ksUIlYAkA5MkwgrP1NWEV8gWb7WQElw4zFFP3H0aNHsXTpUsycOROBgYHYvXs3PvjgAxw7\ndgw+Pj5V2ufk5GDy5Mlo164d1q1bh3/++QfffvsteDwe3n//fQCARCLBw4cP8dFHH+E///mP+tza\nfDAmhDRuLVu2xM2bNzFu3DiD2v/99990/0MahdJyKU4mKKdH8rgcTBjsB3enqjXxdFElwVQrQVam\nqq1XXFwMDocDhmHUX/URiURVHkwR07hy5QomTpyIBQsWmOw9rl+/jv/7v/8z2fUJIcZHn3QMYGdn\nh7CwMISFhaGoqAjnzp3D+fPnERMTgzVr1qBjx44YOnQohgwZAieaXkbMpEwshSoZJhLV72mWUKA5\nP6dQf/0LYjhj9h8Mw2DDhg0IDw/HzJkzAQC9evVCSEgIdu7cicWLF1c5Z+/evVAoFNi8eTNEIhH6\n9OkDiUSCLVu2YOLEieDxeLh//z5kMhkGDhyIVq1ameTfgRDSsIWFhWHlypUYM2YMOnfuXGPbGzdu\n4JdffsG0adPMFB0hdcMwDA6cvYOScuUI+j7BLxmcCAOq1girjeqSYhWTZrqIRCKdiTdSd9bW1jof\nGBqDRCLBrl27sH79etjY2EAqrV/pEkKI+dD49lqyt7dHly5d0KVLF/j7+4NhGCQlJSEyMhJ9+vTB\np59+Sn/AiFmUieXqbStR/X6VK04TyC0qq9e1SPXq2388fvwYmZmZGDBggHofn89Hv379cOnSJZ3n\nJCQkoGfPnlo1SQYOHIiCggLcunULAJCamgorKyua/k2IBRs5ciTatWuHDz74ANHR0cjLy6vS5sWL\nF4iOjsaUKVPg7u6OCRMmsBApIYaRSOVYt/8GDp+/p973Tv9XanUNcYWSFHWlSnoZOg2vX79+8Pb2\nplpiRjR+/Hjs27cP+fn5Rr/2xYsXERMTg08++QTjx483eBVRQgj7aGSYgR49eoRTp07h9OnTSE5O\nBpfLRbdu3fDFF19g0KBBAJTTl1avXo1nz55RUVlicmKx5o+tjUhQQ0v9rISaZFhBUf1v/Ig2Y/Uf\njx49AoAqSSsfHx+kp6frfNL8+PFj9OjRQ2ufr6+v+nqdOnVCamoqHB0dMWfOHFy+fBkcDgchISFY\nuHAhbG3rvkopIaTxsLKywubNmzF37lysXbsW33zzDXx8fODi4gKFQoGcnBw8efIEABAUFIQ1a9bQ\naHjSYN1Jy8P6/TfwOKtIvW/0G+3QwsuhVteprhaYoVMh6yIgIAABAQG0qqQRTZ06Fb///jveeOMN\nBAcHw8VF9+qhK1eurPW1AwMDERcXBzs7O2zYsKG+oRJCzIiSYXps2rQJp0+fxp07dwAAHTt2xMKF\nCxESEgIPDw+ttpMmTcIff/xBBaeJWUjEmqSHjZWwXteyEmqSaUWl9SvGTzSM3X+oRo1VTlDZ2tpC\noVCgtLS0yrHi4mKd7SteLzU1FTk5OfDz88PEiRORnJyM9evXIyMjAzt37qz9N04IaZQ8PDywZ88e\nnD59GidPnsQ///yDlJQUcLlcuLm5Yfjw4XjjjTe0RqcS0hAUlUrw/akUXE16CpmcQWGJGIp/c1UC\nPhcRozphQFdfg6+XlJSEixcvorS0tMZ2qtFbumqG6ZsmSSO/zCcmJgaXL18GAFy+fBk8nvbCCar/\nX3VJhlW+nyOENB6UDNNjw4YNeOWVVzB37lyEhoaqR1RUJzg4GIGBgWaKjlgymUTzh9xeZFWva9mI\nhACUTz9LS2l4t7EYu/9Q3VRXN9VC1+pINdUlUe3/+OOPIZPJEBAQAADo0qULnJ2dMW/ePPz555/o\n2rVrjXETQpoOLpeLwYMHY/DgwWyHQohBrtzKxMaDf6OwRFLlWHNPe8wd0xkv+zSr1TX11QoTCoVw\ncXFBr169wDAMrly5Al9fX6Snp1f5mpubi/LycvU5qv20iqT5xMbGIjg4GKtWrdJ7L0YIsRyUDNPj\np59+Qtu2bavsLywshIND1aHWtCoMMRe5WDmaiyMQQ8AzvBisLlZ8IYBSAFyIy+u/tDRRMnb/YW9v\nDwAoKSmBs7Ozen9JSQl4PB6sra11nlNSor0oguq16nrt27evct5rr70GQDlqjJJhhBBCGqL07CKs\n2v0nZHLlwyI+jwtHOyGEAh76dfbBqIGvQMDn6bmKZtVIVaKqsLBQZzvVqC47Ozutv9mqh0m6bNy4\nEeXl5VXOIeZTUlKC4cOHUyKMEJYUFxdXW984KCgIjo6OZo5IiZJherRt2xb79+9HTEwMduzYoe5E\nV65ciYSEBHz88ccYMmQIy1ESSyOXK8DIlFMjucL6r1rD5XLBEUjBSEWQSmhdDWMxdv+hqhWmeuKs\nkp6eXu0qkC1atEBaWprWvvT0dABAq1atIJfLcezYMfj5+cHPz0/dpry8HACoJhAhhJAGiWEYfHck\nUZ0I69zOHTNGdoSHc+0fEKpGgmVnZ+usE6ZKglUcEWaofv364cqVKzQSjEXdu3fHjRs38O6777Id\nCiEW6f79+zhx4gQ8PT219mdlZWHhwoXqh/DmRskwPQ4fPozPPvsM3bp105pf/uabbyIrKwvz58+H\nQCBQF8EmxBxKxOXqbb7QODW+OHxlMoyRCiCVyiAQUPdQX8buP1q2bAkvLy+cOXNGfVMtlUoRHx+P\n/v376zynZ8+e2L9/P8rKytQjx86ePQsnJyf4+fmBx+Nhw4YN8PPzQ1RUlPq8X3/9FXw+H8HBwXX9\n9gkhhBCTuXTzCRLvvQAAuDazxsKJ3WAlqt29S1JSEi5duqRePbW6gvkqAoGg1qO7VAXxCXvmz5+P\nKVOmYOHChRg4cCCcnZ3B51f9WQkKCmIhOkIsg6enZ4NbuZ4+7eqxc+dOvP7669i4caPW/n79+qFf\nv3748MMP8d1331EyjJhVsVgMQFl4VSgyTo0vLl/6b9UwDjIL8tHC1dUo17Vkxu4/OBwOpk6dii++\n+AIODg7o3Lkz9uzZg4KCAkyaNAkAkJaWhtzcXHTq1AkAMHbsWOzZswfTpk3D+++/j5SUFMTExOCj\njz5S3whOnz4dS5cuxfLly9G/f3/cunULUVFReO+99+Dl5WW8fxBCCCGkHhQKBvHX0xH3ZzqS7mtq\nek0JC6hVIkw1LbKwsLBKKQFd2Ch6LxKJtArzG1Ks35SrXDZmw4YNA6Bcufvo0aM623A4HCQnJ5sz\nLEIIyygZpkd6ejomTJhQ7fE+ffrgq6++MmNEhACl5VKok2H1W0hSjceXQzXGLDMnj5JhRmCK/mPs\n2LEQi8WIjY3Frl274Ofnh23btsHHxwcAEBUVhWPHjqlv6Nzc3LBjxw4sX74cs2fPhqurK+bOnYvJ\nkyerrzl69GgIBALs3LkTBw4cgJubG2bOnIlp06bV4bsmhBBCjO9+Rj42H05Ealqe1v7gtm7oFVi7\nBzf6CuSriEQi1oreq6ZXVnxvfcX6KxftJ0orVqwwy/twOJxqFy0ihDQ8lAzTw9nZGbdu3ap2jvm9\ne/dYK/hGLFe5WDOM31pknBpfPJ7mmk/zdReOJbVjqv5j8uTJWsmsiiIjIxEZGam1LyAgAD/88EON\n13znnXfwzjvv1DoWQkjTxjAMioqKIJXqrk/p4uJi5oiIJbqbnodPNv4GqUxzr+LiaIVeQd4YH9K+\nVgmIgoIC5Ofn6zxWeYRVXaZFGkt10ysNmXK5Zs0aU4TUaI0YMcIs7xMREYGIiAizvBchpP4oGabH\nsGHDEB0djbZt2yI8PBzCf4fhSCQS/Pjjj9i3bx/ee+89lqMklkYrGWYlMMo1+RWSYc/zi41yTUtH\n/QchpLEqKirCp59+iri4OIjFYp1taFoRMYdyiQxr9l5XJ8K8XW0x9e1AdGnvXutROJcuXUJ8fLze\n2mAq5pwWaUyVp1hW/tpYvy9CCDEmSobpMWPGDNy6dQvLly/HqlWr4OnpCYZhkJ2dDalUih49emD2\n7Nlsh0ksjESsGQ1mKzLOPEk+T1NjIqew1CjXtHTUfxBCGqsVK1bg5MmT6NmzJ/z9/XV+eKbpQMQc\ndv1yG0+eKx/StfRywOrZfSAS8PScVdXDhw8RFxen81jlZJGtrS0cHBwa7QqQladYVv7aWL8vQggx\nJkqG6SESibB9+3acP38eFy5cQGZmJuRyOXr06IF+/fph4MCBdDNIzE4m0STD7K2sjHJNfoXZlvnF\nukcBkNqh/oMQ0ljFxcVhxIgRZqu1Q4gu/zzIwc+/PQQA8HlczB/XpU6JsJycHBw4cKDK/srJr4rJ\nIn9//3rHzxZawZIQQvSjZJiB+vfvj/79+7MdBiEAALlEOTWSw5dAwLMxyjWFXE1SprBYd20YUjfU\nfxBCGhupVIrg4GC2wyAWTKFgEHPslvr1+JD2aOnlUKtrJCUl4dKlSygsLNRZUF4oFMLFxaXRJ78I\nIYTUHiXDDKBQKHD9+nXk5ORALpfrbBMaGmrmqIilUjAMGKlyaiRXKDHadQU8zdCw0lLdP+ek9qj/\nIIQ0Rq+++iouXbqEUaNGGfW6Bw4cwNatW5GdnQ0/Pz8sWLAAnTp1qrb9f//7X8THx1fZf+PGDVhb\nWxs1NtKwnP8rHfczCgAAvh52eLtvm1qdn5GRgWPHjkEmk1U5phoRZmdnx1qBfGI+YrFYb520s2fP\n4vXXXzdTRISQhoCSYXqkpKRg+vTpyM7OrrYNh8OhD7PEbMQKOQDlKC6+yHhJKxFHkwwro9W4jYL6\nD0JIY5GYmKj1esiQIViwYAHmz5+P0NBQuLi4gMutunpxUFCQwe9x9OhRLF26FDNnzkRgYCB2796N\nDz74AMeOHYOPj4/Oc1JTUzFx4kQMGTJEa7+VkUoEkIapXCxD7AnN4gzvDwsAj2f46tnPnj3Dnj17\ndCbCADT6mmCkdqZMmYItW7bAxqbqbIqMjAx8+eWXuHDhAi0IQoiFoWSYHitXrkRhYSHmz5+P9u3b\nq1eDI4QtYrmm0L1AaNhqSIbgcrjg8KRg5AJIxYbfcJLqUf9BCGks3n33XZ37f/nlF/zyyy86j9Vm\nNUmGYbBhwwaEh4dj5syZAIBevXohJCQEO3fuxOLFi6ucU1hYiKdPn+K1116rVdKNNH7f/5qK3ELl\nk7nO7dzR1c9D7zl///03Ll68CD8/P9y4cUPnKqgikYimRVqgW7duYeLEidi6dSscHR0BKKeCb9++\nHZs3b4ZEIqm2DySENF2UDNPj5s2bmD59ulGHUCsUCuzatQsHDhxAVlYWvL29MXbsWIwbN85o70Ga\nLkmFZJixV8bmCWWQlQkglwigUCh0jgIghjNF/0EIIaZg6kL5jx8/RmZmJgYMGKDex+fz0a9fP1y6\ndEnnOampqQCAtm3bmjQ20rDcScvDsQv3AAB8Hgfvv1Vz0iopKQlxcXHIz88HwzC4fPlylTZcLhcK\nhYKmRVqoHTt24L///S/Gjx+PHTt24P79+1i2bBkePHiA4OBgLFmyBB06dGA7TEKImVEyTA87Ozs4\nONSuWKc+mzZtQkxMDGbOnImOHTvizz//xIoVK1BWVoYpU6YY9b1I0yORawrdW4lqv6JSTQRCOWRl\nABgucoqL4Wbkn31LY4r+gxBCTGHEiBE695eXl0MkEqlXvk1PT4erq2ut63U9evQIANCiRQut/T4+\nPkhPTwfDMFVW101NTYVQKMS3336Lc+fOQSwWo2/fvliyZAlcXV1r9f6k4XuRX4a0rCJs/SkJin+f\n+707sC1aeFb/d1ShUOD48eOQSHTXUBUKhXB1ddVaJZJYnuDgYHz//ff44IMPMGTIEBQUFMDV1RVf\nffUVwsLC2A6PEMISSobpMWzYMBw5cgTh4eEQCAT1vp5cLsfOnTsxZcoUTJ8+HQDQo0cP5ObmYvv2\n7ZQMI3rJZJrRWjYi4/4Ki0QMyv7dfpKTR8mwejJ2/0EIIebCMAy++eYb7N69G0eOHEGrVq0AAOvW\nrcOvv/6KiIgITJs2zeDrFRcXA1DWaqrI1tYWCoUCpaWlVY6lpqZCIpHA3t4emzZtQnp6Or799ltM\nnDgRR48epannTUBpuRSnrz7GxZtPcC89X+tYSy8HjBxY/ajAW7du4eTJk1USYVZWVuBwOCgrK4O9\nvT2NBCMAgDZt2mDfvn2YMmUKioqKsGHDBloxlxALR8kwPYKCgnD69GkMHToU/fv3h7Ozc5UnlwAM\n/kNbUlKC4cOHY9CgQVr7W7ZsidzcXJSXl1NRWFIjmUwzGszGyrgJFmsrDlS3opl5+ejUqkWN7UnN\njN1/EEKIuWzbtg3R0dEICwuDvb29ev/EiRNhZWWFtWvXwtHREeHh4QZdj2GUQ3109YEAdE7Lnzx5\nMsLCwtC1a1cAQNeuXdGmTRu8++67OHnyJI3oaMTkCga//v4Ye08lo6C46qguPo+L/4V3goCv+blI\nSkpCQkICfHx8cP/+fRQWFmoVyOfz+XBzc0Pv3r3BMAyuXLlCI8Es1GeffVZtX/PKK6/g3r17+PDD\nDxESEqJ1bOnSpWaIjhDSUFAyTI958+apt3fu3FltO0M/zDo4OOgsEnv+/Hl4eXlRIozoJZdqEmB2\nIuP+vNhaaW46s/OLjHptS2Ts/oMQQszl4MGDeOedd7B8+XKt/YGBgQgMDIRMJsOePXsMToapEmol\nJSVwdnZW7y8pKQGPx9M57bJ169Zo3bq11r6goCA4ODio64mRxqe4VIKvdv+Jm3eea+1v7mmPTm3d\n4ONuj4DWLvD1sNc6fubMGfWiCro4OjpqjVYMCAgwfvCkUdi/f7/eNvn5+di3b5/WPkqGEWJZKBmm\nx9mzZ03+HgcPHsSVK1ewZMkSk78XafwUqmQYTwohr3Y1W/RxsNJ0CS8KSox6bUtkjv6DEEJMISsr\nC4GBgdUe79ixI06cOGHw9VS1wtLT0+Hr66ven56erp6CWdkvv/wCDw8P9cgwQDnCTCKRwMnJyeD3\nJg1H5vNiLNt6FZkvNPcY3f09MS6kPVp5O1Z7XmpqKgoLC3Ue4/P5cHd3p1FgRC0lJUXrtUQioWnV\nhJAqKBmmh4+Pj9ZriUQCHo8HHs84hct/+uknLF26FCEhIbSaJNFLLleAkSr/mHOFkmqHgNdVMxsR\nAOVUFtWS5qTuTN1/EEKIqXh7e+Pq1asYPXq0zuM3btyAu7u7wddr2bIlvLy8cObMGXXSQiqVIj4+\nHv3799d5zvfff4/S0lIcOXJE/ffuwoULKC8vR7du3Wr5HRG2ZeeWYmHUZfX9hZ21AB+N74Iu7T1q\nPC8hIaHGh0uOjo40wprUaNiwYRgzZgwmTZrEdiiEkAakaoEGUsXTp0+xYMEC9OjRAx07dsQff/yB\nP//8E5MnT8atW7fqfN0dO3bgk08+Qf/+/bF69WojRkyaqtyyMqh+bflCWc2N68DZVjPtUlcND1J7\npuo/CCHElEaNGoVTp04hMjIS6enp6v1PnjzBN998g59++gkjR440+HocDgdTp07Fvn378M033+DC\nhQuYMWMGCgoK1B9Q09LScPPmTfU506dPR3JyMj766CNcvnwZe/fuxSeffII333wTnTp1Mtr3Skwv\nv0iMT7ckqBNhL7nZYc2cPnoTYSUlJTh37py65lxFdnZ28Pb2rjaZSohKZmYmbGxs2A6DENLA0Mgw\nPdLT0xEeHg6JRIKuXbsiPj4egHKYfmJiIiZMmIDY2FgEBQXV6rpr165FdHQ0hg8fjuXLl+ssHEtI\nZVmFmmkFAqHC6Nd3s7MB/l1PsrhEbvTrWxpT9R+EEGJqkyZNwoMHD7Bz507s3LlTfZ+iUCj/9gwf\nPrxWq0kCwNixYyEWixEbG4tdu3bBz88P27ZtU4+ijYqKwrFjx5CcnAwA6NOnD6KiohAVFYWIiAjY\n29vjnXfewZw5c4z4nRJTk8kV+HLH7+qpkV4utlg5ozecHGque3rlyhWcO3dO/TOnYmdnBwcHB/Tq\n1Qv+/v4mi5s0HYMGDcKxY8cQEhICB1opnRDyL0qG6fH111+Dx+PhxIkT4PP56qH93bp1w4kTJzB2\n7FisX78eW7duNfiau3btQnR0NCZOnIiFCxeaKnTSBL0oLlNvC0TGv767nS2AHABAWVnVp7CkdkzR\nfxBCiDlwuVx8+eWXmDBhAi5evIjMzEzI5XJ4eXmhX79+8PPzq9N1J0+ejMmTJ+s8FhkZicjISK19\nAwYMwIABA+r0XqRh2H/mDlIf5wEAnB1E+Hx6T72JsPT0dJw5c0ZrRJhAIICbmxslwUitOTo6Ii4u\nDq+++ipefvllODk56RyIEBMTw0J0hBC2UDJMj6tXr2LSpElwd3dHbm6u1jEPDw+MGzcOW7ZsMfh6\nz549w+rVq9G2bVuEhoZqTQcAlKs0UT0hUp2cEjEAZQF9kdC49cIAQMjngcOXgpEJICmnn8P6Mnb/\nQQgh5tauXTu0a9eO7TBII3X7YQ4OnFWu/MnlAJ+81w2eLrbVtlcoFDh9+jT++OOPKsccHByoNhip\nk/j4ePWiG/n5+cjPz2c5IkJIQ0DJMD2kUikcHatf3YbD4UAiMby20m+//QapVIq7d+9WWY6cw+Hg\nypUraNasWZ3jJU1bQakUqmSYtcg0v74CkRwSmQAKqQAymRx8PiXF6srY/QchhJhKTEwMBgwYgDZt\n2gAAoqOjDVqkhZITRJfiUgkOxd3F8UsPoPh3cNeogW3RoZVLlbYMw+DevXs4c+YMXrx4UaU+mK2t\nLRwdHWm1SFJncXFxbIdACGmAKBmmh7+/P06ePKlzpUexWIwjR47UaqrAiBEjMGLECGOGSCxIcZmm\nboa1SGCS97CyBiQlABgunuTnooWrm0nexxIYu/8ghBBTWbNmDTw9PdXJsLVr1xp0HiXDSEWFJRIc\nv/QAP126j9JyzUI/7Zo7YfSgqiMMHz9+jOPHjyMnJ6fKMS6XCw8PD/Tu3ZumRRKjYBgGKSkpyMzM\nhEAggIeHB418JcSCUTJMj1mzZuH999/HBx98oK5Zcfv2baSlpSE2NhYPHjygaU7EbEo1JcNgKzRB\n0TAAdrZcFL5Qbqe/oGRYfVD/QQhpLM6ePQsXFxet14QY6nrqMxy7eB9/33kOuUIzsovH5SCkZ0uM\nD2kPPk9Toyk7OxtHjx5FdnZ2tdds1qxZrRdpIKQ6Fy5cwLJly5CZmam139vbG0uWLKFVSQmxQJQM\n06N79+6IiorCsmXL8MUXXwBQFsUGABcXF3z99dfo06cPmyESCyIWq24kGdiIai4+W1cOdgKobhMy\ncwtM8h6WgvoPQkhjoVrRsbrXuiQmJhrUjjRt11Of4bPoK1r7uFwO+nX2wZhB7bRqhJWVleHQoUN4\n8OBBleuIRCI4OzujefPmSE9Pp2mRxGj+/PNPzJw5E66urpg/fz5at24NhUKBhw8f4vvvv8esWbOw\na9cudOnShe1QCSFmRMkwA/Tt2xdnzpxBcnIy0tLSoFAo4OXlhcDAQAiFQrbDIxZEJlZOjeQIJOBz\nqy9AWx/ODlYAxACA7Pwik7yHJaH+gxDSmCQmJiIxMREMw8DPzw9du3at0qakpARr167Fvn378M8/\n/7AQJWkoCorF+PaH6+rXzg4i9Ar0xrA+reHtaqfV9u7du/jpp59QXFystZ+vIde3AAAgAElEQVTP\n58PNzY2mQxKTWb9+PXx8fHDo0CHY2Wn/XI4dOxYjR45EVFQUtm3bxlKEhBA2UDLMQDweDwEBAQgI\nCGA7FGKhSsXlYGTKZBhPKDXZ+7g52kKVDMspKKu5MTEI9R+EkIauuLgYs2fPxuXLl7X29+7dG1FR\nURCJlFPzz58/j2XLliErKwstWrRgI1TSQDAMgw0HbiKvSHnPEPSyKz6f3gs8btWFFw4cOIDk5GSd\n13F0dKTpkMSkbt26hYiIiCqJMACws7PDqFGjsHnzZhYiI4SwiZJhekyZMqXG1ZQYhgGHw0FMTIwZ\noyKW6NGzF+ptvlBusvfxcLIHkAsA6htcUjfUfxBCGot169bh8uXL6Nu3L8LCwmBtbY1Lly5h//79\n+Oqrr7BkyRKsXLkSsbGx4PP5mD59OmbOnMl22IRFcX+m4/d/sgAAdtYCzB3TWWci7OHDh1qJMD6f\nD5lMBjs7Ozg4ONB0SGJyXC4Xcnn1985yuRwKhaLa44SQpomSYXroqmkgl8uRn58PsVgMb29vtG3b\nloXIiKV5/CJXvS0Qmu4Pto+zE4DHAICiEtONQLME1H8QQhqL8+fPo0ePHlqLevTv3x9ubm7YsWMH\n7O3tERsbi6CgICxfvhyvvPIKi9EStuUWliPmWJL69cxRHeHazLpKO7FYjJ9++kn92traGoMHD8bV\nq1fRq1cvmhZJzKJLly7Yt28fRo0aBScnJ61jubm52LdvH4KDg1mKjhDCFkqG6REXF6dzv1wuR3x8\nPBYtWoRJkyaZNyhikZ7k5Ku3hcLqRxvVV3NXZ/V2aQlTQ0uiD/UfhJDG4sWLF5g4cWKV/YMGDcK6\ndesQHR2NKVOmYO7cueDxeCxESBoKhmEQdehvlJQpH5j1DvLGqx1fqtImIyMDP//8M/LzlfcvAoEA\noaGhCAgIQGBgoNnjJpZrzpw5GD16NEJCQjBixAi0atUKgPKh5dGjR1FWVoZvv/2W5SgJIeZGybA6\n4vF4GDhwIMLDw7F69WocOnSI7ZBIE5edW6LetrLi1tCyfpzt7QCuHFDwIC433ftYMuo/CCENTXl5\nOZo1a1Zlv2oUxdChQ/HRRx+ZOyzSwCgUDHb8/I96eqS9jQDTR2gSW2KxGLdv38aFCxdQUKC9IvX0\n6dPh4uJi1ngJAYAOHTogNjYWX375JXbs2KF1zN/fH4sWLUJQUBBL0RFzKigoQGJiYrXHg4KC4Ojo\naMaICJsoGVZP3t7euHPnDtthEAugLGavfBpvLRKY7H04HA7+P3t3Hh5VeT58/DtLJpkkM9nJvrBI\ngoQQdoJsCS5IXXBp3VAEhWKtpa8/qVK1tVWrVm3BSrGiAgoqFkWtiMgiiIIgiwTCkkAIBEIWskwy\n2WfmvH8EBmP2yCxJ7s91cXHynO0+Z2bumbnnOc/R6ixYajRYarX2ca3EpSf5QwjRVfziF79wdQjC\nxeotNha8v5ev952xt82+KQmjtwerVq0iKyurxXGZfH19pRAmXCo5OZnVq1dTVFREXl4eiqIQGRlJ\nSEiIq0MTTpSens5zzz1HWFgYAAaDAbVajc1mIysri/nz5zNu3DgXRymcRYphP0NpaSmrV68mNDTU\n1aGIHqCsop4LxTBfT51D9+Wpt2GpAWxaiisqCDYaHbq/nkjyhxCiK7lwN0nRM9VbrDy/fDe7DjX0\nCFOp4P4bExmbFMbq1as5cuRIs+t5enoSEBDA2LFjnRmuEI1cf/31TJw4kfHjxzN06FApgPVwYWFh\nzd4N+UKBTPQcUgxrw7XXXttsr5i6ujry8/OxWCw88cQTLohM9DSVPxq/y9fLsV9KfH1VVJY2TGcX\nFkkxrJMkfwghujrpGSzOlVWzaPV+dh8uAECrUfPIXcMYNbAXb7zxBgUFBU3W0ev1+Pv7c8UVV8gg\n+cLlYmJieO+991iyZAm+vr6MGTOGcePGMX78ePlRUlxSrV2GKZdguh8phrUhODi42Xa1Ws3gwYPt\nvzQI4Wi11efH79LUo9N6O3RffgYPLny0PVVUysh+Dt1dtyX5QwjRlcybN4958+Y1O2/GjBn2aZVK\nZb+E/vDhw84KTzhZQUkVb3xygF0Z+djO/x7noVXzxIxRJPcPZvXq1Y0KYYGBgVx22WXk5ubKnSKF\nW1m0aBFWq5X09HS+/fZbtm/fzl/+8hcsFgvx8fGMHz+e8ePHM2LECFeHKi6B1gpSZrPZoftOT0/n\ngT+/gzEkrlF7eVEOi/9yt1yC6WakGNaGd955x9UhCEG9xYK1tmGcMLVHrcP3F+yvJ5M6AM4Um9pY\nWrRE8ocQoquYOnVqh9eRXmPd1+ETJTy7bCcmc529zVOn4YkZIxl8WQifffZZo0Ko0WjkoYceckWo\nQrSLRqNhyJAhDBkyhN/+9rdUVlaybt06XnvtNZYsWcIbb7whxf1uorWC1JybHX+jBGNIHEFR8mNA\nVyDFMCG6gNziUqChZ5jao97h+wsPNADFABSWVra+sBBCiC7v+eefd3UIwk1s2Xuahe/vw2K1AeDn\nq+OqkbFcmxJHkJ8na9as4cCBA0BDQTQgIIC0tDRXhixEu+Tm5rJ371727NnDnj17yM7ORlEUoqKi\npFdYNyMFKdEeUgxrQ1paWqNfPhWloZ/4T9su/H1hetOmTc4NVHRrOYXn7NNaD4vD9xcTHMiFYliJ\nyfE90boryR9CCCG6CkVRWLUxk5VfXBwMf2CfIOZPH4GfryfV1dX85z//oaioyD7/hhtuIDk52RXh\nCtFuv//979m3bx8FBQWoVCr69u3L8OHDeeCBBxgxYoSMGyZEDyXFsDbcdNNNfPLJJ5w5c4aUlBR6\n9+6NTqfj9OnTbNmyBZVKxaRJkxqtI5cNiEvt9LlS+7RW2/xtyy+l3qHBQBYAFWbH76+7kvwhhBCi\nK8gtqOCNTw6y92ihvS1teDS//eVgPLQaCgoKWLVqFaWlFz+P3HLLLSQmJroiXCE65IsvvgAgNDSU\nadOmkZaWRt++fV0cVfcmA8mLrkCKYW3Q6/VUVFSwevXqJgOBnj59mrvuuou+ffvy29/+1kURip4g\nv6TCPu2hsTl8f9FBQaCygaKmqkqKM50l+UMIIYQ7M5lreX/DUdZtz8Fqu3jX6rsmJ3Dblf2pqKhg\ny86d7Ny5E6v14o9jRqNRCmGiy/j000/5/vvv2blzJ8uWLePll18mKCiIYcOGMWzYMEaMGMGAAQPk\nB8lLSAaSF12BFMPa8PbbbzN9+vRm74gTFRXFPffcw1tvvSVfZoVDFZXV2Kc9nPCq1Wo0aDzrsdZ4\nUl+jbXQpn2g/yR9CCCHc1cZdp3j94wNU114cfiHQ6Mnsm5IYdXkvtmzZwjfffIPNdvFHOLVaTXBw\nMOPHj3dFyEJ0Sv/+/enfvz933XUXAMeOHWPXrl3s2bOH5cuX89xzz+Hr68vu3btdHGn3IuN2CXcn\nxbA2VFZWotW2fJqqqqqoqalpcb4Ql0JJeS3gCYCXWu2UfXrpbVTWAFYt5yrKCTFKd+aOkvwhhBDC\nHe04kMcrH+zj/FCWeGjVTJ3Ql19O6k9ZSRELFy7EbDY3Wc/f358HHnjAydEKcWld+IHXYrFQXV0N\ngE6nc2VIQggXkGJYG0aMGMGyZcsYP348CQkJjebt3r2bZcuWkZqa6qLoRE9RUXHx0gRPtXNetr6+\nairPDw1yPL9IimGdIPlDCCGEuzmSU8JLK/bYC2FjB0cw8/pEQgL0ZGZmsnr1aurrG9+52tvbG39/\nf8aMGeOCiIX4ebKzs9m1axc7d+7k+++/59y5c2i1WgYPHsz06dMZO3asXPYrRA8kxbA2/OEPf+CO\nO+7gpptuIjk5maioKBRFIScnh4MHDxIXF8djjz3m6jBFN1dT6QGAyqMODyf1DAsw6ig4P32yqJjR\n/fs5Zb/dieQPIYQQ7kJRFD7/9gRv/S+DOkvDpY9XDI5g3rThWCz1bN68mW3btjVaR6/XExAQwJgx\nY5q95F+IrmDKlCkAREREkJaWxrhx40hJScHX19fFkQkhXEmKYW3o06cP//vf/1iyZAlff/01GRkZ\nqFQqYmJi+M1vfsN9992Hj4+Pq8MU3VhBqQnF0lAM8/Suc9p+g/31QMMlfHnF5U7bb3ci+UMIIYQ7\nKC2vYeGqfew5cvFukZf3DuSBqQPYtWsnX3/9tf1yMQCNRkNISAhjx46VIpjo8ubPn8/YsWNbvIOk\nzWbj7NmzREZGOjkyIYQrSTGsHXr16sXjjz/O448/7upQRA+Ufuq0fdrg4/g7SV4QEWjgQjGssKzK\nafvtbiR/CCGEcAWrTeFUfjkZ2cW89+VRyisv/qB29agYQpQsFvxzc7Pr+vn58etf/9pZoQrhUM89\n9xwvvvhii8WwDz/8kOeff549e/Y4OTIhhCtJMayddu7cydatW8nPz2fOnDno9Xr27dvHtddei4eH\nh6vDE93YkdMF9ukgowfUW1pZ+tKJDgkEigAoNdU6ZZ/dleQPIYQQzlRsquapJd+Rc7Zxz26jj44H\nbrqc00d3cPTo0Sbr/fiySCG6qry8PD777DNUKhXK+cHxtmzZQn5+fpNlFUXhyy+/RO2kYUiEEO5D\nimFtsFqtzJs3j88//9x+55Ff/vKXmEwm/vCHP/Dee+/x+uuvYzAYXByp6K5OnC0DNABEBnhztrC6\n9RU6qb62lszMTPvflopK+3SZqY709PQm6/Tp00fGW2iF5A8hhBDOZjLX8uR/tpNb0PhukMPig5k0\nUMv3Wz6ivPxikczT05OxY8dy+PBhGRtMdAthYWGsX7+ejIwMe9vatWtZu3Zts8ur1WoeeughZ4Un\nhHATUgxrw2uvvca6det48sknmTBhAldeeSUAkyZN4oknnuD555/n1VdfZf78+S6OVHRX+QV1gB6A\n/qEBnC0sdsh+yktL+fD0GWIKKwCwKQqoAkBRU12tZuU3jbuOlxWd48GbriEpKckh8XQHkj+EEEI4\nk7mqjqfe+M5eCAv21zNlTCzetiKyj+xl88YS+7JarRaj0UhaWhoDBw5k7NixrgpbiEtKrVazfPly\nysrKALjyyiuZP38+kyZNarKsRqPB398fvV7v7DBFD2QymewdDE6dOoXFYkGr1RITE0NSUhJ+fn4u\njrBnkWJYG9asWcMtt9zCXXfdRUnJxQ8QHh4eTJs2jZycHDZu3ChfZoVD1FksVJQ1XEan9qgjwsE9\niPwCggiLirL/nXm6BFudF7Y6T0Ijg+29m0T7SP4QQgjhLGUVtfzp9e2cyGvo9RXpa2ZUdDnHv99L\nbW3j4Q769u3L5MmTCQ4OdkWoQjicr6+v/eqF5cuX069fP4KCglwclejp0tPTee655wgLC8NgMKBW\nq7HZbKxcuZL58+czbtw4V4fYo0gxrA0FBQUMGjSoxfn9+vVj1apVToxI9CQ7s7LA1vAyDQiyOX08\nA41nPbY6L7BpqbHUoPeQX806QvKHEEIIZzh2uowX39lN3rlKQCHOt4hgdR6nTjZeLjo6mtTUVHr3\n7u2SOIVwhVGjRmG1Wvnoo4/YsmULBQUFPP744+j1ejZu3Mhdd92F0Wh0dZiihwgLCyM2NrZJm3A+\nKYa1ISwsrNkBRi/YvXu3PHmFw2zLOGGf7hft/G6zHjob9eenK2pqpRjWQZI/hBBCOFJ5ZR0ffZXF\nmq3HsdkUQOEyYwF+XBwo3MvLi9jYWEaOHEnv3r2ll7focaqqqrj//vvZu3cvfn5+mEwmKisrKSgo\nYOHChXz88ce888479OrVy9WhCiGcSG6b0Yabb76ZDz74gE8//RSbzWZvr62t5dVXX+Wzzz7j+uuv\nd2GEojs7mmOyT48b5PxfcT29FPt0ZXVdK0uK5kj+EEII4Qgmcy3/Xr2fGX9dz4dfHcNmU/BSVzPY\n73ijQtiVV17JvHnzuP322+nTp48UwkSPtHDhQg4cOMB//vMf1q1bZ2+/6qqrWLx4MYWFhSxYsMCF\nEQohXEF6hrVh1qxZHDt2jD/84Q9otQ2n6+GHH6a8vByr1cr48eOZM2eOi6MU3VGdxUJpYcN4YSqN\nhTHxfTl86JBTY/Dyulgvr6qxOnXf3UFPzR82m63J+DRt0el08iVNCCHa4YfMQv753l5KyhvyrAYL\nEV4FhHoWg3Lxh5cpU6YwYsQIV4UphNtYt24dd955JxMmTGg0hitAamoqd999N5988omLohNCuIoU\nw9qg1Wp5+eWXufXWW9m4cSOnTp3CZrMRHh5Oampqs3clEeJS2HE0C8XaUAwLDLHgoXX+y9XbS2Of\nrqlRWllSNKen5o+Dh47w1c5MNJr2PWcrzWX88rrxBAYGOjgyIYTomqxWG9+m57H+u5OkHzsHKPhp\nywn2NBHoUY5is8D5t+mAgACmTp1KTEyMS2MWwl2UlpbSp0+fFueHhoY2KZIJxzGbzWzbtq3F+XJX\nReEsUgxrwyOPPMLkyZO58sorSUlJcXU4ogf5JiPHPt2/t2sG9TToPe3TtTVyVXVH9dT8oSgQGBqD\nh4euXcurCvMcHJEQQnRdxaZqXlyxh4zsYgB0qlri9LkYtWbgYmcwjUbDqFGjmDBhAjpd+/KvED1B\nbGwse/bs4bbbbmt2/tdffy3FYyc6fvw4n3/+ebPj5ubn58tdFYXTSDGsDV9++SVDhgxxdRiiB8o8\nWQE0FKOuGBjb+sIOYvDyAuoBFZZaTVuLi5+Q/CGEEKIzyivr2HOkgMxTpXy97wwVlbUEasvw9zDh\n71GOmouXQ+p0OgYMGMCECRMICAhwYdRCuKe77rqLv/71r/Tu3ZuJEycCYLVaOXHiBK+//jpbt25l\n/vz5Hd7uBx98wBtvvEFBQQEDBgzgscceIzk5+RJH3z01d0dFIZxNimFt6N+/PxkZGa4OQ/QwNpuN\nsnPne2JpLKT0u8wlcXhodKh0ZpQ6Lyy18itzR0n+EEII0V6KorD3aCHrtuew50gBFmvDdY8GTQWX\n+5xGr2k8FqPBYOCqq64iISEBDw8PV4QsRJdwxx13cPbsWV555RUWLlwIwP3332+ff9tttzF9+vQO\nbXPNmjU89dRTPPjggwwaNIh33nmH++67j08++YSoqKhLGn9nmUwm0tPTm50nlyIKIcWwNk2dOpWX\nX36ZrKwshg0bRmBgYLODPM+aNcsF0Ynu6vCZs9gsDR9sDf516Fz4IVfjWY+lzgusHlTX1aGXSy/a\nTfJH+1jq6ykoKKCuruGOpWXmOk6cNXOqoIqKqnqq66x4aNX4eGoI9vciMljP4IQo/AzeLo5cCCEu\njb1HC1n6vwxyzpbjoarHX2vCT1eBj6YKnbq+0bKenp4kJSWRlpaGl5eXiyIWomt5+OGHufnmm9m8\neXOTMVwTEhI6tC1FUfjXv/7FbbfdxoMPPgjAmDFjmDx5MsuWLeOJJ55wxCF0WHp6Og/8+R2MIXGN\n2suLclj8l7vlUkTR40kxrA3PPPMMAAcOHODAgQMtLtfTv8yKS+vbI8fs09ERehdGAjpPK5aKhumK\nmmophnWA5I/2MZvLWfdNGVX4cyK/isKyujbX0aiPcFm0P8MHhDJ6UDgxoQa5G6UQokvasPMkr/53\nH0aNif7e5+xjgf1UWFgYaWlp9OnTB41Ghi4QoqPi4uKYOXPmz97OyZMnycvLIy0tzd6m1WqZOHFi\nqwPDu4IxJI6gqIGuDkMItyTFsDZs3LjR1SGIHigrtxRo+GI/MK6XS2Px8oKq89MV1TX0MkqX6vaS\n/NE2RVE4W6aQVaimqras3etZbQpHTpZy5GQpK744QnSogdRhUUwcGk1IgGsLyEII0V4ffXWMlWv3\nEu+dg4+musl8rVZLeHg4SUlJDB06FLVabmYjRFs+//zzTq03ZcqUdi2Xk5MD0GTMq6ioKHJzc1EU\nRX6gE6ILkGJYG9zlmm/Rs+SfqwEavtAn93btc1DvdTFNVNVYXBhJ1yP5o3U1tRY278nlRJ4CKPZ2\no4+O3hFGegV4Y/TRofPQYLXaqK61Umau4XR+KRXVNs6ZLo6fk1tQwdufH+addYcZfFkIV42MYXRi\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XaENgDTqdew/Sa9B7ofE+h7XKG0ulLwdPn2RQdJyrwxKiy/H30ZA0MJorkiLIyi3jcE4J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\nEBaLhdmzZ9t7ZFVVVbFnzx6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6wtPTE56enjA2NkZubq5GfmhsbERUVJT602FXr16t/gS1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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x179b8da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "CT_mat_file = ('../../../../GitHub/NSPN_WhitakerVertes_PNAS2016/CT_MT_ANALYSES/COMPLETE/CORR_MATS/COVARS_ONES/Mat_CT_Corr_ALL.txt')\n", "\n", "for cost in [ 10 ]: \n", " # CT network\n", " plotting_dict = None\n", " plotting_dict = show_network(CT_mat_file,\n", " n_rand=2,\n", " cost=cost,\n", " plotting_dict = plotting_dict,\n", " sep='\\t',\n", " header=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plotting_dict = results_dict['pathology_cost_10']\n", "\n", "print np.min(np.where(plotting_dict['rc']>0.8))\n", "\n", "plotting_dict.keys()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('Corr_all_ModuleItems_Cost_10.csv', header=None)\n", "df.T.to_csv('testing.csv', index=None, header=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "corr_mat_df = pd.read_csv('Corr_all.csv')\n", "M = corr_mat_df.values\n", "M_abs = np.abs(M)\n", "G = graph_at_cost(M_abs, 10)\n", "M_cost = nx.to_numpy_matrix(G)\n", "for u,v,d in G.edges(data=True):\n", " d['weight'] = 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pos = nx.spring_layout(G)\n", "fig, ax = plt.subplots()\n", "nx.draw_networkx(G,\n", " pos=pos, \n", " node_color=plotting_dict['nodal_colors'],\n", " with_labels=False,\n", " ax=ax)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print results_dict['all_cost_10']['pos_spring'][1]\n", "print pos[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "thr_M = np.copy(M_abs)\n", "thr_M[np.diag_indices_from(thr_M)] = 0\n", "thr_M = thr_M*-1\n", "\n", "G = nx.from_numpy_matrix(thr_M)\n", "\n", "mst = nx.minimum_spanning_tree(G)\n", "mst_edges = mst.edges(data=True)\n", "G_edges_sorted = [ edge for edge in sorted(G.edges(data=True), key = lambda (a, b, dct) : dct['weight']) ]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mst" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mst = G.remove_edge" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cost = 10\n", "\n", "for cost in [ 5, 10, 15]:\n", " for measure in [ 'a', 'M', 'E', 'C', 'L', 'sigma']:\n", " row = []\n", " for group in [ 'pathology', 'personality', 'all']:\n", " real_mean = np.mean(results_dict['{}_cost_{}'.format(group, cost)]['global_measures'][measure])\n", " real_l = np.percentile(results_dict['{}_cost_{}'.format(group, cost)]['global_measures'][measure], 5)\n", " real_u = np.percentile(results_dict['{}_cost_{}'.format(group, cost)]['global_measures'][measure], 95)\n", " rand_mean = np.mean(results_dict['{}_cost_{}'.format(group, cost)]['global_measures']['{}_rand'.format(measure)])\n", " rand_l = np.percentile(results_dict['{}_cost_{}'.format(group, cost)]['global_measures']['{}_rand'.format(measure)], 5)\n", " rand_u = np.percentile(results_dict['{}_cost_{}'.format(group, cost)]['global_measures']['{}_rand'.format(measure)], 95)\n", " if not measure == 'sigma':\n", " row += [ '{:2.2f} ({:2.2f} [{:2.2f},{:2.2f}])'.format(real_mean, rand_mean, rand_l, rand_u) ]\n", " else:\n", " row += [ '{:2.2f} [{:2.2f},{:2.2f}] ({:2.2f})'.format(real_mean, real_l, real_u, rand_mean) ]\n", " print '|'.join(row)\n", " print '------'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for corr_mat_file in [ 'Corr_pathology.csv', 'Corr_personality.csv', 'Corr_all.csv']:\n", " corr_mat_df = pd.read_csv(corr_mat_file)\n", " M = corr_mat_df.values\n", " print os.path.basename(corr_mat_file).split('_')[1].replace('.csv', '').capitalize(), M.shape\n", " M = corr_mat_df.values\n", " M_abs = np.abs(M)\n", " print M.shape[0]/30.0\n", " \n", " print np.mean(M_abs[np.triu_indices_from(M_abs, 1)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_corr_dist(M)\n", "M[np.triu_indices_from(M, 1)].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "\n", "nodal_partition = calc_nodal_partition(G)\n", "module_partition, module_order, module_colors, nodal_colors = color_by_module(nodal_partition)\n", "\n", "n_modules = len(module_partition.keys())\n", "print n_modules\n", "print len(nodal_partition)\n", "\n", "fig, ax_list = plt.subplots(3, 6, figsize=(18,18))\n", "\n", "plt.subplots_adjust(hspace=0, wspace=0, left=0.1, right=0.98, top=0.98, bottom=0.02)\n", "\n", "ax_list = ax_list.reshape(-1)\n", "ax_id = 0\n", "\n", "for i, module in enumerate(module_partition.keys()):\n", " item_list = ['{}'.format(module)]\n", " for item in module_partition[module]:\n", " item_list += [corr_mat_df.columns[item]]\n", " \n", " n_cols = len(item_list)/30 + 1\n", " \n", " for col in range(n_cols):\n", " ax = ax_list[ax_id]\n", " ax.text(0.1, 0.95, \n", " '\\n'.join(item_list[30*col:30*(col+1)]), \n", " horizontalalignment='left',\n", " verticalalignment='top',\n", " fontsize=10)\n", " ax_id += 1\n", " \n", " ax.add_patch(patches.Rectangle((0, 0), 1, 1, facecolor=module_colors[i], edgecolor='none', alpha=0.7))\n", "\n", "for ax in ax_list:\n", " ax.axis('off')\n", "\n", "fig.savefig('testing.png', bbox_inches=0, dpi=50)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "corr_mat_reorder_df = corr_mat_df.iloc[module_order, module_order]\n", "M_reorder = corr_mat_reorder_df.values\n", "M_mask=np.ma.array(M_reorder,mask=np.triu(M_reorder))\n", "\n", "M_cost = nx.to_numpy_matrix(G)\n", "M_cost = M_cost[module_order, :]\n", "M_cost = M_cost[:, module_order]\n", "M_cost = np.tril(M_cost)\n", "M_mask=np.ma.masked_where(M_cost==0, M_cost)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "1/8" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.imshow(M_reorder, cmap='RdBu_r', interpolation='none')\n", "plt.imshow(M_mask, cmap='Greys_r', interpolation='none')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "global_measures_dict = calculate_global_measures(G, n_rand=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sns.distplot(M.reshape(-1), bins=np.arange(-1, 1, .1), label='raw')\n", "sns.distplot(np.abs(M.reshape(-1)), bins=np.arange(-1, 1, .1), label='abs')\n", "plt.legend()\n", "plt.title('Correlations')\n", "plt.gca().set_xlim([-1, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "thr_M = np.copy(M)\n", "thr_M[np.diag_indices_from(thr_M)] = 0\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.arange(-1, 1, .1)\n", "np.linspace(-1, 1, 20).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_corr_dist(M)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:python27]", "language": "python", "name": "conda-env-python27-py" }, "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
openp2pdesign/Labs-Survey---Analysis
Q026.ipynb
1
99207
{ "metadata": { "name": "", "signature": "sha256:07767d2d536906f23c0238678be1dff28b712ef323dee4565ba4424452e024ce" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Q026 - Quali sono le modalita\u0300 di accesso al laboratorio?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# -*- coding: UTF-8 -*-\n", "\n", "# Render our plots inline\n", "%matplotlib inline \n", "\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn\n", "import shutil\n", "\n", "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier, overridden by seaborn\n", "pd.set_option('display.max_columns', None) # Display all the columns\n", "plt.rcParams['font.family'] = 'sans-serif' # Sans Serif fonts for all the graphs\n", "\n", "# Reference for color palettes: http://web.stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html\n", "\n", "# Change the font\n", "matplotlib.rcParams.update({'font.family': 'Source Sans Pro'})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Load csv file first\n", "data = pd.read_csv(\"data/lab-survey.csv\", encoding=\"utf-8\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Check data\n", "#data[0:4] # Equals to data.head()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Range: D26[SQ001] - D26[SQ008] - D21[other]\n", "\n", "lab_columns = ['D26[SQ001]','D26[SQ002]','D26[SQ003]','D26[SQ004]','D26[SQ005]','D26[SQ006]','D26[SQ007]','D26[SQ008]']\n", "lab_options = ['Su prenotazione',\n", " 'Su invito',\n", " 'Per partecipazione a corsi o eventi',\n", " 'Accesso libero gratuito (senza registrazione)',\n", " 'Accesso libero gratuito (con registrazione)',\n", " 'Accesso libero con tessera mensile',\n", " 'Accesso libero con tessera annuale',\n", " 'Accesso libero con tessera (altra durata)']\n", "lab = data[lab_columns]\n", "lab.replace(u'S\u00ec', 'Si', inplace=True) # Get rid of accented characters \n", "lab_other = data['D26[other]'].str.lower().value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "-c:13: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#lab[0:4]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%%capture output\n", "\n", "# Save the output as a variable that can be saved to a file\n", "# Gather data\n", "lab_b = {}\n", "\n", "for k,i in enumerate(lab_columns):\n", " lab_b[k] = lab[i].value_counts(dropna=False)\n", " print \"Data:\",lab_options[k]\n", " print lab_b[k]\n", " print\n", " print \"Data %:\",lab_options[k]\n", " print lab[i].value_counts(normalize=True,dropna=False)*100\n", " print" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Save+show the output to a text file\n", "%save Q026-Modalit\u00e0Accesso01.py str(output)\n", "shutil.move(\"Q026-Modalit\u00e0Accesso01.py\", \"text/Q026-Modalit\u00e0Accesso01.txt\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The following commands were written to file `Q026-Modalit\u00e0Accesso01.py`:\n", "Data: Su prenotazione\n", "No 35\n", "Si 35\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Su prenotazione\n", "No 50\n", "Si 50\n", "NaN 0\n", "dtype: float64\n", "\n", "Data: Su invito\n", "No 53\n", "Si 17\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Su invito\n", "No 75.714286\n", "Si 24.285714\n", "NaN 0.000000\n", "dtype: float64\n", "\n", "Data: Per partecipazione a corsi o eventi\n", "Si 42\n", "No 28\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Per partecipazione a corsi o eventi\n", "Si 60\n", "No 40\n", "NaN 0\n", "dtype: float64\n", "\n", "Data: Accesso libero gratuito (senza registrazione)\n", "No 51\n", "Si 19\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Accesso libero gratuito (senza registrazione)\n", "No 72.857143\n", "Si 27.142857\n", "NaN 0.000000\n", "dtype: float64\n", "\n", "Data: Accesso libero gratuito (con registrazione)\n", "No 57\n", "Si 13\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Accesso libero gratuito (con registrazione)\n", "No 81.428571\n", "Si 18.571429\n", "NaN 0.000000\n", "dtype: float64\n", "\n", "Data: Accesso libero con tessera mensile\n", "No 62\n", "Si 8\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Accesso libero con tessera mensile\n", "No 88.571429\n", "Si 11.428571\n", "NaN 0.000000\n", "dtype: float64\n", "\n", "Data: Accesso libero con tessera annuale\n", "No 40\n", "Si 30\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Accesso libero con tessera annuale\n", "No 57.142857\n", "Si 42.857143\n", "NaN 0.000000\n", "dtype: float64\n", "\n", "Data: Accesso libero con tessera (altra durata)\n", "No 63\n", "Si 7\n", "NaN 0\n", "dtype: int64\n", "\n", "Data %: Accesso libero con tessera (altra durata)\n", "No 90\n", "Si 10\n", "NaN 0\n", "dtype: float64\n", "\n", "\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "yes = []\n", "no = []\n", "nanvalue = []\n", "\n", "for k,i in enumerate(lab_columns):\n", " lab_presents = lab_b[k].index.tolist()\n", " \n", " # Convert NaN to \"NaN\"\n", " for o,h in enumerate(lab_presents):\n", " if type(h) is float:\n", " lab_presents.pop(o)\n", " lab_presents.append(\"NaN\")\n", " \n", " # Reassign new list with \"NaN\"\n", " lab_b[k].index = lab_presents\n", " \n", " # Check for empty values, and put a 0 instead\n", " if \"Si\" not in lab_presents:\n", " yes.append(0)\n", " if \"No\" not in lab_presents:\n", " no.append(0)\n", " if \"NaN\" not in lab_presents:\n", " nanvalue.append(0)\n", " \n", " for j in lab_presents:\n", " if j == \"Si\":\n", " yes.append(lab_b[k].ix[\"Si\"])\n", " elif j == \"No\":\n", " no.append(lab_b[k].ix[\"No\"])\n", " elif j == \"NaN\":\n", " nanvalue.append(lab_b[k].ix[\"NaN\"]) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the data\n", "plt.figure(figsize=(8,6))\n", "plt.xlabel(u'Modalit\u00e0 di accesso', fontsize=16)\n", "plt.ylabel(u'Lab', fontsize=16)\n", "plt.title(u'Quali sono le modalita\u0300 di accesso al laboratorio?', fontsize=18, y=1.02)\n", "plt.xticks(range(len(lab_options)),lab_options,rotation=90)\n", "ind = np.arange(len(lab_columns)) # the x locations for the groups\n", "width = 0.25 # the width of the bars\n", "\n", "my_colors = seaborn.color_palette(\"Set1\", 3) # Set color palette\n", "rect1 = plt.bar(ind,yes,width,color=my_colors[1],align='center') # Plot Yes\n", "rect2 = plt.bar(ind+width,no,width,color=my_colors[0],align='center') # Plot No \n", "rect3 = plt.bar(ind+width*2,nanvalue,width,color=my_colors[2],align='center') # Plot NaN \n", "plt.legend( (rect1, rect2, rect3), ('Si', 'No', 'Nessuna risposta') )\n", "plt.savefig(u\"svg/Q026-Modalit\u00e0Accesso01.svg\")\n", "plt.savefig(u\"png/Q026-Modalit\u00e0Accesso01.png\")\n", "plt.savefig(u\"pdf/Q026-Modalit\u00e0Accesso01.pdf\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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DBw+idu3aqF27drlet1q1ajo1JyQklPiZBlDkc1jcZ0Tf7bek7wCFQlHsd0B5Lger6Lr6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\n/3y30aNHm4bRjBw5EkARc6KbQqPRmFqo7v5vtbGWCkO3bt3Yv38/Xl5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"text": [ "<matplotlib.figure.Figure at 0x111b5b790>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "%%capture output\n", "\n", "# Save the output as a variable that can be saved to a file\n", "# Get \"other\" data\n", "lab_other = data[\"D26[other]\"].str.lower().value_counts()\n", "print \"Data:\"\n", "print lab_other\n", "print \"\"\n", "print \"Data %:\"\n", "print data[\"D26[other]\"].str.lower().value_counts(normalize=True) * 100" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# Save+show the output to a text file\n", "%save Q026-Modalit\u00e0Accesso02.py str(output)\n", "shutil.move(\"Q026-Modalit\u00e0Accesso02.py\", \"text/Q026-Modalit\u00e0Accesso02.txt\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The following commands were written to file `Q026-Modalit\u00e0Accesso02.py`:\n", "Data:\n", "fab lab pomeriggio lavoratori di giorno 1\n", "macchine grosse su prenotazione e gratuite in parte 1\n", "siamo alla fase preliminare non \u00e8 ancora attivo lo spazio comune ufficiale e dunque le regole non sono definite 1\n", "6 mesi 1\n", "dtype: int64\n", "\n", "Data %:\n", "fab lab pomeriggio lavoratori di giorno 1.428571\n", "macchine grosse su prenotazione e gratuite in parte 1.428571\n", "siamo alla fase preliminare non \u00e8 ancora attivo lo spazio comune ufficiale e dunque le regole non sono definite 1.428571\n", "6 mesi 1.428571\n", "dtype: float64\n", "\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot bar\n", "plt.figure(figsize=(8,6))\n", "plt.title(u'Quali sono le modalita\u0300 di accesso al laboratorio? Altro', fontsize=18, y=1.02)\n", "plt.xticks(range(len(lab_other.index)),lab_other.index,rotation=90)\n", "plt.xlabel(u'Modalit\u00e0 di accesso', fontsize=16)\n", "plt.ylabel('Lab', fontsize=16)\n", "ind = np.arange(len(lab_other)) # the x locations for the groups\n", "width = 0.35 # the width of the bars\n", "\n", "my_colors = seaborn.color_palette(\"husl\", len(lab_other)) # Set color palette\n", "rect1 = plt.bar(ind,lab_other,width,color=my_colors,align='center')\n", "plt.savefig(u\"svg/Q026-Modalit\u00e0Accesso02.svg\")\n", "plt.savefig(u\"png/Q026-Modalit\u00e0Accesso02.png\")\n", "plt.savefig(u\"pdf/Q026-Modalit\u00e0Accesso02.pdf\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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AAFgUJQ4AgEVR4gAAWBQlDgCARVHiAABYFCUOAIBFUeIAAFgUJQ4AgEVR4gAA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\nFdXV1Sk5OVmSZLPZNHz4cMOpAB6AAgCWunTpYjoCcFmMxAEAaKUYiQO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"text": [ "<matplotlib.figure.Figure at 0x111b508d0>" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 } ], "metadata": {} } ] }
gpl-3.0
mirthbottle/ghg
combine ghgfins with targets.ipynb
1
1536733
null
mit
phoebe-project/phoebe2-docs
2.1/examples/single_spots.ipynb
1
249928
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Single Star with Spots\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**IMPORTANT NOTE:** if using spots on contact systems or single stars, make sure to use 2.1.15 or later as the 2.1.15 release fixed a bug affecting spots in these systems.\n", "\n", "Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install -I \"phoebe>=2.1,<2.2\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "As always, let's do imports and initialize a logger and a new bundle. See [Building a System](../tutorials/building_a_system.html) for more details." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import phoebe\n", "from phoebe import u # units\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "logger = phoebe.logger()\n", "\n", "b = phoebe.default_star()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Adding Spots\n", "---------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add one spot to our star. Since there is only one star, the spot will automatically attach without needing to provide component (as is needed in the [binary with spots example](./binary_spots)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ParameterSet: 4 parameters | qualifiers: colat, radius, long, relteff>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.add_spot(radius=30, colat=80, long=0, relteff=0.9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spot Parameters\n", "-----------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A spot is defined by the colatitude and longitude of its center, its angular radius, and the ratio of temperature of the spot to the local intrinsic value." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ParameterSet: 4 parameters\n", " colat@spot01@feature: 80.0 deg\n", " long@spot01@feature: 0.0 deg\n", " radius@spot01@feature: 30.0 deg\n", " relteff@spot01@feature: 0.9\n" ] } ], "source": [ "print b['spot']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 'colat' parameter defines the colatitude on the star measured from its North (spin) Pole. The 'long' parameter measures the longitude of the spot - with longitude = 0 being defined as pointing towards the observer at t0 for a single star. See the [spots tutorial](../tutorials/spots.ipynb) for more details." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ParameterSet: 4 parameters | contexts: compute, dataset>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times = np.linspace(0, 10, 11)\n", "b.set_value('period', 10)\n", "b.add_dataset('mesh', times=times, columns=['teffs'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ParameterSet: 34 parameters | qualifiers: xyz_elements, uvw_elements, teffs, times>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.run_compute(distortion_method='rotstar', irrad_method='none')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd4ee3d9c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "afig, mplfig = b.plot(x='us', y='vs', fc='teffs', \n", " animate=True, save='single_spots_1.gif', save_kwargs={'writer': 'imagemagick'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![animation](single_spots_1.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we set t0 to 5 instead of zero, then the spot will cross the line-of-sight at t=5 (since the spot's longitude is 0)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "b.set_value('t0', 5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Mon, 15 Oct 2018 17:38 BUNDLE WARNING overwriting model: latest\n" ] }, { "data": { "text/plain": [ "<ParameterSet: 34 parameters | qualifiers: xyz_elements, uvw_elements, teffs, times>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.run_compute(distortion_method='rotstar', irrad_method='none')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd4ddd90f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "afig, mplfig = b.plot(x='us', y='vs', fc='teffs', \n", " animate=True, save='single_spots_2.gif', save_kwargs={'writer': 'imagemagick'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![animation](single_spots_2.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And if we change the inclination to 0, we'll be looking at the north pole of the star. This clearly illustrates the right-handed rotation of the star. At time=t0=5 the spot will now be pointing in the negative y-direction." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "b.set_value('incl', 0)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Mon, 15 Oct 2018 17:38 BUNDLE WARNING overwriting model: latest\n" ] }, { "data": { "text/plain": [ "<ParameterSet: 34 parameters | qualifiers: xyz_elements, uvw_elements, teffs, times>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.run_compute(distortion_method='rotstar', irrad_method='none')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n", "WARNING: pad_aspect not supported for animations, ignoring\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd4cf93e410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "afig, mplfig = b.plot(x='us', y='vs', fc='teffs', \n", " animate=True, save='single_spots_3.gif', save_kwargs={'writer': 'imagemagick'})" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "![animation](single_spots_3.gif)" ] }, { "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": "2.7.15+" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
eaton-lab/toytree
sandbox/SVG-PDF-render-check.ipynb
1
16085
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], "source": [ "c, a, m = tre.draw(ts='p', admixture_edges=[(4, 5), (6, 9)]);" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import toyplot.svg, toyplot.pdf\n", "\n", "toyplot.svg.render(c, \"/home/deren/Desktop/toytree-n.svg\")\n", "toyplot.pdf.render(c, \"/home/deren/Desktop/toytree-n.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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-3-clause
jakevdp/sklearn_tutorial
notebooks/03.1-Classification-SVMs.ipynb
1
11951
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supervised Learning In-Depth: Support Vector Machines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Previously we introduced supervised machine learning.\n", "There are many supervised learning algorithms available; here we'll go into brief detail one of the most powerful and interesting methods: **Support Vector Machines (SVMs)**." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "\n", "plt.style.use('seaborn')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivating Support Vector Machines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for **classification** or for **regression**. SVMs are a **discriminative** classifier: that is, they draw a boundary between clusters of data.\n", "\n", "Let's show a quick example of support vector classification. First we need to create a dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets.samples_generator import make_blobs\n", "X, y = make_blobs(n_samples=50, centers=2,\n", " random_state=0, cluster_std=0.60)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A discriminative classifier attempts to draw a line between the two sets of data. Immediately we see a problem: such a line is ill-posed! For example, we could come up with several possibilities which perfectly discriminate between the classes in this example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xfit = np.linspace(-1, 3.5)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n", "\n", "for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\n", " plt.plot(xfit, m * xfit + b, '-k')\n", "\n", "plt.xlim(-1, 3.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are three *very* different separaters which perfectly discriminate between these samples. Depending on which you choose, a new data point will be classified almost entirely differently!\n", "\n", "How can we improve on this?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Support Vector Machines: Maximizing the *Margin*\n", "\n", "Support vector machines are one way to address this.\n", "What support vector machined do is to not only draw a line, but consider a *region* about the line of some given width. Here's an example of what it might look like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xfit = np.linspace(-1, 3.5)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n", "\n", "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n", " yfit = m * xfit + b\n", " plt.plot(xfit, yfit, '-k')\n", " plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none', color='#AAAAAA', alpha=0.4)\n", "\n", "plt.xlim(-1, 3.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice here that if we want to maximize this width, the middle fit is clearly the best.\n", "This is the intuition of **support vector machines**, which optimize a linear discriminant model in conjunction with a **margin** representing the perpendicular distance between the datasets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fitting a Support Vector Machine\n", "\n", "Now we'll fit a Support Vector Machine Classifier to these points. While the mathematical details of the likelihood model are interesting, we'll let you read about those elsewhere. Instead, we'll just treat the scikit-learn algorithm as a black box which accomplishes the above task." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVC # \"Support Vector Classifier\"\n", "clf = SVC(kernel='linear')\n", "clf.fit(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_svc_decision_function(clf, ax=None):\n", " \"\"\"Plot the decision function for a 2D SVC\"\"\"\n", " if ax is None:\n", " ax = plt.gca()\n", " x = np.linspace(plt.xlim()[0], plt.xlim()[1], 30)\n", " y = np.linspace(plt.ylim()[0], plt.ylim()[1], 30)\n", " Y, X = np.meshgrid(y, x)\n", " P = np.zeros_like(X)\n", " for i, xi in enumerate(x):\n", " for j, yj in enumerate(y):\n", " P[i, j] = clf.decision_function([[xi, yj]])\n", " # plot the margins\n", " ax.contour(X, Y, P, colors='k',\n", " levels=[-1, 0, 1], alpha=0.5,\n", " linestyles=['--', '-', '--'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n", "plot_svc_decision_function(clf);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the dashed lines touch a couple of the points: these points are the pivotal pieces of this fit, and are known as the *support vectors* (giving the algorithm its name).\n", "In scikit-learn, these are stored in the ``support_vectors_`` attribute of the classifier:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use IPython's ``interact`` functionality to explore how the distribution of points affects the support vectors and the discriminative fit.\n", "(This is only available in IPython 2.0+, and will not work in a static view)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from ipywidgets import interact\n", "\n", "def plot_svm(N=10):\n", " X, y = make_blobs(n_samples=200, centers=2,\n", " random_state=0, cluster_std=0.60)\n", " X = X[:N]\n", " y = y[:N]\n", " clf = SVC(kernel='linear')\n", " clf.fit(X, y)\n", " plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n", " plt.xlim(-1, 4)\n", " plt.ylim(-1, 6)\n", " plot_svc_decision_function(clf, plt.gca())\n", " plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none')\n", " \n", "interact(plot_svm, N=[10, 200], kernel='linear');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the unique thing about SVM is that only the support vectors matter: that is, if you moved any of the other points without letting them cross the decision boundaries, they would have no effect on the classification results!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Going further: Kernel Methods\n", "\n", "Where SVM gets incredibly exciting is when it is used in conjunction with *kernels*.\n", "To motivate the need for kernels, let's look at some data which is not linearly separable:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets.samples_generator import make_circles\n", "X, y = make_circles(100, factor=.1, noise=.1)\n", "\n", "clf = SVC(kernel='linear').fit(X, y)\n", "\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n", "plot_svc_decision_function(clf);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, no linear discrimination will ever separate these data.\n", "One way we can adjust this is to apply a **kernel**, which is some functional transformation of the input data.\n", "\n", "For example, one simple model we could use is a **radial basis function**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "r = np.exp(-(X[:, 0] ** 2 + X[:, 1] ** 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we plot this along with our data, we can see the effect of it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mpl_toolkits import mplot3d\n", "\n", "def plot_3D(elev=30, azim=30):\n", " ax = plt.subplot(projection='3d')\n", " ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='spring')\n", " ax.view_init(elev=elev, azim=azim)\n", " ax.set_xlabel('x')\n", " ax.set_ylabel('y')\n", " ax.set_zlabel('r')\n", "\n", "interact(plot_3D, elev=(-90, 90), azip=(-180, 180));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that with this additional dimension, the data becomes trivially linearly separable!\n", "This is a relatively simple kernel; SVM has a more sophisticated version of this kernel built-in to the process. This is accomplished by using ``kernel='rbf'``, short for *radial basis function*:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "clf = SVC(kernel='rbf')\n", "clf.fit(X, y)\n", "\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here there are effectively $N$ basis functions: one centered at each point! Through a clever mathematical trick, this computation proceeds very efficiently using the \"Kernel Trick\", without actually constructing the matrix of kernel evaluations.\n", "\n", "We'll leave SVMs for the time being and take a look at another classification algorithm: Random Forests." ] } ], "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": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
pcmagic/stokes_flow
head_Force/CompareEcoliTable_tmp.ipynb
1
5195571
null
mit
unmrds/cc-python
.ipynb_checkpoints/Step Through Variables and Data Types-checkpoint.ipynb
1
19649
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples: Variables and Data Types\n", "\n", "## The Interpreter" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The interpreter can be used as a calculator, and can also echo or concatenate strings.\n", "\n", "3 + 3" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3 * 3" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "27" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3 ** 3" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.5" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3 / 2 # classic division - output is a floating point number" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'dogs'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use quotes around strings\n", "\n", "'dogs'" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'dogscats'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# + operator can be used to concatenate strings\n", "\n", "'dogs' + \"cats\"" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World!\n" ] } ], "source": [ "print('Hello World!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Try It Yourself\n", "\n", "Go to the section _4.4. Numeric Types_ in the Python 3 documentation at <https://docs.python.org/3.4/library/stdtypes.html>. The table in that section describes different operators - try some!\n", "\n", "What is the difference between the different division operators (`/`, `//`, and `%`)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variables\n", "\n", "Variables allow us to store values for later use. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = 5\n", "b = 10\n", "a + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variables can be reassigned." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "38764294.1097" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = 38764289.1097\n", "a + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ability to reassign variable values becomes important when iterating through groups of objects for batch processing or other purposes. In the example below, the value of `b` is dynamically updated every time the `while` loop is executed:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b=10\n", "b=9\n", "b=8\n", "b=7\n", "b=6\n" ] } ], "source": [ "a = 5\n", "b = 10\n", "while b > a:\n", " print(\"b=\"+str(b))\n", " b = b-1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variable data types can be inferred, so Python does not require us to declare the data type of a variable on assignment." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = 5\n", "type(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "is equivalent to" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = int(5)\n", "type(a)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'str'>\n", "<class 'str'>\n" ] } ], "source": [ "c = 'dogs'\n", "print(type(c))\n", "\n", "c = str('dogs')\n", "print(type(c))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are cases when we may want to declare the data type, for example to assign a different data type from the default that will be inferred. Concatenating strings provides a good example. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "must be str, not int", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-21-7975542f30cb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mcustomer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Carol'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mpizzas\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcustomer\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' ordered '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mpizzas\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' pizzas.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: must be str, not int" ] } ], "source": [ "customer = 'Carol'\n", "pizzas = 2\n", "print(customer + ' ordered ' + pizzas + ' pizzas.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above, Python has inferred the type of the variable `pizza` to be an integer. Since strings can only be concatenated with other strings, our print statement generates an error. There are two ways we can resolve the error:\n", "\n", "1. Declare the `pizzas` variable as type string (`str`) on assignment or\n", "2. Re-cast the `pizzas` variable as a string within the `print` statement." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Carol ordered 2 pizzas.\n" ] } ], "source": [ "customer = 'Carol'\n", "pizzas = str(2)\n", "print(customer + ' ordered ' + pizzas + ' pizzas.')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Carol ordered 2 pizzas.\n" ] } ], "source": [ "customer = 'Carol'\n", "pizzas = 2\n", "print(customer + ' ordered ' + str(pizzas) + ' pizzas.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given the following variable assignments:\n", "\n", "```\n", "x = 12\n", "y = str(14)\n", "z = donuts\n", "```\n", "\n", "Predict the output of the following:\n", "\n", "1. `y + z`\n", "2. `x + y`\n", "3. `x + int(y)`\n", "4. `str(x) + y`\n", "\n", "Check your answers in the interpreter.\n", "\n", "### Variable Naming Rules\n", "\n", "Variable names are case senstive and:\n", "\n", "1. Can only consist of one \"word\" (no spaces).\n", "2. Must begin with a letter or underscore character ('\\_').\n", "3. Can only use letters, numbers, and the underscore character.\n", "\n", "We further recommend using variable names that are meaningful within the context of the script and the research.\n", "\n", "## Lists\n", "\n", "<https://docs.python.org/3/library/stdtypes.html?highlight=lists#list>\n", "\n", "Lists are a type of collection in Python. Lists allow us to store sequences of items that are typically but not always similar. All of the following lists are legal in Python:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Separate list items with commas!\n", "\n", "number_list = [1, 2, 3, 4, 5]\n", "string_list = ['apples', 'oranges', 'pears', 'grapes', 'pineapples']\n", "combined_list = [1, 2, 'oranges', 3.14, 'peaches', 'grapes', 99.19876]\n", "\n", "# Nested lists - lists of lists - are allowed.\n", "\n", "list_of_lists = [[1, 2, 3], ['oranges', 'grapes', 8], [['small list'], ['bigger', 'list', 55], ['url_1', 'url_2']]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are multiple ways to create a list:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create an empty list\n", "\n", "empty_list = []\n", "\n", "# As we did above, by using square brackets around a comma-separated sequence of items\n", "\n", "new_list = [1, 2, 3]\n", "\n", "# Using the type constructor\n", "\n", "constructed_list = list('purple')\n", "\n", "# Using a list comprehension\n", "\n", "result_list = [i for i in range(1, 20)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can inspect our lists:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "empty_list" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_list" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result_list" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['p', 'u', 'r', 'p', 'l', 'e']" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "constructed_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above output for `typed_list` may seem odd. Referring to the documentation, we see that the argument to the type constructor is an _iterable_, which according to the documentation is \"An object capable of returning its members one at a time.\" In our construtor statement above\n", "\n", "```\n", "# Using the type constructor\n", "\n", "constructed_list = list('purple')\n", "```\n", "\n", "the word 'purple' is the object - in this case a word - that when used to construct a list returns its members (individual letters) one at a time.\n", "\n", "Compare the outputs below:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'int' object is not iterable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-53-b6b8bc4a05b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtyped_list_int\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m123\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: 'int' object is not iterable" ] } ], "source": [ "constructed_list_int = list(123)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['1', '2', '3']" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "constructed_list_str = list('123')\n", "constructed_list_str" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lists in Python are:\n", "\n", "* mutable - the list and list items can be changed\n", "* ordered - list items keep the same \"place\" in the list\n", "\n", "_Ordered_ here does not mean sorted. The list below is printed with the numbers in the order we added them to the list, not in numeric order:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 2, 7, 1, 19, 0]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ordered = [3, 2, 7, 1, 19, 0]\n", "ordered" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 7, 19]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# There is a 'sort' method for sorting list items as needed:\n", "\n", "ordered.sort()\n", "ordered" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Info on additional list methods is available at <https://docs.python.org/3/library/stdtypes.html?highlight=lists#mutable-sequence-types>\n", "\n", "Because lists are ordered, it is possible to access list items by referencing their positions. Note that the position of the first item in a list is 0 (zero), not 1!" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "string_list = ['apples', 'oranges', 'pears', 'grapes', 'pineapples']" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'apples'" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "string_list[0]" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['grapes', 'pineapples']" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can use positions to 'slice' or selection sections of a list:\n", "\n", "string_list[3:]" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['apples', 'oranges', 'pears']" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "string_list[:3]" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['oranges', 'pears', 'grapes']" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "string_list[1:4]" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# If we don't know the position of a list item, we can use the 'index()' method to find out.\n", "# Note that in the case of duplicate list items, this only returns the position of the first one:\n", "\n", "string_list.index('pears')" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "string_list.append('oranges')" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['apples', 'oranges', 'pears', 'grapes', 'pineapples', 'oranges']" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "string_list" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "string_list.index('oranges')" ] }, { "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 }
apache-2.0
mne-tools/mne-tools.github.io
0.21/_downloads/32ac0c2b9b302521df6384e100fdf1fd/plot_eeglab_head_sphere.ipynb
1
6490
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# How to plot topomaps the way EEGLAB does\n\nIf you have previous EEGLAB experience you may have noticed that topomaps\n(topoplots) generated using MNE-Python look a little different from those\ncreated in EEGLAB. If you prefer the EEGLAB style this example will show you\nhow to calculate head sphere origin and radius to obtain EEGLAB-like channel\nlayout in MNE.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Miko\u0142aj Magnuski <[email protected]>\n#\n# License: BSD (3-clause)\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimport mne\n\n\nprint(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create fake data\n\nFirst we will create a simple evoked object with a single timepoint using\nbiosemi 10-20 channel layout.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "biosemi_montage = mne.channels.make_standard_montage('biosemi64')\nn_channels = len(biosemi_montage.ch_names)\nfake_info = mne.create_info(ch_names=biosemi_montage.ch_names, sfreq=250.,\n ch_types='eeg')\n\nrng = np.random.RandomState(0)\ndata = rng.normal(size=(n_channels, 1)) * 1e-6\nfake_evoked = mne.EvokedArray(data, fake_info)\nfake_evoked.set_montage(biosemi_montage)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate sphere origin and radius\n\nEEGLAB plots head outline at the level where the head circumference is\nmeasured\nin the 10-20 system (a line going through Fpz, T8/T4, Oz and T7/T3 channels).\nMNE-Python places the head outline lower on the z dimension, at the level of\nthe anatomical landmarks :term:`LPA, RPA, and NAS <fiducial point>`.\nTherefore to use the EEGLAB layout we\nhave to move the origin of the reference sphere (a sphere that is used as a\nreference when projecting channel locations to a 2d plane) a few centimeters\nup.\n\nInstead of approximating this position by eye, as we did in `the sensor\nlocations tutorial <tut-sensor-locations>`, here we will calculate it using\nthe position of Fpz, T8, Oz and T7 channels available in our montage.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# first we obtain the 3d positions of selected channels\ncheck_ch = ['Oz', 'Fpz', 'T7', 'T8']\nch_idx = [fake_evoked.ch_names.index(ch) for ch in check_ch]\npos = np.stack([fake_evoked.info['chs'][idx]['loc'][:3] for idx in ch_idx])\n\n# now we calculate the radius from T7 and T8 x position\n# (we could use Oz and Fpz y positions as well)\nradius = np.abs(pos[[2, 3], 0]).mean()\n\n# then we obtain the x, y, z sphere center this way:\n# x: x position of the Oz channel (should be very close to 0)\n# y: y position of the T8 channel (should be very close to 0 too)\n# z: average z position of Oz, Fpz, T7 and T8 (their z position should be the\n# the same, so we could also use just one of these channels), it should be\n# positive and somewhere around `0.03` (3 cm)\nx = pos[0, 0]\ny = pos[-1, 1]\nz = pos[:, -1].mean()\n\n# lets print the values we got:\nprint([f'{v:0.5f}' for v in [x, y, z, radius]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare MNE and EEGLAB channel layout\n\nWe already have the required x, y, z sphere center and its radius \u2014 we can\nuse these values passing them to the ``sphere`` argument of many\ntopo-plotting functions (by passing ``sphere=(x, y, z, radius)``).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# create a two-panel figure with some space for the titles at the top\nfig, ax = plt.subplots(ncols=2, figsize=(8, 4), gridspec_kw=dict(top=0.9),\n sharex=True, sharey=True)\n\n# we plot the channel positions with default sphere - the mne way\nfake_evoked.plot_sensors(axes=ax[0], show=False)\n\n# in the second panel we plot the positions using the EEGLAB reference sphere\nfake_evoked.plot_sensors(sphere=(x, y, z, radius), axes=ax[1], show=False)\n\n# add titles\nfig.texts[0].remove()\nax[0].set_title('MNE channel projection', fontweight='bold')\nax[1].set_title('EEGLAB channel projection', fontweight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Topomaps (topoplots)\n\nAs the last step we do the same, but plotting the topomaps. These will not\nbe particularly interesting as they will show random data but hopefully you\nwill see the difference.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(8, 4), gridspec_kw=dict(top=0.9),\n sharex=True, sharey=True)\n\nmne.viz.plot_topomap(fake_evoked.data[:, 0], fake_evoked.info, axes=ax[0],\n show=False)\nmne.viz.plot_topomap(fake_evoked.data[:, 0], fake_evoked.info, axes=ax[1],\n show=False, sphere=(x, y, z, radius))\n\n# add titles\nax[0].set_title('MNE', fontweight='bold')\nax[1].set_title('EEGLAB', fontweight='bold')" ] } ], "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": 0 }
bsd-3-clause
napsternxg/ipython-notebooks
Company Mission Statements.ipynb
1
83526
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline \n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.set_context(\"poster\")\n", "sns.set_style(\"ticks\")\n", "\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.cross_validation import cross_val_score\n", "from sklearn_pandas import DataFrameMapper, cross_val_score" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('https://query.data.world/s/d3gals7qk5sz9l2o50oirszif')\n", "df = df.rename(columns={\n", " \"Revenues (M)\": \"Revenues\"\n", " })\n", "df = df.assign(Revenues=df[\"Revenues\"].apply(lambda x: float(x[1:].replace(\",\", \"\"))))\n", "df[\"Mission\"] = df[\"Mission\"].fillna(\"\")" ] }, { "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>Name</th>\n", " <th>Rank</th>\n", " <th>Revenues</th>\n", " <th>Mission</th>\n", " <th>Mission Link</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Walmart</td>\n", " <td>1</td>\n", " <td>482130.0</td>\n", " <td>We save people money, so they can live better</td>\n", " <td>http://corporate.walmart.com/</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Exxon Mobil</td>\n", " <td>2</td>\n", " <td>246204.0</td>\n", " <td>Exxon Mobil Corporation is committed to being ...</td>\n", " <td>http://corporate.exxonmobil.com/en/company/abo...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Apple</td>\n", " <td>3</td>\n", " <td>233715.0</td>\n", " <td>Apple designs Macs, the best personal computer...</td>\n", " <td>http://www.inc.com/jim-schleckser/apple-s-bori...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Berkshire Hathaway</td>\n", " <td>4</td>\n", " <td>210821.0</td>\n", " <td></td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>McKesson</td>\n", " <td>5</td>\n", " <td>181241.0</td>\n", " <td>Together with our customers and partners, we a...</td>\n", " <td>http://www.mckesson.com/about-mckesson/who-we-...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Rank Revenues \\\n", "0 Walmart 1 482130.0 \n", "1 Exxon Mobil 2 246204.0 \n", "2 Apple 3 233715.0 \n", "3 Berkshire Hathaway 4 210821.0 \n", "4 McKesson 5 181241.0 \n", "\n", " Mission \\\n", "0 We save people money, so they can live better \n", "1 Exxon Mobil Corporation is committed to being ... \n", "2 Apple designs Macs, the best personal computer... \n", "3 \n", "4 Together with our customers and partners, we a... \n", "\n", " Mission Link \n", "0 http://corporate.walmart.com/ \n", "1 http://corporate.exxonmobil.com/en/company/abo... \n", "2 http://www.inc.com/jim-schleckser/apple-s-bori... \n", "3 NaN \n", "4 http://www.mckesson.com/about-mckesson/who-we-... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fe00cb42f28>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AynLHR5J2u512u50k6XQ6fZ4GAAAA9odOp5P19fXuutVqpdVq7eo5hI8kCwsLmZ+f765H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\nNH2eBgAAAPaHpmk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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe00cb617f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.log10(df.Revenues).hist(bins=100, log=True)\n", "plt.xlabel(\"$log_{10}(Revenue)$\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mapper = DataFrameMapper([\n", " ('Mission', [CountVectorizer(), TfidfTransformer()]),\n", "])\n", "mapper.fit(df)\n", "X = mapper.transform(df)\n", "y = df.Revenues" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = LinearRegression()\n", "model.fit(X, y)\n", "y_pred = model.predict(X)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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eeQXjxo3T+3vHxMQgNjYWP/30E2pqauDg4AAvLy8sWrQIMpnMkI+SiMgoCKIoil3dCGP0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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe00906c128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8,4))\n", "ax = plt.subplot(121)\n", "ax.hist(y - y_pred, bins=100)\n", "ax.set_xlabel(\"error\")\n", "ax.set_xscale(\"log\")\n", "ax = plt.subplot(122)\n", "ax.scatter(y, y-y_pred, marker=\"+\")\n", "ax.set_xlabel(\"Revenue\")\n", "ax.set_ylabel(\"Error\")\n", "ax.set_xscale(\"log\")\n", "fig.tight_layout()\n", "sns.despine(offset=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:python3]", "language": "python", "name": "conda-env-python3-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": "3.4.5" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
tensorflow/docs-l10n
site/ja/tensorboard/tensorboard_in_notebooks.ipynb
1
16533
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "TsHV-7cpVkyK" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "atWM-s8yVnfX" }, "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": "TB0wBWfcVqHz" }, "source": [ "# ノートブックで TensorBoard を使用する\n", "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td> <a target=\"_blank\" href=\"https://www.tensorflow.org/tensorboard/tensorboard_in_notebooks\"> <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\"> TensorFlow.org で表示</a>\n", "</td>\n", " <td> <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/tensorboard/tensorboard_in_notebooks.ipynb\"> <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\"> Google Colab で実行</a>\n", "</td>\n", " <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/tensorboard/tensorboard_in_notebooks.ipynb\"> <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\"> GitHubでソースを表示</a></td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/tensorboard/tensorboard_in_notebooks.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "elH58gbhWAmn" }, "source": [ "TensorBoard は、[Colab](https://colab.research.google.com/) や [Jupyter](https://jupyter.org/) などのノートブックエクスペリエンス内で直接使用できます。そのため、結果の共有や既存のワークフローへの TensorBoard の統合を行い、ローカルに何もインストールせずに TensorBoard を使用することができます。" ] }, { "cell_type": "markdown", "metadata": { "id": "VszJNloY3ZU3" }, "source": [ "## セットアップ" ] }, { "cell_type": "markdown", "metadata": { "id": "E6QhA_dp3eRq" }, "source": [ "TF 2.0 をインストールし、TensorBoard ノートブック拡張機能を読み込んで起動します。\n", "\n", "**Jupyter ユーザー:** Jupyter と TensorBoard を同じ virtualenv にインストールしている場合は、このまま先にお進みください。異なる Conda/virtualenv 環境に対してグローバルの Jupyter インストールとカーネルがあるといったより複雑なセットアップを使用している場合は、`tensorboard` バイナリが Jupyter ノートブックのコンテキスト内の `PATH` にあることを確認する必要があります。これには、環境の `bin` ディレクトリを `PATH` に付加するように `kernel_spec` を変更する方法があります。[こちらをご覧ください](https://github.com/ipython/ipykernel/issues/395#issuecomment-479787997)。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9w7Baxc8aCtJ" }, "source": [ "**Docker ユーザーの場合:** <a>TensorFlow のナイトリーを使用する Jupyter Notebook サーバー</a>の [Docker](https://docs.docker.com/install/) イメージを実行している場合は、ノートブックのポートだけでなく、TensorBoard のポートも公開する必要があります。次のコマンドでコンテナを実行します。\n", "\n", "```\n", "docker run -it -p 8888:8888 -p 6006:6006 \\\n", "tensorflow/tensorflow:nightly-py3-jupyter\n", "```\n", "\n", "上記の `-p 6006` は TensorBoard のデフォルトのポートです。これにより、1 つの TesorBoard インスタンスを実行するためのポートが割り当てられます。同時インスタンスを実行する場合は、さらにぽポートを割り当てる必要があります。また、`--bind_all` を `%tensorboard` に渡してコンテナの外部にポートを公開します。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "8p3Tbx8cWEFA" }, "outputs": [], "source": [ "# Load the TensorBoard notebook extension\n", "%load_ext tensorboard" ] }, { "cell_type": "markdown", "metadata": { "id": "9GtR_cTTkf9G" }, "source": [ "TensorFlow、datetime、および os をインポートします。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mVtYvbbIWRkV" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import datetime, os" ] }, { "cell_type": "markdown", "metadata": { "id": "Cu1fbH-S3oAX" }, "source": [ "## ノートブックにおける TensorBoard" ] }, { "cell_type": "markdown", "metadata": { "id": "XfCa27_8kov6" }, "source": [ "[FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) データセットをダウンロードし、スケーリングします。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "z8b82G7YksOS" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", "32768/29515 [=================================] - 0s 0us/step\n", "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", "26427392/26421880 [==============================] - 0s 0us/step\n", "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", "8192/5148 [===============================================] - 0s 0us/step\n", "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", "4423680/4422102 [==============================] - 0s 0us/step\n" ] } ], "source": [ "fashion_mnist = tf.keras.datasets.fashion_mnist\n", "\n", "(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()\n", "x_train, x_test = x_train / 255.0, x_test / 255.0" ] }, { "cell_type": "markdown", "metadata": { "id": "lBk1BqAZKEKd" }, "source": [ "非常に単純なモデルを作成します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OS7qGYiMKGQl" }, "outputs": [], "source": [ "def create_model():\n", " return tf.keras.models.Sequential([\n", " tf.keras.layers.Flatten(input_shape=(28, 28)),\n", " tf.keras.layers.Dense(512, activation='relu'),\n", " tf.keras.layers.Dropout(0.2),\n", " tf.keras.layers.Dense(10, activation='softmax')\n", " ])" ] }, { "cell_type": "markdown", "metadata": { "id": "RNaPPs5ZKNOV" }, "source": [ "Keras と TensorBoard コールバックを使ってモデルをトレーニングします。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "lpUO9HqUKP6z" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/5\n", "60000/60000 [==============================] - 11s 182us/sample - loss: 0.4976 - accuracy: 0.8204 - val_loss: 0.4143 - val_accuracy: 0.8538\n", "Epoch 2/5\n", "60000/60000 [==============================] - 10s 174us/sample - loss: 0.3845 - accuracy: 0.8588 - val_loss: 0.3855 - val_accuracy: 0.8626\n", "Epoch 3/5\n", "60000/60000 [==============================] - 10s 175us/sample - loss: 0.3513 - accuracy: 0.8705 - val_loss: 0.3740 - val_accuracy: 0.8607\n", "Epoch 4/5\n", "60000/60000 [==============================] - 11s 177us/sample - loss: 0.3287 - accuracy: 0.8793 - val_loss: 0.3596 - val_accuracy: 0.8719\n", "Epoch 5/5\n", "60000/60000 [==============================] - 11s 178us/sample - loss: 0.3153 - accuracy: 0.8825 - val_loss: 0.3360 - val_accuracy: 0.8782\n" ] } ], "source": [ "def train_model():\n", " \n", " model = create_model()\n", " model.compile(optimizer='adam',\n", " loss='sparse_categorical_crossentropy',\n", " metrics=['accuracy'])\n", "\n", " logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n", " tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n", "\n", " model.fit(x=x_train, \n", " y=y_train, \n", " epochs=5, \n", " validation_data=(x_test, y_test), \n", " callbacks=[tensorboard_callback])\n", "\n", "train_model()" ] }, { "cell_type": "markdown", "metadata": { "id": "SxvXc4hoKW7d" }, "source": [ "[magics](https://ipython.readthedocs.io/en/stable/interactive/magics.html) を使って、ノートブック内で TensorBoard を起動します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KBHp6M_zgjp4" }, "outputs": [], "source": [ "%tensorboard --logdir logs" ] }, { "cell_type": "markdown", "metadata": { "id": "Po7rTfQswAMT" }, "source": [ "<!-- <img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/notebook_tensorboard.png?raw=1\"/> -->" ] }, { "cell_type": "markdown", "metadata": { "id": "aQq3UHgmLBpC" }, "source": [ "スカラー、グラフ、ヒストグラムなどのダッシュボードを表示できるようになりました。一部のダッシュボード(プロファイルプラグインなど)はまだ Colab では使用できません。\n", "\n", "`%tensorboard` マジックのフォーマットは、TensorBoard コマンドライン呼び出しとまったく同じですが、先頭に `%` 記号が付きます。" ] }, { "cell_type": "markdown", "metadata": { "id": "NiIMwOG8MR_g" }, "source": [ "トレーニング前に TensorBoard を起動すると、その進捗状況を監視することもできます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qyI5lrXoMw9K" }, "outputs": [], "source": [ "%tensorboard --logdir logs" ] }, { "cell_type": "markdown", "metadata": { "id": "ALxC8BbWWV91" }, "source": [ "<!-- <img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/notebook_tensorboard_two_runs.png?raw=1\"/> -->" ] }, { "cell_type": "markdown", "metadata": { "id": "GUSM8yLrO2yZ" }, "source": [ "同じコマンドを発行すると、同じ TensorBoard バックエンドを再利用できます。異なる logs ディレクトリが選択されている場合、新しい TensorBoard インスタンスが開きます。ポートは自動的に管理されます。\n", "\n", "新しいモデルのトレーニングを開始すると、TensorBoard が 30 秒ごとに自動更新されます。または、右上にあるボタンを使って再読み込みすることもできます。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "ixZlmtWhMyr4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/5\n", "60000/60000 [==============================] - 11s 184us/sample - loss: 0.4968 - accuracy: 0.8223 - val_loss: 0.4216 - val_accuracy: 0.8481\n", "Epoch 2/5\n", "60000/60000 [==============================] - 11s 176us/sample - loss: 0.3847 - accuracy: 0.8587 - val_loss: 0.4056 - val_accuracy: 0.8545\n", "Epoch 3/5\n", "60000/60000 [==============================] - 11s 176us/sample - loss: 0.3495 - accuracy: 0.8727 - val_loss: 0.3600 - val_accuracy: 0.8700\n", "Epoch 4/5\n", "60000/60000 [==============================] - 11s 179us/sample - loss: 0.3282 - accuracy: 0.8795 - val_loss: 0.3636 - val_accuracy: 0.8694\n", "Epoch 5/5\n", "60000/60000 [==============================] - 11s 176us/sample - loss: 0.3115 - accuracy: 0.8839 - val_loss: 0.3438 - val_accuracy: 0.8764\n" ] } ], "source": [ "train_model()" ] }, { "cell_type": "markdown", "metadata": { "id": "IlDz2oXBgnZ9" }, "source": [ "`tensorboard.notebook` API を使用すると、もう少し制御できるようになります。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "ko9qeSQHLrEh" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Known TensorBoard instances:\n", " - port 6006: logdir logs (started 0:00:54 ago; pid 265)\n" ] } ], "source": [ "from tensorboard import notebook\n", "notebook.list() # View open TensorBoard instances" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hzm9DNVILxJe" }, "outputs": [], "source": [ "# Control TensorBoard display. If no port is provided, \n", "# the most recently launched TensorBoard is used\n", "notebook.display(port=6006, height=1000) " ] }, { "cell_type": "markdown", "metadata": { "id": "za2GqzKiWY-R" }, "source": [ "<!-- <img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/notebook_tensorboard_tall.png?raw=1\"/> -->" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "tensorboard_in_notebooks.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
rsignell-usgs/notebook
pyugrid_test2.ipynb
1
17913
{ "metadata": { "name": "", "signature": "sha256:3cb6ef125dd8f93cb8e61f24e034ddcc5451aaa7623ddbcbc0357cc1659372a1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Pyugrid test: extract elevation at nodes " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import netCDF4\n", "import pyugrid\n", "import matplotlib.tri as tri" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# dataset form FVcom...\n", "# big\n", "url = 'http://comt.sura.org/thredds/dodsC/data/testbed/inundation_tropical/UND_ADCIRC/Hurricane_Ike_2D_final_run_with_waves'\n", "# little \n", "#url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_GOM2_FORECAST.nc'\n", "url='http://geoport.whoi.edu/thredds/dodsC/usgs/data2/rsignell/estofs/estofs.ncml'\n", "# get the datasets:\n", "# note: this reads the whole thing in to memory at once: maybe we don't want to do that.\n", "print \"Loading data: This could take a while...\"\n", "ug = pyugrid.UGrid.from_ncfile(url)\n", "\n", "# What's in there?\n", "\n", "print \"There are %i nodes\"%ug.nodes.shape[0]\n", "print \"There are %i edges\"%ug.edges.shape[0]\n", "print \"There are %i faces\"%ug.faces.shape[0]\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Loading data: This could take a while...\n" ] }, { "ename": "NameError", "evalue": "name 'pyugrid' 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-2-d2e60be69c8c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m# note: this reads the whole thing in to memory at once: maybe we don't want to do that.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Loading data: This could take a while...\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mug\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpyugrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUGrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_ncfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;31m# What's in there?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'pyugrid' is not defined" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "lon = ug.nodes[:,0]\n", "lat = ug.nodes[:,1]\n", "nv = ug.faces[:]" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'ug' 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-3-e48852a0b395>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mug\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\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[0m\u001b[0;32m 2\u001b[0m \u001b[0mlat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mug\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mnv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mug\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfaces\u001b[0m\u001b[1;33m[\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 'ug' is not defined" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "triang = tri.Triangulation(lon,lat,triangles=nv)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'function' object has no attribute 'Triangulation'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-4-5a998c08168e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtriang\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtri\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTriangulation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlat\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtriangles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'function' object has no attribute 'Triangulation'" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "nc = netCDF4.Dataset(url)\n", "ncv = nc.variables" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "nc.variables.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[u'depth',\n", " u'element',\n", " u'ibtype',\n", " u'ibtypee',\n", " u'max_nvdll',\n", " u'max_nvell',\n", " u'nbdv',\n", " u'nbvv',\n", " u'nvdll',\n", " u'nvell',\n", " u'x',\n", " u'y',\n", " u'neta',\n", " u'nvel',\n", " u'time',\n", " u'zeta',\n", " u'adcirc_mesh']" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "print ncv['zeta']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'netCDF4.Variable'>\n", "float64 zeta(time, node)\n", " long_name: water surface elevation above geoid\n", " standard_name: sea_surface_height_above_geoid\n", " units: m\n", " _FillValue: -99999.0\n", " coordinates: time x y\n", " location: node\n", " mesh: adcirc_mesh\n", " coverage_content_type: modelResult\n", "unlimited dimensions: \n", "current shape = (192, 254565)\n", "filling off\n", "\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "#z = ncv['zeta'][700,:]\n", "z = ncv['zeta'][10,:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "print z.min()\n", "print z.max()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-1.00254822095\n", "6.33337428446\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "figure(figsize=(12,8))\n", "levs=arange(-1,5,.2)\n", "gca().set_aspect(1./cos(lat.mean()*pi/180))\n", "tricontourf(triang, z,levels=levs)\n", "colorbar()\n", "tricontour(triang, z, colors='k',levels=levs)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'lat' 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-10-3e8f125cd203>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mlevs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_aspect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mcos\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m180\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mtricontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtriang\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mcolorbar\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 'lat' is not defined" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xRDEAAOOJYgAAxhPFAACMJ4oBABhPFAMAMJ4oBgBgPFEMAMB4ohgAgPFEMQAA44liAADG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\n0v8DdGxAe/JILoYAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x2c9cd90>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
herruzojm/udacity-deep-learning
embeddings/.ipynb_checkpoints/Skip-Grams-Solution-checkpoint.ipynb
2
923632
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Skip-gram word2vec\n", "\n", "In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealing with things like translations.\n", "\n", "## Readings\n", "\n", "Here are the resources I used to build this notebook. I suggest reading these either beforehand or while you're working on this material.\n", "\n", "* A really good [conceptual overview](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) of word2vec from Chris McCormick \n", "* [First word2vec paper](https://arxiv.org/pdf/1301.3781.pdf) from Mikolov et al.\n", "* [NIPS paper](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) with improvements for word2vec also from Mikolov et al.\n", "* An [implementation of word2vec](http://www.thushv.com/natural_language_processing/word2vec-part-1-nlp-with-deep-learning-with-tensorflow-skip-gram/) from Thushan Ganegedara\n", "* TensorFlow [word2vec tutorial](https://www.tensorflow.org/tutorials/word2vec)\n", "\n", "## Word embeddings\n", "\n", "When you're dealing with language and words, you end up with tens of thousands of classes to predict, one for each word. Trying to one-hot encode these words is massively inefficient, you'll have one element set to 1 and the other 50,000 set to 0. The word2vec algorithm finds much more efficient representations by finding vectors that represent the words. These vectors also contain semantic information about the words. Words that show up in similar contexts, such as \"black\", \"white\", and \"red\" will have vectors near each other. There are two architectures for implementing word2vec, CBOW (Continuous Bag-Of-Words) and Skip-gram.\n", "\n", "<img src=\"assets/word2vec_architectures.png\" width=\"500\">\n", "\n", "In this implementation, we'll be using the skip-gram architecture because it performs better than CBOW. Here, we pass in a word and try to predict the words surrounding it in the text. In this way, we can train the network to learn representations for words that show up in similar contexts.\n", "\n", "First up, importing packages." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "import utils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the [text8 dataset](http://mattmahoney.net/dc/textdata.html), a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the `data` folder. Then you can extract it and delete the archive file to save storage space." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Text8 Dataset: 31.4MB [00:16, 1.88MB/s] \n" ] } ], "source": [ "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "import zipfile\n", "\n", "dataset_folder_path = 'data'\n", "dataset_filename = 'text8.zip'\n", "dataset_name = 'Text8 Dataset'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile(dataset_filename):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc=dataset_name) as pbar:\n", " urlretrieve(\n", " 'http://mattmahoney.net/dc/text8.zip',\n", " dataset_filename,\n", " pbar.hook)\n", "\n", "if not isdir(dataset_folder_path):\n", " with zipfile.ZipFile(dataset_filename) as zip_ref:\n", " zip_ref.extractall(dataset_folder_path)\n", " \n", "with open('data/text8') as f:\n", " text = f.read()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocessing\n", "\n", "Here I'm fixing up the text to make training easier. This comes from the `utils` module I wrote. The `preprocess` function coverts any punctuation into tokens, so a period is changed to ` <PERIOD> `. In this data set, there aren't any periods, but it will help in other NLP problems. I'm also removing all words that show up five or fewer times in the dataset. This will greatly reduce issues due to noise in the data and improve the quality of the vector representations. If you want to write your own functions for this stuff, go for it." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english', 'revolution', 'and', 'the', 'sans', 'culottes', 'of', 'the', 'french', 'revolution', 'whilst']\n" ] } ], "source": [ "words = utils.preprocess(text)\n", "print(words[:30])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total words: 16680599\n", "Unique words: 63641\n" ] } ], "source": [ "print(\"Total words: {}\".format(len(words)))\n", "print(\"Unique words: {}\".format(len(set(words))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here I'm creating dictionaries to covert words to integers and backwards, integers to words. The integers are assigned in descending frequency order, so the most frequent word (\"the\") is given the integer 0 and the next most frequent is 1 and so on. The words are converted to integers and stored in the list `int_words`." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "vocab_to_int, int_to_vocab = utils.create_lookup_tables(words)\n", "int_words = [vocab_to_int[word] for word in words]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subsampling\n", "\n", "Words that show up often such as \"the\", \"of\", and \"for\" don't provide much context to the nearby words. If we discard some of them, we can remove some of the noise from our data and in return get faster training and better representations. This process is called subsampling by Mikolov. For each word $w_i$ in the training set, we'll discard it with probability given by \n", "\n", "$$ P(w_i) = 1 - \\sqrt{\\frac{t}{f(w_i)}} $$\n", "\n", "where $t$ is a threshold parameter and $f(w_i)$ is the frequency of word $w_i$ in the total dataset.\n", "\n", "I'm going to leave this up to you as an exercise. Check out my solution to see how I did it.\n", "\n", "> **Exercise:** Implement subsampling for the words in `int_words`. That is, go through `int_words` and discard each word given the probablility $P(w_i)$ shown above. Note that $P(w_i)$ is that probability that a word is discarded. Assign the subsampled data to `train_words`." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import Counter\n", "import random\n", "\n", "threshold = 1e-5\n", "word_counts = Counter(int_words)\n", "total_count = len(int_words)\n", "freqs = {word: count/total_count for word, count in word_counts.items()}\n", "p_drop = {word: 1 - np.sqrt(threshold/freqs[word]) for word in word_counts}\n", "train_words = [word for word in int_words if p_drop[word] < random.random()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making batches" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now that our data is in good shape, we need to get it into the proper form to pass it into our network. With the skip-gram architecture, for each word in the text, we want to grab all the words in a window around that word, with size $C$. \n", "\n", "From [Mikolov et al.](https://arxiv.org/pdf/1301.3781.pdf): \n", "\n", "\"Since the more distant words are usually less related to the current word than those close to it, we give less weight to the distant words by sampling less from those words in our training examples... If we choose $C = 5$, for each training word we will select randomly a number $R$ in range $< 1; C >$, and then use $R$ words from history and $R$ words from the future of the current word as correct labels.\"\n", "\n", "> **Exercise:** Implement a function `get_target` that receives a list of words, an index, and a window size, then returns a list of words in the window around the index. Make sure to use the algorithm described above, where you chose a random number of words to from the window." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_target(words, idx, window_size=5):\n", " ''' Get a list of words in a window around an index. '''\n", " \n", " R = np.random.randint(1, window_size+1)\n", " start = idx - R if (idx - R) > 0 else 0\n", " stop = idx + R\n", " target_words = set(words[start:idx] + words[idx+1:stop+1])\n", " \n", " return list(target_words)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's a function that returns batches for our network. The idea is that it grabs `batch_size` words from a words list. Then for each of those words, it gets the target words in the window. I haven't found a way to pass in a random number of target words and get it to work with the architecture, so I make one row per input-target pair. This is a generator function by the way, helps save memory." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_batches(words, batch_size, window_size=5):\n", " ''' Create a generator of word batches as a tuple (inputs, targets) '''\n", " \n", " n_batches = len(words)//batch_size\n", " \n", " # only full batches\n", " words = words[:n_batches*batch_size]\n", " \n", " for idx in range(0, len(words), batch_size):\n", " x, y = [], []\n", " batch = words[idx:idx+batch_size]\n", " for ii in range(len(batch)):\n", " batch_x = batch[ii]\n", " batch_y = get_target(batch, ii, window_size)\n", " y.extend(batch_y)\n", " x.extend([batch_x]*len(batch_y))\n", " yield x, y\n", " " ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Building the graph\n", "\n", "From Chris McCormick's blog, we can see the general structure of our network.\n", "![embedding_network](./assets/skip_gram_net_arch.png)\n", "\n", "The input words are passed in as one-hot encoded vectors. This will go into a hidden layer of linear units, then into a softmax layer. We'll use the softmax layer to make a prediction like normal.\n", "\n", "The idea here is to train the hidden layer weight matrix to find efficient representations for our words. This weight matrix is usually called the embedding matrix or embedding look-up table. We can discard the softmax layer becuase we don't really care about making predictions with this network. We just want the embedding matrix so we can use it in other networks we build from the dataset.\n", "\n", "I'm going to have you build the graph in stages now. First off, creating the `inputs` and `labels` placeholders like normal.\n", "\n", "> **Exercise:** Assign `inputs` and `labels` using `tf.placeholder`. We're going to be passing in integers, so set the data types to `tf.int32`. The batches we're passing in will have varying sizes, so set the batch sizes to [`None`]. To make things work later, you'll need to set the second dimension of `labels` to `None` or `1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " inputs = tf.placeholder(tf.int32, [None], name='inputs')\n", " labels = tf.placeholder(tf.int32, [None, None], name='labels')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Embedding\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The embedding matrix has a size of the number of words by the number of neurons in the hidden layer. So, if you have 10,000 words and 300 hidden units, the matrix will have size $10,000 \\times 300$. Remember that we're using one-hot encoded vectors for our inputs. When you do the matrix multiplication of the one-hot vector with the embedding matrix, you end up selecting only one row out of the entire matrix:\n", "\n", "![one-hot matrix multiplication](assets/matrix_mult_w_one_hot.png)\n", "\n", "You don't actually need to do the matrix multiplication, you just need to select the row in the embedding matrix that corresponds to the input word. Then, the embedding matrix becomes a lookup table, you're looking up a vector the size of the hidden layer that represents the input word.\n", "\n", "<img src=\"assets/word2vec_weight_matrix_lookup_table.png\" width=500>\n", "\n", "\n", "> **Exercise:** Tensorflow provides a convenient function [`tf.nn.embedding_lookup`](https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup) that does this lookup for us. You pass in the embedding matrix and a tensor of integers, then it returns rows in the matrix corresponding to those integers. Below, set the number of embedding features you'll use (200 is a good start), create the embedding matrix variable, and use `tf.nn.embedding_lookup` to get the embedding tensors. For the embedding matrix, I suggest you initialize it with a uniform random numbers between -1 and 1 using [tf.random_uniform](https://www.tensorflow.org/api_docs/python/tf/random_uniform)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_vocab = len(int_to_vocab)\n", "n_embedding = 200 # Number of embedding features \n", "with train_graph.as_default():\n", " embedding = tf.Variable(tf.random_uniform((n_vocab, n_embedding), -1, 1))\n", " embed = tf.nn.embedding_lookup(embedding, inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Negative sampling\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For every example we give the network, we train it using the output from the softmax layer. That means for each input, we're making very small changes to millions of weights even though we only have one true example. This makes training the network very inefficient. We can approximate the loss from the softmax layer by only updating a small subset of all the weights at once. We'll update the weights for the correct label, but only a small number of incorrect labels. This is called [\"negative sampling\"](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf). Tensorflow has a convenient function to do this, [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss).\n", "\n", "> **Exercise:** Below, create weights and biases for the softmax layer. Then, use [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss) to calculate the loss. Be sure to read the documentation to figure out how it works." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Number of negative labels to sample\n", "n_sampled = 100\n", "with train_graph.as_default():\n", " softmax_w = tf.Variable(tf.truncated_normal((n_vocab, n_embedding), stddev=0.1))\n", " softmax_b = tf.Variable(tf.zeros(n_vocab))\n", " \n", " # Calculate the loss using negative sampling\n", " loss = tf.nn.sampled_softmax_loss(softmax_w, softmax_b, \n", " labels, embed,\n", " n_sampled, n_vocab)\n", " \n", " cost = tf.reduce_mean(loss)\n", " optimizer = tf.train.AdamOptimizer().minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Validation\n", "\n", "This code is from Thushan Ganegedara's implementation. Here we're going to choose a few common words and few uncommon words. Then, we'll print out the closest words to them. It's a nice way to check that our embedding table is grouping together words with similar semantic meanings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with train_graph.as_default():\n", " ## From Thushan Ganegedara's implementation\n", " valid_size = 16 # Random set of words to evaluate similarity on.\n", " valid_window = 100\n", " # pick 8 samples from (0,100) and (1000,1100) each ranges. lower id implies more frequent \n", " valid_examples = np.array(random.sample(range(valid_window), valid_size//2))\n", " valid_examples = np.append(valid_examples, \n", " random.sample(range(1000,1000+valid_window), valid_size//2))\n", "\n", " valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n", " \n", " # We use the cosine distance:\n", " norm = tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keep_dims=True))\n", " normalized_embedding = embedding / norm\n", " valid_embedding = tf.nn.embedding_lookup(normalized_embedding, valid_dataset)\n", " similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# If the checkpoints directory doesn't exist:\n", "!mkdir checkpoints" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10 Iteration: 100 Avg. Training loss: 5.6559 0.1018 sec/batch\n", "Epoch 1/10 Iteration: 200 Avg. Training loss: 5.6093 0.1028 sec/batch\n", "Epoch 1/10 Iteration: 300 Avg. Training loss: 5.5315 0.1023 sec/batch\n", "Epoch 1/10 Iteration: 400 Avg. Training loss: 5.5730 0.1030 sec/batch\n", "Epoch 1/10 Iteration: 500 Avg. Training loss: 5.5062 0.1014 sec/batch\n", "Epoch 1/10 Iteration: 600 Avg. Training loss: 5.5396 0.1025 sec/batch\n", "Epoch 1/10 Iteration: 700 Avg. Training loss: 5.5646 0.1033 sec/batch\n", "Epoch 1/10 Iteration: 800 Avg. Training loss: 5.5273 0.1035 sec/batch\n", "Epoch 1/10 Iteration: 900 Avg. Training loss: 5.5067 0.1030 sec/batch\n", "Epoch 1/10 Iteration: 1000 Avg. Training loss: 5.4201 0.0999 sec/batch\n", "Nearest to for: hoffman, rogue, jehoiakim, montinari, aldington, silos, explains, ilayaraja,\n", "Nearest to would: louisiane, lampoon, albertina, bottle, olin, allahabad, disobey, tcl,\n", "Nearest to known: homicide, intervening, tori, satrapies, mated, rtgs, lodbrok, assistants,\n", "Nearest to used: contributing, brazil, institutionalization, ceilings, breed, gilchrist, superstitious, hawat,\n", "Nearest to at: squaresoft, taya, buffalo, ferraris, poststructuralism, osiris, bathory, fina,\n", "Nearest to such: expellees, wanderer, monopolistic, seldom, nanda, imperii, portnoy, heseltine,\n", "Nearest to called: ramp, philology, lacklustre, stoner, purification, nuisances, implementing, vegetative,\n", "Nearest to when: benguela, edinburgh, sul, tze, konkani, fo, gigue, iranic,\n", "Nearest to taking: leopards, arlene, disembodied, maharishi, offal, krulak, sidgwick, rational,\n", "Nearest to consists: lippe, karaca, anthropic, gramophone, squids, cbd, buildup, detox,\n", "Nearest to scale: exposed, shrek, allude, chappell, foretells, childe, sheltered, escola,\n", "Nearest to units: experimenter, lawn, fortieth, jagdish, mileposts, summit, danse, decorations,\n", "Nearest to ice: pediment, witnessing, staining, plasmodium, habibie, riggs, detection, reconstruction,\n", "Nearest to instance: caesarean, healthy, wong, resize, corals, movers, attitudes, buena,\n", "Nearest to channel: creditors, tritium, bouchard, mastercard, gli, dray, stringy, frees,\n", "Nearest to report: conscious, hellas, candlestick, midwinter, presidents, girls, bathyscaphe, haryana,\n", "Epoch 1/10 Iteration: 1100 Avg. Training loss: 5.4772 0.1044 sec/batch\n", "Epoch 1/10 Iteration: 1200 Avg. Training loss: 5.4192 0.1002 sec/batch\n", "Epoch 1/10 Iteration: 1300 Avg. Training loss: 5.3636 0.1020 sec/batch\n", "Epoch 1/10 Iteration: 1400 Avg. Training loss: 5.2318 0.1000 sec/batch\n", "Epoch 1/10 Iteration: 1500 Avg. Training loss: 5.1699 0.0994 sec/batch\n", "Epoch 1/10 Iteration: 1600 Avg. Training loss: 5.1744 0.0986 sec/batch\n", "Epoch 1/10 Iteration: 1700 Avg. Training loss: 5.1248 0.1007 sec/batch\n", "Epoch 1/10 Iteration: 1800 Avg. Training loss: 5.0379 0.1045 sec/batch\n", "Epoch 1/10 Iteration: 1900 Avg. Training loss: 4.9862 0.0994 sec/batch\n", "Epoch 1/10 Iteration: 2000 Avg. Training loss: 4.9961 0.0995 sec/batch\n", "Nearest to for: hoffman, rogue, explains, cited, dod, listed, census, oxford,\n", "Nearest to would: louisiane, still, bottle, nyquist, lampoon, introduced, disobey, feet,\n", "Nearest to known: homicide, intervening, tori, assistants, lodbrok, mated, millions, justified,\n", "Nearest to used: contributing, ceilings, institutionalization, brazil, pre, question, superstitious, incorporates,\n", "Nearest to at: squaresoft, help, taya, good, degree, their, melody, ferraris,\n", "Nearest to such: school, seldom, noise, distances, desired, wanderer, heseltine, next,\n", "Nearest to called: purification, implementing, industry, ramp, stoner, philology, cost, vegetative,\n", "Nearest to when: edinburgh, tze, preservation, sul, five, order, benguela, fo,\n", "Nearest to taking: rational, death, disembodied, countless, krulak, quaternions, carpal, audited,\n", "Nearest to consists: gramophone, karaca, whigs, squids, brighton, anthropic, heterosexuals, increase,\n", "Nearest to scale: exposed, formation, shrek, full, childe, sheltered, aggregated, speciation,\n", "Nearest to units: summit, begins, independent, dod, asserted, appoint, lawn, experimenter,\n", "Nearest to ice: pediment, witnessing, reconstruction, habibie, aiding, riggs, inflammable, detection,\n", "Nearest to instance: healthy, wong, census, attitudes, believed, buena, corals, husband,\n", "Nearest to channel: creditors, tritium, mastercard, bouchard, frees, stringy, bypassing, nietzsche,\n", "Nearest to report: conscious, presidents, hellas, but, girls, cooper, lineage, publishing,\n", "Epoch 1/10 Iteration: 2100 Avg. Training loss: 4.9267 0.0995 sec/batch\n", "Epoch 1/10 Iteration: 2200 Avg. Training loss: 4.9097 0.1014 sec/batch\n", "Epoch 1/10 Iteration: 2300 Avg. Training loss: 4.8684 0.1004 sec/batch\n", "Epoch 1/10 Iteration: 2400 Avg. Training loss: 4.8427 0.1060 sec/batch\n", "Epoch 1/10 Iteration: 2500 Avg. Training loss: 4.8111 0.1087 sec/batch\n", "Epoch 1/10 Iteration: 2600 Avg. Training loss: 4.8307 0.1029 sec/batch\n", "Epoch 1/10 Iteration: 2700 Avg. Training loss: 4.7947 0.1068 sec/batch\n", "Epoch 1/10 Iteration: 2800 Avg. Training loss: 4.8068 0.1025 sec/batch\n", "Epoch 1/10 Iteration: 2900 Avg. Training loss: 4.7837 0.1026 sec/batch\n", "Epoch 1/10 Iteration: 3000 Avg. Training loss: 4.7842 0.1076 sec/batch\n", "Nearest to for: hoffman, rogue, searchable, housed, cited, explains, dod, silos,\n", "Nearest to would: louisiane, still, concentrate, lampoon, disobey, nyquist, bottle, kaiju,\n", "Nearest to known: homicide, intervening, tori, millions, justified, mated, lodbrok, satrapies,\n", "Nearest to used: contributing, ceilings, brazil, institutionalization, breed, superstitious, incorporates, tends,\n", "Nearest to at: squaresoft, melody, ferraris, buffalo, competed, emi, taya, kids,\n", "Nearest to such: seldom, desired, school, noise, distances, wanderer, rays, unions,\n", "Nearest to called: ramp, philology, implementing, purification, industry, lacklustre, stoner, strategic,\n", "Nearest to when: edinburgh, attractive, preservation, fo, sul, itv, tze, scotland,\n", "Nearest to taking: rational, disembodied, india, death, arlene, exercised, quaternions, countless,\n", "Nearest to consists: gramophone, karaca, anthropic, brighton, buildup, whigs, squids, fascist,\n", "Nearest to scale: exposed, formation, coral, curved, childe, chappell, unusable, shrek,\n", "Nearest to units: lawn, summit, appoint, begins, dod, laid, independent, experimenter,\n", "Nearest to ice: witnessing, reconstruction, detection, pediment, aiding, inflammable, drugs, habibie,\n", "Nearest to instance: healthy, wong, buena, census, attitudes, implementations, caesarean, corals,\n", "Nearest to channel: creditors, tritium, mastercard, bouchard, frees, bypassing, nietzsche, dray,\n", "Nearest to report: conscious, presidents, hellas, cooper, ts, girls, isomorphism, credibility,\n", "Epoch 1/10 Iteration: 3100 Avg. Training loss: 4.7704 0.1056 sec/batch\n", "Epoch 1/10 Iteration: 3200 Avg. Training loss: 4.7655 0.1045 sec/batch\n", "Epoch 1/10 Iteration: 3300 Avg. Training loss: 4.7184 0.1032 sec/batch\n", "Epoch 1/10 Iteration: 3400 Avg. Training loss: 4.7202 0.1049 sec/batch\n", "Epoch 1/10 Iteration: 3500 Avg. Training loss: 4.7368 0.1028 sec/batch\n", "Epoch 1/10 Iteration: 3600 Avg. Training loss: 4.7046 0.1022 sec/batch\n", "Epoch 1/10 Iteration: 3700 Avg. Training loss: 4.6942 0.1021 sec/batch\n", "Epoch 1/10 Iteration: 3800 Avg. Training loss: 4.7397 0.1023 sec/batch\n", "Epoch 1/10 Iteration: 3900 Avg. Training loss: 4.7120 0.1021 sec/batch\n", "Epoch 1/10 Iteration: 4000 Avg. Training loss: 4.6501 0.1022 sec/batch\n", "Nearest to for: hoffman, rogue, searchable, housed, silos, cited, dod, jehoiakim,\n", "Nearest to would: louisiane, lampoon, concentrate, disobey, nyquist, still, albertina, bottle,\n", "Nearest to known: homicide, mated, tori, intervening, justified, satrapies, millions, lodbrok,\n", "Nearest to used: ceilings, contributing, institutionalization, brazil, breed, gilchrist, hawat, superstitious,\n", "Nearest to at: squaresoft, emi, buffalo, melody, worded, polls, competed, lander,\n", "Nearest to such: desired, seldom, distances, wanderer, noise, license, expellees, heseltine,\n", "Nearest to called: ramp, philology, implementing, purification, lacklustre, vegetative, industry, intimidated,\n", "Nearest to when: edinburgh, sul, preservation, fo, attractive, tze, launchers, benguela,\n", "Nearest to taking: leopards, maharishi, india, rational, forge, concordat, arlene, disembodied,\n", "Nearest to consists: gramophone, buildup, karaca, coronets, brighton, terminals, efficiencies, anthropic,\n", "Nearest to scale: exposed, chappell, childe, formation, allude, sheltered, embroiled, unusable,\n", "Nearest to units: lawn, experimenter, summit, typewriter, fortieth, torsion, independent, jagdish,\n", "Nearest to ice: witnessing, reconstruction, detection, pediment, habibie, aiding, pyotr, inflammable,\n", "Nearest to instance: healthy, wong, attitudes, resize, buena, implementations, synapses, census,\n", "Nearest to channel: creditors, tritium, mastercard, odor, frees, bouchard, dray, speculators,\n", "Nearest to report: conscious, candlestick, hellas, presidents, haight, credibility, cooper, isomorphism,\n", "Epoch 1/10 Iteration: 4100 Avg. Training loss: 4.6614 0.1032 sec/batch\n", "Epoch 1/10 Iteration: 4200 Avg. Training loss: 4.6734 0.1022 sec/batch\n", "Epoch 1/10 Iteration: 4300 Avg. Training loss: 4.6329 0.1024 sec/batch\n", "Epoch 1/10 Iteration: 4400 Avg. Training loss: 4.6284 0.1037 sec/batch\n", "Epoch 1/10 Iteration: 4500 Avg. Training loss: 4.6296 0.1047 sec/batch\n", "Epoch 1/10 Iteration: 4600 Avg. Training loss: 4.6149 0.1042 sec/batch\n", "Epoch 2/10 Iteration: 4700 Avg. Training loss: 4.5956 0.0812 sec/batch\n", "Epoch 2/10 Iteration: 4800 Avg. Training loss: 4.5381 0.1114 sec/batch\n", "Epoch 2/10 Iteration: 4900 Avg. Training loss: 4.5008 0.1046 sec/batch\n", "Epoch 2/10 Iteration: 5000 Avg. Training loss: 4.5004 0.1017 sec/batch\n", "Nearest to for: hoffman, rogue, searchable, housed, cited, explains, appropriately, silos,\n", "Nearest to would: lampoon, concentrate, disobey, nyquist, louisiane, albertina, still, bottle,\n", "Nearest to known: homicide, mated, assistants, satrapies, justified, tori, uppercase, rtgs,\n", "Nearest to used: ceilings, contributing, institutionalization, gilchrist, mollusks, breed, hawat, tends,\n", "Nearest to at: squaresoft, taya, emi, melody, buffalo, lander, awarding, polls,\n", "Nearest to such: desired, noise, distances, seldom, license, heseltine, expellees, plosives,\n", "Nearest to called: ramp, philology, lacklustre, purification, implementing, vegetative, bakunin, intimidated,\n", "Nearest to when: edinburgh, attractive, preservation, fo, sul, tze, launchers, ragga,\n", "Nearest to taking: leopards, arlene, rational, sidgwick, concordat, india, maharishi, representational,\n", "Nearest to consists: gramophone, efficiencies, karaca, buildup, coronets, coasts, terminals, anthropic,\n", "Nearest to scale: exposed, chappell, allude, formation, childe, fuse, aggregated, curved,\n", "Nearest to units: torsion, lawn, fortieth, experimenter, typewriter, overlordship, jagdish, latest,\n", "Nearest to ice: reconstruction, witnessing, detection, plasmodium, pinstripes, habibie, pediment, pyotr,\n", "Nearest to instance: healthy, resize, synapses, attitudes, lenses, wong, implementations, corals,\n", "Nearest to channel: tritium, creditors, mastercard, speculators, gli, dray, bouchard, frees,\n", "Nearest to report: candlestick, conscious, haight, hellas, presidents, leaped, credibility, cooper,\n", "Epoch 2/10 Iteration: 5100 Avg. Training loss: 4.5328 0.1027 sec/batch\n", "Epoch 2/10 Iteration: 5200 Avg. Training loss: 4.4976 0.1024 sec/batch\n", "Epoch 2/10 Iteration: 5300 Avg. Training loss: 4.4784 0.1023 sec/batch\n", "Epoch 2/10 Iteration: 5400 Avg. Training loss: 4.5429 0.1024 sec/batch\n", "Epoch 2/10 Iteration: 5500 Avg. Training loss: 4.5072 0.1021 sec/batch\n", "Epoch 2/10 Iteration: 5600 Avg. Training loss: 4.4743 0.1062 sec/batch\n", "Epoch 2/10 Iteration: 5700 Avg. Training loss: 4.4699 0.1040 sec/batch\n", "Epoch 2/10 Iteration: 5800 Avg. Training loss: 4.3911 0.1088 sec/batch\n", "Epoch 2/10 Iteration: 5900 Avg. Training loss: 4.4513 0.1101 sec/batch\n", "Epoch 2/10 Iteration: 6000 Avg. Training loss: 4.4301 0.1096 sec/batch\n", "Nearest to for: rogue, hoffman, searchable, appropriately, cited, meats, silos, housed,\n", "Nearest to would: disobey, nyquist, concentrate, lampoon, louisiane, whyte, still, albertina,\n", "Nearest to known: homicide, mated, satrapies, rtgs, justified, tori, ctor, millions,\n", "Nearest to used: ceilings, contributing, mollusks, institutionalization, hawat, user, breed, weight,\n", "Nearest to at: squaresoft, taya, emi, awarding, buffalo, melody, lander, polls,\n", "Nearest to such: desired, license, seldom, distances, noise, heseltine, plosives, consumers,\n", "Nearest to called: ramp, vegetative, lacklustre, philology, implementing, bakunin, supersessionism, purification,\n", "Nearest to when: edinburgh, fo, attractive, ragga, preservation, tze, be, benguela,\n", "Nearest to taking: leopards, arlene, rational, sidgwick, concordat, bhagavan, vicar, applause,\n", "Nearest to consists: efficiencies, gramophone, karaca, buildup, coasts, coronets, cbd, terminals,\n", "Nearest to scale: exposed, chappell, formation, allude, childe, curved, fuse, coral,\n", "Nearest to units: torsion, typewriter, fortieth, lawn, latest, experimenter, torrens, arched,\n", "Nearest to ice: reconstruction, detection, plasmodium, witnessing, staining, soils, pediment, habibie,\n", "Nearest to instance: healthy, resize, synapses, implementations, lenses, attitudes, spreads, what,\n", "Nearest to channel: tritium, speculators, creditors, dray, restructured, mastercard, gli, frees,\n", "Nearest to report: candlestick, haight, conscious, leaped, credibility, presidents, hellas, standish,\n", "Epoch 2/10 Iteration: 6100 Avg. Training loss: 4.4451 0.1131 sec/batch\n", "Epoch 2/10 Iteration: 6200 Avg. Training loss: 4.4053 0.1095 sec/batch\n", "Epoch 2/10 Iteration: 6300 Avg. Training loss: 4.4466 0.1095 sec/batch\n", "Epoch 2/10 Iteration: 6400 Avg. Training loss: 4.4000 0.1088 sec/batch\n", "Epoch 2/10 Iteration: 6500 Avg. Training loss: 4.4273 0.1082 sec/batch\n", "Epoch 2/10 Iteration: 6600 Avg. Training loss: 4.4487 0.1098 sec/batch\n", "Epoch 2/10 Iteration: 6700 Avg. Training loss: 4.3700 0.1094 sec/batch\n", "Epoch 2/10 Iteration: 6800 Avg. Training loss: 4.3856 0.1091 sec/batch\n", "Epoch 2/10 Iteration: 6900 Avg. Training loss: 4.4200 0.1091 sec/batch\n", "Epoch 2/10 Iteration: 7000 Avg. Training loss: 4.3654 0.1083 sec/batch\n", "Nearest to for: hoffman, rogue, searchable, cited, appropriately, silos, caller, jehoiakim,\n", "Nearest to would: disobey, nyquist, lampoon, concentrate, louisiane, whyte, still, olin,\n", "Nearest to known: mated, homicide, satrapies, tori, rtgs, assistants, grady, oak,\n", "Nearest to used: ceilings, mollusks, institutionalization, contributing, user, breed, gilchrist, negating,\n", "Nearest to at: squaresoft, taya, emi, awarding, room, bathory, berke, melody,\n", "Nearest to such: desired, license, noise, seldom, plosives, distances, itself, techniques,\n", "Nearest to called: ramp, vegetative, bakunin, lacklustre, philology, supersessionism, intimidated, sealand,\n", "Nearest to when: edinburgh, ragga, attractive, benguela, be, fo, preservation, launchers,\n", "Nearest to taking: leopards, rational, arlene, concordat, sidgwick, bhagavan, vicar, tents,\n", "Nearest to consists: karaca, gramophone, coasts, efficiencies, cbd, buildup, anthropic, eee,\n", "Nearest to scale: exposed, chappell, formation, childe, speciation, allude, curved, coral,\n", "Nearest to units: torsion, typewriter, fortieth, force, experimenter, arched, latest, teletype,\n", "Nearest to ice: reconstruction, detection, plasmodium, staining, soils, witnessing, pediment, robotics,\n", "Nearest to instance: synapses, resize, healthy, implementations, lenses, attitudes, spreads, krugerrand,\n", "Nearest to channel: tritium, speculators, creditors, curler, mastercard, restructured, dray, almohades,\n", "Nearest to report: candlestick, presidents, haight, leaped, conscious, standish, credibility, tillman,\n", "Epoch 2/10 Iteration: 7100 Avg. Training loss: 4.3969 0.1102 sec/batch\n", "Epoch 2/10 Iteration: 7200 Avg. Training loss: 4.3768 0.1086 sec/batch\n", "Epoch 2/10 Iteration: 7300 Avg. Training loss: 4.3602 0.1087 sec/batch\n", "Epoch 2/10 Iteration: 7400 Avg. Training loss: 4.3689 0.1125 sec/batch\n", "Epoch 2/10 Iteration: 7500 Avg. Training loss: 4.4073 0.1099 sec/batch\n", "Epoch 2/10 Iteration: 7600 Avg. Training loss: 4.3354 0.1114 sec/batch\n", "Epoch 2/10 Iteration: 7700 Avg. Training loss: 4.3640 0.1068 sec/batch\n", "Epoch 2/10 Iteration: 7800 Avg. Training loss: 4.3759 0.1094 sec/batch\n", "Epoch 2/10 Iteration: 7900 Avg. Training loss: 4.3205 0.1064 sec/batch\n", "Epoch 2/10 Iteration: 8000 Avg. Training loss: 4.3363 0.1084 sec/batch\n", "Nearest to for: hoffman, rogue, silos, searchable, housed, entities, appropriately, jehoiakim,\n", "Nearest to would: disobey, nyquist, lampoon, louisiane, zubaydah, habilis, concentrate, despaired,\n", "Nearest to known: satrapies, mated, oak, homicide, demographically, justified, conglomerates, uppercase,\n", "Nearest to used: ceilings, mollusks, institutionalization, gilchrist, bp, negating, nazca, contributing,\n", "Nearest to at: emi, awarding, taya, bathory, squaresoft, sharps, motivates, room,\n", "Nearest to such: desired, license, seldom, plosives, noise, assumes, techniques, furtherance,\n", "Nearest to called: ramp, vegetative, bakunin, lacklustre, reintroduce, philology, purification, supersessionism,\n", "Nearest to when: edinburgh, ragga, refuse, attractive, be, benguela, tze, fo,\n", "Nearest to taking: leopards, rational, concordat, sidgwick, arlene, anoxic, bhagavan, vicar,\n", "Nearest to consists: karaca, cbd, coasts, gramophone, brighton, eee, circumcising, efficiencies,\n", "Nearest to scale: exposed, chappell, formation, speciation, curved, allude, childe, coral,\n", "Nearest to units: torsion, fortieth, typewriter, force, arched, experimenter, latest, torrens,\n", "Nearest to ice: soils, plasmodium, reconstruction, staining, detection, golem, hartsfield, witnessing,\n", "Nearest to instance: synapses, resize, healthy, lenses, implementations, illogical, krugerrand, attitudes,\n", "Nearest to channel: speculators, tritium, curler, creditors, mastercard, restructured, almohades, odor,\n", "Nearest to report: haight, candlestick, presidents, leaped, corte, conscious, tillman, standish,\n", "Epoch 2/10 Iteration: 8100 Avg. Training loss: 4.3422 0.1105 sec/batch\n", "Epoch 2/10 Iteration: 8200 Avg. Training loss: 4.2877 0.1093 sec/batch\n", "Epoch 2/10 Iteration: 8300 Avg. Training loss: 4.3619 0.1113 sec/batch\n", "Epoch 2/10 Iteration: 8400 Avg. Training loss: 4.3875 0.1123 sec/batch\n", "Epoch 2/10 Iteration: 8500 Avg. Training loss: 4.3750 0.1136 sec/batch\n", "Epoch 2/10 Iteration: 8600 Avg. Training loss: 4.2679 0.1082 sec/batch\n", "Epoch 2/10 Iteration: 8700 Avg. Training loss: 4.3009 0.1120 sec/batch\n", "Epoch 2/10 Iteration: 8800 Avg. Training loss: 4.3798 0.1139 sec/batch\n", "Epoch 2/10 Iteration: 8900 Avg. Training loss: 4.2172 0.1133 sec/batch\n", "Epoch 2/10 Iteration: 9000 Avg. Training loss: 4.2966 0.1099 sec/batch\n", "Nearest to for: hoffman, rogue, searchable, silos, serrated, appropriately, emeryville, jehoiakim,\n", "Nearest to would: disobey, nyquist, habilis, whyte, zubaydah, despaired, replied, concentrate,\n", "Nearest to known: mated, satrapies, rtgs, uppercase, oak, homicide, demographically, very,\n", "Nearest to used: ceilings, mollusks, bp, comprehensible, institutionalization, gilchrist, nazca, negating,\n", "Nearest to at: emi, taya, bathory, squaresoft, awarding, motivates, room, summer,\n", "Nearest to such: desired, license, heseltine, furtherance, seldom, techniques, monopolistic, plosives,\n", "Nearest to called: ramp, vegetative, lacklustre, bakunin, philology, purification, supersessionism, reintroduce,\n", "Nearest to when: edinburgh, ragga, be, refuse, benguela, attractive, tze, bursa,\n", "Nearest to taking: leopards, rational, concordat, sidgwick, bhagavan, go, arlene, garis,\n", "Nearest to consists: eee, karaca, cbd, efficiencies, coasts, brighton, coronets, circumcising,\n", "Nearest to scale: exposed, chappell, formation, allude, curved, speciation, fuse, coral,\n", "Nearest to units: torsion, fortieth, typewriter, force, torrens, arched, teletype, experimenter,\n", "Nearest to ice: soils, plasmodium, reconstruction, staining, golem, detection, hartsfield, pyotr,\n", "Nearest to instance: synapses, resize, healthy, lenses, krugerrand, illogical, implementations, spreads,\n", "Nearest to channel: tritium, speculators, curler, mastercard, restructured, creditors, almohades, dray,\n", "Nearest to report: haight, leaped, candlestick, presidents, standish, corte, conscious, credibility,\n", "Epoch 2/10 Iteration: 9100 Avg. Training loss: 4.3073 0.1099 sec/batch\n", "Epoch 2/10 Iteration: 9200 Avg. Training loss: 4.3067 0.1088 sec/batch\n", "Epoch 3/10 Iteration: 9300 Avg. Training loss: 4.3305 0.0503 sec/batch\n", "Epoch 3/10 Iteration: 9400 Avg. Training loss: 4.2538 0.1096 sec/batch\n", "Epoch 3/10 Iteration: 9500 Avg. Training loss: 4.2195 0.1093 sec/batch\n", "Epoch 3/10 Iteration: 9600 Avg. Training loss: 4.2297 0.1091 sec/batch\n", "Epoch 3/10 Iteration: 9700 Avg. Training loss: 4.2225 0.1116 sec/batch\n", "Epoch 3/10 Iteration: 9800 Avg. Training loss: 4.2412 0.1091 sec/batch\n", "Epoch 3/10 Iteration: 9900 Avg. Training loss: 4.2439 0.1091 sec/batch\n", "Epoch 3/10 Iteration: 10000 Avg. Training loss: 4.1912 0.1096 sec/batch\n", "Nearest to for: rogue, hoffman, searchable, silos, caller, converged, appropriately, pokey,\n", "Nearest to would: disobey, nyquist, whyte, habilis, zubaydah, concentrate, lampoon, weaponry,\n", "Nearest to known: mated, rtgs, conglomerates, demographically, oak, uppercase, satrapies, assistants,\n", "Nearest to used: ceilings, mollusks, bp, negating, comprehensible, institutionalization, cages, bleaches,\n", "Nearest to at: emi, taya, bathory, awarding, room, summer, squaresoft, sharps,\n", "Nearest to such: license, desired, heseltine, plosives, afips, furtherance, expellees, techniques,\n", "Nearest to called: ramp, bakunin, philology, vegetative, lacklustre, supersessionism, purification, reintroduce,\n", "Nearest to when: edinburgh, ragga, refuse, benguela, attractive, remove, be, falklands,\n", "Nearest to taking: leopards, rational, concordat, go, sidgwick, garis, bhagavan, applause,\n", "Nearest to consists: eee, cbd, coasts, efficiencies, karaca, brighton, coronets, located,\n", "Nearest to scale: exposed, chappell, coral, allude, curved, formation, fuse, speciation,\n", "Nearest to units: torsion, fortieth, force, typewriter, teletype, torrens, pucker, arched,\n", "Nearest to ice: soils, plasmodium, staining, reconstruction, detection, golem, pyotr, pinstripes,\n", "Nearest to instance: resize, synapses, healthy, lenses, krugerrand, illogical, attitudes, caesarean,\n", "Nearest to channel: speculators, tritium, curler, mastercard, restructured, creditors, bypassing, almohades,\n", "Nearest to report: candlestick, standish, credibility, haight, leaped, presidents, conscious, corte,\n", "Epoch 3/10 Iteration: 10100 Avg. Training loss: 4.2465 0.1103 sec/batch\n", "Epoch 3/10 Iteration: 10200 Avg. Training loss: 4.2411 0.1091 sec/batch\n", "Epoch 3/10 Iteration: 10300 Avg. Training loss: 4.2232 0.1098 sec/batch\n", "Epoch 3/10 Iteration: 10400 Avg. Training loss: 4.1565 0.1094 sec/batch\n", "Epoch 3/10 Iteration: 10500 Avg. Training loss: 4.1659 0.1097 sec/batch\n", "Epoch 3/10 Iteration: 10600 Avg. Training loss: 4.1560 0.1100 sec/batch\n", "Epoch 3/10 Iteration: 10700 Avg. Training loss: 4.1616 0.1101 sec/batch\n", "Epoch 3/10 Iteration: 10800 Avg. Training loss: 4.1829 0.1101 sec/batch\n", "Epoch 3/10 Iteration: 10900 Avg. Training loss: 4.1989 0.1096 sec/batch\n", "Epoch 3/10 Iteration: 11000 Avg. Training loss: 4.1676 0.1097 sec/batch\n", "Nearest to for: hoffman, rogue, searchable, caller, silos, appropriately, typeface, converged,\n", "Nearest to would: disobey, nyquist, whyte, weaponry, habilis, zubaydah, concentrate, despaired,\n", "Nearest to known: rtgs, demographically, mated, satrapies, very, conical, usability, uppercase,\n", "Nearest to used: ceilings, mollusks, negating, bp, institutionalization, grams, cages, painstaking,\n", "Nearest to at: emi, taya, awarding, room, squaresoft, sharps, bathory, italia,\n", "Nearest to such: license, desired, plosives, techniques, heseltine, undercurrent, imperii, procedure,\n", "Nearest to called: vegetative, ramp, supersessionism, bakunin, sealand, philology, purification, reintroduce,\n", "Nearest to when: ragga, edinburgh, attractive, refuse, be, benguela, remove, falklands,\n", "Nearest to taking: leopards, rational, go, concordat, garis, sidgwick, carpal, anoxic,\n", "Nearest to consists: eee, cbd, coasts, located, condorcet, circumcising, gramophone, brighton,\n", "Nearest to scale: exposed, chappell, fuse, childe, curved, allude, formation, speciation,\n", "Nearest to units: torsion, force, fortieth, typewriter, teletype, latest, unit, prefixes,\n", "Nearest to ice: soils, plasmodium, staining, detection, reconstruction, pinstripes, fracture, golem,\n", "Nearest to instance: resize, synapses, lenses, implementations, healthy, illogical, oscillators, krugerrand,\n", "Nearest to channel: curler, speculators, tritium, restructured, creditors, bypassing, mastercard, dray,\n", "Nearest to report: credibility, presidents, candlestick, standish, leaped, haight, corte, conscious,\n", "Epoch 3/10 Iteration: 11100 Avg. Training loss: 4.1830 0.1103 sec/batch\n", "Epoch 3/10 Iteration: 11200 Avg. Training loss: 4.2133 0.1089 sec/batch\n", "Epoch 3/10 Iteration: 11300 Avg. Training loss: 4.1865 0.1096 sec/batch\n", "Epoch 3/10 Iteration: 11400 Avg. Training loss: 4.1479 0.1090 sec/batch\n", "Epoch 3/10 Iteration: 11500 Avg. Training loss: 4.2011 0.1093 sec/batch\n", "Epoch 3/10 Iteration: 11600 Avg. Training loss: 4.1720 0.1095 sec/batch\n", "Epoch 3/10 Iteration: 11700 Avg. Training loss: 4.2111 0.1095 sec/batch\n", "Epoch 3/10 Iteration: 11800 Avg. Training loss: 4.1659 0.1095 sec/batch\n", "Epoch 3/10 Iteration: 11900 Avg. Training loss: 4.1315 0.1091 sec/batch\n", "Epoch 3/10 Iteration: 12000 Avg. Training loss: 4.1508 0.1092 sec/batch\n", "Nearest to for: hoffman, rogue, given, searchable, silos, census, converged, caller,\n", "Nearest to would: disobey, habilis, nyquist, zubaydah, whyte, despaired, weaponry, preeminence,\n", "Nearest to known: rtgs, mated, satrapies, uppercase, usability, conical, very, oak,\n", "Nearest to used: ceilings, mollusks, bp, negating, institutionalization, decorator, supplementation, cirth,\n", "Nearest to at: emi, taya, awarding, habr, squaresoft, sharps, coronets, dini,\n", "Nearest to such: desired, techniques, plosives, license, pollutant, procedure, unfair, lysenkoism,\n", "Nearest to called: ramp, vegetative, supersessionism, bakunin, philology, sealand, reintroduce, denunciations,\n", "Nearest to when: ragga, edinburgh, attractive, be, refuse, benguela, bush, remove,\n", "Nearest to taking: leopards, rational, concordat, sidgwick, arlene, garis, carpal, anoxic,\n", "Nearest to consists: eee, cbd, coasts, gramophone, located, morisot, condorcet, brighton,\n", "Nearest to scale: exposed, chappell, curved, allude, formation, fuse, speciation, childe,\n", "Nearest to units: force, torsion, fortieth, typewriter, teletype, unit, prefixes, pucker,\n", "Nearest to ice: soils, staining, plasmodium, fracture, pinstripes, reconstruction, pyotr, louth,\n", "Nearest to instance: resize, lenses, synapses, implementations, illogical, healthy, krugerrand, oscillators,\n", "Nearest to channel: curler, tritium, speculators, restructured, mastercard, creditors, bypassing, almohades,\n", "Nearest to report: credibility, presidents, standish, candlestick, leaped, annotated, haight, serviced,\n", "Epoch 3/10 Iteration: 12100 Avg. Training loss: 4.1912 0.1103 sec/batch\n", "Epoch 3/10 Iteration: 12200 Avg. Training loss: 4.1658 0.1091 sec/batch\n", "Epoch 3/10 Iteration: 12300 Avg. Training loss: 4.1775 0.1089 sec/batch\n", "Epoch 3/10 Iteration: 12400 Avg. Training loss: 4.1726 0.1093 sec/batch\n", "Epoch 3/10 Iteration: 12500 Avg. Training loss: 4.1599 0.1099 sec/batch\n", "Epoch 3/10 Iteration: 12600 Avg. Training loss: 4.1498 0.1099 sec/batch\n", "Epoch 3/10 Iteration: 12700 Avg. Training loss: 4.1615 0.1097 sec/batch\n", "Epoch 3/10 Iteration: 12800 Avg. Training loss: 4.1188 0.1095 sec/batch\n", "Epoch 3/10 Iteration: 12900 Avg. Training loss: 4.1679 0.1098 sec/batch\n", "Epoch 3/10 Iteration: 13000 Avg. Training loss: 4.2005 0.1100 sec/batch\n", "Nearest to for: hoffman, rogue, emeryville, census, given, scriptwriter, searchable, converged,\n", "Nearest to would: disobey, habilis, despaired, zubaydah, amontillado, preeminence, whyte, replied,\n", "Nearest to known: satrapies, mated, rtgs, oak, grady, tori, demographically, usability,\n", "Nearest to used: ceilings, bp, negating, cirth, decorator, supplementation, comprehensible, hyphen,\n", "Nearest to at: emi, taya, italia, habr, bathory, dini, nde, awarding,\n", "Nearest to such: desired, unfair, expellees, eudicots, actus, nanda, plosives, license,\n", "Nearest to called: supersessionism, bakunin, reintroduce, excommunicating, faithless, denunciations, ramp, vegetative,\n", "Nearest to when: edinburgh, ragga, refuse, attractive, bush, be, benguela, convinced,\n", "Nearest to taking: leopards, rational, sidgwick, concordat, go, garis, anoxic, arlene,\n", "Nearest to consists: eee, cbd, condorcet, located, coasts, brighton, morisot, circumcising,\n", "Nearest to scale: exposed, chappell, allude, curved, fuse, speciation, hashes, sheltered,\n", "Nearest to units: force, torsion, fortieth, typewriter, teletype, unit, pucker, prefixes,\n", "Nearest to ice: staining, plasmodium, soils, pinstripes, pyotr, fracture, louth, golem,\n", "Nearest to instance: resize, synapses, lenses, illogical, implementations, unappreciated, healthy, krugerrand,\n", "Nearest to channel: curler, tritium, restructured, speculators, creditors, mastercard, bypassing, dray,\n", "Nearest to report: presidents, credibility, leaped, standish, candlestick, focusing, haight, corte,\n", "Epoch 3/10 Iteration: 13100 Avg. Training loss: 4.2402 0.1103 sec/batch\n", "Epoch 3/10 Iteration: 13200 Avg. Training loss: 4.1416 0.1096 sec/batch\n", "Epoch 3/10 Iteration: 13300 Avg. Training loss: 4.1287 0.1098 sec/batch\n", "Epoch 3/10 Iteration: 13400 Avg. Training loss: 4.1439 0.1095 sec/batch\n", "Epoch 3/10 Iteration: 13500 Avg. Training loss: 4.0455 0.1098 sec/batch\n", "Epoch 3/10 Iteration: 13600 Avg. Training loss: 4.1497 0.1102 sec/batch\n", "Epoch 3/10 Iteration: 13700 Avg. Training loss: 4.1528 0.1098 sec/batch\n", "Epoch 3/10 Iteration: 13800 Avg. Training loss: 4.1375 0.1094 sec/batch\n", "Epoch 4/10 Iteration: 13900 Avg. Training loss: 4.1982 0.0209 sec/batch\n", "Epoch 4/10 Iteration: 14000 Avg. Training loss: 4.1256 0.1089 sec/batch\n", "Nearest to for: hoffman, rogue, given, converged, searchable, scriptwriter, typeface, emeryville,\n", "Nearest to would: disobey, habilis, nyquist, whyte, zubaydah, busting, amontillado, gimme,\n", "Nearest to known: rtgs, very, perihelion, uppercase, satrapies, usability, fervour, conglomerates,\n", "Nearest to used: ceilings, bp, bleaches, cirth, negating, supplementation, institutionalization, stds,\n", "Nearest to at: emi, taya, travelling, seated, bathory, coronets, breach, awarding,\n", "Nearest to such: license, pollutant, techniques, desired, conceals, actus, procedure, unfair,\n", "Nearest to called: ramp, vegetative, supersessionism, reintroduce, faithless, ripples, sealand, joliot,\n", "Nearest to when: edinburgh, ragga, attractive, bush, refuse, be, benguela, bursa,\n", "Nearest to taking: leopards, rational, sidgwick, garis, anoxic, go, concordat, carpal,\n", "Nearest to consists: eee, cbd, located, brighton, condorcet, chamber, appoints, coasts,\n", "Nearest to scale: exposed, allude, curved, fuse, chappell, mellin, capricornus, gears,\n", "Nearest to units: force, torsion, fortieth, unit, prefixes, typewriter, teletype, pucker,\n", "Nearest to ice: staining, plasmodium, soils, pinstripes, pyotr, louth, hawk, golem,\n", "Nearest to instance: resize, synapses, illogical, lenses, krugerrand, healthy, unappreciated, oscillators,\n", "Nearest to channel: curler, creditors, tritium, dray, restructured, bypassing, mastercard, speculators,\n", "Nearest to report: credibility, presidents, leaped, standish, candlestick, annotated, haight, targeted,\n", "Epoch 4/10 Iteration: 14100 Avg. Training loss: 4.0816 0.1103 sec/batch\n", "Epoch 4/10 Iteration: 14200 Avg. Training loss: 4.1231 0.1090 sec/batch\n", "Epoch 4/10 Iteration: 14300 Avg. Training loss: 4.0923 0.1093 sec/batch\n", "Epoch 4/10 Iteration: 14400 Avg. Training loss: 4.0457 0.1082 sec/batch\n", "Epoch 4/10 Iteration: 14500 Avg. Training loss: 4.0987 0.1090 sec/batch\n", "Epoch 4/10 Iteration: 14600 Avg. Training loss: 4.0307 0.1086 sec/batch\n", "Epoch 4/10 Iteration: 14700 Avg. Training loss: 4.0652 0.1095 sec/batch\n", "Epoch 4/10 Iteration: 14800 Avg. Training loss: 4.0900 0.1090 sec/batch\n", "Epoch 4/10 Iteration: 14900 Avg. Training loss: 4.1109 0.1091 sec/batch\n", "Epoch 4/10 Iteration: 15000 Avg. Training loss: 4.0441 0.1098 sec/batch\n", "Nearest to for: rogue, given, converged, census, autrefois, hoffman, silos, searchable,\n", "Nearest to would: disobey, nyquist, habilis, whyte, gimme, busting, preeminence, amontillado,\n", "Nearest to known: rtgs, oak, usability, very, perihelion, mated, satrapies, fervour,\n", "Nearest to used: ceilings, bp, grams, alliances, pacemakers, stds, epoxy, mollusks,\n", "Nearest to at: emi, seated, travelling, aviators, coronets, taya, italia, awarding,\n", "Nearest to such: desired, license, undercurrent, hinges, pollutant, unfair, techniques, heseltine,\n", "Nearest to called: ramp, vegetative, supersessionism, reintroduce, sealand, denunciations, faithless, purification,\n", "Nearest to when: ragga, edinburgh, attractive, bush, be, refuse, benguela, remove,\n", "Nearest to taking: leopards, rational, garis, sidgwick, concordat, go, nba, anoxic,\n", "Nearest to consists: eee, cbd, located, chamber, coasts, twos, consist, morisot,\n", "Nearest to scale: exposed, allude, curved, capricornus, mellin, fuse, chappell, sheltered,\n", "Nearest to units: force, unit, torsion, fortieth, prefixes, teletype, typewriter, pucker,\n", "Nearest to ice: plasmodium, soils, staining, pinstripes, pyotr, louth, golem, gskola,\n", "Nearest to instance: resize, lenses, illogical, synapses, krugerrand, healthy, unappreciated, caesarean,\n", "Nearest to channel: curler, restructured, bypassing, creditors, dray, tritium, speculators, mastercard,\n", "Nearest to report: credibility, presidents, spirituality, leaped, focusing, standish, annotated, targeted,\n", "Epoch 4/10 Iteration: 15100 Avg. Training loss: 4.0226 0.1103 sec/batch\n", "Epoch 4/10 Iteration: 15200 Avg. Training loss: 4.0229 0.1098 sec/batch\n", "Epoch 4/10 Iteration: 15300 Avg. Training loss: 4.0029 0.1098 sec/batch\n", "Epoch 4/10 Iteration: 15400 Avg. Training loss: 4.0458 0.1080 sec/batch\n", "Epoch 4/10 Iteration: 15500 Avg. Training loss: 4.0678 0.0983 sec/batch\n", "Epoch 4/10 Iteration: 15600 Avg. Training loss: 4.0606 0.1029 sec/batch\n", "Epoch 4/10 Iteration: 15700 Avg. Training loss: 4.0898 0.1005 sec/batch\n", "Epoch 4/10 Iteration: 15800 Avg. Training loss: 4.1047 0.0983 sec/batch\n", "Epoch 4/10 Iteration: 15900 Avg. Training loss: 4.0668 0.1013 sec/batch\n", "Epoch 4/10 Iteration: 16000 Avg. Training loss: 4.0396 0.1101 sec/batch\n", "Nearest to for: given, census, hoffman, rogue, converged, parliamentary, autrefois, tomo,\n", "Nearest to would: disobey, whyte, nyquist, habilis, gimme, despaired, busting, relegated,\n", "Nearest to known: rtgs, banach, pisin, perihelion, oak, satrapies, mated, usability,\n", "Nearest to used: bp, ceilings, grams, cirth, stds, bleaches, pacemakers, primary,\n", "Nearest to at: emi, travelling, degree, taya, dominants, aviators, habr, awarding,\n", "Nearest to such: desired, actus, plosives, lysenkoism, hinges, license, pollutant, conceals,\n", "Nearest to called: supersessionism, reintroduce, denunciations, vegetative, faithless, ramp, core, sealand,\n", "Nearest to when: ragga, edinburgh, attractive, be, refuse, bush, remove, painda,\n", "Nearest to taking: rational, leopards, garis, sidgwick, concordat, go, anoxic, carpal,\n", "Nearest to consists: eee, chamber, cbd, located, consist, morisot, condorcet, coasts,\n", "Nearest to scale: exposed, mellin, allude, capricornus, fuse, childe, visualizing, curved,\n", "Nearest to units: force, unit, fortieth, torsion, prefixes, teletype, typewriter, pucker,\n", "Nearest to ice: plasmodium, staining, soils, pinstripes, louth, fracture, pyotr, detection,\n", "Nearest to instance: resize, synapses, lenses, implementations, unappreciated, illogical, caesarean, oscillators,\n", "Nearest to channel: curler, creditors, bypassing, restructured, mbit, tritium, dray, speculators,\n", "Nearest to report: credibility, presidents, leaped, standish, spirituality, focusing, annotated, candlestick,\n", "Epoch 4/10 Iteration: 16100 Avg. Training loss: 4.0831 0.1100 sec/batch\n", "Epoch 4/10 Iteration: 16200 Avg. Training loss: 4.0817 0.1094 sec/batch\n", "Epoch 4/10 Iteration: 16300 Avg. Training loss: 4.0709 0.1093 sec/batch\n", "Epoch 4/10 Iteration: 16400 Avg. Training loss: 4.0693 0.1013 sec/batch\n", "Epoch 4/10 Iteration: 16500 Avg. Training loss: 4.0710 0.1000 sec/batch\n", "Epoch 4/10 Iteration: 16600 Avg. Training loss: 4.0771 0.1090 sec/batch\n", "Epoch 4/10 Iteration: 16700 Avg. Training loss: 4.0465 0.1083 sec/batch\n", "Epoch 4/10 Iteration: 16800 Avg. Training loss: 4.0753 0.1018 sec/batch\n", "Epoch 4/10 Iteration: 16900 Avg. Training loss: 4.1115 0.1103 sec/batch\n", "Epoch 4/10 Iteration: 17000 Avg. Training loss: 4.0615 0.1194 sec/batch\n", "Nearest to for: given, scriptwriter, census, rogue, emeryville, hoffman, autrefois, converged,\n", "Nearest to would: disobey, nyquist, habilis, whyte, busting, gimme, despaired, maecenas,\n", "Nearest to known: satrapies, fervour, pisin, sixteenth, banach, with, perihelion, oak,\n", "Nearest to used: ceilings, cirth, bp, alliances, stds, grams, machining, hyphen,\n", "Nearest to at: emi, travelling, breach, dominants, taya, dini, bathory, degree,\n", "Nearest to such: plosives, pollutant, desired, hinges, lysenkoism, undercurrent, actus, characterised,\n", "Nearest to called: supersessionism, reintroduce, vegetative, denunciations, faithless, ramp, sealand, purification,\n", "Nearest to when: ragga, edinburgh, attractive, refuse, be, painda, bush, manor,\n", "Nearest to taking: leopards, rational, sidgwick, garis, concordat, templar, anoxic, carpal,\n", "Nearest to consists: eee, chamber, cbd, morisot, consist, located, brighton, trending,\n", "Nearest to scale: exposed, mellin, capricornus, allude, curved, regolith, fuse, speciation,\n", "Nearest to units: force, unit, fortieth, torsion, prefixes, typewriter, teletype, pucker,\n", "Nearest to ice: plasmodium, pinstripes, soils, pyotr, staining, louth, gory, fracture,\n", "Nearest to instance: synapses, lenses, resize, unappreciated, implementations, illogical, placed, oscillators,\n", "Nearest to channel: curler, restructured, creditors, mbit, bypassing, dray, dts, tritium,\n", "Nearest to report: presidents, credibility, annotated, standish, spirituality, leaped, focusing, targeted,\n", "Epoch 4/10 Iteration: 17100 Avg. Training loss: 4.0576 0.1166 sec/batch\n", "Epoch 4/10 Iteration: 17200 Avg. Training loss: 4.0014 0.1178 sec/batch\n", "Epoch 4/10 Iteration: 17300 Avg. Training loss: 4.0085 0.1100 sec/batch\n", "Epoch 4/10 Iteration: 17400 Avg. Training loss: 4.0609 0.1082 sec/batch\n", "Epoch 4/10 Iteration: 17500 Avg. Training loss: 4.0888 0.1111 sec/batch\n", "Epoch 4/10 Iteration: 17600 Avg. Training loss: 4.1041 0.1124 sec/batch\n", "Epoch 4/10 Iteration: 17700 Avg. Training loss: 4.1330 0.1147 sec/batch\n", "Epoch 4/10 Iteration: 17800 Avg. Training loss: 4.0638 0.1094 sec/batch\n", "Epoch 4/10 Iteration: 17900 Avg. Training loss: 4.0446 0.1126 sec/batch\n", "Epoch 4/10 Iteration: 18000 Avg. Training loss: 4.0699 0.1122 sec/batch\n", "Nearest to for: given, scriptwriter, rogue, census, autrefois, emeryville, converged, first,\n", "Nearest to would: disobey, whyte, habilis, nyquist, busting, gimme, relegated, maecenas,\n", "Nearest to known: satrapies, banach, rtgs, perihelion, pisin, quetzal, fervour, with,\n", "Nearest to used: ceilings, cirth, machining, bp, stds, alliances, ido, okinawan,\n", "Nearest to at: emi, travelling, breach, bathory, italia, dominants, dini, taya,\n", "Nearest to such: hinges, cc, actus, plosives, desired, conceals, license, eudicots,\n", "Nearest to called: supersessionism, reintroduce, ramp, faithless, denunciations, sealand, excommunicating, vegetative,\n", "Nearest to when: edinburgh, ragga, attractive, refuse, be, bush, remove, painda,\n", "Nearest to taking: rational, leopards, sidgwick, garis, anoxic, concordat, go, nba,\n", "Nearest to consists: eee, chamber, cbd, appoints, consist, morisot, located, condorcet,\n", "Nearest to scale: exposed, mellin, capricornus, allude, curved, fuse, regolith, speciation,\n", "Nearest to units: unit, force, fortieth, prefixes, torsion, si, typewriter, teletype,\n", "Nearest to ice: pinstripes, soils, louth, pyotr, plasmodium, staining, gory, rink,\n", "Nearest to instance: illogical, resize, lenses, unappreciated, synapses, oscillators, implementations, krugerrand,\n", "Nearest to channel: curler, restructured, dray, creditors, mbit, bypassing, mastercard, tritium,\n", "Nearest to report: presidents, credibility, spirituality, leaped, annotated, standish, focusing, reports,\n", "Epoch 4/10 Iteration: 18100 Avg. Training loss: 3.9760 0.1089 sec/batch\n", "Epoch 4/10 Iteration: 18200 Avg. Training loss: 4.0450 0.1039 sec/batch\n", "Epoch 4/10 Iteration: 18300 Avg. Training loss: 4.0234 0.1026 sec/batch\n", "Epoch 4/10 Iteration: 18400 Avg. Training loss: 4.0367 0.1004 sec/batch\n", "Epoch 4/10 Iteration: 18500 Avg. Training loss: 4.0817 0.1018 sec/batch\n", "Epoch 5/10 Iteration: 18600 Avg. Training loss: 4.0321 0.0936 sec/batch\n", "Epoch 5/10 Iteration: 18700 Avg. Training loss: 4.0089 0.1002 sec/batch\n", "Epoch 5/10 Iteration: 18800 Avg. Training loss: 3.9820 0.1098 sec/batch\n", "Epoch 5/10 Iteration: 18900 Avg. Training loss: 4.0002 0.1016 sec/batch\n", "Epoch 5/10 Iteration: 19000 Avg. Training loss: 3.9676 0.1011 sec/batch\n", "Nearest to for: given, scriptwriter, rogue, census, autrefois, converged, to, emeryville,\n", "Nearest to would: disobey, whyte, habilis, nyquist, maecenas, busting, gimme, relegated,\n", "Nearest to known: perihelion, rtgs, banach, satrapies, pisin, fervour, oak, quetzal,\n", "Nearest to used: ceilings, stds, cirth, machining, bp, alliances, grams, common,\n", "Nearest to at: emi, travelling, dominants, breach, italia, taya, bathory, seated,\n", "Nearest to such: hinges, actus, undercurrent, pollutant, lysenkoism, desired, cc, license,\n", "Nearest to called: supersessionism, reintroduce, keno, faithless, bother, sealand, vegetative, denunciations,\n", "Nearest to when: edinburgh, refuse, attractive, ragga, bush, be, remove, painda,\n", "Nearest to taking: leopards, garis, rational, sidgwick, go, anoxic, nba, boosts,\n", "Nearest to consists: eee, chamber, cbd, consist, located, morisot, twos, appoints,\n", "Nearest to scale: exposed, capricornus, curved, allude, mellin, regolith, fuse, gears,\n", "Nearest to units: unit, fortieth, prefixes, force, torsion, typewriter, si, irl,\n", "Nearest to ice: soils, pinstripes, plasmodium, louth, rink, pyotr, staining, joaquin,\n", "Nearest to instance: illogical, synapses, lenses, resize, krugerrand, healthy, placed, caesarean,\n", "Nearest to channel: curler, restructured, dray, creditors, bypassing, mastercard, wb, mbit,\n", "Nearest to report: credibility, spirituality, presidents, reports, annotated, standish, focusing, leaped,\n", "Epoch 5/10 Iteration: 19100 Avg. Training loss: 3.9968 0.1027 sec/batch\n", "Epoch 5/10 Iteration: 19200 Avg. Training loss: 3.9635 0.1035 sec/batch\n", "Epoch 5/10 Iteration: 19300 Avg. Training loss: 4.0181 0.1107 sec/batch\n", "Epoch 5/10 Iteration: 19400 Avg. Training loss: 4.0267 0.1175 sec/batch\n", "Epoch 5/10 Iteration: 19500 Avg. Training loss: 4.0411 0.1127 sec/batch\n", "Epoch 5/10 Iteration: 19600 Avg. Training loss: 3.9779 0.1149 sec/batch\n", "Epoch 5/10 Iteration: 19700 Avg. Training loss: 3.9253 0.1095 sec/batch\n", "Epoch 5/10 Iteration: 19800 Avg. Training loss: 3.9642 0.1090 sec/batch\n", "Epoch 5/10 Iteration: 19900 Avg. Training loss: 3.9214 0.1154 sec/batch\n", "Epoch 5/10 Iteration: 20000 Avg. Training loss: 3.9692 0.1104 sec/batch\n", "Nearest to for: given, census, to, scriptwriter, first, converged, emeryville, autrefois,\n", "Nearest to would: disobey, relegated, whyte, habilis, nyquist, capitalistic, busting, maecenas,\n", "Nearest to known: rtgs, banach, oak, perihelion, satrapies, with, nbi, hoosiers,\n", "Nearest to used: ceilings, grams, cirth, machining, bp, stds, nazca, epoxy,\n", "Nearest to at: emi, dominants, travelling, the, italia, degree, breach, surrounding,\n", "Nearest to such: undercurrent, actus, cc, hinges, license, lysenkoism, group, techniques,\n", "Nearest to called: supersessionism, vegetative, the, reintroduce, core, bother, denunciations, sealand,\n", "Nearest to when: edinburgh, ragga, attractive, be, refuse, remove, down, itv,\n", "Nearest to taking: leopards, rational, garis, go, anoxic, sidgwick, nba, carpal,\n", "Nearest to consists: eee, chamber, consist, located, cbd, morisot, leblanc, appoints,\n", "Nearest to scale: exposed, mellin, capricornus, allude, fuse, curved, townes, gears,\n", "Nearest to units: unit, force, prefixes, fortieth, torsion, typewriter, si, teletype,\n", "Nearest to ice: plasmodium, pinstripes, louth, soils, pyotr, staining, cools, rink,\n", "Nearest to instance: lenses, resize, placed, synapses, bookstore, illogical, oscillators, unappreciated,\n", "Nearest to channel: curler, restructured, dray, creditors, wb, channels, hearsay, dts,\n", "Nearest to report: credibility, presidents, spirituality, reports, annotated, standish, leaped, timeline,\n", "Epoch 5/10 Iteration: 20100 Avg. Training loss: 3.9983 0.1107 sec/batch\n", "Epoch 5/10 Iteration: 20200 Avg. Training loss: 3.9932 0.1185 sec/batch\n", "Epoch 5/10 Iteration: 20300 Avg. Training loss: 3.9784 0.1098 sec/batch\n", "Epoch 5/10 Iteration: 20400 Avg. Training loss: 3.9886 0.1104 sec/batch\n", "Epoch 5/10 Iteration: 20500 Avg. Training loss: 4.0409 0.1045 sec/batch\n", "Epoch 5/10 Iteration: 20600 Avg. Training loss: 3.9733 0.1048 sec/batch\n", "Epoch 5/10 Iteration: 20700 Avg. Training loss: 3.9866 0.1072 sec/batch\n", "Epoch 5/10 Iteration: 20800 Avg. Training loss: 4.0136 0.1085 sec/batch\n", "Epoch 5/10 Iteration: 20900 Avg. Training loss: 3.9813 0.1100 sec/batch\n", "Epoch 5/10 Iteration: 21000 Avg. Training loss: 4.0106 0.1119 sec/batch\n", "Nearest to for: given, census, scriptwriter, first, to, cited, autrefois, awards,\n", "Nearest to would: disobey, whyte, relegated, nyquist, maecenas, habilis, lege, forbid,\n", "Nearest to known: banach, rtgs, pisin, satrapies, nbi, hoosiers, sixteenth, perihelion,\n", "Nearest to used: cirth, bjarne, ceilings, alliances, grams, bp, machining, stds,\n", "Nearest to at: emi, travelling, dominants, degree, breach, their, the, awarding,\n", "Nearest to such: lysenkoism, actus, hinges, desired, cc, unfair, plosives, license,\n", "Nearest to called: supersessionism, bother, reintroduce, the, screenname, denunciations, ripples, core,\n", "Nearest to when: edinburgh, be, ragga, attractive, refuse, itv, retrospect, remove,\n", "Nearest to taking: rational, garis, leopards, go, sidgwick, anoxic, salim, nba,\n", "Nearest to consists: chamber, eee, consist, morisot, leblanc, cbd, located, hydrohalic,\n", "Nearest to scale: mellin, exposed, capricornus, townes, speciation, allude, fuse, curved,\n", "Nearest to units: unit, force, prefixes, fortieth, torsion, typewriter, si, kilogram,\n", "Nearest to ice: louth, pinstripes, rink, pyotr, plasmodium, staining, joaquin, sweden,\n", "Nearest to instance: lenses, bookstore, unappreciated, resize, illogical, synapses, placed, caesarean,\n", "Nearest to channel: curler, restructured, wb, dray, creditors, bandwidth, bypassing, mbit,\n", "Nearest to report: reports, credibility, presidents, spirituality, annotated, standish, leaped, timeline,\n", "Epoch 5/10 Iteration: 21100 Avg. Training loss: 3.9997 0.1121 sec/batch\n", "Epoch 5/10 Iteration: 21200 Avg. Training loss: 3.9752 0.1114 sec/batch\n", "Epoch 5/10 Iteration: 21300 Avg. Training loss: 4.0002 0.1109 sec/batch\n", "Epoch 5/10 Iteration: 21400 Avg. Training loss: 3.9800 0.1107 sec/batch\n", "Epoch 5/10 Iteration: 21500 Avg. Training loss: 4.0198 0.1114 sec/batch\n", "Epoch 5/10 Iteration: 21600 Avg. Training loss: 4.0034 0.1111 sec/batch\n", "Epoch 5/10 Iteration: 21700 Avg. Training loss: 3.9504 0.1112 sec/batch\n", "Epoch 5/10 Iteration: 21800 Avg. Training loss: 3.9446 0.1112 sec/batch\n", "Epoch 5/10 Iteration: 21900 Avg. Training loss: 3.9754 0.1101 sec/batch\n", "Epoch 5/10 Iteration: 22000 Avg. Training loss: 4.0392 0.1137 sec/batch\n", "Nearest to for: given, census, scriptwriter, first, to, emeryville, unusually, from,\n", "Nearest to would: disobey, relegated, whyte, nyquist, maecenas, habilis, in, lege,\n", "Nearest to known: satrapies, banach, rtgs, pisin, with, oak, yemenite, aalborg,\n", "Nearest to used: cirth, grams, machining, common, bp, ceilings, other, alliances,\n", "Nearest to at: emi, travelling, degree, dominants, the, breach, italia, their,\n", "Nearest to such: lysenkoism, cc, actus, hinges, license, desired, baa, undercurrent,\n", "Nearest to called: supersessionism, bother, reintroduce, denunciations, sealand, vegetative, ripples, faithless,\n", "Nearest to when: attractive, edinburgh, refuse, ragga, be, remove, painda, itv,\n", "Nearest to taking: rational, leopards, garis, go, sidgwick, anoxic, salim, kessinger,\n", "Nearest to consists: chamber, eee, consist, cbd, located, morisot, leblanc, sint,\n", "Nearest to scale: exposed, mellin, capricornus, speciation, accede, allude, gears, fuse,\n", "Nearest to units: unit, prefixes, force, fortieth, typewriter, si, torsion, irl,\n", "Nearest to ice: louth, rink, pinstripes, plasmodium, cools, pyotr, soils, staining,\n", "Nearest to instance: lenses, placed, illogical, synapses, unappreciated, bookstore, krugerrand, oscillators,\n", "Nearest to channel: curler, bandwidth, restructured, dray, wb, channels, mbit, dts,\n", "Nearest to report: reports, credibility, presidents, annotated, spirituality, standish, focusing, lebanon,\n", "Epoch 5/10 Iteration: 22100 Avg. Training loss: 3.9926 0.1178 sec/batch\n", "Epoch 5/10 Iteration: 22200 Avg. Training loss: 4.1086 0.1140 sec/batch\n", "Epoch 5/10 Iteration: 22300 Avg. Training loss: 4.0173 0.1238 sec/batch\n", "Epoch 5/10 Iteration: 22400 Avg. Training loss: 4.0545 0.1200 sec/batch\n", "Epoch 5/10 Iteration: 22500 Avg. Training loss: 3.9600 0.1167 sec/batch\n", "Epoch 5/10 Iteration: 22600 Avg. Training loss: 3.9318 0.1150 sec/batch\n", "Epoch 5/10 Iteration: 22700 Avg. Training loss: 3.9985 0.1157 sec/batch\n", "Epoch 5/10 Iteration: 22800 Avg. Training loss: 3.9130 0.1197 sec/batch\n", "Epoch 5/10 Iteration: 22900 Avg. Training loss: 3.9757 0.1174 sec/batch\n", "Epoch 5/10 Iteration: 23000 Avg. Training loss: 3.9773 0.1208 sec/batch\n", "Nearest to for: given, to, first, scriptwriter, census, the, from, have,\n", "Nearest to would: disobey, whyte, relegated, nyquist, busting, gimme, habilis, in,\n", "Nearest to known: banach, rtgs, satrapies, pisin, with, perihelion, usability, oak,\n", "Nearest to used: cirth, common, grams, machining, use, bp, ceilings, phenol,\n", "Nearest to at: travelling, degree, emi, the, dominants, breach, italia, awarding,\n", "Nearest to such: cc, multinationals, lysenkoism, unfair, senegal, group, undercurrent, actus,\n", "Nearest to called: the, supersessionism, bother, core, ripples, sealand, reintroduce, macedonian,\n", "Nearest to when: attractive, ragga, edinburgh, remove, be, refuse, itv, retrospect,\n", "Nearest to taking: go, garis, rational, sidgwick, leopards, salim, anoxic, nba,\n", "Nearest to consists: chamber, eee, consist, leblanc, morisot, cbd, located, appoints,\n", "Nearest to scale: mellin, exposed, townes, fuse, gears, curved, capricornus, allude,\n", "Nearest to units: unit, prefixes, fortieth, force, si, typewriter, torsion, irl,\n", "Nearest to ice: louth, rink, pyotr, pinstripes, plasmodium, joaquin, soils, gory,\n", "Nearest to instance: lenses, illogical, placed, synapses, bookstore, unappreciated, healthy, resize,\n", "Nearest to channel: dray, curler, wb, channels, dts, bandwidth, hearsay, restructured,\n", "Nearest to report: reports, credibility, presidents, annotated, spirituality, binge, standish, leaped,\n", "Epoch 5/10 Iteration: 23100 Avg. Training loss: 3.9697 0.1115 sec/batch\n", "Epoch 6/10 Iteration: 23200 Avg. Training loss: 3.9797 0.0768 sec/batch\n", "Epoch 6/10 Iteration: 23300 Avg. Training loss: 3.9693 0.1202 sec/batch\n", "Epoch 6/10 Iteration: 23400 Avg. Training loss: 3.9590 0.1265 sec/batch\n", "Epoch 6/10 Iteration: 23500 Avg. Training loss: 3.9599 0.1224 sec/batch\n", "Epoch 6/10 Iteration: 23600 Avg. Training loss: 3.8895 0.1215 sec/batch\n", "Epoch 6/10 Iteration: 23700 Avg. Training loss: 3.9265 0.1228 sec/batch\n", "Epoch 6/10 Iteration: 23800 Avg. Training loss: 3.9374 0.1243 sec/batch\n", "Epoch 6/10 Iteration: 23900 Avg. Training loss: 3.9506 0.1151 sec/batch\n", "Epoch 6/10 Iteration: 24000 Avg. Training loss: 3.9664 0.1254 sec/batch\n", "Nearest to for: given, first, to, scriptwriter, the, census, from, converged,\n", "Nearest to would: whyte, relegated, disobey, busting, in, habilis, gimme, maecenas,\n", "Nearest to known: rtgs, banach, hoosiers, pisin, nbi, oak, which, perihelion,\n", "Nearest to used: grams, cirth, common, epoxy, bp, use, machining, commonly,\n", "Nearest to at: travelling, the, emi, degree, dominants, their, breach, italia,\n", "Nearest to such: lysenkoism, group, cc, undercurrent, multinationals, actus, hinges, baa,\n", "Nearest to called: supersessionism, the, bother, reintroduce, denunciations, ripples, systematized, keno,\n", "Nearest to when: attractive, edinburgh, remove, ragga, refuse, bursa, painda, be,\n", "Nearest to taking: go, rational, garis, leopards, salim, sidgwick, anoxic, nba,\n", "Nearest to consists: chamber, consist, eee, located, leblanc, cbd, sint, hydrohalic,\n", "Nearest to scale: mellin, townes, exposed, capricornus, gears, diatonic, curved, allude,\n", "Nearest to units: unit, prefixes, fortieth, si, typewriter, force, torsion, irl,\n", "Nearest to ice: louth, rink, soils, joaquin, pyotr, pinstripes, plasmodium, cools,\n", "Nearest to instance: lenses, bookstore, illogical, placed, synapses, unappreciated, caesarean, healthy,\n", "Nearest to channel: curler, wb, dray, creditors, dts, channels, mbit, restructured,\n", "Nearest to report: reports, credibility, spirituality, annotated, presidents, standish, lebanon, binge,\n", "Epoch 6/10 Iteration: 24100 Avg. Training loss: 3.9397 0.1236 sec/batch\n", "Epoch 6/10 Iteration: 24200 Avg. Training loss: 3.9810 0.1160 sec/batch\n", "Epoch 6/10 Iteration: 24300 Avg. Training loss: 3.8346 0.1265 sec/batch\n", "Epoch 6/10 Iteration: 24400 Avg. Training loss: 3.9313 0.1289 sec/batch\n", "Epoch 6/10 Iteration: 24500 Avg. Training loss: 3.8972 0.1195 sec/batch\n", "Epoch 6/10 Iteration: 24600 Avg. Training loss: 3.8997 0.1186 sec/batch\n", "Epoch 6/10 Iteration: 24700 Avg. Training loss: 3.9321 0.1139 sec/batch\n", "Epoch 6/10 Iteration: 24800 Avg. Training loss: 3.9608 0.1289 sec/batch\n", "Epoch 6/10 Iteration: 24900 Avg. Training loss: 3.9414 0.1107 sec/batch\n", "Epoch 6/10 Iteration: 25000 Avg. Training loss: 3.9407 0.1113 sec/batch\n", "Nearest to for: given, to, first, the, scriptwriter, have, from, census,\n", "Nearest to would: relegated, whyte, disobey, busting, nyquist, in, habilis, coastlands,\n", "Nearest to known: rtgs, banach, hoosiers, with, which, pisin, charcoal, oak,\n", "Nearest to used: cirth, grams, common, epoxy, is, use, invented, commonly,\n", "Nearest to at: the, degree, travelling, emi, dominants, of, awarding, their,\n", "Nearest to such: cc, group, lysenkoism, hinges, multinationals, undercurrent, actus, baa,\n", "Nearest to called: the, supersessionism, core, bother, denunciations, keno, reintroduce, systematized,\n", "Nearest to when: attractive, be, edinburgh, remove, ragga, refuse, retrospect, itv,\n", "Nearest to taking: go, rational, leopards, garis, salim, sidgwick, anoxic, carpal,\n", "Nearest to consists: chamber, consist, eee, located, leblanc, calderon, sint, cbd,\n", "Nearest to scale: mellin, gears, townes, exposed, capricornus, diatonic, fuse, effects,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, torsion, hubei,\n", "Nearest to ice: louth, rink, joaquin, pyotr, plasmodium, soils, pinstripes, cools,\n", "Nearest to instance: lenses, placed, bookstore, resize, synapses, unappreciated, jimbo, illogical,\n", "Nearest to channel: dts, creditors, mbit, curler, wb, bandwidth, channels, hearsay,\n", "Nearest to report: reports, credibility, annotated, presidents, spirituality, binge, standish, focusing,\n", "Epoch 6/10 Iteration: 25100 Avg. Training loss: 4.0258 0.1102 sec/batch\n", "Epoch 6/10 Iteration: 25200 Avg. Training loss: 3.9340 0.1118 sec/batch\n", "Epoch 6/10 Iteration: 25300 Avg. Training loss: 3.9212 0.1136 sec/batch\n", "Epoch 6/10 Iteration: 25400 Avg. Training loss: 3.9460 0.1095 sec/batch\n", "Epoch 6/10 Iteration: 25500 Avg. Training loss: 3.9257 0.1138 sec/batch\n", "Epoch 6/10 Iteration: 25600 Avg. Training loss: 3.9545 0.1245 sec/batch\n", "Epoch 6/10 Iteration: 25700 Avg. Training loss: 3.9430 0.1241 sec/batch\n", "Epoch 6/10 Iteration: 25800 Avg. Training loss: 3.9479 0.1211 sec/batch\n", "Epoch 6/10 Iteration: 25900 Avg. Training loss: 3.9151 0.1171 sec/batch\n", "Epoch 6/10 Iteration: 26000 Avg. Training loss: 3.9370 0.1135 sec/batch\n", "Nearest to for: given, first, to, scriptwriter, by, from, have, the,\n", "Nearest to would: in, disobey, whyte, relegated, preeminence, lege, nyquist, that,\n", "Nearest to known: banach, pisin, rtgs, hoosiers, satrapies, which, named, oak,\n", "Nearest to used: cirth, alliances, invented, machining, is, common, use, grams,\n", "Nearest to at: the, travelling, degree, emi, dominants, of, their, awarding,\n", "Nearest to such: group, cc, lysenkoism, hinges, unfair, actus, baa, multinationals,\n", "Nearest to called: supersessionism, bother, the, denunciations, core, sealand, reintroduce, anakkale,\n", "Nearest to when: attractive, edinburgh, refuse, ragga, be, remove, painda, itv,\n", "Nearest to taking: go, rational, sidgwick, garis, salim, leopards, carpal, dedicates,\n", "Nearest to consists: chamber, consist, eee, leblanc, calderon, morisot, sint, located,\n", "Nearest to scale: mellin, townes, exposed, capricornus, effects, accede, allude, correlations,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, torsion, hubei,\n", "Nearest to ice: louth, rink, plasmodium, pyotr, joaquin, soils, cools, pinstripes,\n", "Nearest to instance: lenses, placed, resize, bookstore, unappreciated, illogical, synapses, consented,\n", "Nearest to channel: curler, creditors, mbit, dts, bandwidth, wb, dray, restructured,\n", "Nearest to report: reports, credibility, presidents, annotated, santer, haight, standish, lebanon,\n", "Epoch 6/10 Iteration: 26100 Avg. Training loss: 3.9495 0.1184 sec/batch\n", "Epoch 6/10 Iteration: 26200 Avg. Training loss: 3.9339 0.1132 sec/batch\n", "Epoch 6/10 Iteration: 26300 Avg. Training loss: 3.9436 0.1120 sec/batch\n", "Epoch 6/10 Iteration: 26400 Avg. Training loss: 3.9021 0.1305 sec/batch\n", "Epoch 6/10 Iteration: 26500 Avg. Training loss: 3.9170 0.1217 sec/batch\n", "Epoch 6/10 Iteration: 26600 Avg. Training loss: 3.9391 0.1154 sec/batch\n", "Epoch 6/10 Iteration: 26700 Avg. Training loss: 3.9181 0.1176 sec/batch\n", "Epoch 6/10 Iteration: 26800 Avg. Training loss: 4.0194 0.1174 sec/batch\n", "Epoch 6/10 Iteration: 26900 Avg. Training loss: 4.0194 0.1122 sec/batch\n", "Epoch 6/10 Iteration: 27000 Avg. Training loss: 3.9875 0.1128 sec/batch\n", "Nearest to for: given, first, scriptwriter, from, to, the, have, census,\n", "Nearest to would: disobey, relegated, whyte, in, lege, that, maecenas, coastlands,\n", "Nearest to known: hoosiers, banach, pisin, oak, with, named, nbi, millions,\n", "Nearest to used: cirth, invented, use, bunyan, commonly, machining, common, paused,\n", "Nearest to at: travelling, the, emi, degree, dominants, of, breach, leadbelly,\n", "Nearest to such: actus, cc, lysenkoism, unfair, hinges, baa, musical, plosives,\n", "Nearest to called: bother, supersessionism, the, anakkale, keno, denunciations, reintroduce, distinctive,\n", "Nearest to when: edinburgh, attractive, refuse, painda, remove, scotland, trouble, ragga,\n", "Nearest to taking: go, sidgwick, rational, salim, garis, leopards, anoxic, dedicates,\n", "Nearest to consists: chamber, consist, eee, leblanc, sint, calderon, morisot, located,\n", "Nearest to scale: mellin, diatonic, exposed, accede, effects, gears, capricornus, townes,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, hubei, trucial,\n", "Nearest to ice: rink, louth, pyotr, joaquin, plasmodium, pinstripes, gory, soils,\n", "Nearest to instance: lenses, placed, illogical, bookstore, consented, unappreciated, philos, contacts,\n", "Nearest to channel: creditors, curler, channels, dray, restructured, hearsay, mbit, dts,\n", "Nearest to report: reports, credibility, presidents, annotated, santer, lebanon, standish, haight,\n", "Epoch 6/10 Iteration: 27100 Avg. Training loss: 3.9083 0.1172 sec/batch\n", "Epoch 6/10 Iteration: 27200 Avg. Training loss: 3.9032 0.1138 sec/batch\n", "Epoch 6/10 Iteration: 27300 Avg. Training loss: 3.9424 0.1262 sec/batch\n", "Epoch 6/10 Iteration: 27400 Avg. Training loss: 3.8443 0.1288 sec/batch\n", "Epoch 6/10 Iteration: 27500 Avg. Training loss: 3.9509 0.1284 sec/batch\n", "Epoch 6/10 Iteration: 27600 Avg. Training loss: 3.9196 0.1230 sec/batch\n", "Epoch 6/10 Iteration: 27700 Avg. Training loss: 3.9078 0.1216 sec/batch\n", "Epoch 7/10 Iteration: 27800 Avg. Training loss: 3.9767 0.0466 sec/batch\n", "Epoch 7/10 Iteration: 27900 Avg. Training loss: 3.8898 0.1218 sec/batch\n", "Epoch 7/10 Iteration: 28000 Avg. Training loss: 3.9203 0.1215 sec/batch\n", "Nearest to for: given, scriptwriter, first, to, the, census, have, from,\n", "Nearest to would: disobey, whyte, relegated, coastlands, lege, that, busting, atomic,\n", "Nearest to known: with, hoosiers, banach, named, pisin, which, rtgs, oak,\n", "Nearest to used: cirth, commonly, use, machining, stds, invented, netbios, is,\n", "Nearest to at: travelling, the, degree, dominants, emi, of, breach, leadbelly,\n", "Nearest to such: lysenkoism, multinationals, actus, group, unfair, hinges, cc, baa,\n", "Nearest to called: the, bother, supersessionism, anakkale, systematized, keno, denunciations, core,\n", "Nearest to when: attractive, refuse, edinburgh, painda, remove, be, scotland, trouble,\n", "Nearest to taking: go, rational, chinguetti, garis, nba, anoxic, boosts, salim,\n", "Nearest to consists: chamber, eee, consist, leblanc, located, sint, calderon, cbd,\n", "Nearest to scale: diatonic, mellin, gears, townes, effects, accede, fretting, capricornus,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, kilogram, sumo,\n", "Nearest to ice: rink, louth, pyotr, plasmodium, joaquin, pinstripes, gory, zubr,\n", "Nearest to instance: lenses, placed, illogical, bookstore, resize, attitudes, oscillators, unappreciated,\n", "Nearest to channel: channels, curler, wb, creditors, dray, mbit, dts, hearsay,\n", "Nearest to report: reports, credibility, annotated, presidents, spirituality, standish, haight, comprehensive,\n", "Epoch 7/10 Iteration: 28100 Avg. Training loss: 3.8978 0.1224 sec/batch\n", "Epoch 7/10 Iteration: 28200 Avg. Training loss: 3.9022 0.1212 sec/batch\n", "Epoch 7/10 Iteration: 28300 Avg. Training loss: 3.9255 0.1210 sec/batch\n", "Epoch 7/10 Iteration: 28400 Avg. Training loss: 3.9095 0.1189 sec/batch\n", "Epoch 7/10 Iteration: 28500 Avg. Training loss: 3.8764 0.1190 sec/batch\n", "Epoch 7/10 Iteration: 28600 Avg. Training loss: 3.9017 0.1203 sec/batch\n", "Epoch 7/10 Iteration: 28700 Avg. Training loss: 3.9144 0.1210 sec/batch\n", "Epoch 7/10 Iteration: 28800 Avg. Training loss: 3.9431 0.1213 sec/batch\n", "Epoch 7/10 Iteration: 28900 Avg. Training loss: 3.8440 0.1219 sec/batch\n", "Epoch 7/10 Iteration: 29000 Avg. Training loss: 3.9068 0.1244 sec/batch\n", "Nearest to for: to, given, the, first, have, from, and, scriptwriter,\n", "Nearest to would: relegated, coastlands, disobey, that, whyte, in, habilis, lege,\n", "Nearest to known: with, hoosiers, pisin, banach, which, oak, named, rtgs,\n", "Nearest to used: use, cirth, commonly, is, grams, machining, epoxy, invented,\n", "Nearest to at: the, travelling, dominants, emi, of, degree, two, meeting,\n", "Nearest to such: multinationals, unfair, lysenkoism, group, pashtuns, many, actus, hinges,\n", "Nearest to called: the, supersessionism, bother, anakkale, core, denunciations, systematized, keno,\n", "Nearest to when: attractive, remove, refuse, retrospect, edinburgh, be, painda, itv,\n", "Nearest to taking: go, rational, salim, nba, chinguetti, anoxic, garis, levees,\n", "Nearest to consists: chamber, consist, eee, located, leblanc, calderon, sint, cbd,\n", "Nearest to scale: diatonic, mellin, capricornus, townes, suggests, motherhood, accede, effects,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, dera, sumo,\n", "Nearest to ice: rink, louth, pyotr, plasmodium, joaquin, pinstripes, zubr, cools,\n", "Nearest to instance: placed, lenses, bookstore, resize, unappreciated, contacts, illogical, envisage,\n", "Nearest to channel: channels, curler, creditors, wb, dray, bandwidth, mbit, restructured,\n", "Nearest to report: reports, credibility, annotated, spirituality, presidents, comprehensive, focusing, html,\n", "Epoch 7/10 Iteration: 29100 Avg. Training loss: 3.8945 0.1254 sec/batch\n", "Epoch 7/10 Iteration: 29200 Avg. Training loss: 3.8284 0.1224 sec/batch\n", "Epoch 7/10 Iteration: 29300 Avg. Training loss: 3.8781 0.1231 sec/batch\n", "Epoch 7/10 Iteration: 29400 Avg. Training loss: 3.9094 0.1229 sec/batch\n", "Epoch 7/10 Iteration: 29500 Avg. Training loss: 3.8962 0.1207 sec/batch\n", "Epoch 7/10 Iteration: 29600 Avg. Training loss: 3.8959 0.1095 sec/batch\n", "Epoch 7/10 Iteration: 29700 Avg. Training loss: 3.9419 0.1060 sec/batch\n", "Epoch 7/10 Iteration: 29800 Avg. Training loss: 3.9093 0.1057 sec/batch\n", "Epoch 7/10 Iteration: 29900 Avg. Training loss: 3.8714 0.1004 sec/batch\n", "Epoch 7/10 Iteration: 30000 Avg. Training loss: 3.8931 0.1013 sec/batch\n", "Nearest to for: given, first, scriptwriter, to, the, have, census, from,\n", "Nearest to would: relegated, that, disobey, lege, whyte, coastlands, in, nyquist,\n", "Nearest to known: banach, with, pisin, which, hoosiers, rtgs, nbi, first,\n", "Nearest to used: is, use, commonly, cirth, netbios, invented, grams, common,\n", "Nearest to at: the, travelling, dominants, emi, of, degree, surrounding, aviators,\n", "Nearest to such: lysenkoism, unfair, cc, other, actus, hinges, desired, group,\n", "Nearest to called: the, supersessionism, bother, core, systematized, denunciations, rearranged, eusocial,\n", "Nearest to when: be, attractive, remove, edinburgh, refuse, trouble, itv, retrospect,\n", "Nearest to taking: go, rational, salim, xo, anoxic, garis, chinguetti, nba,\n", "Nearest to consists: chamber, consist, eee, leblanc, calderon, conscience, hydrohalic, located,\n", "Nearest to scale: diatonic, mellin, capricornus, suggests, townes, correlations, accede, motherhood,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, dera, hubei,\n", "Nearest to ice: rink, louth, pyotr, plasmodium, joaquin, pinstripes, zubr, gory,\n", "Nearest to instance: placed, lenses, bookstore, contacts, envisage, geometrically, consented, illogical,\n", "Nearest to channel: creditors, curler, wb, hearsay, channels, transmitters, dts, mbit,\n", "Nearest to report: reports, credibility, annotated, spirituality, santer, presidents, comprehensive, lebanon,\n", "Epoch 7/10 Iteration: 30100 Avg. Training loss: 3.9198 0.1057 sec/batch\n", "Epoch 7/10 Iteration: 30200 Avg. Training loss: 3.9272 0.1015 sec/batch\n", "Epoch 7/10 Iteration: 30300 Avg. Training loss: 3.9112 0.1014 sec/batch\n", "Epoch 7/10 Iteration: 30400 Avg. Training loss: 3.8940 0.1035 sec/batch\n", "Epoch 7/10 Iteration: 30500 Avg. Training loss: 3.9486 0.1055 sec/batch\n", "Epoch 7/10 Iteration: 30600 Avg. Training loss: 3.9379 0.1060 sec/batch\n", "Epoch 7/10 Iteration: 30700 Avg. Training loss: 3.8933 0.1067 sec/batch\n", "Epoch 7/10 Iteration: 30800 Avg. Training loss: 3.8929 0.1102 sec/batch\n", "Epoch 7/10 Iteration: 30900 Avg. Training loss: 3.9001 0.1094 sec/batch\n", "Epoch 7/10 Iteration: 31000 Avg. Training loss: 3.8601 0.1133 sec/batch\n", "Nearest to for: given, the, to, first, scriptwriter, by, in, of,\n", "Nearest to would: relegated, that, disobey, coastlands, lege, whyte, in, maecenas,\n", "Nearest to known: with, which, first, banach, hoosiers, pisin, aalborg, millions,\n", "Nearest to used: use, cirth, commonly, common, invented, is, netbios, grams,\n", "Nearest to at: the, travelling, of, dominants, degree, emi, as, to,\n", "Nearest to such: lysenkoism, unfair, cc, hinges, group, plosives, other, baa,\n", "Nearest to called: the, bother, supersessionism, denunciations, anakkale, keno, distinctive, eusocial,\n", "Nearest to when: attractive, be, edinburgh, remove, scotland, trouble, refuse, painda,\n", "Nearest to taking: go, rational, anoxic, salim, xo, sidgwick, boosts, regrettable,\n", "Nearest to consists: chamber, consist, leblanc, eee, calderon, morisot, conscience, sint,\n", "Nearest to scale: diatonic, mellin, effects, capricornus, suggests, correlations, agglomeration, motherhood,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, dera, hubei,\n", "Nearest to ice: rink, louth, joaquin, pyotr, plasmodium, zubr, sweden, soils,\n", "Nearest to instance: placed, bookstore, husband, lenses, contacts, pasts, wong, envisage,\n", "Nearest to channel: creditors, curler, hearsay, channels, dray, restructured, wb, mbit,\n", "Nearest to report: reports, credibility, santer, annotated, standish, presidents, spirituality, comprehensive,\n", "Epoch 7/10 Iteration: 31100 Avg. Training loss: 3.9213 0.1056 sec/batch\n", "Epoch 7/10 Iteration: 31200 Avg. Training loss: 3.8905 0.1058 sec/batch\n", "Epoch 7/10 Iteration: 31300 Avg. Training loss: 3.8990 0.1132 sec/batch\n", "Epoch 7/10 Iteration: 31400 Avg. Training loss: 3.9640 0.1252 sec/batch\n", "Epoch 7/10 Iteration: 31500 Avg. Training loss: 3.9684 0.1159 sec/batch\n", "Epoch 7/10 Iteration: 31600 Avg. Training loss: 3.9861 0.1196 sec/batch\n", "Epoch 7/10 Iteration: 31700 Avg. Training loss: 3.9020 0.1109 sec/batch\n", "Epoch 7/10 Iteration: 31800 Avg. Training loss: 3.8697 0.1079 sec/batch\n", "Epoch 7/10 Iteration: 31900 Avg. Training loss: 3.9195 0.1062 sec/batch\n", "Epoch 7/10 Iteration: 32000 Avg. Training loss: 3.7972 0.1137 sec/batch\n", "Nearest to for: given, to, the, first, scriptwriter, by, and, have,\n", "Nearest to would: that, relegated, coastlands, disobey, to, lege, in, busting,\n", "Nearest to known: with, which, hoosiers, pisin, first, banach, millions, aalborg,\n", "Nearest to used: use, commonly, common, cirth, netbios, is, bunyan, invented,\n", "Nearest to at: the, travelling, emi, of, degree, dominants, to, s,\n", "Nearest to such: unfair, cc, other, lysenkoism, group, pashtuns, hinges, multinationals,\n", "Nearest to called: the, supersessionism, bother, denunciations, anakkale, is, keno, instituted,\n", "Nearest to when: be, remove, attractive, edinburgh, trouble, refuse, painda, scotland,\n", "Nearest to taking: go, rational, salim, boosts, xo, anoxic, sidgwick, regrettable,\n", "Nearest to consists: chamber, consist, eee, appoints, leblanc, calderon, conscience, couturat,\n", "Nearest to scale: diatonic, mellin, effects, motherhood, suggests, capricornus, correlations, townes,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, dera, kilogram,\n", "Nearest to ice: rink, louth, pyotr, joaquin, plasmodium, sweden, indoor, zubr,\n", "Nearest to instance: placed, lenses, bookstore, contacts, philos, illogical, envisage, kruskal,\n", "Nearest to channel: creditors, hearsay, curler, wb, channels, dray, mbit, bandwidth,\n", "Nearest to report: reports, credibility, annotated, santer, presidents, spirituality, haight, focusing,\n", "Epoch 7/10 Iteration: 32100 Avg. Training loss: 3.9153 0.1189 sec/batch\n", "Epoch 7/10 Iteration: 32200 Avg. Training loss: 3.9433 0.1161 sec/batch\n", "Epoch 7/10 Iteration: 32300 Avg. Training loss: 3.9029 0.1209 sec/batch\n", "Epoch 8/10 Iteration: 32400 Avg. Training loss: 3.9170 0.0138 sec/batch\n", "Epoch 8/10 Iteration: 32500 Avg. Training loss: 3.8952 0.1250 sec/batch\n", "Epoch 8/10 Iteration: 32600 Avg. Training loss: 3.8827 0.1306 sec/batch\n", "Epoch 8/10 Iteration: 32700 Avg. Training loss: 3.8966 0.1219 sec/batch\n", "Epoch 8/10 Iteration: 32800 Avg. Training loss: 3.9122 0.1221 sec/batch\n", "Epoch 8/10 Iteration: 32900 Avg. Training loss: 3.8753 0.1216 sec/batch\n", "Epoch 8/10 Iteration: 33000 Avg. Training loss: 3.8522 0.1206 sec/batch\n", "Nearest to for: to, given, the, and, first, by, in, have,\n", "Nearest to would: that, in, relegated, coastlands, to, disobey, whyte, lege,\n", "Nearest to known: which, first, with, hoosiers, most, millions, pisin, many,\n", "Nearest to used: use, commonly, common, is, netbios, cirth, other, for,\n", "Nearest to at: the, travelling, of, to, dominants, later, as, s,\n", "Nearest to such: other, group, lysenkoism, multinationals, unfair, hinges, cc, actus,\n", "Nearest to called: bother, the, supersessionism, is, denunciations, instituted, keno, ripples,\n", "Nearest to when: remove, be, attractive, edinburgh, refuse, painda, trouble, retrospect,\n", "Nearest to taking: go, salim, levees, boosts, xo, nba, anoxic, nsaids,\n", "Nearest to consists: chamber, consist, eee, conscience, sint, couturat, leblanc, calderon,\n", "Nearest to scale: diatonic, mellin, capricornus, motherhood, gears, suggests, agglomeration, tuning,\n", "Nearest to units: unit, prefixes, fortieth, si, typewriter, hubei, force, dera,\n", "Nearest to ice: rink, louth, pyotr, joaquin, plasmodium, sweden, gory, zubr,\n", "Nearest to instance: placed, bookstore, husband, lenses, illogical, attitudes, pasts, herders,\n", "Nearest to channel: creditors, wb, mbit, curler, channels, bandwidth, hearsay, transmitters,\n", "Nearest to report: reports, credibility, annotated, standish, spirituality, presidents, santer, focusing,\n", "Epoch 8/10 Iteration: 33100 Avg. Training loss: 3.8330 0.1218 sec/batch\n", "Epoch 8/10 Iteration: 33200 Avg. Training loss: 3.8716 0.1212 sec/batch\n", "Epoch 8/10 Iteration: 33300 Avg. Training loss: 3.8915 0.1208 sec/batch\n", "Epoch 8/10 Iteration: 33400 Avg. Training loss: 3.9107 0.1212 sec/batch\n", "Epoch 8/10 Iteration: 33500 Avg. Training loss: 3.8661 0.1210 sec/batch\n", "Epoch 8/10 Iteration: 33600 Avg. Training loss: 3.8355 0.1189 sec/batch\n", "Epoch 8/10 Iteration: 33700 Avg. Training loss: 3.8342 0.1208 sec/batch\n", "Epoch 8/10 Iteration: 33800 Avg. Training loss: 3.7842 0.1212 sec/batch\n", "Epoch 8/10 Iteration: 33900 Avg. Training loss: 3.8311 0.1226 sec/batch\n", "Epoch 8/10 Iteration: 34000 Avg. Training loss: 3.8845 0.1218 sec/batch\n", "Nearest to for: to, the, given, and, in, have, first, by,\n", "Nearest to would: that, relegated, to, in, with, coastlands, yet, accelerations,\n", "Nearest to known: with, which, first, hoosiers, most, many, millions, banach,\n", "Nearest to used: is, commonly, use, common, grams, for, other, cirth,\n", "Nearest to at: the, of, travelling, dominants, to, as, degree, two,\n", "Nearest to such: other, and, as, group, can, cc, exotic, actus,\n", "Nearest to called: the, is, supersessionism, bother, of, denunciations, a, rearranged,\n", "Nearest to when: be, remove, attractive, refuse, tire, initial, painda, headers,\n", "Nearest to taking: go, rational, levees, xo, nsaids, salim, boosts, nba,\n", "Nearest to consists: consist, chamber, calderon, eee, conscience, located, couturat, leblanc,\n", "Nearest to scale: diatonic, mellin, suggests, capricornus, motherhood, gears, townes, effects,\n", "Nearest to units: unit, prefixes, fortieth, si, typewriter, force, hubei, dera,\n", "Nearest to ice: rink, louth, pyotr, plasmodium, joaquin, sweden, detection, ussr,\n", "Nearest to instance: placed, bookstore, lenses, oscillators, resize, xa, philos, barcodes,\n", "Nearest to channel: creditors, channels, mbit, wb, curler, dts, restructured, dray,\n", "Nearest to report: reports, credibility, annotated, santer, presidents, standish, spirituality, focusing,\n", "Epoch 8/10 Iteration: 34100 Avg. Training loss: 3.8751 0.1228 sec/batch\n", "Epoch 8/10 Iteration: 34200 Avg. Training loss: 3.8528 0.1223 sec/batch\n", "Epoch 8/10 Iteration: 34300 Avg. Training loss: 3.9067 0.1178 sec/batch\n", "Epoch 8/10 Iteration: 34400 Avg. Training loss: 3.8909 0.1161 sec/batch\n", "Epoch 8/10 Iteration: 34500 Avg. Training loss: 3.8444 0.1158 sec/batch\n", "Epoch 8/10 Iteration: 34600 Avg. Training loss: 3.8552 0.1208 sec/batch\n", "Epoch 8/10 Iteration: 34700 Avg. Training loss: 3.8861 0.1260 sec/batch\n", "Epoch 8/10 Iteration: 34800 Avg. Training loss: 3.8621 0.1159 sec/batch\n", "Epoch 8/10 Iteration: 34900 Avg. Training loss: 3.8820 0.1110 sec/batch\n", "Epoch 8/10 Iteration: 35000 Avg. Training loss: 3.9116 0.1115 sec/batch\n", "Nearest to for: to, given, the, and, by, have, in, first,\n", "Nearest to would: that, to, relegated, in, accelerations, yet, than, it,\n", "Nearest to known: which, with, first, pisin, most, hoosiers, banach, millions,\n", "Nearest to used: is, use, common, commonly, cirth, occasionally, for, invented,\n", "Nearest to at: the, travelling, of, dominants, to, as, degree, s,\n", "Nearest to such: other, as, and, can, group, lysenkoism, cc, hinges,\n", "Nearest to called: the, bother, supersessionism, is, denunciations, rearranged, anakkale, timbres,\n", "Nearest to when: be, remove, attractive, painda, refuse, trouble, edinburgh, initial,\n", "Nearest to taking: go, rational, salim, levees, nsaids, xo, pia, regrettable,\n", "Nearest to consists: consist, chamber, calderon, conscience, leblanc, couturat, eee, sint,\n", "Nearest to scale: diatonic, mellin, suggests, capricornus, motherhood, trillions, correlations, effects,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, hubei, dera,\n", "Nearest to ice: rink, louth, pyotr, joaquin, plasmodium, sweden, ussr, pontine,\n", "Nearest to instance: placed, bookstore, lenses, contacts, geometrically, pasts, oscillators, robby,\n", "Nearest to channel: creditors, curler, mbit, wb, restructured, dts, dray, channels,\n", "Nearest to report: reports, credibility, santer, annotated, focusing, html, standish, comprehensive,\n", "Epoch 8/10 Iteration: 35100 Avg. Training loss: 3.8544 0.1112 sec/batch\n", "Epoch 8/10 Iteration: 35200 Avg. Training loss: 3.8741 0.1111 sec/batch\n", "Epoch 8/10 Iteration: 35300 Avg. Training loss: 3.8893 0.1121 sec/batch\n", "Epoch 8/10 Iteration: 35400 Avg. Training loss: 3.8901 0.1112 sec/batch\n", "Epoch 8/10 Iteration: 35500 Avg. Training loss: 3.8736 0.1117 sec/batch\n", "Epoch 8/10 Iteration: 35600 Avg. Training loss: 3.8698 0.1114 sec/batch\n", "Epoch 8/10 Iteration: 35700 Avg. Training loss: 3.8237 0.1114 sec/batch\n", "Epoch 8/10 Iteration: 35800 Avg. Training loss: 3.8605 0.1120 sec/batch\n", "Epoch 8/10 Iteration: 35900 Avg. Training loss: 3.9338 0.1116 sec/batch\n", "Epoch 8/10 Iteration: 36000 Avg. Training loss: 3.8586 0.1116 sec/batch\n", "Nearest to for: given, the, to, and, in, first, scriptwriter, by,\n", "Nearest to would: that, to, in, relegated, coastlands, yet, lege, with,\n", "Nearest to known: which, with, first, hoosiers, millions, seventeenth, banach, pisin,\n", "Nearest to used: is, common, commonly, use, cirth, netbios, often, invented,\n", "Nearest to at: the, of, travelling, as, s, to, later, in,\n", "Nearest to such: other, as, lysenkoism, actus, cc, group, hinges, types,\n", "Nearest to called: bother, the, supersessionism, denunciations, keno, is, timbres, anakkale,\n", "Nearest to when: be, the, painda, edinburgh, remove, scotland, refuse, trouble,\n", "Nearest to taking: go, salim, pia, nsaids, xo, rational, levees, diva,\n", "Nearest to consists: consist, chamber, calderon, eee, sint, conscience, couturat, leblanc,\n", "Nearest to scale: diatonic, motherhood, capricornus, mellin, suggests, effects, correlations, trillions,\n", "Nearest to units: unit, prefixes, fortieth, si, typewriter, force, dera, hubei,\n", "Nearest to ice: rink, joaquin, louth, pyotr, plasmodium, sweden, ussr, hockey,\n", "Nearest to instance: placed, geometrically, bookstore, philos, oscillators, kruskal, pasts, lenses,\n", "Nearest to channel: creditors, mbit, channels, curler, wb, bandwidth, restructured, hearsay,\n", "Nearest to report: reports, credibility, santer, focusing, annotated, comprehensive, standish, html,\n", "Epoch 8/10 Iteration: 36100 Avg. Training loss: 3.9513 0.1133 sec/batch\n", "Epoch 8/10 Iteration: 36200 Avg. Training loss: 3.9537 0.1111 sec/batch\n", "Epoch 8/10 Iteration: 36300 Avg. Training loss: 3.8965 0.1114 sec/batch\n", "Epoch 8/10 Iteration: 36400 Avg. Training loss: 3.8243 0.1119 sec/batch\n", "Epoch 8/10 Iteration: 36500 Avg. Training loss: 3.8824 0.1117 sec/batch\n", "Epoch 8/10 Iteration: 36600 Avg. Training loss: 3.8074 0.1114 sec/batch\n", "Epoch 8/10 Iteration: 36700 Avg. Training loss: 3.8481 0.1124 sec/batch\n", "Epoch 8/10 Iteration: 36800 Avg. Training loss: 3.8889 0.1118 sec/batch\n", "Epoch 8/10 Iteration: 36900 Avg. Training loss: 3.8722 0.1119 sec/batch\n", "Epoch 8/10 Iteration: 37000 Avg. Training loss: 3.8919 0.1121 sec/batch\n", "Nearest to for: to, given, the, and, by, in, scriptwriter, have,\n", "Nearest to would: that, to, with, relegated, coastlands, lege, yet, maecenas,\n", "Nearest to known: which, with, most, hoosiers, many, the, first, pisin,\n", "Nearest to used: commonly, use, is, netbios, common, other, cirth, for,\n", "Nearest to at: the, travelling, to, as, dominants, s, of, emi,\n", "Nearest to such: as, other, many, group, and, exotic, pashtuns, cc,\n", "Nearest to called: the, bother, supersessionism, of, denunciations, keno, philology, systematized,\n", "Nearest to when: be, remove, attractive, was, painda, marysville, edinburgh, the,\n", "Nearest to taking: go, levees, xo, nsaids, nba, boosts, salim, pia,\n", "Nearest to consists: chamber, calderon, consist, conscience, couturat, eee, appoints, leblanc,\n", "Nearest to scale: diatonic, mellin, accidentals, motherhood, capricornus, suggests, gears, scales,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, dera, kilogram,\n", "Nearest to ice: rink, joaquin, pyotr, louth, sweden, hockey, plasmodium, ussr,\n", "Nearest to instance: placed, bookstore, pasts, geometrically, oscillators, philos, kruskal, husband,\n", "Nearest to channel: creditors, mbit, curler, channels, wb, hearsay, bandwidth, dts,\n", "Nearest to report: reports, credibility, annotated, santer, focusing, standish, html, comprehensive,\n", "Epoch 9/10 Iteration: 37100 Avg. Training loss: 3.8941 0.0937 sec/batch\n", "Epoch 9/10 Iteration: 37200 Avg. Training loss: 3.8418 0.1114 sec/batch\n", "Epoch 9/10 Iteration: 37300 Avg. Training loss: 3.8491 0.1207 sec/batch\n", "Epoch 9/10 Iteration: 37400 Avg. Training loss: 3.8795 0.1237 sec/batch\n", "Epoch 9/10 Iteration: 37500 Avg. Training loss: 3.8064 0.1177 sec/batch\n", "Epoch 9/10 Iteration: 37600 Avg. Training loss: 3.8517 0.1224 sec/batch\n", "Epoch 9/10 Iteration: 37700 Avg. Training loss: 3.8122 0.1167 sec/batch\n", "Epoch 9/10 Iteration: 37800 Avg. Training loss: 3.8771 0.1231 sec/batch\n", "Epoch 9/10 Iteration: 37900 Avg. Training loss: 3.8810 0.1157 sec/batch\n", "Epoch 9/10 Iteration: 38000 Avg. Training loss: 3.8750 0.1181 sec/batch\n", "Nearest to for: the, to, and, in, given, by, first, a,\n", "Nearest to would: that, to, with, relegated, in, than, coastlands, asians,\n", "Nearest to known: which, most, with, hoosiers, first, and, many, name,\n", "Nearest to used: commonly, use, is, common, netbios, cirth, as, other,\n", "Nearest to at: the, of, two, as, and, travelling, to, s,\n", "Nearest to such: other, as, can, group, lysenkoism, exotic, many, american,\n", "Nearest to called: the, bother, supersessionism, hardin, is, of, anakkale, eusocial,\n", "Nearest to when: be, was, painda, attractive, initial, trouble, remove, but,\n", "Nearest to taking: go, pia, salim, xo, levees, nba, boosts, fugees,\n", "Nearest to consists: chamber, calderon, consist, conscience, couturat, eee, sint, appoints,\n", "Nearest to scale: diatonic, motherhood, capricornus, correlations, mellin, chords, gears, trillions,\n", "Nearest to units: unit, prefixes, fortieth, si, force, typewriter, hubei, dera,\n", "Nearest to ice: rink, joaquin, pyotr, louth, hockey, sweden, ussr, plasmodium,\n", "Nearest to instance: placed, bookstore, pasts, philos, accepts, geometrically, oscillators, kruskal,\n", "Nearest to channel: creditors, curler, wb, restructured, channels, mbit, dts, bandwidth,\n", "Nearest to report: reports, credibility, annotated, focusing, santer, standish, html, spirituality,\n", "Epoch 9/10 Iteration: 38100 Avg. Training loss: 3.8705 0.1189 sec/batch\n", "Epoch 9/10 Iteration: 38200 Avg. Training loss: 3.7634 0.1132 sec/batch\n", "Epoch 9/10 Iteration: 38300 Avg. Training loss: 3.8207 0.1136 sec/batch\n", "Epoch 9/10 Iteration: 38400 Avg. Training loss: 3.7974 0.1140 sec/batch\n", "Epoch 9/10 Iteration: 38500 Avg. Training loss: 3.8033 0.1138 sec/batch\n", "Epoch 9/10 Iteration: 38600 Avg. Training loss: 3.8553 0.1134 sec/batch\n", "Epoch 9/10 Iteration: 38700 Avg. Training loss: 3.8482 0.1135 sec/batch\n", "Epoch 9/10 Iteration: 38800 Avg. Training loss: 3.8287 0.1131 sec/batch\n", "Epoch 9/10 Iteration: 38900 Avg. Training loss: 3.9033 0.1122 sec/batch\n", "Epoch 9/10 Iteration: 39000 Avg. Training loss: 3.8907 0.1133 sec/batch\n", "Nearest to for: the, to, and, in, given, have, by, a,\n", "Nearest to would: to, that, relegated, with, than, coastlands, in, it,\n", "Nearest to known: which, most, with, hoosiers, first, banach, the, in,\n", "Nearest to used: commonly, is, use, common, for, occasionally, as, invented,\n", "Nearest to at: the, of, to, two, travelling, as, dominants, and,\n", "Nearest to such: as, other, and, can, many, exotic, lysenkoism, types,\n", "Nearest to called: the, is, bother, supersessionism, eusocial, of, rearranged, a,\n", "Nearest to when: be, was, attractive, remove, initial, edinburgh, painda, time,\n", "Nearest to taking: go, levees, pia, xo, nba, fugees, nsaids, boosts,\n", "Nearest to consists: consist, chamber, calderon, conscience, couturat, located, leblanc, eee,\n", "Nearest to scale: diatonic, suggests, trillions, motherhood, mellin, correlations, capricornus, effects,\n", "Nearest to units: unit, prefixes, fortieth, si, typewriter, force, hubei, dera,\n", "Nearest to ice: rink, pyotr, joaquin, louth, sweden, hockey, plasmodium, frozen,\n", "Nearest to instance: placed, geometrically, philos, bookstore, pasts, accepts, oscillators, contacts,\n", "Nearest to channel: curler, creditors, wb, restructured, channels, mbit, bandwidth, hearsay,\n", "Nearest to report: reports, credibility, focusing, annotated, santer, standish, binge, html,\n", "Epoch 9/10 Iteration: 39100 Avg. Training loss: 3.8177 0.1132 sec/batch\n", "Epoch 9/10 Iteration: 39200 Avg. Training loss: 3.8758 0.1144 sec/batch\n", "Epoch 9/10 Iteration: 39300 Avg. Training loss: 3.8498 0.1183 sec/batch\n", "Epoch 9/10 Iteration: 39400 Avg. Training loss: 3.8540 0.1166 sec/batch\n", "Epoch 9/10 Iteration: 39500 Avg. Training loss: 3.8741 0.1142 sec/batch\n", "Epoch 9/10 Iteration: 39600 Avg. Training loss: 3.8607 0.1127 sec/batch\n", "Epoch 9/10 Iteration: 39700 Avg. Training loss: 3.8709 0.1122 sec/batch\n", "Epoch 9/10 Iteration: 39800 Avg. Training loss: 3.8405 0.1132 sec/batch\n", "Epoch 9/10 Iteration: 39900 Avg. Training loss: 3.8565 0.1126 sec/batch\n", "Epoch 9/10 Iteration: 40000 Avg. Training loss: 3.8557 0.1125 sec/batch\n", "Nearest to for: given, the, to, in, by, and, of, have,\n", "Nearest to would: that, to, than, with, manorialism, coastlands, relegated, lege,\n", "Nearest to known: which, with, most, first, name, this, by, pisin,\n", "Nearest to used: is, use, commonly, common, other, for, as, occasionally,\n", "Nearest to at: the, of, travelling, dominants, to, two, as, in,\n", "Nearest to such: as, other, types, and, lysenkoism, exotic, many, american,\n", "Nearest to called: the, is, bother, of, supersessionism, rearranged, a, eusocial,\n", "Nearest to when: be, initial, the, attractive, painda, time, was, scotland,\n", "Nearest to taking: pia, go, levees, novels, xo, fugees, salim, neustria,\n", "Nearest to consists: consist, chamber, calderon, leblanc, conscience, located, couturat, composed,\n", "Nearest to scale: diatonic, suggests, correlations, capricornus, motherhood, trillions, mellin, effects,\n", "Nearest to units: unit, prefixes, fortieth, si, typewriter, dera, force, hubei,\n", "Nearest to ice: rink, pyotr, joaquin, louth, plasmodium, ussr, sweden, hockey,\n", "Nearest to instance: placed, geometrically, philos, accepts, kruskal, pasts, bookstore, barcodes,\n", "Nearest to channel: creditors, curler, mbit, bandwidth, wb, restructured, channels, broadcasts,\n", "Nearest to report: reports, credibility, santer, annotated, focusing, zangger, html, standish,\n", "Epoch 9/10 Iteration: 40100 Avg. Training loss: 3.8686 0.1133 sec/batch\n", "Epoch 9/10 Iteration: 40200 Avg. Training loss: 3.8666 0.1148 sec/batch\n", "Epoch 9/10 Iteration: 40300 Avg. Training loss: 3.8254 0.1171 sec/batch\n", "Epoch 9/10 Iteration: 40400 Avg. Training loss: 3.8455 0.1171 sec/batch\n", "Epoch 9/10 Iteration: 40500 Avg. Training loss: 3.8998 0.1156 sec/batch\n", "Epoch 9/10 Iteration: 40600 Avg. Training loss: 3.8319 0.1151 sec/batch\n", "Epoch 9/10 Iteration: 40700 Avg. Training loss: 3.9923 0.1180 sec/batch\n", "Epoch 9/10 Iteration: 40800 Avg. Training loss: 3.8747 0.1179 sec/batch\n", "Epoch 9/10 Iteration: 40900 Avg. Training loss: 3.8889 0.1259 sec/batch\n", "Epoch 9/10 Iteration: 41000 Avg. Training loss: 3.8198 0.1099 sec/batch\n", "Nearest to for: the, given, to, in, of, have, and, by,\n", "Nearest to would: that, to, coastlands, with, manorialism, relegated, yet, asians,\n", "Nearest to known: with, most, which, this, name, first, by, hoosiers,\n", "Nearest to used: commonly, is, use, common, for, invented, netbios, or,\n", "Nearest to at: the, of, travelling, as, and, where, dominants, to,\n", "Nearest to such: as, other, many, types, can, american, lysenkoism, dodging,\n", "Nearest to called: the, is, bother, of, supersessionism, hardin, a, eusocial,\n", "Nearest to when: be, painda, was, initial, remove, refuse, edinburgh, scotland,\n", "Nearest to taking: go, pia, levees, xo, fugees, novels, reestablishing, boosts,\n", "Nearest to consists: chamber, calderon, consist, conscience, leblanc, judicial, couturat, mayors,\n", "Nearest to scale: diatonic, suggests, mellin, correlations, capricornus, motherhood, trillions, accidentals,\n", "Nearest to units: unit, prefixes, fortieth, si, dera, force, typewriter, kilogram,\n", "Nearest to ice: rink, pyotr, hockey, joaquin, ussr, plasmodium, louth, sweden,\n", "Nearest to instance: placed, geometrically, philos, pasts, accepts, bookstore, kruskal, oscillators,\n", "Nearest to channel: creditors, curler, channels, restructured, mbit, hearsay, wb, bandwidth,\n", "Nearest to report: reports, credibility, santer, commission, annotated, zangger, focusing, binge,\n", "Epoch 9/10 Iteration: 41100 Avg. Training loss: 3.7843 0.1144 sec/batch\n", "Epoch 9/10 Iteration: 41200 Avg. Training loss: 3.8725 0.1137 sec/batch\n", "Epoch 9/10 Iteration: 41300 Avg. Training loss: 3.8033 0.1140 sec/batch\n", "Epoch 9/10 Iteration: 41400 Avg. Training loss: 3.8783 0.1153 sec/batch\n", "Epoch 9/10 Iteration: 41500 Avg. Training loss: 3.8427 0.1154 sec/batch\n", "Epoch 9/10 Iteration: 41600 Avg. Training loss: 3.8499 0.1160 sec/batch\n", "Epoch 10/10 Iteration: 41700 Avg. Training loss: 3.8824 0.0667 sec/batch\n", "Epoch 10/10 Iteration: 41800 Avg. Training loss: 3.8163 0.1239 sec/batch\n", "Epoch 10/10 Iteration: 41900 Avg. Training loss: 3.8315 0.1177 sec/batch\n", "Epoch 10/10 Iteration: 42000 Avg. Training loss: 3.8348 0.1208 sec/batch\n", "Nearest to for: the, to, given, and, in, a, by, as,\n", "Nearest to would: that, to, coastlands, with, relegated, than, lege, in,\n", "Nearest to known: most, which, with, the, by, first, name, in,\n", "Nearest to used: commonly, use, is, common, or, as, invented, cirth,\n", "Nearest to at: the, of, as, travelling, to, in, where, and,\n", "Nearest to such: as, other, types, can, any, and, lysenkoism, musical,\n", "Nearest to called: the, is, bother, of, a, supersessionism, systematized, hardin,\n", "Nearest to when: was, be, initial, the, painda, then, in, remove,\n", "Nearest to taking: levees, boosts, go, fugees, xo, pia, ukrainians, salim,\n", "Nearest to consists: chamber, consist, calderon, conscience, leblanc, couturat, sint, judicial,\n", "Nearest to scale: diatonic, capricornus, suggests, accidentals, mellin, motherhood, specifying, scales,\n", "Nearest to units: unit, prefixes, fortieth, si, measurement, kilogram, dera, force,\n", "Nearest to ice: rink, pyotr, joaquin, ussr, louth, hockey, plasmodium, sweden,\n", "Nearest to instance: placed, pasts, geometrically, bookstore, philos, herders, kruskal, oscillators,\n", "Nearest to channel: creditors, curler, channels, mbit, wb, hearsay, bandwidth, restructured,\n", "Nearest to report: reports, credibility, annotated, commission, focusing, santer, binge, zangger,\n", "Epoch 10/10 Iteration: 42100 Avg. Training loss: 3.8185 0.1217 sec/batch\n", "Epoch 10/10 Iteration: 42200 Avg. Training loss: 3.8360 0.1214 sec/batch\n", "Epoch 10/10 Iteration: 42300 Avg. Training loss: 3.8103 0.1212 sec/batch\n", "Epoch 10/10 Iteration: 42400 Avg. Training loss: 3.8191 0.1210 sec/batch\n", "Epoch 10/10 Iteration: 42500 Avg. Training loss: 3.8747 0.1212 sec/batch\n", "Epoch 10/10 Iteration: 42600 Avg. Training loss: 3.8540 0.1210 sec/batch\n", "Epoch 10/10 Iteration: 42700 Avg. Training loss: 3.8766 0.1211 sec/batch\n", "Epoch 10/10 Iteration: 42800 Avg. Training loss: 3.7192 0.1214 sec/batch\n", "Epoch 10/10 Iteration: 42900 Avg. Training loss: 3.8094 0.1219 sec/batch\n", "Epoch 10/10 Iteration: 43000 Avg. Training loss: 3.7974 0.1225 sec/batch\n", "Nearest to for: the, to, and, given, a, in, of, by,\n", "Nearest to would: that, to, relegated, than, coastlands, in, because, with,\n", "Nearest to known: most, which, with, by, in, first, the, this,\n", "Nearest to used: is, commonly, use, common, as, for, or, occasionally,\n", "Nearest to at: the, and, two, as, of, degree, in, s,\n", "Nearest to such: as, other, can, and, types, many, any, american,\n", "Nearest to called: is, the, a, of, bother, and, supersessionism, systematized,\n", "Nearest to when: be, initial, was, then, remove, time, the, before,\n", "Nearest to taking: go, pia, fugees, levees, nsaids, boosts, xo, ukrainians,\n", "Nearest to consists: consist, chamber, conscience, calderon, couturat, composed, leblanc, the,\n", "Nearest to scale: diatonic, suggests, motherhood, capricornus, mellin, accidentals, specifying, trillions,\n", "Nearest to units: unit, prefixes, fortieth, si, measurement, hubei, dera, kilogram,\n", "Nearest to ice: rink, pyotr, joaquin, ussr, plasmodium, detection, jabir, louth,\n", "Nearest to instance: placed, philos, geometrically, kruskal, pasts, accepts, xa, oscillators,\n", "Nearest to channel: creditors, wb, channels, hearsay, curler, mbit, restructured, carnivores,\n", "Nearest to report: reports, credibility, annotated, santer, focusing, commission, binge, html,\n", "Epoch 10/10 Iteration: 43100 Avg. Training loss: 3.7622 0.1223 sec/batch\n", "Epoch 10/10 Iteration: 43200 Avg. Training loss: 3.8084 0.1211 sec/batch\n", "Epoch 10/10 Iteration: 43300 Avg. Training loss: 3.8268 0.1220 sec/batch\n", "Epoch 10/10 Iteration: 43400 Avg. Training loss: 3.8140 0.1209 sec/batch\n", "Epoch 10/10 Iteration: 43500 Avg. Training loss: 3.8296 0.1220 sec/batch\n", "Epoch 10/10 Iteration: 43600 Avg. Training loss: 3.8960 0.1191 sec/batch\n", "Epoch 10/10 Iteration: 43700 Avg. Training loss: 3.8529 0.1213 sec/batch\n", "Epoch 10/10 Iteration: 43800 Avg. Training loss: 3.8322 0.1238 sec/batch\n", "Epoch 10/10 Iteration: 43900 Avg. Training loss: 3.8167 0.1228 sec/batch\n", "Epoch 10/10 Iteration: 44000 Avg. Training loss: 3.8544 0.1259 sec/batch\n", "Nearest to for: the, to, and, given, in, a, of, by,\n", "Nearest to would: that, to, than, relegated, in, coastlands, asians, it,\n", "Nearest to known: most, which, with, this, in, first, the, by,\n", "Nearest to used: is, commonly, use, common, occasionally, other, often, for,\n", "Nearest to at: the, of, as, degree, and, travelling, in, dominants,\n", "Nearest to such: as, other, can, and, any, types, the, american,\n", "Nearest to called: is, the, bother, a, of, systematized, rearranged, supersessionism,\n", "Nearest to when: be, initial, attractive, was, painda, time, tire, somehow,\n", "Nearest to taking: pia, go, fugees, levees, nsaids, reestablishing, boosts, nba,\n", "Nearest to consists: consist, chamber, conscience, calderon, leblanc, couturat, composed, hydrohalic,\n", "Nearest to scale: diatonic, suggests, capricornus, correlations, mellin, motherhood, trillions, townes,\n", "Nearest to units: unit, prefixes, measurement, fortieth, si, force, moller, remembrance,\n", "Nearest to ice: rink, pyotr, joaquin, ussr, plasmodium, sweden, jabir, frozen,\n", "Nearest to instance: placed, pasts, geometrically, accepts, kruskal, philos, barcodes, bookstore,\n", "Nearest to channel: creditors, wb, curler, channels, mbit, hearsay, bandwidth, broadcasts,\n", "Nearest to report: reports, credibility, santer, annotated, zangger, commission, binge, focusing,\n", "Epoch 10/10 Iteration: 44100 Avg. Training loss: 3.8485 0.1220 sec/batch\n", "Epoch 10/10 Iteration: 44200 Avg. Training loss: 3.8296 0.1186 sec/batch\n", "Epoch 10/10 Iteration: 44300 Avg. Training loss: 3.8256 0.1181 sec/batch\n", "Epoch 10/10 Iteration: 44400 Avg. Training loss: 3.8264 0.1154 sec/batch\n", "Epoch 10/10 Iteration: 44500 Avg. Training loss: 3.8798 0.1159 sec/batch\n", "Epoch 10/10 Iteration: 44600 Avg. Training loss: 3.8181 0.1083 sec/batch\n", "Epoch 10/10 Iteration: 44700 Avg. Training loss: 3.8231 0.1113 sec/batch\n", "Epoch 10/10 Iteration: 44800 Avg. Training loss: 3.8373 0.1067 sec/batch\n", "Epoch 10/10 Iteration: 44900 Avg. Training loss: 3.7952 0.1103 sec/batch\n", "Epoch 10/10 Iteration: 45000 Avg. Training loss: 3.8190 0.1097 sec/batch\n", "Nearest to for: the, to, in, given, of, by, and, a,\n", "Nearest to would: that, to, than, with, in, it, relegated, coastlands,\n", "Nearest to known: most, with, which, first, in, by, the, this,\n", "Nearest to used: is, use, common, commonly, other, often, for, to,\n", "Nearest to at: the, of, in, as, two, three, degree, and,\n", "Nearest to such: as, other, and, types, any, can, many, american,\n", "Nearest to called: is, the, bother, a, of, eusocial, identical, rearranged,\n", "Nearest to when: be, initial, the, attractive, remove, time, before, was,\n", "Nearest to taking: pia, go, nsaids, fugees, boosts, neustria, reestablishing, xo,\n", "Nearest to consists: consist, chamber, calderon, leblanc, conscience, composed, couturat, located,\n", "Nearest to scale: diatonic, suggests, capricornus, motherhood, correlations, mellin, trillions, accede,\n", "Nearest to units: unit, prefixes, fortieth, measurement, remembrance, force, si, dera,\n", "Nearest to ice: rink, pyotr, ussr, joaquin, sweden, hockey, plasmodium, louth,\n", "Nearest to instance: placed, pasts, geometrically, kruskal, philos, accepts, barcodes, xa,\n", "Nearest to channel: creditors, channels, curler, mbit, wb, bandwidth, hearsay, restructured,\n", "Nearest to report: reports, credibility, santer, annotated, zangger, commission, focusing, lists,\n", "Epoch 10/10 Iteration: 45100 Avg. Training loss: 3.8512 0.1079 sec/batch\n", "Epoch 10/10 Iteration: 45200 Avg. Training loss: 3.8194 0.1076 sec/batch\n", "Epoch 10/10 Iteration: 45300 Avg. Training loss: 3.9229 0.1111 sec/batch\n", "Epoch 10/10 Iteration: 45400 Avg. Training loss: 3.9125 0.1113 sec/batch\n", "Epoch 10/10 Iteration: 45500 Avg. Training loss: 3.8759 0.1216 sec/batch\n", "Epoch 10/10 Iteration: 45600 Avg. Training loss: 3.8293 0.1217 sec/batch\n", "Epoch 10/10 Iteration: 45700 Avg. Training loss: 3.8020 0.1224 sec/batch\n", "Epoch 10/10 Iteration: 45800 Avg. Training loss: 3.8479 0.1217 sec/batch\n", "Epoch 10/10 Iteration: 45900 Avg. Training loss: 3.7367 0.1218 sec/batch\n", "Epoch 10/10 Iteration: 46000 Avg. Training loss: 3.8804 0.1215 sec/batch\n", "Nearest to for: the, and, to, a, given, of, in, from,\n", "Nearest to would: that, to, than, coastlands, asians, relegated, with, because,\n", "Nearest to known: most, with, which, the, first, by, in, this,\n", "Nearest to used: commonly, is, use, common, or, often, other, as,\n", "Nearest to at: the, in, of, as, degree, s, to, two,\n", "Nearest to such: as, other, and, types, many, can, any, exotic,\n", "Nearest to called: the, is, of, a, bother, identical, rearranged, hardin,\n", "Nearest to when: be, the, was, initial, remove, laga, then, painda,\n", "Nearest to taking: pia, go, fugees, ukrainians, reestablishing, xo, malm, boosts,\n", "Nearest to consists: chamber, consist, calderon, leblanc, conscience, judicial, composed, couturat,\n", "Nearest to scale: diatonic, suggests, capricornus, accidentals, mellin, motherhood, trillions, accede,\n", "Nearest to units: unit, prefixes, measurement, fortieth, si, remembrance, force, dera,\n", "Nearest to ice: rink, pyotr, ussr, joaquin, hockey, sweden, louth, plasmodium,\n", "Nearest to instance: placed, pasts, geometrically, kruskal, philos, lenses, barcodes, oscillators,\n", "Nearest to channel: creditors, channels, curler, hearsay, mbit, wb, carnivores, bandwidth,\n", "Nearest to report: reports, credibility, annotated, commission, zangger, santer, focusing, lists,\n", "Epoch 10/10 Iteration: 46100 Avg. Training loss: 3.8255 0.1184 sec/batch\n", "Epoch 10/10 Iteration: 46200 Avg. Training loss: 3.8518 0.1119 sec/batch\n" ] } ], "source": [ "epochs = 10\n", "batch_size = 1000\n", "window_size = 10\n", "\n", "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " iteration = 1\n", " loss = 0\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for e in range(1, epochs+1):\n", " batches = get_batches(train_words, batch_size, window_size)\n", " start = time.time()\n", " for x, y in batches:\n", " \n", " feed = {inputs: x,\n", " labels: np.array(y)[:, None]}\n", " train_loss, _ = sess.run([cost, optimizer], feed_dict=feed)\n", " \n", " loss += train_loss\n", " \n", " if iteration % 100 == 0: \n", " end = time.time()\n", " print(\"Epoch {}/{}\".format(e, epochs),\n", " \"Iteration: {}\".format(iteration),\n", " \"Avg. Training loss: {:.4f}\".format(loss/100),\n", " \"{:.4f} sec/batch\".format((end-start)/100))\n", " loss = 0\n", " start = time.time()\n", " \n", " if iteration % 1000 == 0:\n", " # note that this is expensive (~20% slowdown if computed every 500 steps)\n", " sim = similarity.eval()\n", " for i in range(valid_size):\n", " valid_word = int_to_vocab[valid_examples[i]]\n", " top_k = 8 # number of nearest neighbors\n", " nearest = (-sim[i, :]).argsort()[1:top_k+1]\n", " log = 'Nearest to %s:' % valid_word\n", " for k in range(top_k):\n", " close_word = int_to_vocab[nearest[k]]\n", " log = '%s %s,' % (log, close_word)\n", " print(log)\n", " \n", " iteration += 1\n", " save_path = saver.save(sess, \"checkpoints/text8.ckpt\")\n", " embed_mat = sess.run(normalized_embedding)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Restore the trained network if you need to:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " embed_mat = sess.run(embedding)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the word vectors\n", "\n", "Below we'll use T-SNE to visualize how our high-dimensional word vectors cluster together. T-SNE is used to project these vectors into two dimensions while preserving local stucture. Check out [this post from Christopher Olah](http://colah.github.io/posts/2014-10-Visualizing-MNIST/) to learn more about T-SNE and other ways to visualize high-dimensional data." ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import matplotlib.pyplot as plt\n", "from sklearn.manifold import TSNE" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "viz_words = 500\n", "tsne = TSNE()\n", "embed_tsne = tsne.fit_transform(embed_mat[:viz_words, :])" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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D6wVA0uklrmxtbVtkuSpfX98GAVDdM0wmk7Zv397ofeHh4Q0CIEkKDg5WcHCw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\ncWFhYZibmxMTE0NMTAz29vYEBQURFhbG9OnTm72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Ir+/XsWPHmD9/PmvWrGHevHkG17h4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VcD1q9fz7asLBoaGnBzcyM8PBxfX1+l4xsaGti5cyexsbGUlJQgkUhwdXUlNDSU4cOHK47b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\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PPlERXhQKBaWlpYI30/3gt99+IzY2Fh8fH/r06SOUTcnLy1OJXoWmTKcPP/wQmUxG7969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xIhUV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rHbp3QkNDiYyM5OeffyYkJAQTExMi4q/xyf4EsqL2qK17O9aGfxZOnL6IvedDOPo8LIwVJJ+g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+fj5eWlsbF3ZmYmycnJjBw5UkUAgoYgy9y5c/nwww8FixRoCNDExcUREhLCggULhO3p6elk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uyiZvRwd0dHQUQaCKigquX7/OtGnTFDMToqOjcXBwICYmBlA/S0HH1I6D4TeorJZRU3GL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uwoUIg6Ojo6jB8/nn379hEZGamSYRYUFATApEmT1LZvb5ZrSUkJx44do0ePHioCkGLfS5cuJTY2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\nge4D4/s5YWtm2K7JuJezBUHDu4vvkYeMtuPKUE8bK4duQKvw01YE6tatG35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1a6xYsULK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U1khG+h+mYehuadGRIwnJ62huzYseOOQdT2Amlubm5qLUGU95m9vb1KKxNoreBpkuvTUNOa\nDFBfUUxLUyPG1o5o6xlKy9n1HU1lfiYVeRk0VJdTnpvKDTc7Xpk5EycnJ/73f/+XHj164OjoyPLl\ny9myZQtRUVHI5XIaGxvx8PDAwMCA1NRUDhw4IFXhKK9f7969cXR0JDMzk9jYWIyMjHB0dOSFF17Q\neP4drbYCMLF15mZGLNXFeXzddBk3c20iIyNpaWnhrbfekgTpsWPHkpGRQWhoKLNnz6Zfv37Y2NhQ\nWVlJQUEBiYmJjBkzhrfeegto/e1csGABn3/+OYsXL8bHx6ddAWvUqFHs2bOHDRs2kJCQQJcuXcjL\ny+PChQsMGTKEyMhIteMeO3YsazZs4UbiKWpLC9DvZEV9RQkV+Rl0cuxF2dXLrdfXzQsDc1su7/+O\nvLijyLV1kevqI9czQq6tQ0tzE3qGJlh16y9te2LQGxxbn0F+fj4ymYyNGzdiaWlJeXk5eXl5JCcn\nM336dJ5//nnWrl3Lr7/+SlRUFOHh4WhpaWFubo6bmxtBQUHttpt51ElMTGTatGkaE0nmz5/PsGHD\nNFYV7Nu3T0oo8fb2ZuvWrWrjLjs7m4ULF7J582Y++ugjaXrClZsk55ZyLu0kdl4B6Pf252bGWupp\nxKz/M1w7u0ulKkJLS0ulak0ZbGwvs1pZ6dM207umpkZl3q1YWFhQVFQkiUAdCWgKBLfSXkV5fVUZ\nubmlOPXw1NjOysrKSmofrOTy5csEBweTkpJCWVkZTU1NKvNv3rypJgK19/tnZmZGXV2dxop/S0tL\nFQGqvr6e7OxsTE1N2217qKOjI9oYP6S0l9Qm123tDOE6+T209fR5e5KXmofgvaB8xigrK5P8GdtS\nWlp6T9vX0tLiqaee4qmnnqK8vJykpCQiIyM5deoUV69eZd26dQ/Up6qmvumOy8hkMqx7DsK65yBp\n2oiAHhgZGWlM9BEIBALBbzy+0UiBQHBXtA0SGti7U5qcypw5cxgxYgSenp64u7urVQVNmDCBX3/9\nVcpshtZKobNnz+Lo6KiS+Tl8+HCCg4NZvnw5Q4cOpW/fvri7u2ssMb8daWlpUtaRJm8YZea4ppcq\nTaX4yn7OmoKhynk3b96Upnl6emJpacmuXbvIzMzEx8cHd3d3jes/bnSk3ZKhuR3auvqU56Zy8Xoj\nz0/+TShUBqqUme5t2+R0796d3r17c+bMGcLCwhg7dqzatnNycjA3N1e7Tx8VwsLC+Pbbb9HR0cHX\n1xcrKyvy8vI4fPgwUVFRrFy5UgpUHD58mLNnz9KnTx/69u2LQqEgIyODX3/9lYsXL/LVV1+pBTAA\nlXZUTz755G2zCZ988kl2797N4cOH6dGjB+vXr8fQ0JAVK1bg5OTEt99+i6urK0uXLpWy0+/WL0LJ\n7YxnV61ahbW1NZ06dcLf35+9e/eipaWFvbU5XZwNMR/0DBdTrmLS2YWCpDNoyeVY9/LFycqYyvSz\nNNeptqpIT08nOjoaCwsL3nzzTRQKBadOnaK8vJwzZ86oid/bt2/nyy+/pKCgAD8/P8aNG8fVq1eJ\njY3FwcEBf39/jh07xtChQ6Uqx+bmZlJSUmhqasLf35+RI0fi7OzMqFGj+Mc//sHly5cpLCzE1NSU\n8ZOfISK1jKqCHDrZ98Bl2PNkhG3m2sXDdHLoiY5Bq6BXePks1TfzMHNyx3X4C8z7f/642Jjw/PPP\nSz4qt3KnQFrPvuoDVnn+ykDErbQoZKBo/Q5ubqwHQMdANYvbuocP1j18qCsvJjlkHSa2LgS9vZjn\nhneXBB1lUNjDw4PPP/9cWnft2rUcOXKEqqoq7O3tOX36NKdPn1bZvoGBAU888YRKKyBNKAXObZHq\nLVuc/Z7C2e8ptem6RuY4DppIXmwEF04d47qZPl27diUwMJD+/furLPvmm2/i4+PDwYMHiYuLo7q6\nGmNjY6ytrXn22WcZOXKkyvIDBw7km2++YdeuXcTFxbUrYFlYWLBixQo2bdpEcnIyMTExODg48Oab\nb9K3b1+NIlCnTp14fvZ8Vq/7F1WFV6gqvIKhhR1dR71MQ1WZJAIBGJjb0m3MdEqzE6m+eZ3G2koU\nLc3oGppi2qUb1r0Go2fcen1kMnh9yhC+e+cyx48fJzw8XDJKNzU1xdbWlpdffllKsOjUqRMzZsxg\nxowZt7020DrGb/U8UqKsPntUsLGxUUskGT16ND///DONjY3tVhW0TTJp77fL1dUVLy8vYmNjaWpq\nQltbm0OxV1mxO4aK2gb0jM3o7DkcaPUPA8hrMCCrrAX5+d/ap7a0tFBZWSk92ygzvNsLKiqnt80E\nV34vlJaWanx+KykpUVlH+a+yFaSSuro6pk6dSn5+vkoAvqGhgcDAQBobG5k3b57KGAoNDWX9+vW8\n8847Ks8Azc3N7N69m/DwcIqKijAzM8Pf35+XX35ZLeHg3LlznD59mrS0NOnZzsHBgdGjRzNp0iSV\na9Q24URZsQqt11oEIh8cd6oor6htIOLyTQ5fylULvsvlchRtVjx79iyfffYZurq69O3bFzs7O/T1\n9ZHJZCQkJJCYmKixgr+93z+5XH7byoi21atVVVUoFArKy8vZvn37nU5b8BBxu6Q2Iyt7am7mSd56\nmjwE7wU3NzfOnj1LcnKyynsQtFaZFxd3PKnlTnTq1Ak/Pz/8/PyoqKggPj6eK1euqLSxu98Y6v2+\n8OTvXU8gEAgeN8S3pUAguC2ag4QOlNsPp/xGIld27MLUYB8ymQxPT09eeeUVSUzp3Lkz/fv3l/rX\n29nZERERQWNjo1oVUI8ePVixYgX//e9/OX36NMeOHQNas+2CgoIYMWJEh45X6Q2Tnp6usRezklvL\n8EHzS50y4Hm7Mv22WYOGhoasXLmSbdu2cf78eWJiYoDWLO8JEybw0ksvPZbVQB1ttyTT0sLYxpmy\na6k0ApYOv71o2NjYYGdnR35+PlpaWmrtYxYsWMAHH3zAmjVrCAkJoWfPnhgZGVFcXExOTg5Xrlxh\n5cqVj6QIdP36db777jtsbW357LPPVMxm4+LiWLJkCT/88IPUa/6FF17gzTffVBMew8LCWLNmDQcO\nHFALSEJrS8Rly5bd0ZMEoAYDjDp3Jfx0NHlVa6isqeeVV17BycmJ2tpaTpw4gZWVFQMGDJCO4279\nItqiyXjWxcWFY8eOSaKTubk5aWlpmJqa4u7uTlxcHONH5vP3+VO4lOPHqn/moC2X8eO6pbjYmEjG\n4W1JSEjAxMSE//znP/Tt2xdobUP59ttvo1AoVAJI8fHxbNu2jdraWkaMGMHWrVul7wVldZWnpycy\nmYzjx49LIpCyndrgwYP597//LWVV5uTkcPnyZamybejQofzP//wPCzafJf7KTZob6tDW1aezVwBZ\nJ3ZQlnsZ6x6tnh43My8hk8mw7zcGbxdLqX2Ora0tkydPVgsydSSQtv/iVcZqCKTdDm35b0FSuU5r\nW6/GOs094RtrK6Xlbn2Bv7XFnxLld7Gm1qG/l44ED/SMzej/8jLpb7eAQN4c58HTg9Tbb7Vl4MCB\nku9KR3BycpKqxm6Ho6MjS5Ys0TivPXHEycmJbqP+pj7DFiy79lWZZGrXVa3l4K3IZPDeJC8puDVy\n5Eg1Yetx435W1ZmZmakF9C5cuMDBgwfJyMigoqJCrSViRUUFV8pbVMa2gZktsv/bt4GFHTUlN6gq\nuoqOgSlJ2deIzS6mn6sVqampKtszMDDAzs6OGzdukJeXR5cuXVT2FR8fDyC1NFSeZ2ZmJomJiWoi\nUH5+PsXFxdja2krjWLluYmKiinCjr6+Ps7MzFy5cUKlESk5OloLycXFxKvdbXFwcoN6KbuXKlSQl\nJTFgwAAMDQ2Jjo5m9+7dlJWVqQnkmzZtQktLi549e2JpaUl1dTXx8fH88MMPpKenq4zNqVOncu7c\nObKzs5kyZYqasCW4/3SkohwABR0Kvm/duhUdHR2++eYbNU+tdevWPdBWhMr7xM3NjdWrVz+w/Qju\nP7dLarPuMYibGTEq3nptPQSbmppITU2ld+/ev2vf/v7+7Nixg5CQEMaMGSO1iVUoFGzevPmu26+1\npbGxkYyMDKkNt5Kmpiap4vNBt2vt6/L7xLLfu55AIBA8bjx+kUiBQNBhbhcktHTzBjdvmhvreKKn\nHpRkExYWxrJly1i/fr0UaH/yySe5ePEiR44cYcaMGRw+fBhdXV1GjRqlts1evXqxdOlS6SE0JiaG\nkJAQvvzyS0xNTaWA7O1QvlRp8uT5o7CysuKdd95BoVCQm5tLXFwcBw4cYMeOHSgUCl5++eU/5bj+\nTO6m3ZJxZ1fKrqUi19WnXEtVsPH29iY/P59u3bqpBVqsrKxYtWoVISEhnDlzhuPHj9PS0oKZmRlO\nTk5MmjQJZ2fn+3I+fzQHDx6kqamJ2bNnqwhA0PqZ+Pr6EhUVRW1tLQYGBu0aVI8ZM4Yff/yR2NhY\njSKQr6/vHQWgtsJwuZYTucWnuXI4DC1tXUKzwCm7mILUaOrq6njuuedUAp936xehpD3j2evXr0vn\npRSVAgIC2LhxIykpKeTm5rJ161bGjRvH04NcCenc2mrqdqbSdXV1dOvWTUVkdHR0xMPDg4sXL6qI\nuCEhIdTV1WFvb4+pqSm//PKLyrYaGxvZvn07nTt3Vqs+dHJyolu3bhrbaigz7Kurq9m2bRsWpZXc\nSMiQ5jfVtbZcqitvHVfNjfXUV5aga9QJfVMLgoarVjX26dNHRQS634G0tlga60v/1zO1Qktbh9rS\nApr+T8BqS1VBDgAGlnYdfoFv2/rxfolAj0vQ4X4er5dz6312v7KbH3UeRFXdrZUDwcHBbNiwAWNj\nY/r27Yu1tTV6enrIZDJJjGhqauLnk5kqY1uu+5vAZOHqxc2M/8/efUdFdeaPH38PHYYOgkoRUCxI\nVRS7WKOxl9iSKIma/RqzxiSaXc0mmmZi4mY1ZU1ZN8Yo6i+aGGwYQVHWAhZEmgIigtIEFIahw/z+\nYGeWcYYqGojP65ycE+/cOsCduc+nPDHkxkeia2CEQqEgODIFLydLduzYoXEOY8eO5ccff+Tf//43\na9euVd3Pi4uL2bNnD4Ba8GbcuHEcP36cPXv2MHDgQNV3wdraWrZt24ZCoWD8+PGq9QcNGoSpqSmn\nTp1i8uTJahXZJSUlVFVVqZJ7oC7Qo0wCUQZ9oG4ANC4ujs6dO2t8/mVnZ/PVV1+pWt09//zzrFix\nghMnTrBo0SK1hIR169ZpfNYoFAo2b97MiRMnmDRpkuoetGDBAvLy8rh58ybTpk1r8HNXaDvNqShX\nUihQG3zXJjs7G2dnZ40AkEKhICEh4WFOtUlGRkY4OzuTkZGBTCZTa8UotF9NJbUZWdiq5tZLOvQ1\n5l26c9vcBqu8C9SWF5OYmIi5uTlff/11q47fpUsXnn32WXbs2MGf//xnhg8fjlQqJSYmBplMhqur\nK+np6a3ad2VlJW+++SZdunShR48e2NnZUVlZyZUrV8jMzCQgIEDjb6WtudiZ4eVs3ex5GqHu+0hj\n3+sFQRCE/xFBIEEQtGruIKGuvhGHb8JHz85HoVBw/PhxEhISVO3fBg4cSKdOnTh+/Dje3t7cuXOH\n0aNHNzrPj76+Pn369KFPnz507dqVzz77jKioKFUQSDkIoS3bqWfPnkgkEhITE1t55W1HIpHg7OyM\ns7MzgwcP5oUXXuD8+fNPZBCoOT2elex6B2DXu25wt7xK/We8fPly1Rwa2hgbGzNnzhytlSQPGjNm\nDGPGjNFY3l7auNTPKD90MorSimri4+O1VrgVFRVRW1vLnTt36NGjB9XV1YSGhnL69GkyMzORy+Vq\nFSz1WxjW17Nnz0bPKS23mDW7olT3BfOu7hiaWlF48ypSW0du3Kthza4ojJN+RldXV22wD1o+X4RS\n/f+v/74kpGZQU6tQGzicPn065ubmHDlyhOjoaLKzs5k/fz6+vr4UFxc3OeeInp4eVlZWGhV7tra2\n6Ovrq1X+Xbt2DYVCQWFhIYWFhVy4cEFtm8LCQsrKyjAzM6OsrEy1XFnR9OD74OzsjJubG//5z3+4\nf/8+2dnZREZGIpVKMZZVcDOvWO2eXFtVCUBNZV1lo76xVK0yQ+nB47T1QFp9psb6WEnrMkV1dHWx\ndvUiP+Uy2bEncRowUbVehayQu9ej0dHVZdjwkc1+gA8ICKBLly4cPnwYb29v/P39Nda5du0arq6u\nzc5YfVIGHVp7ncsneKpVt/i62Ha4a3+UHlVVXX01NTUEBwdjZWXF5s2bNebpUc5zknG3pNGfr5m9\nC7bu/clPuUT5/bp2n0d/2UNW+L+wt7HA2tparQpv5syZXLp0iaioKP785z/j7+9PRUWFqkXmrFmz\n8PDwUK3fp08fZs2axf79+1m+fDlDhw5Vzft269YtPDw8mDlzpmp9IyMjXnnlFTZu3Mhf//pXhg8f\njpWVFYmJidy5cwczMzOKi4tV68fGxtKjRw+GDBnC119/zZ07d3BwcCAtLQ2ZTKY2/6RSUFCQ2gC7\nkZGRKps+NTVVrVJPW7KBRCJh6tSpnDhxgpiYGFUQSHi8mltRXt/VW4Wk58kavF/Z2dmRlZVFYWGh\n6m9KoVAQHBz8WObimT59Op9//jlbtmzhtdde00huKikpITc3V63aTvh9NSepTTm3Xl7SeWS5N5Hl\n3OBoaRre7s4MHTqU4cOHP9Q5PPPMM9ja2nLgwAHCwsIwNjamX79+vPDCC7z99tsNJhY0xdDQkKCg\nIOLi4khKSuL8+fOqitCXX35Za6vtR+HZEe5qzxqNkUjQSHwSBEEQGiaCQIIgaNXYIKEs5yam9i6q\ngQLlIKHZf3u61x94k0gkTJgwgR9//FHV7mDixIka+0xKSqJ79+5q7Z7gf33i6+9TOYh79+5djf1Y\nWFgQGBjIyZMn2bNnD3PmzNFowaJsJ2Zvb9/oe9AaGRkZmJuba0xyrMzsf9Rl9O2V6PHcfNoyyhOu\nZ1IhK+SDLdtwsJFiYWKgdVtlm8NPPvmEc+fO0blzZwICArCyslJVnISEhGjtcQ+NT8JdVFpJQmoe\n7vW6X0kkEmzc+1GQFktVmYyqUhkVxQVcvhTHM5PHagxUtnS+iPq0vS/pufcpKavk36fSsXLNVwUq\nRo8ezejRo6mqquLy5csMGzaM8+fPqwWoG2JkZIRMJlPNraGkq6ur8b4pM9Tv3LmDlZWVxrxiDg4O\nQF1gsX6WuJmZGSUlJRotz3R0dPjwww9Zs2YNBw4cUE3UbmxszNSpU/EZOYn90ZlcvaU+EKZrYIS5\niQF9uxhrHWSuP6fHoxhIe1D3zuaUS+o+G7r6jqEkL4O716MpLcjC1N6FmopS7mUkUltVgdPAiSyd\n3PyKHj09PdauXcs777zDu+++S58+fVQBn/z8fFJSUsjJyWHHjh0tut8+KYMOrblOFzszEfRpwKOs\nqquvuLgYuVyOj4+Pxn21vLxcVWUZn6k9wF+f08BJGJnbcuNkMBWyQgrT4zEbH8j777xOUFCQ2r1Z\nT0+P999/nwMHDnDq1CkOHTqEjo4Orq6uvPTSS1pb9QYFBeHm5sahQ4c4ceIENTU1dO7cmeeff57p\n06drBNiHDh3Ke++9x9Z/bWfvr6Ggo4ube2/Wvv8pryx+VhUEksvl3Lhxg1mzZqnmwoiNjcXBwUHV\nmu7BOTJA+3yPynmGlC2OlGQyGT///DMXL14kJydHo3VwQwkUwqPXkoryB7dr6P41ffp0vvrqK1as\nWMHQoUPR1dUlKSmJjIwMBg4cSHR09MOccpPGjRtHamoqR44cYenSpfj5+WFnZ4dMJiM3N5f4+HjG\njh3baPKT8Hg1N6nN2MpebV7BRYE9tX5v+OijjxrcR0PJaqC9/WppaSk5OTmqeTiVvLy8tLaJffDY\nenp6zJo1i1mzZjV4TvU1ljTX2Jx+TfFztWXlJK8mP1sfbEkrCIIgNO3JG10TBKFJTQ0S3jz9/9DR\nM8DE1gFDU0sUCrh+9BbdTSvw7ttbo9XT+PHj2b17NwUFBbi4uNC7d2+Nfe7fv5+rV6/St29f7O3t\nMTY25tatW1y6dAlTU1Oeeuop1bpeXl5IJBJ++OEHbt26paoqmjt3LgD/93//R1ZWFrt27eLkyZN4\neHhgaWlJYWEhmZmZpKSksHr16kcSBIqJieH777+nd+/edO3aVdXTPyoqColEopYB+yR5UtotPayG\nMsp1/9tGy3XKa+gZGvHKZO8GM8pTUlI4d+4cvr6+rF+/XtVyCOoyXPfv39/g8RuahwXgToEctFTR\n2HT3Q9/o/1EpL6I4K5Xy4rsoFFBspvmw29L5IpRuF5RoHbjW0a37GnMtI4c1u6J47YH3RSaTYWlp\nyauvvoqpqSlXrlzRmID8QVZWVigUCq2T7j7YssXExAQjIyNGjRqFoaEh27Zta9acX429z6ampqxY\nsYL09HS8vLwYNWoUR48e5dChQ8jlcj59/XWNeUd8XWzZkBNKTk6Oav61+uLi4lT//ygG0h5kY2bE\nM/99gNczNKHnU4vJTfgP9zOSuHvtHDq6+khtumLvMYR1L81o8QO8i4sLX3zxBQcOHCA6OpqwsDB0\ndHSwsrLCzc2NBQsWNFnx9aAnZdDhSbnOx+VRVtXVZ2lpiaGhIampqZSXl2NkVPeZUF1dzbfffqsK\nlJRV1jS2G6Du/mPXZxBFt68hy72F18zXGRHYk6KiIsrLyzXa/RgYGDS7wlZpxIgRzZ7LMeZmPrti\ny0i3H4e5fV2meT7wwdFbmHTrh2VpNoaGhsTHx1NbW4uPjw9OTk5YW1sTGxvL008/TWxsLBKJRGur\n0cbmdKxfUS6Xy3nttdfIzc2lZ8+eqqp1XV1d5HJ5owkUD8rLy2Px4sWMGTNGY94hoXVaUlHe3O0m\nTJiAvr4+v/76K+Hh4RgYGNC3b19effVVzp49+8iDQADLli3D39+fo0ePEhsbi1wux9TUlE6dOjFz\n5swnfp619qY9JLUVFRUhlUrVvm/W1NSwbds2KisrGTx4cJsd6/cywc8Ze0sTgiNTNBKfQLSkFQRB\naC0RBBIEQUNTg4RdfMcgy75BWWEOxVmp6OjqYSC1wH/0FNa/+oLGIKilpSX+/v6cP3+eCRMmaN3n\npEmTMDU1JTk5mcTERGpqarC1tWXSpElMnz5dLYveycmJ1157jV9++YUjR45QWVnXEkkZBDIxMeHj\njz8mNDSUU6dOcfbsWSorK7G0tKRr164sWbIEPz+/h3mLGtSvXz/u3r1LQkICUVFRlJaWYm1tja+v\nL9OnT9eYbPNJ8aS0W3oYjWWUS20dKC3IouRuBhYOPRvNKM/OzgbqWjHWDwABJCcnq/5eWiI9T0Zx\nWSVmWsbV9Y2k2Pb0586l37h9MRQkYGhqTa6kk6p6JD8/H1tb2xbPFwFQICsnIfMefbVMVWQgrau4\nKy3MQuHmwz8OXeV+VhpzJo4gJydHLaikDP40FoCBugBDcXExP/74Ix9++KGqOrG8vJysrCy1VkC9\ne/fmwoULjB07lrCwML799luWLFmiUdFYWFiIXC5vdi91d3d3+vbtS3x8PKNGjeLjjz/m2Wef5fz5\n83XnaGcGpQVYWdmp3kPl3B3bt2/nr3/9q+o6c3Nz1TJAmzOQZmhqSb/n1qktq7+dtoxSpfqZofUf\n4B38xuLgN1b1WkMP8A1lrD7IwsKCRYsWsWjRoibXba4nZdDhSbnOR+1xVNUpSSQSpkyZwr59+1i+\nfDmDBg2iurqaq1evIpPJ8Pb25urVqxgb6Da5r6qyEvSM1AMj+pIavvvuO4DHOoDYVCu9UpPO3EhO\n4Lt9xzGvKcTAwED1Pcrb25tLly5RVVVFQkICzs7Oqvtha/z222/k5uYyf/58jQz2a9euERIS0up9\nCw+vOYPo2j676m+nreqioWoLFxcXrZUMzf38e1BjFR8DBgxQa0sotF85bkAlAAAgAElEQVTtIant\n7Nmz7Nq1Cx8fHzp16oRMJiMhIYE7d+7g5ubGlClT2uxYvyc/V1v8XG21Jj49Sc+HgiAIbUkEgQRB\n0NDUIGGnnv506qk5D4PP0J4YGxtrLFcoFNy8eRNDQ8MGM9r8/PxaFJjRVgZfn56eHpMnT2by5MlN\n7quxQUc7O7tGH/gefM3JyYklS5Y0ecwn0ZPSbqm1Gsso79RzIAWpl7lz6TcMzawxMrdVyyivrq7m\n+vXrqko6gPj4eLUHwaKiIrZu3dqqc2syMOwzioIbV5DlpqOoqcbazZfs2JNs+PQSJlX3MDExYcOG\nDS2eLwLgRk5xA0cFU/tu5KdeIj/lMp09R6BvJGXde+9zZK8DWVlZ3L17l65du/L666+TkpKCra0t\nurq6VFZWagRqlJycnDA1NSUqKopXXnmFgIAAampq2L9/v8b9bdq0aVy4cIGMjAy8vLw4evQo0dHR\neHt7Y2NjQ35+PteuXSM3N5eFCxc2GQTKzc1FoVDQuXNnVq1axVtvvcXnn3/OTz/9RHJyMiYmJmza\ntIn09HRu3brFpk2bVIOeM2bM4Pz585w9e5ZXX32Vfv36IZfLiYyMxNPTk6ioKODxZrF2xAf4jnjO\nrfGkXOej9Diq6up77rnnsLCw4LfffiM0NBQTExP8/Px47rnnCA4OBsDTyQYu5DW6n7xrUdxLj6O8\nKI9KuYxbZ3/l/yWWUl5SRP/+/Rk6dGirrqulmtNKz6yzK1kK2PZLGF5WlfTu3Vt17/bx8SEiIoIj\nR45QXl6utQqoJbKysgC0tgyNj4/Xuo2y3XBNjXoFlrW1NVu3bm313ByCpvYw+C4I7SGprVevXnh4\neJCQkKBqS2xvb8+cOXOYPXt2g99vOyrRklYQBKHtiCCQIAga2nqQ8MyZM+Tm5jJx4kTxQPwEE22I\nGtZURrmRhS3OAVPJiAoh6dDXmHfpzm1zG6zyLlBbXkxiYiLm5uZ8/fXXuLu706dPH86ePcvq1avx\n8PDg/v37XLp0CQcHB435JJqjqcCwaScnLB17UZx7kyp5EYraavKSznJN3plRAd5q1T0tmS8iPU/G\nPXlFw++LuQ2GptZUl8m4dmgrls4elOmYcjU+kYK7uRgbG3Pv3j3VZLeFhYWEhISwbt06+vbtS1JS\nktp8OVCXcf/Xv/6Vffv2ERYWxqFDh7C2tqZnz57cv39frR2Qj48PixYtYseOHRgYGNC5c2eys7MJ\nDg5GLpdTVlZG165dWblyJYGBgU2+zzdv3mTDhg24u7vj5OTEgAEDOHPmDKdPn6a4uJhu3bqRlJSE\ns7MzkydPplu3bqpt9fX1+eCDDwgODiYyMpKQkBDs7OyYO3cugwcPVgWBfo+BtI74AN8Rz7k1npTr\nfBQeZ1Ud1LUwmz59OtOnT9dYd+XKlaq2Y14Xc4nLKNR6bADzLq6U3ctBUVuDnqEJtfkpdO3pzchn\nZjJ16tQmqyXbSnNa6ZlYdUHPwIiizOtculPF7Cn/qyZXtuv86aef1P7dWsoEiri4OFxcXFTL09LS\nVMd4kLI96N27dzXmUnJ0dHyo8xHUtYfBd0GA3z+pzc3NjbVr17bpPgVBEIQngwgCCYKgoa0GCfft\n24dMJuPYsWMYGRnxzDPPtMXpCR2YaEOkXXMyyq3dvDG2sicv6Tyy3JvIcm5wtDQNb3dnhg4dyvDh\nw4G6zOS3336bnTt3cvHiRQ4ePIiNjQ3jx49n7ty5vPzyyy0+v+YEhq27+1J6L4dOPfrjOqLub33Z\nUx5MH+iqsW5z54u4kp5P3+mvNvh6F+9AungHUpgeT/71aApvxqKoraWzRy/W/GU106dPV8uILC8v\np6qqiujoaBITE6mtrWX27Nka+9XT02PevHnMmzdPtWzz5s2Eh4ezc+dOtfaUs2fPxsPDg4MHD5KY\nmIi+vj69evXCxsYGb29vRo4cqTExubGxsSpzv74ePXowe/Zs4uPjuXTpEiUlJVhYWDBv3jymTJlC\n//5aeuLVY2JiwpIlS7RWI9YfbBYDaYLw8NrD3BDaNDVAadbZDbPObkDdAOVHzwY89s/c5rbSk+jo\nYGrXjfu3r1MF2Dj2UL1mZ2dHly5dyM7ORkdHB09Pz4c6p9GjR/Pzzz/z3XffERcXR9euXcnKyuLC\nhQsMHjyYyMhIjW18fHz4+eef+fLLLxkyZAjGxsZIpVIGDhwo5gR6BH7vwXdBAJHUJgiCIHRcIggk\nCIKGtsq2++GHH9DT08PJyYkXX3yRTp06tfWpCh2QaEOkqbkTHhtb2dNtyDTVvxcF9tQ6yGFmZsay\nZcu07kNbz/qGeuIr+brYas0or6+sMAcA257/C1Q8bBuW5r4v1i6eWLv8bwDw+cCezNHyvhgZGfHy\nyy83GAhrLCu/fqb9gzw8PPDw8GjWuTY2Z4CtrS0LFy5s1n4ehhhIE4SH117bU3WEAcqWtNIz7ezK\n/dvX0TUwokhHfc4fHx8fsrOz6dGjB1KptIE9NI+1tTUbN25k+/btJCYmcvnyZRwdHVm2bBm+vr5a\ng0D9+vVj8eLFHDt2jF9//ZXq6mrs7OwYOHDgQ52LoF1H+N0WngwiqU0QBEHoiEQQSBAErdpikLA5\nk3sLTy7Rhuh/2mtGuVJTgeFKeRH3bsVjZNEJU/u6yp+2qB5p7+9LRyUG0gTh4bXn9lTtfYCyuQF+\nALveAdj1DgCgvKpW7bXly5ezfPlyrdt99NFHDe6zocQHJycn3n77ba3bNPSdVluLvry8xudlElqv\nvf9uC08OkdQmCIIgdDRilEQQBK3EIKEgPD7tNaO8Pm2B4cKbcVTICriXHk9tTTVdfUYhkUjarHqk\nI7wvHZUYSBOEh9eeq+ra8wClCPALD6M9/24LT57HndS2Zs0a4uPj1QLTcXFxrF27lvnz57NgwYJW\nrSsIgiD88Ylv0oIgNEgMEgrC49GeM8qVtAWGC1IvUZKXgb6JOY79n8LSuU+bBoY7wvvSkYmBNEF4\nOB0hYaY9Vt3+0QL8D95DHaXNiAoKD609/m4LgiAIgiC0VyIIJAhCo8QgoSA8Hu05o1zpwcCw+7gg\ntdcfRWC4I7wvHZ0YSBOE1hMJMy33Rwnwx9zMZ9fpFI3rqCi5T2bmPXoVlPxOZyYIwh/V66+/TkVF\nRau379mzJ1u3bsXc3LwNz0oQBEHoCEQQSBCEZhGDhILwaHWEjHJ4/IHhjvK+CILw5BIJMy3X0QP8\noTEZjX4uFZdVcuhSBuOuZPKUr9PjPTlBEP6wOnXq9FDbGxoa4ujo2EZnIwiCIHQkIggkCIIgCO1E\nR8oof5yB4Y70vgiC8OQSCTPN15ED/DE385s8bwAU8I9DV7GzMG5X5y8Iwu+jvLyc+fPn4+7uzief\nfKJaXllZybx586iqquL1119n1KhRqteOHDnC1q1bWbFiBePGjdM6z09LNDQnUGpqKidOnCAuLo78\n/HwqKiqwtbUlICCAuXPnYmpqqraf8PBwNm/ezMqVK7GxsWH37t2kpaVhYGDAgAEDWLp0KVKplLS0\nNHbu3EliYiI1NTV4e3vzpz/9CTs7u1advyAIgtB6IggkCIIgCO2IyCjXTrwvgiAIfywdNcC/63RK\nsyqYABQKCI5MaXfXIAjC42dkZIS7uzvJycmUlZVhbGwMQGJiIlVVVQDExsaqBYFiY2MB8PHxeaTn\nduzYMc6dO4eXlxe+vr4oFApSU1M5cOAAly5d4u9//7vqfOuLioriwoULDBgwgIkTJ5KUlER4eDh5\neXksWrSIt956i759+zJ+/HjS09OJjo4mJyeHL7/8EolE8kivSRAEQVAngkCCIAiC0A6JjHLtxPsi\nCILwx9HRAvzpebIWzWUEcPVWIel5snZ5PYIgPF4+Pj4kJSURHx/PgAEDgLpAj46ODp6enqqgD4BC\noSAuLo7OnTs/8sqZZ555hmXLlqGjo6O2/Pjx43z++eccPnyY2bNna2wXFRXFhx9+iKenp+qc33nn\nHa5cucL69et55ZVXCAwMVK3/+eefc/z4caKjowkICHik1yQIgiCo02l6FUEQBEEQBEEQlNasWcOU\nKVN+79MQhD8MFzszpg90ZcFwd6YPdG23AZMr6fmPdTtBEP5YlBU99YM9sbGx9OjRgyFDhpCfn8+d\nO3cASEtLQyaTPfIqIAA7OzuNABDA2LFjMTExISYmRut2I0eOVAWAACQSiaqSqVu3bmoBIIDRo0cD\nddcmCIIgPF6iEkgQBEHocBYvXgzAtm3bfuczadjD9uxuLzZv3kx4eDjbtm0T/bsFQRCEJ1ppRXWT\n6xiaWtLvuXUt3k4QhD+eB6scPR0dMDAwUAWB5HI5N27cYNasWXh7ewN1QSEHBweuXr0KoFr+KFVX\nVxMaGsrp06fJzMxELpejqNf3sqCgQOt2PXr00FhmbW3d4Gs2NjYA5OeLwLggCMLjJoJAgiAIgiAI\ngiAIgtAEE8PWPT63drvG5OXlsXjxYsaMGcPKlSvbfP+CILRezM18dp1O0do+srjKnPykFIqKirh2\n7Rq1tbX4+Pjg5OSEtbU1sbGxPP3008TGxiKRSB5LJdAnn3zCuXPn6Ny5MwEBAVhZWaGvrw9ASEiI\nas6iB0mlUo1lurq6AJiYmDT4Wk1NTVuduiAIgtBMIggkCIIgCIIgCIIgCE3wdbF9rNsJgtDxhMZk\nsPlwHPUKadSUmnTmRnIC3+07jnlNIQYGBvTp0weoq/q5dOkSVVVVJCQk4OzsjIWFxSM935SUFM6d\nO4evry/r169XBWqgbo6f/fv3P9LjC4IgCI+HCAIJgiAIgiAIv7v6We1z585l+/btxMXFUVVVRe/e\nvVmyZAndunWjqKiIH3/8kejoaEpKSnBxcSEoKEitXUpjbQzj4uJYu3Yt8+fPZ8GCBWqvyWQyDhw4\nwPnz58nJyUFPTw87Ozv8/f2ZO3cuRkZGauvX1NSwf/9+wsLCuHv3LpaWlowcOZLnnnsOPT3xNVsQ\n/mhc7MzwcrbWmt3fEO9u1u12jiNBaE8UCgUHDx4kNDSUnJwczMzMGDx4MM8//zwrVqwANFtBnz59\nmtDQUNLS0qisrMTe3p7AwEBmzpypqmSpLzY2lp9//pnk5GTKy8uxs7NjyJAhzJ49W6OqRdna+Zdf\nfmHfvn1ERESQm5vLyJEjVdV3crmc4OBgzpw5Q3FxMTpG5tzAEQvH3iT8+jk2br50GzJNbb9SW0fK\niwvZ8MG7mNTKMDMxZu3atUydOhUfHx8iIiI4cuQI5eXlj6UKKDs7G4CBAweqBYAAkpOTqaysfOTn\nIAiCIDx64ulUEARBaJcUCgWHDx/myJEjGg+CDWnOg2BBQQEvvPACrq6ubNmyRet+1q9fz6VLl/jy\nyy/p1q2bavn169f5+eefSUxMpKSkBEtLS/z9/Zk/f76q/3Vzris0NJTjx4+TmZmJQqHA2dmZsWPH\nMnHiRCQSidr6U6ZMwdPTk9WrV7N9+3YuX75MWVkZTk5OzJgxg5EjR2o9zuXLlwkJCSE5OZmysjJs\nbW0ZPHgwc+fO1dq64cqVK+zevZsbN26gr69P3759CQoKatY1CUJbys3N5Y033sDJyYkxY8aQl5fH\nuXPnWLNmDZs2bWLdunWYmJgwfPhwZDIZkZGRrF+/nm+++YZOnTo91HHXrl1LXl4ePXr04Omnn0ah\nUHDnzh0OHDjAxIkTNYJAmzZtIiEhgf79+2NiYsLFixfZv38/9+/fF+2ZBOEP6tkR7qzZFdVgln99\nEgksGO7+6E9KEP4Avv76a44cOYK1tTUTJkxAT0+PqKgokpOTqa6u1kiu2LJlC2FhYdja2jJkyBCk\nUinXr19n586dxMbG8v7776sFNUJDQ/nnP/+JoaEhw4YNw9LSkri4OPbt20dUVBSffvqp1u/IGzZs\nICUlhf79+zNo0CBVZU5lZSVvvfUWN27cwM3NjcDAQH4Mjycn9j+U5GVovcbqynJuXzxGZck9aqrK\n0TUyYsLQoRQXF/Ppp58yYcIEAH766Sfg8cwHZG9vD0B8fDxTpkxRLS8qKmLr1q2P/PgtJVphCoIg\ntI4IAgmCIAjt0nfffcfBgwdVD4K6urpt8iBoY2ODr68vMTExpKen4+LiorafwsJCYmJi6NGjh1oA\n6Pjx43z55Zfo6+sTEBCAra0tWVlZHDt2jOjoaDZt2tSsAei///3vnDp1CltbW8aPH49EIuHcuXNs\n3bqVxMREVq1apbFNSUkJq1evRiqVMnbsWORyOZGRkWzatImCggJmzpyptv7u3bsJDg7GzMyMAQMG\nYGFhQXp6Or/88gsXL15k06ZNan26z5w5w8aNG9HX12f48OFYWVmpzsXV1bU5Py5BaDPx8fE8//zz\nzJkzR7Vsz5497Nq1izfeeINhw4bx8ssvqwKmfn5+fPbZZ/z6668sWbKk1cfdtGkTeXl5LFy4kGee\neUbtteLiYo0AENRlz3711VeYmdVl+SuzlU+cOMGiRYuwsrJq9fkIgtA++bnasnKSV6PtnqAuAPTa\nZG/8XB9vK7iKigpCQkKIjIwkKysLiURCt27dmDp1KiNGjFBbVzkZ/MWLF8nIyODevXsYGRnRvXt3\nZsyYQf/+/bUe4/Lly+zZs4e0tDS1xJF9+/ZpVGE2Vn0JsHjxYkCzwgNaXuUhdFwJCQkcOXIEBwcH\n/v73v6uCMQsXLuRvf/sbhYWFapW94eHhhIWFMXjwYFatWoWBgYHqteDgYHbv3s3hw4eZOnUqUBc4\n+OabbzAyMuKzzz7D0dFRtf7WrVs5cuQI33//Pa+88orGud29e5evvvoKc3NzteU///wzN27cYMSI\nEaxatYpbd0vYndmJ3g79uHb0W63XeefiMcru5WDVzZPqyjIA5r/4MsP9Pfnwww85duwYUqmUoqIi\ndHR08PT0bOU72nzu7u706dOHs2fPsnr1ajw8PLh//z6XLl3CwcGh2YlugiAIQvum83ufgCAIgiA8\nKCkpiYMHD9KlSxe+/PJLXnrpJRYvXsyXX36Jjo4OhYXqbVjqPwh+8803rFixgsWLF/PJJ58wf/58\n4uLiOHz4sGr9sWPHAnDixAmNY0dERFBbW8vo0aNVy+7cucM///lP7O3t+eabb1i9ejUvvPACb731\nFu+//z737t3j22+1P+zVd/r0aU6dOoWbmxtbt25l6dKlLFmyhK+++ooePXpw6tQpTp06pbFdeno6\nPXv2ZMuWLQQFBbF8+XK2bNmCqakpP/74Izk5Oap1r169SnBwML179+a7777jtdde48UXX+S9995j\n5cqVZGZmEhwcrFq/vLycr776Ch0dHT7++GNWrlzJokWL2LhxI2PHjiU+Pr7J6xKEtmRnZ8fs2bPV\nlo0ZMwaAqqoqXnzxRbWKuZEjR6Krq0taWlqrj5mamsq1a9dwc3PTODaAubm52gCTUlBQkCoABGBk\nZMTIkSNRKBSkpqa2+nwEQWjfJvg589GzAXh30z446t3Nmo+eDeApX6fHel5yuZw333yTHTt2oKOj\nw7hx4xg9erSqyuDHH39UW18mk/Htt99SVlaGr68v06dPJyAggLS0NNavX89vv/2mcYzTp0+zfv16\nbty4wbBhw5gwYQJyuZxVq1aRm5vbZteyZcsWPv30U7KzsxkyZAiTJk3CzMyMnTt3sm7dOjGx/B9A\nep6MA9E3CY5M4R///n+UVlQzZ84ctWocPT09Fi1apLFtSEgIurq6vPrqqxqfz/PmzcPMzIyIiAjV\nsoiICKqrq5k8ebJaAAjqEjiMjY05efIkVVVVGsd67rnnNAJAUPccIZFIWLRoERKJhCvp+QAYSC2w\n6z1IY/3qilIK068itelKZ++6Sn5dAyOKdCwwMDAgKCgIhUKhSnTr0aOH1sqktqajo8Pbb7/N008/\nTWFhIQcPHiQxMZHx48fz3nvvifa2giAIfxDibi4IgiC0O2FhYQDMmTNHbYDVwMCARYsWsXbtWrX1\nm3oQPHToEBEREapswEGDBiGVSomIiCAoKAgdnf/lRISHh6Onp6fWZu3o0aNUV1ezdOlSbGxs1Pbv\n4+NDQEAA0dHRlJWVYWxs3OB1HT9+HKgbOK5fVWBkZERQUBB/+9vf+O233zRavOno6BAUFKQ28G1v\nb8+UKVPYvXs3J0+eZP78+QAcPHgQgD//+c8aD45jxowhJCSEiIgIVcXE+fPnkclkjB49Gnd39ZY1\n8+fPJywsDLlc3uA1CcLDSM+TcSU9n9KKairl9ymtqMbNzU3tbxJQZaE6ODho/I3p6OhgaWlJfn5+\nq8/j+vXrAPTr10+jJWNjHvybAVQVgSUlJa0+H0EQ2j8/V1v8XG3V7mMmhnr4utj+bnMAfffdd6Sl\npREUFMSsWbNUyysrK/nwww/56aefGDp0KG5ubgCYmpry73//G1tb9WolZTDp+++/JzAwUPXdqqys\njH/+85/o6uqyadMmtWrhH374gX379rXJdbS0ykPoWGJu5rPrdIra3FrXzl6htLCAn+JKsXLNV6ug\n69Wrl1pbt4qKCm7evIm5uTm//vqr1mPo6+uTmZmp+veNGzcA7e3VTE1N6d69O/Hx8dy+fVujCl7b\nZ31paSnZ2dnY2tqqKpRKK6pVr0s7aQaASwuyUNTWAlBTWUaX/waCwo/8SnlaZ1Vgs2fPnrzzzjta\nr+ujjz7SWObl5aX6/t/adc3MzFi2bJnWY2qr0hszZowqQae5x4C6RJ+GXhMEQRAeLREEEgRBENqF\n+oMov525TGlFtdYWCB4eHmoDxK15EDQwMGDYsGEcO3aMy5cv4+/vD9RVA2RkZDB48GC1jL9r164B\ndW2qUlJSNPZfVFREbW0td+7coUePHg1e440bN5BIJHh5eWm85unpiY6Ojuohtb5OnTqp+nXX5+Xl\npZrHp/656unp8Z///EfrOVRVVVFUVIRMJsPMzEy1rbb3WiqV4urqKqqBhDanbQCoouQ+CbcKqE24\ny9M31QeAlIM/9dsY1qerq/tQWeHKQGdLW55oy9BVnmvtfwd6BEH4Y3OxM/vdgj71yWQyTp48ibu7\nu1oACFBVGVy+fFlVkQx1348eDABB3b1t3LhxbNu2jeTkZNV3hPPnzyOXyxk7dqzGQPncuXM5evRo\nmySOtDS5R+g4QmMytLZSrKmqACCloIo1u6J4bbK3qpJOR0dHLSmspKQEhUJBUVERu3fvbtZxm/qc\nV7Zv1fb7q621a2lpqcZrJob/G17TNzLV2Ka6om4beUEW8oIs1fLoHDMyYv73/aa8vLzhCxHU3L59\nm+3bt5OQkEBVVRVubm7Mnz8fPz+/3/vUBEEQ2h0RBBIEQRB+V9oGgxNSs6mQFfLxwWssGqunMRhc\nP0DTmgdBqMtgO3bsGOHh4aogkLI93IOZbcXFxUBd7+/GNPXQJpfLMTMz09pWQXldRUVFGq9ZWlpq\n3Z/ywVP5IAp1g0A1NTVNvhdlZWWYmZmpHnabOoYgtJWGBoCUsu+VagwAtZSymkdbYEjbAI8ymPNg\nq0lBEIT24sGKI0ep+k00OTlZFXyu3/ZVSXk/rJ8UA5CRkcHPP/9MfHw89+7do7KyUu31+vdFZdtN\nDw8Pjf0bGRnh5uZGXFxcK67uf1qT3CN0DDE38zU+/ytK7pNwYAvVFaXoGZpQXS5HV9+Afxy6ip2F\nMX6uttTW1iKTyVTV+MrPbDc3N7Zs2dKsYyu3uXfvHs7Ozhqv37t3D9CebPJghXBcXBx/+ctfyMnJ\nUQui+rr87/+ryjWrgXUN6roA2PUZhGP/p1TLv/nTiHYRSO5ocnNzWbVqFS4uLkyYMIF79+4RGRnJ\nunXrWL16NcOHD/+9T1EQBKFdEUEgQRAE4XfT0GCwrr4hAFdSbnMtV642GFxTU0NxcbHqoas1D4IA\nffr0oWvXrkRHRyOXyzE0NOTUqVOYm5trTISsPMbevXsbrERoDqlUikwmo7q6WiMQpLwubfu/f/++\n1v1pe2A1MTFBoVA0OyCmvLamjiEIbUHbAJA2CgVqA0AtZWpal4F79+5dunTpovaatmq+Xr16AXWT\nnS9cuLDBlnDXr19n1apVFBcXa50fAGDz5s1cuHBBLTh7+fJlQkJCSE5OpqysDFtbWwYPHszcuXM1\nqomUk6R/8cUXBAcHc+7cOQoKCpgzZw5VVVXs27ePlStXam3DkpqaymuvvcaAAQMabCUjCELHoi1Z\nBuoGzzMz79GroG6wWSaTAXX3OG33OaX6CSvXr19n7dq11NbWqtrbmpiYIJFISEtLIyoqSm2OlKYS\nRxpa3hKtTe4R2r9dp1Ma/Pw3MDantraakrsZGJpZoVBAcGQKfq62XL9+XS2pw8jICGdnZzIyMlSV\n7U1xc3Pj7NmzxMXF4ePjo/aaXC4nLS0NAwMDnJyal3yiq6uLhYUFBQUF5OXlYWdnh4udGV7O1sRl\nFCK/qxmkNLFxQCKRIM/LUC3z7mYtAkCtFB8fz4wZM3jxxRdVyyZNmsTq1av56quv6N+//0M9twmC\nIPzR6DS9iiAIgiC0vQcHg7OvRnB557vIctMxsa4btC3Ju6UaDI65WTffR2JiolqbpQcfBFtizJgx\nVFZWEhkZycWLFykuLiYuLo63335bbT3lAHFCQkJrLxeoewBVKBRa95OQkEBtbS3du3fXeO3u3bvk\n5eVpLFdm29bfpnfv3pSUlJCRkaGxvjbKbbW1fJPL5dy8ebNZ+xGE5mhsAOhBygGg1ujZsycAx44d\nU1uenp5OSEiIxvo9evSgT58+pKWlaZ3TQiaTUVlZSa9evXBwcCA7O5vq6mqN9ZKTk8nPz8fKyko1\n8LB7927WrVtHcnIyAwYMYMqUKXTp0oVffvmF1atXqwWLlKqrq3nrrbc4f/48fn5+TJ06FXt7eyZO\nnIhEItG4LqXQ0FAAJk6c2MQ7JAhCRxAak8GaXVEaASCl4rJKDl3K4NiVTFVAedq0aRw8eLDB/zZs\n2KDafu/evVRWVvLee++xfv16li5dyrPPPsuCBQtU333qU97XGnaAzWoAACAASURBVEoc0ba8scpM\n0KzOrJ/c09h1iHlFOpb0PFmDv8cAJjZdAciNj6S6si5QefVWIalZ99ixY4fG+tOnT6e6upotW7Zo\nrfAtKSlRa5c8atQo9PT0OHToENnZ2Wrr7ty5k9LSUgIDA9HX12/2NfXt2xeFQsEPP/yA4r9fbp4d\n4U5VaRF5185rrK9vJMXKxQt5QRbZcadAUcuC4erzDWVnZ5Obm9vsc3iSSaVS1ZyoSu7u7gQGBiKX\nyzl37tzvdGaCIAjtk6gEEgRBeEKsWbOG+Pj4dvPQ3NhgsHV3X/JTL5MTH4mFY0/0DE0Ijkyhr4M5\nP/zwg8b606dP5/PPP2fLli289tprGpn1JSUl5ObmagRYRo8ezc6dOzlx4oQqe1U5qXt9kydP5tix\nY/zrX/+ia9euODg4qL1eXV3N9evX6du3b6PXPG7cOGJjY/nhhx/46KOPMDSsq3iqqKhg+/btqnUe\nVFtby/fff8+bb76pGkzJzc3l4MGD6OrqEhgYqFp32rRpXLhwgS+++II1a9Zo9D4vLy/n1q1bqsGd\nQYMGYWpqyqlTp5g8ebLa5Le7d+9uk97+ggBNDwBpc/VWIel5shZnyQYEBNC1a1dOnz5NQUEBPXv2\n5O7du0RFRREQEKB1zqw33niDNWvWsGPHDs6ePYuXlxcKhYKsrCxiYmL4+uuvsbOzY8yYMYSFhVFQ\nUKCxj/DwcABVpeLVq1cJDg6md+/erF+/Xu3eFB4ezubNmwkODmbJkiVq+yksLMTJyYmPPvoIIyMj\ntdf8/f25cOECt27dolu3bqrlZWVlnDp1CltbW41qRkEQOp7mVk7y32SZtVP6IJFISExMbPYxsrKy\nMDMz0zpXobbkEOX3qMTERI3vK+Xl5ap2cfUpKzPz8/M1XsvOzkYul6vdG1tT5SG0f1fSNX/+9Rma\nWWNk2Yn8lEtcO7QVS+c+SHR0WB6zi74unbG2tlar0h03bhypqakcOXKEpUuX4ufnh52dHTKZjNzc\nXOLj4xk7dizLly8HwM7OjqVLl7J161ZeffVVhg0bhoWFBfHx8Vy7dg1HR0eCgoJadE0DBw5EV1eX\n06dPc/v2bfr164dcLkdx5RimnZy5n3kNHigsdhowkQpZITlXI3CsvcPpkDSuWlpSWFhIZmYmKSkp\nrF69WutcoE+qhlphdu/eHWNjY431vby8CA8PJy0tTWvVtCAIwpNKBIEEQRCEx66pwWDTTk7Y9Q4g\n71oUSYe/xsrZg9uXdLh9/Fu6dLLSCGy09EFQydbWFm9vb2JjY9HV1cXFxYX09HSN83F0dGTFihV8\n/vnnLF++nH79+uHg4EBNTQ15eXkkJiZibm7O119/3eh1jxw5kvPnz/Of//yHl19+mcGDBwN1Ey3n\n5uYyfPhwtYCOkouLC8nJyaxcuRI/Pz/kcjmRkZHI5XJeeOEFtXZXPj4+LFq0iB07dvDSSy/h7++P\nvb095eXl5OXlER8fj4eHB++++y5QN9jyyiuvsHHjRv76178yfPhwrKysSExM5NatW3h6emodCBKE\nlmpqAKix7VoaBDIwMODDDz9k27ZtXLlyhZSUFLp168aqVaswMzPTGgSyt7dny5Yt7N+/n/Pnz3Po\n0CEMDAyws7NjxowZWFhYAHXZxBKJRGNAs7q6msjISKRSqSpwowy6//nPf9YITo8ZM4aQkBAiIiI0\ngkBQ1xbuwQAQ1FX5XLhwgdDQUP70pz+plp86dYry8nJmzZqFjo4o9heEjq6llZMHY3MJDAzk5MmT\n7Nmzhzlz5mjcC7Kzs9HR0VENMNvb23Pnzh3S09NxcXFRrXf8+HEuX76scZyAgACkUikRERFMnToV\nV1dX1Wt79+7Vmjji6OiIiYkJUVFRFBUVqe6llZWVfPPNN1qvp7XJPUL7k5yczC+//MLhk+e5npGD\nroExxpZ22PToh1U39eQpO4+hFN1O5l5GIvmpl9E3NsV13ATef/99goKCVN93g4OD2b17Nxs2bMDf\n35+jR48SGxuLXC5HT0+PS5cuMXjwYKZNm6ba9+bNmwkPD2fFihX861//4ssvv6SkpAQ7OzteffVV\n5syZg1QqJSYmhoMHD5KcnEx0dDRlZWV88MEHTJ48GV9fX7Xz1dPTY+nSpaxZs4aQkBB++ukn7O3t\nefHFF3HyGMgbq95QtbhW0jUwYtaS13CuyeT29RjOnj1LZWUllpaWdO3alSVLluDn5/eIfhodS1Ot\nMLt7aq/aUib2iUQ2QRAEdSIIJAiCIDx2zRkMduj/FIZm1txNvkB+ykV0DU2wGB/I+++8zooVKzTW\nX7ZsmcaDoKmpKZ06dWLmzJmMGjVK63HGjBlDbGwsNTU1jB49mn//+99a1xs1ahSurq4cOHCAq1ev\nEhMTg5GREdbW1gwdOrTZk4+++eabeHl5cfz4cY4ePQqAk5MTM2bM4Omnn9a6jampKe+++y7ff/89\nYWFhlJaW4uTkxMyZMxk5cqRqvby8PBYvXsyYMWP4+OOPOXjwIImJiURFRWFiYoKNjQ1PPfWU2jYA\nQ4cO5b333iM4OJjIyEj09fXx9PRk06ZN7Nu3TwSBhDZRWqHZPq0+Q1NL+j23rsHtGqti3LZtm8Yy\nW1tb/vKXv2hdv6F9mZmZERQUpJYNrMxA3R+dgYmhHr4utsybN48rV66QmZmpmj8gOjoamUzGvHnz\nVEGd7du3o6enpzXoBFBVVUVRUZFGtruBgYHagGx9ysDuyZMnCQoKUlUUhoaGoqury/jx47VuJ/w+\nlBVfD87jpJz7Sdvvrjb17+8rV658JOcqtB+trZx88flnycrKYteuXZw8eRIPDw8sG6kymDp1Kpcv\nX+bNN99k2LBhSKVSUlNTSUhIYOjQoZw5c0btGCYmJvzf//0fn332GatXr2bYsGFYW1uTlJTEzZs3\nVYkj9Ss29PT0mDp1Knv27GHFihUMHjyYmpoarly5grW1tUZiD7Q+uUdoX44dO8Y///lPdHR06Nbd\nnXtSV6rK5ZQVZJOffEEtCFQpv0/KsW0YmFrhOnw2NRVl3LuVQG5mGufOnaO8vFzrfD0DBgxgwIAB\nqn8r75UDBgzA0dFRY/1z584hkUh46aWX6NSpEzo6Ojz//PMA7Nq1iz179mBkZMTgwYMZP348hYWF\nJCUlERERoREESk1NZf/+/Xh7ezNjxgzu3r3LmTNniIiIYKa1NR6OVsyePwoLNw9VFYuvi229xJYF\nbfAu/zE1NG+sUnFZJQfPJjHxSqZq3lglZVvKB4PHgiAITzoRBBIEQfgDiIqKIiQkhMzMTGQyGebm\n5nTt2pXhw4fj7++vGmwCmDJliur/PT09+eijj4C6tkWnT58mMTGR/Px8ampq6Ny5M8OGDWPWrFkY\nGBioHbN+Fl5xcTH79+/n1q1bGBgY4Ofnx+LFi7GxsdE419TUVHZs3UzshSuABBMbB7r6BGqsJ5FI\n6NRrIPom5tzPSKS0IIvLUWdYtCgWR0dHxowZg0KhUBtoGDBgAGfOnKGoqIjvvvuOCxcu8Ntvv3Hg\nwAESExNV11pdXc2+ffsIDw8nPz8fOzs7AgMDmTx5coNBIKiryGnu4JvyWNqu6+mnn24w4NMQa2tr\n3njjjWav7+HhgYeHR7PX9/X1xdfXl7i4ONauXYubmxuOjo6sXLlSDDgKbcLEsHVfO1u73cNqKAMV\nwLK6C0Wl0YSHh6sCRspWcPUH+mUyGTU1NU1Obl5WVqYWBLKwsFC7t9UnkUiYMGECP/zwA5GRkYwd\nO5bU1FRu3LjBoEGDtA6oCoLQsbS2cvJ6Xikff/wxoaGhnDp1qskqg/79+/POO++wd+9eIiMj0dXV\nxd3dnQ0bNpCbm6sRBAIIDAzEzMyMPXv2aCSOKL9DPTgZ+4IFCzA0NOTYsWMcO3YMS0tLRowYwYIF\nC3j55Ze1Xktrk3uE9iEzM5OtW7diYmLCxo0bqTWy4k/fnFa9XikvUltflpuOXe9BOPQfr/r8s3Lx\nxCjlCB988AFmZmaqCvqHcePGDbZs2aLRbi0mJoY9e/Zgb2/Pxo0bNZ5htLUzvHDhAi+++CIzZsxQ\nLQsNDeWzzz7jyy+/xN7eninjA8Xncgs1txVmaWE2m365gJ2FMX6utqrlyjlT3dzcHuVpCoIgdDgi\nCCQIgtDBhYaG8tVXX2FlZcXAgQMxNzfn/v37pKenExYWxsiRI5k/fz7h4eHk5eWpTaBZ/wFo//79\n3L59m969e+Pv709VVRWJiYkEBwcTFxfHBx98oLXF0JEjR1TzbHh6epKcnExkZCQ3b97k888/V5tg\nNSkpib/97W/cvluMeVd3DM2sKC3MISXsB0ztXTX2DZAVEwYSHUxsHBgU0Jve9iZcvXqVb7/9lpSU\nFF5//XWt23377bckJibi7++Pv7+/6twVCgUff/wxUVFRdOnShcmTJ1NdXU1YWBi3bt1q1c9AEISm\n+brYNr1SG273MJrKQC006EpKXhm79h9i4cKFyGQyLl26hKurq1p7JBMTExQKRZNBoAc1FABSGjdu\nHMHBwYSGhjJ27FhCQ0MBmDBhQouOIzx6gwYNYuvWrVhZWf3epyJ0IE1VToL26snSimr09PSYPHky\nkydPbtaxHqykUPL09GxwPo3+/ftrzD1WW1tLeno6VlZWGhn4EomE2bNnM3v2bI19NVYN19C5Ce3f\nkSNHqKmpYd68eTg7OwPg5WytSqwwkFqorW9oaolER4eEA1sws3dBz9gMO+NaMm+lUVpaytKlSxk6\ndOhDn9esWbO0zrejrBBuKIlNOddffX369OHMmTOcPn2aHj16IJVKyc7OJj4+HiMjI9544w0RAGqF\n5rbCrK4sJ/vqKYIju6iCQCkpKURERCCVStskaCgIgvBHIoJAgiAIHVxoaCh6enp88cUXqj7rSsXF\nxUilUhYsWEBcXBx5eXksWKC99cCyZcuwt7fXGHzcuXMne/fu5cyZM1pbnl26dInPPvtMrXXRp59+\nyunTp4mKimLYsGFAXfBly5YtVFZWsmbNGr6+WKZaP+9aFLcvhmo9r+6jFmBoVvcA9fqfRuBiZ4ZC\noWDz5s2cOHGCSZMm0atXL43tGsr0U55Xr1692LBhg6rCacGCBQ0GlBqjrJyZP39+g++tIAjgYmem\nNgDUHN7drFs8H9DDak4Gqo6ePpbOHsSmXGbP4ZNIa0uoqanRGDDt3bs3Fy5cICMjQzUI1hYsLCwY\nOnQoERERJCUlcerUKezt7enXr1+bHUNoG1KpVLSkEVqsPVdOKuddUbaihLrveHv37uXu3bstrnQW\n/hiUrVOVbc8uxNRVY9QPFj47wp01u6K0fr4aW9pj3rU7ZffzKM6+QU1lGZ272WJubo6lpSV/+9vf\nmkyQaI6ePXtqXX79+nUkEolGcLMx7u7uODg4cOLECc6cOUNpaSlGRkbY2tri4uKiNegpNK4lrTDN\n7LtRkBrDvu+y6Fw0Bt2aciIjI6mtrWX58uUaFYmCIAhPOhEEEgRB6IDqP2jdyCmiulqBrq6uxnrm\n5ubN3mfnzp21Lp82bRp79+7l8uXLWoNAU6ZM0Zi74qmnnuL06dMkJyergkDXrl3jzp07eHp6MmPi\naM7knVN9ye/UcwB3r0dTIdP80q8MANUfDJZIJEydOpUTJ04QExOjNQjUUKZfWFgYAAsXLlRrcWdm\nZsa8efPYvHmzxjYdeS6GO3fuEBYWxpUrV8jLy6O0tBQrKyv69evHvHnz1DIblZPmAuzevVutemHD\nhg14eXmp/n369GlCQ0NJS0ujsrISe3t7AgMDmTlzplr1lyDU19gA0IMkElgw3P3Rn9QDmpuBat3d\nl/zUy3y76wA+9jro6uoSGBiots60adO4cOECX3zxBWvWrNHICC4vL+fWrVta72FNefrpp4mIiGDj\nxo2Ul5czZ86cNhkga8/q34tnz57N9u3bSUhIoKqqCjc3N+bPn68xoXZVVRW//vorERERZGdno6ur\ni6urK1OmTFF9PtXXWHvV+oPbOTk57Nu3j6tXr1JQUICBgQE2Njb06dOHhQsXqtr7NTQnkJJcLufH\nH3/k3LlzyGQyOnfuzMSJE5k8eXKzf54VFRWEhIQQGRlJVlYWEomEbt26MXXqVEaMGNGSt1hoJ9pz\n5eS1a9f45JNPVPP0lJeXc/36ddLS0rC1tRUJMU+YhlqnJlxMxbC6hEyZAuXMPH6utqyc5KU10ULX\nwBizzm6YdXZDIoHXJnvzlK8Ta9asIT4+Hj29thm6aqgqU9lu8MH2142RSqVaWzzXb8MttExLWmEa\nSK1wGjiJrJhwDoQcxs7cgO7duzNv3jyRFNNCbfWsq/x7rT/3pkiYFIT2QwSBBEEQOhBtD1p5Og7c\nTk4g4KlnmDVlPE8HDqZPnz4aVUFNKS8vJyQkhPPnz3Pnzh3KyspQ1HtCKygo0Lqdu7vmIG2nTp0A\nKCkpUS1LTU0F6tqLgPpgsERHB9NOzlqDQNUVpeQlncMqtZhnDn5GeXm52usNnVdDmX43btxAIpFo\nnS+nfpCjvWhoAvvmOnfuHEePHsXLy4s+ffqgp6dHRkYGv/32G9HR0fzjH/9Qtb0YNGgQUDdo6enp\nqfZ+1A+obdmyhbCwMGxtbRkyZAhSqZTr16+zc+dOYmNjef/997UGJQWhsQGg+pQDQPV7vD8OLclA\nNe3khKGZNUlXL6LraEng8CEa910fHx8WLVrEjh07eOmll/D398fe3p7y8nLy8vKIj4/Hw8ODd999\nt8Xn2qdPH1xdXbl58yZ6enqMGzeuxfvoqHJzc1m1ahUuLi5MmDCBe/fuERkZybp161i9erUqYaG6\nupp33nmH+Ph4HB0dmTRpEhUVFZw5c4aNGzeSlpbGwoULVfttqr2qcqCvsLCQ119/ndLSUvz9/Rky\nZAiVlZXk5uZy8uRJJk+erDbHU0Oqq6t5++23KSkpYcSIEVRXV3P27Fm+/fZbbt++zbJly5rch1wu\nZ+3ataSlpdG9e3fGjRtHbW0tMTExfPrpp9y6dUs16bnQcbTnyklHR0cGDBhAUlISFy9epKamBltb\nW6ZMmcKcOXNa/P1T6Lgaa52qZ2BEsayQNf8+wZpnR/OUrxMAE/ycsbc0ITgyhau3NH+/vbtZs2C4\ne6Of/8oWzzU1NRqv1X/20Kah4LpUKkUmk1FZWdmiQJDQtlrTCtMtcB6LAnv+LolDgiAIHYkIAgmC\nIHQQDT1o2fUZjK6hCfnJF/l6+x5+O3oYOwsTPD09eeGFF7QGaR5UXV3NW2+9RXJyMt26dWP48OFY\nWFioBvJ3795NVVWV1m21tblRbldbW6taVlpaCoClpSWgORisZ2yqeV6V5VwP/ReO0hocXHzo0aM/\npqam6OrqIpfLCQkJafC8Gsv0MzMz05pRqDy3P5JRo0Yxbdo0jeqcmJgY1q1bx969e1WTMg8aNAip\nVEp4eDheXl5as7XCw8MJCwtj8ODBrFq1Su1BOTg4mN27d3P48GGmTp36aC9M6LDaYgDoUWnpZOw2\nbj5kxZ6kuKyywbkzZs+ejYeHBwcPHiQxMZGoqChMTEywsbHhqaeeYuTIka0+37Fjx/L/2TvzgKqq\n9e9/mOcZmRUEQVBGJ5zFWVPTShO9DtzU917z3rLMfmWDvW9pWd2befV60+xazqmlOGGCA6gICKIM\nGiAIKCAi0+HILO8f/M6JwznAgTRB1+efcu299l57b/Y+e6/neb7frVu3EhgY+FQ+v1oiOTmZF154\ngVdeeUXeNmXKFFauXMmmTZvo378/hoaG/PzzzyQnJ9O/f38++OAD+W+TTP5z//79DBw4EC8vL6Bt\neVUZFy5cQCKRsGTJEqVnXVVVlUr/PFUUFxdja2vLpk2b5M9o2diOHz/OiBEj5IkTLbF161YyMzMJ\nCQnhpZdekrfX1NSwZs0a9u/fz7Bhw4RBdheks1ZO2tra8tZbb/0h+xJ0XtqSTjW0dkJ6P4+yOxl8\nddQaGzMD+e96QE9rAnpac6tQwtmEG2w4b0yApy0f/K/sc1vIvj2KipR/s2VJZ+2ld+/exMXFER8f\nL7xkniCdWQrzacbS0pLNmzf/bgm9N998k+rq6kc0KoFA8KgRT0qBQCDoArT1oWXl6oeVqx91NVU8\nKMrFy7aS5IRoVq9ezebNm9vMyoyJiSEtLU1lCXhxcXG7Tc1VIXupLC0tlbc1nQzOvqicuWdSlo6r\nGfx10StKAYkbN24QGhra4v7ayvSrq6tTCgQ1HZsMWWADGgMgMrk0gOXLl2NjYyP/d2ZmJjt27OD6\n9evU1tbi4eHBggUL5BOMTamvr+fkyZOcPn2anJwc6uvrcXJyYvz48UyZMkVh/E1L9GfNmsXOnTtJ\nSkqivLycNWvWyCt2KioqyM3NZf/+/URFRaGtrU2vXr2YOXOmkkRSQEAAzs7OJCQktHgOVREaGoqW\nlhavv/66UqZkcHAwR48e5ezZsyIIJGiVphNATT0E/F2s/3APoKaok4HaFDufkdj5jGRhkAdDh7Y8\nAdunTx+V1YeqaM0kvTmZmZkATJ48We0+XYnmfx9ORo0/gkZGRsyZM0dhXXd3d4KCgoiIiCA6Opqx\nY8dy6tQpNDQ0WLx4sUJ1opmZGcHBwWzYsIFffvlF4RmtpaWltryqqmxxfX39dh3jwoULFYL0TaVJ\nw8PDWw0CSSQSzpw5g7u7u0IASDa2kJAQEhISOHfuXJcJAnVl+dVHTWevnBQ827QlndrNYwBF6fEU\nJEdi6uDG7qh0hb/RoqIiXGysea6fMwctjfBxtlL7919W7R8eHs7o0aPlz+yioqIOf7NMmzaNuLg4\ntm3bhoeHh7xKXsb9+/eV2gSPns4shfk0o62tjZOTU9srtoFMDUQgEHRORBBIIBAIugDqelRo6+pj\n6uDOQ2dLxlkacerUKVJSUhg6dKg8M/nhw4dKWcr5+fkADB06VGmbycnJv/8AgF69eqncXkBPa/yc\nLbl9agsZFSY8P8AZzz598Hex5tj+W4Rl6T7Scbm5uZGYmEhqaiq+vr4Ky5KSkpTW9/HxkVcd9ezZ\nUy6ZBtCzZ0+kUinQmHl48OBBPD09mTBhAvfu3ePChQu8//77bNiwAUdHR3m/uro6Pv74YxISEnB0\ndGTUqFHo6upy7do1vvnmG9LS0njzzTeVxpKfn8+KFStwdHQkKCiIvKIyzqffJ6k0nRppKT9v+Zz8\n/Hy8vb2ZPHkyVVVVxMbGsmzZMpydndHU1KSiokKhQqs9GuvV1dVkZWVhamrK4cOHVa6jo6NDbm6u\n2tsUPNu42Jg80aBPc7pSBmpRURGRkZF0795d6VnW1WnJY6K6opTc3BKChvXCwMBAqZ+Pjw8RERFk\nZmYydOhQ8vPzsbKyUjmxITtnskBaYWGh3Nj71VdfZeTIkXh7e6uUVw0MDOSHH37gP//5D1euXCEg\nIIA+ffrQvXv3dvkyaWlpqUwSkAX2ZWNribS0NPnzfPfu3UrLZVJJ4pncdenMlZOCZxd1pFP1zbrR\nfeBkcmOPceP4N+Rf88RekoQetaSnp2NoaMjatWs7tP/evXvj7e1NcnIyb775Jn5+fpSWlhIbG0tA\nQADnz59v9zYDAgKYPXs2+/btY+nSpQwePJhu3bpRUlJCamoqnp6ez3xg+o+gM0thdmXS0tL4+eef\nSU1Npby8HBMTE5ydnZk4cSLDhw9XmYCxevVqEhIS2LBhAz179lTaZlRUFJ9//rlCZbYqTyCBQNB5\nEEEggUAg6OS09aElKcjC2NZFYeLpWnYx9RV3AdDT0wN+y2K+d++egr8LIK9mSUpKYtCgQfL2goIC\ntm/f/kiOw9PTE0dHR5KTk4mJiSEwMFC+7OjRo1SUFmFnbsiU/s74+DS+aMrGmZSUhIuLi3z9zMxM\n9u/f36FxjBs3jsTERHbs2MGaNWvkmdwSiYR9+/Ypre/j44OtrS2hoaG4uroqVSTJAkdxcXFKxt8y\nf4nQ0FAFb4cff/yRhIQEpk6dypIlSxQCdBs3buTUqVMMGzZM4RwBpKamMmvWLHxGPNc4QVpbDClS\nII30U9spz7uJnqkNAUNHs3jxYgA2bdpEVFQUd+7cYcmSJTg5OcmPOSIigsLCQrXPXUVFBQ0NDZSV\nlT2S6jCBoLPRFTJQz507x507d4iMjKS2tpZ58+a1K/DQ2WnNYwKgvLKG8zfLOJmYK/eYkCGTxJNK\npfIAvaWlpcrtyCRDm/pH2NnZ0aNHDwwMDAgNDeXw4cNoaGgoyava2Njwz3/+k927d5OQkMDFixcB\nsLa25sUXX2TatGlqHaupqalK6bimx9EaEokEgPT0dNLT01tcr7mXnqBr0VkrJwXPLupKp1q798fA\n3Ia716OpuHuLH/fn0dvZDhcXFyZMmPC7xvD+++/z3XffERMTw5EjR3BwcCAkJIR+/fp1KAgEMG/e\nPDw9PTly5AhxcXFUVVVhbm5Or169GDNmzO8ar0B9OqsUZlfl5MmT/Pvf/0ZTU5PAwEAcHBwoLS0l\nIyODY8eOMXz4cJX9xo4dS0JCAqdPn2bRokVKy2XqGC3JIQsEgs6HCAIJBAJBJ6etD62syB/R1NbF\n0NoRPWNzGhpAWphNsZaE4QN88fPzAxoNys+fP8/atWsZMGAAurq62NjYMHr0aAYNGoS9vT2HDh3i\n1q1buLm5ce/ePWJjYxk4cCD37t373cehoaHB66+/zvvvv8/atWsZOnQo9vb2ZGZmcvXqVfr37098\nfLxCnzFjxvDTTz+xdetWkpKScHBwIC8vj7i4OIYMGUJUVFS7xzFy5EiioqKIiYnhb3/7G4GBgdTX\n13PhwgXc3d3Jz8+nTFrDodgsJQmi1vDy8lJ6CR43bhz/+c9/SEtLk7c1NDRw9OhRLCwsWLx4scIE\noKamJosWLSI8PJyzZ88qBYHMzc2x9Bym9GH0oKQAyd1sTB3dkRRkcTQ+h/GJuQzuacrJkycZMGAA\nUqmUfv36yY3NASIjI9t17mQa7K6urnz99dft6isQdAW61nwcoAAAIABJREFUQgZqWFgYKSkpWFtb\ns3jxYpWVkl2VtqRPZdRWSvnq6DUFjwn4TdLTyMhI/rwqKSlRuQ1Ze3NfO3d3d5YvX45UKuX69etE\nR0dz6tQpJXnV7t278z//8z/U19eTlZVFYmIiR48eZcuWLejr6zN+/Pg2j7e8vFxldW7T42gN2fLp\n06fLA/9dmbbkV5/1iabOVjkpeHZpj3SqUbfuuHZrDNgvDPJQmrC3sbFptWrg008/Vb1dIyP+/ve/\n8/e//11pmartLV++XK1KngEDBjBgwIBW1/Hx8Wl1zO2RdRUoI6QwHx25ublyr59169bRo0cPheWq\nfLVkyLxiz549S0hIiIJUbklJCVeuXMHNzQ1nZ+fHNn6BQPBoEUEggUAg6OS09aFl7z8WSf5NKosL\nKM/LQFNLG10jM4ZNeIG1by2Sy31NmDCBwsJCIiMjOXjwIPX19Xh7ezN69Gj09fVZu3Yt27dvJykp\nidTUVGxtbQkODmbGjBkdCraowsvLi3Xr1rFjxw4uX74MNEo6fPrppyQkJCgFgSwtLVm3bh3bt28n\nNTWVhIQEnJycWLp0Kf7+/h0al4aGBu+88w4HDhwgPDyco0ePYmlpybhx4+gdOI7/HggjpSyHm91S\n5X1kEkS97yv7FsmQZYg3RVtbG3Nzc4VM8zt37iCRSHBwcFBZeQSNXg6q5HsMLGzZ+MsNpQ8i6b3b\nADysraZacp/yvAxWrtnAWHdjbt++TZ8+faiqqlLYZlFREQUFBUr7aFqV1Bx9fX169OhBTk4OEokE\nExMxGSV4+niSGaiq5DjWr19PREQE27Ztw8bGpsUJsfYQERHB+vXrO93EurrSp5XF+dTVVCt5TMgq\nM11dXTEwMMDe3p6CggLy8vJwcHBQ2Ma1a9eARolQVRgZGcknAxsaGhTkVZuipaVFr1696NWrF15e\nXrzzzjtER0erFQSqr6/n+vXr9O3bV6G96XG0hoeHBxoaGqSmpra6XlehLflVgUDQOehK0qmCromQ\nwnw0HD9+nPr6eoKDg5UCQNBYwdwSurq6DB8+nJMnT5KQkMDAgQPly86ePcvDhw871TukQCBoG/Er\nLBAIBJ2ctj6YunkMoJuHcsZa0MQ+Cp4JmpqaLFiwgAULFqjcjrW1NW+99ZbKZaqy3ebOnaskjSaj\ntay+Xr168X//7/9Vavf09FS5ve7du/PBBx+oPS51Mv20tbUJDg4mODhY3hZ2JYcPf0yg10vvquxT\nXlkjr7BpLkEELWdsa2lpKQRUZPI9eXl5rUqqVVZWKrWlF9Wiq8Jvs76mcV3J3Wyqyu/zsK6W2soK\nfr6hSWXhHcrKyvDy8pJvs6qqio0bN8r9IprSVDZQFTNmzGDDhg18/fXXvPHGG0rHXVFRwd27d1uc\nWBUIOjtdIQNVVbCoKUlJSaxatYo5c+a0+JzubNwqlHA5NZOUQ19j5eqPnc8I7lwJp+JuNg0P6zCy\ndqKb1xAAaqukpBzeQKqGJkn7jOnTuxejR4/m7NmzGBkZMWTIEIqLizEwMCA1NZXnnnsOFxcXzMzM\n8Pb2ZsqUKezduxdAIVhTXl5OQ7OL3tDQQHh4OLGxsezatYsBAwaQk5ODvb09+vr6nDx5ktOnT5OT\nk0NhYSHZ2dlYWVnR0NCglkzf999/z5o1a9DR0QEUpUnHjRvXal8zMzOCgoI4c+YMe/fu5eWXX1bp\n+aepqakkA9sZaUt+VSAQdA66gnSqoOsjpDA7RtPzdexcLA+q6+jfv3+HtjV27Fj5e07TIFBERATa\n2tqMGjXqUQ1bIBD8AYggkEAgEHRyxIfW40ddCSIaUClB1B4MDQ0BGDJkCKtWrVK734PqOvLrKlFV\ncK+l0+j7ZO8/Bs3kKKxc/XEeOh2AfnVXSE6IwdnZGRMTEzZs2EBiYiK6urq4uroqGY87OjpiZWVF\nZGQkWlpa2NjYoKGhwejRo7GxsWH8+PFkZGRw/PhxlixZQkBAADY2NkgkEu7evUtycjLjxo1j2bJl\nHTo/AkFnoDNloC5YsICZM2e26G3ztNBU+rRGWsKvYdvQN7PG0tWPGmkpZbk3kBTm8LCulpoHZVSW\nFGBgYYe01ohTp06xb98+fHx8WLFiBYaGhly+fJmcnBzs7OwoKytDIpGgpaXFrl272LhxI7169WL+\n/Pn06dNHvt+MjAz27NlDVVUVtra21NbWsmPHDtLT0/H29mb9+vXo6Ohw5swZTpw4QVFRESUlJVhb\nW2NmZkZ5eTmamppkZ2fz1Vdf8eabb7Z6zJaWltTV1bFs2TIFadLi4mKee+45vL292zxvf/3rX8nL\ny2PXrl2cOXOGPn36YG5uTnFxMbm5uaSnp7Ny5couEQQSCARdg64gnSp4ehBSmOpxJauo0TO2yX2Z\nknaHakkxX5zIIGScfrvfW728vHB0dCQmJoaKigqMjY25efMm2dnZDB48WJ48KBAIugYiCCQQCASd\nHPGh9fhpS4JIls3d0PCQhgaUJIjag5OTE0ZGRvz666/U1dXJ5fraoryyBoxVLzO0dgTgQdFtpWX9\nxs+kTy9noqKiOHbsGGZmZgwaNIh58+axdu1apfU1NTV577332L59OxcuXKCyspKGhgb69OmDjY0N\nAEuXLmXAgAGcOHGCq1evIpVKMTY2plu3brz44ouMHj1azbMhEHReOksGqqWl5VMfAAJF6VPJ3Wwc\n/Mdg5z1C3pafdI478aeQ3svBrLsnvSctJj/xNHX1xVhYWPDgwQMGDhzIiBGNffz8/Ni9ezdaWloc\nOnSIc+fOUVBQgIODA5WVlfTo0YOQkBCFMTg5OWFtbc3Nmze5dOkSaWlpVFdXM3/+fD7++GN5tc7I\nkSO5ePEiV65cwdraGgsLC6ytrQkKCmL69OkcOXKEU6dOMWzYsFaPWVtbm48//pgffviByMhIysvL\nsbOzY+bMmUydOlWt82ZoaMhnn31GWFgY586d4+LFi9TU1GBubo6DgwOLFy8mICBArW09CZrfX+p4\n8AkEgifPk5ROFQgEioRdyVGZ0Kitq081kPhrNu/elfLGVF+VihatMWbMGHbs2EFUVBSTJ0+W+/UJ\nKTiBoOshgkACgUDQBRAfWo+PW4WSNgNsWroGaGhoUPugDIBr2cXcKpR0aCJYS0uLadOmsXfvXrZs\n2cLixYvR1dVVWKe4uBipVEr37r+9pNc/bPniG1k5YmzjTMXdWzgPfh6rXr9N+NU2aDF//nxGjBiB\nhYWF3NQcWjbbdXd3Z82aNa0ex8CBAxVkAQSCtiTKuipPOgO1uSfQ7t275VKSERER8o9xaJTDTEpK\nkrft2bNHQXZy7dq1+Pj4tLq/oqIiDhw4wOXLl7l//z4GBgZ4eXkRHBys0vvs99A0AJBRUCZv1zM2\nx7aPYgDFytWfO/GnaGhowMypNwbmNrgGBbN0Yh+eH+DMiy++qCBx2fRZ9/LLL/Pyyy/L//3xxx9z\n5coVpUC8jY0NY8eOZe7cuaxevRpNTU1ef/11goKCFMbi4eFBdXU1I0aM4L///a+CWTLAokWLCA8P\n5+zZs/zP//yPyomSpsbhS5cuZenSpa2eq9ZkVrW1tZk6daragaPOgKqMZVDPg08gEDx5uoJ0qkDw\nLNCaooWhtRPS+3mU52Wgb2bdIUWLMWPGsHPnTiIiIhg/fjyRkZGYmpoyYICyHL1AIOjciCCQQCAQ\ndAHEh5Yij3KyuakEUUto6ehiaOVIRWEOt87/hJ6pFRu3ZvK3P03r0D5nz55NVlYWJ06cIDY2Fl9f\nX6ysrCgrKyMvL4/U1FQWLFigEATS0mzdW8Jl2AtkROwg+1Io936NxdDaES1dfX65d4GL+8rJzs7m\nyy+/VJgYFQgEXQ8fHx+kUimhoaH07NmTwYMHy5f17NlT7tMVERGBt7e3QtCnLUmwmzdv8sEHH1BR\nUUG/fv0YOnQo5eXlXLp0ibfffpv33nvvkXz0txQAkGFgYYdGM28bHYPGUkhNbV00NX/7hPF3sUZT\nUxNzc3OKihSf53FxcZw4cYKMjAzKy8uVfNDKy8uVqqxu377NypUrqaqq4qOPPsLPz09pfHfu3EEi\nkeDg4CD372mOrq4uubm5LZyBZ5uWMpZltOXBJxAIOgedSTpVIHhWaU3RopvHAIrS4ylIjsTUwQ19\ns24KihZFRUVYW7d+f1pbW+Pn50diYiJHjhyhrKyMadOmqa1mIRAIOg/irhUIBIIuQlf80Gqewd4Z\naSpB1Bouw17g9uWTlOffpD47mdN5Rkwe3KdDx6Wtrc17773H2bNnCQ8PJy4ujqqqKkxNTbG1tWXe\nvHlKmeemBrpIWtmmrpEZvScv4d6vsZTmXKfkVhINDQ1U6rvRs7cbU6dOxdlZlaOQQCDoSvj4+GBr\na0toaCiurq7MnTtXYbmrqytGRkZERETg4+OjtLwl6uvrWbduHVVVVaxdu1bBj6a4uJg33niDDRs2\nsG3bNrksWkdoKwAAoKWjr9Smoan1v//9LTjUVPpUS0tLIcgTGhrK1q1bMTY2xt/fn27duqGnp4eG\nhgaXLl0iKyuLujrl539eXh4SiQRXV1fc3NxUjk8ikcjXbVpp1ZzKysqWD7KLExERwfr161m+fLna\nkjDr16/npyMn0Bq4AF0jc5XryOVXHz783R58AoHg8dNZpFMFgmeRthQt9M260X3gZHJjj3Hj+DeY\nOXmSl2iJSd5FigtyMTQ0VCkP3pwxY8aQmJjIDz/8AAgpOIGgqyKCQAKBQNCFEB9ajx5DPfV+CvVM\nLHEbPUf+76UT+zB2UE+AFiV6QFHypykaGhqMHj1aLf8cGxsbIn45wVvfR7f6oq+lo4ed9wi5j4av\nsyVfLBjS5vY7G9OmTcPb27tFuTqBQNBxjhw5wtatW7l8+TLvv/8+FRUVTJ8+ncuXL5Ofn88LL7yg\nEACCRl+il156ia1bt3L16tUOVwO1JlnSXlqTPq2vr2f37t1YWFiwfv16pWqfGzdutLjdQYMG4ejo\nyA8//MB7773HJ598gomJ4u+roaEhAEOGDGHVqlW/80ieLe7cl9K9tQBgE/nV3+vBJxAI/jietHSq\nQPAsoo6ihbV7fwzMbbh7PZqKu7cou32DM1UOjBzgzYQJE9Taz9ChQ/nPf/7DgwcPcHZ2bjFJRiAQ\ndG5EEEggEAi6IM/yh1ZrfhjLli1j06ZNLcrE1dbWsnDhQgC+//57dHR0kOYkk7DzY5yHTEdb35CC\n5PNUlhSgqamFsV1PHPzHom9qpbQtL3sT9u/fT1RUFHl5eWhoaODs7Mzzzz/PyJEjH8uxC28owZMk\nLS2Nn3/+mdTUVMrLyzExMcHZ2ZmJEycyfPjwFvvduXOH8PBwEhMTKSws5MGDB1hYWNCvXz+Cg4OV\nZCgaGho4ffo0YWFh5OXlUVlZiZmZGd27d2f8+PGMGDFCvu6tW7fYv38/N27coLi4GENDQ6ytrfH2\n9ubPf/5zl5CqaBrUr5GWql2d2BEiIyPZsmULOjo62NraMnr0aDw9PYHfAiP37t1j9+7dSn3z8vIA\nyM3N7XAQqDXJEnXQ1NbBru8wXIZNb1X6tLy8HKlUip+fn1IAqKqqips3b7a6n1mzZqGrq8u3337L\nu+++yyeffIK5+W+VK05OThgZGfHrr78q+Qo9KwwePJjNmzdjYWGhdp9iSRXllTWtrtNcfjX/mhXO\nVb8ydUIQLi4uv3PUAoFA8Ph5Wj0SBZ0Pdd8Zjbp1x7Xbb/KqC4M8FL4TW/MdBNDT02tR/rYpqhL4\nfHx8Wt22QCD443j2vlgEAoHgGaHpB8jMmTPZvn07KSkp1NbW4urqypw5cwgICJCvL5VKOXnyJPHx\n8dy5c4eysjIMDQ3x9PRk1qxZ8onCpsgqNt5++2127NhBfHw8JSUlvP7666xfv16+3qJFi+T/b2Nj\n02J1jDq05ofh4eGBvb0958+fZ8mSJXJvDBkXL15EIpHwwgsvyOWM7CwMMTXQpTT3OuV5NzHv7omJ\nrTMPigsozblOxd1sPCb+GX3T3yYbPe0M+PeXH5OZmYmbmxvjx4/n4cOHXLlyhS+++ILs7Gzmz5/f\n4WNsCeENJXhSnDx5kn//+99oamoSGBiIg4MDpaWlZGRkcOzYsVaDQNHR0Zw4cQIfHx+8vLzQ1tYm\nJyeHX375hdjYWL766iusrH4LtO7YsYP9+/dja2vL8OHDMTIyori4mPT0dM6fPy8PAt26dYsVK1YA\nEBgYiK2tLQ8ePCA/P5/jx48zf/78Tj05r8oXp7qilJTs+zyMvcWorKJHfg/HxcUBMH/+fL777jvG\njh1L7969gcbACcD58+db3UZVVVWH9t2WZIm6dDM14NM/BbZ6bszNzdHT0yMjI4Oqqir09Rvl5erq\n6tiyZYv8WFtj+vTp6OrqsnnzZt555x3Wrl0rDyhpaWkxbdo09u7dy5YtW1i8eDG6uroK/YuLi5FK\npQrebk8TRkZGSr+xbXGnWKrWes3lV78viMfLrbsIAgkEAoFA0AR1FS0eVT+BQNC1EXe+QCAQPOXc\nvXuXt956CxcXFyZNmkRJSQlRUVGsXr2alStXyidUb9++zY4dO+jbty8DBw7E2NiYwsJCYmNjiY+P\n54MPPqB///5K26+oqOCtt95CX1+foUOHoqGhgbm5OXPmzJH7Ljz//PPyyaL2Tho1py0/jMmTJ/Pd\nd99x5swZpk6dqrAsLCwMgIkTJyq0O1oZceNOGq5BczBz9JC3F96I4fblMHJjj+M+bgHQGGDRybnI\nr5mZhISE8NJLL8nXr6mpYc2aNezfv59hw4bh6ur6u45VFV3RG0rQtcnNzWXz5s0YGhqybt06evTo\nobC8qKh1KYrRo0czffp0JR+ZK1eusHr1avbt28err74qbw8LC8PKyopNmzahp6en0Kfp5H1ERAQ1\nNTW8//77BAYGKqxXUVGh1Lcz0ZYvTn7JA97dFcMbU30f6X6LixufGaampkrLZM9mVefzUaCOZIkq\nhnna0svODEM9bf4VZcUgH8c2n28aGhpMmzaNAwcOsGzZMgYPHkxdXR3Xrl1DIpHg6+vLtWvX2tz3\n5MmT0dXV5euvv+add95hzZo1dOvWDYDZs2eTlZXFiRMniI2NxdfXFysrK8rKysjLyyM1NZUFCxZ0\niiBQTEwMoaGh5ObmIpFIMDU1xcHBgREjRvDcc88BkJGRwenTp0lKSqKoqIjq6mqsra0JDAxk9uzZ\nGBsbK2yzNU+gxMRE9uzZw82bN9HR0aFv376EhIRQU/dQrfE2l19dGOTBWFHZKhAIugiWlpby9yaB\n4HHi79Kx772O9hMIBF0bEQQSCASCp5zk5GReeOEFXnnlFXnblClTWLlyJZs2baJ///4YGhri5OTE\n999/rzQ5WFRUxIoVK/j2229VBoFu3brF6NGjef3119HS0pK39+/fn8LCQrKyspg+fTo2NjaP7yCb\nMG7cOHbu3ElYWJhCEOjOnTskJyfj6+uLo6OjQh8zQ13GDBtEqZOHwqRsN4+B3Ps1FklBFtUVpeib\nmPOX0a5s/3wL7u7uCgEgAF1dXUJCQkhISODcuXOPJQgEj8Ybqmml2OzZs9m+fTtJSUnU1tbi6enJ\n4sWLcXZ2pqysjB07dhAbG0tFRQUuLi6EhITg66s4OS2VSjlw4ADR0dEUFhaiq6uLh4cHL774Iv7+\n/kr7r6ur48CBA0RERFBUVISlpSVBQUEEBwe3OOb6+npOnjzJ6dOnycnJob6+HicnJ8aPH8+UKVPk\nhuLNj2/WrFns3LmTpKQkysvLWbNmDT4+Prz77rskJydz6NAhDh48SHh4OPfu3cPc3JxRo0Yxb968\nTl1J8kdx/Phx6uvrCQ4OVgoAAUpybs1pWuXTlICAAJydnUlISFBapqWlhaamplK7quBF8woMQGnC\nujOhri9OQwN8dfQa3tUVSstk5+bhQ9WT6s2XN5XRhMZAT2ZmJu+//z5jx47l0qVLJCQkcO3aNZYt\nW4aLiwtOTk6MHTuWqVOnKtxbAOvXryciIoKtW7cSFxfHL7/8Ql5eHh4eHi16eXVU5q6XnZlcsmRb\nOzJX582bh5mZGb/88gthYWEYGhoSEBDAvHnzVMrdtcTYsWPR0dHhn//8pzwQZGdnh7a2Nu+99x5n\nz54lPDycuLg4qqqqMDU1xdbWlnnz5hEUFNTew33khIWFsWnTJiwsLBg0aBCmpqaUlpZy69YtwsPD\n5UGgkydPEh0djY+PD/7+/jQ0NJCRkcGhQ4eIj4/nH//4BwYGBm3u78KFC6xbtw4dHR1GjBiBhYUF\nqampvPXWW9TqqS8d1xSRsSwQCLoS2traODk5PelhCJ4BXGxM8Olh2a5Ka19ny2dWVl4geNYRb9QC\ngUDwlGNkZMScOXMU2tzd3QkKCiIiIoLo6GjGjh3bYoWOtbU1w4YN48iRI9y7d0+eBS1DW1ubRYsW\nKQSAHgfq+maYmJgwfPhwTp8+zfXr1/Hy8gJ+qwKaPHmyyn4zxg+nd2CgQoWNhqYmxt16UC0pxlHv\nAW/9aSIPi7MVJlabU19fDzRWTzxuHoU31N27d1mxYgXdu3dn7NixFBYWEh0dzbvvvsuXX37J6tWr\nMTQ0ZMSIEUgkEqKiovjoo4/45ptv5H8LUqmUlStXkpubi7u7O9OnT6esrIzz58/z4Ycf8uqrrzJp\n0iT5PhsaGvjss8+IiYnB3t6eqVOnUldXR3h4ONnZ2SrHWVdXx8cff0xCQgKOjo6MGjUKXV1drl27\nxjfffENaWhpvvvmmUr/8/HxWrFiBo6MjQUFBVFdXK2Vmfvnll6SkpMgDopcvX+bgwYOUlpY+s1ru\nTe+3Y+dieVBdpzIIrA4NDQ2cPXuWiIgIsrKyqKioUAheNA+0BQUFceTIEV599VWGDx+Ot7c3np6e\nSs+oESNGEBoayieffMKwYcPw9/fHy8sLe3v7Do3zj6I9vjgNDZCQWUTzp7OxsTEaGhrcu3dPZT9Z\nsEy23MfHB2is3igsLGT06NFUV1czevRoALZv3w6Avb09dXV1uLm5UVZWxpYtW0hPT5ffWzdu3KBn\nz57y/WzZsoXU1FQGDBjAgAEDVAbuZKgzka9nbE6/eatb7NeapnxzmVEtLS1mzJjBjBkzlNZdvny5\n0r3dmh7+yJEjVXq9aWhoMHr0aPl57IyEhYWhra3Nv/71L8zMzBSWNa2smzVrFkuXLlW6hqdOnWLD\nhg0cO3aMmTNntrqvqqoqNm3ahKamJp999hnu7r9V73z77bfs+fFgh45BZCwLBIKuhCpPoPb6IyYl\nJbFq1SrmzJnDwIED2blzJzdu3EBDQwM/Pz+WLFmCtbU1BQUF/PDDD1y9epWqqip69+7NkiVLFH6r\nZVRXVxMaGqqWn2l7/BkFTxbhGSsQCNRFBIEEAoHgKaF5VYiTUeOboJubm8rsXR8fHyIiIsjMzJRL\nuVy/fp3Q0FBu3LhBaWkpdXWKgZb79+8rBYFsbW2VJpYeJR3xzXjuuefkHy5eXl7U1tYSERGBmZmZ\ngodQU8zNzVVW2Fyq8yClPpdXRrkS0NOas9nJAKSnp5Oent7iuDvqm/FHk5yczPz583n55ZflbXv3\n7mXXrl2sWLGC4cOH8+qrr8orAQICAvjnP//J4cOHWbx4MdA4gZybm8ukSZMU1p05cyZvvPEG33zz\nDf369ZNXg0VGRhITE0Pv3r1Zu3atvJJj7ty5KgM5AD/++CMJCQlMnTqVJUuWKFQ6bNy4kVOnTjFs\n2DAlGavU1FRmzZrFggULWjwH+fn5bNq0CROTxoDa/Pnzee211zh9+jQLFy5sl/F5V0fV/ZaSdodq\nSTFfnMggZJx+u6UGt23bxuHDh7G0tKRfv35YWVnJr7ksKNGUxYsXY2trS3h4OAcOHODAgQNoaWkx\nYMAAFi1aJA/yeHh4sG7dOn788UcuXLjAmTNnAHB0dGTu3LkqJ+2fNB3xxckrfoBjveKzWF9fHw8P\nD1JSUvjyyy9xdHSU+zW5uLjg6OiIlZUVkZGRaGlpYWNjg5aWljyQNnbsWJKSkuTP/tWrV2Nvb8+t\nW7f48MMPSU1NxdPTEz09PX744Qfy8/MpLS2VTzbJuHnzJl9//TW2trZtHoeQLHlyaGlpqUzUaFpZ\n11K17rhx4/j222+5cuVKm0GgS5cuIZFIGDNmjEIACGDOnDmEh4djWihp19hFxrJAIHgaaK8/ooz0\n9HQOHjyIt7c3EydO5NatW1y8eJHs7Gzef/993n77bZycnBgzZow8keuDDz7g22+/lfvhQWPC1qpV\nq9T2M1XXn1Hw5BGesQKBQF1EEEggEAi6OKombaExUJKbW4Kbt47Kfubm5kDjRwE0fpx8+umn6Orq\n4u/vj729Pfr6+mhoaJCUlERycjK1tbVK23mcE+Tt8c2Y6P+b70Lv3r1xdXXl/PnzLFmyhPj4eCQS\nCTNnzmxR3qu0tFT+/00rbArjtcnS01byNJo+fbo8CNKVsbGxUZrYGzt2LLt27aK2tpZXXnlFQQpq\n1KhRfP3112RmZgKNFTpnzpxBX1+fBQsWKKzr4ODAtGnT2LdvH6dPn5ZLvYWHhwOwYMECBSkvExMT\ngoODWb9+vcJ4GhoaOHr0KBYWFixevFghU11TU5NFixYRHh7O2bNnlYJAMn+q1ggJCZEHgKBxgn3U\nqFHs3buXjIwMBg4c2Gr/p4WW7jdtXX2qgcRfs3n3rlTpfmuNsrIyQkNDcXZ25osvvlAKSEdGRir1\n0dTUZPr06fKKspSUFKKiojh//jw5OTls2rRJ7i/k6enJhx9+SG1tLRkZGSQkJHDkyBG++OILTE1N\nVUoRPkk66otTXlmj1LZixQq2bt1KQkICkZGRNDQ0YG1tjYuLC5qamiz86+ts+uZbdh8K42FdDSb6\nOvTs4ahi68gDay4uLvzrX//i0KFDxMbGIpFIKCwsJCEhgXHjxjF37lyFwMFLL72kVgAIhGTJH0Xz\nhJA+/oHcvHmTV199lZEjR+Lt7Y2Xl5dS8kZdXR2aJs3dAAAgAElEQVRhYWFERkaSm5uLVCqlocnD\n4P79+23u++bNmwB4e3srLTMyMqJnz57k3Suhmbpgi4iMZYFA8LTQXn9EGZcvX2bFihUK8qIbNmzg\n1KlTrFy5khdeeEFlItcvv/zC888/L2/funUrme3wM1XXn1HQORCesQKBQB1EEEggEAi6MG0FScor\nazhy8TqTE3OVJm1lQQ9ZUGPnzp3o6Ojw1VdfKRlZb9q0ieTk5Ed/AK3Qmm+GLNDQ0PBQ7pthY2ag\n8GI7ZcoU/vWvf3H69Gmio6PR0NBg4sSJLe4vKSlJyY/m4cOHpKamAsg/ijw8PNDQ0JC3dxVaqhRz\ndXVVkv+xtLQEGisqmk/aa2pqYm5uTlFR42T27du3qa6uxsvLSyGQIsPX15d9+/bJJwehcaJQQ0OD\nPn36KK0vk61qyp07d5BIJDg4OLBv3z6Vx6erq6tSgq9nz55KH9zNaZ6xDsgr3ioqlP1YnkZau98M\nrZ2Q3s+jPC8DfTNrlfdbSxQUFNDQ0EBAQIDS31JRUREFBQWt9jczM2Po0KEMHTqU8vJyrl27RnZ2\nNr169VJYT0dHBy8vL7y8vHBwcOCf//wnMTExnS4I1JYvjio5NOeh01kY5KFUqWFvb8+HH36otA2F\nxADX5zBrYk2WFLMPvcoaxo4dK68CApBIJPz0009cvnyZgoICeSWjrq4u/fv3Z9KkSSxbtkxpXx4e\nHm0ec1OEZMnjo6WEEDDFrM8EKL5BaGgohw8fRkNDA29vb/785z/Ln3+ff/450dHR2NnZERgYiIWF\nhfzZGRoaqjIJpDmypBJZkklzLCwsMDPU5U/jvdgenS8ylgUCwTNDR/wRAfr06aPkLzdmzBhOnTqF\noaGhUiLXmDFj2LVrlzxZCxp/48+cOdNuP9P2+DMKnjyPwjNWIBA83YggkEAgEHRR1DUXf1Ccz5c/\nxylN2iYlJQG/BTfy8/Pp0aOHUgCooaGBlJSUDo1R9uEg88lpD635ZmjpGqChoUHtg7L/HSPsjkpX\nOL5Ro0bx3XffcfDgQYqLiwkICMDOzq7F/V27do24uDiFqo+jR4+Sn5+Pr6+vfALWzMyMoKAgzpw5\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UFxfj6+vLyJEjKSoq4vz588TFxbFq1Sq5h1jfvn3R1tbm6tWrCkGgq1evKvy/7Bo0NDSQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5f8c12e358>" ] }, "metadata": { "image/png": { "height": 793, "width": 832 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(14, 14))\n", "for idx in range(viz_words):\n", " plt.scatter(*embed_tsne[idx, :], color='steelblue')\n", " plt.annotate(int_to_vocab[idx], (embed_tsne[idx, 0], embed_tsne[idx, 1]), alpha=0.7)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
RayleighChen/SummerVac
wu/Untitled1.ipynb
4
314
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-2.0
reachtarunhere/aima-python
grid.ipynb
4
808
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import grid\n", "\n", "print(grid.distance_squared((1, 2), (5, 5)))" ] }, { "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 }
mit
ocefpaf/virtual_machine_ipynb
notebooks/Climatologia/wind_stress.ipynb
1
3006
{ "metadata": { "name": "", "signature": "sha256:939d2d267e62af48507a51a287368268a16602383021aaca196b0ba36cf894cf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Tens\u00e3o de Cizalhamento do vento -- climatol\u00f3gia global de [*Hellerman and Rosenstein*](http://journals.ametsoc.org/doi/abs/10.1175/1520-0485%281983%29013%3C1093%3ANMWSOT%3E2.0.CO%3B2)\n", "\n", "obs: 35 milh\u00f5es de observa\u00e7\u00f5es 1870-1976" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from netCDF4 import Dataset\n", "from mpl_toolkits.basemap import Basemap\n", "\n", "def make_basemap(projection='robin', figsize=(10, 5), resolution='c'):\n", " fig, ax = plt.subplots(figsize=figsize)\n", " m = Basemap(projection=projection, resolution=resolution,\n", " lon_0=0, ax=ax)\n", " m.drawcoastlines()\n", " m.fillcontinents(color='0.95')\n", " parallels = np.arange(-60, 90, 30.)\n", " meridians = np.arange(-360, 360, 60.)\n", " m.drawparallels(parallels, labels=[1, 0, 0, 0])\n", " m.drawmeridians(meridians, labels=[0, 0, 1, 0])\n", " return fig, m" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# nc3tonc4 -o --complevel=9 \"http://iridl.ldeo.columbia.edu/SOURCES/.HELLERMAN/dods\" winds_hellermanAndRosenstein.nc\n", "filename = '../../data/winds_hellermanAndRosenstein.nc'\n", "\n", "if os.path.isfile(filename):\n", " nc = Dataset(filename)\n", "else:\n", " nc = Dataset(\"http://iridl.ldeo.columbia.edu/SOURCES/.HELLERMAN/dods\")\n", " \n", "lon = nc.variables['X'][:]\n", "lat = nc.variables['Y'][:]\n", "time = nc.variables['T'][:]\n", "taux = nc.variables['taux'][:].mean(axis=0) / 10\n", "tauy = nc.variables['tauy'][:].mean(axis=0) / 10\n", "lon, lat = np.meshgrid(lon, lat)\n", "tau = np.sqrt(taux**2 + tauy**2)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, m = make_basemap(projection='robin', figsize=(14, 8), resolution='c')\n", "cs = m.pcolormesh(lon, lat, tau, cmap=plt.cm.rainbow, latlon=True)\n", "fig.colorbar(cs, extend='both', orientation='horizontal', shrink=0.65, pad=0.05)\n", "Q = m.quiver(lon[cut], lat[cut], taux[cut], tauy[cut], latlon=True)\n", "qk = m.ax.quiverkey(Q, 0.05, 0.05, 0.1, r'1 N m$^{-2}$', labelpos='N')" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
QuantEcon/QuantEcon.notebooks
insurance_incentives.ipynb
1
46917
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Insurance and Incentives\n", "\n", "*By Sebastian Graves and Thomas Sargent*\n", "\n", "This notebook computes optimal contracts for the three examples that lead off chapter 21 of \n", "**Recursive Macroeconomic Theory, Fourth edition** by Lars Ljungqvist and Thomas Sargent.\n", "\n", "The examples illustrate different sorts of tradeoffs between insurance and incentives that emerge under different\n", "limits on enforcement and information. \n", "\n", "In each of the three economies, a planner or money-lender designs an efficient contract to supply insurance to a risk-averse consumer who receives an exogenous random stream of a non-storable endowment.\n", "\n", "The only way that the consumer to smooth consumption across states and time is to interact with the planner. \n", "\n", "The three models differ in the constraints that they impose on the planner. \n", "\n", "These constraints express the planner's limited ability either to enforce a contract or to observe the consumer's endowment\n", "\n", "Each of the examples uses a version of what we have nicknamed **dynamic programming squared**\n", "\n", "In a dynamic programming squared problem, a value function from one Bellman equation is an argument of another Bellman equation. \n", "\n", "In the examples below, a planner or money lender's value function will have as an argument the value of a villager\n", "that satisfies a Bellman equation\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Three models of a villager and a money lender\n", "\n", "Imagine a village with a large number of ex ante\n", "identical households. Each household has preferences over\n", "consumption streams that are ordered by\n", "$$ E_{-1}\\sum_{t=0}^\\infty \\beta^t u(c_t), $$\n", "where $u(c)$ is an increasing, strictly concave, and twice\n", "continuously differentiable function,\n", " $\\beta \\in (0,1)$ is a discount factor, and $E_{-1}$ is the mathematical expectation\n", " not conditioning on any information available at time $0$ or later. \n", " \n", "Each household\n", "receives a stochastic endowment stream $\\{y_t\\}_{t=0}^\\infty$,\n", "where for each $t \\geq 0$, $y_t$ is independently and\n", "identically distributed according to the discrete\n", "probability distribution ${\\rm Prob} (y_t = \\overline y_s) = \\Pi_s,$\n", "where $s \\in \\{1, 2, \\ldots ,S\\}\\equiv {\\bf S}$ and\n", "$\\overline y_{s+1}>\\overline y_s$. \n", "\n", "The consumption\n", "good is not storable. \n", "\n", "At time $t \\geq 1$, the\n", "household has received a history of endowments\n", "$h_t = (y_t, y_{t-1}, \\ldots, y_0).$\n", "\n", "Endowment processes are distributed independently and identically\n", " both across time and\n", "across households.\n", "\n", "\n", "##### Competitive equilibrium\n", "\n", "In this setting, if there were a competitive equilibrium with\n", "complete markets, at date\n", "$0$ households would trade history- and date-contingent claims. \n", "\n", "Since households are ex ante\n", "identical, each household would consume the per capita\n", "endowment in every period, and its lifetime utility would be\n", "\n", "$$ v_{\\rm pool} = \\sum_{t=0}^\\infty\n", "\\beta^t \\, u\\!\\left(\\sum_{s=1}^S \\Pi_s \\overline y_s\\right) =\n", " {1 \\over 1-\\beta}\\, u\\!\\left(\\sum_{s=1}^S \\Pi_s \\overline y_s\\right) .\n", " $$\n", " \n", " Households would thus insure away all\n", "risks from their individual endowment processes.\n", "\n", "But the\n", " incentive constraints that we are about to specify make\n", "this allocation unattainable. \n", "\n", "For each specification of incentive\n", "constraints, we shall solve a planning problem for an efficient\n", "allocation that respects those constraints.\n", "\n", "\n", " Following a tradition started by\n", "Edward Green (1987) [*Lending and the Smoothing of Uninsurable\n", " Income*, in Edward C. Prescott and Neil Wallace, editors, **Contractual Arrangements for\n", " Intertemporal Trade**, Minnesota Studies in Macroeconomics series, Vol.\n", " 1, Minneapolis: University of Minnesota Press, pp. 3--25], we assume that a *moneylender* or *planner* is\n", "the only person in the village who has access to\n", "a risk-free loan market outside the village.\n", "\n", "The moneylender can borrow or lend at a constant one-period\n", "risk-free gross interest rate $R=\\beta^{-1}$.\n", "\n", "Households cannot borrow or lend with each other,\n", "and can trade only with the moneylender. \n", "\n", "Furthermore,\n", "we assume that the moneylender is committed to honor his\n", "promises.\n", "\n", "We will study three distinct environments in which there are three alternative types of incentive constraints.\n", "\n", "\n", "**Enviroment a.** Both the money lender and the household observe the household's history of endowments at each time $t$.\n", "Although the moneylender can commit to honor a\n", "contract, households cannot commit and at any time are\n", " free to walk away from an arrangement\n", "with the moneylender\n", "and live in perpetual autarky thereafter. They must be induced not to do so\n", "by the structure of\n", "the contract.\n", "This is a model of *one-sided commitment* in which the\n", "contract must be *self-enforcing*. That is, it must be structured to induce the household to prefer to\n", "conform to it.\n", "\n", "**Environment b.** Households *can* make commitments and enter\n", "into enduring and binding contracts with the moneylender,\n", "but they have private\n", "information about their own incomes. The moneylender\n", "can see neither their income nor their consumption. Instead,\n", " exchanges between the moneylender and a household must\n", "be based on the household's own reports about income\n", "realizations. An incentive-compatible contract induces\n", "a household to report its income truthfully.\n", "\n", "**Environment c.** The environment is the same as b except that now households have access to a storage technology that\n", "cannot be observed by the moneylender.\n", "Households can store nonnegative amounts of goods at a risk-free\n", "gross return of $R$ equal to the interest rate that\n", "the moneylender faces in the outside credit market.\n", "Since the moneylender can both borrow and lend at the interest\n", "rate $R$ outside of the village,\n", "the private storage technology does not change the economy's\n", "aggregate resource constraint, but it does affect the set of\n", "incentive-compatible contracts between the moneylender and the\n", "households.\n", "\n", "\n", "#### Preview\n", "\n", "\n", "When we compute efficient allocations for each of these three\n", "environments, we find that the dynamics of the implied\n", "consumption allocations differ dramatically. \n", "\n", " \n", " We shall see that the dynamics\n", "of consumption outcomes evidently differ substantially across the\n", "three environments, increasing monotonically and then flattening out in environment a,\n", "stochastically heading south in environment b, and stochastically heading north in\n", "environment c.\n", "These sample path properties will reflect how the optimal contracts cope with the three different frictions that we have put into the environment. \n", "\n", "Chapter 21 of RMT4 explains why sample paths of consumption differ\n", "so much across these three settings." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Three computed contracts\n", "\n", "\n", "For all three environments discussed, consumers have a utility function:\n", "\n", "$$u(c) = - \\gamma^{-1} \\exp(-\\gamma c)$$\n", "\n", "We set $\\gamma = 0.7$, and the discount factor, $\\beta$ to 0.8. \n", "\n", "The consumers receive an iid endowment that can take any integer in the range $[\\bar y_1,...,\\bar y_{5}] = [6,...,10]$. \n", "\n", "The probability of each realisation is $\\Pi_s = \\frac{1-\\lambda}{1-\\lambda^{5}}\\lambda^{s-1}$ with $\\lambda = 0.4$.\n", "\n", "As mentioned above, an interesting benchmark case is a complete markets environment.\n", "\n", "Because all households are *ex ante* identical, in a complete markets economy each household would consume the per capita endowment in every period, and its lifetime utility would be:\n", "\n", "$$ v_{pool} = \\frac{1}{1-\\beta} u \\left( \\sum_{s=1}^S \\Pi_s \\bar y_s \\right) = \\frac{u(c_{pool})}{1-\\beta} $$\n", "\n", "Later we will compare the consumption paths for each environment to that which would occur in the complete markets environment. \n", "\n", "In each environment, we compute allocations for the situation in which the planner or money lender just breaks even.\n", "\n", "## Environment a\n", "\n", "The first environment is one in which the planner is able to commit, but households are not.\n", "\n", "At any time households are free to walk away from an arrangement with the planner, and live in perpetual autarky thereafter.\n", "\n", "RMT4 shows how this problem can be written in a recursive form. \n", "\n", "Equations 21.3.4 to 21.3.8 in RMT4 express the planners's problem as:\n", "\n", "\\begin{align}\n", "\t&P(v) = \\max_{c_s,w_s} \\sum_{s=1}^S \\Pi_s \\left[ (\\bar y_s - c_s) + \\beta P(w_s) \\right] \\\\\n", "\t&\\text{subject to} \\\\\n", "\t&\\sum_{s=1}^S \\Pi_s \\left[ u(c_s) + \\beta w_s \\right] \\geq v \\\\\n", "\t&u(c_s) + \\beta w_s \\geq u(\\bar y_s) + \\beta v_{aut} \\text{ , s = 1,...,S} \\\\\n", "\t&c_s \\in [c_{min},c_{max}] \\\\\n", "\t&w_s \\in [v_{aut},\\bar v]\n", "\\end{align}\n", "\n", "where $w_s$ is the promised value with which the consumer will enter the next period, given that $y = \\bar y_s$ this period. \n", "\n", "The first constraint is a promise keeping constraint, while the second set of constraints are participation constraints. $[c_{min},c_{max}]$ is a bounded set, while $\\bar v$ just needs to be a very large number. \n", "\n", "\n", "The value of autarky to the households is:\n", "\n", "$$ v_{aut} = \\frac{1}{1-\\beta} \\sum_{s=1}^S \\Pi_s u(\\bar y_s) $$\n", "\n", "Below we solve the moneylender's problem in this environment by approximating $P(v)$ using Chebyshev polynomials." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.optimize import minimize, fsolve\n", "from scipy.interpolate import UnivariateSpline\n", "import matplotlib.pyplot as plt\n", "import numpy.polynomial.chebyshev as cheb\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Parameter values\n", "gamma = 0.7\n", "beta = 0.8\n", "lamb = 0.4\n", "S = 5\n", "y_grid = np.linspace(6,5+S,S)\n", "prob_grid = np.zeros(S)\n", "for i in range(S):\n", " prob_grid[i] = (1 - lamb)/(1-lamb**S)*lamb**(i)\n", " \n", "# Utility function\n", "u = lambda c: -gamma**(-1)*np.exp(-gamma*c)\n", "u_inv = lambda u: np.log(-gamma*u)/(-gamma)\n", "\n", "# Calculate complete markets consumption \n", "c_pool = np.dot(prob_grid,y_grid)\n", "\n", "# Calculate value of autarky\n", "v_aut = 1/(1-beta)*np.dot(prob_grid, u(y_grid))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Functions used in each environment\n", "\n", "# Nodes and basis matrix for Chebyshev approximation\n", "\n", "def Cheb_basis(order,lb,ub):\n", " # Calculate roots of Chebyshev polynomial\n", " k = np.linspace(order, 1, order)\n", " roots = np.cos((2*k - 1)*np.pi/(2*order))\n", " # Scale to approximation space\n", " s = lb + (roots - -1)/2*(ub-lb)\n", " # Create basis matrix\n", " Phi = cheb.chebvander(roots, order-1)\n", " return s, Phi\n", "\n", "# Value Function Iteration\n", "\n", "def Bellman_Iterations(s, Phi, P_fun, x_store, coeff, tolc=1e-6, bnds=None, cons=(), max_iters=100):\n", " global x, c\n", " c = coeff\n", " order = Phi.shape[1]\n", " iters = 0\n", " diff = 1\n", " \n", " while diff > tolc:\n", " # 1. Maximization, given value function guess\n", " P_iter = np.zeros(order)\n", " for i in range(order):\n", " x = s[i]\n", " res = minimize(P_fun, x_store[i], method = 'SLSQP', bounds = bnds, constraints=cons, tol=1e-15)\n", " x_store[i] = res.x\n", " P_iter[i] = -P_fun(res.x)\n", " # 2. Bellman updating of Value Function coefficients\n", " c1 = np.linalg.solve(Phi, P_iter)\n", " # 3. Compute distance and update\n", " diff = max(abs(c1 - c))\n", " print(diff)\n", " c = np.copy(c1)\n", " iters = iters + 1\n", " \n", " if iters >= max_iters:\n", " print('Convergence failed after {} iterations'.format(iters))\n", " break\n", " \n", " if diff < tolc:\n", " print('Convergence achieved after {} iterations'.format(iters))\n", "\n", " return c" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.02046457448057115\n", "0.01420417271952934\n", "0.007919316517300268\n", "0.004362488108637486\n", "0.0024521591210679983\n", "0.0014181717553074513\n", "0.0008450151371991454\n", "0.0005180673399127755\n", "0.0003258997519962614\n", "0.0002096164890845742\n", "0.00013734153609978872\n", "9.134953200051754e-05\n", "6.149551030254496e-05\n", "4.179812741511579e-05\n", "2.8630491596959295e-05\n", "1.973504389185532e-05\n", "1.367544312058655e-05\n", "9.5197594243146e-06\n", "6.6539162798529006e-06\n", "4.66802753196216e-06\n", "3.286358093546049e-06\n", "2.323138710569328e-06\n", "1.666813717604576e-06\n", "1.1999303339838008e-06\n", "8.667152331387484e-07\n", "Convergence achieved after 25 iterations\n" ] } ], "source": [ "# Value Function Approximation\n", "# Set bounds and approximation order\n", "v_min = v_aut\n", "v_max = -0.065\n", "c_min = 0\n", "c_max = 50\n", "order = 70\n", "\n", "# Calculate nodes and basis matrix\n", "s, Phi = Cheb_basis(order, v_min, v_max)\n", "\n", "# Bounds for Maximisation\n", "lb = np.concatenate([np.ones(S)*c_min, np.ones(S)*v_min], axis=0)\n", "ub = np.concatenate([np.ones(S)*c_max, np.ones(S)*v_max], axis=0)\n", "\n", "# Initialize Value Function coefficients and goess for c,w\n", "y = (c_pool - u_inv(s*(1-beta)))/(1-beta)\n", "c = np.linalg.solve(Phi, y)\n", "x_init = np.concatenate([np.ones(S)*c_min, np.ones(S)*v_min], axis=0)\n", "\n", "# Function to minimize and constraints\n", "def P_fun(x):\n", " scale = -1 + 2*(x[S:2*S] - v_min)/(v_max - v_min)\n", " P = np.dot(cheb.chebvander(scale,order-1),c)\n", " P_fun = - prob_grid.dot((y_grid - x[0:S]) + beta*P)\n", " return P_fun\n", "\n", "def cons12(y):\n", " global x\n", " return prob_grid.dot(u(y[0:S]) + beta*y[S:2*S]) - x\n", "\n", "cons1 = ({'type': 'ineq', 'fun': lambda y: u(y[0:S]) + beta*y[S:2*S] - u(y_grid) - beta*v_aut},\n", " {'type': 'ineq', 'fun': cons12})\n", "\n", "bnds1 = np.concatenate([lb.reshape(2*S, 1), ub.reshape(2*S, 1)], axis = 1)\n", "\n", "# Bellman Iterations\n", "NBell = 5\n", "tolc = 1e-6\n", "diff = 1\n", "iters = 1\n", "x_store = {}\n", "for i in range(order):\n", " x_store[i] = x_init\n", " \n", "c = Bellman_Iterations(s, Phi, P_fun, x_store, c, bnds=bnds1, cons=cons1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Time Series Simulation\n", "T = 100\n", "np.random.seed(2)\n", "y_series = np.random.choice(y_grid,size = T,p = prob_grid)\n", "c_series = np.zeros(T)\n", "w_series = np.zeros(T)\n", "resid_series = np.zeros(T)\n", "pval_series = np.zeros(T)\n", "\n", "# Initialize v such that P(v) = 0\n", "v_find = lambda v: cheb.chebvander(-1 + 2*(v - v_min)/(v_max - v_min),order-1).dot(c)\n", "x = fsolve(v_find,v_max)\n", "\n", "res = minimize(P_fun,x_init,method = 'SLSQP',bounds = bnds1,constraints = cons1,tol=1e-15)\n", "c_series[0] = res.x[np.where(y_grid == y_series[0])[0][0]]\n", "w_series[0] = res.x[S + np.where(y_grid == y_series[0])[0][0]]\n", "\n", "# Simulate\n", "for t in range(1,T):\n", " x = w_series[t-1]\n", " res = minimize(P_fun, x_init,method = 'SLSQP',bounds = bnds1, constraints = cons1, tol=1e-15)\n", " c_series[t] = res.x[np.where(y_grid == y_series[t])[0][0]]\n", " w_series[t] = res.x[S + np.where(y_grid == y_series[t])[0][0]]\n", " \n", "plt.plot(c_series, label = 'Environment (a)')\n", "plt.plot(np.ones(T)*c_pool, label = 'Complete Markets')\n", "plt.ylabel('Consumption')\n", "plt.xlabel('Time')\n", "plt.legend(loc = 'best');\n", "plt.title('Environment (a)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above simulation is equivalent to Figure 21.2.1.a in RMT. \n", "\n", "The discussion in RMT4 confirms that the household's consumption ratchets upwards over time. \n", "\n", "The consumption level is constant after the first time that the household receives the highest possible endowment.\n", "\n", "## Environment b\n", "\n", "The second environment is one in which households *can* make commitments to enter into binding contracts with the planner, but they have private information about their incomes. \n", "\n", "Consequently, incentive compatability constraints are required to ensure that households truthfully report their incomes. \n", "\n", "Equations 21.5.1 to 21.5.5 in RMT4 express the planners's problem. \n", "\n", "\\begin{align}\n", "\t&P(v) = \\max_{b_s,w_s} \\sum_{s=1}^S \\Pi_s \\left[ -b_s + \\beta P(w_s) \\right] \\\\\n", "\t&\\text{s.t.} \\\\\n", "\t&\\sum_{s=1}^S \\Pi_s \\left[ u(\\bar y_s + b_s) + \\beta w_s \\right] = v \\\\\n", "\t& C_{s,k} \\equiv u(\\bar y_s + b_s) + \\beta w_s - [ u(\\bar y_s + b_k) + \\beta w_k ] \\geq 0 \\hspace{2mm} \\forall \\hspace{2mm} s,k \\in S \\times S\\\\\n", "\t&b_s \\in [a - \\bar y_s,\\infty ] \\\\\n", "\t&w_s \\in [- \\infty, v_{max}]\n", "\\end{align}\n", "\n", "Here $b_s$ is the transfer that the moneylender gives to a household who reports income $y_s$ if their promised value was $v$. \n", "\n", "The promise keeping constraint remains, while the participation constraint has been replaced by a large set of incentive compatibility constraints. \n", "\n", "RMT4 shows that we can discard many of the incentive compatibility constraints.\n", "\n", "In solving the model below, we keep only the local upward and downward incentive compatibility constraints." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0356136025755589\n", "0.030371315540371313\n", "0.02407399182513359\n", "0.01913266149864512\n", "0.015222370598273471\n", "0.012114987856719495\n", "0.009631147461220735\n", "0.007672223008313495\n", "0.006105497493145151\n", "0.00486238930683669\n", "0.003874043097987112\n", "0.0030852738234585786\n", "0.002458861972733928\n", "0.00195938541418883\n", "0.0015613577112887356\n", "0.0012446135038572947\n", "0.0009919573701395734\n", "0.0007906335097516148\n", "0.0006302442740278025\n", "0.0005023133549428849\n", "0.0004003561312018178\n", "0.0003190925702796221\n", "0.0002542816361241762\n", "0.0002026263867875855\n", "0.0001614498820146082\n", "0.00012861680815490217\n", "0.0001024510164100434\n", "8.159488184134034e-05\n", "6.497036918773347e-05\n", "5.17247087685746e-05\n", "4.116982454860363e-05\n", "3.2760247570706724e-05\n", "2.6062380570124333e-05\n", "2.072756330306902e-05\n", "1.6479524738599594e-05\n", "1.3097959921992697e-05\n", "1.0406276047092433e-05\n", "8.26447011803566e-06\n", "6.560744409966901e-06\n", "5.205745019054575e-06\n", "4.1285380945055294e-06\n", "3.27246526410363e-06\n", "2.5923429660679176e-06\n", "2.052268719410222e-06\n", "1.6235840192280193e-06\n", "1.283467490509338e-06\n", "1.0137738115645334e-06\n", "8.000368652005818e-07\n", "Convergence achieved after 48 iterations\n" ] } ], "source": [ "# Set bounds and approximation order\n", "b_min = -20\n", "b_max = 20\n", "w_min = -150;\n", "w_max = -0.04; \n", "v_min = -150;\n", "v_max = -0.04;\n", "v_pool = u(c_pool)/(1-beta)\n", "order = 70\n", "\n", "# Calculate nodes and basis matrix\n", "s, Phi = Cheb_basis(order,v_min,v_max)\n", "\n", "# Bounds for Maximisation\n", "lb = np.concatenate([np.ones(S)*b_min,np.ones(S)*w_min], axis=0)\n", "ub = np.concatenate([np.ones(S)*b_max,np.ones(S)*w_max], axis=0)\n", "\n", "# For initial guess, use upper bound given in RMT:\n", "cbar = np.zeros(order)\n", "upper = np.zeros(order)\n", "for i in range(order):\n", " cbar[i] = u_inv((1-beta)*s[i])\n", " upper[i] = np.dot(prob_grid,(y_grid - cbar[i])/(1-beta))\n", "c = np.linalg.solve(Phi,upper)\n", "\n", "# Function to minimize and constraints\n", "def P_fun2(x):\n", " scale = -1 + 2*(x[S:2*S] - v_min)/(v_max - v_min)\n", " P = np.dot(cheb.chebvander(scale,order-1),c)\n", " P_fun = - prob_grid.dot(-x[0:S] + beta*P)\n", " return P_fun\n", "\n", "def cons23(y):\n", " global x\n", " return prob_grid.dot(u(y_grid + y[0:S]) + beta*y[S:2*S]) - x\n", "\n", "cons2 = ({'type': 'ineq', 'fun': lambda x: u(y_grid[1:S] + x[1:S]) + beta*x[S+1:2*S] - u(y_grid[1:S] + x[0:S-1]) - beta*x[S:2*S-1]},\n", " {'type': 'ineq', 'fun': lambda x: u(y_grid[0:S-1] + x[0:S-1]) + beta*x[S:2*S-1] - u(y_grid[0:S-1] + x[1:S]) - beta*x[S+1:2*S]},\n", " {'type': 'eq', 'fun': cons23})\n", "\n", "bnds2 = np.concatenate([lb.reshape(2*S,1),ub.reshape(2*S,1)], axis = 1)\n", "\n", "x_store = {}\n", "for i in range(order):\n", " x_store[i] = np.concatenate([np.zeros(S),np.ones(S)*s[i]], axis=0)\n", "\n", "c = Bellman_Iterations(s, Phi, P_fun2, x_store, c, tolc, bnds=bnds2, cons=cons2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Time Series Simulation\n", "T = 800\n", "np.random.seed(2)\n", "y_series = np.random.choice(y_grid,size = T+1, p = prob_grid)\n", "c_series = np.zeros(T)\n", "w_series = np.zeros(T)\n", "\n", "# Initialize v such that P(v) = 0\n", "v_find = lambda v: cheb.chebvander(-1 + 2*(v - v_min)/(v_max - v_min),order-1).dot(c)\n", "x = fsolve(v_find,v_aut)\n", "\n", "x_init = np.concatenate([np.zeros(S),np.ones(S)*x],axis=0)\n", "res = minimize(P_fun2,x_init,method = 'SLSQP',bounds = bnds2, constraints = cons2,tol=1e-10)\n", "c_series[0] = y_series[0] + res.x[np.where(y_grid == y_series[0])[0][0]]\n", "w_series[0] = res.x[S + np.where(y_grid == y_series[0])[0][0]]\n", "x_init = res.x\n", "\n", "# Simulate\n", "for t in range(1,T):\n", " x = w_series[t-1]\n", " res = minimize(P_fun2,x_init,method = 'SLSQP',bounds = bnds2,constraints = cons2,tol=1e-10)\n", " c_series[t] = y_series[t] + res.x[np.where(y_grid == y_series[t])[0][0]]\n", " w_series[t] = res.x[S + np.where(y_grid == y_series[t])[0][0]]\n", " x_init = res.x\n", "\n", "# Plot \n", "plt.plot(c_series, label = 'Environment (b)')\n", "plt.plot(np.ones(T)*c_pool, label = 'Complete Markets')\n", "plt.ylabel('Consumption')\n", "plt.xlabel('Time')\n", "plt.title('Environment (b)')\n", "plt.legend(loc = 'best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This simulation reported in the graph above confirms that in environment **b** the incentive compatibility constraints induce the planner to introduce a downward tilt into consumption paths.\n", "\n", "## Environment c\n", "\n", "The third environment is the same as in (b), except for the additional assumption that households have access to a storage technology. \n", "\n", "A household can store nonnegative amounts that cannot be observed by the planner.\n", "\n", "The text of RMT4 chaper 21 shows that the solution to this problem is the same as in an economy in which each household can lend *or borrow* at the risk-free gross interest rate R, subject to the natural debt limit.\n", "\n", "Thus, the planner enables the household to relax the no-borrowing constraint implied by the restriction that it can store only nonnegative amounts \n", "\n", "We can find the natural debt limit by iterating forward on the households budget constraint:\n", "\n", "\\begin{equation}\n", "\tc + k' = y + Rk\n", "\\end{equation}\n", "This iteration gives:\n", "\\begin{equation}\n", "\tk = \\frac{1}{R} \\sum_{j=0}^\\infty \\frac{c - y}{R^j}\n", "\\end{equation}\n", "\n", "Imposing non-negativity on consumption:\n", "\n", "\\begin{equation}\n", "\tk \\geq - \\frac{1}{R} \\sum_{j=0}^\\infty \\frac{y}{R^j}\n", "\\end{equation}\n", "\n", "Finally, the natural debt limit is found by choosing the lowest possible value of the endowment, so that for any possible endowment stream the household can always pay back its debts:\n", "\n", "\\begin{equation}\n", "\tk \\geq - \\frac{1}{R} \\sum_{j=0}^\\infty \\frac{\\bar y_{min}}{R^j} = - \\frac{\\bar y_{min}}{R-1} \\equiv \\phi\n", "\\end{equation}\n", "\n", "A recursive presentation of the household's problem is then:\n", "\\begin{align}\n", "\t&V(k,y) = \\max_{c,k'} u(c) + \\beta E [V(k',y')] \\\\\n", "\t&\\text{s.t.} \\\\\n", "\t&c + k' = y + Rk \\\\\n", "\t& k' \\geq \\phi\n", "\\end{align}\n", "\n", "As income is iid, we can re-write the household's problem with only one state. \n", "\n", "Define a = k + y. \n", "\n", "Then\n", "\\begin{align}\n", "\t&V(a) = \\max_{c,k'} u(c) + \\beta E [V(Rk' + y')] \\\\\n", "\t&\\text{subject to} \\\\\n", "\t&c + k' = a \\\\\n", "\t& k' \\geq \\phi\n", "\\end{align}\n", "\n", "Below we solve this latter problem using Value Function Iteration, again with Chebyshev polynomials." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Update parameter values\n", "# Set bounds and approximation order\n", "R = 1/beta\n", "k_min = - y_grid[0]/(R - 1)\n", "k_max = 100\n", "a_min = R*k_min + min(y_grid)\n", "a_max = R*k_max + max(y_grid)\n", "order = 150\n", "\n", "# Calculate nodes and basis matrix\n", "s, Phi = Cheb_basis(order,a_min,a_max)\n", "\n", "# Create bounds\n", "bnds3 = np.array([[k_min,k_max]])\n", "\n", "# Value function\n", "def P_fun3(kprime):\n", " global x,c\n", " # Function to minimize\n", " scale = -1 + 2*(R*kprime + y_grid - a_min)/(a_max - a_min)\n", " P_fun = -(u(x - kprime) + beta * prob_grid.dot(cheb.chebval(scale, c)))\n", " return P_fun\n", "\n", "# Initialize guess and VF coefficients\n", "c = np.zeros(order)\n", "\n", "x_store = {}\n", "for i in range(order):\n", " x_store[i] = k_min\n", "\n", "c = Bellman_Iterations(s, Phi, P_fun3, x_store, c, bnds=bnds3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Time Series Simulation\n", "T = 800\n", "np.random.seed(2)\n", "y_series = np.random.choice(y_grid, size = T+1, p = prob_grid)\n", "\n", "a_series = np.zeros(T+1)\n", "c_series = np.zeros(T)\n", "\n", "# Initialise at v_aut\n", "def k_find(k): \n", " scale = -1 + 2 * (R * k + y_grid - a_min)/(a_max - a_min)\n", " return prob_grid.dot(cheb.chebval(scale,c)) - v_aut\n", "k0 = fsolve(k_find,0)\n", "a_series[0] = k0 + y_series[0]\n", "\n", "# Simulate\n", "for t in range(T):\n", " x = a_series[t]\n", " res = minimize(P_fun3, k_min,method='SLSQP', bounds=bnds3,tol=1e-15)\n", " c_series[t] = a_series[t] - res.x\n", " a_series[t+1] = R * res.x + y_series[t+1]\n", "\n", "# Plot\n", "plt.plot(c_series, label = 'Environment (c)')\n", "plt.plot(np.ones(T)*c_pool, label = 'Complete Markets')\n", "plt.ylabel('Consumption')\n", "plt.xlabel('Time')\n", "plt.title('Environment (c)')\n", "plt.legend(loc = 'best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the introduction of a storage technology for the household means that the consumption path now has an upward trend. \n", "\n", "This occurs because our parameter values satisfy $\\beta R = 1$." ] } ], "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 }
bsd-3-clause
hpi-epic/pricewars-merchant
docs/Handling products and offers.ipynb
1
13481
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulation API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A merchant wants to offer products and maximize profits. Just like on a real online marketplace, he can look at all existing offers, add some own, restock or reprice. First, it has to order products from the producer, which comes with costs. All Pricewars entities are implemented as services, and their interfaces (REST) are described in detail [here](https://hpi-epic.github.io/pricewars/).\n", "\n", "This notebook will present how to use the Pricewars APIs to do all these tasks easily. From registration to buying products, offering them and repricing them. Using this, it is possible to build a powerful merchant.\n", "\n", "*Note*: The code is type-hinted, so using an IDE (e.g. PyCharm/IntelliJ or an IPython/Jupyter notebook) provides you with auto-completion.\n", "\n", "If you want to try the following examples, make sure that the Pricewars plattform is running.\n", "Either by deploying them individually or by using the docker setup." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following step is specific for this notebook.\n", "It is not necessary if your merchant is in the repository root." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append('../')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize Marketplace API" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from api import Marketplace\n", "marketplace = Marketplace()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the marketplace doesn't run on the default URL, you can change it with the `host` argument" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Register as merchant" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to act on the marketplace, we need to be a registered merchant. Usually you use the Management UI to register a merchant and remember/keep the merchant_token. However, you can also use an API call.\n", "\n", "You will also have to provie an API endpoint for your merchant, which will be called upon sales of products. We will simply use an invalid one, since this is only an example." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'merchant_token': 'Qb3JxrP4T1UWyKWHarmPGjsOEkWnkRvA2xT0pt4ItYptsMATEwsTglruWOiHpeRr', 'algorithm_name': 'human', 'merchant_name': 'notebook_merchant', 'api_endpoint_url': 'http://nobody:55000/', 'merchant_id': 'autUGujAXiMPaZCwJcRTI1/8hFDXqH1khg6G8fl2BNU='}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "registration = marketplace.register(\n", " 'http://nobody:55000/',\n", " merchant_name='notebook_merchant',\n", " algorithm_name='human')\n", "\n", "registration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It was not possible to connect to the marketplace if you got the following error:\n", "```\n", "ConnectionError: HTTPConnectionPool(host='marketplace', port=8080)\n", "```\n", "In that case, make sure that the marketplace is running and host and port are correct.\n", "If host or port are wrong, you can change it by creating a marketplace object with the host argument:\n", "```\n", "marketplace = Marketplace(host=www.another_host.com:1234)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check offers on the market" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'price': 22.75, 'amount': 1953, 'offer_id': 2, 'quality': 1, 'product_id': 1, 'uid': 11, 'prime': False, 'shipping_time': {'standard': 5, 'prime': 1}, 'signature': '', 'merchant_id': '79Qj3UKaNep4GpXXtKLFt8Y1hEMTH1KQd+p+wFwvt/I='},\n", " {'price': 22.65, 'amount': 1934, 'offer_id': 1, 'quality': 1, 'product_id': 1, 'uid': 11, 'prime': False, 'shipping_time': {'standard': 5, 'prime': 1}, 'signature': '', 'merchant_id': '9vLjL+h81Nql8ZLBBxnm70SDZZE98IAGAuMaj1JRmC8='},\n", " {'price': 22.55, 'amount': 20, 'offer_id': 3, 'quality': 1, 'product_id': 1, 'uid': 11, 'prime': False, 'shipping_time': {'standard': 5, 'prime': 1}, 'signature': '', 'merchant_id': '8Ezxj8Q/GvFcwa0CT3zoNyr5Hg3ZuNIs+E/LbVn9R3U='}]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "offers = marketplace.get_offers()\n", "offers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result is a list of Offer objects." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "models.Offer.Offer" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(offers[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want a state-less merchant, you can set the argument `include_empty_offers` to True. This will add your own, but out-of-stock offers to be added to the response." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize Producer API\n", "To be able to call authenticated functions (like ordering products), we must provide our merchant token." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from api import Producer\n", "producer = Producer(token=registration.merchant_token)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Order products" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Order any amount of units of a product:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'billing_amount': 160, 'product': {'amount': 10, 'quality': 1, 'product_id': 1, 'name': 'CD_1', 'time_to_live': -1, 'uid': 11, 'signature': 'g0UFLCYfY5aFni311PuOfwLsAKFyiGW9mVK0HrgFRA0TchE0/CCoAjIXZfgozjMcjJb6+THRJBQQcoYEbqIHIt13Hfomx5yAMP2ndbGvz4tm6FvxNK1m5dmWsIRb9fPiZuhb8TStZuXZlrCEW/Xz4mboW/E0rWbl2ZawhFv18+I=', 'start_of_lifetime': -1}, 'left_in_stock': None, 'stock': -1}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "order = producer.order(amount=10)\n", "order" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The order contains 10 units of a product.\n", "The `billing_amount` is the total cost that the merchant must pay for this order." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "models.Order.Order" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(order)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add product to marketplace\n", "\n", "To create a new offer, you need a product, a selling price for that offer and guaranteed shipping times.\n", "\n", "Let's use a price of 35€ and any shipping times." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'price': 35, 'amount': 10, 'offer_id': -1, 'quality': 1, 'product_id': 1, 'uid': 11, 'prime': False, 'shipping_time': {'standard': 5, 'prime': 2}, 'signature': 'g0UFLCYfY5aFni311PuOfwLsAKFyiGW9mVK0HrgFRA0TchE0/CCoAjIXZfgozjMcjJb6+THRJBQQcoYEbqIHIt13Hfomx5yAMP2ndbGvz4tm6FvxNK1m5dmWsIRb9fPiZuhb8TStZuXZlrCEW/Xz4mboW/E0rWbl2ZawhFv18+I=', 'merchant_id': None}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from models import Offer\n", "\n", "price = 35\n", "shipping_time = {'standard': 5, 'prime': 2}\n", "\n", "offer = Offer.from_product(order.product, price, shipping_time)\n", "offer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Send the offer to the marketplace. The accepted offer with its new offer ID is returned." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'price': 35, 'amount': 10, 'offer_id': 4, 'quality': 1, 'product_id': 1, 'uid': 11, 'prime': False, 'shipping_time': {'standard': 5, 'prime': 2}, 'signature': '', 'merchant_id': 'autUGujAXiMPaZCwJcRTI1/8hFDXqH1khg6G8fl2BNU='}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "offer = marketplace.add_offer(offer)\n", "offer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see, if we can find the new offer on the marketplace:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'price': 35.0, 'amount': 10, 'offer_id': 4, 'quality': 1, 'product_id': 1, 'uid': 11, 'prime': False, 'shipping_time': {'standard': 5, 'prime': 2}, 'signature': '', 'merchant_id': 'autUGujAXiMPaZCwJcRTI1/8hFDXqH1khg6G8fl2BNU='}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[market_offer for market_offer in marketplace.get_offers() if market_offer.offer_id == offer.offer_id][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Update product on marketplace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Updating an offer, e.g. changing its price, is a limited API request. According to your simulation/marketplace settings, we can only call it N times per second." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "offer.price = 28\n", "marketplace.update_offer(offer)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'price': 28.0, 'amount': 10, 'offer_id': 4, 'quality': 1, 'product_id': 1, 'uid': 11, 'prime': False, 'shipping_time': {'standard': 5, 'prime': 2}, 'signature': '', 'merchant_id': 'autUGujAXiMPaZCwJcRTI1/8hFDXqH1khg6G8fl2BNU='}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[market_offer for market_offer in marketplace.get_offers() if market_offer.offer_id == offer.offer_id][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unregister the merchant" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should keep your merchant and the token as long as possible, because it is the reference to all market data (sales, profit, marketshare), offers and products.\n", "\n", "However, if you just try things out, like in this sample and don't want to pollute the database with lots of merchants, unregister it. This also removes all offers and products." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "marketplace.unregister()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, it shouldn't be possible to do authenticated actions." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I can't do that\n", "\n", "Status code: 401\n", "URL: http://marketplace:8080/offers/4\n", "Text: {\"message\":\"Not authorized!\",\"code\":401}\n" ] } ], "source": [ "from api.ApiError import ApiError\n", "\n", "offer.price = 35\n", "try:\n", " marketplace.update_offer(offer)\n", "except ApiError as e:\n", " print(\"I can't do that\")\n", " print(e)" ] }, { "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
robertclf/FAFT
FAFT_64-points_R2C/nbFAFT128_offset_xyzu_4D-Axes1.ipynb
1
555236
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4D Fast Accurate Fourier Transform - 64 points\n", "## Ini from Axes-1" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import ctypes\n", "from ctypes import *\n", "\n", "import pycuda.gpuarray as gpuarray\n", "import pycuda.driver as cuda\n", "import pycuda.autoinit\n", "from pycuda.compiler import SourceModule\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.mlab as mlab\n", "import math\n", "\n", "import time\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading FFT routines" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "gridDIM = 64\n", "\n", "size = gridDIM**4\n", "\n", "axes0 = 0\n", "axes1 = 1\n", "axes2 = 2\n", "axes3 = 3\n", "\n", "makeC2C = 0\n", "makeR2C = 1\n", "makeC2R = 1\n", "\n", "axesSplit_0 = 0\n", "axesSplit_1 = 1\n", "axesSplit_2 = 2\n", "axesSplit_3 = 3\n", "\n", "segment_axes0 = 0\n", "segment_axes1 = 0\n", "segment_axes2 = 0\n", "segment_axes3 = 0\n", "\n", "\n", "DIR_BASE = \"/home/robert/Documents/new1/FFT/code/\"\n", "\n", "# FAFT\n", "_faft128_4D = ctypes.cdll.LoadLibrary( DIR_BASE+'FAFT128_4D_R2C.so' )\n", "_faft128_4D.FAFT128_4D_R2C.restype = int\n", "_faft128_4D.FAFT128_4D_R2C.argtypes = [ctypes.c_void_p, ctypes.c_void_p, \n", " ctypes.c_float, ctypes.c_float, ctypes.c_int, \n", " ctypes.c_int, ctypes.c_int, ctypes.c_int]\n", "\n", "cuda_faft = _faft128_4D.FAFT128_4D_R2C\n", "\n", "# Inv FAFT\n", "_ifaft128_4D = ctypes.cdll.LoadLibrary(DIR_BASE+'IFAFT128_4D_C2R.so')\n", "_ifaft128_4D.IFAFT128_4D_C2R.restype = int\n", "_ifaft128_4D.IFAFT128_4D_C2R.argtypes = [ctypes.c_void_p, ctypes.c_void_p, \n", " ctypes.c_float, ctypes.c_float, ctypes.c_int, \n", " ctypes.c_int, ctypes.c_int, ctypes.c_int]\n", "\n", "cuda_ifaft = _ifaft128_4D.IFAFT128_4D_C2R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initializing Data" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Gaussian" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def Gaussian(x,mu,sigma):\n", " return np.exp( - (x-mu)**2/sigma**2/2. )/(sigma*np.sqrt( 2*np.pi ))\n", "\n", "def fftGaussian(p,mu,sigma):\n", " return np.exp(-1j*mu*p)*np.exp( - p**2*sigma**2/2. )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Amplitude x = 5.0\n", " Amplitude p = 6.0\n", " \n", "mu_x = 1.5\n", "mu_y = 1.5\n", "mu_z = 1.5\n", "mu_u = 1.5\n", "sigma_x = 1.0\n", "sigma_y = 1.0\n", "sigma_z = 1.0\n", "sigma_u = 1.0\n", " \n", "n = 64\n", "dx = 0.15625\n", "dp = 0.1875\n", " standard fft dp = 0.628318530718 \n", " \n", "delta = 0.0046627424734\n", " \n", "The Gaussian extends to the numerical error in single precision:\n", " min = 5.07874657504e-39\n" ] }, { "data": { "image/png": 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3IvJ3gX8LfKOqvnXI80UxPAj6hDEUWe6KHifG3XNP8es0dInhHUhvGdc4OyoY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\ntS1yNbjidZ7rcK53Bcl3j/uEsEsY7RiiL4Yhwbu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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0ad46dfc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Gaussian parameters\n", "mu_x = 1.5\n", "sigma_x = 1.\n", "\n", "mu_y = 1.5\n", "sigma_y = 1.\n", "\n", "mu_z = 1.5\n", "sigma_z = 1.\n", "\n", "mu_u = 1.5\n", "sigma_u = 1.\n", "\n", "# Grid parameters\n", "x_amplitude = 5.\n", "p_amplitude = 6. # With the traditional method p amplitude is fixed to: 2 * np.pi /( 2*x_amplitude ) \n", "\n", "dx = 2*x_amplitude/float(gridDIM) # This is dx in Bailey's paper\n", "dp = 2*p_amplitude/float(gridDIM) # This is gamma in Bailey's paper\n", "\n", "delta = dx*dp/(2*np.pi)\n", "\n", "x_range = np.linspace( -x_amplitude, x_amplitude-dx, gridDIM) \n", "p = np.linspace( -p_amplitude, p_amplitude-dp, gridDIM) \n", "\n", "x = x_range[ np.newaxis, np.newaxis, np.newaxis, : ] \n", "y = x_range[ np.newaxis, np.newaxis, :, np.newaxis ] \n", "z = x_range[ np.newaxis, :, np.newaxis, np.newaxis ] \n", "u = x_range[ :, np.newaxis, np.newaxis, np.newaxis ]\n", "\n", "f = Gaussian(x,mu_x,sigma_x)*Gaussian(y,mu_y,sigma_y)*Gaussian(z,mu_z,sigma_z)*Gaussian(u,mu_u,sigma_u)\n", "\n", "plt.imshow( f[:, :, 32, 32], extent=[-x_amplitude , x_amplitude-dx, -x_amplitude , x_amplitude-dx] )\n", "\n", "axis_font = {'size':'24'}\n", "plt.text( 0., 5.1, '$W$' , **axis_font)\n", "plt.colorbar()\n", "\n", "#plt.ylim(0,0.44)\n", "\n", "\n", "print ' Amplitude x = ',x_amplitude\n", "print ' Amplitude p = ',p_amplitude\n", "print ' '\n", "\n", "print 'mu_x = ', mu_x\n", "print 'mu_y = ', mu_y\n", "print 'mu_z = ', mu_z\n", "print 'mu_u = ', mu_u\n", "print 'sigma_x = ', sigma_x\n", "print 'sigma_y = ', sigma_y\n", "print 'sigma_z = ', sigma_z\n", "print 'sigma_u = ', sigma_u\n", "print ' '\n", "\n", "print 'n = ', x.size\n", "print 'dx = ', dx\n", "print 'dp = ', dp\n", "print ' standard fft dp = ',2 * np.pi /( 2*x_amplitude ) , ' '\n", "print ' '\n", "print 'delta = ', delta\n", "\n", "print ' '\n", "\n", "print 'The Gaussian extends to the numerical error in single precision:' \n", "print ' min = ', np.min(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $W$ TRANSFORM FROM AXES-1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### After the transfom, f_gpu[:, :, :, :32] contains real values and f_gpu[:, :, :, 32:] contains imaginary values. f33_gpu contains the 33th.. complex values " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Matrix for the 33th.. complex values\n", "\n", "f33 = np.zeros( [ 64, 64, 64, 1 ], dtype = np.complex64 )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " GPU memory Total 0.499267578125 GB\n", " GPU memory Free 0.120002031326 GB\n" ] } ], "source": [ "# Copy to GPU\n", "\n", "f_gpu = gpuarray.to_gpu( np.ascontiguousarray( f , dtype = np.float32 ) )\n", "f33_gpu = gpuarray.to_gpu( np.ascontiguousarray( f33 , dtype = np.complex64 ) )\n", "\n", "print ' GPU memory Total ', pycuda.driver.mem_get_info()[1]/float(2**30) , 'GB'\n", "print ' GPU memory Free ', pycuda.driver.mem_get_info()[0]/float(2**30) , 'GB'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(64, 64, 64, 64)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_gpu.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forward Transform" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "computation time = 6.34740400314\n" ] } ], "source": [ "# Executing FFT\n", "t_init = time.time() \n", "\n", "cuda_faft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, delta, segment_axes1, axes1, makeR2C, axesSplit_1 ) \n", "#cuda_faft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, delta, segment_axes0, axes0, makeC2C, axesSplit_1 )\n", "#cuda_faft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, delta, segment_axes2, axes2, makeC2C, axesSplit_1 )\n", "#cuda_faft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, delta, segment_axes3, axes3, makeC2C, axesSplit_1 )\n", "\n", "t_end = time.time() \n", "\n", "print 'computation time = ', t_end - t_init" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cuda_faft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, delta, segment_axes0, axes0, makeC2C, axesSplit_1 )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cuda_faft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, delta, segment_axes2, axes2, makeC2C, axesSplit_1 )" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cuda_faft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, delta, segment_axes3, axes3, makeC2C, axesSplit_1 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Real Parts" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Getting the real-value data from f_gpu and joining to the f33_gpu real-value data \n", "\n", "f_real = np.append( f_gpu[:,:,:,:32].get(), f33_gpu.get().real, axis = 3 )" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1UUTGgP8ds+zrhzBrY/8cG9fG/m0ReSvGdbZrY6uILIrIQ8BjmLWxf8F5zysx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\nKButx6RIutZjMoPtJrqSv4/k5+/piXe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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0ad46dfed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_real[:,:,32,32]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Re \\\\mathcal{F}(W)_{uz}$', **axis_font )\n", "\n", "plt.xlim(-p_amplitude , p_amplitude - dp )\n", "plt.ylim(-p_amplitude , p_amplitude - dp )" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FmNaFqB+GIWh3acnPIC62iOwHGqq6KCJjwP+CWfb1w5i1sX+RjWtj/56IvBXj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\nc7LwfTMCmTxWFv2+T/HC6DJYmU9r3eqYixjR3NV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa4969350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_real[:,32,:,32]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Re \\\\mathcal{F}(W)_{uy}$', **axis_font )\n", "\n", "plt.xlim(-p_amplitude , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oa08JRQ2AS3QlEP8kYka//hl97SnA31M9axPGQPSTjRl1jBlIn4ciKZ5F+DSWjDhgNBzV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\nRpWfh2ReSOaFeD6ysbBfqrA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TwL8APuBmish1wNOB64CnAb8pdmtl4LeAH1PVa4FrReQp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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa4969610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_real[:,32,32,:]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Re \\\\mathcal{F}(W)_{ux}$', **axis_font )\n", "\n", "plt.xlim(0 , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EBEQ0yRuXmhx1iuQpUSNERFFpxyTDMCTIRUgQGXc/y7VOWoxpXYj6YRiCtkdL\nfgZxsUXkINBQ1UURGQP+D8yyrx/CrI39c2xcG/t3ROStGNfZro2tIrIoIs8EHsasjf0LznteiWnY\nPZS1sb1ADpPNfCmyHAu3lCcZc7Ti6JbyrNB2q5eB1RCqNtZYg7AKWk28YZXOtRXsxTsV4UEeJA+F\nPIzlYFqMtTgLHAGOKXIkorx/ndL+dUpT64yPrzBRXGIit8Q4K4yxTpkqY6y3hDGIrdJGbDE2KJCn\nSUDUStiE5GJ3u4DE2zd8Vsma0G5L1/YSyD0oZtvBgDHIo8C74jhkALxPVT8sIn/NLl4b2wvksOi3\ntCfry5qsdUx25XFdareUx3Wrl9WIY7UB9QY0VyFaAbU7u0WRbqtwayaCScwUjDjm8pDPQ0VgOoCD\nGIE8qnBMCY6GlCarTE0vMjm5xGRpicnCElO5RcZZoUyNEuuUqbWE0dyqUKdIjRJ1iuQIY3E0CRsj\njsVYUG2kMuPzylqudrMWpBfJngxSB6mqXwCenrJ9HviWjPf8DPAzKdv/BvhHKdtrxAI7LLxADoNe\n9XhJ0r6waaU8blLGnSGTLOWxMccVjFtdb0Bt3cQbdTl+cZnOSnF7EdZyLMc/50ByxoIMrAWJScoc\nBGYVjrQFslxZY7KyyMHKDaZzC0zJEtOyyATLsfwZGRS0JXchOdYpt4a1EE02O98SzVxsPYqrclm1\noVmW42ZcbC+SXRmkDvJuZfTueLvZrEhmZa3dudFu0wl3dowt5VlTk61ej93q5qojju5wsztWGN1p\nMWPABBTGYawUu9bAUUVOKJxSCsfrFGfrFGZqVCZXOVS6zmxpjkP560wHi0yxyBRLjLNKgQbF2JG2\nwhiSo0GBAo2W222txrYwhm2LUzE1mFE8Qklfx9v2sEwTyK3+vrxgduCnGnp2FtfCSVtgy407ptU7\nrhDXOEbthExYjd1q1/d2447Wb7ftwF1xjAsbi+MwVYaZPBwCORUhZyPkbEh5do2pmUUmp5fYV15g\nNn+d2dwch7jONItMsswEy1RYayVlcoQtYTQZ61Ir5ggmHlmj1NrfblfEzKCxwth0BxtF0v0n06u0\nxwvjpqlnlPnsZbxA3knS4o7JrLVrPVqR7JhGGMcda01o1OJSnhXabrX1w23c0Q5bc5Ss/p6GYhkm\n83AoBycUOR0RnGki9zQp71tlX2We2cp1DpVvMBvMMRtcZ1auM8UiE6wwwSpjrGFmUJt5MlYETVRy\nrCMhU6PBsFHiAAAgAElEQVREkXorWZOMVxrrMYitR9nY/bzb+t1ZpTe9QiJeJDcwSAzybsUL5E6S\nLOVxrce09WTSLEjXxV7B1Dqux3WOTbcwspXWjt+wTqdqWHGM63eCcQgmzJgomNrGY0pwKiJ/skHh\nZI38iRrTE7c5lJ/jeP4yx4IrHNIbZjRvMMkyFV2jwhpl1juSVjUpsi5jrEuZgjSd+sdipzhqWxwj\nAqIoQEPTsIJGsFEc3bV3ko1+s7DimLXOuBfHVHwM0jN8upXzpLnVblImbSqh28JsHVPOE8ZF4Bsm\nXttaR7segtuhZ5LWHMFgBsanoVKG8QA5GRHcEyFPCimcrTN1ZJHJmQWmSoscyV3lRHCJE1ziqF5j\nX22RmdoC+2qLjIVVSlGNkq5ToNHRaDdXUKQgUIQwH1BgjHwchzQfh7Qz2FqgERWoa4FmI09YD9Ca\ndM47Ty4T0aTTIndJqy4QtqdecVhdnHYhPgbp2R6S7l5aBjZZDJ4mksmGty2BrMUzZNxyHjtbxgok\ndHaZmMBUex+CYD9UxuBgGQ4IckYJ7mmSf0qD8uk19k3d4vD0NQ6XrnE8d9kIpFziSDTH+PoaE8tV\nxlfWKDYa5MMmudDEElvdxYuQK6vx4gMjkK7VmKx/bEZ5GmGBRlSk0cgT1nPounSKY9pyte5nCxtd\n6eRsyu0Qx2F0cdqleIH0bB/dylOyFtpKS8644riO6eVIPRZIG3NsNX2k7Xvaekc7qXoCY0EehNwB\nGM/BgTwcD5CzDXJPbpK/r07pzBozuXmO5a9wJn+Ok3KBE1ziJBeZ1euUaiHFlSalW02CmiJNhaYi\nqh3LLeQmFXKgRWjSRSA1T0PzxoJsWgsyl25BJpeqdT/rZDu0bi71sNmD4gg+BukZNmnudVpSJivu\nmLqmjJpmtw2FMMK0K0sucG1rXuKOPC11sC14JiB/AIpTUKzAZBE5AXI2IjgbMnZ2lcmjS0zMLLK/\ncosz4TnORk9wdv0cRxtXma1fZ399gcnqKvl5pTAfkZ+PkDrtGKA93YS5vyAfEZQjclG7hKdd3lNo\nVUyuU6beLNGoFQlrBcLVPLoaoGvBxvCCG4e0LdAs9pYD57lrWQ6bpBjvQeqU7vQl7DheIHcCt9bR\nFcduZT1p2WsrkPXINLmlSWfTCTfeaMWxQDshE8cc2WfEcXoKpkpwUAieFJK7t0nu3iZTRxeZPXSN\n2co1jupVTjUvcrp+gdO1i+xfmWdqeYnxpXUKSxG5BUUWFBboXNCrQHuad4AxWmPdduONrjBWKVPV\nMrVGmcZ6kXC1iK7k0ZUcuiKdTX/X28dLXb/b9t2whrN9zccct4x3sT3Dx42JufOHkzNAstzr5MJb\nbmJG3XbiaQLprrY1RivmyEEoVYw4zhaR40JwNiR/X4PC/XWmZhY4MnaVM2PnOM15TjQvc7J6mZOr\nlxmfX6V0vU7xRp3CzQhZBlmKm2NAu+fFGG1xLGAaXVjBhFa8sS2QpvxnXceoNUs0qiXClQLRSh5d\nCWBF4plCzmeTLOtJutM2LwXtpMww2eMxxyTexfYMTtqXsJ/mCsluPcnRsiY1XkPG6Qbe4VZDe3aM\nLQIvYUp5ZkxCJjhoSnkOSiyOEcWzdcbOrlI+u8qB8g2O6hXORE9wT/1xjq3OcXRxjmOLcxSuNdDL\nwBXQOZC4JlNXQfOYhbnGQMZp63IsjhrGSyq0xNEIY1XHqGqFNR1jrVmhXivTrBaJlgvoUq4zOe+2\nskzOlnHFMZeyLfnPaliMgPUIvszHs1W6fdlctzrNanTr+JLJGjcm2RFvU7NkQoe1aE01d251EZM6\nrpg6x/FpqFRgPIecgOBJIcHZkNKZOvtP3OTA9A32525yunmee9af4NT6JY6uzjFzfYHKjSpyIyKa\ng3AOmnMQ3QJZj0cNghLkJuK2jXYVRMzlhLkcjaBIVcZYZYIVJllikkWmWQ6nWG5MsdqYoro+QW1x\njOZCAV2Qdr27Lel0Pw+33tEVRteatFasfdxs+7M0RsitdvEutmfrbNZyTBPDfkYTs/pgawUre2Jn\noa0WdvrghBHIVilPDjkbkbu3Sf6+BuUzqxzYd4OT+y5wMneBU7WLnFq5yOmlSxydn6NyucrY5SrB\nJSW6AY2bUL8JzQUImmZIE/LjoBHkBYIybYHMQZQPqAcFqjLGCuPxhMQplphmKZxmpTbFyvoUayuT\nNBcLRiBv054paZPzbocj2Gg1Ku01u11BzBqbZcTcahcvkFtERF4AvA3zJ/kOVf25YRz3riMra90t\n7phlObrWY7Ler2N5v2RCxgpljnbr72nIjcN4AAcCOB4QnDVJmcL9dcbOrHIgd4OTuQs8JfdFTjYu\nc2z1GsdvX+Pg3DzBBSV4IiJ4IqJ5AxoLsH4bGivGWszFQ2sgArkCRpdTLMh1x4JcZpIlplgKp1mu\nT7G6OsXa8gS6KMZ6vC2dk4GqdGatoZ2ptrcNbcFM6+ozDAvSnmPE8AK5BeIGmL8EPBe4AjwsIn+k\nql8c9NiZdPsDv9N/uGnTCZMimVUD2Uxsd+cXdzRhcBXBXX0wXnJY4pEfN40nimVTynMyQs4ocqbB\n2NlVpo6aGTIHx25wqnmBU/WLnFy9zNEbcxy4cpuJK2sULjdoXITaJWhcM8K4vgLVVWiut1d+LWKW\njIkEtABaAq2ATgo6DbWJAivlCgu5fdzSA9yKDnAzOsjN6CCLa/tYXZqgvlAiup03rU5vAfPE0ylp\n1727n4X9GHJ0hl+tOKbFG7Pc4n7nZ+/xUp5u1AYo8xGRE5gVCA9jfju/rqq/MArrYj8TeExVzwOI\nyHsxyy9un0Bakn/4u4k0q8UVx14WZbIBQ+terXlkxRHnedTuBB7koVKCyTJM5eEABPdEBPc0yT25\nyWRcynN47BrH9LKJOa5c5PjqNWauLDBxfpXC+QbRRVifg5U5WInFcX3d9MaIMPmXVrI6MIXgtmta\nNCVEM0J4UKjuK7FUmeRWfj/XdZbr4WFuNGe50ZhlcXWGtYVxGjfz5s96nrZA2mnk6xhL2rUA3aiC\nLeexfThyzr5pRePJmkkyXkt7fUQZ0IJsAj+iqn8rIhPA34jIg8D3s8fXxW4ttRhzCSOa20uaq7Rb\n/nh7xb2yxLHXsgGt+7XKYOtYnA4NthN4EC+VsN905ZFjSvCkkPxTGuTvqzM5s8ThyjXOjj3Bmegc\np9ZNzPHY7WuMX1mjeL5B4csN9DxUF2BhAeYXoFqHetNUGtnp5HaJrzAHURF0DHQSommhORPQPCBU\nJ4ss5yeYz+/nOoe5Hs5yoz7LzdosqysTNBZKNG8WzJJNtzHieBtjPdqstbv4os1FFdhoPSY+kg6y\nYojdxHFEY45JBhFIVb0GXIufr4jIoxjh8+tit/jgm9rP738AnvrA4MfcztkRmzm/+3NSDNNc7bRS\nn15rqggmyKfWgsQIon0tELNEQiFeKmEaU/Z4AuRUSP6smTpYPltlZuwWR7jKaT3HPfWvmFKe23Mc\nujZP/nIDvQjROaidh9UqLK7BzTXTl9fqeQCUBMYF1Hr4rbaSQjidoz6dZ306z/LYBPPs4wYHmQsP\nc7N+iFtrB7m9tp/6QhnmBW4EMIcpOl+MH+2MGVvimeZWu0mabu3O0qzG1udKdwtxJ6zHYYWOHnkI\nHn1osGtJYVh1kCJyBvha4K+BPb8u9mXglPOzuwxjJ9/9piGcbheTJY5JtzotHpnVBdv98reS1AIa\n/7EG6rwmUMnBmJjqnqNqmt2eVgonTVeefVO3mMnd5mx4jtPNi5xsXmmX8lyqIhcjGrFbXV004niz\nBrebsKTtGX0CFARKBSgXYawI5f1QmIXgKERHA1b3V1isTLAYTHJJT5gRHedK4yjzS/tZm68Qzefg\nmsBVgasYC7K1xg7GerSKbDPUdvFFm5vqt3u4+5nax26C2Guf7WSQf/xPfaDT+PiDNw9+PWTXQT7x\n0AXOPdRfqC92rz+AiSmuiEiaiTEsBv7NDUMgHwaeLCKnMX/iLwVeNoTj3l30cqfTxDA5khYnbBTI\nAsaCVIzZVlBjuRWAspjVB6cCmAY5ochZ0+w2f3KdqZkFDk9d41jOFIGfrl3g5Pplji3MUblRZexS\nleBxpXbZxBwXF2BhzYjj7aZZE8waikWgKFAqQrkCYxUoHYB8LJDNYwFr+yvcGjvAXHCIi3qSS9EJ\nLoUnuFI7xsrSFNXr40RXcjAn8cDEIN3CcLeNmT25/YysIZ3MVPdDr5jknXSr0/5J7gKyXOxTD5zl\n1ANnWz9/4s2fTN1PRPIYcXyPqtrlXff2utixKfuDwIO0y3weHfS4dxX9xhzT3Os0kexwqel0K/MY\nYdSc2a+gJpdXxvi6B2iP04qcDZF7mhRO1JgsLXC4dI0zedN44lT9IidWL3N0cY7guhJcjggej2jE\nCZlbC8atXlIjjkvE2er4dAUrkONQmYLifswSDbEFubZvnPnKAS4Hx2Lr0Qjk1fpRGsslmjdK6MXY\ngryBGfN0di6KEp9DsrJpQxJrE9hj9rIidxL372aXCKNlCGU+vwk8oqo/72zb++tix+vSPmUYx9o0\nd9L9ST63P/caafu72C+utZBsIkKBnLT3AWM1jmNc6ik1y7LGo3C8Tnl2jfLMKvsmbnM0Z/o5npSL\nHG1cZf/KPBPzqxTnGkTXIboOzRumlKe6YhIy1ajdarGA8d4nA5jOwb4STO6D8iHIHYTweI76kSKN\ngwWW9k1wdfwwl4rHOc8ZLtVPcGNllqWVfVTnK+iFPNGFAnohMFbjbTVjkc4ploqJuyLm3u0/iTRR\nTLMK3W4+yZGV3e4Vj9wpdplI1gZYk0ZEngV8L/AFEfk85u7eiBFGvy720MkqxdhJXHFLiuZmXT5L\nUhytGEC7naObrbVLycTrbXEEs271EaU4W2NqZpF9lXkO5edMo9u4G/hs/TrTS4sUr9fRy2b6YONW\nXAS+YlaNrTc7s9Q5YDIH+wtmzIzDxAEYOwbBCVg/VWT5yARL+ye4Nbmfi8UTnMuf5nF5ElerR5m/\nedC41VcL6Lkcel7gArCgsBKZsaqd0y9VwK41r5K+7kyaEFqLO3AeNyOQng0MMhdbVT8JmSaoXxd7\nW7iTf8i9BHArApm0fOzkGOis7HGb9NjJMlPAfuCYtkZxpsbU9BKzY9c5nrvMcTGdwE9ykZn6IhPL\nVYrX63AFmtfM9MH121BdM3WOtdBolA39lYDpwIjjbBlmJqBwAArHQM5C/USBpSMT3Nh/kCuTR7gQ\nnOB8cJonOMut6kFWbk1RvTBOdK4AFwW9ICbnuKKmx+V6CI0oEXII4t+zmHt3+/8mPzdXHJPPk4+u\nqHpx7As/k+ZuYaf/iHu50Wn7Z1mXaSSFMZl8cKfNQcdSBkzRavHIAZAjEcHREDkaMja5xnT5NofK\n1zmWu8IxvcpRvcbhcI6J6hqFxYjCrQiNG080F6C+YhZHDJ1LK4qJNxYF9hWN5TgzAfsOQHRY0OMB\nzdPC6tEK8wdnuDp9mAtjJ7jYNONS/QQrC9M0rpVonC+hX8nBFTXjWmRWZQybZkRu51sbaLQfDBvj\ntND5uW1m7HbrcTddC14gPf3QTey6xRh7xRrd2Fqyvi8pBmXaXcymMZbjfpD9EeX965SmqpQqVWZL\n15ktXGc2uM4hvcG+2iLjtSrFWkh+Xk2z2yVgFdORp2HmVMfN0QgxdY6lAhSLJiEzOQ0TB43lGB0O\nqJ4tUT0xRvVwiSszxzg3foonCqc53zzF5cUT3F44QG2hQvNckfDLefSJAC4qzDdguW5WY4yaZqht\n2eZmpArOB0S6tZgc1rp23ey7xa3eLdeRgu8H6elON6twM1ZlWpbaFUk3S2v/JgNnm7PWCzO0stbB\ngYjS/nWmphaZrCxyqDTHbO46h4PrHIpusG99kfHlNUorTfK3IoIFRZbVCGTNdOXJxVVD4/GpGmLq\nHMuVeMyamGPhGETHheqJMRZOTnH7yDQXJ4/xePkMXy7cw/nwDLcWDnL70gHWLxmBjB7PoY8LXFZY\nq8PaGjTW4ua/ViCVtnmcjC84n1eWKOYT2+2+uzkZkyRZcrRL8P0gPdlsVvy0y2uWpDi6+1i3Ok+n\nINhu3ePx437gIPHihEp5ep3JySUOVm5wKH+dQ1xnVuY41IwtyJUqxVuhWUNmEVgCjS3IoGnqzm1C\nphRf41gRxsZhbAryhzC9JM9C41RA9UiZ20emmTt8iIul45wLTvNleTLnqmepLo5TvTxO7UsVoidy\nJuZ4HpgLIYqXqo0WMY1/7SR0xSi//Q9gb1rbn1kvCzI5sizIbiU+d5pdeF3exfa06VbKs1Xc0Fra\nF921LJMxSDt7xJp348AEyExIMBOR2xdSmq4yPr7CVGmJ6dwC+4IFpllgiiUmdZlKWKVYbxCsRwR2\nhgqmpDIoxf0ca5CLIJ+DYpy+Lu+HUjzCEzlqp4o0ThZYOVbhysxRLk4e41LpGOfCM1xZOc7N6iGW\n5vfReKJI44ki0RMBejmEGw1YbkCthqmqtKNJZ4Fjvn1x7udlrUP7WSQf7XP35yyhdMVyt7CbriWF\n+gBlPncrXiB7sZlki/seSz+1dk6IrfXldY9jRcGmkp3FCXMzIYXpOoWpGpXJVSaKS0wWFpmSxbhn\n9woTrFBhjaKukw+bZmlWW4BdABmDoAH5CEQgCiAqYDrzVMz0wfwhYBbqR+JSniMTzB+c4YnxU5wr\nn+GJ4DRXVo4xd/0oy9f3Ub9SIvxynugrOfS8mvmKS6smC9SaJmOHFUb7nyBq/yyBqX8MpFMA3URV\ngY3CmOVyu0LpCqRs4fc8YvgYpKeTrdYyJkmKoe0P5lqPdj+lHTOzuIJQxmSu4xHsCynsq1GeWmN8\nYomJ3BKTuSWmZclYjiwzzgoVXaMU1chHTcR6slaPysZqlAByRdA8qI1zTkFwBIJjIEegfrDA0gFT\nynN1+jDnCqd5rHAPX5Ync2vtEEtz0yw9Pk3j8TLROUHPCVzQOCGzBLV52l0o7CpktpCoQKdSxQIZ\nBO1/Eq5AJkWxwEaxtPHJZPlPmovtRbIrPgY5ynRLugxCmludtBytAeWe07VwrOVYAsbUiOM0MK0E\n0yHFyRpjE2tUKu3FDMxYocIqY1Qps06RBjkNQbVlPVIGmQApYJZJmACNH5kA3SdER4XwWEDjWMDK\ntCkCvzJ5mAtjJzkfnuJceIYn1p/E8u1p6ldLNB4v0/xSAS6HZlwLob6Kcadto0e3VbqbmbJuXFzr\nJDnIBabOKGk5WpG0j2mWo33sVuKTxItjKj4G6TEMQxSzjuGU9G3YP5mksV9sK45loAIyEcFEhEwo\n+bE6hWKNUlBtCWGJGiVqFGiQIyQgamfAnYa2rYWvinT0o9SK6eWoU0JzX461mQqr+yusTVe4On6E\ni6UTXAhMjePlxRPMLx5kfbFC43FTyhOdE7gSwvyayVRHa7SbPMZ1RR1TYuyHYoOsxfYNB0XTuq0U\nmFlDbgbfDvv5uELZLe44yO9vhPEC6dlazDHtGGm4lqO7LW1esfslL9EWhQowERFMhshkSL5Sp1is\nUc6tU6HKGOuUqVGiToEGeUIEbSc5XIG0lupY4vInTSfw5kxAbTrPQmWSW5X9zFf2cymePng+OM2l\n5nHm41Ke6uVxk5D5Ss641ZdDI45r8xDdxkyytkmZVefmLVYgC7QVr2SyR4WcKcjsJo5ZApnmVidP\nncSLYyo+BjnqDMut7oYb+3ILwZOJHTc7a6texoBxRSYUmQgJJhvkS3WKBSOQbQtyPWFBxgd3LUg7\nybqAEUvHHdV9EB4QmgeF2lSBxWCCueAQl4NjnJczPM6TeELOcrl2gvXbFWqXKqx/qWKy1efVxByv\nhRCtGnGMrmGE0bboqdGpYpBtQRbiz0DSxdE+9iuOuzxTvJvxMchRYztKeWCji5Ymhsm4o90P2sLl\nCqQzgnJErtQkX2xQKNQp5OsUpN4SxFw8FUcRQnI0KFCXIrmCkhuLCMKIIK9xizSgAWE+R5QLiPIB\ntYki1X1FqpMllioTXNITpp8jJ7hUO8HV6lFuVQ+yvDBN83yRxrki4RM5YzXerMUJmVXarcGt1Wjr\nHd1KeJt5GqO9hnc5DormjGudeLlDKK04JpM2SZHcTFG4d7FT8WU+o8ow3Ork8VySX8ysbKlbF+lm\nY62lVAYpK0ExIl8MKRQaFPINCkGTgjTb8UasOOZpUKBGiWpQhoIgFXP8YCxqLWUQhQGNoEA9V6QR\nFFgpj7NcmWApP8FtZoxARqaf483lWW7dOsDKrSkacyXCx/Lo4zk4L3CzAUsrULctwW3McY1OcXSt\nRat+trBzwghkoWDKe6wwuo9WIJPudVZyxq0SyPoddfv9eQDvYo8m2+1WWzF0s9nuud393LIfNzvr\nJmnKEJQi8oUmxXyDQq5BQRrkUwUy1xLIdRlDCgKBokUliKJWniTSHFUpsy5jVGWMhfw08/n93Mrv\n5waHuBQd51Jomt0ur0yzNjdB9eK4aTzxRGCmD16gbTnW5jHiaK3HNdri6Aqkk3lq9W2bgFzZrKtT\nlI3WY1IkXesxmcF2E13J30fy8/f0xLvYo8h2iKMVwaTr3M29S06dc0tYitoSSSkqQSEiVwjJ55sU\ncm1hTBPHOkXWKZOXJlEhICwIIYFJ3Dj7rjLOKhOsMs5NPWiWZmWWufAwVxrHuFI3Y/32GNG1AtG5\ngunKc1HhUhxzrNcwReALmHIeWwhuO9+6rYqs9VihNTVIxiGoQL4AxXxn7DXpXm8mOZOsGnB/N56+\n8VnsUWQY8aYs4UsmYNJcb/toQ3LJedcdcTWFvJpH0Q6Riwg6LUbKrZgkmD/uKmMUqVCkDkhLVhsU\n4vk2Zt7NzeigWZY1Xpp1fmk/K8tTNJdK6IU8ei4HF8W0K5tvmNbjUR2zytYq7bVa7XRBe2PWvCti\npgLFQyYhGI/nOxZgLA+VwCwhMUG7Y3pa7DFrWmGveGOvUiwvoBsYRCBF5B3AtwNzqvq/xdtmgPcB\np4FzwItVdTF+7Q3AqzCux+tU9cF4+9Pp7Cb+7+PtRcwSsM/A9Kh/iape2PIFx3iBHPY867Tt3b6I\n9jFpQW6YMueIYxBhFoPrFMgmeZrkW1ZjLi7xUYQm+fhVM+z+jXj/5da8myluRQe53pjlRmOW+bUD\nrM2Ps3Z9nOaNEtGFHHo+MI0nriosxV15dI12MmYdI5C2xtGql5OdZpLWdCCZhFwF8mUoFqAcGIGM\nPe4OgXRFslvdY6+kjI9BbpoBLch3Ar+IETHL64GPqepbROTHgDcArxeRp2I6g9+PWZTrYyJyb7zk\nwtuBV6vqwyLyYRF5vqp+FHg1MK+q94rIS4C3MNJLLuxGuolk1vbkrBq37Vmr1EcdgYyQoLcFWaDR\nEseQHHWKLTc8IGwJqRXTJaZZZIolprkVHeRGc5YbtVkWVmeIbuWJruSILsYdeWwn8GsRNOpQj7vy\ntASySrsKHTqz1TZ1bgVyn3Grc2OOQEorZ5NqQVrXOi1rvZmCcM+mqJn+TltCVf8yXvnU5TuAZ8fP\n3wU8hBHNFwHvVdUmcC5eY+aZInIemFTVh+P3vBv4TuCj8bF+Mt7+AeCXtnyxDl4gB3WntvpFTIqj\nK5Bp0+Oc/VUAkVj8AprkHMux1IpJWiuxTrElmFZU2654iXUdYymcYimcZimcYrE6w8LKDKsrk9QW\nxtrLstrVB2+rWeawGje6DeumDVBLFO1NuB9OiXYgcdxYjTIJUoFiOV4eMQdjQasRxwaBdN3rtAYV\n7mfZz+efFn/21mMm2xCDnFXVOQBVvSYis/H248CnnP0ux9uawCVn+6V4u33PxfhYoYgsiMj+O77s\n613Pdmav3efJjDV0FjAnG7x2qd8zFmPbarQimKNJQBifTloiWKROjhCND6TQFkfKVHWMlcYUy7Vp\nlutTrC5NsLYwTmOxCLcErsYCeYP2Alu1KF4iwTa6TemA0aHyNtMSJ2SCceNW58ZMy/JxG3fEGJY2\nRGkF0rrXbuwxGX9Mc6uzstZZwugFMpMsgaw+9BmqDz2c+tomGXY18sB4gdwO0rLWab8u1+JxE7xZ\nLbmwf0HSEsm2QBYIKMXyZ8SzTrEVc8wRErbKyANTGxnPvalqhZXGFKvrU6ysTlJfKNG4WaB5swA3\nrEDSth5XIrOiVxiLY6pAukmZPB1lPBIXg+fjUc5BRWBSOnI3TLKxvKdbQXg/1QJpX8Fh18HuUbLq\nIAsPfAOFB76h9fPtN7+930POichhVZ0TkSPA9Xj7ZeCks9+JeFvWdvc9V0QkB0wNaj2CF8iNJTnD\nOl4/JMUxWbuX1tRVBVUh0oBIA0I1cccAm7gRolg08zQJiEyyRtVJ0eSpaYmqjlHVMdaaFarrE6yt\nTrK2NEk0nzN5wDnMn+x1jDjOA4sKaxHUQ4gi2k0nbLGn9XWVzmJOtxA8LuUpFIz1OBZ0hiVd93qM\nzuRMskGuK5AuSYu9n1lTXhy7MoQ6yORf84eA78Osjf1K4I+c7b8jIm/FuM5PBj6jqioiiyLyTOBh\n4BXALzjveSXwaeB7gI8PerHgBbLNTpZ2SMbIQsVcWwREQhQGhM0czWaBQE3SRsQ0pLDi2BJNtFUf\n2dQ8jahAU/PUmiXqjTK1ZolarUxtcYzmYgFdknZHsoV42OodO43aNgAHOjtg2LZlNnvtzKsOShCM\nmSJwW8pTDkxCxgqj61InY472UG4CK/mPJMs6tI/20jxbYsAyn/8KPAAcEJELmITKzwLvF5FXYRbj\neDGAqj4iIr8HPIIJbL8mzmADvJbOMp+PxNvfAbwnTujcYggZbABpn3d7ERHlt3fZv+ikW7XdjSqg\ne3LGtYZcS6mksc4oUozIlevkyzXTqCJfp5BrUAzMY46QPCF5mq2EjKCoCo2wYEaUp1Er0Vgv0lgv\n0Vwr0lwoxCMPtwJjQd7E/Km5z9ea8aLZTWg2aDegWGPjcoxONXeuYDLUhbwpAq8E7TEpndZj2pxr\nW2Rbi6oAABK4SURBVNbTrTt40lVO/hyR/Tvfid/9neLlgqoOFJMTEZ3V833te11OD3y+3YK3IGGj\n9bYTX5RNWZDxiASNAjQMCJv5uGAcVAQNTDzSuNTtWTXm7cYlb2iBelSg3iwS1gqEq0XC1QLRUh5d\nEPS2wG0x1uM8poXjAu3qHdeCVHsTtoTHfmiu6rsdNgLIi5k+OCbtInA7bMzRJmSSXXusBZkVgugI\nQ6R8fmnraXs2Ra3um1WMFt2+VNt1PmtcZWVcXVpWjpgveAiEijYDNMgRiiIREAoa5ghzzdilNm61\n+Scexywjodko0GjkaTbyhKt5dCVPtFJAl3LtadPLtJeMsSsiNHDW1RLQANS2Qi/SFkpAgnjkTLPb\noGRalpWCtvDZfpTJ4brWtp48WQzeb1dw9zPsVsrjBbNvwuboycXo3fGdIDljpp8vtqUVe8QIZFNQ\nCYjIIyo0wwBt5ojyIUFQaNU6CmoSOpG0LM+wFhDWc4T1HLoWoCs5dEWMKLrCuIYRRlvaaMVR3Juw\nz604FuKnAoGYZRLyedPsNi+dTSds857kcEt5ksXgWVbjZjPWydd99rpvwqafi70pROQtwD/HfJW+\nAny/qi4N48L2DO6XOE0c077gybiYFUdbKB4fSDVAwxxRLkIaERJE8SHjb3ssjoRirM6amLEu6Jqg\nKwGsiBFGK5BrtC1Hu2RMiNN4Q2IL0S3gjP3X1iYxa8iUAtMJ3HZEr2SMZDMKOyPRtjDb7DTCftjr\nccdtwAvk5nkQeL2qRiLys5i5lG8Y/LL2IK4gZn3Bk66fFccARySt22x+1lwEgbaHe5xIoCkQBtAQ\nI3p2rNG5+mqWOCb72+ak7fK3RDN+TDbYSPZwTM6pdhMxyRZmtkJoK2518rNMe82L46ZpNrxAbgpV\n/Zjz418D3z3Y5dxhhpmsSQpivyQtR6HdFMfdp+V2S6fr7rqMobEeaWLEzrrNdsXVmjPi5rkdJTxW\nFG0lTxj/3Ix/brndtEsg3WVZXdFLNr21CRh3fnWWxdhLHNNKeZJWuC/zGZgoHL2I3DDv+FXAe4d4\nvDvDMESylwudRZo4pl2PtSpbbrc48UHnGHHMcoNA1jACWXW22YbfrWQMGwXSHjNZG24F2u5nBdIV\nwGTBd9aKhGnNJ3pZ3WmlPFni6GOOW8e72BsRkT8FDrubMH9eP66q/y3e58eBhqr+164H++Cb2s/v\nfwCe+sAmL3eHcC2xQY/TL1ZsXHF0LTR3P3el1OTJXEFoOqNOp8VoXW0rjm68MWlBFpyfw8Q5XAsv\n2QW9nDKSSZhuc6s3m63uRxxHQRgfeQgefWj4x10fPQty4EJxEfk+4N8Az1HVWpf9dl+heDe2WkDc\nK8OafD2r9CfpXvYbh7OutyuQ1nW24lin7Wa7j67rnWZZuqsmWEF36xJd99rtblZKeSw6I00ck41v\neyWzuonjKBSDd2NIheL8fZ8f3tMGP99uYdAs9guAHwW+qZs43tVI4rlm/GyfbzWBYN1md59kvMz9\ngqe58G7Gu1UWRFsk3dGk01q0x3L7UbrtHPN0ZrPt/mmzf6zopYlisnxnK5ZjvzFHz3Bp3ukL2HkG\ntZl/EfNn/6ciAvDXqvqaga9qt5AmQskva7fX0uj2xXVF0s1euxab6/6mHTtMDCuEDdputJuMSWaj\nXYF0z2mHe+7k9EgrdFYkk250ljvdbS3rQWKOnuHiBXJzqOq9w7qQXcd2OwhZX+CkSFqR6Mc6tW51\nUhxdS9LdlhRfaxFGtOOOARtFJ62PZUcHdDpXY8xyp9339utWu/fay632DJfGnb6AnWf0oq53A0l3\nG7IFUhPPXevRnYGTdI8tbrwzz0Zr0k3K2O3ue1yBSwpkVoyxlyB2E7d+Yo5pn48XzcFJlpuNAF4g\ndxPuF9gVSTfDnfU+O1xhjFJ+TlqMruWYJo6uC+6+liaQrji6NZFZPRyTiaeszyK5fTPi6AqkZzC8\ni+2543QTyaz9swQySxxdrOApndZcjs64oz1XWou2LIF0BTH5mDVDJuuzcD+TzbrUXhyHw/qdvoCd\nxwvkbqBX4qaXW+26nWmCmLSi0txku92KsmupuVjxTC4RkRaHTPZudLcl3erNuNebsR57ieNmxHNP\nFK4MwIAWZFz18jbMX847VPXnhnBV24oXyDvJViybpNglY3JpFpYlrc7SFces47h0a/LrimSaQPbK\nUKfdZ3JbL6txqy51t/1HXRgtAwikiASYpVifC1wBHhaRP1LVLw7n4rYHL5B3mrQvdC9Xs5sV1c1y\ntMd23Wi7zRXGrOuCjQKZ1gndPiaF1H1Mu8/k/fVz7/2+rxvdPqu056PKYBbkM4HHVE1bchF5L2Yt\nay+Qnk3Sj1uYJYj9imOQ2J5WzpM1EygZP0xak2n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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa4969210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_real[32,:,:,32]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Re \\\\mathcal{F}(W)_{zy}$', **axis_font )\n", "\n", "plt.xlim(-p_amplitude , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ltcVKe4vl9hYrre2g3k8BgfRQbKzLUufzhuaKusR5s+LcaDZZKYvwDXtuGpYq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\nspFlYPOt+gyRZ10r3XZJ71v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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa3d95090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_real[32,:,32,:]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Re \\\\mathcal{F}(W)_{zx}$', **axis_font )\n", "\n", "plt.xlim(0 , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dBTK+jSxN501pL+q3t9OZdUlvVl2SSu2sGTvujbvO6+j1192JNW5I+/Befz0d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\nS7qEl3psVPtKi3KpRSVqUS0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa4969f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_real[32,32,:,:]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Re \\\\mathcal{F}(W)_{yx}$', **axis_font )\n", "\n", "plt.xlim(0 , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imaginary Parts" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Getting the imag-value data from f_gpu and joining to the f33_gpu imag-value data \n", "\n", "f_imag = np.append( f_gpu[:,:,:,32:].get(), f33_gpu.get().imag, axis = 3 )" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9fRyCHIDvEZG/Dvwe8A9UdQujo/64s8/VaF0XeNpZ/3S0nuj/UwCqGojIpois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\ngQwEAoEEgoEMBAKBBP4PU8AJCdPUxi8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa3e60510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_imag[:,:,32,32]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Im \\\\mathcal{F}(W)_{uz}$', **axis_font )\n", "\n", "plt.xlim(-p_amplitude , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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I3wX+APgnqrqF0VF/zNnnSrSuCzzlrH8qWk/0/0kAVQ1EZFNE1lR1/TiNK4gF\nmXdrn0b0u4OHd6tja8zmm9ME6RKshRvnS5NjlU7PjbYxxqz/tSiWWHMsSDdJ4xKw6/53qCDEbr2l\nUWM9xi1NW85ZhJg1vNK9zjyXOv1DMXlM/r5O/rBM9ppac7XM9R95MOSjHw6dNWHmfhn4D8APqaqK\nyA8DPwZ8x7EaGWMsXV4AgixufHASOD45ZglxKr2ETHpoYRrJeGOSIGtRMsVajPNRssUudQ6Y68Uj\n2wmCtNHQ+ColQegeNYeUkmEDcSzIZEtdQXySHEcZOqn0l7+w67K2jReTv6/dO2YaCPzsIORXvszn\nK18Wv//Rf3kw0vFU9Tnn7U8Dvx69vgLc5Wy7M1qXt979zNMi4gPLx7UeoQAE2X9rZyPrFig6nY5P\nypMkx67jFLuZapcg7bGyEzFJYoxjja0eGc6zzwJ7zKtDkrpvyFHbPSvS1/jsIslr70qFQAx9JixH\n8fCduGNaqJVFiLZfNPUDMMhGc8+Zt2186HeOJ+8V5UVcJwNXjnVEJL5qEbk1GqoM8C3An0av3wP8\nRxH5NxjX+T7g45GluSUiLwIeAl4L/ITzmddhRvG9EvjQcRsLBSDIw8PN7p0kh3y0mGNMjv26xkHk\nmBSG58l3ApIuddJatBbjAnu9ZZ595sMD5oMDGuEBtbBDJez2Fp8QT6OhhaLmK/FAPcH3la4f4vs+\nSJzJ7hI4LnVMhO54nX6JknXX7dX0C8PzSG+6CRrXaiz6T/jh0D0GQYrIfwIeAM6LyBOYhMpLROQF\nGJ/8ceA7AVT10yLyS8CngQ7wXRqPaPlu4Ocx5Xvfq6rvi9a/A3hXlNC5wRgy2HDiCDL5QJwUKMe1\nHN2YYzY5DrMe+93pOBFjydElxkV2WWSXBd1jPmiy0D1gvntApRvgdQO8bogfhIhGBKyhUdX6GIKs\ngF8N8WsBXfEIPaErPhUq+FRxp2GIY43uKBr3GuP1miJI83mi6xzsNk+eKE93uCg4Bl2o6t/KWP1z\nA/Z/C/CWjPWfAL44Y30LIw0aK04UQbqSjpOD8Uh5ssgxLQJPx+V84ko8ySN0ezFEm4CxpLjAHkvs\nsMhu7/9C2GS+22Sh3cTrhEhboQ3SAVRBQRRDjpFYWKvga4jvCX7FI1TPDFSUbCmQ+wORHL9TyUw8\nZUXekj3bn8meNIorJxsPxuBinzicKIKMb8HRo5XTda+yz54XP+3PKbtutZcix9iKzHKr0wkL+y5N\nikau02JuEb+9AAAgAElEQVSe/Z6VuKiGDO2yEOyxEOyz0N1joXNAvdVmrtmm2uriReRIGyO6cFEB\nakAVdA48CfF8wa8qnkSJmOirS48OsmTojvTuUM25zmwpj5v3ziOp/rjk6MKaQcQXf9PTiwlOGyVB\nnipMXmKRj/7UiNuqtIQlWfnbtabcKouVhAWZF3d0fxDsXllJGGsh2mWZbVbYYlm3me8eUD9o0Wi2\nmDtoUzvoUjkI4CAix060dDFudRR3pIap+FIHQhBfkRpICBKqcb3VJGlccrRjc7KW/muM+9H2sP2e\nY/qMI5WDvqE8OdCo324Sp5cYLVpky3xOM04pQc42FuRKedKtsv/7kxP9iYpBwnBLlHF2N447unZp\n2nq0GWpLkMtss8w2q2yyolus6ib1TovaQZfadkB1t4u3F+LvK7JPkiADzB3kR0sdaETrFaiBdBQv\nAPGM9eiGEtKWY9sZ1OhakO4ARrcv0wELdSjUkti4SXLa0poi4TgxyJOKGVxxtvhlnBhVOjQODJPy\n9O+fJsmsOt3ZyRo3JjfItbZb09lqS45L7LCkkdWoW4YYwy1Wwk1Wwy1q+x0q20plI8TfBPaiZZfY\ncuxGF1/F3EXVaJvlIx8Tp+wCoYKKsR41aTm61mMrUlm6o72D1LUmezopF/eQXgM8Qqdf+mOS7jHi\n7y1rIGP6/XilNaPL14a1bfIoXeyp4jTKIfJjjnkyn6TlmD1s0P2fRY7uEd2Yo1t1Z579nrVoiHGb\n1WCL1WCTle4O88095pstqs0Af0vxNhTZALaAfQxB7mOIMXQucs5ZPAxRzpl9FCHwhMAXOn6Ftlej\nJXM0nTo/WbWA3BikK3+KaS7758Xt6XRCLG3x9ad6ht2D9kiTghuOGXaWSbclGyVBTg2nTw7h5k/T\nt246GuqSozvor7+sWXasMWsYoetS23HSboZ6gb3YlcZYjOe6W5zrbLLc2qW23aa23aa6E+JvKLKh\nsElMkPvAAbGFaOOODWAe41b7GHLsgoaxKLzr+3S9Ch2vSltqiYJp7lidfRo0aSTK88YEGRNduuc8\nBN+xoPMUA+nIcPp1HpJpoUncry5VD6a9ybclH8fRQZ5UzIQgT58cInar85BlPcZD5voL3qaJMsul\ndpMyLkG6brXVN7qJmHNssBZucq67xVpni6WDXbxtRa6HyPXIcrTLFtDEkKMdQWb1jtatDjDEWSXO\nbocQqhCKT+D7dPxqwo22VqQlxv3Izm1ST9S17A8hxJRpe0YJoSc+14R77UYo00ms0eKIk485ji5f\nm238s4xBTg3D5RD9EZfi5AgHSXmyEjNJlzpZNbF/XbalOCjWaMeZWLfaLovsssy2IUfdZjXc5Fyw\nyVq4ycrBDot7+9T3mtS2u/AccA14DnQDdDNatoGWs3jgVUEqIK5bXScO9VVAa0JQqdD2qxxIjX1p\nsMc8uyywyyI7LPZy6PsssM88B1FJXjdTn+7PWAAVRttiracpfBH2KESdJrnfRvytZVGSJl67ruwk\n3etRXGvJeT0tlC52IeHe0sUhyUFSHsjOWOcTpc1ie459lF2tJhk3i6U81nLMijmuYBIxa91N1jqb\nrLW3WNjZp7HRpLIZwDoxQV4zxBhuRUuUmNFo8Srgz5lFAnouNSE9cjRSH6Fbq9D06+xLPZKhG0Lc\nclq2zXKPHA9o0GIuYTXHPWyL+Xbx8RwRfPxjkbYa3e8kSXT9rnb2t3u6wkDHRbuU+RQNo8dmpo1B\npH14cuwnyjQxunCP5hastTpHG3NcYsfEG9nknG4agmxtstbcZG6rTeVGl8q1IEGOXAPdgnAbgm0I\n9kCDKKYYgF8DGiCNaL6OeYybrZgVkVhc60K36tPy53qWo7UabapoKyJJaznayRnc/nB7zKhCTVVJ\nNznlkWdx5/fbaORYEqOLMgZZMLjRosOMnrGfGH873KO7lkrWZ/qtP5cc3Rhkcpqr/DqHkHStPcKe\nCNzqHBMxRzUynnNsci7YYLW1zcreNst7u1TWA0OIV0Gvgj4XL91t6O5CZxeCA9MBGi3VuhF++2Bc\nayv3EVAftIaxHuse7VqNA7/BrrikuMKWrli7lm2WjeWocSE114r2JIzHEElANRFiMEtISDigz9xv\nsZ/0Ji/lmTYmRetlDPLEw3WJJnWbyJGOnkWW/fZM/iPpyljc/LZbdKLBQULKsxpGVmN3k9XWNgsb\n+9Q2OiZD/SxwFXga9FlobUB7Hdqb0N6HdhM6HQhC8BU8jbXg4kHFjruew1iRixAueXQXPbpLPs3F\nObbrS2xUVrkua1znJp7jAte5iRucZ0eX2dYldnSZZlinrTXaYY2uVlEBRFABXwIqXtcs0un1UUyQ\nptqkZnwv9ufLS9wXw76lovkqx8F4beAyBnmikaSfSZ1hUHh/0OcGk+Ngpy+91zCto5XynAuimGNr\nk5W9bWobHeautZFrasgxIsjwGrR3YHcbdneg2YZ2B1oRQdbU8KCNQFUE5qw7PYeR+ixCsCR0lqq0\nFqvsLTbYqiyyUV3tkaNd1lljV5fYCRfZDZdoBzU6QZVOUKOrvqkrGY28qfhdqn6bmnQSMcnY5fYJ\nI5e739MYZIv39/HpijmO3wYuCfIEY/LSoeFSniy4pGj/9y92QPPgmCPQy1a7ch53hExayrPW3mKt\nucnS7q4Rfz+ryBWFp+mRpD4HrX3YPYCNA9gPoKXQVAg0ljrOE0kdBQJrQUYxSRYhXPRoL1VoLtXY\nXWiwLUtsyCo35DzXuam33OA8e7rAXrjIXrBAu1uj260QdCsEgY94IZ6niBdSrXYIxEc1HmtuR6gH\nBD2ZVHY4wu27QffF6Ys5ji4dGh1lDPJEY7h0aNin85F+xFzhSPo4cWxymMWYd6aYNmO32kYo3eIT\nVTqJ2o1LumNijuFWT8qzsLPP3FaL6npg3OpngKeh8yx0rkNnA5o7sNGCjTZsdAwx2uHWdjQhRMOu\nffDq4C2CroKuATeB3gKt8zV2lhbYmlvmRuUcz3ALV7mVp7md68FNrHfPs949z1Z3lYNug2anQbPb\nMOQY+ASBIUKphIQVRSqKhMZF9rwQzwupSDfWhUpMaZKghHTgQhNkmdyedKsn7V5PT742mnToMGgz\nN7ZjnRScIoIcF7LsiNHc6iQ5JteZpEMWSSZHw7jk6L52x1fPRRNnzdFKVOVZJho+2DWjZBb3IinP\njSgh41iNneuwuwk7e7DThK0ubAWwpcnRhNZmqAINgXoVqvPgrQDnQW+G8FYhvF3YPz/H5tIK12oX\neIZbeJrbucIdXOF2Nrrn2TpYZetgld3mEp12lXarRrddIwg9VD1QY0nrnBjLtKaEYUgoIYEf0PUD\nAvFNNSBx+y6fAJMkmUWOs3Kr0/RdfJQu9pnHaAmTLKTJcdDjmLQss1thdX3JErJBbwihKwbvuda6\nxWpghg+udTap77apbEZSHifmyFXobMLOLtzYhfUm7CjshLCDIUc7WGYu6omqGIJsuAR5E+gFIbhV\nCG4XDpbm2Jxb4dnazRExmuVpbmeru8rewRJ720s0d+YJmh5hyydsemjooQIqAr5AXdEG0MCQYyXA\nq1UIwi6BZwjS7btYCzmcGO06Uuumi6PfZ7NE6WJPHJPMLh8e2QIPOKxr4loAg8gxbVn2HyX9ENNz\nq20lyKoj52lw0Ct226vM091mpbXDcnOX6k4sAterJlut18zS3DWW43oTnuvEBXv2SJZ2FIxbXfOg\n4cPcPFSXwVsz1mP3Zp/WhQrtm3y2Gktc5zxX5TaeDO/iangbV8PbeCa8lb29ZZpbDVob83Q26/Hw\nxWZ0+bZkmlMVSAH1fcKacb8D9QmjqkB2hrBBlqOXYbPbdaMR5GTIqyjytcOilPlMGMX9zXRbNsnW\nxbSYlabRnmObHGOdnn3QnUOm51pjSpXNN/epbreRbe0NHeRalIjZMNnq9oGJOW51jdW4hxlFaCWN\nFYycZwFY9mCxDo15qM1D5Xbw7gK5G4KLPrsXFtlcXGLTX+IJLvJYeA+P6T080b3I+v55NvbPc7C/\nSHurTrBZI9zwjJlqq5J3iBnZVgKyI3JqxOO81fab7bv0+KP+ekh5Uvx0yiyJIqZq3DDA7FpXutgT\nRxFlFNN3d/LiYZCsSmMXtyK4nS5hwRm8t4IpdLsSbDHfbFHb7iDXNTFCJnzO6Bx3t6NsddvEHLfV\nEKRb4tHqHZeAFQ8WG9BYhdo5qNwREeQlCC957K4tcm3pZp72b+FxvcTnw3v4fHAvT7XuZH9zkb31\nRfbXF+ls1gg2K+iWb+pKmvoScZELWzLNpsprUSNS5dWScdlB1TTzF3ucbOjArdPHLOOkSZQEeUSI\nyMuBt2FsgXeo6v+VuV+BbjuLScghhp2xnyDNQGa3f9w6Py45Ws2jO5zQjphZDbeoNgOq22EfQepz\n0No0OseNfdjomkI9OxFBut+MtSAXSRHkLcaClLuAS5EF2VjkWuMCl/3Iegzv4bHuPTzdvJ1gq0bw\nTI3ulSq66cG2oNtiTuh2dx1DjA3itHmduFJQVKHctapdYnQTWaNZkIO+HYti+DmTl6+NjpIgjwAR\n8YB/B7wMkwJ4SER+TVU/27fvkGMlb4Hx3xbZEovxyyGSxydxbCG2UeLctpewEkzcMa7S485fnSbH\nhWDPzCHTaVHb7+BvKf6mIhug6/Sq8gTbZoRMs2V0jlbKo8QJGTt7wooPq1VYrcDyAizcArU7wb8L\nOherNG+p01ybY2txhcveRR7Xe/l8+3k8eXAn1/ZuY2t3lYPNBfSKj16poFd82JK4KnnTOZlP3BDB\nWI5pq1EUTwI8MTKfirgF0cySrqDpVjnKStTko2g/48Pla9OSDrVKmc+R8CLgYVW9DCAi7wa+Cegj\nyNHQn6YYPyYrscjLVBtyTCZizNiQMLG3kfPEo2Ws5VinmZi3eokdFrr71A/MHDKVbVsJXCEqWRZG\nhSc6u9BpQrvbT47Wm7XLahXWFmB1AVZWoX4n1O4GuRead9S5cfMaN5bO8Wz1Fh4J7uPhzn080rqP\n5zYvsPHcOZrXF9HnKuhVD70qRnu5Q5yY6RAPzekNz6GfHMWSY4jvhdFww1gkHxNkkEmU/WVAsgny\npMhshmOy9/VxLchRPc0iYRwEeQfwpPP+KQxpHgmT14ZNNuaYJMasFIDZal4bQY3Xa5VbhCJOzFjr\n0U3OWHH4QrBH46BFbbtLZT0mR5cgg52o+EQ0fLCFyY9YOc8cJiGzSBR3rMK5BTh3DpZuMXFH/x6Q\n+6F5oc6N1TWeWLqTy9WLPBrcxyOd+3m0dT/b68u0np6j+WTdWI7XxIjTr2GIse2c2LrUDZI1JbvE\nk35ZgvRCfC9IEGTSehxMjGkrctD3dnIx+Vj6cQjyMJ5mkTDVJM2H3/SR3utLD1zk7gcuZewV6wnz\nkSXQGQ2jSyyOjywpj3v2OHOdJEg/kvPUouRMYry1mvKyi+yxqLssdA6Ya7ao7pi5ZNiit+i2qecY\n7JmqPN0wWqJm2FgjwJIHq2JijsvzxnJcvhnmb4fgLo/wLp/mJY+tlWWerd/M5bmLPMzzeLxzL5d3\n7+apnbtoPVs3P4+PAZcVbijcAG6EhpUDga6Yi29LbC3WxBCmfe90kPiGIHsFK5wfDmtF2v9ZbvYw\nF9t+R4JOjCSz7s5xu/HunXX5wcs88eDlMZ/h2DrIMXua08E4CPIKcNF5f2e0rg9f+6YXj+F0aQyn\n01kgfhht1DF7H4ukVKUbkaMpANY3vVV4wHxwwELYpN5qUzsI8PbDWMho549pYYrdBqBqqvLMqeEi\nt/RpxTdSnsWGWRZuMW61fwcEd/rs3LXIzoVFdhYWebxyiUeC+3jk4D4e79zDtWu3sHttifA5H55S\neCqEJ0N4JoTtLuwE0OpCx4OwYgZxez6EHgTRYovt2kBolZ77LVWl4gdUpdMbQWSHWbqv7RKTY3I+\nRLfPZ2ctTkdOdumBu7n0wN299x998++M5bjH1EGO1dOcFsZBkA8B94nIJcxYjdcA3zaG4w6FK8Eu\nQpYPkg9gOvqYtW9StmJmojFWUZu5RCmzyIpUQ47z3Sbz3SZzzTaVgwB/X00CxJJjRJAaESQRQdYw\nBAnRjK0CNd/oHOdXo2x1FHP07zHW4/aFRZ65cAvPLN7MY9zLw+37eLR9P0/sXmLn6jK7l5cInvDg\naYVnA3gmgBtdkxFqtUx5oNAHnYOwBlqDoGJc6a4Xd411tS1BzoFXDfErXaoSE2KaGO17K6hPu9nS\nY2Ccn6xpk+TJHD3jIs/FfvzBy1yegMVaBBybIFU1EJHvAT5AHHz9zLFbNvzM5A/Wmy2y25QmSTs0\nzsp54qm6Ko4FWacZTXEVz2s9Hx4w3z1god2k0uoiByB72mdBajsmSNV46GCDeGx1Q6DumREytXNG\nyuPdZRIy3v1wcNFnZ3GJZxZv5tGFu3m09TweObiPRw/u58rGXQRPe4Sf9wk/5xtyvBHCehe2WxAe\ngO6b/7a6rkYUFWBc7kAhlNiCdKxH5kBqiu8HVD3THzVaVFPkmLYe+wlSo7vFS3wj0yTJ6cvJxo88\ngrzrgXu564F7e+9/580fzdptZE+zSBhLDFJV3wf8hXEca1RIzus0Ji0dGgaXLC1FarTO1TqaB7vb\nG2/txiDnHB1kjRbVsEOl28XvhPgtjZMfzoyCYEbjeRUzTUJlzpCjzVxXKqbwRKMCcwtG31i5Hap3\nQPtileYddZoX5thaXeFy5SKPcS+Ptp/HE9uXuPbcLWw9t0Lzah0e78KTXbjaghst2GnBXhPaLUza\n2s721SCmaJIzI84RayHnFW8+ROYDvPmQubkmc9Umde/AGYPecvojJkhXBxlLqNxQR9j7RrIGek4W\no8jJ0ndrsX78W8ebk2ZmnuZxcEYGV05DOjT87OnX6bijzcoaYuxkupFz2qESdvG6IbQ1rktmRdX2\n6fdBqmZyLRogagiz4pl6jn7dFJ2oNqC6YqxG7y7gTmjeGkl5Vs/x7NwtPBLex8Pt+3ikeR/XnruF\njSfO03qybmKNT3Xg6SbcaMLOPjT3TVaIJrHK27q4ztSHvpjkjC02GaXRZUnxFrtUFzv4C23qjX0a\ntX0a3oEz72EzsqwtQbap9AgyLenJy1rHd0HxMtiuFqI4OE4Mcnae5vFwJghy8tKh4cgqQuGOJXZn\nJky7jn2xt6CL3w2RtOXoZoGj6VmZA2kYPqr4UU7EB1kEbxn8FfDOm6GDcjdwCZrn6txYWuOJ5Tu5\nPHeRRw/u45H2/Tx6cD/bz63QfLJO88/r8JjC9TZc34cbO9Dage6OmdSGA9OIXr6+Qk99KV5EkMSW\n40K0LCr+YpfqQovaQpN6fY+Gt0/D34+0oAcOObZ6lnaVbu5omUGDCu0IpuJQUXGGFqZxXB3kLDzN\n42KmBJmVtpgMRpEOTeas2S2xiZk4VlbpEWS3jxwTr7WDr128IISOJofjudngKsgc+NaqrGG+7WjR\nqFxZr6bjJZ/gkkdwyWNrYZlnqzdzuZqS8mzeRetqHZ5U+LzCox1TEmhnB3Yi0SXbGJ3RAXEwsUav\nRI8Anpj6aXWBBYFFNSLMZZBlpbLYNeTY2DPWY1S5qJGyIK31aGQ+nV64QnqutPu9ZytSrXR/nCR5\nnPs63vNwo2fy1o0T5VDDmWKQQ3SyMKgQQlyz0HWvrQUZZ2FdfV9y1EgXX0NENe6sdIKjgbEqPed9\nQFzDbA50DfQC6M1C94LP7s2L7K4tsltf4LJ3iUdCMzrm8VYk5bm6RHjVh8e6xq2+3jbkeLAOnXWM\nOt2m0ZsYc7ZKL+4oc+DVjFk7V4EFH5YFzgFrCmuKrCneuQ7VxSaN2gGLXjRaKBo9ZEu8WYKM+6UT\nJWbiuKOrLO3/VuJ7bfLO7KTv62lVoirrQc4MyVqJRXF1jocsi0RyyNFLCZxdMuwjx0jjJxoirjzG\nEqRb5MFOy2rL9Nh4XwO4yVQCD24VWhcqbC4ucW3xZp5rXOAxvZeHO/fxaCeS8lyLpDyf9+CpLlxp\nwo19Yzl216F7A/QG8TjCtnPCaGYvqUfZoirUKjAvppbaGrAGcj5Ezgf45zrUFlvU5/ZZlJ14xFBE\nkDabP0eLCh0qju7RDV9o5Nrb0UqGMtNk5VqZ48fk7+vpSofKepAzgb2JTgcxukhbkl4uOYaZ5OiS\nZDxaxFiQHkMsSCVOW9sxhYvxojebaRKC24XWTRU2K8s87d/CZf8in289j0eC+3i0dT9Xtu8iuOYR\nXo6kPM804XrTxBx3N0E3DDnqdWJiVOLJGiIL0puLCXKuYoh6mYgcFVkLkbUA71yXaq1Fo7bPoiTJ\ncZ79ROGOtCDchRIP5AwdJzomksmS4zTu62lLh0oXewbIkuEM37NYtma2ODy53f73EkvsXrvVZ9Kl\nu/rGEotGpKgxKVrLUYgLP1g+ENCKmbc6WBTCJY/W+RoHN81xsDLH1vwST3AXj+slHtN7eLJ5F89t\n3czW+grNZ+twReGqGgH49XaUrd6BYAvjVtuB1gFxiR5b3HEJWIHKIszXYb5ixjTehFkugLcWUF1p\nUV1o0ajvsVTZYcnf6SNIm5yZo9XTPVodqaU/SA8fTNbbpLcPzuvj30lZAp2kwGsSGEU6ND60jyfz\nOZGYOUEeFkUcPQP5cUdxHtvs4q6Dqs/YJSWId+uTuW61W3y2kdxPa0J3yaOzVKW96LOztMjm8gpb\ntWWe4yYeC+/h81E9x2u7t7Lx3DlaT8+ZAWFPhoYc10MTd2zumQoYbGNijrY2kK10a83YZWAVWIPq\nIizOw7kKXABujZZboHK+S2O5SaOxy2J1mxVviyVvJ0GONvbojqJJy3lclzZEEumZ6dDIbOVkk0YZ\ngyw4YnIsDjG6yLcebdY6TZBBIgbZT57qEKW1HoldamspzmHijESvg+i9s13r0F30aS1VaS7V2Jxb\n5lrtAs/Wbuaq3MZjeg+fD+7hse69bO+ucnB9kdaTc0bK81RoRsmsd2G/Cd39SMqzhUnI2BI9Ljku\nkSTIBixW4HylR4x2qZwPqC83WWrssFLdZEW2WJbtXoLGJUg3cZX+4XCTMv0FKiZ/zxRBTjZJlDHI\nwmN41Gh60qG8o/c7W25yxpJdchRNXmmufgtJEdQT1AetCjqnsfaxgpnsKqRXJUfrgtYhqPs0F+fY\nW2ywt9hg3TfzVl/hDp4M7+KJ7kWeal3kSusOmlvz6LUK4ZUKXAauhnC9C1tt6LQwLrUd9G2tR8vG\nDXqaHW8ZJPo/X4MVjPV4myK3KHKzIjcptdUW84t7LNe3Wa1ssowhx0V2e+TYiIZcunHaZK8bcvQI\nCfH6CHLYXTBKOd3BFOvGHE8rQZYW5CnDLKVD6jwyh1+sYxiXsogccanQ9RW/FsUsBcTGIjugGlNr\nUKnQrfp0axXatRrb9SW2K4tsyyJXubU3b/XV8DbW98+zv7lAsFUzlcCf8eBZMSXLdrqm6ITaEkE2\n62Nn2iJq7wLGaoxIsbEKjQbMe3Abpp7LHSC3K9Wb29TOtagut1mZ32CltsmKv8Uy2z2CXGAvshxb\nzEXutRuOsL2cV7LsKL0+mjOeTaTF9GvGh5IgTxFmKx1yJcr9lowbqcoixqTFI5ET7vVGa/uVkC4B\nvufh+SFSBW9OIYBAhFA8Qs+n7dVo+jVa/hwHlXk2KitsVM+xwQpXua03b/XV4DY29s+zt75I8EzV\nFLu9KqbQbYIg94kKOxITpE3KVDBu9SqwYibOnq/DuQackx45cqchyNrNLebXdplf2mO1vsFKdYMV\nb5MVtnrW4yK7veGENjGTLECRFE1l0VYeGWY74e63k4dse/Q0xhzTKGOQpwazlQ7FucXkI5ie2D7p\n1PXHy1y67JWzkAq+H+CJh1/x8KuKBKChcbUDX+j6PoHncyBV9qXBvsyzwxLX5Tw35Cauy/nIeryd\np7mdZ8Nb2d9bZP/GIsEVY0FylYgggVYQlSzLsiDnnGWFnrDRWzalgs55cKsHtxNbkHeEVM+1mT+3\nx/JSRI6yaRa2ehXTF9hLjCSq0E30myIE+FgRD8T0lm9JehnfxCjBG3uss0GGWShjkFNAlqxn3K7J\n6NKhSZwtHqPhPk7u9n6nLv1o2miltRor+AR0qGITNeoJqoIniniKhIqq0PUrdLwKXb/Cvsz3ZNbb\nLHOdm3iOC1znJq4HF1jvnmeru8ru3jLt7TqdzRq64ZkJtuygmBbQETOAO1GD3Ae6ZoSMXapLJltd\nbZiYo3WrbwfvjgD/1i7+hS5z51osL22xUt9ktbrBqr+RsByzkjJWJB/3EASOax1/G3FvZluO8X4k\njtb/On1fxvHFccYZiytfS6OU+UwN6d/7ot4Sh4H7WA7eL5mJjy0e15JJFkGrJOoahngEYtYLasY2\nC4QqdLwabc+M3N5jPqKcJbZY5jku9AhyvbvG9sEqewdLtLYadDdqBFsVdJuUtFFMcduwBjpPTJJR\nZQyvZgTgfs241AvzJlu9SuxW3wH+rV3qtxxQP3/A/Moeq/V1ztXWWZV1VtjqEWQcd2z2rEebmLGS\n70HWnv1J6p9oIUmao3xTMfJ/ysYF18kvajSzdLGnhNMnh0g+doPQ73pnb7Vxxy4+YokQ8+Bb0uyI\nSZCoGGsywKctNVpi5NR7LETpjiW2WElYkOvd82wdrLK3vURzfZ5w00O3fLDzVjcxCequmKkS1A7L\ncYbniJix1ZWqKTA5X4W1SMpzgUTcsXJTl/r5AxbXtlla3mK1ss6qv8452eglZZbZZoG9RNzREqOt\n8RgLoLLigPlpMfdbGtWdtt9JMoEzfrg/jUVG6WJPDXGg/GSi34kfbJO49rJLpJraK5l0CPB7lqP9\nRCgSudyGNFSS+zd7Od86OyxGOeFltnSFG5xnnTVucJ6t7jl2D5Zo7szT2aobzbdba8IKzwUQ31iK\nGl2dSESOnhlXPVcx/1clFoHfpsjtitxh/tfOtZhf2WNpZYvVhXVW2WCVTVbZTCRl5tnvm2PG2tNZ\n8QCTnWEAAB4nSURBVEW335IWeP6Sjeyft2TMcVL363DKnrZ8LQtlFrvEIZFPdi7i3Gjayux/qGOy\n8xD8BCnYpIQ7VYOroOxSiab3MiUdEgTJCju6zK4usaeLHHTqdNo1gqafHCkI8YCYBmb2wcCL3Gyi\n0TlilqrAvG8KTyxghg5GInC5JZLyXGhRPddmeWmL1bqxGlcxMUdXzuMOJXTHoVs5T1acMd1vWeOU\n+qX3edqB/m9t8sR4VOS1ebIoCbLEITCaW90f+XKPYPbIfuB9Aocc7QPu91Th5njuYMUOVWf+wzo7\nLLHdo6IVtnWJnXCRvXCBZrdBu10lbHpJixGSmu8uEUFi4pEVTCXwGqae45KYqjzLGOvxFuBWkAtK\nbS2S8pzbY6W+aWKO/jqrbPb0jumkzBytRGUjm63u7/E0OYpDgtlkmW9V9n9rk3arj4o4jADTbltJ\nkCVGhutWD0YyMtbvVve71ma4nB3b7BKkTzdFGIGTyOlQ7ZWV3acREWREjiwbCzJcYjdYoNVp0G3X\nYoK0FqQdxmjHcytm5sHAMxNsVaP1dUyx23MYZc854GZ6wwflppDqUpv5ZSPlWa1ssCrrnPM2+txq\nt8ZjjXaC1vJiiW6/JMlRDkGM2VG/4kp5Zitfa9n5hM4QCkyQWfKZ0fbsXzeJtrguc/a++RZm/0Pq\nDpMz2r6YOG0MrhutcXV81nLsUqFNFTtzyz6NZJlZbdAM67SCOTrdGt2gQhB4hBoVlHUn0JrHDuQx\nE9jYoYuhxNvnMZXA1xTOA2uKfz6gcr6Lfz5gbrXFyvwmqw2jc7RSnrRb7WasrZTH7ZmsHw93FLtr\nQdvQRHrQZjZJZn+DcWy8eK716PK1yTjfpQVZSKTthjxMQzo0Wlv67Rz7abCSZkk8hNp7kN2YY5g5\nrthKWMxDby1HO2ONa0EeMB/NqG0SN22t0Q2qdLsVgsBH1TOnt/NRW/KDOA7ZIu5KIbIco2UJUwn8\nfIishVRXWtSXmzSWm8wv7LMyt8FKZaMnAM/TOho5TzyftdvbWfHZ/jpILjm603Z5iW8haTWmv8HJ\nS3mmjXFnxUuCLBxGi/PFWyd5c4/elqRb7SKmv7xEg/20Kdfl5SQpYtfRJccWcwlyTM/i0g5rdCKC\nDCOCVJH+auSe875LPJrQJ65FYeeQWVNkLUDOB1QXWsw3dllq7LBcN2XLVnwzQsaIjXZ6ccd4Ctdm\nz3K01mO6x9PkmKy/7pJkbFUOGrfUf48UN+Z4VLjpv3Gh1EEWDHnjHvL2jt2jo2AU/eKobbHHyztm\ndvY6/qQhR0EJnJvcntktqWsJ0s4UbQgxJsVWNKt2hyodrdINDTmGoRdJdSS2Hu2siDbO6L6vYu6W\nBYVFRZZAlkK8cx28tS7+uQ71+r6p51jd7FXlsZZjeuoEd17rdFHgPMsxnxiTLnd/Jc0kOfa71dOQ\n8rhnG/R+fOcZt7lQ6iBLRMiLLY56M7t2ppL30Lmk6JKgebTtuyQ5GsKIa5C7FmTHURBaoYxLHMad\nVjM0saJoDWioIUI7M2uNKHONIUclrlpeBW8hxFvs4i92qSx2qS62qC02qdZaLFV2jNUo/VV5XBlP\nPH1ETIxuz6eTLfY6+pdkIiavmuYobvVsLMfj/aRPG6WLXaJnZyTX0bduEPofxuEkaS0n61zHbbFH\nMI+SHUljZ7CxBOn+d8nREqQiqAgiinghUgmh5qF1DBFacqxjyNF2g5CYvVUWAqoLHUOM803qcwc0\n5vZp1PZZ8ndY8nZ6xW6zMtWjEGQeOSavK6uSZpIck5HbtLM5W7c66ewXewSNRUmQZx7ji9scliTj\nR1nIemDihz0mjW6KIPOsR/sfAA88LySsKMwpaqU91s3uOCe1CRxbrKdmLMjKQpvaQpNGY8/MGyO7\nLEZTJCzKbo8Y3ao86ekS0gQ5zKXuJiKVeZNUuJZjf8We9Pczu5jjyRhamEarPZliFSLyrcCbgOcD\nX66qn4zWXwI+A3w22vX3VPW7om0vBH4e85P+XlX9P6L1NeCdwJcB14FXq+oT0bbXAT+A+QJ+RFXf\nOaxtUyXIPGHM7KCJ1667c/RWpknRXZc9VM5uEfonJo1tjXjW535rKhmPc8nBHsEjxPcCKn6XarWD\nhEKoSoiilSB2q6NC3eJpZG0qUlOkqnjVkFq9SaOxT72+Z6xGx0q0ZOhKeOaiKGhyZsak5ZgmsyzL\nMZ7zMZmUybruLHJMxzVnKeWRnNdpTF6+djgE3YnRxZ8AfxP4qYxtj6jqCzPWvx14vao+JCLvFZGv\nV9X3A68H1lX1fhF5NfBW4DUicg74F8ALMZ35CRH5NVXdGtSwKRPkbIWueZimu6PYCUj742Emu21f\nx61JPtzJGWwGJSfsGT1CfAmoeF2qfodAzHDFQAJCPyCc8+OYY2gcec8L8TzF8wJ8P8CvGHKdqzZp\n1PapewfMR2SYJsb0yJhKDjFmkVkeOaYtyDQ5Zv04DCLHot2D+ZiGfG00BN3JuNiq+jkAEcm6uL51\nInIrsKSqD0Wr3gl8M/B+4JuAN0brfxn4yej11wMfsIQoIh8AXg784qC2HYsgReStwN/AqOUeBf6e\nqm7n7a9OUqIomIQcYhjyHs5YH+m2yCUP8z+eJLY/BucWhLWftpRa8bpUpU2oHp4XEvgBQS0gCPzY\n51TwxFibPVL1OlSlQ8Xr0PAOzOIbQVEWMbrk6LrUw0gy7Vr3k2O2xWxf50l6sq3Hk4EiaTMnRZBD\ncLeIfBIzQ9wPqupHMTWinnL2eSpaR/T/SQBVDURkS0TW3PURrjifycVxLcgPAN+nqqGI/CjwhmjJ\nwbAvuZ+kxntbZEeipnX7uWdJZ6eTa/uFzcn4XPYkse6V2P+9icGkS0U8aniA4HsBXT+gGwb4mvzh\nssRol1rkJtdoUafpKCwPesRos9TukhdvdK89nX3ujzkm/2dZjolE1EjkOHuyGR2jkPp0pEPdTjZB\n6u/+Dvo/PjLwsyLyW5iBqL1VmIb+gKr+es7HngYuqupGFHP8VRH5okM2+1hf9rEIUlU/6Lz9PeB/\nOc7xnCMnHMzJYDq2hPbuAyFNh1nudtqdSscfNUWG/bav9vbqWY69QYrmigXFly5VMVMzhOoRU0pE\nkNKlImZ61VpPSNSO6gQd9P6746jTFmOetZjsG3IJ0s7BM8ytHsVyPHnEeFRM7r4Ogxy6+IqXmsXi\nx97S3yrVv3rY86lqB9iIXn9SRB4FvgBj/d3l7HpntA5n29Mi4gPLqrouIleAB1Kf+e1hbRhnDPLv\nA+8+/mGSdtAkMG2JxagkmUWOdp/0ZLGujMV9+ONIpRHBhFHdcQtrUVbxCMUjEM9M3eDQSUXicmOW\nIG2uPG0lWqJ0hd9utHBQzNFdly0E9zLd6rTE57S51UfFxO/r6bjYvS9KRG7CJFxCEbkXuA/4vKpu\nRq7zi4CHgNcCPxF97D3A64DfB14JfCha/37gR0RkBaPN+KvA9w1rzFCCHMU0FpEfADqq+p8GHesj\nb/pw7/XFBy5x6YG7+8/HpPN1s5FYGCJ025Dcln7A+7cn3ek4GZO0s+NopaUcN5sdE6QihBKdUyQh\nkHHJ0S7WMpyL3Oz0fzt/jJutznOp06/T7nJMjnnZ6qO41acd8dVffvAylx98YvynaE4mpysi34xJ\nptwE/IaI/JGqvgL4GuCHRKSNSSF+p6puRh/7bpIyn/dF698BvEtEHsZMOfcagMhN/5fAH2A6683O\nsfLbpno8ohCRbwf+AfBSVW0N2E/foD845GhuKmLQXv2fGjVWk3yMpg33kc0iyeyWZdU2dEkkmcyI\n6S224/o1hOlWuBUUk0fpJ0hjKdpBjK3MZEw6gpjXE9aCzLIQ3WRU2q3uT8oMylbPmiCz5GTDRW9u\neOU49/UPy1tQ1WN1gogofzYiV/xFOfb5ioLjZrFfDvxT4GsGkePkkMz0ngwYhzp9ww8aCpdtFSX3\niq1DN/7oqv0M1VjiSes1syxIu1jyi13tdqRtbDuxxsD5ceuPObo/CGlyzKrMk15/GqQ80w7tjB3d\nWTdg+jiuzfyTmEFovxVJmHpK98nDfRSL9SAMQjq+mI/sh98eIyklT8YezWQNZjx32KMhS3/daA9w\nrZm4QFjYs/zy3Gx37E4lmjfGpa34WuOYYxZB2m155Ji2GE+ylMdt6YlFSZCHg6reP66GHPrcvVeH\nlUDM8tHJP/OgoXDp2J17nCyKMNMyxIVnbSFed09JHDnpxLuOeTJRE5NiNRroaK3HOBmTlC/ZYZTJ\na0smndKutGtZnh4pz/CwTpHu1Ex0hu9y2nCGxmLn2V9Fg+uIZbvUNuUTk5uiEd3gfCp76J3Zw6WW\n9Awu6cilnV0wK3+czFL3E1Y/OZrz91uKw4pPDLcci0mMR4X7c1MQ9IeSTz3OCEGmHdSiIkliaXK0\nr9PusbXF0kex2eosFz1tQaZTIq6rnU7z9MvTk9dgnPtkKbf0dcWfHkSOsZV5Et3qo8JN4xTqmkoX\n+3Ri8tKh8SDLrc5LzJi9iBznJDkqkkuO7n72EczKGSed3uwaOkmhEakj9luO6eRM9jStydkJzxo5\nuj9dhUNz1g2YPmZMkMmHaHIB7OGSimkjT6qU71bn989wi1F6VmP6M1kWZP//gKTF6B4xea4QM1bH\nbWu8bRBB2iuQQ5EjiaUoOPp9nf7kaHtOydYsLchZIZmCOFtIup6D9sqK6dltWeSYzpiLs7+7NW3D\n5VdajK3GJEl5ETG6tJUeQplsW5YrrUji9SjkWOw7ZvL3ddwz5jwTRUmQs8BJiQ9OAkmqGmXvLGSR\nY1yXPD52kmrys9iDlrRLHSJRtNHM5a30E2SWSzwoATNoLpmT41ZP/r52e2YqKAly+uhPQWQj6xY4\naXQ6SMoz6GHPclfzHwrj+noZx8uqsZ1FkO77pG2XzFjbo4akqcxNzGQvLjlaMXn2vidJyhNj1Pv6\neMgLxkwIpcznJMC1uIr7gAzG8GtwXer0IzDY3bbEm7ZLY+Kyx8izIONooGud5D+KhpKl7xzZxBYL\nyLPlO8nQwEkjxlONUuZTdBzOJS0mRr+Go5Kk1UmmCcu1ZoYRZJqukp92z0SvhZYm7do8crQEOYwc\n3f1Phlt9ylG62MVG0iU9mTjsNQwiqDw3O5m7po/o0uuy4oxZtDXIgsy6njSxDXK5kw56/jFKzBCl\nzKfoGMXFmpZ0aDgGSXkGxxyzj5N1LVlWpPs+ixzd9S415SGOeybbk9+q+HPWPnXfJ7f1xxr7z10U\n13rUe+mk/4znoLQgTxPcR3HWSNpyRz1Cnkvtrk+TY3qduy0mxrQr3g+33TbOmT5bcv/s4ZLZBNn/\n+XH02aQw/Ec3vspThZIgTwuSj+RsMb646TACy4ri5bvh/SmQYcgiyUEklhV/zNo2iL6LZoe5/Xbm\nUBLkNJCdeR0npiOxyMZRpTyDMPhTWbFFs77fpku+H8XFzu9BQ5FpEsw7Q/627JYWQ6mQ/W2O06Y9\nUfK1UuYzTbgWXmFviWNApvaQ91szo7mAgyO5+Vtjl77fbU7vl2cJZn/r0+uzw2PSaaIiX3uEUuYz\nLRTJBZ4Epu8ijuryJW22wZ/Jinm6ZGjjonmfzbMQh0VFi0YQh+mzo5+hmNeeQJnFng7i38jTSI5F\nliMdPn42zJIctO0wDulp6rOjnQGKd+0plDHIaSHbrjiJOKqUZ1wYPT6ZdBH7P2djisOPOgr63UVJ\nbctqWRHcy8P02bhwQuRrZQyyxNFRdFnKYBdx3BbM4Bx6smUntc9mgxnK18oYZImjobgxpMPIUkYj\ntXHhdPTZdDHj2P0ZdLG94bvMBppahrsfybzoJB+59Nn6ZSmzchXTvZaMAua3LL11/Mvp67PpIy1f\ny2tX/7MzJnRHXA4JEfkhEfljEflDEXmfiNzqbHuDiDwsIp8Rka9z1r9QRD4lIn8uIm9z1tdE5N3R\nZz4mIhedba+L9v+ciLx2lLYVliCTGDYYzkIzHsVJIv0YFQlFHb1c9tn0MObQRWfE5fB4q6p+iap+\nKfCbwBsBROSLgFcBzwde8f+3d/4he5VlHP98p4WRP1ADBec2h41KBRs5C8HWamwYTf+xJsGMBmWZ\nCYqIuvyRWiaFQqH/ZOoEWVLiFJZuMicFmtNpTZ02y71uM1u6+YIo5tzVH+d+fc7z+zzv+XU/z3N9\n4PCe537Oj+tc77mv5/5x3dcF3KaQXxq4HVhpZvOAeZKWhPKVwN6QcfVW4OZwrSOBq4HTgNOBayQd\n0U+w6A1ks7NxP8+97KtC8qMW2eIh6xrr6nGdVUeWOjMg72fcBsTM3kl9/CSNJEvLgDVmtt/MdgDb\ngQWhhXmYmW0Ox60Gzgn7ZwN3h/0/AIvC/hJgvZlNmtnbwHpgaT/ZIh+DnOqe9f8nV+06FK9rRqzj\nZ66zKilF1yWOQUq6AVgBvA18JRQfBzyROmx3KNsP7EqV7wrlU+fsBDCzDyVNSjoqXd5yrZ5UbCAH\n9cFL0+8fXZ7rkLulDI7rrAisbT+7fCXoulv3ec8m+O+m3tJIG4Bj0kUkD3WVmT1kZquAVZIuBy4C\nrs0rbuo+06ZSA9ltXcXw4G4pg+M6y0s0cnZz8zl6YbJNse26tkPMbHHGu9xLMg55LUkr7/jUdzND\nWbdyUt+9Lukg4HAz2ytpN7Cw5ZzH+glT6Rhkax7l4SLu8bPmCapYcJ3lJSo5y5vFPjH18RzgpbD/\nILA8zEyfAJwIPGVmbwCTkhaESZsVwNrUOeeH/XOBjWH/EWCxpCPChM3iUNaTiluQeUi/HOVXtvYu\nYqfVIbEQowlqdbqKT7oYddbOdOUswZiWNwZ5k6R5JJMzE8AFAGb2oqT7gBdJOvg/NLOpB7sQuAs4\nBFhnZg+H8juAeyRtB94Clodr7ZN0PfA0iXKuC5M1PVHjfuUiyVbZFZmPb/jLNfzouo8UGY1kocWQ\nrtztXcSYqlXxz14EjU5hCbOpuYlHZ4meZnTR0fTkbH1ff6YbMbNcjyrJ+FpGW/Goct8vFgrpYku6\nVNKBMFtUKPV2MeLtIjpOb0qoMyW5+cRM7i62pJkk/fmJ/OJ0uQdGtU48fHSvON1SHCcLBf+s+1LD\naXELcFkB12mi3Smk1eWheVlYOXRbOlcnVT374HRaGhqPdMNAMf/b0t7W8lbSREuuFqSkZcBOM9va\nWAFUJTVGNqmdWJ89PWab/HUGJdL/rUfzaaeHg+cq4EqS7nX6u648fu2fP9qfvXAWcxbOHkTWFkY9\nKnkv4n32WH0eh4f8/9sdmyaY2PRakUIljGEXe9qz2JJOBh4F3iUxjFPOmgvMbE+H4weaxW6lNcZK\nr4D+071+M1nXgFdB65qiYp99ugyTzlq/i2Xqrez3GuAG/byYWezPZrQV20ZnFnvaXWwzex5IhyV6\nFZhvZvuKEKwTDY2XHbEnCrfcDqRbFrFJF7vOmomp9lb3XudkxMYXs1Cko3hjqrk0rOVlKusOMY6f\nxdytjltnrcSnw/Lf60IYMReeLBRmIM1sblHX6nOnEq+ddmyOi86z+THgOiuGIZBxDMcgIw931qD8\nKD2tXcSqK3z3CpIOAhyPVOA6y0/MsrXhXexxJC63lM6+bzFU8s6dVdfZGOFuPuNHTG4p8SaL8gRb\nDt7FHnU6D9eX6ZYySKVtdvgom8Elg2pWEsWrs7HHDeQ4UbVbSv9KXE8lz9L2qsuvMVadjSk+Bjke\nVO2WksUU1zPb2r/tVVe3Ol6djTHeghwHqnZLiXeMLN5oRfHqzBkvxtBATtHq115GZaw2IdRgo3dV\nRyjKKl28Opsusf38ONkZQwNZ5YB+XStfso0r1jG10V+ymHU2yNXAu//DzxgaSKivElaBu+QMTtE6\nG9WImOM3SzNWBjIuc9GdPFWqfJec6UpXrktO/TpLSyBGyzBOMX6zNGNlIIeTQdtb1Y0pDnqX6n6g\n6tRZzBGX8uItSCciBnVgr65bPXg3uaruZr06izfiUjG8V7cAleMGMlo8Ss7g1KuzePVSFN6CdEok\ne7WpOkrOdCSLbUlkFTob/uhB+Ri/MchC8mI7g9Cca7vXVkf4sH5S1WMA4tJZPHqpmnLTGkq6VNIB\nSUeFz7MlvStpS9huSx07X9LfJf1D0q2p8o9LWiNpu6QnJM1KfXd+OP5lSSuyyOQtyIqJKXpQM/GO\nn8Wks3hdlaqgvBakpJkkCQAnWr56xczmdzjldmClmW2WtE7SEjN7BFgJ7DWzT0v6FnAzsFzSkcDV\nwHySX9FnJK01s8lecnkLMic2wNYePSjrVoY0nSRLrlG0VHmkLF9ng0vXT5rRpdQW5C3AZR3K21Qq\n6VjgMDPbHIpWA+eE/bOBu8P+H4BFYX8JsN7MJs3sbWA9sLSfUN6CLJQs60SqYtD2Vl1uKTHprPmu\nnfQ32gawH+XMYktaBuw0s61Sm4bnSNoCTAI/MbO/AMcBu1LH7AplhL87AczsQ0mTocv+UXlgd+qc\nrriBLIgs418xRQ9qPp6Bji+KmHTWetfhSPZVNd262FuAZ3ueKWkDcEy6iESZq4ArSbrX6e8AXgdm\nmdk+SfOBByR9bkChc708biALISaXnGEZI4tJZ82MvrvOdOnWfT4lbFPc2XaEmS1uKwQknQzMAf6m\npPk4k2R8cIGZ7QH2hfO3SPonMI+k9Xd86jIzQxmp716XdBBwuJntlbQbWNhyzmNdHzXgY5BdGGSM\nrN29pOgxsuyjdumrDzpqV+WIZ0w6ax1fbNXleI0z9mJ/xi07Zva8mR1rZnPN7ASS7vLnzWyPpE9J\nmgEgaS5wIvAvM3sDmJS0IBjVFcDacMkHgfPD/rnAxrD/CLBY0hFhwmZxKOuJtyB7kjUqTvK3bEYn\nEk5MOmslxjZtLFTiKG40XowzgZ9K+h9wAPh+mGABuBC4CzgEWGdmD4fyO4B7JG0H3gKWA4Ru+vXA\n0+Ee16Wu1RU3kD1otHbqrzLxupd49KDxoXxHcTObm9q/H7i/y3HP0Nyvnyp/H/hml3PuIjGqmXED\nGeg8JO8JvbIc2+ooVB7x6mw88KWGDjD4PHBeYk1O5Qm9nDQerGLs8YRejbt6Qi+nGW9BjjlVu57E\nO0bW3HWOiXh1NvqMX7AKN5ApmqtcVsMw3Yoab3IqT+g1dbfsxPYzUg7j14LM7Qcp6SJJ2yRtlXRT\nEUJVyY5NEwVdKWu8mfYoMOXK1Uq2iDidWtETm3aUJFNWyTrrrDxd9Zasn1ksX66qKd4PMnZyGUhJ\nC4FvAKeY2SnAL4sQqkomNr1WwFXS1eZAxq33OFoxcrWSruAzemydK/5rJVb4hkvO4DorR1dpyTrp\nrL+RLFeuOig33FmM5O1i/wC4ycz2A5jZm/lFGj4aVSRul5L2lSsxEadLTm+dGdahdHQZrdZhFvJ2\nsecBZ0p6UtJjkr5QhFCO48TIexm30UFmfdwlekfhuBHYaGYXSzoN+H3aE77lOj7t6Dg1YWa5GrqS\ndgCzMx4+YWZz8twvFvoayJ4nS+uAX5jZ4+HzK8DpZvZWQfI5juPURt4u9gOEiL2S5gEfc+PoOM6o\nkHeS5k7gd5K2Au+ThB1yHMcZCXJ1sR3HcUaZygPmxupY3ppysm4k3Rz09JykP0o6vGZ5lkp6KaTN\nvLxOWaaQNFPSRkkvhPfpx3XLNIWkGSFV6YN1y+JMn0oNZKyO5T1STtbJeuAkMzsV2A5cUZcgIarz\nb0gyw50EnCfpM3XJk2I/cImZnQR8CbgwErkALgZerFsIJx9VtyBjdSzvlnKyNszsUTM7ED4+SZJD\noy4WANvNbMLMPgDWkKTXrBUze8PMngv77wDbyJCprmzCD+5ZwG/rlsXJR9UGMjrH8nTKybpl6cF3\ngT/VeP/WlJnpNJtRIGkOcCrw13olARo/uD7AP+QUHs2nj2P5wcCRZvbF4Fh+H9DRsbxCmbqlnCyd\nHnJdZWYPhWOuAj4ws3urkmvYkHQoSZL4i0NLsk5Zvg78x8yeC0NK47MScQQp3EB2S+8IIOkCQo4J\nM9scJkWOLtt3MkfKyVLppasg33dIumqLypalD7uBWanP6TSbtSLpYBLjeI+Zre13fAWcASyTdBbw\nCeAwSavNzF3ghpBK3XwkfQ84zsyuCY7lG8ws6/Kl0pH0KjDfzPZFIMtS4FfAmXU734f8wi8DXwX+\nDTwFnGdm2+qUC0DSauBNM7ukbllakfRl4FIzW1a3LM70qHoM8k5gbnAsv5f4HMtjCs7ya+BQYENw\nF7mtLkHM7EPgRyQz6y8AayIxjmcA3wYWSXo26Glp3XI5o4M7ijuO43Shckdxx3GcYcENpOM4Thfc\nQDqO43TBDaTjOE4X3EA6juN0wQ2k4zhOF9xAOo7jdOH/03q5SMyeEcwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0a98d00510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_imag[:,32,:,32]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Im \\\\mathcal{F}(W)_{uy}$', **axis_font )\n", "\n", "plt.xlim(-p_amplitude , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RiDgSYiIiiYglIo4iVBLjBy9GwoslvPSNuj9cDDKTixl+qwN1Vza5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\nHqaR9MUEL9o/V8xZcTgoCKTfFZQRPd2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0a98db3fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_imag[:,32,32,:]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Im \\\\mathcal{F}(W)_{ux}$', **axis_font )\n", "\n", "plt.xlim(0 , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5vogI8GrgV51jXhOtvxz4yDjuuSASZNYDPq6n0pU/091knB1nVh7VUeSP2XihDy8ZTauW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\nfzbfQRULyWcrxXi92Gdn/r0Q+KFbvxO4yPVMvxk4G/iWmT0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0a98d6a090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_imag[32,:,:,32]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Im \\\\mathcal{F}(W)_{zy}$', **axis_font )\n", "\n", "plt.xlim(-p_amplitude , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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edHrQtnmnB/0EBmoN82psdG67DjSAptqcNJ8n5jROnw6lGLgQ2L/JaAjst4jI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\nv3sx0UftBvmPQPnKvheQ9EG9D5gXiu3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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0a98d00c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_imag[32,:,32,:]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Im \\\\mathcal{F}(W)_{zx}$', **axis_font )\n", "\n", "plt.xlim(0 , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 5.8125)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0a98b814d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_imag[32,32,:,:]/float(np.sqrt(size)) ,\n", " extent=[-p_amplitude , p_amplitude-dp, -p_amplitude , p_amplitude-dp] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-p_amplitude/2. , 1.1*p_amplitude, '$Im \\\\mathcal{F}(W)_{yx}$', **axis_font )\n", "\n", "plt.xlim( 0 , p_amplitude - dp)\n", "plt.ylim(-p_amplitude , p_amplitude - dp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inverse Transform" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "computation time = 3.96876811981\n" ] } ], "source": [ "# Executing iFFT\n", "t_init = time.time() \n", "\n", "cuda_ifaft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, -delta, segment_axes3, axes3, makeC2C, axesSplit_1 )\n", "#cuda_ifaft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, -delta, segment_axes2, axes2, makeC2C, axesSplit_1 )\n", "#cuda_ifaft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, -delta, segment_axes0, axes0, makeC2C, axesSplit_1 )\n", "#cuda_ifaft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, -delta, segment_axes1, axes1, makeC2R, axesSplit_1 )\n", "\n", "t_end = time.time() \n", "\n", "print 'computation time = ', t_end - t_init" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cuda_ifaft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, -delta, segment_axes2, axes2, makeC2C, axesSplit_1 )" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cuda_ifaft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, -delta, segment_axes0, axes0, makeC2C, axesSplit_1 )" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cuda_ifaft( int(f_gpu.gpudata), int(f33_gpu.gpudata), dx, -delta, segment_axes1, axes1, makeC2R, axesSplit_1 )" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-5.0, 4.84375)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SNeA6C1Fy/k72G5K9inS/Ih1XpIOKbGC2eV4wyGcM0oKBzMgp21K0UlO34uTG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\nUKe/fEto9ZWqFSbdRIR6R/9uWM6By5I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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa3e29510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_gpu[:,:,32,32].get()/float(size) ,\n", " extent=[-x_amplitude , x_amplitude-dx, -x_amplitude , x_amplitude-dx] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-x_amplitude/2. , 1.1*x_amplitude, '$Re \\\\mathcal{F}(W)_{uz}$', **axis_font )\n", "\n", "plt.xlim(-x_amplitude , x_amplitude - dx)\n", "plt.ylim(-x_amplitude , x_amplitude - dx)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-5.0, 4.84375)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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eL0rWenItp9Sp43dwdLlzZytKsJUY0zOAtwAH6ypGYdpp/PhRXz1fiI4jSCFx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\nFCl0HrsR78bQpelUhKYdYtKMhGk+MMm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0a98f4fb90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_gpu[:,32,:,32].get()/float(size) ,\n", " extent=[-x_amplitude , x_amplitude-dx, -x_amplitude , x_amplitude-dx] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-x_amplitude/2. , 1.1*x_amplitude, '$Re \\\\mathcal{F}(W)_{uy}$', **axis_font )\n", "\n", "plt.xlim(-x_amplitude , x_amplitude - dx)\n", "plt.ylim(-x_amplitude , x_amplitude - dx)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-5.0, 4.84375)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T12A96+r51pNrOaVOHb+Do8udO1sowVZiTM8A3gQcrKsYwbTT8uNHffV8EB0H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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0a99112610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_gpu[:,32,32,:].get()/float(size) ,\n", " extent=[-x_amplitude , x_amplitude-dx, -x_amplitude , x_amplitude-dx] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-x_amplitude/2. , 1.1*x_amplitude, '$Re \\\\mathcal{F}(W)_{ux}$', **axis_font )\n", "\n", "plt.xlim(-x_amplitude , x_amplitude - dx)\n", "plt.ylim(-x_amplitude , x_amplitude - dx)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-5.0, 4.84375)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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m26IcUBRDKGqYNegkQ8cpOgEdCQwFtUH7JYtJVgcTzz92cVIU1CRizi2nrsF6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\nTWqmFamahKYBmgap29Vx9xSdKFLoPHY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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0a98f66290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_gpu[32,:,:,32].get()/float(size) ,\n", " extent=[-x_amplitude , x_amplitude-dx, -x_amplitude , x_amplitude-dx] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-x_amplitude/2. , 1.1*x_amplitude, '$Re \\\\mathcal{F}(W)_{zy}$', **axis_font )\n", "\n", "plt.xlim(-x_amplitude , x_amplitude -dx)\n", "plt.ylim(-x_amplitude , x_amplitude -dx)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-5.0, 4.84375)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6clFRUTunU84ucBydevkmEXmiiLxKRP5IRN7YtyRLVFTUBdYplm86yaVOqwp4vqo+KCJX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\nnnoiTZuyAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa3d57950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_gpu[32,:,32,:].get()/float(size) ,\n", " extent=[-x_amplitude , x_amplitude-dx, -x_amplitude , x_amplitude-dx] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-x_amplitude/2. , 1.1*x_amplitude, '$Re \\\\mathcal{F}(W)_{zx}$', **axis_font )\n", "\n", "plt.xlim(-x_amplitude , x_amplitude - dx)\n", "plt.ylim(-x_amplitude , x_amplitude - dx)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-5.0, 4.84375)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FRW2mOFYuKipq5xTHykVFRe2cIpiioqJ2TtGVi4qK2jlFiykqKmrnFMEUFRW1c4qLEURF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\nbE2R0hQJDDMmSUUuM7LMrCmXSN1CqZ3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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0aa3d57590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow( f_gpu[32,32,:,:].get()/float(size) ,\n", " extent=[-x_amplitude , x_amplitude-dx, -x_amplitude , x_amplitude-dx] )\n", "\n", "plt.colorbar()\n", "\n", "axis_font = {'size':'24'}\n", "plt.text(-x_amplitude/2. , 1.1*x_amplitude, '$Re \\\\mathcal{F}(W)_{xy}$', **axis_font )\n", "\n", "plt.xlim(-x_amplitude , x_amplitude - dx)\n", "plt.ylim(-x_amplitude , x_amplitude - dx)" ] }, { "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": 0 }
bsd-3-clause
BinPy/BinPy
BinPy/examples/notebook/Sequential/FlipFlop/JKFlipFlop.ipynb
5
15636
{ "metadata": { "name": "", "signature": "sha256:4dd7afb32f009d7d600fba4682a88dd9002e84c87731f74d308ebf4258dda772" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example for JKFlipFlop" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function\n", "from BinPy.Sequential.sequential import JKFlipFlop\n", "from BinPy.tools.clock import Clock\n", "from BinPy.Gates import Connector\n", "from BinPy.tools.oscilloscope import Oscilloscope" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "j = Connector(1)\n", "k = Connector(0)\n", "\n", "p = Connector(0)\n", "q = Connector(1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Initialize the clock\n", "clock = Clock(1, 4)\n", "clock.start()\n", "\n", "# A clock of 4 hertz frequency initialized to 1\n", "clk_conn = clock.A\n", "\n", "enable = Connector(1)\n", "\n", "jkff = JKFlipFlop(j, k, enable, clk_conn, clear=enable)\n", "\n", "# To connect outputs use s.setOutputs(op1,op2)\n", "jkff.setOutputs(A=p, B=q)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Initiating the oscilloscope\n", "\n", "o = Oscilloscope((clk_conn, 'CLK'), (j, 'J'), (\n", " k, 'k'), (p, 'OUT'), (q, 'OUT!'), (enable, 'ENABLE'))\n", "\n", "o.start()\n", "o.setScale(0.02) # Set scale by trial and error.\n", "o.setWidth(100)\n", "o.unhold()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\u001b[0m\n", "\u001b[0m\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "print (\"SET STATE - J = 1, K = 0\")\n", "\n", "# Set State\n", "j.state = 1\n", "k.state = 0\n", "\n", "# The same thing can also be done by --> jkff.setInputs(j = 1, k = 0)\n", "while True:\n", " if clk_conn.state == 0:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break\n", "print (jkff.state())\n", "\n", "# Sending a positive edge to jkff\n", "while True:\n", " if clk_conn.state == 1:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "SET STATE - J = 1, K = 0\n", "[1, 0]\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "print (\"RESET STATE - J = 0, K = 1\")\n", "\n", "# Reset State\n", "j.state = 0\n", "k.state = 1\n", "\n", "# The same thing can also be done by --> jkff.setInputs(j = 1, k = 0)\n", "while True:\n", " if clk_conn.state == 0:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break\n", "\n", " print (\"[Printing the output using the output connectors:]\\n\", p(), q())\n", "\n", "# Sending a positive edge to jkff\n", "while True:\n", " if clk_conn.state == 1:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "RESET STATE - J = 0, K = 1\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "print (\"TOGGLE STATE - J = 1, K = 1\")\n", "# Toggle State\n", "j.state = 1\n", "k.state = 1\n", "# The same thing can also be done by --> jkff.setInputs(j = 1, k = 0)\n", "while True:\n", " if clk_conn.state == 0:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break\n", "print (jkff.state())\n", "\n", "# Sending a positive edge to jkff\n", "while True:\n", " if clk_conn.state == 1:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "TOGGLE STATE - J = 1, K = 1\n", "[1, 0]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "print (\"NO CHANGE STATE - J = 0, K = 0\")\n", "# No change state\n", "j.state = 0\n", "k.state = 0\n", "# The same thing can also be done by --> jkff.setInputs(j = 1, k = 0)\n", "while True:\n", " if clk_conn.state == 0:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break\n", "print (jkff.state())\n", "\n", "# Sending a positive edge to jkff\n", "while True:\n", " if clk_conn.state == 1:\n", " # Falling edge will trigger the FF\n", " jkff.trigger()\n", " break\n", "\n", "# To connect different set of connectors use s.setInputs(conn1,conn2,enab)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "NO CHANGE STATE - J = 0, K = 0\n", "[1, 0]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# Display the oscilloscope\n", "o.display()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\u001b[0m===================================================================================================================\n", "BinPy - Oscilloscope\n", "===================================================================================================================\n", " SCALE - X-AXIS : 1 UNIT WIDTH = 0.02\n", "===================================================================================================================\n", " \u2502\n", " \u2502\n", " \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n", " CLK \u2502 \u2502 \u2502 \u2502 \u2502 \u2502 \u2502 \n", " \u2500 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n", " J \u2502 \u2502 \u2502 \u2502 \u2502 \n", " \u2500 \u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n", " k \u2502 \u2502 \u2502 \n", " \u2500 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n", " OUT \u2502 \u2502 \u2502 \u2502 \u2502 \n", " \u2500 \u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502 \u250c\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n", " OUT! \u2502 \u2502 \u2502 \u2502 \u2502 \n", " \u2500 \u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502\n", " \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n", " ENABL \u2502 \u2502 \u2502 \n", " \u2500 \u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u2502\n", " \u2502\n", "\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\u2502\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "\u001b[0m\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Kill the oscilloscope and clock threads\n", "o.kill()\n", "clock.kill()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 } ], "metadata": {} } ] }
bsd-3-clause
SzymonPrajs/astrotools
notebooks/make_color_figures.ipynb
1
1765022
null
mit
dream-olfaction/olfaction-prediction
opc_python/paper/fig2cef-SubjectVariance.ipynb
1
431430
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Figure 2" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Preliminaries to work with the data. \n", "%matplotlib inline\n", "%run __init__.py # Add the current directory to the path. \n", "from utils import loading, scoring\n", "from gerkin import dream,params\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subject Clustering" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the observed perceptual descriptor data for the high concentration (low dilution) of each pair. \n", "data = loading.load_perceptual_data(['training','leaderboard','testset'])\n", "data = dream.filter_Y_dilutions(data,'high')\n", "descriptors = loading.get_descriptors(format=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "49 (# subjects) by 42 (2 times the # of descriptors)\n" ] } ], "source": [ "# Compute means and stdevs\n", "y = data.unstack('Descriptor').T\n", "means = y.mean(axis=1).unstack()[descriptors].values\n", "stdevs = y.std(axis=1).unstack()[descriptors].values\n", "y_moments = np.hstack((means,stdevs))\n", "print(\"%d (# subjects) by %d (2 times the # of descriptors)\" % y_moments.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Cluster the data. \n", "from scipy.cluster.hierarchy import fclusterdata,dendrogram,linkage\n", "fclusterdata(y_moments,1)\n", "Z = linkage(y_moments, 'ward')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SJKlDTM4kSZI6xORMkiSpQ0zOJEmSOuT/BzLGo/sq3cCLAAAAAElF\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1103fc978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the dendrogram\n", "plt.figure(figsize=(10, 4))\n", "plt.title('Hierarchical Clustering Dendrogram')\n", "plt.xlabel('Subject number')\n", "plt.ylabel('Distance')\n", "d = dendrogram(\n", " Z,\n", " leaf_rotation=90., # rotates the x axis labels\n", " leaf_font_size=8., # font size for the x axis labels\n", " distance_sort = 'ascending'\n", ")\n", "\n", "# Create a copy of the data that is sorted by subjection position in the dendrogram.\n", "y_dend = y_moments.copy()\n", "for i in range(49):\n", " y_dend[i,:] = y_moments[int(d['ivl'][i]),:]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Swap the red and cyan branch for visualization since the ordering is arbitrary\n", "divl_ = np.array(d['ivl'])\n", "divl_[5:30] = d['ivl'][24:]\n", "divl_[30:] = d['ivl'][5:24]\n", "y_dend_ = y_dend.copy()\n", "for i in range(49):\n", " y_dend_[i,:] = y_moments[int(divl_[i]),:]\n", "y_dend = y_dend_" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a version of the same that is normalized to the mean value for each descriptor. \n", "y_dend_norm = y_dend / np.mean(y_dend,axis=0,keepdims=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fig. 2C and some auxiliary plots" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TubKxpkgongocWjXqHuBFwF9KemUx7BLgjZImFfM6WtLUGov6LvBs4LRiOjMz\ns1ELRD8ddXnZ2HG+YWZmzaqeuUYj5xuN3hJj8B5VqFzdOCci+iV9FfihpBuA5cCt1RNFxHZJzwd+\nKmkblasdS4BrJQlYC7xwuAVGxB5JvwQ2RUT/mKyVmZm1pQH3idGonG+YmVlLaIdco6ErMSKis8bw\ndcAZNSY7sRhnE5WrG4PeVbyqLSteDys62DodeMk+F9jMzKyGdnnsWTNyvmFmZq2gXXKN1l/DfSDp\neOBO4OcRccdEl8fMzMxaj/MNMzOz/dfQLTHGW9G7+OH7Mk3/5E42nTD8E0q2Lc7riLp25POe9lBv\nGt8xf9gLRw9b+MsR1FFt2JTHO2vPo2vKZJg0KZ38/pcuSeOTSrbBjHv2pPGuHX1pPJLyA+xc0J3G\np9+9rXZw0VzWPWZGOv28r/4+X/7TH5XGe9fsSuPaU3v9O3buYfuh02tPvKCHDcdm+9C80mrOgy/Z\nUrtsQN/U2vtHP7BrXr79p/4h2UEOmAOdtTsu6gfWnTwtnf+s23fXjO15zBFsPqInnX72rbXL1w9M\nWlt7/+mYN5sdh89O518muvKOm6besDqffnK+fnHvfWlcXfkpRL2159+VxB5e/qL5+fy37aw9/1kz\nYdvQfhQfadejD03jvXevy6c/fF4a56ePfBuoLTrbsnL7k2/s6O/m+k2Lh40dMLn2sRjgvp35sWbV\njuRcAczrzU/WA3NLztV/yJ+sMtCd91k60DPAkk//a834Ux67Jp3+0CduTOMrt+TbZ+fe/FjX2ZGX\nf/7UJJcA7l43N42fckh+LL7toqPT+MLnPZjGV23Jc5m9fXm++b0HT0rjR83Ij6XPOGNFGr/sD0ek\n8d6FW9P43Cn5ueDWNQvS+JRptXMFgDW78+/Pv9zxzDR+1Nx8+9ypfP9auy7//BYvyedfpnPWQBpf\nubLkXD05z9WnLp/Mo/7PR2vGe6fkyeieXbW6HarYsSjffh2d+fptXJ1/vp0npGHmHZdv/zUPzUrj\nCw7bkC8AWFn1d7vkGq7EaGejqMAA2rsCAxq6AgPIKzCgpAKDUVVgQF6BAaOswIC0AgNGV4EBjKoC\nA/IKDKCtKzBGYjQVGMDEV2DU0A6PPTMzM7OJU+dcY56k5VXvlxZPzppQrsQwMzMbBxHQ3wadbZmZ\nmdnEGINcY11EnFrPGdaDKzHMzMzGhRig9Zt4mpmZ2URpj1zDlRhmZmbjIHBLDDMzMxs77ZJrNF0l\nhqR3A69SIQzwAAAgAElEQVSk0m/eAPD6iPhdnZdxFrAnIn5bz/mamVl7a4fHnrUK5xtmZtaM2iHX\naKpKDElnAM8HTomI3ZLmAXnvgPvnLGAb4KTCzMyszTjfMDMza1zNVk2ziErnIrsBImIdsFjSdwAk\nvUDSTkndknol3V0MP0LSTyRdI+nXko4ths+X9G1JVxevJ0paArwBeIuk6ySdOREramZmrSUQA1Gf\nl4055xtmZtZ06plrNHK+0VQtMYBLgfdIuh34GfAN4HLg5CJ+JnAjcBqVdRts9rkUeENE3CHp8cCn\ngLOBC4CPRsRvJB0CXBIRx0n6DLAtIoZ9KLmk84DzALqnjO4xiWZm1j7aoYlni2i4fKN3Yf7YbDMz\nM2iPXKOpKjEiYpukx1JJHp5KJal4B3CXpOOAxwH/DjwZ6AR+LWka8ATgW9LDtUk9xf9PB46vGj6j\nGL+sHEupJCpMm3tw1GHVzMysxQUw0AadbbWCRsw3Zh670PmGmZml2iXXaKpKDICI6AeWAcsk3QCc\nA1wGPAfYS+WKyZeoJBV/T+WWmU0RcfIws+sATo+IXdUDq5IMMzOzOhH9bfDYs1bhfMPMzJpPe+Qa\nTVVNI+kYSUdVDToZWAn8Gvg74IqIWAvMBY4BboyILcA9kl5SzEOSTiqmvxR4c9X8BxOPrYDbbZqZ\nWd0MXh2px8vGlvMNMzNrRvXMNRo532jckg1vGnChpJslrQCOB95H5V7UhVSukACsAG6IiMGml68C\nXifpeuAm4AXF8L8BTpW0QtLNVDrYAvgh8CJ3tGVmZvXUX1whGe3LxpzzDTMza0r1yjUaOd9oqttJ\nIuIaKvebDqenarzzhkx3D/DsYea3DnjZMMNvBx49qsKamZlViVBDX9WwP3K+YWZmzahdco3WX0Mz\nM7M2JGmWpIsk3SrpFklnSJoj6aeS7ij+9yO2zMzMrJZ5kpZXvc4rn2TsNVVLjEbUuX0vs69eM2xs\nypo8N+zauieNbzgx77h80vaSjsr7+9NwxEAe37ojj+/Jy7/4i/n0REn5B0rKt3t3GldPTxrvXjAv\nX35/vvwFP9uaT18y/96HdqbxznVb8vl35nWQUy+9N41PXnVUGu+fnB8eOv6wKo1PWjg3j19/dxrn\nwIVpWOs2pPF5HJjGO+9fl8bn7F6Qxjt296XxeHB1Gp/8QL79NLk3jdNVcvgu+f6wPt9+Azvz/bNj\nypQ0HttLvv8l36+Bks+3o2z7AOqo/R3pufLW0mNEtg26V62h44B8HxlO//heHbkA+ElEvFhSNzAF\neBfw84g4X9I7qDxx4+3jWSjbPwMhtu/tHjY2a0b+fV030JnGX754eRrfFZPS+P0HzEzjqzryuNbl\n38U51+fl/9WUo9M4A3mT6OjP470P5cfb3Qfk54Mph+X50swpu9L4yi15PtnzjLVp/OBpm9L4nr58\n/bo683zy3pvy8+2S0/Pj+YyufP337s0//yPm5efzm+46KI0fc/iDafzOVfPTeG/n3jS+ty8v/+zu\n/Hx5/Lx8//rt6iPT+AO35+eqrvn58aO7O19+76z889u1YXIaX7g8X/5dL86PD2V3PEzanJ93Z/8o\nz2fWPC7/vTIwOc9n1m3Mf89pW75/dHaU5HPDqHOusS4iTq3nDOvBlRhmZtZysgoMKK/kLK3E2Y8K\njAAGxun+UkkzqTz+81yAiNgD7JH0AuCsYrQLqTx9w5UYZmZmLWA8c42J5EoMMzOzcaHxbIlxGLAW\n+GLxhIxrgL8FFkbEQ8U4q6h0UmlmZmYtYVxzjQnjSgwzM7NxUHnsWd2ujsyTVH0PwNKIWFr1vgs4\nBXhzRPxO0gVUbh35Y3kiQlLJfX1mZmbWLOqcazSslqqmkdRfPKZs8LVkH6f/nKTji7/fNRZlNDOz\n9tVPR11eFPeoVr2WDlnU/cD9EfG74v1FVCo1VktaBFD8P3ynTpZyvmFmZo2qXrlGfwNXFbRaS4yd\nEXFyraCkroio2TtNRPxV1dt3AR+qZ+HMzKx9BRq3qyMRsUrSfZKOiYjbgKcBNxevc4Dzi/+/Py4F\naj3ON8zMrOGMZ64xkRq3eqVOJJ0r6QeSfgH8XNJZkn5UFf+EpHOLv5dJOlXS+cDk4urKVyeo6GZm\nZqPxZuCrklYAJ1P5oXw+8AxJdwBPL95bHTjfMDMzGx+t1hJjsqTrir/viYgXFX+fAjw6IjZIOqts\nJhHxDklvqnWVpXg+7nkAvV0z6lBsMzNrBwPjeO0gIq4Dhnss2tPGrRCta9zzjZ4F0+tQbDMza3Xj\nmWtMlFarxKjVvPOnEZE/pHofFPceLwWY2XuAO0UzM7NSEdDfBk0828S45xvTj3G+YWZmuXbJNVqt\nEqOW7VV/9/HI22h6x7ksZmbWptrhPtU253zDzMwmVDvkGu1SiVFtJXC8pB5gMpVmtb8ZZry9kiZF\nxN5xLZ2ZmbWkSmdbrd/E0x7mfMPMzMZVu+QabVeJERH3SfomcCNwD/D7GqMuBVZIujYiXjVuBTQz\ns5bVT+tfHbEK5xtmZjYR2iHXaKlKjIiYNsywLwFfGjLsH4B/GGbcs6r+fjvw9nqX0czM2lPQHk08\n24HzDTMza0Ttkmu0flsTMzMzMzMzM2sJLdUSYyJEVyd984Z/7FnPnWvSaQfWrk/jc2/Ml63u7nyE\nKZPTcP+Gjfn8uyaVFCCvA4s9e0riJbf/xkAe7u9P4509PWm87+6Vabxjct4H28COHWm8bPt0PLQ6\nn3/X6L6eZeXrvLVk/XfvTuN9u/K4Svavjsn5/jlw+11pvOzz18bN+fy78/27c9v2NN6/ZWu+/I68\nFrys/GzblsdLlH5/S75fpfr68uWXHJ/6d+4a1eLLjh8BRF8yzgi2b+f02o+0jLXrGdjndWiP+1Rt\nbBzWs54vH/OVYWO/2nlYOu2XNpyRxr+3e9gnvD7sgMlb0vhLD742jX/5+89J4xsfn59PNp+VH68O\nW5DnUytXzU3j/Tvz8+2uA/fS+2DtY+oZj7ojnf7qy45L44eedl8aB9i6u3ZOsnHF/HTa647Nj8cL\npufns66OfPvPvC0/rnWekT9Y56q1h6Zx3Tsljd+0Lp/+hEfl+c5NNx6Sxucvz9fvV089Ko0fc8iq\nNL53oDONX3n1MWmcqXk+MfW+ks/nrqlpfOGVeT5051/n5VdXvv/c+ZpOjvxi7XXoPSg/Xx+7IP+9\ndfMvjkzja0/N90/NzY9Pk2/I89lFv03DzPtwfvy46u58//5T7ZFruBLDzMxaTlqBMQJZBQawHxUY\nxXRtcJ+qWSu67f++pWbsFVeeN+bLv+rZH6oZO/L6j4758s3G0s9+9a6aseO++/5xLElraIdcw5UY\nZmZm46Bdnt1uZmZmE2MMco15kpZXvV8aEUvruYD94UoMMzOzcdIOTTzNzMxs4tQ511gXEafWc4b1\n0LLZlKR+SddVvZZIOlXSx5JpzpL0o/Esp5mZtYfKs9vr87LG4XzDzMwaRT1zjUbON1q5JcbOiBja\nU9W9wPJhxjUzMxtz7XCfahtyvmFmZg2jHXKNlm2JMZzqKx+SnlJ11eT3kgZ7cZsm6SJJt0r6qqTW\n3wvMzGzMDT67vZWvjFiF8w0zM5sI9cw1GjnfaOWWGJMlXVf8fU9EvGhI/G3AX0fE5ZKmAYNdzT8G\nOAF4ELgceCLwm+oJJZ0HnAfQ2z1zjIpvZmZmTWBc8o0DF7fVdSczM7OaWrkSY7jmndUuB/5d0leB\n70TE/cVFkKsi4n6AIilZwpCkouiRdSnAjGmL84cLm5mZFdyxZ0sal3zj0Y+e5HzDzMxKtUOu0fpr\nWENEnA/8FTAZuFzSsUVod9Vo/bR2RY+ZmY2XNmjeaX/K+YaZmY2bOuYajZxvtO0JU9IREXEDcIOk\n04BjgU0TXCwzM2tRQXt0tmWP5HzDzMzGS7vkGm1biQH8naSnAgPATcD/AGdMbJHMzKyVNfJVDRsz\nzjfMzGzctEOu0bKVGBExbZhhy4Blxd9vHmayh+PFOG8ak8KZmVnbGewx3FqL8w0zM2sU7ZJrtGwl\nhpmZWaNph8TCzMzMJk475BquxBilPTM7WPm8qcPGDvxNdzpt75TJaVybtuQLn1Ty8e3ek8+/szOf\nPgbyuEr6he3Pp4++vWm8ozvfftHfn8ZVtn137MiXP234z3XEyta/pPwdHfn2Hdi5c5+L9MgClHR0\nr/wA2NHbM6rFa+qUPD6Qbz/19aXxjvnz8gLs2p2GB7Ztz6cvoZ58+8TOXWl81Pt/R8kJrLNk/iX7\nVwyU7D9ln19J+UqPTyXU3V26jbJj2MDOXaXHqK4F8/P5r87DZvuiW50c0jV92NiBXRvTaZ+64I40\nfsOWxftdLoBfrDsmjW89NJ++Z2V+vNw7PT/e3L1jYRo/9tP58ey21+XHw0WXiTN+/m814wf89aR0\n+q6S03VvZ34+u331Ao761gdrxvfOz8vfd//w+82g1SUf/547ZqTxmfnplOWrD07j27bnn//AwfkG\nLHtKQXdnfi7oXpjPf+1peT555tF3pvH7t89M48tuzL8/U1fna7j7UXk+EV35/rnlhPwD3HpYvv6x\no+R8PyXfvw/4cTdnXFr7+7X7cfn+e1N/Sb5ckq4OTC7J13fkv7d2z8mPT3e9JC/A1s8encaPfe3K\nNA5wb+kYrceVGGZm1nJGU4EB5ZWspRUYw82Txu7p28zMzJpbu+QarsQwMzMbJ+3QY7iZmZlNnHbI\nNVyJYWZmNh6iPe5TNTMzswnSJrlG2W1kDUnSQkn/LeluSddIukLSi/ZxHtuK/w+UdNHYlNTMzKxi\nsMfwerxsfDjfMDOzZlLPXKOR842ma4khScD3gAsj4pXFsEOBPxvh9F0R8XAPMxHxIPDisSirmZlZ\ntUZOCOyRnG+YmVkzaodcoxlbYpwN7ImIzwwOiIiVEfFxSUsk/VrStcXrCQCSziqG/wC4uXpmxTQ3\nFn93SvpXSTdKWiFpuGe7m5mZ7bPBzrZa+cpIi3G+YWZmTaWeuUYj5xtN1xIDOAG4tkZsDfCMiNgl\n6Sjga8CpRewU4MSIuCeZ93nAEuDkiOiTNGe4kSSdV4xL18zZ+74GZmZm1ugaKt84ZHEzpmxmZmb1\n1/RnREmfBJ4E7AGeDnxC0slAP1D94N2rShIKiuk/M9j8MyI2DDdSRCwFlgL0Lj44fziwmZlZIRr4\nqoblJjrfOPWkXucbZmZWqh1yjWasxLgJ+P8G30TEX0uaBywH3gKsBk6icqvMrqrpto9nIc3MzIZq\nh8eetRDnG2Zm1nTaIddoxj4xfgH0Snpj1bApxf8zgYciYgB4NdC5j/P+KfB6SV0AtZp3mpmZ7asI\nP52kyTjfMDOzplLPXKOR842mq8SIiABeCDxF0j2SrgIuBN4OfAo4R9L1wLHs+9WQzwF/AFYU83hl\n/UpuZmbtLkJ1eY2EpHsl3SDpOknLi2FzJP1U0h3F/+7YqQbnG2Zm1ozqlWs08m0pzXg7CRHxEPDy\nGuFHV/399mL8ZcCyIfOYVvx/L3Bi8Xcf8NbiZWZmVkcTclXjqRGxrur9O4CfR8T5kt5RvH/7eBeq\nWTjfMDOz5tLYLSjqpSkrMczMzJpRA1zVeAFwVvH3hVR+cLsSw8zMrEU0QK4x5lyJMUqdu2D2bcN3\nGD5p6950Wg0MpPEtZxyaxmdctzqN9x+QtxIuu4G3f936fITIy6/u3nz5k/LdT/Pm5ou/7/403nfY\nonz5C0vm/4eH0rh6e9L42j8/Lo3PvnVHGl93/JQ0vuC/V6Rx9eTloyvfA9TTXTJ9yeFjb77/Dyye\nn8Y7NuXrz+ateXzPnjTcd/RBabxr3bY03rF5SxqPnbvSeEfJ/qOy7ZuvXilNzr+fpfcaKj9BatKk\nfP5TSz7fkvXvW7Umnx7oKFlH+vpqhtTdzUCyD/WtWUv/Ux+bz3/IITqgnldH5g3eIlJYWjzJYugi\nL5UUwGeL+MKidQHAKmBhvQpkY2t1fzcXbFwybGzFtsXptPdsyc93Zy+8PY1f/MAJafywmXm+0Hlk\nfrye+tPpaby/O//ebD4xj9/2uqlpfP6hwz4c5mHbF+bnqxmT8uP9UWffncZvuCs/H6krz7fe9cQf\np/Grtx6Wxk+fcVca/+CDL0jjG87Kz/eTduX5RMnphJ7J+fz37M7PN13Kt9+caXk+tmZ3ni9dfvfh\nafzMI/LtG4fmG+CB6bPSeP/GPJ/YPbvkwUY78/Xr3JFnBNqex/f29qfxtY8p2QFKwrs35+vfdcjO\nNN7bXTsXAFj4xclp/L5n5Nu3d22+faasyhO6I6etS+ND1TnXqDtJU6nckrkHWBYRX92f+bgSw8zM\nWs5oKjCAtAIDKK/AGHvrIuLUknGeFBEPSFoA/FTSrdXBiIiigsPMzMxsv0j6AvB8YE1EnFg1/NnA\nBVSunX8uIs4H/hy4KCJ+KOkbwH5VYjRdx55mZmZNKSq9htfjNaLFRTxQ/L8G+C7wOGC1pEUAxf/l\nTVrMzMysOdQx1xhpvgF8CXh29QBJncAngecAxwOvkHQ8cBBwXzFa3kwn4UoMMzOzcTKA6vIqI2mq\npOmDfwPPBG4EfgCcU4x2DvD9MVpVMzMzmwD1yjWKfGOepOVVr/OGLi8iLgOG3pf3OODOiLg7IvYA\nX6fSL9f9VCoyYBR1EU1zO4mkd1N5BFk/MAC8PiJ+V2PcNwA7IuLL41hEMzOzmoJx7WxrIfBdVW42\n7wL+OyJ+Iulq4JuSXgesBF46XgVqFs43zMysWY1BrjGS21eHs5g/triASuXF44GPAZ+Q9Dzgh/tb\nqKaoxJB0BpX7bE6JiN2S5gE1ewmKiM+MW+HMzMxGZPweexYRdwMnDTN8PfC0cSlEE3K+YWZmza2x\nH7EaEduB14x2Ps1yO8kiKrVAuwEiYl1EPCjpXkn/IukGSVdJOhJA0vskva34+0hJP5N0vaRrJR1R\nDP97SVdLWiHp/cWwqZJ+XIx7o6SXTdD6mplZCxrne1Rt3znfMDOzpjYBfWIM5wHg4Kr3BxXD6qJZ\nKjEuBQ6WdLukT0l6SlVsc0Q8CvgE8B/DTPtV4JMRcRLwBOAhSc8EjqJyr87JwGMlPZlKhyQPRsRJ\nRc+qPxmuMJLOG7wvqG/39rqtpJmZtbYI1eVlY6Zh843tG/LHTJqZmUH9co1R5htXA0dJOkxSN/By\nKv1y1UVTVGJExDbgscB5wFrgG5LOLcJfq/r/jOrpik7NFkfEd4v57IqIHVQ6OHsm8HvgWuBYKknG\nDcAzJP2zpDMjYnON8iyNiFMj4tSunvzZ42ZmZtYcGjnfmDpnUh3X1MzMbERKO/aU9DXgCuAYSfdL\nel1E9AFvAi4BbgG+GRE31atQTdEnBkBE9APLgGWSbuCPvatXN3QZaaMXAR+OiM/+SUA6BXgu8EFJ\nP4+ID+x/qc3MzCoqTTPdiqLROd8wM7NmNQa5RmnHnhHxihrDLwYurmdhBjVFSwxJx0g6qmrQyVR6\nVQd4WdX/V1RPFxFbgfslvbCYT4+kKVRqhF4raVoxfLGkBZIOpNLL+FeAjwCnjNlKmZlZ2xkI1eVl\nY8P5hpmZNbt65RqNnG80S0uMacDHJc0C+oA7qTT1fD4wW9IKYDcwXC3Qq4HPSvoAsBd4SURcKuk4\n4Iri8XPbgL8AjgQ+ImmgGPeNY7taZmbWTtwpZ8NzvmFmZk2tHXKNpqjEiIhrqHSS9QhFQvCRiHj7\nkPHfV/X3HcDZw8zzAuCCIYPvonLVxMzMrO58O0ljc75hZmbNrh1yjaaoxDAzM2t2gZ8sYmZmZmNn\nDHKNeZKWV71fGhFL67mA/dHUlRgRsWSiy9C1eRezf3jzsLFbP3hcOu3RF/bn894xkC9867Y0HPf8\nIZ/+0IPTcAyUtEWKvHz9W7fm06ukS5Zt+fp1zpyZxmNn/ji6/hW35MsvK1/J+s/53G/TeNeSQ9P4\nwrtX54vv7Ezj/Zs25dOfdmIa77j+9jQ+cNLRaXzl8/Mn9xxxYb5+bMsfXzxQEu87/fg03vGra9N4\nf8n2jf78+1umo7s7jWv+3HwGW/LvR9nn39GZ798DO3fm0/f0pvHo3w3JMmLvXgZ27NjvZXR0dzOw\ne1fN+MCOHajsM8yOcepAHbWTgK7LrqNj+vR0/sMuc5+nsEbQCPnG2p3T+eQNTx42dtkTP5VO+x/d\nT0zjc7ry48mOPfmTUW7/Qkm+8+q70/h9nfl3acHH8/PpgjQKW195Rj7Cb+bR/6p1NcPbT8+PVTO6\nah+LAO5791FpfM7h+fkAYOOZtZfxzTc+O522930PpfEvbvyTxkeP0DF7TxqfvGJyGn/hK36dxr/6\nm3z5L31S/vmfO+fKNP6P9/1ZGt87UJLvrcnPd697xi/y6YHbty+sGVt/8eJ02iN/lOdL24/pSeMP\nPSH/QXv40Q+m8ZXr5qRxgLhzWu3Y3nz77p3bx6T1tX+SRsfozpyLvpl/v+5/Wr79ui++Ko1PPuH0\nfPotaZj1x+flu+SeY/MZDKPOuUZpx54ToakrMczMrEGVVZKMogIDSCswgNFVYEBagQHsVwWGmTWu\nq5/zoZqxI77xT+NYkuHd8xfvrBl72hc+PI4lsf1x4eM+XzP2qG98dBxLMjbueMdbasYO/cK/lE5/\n59+/tWbssI//236VyVqbKzHMzMzGgx+xamZmZmOpTXINV2KYmZmNF99PYmZmZmOpDXKNkpvAmo+k\nAyR9XdJdkq6RdLGkYW/el7RE0o01YsskNdz9P2Zm1rwiVJeXTTznG2Zm1ojqlWsU+cY8ScurXudN\n9PpBi7XEUOUZaN8FLoyIlxfDTgIWAnkvhWZmZmOsHZ7d3g6cb5iZWaOqc67hjj3HwVOBvRHxmcEB\nEXG9Kj4CPIdKA5sPRsQ3qieUNBn4InAScCuQd7VsZma2D4L2uE+1TTjfMDOzhtMuuUarVWKcCFwz\nzPA/B06mkjDMA66WdNmQcd4I7IiI4yQ9Gsifv2hmZrYvAmiDxKJNON8wM7PG0ya5Rsv1iVHDk4Cv\nRUR/RKwGfgWcNmScJwNfAYiIFcCKWjOTdN7gfUF7BnaOVZnNzMysuYxZvtG/ZftYldnMzKyptFol\nxk3AY8d6IRGxNCJOjYhTuzvcCtTMzEYmoj4vm3Djnm90zpg61oszM7MWUK9co5HzjVarxPgF0FPd\na2rRVHMT8DJJnZLmU7kKctWQaS8DXllMcyLw6PEpspmZtY2o08smmvMNMzNrTPXKNRo432ipPjEi\nIiS9CPgPSW8HdgH3An8HTAOup/Jx/ENErJK0pGryTwNflHQLcAvD3+tqZma2n/x41FbhfMPMzBpT\n3XONeZKWV71fGhFL67mA/dFSlRgAEfEg8NJhQn9fvKrHvZdK51xExE7g5WNdPjMza2MNfFXD9o3z\nDTMza0h+xKqZmZnVRbTHY8/MzMxsgrRJrqFo5B47msDkAw6OI1791mFjXSUPLplzy+403r1maxrf\ntXhGGt89O6+jmvXLu/LpH3VIGu+5+YE0TvekNBzT8k5RtW5TPv3evWm8/+i8/JR8v/fM6UnjHbsH\n0njvLQ/mC5jcm4Z3HDMvjfeszXewznseypc/a2Ya3nbc3DQ+7cY1aTym5utXZuCWfP/sXJBvnx2P\nPiiN9/7qxjQee/vy5c+ZlU/fl08/sHlLPv/DDk3jZQZmTslHuOH2fPqS9VdnZ2kZOkr2cU2flpdh\n3fo8vmdPyfJLjjFd+TGyf2t+DN7+4tPT+BXfets11Vcveg47KBa9/03pNCO18px3XtOIV0Zs7Bx8\n4ox467eG3+d2DHSn067Ykh8P79k8J40fMzs/3s/r2ZbGv7viMWn8jKPz4/2Vdx6Wxrun5PnA7Kn5\n+XLV6vx8yI78WHHc8fel8S7l+cK83nz77erP86mr7l2SxqdNy9f/lIX3p/H7t+fnuzvuWZTGZ87P\nj6WPP+APafzyB/LPf9aU0T0pcNsPD0jjW0/P53/mkfn+e8WlJ6bxyevSMJtPyc917MjPx3Ouy+N9\nz9uYxqX8t+K8qfmTk9b8+OA0vuDaXWl8w7F5LrH56JLfsgvy+U+5Ls8VDv7eqjR+96vz/Wfv9Lx8\nxyzNd4Ajv7IyjQN84rH//XBOUM9cAxo333BLDDMzq7t2r8BIlryf05mZmZmNROvnGq32dBIzMzMz\nMzMza1FuiWFmZjZefAenmZmZjaU2yDVariWGpHdLuknSCknXSXr8RJfJzMwMaPnntrcT5xtmZtaQ\n6pVrVPKNeZKWV73OG9d1qaGlWmJIOgN4PnBKROyWNA/Ie7sqn2dXROQ93JmZmZUJoA16DG8HzjfM\nzKwh1T/XaMhHrLZaS4xFVDb0boCIWBcRD0p6mqTfS7pB0hck9QBIurdIPJB0qqRlxd/vk/Rfki4H\n/muC1sXMzFpMRH1eNuGcb5iZWUOqV67RyPlGq1ViXAocLOl2SZ+S9BRJvcCXgJdFxKOotD554wjm\ndTzw9Ih4xdgV18zM2opvJ2kVzjfMzKwx1fd2kobUUpUYEbENeCxwHrAW+AbweuCeiLi9GO1C4Mkj\nmN0PImLYB0NLOm/wvqD+Hfmzkc3MzB4Wqs9rhCR1Fi0DflS8P0zS7yTdKekbkkZ1C0S7moh8Y/uG\nvXUouZmZtbx65RoNfAtsS1ViAEREf0Qsi4j3Am8CXpiM3scft0HvkFjN2omIWBoRp0bEqZ1Tpo6u\nwGZmZmPnb4Fbqt7/M/DRiDgS2Ai8bkJK1QLGO9+YOmfS6ApsZmbWIlqqEkPSMZKOqhp0MnAXsETS\nkcWwVwO/Kv6+l8qVFID/b1wKaWZmbUtRn9eIliUdBDwP+FzxXsDZwEXFKBeS//C2GpxvmJlZo6pX\nrjHSfGMitFQlBjANuFDSzZJWULnP9B3Aa4BvSboBGAA+U4z/fuACScuB/okosJmZtYnxv0f1P4B/\noHLeA5gLbKp6Asb9wOJRrFE7c75hZmaNp565RgNXYrTUI1Yj4hrgCcOEfg48Zpjxfw0cPczw99W9\ncExtTJsAACAASURBVGZm1ubqen/pvOIH8aClEbH04SVJzwfWRMQ1ks6q10KtwvmGmZk1psbuy6Je\nWqoSw8zMrKHV76pG2XPbnwj8maTnUumDYQZwATBLUlfRGuMg4IG6lcjMzMwmXgO3oKgXV2KMVoBq\nNAzddmi+By36yfp83v15i9Oe1fnHN/mmTfn8J+WdhPX8IZ8+du9O45Xbr2sbuO2eNN555JI0zqq1\n+fTb8/LFbXen8SmHHZIvf+PmfP59fWlcAwNpfMoND+bzL1k+06fl06/fkC//J/fl0/f05Mtfk+//\ncfShabxjRl5+pkzOp+8b2yN47N6Tx/fmTxLonDsnn37VmrwAXfn3X+s2oO7kOz5rJn1r82NQx+Sh\n/Q8O0V97H449e1EyfezYSezYkc5+YE++jdVVe/1ibx+aVHsbRV8fA7uSY4Tyuy2nfvsqNrz29HSc\n4Re875Psj4h4J/BOgKIlxtsi4lWSvgW8GPg6cA7w/fEpkY1Wj/o4vGf448IBnfn54JJVx6Xxzo78\nfNQX+ffhVw8emcbnzNuaxtftyjtJ7+jKvzg9k/Lz7ZbLFqTxE56V5yM3/2FRGp/WlR+rVvz8Txrh\nPELfU+5N4/esm5vGy0TJVdlbNhyQxlc9ODuNz1qQf75bNufn62UrTk7jexbl59Ptq/J84cyTb03j\nl58+M40ftmhdGj+od2Ma75ua77+795ZdNS85ceRfX7Y/M/98dj84I41rar79d+3Jf0/Mfigv4F0v\nyafv3F6ST87L8/3uu/L9b+90mJRsontekX8/9szK1+/gn+XxDY+dl8ZPnb4sjQ+rvrlG2vJzorgS\nw8ysBaUVGDCmFRhAWoEBjGkFBpBWYAB5BcYI7FcFBjTC1ZG3A1+X9EHg98DnJ7g8ZmZmE+rGf3lL\nzdix7/3oOJakTuqba5S1/JwQrsQwMzNrYRGxDFhW/H038LiJLI+ZmZnZaLgSw8zMbDwEbdHZlpmZ\nmU2QNsk1mrYSQ1I/cAMgKo8re1NE/LZO8z4ZODAiLq7H/MzMzKCxn7luw3O+YWZmzaQdco28p6bG\ntjMiTo6Ik6h0XvbhfZlYUmcSPhl47mgKZ2Zm9ida/LntLcr5hpmZNY965RoNnG80cyVGtRnARqj0\nwi7pR4MBSZ+QdG7x972S/lnStcBLJC0r3l8l6XZJZ0rqBj4AvEzSdZJeNgHrY2ZmZo3H+YaZmdkE\na9rbSYDJkq4DeoFFwNkjnG59RJwCIOkNQFdEPE7Sc4H3RsTTJb0HODUi3jQmJTczs7bUDk08W5Dz\nDTMzaxrtkGs0cyXGzog4GUDS/8/evcfZVdf3/n+9ZzKT2+SeEJIACeEqdzQgoFC0avHSgj14P1as\nldpWj9rjsZ72VLE/W7FaObVaPJEqXsG7IlrwVgS5ByQkEOQaIBDI/X6ZzMzn98deg5th9mcnmT0z\n+/J+Ph77kb3XZ63v+q699+z1yWd911qnA1+RdNxeLPfNAa+/V/x7B7Bgb1Ys6ULgQoCOSfm9s83M\nzJ7RAhfbakJ1kW/MmpvfVtjMzAxoiVyjKU4niYibgZnALKCHZ2/XuAGzbx/wenfxby97WdSJiMUR\nsSgiFrWPn7gfPTYzs5bTAueoNrvRzDcmT2/k405mZjYiaplr1HG+0RRFDElHA+3AeuBR4BhJYyVN\nBX5/P5rcCkyqYRfNzMyswTnfMDMzG32NXNbvP0cVSrc9e1tE9AKPS/oWsBx4BPjNfrT9X8CHivY/\nHhEDh4SamZntuzo+qmEVOd8wM7PG0QK5RsMWMSKi4i3LIuKDwAcHmb5gwOuzy56vozhHNSI2AKfU\npqdmZmYlrXCxrWbjfMPMzBpJjXONmZKWlL1eHBGLa7qG/dCwRQwzM7OG4yKGmZmZDafa5hrrImJR\nTVusARcxhkh90DHw0l2FOd/dli67+9AZabzzjofSeFvk39BNZx6SxqdcvTyNa8+eNE5fvv6+jZvy\n9jvzK633PrgyjUdvbxof05VfdFUHzU3j7Nqdxyd15fF1G9Jw91H5+nvGVzz4B8D4Wyt88fpV+fw0\nbWoa7zvi4DTetqHK+oEYW/knRt09dM9K3sMTF9KxaWfltgE98mTF+Nh1G9j+e0dVjPe84gS6bnu0\n8vqBvk2bK6+/uxt1dlaMq6MDVfkOVv0bGTc2j48fn7effAfbOjvh2MPS5ds25Z9xrF1fOdjbiyZX\nPtVfEyfQ8+RTlZdXG0Rf5XX37CnNUyle5fehtIr9v3r3jMtvZdObT933BV3EsP20J9p5Ys/gd0T7\nxtOnpcseN211Gv/xXSek8R0H5Pvr1x+an0nzheteksY3z8x/y3p35unqjsfyO8X1za78WwLw6I8O\nTeOzV+d/uPe+bnYan3bqmjS+euvkND51YuV9IcDqjfn2n3Z4vq+b3LErjX/n6ZPT+JYt+ed3wIyt\nafzQhSvT+BPbp6TxiR3daXxSle07cf4Tafw3989P46fNWJnGO+bn/x/Y01cln9yd54MT5+Xt71yZ\nf7+YmueL47vyfLhnef75zPurB9J47+Y8H1372PQ0PqFK/6bdXTlX2xtzbsm/Xztn5b+PWw7Jf7+q\n3Uhkbc9+XDapBXINFzHMbFhkBQwgL2BAWsCAvIABpAUMYEgFDCAtYAB1XcAAhreAAWkBA8gLGJAW\nMEorGNp1qYdSwAD2q4Ch8OkkZmZmNnxaJddwEcPMzGyktMC9283MzGwUtUCu0RS3WDUzMzMzMzOz\n5ueRGGZmZiOlBYZ4mpmZ2ShqgVyjKUdiSDpPUkg6usp8/yDpZYNMP1vS1cPXQzMza0X956oO9WH1\nwfmGmZnVm1rlGvWcbzRlEQN4E/Dr4t+KIuLDEfHzkemSmZm1vKjRw+qF8w0zM6svtco16jjfaLoi\nhqQu4MXAO4A3lk3/G0nLJC2VdHEx7XJJ5xfPz5F0n6Q7gT8ejb6bmVkTa4EjI63E+YaZmdWdGuYa\n9ZxvNOM1Mc4FromI+yWtl/QC4IBi+gsjYoekZ91wWNI44AvAS4EHgW9mK5B0IXAhQEdXfm9uMzOz\nZ9RxQmD7bETzjelz89sum5mZAS2RazTdSAxKQzqvLJ5fWbx+GfCliNgBEBEbBixzNPBIRDwQEQF8\nLVtBRCyOiEURsWjMuIm17b2ZmTWvJh/e2WJGNN/omtZR296bmVlzaoHTSZpqJEZxxOOlwPGSAmin\n9PZ/e1Q7ZmZmZk3D+YaZmdnoabaRGOcDX42I+RGxICIOBh4BNgNvlzQBnkk+yt0HLJB0WPE6vUCX\nmZnZ/mj2c1RbiPMNMzOrS61wTYxmK2K8Cfj+gGnfBeYAVwFLJN0FfKB8hojYRemc0x8XF9paMwJ9\nNTMzs8bkfMPMzGyUNNXpJBHxkkGmfabs5cUDYheUPb+G0rmqZmZmw6OOj2rY3nO+YWZmdasFco2m\nKmKMho4dvcy6Y3PF+Mpzp6TL94xPvmV/8DwOv3JLxXAA60+anLY/87v3Vg62t9O96PB0+c7b7k/j\nvVu3pvFqxsw/OI33PbW2Ykzt7UTPnorx3tVPoeOOyttfmrw/QHtXV778rt1pvG1c5avJdyy5n1Xv\nOiFdvuuJvoqx3X9wFFPuy78fenR15cY3bkJTK38/29dugW3b0/7RW7l/AvrmH1gx3rF5F70TKl+o\nrndiJ2PufbTyutuUrn/idfehmQNHcpeZMJ6+1U9Xjkv07dhROb5zJygZzLZlK+1dlS/8q/Z2erP3\nd+cu1N6etL+NtokTKrc/biyxc2fl5Zc/AEn7fVT/fhOV33+2bk3/ftonTqB3e/L+Zu/tM7MojUdf\n5d/X6Iu8/1VM+erNtE+dum8L1fnQTKtvW3vHccPGIwaN/dmBv0qXHafK+0qA9c/L93Uvn3FPGv/Y\nza9J4+e/+LY0/u3bF6XxYz7yWBrvPeSANL7xw8lvIbBpx6w03r47/605ZlayLwHW//38NP7U6/J0\nfOLKZF8AaH5vGn/19KVpfGX3zDT+wiNWpvHbHzkkjbe35b+1dzyR54K9Pfn+4NT5Sa4A9EX++S19\ndF4ar/bDfcPaw9L4nlX5TQAO+16yLwS6p3am8ZWvzds/cEkaZuv8vP3tC/P3b3z+9eOuW/L/axyw\nJH9/5wCrf6/yPDtW579fk/PNo3NrH2f90ScrxrctyBsYsyvv/4x7duXLb+1O45895Tk180H89HdP\nWyTXcBFjGA2pgAFpAQOGWMCAhi5gAGkBA6jrAgYwpAIGkBYwoEoBA9ICBjCkAgbkBQwgLWAAeQFj\nL9afFjAgL2BAXsCAqv/JzgoYQF7AgLyAAWkBA8gLGJAWMGCIBQyq//2kBYy9MJQCRjHDkNa/zwWM\nZ9Y7pNWamZm1nJV//oGKsQWXfmoEe9IgaptrzJRUXgpbHBGLa7qG/eAihpmZ2UhxEcPMzMyGU21z\njXURkQ+XGwUuYpiZmY0A0RpDPM3MzGx0tEqu0Wx3JzEzMzMzMzOzJtVwRQxJl0h6X9nrayVdVvb6\nXyT99ej0zszMLBE1etiwcq5hZmYNq1a5Rh3nGw1XxABuBM4AkNQGzASOLYufAdxUrRGVNOL2m5lZ\nIyquGF6LRzWSxkm6TdJSSfdI+mgx/VBJt0p6UNI3JVW5bnvLcq5hZmaNp4a5Rj2fltKIO9abgNOL\n58cCy4GtkqZJGgs8D7hX0i8k3SlpmaRzASQtkPRbSV8pljtY0jZJnyySvJ9LOlXSdZIelvRHo7GB\nZmbWpEbuyMhu4KURcSJwEnCOpNOATwCXRMThwEbgHTXasmbjXMPMzBqTR2LUn4h4EuiRdAilIyE3\nA7dSSjYWAcuAHcBrI+L5wEuAf5HUfz++I4B/j4hjI+JRYCLwy4g4FtgKfAx4OfBa4B8G64OkCyUt\nkbSku2dotwk0M7MWMkJJRZRsK152FI8AXgp8p5j+ZeC8oW9U86mHXAOenW/s3rRrODbVzMyaTQsU\nMRr17iQ3UUoqzgA+Dcwrnm+mNARUwD9JOgvoK+Kzi2UfjYhbytrqBq4pni8DdkfEHknLgAWDrby4\nN+5igCkT59bxx2tmZvWkhkMzq963XVI7cAdwOPA54CFgU0T0FLOsorR/tMGNaq4Bz843pj9vlvMN\nMzOrqp5PA6mVRi1i9J+rejyloZqPA/8T2AJ8CXgLMAt4QZEkrATGFctuH9DWnojo/6j7KA3BJSL6\nJDXq+2NmZvWodolF1fu2R0QvcJKkqcD3gaNrtvbW4FzDzMwaTwsUMRrudJLCTcBrgA0R0RsRG4Cp\nlIZ53gRMAdYUScVLgPmj11UzM7PRExGbgP+itI+cWvaf5oOAJ0atY/XPuYaZmVkdatQixjJKVwq/\nZcC0zRGxDvg6sKgYpvknwH0j30UzM7MyI3iOqqRZxQgMJI2ndP2FFZSKGecXs70N+GEtNq1JOdcw\nM7PGUstco45HdDTkEMZiiOzkAdMuKHu+jt9dVXyg4wYs11X2/KJKMTMzs6EawfNU5wBfLq6L0QZ8\nKyKulnQvcKWkjwG/Af5jxHrUYJxrmJlZI/I1MczMzKx2RiixiIi7gZMHmf4wcOrI9MLMzMxGnIsY\nVs2uGe3c/7ZJg8YmPpYve9Avd6fxtjWb0vj05fnZQDvOPCqNT7z5oTQehx6UxsesXpvG1/7hEWl8\n5q3r8/Ufny/f/vjTaZx1+fsXi47Pl394Vb788Yfl8Tt/m8YP/vrDabzvgOlpXN09eXz8+DQeYzvT\n+M7nHZDGJ9ySf3/aqnw+bRPy/jFtahruW7U6X37NujSstvzvZ8ycA/P1b9qcx3fmt0NsnzihyvI7\n0zgH5/3bcVj+/nXdk//9qiPfPfQ98Ei+/KT84HJ7e3saj+7uvP0x1Xdfmjz4b3O/3qfz9yB69lRe\ndtMm4szn1Aie7fpB+tQCiYUNj+ljtvPGA24bNPZAd/57cMPGfH/6myfzm9Rs25PvL845bnka/86d\nL0jjhx6a7y9W/FOej/zpC25M49eufl4aP/yMlWn8t6tmp/G7nsjfv8M/UmV/9dt8+478wwfT+Iqf\nH57G33/r69P4/Nl5PrarpyONt3f0pfG+UBo/e36+fdfefWwav/m3eT42fnK+P548Jd/f7r4lz8dW\nbcn//qiy/Q+/p0o+8kD+/k+7K1/92kX5jmf63VXiZ+f51Mkn5ZdWumH1wjQex+b57I5rDuCE919S\nMd7+wjxfeOpF+e9Xx+Y8n+idmH+/2w6s8v359cQ0fuAX8zMRTzkszxcBHh3wuhVyDRcxzMys6Qxn\nAQOoXsCouOD+LWZmZma2V1og13ARw8zMbCTU+UWyzMzMrMG1SK7RqHcnMTMzMzMzM7MW0zBFDEkh\n6Wtlr8dIWivp6v1sb6WkmbXroZmZWWWq4cOGj/MNMzNrVLXMNeo532ik00m2A8dJGh8ROynd8z6/\nkoyZmVk9aYEhnk3A+YaZmTWuFsg1GmYkRuEnwKuL528CrugPSLpI0gfKXi+XtEDSREk/lrS0mPaG\n8gYljZf0n5LeKekfJL2vLPaPkt47zNtkZmYtQlGbhw075xtmZtaQapVr1HO+0WhFjCuBN0oaB5wA\n3LoXy5wDPBkRJ0bEccA1ZbEu4EfAFRHxBeCLwJ8ASGoD3gh8bUB7SLpQ0hJJS3q3bR/SBpmZWQuJ\nGj1suNVdvrFlQ34bQjMzM6B2uUYd5xsNVcSIiLuBBZSOivxkLxdbBrxc0icknRkRm8tiPwS+FBFf\nKdpfCayXdDLwCuA3EfGcm2dHxOKIWBQRi9q78nv/mpmZPaPJk4pmUY/5xuTpjXQGsJmZjRoXMerS\nVcCnKBvaWejh2dszDiAi7geeTym5+JikD5fNcyNwjqTy65ZcBlwAvJ3SkRIzM7Oha4HhnU3G+YaZ\nmTWWGuYa9ZxvNGIR44vARyNi2YDpKyklD0h6PnBo8XwusCMivgZ8sn+ewoeBjcDnyqZ9n9KQ0FOA\na4eh/2Zm1qqa/MhIk3G+YWZmjccjMepPRKyKiM8MEvouMF3SPcC7gfuL6ccDt0m6C/gI8LEBy70X\nGC/pn4v2u4H/Ar4VEb3DsQ1mZmZW35xvmJmZ1aeGOcEyIroGmXYdcF3xfCel80oHWskgRzgiYkHZ\ny7f3PykusHUa8LohdNfMzOw56nloppU43zAzs0bWiLmGpIXA3wFTIuL8avM33EiM4STpGOBB4BcR\n8cBo98fMzJpMkw/vtL3jfMPMzIbNCJ9OIumLktZIWj5g+jmSfivpQUkfSrsc8XBEvGNvN7FhRmKM\nhIi4F1i4L8uMW9/LkZdvGTTWM2lsumznfavSeN+cWWk82vIa1IQbfpsv35uPXtVT69J43+bBt7vf\njCuX5u1PnJDG22dMS+OxY2fe/vSpefurn3Mh+Ge335f/5bYvfzhfvk1pnCqfn6p9Ptt35Ouvdvvf\nKvFxnfnPQ9/WrXn7VbSpyvszpsr6u7vTuHr2pPFq3/+2PUO7nWFblTsXRZX+V7Xq6TQ88dEn8+XH\n5b9PvWvzv3+1t6fxan+fsWt3Gif68niV70dsyb+fbePHET2VP2N1jMm38TcPoIPmpOsYtF0XIIz9\nyze2943l5m2HDxqb07kpXfaRzTPS+B8fke+vp4zJ9zdfWnF6GldH/nu7q6cjjbdtyuNf/M0ZaXzW\nzDxfmT99Qxp/sCPPx46buzqN7+rNf6/Gz8zf32VP5L81PYfk+7uj5q5J40dPzvcnD2+bmcY3bsvz\nuWrx6Qfm+Ujb9nx/o748n5g+N/98J3Tk++P1a6bn6++tfkx41+zKP/69GzvTZXsPyP9+2o/flsb7\ntoxL49vn5fnA9q15PnPNxuel8cldeT4w9l/zfH/TKWmY9iq/L31Vvh99nfmOue3AKvlM5O1vPj7P\nJ7cuPiqNv2LsijQ+mFHINS4HPgt85Zk+SO2UrgP1cmAVcLukq4B24OMDlv/TiMh/qAZwEcPMzFpO\nVsCA6kWa/SlgeBSFmVlrevi9f10xtuDST41gT6zpjUKuERHXS1owYPKpwIMR8TCApCuBcyPi48Br\nhrpOn05iZmY2Unw6iZmZmQ2n2p5OMlPSkrLHhXvZi3nA42WvVxXTBiVphqTPAydL+t/VGvdIDDMz\nsxEgfDqJmZmZDZ9hyDXWRcSimrY4iIhYD7xrb+ev25EYkv5O0j2S7pZ0l6QXDsM6zpaUn0hpZmZm\nTcv5hpmZWc09ARxc9vqgYlpN1OVIDEmnUzpX5vkRsVvSTCC/6s3+ORvYBtw0DG2bmZk9m0di1BXn\nG2Zm1nTqI9e4HThC0qGUihdvBN5cq8brdSTGHEpDV3YDRMQ6YJ6k7wFIOlfSTkmdksZJ6r9gyGGS\nrpF0h6QbJB1dTP9DSbdK+o2kn0uaXVx85F3A+4sjL2dKekRSR7HM5PLXZmZmQ6WImjysZpxvmJlZ\nU6lVrlHkG1WviSHpCuBm4ChJqyS9IyJ6gHcD1wIrgG9FxD212sa6HIkB/BT4sKT7gZ8D3wRuBE4q\n4mcCy4FTKG3DrcX0xcC7IuKBYjjovwMvBX4NnBYRIenPgA9GxP8sLh6yLSI+BSDpOuDVwA8oVYu+\nFxHPuW9V8eFdCDCuc0qtt93MzJqRL8pZjxom35g8Z3ytt93MzJpN7XONqtfEiIg3VZj+E+AnNe1N\noS6LGBGxTdILKCUPL6GUVHwIeEjS8yjdsuXTwFmU7jV7g6Qu4Azg29Iz9+vtv/HxQcA3Jc2hNEz0\nkQqrvgz4IKWk4u3AOyv0bzGlBIYpE+c6JTUzs73iC3vWl0bKN+YcO83fHjMzq6oVco26LGIAREQv\ncB1wnaRlwNuA64FXAnsoHTG5nFJS8b8onRqzKSJOGqS5fwM+HRFXSTobuKjCOm+UtKCYpz0iltdw\nk8zMrNW1QGLRaJxvmJlZU2mBXKMur4kh6ShJR5RNOgl4FLgBeB9wc0SsBWYARwHLI2IL8Iik1xVt\nSNKJxfJT+N3VUN9W1u5WYNKA1X8F+AbwpRpukpmZGYraPKw2nG+YmVmzqVWuUc/5Rl0WMYAu4MuS\n7pV0N3AMpaMZtwKzKR0hAbgbWBbxzFXO3gK8Q9JS4B7g3GL6RZSGfd4BrCtbz4+A1/ZfaKuY9nVg\nGnDFcGyYmZmZ1Q3nG2ZmZpVVvbDnaKjL00ki4g5K55sOZmzZfM96EyPiEeCcQdr7IfDDQabfD5ww\nYPKLge9ExKZ97LaZmVmujo9qtCLnG2Zm1nRG+MKeo6EuixijRdK/UToH9lWj3RczM2sydT4000aO\n8w0zMxsWLZJruIhRJiLes6/L7J7azsrzpg4am3ZfX7rsrpkL0/jEx3ak8THrtqbx7WcdlcbH/yK/\njlhs3ZbG22bNTON0Tcjbf2ptHn/siTSuzs58/b35+9/z5FNpfMy8OXn73d1puG/9hjQe27bn8XX5\n8poxLV++Sv902CFpvG3jljROlc+/2vvbNi2/PfGeWV1pvGN3vn1MzG9H2PvE6nz5NuXx3t40rM6O\nobUf+R6or8r3Ryccmcef3pjGx8w+II33rluftz8x//vv25b/vqi9PY3H7t1pHKBt0sBLEJS139lJ\nz4b8PSCS35D7HqDnZVUOTKwYrM18EWsN+5NvTG7fySsn3z1o7J7d89Jlzz/kzjS+bOtBafz27fn+\n4m1H35rGv/Crs9P4mg35/mTqEfn+cHZXng+tWJnvz69ZeWIap6snDa/aku/Pen8wK41POG9dGt8W\nY9P4hGX5/ua37fn2P/TU/DTedWz+/nevmpjGT3zBw2n8utVHpPEpC/PBSm1XTU/jk0/elcYP78rf\n/5+8NP9+zpqa7892/ucBnPi+SyrG2w/Jz+7v2JLnC72z8uXbO/N8ePf0fMfUedfkNH7aq+5J4/es\nPzCNr3nnzjQ+7pf59zvG5vng+AfyfHJn/ueJ7su/3z1H5f9fO/BXeT4z6Ru3pfGpd+1H4tACuYaL\nGGZm1nSyAgYwtAIGVC9gDEK0xtERMzMzGx2tkmu4iGFmZjZSqoywMTMzMxuSFsg16vXuJGZmZk1n\npG55JulgSf9V3HXjHknvLaZPl/QzSQ8U/+bnpZmZmVlDqfEtVuvy7iQjXsSQdJ6kkHT0SK97MJLe\nJelPRrsfZmbW5KKGj+p6gP8ZEccApwF/JekY4EPALyLiCOAXxeum41zDzMxaUi1zjVK+sS4iFpU9\nFo/k5lQyGiMx3gT8uvh31EXE5yPiK6PdDzMzs1qJiNURcWfxfCuly4zOA84FvlzM9mXgvNHp4bBz\nrmFmZtakRrSIIamL0n3R3wG8sZh2tqRfSfqhpIclXSzpLZJuk7RM0mHFfJdLulTSLcV8Z0v6oqQV\nki4vW8ebiuWWS/pE2fRtkv5R0tKijdnF9IskfaB4/k5JtxfzfFdSfnl9MzOzfaC+2jzYh+GdkhYA\nJwO3ArMjov/WPE8Bs4d3i0eecw0zM2tltco1lF/jfFSN9EiMc4FrIuJ+YL2kFxTTTwTeBTwPeCtw\nZEScClwGlN+GbBpwOvB+4CrgEuBY4HhJJ0maC3wCeClwEnCKpP6jTBOBWyLiROB64J2D9O97EXFK\nMc8KSgnQc0i6sD9x7N2e3+bQzMzsGSM8vLP4D/13gfdFxLPumxwRe39ySmNpilwDnp1vbF6f39bZ\nzMwMGOnTV0fFSBcx3gRcWTy/kt8N87y9GPq6G3gI+GkxfRmwoGz5HxVJ1zLg6YhYFhF9wD3FfKcA\n10XE2ojoAb4OnFUs2w1cXTy/Y0C7/Y6TdIOkZcBbKCUtzxERi/sTx/aJ+b2DzczM+o3UhT0BJHVQ\nKmB8PSK+V0x+WtKcIj4HWDMc2znKmiLXgGfnG1NmtO/VxpuZWWur8YU969KI3WJV0nRKRy2OlxRA\nO6X6zo+B3WWz9pW97hvQx92DzFM+356kC3uKpASgl8G3/XLgvIhYKukC4Ox0o8zMzPZWMGK3PZMk\n4D+AFRHx6bLQVcDbgIuLf384Ih0aIc41zMyspdU+15gpaUnZ68X1cHHPkRyJcT7w1YiYHxEL5t6l\nqQAAIABJREFUIuJg4BHgzBqu4zbg9yTNlNRO6ejLr/Zh+UnA6uLo1Vtq2C8zM7ORPDLyIkqnTLxU\n0l3F41WUihcvl/QA8LLidTNxrmFmZi2txiMx6vLuJCM2EoPSTv4TA6Z9F/gLSsM6hywiVkv6EPBf\ngIAfR8S+HGX6e0oXPltb/DupFv0yMzMDRuz80oj4NaX94GB+f2R6MSqca5iZWWur49NAamXEihgR\n8ZJBpn0G+MyAaWeXPb8OuK54fkHZ9JXAcWWvy2NXAFcMsq6usuffAb5TPL+obPqlwKV7uUlmZmZW\nR5xrmJmZNb+RHIlhZmbWskR9XyTLzMzMGlur5BouYgzR2DW7WfjZ+weNbXjFEemyk297PI3vPurA\nNN7W3ZPGJ97ycBrvPvXoNN65eksa75mW35mlWv/aZkxL4/HEU3l8x4483t2dxsccPDdffvPWPL4n\nu7YbaExHGkeVRnqX9O3elS8+LR+BrK3b8vjajWm8+vblPx9V39+n16Xxji15/xnbmcc78ve//aAq\n/Vufvz+aOiWN923YlMZpzy9JFHvyv5/oyT8f7h78d+kZc2fn7W/bncbbpkzOl9+xM19+/Pg0rirt\n9zyZ/z70bdiI2ivfzUHt7URvcstKtdGe9KF9yYP0HXlw2ofniBixC3ta83lsx3T+cumbB4196Jhr\n0mW//tipafwP5qzY734BfO3+U9L4W8+8MY0v35L/Hi+YuD6N7+zNf+/75uf72wfuPCSNj9k4No2v\n3ZXnMwte/0Qaf/SpGWk8duT72z0L+tJ4+9h8f7Lgx/nte2ed9XQav3l9Vxp/eOP0NL59R/7+Thif\n53OTX/9kGr//yXx/9/TkPJ+aPnl7Gp8xPo/vOjfP9zc/mvdvytF5PrrxoSr5dGe+3xm7Nf/7iDGg\n5Ct0+zUVb7AEwIEvzr//mzZNSON7zszf39g+Lo33npx/f+bMzfO9nisPSONbe/P+Vzu34+ErTkrj\nx/Qur9L+wNW1Rq7hIoaZmTWdrIAB5AUMSAsYwL4XMAqtcHTEzMyay30ffX/F2NEfvWQEe2J7oxVy\nDRcxzMzMRkoLJBZmZmY2ilog13ARw8zMbIS0wtERMzMzGz01zjVmSlpS9npxPdxmddSKGJJ6gWVA\nB9ADfAW4JCLyE/tGkKRt5VcaNzMz228B9LmKMdKcb5iZWcuofa6xLiIW1bLBWhjNkRg7I+IkAEkH\nAN8AJgMfGcU+mZmZWXNxvmFmZtZE8svjj5CIWANcCLxbJe2SPinpdkl3S/rz/nkl/Y2kZZKWSrq4\nmPbOYt6lkr4raYKkSZIekdRRzDO5/7WkwyRdI+kOSTdIOrqY51BJNxftf2w03gszM2tiUaOH7Rfn\nG2Zm1vRqlWvUcb5RF0UMgIh4GGgHDgDeAWyOiFOAU4B3Fjv8VwLnAi+MiBOBfy4W/15EnFJMWwG8\nIyK2AtcBry7meWMx3x5gMfCeiHgB8AHg34t5/hW4NCKOB1YP7xabmVmrUdTmYfvP+YaZmTWzWuUa\n9Zxv1OuFPV8BnCDp/OL1FOAI4GXAlyJiB0BEbCjixxVHMqYCXcC1xfTLgA8CPwDeTik56QLOAL4t\nPXNf5P4bVL8I+G/F868Cnxisc5IupHQkh3FtPoXVzMz2Ugvcu73BNEy+0TFrypA21MzMWkQL5Bp1\nU8SQtBDoBdYAonTk4toB8/xBhcUvB86LiKWSLgDOBoiIGyUtkHQ20B4RyyVNBjb1nx87iKqfenFF\n1sUAUzoOaP5viZmZ1UQ9H9VoFY2ab4w/fK6/PWZmVlUr5Bp1cTqJpFnA54HPRkRQOrLxF2Xnlx4p\naSLwM+DtkiYU06cXTUwCVhfzv2VA81+hdBGvLwFExBbgEUmvK9qQpBOLeW+kNAyUQdoxMzPbfy1w\njmq9c75hZmZNrZa5Rh3nG6NZxBgv6S5J9wA/B34KfLSIXQbcC9wpaTnw/4AxEXENcBWwRNJdlM4v\nBfh74FZKScF9A9bzdWAacEXZtLcA75C0FLiH0nmvAO8F/krSMmBezbbUzMxangBF1ORh+8T5hpmZ\ntYRa5hr1nG+M2ukkEdGexPqAvy0eA2MXAxcPmHYpcGmF5l4MfCciNpXN/whwziBtPwKcXjbp/ySb\nYGZmtm/6RrsDrcf5hpmZtZTa5hozJS0pe724ONVxVNXNNTGGg6R/A14JvGq0+2JmZmbNyfmGmZk1\nqXURsWi0OzFQUxcxIuI9w72OniljWX/OEYPGZly/Kl02Nm9J42NXVCmjTZqYt7+nJ413LLk/jffu\n3JnGlUarn0bVq/xsJnXkX0+1d+btb9+RxuPJp9N42/hx+fLde9J4tf7T25uGxxwwK19+V77+OGRO\nHs9bRxvy72fP/NlpfMwD+fe/2pWT+zZsSuNR5f0b6udXrX1Vifd1d6fxts78+1v9+1/x4HJJVPn9\n2LkrX7xK/6t9fwE0YULl4Jgx9Ga/gdt3VN2G9D2IPkh+Y9Tenn7GvZu3oLbkV+6Oe2k/MP8bGHS9\ndTw00/bfSOQbCyes48rnXzZo7NNPvTxdduuusWl86ZaD0vhhE9em8Unjd6fxbz9wchrveXBSGr9v\n2+FpPKokJHumVNnfzMx/79SVx7tuzvv/6K58fzzuoG1pfNeGfH8R0/P+je3M80F9NN/fzuzcnsbf\nevItabxd+W/50s359+/Vs+5O4195/LQ03tWV57PrV09O42M25/vj7Qvzz2fHpvF5+2s70vjGMXm+\nP3VFnk9vPCXPd7oXVsmH9rSx4PJBb6AEgKbn7097W/75R5U/4N41+fs3beHGNM5/zUjDu5nN2D+u\n/H+CzYfn/evryH9fts7Ll297OMmVgDXz89+XwbRCrtHURQwzMxsdaQED8gIGDK2AAWkBA/aiSJUV\nMGC/Chj1fpEsMzOzVnTzKy6uGDtyySUj2JMaaJFcw0UMMzOzEREtce92MzMzGy2tkWu4iGFmZjZC\nWuHe7WZmZjZ6WiHXGM1brO43SZdIel/Z62slXVb2+l8k/fUQ13G5pPOH0oaZmdmzRNTmYSPC+YaZ\nmTWcWuUadZxvNGQRg9L92c8AkNQGzASOLYufAdw0Cv0yMzMbXID6avOwEeN8w8zMGkcNc416zjca\ntYhxE7+7v/qxwHJgq6RpksYCzwN+I+mTkpZLWibpDQAqqTT9s5J+K+nnwAGjsF1mZmZWP5xvmJmZ\n1ZmGvCZGRDwpqUfSIZSOgtwMzKOUaGwGlgGvAU4CTqR05OR2SdcX8w82/XTgKOAYYDZwL/DFkdwu\nMzNrcnU8NNOey/mGmZk1nBbINRqyiFG4iVKCcAbwaUpJxRmUkoobgRcDV0REL/C0pF8BpyTTzyqb\n/qSkX1ZasaQLgQsBOidOG6bNMzOzptP8eUUzqot8Y868KrcVNjMzg5bINRr1dBL43Xmqx1Ma3nkL\npaMbw35+akQsjohFEbFozLiJw7kqMzNrIoqoycNGVF3kG9OmN3LKZmZmI6VWuUY95xuNvEe8idIQ\nzg0R0RsRG4CplBKLm4AbgDdIapc0i9KRj9uS6deXTZ8DvGTkN8nMzJpak18tvEk53zAzs8bRAncn\naeTTSZZROsf0GwOmdUXEOknfp5RgLKU0qOaDEfFUlekvpXRu6mOUzns1MzOrjQDq+ErfVpHzDTMz\nawy1zzVmSlpS9npxRCyu6Rr2Q8MWMYpzSScPmHZB2fMA/lfxYC+nv3uYumtmZi1O1PfQTBuc8w0z\nM2sUw5BrrIuIRbVssBYa+XQSMzMzMzMzM2shDTsSo16oD8bsGrza1X3orHTZODy/NXzn09vy5Tvy\nK5W3deQfb+/WrXn7fXkVr23c2DSu8ePy9nfuSuPV9O3cmcar9a/a8tE9tCvBD3X7NX1qvoKt2/P4\n+o15+1On5Mv39KThMRvz9fdsyNff1tmZr789r7EqqoyVa88/v+it8v2r0n50d6fxan9/1VRrX2Pz\n7ze0Ebt3V4z2bthI2/jxldtvb6d3W/4b1Da28nc8du5CnR0V4+1dE+nbsSNpvR2UfweitzeJ9qLk\nO6D29qqfcbb+3qfX0j53dr78YEZoJIakL1K6jsOaiDiumDYd+CawAFgJvD4i8j9Uqxu9tLGpd/C/\nuZdOXZEue8605Wl8+c6D0viE9sq/JQDjOvak8U335fnQ+PVpmK0n5uufNWtLGl+7dnIarybunZTG\nt5+U708WXpa3/9CbJ6TxMbuUxqfPzPO5NWvy/f2JC59M449sn5HGb3p6QRo/edYTaXz9rnz779kx\nL43v+MacNL75tGxfAe1d+fe3d0++LxrXmedLuzZW3hcCjNmRf75tj+X55MYT8u2L3rz9cQ/k7e8+\nIv9+d2zN35/H1k1P43O/n78/q87J99U7dlXJJ/OvB92Tg4WXfLpifNoj+fu3+fA83ls51QKgr7PK\n/7e0H3lDC4z69EgMM7MmlBUwgLSAAQypgAGkBQygSgGDIRYwSAsYpQb2v4AB7F8BA0byQluXA+cM\nmPYh4BcRcQTwi+K1mZmZNZMWuLCnixhmZmYjof9iW7V4VFtVxPXAhgGTzwW+XDz/MnDeELbGzMzM\n6k0tc406vhi5TycxMzMbITW82Nb+XC18dkSsLp4/BezncBIzMzOrV61wEfGGLWJImg1cApwGbAS6\ngX+OiO+PasfMzMwqqV1iMaSrhUdESPtzom3rcb5hZmYNpQWKGA15OokkAT8Aro+IhRHxAuCNwEED\n5mvYIo2ZmTWbUT9H9WlJcwCKf9fUbNOalPMNMzNrLDXMNeq4GNKQRQzgpUB3RHy+f0JEPBoR/ybp\nAklXSfol8AtJXZJ+IelOScsknQsgaaKkH0taKmm5pDcU0y+WdK+kuyV9anQ2z8zMmk4w2knFVcDb\niudvA35Yi81qcs43zMyscdQy16jjIkajHjk4FrgziT8fOCEiNhRHR14bEVskzQRukXQVpau2PxkR\nrwaQNEXSDOC1wNHFUNsq97g0MzOrP5KuAM6mdO2MVcBHgIuBb0l6B/Ao8PrR62HDcL5hZmZWZxq1\niPEskj4HvJjSeaqfA34WEf1XZRfwT5LOonSN1XmULma2DPgXSZ8Aro6IG4oEZBfwH5KuBq6usL4L\ngQsBOidMG74NMzOz5jJCV/qOiDdVCP3+yPSgOY1mvjF7blOkbGZmNtzq+K4itdKop5PcQ+noBwAR\n8VeUErNZxaTtZfO+pZj+gog4CXgaGBcR9xdtLAM+JunDEdEDnAp8B3gNcM1gK4+IxRGxKCIWdYyd\nWNstMzOzpqWImjxsxNRNvjF1Rnttt8zMzJpSrXKNes43GrWI8UtgnKS/KJs2ocK8U4A1EbFH0kuA\n+QCS5gI7IuJrwCeB50vqAqZExE+A9wMnDtsWmJlZ62nyc1SbkPMNMzNrLL4mRn0qzh89D7hE0geB\ntZSOhvwNMH7A7F8HfiRpGbAEuK+YfjzwSUl9wB7gL4BJwA8ljaM0LPSvh31jzMysNQTQV78JgT2X\n8w0zM2soLZJrNGQRAyAiVlO6zdlgLi+bbx1w+iDzrASuHWT6qUPtm5mZ2XPV91ENG5zzDTMzaxyt\nkWs0bBHDzMys4bRAYmFmZmajqAVyDRcxhqitu4+JT+waNLbhmEqnzZZMeWR3Gt8zI79oaF9HfkmT\nzvu2p/H2GdPz9jdtTuNtk7rSOJ2daTh2Dv6+PaO3N1++ylCptjH511vt+UXSNG5s3v7YfPvW/NER\naXzyo915+7vz7e9oVxrXnj1pPLZsSeN9RxycxunN3/8xB8/Nl+/oSMOxcVMa17gpabxvVn7nIN33\ncL7+nvzSzqry/d4bbXNmp/G+VU9WDvb0oPEDR7P/jjo6oC/fht5tyW+E2uj7vZMq9w3ouPuRtH26\nK38H28aPp3fbtqx3jJk7J22+r8p3pCrlf0PputdtoO3AA4a2frN9sL6ni6+ue9GgsVMn579nd25f\nkMZ7+vL94bbefH+4ann+tzrmyOxvHXbfl+cTnRPy/Vlv5PlQ28b897pjc/5bMHllvr9be0Ceb6w7\nIY9He759bQvzfO7ChTek8fvnHJjGq32+u3ry/m/dMS6N//L+I9P4mYc/lMbX7J6UxuP89Wn8kM48\n33p8dZ4PT5q7NY0fNjVf/127Z6Tx8U+nYTYfWSXf3Zl//+ccl69g7dP59yP25O13z+xJ44d8Nf9+\nnXjRb9L4E/ccn8Z3rc3/v8WBvYx7qvJv3PiFeT68qXdyGo8q11zuHp9/flElFVm5Jf9+joCZkpaU\nvV4cEYtHrTcFFzHMzEbBkAoYkBYwSg0MoYABaQEDhlbAKK0//09NPRcwgP0vYLTA0REzM7N6ct9H\n3l8xduwPLxq5joyU2uYa6yJiUS0brAUXMczMzEZCi1xsy8zMzEZJi+QaLmKYmZmNiIDIR8iYmZmZ\n7b/WyDUaooghqRdYVjbpPGAB8IGIeM2odMrMzGxf+XSSuuZ8w8zMGl4L5BoNUcQAdkbEs07QlrRg\nJFYsaUxE5FesMTMzq6ZFhng2OOcbZmbWuFok18gvN9sgJE2X9ANJd0u6RdIJxfRlkqaqZL2kPymm\nf0XSyyW1S/qkpNuLZf+8iJ8t6QZJVwH3juKmmZlZM4mozcNGhfMNMzOre7XKNeo432iUIsZ4SXcV\nj+8PEv8o8JuIOAH4W+ArxfQbgRcBxwIPA2cW008HbgLeAWyOiFOAU4B3Sjq0mOf5wHsjIr8vlJmZ\nmTUL5xtmZmZ1rmFPJxngxcB/A4iIX0qaIWkycANwFvAocClwoaR5wMaI2C7pFcAJks4v2pkCHAF0\nA7dFxKD3EJR0IXAhwLixU4a+dWZm1hrq+KiGAXWcb3QdOHHoW2dmZs2vBXKNRhmJsb+up3Q05Ezg\nOmAtcD6lZANAwHsi4qTicWhE/LSIba/UaEQsjohFEbGoY4yTCjMz2xvNP7yzhQ17vjFu2rjh672Z\nmTWJGuYadZxvNEsR4wbgLVA6vxRYFxFbIuJxYCZwREQ8DPwa+AClZAPgWuAvJHUUyx4pyVUJMzOr\nvQD6+mrzsNHifMPMzOpXLXONOs43GuV0kmouAr4o6W5gB/C2stitQHvx/Abg45SSC4DLKN067U5J\nonTk5LwR6K+ZmbWiOj6qYXvlIpxvmJlZPWuBXKMhihgR0TXItOsoDdkkIjZQIRmIiLeWPb+JstEn\nEdFH6cJcfztgsWfaNjMzq5kWSCwamfMNMzNreC2QazREEcPMzKzxRUvcu93MzMxGS2vkGi5iDJHm\n9dD2j2sHjW29cX66bM/4sWl85tKdaXzcyg1pfNW7Tk7jAFuP7E3jh13RnTfQpjy8O2n/4JlsWTgh\nbz9vnikPVLweGn1A+7qtFePtwM7DZ6btt2f9B/Z0Vf4TmvhUDxuPqBxfd1wnE5/KzjUbw9ZDssvW\njGf6fUn/Dp9G58b889tyaH6huOl3b07ju7Kr5c+aUzovL/HE2R1JdA5dj+fLT30g376NR3VWDp71\nfOb8amPFsIDuGfn3s3Nd5e8fgLYmf8N9wY5jZ1eOP+8Axq7bVTEcwO5p+W/I+Cfy7//2hfndlSL5\n+9t91pFMuvXRyjOMHQs9PRXDY8aPY9PvH56uf/ID2yoH58xIl90bbdsqv78AbNpSObZ9B6velPef\nSwa8DigdkDfbd53q4aBxg/9m/WrjUemyG3bnv2UPrsv3hbvXj0/jJy96KI3PGZ/vS37Rlt9dtk35\nzmTbjvy38KDjVqfxap48usqd6Dbm+9J4WeV9DcCEnvY0Pn7snjT+47UnpPG1O/PLr8yZUHlfAbBm\n+3MGKD2Lqnw+8w9cn8avfyD/LZ0ydUcar3bQ+dBJeb48qWN3Gn9s07Q0vqMny2Vgz7y8/d1H5/ui\nvi1VLuq7Lf/v3PbuJBcC+o7Ic5kpE/L+b96Qf7+2/0X+/brl6fz/S+1P5/1v787/szDrlKd40c/+\npmJ8544qf9/zquQKVfTuyD+fjnX592fm+PzzeY4WyTWa5cKeth9GtYABw1rAgLyAAcNbwADSAgZQ\npYBBlQIGeQEDRreAAUMsYDC8BQxICxgwzAUMyAsYkBYwYGgFDBhaAQPICxiQFjCAoRUwamBIBQyo\nXsAwMzMzs2HhkRhmZmYjpQWGeJqZmdkoaoFcw0UMMzOzkdICF9syMzOzUdQCuYaLGGZmZiMhoq7v\nuW5mZmYNrkVyjRG/JoakAyVdKekhSXdI+omkCyVdPUzru2k/l7tI0gdq3R8zM2thEbV5WFXON8zM\nrCXVKteo43xjREdiSBLwfeDLEfHGYtqJwB8N1zoj4ozhatvMzGxfRAscHakHzjfMzKxVtUKuMdIj\nMV4C7ImIz/dPiIilwA1Al6TvSLpP0teLBARJL5D0q+IoyrWS5hTTr5N0iaQlklZIOkXS9yQ9IOlj\n/e1L2lb2/G8kLZO0VNLFxbR3Srq9mPZdSVVumWFmZrY/mv/ISB1xvmFmZi2ohrlGHecbI13EOA64\no0LsZOB9wDHAQuBFkjqAfwPOj4gXAF8E/rFsme6IWAR8Hvgh8FfFOi6QNKO8cUmvBM4FXhgRJwL/\nXIS+FxGnFNNWAO+othHFcNQlkpZ0b85vo2hmZmYjrunyje1VbpttZmbWKurpwp63RcQqAEl3AQuA\nTZSShJ8VB0ragdVly1xV/LsMuCciVhfLPwwcDKwvm/dlwJciYgdARGwoph9XHEmZCnQB11braEQs\nBhYDTDlqdv2WqMzMrH4ELXHbswbQkPnGvGOn+stjZma5Fsk1RrqIcQ9wfoXY7rLnvZT6JkrJwulV\nlukbsHwfe79tlwPnRcRSSRcAZ+/lcmZmZvsmmv881TrhfMPMzFpTC+QaI306yS+BsZIu7J8g6QTg\nzArz/xaYJen0Yt4OScfu57p/Bry9/xxUSdOL6ZOA1cVQ0rfsZ9tmZmapAKIvavKwqpxvmJlZy6ll\nrlHP+caIFjEiIoDXAi8rbnl2D/Bx4KkK83dTOpLyCUlLgbuA/br6d0RcQ2k46JJi+Gj/7cz+HrgV\nuBG4b3/aNjMzqyqidHSkFo+9IOkcSb+V9KCkDw3z1tUV5xtmZtaSaplrjOCIDknnSfqCpG9KekW1\n+Uf8mhgR8STw+kFCXyib591lz+8CzhqknbPLnl8HXFch1lX2/GLg4gHtXApcOkj7F6UbYmZmto9G\n6qiGpHbgc8DLgVXA7ZKuioh7R6QDdcD5hpmZtaKRHkEh6YvAa4A1EXFc2fRzgH+ldJ2py4p946Ai\n4gfADyRNAz4F/DRbZz1d2NPMzKy5jdxRjVOBByPiYQBJV1K6Y0bLFDHMzMxa0shfE+Ny4LPAV/on\nVDqYQqmg8fEBy/9pRKwpnv+fYrmUoo7v/9oIJK0FHi1ezgTWJbM77rjjjjveOvH5ETGr/4Wka4r5\namEcsKvs9eLiThb96zofOCci/qx4/VZKt/x8N9aQnG847rjjjjteIf5MvlHjXAOq5Bv9JC0Aru4f\niVFcY+qiiPiD4vX/BoiIgQWM/uVFaQTjzyLi51V7FRF+1OgBLHHccccdd9zx0X5Qur7DZWWv3wp8\ndrT75UfNPt+6/r477rjjjjs+OvHRelC6Xfnystf7lIcA/wO4A/g88K5q6/PpJGZmZs3nCeDgstcH\nFdPMzMzM6kpEfAb4zN7OP9K3WDUzM7PhdztwhKRDJXUCb6R0xwwzMzOz4TasB1NcxKit55wf5Ljj\njjvuuOMjLSJ6gHcD1wIrgG9FxD2j2yurodH+PjvuuOOOO16f8XoxrAdTfGFPMzMzMzMzM9tnkq4A\nzqZ0QdGngY9ExH9IehXwfyndkeSLEfGPNVunixhmZmZmZmZm1gh8OomZmZmZmZmZNQQXMczMzMzM\nzMysIbiIYWZmZmZmZmYNwUUMMzMzMzMzM2sILmKYmZmZmZmZWUNwEcPMzMzMzMzMGoKLGGZmZmZm\nZmbWEFzEMDMzMzMzM7OG4CKGmZmZmZmZmTUEFzHMzMzMzMzMrCG4iGFmZmZmZmZmDcFFDDMzMzMz\nMzNrCC5imJmZmZmZmVlDcBHDzMzMzMzMzBqCixhmZmZmZmZm1hBcxDAzMzMzMzOzhuAihpmZmZmZ\nmZk1BBcxzMzMzMzMzKwhuIhhZmZmZmZmZg3BRQwzMzMzMzMzawguYpiZmZmZmZlZQ3ARw8zMzMzM\nzMwagosYZmZmZmZmZtYQXMQwMzMzMzMzs4bgIoaZmZmZmZmZNQQXMczMzMzMzMysIbiIYWZmZmZm\nZmYNwUUMMzMzMzMzM2sILmKYmZmZmZmZWUNwEcPMzMzMzMzMGoKLGGZmZmZmZmbWEFzEMDMzMzMz\nM7OG4CKGmZmZmZmZmTUEFzHMzMzMzMzMrCG4iGFmZmZmZmZmDcFFDDMzMzMzMzNrCC5imJmZmZmZ\nmVlDcBHDzOqapL+VdNlo98PMzKyVSbpI0teK54dI2iapvcbrWCnpZbVss1FJOlPSb0e7H2b1yEUM\nswYj6cWSbpK0WdIGSTdKOqWIXSDp1wPmv1xSt6StxWO5pI9LmjJM/QtJ24vk5glJn97bJEfS2ZJW\nlU+LiH+KiD8bjr6amZnVi+I/8GskTSyb9meSrhvFbg0qIh6LiK6I6B2pdUo6SNJ3Ja0rcqDlki4o\nYguK/GNM2fwXSOot8pFtkh6R9CVJRw5T/66TtKtY1zpJ35M0Zx+WD0mH97+OiBsi4qjh6KtZo3MR\nw6yBSJoMXA38GzAdmAd8FNhdZdF/johJwCzg7cBpwI3liVKNnRgRXcDvAW8A/nSY1mNmZtZM2oH3\nDrURlTRbnv9V4HFgPjADeCvwdJVlbi7ykSnAy4CdwB2SjhumPr67WN/hQBfwqWFaj1lLa7YfN7Nm\ndyRARFwREb0RsTMifhoRd0t6HvB54PTiKMCmgQtHxK6IuB34I0oJwNv7Y5L+VNIKSRslXStpfjH9\nUknP2glL+qGkv67W2Yh4ELgROKls2bcX69kq6WFJf15Mnwj8JzC37KjJ3AHDV/uPtLwisqhHAAAg\nAElEQVRN0mPFkY6/K2t7vKQvF9uwQtIHB47sMDMzq2OfBD4gaepgQUlnSLq9GIlwu6QzymLXSfpH\nSTcCO4CFxbSPFSM4t0n6kaQZkr4uaUvRxoKyNv5V0uNF7A5JZ1boxzMjHyT15x39j12SVhbztUn6\nkKSHJK2X9C1J08vaeaukR4vY3w22rjKnAJdHxPaI6ImI30TEfxax64t/NxV9OL18wSJneigi/hL4\nFXBRWR9OK96fTZKWSjq7mP4GSUsGbPf7JV1VpZ9ExCbgBzw7/zlV0s3FelZL+qykziLW3/+lRf/f\noAGjU1UaqfMBSXcXn/83JY0ri3+waPdJlUbwPGtkh1kzcRHDrLHcD/QW/1F/paRp/YGIWAG8i+Ko\nQ0QMmgAV824FfgacCSDpXOBvgT+mNFrjBuCKYvYrgDdIUjHvNOAVwJXVOivp6GIdD5ZNXgO8BphM\nqYhyiaTnR8R24JXAk0X/uyLiyQpNvxg4Cvh94MNFAQfgI8ACYCHwcuC/V+ujmZlZHVkCXAd8YGCg\n+M//j4HPUDoQ8Wngx5JmlM32VuBCYBLwaDHtjcX0ecBhwM3AlyiN6FxBad/Z73ZK//GeDnwD+Hb5\nf5QHExH9eUcXMA24ld/lEO8BzqM0MnMusBH4XLE9xwCXFn2bW2zTQcmqbgE+J+mNkg4ZEDur+Hdq\n0Zebk3a+x+/yn3mU3tOPFdv8AeC7kmYBPwKOknRE2bJvpvS+pIrP5I95dv7TC7wfmAmcTimH+UuA\niOjv/4lF/79ZoenXA+cAhwInABcU6zsH+GtKo00OB86u1kezRuYihlkDiYgtlP4DH8AXgLWSrpI0\nez+ae5LSDhtKxY+PR8SKiOgB/gk4SaXRGDcU6+s/GnM+pUJJpQIDwJ2StlNKjq4D/r1sG35cHA2J\niPgV8NOytvfWR4tRKEuBpcCJxfTXA/8UERsjYhWlRM/MzKyRfBh4T/Ef6XKvBh6IiK8WIxGuAO4D\n/rBsnssj4p4ivqeY9qViv7uZ0ojHhyLi58X+/tvAyf0LR8TXImJ9sfy/AGMpHTTYW58BtgL9oyre\nBfxdRKyKiN2URkCcr9K1K84Hro6I64vY3wN9Sduvo5ST/D3wiKS7VFwTbB+V5z//HfhJRPwkIvoi\n4meUCkmviogdwA+BNwEUxYyjgWwkxmckbQbWUSpWvKc/EBF3RMQtxXu7Evh/lIo7++IzEfFkRGyg\nVGTpH+nxekqf8z1Fvy/ax3bNGoqLGGYNpig0XBARBwHHUTp68X/3o6l5wIbi+XzgX4shjpuK6QLm\nRURQGnXxpmLeNwNfr9L28ymdC/oG4IVA+UXKXinpFpUuSroJeBWlHf2+eKrs+Y5iXVB6Lx4vi5U/\nNzMzq3sRsZzS9a8+NCA0l9+Nruj3KKX9eb/B9nvl143YOcjr/n0oxekKK4rTFTZRupbEXu2jVTo9\n9GzgzRHRX4yYD3y/LL9YQWlEwmwG7LOLEZnrK7VfHKD4UEQcWyx/F/CD/pGi+2Bg/vO6/v4VfXwx\n0H9Bzm/w7PznB0WRoJL/ERFTKI2SmEbZyBJJR0q6WtJTkrZQOmDk/MdsP7iIYdbAIuI+4HJKxQwo\njZioSlIXpSGHNxSTHgf+PCKmlj3GR8RNRfwKSkdO5lMqSnx3L/oWEfEtSsNWP1ysd2yx7KeA2cUp\nLz+hVDDZ6/4nVvPsoagHD7E9MzOz0fAR4J08u0DxJKX/dJc7BHii7PV+70eL6198kNJR/WnFPnoz\nv9tHV1v2/wPOLUaN9nsceOWA/GJcRDxBaZ99cFkbEyidUlJVRKyjlEvMpTSqYl+2+7U8O//56oD+\nTYyIi4v4z4BZkk6iVMyoeipJ0b9llE5R+VxZkeVSSiNnjoiIyZRO493XAkwlzn/+f/buPEquukwf\n+PN09b4lne4me9LZIIQAIQQEEQiILA6D4uggjg6MjlGOiujgOvNzxBlncPToqDPKZFzAFWQbQQVE\nILLIln0hgYQsZE86SXd6X6rf3x91OxZN1XuTdHWtz+ecOt19n7q37q3tvv29936/UlDUiCGSQ0jO\nJvkPJCcFf09GbKf6XHCXvQAmDXYUlWD+MpJnItbZ1CHErokFYh2CfoHkKcH9RpF8z+B8ZrYCsVMj\nfwDgkaDDqqN1K4APkxwHoBSxU1P3A+gneQVi/WsM2gugnsc//Ouvgu2oC65z/fhxLkdERCRjgo6x\n7wJwY9zk3wE4keT7GOtQ8xoAcxA7ayMVagD0I7aPLib5JcT6r3IFtcivAPytmb0yJL4NwFf5587C\nG4N+uADgHgBXMjZ0fCmAr8D534Tk10jODba9BsANADaZ2YFgnQcQ6xMr0bwRktNIfhexs0VuCaKf\nAfhLkpcF9ykPOtScBADBJTl3I9bh6hjEGjWO1h2InTFyVfB3DYDDANqDPsNuGHL/vcnW/yj8CsDf\nkTw5aAz6f8e5HJGcoEYMkdzShtiZEM8HfU48B2AtgH8I8scBrAOwh2Rz3HyfJdmG2GmaPwGwDMCb\ng1M3YWb3A/gagDuDUxzXItbJZrxfIHb2xlEdhRgUHI14EsBngg5Fb0RsZ3sIsVMzH4i77wbEzvrY\nHJzWOeFYHguxAmgHgC0A/oBYgRQ2/KyIiEg2+griLscM/lm/ErF9/gHEzpq4MjgrIRUeAfAwYp2I\nbwPQjaO7LOGtiP2zfg//PELJuiD7NmL7+d8HdchziNUxMLN1AD6GWF2xG7G6wBtRrBLA/QBaAGxG\n7KyUq4JldQL4KmLDx7eQPCeY51yS7Yg1HixBrFHmrKA2gZltBzDYufn+YHs/g9f/jzRY/9wd9CNy\nVMysN9j+wQaFmxGre9oQ69dsaOedXwZwR7D+f320jxM81kOI9UfyBGKdiQ4e3FINJHmJscvdRUTy\nD8kbALzXzI614ywRERGRnBSM2rYWQNmxNLyI5AqdiSEieYPkeJLnMTYu/UmIHa26P9PrJSIiIjKS\nSF4dXDZch9jZtQ+qAUPylRoxRCSflCI2ZFkbYpfW/Bpxw7uKiIiI5KmPANgH4FXERoAZ2ueGSN7Q\n5SQiIiIiIiIikhN0JoaIiIiIiIiI5ITiTK9ArovUVFlxQ13CrK6y05037CSYfou4eUNJm5t3D5S4\neddAwlE4j6gvbnfzyvBRw910d3+Fmx/oqnTz0pKom48p6XDzjmiZm/eFPP+1xd1uzpAhy8Ne34qi\nXjdvDXn+wta/p9//+Iet/0lVfmfoe/qr3XzA/PfHlJDXb3OPP+pbn/lttKNLuty8ta/czcsi/vuv\nO+o/v9XFfofhh3v9xy8q8l+fYg64eWXEf3+FnaPXGfW/P7p6/e+f2nL/89PR538+x5T57w8AaOn1\nv0OKQp6j2hJ/HbtCnoODG5qbzaxx8O/LLqqyAwf9983RWra65xEzuzwlC5OcUDqqwirGJf7eKw35\nPgoTVo9EQj4rFrK/D8vDlh+2PwrTH7I/6B3wv6/D1i/s+zZsfzfc5ydM2PKL4C8/GvL8hS0/bPvD\nlEf63DysngpTQv/z0zvgL38gZPvD3h9hz2/Y+z/s8SP0548OhBzTDpk/7NUN29eGGQh5fqIh76+S\nopDnP2T7I0X++yP89fOFPT92FJ+fAxsOHKk3UllrANlbb6gRY5iKG+ow/p8/kTB79xnL3XnDPnT7\nemrc/ENjn3TzDT3+6JTrO/z8bxuecfPTSkI+9PTzW/af6uY/X3OWm08dd8DN3ztxqZv/qXWmmzd3\nV7n5RY0vu3nYTrG5z39951Z4o4wBvzt4mr/8kPXf1Nzg5qXF/vo/uuAHbv71/W9287B/AL894UU3\nv2bzW918b5ffiHLVhNVu/tCeU9x8Ro3//nultdHNz2nc6uaP7jzJzatK/UaIxgr/n/z5o15z87BG\nsGWHprj5uh3j3fzSE9e7+XN7mtz8mmnL3PyBHadhspNXlvjP32Vj/fVb1TbJzQHgF+f8YFv83wcO\nRvHCI/7zdrQi4zf6H2DJOxXjanHO4msTZlOrDrnzhv2T0xfyT9qYUv/7pD9k/q6Qgyphjcpl9P+J\nDXOgz98fbu9IfDBqUG2p36DZEPL8tIfs78IaUUaFPD9hwl6fipBG7cP9fqN62Pp39vvbXxTyT/Ks\n6n1ufrDXf33D3v/jy1rd/LWuMW4e1shRV+of1Gzt8w9KlRb5fXN2R/3PV03IQZNDIQ3+xSH/xIc1\n0lSFPH6Yjn7/oEZLr//8ja3wD/q2hsxfX+Yf1D0U8v4Le/5qQg6KhtXLAPCTN/3oSL2RyloDyN56\nQ40YIiIiaWAABkKOeIqIiIgcr0KpNdSIISIikhaGqOV/YSEiIiKZUhi1RsY69iTpn5sTu89NJP1z\nnI79cSeQvCf4fR7Jt6dy+SIiIonEjo5YSm5y9FRviIhIoUhlrRHUGw0kl8bdFmV4EwFk/5kYNwH4\nGQD/YrJjYGa7ALw7+HMegAUAfpeq5YuIiCRTCKd45ijVGyIikhdSXGs0m9mCVC4wFTI+xCrJhSSX\nkLyH5AaSP2fMjQAmAHiC5BPBfS8l+SzJ5STvJlkdTN9K8pZg+hqSs4PpF5JcGdxWkKwh2URyLclS\nAF8BcE2QX0NyI8nGYN4ikpsG/xYREZHcpXpDREQkP2S8ESNwBmJHQeYAmA7gPDP7DoBdAC4ys4tI\nNgD4JwCXmNl8AEsBfDpuGc3B9O8DuDmYdjOAj5nZPADnAzjSvbOZ9QL4EoC7zGyemd2F2FGYvwnu\ncgmAVWa2f+jKklw0eEpNtC18mD8RERGDIWqpuclxy9l6o7d1eCNUiIhI/ktlrZHN9Ua2NGK8YGY7\nzGwAwEoATQnucw5iRcczJFcCuA7A1Lj8vuDnsrj5nwHwzeAoy2gz88coAn4E4G+D3z8I4MeJ7mRm\ni81sgZktiNT4w+qIiIgMUp8YGZez9UbpKH8YQBERESDlfWJkpWzpEyN+AOEoEq8XATxqZokHSf/z\nMo7Mb2a3kvwtgLcjVoxcBiDpYLxmtp3kXpIXAzgbfz5KIiIiMiwGIJrFBUGBUL0hIiJ5q1BqjWw5\nEyOZNgA1we/PATiP5EwAIFlF8kRvZpIzzGyNmX0NwIsAZjvLH/QDxE7zvNvMosPdABERkUH5fmQk\nh6neEBGRvFAIZ2JkeyPGYgAPk3wiuFb0egC/JLkawLN4Y5Ew1E1Bp1qrAfQBeGhI/gSAOYMdbQXT\nHgBQjSSndoqIiBwPA/L+GtUcpnpDRERyXiprjWyuNzJ2OYmZVQc/lwBYEjf943G/fxfAd+P+fhzA\nWQmW1RT3+1IAC4PfP5HgobcCmBvkBxMs73TEOtjacCzbIyIiEkYDrKaf6g0RESkkhVBrZEufGFmB\n5OcB3ABdmyoiIiIjRPWGiIjI8VMjRhwzuxXArccyD/uIkj2lCbMtHfXuvFc1rnTz/9q90M2/H73Y\nzV9t8R//4LbRbt53tn+10Q2NS9w8Crr5FbWr3Pye9Re4+WuRMW6OiX78x5dn+Xfojrjxq80Nbn7H\nmf4Zwp/c9l43X13lb8CqNU1uXjSq180HOkrcvK+qz81v3nG5m7986AQ337PXf//Vl7a7+bjyw26+\ndNlMN//+/jo37+/0vx4Pjat085ZWP9/dWuvmPbv9kY9sjf/5bAkZyGDF2ZPc/Myp2938lSXT3Xz6\no51u/oe/mO/m/XX+4A637Vno5gBQcsB/jw+UJT9N8jZMQrTaX4eKHf7yhzJYQXS2JeGOp94AgWIm\nPr5WW5K0D1EAQF2xPxz8zh7/+zhC/33bZf73UWuv/4XUEPJ9X1cyvOHsRxX7w9O+3Orvr06oaHPz\nxlI/b+nwl98/4NcbvQP+/mh21W4339zV6OYlRf5x2/a+cjevLPbrjdIiv8uX8ohfbwyYX08Whyy/\ntW94I/uML291843t/uvb0V/m5mHbV1bk74sGQj5/AyH1eJiXD/rbV1bsr9+Jo98wevTrNJb5n/8t\n7f7/MxteneDmc2fucPPqEv/9G/b+6ez3a4HiIv/zHfb6N3cf20iYhVJrqBFDRETyznAaMACkvAED\nAGBANP/rChEREcmUAqk11IghIiKSBobCuE5VREREMqNQag01YoiIiKQFQy+zExERETl+hVFrhA6x\nSjIaDAm2luTdJCuD6f4FTCOMZBPJ92VyHURERI6WARiw1NzykeoNERGR4UllrRHUGw0kl8bdFmV2\nC2NCGzEAdJnZPDObC6AXwEdHeJ2OVhMAFRUiIpIzosERkuHe8pTqDRERkWFKVa0R1BvNZrYg7rY4\n09sHHF0jRrynALyhy3+SnyH5IsnVJG+Jm/5/JJeRXDfYakMyQvL24EjLGpKfCqZ/OFjGKpL3xh2B\nuZ3kd0j+ieRmku8OFn8rgPODozafInk9yftIPkxyI8n/iFuPS0k+S3J5cHSnOph+K8mXgvX+RjDt\nPcG6rSL55DE+PyIiIjJ8qjdEREQkoaPuE4NkMYArADw8ZPqlAGYBOBsAATxA8gIzexLAB83sIMkK\nAC+SvBexIxoTgyMtIDk4rtd9Zva/wbR/BfAhAN8NsvEA3gJgNoAHANwD4PMAbjazK4N5rgcwD8AZ\nAHoAvEzyuwC6APwTgEvMrIPk5wB8muR/A7gawGwzs7j1+BKAy8xsZ9w0ERGRYTGEDz2daiQjAJYC\n2GlmV5KcBuBOAPUAlgH4gJn548ulmeoNERGR45OJWiMTjuZMjAqSKxErgl4D8MMh+aXBbQWA5Yjt\n+GcF2Y0kVwF4DsDkYPpmANNJfpfk5QAOB/edS/IpkmsA/A2AU+Ie4//MbMDMXgIw1lnXx8ys1cy6\nAbwEYCqAcwDMAfBMsB3XBdNbAXQD+CHJdwHoDJbxDIDbSX4YQMKBfUkuGrwuKNoxvLHLRUSkcAwY\nU3I7Bp8EsD7u768B+JaZzQRwCLF/4LOF6o0h4uuN3pYuZ3VERERiUlVrHGO9kVZHcyZGl5nNc3IC\n+Hcz+5/XTSQXArgEwLlm1klyCYByMztE8nQAlyF2vetfA/gggNsBvNPMVgVHORbGLa5nyOMlE3+/\nKGLbRwCPmtm1b1hx8mwAbwXwbgAfB3CxmX2U5JsA/AWAZSTPNLMD8fMF1wItBoDySZPztIs1ERFJ\npXQfHSE5CbF92VcROyOAAC7Gn/t3uAPAlwF8P20r5VO94dQbo2aPVb0hIiIunYlx9B4B8MG46z4n\nkjwBwCgAh4KCYjZiRyhAsgFAkZndi9hpl/OD5dQA2E2yBLEjI2HagnnCPAfgPJIzg8evInlisL6j\nzOx3AD4F4PQgn2Fmz5vZlwDsR+yIjoiIyLAYiCiKUnLD0fUW/p8APos/DxlfD6DFzPqDv3cAmDji\nG546qjdEREQcqaw1oilpKhgZR90nRjJm9nuSJwN4NnaQB+0A3o/YtawfJbkewMuI7dyBWMH0Y5KD\nz8oXgp//D8DziO3In0d4wbAaQDQ4ffR2xE6LTbR++4MjLb8kWRZM/ifEipJfkyxH7OjJp4Ps6yRn\nBdMeA7Aq7DkQERE5Gik8NbPZzBYkC0leCWCfmS0LzlTIeao3REREwmXzZSCpEtqIYWbVYdPN7NsA\nvp3gblckWez8oRPM7PtIcEqrmV2f6HHNrA+x02Lj3R53vyvjfn8cwFkJ1uPsBI/3riTrLCIictzS\nfIrneQCuIvl2AOUAahHbT48mWRycjTEJwM50rVAY1RsiIiLDUyiXkwz7TAwRERE5GkTU0nNqppl9\nAcGZB8GZGDeb2d+QvBuxfhnuRKzjyV+nZYVEREQkDdJXa2SSGjGGyYqAvpqBhNny5TPceXfOGuXm\nB15pcPOD/X7e9GC3m9cV9bn5msdOd/Ob7/OXv+8Tb3bzcU+3uPmok/0+zKI7Ktz8Z3f+pZuPrfE/\n4F31fitm7bZKN//w0ze6edXuxO+bQVtPOMHN6/rdGGPe3ezmrz3nX3499af+qIubxs5x81GbD7t5\n/xklbn7H4QvcfMwq//WrrnVjRKb7Pf2X/a7OzSv2+w/QO9ffvsZV/vPbOsPfvormqJtX7ulx88YV\n/vu75Un//TPtVP/7BwDQn/w9PvMnnbBSfxfUPtN/jtsn+s/RhLs3u7k1+KNatpzqvQcMNZs7nTx2\nXUMW+hyAO4OhRVfgjSOASJaqK+7E1eNWHNe8k0sOuHlDcaOb91nCwVOOuHN/0iubAADF9Pd3RfT3\n91vQgLNGb0ma7+n1P8uPbJvt5vPH7XDzkpD1X3V4kpvXlvj7m4kVfj20sd2vB5pL/SuiWvv8eqky\n4teDkyoTXkF1xF+O9t+Xq7qmuvk9O85w8+5qf3/60n5vICHgvIn+vmBqmb+/W97ur//s6j3+49e8\n4uZ3Nyc6eevPtrTXu/nZY7a5+Z+ap7n53NG73TzM9jb/8/fqYX/9X0U9zmlMvg2vHfTrsbJav97Z\ncnCMm5861n/9Jlf47/+7l57j5mUT/JEs3zTJf/1auvzPb6FSI4aIiKSe04ABIMcbMBDagJHwMQEM\nZKCTLDNbAmBJ8PtmJLi0QSTb3XzyI8mzVdekcU1EJNW+cfpdSbPfbr4ljWuS+zJVa6SbGjFERETS\npBCuUxUREZHMKYRaQ40YIiIiaWBWGNepioiISGYUSq2R1VtIMkpyJclVJJeTdDtZINlEcm261k9E\nRORYDIApuUlqqd4QEZF8kapaI5vrjWw/E6PLzOYBAMnLAPw7gAtH4oHihpwTERFJudiwZ1l97KCQ\nqd4QEZGcVyi1Ri5tYS2AQwBAsprkY8HRkjUk3zH0ziSnk1xB8iySEZJfJ/kiydUkPxLcZyHJp0g+\nAOAlkl8heVPcMr5K8pPp2kAREclnsVM8U3GTEaV6Q0REclTqao1srjey/UyMCpIrAZQDGA/g4mB6\nN4CrzewwyQYAzwWFAQCA5EkA7gRwvZmtIrkIQKuZnUWyDMAzJH8f3H0+gLlmtoVkE4D7APwnySIA\n74V6cRcREcl3qjdERERyRLY3YsSf3nkugJ+QnAuAAP6N5AUABgBMBDA4SHQjgF8DeJeZvRRMuxTA\naSTfHfw9CsAsAL0AXjCzLQBgZltJHiB5RrC8FWb2hsHVgyJlEQBE6vxh+ERERIDCGfYsR2V9vTFm\nQlmqt1lERPJModQa2d6IcYSZPRscBWkE8Pbg55lm1kdyK2JHTwCgFcBrAN4CYLCoIIBPmNnrBhkn\nuRBAx5CH+gGA6wGMA/CjJOuyGMBiACibMtmGs10iIlI4opa9nWRJTLbWG01za1RviIhIqEKoNXKm\nmYbkbAARAAcQO7KxLygoLgIwNe6uvQCuBvC3JN8XTHsEwA0kS4JlnUiyKslD3Q/gcgBnBfOJiIgM\nm4GIoiglNxk5qjdERCRXpbLWCOqNBpJL426LMr2NQPafiTF4jSoQO7pxnZlFSf4cwIMk1wBYCmBD\n/Exm1kHySgCPkmxH7GhHE4DlJAlgP4B3JnpAM+sl+QSAFjOLjshWiYhIQRrI4k6yCpzqDRERyQsp\nrjWazWxBKheYClndiGFmkSTTmwGcm2S2ucF9WhA7ujHoi8Et3pLgdkTQwdY5AN5zzCssIiKSRKEM\ne5aLVG+IiEg+KJRaI6sbMdKN5BwAvwFwv5ltzPT6iIhI/jCwIK5TlXCqN0REZCQUSq2hRow4Qe/i\n049lntKKPkw7eVfC7Mz6He68F9euc/Nvl1/i5qeP3unmD7a+2c1PWNHv5lbkfwA2/veb3PxPV33d\nzc8/9+NuHike2gfa602uP+Tmm3c1uvnMifvcfO/WcW5edmmrm1f9pN7N9/gvD6zcf33Q778+Vzdu\ncvONFx9285a3VLj5jv3+87tzd62bj5u5381PLO9y89ZZ/vod2uCvX3lfwgOvR0SufcNAAa+zq7Pc\nzXsP+q9P+xw/nzbV//7Y11bj5i0AostHuffpHeP0E3j1uZh+b/LXoB+AFfst/Yz6/RAeOMV/DTsm\nuTGK+pJnWxZNR5X/FYmDpw44qSHSnfw1aj69EpGukCLhWT+WwnU89UaPFWNT99iEWU2k2523Y8Af\n2WRfr/99XV3sL39UqZ9v3Od/H+9vr3bz+eN24APP/33S/MK6xHXYoM5JJW4egf9dVVXc4+ZF9L5L\ngNEl/v6sudff/mlV/v7ot1tPcfPTxvrPz/4e//EHQv4hWtrpv5U7B0rdfOFYvx2vPeq/f0vH+vVS\nbcj7d0u3//6sjDg7GwCbOxvcPGz960s73bw60uvmO7pHu/m0moNuXhGyfRMqWty8s9//fNWGfD88\n+8oMNP3ka0lzRvz3j0X992fj+HY37x3w68H1h/3/Bxpn+p/PWXV+vdvZ72/fuOo2Ny9UasQQEclD\nw2rAANwGDCC7GzAADLMBA24DBoDwBowkCmHYMxEREcmcQqg11IghIiKSBmZAVB17ioiIyAgplFpD\njRgiIiJpQQwg/69TFRERkUwpjFoj5xoxSP4jgPcBiAIYAPARM3s+xY+xEECvmf0plcsVEZHCZSiM\noyP5QvWGiIjkmkKpNXKqEYPkuQCuBDDfzHpINgDwe0M5PgsBtANQUSEiIilTCMOe5QPVGyIikqsK\nodbItS0cD6DZzHqAI+O3TyR5HwCQfAfJLpKlJMtJbg6mzyD5MMllJJ8iOTuY3kjyXpIvBrfzSDYB\n+CiAT5FcSfL8TGyoiIjkFwMxYKm5yYhTvSEiIjknlbVGNtcbOXUmBoDfA/gSyVcA/AHAXQCeATAv\nyM8HsBbAWYht2+Bpn4sBfNTMNpJ8E4DvAbgYwLcBfMvMniY5BcAjZnYyydsAtJvZN9K1YSIikv8K\n4ehInlC9ISIiOakQao2casQws3aSZyJWPFyEWFHxeQCvkjwZwNkAvgngAgARAE+RrAbwZgB3k0da\nkwYHbL4EwJy46bXB/V0kFwFYBABlJ9SkYMtEREQkW2RjvVE73h+WWEREpFDkVCMGAJhZFMASAEtI\nrgFwHYAnAVwBoA+xIya3I1ZUfAaxS2ZazGxegsUVATjHzLrjJ8YVGcnWYTFiRz3xqtkAACAASURB\nVFtQc9I4O/6tERGRQmEABgqgs618kW31xvhT6lRviIiIq1BqjZzaQpInkZwVN2kegG0AngJwE4Bn\nzWw/gHoAJwFYa2aHAWwh+Z5gGSR5ejD/7wF8Im75g4VHGwCdYiEiIilERFN0k5GlekNERHJT6mqN\nbK43cqoRA0A1gDtIvkRyNYA5AL6M2LWoYxE7QgIAqwGsMbPBoxZ/A+BDJFcBWAfgHcH0GwEsILma\n5EuIdbAFAA8CuFodbYmISKoMHh1JxU1GnOoNERHJOamsNbK53sipy0nMbBli15smUhZ3v0VD5tsC\n4PIEy2sGcE2C6a8AOG1YKysiIjJENh/VkD9TvSEiIrmqEGqNnGrEEBERyVVmTNtRDZLliJ0tUIbY\nvv4eM/tnkrcDuBBAa3DX681sZVpWSkREREZUOmuNTFIjxjD1dpVg60sTEmYHmyrdeZ+IzHTza6Yu\nd/OdPaPdPFrqxmDfgJuXd0fdfOYv/fxdz9zs5uM7/ccP+/z19pf7yy/1F/DqeZPdvLjHb8Vs29Lo\n5t0L/eenYmfEzYt6/fXvbvD7eHvwmwvdvOWKTjevq/FzrvQv446M9tev754T3Py1tx92c1tZ6+as\n9R+/f90oN28e1+fmRWX+68uKftQu9d+jdS8nfwzDWOw/rcRfB2cVigDQyUsPE0X9yfPm0ysx7pfr\n3cfvnzPVzUt2tybNxu1qQd/45K9B/Vqg+ID/Hjx0Zr2be8ObNy4lzP8Ius8PYOgec+xHOqLpKyx6\nAFwcjLJRAuBpkg8F2WfM7J50rYikBgGUJPlQ1xV3uPNG4O9vz67e7Oa9IR+WPTX+9+meDn9/ER3w\nPxfLdk9y833d/kAvHX1+QVRM//npG/C3vzTiflngzQ1b3HxMif/6tUfL3Pw901e4+Z5ef3/ZO+D/\nOxC2fk8d8OvZixtedvPKkh43X9Xh12sTK1rc/OW2sW7+1np/X/dSZ+I6f9DUyoNu3hPy/IZ9fktK\n/HpjTdtEN1970K+3Tm/c5eZh7/8xZf6+uj9kv1exMeQflvl+Pdi1t8rND7b7/4/t3DnGzU+e7j8/\njZXtbn64168Fw56fhjL//ZFIGmuNjFEjhojICBhOAwaAYTVgAH4DBhD2DzpGtAEDgNuAAYxsAwaA\nYTZg4LgaMNIp6KNhsLIqCW4a3UJERERyXv4304iIiGQBAzAApuQGoIHk0rjboqGPRzJCciWAfQAe\nNbPng+irQQeT3yLpH+IVERGRnJHKWmMgi/vW0JkYIiIiacFUnuLZbGYLvDuYWRTAPJKjAdxPci6A\nLwDYA6AUwGIAnwPwlVStlIiIiGRSSmuNrJVXW0gyGgxTNnhrOsb5f0ByTvD7F0diHUVEpDDFhj1j\nSm7H9LhmLQCeAHC5me22mB4APwZwduq3NP+p3hARkWyUylrjWOuNdMq3MzG6zGxespBksZklvdLZ\nzP4+7s8vAvi3VK6ciIgUtmiajh2QbATQZ2YtJCsAvA3A10iON7PdJAngnQDWpmWF8o/qDRERyUrp\nqjUyKe+3kOT1JB8g+TiAx0guJPmbuPy/SF4f/L6E5AKStwKoCI6u/DxDqy4iInnEkNYjI+MBPEFy\nNYAXEesT4zcAfk5yDYA1ABoA/OuIbXCBUb0hIiKZlspaQ2dipE9F0IkZAGwxs6uD3+cDOM3MDpJc\nGLYQM/s8yY97R1lERESO1UCajh2Y2WoAZySYfnFaViD/qd4QEZGslK5aI5PyrREj2emdj5qZP4jz\nMQh6gV8EAJG6ulQtVkRERHJD2uuN2vEVqVqsiIhITsu3RoxkOuJ+78frL6MpP9aFmdlixHp1R9mU\nyTa8VRMRkUJgBkSz+NRMSYkRqzcmnFKnekNERFyFUmsUSiNGvG0A5pAsA1AB4K0Ank5wvz6SJWbW\nl9a1ExGRvJXN15dKyqneEBGRtCuEWqPgGjHMbDvJXyHWI/sWACuS3HUxgNUkl5vZ36RtBUVEJC/F\nOtvK/+tUJUb1hoiIpFuh1Bp51YhhZtUJpt0O4PYh0z4L4LMJ7rsw7vfPAfhcqtdRREQKVxT5f3Sk\nEKjeEBGRbJXNtQbJKgDfA9ALYImZHdfIXPnfTCMiIpIFDMj7Ic9EREQkc1JZaxxtvUHyRyT3kVw7\nZPrlJF8muYnk54PJ7wJwj5l9GMBVx7udeXUmRkZEDFbbnzDqf2qMO2s/gNrtA0nz+3EJikKukG2f\nkLwdqgJASUfyfsDaJ5Wg/mfL3OUXTZnk5p0nNSTNylqj6KqPJM17a4swZsUhd/kwvx+z6NqX3fzw\n+89NmjUuA0avaXXn755Q5ealD73o5kVnznXzaGWJm/fV+nlnY/LnFwAaHnrVyYC2c6c5c5ejpCOa\nNK1CL8p2HXYf34r9r5iWU0clzWrvr8Hodd7r0wJu2+0un9X+63foLZOTh+uL0VfhfXmXoPH5A07e\ngb4xle7jl+5tS5pN2QqgvdOdf6DZHwSh782nuHlRf/Lvn/5TmsCnkp39DvDpQ4icOjtpbpVlYHfy\nL7CSvW2wUuf9WwQg+eqhbtkBREf7ozUUb9rp5gOH29388DuTj3pZudcweuled/7Vb5hSGKd4ysho\niLTjQ3XPJcxe7vNHStvQM8HNN/aMdfPppfvc/PSq7W7++B/OdvPqy/zP0l9OWevmk0r978IH9p7u\n5v0hn8sy9KO9tyxp/p6Jy935H9nvfxfPrd3l5nXFnTjUn3x/subwRH/+Un9fMr7Mr4WmljW7+T0v\nvWE059c5Z8wWN9/dm7wWAICNhxvdvLLYL5Yva1zn5o82z3Hz5eub3Lz2BH9f8pETn3Lzmki3m/9i\nl//5mVbt1SJARan//Dz92nQ354oaN7/k6qVu3jPg14Lnv2Mlnvp18v3t9Hp/+1qq/fd3ZUmvm/eP\n8j//4yr8WveJDSf5j78++XcHAHz4Aw+5+S+2LnDzN8pIrXE7gP8C8JMja0FGAPw3gLcB2AHgRZIP\nAJgEYE1wt+T/aIRQI0YGeQ0YAIbVgAH4DRgARrQBA4DbgAEgow0YQGE3YABhDRhwGzAAjGgDBoCQ\nBgyMbAMGENKAgZAGDAyrAQNARhswALgNGADcBgwAbgMGAL8BA3AbMABktAEDQGgDRtLHzeJTPEUk\nuWfe9rWk2fdevmjEH/9783+WNHvPn24Y8ccXGUnr/+VTSbO/ePLGNK5Jfkh3rWFmT5JsGjL5bACb\nzGwzAJC8E8A7EGvQmARgJYZxVYgOCYmIiIiIiIjIUA0kl8bdFh3lfBMBxJ+mtyOYdh+AvyL5fQAP\nHu9K6UwMERGRNCiUsdtFREQkM0ag1mg2s2O9piUpM+sA8HfDXY4aMURERNJEfWKIiIjISMqSWmMn\ngPhrtycF01IiK7ZwJJCMklwZd2siuYDkd5x5FpL8TTrXU0RECkNs7HaNTpJvVG+IiEi2SGWtMcx6\n40UAs0hOI1kK4L0AHkjJRiK/z8ToMrOhPbNtBeB3oSsiIjJC1LFnXlK9ISIiWSPdtQbJXwJYiFj/\nGTsA/LOZ/ZDkxwE8AiAC4Edm5g8VdAzyuRHjDUguBHCzmV1J8kIA3w4iA3BB8Hs1yXsAzAWwDMD7\nzUKGyRAREQkxOHa75D/VGyIikgkjUGs0kIxvlF9sZotf95hm1yZcF7PfAfhdKldmUD43YlSQXBn8\nvsXMrh6S3wzgY2b2DMlqAIODNJ8B4BQAuwA8A+A8AE/Hzxj0yroIACL1o0do9UVEJN9kyXWqklpp\nqTcmTNR7R0REwqW41khpx56pks97xC4zmxfchhYUQKxg+CbJGwGMNrP+YPoLZrbDzAYQG7+2aeiM\nZrbYzBaY2YJITdWIbYCIiIhkvbTUG/Vj8rlkExEROXoFu0c0s1sB/D2ACgDPkJwdRD1xd4siv89W\nERGRdMmOjrYkzVRviIhI2qSw1sjmeqNgd5gkZ5jZGgBrSJ4FYDaAlgyvloiI5CmDOvYsRKo3REQk\nXQql1ijYRgwAN5G8CMAAgHUAHgJwbmZXSURE8lk2H9WQEaN6Q0RE0qYQao28bcQws+oE05YAWBL8\n/okEsx3Jg/t8fERWTkRECo5GJ8lPqjdERCRbZGJ0kkzI20YMERGRbKNGDBERERlJKa41snJ0EjVi\nDFNtRRcuP2VtwuzR0pPdeTsnl7l5xW6/39X+Cn/divr8N3DRtCn+AqIDbsx+fzj7iv39bm6btrl5\n/9n+81c6a4abd9X721834G9fxzj/49H3nnPcHP7Tg6odXW4eLfXXf8yaNv8BKvw3SKTPX8HOsSVu\n3ldVN6zl91b723f4pFo3L2+o9OefUuo//ig3xvin/efXSiJu3ja13M1Hd/qfj86T6928/MBYNw/T\nO9p/favGjwtZgL/+6Op2Y6vxh6fuq/Ofv/4K//nvqZ+O8n3+Z8wiyb9jq1/rQvErO9z5286f6ebY\nNOTxkN2dZEnuqmSvm08v3efmu/r87/OOAb9e2d3nf54HLmh180Pt/vf55s4GN9/a6X9fbn1ompvP\n/ItX3fyV/Y04+f5bkuaLZvvfpzva/B3OaaN2uvnh/nJ8csW1SfNx5X69+FKrv78ool8PPbF3lj9/\nxJ//lQ7/8ceWHXbzGTXNbt4V9ff3h/r9kQSbqg64eekpUTc/uWa3m0dDxlG4fbt/dVl3v//+Gl3n\n7+vCXDDFf//vbvTrsV3d/vu7rrTTzf+wZg6a1vxH0ryybow7f1eH//004QS/C6IJVf77L2y40mkT\n97t56RT//fOdxy5z87eds9rNASD+NIlCqTUKdnQSERHJX8NpwAAw/AYMERERERkROhNDREQkTQqh\nx3ARERHJnEKoNdSIISIikg6mPjFERERkBBVIrZGTl5OQHEvyFyQ3k1xG8lmSVx/jMtqDnxNI3jMy\nayoiIhIz2GN4Km5hSJaTfIHkKpLrSN4STJ9G8nmSm0jeRdK/mLzAqd4QEZFckspaI6g3Gkgujbst\nyvAmAsjBMzFIEsD/AbjDzN4XTJsK4KqjnL/YzI70SGdmuwC8eyTWVUREJF4aj470ALjYzNpJlgB4\nmuRDAD4N4FtmdifJ2wB8CMD307VSuUT1hoiI5KJCGJ0kF8/EuBhAr5ndNjjBzLaZ2XdJNpF8iuTy\n4PZmACC5MJj+AICX4hcWzLM2+D1C8hsk15JcTTLR2O4iIiLHbLDH8HSciWEx7cGfJcHNENuHDp4N\ncAeAd47EtuYJ1RsiIpJTUllrZPNlKTl3JgaAUwAsT5LtA/A2M+smOQvALwEMthzNBzDXzLY4y14E\noAnAPDPrJ5lwTJ/gNJpFAFA9zh+2SUREZJClsSAgGQGwDMBMAP8N4FUALXFnB+wAMDFtK5R7sqre\nmDAxF487iYhIuqWz1siUXGzEeB2S/w3gLQB6AVwC4L9IzgMQBXBi3F1fCCkoEMx/22CBZ2YHE93J\nzBYDWAwAjXPqbXhbICIihSKFPYY3kIwfGn5xsG86wsyiAOaRHA3gfgCzU/XghSjT9cZpp5Wo3hAR\nkVAanSQ7rQPwV4N/mNnHSDYAWArgUwD2AjgdsUtluuPm60jnSoqIiIygo75G1cxaSD4B4FwAo+P6\napgEYOdIrmSOU70hIiKShXLx3MTHAZSTvCFuWmXwcxSA3WY2AOADACLHuOxHAXyEZDEAJDu9U0RE\n5FiZpXV0ksbgDAyQrADwNgDrATyBP3cueR2AX4/Q5uYD1RsiIpJTUllrZHOfGDnXiGFmhlhHZBeS\n3ELyBcQ6J/scgO8BuI7kKsROmz3WoyE/APAagNXBMt6XujUXEZFCZ8aU3I7CeABPkFwN4EUAj5rZ\nbxDbV36a5CYA9QB+OGIbm+NUb4iISC5KVa2RzX1r5OLlJDCz3QDemyQ+Le73zwX3XwJgyZBlVAc/\ntwKYG/zej9jwc59O5fqKiIgA6TuqYWarAZyRYPpmAGenZSXygOoNERHJLSmvNUL74MqEnGzEEBER\nyUXZfFRDREREcl+Ka42j7oMrndSIMUztvWV4cvuMhJntLnfntbIBN591xatuvuHxxI876OD8fjcv\na21w87oVB9y8uKPPf/xTKt2896rT3byr3r/aaXSZn3ed4MbYcMMoN5/4mN8RfE+t/wXxw3/+lptf\n8yP/AFzJ/BY3t5/WunnHOX5e3Olv30Cpv33tE/1LwMv81UfbNP/xD43137+jlpW6eeU+//PVfoV/\n9ndzR42bD4R8ezLq50U9/uent8Z/f5c3+89fNOTzcXiyvwGlh8a5eVGvv4Es91+f5vn+56/HjzH5\nZ5v8OwDomTPZzYs7epNm0RkTENnRnDSveeE1bP7INH8F7n/9nwZk9fWlkt0OD5Th8c7E+/3dfaPd\neUtCvpBmlu1x8xc6/HpjbMlhf/n1yT9LALBqyyQ3X7vf/z46rXG3m09/+2Y3H1Pm7w9GV1W7+cH+\nKje/tmmpmz+23x84qLakx81vnviwm98dOcvNT6n0+/fd1uF32TJrml+vdkdL3Hxvj1+vjC7pdPOu\nqL+/6TO/XplRvs/N2/r9en7ZoSlufvXYFW4+qarVzcsifj20u9vfYe5s9r8fGiv89/9rrf78NeX+\n+7O2pMvNRzW2uXlvn1+vFJf6329NtQkHfzpiTMj763dPnOnPP8f/fmvvLnNzRv26YEzpsV2tWCi1\nhhoxREQk7wynAQOA24ABILwBIxGLdbglIiIiMiIKpNbIuY49RURERERERKQw6UwMERGRNBlA/p/i\nKSIiIplTCLVGzpyJQfIfSa4juZrkSpJvcu77UZJ/m871ExER8RjSOsSqHCfVGyIikqtSWWtkc72R\nE2dikDwXwJUA5ptZD8kGAEl78TGz29K2ciIiIkclfUOsyvFRvSEiIrmtMGqNXDkTYzxiw7v0AICZ\nNZvZLpJbSf4HyTUkXyA5EwBIfpnkzcHvM0n+geQqkstJzgimf4bki8GRlluCaVUkfxvcdy3JazK0\nvSIikofMUnOTEaN6Q0REclqqao1srjdy4kwMAL8H8CWSrwD4A4C7zOyPQdZqZqcGp3P+J2JHUOL9\nHMCtZnY/yXIARSQvBTALwNkACOABkhcAaASwy8z+AgBIJhyziOQiAIsAoLgxZBxAERGRQDafmikA\nsrjeqJ/gD9MnIiICpLzWaCAZP070YjNbnMoHOB45cSaGmbUDOBOxHfl+AHeRvD6Ifxn389z4+UjW\nAJhoZvcHy+k2s04Alwa3FQCWA5iNWJGxBsDbSH6N5PlmlnDgZjNbbGYLzGxBcW1lCrdURETyVeyo\nRn5fo5rrsrneqK7LleNOIiKSKamsNYJ6o3lwPxTcMt6AAeTOmRgwsyiAJQCWkFwD4LrBKP5uR7k4\nAvh3M/ufNwTkfABvB/CvJB8zs68c/1qLiIhILlG9ISIikt1y4kwMkieRnBU3aR6AbcHv18T9fDZ+\nPjNrA7CD5DuD5ZSRrATwCIAPkqwOpk8keQLJCQA6zexnAL4OYP6IbZSIiBScAWNKbjIyVG+IiEiu\nS1Wtkc31Rq6ciVEN4LskRwPoB7AJsVM9rwRQR3I1gB4A1yaY9wMA/ofkVwD0AXiPmf2e5MkAniUJ\nAO0A3g9gJoCvkxwI7nvDyG6WiIgUkmzuJEsAqN4QEZEcVwi1Rk40YpjZMgBvHjo9KAi+bmafG3L/\nL8f9vhHAxQmW+W0A3x4y+VXEjpqIiIiknPqzyG6qN0REJNcVQq2RE40YIiIiuc6gTjlFRERk5BRK\nrZHTjRhm1pTpdShqiaD6/2oTZqtv/Z4775xnrnPz9r5SN6/e7q/b9J/sc/PuGQ3+Ag4ccmNu2Ojm\n9U/7iy+eOMHNq3fucvPOvzrHzQdK/XOpTr5li5sjEnHj/l273fzzT1zj5iXv9B9+4NnR/vxtvW4+\nfl3Czu6PWH/jGDeffm+/m+/4SJ+bn9v0qpsvefx0Ny/b5r//6172t3/Pm/z5p7x7jZtHTj7RzW3b\nDjcvGh0y/HJttRt3jPd3QKM2+u/v0odf9B/+yrPdnE+vdHM7x3/9Ii0tbn7Cg81u3jfL/34YaDns\n5iV/WgfOmurehwf9ZYDJX4Ppi7eic94kd/ZXEkwrgDM881I21Bu9VoItPY0Js1sa17nzLuv1vy9r\n6efruye6+fdfOt/Nz5wYUrB0+OXoCddtcPM9/tKx6SczQu4BLDwp0Sc25uzGbUkzAFhQ5dcTNz7x\nPjdnyYCbA8CbZm1Nml373N+789502uNu3md+vTOmrNPNl+/1vwu/cco9bv6DPRe6+fvHPevm4yI9\nbv5i9zg37za/XuiJ+u/Pvx7n728BoM+SL2NN83j/8Z/367WuGf7nd1R9u5tf0eDXQ3f3nenmANDh\n/M8Sob/nO2vcdry4Z3LSfMoY//+RTXsSfy8OenqNX8+ddtJrbl65M6RBYI4fdx0u9+8wxn/99vbU\n+PMnUAi1Rk43YoiIiCQykg0YAEIbMBKywjjFUyQX/fisHyfNPrkiURco6XXXubclzWZu/9c0rokc\njw+emPzI3nc2/r80rsnIePqS/0ia/f3S60PnX3XlvyTNLvvjTcezSoWrQGoNNWKIiIikSyEcHhER\nEZHMKYBaIyeGWBURERERERERybtGDJLjSN5J8lWSy0j+jmTCi6FINpFcmyRbQnLByK6tiIgUEjOm\n5CaZp3pDRESyUapqjaDeaCC5NO62KNPbB+TZ5SSMjYF2P4A7zOy9wbTTAYxF4j7WRERE0qYQxm4v\nBKo3REQkW6W41mg2s6xraM+rRgwAFwHoM7MjvR+Z2SrGfB3AFYhdJfSvZnZX/IwkKwD8GMDpADYA\nqEjfaouISL4zFEZnWwVC9YaIiGSdQqk18q0RYy6AZQmmvwvAPMQKhgYAL5J8csh9bgDQaWYnkzwN\nwPJkDxKcRrMIAEqr6lKx3iIiku8MQAEUFgUi7fVG7Xi1dYiISIgCqTXyrk+MJN4C4JdmFjWzvQD+\nCOCsIfe5AMDPAMDMVgNYnWxhZrbYzBaY2YLi8qqRWmcREckzZqm5SdYasXqjqq5spNZZRETySKpq\njWyuN/KtEWMdgDMzvRIiIiIJWYpukmmqN0REJDulqtbI4noj3xoxHgdQFt9ranCqZguAa0hGSDYi\ndhTkhSHzPgngfcE8cwGclp5VFhERSS2Sk0k+QfIlkutIfjKY/mWSO0muDG5vz/S65ijVGyIiIhmS\nV31imJmRvBrAf5L8HIBuAFsB3ASgGsAqxNqUPmtme0g2xc3+fQA/JrkewHokvtZVRETkOKV1eNR+\nAP9gZstJ1gBYRvLRIPuWmX0jXSuSj1RviIhIdiqModjzqhEDAMxsF4C/ThB9JrjF33crYp1zwcy6\nALx3pNdPREQKWJpOzTSz3QB2B7+3Bf8wT0zPoxcG1RsiIpKVsvgykFTJt8tJREREspPFhj1Lxe1Y\nBGcBnAHg+WDSx0muJvkjkhpiS0REJF+ksNbI5jM6aNnc7WgOGD37BLvwB4kOxAC72mrdeYsjA26+\na/sYNz9nzmY339RS7+b2QIObd7ytzc37t1a7OaNuDEb9D8aYl/z3Zl+VP3/rhV1u3ljnb9+efaPc\nfEx9u5u3vOy/fuXN/vp3zuxz8+KqXjeveC5k5JyQJszDc/zHL+qMuDn9tzcQ8vrPuNd//bZdXhny\nAL6G1f4K1rx62M33nTPazftCVm/yXdvc/JUbp7j5xCf9D9j2t/kv8NTf9rt5xSv73RwW8gKXlrrx\npuvHuvmEp/z3X8UzG/zHBxCdO93NB0r856h0/XY333TTiX7+xU8vM7MFg3+XTZtk42/5uDvP0dp2\n3Re2AWiOm7TYzBYPvR/JasRGyPiqmd1HcmwwnwH4FwDjzeyDKVkpGVFNc6vtS/fNS5h1DviftyL6\n+9N9fX69Mq3M/z5oi5a7+WMHTnbz88ZscvM9vf73bTSk0B4IyTcc9r+PKov976Nz6/x6rDrS7eaH\n+v399ahIp5u/1uPXewf7/OVPKT/o5uVF/vavapvk5kUhh4VnVe1z87D3d5h+8+uVB189xc0vbXrZ\nzaPw319rD4138+17/HpxwbTX3Lw84r8+z/xpjpu/46Kh3fYMmX+Pvy+9eMIrbv7wdv/z37rZ/3yH\nsTK/HnnHWUlHsQYAPB2yfX2/9/9f6j7P/38gEvL/nq32v38vu8p/fQDgO/PvPFJvpLLWAIBt133h\ndbVMtsi7y0lEREQy3YCRXMqOajSHFRUkSwDcC+DnZnYfAATDfg7m/wvgN6laIREREckG2XsGRaqo\nEUNERCRd0nTyI0kC+CGA9Wb2zbjp44P+MgDgagBr07NGIiIikhYFcKGFGjFERETyz3kAPgBgDcmV\nwbQvAriW5DzESpytAD6SmdUTEREROT5514hB8h8RG389CmAAwEfM7Hl/LhERkTRI3+gkTyPx+aS/\nS88a5D/VGyIikpVSW2s0kFwa93fCPrjSLa8aMUieC+BKAPPNrIdkA4Bh9QZEstjM/B7wREREwhiA\nLO7pW46e6g0REclKqa81QvvgyoR8G2J1PGJPdA8AmFmzme0i+VaSK0iuCYaUKwMAkluDwgMkF5Bc\nEvz+ZZI/JfkMgJ9maFtERCTPmKXmJhmnekNERLJSqmqNbK438q0R4/cAJpN8heT3SF5IshzA7QCu\nMbNTETv75IajWNYcAJeY2bVDA5KLSC4lubS3xR8GUkRE5AhL0U0yLe31RvshnaQhIiJHIVW1RhbX\nG3nViGFm7QDOBLAIwH4AdyHWadkWMxscxPgOABccxeIeMLOELRRmttjMFpjZgtLRFSlYcxERKQjG\n1NwkozJRb1TX5dUVwCIiMlJSVWtkcb2Rd3tEM4sCWAJgCck1AD7m3L0ff27IKR+SdaR+7UREpJAx\ni49qyLFRvSEiItmoEGqNvDoTg+RJJGfFTZoH4FUATSRnBtM+AOCPwe9bETuSAgB/lZaVFBGRwlQA\np3cWCtUbIiKSlVJZa2RxvZFXjRgAqgHcQfIlkqsRu8708wD+DsDdwZGSLMVWxgAAIABJREFUAQC3\nBfe/BcC3g2FjoplYYREREck5qjdEREQyJK8uJzGzZQDenCB6DMAZCe7/FIATE0z/cspXTkREClx2\nX18qR0/1hoiIZKfCqDXyqhFDREQkq2XxqZkiIiKSBwqg1lAjxjCVFkUxpfJQwizZ9EEPP7LAzYtL\n/XfgC5ununnNsqF9hw1ZvpsCo/+v2s1bp/mtfP2V/vKbftPp5tsu9xdQethffvRgqZuX/bTOzavm\nlrh5D8vcvDzkCS5p8/PaNf7jl7X4D9A6w19+/doBNz9hmX/G84FT/PWrOOAvf895/vt7zzn+6987\npcfNyzf7r09Yp0cDZf72hTVyV+/2H2Db+/3Pb/3akBUMefzajf7VghXrdrl5dGK9//DR8D1ktCL5\ne3TqQ53oqU/+Gg2UFaH8188nXzYAnHu6+/g9Df57oGp9c/JwVC0wkPw9PPOHu7D+5rHu8hMqgMJC\nRkYpo5hYcvC45n22Y5ablxX1uXn3gP99+FLnBDcfXerv758/NN3NZ1Tvd/PaSK+b37vV/664aupa\nNz/c79dTJfT3l7/acaabn1G/w82b+/x6LOzxi0K+ePb01rr5oV5/fzyzyn99VrdOdPMHt8918zMa\nd7r53u4aNz+/fqObX9r0spvPrfJfny09jW5eU+LXK9U13W5eF/L5OdBb5ebvujj5vhQAtnf69XBN\nmb9+B0Mev3XzaDevnOIXxNGoX89Ulvuf/w2H/X11Q2UHmjudbbjY/39ucnW7m7/6mv/4Nsn//m0q\nP+DmiRd67LPkGjViiIhIynkNGADcBgwAbgMGgJFtwADcBgwAx9eAARREYSEiIpJLll3x1aTZaQ9+\nKY1rkiIFUGuoEUNERCQdDAVxnaqIiIhkSIHUGmrEEBERSZNCGLtdREREMqcQao2cHWKVZJTkSpKr\nSC4nmaiX8ONd9jySb0/V8kRERCQ3qd4QERHJLjnbiAGgy8zmmdnpAL4A4N+PZWaSESeeB0BFhYiI\npJal6CbppHpDRERyR6pqjSyuN3K5ESNeLYBDAEByIcnfDAYk/4vk9cHvW0l+jeRyAO8huST4+wWS\nr5A8n2QpgK8AuCY48nJNBrZHREREso/qDRERkQzL5T4xKkiuBFAOYDyAi49yvgNmNh8ASH4UQLGZ\nnR2czvnPZnYJyS8BWGBmHx+RNRcRkYJUCNep5iHVGyIikjMKodbI5UaMLjObBwAkzwXwE5L+QNMx\ndw35+77g5zIATUfzwCQXAVgEANXj/LGzRUREjiiAHsPzUFbUGydMyOWSTURE0qYAao28uJzEzJ4F\n0ACgEUA/Xr9d5UPu3jHk757gZxRH2ahjZovNbIGZLaioG7p4ERGRBArgGtV8l8l6Y9QYNWKIiEiI\nVNYaWVxv5EUjBsnZACIADgDYBmAOyTKSowG89TgW2QagJoWrKCIikvdFRb5TvSEiIlkvtY0YDSSX\nxt0WpXVbksjlZv3Ba1QBgACuM7MogO0kfwVgLYAtAFYcx7KfAPD5YPn/bmZDTwkVERGRwqB6Q0RE\nClWzmS3I9EoMlbONGGaWdMgyM/ssgM8mmN405O+Fcb83I7hG1cwOAjgrNWsqIiISUwidbeUb1Rsi\nIpJLCqHWyNlGDBERkZxTAIWFiIiIZFAB1BpqxBimnoFibG6vT5hteLHJnXfMvP1uzrsa3Lyl2+9U\ntOctbW4+4ZY+N9//pjFuHul1Y4x/1r9DX02Jm0//1QE3514/33XtiX5+ftKDawCAyj1ujJ5Rfl7W\n4udt0/xvmIHqqJtP+Y2//N5a/+NNf/HY9vYyN49WDLh5Z6vf5Y6N6nHzjvn+8kc9V+HPP9GN0Xyq\nv36TDoS8P/b769c63V/+xD8O7fPv9Q7P8Ec+Cut4un69//yu//wUN29Y7j9AzXb/+wMALJJ8GZHu\nAVSs2ZF85gnj0b9rd/L82VUoHj8uaVy8FbDOruTrBsCa/DdJUU/ybTz5Owex6QON7vxJH1jkOBiI\nXkv8vb62e5I77ykVzmcNwH37z3Tzk6r3uvm5NZvc/JYVV7r5eU1b3Lw/+ckwAIBHdp/s5nUVyb8L\nAOAXL/lnShdt8b+P33bpMj8ft8HNd/aMdvO64k43P9BX5eYzK/e5eWWRv794sO00//FL/ccP866p\nK928nP1uPqn8kJuPjvjP35jqbW7+Qts0N58c8vjz67a7+f7Oajff2+13mzOrxv9/4tHtfj08u96f\nv7jIr3fWHUq+LwaAGy95xM2XHp7q5lsP+/+PlBT5Be0rqye7OQDgwuRR+6v+57N/f52bl85vd/O+\nbr9eb4sexyASBVBrqBFDRERSzmvAAOA3YAB+AwbgNmAAfgMGMLwGDADH1YBBK4xTPEVERCQzCqXW\nyIvRSURERHKCMTW3ECQnk3yC5Esk15H8ZDB9DMlHSW4MfvqHkERERCS3pKrWOIp6I1PUiCEiIpIu\n6RtitR/AP5jZHADnAPgYyTkAPg/gMTObB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Eg6lFJRd3FEPLwrw6zfOIarbzqyaj5q9qZ8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"text/plain": [ "<matplotlib.figure.Figure at 0x10b7885c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "from matplotlib.colors import LogNorm\n", "fig,axes = plt.subplots(2,2,figsize=(15,10),sharex=True,sharey=False)\n", "im = {}\n", "im[0] = axes[0,0].pcolor(y_dend[:,:21].T)\n", "axes[0,0].set_title('Mean Rating')\n", "y_ = y_dend_norm[:,:21]\n", "im[1] = axes[0,1].pcolor(y_.T,norm=LogNorm(vmin=y_.min()+0.01, vmax=y_.max()))\n", "axes[0,1].set_title('Normalized Mean Rating')\n", "im[2] = axes[1,0].pcolor(y_dend[:,21:].T)\n", "axes[1,0].set_title('StDev Rating')\n", "y_ = y_dend_norm[:,21:]\n", "im[3] = axes[1,1].pcolor(y_.T,norm=LogNorm(vmin=y_.min()+0.01, vmax=y_.max()))\n", "axes[1,1].set_title('Normalized StDev Rating')\n", "\n", "for i,ax in enumerate(axes.flat):\n", " if i>=2:\n", " ax.set_xlabel('Subject')\n", " ax.set_xticks(np.arange(49)+0.35)\n", " ax.set_yticks(np.arange(21)+0.5)\n", " ax.set_yticklabels(descriptors)\n", " ax.set_xticklabels(d['ivl'],rotation=90,size=7)\n", " ax.set_xlim(0,49)\n", " ax.set_ylim(21,0)\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size = \"5%\", pad = 0.05)\n", " plt.colorbar(im[i], cax=cax)\n", "\n", "plt.tight_layout()\n", "plt.subplots_adjust(hspace=0.2,wspace=0.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mean vs StDev for each descriptor; each point is one subject (Colors are as in dendrogram)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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UKfa1jtSMDFx5RQeQuLHTW4auyQKGAzcdwMdWldfNJ93ME+kbyavVuMi6gLio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"text/plain": [ "<matplotlib.figure.Figure at 0x10c7617f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib as mpl\n", "mpl.rcParams.update({'font.size': 12})\n", "fig,axes = plt.subplots(3,7,figsize=(15,8))\n", "colors = ['k']*49\n", "for i in range(0,5):\n", " subject = int(d['ivl'][i])\n", " colors[subject] = 'green'\n", "for i in range(5,27):\n", " subject = int(d['ivl'][i])\n", " colors[subject] = 'red'\n", "for i in range(27,49):\n", " subject = int(d['ivl'][i])\n", " colors[subject] = 'cyan'\n", "for i,ax in enumerate(axes.flat):\n", " ax.scatter(y_moments[:,i],y_moments[:,21+i],color=colors)\n", " ax.set_xlim(0,100)\n", " ax.set_ylim(0,50)\n", " ax.set_title(descriptors[i].split('/')[0])\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load predictions from previously run models\n", "from scipy.io import matlab\n", "yg = matlab.loadmat('../../data/sc2_yg.mat') # Load Yuanfang's predictions. \n", "yg = yg['a2']\n", "resort = [sorted([str(i) for i in range(1,50)]).index(str(s)) for s in range(1,50)]\n", "yg = yg[:,resort]\n", "#y = np.ma.dstack([Y['subject'][i] for i in range(1,50)])\n", "\n", "rg = np.load('../../data/rg.npy') # Load Rick's predictions. \n", "\n", "# Take the mean of these two sets of predictions\n", "pred = rg*0.5 + yg*0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prediction quality vs StDev for each descriptor; each point is one subject (Colors are as in dendrogram)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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mxth2d+BZ59wK51wJfoCf3ma2STOUU1KT4kvCphiTMCm+JGyKMQmT4kvCphiT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bx9++05tw8FFvbsmXPbz+K8gJQtHcMkSWWfryOPhIsvhmWXxdzJ1xTchTDrxVvA54QeAcslWjGJ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\nI6ekNhbptp1ryU1JLCKPisgsETka4b2lSTMgR0Q2+p1bjfm2r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rKy5L1I6zB5si5yGIa2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P51+/9dZxD3aWR4n7w/oTVykugZy5Rwr7PjGrSRMx3WITMRryZctV2eLcCGxADPxzFtGNXl25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\nqu3bx/4iuHWrr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"text/plain": [ "<matplotlib.figure.Figure at 0x10d9ecba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mpl.rcParams.update({'font.size': 12})\n", "rs = np.zeros(21)\n", "fig,axes = plt.subplots(3,7,figsize=(15,9)) \n", "for i,ax in enumerate(axes.flat):\n", " y = data.loc[descriptors[i]]\n", " ax.scatter(y.std(axis=0),pred[i,:],color=colors)\n", " ax.set_xlim(0,50)\n", " ax.set_ylim(0,1)\n", " ax.set_xticks([0,10,20,30,40,50])\n", " rs[i] = np.ma.corrcoef(y.std(axis=0),pred[i,:])[1,0]\n", " ax.set_title('%s\\nR=%.2f' % (descriptors[i].split('/')[0],rs[i]))\n", "plt.tight_layout()\n", "axes.flat[7].set_ylabel('Correlation between data and prediction',size=16,labelpad=20)\n", "axes.flat[17].set_xlabel('Data standard deviation',size=16,labelpad=20);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Compute two-tailed for $\\alpha=0.05$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold for p<0.05 is 0.283\n" ] } ], "source": [ "alpha = 0.05\n", "n_iter = 1000\n", "n_desc = len(descriptors)\n", "n_subjects = 49\n", "rs_shuffle = np.zeros((n_desc,n_iter))\n", "for j in range(n_iter):\n", " randos = np.argsort(np.random.random(n_subjects))\n", " for i,descriptor in enumerate(descriptors):\n", " y = data.loc[descriptor]\n", " rs_shuffle[i,j] = np.ma.corrcoef(y.std(axis=0),pred[i,randos])[1,0]\n", "threshold = sorted(rs_shuffle.ravel())[int((1.0 - alpha/2)*n_desc*n_iter)]\n", "print(\"Threshold for p<0.05 is %.3f\" % threshold)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fig. 2E and 2F" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QkWwRmSQifTuuHZ2gPiS1YcdruIRmF3Xl4oZhGIaRzrzzzjs8/PDD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\nLenjuL9Kt5n9NXF6Eh57JRKpCkk9kvaq8trdJD1RPmfF5fZxMI80B1E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"text/plain": [ "<matplotlib.figure.Figure at 0x10d313080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,4))\n", "\n", "# Colors for the dots\n", "# Indices are determined by the sort order of the correlations\n", "colors2 = ['k']*21\n", "colors2[20] = 'red'; colors2[7] = 'darkgoldenrod'; colors2[19] = 'green'; colors2[0] = 'cyan'\n", "\n", "# The sorted correlation values\n", "ax1 = plt.subplot2grid((2,3), (0, 2), rowspan=2)\n", "ax1.scatter(sorted(rs,reverse=True),np.arange(1,22),marker='o',s=30,color=colors2)\n", "ax1.set_xlim(-0.3,0.65)\n", "ax1.set_ylim(0.5,21.5)\n", "ax1.set_xticks([0,0.2,0.4,0.6])\n", "ax1.plot([0,0],[0,21],'--',color='k')\n", "ax1.plot([threshold,threshold],[0,21],'-.',color='k')\n", "ax1.set_xlabel('Correlation between SD\\nand Prediction Quality')\n", "ax1.set_ylabel('Descriptor rank')\n", "ax1.yaxis.tick_right()\n", "ax1.yaxis.set_label_position('right')\n", "\n", "# Prepare the subplots for specific descriptors\n", "ax2 = plt.subplot2grid((2,3), (0, 0))\n", "ax3 = plt.subplot2grid((2,3), (0, 1))\n", "ax4 = plt.subplot2grid((2,3), (1, 0))\n", "ax5 = plt.subplot2grid((2,3), (1, 1))\n", "\n", "# Plot the data for specific descriptors\n", "for ax,i,color in [(ax2,0,'red'),(ax3,1,'green'),(ax4,9,'darkgoldenrod'),(ax5,19,'cyan')]:\n", " y = data.loc[descriptors[i]]\n", " ax.scatter(y.std(axis=0),pred[i,:],color='k')\n", " ax.set_title('%s (r=%.2f)' % (descriptors[i],rs[i]),color=color)\n", " ax.set_xlim(-1,50)\n", " ax.set_ylim(-0.05,0.75)\n", " ax.set_yticks([0,0.2,0.4,0.6,0.8])\n", " if ax in [ax2,ax3]:\n", " ax.set_xticklabels([])\n", " if ax in [ax3,ax5]:\n", " ax.set_yticklabels([])\n", "\n", "# Finish the plot\n", "ax4.set_xlabel('Standard deviation (SD)\\nacross molecules')\n", "ax4.set_ylabel('Prediction quality')\n", "plt.tight_layout()\n", "ax4.xaxis.set_label_coords(1.1, -0.2)\n", "ax4.yaxis.set_label_coords(-0.2, 1.1)\n", "plt.savefig('../../figures/subject_stdev.eps',format='eps')" ] } ], "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 }
mit