{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# 第二章 数据科学的编程工具\n", "Python使用简介\n", "\n", "\n", "王成军\n", "\n", "![](./images/author.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 人生苦短，我用Python。\n", "\n", "Python（/ˈpaɪθən/）是一种面向对象、解释型计算机程序设计语言\n", "- 由Guido van Rossum于1989年底发明\n", "- 第一个公开发行版发行于1991年\n", "- Python语法简洁而清晰\n", "- 具有强大的标准库和丰富的第三方模块\n", "- 它常被昵称为胶水语言\n", "- TIOBE编程语言排行榜“2010年度编程语言”\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 特点\n", "- 免费、功能强大、使用者众多\n", "- 与R和MATLAB相比，Python是一门更易学、更严谨的程序设计语言。使用Python编写的脚本更易于理解和维护。\n", "- 如同其它编程语言一样，Python语言的基础知识包括：类型、列表（list）和元组（tuple）、字典（dictionary）、条件、循环、异常处理等。\n", "- 关于这些，初阶读者可以阅读《Beginning Python》一书（Hetland, 2005)。\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Python中包含了丰富的类库。\n", "众多开源的科学计算软件包都提供了Python的调用接口，例如著名的计算机视觉库OpenCV。\n", "Python本身的科学计算类库发展也十分完善，例如NumPy、SciPy和matplotlib等。\n", "就社会网络分析而言，igraph, networkx, graph-tool, Snap.py等类库提供了丰富的网络分析工具" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Python软件与IDE\n", "目前最新的Python版本为3.0，更稳定的2.7版本。\n", "编译器是编写程序的重要工具。\n", "免费的Python编译器有Spyder、PyCharm(免费社区版)、Ipython、Vim、 Emacs、 Eclipse(加上PyDev插件)。\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Installing Anaconda Python\n", "- Use the Anaconda Python\n", " - http://anaconda.com/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 第三方包可以使用pip install的方法安装。\n", "- 可以点击ToolsOpen command prompt\n", "- 然后在打开的命令窗口中输入：\n", " - pip install beautifulsoup4 \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "> pip install beautifulsoup4" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- NumPy /SciPy for scientific computing\n", "- pandas to make Python usable for data analysis\n", "- matplotlib to make graphics\n", "- scikit-learn for machine learning\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:26:43.144421Z", "start_time": "2021-05-14T03:26:38.835635Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: flownetwork in /opt/anaconda3/lib/python3.7/site-packages (3.1.0)\n", "Requirement already satisfied: peppercorn in /opt/anaconda3/lib/python3.7/site-packages (from flownetwork) (0.6)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install flownetwork" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:27:30.848668Z", "start_time": "2021-05-14T03:27:27.830316Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "$version = py3.0.1$\n" ] } ], "source": [ "from flownetwork import flownetwork as fn\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "print(fn.__version__)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:27:44.448648Z", "start_time": "2021-05-14T03:27:44.444849Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function constructFlowNetwork in module flownetwork.flownetwork:\n", "\n", "constructFlowNetwork(C)\n", " C is an array of two dimentions, e.g., \n", " C = np.array([[user1, item1], \n", " [user1, item2], \n", " [user2, item1], \n", " [user2, item3]])\n", " Return a balanced flow network\n", "\n" ] } ], "source": [ "help(fn.constructFlowNetwork)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:28:15.798546Z", "start_time": "2021-05-14T03:28:15.795725Z" } }, "outputs": [], "source": [ "# constructing a flow network\n", "demo = fn.attention_data\n", "gd = fn.constructFlowNetwork(demo)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:28:23.892117Z", "start_time": "2021-05-14T03:28:23.593815Z" }, "code_folding": [] }, "outputs": [ { "data": { "image/png": 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PAkYeSJ4x/VSdFgZa1somrOKQvTRgyL3xEpF/zynlznlQgNIQUMpIqSpO/xPNGX97yD1qAGg/Wwrx+ykbAsja1bazJGjNHZrZRqpQUrKs4UXSAWv0Oe9bzBItJH49pwTi9p82oHWdNBwpjXo9KqKGpVgWtzeTy0wdGH5G1olGugBF824sliEXuehSydJYBpB6DtBiQGK1dLmaCaMI5I5IhjW8SDrcqB6+L6KFpF7nlNHngNgymc6fq/HnlPgqILvf3gimyGxMKDn35yZaoBiskjcs085G7pIMRHAeF4e5KqVlrWrrZllPVp6mM4pAajegReXiMtOAFbCrCxySclfR5WypSOSWepxT5husVjun6Bkg9TwDVqJ5YLBK3iqlJCNSHAEine5kWS1D1riFknZ5qio7dI2inWGN2BnWWayYsSwpc5U7CqgRWb8WiNdv/ERU23zOKXMNVk93TpkSsK4GQm2zew2iBYzBKnnPMoH0AcmIqBoQTDrTN9vUpXOVaUgXmfHZ1GrKAasaBlrWzH6DhKnL0oDCgGwCiS5nP3AiN8z1nDLbYHU25xQ9K+cTBqxEs8ZgldxhmZJZyB4B1CDQsg6Idk/8Gj0rn8/sBywdCLRIe9T50rNSS1UJyK7c2PKZtzs0S3IBU0NzC1jLr585CJh5WcsaXjS7pQVENDezPafMNFid6zlFz9oZVh0SsK6S2s1ENC0Gq+S8wiAw+DiQPzHugwrQ/YqpASsg2Yp8P5DZK1kLKBLgahHJck4X6FkWYBakBJVZAmBJViW+WtaPziWzORawBqVk1VxL0BQGgOxheZ7YChYIJ3LLTM8p1YLVep9T9JydYdXlcXyVVDMgopoYrJJz9AyQOSRBavbQ1M9HlwPdL5v+OcobFopDQHFQ1qPBhBTdnswCoEoQGOqQjEW9Nm+ZJcmIKBqQWDv3nf6mAeSPAfmTMgUY7QG00PzHR0QzM905Jb1XbqC1cpbUoXPKlIC1T5YKEVFVDFbJGaVRaUlq6nIBsHS7P3ZashIAEGwHeq6c3fOaBmDk7OczIRcTRbKdSkB28DtVMsrU5QIz34AVkItV9pBcNKPdQGQJi4YTeWH8OWXoaam1HGx1/pxi5CWTa5XkMQNWopoYrJIzRp4FDHu9WHEY0Edld64WkcAMkPVai17i7Thny9QlwwpF1t3O9yJWHJKlAVBkaQBL2hB5Z+gpOS8FW915vckBa2wly90RVcFUDjnDyMv0WmlU1moWhiQTqtjru9QgEFnm7RjnQg1IkApLglbTmN/zhdqlC1aoA0i/INloo1CXoRKRz2kRoHWDbOAEgOwBIH/K2zER+RCDVXKGGpJuMgAQWWRvIFAqmxGiS2e+I99v1ADQsl7+nXpOsq3zoaiyqSO5BYACjOyQjLRlznuoRORzWljOJ2MB60EGrESTMFglZ0xefxlMSkCmRSqbFLSwN2OrB1WzM6yKnWGdZ8AK2BetNVJxoDgMDG+XSgpE1Ny0MNCyYVLAetLbMRH5CINVqr/CoKxXTayeWAJGDcnjyGIAyvz6b/tBOWBV1PpkWMuCrUBys/w/ZQ4Ao7tlQxYRNS8tZAes9k189hCQOzH99xAtEAxWqb7MkmQFAJkuj6+QKf9ypjW6TP4dWdIc3ZzGAlbNDlhL9XleRZEqAW1nSJA/+qyUAZvvGlki8i8tZC8JsAPW3GEgd9zbMRH5AKsBUH2l9thFtycxdQm6Iosko9psbQYt0y7VVQJa18sGsnoqpeUmwCxJfdpwJ7tgEdWb29UAaik3IjHz8ji6TG76iRYoBqtUP4UBaWtYjRoBkpuau5aoZQKpFwCz6EzAallAoR/IHZHMS7xX1v4SUX34JVgFGLASjdPEkQO5yihW71IFQHpg9zV3oArI+2tZY0/b75b/k7o+vyKZ6eQZEqSO7gbS++u39ICI/EMNyk2vFpXHuaNA9qi3YyLySJNHD+SazAHpUFVNpHvhZADLAasWkTWs9Q5YAXstcC/QulF6lo/skI0YnCQhai5qUNawlgPW/DEpa0e0wDBYpfnLn5IOVdVo0YU3daWoQKIcsDqQYS0LxKSgeGwFkD8BjOy0+5wTUdMo13XW7LrU+eN21zuihYPBKs2PUZjmxKkA8VULcyOQotgBa8wOWB3sShXutLtgJaXma3qvcwEyEbmv3DlvLGA9IdVBiBYIBqs0d5Zlb6iq0WkpuhQIRN0ckb8oil1rNm4vCXAwYFU1INYj9VlNw14acIxdsIiaxViG1V5SVTjJgJUWDAarNHf5k4Cerv45LS5rVRc6xc4ulzdEGXlnX0+LAK3rZEdzoV+WBhSHnX1NInJHua7zhID1oLdjInIBg1WaGyMvJZSqKu/+X4DT/9WUA9Zgi5SicTpgBaSObXILEOoA0vuA0efdeV0iclY5YC13ACyckg2uRE2MwSrNnmVJySTU2H0eXS4ZPqpQFCDeJ/Ub3WqfqqhAbJkErYoqWdbsEXbBImp0UwLWfjknsyIINSkGqzR7+ROAkan+uUBCetrTVIoCxFdK1jP1nDsBK2C3cFwDtKyVJQEjO4DCoDuvTUTOUFQ7YG2Rx8UBu4QgA1ZqPgxWaXb0nBSnrkqV7CGn/2ubErBm3XvtYKtswIoskdato7vdfX0iqi9FlZvQgN1xq2h3EWTASk2GwSrNnGUBmX2oOf0f6wG0sKtDaljxlUCoXTapnebCYpom6tYVWVGA6BJZGqCGgdFdskHD1Ovz/ETkrnIjkrGAdVDO0wxYqYkoVt2ugtT0skelg0o1gRZpDUizY5nTtqEdHh7GHXfcgZ6eHnR2dmLjxo1ob2+v3+uX0tIm1yxK7/FwFzPjtHANPSWVNIKtXo9k9ixTaiyXRuRxqH3h1rmmpsPMKs2MnpHOKdUomkz/0+xNE6gCQDwex8UXX4x8Po8HHngAd999N4aGhur3+sGEtG2NLpPlHaO7JIAlosZS7pwXTMrj4pAEr8xHURNgsEqnZ5mn2f3fI5t4qO4CgQDOOOMMbNq0CaZpYsuWLYjH4/V9EUUBIotkaUC5gUF6P2CW6vs6ROSscue8YJs8Lg0D6RcYsFLDY7BKp5c7Cpg1anQGk0Cky93xLCCKoiCbzeK///u/sXbtWmzcuBGhkEM3BmoAiPdKptUs2F2wTvBCR9RIyp3zQvZyodKIHbCymx01LgarNL1SWkpVVaNoslGIHHX//ffDMAxceOGFSCaTzr9gIAa0bgBivfKzH9kJlEadf10iqo9yI5LxAWuKASs1LgarVJtlShmUWmK9gBp0bTgLSXnf4549e/DEE0/goosuwvLlyyd8znHhDrsLVhuQ2iMXO6PozmsT0fyMBawd8lgfZcBKDYvBKtWWPSLTwdWE2iWYIUcoioJSqYR7770Xq1evxubNmxEMBsc+5xpVA2LLpT6rZcrSgOxRXvCIGkG5c16oUx7ro3Ljyd9fajAMVqm6UgoonKz+OSUgWVWqu507dyKXk85Wv/jFL5DNZnHhhRfWt1zVXGgRoHWdlPUpDkrQWhz2dkxEdHrlRiRhe2+BngJSz7PtMjWUgNcDIB8yjemn/+MrZTMO1VWxWMRjjz2Ge++9Fy9+8Yvx2GOP4eKLL8aKFSsAyPS/q1nVakJtUoMyfwJI75P2uvEVEswSkT+VA1YAKPQDehpI7wESa2X2hMjnmFmlqXKHpUh8NaEOCVio7kKhEK677jq86EUvwq9+9SuYpom1a9ciHJauYJ4HqmWKCkSX2l2wNNmAlT3MTA2R38VXAuFF8m89zQwrNQx2sKKJiiNyx12NEqwEKOSo0dFR3Hnnnejv78eVV16JLVu2eD2k2kopadlqGbK+Ndzp9YiI5qaRO1jNRuZQZZmXFgda1vG8Tr7GYJUqTF2yZFaNYvCJtUDIhdJJNObhhx/GwMAAXvva13o9lOlZFpA/Ke141YjUaw3EvB4V0ewslGAVmBSwxuyAlcu7yJ8YrFJFep9snqkm3MWaqh6ZslbVMgCjBAR8uE7ULEkVieKgHDPRZbwAUuNYSMEqIMt3ynW0GbCSj3HNKonicO1AVQ1JS1XyxNS1qgowugsoDHgynmmpQSDRJ00F9Awwsh3In2IXLCI/ivUAkW75t5GVVsum7u2YiKpgsEpycsocqP35eB/XM/mJoso0e+aAPwNWAAjEpW1rtEfa9Y4+K93QiMhfYsuByFL5t5GzA9YaS8GIPMJglYDsQcCqcTcdXgwEW9wdD51euENuIjIHgHy/16OpTlGASBeQPAMItMhFML2PF0Iiv4ktkyU7gASsowxYyV8YrC50hUGgOFT9c2pY7rrJn8Idsr4ue1Cm2v1K1aQWa+smKYk2vB3IHefSACI/iS6tBKxmngEr+QqD1YXMLEmgU0u8T6acyb9C7XbAesjfASsABKKyljW+UioHjOwESqNej4qIyqJLgaidoDDzwOhuwKhRc5vIRYxEFrLMAdlZXk1kCRBMuDsemptQO5BYbQesNVrk+km4A2g7Q5pLpPYAqRcAo+D1qIgIAKLdlQ21ZkGW7zBgJY8xWF2o8v1AaaT659RIZTqIGkOoDUiskVI0uRNej+b0FFWWmCS3ALCAkR1A9ihgmV6PjIiiS4CYtHmWgJUZVvIWg9WFyChKS9WqFCk9xOn/xhNKSsCaOyJrQhuBFgZa1sq4i4MStNZaQ01E7oksBmK98m+zaAesnAEhbzAiWYgy+6eZ/u+WskPUmEJJCf5yx+RPowglgeRmaSSQ3i+bO4y816MiWtgiiyYFrM8xYCVPMFhdaPKnAD1V/XNaVBbYU2MLttoB6/HGClgVVY6/5BbpojOyU1pCmjVurIjIeZFFQMzuXmgW7U1XvJEkdzFYXUgsU9YFVqUA8VVSG5MaX7ClErDW/Jn7lBaSDWMt6+TGamS7f5sfEC0EkS6pDgMAVokzH+Q6xbJY7HDBsMxKwWdM2sgSXcasajMqpYHU8/b6swasmWtZQOGUdMFSI9K5KxDzelTUrEppQItIZp+mKgzIMjIAUAJAy3opSUfkMAarC41lArrdAxr2j16LS/1LZlWbUykNpPfIetBYj9ejmRtTl41jhQEg3Cm1IBlQELmvMAhk9sm/lQDQsgEIRLwdEzU9BqsLkWXYAevz8ji5WbIJ1Lz0jPy8Q53STapR6Rl7HWteZgPCi2Z0k5XNZlEqlWAYBjo6OlwYKFETKw5J62TAXmMeYrKDHMVgtUmZpglVnWZJsmkARgbQc1JTj5pfswSsgNQJzh0B1KDsVp6mgUWxWMTNN98MXdcRj8eRz+dx5ZVXore318UBEzWZ4lDl+qFoNb/sxIkTOHbsGDKZDDZs2ICuri4XB0nNghusmtC2bdvw+OOPY9r7EFUDAglZy0gLQyAua8yKA0Bmmja7jSDSBSTPAAItdjmd2ps9AoEArr76alxxxRUolUqwLAuaVvviSkQzEGqXfQ7TBKoAkEqlcODAARw/fhzf+ta3cP/997s0QGomDFabzEMPPYT7778fra2tUE43LaOonLpZaAIxO2Adkna7jUzVJEPculmmIWt9maqitbUVa9euxcjICFavXo1Fixa5OFCiJjWD68fatWvx+te/Hq9//evxxje+ETt27MBvfvOb6ZMpRJMwWG0iv/zlL/Hggw/i6quvxoYNG7weDvnVWMA6LAX4G/2iEYhM23GtfFF85plnkM/nsXbtWoRCtYNbIqqf8u+fqqpYs2YNli9fjmeffZbBKs0Kg9Um8fjjj+PBBx/EDTfcgHXr1o19vFQqIZPJeDgy8qVAVALW0ohkWBfAhePxxx9HT08PFi+WpS+8WNJ4P/zhD6c9V46MjOCOO+5APs/6orM1fg9FLpdDIBBAsVj0eFTUSBisNgHLslAqlaAoCrZv3z728XvvvRff/e53cdNNN+Huu+/Gnj17PBwl+U4gKiXLSqN2C97mDN4URcHg4CAOHDiAzZs3I5FIjH2cqOypp55CoVC7lWhraysOHjyIvXv3ujiqxmSalTreiqKMBaoPPPAABgcHsWzZMkQirEBDM8dChU1AURScd955UFUV9913HxRFwfHjx5HL5dDb24uVK1di+/btGBgYQCQSQU9Pg9bapPrTIkDremkUkdnnbBcz0wCMLGDpdmBsAQsps7gAACAASURBVFDk9ZQAoMVkHWodWZYFRVHwxBNPIB6Po7e3d/oqGbSgZbPZaZeIrFixAi+88AI2b97s4qgaT/l3rL+/H8ePH8ehQ4dw4MAB9Pf346KLLsL555/v8Qip0TBYbRKapuHcc8+Fruv4+c9/jp6eHrz1rW9FS0sLVFXFypUr8f3vfx/Hjh1jsEoTaRHJsKbqHLCW0lIuqzgkFQhKKYwFqFPYHw+2SGmtULtUL5imJNVMKIoC0zTx9NNPY/369Whvb5/X81Fz++Y3v3nar0kmky6MpDGNjIzgkUcewfDwMI4dO4ZisYhAIIC2tjacddZZ6Ovrw7Jly7weJjUgBqtNRNM0nH/++ejo6EB3dzeSyeTYurx169YhmUzi6NEG6xNP7tDCsoY19RyQ3gskVkvAalmzC1xNXWqgZvbKelgoUgtVi0gHremey7IAsyD1UzP7AVhAMAnEV0upqll0rEqn03jggQewfv36sccbNmzg1CNN65WvfCXi8fhpv84wDJY/m8SyLDzxxBP4zW9+g+7ublx55ZVYvHgxQqEQf+9o3hisNplAIIANGzaMrccr/51KpRAMBlkInWrTwtI6MbUbSL8ARJYBozuAlrWS6ZyOngWydpBp6pIhnW0NX0WRoHZ8NzU9Bww/IcsE4n1AbLlUMziNUCiEcDiMO++8cyywCAR4uqPpbdy4kR3O5khRFFx66aWIRCL4+c9/jl27dmHp0qUMVKku2MFqASiVSti2bRuefvppvP3tb2eNSZqeUQSGnwFSewDLBDrOAZKbqn+tZUr5q9Hdst40mJxVBnTGTF0ytaYBtG4EEiunLVc13t69e7Ft2zbs3bsXy5cvx9ve9raxTVaV9zHLDDI1nS984Qt417vexWn+OhgaGsL3v/99DA8P4/LLL8fZZ5/t9ZCowTFYbQbTXGiffvppPP/889i9ezeuvfZaZlbp9Iw80P+oBKyhdiCyCFjysqlBaCklX1McASKdp+1kUxeWAeQHgFASaDtTMrgzlMlksHPnTpx33nmTntME8icAswhElzsTbBMtQA8//DC2bduGyy+/HGeddVblE7w5pFlisNroTAMw84AWrZppGhoawiOPPIKtW7diyZIlHgyQGk72KDD8tEztFwYAswSsfHNlWn8sm7qrLpug5qS8eat1I5Dom3GWtSY9J/VmzTwQXQaEF/FiSlQH+XwexWIRra2tlQ9ahvwOh5jFpplhsNroMgeAwqCUH6oRsJbL9xDNmJ6VDleZg8DQk0ByI7DkUpmOH34GyB2TTU9uZFNrsQzZzBVdKlnWemRECwNA9rBsCoutmFXmlohmwDJkfXvhlFQeCXONMJ0eg9VGVhwB0uVC/6qUH9Kmbz1JNGulDGCVJIs69JQEdJEur0dVke8Hwp1A+9kSZM6XaQD5Y0D+JBBqA6I9gMb2rER1YZlyw2vp8jjeJ7+/RNNgsNqoTB0Y2SlBRJmiAi0bZVc3A1aqJ1OXDGthUNan+k1+QDI07efUb82pkZfMsp4Bot1AZAl/r5qdZWKsUQU5x8hLI5Ly9YsBK50Gz7yNKntoYqAKyIk2tVumWYjqpZwJKQz4M1AFZFyFATtjY57+62ei3N0r0QcU+uXmsDhSn+cmfxreAegpr0fR/Mq/W4o9E5LZLzMkRDUwWG1ExWGgOFj9c4oG/liprtL7K2tU/SzSJeNMH6zv84bageQWINQh9WdTewCjdg95amR1utGh0yt3zlPtJTbZA0D+lLdjIt9iVNNoTF02VdUS76t7f3VawEop2fXv90C1LNIFjO60W7vWkaICsWUStEIBRnbIJpF6ZXGJFqJy57yxgPWgrBUnmoTBaqPJHKgsTJ8svJi7l6l+ytP/gbi3u/5nQ9FkvPVcDjCeFgZa1khXr+IwMLxd1vES0dyUO+eNBayHGLDSFAxWG0lhECgNV/+cGpZWlET1kt4vazS9qKM6H8GEXSmjzssBJrxGK5DcLJuuMgekg5eec+71iJqZFrID1rA8zh4Ccie8HRP5CoPVRmGWZIqklngfdypT/ehZCcD8uqHqdCKdwOiz8j6coihAdAnQdoZkhUafBTKHpPQVEc2OFrKXBNgBa+4wkDvu7ZjINxjdNIrMgdq7/CNLGi/7Rf6WPSJrnxtl+n8yRZPxZ484/1pqEEiskgutngZGtsvOZlYFJJodLWRvuorI49wR2TRJCx6D1UaQ7wdKNUrmqBFpD0lUL6YupWSCDd4KMZiU92HWWONd99dLSPvX6DLJCo3ukhqtRDRzalDKWo0FrEelBTQtaAxW/c4oyoWvKqU+fdGJxsv3S4BXr+L6XlEDshnRzfqNigJEFgHJM2Sj1+huWftrlk77rURkU4N2R8aoPM4fY8C6wDHK8bvM/mmm/7vlgkhUT5m9jlaVGBgcxm133odr3vs3uPPH9zv2OgCAQIu8H7epASDeK5lWsyClrnInuDSAaKbUgCytmRCwurCsh3ypwVMnTS5/qnY3FS0KRJe6Ox5qfqW0LDmJLK77U/962+P433/3Jfz2se0wTSkr9bKLX1T315kgEJUyOKW0N+u6AzHJEBUG5EJb6JcgliXmiE6vHLCmngeMLJA/DsACYj1ej4xcxsyqXxkFIDvN9H98FftXU/3pGQDOHFdPbn8O6UwOf/nBd+LDN17jyGtUp3i/djTcaXfBSsqFN71XlvgQ0fTUANCyDtBi8jh/Qqpu0ILCYNWPLEum/2u1/osulYwRUb0Vh2S9mAPe/+634qkHb8fff/xPcOamtY68RlVqUN6X11RNMkLJzVLeamSH7HRmFyyi6Y0tCbCXvRVOMmBdYBis+lH+pJTAqUaLy1pVIicUB6RntwM0zaMyWFoEKPqoy5QWAVrXSbmrQj8wslO6YRFRbapmZ1jHB6wONv4gX2Gw6jdGXmrLVVXe/c/pf3KAaQClVKUod7NQw/K+/FasP9QmSwPCnUB6HzD6vPz+E1F15YA1YK8/L5ySGuTU9Bis+ollSZkb1NgxHF3uWNaLCEYWgNV8N0OKAsAEDB+2Q1VUWdaT3CL/Htkpa9X9FlgT+cWUgLVfrpustNHUGKz6Sf44YNTYCBJIOLJDm2iMpcOpzVUTFEckIwK4eIFR7PfnU1oIaFkDtKyV/5+RHUDBR0sXiPxEUe2A1a6qURywuzwyYG1WDFb9Qs9N01ZOBeJ9zZfxIn9x40Sv56UjTblIvunitHcjbGQKtsoGrMgSIHtQmgroWa9HReQ/iio3d4FWeVwcsOuSM2BtRgxW/cCygMw+1Jz+j/UAWpOtIyQfcuEkX5zcTcrNG7AGuYgpChBdIksD1LC0bc0cdK9tLFGjUFSZkRgLWAflWsqAtekwWPWD3LHa6+kCLdK+kchxDgeOel42Oo3n6mauBpuZUIOyobJlvdSJHdkhjUJ4ISaqKAeswaQ8Lg4xYG1CDFa9pmfsrhxVKJpM/xO5wellJoXJWVUXXnPCazXo6S6YkLat0WWyhGJ0l3TkIiKhqEBiUsCa3suAtYmw3aqXLPM0u/97ZOMFkRuUABybKtfz2PnsLvzsoWcAAI8+vR8AcP+vH0E6I2sy/+DSC7B542pnXh+W/f4alKLIDEuoXQLW1HNAqAOILXesiQNRQ1EUCVjTe4HSsPxJvyAf436PhqdYFm89PJM9LK3jqgkmZfE4Ub1ZZvUso2kAx38OhLvqf3LPHMK377gX1//F12t+ybf+7ZN419Wvre/rApJdKQwA3ZdJ2ZtmoGeB7CFZPhRZKpVCeEGev6GnpFlDsNXrkdBclfeAlLvWBZNAYnXjzqwQAAar3imlgdTu6p9TNHtzBTMmVGcjOyXA0aJSDi0Ql57b5fa9p7bJ3/Ws56vn7PbBNi0sFw+3GHkACrDoIvde0y2FQbnpVTQgvoJB1nwxWG0O5Zbl5c51gVZZ18qAtWE18LxYA7PMiRfvyWK9DFSp/ixLAtX0fgngLAuAnWXtOA9IbgBCndJBrZ7Bav7UxMfhrvo990wYeWmo0YzCHZI5yh8HUnvk37EVXD5EC5uiVPZ7FAcBfVR+P1rWMmBtUPypeSF7GDAL1T8XapcLEFE9WJZMGedOyFquzAEgd9j+9z5Z/2jplXqnofZKDdR60LMTG11oYfezVmZJ3lezUjVZu5rcLDfCIzuA7NHGqCtL5JRywBrqlMd6SgJW/l40JGZW3VZKVbr3TKYEJKtKNFeWJW1TSylAT8sfa1zrTi0CqBEAo/ZarlVAfCXQdqZ8PhBHXTdZ5SdVAAh7UYbNst9Xk9MiQOs6oDgsN8TFAcmyhtq8HhmRNxRFzm+KItVI9BSQeh5IrG2e9esLBINVN5nG9NP/8ZWAyh8JzYJlSfkzPV0JUFEtc6DI2tR4H6CEgUi3ZONiPUC0u/JlwYQEsXquso51rqZkVSNAsGV+zznrMeTk/QQT7r6ul0Jtkr3OnwDS+2RtcnxFfZd2EDWKcsAK2AFrGkjvYcDaYBgZuSl3GDCL1T8X6mAGhE7PMicFpxnUDE4DcQlUgi2AFpcTs2lIJySrQwrOV5sej68Ghp6Yf7A6Za2qB1lVPQW0bXX/db2mqEB0qb0G+bBsrIsslsoBvEDTQhRfCUCRmU09LRnWlnX8fWgQDFbdUhypXhQdAJQgp/+punJwWkpJ4KVnUH2avhyctkhwGohX30igakDbGZKRrXWSjnRJht/U557p1zOyHKFMi7qf3TR1WVoTcXlDl59oIam8UEpJy9bCoGTUw51ej4zIffFeSMB6UmZ9GLA2DAarbjB12dhSS3wlf1lIWObEKf2awakqAWmwpVKCaqa7XBV1+s6jakCWC2T2zT2omZxV9SJgLI0A8VVcWgPIcZLcLFml7CH5+cR7gUDM65ERuSu+QpYG5E/YAetzdsDK84Sf8afjhuwhwKqxwzrcBYSS7o6H/MM07KA0JbV3jSxqB6eJScGpg0XgY8vtnbOG1PCcDT0tJbLKyjVd3WQZ8n8ba9KSVXOhKLIUINQOZI9I29Zwl7Rx5YWaFpJYj/ydPyHn3LEMK38P/Io/GacVhyuFiSdTQ9JSlRYOU6/s0i+lJk6Vj6dodlBaXnMac7dDUSAGtG4ARncD0cWz+97JFQAiHqxVzQ8ArZuYOaxGDcp6ZX2RLA0Y2S51aJ3oXEbkV7EeAIrUKDaydoZ1PQNWn+JPxUmnnf7v4/R/szP1StZUT03MOI6naLLedCw4jXofOCT65EReSs98vWmpWlbV5bJRpbTMViS4DnxagTiQ3CQ3F7kjskQg1ruwKifQwhZbDglYj8l5a2xJAJvy+A2DVSdlDkjB9WrCi90v40POM0uVrGkpVSm2P5kSGDet3zL/nfdOUFSpv3ryIRnfTJYDTK4h7HZW1TJkne/iS9ipZqYiXbI0IHdULtahdsk68YJNC0FsmSQGckclYB19Dmhdz+PfZxisOqUwCJSGq39ODXMtXbMwihPXnNYMToOSsSpnT/0YnFYTbAFaN8r6xuiS6b+2lJLWpmVazP2sar4faN3MG8HZUjXZeBLuArIHgeHtUvoqssT7DD+R06JLASgyw2DmGbD6EINVJ5glOeHXEu9j1qdRGUU7MLV369dqm6sEK1nTYKKxC7In+mRnff7k9Lv6vc6q5vvlosPp/7kLRGWtcmFQumAVBiSIdbtFLpHbys1RxgLW3bKGVQt5Oy4CwGDVGZkDE1tcjhdZwjVhjcQojCsjlard1EENTVpzGnZ3nE4qLwcYelI2LkWqlLMqpeT/qkyLu7u5KT8gZbbazuSNYD2E7SYluWNSFSKYlKUBzXRcE00W7YZkWA9LIqK86YoBq+cYrNZbvl+yUNWoESkTQ/MyMjKCvXv34sCBAyiVSti4cSO2bNkCVa1DkGLkK8FpKVW75JgaHldGqqX5T2ZqAGg/Gxh6So7xyRlWL7Oq+X4JVNvP5k7eelJUu4FAl5TfG9khbXqj3bwhoOYVtZe+ZA/ZAWs5w8obNS8plmVVK+pIc2EUgdGdNbKqikyvub2Gr8mMjo7igQcewOjoKNra2qDrOp599llccMEFuOSSSxAKzTJo1HMT15zWDE4jlTWnwZaFu5bJ1IHhZyTjFumSTVelUanbWVbuRe80y6hM/bedyUDVacURuYDDkixrtVa9jWp4p1S+SL0gx1O4E+jY2tjLd2h+8qcqy/nUEANWj/HsXk+Z/dNM/3czUK2Dp59+GqFQCK95zWvQ0dEBAPjpT3+KHTt2YO3atVi5cuX0T6BnJ3aIqlWtoVzIvpw9XajB6WTlDGswKZuutNjUrGrYhaxqye7u1bpZ1qgy0+e8UFJ+H/IngfR+IGB3wWqGgC6QAIpDcnwbGQCdMntCC1d5dih7UJZ/jS0J4HHhBQar9ZI/Jdm5arSovduQ5uv8888fy56apglVVbFq1Srs2bMH2WyVAvuWJRdXvRyc1riZ0KKVrGkgwSzddBQVaFktJ/OT26QLTDApHw+0AAEHgxfLkPWpoaSUp+Kuf3cpqiwDCHXIur6RnXJzEl3W2DWjY0uBdLsErADQspZVEMgOWBUge0AC1tHdUiWgGW7QGgyvyPVgFGTnbFWK9Cfnia8uyoHq+NUrhmGgUCigu7u7yndYQP4oYJkTP6zFxtU4TTT2hdYrgYSsabSKkmlTNCDq0PS/qctacNOQzlTMpnpLCwGJ1TJDkT0kXbBiPTJ93ojUIJBYCww+DsRXyjpdIsBe7qTYM6elSlkrBqyuYrA6X5YlBzHM6p+PLm2cmpoNRFEUKIoCy7Lw6KOPYuXKlWhvr7KGzjIB1f7/H9sQxeC0LoqDEqjGlkuQYuTlcf5k/RodlJdtKAG56YstZwtVPwm2yM1D4ZQErXl7aUAj/oxiSyVrzKwqTRbuBKAAmX12wGpvuuK13TULc4OVaUgvYEuXYBMWAEVOUEpAsm4zDWZyJ2Q6rBotLpuqeOJzzEMPPYTf/OY3eN/73ofW1iq1IMs/X2bh6suyZHf4+DqzrZslQ5XvBzJ77aoYinxMi8gawOl+FyxLns/IS61iWLK8IL5ashtcmuFvpm63bbXLiEWXN8bPbPz1oJS1l7HM8XpAza0waCenLDk2nAxY6xmnNIEGOJPUQXkzRnEIKA7I1FX5Bz+F/fFgCxDqlB2vgXj12qhGXk7OVSlSTJ2B6txZ1rT/f/v27cPDDz+MV7/61dUDVcD+fv4M6q4wMDFQDbVXTtqxbvkz4fduUL4HJmr/3qnyexddPv3vHfmTGqhMoWfspQHRZbKm1U/nQaeuB9T8wh1yLKf3SRCZeg5oWSczCaYhSZG5Hus8LqfVvJlVU3c2w2NZMhVgZKo/R7Tn9O0paSLTkP/PUkqm76NLa2Zm0uk0brnlFvT29uLVr361ywNd4CxLApHxDRKSW06/hss0pPe2pdtriMuZAtXOFEQXVKag6RUGZC2/GgRiK7zdDOf09YAWluIwkN4LybBqQKgLKJyUc1hy08yfh8fljDVfsKpnpeZjZr8cCMF6rZ3LyY5yJVBpl1oarv61gYRMD/gpm+BHpjGxxqmRhQQwkLp2yc1yIhjHsiwoioKf/OQnGBkZwVve8haEQiGk02mk02nEYrHaWVaqj3y/7I4tC3UAiVXejYf8yzSA3FFZ0xpql6y5mw003LoecC31wlMOWIvDUndai8h67dZNp89w8ricteYJVi1TdiSP7pbsTDDpzJ2FqVfqDCZWSvZvwnpIVYIs1mKbytQn1jg1qpSaAiRADSSAxJqqAf+TTz6JH/3oR1iyZAni8TiOHTuGXC6HeDyOP/iDP8BZZ53l8BtZwOaaVaWFTc/JBiw9I+fMyBJnb+bdvB6MVanYKNcEro9fONIHgFP/IzGBkQU6fk/Oh/He6l/P43LOmiNPXEpJV53iiPQtVxycSlQ0wMxLncfMAZnqallTKfjP/tkVpm5nTcvBaa761ymBiQX4tei0FzLDMLBkyRIsW7YMbW1teMlLXjIWuJLDCv0TA9VQJwNVOr1AVMr9FIdkaUChX5YGhJL1fy03rwdqQDaTWYY0ycgfk25qrP/b/Exd1pZqQbkJgwWM7AKCbXJsT76G8bicl8bOrI7dpexyb3Fx/pScaMv0rKwZia+Unf/Jjc6Pwa/MUiUwLaUkqK9GCYwrI1Wn6Q9ynmUCw9vHtaRV7Kwqb85oFiwTyB2X9qbBVrmw1+MY8uJ6MNlYZ7WN9gbbxs5m0TQsU5pi6BnJrI4+J9e82Epg2eWVDlg8LuuicTOr1XqUO03P2TuaxwnEJLOUPQRElsq4mmxhc01GsdIZqpSauDt8PCVYCU6DLczENapC/7hAFXLnzkCVZktRgdgyOX6yh6QEWmRJlSVVs+DF9aCaYEJuvkd3yTRs25kL53qw0Cj2kr/SqBy/4Q4gtReAaXezXMTjso4aM7NqloChpyRwjLjUacQypVzF+CnQ8SJLZZdzuFN6pzdjL3mjMG7Naar2/4UaqmRNgy0MaJpB1azqGe5ulqHmVByRoBWWLKMKVWnuMR0vrgczke9v7usBTWRZspGwlJLubpbO47KOGi9YNXVg6EkpzhtxsbVf/oS8ZjWBBBC320zmB+QOq/2chrtzmcLI29MHdva0ZnAaHrfmtIUBTDOa3PwivKj2JgKi2bIsOcfmjslUaWyFZIIsU6ZYI4urZ129uh7MVDNdD2jmeFzWXWMFq5Ypdyr5k+7eqehZ2UxVjaLKrvXxP/B8v5xc289urLUhRr6SNS2lJ075jqdG7GmF8oYoBqdNzTJlKsvS7Q8wq0oOMUuyAas4JDdEUIDhJ4G2sysJgTKvrgez1ajXA5obHpeOaIyQuiy9X+683Sy2b5lSJ7CWSPfUO5NIl4wz2A609Dk6vHkp12QrT+2PBSOTqBHJmpbXnTbQ1AHVQf7kxGMjvIiBKjlDDUrN3tIiIL0HGHgM0BLA6E7JBAXGVfzw4nowF41yPaD64HHpiMYJVkspWRzs9p1K/qTdHaKKYEvt0iuRLjnBRjr9US7CsqR0VHm3vp6uHZxqUXu9qZ09bZBpAnKAacj07BgFiHZ7NhxaIIIJQI3J5kwYQGqfnIu6LpSSQF5dD+bKb9cDcgaPS8c0RhRSnoYMxN3dTVfu01uNoklWtRZFk/EOPwN0XeB+mt2ypEjx+DWnllH9a7XYuFJSCQanVFGoklVlZp2cZllSwzK6RDZ2Gnlg8HcybZlY7c31YD68vh6Q87yKU+ajgY7LxohK0vtlx2h0sXuvaRqSIq9lmr71Y4IJIHcSSB90Ps1uWRJcj+8QBbPKFyp2cDpuzSn7sVM1U7KqKrOq5A5FAbpeLBtVLENumPS0FFz34npQD25eD8h9PC4d5f9gVc9KazK3d9QVppv+b515yjzSCYw+KwdwPXv0Wqb834x1iMqgZnAaiI+rcRpncEozUzg5MRsfYVaVXKSoE9dGB1u8ux7Ui1PXA/IWj0vH+T9YzR6R4MrNtHopDRSHq39OCUw//T/l6zUZf/YI0Lpu7mOyTAlIx685RbVCDuXgtLwhKu7r1D75VLWs6myOeyIneHE9qKd6XQ/IX3hcOs7fwaqpA5n9QNCB/tHTveZpp/9neUAGk/I+Eqtmvh7UMidO6Zd7D0+h2i3cymtOGZxSHeRPTMqqLuZaZvKWF9cDJ8zlekD+xePSFf4b0Xj5fvfbl+ZP1N4lH2qbW19fNSDPme8HYjWyU6ZhB6V2jVMji9rBaWJScKrMfkxEtZh6layqz8uwUPPz4nrghJlcD6hx8Lh0hb//dzN73S2nUBqVP9WoQSA8j4XTgRZ5P+WDwNQr0/mllB2cVqFolV36wRbZHMXglJyUP4EJ658jSxr/REyNz+3rgZMmXw+ocfG4dIV/54tLaaA0Ii333GDqQO547c/PZfp/PDUI5I4AI7uAkZ3A8FNA+gUJDMYHqoomO16jPUDrJunc0rJWdmEzi0pOM3WpLVymaMyqkvccvh4onS+q+ufiV/+RI6+HQFTeTyntzPOTO+p4XG5/dg+61r0C7/3wZ+b0/b/6n8egdL4If/OZL899ED4+Lv2bLtEzAFwMzHLHa9chDbVP7JwyE6YuQaielfdiFqVmazAJhMftGFQC46b0W9wLzomqyR/H1Kxqg24aoObhwvXgvK2bcfWbr5jwseVLnSxDpMj7msvSMvKHOh6XQ8MpDA2nsP/gNHtmXOHP49K/wWpxyL0yOcURWStajRqUzSWnY5QkOC0HqGZx6tcoAaCUA1raK7v1tcj8xk5UL2YJyJ+qPFa0+S19IaoXF64Hmzesxofed7WjrzGBGpT35fe2nLM0PDyMhx56CPv27UMqlUJbWxsuuOACnHvuuV4Prf7qeFxecuFWHN3xX+ho93ijlk+PSx8HqwPuBHKmbmeTaoguAxQVlmVBGT8Fb5QAIyOBqZGtXZNVDUr7Ui0ORJcDWlg6sBD5TW5yVrWbWVXyB7euB27SIkBx0OtR1N3999+PaDSKq666CqFQCNu3b8c999yDcDiMM844w+vh1Vedj8sli31Qp9Wnx6U/16yaBvTcMG76xt04+/ffjviKi9G59hV49R9+EMXixKDwu3f8BBddcQNaen8fsZ6XYOulV+OLX/9PWNbEnfS11h996jP/AmXVNfjVb58d+9ilb/8MlNXvwJFBE69860cQ6r4AX/jKLUBhGMgexdOP/gJvu+FD6D7zTQiveSN6L/wTfOZLP5JvVoNAMIkHHj+OS6/9POKb34XWTVfjims/jsd3HJbNVGaN5QZEXjFLQGF8VjUgrVWJvGYact5Uw16PpL7UcFNeD6644gpceeWV6O3tRXd3Ny677DIsXrwYzz777Om/uZHM8rjUdR03ffWOmjFNec3pp/7xq2Pf8+3b7oHS+SJ84z9+iLt/8ktsvfRqRJZdhL5zXosvfeN7p33Nk6cGsWrr6xBZdhF++dBjM3tfPj0u/ZlZNbK48X9/EV+/9T686rKL8PY3Ujid2gAAIABJREFUXo6T/YP48X0PolgsIRSStPt7P/wZfO07d+OMTWvwJ3/0FiiKgp/+/H/wgb/6Jzz+1C5864ufnP51CsPVp+tt7/mLLyIWUvCpP30TNvaEgPwx3PvLJ/GmG2+Coij4wytfjHWrl+GFg4N4+MmDQGItoAVx25334R1//AlsWt+Hj/zJO5DN5fHt2+/BJa99Dx79yb/gjEU5QPXXehBa4HLHMKFUGteqkl+Uy/g5vLk0ncniwKFjSMSj6Oxoc/S1ANjvxwSM5roeJBJT30soFJqSQPI9PQdkD0lHp8iSqdP9szwub/zoP+Lr37172pimlh/f92v87qnduPYtr8LLLzkPX/vO3Xj/X34WG9f14RUvPb/q9+TzBbzumg/j4OHj+M9v/j1edsmLZjROvx6X/gxWLR23/uCX2LB2Je79z5vGPvy5T39obCr+nvsexNe+czde/cqX4O7vfm7sh/23f/VevPbqP8O3b78H17z5Clx26Yurv4apA4VxtSTNopStMnKAUQAAtCcCuPVfbxz7ktFUDu/4868iEglh249uwuYtW8ZK+hQKRUAL4lT/EP7Xhz+Dl170e/jZXV9EICCff887rsKZl7wNf/v52/D9i99et/8qonkzikChv/JYCcxsnTaRGywdbmy2veueB3DXPQ8AAJYu6cL7rn8TPvah6xEMOnmZVGrX9W4Sg4ODOHr0KM477zyvhzI7xYFKZZT8KWk3PT5oneVxeeud/zVtTDOdhx99Gju2fW9smcClLzkXr7vmw/j3b99VNVi1LAvX3fhJPPL4dnzlc3+FN7/ushmPU/jvuPRpsGphyaJ2HDl2Co8/+SzOPWcTAEBVK6sWvnPHTwAA//SpP51wVxIKBfHxj7wbP/vlb3HnPb+oHawWh6VLlCmBKfL9QKFV/m23TPvT6y+XNaZaDAjEcOePf4HB4TT+8ZMfwOYzz57wdOGw9LD+3g9/jkwmh3e/4yocPlopARQKBbGmrwcPPrJdXpfIL/LHMTGr2s0uaOQfLmTkvvGFv0FrPIJSph/7Dh3Dzd/7FT75D1/Frp3bcdtNH3TuhYtDQKAVCDVY96N4HxA8fYWcUqmEu+66Cz09PTjzzDOnfoFlAqO7/XlNzB0DMgdkeZQWk6ZAgTjQsVX2nczyuFyyqHPamGY6N1zzugnrWV9z+SUIh0PYs+9Q1a//2Ke/iO//6H787V+9F++7/s2zGucYn/1M/BmswsLnP/keXPvBz+G8y67Dqy67CB94zx/iildcNPYVzz63H7FYBJs2rJry3WdsWgMAeGHf4dovUZ7+1zPyd+EkUEhKmSpVAs/1Z74ESLSPfctTO18AAFx8wTk1n3bHrr0AgGve+zdVPy8HZ4NNh1DzmpJVDUoGgcg3nD9f/tG1VwGFobHNtn92/Stx0Zs/jdt/vA1/feNrsGV9jzMvbBYBMweYjbYe9/TrGUulEm6//XYUCgVcf/31tQMzs1C7bKSXzIL98ykB5ghgpCWzmtprb5Ke3XH5+f/zZ7j2jz9eM6aZzpq+icefoihY3NWO4ZGp9VB/8JNf4tnn9uGGa16HT3z0PbMa40T+ilN8GqwquOqKi7Dn/92NL998J7723bvxqrd+EFe84iL8+NbPIxgMwDAMqIo6bQpd06ZZc6fFpPht0M6mBtuA+Cp7+lPuKBLKEJA3pPapFkW+IFnYULD2+hLTvhv5/N/9GVb1Lp/6BaURuFo/lmg6+UlrVaPMqpLfuHS+DLZK0GTpiIWAa974Cjz1mW9h+94hbDmjSlawHixLysOFXFgjW092QqeWYrGI22+/Hel0Gu985zsRj9fKwipAqAt+C4wAyFLBUkoyjKE2qZEebAXaz7K/YHbH5VVXXjptTDOdarGMqqpV1wF3L+7EC/sP4+FHn0b/wDC6Oud6bPkrTvFnsGoHoN1LuvDpj70PH/vQu/CeD30Gt975X7jtzvvwzre/BuvW9GL3ngPYs/cQ1q5eMeHbd+ySDOim9X1jH4tGwxgYHKl8UbgNMDI4ctKurxqIAfEeINEngWz5Y3pmLPO0qls+/rsnnsKLztlY9aK+qncZAKBn2RJcdeWlU99b/hSDAfIHowAUBiqPlSAQ7vJuPETVuNW1T9WASOX4z5Tk8hhvWyo3cU5QNCC+ovGC1WkUCgXceuutKJVKuP766xGLxWp/saLIddeP7OWAaFkjQWp02cS2qnM4LqeLaerlovPPwrvfcRWufd/HceXb/hS/uPsrSCSm+RnU4rM4xV+jKVMCOHSkst4zGo3gja95GQDg2AkJHK9+0+UAgI9+8gsolSoLgUslHX/3zzdDURRc94dXjn18w9qVeGH/YRw9VinPkzbbcc8DT8uD1o2AkQf0fOWGItIFJFYCrRuAeC/e/NpXQNNU/N3nv41jex4B0vtlAbaeRmpUgt7XXfFSAMDf/8vNSKfHtVEFMDySkvel+PMegRaYyRUAokt9d4IikvOlc5m3/oFhmObE9XmHj5zAV7/zA7Qk4rjkwq2OvbbsJm+e60GhUMB//Md/IJPJ4HWvex3y+TwGBwcxODiITCbj9fBmJ7IEiK+UzLeRkyyrPr41+uyOy0NHKvXcq8U09XT1m6/A//nY+/Do73bgTe/6iwkx0sz477j012jKtBjWXfJuvOHVL8OWjauRyxfw7dt/gmg0jDfY2cq3vfFy/OAnv8SdP/4Fzn35tXjNH1wCALj3/m14avtz+PTH3oetZ20ce8p3X3sV3v+Xn8UbrvsIPv6Rd8MwDPzff/02WlsTOHFqUFqLBVuB/NGpx5+iAIEY1m46C//34+/HX3zqJmy54n/jba97KZYvbsWO3fvQP5TCz279FDatjOHD730rPv/V72HLS96KN7325ehe3Indew7grnsewC03fRQrzmVLVfKYkZfdrmVqiFlV8ictBtmd7Ez5qlu+fy++fPOduOLlF2JZ9yLsO3gEt9/1M2SyOdx80yeQbHWofI9lAVClaUyTOHbsGA4flr0iX/3qVyd87pxzzsFVV13lxbDmRtUko54/BQw+DqRekGUiWlyC2OQmzOa4XHfeG/GGKy/Flg3VY5p6++s//yPsO3gU37zlR7juxk/gtq99ZkaVB/x6XPozWFU1XP+2K/FfD/wWd9/7K3S2J3HxBefgrz98Azas6wMgC4zv+Mbf44vf+B5uvvXH+Jd/vw0BTcPWszbgB9/5J7zBvmspu/GP3oKR0TS+8q278MZ3fhR9vcvw5zdeg2Mn+vG3n/26fFFkCZDZB1g1ulEB+OgHrsP6Nb345y/fiu/e9QsoULB+TS/+7L1vlhaqRhb//Fevx/oVcXzltgfwlW/diVg0gtUrl+NTH70BL7/0YtavJO/lJvWfjix1b7qVaDZUTaZfzYIjXaxefO4ZuPunv8Jtd92HkdE02pIteNnF5+KjH7hu2s2082YW5H010fWgr68Pn/rUp7wextxZlvxcjLz9JydLpTKHgHCH3ZZ9QPa2zPK4vP7tr8V//eJh3P3T6jGNE/79nz+Gg4eP444f/AyLOttx0z989PTf5NPjUrH8Wql3ZBeQO+L+Wh49DWQOy51TYI53FpYl6wGNbKUdq2XIzsL4KqDjHAlsVX/eK1CTM/LAyI7KYzUEJM9gsEr+5dX1wEnFYWnBndx4+q+l+hoLSnOVoLQcoMKS9ftaRLKLSgAYeESu4eFOoO0sCVwBHpcu8m+0FGoHMvvdf91AQg683FEgsWpua/gUBQhE5E/5oNbzdjeMOJA5KAV31YjcwQRb5HUnd8ggckLu6MTHzKqS33l1PXCSWZL3Rc6xTDtxZAejZl46U5kFAJbcqKsRCUzDrXaAGpmaSMoul5rrrRvk7zIel67xb7AaiMOzchaRxUB6rxQDjiypz3MGIlL4OblZ1scaeXvBdlqmGKySHbwmJOsaSADa9OVBiGZNz0kh8jI1LNkCIj/z8nrgGMt+XzRvljk1Q2rkKk1/1JBkSbUIEE3aAWlk5lPdHedIhnXyTT2PS9f4N1gNJqSumZ6b+3T8XCmqlKnIHLQDxzmUfZhMz9l12uzF+uU7ONgF2I2CHbymZFrBLEogEUjYmdcWBq80f5OzqlFmVakBeHk9cMLk6wHNjGlIdnRCYJqrNPlRw5WgNNRW+fdcq5xYliSujKydhQ3anZ0sINbH49JF/g1WASC+Ghh6wpuDIBADwu32coDV8y/po6eAtmlKoGhhe3rB3pFtFCTrWkrJZhhzv/yylLOuwZaJ0xFEp6NngdJw5bEaAUId3o2HaDa8vB7U2+muBwudaUxdT2rm7aBUsYPSiN0GtcMOSsP1L71nGXLOzB6Va7GRkyV8WgRoHQK6zudx6RJ/B6uRLlk7YurebEYKLwJKaamlOp+i0KYuUwiRWZQGKgev5SlaoyjBq56SloDZA7IIPDg+eK3/TllqIpMrADCrSo3E6+tBvczletCsTH3q1L2RtyvyKJUZyGAC0Loq60vdOG9Zllxz8/1A5kBl+ZQWliC53Jaax6Ur/P0/qwaAeJ+Uk/JiXd3YcoADdlA4x3UcpRGpAjCfA1kLAVpHZcOWWaqsec2fBLIH7eB13JrXZrjTo/qomlX13yJ6opq8vh7USz2uB41mrOiQ/bdlynW1NAyp6VkOSltkz4gWkeypFzfTelbKVRUHJYuqqDLTWhwCAq2ySz65CYj3ytfzuHSFP0c1Xmw5kNoj6XjFg7pfgagEiLlj9g9ylmOwDJnSiC2v77jUoIxrLHjVJetaSsvGsOxBuUsaW/OasMtwMJO2IE1eqxpbxmOBGo/X14P5cup64BemLokUsyh/W/a/LROAUtmkpGjShCTWI8vbvD4XmaVKgGrkJn4u3CU/t2BSxhtdJi1yx+Nx6Tj/B6uBmJSLGN0NRBd7M4bwIslgFk7K1Ols5AeA1k312aQ1HTUgmbJytszUK2teCwNSNkvR7Ixriz2tEvP+JEHO0zNy11ymRZlVpcbkh+vBfLh1PXCaUZg6dW/mK8GaGpFET3kGR434b4OwZUpN0cIAoI9O+qQq4w53SrInvV86/oW7pgaqAI9LF/i3KcB4lgn0/1bufrzapabnpZ5arGfmYyilJQPadYH3PddNo7LmtZSW3Y2KKq3jyrVeGbw2p9Hn/397dx4kx33dB/zbPfe5J4DFvQAJggQIAhQpBaJIMRSpw5JFUbTsSHEcmlGkVBTZOSqppHI6laTiUpUrdpw4oRU5smSHiUJTRdFSUiYlqkgwpk1ZIgkSIkHcNxZ7zc49092//PG6d2Z2ZoE9pqe7Z76fKhYxWADbC8z++vX7vd97rYtx+qb+amJNg8UP94O18NP9YCWUkqxox8b5lmRJncb5zjZ+KOH/fuH1vGRQa3MSXDcLZyRAjY60/hs5fxfXO9TM96WrghGsAvIGm3pJin+9SrNXpuUNnl5BXYcy5ddvvE8CQb+xTMAsNupejSIAzS4bcOpeUwxeg65eAPLvNF6HkvZMa6IA88P9YDX8fD9om+bUFJi2THNqDkwTvq1t7Mis2tv8M402Vw49bgeoo+vP/vJ96ZrgBKsAkD8FLLwNJLrUqH+1lJLsqh69cW1H+SqQ3QdkJntxZeunLHu72O71uhi8pprqXlO+fvKiDhaO2/OsbembZTgFUdB5fT9YDT/cD5RaMsVpyTQnLdIajC43zSkoLNPOoM5KQqaZFm5s83e7AT7fl64I1rswPSm1d5Upb9oraJoUVxdOS1C33JNIZVpqW9M7ent966HpjXIAoBG8OnWvlatYnGzh1LyG0wxe/cx58HCEkgxUqX94fT9YqV7fDxanOS3Zurcq8vHmaU7xbKO2dLWHh/1IKaC+IBnU2jxap0tpckgqNib/d2vXkO9LVwQrswrIwaG514DqLBD3qE1Exd5OSO9uf+qszMgJ/ZFDwX0i7USp1uDVKABQEgA53QbC6f5Y8PrFwjutGQVmVanf+OF+cD1u3g9apjk1H3RyRozG2rfu1zPNyc+MkmRQqzPSbqpZKNnY5u/VPXmQ35cuCV6wCkgB89zr8sb04slFKekRp4flwJWjMi3fFCMH/V9kvl5KySGtxZrXgtS/LAavGdeD17m5OeRyOcRiMYyNjSEa9dlpUy/V80D+eON1KCX9AYn6TRfuB6ZpolAooFQqIZvNIpXqwtZwt+4HTlDasnXfaZpT80GnPg1Km1l1CQZrM+3tprSI/N3HxnozLMdJ5uRPym7W6N1y7/MyTllOQOOUYAargDy5zB+V/qdeFDObNZkZnNgsW+JOSn34QGCeVLpKKVkwFoPXfCN4be712oW/m1qthieffBLVahW1Wg2VSgWf+cxnMDk5uf6vo1/k3pYDdI7MHiCS9e56iNy0jvtBvlDAiXffxenTp7GwsIAHH3oI27dtu/FvXI5zaGW19wPLaN+6N8vt05ya20L1apqTXyhLttirM63t+ABIu6lhuw4105u/F6MEVO2D16WL0iIyFAfG7wGye7yPU5qt9X3pE8G62mZ6WJ4MIkNSzBxO9bZdRCgqkzYKp+T/Q3dI7Ue/P80uR9OkR1s4CcAuLDfKdqusvGSilSFP/y3B69qe7O6++25omobXXnsNuVwO6bT82yuloA3S4t1JfaE1UA2nGahSf1vj/UAphR98//uoVCrYtGkTZmdnoSxr7ddRtzurZPctfz+w6h1qSu2Z84tBaUKuP77B22lOflEv2HWo12k3FRnubRmaZch7rb4g51jKF+XfO7MHiNv92L2OUxwreV/6XHCDVcB+Y+yWb+j5o0B5SupDevH0okx5s4bTMnYtAKfpei6ckP/idpNko9zIuhbPS8ZAjzcC10hmRcFrNBrF/v37AQBvvvkmEokEEgkZLTvwgSoAlJZMq0ps8eY6iHppDfcDTdPwoQcfRCadRr1ex/Hjx1GpVFb/uZUpdYDRoUYbIKsu693SbKky0BgxmpAHyfhGux2UD6Y5+YVZbdShOnW4Dj1m16GOeTdsQNPlflW5CpQuyM9FR4DMXiCabv11XsYpS9+XARXsYNURyUhD28JZeXrRQ/Ik40aa2zJk+8EyZeJDYgLIvy3pdT/VpfiRE7xig7w2K/YTX16+2VVdFiEneI2OLrtwW5YFXddx9epVTE5Osl7VUcstyapmAr1AEa3aKu8HmXQaTjVcKBRCuVxe+Q6NZUgwYJaA1E65B5Qutk9zCsUl8xd3aku5XnVkmZI9rc10aDcVknuCG+2m1kLTpcwNyp5ymQOG9klQ2omXcUpAs6nN+iNYBeynl13S26x0UfqhKsM+6JNY/59vlOSbRwsDqV3SZ9UZTZbYBpQvyBMyF6GVc2qwYAf5ZrVR81qdsacsdX761HUd9XodCwsLGB0dRTjcP2/ldSkzq0q02vuBpmmIRCKIxWIolUowTXP5NUVZsu1bm7fnqU8Aqa1ANCufNzbaqC3l/eDGlJKERdVpN9VchqHJfXWx3ZSPAq7ieaA+DyR32HXEe2SLPzq6/O/xMk4JuP67w4eTUtic3iXZzuIp6XcGTVL2K6n/WZzoUZGtHCj5Rhm+U56clz4JxcflTVs8A2RvcfGL63OhmD3Ozg5eb3D2b2FhAYZhYHh4mNv/gCz0ZqnxOpwN1tg/om5b4f1AQQLWZDKJUqkEq7luden9QCm5B4zfI8FAZO219wPNKEsGtTprHyJr4kW7qdUongOq1+TH0VFg+KDUrIZT1x/J6vAiTgm4/vpqmulheeJNTjSKi2tzjRoYWAA6vREUALtBfmKr1KCspCg6tRPIvQVUri2/DUCrc4MA9Nq1a9B1HZkMt7kBtGdVkz7LqlqmBNPKsB9EFABN/p21sNyg2KeX3HCj+4GyAE3DSNJEvXQVZukqYMXtcoA13A+oM8to/J03P1gDdrupUalD7UaW0S3NgWo4Lf2r9RAQWUOiqtdxSoD1b7DaLGLPu3fGn1lm4/SlstC4aer2TTOx+pumHpHtgOJZuxxgBU9XtCZOPdnVq1eRSqWQTCZbfn4g1eZaew1Ghryv62pZfGekxMP5Xmtj/3wkIzerAVh8qQeapzmFkkA43vl+YJQAmDCyOZw/fxa19EEkhkcAhCQLxoeotWtpN7WA1qlSHrSbWo/iWWlVBbQGqt3QizglwAYjWF1KDwG6CzfB2KjcmItngMwt/v/GCyDLsmBZFsLhMGZmZjA6OroYrHakTDkdr0clEAol+u/fRakOHQA2e3MtltHY1qrn0LKtFRtf2bZW2a7lcra1Urv7cluLumgxKC1j2WlOyW0AOjSI10PQorI7k90widzxi7Aiw0B0uONjFa2QUbTrUGc7tJtKNx5KgxBwOYOAajPyOpwBMje7W0PrVpwSUFz9u22xHGCq8YREXXPs2DE899xzSCaTuHLlCsbHx3H+/Hls3rx5mXIAXQKk2qwcgtNCjdGwkYxkW4IevNbmGnO/AW+yqkapcWDAMuTv1mlZtlJaU+PzxT+3DMz/xD4wMNlXBwZoDZZOc3ICU6smH3dO3ocSUku4wmlO09PTmJubw6VLl5DP5/Htb38bhmFgYmICDz/8cA++sD5h1uw61Ou1mxoN1s6jUrKu1WbldS8CVWoT3AlWflabk9OiQ/t6M+ptgBiGgcuXL+PixYvI5XK4cOECpqamoJTCl7/8ZWSz12l+b5mN0bD1vF0zpbUOKQinghW8KgXkjrUGq9nbehfQKQsonAEW3ulhK5ZbgfRO3iz62eI0p6WN8+1pTnqsMcVpcZpTbE3vCaUUnnzySZw7dw4jIyPYtGkTEokEstkstm7dih07dnT/6+snlikHjKszcqq/mRaS7Gl0LJglPW2BahbI3MS1xwMMVt1SOC2tmLJ7gxX8BJRpmgiFVrmdpCw7cLV7vRpFSPCaWhK8+nhhqs4CxdON15FhWUx7oZ6XJte1nDdNrocPsIds0FlG+9a9WVkyYrR55n2C05z84LrtpiAPrX5sN7UaSsnaWpuT1wxUPcVg1S2WIRmv+Abv6gdpdZQlAavT69VpSh1ONZrr+yl4VUpKTpq327L73D9Ju5hN9cP4wFuB9KR//k2oM6vePvO+bZpTc2DKaU6+ZFYkQK3OdGg3lZAMamw0+K28lJJR6vV5eR0ZAtI38f3oIQarbqrlgMJJe1vWx604qDOlJCAy8nZwVACg7OC1qXTAq0CpOmMfRLJFR4D0bnc/p2XYIwMvy6GnXmRTl6NMOcyV2CxZVh7A8p5Za60pdf6vTEhQmuiQLQ1Q/eIgum67qXCjDrVfasnbAtVhWVcZqHqKwarbCmfkGzx7G9/sQaeU/FvW843sK5Qc0oqk7Skk6d6cbvUiq2rVgbnX5ablp9HClWm5YY4cDH5GJyjMavvWvVkGYMkDjBOMttSUcppTYCjV1G4qh9Z2U5q0m4qOSZvGfrqvKcsOVHPymoGqbzBYdZtlAgvH5Bvbb03aaX0Wg1en5rUgGaRQsqnmNe1Oxq8yDZTONl5HR2UailssA5h7TWpk42PufZ61qszI9uPIIWZYu6V5Qs7SwBSW3esx3p4t5QNDcC22m5qzSzSaBK3d1GopC8ifBIwFeR0dkZGlDFR9gcFqL9QXgPwJOWzldaN2co9SckN3ug0YBVnwQwm75tXOvq43mFIKyL3ZaNcDAEP73es8oSzJqFam/JVRXaoyLe2yRg6yhnU1FoPSDgedoGSyUMeDTnwo6AtmrbHN39xVBJC64eiY7Fz0c7lGW6A6Kq3yGKj6BoPVXimekwBm6DbeSAeJUW5kXet5CV71uGRenezrajNRlWtA6VzjdXRMDhm5JX9KDlMFoW9w+aqUQ2Qmvb4S/1GWvX3fqXG+ksBEX3rQKc6gtB8pS7Kn1203NToY3TaUJckk5++BgaovMVjtFWVJd4DosD1JhQaSWWlkXet5OVGrx1trXq9X26csYP7NppO4mp1VdSnrUc8DUy95f5hqpZxDVxvvG4wbbSdt05ycxvnONKdoh637eH9u7VKrer6xzb+03VQ4ax+WGh6chEpboDomg30YqPoOg9VeqheA/HEZxRrEBsnUfWbVDl7tANaq2Q3PnW4DmdbgtTIFlM43XsfGZXF1g7KA6VfkYFWQ3q/1gmSrxw/3903Xmea0tHH+4jSnWPvW/QqmOVGfcdpN1WZbS4cAeUiJ2dv8g1ZrbJlA4USjRaGbaymtG4PVXitdkCbKQ/t406B2liGHHGpzdvBalUxY2B4NWzrX9NTvclY1f0omUyVWOTbVD8pT0oGjH8oBLLPD1n3FDjzsaU5tB53WNs2J+oRlNLb5zWLrx7SwbHXHxvqn3dRqWSaQf7fxdxPbAKQ4qczPWIzUa4kt0n+1dIHfHNROD8t0puiQvDZr9oCCvHSVKF2Qm004aZeTuPSsaZQkUPXjyf+ViI8BCz+VQDsoN+SWaU7NjfObpznZJSOhcU5zolYrajc1ak+VGuD3DAPVQGKw2muaLodhFt6RIvZBraujlQlFgdCo3Ghq8xKgGEW7p6UuvVa1SGvNazd6rZYuSg1jEOpUO9FCcv2li0B2j9dX08qqt2/dd5rmFMlKd4NQnEEpLc8oNbb5l7abCqXsOtQRHpQD7ED1eGO4QWwjkNru7TXRirAMwCuli7K4ZPfxYAPdWPkqUL7QeO1kAyyjacJWXgIfLdw6YSuUaA90lCU3uNh4+8csA7j6ggRLQb7BWYa0jdv0gDdfx9JpTk5g2jbNqfmgE0eM0gpY9UaAapZbP6ZHG9v8brWzCyLLsDOqdqAa38TDzgES4DtRwCW2yFZN+QKLuun6lLIfaDTI1p4GxCfkY3rYbjMzIq8twy4bKMjNrHResozhdKPXaygp9WyzrwLJnUD21tYHpsq0/DlBDlQBuX5lyNeTnHDv85idGuc3TXNypjhFhmU0LKc50VooS3ZXqjONfqCLdFkDYmPcrevEMuyMqh3YxyeA5FZvr4lWhZlVLxkl6V+ZvqlRo0g4GzZSAAAgAElEQVTUiTIlKMqfsCerrHDryjIbNa/1gmQVNF0mPpXOST1n6iZg5EDjoNa1/yc3RhdGt5ZKFfz6b30d//Pbf4JzF64gmYjjQ/fdjae+/pWufy4AktXUdGDDPev7c5SSA00dG+d3mubkNM4fsBPW1H31vGRQa3N2Vr5JOCuT26IjPFC3nLZAdTOnSQZQwFMnARdOSqaleBYI7wt+Jovco4UkABraj1UdqtJDrQe2LFMOFhTtkoJ6Cbj6A5nesvXjsgVdz0mtZJfNzS/g/k9+ET89fho//6mH8PlffBjzuQLeeudk1z/XonBC2n3VCytrv9U2zakpMG2Z5hSXEgpOcyI3mFV7m39m+XZT0VFm6G/EqgMLxxuTuRioBhZXWK/FJ2Rrp3Te3dnuFHyaLpUA66GHANjZv9iY1LdlJgE9KR+r57H+T9LZ53/13+DkmQt44ZkncO/hQ658js40OZTWHKwq1WgB5dSUGk7jfCcodf6eNnKaE7nPMu0M6myj96dDCze2+Tmye2WWBqqJLZIcokDiyus1TZMgNXcMqI3IqW8aaEopaG4estHCwPDtEnyF060BWPGcK1vXf/4Xb+Lb330B//5ffLm3gaqyAMsCCmekftXJljo3sOZpTvGsZGI5zYl6RSk5BFibkaTF0nZTkSG7DnXA202tllWXjjvO1LbEViDhYt06uY7Bqh+E4vLNVDzbHjzQQKjVaiiXy4hEIkgmXe4LqmnLL9y1GVdOEP/hU/8Xmqbhb/7SIwCAmdl5hMNhDGW7NBnLMmW71Kra2/jOj+vy4/qM9F4NJeSBkNOcyEtGSTKo1ZkO7aaSjW1+3gtWz6xJjepioLoNSGzy9ppo3fid4BfxjUB9XgLWzE1eXw31UC6Xw7PPPourV6+iVqvhgx/8ID7wgQ/0/kIsU8oAYuNd/6P/9NU3sHtyK06duYD7P/lFHHvnFADg8N0H8NXf/Ge4/babV36NTjCqqoBRBVRNglJokhV2JjpFsvJjPSKHU1K7mDEl71h1oDorD4RL201pkcbYU7abWjsGqn2L3QD8xKxKOUBqp5zwpIHwO7/zOxgdHcW2bdtQKpXwp3/6p3j00Udx4MCB3l5IPQ9cO+LK4aqxmx/E8FAapXIFf/XnPoY7D+zFKz86iv/y3/8I42PDePPI/8KG8ZHGb7AM+/S9kymtymtlQKY5RWULX4/Zo0Wj9v+X2SqtTAEb7lvZISuiblHWkqlSzXTJ8sfGpK0ct/nXx6zagap9IC253ZW1jLzBzKqfhGLS+610Tnrlse1N3/vhD3+ISCSCT3/604jFYjBNE1NTU8jlcrAsC/l8HgAwNNSD1mZOIOiChXwBs3M5/N5//Jd4/BcfBgD8tV/4ODaMDeHXvvLf8MTX/gD//Fd/vhGcKlOuJRSToDSckkleenSNjfO19u1WIrfUC3Ydaqd2Uxm7DnWYmf5uaQtUdwDxDd5eE3UVg1W/iW+UQvviWSCzwq1RCiTTNBEOh3HHHXcgEolAKYVQKISJiQlcvnwZX/va13DlyhVs3rwZDzzwAG66yeXyEBc3WcLhMKLRCB773M/KT5QvA/UFPPaz+/BrXwFe+dFRQP2cHZTaXQr0SHezTcrq3p9FtJRZbdShOtvQDj1m16GOsd1Ut5kVOfWv6vI6uROId7+UibzFYNWPUpPAwjGZvMNvur4VCoVw6NAhFAoF6HrjoM/rr7+OTZs24T3veQ+y2SyeeeYZvPbaa+4Hq6vp37pKmzaMolypNr7OSAYIZzAxKVNkyvVQD/ofsuKJuswyJXtam+nQbirUGHvKdlPuYKA6MBis+lEoKoXh5QtySIRP4n0rnU4jnW7UUT777LNIJpP41Kc+hUxGxiYePHgQJ0+ehGVZLUFt97lXM/feO/fhqe98H1euTmNi07h0vQBw6qQctNq5vRdtZVgTSF2glEyEqzrtppoz9pqs2YvtpthtwjVLA9XUpPy9U1/id5Jfxe0bevGM11dCPXTnnXfis5/97GKg6pQGDA8PwzTNG/zudXLxgMcvfuZnAAD/+itfbfn5r/z2NwAAn3n4Qdc+9yIGDrQeRhkoXQDmjwL5d2XL3wlUQ0k50DN8h5Rvcfypu4yy9FFdDFR3MVDtc8ys+llqJ5B7C6hcY7H4gNi2bRuaG3TMzs7ijTfewL333otIxOUDd1oYbm2VP/KJv4yf/eh9+K9f/yOcvXAZh+8+gCOvvIbnfvhn+OyjH8HHP3yvK5+3QdlfH9EqWEajDtUstX5Mi0jXluiYDJOg3jDKcpjKORCa3iUPB9TX2LrK76qzcthqaJ+cjKaB8aMf/QhHjx5FPB7H5z73Ofc/oWUCV56TPqsuZFkrlSr+7W98Dd/81vdwZWoGO7ZN4PO/+Cn8o1/5JYRCLp6KVkqCjYmHePqabqyl3dQCWh/g2G7KU0ZJstoMVAcOg9UgyJ+Ub87MLVwcB4RSCj/4wQ8wPz+PRx991N3xq82uvSz/76fG5GYFgAZsuMfrKyE/M4p2Hepsh3ZTacmgRkf4wOMVo2RnVO22dundHE8+QBisBoFlSDlAfILTOPqRUss+hBiGgXC4h9vXubeB8sX+ugnU5mWc8dCtXl8J+Y1Zk5P81203NcpdLa8ZRTujykB1ULGIKwj0MJDaARROA9Gh/sp6EVC5Ym+9h9oOZfQ0UAUkc9Rvh/qsOrcKqcEyZbR1dUZO9TfTQvJeiY5x2plf1AtA4URToHqT3AdpoDBYDYroCBCdBwpngOxelgP0C7MqDfIrVyVbEE7JDdMr4RT6rx+pYp/LQcd2U8FUL0hGFRYATTotRLJeXxV5gMFqkCS3A7ljkolLbPb6aqgbypcBKMka5N8FMnu9zehE0nLDNsr9ccLZKMvXwyzZYDIrEqBWZxptjhyhhGRQY6Mcbe1H9TyQPwEJVHUgcxMD1QHGYDVI9LC0syqclLnS/RBMDDJnPKNDjwLhpHfX40jtBuZ+0h/vLyMPDN/p9VVQL1233VS4Mfa0H97f/aotUL1Zpt7RwGKwGjTRIVlsi6eB7G0sBwiy8iW0bLnHJ/yxBRkflwcjy5D/B5VlSHDC8Yv9T6mmdlM5tJayaHIYJzommTmumf5WX5AOOAxUqUmA70QDLLENWDgmW8iuz1MnV5iV9qxqzCdBlR6W0YXF08GeClPPyWSbIAfcdH2L7abm7N6bTdhuKnjqC3ZGVUEC1T0s4SEADFaDSQ9JOUD+hGRaeXgkeMqXW1/HN/sr45PcKu8vZXp74GutlCmnvpNbvb4S6jaz1tjmtyqtH9Ojdh3qGNtNBU0tJyVuULLmZPbw3kaLGKwGVSQrmbjCGWDoNn9sH9PKGOUlWdWY/zKY4aR0nVh4B0hs9PpqVq8yI2UyfqgBpvVTlmRP2W6qP9XmgcIpMFCl5TBYDbLkNukOUL4kP6ZgqCzJqiZ8llV1pCel80S9EKwgoF6QHYf0Dq+vhNarnm9s87e0mwIQtttNRYf5sB5kbYHqLXzIpDYMVoNM06W2MH9cugMEKaAYVEbZvvHa9JhMyPEjTQeGDwBTL8nJ6SCUAyhT6hg33scAJqicdlO1WcCqtX6M7ab6S21Oht1AyWHIzB4GqtQRg9Wgi6SB+EaZOjS0jzdovytfan2d2OLPrKojkgGytwILbwdj1G9lGsju4+nhoLGMxja/WWz9mBaWB7rYGAOZflKdtaflOYHqLWwnRstisNoPElukOL1ylcMC/MwoyZhHhx4PxhjQ9KScrK9M+bsNVGVa3v/c/g+GFbWbGrWnSvn4gY5Wrzor3UYABqq0IgxW+4GmA+ldchJWKS7sfrU0q5r0eVbV4ZQDzL0mB5fiPjsMBsh1xcbkOrm74DrDMBAOr/H2YZQa2/xL202FUnYd6ghbjvUrZQGlc/JjLQJkbwFCcW+viXxPU0r12yDwwXWDQPXEiROwLAtbtmxBOs361p4yirKV7gglpGwjSKw6MPe6BBp+yrBWpiXAGTnIOkaXLCws4M0338Rbb72FarWKyclJ3H333ZiYmFjZH2DVGwGqWW79mB5tbPMzaAm8qakphMNhZLPZzg80TmeH0kUGqrRifHTtJ9cJVC3LwrVr1/Diiy9iz5492LlzJ+66664eXtyAKy2tVQ1guYYeAUYOAfNHpU9sfNzbQ1fKbGz9Dx9gJs4ltVoNR44cwfz8PPbv349IJIKXXnoJly9fxic/+cnlA1ZlyUnv6gxgLCz5oC7Z09gY64v7RLFYxNNPP43Z2VlUq1Xs3r0bhw8fxrZtSzrVaPa/fTgDhKLeXCwFDlf3AaHrOt7//vdj69atePrpp/Huu+9ifHwcO3fu9PrS+l+90HqzDiWCUavaiR6WDGZkSDLF4ZQ3XSjqBclWZ/dJjSq3/l1z/PhxVCoVPPjgg9i0SQ7ZjY2N4ZlnnsGJEyfag1VlAaXz9lQps/Vj4ayc5I+O8N+sj5w/fx7f/e53sWHDBnzgAx/AlStX8Oqrr+LIkSP49Kc/jVhsyYAGTWegSqvCYHUAKKWg2VnXEydOoFAo4EMf+hBGR33aMqnfLJ1WlQj4iFxNBzK7gfgGO8s6JXWsvciyKlPqU6ND0p6KWTnX7d27F/v27YOuN4LLjRs3QinV8nMt6guNQFWP23WoowxQ+tTZs2exfft2fPjDH0Y0GsXu3btRrVZx9OhRFAqF9mCVaJUYrA4AJ1B9/vnn8fLLL+OBBx7AoUOHkEyyDYzr2rKqSTnl3A8iGWD8MFA4K1lWPSQZVze24y1DToxbpkymYja1ZyIRqQN2HnqVUjBNE8ViEVu3dhhnq5S8DwC73RQnEfUzy7IwNzeH22+/HdFodPF9cvDgQbz00kswDOPGfwjRDTBYHRAvvPACXn75Zdx///246667GKj2Sqe+qv1E04HMLunBWroofROVIfVo3WhFY5QAoyDtbVK7gORW9tr0iKZpME0ToVAIR44cwcTExGJZQAs9BCS3B6PTBa2bruv46Ec/imhUsuZOcqRQKCCRSCCRSLTs7hGtBYPVPlQsFpFKNbIZL774Il588UXcd999uPvuu1s+Ri6q51vnmIdSsn3dj8JJILtHWqhVpoHiKenLCk0OZoXiMq3rejcspQCrKhOMrDoAO0M3fKcc5uIBKk8ppRAKhXD58mW89dZb+MhHPoJ4PN45EGFgMlCcQBVoZODn5+cRCoVYAkBdwdW/zxiGgaeffhobN27ERz/6URw5cgQvvPAC7rnnHrzvfe9jy6pe6tRXtd/pYSA5If85h6Bqc9KyqDoDme/eKZBRAHQpLUhstU8Le3R4izpySgCef/55bN26FYcOHYJlWcvXrdJAu3jxIjZu3NgSrDqZeaLVYrDaZ8LhMD7ykY/gd3/3d3H27FlcvXoV73//+3H48GEGqr1UX5Dta0c4DUSy3l2PFyJp+c8Z02qZ0mNTGXJiHAqAJqUEWli6JOi8kXnqBr2a/+Iv/gJTU1P4whe+AAAMVKmNk2W/ePEi9u/fD0CSKK+88grK5TIeeughlgTQqjFY7UObNm3Cl770JXzjG99AJpPB4cOHkcnw1HRPtfVVHYCs6o3oIUDnA5Mv1QtAzZ4CFu78b1QsFvHjH/8Y999/PyKRCN555x2cPHkSxWIR991338oHBFBfsywLhUIBuVwO27Ztw/nz5/HHf/zHyOVy+NznPsdAldaEE6z62Pz8PL761a9iz549eOSRR7y+nMFRywGFE43X4YxMaiHyE7PaKM+wqvJz8QkZstCh08L3vvc9vPrqq8hkMigWi4hGo5iYmMCBAwcWT4ITAcCFCxfw1FNPYXJyEm+88QYOHTqEhx9+2OvLogBjZrWPDQ8P44tf/CJ+//d/HxcvXuzcZoa6r60DQACnVVF/sky7hnimtUwFkD65WgidaoqVUhgeHsb+/fuxd+9e7NixA0NDfXpYkNbt0qVLyOVyuHz5Mh577DEOn6F1Y2Z1ABiG0XlGM3VfbR4onGy8ZlaVvKaUdKWozsj7E1bTBzWppY6NAZFhnuKn1VFmx2EgU1NTOHfuHO6++24PLor6EYNVom7KHZNDRI7MXp5oJ28YZcmgVmcBVW/9WCjZmCrFlmC0FsVzgFWTdnW9mF5HA42r1CBSptzIGER1V22+NVANZ/l3TL1lGY06VLPU+jEtAsRGgehYdwY20OAqngOq1+THRkkO5TErTy5isDqIzCqQfxcY2geE2LC5awaxryp5T1kyirY6Iy3T0LxZpst439iYlKQwoKD1Kp4FqtPy43BasvR8X5HLGKwOonBS6tSKZ4DMLVxouqE215pVjQxxJjq5yyjadaizslvSLJyWDGp0hL1rqTuUkkC1NiOvwxkgc3PHzhFE3cZgdVCldgK5t2QkZqLDfG9aOaXYV5V6w6zZdahN7aYceqxRh8odE+ompSS5UZuV1wxUqccYrA4qPQykdgCF0zKvPhT3+oqCqzYHWJXG68iwZK+JusEygfq8BKhGvvVjWkiyp9Ex1keTO9oC1SyQuYmBKvUUg9VBFh0BovNA4QyQ3ctygLVQCihfbv059lWl9Vpxu6khBg3kHqWA4ml5IAcYqJJnGKwOuuR2abdUucIgay1qs61Z1egIs6q0dmZFAtTqTId2UwnJoMZGAT3izfXR4FAKKJySrD4gD0bpm5jUIE8wWB10eljqVwsn7e1rtrRZsU5Z1TgDfloltpsiv2kLVIeB9G4GquQZBqskNauxMdnuyd7GBWmllh5yiY4woKCVUaqp3VQOre2mNGk3FR2T7X5+P1IvKcsOVHPymoEq+QCDVRKJbcDCMckUsj/ojSkFVJbWqvLvjW5gsd3UHKCM1o+x3RR5TVlA/iRgLMjr6AiQ2sVAlTzHYJWEHpJygPwJybSyR+j1Vadl1KAjOsqOCtSZWWts8zfXNwOAHrXrUMfYboq81RaojgKpSQaq5AsMVqkhkgVi49IdYOg2nvhcjlJyIG2RxqwqtVKWZE/ZboqCQFmSqHDeq9ExSV4wUCWfYLBKrZLbpDtA+ZL8mNp1zKoyK0YA6vnGNn9LuylI25/YmNSj8kGQ/IKBKgUAg1Vqpemy9ZM/LoX1zPy0UtaSDgAaW34NOqfdVG229SEGYLsp8jfLBAonAKMgr2PjEqgS+QyDVWoXSQPxjTK1ZGgfs0DNqtOt/S9ZaziYLKOxzW8WWz+mhSXbHhtjz13yL8sE8u823r+xDTLVkMiHGKxSZ4ktQC0HlC5wAXMoCygvqVVlX9XBwXZT1C8YqFLAMFilzjQdSE8CC+/IYZBIxusr8l7l2pKs6jgQinp3PdQbRqmxzb+03VQoZdehjsiADSK/s0wp83IGUMQ2Aqnt3l4T0Q1wdaXlhVNAfELKAbL7Brv3o7LaOwDEJzy7HHKZVW8EqGa59WOL7abYrowCxjLsjKodqMY38SAtBQKDVbq+xGYZuVe+MNiF95Wp1qxabAOzqv1GWUBt3m43tbDkg7pkT2Nj3GWgYLIMO6NqP3zFJ4DkVm+viWiFGKzS9WmadAdYeFu6A0SHvL6i3rNMoHK16Sc0IMGsat+4YbupUQlUedCQgqotUN3MSYUUKAxW6cbCScmwFs8C4X2DV5tX7ZBVZRuiYDOrdoA6095uSo/bdaijzJ5T8Fl1YOF4Y3oaA1UKoAGLOmjN4hOyRVo6D6R3eX01vdOWVdWZVQ0qy5Qa1Npso6+kY7Hd1ChHDVP/WBqoJrawLzQFEoNVWhlNkyA1dwyojUibnkFQuQoos/E6zqxqoCgF1Bckg1qbR1u7qciQXYc6xHZT1F+sunRzsaryOrGVD9oUWAxWaeVCcVnwimeBcLr/ywEsQ0oAFunsABAURkkyqNWZZdpNjUomtd/fwzSYzJrUqC4GqtuAxCZvr4loHbhS0+rEN0p3gOJZIHOT11fjrsrUkqzqRgY3fmbVgeqsZFGXtpvSIpJBjY2x3RT1Nwaq1Id456XVcboD5I5JYBAb9fqK3GEZ7bWqcS74vuO0m6rN2lOlmulSrhIbA8IZbvNT/zOrdqBqHxpMbpeHbKKAY7BKqxeKSX++0jnpOdmPNZyVq2hpYxTfxKyqn9QLdh3qXGv2G5DANDYmrdYGeZAFDZa2QHWH1NgT9QHefWlt4hslo1U8C2Ru9vpqussypATAoYWYVfUDs9qoQ3W2OB16zG43NcZ2UzR4zIqc+nfGQSd3AvFxb6+JqIsYrNLapSaBhWNAZbq/FsbKFbRnVZmh84RlSva0NtOh3VTIbjc1xnZTNLgYqNIAYLBKaxeKSvF++QIQyfZHRsuqA5VrjddaCIix5qunlAIMZ6rUPFqnSmnyXnO2+VmHSoNsaaCampTvDaI+w2CV1ic+bncHOANkb/H6atavvDSrOsGsaq8YZcmgVmcbN19HKNmYKsXaYSL5fskfb7RmS+3q3wOvNPC46tP6pXYCubckIxnkgn6rDlSbs6phGa1K7rGMRh2qWWr9mBax+6GOAeGEN9dH5Ectgao9sCU64vVVEbmGwSqtnx6Rk6fFs3Y5QMzrK1qb8mW0TDhirao7lCVtpqozMl2qZaoU200RXZdRAvLvMlClgcJglbojNioHYYpngMwtwQsyzBpQnW681sLsT9htRtGuQ53t0G4qLRnU6AgfEIiWY5TsjKoJCVR3D87oaxpoDFapexbLAaaCNzGlcgWtWdUJQNM9u5y+YdbsOtTrtZsaDW42nqhXjKKdUXUC1ZuA6JDXV0XUEwxWqXv0MJDaARROyyIalLGWbVnVSLBrb71mmXLorjojp/qbaSHJnkbHgEjam+sjCpp6ASicYKBKA4vBKnVXdASIzgOFM0B2bzDKASpLalUTzKqu2orbTQ3x75ZoNeoFyajCAqDJEJZI1uurIuopBqvUfcntQO6YbK0nNnt9NddnViXAcmgRIMaG2itmVuTvrzrTod1UQjKosdH+HMlL5LZ6HsifgASqOpC5iYEqDSQGq9R9eljqVwsnpXG7n9sOLe0AkNjMzN+NsN0UkfvaAtWbgUjG66si8gSDVXJHdEi2fYungext/iwHMCty+MehR5lVXY5STe2mcmhtN6XJieTomGR9/PhvTRQk9QUgfxIMVIkEg1VyT2IbsHBMspfJLV5fTUN9oX37HwDimxloLbXYbmquMSnHwXZTRN1XX7AzqgoSqO7hYUQaeAxWyT16SMoB8ick0xpOeX1FQC0np2rNaqNrQWxcro0ztYVZa2zzW5XWj+lRuw51jO2miLqtlpPyKSjpnJHZ4491k8hjDFbJXZGsBIOFM8DQbd7XgzqtlJxasNq83CCG7/D0sjynLMmest0UkTdq80DhFBioErVjsEruS26T7gDlS/JjLykFGBXpVKDHASgJpq2q9FodtP6q9Xxjm7+l3RSAsN1uKjrs/UMGUT9rC1RvAcJJr6+KyDcYrJL7NB1ITcqYwMiwx9k5JUGpHpesb/mSzNbWtPYRoP3KaTdVmwWsWuvH2G6KqLdqc1KSBCVjnjN7GKgSLcFglXojkgbiG4HiGWBon3eZOqMs29xWBahWJYhObAFCKbm+fmUZjW1+s9j6MS0sI09jY7xJEvVSdVbWxMVA9Ra2fCPqgMEq9U5ii32A4IwET2YZGD3Y22swFuT/4azUgyUm7P+29l8nALabIvKv6qy09gMYqBLdAINV6h1Nl9P3l/8E0GNAfJMEVKsJlCxTGtErQ34vFABN/gwtDISS12+jFBsH8I5kesNpYOQ9QGx4nV+Yzxilxjb/0nZTIbvrQXREhjcQDbr1rilrUZ2xM6qQQRrZW4BQvLufg6iP8G5FvVOZAubfkMVZ06R20qpef5GuF6TXZ21OGvjX81i8mbSxfz6SafT/DKdaa2TTuwDogFEA0rv7J5Nh1RsBqllu/dhiu6lR3hCJur2mrFZlGiidlR8zUCVaEU0ppW78y4i6wKzIVJbCaaB8UU697vyF9lnXliELevGUvX2tyWGfUFwystfLxColAbBZkQAOCogMAandQHy8v7KJym69VZ1plDcs0uXGGhvj5Bsiv6wpbYHqXvYrJloBBqvUe7V5YO4oMP8TYOsn7WwnZPu6dFG2xyxDgqxuZD6dQ1VaWA5UJbcG+yAR200RrYyf1pTKNaB0Tn6sR6VGlYEq0YowWCVvKCUHrWJ2o/nCGWDhHakNiwy5kwG1DMmqWCaQvRVI7wxOQOeMh63NtLeb0uN2gDoKhKLeXB+RnyjLX2tKZQoonZcfM1AlWjUGq+Steh6YPypdAuJjUhrgNmUClRk57DV8wL/b5JYpNai1WamxbbbYbmqUU26ImvltTSlfBcoX5Md6zA5U+VBJtBoMVskbi5mPt9d/YGGtnIMW2VuB9KQ/sqxKAfUFyaDW5tHWbioyZGejh9huiqiZH9eUyjUGqkRdwGCVes8yJPNRviwHFHqR+ViOMuXQQ2KzZES8OoBllCSDWp1huymi1fLjmqJFgGhWvmf1mBym4lQ4ojVhsEq9ZdWBudclKIuPe301DZVpCQhHDvbuhmLVpTF4baa93ZQWkeuJjbGtDdH1+HFNqUw3xksPHwCG9zNQJVoHpmmodyzDvqnM+uem4oiPS83Z3OvAyCH3MphOu6narN1Cp5nTbmoUCGe4zU90I35cUyrXgOq0fC8bZftBlN/LROvBzCr1hrLkplKZ8s9NpZPKNBDfKBnWbtaw1gt2HeqcbBM2C2fsOtTh7k/KIepXflxTKlOS4QXktH9yhzycurGmEA0QZlapNwpnpJ4sscnrK7m++LhcZ2QEyEyu788yq406VKva+jE9ZtehjvHABdFa+G1N6RSo6uHurilEA4rBKrmvnpcTun7JftxIfBxYOCZtb1bb1soyG2Mc29pNhex2U2NsN0W0Hn5bUypXpRQBkBrz5PbWUqL1rClExDIAcpmygOlX5BCEF61k1qpekAMR44dvvHWnlEyzqTrtppqnSmkyTtbZ5mcdKtH6+G1NKb0/UpQAAAwRSURBVF+xp8nBDlR3dC7nWc2aQkQtmFkldxXOSHPuxEavr2R1ImmgPAUUzi2/dWeUJYNanQVUvfVjoWRjqhTbTRF1j5/WlJZANWFnVJepO1/JmkJEHfEuSu4xSjLuMD7m9ZWsTXwMWPip3BSdud+W0ahDNUutv16LyEn+6Fh35o8TUSs/rSnly/ZOCm4cqDo6rSlEdEMMVsk9pYuyeHvZoHs9tJBcf+m8nOatzsh0qZapUjoQHbbrUNluishVfllT1hKoAk1rykUgu8fdayTqIwxWyR2WARTPyFjQoDLKgFkBrrwAjN7Zup0fTksGNTrCdlNEveCHNUUpCVSdHsmhJJDavroa1MiQfB3pXSwRIlohfqeQOyrTcnMJ2mJs1uVGVJ+XAxyA1KPW5oHk1kYdaijm7XUSDRqv1xSlgPIle3cFawtUAbl+ZcjXk5zo/nUS9SEeSSR3FE+52qLlqe88j4/83N/B6E0fQnTiMPa+71H8xn/+A1iWdePfvJRlSjBaOAsUTgDVa41AVdOBxGbZvhu+XX7MQJWo91xaUxYWCvhXv/4EDtz7V5DZ8UFs3Pth/Mwv/Cr+7EdvNn5RW6CaWlug6ghn5OshohVhsErdVy9IdtKlQ0Zf/ca38fOP/xPU6nX83b/1WfzjX30Muq7jH/7L38Q/+Of/YWV/iFLSB7V0Eci/K1t7iwemNNnmT24FMntku07V5esiot5zcU059s5p/OevfQvvf+8d+Kd//3E8+rMP4KVXfoJ7P/F5vPj/fmwHqheXBKrb1td+Kpywd3C4phCtBPusUveVrwJzPwHiG1z547/5v76LDeMj+NiD9zQ+ZbmCOz74OZw+ewlT7/wJRkeWqWszq3KTqOVkK65ZKAaEh4DoUPtWY+UaMHKnf6bl0IrMz8/jpZdewunTp5HP5zE8PIzDhw/jrrvu8vrSaDVcXFPmc3nomoZsttGz9XvPHcEnPvv38JmHH8T//k+/In2UAfshdlt3DlJyTSFasYAVFFIg1Oak+bVLfumvfKLt5xKJOD7x4XvxW088ieMnzuHwew80PmgZkhWp5+TAVDMtDESzEqSG48t/Uj0iXxdvLIHy/PPPI5FI4JFHHkE0GsWbb76JZ599FrFYDLfffrvXl0cr5eKaMjzUXlrwwfe/BwBQzM81BaoZ2W3pVscPrilEK8ZglbqvNiOTXHqsXpdM6ehItrHNX8vZY0+bNxA0adAdGZaxpyu5+YTi0l+VAuVjH/sY0ulGxmxiYgLHjx/HT3/6UwarQdLjNeVHP3kLAHDPnbvkJ7odqAJcU4hWgcEqdZdlytzuWG9ndluWhe89/zJ2bp/AzZsjUoeqzNZfFEpIgBrJrL7dlB6TPquWyVZVAdIcqDqi0ShY/eQjypIMYzjVOSDtwZpSKJRwbWYOM7M5vPKjN/DvfuO/4YPv24u/9/jHZL1IdDlQBbimEK0Cg1XqLrMEQPW2Ob5Zx2/+p9/DmXOX8Nu/9tehG7nGx/SI9DWMDAGh6No/h6YBsACzDOg+mEdOazI7O4tLly7hve99r9eXQo7yFaB0QQK26AgQ39x6kKoHa8pT3/k+Hv+Vf734+uc//j789r/660gPbwQSW9z53FxTiFaMB6you2pzwPSfuXa4qkX5KlC+jO+//Bp+5gv/BQ/8pT34P1/929D1sByEiAx1d+uwOgOM3iUHsMgnNJkutoJgol6v4+tf/zpCoRB++Zd/Gbq+5DS3WeO2rBdKF4DCKWkfFxuT793YCDB0u4wk7cGacu7CFfz49WOYvnQab584g99/+ggsC/jW7/06Hrz/L7n2eVG5Bowflil4RLQsBqvUXdVZYObP3Q9WjQpw9Qf486MX8OAXvo7JLcN46ZtfxvDoBrnBraetzHJqc8DQfgarvqIDIwdv+O9dr9fx5JNPYmFhAY8//jhSqVSHX1QA8u+4dJ20rPIVoHgOqE7L61BUtvxTu4CJD/VuTanOA5XLAIBLMwbu+sQ/BDTgzE+eRSy2jl2Z66lcA8beB8RG3fnzifoEywCoy3r07KNH8NqpEj72t7+JLRuH8dwf/DMMb3K5TlbT7f9YX+YfOoDrZ1VrtRqefPJJFAoFPPbYY50DVQf/bXtv8ftKlzrO6LD0Mk1ssX9Bj9aUUNxuX5fGlt0b8amP348nvv403n73DA7efouLn5j5IqIbYbBKXdabWtW3jp/Bhz//X7Fhwwa88MwTmNjcg7KDyjVg+A5mQQKkWq3iD//wD1Gv1/H4448jmUwu/4sjaWDkUO8ujkR0VA5XKSX/BtExIDHRVMLTo/r3cBxI7158aZoyDa+tXKTreljfTxRQnGBF3dWDg1XHT5zFQ49+CaPDWbzwzBPY0otA1eFGeQG5olqt4pvf/CaKxSIefvhhVCoVzM7OYnZ2FsVi0evLI0d0VALU1E4ps0lPttaau7ymvPLq0bafO3XmAp76zvexfesm7L91d4ff1UVcU4huiJlV6i4tDDe3tRYWCnjw01/C1alZ/NIvfBzfeua5tl/zN/7qwy3TaLpH2V8fBcHly5dx4cIFAMATTzzR8rFDhw7hkUce8eKyaKlIGhjev/zHXV5T/sm/+W3MzObw8Yc+gNGRLE6euYj/8Uf/F4Zh4ltf+/cuZ1a5phCtBA9YUXdZJnDlOTkg4UJG5My5S9h158PX/TWnf/IdTO7Yct1fs2pKSTeAiYfYE5Gol1xeU5753g/xW0/8Txw7fhozs/PYMDaCv3zvXfinf/9x3H7bzV3/fIu4phCtGINV6r5rL8v/PZhi5RqzAkADNtzj9ZUQDR6uKUQDjcUy1H3RMXsh7iNmRWrriKj3uKYQDTQGq9R90RHAqnt9Fd1l1eXrIqLe45pCNNAYrFL3hVPov96Byv66iKjnuKYQDTQGq9R9EXvUqVH2+kq6wyjL1xPh/G4iT3BNIRpoDFbJHandQD3v9VV0h5GXr4eIvMM1hWhgMVgld8THAT0MWIbXV7I+liF9EOMuj3IlouvjmkI0sBiskjv0MJCaBOo5r69kfeo5+Tp0Nu4m8hTXFKKBxWCV3JPcKg29len1layNMuX6k1u9vhIiArimEA0oBqvknnASyO4FKjNeX8naVGaA7G3ydRCR97imEA0kBqvkrvQkEB0C6gWvr2R16gW57vQOr6+EiJpxTSEaOAxWyV2aDgwfAIxicLbulCnXO3xArp+I/INrCtHA4XcNuS+SAbK3ApVpr69kZSrTQHafXDcR+Q/XFKKBwmCVeiM9CSQ2+//mUpmW6+RWHZG/cU0hGhgMVqk3nK272Jh/D0dUZuT6uFVH5H9cU4gGBr97qHf0MDByEIiN+i8bUpmW6xo5yP6HREHBNYVoIGhKKeX1RdCAsQxg/ihQvixTXLSQd9eizMY23fAB3lSIgohrClFfY7BK3lAWUDgDLLwNhFNAJN37a6gX5IRudp/Uk3Gbjii4uKYQ9S0Gq+Stel4yIrUcEB/rTUZEmVJLFh2SzAdP6BL1D64pRH2HwSp5T1lA4axkRPQQEBlyZ+vMMmQut2XKFBlmPoj6E9cUor7CYJX8wygBpYtA8QygDCCcAcKJ7vy5RgHQwkBqUuZyc9whUf/jmkLUFxiskv9YhhxQKJ6SrAU0QI8AoTigxwBNW/73KgVYVcCsAFYdgJKsSmq3HLzgYQeiwcM1hSjQGKySvzkHFmpzQG1W6tFgAeh0c1EAdKkXi44C0RHvDloQkT9xTSEKHAarFCyWCZhl2dJTFuRmokmdmBYGQgmpUSMiWgmuKUS+x2CViIiIiHyLxxaJiIiIyLcYrBIRERGRbzFYJSIiIiLfYrBKRERERL7FYJWIiIiIfIvBKhERERH5FoNVIiIiIvItBqtERERE5FsMVomIiIjItxisEhEREZFvMVglIiIiIt9isEpEREREvsVglYiIiIh8i8EqEREREfkWg1UiIiIi8i0Gq0RERETkWwxWiYiIiMi3GKwSERERkW8xWCUiIiIi32KwSkRERES+xWCViIiIiHyLwSoRERER+RaDVSIiIiLyLQarRERERORbDFaJiIiIyLcYrBIRERGRbzFYJSIiIiLfYrBKRERERL7FYJWIiIiIfIvBKhERERH5FoNVIiIiIvKt/w88ueVdd3ohFwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# drawing a demo network\n", "fig = plt.figure(figsize=(12, 8),facecolor='white')\n", "pos={0: np.array([ 0.2 , 0.8]),\n", " 2: np.array([ 0.2, 0.2]),\n", " 1: np.array([ 0.4, 0.6]),\n", " 6: np.array([ 0.4, 0.4]),\n", " 4: np.array([ 0.7, 0.8]),\n", " 5: np.array([ 0.7, 0.5]),\n", " 3: np.array([ 0.7, 0.2 ]),\n", " 'sink': np.array([ 1, 0.5]),\n", " 'source': np.array([ 0, 0.5])}\n", "\n", "width=[float(d['weight']*1.2) for (u,v,d) in gd.edges(data=True)]\n", "edge_labels=dict([((u,v,),d['weight']) for u,v,d in gd.edges(data=True)])\n", "\n", "nx.draw_networkx_edge_labels(gd,pos,edge_labels=edge_labels, font_size = 15, alpha = .5)\n", "nx.draw(gd, pos, node_size = 3000, node_color = 'orange',\n", " alpha = 0.2, width = width, edge_color='orange',style='solid')\n", "nx.draw_networkx_labels(gd,pos,font_size=18)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:29:13.581294Z", "start_time": "2021-05-14T03:29:13.577521Z" } }, "outputs": [ { "data": { "text/plain": [ "'Name: \\nType: DiGraph\\nNumber of nodes: 9\\nNumber of edges: 15\\nAverage in degree: 1.6667\\nAverage out degree: 1.6667'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nx.info(gd)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:29:21.862691Z", "start_time": "2021-05-14T03:29:21.857635Z" } }, "outputs": [ { "data": { "text/plain": [ "matrix([[0., 5., 1., 0., 2., 1., 0., 0., 0.],\n", " [0., 0., 0., 0., 1., 0., 0., 3., 1.],\n", " [0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 3., 0., 0., 0.],\n", " [0., 0., 0., 2., 0., 0., 2., 0., 0.],\n", " [0., 0., 0., 2., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 2., 0., 0., 0., 0., 1.],\n", " [0., 0., 0., 2., 0., 0., 0., 0., 0.]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# flow matrix\n", "m = fn.getFlowMatrix(gd)\n", "m" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:29:26.134764Z", "start_time": "2021-05-14T03:29:26.130022Z" } }, "outputs": [ { "data": { "text/plain": [ "defaultdict(.()>,\n", " {0: [0, 5, 5],\n", " 1: [0, 3, 2],\n", " 2: [2, 4, 1],\n", " 6: [1, 1, 1],\n", " 3: [2, 2, 0],\n", " 4: [2, 3, 0],\n", " 5: [2, 2, 0]})" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fn.networkDissipate(gd)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:30:19.089882Z", "start_time": "2021-05-14T03:30:19.086650Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "import random, datetime\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import statsmodels.api as sm\n", "from scipy.stats import norm\n", "from scipy.stats.stats import pearsonr" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:34:32.678365Z", "start_time": "2021-05-14T03:34:32.670475Z" } }, "outputs": [], "source": [ "with open('./data/the_republic_plato_gutenberg_pg1497.txt', 'r') as f:\n", " lines = f.readlines()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:34:45.419000Z", "start_time": "2021-05-14T03:34:45.414822Z" } }, "outputs": [ { "data": { "text/plain": [ "24692" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(lines)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:34:53.866764Z", "start_time": "2021-05-14T03:34:53.863472Z" } }, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(lines)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:45:53.310818Z", "start_time": "2021-05-14T03:45:53.306007Z" } }, "outputs": [ { "data": { "text/plain": [ "['\\n',\n", " '\\n',\n", " '\\n',\n", " '\\n',\n", " 'BOOK I.\\n',\n", " '\\n',\n", " 'I went down yesterday to the Piraeus with Glaucon the son of Ariston,\\n',\n", " 'that I might offer up my prayers to the goddess (Bendis, the Thracian\\n',\n", " 'Artemis.); and also because I wanted to see in what manner they would\\n',\n", " 'celebrate the festival, which was a new thing. I was delighted with the\\n']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lines[8520:8530]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Variable Type\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:49:21.862606Z", "start_time": "2021-05-14T03:49:21.859090Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "'3'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# str, int, float, bool\n", "str(3)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:49:36.965025Z", "start_time": "2021-05-14T03:49:36.961704Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "'chengjun wang'" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"chengjun wang\"" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:49:41.341000Z", "start_time": "2021-05-14T03:49:41.337258Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# int\n", "int('5') " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:50:18.235094Z", "start_time": "2021-05-14T03:50:18.231377Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "7.1" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# float\n", "float(str(7.1))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:50:31.940144Z", "start_time": "2021-05-14T03:50:31.936700Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "range(0, 10)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(10)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:50:41.810207Z", "start_time": "2021-05-14T03:50:41.806428Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "range(1, 10)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for i in range(1, 10):\n", "# print(i)\n", "\n", "range(1,10) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## dir & help\n", "\n", "当你想要了解对象的详细信息时使用" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:11:24.391964Z", "start_time": "2020-09-22T01:11:24.387545Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "['__add__',\n", " '__class__',\n", " '__contains__',\n", " '__delattr__',\n", " '__dir__',\n", " '__doc__',\n", " '__eq__',\n", " '__format__',\n", " '__ge__',\n", " '__getattribute__',\n", " '__getitem__',\n", " '__getnewargs__',\n", " '__gt__',\n", " '__hash__',\n", " '__init__',\n", " '__init_subclass__',\n", " '__iter__',\n", " '__le__',\n", " '__len__',\n", " '__lt__',\n", " '__mod__',\n", " '__mul__',\n", " '__ne__',\n", " '__new__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__rmod__',\n", " '__rmul__',\n", " '__setattr__',\n", " '__sizeof__',\n", " '__str__',\n", " '__subclasshook__',\n", " 'capitalize',\n", " 'casefold',\n", " 'center',\n", " 'count',\n", " 'encode',\n", " 'endswith',\n", " 'expandtabs',\n", " 'find',\n", " 'format',\n", " 'format_map',\n", " 'index',\n", " 'isalnum',\n", " 'isalpha',\n", " 'isascii',\n", " 'isdecimal',\n", " 'isdigit',\n", " 'isidentifier',\n", " 'islower',\n", " 'isnumeric',\n", " 'isprintable',\n", " 'isspace',\n", " 'istitle',\n", " 'isupper',\n", " 'join',\n", " 'ljust',\n", " 'lower',\n", " 'lstrip',\n", " 'maketrans',\n", " 'partition',\n", " 'replace',\n", " 'rfind',\n", " 'rindex',\n", " 'rjust',\n", " 'rpartition',\n", " 'rsplit',\n", " 'rstrip',\n", " 'split',\n", " 'splitlines',\n", " 'startswith',\n", " 'strip',\n", " 'swapcase',\n", " 'title',\n", " 'translate',\n", " 'upper',\n", " 'zfill']" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(str) " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:19:18.203635Z", "start_time": "2020-09-22T01:19:18.199931Z" } }, "outputs": [ { "data": { "text/plain": [ "'Cheng'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'cheng'.capitalize()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T06:21:19.950231Z", "start_time": "2018-04-27T06:21:19.945950Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "['rstrip',\n", " 'split',\n", " 'splitlines',\n", " 'startswith',\n", " 'strip',\n", " 'swapcase',\n", " 'title',\n", " 'translate',\n", " 'upper',\n", " 'zfill']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(str)[-10:]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:11:55.262050Z", "start_time": "2021-05-14T06:11:55.235947Z" }, "scrolled": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class str in module builtins:\n", "\n", "class str(object)\n", " | str(object='') -> str\n", " | str(bytes_or_buffer[, encoding[, errors]]) -> str\n", " | \n", " | Create a new string object from the given object. If encoding or\n", " | errors is specified, then the object must expose a data buffer\n", " | that will be decoded using the given encoding and error handler.\n", " | Otherwise, returns the result of object.__str__() (if defined)\n", " | or repr(object).\n", " | encoding defaults to sys.getdefaultencoding().\n", " | errors defaults to 'strict'.\n", " | \n", " | Methods defined here:\n", " | \n", " | __add__(self, value, /)\n", " | Return self+value.\n", " | \n", " | __contains__(self, key, /)\n", " | Return key in self.\n", " | \n", " | __eq__(self, value, /)\n", " | Return self==value.\n", " | \n", " | __format__(self, format_spec, /)\n", " | Return a formatted version of the string as described by format_spec.\n", " | \n", " | __ge__(self, value, /)\n", " | Return self>=value.\n", " | \n", " | __getattribute__(self, name, /)\n", " | Return getattr(self, name).\n", " | \n", " | __getitem__(self, key, /)\n", " | Return self[key].\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __gt__(self, value, /)\n", " | Return self>value.\n", " | \n", " | __hash__(self, /)\n", " | Return hash(self).\n", " | \n", " | __iter__(self, /)\n", " | Implement iter(self).\n", " | \n", " | __le__(self, value, /)\n", " | Return self<=value.\n", " | \n", " | __len__(self, /)\n", " | Return len(self).\n", " | \n", " | __lt__(self, value, /)\n", " | Return self int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | string S[start:end]. Optional arguments start and end are\n", " | interpreted as in slice notation.\n", " | \n", " | encode(self, /, encoding='utf-8', errors='strict')\n", " | Encode the string using the codec registered for encoding.\n", " | \n", " | encoding\n", " | The encoding in which to encode the string.\n", " | errors\n", " | The error handling scheme to use for encoding errors.\n", " | The default is 'strict' meaning that encoding errors raise a\n", " | UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that can handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(self, /, tabsize=8)\n", " | Return a copy where all tab characters are expanded using spaces.\n", " | \n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub[, start[, end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> str\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | format_map(...)\n", " | S.format_map(mapping) -> str\n", " | \n", " | Return a formatted version of S, using substitutions from mapping.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub[, start[, end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found, \n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Raises ValueError when the substring is not found.\n", " | \n", " | isalnum(self, /)\n", " | Return True if the string is an alpha-numeric string, False otherwise.\n", " | \n", " | A string is alpha-numeric if all characters in the string are alpha-numeric and\n", " | there is at least one character in the string.\n", " | \n", " | isalpha(self, /)\n", " | Return True if the string is an alphabetic string, False otherwise.\n", " | \n", " | A string is alphabetic if all characters in the string are alphabetic and there\n", " | is at least one character in the string.\n", " | \n", " | isascii(self, /)\n", " | Return True if all characters in the string are ASCII, False otherwise.\n", " | \n", " | ASCII characters have code points in the range U+0000-U+007F.\n", " | Empty string is ASCII too.\n", " | \n", " | isdecimal(self, /)\n", " | Return True if the string is a decimal string, False otherwise.\n", " | \n", " | A string is a decimal string if all characters in the string are decimal and\n", " | there is at least one character in the string.\n", " | \n", " | isdigit(self, /)\n", " | Return True if the string is a digit string, False otherwise.\n", " | \n", " | A string is a digit string if all characters in the string are digits and there\n", " | is at least one character in the string.\n", " | \n", " | isidentifier(self, /)\n", " | Return True if the string is a valid Python identifier, False otherwise.\n", " | \n", " | Use keyword.iskeyword() to test for reserved identifiers such as \"def\" and\n", " | \"class\".\n", " | \n", " | islower(self, /)\n", " | Return True if the string is a lowercase string, False otherwise.\n", " | \n", " | A string is lowercase if all cased characters in the string are lowercase and\n", " | there is at least one cased character in the string.\n", " | \n", " | isnumeric(self, /)\n", " | Return True if the string is a numeric string, False otherwise.\n", " | \n", " | A string is numeric if all characters in the string are numeric and there is at\n", " | least one character in the string.\n", " | \n", " | isprintable(self, /)\n", " | Return True if the string is printable, False otherwise.\n", " | \n", " | A string is printable if all of its characters are considered printable in\n", " | repr() or if it is empty.\n", " | \n", " | isspace(self, /)\n", " | Return True if the string is a whitespace string, False otherwise.\n", " | \n", " | A string is whitespace if all characters in the string are whitespace and there\n", " | is at least one character in the string.\n", " | \n", " | istitle(self, /)\n", " | Return True if the string is a title-cased string, False otherwise.\n", " | \n", " | In a title-cased string, upper- and title-case characters may only\n", " | follow uncased characters and lowercase characters only cased ones.\n", " | \n", " | isupper(self, /)\n", " | Return True if the string is an uppercase string, False otherwise.\n", " | \n", " | A string is uppercase if all cased characters in the string are uppercase and\n", " | there is at least one cased character in the string.\n", " | \n", " | join(self, iterable, /)\n", " | Concatenate any number of strings.\n", " | \n", " | The string whose method is called is inserted in between each given string.\n", " | The result is returned as a new string.\n", " | \n", " | Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n", " | \n", " | ljust(self, width, fillchar=' ', /)\n", " | Return a left-justified string of length width.\n", " | \n", " | Padding is done using the specified fill character (default is a space).\n", " | \n", " | lower(self, /)\n", " | Return a copy of the string converted to lowercase.\n", " | \n", " | lstrip(self, chars=None, /)\n", " | Return a copy of the string with leading whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | partition(self, sep, /)\n", " | Partition the string into three parts using the given separator.\n", " | \n", " | This will search for the separator in the string. If the separator is found,\n", " | returns a 3-tuple containing the part before the separator, the separator\n", " | itself, and the part after it.\n", " | \n", " | If the separator is not found, returns a 3-tuple containing the original string\n", " | and two empty strings.\n", " | \n", " | replace(self, old, new, count=-1, /)\n", " | Return a copy with all occurrences of substring old replaced by new.\n", " | \n", " | count\n", " | Maximum number of occurrences to replace.\n", " | -1 (the default value) means replace all occurrences.\n", " | \n", " | If the optional argument count is given, only the first count occurrences are\n", " | replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub[, start[, end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub[, start[, end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Raises ValueError when the substring is not found.\n", " | \n", " | rjust(self, width, fillchar=' ', /)\n", " | Return a right-justified string of length width.\n", " | \n", " | Padding is done using the specified fill character (default is a space).\n", " | \n", " | rpartition(self, sep, /)\n", " | Partition the string into three parts using the given separator.\n", " | \n", " | This will search for the separator in the string, starting at the end. If\n", " | the separator is found, returns a 3-tuple containing the part before the\n", " | separator, the separator itself, and the part after it.\n", " | \n", " | If the separator is not found, returns a 3-tuple containing two empty strings\n", " | and the original string.\n", " | \n", " | rsplit(self, /, sep=None, maxsplit=-1)\n", " | Return a list of the words in the string, using sep as the delimiter string.\n", " | \n", " | sep\n", " | The delimiter according which to split the string.\n", " | None (the default value) means split according to any whitespace,\n", " | and discard empty strings from the result.\n", " | maxsplit\n", " | Maximum number of splits to do.\n", " | -1 (the default value) means no limit.\n", " | \n", " | Splits are done starting at the end of the string and working to the front.\n", " | \n", " | rstrip(self, chars=None, /)\n", " | Return a copy of the string with trailing whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | split(self, /, sep=None, maxsplit=-1)\n", " | Return a list of the words in the string, using sep as the delimiter string.\n", " | \n", " | sep\n", " | The delimiter according which to split the string.\n", " | None (the default value) means split according to any whitespace,\n", " | and discard empty strings from the result.\n", " | maxsplit\n", " | Maximum number of splits to do.\n", " | -1 (the default value) means no limit.\n", " | \n", " | splitlines(self, /, keepends=False)\n", " | Return a list of the lines in the string, breaking at line boundaries.\n", " | \n", " | Line breaks are not included in the resulting list unless keepends is given and\n", " | true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(self, chars=None, /)\n", " | Return a copy of the string with leading and trailing whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | swapcase(self, /)\n", " | Convert uppercase characters to lowercase and lowercase characters to uppercase.\n", " | \n", " | title(self, /)\n", " | Return a version of the string where each word is titlecased.\n", " | \n", " | More specifically, words start with uppercased characters and all remaining\n", " | cased characters have lower case.\n", " | \n", " | translate(self, table, /)\n", " | Replace each character in the string using the given translation table.\n", " | \n", " | table\n", " | Translation table, which must be a mapping of Unicode ordinals to\n", " | Unicode ordinals, strings, or None.\n", " | \n", " | The table must implement lookup/indexing via __getitem__, for instance a\n", " | dictionary or list. If this operation raises LookupError, the character is\n", " | left untouched. Characters mapped to None are deleted.\n", " | \n", " | upper(self, /)\n", " | Return a copy of the string converted to uppercase.\n", " | \n", " | zfill(self, width, /)\n", " | Pad a numeric string with zeros on the left, to fill a field of the given width.\n", " | \n", " | The string is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\n", " | \n", " | maketrans(x, y=None, z=None, /)\n", " | Return a translation table usable for str.translate().\n", " | \n", " | If there is only one argument, it must be a dictionary mapping Unicode\n", " | ordinals (integers) or characters to Unicode ordinals, strings or None.\n", " | Character keys will be then converted to ordinals.\n", " | If there are two arguments, they must be strings of equal length, and\n", " | in the resulting dictionary, each character in x will be mapped to the\n", " | character at the same position in y. If there is a third argument, it\n", " | must be a string, whose characters will be mapped to None in the result.\n", "\n" ] } ], "source": [ "help(str)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:55:38.921530Z", "start_time": "2021-05-14T03:55:38.917817Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "' '.isspace()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:53:49.045706Z", "start_time": "2021-05-14T03:53:49.042142Z" } }, "outputs": [ { "data": { "text/plain": [ "'socrates the king is the greatest.'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'socrates the king'.__add__(' is the greatest.')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:59:12.870300Z", "start_time": "2021-05-14T03:59:12.865426Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "['rstrip',\n", " 'split',\n", " 'splitlines',\n", " 'startswith',\n", " 'strip',\n", " 'swapcase',\n", " 'title',\n", " 'translate',\n", " 'upper',\n", " 'zfill']" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = ' Hello WorlD '\n", "dir(x)[-10:] " ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:59:15.825497Z", "start_time": "2021-05-14T03:59:15.822225Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "' hello world '" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lower\n", "x.lower() " ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:59:24.028262Z", "start_time": "2021-05-14T03:59:24.024581Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "' HELLO WORLD '" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# upper\n", "x.upper()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:59:30.148025Z", "start_time": "2021-05-14T03:59:30.144262Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "'Hello WorlD '" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rstrip\n", "x.lstrip()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:22:38.713347Z", "start_time": "2020-09-22T01:22:38.709491Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "'Hello WorlD'" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# strip\n", "x.strip()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:59:44.103945Z", "start_time": "2021-05-14T03:59:44.100337Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "' Hel WorlD '" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# replace\n", "x.replace('lo', '')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T03:59:50.859699Z", "start_time": "2021-05-14T03:59:50.855971Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[' Hel', ' WorlD ']" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# split\n", "x.split('lo')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T04:00:00.310907Z", "start_time": "2021-05-14T04:00:00.307439Z" } }, "outputs": [ { "data": { "text/plain": [ "'a,b'" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# join \n", "','.join(['a', 'b'])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## type\n", "当你想要了解变量类型时使用type" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:24:35.607702Z", "start_time": "2020-09-22T01:24:35.604262Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = 'hello world'\n", "type(x)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Data Structure\n", "list, tuple, set, dictionary, array\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:05:22.880431Z", "start_time": "2021-05-14T06:05:22.874747Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 3] (1, 2, 3, 3) {1, 2, 3} {'a': 1, 'b': 2, 'c': 3} [1 2 3 3]\n" ] } ], "source": [ "l = [1,2,3,3] # list\n", "t = (1, 2, 3, 3) # tuple\n", "s = {1, 2, 3, 3} # set([1,2,3,3]) # set\n", "d = {'a':1,'b':2,'c':3} # dict\n", "a = np.array(l) # array\n", "print(l, t, s, d, a)\n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:31:11.127410Z", "start_time": "2020-09-22T01:31:11.123452Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 3, 4]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l = [1,2,3,3] # list\n", "l.append(4)\n", "l" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:06:53.847121Z", "start_time": "2021-05-14T06:06:53.843194Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['a', 'b', 'c'])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {'a':1,'b':2,'c':3} # dict\n", "d.keys()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:06:59.161625Z", "start_time": "2021-05-14T06:06:59.157718Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "dict_values([1, 2, 3])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {'a':1,'b':2,'c':3} # dict\n", "d.values()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:07:25.993162Z", "start_time": "2021-05-14T06:07:25.988921Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {'a':1,'b':2,'c':3} # dict\n", "d['b']" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:32:27.607384Z", "start_time": "2020-09-22T01:32:27.603091Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "dict_items([('a', 1), ('b', 2), ('c', 3)])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {'a':1,'b':2,'c':3} # dict\n", "d.items() " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 定义函数" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:15:49.478939Z", "start_time": "2021-05-14T06:15:49.476320Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def devidePlus(m, n): # 结尾是冒号\n", " y = m/n + 1 # 注意：空格\n", " return y # 注意：return" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## For 循环" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:16:03.929223Z", "start_time": "2021-05-14T06:16:03.925842Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "range(0, 10)" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(10)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:16:12.065379Z", "start_time": "2021-05-14T06:16:12.061598Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "range(1, 10)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(1, 10) " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:16:25.378463Z", "start_time": "2021-05-14T06:16:25.373009Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0 0\n", "1 10 1\n", "2 20 4\n", "3 30 9\n", "4 40 16\n", "5 50 25\n", "6 60 36\n", "7 70 49\n", "8 80 64\n", "9 90 81\n" ] } ], "source": [ "for i in range(10):\n", " print(i, i*10, i**2)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:16:29.065853Z", "start_time": "2021-05-14T06:16:29.061400Z" }, "code_folding": [ 0 ], "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "10\n", "20\n", "30\n", "40\n", "50\n", "60\n", "70\n", "80\n", "90\n" ] } ], "source": [ "for i in range(10):\n", " print(i*10) " ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:38:25.096618Z", "start_time": "2020-09-22T01:38:25.092487Z" }, "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n", "1.5\n", "2.0\n", "2.5\n", "3.0\n", "3.5\n", "4.0\n", "4.5\n", "5.0\n", "5.5\n" ] } ], "source": [ "for i in range(10):\n", " print(devidePlus(i, 2))" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:19:10.484026Z", "start_time": "2021-05-14T06:19:10.479691Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 列表内部的for循环\n", "r = [devidePlus(i, 2) for i in range(10)]\n", "r " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## map函数" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:20:40.810819Z", "start_time": "2021-05-14T06:20:40.806477Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32.0 72.5 104.0 212.0\n" ] } ], "source": [ "def fahrenheit(T):\n", " return ((float(9)/5)*T + 32)\n", "\n", "temp = [0, 22.5, 40,100]\n", "F_temps = map(fahrenheit, temp)\n", "\n", "print(*F_temps)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T07:59:39.584810Z", "start_time": "2019-06-07T07:59:39.579618Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.0 4.0 1.4\n" ] } ], "source": [ "m1 = map(devidePlus, [4,3,2], [2, 1, 5])\n", "print(*m1)\n", "#print(*map(devidePlus, [4,3,2], [2, 1, 5]))\n", "# 注意： 将（4， 2)作为一个组合进行计算，将（3， 1）作为一个组合进行计算" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:22:49.133689Z", "start_time": "2021-05-14T06:22:49.129613Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 7 11 15 19\n" ] } ], "source": [ "m2 = map(lambda x, y: x + y, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10])\n", "print(*m2)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "ExecuteTime": { "end_time": "2020-09-22T01:41:22.820936Z", "start_time": "2020-09-22T01:41:22.816574Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 4 9 13 14\n" ] } ], "source": [ "m3 = map(lambda x, y, z: x + y - z, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10], [3, 3, 2, 2, 5])\n", "print(*m3)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## if elif else" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T06:40:35.103035Z", "start_time": "2018-04-27T06:40:35.097057Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "余数是1\n" ] } ], "source": [ "j = 5\n", "if j%2 == 1:\n", " print(r'余数是1')\n", "elif j%2 ==0:\n", " print(r'余数是0')\n", "else:\n", " print(r'余数既不是1也不是0')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:02:13.690924Z", "start_time": "2019-06-07T08:02:13.682806Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 0 10\n" ] } ], "source": [ "x = 5\n", "if x < 5:\n", " y = -1\n", " z = 5\n", "elif x > 5:\n", " y = 1\n", " z = 11\n", "else:\n", " y = 0\n", " z = 10\n", "print(x, y, z)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## while循环" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:03:12.473818Z", "start_time": "2019-06-07T08:03:12.469141Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n" ] } ], "source": [ "j = 0\n", "while j <10:\n", " print(j)\n", " j+=1 # avoid dead loop\n", " " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T06:43:15.236750Z", "start_time": "2018-04-27T06:43:15.231493Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "9\n", "25\n", "49\n", "81\n" ] } ], "source": [ "j = 0\n", "while j <10:\n", " if j%2 != 0: \n", " print(j**2)\n", " j+=1 # avoid dead loop " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T06:44:41.340319Z", "start_time": "2018-04-27T06:44:41.333449Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "9\n", "25\n", "49\n", "81\n", "121\n", "169\n", "225\n", "289\n", "361\n", "441\n", "529\n", "625\n", "729\n", "841\n" ] } ], "source": [ "j = 0\n", "while j <50:\n", " if j == 30:\n", " break\n", " if j%2 != 0: \n", " print(j**2)\n", " j+=1 # avoid dead loop\n", " " ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:32:59.789099Z", "start_time": "2021-05-14T06:32:59.785398Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "3\n", "2\n", "1\n" ] } ], "source": [ "a = 4\n", "while a: # 0, None, False\n", " print(a) \n", " a -= 1\n", " if a < 0:\n", " a = None # []" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## try except " ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:38:19.884566Z", "start_time": "2021-05-14T06:38:19.880063Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.0\n", "0 division by zero\n", "1.8\n" ] } ], "source": [ "def devidePlus(m, n): # 结尾是冒号\n", " return m/n+ 1 # 注意：空格\n", "\n", "\n", "for i in [2, 0, 5]:\n", " try:\n", " print(devidePlus(4, i))\n", " except Exception as e:\n", " print(i, e)\n", " pass" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T06:52:32.471043Z", "start_time": "2018-04-27T06:52:32.463396Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.0\n", "10.0\n", "division by zero\n" ] } ], "source": [ "alist = [[1,1], [0, 0, 1]]\n", "for aa in alist:\n", " try:\n", " for a in aa:\n", " print(10 / a)\n", " except Exception as e:\n", " print(e)\n", " pass" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T06:52:20.430949Z", "start_time": "2018-04-27T06:52:20.423433Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.0\n", "10.0\n", "division by zero\n", "division by zero\n", "10.0\n" ] } ], "source": [ "alist = [[1,1], [0, 0, 1]]\n", "for aa in alist:\n", " for a in aa:\n", " try:\n", " print(10 / a)\n", " except Exception as e:\n", " print(e)\n", " pass" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Write and Read data" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:45:37.378528Z", "start_time": "2021-05-14T06:45:37.373805Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[[0, 0, 0],\n", " [1, 1, 1],\n", " [2, 4, 8],\n", " [3, 9, 27],\n", " [4, 16, 64],\n", " [5, 25, 125],\n", " [6, 36, 216],\n", " [7, 49, 343],\n", " [8, 64, 512],\n", " [9, 81, 729]]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data =[[i, i**2, i**3] for i in range(10)] \n", "data" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:45:37.926457Z", "start_time": "2021-05-14T06:45:37.922101Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0\t0\n", "1\t1\t1\n", "2\t4\t8\n", "3\t9\t27\n", "4\t16\t64\n", "5\t25\t125\n", "6\t36\t216\n", "7\t49\t343\n", "8\t64\t512\n", "9\t81\t729\n" ] } ], "source": [ "for i in data:\n", " print('\\t'.join(map(str, i))) " ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:45:40.413504Z", "start_time": "2021-05-14T06:45:40.409671Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:45:43.901263Z", "start_time": "2021-05-14T06:45:43.897405Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(data)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:45:45.508699Z", "start_time": "2021-05-14T06:45:45.504734Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[0, 0, 0]" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[0]" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:45:53.790335Z", "start_time": "2021-05-14T06:45:53.786988Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function write:\n", "\n", "write(text, /) method of _io.TextIOWrapper instance\n", " Write string to stream.\n", " Returns the number of characters written (which is always equal to\n", " the length of the string).\n", "\n" ] } ], "source": [ "help(f.write) " ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:47:52.384851Z", "start_time": "2021-05-14T06:47:52.359107Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# 保存数据\n", "data =[[i, i**2, i**3] for i in range(10000)] \n", "\n", "f = open(\"data_write_to_file1.txt\", \"w\")\n", "for i in data:\n", " f.write('\\t'.join(map(str,i)) + '\\n')\n", "f.close()\n" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T06:58:58.363840Z", "start_time": "2021-05-14T06:58:58.357336Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "['0\\t0\\t0\\n', '1\\t1\\t1\\n', '2\\t4\\t8\\n', '3\\t9\\t27\\n', '4\\t16\\t64\\n']" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('data_write_to_file1.txt','r') as f:\n", " data = f.readlines()\n", "data[:5]" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:00:39.802681Z", "start_time": "2021-05-14T07:00:39.798076Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "77" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('data_write_to_file1.txt','r') as f:\n", " data = f.readlines(1000) #bytes \n", "len(data) " ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:01:10.254322Z", "start_time": "2021-05-14T07:01:10.249892Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0\t0\n", "\n" ] } ], "source": [ "with open('data_write_to_file1.txt','r') as f:\n", " print(f.readline())" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:05:35.468788Z", "start_time": "2021-05-14T07:05:35.464494Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1\n", "1 2\n", "2 3\n", "3 4\n", "4 5\n" ] } ], "source": [ "f = [1, 2, 3, 4, 5]\n", "for k, i in enumerate(f):\n", " print(k, i)\n", "\n", "# with open('data_write_to_file1.txt', 'r') as f:\n", "# for i in f:\n", "# print(i)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:09:42.081128Z", "start_time": "2021-05-14T07:09:42.058482Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1000\n", "2000\n", "3000\n", "4000\n", "5000\n", "6000\n", "7000\n", "8000\n", "9000\n", "2499833358330000\n" ] } ], "source": [ "total = 0\n", "with open('data_write_to_file1.txt','r') as f:\n", " for k, i in enumerate(f):\n", " if k % 1000 ==0:\n", " print(k)\n", " total += sum([int(j) for j in i.strip().split('\\t')])\n", "\n", "print(total)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:21:43.923393Z", "start_time": "2019-06-07T08:21:43.912523Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0\t0\n", "\n", "2000\t4000000\t8000000000\n", "\n", "4000\t16000000\t64000000000\n", "\n", "6000\t36000000\t216000000000\n", "\n", "8000\t64000000\t512000000000\n", "\n" ] } ], "source": [ "with open('../data/data_write_to_file.txt','r') as f:\n", " for k, i in enumerate(f):\n", " if k%2000 == 0:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:25:49.299881Z", "start_time": "2019-06-07T08:25:49.290974Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[[0, 0, 0]]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = []\n", "line = '0\\t0\\t0\\n'\n", "line = line.replace('\\n', '')\n", "line = line.split('\\t')\n", "line = [int(i) for i in line] # convert str to int\n", "data.append(line) \n", "data" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:25:32.931614Z", "start_time": "2021-05-14T07:25:32.856992Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[[0, 0, 0],\n", " [1, 1, 1],\n", " [2, 4, 8],\n", " [3, 9, 27],\n", " [4, 16, 64],\n", " [5, 25, 125],\n", " [6, 36, 216],\n", " [7, 49, 343],\n", " [8, 64, 512],\n", " [9, 81, 729],\n", " [10, 100, 1000],\n", " [11, 121, 1331],\n", " [12, 144, 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924010424],\n", " [975, 950625, 926859375],\n", " [976, 952576, 929714176],\n", " [977, 954529, 932574833],\n", " [978, 956484, 935441352],\n", " [979, 958441, 938313739],\n", " [980, 960400, 941192000],\n", " [981, 962361, 944076141],\n", " [982, 964324, 946966168],\n", " [983, 966289, 949862087],\n", " [984, 968256, 952763904],\n", " [985, 970225, 955671625],\n", " [986, 972196, 958585256],\n", " [987, 974169, 961504803],\n", " [988, 976144, 964430272],\n", " [989, 978121, 967361669],\n", " [990, 980100, 970299000],\n", " [991, 982081, 973242271],\n", " [992, 984064, 976191488],\n", " [993, 986049, 979146657],\n", " [994, 988036, 982107784],\n", " [995, 990025, 985074875],\n", " [996, 992016, 988047936],\n", " [997, 994009, 991026973],\n", " [998, 996004, 994011992],\n", " [999, 998001, 997002999],\n", " ...]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 读取数据\n", "data = []\n", "with open('../data/data_write_to_file.txt','r') as f:\n", " for line in f:\n", " line = line.replace('\\n', '').split('\\t')\n", " line = [int(i) for i in line]\n", " data.append(line)\n", "data\n" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:25:55.281024Z", "start_time": "2021-05-14T07:25:55.278651Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-05-11T22:18:54.212496", "start_time": "2017-05-11T22:18:54.207441" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function read_csv in module pandas.io.parsers:\n", "\n", "read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='\"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=False, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)\n", " Read CSV (comma-separated) file into DataFrame\n", " \n", " Also supports optionally iterating or breaking of the file\n", " into chunks.\n", " \n", " Additional help can be found in the `online docs for IO Tools\n", " `_.\n", " \n", " Parameters\n", " ----------\n", " filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)\n", " The string could be a URL. Valid URL schemes include http, ftp, s3, and\n", " file. For file URLs, a host is expected. For instance, a local file could\n", " be file ://localhost/path/to/table.csv\n", " sep : str, default ','\n", " Delimiter to use. If sep is None, will try to automatically determine\n", " this. Regular expressions are accepted and will force use of the python\n", " parsing engine and will ignore quotes in the data.\n", " delimiter : str, default None\n", " Alternative argument name for sep.\n", " header : int or list of ints, default 'infer'\n", " Row number(s) to use as the column names, and the start of the data.\n", " Default behavior is as if set to 0 if no ``names`` passed, otherwise\n", " ``None``. Explicitly pass ``header=0`` to be able to replace existing\n", " names. The header can be a list of integers that specify row locations for\n", " a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not\n", " specified will be skipped (e.g. 2 in this example is skipped). Note that\n", " this parameter ignores commented lines and empty lines if\n", " ``skip_blank_lines=True``, so header=0 denotes the first line of data\n", " rather than the first line of the file.\n", " names : array-like, default None\n", " List of column names to use. If file contains no header row, then you\n", " should explicitly pass header=None\n", " index_col : int or sequence or False, default None\n", " Column to use as the row labels of the DataFrame. If a sequence is given, a\n", " MultiIndex is used. If you have a malformed file with delimiters at the end\n", " of each line, you might consider index_col=False to force pandas to _not_\n", " use the first column as the index (row names)\n", " usecols : array-like, default None\n", " Return a subset of the columns.\n", " Results in much faster parsing time and lower memory usage.\n", " squeeze : boolean, default False\n", " If the parsed data only contains one column then return a Series\n", " prefix : str, default None\n", " Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n", " mangle_dupe_cols : boolean, default True\n", " Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'\n", " dtype : Type name or dict of column -> type, default None\n", " Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}\n", " (Unsupported with engine='python'). Use `str` or `object` to preserve and\n", " not interpret dtype.\n", " engine : {'c', 'python'}, optional\n", " Parser engine to use. The C engine is faster while the python engine is\n", " currently more feature-complete.\n", " converters : dict, default None\n", " Dict of functions for converting values in certain columns. Keys can either\n", " be integers or column labels\n", " true_values : list, default None\n", " Values to consider as True\n", " false_values : list, default None\n", " Values to consider as False\n", " skipinitialspace : boolean, default False\n", " Skip spaces after delimiter.\n", " skiprows : list-like or integer, default None\n", " Line numbers to skip (0-indexed) or number of lines to skip (int)\n", " at the start of the file\n", " skipfooter : int, default 0\n", " Number of lines at bottom of file to skip (Unsupported with engine='c')\n", " nrows : int, default None\n", " Number of rows of file to read. Useful for reading pieces of large files\n", " na_values : str or list-like or dict, default None\n", " Additional strings to recognize as NA/NaN. If dict passed, specific\n", " per-column NA values. By default the following values are interpreted as\n", " NaN: `''`, `'#N/A'`, `'#N/A N/A'`, `'#NA'`, `'-1.#IND'`, `'-1.#QNAN'`, `'-NaN'`, `'-nan'`, `'1.#IND'`, `'1.#QNAN'`, `'N/A'`, `'NA'`, `'NULL'`, `'NaN'`, `'nan'`.\n", " keep_default_na : bool, default True\n", " If na_values are specified and keep_default_na is False the default NaN\n", " values are overridden, otherwise they're appended to.\n", " na_filter : boolean, default True\n", " Detect missing value markers (empty strings and the value of na_values). In\n", " data without any NAs, passing na_filter=False can improve the performance\n", " of reading a large file\n", " verbose : boolean, default False\n", " Indicate number of NA values placed in non-numeric columns\n", " skip_blank_lines : boolean, default True\n", " If True, skip over blank lines rather than interpreting as NaN values\n", " parse_dates : boolean or list of ints or names or list of lists or dict, default False\n", " \n", " * boolean. If True -> try parsing the index.\n", " * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n", " each as a separate date column.\n", " * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as\n", " a single date column.\n", " * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result\n", " 'foo'\n", " \n", " Note: A fast-path exists for iso8601-formatted dates.\n", " infer_datetime_format : boolean, default False\n", " If True and parse_dates is enabled for a column, attempt to infer\n", " the datetime format to speed up the processing\n", " keep_date_col : boolean, default False\n", " If True and parse_dates specifies combining multiple columns then\n", " keep the original columns.\n", " date_parser : function, default None\n", " Function to use for converting a sequence of string columns to an array of\n", " datetime instances. The default uses ``dateutil.parser.parser`` to do the\n", " conversion. Pandas will try to call date_parser in three different ways,\n", " advancing to the next if an exception occurs: 1) Pass one or more arrays\n", " (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the\n", " string values from the columns defined by parse_dates into a single array\n", " and pass that; and 3) call date_parser once for each row using one or more\n", " strings (corresponding to the columns defined by parse_dates) as arguments.\n", " dayfirst : boolean, default False\n", " DD/MM format dates, international and European format\n", " iterator : boolean, default False\n", " Return TextFileReader object for iteration or getting chunks with\n", " ``get_chunk()``.\n", " chunksize : int, default None\n", " Return TextFileReader object for iteration. `See IO Tools docs for more\n", " information\n", " `_ on\n", " ``iterator`` and ``chunksize``.\n", " compression : {'infer', 'gzip', 'bz2', None}, default 'infer'\n", " For on-the-fly decompression of on-disk data. If 'infer', then use gzip or\n", " bz2 if filepath_or_buffer is a string ending in '.gz' or '.bz2',\n", " respectively, and no decompression otherwise. Set to None for no\n", " decompression.\n", " thousands : str, default None\n", " Thousands separator\n", " decimal : str, default '.'\n", " Character to recognize as decimal point (e.g. use ',' for European data).\n", " lineterminator : str (length 1), default None\n", " Character to break file into lines. Only valid with C parser.\n", " quotechar : str (length 1), optional\n", " The character used to denote the start and end of a quoted item. Quoted\n", " items can include the delimiter and it will be ignored.\n", " quoting : int or csv.QUOTE_* instance, default None\n", " Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n", " QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n", " Default (None) results in QUOTE_MINIMAL behavior.\n", " escapechar : str (length 1), default None\n", " One-character string used to escape delimiter when quoting is QUOTE_NONE.\n", " comment : str, default None\n", " Indicates remainder of line should not be parsed. If found at the beginning\n", " of a line, the line will be ignored altogether. This parameter must be a\n", " single character. Like empty lines (as long as ``skip_blank_lines=True``),\n", " fully commented lines are ignored by the parameter `header` but not by\n", " `skiprows`. For example, if comment='#', parsing '#empty\\na,b,c\\n1,2,3'\n", " with `header=0` will result in 'a,b,c' being\n", " treated as the header.\n", " encoding : str, default None\n", " Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n", " standard encodings\n", " `_\n", " dialect : str or csv.Dialect instance, default None\n", " If None defaults to Excel dialect. Ignored if sep longer than 1 char\n", " See csv.Dialect documentation for more details\n", " tupleize_cols : boolean, default False\n", " Leave a list of tuples on columns as is (default is to convert to\n", " a Multi Index on the columns)\n", " error_bad_lines : boolean, default True\n", " Lines with too many fields (e.g. a csv line with too many commas) will by\n", " default cause an exception to be raised, and no DataFrame will be returned.\n", " If False, then these \"bad lines\" will dropped from the DataFrame that is\n", " returned. (Only valid with C parser)\n", " warn_bad_lines : boolean, default True\n", " If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n", " \"bad line\" will be output. (Only valid with C parser).\n", " \n", " Returns\n", " -------\n", " result : DataFrame or TextParser\n", "\n" ] } ], "source": [ "help(pd.read_csv)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:26:33.625857Z", "start_time": "2021-05-14T07:26:33.614527Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " a b c\n", "0 0 0 0\n", "1 1 1 1\n", "2 2 4 8\n", "3 3 9 27\n", "4 4 16 64" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data_write_to_file1.txt', \n", " sep = '\\t', names = ['a', 'b', 'c'])\n", "df[:5]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 保存中间步骤产生的字典数据" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:29:01.225030Z", "start_time": "2021-05-14T07:29:01.219271Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import json\n", "data_dict = {'a':1, 'b':2, 'c':3}\n", "with open('./data/save_dict.json', 'w') as f:\n", " json.dump(data_dict, f)" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T07:29:06.950639Z", "start_time": "2021-05-14T07:29:06.946670Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "{'a': 1, 'b': 2, 'c': 3}" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dd = json.load(open(\"./data/save_dict.json\"))\n", "dd" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## 重新读入json" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 保存中间步骤产生的列表数据" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T07:11:56.415515Z", "start_time": "2018-04-27T07:11:56.411564Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "data_list = list(range(10))\n", "with open('../data/save_list.json', 'w') as f:\n", " json.dump(data_list, f)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "ExecuteTime": { "end_time": "2018-04-27T07:12:32.842754Z", "start_time": "2018-04-27T07:12:32.837820Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dl = json.load(open(\"../data/save_list.json\"))\n", "dl" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 使用matplotlib绘图" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:31:51.190898Z", "start_time": "2019-06-07T08:31:50.419496Z" }, "code_folding": [ 0 ], "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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ImiNptaR7B22fKukBSSsknTXcGBHx04g4CTgZeH+W9Vr2qq2ztnZtMmPtYx+Db3wD9toLfvnLvKs1s8EKGTbAXGBq5QZJo4DLgEOBScAxkiZJ2kvSzwa9dqj46Dnp56xLbbklXHopLFoEo0fDwQfDzJnw+ON5V2ZmAwoZNhGxCFg7aPMUYEVEPBgRLwDXAYdHxD0RMW3Qa7USFwI/j4g7hzqOpFmS+iT1rVmzJttfyjL3trclC3uefTZcdVVyc+hPfpJ3VWYGBQ2bKnYEKhcu6U+3VXMq8C7gaEknD7VDRMyOiN6I6B0zZkzrKrXcbLIJnHce3HFHsrrAkUcm27z6gFm+Oils6hIRF0fEfhFxckR8q9p+Xq6mO+27bxI4551XfWq0Vx8wa59OCpuHgZ0qfh6fbmuKl6vpXhttlJxSM7P8dVLYLAF2k7SLpNHADKDpR225szEzy14hw0bStcBiYKKkfkkzI2IdcAqwELgfuCEiljV7LHc25fbAA3lXYFYOG+ZdwFAi4pgq2xcAC1p5LEnTgek9PT2tHNY6xOTJ8IUvwBlnJKfdzCwbhexs2smdTfertvrAmDHJw9nOPjt5jMFdd7W3LrMyKX3Y+JpN96u2+sDq1TBvXvJ65BHYf3/47Gfhuefyrtis+yj8kHcAent7o6+vL+8yLCdr1yan0ubOTR7ctn79q/cZOzYJLjN7maSlEdE70n7ubNzZGLDttnDllbBw4dBBA74vx6wZpQ8bX7OxSgd7fXCzTJQ+bMzMLHsOG7M6zJuXdwVmnan0YeNrNlaP974XjjoKHn0070rMOkvpw8bXbGywavfljB0LF1wAN9+cPL5gq628mrRZrepeQUDSu4F3AwcCE4DtgWeB1cAfgd8A8yOi6UUyzfIw0vTm97wHTjwRbrll6Pc9a83s1Wq6z0bSZsAngI+QBIzSt54jecjZpsDWFdvXATcBX42IxS2uORO+z8bq8dJLyf041fj2NSuLlt1nI+nDwHLgPJIO5osknc3WEbFZRIyPiO1IuqRJwIeBH5E8vvlWSddLmtD4r5ItX7OxRmxQ+hPQZvUZsbOR9BLwU+D8iFhS88DSa4DjgLOAb0fEl5opNGvubKxeUvX3XnjBC3taObRyBYHeiDiynqABiIi/R8QlwOuBG+r5rFmn6+2FpUvzrsKsOEYMm4i4s/JnScOcqR7y889FxJ/rLcys6KrNWtt6a1izJllJeostPGPNDBqb+vw/Wl6FWQeqtpr044/DfffB8cfD008P/VnPWLOyaSRsTpf0npF2krRtA2ObdYWtt4bvfCfvKsyKo5GwuQmYI2nXajtIej3JY50Lz7PRzMyy10jYHAc8CfxI0saD35Q0hSRodm6ytrbwCgKWl8cey7sCs/apO2wi4gng/ST31Hyz8j1JR5CsILAhMLUVBZp1q0mTktNtnkBgZdDQrWkRcTtwNnC8pBMAJJ0G/BBYA7w1In7XqiLNOlW1GWvbbQc77wzVzt56AoF1mxHXRpM0A+iLiBWV2yPia5LeDlwq6SDgQ8BSYFpE+D8VM4ZfZ23dOt/4aeVRy0KcPwBC0t+Bu0gCZeB1AnAn8EGSiQPHRMQzGdVq1lU2rHsZXLPOVcvX/Qxg3/R1IHAQMLDGzT/S19+AecDOkv4ctazumSFJbwROI1mR+tcRcXme9Zg1Yty4oU+njR078srUZkVT06rP/9w5Wf15b5Lg2S/95xtJQmtgoGeBe4A7I+LjdRckzQGmAasjYs+K7VOBbwCjgO9ExAU1jLUBcHVEfHCkfb02muVhuPXVhuNVpa0oal0bra5GPj1Fdlv6GjjQxsBkXhlA+wJTgLrDBpgLXApcXXGMUcBlJKtN9wNLJM0nCZ7zB33+wxGxWtJhwEeBaxqowawtxo4dunvZaqvqkwfMOlHTZ40j4nngjvQFgKSNgL0aHG+RpNcN2jwFWBERD6bjXwccHhHnk3RBQ40zH5gv6WaS606vImkWMAtgwoTCPgXButhwp8Ma7XrMiiiTS5QR8SLJxIFW2RF4qOLnfuCAajuns+OOBDYGFlTbLyJmA7MhOY3WikLNzOzVapn6vGlEPNvMQVoxRj3Se3x+V8u+kqYD03t6erIsyaylhup6PHHAiqyWmzr/r6TThlqaZiSSJku6Efhv9Zf2Cg8DO1X8PD7d1jQvV2NFVe2G0Gp8I6gVWS1hsxD4GvCopMslvUPSptV2lrSrpI9KWkxyKm0y8Nsm61wC7CZpF0mjgRnA/CbHBLwQpxVXtUcYmHWimqY+S9ofOBd4Z7ppPXA/8CjwOLAJsB0wkeTeFgGrgYuAr6eTCGorSLqW5F6e7YFVwL9HxHcl/Ws63ihgTkScW+uYtfDUZ+sU9U4c8Ok1y1KtU5/rvc9mN2AmSejsTfIXf6U1wCLgR8CP0okChVZxzeak5cuX512O2YgamaXmjsiykknYDDrAZiSzxLYjuZFzdUQ82tBgBeDOxjqFw8aKJJObOiulN3guT18dy7PRrNNUuxHUrMgaesSApPGS/kPSzyQtlHSFpKPTi/cdxbPRrNM0MnHAz8uxvNXd2aQ3TN5MMimgsqH/MPCfkk6PiBtbU1723NlYGbkzsnZrpLO5ENiUZP2yyST3vPxLun0L4MeSPt2yCjPmzsa6Rb335Zi1UyNhMxn4ZUScFhH3RMQjEXFbRHwW2B34JXCBpHcOP4yZtdJQp9eG41Nr1k6NhM1zVFn3LCIeB44iuf/mM03UZWZt5lNrlqVGwuZPwBuqvRkRTwM3Am9utKh28goCZmbZayRsvgn8m6S3DLPP87z8MLVC8zUb62b1XsfxaTXLSiNh8xZgBbBA0rGD30xv9jwM+H2TtZlZk+q9jlPJp9WslRq5qfPUin+/WtK5JIt1/hXYGjg6fe8TzZVmZmbdopGw+S+88vHPk4CT0veC5N6bPwKfknQXcBdwT0S80Hy5ref7bKxs6lmBYPDSOF7U0xrV8Npo/xwgWTVgL14On/3SnweefxPAOuDPETG5qYNlyGujWVnVu9aa11mzSpmvjTYg7ViWpq+Bg48C9uCVAfSmZo9lZvmrDCd3OlarpsNmKBGxnmSK9J+AuQBSI2vVmlnWmlnY05MIrFaZhM1QotnzdWaWiaE6E/+vobVaQ6s+m5kN8L05VovSh41XEDB7tUYX9fRpNaum9GHjFQTMXm3wzaD1cJdjQyl92JjZyBrpdNzlWCWHjZmNqNFOx9dzbIDDxszawp1OuTlszKxujU4gcJdTXl0bNpI2l9QnaVretZh1m8rTao1wl1M+hQsbSXMkrZZ076DtUyU9IGmFpLNqGOpM4IZsqjSzAe5yrBZtW0GgDnOBS4GrBzaka61dBrwb6AeWSJoPjALOH/T5DwOTgfuATdpQr1mpDV6BoN7VB9zllEPhwiYiFkl63aDNU4AVEfEggKTrgMMj4nzgVafJJB0EbE7y+INnJS2IiJeyrNvMEo2stTYQUF7Ys3sVLmyq2BF4qOLnfuCAajtHxOcAJB0PPFYtaCTNAmYBTJgwoVW1mpVaZVi4y7EBnRI2DYmIuSO8P1vSo8D00aNH79eeqszKw12ODSjcBIEqHgZ2qvh5fLqtaV6uxiw7zcxac5fTXTolbJYAu0naJX0y6AxgfisG9kKcZu3RyKw1z1brHoULG0nXAouBiZL6Jc2MiHXAKcBC4H7ghohY1orjubMxa49Guxx3ON2hcNdsIuKYKtsXAAtafTxJ04HpPT09rR7azKqo91qOr+N0vsJ1Nu3mzsas/ZrpcnxqrTOVPmx8zcYsX358QTmUPmzc2Zjla6DLqZeXu+kspQ8bdzZmxdDMo6gdOsVX+rBxZ2NWDF5JuruVPmzMrHiaWUnaHU4xOWzMrHAGupxGJw/4ek7xlD5sfM3GrLh8aq17lD5sfM3GrDP41FpnK33YmFlnaMWpNYdOfhw2ZtZRGr0vBxw6eSp92PiajVlnavS0Gjh08lD6sPE1G7POVDl5oJkbQq09Sh82Ztb5mjm15u6mPRw2ZtY1PHmguBw2ZtY1PGOtuBw2ZtZ1HDrFU/qw8Ww0s+7VbOg4cFqn9GHj2Whm3a/RR0m7y2md0oeNmZWD78vJl8PGzEqhmVNqA3xqrXEOGzMrlWZDx11OYxw2ZlZKrQgdB07tujJsJB0k6RZJ35J0UN71mFlxrVzpLqcdChc2kuZIWi3p3kHbp0p6QNIKSWeNMEwATwGbAP1Z1Wpm3cFdTvYKFzbAXGBq5QZJo4DLgEOBScAxkiZJ2kvSzwa9dgBuiYhDgTOBL7a5fjPrUL4ZNDsb5l3AYBGxSNLrBm2eAqyIiAcBJF0HHB4R5wPThhnucWDjLOo0s+61cmUSGo2sCj3Q5TR6b0+3KmJnM5QdgYcqfu5Ptw1J0pGSvg1cA1w6zH6zJPVJ6luzZk3LijWzzufVB1qrcJ1NK0TEj4Ef17DfbEmPAtNHjx69X/aVmVmnabTLGTitBklglb3T6ZTO5mFgp4qfx6fbmublasxsJJ5A0LxOCZslwG6SdpE0GpgBzG/FwF6I08xq1ew06TIHTuHCRtK1wGJgoqR+STMjYh1wCrAQuB+4ISKWteJ47mzMrB6esdYYRaPPUu0SkqYD03t6ek5avnx53uWYWQdpdMbagG64liNpaUT0jrRf4TqbdnNnY2aNaua0GpTr1Frpw8bXbMysGZ48UJvSh407GzNrBV/LGV7pw8adjZm1kmesDa30YePOxsxarRVdTrd1OqUPGzOzrHgCwcscNmZmGfKTQROlDxtfszGzdih7l1P6sPE1GzNrl1YETqd2OaUPGzOzdmr2tBp0ZpfjsDEzy0HZbgYtfdj4mo2Z5aksN4OWPmx8zcbMiqDZm0GLHjqlDxszs6Lo5mnSDhszs4LpxmnSDhszswLqtsBx2JiZFVQ3nVYrfdh4NpqZFV03dDmlDxvPRjOzTtDpXU7pw8bMrJN0aug4bMzMOlArTq2NGtW6ekbisDEz61DNdjkvvdS+LsdhY2bW4TphAkFXho2kDSSdK+kSScflXY+ZWdaKflqtcGEjaY6k1ZLuHbR9qqQHJK2QdNYIwxwOjAdeBPqzqtXMrEhacVotK4ULG2AuMLVyg6RRwGXAocAk4BhJkyTtJelng147ABOB2yLiU8BH21y/mVmumu1ysrBh3gUMFhGLJL1u0OYpwIqIeBBA0nXA4RFxPjBt8BiS+oEX0h/XZ1etmVkxrVz58r+PGpVt11KLInY2Q9kReKji5/50WzU/Bg6RdAmwqNpOkmZJ6pPUt2bNmtZUamZWMOvX59/pFK6zaYWIeAaYWcN+syU9CkwfPXr0ftlXZmaWj5Urkxlnq1blc/xO6WweBnaq+Hl8uq1pXq7GzMpiYALBBlX+5q+2vRU6JWyWALtJ2kXSaGAGML8VA3shTjMrm/Xrk9AZ/Fqf4RXuwoWNpGuBxcBESf2SZkbEOuAUYCFwP3BDRCxrxfHc2ZiZZa9w12wi4pgq2xcAC1p9PEnTgek9PT2tHtrMzFKF62zazZ2NmVn2Sh82vmZjZpa90oeNOxszs+wV7ppNuw1cswGekXT/MLtuBVRrf7YHHmt1bW0w3O9U5GM1M1a9n611/1r2G2kff8eKcZwifr9q2Tev79fONe0VEX5FAMxu9H2gL+/6s/idi3qsZsaq97O17l/Lfv6OdcZxivj9qmXfon+/Sn8arcJNTb7fidr5O7XyWM2MVe9na92/lv38HeuM4xTx+1XLvoX+filNNWuCpL6I6M27Dute/o5Zltrx/XJn0xqz8y7Aup6/Y5alzL9f7mzMzCxz7mzMzCxzDhszM8ucw8bMzDLnsMmApM0lXSXpCknH5l2PdRdJu0r6rqR5eddi3UnSEenfX9dLOrgVYzpsaiRpjqTVku4dtH2qpAckrZB0Vrr5SGBeRJwEHNb2Yq3j1PP9iogHI2LEJ9GaVarzO/bT9O+vk4H3t+L4DpvazQWmVm6QNAq4DDgUmAQcI2kSyZNEH0p3y/BxRNZF5lL798usEXOp/zt2Tvp+0xw2NYqIRcDaQZunACvS/9N8AbgOOBzoJwkc8J+x1aDO75dZ3er5jilxIfDziLizFcf3X4TN2ZGXOxhIQmZH4MfAUZIupztpbuvvAAACW0lEQVSXILH2GPL7JWk7Sd8C9pF0dj6lWZeo9nfYqcC7gKMlndyKA5V+1ecsRMTTwAl512HdKSL+RnIu3SwTEXExcHErx3Rn05yHgZ0qfh6fbjNrBX+/LGtt+445bJqzBNhN0i6SRgMzgPk512Tdw98vy1rbvmMOmxpJuhZYDEyU1C9pZkSsA04BFgL3AzdExLI867TO5O+XZS3v75gX4jQzs8y5szEzs8w5bMzMLHMOGzMzy5zDxszMMuewMTOzzDlszMwscw4bswKS9ElJIekDeddi1goOG7Ni2i/9Z1+uVZi1iG/qNCsgSfeRrL67dfg/UusC7mzMCkbS5sBE4C4HjXULh41Z8exN8t/m0sqNkraRdGN6LeciSRvlU55Z/fw8G7Pi2Tf95z/DRtIBwPXANsB7I2JeHoWZNcqdjVnxDEwOWAog6VPALcDfgV4HjXUidzZmxbMv8A/gMUk3AocBVwEfjYhnc63MrEGejWZWIJI2IQma1cA6YAfg1Ij4Tq6FmTXJp9HMimUyyRmHjYEJwDwHjXUDh41ZsQxMDjgD+BnwQUmn51iPWUs4bMyKpXLlgBkkkwT+p6Qj8yvJrHm+ZmNWIJLuBN4AbBkR6yWNA/5Acu3mv0bEH3It0KxB7mzMCkLSaGBP4J6IWA8QESuBfwWeB26S1JNjiWYNc9iYFcdewEbAXZUbI+I+4EhgK+DnkrbPoTazpvg0mpmZZc6djZmZZc5hY2ZmmXPYmJlZ5hw2ZmaWOYeNmZllzmFjZmaZc9iYmVnmHDZmZpY5h42ZmWXOYWNmZpn7/08ylgZeehmMAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "x = range(1, 100)\n", "y = [i**-3 for i in x]\n", "plt.plot(x, y, 'b-s')\n", "plt.ylabel('$p(k)$', fontsize = 20)\n", "plt.xlabel('$k$', fontsize = 20)\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.title('Degree Distribution')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:32:20.805797Z", "start_time": "2019-06-07T08:32:20.592198Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "# red dashes, blue squares and green triangles\n", "t = np.arange(0., 5., 0.2)\n", "plt.plot(t, t, 'r--')\n", "plt.plot(t, t**2, 'bs')\n", "plt.plot(t, t**3, 'g^')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:33:26.540992Z", "start_time": "2019-06-07T08:33:26.292284Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# red dashes, blue squares and green triangles\n", "t = np.arange(0., 5., 0.2)\n", "plt.plot(t, t**2, 'b-s', label = '1')\n", "plt.plot(t, t**2.5, 'r-o', label = '2')\n", "plt.plot(t, t**3, 'g-^', label = '3')\n", "plt.annotate(r'$\\alpha = 3$', xy=(3.5, 40), xytext=(2, 80),\n", " arrowprops=dict(facecolor='black', shrink=0.05),\n", " fontsize = 20)\n", "plt.ylabel('$f(t)$', fontsize = 20)\n", "plt.xlabel('$t$', fontsize = 20)\n", "plt.legend(loc=2,numpoints=1,fontsize=10)\n", "plt.show()\n", "# plt.savefig('/Users/chengjun/GitHub/cjc/figure/save_figure.png',\n", "# dpi = 300, bbox_inches=\"tight\",transparent = True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "plt.figure(figsize=(5,5))\n", "sns.set(style=\"whitegrid\")" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:34:00.646076Z", "start_time": "2019-06-07T08:34:00.215086Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1)\n", "plt.subplot(221)\n", "plt.plot(t, t, 'r--')\n", "plt.text(2, 0.8*np.max(t), r'$\\alpha = 1$', fontsize = 20)\n", "plt.subplot(222)\n", "plt.plot(t, t**2, 'bs')\n", "plt.text(2, 0.8*np.max(t**2), r'$\\alpha = 2$', fontsize = 20)\n", "plt.subplot(223)\n", "plt.plot(t, t**3, 'g^')\n", "plt.text(2, 0.8*np.max(t**3), r'$\\alpha = 3$', fontsize = 20)\n", "plt.subplot(224)\n", "plt.plot(t, t**4, 'r-o')\n", "plt.text(2, 0.8*np.max(t**4), r'$\\alpha = 4$', fontsize = 20)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def f(t):\n", " return np.exp(-t) * np.cos(2*np.pi*t)\n", "\n", "t1 = np.arange(0.0, 5.0, 0.1)\n", "t2 = np.arange(0.0, 5.0, 0.02)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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QwocKP+uAARnF3jG4/b0oi4Iuy91OaZ/XzQuF0xer0uRzU+F3BCYUev1w0VU+8DJwbaHX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\n2LPH5PWYPj12HYuxwpEjULO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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1)\n", "plt.subplot(211)\n", "plt.plot(t1, f(t1), 'bo')\n", "plt.plot(t2, f(t2), 'k')\n", "\n", "plt.subplot(212)\n", "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-05-13T09:23:13.850060", "start_time": "2017-05-13T09:23:13.255032" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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Kbnk5ObzYvTtZR4+WrxwR5BxU2NZBVfc+2DBh0qguqQHgwLEDHDh2wPX15555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\nBaVFXVPnpXJe9/NivtNttMRqh9R47S9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.gridspec as gridspec\n", "import numpy as np\n", "\n", "t = np.arange(0., 5., 0.2)\n", "\n", "gs = gridspec.GridSpec(3, 3)\n", "ax1 = plt.subplot(gs[0, :])\n", "plt.plot(t, t**2, 'b-s')\n", "ax2 = plt.subplot(gs[1,:-1])\n", "plt.plot(t, t**2, 'g-s')\n", "ax3 = plt.subplot(gs[1:, -1])\n", "plt.plot(t, t**2, 'r-o')\n", "ax4 = plt.subplot(gs[-1,0])\n", "plt.plot(t, t**2, 'g-^')\n", "ax5 = plt.subplot(gs[-1,1])\n", "plt.plot(t, t**2, 'b-<')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:35:14.708956Z", "start_time": "2019-06-07T08:35:14.695191Z" }, "code_folding": [ 1 ], "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "\n", "def OLSRegressPlot(x,y,col,xlab,ylab):\n", " xx = sm.add_constant(x, prepend=True)\n", " res = sm.OLS(y,xx).fit()\n", " constant, beta = res.params\n", " r2 = res.rsquared\n", " lab = r'$\\beta = %.2f, \\,R^2 = %.2f$' %(beta,r2)\n", " plt.scatter(x,y,s=60,facecolors='none', edgecolors=col)\n", " plt.plot(x,constant + x*beta,\"red\",label=lab)\n", " plt.legend(loc = 'upper left',fontsize=16)\n", " plt.xlabel(xlab,fontsize=26)\n", " plt.ylabel(ylab,fontsize=26)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:35:29.415424Z", "start_time": "2019-06-07T08:35:29.157757Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.random.randn(50)\n", "y = np.random.randn(50) + 3*x\n", "pearsonr(x, y)\n", "fig = plt.figure(figsize=(10, 4),facecolor='white')\n", "OLSRegressPlot(x,y,'RoyalBlue',r'$x$',r'$y$')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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qFI8//jjV1XKWv7Byly9DSgrY2MDUqWqnsViZwR7oNBrYuRMuXlQ7juggdqYW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\noLYWbG3VTiWukRFFhGjM1q1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(7, 4),facecolor='white')\n", "data = norm.rvs(10.0, 2.5, size=5000)\n", "mu, std = norm.fit(data)\n", "plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')\n", "xmin, xmax = plt.xlim()\n", "x = np.linspace(xmin, xmax, 100)\n", "p = norm.pdf(x, mu, std)\n", "plt.plot(x, p, 'r', linewidth=2)\n", "title = r\"$\\mu = %.2f, \\, \\sigma = %.2f$\" % (mu, std)\n", "plt.title(title,size=16)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T08:36:49.566655Z", "start_time": "2019-06-07T08:36:49.550926Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " a b c\n", "0 0 0 0\n", "1 1 1 1\n", "2 2 4 8\n", "3 3 9 27\n", "4 4 16 64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('../data/data_write_to_file.txt', sep = '\\t', names = ['a', 'b', 'c'])\n", "df[:5]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-05-11T22:21:09.150592", "start_time": "2017-05-11T22:21:08.103623" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot.line()\n", "plt.yscale('log')\n", "plt.ylabel('$values$', fontsize = 20)\n", "plt.xlabel('$index$', fontsize = 20)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-05-11T22:32:42.105831", "start_time": "2017-05-11T22:32:41.789998" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot.scatter(x='a', y='b')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-05-11T22:26:59.893837", "start_time": "2017-05-11T22:26:59.528857" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot.hexbin(x='a', y='b', gridsize=25)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-05-11T22:28:39.025521", "start_time": "2017-05-11T22:28:38.630099" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['a'].plot.kde()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-05-11T22:37:06.791001", "start_time": "2017-05-11T22:37:06.226303" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: \n", "The default value for 'return_type' will change to 'axes' in a future release.\n", " To use the future behavior now, set return_type='axes'.\n", " To keep the previous behavior and silence this warning, set return_type='dict'.\n", " if __name__ == '__main__':\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bp = df.boxplot()\n", "plt.yscale('log')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2017-05-11T22:39:16.120016", "start_time": "2017-05-11T22:39:15.913844" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot.hist(stacked=True, bins=20)\n", "# plt.yscale('log')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "> To be a programmer is to develop a carefully managed relationship with error. There's no getting around it. You either make your accommodations with failure, or the work will become intolerable.\n", "\n", "Ellen Ullman （an American computer programmer and author）\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## This is the end.\n", "> Thank you for your attention." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernel_info": { "name": "python3" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nteract": { "version": "0.15.0" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "48px", "left": "1263px", "top": "0px", "width": "159px" }, "toc_section_display": false, "toc_window_display": false }, "toc_position": { "height": "339px", "left": "1245.99px", "right": "20px", "top": "120px", "width": "196px" } }, "nbformat": 4, "nbformat_minor": 1 }