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rfinn/capella
matchNSAtoAGC.ipynb
1
19591
{ "metadata": { "name": "", "signature": "sha256:bf5e6f81415adf5b2a6d8118f874313ca1b31599ceff9832a7b3b968d7bf7cd3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this program is to match the NSA galaxies to the AGC catalogs.\n", "\n", "Ideally, there will be a one-to-one match, but of course, nothing is ideal...\n", "\n", "So what do we do with galaxies that don't have an NSA match? We will need to \n", "get their SDSS colors another way so that we can properly separate them into \n", "blue and red, and so we can estimate their stellar masses." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import pyplot as plt\n", "import numpy as np\n", "from astropy.io import fits\n", "import glob\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "def findnearest(x1,y1,x2,y2,delta):#use where command\n", " matchflag=1\n", " nmatch=0\n", " d=np.sqrt((x1-x2)**2 + (y1-y2)**2)#x2 and y2 are arrays\n", " index=np.arange(len(d))\n", " t=index[d<delta]\n", " matches=t\n", " if len(matches) > 0:\n", " nmatch=len(matches)\n", " if nmatch > 1:\n", " imatch=index[(d == min(d[t]))]\n", " else:\n", " imatch=matches[0]\n", " else:\n", " imatch = 0\n", " matchflag = 0\n", "\n", " return imatch, matchflag,nmatch" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def compare_samples(x1,y1,x2,y2):\n", " plt.figure()\n", " plt.plot(x1,y1,'bo')\n", " plt.plot(x2,y2,'ks')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# match radiuss = 3 arcsec\n", "matchradius=3./3600 # convert to degrees\n", "\n", " \n", "clusterfiles=glob.glob('*_AGC.fits')\n", "for cfile in clusterfiles:\n", " agcdat=fits.getdata(cfile)\n", " name=cfile.split('_')[0]\n", " nsafile=name+'_NSA.fits'\n", " print nsafile\n", " nsadat=fits.getdata(nsafile)\n", " imatch=np.zeros(len(agcdat.RA),'i')\n", " matchflag=np.zeros(len(agcdat.RA),'bool')\n", " nmatch=np.zeros(len(agcdat.RA),'i')\n", " for i in range(len(agcdat.RA)):\n", " imatch[i],matchflag[i],nmatch[i] = findnearest(agcdat.RA[i],agcdat.DEC[i],nsadat.RA,nsadat.DEC,matchradius)\n", " \n", " outfile=name+'_matchNSAtoAGC.fits'\n", " \n", " orig_cols = agcdat.columns\n", " new_cols = []\n", " for col in nsadat.columns.names:\n", " print col\n", " base=nsadat[col]\n", " print base.shape,matchflag.shape\n", " a=nsadat[col][imatch]*matchflag + np.zeros(len(nsadat[col]))*matchflag\n", " #new_cols.append(a)\n", " #print a.shape\n", " \n", " t=fits.Column(name=col, array=a)\n", " new_cols.append(t)\n", " fits.ColDefs([new_cols])\n", " hdu = fits.BinTableHDU.from_columns(orig_cols + new_cols)\n", " \n", " hdu.writeto(outfile,clobber='yes')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Abell2063_NSA.fits\n", "IAUNAME" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(949,) (992,)\n" ] }, { "ename": "ValueError", "evalue": "Can only multiply by integers", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-fc8cfcdc6f1c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnsadat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmatchflag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnsadat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimatch\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmatchflag\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnsadat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmatchflag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;31m#new_cols.append(a)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;31m#print a.shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rfinn/Ureka/variants/common/lib/python2.7/site-packages/numpy/core/defchararray.pyc\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, i)\u001b[0m\n\u001b[1;32m 1954\u001b[0m \u001b[0mmultiply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1955\u001b[0m \"\"\"\n\u001b[0;32m-> 1956\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1957\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1958\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__rmul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rfinn/Ureka/variants/common/lib/python2.7/site-packages/numpy/core/defchararray.pyc\u001b[0m in \u001b[0;36mmultiply\u001b[0;34m(a, i)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0mi_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only multiply by integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 311\u001b[0m \u001b[0mout_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_num_chars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_arr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m return _vec_string(\n", "\u001b[0;31mValueError\u001b[0m: Can only multiply by integers" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure()\n", "plt.hist(agcdat.VOPT,color='b',bins=np.arange(0,12000,500),label='AGC')\n", "plt.hist(nsadat.ZDIST*3.e5,color='g',bins=np.arange(0,12000,500),label='NSA')\n", "plt.xlabel('Recession Velocity')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "<matplotlib.text.Text at 0x109ecff50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109ecf310>" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
palandatarxcom/sklearn_tutorial_cn
notebooks/02.1-Machine-Learning-Intro.ipynb
1
238658
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "这个分析笔记由[Jake Vanderplas](http://www.vanderplas.com)编辑汇总。 源代码和license文件在[GitHub](https://github.com/jakevdp/sklearn_tutorial/)。 中文翻译由[派兰数据](http://datarx.cn)在[派兰大数据分析平台](http://www.palandata.com)上完成。 源代码在[GitHub](https://github.com/palandatarxcom/sklearn_tutorial_cn)上。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Scikit-learn简介: 基于Python的机器学习\n", "\n", "在本节中会介绍Scikit-learn的基本原理,它是一个集成了很多机器学习工具并被广泛使用的包,用Python实现。详情请参考http://scikit-learn.org 。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 概述\n", "\n", "**主要目标**:介绍机器学习的中心思想以及它们是怎样通过Scikit-learn集成进Python的。\n", "\n", "* 机器学习的定义\n", "* Scikit-learn中的数据表示\n", "* Scikit-learn的API的介绍" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 关于Scikit-Learn\n", "\n", "[Scikit-Learn](http://github.com/scikit-learn/scikit-learn)是一个采用**简洁并灵活的 API**,向用户提供**广为人知**的机器学习算法的Python包。它由上百个贡献者所开发,并且已经广泛运用至工业界和学术界中。\n", "\n", "Scikit-Learn依赖于Python的 [NumPy (Numerical Python)](http://numpy.org) 和 [SciPy (Scientific Python)](http://scipy.org)库,它们为Python中高效的数值和科学计算提供了支持。scikit-learn本身并不是为极大的数据集量身定做的,但是也有[一些工作](https://github.com/ogrisel/parallel_ml_tutorial)是基于此的。\n", "\n", "在这个教程中,我将会主要关注于Scikit-learn中,运用于中小型数据集的问题。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 什么是机器学习?\n", "\n", "在这一节里面我们会去探索机器学习的本质。机器学习是一种构造程序的过程,让机器根据**已有的数据**,自动适应和调整程序的**可变参数**(一般来说是一个浮点数的列表)以提高程序的某种行为。\n", "\n", "机器学习可以看成是**人工智能**的一个分支。这些算法可以让电脑变得更加智能,从某种程度上电脑会自动**生成**数据,而不是仅仅像一个数据库一样进行数据存储和数据获取。\n", "\n", "我们在这里会举两个特别简单的关于机器学习的小任务。第一个是一个**分类**的任务:从图片上我们可以看出有一组两维的数据,根据类别分成了两种颜色。这个分类的算法可以在两类数据之间画出分割线:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# 设置 seaborn 绘图库的默认参数.\n", "# 可以安全地注释掉\n", "import seaborn; seaborn.set()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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CS85FolJxzxEiooQF8YABA7BrVxms1jRUVByMGHrPP/9iIIiXLXsW+/fvDWmj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\nMex77nVwVu7z13uYNiMXfc++pss2riD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"text/plain": [ "<matplotlib.figure.Figure at 0x10b8c1c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 导入样例\n", "import matplotlib.pyplot as plt\n", "from fig_code import plot_sgd_separator\n", "\n", "plot_sgd_separator()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这些可能看起来是很小的任务,但是它体现了一个非常重要的概念。通过画出分割线,我们已经学习了一个可以**生成**新数据的模型。如果您往这张图上添加一个没有被分类的点,这个算法现在可以**预测**它应是一个红色的点还是一个蓝色的点。\n", "\n", "如果你希望看到生成这个的源代码,你也可以在`fig_code`文件夹中打开代码,或者你可以用`%load`命令加载这段代码。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下一个简单的例子我们看一个**回归**的算法,为一组数据拟合一条最佳的直线。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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2g104F46UykhYTWbERSrQqP9pcMdGhsBqMos+Z4YOMz7+rhzfn7EXzbImJODn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\nvdKMgsIbRbNf/iwd92X0XDT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"text/plain": [ "<matplotlib.figure.Figure at 0x115107ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from fig_code import plot_linear_regression\n", "plot_linear_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这也是一个从数据中建立模型的例子,所以这个模型可以被用来生成新的数据。这个模型从训练数据中被**学习**出来,而且可以用来预测测试数据的结果:我们给出一个点的x坐标值,这个模型可以让我们去预测对应的y坐标值。同样的,这看起来是一个简单的例子,但是它是机器学习算法的一个基础的操作。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scikit-learn中的数据表示\n", "\n", "机器学习是从数据中建立模型的,我们将会从怎样让用电脑理解的方式去表示数据开始。同时,我们会用matplotlib的例子讲解如何将数据用图表的形式显示出来。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在Scikit-learn中,大多数的机器学习算法的数据在二维的数组或者矩阵中存储。这些数据可能是``numpy``数组,在某些情况下也可能是``scipy.sparse``矩阵。数组的大小应该是`[样本数,特征数]` (【译者注】sample - 样本,feature - 特征)\n", "\n", "- **样本数(n_sample):** 样本的数目。每一个样本都是一个需要处理的独立个体(例如:需要被分类),一个样本可能是一个文档、一幅图片、一段音频、一段视频、一个天文学数据、数据库或者CSV文件中的一行,或者任意一个确定的数值的集合。\n", "- **特征数(n_feature):** 特征的数目,特征是描述一个样本的数值表达。特征一般是实数,不过在某些情况下也会是布尔值或者是离散数据。\n", "\n", "特征数必须提前确定。但是对于给定的样本,特征可以是很大(百万级)的一个零占大多数的集合。这种情况下,`scipy.sparse`矩阵就派上了用场,用这个矩阵比numpy矩阵在存储上会更加高效。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Data Layout](images/data-layout.png)\n", "\n", "(图片来自 [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook))\n", "!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 一个简单的例子:Iris 数据集\n", "\n", "作为简单数据集的例子,我们将会介绍scikit-learn中存储的iris数据集。数据由3种不同品种的鸢尾花组成。下面是数据集中的3个品种,我们可以通过下面的代码显示出它们:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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T3Mjj93NOUgsM9KrBuaeCSa+sR0s3zpAuFQ25wNucnvVSfRrqHJKhh7Vt2lt5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\nR+NS3ibZDjvXJWjaVwZScYNRMasSDjNQMKhEn//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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Iris Setosa\n", "\n" ] }, { "data": { "image/jpeg": 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HB+4waiiwuCwBIJUsfQdQfp/Sjsx2tGlAe5ooJxtLq3KKPPjz35FAfFd1zMFI\nBiVWDA8lsknH0xQWVZFkaW1boSMY6j+9Q3N4bm32zwgTMBvdTwcDAO09CABQDh+60MuJNlFoybpb\nbaciRZpAB2BRRz9W/eiNrppdWV5BvkBDKF4AJyRn5cfKqumTQrCiq4ZwgUlhggAAAftn51dfULax\nia4up44YEyxZ2Az8vU+1SSWN+KVnPAGisv8AxM16QPNpFq5WaQn81tP6VxgR/MjO72OO5ATNKswu\nCRV7WZxq+v31+kZjS5maQK3VQTxn3qYAQxYHHFcT1LKfkSm+BoLKleXuJKjvZhDEQD0pN1G5NxOQ\nDkA0T1u9yGRTzQa3Qs2TSJHaEKrVmFNq800+HfBOua8EltbQw2rAsLm4ykZHqvGT9Aa0r8KPBumx\naFbazqECXV7dAvGJkDLCoYgFVPBJwDu9CMdydG3/AM5iDkYGT71s4fRw9ofKf+E7HjircVlum/hG\nywMl3rCB3wT5VvuHQcAkj1POKqn8GN2o2zyasJLAOPPRYSkjLnkK2SPTr7/KtXEpzkEDArxl2wrk\njJ6muhhhELeyPQTAAaKCz6//AArspwRNqdysaFliCxrkAgY3epHPTGfavzL4o32upXVlIQZLeZ4X\nK9CysVOPqK/al9qMFtY3FxdMEt4o2kdv9KqCSfoAT9K/DmsXp1XWb29ZBGbqeSYoDkLuYnGfbOKz\nc+FjAHVtLzNGiutPi3MDimiyjwBx2oPpkPTimG1TAGK5jKkspFxsq1GoGPWiOnD+eBVJeBxVvTT/\nAOoFZ3JUN2U4Wq5gwaG3cf8AOPFF7QfyPpQ65X+cao4WUxVpGtm4wakYlWypxiubZCCeKlIBJPen\nYx3ypcBd22pSROAx4B60xWWqhlG49qS5fhkIFXLdm+HaSKalPaVa0/290srKFIOTXFy26diKDaFI\n7TgsThRmikjAK7N6E0uHB50pCpaVcXNrqb3NlPLBLkjdGxUkehx1HseKetJ8ZXj31tbX7wusjhPN\nK7SCeBnGB1x2FZ/bzLFGWJ5PNL2v6yyNiNyHB4YHBB9a0sPKkx3Nomr2PCsyQtOiv1JZkSAHlz6n\n/eKsSfCuQmPtS14G1Yat4X07UJZVDzQhpMEABhwf3Bq7q3iLRtNYx3upW0MgAYq0wJAIJHwg55AJ\nHHIBPauyc4D3E6Ws3Y0rsEQkLFiThj1PyP1qb8qmP0g/WllvHvhe2Vw+rKCpy4MMmAOef09ODz0q\nST8RPDkLMBdTyFZPJYR27gBsAkZIAJwc9/uDQnZLBvuCM1pA4R6S1QjG0Y+VUp7JcZjLRt6g0Ot/\nxG8K3LqovZoS23/Ft5FHIyOduOhB9hzxRsanp7sVF1GG6YcMuPqQBXhmsbVuCt3Bp92kuX8UoVBI\npcRyLIrA4KkEEEfUUuXepS6RrNlfhlWzJMFwik8xgHAOe4HPzB+uhzwRTxCSNldGyA6EMD64I4NK\nXiPTQ1vMrj4XUqy84ZSMEHHajPEc9EacOCjtDZBQRO8Q4iycvBJ5bH1AHBP0xUBcC2Z8AkElgO4y\ncmuba+W+0kOrhZogscqsehAyD8jgkH5jtQu5uSkEkZOGLYHPc1owguaPlJOBaSCp4pSqx4BGQxGP\nQ9P61GriVEAAPGT/AN6pfmEw5UbmUbUXPOaL6Fp+6Bd4yvJPPBOecY+tFkc1gtyhjS80F7T4XuiS\niFYgeOOT6miMnh6zu5Vlu7RbhwNo81ywA9lzgfPGaMQwLDGCAFQDlugH9qkjuYWOElgOOoEi5H71\nmy5DH6NI/YwaNFDYvD2nIAF0jT/n5Cn98VONB0pY3RtHsSrAgk26McEY4JGR9MUUimRwShD7eoQg\nkfapRKvQ4I9KWLI3f7QqlgPgIDL4W8NXQUXGiaecDaP5IXAHoR0+fU856mqN1+HXhYXCXa6WIwHD\nGOOZxG/BGGUscDkHAx0HbILcQsgxwB+4obrcxtdPnYuOMYBwOcjp+9U/TRSEAtCE6NvwvtvLBawx\n28CrHBDGI0jBOFUAAAZ9BxUf50b3wSBkUotqTNKzkkrgAfeuJNTITKklsnA/atH0A3SGbTSL9Cuc\n8k4GeleW9DBixO0DgZ/elq2j1C5RClpJtHQsNoP3xU99NDotjJe65dxwRxgsVzuOB6Ack+wzVXdo\n8qKKVvx18TDTPCD2ULqLrUAbcJ3EZH8xsfLC+xcV+b7KMvICaOeP/E8vizxA90VMdtGPKt4SQSiA\nk5bHcnJPzx0AqppkHQkfeuY6pkh7jXASc7rRawiwF4oxAuB0qnbIABiiMeAtcpM6ykwLXWMepqxp\njEXIyCOam0+ESvzzmjsGmpuBwK9HGSLVw03YRay/wR8qHXI/nNRS3j8uLB6AUPucecaULqJtHA0l\nU2c0a58skY6gZqoVZWO5SPmKa7C/jinUTqChOCcUzjS7K7jV1RSGHHFaGKQ8l4P/AAlxR2Fjl2Qs\nh7Vd0/4lBNP2peCrW6LGMAMO44qtp3gOdmbddCCAZzI0MkuCOx2KcfMkd8ZxTr4ZJyBGLKu1jnGg\nFQ0RcQyP6/CKuXEUs0bR26F5COi9hkDJPQDJAyeORTXonh7SEaK2tNX0y6lBZviulcS4C8BUYFSC\nSM/Fjg7TnAO3eg6wtor2llpVoIZC7JLfvLDKhBDRtmAYUjp6MAcHGKK3pb4m/uGinI8Mn+ZpZa/h\nu/2XD3Ui20MDCOZsFtjHoGIB2jpliCoByaiu9AsNN8l7q2lEE0kajUJ0EsZDg4ZWVmUYYqMsACuS\nByCHyKOw1Odp4NOvrDULdcJeabM9xbkA/pWSEOm3kkqyAjOdvNfNM0zNvJFp99qslvI8rSIsatEN\nzEkDzYAuPiOVDAZzkE5obi1mhqk8yCNmwNpYnvNT0izexCzfyLgOjW6G4zEzDcrAKWQgFmBKnOBg\ntk5BzWJurq7uYibtN4Y3UTrMCwYELNGxDFshgWGWIY4wSc6FF4YECp+WkuVhBDCIyQKhPbagiaME\nHAyCDnHyM13olqxZ5bgNKAC8rlEkVQvIVkQHHxZyPTIP6hRv1L5QB3EgIwocBZoYmVFNoptnhcuL\nO9jZARxuETEAmMgcAqcbR+nAFd/k5JI2tgk0LqUEaXQBWRRlhE7AsCR0DBs4YA5I2l6urayskmia\neSRFJbbLIpYgcEEkYJwACOTjB9KC32swRwNHaqbFgCCkUhhOSAFJIIyCcYI6YORyRXg5oPuKsCgm\nnQCOUBo2Agcb1njJlhjOBll+EPGCMZUkAAjJDcP3h26F1Y7GJMkBKEN1wDgc98cDd34PU0lJ4jmj\nZ0ilSVICAxEYV2Vv84UdSGBPAJxwATVWfxDLK8dw0Uc0/lB4THLztKjcMg84JA4I6rg9QbO7ZB22\nfpUlj9UUUzQaveadcy3Gm3MkBeZiyryGAJ6qeDx6inLR9fi12GeK8SKKaOPzC4O1GXOCeehGV74O\nT0xWYJqCTTFbdvzCFHlDYbMgC7yw4JySSpBz8Q7cgS6NfCLWFhlBjWctbtE42kq4KkMCegBB+Yre\nysuN7GPiOwACP/KJBGWtLTyOCm24L6VrhiJ2w3QMJ3cYbqufQ5AHyYig+s6gsRgBcjrnJxjHAJ+/\n7Gh2jau+r+GGtrgmS/06NGRmJJkiAG0k9yCApPoy98mgev6kj3WYzxyAB6kkgfsa0sDqLSwgnYV5\noy8g+eCmeHUrdP5t7KYrSIgMVUsxycHCjkkn4QOOepAzTTrXiKSyuFsdMCW5UbHYgM6uCAVycgAc\ngkc5Bwcc1ktveBb+zQklYJFmIJyWYDIJ9Rnbx/xGilveSzzKWYAAlmHmbTt5wu49CQDz8++KRzeo\nevKGtNNHP2rNgDG2Uwa3dSajdqjPLcmEElnfeQevJJwOOeccZqvH+Vh2iU/GQGBALD/MMAAAnI3A\ng9j3BU0uTakqQRIhcRyBiyxgKWhK5PwggDJLDqOGHOVIFG6u5ZGuJWQq8iEDnEajI4OcErwoYjrj\nHQADHmka+UyeTxvwlmQtjcX3spni/h0VwlxHLPujkeRWZtu44B7EZUAqSoHVQDwAAZtdXv4mCLq1\n5v8A1OjSFgB0+EEFRggj9PYk8/FWZTa7As8ZFxDI8YbBx5gDEksSBwSW75A7cZNSxa1LJbrFHyoj\n2hiTuY7du4n1wSMdOTxXi+ZrDJRAHlWfK1otxWwWnjHVbW7LTPa30DAYjB8uRMZBG7gZ47r1B5wR\ni5rnihNS04Qw2tyGZg7owXdGBkHdhsY5HesptdemErNLbRtEyhfKSQoCASRk4JOc4POMADHXJCHx\nHarDF+aLiQFciNNuCCOdwbOeuCMDJ5HTEwdYew0CD+UASxPOinSZWWyldAWkAyFyRkk49D/T50Jj\nWTzWla5u2fj4VuxEFGecYaLt9fWpbG2kugGsbmSRM7hCyCTA4y24dSCQf1D9XGcGjem27giNo/MV\nQcCIhuAeSFY846HDHHTGc4rndWmko7A+uEVob8IadU1qG22QPIbiVlWJvy8sscS9TI7AsXxggKG6\ngZONxWWC+0/TNPR7nxfPMtuhd1eSETSnqQCVEmSemGBGevcNdlp1tPbPKojDk/zSgMZBxnLj9Q6d\nDkAYytfZbS/hZX05o7pSMiKWZkYrgjcpG5GwGJwFQ4Izk/CU2dS7iA82PypoeAk+DTdB8Qb2ntdP\nvCygvvmh8u3bBYL5vEjnnkgFewb4aD6l+HekyO6aDcD82CR5VoJLyIMBnEjAEx5685A78c0467rG\ns2kK3c3h6x1Gzi+CS8iLXT2/HJeIosgx1ZQvQ8suKvo6apaxmG9u9fjkjGINOt7U2kfOQQJcgEFe\njOzD0Fb7IY8hgLgCCl3xtd/ILEtX0O/0K5eDUYAjKdu9GDoSecbhxnjocH2qiZOQM96/Ql94f1DW\nNLuLe9jFvbSgrI2pXPmNGgIYbIosRoQR+sksMDOayjX/AADd6chvNJu4da05SQ8tsPjjI67kySQP\nUZGOTgYzh53RzH74djz9LOmxiw23YQvSyVwRTDa3WcBu1A7NNsQIq1G+0gk8VnNaQEBpopiMo8rI\n9KCXM5849a8+oIqYJqDzEk+LjmsqSMueUYghCJpsng06+B78z27W0h+NORnuKQC2X+tGfD90bO+h\nlU4UEAj1FHicInApFhIK050Z2RIyAzkjOcEAAliCeAQFOCSBnGSBRdobK0ggMPinSrSXYQlx5UKs\nwIySuHBIOASCSCR0pavjG+oaShlIWd5AiLbrOJGKgAMjEAqM5IBB4ByADRq01S1s7v8ALww6LoLL\nF5k1/Lp0kKsRxt2sIwhxzkyMPTPOO96ZD+wHtGzzq1sYjR235Vi58V6ZKJrTXorPVtOClnvrG2a8\nhBGMebEFcxnng5YEjqOg+aZpOgzD8zo+hW8aAiQSzaYLKJRgnJEiBjwc5VfYkAnMx12zvDLf2s+p\n+JBb5aJYoRHZxMAACrkKsj5Ix8UjAn4VFGzDNdskd2GibCzGDdu8oEnBYjhpCQQByAQSMkAnK6xk\nmP8AbZr5TzBpVYoVvF3u8l0i5JklUpAvoEj746qRnjnecjMkqEyqojZnO7YpCtIw/wBSjO1QM9SO\nvHXrcmA2RpHArlyEt4ySUAxyzDnIGT16kgcEg0PligPnkNMbcsJriXdlrokkKp4yVLcKoIBxjoRu\n5ouJPaOVavKoXoCwtJcSpIvBVeGjZskAbsBmP+b4QOhwetIPi/WIrORIopwZHIHlYeORFB5JyzEj\njHPBzkZ5yZ/EzX5fD2nyM0TjUpW8m3YsNqkqCzrjkheVOcZYEnIIC4Wk0sk7SyO0kjks7McksepJ\n7k0+yMxsBPJ5/CSyMr0/a3lPQujdJLFNI7W8x+KLPAz12nqPoRjtijun6Dol1Com06KTHTe7tj5Z\nbikSyuiAATxTh4fvuQM96kUR7UkJ3uOyUSn8G6HKDttDE2c7kkJIPqN24ftQLV/BItbSSbTJpZCg\nZjER8RB67T0J9QFGexzinlXDKD610GweuD1zVWkh3KK2d7TysbtSYcje8kADbRx8O7qck9yScH4T\nkknB5IeSNTvo5FlNtfgFyFjO1pBtwm0cgFVZtwHcfCecEvEWigXct7bQGGQsW2KAA3OcA9Bnk4wR\nz2pdu5xb6npZVzGXkblvhIIGMHPXG4HHt2zWrLjSR0WmwRz8LVhm7qIKh029n03xAEmieOR1ddjE\n4ZWHBOcdAx6dCKDXs3nXSJmQKWDE5wBwCefYlhV7V9Qu7q0uYdRvY47qykCxwi1OZsgkyM/AUnPI\n5yxwRwMDI1dkgCBTIxVRyRhiWyOR6HP0Pyr0T/SB3tPMkDgLCK2Ti4kJTO6QnaoBJC5Hp7cVZkZn\nl2JkxsSu5TneQWUADntnn7d8caar6fbl5iieUCoYsFDHA4GRjgHPyI49bWgiWTTwIhIJmJV7hjgA\nbmPw559B396Xj9QsJsAE8n4QZ5qF3QClgt5VwwAjZzngAnIJ+wHQDrx16Uo+LL1Lq8/L24UwQNtJ\nDZLN0JPc4xgZz39aatcnNlaNFanfdzAqGJC4AHLE9AAO59qW7DSEtQlxdSKeTtQqVyP9XJBwPuSe\nlaXToo3P9TmtD7Pysv1PUPcOBwqdlbpb2/xYaYjcy9l9mOfT+/FFLGMquXJLHAyRjp7dh7VWuPMk\nIghUuwPDbcAD1I4weTyR9aK2sBWNVUEhRgcUTr2R6cAibyeUHKfTQB5XZIVOoqkxMsgHar01rO4w\nqHmrFjpFwTuKEVy0UZ5KQV/w/e3OlyCW2fAHVGJ2n5gEc8dRg1pHhXWk1+3LtCLWcscxGINFJjoR\ng5J4wDjcCepArPo9NmKlAuCRjNNlrbpY6fHAgGxFAPue9Mh5aaI18JvHmcwfS0BbiBQkt07whT5Q\nuyVZ4CSPglPdMkDcRjpu5+KjMYU+bFdosMsKbnji+FWAP+IhAyOOMA5B454JTfCd8NWinhu3Ia1j\n8uVh1kifdgk+qEMQfRm4yc0yW35n+HAJl76xAkjXH6hkgxc9sq6D/lRjk0jlQ+mQ9vBWpG8PFhVr\n9J4HW483bLBteO/Vdo8sZIE6oQJIz8QJA+E/FgD4qpalP4cluBP4u0SOwuNnF9LD5kDqeQVuoxgA\n9gxRuf080yeeqwxXNq7SwFRPHIerK2SV6ADI5Az1x0wMrWt6uvhcRzpBHqHhfUn2ERSoBasynhdz\nBDFIR0LKAxIGdwUdL0Nz9s39KjzvS4tV8LxObrTr/WY3RCqSqs8wC452GVGGOcZFE7XWLYXEFqlz\nquoXKkGJbmzjUqwGcjeqnI65B7UGtPJhd4fDurXOkz7AyaTqEeIgMk/CrcgHJ/w2KiurzWdVjt/I\n1vRrcoDzMkxeME9CVCkgY5zz0rqAzuNbP5/+Kt2qHivwiZI5b7TrW5hmyWeGSAKrdztKkgH24B6D\nJ651cN8PwkVqthJcshe3S/i2oAps78SxjnPClhgfLFJ3jnSXiH8WjikiSaUxzRSKFKyHOGGOCDtJ\nOOh7ndxz3Wen9kZnjGxyAkp4QPe1IdyZCSQcCqv5+WP4R0FXr5wsZ6dKXppCZCa5KAl9kpaSU0EZ\nhGW+tFbZeKG2y5aie4RREn0pV23AJNosogmtmaCzsjEZbmCZvL4yNpU5J9AAMfXHem/TtQtbtxHq\nyar4hvsFktFbdFjqf5ZIUgY/zE9up65TAXl1GFYYGmnmJVM8IMDneTxjLKefQDnodd8I3raTbx/l\nYze6lM4hAQA+a5PALHoo68DpnNfSuiQu/RNcQbpa2KDVp203Uri/uLy41m2jsLLSgGZTcCU+Zs3l\nnIGBsQggDIywOcquCcRby41kifz7pvMkAAIjyOA3sFAX3IHrS2LUS2qWLTLdKJzDPLGxAur2U5kI\nHpEm5gDnBAHBQ0TvdVSfRdVutNndpmjKxlRyHkUCMrxnBBRh/wA1YvUsXuk7xwE801pWGcTMm15E\ne/k/LQgscrAoJLLjoSFYg9csuemKhCG71OBBKIEYyzqBjJEciRYUcYwhYZ7GTIGQK6uAbfU7MQqI\n4bWOKJVBAG2SQIQM9ANq479qF3cLzz2c0UCPeWTTiAEkZLSMGU56BxAyA/8A8g+uT0vAMs3dJxsq\n73UKCyv8ap3uZNBtjI0k8ccjNuHJMhV8579cEdip9aVLiwSy0xAwBmbB/wC9Ovi6G3vLiylhLNFF\ntKl1IKg5XaQeQRtAI7EUI1WyNxDvUZ246egrp5cAelJJVkih9LLkj7i5x5Sd/MDqFBpg0aZ4pFJz\nioo7RRyRVoKqL8IrmooS3lKDSfdKuBLEBnJxRDNJmg32xwjHv603xsGQEd6G8AnSKDYXbKrDDAFT\n2NZ1+Jnh24dbXUtLgaaO3D/mIY8bgDtIYAg5A2nPBI4OMZI0MNmq2qvs0q+c44t5D/8AFqmOZ0Ww\ndfCJFIY3AhYZeMl1ZPcRlmZkCuzDk4P6sgDg4PbuO+aisZBNqMDKuXWQNsjHH6c4BHfnGe2faj+u\nWttDbzXQzEzZVyvG8ZPUcZwQD9KXhK1pMCu9VmLKVHGOBkEdxx9avHJ6gJAO1rtnBFhMljNdm6ZL\nGYFsEyMUDIpO4E85HcjoG9D1yZCx2lskMIwijAGcn1JPuTk0P0CQslwzOzNlSSxycEHvXzUrg8Io\nJZiFAz6/0GM80tLM6QhtUAsvJldK8M8Ia7/nNUKumYgSAzFcEDkkZ4655PAOODkYMDTneESQxfzA\nN3mRmS4JwenwqVA9uRVLT1K3Jd0RWJy0i7mJA4wDtwAPTjHPOc1Z1CeKXe4DGPHwvhgWYnHHxHj9\nX/tPbru407IYweEyGhoACGQwGGWd2fOBtyRjJJxwMDHA7jPyq9bukYGTVa6YtsBGBksMDt0GPbrj\niqszZ4yay+oZJyJx9CkjkOJfXwmK1uIiwBIphsZoMAZFIdjFk5IoxACoyCahpoUgWnkGJomKAFgK\npX0m1SM4rjw8D/DJ53JJZtoz2AqlqEx2vk0KR12UUcBMH4ZOG13URKf5LWUmR6kEfbgtzT1PI6SR\ntGB5zyXyRKzFQ0glLhSR2zG30zSR+EzINS1Ked1SBIQsrMcAKSWJY+gCE/amLUL5x/B5CAgKTX0g\nc4ZGlhndR7YxIDx2FaDMP18ZvyE/juIaEW0xw1hdRwkEW07iPgH4JFWUD5DzFA6cKO2cr8LQaLp7\nPcKLvwxdlobiF18xbWQsVZ9pHMUhwWU/pZtwGGOLPg7UEu7nUirA/HaK5GWw5t488/Jl579OtLSa\nuzaYltKuZmmKvaQvtM6TwKxLAkjAL8n4sAds1tdLxHNPbX5RSbKu6hCPDiGG5s/4p4Rk2mGNiZWt\nCcfpJydh6g7uM8Y4riCeCMq2hagz2+3L6fqDsoA6/Cx4HtgmqOj6odDD6R5qzaZOWWC55Pkkgkow\nI5+fHQ/Wm0MFjdRxshksJGyrIR5lu5A4DcZBz0xj+3SRQnYd/wAfakbTHbwaVdAGzDWWqYyYpAGD\nH3B4cdeRXrxUmtLmwvLR7d51KBok/kSccMuRkEEAj1I9KD3Fn5UZExDREhlkCggjPVgOB8xjHf1o\nhuntYERp5liblAXDxMR0GTnn2OOnU1SfHD2lhNg/KhwBCx7U5iw68exoYoJz86NeMFP/ANS6goQo\nGlL7cAYyAe3GOaGBCP8AzXzY4xhc5nwaWQR7imCKBopWjcYZTg191FysRAo5q9uGVLqMckbWxS3q\nLEsqD1rMjiLpe1D7O0kJn8BWFoyy3VzbvdS5EcUJciMvgkE9v8w+WDx1NaIlvc6ZfGKGdReS2589\n0AxaRk9I+OXJJAAHJ59gveEXS00m2mjiMT7TIC3IjBJwScckgLwOSAKJxu9uEnAkkmXbKwJ+KSU8\nIMdAMkAD0Hqc19ggxzFA2IcALZib2sARvVLgI+l2OmIYFtLiG0xEDKyTSf4pDYySkAlJY95dxOVq\nxqkjDTYXmVg013YMr5AJiM8RCkD0ZmHyJ9sj7S4XTr8xLMkz6bZy75SR8dzICzHOc8FAMjoGxxQf\nX7y4u/wyuriZNj2kMN3E6Mpx5YjkG0nBBbA7ZBbHIArJycXurWj5U+U2LcRajdyWUN2kkt1ZS+TK\nMEJJDNgjg9UaRQR6r86oRXbXF1dSvOIINTRZoCwANrOMRtESemJlU5PAdscluFnV9auY/F+n3MwV\nWhmaezaIFFmtZ0UZYnIJV1UPjorbgBjgjqF0J5IppEjSDUfikgmGBbXONgWUYIVZADE5yQHVSMMa\nFBiej45Um0r6wZbnVLyKYGOdTmeJeiyj9YyRnnORnqCDmvWJSWFxnIIP/Sj2o2El6ou1lVJATAsd\n0VQqR/8AjLkfDIDxtfg5BU4IVVZ1vdJ1Jorm3njz1SdDjHorjIPfndj1FPRcFh8pdzSCQeEFlO2V\n1z0JFcM3HrUd1Oj3crRMGQnIYdxUDyHHBrhcudsL3M8glZ9UV3FdGC4VweM80+6JeieBRnPFZnMx\nJxmmHwvfFGCMenvWZFIQd+VYHaf896qa0vmaJqKDq1tKPrtNTRyhowQete4YFG5VuCPUHrTPCtdL\nEPFN0TaLCT8TZbk9txJPz/70Ba6M0kZwAIm3dPkKOeJoDHHcRTf4kOYyf+JWOefrmgVhbytBkjO8\nhVQckk4bge/FN45AjKfY4dqffC8EjaHcXbgjzJAo47KOv3b9qBalP518kJyc8YyADnrk9h0z9u9a\ne2lJpvh6C0JAW3h/mN6nkufuWNZFLIZtUSVSyMPjGDtCjrycYJzkD2A6Uu1oJJS8Q75C5Mkc0csq\nRbVMaIXKgHHAOACc8FhnjA46c1ZuzGSqKSEjGSWGMPyuR6gHec0KsJCtuZ5cckswzuJ59fcJnr3+\n1czmSdI1YkseSAOQBgk/Xdj5n5VQ2Xc8Jl5V+dlLZQAKoCgD0AqsFLSAVO3SureP4s1SFpcS4rNd\nskq5aRgAYq1KwSM46mvkK7VFetVN1qttbjkFgSPYc0wdBDAvScrWH8rodvGxwxG4j3NLurS9ADTP\nqjBYUA+VBtJ0WbW9TKbJBZRnM8ykKEXBP6jxn7+uKHHG6VwY0WSUxRJACY/AlkD4UvBIpH8WuBZj\nBwTGAMsD6AeaWz1CEdSMzeLZxcs1uhSBr2IzOS2zyopJFijPsPLWUkd2fA5ar9/cWmnpb2jBItOt\n7co2MlzFhcggc5kIUHPIUAdZRSX4ivrm71O6lt5Qt/qipHnoba2AP+btgMzded4bgiu2w8Qxsazw\nB/lPMFABFfCMqtoWsamQVhuL24v4QyjayoAsWMdACiYA9MDigcklxCbmGQuJrcQyIEyqgtDGDuPu\nF6DH9RRPXNUt9JjtdN0iONVCCNnBJIEYIVcnqAxB69VIPfIK4uGudQuLmdM72VWZSSAAoAAJ9MD7\n1r9Pidt5FAnSuBZVu1QT2gQpGu7LJIOuevPy6Y9zRWwVHgWURGWIqRNbEZwO5U9senPt6UHRdt47\nRxFVdQ4X1I4JH7cUXtZljlieM4RwWVwf0MOoPsev0NaDxfCIArthIbbBbElrkbZCMlR6H146gir0\n0KwliQDaSHAaI8KxyR7EH9qrARENIykDIEyqcEH/AFL7/wBcVahUxRtEpDREZKsMhlPdff1FKyfK\nhyzTx9DEviYGPBdreNnwhUbviXjPX4VU59c+lAWHPSmHxyyt4puFjlMiRxxRgsP04RSR/wC4n70A\nY89q4LLAdO4j5WU8040n2yYXFo8bDII4pVn/AJOqpvXIjkDMvqAcn9hRnw5dCSMjOTiotdtP/XpI\ng4mHln23EL//AGrO6cz1cljCNkhQR3EJ40Jd1nbQ3D+VFDEsrqOhIHwrxz1Gf/1q9nc5YhSyfEow\nSA3Yn3AyfrXFpEYoVGdrYDbW4JPUA+wHPzPtXN2JF0xl3HLLyexZiATx2y39PSvrA5Wr4pV9OiSS\nwnCqROYZJyoBUBCCSMDg8BcDoMH2FGNOhiuPBFxazLn8xAImIZW5MCKMc5HCqMHnPr1oPdyy2lpO\nltsjbymj3KM8AEZ+VLr6nPZ3EEpcRvCFxgYDrgYV8YyAAR0ycjJ4FAycZ0rbCoQVxZzHUPCuiSXF\n3ctb2kL2V4seTJGDxnkYOAACB2P2PWN5cSwXFsZIbu/jHkz27hSb2LaAssZJI8wxgBhgq2BkDGaV\nZWiGo6lbadK62Vy/mgbiMq3ZgMdCGxnsfrX2RUtY7YiQi4EmxgGyQmMqw7ZB3cD1NUOP3ijr4Uja\nbl1EzQiW3nZ2xsBKbpAo48uWBiTIoHGeWXPDHOAI/PXKWhNrKBbFyJIFBltgcnhF/VEMcFTjg9DQ\nqaYzXzSzRRXAY7pFPwiU4wWXH6Wx3AGamlItyk9tNIQSNsi8SoASCrDGJAPXg/0qGwBvItTV6QfW\nrW4M4uIbW2Erks6x3CKCCe6ls8diQD86HO2CwPDA4IPUH0ptaASbI7q3RoJSfJliyqSk9eOmevBF\nBNc0ySAvPFGPIGFJU8KRxjb246D2+Qrmv6g6Q2WM5EY945ryErPCALCAuct1qxaz/lpVcHHPNVxy\n2TUNw/UCuEAs0kuNrTNDvRPCoznijAzjNZ14SvyrBGPTin4Tgxb+oxmmyfbZVgbFrN/F+nNcT30q\ngASSSH05/wBgVz+H2jKNfgMwBS1QzAAcFgQAfoWz9BTDrVk0ylSSNxLHHucmrHhaFLKS9d+CI1yf\nYE5/tSkOX3e2/KY76bSv+NLgReHbwBwjuBGPUkkcfbNYw433Fy4A2rnGSCOnQZxx06ZPWtI8dXLN\n4anMUUlxdM6uEUZKAZJOOvA9O1ZtZPE0tqJVXzCQzsMHOOTj24xgZFacLT6fqeCmYWBotEL12t4v\nKQjavAAA5AGM9fZv9mvumJy0rkbh8I79ySc/M/vVG8dpp1AILcAc/XPvyT96JxARxqi84H3qO221\n5KBM+hXkq0rFmxV21XoaoQISQSKJqwRc8jFELQ0UkSppZAkZOR0q54IAn1eeY8iJMD5mlnUb0KpG\naY/AMcsOmT3lyDDHcONjMMFkHUqMc98H7dKvBiyZR7Ixdq8TS5wATzNa/mnUM6IgHO5gv1OSDjr0\n5qa81S20W3NvbqLmRTlYgAIlfoNyg54PRT8RxyR2Aahqpkg8i3jEcBO7auVLdMZAPsMDjpnqKFKU\nkcnEhxjepBGCeiL2x0zx9u3cYPSIsVo1Z8labIg38qW5n83z7m5leWSR/NKnDCSTqSSMEjJOAOCS\nTwMZqxTzWdy10xWS8k+EtKCQo64x0468DrV6O3Dys82Bs5IUdMf7+VVWQzyhsAI44B67R3+tafaC\nKrSudqsA0pSWRy8hbC5JyF5IHPuSfmTRBIxIswXqecY74HPvXf5Q/wAkbRjO459Mf9xVmBcpI7Eh\nGbCkc4IAH9qLYAFKQq8bfybe7AKmPHHqD1B/32oqlqYpV2sPImOQeoWT/of99apaep8koeQWKkex\nJw2KP2UQktkjfBjkGASOjDt+3FUe+jatdLuOIsF2ABh8GGHAb/ST6HtVyMbwscJ2oxAIkz/KPc57\nYr7GH+FyM5HlyqDjOOh+ef7UL8S+d/C7yK1co86+W7DIOO59iRwfnSGXkNjjLnHSHI6gSVlbzfmL\niaYuZDJIW3Mck5JOajbr1rowtbsUYEEVC+d1cK55cbWSXIj4ZuDHMykkZp5sIkvL7T0eMSAShiCO\nu3kfc7R9azu1HkX5Q8HJrRvB7iS4UYO8A7SOxPBP2JofSCG50d/KNFtwTW2BGFDb3mYKpOBlc5Y+\n2cE/aodQLsE3gBWkRVwe4bJ7+xqaWRI7svFzGkZVAACASeR7cAVULNM8Bw23eSgx1AB5/wB9q+oN\n+VpFQaqQtpOSSoVCMkZ5Azxn6Uoaow3RKpw4KgqckjAIwT9RTJq0pa0cEAMyMxGccH/YpQu8zKhy\nTkoSe/Ue/vRmu8FVXyEr+ZgeMHLhlZuw6Efvn71da23BgChZjtDHGQwOQfv/AFqO2UO1uiOSCCD6\nD4TxUywiOZEyWYsMDPBGeftVCRel4LhoioQsDsbnJwcHPXrxVhLUJKrqmWY5wRjkDkfXr9DU8UZM\nTQjarRkhW6jHUA/QivlsTJFJBgCdDlAT0IwQM/74NV8lWC9EQWJWISqw/nW7EgNjvx0I5wRU8yx3\nEDAObiCRdpWTIlVemDjO4Dp3Ix0HaC5IkhW7tfglXkoRyp7j75yO/bFenkiaHztjRkDdJGhJPP8A\nnU/T2oUlELzkkX0P5S5miDh1VjtYEHKnkHjvihMr7nxnvV7VJ8zynduUHCsMcjr2+ZofaqZJePWv\nlmZGxmTIGcAmljy6JARfSQY5FcZHrT1a3Ba1Azz3pOtYwoHajWnTE20jdgdorNyJCGEBeZo0iF1N\nu5J6UPnu/J3DJAcAHHfB6fU4qKaY4A70F1C4J1KGAHonmN7c4A++T9KRxoSXUjg3sqXV7h5S77se\nUhc8E9B6etJ2nsJpXdyCUTAZeM5zx9uMcU5RRtPFdoBhpEZB9jzS5Yae0OnpkAyEDdn1/wC1dAXC\nOMNPPCZjee0hcaVC0088xUlYxjLHoT/fvV9QA2BTHpmki30AHHxSEsT7Dgf3+9LEgdLhkwRg0VpF\nWlJnW5EIgFHOKhvrgKhCnnFfA2xMk80Iv7nJPNC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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Iris Versicolor\n", "\n" ] }, { "data": { "image/jpeg": 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mg6Z4s0pPD2uskFoH/wBC1Zk3S6c2OAD1MecZXI+vFfM/iPwTrHhOSbTNQ0Qw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naQdh\n+UKR0IP9Ohptlpyt8lx+6nGCrL0rWTaqiJ1Ab88/jUKdmD1M69sra/0i60e/s4dV0e5O6WylX5Sf\n7y/3XAz8w/KvMdQ+Gd74JurttJmk1jwFjzoGkJa7sG7xyjqUB6MPfivZHtdrgHGWGAwHBFMTzrG6\nSeFlSaL7hI4+hHQj2Nd1Ku4tXZcJyg7o+Yriya91i51CF1mtjFwEbKyev4cCsnSvh/BrWrtreoWo\nt4Y+Qikrk9gR36frX0TqHw+s7m9nu9Dt7fTL6Rjc3NpJ/qp37mMfwsf7vf2xz5rr/iK2e+uojGRJ\nBhp4mTy3jHPVOvavYhUUloehCqpaPc5tNR8vVJEtrEoI0DIWyM884PatJJdJ1n7TFeqY2ikDSYXJ\nwOh/nXMHxNNc3huLdWltznhBllX6cdf6VneJtZMN1a6lp8csDRjbNFOMeaDwfpWjuzqUlY6uOVXu\nbiCN0kjU7oXHBdPXFXLG8eJkbcYzn76nnFcnrGuWmkjT7lkZZVTyhsHLg4PT05roYZ1uYkkjYFWX\nO8cge34f1rz69Lkdzzqr5Z3iO8RadJqely3T7prsvshlboR/dJ7HjiuD0FpLTV40KMuA+zcMEAgg\ng++cce3vXrXh29h3/ZLwB7K5XZKD2I6MPQjPWuW+IPhmTwzrazJIstqxDsyjLAnoT9f6V5VdtLmS\nPUwuKUlyyZPNeNGHwwB8tip+uMiuR8R+GJfFOqaRaG6WKxgGZ2/iI6nH1rV1KbBUsxZlJVivpwak\nsreRraCYAh23u2eyHAHP4GhVHJppnqe2UIs0INQhh0sadbQqtlb7fs8ajoRkFs98jNVreQRTyMBu\nDEMI9vORnAH09azLrVF0uPEcm5gOWboBgmur8JaattYw3M+XublBLlv4QemK2nUUVZHk18Sc946h\nuvD/AIWgv5nEM97K4EI4OAAST+leSajqg1UQiFmWd/lfB4K8d69p+Od3a2eh+GjdgsZJLhUB/wB1\nOc/lXguqalbxQBoNqSR9h/FwK93AxTjdnmxquVyxNcQW8gIZtqDynZeeOw98Eda96/YU12/0n9pK\nO00q4t7TVPEGjTWemXN1F5scN0pUiVk6Eqm4gHjKgnPQ/L81zK1wxjDEE/6sHjOOteh/B7xfN4K+\nJ3gDWY0JurXVrYNjb9x38thtY4PD/nj8fY5eiMqjaP1ztLO28YRN8Nfh1etrHhjSboyeL/EmoBrl\n5p/lkFtDKAEluC+JJNuFQBRwXADPG0N9YfCXWfAGlXlv4n8cz3CRXNstyPIt1a5MrTyMRwRHGx2k\n7sgADHJx/F8fibRo9N8J+B72LwfpTaobRrXQrQsZpJtzSnz5AS2CzMzqoA2sOSFFdl4T8E+H/hx4\nX1zwv4ZtZo/DXhC1uru51O9uTcSX2ozRs8nmSNuLMqs27JDAtGMAAZzlFw0k9DNPm1W51Xxj1Cxf\nwfpnxEtTMT4UZNYjlCMEms5E23KlepBgdzjsyr6Vm69pEOjaN4WlsJLXVvAerTNpt6jxcCwvABAN\n+ckLKwAbjCykY7112niPwZ8OfCeia3E17Jdw2ehyxHkOzR+W2Qc5GAxOevrXInSkj/Zw8R6FYyNM\n/h+K+sofNGHH2SVzCD7lY4sfga4Z04T1aNoykij/AMILPJpVx4PTUhYeJfC0zah4T1CeXaVtjlYt\n+P8AWRqN0LrggrtJGSMQazrpkkn8e6Db3N1rWjyJpPibQ7dcNJGChc7CDukiDlkYHlSy89R0Xi7V\nlm8F+CvihI0dpPp0Ntd3ZChv9DuVjFwv/AdwcHsY/fi34ultfAHxCsvE4njh0/Vkg0rWEB+UMWYW\ndw2TgDc7ox9GX+7XzmY5bDEp6HRTquO5gakmga5dKlra3sdvrbJLaarGoe0SYRbkPcKSPlYEYJ46\n1VuNWFl4rstY07StRm8RpZPFcpE2LW5KbftFtICQgmCqrRluvqVBFZth4Yt/h/4s1z4fahNJH4R8\nRLcaro12jMgs3Ug3FszE4wCFkXHbcPermnWOoT35e51M6dIk0EUdzd+WZFuQu22kK4JZWJ2N03+o\nxg/FYOjPBYpRXc9SbVWndnUeLV8L+OL7w1rmj+IZNE8S6hBKmi61ZSACcKQ5t5g2VkXdz5Tjg78Y\nINYVn44udZ1nTvAPxTtV8M+MGnFzo+q6VIw06/kTlDbyt/y1AY7oHGcMpAIPGadd0rXNKs7nVp2g\n0DxNqLWsz20fkPoutJIVWVCSxRm28nO3cCxGJGFbWpeGZPHizeAPibLbPcxoLvR9d09ja3U7IObi\nIg4ilTeQyLxhiQNhIH6zB3VzxLWbKnjN/CHja5tRq3jbS9M8Q+H4mW18Wafq1tb3FvOTh4nhMhA+\n4Cyt8pxjjmvGfidqWo6DHN/asHwy+KdnfSGSK7uXWG8nO7jOz5QTu/hOOvSt3UtTlvtH8faH8R/C\n51n4geFnjntta8PackF5q1mXXZeRRNwxQY8wAlcqwA+U15YPgzYfHHRL260HXtG1eVrTzY01iL7J\nfxzGXmCTYww3ykjgjDDAGTn6HAUYVFepKyOSb1NLXfhX4dh0FbuH9n3RNO1G5hEr3SeK41SNs+gY\nEeuM8V5ZrXhfS9L0oXWteEp7ON5t82qR+IFuYgR0QIrkntjOelWtV+A7xW802tfCnxDpUdvKbVL/\nAErU/Mgd+7eXKMnd6+1cv8Rbnw7bWP8AZuiQaxpkkMaxSaZ4j02NAegaWOVMc/ga+nw1KNGD5NU+\nplJcz0PSvCtzLf8Ahu1lkUIJPubTxtCJtOOMHHtUmrWhvdK1SInPmWrYGP4lwQf0P51Z8N2EWm+G\ntLtoxhVs4m+Y5bJQcmnow3CNuA5KkeuVIx+tfkmPlzYuUlsWlY8s0hFktIzz+8bB/wA/jXSNFtTC\ndDtQD1NUNKs1tHe2ZeIHZOexrSOI2DvwIVMv5Dj+deba8hnm1zcWqWTXF0FVIMgANwp7DtWJ4g1l\nra0a6jbbE8YURKM+YT0B9O/NXE8W2crR6f4i0iG6V2BSYDbub3P5V0V/DpOrWMkTaW9v+7IWRCCO\nBxgcfnmve1Wp7s7Hl2s6IZLWGSCMw4dVfYBgA4zzV3TLp7jxIkIfdBa2zOVB53dF/kavrpc/9lLY\noZJb2BDJH2EwByePUcfnXC6Z4puZNVupUtPLeOLy5wOMDJwc44PXitFr0C9kel+F0NhJean/AGnZ\nxxbNt3ZN8occ4Jx0PJ+tdf4O+Hf9qyNeXjG00cOJf7OLhZrlcHbtGQNvJ6msP4UfDKKHHiXxDpvn\nSXBD2+n3Em1WXs0nPIPGBj1r1bxJ45EpAOnaelsvyiw1NUmTp0QgAqOOBmvqMvwcqVqjdjysTXVT\n3YkkOqQ6TpgFzp89pZRNtEd7ZK8UacbSsmQVPHY9x6UzxHrGo6/pm+PTTZ2jRiVYZLqZnlH3R1bA\nBz2HP4Vk2FrHqU4u9cWz03RI4vtB0y13ZmYMAu7J6DPTHeun1HxfDcS7rkG2tioByv8AqVPCHp91\nTg/jXsz/AHnuo4FHqzhPDvjDTbsPGpFnIrmM28vZl4IU9xkd60tRuY5k/wBX+7Oct2Fch488Mrp/\niWRLRopkvGyphPyuwHJ/Hk+2axbPU9U8PttEjQ7fvxScoPbFfmmZYOeErO+zNYarQ7QQyoQEBYeg\nPBrptLiQwr8uyUdc9q4Oy8Y2sskYuIjZseTKvKk/TsK63TNRjvFV4ZVmx0ZGz+nWvDmkaJXOhQ7S\nEfJQ9+9W7eYgbH+aIdG71nw3Pmpl0ZiOOOD+VXUkVSCPuDrxyPwrladxmgMLGBu3oeh7inlVMY34\naM9+4qpbN5wZrb96D1U8Z/wqzGMDeoJ7FGrVaAV7iIqUDZYZyGHB9sHtXJfET4dW/juYXy3A07xX\ngRxaqRkXSdoZRwP91u2Wzmu8UpICuPlH8J7VE1spDBhujIwynoR6V2UqzgxczjqfINz401LwR4xn\n0C6sntLsEw4RAxV/xHKnsRWn45h8Q6laR2jXcTusYkdUhG7GM9q+h/FfgzSPGhsH1OCAaxYTLJZa\nlImCFH/LJyOq9MZ968d8VWVx4U8Raj57zyCeTzW43CFu2P8AZPpXt06vPE76NRT2PO/GejwX2m+G\n79nYSmBVfbwwcdiK2NC8+OR7VoisZhSePHTnO7j8BXSzJZ3tta3FtLFcTxMxWIruyzAcY/Cui8L/\nAA+0/QbE6z4hvYZrpl2bCSoiHJIxng9OKJJTi1I2lT5kcrC5VSd2wFMj2Pao/G2tM0tw8zB0azSN\ngf4sBjkfl+tILq3uppGtX822diEYjbjngYrG+IGT4VNzGB5yN5Tkj1BA/U149SkkzgpNwnYg1C0I\niVkYkbEOf7y85H/16ZqmqrDZ29lZygg7VJzyBzkVz8Hib7RbhfmZI49rDP3GPUfmKwrC/kW/3DJj\nYk884x/+uuWVFo9eU7o2NUg8+K3t9+5ru4jt1VeSNzhf0BJ/CvZ44RBFFEFK+UiRAHthRXlfgrRp\nNW8TabcPn7PYA3s/HAAO1Pxy36V6jLfRQXscE0qC5uHIijZuXbHQflSknL3Yq55deabPOv2o55bf\nwx4TeMZAuJ13Y6HalfPCOb1pCSVYDkt9BX1P8ebC0v8AwPotpdFllmuZPJIXJB4AOK+Z73Q7m3uG\nhidLhdo3SL6jgivpcFpAiknYrWV4hvI0ORuJIYdzjpWjoMjapqKOCyrGN6sHI2OmWUjHfcox71p2\nWl20Vha3cwTzXkESoRjJ55zWraaN/wAI74kthbhiJgoaJR13ZzXpKaT1NJwctj9Y/CGu3fjDTdPX\nRdQa08b6t4fWbXvEt7dv/wAU/pZ3BBArZi813iLBTtBCs7HCimapqmneMvDPhL4eeBdJ1iz8G3vi\nWK1k1QMxk1i3gJkvJySN3leYiqZG4c52jbtJ87/Zsv8ASfF/7NuiXXi3UrY+AfDy3LeIbZ8tNqly\nkhW2tnj/AI41XB2Hdvbbgc8ezaB4r8SWusJ4u1vT4tL8c+IIVl0/wnqKhn0TQoWjE5kdHwrlpFd3\nPcqgUEGnK8pHPH3Uz1fUdXtfE/xm07QYZA50CxbV7kY3ASTAwwAc/KQolbn2pPBLCG2+JEt2I7m2\nOuXLeW33Si2sAKk9uVbPua838IzrrVjaePNJWWxXxfr66vc6kynLaPbIzQ72GQqGNVZVz/y0PcEV\n1HgzU7LXPgj4h8WWbymw1ddW1RUZvlljd5Qh6d0VCD79K55Q6lKV9B/gPT7bW/g5o/hDVIzJpOoe\nF4Ee7dd0P7xShUnPJ+aPA44B5qGWxsL/AMJeGdD8Ua/Y3T3MP/CKalFBD5lvd3ohLAliQUKtC5Xv\nuYD3rE8WeHoT+zHYarDc3MOp2PhKCOERS4G0xxMMr3IaPg/X1rT8R6doGh678RX1ixkvdJWzsPFg\ntbdf3omTzkkkQdnzbxk49feolFcpSbucJ4s8UX3iL4W+H9Ws5JtM8Q+F7gw31/qMe+0gNqds4lHJ\nJlQHA4yG74rc8S6rZ+LfGd/o1nZxvfajBETrG7Z+7ZPNtfL4+baduG45z61o20ehprHjjTdTLWui\n+IYbPxDDHGd0k3nRskqBB97mIHjpn8/BrDWLeTR/AL6Vrgh1E3NtpDm5WSOSEAyCPdIw2kHap46Y\nwexrxa2VRqTVRHXGrZWPavCN7L8StbjsZNEiuPAXjjSbiTUYJGEcljqNvIIp2Ax1clCCMENGG7Vj\nWPiO30L4OX9p8V77U9Y1rwzrk2kt4n0+HN5YLKpNtqGVO6NRFMgMgz3yDlqf8SNevtP0qXUNGs47\nTUvC/jO2kmlsLgC2S3uUQys2QNysJeV9663VL2Dwz+0nLcxzxT2XiXwncG7tAwYPPZyKVJHfMczj\nOOgORyMe1TptaI5W9dTzTxV4Y1/xf4L0LTh4mtNY+IWkPPfeBPiDbSgQX7YBaCZoyVO8AQsu47/L\nBOSpLeI+LfEF18ZZLPx7qXg2zg1vwzcHTvF3h/SONVaQFD5hQhGdCN2HGDgdOK1/HmraZpejeJvF\n3wquBpGmWzgeJ/B0Nwxht22gQ6nZAZEUiBeVjCqR15BJ858S+JrPxd4zi8UeMdXv/BevahZ28q6v\npEAK3CooWO4VwRuLALncMZzX1eX4ST95nJJpux0vxa/4Qv4gyRH4f+ONZ8GT2kIRfDviie4FpNIS\nM7TI5MTc4zk9uK800XTNTX4h/ZfEcLXNro1jLqUjSXHnxvHGBsCtk5BY9f0rZ+J3xH8aWGhXMGu6\n3Y+KPCt9NHIfED2Ufmbl+4k2BkE56g889Mc87oOlw6b4A8f6zZjyl1C8ttMtTFL5kZUbpJdhP8Pz\nIMV7lVfUcG7suyse0eGtfh8SeHrHUYsJ5kSrKndZAB8v5Yqe8+WTdxkYIx2ORXmPwg1k2uq3elsf\nlvI/MQN084ZyAO2Rj8q9LuCXjVtoJCEE+pr8SqTc6kn5iMHxBAtvrtyEGFmcS8e4FZGvXJttJuHP\nLyMsY7fLzn+lbOsuJL+CZvum1VT7EZyf5VzHiWQiKzhfhvLMhH1xj+VXStJ3E9jlV8ORW0UkmsxR\nS3ZcLFbg52EdD+P9K5z4jatqGnS2qppr3VohX5IWKKPU556cV0c8dnqSzLPqEayqQxkR8sp9Saxr\nzxDYMJIn1GO6iQYKo2Rn+le09Ue/ZnNfbtWsr2112ASXsMcwCBiN0an7ynnp07dq6zSvBOg3mvXX\nim9Ny2lErOunjhZZhnIb1UEjjvmma1ouiaR4btdWs1WVpXDTRR7jlR1/nVOf4g2GtqG0v5rOIbAk\nJ+4e+R/npXrZfCkpKVVnLXlKXuxR311rQ1yB5oryKDADJbvLnYOygEcDjgVysWtiGbfJMl0hYq6S\nAEqexB/OsqW7kubKFrGOO6lYnO9gHX8K5DxNq0umapZWhdLYXWMvIMBT6V9HLMqFNct9DjjhJXue\ns3/jM/2YY4/vzKLZN43OwB3ZHtwK3tJ1ttZto5bi4k+zrA8EoODmUbSv+fbvXlNtoii+tpJby5jT\nDDz4eSrY4IHoc/pUV34nNlrBzOJJJSIJFAxuAGAx9+tTSzHDt6DlhWlqexahJH4jtoIHRDcW6PIp\njO1hIVPI+vGT7DivAfGOq6z4UvzcRXUl/O8qRS20x3blIAOB27816HY+KZNLvJ4bu0MDi2VopVG4\nOM4xkdM561HDa+FNT1qxvNWgluZ4odiQ24IMjgsfmJ9OKxzNUMTSc09RUoSg7WK0MTyRxTgFSyA9\nd23/AGQfb6VNaXM1pcLPGzQOp/1sTYapvEWuveS2hsbG30uKQMgiUcqR3b1zWDba0vmSWtydt4rY\nwBwwxnIr4KtQXQ2nR6o7jTfiRqGnSeXfW8epW27Jm+7MAfzzXoOh+I7DWirabci74yYDxIv1Brxe\nNkuduf3aSfdk7n/CopYZLOYOGYFeVkXg5+orwq0JQ2RzyXLoz6HSQB/3BKnPKp1H4VpwOLtM7jHM\nvQN3/CvHfDPxNuLMRQ6mPtkQGPtCD96g9x3H5fjXpthe2msWaXVlcpNC2ALiLkA/7XcVnCV0TZm7\nEd5Jk+R+h96kRi26Ip83B57is+3uSJPKuBhwfXg++fStAzZYBzmPs4HNWrtky2sV721RxnBKPxtH\nG0+tc54n8KxeIY2icLDqpj22163CSkfdjl+vQH3NdaXZmK4yx/Wqt5amSFlYK/Yq4yv4+o/+tXZT\nqOD3Ju4/CfKA1TVvBfiO7mlsIodSspfJure4Xi2YdW9+owao/E7xBqHijS5it8wDbSptiAm8nk+/\nQZ+te+fEj4aQ+MUS7tiw8RWkBitgzcX8Y58mT1YY+VvcjFfKMejtNrlxbabHcw3DBvMs3U/uJh1G\nO3T+Ve5SlGcbnpUq+nKzvdOuvNa2Xf8AI8KiSM9VdRww475NbaaT/wAJDY6jpdwOJ7V3jyP+WifM\npH6/lWRpd5PDpMMzXCRXkcOyaCZeS3qDitXRrn7QsVwjCNtp+6c4bGP61yV1ye8TVp2d0eUXWily\n7226MNjzEHrjk/nn86uaNoxcoPLLSMwSBR/E5IGP1z+FdRfQqmo3S7eo3ELzgY/+t+taHhrSGm16\nwMasFtoxeH0XBA5rwZ4qVV8g3dK51ui+Grbw7pD2cH+vZf39wTwSM8n0UE+teQeP/F8V3rGseXNs\nexe2OnzwcgGNsOAe+d5Of8K774i6pcTJqdtHN5GnRfKzRNzMc5YA+nIrwu5ginkuzJJ5RGPLhQdQ\neAPqOv4V9hkuC/5e1UclSz0Z32leI7/4keGYVvZmaXR9W3Zz8ywvErYz9VP51y8ukWmoXV0+kxyM\nRIwIYjbnufb/AOtWT4Q1q70i01SztWCyXsG0k9d8ecfmKveHZQty1uyxtA6s8298FHBPQ131qHs5\nOSR0UuVq1zYvvC9s+hW8Ek7QTxSCQbAMk/nT55tHilgS6u7k6qEwkr8A49B+P6VyvjvxO8a2a28i\nSsG2fu88Djqaz3099XT7ZFcF7iPhl6lR3IrKK1NOfl0R9ffsja3pKW2taXrumX3iRvDWpW+uaX4e\nsAQ2p30key2LDIG0SA9c8sMjGa+rvGPim905l8DXXiK1174oeJ7z/ipL23jRI/DmkOI5J7eNhkLG\nsQG3czMS24/w4/PD9lzxBfaf8bIYWv306LxBZyaat+wObRlPmRy4HQrzgnp1HPT6W8X65o2u6zqm\no6bFP4ftNRthpsst0CHeyRh594zDrJMwAGQuce5r6TC5bDGtO+nU8+VS19D17xd+1JH460NvAfw0\n0u0tdG1iP/hHtNm+YMiuTBG0KcBRt3sM9AvvXoXjbV4PB/7O/i3wZo8g8zSrKLwzYIsg8242IizT\n7eMD942f+uZrxb4U+Era0+JvhzxDqEEWkX1wjeILfQ5VA+wwbPs1kkgUna0pdZDwMbRxwTXS6PpO\nmeILLxZq0l5Fq+tavqsOhabJDIxivJfOWS8nQEDgAshI6CNuTmqxWCoxaUFojP2jPbfFVsbLwla6\nU7wvaanBpHh3R4kbLSgupuD17ICfbYfWtP4oata6b461+ea7t44k8F3Cus7YTdJOEj3H3JYY75ry\nbWPiFb+IfiD4De4vrTwzp/hmzvbrzjavNafankaCFgBjI2qWBJHVjWZ8YfGA8beIriysrRrmfVLi\nxtre1XEIlgtmeR2IOcKzSBsHoEzk9K8N4WSm4yRqpq2pv/ELVraLxBpltZxtZvaeDIIbjaSP7ODS\nqRuYdGADD3BNecyaS2v/AA8+H8091bSLrXiW3+zwQ2q25WKGVyGG0ZYMi5yf735838VrhrJJdA00\n302peIGRWvZ7kl2Vd3UgcqWbgHGQvatie51Lwr4ktvs/kSaN4GtIka4uGwPPlTaVHJ+bJwoHUk9K\n7IUdOVC5rnofxLurqPwZ8VbW0063Il1Gz08WEcjBxKlqjAxsOsgCqRkfw+9cp44a4k8bape2Hia9\nTXPBbWyaJfTysftF5cxiS4tJiCAysBCpHbn6V4/B8RNSstKvI9Q1MahHPqo8R21zI/lFrtBseFmy\ndwGACP8AZrz/AFf4i3q6bZWWp2+br+2JNQvZS5G4s3yhSCckJ0J6ZHpXpYfBTvqRq3od3rfjDTNL\nn/4TLQkGgWurlrPxJ4fgufMij3MyzxKpBPJyQwHQ9BivFdc8T6jo1rBoMlpHNoDpI2kyTHc32Itz\nDuBOApB46j2zV6x1CW60y70eVUtIdSvW1PTbyRAZXdGO6MS8ZwCMqRzXIRxat8TPEWleGtMt2vPE\nE0zw20caCOO3XO6WV8cKuCWP4cmvrcNTVKN2jWMUnc29DstT8UyWfh3wzPLcJdyoLu3mJI08KGJZ\nieAm0kgn0r0+N9Hv9Jh8PaHGY9B0yBo4ZQP+Pmcn5pvxK4rznx5490f4bPN8JPBGoDUry9WO38R+\nJEXMk9wW+aCJuyKMjOfwrtPh8ljDYTrLMbdbaRII/L6IgJVePfmvh8+zJyh7GApJXuc9bXs2hXy3\ntq5EtvLvznkEEA19BxXK3FvHLHykirKvPYivCvE2nGx1ae3OcStu56MpJGf0zXpvg7UzceCLG4zh\noUaJuf7pwBX5Sm1UafUgezteQwr1LzGIfTIrnvFF0t3q90yEBEPlL34Uf/XroNKkWGGaRiGFu7SD\nPHJFchs80MTksQSSe5J616duWOgnscbayQwugFmhYoqyMycMMc55/wA5rA1fwrpula1LJpCqyXAV\npEUcZ5x3Pqal8O6o+seJDJrF41+rdfKAUAH1x9K9Bt/7EuPPFpbQwhB8kkp6kZr17qO57rg90zl9\nE8Taj4b02VpoLWQRHKQy4Ib2PHeub8U/2Z4oC63BpKeHdWzl4bE4im98cf5NdZDosOrO2+NpC0mT\n5n3TjsK5P4hXdvol7AY1aCNxjY6nAxjgVSvITTRQ0O4m04OY7NHuJGB85pOV69B+NYF/4xuILC4k\n1KyW9tVk8ljKmSvPBB7d/wAqNbN1A0F7GGa0kB+ZTnBxxxV/TNPn1TwnYJeolvLeyHcJflBz0z9M\nfrWlo22J5pLU7nw5r9sYbcJMkqiBWQ9cgdj9M1Nq9zoUs0R1CFLV5SGilGBvPcH9K8T8HaPqGieO\nH0Oa5lW2tXM7zRnICE/dB98fpXu138MLLxBcXGpNAmr6THF5guoZdzxOgyVYDoRkVk5WZup3WpQg\nsHW8ngUxzSiNTG0xO3aDkd66jR7Ow1WCW1tkEGtCMtJFvyCpzyM4/SvKr/xfOLyBbJXa5im8hV6D\nbgZDD8sH61neI0m8QWS3Npdywaja3QV5Y5NrrGWXIz3HHSnzN7C0a0Oj8TeGfEWlCzWSRZLcMR5i\nvnyh7/n+lV7XRdZ1aG60YSAC5kQJeqoDKP4sEmtLxT45uNL12XTCuSEUrkc7So5Hqah0fxTdT2sm\nnG6Gn3kp/cXHlhg3+8T07U9LXZkzsILIaF4Yv9LLi6bTpd6MSHkC4GRkH2JptvOl3ZwzQutxbToJ\nFPoDXOaDbzaTrMU886Ty3+UnEZBBYDqfbk/nVT4eap9m17U9AmkQZlaa0UnqmTkCuepCMlsc+Ip+\n7dHTvpm7LWzYP8SN0NT6FrN3o19DcWdw1pco+NmcI49GXpg1oGHaf3Y2sRuBPcdv61K0Vlq8aw3P\n+h33RWB+R8etePXwq3icEZdz03wv45sfFUv2KdE07UmBPkO2I5D6o39PcV0iSNaHy5VJHoRjFeH2\n2nx27/Zbpd4zlVbgfXP8q7rw54tl09Ftr6aW9sF4SWT5pI89ie46fSuWnSnBPmE3c9EDeZGGyXX1\nUdKe8Y4XnpnB7/jWfZXIUJLC/m2rcq6HIq/G6vGQrZQ846mr2JKd1bgxMTnYwwHXh0PYqex968o+\nNPw71LxJG3iHw0Ug8Q2sW67tYAEN7GBzJ7sAOfXdXrryKrgNwx4GehNV50dJEeMkSRtuBXjn0PqP\nauqjWcNwu1qj4wl+JHibS9EEzafb3scKBnjuoPl25PBPY/nXZ+D9el8S6Z9tOnW1jatCZQIUKYPp\nt/rmuk+N/hxvDEUviOxtY5dBuD5ep2rpuEDsfllA/hXO7PXHy+tZNqrDSTKhSO3aGNI4142rySf5\ncV2Yipz0W0ejGfMtTGeNPtkkpB3FcPt5O31x/nrW9ZSN4d0X+0DCGvb0YiRj9xOQD9Bnn6isXw7p\nkmv6+0LErBEn2m4kBx5cSnn+nFbV1dLr+vX135DNaxAJaxq4VVQccg9j3+lfP5fQc6ntZbEt/cZG\nt6f9o8Pzi3hlvJPIaNE3DA6EsP6GvAL9iupokmUQjY394PjAz+f6V7x4u1ePTdKmV18oISqeW3OT\n2yOgP9K8BuJzfXuTxGZDudjkIR6n86/VsGnClscL95l+2t919YQW/wA0puBGQOpLDBOe/c1z99ot\n1JrGoMS8luLqVYzECSfnI/XArutI0i+02CXxDbWkklhp7LGbnGFM0h4A9eB17ZqLQNXfRpSCpe1m\nyzgruZJOdwz7nn8a58TWjLSJtRp36nNweHNYsNKmlmtWaIYfy2+ZgvOT7dasqYdHtLWaSGWJJTkz\nx84U+or0/wAJa7pV/dXFtDcQQ6ky5MVycBxzxk1z2u+DdN1GeXK3lpNI4zE7fuiefu+1eYqlzt9m\n0tDFj8bx+EPEfhfX03j7JfxXEkYByYAcOfxUnsa+w/Hl9p11eQR3E15qWnSMNWvZgAEuJ5drWtuM\nf8sgiZPoe3NfHvjXwjJrGn6bJtSMW8ZibnHsQT/ulq96+BOsv4l+Gmlrf3itNok0mm3I3Z8wAqIA\nB3OCBn0Br6zJMRyuzOGvTakmep/B4654v8Vy6ZYIJPEvjbcl1fTMSulWi/LvB6krEJFC8dc8V6Xa\n6LZ6G114ftNSktb22vptH8L3jAmNUREjvb58cKqhmVecnB5yePNvhtBpmqy+J4rbVrzRLzUZWXWd\nagHkppmhxAG5eOXP7uRh8vHPJwea7PXvG9rpWjXVvpFo66fqOkxaXounNAZBpulJJlWkkzkvcsX+\n8SWI57V6WI55YhRjsc0krFjw54jTTl0e8n1L7Z4Z12e7WQ3I+aSxsx5UKIv8IkkD4zyNzcHNYOp6\n/qDQt4luraN9e1ZfLtbNMgQRyc7WP8IO0knHAUDvmuPhsLmx0LRmuIS9xqOoXQsELZWK3tRzHGpP\nyqJS3HQHPFcV458b6vNMJBdyyXxzDDHD02EEEfXBwa0q4Z3bkzOMOZ7npa+LLPTDPrkEryLp4NrZ\ns7eY1/fOoBkAPSOPG0EcYYVxy+Mkm054beZpLLTrtTM07lmuLsp5kkjeojGdnXkdq8d1fxvq7XFn\nbSL9kCIIolxtWNBglUPqccn6elVNDnmni1hY2kaI2b3TR5+YOuFxj6N19M0U8NFO6OhRUep2+vTt\naeG/D2pzTC809r6JPsrsN7gAsJMY9GG71JzSeLdZ05/iLdWdyftgvrGJ4EtY8rJcBcKAo6E8fTHv\nXP8Aia8sbi00jTtKZ7m8i1KIyO/+qNs8eVI/ukFiPyre0gR+DLgtp8hn1bc27UZcO6Rt/wAs19OO\n/vWmKxdHAx5puxVk9jV1P4bxaT4R0i78Q3jPrgCGDTbJsLanc27ccnD4IB4rnPFfiW38HeE7lNPj\nfSW1GX7HLd2z/wCklW4b95jPK5H41tPcyPChklZwDn5jkj/GuF+ImmR69Y6PYuXImneRthwQgwSf\n0/Wvgq/EVTHVeSnoiowdzgfBPhG2h+Idhc2F5Ld2dqr3LGYfOrYxyc8/WvevDd2oS6jOESWIHHrz\n/n86858A+Hm06LxPqoiaGxAS1tCxySOckn8q67R5gb3Yx4kRgB7BQR/WvncfVblvcUk0zuvFpNzo\nel3j8SxkwM/quMr/AFrT8G3bJ4avrVTwLhXAz0BA/qDTL6FNR+GxZBuaPEx9sYyKp+C2LJeDsYg3\n15JH86+fv76bMzo5pPLtNRxkKzqB+RrM5CxEDksqgfnk1sarGsXh6SU8b5E5rCivA1wmBkLk9a9W\n65RPVHiOu/EuF9TE1hpEFvAPu7E2FvqKXwdrzeIbi4FwPs0COJNqtgNjPWm6dotvqenXe+3aOa1y\nNuMs47mqdhc6JoNkBK89xLIxzsjwkYPZjn/OK9lK+59BfWyO7bxU0VxIC32fKkx+VyOPu/1rp7XT\n4/iDpNm0kXmSRoVlMhAAI71xmk22n6zBazQMVlj3FlI7DGMevepIPETzefHYzW9nDG+2ZJ5MO49h\n26e9TezE30KuuNaeGNfXTTcW00DdFX5sHt/WuL+JWoSanbwzRSjbbyYEcR+70xxXo9h4Q8O+JQ7O\nrSrF87Op2mP/AIF36Uvi74Z6VDpKTaDp5Zkw8habzDL6dhjoa0U0S4ux5J4LsfEGtXirKsy3c48t\nPIT52Qd/5V6h4O1fxN8KfHNpqug2Sz3Jk8vVNLnb5LmEghiVPG73rN+Huv6lY+I7eGO0e0vix/eM\nnEaiumuNT1LxBd3l7FZi8l3EGVOWkIz0A9Pr3qJPm0BaI6Xx78K9H+Kt3H4t8AW6W93BIJNQ0i4k\n8uVDjBIHp17dq8zbwBF4d1ATa40qo8ibLSD727cSC3+GO1XLfxZrekXn9p+HZLbSdctWBliuTkyg\nHlSM8/8A169o1aTRvibb6dD4nij8OeJ5o0uobqJv3LH0Y8f5zWak0+UJQW8WfPXxQ1GxTxqY9QlW\nAyhTaXCH/Vvj7re3Sm2d7puqwC7uJ082zbM4jOVYjoR+tUfj14EvbPxpPJfhLiFQU325yjjAw49K\n4Twj4Ju7e0v9Xe8WDTo38qCJ3P7445P6iui11cyvZ2Z1PiHxNcRXFtPp0ZikVvNiZwQGx6+xB6VD\nZ6lcuP8AhKLSFYhZtnafvbj97B9PQY713HwT+GEvxW8VFdbvdmmWChvLRdouDztUHPbB5960PjT4\naj8K2skMWmf2bZeYdiqcgt2ye9QuV6IH7ytI6zQdctfEmkW2oWsnmQyoD8v8LdwatzKGBSTv0rxr\n4Q6t/wAI3qx0eSRXtb0GRQW4jfuPxz+lewyFSOQMqMcGsZwueTL3WWYL0LEtvqG6S2X7k3VlPofU\nfyxVuOWW2xJGontSMZ/+tWUk6LgSHCN8pYdRn0rJh1+98JahLBqMizWUrAR3eOMHOA3pj1rmdNNa\nAlfY9E8PeI5NJPm2jAwOfngz8uPb0PP416HpWsW2o2v2ixf5RgyQtw6epxXjKCNyt1ZSLgnJTOQf\nce1aOmatLbXwu7aUxXsf3dxyG/3h3rhnBbCem57O/lXkQV34blSByD2NIAdphlID+o6n3BrnPDni\nmPXUkK7YdQiYie2bjf8A7Sfrx24roYpFkQMpDbecnt7VzSTQ7XRn31nHJFPFcQx3UE0bQzwyDKyR\nH7ysO/QH8K8M8UeGT4WuLyzQMbKRFezkPeMZ6+4yB9PpX0C6+emVxvz6dP8AGvOPifpzajd6TZK3\nliVmY452xgfN+mfz9q1U24OJpC97HmGiu2neEJbtBsu9Yl/eOxwFtkJA/NiR71DrHiBNPWCCIRCW\n4XHykYZSMYBPfj8Me9dTq2gL4hcwQJFZWtvEscZf7yIvUr69s/SuO8RaDatBciGViYXSS3EoDhxg\n7wenfBH1r1ctUYuMWdVaKUbxZxXxFcSW1jDGpjikTZNsYPgr0BI+p5rlND0VUhlvbiLZZwNkkjDN\n14Hr9feummFtqcrRW9q1taZAkfOAzc5wMcfnWnpOkDWdRtdORW+zK+cEZwoIzX0mIx0YR5IHFTp3\nvJnY3tobH9mvUpPL2thbxxIMEbnAGR9FAzXzbrHjU290RAiRqGyQozX2D4vtYr34PeMrd1/cC1wg\nHZVOR/I/nXx//wAILeRq8jxoYQ3DMw5H514WErOs5Nm8L2aRdsYtM8VyiXTpBbaqQMxyvt3n2P8A\nnrWt4d1vWrCaSyvpYrhEJ/1zEuhHfpyK4P8As0XeoQtbu1nPEQ688ZB7N+FemS28+vWkeo2xRdQV\nPKmKjcGHcmu+SSeh0x5luU7mGbUpLya4lWRXfcYQ+FKHrgV2P7M8sFl4y1Lw/dlorHVUR4xnaTIj\nEZB7HY7c/wCzmuM8P+GLt5GM0GYoiXEsbff/ANmuo8ECDxNeJdGKXTdQ0i+SaG46LhTkh/UZC/hn\n1rvwdf2FRPoFVKcdD6Kg1G/0LS9SW5Way8K285gaN4gP7ZuxKTBbITkMNw+c4IwDkcVeuvipqmn6\nVOmozzXTy3h13xEYYFSH7Qqr5EEZHComB8g43ZrC+Il1rOovpUVzP9o0bQ7CQ6XCv3N7tlnBGdzs\nSvzdRz61Fb2surQ6V4Mt763exmgXVtVuJTgrIoaVoy3YBVYkc8n3r9IpQjUXtJHlSikbEOl3d1pH\nh7Vri4Eh0m2i1K7gDcQi8uXIAGeu0gkdyT0pvh9PD8LXpYmS/LBnEq5dG5ztPcYIzWRqHiT+0b2e\n5WM+VqU0SNEVxtWE5jwvoUKf5HNa9VGuhd2vmxXcM7xR47ozHjH4gVeIw6r03BuwU1bcyPiumkLr\nVvayok1je2Cz7IOGDBiAytjg9cj6elcToeh2P9pyJp2qoZ5Q0YhmbDlWABU9e2auftS2epaZqXhe\nxs5fLaLR9srRDAZvMbgj149e9cx8FrOe81GG+Yt+4gklzwfm4GK/Oq+LnlfMoS5jsUIuOj1Oss/D\n1v4fhvFidZ3muMbv4QoA5A7c5/Ko/MHnkD5VDYwO/wDkYq7eSiOKM4znNZcLHzQc/wAVfm+Nzarm\nE23LQlrlRr3DgQMB/drB1i/t7LVbcyuv7q22AE9zx+uf0rWuX3yRoDy52j/P4V5xNFL4u+JsyzDG\nm6RLul2/xmPOAfxrTL4uKciYys9T1HxReQWGh2GkwqI3d1klx3wo4x+Nc7DfeRfQOOCnv14Ix/n0\nqHxDqRv9Yhdz+8VA7D3P/wBYCs27diAFPz4AB9+ea0m+aV2RJ3Z774H26h4c1K0YjgNEM9vlBNVf\nBcTSWs5HG8iIHHYE1n/C/U4p4ZwzYEhUsPfbiur8B2X/ABLkdl+Zp5CPorVxXUpaElzx86Wnh1IQ\nMb5UVcfrXHWmV3SMMDg11Hj+f7XFDbow+WTzCevTtXOxqBEpbv2r0U7pAeC6fPdzPPLCfskwG8yA\nnkjsfrVrTddsnWe6S2WYnMN1CVyjep9jWZpcz6VqkunXNyixh8xSOcKw9T61m+ODb2WrTS2l0vky\nKDII2wpPrX0CR7TdtTufD+qR2MLCzt9lqMlWc4ZQPf8AGpNVh0fVBHf/AGA3F9kDer7VPueOawoR\nFfaTot7YyBg+I5owcAj39elegaYtvZWu+WWC2ZV3ABN5Uei+prOVuhpFKWrNCzt5PJS1m0icbkV/\nMtvuNj1xR4iZtJgt59HllihmUeZEM7g3f6Vd0z4pwXc8C2Us84hZVYLGAXznhvyNXNa+KOntG8M+\nhldh2ySoPmj9+lZtjt2ILTQJZ2gv5Z2MQTEvA3nI79Kra49/babJa+H/ACNHs9vloyjMrserZ/z1\npbbxj4UuLcpd/wBp3CqdxKccf0pdVn0rVNIuIvD+oSRCSJvLNzGVaM+uec4/Ckm2xNW1ZxumeH08\nI2wGsW0F5e3LbiztulJHO7Pvnp7VYvPiNYpaxR3zgu7rGI878DOAPbrn8Kyn8B+KtThguFeLWGhQ\nq0ls+5j71h2ngq8ku/8AiY2E2m2lopZ52UnzG7c+1bcq3EpI6fxtp0um+G4J2PmrNGWKsxLMu4jH\n5AVxmgyXFl4citJIj5o3FImXI2Fs4I9cY/KvU7CybX/DSaXK7tLEnmQXDDgY9T6dK6r4efDnxVqi\niSa6tYrYLxIsIOevIJ60lLWw9F7w39m3xFJJ4yW0nshaRkhIoym0kAHJIr0/4m/A+08WeMfNurqY\n6SUE0lrHyWb8/wDOawtV8PnwB4g8N3b35vZxKsU07KBwe/A4ru/HPi+28G63Hquol3tnhUQpE2ST\nzn+lZXaZne7uzzX4g/s+eBZo7S+06CXRbmEZBJ+ZmxxnpnpXBQzBrPdvD7GKF1OckcU74qeMPGHx\nCk+26Z/xL9OJwjLiTaOmWx/hXGeAdN1LRbaW3vLr7WpY5JPJJ7gVd9NTCrSjJNo6t33pnqR2zUOo\n2yazpM1mcJJtPlOf4T7+3tTmPlPsb5Tj86azBMkcj3rkTadjy7uLsjlPCd/qnhKT7PeTtcohx5GM\nrHnup9D6e1ejRSR6pbieBsOV4HQ1zuq6Yl3i+gYRzIm2RcEhvwH41BYT3JvrZ7VmaAriQNGVxjvz\n+NU6aktDsjBTjdHaWmoGSUASmG6Qkxzjgo3v7cV6B4c8UyXg8mXCXqqN6KciU92H+FeXMyzBnXaZ\nkPUHgj3FXbW+FwUjO6CeMhkdGwd3bB9PUVw1IWOeScXZntcF+jxh1weDkZ71zPjWNFvLO6DgmOB4\n8emcc1naHrxu3NvKuy6C7mXOC/qQKpeILlrm+CDJJTCjPHOKVCOor9jA1y8lt9KKxykS3j7SB1CK\nAOPzrKu/Ddr4i05bR28ubaUimHG1j6+tWNXbzdXl2jdHEoiU54z3P8ql05XgZSGOQM4rWUWneLsa\nwlrqeWXulz+FlexvYwLkEhGPCPj+LP49K7b4a6UbLRW1CT55rl8Ix/hUZ/nXU+JdBt/GehSw3C7n\nj2Oj45BLqrAfVSRWhqWmRaRqt9p8MYihtmESKOwVQB+mPyrirSnGLuzab090j1eEyfCvxxHGpLf2\nZKVHXJAJFfFn/CVrcWVtcTaesgVVLqrEk5HpX3do1v8AbfDGv22zcJrN1IzgHgj+v6V8lSfBm30e\nznsJNVhaV/nEmSAAOxGfevSyyfKrMzoqTOMtfEWhy3sL3GmXHkKpVoQcHnoRXTaTPcTeHGn8OuhS\nOYmSOX5XZey/zrnIdCmsfFNpZz20KW5OBcJzvHbn8P1qaTV7fT7W+tbZpQ6uSAByj845717vxPQ6\nFLld2dHZSW3jyxubWG7k0vUVUj7PuKAOOmK3PhP4e17wjPLDqwDLJKrl5PmOM+vrXmnhK9TU9WMt\nzerZXMq/KScEyL07e9emeG/Hlrc3jaXqFwTdovzXRb5N2Rjms5PlNKaUndnbaL4/u/DvxUh8Ks7a\nhoWpTiE2Jf5oSy/NIhwcE9x7DpivadY+HjvZ35sot3mRiN2RcF1LjdnnuOvtXgPw70OPUvjfpuoR\noztGj3BnbkFVGAR9ea+w9DcJpilupy+4nPXnn/PauyhnNbBNR+JHm4hLmsjzTWdIvLs6ZKNPX7RB\n+8LhdoYiUfLjsNqgD0qxoWhDSbt76+aB5hKZEi3dW3Aj6V2Gpy5uYSvyxqS7MOhGOlcqk3mktGw+\ncE5A754rsrcTzqwcIwszJQaVzwL9qOSb/hIPCarcMvmWcvmvjO6RnX/OPao/g2sVlY63sJK29osX\nPd2L5+nQVZ/aWtGuvEvhK425W1t2aQDuSeP8+1U/hxKv/CK6ndhcfaLrPpuCgj+tfIYqrJ0HJu7Z\n1RdkXtQkIRAex/wqjCctweM5zUmoy7tgPOEGfeq1u+WAyB6A18VCLRDd2XWmVZ0d22hCWz6YU81y\n3gdD/Zk1867W1e8knYnr5ZY/z/pW9EyXF6yzoTCiNv2nPXH/ANeqVzJDbQyywr5drbr5USDsv8P8\nzXs024R5e4nbYyGuftWpTSE4w20e4FTJ++n2n7o5rKssoqluXAya0tOky7seSRgCuuppHQg6/wAJ\n6x/YmoRSO/7mQgOOmB6/h/WvftIgNj4SsWjUl5t7AjrhjkV8xwK1zdQ2/wDz1ZY8e5YD/GvrazVb\nWztIFXm3jCYPoBiuWhHdk3RyGr6RK8lv5hwWXdjrSxaNF+6Qrk56+lauuHF9bqDwEPH4ip7CAG6Y\nHtxXoxiguj5u8PaFpep6bbzTn+0WxhJphzk9sVZ1Dwz4fvB5Wp2KjacHylIJHtgGqFr4GuFnSW1v\n7i1WQ8QQcruHt+NTN/bXhi+mS4U39zHhkOMBweoIz24/Ovei09D25LQTTvDOg+HriFLKW+khkBSO\nKVSUjJ7g1jap4n1DTbu40y6f7LaLLmB3j4Pr8/5cV1w8Q6lcRBobCGF3wTE5xz6jOant/DVtr84u\ntc+dU48jcAh96iTimEeboZWg6do2lajYX1hOyyTnfPG75y4xgj8zxXVxaKdUv7uW5BVJT8wC8H9a\no6tpnh2ys0Flb4licOCpzgDrz3oh159T/cQlpnOSqrxge9S0mtC7tPUr376Z4TaWO9eDzMARKpyX\nH4en9aSeaXUY7MWl89hHJIodoQM7fTmpf7KstDmNwbVbydxljJ8xDegBrC8TX8vn2FvDH5F15wco\nDwq/lUWsNu+h0Ec19pOq3Fsl01tOF3xXEfy7x2BH9avQeIdSvUmtZ7oMyxlmeHDbvYA96yvFumT6\nnpFrrdhIZb6zzGwBwrKR1Prj+tbWlaBJFoOkJcWxtrqSDMjd2JOSf5Um2JI0PC2l3Sz2t3rlnc/2\nU+GMKsFM4HZyM8e3vXv2neO9Hk04Kog0yxhXYsCkbUHtx7V86Ra5PpsVxFcXUsM1uuIpVJZHH91l\n/rnua5/xD4lnsNHj1lo+YZCLiEHAlj4yR7+1Lpcq1z034seOdL8ZPNpGn3jAwr/roV+5KORk8ZHH\n61saDqsPxw+HLaTfqLXxFYApFJEB+9CjHGfpzXJWNlovxG8Mw6rol4reZHlnjAAVu6sOCCK5vwPN\nq/gS8Ecqyi8srtpUA/5bRHrj68euMe9NK5Eoq1ij4X8JeLtHu7kRzSWSqxjBIG1gCcEqfqa0vEGi\n38s5c2iyahtUSS23yhhzzxXV/EW6/sy40/xLbebe6Zdx+W8Zb5Uc9Rx3Fcda/FJfs09vphgkmVG3\nKU+aMd+Seadgjr7qI5LcwwJE7bpUPOevPv36VCkob5e4yDXE+O/F2oWh0+8R2NruBJx949wfT9a6\nW21GO9gju4WDxygHjoD3H4VjOLtoebXp8krm1Z3BhkBAyD1GetdPceJbc2Je4tI7dIY+HJwGPpn1\nrjFkDqMAfKep5qTUtMHiXQL3S2kaN7hf3bKejc4NRFtPUijUdNnL3euSQ65HJa3ayXM2XKLyqKP4\nT7812en6j/atmtyqmNwcFV5OfUdK8LbUx4cSSyvE8nUISYWfHLbf4q6jRfHJn8JwvAcSOwAlXqCC\ncg/XitZU7o7JRjUV+p7Tpc8l7f20TFhOHBSUHkAdcH8sitq+ux9onud33VZzx3AP8yRXHfDjxDD4\nluhOsfkz2kR85B0B4AIPvzXV6hbSPZTFVyWZI5AOwDEt/T86xjT5E7nmz912OetoZFEe7mQ5Lk/3\njgn+dadrGWfJGDjAp8VmJLmQgZ578Z9/8+lalnp+ZkwQADzmpauSpI3vCWnrdXkEDLkNLEMf9tFz\n/PP4Vn+IiJfFmrnOd93Ku72DYH8q7LwBbf8AE4SYrlIQzH8BkVwMsv2m5kmzlpJnkJ+rHivHxzt7\npupXVjo/BkSy2eoIw+9G6AdycGvjbW5tQ1yW6hM7wyRSOgI4YYcj+nSvtXwPGpBDcBpMcdehr4a8\nQ+ILjSfE2u2C7Fa2vpgXYckbs/1roy92W5tRdtDO8apdadDZeUGaRArGbqzHIzmr2reGpbjVUa32\nGK4US5TqGwM5FXPDPjGz1OO4gu4Q0zAbS3zBcZzgY57VF/wlLaTrIuhArKzFQucfLxzX0kWzs5U0\nR6r4L0jTmgkknkjvSuVWIcM59ahvNMg8P2ttG9t9ou7gh3Uv90ZHX/PetzxFrtleaZbXK4RllyzE\nc89vaufmvw5kjjtwGwBGw+ZpCTwM/XBp25tWZyfItD239mzTXij1i+LySQE/ZrRpOSqliXAPfBGK\n+n7CcLp7p2C8DPQV5F8OdIGiaHpelooQ28QMoA/5aEZY/qK9DXUFgUhuA3y4ryazep50velqT6pd\n7bOQjC7UJwT19qxhIY9Ksotqq4BdyFwcYNWrl/OkZNoZAuSfSsaSYvcSfMduwKMn9KwprRtku549\n+0PDLPPCyHMtskSIgPLbgayPDFq2meBdNgbIkePzJAeCC3NaPxw1Ii48STLhvs1xAEPdQABwfqT+\nVVoyyeH9LVyS5toixPXJGa5cdNxw6NoNMz7+b9/jHRQKitpMMSFyeMZ7Uy+fEzH0wKSCQKJDzwua\n+fpu6BpXJEZo4bqfJAkZY1I4yOc/0/OsXxDemLSUiXh5pMk+ijpW0zqbC3hBzjc5+pxgVynimVf7\nW8kHCwxhT9a9Gk+Zq5jJNakVuxjibnNa9kBHGOxxnNYkZI8pfUCtoTeXEuBz/wDWNdlV6WJXmb/h\nRUvPF+hW7nMck5nYeqxjdn8yBX1B4fum1COSZ/4uVPt1H88fhXzt4R8OXtp4osNRlgZLNdIKxORj\ndI7AEfkK+kfDVp9m0i0Vl2sRz/hVpKOgmr7GTrYEmsLhvuoM/j/+qtCwQPMzDoQSR+FZ+pgSanMR\n8pzj16Vd04mOCVup2ECuqCuJRZ4b4W1qfw+8V08sN021mWADLKpxyayda8UyNcs5iVxc7goB+dc4\nya1fEF7btqP2eNkgiVRCGiUZPsT3rziK40jRvFN5pXiOSazDrm3kwRuznjP5fnXrR0PoLkV9rh07\nVhPcnbaQxsMO/LHipbLxlca9FCmlYRJSVzcNgEj09a5bV/At7fakt1bOt5YLJ+6EsvT1z+lc/fPe\nR61HCjxj7NJkrbtwvr+f9K2SjIydRo9F0ie4v9fWzuJnJjG9hGcLntXTab4pi0Ge5VIyJ+/GN1cL\nojSxXBuYW82JnEJZeSG/P3r0yx8IWdtGLq+KImBukc9z6Cs5e6axfMQtqZ8RSwfZXeGQglxj7vTn\nPetF0s0tz55M0+AplCZbHqTniuXtmtYrS+vLKQr5EwTcpz8pz/hW7pep2jSGZ3PkbRuKjg/41nfS\n47GxqGi/2N4CVldsSykjOScfT8vyqC38c3txZ2puIjM2mxYlKcFlPQgfhVzT/El34m1A3Vifs2n2\nqBIGkHDf3jg9egqDUdYm1+6ItdGae5A2/bIYSoZh/e7VHMh2Zz0fjuCeeWaG1uZfMG4xGLOOeM16\nPDoUeqeHpLjWbVXtJRuWFh8p4/Q02y0GKxaBJ0IPBmIIyW4yOnFS69d22tahLbXjXNm0blLU/diI\nIGDjv0FUpK4NXRw+r/8AFDXNpr+izx6ZFbn95Yynal0v90j1684713/hT4geGvi5pUctmwjuYj88\nTjbPE46gdMr/ADrnrbwJYeNbN01OOWPxFayFYxO2beePsU9Dx79a57Q/hJJ/ad1b2d68VzExlSIS\nbJh64PcdKlrW5HNbRnW2t3DoEup+DPEDlIbpvtdnPn5VPPHtnI/KvLfFHgy48B60jMzutzHvgl+9\nG7ehcfUVZ8W2epQXBubn7TPcxELiRSx29ziu30Kb/hJvCsek6gzz2kjALMFysbkHaM9uh4oT1Ha6\n0PLdNu31eObStXgFuxy0c0XZj6cc9q0PDlne6Usmnyyrd2aAss7HZICexH5Y59a2V8F/8I1fXJml\nkN5t2LDM2TEScZx7+vtVvxTpelW+h+RNLviWMmSYth2k45z6DNabmMlzJpiWsjQlYmUqpUEFqvQs\n8bcNtzxkV518P/Eba7Dd6dOCt5ZyEJIf+WsXYg9+nSu6spw20EHI9azlC2p5NuVtM5/4p/D5vGEE\nOpWG1b+MCN1A++OOf0P51i+AfBf/AAi2n6haam8VwZH8xoVO7yge2fU8/lXqFncCNgNxAJ7Vz9/B\nbeHGkk8tXEknnGSXkk9+fbOcVpGq+XlaOnDNN6s7P4cabFBa6lcKsUcO5IF2D+AZO4n16cV3Gkpt\nitzICWeFriXIzjcxA/Rf1rj/AABaFPhpaTEszapvuUyeoc4XH5GvTYdPjFzOrZzblYgqnG5Ag/qT\n+VYt3ZjXtzaFDSdIiur6Qso+UN7Zxjj9a6PT/DsCyL+7+YsOvNQ6HYAMpdgHxsyeMknk/oK7SGwc\nXOVZf3eQcc54A/rn8KpJM5iDSLSG0tJnwI1WOQsyj2OP5V4tZZktYSRtLLvx6Z5x+te2eJmXRPCG\nsTKdohtJNpP0IJ/EkV4zZbZbeF4/uOgbPpkA18/jotu5vE7PwlGEton9Xzn05r4E+NlrLp3xb8XR\nqoCPfM30zjH9a+//AA2uNMXB2kHr+NfI37SnhhYfidrF0kQHnKkrgnGcjg/zqsrleVjQ8Xsb6VNS\ntAI1ikiXIxyGHfJ/Koby6bVpby4LN5kR4UDpnsPyqCK0muvtkMBO6FgQScHHsa1I9M8s211Gsg4A\nkVlwD+NfY9tDVSaKmkX7x74LmEvaTBfNTOSMZ+Yfn0r1X4X+B4NS8TwahJK8umaftkUMuMykHap5\n7c/nXl1wsVvfvFEzvIcmPA6nsB+OK+mPh9oEnhrQbLTZyPta/vbkjnErdR74AH5muavL2exEpt7n\nqnheLyo2djlwm3ntnNaV/chPI3HjOD7kVX05Bb2kanCu3XJ61NZ2zalq0cQTfDD88jeleXL3mc78\nizdznSfDFzey8PMPlB9ewrF8PSfb722SRtw81cn2HJqP4sap/pNjpcRwIgHkjB79hWPoN+bB2lZs\nCG3kc/XacfzFTOXK0hK55n42j/ty28RqXH+kTOwJHpKMfoP1qxqa7Y7eMcbI41x6YUD+lJbWiarF\n5TKdsmHbHX1JpuqTBrkgnqN49h0A/SvFzGf7uMTaGi1MS6k/fPgZANRmQx20jEjO04qOVwWZSc85\nzVa8mVVhiGcu4H0FeZSQN9jWiwroWOFVQW+gGc1wNzcf2hqcs7fdmdm/Dt/Kux1+6W00m9fO1pB5\nafj1/lXFQrhHIAGADtzyBXq4eJlKV1Yv23zXarnIXAzW/oemNr+tafp68GecDrgAD1rnNL+cM+cu\n3AU/41oz+KLnwZbxa3ZEC8hmjjiDruVs53Aj6AVu1z1FFAldWPrvWdKSM6JbhVECgFdo4IAxj8zX\nUQAiOEdBtBryH4I+Otb+J2gjWtdS3jka4f7NHbJtCRDAA68855r193MFuDn7qVrNe87Aorozlrj9\n7fzsDgbqh8Q6ouh+E9Svnyqw27ncD0+U0sTGTLOeSzGvLP2nfFp0H4dLYW+Wub+dYQB6d66qUW9j\nWKuzlZoLTUNQt4ri4EKS/wCrkJ2qZM9M/l+dQeP9LsvEHhsx6k5j1WzfEc3Tf7Z79P1q3Aja7o19\nb3scUAgmW6hWE4Ixnjpz2qt4e1az8dXF/Y3Mbx31qS22QAbk7OB+B4r0r2Z6zV0cFo/hzVb63ntr\nW6EcAXeCrfeYZyD6VyulNHBDc25gaa/lkZGOMlcHnn0r13Ulm0++OlyK8cTfcurdcIc9z/hUn/Cs\n/N1Nre2kMUbbfNumGAV/iwexPFauWljHls7nVeGbHRb3RLLUH0qG1vYEAMwGxRgdcevFc74s16W4\naRo4mktI2zlx8xyOuPSm/EbVpND0X+yNMBHybIw7ckAcE/ma2fDt/pGqRaLp1+sc73drhm3YZHUD\nIJx/nFc7XVm1rq63PPdF8X6faXNwrQMFfhkC5DHtkfnXa6Nb2uvaaHsoTFAQQQxwM/0rK8c+D7LT\nNQElqksVmyFkd+/pz+daXh+6EGmQWME8YsyQ7uoy7vz8oqXrsXF23Jn1q4l1/TfDuj+S6x/NcOnI\nUcZAP517x4TnGnwpApPkA/KrYx7npXlXhvwnBo0zXszGO7uzvYtwI15wMdjXYDXI7C3MskqFY+Aq\nt1A/yKizNOlzk/ir8QLbSPGdpo8DAyX05LmMcIApJP8AKt2a4m1PSrOEvvwoAmkTIAIB6f56V5xa\n/b9Z11zqlvCkBnMkd6mDlSeFz+Hr3r13Rr/SdKtjJNOYLtQfJiugAcDH3fbpzVbEX7EuleEb64gX\nzRHa268/aHfAUeozWTqekQzSyXuj6hGdatWHlXBbO4fxAn3wKyfF/ju91xZbaCRWhAKqFP3jx371\nm2V9FoVrF+7XbIoNxKexHQAfie9LVlqMXuegQST+K9MLapFDaaio5lUABvx715/Y6mYI38Iw3EcM\ns9wXyB8qnIO7P4GrlxrE06JeWYlmh3qskbfdAJ4I/WuL8RyxWviVJmuIoZmAkWGN/nBBIGR9P51U\ndXoTJW2O+8dXSa5eL5USsVVbc3ZGGmVep/nXkHjrWrKS5ktGxJEMhVxkYxgc/UV6FqOuPaI966oI\n1AQRBexHb9a8X1jTZNX1idoZFtrdWLqJDnK4J2j34rWzM5WtoZ/w4029vdRZreRYbqwjaQRk/fOT\n8o9eAPzr1q2aV7WK5kj8pphuZSejdxXKSaa3hG10y+sI0WZ4vNeSQZYvjPT3/pWfaeL9RsNeiE6l\n7K/lVZISOY3IyGX256cVpNOS0OOpTTVz0uzlWRO/Bxn3pPFGknxD4fu7dMLcLGzxMf7wB4/GoP3l\ns49+SMYrQjuGddp+v6Ef1rkPNV4vlPQPB+mi20LwLozD5oYIFlVe2FJP6j9a7yKFXmyOGbJbnOfm\nP9K4O0vja6pprIVzFaHacdOACa6vwq63M429hknOQc5qVqxtaHVWduEVFVVYEgkt2xXV2GZmlmyv\nUb8DsSMfy/WsHToXmRspt8pWYbu/Suj0Cze4bDbVhh/fSt6gdvxJFW/c1ZC3OR+LN80HhfUrNshp\nykOB6s4JH5c15R4bdJ7GW1OTLbncg9UOf5YruPjLrqi90VXbCzPLcSDPbAVfyJP5e9edRMdC1yOY\nHNvwRz1Rj0/WvLklUTuapq56joJB0qAqPlcbh79q+bP2tdN8/wAb6WqsY2n04KWBxkg19M6YqLAo\nTG1SNh7FeoNfPP7Yul+Zc+FtQJKsm+LKnGehrzsG/ZVrG6XU+ZGsxFa+Wysk7OPY/wCeKfPrJiS4\ns55NwEZVAONpq/qO26tg3mMJiuRjv/nNY1ro01zqFqYgbyeVxGkGeS56Z9B3z7V9spLlTZq/dVzo\nvgz4eu9Q8TTXt2Fez0tQ2W5DyZ4H6H9K+mvC1i80xlly4DAFjznArhvCPhqPwvpNvpNuBPKJCZX2\n4MkrfeP06D8K9cs9PXSLWK1V9zqu9zjv3rzJzc20ckld3L0rqm13PyoCc+1dFoHkaLoc2p3OVj5n\nck9EHT9TXLRINSvLe3AOJTggeneofihr/wBnsYdEhbbEUBnUHsOi/wA6weiuScNrGtPq2qyXspJe\neQvnrwen6YpNRvzBoV8+SrT7baLjlmJz/JTWVKQ1zCS3QHcemelZnifWBY6z4T0ssWefzbw+gGNq\nZ/Nj+FYxkpu7Cza0Op8ARRnxGGlXdCkLEjHGD/n9K5nxDCLPU7mHHCMwX/dPSu6+FKL/AGrcI+Di\n1K7T35A6/rWJ8XdGGi66kiYMV3CHUjswOCP1rzsdBSpq26Ki29Dz1tq5J6ZAqLAk1qNBgqoyw6/j\n+lK8mwqM469e9N0mETai7pxtwGye3Jz+n615lJFNWKvisyTG0sYonnupSClvGuWZieAB+Br0Lwn8\nBZWs1n8Uv9ngdfNktI3wTGOoY9j7Vp/ATw/FqfiTWPGF6N7Wkn2bTxniM4+ZvcjjH413nxJlubjw\nzriws0l5JZSEMfvHHOf1NfQ4emlBsx3Z89+MtSsNY8R3X9l20Vpo9sBbWggTgqv8Z9STxXnnxN1A\nx3uk6cjbVSPLx5+7K+AAfXgfrXWaPCI4oYiQsMYJfPGFXr+tcFp+gTeMfGlvP9o8wz3ZmdeoRF6H\n/PrVYSHNJyfQ2slE+0PgfpI0Twdo9tGoQLESVx0PBr0/UHP2WQnGPLx9K5H4ewBNE05hg7oVb8wK\n6jVZAlrIWGQF9cVhN++ZxTOYmnSBVLHC4AJ9PevnX9ofWItT13SbCS4S3WJDduj8k9QoH5ZzXs+q\n37zziNW/dYKj3yD/AIV8nfG3Um134h3syvgWxjtx7ALzXoUN0bQ1Z694heGPV7QQeZHbtJmUhT8w\n/un0qrqsGn/2lc3EenNbX9nblotSt2Ks/opH4dc11FxrizRSW91OrxEZztAZfT/PtWfaaJHqdjdS\nC4M1qmUYu2CeOgruST3PUVzL0XxFH4xuIFvRLGoOG8xdoOMZPvjj86f4++LVr4NtzaWKG6l+8VVg\nQD2HTmptVCyeF9HmsreKZLaRoZ4WO0ypxkf/AF68z8TeA7bVZGns7pba23FmRskofQf57UKN9wex\nq6LDdeKgdRvrktPIS6xTNg/QV0vwv0K4Pii91G4twiWluY4hIPlLNkZrzzw1Nc3Mt3bWqiaW1A8t\nc4ZuvNek/DHXNQOgavDrKPbzKwWNpV2k8k8f570mnblQJ6aGJ4ivNRnto4XklmSKQqkO7ORu6f59\na7CC103w9daTc3UjQ7VEogA6PxXKXxg1q9J09JXWMrvb0YE5x9c/pVjxWsv/AAkH9lqrXN39lWco\nvJVMdTRy6WNFZas0/iB8ZYrLVksIbWW6byjIJCcAtxgdDXO+HfFp8VqYda+TB3PDFJhXB6KePasj\nQ/CUniPUTNEzOY+WRVJOP9r06VoeG9Di+33VutoZY9x3TgHap5wM0vZruHtG3ZI3r/xRpk9x/wAI\n3FtslnjKW5iU7AeMHOevvVjxTrsTeBrLSL6Qz6kg8uO+XJIKnhS3visa+8FSXMdgbXy1uLMEeey4\n3dcHr/nFU/DeleJptVbTbjTbjVbCQkOqrnBxjcvH170ezJlUtodhpc9te+F9RuzC8eoacyplX46Z\nJ24+nerdtqs80MDm3j1OwuowWKDDRn3/AM9qsaP8F/ErC6geZdN0iaUTSCZ/35UDG3jNby6Fofh2\nJlvtcRVHEe87AnsB3NPlsiVO5naJd3SXKXFhA1vpxTbIszYVjyOOO39ayPEfhC30vVbrV5LX7TqN\n/IipK3zCGM9Qv19a6TWPE1npmiRWumlZFkwiNKmQw/ibHbqKy/B3iOKfzLG6mjlO9gguDkgDqQfa\nlC0UW22tDB8dXNvDY3FlcXhjm8sYIGdoxxXFeErAXWoWaXLiWKJWMZfnBIwGb1r0PxD4Ej8ZTLq+\nialBqSxqyyWaycsR2B/+tWD4Y0G/UPDe2MlrcKxjeGX5SkbdOfw4q7pmSTvqYnxD1+LTr+zsIUMi\n7F+0h/4WAI+U+nNUtL8QJZYM1h9o2sWjuB1XIGOPwqbx94ae8uYIrRmuVt8JJOOgHcn8qybUQrbG\n0nuY2uivyQx/MwGeDntT96+hbSsdL4c8bxah4km0uabzDcqJomP/ACybncjfpzXaq+0ODw2CM+hr\nzB/D1roVnK0IKzNJvMxPKnHHP511Hg7xWPESPZ3BEd/bjkY/1i8/OP6+mRSlC2qPNqwV7o9h0S42\n3enyuFctaMpVj1zgda9L0/SWtZI4ICUt0RWYJ/ET7143p9wz6ZpFxtw6iWMt1BwVxXuvh66dtKtn\nfbPMyCQJFycEcfyqY+87I5ZbGzbv5XmQll+0FANg5IznAP5H8q6G2Pl6TCC21Z2DkjjcOgH51xNh\naw+GlaFYZLnVroGURPJukjL92PYAA4+proYr0W9qoVWFvbQFvrtBYt9Mj9K4q1TmlyxNoxSV2eB/\nETxPHrnxE1q0jIP9llLVlHzAEruY/maq2DnUbJYZSDPbsdo/vIf/ANVfP1l8TtUf4l3eqXisdN1C\n8leRgvVGc4bPfAAFe0W10beWG6t3PlnEiH+9Ge1ZThyIzmrHs3ga7+3eH4txybY7Ce+OcGvOf2od\nKjvfCWl3TRmUW1xjAPJ3DFdR4A1UR6ssKj9xffKBngNzgfzqt+0Zpxu/hfcNkB7e5iO7HI5IryJp\nwrKSN6b5lY+N9eiOlWcd21uRECFKZ5GO544Fdf8ACrw+4s/+EluYkhursFLSHqUQ9XPueMVB4V0W\n78Ste+H5sLHLzdykZYQg5x7E4617XoHh5ZZElljVYIsJHEv8IAwB+n619JVqNxikOrLSyLPgjQi0\nzahOhSOMHyVbk8etbV2+EZkI3sM/MevrVm+uRDbGKPCgdQP5Vzmp3JlkSNPvAZI/ujBqemhy69Tp\n/BV5EG1LU5FKWtqhjSRh1buf0ry/xLrb6pqk9zIcmR8g5/h7V33iq8Tw/wDD7SdPQ4mvlE0g6EjP\nPH4ivJrx3uJ5Qq9DhRnovauSrK2hSLkEb3VykagFpHVFJ+oz+gNcBrusW3iv4h295Zy4Gn3BtEUH\ngxqTz+efzrrb2+fTNE1G+QktBAUTHeRgQAP1/KvGtE0S98PXOlXtwCkU86W6An5ndjzn/GtqNNcr\nbNKa0Z9OeCbn7FczTnCkRZPpjPStT42QpL4Y0i/CjKzBPqDXOaFIGbVhnCCTyAPQgc/l/Wtj4i34\n1H4T2hUbmivUTdnr1rx6z5lJFRVjxm8O0bTztGCfXn/69OsZhY6be3GMElYY8+pNQXkm4ucdOcVD\nq8vk6bbW38Zdrhvy+UfzrkoRV9SZnuHwDuI3+Hyx4Cyrdy7sHqd1bfinVjpusWk8hDIgdJAeA6su\nNteefAHUlj07UbRn5ilEp98jmuk8aXK3+pEKN6LjHPU9a92LShZGMV0PFvHsQ0yHWoYf3S3cxSH1\n2Mdxx+ePwrm/AE9ppOn6pcRJi4Rfs0THsW//AFfrXZ/FbTE1LU9O066LRrc25WKaPqr5znH4j8q8\n316NfD+iCxtizBCqPJnlmLDk/l+tawtCDUepu4vlPu34bKT4S0Xeu2RbOEOMd9gzWp4rn8rSrpwQ\nNq9CetReE4fsei6bCf4bSEflGtZ3jO4MkZtkOd2Mg968v4pMhaI4RphEZLpwRBbIZnz0wBk8/jXz\nn+0toA0nx/Hq2mgNper2yXEQTuejDP5V7v8AFm+Xw18NNYkZikt6v2VMHpvBBrwzxTdS+IfgN4Y1\nIHzL3QbxraY5yfLJGM/lXrUE0XT2PcYfCclhZSbLaN93zPJIPmPv1rP8PSvb6zqOmSqIUKLOq9VP\nXmux8XfCPxLrmtW17pt2ytbgMYM5Rx6ZzXH6X4I17Sdd1W7v9PuA04+TYdy8ZyB+ldtj0XNI5Xxc\nL8RSWlvENPPnB2mDYBX1xWTpd88sF4fLbyEH+vccSEZ6DH+c12F5H4j1+GSC68K+SEYhXZx8y+/5\nfrU8PhjWobCNYY7O3CHP2eTDD9DTugU1I86t9L0/XR9us5jZ6kjAMYBtJ57j8K27251AFI9Ruk8r\nJITGWPTvXTXvh7U5rCcKunWt6wx50Qxj8K42T4K3s0i3E/iVfMJBIKlgD+dJvQu8UXtIe9s5S9rb\nKlvK2GMpC/iK6nV9OiEqeLIWZ55IDZykDhAPf8T27Vm2XgK8lkja/ntmSHhJUnA3EdCVzW9N4O0+\n9s44tX11xAr+Y9va5VTjt71K13G5aaHPW2sRRCPS9Jhe4u5gA4tlYuR9R9e9dz4P+GmqTQNNfRJo\nFkCTgkNK3vjdx+Nbdpr9loNqkPhjRIlbaAby5GDj+vesLW7l9SO/Vry9vo+c29qCic9iRkmqtFa3\nM1KT6F3UdQ8FeE2xLdPqlyOiD96SfYDiuevfiT4i1CVZPDngm7CLlY5XUoPrjirNr4xTw1Mkll4V\nOFG1UWDH4kkGsnxB+0n4hS5MEegLYxAEeZIp5+nAp30uiZXJrix+L/iCDzLpBZ2zA7kUhdoP05rJ\nX4P6xqfzarcRxqP7zFyx9eaLf4iaveW63Gp69b2KTrvWGIlpR9BnijVfircQWgtbCP8AtGdYvN+0\n3LEHntgfSocnY0jGTWr0OosPh3aWFlHb6rrX+jx/d8xfmHsDmt/R9J+GdqU8jzrmdOS6yZy3cdK8\nN8JeJdW8c65PHqZFvGqAkc4JJwBya721K+FZJoo7dJZUXcueM88nP5Vk5y7GqjF6XOT+L95a+CfE\nEGpeEJm0fzQRNZOT5UuMfMPfk/nXMWnx113W7JlvLdGe3kAdl+8yHOCTj2PHvXpXjjwjD8QYbfT7\nmR7ViRKkiYyOnP0rlbr9nnxL4ctLmHTTFq1rPh5J1OJAOeMc+v6VcbPVmbc4PbQ5Txhqd5c2jtZz\nSINTiARHbAU57fnWj4R8NQ6BAb69YPd+WF3SDOPYVgXlvqOlhtOv4ZIzaoWi81eRj0P5VDN4xv20\nQyLEJJyuBk9f04rqiYOV5HXf2nbzi9S7uEgtYka4lZl3AADgde9eZaHreozXj6kl41nIrHyFXg4z\nkKfY4/HPtXUaZ4fvdZt54mjDm7VFlDHgKASR+orW8NaJYaBrUJnVWt/K2FpRkE59Pwx+NW7dRW1P\nTPh14xtPFHhe8u4ZMtY3iNNE3BjYqQ3H90/0r6e8F+Tpnhi1gjKl0j/ec5JbOT+Ar5M+H+iNa+Jd\nVZIAljq0B2x9A+3cce5wa9Z8OeIruWysmFxmUjyfLHBycj5h+ZryalT2TdzmqUbvmPRdGvWvtbnv\nvnluLhj5ZT+CMEgE+3XitHx1evoPw38W30a7Gt9Mn8st1AKkE+w5p/hK2Sa3jvo08hDH5KI4wcLk\nfrya8y/a68ct4U+E0mlWu17/AFyX7GyD7yQAZcn8xj8a5aUG5czM5uysfFGn+J5dKiFnJCAqZR1l\nGcYByAfr/Ovb/hH4rXxXodzZuVWbTHCH1aIqCG/mK+Y9euDJdu7udzkFgPT0/Wun+GPiS48I+K7D\nUJWkTTmPk3LfwlG7n6cV69ekpUzVqLR9jeF9RazuxEGxLEVmjHoQef0/nXp3xl02PVPhvqskY3x3\nEKXcYHYAg4/nXi0kq215a3kT7otwYSLyCD/Fn0x2r2zSbyHXPAF5bXB3Rxo7AnshxgfTg/nXzk9H\nZmCvFnzp8JrCWfw/qOuTReXcatc4TcOfIUcfmWP5V2Y1F4mRISMqRvI4ziqr3ogtrS1gUJHbxCJQ\nowABVNJREWIPJOTXpxk5RTJcr7mvcaiSrs7Zyc4/pVbT4muZk3jLzSBce2f/ANdZ5mE1yYyflADZ\nrY0V/wB8s56Rgvj0wD/jW6fKrsn4tEZHxC1o33iOfDZgtQLaNR0AUc/zriTMdrOeC5K59h/9bNWL\n28a782UcmRixyehJOf6VUnmhtoZrq5ObS0i82U9AQO349K4nactB7aHH/EjxONLTTtOWcKioLueL\nuWP3FPv1P4157/b97q2qaFLMzOYrxXEZPByeuO3atTWPEC+Jpbqe78iGW+l8/ey5K9gB6cBfyqr4\na0zb4ngeJxcxwkMT2HIx/X8q9JpU6TbOl+7DQ+mpbM6HZANhXlAlJ9SetU/FGpA/Du1tgRvkvA+z\nPYf/AK6f8Q9TjaWCBHyqRIgPvgZNchqTySWyjdkKPkXP0ya+ZrTUVIiN7mEYvMnYD5gHOT6gVk6p\ncG6u3c8DG1foP/11q3UpsI5ZVG4klF/qawJpN4bI+YjFLDxurhI6z4aambDVtQgjbDTIrZ/DH9K9\nElu/Omh7nIYj1ryLwM3/ABP5X6EQn8cf/rr0VXZTCQ2GBwfevTg1axm0rFj4j6CsnhyPXI0Ly6fl\ngw/2sf4V4Jba8zanLZzQLcWs0i+YCvzbs5yPYZr7Ij0iLVPDQsZAPKu7Zg4PPUH+oFfJyaF5/iXT\ndPI8uaO4+zzMvBypJI/LFdSStdDW25926WFtrVcHEaRKoPbAUAfyrnbthfXTXDr8gYKPbrV+/uGS\nAQpkF2KhR0AHFZuoyLp2nrv4YEn615K0mxKzPBf2rtZkh8N6Lp8Rw81y0rLnqAABx/wKvK/hjqj6\nhoPjDw82HgurT7TChGdrjAOP510X7Qdxdanr9pPtaWGCEsQvO3P/AOqvMvBusLpPivT5YyfLciKY\nqcfIxAOa+how9y5vCyP0js9XurBDBx5DhTGVfLnjkY/L86rXmpR2yzAoI1bGFc5YHnmuOPiAafqM\nN5qO94mUbGg5x7H0rV8V6lF4vsUu9CGyWFNrrPw5PHOO/SpSae56Nr9Dmnlja7ljubqG3kkbGWU8\n+mOap30B0qVo2dXB6M2Afyo1bS77SrSLUNR2JJKMRoR3Hf8AWodAtLXWtUNq8rvPLG2yVj8ofGdp\n+uOPxqW3cpJWMq6WPcx+0gkgsNo/n6VkJBBKjiSZ5Hc/KADgH8K2PtS2d3dRfaI4bmH900UnBz34\nxzWJqlx59jIRrHkyAjKREKW9gKrVkuyIL+4XQYw77mcnG19uCe3bNTW9/d6hK9vHbYjEYeSbGET8\nTT9L+Bd5Ko1S5uZZ71186GJnypHUA81nazpWo6s6RXN9Fp+l/wCq+ywtmWQnjjGPSk436gp6bHN+\nJPirJp90lhoqm7cnymuHUlFb2/WuNf4j+JRdi2luXhLH7yEhQfpXpsPw20fwbpaXmoJcSgSN9kt5\nOCW7kj8q4V/D7314bme1MMIY4Kfd9+fXpWkIpbg5dh2jfELxBJqKpLq00kSna4J4x+NbWueIL7VN\nbtoJpRPZBVZeBkFs9+/SsYxyWDLJZaak6I/zs5UZXuME1faGO7liuLIxw3Ma8wlxtPoM9u9U7ILJ\nmBc+F3TWVKAeecs25v4fY/0q1NaTafDF5SHcDtbrkr6//Wq6072Ij+0wuWDBlmY4AbuM9xWld+Ig\n8MU9sFXdIFdioIHrUpph5GZpzt9mme2cxSI6Hcewzn9a9Av7ka5Yrcq8ZlU+XJGWwwx1x69RXMaD\nONaiukkg+Ql0LjgMOMfyqfw94ZudUvlaOcRW/mDz3LYwo6j60SirApcpR+J/jUeHLrwZqEUmAjmC\nfaeNmRyfyr07SPi5ZxX0VkbkI0y74PM4Df7INeG+K/hpq/iHU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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Iris Virginica\n" ] } ], "source": [ "from IPython.core.display import Image, display\n", "display(Image(filename='images/iris_setosa.jpg'))\n", "print(\"Iris Setosa\\n\")\n", "\n", "display(Image(filename='images/iris_versicolor.jpg'))\n", "print(\"Iris Versicolor\\n\")\n", "\n", "display(Image(filename='images/iris_virginica.jpg'))\n", "print(\"Iris Virginica\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 问题:\n", "\n", "**如果我们想设计一个算法去分辨iris的品种,数据可能是什么?**\n", "\n", "记住:我们需要一个2D的数组,其大小为`[样本数 * 特征数]`\n", "\n", "- `样本数`指的是什么?\n", "\n", "- `特征数`指的是什么?\n", "\n", "记住每一个样本的特征数必须是**固定**的,而且对于每一个样本,特征数``i``必须是一个数值型的元素。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 用scikit-learn 加载 Iris 数据\n", "\n", "Scikit-learn对于Iris数据有一个非常直接表示。数据表示如下:\n", "\n", "- Iris 数据集的特征:\n", "\n", " 1. 萼片长度(cm)\n", " 2. 萼片宽度(cm)\n", " 3. 花瓣长度(cm)\n", " 4. 花瓣宽度(cm)\n", " \n", " \n", "- 预测的目标类别\n", " \n", " 1. Iris Setosa\n", " 2. Iris Versicolour\n", " 3. Iris Virginica\n", " \n", "``scikit-learn``嵌入了一个iris CSV文件的拷贝和一个帮助函数去从numpy数组中加载它:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iris.keys()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 4)\n", "[ 5.1 3.5 1.4 0.2]\n" ] } ], "source": [ "n_samples, n_features = iris.data.shape\n", "print((n_samples, n_features))\n", "print(iris.data[0])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 4)\n", "(150,)\n" ] } ], "source": [ "print(iris.data.shape)\n", "print(iris.target.shape)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2]\n" ] } ], "source": [ "print(iris.target)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['setosa' 'versicolor' 'virginica']\n" ] } ], "source": [ "print(iris.target_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这个数据是四维的,但是我们可以使用简单的scatter-plot一次显示出两维的数据:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { 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mjdJz5cEN7JjwMOW79qFQKkm84Rq6B/kEHpoIPf2WvUP2opWU70pDpQ+n+dgRRPXuGujQ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\nGtAVA/vfDIdDRKtWcSgtrd89aKczAU5nc3CcBMYUkEu9dUJI46GkTUg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mzRrcFyEk9Ojy\nOCER0K5dO3z11VcYOXIkxowZg+HDh9crYQNAQkIC7rzzTnz77bcNimnbtm246667KGETEsXoTJsQ\nQgiRCTrTJoQQQmSCkjYhhBAiE5S0CSGEEJmgpE0IIYTIBCVtQgghRCYoaRNCCCEy8f8oeoZGeklr\nwwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b8057f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x_index = 0\n", "y_index = 1\n", "\n", "# 这段代码使用iris的名字来标注颜色条(colorbar)\n", "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n", "\n", "plt.scatter(iris.data[:, x_index], iris.data[:, y_index],\n", " c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3))\n", "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n", "plt.clim(-0.5, 2.5)\n", "plt.xlabel(iris.feature_names[x_index])\n", "plt.ylabel(iris.feature_names[y_index]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 快速练习:\n", " \n", "**在上面的脚本中改变** `x_index` **和** `y_index`**, 找到一种可以最大化分隔出三个类别的它们的组合。**\n", "\n", "这个练习是**降维算法**的一个预告,我们在之后会看到。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 其他数据\n", "\n", "它们分为如下三种:\n", "\n", "- **包内置数据:** 这些小的数据集已经被集成在scikit-learn的安装包里面了,可以用``sklearn.datasets.load_*``去下载它\n", "- **供下载数据:** 这些较大的数据可以供用户们下载,scikit-learn里面已经包含了下载这些数据集的流通道。这些数据可以在``sklearn.datasets.fetch_*``中找到。\n", "- **生成数据:** 通过随机种子,可以通过现有模型随机生成一些数据集。它们可以在``sklearn.datasets.make_*``中找到\n", "\n", "你可以通过IPython的TAB自动补全来发现可能的数据集生成和加载工具。在从``sklearn``导入``datasets``之后,\n", "键入\n", "\n", " datasets.load_ + TAB\n", "\n", "或者\n", "\n", " datasets.fetch_ + TAB\n", "\n", "或者\n", "\n", " datasets.make_ + TAB\n", "\n", "可以看到一列函数的组合。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import datasets" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Type datasets.fetch_<TAB> or datasets.load_<TAB> in IPython to see all possibilities\n", "\n", "# datasets.fetch_" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# datasets.load_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在下一节,我们将会使用一些数据集来研究机器学习的基本规则。" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
szitenberg/ReproPhyloVagrant
notebooks/Tutorials/Basic/3.8 Building a supermatrix.ipynb
1
31791
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This section shows how to build a supermatrix by providing minimal requirements for gene content per taxon (OTU). This approach is more suited for *small scale* analysis, because it relies on manual decisions, whereas *large scale* suprematrices are better constructed with the parameter space and data explorations tools of ReproPhylo. However, these are not addressed in this section. First, lets load our `Project` with the trimmed alignments:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:Cloud:Log file (/home/amir/.picloud/cloud.log) opened\n" ] } ], "source": [ "from reprophylo import *\n", "pj = unpickle_pj('outputs/my_project.pkpj', git=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.8.1 Sorting out the metadata\n", "The main decision to make when building a supermatrix is what metadata will be used to indicate that sequences of several genes belong to the same OTU in the tree. Obvious candidates would be the species name (stored as `'source_organism'` if we read a GenBank file), or sample ID, voucher specimen and so on. Often, we would be required to modify the metadata in our `Project`, in a way that will correctly reflect the relationship between sequences that emerged from the same sample. \n", " \n", "In the case of the `Tetillidae.gb` example file, sample IDs are stored either under `'source_specimen_voucher'` or `'source_isolate'`. In addition, identical voucher numbers are sometimes formatted differently for different genes. \n", " \n", "In the file `'data/Tetillida_otus_corrected.csv'`, I have unified the columns `'source_specimen_voucher'` and `'source_isolate'` in a single column called `'source_otu'` and also made sure to uniformly format all the voucher specimens:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5llac/yjYLmDUuOblLc0je+YzOgOBlCeEVpqhlZprnDFdUUZanpax/F/Nt3Rx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\nSsjKG1oiuMbFTz1zjdeLPJu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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": { "image/png": { "width": 400 } }, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('images/fix_otus.png', width = 400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our `Project` has to be updated with the recent changes to the spreadsheet:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pj.correct_metadata_from_file('data/Tetillida_otus_corrected.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Such fixes can also be done programmatically (see section 3.4)\n", "## 3.8.2 Designing the supermatrix\n", "Supermatrices are configured with objects of the class `Concatenation`. In a `Concatenation` object we can indicate the following:\n", "\n", "1. The name of the concatenation\n", "2. The loci it includes (here we pass `locus` objects rather than just `Locus` names)\n", "3. The qualifier or metadata that stores the relationships among the records\n", "4. What loci all the OTUs must have\n", "5. Groups of loci from which each OTU must have at least one\n", "6. Which trimmed alignment to use, if we have more than one for each locus in our `Project`\n", "\n", "Here is an example:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "concat = Concatenation('large_concat', # Any unique string\n", " \n", " pj.loci, # This is a list of Locus objects\n", " \n", " 'source_otu', # The values of this qualifier \n", " # flag sequences the belong to the same\n", " # sample\n", " \n", " otu_must_have_all_of=['MT-CO1'], # All the OTUS must have a cox1 sequence\n", " \n", " otu_must_have_one_of=[['18s','28s']], # All the OTUs must have either 18s or 28s or both\n", " \n", " define_trimmed_alns=[] # We only have one alignment per gene\n", " # so the list is empty (default value)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we print this `Concatenation` object we get this message:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Concatenation named large_concat, with loci 18s,28s,MT-CO1,\n", "of which MT-CO1 must exist for all species\n", "and at least one of each group of [ 18s 28s ] is represented.\n", "Alignments with the following names: are prefered\n" ] } ], "source": [ "print concat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.8.3 Building the supermatrix\n", "Building the suprematrix has two steps. First we need to mount the `Concatenation` object onto the `Project` where it will be stored in the list `pj.concatenations`. Second, we need to construct the `MultipleSeqAlignment` object, which will be stored in the `pj.trimmed_alignments` dictionary, under the key `'large_concat'` in this case:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Concatenation large_concat will have the following data\n", "OTU 18s 28s MT-CO1 \n", "NIWA_28507 JX177975.1_f0 JX177943.1_f0 JX177896.1_f0 \n", "ZMBN_85230 HM592765.1_f0 HM592717.1_f0 \n", "NIWA_28910 JX177982.1_f0 JX177865.1_f0 \n", "VM_14754 JX177986.1_f0 JX177960.1_f0 HM032751.1_f0 \n", "ZMBN_85239 JX177987.1_f0 JX177959.1_f0 HM592669.1_f0 \n", "ZMBN_81789 HM592753.1_f0 HM592667.1_f0 \n", "QMG_315031 JX177974.1_f0 JX177942.1_f0 HM032749.2_f0 \n", "NIWA_28617 JX177980.1_f0 JX177912.1_f0 \n", "RMNH_POR_3206 JX177925.1_f0 JX177892.1_f0 \n", "UFBA_2021_POR JX177921.1_f0 JX177907.1_f0 \n", "NIWA_28586 JX177978.1_f0 JX177953.1_f0 JX177918.1_f0 \n", "QMG_320270 JX177963.1_f0 JX177931.1_f0 HM032741.1_f0 \n", "QMG_318785 JX177985.1_f0 HM032752.3_f0 \n", "NIWA_25206 JX177981.1_f0 JX177917.1_f0 \n", "QMG_320216 JX177966.1_f0 JX177902.1_f0 \n", "MHNM_16194 HM629803.1_f0 JX177941.1_f0 JX177905.1_f0 \n", "ZMA_POR_16637 HM592820.1_f0 HM592745.1_f0 \n", "SAM_S1189 JX177929.1_f0 JX177910.1_f0 \n", "TAU_25529 JX177970.1_f0 JX177939.1_f0 JX177906.1_f0 \n", "LB_1756 JX177933.1_f0 JX177886.1_f0 \n", "MNRJ_576 JX177957.1_f0 HM032742.1_f0 \n", "NIWA_28877 JX177977.1_f0 JX177950.1_f0 JX177864.2_f0 \n", "NIWA_28524 JX177976.1_f0 JX177945.1_f0 JX177895.1_f0 \n", "QMG_316342 JX177983.1_f0 JX177955.1_f0 HM032747.2_f0 \n", "TAU_25568 JX177969.1_f0 JX177940.1_f0 JX177904.1_f0 \n", "NIWA_28929 JX177951.1_f0 JX177863.1_f0 \n", "DH_S271 JX177965.1_f0 JX177935.1_f0 JX177913.1_f0 \n", "ZMBN_85240 HM592754.1_f0 HM592668.1_f0 \n", "QMG_321405 JX177930.1_f0 HM032743.1_f0 \n", "NIWA_36097 JX177944.1_f0 JX177866.1_f0 \n", "UFBA_2586_POR JX177958.1_f0 JX177898.1_f0 \n", "NIWA_52077 JX177948.1_f0 JX177916.1_f0 \n", "QMG_316372 HE591469.1_f0 HM032748.2_f0 \n", "QMG_320636 JX177971.1_f0 HM032745.1_f0 \n", "NIWA_28496 JX177946.1_f0 JX177897.1_f0 \n", "QMG_314224 JX177924.1_f0 HM032744.1_f0 \n", "QMG_320143 JX177973.1_f0 JX177922.1_f0 HM032746.1_f0 \n", "DH_S124 JX177938.1_f0 JX177903.1_f0 \n", "SP_DH_S192 JX177961.1_f0 JX177956.1_f0 JX177901.1_f0 \n", "SP_DH_S193 JX177926.1_f0 JX177900.1_f0 \n", "NIWA_28957 JX177949.1_f0 JX177867.2_f0 \n", "RMNH_POR_2877 JX177920.1_f0 JX177909.1_f0 \n", "\n" ] } ], "source": [ "pj.add_concatenation(concat)\n", "pj.make_concatenation_alignments()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'outputs/my_project.pkpj'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pickle_pj(pj, 'outputs/my_project.pkpj')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that this supermatrix is stored as a trimmed alignment in the `pj.trimmed_alignments` dictionary, we can write it to a file or fetch the `MultipleSeqAlignment` object, as shown in section 3.7.\n", "## 3.8.4 Quick reference" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Design a supermatrix\n", "concat = Concatenation('concat_name', loci_list, 'otu_qualifier' **kwargs)\n", "\n", "# Add it to a project\n", "pj.add_concatenation(concat)\n", "\n", "# Build supermatrices based on the Concatenation\n", "# objects in pj.concatenations\n", "pj.make_concatenation_alignments()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
XiaowenLin/cs598rk
scripts/red_opal.py.ipynb
1
44037
{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark import SparkContext\n", "from nltk.tokenize import word_tokenize\n", "from nltk.stem import WordNetLemmatizer\n", "import json\n", "import os\n", "sc = SparkContext()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_rdd(base, input, num_part):\n", " base_dir = os.path.join(base)\n", " input_path = os.path.join(input)\n", " file_name = os.path.join(base_dir, input_path)\n", " rdd = sc.textFile(file_name, num_part)\n", " rdd_j = rdd.map(json.loads)\n", " rdd_j.cache()\n", " return rdd_j" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def tf(tokens):\n", " res = dict()\n", " addon = 1.0 / len(tokens)\n", " for tok in tokens:\n", " res[tok] = res.setdefault(tok, 0) + addon\n", " return res" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def idfs(corpus):\n", " N = float(corpus.count())\n", " uniqueTokens = corpus.flatMap(lambda x: x[1]).distinct()\n", " tokenCountPairTuple = corpus.flatMap(lambda x: set(x[1])).map(lambda x: (x, 1))\n", " tokenSumPairTuple = tokenCountPairTuple.reduceByKey(lambda a, b: a + b)\n", " return (tokenSumPairTuple.map(lambda (tok, num): (tok, N / num )))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def tfidf(tokens, idfs):\n", " \"\"\" Compute TF-IDF\n", " Args:\n", " tokens (list of str): input list of tokens from tokenize\n", " idfs (dictionary): record to IDF value\n", " Returns:\n", " dictionary: a dictionary of records to TF-IDF values\n", " \"\"\"\n", " tfs = tf(tokens)\n", " tfIdfDict = {key: idfs[key] * tfs[key] for key in tokens}\n", " return tfIdfDict" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PythonRDD[7] at RDD at PythonRDD.scala:43" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_part = 4\n", "revs = get_rdd('../data', 'reviews_electronics5000.json', num_part)\n", "rev_texts = revs.map(lambda x: (x['asin'], x['reviewText']))\n", "rev_agg_texts = rev_texts.map(lambda (asin, text): (asin, [text])).reduceByKey(lambda x, y: x + y)\n", "rev_agg = rev_agg_texts.map(lambda (asin, revs): (asin, ' '.join(revs)))\n", "rev_agg = rev_agg.map(lambda (asin, rev): (asin, word_tokenize(rev)))\n", "rev_agg.map(lambda (asin, toks): (asin, tf(toks)))\n", "rev_agg.cache()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# tf\n", "tfs = rev_agg.map(lambda (asin, toks): (asin, tf(toks)))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# idf\n", "# use the whole category as idf corpus\n", "idfs_cat = idfs(rev_agg)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'DVD+R', 299.0),\n", " (u'1,2', 299.0),\n", " (u'four', 13.0),\n", " (u'gag', 299.0),\n", " (u'recommended.UPDATE', 299.0)]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idfs_cat.take(5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[299.0]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idfs_cat.lookup('1,2')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "ename": "Exception", "evalue": "It appears that you are attempting to broadcast an RDD or reference an RDD from an action or transformation. RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(lambda x: rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mException\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-21-5fc72a66dedb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrev_agg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0masin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtoks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0masin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtfidf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtoks\u001b[0m\u001b[1;33m,\u001b[0m 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Same as in the big comment below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 550\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmemo\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/anaconda/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m 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self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/cs598rk/spark/python/pyspark/cloudpickle.pyc\u001b[0m in \u001b[0;36msave_function\u001b[1;34m(self, obj, name)\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthemodule\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 198\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m 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Same as in the big comment below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 550\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmemo\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/anaconda/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/anaconda/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_list\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 598\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_appends\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 602\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mListType\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/anaconda/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36m_batch_appends\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m 634\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAPPENDS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 635\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 636\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 637\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAPPEND\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 638\u001b[0m \u001b[1;31m# else tmp is empty, and we're done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/anaconda/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/cs598rk/spark/python/pyspark/cloudpickle.pyc\u001b[0m in \u001b[0;36msave_function\u001b[1;34m(self, obj, name)\u001b[0m\n\u001b[0;32m 191\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mislambda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__code__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_filename\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'<stdin>'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mthemodule\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;31m#print(\"save global\", islambda(obj), obj.__code__.co_filename, modname, themodule)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_function_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 194\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/cs598rk/spark/python/pyspark/cloudpickle.pyc\u001b[0m in \u001b[0;36msave_function_tuple\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[1;31m# save the rest of the func data needed by _fill_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf_globals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/anaconda/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/anaconda/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 648\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 649\u001b[1;33m 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\u001b[1;32mif\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 306\u001b[1;33m \u001b[0mrv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 307\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 308\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"__reduce__\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/cs598rk/spark/python/pyspark/rdd.pyc\u001b[0m in \u001b[0;36m__getnewargs__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 204\u001b[0m \u001b[1;31m# This method is called when attempting to pickle an RDD, which is always an error:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 205\u001b[0m raise Exception(\n\u001b[1;32m--> 206\u001b[1;33m \u001b[1;34m\"It appears that you are attempting to broadcast an RDD or reference an RDD from an \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 207\u001b[0m \u001b[1;34m\"action or transformation. RDD transformations and actions can only be invoked by the \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[1;34m\"driver, not inside of other transformations; for example, \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mException\u001b[0m: It appears that you are attempting to broadcast an RDD or reference an RDD from an action or transformation. RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(lambda x: rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063." ] } ], "source": [ "rev_agg.map(lambda (asin, toks): (asin, toks)).take(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ML4DS/ML4all
R2.kNN_Regression/regression_knn_student.ipynb
1
32110
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# **The *k*-nearest neighbors (*k*NN) regression algorithm**\n", "\n", " Author: Jerónimo Arenas García ([email protected])\n", " Jesús Cid Sueiro ([email protected])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ " Notebook version: 2.2 (Sep 08, 2017)\n", "\n", " Changes: v.1.0 - First version\n", " Changes: v.1.1 - Stock dataset included.\n", " Changes: v.2.0 - Notebook for UTAD course. Advertising data incorporated\n", " Changes: v.2.1 - Text and code revisited. General introduction removed.\n", " Changes: v.2.2 - Compatibility with python 2 and 3." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# Import some libraries that will be necessary for working with data and displaying plots\n", "\n", "# To visualize plots in the notebook\n", "%matplotlib inline \n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pylab\n", "\n", "# Packages used to read datasets\n", "import scipy.io # To read matlab files\n", "import pandas as pd # To read datasets in csv format\n", "\n", "# For the student tests (only for python 2)\n", "import sys\n", "if sys.version_info.major==2:\n", " from test_helper import Test\n", "\n", "# That's default image size for this interactive session\n", "pylab.rcParams['figure.figsize'] = 9, 6 " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 1. The dataset\n", "\n", "We describe next the regression task that we will use in the session. The dataset is an adaptation of the <a href=http://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html> `STOCK` dataset</a>, taken originally from the <a href=http://lib.stat.cmu.edu/> StatLib Repository</a>. The goal of this problem is to predict the values of the stocks of a given airplane company, given the values of another 9 companies in the same day. \n", "\n", "<small> If you are reading this text from the python notebook with its full functionality, you can explore the results of the regression experiments using two alternative datasets:\n", "\n", "* The \n", "<a href=https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength>`CONCRETE` dataset</a>, taken from the <a href=https://archive.ics.uci.edu/ml/index.html>Machine Learning Repository at the University of California Irvine</a>. The goal of the `CONCRETE` dataset tas is to predict the compressive strength of cement mixtures based on eight observed variables related to the composition of the mixture and the age of the material). \n", "\n", "* The `Advertising` dataset, taken from the book <a href= http://www-bcf.usc.edu/~gareth/ISL/data.html> An Introduction to Statistical Learning with applications in R</a>, with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. The goal of this problem is to predict the sales of a given product, knowing the investment in different advertising sectors. More specifically, the input and output variables can be described as follows:\n", "\n", " - *Input features:*\n", " * TV: advertising dollars spent on TV for a single product in a given market (in thousands of dollars)\n", " * Radio: advertising dollars spent on Radio\n", " * Newspaper: advertising dollars spent on Newspaper\n", " \n", " - *Response variable:*\n", " * Sales: sales of a single product in a given market (in thousands of widgets)\n", "\n", "To do so, just replace `stock` by `concrete` or `advertising` in the next cell. Remind that you must run the cells again to see the changes. \n", "</small>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# SELECT dataset\n", "# Available options are 'stock', 'concrete' or 'advertising'\n", "ds_name = 'stock'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Let us start by loading the data into the workspace, and visualizing the dimensions of all matrices\n", "if ds_name == 'stock':\n", " # STOCK DATASET\n", " data = scipy.io.loadmat('datasets/stock.mat')\n", " X_tr = data['xTrain']\n", " S_tr = data['sTrain']\n", " X_tst = data['xTest']\n", " S_tst = data['sTest']\n", "\n", "elif ds_name == 'concrete':\n", " # CONCRETE DATASET. \n", " data = scipy.io.loadmat('datasets/concrete.mat')\n", " X_tr = data['X_tr']\n", " S_tr = data['S_tr']\n", " X_tst = data['X_tst']\n", " S_tst = data['S_tst']\n", "\n", "elif ds_name == 'advertising': \n", " # ADVERTISING DATASET\n", " df = pd.read_csv('datasets/Advertising.csv', header=0)\n", " X_tr = df.values[:150, 1:4]\n", " S_tr = df.values[:150, [-1]] # The brackets around -1 is to make sure S_tr is a column vector, as in the other datasets\n", " X_tst = df.values[150:, 1:4]\n", " S_tst = df.values[150:, [-1]]\n", "\n", "else:\n", " print('Unknown dataset')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Print the data dimension and the dataset sizes\n", "print(\"SELECTED DATASET: \" + ds_name)\n", "print(\"---- The size of the training set is {0}, that is: {1} samples with dimension {2}.\".format(\n", " X_tr.shape, X_tr.shape[0], X_tr.shape[1]))\n", "print(\"---- The target variable of the training set contains {0} samples with dimension {1}\".format(\n", " S_tr.shape[0], S_tr.shape[1]))\n", "print(\"---- The size of the test set is {0}, that is: {1} samples with dimension {2}.\".format(\n", " X_tst.shape, X_tst.shape[0], X_tst.shape[1]))\n", "print(\"---- The target variable of the test set contains {0} samples with dimension {1}\".format(\n", " S_tst.shape[0], S_tst.shape[1]))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 1.1. Scatter plots\n", "\n", "We can get a first rough idea about the regression task representing the *scatter plot* of each of the one-dimensional variables against the target data. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "pylab.subplots_adjust(hspace=0.2)\n", "for idx in range(X_tr.shape[1]):\n", " ax1 = plt.subplot(3,3,idx+1)\n", " ax1.plot(X_tr[:,idx],S_tr,'.')\n", " ax1.get_xaxis().set_ticks([])\n", " ax1.get_yaxis().set_ticks([])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 2. Baseline estimation. Using the average of the training set labels\n", "\n", "A first very simple method to build the regression model is to use the average of all the target values in the training set as the output of the model, discarding the value of the observation input vector.\n", "\n", "This approach can be considered as a baseline, given that any other method making an effective use of the observation variables, statistically related to $s$, should improve the performance of this method.\n", "\n", "The prediction is thus given by" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Mean of all target values in the training set\n", "s_hat = np.mean(S_tr)\n", "print(s_hat)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "for any input ${\\bf x}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##### Exercise 1\n", "\n", "Compute the mean square error over training and test sets, for the baseline estimation method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# We start by defining a function that calculates the average square error\n", "def square_error(s, s_est):\n", " # Squeeze is used to make sure that s and s_est have the appropriate dimensions.\n", " y = np.mean(np.power((s - s_est), 2))\n", " # y = np.mean(np.power((np.squeeze(s) - np.squeeze(s_est)), 2))\n", " return y\n", "\n", "# Mean square error of the baseline prediction over the training data\n", "# MSE_tr = <FILL IN>\n", "\n", "# Mean square error of the baseline prediction over the test data\n", "# MSE_tst = <FILL IN>\n", "\n", "print('Average square error in the training set (baseline method): {0}'.format(MSE_tr))\n", "print('Average square error in the test set (baseline method): {0}'.format(MSE_tst)) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Note that in the previous piece of code, function 'square_error' can be used when the second argument is a number instead of a vector with the same length as the first argument. The value will be subtracted from each of the components of the vector provided as the first argument." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "if sys.version_info.major == 2:\n", " Test.assertTrue(np.isclose(MSE_tr, square_error(S_tr, s_hat)),'Incorrect value for MSE_tr')\n", " Test.assertTrue(np.isclose(MSE_tst, square_error(S_tst, s_hat)),'Incorrect value for MSE_tst')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 3. Unidimensional regression with the $k$-nn method\n", "\n", "The principles of the $k$-nn method are the following:\n", "\n", " - For each point where a prediction is to be made, find the $k$ closest neighbors to that point (in the training set)\n", " - Obtain the estimation averaging the labels corresponding to the selected neighbors\n", " \n", "The number of neighbors is a hyperparameter that plays an important role in the performance of the method. You can test its influence by changing $k$ in the following piece of code. In particular, you can sart with $k=1$ and observe the efect of increasing the value of $k$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# We implement unidimensional regression using the k-nn method\n", "# In other words, the estimations are to be made using only one variable at a time\n", "\n", "from scipy import spatial\n", "\n", "var = 0 # pick a variable (e.g., any value from 0 to 8 for the STOCK dataset)\n", "k = 1 # Number of neighbors\n", "n_points = 1000 # Number of points in the 'x' axis (for representational purposes)\n", "\n", "# For representational purposes, we will compute the output of the regression model\n", "# in a series of equally spaced-points along the x-axis\n", "grid_min = np.min([np.min(X_tr[:,var]), np.min(X_tst[:,var])])\n", "grid_max = np.max([np.max(X_tr[:,var]), np.max(X_tst[:,var])])\n", "X_grid = np.linspace(grid_min,grid_max,num=n_points)\n", "\n", "def knn_regression(X1, S1, X2, k):\n", " \"\"\" Compute the k-NN regression estimate for the observations contained in\n", " the rows of X2, for the training set given by the rows in X1 and the\n", " components of S1. k is the number of neighbours of the k-NN algorithm\n", " \"\"\"\n", " if X1.ndim == 1:\n", " X1 = np.asmatrix(X1).T\n", " if X2.ndim == 1:\n", " X2 = np.asmatrix(X2).T\n", " distances = spatial.distance.cdist(X1,X2,'euclidean')\n", " neighbors = np.argsort(distances, axis=0, kind='quicksort', order=None)\n", " closest = neighbors[range(k),:]\n", " \n", " est_values = np.zeros([X2.shape[0],1])\n", " for idx in range(X2.shape[0]):\n", " est_values[idx] = np.mean(S1[closest[:,idx]])\n", " \n", " return est_values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "est_tst = knn_regression(X_tr[:,var], S_tr, X_tst[:,var], k)\n", "est_grid = knn_regression(X_tr[:,var], S_tr, X_grid, k)\n", "\n", "plt.plot(X_tr[:,var], S_tr,'b.',label='Training points')\n", "plt.plot(X_tst[:,var], S_tst,'rx',label='Test points')\n", "plt.plot(X_grid, est_grid,'g-',label='Regression model')\n", "plt.axis('tight')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 3.1. Evolution of the error with the number of neighbors ($k$)\n", "\n", "We see that a small $k$ results in a regression curve that exhibits many and large oscillations. The curve is capturing any noise that may be present in the training data, and <i>overfits</i> the training set. On the other hand, picking a too large $k$ (e.g., 200) the regression curve becomes too smooth, averaging out the values of the labels in the training set over large intervals of the observation variable.\n", "\n", "The next code illustrates this effect by plotting the average training and test square errors as a function of $k$. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "var = 0\n", "k_max = 60\n", "\n", "k_max = np.minimum(k_max, X_tr.shape[0]) # k_max cannot be larger than the number of samples\n", "\n", "#Be careful with the use of range, e.g., range(3) = [0,1,2] and range(1,3) = [1,2]\n", "MSEk_tr = [square_error(S_tr, knn_regression(X_tr[:,var], S_tr, X_tr[:,var],k)) \n", " for k in range(1, k_max+1)]\n", "MSEk_tst = [square_error(S_tst,knn_regression(X_tr[:,var], S_tr, X_tst[:,var],k)) \n", " for k in range(1, k_max+1)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "kgrid = np.arange(1, k_max+1)\n", "plt.plot(kgrid, MSEk_tr,'bo', label='Training square error')\n", "plt.plot(kgrid, MSEk_tst,'ro', label='Test square error')\n", "plt.xlabel('$k$')\n", "plt.ylabel('Square Error')\n", "plt.axis('tight')\n", "\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "As we can see, the error initially decreases achiving a minimum (in the test set) for some finite value of $k$ ($k\\approx 10$ for the `STOCK` dataset). Increasing the value of $k$ beyond that value results in poorer performance." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Exercise 2\n", "\n", "Analize the training MSE for $k=1$. Why is it smaller than for any other $k$? Under which conditions will it be exactly zero?\n", "\n", "#### Exercise 3\n", "\n", "Modify the code above to visualize the square error from $k=1$ up to $k$ equal to the number of training instances. Can you relate the square error of the $k$-NN method with that of the baseline method for certain value of $k$? " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 3.1. Influence of the input variable\n", "\n", "Having a look at the scatter plots, we can observe that some observation variables seem to have a more clear relationship with the target value. Thus, we can expect that not all variables are equally useful for the regression task. In the following plot, we carry out a study of the performance that can be achieved with each variable. \n", "\n", "Note that, in practice, the test labels are not available for the selection of hyperparameter\n", "$k$, so we should be careful about the conclusions of this experiment. A more realistic approach will be studied later when we introduce the concept of model validation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "k_max = 20\n", "\n", "var_performance = []\n", "k_values = []\n", "\n", "for var in range(X_tr.shape[1]):\n", " \n", " MSE_tr = [square_error(S_tr, knn_regression(X_tr[:,var], S_tr, X_tr[:, var], k)) \n", " for k in range(1, k_max+1)]\n", " MSE_tst = [square_error(S_tst, knn_regression(X_tr[:,var], S_tr, X_tst[:, var], k)) \n", " for k in range(1, k_max+1)]\n", " MSE_tr = np.asarray(MSE_tr)\n", " MSE_tst = np.asarray(MSE_tst)\n", "\n", " # We select the variable associated to the value of k for which the training error is minimum\n", " pos = np.argmin(MSE_tr)\n", " k_values.append(pos + 1)\n", " var_performance.append(MSE_tst[pos])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "plt.stem(range(X_tr.shape[1]), var_performance, use_line_collection=True)\n", "plt.title('Results of unidimensional regression ($k$NN)')\n", "plt.xlabel('Variable')\n", "plt.ylabel('Test MSE')\n", "\n", "plt.figure(2)\n", "plt.stem(range(X_tr.shape[1]), k_values, use_line_collection=True)\n", "plt.xlabel('Variable')\n", "plt.ylabel('$k$')\n", "plt.title('Selection of the hyperparameter')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 4. Multidimensional regression with the $k$-nn method\n", "\n", "In the previous subsection, we have studied the performance of the $k$-nn method when using only one variable. Doing so was convenient, because it allowed us to plot the regression curves in a 2-D plot, and to get some insight about the consequences of modifying the number of neighbors.\n", "\n", "For completeness, we evaluate now the performance of the $k$-nn method in this dataset when using all variables together. In fact, when designing a regression model, we should proceed in this manner, using all available information to make as accurate an estimation as possible. In this way, we can also account for correlations that might be present among the different observation variables, and that may carry very relevant information for the regression task.\n", "\n", "For instance, in the `STOCK` dataset, it may be that the combination of the stock values of two airplane companies is more informative about the price of the target company, while the value for a single company is not enough.\n", "\n", "<small> Also, in the `CONCRETE` dataset, it may be that for the particular problem at hand the combination of a large proportion of water and a small proportion of coarse grain is a clear indication of certain compressive strength of the material, while the proportion of water or coarse grain alone are not enough to get to that result.</small>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "k_max = 20\n", "\n", "MSE_tr = [square_error(S_tr, knn_regression(X_tr, S_tr, X_tr, k)) for k in range(1, k_max+1)]\n", "MSE_tst = [square_error(S_tst, knn_regression(X_tr, S_tr, X_tst, k)) for k in range(1, k_max+1)]\n", "\n", "plt.plot(np.arange(k_max)+1, MSE_tr,'bo',label='Training square error')\n", "plt.plot(np.arange(k_max)+1, MSE_tst,'ro',label='Test square error')\n", "plt.xlabel('k')\n", "plt.ylabel('Square error')\n", "\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In this case, we can check that the average test square error is much lower than the error that was achieved when using only one variable, and also far better than the baseline method. It is also interesting to note that in this particular case the best performance is achieved for a small value of $k$, with the error increasing for larger values of the hyperparameter.\n", "\n", "Nevertheless, as we discussed previously, these results should be taken carefully. How would we select the value of $k$, if test labels are (obvioulsy) not available for model validation?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 5. Hyperparameter selection via cross-validation\n", "\n", "### 5.1. Generalization\n", "\n", "An inconvenient of the application of the $k$-nn method is that the selection of $k$ influences the final error of the algorithm. In the previous experiments, we kept the value of $k$ that minimized the square error on the training set. However, we also noticed that the location of the minimum is not necessarily the same from the perspective of the test data. Ideally, we would like that the designed regression model works as well as possible on future unlabeled patterns that are not available during the training phase. This property is known as <b>generalization</b>. \n", "\n", "Fitting the training data is only pursued in the hope that we are also indirectly obtaining a model that generalizes well. In order to achieve this goal, there are some strategies that try to guarantee a correct generalization of the model. One of such approaches is known as <b>cross-validation</b> " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 5.2. Cross-validation\n", "\n", "Since using the test labels during the training phase is not allowed (they should be kept aside to simultate the future application of the regression model on unseen patterns), we need to figure out some way to improve our estimation of the hyperparameter that requires only training data. Cross-validation allows us to do so by following the following steps:\n", "\n", " - **Split** the training data into several (generally non-overlapping) subsets. If we use $M$ subsets, the method is referred to as $M$-fold cross-validation. If we consider each pattern a different subset, the method is usually referred to as leave-one-out (LOO) cross-validation.\n", " - Carry out the **training** of the system $M$ times. For each run, use a different partition as a <i>validation</i> set, and use the restating partitions as the training set. Evaluate the performance for different choices of the hyperparameter (i.e., for different values of $k$ for the $k$-NN method).\n", " - **Average** the validation error over all partitions, and pick the hyperparameter that provided the minimum validation error.\n", " - **Rerun** the algorithm using all the training data, keeping the value of the parameter that came out of the cross-validation process.\n", " \n", "<img src=\"https://chrisjmccormick.files.wordpress.com/2013/07/10_fold_cv.png\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "### This fragment of code runs k-nn with M-fold cross validation\n", "\n", "# Parameters:\n", "M = 5 # Number of folds for M-cv\n", "k_max = 40 # Maximum value of the k-nn hyperparameter to explore\n", "\n", "# First we compute the train error curve, that will be useful for comparative visualization.\n", "MSE_tr = [square_error(S_tr, knn_regression(X_tr, S_tr, X_tr, k)) for k in range(1, k_max+1)]\n", "\n", "## M-CV\n", "# Obtain the indices for the different folds\n", "n_tr = X_tr.shape[0]\n", "permutation = np.random.permutation(n_tr)\n", "\n", "# Split the indices in M subsets with (almost) the same size. \n", "set_indices = {i: [] for i in range(M)}\n", "i = 0\n", "for pos in range(n_tr):\n", " set_indices[i].append(permutation[pos])\n", " i = (i+1) % M\n", " \n", "# Obtain the validation errors\n", "MSE_val = np.zeros((1,k_max))\n", "for i in range(M):\n", " val_indices = set_indices[i]\n", " \n", " # Take out the val_indices from the set of indices.\n", " tr_indices = list(set(permutation) - set(val_indices))\n", " \n", " MSE_val_iter = [square_error(S_tr[val_indices], \n", " knn_regression(X_tr[tr_indices, :], S_tr[tr_indices], \n", " X_tr[val_indices, :], k)) \n", " for k in range(1, k_max+1)]\n", "\n", " MSE_val = MSE_val + np.asarray(MSE_val_iter).T\n", " \n", "MSE_val = MSE_val/M\n", "\n", "# Select the best k based on the validation error\n", "k_best = np.argmin(MSE_val) + 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Compute the final test MSE for the selecte k\n", "MSE_tst = square_error(S_tst, knn_regression(X_tr, S_tr, X_tst, k_best))\n", "\n", "plt.plot(np.arange(k_max)+1, MSE_tr, 'bo', label='Training square error')\n", "plt.plot(np.arange(k_max)+1, MSE_val.T, 'go', label='Validation square error')\n", "plt.plot([k_best, k_best], [0, MSE_tst],'r-')\n", "plt.plot(k_best, MSE_tst,'ro',label='Test error')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "##### Exercise 4\n", "\n", "Modify the previous code to use only one of the variables in the input dataset\n", " - Following a cross-validation approach, select the best value of $k$ for the $k$-nn based in variable 0 only.\n", " - Compute the test error for the selected valua of $k$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 6. Scikit-learn implementation\n", "\n", "In practice, most well-known machine learning methods are implemented and available for python. Probably, the most complete module for machine learning tools is <a href=http://scikit-learn.org/stable/>Scikit-learn</a>. The following piece of code uses the method\n", "\n", " KNeighborsRegressor\n", " \n", "available in Scikit-learn. The example has been taken from <a href=http://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html>here</a>. As you can check, this routine allows us to build the estimation for a particular point using a weighted average of the targets of the neighbors:\n", "\n", " To obtain the estimation at a point ${\\bf x}$:\n", " \n", " - Find $k$ closest points to ${\\bf x}$ in the training set\n", " - Average the corresponding targets, weighting each value according to the distance of each point to ${\\bf x}$, so that closer points have a larger influence in the estimation.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Author: Alexandre Gramfort <[email protected]>\n", "# Fabian Pedregosa <[email protected]>\n", "#\n", "# License: BSD 3 clause (C) INRIA\n", "\n", "###############################################################################\n", "# Generate sample data\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn import neighbors\n", "\n", "np.random.seed(0)\n", "X = np.sort(5 * np.random.rand(40, 1), axis=0)\n", "T = np.linspace(0, 5, 500)[:, np.newaxis]\n", "y = np.sin(X).ravel()\n", "\n", "# Add noise to targets\n", "y[::5] += 1 * (0.5 - np.random.rand(8))\n", "\n", "###############################################################################\n", "# Fit regression model\n", "n_neighbors = 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "for i, weights in enumerate(['uniform', 'distance']):\n", " knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n", " y_ = knn.fit(X, y).predict(T)\n", "\n", " plt.subplot(2, 1, i + 1)\n", " plt.scatter(X, y, c='k', label='data')\n", " plt.plot(T, y_, c='g', label='prediction')\n", " plt.axis('tight')\n", " plt.legend()\n", " plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors,\n", " weights))\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Exercise 5\n", "\n", "Use scikit-learn implementation of the $k$-nn method to compute the generalization error on the `CONCRETE` dataset. Compare the perfomance when using uniform and distance-based weights in the computation the estimates. Visualize the regression curves and error for different values of $k$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
james-prior/cohpy
20141207-dojo-dictionary-stuff.ipynb
1
2492
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{2.718281828: 'world', 'hello': 3.1415926}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = {'hello': 3.1415926, 2.718281828: 'world'}\n", "a" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{98237: 'eggs', 'spam': 43983872}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = dict([('spam', 43983872), (98237, 'eggs')])\n", "b" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('name', 'Bob'), ('job', 'dev'), ('age', 40)]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z = zip(['name', 'job', 'age'], ['Bob', 'dev', 40])\n", "z" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('name', 'Catherine'), ('job', 'dev'), ('age', 20)]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z = zip(['name', 'job', 'age'], ['Catherine', 'dev', 20])\n", "z" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'age': 20, 'job': 'dev', 'name': 'Catherine'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = dict(z)\n", "d" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
TANAV/predictorsAndDB
predictorNotebooks/SGDClassifier_Arts.ipynb
1
12815
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cPickle\n", "from scipy.io import loadmat\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.cross_validation import StratifiedKFold, train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.grid_search import GridSearchCV" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load processed data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "productCategory='Arts'\n", "tfIdfArr=loadmat('/home/hencrice/Downloads/AsterixDBClassData/processedData/TfIdf_{0}.mat'.format(productCategory))['data']\n", "scores=load('/home/hencrice/Downloads/AsterixDBClassData/processedData/score_{0}.npy'.format(productCategory))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split data into training+validation (used gridSearch to pick hyper-parameters), and test set (evaluate model performance)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tfIdfArr_trVaSet, tfIdfArr_teSet, scores_trVaSet, scores_teSet = train_test_split(tfIdfArr, scores, test_size=0.1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "hist(scores_teSet)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "(array([ 280., 0., 151., 0., 0., 224., 0., 288., 0., 255.]),\n", " array([ 1. , 1.4, 1.8, 2.2, 2.6, 3. , 3.4, 3.8, 4.2, 4.6, 5. ]),\n", " <a list of 10 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x499ead0>" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pick hyper-parameters for SGDClassifier using grid search:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hyperParam={'n_iter':range(5, 20, 5),\n", " # strength of regularization\n", " 'alpha': logspace(-5, -3, 10)\n", " }\n", "clf = GridSearchCV(SGDClassifier(loss='log', class_weight={1:0.3, 2:0.8, 3:0.05, 4:0.1, 5:0.25}), hyperParam, n_jobs=8, verbose=1)\n", "clf.fit(tfIdfArr_trVaSet, scores_trVaSet)\n", "bestClf=clf.best_estimator_\n", "clf.best_params_" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "[Parallel(n_jobs=8)]: Done 1 jobs | elapsed: 0.5s\n", "[Parallel(n_jobs=8)]: Done 50 jobs | elapsed: 5.9s\n", "[Parallel(n_jobs=8)]: Done 76 out of 90 | elapsed: 8.7s remaining: 1.6s\n", "[Parallel(n_jobs=8)]: Done 90 out of 90 | elapsed: 9.8s finished\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "{'alpha': 1.0000000000000001e-05, 'n_iter': 15}" ] } ], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prediction accuracy of each class:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "te_cm=confusion_matrix(scores_teSet, bestClf.predict(tfIdfArr_teSet))\n", "te_cm.diagonal()/sum(te_cm,1,dtype=float32)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "array([ 0.80427046, 0.48630137, 0.0913242 , 0.37354086, 0.86101695])" ] } ], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the resulting model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('/home/hencrice/Downloads/AsterixDBClassData/models/clf_{0}.pkl'.format(productCategory),'wb') as fp:\n", " cPickle.dump(bestClf, fp, -1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 } ], "metadata": {} } ] }
apache-2.0
UCIDataScienceInitiative/IntroToJulia
Notebooks/Clustering.ipynb
2
12263
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Clustering Task\n", "\n", "Use the following dataset:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPackage RDatasets is already installed\n", "\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mMETADATA is out-of-date — you may not have the latest version of RDatasets\n", "\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mUse `Pkg.update()` to get the latest versions of your packages\n", "\u001b[39m" ] }, { "data": { "text/html": [ "<table class=\"data-frame\"><thead><tr><th></th><th>SepalLength</th><th>SepalWidth</th><th>PetalLength</th><th>PetalWidth</th><th>Species</th></tr></thead><tbody><tr><th>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr><tr><th>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>setosa</td></tr><tr><th>7</th><td>4.6</td><td>3.4</td><td>1.4</td><td>0.3</td><td>setosa</td></tr><tr><th>8</th><td>5.0</td><td>3.4</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>9</th><td>4.4</td><td>2.9</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>10</th><td>4.9</td><td>3.1</td><td>1.5</td><td>0.1</td><td>setosa</td></tr><tr><th>11</th><td>5.4</td><td>3.7</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>12</th><td>4.8</td><td>3.4</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>13</th><td>4.8</td><td>3.0</td><td>1.4</td><td>0.1</td><td>setosa</td></tr><tr><th>14</th><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>setosa</td></tr><tr><th>15</th><td>5.8</td><td>4.0</td><td>1.2</td><td>0.2</td><td>setosa</td></tr><tr><th>16</th><td>5.7</td><td>4.4</td><td>1.5</td><td>0.4</td><td>setosa</td></tr><tr><th>17</th><td>5.4</td><td>3.9</td><td>1.3</td><td>0.4</td><td>setosa</td></tr><tr><th>18</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.3</td><td>setosa</td></tr><tr><th>19</th><td>5.7</td><td>3.8</td><td>1.7</td><td>0.3</td><td>setosa</td></tr><tr><th>20</th><td>5.1</td><td>3.8</td><td>1.5</td><td>0.3</td><td>setosa</td></tr><tr><th>21</th><td>5.4</td><td>3.4</td><td>1.7</td><td>0.2</td><td>setosa</td></tr><tr><th>22</th><td>5.1</td><td>3.7</td><td>1.5</td><td>0.4</td><td>setosa</td></tr><tr><th>23</th><td>4.6</td><td>3.6</td><td>1.0</td><td>0.2</td><td>setosa</td></tr><tr><th>24</th><td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td>setosa</td></tr><tr><th>25</th><td>4.8</td><td>3.4</td><td>1.9</td><td>0.2</td><td>setosa</td></tr><tr><th>26</th><td>5.0</td><td>3.0</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>27</th><td>5.0</td><td>3.4</td><td>1.6</td><td>0.4</td><td>setosa</td></tr><tr><th>28</th><td>5.2</td><td>3.5</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>29</th><td>5.2</td><td>3.4</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>30</th><td>4.7</td><td>3.2</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>" ], "text/plain": [ "150×5 DataFrames.DataFrame\n", "│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │\n", "├─────┼─────────────┼────────────┼─────────────┼────────────┼─────────────┤\n", "│ 1 │ 5.1 │ 3.5 │ 1.4 │ 0.2 │ \"setosa\" │\n", "│ 2 │ 4.9 │ 3.0 │ 1.4 │ 0.2 │ \"setosa\" │\n", "│ 3 │ 4.7 │ 3.2 │ 1.3 │ 0.2 │ \"setosa\" │\n", "│ 4 │ 4.6 │ 3.1 │ 1.5 │ 0.2 │ \"setosa\" │\n", "│ 5 │ 5.0 │ 3.6 │ 1.4 │ 0.2 │ \"setosa\" │\n", "│ 6 │ 5.4 │ 3.9 │ 1.7 │ 0.4 │ \"setosa\" │\n", "│ 7 │ 4.6 │ 3.4 │ 1.4 │ 0.3 │ \"setosa\" │\n", "│ 8 │ 5.0 │ 3.4 │ 1.5 │ 0.2 │ \"setosa\" │\n", "│ 9 │ 4.4 │ 2.9 │ 1.4 │ 0.2 │ \"setosa\" │\n", "│ 10 │ 4.9 │ 3.1 │ 1.5 │ 0.1 │ \"setosa\" │\n", "│ 11 │ 5.4 │ 3.7 │ 1.5 │ 0.2 │ \"setosa\" │\n", "⋮\n", "│ 139 │ 6.0 │ 3.0 │ 4.8 │ 1.8 │ \"virginica\" │\n", "│ 140 │ 6.9 │ 3.1 │ 5.4 │ 2.1 │ \"virginica\" │\n", "│ 141 │ 6.7 │ 3.1 │ 5.6 │ 2.4 │ \"virginica\" │\n", "│ 142 │ 6.9 │ 3.1 │ 5.1 │ 2.3 │ \"virginica\" │\n", "│ 143 │ 5.8 │ 2.7 │ 5.1 │ 1.9 │ \"virginica\" │\n", "│ 144 │ 6.8 │ 3.2 │ 5.9 │ 2.3 │ \"virginica\" │\n", "│ 145 │ 6.7 │ 3.3 │ 5.7 │ 2.5 │ \"virginica\" │\n", "│ 146 │ 6.7 │ 3.0 │ 5.2 │ 2.3 │ \"virginica\" │\n", "│ 147 │ 6.3 │ 2.5 │ 5.0 │ 1.9 │ \"virginica\" │\n", "│ 148 │ 6.5 │ 3.0 │ 5.2 │ 2.0 │ \"virginica\" │\n", "│ 149 │ 6.2 │ 3.4 │ 5.4 │ 2.3 │ \"virginica\" │\n", "│ 150 │ 5.9 │ 3.0 │ 5.1 │ 1.8 │ \"virginica\" │" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pkg.add(\"RDatasets\")\n", "using RDatasets\n", "iris = dataset(\"datasets\", \"iris\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use Clustering.jl to cluster using the `SepalLength`, `PetalLength`, and `PetalWidth`features via K-means clustering. Make a scatter plot of the resulting clusters.\n", "\n", "**Hint: You will need to index the dataframe, convert it to an array, and transpose it. In addition, you will need to use the `assignments` field of the return to get the cluster assignments**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Clustering Task\n", "\n", "For the the example presented here, we will use a subhset of Word Embedding, trained using [Word2Vec.jl](https://github.com/tanmaykm/Word2Vec.jl).\n", "These are 100 dimentional vectors, which encode syntactic and semantic information about words." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "using Embeddings\n", "countries = [\"Afghanistan\", \"Algeria\", \"Angola\", \"Arabia\", \"Argentina\", \"Australia\", \"Bangladesh\", \"Brazil\", \"Britain\", \"Canada\", \"China\", \"Colombia\", \"Congo\", \"Egypt\", \"England\", \"Ethiopia\", \"France\", \"Germany\", \"Ghana\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Ireland\", \"Italy\", \"Japan\", \"Kenya\", \"Korea\", \"Madagascar\", \"Malaysia\", \"Mexico\", \"Morocco\", \"Mozambique\", \"Myanmar\", \"Nepal\", \"Nigeria\", \"Pakistan\", \"Peru\", \"Philippines\", \"Poland\", \"Russia\", \"South\", \"Spain\", \"Sudan\", \"Tanzania\", \"Thailand\", \"Uganda\", \"Ukraine\", \"Usa\", \"Uzbekistan\", \"Venezuela\", \"Vietnam\", \"Wales\", \"Yemen\"]\n", "usa_cities = [\"Albuquerque\", \"Atlanta\", \"Austin\", \"Baltimore\", \"Boston\", \"Charlotte\", \"Chicago\", \"Columbus\", \"Dallas\", \"Denver\", \"Detroit\", \"Francisco\", \"Fresno\", \"Houston\", \"Indianapolis\", \"Jacksonville\", \"Las\", \"Louisville\", \"Memphis\", \"Mesa\", \"Milwaukee\", \"Nashville\", \"Omaha\", \"Philadelphia\", \"Phoenix\", \"Portland\", \"Raleigh\", \"Sacramento\", \"San\", \"Seattle\", \"Tucson\", \"Vegas\", \"Washington\"]\n", "world_capitals = [\"Accra\", \"Algiers\", \"Amman\", \"Ankara\", \"Antananarivo\", \"Athens\", \"Baghdad\", \"Baku\", \"Bangkok\", \"Beijing\", \"Beirut\", \"Berlin\", \"Bogotá\", \"Brasília\", \"Bucharest\", \"Budapest\", \"Cairo\", \"Caracas\", \"Damascus\", \"Dhaka\", \"Hanoi\", \"Havana\", \"Jakarta\", \"Kabul\", \"Kampala\", \"Khartoum\", \"Kinshasa\", \"Kyiv\", \"Lima\", \"London\", \"Luanda\", \"Madrid\", \"Manila\", \"Minsk\", \"Moscow\", \"Nairobi\", \"Paris\", \"Pretoria\", \"Pyongyang\", \"Quito\", \"Rabat\", \"Riyadh\", \"Rome\", \"Santiago\", \"Seoul\", \"Singapore\", \"Stockholm\", \"Taipei\", \"Tashkent\", \"Tehran\", \"Tokyo\", \"Vienna\", \"Warsaw\", \"Yaoundé\"]\n", "animals = [\"alpaca\",\"camel\",\"cattle\",\"dog\",\"dove\",\"duck\",\"ferret\",\"goldfish\",\"goose\",\"rat\",\"llama\",\"mouse\",\"pigeon\",\"yak\"]\n", "sports = [\"archery\",\"badminton\",\"basketball\",\"boxing\",\"cycling\",\"diving\",\"equestrian\",\"fencing\",\"field\",\"football\",\"golf\",\"gymnastics\",\"handball\",\"hockey\",\"judo\",\"kayak\",\"pentathlon\",\"polo\",\"rowing\",\"rugby\",\"sailing\",\"shooting\",\"soccer\",\"swimming\",\"taekwondo\",\"tennis\",\"triathlon\",\"volleyball\",\"weightlifting\",\"wrestling\"]\n", "\n", "words_by_class = [countries, usa_cities, world_capitals, animals, sports]\n", "all_words = reduce(vcat, words_by_class)\n", "embedding_table = load_embeddings(Word2Vec; keep_words = all_words) \n", "@assert Set(all_words) == Set(embedding_table.vocab)\n", "\n", "embeddings = embedding_table.embeddings\n", "all_words = embedding_table.vocab\n", "classes = map(all_words) do word\n", " findfirst(col -> word ∈ col, [countries, usa_cities, world_capitals, animals, sports])\n", "end;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can download the datased from [here](http://ucidatascienceinitiative.github.io/IntroToJulia/Html/ForwardDiff), and load it up with [JLD](https://github.com/JuliaIO/JLD.jl) as shown below. (or just load it directly if you have cloned the notebooks)\n", "\n", " - Use Affinity Propagraion from [Clustering.jl](https://github.com/JuliaStats/Clustering.jl), to cluster word2vec word embeddings, according to meaning.\n", " - Done right this will seperate locations from sports\n", " - Done finely and it will seperate ball-sports from other sports, and will seperate locations according to regions, etc \n", " \n", " - Affinity propagraion requires a similarity matrix, which you can set as a negated distance matrix. \n", " - For this you'll also want [Distances.jl](https://github.com/JuliaStats/Distances.jl) for all your distance metric needs. \n", " - It is traditional with word2vec to use cosine distance.\n", " - You will as also need to set each item's availability. This is the diagonal of the similarity matrix. Decreasing it roughly corresponds to decreasing the amount each node wants to be in a cluster on its own.\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Julia 1.0.0", "language": "julia", "name": "julia-1.0" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.0.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
babebe/Yummly
BB/BB_DataCollection-2.ipynb
1
13873
{ "cells": [ { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from yummly import Client\n", "import json\n", "import requests\n", "import pandas as pd\n", "import numpy as np \n", "import re" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# API call for the first 500 BB recipes labeled as such only!\n", "header= {'X-Yummly-App-ID':'79663a75', 'X-Yummly-App-Key':'02b233108f476f3110e0f65437c4d6dd'}\n", "url='http://api.yummly.com/v1/api/recipes?'\n", "parameters={\n", " 'allowedCourse[]':'course^course-Breakfast and Brunch',\n", " 'excludedCourse[]': ['course^course-Main Dishes','course^course-Appetizers', 'course^course-Salads', 'course^course-Lunch',\n", " 'course^course-Side Dishes','course^course-Desserts','course^course-Breads',\n", " 'course^course-Soups', 'course^course-Beverages', 'course^course-Condiments and Sauces',\n", " 'course^course-Cocktails', 'course^course-Snacks'],\n", " 'maxResult': 500,\n", " 'start': 1000\n", " }\n", "\n", "response=requests.get(url, headers = header, params = parameters)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response.status_code" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<type 'dict'>\n", "[u'matches', u'totalMatchCount', u'attribution', u'facetCounts', u'criteria']\n" ] } ], "source": [ "BB=response.json()\n", "\n", "print type(BB)\n", "print BB.keys()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "500\n", "<type 'list'>\n", "[u'flavors', u'rating', u'totalTimeInSeconds', u'ingredients', u'smallImageUrls', u'sourceDisplayName', u'recipeName', u'attributes', u'id', u'imageUrlsBySize']\n" ] } ], "source": [ "#only interrested in the information under matches. \n", "print len(BB['matches'])\n", "print type(BB['matches'])\n", "print BB['matches'][0].keys()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'attributes': {u'course': [u'Breakfast and Brunch']},\n", " u'flavors': None,\n", " u'id': u'Gluten-Free-Waffles-1629571',\n", " u'imageUrlsBySize': {u'90': u'https://lh3.googleusercontent.com/JQInkF_UXHKe_nUZGpjg9IJdrsaivrAr35PY6OhMVzvAn3NzhhroVkC5vlm121MScZlPw06pX4rc6YDtZeIzlIM=s90-c'},\n", " u'ingredients': [u'gluten free blend',\n", " u'ground flax',\n", " u'cane sugar',\n", " u'sea salt',\n", " u'gluten-free baking powder',\n", " u'eggs',\n", " u'melted butter',\n", " u'glutenfree vanilla',\n", " u'milk'],\n", " u'rating': 4,\n", " u'recipeName': u'Gluten-Free Waffles',\n", " u'smallImageUrls': [u'https://lh3.googleusercontent.com/A6-u0cpl4WVbuRY9l03vR0_gcMvPTvNQIjlcADM7S6cgVxZE1L9Vvx2YmWuGBl--joXRGT9bCcIA3ORNpzVz=s90'],\n", " u'sourceDisplayName': u'Dinner Was Delish',\n", " u'totalTimeInSeconds': 1800}" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#checkout one recipe\n", "BB_matches=BB['matches']\n", "BB_matches[0]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Healthy-Chocolate-Porridge-1711204\n", "Fruity-Whole-Grain-Breakfast-Porridge-1706525\n", "Gluten-Free-Waffles-1629571\n", "[]\n", "[]\n" ] } ], "source": [ "#import previous list of recipes collected\n", "df=pd.read_csv('BB_main.csv')\n", "df1=pd.read_csv('BB_main_1.csv')\n", "BB_ids=df.id\n", "BB1_ids=df1.id\n", "print BB_ids[0]\n", "print BB1_ids[0]\n", "BB2_ids=[]\n", "for recipe in BB_matches:\n", " BB2_ids.append(recipe['id'])\n", "print BB2_ids[0]\n", "#check if there are dupplicate recipes\n", "print [i for i, j in zip(BB_ids, BB2_ids) if i == j]\n", "print [i for i, j in zip(BB1_ids, BB2_ids) if i == j]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# #remove duplicate recipe from the recipe\n", "# BB_matches[:] = [d for d in BB_matches if d.get('id') != 'French-Toast-with-Vegan-Nog-964692']\n", "# BB_matches[:] = [d for d in BB_matches if d.get('id') != 'Quick-and-Easy-Waffles-1537027'] \n", "# #'Quick-and-Easy-Waffles-1537027'\n", "\n", "# # check to see if recipes have been removed\n", "# BB2_ids = []\n", "# for recipe in BB_matches:\n", "# BB2_ids.append(recipe['id'])\n", " \n", "# print [i for i, j in zip(BB1_ids, BB2_ids) if i == j]\n", "# len(BB_matches)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#forming lists to create dataframes of the features we want. \n", "main_list = []\n", "ingredients_list = []\n", "attributes_list = []\n", "\n", "for food in BB_matches:\n", "\n", " _d1 = {}\n", " _d1['id'] = food['id']\n", " _d1['rating'] = food['rating']\n", " _d1['recipeName'] = food['recipeName']\n", " _d1['sourceDisplayName'] = food['sourceDisplayName']\n", " main_list.append(_d1)\n", " \n", " _d2 = {}\n", " _d2['id'] =food['id']\n", " _d2['course']= 'Breakfast and Brunch'\n", " _d2['ingredient_list'] = food['ingredients']\n", " \n", " for i in food['ingredients']:\n", " i = i.lower() # additional code to conver to lowercase\n", " i = re.sub(r'\\d+%\\s', '', i) # additional code to remove 1%, 2%, etc\n", " i = re.sub(r'\\xae', '', i) # remove '\\xae' characters\n", " i = re.sub(r'shredded\\s', '', i)\n", " i = re.sub(r'chopped\\s', '', i)\n", " i = re.sub(r'diced\\s', '', i)\n", " i = re.sub(r'crumbled\\s', '', i)\n", " i = re.sub(r'fresh\\s', '', i)\n", " i = re.sub(r'grated\\s', '', i)\n", " i = re.sub(r'fat free\\s', '', i)\n", " i = re.sub(r'boneless\\s', '', i)\n", " i = re.sub(r'boneless skinless\\s', '', i)\n", " i = re.sub(r'minced\\s', '', i)\n", " i = re.sub(r'sliced\\s', '', i)\n", " i = re.sub(r'(?!ground beef)ground ', '', i)\n", " i = re.sub(r'^dried\\s', '', i)\n", " i = re.sub(r'^cooked\\s', '', i)\n", " \n", " _d2[i] = 1\n", " ingredients_list.append(_d2)\n", "\n", " _d3 = {}\n", " _d3['id'] = food['id']\n", " for k, v in food['attributes'].items():\n", " for i in v:\n", " _d3[i] = 1\n", " attributes_list.append(_d3)\n", " \n", "flavors_dict = {}\n", "\n", "for food in BB_matches:\n", " flavors_dict[food.get('id')] = food.get('flavors') \n", " " ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# read in dictionary for course and cuisine and create list of possible values for each\n", "cuisine_df = pd.read_csv('cuisine_headers.csv', names=['cuisine'])\n", "\n", "cuisine_list= cuisine_df.cuisine\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create dictionary of cuisine and course for each recipe\n", "cuisine_dict={}\n", "for food in BB_matches:\n", " cuisine_dict[food.get('id')]= food['attributes'].get('cuisine')\n", "\n", " \n", "_cuisines= {} \n", "\n", "for k, v in cuisine_dict.iteritems():\n", " cuisine_val = {}\n", " for course in cuisine_list:\n", " try:\n", " if course in v :\n", " cuisine_val[course] = 1\n", " else:\n", " cuisine_val[course] = 0\n", " except TypeError:\n", " cuisine_val[course] = 0\n", " \n", " _cuisines[k] = cuisine_val\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# second api call to get other features for each recipe\n", "key_id= '_app_id=79663a75&_app_key=02b233108f476f3110e0f65437c4d6dd'\n", "url='http://api.yummly.com/v1/api/recipe/'" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# retrieve other features for all recipes\n", "\n", "def get_recipe(_id):\n", " response = requests.get(url + _id + '?' + key_id)\n", " return response.json()\n", "\n", "recipes=[]\n", "for _id in BB2_ids :\n", " recipes.append(get_recipe(_id))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response.status_code" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "500\n", "[u'totalTime', u'ingredientLines', u'attribution', u'name', u'prepTimeInSeconds', u'rating', u'cookTimeInSeconds', u'numberOfServings', u'yield', u'nutritionEstimates', u'source', u'flavors', u'images', u'attributes', u'cookTime', u'id', u'prepTime', u'totalTimeInSeconds']\n" ] } ], "source": [ "print len(recipes)\n", "print recipes[1].keys()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#for each recipe create a new dictionary of selected attributes and append into a list\n", "\n", "recipe_details=[]\n", "for recipe in recipes:\n", " _dict={}\n", " #import pdb; pdb.set_trace()\n", " _dict['id']=recipe['id']\n", " _dict['ingredientCount']= len(recipe['ingredientLines'])\n", " _dict['numberOfServings']= recipe['numberOfServings']\n", " _dict['prepTimeInSeconds'] = recipe.get('prepTimeInSeconds')\n", " _dict['cookTimeInSeconds'] = recipe.get('cookTimeInSeconds')\n", " _dict['totalTimeInSeconds']= recipe.get('totalTimeInSeconds')\n", " \n", " recipe_details.append(_dict)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create dataframes, arrange column index and save into csv\n", "df_main = pd.DataFrame(main_list)\n", "df_main.to_csv('BB_main_2.csv', encoding ='utf-8')\n", "\n", "df_ingredients = pd.DataFrame(ingredients_list)\n", "df_ingredients = df_ingredients.fillna(0)\n", "cols = list(df_ingredients)\n", "cols.insert(0, cols.pop(cols.index('id')))\n", "cols.insert(1, cols.pop(cols.index('course')))\n", "df_ingredients= df_ingredients.ix[:,cols]\n", "df_ingredients.to_csv('BB_ingredients_2.csv', encoding ='utf-8')\n", "\n", "df_attributes = pd.DataFrame(attributes_list)\n", "df_attributes = df_attributes.fillna(0)\n", "cols = list(df_attributes)\n", "cols.insert(0, cols.pop(cols.index('id')))\n", "df_attributes = df_attributes.ix[:,cols]\n", "df_attributes.to_csv('BB_attributes_2.csv')\n", "\n", "df_flavors = pd.DataFrame(flavors_dict).transpose()\n", "df_flavors.reset_index(level=0, inplace=True)\n", "df_flavors.to_csv('BB_flavors_2.csv')\n", "\n", "df_cuisines = pd.DataFrame(_cuisines).transpose()\n", "df_cuisines.reset_index(level=0, inplace=True)\n", "df_cuisines.to_csv('BB_cuisines_2.csv')\n", "\n", "df_details=pd.DataFrame(recipe_details)\n", "cols = list(df_details)\n", "cols.insert(0, cols.pop(cols.index('id')))\n", "df_details=df_details.ix[:,cols]\n", "df_details.to_csv('BB_details_2.csv')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
metpy/MetPy
v1.1/_downloads/5f6dfc4b913dc349eba9f04f6161b5f1/GINI_Water_Vapor.ipynb
1
3223
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# GINI Water Vapor Imagery\n\nUse MetPy's support for GINI files to read in a water vapor satellite image and plot the\ndata using CartoPy.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport xarray as xr\n\nfrom metpy.cbook import get_test_data\nfrom metpy.io import GiniFile\nfrom metpy.plots import add_metpy_logo, add_timestamp, colortables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Open the GINI file from the test data\nf = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))\nprint(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get a Dataset view of the data (essentially a NetCDF-like interface to the\nunderlying data). Pull out the data and (x, y) coordinates. We use `metpy.parse_cf` to\nhandle parsing some netCDF Climate and Forecasting (CF) metadata to simplify working with\nprojections.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = xr.open_dataset(f)\nx = ds.variables['x'][:]\ny = ds.variables['y'][:]\ndat = ds.metpy.parse_cf('WV')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the image. We use MetPy's xarray/cartopy integration to automatically handle parsing\nthe projection information.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(10, 12))\nadd_metpy_logo(fig, 125, 145)\nax = fig.add_subplot(1, 1, 1, projection=dat.metpy.cartopy_crs)\nwv_norm, wv_cmap = colortables.get_with_range('WVCIMSS', 100, 260)\nwv_cmap.set_under('k')\nim = ax.imshow(dat[:], cmap=wv_cmap, norm=wv_norm,\n extent=(x.min(), x.max(), y.min(), y.max()), origin='upper')\nax.add_feature(cfeature.COASTLINE.with_scale('50m'))\nadd_timestamp(ax, f.prod_desc.datetime, y=0.02, high_contrast=True)\n\nplt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
pmorissette/bt
examples/PTE.ipynb
1
227540
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import ffn\n", "\n", "#using this import until pip is updated to have the version of bt with the targetVol algo\n", "# you will need to change this be wherever your local version of bt is located.\n", "import sys\n", "sys.path.insert(0, \"C:\\\\Users\\JPL09A\\\\Documents\\\\Code\\\\pmorissette\\\\bt\\\\\")\n", "\n", "import bt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Fake Index Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1dc7e64b6d8>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names = ['foo','bar','rf']\n", "dates = pd.date_range(start='2015-01-01',end='2018-12-31', freq=pd.tseries.offsets.BDay())\n", "n = len(dates)\n", "rdf = pd.DataFrame(\n", " np.zeros((n, len(names))),\n", " index = dates,\n", " columns = names\n", ")\n", "\n", "np.random.seed(1)\n", "rdf['foo'] = np.random.normal(loc = 0.1/252,scale=0.2/np.sqrt(252),size=n)\n", "rdf['bar'] = np.random.normal(loc = 0.04/252,scale=0.05/np.sqrt(252),size=n)\n", "rdf['rf'] = 0.\n", "\n", "pdf = 100*np.cumprod(1+rdf)\n", "pdf.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build and run Target Strategy\n", "\n", "I will first run a strategy that rebalances everyday.\n", "\n", "Then I will use those weights as target to rebalance to whenever the PTE is too high." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target\n", "0% [############################# ] 100% | ETA: 00:00:00" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2054: RuntimeWarning: invalid value encountered in minimum\n", " negative_returns = np.minimum(returns, 0.)\n", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2056: RuntimeWarning: divide by zero encountered in true_divide\n", " res = np.divide(er.mean(), std)\n" ] } ], "source": [ "selectTheseAlgo = bt.algos.SelectThese(['foo','bar'])\n", "\n", "# algo to set the weights to 1/vol contributions from each asset\n", "# with data over the last 3 months excluding yesterday\n", "weighInvVolAlgo = bt.algos.WeighInvVol(\n", " lookback=pd.DateOffset(months=3),\n", " lag=pd.DateOffset(days=1)\n", ")\n", "\n", "# algo to rebalance the current weights to weights set in target.temp\n", "rebalAlgo = bt.algos.Rebalance()\n", "\n", "# a strategy that rebalances daily to 1/vol weights\n", "strat = bt.Strategy(\n", " 'Target',\n", " [\n", " selectTheseAlgo,\n", " weighInvVolAlgo,\n", " rebalAlgo\n", " ]\n", ")\n", "\n", "# set integer_positions=False when positions are not required to be integers(round numbers)\n", "backtest = bt.Backtest(\n", " strat,\n", " pdf,\n", " integer_positions=False\n", ")\n", "\n", "res_target = bt.run(backtest)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1dc6c3d70b8>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res_target.get_security_weights().plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use the PTE rebalance algo to trigger a rebalance whenever predicted tracking error is greater than 1%." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PTE\n", "0% [############################# ] 100% | ETA: 00:00:00" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2054: RuntimeWarning: invalid value encountered in minimum\n", " negative_returns = np.minimum(returns, 0.)\n", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ffn\\core.py:2056: RuntimeWarning: divide by zero encountered in true_divide\n", " res = np.divide(er.mean(), std)\n" ] } ], "source": [ "# algo to fire whenever predicted tracking error is greater than 1%\n", "wdf = res_target.get_security_weights()\n", "\n", "PTE_rebalance_Algo = bt.algos.PTE_Rebalance(\n", " 0.01,\n", " wdf,\n", " lookback=pd.DateOffset(months=3),\n", " lag=pd.DateOffset(days=1),\n", " covar_method='standard',\n", " annualization_factor=252\n", ")\n", "\n", "selectTheseAlgo = bt.algos.SelectThese(['foo','bar'])\n", "\n", "# algo to set the weights to 1/vol contributions from each asset\n", "# with data over the last 12 months excluding yesterday\n", "weighTargetAlgo = bt.algos.WeighTarget(\n", " wdf\n", ")\n", "\n", "rebalAlgo = bt.algos.Rebalance()\n", "\n", "# a strategy that rebalances monthly to specified weights\n", "strat = bt.Strategy(\n", " 'PTE',\n", " [\n", " PTE_rebalance_Algo,\n", " selectTheseAlgo,\n", " weighTargetAlgo,\n", " rebalAlgo\n", " ]\n", ")\n", "\n", "# set integer_positions=False when positions are not required to be integers(round numbers)\n", "backtest = bt.Backtest(\n", " strat,\n", " pdf,\n", " integer_positions=False\n", ")\n", "\n", "res_PTE = bt.run(backtest)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1,ncols=1)\n", "res_target.get_security_weights().plot(ax=ax)\n", "\n", "realized_weights_df = res_PTE.get_security_weights()\n", "realized_weights_df['PTE foo'] = realized_weights_df['foo']\n", "realized_weights_df['PTE bar'] = realized_weights_df['bar']\n", "realized_weights_df = realized_weights_df.loc[:,['PTE foo', 'PTE bar']]\n", "realized_weights_df.plot(ax=ax)\n", "\n", "ax.set_title('Target Weights vs PTE Weights')\n", "ax.plot()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "trans_df = pd.DataFrame(\n", " index=res_target.prices.index,\n", " columns=['Target','PTE']\n", ")\n", "\n", "transactions = res_target.get_transactions()\n", "transactions = (transactions['quantity'] * transactions['price']).reset_index()\n", "\n", "bar_mask = transactions.loc[:,'Security'] == 'bar'\n", "foo_mask = transactions.loc[:,'Security'] == 'foo'\n", "\n", "trans_df.loc[trans_df.index[4:],'Target'] = np.abs(transactions[bar_mask].iloc[:,2].values) + np.abs(transactions[foo_mask].iloc[:,2].values)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "transactions = res_PTE.get_transactions()\n", "transactions = (transactions['quantity'] * transactions['price']).reset_index()\n", "\n", "bar_mask = transactions.loc[:,'Security'] == 'bar'\n", "foo_mask = transactions.loc[:,'Security'] == 'foo'\n", "\n", "trans_df.loc[transactions[bar_mask].iloc[:,0],'PTE'] = np.abs(transactions[bar_mask].iloc[:,2].values)\n", "trans_df.loc[transactions[foo_mask].iloc[:,0],'PTE'] += np.abs(transactions[foo_mask].iloc[:,2].values)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "trans_df = trans_df.fillna(0)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1,ncols=1)\n", "trans_df.cumsum().plot(ax=ax)\n", "ax.set_title('Cumulative sum of notional traded')\n", "ax.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we plot the total risk contribution of each asset class and divide by the total volatility, then we can see that both strategy's contribute roughly similar amounts of volatility from both of the securities." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weights_target = res_target.get_security_weights()\n", "rolling_cov_target = pdf.loc[:,weights_target.columns].pct_change().rolling(window=3*20).cov()*252\n", "\n", "weights_PTE = res_PTE.get_security_weights().loc[:,weights_target.columns]\n", "rolling_cov_PTE = pdf.loc[:,weights_target.columns].pct_change().rolling(window=3*20).cov()*252\n", "\n", "\n", "trc_target = pd.DataFrame(\n", " np.nan,\n", " index = weights_target.index,\n", " columns = weights_target.columns\n", ")\n", "\n", "trc_PTE = pd.DataFrame(\n", " np.nan,\n", " index = weights_PTE.index,\n", " columns = [x + \" PTE\" for x in weights_PTE.columns]\n", ")\n", "\n", "for dt in pdf.index:\n", " trc_target.loc[dt,:] = weights_target.loc[dt,:].values*(rolling_cov_target.loc[dt,:].values@weights_target.loc[dt,:].values)/np.sqrt(weights_target.loc[dt,:].values@rolling_cov_target.loc[dt,:].values@weights_target.loc[dt,:].values)\n", " trc_PTE.loc[dt,:] = weights_PTE.loc[dt,:].values*(rolling_cov_PTE.loc[dt,:].values@weights_PTE.loc[dt,:].values)/np.sqrt(weights_PTE.loc[dt,:].values@rolling_cov_PTE.loc[dt,:].values@weights_PTE.loc[dt,:].values)\n", "\n", "\n", "fig, ax = plt.subplots(nrows=1,ncols=1)\n", "trc_target.plot(ax=ax)\n", "trc_PTE.plot(ax=ax)\n", "ax.set_title('Total Risk Contribution')\n", "ax.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the Target strategy's and PTE strategy's Total Risk they are very similar." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1,ncols=1)\n", "trc_target.sum(axis=1).plot(ax=ax,label='Target')\n", "trc_PTE.sum(axis=1).plot(ax=ax,label='PTE')\n", "ax.legend()\n", "ax.set_title('Total Risk')\n", "ax.plot()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2015-01-06\n", "2 2015-01-07\n", "4 2015-01-08\n", "6 2015-01-09\n", "8 2015-01-12\n", "10 2015-02-20\n", "12 2015-04-07\n", "14 2015-09-01\n", "16 2017-03-23\n", "18 2017-06-23\n", "20 2017-10-24\n", "Name: Date, dtype: datetime64[ns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transactions = res_PTE.get_transactions()\n", "transactions = (transactions['quantity'] * transactions['price']).reset_index()\n", "\n", "bar_mask = transactions.loc[:,'Security'] == 'bar'\n", "dates_of_PTE_transactions = transactions[bar_mask].iloc[:,0]\n", "dates_of_PTE_transactions" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1dc7fa8d828>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1,ncols=1)\n", "np.sum(np.abs(trc_target.values - trc_PTE.values))\n", " #.abs().sum(axis=1).plot()\n", "\n", "ax.set_title('Total Risk')\n", "ax.plot(\n", " trc_target.index,\n", " np.sum(np.abs(trc_target.values - trc_PTE.values),axis=1),\n", " label='PTE'\n", ")\n", "\n", "for i,dt in enumerate(dates_of_PTE_transactions):\n", " if i == 0:\n", " ax.axvline(x=dt,color='red',label='PTE Transaction')\n", " else:\n", " ax.axvline(x=dt,color='red')\n", "\n", "ax.legend()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the Predicted Tracking Error of the PTE Strategy with each transaction marked." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
SlipknotTN/udacity-deeplearning-nanodegree
language-translation/dlnd_language_translation.ipynb
1
1177502
null
mit
wdbm/Psychedelic_Machine_Learning_in_the_Cenozoic_Era
Keras_CNN_newsgroups_text_classification.ipynb
1
117405
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 20 newsgroups classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use the [20 newsgroups text dataset by Ken Lang](http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html), which is a dataset of 20,000 messages from 20 different newsgroups. One thousand messages from each newsgroup were sampled randomly and classified by newsgroup.\n", "\n", "The standard GloVe (Global Vectors for Word Representation) word vector model of the Stanford NLP Group is used for this task.\n", "\n", "- reference: [*GloVe: Global Vectors for Word Representation*, Empirical Methods in Natural Language Processing (EMNLP), J. Pennington, R. Socher and C. D. Manning (2014)](http://www.aclweb.org/anthology/D14-1162)\n", "\n", "```Bash\n", "wget http://nlp.stanford.edu/data/glove.6B.zip\n", "unzip glove.6B.zip\n", "```\n", "\n", "We use this data to train 1D convolutional neural networks in Keras to classify the messages into one of two newsgroup classes.\n", "\n", "For the case of one of the models, a dropout probability of 0.1 was applied to convolutional layers in towers and the more standard approach of a dropout probability of 0.5 was applied to the more output dense layer [[ref](https://stats.stackexchange.com/a/317313)].\n", "\n", "## references/inspirations\n", "\n", "- [reference](https://www.kaggle.com/carlosaguayo/deep-learning-for-text-classification)\n", "- [reference](https://docs.google.com/presentation/d/1NQpJtkD8PhMmER4fw4WO0VwaD9I-s20gWnypwbkx5wk)\n", "- [reference](https://github.com/pavansolapure/opencodez-samples/blob/master/keras-text-classification/20_news_group_classification.py)\n", "- [reference](http://ai.intelligentonlinetools.com/ml/text-classification-20-newsgroups-dataset-using-convolutional-neural-network)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(120000)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Autosaving every 120 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%autosave 120\n", "import numpy as np\n", "np.random.seed(1337)\n", "from IPython.display import SVG\n", "from keras.models import Model\n", "from keras.preprocessing.text import Tokenizer\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.layers import (\n", " Concatenate,\n", " Conv1D,\n", " Dense,\n", " Dropout,\n", " Embedding,\n", " Flatten,\n", " Input,\n", " MaxPooling1D\n", ")\n", "from keras.utils.np_utils import to_categorical\n", "from keras.utils.vis_utils import model_to_dot\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from pathlib import Path\n", "from sklearn.datasets import fetch_20newsgroups\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "def summary_and_diagram(model):\n", " model.summary()\n", " return SVG(model_to_dot(model).create(prog='dot', format='svg'))\n", " #SVG(model_to_dot(model, show_shapes=True, show_layer_names=True).create(prog='dot', format='svg'))\n", "\n", "def model_training_plot(history):\n", " plt.plot(history.history['acc'], marker='.', label='train')\n", " plt.plot(history.history['val_acc'], marker='.', label='validation')\n", " plt.title('accuracy')\n", " plt.grid(True)\n", " plt.xlabel('epoch')\n", " plt.ylabel('accuracy')\n", " plt.legend(loc='best')\n", " plt.show();" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.rcParams[\"figure.figsize\"] = [10, 10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of training samples: 1079\n", "\n", "example training sample of category soc.religion.christian:\n", "\n", " WASHINGTON, April 19 -- A symposium on the Dead Sea \n", "Scrolls will be held at the Library of Congress on Wednesday,\n", "April 21, and Thursday, April 22. The two-day program, cosponsored\n", "by the library and Baltimore Hebrew University, with additional\n", "support from the Project Judaica Foundation, will be held in the\n" ] } ], "source": [ "categories = ['alt.atheism', 'soc.religion.christian'] \n", "\n", "newsgroups_train = fetch_20newsgroups(subset='train',\n", " shuffle=True, \n", " categories=categories)\n", "\n", "print(f'number of training samples: {len(newsgroups_train.data)}')\n", "\n", "example_sample_data = \"\\n\".join(newsgroups_train.data[0].split(\"\\n\")[10:15])\n", "example_sample_category = categories[newsgroups_train.target[0]]\n", "print(f'\\nexample training sample of category {example_sample_category}:'\n", " f'\\n\\n{example_sample_data}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# data preparation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20030 unique tokens found\n" ] } ], "source": [ "labels = newsgroups_train.target\n", "texts = newsgroups_train.data\n", "max_sequence_length = 1000\n", "max_words = 20000\n", "\n", "tokenizer = Tokenizer(num_words=max_words)\n", "tokenizer.fit_on_texts(texts)\n", "sequences = tokenizer.texts_to_sequences(texts)\n", "\n", "word_index = tokenizer.word_index\n", "#print(sequences[0][:10])\n", "print(f'{len(word_index)} unique tokens found')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data tensor shape: (1079, 1000)\n", "targets tensor shape: (1079, 2)\n" ] } ], "source": [ "labels = to_categorical(np.array(labels))\n", "data = pad_sequences(sequences, maxlen=max_sequence_length)\n", "\n", "print(f'data tensor shape: {data.shape}\\n'\n", " f'targets tensor shape: {labels.shape}')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training samples shape: (756, 1000)\n", "validation samples shape: (756, 2)\n", "\n", "training samples positive/negative reviews: [330. 426.]\n", "validation samples positive/negative reviews: [150. 173.]\n" ] } ], "source": [ "indices = np.arange(data.shape[0]); np.random.shuffle(indices) \n", "data = data[indices] \n", "labels = labels[indices]\n", "cross_validation_split = 0.3\n", "nb_validation_samples = int(cross_validation_split * data.shape[0])\n", "\n", "x_train = data[:-nb_validation_samples] \n", "y_train = labels[:-nb_validation_samples] \n", "x_val = data[-nb_validation_samples:] \n", "y_val = labels[-nb_validation_samples:] \n", "\n", "print(f'training samples shape: {x_train.shape}\\n'\n", " f'validation samples shape: {y_train.shape}\\n\\n'\n", " f'training samples positive/negative reviews: {y_train.sum(axis=0)}\\n'\n", " f'validation samples positive/negative reviews: {y_val.sum(axis=0)}')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "word vectors: 400000\n" ] } ], "source": [ "embeddings_index = {}\n", "with open('glove.6B.100d.txt') as f:\n", " for line in f:\n", " values = line.split(' ')\n", " word = values[0]\n", " embeddings_index[word] = np.asarray(values[1:], dtype='float32')\n", "print(f'word vectors: {len(embeddings_index)}')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "embedding matrix shape: (20031, 100)\n" ] } ], "source": [ "word_vector_dimensionality = 100\n", "\n", "embedding_matrix = np.random.random(\n", " (len(word_index) + 1, word_vector_dimensionality))\n", "\n", "for word, i in word_index.items():\n", " embedding_vector = embeddings_index.get(word)\n", " if embedding_vector is not None:\n", " # Words not in the embedding index are all zero elements.\n", " embedding_matrix[i] = embedding_vector\n", "\n", "print(f'embedding matrix shape: {embedding_matrix.shape}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# model: convolutional neural network" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 1000) 0 \n", "_________________________________________________________________\n", "embedding_1 (Embedding) (None, 1000, 100) 2003100 \n", "_________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 996, 128) 64128 \n", "_________________________________________________________________\n", "max_pooling1d_1 (MaxPooling1 (None, 199, 128) 0 \n", "_________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 195, 128) 82048 \n", "_________________________________________________________________\n", "max_pooling1d_2 (MaxPooling1 (None, 39, 128) 0 \n", "_________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 35, 128) 82048 \n", "_________________________________________________________________\n", "max_pooling1d_3 (MaxPooling1 (None, 1, 128) 0 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 128) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 300) 38700 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 300) 0 \n", "_________________________________________________________________\n", "preds (Dense) (None, 2) 602 \n", "=================================================================\n", "Total params: 2,270,626\n", "Trainable params: 267,526\n", "Non-trainable params: 2,003,100\n", "_________________________________________________________________\n" ] }, { "data": { 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text-anchor=\"middle\" x=\"103.5\" y=\"-14.8\">preds: Dense</text>\n", "</g>\n", "<!-- 140668844230080&#45;&gt;140668843936176 -->\n", "<g class=\"edge\" id=\"edge11\">\n", "<title>140668844230080-&gt;140668843936176</title>\n", "<path d=\"M103.5,-73.4551C103.5,-65.3828 103.5,-55.6764 103.5,-46.6817\" fill=\"none\" stroke=\"#000000\"/>\n", "<polygon fill=\"#000000\" points=\"107.0001,-46.5903 103.5,-36.5904 100.0001,-46.5904 107.0001,-46.5903\" stroke=\"#000000\"/>\n", "</g>\n", "</g>\n", "</svg>" ], "text/plain": [ "<IPython.core.display.SVG object>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embedding_layer = Embedding(len(word_index) + 1,\n", " word_vector_dimensionality,\n", " weights=[embedding_matrix],\n", " input_length=max_sequence_length,\n", " trainable=False)\n", "\n", "inputs = Input(shape=(max_sequence_length,), dtype='int32') # inputs\n", "x = embedding_layer(inputs) # embedded sequences\n", "x = Conv1D(128, 5, activation='relu')(x)\n", "x = MaxPooling1D(5)(x) \n", "x = Conv1D(128, 5, activation='relu')(x) \n", "x = MaxPooling1D(5)(x) \n", "x = Conv1D(128, 5, activation='relu')(x) \n", "x = MaxPooling1D(35)(x) # global max pooling\n", "x = Flatten()(x)\n", "x = Dense(300, activation='relu')(x)\n", "x = Dropout(rate=0.5)(x)\n", "preds = Dense(2, activation='softmax', name='preds')(x)\n", "model = Model(input=inputs, output=preds)\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='nadam',\n", " metrics=['acc'])\n", "summary_and_diagram(model)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 18min 12s, sys: 53.3 s, total: 19min 5s\n", "Wall time: 2min 41s\n" ] } ], "source": [ "%%time\n", "history = model.fit(x_train, y_train, validation_data=(x_val, y_val),\n", " epochs=60, batch_size=32, verbose=False)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "max. validation accuracy observed: 0.9535603715170279\n", "max. validation accuracy history index: 4\n" ] } ], "source": [ "model_training_plot(history)\n", "print(f'max. validation accuracy observed: {max(model.history.history[\"val_acc\"])}')\n", "print(f'max. validation accuracy history index: {model.history.history[\"val_acc\"].index(max(model.history.history[\"val_acc\"]))}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# model: convolutional neural network with multiple towers of varying kernel sizes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_2 (InputLayer) (None, 1000) 0 \n", "__________________________________________________________________________________________________\n", "embedding_2 (Embedding) (None, 1000, 100) 2003100 input_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_4 (Conv1D) (None, 998, 128) 38528 embedding_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_5 (Conv1D) (None, 997, 128) 51328 embedding_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_6 (Conv1D) (None, 996, 128) 64128 embedding_2[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 998, 128) 0 conv1d_4[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_3 (Dropout) (None, 997, 128) 0 conv1d_5[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_4 (Dropout) (None, 996, 128) 0 conv1d_6[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_4 (MaxPooling1D) (None, 199, 128) 0 dropout_2[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_5 (MaxPooling1D) (None, 199, 128) 0 dropout_3[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_6 (MaxPooling1D) (None, 199, 128) 0 dropout_4[0][0] \n", "__________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 597, 128) 0 max_pooling1d_4[0][0] \n", " max_pooling1d_5[0][0] \n", " max_pooling1d_6[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_7 (Conv1D) (None, 593, 128) 82048 concatenate_1[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_7 (MaxPooling1D) (None, 118, 128) 0 conv1d_7[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_8 (Conv1D) (None, 114, 128) 82048 max_pooling1d_7[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_8 (MaxPooling1D) (None, 3, 128) 0 conv1d_8[0][0] \n", "__________________________________________________________________________________________________\n", "flatten_2 (Flatten) (None, 384) 0 max_pooling1d_8[0][0] \n", "__________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 128) 49280 flatten_2[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_5 (Dropout) (None, 128) 0 dense_2[0][0] \n", "__________________________________________________________________________________________________\n", "preds (Dense) (None, 2) 258 dropout_5[0][0] \n", "==================================================================================================\n", "Total params: 2,370,718\n", "Trainable params: 367,618\n", "Non-trainable params: 2,003,100\n", "__________________________________________________________________________________________________\n" ] }, { "data": { "image/svg+xml": [ "<svg height=\"994pt\" viewBox=\"0.00 0.00 665.00 994.00\" width=\"665pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g class=\"graph\" 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-->\n", "<g class=\"edge\" id=\"edge1\">\n", "<title>140668440353368-&gt;140668440353424</title>\n", "<path d=\"M328.5,-949.4551C328.5,-941.3828 328.5,-931.6764 328.5,-922.6817\" fill=\"none\" stroke=\"#000000\"/>\n", "<polygon fill=\"#000000\" points=\"332.0001,-922.5903 328.5,-912.5904 325.0001,-922.5904 332.0001,-922.5903\" stroke=\"#000000\"/>\n", "</g>\n", "<!-- 140668440353872 -->\n", "<g class=\"node\" id=\"node3\">\n", "<title>140668440353872</title>\n", "<polygon fill=\"none\" points=\"84,-803.5 84,-839.5 207,-839.5 207,-803.5 84,-803.5\" stroke=\"#000000\"/>\n", "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"145.5\" y=\"-817.8\">conv1d_4: Conv1D</text>\n", "</g>\n", "<!-- 140668440353424&#45;&gt;140668440353872 -->\n", "<g class=\"edge\" id=\"edge2\">\n", "<title>140668440353424-&gt;140668440353872</title>\n", "<path d=\"M283.264,-876.4551C258.1892,-866.4525 226.824,-853.9407 200.358,-843.3833\" fill=\"none\" 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id=\"edge18\">\n", "<title>140668439256312-&gt;140668439047360</title>\n", "<path d=\"M328.5,-292.4551C328.5,-284.3828 328.5,-274.6764 328.5,-265.6817\" fill=\"none\" stroke=\"#000000\"/>\n", "<polygon fill=\"#000000\" points=\"332.0001,-265.5903 328.5,-255.5904 325.0001,-265.5904 332.0001,-265.5903\" stroke=\"#000000\"/>\n", "</g>\n", "<!-- 140668438616552 -->\n", "<g class=\"node\" id=\"node18\">\n", "<title>140668438616552</title>\n", "<polygon fill=\"none\" points=\"277.5,-146.5 277.5,-182.5 379.5,-182.5 379.5,-146.5 277.5,-146.5\" stroke=\"#000000\"/>\n", "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"328.5\" y=\"-160.8\">dense_2: Dense</text>\n", "</g>\n", "<!-- 140668439047360&#45;&gt;140668438616552 -->\n", "<g class=\"edge\" id=\"edge19\">\n", "<title>140668439047360-&gt;140668438616552</title>\n", "<path d=\"M328.5,-219.4551C328.5,-211.3828 328.5,-201.6764 328.5,-192.6817\" fill=\"none\" stroke=\"#000000\"/>\n", "<polygon 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"<title>140668438351096</title>\n", "<polygon fill=\"none\" points=\"285,-.5 285,-36.5 372,-36.5 372,-.5 285,-.5\" stroke=\"#000000\"/>\n", "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"328.5\" y=\"-14.8\">preds: Dense</text>\n", "</g>\n", "<!-- 140668438765128&#45;&gt;140668438351096 -->\n", "<g class=\"edge\" id=\"edge21\">\n", "<title>140668438765128-&gt;140668438351096</title>\n", "<path d=\"M328.5,-73.4551C328.5,-65.3828 328.5,-55.6764 328.5,-46.6817\" fill=\"none\" stroke=\"#000000\"/>\n", "<polygon fill=\"#000000\" points=\"332.0001,-46.5903 328.5,-36.5904 325.0001,-46.5904 332.0001,-46.5903\" stroke=\"#000000\"/>\n", "</g>\n", "</g>\n", "</svg>" ], "text/plain": [ "<IPython.core.display.SVG object>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embedding_layer = Embedding(len(word_index) + 1,\n", " word_vector_dimensionality,\n", " weights=[embedding_matrix],\n", " input_length=max_sequence_length,\n", " trainable=False)\n", "\n", "inputs = Input(shape=(max_sequence_length,), dtype='int32') \n", "x = embedding_layer(inputs)\n", "\n", "convolutional_layer_towers = [] \n", "for kernel_size in [3, 4, 5]:\n", " _x = Conv1D(filters=128, kernel_size=kernel_size, activation='relu')(x)\n", " _x = Dropout(rate=0.1)(_x)\n", " _x = MaxPooling1D(5)(_x)\n", " convolutional_layer_towers.append(_x)\n", "x = Concatenate(axis=1)(convolutional_layer_towers)\n", "x = Conv1D(128, 5, activation='relu')(x) \n", "x = MaxPooling1D(5)(x) \n", "x = Conv1D(128, 5, activation='relu')(x) \n", "x = MaxPooling1D(30)(x) \n", "x = Flatten()(x) \n", "x = Dense(128, activation='relu')(x)\n", "x = Dropout(rate=0.5)(x)\n", "preds = Dense(2, activation='softmax', name='preds')(x)\n", "model = Model(input=inputs, output=preds)\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='nadam',\n", " metrics=['acc'])\n", "summary_and_diagram(model)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1h 30min 55s, sys: 4min 39s, total: 1h 35min 35s\n", "Wall time: 13min 1s\n" ] } ], "source": [ "%%time\n", "history = model.fit(x_train, y_train, validation_data=(x_val, y_val),\n", " epochs=100, batch_size=32, verbose=False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "max. validation accuracy observed: 0.9566563467492261\n", "max. validation accuracy history index: 9\n" ] } ], "source": [ "model_training_plot(history)\n", "print(f'max. validation accuracy observed: {max(model.history.history[\"val_acc\"])}')\n", "print(f'max. validation accuracy history index: {model.history.history[\"val_acc\"].index(max(model.history.history[\"val_acc\"]))}')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
vravishankar/Jupyter-Books
Classes+and+Objects.ipynb
1
43697
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Object Oriented Programming" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to Wikipedia, \"Object-oriented programming (OOP) is a programming paradigm based on the concept of 'objects', which may contain data, in the form of fields, often known as attributes; and code, in the form of procedures, often known as methods.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classes & Objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A **class** is a template for defining objects. It specifies the names and types of variables that can exist in an object, as well as \"methods\"--procedures for operating on those variables. A class can be thought of as a \"type\", with the **objects** being a \"variable\" of that type.\n", "\n", "For example, when we define a Person class using the class keyword, we haven't actually created a Person. Instead, what we've created is a sort of instruction manual for constructing \"person\" objects." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class Person:\n", " pass\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the class statement did not create anything, it just the blueprint to create \"Person\" objects. To create an object we need to instantiate the \"Person\" class." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class '__main__.Person'>\n" ] } ], "source": [ "P1 = Person()\n", "print(type(P1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we created a \"Person\" object and assigned it to \"P1\". We can create any number of objects but please note there will be only one \"Person\" class." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simple Person Class\n" ] } ], "source": [ "# Doc string for class\n", "class Person:\n", " '''Simple Person Class'''\n", " pass\n", "\n", "print(Person.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Attributes & Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Classes contain attributes (also called fields, members etc...) and methods (a.k.a functions). Attributes defines the characteristics of the object and methods perfom action on the object. For example, the class definition below has firstname and lastname attributes and fullname is a method." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Person:\n", " '''Simple Person Class\n", " \n", " Attributes:\n", " firstname: String representing first name of the person\n", " lastname: String representing last name of the person\n", " '''\n", " def __init__(self,firstname,lastname):\n", " '''Initialiser method for Person'''\n", " self.firstname = firstname\n", " self.lastname = lastname\n", " \n", " def fullname(self):\n", " '''Returns the full name of the person'''\n", " return self.firstname + ' ' + self.lastname" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inside the class body, we define two functions – these are our object’s methods. The first is called \\__init\\__, which is a special method. When we call the class object, a new instance of the class is created, and the \\__init\\__ method on this new object is immediately executed with all the parameters that we passed to the class object. The purpose of this method is thus to set up a new object using data that we have provided.\n", "\n", "The second method is a custom method which derives the fullname of the person using the firstname and the lastname." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">**\\__init\\__** is sometimes called the object’s constructor, because it is used similarly to the way that constructors are used in other languages, but that is not technically correct – it’s better to call it the initialiser. There is a different method called **\\__new\\__** which is more analogous to a constructor, but it is hardly ever used." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may have noticed that both of these method definitions have self as the first parameter, and we use this variable inside the method bodies – but we don’t appear to pass this parameter in. This is because whenever we call a method on an object, the object itself is automatically passed in as the first parameter (as self). This gives us a way to access the object’s properties from inside the object’s methods." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Instance Attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the attributes that are defined on the Person instance are called instance attributes. They are added to the instance when the \\__init\\__ method is executed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Class Attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can, however, also define attributes which are set on the class. These attributes will be shared by all instances of that class. In many ways they behave just like instance attributes, but there are some caveats that you should be aware of.\n", "\n", "We define class attributes in the body of a class, at the same indentation level as method definitions (one level up from the insides of methods)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "('%s is not a valid title.', 'Mister')", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-23-a6f2f6f9628f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirstname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlastname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mJohn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPerson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mister'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'John'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Doe'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-23-a6f2f6f9628f>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, title, firstname, lastname)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m'''Initialiser method for Person'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtitle\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTITLES\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not a valid title.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirstname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfirstname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlastname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlastname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: ('%s is not a valid title.', 'Mister')" ] } ], "source": [ "class Person:\n", " '''Simple Person Class\n", " \n", " Attributes:\n", " firstname: String representing first name of the person\n", " lastname: String representing last name of the person\n", " '''\n", " TITLES = ['Mr','Mrs','Master']\n", " \n", " def __init__(self,title,firstname,lastname):\n", " '''Initialiser method for Person'''\n", " if title not in self.TITLES:\n", " raise ValueError(\"%s is not a valid title.\", title)\n", " self.firstname = firstname\n", " self.lastname = lastname\n", " \n", " def fullname(self):\n", " '''Returns the full name of the person'''\n", " return self.firstname + ' ' + self.lastname\n", " \n", "John = Person('Mister','John','Doe') # this will create an error\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name : Zara , Salary: 2000\n", "Name : Manni , Salary: 5000\n", "Total Employee 2\n" ] } ], "source": [ "class Employee:\n", " '''Common base class for all employees'''\n", "\n", " empCount = 0\n", " \n", " def __init__(self, name, salary):\n", " self.name = name\n", " self.salary = salary\n", " Employee.empCount += 1\n", " \n", " def displayCount(self):\n", " print(\"Total Employee %d\",Employee.empCount)\n", "\n", " def displayEmployee(self):\n", " print(\"Name : \", self.name, \", Salary: \", self.salary)\n", "\n", "\"This would create first object of Employee class\"\n", "emp1 = Employee(\"Zara\", 2000)\n", "\"This would create second object of Employee class\"\n", "emp2 = Employee(\"Manni\", 5000)\n", "emp1.displayEmployee()\n", "emp2.displayEmployee()\n", "print(\"Total Employee \", Employee.empCount)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please note that when we set an attribute on an instance which has the same name as a class attribute, we are overriding the class attribute with an instance attribute, which will take precedence over it. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Class Decorators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Class Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like we can define class attributes, which are shared between all instances of a class, we can define class methods. We do this by using the __@classmethod__ decorator to decorate an ordinary method.\n", "\n", "A class method still has its calling object as the first parameter, but by convention we rename this parameter from self to cls. If we call the class method from an instance, this parameter will contain the instance object, but if we call it from the class it will contain the class object. By calling the parameter cls we remind ourselves that it is not guaranteed to have any instance attributes.\n", "\n", "Class methods exists primarily for two reasons:\n", "\n", "1. Sometimes there are tasks associated with a class which we can perform using constants and other class attributes, without needing to create any class instances. If we had to use instance methods for these tasks, we would need to create an instance for no reason, which would be wasteful.\n", "\n", "2. Sometimes it is useful to write a class method which creates an instance of the class after processing the input so that it is in the right format to be passed to the class constructor. This allows the constructor to be straightforward and not have to implement any complicated parsing or clean-up code." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['92', '-15', '99', '101', '77', '65', '100']\n" ] } ], "source": [ "class ClassGrades:\n", "\n", " def __init__(self, grades):\n", " self.grades = grades\n", "\n", " @classmethod\n", " def from_csv(cls, grade_csv_str):\n", " grades = grade_csv_str.split(', ')\n", " return cls(grades)\n", " \n", "class_grades = ClassGrades.from_csv('92, -15, 99, 101, 77, 65, 100')\n", "print(class_grades.grades)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Static Methods\n", "\n", "A static method doesn’t have the calling object passed into it as the first parameter. This means that it doesn’t have access to the rest of the class or instance at all. We can call them from an instance or a class object, but they are most commonly called from class objects, like class methods.\n", "\n", "If we are using a class to group together related methods which don’t need to access each other or any other data on the class, we may want to use this technique. \n", "\n", "The advantage of using static methods is that we eliminate unnecessary cls or self parameters from our method definitions. \n", "\n", "The disadvantage is that if we do occasionally want to refer to another class method or attribute inside a static method we have to write the class name out in full, which can be much more verbose than using the cls variable which is available to us inside a class method." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got grades: ['90', '80', '85', '94', '70']\n", "Invalid!\n" ] } ], "source": [ "class ClassGrades:\n", "\n", " def __init__(self, grades):\n", " self.grades = grades\n", "\n", " @classmethod\n", " def from_csv(cls, grade_csv_str):\n", " grades = grade_csv_str.split(', ')\n", " cls.validate(grades)\n", " return cls(grades)\n", "\n", "\n", " @staticmethod\n", " def validate(grades):\n", " for g in grades:\n", " if int(g) < 0 or int(g) > 100:\n", " raise Exception()\n", "\n", "try: \n", " # Try out some valid grades\n", " class_grades_valid = ClassGrades.from_csv('90, 80, 85, 94, 70')\n", " print('Got grades:', class_grades_valid.grades)\n", "\n", " # Should fail with invalid grades\n", " class_grades_invalid = ClassGrades.from_csv('92, -15, 99, 101, 77, 65, 100')\n", " print(class_grades_invalid.grades)\n", "except: \n", " print('Invalid!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference between a static method and a class method is:\n", "\n", "* Static method knows nothing about the class and just deals with the parameters.\n", "* Class method works with the class since its parameter is always the class itself." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Property\n", "\n", "Sometimes we use a method to generate a property of an object dynamically, calculating it from the object’s other properties. Sometimes you can simply use a method to access a single attribute and return it. You can also use a different method to update the value of the attribute instead of accessing it directly. Methods like this are called getters and setters, because they “get” and “set” the values of attributes, respectively.\n", "\n", "The @property decorator lets us make a method behave like an attribute.\n", "\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "John Doe\n" ] } ], "source": [ "class Person:\n", " '''Simple Person Class\n", " \n", " Attributes:\n", " firstname: String representing first name of the person\n", " lastname: String representing last name of the person\n", " '''\n", " def __init__(self,firstname,lastname):\n", " '''Initialiser method for Person'''\n", " self.firstname = firstname\n", " self.lastname = lastname\n", " \n", " @property\n", " def fullname(self):\n", " '''Returns the full name of the person'''\n", " return self.firstname + ' ' + self.lastname\n", " \n", "p1 = Person('John','Doe')\n", "print(p1.fullname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also decorators which we can use to define a setter and a deleter for our attribute (a deleter will delete the attribute from our object). The getter, setter and deleter methods must all have the same name" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "John Doe\n", "Jack Daniels\n" ] } ], "source": [ "class Person:\n", " '''Simple Person Class\n", " \n", " Attributes:\n", " firstname: String representing first name of the person\n", " lastname: String representing last name of the person\n", " '''\n", " def __init__(self,firstname,lastname):\n", " '''Initialiser method for Person'''\n", " self.firstname = firstname\n", " self.lastname = lastname\n", " \n", " @property\n", " def fullname(self):\n", " '''Returns the full name of the person'''\n", " return self.firstname + ' ' + self.lastname\n", " \n", " @fullname.setter\n", " def fullname(self,value):\n", " firstname,lastname = value.split(\" \")\n", " self.firstname = firstname\n", " self.lastname = lastname\n", " \n", " @fullname.deleter\n", " def fullname(self):\n", " del self.firstname\n", " del self.lastname\n", " \n", "p1 = Person('John','Doe')\n", "print(p1.fullname)\n", "\n", "p1.fullname = 'Jack Daniels'\n", "print(p1.fullname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspecting an Object" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'fullname', 'name', 'surname']\n" ] } ], "source": [ "class Person:\n", " def __init__(self, name, surname):\n", " self.name = name\n", " self.surname = surname\n", "\n", " def fullname(self):\n", " return \"%s %s\" % (self.name, self.surname)\n", "\n", "jane = Person(\"Jane\", \"Smith\")\n", "\n", "print(dir(jane))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Built In Class Attributes" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Employee.__doc__: Common base class for all employees\n", "Employee.__name__: Employee\n", "Employee.__module__: __main__\n", "Employee.__bases__: (<class 'object'>,)\n", "Employee.__dict__: {'__module__': '__main__', '__doc__': 'Common base class for all employees', 'empCount': 0, '__init__': <function Employee.__init__ at 0x1022c4840>, 'displayCount': <function Employee.displayCount at 0x1022c47b8>, 'displayEmployee': <function Employee.displayEmployee at 0x1022c4730>, '__dict__': <attribute '__dict__' of 'Employee' objects>, '__weakref__': <attribute '__weakref__' of 'Employee' objects>}\n" ] } ], "source": [ "class Employee:\n", " 'Common base class for all employees'\n", " empCount = 0\n", "\n", " def __init__(self, name, salary):\n", " self.name = name\n", " self.salary = salary\n", " Employee.empCount += 1\n", " \n", " def displayCount(self):\n", " print(\"Total Employee\", Employee.empCount)\n", "\n", " def displayEmployee(self):\n", " print(\"Name : \", self.name, \", Salary: \", self.salary)\n", "\n", "print (\"Employee.__doc__:\", Employee.__doc__)\n", "print (\"Employee.__name__:\", Employee.__name__)\n", "print (\"Employee.__module__:\", Employee.__module__)\n", "print (\"Employee.__bases__:\", Employee.__bases__)\n", "print (\"Employee.__dict__:\", Employee.__dict__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overriding Magic Methods" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Jane Doe, born 1992-03-12\n", "Address: No. 12 Short Street, Greenville\n", "Telephone: 555 456 0987\n", "Email:[email protected]\n" ] } ], "source": [ "import datetime\n", "\n", "class Person:\n", " def __init__(self, name, surname, birthdate, address, telephone, email):\n", " self.name = name\n", " self.surname = surname\n", " self.birthdate = birthdate\n", "\n", " self.address = address\n", " self.telephone = telephone\n", " self.email = email\n", "\n", " def __str__(self):\n", " return \"%s %s, born %s\\nAddress: %s\\nTelephone: %s\\nEmail:%s\" % (self.name, self.surname, self.birthdate, self.address, self.telephone, self.email)\n", "\n", "jane = Person(\n", " \"Jane\",\n", " \"Doe\",\n", " datetime.date(1992, 3, 12), # year, month, day\n", " \"No. 12 Short Street, Greenville\",\n", " \"555 456 0987\",\n", " \"[email protected]\"\n", ")\n", "\n", "print(jane)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create Class Using Key Value Arguments" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "John\n", "Doe\n", "['firstname=John', 'lastname=Doe']\n" ] } ], "source": [ "class Student:\n", " def __init__(self, **kwargs):\n", " for k,v in kwargs.items():\n", " setattr(self,k,v,)\n", " \n", " def __str__(self):\n", " attrs = [\"{}={}\".format(k, v) for (k, v) in self.__dict__.items()]\n", " return str(attrs)\n", " #classname = self.__class__.__name__\n", " #return \"{}: {}\".format((classname, \" \".join(attrs)))\n", " \n", "s1 = Student(firstname=\"John\",lastname=\"Doe\")\n", "print(s1.firstname)\n", "print(s1.lastname)\n", "print(s1)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The value of my_name is Sammy\n", "The value of your_name is Casey\n" ] } ], "source": [ "def print_values(**kwargs):\n", " for key, value in kwargs.items():\n", " print(\"The value of {} is {}\".format(key, value))\n", "\n", "print_values(my_name=\"Sammy\", your_name=\"Casey\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Class Inheritance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inheritance is a way of arranging objects in a hierarchy from the most general to the most specific. An object which inherits from another object is considered to be a subtype of that object.\n", "\n", "We also often say that a class is a subclass or child class of a class from which it inherits, or that the other class is its superclass or parent class. We can refer to the most generic class at the base of a hierarchy as a base class.\n", "\n", "Inheritance is also a way of reusing existing code easily. If we already have a class which does almost what we want, we can create a subclass in which we partially override some of its behaviour, or perhaps add some new functionality." ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__']\n" ] } ], "source": [ "# Simple Example of Inheritance\n", "\n", "class Person:\n", " pass\n", "\n", "# Parent class must be defined inside the paranthesis\n", "\n", "class Employee(Person): \n", " pass\n", "\n", "e1 = Employee()\n", "print(dir(e1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Person:\n", " \n", " def __init__(self,firstname,lastname):\n", " self.firstname = firstname\n", " self.lastname = lastname\n", " \n", " def __str__(self):\n", " return \"[{},{}]\".format(self.firstname,self.lastname)\n", " \n", "class Employee(Person):\n", " pass\n", "\n", "john = Employee('John','Doe')\n", "print(john)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Jack,Doe,12345\n" ] } ], "source": [ "class Person:\n", " \n", " def __init__(self, firstname, lastname):\n", " self.firstname = firstname\n", " self.lastname = lastname\n", " \n", " def __str__(self):\n", " return \"{},{}\".format(self.firstname, self.lastname)\n", " \n", "class Employee(Person):\n", " \n", " def __init__(self, firstname, lastname, staffid):\n", " super().__init__(firstname, lastname)\n", " self.staffid = staffid\n", " \n", " def __str__(self):\n", " return super().__str__() + \",{}\".format(self.staffid)\n", "\n", "john = Employee('Jack','Doe','12345')\n", "print(john)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Abstract Classes and Interfaces\n", "\n", "Abstract classes are not intended to be instantiated because all the method definitions are empty – all the insides of the methods must be implemented in a subclass.\n", "\n", "They serves as a template for suitable objects by defining a list of methods that these objects must implement." ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "ename": "NotImplementedError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-102-33b983e0d6d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0msh1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshape2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0msh1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-102-33b983e0d6d3>\u001b[0m in \u001b[0;36marea\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mshape2D\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0marea\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mshape3D\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNotImplementedError\u001b[0m: " ] } ], "source": [ "# Abstract Classes\n", "\n", "class shape2D:\n", " def area(self):\n", " raise NotImplementedError()\n", " \n", "class shape3D:\n", " def volume(self):\n", " raise NotImplementedError()\n", " \n", "sh1 = shape2D()\n", "sh1.area()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class shape2D:\n", " def area(self):\n", " raise NotImplementedError()\n", " \n", "class shape3D:\n", " def volume(self):\n", " raise NotImplementedError()\n", "\n", "class Square(shape2D):\n", " def __init__(self,width):\n", " self.width = width\n", " \n", " def area(self):\n", " return self.width ** 2\n", " \n", "s1 = Square(2)\n", "s1.area()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple Inheritance" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<class '__main__.Employee'>, <class '__main__.Person'>, <class '__main__.Company'>, <class 'object'>]\n" ] } ], "source": [ "class Person:\n", " pass\n", "\n", "class Company:\n", " pass\n", "\n", "class Employee(Person,Company):\n", " pass\n", "\n", "print(Employee.mro())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Diamond Problem\n", "\n", "Multiple inheritance isn’t too difficult to understand if a class inherits from multiple classes which have completely different properties, but things get complicated if two parent classes implement the same method or attribute.\n", "\n", "If classes B and C inherit from A and class D inherits from B and C, and both B and C have a method do_something, which do_something will D inherit? This ambiguity is known as the diamond problem, and different languages resolve it in different ways. In our Tutor class we would encounter this problem with the \\__init\\__ method." ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<class '__main__.M'>, <class '__main__.B'>, <class '__main__.A'>, <class '__main__.X'>, <class '__main__.Y'>, <class '__main__.Z'>, <class 'object'>]\n" ] } ], "source": [ "class X: pass\n", "class Y: pass\n", "class Z: pass\n", "\n", "class A(X,Y): pass\n", "class B(Y,Z): pass\n", "\n", "class M(B,A,Z): pass\n", "\n", "# Output:\n", "# [<class '__main__.M'>, <class '__main__.B'>,\n", "# <class '__main__.A'>, <class '__main__.X'>,\n", "# <class '__main__.Y'>, <class '__main__.Z'>,\n", "# <class 'object'>]\n", "\n", "print(M.mro())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Method Resolution Order (MRO)\n", "\n", "In the multiple inheritance scenario, any specified attribute is searched first in the current class. If not found, the search continues into parent classes in depth-first, left-right fashion without searching same class twice.\n", "\n", "So, in the above example of MultiDerived class the search order is [MultiDerived, Base1, Base2, object]. This order is also called linearization of MultiDerived class and the set of rules used to find this order is called Method Resolution Order (MRO).\n", "\n", "MRO must prevent local precedence ordering and also provide monotonicity. It ensures that a class always appears before its parents and in case of multiple parents, the order is same as tuple of base classes.\n", "\n", "MRO of a class can be viewed as the __mro__ attribute or mro() method. The former returns a tuple while latter returns a list." ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Person\n" ] } ], "source": [ "class Person:\n", " def __init__(self):\n", " print('Person')\n", "\n", "class Company:\n", " def __init__(self):\n", " print('Company')\n", "\n", "class Employee(Person,Company):\n", " def _init_(self):\n", " super(Employee,self).__init__()\n", " print('Employee')\n", " \n", "e1=Employee()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mixins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we use multiple inheritance, it is often a good idea for us to design our classes in a way which avoids the kind of ambiguity described above. One way of doing this is to split up optional functionality into mix-ins. A Mix-in is a class which is not intended to stand on its own – it exists to add extra functionality to another class through multiple inheritance." ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [], "source": [ "class Person:\n", " def __init__(self, name, surname, number):\n", " self.name = name\n", " self.surname = surname\n", " self.number = number\n", "\n", "\n", "class LearnerMixin:\n", " def __init__(self):\n", " self.classes = []\n", "\n", " def enrol(self, course):\n", " self.classes.append(course)\n", "\n", "\n", "class TeacherMixin:\n", " def __init__(self):\n", " self.courses_taught = []\n", "\n", " def assign_teaching(self, course):\n", " self.courses_taught.append(course)\n", "\n", "\n", "class Tutor(Person, LearnerMixin, TeacherMixin):\n", " def __init__(self, *args, **kwargs):\n", " super(Tutor, self).__init__(*args, **kwargs)\n", "\n", "jane = Tutor(\"Jane\", \"Smith\", \"SMTJNX045\")\n", "#jane.enrol(a_postgrad_course)\n", "#jane.assign_teaching(an_undergrad_course)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now Tutor inherits from one “main” class, Person, and two mix-ins which are not related to Person. Each mix-in is responsible for providing a specific piece of optional functionality. Our mix-ins still have \\__init\\__ methods, because each one has to initialise a list of courses (we saw in the previous chapter that we can’t do this with a class attribute). Many mix-ins just provide additional methods and don’t initialise anything." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Composition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Composition is a way of aggregating objects together by making some objects attributes of other objects. Relationships like this can be one-to-one, one-to-many or many-to-many, and they can be unidirectional or bidirectional, depending on the specifics of the the roles which the objects fulfil.\n", "\n", "The term composition implies that the two objects are quite strongly linked – one object can be thought of as belonging exclusively to the other object. If the owner object ceases to exist, the owned object will probably cease to exist as well. If the link between two objects is weaker, and neither object has exclusive ownership of the other, it can also be called aggregation." ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "class Student:\n", " def __init__(self, name, student_number):\n", " self.name = name\n", " self.student_number = student_number\n", " self.classes = []\n", "\n", " def enrol(self, course_running):\n", " self.classes.append(course_running)\n", " course_running.add_student(self)\n", "\n", "\n", "class Department:\n", " def __init__(self, name, department_code):\n", " self.name = name\n", " self.department_code = department_code\n", " self.courses = {}\n", "\n", " def add_course(self, description, course_code, credits):\n", " self.courses[course_code] = Course(description, course_code, credits, self)\n", " return self.courses[course_code]\n", "\n", "\n", "class Course:\n", " def __init__(self, description, course_code, credits, department):\n", " self.description = description\n", " self.course_code = course_code\n", " self.credits = credits\n", " self.department = department\n", " #self.department.add_course(self)\n", "\n", " self.runnings = []\n", "\n", " def add_running(self, year):\n", " self.runnings.append(CourseRunning(self, year))\n", " return self.runnings[-1]\n", "\n", "\n", "class CourseRunning:\n", " def __init__(self, course, year):\n", " self.course = course\n", " self.year = year\n", " self.students = []\n", "\n", " def add_student(self, student):\n", " self.students.append(student)\n", "\n", "\n", "maths_dept = Department(\"Mathematics and Applied Mathematics\", \"MAM\")\n", "mam1000w = maths_dept.add_course(\"Mathematics 1000\", \"MAM1000W\", 1)\n", "mam1000w_2013 = mam1000w.add_running(2013)\n", "\n", "bob = Student(\"Bob\", \"Smith\")\n", "bob.enrol(mam1000w_2013)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* A student can be enrolled in several courses (CourseRunning objects), and a course (CourseRunning) can have multiple students enrolled in it in a particular year, so this is a many-to-many relationship. A student knows about all his or her courses, and a course has a record of all enrolled students, so this is a bidirectional relationship. These objects aren’t very strongly coupled – a student can exist independently of a course, and a course can exist independently of a student.\n", "* A department offers multiple courses (Course objects), but in our implementation a course can only have a single department – this is a one-to-many relationship. It is also bidirectional. Furthermore, these objects are more strongly coupled – you can say that a department owns a course. The course cannot exist without the department.\n", "* A similar relationship exists between a course and its “runnings”: it is also bidirectional, one-to-many and strongly coupled – it wouldn’t make sense for “MAM1000W run in 2013” to exist on its own in the absence of “MAM1000W”." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inheritance Methods" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "True\n", "True\n", "True\n" ] } ], "source": [ "class Person:\n", " pass\n", "\n", "class Employee(Person):\n", " pass\n", "\n", "class Tutor(Employee):\n", " pass\n", "\n", "emp = Employee()\n", "\n", "print(isinstance(emp, Tutor)) # False\n", "print(isinstance(emp, Person)) # True\n", "print(isinstance(emp, Employee)) # True\n", "print(issubclass(Tutor, Person)) # True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Links to Topics\n", "\n", "[Python - Object Oriented Programming](http://python-textbok.readthedocs.io/en/1.0/Object_Oriented_Programming.html#introduction)\n", "\n", "[Excellent Introduction Tutorial on Object Oriented Programming](https://jeffknupp.com/blog/2014/06/18/improve-your-python-python-classes-and-object-oriented-programming/)\n", "\n", "[Mozilla - Introduction to Object Oriented Programming](https://developer.mozilla.org/en-US/docs/Learn/Drafts/Python/Quickly_Learn_Object_Oriented_Programming)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ToqueWillot/M2DAC
FDMS/TME2/Modèle linéaire régularisé L1.ipynb
1
85054
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## FDMS TME2\n", "Florian Toque & Paul Willot\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import sklearn\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "import random\n", "\n", "from sklearn.datasets import fetch_mldata\n", "from sklearn import cross_validation\n", "from sklearn import base\n", "#mnist = fetch_mldata('iris')\n", "\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = sklearn.datasets.make_classification(n_samples=1000,\n", " n_features=20,\n", " n_informative=10,\n", " n_redundant=5,\n", " n_repeated=2,\n", " n_classes=2,\n", " n_clusters_per_class=2,\n", " weights=None,\n", " flip_y=0.01,\n", " class_sep=1.0,\n", " hypercube=True,\n", " shift=0.0,\n", " scale=1.0,\n", " shuffle=True,\n", " random_state=None)\n", "X= ds[0]\n", "y= ds[1]" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mnist = fetch_mldata('iris')\n", "\n", "X= mnist.data\n", "y= mnist.target\n", "\n", "for idx,i in enumerate(y):\n", " if (i==2) or (i==3):\n", " y[idx]=-1" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.30134275 4.48965222 -0.45212721 2.86652643 1.08376722 3.12443608\n", " 1.48065915 -1.1379765 0.92573125 0.69962513 2.80682152 1.46842328\n", " -3.37984414 -3.37984414 1.84762307 0.36497919 1.99927759 -0.63767391\n", " 2.80682152 2.09540991]\n", "-1\n" ] } ], "source": [ "# labels: [0,1] -> [-1,1]\n", "for idx,i in enumerate(y):\n", " if (i==0):\n", " y[idx]=-1\n", "\n", "print(X[0])\n", "print(y[0])" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class SimpleGradientDescent:\n", " def __init__(self,theta,eps=0.01):\n", " self.theta=theta\n", " self.eps=eps\n", " \n", " def fit(self,X,y,nbIt=10000,printevery=1000):\n", " l=len(X)\n", " xTrans = X.transpose()\n", " for i in xrange(0,nbIt):\n", " hypothesis = np.dot(X, self.theta)\n", " loss = hypothesis - y\n", " cost = np.sum(loss ** 2) / (2 * l)\n", " if i%printevery==0:\n", " print(\"Iteration %s | Cost: %f\" % (str(i).ljust(6), cost))\n", " gradient = np.dot(xTrans, loss) / l\n", " self.theta = self.theta - self.eps * gradient\n", " def predict(self,x):\n", " #print(\"Product: %f\"%(np.dot(x,self.theta)))\n", " return 1 if np.dot(x,self.theta)>0 else -1\n", " def score(self,X,y):\n", " cpt=0.0\n", " for idx,i in enumerate(X):\n", " cpt += 1 if self.predict(i)==y[idx] else 0\n", " print(cpt,len(X))\n", " return cpt/len(X)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "### Pseudo-code descente de gradient L1\n", "\n", "procedureGradientL1(θ,X,Y,I;ε)\n", " for it = 1, I do\n", " \n", " for i = 1, n do\n", " idx ← random(l)\n", " θ′ ← θ − ε∇θ L(θ)(idx)\n", " for j=1,n do\n", " if θj′ ∗ θj < 0 then\n", " θj ← 0\n", " else\n", " θ j ← θ j′\n", " end if\n", " end for\n", " end for\n", " Afficher L(θ) et accuracy(θ) pour controle\n", " \n", " end for\n", " return θ \n", "end procedure\n", "\n", "L(θ) = 1/l * somme de 1 a l:( yi − fθ(xi))2 + λC(θ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### L1" ] }, { "cell_type": "code", "execution_count": 226, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class GradientDescent(base.BaseEstimator):\n", " def __init__(self,theta,lamb,eps):\n", " self.theta=theta\n", " self.eps=eps\n", " self.lamb=lamb\n", "\n", " def fit(self,X,y,nbIt=1000,printevery=-1):\n", " l=len(X)\n", " xTrans = X.transpose()\n", " \n", " for i in xrange(0,nbIt):\n", " index = np.random.randint(l)\n", " loss = np.dot(X, self.theta) - y\n", " #cost = np.sum(loss ** 2) / (2 * l) + (self.lamb*np.linalg.norm(self.theta))\n", " \n", " \n", " cost = np.sum(loss ** 2) * (1 / l) + (self.lamb*np.linalg.norm(self.theta))\n", " gradient = np.dot(xTrans,(np.dot(theta,xTrans)-y))+np.sign(theta)*self.lamb\n", " thetaprime = self.theta - self.eps * gradient\n", " \n", " for k in xrange(0,len(theta)):\n", " theta[k] = 0 if thetaprime[k]*theta[k]<0 else thetaprime[k]\n", "\n", " if printevery!=-1 and i%printevery==0:\n", " print(\"Iteration %s | Cost: %f | Score: %.03f\" % (str(i).ljust(6), cost,self.score(X,y)))\n", " print(\"%d features used\"%(self.nb_used_features()))\n", " \n", " def predict(self,x):\n", " #print(\"Product: %f\"%(np.dot(x,self.theta)))\n", " ret=[]\n", " for i in x:\n", " ret.append(1 if np.dot(i,self.theta)>0 else -1)\n", " return ret\n", " \n", " def score(self,X,y):\n", " cpt=0.0\n", " allpred = self.predict(X)\n", " for idx,i in enumerate(allpred):\n", " cpt += 1 if i==y[idx] else 0\n", " #print(cpt,len(X))\n", " return cpt/len(X)\n", " \n", " def nb_used_features(self):\n", " cpt=len(self.theta)\n", " for ii in self.theta:\n", " if ii==0:\n", " cpt-=1\n", " return cpt" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#theta = np.zeros(len(X[0]))\n", "theta = X[1]\n", "lamb=400\n", "eps=0.00001\n", "\n", "#gd = SimpleGradientDescent(theta,eps)\n", "gd = GradientDescent(theta,lamb,eps)" ] }, { "cell_type": "code", "execution_count": 248, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0 | Cost: 28.995022 | Score: 0.703\n", "12 features used\n", "Iteration 10000 | Cost: 28.168047 | Score: 0.709\n", "16 features used\n", "Iteration 20000 | Cost: 28.293302 | Score: 0.705\n", "16 features used\n", "Iteration 30000 | Cost: 28.140175 | Score: 0.698\n", "13 features used\n", "Iteration 40000 | Cost: 28.195999 | Score: 0.711\n", "16 features used\n", "Iteration 50000 | Cost: 28.316028 | Score: 0.704\n", "16 features used\n", "Iteration 60000 | Cost: 28.128271 | Score: 0.700\n", "13 features used\n", "Iteration 70000 | Cost: 28.164374 | Score: 0.709\n", "16 features used\n", "Iteration 80000 | Cost: 28.323631 | Score: 0.705\n", "15 features used\n", "Iteration 90000 | Cost: 28.163377 | Score: 0.701\n", "13 features used\n" ] } ], "source": [ "nbIterations = 100000\n", "gd.fit(X,y,nbIterations,printevery=nbIterations/10)" ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.703" ] }, "execution_count": 246, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gd.score(X,y)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross validation scores: [ 0.8 0.85 0.85 0.9 0.8 ], mean: 0.84\n" ] } ], "source": [ "scoresSvm = cross_validation.cross_val_score(gd, X, y, cv=5,scoring=\"accuracy\")\n", "print(\"Cross validation scores: %s, mean: %.02f\"%(scoresSvm,np.mean(scoresSvm)))" ] }, { "cell_type": "code", "execution_count": 279, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lamda: 0.00 | Cross val mean: 0.74 | Features: 20\n", "Lamda: 50.00 | Cross val mean: 0.74 | Features: 16\n", "Lamda: 100.00 | Cross val mean: 0.75 | Features: 14\n", "Lamda: 150.00 | Cross val mean: 0.74 | Features: 13\n", "Lamda: 200.00 | Cross val mean: 0.74 | Features: 11\n", "Lamda: 250.00 | Cross val mean: 0.73 | Features: 12\n", "Lamda: 300.00 | Cross val mean: 0.71 | Features: 13\n", "Lamda: 350.00 | Cross val mean: 0.70 | Features: 10\n", "Lamda: 400.00 | Cross val mean: 0.70 | Features: 8\n", "Lamda: 450.00 | Cross val mean: 0.71 | Features: 10\n", "Lamda: 500.00 | Cross val mean: 0.70 | Features: 12\n", "Lamda: 550.00 | Cross val mean: 0.71 | Features: 13\n", "Lamda: 600.00 | Cross val mean: 0.71 | Features: 9\n", "Lamda: 650.00 | Cross val mean: 0.71 | Features: 9\n", "Lamda: 700.00 | Cross val mean: 0.70 | Features: 10\n", "Lamda: 750.00 | Cross val mean: 0.70 | Features: 7\n", "Lamda: 800.00 | Cross val mean: 0.69 | Features: 6\n", "Lamda: 850.00 | Cross val mean: 0.70 | Features: 4\n", "Lamda: 900.00 | Cross val mean: 0.70 | Features: 5\n", "Lamda: 950.00 | Cross val mean: 0.68 | Features: 2\n" ] } ], "source": [ "eps=0.00001\n", "la = []\n", "cross_sc = []\n", "used_features = []\n", "\n", "for lamb in np.arange(0,1000,50):\n", " theta = np.zeros(len(X[0]))\n", " gd = GradientDescent(theta,lamb,eps)\n", " nbIterations = 5000\n", " gd.fit(X,y,nbIterations)\n", " scoresSvm = cross_validation.cross_val_score(gd, X, y, cv=5,scoring=\"accuracy\")\n", " print(\"Lamda: %.02f | Cross val mean: %.02f | Features: %d\"%(lamb,np.mean(scoresSvm),gd.nb_used_features()))\n", " cross_sc.append(np.mean(scoresSvm))\n", " la.append(lamb)\n", " used_features.append(gd.nb_used_features())" ] }, { "cell_type": "code", "execution_count": 280, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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mCCFERxqRZGdEkh2XVaO0UWdggo3T4m2UNRq4bKGPsstIRic0zKnRy654qjjA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"text/plain": [ "<matplotlib.figure.Figure at 0x1044fe750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots()\n", "\n", "ax2 = ax1.twinx()\n", "ax1.plot(la, cross_sc, '#6DC433')\n", "ax2.plot(la, used_features, '#5AC8ED')\n", "\n", "ax1.set_xlabel('lambda')\n", "ax1.set_ylabel('Cross val score', color='#6DC433')\n", "ax2.set_ylabel('Nb features used', color='#5AC8ED')\n", "\n", "ax1.yaxis.grid(False)\n", "ax2.grid(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### L2" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class GradientDescentL2(base.BaseEstimator):\n", " def __init__(self,theta,lamb,eps):\n", " self.theta=theta\n", " self.eps=eps\n", " self.lamb=lamb\n", "\n", " def fit(self,X,y,nbIt=1000,printevery=-1):\n", " l=len(X)\n", " xTrans = X.transpose()\n", " \n", " for i in xrange(0,nbIt):\n", " index = np.random.randint(l)\n", " loss = np.dot(X, self.theta) - y\n", " cost = np.sum(loss ** 2) / (2 * l) + (self.lamb*(np.linalg.norm(-self.theta)**2))\n", " gradient = np.dot(xTrans,(np.dot(theta,xTrans)-y))+np.sign(theta)*self.lamb\n", " thetaprime = self.theta - self.eps * gradient\n", " \n", " for k in xrange(0,len(theta)):\n", " theta[k] = 0 if thetaprime[k]*theta[k]<0 else thetaprime[k]\n", "\n", " if printevery!=-1 and i%printevery==0:\n", " print(\"Iteration %s | Cost: %f\" % (str(i).ljust(6), cost))\n", " \n", " def predict(self,x):\n", " #print(\"Product: %f\"%(np.dot(x,self.theta)))\n", " ret=[]\n", " for i in x:\n", " ret.append(1 if np.dot(i,self.theta)>0 else -1)\n", " return ret\n", " \n", " def score(self,X,y):\n", " cpt=0.0\n", " allpred = self.predict(X)\n", " for idx,i in enumerate(allpred):\n", " cpt += 1 if i==y[idx] else 0\n", " print(cpt,len(X))\n", " return cpt/len(X)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0 | Cost: 0.500000\n", "Iteration 2000 | Cost: 0.190335\n", "Iteration 4000 | Cost: 0.189843\n", "Iteration 6000 | Cost: 0.190266\n", "Iteration 8000 | Cost: 0.190468\n", "Iteration 10000 | Cost: 0.190555\n", "Iteration 12000 | Cost: 0.190593\n", "Iteration 14000 | Cost: 0.190609\n", "Iteration 16000 | Cost: 0.190617\n", "Iteration 18000 | Cost: 0.190620\n", "(93.0, 100)\n", "Score: 0.93\n", "Cross validation scores: [ 1. 0.85 0.95 0.9 0.95], mean: 0.93\n" ] } ], "source": [ "theta = np.zeros(len(X[0]))\n", "lamb=0.05\n", "eps=0.00001\n", "gd = GradientDescentL2(theta,lamb,eps)\n", "\n", "nbIterations = 20000\n", "gd.fit(X,y,nbIterations,printevery=nbIterations/10)\n", "\n", "print(\"Score: %s\"%gd.score(X,y))\n", "\n", "scoresSvm = cross_validation.cross_val_score(gd, X, y, cv=5,scoring=\"accuracy\")\n", "print(\"Cross validation scores: %s, mean: %.02f\"%(scoresSvm,np.mean(scoresSvm)))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lamda: 0.00, Cross val mean: 0.93\n", "Lamda: 0.50, Cross val mean: 0.93\n", "Lamda: 1.00, Cross val mean: 0.92\n", "Lamda: 1.50, Cross val mean: 0.91\n", "Lamda: 2.00, Cross val mean: 0.90\n", "Lamda: 2.50, Cross val mean: 0.89\n", "Lamda: 3.00, Cross val mean: 0.89\n", "Lamda: 3.50, Cross val mean: 0.89\n", "Lamda: 4.00, Cross val mean: 0.89\n", "Lamda: 4.50, Cross val mean: 0.89\n", "Lamda: 5.00, Cross val mean: 0.88\n", "Lamda: 5.50, Cross val mean: 0.88\n", "Lamda: 6.00, Cross val mean: 0.87\n", "Lamda: 6.50, Cross val mean: 0.86\n", "Lamda: 7.00, Cross val mean: 0.85\n", "Lamda: 7.50, Cross val mean: 0.85\n", "Lamda: 8.00, Cross val mean: 0.86\n", "Lamda: 8.50, Cross val mean: 0.86\n", "Lamda: 9.00, Cross val mean: 0.86\n", "Lamda: 9.50, Cross val mean: 0.86\n", "Lamda: 10.00, Cross val mean: 0.86\n", "Lamda: 10.50, Cross val mean: 0.86\n", "Lamda: 11.00, Cross val mean: 0.85\n", "Lamda: 11.50, Cross val mean: 0.85\n" ] } ], "source": [ "eps=0.00001\n", "la = []\n", "cross_sc = []\n", "\n", "for lamb in np.arange(0,12,0.5):\n", " theta = np.zeros(len(X[0]))\n", " gd = GradientDescentL2(theta,lamb,eps)\n", " nbIterations = 5000\n", " gd.fit(X,y,nbIterations)\n", " scoresSvm = cross_validation.cross_val_score(gd, X, y, cv=5,scoring=\"accuracy\")\n", " print(\"Lamda: %.02f, Cross val mean: %.02f\"%(lamb,np.mean(scoresSvm)))\n", " cross_sc.append(np.mean(scoresSvm))\n", " la.append(lamb)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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eChHZl9mV/kcffYT58+fj+PHj2LRpEwuf6CGJRCLTap/XNyAiIZhd6X/wwQf2\nyEHkEiaOCkJEiDfOXq7DkpkjeZU+IrIrm95lj4gGurvaNxqBXK72icjOWPpEdvbYGDlCgzxR+J0W\nDS3dQschIhfC0ieyM7FIhPSkKOgNRhwp4i2jich+zJb+2bNnTbe//dvf/oa3334b1dXVNg9G5Mym\nxYVA7u+Bk2W30NzeK3QcInIRZkt/48aN8PLywvXr1/HBBx9gxIgRWL9+vT2yETktiViMtKQo6PRG\nfMHVPhHZidnSl0gkEIlEyM/Px8qVK/Hiiy+ira3NHtmInFpy/HAE+rrjxIUatHX2CR2HiFyA2dLX\n6/UoLS3Fl19+iaSkJNOfEdGjkUrEWJQYiT6dAV+e4yEzIrI9s6X/yiuv4O2338akSZPwgx/8ADdv\n3kRkZKQ9shE5vR9OCIWflxvyvtGgo7tf6DhE5OTM3mVvKOFdmSzDO1hZxl7z9EVRFfZ/dQNLZo7E\nkpkjbf5+tsDfKctwnizDebLcYO+yZ3al/6c//Qnt7Xcmf+3atVi4cCFOnjz5cOmI6B6PTx4B72Ey\n/L24Gt29OqHjEJETM1v6n332GXx8fHDmzBk0NTXh3XffxX/913/ZIxuRS/Bwk2L+1Ah09ujwVUmN\n0HGIyImZLX2x+M6QoqIipKenIyEhAU50RIDIIcx9LBzD3KU4erYKvX08UZaIbMNs6Xt4eGDXrl04\nfPgwZs6cCYPBgP5+nnBEZE2eHlLMTQhHe1c/TpTeEjoOETkps6W/ZcsW3L59G2vXroVcLkd1dTUW\nL15sj2xELmXB1Ai4yyT4okiNfh1X+0RkfRafvd/V1QUA8PR03FuB8mxPy/DMWMsIMU/7v7qBL4qq\n8NOFY/DE5DC7vvej4O+UZThPluE8Wc7qZ+9XVVVhxYoVSExMRGJiIlauXMlr7xPZyMKpEZBJxcgt\nVEOnNwgdh4icjNnSf/vtt7FixQqUlpaitLQUy5cvx9tvv22PbEQux8/bHbMmjkBjWw/OfFcndBwi\ncjJmS7+pqQmZmZkQi8UQi8V46qmn0NjYaI9sRC5pUaISErEIqsJKGAz8pgwRWY9FN9wpLy83Pb55\n8yakUqlNQxG5skBfD8wYH4q65m6cvcLVPhFZj9n2/tWvfoWf/OQniI2NBQBcuXIF27Zts3kwIleW\nmhSJU2W1UBWoMW2sAmKRSOhIROQEvrf0DQYDQkJCcPjwYZSWlkIkEmHixIkIDAy0Vz4ilxTiPwzT\nxylQcFG8koFCAAAgAElEQVSLkmsNSBgjFzoSETmB7929LxaLsXbtWgQFBWHOnDl44oknWPhEdpKW\nFAkRgMMFlbwKJhFZhdlj+pGRkfyKHpEAQoO8MCU2BOq6dnx7s0noOETkBMwe0+/o6EBGRgYSEhJM\nF+YRiUT47//+b5uHI3J16clROHflNg4VVGB8dCBEPLZPRI/AbOlnZGQgIyNjwJ9Z+hdPfn4+3n33\nXRgMBmRmZuKFF14Y8HxTUxPWrl2LhoYG6PV6PPfcc3jyyScBAO+//z5ycnIgFosxevRobNmyBW5u\nbpb+XEROISLEG5NGBePCjQZcqWrB2MgAoSMR0RD2wNLX6XTo6+szlfBdXV1dFpWvXq/Hxo0b8cEH\nH0ChUCAzMxNz585FTEyMacy+ffsQFxeH1157DU1NTVi0aBEyMjKg1Wqxf/9+HDlyBG5ubvjlL38J\nlUqFZcuWPcKPSjQ0pSdH4cKNBhw6XcHSJ6JH8sBj+r///e9x+PDhe/5cpVJh+/btZjdcVlYGpVKJ\n8PBwyGQypKWlIS8vb8AYuVyOjo4OAEBnZyf8/f0hlUrh7e0NqVSK7u5u6HQ69PT0QKFQDPZnI3IK\n0SN8MW5kIK5UteC6pkXoOEQ0hD1wpX/mzBm8/vrr9/z5k08+iYyMDLzxxhvfu+G6ujqEhoaaHisU\nCpSVlQ0Ys2LFCjz77LOYOXMmOjs7sWPHDgCAv78/nnvuOTz++OPw8PDAzJkzkZycPKgfjMiZLE6O\nwncVTcg5VYGV80ZbZZtyPw+4ySRW2RYRDQ0PLH29Xg+J5N6/ECQSCcRisyf9W3Tcf+fOnYiNjcXe\nvXtRVVWFVatWIScnB42Njfjwww9x/Phx+Pj44JVXXkFOTs495xYQuYrREf4YE+GP7yqbsWF3kVW2\nOSrMD2/95DGeHEjkQh5Y+r29vejq6rrnVrqdnZ3o6+szu2GFQoHa2lrTY61We88u+pKSErz44osA\nYDoUUF5eDo1Gg8mTJyMg4M7xy/nz56OkpMRs6Q/2FoOujHNlGUeap1d+/BhyT1dAb4Xv7F9VN+NG\nTSs0TT14LDbECukca64cGefJMpwn23hg6aempuLNN9/E5s2b4eNzZ/Lb2trw9ttvIyUlxeyG4+Pj\noVarodFoEBISgtzc3HvOBYiOjkZhYSESEhLQ0NCAiooKKJVKuLm54b333kNPTw/c3d1RWFiICRMm\nmH1P3n/ZMrxXtWUcbZ48xMCTPxxplW1V1bXjtx+cw94jlxAe6PHIq31HmytHxXmyDOfJcoP9cPTA\n0v+3f/s3vPXWW5g1axYiIyMBAGq1GnPmzMHLL79sfsNSKTZs2ICsrCzTV/ZiYmKQnZ0NAFi5ciVW\nr16NdevWISMjA0ajEWvXroW/vz/8/f2xZMkSPPXUUxCLxYiLi8OKFSsG9YMR0YMpFT6YGBOE0vJG\nXKtuwRglvxVA5ApERjPX96ysrMSlS5cAAHFxcYiKirJHrofCT4aW4adoyzj7PJXXtGLz3vOIiwrA\n6ysnP9K2nH2urIXzZBnOk+WsttK/KyoqyqGLnogeTkyYH+KiAnCpshnlNa2ICfMTOhIR2Zj50/CJ\nyGktTo4CcOemPkTk/Fj6RC5sjDIAo8P9UFreCLWWu1OJnB1Ln8jFpc+IAgCoCiuFjEFEdsDSJ3Jx\n46ICMTLUB+ev1qOmoVPoOERkQyx9IhcnEomQnhwFI7jaJ3J2LH0iwsRRwQiXe6PoUh3qmruEjkNE\nNsLSJyKIRSKkJ0fCaARyC9VCxyEiG2HpExEAYMqYEAwP9ETBRS0aW3uEjkNENsDSJyIAgFgsQlpS\nJPQGI44UcbVP5IxY+kRkkhinQLCfB/JLa9HS0St0HCKyMpY+EZlIJWKkJUVCpzfg6NkqoeMQkZWx\n9IlogOT4UAT4uOOrkhq0d/UJHYeIrIilT0QDyKRiLEpUoq/fgC/PVQsdh4isiKVPRPeYNXEEfD1l\nyDuvQWdPv9BxiMhKWPpEdA83mQQLE5Xo6dMj77xG6DhEZCUsfSK6r8cnhcHLQ4pj56rR3asTOg4R\nWQFLn4jua5i7FPOnRqCzR4evL9QIHYeIrIClT0QPNC8hHMPcJThaVIW+fr3QcYjoEbH0ieiBPD1k\nmJsQjraufuSX3hI6DhE9IpY+EX2v+VMi4CYT40hRFfp1BqHjENEjYOkT0ffy8XTDE5PD0Nzei4KL\ntULHIaJHwNInIrMWTlNCKhFDVaiGTs/VPtFQxdInIrP8vd0xa2IoGlp7UHSpTug4RPSQWPpEZJFF\niZGQiEVQFaphMBiFjkNED4GlT0QWCfLzQHL8cGibulB89bbQcYjoIbD0ichiqUmREImAwwWVMBi5\n2icaalj6RGQxRYAnpscpoKnvROmNBqHjENEgsfSJaFBSk6Igwp3VvpGrfaIhxaaln5+fj5SUFCxY\nsAC7du265/mmpiZkZWVhyZIlSE9Px4EDBwAAN2/exNKlS03/JCQkYM+ePbaMSkQWCgv2QsIYOSpq\n2/FdZZPQcYhoEKS22rBer8fGjRvxwQcfQKFQIDMzE3PnzkVMTIxpzL59+xAXF4fXXnsNTU1NWLRo\nETIyMhAdHY3PP/8cAGAwGDBr1izMnz/fVlGJaJDSk6NQfLUeh05XYlxUoNBxiMhCNlvpl5WVQalU\nIjw8HDKZDGlpacjLyxswRi6Xo6OjAwDQ2dkJf39/SKUDP4cUFBQgIiICoaGhtopKRIOkVPhgYkwQ\nrmtaca26Reg4RGQhm5V+XV3dgKJWKBSoqxt4UY8VK1bgxo0bmDlzJjIyMrBu3bp7tqNSqZCenm6r\nmET0kNKTowAAOacrBc1BRJazWemLRCKzY3bu3InY2FicOnUKBw8exDvvvGNa+QNAX18fvvrqKyxa\ntMhWMYnoIcWE+SEuKgCX1c24ouaxfaKhwGbH9BUKBWpr/3lzDq1WC4VCMWBMSUkJXnzxRQAwHQqo\nqKjA+PHjAdw5EXDcuHEIDLTsmKFc7mOl9M6Pc2UZztP3+0lqHNa9dxr7/34Nb2dNFzrOkMDfKctw\nnmzDZqUfHx8PtVoNjUaDkJAQ5ObmYvv27QPGREdHo7CwEAkJCWhoaEBFRQUiIiJMzw921359fbvV\n8jszudyHc2UBzpN5w33dMTrcD+cu1aH421uIHM6/qL8Pf6csw3my3GA/HNls975UKsWGDRuQlZWF\ntLQ0pKamIiYmBtnZ2cjOzgYArF69GhcvXkRGRgZWrVqFtWvXwt/fHwDQ1dWFgoICnrVP5ODSZ0QB\nAFSFlULGICILiIxOdHUNfjK0DD9FW4bzZBmj0Yjf/aUEN6pb8M7PExEW7CV0JIfF3ynLcJ4s5zAr\nfSJyDSKRCD+aNxpGcLVP5OhY+kT0yKbGDUe43BtFl+pQ19wldBwiegCWPhE9MrFYhPTkSBiNQG6h\nWug4RPQALH0isoopY0IwPNATBRe1aGztEToOEd0HS5+IrEIsFiEtKRJ6gxFHirjaJ3JELH0isprE\nOAWC/TyQX1qLlo5eoeMQ0f/C0iciq5FKxEhLioROb8AXRVVCxyGi/4WlT0RWlRwfigAfd3x9oQZt\nXX1CxyGif8HSJyKrkknFWJSoRF+/AcfOVQsdh4j+BUufiKxu1sQR8PWUIe+8Bp09/ULHIaJ/YOkT\nkdW5ySRYmKhET58eeec1Qschon9g6RORTTw+KQxeHlIcO1eN7l6d0HGICCx9IrKRYe5SzJ8agc4e\nHb4uqRE6DhGBpU9ENjQvIRzD3CU4erYKvf16oeMQuTyWPhHZjKeHDHMeC0dbVz/yS28JHYfI5bH0\nicimFkyNgJtMjC+KqtCvMwgdh8ilsfSJyKZ8PN3wxOQwNLf34vTFWqHjELk0lj4R2dzCaUpIJWLk\nFqqh03O1TyQUlj4R2Zy/tztmTQxFQ2sPii7VCR2HyGWx9InILhYlRkIiFuFwoRoGg1HoOEQuiaVP\nRHYR5OeB5PjhqGvqQvHV20LHIXJJLH0ispvUpEiIRMChgkoYjFztE9kbS5+I7EYR4InEOAVq6jtR\ner1B6DhELoelT0R2lZYUBRHurPaNXO0T2RVLn4jsKizYCwlj5KjUtuO7iiah4xC5FJY+EdldenIU\nACCHq30iu2LpE5HdKRU+mBgThBuaVlytahE6DpHLYOkTkSDurvYPFVQKmoPIlbD0iUgQMWF+iIsK\nwGV1M27UtAodh8glsPSJSDCL/7HaP8zVPpFd2LT08/PzkZKSggULFmDXrl33PN/U1ISsrCwsWbIE\n6enpOHDggOm5trY2rFmzBosWLUJqaiouXLhgy6hEJIDREf74Qbgfysoboda2Cx2HyOnZrPT1ej02\nbtyI3bt3Q6VSQaVSoby8fMCYffv2IS4uDgcPHsSePXuwdetW6HQ6AMDmzZsxa9YsHDlyBDk5OYiJ\nibFVVCISiEgk+udqv7BSyChELsFmpV9WVgalUonw8HDIZDKkpaUhLy9vwBi5XI6Ojg4AQGdnJ/z9\n/SGVStHe3o7i4mJkZmYCAKRSKXx8fGwVlYgENG5kIKKG++D81XrU1HcIHYfIqUltteG6ujqEhoaa\nHisUCpSVlQ0Ys2LFCjz77LOYOXMmOjs7sWPHDgCARqNBYGAg3nrrLVy5cgXjxo3D+vXrMWzYMFvF\nJSKBiEQiLJ4Rhf/76bdQFarxQsY4oSPZxBV1Mwou30ZHR6/QURyet7e71eYpVukPpYKLxrtsVvoi\nkcjsmJ07dyI2NhZ79+5FVVUVVq1ahYMHD0Kn0+HSpUvYsGEDJkyYgM2bN2PXrl145ZVXvnd7cjn/\nw1qKc2UZzpPlHmWu5gd741CBGmcv12FVRjxGyL2tmEx4DS3d2L6/FDq9QegoLifQ1x1/XDcfbjKJ\n0FEcgs1KX6FQoLa21vRYq9VCoVAMGFNSUoIXX3wRAEyHAioqKjB8+HAoFApMmDABALBw4UL88Y9/\nNPue9fU8EcgScrkP58oCnCfLWWOuUqZFYOfB77A39xKeSx1rpWSO4S/HrkGnN2D53B9A4echdByH\n5+s7DG1t3Y+8nW+u1aPgohafH7+GJx4Lt0IyxzPYD9s2K/34+Hio1WpoNBqEhIQgNzcX27dvHzAm\nOjoahYWFSEhIQENDAyoqKhAREQF/f3+EhoaioqICI0eORGFhIUaNGmWrqETkAKaMCcHwwAoUXtQi\nY0YUgv2c43Bea2cfTpTeQpCvO55eGIvmpk6hIzk8a33gjgnzw7krt5F7Ro0fThwBqYTfUrfZDEil\nUmzYsAFZWVlIS0tDamoqYmJikJ2djezsbADA6tWrcfHiRWRkZGDVqlVYu3Yt/P39AQAbNmzA66+/\njoyMDFy9etW0R4CInJNYLEJaUiT0BiOOFFUJHcdqvjxbhX6dAanTI1k6dubn5YbZE0egsa0Xhd9p\nhY7jEERGJ7rbBXfFWoa7rS3DebKcteZKpzdg3a4zaOnow9YXkxDg426FdMLp6O7H2j8UwMNNgm0v\nJmFEqD9/pyxgzf/3mtp68B87CxHk54F3n58Osdj8+WZDyWB37/NjJxE5DKlEjNSkSOj0Bhw9O/RX\n+38vrkZvnx6Lpikhk/JEMiEE+npg5oRQ3G7uxtnLdULHERxLn4gcyoz4UAT4uOPrCzVo6+oTOs5D\n6+rR4e/FGngPk2H2pDCh47i0RdMjIRaJcLhQDYPz7Nx+KCx9InIoMqkYixKV6Os34Ni5aqHjPLTj\n32jQ1avDwmkRcHfjKl9IIf7DMH2cArcaOlFyrV7oOIJi6RORw5k1cQR8PWXIO69BZ0+/0HEGrbdP\njy/PVcPTXYo5TvpVsaEmLSkSIty5lbMTnco2aCx9InI4bjIJFiYq0dOnR16xRug4g/b1hRp0dPdj\n3pRwDHO32TejaRBCg7wwdWwIquo68O3NRqHjCIalT0QO6fFJYfDykOJYcTW6e3VCx7FYv06PL4qq\n4O4mwbwpEULHoX+RlhQFADh02nVX+yx9InJIw9ylmD81Ap09OnxdUiN0HIudLKtFa2cf5jwWBu9h\nMqHj0L+ICPHG5B8Eo/xWG66om4WOIwiWPhE5rHkJ4RjmLsHRs1Xo7dcLHccsnd6AI2fUcJOKsXCq\nUug4dB/p/7iV86GCSkFzCIWlT0QOy9NDhjmPhaOtqx/5pbeEjmNW4UUtGtt6MWvSCPh6uQkdh+5j\nZKgv4kcG4kpVC65rWoSOY3csfSJyaPOnRsBNJsYXRXcuZ+uo9AYDVGfUkEpESJnGVb4jc+XVPkuf\niByar6cbnpgchub2Xpy+WGv+BQI5d/k2bjd3Y+b4UAT68k56jmx0hD/GRPjj4s0mVNS2CR3Hrlj6\nROTwFk5TQioRI7dQ7ZD3pDcYjThcqIZYJELq9Eih45AFFs+IAgAcdrHVPkufiByev7c7Zk0MRUNr\nD4ouOd7107+5Wo9bDZ1Iilcg2N85bgns7MZGBiBmhC9KrjdAc7tD6Dh2w9InoiFhUWIkJOJ/XD/d\n4DjfsTYajThcUAkR/vk9cHJ8IpHIdGz/cGGlkFHsiqVPRENCkJ8HkuOHo66pC8VXbwsdx6SsvBFV\ntzswdWwIhgd6Ch2HBmFCTBCUId44d/k2ahs7hY5jFyx9IhoyUpMiIRLdOevaEe6WZjQaTWeAp3OV\nP+TcXe0bAeSeUQsdxy5Y+kQ0ZCgCPJEYp0BNfSdKrzcIHQeX1c24easNk38QjPAQb6Hj0EN4bIwc\noUGeKLxYh/qWbqHj2BxLn4iGlLSkKIe5W9rdM7/vHhumoUf8j9W+wWjEERdY7bP0iWhICQv2QsIY\nOSq17fiuokmwHNeqW3ClqgXx0YEYGeorWA56dNPGhiDEfxhOfVuLprYeoePYFEufiIacuyvrHAFX\n+3fP+M5IHinI+5P1SMRipCVFQqc34ouzVULHsSmWPhENOUqFDybGBOGGphXXqu1//fSK2jZcvNmE\nWKU/RoX72f39yfqS4ocjyNcd+RduobWzT+g4NsPSJ6IhybTaP11p9/e+eyx/MY/lOw2pRIxF0yPR\npzPgy3POu9pn6RPRkBQT5oe4qABcVjfjRk2r3d63+nYHSq43ICbMF7GRAXZ7X7K9H04IhZ+XG45/\nU4OO7n6h49gES5+Ihqy7K217Xj9dVVhpem+RSGS39yXbk0klSElUordPj78XVwsdxyZY+kQ0ZI2O\n8McPwv1QVt4Itbbd5u9X29iJc5dvQ6nwxvjoIJu/H9nf45PC4D1Mhr8Xa9DVoxM6jtWx9IloyBKJ\nRP9c7RdW2vz9cgvVMIKrfGfm7ibBgqkR6OrV4asSjdBxrI6lT0RD2riRgRgZ6oPzV+tRU2+7u6XV\nt3Sj8Ls6hAV7YfJouc3eh4Q357FweLpLcfRsNXr79ELHsSqWPhENaf96tzSVDa+oduSMGgajEWnJ\nkRBzle/UPD2kmDclHB3d/ThxoUboOFbF0ieiIW/iqGCEy71RdKkOdc1dVt9+U1sPTn1bC0XAMEyL\nVVh9++R45k2JgLubBEfOVqFf5zyrfZuWfn5+PlJSUrBgwQLs2rXrnuebmpqQlZWFJUuWID09HQcO\nHDA9N2fOHCxevBhLly5FZmamLWMS0RB35/rpkTAa7xx3t7YvzlZBpzciNSkSYjFX+a7Ae5gMcyaH\nobWjD6fKaoWOYzU2K329Xo+NGzdi9+7dUKlUUKlUKC8vHzBm3759iIuLw8GDB7Fnzx5s3boVOt0/\nz5bcu3cvPv/8c3zyySe2iklETmLKmDv3sy+4qEVDq/Xultba2YcTF24hyNcdSeOGW2275PgWTFNC\nJhUj94waOr1B6DhWYbPSLysrg1KpRHh4OGQyGdLS0pCXlzdgjFwuR0fHnRNvOjs74e/vD6lUanpe\n6DtoEdHQIRaLkJYUCb3BiCNF1rui2pdnq9CvMyB1eiSkEh4RdSV+Xm6YPXEEGtt6UXhRK3Qcq5Ca\nH/Jw6urqEBoaanqsUChQVlY2YMyKFSvw7LPPYubMmejs7MSOHTtMz4lEIqxatQpisRgrV67EihUr\nbBWViJxEYpwCB09V4GRpLeYlhMPH0+2Rttfdq8Pxkhr4ebth5oRQ8y8gp5OSqMRXJTVQFaox8QfB\nVjmJ08NNItgHSJuVviXfYd25cydiY2Oxd+9eVFVVYdWqVTh48CC8vb3x8ccfIyQkBE1NTVi1ahWi\no6MxZcoUW8UlIicglYiRmhSJPV9cxfo/Flltu8tmjoRMKrHa9mjoCPT1wMwJoThx4RZ++X9OWWWb\nYcFe2PjzRKtsa7BsVvoKhQK1tf88+UGr1UKhGHjWa0lJCV588UUAMB0KqKiowPjx4xESEgIACAwM\nxPz581FWVma29OVyHyv/FM6Lc2UZzpPlHGWuls+PxfL5sULHeCBHmSdH50jz9PpPp+L1nwqdwjps\ntn8hPj4earUaGo0GfX19yM3Nxdy5cweMiY6ORmFhIQCgoaEBFRUViIiIQHd3t+lYf1dXF06dOoXR\no0fbKioREZFLEBlteLbciRMn8O6778JgMCAzMxOrV69GdnY2AGDlypVoamrCunXrcOvWLRiNRrzw\nwgtYvHgxqqur8fLLLwO48y2AxYsXY/Xq1baKSURE5BJsWvpERETkOPj9EyIiIhfB0iciInIRLH0i\nIiIX4RSlb+4a/wTU1tbipz/9KdLS0pCeno49e/YIHcmh6fV6LF261PSVUrq/trY2rFmzBosWLUJq\naiouXLggdCSH9P777yMtLQ2LFy/Ga6+9hr6+PqEjOYy33noLycnJWLx4senPWlpasGrVKixcuBDP\nPfcc2traBEzoGO43T1u3bsWiRYuQkZGBl19+Ge3t7Wa3M+RL35Jr/BMglUqxbt06qFQq/PWvf8W+\nffs4T99jz549iImJETqGw9u8eTNmzZqFI0eOICcnh3N2HxqNBvv378dnn32GQ4cOQa/XQ6VSCR3L\nYTz11FPYvXv3gD/btWsXkpOTcfToUUyfPp2LOdx/nmbOnAmVSoWcnBxERUXh/fffN7udIV/6llzj\nn+7c52Ds2LEAAC8vL8TExOD27dsCp3JMWq0WJ06cwPLly4WO4tDa29tRXFxsugumVCqFj4/jXFDF\nUXh7e0MqlaK7uxs6nQ49PT33XKjMlU2ZMgW+vr4D/uz48eNYtmwZAGDZsmX4+9//LkQ0h3K/eZox\nYwbE4js1PnHiRGi15u8PMORL/37X+K+rqxMwkePTaDS4fPkyJkyYIHQUh/Tuu+/ijTfeMP3PRPen\n0WgQGBiIt956C8uWLcOvf/1rdHdb7+52zsLf3x/PPfccHn/8cfzwhz+Ej48PkpOThY7l0BobGxEc\nHAwACA4ORmNjo8CJHN+nn36K2bNnmx035P9Ws+Qa//RPnZ2dWLNmDdavXw8vLy+h4zicr776CkFB\nQYiLi+NdHs3Q6XS4dOkSfvzjH+Ozzz7DsGHDuBv2PqqqqvDhhx/i+PHjOHnyJLq6upCTkyN0rCFD\nJBLx73kz/vCHP0Amkw043v8gQ770LbnGP93R39+PNWvWICMjA/PmzRM6jkMqKSnB8ePHMWfOHLz2\n2ms4c+YM3njjDaFjOaThw4dDoVCY9hgtXLgQly5dEjiV47l48SImT56MgIAASKVSzJ8/HyUlJULH\ncmhBQUGor68HANy+fRuBgYECJ3JcBw4cwIkTJ/Cf//mfFo0f8qVvyTX+CTAajVi/fj1iYmLws5/9\nTOg4DuvVV1/FiRMncPz4cWzfvh3Tp0/Htm3bhI7lkORyOUJDQ1FRUQEAKCwsxKhRowRO5Xiio6NR\nWlqKnp4eGI1GzpMF5syZg88++wwA8Pnnn3OR8gD5+fn4n//5H7z33ntwd3e36DVOcRne+13jnwYq\nLi7GT37yE4wZM8a0q+zVV1/FrFmzBE7muM6ePYs//elP2Llzp9BRHNaVK1ewfv169Pf3Q6lUYsuW\nLTyZ7z7++Mc/4vPPP4dYLEZcXBw2bdoEmUwmdCyH8Oqrr+Ls2bNoaWlBUFAQ1qxZg7lz5+KXv/wl\namtrERYWhh07dtxzEpur+d/z9O///u/YtWsX+vv74efnBwCYNGkSfvvb337vdpyi9ImIiMi8Ib97\nn4iIiCzD0iciInIRLH0iIiIXwdInIiJyESx9IiIiF8HSJyIichEsfSIXFBsba9Xr5B84cABr1qyx\n+lgisi6WPhE9Ml4bnWhokAodgIiEtXXrVpw7dw79/f0ICAjAu+++ixEjRkCj0eCpp57Cj370I5w8\neRI9PT3Ytm0bPv74Y3z77bcYNmwY3nvvPdPd0Do6OvCLX/wCVVVVCA4OxrZt26BQKNDX14dNmzah\nqKgIAQEBpls8A8DVq1fxzjvvoLu7G729vVixYgWeffZZoaaCyOlxpU/k4l544QV88sknOHjwINLS\n0gbcuKO1tRUJCQn47LPPkJmZiVWrVuGZZ57BoUOHMG7cOHz00UcA7tzb4fz58/iP//gPqFQqTJ06\nFZs3bwYA/PWvf0VNTQ1yc3Px5z//GWVlZaY9A+Hh4fjggw9w4MAB7N+/H/v370d5ebn9J4HIRXCl\nT+TiTpw4gY8//hhdXV3Q6XQDnvP09DTdozsuLg6hoaGIjY0FAIwbNw4FBQWmsVOmTEFUVBQAYPny\n5cjIyAAAFBUVYdmyZZBIJJBIJMjIyMD58+cBAN3d3fjNb36Dq1evQiwW4/bt27h69SpiYmJs/WMT\nuSSWPpELq6mpwe9+9zt8+umnCAsLwzfffIPXX3/d9Lybm5vp38Vi8T2P//VDwoNu4yESiQY896//\nvn37doSEhGDbtm0Qi8XIyspCX1+fVX42IroXd+8TubCOjg7IZDIEBwfDYDAgOzv7obf1zTffQK1W\nAwA+/fRTTJ8+HQAwffp0HDx4EHq9Hj09PTh8+LBp935HRweGDx8OsViMa9euobi4+NF/KCJ6IK70\niVzQ3dIdM2YMUlJSkJqaioCAAMyePdu06/1fx9399wc9FolESEhIwNatW6FWqyGXy7Ft2zYAwIoV\nK6wutFUAAABqSURBVHD16lXTe0yYMAGNjY0AgF/84hd444038MknnyAqKgpTp061+c9O5Mp4a10i\nIiIXwd37RERELoKlT0RE5CJY+kRERC6CpU9EROQiWPpEREQugqVPRETkIlj6RERELoKlT0RE5CL+\nP7t69hgT0Zu+AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x113ca4c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot(la,cross_sc)\n", "plt.ylabel('Cross val score')\n", "plt.xlabel('lambda')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
marcolivierarsenault/AdventOfCode2016
06/Day6.ipynb
1
3582
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advent of code Day 6\n", "Look for the most/Least used Char in the messages\n", "Ref: http://adventofcode.com/2016/day/6" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Create the Matrix of char\n", "data <- matrix(unlist(strsplit(readLines(\"data.txt\"),\"\")),ncol=8,byrow=TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Look for the most used per row" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\"qzedlxso\"" ], "text/latex": [ "\"qzedlxso\"" ], "text/markdown": [ "\"qzedlxso\"" ], "text/plain": [ "[1] \"qzedlxso\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q1 <- paste(apply(data, 2, function(x) names(sort(table(x), decreasing = TRUE)[1])), collapse='')\n", "q1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Look for the least used per row" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\"ucmifjae\"" ], "text/latex": [ "\"ucmifjae\"" ], "text/markdown": [ "\"ucmifjae\"" ], "text/plain": [ "[1] \"ucmifjae\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q2 <- paste(apply(data, 2, function(x) names(sort(table(x), decreasing = FALSE)[1])), collapse='')\n", "q2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final Answers" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\"Q1 -- Code with most frequent char qzedlxso \n", "\"" ], "text/latex": [ "\"Q1 -- Code with most frequent char qzedlxso \n", "\"" ], "text/markdown": [ "\"Q1 -- Code with most frequent char qzedlxso \n", "\"" ], "text/plain": [ "[1] \"Q1 -- Code with most frequent char qzedlxso \\n\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\"Q2 -- Code with least frequent char ucmifjae \n", "\"" ], "text/latex": [ "\"Q2 -- Code with least frequent char ucmifjae \n", "\"" ], "text/markdown": [ "\"Q2 -- Code with least frequent char ucmifjae \n", "\"" ], "text/plain": [ "[1] \"Q2 -- Code with least frequent char ucmifjae \\n\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "paste(\"Q1 -- Code with most frequent char\", q1, '\\n')\n", "paste(\"Q2 -- Code with least frequent char\", q2, '\\n')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.3.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
trangel/Insight-Data-Science
scrapping/scrapping-medhelp.ipynb
2
20162
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests # to make GET request\n", "from bs4 import BeautifulSoup # to parse the HTML response\n", "import time # to pause between calls\n", "import pandas as pd # to see CSV\n", "import os\n", "\n", "os.chdir('../data/')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "columns=['post id','title','text','href','user id','mother post id']\n", "df = pd.DataFrame(columns=columns)\n", "\n", "columns=['user id','user description']\n", "df_users = pd.DataFrame(columns=columns)\n", "\n", "# Initialize post index\n", "post_id=0" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def parse_post(title,url):\n", " global df,df_users,post_id\n", " response = requests.get(url)\n", " page_source = response.text\n", " soup = BeautifulSoup(page_source, 'html5lib')\n", " post_data=soup.find(\"div\", class_=\"post_message\").text\n", "\n", " post_answer=soup.find(\"div\", id=\"post_answer_body\")\n", " post_entries=post_answer.find_all(\"div\", class_=\"post_entry\")\n", " post_id_mother=post_id + 1\n", " for post_entry in post_entries:\n", " post_id = post_id + 1\n", " subj_user=post_entry.find(\"div\", class_=\"subj_user\")\n", " user_id=subj_user.find('a')['id']\n", " user_name=subj_user.find('a').text\n", " post_message=post_entry.find(\"div\",class_=\"post_message\").text\n", " #\n", " # Add post data to dataframe\n", " #\n", " newrow={\"post id\":post_id,\n", " \"title\":title,\n", " \"text\":post_message,\n", " \"href\":href,\n", " \"user id\":user_id,\n", " \"mother post id\":post_id_mother}\n", " df.loc[len(df.values)]=newrow\n", " #\n", " # Update user dataframe:\n", " #\n", " newrow={\"user id\":user_id,\n", " \"user description\":user_name}\n", " if user_id not in df_users['user id'].values:\n", " df_users.loc[len(df_users)]=newrow\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37\n", "56\n", "74\n", "112\n", "139\n", "168\n", "196\n", "216\n", "244\n", "269\n", "283\n", "311\n", "333\n", "345\n", "364\n", "380\n", "401\n", "417\n", "439\n", "465\n", "492\n", "526\n", "552\n", "598\n", "627\n", "660\n", "693\n", "716\n", "749\n", "773\n", "807\n", "840\n", "911\n", "946\n", "998\n", "1060\n", "1108\n", "1163\n", "1223\n", "1268\n", "1313\n", "1342\n", "1363\n", "1401\n", "1477\n", "1510\n", "1594\n", "1669\n", "1708\n", "1778\n", "1813\n" ] } ], "source": [ "source=\"http://www.medhelp.org/forums/Autism--Aspergers-Syndrome/show/187\"\n", "\n", "\n", "\n", "for page in range(1,52):\n", " page_suffix=\"/?page={}\".format(str(page))\n", " if ( page == 1 ):\n", " page_suffix=''\n", " url=source+page_suffix\n", " response = requests.get(url)\n", " page_source = response.text\n", " soup = BeautifulSoup(page_source, 'html5lib')\n", " medhelp_path=\"http://www.medhelp.org/\"\n", " subjects_list=soup.find(\"div\",class_=\"subjects_list\")\n", " new_subject_elements=subjects_list.find_all(\"div\", class_=\"new_subject_element float_fix\")\n", " for new_subject_element in new_subject_elements:\n", " subject_summary=new_subject_element.find(\"div\", class_=\"subject_summary\")\n", " # Get href. for further reading\n", " href=subject_summary.find('a')['href']\n", " href=medhelp_path+href\n", " title=subject_summary.find('a').text\n", " excerpt=subject_summary.find(\"div\", class_=\"excerpt\").text\n", " # \n", " # Now that we have extracted the title and href of each post\n", " # Let's extract now the question and answers in that post.\n", " #\n", " parse_post(title,href)\n", " time.sleep(1)\n", " print(len(df))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df_users.to_csv('MedHelp-users.csv',index=False)\n", "df.to_csv('MedHelp-posts.csv',index=False)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>user id</th>\n", " <th>user description</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>user_340688</td>\n", " <td>Rachel Thompson, Ph.D., BCBA</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>user_1566928</td>\n", " <td>CirclesLady29</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>user_15010831</td>\n", " <td>Rosseau</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>user_340657</td>\n", " <td>Myrna Libby, Ph.D., BCBA</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>user_391640</td>\n", " <td>babesmissy</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>user_388553</td>\n", " <td>Grandmother1941</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>user_393618</td>\n", " <td>fidgit</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>user_395246</td>\n", " <td>sljenkins</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>user_347888</td>\n", " <td>MaryannesMom</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>user_397233</td>\n", " <td>aspiemom</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>user_436623</td>\n", " <td>autistic_mom07</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>user_9757959</td>\n", " <td>Alainee</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>user_340676</td>\n", " <td>Jason C Bourret, Ph.D., BCBA-D</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>user_9351486</td>\n", " <td>vincentcausse</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>user_9280615</td>\n", " <td>mentor4succes</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>user_8221281</td>\n", " <td>Pantx</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>user_6976850</td>\n", " <td>Akita0419</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>user_1785966</td>\n", " <td>Regret2011</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>user_7980329</td>\n", " <td>Kamsthere</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>user_7329383</td>\n", " <td>Hannahvictoria98</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>user_7037769</td>\n", " <td>FollowerofChrist</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>user_4447834</td>\n", " <td>gabrielamg</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>user_6554120</td>\n", " <td>ZapCat</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>user_7154461</td>\n", " <td>PresleyNic</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>user_6929994</td>\n", " <td>Jane1211</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>user_6573002</td>\n", " <td>Jellybean1986</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>user_6734966</td>\n", " <td>amethyst111</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>user_6591769</td>\n", " <td>lillibetlayne</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>user_6506090</td>\n", " <td>megamomz6</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>user_6333487</td>\n", " <td>exfulgere</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>475</th>\n", " <td>user_518366</td>\n", " <td>troisboyz</td>\n", " </tr>\n", " <tr>\n", " <th>476</th>\n", " <td>user_356327</td>\n", " <td>angelinamarina</td>\n", " </tr>\n", " <tr>\n", " <th>477</th>\n", " <td>user_92911</td>\n", " <td>LukeL</td>\n", " </tr>\n", " <tr>\n", " <th>478</th>\n", " <td>user_9995</td>\n", " <td>wmac</td>\n", " </tr>\n", " <tr>\n", " <th>479</th>\n", " <td>user_380759</td>\n", " <td>jaipur</td>\n", " </tr>\n", " <tr>\n", " <th>480</th>\n", " <td>user_333573</td>\n", " <td>crayons</td>\n", " </tr>\n", " <tr>\n", " <th>481</th>\n", " <td>user_371209</td>\n", " <td>barbsbit</td>\n", " </tr>\n", " <tr>\n", " <th>482</th>\n", " <td>user_282524</td>\n", " <td>rebbecca</td>\n", " </tr>\n", " <tr>\n", " <th>483</th>\n", " <td>user_304011</td>\n", " <td>jerry9798</td>\n", " </tr>\n", " <tr>\n", " <th>484</th>\n", " <td>user_368946</td>\n", " <td>bweebles</td>\n", " </tr>\n", " <tr>\n", " <th>485</th>\n", " <td>user_337410</td>\n", " <td>tomcat47</td>\n", " </tr>\n", " <tr>\n", " <th>486</th>\n", " <td>user_362088</td>\n", " <td>aver</td>\n", " </tr>\n", " <tr>\n", " <th>487</th>\n", " <td>user_367032</td>\n", " <td>EdieMarie</td>\n", " </tr>\n", " <tr>\n", " <th>488</th>\n", " <td>user_474322</td>\n", " <td>Presidents</td>\n", " </tr>\n", " <tr>\n", " <th>489</th>\n", " <td>user_367308</td>\n", " <td>JollyHolly1221</td>\n", " </tr>\n", " <tr>\n", " <th>490</th>\n", " <td>user_365670</td>\n", " <td>bellemom</td>\n", " </tr>\n", " <tr>\n", " <th>491</th>\n", " <td>user_317629</td>\n", " <td>BlueEgg</td>\n", " </tr>\n", " <tr>\n", " <th>492</th>\n", " <td>user_287540</td>\n", " <td>littlebartie</td>\n", " </tr>\n", " <tr>\n", " <th>493</th>\n", " <td>user_361661</td>\n", " <td>jrobb1564</td>\n", " </tr>\n", " <tr>\n", " <th>494</th>\n", " <td>user_596996</td>\n", " <td>keloz</td>\n", " </tr>\n", " <tr>\n", " <th>495</th>\n", " <td>user_336901</td>\n", " <td>Atto786</td>\n", " </tr>\n", " <tr>\n", " <th>496</th>\n", " <td>user_681771</td>\n", " <td>smettoh</td>\n", " </tr>\n", " <tr>\n", " <th>497</th>\n", " <td>user_726652</td>\n", " <td>eloisa7</td>\n", " </tr>\n", " <tr>\n", " <th>498</th>\n", " <td>user_359999</td>\n", " <td>Danalou275</td>\n", " </tr>\n", " <tr>\n", " <th>499</th>\n", " <td>user_355729</td>\n", " <td>PATTI143</td>\n", " </tr>\n", " <tr>\n", " <th>500</th>\n", " <td>user_364792</td>\n", " <td>losifat</td>\n", " </tr>\n", " <tr>\n", " <th>501</th>\n", " <td>user_360460</td>\n", " <td>TammyLynn1976</td>\n", " </tr>\n", " <tr>\n", " <th>502</th>\n", " <td>user_361230</td>\n", " <td>beancounter68</td>\n", " </tr>\n", " <tr>\n", " <th>503</th>\n", " <td>user_212002</td>\n", " <td>Susie2007</td>\n", " </tr>\n", " <tr>\n", " <th>504</th>\n", " <td>user_345311</td>\n", " <td>Claudinne</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>505 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " user id user description\n", "0 user_340688 Rachel Thompson, Ph.D., BCBA\n", "1 user_1566928 CirclesLady29\n", "2 user_15010831 Rosseau\n", "3 user_340657 Myrna Libby, Ph.D., BCBA\n", "4 user_391640 babesmissy\n", "5 user_388553 Grandmother1941\n", "6 user_393618 fidgit\n", "7 user_395246 sljenkins\n", "8 user_347888 MaryannesMom\n", "9 user_397233 aspiemom\n", "10 user_436623 autistic_mom07\n", "11 user_9757959 Alainee\n", "12 user_340676 Jason C Bourret, Ph.D., BCBA-D\n", "13 user_9351486 vincentcausse\n", "14 user_9280615 mentor4succes\n", "15 user_8221281 Pantx\n", "16 user_6976850 Akita0419\n", "17 user_1785966 Regret2011\n", "18 user_7980329 Kamsthere\n", "19 user_7329383 Hannahvictoria98\n", "20 user_7037769 FollowerofChrist\n", "21 user_4447834 gabrielamg\n", "22 user_6554120 ZapCat\n", "23 user_7154461 PresleyNic\n", "24 user_6929994 Jane1211\n", "25 user_6573002 Jellybean1986\n", "26 user_6734966 amethyst111\n", "27 user_6591769 lillibetlayne\n", "28 user_6506090 megamomz6\n", "29 user_6333487 exfulgere\n", ".. ... ...\n", "475 user_518366 troisboyz\n", "476 user_356327 angelinamarina\n", "477 user_92911 LukeL\n", "478 user_9995 wmac\n", "479 user_380759 jaipur\n", "480 user_333573 crayons\n", "481 user_371209 barbsbit\n", "482 user_282524 rebbecca\n", "483 user_304011 jerry9798\n", "484 user_368946 bweebles\n", "485 user_337410 tomcat47\n", "486 user_362088 aver\n", "487 user_367032 EdieMarie\n", "488 user_474322 Presidents\n", "489 user_367308 JollyHolly1221\n", "490 user_365670 bellemom\n", "491 user_317629 BlueEgg\n", "492 user_287540 littlebartie\n", "493 user_361661 jrobb1564\n", "494 user_596996 keloz\n", "495 user_336901 Atto786\n", "496 user_681771 smettoh\n", "497 user_726652 eloisa7\n", "498 user_359999 Danalou275\n", "499 user_355729 PATTI143\n", "500 user_364792 losifat\n", "501 user_360460 TammyLynn1976\n", "502 user_361230 beancounter68\n", "503 user_212002 Susie2007\n", "504 user_345311 Claudinne\n", "\n", "[505 rows x 2 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_users" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1813\n" ] } ], "source": [ "print(len(df))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
rubensfernando/mba-analytics-big-data
Python/2016-08-05/aula6-parte1-post.ipynb
1
3136
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Postando uma mensagem simples!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import facebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "O token de acesso, deve ser gerado conforme apresentado nos slides 36 até 46.\n", "\n", "Lembre-se da validade do token de acesso!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "access_token = 'EAACUzLmOZC7kBAODqMlhUdJXEtRJJoDWKJMCC32vC1cd9ou4FBrQNUT1pNxHJZBXZBmCl0dwSNPDl6w3YhjI6DLLwquy9AbWCZAlky2ZCiQRboCJkiuiITS8iZAZALVYZAlQP9d1W47s0j60rYeFk94ctCqrZAxST93KxGZBS5L4cRrgZDZD'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "api = facebook.GraphAPI(access_token)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'access_token', 'debug_access_token', 'delete_object', 'delete_request', 'extend_access_token', 'fql', 'get_access_token_from_code', 'get_app_access_token', 'get_connections', 'get_object', 'get_objects', 'get_version', 'proxies', 'put_comment', 'put_like', 'put_object', 'put_photo', 'put_wall_post', 'request', 'timeout', 'version']\n" ] } ], "source": [ "print(dir(api))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'id': '306405989707205_308011842879953'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "api.put_wall_post(\"Programação com Python + FIA\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "api.put_wall_post?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
drcjar/pypf
.ipynb_checkpoints/pypf-checkpoint.ipynb
1
28449
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import xlrd \n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "workbook = xlrd.open_workbook('./data/eng/Lung disease tables FINAL.xlsx') #what ONS sent part one\n", "print workbook.sheet_names()\n", "df = pd.read_excel('./data/eng/Lung disease tables FINAL.xlsx', 'Contents') #lets load the contents worksheet\n", "df.head() #lets look at the contents " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[u'Contents', u'Table 1', u'Table 2', u'Table 3', u'Table 4', u'Table 5', u'Table 6']\n" ] }, { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Contents</th>\n", " <th>Unnamed: 1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Table 1</td>\n", " <td> Number of deaths in each standard region of En...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Table2</td>\n", " <td> Number of deaths in each standard region of En...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Table 3</td>\n", " <td> Number of deaths in each standard region of En...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Table 4</td>\n", " <td> Number of deaths in each region of England and...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 2 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " Contents Unnamed: 1\n", "0 NaN NaN\n", "1 Table 1 Number of deaths in each standard region of En...\n", "2 Table2 Number of deaths in each standard region of En...\n", "3 Table 3 Number of deaths in each standard region of En...\n", "4 Table 4 Number of deaths in each region of England and...\n", "\n", "[5 rows x 2 columns]" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "df.columns = ['Name', 'Desc'] #lets have Name and Description columns for the worksheets\n", "df.fillna('n/a', inplace=True) #get rid on NaNs\n", "df = df[df['Name'].str.contains('Table')] #get rid of rows that aren't about our worksheets of interest\n", "df['Name'] = df['Name'].str.replace('Table2', 'Table 2') #correct ONS typo\n", "sheets = df.to_dict(outtype='records') #lets save our work as dict" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#lets make a shorthand table-disease lookup\n", "dis_lookup = {'Table 1':'IPF', 'Table 2':'Asbestosis', 'Table 3':'Pulmonary Mesothelioma', \n", " 'Table 4':'IPF', 'Table 5':'Asbestosis', 'Table 6':'Pulmonary Mesothelioma'} \n", "\n", "#a few transforms needed to make the ons data usable\n", "transforms = {'YORKSHIRE AND THE HUMBER':'YORKSHIRE AND THE HUMBER',\n", " 'YORKSHIRE & HUMBERSIDE':'YORKSHIRE AND THE HUMBER',\n", " 'YORKSHIRE & HUMBER':'YORKSHIRE AND THE HUMBER',\n", " 'NORTH WEST':'NORTH WEST',\n", " 'EAST MIDLANDS':'EAST MIDLANDS', 'WEST MIDLANDS':'WEST MIDLANDS',\n", " 'WEST MIDLANDS (REGION)':'WEST MIDLANDS', 'EAST':'EAST', \n", " 'SOUTH EAST':'SOUTH EAST', 'SOUTH WEST':'SOUTH WEST', 'WALES':'WALES', \n", " 'NORTH EAST':'NORTH EAST', 'LONDON':'LONDON', \n", " 'EASTERN':'EAST', 'EAST OF ENGLAND':'EAST', 'EAST ANGLIA':'EAST', 'NORTH':'NORTH EAST', \n", " 'YORKSHIRE AND HUMBERSIDE':'YORKSHIRE AND THE HUMBER'}\n", "\n", "sex_transforms_74_80 = {'Unnamed: 2':'Male', 'Unnamed: 3':'Male', 'Unnamed: 4':'Male', 'Unnamed: 5':'Male',\n", " 'Unnamed: 6':'Male', 'Unnamed: 7':'Male', 'Unnamed: 8':'Male', 'Unnamed: 10':'Female',\n", " 'Unnamed: 11':'Female', 'Unnamed: 12':'Female', 'Unnamed: 13':'Female', 'Unnamed: 14':'Female',\n", " 'Unnamed: 15':'Female', 'Unnamed: 16':'Female'}\n", "\n", "sex_transforms_81_2012 = {'Unnamed: 2':'Male', 'Unnamed: 3':'Male', 'Unnamed: 4':'Male', 'Unnamed: 5':'Male',\n", " 'Unnamed: 6':'Male', 'Unnamed: 7':'Male', 'Unnamed: 8':'Male', 'Unnamed: 9':'Male',\n", " 'Unnamed: 10':'Male', 'Unnamed: 11':'Male', 'Unnamed: 12':'Male', 'Unnamed: 13':'Male',\n", " 'Unnamed: 14':'Male', 'Unnamed: 15':'Male', 'Unnamed: 16':'Male', 'Unnamed: 17':'Male',\n", " 'Unnamed: 18':'Male', 'Unnamed: 19':'Male', 'Unnamed: 20':'Male', 'Unnamed: 21':'Male',\n", " 'Unnamed: 22':'Male', 'Unnamed: 23':'Male', 'Unnamed: 24':'Male', 'Unnamed: 25':'Male',\n", " 'Unnamed: 26':'Male', 'Unnamed: 27':'Male', 'Unnamed: 28':'Male', 'Unnamed: 29':'Male',\n", " 'Unnamed: 30':'Male', 'Unnamed: 31':'Male', 'Unnamed: 32':'Male', 'Unnamed: 33':'Male',\n", " 'Unnamed: 35':'Female', 'Unnamed: 36':'Female', 'Unnamed: 37':'Female', 'Unnamed: 38':'Female',\n", " 'Unnamed: 39':'Female', 'Unnamed: 40':'Female', 'Unnamed: 41':'Female', 'Unnamed: 42':'Female',\n", " 'Unnamed: 43':'Female', 'Unnamed: 44':'Female', 'Unnamed: 45':'Female', 'Unnamed: 46':'Female',\n", " 'Unnamed: 47':'Female', 'Unnamed: 48':'Female', 'Unnamed: 49':'Female', 'Unnamed: 50':'Female',\n", " 'Unnamed: 51':'Female', 'Unnamed: 52':'Female', 'Unnamed: 53':'Female', 'Unnamed: 54':'Female',\n", " 'Unnamed: 55':'Female', 'Unnamed: 56':'Female', 'Unnamed: 57':'Female', 'Unnamed: 58':'Female',\n", " 'Unnamed: 59':'Female', 'Unnamed: 60':'Female', 'Unnamed: 61':'Female', 'Unnamed: 62':'Female',\n", " 'Unnamed: 63':'Female', 'Unnamed: 64':'Female', 'Unnamed: 65':'Female', 'Unnamed: 66':'Female'}\n", "\n", "year_transforms_74_80 = {'Unnamed: 2':1974, 'Unnamed: 3':1975, 'Unnamed: 4':1976, 'Unnamed: 5':1977,\n", " 'Unnamed: 6':1978, 'Unnamed: 7':1979, 'Unnamed: 8':1980, 'Unnamed: 10':1974,\n", " 'Unnamed: 11':1975, 'Unnamed: 12':1976, 'Unnamed: 13':1977, 'Unnamed: 14':1978,\n", " 'Unnamed: 15':1979, 'Unnamed: 16':1980}\n", "\n", "year_transforms_81_2012 = {'Unnamed: 2':1981, 'Unnamed: 3':1982, 'Unnamed: 4':1983, 'Unnamed: 5':1984,\n", " 'Unnamed: 6':1985, 'Unnamed: 7':1986, 'Unnamed: 8':1987, 'Unnamed: 9':1988,\n", " 'Unnamed: 10':1989, 'Unnamed: 11':1990, 'Unnamed: 12':1991, 'Unnamed: 13':1992,\n", " 'Unnamed: 14':1993, 'Unnamed: 15':1994, 'Unnamed: 16':1995, 'Unnamed: 17':1996,\n", " 'Unnamed: 18':1997, 'Unnamed: 19':1998, 'Unnamed: 20':1999, 'Unnamed: 21':2000,\n", " 'Unnamed: 22':2001, 'Unnamed: 23':2002, 'Unnamed: 24':2003, 'Unnamed: 25':2004,\n", " 'Unnamed: 26':2005, 'Unnamed: 27':2006, 'Unnamed: 28':2007, 'Unnamed: 29':2008,\n", " 'Unnamed: 30':2009, 'Unnamed: 31':2010, 'Unnamed: 32':2011, 'Unnamed: 33':2012,\n", " 'Unnamed: 35':1981, 'Unnamed: 36':1982, 'Unnamed: 37':1983, 'Unnamed: 38':1984,\n", " 'Unnamed: 39':1985, 'Unnamed: 40':1986, 'Unnamed: 41':1987, 'Unnamed: 42':1988,\n", " 'Unnamed: 43':1989, 'Unnamed: 44':1990, 'Unnamed: 45':1991, 'Unnamed: 46':1992,\n", " 'Unnamed: 47':1993, 'Unnamed: 48':1994, 'Unnamed: 49':1995, 'Unnamed: 50':1996,\n", " 'Unnamed: 51':1997, 'Unnamed: 52':1998, 'Unnamed: 53':1999, 'Unnamed: 54':2000,\n", " 'Unnamed: 55':2001, 'Unnamed: 56':2002, 'Unnamed: 57':2003, 'Unnamed: 58':2004,\n", " 'Unnamed: 59':2005, 'Unnamed: 60':2006, 'Unnamed: 61':2007, 'Unnamed: 62':2008,\n", " 'Unnamed: 63':2009, 'Unnamed: 64':2010, 'Unnamed: 65':2011, 'Unnamed: 66':2012}\n", "\n", "#what we're expecting\n", "REGIONS = ['YORKSHIRE AND THE HUMBER', 'NORTH WEST','EAST MIDLANDS', 'WEST MIDLANDS', \n", " 'EAST', 'SOUTH EAST', 'SOUTH WEST', 'WALES', 'NORTH EAST', 'LONDON']\n", "\n", "AGEGROUPS = ['ALL AGES', 'UNDER 25', '25-34', '35-44', '45-54', '55-64', '65-74', '75-84', '85+']\n", "\n", "SEX = ['Male', 'Female']\n", "\n", "UNDERLYING_CAUSE = ['IPF', 'Asbestosis', 'Mesothelioma', 'Mesothelioma_other', 'Mesothelioma all']\n", "\n", "YEARS = ['1974'] \n", "for year in range(1975, 2013):\n", " YEARS.append(str(year))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#lets load the worksheets of interest into lists of dataframes using our dict\n", "df_list1 = [pd.read_excel('./data/eng/Lung disease tables FINAL.xlsx', sheet['Name']) for sheet in sheets] #74-80\n", "df_list2 = [pd.read_excel('./data/eng/Lung disease tables FINAL.xlsx', sheet['Name']) for sheet in sheets] #81-2012\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "for i, item in enumerate(df_list1):\n", " df_list1[i].rename(columns={'Contents Page':'Region'}, inplace=True)\n", " df_list1[i]['Region'] = df_list1[i].icol(0).fillna(method='pad') #fix the first column\n", " df_list1[i]['Region'] = df_list1[i]['Region'].str.strip()\n", " df_list1[i]['Region'] = df_list1[i]['Region'].str.upper()\n", " df_list1[i]['Region'] = df_list1[i]['Region'].map(transforms.get) \n", " df_list1[i] = df_list1[i][df_list1[i]['Region'].notnull()] #throw away rows that don't contain a region\n", "\n", " df_list1[i].rename(columns={'Unnamed: 1':'Agegroup'}, inplace=True)\n", " df_list1[i]['Agegroup'] = df_list1[i]['Agegroup'].str.strip()\n", " df_list1[i]['Agegroup'] = df_list1[i]['Agegroup'].str.upper()\n", "\n", " df_list1[i] = df_list1[i].dropna(axis=1, how='all') #throw away columns that don't contain data\n", " \n", " df_list1[i] = pd.melt(df_list1[i], id_vars=['Region', 'Agegroup'])\n", "\n", " df_list1[i]['Sex'] = df_list1[i].variable.map(sex_transforms_74_80.get)\n", " df_list1[i]['Year'] = df_list1[i].variable.map(year_transforms_74_80.get)\n", " df_list1[i].rename(columns={'value':'Deaths'}, inplace=True)\n", " df_list1[i] = df_list1[i][['Region', 'Agegroup', 'Deaths', 'Sex', 'Year']]\n", " df_list1[i]['Underlying Cause'] = dis_lookup[sheets[i]['Name']]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py:2175: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame\n", " **kwargs)\n", "-c:10: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_index,col_indexer] = value instead\n", "-c:11: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_index,col_indexer] = value instead\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "for i, item in enumerate(df_list2):\n", " df_list2[i].rename(columns={'Contents Page':'Region'}, inplace=True)\n", " df_list2[i]['Region'] = df_list2[i].icol(0).fillna(method='pad') #fix the first column\n", " df_list2[i]['Region'] = df_list2[i]['Region'].str.strip()\n", " df_list2[i]['Region'] = df_list2[i]['Region'].str.upper()\n", " df_list2[i]['Region'] = df_list2[i]['Region'].map(transforms.get) \n", " df_list2[i] = df_list2[i][df_list2[i]['Region'].notnull()] #throw away rows that don't contain a region\n", "\n", " df_list2[i].rename(columns={'Unnamed: 1':'Agegroup'}, inplace=True)\n", " df_list2[i]['Agegroup'] = df_list2[i]['Agegroup'].str.strip()\n", " df_list2[i]['Agegroup'] = df_list2[i]['Agegroup'].str.upper()\n", "\n", " df_list2[i] = df_list2[i].dropna(axis=1, how='all') #throw away columns that don't contain data\n", " \n", " df_list2[i] = pd.melt(df_list2[i], id_vars=['Region', 'Agegroup'])\n", "\n", " df_list2[i]['Sex'] = df_list2[i].variable.map(sex_transforms_81_2012.get)\n", " df_list2[i]['Year'] = df_list2[i].variable.map(year_transforms_81_2012.get)\n", " df_list2[i].rename(columns={'value':'Deaths'}, inplace=True)\n", " df_list2[i] = df_list2[i][['Region', 'Agegroup', 'Deaths', 'Sex', 'Year']]\n", " df_list2[i]['Underlying Cause'] = dis_lookup[sheets[i]['Name']]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "df1 = pd.concat(df_list1) #combine sheets 1-3 for 1974-1980\n", "df2 = pd.concat(df_list2) #combine sheets 4-6 for 1981-2012\n", "df_list = [df1, df2] \n", "df = pd.concat(df_list) #combine sheets 1-3 and 4-6\n", "ons1 = df #save result\n", "#df['Country'] = 'England & Wales' #add a label for country\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "df.tail()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Region</th>\n", " <th>Agegroup</th>\n", " <th>Deaths</th>\n", " <th>Sex</th>\n", " <th>Year</th>\n", " <th>Underlying Cause</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>5051</th>\n", " <td> WALES</td>\n", " <td> 45-54</td>\n", " <td> 1</td>\n", " <td> Female</td>\n", " <td> 2012</td>\n", " <td> Pulmonary Mesothelioma</td>\n", " </tr>\n", " <tr>\n", " <th>5052</th>\n", " <td> WALES</td>\n", " <td> 55-64</td>\n", " <td> 0</td>\n", " <td> Female</td>\n", " <td> 2012</td>\n", " <td> Pulmonary Mesothelioma</td>\n", " </tr>\n", " <tr>\n", " <th>5053</th>\n", " <td> WALES</td>\n", " <td> 65-74</td>\n", " <td> 0</td>\n", " <td> Female</td>\n", " <td> 2012</td>\n", " <td> Pulmonary Mesothelioma</td>\n", " </tr>\n", " <tr>\n", " <th>5054</th>\n", " <td> WALES</td>\n", " <td> 75-84</td>\n", " <td> 0</td>\n", " <td> Female</td>\n", " <td> 2012</td>\n", " <td> Pulmonary Mesothelioma</td>\n", " </tr>\n", " <tr>\n", " <th>5055</th>\n", " <td> WALES</td>\n", " <td> 85+</td>\n", " <td> 1</td>\n", " <td> Female</td>\n", " <td> 2012</td>\n", " <td> Pulmonary Mesothelioma</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 6 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " Region Agegroup Deaths Sex Year Underlying Cause\n", "5051 WALES 45-54 1 Female 2012 Pulmonary Mesothelioma\n", "5052 WALES 55-64 0 Female 2012 Pulmonary Mesothelioma\n", "5053 WALES 65-74 0 Female 2012 Pulmonary Mesothelioma\n", "5054 WALES 75-84 0 Female 2012 Pulmonary Mesothelioma\n", "5055 WALES 85+ 1 Female 2012 Pulmonary Mesothelioma\n", "\n", "[5 rows x 6 columns]" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#df.Year = pd.to_datetime(df.Year.astype(str))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#df.index = df.Year" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "#df.groupby('Year').Deaths.sum().plot()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "#load meso 2 table" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "workbook = xlrd.open_workbook('./data/eng/Mesothelioma table 2 FINAL.xls') #what ONS sent part two\n", "print workbook.sheet_names()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[u'Table 1', u'Table 2']\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_excel('./data/eng/Mesothelioma table 2 FINAL.xls', 'Table 1', skiprows=3)\n", "df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Area of usual residence</th>\n", " <th>Agegroup</th>\n", " <th>2001</th>\n", " <th>2002</th>\n", " <th>2003</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>Unnamed: 14</th>\n", " <th>2001.1</th>\n", " <th>2002.1</th>\n", " <th>2003.1</th>\n", " <th>2004.1</th>\n", " <th>2005.1</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> North East</td>\n", " <td> All Ages</td>\n", " <td> 124</td>\n", " <td> 118</td>\n", " <td> 109</td>\n", " <td> 114</td>\n", " <td> 99</td>\n", " <td> 123</td>\n", " <td> 77</td>\n", " <td> 146</td>\n", " <td> 128</td>\n", " <td> 145</td>\n", " <td> 134</td>\n", " <td> 132</td>\n", " <td>NaN</td>\n", " <td> 21</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 21</td>\n", " <td> 17</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> NaN</td>\n", " <td> &lt;25</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> NaN</td>\n", " <td> 25 to 34</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> NaN</td>\n", " <td> 35 to 44</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> NaN</td>\n", " <td> 45 to 54</td>\n", " <td> 4</td>\n", " <td> 2</td>\n", " <td> 5</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 5</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td>NaN</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " <td>...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 27 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ " Area of usual residence Agegroup 2001 2002 2003 2004 2005 2006 2007 \\\n", "0 North East All Ages 124 118 109 114 99 123 77 \n", "1 NaN <25 0 0 0 0 0 0 0 \n", "2 NaN 25 to 34 0 0 0 0 0 0 0 \n", "3 NaN 35 to 44 1 0 0 0 0 0 0 \n", "4 NaN 45 to 54 4 2 5 3 1 5 2 \n", "\n", " 2008 2009 2010 2011 2012 Unnamed: 14 2001.1 2002.1 2003.1 2004.1 \\\n", "0 146 128 145 134 132 NaN 21 10 11 21 \n", "1 0 0 0 0 0 NaN 0 0 0 0 \n", "2 0 0 0 0 0 NaN 0 0 0 0 \n", "3 1 0 0 0 1 NaN 0 0 0 0 \n", "4 1 3 2 0 0 NaN 1 0 0 2 \n", "\n", " 2005.1 \n", "0 17 ... \n", "1 0 ... \n", "2 0 ... \n", "3 0 ... \n", "4 2 ... \n", "\n", "[5 rows x 27 columns]" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "df_list3 = [df]\n", "\n", "#this works because years are dupilcated in the dataset for women and pandas applies a .1 to indicate it's a duplicate\n", "def sex(year):\n", " year = year % 1\n", " if year == 0.0:\n", " return 'male'\n", " else:\n", " return 'female'\n", "\n", "for i, item in enumerate(df_list3):\n", " df_list3[i].rename(columns={'Area of usual residence':'Region'}, inplace=True)\n", " df_list3[i]['Region'] = df_list3[i].icol(0).fillna(method='pad') #fix the first column\n", " df_list3[i]['Region'] = df_list3[i]['Region'].str.strip()\n", " df_list3[i]['Region'] = df_list3[i]['Region'].str.upper()\n", " df_list3[i]['Region'] = df_list3[i]['Region'].map(transforms.get) \n", " df_list3[i] = df_list3[i][df_list3[i]['Region'].notnull()] #throw away rows that don't contain a region\n", "\n", " df_list3[i].rename(columns={'Unnamed: 1':'Agegroup'}, inplace=True)\n", " df_list3[i]['Agegroup'] = df_list3[i]['Agegroup'].str.strip()\n", " df_list3[i]['Agegroup'] = df_list3[i]['Agegroup'].str.upper()\n", "\n", " df_list3[i] = df_list3[i].dropna(axis=1, how='all') #throw away columns that don't contain data\n", " \n", " df_list3[i] = pd.melt(df_list3[i], id_vars=['Region', 'Agegroup'])\n", " df_list3[i]['variable'] = df_list3[i]['variable'].convert_objects(convert_numeric=True)\n", " \n", " df_list3[i]['Sex'] = df_list3[i]['variable'].map(sex)\n", " \n", " df_list3[i].rename(columns={'variable':'Year'}, inplace=True)\n", " df_list3[i].rename(columns={'value':'Deaths'}, inplace=True)\n", " df_list3[i] = df_list3[i][['Region', 'Agegroup', 'Deaths', 'Sex', 'Year']]\n", " df_list3[i]['Underlying Cause'] = dis_lookup[sheets[i]['Name']]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
agpl-3.0
grezesf/Research
Fun/Costly-Search-Task.ipynb
1
392805
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Task: Guess an integer between 0-X\n", "# guessing the number v costs v\n", "# after each guess we are told if the answer is bigger or smaller\n", "# goal: minimize total cost of guess" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "import pylab" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "max_val = 100" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def CST_binary(target_val=50, max_val=100, ratio=0.5, verbose=False):\n", " # simple binary search by ratio, returns the cost\n", " \n", " if ratio > 1:\n", " return None\n", " \n", " floor = 0\n", " ceil = max_val\n", " \n", " if floor == ceil:\n", " cur_guess = floor\n", " else:\n", " cur_guess = math.floor(float(ratio)*(ceil-floor))\n", " cost = cur_guess\n", " \n", " nb_guesses = 1\n", " \n", " if verbose:\n", " print floor, ceil, cur_guess, cost, nb_guesses\n", " \n", " while cur_guess != target_val:\n", " \n", " if cur_guess > target_val:\n", " ceil = cur_guess-1\n", " if cur_guess < target_val:\n", " floor = cur_guess+1\n", " \n", " if floor == ceil:\n", " cur_guess = floor\n", " else:\n", " cur_guess = floor + math.floor(float(ratio)*(ceil-floor))\n", " \n", " cost += cur_guess\n", " \n", " nb_guesses +=1\n", "\n", " if verbose:\n", " print floor, ceil, cur_guess, cost, nb_guesses\n", " \n", " if nb_guesses>max_val+1:\n", " print target_val, ratio\n", " return None\n", " \n", " return cost" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 100 10.0 10.0 1\n", "11.0 100 19.0 29.0 2\n", "20.0 100 28.0 57.0 3\n", "29.0 100 36.0 93.0 4\n", "37.0 100 43.0 136.0 5\n", "44.0 100 49.0 185.0 6\n", "50.0 100 55.0 240.0 7\n", "56.0 100 60.0 300.0 8\n", "61.0 100 64.0 364.0 9\n", "65.0 100 68.0 432.0 10\n", "69.0 100 72.0 504.0 11\n", "73.0 100 75.0 579.0 12\n", "76.0 100 78.0 657.0 13\n", "79.0 100 81.0 738.0 14\n", "82.0 100 83.0 821.0 15\n", "84.0 100 85.0 906.0 16\n", "86.0 100 87.0 993.0 17\n", "88.0 100 89.0 1082.0 18\n", "90.0 100 91.0 1173.0 19\n", "92.0 100 92.0 1265.0 20\n", "93.0 100 93.0 1358.0 21\n", "94.0 100 94.0 1452.0 22\n", "95.0 100 95.0 1547.0 23\n", "96.0 100 96.0 1643.0 24\n", "97.0 100 97.0 1740.0 25\n", "98.0 100 98.0 1838.0 26\n", "99.0 100 99.0 1937.0 27\n", "1937.0\n" ] } ], "source": [ "print CST_binary(target_val=99, max_val=100, ratio=0.1, verbose=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "pylab.rcParams['figure.figsize'] = (20.0, 8.0) #adjust to your screen" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plot CST binary distribution\n", "steps = 100\n", "ratios = [x/float(steps) for x in range(steps+1)]\n", "# print ratios\n", "# CST_bin_dists = [[CST_binary(x, max_val, y) for x in range(max_val+1)] for y in ratios]\n", "\n", "CST_bin_dists = {ratio: {x:CST_binary(x, max_val, ratio) for x in range(max_val+1)} for ratio in ratios}\n", "\n", "# stats:\n", "CST_bin_dists_averages = {ratio: sum(CST_bin_dists[ratio].values())/float(max_val) for ratio in ratios}\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sSZI0d8pwyu0oXUl7AYsA1wO/B/7qnCRJktoWOx7zIXrecR2r3t+bB3e/iJd6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\n9U4W9z5NZ04oaAaHswpEWmAZd2OxCZpZYEZJi0ZVEdkFczCsj9V/K5r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9ltd8vGY0t0mZ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UOGDBmaLZIFbW1kaVsK5Sv1RqqmR1CVpCdryklKbWyAggTnQYO1ie6UNIlLlUGWBgKy182PJL/n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\nr+WH+0eNj3y85bxGU0Imb5tmKPHw4ShgzfvFPtU//VG+XEOBbcn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EgDy1m5mKpD/W1hbYRWC8Ag2XowKPKP10GWE4E6+HK+FgOYTyl+ZFksUzs1BVOnnAE7v9uB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\n+JIbhbwvMXhrSMYAdKcp7STFN3r725v8a6HG2Wmsc0wyU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JXEBVxWyTBwk6rNCgXNIUql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\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trYtCeRyNyyoqiiegej2ax+U9uKHszedTdxGeNZ3bwxsjlT/kt7M7GbMznTDxAh7WmcRs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okA4oIGTTruFrRBzgUGnz5JQewhNw4yLxHaMwPhJMqFG66LXDQJdiVmXzV1TOE6wOhZCrvlFehx\nr6qp70ekOtf7eO1jAl0SxTZsxy7ZqY291VfjO/LzkPbIGmuWsoRaqK84WXTLOI3MX78ACn0Tjj3c\ngtKpBDorivvPAFYlozahjBDgbWTt9LacMa9V3YFWLcm7oiRSX/VZIeKpKOD1bKY8bpuYBt6EbshS\neB1fjVr/JChbVeLPa9/jaJ8dmFgTj8EJBIs9XXGupBYCz0HUUk67MTrsywYRzYHQFArBcVNA8cuK\n2T7nhGHsS+qLFjQbmQhCe3EO7TbxIAnNFEyZw4qlka3v4u2GCTj18Raie/XB99pKRJQUI9XNET71\nOvTbuQOhGXmCW1UJLM+eIjEji1Epb2Do7j1CB9+PzM2C8biwbgl1C80m4UMfQmF7B0/3lEEtAH79\nneFV5UanPtKT0eVFWD5lBQ592x19Yksx/PYh9GnMhUt1Ay2wdxD2jxmB0wPHsrP+Aur173Gjfg9M\nyToQBYt7eiOqVAxOhErhYDcQJp8mPDh+HDuZJ0KCScc8bdMRFV0kQLc6ODTy6P3CgI+X26GPsRob\nMBOE0eOzbB1ynD+glLmLmCUNEAeWQp/riC0tfoJcMMCbFKK4qAuSYh9D7X0TY8//hfG13tBwBnwR\nshHv+xHt19bhbNkz2PjMgbOjH8KKG5F2KBqTZy0EK83B9gMcKhMJAqV+6Gkagh6yAfgkM+OZN8VK\nq2non16HNwtlYDIiQTL7AJOugvEvg81NbwSfyUAnQYQ1OIdciHAN7riJgQhlQ2HtUIs+o4bB6E+R\n5eiDnkeqoHbah+fvG2FhCDTzlkPfrivweTpcXwPsaxMiuwfRx6ODiMVjBIY/jEXv4Jd4ROR4UpQK\nSaOJDjv7K7nf+COmjt+FHuF3UbKmJ3aR2Si2BEKEU1hGTmACfYtEEo6NPdog+/NW9B50GSkTbVH1\nQAT7OAb1+QNA2E5g+pZibVdHlCx3x6mRXjCP6QiSshZo1IPaAGKlFyzeYyFp7A+yrBu2+M/Bn/xY\n/NRQAyujBD6NCpSjEk8RgykoA4MmLJX6QR18G/l91mDWGwabPvGIm6zC4qEH8fum36CtaMTNcUvx\nskcriEufw//LcaT6UERjDsbtj4Pt71mgbwV8trjDvV8BXO104A+HCiec05luaUCP/M6o0/6G9Quz\n0cBmYteJSLBrfoferQH+7y0w32+Fk58Y1Fs/xzhnT3QuaIntY7/DgYnecChrhJk7T3uUJOJt0HBT\nrVMH2dUNe/CO64LFGacRsfcQItRatNmXjk6adjAxGrzzP42ogkngbAPxLPQJXgQcRLf03nj17QzM\nlByHszkDeZuy8CRPgEs3AS/eAMdEo+jjqeMIWr7DzbD++GsWDxccw9qiH6GJfIf8wtmYOWOFcOTA\ndsb1z9tC67xsRnvrJhamb8WCdeUItH6I+HU3oHf/EbaWkahXrBZo8GRKkwawv66YjyqRDF1O2sH7\nSz94mn5F28bJ0It7g7K9MJiJwTfmiXjf/gPsJYB/RTiEXxbh3n0n/k7vs6xwsyXoU2eM9D+O6b77\n4BOSAWMKxepMiteV1hjo24+6rryJ/upW5FyNE71YLiY9NAI+hpUKTbICRlw0U+ha0JJMHneCzFnw\nEraOTggaasQnRwoFBLRylOOHVYf4BO8k9b4R+2yE1Kninl1v0Lgze6ibvoC0O1BCJuZcRe91PBSN\nWtxAuDAAJcwTphfd0/NTw9L0Ytt9/cSYXhBK8l6n4CoYaC6cRo2DA/gKhXDl0CLmZQuFJSFvPh/2\n6Qfp4Qjnaqzb5aJM1qJp62Ds5FLpp9H/i7uzjI7j6tb0e6qqudWSWswMloySmWXHHDPEEDumGGOG\nxImZIWbmOIY4ZoxBhphJlmzZkixmxmaqqjM/lO/Od+/Mnfl/e629zj7Vq3ut6v7T5+m9n20hH9rZ\n8POGV/am1Q2OcdPSpenmQ5KgZ3z1+9KWHn2nKrCsKSOqQhqmmsYTAAAgAElEQVTI/l/+EKcnu7HO\nvj9geXS9/Y/PLlxWzQ6GjyjIm7lWEbR51zHmnGu48d7r007Rw0Ow8txWHJ/N6d36XrjR3JI1vHbL\nxnutn5j6f4jUSfPjPFH96GDDve8SXe6dkuJTu1ai0+DH/LTpnaggvJD269o1P7tpSei+hCokoTXW\nWZbztiU8J/92mBDtYWdWRQnk7dpFYo0mmCZM/Zmv1etln68nIDipNyLsEfREjw/06eSO5MicI+Kc\n8uPMxEVzYdTk4Pjx1fyGCXslXhU8H3O8mk3ZwkFrE8nXP2dj0K6Vtjd1RRL9mj2EAWgEgL69etA5\nuanMk0MK8uPDCGrf84wcnhMJ7d7vq+571Xpu5/sClS1AYq/QpsFPSOm1K9iGwTQBd0kT2VkophfS\nwKE6LMzd5+ixsZzzzzMLtd5yenz+YOm64MkwTSgCZlfXKfZ9rx3gyK65jE73qZSOQPs4gnF+QLba\nojp3W+MhV6VV5hXG/IGn9famU7RTRhfCJbXvA9OF9d1MiJHYu+YUk1cRbpyjXql1/dkgLvnNaWoo\nKXjhtDJ49cC1VlFcICZyk5mt+zeRCeetbJpPIWuQR2DDyZmYgNG8eWGZ2Pvxt9LUbl3LKrop/fau\nXZihbhYfFvM6Vypz8KjoLBNGeefSlsUS7uwfcvEed5AJa5Fd2SHrF232OOnSpi12e8LbaxlbmC8I\nR0+U2dPSHkuAER89VfZf/Zu42MKSV13sdXD0AafNfsqUr1QBW8cKTOPZ9NmsS2XcGrd57a6VD7Hq\niryUs1rsqK2/+s0jx5GZgwJhvvei28i+t+prpH38ZGLbB5duqR3qHn+35CqPLbSElbvZxYCp3woH\nDzUQkVUxT1KaC5+cgrgpTe41LJl+yfngwT6CsKDNU+WXDT0z1bRupq2LBPx8NeghEND1N3G6xwhM\nibXCNg4gB8Fs9ukSzNLV1eHSHwZs/5QrkzVz/L3MRAfOEgZWZP716u9n0TUO/yQsCBniDZmyr8Ou\niPOh5PIDr8yL9yqjrkaCp8wue4noU7o+bt8uZ8+07VuvHpYPyBpVdxpc7lnJm5aHAsZKDiz5Sujj\neoedNSlFPHy0VdaIublxDI+q97M8c0siXVtsXH9MTCkIoeyge2y3YFv1zGMh7nNt09PKHU9igIyx\nmOfxO5QZDdg9QxMVNNY0JPM7bX/RgBzVStt8JknecaEMo845mYIdNc/DymN7ZfGbDStCJki6tJPo\nqkiYz+lzF6H07W7xmzlQkYXf0E8Y8HxJ2wud+bIAILC4/EC2fcv1PG4jSqjSlfbGdJ4/2trRZorr\nw/bM5/BPQ+Z2OHUVZT4WXH9yFM08Irh+Yj/XS9fJvBZ/ocPNboXl7uVev80wyK32ctOIL1+OMpT6\nzr16Za/WqfrxyW3dOSnlcOVRPG1bVHy1x4c3w54HEdyLGnRg1Pv2Pwpu1bHDUle/6dZ+zLpfvk9Z\nnpcdbpiZb1TDs5B0yW+N1d8nIudVr4XTNl/ciX97/HfVRnTV/4Y+/+PgUbGL6m8ADPlHek3JPwJs\n+h8SbJYAhBUFkRUEUcLzAkupCEAkFBSNsEgEQEnjKtDG1icpJZDiH4DEM4yEZzkpz3Gcg+MkPMtx\nPMtxBJRyPG+XOOwWmcNhlTrsZgawMBQmhsLMijBxIkzMP21rFJDzDFQCgcokl6oblCq1QaVUGxUK\npVmmUJgVcplFJpfxLCfRmC2ck8XKSh02nnPYeM5h5RnexhPRKhLeKtg4ASYpYGdAvEyAtxGc1gIl\nK4LYONSIBBWciEI5jyLyvwFHFQAt/q01SgQCql1dAws9PZ0zA/wbMgIDzJkBfmKetxcnt1mEgMpS\nMbC8BKFlJUx4STEXVFklU9sFuZyHVOmARCqAMUlAWQrIeMDKwSIw0DMUNXIeFRIRNfg/nUcKNDpS\nmqERBlkp8LlaiYI3/tDdiIJ4LRpONSqEAfAkFA3hdTB1KgLiyziZXBHmUeMZ6VnsE6T9EBFJPoaH\nE5nDbnU2GqtqnZ097RwnCy0tNsfk5wots7PY1lnZkraZuXKpwyLUyiFWyL2Ijg1ltfZKa6C5kNfa\nBDmhsDONn1EFgAqeYSryfXz0H8LDrZ9CQ4XY/HzfdhkZzfyrq+M5UcxCI4x5DOAZWQ0CoA0aq53a\notHf4w9AAmVwFXHv6tAo4zUW1whtQGWFfcDrN3TA67eI+5L2tsxFlrvpm4ERj9p0aVLrHuDGVj6l\n9tpH5WhIfgqI7wC8A5CC1dQFjUCnDEDx2g4KabTO2t/LiK6eJsS56f0jamlHroztTEQ+lGtQVYka\nizMvkz7LaWm+nONMi93RCIrcKFBsg6y0El4NWYi0lMCf7Yu7/t6oiCGNAuk/APxFVkMEMAIgc6GO\nDJD6DUjq5CnVppNmhZVWlEGfbkXdW8at9J3LwicNncanIqZcDXZnB9guRSGHNcsMDpXDLkrEIKjC\n/OE72AzPBCVjrq0LSknL6XUxV9ekQKr1NfE+XqjykMFqZZyK379tUms7MnhwfHrL4T6Er9I31f/x\nfrLyVdmz82h/7x5Cp06F8PXXsFos+JyUhLdfzqChfTZpGY8WHdQY6FGPtqRQmYuzUU/sL5snWh1u\ndrlKVEm+S5yAHlkJJNn9EjXKryDKzJJYg5zGWA2wUQXNJJGONLE5zZV4Vr7reFf5oUW6q+5psIAP\nuldA2R8JCbi/ciW+BrAQjQDzKIAJEOEXtFuS6nOD6b+JXZi7/uuSWDH0uQ2WTTfRjnrDx7U9Kips\nkEgc8PZWgGH0AD7EpBcZl14+17+0KDzpl7zdZRDRErgSDqwUAQ0L/405WKMxItgUBAZbARyg3btb\n/gFQ/r+sw4IuzzCr0hulHlWoVdjgXekJl0XbwXiXiXldV9y/dF19UV7ULi+BRKCtgwWDQjhIGCqJ\nCh5R9YGmKXfnOIUXR0tuBRjoxdIehKMSOkB+zzbZdoI2M6fIP9AWgDcn1LlGOmh5O/nGaUvQOqai\nvp2ns/HIXol3Tg4k3Oa1llrXSGUXPE1biJ23NdQw3uUtKlwuQdMwHKG1voQcOfgTvVCdRxHwpwB/\nSOAHBHMMfu0Aev9enFV2YY60f6Uzo2Y+UbX6FfGUfiDuuipUq1yQ6xyKMl8J3db2DfHxEVF1RU4/\nfqGEa9MK5k85QHAERY+RdoTkmZC5T+udLDrkmVJuRpsE8ciQkWl5HuEh4HROKNZb8MqWh1cXnZH1\nwlciBjAiuvAiejAyV28idPoNcUIqbiZmkW9cNtqJboB0rFgGZ8aI2tbJosrgyqiyIwXWLpKUeDvJ\nCDJXvu3vISkItN+n68yleNtrtvqrMnmMYhN5+2gO5NprGCJsEoSCteztub0RGfwBz1dNR3orHqsp\nqJUFmTKFgV6vwrtXrPjobyNTaWfEPbXbjD4O6N/3fiH91WOMp0k8Bhp+D5zOHbxTHbjylhC0BVBY\ntNj7+ybcdrPhAZcPa1kifJlMcaW0kulnEJAFBi0FAa+CCF5JI2h18UDiNr0I7mFFWLp7DUKNFEvL\n7TAin15nq/FSXE5mkyy0jb8J9coN2PV5Bu7V7EFEgYiH9ykeRChRqHXHyEwjho33x9ozmRhYZIOe\nAYioQl90AU8SEfL9L0iq7oWRXy2AXpmM59UsisyA+oUT+Ld2TEnoBc/qEfAp9MaOCbmIfXUPHVMS\nEejpg6YFhUgLU2Bve4rHQ/YjNEPA/IMy1LkbsX6VM/ox9/Bk4i9QqV6hvCoQivEZeJq4Dg9DrWJp\ndBDd65LHRlZsR9blYSBbUhHqsCI+iUebx0DTIgfOQYEMRolvIodhQqEOQ6b+iJ/PlkNtbQ82OgnG\nlYdQSXyRKO0B4WUEXvu1x6QL6Wj71AHW0QJXYhJxovc++ORsQfXkIBidFGAzZZBVzEU/cx5aftUC\nq6S/oP/fPyPlXjKq0/dhw9oTcNHpYTswCde73wU8dGipbIXebW/DQu2w/6VD0U1gZD3FgW/U6DrZ\njudF/SHbocbAjJc4Sux4woRgtTAfGcpcRNs8IBEeID1wKNa77oCMWNDBTcS9N3bwllpEtYjD3G+e\noU2qFNXj7diSOwPP3ePBfl6CEY+7YsjHkfjuiAU+Ka8R5H4Gk2MaMO3oVDTrJ2Ky8hYi1Ho0LOmC\nNfnbkMWGo4XrGwyquYOJ7BHs8bBhR5kV33j8hNv6CBiE3gj7bgmyvG5DkhpJaewH0rXKRZxy3sDM\nEuWwRy6Bpc8eyE4vBa1MhKxTOcyLfoBnThoqgmPhXGaBwewPejEKbEooQpk8/NSkLVImOWHgxh3Q\n6nJgorFwUDWeUnd4++8Sl1ftYLq1DEOvL97oixIMG2lFCsuAsG0g6bkYglgJ8dNvoEIKnIrUMCZ9\nC9rhPZgWUnQrH44Ltxcgc7UIx3sZRSQVfa4K7KcsBpNHUMxu0xabFcuw5PBT9L3aCUZRjbVSJZYw\nC9HC2YN3xmtObTBgomyPqHV6ywyt7IIu4lx8v2QWmrS+i71Xt6ChjxKCjwNIU+vDdtk0r/IGYkf7\nWBrlJCOTZ89C3/G/ItuQhG/9FuJZ6yS0TeuKv+IeoyRUirp2SzBl9nt0VJ6A2tMJk5ZvxBn7FNzL\ncqLFR7+QF9kUI1SEfjtNhQEJ18j3h6rxors7otPTUeYJDI08gyj3VJw6mYAzt25C0vQR/CsmoFtI\nuH1NKifN8RuJvSuO0RslJWSKpRbn9xCYev5FZffaEKmUF3u51TCjrK9El1PTmfQKT6obP456iO7M\ncqxEHbkOeSsPjPHfJIgxqeTr1mWMcuZxHFTtE+ImqPj1m67JRCUDaZNqKr52I51HNreGO2fL3t4E\nKVcQOn4cpfkXZPUp2X7ar7r2J/FzD+FVrYfj9/tTGOJ/mnV5sgCcyxfHT098irlfD4XeSJqAR0wK\n4PcIS7IFNH/ciSpc88jswQKiK8LoC+vLLyuVEC56to5Nd+gt8v1v5Tzbhlm8Ig/n2u/EzAMPsPDG\nDZgBzJNMe3FLeJB8jSmcHSgXGRlkONTMgV2vXOEpd0a4vANe//5NTbVE44Zz/iboZAy8S3jyRy8N\ns26qZciT6qJbd7dHjeGq6D1TCPmVJtGZx+qpw36bBKXlCpndFgj4ri8bPyGZ/Ph8KVspf4oTYU56\naeFpjcWiMW9rcSJf80dCbKhsDzLsq6k46voDcmVQj5rWO6yL589Qvlk6kcQNkaAsiadBz/3IHtlh\nx9MEVtLvhV7MY7IatsiPOpXs2imR/LgczYrthnGtspzWdNCI+n1ZOLyjiyE5L9PZy4vUHWehHXJj\nNlVkDqO/cd4M/3OyldQ8Fxv2HSoms7jIaKuKrO+hQ0kxEY/9HkfetqnHrstzyTWPa7x3m6n1A2/4\nuE/2aEazU9tfrobzyG6yxw6HKLF2CvnZ6Yjud1p6QI+b638sVKQPDep6fBipPe8sehYzZOSO5Twu\nXyyo/ViWEj92zIjby5czl35xFpK86yubSszeXy6qxKe3zeJv9rnEKPTmnoS+x52I9LIlrwf4FYd6\nUEnWKscW0yvRkxHlFe4RQlXdF8LdfoQZ5CAdyl9j1Z+pubyFSjnZfoH6nH8ipl1cxIJKBbQ+zD56\n1eYRAbmdEJHwAVVIhJ4wkFNhzFLcDchb1u3S6Z/Ld0le+LVmBsrHrXZ8qpAi9sti5E1lN/ge5Zep\nY5gUdHRf+OGc8UnLnkvx1zsZ+ne0zTNW1QgXL+87MekNd8GxYswm+ik8WSp17kzPrfgp76hBE3on\nfttHpsWFlr3ZUzWLjgakDz6V23XMsyT+0PEj3MPBTWlxc2f4P5YsH9E8a4NZbq/7Y3tPbQ1eoEno\nCqHaTVe8Yr5nQI5PwBO6bRuDN29aOpnNYXqr9ToF4mtVUPgH7EFY9sgBS9Svv3XzNI6R7ZkprKvb\nvmfduMjsKY9zPy/DpofPaSeL6k+V0/GvB5Cg8l8uHQr/2LVg8QFnMbn1HdEpy6cu6Odoc/WyGQmV\n0VtFoPBTFDr9tLUSfpxR9Ns81UYtUC5ervgyZGh2tP/K3T2X+165wHGCW7BbwVbVxIlL3nuMJq7Z\ndcI4/GalkgtKOG4AmFwA8NEAxgHYA4yyE+epLjfdSmiKzousMcXaPeZ0hklVwerBPwPwBFdO/Yms\nr9Nlk7vldovIiPj7qID9G4GzBTJx/Brb7pH1mGdSe2W+Zc41KYio3/577PZF3cqSK+Mf2FwC0PSv\nJqR06L7ZfdBpyKW63cZF2poLrcEVSIzrNwz50+O6pF/i6Wm+yy0bYelWVyNpf0Vl37FMMTdw5mY2\nYFDXJvcK2/4gvMixYU8UXK7V4ZST61fHCxzLbjXV3YDUcwS+s/YRc80uvnDdf1hOdrwIq3u6OU27\nry0e+ucf9imftDfGKyGDHnkTQgq37ccXU+kzqOd2lk7rSajTXfNqYYSYyUX/9khc0brcivY0xR/D\nvbo6vh/wlyQ7a2QX331frde413Yjc/ZUD93I99ZVmQs13J3vDGbPnVETZsDo/7epJC9W6qTTFlxz\n9Y6wXBqOy2PtKJA8uD+7zeuBplLXwv03F3mHupTCxeV8Eecf/dfD5i0mFqYulq870hbj6Uto7cYx\nLKV/4h9QM2nAhMLx398MPLZ/Pu7ocmhD10TiWtyEHumXQ4yFnQInrjpf/P+rNvqvvOV/FDwaNgq1\nIoEoElAKUPpPLhKIlED85xoAgKEgoCAMwDAUDGkMMPiPnLAUjEAgOlg4eAYOngHPM3D8a+9gYHew\ncNgbVzsBiNIBlZyH4p+Qy3jIZQKkUgEyGQ+pnAencEDgKKhJAs7GQbSxsFs52O0sbDYOVhsLq5WD\n1crBbJbAYpbAZJDBoJdBT8l/uI3MaKwi+q97Ho3tZb4AfLRmBAboEeRthK+7GR7eBkiCdTAFN8Du\nYwCtl4MvcoGY7wLkuYLNc4WswAXKKhUkIvMfLWuGf+LfvUYN/2XVAWjwMMHYJwd8rhZOb/wQKDJo\nAvxHRKDRJZTxX8KMRidPNKGICm5A05hqhLWsgL1tKczNK8H56eFsZ1ErEtTIeYQIBHylGhW5rqhP\n9YLptT/sLwPBFGugBiUeUPh5QOGtgrm8Aca6Gtg1RpjdLDD6OKALBKkPYt1qPJRedUqNu1Hu4mwT\n1AaPkvry8A+GkqhkidE73w9ADRj+bxD6HI3TyjL/nb4CwPaORKmxYYy3EYODGtAmrB5e6R6gKd4o\nS/aQJd9wbvkyyOD4ePJtsjqqFs3Jv8mxzTLZ6wODB1cdGDJEk6/VdgQhURBFgvLyIiQnP8bFi3dg\nqCzACKgQgkgI0g6waXpCaghEXSRgca2HskYNp3IZOCuBwccOk7sJRhc90cuM0RUmvntZqaKjzuIa\n5HBXV7nkmrKCeC7Tz6oqc0RU5ZaPbCjMmMsKdvdANALNgn+iGkCcFrWxU3C8bCJ7WB5ICrxuRIvi\njWhk3I7AOqMMN9DtsQKNVTOFSEgwn4RzyzagK0Nh7JMGte4w3G2JEJxrFLVOdlcVq3ANFC0ON+JT\nN8jA1A8prRA8OasEfuhSY8egMhFRBjWy1R/wxu3qRPnR4nq955h7pE9vYUwZFyH9QsdJD9XVVKVW\n3smHMtOAIGKHQ63AdYsMJwOVyGsjU44llAzSsdYmxRZICo0MEcwcM/BtDMZ+7EBdG7oRjpRCLymA\n3N4DnkiEO/uno1ZuKwFlkkMtuodELmZkLoG1OgF+aKxSCkCjf+lWwhNEQsRhmCDDRRShCB0ApEil\nuHLxF2j93DFf+w5Vzufw4LIddSluCJ3EYZTAg07V9Xek8bM5oBvLuaZZnL8qkuis1Rb+tcMZ5pZk\nhj2JrnFsJjO1u6q+BAaVdfW9K7aPuq8NafHRk/rYlGyJxP5y09RHm3I20IYwrivmZdsQaVSCFQvB\nwH3aUch736fMhoWlDz82sRvFakdvFBcb0badt/TyDR3/+0kXD7jbYhTBXMyQTLmvrquj+P1w8jog\ng0mv8CK0ciiROXxoCKs3dld+sffmazVqzsi5d9oDscNbYmgCOLRAwYt45Gw6hFhGB1VYJp56N4in\nOySSKuEZISUKW7TX7Efjn/RucM0vCTm24XN8sk9XXn3iUH2s/bZ3RFcwtfVOtFcZSOs+BqCIxYe7\nMeZXFaNIF85ZEb16BcTDbak0sZ6EyTLgRkwwRAP1kbDXR8Koa8LJHK5EUWXgGHepiPzTfbHbPQ11\nuhCx7vYOBp67TCg9xyHAz4TKChdMHkdQUCogNY3Dtq2lLeszFJNuvnZ9Sz7Ts+ZKgsTVAKZCA558\nx5SgLWdDtV2Jp5wTkmUMdbbVkEShH6aEjxL+allLB8W9Kl4UVBZoe9Oe5P/VjeEi8gS33kmsPLoY\nd95FYK/HDMgM1ULC8xdon9uP8ShvR47Vh+OT6I4+KEGYXw+80uSjoN84NLQaCGZBJ/i7HkV5+XqM\niKfo7RSNazUxGLMwEfq7TanbCZYIeI2LCglirYch0ChsRgU6SJ/j2TIWlPwBziEDjyiQzzasvdMf\npXw5TqhMYnhEAPMh/2uIJn+o3AowOCgRkiIWl8pjYUJ7QGkEG3IfY7nDmNvnFT5HUSzew6BzPgOF\n/0i0rG6BO/pKpGsmY/+RDtj72QXFd6pRW+0GcYISHlV2LHi0AS0/a0Eg4n3fWVgVXwafK0NoTukS\nQQgL46BpQNPi7tAUlCKhI3CqPdCCUWBYOuBxV8R3vICx6I58ZMCP1GMScUVrsQGZbv648FVH3OkQ\nj6TIICgrP6PN6+MogxXrz+7E2YTTGIbX8BziwDzfrQgh+Zhfuxd1Zk+cubYY6SQcM9XHsadva1T4\nxoDVM/A5mwfbm77oOHomVh6vhI1w2KmbhavMQIRuOIrd1y9BI6UoTKiDpnURaj77Q3VkC74EOOFW\ncBraXmiONjYbtByH0gAVZI4viCn7AzOEvWB7z0NS4FNIgxaheQsF3OqvIT2jBN/umYJ9u8JhvXYJ\nKCiBo3UoTn5R4mbKWnRbPQutNC9QpVXBLGphrFUhq7QTuhwehQ3DUmHxr6Zb7adI98PlmDV2FK6K\n8zC74SB6XPwDwwU7tuI2qlyysNn0E04IX0Mt/x675zNQBF9Cw9WzqHW4YVj3Kly5cBXErsLcyLsI\n+/YITi9shxu6u4jbPApPAgZBTF4Md5dRqPbxBnNpOY4O5yEYOHz4MB2XLy3AhpjLqGADcSnNFz9W\n7kQX8QUcHXQ4ECHHUQ83xApzkProMeyplQgIWweinYH8lGLQkc3AR36CRDoOyodF0D9+jAAJxcDp\nBEfTgmFrVQP1b48hr2uCNqpMpHV8CFVKjPilug0TDDsGyGvxtv9yJKfMxLdVH/G99yZwE1kYDu2D\nziSCWDXY29Isfs7tyHTtshsLr+2Au0aKkQ1vIOmVh9KoI6iueQghggKChOfeiVzwR4Ip1Z5YTfPA\ny2tBl4ZAjJ4DqWsXLLr1QOzVby/jqJZiX/Iy+02v3VKZrBkVWswn8qytUN7OxKs7fZHPjMGlaeeE\nfm5vBI96PXe48gfbcOk1eeCVIeS9izsyyVphlr2BdSYErbYfQtninhADHlK+4CsbuAbpft1PTL0P\ng4FIw9LQmShqU4K8vUfgPIwDyeLo1swpeBBfRJpmdUaOQk+PT/Mioq6l0MRyqSSvWXSQmpOiS7EO\n86KX4v0lmfi0rtb6VQfICqu+YZPdR2LhMSB93m9iTPA7Jq8iHFZ7AP5070JLVJ4kcPMTwdqCRVX/\nLuzwJ4/LEi77eMZZ3rAHttwkx8pYXGgu0NO3QR5cUYgrVOcZlUOKu63eGeNGH5S/u11Rd/8B6+lO\nXbGz4Wc8R754yf8SKSx/QebMW4VACcvfLWzDjD3vyyx2jbJSQOYYAmLzNcPnrIPW8gd5Y9lViWuQ\nXnD9Sm7O87Y6bvlD+ezF1PqDl997T7J0IrpeJRUD5l/33nJwHZJS4yg3cgI5fv6bjw8jnWIGL/pV\nMvoBA8e7eIdb1kbJtg19efcVUZy6agVeNTtMtw55gCM+sP9ugeRlNZtn21oS6u0QMHpBLHPwoB67\nIptgzoYNcCsuQ+rc+bSEdxBPEDgRVlw+wI855sGLdaSSId4DaLBnX9KwaAldppxvO76y94+ZkWQX\nWEqxd3OaX/IL0T7B2uzsvoPMTq4VrVHK8S5WBad3heJWFWVn/26wA7zo/XNcZvBHSdNXt9LZgfdu\nipd27yR+s1Sk4dpD4c+9Xz9wdanpwE7faRDymvjhhwM3MORGpOfhEHfv9EJt1I7TGH/iPNMk5b0t\nq2iu4VfrL9oIuT/9ye3Y9WcBc5pYgr5EX8/rAHbiD+Tw7Ay+O7ubq/KU/z1ncXW3lDe9efX9o5ID\nB1uUHXnU0ymRe+fkd/Rv01FJukrefdtLfUFqx6E7/6wTFy+p+7X4o2LxPImfIt2p1qLVcdAqnIM/\nDLXtuj1MNmHQ2QuG+bNG/THO+uaVwqv5HmN0Yl1Vu64OVDvLYaEiR5kZA+zGWst+3ayJa7yl046w\ndQNfX/ces7mPdqYgvTJ4tHnjyElqCoLb2xbaO//9mXl+VeBeV7EVbeQqb4VAng+e2BC81ga/TnSv\nJZfRKL1EH5zUbq8zTR9S5WIpDr21eyNRqCApt8AgCNZ36KbrIF2VIh1w/q5+SKcbrrOyDlT5tdKJ\nwujV3rmaozzqmnGY0t0R/3ngtl/fJEwH0CZhQUJ/PMM+dMPOx4dvvPsxXHE21uK4584k9+pae/RF\nZ33hh9jVmBtuwifdennur0z8kCRfWvSpy8tP1dXomV+Pz4HDEF9e6/Myo/3MoBU7V3p3y+jClBnW\n0nWHi1+OOa8pCUtU9J+IkxyZnCtls3LZZmO2z7k2+9a22qCPko77f5VsWf6jPcLD96pc2fT2ueLn\nB4511Kskmmqb/5EL8nd1XwsTf15qHXusp1zuf53pk7N3kbKB7gEgp0BPAJtsGni8cIXHn5YDpQOq\nmrlrwJ8dOf5G75Gjs2QBylqJAhZ3EUwVgMrFlvXBbVYEMFEAACAASURBVHMKFDmlfs9r2+e3OyNf\nNMhdVF4R5+whirwQKxr/nL8MIMUIZlTaV9XaTcsgHBJ+YAqzgy+fX/h6aFAP2M38Gi49XfEpMtI5\nbsWK6dS2Ci8TvyzscMLwu3jAukdgwSZ9M6RTCW5oRkLsZgVSVgBoBsjHAj+xPQNGVs8trvXeFVv1\nJrlkQuslAzqN2xX74HyNnerReIb9gAcbYxUmHx+PhGkoFh1sX4IL43ty38z4bczMDZdOr5kFuF0L\n2kqUFa0Zc8QPeeej0kP2X4ExjXSqm9iufeC2ZWds+Yao6vIjYI4vWOZHv2tPZ01faL385xKpxS5l\nG5YVU5V//TaT+dMS+8p11jF5Y3QXb09OWPpTzUf2Uwa/CgG2UGxKnR28qaOb4CoGFZPBt/3P33he\nckKaBLql7wA+jFDPYRPnVjO/X4z8Y9iAottqhp5RvemEuR+jHuUnzumRKB1Ev7KmmvmN50X5jtlO\n9o5OYGLqKgWBd0ImqxwToISnJLp8xf633ndWDtIrBJHXrlhE0lpUK6MnrZeWFUW9XnZyh7NUrY+J\n1v5MisszKj/PynpKqt3DqVJstY70L3r7THw/OKvfUDbWBp8pG8Dr3LH3ck88evGL+caJWJk+JXrv\nqmaj5js9eFfk/0FRU5beO+6Cari5nLPU+ugknfsGbd3xpfCb4eum/oj2fe8g53VCxxkX4IbWdZcR\n+EU60hYuDnTRzveK+X9XGwFAXOTWzdmlXX40mjv+z5q2BlA58B9VRuT/kf/LfSTiv3cj/Stn8X+2\nrf13IeL/7iyyAOApBSVryL9a21gAVrqK/jMKHSz+cSX9X9b/36S1f78mRePh/z+1UP0TtVhNFAB8\nIMIHeoSBhRlK1ICFDnpfM14tUCNzkDPqwzxB2X8XNvv/c3//Akr6/5ybLEC5ABQLjcPZ3GxAggVQ\nOwNwBeAKxqGF5yc/eH72gVuWO7S5GmhzFZCYAb1fHczuBbC6fILN+SV42T3696qSf32/7QlxSgB6\nskDTa4A1rfFe3SUS+LIs401phJ8gNHUThGgV0AyENIMohjON3Xk1AiEGE6W2GkAoA7h8QJsJhGYC\nkko0Ai0dGtv2Gie3ESEGXqktEfDSDSGJFgS8lEJqYlDjWoFKoQzO4OFp94JSHwSLtgFVzWpR1Mmi\nyO7CdSmzeXaibzx64BFphRRGBEPSEWMoQuBnHZxv+6PkZF96t4wQ4gdglEKBKQBCOvXXiGHhjNJQ\n3GApK4O1ogJiTQ3DNjR0U/P8eA4YShgmVU+5S19o6Ik8eJhYpgpKaR28RL3Gz2EP8OS4IEGpDHLI\n5UGMhPOTUPhyFnMgrFYXyOUlcPDFDqOYyqDJUwdtnmqGb5WasbJvW+kct47cRE5cBTwBePEEqc2+\nDcMXD3YxFCWt/VL7Wkc8CVOPsySKwTSfveLXXLjsrSIFxTohpK5U+p21DH0h4ioixFMYaMpHF5MF\n4Q4zfDkrNC4UxASQXBbVNQrcaGLD04Du8DdNRmfRG4r7AO6fmMqnfhiXPqMKnuNYCJweGrNSsBSz\nxwPTKs637UEp7gLdSwiez4gn8We+avaV5UPwx+6ZvlmxDeo6ZUBNALR6L1FmUxGbnCMfk+eSUK9q\numTRdFpmZStyivxfRZ1tpXLROcV/iLj9MVTSnETSsHg2MsfIdHqZT3zKXUEQDqDMAS6zFH7V5fDh\n2+KtDwuxM4AndhFXRr+Gtt6BH8EjEc9QH/4Jg2vq4C91Am8SYLeZIXONgEU3BKruT13o6jee9ngU\nS8vRz/aBnSW/rfZ1PNf7mFzlerW/77uiWfolnoH6EvVg4iDSWIJW3TyorGu8WKqMctRYPAuymfCn\nw4Srzfqrb7a7IQwit8uH2x13/WpqS31U4te1GkmIjok8n0ZzspcQrsICcz1AEzpbydyfJKqnySab\nwkvV5HNLMTCD8g2aKtmXTx14k9mJjWKKxVh1EYl2SbYnG08rkhpSyVkGtJmPTJcT4mH+LB+uVab2\nk5f6sLgws04sCASdtd6dda4XsYP6UrNFQeY1/V24+tdllpMp0bRVlHCrzW3BJtosxndWladayzhi\nKKnRmoiUuiIqs6ktO7efZHhQvm3i6MMKdY5KtEfXM573GNR8xSP0MMDVyFAWLVoLQnmBi6eqBiMj\npqWp6acqDZsWNUosju5OWJ4HceKIP1Nu759USJM/vGDz3PJNhrP7kqT1YZ38nEczBeZPUhsrB2FZ\nOFROEOrqAK8WNLpHFvnrjg65IU1xXrKf9vkkkkt+brhj9oWhzAVa/zJ0UqUiobYCMyp+Qi7zPXKG\nEkhGn4Ky2oiK+zbMlhN0y+wjPmhVTBTmOhJvDUZ36gN/dxELW8dDZ+Xxo/0YgsJl8FZn49XHQeiY\nroFM04DEPiJOyEej9akf8PBPKQThKng+FnL8jtmqDVhiqUGVmwNl2yk+PpXg8L01KKz8AURTiG38\nJ/g3KLAF11FMHqKeq4QIAi+2CSZaf4A7E4I1bh44eKwDDhfaIeMDIbq1xvusGah72QSs6IBPaQZK\nc/dBxqWAOCoR7uqJSFkplk+iKAoDxpcCVAQYowSME8WmGDle/jUbLhfTcNZ0GzsxBTemDsX1Fofh\nkf8MnX5fgje102CBErbYBzB9vx/UEQX6KQOSkEfwAAvT73Y0LwCWi0CRRAJdMwdWfAQGCQrMJb5o\nQquRDS9wLQnKchYDDh8caXcTN7u9QNQLT/gmA9MNP+DY0JUwqfMxonY0mk09A6XFDu8ZPui7ZRtq\n4AHjs2CQ7pWAtw1NstMw++41/NZCjpfcR6ifO6O7dQnyddHo4zwI4tvBOCzZj6YdH2BZ5znoc7IA\nL48CtV9C8FfyMHp+RFtCGrKxdo0rWhVHoEBmhYfZBbeUIvImbkNS7wWYs+gT5ueuwMsO7bEiuAxf\nbJnoSbzwIKwK7CkGQvwQqMb3w+2TM2Gf6UD91g2oro1Heo/jqChpARtxgiKLRXevDdAsq8Wmov4I\n+jQLk67YwO6dCv6TGpUHvTCq8hVWgcNqOYN2y4/yX6/5i+nm6MccxEx0d9Vim7EcXt+vxJqLkZDH\npmPeV/dRY/qICcVdxW6FbRn1q8744P0cNUP3IDySh3RjP3RvdRn7Zg/CH+JomBkpTj7bAa/ObwDW\nDv+DQMabAbjZviuuPxuF2gQ7hMn5wKvLcPo7EDFdC8Wfup9nfuD3oO4hINk9DAq2O+r5D/hm4lDc\njukEsno1THYRVtYBRHiCKbXj1HiKL1wNrubHQMGVIzmyAdGnbiCysDmSIBc6MDqo6U32gnoHxIlu\nsAfJId8KsLpbuG4fjrednsHWJYB2ubicmGbuoMfqVbiz4TnJQiBuqWw0eLMzmZE6HKUXNmBq2GYs\ny9qGmSyxBpns7PgAwjwcNpQ9c2cpLclsRn6RPsfagDw4RswC2p5ASBIPtnk9tdgI9HeSeGP4NQlt\n9Su451vsp7uUSau2LUZNnRbf5H1GnWtHerijXldQESn7tvtvn2OOf93aqTaLJDXdZfnJwKBDuFVx\nMluKJ1w3jJq1CXRZLKQSg10DszTL0hyjlvyM01s20VopRS/RQMpn/ADExjkkSUttgnOtukNeB/27\nkPca+6cJAKmZixnzd+KsP6vgf8LvyfUYu/dnbLiXiLi2h9Bqbj3+dg3A9O0HsbDyhC0++J6sNikK\n6+7MRr/i5hCD76Dz0nXGhgN+7MuYwZY2Wbl87xf3shwSrkOZ4QJbFL9SdK5OZkpXQoxObUOYk1P4\njdE/SLI/qSxWqaE6Okr0ffVewnVk5eXjl9ncbjWdKRVWfzaNS56iYsYcpQtfmFBccpeg3ResfSHB\nFzC2qB83SAVjHL/u69YS7eRfaxuqfnSjriqHaNYYm3Raqspu/5d49oKk7oz2IP/tkpWB7967Vx/Y\nUOyxBedwZtAZ+9JZZ6Wz32jEmsQlopCwhpvtr4VdsNiOLDVwMBNW6vMas0YeRM+UAOAJaB/sFDZ1\nlek29Kh1dXHgU+Y2iW+C/YNbl/bnmfFD1mPZ/kAhx2hE7XIXxhS1UwirrOTSp8xApdK3Puq37S60\n8C0sickEvMMh6fiJW/dukzHm3kzdZJnc/5D9VMWZYYHznmlelBhO/ny/X7u+gkJbr4mz2gwv/GVp\nac/3ts1xTbOjsL+ke0OhLapjNQqiFJdfHWs7Xh72xla13yTt/vhl9awdyW5VLoXsD2Oq9ZKTKZvu\n3/DpALOiH7IjnqNFaksAYvx3UlOqeYJ752VZMiZ0EVM6eqItg1nEzLDHWbPQX0NEWCgDRYdWoNYJ\nJxH6xq3w5B/fBc3t7RB/fmv0rp6I0t4ukPid2tGQlTvflZnYBZ6Zqkw306OInaNa88KGhUwcnUTH\nrF/z86Po6AVeI0YwXSd10l/0ehQJRQBVsMvejVrcta2b213b/tNNxBb5+bpSTczRw9Ow4BehmbSz\nvWrkJgy6boY/vYO1QyeNX3d+aHq7Z9N7v+uVEyIxxk5blInT3+YUFBiGf73Fxv22v3WWtsHoPn5R\nqrhzFufWsZODz1USiXb+/lynY7PcVq/GmLdPcOc551N/ODqo7K0lLXIZ6dS/Q1X6vaDW4WKb58+4\nbLlDrNfjHdDmT0hebyMH3tFQn0+63HnORuao1llT8kVjmLCa8fJOEcsq3lvg71mKnstkj0+tPgig\nx+qRq/s9iX3S/9qWa3/zFo/MsVyc2/mNXfndj0NkVxNvNHPlrU8iNyGlRkQf5iD0cXVrvyv131m+\nen39y8RnmHnmOA5vGQ7L3FBwkLkjodj7/P6Dn799j9N2M6JvhMDYfT42awtbJa2xdGfXTr/Wjt+f\nf7OuAXHVn6Vzng7/2TGzLsgqPl45NztPrfhjai+31aqMsfvCWG52Rn8vMuVLlmXMxT+l+vUuqYoF\n22Oj7dsEL/558D/nkc9mX6xna9jfPtk3o55tmjxX6Bj8BZrOUDv1lJ08uOmU+yKlGco8M5R+88Ud\n5rjvl8jK1q1Ul9SHDERo1hnUPLl60JT8MUhbvEM2d2ceU+ldBsBTlNrcrk1OVN0bGan4KiczX/5F\n//BhTLvhfY98f/7PNMzoMVriOHOmXrpnTzPKOPKTTu1Gm4Jcf0SH+m1dnrv5NYBLT7qg2WpNwHvc\nNsqBODvgVAtc8wLxZnbAKIQOOi84z9/uNHs2qnx9cWpsr5ix31sytAJoJYA6RuCCqN1JLf/gKlgf\n5/HUDp/dZ7zSk6tlsnMLipamAr+GslLZi9Ce9d2y77jmKSFbl4DE3x9VJ6zY1sXUKagodax4rku7\nHZdHDNTUX14cuQb8wSZUMe0zaefxAAd2766eP29uVZL9pG9lZu5e9Xn1L6uwavDyv+IX7x9Y1E4n\n1FrDEaH58zu5+c9xXMOSbXhZkDi6RwohHka/Rc++/XZJ3zO/E0OvCfG2YT2SFQ02gl2JzbH08Mab\n74k94TdNhbA8ePr1bfTtt8Uk9BW8FtHw6g/xOYo0BSuLQIJXC53VKxNr2qTIdKuQ82LEttkDUouf\nWF93u58R++7P7LffHP8DARja9ygdWCOKH3nX+rXPZokIxECfqYoXVcWBnFARmP14YWLUsMOd+dMj\n0piiNwn0p9xM4mH2fJ9791F+l6C33OpdvYf8drOP/VXCQKEsf4W3/LNHvcH3PfPLW5HOfMOQX8Tp\n0HWUObzE0j7tetofufrnwMS7S7/5AaJre+2HTQNqm0Y0TcX/q9rI3f13ZxVHyoqreioTWu7Ao5Tt\n/9PgkeAA6D9giP4DgP7T/l85APJvUIn8Cy79c52QxucI0/hTWuQBUWhc6X+Nf5xH1NH4OqIAiAxg\nZAAjBVgZwEoa3593NIaDb+QwnATgOEDCAQwD2AXARgGrSIgNgIUCFgJYKCEOO6W8mVJqBOg/lUBM\nA8DWA/JaQF0DeFQCWhNQHQSYwgEaBHA+gMKjEeLIZEANT0gpKC3hCKkWAVdCqT/TyIc8AFSLhJTb\nCam0ElJhpLRSR2l5DaXFNQAvZVmVO8PIXQClhlKVShQVclFUSQENYRhngWGcKcNoIIo84flalmFM\nZoCvFkVSJIGkVAN1aTD8y2Phrg+DmQmBidNBwt2DtyQJrppKCOEUfCtAFQoY7ISk2RkmSSGKKTIX\nlzSjr281L5HESICmMqs1RtrQ0Ay1tTFQKHS8l1ee3te3sDIwMLcwJORzTlzcoyK1uta/pAQtS0sR\nXV4Ot+xsGPLywJeXQ242QyaVolgU8dluxxdCoJZKEcgw8BdFePE83Ch1YhSKWJtU2pzC2YdzeNul\nVvdKlhqdRbHIH6RM65DapKVaqiyJgldZPFwsoTArA2DWqCF4MbD52MA2vIR38X14k49QBpjAhTBM\nko2Qm8qWLR9YhwxJNrZtK56USvEHGqeEeZ0792Only8HjcrPj+2pVBocnTpdKG3bcR+htMjH0CBq\nq2oIYzYT0ddLYDycJXovhUbvQ30siqpggqJAOQqDXFHhLUOVZxFs8gIGFqXobGqm6HA5m2tzk2Rw\nppCUarh8SAZN5sGYwgAxGNSpQWGGUzPeIC3QCLAQSbEPVXywOfjSWljqqEQm7+xowjbHEJtOMtT+\ngviREliJHHvoXHpUMdlS568UEWyWwYkvgFR8Awe52/Ve5oMnhl6ujkZ5dqkE+EgapevLCZiBcaqO\nSfO+ivP363cvSnQy0S8fRtUf8Rqg0LMS0uKxvKTVB1ollGSr94sNzfQYx3ZmysSvXD/z9d4ZbKFP\nOlvM6cUS1mEod1LlU7NbOt7MVaGyeVeAmdsVfz/rE3lpeFDHt0NVrdLaaCLMMtIgoYKHAB1PzMVW\nYrMURjuFvOrGoShcuBCvNd7q50YcCrkMIp8NRqIDFfzdK05dGMKfDW+mQfsIJ3i/rgVO5cOqb4Bs\n3T2ZLaWu+fv8aifPKGUUKWhfEPgm6o188Y3F9qCy6FxeaqsTWj0LaqW74x9UUo7iMSIaetshz2cR\ntVkGnmiE6rayyzpd4YVeD6A0EIwGRVf4yvPQM9KOLnG+DMs6Yks/1vTik2Nq80V54X2QygoWuYiA\nNGYuNU2OA3wdtOlps9lXJVE9GicS4aCFSp+2EajNmfgFZtPmbR9zJX5uaFaeKc5MPo9WhdlMg68M\n1W4a4W14jP2YI4xJunNV1iRqMJjvhiMvkhEtEo4fc8FRO+S81MMisZBybZX912mZkmmvihl6PQDr\n6FImNmqOdcfMFJv00FTekhmrvRByhWQ2y0CwPgix+gAhKjeuodAQ5rKCNGcHxz3iR/TczDC6NMbD\nYAU/wAiHBBAsDOXcKVFksXD7BFjStHiazjjuNG+Nv4f1kth8vNH3yhVx7PtE6NR6kqeJEP8KiKgz\nhEVaOZegAJ/098h0vo3hJd/AhcmC7W0UkoqV8GXuorvUglXxR1DTcQmkai1mZYzBimfzYHez48b3\ncXASq/D6HYMHJh8YB00SYxsKmHNrNqJgFIe6wRYYSzWQ72kHSXI/UDSBQVWJbO98hFU0hdqmAK+t\nQ2lkMt6rnuKPjAXQr2FArfVodX0Npo6lCPHUo8HK4v3ngdjfdBxsc+dAWuQBGzkOtGQAtRFIigDp\nOw009jJU79vC49nXYDx6gze4YpahBh1d3sNSE4qU4M9wrwjBantb1BMLIDphjHMp+jlqcXryBoTE\nv4abkyu2sDsgqTTCObcQPx7ugtvG07hH7gJuWsRahiFVl4zAgGr0bNMf958646BhOXwWAHX+wGUB\nsJs16C13QqABMK+IRh96DgzGIUxtRdYva+BRb0NV5H2IeWcQVDsCHuFPkCo2gHeYQNMToIINc+vT\nMeqzDdYqPcaJFMEOBzZBwDwAHSXAQmcg1ykWM75dB/6GHyyfIyAf9B2FeyZZ/8dGPIIvIgQLfGgx\ndip2wtHyI6gfEKNXIUAxGRfvjUWxoRl6sI+RsYpC6VKFjX+eQIBPOnq1kKH17ZE49+ksJg3sjIM3\nbuKhE4s1zk8Q4FGFeoMbmjZ5jeZtHuDbNR+xo3MgDrTNgBg6FdQ1Acor5Qh8HYySL10Q1Pwqcr6e\nhq+/DMa4i9/gnfIZEuc4kNdyGGavvof2eZsRolUiYZQMtXIDfL64YV6eFdt/OYJDW3+FS4Y7rJ1i\nQebuw+ob4/G2/RT42DLQLuM6ZIxcbPBuxtxp3R4umQ7Mk+xAuzuxKCjpiu2zDJB9DEbgISMu8kOR\nLpuA7zeMwU/b7bhkPYbcflLcv3kLEWNGQzj9G3pMlyHo5ST0yuwKQgiq5PW42ywRr/WpKHxfAumk\nb3C5ayIkT+Nx+OYbbHErxe+L+kCmsmDU7eco7+EF6fMw2B4NQaEkEmdDf+flkm7c82f9UO81h+Ln\nmQRuSiCvEF8X7YE9dRpSkgbiF2MhAvECo9g1EBkGbI/TcNe+rYpIf+XWt2aYbWXNNqXgRnF+Symw\nlhOVI1XMRLsATV0EzfdKJy6PEvl+L3y5D84KvqD3YdYeuJEIlf6CpN8mGlJfxK2a+RQLmd/wUWyF\nd4FO2J2gRmhuE7Hi5TgmXnyNl4hFLZKScjt/ibcuGEvmPjmHQ6eOYOKKxRhjuGQx+0OWpwlktqw+\njcKiJlTjVF1zzuWOxy0mQNwTdolBi6tAx1NwWbpdsJfk8ZZlkTKqEGjLN0CqdyZZlz4TcaNOY3BW\nJeY5rHT8rqWkIVJjMw96dlu+e8owb3pVWPLDmfoj/4u7t4qOIu3evq+7qqu9Ox13IUKChBDcSYK7\nuw3OYMPA4Azu7gwyDAwOg7smuEtIQkKEuKe7k3arur+DeZ5v/df7vt/6zt9a61p771p1cB/VwW/t\n+7oOw+vWBCl+O+HAIlMnupA8J0vHzcLehj9R5+EIsj1igeBvK+HDPnxlGtnK2CSNEuaO/vR7vAWI\n308gU5hlE0bnsR62+rYftlyXqPgNJupHoW+RAnVS+E2082+k09jRrVc7ckfV445920pM9T8iXZyE\n674dhXjtEVKaPpNO2RLGHOy7U+jCdiUNk2PJ+cmzXJM75BoDZsBRqsSNfb9gxKG1PVRvZAP46WOW\nsh8PMICjEyk49srqn2GxW7+Zb476grG1tUBcNB7GhY1sqG0YxOy5st3r/TaJeIZrP8bOfsO3zOvL\nHvNZ6LxVm+D0dSSIj8GT7m48vmruUhI4SHEUwqqVvCS1P3N81YHkbXfOh38u7RCsqrGaX0rDVb3I\n58JSf4/AlppXwve0NiJP8xy7WpMlnUN/+3h/zIQWXXq6yNzbUQL7fYfjwtL+kim3BVfVFzAoI+z8\n9lMsJ8/skJ88G+ZYMfc5u69ih7MtTkp3tRa/+fmjvbHDFSwLZVLZQ0daYMkii2VJ8+ayefnfDZ6h\n2S6PQfVyskJ2t/Gw2Vw6tRtLBJ7QB2cq6MmLPgjd7lKMvSvu+UjpOHvv0ejlsroz18Re+sN1F5hj\nOCYrQlFd+S8fNbs/2yTiUqBGd9ps37S9ZtrspyH8vEwBuxqIV/H5TC7c+CtyCax7/ilDTrVUlDzT\nu4vxMf75PtbWaqDEkOkcT5IXHPUD8BhABwCbGy/HGY9XeB05O9hNKPVD4ZBNX+LumCLuXhulume7\nbt4d+3Pl18q8iIkBMLWU7y1auDq84fch05znTbPY3xZvTIfYvNp8BNN37UCXtZmwTzrXW3J66B39\n+hbIXDcbHRZMRq3x5q+avhnBOe1c85+TJ09U7O3bXU/s2MGOu31WCZnvm/5JXdxGc/cbrdzAMluu\n/l75OjZ28dZRo9Zu+w3bAr6L9k43tTp3E69CAKQkIkHT1ftR/Cyzuf2aFr78ltWddeIla12o8Rrh\n2LwoOfeMgfb6oCkNrawxTRse6dF5XLF7mM0u+ed0CB10+WRfJCcOBFA8fjTmNqmA1way+UPVvSWT\nY7t3urYAacqrgWLNoLEjxNbsvXuvXsVQAPkAJdDYW6n/uccaWHVpjJAlzfspQwG9nTvuHMRMta9Z\nbMPrxRjf1csvv23MuefdLgDYkUATTqWQlI1HUa/rJSag4N65gGHrU1o+fnLwTlcQ8uxuJK7N/xmb\n1Ha43i3DrHnzsLJhQ+imleFpYCXmV6vhdMhwEWKvzqTlmUcfuvUY5AUfdRYuCC9QaVmPZUU9xvyQ\nNSyShPd93cLZwfW25Us8JACu30xwOnb+Vhs57MRlqq8uoin1xLYE/9U1ya2ZEFp7Q1DWfG798rcn\nybl9wGpi+uxm9k5d3MC54643niebEN6nyrtxLKev9spRSVyJ+qdzGAi+AIIBsght2xa1W9+LTmBO\nKRdim9Zw6OzbbRdrGn0e2VV6dnrblyi7+QCebQ/AWuo561ZZ+uB2lwKYOfsYtPhgNM89wJwW9Wau\nS4aKb/VW6q0hTxvO2NqgIun9q/ETj+w5ORAQBg7y4wyGSuPD61Q+YhzHJBWso6NTFrMAkEJS8gHk\nJj7CdwyMGAtTsgpoRyANcLSSHjT9bozyUF4ZezGhv3Hk8OFky9u3mPlX4rh1j9teX7+xwiACIMSw\nUmfkq5GGO/cX+AhCsyeUOrrM29y+c2Kz1ynbr3Xo9eXQ81N1gJIBpLeAnDFuuGaYI54xsGKyasbw\no9kSztV81Zdd+YFa7bU+Zw+PubdgFpccXl805OpTh+eLlLI5RaWF+/v36rQq6pKJEoTEro696QEP\nn6fJq5f5Pf2+esLq/MDzG5ukF7YNuBxSCOpIub9LemIzEhu0O3Ei68qksLCjIW0bb1n24iOZETQ+\nSv/jsdJtoFj/Tlcwfsvo0tZX78ddmfr3mhYfOJ37Z/OUBKvTvTARPyW9GqPrfTfbrDW/9/02eNyL\ngEFzJjy9WeyO9R4Tkg8KUntJxpQr9rn7D38JVda0nB7y1OQnMteaiEew3Pe495QHVydGhnotWLaG\nCZicq7NQl0zOvkqMPznr0cW3FlfUvrchVFUYZbux6K4iMZHGgDN+XT5xDNulw2f7bO0A5NYedzjz\nbHrRtfahTs/f0SShH/467aAyHgUE6DUudnibPUDn8AAAIABJREFUNdNzTmhrPC2sze1378bvttuq\ngsmd5FAopP/f20bp+YMXqxQf0aPlXevttzWBBuNt3f9V8CgqClX//1/+i2lY9n8TYZh/UQ7DgIhE\nICwLIgigTue/crmA/9b/iLhcAM+DuFwghICKRKAcB4jFgJgDxGJCJGIQkYhjOJGUYUUylmGkjFjM\nMHKFhcgVNpFSYWXEEqdTIqEOkQgOsRg2joNdLIaV42AjBILJBE+jER4mE5Q6HZxaLWxaLVx6PYTa\nWjAmEziLBTKXC5xEDLtaTWyeHoT38Qbj6weJr58gUSnkTsZVzyxYomz22hhHXU0YVblpLd6B2WaP\ngHS7wvubyyFYZVYbVHY7FDYb5HY75HY7pDYbWI4DZDJYZDKYZTLUKZWoVSqgU0skJpnFkzI1PiJU\n+spQ5aMC5/R0eJV5lDLF6hJnpbi4wskUfZfSomLQ4moboQKhPiJPk6fTr0YsSAStuNyvQqhSuOAi\nwd5SIShU4D0D/VkGTV1GfRtjSVlra2FZtNhkUUnkYmuhhtgLAhx8UazDVt0Z1bYA2BT4l355/Y8q\nB5COf32C3iPxSRpWrvMEEE0caGTSIr6oFI1LtQgqKwOjUIN10xCbTCM2SD3kZsbbw2xS+LpqBD/W\nVRmkkhT4aTQ53krf725if3GhoIj4whQGvBXSagvIp7cSPjXDAepiHL6Cf6avK+hNYzR+PBAD3+cg\nJ/ge7k3MkaT1KhbKAls08kZweAdqyB1j+5TVjhgcCjaOTdU183juKHPzEu7p+6otVrXcLbbIENao\nkAmV2ZVeeqnEu4rCp8pFvKsAb5sDTrWVlos9SI3K6LCjokBiNrz1NHq+iqiI+MhStrA9+ls5GEcD\nmAYgwITwwgKMV+rQOlyC6m9qtxdn7b8fLSxo6hafU6UYWZpZHf6kiNIfLpfgrYctVkT5eiFQ+rvD\nGs7CFWL0VQqVbShfUc+lMFc/qRLVbLr8YpA/NyZP1XPA8U5qd+1Arg6l4veKvDelicrXoU3rv4+O\n9tWp1Vzr9HRb5McPrLfDKaoIDsa70FB7XlgwH2N/JiWnTzGpb23Ev0uDEpnHlLq4VN/6LUsYkdIl\npgVeEnJfdJt8zj0NTfOpMEqmU9tbjQCzGwN8tgGHAXy1u6MWkWjtLMYahQI8sw4rHW3wTumPcqZU\n5Gl72cgl+hgVba+r31SLQKosswW7fQhpyNQqVKA6STWo7VuPt+XGYdekcT5asded+FvFpzuelplk\npkB4dapB5BwVp62oaXTi7rfYbxL3dg0s9Ru0K3P/EvMde3IZKOvchfYPBplvt36gcKgs6Ce0s0TE\nZCjCg3OhlFKSj0gU5wWYfE740zHv76tKEGip5yqUnSPD9Q/pEvkUJp9Njn2Yu6/vDrlT5AwQG2Bl\n0iGxZ0FCawDIOcDIg/VT8w0DSW2XlkZ19Pc+QtyDdhWN6BFfKSqlK+NWYO/EzrDJGTT53YWE2mQa\n5XuFNEv6DNqbwnOHxn4nKMl0oWeCND0sTCEzVKBOa6FCeBRxt9kEs0jBRKZ9dBbsO2YXG8VCUsCv\nbHrYU3l249tE/r0bbfJ+LDIjzxaIm7wsrhMs8c5zdaoOznb0h/E0qe/1QfDp8DuepBQwe5v0RkBq\nR3DyGqDBN9wNVGDrtR1oNHIONTdvALM6isw5cRAvCIub/WMRm+sFm/tl5ApisCEL4Eck6P2sgj7p\n4E8YYsGi86cx4PETfOZduEUANwo0JUAbBnCJGLwNDAY6ynHNwx3XxOnokzUXd0d2RZ2UBRwMxIpa\neLxbCLcqMTb9uQaf8Bb2nucwt6oKaq0Fl3s0xR85fqiWGNFK8Mb2L8+g6+pEdislDh/TY0aGE+1V\nAvJ2C3B4cCAPk2DPawxXx+eQNUyHpaAh7vk1wlOuObL4BsA7CVx6GdAmB8zSpaDaWtCwpsCWXxC6\n5W/MfjcEBxvEwdJiOeqCHiO0Igzh78fjdcYstGEqoeOV+EGkSHTbjy5rV8KtQI6AvX8BSfsxLnMO\nRnt8QHRtPTiWHoQlsz0ijvfEjGlLEFXPjqVB1TBOPYQTLjHujP0ZUVf9hHJtBtOhXiBGqvtjT9/O\n0DosqDWYYTp0EZ4SAQf3fUb4RQYr7s3HsiU7IVEI4NOagya9hGO2N6bY3KFDA7jwF3x9JiHfJIEI\nWxHd5z1yhmXAVXIGDkVzJDzuBFdKPHLcCbxGt0RpLQvRBQns5QQtaQvk4iNEMCIAUozvFwzfmT9w\n9GYnJLV9BsP9Qdin+Y4apxwer5dB6v4Gv5Z4IivsMzzqXcHLP+NRh7HIVbSH9+huGPLDjODn55GR\nKCA1bheu/vURlRp/zJ81hLrwjJrqPjMnjg+BuSYFERILBg2eAGP1GehC9LD/8wGcSE79I4dUJ5jT\nffpWAr1+Hg8hZiTw1hs+Xx5h408b4LK6g1ARTn/sL7y3voMQ/JJZrvGmHSKMZMmG48ju4YSnvydG\nHixHUdYcLJRYcaoFsDYZWDhsGO40bY1lqygYdw1Od9mME1WFKJjFYuPa0XjQeAnomAqw3xVIeJiB\nTYalMC6sgt9zF+48ZOBeehpvOvrjTBslZA3LbZv/uScuq89Xv9EP8RxwgxMtWu/EzoObEf7tOy43\nl0DV4gf2bGcw2WcmspqngYENkfnxyEz7hkzZJ1SPX8MLTEu296NEzFzNgd23ED9aXEB7abYg1YEU\nRYHQMDeIhl7G0+inwtdG78jNrNNksPU6JA10ePBmAKq8V5mhKTT7fWktl8gnKUMa5GBxh+3C2/uJ\nzMNW2ejyOBK7y7fAxXtgMzmMZ53CzFef58rgnMC07e7AwukiIWJeIuNV8wgb1hLkKAYLj6mdCIav\nBN5bAecFoPox2tZ2KMrddzdw0Bg5nnfaw8of/YPMM6OFeqogfKrrz+RhBgwoRB8cwCxMm78W4euJ\nZJxMdLqc3F61CNZ1GRj/awGcWhVm75pEiy316J1lS4jUan9eqfbutOD4cMGtOMsZv2gXtxCxyJ3V\nmhF8VALTeBsjstZRW9ZkgmzGEnPnmczQbTlpV6u2dZyXL/325atwrM7FnNdrhLA/VzNmhMNXua/u\n2OgnC3b68MeO6oFRZ0XYZ2eL9juZ4LPWRq62zCeu+a7T0Hl40M8zRkLrcLfXd+ZLe0KCvEgntR08\ndr00ZXMrmPzd0GOxAk5HBgYNOoM//niAwIjnMMlkeLfBBkk7CWsbIPx0pJTZYB7PR62/yffLN4on\nyg7yUxLHW8bsdZPu/LmcCzzhIO73+jhWSN6JViwfwbhbDwvz97oxVT9vtbxr/Iy1C3AM8hFJ2DGn\n8KjBI3F6UB5GW9x1fe9fc78qpbtjL6CJMg8dTUYcphwymh3Bkqyfmbp6JxFnCORMH5Jgf+U10P2R\nbXjR/JklQXJ4i2ayPzun8NNLHcTuIVl4QFnVsh/z9V5Hp8EQXdvykr50avLYBEpJ5rgVn76Wfojt\nTgTBQeWUkwgO/ZQJyyV9Ol4kdRM27hqFSQs2MBssH4LrZE0X/cUZJTVclpmitQfw+218wxnSoCUb\nL6wRrWLmKd0tHTw/CAMX7XfNmvJJ/dI1OjUId5rZ4MdPk+56+Tk8pFOU352yr0/CfJ4ojrwJ+2th\nrGjcCCIaQxWe+kknS4d3naQuyQablnpAf/nqz6Ren0tMz4zhIYkTX1Q+39Jm+ZUlS3+pWDm2gxQN\naqqCLpajvN+caH7TmQHYMk6DbUvWkwWllp03apN/TT52bOmkM9enN0a3EjB3owTqVHN05u77aBrS\nVT2lXZoq2tw0bv2+H/uGHr6cxuKXgfP8WBR2nEkvXTySkkJIQgKlVQqy8V4kmfl7/7FuqzZ0N0x+\nENwOZjb13G/jS1vlh/svpLu/jq0fG8xkHVr/YEDgnoqWn+jaW/eYjl8vvdYvCt4LYDpdjYNpi7B7\nrD98vtaCNtXg+PIGmHrzPB6n3EVXYZj8zan9N3wbYKPvrqkRcVuGDn3nv3ixcpNG88uH16/pJ6/E\ng9PJQvwUHEbfvB75uRV915KkpLwGsHntUvKX9q2fYg+NjgWgiYf+7jaSinn9L2rSGxDj4x6HRjIm\n+VmMO3Ubbd6IamftGBY8183lYXTk/7rZGf1LuB0Z26L43i/0YIx9xAXJJ+MBXPvjD+x7dAlbtwmR\nfHv0WXMKf6zextgLlwUrw3YfOk1kMlN0WeLYDQB6AX8thWbUcrZvsXfIpGdVRfs7+fJXujqxfim3\nY42Tj3GGz+2D3rVQF59E0opbydcmbgRwC8BgADe6o9OPtqrUtKlDNk3yHn31fo9uzp4gZAKA4eI1\nCFSKENssD7ap/SCZkY5TRh4T+ktArq6BHmJEYAF3B+FT4yrnnP0jtbbup2J01xxDqvCXsuJbm1mI\nbf6HGE+MphwqcLMoxcMUkvLlXQuk3Aovm/N8HGGQcwZs9K+6uAzWI3N/MyrrF39S5/3D8stq5K1g\nsC37JPa4sttOYtctUnkjxVVGetlaKYap+na1kqQ61+EVyegajrx2+Qj/DqAhQA7g1Kn6CAqKQnHx\nXoz/aeJLtFJ8dm+8dvaVMfMBtIP21UuI1K/IoJ8fHV02eEVE82e+PEj14m1npR/XQQFL8e3k/lEC\ngAcnF93t9yMgIGrk6XmZjAGt5hXBJ8gDqZf0aPLi57mu6BaXXJx/uSohgfIpJKUHgDsnxqPbySHC\neQxo6g5xRR6uVnvc7+Ngj4j3auovuPXrxo1075YtRLx7N8zN48mU34aGHZ5TqPtAQcv3xbKDF24w\nFKamPw5wuQqvUzp+BAAsOBhWo5Q4HWunlP09AYiYCnztAMwCEBU/u1v1ij7JEpXYFdEtif5YOGPG\nqhvt2y9qvvPUkRXFb+b93Xn0qZkPj44bEj0hJrqr74MDu7cHNZvmupy3hw7vR/pFPsOz7GayDh1T\n7iy8AGAdgGUA6m9Ment6hWLDkEXxTfM3PXsW4el5xsJxltIZM6ZFXzrD2TUipbXcbFSt95jnNqe4\nee54O6sYYPa+nJgMWYsPdzxbrnDreNi++IzQa54akfe7QaZX4Nmyn+5WL5hvawyJZh6U1j53bu12\nNmn7ViaJaxSZ+c8Q9+t/Rr7rc9eiEFAVd7Pf3Hu7b21dMmb89mPnTnbqOPDvy02vXoNDenmIwsc4\nu11RZr/d3ZpZpHZuoiYv4NgfJeWk88BlqBm1AdWed7vNGdnpMzXIbFUJ1HTgYebCpJ+e1jBuP92P\nP3G0SlTXafNmSCdSRGcBu/d2GdV+2vTHbfLSGkFUF1rl8aksemDXB8b/3oL67/O/bhvVCUW7P3y5\n+Ot/ecv/VfDoUf/5XynIfw2yAUqY/2wYAQDz7wxAYCgERgDPClRgBMKzPARG+M87nvCsAJ7lKc/w\nYKgILC+hnEsMluf+K8LwIrD/EcOzhHWxRGBZ2DmWcYhZwcUJlBe5eJ4TqCDiXQLHCwLHuwSx4AQn\nELuYig0SkcQoEYvNnAwgAs/CSMm/3kKEQidyQUv+9RSy4j9X1wSWVwjudRrqoXen7rUqojIoiLpO\nShRGCaM2cLzEzDBmN95V5+F0GTyddpOn02rycpptXi4zJ6EWOWBWgDErwNikEHnVwBlYCsG7GmK3\nOqgIhcCzKGV55Il45BO4iuUoNKhl7wSXzUNqpeGednj5OKEIcIq4cEYgYTwLXu8OQ7k/HEUhYPLr\nQVnjBZiUMJqU0BtV0BO3mrpoLp1t5MpShLl+qOX6Mk9DudajptKudDop8QmQmOT1PCtsHmE6rT6a\nGosbSFw/6itUZW4aTy1UXjVU6lsJRmUEzArwNikx8SxqRC6UqIzIU1hQjH+TpqqVyHX64LG7HEXB\nRkQ1siAsyoKgQCsC3TkYBKkoF7U+NWxmNGN50Vrj+NAojNP5yuSMlVC/SgPT8LvdFZHDWYMLlA6f\nMqVIrZepzDKHvsJDX/LDJ6fia+AbXVGoKL2w05hcs6dvPTnM8bH0a6sYmhkaUvyKr0st4D5+ZF0Z\nX1yMYKeCS6Ci2IYM7dIdTAPfQBSa4i15+o521hgscqtyk8jzVWJDngf5zGvgEgno7axCC2hhVxhd\ntQprba3UWQjBli120AwPo9fXYmNk5RFEkGyoXNh7oBYhdD7kwcPhsohRdpW0+HjXufyJydAjD746\nGd4Tih0BJtwiq0EBeEdURDQc93T82EZlzQZbFCLFP8MY9kGzFyZH9TlpOM01NVTDWmmHV40d6dZq\nzbPfLv4ua1rQtDdAys3Ie6fAnRA/FDYLRXZAHTRQwehSwkAECc0v7wW+sgc8TRHw4gzQmgywrL/i\n6f9VGi8JGdrKonTjxeLiYkdC3Seubb1csVslD+4qxaVq4G+tCOVVBAj3pIgidkiNFK9NMtSBh0jF\nM+0TdW5tEiXmuBi123dK5UeDGG1uKDNH2I8QWoSl2IhE1TWHrNFTZISo2bqmGmdtjI+4ThnMiFhO\n8CNanZoYCv2cVbpE15NmCefyrc3PVbrfcg14+RNOupmhjAbsDzvhSeU4VLcKhHfcM+kzh8whk4bS\nMJEfE0gq/An0bLUjqfgRd1fw4v8Qd2ECW5dClrCTvBZ/JZ3UcrQzRfG52TbkFYrIj64LnOURYTxN\nuVQBj4t+4GplPXN5bHrEkE9+Ak33JchVuJE0ZT1MeTsMIdpAvIrb5hBx6aWpjMr5tUIa4cjVQUZE\ntloTLxN5qEkHTQ/yi6MbdJs3wa4Azv25CqbbTTCensIwcgqeghY5kgh8UTbkbZMroGhUyC58FYQS\neRlk7xLBfK2Gk82As1cUSGhDkIoCEAWLgPexjohvUeLvwZUokx4AvhdCFN6FtitciqFGN6IHh5Oo\nBzk+oxhDUV/Zl9aMa0gC8RbiY2NRSvzBDBkOV0wRIr2BfAvFj0/9wF89hkUBQ7Gs6DnSAgMwsfFR\n5ExUwKewFOp13hDbtsJoz4RbTwnyeraCOGA4grU6NP5sRu8rMSgNBu4O+IYGhR/R81UhTHIgo2E4\nriV0gqvOE0PPU2hqCrFmzGKMCST4p5BDrw990S7WivKAOlxIs8CdBw790w782PM4H1iO038B1cXA\nfpka4yVmfJ3rBefHLojPvIDKUILx7XvgfcxwLDqxE5M/FSO3dyQUbYvh2t8cV6d7QBclBp/F4fsb\nP3R9GY6uZR7Y/XsximUqlJwohEtdAVv8RDiTyoEda4HFi4E/JGAKWKyPe4CWye2weY0OXkIdxm0K\ngrLWCa1TjZOMBG35R2hY7wA4WzsYg/ygHvEZhXoeC8/twJSZl9C24SWkb5qLkOwYeBrccWDUWsz9\nlgbuVynOnhyJz4IOLT+ocL/mHsRiB2rkDKjeCsoCjLsE5p0bQLUvEFLuhZq9H0Hsnti/2IbQqHTk\nnkuiXh0uEPc4O07OkCO7RobvRIutvVVIc5+JfafXQulWCXjMh8X0As6gdUBoV4gNmeiYfhpPyw6A\nQRe4+xTDd0QNSlUSLL8zE5e/vIGNc8DiaaZcR7FdG+cp7h+axnTxrsGRr+54Vy2giywM2pj+SLk3\nAdxFfyR5HIZLpMCLqsHgpQ7wtgvwGc5j09Dl+PWJHcteEKiqx8GzbhSOdLPD8pmhseUe5MLPfbHn\nbA/0K7uMo3BB4OLRw8eCBRNmg1xg8bHr74JZaSWJmRTnHwgkafJhfE2oD+ZQKJTeViSM3AVF7QHI\nWdABQRBy9CJnjLtL8lceyL0agk7Jy+mL5CWkWbvuePPrTDTMlmHttnw8saxEhdOBGs8gvDm8Hzt+\nE/DB7wb+SjjDh+9/z243pKJeq8moWOnAxn86OvNfjhdMshui4/Meshq1BeHbgex48I/ipOy+62Nw\n9Oko+tcMloiLJcKNcS6jMkciPbnGIlm7kiIt1kUf/vIbxvz4SnbvAz7fjqb7UgqJw2rD+KZdqUvp\npDe/PGc4o7urxldJ6aH9XPBoCWosqWi+aisWNTfAMnc3Nvjdobw8w7VuSi2HA7PwQOfOJ+VGC1OC\nnFyIriVy/v7m3Lb7tOiFKI6kf+iEQZZ/sJ+Zj7H9txi0Ly+RKeup6oh0gOVuzhH5utvDkfauBHe9\nnsNS40n1yj0ExjFQBQqVO9f5+spv98I+yUs65XkpjTdSU+F5Tn10v9nxvJ2XyWSv8wAj0uMf29wR\nXq79fbvB7ZelBB3bx7uuL91AWk05w7yvWCkkBB9/9rhwS8IQXK6+g27H7FD1FEncGtOYHHG85Y2z\nW+5pUfyKSmdGLEfXjbvNymEVERd4BsKlOqIeGt7vBVHP/eTq/fnYs+ybrbpNSp5QMiTC5LANGxMO\nz1iBuoqdpFLLSU69IUl6ZfVk2WfvEQvGO3qRTuJ57e4IUzeD1HQGc/15QLpHpr9x/dyPd246sFZl\nAj3XlTAx0/wcfqXlXD0NvlRUT1JWiS6FE87F7I+azC/8+gfDgnd+4kLE7UglrR9l037bnzwcpupj\nSBleD9pVLoxqJwLDXAIwFDXVFGe+1yJ5qDvkfapw5oS/Yl4T+uu3I/bWAY9eDz2wKnHyCcF4eTiU\n9Vd6a1M3F3oZptenjJPT3TEWeH5se9Oye+gJpzt7VLb5d8ooB19nzC4I3OVhLpYXS+dOGYHOxXX2\nHrMo92gMDm/WYbJSgaaf92AtL8UgaSVchoZwNNiEz6YfPkEzZ/0amrpxqeGP0+6KFfz6mk5XVH4D\nT5fTTEbMBgthONRgtmP2TqP4ZOZi57hVbbnf2Ei+Q+2j0L/oxNKUFPITgJ8Tt5QYkeae4BFduP7i\nsthJrFM0A98aXoWmdk3imnyR2Cpecbnutmu5Yr540d8FvJvIKN5xEJ9S7nHRKqm3+Lxzr2hZwHJ8\nD53cSpY8+e2eTT2z8kuaR2ceWJo+B6+/X0PjPbdijM/bhn003713vM6Ah0EbsCF2XnK/X5iL58TS\n80fG8ZPI/ZhHO9uXGxqeqWJ6jIcVd8LdPjTJH/pTfepmm97sq7ozKLq9P/+pd4EMb+qbYOIE3FjW\nFcNet4U+mZdeOXUuelr//IKbr5LrplNKcrt1u035xAg5vtqNYNlC7P3RCJOiqxub8qTrxs53HzZp\nT/Whbbuj+925Uz0+rPO3ByNSIyCrrUdXUa3PIuL74TCKR/fqVv06OtH37trmFWKIhyXucTsgqTIq\nC3f2qJdmXcecQ9+AoUjLW/qHQdb9xd2K7sMeBqzd/lef512GPARQEKHDwG8n8eDmOaimf8HjvU3R\n7noZ7i1woU3PTQjQi7E5OSf5OgvT06ZYsE39+NB7trr6yqIpUz6c9faOHxlxmK0tSi/Iy67x/o7p\nmUU0pC1JSRkLYHxAKV7tn8Qu2+OIfl4LcfAWfPXduYC+fHDeuyemxpsBR0hyS9kePOs0AluW9NCt\nXnrzS8gnxXujmp/XWCuIs6MqR2TXBPzYa9cqefNEUHonJYW843lsGDkYV0QGMFOhxCHY9epwp+0N\nD69pa5akXQvt8QWJiUsB/ACYmwCfwHibPUmMgUPat1reNF6KMwdI9xGHzi3F0oQSyKLHKcI+odHF\naMi14uSUhO0AZn+B2/ZfET/tt7BDgklVax+xdxkLoG9CIrIBFLedjO3aGIw28mhUZ4fVSlF52AHa\nVgFHk+V4B+A7VuMyWHlqjNvsXS/mbp3kArwkYOybugra4/W9ZTVPfazISF8CYAqlSEghKfMo0Lw4\nGCMmrsmDoPHkZ5/SmOkHd8P14ijPktWK+gAyYUHDY9vxvVU8OO12HHNbNrwpfd2Rk878Xep2tjYq\nZAbkHcvBpZzEcwA7CWgigDqAvEdg4CKcPn0RSUmdPemM2iKcGGeGQu2TfGkJAC+kzn+HRmuP4G1Q\nR9GqhvdvzB2xZ+D+s9GOFSnBaM+0AXU2Su6m9AJwPL3P0V4rp43IHnjnSJ+tf9++fb49erbNwF8P\nmkSVeOYfaBW1eVg6ca87kJBA/wCAFJLyA0BFYjJ0EIRrYJh+s/bBPPQaP+Li5B7OI8f55zyP7pRS\n2qsXuVlWhkZ7Ega+QsNvSWB5Vvsx5PP4v5O7WyxNTMAnB8BGUwpt1MgZI7ZOPHI+OVkxYN8W018A\nPgO40XZuD/uChI9/bMs3Vb812ibRVfTW4X79RNtGjrR2/vxlxtTT+46GGVxkd0SjdH23Lsuvduhw\nbd3GIbVJ+XgepaUDAKAv6fs+G9mynOQjFwGsBjBlSeL91OM4/m5wlHvBmV07VEaFIrj7pl0jHjw4\nfrJ+/Z3t+4TcHLbrUdq8Xzo2ffxQ223vt2+Lro/rMK/P+NdT/p69H0KZT2G9q7PnlverLBJZRUjE\nT0nP8KPrpQN1W/eOTMObLR0Q695h4NFDOw+2qTT4PFg5b1loXKO74YrINOnbTdtTRMTwccG9NfP/\n3DzaLyT0ednhE8Ellx+8zgOQFLRC+cTdw5SgyW4+thFb+IZSl+yPA/p00nlwYzR/nYbvcemISWso\nF9kubpCLhqzYWse1GzrinwcnTw4DgP/Y5FwFRfHwNcibR7Bdo2HY2+16otnUV0hP7jZ67u6L5/5X\nnvJ/2jaqM9zS/0/e8n8VPGq//qGT/vdk/1YK8m8PUFDQf2cAhJJ/O0r+2xNQEIL/zBQglDACQwXK\nCAJlqEAJTykRBDACBeEFwCUQwlMCFyWEp4S4eIG4HGB4JwPBwQqCgxEEO6HUzlBqI/8RAIvAMLBz\nnJtDJFLZObGSUrGadYiVrFMiEzk5GevgxGK7iPWo5QWFmRcscoYxKlnGKiMEjFMA4wAhdoioDSJq\nZRjYBIbYeY63CRqziZM4nXbO5TKyPF/LUqojlNZQQipdDFNulUpLKjWaotTIyPKAmpoIud3ehGeY\naJtYHMYLygDOqvLkrCpZcIkTEQVWElAm8J5aEbVJKS0LoCQ/jGOyI2SMTWmEBOWCt7nSHlRdbQ2q\nrjaGVlTow8vLaxQ2G6lTKDwMCoWHSSZTG2UyVZ1SiRq12lqt0di1arVLq1ZRnUomYuHk/KoN0kCt\nXixymG12arHXMmZnudhE82V1bIlcJ6XporbOAAAgAElEQVS0Rg6n0wWIKBSBgDKuTqxuYJUqwqQ2\nlb+HwmZzxf7It7ZPz5C0yM5G7I8fGfXKy1PfNGxovd2mjfRV48buuf5BAUq9Oioij3WL+yIYYjKp\nKKCCVdUpLLVVqpIqtUXp5VPn41mnqHHp3b7DJcvkPPlsvr4xh4/WWTi7CM5iFbRZMtgcMmg0PDTh\neliDDeCVTigcIpE+NSJC97JJI2d+vMabDTW4y1xZYqfcAx/sPYX3ZZ1s5jpNLYxcDWyWGshuyKG8\nHQw20wP54sywNwFlPjn+VSVokBWPUNtMPExqjbedLZCTsxhddQCzmGKE+OPfVMBi/Hv3MQJAqi8p\n+zSs64HQ/M7Kjs/jYtX18zMKagy3MnWOF+EWggglhbmOQEIZiQX+fUzwH+ABRiFqey2nbsgND0WU\nNlBxX/7cecWVIqqwMWyYV0/LcBKDjjqN7FNQFj3f6SSTKRJDUdCeehW1tPoXtaj0V2trWjU55f2s\nKCHgo7aleOiQ/bRH68t1lS5bWYbF5rRYDfUaBrnU4T7A2wrgng5oKAUGhANVdpZmG8WCh8hO8LKN\ng/27jThReEZk9q9kqUgkPLfbCUDIQKUvP0FtQIjVzIbrCAS4OSwKteFp+2jrjaQ40WNlW7eyC61l\nokoRoQu+QxWmhb9Fb+Ot1cU52vderpofYvxdAOTrXoIqHyDoQQLCw3uyFRZru8CddR18Tnr0TjGL\nI6oFLBY8TKcxTEExwkXQQlpflEaG8kaqJwqkiTQkXfDAz9578G6WF15ENBZc1y3atvYsoUfHJx5N\nYj5wH6vaOdQOnc3P/bvMrpJyi2xrYMwohvrELrSI9UILY1uhcWoA86R+OVIafAQnONG0WovAGpW9\nRJIEFePJGj/1ZQbrxcxTJh3vWv6OzvE8mhtr4daJQKynYNaMhtU6EAekKXgk9EHvfl8wduwmXDvT\nAGdrYiDEh4BTcJCn5WJhwh1w1IhVK5WwO5IgbWkH3+Y9HGZf8Ld2gq1oBIaWw8nXB5TvIE88Bluj\nO5BaxZj/sQ4hmXbMtXCQ+0Sgl2okIkz1cZr5ih+FhwHud8QmXsa6gle4U+uFkw4BCcEb8TF1ICY1\nmolbwy4j6PlovEvZgYtsf6S3oNg9fDaKNnWDlz0dw+zvUfYTg2c9GmHpmTOo0mhwrE9vaH58Qon5\nAnwfeyGmaiCKQr+gXa0ag94NxakIb9zvboDUpYLHawUWZaahvp0gtdEVLB92BksCeNS6x8LT/APH\nCil8vsdBZlOgNDgL6+KsKC7xxsCDOlxWdsdpSX0MLdLggeYQ6hdnYpcYMPsDr83AaD3Qym8qmC7B\nSB/YEF4ZG9Eh3IhrZAu0CjnoWSPEt5qB2r2gjCqDrq0BPk0/wJOJwO7VTxGn240zzSKxvqcIesMs\noH8EmLNqIM0IP10IZluuoK/vbdTkz4G201twz7ohr1EG2mV9BqdRwlDVGV+aPcOqDpex5thGFIRb\nET59KZw1tagyhsAztwXyOqvQ9IEILq9ihIUWU9ZiISrehGl7d0Ivng4jtSGmcQym5c1HgN4PNxpe\nw9XgfxBX0hwtnZ3QsqQTOK4WVJyB2dWXUOZqgD+aWNBkwS3o9TK8el2fJtQvITuea+GyS1Bi2Yns\nnAnQxOrgeuWLxapUvI58gEcZJ+GQGsBYzXBzXkEAZbEQJnhAAYnGgPurpuNgXTWicwGfdkCTmhi0\nCquG1EOHVyaCP/IExDm9MfTrOHo79ANJ2XMBiwPX4fy4hsj80gXw4cFElqJZbjHCH93E49rrONAj\nic7zziJ6ZRn+uEPwNTMCMaLVICxwsMdahLrysPIOj6kEVJMEupcGMJInSuREeGPAryvBrGjOe/F1\nwgrFas7Z1Iz5UWvhe8bBl/qEsiuXT0Uz/8dYJ6zgW918gizlXcIEgDGXqJERPkqwVObq+DyX+4jT\nB5nwlj/I1rg7UHfuhJavHZhwVEH9+dPVu2dN9LZwIqIr/92VUT9ViC9WiRccOYVDaIKZ7uMcTVs/\n50png9wpDUf3oAJ4fhEQuY/yogrO9VCUJBhOZEpPaivJj2sh2P1iv7Bss5gZfpQTOIWTYQQBmyZV\nlkx/eSvw0MgBpOfjn2nanzp61+LJjJ67CsyZ3/jnOgcLABIJStVtxlTGte/RTFn0DPdP3RYsntkM\nek3BsPY3MIQLBP11M5xJyfDonsyvPTJpxKS1C0/lzDknDawMo9EOq7Bz4j37k1HduC0b6sRPJTr7\n57TO4jk/Lbvke2FaWGaY0IxzS0ebievZfKfBuSZNYC9sXsEuoGdgi8in2d8BhQyOG4ubiJnG+eTn\ne/6wOp187fF8m5ZCcnBBV3tY9+eyNb/bMz4m4rj0IDQ/T8Fvid0kjmbT5OqT5cba5Z7UEwOmF7h3\nH+rvOeo4zaU7JfV4Xb98RJ0C/L4TaFsFbPiT2Bn9J3bpyOYWhhFOal5XaS4O9R17Otla9U/zUp5n\nC6gn30Fax4t37+2oXRK6SuM+u5Wurs1A5ZGLS0oYk9+pkb3OrnLWvyWmqjKIj75B90rvh/PxI1Jg\nULBp8pPO77w32Z/WucqzOyJ46mHw7t0hCVHiQYYB3UQ1ECbGQtT6yQBLQMf70pZzbM6XDoztDNGR\nCJxlsskIN0IpnCyrS3Zr6nZBl0lCV1teb9yIJg4iaY17925h23gJIh3e8F1oQJt4L2Rl1aFs8T3k\nJSbh/FE3tNpLMSkkTa1pEO0Y11r6wtnWuXbMsuIbU7yio/7w1DXXPFFcZrqJnFmns/xUhQ1+u7Hw\nQjNxZa91U+bLkhsHwNvZ3Lxxjz/nEEhObqS3j0j3NehV3OZ3l6+i1dn9ODF/LH6ZBlQcAEpEQMvs\nudjk1MAZtQd3xXX4s04m2x114sSm6uPH/1zZ2s0SFpk2bbFqI7too1jU8ke+xRJSp8hOqi1T9rgW\n9PzRoN1uH7vPi34syZhJOzQGgJQUwgB4KYAcHYhrl25gwHEAZUhMvozmH45h+0Lu7VtEL9kFbTN5\npw8Lqxd2XttujWt0pw/GlVsUaf4kpNNAa5Kl49578lG//rDBRtuNxumtOaKI6A1nBgZt3bufeL9s\nFW3s8uT6k1eDG8xUnpp+uHre4Ta4lfMQPc4hOeUugNOd1yc6nhciSmjafxSqbsxBKhrvWRs66Ldl\n355i29JXzqq9XI/PPbp9Cv+ki6iIGHXv65sV7+QQIgpQNmgCeph8MayqRnls29nf3s8oXt0SQJOU\nZOw9d86fP3IirT9mfWVRr/ILtjaPRYnsaaZ7vST7VNeHpr2STQBOHE5M7NIG7MCmvaZztNWh+yB0\nZo8cvDlynXMLW3ZJRT+cyUy+MvNPAO0TR9fbixHFz9reHlrz6IjD6ym2Gt3qHVH12Pe7ZfrMSlHU\noGRb9MCz5xMS6AyyhiwHUI+uBntrHdqiDSJTa2GdMAfDs//C7UEXkGM4ARmA+u/w514OhrGhOKEK\nOv7LI2txcYI8spFwdYKHdaprpPE0jOaOMIsBMhLJKR8BFAEY1f4hc3XuRk4pA+96m+RavqGbxwqc\nCJNilOY0QKuSO2MT9JpyXBl8FlcHdam5PCiUYwVG7RTnl++f7DW//j+K4t2G0RysSwA0T0nGJAAD\nV61CGD6yod/NkAc25bcr7VjwuBCiZQeGRm8Lm/UKQBskJvYBsAMo2ctGS+OIjW3rKur1AjGx7TDA\nk8PmzWHJSD4J4GIi0+4+xKYiRN9YmZwathPA8m7oZHaJ7JF/q67+9ERj+WnC8akaAJ0TEuhgEHJY\nK0O112LMaaLG9RwzXjodWPbOA74iC5Y0WY77+Dc0JRy7Yo+BsF3pvC85z4EbvlJkdJ6FKxVZLewo\ndhzA19RlAHIAjE1GSjaAnEdJ+KyuhZdeg+ONsrD1Slmk7hvUyVlUPZKsIQcB6CNWo/y1CHv1s7Ct\nbCCGAvg5bj6urnNAdLI3CrRbaTQIGQhgmRTWUXZI3wIXooGR3/Fv2u/aCfizYg3WaEJpYQxJSfEF\nkAVbZQzKb6UhcEgBeg6oAiUF0DgG45/HnqCCDSKFJjkRAPAWwMZ9a56ttXOcYczZZcq4CgRLXXBb\nOGtzxoytLRpzf49pDb/K2wAaJiRQbQpJGQzg/LmRWHRkOpawLtgedoOctH9e7li5Mr9HD0QDWEQp\nvTl7Ngn9+2/k/9SxXpfBU/SPYZELG/+svJFfzLfPzUUBQD8AECjFHAAYtq2euVe9Wt2U4bXvKUX8\n/PmIb9jWrfrBlUW4GLlqIFjXTgCxdBV1TFq8+HpeQEBs/LHTFVPLP7cdGT7ZVzmhyWdlSXL+Z+Gy\nX/VWqAmQBErT/yR/tlmERS/MjeoNsu/fPhyXL0+O3H+vIAiK0mSkhnMPH75ziUQPaELCrsDAfdVW\nqy+u6H18nnde9090WeKY6dWNypXKPEtx8W+Bk8anpHV5DNGY0oQGazuTmfTNvH3rnJsf8FQylFKY\nH0aQbwVid+O0LN17FWub/OtvU0mrjjf8r7/ZIRreYE1VXn7XndOX/7XgP/9HUpvRVueyqsQ/H7dr\najKfpkdG4vc/jmDn4EcqX4NB5aT7SuUAQCaGsWCUJii0BB4VJbhyKh1pA4qkIo9hC3/hfLMiVgqK\nAr3mry1bTABA1hANAT7N4WI8Evwq3PIWMc5JWh37OjaccPMosRVEq/uvvWsE/vdto1q+ePfH1Au/\n/p94y/9V8CjXHaf4f9PVKCWAQED5/9T/CP8zbY2hAEsBRgBh/k1eAyP8x+yIAqwAhmdA7Sx4Bwtq\nF4Ha/0e1iYD/VxzAE4g4HnJOgJTjIeN4SEUCkYp4IhUJjJTjIeYEiDkeHEsBnlAbiGCmhJpBYCaA\niVAYRQIMHI86FqI6g5uftVbl7jAytVTHaFGstEpK1FRZpoKqXCr21IqlPkaO9eVFgjdYhztYu4Ll\nGZePUeQIqiOuIANcwQbwIQZKA40CCTAJjK9ZEHlbBYmbHVKTGI4KOWMrVTF8oZqh+RqGFGggynen\nXL6fSlzh5c3w0hAXxIEU0IlgqXbCYiyFpSKLsNq0QDNf3LQClX2zoRv7FRYJB3WZGmH5RkiyzLB9\nkkPyIQT+uQEIMHggEHJFKBQiH6jtKrCUhS5QQG0oYPWlUqM3E60jJK5W54gzVrviLWVCA3sppxFM\nonL415QjoKQObiYPpjw8iJT4ugsGSabCCxmeYtfLRhrXuyY+bF69eqzRo6ETqnAWnIyF0+yEqdgC\nQy5gzBLDkiGFUe+AVeOAxVsQGwJE4WUNpaFVYaxJ7nAYA8urvf3LfjSpn/65fYOvnzViWnL8OJSP\nbrNtPOuYPhFwRSZCXsFDMGWKrSQnDD6F0dBUxEDsrPYTmO8DBSHjF1aNIltY2GV7kyaXCaVaa16e\nm6GgOJjWeLHurhidO+pVcsiP4ZHWy4LvA50QQjjAQwZKCFBpB35wQKmTwFQyBJ9r5+KFW2vkhFoR\nWKxF1x8lGOAUNAaRm99femV1VssgfVW9d9Lmwl5+LvvUuzWRDc0guo4UHG9CSOoNl7PwM1MZ2pnR\nt+wNZJUBD8opMtxcQKQDzhAhwOrODbDaJT1QRWoghi8seIBqeluazfMtg6m4bWBORcj9dKP9ul1j\ny4m38LRBiAzwtci1Vh0jrnZIlFUSkciu1ENp8ADVWSDWE7R1NBd8gjzsfu0rXY2Cf0jLEUhO07Ei\n0Xdfe8h3rjzvfhyXX+rjO6PPXkuv4bsUIgtlPW4HWOveGRkxtRNlfamjqIGMzQiziC/RYuS7eHZk\nMMr7+OO9WC8rK/mHae8sEDeU8cF2cVah8YfRyX1sEKu50yeTSfiQYPdMFaV9bJfqrFDFNagqsmpo\nxmuI4ITL5gSCQwD/ED7IU4Q5kq/MxFfVhNq8kG+YjzKixne7k36nHsQCL0ECtXkVmamgnJTJbt9R\n+DxMyxyI7opgkx4t02uQbtSgzFsPlas5VDUl+NipHgYnF2BUWg6eIhnnv6ZDpnThp/FAS+9QkIIo\nQKUHjShChVOPa/dlePTQBt6lhlvgHIwu6oM4owVfI+/QPjG7CefBoerrMljyG2OhtQWqBAvkigJs\nddThXftTiB95D6SWwYHD7jCJHFi6yIDSHyz2nQL0Ta1o1x5Y2IDg+NExeG3xQXWTv0HyO4BkdYcQ\n/RiIug18ExCY3hRzAvujQafH8Kp9j8BzdZgEgvciQNYS0KcAYxp7I+7LAGxwbICP53EUsJshG9AZ\nNDQCLJ8EuqEpWjk+4BOao2/iRvwzqyPMFa/Bbp4KTimFsSwUSKzEROEEmlpSsWnKeIjsCnS9yaLt\nNQ7pHpdxeMx+ODgnZLVyiDg5wjg3LL24CmKbCoLKCE+jAubeN3A34hUuOvKw4akKL+JH46VTj7Y3\nL4APpSgdRlDHE0y3CbAR4HqhO/JTG+NMnRYWiRuklRThugLYqBZPBAJ3EKjgxFoJ0Iv64FeHC/F9\nLiN/nhNUcAA5t0AuKwDSGf8Pd28ZHUXWdoHuU1Xt3XFPiENISILrIN24uzMM7sMAgwwOYbCBgcHd\n3d2tG5eBoIGQQCAh3tFOd1pKzv0R5n3n+9a96/7/eq1afVbV8+P0Xqeqz961n+dBCxeI4xnmWO/h\nXYswmNw7QHNnCYbc6IP6GXXhUO5H11ITDjepjvVtF6Px/RS0yXwqhebamfr2D8jR9UaxsiFg90Tm\nkI2Y7hMLW8P2MJhk4O123K9rxeTTAYgqzof73F9Rag6DuzUVhUwl5q6fClTMx6QhC/DqbXOUWz0x\ne85gnJ5ahpt5FEXtGESHyLHxggHJHgl4HPkc/Z4MhY/VDx9CXyEYLxBAytFv9WSYpk5BWTUBrc3+\nEL5GIjFxBPaHDAYTW4HDaRFYdc0KiX0LNLUCo7+CebofjbZVR2rFDCzEW8ShiGbgo1iGBGxCI24b\n7kEOBeyQo0C5CNNkyeg734JLAkWnAA7dvRmpgpGY7V9EfLOoEPdqhjS373ZmXUE07sy5ip3uP6Fn\nwTn0oqwjqNZBtqzoKW4U7GC9IhtU7Ajp5H46dAHeH5VQMVKLUk13Wqg4TI4fY/DEEk3zfR30Wvcs\nZsB20KdxsuKvaUqPc0IrLlF9C8ZfYpCw4BtvjGklmzhvJJq8eIdju1ehW7XjsLxfmxLa6U5cWt4T\nUlBbgZ3qTdh/ZQp9MPtpaYiK90z8/AmmBk0s1i/+Y4Wp9WZLA/vU1dYSsXbjGNy3HsGrVka8n7AH\nPY99RU9jPMbs4KH4e97V8soXHeUiSw5s2o+LUmDaVbdKrrK0UeRxeV9UNzySyoeITOAZBu7vpccP\nPnauPR1rFHehtxxvV2yJmyKETrzNkbD7raTR735mpm/XYfMk0PfKjNRbIfPDj382q6YeGyolW1tW\nCj/+IbG8H/uQvNf0XDpf/HJ5/QqrW1AGGb5g/pydx8KWTerDCj+ORuWYIdluHn1ctjUJkeKYOlje\n7Js1PP2HQp+YFJ8DpWWbDmVL4/2fz9Io3vwk31ic7yxRfYNG9LX3P62RZBWMx1/LzNaYqOtrjebB\n0/YMh2LXCCIbxs8XZ658ntO4scN9dzrR5dz3lU8/+LM0VL6SUXtVYng1A9rVc5e+hN2gk80aprLM\nX5i59x0/cgouhO1h+g3ZMq1yuOda+Yrl1DlyJIhbiFtK2dYf6vS7de8DHPbEQC8vq8NcJMOVKyV9\nz3CeFw89UjodcxlgvdVTuzyX+kfGCCtnPj8wbUbl9cJGLba7Ttn1MD9ZtKhfTEGLjxg4IsMf4Tah\nQa1bT+z3Q5tv2tTcPPXcw8wPvppGiqIuo30dinG7t+2OPq5lRm8fNvYsUxiPHif/WjgZn/UAXkxc\n/spnxYr4EcMmdj7bL4XvMuhnMDv3ga3whvQxDOKA47Cu7w6vYV+0ZX0PH1Wpt3WTBV3EqpBzGFAN\n+CsbNRZNS+gjzcncrG7Yf7/0alcfXVN3j2Vbz5VM7tIFmysr4QujMQtv37bH33NrobxvAdyEhwg9\n+jdcPsuw8e/XcP3WEiEbWEx5pfGI5K6EX9U1ydgZJf9Ian6aMuhE9cnnT3007O5Xk9kRukx6pJmh\nmFxdoXMpE49u3ZfIB+Tt7zJ8DOFrzQXsuf3g3zZqx0j7qn36PW+TY++Hvz4HdVQK5rAOupoQcuwh\nENisqlhvT1Sl/ycD+KkUWNqxa9fEZ1OmPK89g2s9Zswcm2BXPE+Knt103+T0sqJ4cuzL1MsT679O\nf7HGNT089Ukb7YnLuTYC1NNTfSYAmEykPqrSiP4AMApAQxiMqwHkw2gwADhqWIMw3MKsiS3O2kLu\nZXjO91p7s19Fx9YXrcfYkds6mRntGa8N46XfYUXiMzSYNQDH0wfHPHvYdOXPzZcvPFnSvMNB7yPb\nVnzpY/1Nlo0DgR7Y0Wo3Rp+Dlo/ExYePBzj2Hbs9fv/cIl/wSIHWIxT6kQNGHP7r7JoguvZhdTzs\ndhXAxBF3Rsy8XP9yoslSOLj6URgz/CEmDIfdv6BaJ5fcdffgxoMJ7cT2YwBIJiPu8jzmtW+vrwXd\nZQ1CLARp4o1IJjw0VS7EPN+LJs18jV4AVsZ17mw4brcXrWLWfD04K4lAadHc30Xyr+UvDFh2vrEv\n5s7pbHy97iGqut7WN/wa+BAxyY6zSb/Le+SmhrRevPGdKeJj0Olh/d3PhAlTR+8zLASgN9yFGUDa\n3T0YVb8cSZt2o9adjxh05BRWXU6C/7BkDMRiDAPwyAjjXxpklClQcLi2seksdV5e7tg99Eb7W55e\ng9G5SREwlgNlAQwH0ApG0zJUNeRptGyI2uZTiOPjblb+jBU1/VDJPkdbv4kAXgGoZSzv1g/NH6zF\npM0bi2u9DaqcuvFaNZHZuX1rh6yP/p/MyQdeNgXwAsAykxHXAHy7dAnrjxzBb6yAGnYJGZc74Wvs\n32DcUmk0MZkWAoiCwTAcQAHg/hIoqwM8qgn0MGPOtFx8+5BJDx5sYSKmHwAcAhBjiDW/QH7daJRG\naymFSAjeBibstGxOD2zi6dByMBpUAD4D6KA3QAngKLcQz0QGTwCM2HEHD2LmYLykhKdeTysIIScA\nPMWu/ttR/LBsyBty/NDhbM3Oekj4rZO7rHQTAmCz1aQ8n0EIxgPoSim6mojpbFYIPniVYrZVC2d6\nNM7ve9igZxbUHXnKmEgSiQTwDO9Rc8RZZK11A//6GHKUufCvOxoq315QOmtigiOJbgchDKpqoo4n\noJMAPARIDIAxQPupmxG1+iccWKml1iQAICbTDgC5uN+pEA33rsS1eruwvsY0bH5kRPCLFtBEnqBt\nuwwBABMxDQAw6fHgnUmrBw68OeDA5F4bzmSe+7OFdu+9ZseHT1tLXrR3dGpoMpH1ABR6PR1vIiYG\nQA4FLG1vwTpvGZJbGzEUZ3o+hmf5XoMBBQA2A6hFKXW2aEHeSRKKlyUMyJNsytShd/b/xvNINptx\nEaA7UdV520ApUpRdf16+Y9zO2eWZrjHTpuL1jqOemz/nhDZcMf3ZPrpQMQrAVQBX6SK6fm2/fhFL\nhg373P2Gsa4x2SHv0FRd63rDhrsK3w0+6mLFt3QxnAA6gtJOADCJTEo/giNCGcriAhCw0x/+Qx8g\nbY0WdndiNG4BYAQQ023tkthLl44+jIlZN/zDhxX74+JmD01NnXagS5cfG1yaPk8mc+HE9Q5wEWC8\nnupvXY1Q5g369tWtXAzouKkhYTxedzX+xJ/J11J6/yCetXW70u4SVI48vZ7O3rh4kqlG5PXmiowQ\nuX6xSTrwR987Af6vDOcyp8RsWTQ5TSYjA4KCsHPvXqxrs7D5E7R5dBk5oWtR7esMfI3NQrX0EAjY\nAoUwE4srPQD+C9DksHF8QqnY5s6vZ14Nu7x56Z9dAeDYnN6/l9W8P/+3r+UYmtXqz027b87sQ8io\nn4GtwRpO9mFonNBj62vZ/5/b6H/rLf+nxCNMjiwHmCqFiDLfj/8xJlU1skFBJIBQVH1LAJEIQOl/\nxv9coywgyihEGYEoAyQZIMqrxqKcfB8TiAoCQUEgqhjwKgJBxUJQMRBlIiROgCQTIMp5iDIXJDkP\nSARyqxZymxoKCwOVmYeqWIKylEJZQqAoZyC3MlBYWbACgd1Ngs1Lgs2fwhZEUBHKwuYLVGocxKGu\nZBwKm8whK+dcrNUlEcZFIYfcpYFMUEHGK8EJcsgEGWQCB05gIRMYwvGECjIKl4KHS+GES1XJujRW\nzqWrUDm8yzwFrwo/jVXm55Wv8vTOV1vAqbMERpcPm1sJqdA4lUUy1vc94JkGyf0bkZQWkAoVUELA\nygkkTwGS0gWmPJCiOJpK5jqElIaKnMWjlCnzyuKL3d4wov0jg9RCF0xqlk32UShywgmhUaKIUJcL\ngRwHZzUtyn/QMbSeRqb2Zlkuhcqsj52s5Uk5tTorqBLOap6QwjyACDWRRwisl5qhXiInetkBiwyM\njYPapqbuNjeXv8vTHh5ewYfUTuECaj3T6nQWweEKKMkWapRZPwYyRRn+mqzcEPe03AidKLkIyEPC\ncY9Qr/ZTdOz8HA1iZYKM1zgg4xXlVCa7b+orGe/2k95/iWN9Gu6o5GOPiiXeqRq1VcZ7p+uc7m+U\nTKUvT4saVTAWv0p1AEdoO1/KNAjRCEVMNWRbo9mS4njYC2oTbZ43fAop/HKKEVhk5YmUQ7PYNHkK\n3vMfK7OQX1wqG6ggdDhDSU0nJTY3gK0kuO6tpicYIryx8GK+ReRcQhhDmPaiqNAzqBvLog2AxHLA\n6AP5NU9aDTnET5tJo0szSFxOPqWKQJcPH8+4yYvLUyJfXKyUI3+PYU+QnSvt5ftY7qh4RXQukZE3\n6RhKhvxYysBNgyfZTVCabIHd8pVa5Z6weEfxJV7h9pwrBZxwv5kGTG9om5WWt/fKzG/+sdy9xmeZ\nX5mmghHhpIGlPuQlVNggxqM2vmCK/Hypzdv//dWE+HzHD7dj6vneilVqbAyndkoKnc3sLjhKeUHB\nXZLpg07ZozXSp/3QlOdh5RUH/KAvHB8AACAASURBVMspLsQTmu7NSr1yg2AMKyLJHpXMvJf+cIqj\nIM/7Ad5mJV7EFiOj8hqKU4/AzClQw2ZBPZ0ckR56KPi2UBfHo7zhGwRHbUfdi1/wsl44ZrWeiXRb\nDfQsuYLlR3fgeW9/8APzUGSLRJmlGlQWDreFjrhSPQatrznR+xSPs/3VuN2WQ5MnPHo/TUUN7hOK\ns8/CkJKNET5AMgu4qxgMaOMG0SXh6jMe6Z+BH8gPKFbn4u2kLpDXrAf1qnBEfNVicmUpXssUCCY8\nSgQNzsaXoWevjQj2zYb36l/wXKuE3f4Qp9rsQYeaFegWBTjtDJ69ZrDDSeDQ6tAtpjpGe74Eky0D\nVBI+H2mNsrcNYIxNxfvwl2j0uSGap9XE27wjOCy8ByuXQz1vDuL8PdD72R3EXSrCl5KnWMZIOEAp\nqrux2N+6NrYba+Nb0UT4MNFo02orzvMX4MjIBMbPg+5Ba9gGFEEKsgHPbJD/1RrU+29I/fpjeDDB\noPQoyE51Q3Fea4AQfCBafOhow5t6FqRl9gETZYc3C9iKAOdRoFFNFs1aKqDNbYnCSgZM7Gv4bPsJ\nyweuQa0bQLUMJdbaK/DIl+BAq1BqNkvoWZhNwuVKbPSORMCAIAwIfwnjzjqYcz4ZRcG+4ODC+PBd\neGdshvoBf2NK9QV4+nYU+DLgN2YK2g3bine9fBGSlo+s0GoISr2OQO1SfLQxcHeLg7uUgXRlU0hh\nY9FgzwY8e+MALYxFq8YCfrk/ELdqXsfgD3fQyPmZZjJuzhtCfZldNYbUcPgxd4KzkDtxChYe9UXJ\n5+WQhu7Bmo/FuJLQFJLVgikfQ9EpOwT091+wKkcB92VTcbPBDUyaxSI8/yFmr9TA4dgHztOMnQvH\nY8N6ijciwc2vFFl11Xh26yoaO3lcVP6C5cr3uBtGUC5j6LA3IJVKX5RVNsadNirMG95bmmleSfhK\nb7J8dxFcBRGQRa6nbm1TSPG68eCCVkA31x2OcIdkX7MZKHvKoHosDT7Zidj4OVgatYvWcDqEwfmj\nZJOE93An1ytaavcrDnHb5d6l3jBz/ZBavy5UsWF4HbsZLmilClc5E5LdyfnDoR9PGNz8hhZG70f4\nvCNIetQTwYpGWPbpd+y8baDbsq5IokZZjIUL2Ok1H+W2w/2Ev4pb4v3Mi+huV6OHcATn9GftO+rt\nVR3fByzuDOR/ISW24KFD5edunF/jiOb6048WE37XZRzeV6GSctzaDbHgUOxQNC99hozimrYk72NZ\nv67UhGhYu/boudo3H9260V75a4qwc5mVG++sBdvsT3+4NyySF0zo9EFWKP/Tk7h0zjHvUREwhGge\n5ggHnuzjjvqvxe3ochTPWAGFpcTGu96Vy86uDZK8CSY8mYiQrJZ7ZpV07AONvxw9mxD5iZPcGWmw\nq1ni9W8kReua4NrheRL9mGMYmB/rc/qlXyVGiInIfjZNGzT0ppUZencS7fGpF5PH5vIjBgxnrz6W\niH9rFkv6jc91PAsJvCc2LfJcrPeOwEic5M5zPVYssTOEkkMLVsq7zJpLUz8+ZZcdr+k4sKUO3bdu\nqrVZs/M66Wa5XDO+Q8Wu+mWKCgGlY1/AipfDPGBc557014+WxtcDPR2XejoFhUZpMmDMpojA6uof\nCmY1SZacD5vAMvEnb05XwpQej2mz3GIp2HVwre9NInJtJ6bmC2NuaOQ0eYTrAL2h2KqY/M1+/OeQ\nmc9UUpddo3ct63dhXJ9snzs/T35Rs8lA7PzGBY9bvLvD21GyPYYCyefPF6f7jJ9/8JB1QqS+SwPl\nrdfN3ijPNHOWt0FIyDt2z8FmSZPNFfNzvhK/2CPPaBbfxjKka0FUiH9/04wZdwfFxq68nfIuCqCd\nbykvC+yVbm4rhNmbdbKKl5kzu23T60+69KleM8bdWLgp71yyVWLMrfGk7+U9m/fsjjBHGJZEftvI\n8uryed+82wGI+6Pfrh7Xhrcv3zxZ88GrmL8weeygGYsdyI7oiOgFE2A9nA3VyNlgoQBmHlh+MSa/\nsBumrdtc2AaNWhnwngJOFtp6YBYkYk1by7RpI73bIjVPedu5nGHQ0mDAZABpGDOmHQYOvIkBnTmM\n1GjBq8dAU7gWO59WorDNSUT7/Azzq0yUqebJr5lMCiey2BkJ1pj0dHIDfe/1qLOhw72Z/pXSkGbb\ngmCfFTyo3cm/Yx5xxsXGvoj6lPEy5qny1yYH/BD/R3n43/d1qzcNZgdM679EK5cW3rJiccOVmAQg\nmgCtFMBcR5Wz2QigKYDHADZZgZxIuXy2+fr1cQAmNJ1b0XX+vCET0k+0lja1nKRys4l0Rc1eqDWL\nrA5bemUmnVZn4e1Pr9QAfPRUP/affbrJRHYA+AlVwlEKgGwABhgNfgD2L3iHug92o4AwCSlbc5fG\n0oI81XXVfde1sA9vt6xNTTz+tfLk9QkYCyADQOvdGLFvNlaH7xq0gNWO2u7529gn1tCMQFMharfp\nhJK7cyntRAiOAHgJo0lgITTYn9Ou248joYIfdhkPomTIkuRJuV4+H+ipak1JEhkBYNDNJTdnThw9\n8VmGf8bo1BJxwR9+oLso7v2Q+kOYgleE3j51uyYICQXwsrwWYl5uwruxY/EmPe+ADxBDYW1+8XAD\nfnQHgHj/TYOJyUQAvAEwbZjBMH8Z1PWigq7eDe7b6ta7Dco/IlusyM0frPSk48d7AoCJmFYDYAzV\nGuVi6/Pl3JthpQuWDFi36GLL+TU2v/62+JwhYjCaqIxGMhVAG72ediVJZC8rIV1Ygp9PxqFvv/fo\ncGUBht2sCd26dPhiMWoAuA8gphDNR6bit+UitLUNRqQaDbh6D4crkrGryznAg4BK3+c7A0bTO1Sl\nDq1AVav3N6DojUFNmkItNKMZ2qckiawFIDMuNv6CmavMqJn6CaP2+GPZ3DRU6Gr9bM7xtygtQ7I2\nZZ0AIZ0BrEaVa2u9JKF0wADM9fHBJx8feJ5m4abJRJLmC11BTCZ3AJ8A/ACDoSWALcC910C+BpgX\ngDN/uOPw4a701Kmr33G7AeCEocWDZ8hs9RoFCYvh9DgD0Es9Wo4N7vSq87Nx5b2afV+L0wE01hsw\nAMCbv5pg/68dsQTA33n34ZMxAbRZHxr/nV/WRpVgEYWjc/bh04ZOZUttnH44hFdRg3ORdN6DWq0B\nVbFQokqY6mKEKQzAjDcJqAWA83+ruDkUjTvzYNwphQsASBI5AuCl93roblDMD2yCK0RE7buvofxx\nLDwEDhF0Ec3+TnRHAehDQBcDOAkE9gXyZwFSWg6CxwchrwMofQ4AxGSKAXAPmQfiUPbmE+JX2rGw\nzkUseTEElV8roIuZSvX6499x477j3O/3NckXfMrLjZrXS27zsb81TrzbcGjDx94L9VS/2mQiHgBS\nAXTR6+kLEzFNApAkMljKSpgPhb0Q1zqHAAjX62kJIeQ8gMeU0pUjR5Iup0/jQvPm8GQYDHjxApvz\n8lAIoA2l9CMhmAygG4AOiDvBNflxsn1u3TKzVuHa8KlM+fvkUe+LnaURwynFVZJEagEwAahJF9Hi\n7suWfWAlKftcixbt63z6VJT44fmtA27b2wOIpYtRAiAFwM+g9Pp1cn3IKIzaloOc4x7wGLQea1b9\nhFEjAPQGpcnEZNoJoIzq9TOjopLSCgp+CLFa26rd3K5Yvb1fFn75Mi+SmEw3AZwwGlAOYDaABrcN\nhq6BD4aemyJsf91IeSM6VajHlvDVuhhhGgYgE0bDju/3VOJW09GiUaGL7Fn5jU6IkuxR9fiz6++8\nnD51adL89QBw+zZZOmIEfsnLw2iepydIrxa5SHgSALv6C1SVkXhVax698Gr597X5K7B9JjDiPKWy\n8TfmtfviCC8Kz8itGRumzr3vHvHe56up27fJHgdWVMrEyQAa0UXU6kGIx3i1+8cZ9nK/w5oumGXd\nhOZ1N/1/uo2+r1U3ABOxGCv+T4lHMll/FyESqrJ5QAmRQIhEv5+jhPzTcA2gtKrZGqUMqTr+EZYY\nUMoQUAKJsoQhImUYF2VYQWIYnrIsLzEMTzmOB8vylGV5KpPx4DgXAJfE807C8w7K806G511EECgr\nCGAFAZwogmUZiCxLeEpBBJHKlXLGqVKoXGqlp6hSeFOVwpPK5V5ELvNgOM6dYVgPhmFlRKnOh0KV\nB7kyjyhUeaxMns+B2hi7A7zDAcHhgGh3QOB5EI0GcjcdlHIZ7BxLKjgqsylEdaXK5e5S23xFbXkw\noy0NlatLA7XlygKPItU3TZEsT17IFNJCvgTmShsxl/PEUkHh5c5QLze54KlWuWw2hjGXuORF1krW\nTaaCvywAWlmCwLAJoouJlayyEJS6iaxVbeYI5UQvR4AtknKWGLdMe6zfeyEq/I3SLTBbB49SHTxL\nFfAqYWDTAg4F4FYOKJ0gNrnIVrIuSSCVRZzG9cHuzaWUeWiyzZzcmlFqEUosjgAfQRXk61CF+VgU\naqWGKivDiX9WTaLIq4lUZQT94Kem5hAzgawChe5uAsg3ySvriYz79JbJTy/Bx4+AXK6SfHQR9kAm\nzlGdrS5G0mrkvZQuvXQ8V2WUJuvkHv5QhXexipruBbaiRKvwVeMtWeV+Ppy5IEKdKi+2B7rnuEKV\niewH6Qf1B6axqgBayYNwtgCIlMBU5zm9k/iIpPg/BQc3xJUY0ORTc4RlB8K9jKX+umyoQj8RMf6D\nyNRIBRv6lZWcMricMonxsBOGE0mR5IOCSiLlV1QQZVEFiXvDUq9kiVwudheeF/uLrEPN3VNUZ92V\nHG1MPYW21uqs01fmMvZg6P1uIUpX5Qsg95o5OPttZVhBuKfCyVbm5zcW8wrbeQ6t94Tv3OGUSgEq\nt95qUFZ+r4CvUfTcq1ali8sggBcAbwpk+AM5/Qj9EELJ0WOgb94xCOOaVeTVXqSuqFON09TJAWys\nZHuYKonXV3BQJFJEjxbgkcIhL4wgvSPY0Mtg3bbRahlFRFIHClnK+ZKY30Am+2EvDfQ9y/z0+T1G\nvS9GsUKGfTUDcc2rDnTFETBDDbP3N9SVP0drcwa6pQmILOewsmsruqt5dWKtvAaFVIrOfi5oiQ1H\nvnEQVZHQhs+jHBtEGt5jYH6qRsP3eejOZkNbGgEdvQ13PIMTjVCEVhC5SskR8Y18bn+NJAXdRQud\nN2Lcm6PjlZdoejUTn+t4Ifx9KR7P9ITfZxu8U1y4O9ADF+QhyDSFwv+VN7wRgWc/1sXnRB1Cs1wY\nvysTlztVw+MmHBq+f4l16//CCVofB8tqIYc/AndPG+QyC1QyAnu5F0rLLRiuGYe28u5Ii7LjWPgi\nvAkvAfNlEeSPumOYJR9WiDjiiITC8yNGhd5F77i3IEMOg7vQAin3c7Gt5VeYAxloOR5eKgFPMgPB\nKB0YXL0U/aoxYAiHk1J/PP3aHJ3PnMevpsfwdTngVMsht1twtHkiTrYehfshHlAuSEJx7mdo5M1h\nw0n0mjoY42PvwO0cwNcHYncDo+0s3sINrvY9kXNiLiRXOOLIBUzg0nFey+Fpw1Q0/FhIh+W6CdPE\njTIb1BineoN47Sus77MU1KsIE+vwCMiMw7XXo2H3yIJP0GlcV+RCEB2Y7KtE9bwaOOX2EecKXeh1\nArjxSQJhfABnCMrd38A6QkL1u9GoK8bgws/tsJ7dgyarzdB9q8CEab/CVN0XSgePysBoTD+zFb9e\nvgniJSK96BeMZE+hk4vBVGrF6B/nwHqsD9KLAtAm4IK4zjKO7TN/Dp58/oA25afx9NZ8lGpbAYs5\nkFQN2thWQBF+ER8Lf8Kn5n2BabHQZu2Dp7QcPepPxaWGB2GXVWLOudnQmKPEpY4WZr0yk/SQcv2T\no5/gcL07+HLtAoLbX0f6lcHI9m2Jgqzx4AeexemwNKH2Vx8u+vJoBNebhEd3OtBf27gTn4hbkNda\njOyjSzFlqg7RwitMm+XAT4MAT3cW69ynO6n4GW4vLyneXxCQ0ioKg28fQjzU6BGQgnvtPiOwPBdR\n9+pSj0LfAoXvzFx1ia7OskNdmE80FPzoFWJcg0/0Q1Egl2duAFJSDYj8jMbjOlv+1hRl8TpVPGbO\nFVDk4UCsVstG14Hq60CRfdKTTUAxePCSm/749puTQnvW2jvRP83oYLbxZ+l9EkRmcB3x0f9H3IoL\nEh09FrG6S12LRl4McrdxwVyZ5EsGMl2xYLX+nH/U2557nndFQcRILAiYivnmMRaFj9a8zLbC6wNb\n00Mp/0KSHtoQXzzflbpnjmydlycNFdYyGzpeSD4beSweLphbyq9fDNpyYezXshPibbeSiuSRjfkt\na/VlxxSO0EnnM2H4doVpNo7nXJLM/msPO0ZOknEnuUHiid0VE9kLZ3c2bbrp6o2/R/eInHSGrlzl\ngxF8CxdPZc+94UroJPvGHAvx0zbYsD3/3pDmHspJ7RR+pUHSxl1b2P31R+J6WCRsQ0bB6/fxUKpY\nXpPgx/V71P/e8vhH9XC8jhbTNmZAjXSsLFkmd6gvLkIStwbTK0vheYOCmUxBgsoUeLmrHg7MeIx+\nKcOV9EZtN+Vvr4udY56OczvZ8iRt8tmccywZIQ+2YvbPlkOLa1wpt3/s7aaKe/Qo+crWXbXbY4n6\nALuSAWX4HpGT7Q9X1nGrNnrM72Mb/jm1xgcV2z97YCS5dec4JuuawOeJPLjblJuESsXZr7rVwblD\n0Vidnrr2+GFdrdlbQ5ekHy1cPN3DkxNxwQD9ILTPy2O75Ga5zakZerTyhRZAdYWx1fTRI5j+XfJG\na3udOVhRRuyeo01Eff5AgEWdP2t1wfBp49K6kmrizN9PfPoyv9mphh3U1qZZnud7fGjRtg0eO2Wy\nO6sHDGiwZ5ShcwNT1t7dq1b56uz22qRv/y1BUn5szpl7uibArKfATuzZ8zlQHh7SbvdhmSDVFo+8\nXiTD9m2Jv6xZ87jLq1dSB5fLA6BKACXtkJ8998/OStR/ac/PD1OMGfOy2v65PWZfmbtv7jkEfyq+\n8/goCBrjriGdkRj17SW36wE4j6q6iQsANDLs2rIMQb2G3uzm34QTmdsnOnb962y8benGVqjM+wKd\nyQirsR50wW+wcsv1Q7NixDVpOUtfxpfXwXvdB/xafyK2XgMWdoJ2GzBe9hZ/iV4Qe6QZMQ/ASr2e\nXiKEbAJggdFYH5s3eyCvzB/tboXh3B4LXgkj4TvvGHr3NmP3lsEQmL0Aavb8xXTgSV2mS9noevba\n4juvl3OVdle4sDNobNykFigqfTZBH/HF/8tHAH2NpT286Z6RF38a9rM9279IM/rmcFo3I/7CysmT\nOsa7wT6rJnz0BuwD8IkAywHk/Ax02QicRRWZbHMLWFUBjOsNeMFo7APgF3Rq0WP88N+yhkdvLFhe\nscrdN+yTboT7phTjurERC/qPlWNyfTcjTFoAaQAa6Kn+CwCYTMQdQC29nj767hTZqqf6xO/X7gA4\nYDiH5thKBnv+vsM5+koQ+fNRb9537QT5thrrdJNeIiD3N1pICJkDIJ4He6Il7u5LQADp3nfdux6n\n1tc5jBPOMRjktQOoMZDSdEKQCOAaVrxpiCYlKafR+6bla+njkBDct8Dzcp8++T7okduSHgh/QJKI\nHFXEv+fqA6sPzxs0z2tBHVejhSk45mH1XO5ucz8dnR89+uaZm/u/E4+DAN6ZjPB79w7VJk9GAICJ\nHAdjQQK0khLLfB7RpQBATKbhAAayBsOG+yBHj2C9rDfObKiAblqP+XXt8HA7SqdPnwAAJmIKBfBy\nE6LqnR4ipaLHaScq352ER4Phk3q1NsfSyr8n0Xo9TCaiQJVjY7ThLkoBXKhcitMqAYEAWu88D+5R\nCfbtHU7/KXS7HYCFAvOz0aswA2OzJSg7A3j1JzqVBcPxYjulfati0RNAEoC6MJpOA3gEYB4AAe/c\nHmFOQitYZR6UgpIk4osqUaG+cf3pHtgxdil2jrFg+L7A8h3DL/T/YW1nl8zlThdROwghAB4A2Goy\n4jWAa0lJKHnyBPEH92BTz1EYy9kRBVolmBCTaR6AWBgMI6ruTw8r0N4ffi9uY+uqVrR3b+U/PPB/\nuI967crCxZ0eEFTn4PcmeKXyftOGWXGDW1PDie/rTYMqEdKgN6C9BDRgFyNo/VXs7NAC2/M7YHmr\ntnTlvzjmBQDXYTTeReqKJxGpN3Ky3TlPPvtHFfYfPEAFYdJ/YzEdQCMjTD8CyL7SEbMdKhTozvqf\nWI2Yey7KdP5PbNJ3Yeo06tROR/bfIlwsDyE2FG6fhyNDSKLR/yK6CgBfAHQkoBtR1THmdCgysz8j\niuMg+oD+t/AxMZnOAbiBuwZ/1F4/GO4Jvsg67ELoIDUIG071+qJ/YTcNQOProw8/ONSu3dqo3FxV\nkbt72ZpJgS6FXdlWT/Uvv+M2EsBoAM1hMKpRVULDDUA+Jm4+h36nquv1tP13zKIAPAOQSCnNSUwk\nRf7+OFNUhNYsi/wXL+BNKY39jpkMwGsAsynFBabn8At/DTzXtoa3TRp+vq6scPNTO0D8KYXzO26b\nAYh0Ef1l0YgR/bZ1736049OnCx4kJiblffhpmp0T29BFtPd33HqiqkB2HROM3GM8zpuP+dqN2Fg2\nGvNGyFH2Oyit9x2zQFQ1WmowaMcM4fjx41kBATce5eV1b9a9e/+Y81OXBQHYDaCm0QABVSl/6/RU\nf+RsDPcx6asxrKX0QJFb70qNn5/9znxf69X1VF9mMpGVALz0ejp2zcIZx2vHH+kHIpF3KQOMUxev\nb/0d3y4Ato0ahekZGVgCIB7hfaLQ/1YqOCdwvfNJ+vx0/+/4NgNwFhjXEdh2C0ADY9Pl+c4hx23y\nyHRS/roZLXlRbcrIk3s3fsdsD6rqJw+miyglBK2iFMl3ttBxTKzsEzXbaZt6YpkR/y8fkkTaA9gJ\n4DoWY8z/KfHo1sBxrv+6jQggMVVaEWWqxsD3GkdgQCgBBcMQSigjUcpIFAylYCTp+1iirEiJyBLw\nMobwMgY8xzA8x0giS6nEilRiJSpxVJJYSaKsBIAhnJNlOZ5hOBfLcDyhCieVlC5IKgcVFU64ODuc\nrB2STIKKU0uQ5KIkcAKVOJckylxU4iqpIKuEyNkIz1kZnq0gDoWTt3q4S1Y3T8aq9mQtKne5RalV\nWpRKQgmpcKO0zB2kxIsQXi5SNwsDzxKJ+EhlcAUUozLMLDmDzVTyK5KIj1niPIqoXFdClMoyxka1\nYrHoI+QRP5rHBDC5CGK/ccGyXCaIcVawgjI3jyqzsyHLyyMqhnFpPD0tam/vfCrTmKVKXam80Nep\nyvWBfzajcS92emudFj+t0+klKQXW6ktpuR/hKnxkqhI/jZJBBQ0uzUS4+TPg/Cjms2/ZtPACZ4mb\njPExJ/C+ZW0EH1u8zLPMXxmSI2P8C0Cc6kqHiuaVB1s/VLjRNBDYZUVoLLMg0Y0n7ko+5JWlIu4l\nb4lN5xBtVnlWK1PKFDzzuSyAFvFa1PHOABUkqeyT5GJfCFzgc3A1ciFV2MDcUMJ1nwH/joLLcUIe\n5A4p0R1MFxkqGvLyynL3CPaLX5juU1Cw8nVYNN76VSfZ5ZFQJstAQ6yw6+3gYIeitEySlZZTCGVE\nkBcyLF+MxDSN1CTTU0w0e1MVKjmnPJvRFBXR4IIiGmQ1k8cyFX9IbEgvif1lLjRgCLGKIaF37DqP\nEqG0KI6zlEcp1UECF9HuGc1o7UZCCsxS8LNsc1rOt7Jyn49eLp8vHpW6Es7D6u6UO3WWcv/GCmdE\nK90Pb/2Ynqd4ITpfwzgaPxXcBh2WO/2LkFYSC38hh/qkVyeyG93g+hqBO0onnoSwMHTfivr1H+D9\nIw/wN+NROy0eRQnPKNstmfgl8njxJAGHz4/GB4UAhG8BvnwG3krQucsQ6eUp5ZcVMkVlFEwCwDdj\nwHAc5J8jqS7DAHV6H5KDeAhO3++6LQFIGcA9hi4wT2wo/0i6+j9jIn5MgdvdEmhNcsQU87ga6osi\nhQytv5XBw8HgPNce92LdcUt6hhJzMQInjEJTwYV0eTbeMvcAvgS6oPHwF4PwRbgLvuAZuCsL0OR9\nDUywfYUfQlAOHla4gwULG4BwWKAl+djRfC9ONk5GvbQ+GNTxPcKikqseGd84+N8mKOwmoEJF8DYt\nAtq9tTD6wwNckydgmajEMP9nGGznUc1ix5XYrgirZFEa6cT7sI+odZ/Hzl79cbRta+DTI3BHjoJ/\nlQ45xwGVLrAsASEUlQ0jgGnL0PTRNyz+MxEMlePIYAGpIWawV7R49bolKBjUxglMQRoC0BpgHVBM\nnQNbpzRIlMXrjBrY+mYA8qt/QqXCAl1pLKbJnqFp7Yf4IgVh88tyfPhSAfFjIyi6dgISaqNNyhdE\nvmVxWR+CMrUdlYUn0OzALQi0AoFp9XGiPBUMcUFSr4OymTsaRy5BwcdayHnxBZ7WVDBUAgMWEbV1\nuO0/Ari1FF6MDcvxDNV1ZnxwBGO6vRm0Mjva9XiI1FYUk++vQ6tbn7E1siN2tDmAIJ0DE2J4pFiB\nI5kEDbPC4e2ThBc1/RE0az2ahyngPeU6Vn6rRL+vNdB4Xxly3Bi6YWQFGagNRMcQDhXzN+Khr4Tr\nHylySTfMac9j6lOClFqheB8bir5nHsKpUuJSdS/4uOtB+5zHjHWX8DmzFmaV1KdTaT75ZfIkpAsS\nLu7fjwn16+NBulZYl3SLGzPLBj42jKp+2UoCH19HemhnwOYBxZEi6kxSE1lSEMKsF8UvhQuYiR2j\n6eyrb5npbbvQwbeGkMlDJ9NmqZ3JiLs/ITnsDfa02c2n+WRK0flRikUnhmAkOwk1ez/E3H5jIDMF\nOYL+GquUEj5C/NAYtTDbme2klxpyT7s2XLdT8c18EdXSBLS/2hib7DswfGsimgSkgGEonebcYv5l\nw1p2boNXHjN2ziPRlYV0sHwn+yJJjm6rX1Admyb8UUggpzKyr/Ue+4e8Gvzjt9u9HqzR0LPvhrv2\ndWiSgXvXYmP2bsYpL4itjXJpOwAAIABJREFU1T5iaY5dHtxt8PHMWldbo/YmXxydUI7IvEeLVze/\nuNiVswWJ3hT9SiU2c6+D2dOAEXfd/1uy7YnDw0te5LSAnRwhh6ateP3zqrg6f6p8sYd2gFtFo8o8\ndMwnQFAwu1XxVlpDxjF17F/+zE2Jyj78ba/X2l43QwnSH3WVTO3HYqVsMr6SCHIAQ4UN3DjZ/Idy\n2ubuhjfxXyITPus2vtmZfqbOPu+40kJsdD9uuFzoJdRUdLzkq5lY2Zfchvg5I3DdXu8ti/+YPHvH\nhw8fBwVi1/begyOfXBj2eTdTXkidpJF72SxurdsXRLY3wuC9YAFOP3oEk4SCzkq9jfSOS7V3OSzJ\n75bWpO2qvfg82NotNvjXu+kB17stTX3Xba9VM9smDG+lafV3s6Jfnw/z2VJ7QuX1u5XaLi3d3yfX\nk8cOetz/9Fr6xR2NUtqBzU2GOy8AaE0XURshqAfQXQBZQSlOAgBJIo03XsHNcc9xRiYhmQJrDq6s\nhYdf3zt22Ojf3l9gfluCvvZoXJt74WjysLgNc35cPls4tWjhjdZ//dUIO3bs1x27O6MRBojvoLMX\n/O6mCy7MeXT77KnqjTfv1J7pqShgKBlvMMIHuebFmNg8Cp2nWqHMScaxS82x6GthZPawsxt2Lh//\n5NxGs7uyKKeB4c9TAJYCCDJAPwTArD14ZoxAZSmMhoWgSH/Xb1HxrOJVfqs32VvUisOL9JNwrSFy\n9b7zrpwX6xHMXuvyqe+uyy+igF6rtsuyJryLiKBZTefTc/tWgpAgm0KR+uukSarV27YJaoejtazN\ntLeofagcr4YPoLf+aAhAR4AWUKt9VUcvsI2SGe8HrzaL4ounB9jlyyNThw1r9YbSwb0pPVa1F8Qi\nAjp/r9vNb2HnOwTs2rXUkZbaMGf6C519GBrVcoKtC6MpB0AG8i4PQdqfx3Zt3dUyqiDqHqrePBoM\niw1mJKz8BpnH77TbuKUmYjoD0LsHu7de/LUh2CFBUGYrwC17jZJbmrr5sqRf4xrREYkM5d+ZTGQU\ngL56A9YB2BGEgGMBKP7lKfiSJ5fQQtTgCYBgvZ7yhHxPWzl8eAbk8inoP16B0NOxyKw7C3LfQWDZ\nOlizpjudMOESIbgAwHRVYTo6ZwWy3j4PF8hZ/0fCxacN2MGN8jSFsho/I/2nZTTuEEkiowEMGRaG\n1sPv/5jz9U5j91E/TSk7t+qc5rcfZ+WWRadW7xSA2buH0TX4L8GLIcAiAIUU2A+gggBlT4Gyh8Ce\naZROIyYTg+8ET9ut9k/HD4V3brjKkp/+C8kNOUWrxyQ+0zicmot0adyPAGAipqUA/PVUP+Z/79lN\nxPQXgFI91S8BAJOJ6AHs6vkIfcvvwoismhzCwtYgI2PmmCXm/JqKsrJfe9H63/f7bgA++wItbiHh\nXjP87dYOZmMGtM0Ho+bn4yhwS6Y08r/8ABcAXIXR1Lo57pf8joWxALxHmC4Ufd3RthbNVfn8JzaJ\nTAPQ1LjYeHfasGmzXkW8ugag74pDK7Ys67NsplVl1fynMxEhiQCuPTmErvYgnG/bFgpJQvHCCXi+\ncA8GsU54gVILABCTSQEgAxUVXRt0737zGtx02QjntpOAa1vPTWoPlSqGtmuX8S98jgF4alA1N+Ck\nqS2QrcIb/saeubVbBcLRviNtee87bv3x3f1guAtTx3RcvnoYK480wmrP5ZjxpBi+Sf1p8Xfc/iHG\ndSWQMS+w7UcravAcLC8HoEffSKDBfUpffseMoEow2gyjKR/AXwDyACTjcOgvOBp6glq54f/CbQmA\nasbFximIf5uNtb8qYNOU31g0Qbmh88bn1hXW1v8ibAZUEdFYkxGmzExcv3cPXpPj0Td+HuxcJa3+\nn1CTSYcqQa8VDIafAAwEEIquXXPRv38aHTq0zf9aV1Xuo06nA1EaOR9Ppsm5bsPLLl3+UaOQuAA9\n1Zf8J9ZEZgNI0BswBVViaTiADQ/Oo7PghtZ6PX3z3ymTxgBOAKiO62cf40n/SAR0eo/fk4OQnT2Z\nUnrpX+tNiyqR5wcjTOMB2AAI01F7UDI8N1KKzf+eM0kiVwCcjTuBLnPtMMSWobxLJ7jn18Buuoj+\niv8RTH4DEE9A/+kY9ud0/Dn3T8x8B0oH/I9Qk6k5gH14MrA5IKWi9l8f8WmzL+KXlVCDocH/ws0N\nwBcCV72pOzLfh5jNj7+519D/9bN3CQEJ0FO99B0zBlWOxC16Pd1vIqZVAAwAonGj3XXIhLt6Pd36\nL9yWA6hGKR06YABZcO8e5lVUQE4IdlqtKKaUzv0Xbh3wPdUNzVYHerVdmqGVtELWwcN3kKkvoxQD\n/4WZD6qE05Z0Ef3QeOvW8vdhYbph169v3ey1oQGAJLqIXvlnEqhyKh0CpTtNxLTMBttvGmgm62HQ\nA7gLSrf8C7fFAGpQvX5wfPy0eykpf7WIilr0/vMuQzyAewB2UL3+4HfcWgHYB6Dm9TaGhPn38PfJ\nOFwf/op2NBHTUQBv9VS//Dt2nqgS1FssNhnTZvjMclks1SqGNJvsRfV6ajKRMFQ9i3sbDHiEKgfo\nQUrpbtK4313IaSC9f6rG95/ki6oU0ImU0kuEIAlAGKUYvnfAyCGiGzsz6uHnxob3d5z/wkwF4DEc\n7gewsiwSQC9AnKDU9sm1W8/XQNU9vgdAEqrqM//jNvoTQAcAY+gieuP/XNpal6Q1AkslEEpBKAUr\nSYRQCoZKhJXoP+cJZUAFFhBZQGAIRI6hEkNACYHIMJAIASWEiiwLVhSJguch5wVGLghQ8AKR8SKV\n8ZTKXBJkPJVYgVKZIIERBYmBSwJcPEOdIkOdPEsFnhUkkeGpyAgQiZMI1MVQCYQTNUTFq1klr5LJ\nHUql3KlUcg6lirVotKREp6ElOg3KtFrilMngW1aGoJIy+JeWQWsro0pbmcjZK0TO6hBYKxGJVSaJ\ndq3EUy0EeZEgqXIEUVNIiVzgGI6TE0YuB6uUEUYhI4yCMoxKAqugVHSwRKgUOdFhl4mVNpnotDFO\nxsU73KTSyhC52RKlKiiLUxfaI9Q6F8+FyT6zEfIPTCTzAZH0GyKdxfDmnch0I/jsRZGhViJTphW9\nKmRoUuRkG1gs+Cr3wn11HG6ENxT+jo5zmqP8BRIuY4VqjErhojwBpS45ZRUFxRXct28SctI5oSJD\nCZlFHuiSMdXsahKeF4Pw/BrQ2nV4HfwBz6OeIjXiBSSFHbCrKRxKAQ65S14p50MkOamt4dSBSpAn\nKWHs6691iWRrB5k1QArjrdamYYUlrTqcIlEhN7y8rPka+tHbSbM11KFUcjY3wmWqK8jfkoTn2lKS\nQwNgzm4K7puEHwLeoF0ji+geHiPKNTZo1Jlsiq2AvVMo4YWdQssT3q8IxdqPTKntm4y1WDit1QYt\nlYgdPPnqBx9XTS7ar7YrJLQZ9w1xshTWSyyRXW9cV9zTqCf7xhEHxiKhqeOFODj1Btvi0xuSmhBG\ni1uweN3ak9x0dcNTVQLCP5VLuttyobTQj61oI7KljV2ItBSiu+saYl2n8fC2HKm3AxBvbomO6AyH\nXESZp4hgsxYlUjEu0HO4gRuI8ghFVJAfletqo4ytQULivqBzlz2gFOA4HhfOT8DjO33g5paB8jr3\nUN7REwWycPDnDkFR+hR8uR1SCoAagK4R0CQYaF0N8HoUh8h9C+DlfAB/egomVQNckzrjSmVfBJJc\n/ISt8KJnsZ8tQZqGon8vJRJeT4fTzMJV7Q5cES8RVmyFjFKk1VHjaUFvmC52h7XUCxwTjyat7uD1\nYCts1gpQnQ6Svx9Y3gHOKcE3PQXOO9VRlO8GWa+RUMscqPX8F+Q+mIbqdh56ZQYiqQXmsFwca2tG\nqv8ueBfl4cghF1x2YG1DBhcbECSm10WXyiYIDwBkbxKQ/rYIl6kR78XHGCqPwEzJjHChAGmIwRnv\nHqAtbWjQ7iTSXzXEu+vdMDtzBW4o2mJDUA2kGj5CHFz1n06+7oIs8xZc2SKg5MHE1MX/w917RkdV\nve/fn32mJpPeICRA6L13EDihIwgogiggTaRJEVFASoiIItIsCGLhK0gRpYN0MtKU3gIhQCghECC9\nTSYzc87+v5ioNJ/1vP7NWlmLbK7Z2ec6bd/Xvu57U2cqQfnnyTbWxHzPykeLgmme4kZQRDUW4vG7\niY+Sx738GeTSlHP8yW7KkRTYjU8/60pIxF18g/LQdLAnBpOs1WRovbP4GEvYumUsew8OpMHrs9m6\n4BC+HQS28jq+p8pyK6Yjlmp1cO7fjnxwATq5kNEaftcbI3/9nbebz+Pame/43ekkQkRTwDI0OQaN\ndNoD7yOobzXQcYgNi6Mv6V0783BGD0jzJaTyA7JTIlEaZaJ8ckGGeLKYvGGTGGnfwZlXW3B06RBK\nfK5xtvNZ/qhzGB+Pj/xw6wwRotXX3ffuKQkDI9nXsxDtvbHU6uairqEjh8vtINJg5WaJi55loZ85\ngs/ChvHgQjWmzYb9MoSbsghVGcC+aka+97URnOthcZAul18p0pf6f+RxmjVLchsHXXr+j317X+Ob\nXq/RYNJNbXvG2wZfoTNNaer5wTm2eP7iSf45/lXkMgbimPquKFcd8tpY8NlcXSt+9X1DQbdyRM2/\n57m3f+ZaMHSmYr0wRiaYPk6MvDphQ1qtdN92nDePYWK/9505IWnmEiGEqcRP9Dv2upx39oYI08/I\nD+p+ry2/E2u0zjzBbyEjyTtZ4UHtb7sGhXPoyvzGe07tdfw+Mr9xoKjQPVl+6Pu+8uG+YL1aWi2l\nrs8NuXLHFc+sKXWKzEUhxUVfHCmZMcQc3TflFfm6JcL1ztWvi+Or6oUDLhLz4/B2+vgl+zQZdPOc\nqcruC66T4/sdtbUJDHn1gittkD5+9Oi/YtILcj8wLr1vKLt2A3dv/F5U/Apm8rmNjWpEfe7h+u0k\nEpe9ENCSpOht8WXSTc2v5NweVo/INjoDD+iUH+Aidb2bAzV9OJFsZvq7Z9Ydtksl+3TjHdGTXP13\nd7aODVHkgeJungpFQWaTSPd867dKXC5q6l6hn+5hrvv5HL+5c9tum7mhqHDpRt/vryhUO9zZvWTU\nZGNwQWH2vPuDQq/6eGTBTxNe2zSux+rv3ynWAgYvZ9SWHTtCbisvDlJGW/wj+92Xirz3293OQUFo\nZS+QOH8KRQvyxnx3pm6/H7I6dJA5wHkSEvx7s7VHU06fjyO+p46hWwKxF4BTK1dybv16hsG62cLY\nb45t+jn57ukspY3lQP4Iz9tBGXdCMlcvrN5i1js5V3V9wKmUlPVNiZ31E+0+Hjls5VBPv+jqhuyh\nn/1x8vfuL+ys+FfWzaLb2aw+WIvuKxJp/mtNoJKMk/f+cw4TL/bE5LD71hd8gHd76a+zTEE1jqx3\nGb/51qFPqIup8484y+ZnvrVKjljdmf0XX583o+LeZs1Wu0ymvcDa2oNHZian1a6tVS/WzPPH6jmv\nD8hq8P33525ER59JiOUcMDd+Ni3sqn6HjV/bWPu9Ben2MDajiIhX5r27oecsm9+L91fE5aRspH8L\n4Ta2pcv+fUDaHGq/8CIPopqTfQFowKHY4fzVcggffno0llgzkJiQQEXhZshXawJd52rn2ZbV8dFj\n+4t5nzodQ3c2wrhwMdtjP5tYTNU9I3lYL0Bu/FUvslr72UpKNhZarQP9iovXiZeHfk/otT7y++Nh\nCBEGJL8OYzfAKgYPVoz9BmZ7+r5kY8mSda8uXTrg8xs3imMgir/3ShH4gsyIoci08usWua/H7fef\nnJ3x+3YZ3ewkIet1KaYCCLt9OlCXP2KDgQ0JcxIKgUJVqvvE4jrrqf95b4y+oVJVi+3C3hTYUsO3\n+4oW45wzu1bkwcZE/Ds15uKsuClNLOmG5FpyfnOAUjdICt7dnMZIUPJAC4IMewJOIFJV5TuPzV03\nYLGcZffuDxg4UCd96afwWgXatOlHQYEiL1woV3pctfEGQDW+r2zfNOYLpa37UvAuIpxNOrxVofgm\nfhG3pC2o9FoyApeAdxNaG+s7R6ycfdGckV7pUaWg7z7r/+hMLlVeisTnf0NLJ/JCfAM4BPwKrAFq\nSCllRyGGbobvT0FAJykdpby9DMyma9sJsW02bvhkxBtlXCHoKSP6nRj+3fjW5JkqyDda3gOwC3sI\n3mCp2d/uo9J2BbgDdFWleuWfdrv4A/gh9hCjWe5XE80UxCudLq/o/WtNq8JrQ3rIzY/xNgeoICG9\nL9tf3UaP6iO5eDGdRrUS4YMbUi79F0srYB3bj44T/q6PD9GxRjbBh/ouvdSJEyHLZbrPPwG6iBd+\nwK2W11p2H//7+EMDJw40G3TDr8MShvXa1mzbwUeLH73y5I0rdgO/2RMYs3Ahd/bsIejiFGpVXcld\nc7Zs8QTUbp8G1CQ2NnsfAX0aY4ws2zjmlmfOHJt86aWKj2Ptwt4C2NCdth84B6R+wsgbFaN6Nz/x\nQ+GFhhb0IFWqWilnAm8wvyz2D4qRTJLxfN15GW+/VZlKr3WTlZ4crphbytuHJYQm/slGp4E5517i\nyAt5ECgfC+6EwBsYlyuuwdoTF/Fumd6GYc3GcNv2lpTseoy3QLzFotWEOQkT+XBeDC5zxgfpGe1O\nVTv1qYz7V1Ao7fwAsMGeQAkwEFhZ9Qu+jN7KGqSc/hRvU4FGxMbOwBtcb+CjjwbTuvVk2bHj90/x\n1gb4eWN0ap3lr87MZ8/S/Y3rfNNlwaYZJzvK2FZPYO3ib2GqrRrLXCDJEc3Ekz9RjEI5VX0y0BVC\n7AM2kJBgwXF3AHpgfXr1MSFlWSll4ZNY4oAKCdi/AHZKMHamnUFDaSMlN57Axot2wA+VfqNrxm1u\n+FrRC0dx22FinIyTe5/iLQhIyce/SSD5u4DQZKpfq8711Uj5BBel6ZLHgYX8EdsVGES9+XZCWlyQ\nqvoEx6XcfQ4YNo3fbPr6lVfemf/RtaQWCdUvqFJ9/SnemgHbgZrEJgQCN7E4V7Cn+0C8BbXTH+PM\nD68rrf+SJZxYuJDiyEjST5+mGBgspTz1FG/bgSNS8rl4vfcVAu9W4NszR0D8T0p+eYq3yUBHGSd7\nTB437tMj9eqNv3JvQluH0b0diHli+3khmpaOuYadhEDgm8aMHRVAUhIQg5S5j/H2t2uy16gNQy8d\nPDjoaoMGf7Tb9M7c2sBSoK5U1X/6tgv7TuCAKtWlMzqKoen+bHlzW0IFYD9QVZXqP9eG3S4+AFqo\nquzbc+7Wsonlg7JuD1Xdpe+NI8AGVZWLS7lrBfwCVJelYk5puwH4HTgnpZxWytvf9197KUl6+tz+\n893GPwym89T/sfXH3VzrNVhK/q1rJEQZvOJdbWC4mEMAf7uNYIqMKxXC/6+JR+GD6rh14TU46AKv\nCPTUv6UAdAOKZkToCopmFIpuQNEMQkgjiseAIg1CaEYMukFo0iBd0izdmKQbo/ToJq/LSFd0oRt0\ndIOGVDSpKR6EMGLQLBg1E8bS2kLmAokpD0wFpT+FApPDW2/J4Q+OQCgOhOJgKA6RwhGCsTiIoCIb\ngQ4fgorNmN1mkWEwygyTQeYZ3UhjvsCUKzDm65iKNIxFGuYiDyaHB6Nb4Aw0UxJmwRUFrmiJq7zA\nFSJM0qn5Ghx6sF+WCA27L0LDHxgzs8Llg/xAmaMrwmlxCwJvIwOuQsBNRNA9SWAW0rdQGNxGTZo8\nilEKGYhBizAKylkRFf1dhsomo6yeE6BH3vMXtlSz8L0nhW4wUVDeJu+VgftKgbhf7NBvZwRyNyNC\nPMyKVLIyK5BfWJ6SkPJg9oE7WVgNuViNeViNuZrFnO8moNjtLIMsjvb1cQWXlGiG3EcmUZxXs2yh\noV50TkCj8MyQmj4P/Utyy0l5sb6wnGwoLlKfs5X95K1qORQFForMwADpNlmoc3W3Lo/d5ta5mkr6\n/VghpYqvOVOrYMpxNjeYtTqa03TJ5ec57BaGuzLKR3INq9hVUr3SjqLw2tcsd2MKfR/6SaVVFtTQ\nkHdMcDQIEVoEHdJCsQS04kz99pyuVZvIBxlUvXyHiKRsLFcUAvwyMdfLIaNiCNcja3C5Qi1qXJf0\n2JdLowuFVMlKJELswSofghBoeih3le6kiy5kVb5PTq2HMruiTno5hFI2jcJygqPG1qSLSLrJPbR0\nn8CRb5LpqYLy+3LFi2cyWRuUx48t/fFJDKZeUiMquKJINF7lTkW4/0pLXI3qEv7HLSJOWtAKapCe\n0RhNM1AxOonKVRK5kdSEO3dr08X/Np0CHxGVrXCv+kWy5SJ2Vc7lUrQBh18BaH5gcGCVAXQuimFU\n8gv4xh5Gr53I5fNlSDtUgw6noqifn0qQvECe4s9x32gu9iyg8RvJrNtkZNOGEhQBXbubGTbEyYMS\nC4kXm3DjeiVSRSbX7qYir9ylbM3JOM6/Rr6zKuCkKj8zxfAdXYLSyQ2z4kipz9v6lxiljdqWVA7U\nSCSr1WJkRCL+ipFXIkJwa01J1ctSUznM1vxkqhoM5LoV7hTDqBNleP3SPcLyjXyNidVuSYixMukl\n12nv50OXShV54+YtdD0cPVQhJOcGWeEBOHP9mB4cz6+pwxgU/COLCj7g9/pDKZPSAdfkpXzSOJZL\nzooUn05Cj+0ArkLEnZWIkMbI8HYoLhctC/bRrmAvUZ8OIO9+GAscPZhGPi3IRZAPWPFgYhGRHOIa\n0jCIgb0d9C7nTz+rTkzOy3zafS8hwQ8BKCwMYsGCuTLlZrj0FKFk5rfENnwnq5I2kmAu4cdWl3Hp\nILOAICMGSxEvpErqHX+RZT0vYSzXmOaNa9N94w5ZdtN5MTXH+4apBHwcCsm1fNyHI+oWdnp0MXjM\necF553xmhq0m+fPBiM1tydjWGN8uN6j86mFSypXj0y+/4o1DJzgp5/Fg3kfcz2/ByfkLGWa+VXKr\nYooxoCS4pOyjkEK3NHi+aprj2/3FRUEnKjaXW80dpWH6GMXS7h6zWvlwNFsn0leTr/sHyZ+vTFDW\nNq6G853hhFirsyj3PY7lRZKpPHBPqfim6JEhXIVulG4vGNKm690rFdqHGN6UL8hyzaY7Xn51777K\nkZaXr29tx2dDexP84SmteWIe28Ouefp/7HHFVkuzHd7f+cGS5avL/RJRmDPxkQymngmyOmWTtvoh\n0aNPkfRpOmLdO4QsFYwuTKfulKW2wHZT0l7qX0YB84oe7ypVElozdcAkqt9oIyec7kBDbb7rZgWT\nMnr8zG0LJof0GjsgyHRrazVRYcxxfWnlkcqVB6nXCh0y8pFzgu+aLfGGHr3+lMPXHdx5c+ZvnaIe\nxpiGcIo3jg/wxISdlZ9tO+82a+T4tS7nY65vDf/G3SLVOGj9G1u28OKaNUxNdXJJCaPe9lmVnQNH\nXzXpusn8de2BN4c418Wc/Zo3dB9a/rTK9Pa61Ym+s6p8cfnTr3pWDps+vSS14EIKPQnkWpdARoxU\nuHkzSk6dOmRYzOClP/XdZ5KRg6oSXj+JUZMtRA5XGLAlhdUvVSB7tYkRr8+gb98w36Rlgy7P2Ba+\n9NWJO3p916PTHWHUx5TPsy0IfeWPDa6PW0y+3NryNgs/yWHr6N8JStk0YUrTo7VaH1tRo4Pidpna\nTDupaZ/+ry4Xm181VB3iYntCzRb6w+VrT3bMD/omb7qRyUv0i+6rVacwcc9EAhrWpc+eCqzOfwXe\nMHD+z1hCoodxO9P35wFtiLp/JSGBhR99xCyGDq3OkCFHgPJAZ6mq5+12MSA/n/jevQkC9oOSC+4+\nvv4F5fRll1wx93Xj1Y9a6+3a/dalfbPX1y9aFGRxODKLwfABEM8s4yM8WtNF6z4W1Ysq8/pbI2Vh\nYVExyzsZMawrYWpUMQb3ReBPGSc/eu78JV60AtYD1eUcXgMmAq9KuLEhtI8nYsNWY/Nh5P5xr8c3\nM+XH487QxGpAvztk2rSXV3ftegRoAfQ0ut2jKvbqX6Ng7mz32Ivn7qSHhQ38tlevvUC1hFhy8K5k\nro9NoCYPdndmV/0ogptZqTPpveAbf81Z99Vv/kP/p9x4WJaWCcSOAdoSm7AaWA28B1QEgkiIfZ9i\n6wPGf5VCStUWscRGA3/170/LMaO57HcOca2osVbD5+rBeu87GoTZCJw7j5QGDWgROyxBo/uEAtIb\nfSG3/DS9dAJXESnviP79FCLPObjWc7bcvXRB6f/NkxChQBt8fKpQpcqfPHwYwpQpYcenTg2pBm+H\nSbn6yfkgS/xxD4k0FrgeaTbPLJnkmUIDs0TUkJICAGG3BwApJM2byaMDQ2ScbF16LhpSbfJxImK/\nkZ16Tvm7T7uw7zWTsVsv3//jl0bhW6jh3BVuyLGN/S0ymDPNa8u5p//BeuupNFVjeRuviBOmG2l4\neD87gOGqKv98bO7aBNjCnj07OXy4AZ98IgkKqkDFimWRcoq8cOHLx47rWyA/AfvJFYMNS38ZrpWz\nLKryqPVOc8A9fL9Mlv5TH7umXgFmb2tND79b1ZLk6OX+a9XVuflvrDaczmHb7ffl4McIiwQSndDA\nBw4Bg4Az0yBtOCRVk/If10hpQHoKnfl0VGfv/rJMeNmSR4FN9qWl6yPvFMt+res+fi7swv4REKVK\ndcRjbS2BH1Wp1n4CaxcdgBVdDvOJ+7p5PHsC6pWflLd3ZUt3R6uBQFWV7sd4CwZuzISXJxG8uSl3\n3JtodqANyf2dUE5Kr9vmMe4SMOirOHD444ksXbpKHxqe/3KP8eSbWkjJ5Sew8SIeiEyYkxCwqOci\nd73Uend/af3LqJtlb74p4+Qunuw4Flj+x36WeQStS0rY2fxjlob+xSyhe7c1f4y7YCCFBQteK7N7\n9w8TYPGMCRNm0rv3Ctmhw0ye+tiF/VgRhq96Ki8so9PDma/uc38yips7O8n2g5/A2UUbYP3xLGrP\nSOQK8O6oyqzpFcmXL3byBpeP8RaANzDuKuFDHVPyctzvfQi/50nZ7+kxCMFu4HcS7ACdyTe2pE8b\nX6QIl5Lip3h7H2iXItA+AAAgAElEQVSRMCfhY2C7JrSSrjO7hmsGrb6Mk6lPddwK+OXGWOql9SMF\nyGjTG4spnyFIeeQp3vzwOoM6ERu7BKjPrl1mfH2bSVV9Qogp5c3rPur3zSCq72g/9ecld7vdqfmd\nKtW5z2DtYiZQTY1lHbD7xliOpfXjuqrK4c9yIdoD37NiRUtq1Ehj586TLFrkkVJ2fBZLCN5gvlEC\n9q1X8HePo0mYlFR5BuvdUv0YsKTpTuIf5lLp7kA8CCJknCx+Go8QCwCTQF5Q0OZ6MFoENEPKO89A\n7fZXgA843Hko0rOPdgceIgwfSFV9Jj3JLuzlgfN5UVdqL5tYdGHK7DrnzE7fX1Wp/vgc3lYCRaoq\n37UL+ySWTMqh4YW3VVW2eQ5vA4F3geZDhjA0ORnXX3/xKVBByqcFOqrhFUPr0mA1OILLc/2lg0C0\nlOQ/xZuZ0npGMk7uPV2jhrHZG9cWAXkyTs5+Dm9rgDtIObP090lAE6Qc/AzUbn8beAOIlaoqS597\nJ4EFUlV/fYq3usBBoLoq1bzSts3AUVWqi5/izQfv/feqqsoTj7V/DZQD+j4uWpamSyZIKZc81jYb\n6Ii3XpTnMe4+AJpJyfPu4wC8tca602vE/2j84xCgiYyTmU9j7/uLN33drPi5Pu4FbRiYuuRfV13p\n3/+/JR51jW33/2tQitAxCF0aFZ2/fwxCYlAkRoMujEJiEhKDkOhSUCIFLs2AWxPSpRvwaApu3Yhb\nMwiPZsCtGfBoRmE0eAjwL5CBtkKC/AsJ9S+WAT6K8DNZ8DOYhc3og79iQZEWdM0kdY9ZSo9J97hN\nmstl0EpKFLfLJTRXCR63U9dLnJrUSjR0zWOw+ismWwAmX3+3yS/QabIGOhQRlidlUB4iIE8IkwuX\nMwCP20dafPKEopRQeD+UnJuB5KbaSHsQwO2MMHkvL0RmFAaQWxwgit1+WEwO/H2LZbBNo4yfUYT7\nhIpgYyQRWjABDoGtSMfi1HAqOVg9FozCSInJpTtNbrfT4HE4EPkF/rjdgQ6L5lti060lftaATKH5\nuTyPLEHGjAA/Q25Zo1JQzoTuEJS/9YAWiTcJT0/BkfNQTyzw01I94Zo1KhhnlQoGm09NQ4ijvFI7\nSVDnMlhLdIT5KsHaWUL0K/hquTwwNqJAb4bmqo/Ldpui+gl6caNzulLrngiuVGIoKBIkp/rqD/ID\nPPWjs4yVypcoxakK5nOC6OMaUYmw3eTDalpxxtOefE1F0hzBKcINW+ht3MJEz21EMJwL9OesOYZz\nWgvOair5wQ8QlezomdXh0hAMeWH4BaVQPihZj7QmaREB1zyyGsbMytHGW9G1RWo5b7pyxL00Am9l\nYr7rwD/DLYOydZF9+yIXH/1FjFKZV2xdae/yw+aG1AA/ckISqOTaR5OHThLKxrCmZBzHzL1o+9Kv\n9O/0JW4fgcttoazlAUX3wqi9ppCKRwvYXNmPKV3duLIb4Z+ioKWnk5+fgauwhICQIAxR0eQ3isXR\npgOUs6IInWr305i8aTUv7LnJVovKMepilhe53cRB4thXCb+Tw/gfbTS5UQ6BwtXqOqs65HMtz4iS\nXIzrZm3etCbRM8OGvXYCFaLvUKf5bUzNz4GiY7oYxuU/a/JbPZWY/FT6Rm3BLyoP/RIU1VK4nyVZ\nvd7E0dMeNF0gWrYkpN+LaAVF5C7/H0KTiCKQDkl7rGSicV9+Tq7si4KgE+lEUcgaqmJSnDjMTog8\ni7+xgMLM9ujtTiNaT0YWJ+FrgBhfuFoAEkG1vCDG/+nkXO1a/FwhmUnbp9L/nI2K8hf8ucBNXFTE\ngoIvdyxd+ejtDpxo5MuiTz7iTjcXlXpmEXFCo9YKF9nGYIKKikic7kNBOV/SdjYndM9IjtQy8+3F\nLCIqDSGyluR00xl4mrREcTkxr/6ONrm7sDkjGR7TGkvvPcQnS3x/70ibPxycoR+vUZMiDEwmAydX\n9apVKyjvz3ibGR8XUTkdvteqsLxlF1aG7NVfq1ZEdKhHLLznFHqgA+MVIX226mKS1p8vDCvwjL/N\nqIffMn3TSt5rFyyPVnKL8Wcdes8ko75cmSCWiWkG18K/CEp/Vzb0XBeza7gxpRiKstdoPocKw/Ve\nRR2zYoqlbd1rB32+qpVpWFzZRuPVVWTU4RRxwTOb8YYtXJrZXYq6rYTw0+BWmr54xudi0KM8sd4w\nEtOny/FZ9QEho37lnCtEWzJnsWeFIyvHGajlxTUMKRduue7/wegx8sTJqMTFCy/aRq552Wd1wKDI\novffx9QijYmyrayRXbFkaZX7rsud3g7gvcmSCpoUCYmeKM9o95eBnW2n8htzXyS5F1ceTFo3RMiq\nBkpi4WfKFOo8TCakk2dqmVGktRjxTvBDU7e254yer2vT//35HteNG3eq1vOJ/FK+a63wvlHu83Xe\n2dXhtcQNS9b1alTO4E5LkwbaBRdxLmElGXXfxfbrUbT+rRnOYSJ4pXieKejluXOT7z6qmuy7uHy9\nPYYX+anrUL3B3u4GT5mknFolnwYcblg79abr/Z/L3TOP2fySCO6zwirfbr0zjUvvxdSaeNLzkXmy\ndj6jVfEnK5cF9W29P+1R8MQJ6QHK94t/i/eXPw9WNqZrb25MMS/9YNe7fkHVEopzinwyl9U9V31q\nZps1LQfvHgrUAw706cOfgXmk3lJ4K7s5hjOzLWeKL0YefHH+7Wk33uG7Rx156c/9nF/wCZ0nMeJS\nPCstFVf/Gu/wNf6SO2FCkisvrwIbN+r4+naXsbFtj9JmoordrE2NOIhPzn3aJ8D1628x9n0z2qtm\nLFud1ImJZeHCEeScfYEr8dXGp/bY/cXK9dWOsGuXB98+vysyeklgWeX73Fw2y+9u72RVxmYabOzB\n7UV7yi6a3Xt9lfFv8tPMYSVrPy8ssvoNP1/Ax43RA3TfOdbhu3oOXu9pssQ6uqiqSBlMbMJrwL1Y\nYlcYMF7Ygl/AZzicZ5kuLXy4cDPHRwp4WZXqSbtd9AVmd+rEQ01jHQkJ+wGrVNUUu12YS0q4OXMm\nttOn6YF3sp8Eu78KDm4w3hRRqGWEB2VYz1nO7tgW2LRPH7+yhYU/KfBKOym5KAT7KX/0FEPbTjBk\nGCzVPdVJCk9yMF84sFz30HVuAI1+ag9k4S0620LGyZRn5i/xYh/wq4yT35Va7I/gTR3qkUvgi/f9\nw89XKkgL9qcg4AFlH4WRVQFoiJS3Sh00LYCXgVVV7t3rkhUQ4NsmMTFyV6tWi4B8qarTAOzC3gjY\n/f4Cep1u4vqdP1/NpeKQeaR8PX/ujg1XCsLLNF8wlZpSVe/a7cIKJFFsHcGLu9cBJkABGvLZB7Nx\nm15n5ryqqlTvl87BvgGcCQmUN2fS0xWCucVAct+uxP6LRfT54gtiVFU+ABBd35tDza3TuNXRT25f\n+e/kt+fo2cQcnsbl/jaZMOdvV0wIcK07LNnj7z+QgoJaTJuW32bRovy9brfVBpE8NoH2foUaAnnM\nH3fIG6QWraLSnRIM86Xk5ydwdnscUq/E4Y4dgZ7ARUyBR2m5sSGKuZpUvcdWyl074Md2dD4zaoKn\nzd1alP3h3U7Jqc63fFrJ1yo/3m+po+Em0FKNpTwwxJ7A13jTXqo+x9FwkEaN7Hz22Si6dg3ns892\nM2tWN0pKgqX8N3gUgkggsTKFrb9VTh97eVRZV9yawoOzChv1d2KoKCUPHrum/nakfJnQniFH1nQL\nDGt95uKMhxkDc9y0kXHy/JMXobceh/AWsQ4DzpyFL+vDQIN8Kniw27sBi+nS7qtw6/2X2pY5cGjz\n0CYzaJn1oeyoPuEwsQt7MN77qoUq1ZTStoWAQ5XqEwFeqYvmcKGHH146xkI8vNE/hs3DYtjaraMc\nxFOf0pSYEAlVgV2/wkdvwrliKds9i6UzsJQD9t8wEMMV/15MbJSLR6ksJU+eD29KzLVP1346tuX1\nltOy/LLC+k/uH6greriM+9cB8PcggJOOKJae/Jllwk162xeJVjzUQMr7PPURdvtSpHTSocOLQAjb\ntyv4+/eRqnryaaxd2PsCk2NRE4GBqzmRVp7imapUf3sGaxebgFOxfyCB+HXNyYr0YaCqSvtzeHsH\n6CG9da4Ot4ObR+BjKeWq5/DWENjDiJtNGJSazJaoZL6sli4lPZ/BxgtfvOe6d8KchK/OVDoTMOXN\nKbqcI+s9jS3tfCew155AJcsjXm35Gn4CIp6+n0t5mwK0IDZ2I1FR37FmjQMhoqSqPhP3/e0+6vtC\nwuDs1ssO71n4yy2LbnhNlerpZ7DeOlwpxgJav9CLlUd2gObHN6oqNz6HN4E3bWk5CQll6N+/GRkZ\n56WUC55/eCwArAnYfxtJk9438PeVkjHPxcaLXkDc2CwGHLQyONlGSxnnrR30nI6jgEsF+FW9SP16\nbTi+EilrPBdqtxvwOn+GUZJ1BUvobSBcqmrJ8/B2YV+L9121GHiAt2ZZ6jM4uwjHK9x0UFWZWCom\nXVNVufDZ4f5T5+pHKeUPQogPgUgp5fjnHx6fAyFSMkIIBgBvSsmLz8XGi954r+MGgBHv86upjJO3\nn9Nxebw7AzYsxV0CxiHlH89A7XYj3gLXU6Wq7hB2e2+8NcAaS9WbwvcEH8L+I/BAleqHdmFvgtfl\nVFWV6jPiX2lq8yC83Em7XbyONwWxqar+64DyDlnUAw4A1aSU+UKITngXc5pI+a/Dq5Q3X7wiaw8p\nOfdYezdgJbAHeF9K8kS8WADUB3o87tD6u7ZRTA5/JH2Nj1WjATDicUH3/5x41Ov9ZQ5Kj8ibria9\nAy1NY/u7XSq60AVCN4CuSKEbELqC0BXQDAjdgJCKFB5FYNJ0fJyasLo0LC4da4kurC4di0vD4pLC\n4tKFxQ0Wl8Sgu0G6pBQuKXDpunRJXbh1j3SjSTduqUuPRypuzQBCUQw+ulCsCsJiVDSrFbfFKhy+\nNh4G+pEVYCPbz488m790mUwEFuQRnJdDaF4OYXm5RGTniHKPnERmakRkSUIKwGJWkD4mRIEL6cjD\nZckmz6+IrACNbJuvzPPx1XOtNj3Px6YXWm2y0OKH22UUJcUWUezwMZY4zLiK0UuKHeQ685Rcd7bw\nsTr0yqF51K7goMRXx5lpE8E5uqhQVCiqF+VSJ6+ImFzJbZuZZF8zVw2aTHIVE2CElgZoVoCIcMJf\n5QQHq1eWJys35nbZZjhCq4i8CoGgea1iQnoITH6A7WoSyuUTFKReJjs7i8p+4TQzNZK1PPWIKakp\nbJ4AbpiTOclpeaj4hnCZogj2rYPFWgVdiaHYUx7/QAeNGh+iSpWLnDzRjds3GhAa9IjQiOseU8AF\nD6aTStnsm8awXIcILfYIaxHk5YFPkJFHESbyYjyIGFA80TL0XlvqHXhJpIUG80f9VO2U3wWH4XaG\nu6QolUz9gs1VVGD2y1WEK0Onbh1oVh/aeARtjkGZNMm1aCtGo0ZUtpu8HCNz6cAmvS/59MZfZFMt\n4iTRVdII9A8m0lkeoRhJqpjPBWkhPTGGgMyb9IuMY2DaFaplwW+hDTjAaMzlfahiTaX5+du8ULSe\nYxVcxLeTVMnQmfGnjo8HdkUJzlc0ENxEp2pEADdTwtFSIPpeAY1vFhJt8GF5x5dY81JPsssGId0K\nvkccRO6/wIM361Bc3g9DhhV3lBtxzUboCY1OyXn0vlZCuMOHQmsRR2r8RYuUxiiaCavbQklYOj5t\njnIzJZQySe053f0v2r7xJbbQfIzZoPl7nYD5eaH8cbwTB/ZfJS8/ibfeNmM5UIzjmMaXuiBH8z5f\n/A2CN5E0D4S1vl3Yn/YDEbajrDHOJkdGsyhkPOfSuuH2WAgKukTVCvsoKimSKbVPCVfFS3Q4Npm3\n79TnQO8UztbO5LK+DQouY4roSbSxJZ1P3qQoazOTjt8nMNTAjj7NWGafQ2yhgx53sqmRd4w0vwYc\nb10OY/ttDPllPRte6MD03mNwbfgN8+0E2drwmevl8olKK8M6U3K5Othz2rrPPfJxnu1dzt969C+m\nfhdENXdLlomyGCz99dToOyKipp9wPHDL65dcor3Wlnc8U9hINIFNV9F91ldsKzCw6s5LuNbEwdVM\nwODE1tjaaMTPzOo5jqUpJv68NNzh+TXZGpp7QPlFhnIvbCLT+30r74Wmi3I55Wi2rZJ2/PYxw1pz\nmLRENRb7wnz0lRe/URxdHPSL2Fm48ocJJkVIT44VrUv9xT7nb4w2NX3tvLvPGef5j2cW1u/w6yBL\ncoyDT5poMjy3nGTZ+O28ssmxpdzp3uvSpM/i+hRVyrauefTeloGJrc77jt//iSml+C0mir+43s7o\nCky6Zpj5KFzpS5aYwMvuVbx4Zsvw36wBrY/W8Ly30JC+7j3z3XuVtPg5vxlEicbYt95xdm2/uSg4\nr2RQ80HU7VdPzC9Q/EWLGsudCwb5+7pmfQgNrpcQavHQ6Csb236Fe/7buR39FvduHYCVldfMf/H7\noMWjJhx61KQok+vZk/iKJD4p9zvlzq6jYmfA4xd056IyunnV8ncflTQ1ovTsF2IInRsofm7X8cGw\neqsib5/ScqfXnbE+rUqblzn0hv/U9bnmd29xOUo0GasZrx6jlzOL8LLT2HRvOdXYQxf6PJoT5qOg\nX44rMz1o2Q8tfJhe77culxIjfjW/0nFjh1by5WMJ+ukKHbdaLk+snRVIVv8fTA31nyoW7t5x8+7N\nqMQ/xtW4P1EkTSZ6xgVT+qp6Iizfse/BwIo/+RX7ffHD8h/8fmn9y83BkzcvDzLxeqfDjBUeo/3z\n9fOsv7XYYkwPTVuX8sXdIXa7CABOA3NiYzkCnL8ER2tZaHqvD2UsGZToFvKTP+DB3Z18NG0R66fC\nsdY06dqM04eAn0a9O3nIzhYtat7TtJNERt6XHTrk5BDUK4JHPh5M7zFH/AEkEtZuOHXiN3L69CU+\njNcI9XuT9es/xl1QiTMjmxDZYwsVBw+QsbHbXAQnHmfzaAdOZyLOCCsP5CTGvneYT+xN+OJMJm1/\nKC83jhZ2+whgxEHZQVMSazcuqnLNYTFrD4xd9+XN+ywv3NDkZPlp4rOBqiq32IU9Cu/ErmkssfUE\nfpsgRJccbpXArReBeqpUX4N/AtIjx45xfOZMegD1pJQ6wLffik8KC3lvyhR6SelNDxBCzABzY3DE\nVq6cGHDrVj3nhg0Vb61YEZSVkODTBo51kNJ0xIulGbCF6bY7ZDgaEoUPy7iLceJfuEf3ZVTTbnJe\n4QEAES+mAu2AnjLusZXFePEC3lSh6jKu1FkhREO8VvERBdi+BxE+gh+y22NfO47lE4DYvye8wm63\n4t3CeRRe0WkP3oKeh/BOjmtKVX30998rneRmxB6SrUmaW5WMBEOEqe/y7+a/M+vH4fTdulTd8g/W\nLgYA79HxwF50Qx3gL8o82MvX75zhQKdh6ooN/zh+hBBlgctxcUzq1JBPKv1EtHTwXpurLKhWjUkH\nDsiv/8HGzhFU35HHozo/yy2rx/7TPqppNvcbr5c7Vv5TfLa08zklUMUKLzJ1qge7fe/uEyc6d4HP\nFSmfWNn99ysc6Mtd0rEmHSe8MfDCMyKB1wlyg3PvrCb/sgWwU3HIUiq+aZexHd54uk+7sB8J4/Af\ntU1xr97rwc+5W+dNzaHJ1Hay6zfPYO1iLhCmqnJM6e+LgUJVfXZFXAjRFVjIoUM6eXk/sHv3J6xZ\ns0M6HK8/iyUeqJyAPR/gEBFvfk6N88XS0PYZbLxQgR+3t2GOv5G3LuVR970L5LglVR+/Bks7DgGu\nLYL+U2BDDJRcgwAThCOl6ynevAJnruknXm7zGRYth51Hy2CUlR+/1h7jLR6ooEp1mF3YBd56MC+p\nUr30HN46AV93Osw+TTJ8bXMyy/kwTlWfcvx4eQsHkvfDjE7wdTtIPgJfy8fqlzzGm9c5UCv/O745\nu4JPa15mX9ldUjLtaWwpd18IXbgOfXTotbUvrM3/vtP3l2XckzVlHuu8PzDBnkB66FEa1p1FlpCy\n5XOhdnsMcJp+/ZZgs41m1SoDQkT/R0BqAG5sJPqTn4j5ZCdHLcLr4ip4Dm/VgD9/uUvrtals39aa\naCEIU9WnxC4vb2bgigKj3DDEDC9q3mD0GedKKXdrgWQS7A/o26oX2ZadUrLiudh4MRp4OWFOwowh\n44aMSg1PzZZxcurzsAjRCPg9+V2alt3L8MAr1H26bs9jvPkCKXg83Xj4sCNRUU2lqj5zj/7DR6n7\nKNk//3CNgoCjwD91e57B2sUcvG7UccAjoKKqyv/aprwrXmGlHpAM9JXy39pIT2KJxCuw1MRbyPsb\nKdn6XGy8UPA+ryfhXQhIkXFy0X8dH0L8iDflzgNE8x9CDICw28cA3fCKDm9JVe3+X1i7sDcGtgJ9\ngZ9VqT5XlAKw28W4UlwX4D7QSlWfXRzxDlc0AXbh5WIfMF1KefD5WALxCl49gQ+AA1Ly3XOxXpH8\nIN6U21xg2H+Kbt7OPwYqAN9Q+t7lPwQNYbf3xOvWaYB3bjVDquqO52Htwh6Ntx5cA7yFzH9Xpbrs\nuVj7v2nFeNN3DwOdVfUpQf+fIYs1eM/1d6XjGCilPPR8LOOAF6WkhxAE471WY4G3pOTAPzhvavNB\n4JCMk/HPq21U2mEfvKlsm4DpSFn0f048erP2M4sNz/1oKOhSSA8GoUmBGwMaAk0qeKSChoJHCjQE\nHmnCpfvgllbc0geP9MEtfXFLXzyaDbe04db9cOs2FOHC13gPq5KOzfAQf1MWfqZc/I15+BkL8TcU\nST/FiQ0XipDkGizkCR+ZI31FnuZHjieIfGeQkMX+aE5/NI8FTTOj60ZMJgdmkwOzqVhazCWYzU7d\nYnLqZkuRZrUU6WZLvvQxFWA1OhRNOoxFLo+xoEAXuYUa2VmC7GwocUGQzUyI1ZcAqz8+Zj8eFmeS\nmpGNj4+gQvlyhJeJwWqthqekNjk5NeX9B5XEw4zySKkgdYXwwEdEB2VQ3jefSgYXZXU3Hnkfs+cc\nFi7jo9ymjDOTEoORh6aqFMsWuHybYSrrQCl7C5/AFBlku0Ww9Z4IsOaSERqMy2Ci0p37yIfgKPCR\n+c4ALSO/qnYnp65yqzjM+KjEIPI9GThKHqLrxUT4BFLZYqaJ4sBMCXfM+Vwum8a5srlcz2wP1/rA\nzVgMLh80YSO02n1iw65R161TNdVKUJ7O/fBsUknjYs4Niow3CLZlE+Nzj6rBDTBpvSib50tBx9Mo\nbQ4TXCYZZ0YIGReqczYjiCNGBwEhbppXyqVp1C0M0sXh0yZ2/OLgXooGEvz9DTSuYKO3wUx+ps5v\nOS4uO4rx8w1A0xSKHGb8DfMo0XpTTBBWg5sqMWmYbEUkJ9VA0QQGKXFLA7V87xDQdDWGKqtofecu\nAy6CnwccVkGeUTKtK5ypCK0MUEfzpXp2G/SkUHzO3qF1VhKBegHnhE5TKdGB6wYTv9dtyOqXutPQ\nmcWkvT8RkufDpLff4lSDWJw2E6ZiSfU7D2hp+oOajntYVo8k6JGNlRG3OHommfLsZTi9aCVakM19\ntsjtnDWcJMZcj7FVumGZuoRLIpeFe1uRv+djov18GDYsngcPKrBlyzjS06281GMJI0d8y/4FJtb9\nVUCfuoFYY3rg1o9T/fxtjG64Vxdum0JxHy/mz2IHU8Pq8L+SveQX+vPWuJlavQabb3x10cd81Pdu\nBYvbKsrfqy3yD30h3Lk1eKXqan7qPp+i9HpE7BrKEM0PW4v73KrmoOLlioikAua/OoeSh3VQts1n\nhoznA9MWUofrJL4ciM1QTHFmlCs4NMXod0vq9aajpMca9csDfdmc3E75X1RXhQIFZe1ndFFqk308\n++hJ08koejYIZ+BsP9b8BBnbJdcQc/N7eFoUjTf+LCrQzjad285tWrCH4hoMNkuGmzdjpLUSJ8cM\nzxL+uVXdU3vsNNkCdOam1eZR/teIwiDUpnG8E7CFjw610y58MUAj6l4qr6yowl9lXNaTyZaF0kJ9\nZZ52NcQ/KzX9f6E/yqOGN9t1z/566riZ8374Ydu4LZuP7A+ucm1W4dpul2pUpkmXk56YqwsLD7u/\nMt0/X8U2oO7FIxmBs3ZUd+4fU9n3lZjvX3yJW+4jmO+vLVnQuETWCETZnIZn/V2s79cgrnkIS2JJ\nmF7pJu99OQGTu/Ivb3W/9NOiFPqFfkiONh6U5pwU/Vh6bB8DxiVg74vQe7KvSz57u6Aca9zIPG6e\nOUUvezMoJM/qZyq+1HwYl3zu8/aZOeFaRnBgmVEzM0SzjgW/HJz428bcEtsa4mf60qmLwDc4h6VZ\nMTz8tBDvpMwM2h2DQau0c21468yhS0IvOFvmKwj/qhSeO0Zoy1+sbUIiIlZdqVAhK8IZNXD36Zo0\nYzm73h3t17xZG78axuRHztzK4fff9l0n8LhT2NSrBtmukDKbaPVAZxXwnWBtA4wjRlHJKcngPLm0\nMOJqtYOXdhRhk6/xS18t4WhDPOJVOreruYbfDnS2zO38wDXgZo5sHfjAaJrx+lLrl9z3lXxSq14C\n9kjAPmDCG4kP//ywDUkjHbiN6UwOWi9MRW99vuZzf4fFkTF7wOx6Ce3/3dkj9g8eKrqySUjxUDNo\n9RLaU4I3Nz7r72BVCDEnHJo/giauYHx0I7azy5hScyzr38jkqhFu74TGSKkLQXNgy+/GLp3WTGtx\nfkNsbP6dAQN2l8/IqGGj8LYDW66UjAIQ8WII8A5t999CMXYBegBTAR+uzG1GyaMU+e6lJqUv5krA\nqTOs2JZPdUMxxa8c4Ui/rnS9XoMFF/y4dtlf3mgJUFqM9692/HE4Pm/RAO7E+LD6zXs3wgJuT55a\ntsc8MWPMePXSyr/f43Zhn4VXJOovhN8M4K8Edl7CK6T843IAsNtFCynZ1LMnmQ4HM6WUOxs1Eg1m\nz+bs//7HtG3b5OePzSVswDXYuL9FC992vXqtCDcac7dOnZr6OgyeJeXHnz4592ATVfYW0btba36h\nCohFuM9PoGaA92kAACAASURBVOKRr+TJce/9g/Na7M8DM2Sc3PJY+0FgrYyTT6YGCLEMkDpC7cfG\n8EeUWXmEdu8Bm5622ZfWofkYaCBV1VPathTQpao+UXDVLry7yUz/hHl/Nbw/hMQPfxt26X/vtjvM\n5aG31ScEiFLh7QTXqq1j1MoZQAN6bz3P6+sfqq89fMZNIIT4WAjKHzpIx/IbWPJzOSYvWYI7L49K\nz6QndH1vEnV+/ZzL/QLl3kUO0X3iQGpv+omzI/xlQnzxUx0HAddfgB3HhDDESNnzOihGqPh3QeJn\nx8IrwIdANN7J9Nnn4uz2ebgLKnC8Vw+EoYA2u8wYLL3/wwnSHeSC9nS0evALPM5mm8QYrUr1mUCz\ndGU+GW/9iEy8uxKpqiqTn8ObAM4zYsSfDBo0kn790snMHCqlPPAsFn/g2hvceX8kt9aMpvH1ZALm\nS8kzqSUAIl787mvg4K4XmDP7MqlHMtko42T887AIMQOoK6DCfAiaCieQz6bvlPLWHviRrm330eFR\nLB8kp8lYtdPzsHZhD8K7It4SCALWAjVV+RzXiPeaO3Ypj00fXeGdjS0JEoKyqiqf65QQQiwygSUP\nqtugmYQ68jmOHy+Wl4HpHLT/Ttd2A/Eog6TkxHOx8aIicPbbb78dM3nI5DFF1qJvZZy3KPtzOjYC\nyVkt+cDvOi9bsriMlJ8+FwsIu30DLtcpcnKCKFMmQqrqqP/C2oV9EtAqH+PWADxDVal2/U+sXXwB\nGPAKzxNUVXb+zzF4Ba+pwFBgq5TymXSqf7H8XVS9Dt6C242l5Bk3CvzznEsCRuANXCfJOHn4v/pG\niF+BU0BzYAdS/vSfULv9XbwCfD5wXKrqt/+FfWzntcVAc1Wqz6Qm/YP1FjK+AUwHhjwv9erf4Yq/\n05c24g3+o55+tj2J5xvAhZePqKdTr57Axos3gSF4C3f3kXHyGXH1sY5r410guAEsQMrt/wn1Cm+3\n8ApZO6SqLvkvLIBd2BMAP+CEKtV3/hPnFUFO43UVvaCqsuH/V79CiO/w7szWCSgr5b9pqM9ieQsv\nZ7WAGlLy8D+x3h3r9uE9xsUy7lnX2GMd++NNG7sJbEfKz/4T6hXJDwJOIBRo+Tyn298fu7DPx3t9\nRuFNYXvuMwvAbhcvA3PwuqUWq6r84b+HLCrjvUeuAbuk9O7g+Hws5lLc98BovLuITvs7XfsJbLyI\nxHv+vgHe5qnaRo91GoK3mHZbYISAQ/+nxKMlfd4BASC9tY0eW2T65zCFRMGIkEYUaUSRJhTdhKJ7\n24zSjNAMCGkEjwHNpOExl+A2F6EZi9EUB7riQFeKpbd+YLEQOEB3YgJMBGIUAeAJlB63nygpseF2\n+eHRLegYkcKIlEYJCAMlGKUbs16CRSvBqDvRhUtqogSP9OCRbulCCl0RmLBiFH7CiD8Sf0rwR9dt\nGNy+WIot+DqM+BUq+DogOxiygzzk+LnI9nVRaMuTxbZcHIY84XRngjMHY0kWip6J0RKEbo7A4xHI\n/EJpLMwj0JUjI0SOCNcLiXAXE1biEpaiQNy++dwP0rkf9P/aO+8wq6qr/3/WuXV6g2EoQwcpAtKL\nhQNSrLEbsZsY1BhNjCXGaMYxlry+0Rg1Go2994LGLhzsFEUQpPc2MDC93Ln3nrN+f5wLUmYGn7xR\njL/9eZ77MPfOdw77ln3u2Wuv9V1CWa5QnmVRkZmO5xWRWX0AabEeuJltiUoGQc+lsrGczWua2LYp\nl23bBpCIHUokUkHHdnMZ1HYd460g3TcVwbZ8lgfgpbzNfNmmjJrCzdDxE+iwkGgsjS6STr+8PHqk\nZ5IRhHk1jXzdtI2NuoWxG0OcuNTDXp2ky1YPy4NNIViUls7K9FwOTNQxqLqRqkgWs9odwuKCI6jK\n70N2kxCNKd1XQ3o9rCpooEd5Ohs6weouStvtQrvaempHfUlav49p130OGXnbERc/cV9BEkAAdE2E\nym25rK0vYnZlexZsFjYuXUX1mrVoIuF/JqNRrG7diORkElcYFIzz854V9BpfTvkXQ3nksRv4eNtI\nEgQ5gI1M5GMGsIAXg+OZ6Q0nGH2SWNMtHJI+kkFterNhyKuEZQtW4yT6rJtEXW4bNmXOYtyqB5m8\nrpw72x/BsgMHMmnIgwzfso3MFcLqzuksLspl3boMtk/vzOF5m3j0jNOY26MXQ2Y8z4DCBjr0r6N9\n5lqathWydm57piV68dXkCTBrJaMXTefS90+nIbKYLZ1f4vPiOsrrthGL5LAt3Iuj147g4FWDuePI\nO1mVX8dvexQwpNdc1r92NrfO3sTir6ogrZoOB27lhqkhitIT6M3XsTFeTFX+K7zyxf0Ewi7XnRyk\n80fZmlau3tIDE3L5skarY73LnYXgdYdPVsI9oVd0+aojZWSvT4n0uqPpnA9Ob1jsFjX9LTGkcBhV\nVu6Q1933u7y/uvrMU7q1ee5i2dqhzvI+vILoF0fSI6tMl7fZKPFjbiDrS5tj2m51T+/ZSPod1+ht\nhS/FbwnMTOsQ2iivnTB4c97QVeXBxzoGJ763sN/043u5f1z8t7djsYxHb7vt0Fs+eui4DmuTF0af\nODuJ9+yDSvTVJnInhjn0Qovn/6ceN/EEb7+YE6p6/sRo5q3hh67PIe3663VFXS8pZ6ZmUUxvOsk8\nPlnp8sfLbyb+JqOmDmL067NkerF72i82yindNwf+sjTL65QR0pM6bgtce2tgw/KZyT9z3Jkd6P3W\nNVQNcJk4toyveSTzxht/cTLSNi+SxbOxGm/opIm/fe2qq+7a+WXn7/K9U0Xhpl+EHh/wct4Y1UMq\nrYDTpin7mJ89UNH12eOeeLZL8LSl64oqGdg47JFb167rFKr1yl6OWSvuPHBEPmXzqmhjCaMbrtWV\n4jj3Ar8A7nx3AuuCLqeN4rRTPBqderp2C7M5cR8XTrpKb5jpiHN5SnsYM8alo8xh6n0q5/89J1cW\nbMlYy5c97mFM5SD5dMGUwSNpX96W2cOfOHvaS3euX88bwMHMmNGeROJ14ontZKT3U9tuEL/U4lBg\nAqgHzD788Kc++fmpF/zUu+CBeFuKYq/w7MjHM++dOmDALSXl5WP1kEPOGPfww6s+klK5jgTjuYkB\nV708evOQ3JX9L+C+L6pXbLueV39xO51ox7v00RW6GZEDgRnvM37MBP7wKRy7Ff73HPjllTfyh8k/\n46Gtm2k/cIh+0ZhKE5/F853WBO/p7r7LB/nAq1MZ+vzyX5XNYUhllBeKu+u/2teBf8GxPXP7oJN/\ne+oYXrt/CwOfWEg3p+d5088rmvDVBLcqvarbLzf8MgbgODIaeAHoM24mhwHrtUQXOI5cgr8IOHjH\nTnMqCLLkNVh9ZIj3a/vwdu5XbPoLzPmrnwDYbcNuJow8DSxuCEcOLMvPH9WtrGxZW7Y+u422lwHD\nVPENc/2dvulk9Z3DkHvOBzKAt9ny7jBW3BUmWdtVS3YxDxX5g0v0sA95czj+rmNGAR991pebQ0Ea\nu+9hVDkS9OVp3nHbsm74bXvK27517d2VJ8Ul/OZs+6qTdv3Od8RJx9+dPMNW+8PUY3cDSVvt3+x5\njeA48szcuVhXXkkRcPr55/PV6NEs/dnP9s4QEJGpkHaOSNWBN998/OO///2EKXDXJtXVzQRM6As6\nk6sKYsyrfIKMM8/mvVuVuvadmymHsfHL0fppidanjFIfBvrszDr65sD5+Iuwf7pYIwN4XYEioAOq\nu10Ipi5y3wNeUdu+SxynXepv+6tt75bannqdrk0GGDzxPcamNbDuhZPpnd7IiF3Ni3d53Q4DHuOI\nN1eTCPXmhZOj5FWdbNu6l2eGiOQAy265hZdHjeLss8+mYv16rlTVp/fUAsj5o7dT0esNfemxs+Tc\ncWup6fSlvvT4cc1pEbl2GwxrC8e9CrN/Ah+henmzWkCEIP6u7muqXNiiznHaAEuZfc5s8gbn0Os3\nqrbd7OIxlTUztwuPzrOIj1rN+UttHXdSc1oAx5G7gDp8r6kbbVtHtDxeOYNg8CLuuquJiy7qB3TS\nXdpu7/HcLgI96Tk+XXAqo88H6axKVbNaf2H19sPDuOLnc/mLB7aW6JIWBpEJLL8PLjoXrovANege\nhr27yh3nXeoDrxLQ8US9t9S2729J64hTAnTH79jl2mr/oUWtI5OBv3rK3y1hjG3rGS2OwfdrWoS/\nkJ+q2trCHws/+PF3/MBicWpPrXl9qTyKn1FxMVCsJVrdkhaRX+Lv2g8BJqHasmmt4wzD38nfBPxJ\nbfuNlrQ7umDhB55faimbAcBxdnagmgV8bNutBLAkFRiGRmC5qp7f4nMDRLgbP6siW5VBrWpL5Uz8\nwFRHoFBL9i5D2+XAfYGZQBg4ANWWgwSOk4YfLMkERqptN/85TpHKPhoJXGSr/VSrWkduxPd1u9m2\ndS9vpN2HLMfhv39PqOq5rWvpir+Yn6XKXtmBu2lLJYT//EJAx72yA/c++DTgSKCgpSD6TqnfQawE\nGKC2vbA1rSPOMcBr+KXfzWZK7dQ6cgh+xuv1tt1CUHrncKUQ/7V4VVXPaV1LAD9g0qDKIa1pAaRU\n/omfsdVRS5oPNO9y8F8A9+JnbJW1KnWcofgBlklq2++2pk0FyVcCV9tqN5sptVPr7PS5Worvg9fq\ney0idwC9gGN3ZE63rOUM/Pf6AlX2+p7eTVsqY4E7gSt3Zhu1fOCjgZsEBv2ogkcj+5+Bh4WnFiqC\npwH/PhaeBlAVVCzAwyKJJUksXCxJEhAXwSUgSSzxfx+QJK5YJMUiqZYmsXAlgKt+ppInFm6qYbUn\nfpaSm8gQN54hyXgGblMWbjwTN56N25SNl8hFNRNI9wcd2EIwfQPhjM2EszaTnbmJNull5EfKyY9W\nkResIcdqIEKS2kiY2kAGNZpNfaIArzYXrc0jXp9FLJ5GLB4l5kWJB6JEEjGiwQThYJJoOKnhsEs4\nrBoIgQRDNJFNrdtWGhLtJNfKo4tYdIonKaqCDpuFaJNSmd9IIq2CdDaR4a1GvZVsTe9GozWIrJpO\ntA3WUjFyOfH+XxEp/prcwrUILvVrCjS5vKu4ASXUbx1Z7bezelUx8786iFmzh7FoUW8SyTqQTNTt\nD7RHJIFqJZb1OVgVtMmoYmTefA4Kf0FuxnoaMmKsKIBPOsP2dDhsDRy7FLpUw/YMWJ0NK0MWy5pC\nNJVZ9K1QDmpI0N11+TgQ4pNwCNdy6ZxMEIlkUdslylc9euKGx9LTHUgHCshrCNAYUpqkiY0RpaEq\nj5Fz04mGauhkvcehm57hhchElo2soffwOWwvb8vnC49m3srBFOcu5Venvkufgz/EW9WLWSsO47Hg\nRlZkTIOamL8PlJGPxHMZJk0c3cZj+Zdbef1V6J3XmctDlQzf1ITrhkhYIWojETbnZnFvED7eup5g\nqA9t869m6/axHNvrIzplusSzGsl0q+hf/h7Vq2u5LXwxq6sOp8fgNzjlkGs57K1V9F0HM47Kom70\nCNaHDqBf8ityvp5N71fiJFyYPqaQf30+jumFF5D4VYjc7WVMuvtmNm9Y54WL21uzrrie+kQRwduD\nDNn6IJnpr7Buqs2kDwqZ+OUEnh32Bs8Of5mhK0ZwmXM2C7ou4B9nrKVzxfG6+e+TZGSsgn49rmPw\nNdPJqy5k9eOXE+6ylm6n3ca2OUew/INzm6JVofeenfSz+KLcDWO7v32/237eI22+iM2Uy8cImg+3\nvaFcWYAefWTn1dtiRzxjvdrlw/aha597Y6BmlXSZ4IVfed7q5jaRlbshPqd8QHhozzn1C5KXPbl9\n9Zw0yefYQ04+JrBm3OlZV730S/e+odWBNdty0MVjSA6cztW93Ca7c9IhoE88xHnv6uNnP3zcKzr5\nt33uqb9j0efTj0qsO3bboJCVvyyhjw0/+umrPn7owtraNnSk402XFJ56wfC7Hws6T0zd9NGXIzt8\nUNqYdGkMk5Hucv+GafLeZX2G8nlORlF55ulXXZ/91hvzmz79ML76pKajhk/iyptCyMVN0LQay75M\nD52TOocNAp6m69BKTl0zmvqOVQPHHRgoiT4dicVVH/on573/67c+4F8vvkP05f7Ex1cxYdLPyezx\nitq2JyLZBSIvutBXVUdVqW5o5kQ5FbgsSaTrOQVPylPRfrHAyUfoGQsOWXzrjFUDCpmVWcGwp1fz\n87OHzzigAP+C1WX9M//LqvuOBH7J2BmbgNcBG5iitv18qnvOW8BHNuP+qlAqcDeqqxxxzgeuBQ61\n1V4P4DgylersKzn/n3l5l53R2PnF5JyFv8nNcmePtxk7U3Dsw+27X5iZel1+ib8DNYYZM7oB9Wrb\n60XkIvwU74NVfdM/EfqDOv/4x6CNb7+6rNvsN9vc19Rm41nHHDM1c9q0P1oDB37cd8aMU9fBzrTd\nT5jDJt4KuOnvvXpfw6QTljEg8QGHk0sdffSeXXawRa4DRgVIvuMRuBTIvZY/vVVKyVgLHc4u9efi\nOENQ3uKkMUpleALQgL31A367LAvlAP3JITu1jjgZwKLrT7m+bGb/mQcA80csH9G35PmStKAb7D8p\nOWn9rm+h48ijQJlt+6UAjiMj8S/0Rtm2rtpVKyJnZsPvKyAzAEfOhvdsyG6EIaq6bHct3YA5D3Pu\nSefy6Mm9WHbvCnrNBCaoMn83ban0AT5kxJMrSeuwkdiWocw5twAvNkFLdPedfJEIsGA5ly7fyAkV\nISrHjuSM7CCNJ9NM2ro4zkN9+brNPe4l3b60BrS5Rm6ONJLeSW27YU+tI84U4ApgOL7vySf4GQ17\nmUA6jnRTZe6pp9LgeVhPPUVuJMKAPV+z1OsWBBbCg5vgp6OgfQJqx6vq53tqU6/dQ/R6vZgpx4/j\n9vVV1LU/W5VmF4RSKk8AG7REr5ZSmQE8qiX6SHPa1Hw9F3/HfzPwGKo3Nyt1nAPxgxR98VP909Vu\nftfYEScNWHLL1XzcdQ19T3uG1ePUPrE5LYDjyMvMO6iCDw/tySV3RRBGt3SxKyKXpaVx5O9+R9X1\n1zMc36uh2cWjTL7iXAY89QBfTfkJgx9+nc9+3UmdkmazRvCNflf8Fu68DS4TGIxqs9kP3/wJA4A1\nze267qZznFtxY9lYoQFI4I49DVF3xfeh0d8BjSB/bW1x5TjSFb+d8ofADNvWv7U81p0Lx6/wF/OX\ntfK8QvhBkM+BdFWOb/X5+Z+5QqCtlujg1rSIXAKcjZ/90IHWsgMcZyR+MDsL6KW2Xd6S1hEnB//5\nucCRttrzWtQ6O/2aegPn2/Y3XdaaH7LcDfwMuE61lVIfQISz8NtR36/Kxa1qS6U/fnnsO1qiLZb6\npA6cjh+srAD6tFQKs1PuOA4wDGijtr1XadmuOOLcBvwW6NKc/8xuWr+b0//gfx80m1X1zZDFxj9n\nnKGqrQZXRGiHvzD+myotBv4ApFQC+CU8C7VET2tNmzr4Y0BfVIfvU+o4l+BfTxS1lgUC4IgzBn/u\nFdna8mcTwHGkAD8bZbxtN3+e/2a4YuEHFG5Ubf2z6ev5B7BElTv2qS2V8/A3F67clxaR4cBUVH+x\nT6njtAVuA875Fq+bhZ+xdV1zJZJ76R25DHjRtls/H/tDlqOBtaraagDL19IXyFHls31qSyUP6K8l\n+tG+tPjdyoaiuld2abNyx+kDLN3X6wbgiNMO2NpcVuVeWkfSgZhttx4M8ocsFsC+AkffOX7U2ftR\nBY8emHopqoD4Pjqqvinujp8BUPF/VsAj1Yotdd8F8VL+SC6op2AJySC4IcGzBA0KWKABwAKxQEQR\nC99cOx6iKREgHge3KYkbixNM1BJ2K0iLbycjVk5mQw3BZJymSJS6cIT6cISGYDpNgW9uCSuNZCBN\nk1aUhEQkllQa4hZ18Qg1sSwaYoU0NrYn3hjEdRNEJEZOqI6MYIy6JKRrBYWBLXQMbKRYVlLsbaI4\nUUVxrJHiWt8TZ0u6RUGjRzIA67ItNqensT6vIxtzDyIWGohrdSPg5pFZHyG3WqjLVGqzlLRGJU8r\nySpYjXZZS6hoDWkFa0gv2EQ4t8bPzkFBILktg8aKfOqqCqmqKGL12jy+XtWBtWVtKK9Lp7IxiadC\nViREdkaSjMwA6TlhMnOyKO68gYMOnk5+wTqSq6IkFzchFS6BaDG5dOJzq4qngptZ334p2pRHYPlQ\nujdGOHnEYg45dDnhaIykF6KyIcoH22DlumqGzWnitAVQWGfhtE/n5YZ6PqiFdol00glTEILD0oTJ\njXEkms2jgy+mqGwIvVaHeX/gSl5PLGPdxjCRQCWj8gsZ3zCIg9bn8vWAOqK9PmJs/t+JH1hPPFeQ\nL3KZNT9IrobpOTqON6iCQFwpdJSOT8GbbcfwYFNnZq9/l9z09ngHDWBrgUf2nGVUbFnCwPAwTuJE\nkv36ML/zNjaFvvZqNwywVi+aQN+DH6EpupzKlcdRsWYYRx/9T4Yf9CQdlrSjeObhLC1ayttDE1z6\n2su0r02ypE8ugxZWsikDFuW1cWsD9pK64fLhgFPe/OnmhRNy5l1zBI8f086qPyufyOIYTX2ipD9U\n5Z3+r3u/ul3ffjCKtXJi/z5/nrdx6YArb8jms+SJjL93GO3Lcwh6Qf5x2oJkWnEx40Ofbm+sr0h/\n6oUuoa8XnxXNZSMXBMrWj7pwZm3m0a/2S8Yy3ernzn++3TMTbgYWjsPOyiTxp8aD/3JhZPjfrbse\nv6XOa/fi0tuWTxviqsrlB3Dr5GFc2/UR1VSK5S2FdUz66h+Eyk4k//L2Ue/rR2bWeg3dM+LZV86o\nqHpkMENoHNGN8IMfU9jBw/39LWfpK4VH15Zw3bkfrl5+5oo6xp7dhb8PyOF+29ZycZyJ+GZyH75w\nEl9mVjRdcRZnhX4ZLJ9yeWc5W0T/kLWSjcAZ4xl/x2/4TZqij2Q/+qt/avH6N28reUAP+mRj3j+P\nnJxo+Libd0Xl3Usqyeu1/ohQ7gUXX5WINCZum/ZZ8o+3386z+Lt8Z81gxiBgna12RepL4df4O6GX\nAU9yzNS/0PtfvyEoTZmH/X5xUUbodyti2Rfyr+eOJ/pRgIoj79cHH2k21V1ErBa/YPzdxscV6VVN\n337Pt/v18v5bop2GcGV6hPIIcJSot3N3RRznKPzMiAT+ArYT8Ca+oeqhats7L7IccTrgGy3+xFZ7\nVuqxU/HTXW1b7eU7tY4Iytu8dGI36jPaMnxOgpePj3DpXQFqskfap2/aeWGR2iF9GVipqUwDETk2\n9Z4dorp7jb0Iv0pLq7nwuefadLzttsSSiRMn9/nTn14IJZPhoYlEeLeSESmVvigfcDcJtnMrnbmW\ns4hgMUZv2CNl3F/cza0h644cagpf4bg5xzHtWeBIVPcy4hTHuZ0lWYdy0dAEnet7ct/nUaLeZLXt\nT/fUOuIc1xRs+tuxVx+blleXJ4/e/Wg4moyeZKu9V3DFcaQIf+F4MH45zOfAb2xb91rEpj5bn/4V\nMn8OXTtDbRVcoapP7KlNvXb/i59Wfgn+rthjqtzVrLZUSokWTWDEk2OYfeYyYpufbKmbGCITPAIP\nfsTr7YZw8aJMVn2I6l7ZQanXrR3owvu4YMu13JheTmGp2nazpQypTJBP8D0GjgHm2mr/udkxAI4j\nf1mxgoNqakgbMoS5tq2/bkkrIidA9BY4ajm8FNNmOhF9o6UzePPo/4LDolOKQPby19mpLZUi/ABB\nCf5879vizrx/kTsr9RxPA3q2tsMsjnM3kI/vbzFIbXt9S1pHnNPV32kvFDjeVrvFBZPjyAH4pQll\nwHXNfda+GbJE8XdT64B7Vb/xOmpW/7PDNpG3spCNI+bqMy836xOzy8GvBq4E3kF1Lz+gf5dUptYy\noBrovqP0rzlSC6tF+MGY9rba8Za0AI4jjwNTgI623XJWBYD43X/+CgzXZs4pu2s5AXgJ+KkqLZdp\nAFIq3fDfk+u0pOUyjdSBIynt26i2WE61U+4404A0te0WS6R2kCo1PQfota/FlePIkfg+JoW2rXsF\njncfshTjZ9wMVN07GLy7lhB+QOgCVVoup9qhL5UngX9pSevBldTBLwGi6DdlsC1K/bK/I3eY2bdG\nqgvWFbbaLZ6vdmp9o/ubgN/ZdisZPzuHLL8BHlHVZjPXdtfyE2CeKi2eV3ZqS2UAkNSSljOwdjlw\nAdCRFryDdpM6Tgjoqba97+Piv3Y7Nq32qXUkx7ZbyS7bdRwiYd3DC6xlbaompoXvBIPhv4kfnefR\nuENORizBQrFECYgf2LGE1GNgCaBCUgVVwXUDuCq4O+6rhevtCDgJeBYRCRIWIRjwCFhKKOARCChB\na0eHNiUYgGAAAmELKxJAIyHcSBrxaCZN0XQa07JIhqN4auEqWKqkBzwKGyspilWQl6whU2tJC9QT\nDtcTijYQSGvASm9AQgncqhziVVk0VqVTUxNicy2sqm5iWV2QtY0htifDNCQy0FgekrGVtEgluaFq\niqSOTl6Qjk1C+3gT7ZNNtNVGwuF6rGAjSAQ3mcM2K5O1wUxWBTNZHspkTSQTMvLJiOSTY+WRK1nU\nWwm2erVUx2qJlru02xqmsD6NtJBFZrQWspaysWgG5X23UZHpEto2kFFVhzMqHKRH3npCfRdidV9B\n9bZ2VK1JJ2t2BW3mK6G4R7K/izfEZWO7AG/PGcrc2YexduMhxJqGEY6sIatwBWk9aomku1QtjFKz\neRCJRFvyMt+hY4fX0c6vsSrUQLynEkiz6Lkhh87r81nRbjNrezQQ3m6RWAK6Lg86jaBXbi1Tts/n\n9CUNNEk6r/foyZiNmzmgajsv9g7xTD+LjzIHE6nrS3evhoyqRvrXHsXkZT2pTovRtjrKmk5NzO4y\nW5fHH2Rd7nop7w0ytz/2V7/g6iFP03bMXGoG+1nnbT6AkJPNglUDeLn+CM3LWc61lc/K30YId3U5\nnF5zurB5xes0ufUMbnsUbbseWxOQzuv7rV9rRTrP7XHPxQNDA1avYOAHdzQ+2aMwHFlYEqjb3E+6\njXy62HMutgAAFexJREFUOmPE/S/Pa6r+R1mXy1fR/pgzj3klUXrBA5IVL3xRqyufml4zIK24uKKq\ny4IuHT/8sGj0RdMferLbAHr9dConnNo386As64Y/6LJemfLOwqOXFjz09ZMP2GecpAuW3ayfXfsc\nwB+ciScdwNKb02no+ucPxnif/48TCna/Q08YuF1HhTc0FnZbUru0qU2bd96bUvfFF4lM1/29p1q3\nDSbcEOSh0wdTNegGFr4QfeXYP5JTU2fbWv+wzM57ii63zyJ/yii2y1msfePuMy4tn91z9iSEw7me\nVTvqyFM19JcCV+PX8d6k15Plhvhs8c8puqQHTc6LVDCExmEdKb7nI5oGfUzuhhNYsmEKpZsi7V6Z\nwjO34e/IH7Wjy4Q4Tj7+Tsx44AK17bcAHHGuqqHm1+dybqySyhFA1wIK7r2CK3ocxEF1UaJTbLU/\nAXAcGeW61uu/v+zNyHVf3akWnnV53i1cet1FsV4Hzi0PhRKn2bbOT52j0vFr1N9R9U1TUyn3j+Dv\n3J6564WvnHrqdNosGUOHhgZ6nuTyzqwc8pcFWXjqFH3rjmf/DyfLTGCOh5VdxZDqXD7vbPklX4ei\nOn8vuePcie9rUwYciB9qH6N2MyUu4pyM3/liCH452SPARFvtvS4KHUeKca0vuejeEBl1Lv9ztUU4\nMcG2dc7eQ5YC/MDUBfgBkzeAo7WZnaPUhdrrkyY92mnKlFt7Tp36eSKRiB6nyl4dNQCkVC6jhgt5\nmPZcTIIge7dj/ubgQ1P/9+H42T7X0FJJjuNkonzNn/vUcunyTDLcm/fh0TBtRv8ZuUNWDWmb05jz\nmK12iyUHjt8KfAL+1sdi29YrWtKKyOgQvNQRlqyBNap6Xsta8vAXjjPwU2R/0koQJAosIJi9jmRN\nGjB2H+UJz3oEii3cXPxdv71bEO+QOs6vQa8A2QYMbc5MdgeOOCPxA5r1+D4DLR435W2xFD8ftK9t\n617mvt8Md2eHmGH4adqtlkiIcAd+EHj8t0gVvwQ/VfxsLdHHW9MiMgo/C+NaVG9qVeo4Bfjdjl5Q\n257amjYVePsUqLLVPqLVMbCzlfB44MB97ZSKyNn459Yuqfr+lrWTrzieofe/zOyLh+l7t7S64586\nd80HftpcwPb/gjjOtUCZ2vYD+9Kmyjr62mrvM0jgONIbON229fp9jsF/flcC17fmo+JrEfzP2317\ntkxvVu93JfpIS3ZvYd/CwQcD5TSXvbqn1A+8Zaptr9yX1hEnjG/4vHqfYwAcRwpbm6O7jUMkQ1Xr\nv52WoCr7DKwASKnIPsuHDAaD4f8T/quCRyJyBHAH/kXfA7qHyZWI6Kix31z/SCrjSHb9WUgFhdTP\nSvIUFUURVAQVUEllFqVuCQ3QqCFiBEiSCjSJn3frJy6pf7NAPCGSCBL1LDJUyFePdoE4HYJxOoab\naJsZIyc7TjRNCEqASDJIwsuhTvJJxLOIN6aTiKURj6URj4VJuBaulcSz4rjSRDJ1S9BEUmK40kDc\nqqPWbaQ2GaTKC1GnAYJShZdZTUVOgm2ZDTTmlUMwhlS3J1ifR3osk/xkhLYaYpMqlWnb6RhaSd/q\nevquyaVPmUu/qnr6xBpoEFgRCrM8HKFYkwxtbCQeEDanBWmIKlsK+1IVOYyawADCNZ0oXhulLjNJ\nOG6RCAmbiyvICC4kOzmbDdnVzO7ZlbqBaRR2q6VfdBn9ZAmCx9J4d+aGRvFlZU82bGhDuKqeztsq\nGFS1jsr1AZZt7M7aLUOINbWnc4f3OfTA2QzvXUe1tCMjexuNHTezcmUnDc1IyqbyxSzoWMaW9k20\nX53BsJX9tKeOSqTn9nbnZ7wQeWftR1bf/i6TR+Xx4cbB6m1SHV+3UuZmtovN0A6rvE1VjrX9g/kS\n6HBEXd1xR7nu1dGMjBWx44+/471Tzpi5dvGsn43L6Lh8Tf9er33iJa3N9957W3fHOWVypZs4iElX\nBrTTp9Ju0SmJDDe/rn/mokgwX+rDRwW/Hp0/N7mpfNkB75W57ZfW4k1osJaWPGZ1i6uXPq3dyfV9\nFl641QpKXYdG7b623fyv07LuyTh4y8ru5ele+N6R4U0PnXHdNrdgeDHJuvN18kmv7vysO04RfqbA\nBfjmbv87YxxNHlXTC/gkfxp3VpXSdHcRjD6Rjocdx1mEGR3J4/3yzrz6YDRn7XPvvxz9Wb1knHct\nN65cSp9jgfyD+eiiE3h5SlfWZLzLxA+e5ae3VJE3nXHjhkBgWjD4202BQMkBicTHQZGm+a77q2zY\nFIDk1cBLqqoiRIJ4z/eh9pBbWPCvTNxp71N46RN0GRNAK49h01+OZ9Ndttr1AFIqF+CnJE/QEl0q\npXIE8Df8dPffaIku/2aSS1cvyKeLLyH3iRFsOGkR+Qf+g+x4AYu2j+bcbg9/07kg5UPzLH5nitOB\nE/EXby8C16i9e2quI87NZZSddz7nx0cyMvtKrpQw4QcsrOv2XJw6jkxOJMJPX3ThrGjXLovqrrr6\n58FwuOl+oGRPk89Uzfen+Oa22/GzZ/6Bn/68e7vpcdcLXWauRbwCPMulYGU688+aqO/f1OrC9Fsh\n0h9/YVyF7x8wHm3enDHVzelz/HNvG2CC2nazXSEAHHEewfdmGYqfhbRXps1OrSPnUZ9eSjSWTsCb\nYtvaYk25iIwFnsEPXl2orRlECu3Am29ZHp4XvEqVx1rU+l1OpqMchHCDljTfvWmXg9+Mn7FxO6q/\nb1Xqd+uYht/9auo+zBa74ps9v4efCdJywMSRMLDgk09IjBnDENtuubTEH7I8BQwGhu1rgSXCr/H9\nKgaqslf5127aUjkc3zh0uJa0vuOP3174E+BEWij/2in1d5ffBkrVtpsN+u2K47f+/qy5FtZ7aR05\nA8i0bW0xkPfNkGUIMF517/bDe2tpA/xcldYzO2BHyeQvgXtaDbh9c/AT8bNt6vYpdZzhwCq17X0G\nCVJd69RWu/lSsV21fkZDvm03b0a8+3BFgDxVrdiXFkDG/zFHp9/wrXb8EbHY36n7gIjYqnu3QTc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"text/plain": [ "<matplotlib.figure.Figure at 0x8f2ed50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for ratio in ratios:\n", " plt.plot(CST_bin_dists[ratio].keys(), CST_bin_dists[ratio].values(), label=\"ratio: \"+ str(ratio))\n", "plt.xlim(0,max_val)\n", "if steps <= 10:\n", " plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AzJJgaIkIhgAAAIBZEgwtEcEQAAAAMEuCoSUiGAIAAABmSTC0RARDAAAAwCwJhpaIYAgA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\nGACA/RoCoG/NvgBoetyW5OP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"text/plain": [ "<matplotlib.figure.Figure at 0x90f8430>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(sorted(CST_bin_dists_averages.keys()), [CST_bin_dists_averages[ratio] for ratio in sorted(CST_bin_dists_averages.keys())])\n", "plt.xlim(-0.1,1.1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AAADgIMz0+9VnrNXvvf3xM+9bznSl0XH23PaGYQMAAABcqA5se5qVRn95396lvYHel63V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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x90ffa50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# worst cases\n", "CST_bin_worst = {ratio: max(CST_bin_dists[ratio].values()) for ratio in ratios}\n", "plt.plot(sorted(CST_bin_worst.keys()), [CST_bin_worst[ratio] for ratio in sorted(CST_bin_worst.keys())])\n", "plt.xlim(0,1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "steps = 10\n", "ratios = [x/float(steps) for x in range(steps+1)]\n", "print ratios\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
shareactorIO/pipeline
oreilly.ml/high-performance-tensorflow/notebooks/06_Train_Model_XLA_JIT.ipynb
1
2178
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Model with XLA JIT Enabled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the Next Cell to Show XLA JIT Training Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cat /root/src/main/python/xla/train_linear_xla.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the Next Cell \n", "\n", "Note: The cell below will report `Aborted (core dumped)`, but it still generate some useful info.\n", "\n", "Click on the following generated files in File Browser in Left Navigation\n", "* `train_linear_xla.log` **(Log File)** \n", "* `hlo_graph_*` **(JIT Visualizations)** \n", "\n", "Reference of All XLA Operations\n", "* https://www.tensorflow.org/performance/xla/operation_semantics\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "\n", "python /root/src/main/python/xla/train_linear_xla.py &> train_linear_xla.log\n", "\n", "dot -T png /tmp/hlo_graph_1.*.dot -o /root/notebooks/hlo_graph_1.png &>/dev/null \n", "\n", "dot -T png /tmp/hlo_graph_10.*.dot -o /root/notebooks/hlo_graph_10.png &>/dev/null \n", "\n", "dot -T png /tmp/hlo_graph_25.*.dot -o /root/notebooks/hlo_graph_25.png &>/dev/null \n", "\n", "dot -T png /tmp/hlo_graph_50.*.dot -o /root/notebooks/hlo_graph_50.png &>/dev/null " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
serbyy/MozDef
examples/alerts/AlertDevelopment.ipynb
9
6956
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#Step one: \n", "#Copy the 'lib' directory from the alerts directory in the mozdef github repo into the directory with this\n", "#ipython notebook file.\n", "#The directory structure should look like: \n", "# .\n", "# ..\n", "# lib/\n", "# lib/__init__.py\n", "# lib/alerttask.py\n", "# lib/config.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#Step Two: \n", "#Edit the lib/config.py file \n", "#\n", "#In particular: \n", "# Make sure ALERTS={}\n", "# to avoid sending off celery jobs by accident\n", "\n", "# Edit the ES={'servers'} to match your destination elastic search server/cluster\n", "\n", "# Change the timezone to match if needed:\n", "# OPTIONS = {\n", "# 'defaulttimezone': 'US/Pacific',\n", "# }" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "#Step Three: \n", "#Iterate your alerts to search for the right events\n", "#aggregate on the right 'details' field\n", "#and output the right alert text" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#import the alerts library for mozdef\n", "#inheriting the configuration from config.py\n", "#You may receive errors about being unable to connect to celery/kombu\n", "#which you can safely ignore\n", "from lib.alerttask import AlertTask\n", "import pyes" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "ERROR:lib.alerttask.AlertTask:Exception while configuring kombu for alerts: [Errno 61] Connection refused\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "{}\n", "[]\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#setup a base class to begin prototyping our alert\n", "#that does nothing but connect to ElasticSearch\n", "class AlertTest(AlertTask):\n", " def main(self):\n", " self.log.debug('running main')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "ERROR Exception while configuring kombu for alerts: [Errno 61] Connection refused\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "ERROR:lib.alerttask.AlertTest:Exception while configuring kombu for alerts: [Errno 61] Connection refused\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "#instanciate our alert class\n", "testAlert=AlertTest()\n", "#set the logging level\n", "testAlert.log.setLevel('INFO')\n", "#setup a query\n", "#with a time period (X minutes ago)\n", "#and a term search\n", "#and a search for a field that must exist\n", "testAlert.filtersManual(dict(minutes=60),\n", " must=[pyes.TermFilter('summary', 'sometext'),\n", " pyes.ExistsFilter('details.somefield')])\n", "#search for events\n", "testAlert.searchEventsSimple()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "ERROR Exception while configuring kombu for alerts: [Errno 61] Connection refused\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "ERROR:lib.alerttask.AlertTest:Exception while configuring kombu for alerts: [Errno 61] Connection refused\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "#sample the events that matched the search\n", "testAlert.events[0:2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# aggregate on a field in the 'details' section of the json: \n", "testAlert.searchEventsAggreg('http_user_agent')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "#sample the aggregations\n", "testAlert.aggregations[0:2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[]" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#create a test alert\n", "#using the aggregation and events\n", "testAlert.createAlertDict('alert summary goes here','aggregatedAlert','alert',testAlert.events[0:2])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "{'category': 'aggregatedAlert',\n", " 'events': [],\n", " 'severity': 'NOTICE',\n", " 'summary': 'alert summary goes here',\n", " 'tags': 'alert',\n", " 'utctimestamp': '2014-12-12T23:19:13.029914+00:00'}" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#If the events, aggregations and resulting alert are satisfactory\n", "#make a myalertname.py file using some of the github files as a reference that includes your \n", "#'filtersManual', and aggregation choices\n", "# and add the .py file to the config.py ALERTS={} section: \n", "#ALERTS = {\n", "# 'pyfilename.py.classname': crontab(minute='*/1')\n", "# }" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mpl-2.0
daniestevez/jupyter_notebooks
Tianwen/orbit/Project Pluto comparison.ipynb
1
48277
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.dates import DateFormatter\n", "from astropy.coordinates import Angle\n", "from astropy.time import Time\n", "import astropy.units as u\n", "\n", "import datetime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Project Pluto pseudo-MPEC](https://www.projectpluto.com/pluto/mpecs/tianwea.htm) from 2020 Jul 26 16:07:27 UT." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "pseudo_MPEC = \"\"\"2020 07 24 00 00 42 55.456 +15 18 58.20 295982 1.0164 105.2 15.5 .160 126\n", "2020 07 24 01 00 43 33.770 +15 23 21.38 309695 1.0164 105.0 15.6 .155 126\n", "2020 07 24 02 00 44 08.854 +15 27 21.78 323357 1.0164 104.9 15.7 .150 126\n", "2020 07 24 03 00 44 41.107 +15 31 02.29 336971 1.0164 104.8 15.8 .145 125\n", "2020 07 24 04 00 45 10.866 +15 34 25.31 350542 1.0165 104.7 15.9 .141 125\n", "2020 07 24 05 00 45 38.413 +15 37 32.87 364073 1.0165 104.6 16.0 .137 125\n", "2020 07 24 06 00 46 03.992 +15 40 26.69 377566 1.0165 104.6 16.1 .133 125\n", "2020 07 24 07 00 46 27.809 +15 43 08.25 391025 1.0165 104.5 16.1 .129 125\n", "2020 07 24 08 00 46 50.043 +15 45 38.81 404451 1.0165 104.4 16.2 .126 125\n", "2020 07 24 09 00 47 10.848 +15 47 59.46 417847 1.0165 104.4 16.3 .123 125\n", "2020 07 24 10 00 47 30.361 +15 50 11.16 431215 1.0165 104.3 16.4 .119 125\n", "2020 07 24 11 00 47 48.699 +15 52 14.74 444555 1.0166 104.3 16.4 .116 125\n", "2020 07 24 12 00 48 05.967 +15 54 10.93 457871 1.0166 104.3 16.5 .113 126\n", "2020 07 24 13 00 48 22.256 +15 56 00.38 471163 1.0166 104.2 16.6 .111 126\n", "2020 07 24 14 00 48 37.649 +15 57 43.65 484432 1.0166 104.2 16.6 .108 126\n", "2020 07 24 15 00 48 52.217 +15 59 21.26 497680 1.0166 104.2 16.7 .105 126\n", "2020 07 24 16 00 49 06.026 +16 00 53.65 510908 1.0166 104.1 16.7 .103 127\n", "2020 07 24 17 00 49 19.135 +16 02 21.24 524117 1.0167 104.1 16.8 .100 127\n", "2020 07 24 18 00 49 31.595 +16 03 44.39 537308 1.0167 104.1 16.8 .098 128\n", "2020 07 24 19 00 49 43.453 +16 05 03.42 550481 1.0167 104.1 16.9 .096 128\n", "2020 07 24 20 00 49 54.753 +16 06 18.62 563638 1.0167 104.1 16.9 .094 129\n", "2020 07 24 21 00 50 05.533 +16 07 30.28 576779 1.0167 104.1 17.0 .091 129\n", "2020 07 24 22 00 50 15.828 +16 08 38.62 589905 1.0167 104.1 17.0 .089 130\n", "2020 07 24 23 00 50 25.670 +16 09 43.88 603017 1.0168 104.1 17.1 .088 130\n", "2020 07 25 00 00 50 35.088 +16 10 46.25 616115 1.0168 104.1 17.1 .086 131\n", "2020 07 25 01 00 50 44.110 +16 11 45.92 629200 1.0168 104.1 17.2 .084 132\n", "2020 07 25 02 00 50 52.759 +16 12 43.05 642272 1.0168 104.1 17.2 .082 132\n", "2020 07 25 03 00 51 01.059 +16 13 37.81 655332 1.0168 104.1 17.3 .080 133\n", "2020 07 25 04 00 51 09.029 +16 14 30.32 668380 1.0169 104.1 17.3 .079 134\n", "2020 07 25 05 00 51 16.689 +16 15 20.74 681417 1.0169 104.1 17.4 .077 135\n", "2020 07 25 06 00 51 24.057 +16 16 09.16 694443 1.0169 104.1 17.4 .076 135\n", "2020 07 25 07 00 51 31.149 +16 16 55.72 707459 1.0169 104.1 17.4 .074 136\n", "2020 07 25 08 00 51 37.979 +16 17 40.50 720465 1.0169 104.1 17.5 .073 137\n", "2020 07 25 09 00 51 44.563 +16 18 23.61 733462 1.0169 104.1 17.5 .072 138\n", "2020 07 25 10 00 51 50.913 +16 19 05.13 746448 1.0170 104.1 17.6 .070 139\n", "2020 07 25 11 00 51 57.040 +16 19 45.16 759426 1.0170 104.1 17.6 .069 140\n", "2020 07 25 12 00 52 02.958 +16 20 23.76 772396 1.0170 104.2 17.6 .068 140\n", "2020 07 25 13 00 52 08.675 +16 21 01.00 785356 1.0170 104.2 17.7 .067 141\n", "2020 07 25 14 00 52 14.202 +16 21 36.96 798309 1.0170 104.2 17.7 .066 142\n", "2020 07 25 15 00 52 19.548 +16 22 11.69 811254 1.0171 104.2 17.7 .065 143\n", "2020 07 25 16 00 52 24.721 +16 22 45.26 824191 1.0171 104.2 17.8 .064 144\n", "2020 07 25 17 00 52 29.731 +16 23 17.72 837120 1.0171 104.2 17.8 .063 145\n", "2020 07 25 18 00 52 34.583 +16 23 49.12 850043 1.0171 104.2 17.8 .062 146\n", "2020 07 25 19 00 52 39.285 +16 24 19.51 862958 1.0171 104.3 17.9 .061 147\n", "2020 07 25 20 00 52 43.845 +16 24 48.94 875867 1.0171 104.3 17.9 .060 148\n", "2020 07 25 21 00 52 48.268 +16 25 17.44 888769 1.0172 104.3 17.9 .060 149\n", "2020 07 25 22 00 52 52.559 +16 25 45.06 901665 1.0172 104.3 18.0 .059 150\n", "2020 07 25 23 00 52 56.726 +16 26 11.83 914555 1.0172 104.3 18.0 .058 151\n", "2020 07 26 00 00 53 00.773 +16 26 37.80 927438 1.0172 104.4 18.0 .057 152\n", "2020 07 26 01 00 53 04.704 +16 27 02.99 940316 1.0172 104.4 18.0 .057 153\n", "2020 07 26 02 00 53 08.525 +16 27 27.44 953188 1.0173 104.4 18.1 .056 154\n", "2020 07 26 03 00 53 12.240 +16 27 51.17 966054 1.0173 104.4 18.1 .056 155\n", "2020 07 26 04 00 53 15.854 +16 28 14.22 978915 1.0173 104.5 18.1 .055 156\n", "2020 07 26 05 00 53 19.369 +16 28 36.62 991771 1.0173 104.5 18.2 .055 158\n", "2020 07 26 06 00 53 22.791 +16 28 58.37 .00672 1.0173 104.5 18.2 .054 159\n", "2020 07 26 07 00 53 26.122 +16 29 19.53 .00680 1.0174 104.5 18.2 .054 160\n", "2020 07 26 08 00 53 29.367 +16 29 40.09 .00689 1.0174 104.6 18.2 .053 161\n", "2020 07 26 09 00 53 32.527 +16 30 00.10 .00697 1.0174 104.6 18.3 .053 162\n", "2020 07 26 10 00 53 35.607 +16 30 19.56 .00706 1.0174 104.6 18.3 .053 163\n", "2020 07 26 11 00 53 38.609 +16 30 38.49 .00714 1.0174 104.6 18.3 .052 164\n", "2020 07 26 12 00 53 41.536 +16 30 56.93 .00723 1.0175 104.7 18.3 .052 165\n", "2020 07 26 13 00 53 44.391 +16 31 14.87 .00732 1.0175 104.7 18.4 .052 166\n", "2020 07 26 14 00 53 47.176 +16 31 32.35 .00740 1.0175 104.7 18.4 .051 167\n", "2020 07 26 15 00 53 49.893 +16 31 49.38 .00749 1.0175 104.7 18.4 .051 168\n", "2020 07 26 16 00 53 52.545 +16 32 05.97 .00757 1.0176 104.8 18.4 .051 169\n", "2020 07 26 17 00 53 55.135 +16 32 22.13 .00766 1.0176 104.8 18.5 .051 170\n", "2020 07 26 18 00 53 57.663 +16 32 37.89 .00774 1.0176 104.8 18.5 .051 171\n", "2020 07 26 19 00 54 00.133 +16 32 53.25 .00783 1.0176 104.9 18.5 .050 172\n", "2020 07 26 20 00 54 02.546 +16 33 08.23 .00791 1.0176 104.9 18.5 .050 173\n", "2020 07 26 21 00 54 04.903 +16 33 22.84 .00800 1.0177 104.9 18.5 .050 174\n", "2020 07 26 22 00 54 07.207 +16 33 37.09 .00809 1.0177 104.9 18.6 .050 175\n", "2020 07 26 23 00 54 09.460 +16 33 51.00 .00817 1.0177 105.0 18.6 .050 176\n", "2020 07 27 00 00 54 11.662 +16 34 04.57 .00826 1.0177 105.0 18.6 .050 177\n", "2020 07 27 01 00 54 13.816 +16 34 17.82 .00834 1.0178 105.0 18.6 .050 178\n", "2020 07 27 02 00 54 15.923 +16 34 30.75 .00843 1.0178 105.1 18.7 .050 179\n", "2020 07 27 03 00 54 17.984 +16 34 43.37 .00851 1.0178 105.1 18.7 .050 0\n", "2020 07 27 04 00 54 20.000 +16 34 55.70 .00860 1.0178 105.1 18.7 .050 1\n", "2020 07 27 05 00 54 21.974 +16 35 07.73 .00868 1.0178 105.2 18.7 .050 2\n", "2020 07 27 06 00 54 23.906 +16 35 19.49 .00877 1.0179 105.2 18.7 .050 3\n", "2020 07 27 07 00 54 25.798 +16 35 30.98 .00885 1.0179 105.2 18.8 .050 4\n", "2020 07 27 08 00 54 27.650 +16 35 42.21 .00894 1.0179 105.2 18.8 .050 5\n", "2020 07 27 09 00 54 29.463 +16 35 53.17 .00902 1.0179 105.3 18.8 .050 6\n", "2020 07 27 10 00 54 31.240 +16 36 03.89 .00911 1.0180 105.3 18.8 .050 6\n", "2020 07 27 11 00 54 32.980 +16 36 14.37 .00919 1.0180 105.3 18.8 .050 7\n", "2020 07 27 12 00 54 34.685 +16 36 24.61 .00928 1.0180 105.4 18.9 .050 8\n", "2020 07 27 13 00 54 36.356 +16 36 34.63 .00936 1.0180 105.4 18.9 .050 9\n", "2020 07 27 14 00 54 37.993 +16 36 44.42 .00945 1.0181 105.4 18.9 .050 10\n", "2020 07 27 15 00 54 39.598 +16 36 53.99 .00954 1.0181 105.5 18.9 .051 10\n", "2020 07 27 16 00 54 41.172 +16 37 03.35 .00962 1.0181 105.5 18.9 .051 11\n", "2020 07 27 17 00 54 42.715 +16 37 12.51 .00971 1.0181 105.5 18.9 .051 12\n", "2020 07 27 18 00 54 44.227 +16 37 21.47 .00979 1.0181 105.6 19.0 .051 13\n", "2020 07 27 19 00 54 45.711 +16 37 30.23 .00988 1.0182 105.6 19.0 .051 13\n", "2020 07 27 20 00 54 47.166 +16 37 38.80 .00996 1.0182 105.6 19.0 .051 14\n", "2020 07 27 21 00 54 48.593 +16 37 47.19 .01005 1.0182 105.7 19.0 .051 15\n", "2020 07 27 22 00 54 49.993 +16 37 55.39 .01013 1.0182 105.7 19.0 .052 16\n", "2020 07 27 23 00 54 51.366 +16 38 03.42 .01022 1.0183 105.7 19.0 .052 16\n", "2020 07 28 00 00 54 52.714 +16 38 11.28 .01030 1.0183 105.8 19.1 .052 17\n", "2020 07 28 01 00 54 54.037 +16 38 18.97 .01039 1.0183 105.8 19.1 .052 18\n", "2020 07 28 02 00 54 55.335 +16 38 26.50 .01047 1.0183 105.8 19.1 .052 18\n", "2020 07 28 03 00 54 56.609 +16 38 33.87 .01055 1.0184 105.9 19.1 .053 19\"\"\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "lines = pseudo_MPEC.split('\\n')\n", "pluto_t = Time([datetime.datetime(*[int(a) for a in l.split()[:4]]) for l in lines])\n", "pluto_ra = Angle([' '.join(l.split()[4:7]) + ' hour' for l in lines])\n", "pluto_dec = Angle([' '.join(l.split()[7:10]) + ' degrees' for l in lines])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RADEC table generated using GMAT." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "mjd_unixtimestamp_offset = 10587.5\n", "seconds_in_day = 3600 * 24\n", "\n", "def mjd2unixtimestamp(m):\n", " return (m - mjd_unixtimestamp_offset) * seconds_in_day\n", "\n", "report = np.fromfile('Tianwen1_RADEC_GMAT_20200726_105242.txt', sep = ' ').reshape((-1,4))\n", "radec_t = Time(np.round(mjd2unixtimestamp(report[:,0])), format='unix')\n", "radec_ra = Angle(report[:,1], unit = u.deg).wrap_at(360 * u.deg)\n", "radec_dec = Angle(report[:,2], unit = u.deg)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "t_matches = np.array([(j,np.where(radec_t.datetime == pt.datetime)[0][0]) for j,pt in enumerate(pluto_t)\n", " if np.any(radec_t.datetime == pt.datetime)])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (12,6), facecolor = 'w')\n", "plt.plot(pluto_t[t_matches[:,0]].datetime,\n", " (pluto_ra[t_matches[:,0]] - radec_ra[t_matches[:,1]]).arcsec, '.-',\n", " label = 'Right ascension')\n", "plt.plot(pluto_t[t_matches[:,0]].datetime,\n", " (pluto_dec[t_matches[:,0]] - radec_dec[t_matches[:,1]]).arcsec, '.-',\n", " label = 'Declination')\n", "plt.grid()\n", "plt.title('Comparison with Project Pluto pseudo-MPEC')\n", "plt.ylabel('Difference (arcsec)')\n", "plt.xlabel('UTC time')\n", "date_fmt = DateFormatter('%Y-%m-%d %H:%M')\n", "plt.gca().xaxis.set_major_formatter(date_fmt)\n", "plt.xticks(rotation = 45)\n", "plt.legend();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 4 }
gpl-3.0
jseabold/statsmodels
examples/notebooks/formulas.ipynb
3
9821
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formulas: Fitting models using R-style formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the [patsy](http://patsy.readthedocs.org/) package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the ``patsy`` docs: \n", "\n", "* [Patsy formula language description](http://patsy.readthedocs.org/)\n", "\n", "## Loading modules and functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np # noqa:F401 needed in namespace for patsy\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import convention" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can import explicitly from statsmodels.formula.api" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.formula.api import ols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can just use the `formula` namespace of the main `statsmodels.api`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sm.formula.ols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or you can use the following convention" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import statsmodels.formula.api as smf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These names are just a convenient way to get access to each model's `from_formula` classmethod. See, for instance" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sm.OLS.from_formula" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the lower case models accept ``formula`` and ``data`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``data`` takes a [pandas](https://pandas.pydata.org/) data frame or any other data structure that defines a ``__getitem__`` for variable names like a structured array or a dictionary of variables. \n", "\n", "``dir(sm.formula)`` will print a list of available models. \n", "\n", "Formula-compatible models have the following generic call signature: ``(formula, data, subset=None, *args, **kwargs)``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## OLS regression using formulas\n", "\n", "To begin, we fit the linear model described on the [Getting Started](./regression_diagnostics.html) page. Download the data, subset columns, and list-wise delete to remove missing observations:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta = sm.datasets.get_rdataset(\"Guerry\", \"HistData\", cache=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = dta.data[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit the model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical variables\n", "\n", "Looking at the summary printed above, notice that ``patsy`` determined that elements of *Region* were text strings, so it treated *Region* as a categorical variable. `patsy`'s default is also to include an intercept, so we automatically dropped one of the *Region* categories.\n", "\n", "If *Region* had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the ``C()`` operator: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Patsy's mode advanced features for categorical variables are discussed in: [Patsy: Contrast Coding Systems for categorical variables](./contrasts.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Operators\n", "\n", "We have already seen that \"~\" separates the left-hand side of the model from the right-hand side, and that \"+\" adds new columns to the design matrix. \n", "\n", "## Removing variables\n", "\n", "The \"-\" sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiplicative interactions\n", "\n", "\":\" adds a new column to the design matrix with the interaction of the other two columns. \"*\" will also include the individual columns that were multiplied together:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res1 = ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()\n", "res2 = ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()\n", "print(res1.params, '\\n')\n", "print(res2.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many other things are possible with operators. Please consult the [patsy docs](https://patsy.readthedocs.org/en/latest/formulas.html) to learn more." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions\n", "\n", "You can apply vectorized functions to the variables in your model: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res = smf.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a custom function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def log_plus_1(x):\n", " return np.log(x) + 1.\n", "res = smf.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any function that is in the calling namespace is available to the formula." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using formulas with models that do not (yet) support them\n", "\n", "Even if a given `statsmodels` function does not support formulas, you can still use `patsy`'s formula language to produce design matrices. Those matrices \n", "can then be fed to the fitting function as `endog` and `exog` arguments. \n", "\n", "To generate ``numpy`` arrays: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import patsy\n", "f = 'Lottery ~ Literacy * Wealth'\n", "y,X = patsy.dmatrices(f, df, return_type='matrix')\n", "print(y[:5])\n", "print(X[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate pandas data frames: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = 'Lottery ~ Literacy * Wealth'\n", "y,X = patsy.dmatrices(f, df, return_type='dataframe')\n", "print(y[:5])\n", "print(X[:5])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(sm.OLS(y, X).fit().summary())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
prisae/blog-notebooks
MX_BarrancasDelCobre.ipynb
1
826144
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Barrancas Del Cobre\n", "\n", "Maps for <https://mexico.werthmuller.org/besucherreisen/barrancasdelcobre>.\n", "\n", "You can find more explanatory examples in Travel.ipynb, also in this directory." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import travelmaps2 as tm\n", "tm.setup(dpi=200)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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KqLbbLQDT6ZTaQ5IYikLhhMHuLKttzShRjAg0rUMLg9KBOsBYB5qNZ55IpnNN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2wHFi2SctbNN0up6x3v2Hcfb7E6Lx7//d8iJK/VRLD9n/8pz3+5DroXj5PO/h+T13/wA2Ff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\ndgwMwEEH0R/BJfvN47Qzz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83kqVPsffAeXvHmX8Hg0UHRMRAXo4TIy+dYu3U7ydFD9F/3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\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//izdY0/z8Fc+w5tvvJm52YPc+pKr2X75tWilWN7/BEMz27mtcvzJ90tWB5PMZGtEYznb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uZ4m1NoS5bmEjjtcgrryesNnIU8zal2VJiZzvBeyoR9rMXR2Mpxqce1MtL6JElnVTIUC0ehNDZo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Hn8h1z4nBcz2HouH/jLd3LXI3vZPOXY9f3vsHf/YZaXVjht8xx4z8Lue9h96xe57eabOTa/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\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mQvhzQvglJWzTEG0Eb9BRiwk7JlQj1nR0DToTery1UhtjFCZz6EzJ50mAKMm4VAjBneBJ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y8Sbp+CTZRJupwXHS8QY2afnodpJhc0PRsojCkWWGwikfYMyBSBN0btlxGJBjScZbNEcnaeyf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clppf46SH/TORQOIwRp6Tf9cmsxhaVIS4yErLCUhSOqBkROMtbpMTxQRQeCoYqmrvHntRYM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\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inWZe4awWk1UtLLd39XMbIt7PFU5ZayabHKywTknreIcjYK0CXEUyedAW0ogBH3B4mk2d\nHpY2E62uRnFWi6lhwKB5X0bKyPlqKGlAKoezltRGpO8/Ls5TyrhenZqmeKwRbCxEGCd+GVcFzNSR\nc0VJmTJoLiLeEmae3Kvez23MaLxOPLrdHjursN4Tb25jqwl2LZDGg21oV8wfOM6wzMQgxKMlbj7V\nyVQcvxd9xASHlYqwVtHvLvGThTYgxpD6lU5uradabwCHHW8wRjLW1ZRYMLVXhEXUqQxYijeUTuOV\nrA2KB9HNNaVTreBweESzPmU2m9GnRH/nPqZyTI+v4Z2hDVFXKDj8pJA6SKuVTpkMpEFRItEUYt9S\nisHPJ5jGkXqhOzxSA0kdFEMQDdkXckx6Pda1XptuRGmIJZVCIUGyOGPxtf4urVgLQ5twTsXyRXRy\nIF4J2zLyVFVHYwheHcLYwtAVstHvjMabFIoLyCoqTLIb8DNFt/igon8xQk6Cs47KaupCNAXdXo1N\noOQxBaKQkyGEgARYDoVUEjWWocuk2jGfK6l/7/4+fuKp1tZpaocVS18i87knWE/ysHfngCF3TDa3\nmE09iGFRNyyXhdwDviLFnt3bB0zXamL2bCwW9EWnNF0rVI1oVqc11E1D5Q3RVQBsLxPtzhIfM74O\n+GRp6oCzhrYrdENSeHERBoyu+EZ5RBp0gqnMLSFUQAfL2tAESxqLmUXwLCVTKkuN5SBGuiFREhRX\nsAIWQ1uEKmjvqN95deoNgJdCjhbjobZgEYYATkQRFkDuRnlBMDgx5CxYp8P5bHVSlrxG3cwd/PhP\n/Xn+6Zu3+A9++sc5+zM/z/bh73yMIGHMz7x57Qb2woLtWeHdruNf/eu/xLmNKT/35z7Di999j+l8\nykOPPcXpZeTpJ57g9Zde5IlnF9zd3uXY6afZv3ODY1sbvP3q73Nvmohtx6mNKVvzwHzzBK+9cYW7\n+0toZrRMsEao5jOeP2bJaeAb7x/iV5nedBxbtjgMX3vxFT71wjN85Vtv8NSlU9wbEineY/9oRfAa\naNyueqgC9+/f57FzF7l87gFy7Ni5t2RDWtqdA9y58xxzE0JMHMXEK29cY18gVlMWFyc8felRtnfv\n0d64x5e+/jLPPPUkf+qFp/nGy2/AELlzd5/nPvNJDm7c5smL51ju3OXu8ojg7nCw6unaiKyf5U9+\n4hK1RN6+eZ1+2XLy0mnOHjvB9ORprr77Dg8/cJ5yZgK3t3ny08/yxtUr3Lx1n888/wS3tnd58uzj\nnFk7Azmz3NkmVIGbt3YQZ3hV3mKOZ/veLpcfv8C09piceOj0WUKoGdoV1JbKWSaTmsuPXKRppn9k\nzfKDPn6oBdVHQYOgAq+txmuHiVrsS6+iakB5Tk4F6jag0TNGVMNkyii41puNGJ1+2NEHW2LBSCE5\ny3iiKSZ9tN2bPO7i3fdhoiXrhETqCl/k40iZ0itVW6RoEYJFPEif9M9x6g7sExiDbTxOLHZNXXc5\nGg7v3Fdb8Gyhr8GOQvYxIw9vYUhglM9EyRoAKoV8NCDBYLOM+h+vtnKTcL7CSlBoYRaMKzr9KkXF\nwU6t1cbo6tMAORpMn5AA4nS9JjEjKMSRXjVttjhKcBpzo0gfSi5qcBZIRxnjMq7WSJLl0cCwu6R0\nA3ZS4yaBUgyxF4bVIWnZIjHh12dQG9wi6EHYFrrb25RcaI5t4StPKgqnzEdJuU7OEepAcoXcFqy1\nTDcnuMqy2okM7ZFOJkbBP84g1kPOKkb2gp04yqA1dxVqhXQOSRPtjcOIxo6IZKQPyn8SRQRIGkYm\nmEP3ywbDoLmFwVFXFjetiUXIez1+NiF4p4RuyVSTQOxbVh/u8JE9Jq4GsA2IxfqaHNUMEI8ibmOO\nEaMTnuUwShcMoaoxTiNeytDjJjOaRUUaMiYEJCaqrbmupTrNQEx9ARcoWchDJCxqgghFPDn2xNUK\naxoNA/aW3CqwL+dItT7DOUvfDgRfE6YNB3v7lBgp3uDqBc4anDO4UWeWSNiiK2qhAheht4iFJAXp\neqzxhK2pari6TJKBuqph5rXgtkZvxFlNE6bSOCaypWSHb0bgbis4HGYCDJaUGPlQ6lgzYtURacFm\nvaYsek1j9DpWHIZqGskK/rWNIw9Z/flGdW952WKcRmLkPpKWvQ5S7fcBwt6OriU/nkltxjbaeBQi\nKSfEjJO1IdEXQ5Os4iKKRQLYbFhMHJIN21fvUM0aZmFKTBFTHOIKx9cnTNY8d/cyq+0DiiSm0zV8\nQIGXE0eKigA5dnKLaROQMmd754B71+9QHzuOhECJA6kTilkx3Ftiq0DdzOjSkr0MSCKEeswtVQdg\nPlpSgMVsRpgHpO8ZUqRPmcpbbGhYtarbM42nik6zJk1hfTLDGD1762BIg3CwHJAEy2khiOY+HvpC\nPirkVDDWUTl9bieGmIp+HsETUJ2qK9pTDsViayElkPF5hLH4GaHDpRplIlk/e2chG0PMgi9C9oJP\nhmyhS4J1hrX5Br/xpVf4az/9eY4/9ye4XV8jyH1AwDuqjSm29kw3j/HgzzyEu3/Ajz1wns898hif\n/jNzzN4ef/lv/G3+zn/165QirK8vcPWM+dYZloeHtG3Pa3/wVZ59/CFeffcmG1OPlcz3btzH+zuc\n3pyxk9b48U88hp/M2L9ziwHPy69+j8sPnuJfevQ4tvasHzvHN16/wm5vYOMcX7kpPPdjn+WNr36J\nE1tzfBU4ubXGI088ze72ba68+wHPP/sEB93AyemCYX+HHHvub9/mwsULZN8RxfH733iF4iydCCtm\nGNNz9tETnJpvcfXVN3nzOy+z6gceOHuWP/0Tn+NbX/smhyXx0htXCOsLjpnAld0dHr90iTs717nw\nwIMc7OyxMySsdzz86En62PLua2+ylxPVsTlNNWGIkdXuPRYbG3z7Wy+TM1x85CI7t2/x4eEBz376\nOd56/S0ShuMbJ2n3til9x2pvh9ffuwPWcvLYnNl8wfETpzl75iSCYXO6xvat61x596oy3wp0KbIc\nIm0bOegHnnvi8T+yZvlBH39sE6q2bWkcOlnxTm/oTsfdUlTgbGSE/OVR5C36ZTKgPJvaY8dQVymi\nIvQwwjUT2KgCbsHpKilZXRM67VCJegArLyjrl1NUwJ6HgikFWwVAyHi8d5QyEo5T1nfHjk5AIs4r\nzVjQlWTBYUuExZqGL4+EORm5QyYYbBHwkFGIYUkJM37Z9eAXnGhMTSkFYwfKkCFU4CCnqAJOqzZ3\nY0RREqLvKcVhjK41JQriCtRG3S9YiisaPSI6EhebQMK4UgRTecpQNG7F6AGm+VEJR02h0B4tlePU\nRqRAmM4IM0dqI/3ensb1tIeY6YQyJCxCf/+INvZIykgUqrU5dhoog6gWIUYwmTBrsEYLk9SnMWke\nQDg66OjubePqGnFeXXtBo4c8Vl2GVvB1pYJ9EsHVH6Ms7KTCWEsZskJcR02FCNBnxIsytLLFzr1y\npxiz6xi1WSmCbZSufThmR7qGXNT5lcVQBOLeimbrGFAY2pZ6XmONI/kExdDt7REWE7zX6JWjnX1c\n3WihXHnqRQVY4mqEkTZTDQP3KPDWOaqmwhVDKhlrAs5bfDFEI6SlavmWN++RD5Y6vcsdYeMYZW+J\nbC5G4rvHVh43VYCn8475eiBKotvv6PcO8c0cv9hUp5wTuqLi7GwN5ShRKsHPPMYJtgS9pmqD8xZT\nz/U97hOzuad1FiNB4248kISh10w/pBqhQirWtl7DnzHqIPXOYCaO0osypGR0LmZ1AKZBlKqO1ZW2\n0aYrW50MazxUJhVVPzprkJxIPRirYeipTxrPM/E0TU0cNBvSi8Etap12G0PJicQ4+RwiuY8aFdU5\nSpewTos7v0AF+mJwxSoMeJyiF+PZnFfcvnVIziuoA+sn1hgi5NqTh6w3fg/3bx5ok+gNIUx1Omgd\nwQo9eg2vTxzVNLC719H1Pd3OLs3WFsHX3Lxxm3y0xDrPZLGgSMSbAFXEDIXc9QxDxHCoEVFVoK4a\nSs4kYznqdgj3PeIzfafJAq4I1rQUAz44+r1EbbTh3Fzb5KAfyMvCbB7YO8yE2rJa9aRsqaqgxiEj\npFGmmIuhqgzeOCpvSMaQi+jrROiifBwtZFTlAVEjq3LQe4Y3umaVDF0Wnfx/JNfNQrBacHlRgChi\nRsO50HtDnTVdrBL4u7/7Ff7BL/6HvPo/fJGtL/wFmvI2MqhzuGTh8PZ9Dj+8w4lzD/LTP/Xn+Wtf\n+Mv8W//+z7LzjQ+IqfDP/uf/jccevshTTz9FSonNMxcYuhWvf+8NXnjhk7zz9tucngfu2DUeXFgu\nnIice+gSe3duceLsBm/tJZ4Mmes3btMOA5//kWe5+u41sI5pjgyTQ9bSPs9fvsB337rKIw8Grrx6\nha1jC2IunNpc0MXErRtXuX3zPk8/dgkfKuZ9h3WeK1eucnf3gCjCB3e/q6hE3mZlDa1UzDbnPHXp\nFKFuWHjPP/tH/5RcMlubGzzy3FmqzQXeOQ5SoppMuPTIOS5unqPfvs5zTz3OOx/e5vV3PqS8cxPL\neLYuakQiF6YnOf74w7zx4U1OPvgA0VY0znLQW66+fZ3TTz3CJC55+MwZbl17D1dPeOva+yxXHcZb\n/q9vv8hnH3uMvu949b07FIEnHzmvesSDPXb769jFjLWNLT68cYO7qyOcwG6OnDl1nHJwRNdbjp/d\n5N7eig9ub/P0H1Gz/KCPPzaX33K5ZBLM96NYfNE4l1IADSc1Nqi7IAkE0aDWNE52JpUCDotR2CRe\nRbZD1H/nBr3xGjBSkOwwXidbxqgbUI9aGZ/DKQ8lKFPKiMGESrv2DK4kUh81Q5BRSJWVYVNEMK5h\nutVgQuBot8XEpC6/kilpoKSgoc5VpRqeUjDBQzEadWLG1Z+x359UScEUzX7LQ6eAy2rM76sCue2U\nS2SdRupIgTiydWrt1J1XErQJgqHWCWBE1zB/AL4AACAASURBVGIyfhhpXKEyrh6jSsCsCFnANRUi\nmnEoJemN1ECKHRIHxRr7Uc8imRwTw5FQ8oBxHiM94hrVHRhwdUO+f4BrauxmQzlqaTbmtEed8rmM\n1ZBlPKaG3CfKoPgDM4Ioj+4fErdvq0BivhgHm0WL0UEwFmxd6WdvLWXVYY1HGjciJQYNZTYKeM19\nxuCV4yUCWSjG4ExScn8cNFtQdArhpCCV4J3H14Y+Qk4rMIZqXuOcTgSdAcEwOb6BnyhXqNlY4Iyl\nXbbK9Foe4RcL/CxQEnQHS6SN2HmjU8jKkJMlDz3pYEWY1uA9sR2w9YTU9YRJQ7/qCPUEYw3iMmmA\nmDtdbwVHjgFfOdxWjW8q6pm6XfsuUrnAUDIOR1wNxNRTYo+fT79f2FPRHDumwvODjrA2ISdFeZii\nxbx4o/yvlYZ4g7LKXOPxDp2eGfCNY7WM9PeWuHlFRYMtKtpObcR6Fd8bo/9GctJ4mQFyE0AS1nvM\nUhEp0qE5Zh0YW8gYSkl4V2OC6J/bQhrPBAqUIWuDlnUtl73gZAwXzyhGog6oWlEoQ6a9e0C1McfU\nXiG5ZF3nZWVolZHujjWkIWOdpRqRJRIMqS3YqWO2GXRFCMRecRHTynL3cEWMLS5ULDYXGj4dhRyT\nMpYMxCHR95nUFayDzfmUMmSGqKwgKKwtKoaUuHfQElcdJSaas6dZbzwxJqbTY9hTx+mzBpC3fcBY\nR7uzIkkmrpZUVTNO0yJ96ejqHoMlS9GQaQyxtLhRt4Y4qNBzjIJ1FV2oMf3A/n6LS5lmfc7+jkYw\nBWdgqud4L5mD/QiVZSaFtkQmdUBwRFeosmoRTTb0Q+FgKEQL8+zxJhMqxd8EazAV0Mk4qda72ABY\nqyu+6qMECgE3aN1eRlkt6NEeszAxhsEIIUP0hlqEf+9X/jZ7d7f5f/7BfwPA2hf+JmvhReq0Q7EW\nFwJ3793mz/5r/zbSJX77pdcp33yLx89v8b/87/+cX/vlX+DMA+f54u/9Hov5lLZVJ/K3vvUSXUz0\nzQY/9flH6ffvcQIhLQ8I3lMbw1lzQLu/4oknH8W4wN0Pb/DoQxc53N9julgwma9zbD5l9/59SsxU\noeLRi2fpBw1W3zpxjvffeYvoHadPHaOeTPH1lJs3t/mDV76o2mFnETEUazgqhY4aVzuef+FJ1quK\nhREOlwe8/M2XMcbwzGMP8/Sf+BTs79NXnp3du1y+cJr5mRNMC1TG8MXvvk0swuc++0l++oHPce/O\nde45w2S24KVX77DWrGFE+PCdaxylhNvZZuvYMQ5S4upb7/PE889y9f1rzE+sMezc5YGzZ3nrpVdw\nxzZ44U98imnbcj/1vHrrGnlnha8Dj144y3K15Hs37vHZF57iWy+/QVPt8twnj7F7dEisDPNQs7k+\n4V63ol92TLbWEOChRy7y9mtX+CmRP1Sz/KCPP7YJ1Wq1YlJ5rLWIl5FFNRpcrRlJ4iMCYQLSiRYw\njhFwqDRcM97kdO6bwXhMjlpMRdGZrlPCueieb5zojCLyFHWl560iGeQjq4eKo4ejQV1uhbFtGnXh\nbpzoBIsLhnra4CaeGMF5R+p6fFMTj6Lu+aMK1ksXdZoTDULENwETQbzBjwRyhZYW8jCozivrCtO4\n0QgIpKOlAtdHZ5XmYI22eaw+Tyxk6xVgSsBVRWF6RsXUzhXyEBGn3BZrDSaom895/T9IB2XQdZ3z\nU7UwOUtedRphUHloKtWklZ6UW9Kq1ddcGcx8ijOecuuOToyqCjNpcLMJfl4z7C0ZgeMQE4IbtSvj\nCnYQFQpbdV66YJG+UFLCzdYh1IS6VsdmzFR1hZs5Ugv94QEEj0kRY8HPnLoBxwBscQYhYYwHWzQL\nchKQw4gMPWZwmHkzmi/HqKBKtW+uDkrZ9o4UhdR1SJsJ9RgmbdXhlbqCE4Pf9KQO/CJgnaHdHzAp\nQagIxzepfBjjlnqGVUuYTahmNS7bkclWcM5h1hf63jqDDY7cZpxxxN0lNI5+dUSzmCNDJswdufdI\nMeQRIdFszJUJ1Okqtd3rkVLwG4aSveoXJQAd1k0VsVF57LRSHVelYMx6oyE0FbmPeKfMqDIkzblL\nWVfaHqxx2InDFW1+MgqF7YeBtL+HnU10ipULxQntQYt1FleP2PI2Iw3kXshFmyXrDS56ckG/rwkN\nmc4FQ9YVdzS4pgIMKQtE1UW6cUWnVRM4MRTPGCatomk36sqMFLXg9z1YyyCCm06onCW2BaTXxsdm\nXQNHQ3aQu6JNFyNjyVUEGeGq1tD3mb5TF1866jWk2lXc/XAP52uMVITQkLtEDoYuRYwYfHJkCjEY\nnNfiZbo+Q9B1t3OWIRUmjcdYx6odyAjWe8KsITinppNQgdEig2RwtjAPNVVtODQWs1xSV2u4xpGl\n4KYTvPcQC7EvODPoGRAz0+mM+aTi4KjHe0vGkg47vR5sjywHTGORg4HBW7o7B4gUwqTGBqHKM2rX\nce3WXarZgllTMdTqyJaibmURTwlCHTM5FTJCGgzOZKItTKeWefT4sWByAoM3hCw4a+niGLnp1PgU\nC5g4ujHDaBZM4zbeCv1gqC0sS8EWQ3FGkxqcftT/0W/8Y25+8B5vv/xtDv67X6L+S/8JrnxJGWZD\nj6sqjn3mMn4S2B8KG588i2szz7mL/Np//Rv86z97h9l0wvPPPsXL334JGybMas/JuuLExcuUoSXM\nN7n23gc0XjixtQk5ESh0SeDgPjv39nj/7gHDu9dx1jBk4dypm9zc3uXk8U3mledwe5dT5y9w7e03\nePjZF2h37zKfL1gsFnz5O69j3rutZ7wxRGuYr03ZPWrJYsjFUW1tcOLEhCLC3f1dLp27SHv3FmtN\nxenNk7wxvEUqwkk35U53D5Ognkxoj1Y8PN1g9/5tvv3yt9lcm/PY009wrKq5+u5VLl24wOTOh7z0\n6hUkb7LWVNy/9T7v3z1gsTHHNwH6xMXJnDMvPMfXX36dkxfOc3y+QRo6bl17jwfWFsxOb3G6WvDy\nW+9wkBI/+vRzXJu/z5ZreOvtD7DWsHZszldffpNjx9a5eOYU773xJovZhNPHT9KUxH5leX5+nP5o\nh64IUQq3dnZ56plnSSn9oZrlB338UAuqyWTy8c/L5ZJZozlyHx+IRZBgFUApVt1tovRzY3WtoWss\njaogjlMrGe3QRYeUMsqmJBgd11MQ6xU3YEa1ehaKy6OhTAGY4iwuWQhgxtGxCV4nNSapJqnS6AmS\ncqGslY+7tm57Sd5bUhhAhLI81AIh6I1YClCyFmvG6Cqj0w7bYMiDBrraxlH6hLigkS7ZabyIcdjx\nubR2NBATyQBB3ZEuGKSHUgXC1CnZudHiqETVnphaXzPGUFpN+xNnsFVFmHr63ZbiRXU9RqDNmDpg\nGg2rLt2AwVId26DenBBbQWKhM4KJE3wz0bVjcJhBKF2nWYpUhPkMxhVbGVTjYOtawZ3eY2tPXnVI\njth6ghkz6fJRVBi56AFnikWahlDretIOSkAXA+22ujGMVTum8zV2ZiBqVp0wsrxcgU5wEyhJV0om\nATHipxPV15hxKleswkb7jK2cYjUaT1lBPDokxx6cpd6YEVMhDT2TSYWURIyJclAjUvC+odtbEo8O\nCFvruFDhxTIUBSuKs3jvqacNaZlJPiNiKSmS40Azn6u2xGs3XkSJ07ae6HfIZ9r7u7hmCpLGyeL4\nesXQ7xzSnFxjeXcPM/X4SSCvWlbLDkmFPGRsVVGtTxn2E7bW69vEpJwp1yhXTPyoA9P1ch6iZgeW\nBNlgJhbpHSbrer2UoncwDNLo5LA+dZJmVpNKIbdCOmoRiVgzGXWN6sQzWTMnnfEjhkAdi2R18JY4\n4j6cvt/WqzDKZssgg6I0jNP3t4gmMSRFk4gRQuD7371kVJOIAm2HZa8T7JK1z3MVfSzkdtApqtiP\ng3kTokW2MRjv1D1cGyyCrSxlgLwcIOhsvD9sVffXC8UXbF2xvj4dsQ+FPOhUnKTZh81shpRMv8pY\nYxBXIwUkZJLmJ41wWKGNQhwGjK2U/C4OX1DjixdSF9VIY0WDwJvCkCw2ZGxomCwcdeXZX/ZMK0sw\njlgJYSo4W1Mbow2vG6dv3lBXVoPWJ7UmNpU54aTFecveYc+k9prRd3REt7uPlEI/6dlfJiYn1plW\ngZ29XXwzZTprsCqKIy4jdR3oTSIllTRYJ8ReCBRM8jDNNLUQnJoLclYsSx4E34wN22DwcSSyq3+C\nWJS+XhX9O1KAKPRTxdJZUVmGMaAx7SBZ+Hu/8xW++n/8r/z3v/4rrF98nntvfBlXQ6kcOQ2YqLKC\nrUcfIO3MMQ8Ke9zi4N0lb7zzLj//b/4sX/7qN/iXf/xzfPu7b3Dp8iMc3L9N2d/GX3iMt175Njfv\n7fHgyQ1W3UDqWsz6CTYqLe68t5zdnFEQuphZLBoMhmlTcen8aV55/SpN1/P673+dYgw3v/IVVu3A\nZFZTCkyC47BxrA4TxQTEVhy1IL5mfdMyO9bwzPnHeeed77J+5jz3lwfsd0u2Zgvu37/D177+IsZY\njm3O+fbLL3PU9zz3/FNszI7xcOzp9u9y8+ZdptOKw67j2o33WX/gAmvTGhkiu4ctq9VA2Ap8cOdD\n9t+7iQmeixfOcfX+HZ56+jHi3i6vfuu7SOVZLDz7eUXOPedOnibfvMml9ZMs9+6y3F8xO7fBlfff\n5eR8jjOG42e3OL22oG5mlNiREV698gG7+0vs3pLF0ZK10PDMk0/z3puvcfXOHi54qlnF7s4RH9zY\n4XPPf/YP1Sw/6OOHPqEyRnPIDg8PmYzdrj6TfkFhRAiITqF0uT1OlDzK4xnNbXqzA8zofDMfxdXo\ncxija6hi0MlXpYRhzeDLehCIAAFrlK8EhpwTxAExHhuMBpoXi7GG0FgMmgLvnNfRdtCRd+l6ist6\nExGLqaZ6uI43Zq2hPBCxXtH7AmOYs6irUQxlGfVAjgXjIUyUXyUiZApmDJR2wWpYb/7YjkRJgCkY\nEUzRQjP3CURBdiZpoKjDKe/LO7z1FDI5DZSdRIk90jlonBZ0waptfpw+SCpQa96dc0JpDKWymFXB\nrE2Zn5oixbDaj7ipw00a0tKDtXQ7e5g24k8do6RecwJjwU8DxgXKkBBvMG6KnypsVKOGBOuUfi5D\nRHLE1xXGBWwqui1OEdNFsGAnDSVF5dzUFjohxaWaCIoWyrIckFQYJGOrGpMthExYLBQnkfV9LlGw\nqH4ndYkSE6EKDMsBuh58hZGK+dYCO/d024f46YTYFdLeAeH4JsPBEa6Z0HdL0sG+uqnq5mMIqTPq\nJqVkJqc2SGnEZ/S96uDEkdtIG/fVbZkFmU1JXYcxFWEWiG3Ez+cUEylDR9/30He4qeqvZMgk4zi6\nfp96sUHpe0xRoGW6v4PbWOgUsIG4HEjDCmRCM58yxEQzCxQDsVXXq/UqSO8PloSNCbIfVQzuLHRj\nRJSzo9tQc+aMETxQGq8NkhRCsCSXsZMJudUQ7zxGSIkHesFU6FRX9PooySCixgHjKnywZIQcPcEJ\nRTfyKrY3HvFCHtA1utOw4hJ1amGLrvutsRRftNgqQuoz2RSdBhXRBkcMKUdsU2MkM365MTbBAMZZ\nnPeYANIlJDqGEhm6jDhlngVpNLapabAmIZXytOYbjTrRhkJ7uMSFANlTTNKGxChs2BhPXRlkPCfb\nIVGSJXhPVcHh/cOxEfTUjWOIhnrqVYNphAmQgqctmdoFAkIWSz8kJnWDMT3OKhKwYJlPKroE5EI9\nsSqHEEMkKzKCwsmNhvu7LesbDbZyxJVuCULtWMYCtaee1KQuIsXiJpp1WboOXKY7WLG8s4OfNmAT\nw6KnqmpC4xCBZlpjJasTU4TZYgqlULxl6AemWBYpMA2GFphNHLWzGpeaxubRCEkMLipTUFRiOkaR\n6b3Ej8sJv1I6SvSCzxq47I1gs+CcpcvCj/zZP0c9nfKrf+ULrP+F/wLkt6EblH9oNVWibVd0+zus\njhr6d44Qa3nkR57k9Q8+IFrLF7/8VYIxfPlr32BaOT79wvOkbsXpk1t4U9ja3CCmTHNsjel0iqSe\n+/e3EV/xYQnsHi6ZV5737hW6aKhxvPTOTdohEXcPKdaweWqT63dbPvUjz3DtrXfwkwm3790nz07x\n8HMznHXkUthcbHK2qvmw2+P+/hFboWJPPDMGdruBmx/e5BvvXmeIkbZteeLJx3HzBmMCP/b0kzST\nOR++f523r15lY3POyc01rt26y/GLp3ls/QRhsiDfusFXr7zCpQePY73l+LTjve0KD0y2Fty8c5dP\nXb5ATAOvvfo6JRceOXeKk27Klf07rE+3WB7ssb2/4s7Xv8lgYXBCjpGexL3tQ05XDZcfeoT337vK\nqVMekzPDEHn4wZPsny9MpjNO1AsqX/Pa66+wt7dEgCd/5GneevttqhMbVLOGVDKL/1/N8oM+fqgF\nlTGGpmlo25au6whOaeja7IwaHsVJUayFnDFx9C9bRxlkzJNT5AEIdnT1EcCXCrxmkWkbKpSglHIj\nCSFgc6EYdYqJA1N0tafzX0MuCZMS4iuFgBqrZp+gwm9bHEVUSG9ioRiNvCkSwTm8VBTxuMZi6ykp\nDZhcdJ0n438rulFcazV7kDEL0KmdLqxVpDZhvCotjR3dZ94pmLMU1Y9YM3beRd8vdLUhox5LrEIS\nVfiv76OuPVBt2CqCU+hoLiNawVvKoE7G4ANJBJc1a5Ai5KMlkjPNfI6vLaV4hoMVxlicDePNyoEv\nlL6j1FZxD0WQEplsblCaRDWrySmTlglbGbx3CuHLevj5YMgR8qqj5Ijxej3kfsAEwfkGP5ngaiEu\nQUQxCWbqsUadWEhBnEVSHmcODhd0HWKKkGqDnzT6f45J89l6EAq+0teLM7jwUbh1Blsoq0x/FBFf\n8JOG3PaExQzjYbW7pMSEyYnhqMO6irJc6SQlC6SIW5tSNVOcM3SrSO57nBtxATi9nosQd48oLhCm\nhhIHZLWC+Rw/nWqANUYnaXFsBrw6ofx6TbfT06zPIc1odw80imbW4CqPq5WsPxwOkHvEQthaZ358\njYM7++r08w5bNYRpxTBE/MQRk2IknLWY4Imlx7YGZ6oxQFqDo00GN/P6e6xRTaAUsghmKJhgsd5q\ngdVUGhpedIUpRZ12wXlSztghfYxekwqksxipsGQNNQ8WI5aYEtIlfBMQZ9WlZ3SSlwWI6MqQMaIq\nj1Pt8cKPTqddRBWkm8p/HNvkrUVtMplYDF50QpKjkJYdULC1p55XVBNPHCC4rBNT7IgUUE2kw2Id\n5KRFNC7gnWMy0enrasgMq5YiZpzkQgieadXgsfSmwdR5xDoUYtbYJhOE/5e2Nw217MzS9J71DXvv\nM9wxZkmhKSTlICmHyswqqqq7uuzCUBQGYzzjoTHY2I0NjcETGLtpDMbY+EeDwbTb4AnKdjfYP4yh\noNrd1dWVnZlVOaeGVEgKSaGYI27EHc6w9/6G5R9rh6rs/GFMpg8kiJDy3hvn7rP3+tb7vs9bSIw9\n7JzbY3WyJnaBoA7xShlgVCG2dp/ZjFtyX5kfNKQMNRXaLlBwgGMo9hk8nDdk8eRgfx/wjL39XN57\nskAbAkNWsqtT44JtWZ0IOVU2fWbeBIaSGcaEtIHlomPYJNr9XYZtQ+kzrlHGsSBi0Nq8WKKnifi0\nT7Hp0FCIXUM63TCLQpGWWXD0fWEYlNg4msaxHwJFTcFI1ZLVEfOnpglfw1RsIcIELRbGbANncdag\n4UUNoVOtUowokKvdqovw5m/8Fv/sX/53+Rt/9d/glX/1ryDh71FTxgXINTNu1hy+cpXjj+9wev8x\n/9y//C+yvHSRmgoXnzngYuiYlUT1cx4+PCLOl3zzm9/i61/7MqU85Htvf8CXvvQGzXxB6Ga89/bH\nvHD1GZ7cv8sb51uWn/8C77/zNi9/4Xm2/cjRg/scXjjkrU8L8/1zHDYNl7rCpcNdHmw3PPfKS+x1\ncz7/ylUWy11Sv2G22KcOG5pmxh/+wR/y5q98lReeu4yWzK3bj5itNpxI4Gtf/TL3b93lH/mt3+R3\n7/1PXL/+Ac+99pt88cozbDZntLHlxo0bzBYdF69cYne+4BUvPNomvvvgOr/++ld48OAx3bzhxu1H\n/PZv/gXe+e4fs7zY4K5e43RV0PGMb711nRevvYCLkcs7c841kbd+9DbN5T1O9IzD3R2uXOpZdi2r\nUrhTtnzl+dcYU8+91RO2/cgPb7zHhdji1NHtHNANAz9+96cszh9Q/cCDqrz9o+uIE2a7HbMEf/Kt\nH6Hi2Htuh+XODl7kZ2aWn/f1Cx2oAGKMbLdbcs5EPw0ZmG/IiUMSlGAdX9o48xONBR1HS8pgplPF\nJDOSVVJQoPiMFjHisbOKEUmCSP3MtF1FbbcbIx7zVagXS/1NwE6whJJXb2k7NUlMKNZm7w0W6bKh\nBygFidW8GQTwmSrWaSbTKZ1gPpCqBubTp0XMkwTpvENLNVmjVGNbwXRjN4O0DhNZOBhwEpXJZI9t\nxYrF67VUxJts4JxFuJ33NjQWM8trVhsOELz3uKKWYpuSbGDzRC2V0HTghPR4i+aMzGaEzlPx9Kdr\nyrY3f5kTfDMnOmGoQBfJq56yPgNxhOUuzSzilq3JZVtwZGoP2/WKutng9meIBrJWKINRxdUZG6z0\ndqpVM6BmKdSVo0iBJhDEk8dEzsmkQpz5xcZkD95gEigeai0mBXqr6tGkNrQJ1qWXhSoZ97TYN9vW\nBqnQOLqDDoVpK7TARcf6yRqkQHYMJ2eE5ZzgIuoN5pqHFaijaRdIMPChqELxSOeJfuKGOUjrUzRE\n4iJS1oW8WuEP9tk5mFPxpNXarks3FTW7AklIVEMZdDNiG6BVKnvM5ralURWi96gLpMcbu7bGwaCY\n4ZAQIjUWmq6zjWgpCI46Vuow4mYNaVPwXcFnM35bJ6ROkp+a5DYUSJ6cE23X0Z8lJIhBeqdUlBRH\n2mRqshSEBkG3gyEdZtPG1Yl5U6ZhVMTAjsU+bLappuCdIAv7veVNsuE0BJN+UgLMAyMUanGIq9Rs\n/sqq1aT4FBGnkww3Ujwm7/qKbi3E4YPdd9JYYEwUsUobY3A5GKB1yiplxrMBlxTXGey1ZKsIkiYw\n6+zHTyjBO2r19EOiP92gKoQ2gqvkkpkvDTFxMm5Jm0xTW8ZacU7tAJkTbdMQxBHbwHo1MPYjXRPZ\nYmDfnmyk/K1wkjaUIdEuZqRBSVLQ0crGm0ZIqeCct+Rrjay1omOmbYPR4q11yFAvwTEUR1Fhvlxa\n6toDydFj/Khl15CLUPpKdQHvbWMZlgKjEtqOWrfMd3dJSdkerRjXa8ZNT1gsGeoZMUayqFVwDQW/\n7CAHZh7GCC4rzo3krTnMH7mR6BzReWIntArVWR+gb5gaA2zY7sRqqEo2iPSpg91gcNNGwI1KcjDP\nUzpQlGaEEmFe4R/7S3+Zt779Tb71N/4q3/hP/zeefPTXwInVVOXCkwcPqGmcNq52PcwO9lgs5pyb\nLYlj4Sc/+gmunfHowSMund9ndfyEsd/yxktXOH/xCvfu3Oa9t37Mo9MNd+4/4o2XrzDbv2A9pE3D\n9Z++z6uvvUzOhR++8zG/9uu/jJSEOM/HH91gZznj9StX2KxPaMvIrfv3uFfvUKPn9dfOEZsFm6M7\njGNG1qekWuhzZSyVs8dnXPj8Vb57/S3+oV/7VerZE166epl/8L23+OkPfsoecPnCM9R+w04XmF8+\noA4DN46OWU9YjZ2r5/g7f/RNogi/8ubrfHrjQ4LCg5MNL18+z6X989zo7/Dcs6+yeHaNbxdw/hnu\nf/RT2hC59vwVvv/+J1x98zx7Owfc/vhTPrj9iCZ4Xvny51jGlicnR1xuF+ycexaHJW9v3vyYdT9y\ndHSGd46X9g54++bHuN2WK597jnPzOQ9Oj3lw6wnLSwfM95aA47n9czx9Ev7ZmeXnff3CB6qmMVjc\nMAyEYGZo02iwjVKwG2VxTz0SoNm2W9bFhm1zgkO9bbSYYtuMigST/SoTBLIqNfB0DWaJtuhAzIAq\nycji6pzJi2Oxh0sxpo73LSVn89bkYkNa87RU1bZp6iybqyL4RbAV8meIBvOP5I2t6D1qg6AWSl8m\nho1Q1NKOeKWMxsHyU3dhlSnm/VTSxPwYVgMjVnshk8HaGkJt8BgrVW1YE1VSLpbQi1BqRCRY0mby\nt5Q+oTLggnlVyri1sECspLMeWo82DU3TMK5GaurRlK3QdpVAlNAtGJ1QR2fIYZTumQsmAWw3ZK00\njTdgXzZgq2DDm9ubE2JLThnGLXUwrwhNoOaRpwXaJDO3ew1kN9g7MjqTurYJgsf5yVOSMj4GGyAD\nlGoFxg6HREu0lFRodiO5mARdnaHB0nry0FVhqOY5kegIsaWqME6xcx9hPN4gjclPcdcjrsP7QH+2\nIs7mdl32ntm5pXmVsMFX1eHnipsHJBukEK2E5R5lvaFsE2VzijQN3XJGyiCSkKwQK2GmpA3oxthl\nSKEU0KGS3AjOEBl5YpkRJ5noZG2sHi3UseKajrOHZ9TRirmLy/iZEca7RWA86SF40smWMG8N3YHy\nlDCu4qjZcBIkKF6N/1TVZERva3OdakfcBOmso0Jr7DaloE2AMVMyVvrcG4aBPln2JDo0OsqYYAC/\ndEgU6mA+LK29yeXVYLOaDOWh4WngJVgopXqD2gpUKpKdZeOrfQ/DtBijRjdKDQUfGqL3pGoIkbiY\n0TRC3mRCG5AKT44eMw49UsEvIm42t8CG2hDXzC2UMA+OR4/WVKcsz+2w3ibSmC1NGjxRghUiNBbS\n6ceR/uGGZtlQUkYbgxoH9bQz8880ncFGN0ePics5OTiTw3MlNOBCw3a9MpzGcsliERhWmTCLbJNd\ny8M6QVJqo6gE82aVSsLjEqQ6EEMkJWhnjgbHvPEcbQutr1Sd2iOc+VHHNFJLZtyOVjBelNi2lKzU\nSUKMtQKRxnuqCvGgkk4gPXnMeHqCzaB8DwAAIABJREFUa+eUxYKQC2HW0Z3bp501dDGSN6PhHqKw\nG5fEFta5cO/RBoewXDbMx8jB0qwFEhwyGgk9FWgFcpi8Ux6qKqHC2SAsgzL00ETBFdh4aFRRsc1W\nzcoYHT7Bv//f/i1u/uC7/Id/8Z/gS//Bf8enb/9nUHp8jYQQ8UcjXh2L/V1S39MHz/FKePTwHsOd\nU+qQOOcdN258wHOXL3Lrzj0+97kv8OD2J1QRfvjjH/Pa1ct4KuvtyDsf3WF+6wEH5w4YNj37O3Pu\n375DN+/Yq44mRDarY+7cfUBsI8v5nJO7n7J/8RKOwIV5x3L/gm1hS2I4fkQaerPAiOe9d97j7nrL\n5WfPQ/C8engFvTojFNgMI4f7S7yDo0ePOTx/hVu3b/LcM89zthnp7z3hjS+/wXPPzfj+H3+Hb3z1\nK3x6/xbLV55lP7Tcf3Sf569dI69PWUbP4/vHPH/hCs/PIm/dvM8VehbxkbEktfL+jZvcP93S7LWc\nX86JsaVfbfnlX/oiFfh0fcylxT6ffnqPF669yK1PP2HWNuS+Z7GYsdxbcvX5Z+liy1vvvMezz1/G\nhUBoW9778CMO93d49YvXOJzt8MN33uGVVz/HnnesVmtms8X/bWb5eV/u//0/+f/2CsFmtFLK0/YU\n+y45U9OUYks2KFCrgSmDmZdFzQslVa2oOBWr94DJaGzbpwqW3BOohpiyNhjENj7mXLJqGl/tzyl4\nnSQ2sEqYiYckUuyfHZPIblU3Wgo1TGynpx+ylCErWgvi7e8lzjZUogXNSskjaTWiUhBvxHcoiKt2\nw3eC14CfTYbXqhPY0wqTVe0vpCL2PWo1mntWkISPRoemVKSxMulSC2ySDZQaCcHhu2kDCNSSjAaP\nR4IlA2uxZGE+sw+aq0K7WFBrpaQyVfKY2UK8EHd3iNFMk6Xfml8lWIl07QfqJpFPM+OQydU2H6W3\ndJ1vPT405vcq2TYY3psx3TuIEYkR74INiDGQ+kRdZ+p2JPVr0mplfrogCA7nDL9A55Ag1H5Ax0Jw\nHo8zOCSgmhn6kXJ2RtkM6CqRtgNYbzL16TbUq6XRxkI+WaO5ELxjOB1xc0+7syA2U51OEfIwAkLT\nGdaiObBNlgZbhdZ+kpurp64yWpS0HkibQt0m6pgp6zPzcHVxshhOV28x2natagNjVZNu+4JuzHif\n+0RNtl1gnUnryvbOGZttmWL4GR0K2idEC828Rbx1NJa+WlrNYduYYJsi1zZWXVPFSsGdWu1TKQjG\n7JKJ1u2io6onhGBpzcpnW9tSJuu3M2+Xumo/65CtlLxkhmGgDgN10yNTiXgzi8RWqH1FI8Yn22ay\nGrLDJEM3bR4zZRqknQS8GFMuOrHtlxibjV5g2hBXrZRNobgJA5BtG964QBCrVipaaLpAaB3D4y15\n3JLHwmY1ICHSzZbMdg9oZrtIclYSro7oHL7Adj1y/+EZm8ePqX3i6NEp2ycr6nagRk8TW0vZipXx\nbk4HTh6fQpjKv2cOrXbtzWfefFuNJ42Vo3vHaAwslwv2Z4HlTkspI+OY2a42jL2BV/e7SEqZsSaG\n9WAb86QQHbEJtjXLShox3IsXUhVw0Q5/XhmSFROf9JUGsXuxvWVs+8Tq+Jiz9RnjoHSLGZ3zSGjp\nvPHTZvOAd5GiEZktGMQ+YzuLJWHW4HcPkG4HHSp51dvzoQqrx084unPE44cnzHY69paRzkViJwQv\nrE4GullLdo4n68STzcDds5Eno3UwFkx+942wykJOdm2Waov2lIFcOUvY5zQrA8A4lXBX2BamoIeC\nWOfmS1/+Ov/x//C/8of/1u/w8pV/mlysWqyOmTpJpC4Eaq6M6y1HR0ecrBLjoiNcWnLl5WfY2Vvw\n9oc3OTo+5Zvf/g7Pv/YFfv/3fg/vHMena+4eb1ilzBdeukwTHGkYWM4iZ+stNx+eIsBXvvwGdezZ\nrDacnm043N/n9HTFk+MVXbuDjy0/fPcm3c4B2yeP2T64w0/e/YAfX/+UxaJlZ/eA49MNX/ziS5w7\n3OX1a6/w4Ycf8P7N6zx4eJt33/+IsSp7O0t2lgtCWvPSCy/y8Yfvs03W/hCd5/q7b/OFL77B6Z1b\n3P34LlfPXaSJnguXLvPowSM++uhDTrcjmgt56PnRj9+HceAJifOXnqVxgXnT0HQR54TZwYLd2CEK\nrfec3b3H6b17PDvb4cadj9AQuHH3DsdDT7tcstoMjDlPvkM4XZ1y6cI+50LHksDmyRPOn99jtdly\n9PgBP3jrx7SLGc8sdnly/z6plJ+ZWX7u+efn/gr/j5dzNqOZQVKmh4OtrqUIVSyRJNlKNC0iNK2C\ngrc4cgxIqVaZUsH0vmk682qr1WTkY7IVdorHcrE1m7wnisaIjgUXlSrmiaDUCTAohHkEtcFJXEU9\nJk1Wh4aMjgFJJgGRPVWqfV8U3zpKqjjXGEcH24KpWA2FhIBfBCSpebpCNP9TMg6STpA6VPHBUSqT\nbKAIimbzQtUCIg2apjSSt249NNuDe5xSkdHMAqFt8HMPGUtHFuNkSRPNvC2CjsWGvyq41htPJ3q8\n6yyW7hTvAriAUnBzwYlncdiQCPQna8pmsDCjK9QBdOgRKrlfwXrJ/FxDv0nTBsE8KTllXNvhZq2B\nU4vgZw2pTzCM+OUcJ+bFKf1gxvuZo27K9CBY2lak8QgBcrET/2hJvTpWwm5r6abGsBnjamNwvtih\njSXDtFYYbVAsC2fbBz+ZosVZaCEI7WxmfqySJ+9IRVqBrFNS0X6WXBVNGUJDBfJakTaDsz/37cze\n99665QT7+nU0wqvba+mWczRAHb0Nv60nNCZVu0ao6zINMLb98o19BjSboX3UYdrwVoNbpgTzGeIy\n3aUD5gczSlWk8UhpLJVmuSdqn3AxWjVPA5Ls81qmgmDXeMZ+tDJpydQBaO0BPdtpKLkSukD1NoyW\nZMXdZbQCc7F5zFKEiMl/1XyLBCGEhrDToOro5sLxvTPqOBDiDMV/xg9iqmYJjbdi6Wp+JaeAnzZl\nk/RmN9linLRgX8B7R1FDUmg287x6h28VxFGyY1xt6PYa+k2h9itIyvLiLmM2M38ZBXV+ApIq2kAb\nPa7z4Bs2j3uKZpRKe3hIGxw5Z/IkYwfnzfdnBk9wFippYmef61bQUQghIiKs1oNJY01gTD0Oz2xn\nScGTizKMhTJafY+0gYPzC+azllQq29OBXJRCIXaG54gOS1B6RYvDNxWS1RupwxoFJLG/aDjqK4Jt\ndQcUl2wL2Y8jeZ0p3jNvFjhgvhM4OV6DKKtiA0wW+/2IKoFCKrCz23L8aEMdk3Gw0gB+gZDZHB3R\n9XNovP3/5xuGfkOYBbwLNE0keiHuNiyy+ct2m5aihfVQkFyoodBmxzZEZr7iKozO0aDkjbVrNM5s\nJ+rMW9Vm86hlAV+ENB2epcKW6bEjFVXh5a/9Ev/Of/Xf85//pb/IP/pX/jpv3fpfqGyJ3nF6dsrj\nuw+4+MwzMHOEruFg74AGz96soR0zJ/1d84iVSgB+///8uyDCq69cRZOyaISrLz4HwOHl56j9mtuf\n3uLC/g5tE7l48Tyrh3dY7J/nh+/d5Noz+3ipNNFx7drLuNDyo2/+IV978zWO796kne/wgx//lKuX\nD7j3+IyXX34WaiVVZX10yosvv0Qetjx38QC6jsODK/zxd37CjU9ukouyuyd08z2+9yd/YpaK6Hnz\njS8SgC+88RVQODp6wnozMq5O6OZLmmbOMy++wv0P38E5x+ufe4lu5wBSYXvnJo+fe5Fw4z1e2t3n\n1s0H7OwvOdyb03vH7ZM7XFte4njdc/XKOT669YDoHN285eD5y0QXUCCEhhdevIqWwqrfGmPLOZ59\n7kU0jdy/d5tzu7u03Zx36h2u7l8gn1ee3b3Cex+8zbvv3+KLX/tzPzOz/LyvX/hA9Wdftl7EUjJP\nad1q0X481sfmsDVtMRNrqYoEsc1VjeYdmYzMdpV7S2dpMd3GCURn+IJqK3wEVKz7TaKV6JpsVz8b\n8Hxwxr4iG/uq2ibLByOFUzDpsFYkq8mPVVBnZuYKluDx9kFT7MSs6GRyDgbpe+qVGiwKLmGSr8hI\nsQi7ilDSFj/I5PGyE7gL9lePjXlRRBTN5t3yHrKYbBraSBkqbuFwzsp2DcZoJmCKeWDUVVv354IW\nZw/qKsTl3N60FpNTir1/1uzjPoubVi9sbh/TzBu0iQCIC2b+beIE22xpJ27VuFojwcz+6hRpHPNz\nc6QIm3FD2W5IOVklx6whuMlgWiaJNpk3SRpP9GEqlLbhTmuhjKOlxLJDmgYXKr4N5GHEjZ6MASPV\nWWl1CEZ+r7gpNalIVmqc+GM5WbWQ9/ZztAEZFd81eBWkceSplsTNWnseyuQTnH4XKReQAakdOEe3\ntCqStLYqI1y2oW0YqdueeP6QNjY470i1kPve0p3NBPBMGY1i2wscgrGgjI9kcqYlZh1x1lLd0/Sc\nRzc9YXcXYmXIGafBtkVisnN0niJCCA6d+tS0Bw0jOhb8LFgHoNjmSaOfvFCO4Cqy0zBuE2UYcU1D\nWSVCaziBOhYzrNepU3PMmEu4Nc/fhCCIyyXdIjCMhXy25XgFuu2RxdyGnRCsmBUjbVet5I19zkxi\n9FTvEbVmhVKqhRycgzrdKxqDy5W+2LXTAKokSXgNlMERO6swCk0kJbVDVgzELqA4hienxPkMxewE\nWiISYRYjvnMMp5lhPLN7g3h8EwitGber2iGqaIWx0rSe6IvhUKon1Yqj4mcOskn7VIci5OJwzpMV\nmm4ORQnT9ZxVOH18apJfbCCDjy1P1iPjamOlz43ZLVyw+4L9fIL2QgoF2UBoMiBodaQ0EOaBh5vR\nUCdNYwffxipmNttEqhMlHk9KxkapK6UfRuY7CzwybdfNf6qj4LpINw9sU2XMK9xsjqgn1gVVK7Gb\nUUth2Kxx24B0EXpLV8sTxUVHd7hLCIGZFh62hQAcK7iaaZ3nZIL0zruGWWf+sEVj9/0+my/PV4s9\nyYh52Ka+U1/texUvhIlVuFGlKTAEoVMbrlqn/MY//k9x/sIl/r1/8rf5nX/7P+Enx/+AGs7oduas\nT9fwAuYtjY6HY8I5x2kz46cPH6Klkmpl5+KC86Hl3p3HVFX6Buomce94w0vLC3z3O99huZjT9wPb\n7cCV518kPbxPHke2Wbn//gcoyjYVUkqcf/YV+pOH5PUJ1Mr65ISrn3uTWiqX9m9z/dNHuOi5df+I\nk7MVXRtxE2vs/evXGQFtHG/6wK0799jb3ePClcssFi0f37jOtRee48NPbvPqy5cRqYQ4Z3t2yp2b\nN7jx6QMAGuc5ffiAc8909Ef36Lcj1y7t0rYzmEIrWiqvX4woz3Dzo0+4+uwl3vnwFsU74oUOQuDD\njz9ARbh57xGvvniFd27c5vyFPS7tn8OHhrFfkUs2qHWtnD+4zHD8kG3wpO0Z3jfcunPEC4sdTu/f\n5/mdQ+7ff8DLL77C7/3tv017uODwpcsG6P0Fv37hA9XTKc85N53GsdO5itWGFAWpaHUmh00PfEr5\nDCFFqqj3JplN3itxU2UIinNG+BbUTCml4sQM2Kjio6NWN5mzMX9LFQiKS6DOT0Z180qghjzAW2S4\nTnYsHAhm9nb2GbNBbmqvr96bNczJpEYKUgUXLUk0bq0OxIY9rJIjhQkX4VG8Pdxqb0b04KnDiPqK\nSITgaMTRLBpWtzcTt8lOl1bubLKgilGcHcEKgTVjztHBAIZismipiqsJwU9R+2oJwyqojtRTmZKP\nYvwhPOI8sbWOs/GkJ8SI6xqcJmpKNPstw5l50FQzNQfWxyu49wS3nCO+4roZtc80M/t64zahAnm9\nxYWR+TMXQD1pOxgKwTlC01BdRrbTNkc8bmabLilG7FYE5yM+eutRSxlfHKmCBofWZA+ZZbCiWm/I\njiiVkh1BIjUK5aw3Gn8M+BLJdTCmmQqIow49Up2Z2rPi2xbvG7KO1DISdUHVKUk40f79xAIb1oVS\nelz2qE/ooMSdBXhB9iKLvY6zB6dEN4Oh4JqI6wRdV9Iw4jAsgZ97M2Im8yjVqsbQiR6K4lrrjKs5\no6Oa1FJ7yphoQiQ9HqBN1DTiZx0uCbIbkNVocf+pQJvOknahtaojDYKrhTjvbPPaKKCUUairLYpB\nHPOQkQmDULcj6hyxC1ZcnMQgsDTgHG3r7b0qQukLqyFRztZIGwhdR91fEsMUoBA3+biYuvsUKCaF\ntrb9qjWbF28it0uqSCcT3VuQLagUnBNqtK22gWptKBVfqVXIm5GSEk5bQvSErqNfjaSTlQ0kHnQs\nED0+CqH1tMGzORlYHZ3Q7O4iwYrPQ2NMve3JyDhuCTPz3Hnnmc0mavmjNYRq1+1yQe4nan82Kb7p\nPN4rWQJdFy1c4cxHOtbC9mwkq8MzlZgvO7abnnG1JasQFw0heARvtS+hkFLG14ILDVIyxSl5qPgQ\nSKnivBAGGFKhi3N8Jwxa6VQZNpmqhTIUO5Y486x6YBDBu2Bya8GKwb3HOw+d3VfGDKujJwTfgXh8\nG8DN7BkgFbczB292hJJ7alWaOsMtIn4xpxbPWCrb4zO6ZUO3mCG90sysEaElMJOIE8dqPcLMvHQ1\nmgIiE+TVTz6ptCnstg51jkEqC+9IYsXdT/tNUwXvlTMHc5SxOqLCa7/6G/w333qbf+VXX+e3/qV/\nnU/qbdL4KRevXqamp88Vs4hUEc42iVLtntVc2KN4z51NT2k8tSgf3jliPN2yEzzf+va32Q6JdS4E\ngaSWWnx4NvL2zQ+5eukcr73+JT5+8PeoCouDC6we3ebhg4fcenTGmDLKE1L6Ph/cekRRZT5ruXb1\nPL5r8SHw+HTDzvkDHJlXXr7KzXsPuLk64b/867/LzmLBlz5/Da+Fx3tXODjXsDPf4eSdGyxXW8Iz\nDXncEKRy49ZDLp7fg5xp53scPXjEeHbCg8dP+OjeEa8+e0janKJpIBXlxavniQ+PebB3hddeep66\nXtkg1o/EXEg50x4ueLWJuFToh8S1Zy9x484Dst5mZ2fGwf4+92/fYf/wHD+9/hF//td+lYePz7hx\n9wGvvPws5w7Ps9qO/PiTT3j54hXyOPKF56/x8PanHD53yLAdWR2vmHfdz8wsP+/rFz5QPdUhvbeV\ntCuOKkY41gwqlrQy7kegVLXTq7fTlI5ictRTAFV2NjQF7LTjpqJbN91cxaRD69tSq6sR+xq1GEdG\nnw5kE+9IHBNDx7ZaGG3KeCbFGDwSTMaTYG3zaIVUTSoRDBUgYibXRqlep02BUDfV/jlXiB4XI1XN\nPOskU51M3CwzUUsO+Jm9F+rUvr8Eo6OLMtw7meChdrKvOGQQtAXvPE7UPFQuTCG+CQ0du4nbVGxI\n+4ydYNBUopvi8AUtUIctddoAyby1gcqLRf1rtUSVJlQjLtr6VxR8J5SNolshLFrCwhNDYAxCXY9o\nTuabGiCJMKYt+mSNqLK8dIHYRfptptQeLXb6YSiUnFGpxK4jBkfOaluGVtDiLdVWrHux6kgIjaVs\nMkSv5OCp0aSC0AXGbaaW0VJgpaDLFsZK1YS4BmlaSs0wCHERERRpPc5SD1bi69TI6XlAitpmphbb\nBIgB/WU5s+67IRtuI4lVdoxKGQYbSJ0jLhvGjT3oRcI0JE7+v5lHRhtuvbctQy0Vx0R1TgUWEyU/\nKHWYAhq14kOkbgbifE67bFifrqkl47UhNDMoigalbjPVVcQHQjFsQemT+dKwpKRgBxCHI+cRlyvN\n08Lm4G1QpFrPZjD0iVaTOxRMgq9KGUxqbfcaSlVrKEDNuxUCoVsSl1NStXpLs9ZE2ZrPK0QztZds\nRnwbiDw48wwBdkjTCk2Eqe5FnFAL0AiKIxSBifkk1apjUCi5ol4JTUtoHZocw+mGWqtttYKDPlOD\nEtThvW2QHj04IW9WxHZJt4gM1YItFaijMpYRJ54wj8TJz7jdFNLZKdU7GJTZYm5F5kkJmgmdDUFa\nHUhBcmbs7VDoW3sAD5uEDpkybikiuHZm22kUN5vRZTH+UCn4zkI0SSu5KNo1dN668mqvhMYOv5or\nGpVxAN9Mw5xWZjj6MdFvhwnH0pv5cBFwLhBmnuAVfPuZpFqrfNY7SbQt7dmjRyaTt51ZQMTRdi01\nCONpT4gNdW6BBJehkO1QUczcNKqBjWvticslWSIzZ80Rztk9vAY46e0gRcSCAk3AT2EgRQjeyOlr\nqUT1HKpBPvvpligofRG8BxS82VUZHMzsJE92yqVnX+R//uk9/qN/5neow8C5cxfolvNJZDB4dZ3S\nmtULNI7QtuAcuSrFC9pZY0CNgcXBLq0rnHzymOqE+X7HhdmCFy6ew5WR037g2ktXef7qi7z3o+/T\nxEiMHh9bPvjoFvs7M4ZsKKCjzUjoRtoY2I6Z1158lsNzh9z48ENe/sIXuLrpKRX+4O//MT986zo5\nF2Jrw+h62/ODn7zHYjHnwucPOTj3PON2azaerJAS/bBFh4G5vWHcP93g3n2XMo50bcOmH3jjiy8x\n72Y0sx3ee+snfP6FC+wfnuM7P7rOm1/Z4f3Tjr3Nim3OqCrDow15uaDrFizP77I/2+fk/k3euv4p\n65RZUNjZ20fHkX478tHRfV5840Vuf/Qe12/epekiZ6nw3W9/jzCLfOO1z3F5eQACx/du8cFHd/jG\nr3yFP/jeD/nim18mTIn3Pzuz/LyvX/hA9dQp37YtY1aqx27g0YybFIXRfEkqTz1NdsqSLFSfp4I0\nq5AQsW2PZttySbUTL411i4lMxuKkBuKrDn1aKhe9yW3FhhTnPIoB/xAbnmSKsks1lpKIt0i2MlGX\nq/k9akZiMEN8NUkMQEKhVpnqOGw7VjK2mXJqRZ0ByMF8WtVyfKogg/n13eQZ0MHqNVxoTapMZnCv\naQu1mB+rwW7MbUFaTx4yaTtCFup66lcoE1VcJ1YPkySZQVtP9JCr4jNmwkcpfTLuTynG8/JKikKM\njhKUcpIp297iyD0mTyhWwZKVui1oTiwOWqTzlB7KSQ8+kc5688wdtJQhUZ/01FppDw4Js0B1MDxZ\n2yaobakpURADS7YtzkHui/1eJ15W2mzsfezsd+xdQ/CRnM18n8VNxu/EuHH0Z1v7/caIF4+fRZNb\ntiua+RLfWtlurSBtg7hAM3f062JbQ4UyDoZDWG9x884eDNUksSCegkX4S9pahYqC857agp/Fadgp\nNDMrodUK0gi6NYyHGesyQmNbgJSNfB8CZTDZW7yn5orf9ZDMsN/MGpIUK/6ODWWTkWAwv83pFkHN\n6C/eBrHg7LMTlSANoNTgqaMl6BCxShJvg40Uk8bbw4irljSzIQpooWwTYd7aZ0z9xCuaBsohQwjW\nLekdaZupm9H8ZeJweLpFpCwtLi8qts1FKb2AS7bJzZmsSq327/GgbbUpMCToxbyS+SmhXwnRYJcE\nmYjnHrxx7moBbcJnn8eaKy7YBlUypM1g6d/o7X8CIXaGHViNdkjLdm+Jsx1c19CvBxvgtFo8PXg0\nVZrljGVs6LMyrnvbvHcNDiHOGmqBssmExhl/DgOkFpcs3agOTZUQDEeRSrJBQ2G2v2Mb9QkX4MQz\npkyVQs42mGRxdp9KRqEKqpStkIYEOLxOXtdGSL1Y+5QPJKD2sJZCf7pC2oZ0toYYaeczovPk0ZKc\nqQuEqfQ+wZTChuAd6pSjDx+gOTM7f4g0gisWFEDFrpvuqTFekVZI2FadPJI2IzVnXOxQ3+KlI2dB\nVoniC43YJTCPLWOs02FRWG8GUu9sM69KOwt06jkZM9tN5vJux05j0l7KSquGBEmu4ov/jGM4Zmzo\nEdgU6yh0TtgmaOa7/LX//e/zX/yb/wJf+/o3+PCHn/LKV56f7vHZwMZeEO8Is8ak38nzpg6I3u7Z\nwVGDZ73NuMMd8rrn8OplQi18eOce27Mtm23PzTv3+eCjWwD82tffZFidst30bFLBb0fm84bz53c5\n2L9A1Myrr+1wcvQArYWPP/qY47Oe27c/5eGjUx7evMf3fvwei/kCHwMvvPwcq03Pyf3HnG42PHhy\nzM17D/jh3/19Ft2MF194jgvnD/j4xsc82Q6cnqyhFFZHpzzz7AWuvfQioVT+6Nvf58Lhkr3982jq\nef/dt9mOmWf29vjeT67jHOwudji/37Gt+0h9jw9uPUSzMnORmW/Y37nEo4/f4/1P7uKawOvXrnKw\nv0u3PMDhOfnBu7z5pVe5c7Jis97w3NWLzNpIEvjNX/slpFT2Dy7ywVs/4vb9I5IXLjx/ju/86C3G\n0byLTyW/Pzuz/Lyv/18HqiHXP5XNJhAfMg0wuVCnGO3T1FYtGa3+s3Wpk6nzCUCrkY7VIG72mmps\nMCaF4i1Jl2xV4CbUQa0VQalSDBMgE37BeZMEqDbEhenrjSar+cahagXLWuNEfTcAnptuBlXN6+Oq\ng8ZN3KuC1Ipro6V5yqS5IcjMPFQZ8zOIClmLGcWlmNQWxE78AlQhzHagrWhO9mB6KjmuknGjvLN0\nY7KKHRFLmTGM4DzibWNGY9KkTnYWgk4dedWM3nFBHUdiO/W1uURe2wNds5LXa0vOzTLppBJ2Orw6\nks+2zQiRbZ+QoQDOJLvk7dS/a8NO2faU9Slh55Dm/IzYOR7fOqWmtfUANoEYmkl2NH5ZHhTnLKxg\ntSQ2aBM8zaIzmWubYebRPhlUsbUbusRJFpaAoHjnp4ce5M3aBrYuUPtKsY4kgmsIc8f2ZKCMAzVn\nJHbEtiXXhMxamnlnnpQYCY0gCuNpAh0gRkuVjbahDGIQTLaZ7tw+rsWM9EMxv49UkM6kgiCUbZ4S\nlC2+VUsnYc+fmjO0tlkso2nkZcSGrSj4KIxP0mc075wN1EkjkCvSmFxVh0JxoDISY2s0/yyEheOp\nEk41x5kPEfWR8mRk1MFM/xT8vEEHGxjKqHhvniyfpwCCr9A0+GgSombM+7AbcN46Lt1MKAjpdLQK\nFRdN4s0ZrcaUivNAHjNPuz9u/+uIAAAgAElEQVRFnfUM8jT55ylS8FIsnOEtzFJrMXmvm7oEc0Zx\nuDhdTy7aNumpqVsdBKFfD/a7iJNchvHlfOcYNok0bnHDDlkH6/oMcXq/hGbZcPbgMRIatCphZ4YE\nz+l6y3hyQlgsCdEGcXECY6EUNTjnaAdMdZ7YFlIyUK94kEEpTsi5NyiwCGF3DmKf6bj0KJnNWYLW\n0AG1Qm0ENwyk3lM14/AM/Yii0/bEU5GJACM0nVkXdCj0pdg2Z7Ry7Lzq0TExWyynjVAho7TRUTWR\nkyU/kSmQrY7olbNHZ5RSaJd7hCimzDqBYnU+IpZGlujwu3ZdOwHdVHRnzu5sn9X9U9LmmLwS4nwP\n4pYcoA6ZuPG0sxl1z9FshTKMBAn41lNEOT3p8Y2wt5zTtJk8FCLKuIRNVYaqJtMi9KniG+z6To5C\nJTtHFEUGwbdWrKwiLBwMYqrLV3/9L1AFLl1sePDBMacn93j1l79oG2TnDf6q1ZYLKHz2CDNvcM2Z\nYb1CqvUFdud3ebzueXS6gTpCIwxeyE5hFli6wAcff8LDR2eEeIvDgwWzecdzywXzgwvE2PLp9Xdp\nm8D1T+7RJ9ucOie0qw1nrlCDsfskeK599fPM5jO+9NJV7j66y/J45O2PHrA3b5BaeOfd63x48xZf\n+9rrfPrgCcMs8A//ua/zR3/0XQaB177wBj5nXOPIpdK2kfsP7/PxzXu8cHGPk83ATz+6zeULezx4\nsqLxkW0RdrrITx89YW+nY7m/w75vWEark6FWzi0aLj57meXuIe9/cIMXXgqk00cMuXL8+JS7Dx5z\n8MpFijgu7V1mc/SApXjC3gHp5BG3j05YVytNv3d0xpgKV649SzMWci4/M7P8vK9f6EBVa/3sh+u6\nju34tEbFWckuxbwoWj9rm6dMVltzQttDFI+Lau2eHnSoIAXFfTZVTtYnVIQ82oVaNNtg4KeNjKsm\naQm2gq0OF8UGoDaSV8nQAEGpTFA5UUsSFkvEWZGvANY7Jc7ZOtmLPRSZOuoaZzJiUWPzOGdgUyaC\nsyiSDQnw9PQGJg1Iyuho2yPno6XJgELFtUJNyaSMyayuVciY/Om8/SzeObI39IIjUMdkxtpqa24z\niJr0VIttrXJVGJ8WLvsJFBqIy45ysqYOCdeInaYodnr2AS0FaiGdbUAXiJp0Kj6yvf2QeLhrpbnR\nII10nSUXR8MM0DTEnYayqTx58oi6rfjZjslb+vS9NaOsePO/lOph6NGmte5AFaPa94lSMtIGq43p\nR0KMUCHlLSG0SLDTs+r0+80TQLWJtlEc0mc6ujiPxkrqhZoqdQBpO0IToIEmtGi2k6Z4ozb7mVCz\no4zHMCp+x5saLYIki1STQeYtGiyFRjWzvfcBFydmVyk25Af/mfxcVtW2NN4SZrUoXh2ljiZLT4gJ\nSra02zRMx53WZKTjhBaPKx6CEheePFYzmDsDcZZNNd9WSYynJgG3y5Zxk4zZRKGcDSiRsIhQBbsE\nLMBRVlOJsFqPnE7hXQ0R7wu+MfwCDrxY0sJN/21VpnuE/ZltUYRSE1Wm4XcwtIpUk/PDPE6Soz2Y\ndDTvD2CHlwKuNTOynzeUYolFo8NjSdIq6Ha0LZyXad0v5v8arHYHF6bPoSEXNsc9dbsl7i4MqHta\nqNH2v1WVGB39JiPaAJ7YNQQRhu1ILWWqY3n6ObP1eyl2T6xY4tYhRCm4ecNwssGpgW6zZmTIltSb\nd5RSqAVCozAUNDo2x1ukbfHJgKmqajgLB1Uy3gdQ2+KBSfs+GKy1jhUaIfjIcNIbiFgrOQupJMp6\nwM0i7f4uTWOsriEpMWCyvARyzYbFETvUpX5k83DN9vghYe8cfuYt7VjUEA3FDoO1VkQnHa4avXq+\nuyB3DWnTM6SCRkG1tX6+fku+vSXOO2Q5R4pHNhmtZ5yWkSYEYjejcSahp2KojJNqfjkZPDvnW0at\nZtxXIfnKGZ65U7bFrrVSC9EJUc2zWQRcPy3JqzJiqXWn8PV//l9jKfAH/+N/TbN0XOqu8v6ffMAr\nv/QSLmPbYfenKTKtOrUMiDUhBGfPraL4HTu45ZXJ7zI2bNdnzM/tsTk6Q4Jj1QVefuEy234kOeHy\npQvs7u5w/+49Pn74HkdP1rQhsM1mKTh/fkmpcHCw5Pz5C7yUM9+/d4tf/uUv84PvvcXNtz/ka7/9\n51lttgxb4SRVLl+7xv4ycfODu+yfO8fRo0f87t/8Pzi8fIGXvvoqn9y9Z5DfILz1/nVeOtilmUJK\n9083fOm5Z/nas8/jh4FP736fZhHZWcxogiN0S/qP32P3hdc43YwcnG+59sKLliI9ekzYW3Bw6Rk+\n/PguuIf0my1PnhxzcP4AX4SmCZyu1mipjDnRLJfcfHKPecq8870f8fkvXOOHP7rOV15/jT/+6XXm\n5/YJXcPl5/d5+O5H/K0f/x2+8dVf+5mZ5ed9/UIHqj/b1rxcLulHM027xgzgOiriJu+RU+pQkTZS\nhvFPT4gKpJE6mxJ82YCGWgSJVn7qJ3yWMk38Og1b2M1DxdIlIua9cqbJmNyIQ91I3lQqhbhojaKN\nkaAZip3YgnU6qWDDTHATJdDYJD6JeZPUhpmSiiUCg92gnTqIRuBWrK5DpuFEnExVNLa4qmmq5HER\nDQpr67CSpwgGgiV/UMrES3LZ1urqHV4NWDc9xU1GdVjBcJnkgLlt4cpgMqfzAVfsRuUI1KCQM048\nTfScPX5CWO5CbqA1/4Y0rclwXaQOENqWsDTshF44pFalbfZsO9I4QElnGc2JXARCoKYecZ5cKmy3\njMdHLF56EUomrRNVHHVrWqjoNMx5sQi8tvgi1Cn+qL6Qk+K84EVI2YYMFzx5taU7WJKPR4RKCIEh\nZZNyCbi5x1UxOm4x+SrsdpSTLXUEdgTNI27R4NQkH8lC3o6U3qpyZOYJ0hCmUu9ytoJmhvYZ3zVQ\nE9UHpB/ROoJ6tLS2uZhHdMxWnVIKmgRmAYpBMvMgqBZ811CTxfARnSpyEsG3lDRCEdTZhiU7cKWi\naWA4Bd+1FsaIlm71XSANxdKTaqZ5Cc5qTbKA83ixrW7JtjnwvrGgQNfiq2E4DIbqqEOx67l1BLGB\nU6phQspgfkU/a3ClMvYV3wRKyiaD+jr1KaqhuoDQ2ue9TLl166tUSgA3Xcexs8opJ1jisFb8/0Xb\nezTLll13fr+19t7nnMy8ee+7z5YFCkAVTBUIS9CBbRitCE0UodCo2WoNBc31BTTQF9CcIWmggcRQ\nRI80oKLVQbYIgiAcQRRQAMr7V8+/69Kcs83SYJ1bbEVjVugcXVQVnsnMs/daf9s3WlVsdL2VYNCc\nxqoF6rh384J7DLz2RQzV4CnobS5lxzOvmgjdkOj6QC1KyY4MVwQ9XBFU2W8mmlaU5Gccsw7LGgwR\nVVilQAV204QMHbF31MoL1JWW3VwgoRFCYhkFC4kEnH10wf7cOyPLxRlheYguO0JopC7QtoXoKk9a\nAqYMBEeQg6LNQzEuXdRJIqlXdue7OfTW3z/Eh64mipqx3/qZnXNFk+f3teLl6cPR0p81VXabPUPX\ne189wj4XoomjtkGpCmXck3cbwvKI5XrA5u+PFUWCdx/SnJ4Tg2oCFvAGVUE7iA0//1Wpu5Fu0bE7\nPadebCjThtB2sD+gRGHslNQNlJAZzFNd28Gsi4mJqTav+0mZYRN43EeSCH0yuhKYuuJmpsnP09oa\nFoU8gqwjvTIv247ojWqEDDlCtMZZE/7Jv/4Of/2//xmWBp649SRvv3ybZ790k5Q8oufyjhJmFsLb\nfvw199562bkPWCSllooOie12S7y+hGbEq0f85JV3aKXyn3/7D7DtCV235pfvvMxzX/g0//xLL3K6\n2/H+u+9yeLjgmadvcrLdc7A+JKaBn738Dzy4/4h3PvzQYy1iwKaJe2eVzzx9k6O44nvf/yG7ccn6\n1jWkS8T1ggfv3+H+vQesX+351D/9Fi9dv8a7r73F+6cnNCrb2w/oYqQc9vzdy79mMxX+8MtfZFON\nP/79PyJfnFKmLW3ccnzjCX70gx9QgbNd5iev/ZqXnv8sjx7e4+j6l3n80Xt0STm72FGbm85WQ8dy\ndZNPn2843Y4cHx9wRXoW2rM6PkZK5cr5lmm/40tfeI6WR774qVt8gPLUjUP2D7b89Kcvc/3aNbqu\n+49mlk/6+q0OVP9h0qhrqPzhsl3DSoE++pemRmr1qhnLBcMHHrFZCJsiWnyD9bqYOucEza6e4HZ3\ni8AMGcu8UYL64dKHWRtREIveMabigZ74RR1S57oqLbRc/WEyoGuIRTzyMbpD0epswYfYRf+y72fR\nO5fDm6AinicjjlhZLYgZ1kXXp1Sj1ckdhXj2C3NtjKo56hXV6dEUvfIj4UGj0mYxp9DMHWuKW8mt\nzFtP8uR1L+Ny0X5M6mGg2ZyijE5xCX4ZXlIKoMRFz8XFjrBc0a1XMxTvdR9me1pWpA90R5EgkYiS\no0L0qo5pu2O4dkBUICTqyst2h4OevM/UfYTmlEVYJ+ieJIgyFbBxxEJyTVkx6NSp0xjQ0EHOlFlD\no51v9EJAxGt4aEZcL6ljQ3qjbtwBFuPAtG8wFlh4IbYUKM3F8gTxrX/KWDCCiIe3dh1d7Jiya02K\nZaciZrFz33f+2WS/THW1pI2Fblghy566dXrNaLTZJZjHkdAg03sq+Oj0rdkOzT0NcfFsgEDyYVyg\n6zt3tpr4M9IJsg/zoO+DjJrO6fSROk1IjB/3RMY4h4PuDDovg5ZZxGzF0L4hRfxEaC5Q1uZCZjaF\ntO4p1ZciXTanU2MEbfSaqKH6Bdb82Ur9/LnsK9M0ObI7O1wl/GPbAc0vdFVPYDc8Hytohyyb690i\nYF5PY8mQDATBxAe6tpvPgih+CVojTJGgRrYRTdG1WV10GmwshCQzOqLUaZxNsb6IdF2iAXnMlE3G\nomuxtEVYVHIJeDluT0xKM4+qaNtGWwhaxM0sAbanhVagn5PRzbwuqpnQ1ClmIdEtE9WM3bh1PeHF\njpCWlDaxuHnL87ZEGMdCbY3Q9/7ZTr4AIo7qW3OtlCQvg5bo9Kzl4s631mgxIaZo11z/WJU4ABoJ\n/hh5ivzUzTrVnu4woQJaAo/PNwiQ+kskEo86SS7XCKpMuz11N8LQkyRSq59VYT6iawOqkRbKuDHQ\nhjbvTk1dYNpnL3joIq1CSBWVHsSIcUdbX6Hu97TTiXHYoOp6y9oXQreiWyk5NereiCKEEIlRKCES\nFWoXOdtkFsFo1tGSIXtjVxtRPELDaFiJHK4C1MZkQphlKCkKpfnA2u3BegOMnSq/+y//W+698lM+\n/MVPuHbjOnfeOmd/cYfnv/FFTOaUfow5l8ZBhN4X00sq0ObMuLqbPDevCy5oB6iN3YPHxJsHPHPr\nGU5PHvHGr9/g1vEBJcBLz73Avbd+yY/euE1d9FgrnL13l2++9CXe+PWr2NUjfvn621xs93ztxS/T\nJwjHS0+ILyPv3T3l9J2XidcOiUPPlDPD01e5cf2Ap7/2eU5eeYdfvvIq7959yPVbN7h2tEIOV5xK\nY4+xSMrJnTOaCU+/9Fl+/Oab/It/9m2sZFoeeXT3LrF7zMtvfIiJ8MUXnube6Yb7p1se5D277ciP\nf/RD+pSYDK4fLjjfjtw4XNKnHiveIXrn5IJv/9Nvwfk5w+qYB/c/5Ffv3eXalRVFlUcXZzy6c06u\nhatfeAJpxnf/n+9ydHjEM7euE2Pk4uLi/zezfNLXb3Wgmqbp45/7vmfM7oxr0R1BZE8jV3VLrTWH\n5SWo639iQyX61jw/2KhiRBdXB4diwRxlwdELr51pvvkBMrh+xaMPwjx48fHvgbk42Wr1GpgKMLsB\nO8E5Gj/8bHYC1eLITloG1KJPzAoaPSemJcOaB5PKXJ3hRCJY9dmmlYJZ8fDPfXXBYud/frE5z2rW\n04tGpwHn9d0di8xbvbhT0eY+QwISirviss50RcOqu8MkqpcrRxfotp3X01BB9g3roydHB3NXTob+\nygGERkyBPFbKfgPWSKsD+k7QlEDxf5crSCU/fojt9oymhCcOiVFopzu60Lur0dRdXwFartT9HjQy\nzWGnpERaDdgIlpz6CcOcfZQVS9Gb5HG6jYLniyXmNw1sP6Hmg6q1SrdeOg2hxb9XUdEMtWasFjT1\nSKfUCnWT0XWPYJScSckHHNvvkcXiY/2aaEIWwrQvSDPkUFFJxG5B1ZFiFdlNXujcBq83kTajTwHt\ne9wIUHwgLuZoQZzcJddHbxkI6un45vSNzq5SmWw2dHghtawTbdvmIE4lrhZYMdKgTNuMVqFGz/eS\n4BoXa+qfT3EkwwuvlSDiZbyDP1NWjbiMmDn6Y52QVJkEyBntkmdv6dxHMOY5td57+SQ0tOvmLjzX\n8FCdiqn7OQssBmxyITWqxBQIg3jwpAihE0IM7DeVejbOn6N/f20yR6dbwaovB5earWn0C1/UU/VN\nmrvggv96dX6+rXq8CsEXnloLkeixJ7Uh5lVIsgrQImqNiqLiC2MZJ7SqB4/uGrF3feXZyQ6mEWuN\nXKpr+6K6gzFXz+IrQj+4bma/y5Tzc9pUSceHBI0sjwbKzrVK6vuRP7eda6Bybb5zdsLcz4z2wZH6\n0jzNX5qbeqpn/Jk2RxJ3Lq30YNME1qhBmfYTGiKxF/Ybp0WlRfYbo1lGA3R970uMBD+HzI0MsUaq\nFqY8uqSgCCx9WUYNw53IrXpvWt75OUap/p6qMV2ibW2ixUQ3QCeHnJ9tEFPqMLhmlUJYD0CkbPZI\ncZrY2oaLe55x2K+XLNZLJDbKzpdp7Xovccb/vs0KtfgSF5eeBxey0KG0JJyNsIrQpLGMLrIvBULC\n3YgRahGSQQmNKPDUS9/g01/+Ot/78/+Fg6ND1ouON372HkfHiRuffhouOZVZymFjdgpwHqhEPXLB\nfLp1V3udLwf8XiybPe998B5v3b/g+HjJBycbGsK//d53GS/2oMJwZcXVa1e4ao2/euXnDKcj9+8/\n5PR8wwsvfA4To/SJ5649xc9ef5Mrzz7J1SvHbE7OIAgTgb3cIOktDg6NMr3B8eee4fjefbbbkfde\nf5tHBwu6o0OeeuFTXi3VJ+rDC1785pfYTpkriyX/97/9dzy6/5ib147pu47lkPjS559j1UVefv1d\ntrWxq8ZHD8/53c+/wPnpI3a1cLBYMoTItbzn4Pgm1oq7Gt+/x7e++nlW3ZJ2mPj3f/U37Drl1pNX\nKYuevTVuHF/hpc9+ke//9O955vgWP/5/f4gZfO2l5zm52BJC+I9mlk/6+q0OVLvd7uOfh2FgXxum\nniYu6onMNtv2Rb04+FKbJx3eUxedejCbv0y5MBP1IK7bQCuK24d13iZqazP0Pg9S4petFCXQqNHF\n7w2n8GwyDwQ1EJtzfaJACT50IT4gCT6ombi4mUgNRt0W1x6l4DD2vrqJUZQ27SErMswPwZwFZfMB\n6i4l8WR0w6etMovNwYey5knmJB/sLuHfS4TCxP+eau7mYk6F9q3KZqrSqbqWm7eqz3lf/kYqYYhz\n16C6jkZ7QlTCIjFut9jeL9ayG2HMXtTZ93Nml5CnQjnbUvZbbJyoF+eEwyt0qwWWYSoTVGXcn8Oj\n4gaBXUG6ROgiNK8c0D5SzN8PUWipOeInUMaKSSGkwSnhrhEYqNsdxESYw8sE1yxZil4CvR2p0+TD\ncRc8pToEbIKigonTs2FQ2DmtlBY9rUJTI3QJSUrejGhMc8aZ0KYtphE7989KR2VbM0rEerBd9aFR\nI6FbUDMYBa2N0PWkIXgxM+rmitCcJlHBsng/nIU5y0acKsRF2TIaokptFdOCxo6QEm3fICRHPqaG\naSYNPaUYUgv0/SyQ9wFbL+tgzEtv4zJQd+5wMgE9mKkYCWj1S7OZi6glwDQ5vW0i/vuVOTAzN4zq\ndDZCuhJd55bdHNEK7h5kpr3VaX4r7ohTjQieAh9ioJnX19TTwrQ/pWx36HKBSkdsEek8fykM0al1\naYTkBdnTxYgopKFHaqEFt7+LiBf+jnunOA0/Fy5D84JT7f1BZNoZEOkPexfqT06Reqr4vLwFg3IZ\n1+5utRAUS065txYJnZCSI71mRt01DA94jdF1YnmCWiqtRIYbRywWkT4FmgqnZ6csr1/5OH+tqSKT\nRxqYMUdpVOd8w4w8G0zZ8K0jIq3QYiNFz8AywztODRbLnom5Y3Q3IkHoh8Q4jWjfofjQRDM0RPrO\n9ZjO0Blt7nvULtFqY/9oQ82TR5OEiKQ0yxdAzcjuB/DyC3NUzUT9LcziCOYANvVoNCpwfr6liqIt\nE1cr2m5Cw8DieMU4jtTTCzevdAM1gGyjaynVi+5D2MO0o7t+DCGQ72zpe+iXPbXBdjcRVdFJSZ3S\nJ0WCUazSV0eIWoNNhZ5G9Jh1sgjRvwrextGEcd7xSlR+70+/w9/9+Z8h/YIbN59it9vxxk9eRaTy\nuW+8+I9DlFzS1POAVZsXq8/zk4EvVs1DiB1OUqfQj3senm3nxoPIea5c+9yz7O4/5quff56b/RFn\njz7k59/7Gdut6+NSjLSF8sLXXoD9hGjjmc/e5M5HD3h02rh26wgZOoYmPCoXpFB5cHLM1aNrXLla\n+cZ/9k+4+/A+b/7oTXLZMj14zMsf3KFbHfLpz3+Br//+1+ks89O//xkfvfkBXdcRNPD6O+/xlS9/\ngfPdxNluwytvPmKZAi89/wyvPDrh6aef5p033iHnPYurh+ytcXpywu2PHlLaByxS5MrRAa1PHKwP\nsZzZndynCNTceDhW1qnxzPEVQplo08j1q0c8fusj3nrjHX7ny1+iWUVX3W+cWT7p67c6UG02m49/\nXi6XbMfm4tM5w8mKH0hQvddr/m9VcEFraI4uTNm73mKArqLNs5suk0LFvIJDxC23ba5wQXB6rvrl\nrFWxRaUVHOUxp8tMA1735fbXKobMF5h0Do1ZAWkZuojVMvPf/mco+2nOewqUbUZl1qQ0wzQjDWyI\nfgGhaAc2gWS/SCTOAnETgnqFhkXmLc5RgYYgg2/M0vwhE3Pbuen8nGmgteLlWmHWZwVfU4N6vYWJ\nW5ndcm+04oOpa0gMVJm2I7Yr2EKhJaZpR5sykhYO4xe/DMLQU8bJxclaaONEHUeaF1+hfU+3OoTQ\nyGdb6JSy23pujQYs75DYzwgJ1N1Iq3tW65uAx1aUrV++BrMhoKKa/OCdMk2EOl4Q4+B1H7XRxkzL\nda70AKURh0Td7F0LJ2HWm0FrIzYampJXDVWjUNAQ589QSbGbadTqYv9akTzNyd/JtV3qer7WgNMt\nVV3zZ3XCciJdWxBSRKbiyeCxJw2OHlRt2K66Y2/EE/mLH5ChCnnauwZtmr/W6nScrrwL8bLzUlPA\nanbLf4qOPiRDlwOiQt3tSMPgZdlT8edCXeeiC/9+glNmgouVzRx1UcXhkOxiZVDalNGll4O34AaC\nVuu8kPjgz9QI3UA4FKzCOG6BSOzMDSlToYnNvW1O/aoGR1haI5hrleouU8aRmqvXUGVPY49xgTQo\nrSAT7m7VufA6eNSITY50haXHpKARCrTgdF8rntqv5u0GZRaxhFAdcRE4e29Dk5Hh+JhSlTkIALVK\nkUats39GC7afaH1E51DKbEbbQ8kTGtxkYIrTc6NHyUiDGBOahDw1to/PCNrTX1kRVclTY3OWGR89\nIq4WxCTsNplaG0E8H+wygZ3kbsVgnkZZpuI00aXgeSEeGNsElebUrQK1EVNkKn4e17F6WO2YGZPX\nV6UhzIi3/GNHYpwNBXMAZs2ekJ8scH6xYdpeuNMVpR8WLl9tTlmWBpVK3VRI0LJ6CFQR12KGhFCI\nM2jmUYJeA2UqTPtAqwXtIqlf0Uavzor9ASaBtt1Sx0xYBmozOM1kzdAmtOsJByOb7US36BmWK9aH\nHeM0MY6ZPY2+67HBC5FTFPpq7KsjwqEqMUJbQCdGm1GqTfF7JaobcF3Ca7MjXfm9f/UdFOGH/8ef\nkQ4OuLV4jpOzc974yat89uvPE+YAW++rZaZmfCnmUseufseJqbchXDoFxQXyOgR0mdDojkLrlHBj\njVT4q7/6S/79d3/Ecr3kxa++RArCo8cnfOHLL7DQgCwGPvzwDv31NTeeugIKt9++yxPPP8f5+Sna\nRa6sAgfHDxkXL/L6r36EROP42rP8l//Ni7xx+13aySmv//hVTk4fkfue13/9Gu+/+hb37z9g1Q9c\nvXWLfp149Wev8f0f/QOY8JNf/IoYlaHruH1+Qb8+YBU7hvXAy28/Yv/ojFaFJ6+vOcuVlz77FLlk\nXn/3Pt/81ovuAN9d8LNX3sSAs4sLBzPWB/zF371CCpEojes3b/D2m2/Rp45nr6758OyCm5998jfO\nLJ/09VsdqM7Pzz/+eb1ec753G7OM5t+0lfhl5BJOMPGtGFwrBLScvTk+zFtf8SgCpx9AuwjF0Y6P\nv2zMnX8SkM6/Z15NUT0lOZlHLrSCFJnrUBRKpZhTfUQX9DbTOZBTHMu15knoAkag5OpIWqdoCB4o\n2ma0SmYBeheANv8VXYDul7m5NicAIlQMyYpI8W0P//WqNkcGplk3peoOp1JpYmg2wmIBQMMDTLVG\nR7AEt5YHl3Y2jXOFixdIGuo23FkYLptCyxeI+OFeig903eEh0LDJM7rqVNFQqeYiepNZP7GbsGlL\nODwiLJZI6lyL0yWnGEQ9r0kFus6Rkhg940fg8IlrpFWkjJVadk4JYxANspJSNw9Y5XIuJKSO2Puv\nX0t2Q0AAEKQ1ZBmJopTD3st4p2mmjg1K9GE6+lBda0NoqLjbVHqltEoZJ0Q9akFVseRBtBNOeUht\nyND555kCNU/YNGISCMseTd7FFvrkgvdOXA8iUHY7p3yKfw+EhCy8KLcZHg8BVMv+72UegguzqDX4\nRV0rNfthLoNQxooMgQjkzehaMBGm85EoibB0sFWaQmlU8eJqwy99qTMaajYPwDajuga1EYYOaTC1\nLRp7RzmyuY7LPH28v1A076kAACAASURBVOooXxkzZLySqPpXjlp86GqCleyDYe9McB2L//fJaFun\na5vt0b5HZSAMS8IQ58HQP2vM9YJanca30CEzveU5bvMQFNwRp7PJQXs3ZfjwpRC8xFxJNDPaviIr\nYdEfu6swQss+wORSfCkJs4PsJNOscHC4YioVGyFnLzAPMc7fHVzkb4YlRWtjeX3F5tGO/DhjqTKs\nV3OgqqNlzYRGob96TL/saGMlb/fIEDDtiEmJwRHD2ox6UWiD+GdYXWKR8+zaNSX1QtmbF2FLIPYR\nC9UDW7P3LObJnaIaO/LFROi9qsmqIFW9+Fs88sbMUeTQAnEpdCHy+MPHjI9PkOWCg/UhjUIZHXkb\nYqKO2c3TFpCU/HslHngMXiPU94K1BCNkHBUNiNd2mX+u0oRumeYaWKPvl9AvqZbdedeKd5jmDaVk\npHf9V1osGPeFEF1HdbEdabUSDxIaOtTMF6PcqKJsm3E+VqIajci6MzL+/aR3I/iQjZCc7mu4DA8z\nls3vjE1tDC4H5Zv/+jvEYvzt//k/s75yhcPDA+68ccp2cwptx/Pf/NJ/cKeBh+LpHMnx8a32j4jW\nZXWKCtqneZJzbdaj9+/QrZf89Y9/ysvf+zHDcslnnv8MX/3jr3FFlE4Dh4sj2u4CEzh87lOM08jy\n6hV++cFbXH3imFomhtWAaOQLV27x9z//Kbvhx/yTr3+GXC94YnWdl9/8FZ+68QTdrScI28IPfvBT\n3vrb75GSkFLicL3mD/7k2yyPFrzz2rt86vnPsL+44IXnP8tHt++gw8A7r77BG6++xRe+9RWsTNy+\n+5h4uCBNhatP3eTk7iO+9u2vUx6e8s67d5B1wroOTQMfvf8+u1y4e+8+d+49IC0WnKly9eiIk5PH\n1Fr56N5DWjM+86mneffeY4aDgcPF6jfOLJ/09VsdqB48ePDxz9evX+f2JmOX5cKXgZ4hzLqh4Fuw\ngTZ/mMieE0OYNUTFJ3FD/KCsMuuS3MrNXAsDjiFrw8tcZycGSbyOwgSz7IgOQKt+eYGjP77uuTvG\nmDUOzV1LJcz6EEcKqOK2dgCd8690pvFUPbW8NP8zVS8HbaGhRdBqtOgXymXGkqhh2d8ImemrkDrX\njEWgzsXLIqgFYlfR5LanWmZatEXCQQ+tOfUJmLh9W3Lx0uZ2KcDwBF9r0PYVxAi6Ih50hMGpAAk9\n+9MdNBiuREyHj1Eo8G2oZa/JIHZIM5bX1+Sd0x7SJbQZslQqB7TdxvvAJFDrjtWNJdv7Z0jfUyWQ\nzyvt7MIRkNThNRER6XV2bxXCweBIjYnrptTzzOpmRMTNDkajjn6hpCsrp1dKJqRE7Ab/3sjk37VQ\nnSqbKtLNmrnLdvlcHcGQ4EgdRmhKqSOqCamV2A/U1tA5y8XMUTc6SP2CWhs0RTrPoaE5JJ0vtrRS\n3SJeGiwTGtJMN9mMplZYdJCn+dwM1DL5oYkRug50drbZrEGsTp+7E9uo+wkJicZIDAnp51iDWgkJ\nanM6erlest9k0qCUvXlptAkygK5nB+5lrpm66Bvzgazhj6CIuSVfhWpG2bpGT6I4NRgVsjcQmKhn\nwJmgXefuPTM/F3qo+4InzkJaHnl1ifhzly8Knqapc76PnwttrMgQPcVZ/JK1WYzv7syETBVbALOu\nypbNaZUD1wfRitPru4wkoTscYPJBSJtnfWkyrPjiVHaTf+c6Y7FaEwLs3nqALiOyXM6IAj6Q7OZF\noHdKzGpl8/DMC7uXje5gOaN8XsRtIjSrdGmBqTGOE22asAzd0cKjRcSoGfI4ohJnFLfSaqEWI4ZI\nUiXXiVaFukvsL3bQGtpVz5ZTIZfq4aFlcn1bDFguxD547mad0+bVlytapgnE2ZDQpCA74WR3we7e\nI3Q90F9Zk7rAxUVGRi/urcHI6md2VDdxyNyDelnAmlaOzLWdo6Iu4veltFWnxoMkbxSIEAjU0LB9\nJCRoTegO1Hs+tztKVlj2WJ58GT6baLvH9EdLWnXR/NnBAav9im4RUZRBIiUrshR0LIzNWIqSi5EH\nRZuxU6FOjV4CYzCWCFuFWFxXG5JwZrCqHpK6zcYUYRCP7f2Df/XfQSv88M//V5aHRyzXa6xkXv/5\nfWw8QaV6PQ7+qPnhZJh5V+hoAcKCCCSZPKUe43PfeNHvHfEBaxpHfv69HzMsF3zud77If/Uv/oRg\nnvxPq7zz9ttsHj/kuWef4u9+/jqffuFpXv/1G4znEwdPXqVd7Dm6dpXWGn/7859x5doBti08PH/M\nycld3rh4k/PHe44ksN/u2NTG4fqQbn2FZ158muXBmvVqxdef/hS2OeHwqWvc++zTnD14xAvPPcNu\nt6V74joXp+d8+O77sEi8c+ceYei4uP+YeLjk/N59ljdv8Mrrb7I72fD1b7xEXyZONheUkwt+/vZH\n9CFwMe7REHnh6y9y/eaa54cD+vUBD+484NV3PkRVqdWYVHj6+pHran/DzPJJX7/Vgerk5OTjn4+O\njvj1tvgFXQ2o7toKuFvODGmRFvKcjlz8MusCca7GuBRdUyrE4B15hY9RI9TQan652nyZA9S5R7DY\nXHBsWHEHigRcn1Mb7BvSu3WYwqzp8uFDu97jGKyhTbz7zzxJXeaaG89NaU7PJL8YWhaajkiLHqCI\nb/EVg+COqtD1fpiM4KJcHJHLDck4XK9eMQJ4dpW6VktLdDHrXPFhpboOqHn5JNOccaNCs71Tlq3h\na416BYMoHhjqouE8znlCJZBrhf0ODNJywXjuWhQ9XNJOL3zTTz1EI5jQHa9ptqTrO3anG4zJs4RS\nR9dFqk2EGECjRx8MPbuHe+r5ObqG8WIeALLXzpB60qqn7JujPzYiQ0Jao+4r4GieSnVKs7lWrdXi\nSdLZsKmx+/ABoZ8LnJsL9ZtWLCfvh8nmuqAw967VGcGyRqEiY6DqSLBIU6Va9Ytr1t21GObaGtDi\nolxJ6oPW1FwzGJpXL7WMS+zLLJr1oVo0EtLCH3bM9SPVhdP1ZO+oWEg0Df53aR490Eqb+dBMjK6z\nIftqq6bsd9V1X4pfQNUc8RoLktRrUfIe7TqmfaZcXNCqGwckRrSXmQRzxNKRNq8ZanuP1riMAPEw\nfpuLByplNyGmSHHXK7l9rH2U4AiKqKEkwC/GNhWncZovXiodulZPWm9ujKituBM2+qBZVbx9ITd0\n1Tkdb0aZ/JkNzYgxueYkVkr1dG7tDO0SUiCtvUg7qJCbYNuMZe8IZGzkvQvWp6BoZ1xaIFtztFRX\nHSF5LdHZhydAJa0P0eiaz9b8DJM4P5+5uCA8OR3fr2Zt6NiooRHq/AybEjqhqVF3XgSt9MiqgTWm\nyTz0WHE0mEoNhuyrOwCTPy9l9M5MS7Df71yrNvSemdcqbWqOWKp5WXpwVE+aoiF6Qnwx15xqpGV/\nJjVFSp5IMdLFjs3+gvH2Q+LxCh18OLy42HtptkRMA3nrfzcpFZaO/DdRsOy/lwqrxUCOle12g2pP\nqZN38RUv9BYaTRrURs3uYq6jc6+1uTmIVNG0YgzCQmAaJ4aDNaVU9qeP0ezIojz2Cqo6eROCSiAd\nJrb9ggONDB2E7FE9uwkojakZXfBKHekj/WC0ZOxLIxeoaqQiLIOwFMhi7Ezoo+sFa4Y054D1KfCt\nf/kdTITX/vL/4vTeR9y4MWByjWDGB7/+Od36mMW1J0jqBeZxljSAzF3s3qNo1ZHTt39xlzqdITbx\n2W++yEevv0s/9Dz1mU+xeOqYv/ju91j0ytPrA2ppfHj3ES0qt19+DRXh9bduw2pg/fSaYbnkxaee\nIuc9v/roDk88+xTJRp44XrMtW1589nO88vpr3HzuJmhgF4TFzSM+dfAlwrCiWyj7/Zarx8fc3Tzk\n4Zvv8f7dx7Sh48WvvcS9xyfkIGxv36e0AlFZrBfkKHxw5yHdesG1Z59me/s2J/cfEqLSHa55mCee\n6wfO332X104uOHr2JstSOfuHV7hy5Yhvv/QSty/usTi8wq9/9RqnU2bTr+kPrmCpsl4qx7eucX04\n/I0zyyd9/VYHqrOzs49/Pjo64ux+cfG5+SEegvnGmpujAq16H1fLaDfQph3xwA87T42edUdzP59T\nCJf0hx8kyNwoH9zKbE1cHzSnabcC4Poh2ow8JcGai6+V5onR6lUSFqprBuYkY4/GMre0avQtcKYS\nHFHyjZ7sonNCdW1G8IEP838eppkjjwskFJhkzl+ZN97sug9buPjTtEENBJtLn6tXZqAuBK46V+0E\nvwBaNm9Qv0xwV4H5sJE6bzpNZgrJB5Oys1nz6FU1rdocthfR4MXNLpTBD/DoZaeqbtVu5roz1cD2\ndE959BHarSmxEBT2W+acleii9dLQPjKdPkLVk8eteXq2hY7QRfrVgrlayZ2HJoQJsk4eQZEErQEZ\nIkzZ856M2fgQfGitBVEl5wk2O9rVRMmj6+LsksIyr0TpgsdM4INso8yCD4Ua3VgwFlTc3dOsOfqT\nPW1aQnSReKmkw0OvARJDQyDMpdWtFofsQ/SYkOKXSHe48Oyl6uGGjl6MXgyuhtZIu4zTGKsLplvw\n6ptVjwR1LdEIZq7DyDYjSiEgVNLC06LDJLTkNGHLikjvg3qp6ND7wCausZpO93C0RIpRSyWunAqr\n0+Sp7r079SSIv98mlE2BBXgWEo52Tv53IgJimAXv6zPxIFGE6Tx77VPnic0SlDAPYI42O72mtVFs\ndkRmoPfATw2RkJUWhFYzmNL17mo1m/O09kIY/LverXtabYzbPXY+wRCR1EPeo6knrBK1GXmmMq24\nM7ONlWl3Bi0Qu0haL1z2V4zd6QYNgXTtKj4hul7TzGkZdbEehQkrQpBI1zuFHA4Tm4fnc3xBcwdp\ncuNA3mVqyR5UuoyzgB//nqgRQ0e24kOKNV80olcJWRMfqmudXcFKOlqBKGqVvG9ugonNXaMB6izY\n76N47pb58+sNkvjZEUEHo1VBm3B+csL5+7eRsGK5GFw/OvkCJ8EDbmuZ9UTVQ2zDoNh+RpujV++k\nqOymQt168bKUy5DQ5vEfopQ6h23M3wcsEBBMk+tUVfziFQhhQW7QLxNlX4BC7BeMj++RL869ISEt\nqVmYNt7LWXQgbRuTGMPYs1gN9IsO7Rp5Fwh5IqnSdUKhMU5CiO56Na3EqvQr/zMVYF+Nrho7EVLn\ny0RRZ8jHyegSUI2X/uS/YFRYqjABqcEfhtnxrX7lZYMofu+YZ0y767gau8eP+OFf/Buu3riOtKvs\nxz1v/uxDNg/usD44YP3kMTZl9NqK7Wbk17cful5XBA2BqQGtcnzjkMVqgQwHXFsfctyv+du/+3uO\nn79F0Eaoyjsfvs/x1SucT6d0XWTVRSY1ViyoYqSDJY/uK9gJTz/7LIjwzp0H3P3oEYZHYvzsJy9z\ncPMKuRgk5eLsnPX1I46Or3L20QPitRWigWW/4Nx5Xb720lfZTHuuDj1v/eIVHlIJMXDjaMkvfv42\nR09cZXey4cM3XuN3vv4tXv75yzw43zKtrvNHX3mCbiocHhzx2r0PeevuRzz34rO/cWb5pK/f6kD1\n+PHjj38+Pj7m9L2GkPCMEYESaFpnVAgHTaqBROo4IxFFydPkupE544TqD7SlWThu7sSZxUhcOiSc\nfmgzFQdhTmdGDNsH6Gy2788ibsXRqOLJz6Yu+nOdkTvnVOdkcHCEhUuUbe6/kuZak5CcygGkid/H\nOlvVmVPbg/lFbIoG16eUsWHMKFd2kb013xJRz97BvADWdKa7BP9zSnGX1zhnT4VZ/9JFgrkwPohQ\nE9i+uMc3dB49YcE3VYG+6wgxMl7s5ovfs7KaS3iI64UPhkWIi0CIiZwzbRzdWbLoKbtzrEZ0WFK3\n56QrBwxHA9tHW6+b2e1A1d2R1hOvHNAddpADMgZIfpm0KE7jAWqNdpk3VIUQo382yV16LXukhcSE\nWfHy4NZcnNoa9cF9hiee8ctpX90xOl/aoh6+arnOyfQB6ZREB0GdNpr8RAudzHohaG1CmteDxFVP\n3c06sWWinu9d+1UqZoWqCSxQcyWmzj/PoARNxK6bRbaGtkZYGlKUGlyXFQaPD9FOmR7uCMk3hNZA\nO88VqzkTxUMU4ypQzj08lDYXkcdKMedN6pxeHlVoAcwqZZuRNKMR4sJgUsOk0C4mp7zxapMm1ama\n6hUhtvMwXW2VWmxeUiDgidJk1zVJcBebJQgpOOJz4WJto2DTSFyu6ZfRDQYznaVNXW+UovcoNsWa\n66daBM2zMywabR6ubBS0NzRGajHydkPQ3gXFRCw0do/3ftZYI6wWaO+6rFYFlUozxVJBpMOatw3U\nvVH3IxaFuEpo6ihnI0ihNiOkgbjwc6qB9wOao9MxuJuzFYWaSNI8nDQIxRr7BxeU7QUiHbpwo8Ww\nDExnzYep0ojrFapGLZeuaEfKUcPGHcTOh+Ns7qhcBLS4i9pp2jpHIVdaMTLN9WyXCFeotIvM6kZP\nTIlxnz2weD6km10aMDxoVZqwHBZszy84v/0AjQtWN687Il+ahw2XSggdZZoQcR1iSxWt3jlaxTzY\nNiQ3y5iQCkzaXDtY3YGMGGXKSJfm81cAz/kieIRJMHcNtmaemScezJzMWC2XnF/s2T3YITHR33iK\n8ugB1iJg5M1IHSJaM+V+pQ6T55ftR3bTyOG0ghAZNMKgZKuMu0pc9B4V2Bq6TkQxDmLk8c7ZFumU\niHcShghlmlHiIrDwY7ZM0AW4KIZ0cLr1HsNYvDMwiD9/UzO/qBXyrESX4gtHpxCvXeWP//Q7SIK/\n/t/+jG6x5OZiyYdnj6nS8+bP3gX2PP+1L2K5ouuONlVElXC44Imbt1h3wpVhxcU77/HEk9dJsePt\nN3/FrhlPx4HNg0e8ev+U1dUjzi/Oubtr3Lqy4s03b2MSOXjyGi/cuM7PXnuHxeqQJ28+yy5PnL1/\nl3G7JxwvSAc9oBw8ec2R9ebfyVoqi8Uhjx7cx6isrx8zTRNvvfEq06MN/dGKH/zwB1ht3Prcp3j0\n8JTh6ICjG9e50q/RoHz1q7/Dj//6B5xejHz/+3/LWc5srn2OP3nhCCkT75094vbDB8Rlx3m9yoOT\nE577DTPLJ339Vgeq09PTj38+PDxkMxY85QY0Bd9eapxRBJmFwsUdOcmtz3ny1HRj1lwl8TwezLUT\ns6bIxbmz3iN6aB6lQbq0+E408Y4tkwAdkKuLG6tBal63IBCCfmyucLDC3RbNxIekBJdVNNb8UqMV\nH+5CmN1dxek78SGlqXmY6Fz34lSBEELCqJSxYLv2sdVfJrecM86figiWi29kCK1zQbJVP1IdnfKi\nXFEPm3OXocvKLjfKooKMzQdE1ZkCUZBKMyFg1GbsH5265iQA2jsdFY3YeYeh1eYdjFGZtvu53y6S\njhfuSrw3ooeDo3Cpc50PRskjZbP1QY+ITDA8eQQhUiZ8+KhGWnTewH6xm0upk2uJLgeILrkraDv3\nMFoiHjlVUDajV3jERFhE6i678Pb6TUd1UoCxIqGjP3S7bGvqxb2d0HIlNP/cyq4QhuQ1EL1TE1yW\nCksjdgNFC0Gio51asKaeQ5RmBHaulqHzzVi7ROi801EFNIS5MFuo5hquQKTGkXKyI4QO7x4U8skI\nnVBKJnVLF3WnmZaukUomDZ3b+rvoMQISsM4RSlsI7I1mTp/lPsLo0RU6myMwqFsjrn2A15Cwzlxv\nFFxs3op5RYsE6pQRgeEgsX2YXVcW5ueRhuTmQ3tt6CJ5rNvOM5/yuEdCpAVBzcX4GsSDXPE8tjYJ\nrWUMHxDMGmxHTBTrQUskLHpidV1aiMWH4uC/53g2udC8+tJAtI/pMxBC138crXJZI6UW0EX8uG7I\n6t4Hwi5Sc6U/XHrmk4oXNZet05pDpFt5Aa6pB6aaejNCwAvK83lBFz4cappzqGpjfHBO22ywFonH\nS4Z18DDXqTBenKIxIYve31NVUidM24qm4Jf+ZkbNA/48dh1xlSi7PbUa3aIn13mBS3PFSzAolxl9\nXj9joqhUyr4imihjJc7RFRkfpIL68hJSoFfl5KOHbD/4kLBYs3z2KqlTdluvAAphThcvDUnRF0xr\nMJm7dM1oOp/hll0LWD1DquaKaUJmdI3qn2toUGantTSjSCHE5OfYZaRJ8fPP4ZyGVthtJkRg9cRN\nbMps7zyku3aT4WDBNE3kiwvKmYvcUi9kGlOF2DVqFk6KM9dT39PV4C0ZFliHQM2NnSrD+ehhpl0j\ndZFqStwrixRY996NSXAXYDBjsxeSQtfPdaszsBCjI04b871sFAjVWY6p80GrilGqUQMMTRhxel7N\n6Ef4w//6O0R1gODf/I//PYe3nmTRD+Rp4t1f3iOPJzz/9c8ji0RIETPj9oe3WfQD8ZmA9QsuTs94\n+OAh93dbnnjqKmW752S359YTx1y5cpNf//JV/vkf/QFLgwdv3mM8WkNt/PV3fwFp4KXfvepVZBa4\n/sJneOfDD4irAdVAGSdqztT9yP70jNOPHrLf7bm+9oaJmgvn5YS2zxCF5dNXuX71Ovfu3OErX/4K\nhyny6t0HnPUdj07PuPP+Xf7wj3+PYxq/+P5PeHByyrUbV2mHT/DijZGhNt744DabmtkrXJc1f3wr\nUforv3Fm+aSv/2Q5VMvlkv3klmoz7zwSEhbqvL2CqgsP5ZKKaj7IGAZNIamLXVt1XYmB4vA2KXmk\ngM5M8iwmp7lLTYLDwK3ggxYVi94FaMzaVjwEsFmb04U9Y8YTl0Fqm7/tXiXRmruPxMA6QZv6/3/y\nug1pc5GzXiqU/RKy0iAqMbowTho+jASl5AbVt23PmsL1ONFRkVa9akert6b74eq/vOVZdBuSy8UV\nBPW06lxp0Q+yZmUWCAc/gIo5ZSOVomB542hhnlvRY3QBvzSnN0VmVCdTHk1UCkLwUFMR4lFgf0dR\njWiXSMNAtwhst4U2jVid0OUR0NDVoQ9QvTs8L3YbHwhao21d8xEHD6kpIkRRtA9Yg+liJJKwUWiy\nR7PSJs+psqxeajrHZogqtocaMmnZzQGTM1rS/L0OB3OYYXbnISVD8NDPgLsA/S3XOc1evOQbF9Qi\nEFqYgxaFVpyjDivvCGTywVrnlHqrDvnFZaLsBcwvR3Jlu5mo+x3SdejgIvVLipRmtKGjMYc4anI6\nqlYivWdUGVjnFJkuEuV861VBk2G912koOjv3XCHlA576xZW8j9Czy4A9kIRUe8pYaVNhOF6RVsrp\n+xvSesXmzplXy5i5W2w3QRI3TTSBNAdl5koQoe0LYdljU3DnnHgjgQSlNvWKm2ZYzmDRhzor/lx1\nrjcSjDj0hC4wnuxmA8blUNaoLSDJCLH3yBWBXJrrE2sjrjqKOXoWuojJXGkSE3n03jM1jwlpDerY\nCFE5uNZz/837sD4k9gY5Eq/2KEqIgXEqbmCwuSC4C9TJz4q48ggBqxVTJW+b6/dQtDuAeXihJSxW\nTu+dODKnyTVMnaPpdedZfv1iyX6/cU3UYqA1I6ojOtG8TDt2iTaN/n7aiHZzXIk4LS4C2gVihnFX\nPNS3ZNojRfpGaR4REYE6U2sNd0hOU2V35z56sOb46RuIBXYXlbYvxCFSip/XJjhKc3kHlDbX2YjT\n1Dh1pWrUVmHvC1iwQGOaUTj8WZZL3WxAU6FOs3u6upayVR9ktcx6VJeisc8jbTtR60g39Bw8eZP9\nZoctekL0mI1W9mh3RB43aHGtbt61uU/ygMkyU9gT+kjUjsX1JWOBLilU2I2N0Dza5EgCp63ShUqu\njbOtcLBOpASd+uLusYNC3htDwKuOTMguzaO4Lwd1uRgNSHtzJDrAXkB2/r8JNhvShQsVlgV2Ch3G\nn/4P/xMN4dH7r/PLv/lLrty4jpYjPnjtIZ/+ylP+vJnTy6wSb96+x/beqT/HGP3BgvPxlLzNpPWC\n/OA+77R79NdW/Lu/+T51M1LN6PtAPjGe+crn+ezNawxUYivc3uzROnIQlPPzHaVkuqMDrh9d5eH9\nB4gqjx88IqXEe794k8fv3kVEObh5hRvP3iL0Ay0XHjy4RzpY8tZ77xAQHj08pwl86fe/QZTMYeqR\ncc/eAvfvP+Z3Xnqeq9evsloccH52xlgybRk4Olxya3VIKwXdnv7GmeWTvv6T5VCtVisupsfzJuTU\nmpTRUQ5xqzfqicWtee6MRHzTTO5C88vPq0G80BenGbrOEQO9LFctMDupfFoRp+KmMFthm9eV6EzT\nFZmF5x7+5DUAfrmYzNlC7TIo0238NuEUyDzsSPUaglq9E5DieitNHu/gQ5x4+nMQtFR/L8xdi2ow\n2h4pLjp32Nv7tBAXrLtGJTpCJX6oSvNLymwCCaRF9M1MxA/LZlBHzNQdUiZAmp1gLroVDY7y1TkA\ntM7uvtgIvQdaWnFXSTUfHKT4Vt3qiKaBsBoo2z30Qn4wp8LPdnxZdWzOJ8a7D/yC7ZfU/TlSG6m7\n7sLZWhn3I9IKQXrGi61r4SRQavb3IQj0nrFTW/WYAHzItewF1GEI2GjIMiEWaFPBekewghasFXcf\naSId9bRilLzHsqC904vae85VhZle7bDojcYSvPfMqrrDrYckjhq0qdFoXiET1OMeSiCGOd09OfJF\n9V4zQemvdB5N0bzOxvZl7tWrHrdg6jun1Hl4Aov/X3vn1htJciXmLyLyXlkXVvHeHGgX1hpeP1jr\nAeQHwxfAkP+Ef4d+kN/86F+gx109LKQ1ZMNrWWvsQho2m2SxWLe8Z0T4ITJT7JHXmGlOq3uA/IAC\nhz3dzeqTp06cW5wjMfva3SadhOiqcRG/te6iRy0QE+EGsRrVDfoUSN8dgG1muwybG+JptO0uPrh+\nGC0kykqEbd3tq8SHyrjVlZ77LLlp8i3FPxzQuZsy7E1jt4TbV24tjHQrPoTBZZVC93n1ddd8PfeQ\ntcEm9vclV+3GcCD6z4d2jljToowbBWE9232krZtFFSuKQ+0a2aPuYJQCU9puRZV1+9+Eoq3bbgWL\nxQTK3WzFlYxtd088rgAAFR9JREFUJLHH2mUjlUR187ys1BgJ5qjxEo/pJCQ7GNpDgfIjym1BdOEW\ngLetpSo1tmqwwt2AFQh0ppGBcFkAaTqHGue0CG8oy0phXCZKWNqmpc0b9K5EzlJErPF833U05Lib\nk7GibiqqxyPBYg4lCN9lTqS21N1GhbZbqN7fPrW49VymNQhjUN16l1Y4R880liD0kTOJrtyydOkp\nd1vVs1jfEgs3q+3p//wWa1om8zPwFIf7Hd7ETSO3tuuXous5bOmCUqcCbgNGC75AHy1WapRU3Vqg\n1l1UEqpbLC/AU+im846UxbTNsOtOCTfmwLQue66My9KKzsYiLZQSoQI3kLZqKHdv8bwYXWSIwCNc\npXhJRJ0dkDpwOlMVWNvgNQpzrF1ZeJ4QyQStXC9XdqgopEVJS6gCrISJCKhaTXloiJOAnWrwtKDU\nDZ7vEytIJx6pVOgaRGRpNLTa0voS37hgJm8h9N3NcY37OBgJbQWBD7px5cNMw8TAwQevtfhALl3P\nq0VglMUqy+Lmn/Bv/9MPMbbmL//Lf+YkTfjbn/8aYXJAupEhsuucadwMM6Ek6XzG6fmZuz2dFS6j\nGSuapkGEAtO6fsmmqmmakLxYY82C1MDf/u+/I1Ie6Q9uiMOM3+6eePIl9SHjq82e5fkZ//OvfolA\nkIQxrW6p8wohJbutpbz/jetpFYLpzTWLRYSZTWizCjGPoKj59X//H/yrL/8C3xiOu0fOZzH7Q8bf\n/+a3/MkXP0B5Ib/45V9x/sNLziYTCgmLeM7mt3/HP/uXf/7/9Fley0e75RclM5p6jZujYV3Zzlik\n36VntcUzgrpoXN+T30X6/ZBMv3MSTFc6EwLrKTcXSFhMIDC1c+OFcs2/CDdJ3I3z70p/wg1ZEJ5y\nP98ajOp2/9GtnMClvv1AMosEy0QxiXwmgWKWKBaJzzz2mASCwJcEnsJXAk8KkkAS+5IokPi+wBNu\nJU43YYCuUOc23lsXLbfa0hqXum2BsjZsC8OxaDhWhn3RktdwrBp2WUvRGGptKeqGfWkocu1GBQhL\nW1t3q915lkMZUvnKOVpdpEjTzf0RrpEY5Rwg9+4sIoQgjhGALg3aurEI0hdIfHRdYeuulBb74Ht4\nkUdzLDH7A5Q1MtEEscL6kvrhgDUNishNdhYKFU3RwqDzCi8K3K0hgbvOjSAOBCeJTxx7pJ4inQdM\nJh6hUAQYIg/SxGea+MSeIk0UnhQoDKEnCT23TsX3u1/vVvpIKYaJxBa6VSDudk5VayrjosxKu8i8\n0pZDqdllNYfKkNWWQ9FwrOG5ank8tLRZ4w4O1ZVMjCsFWWOoy8plA8CtjMEd2F4co1uNPrqxAFjr\nKjahcgtjPTf3CmO6UQkttnZDHPEEnudSroJuma2nMA0gJCbrhnJa48pYWqFCCa1E50dEGCI9OIk8\npp5kkigiT5AmCl8bZqlPGvrEPkwCjyRwy31DX+IpCKVr9vawBEqiVH+ztpv+baBpLS2WsjHklSE3\nlmOpOVSafaHJW8Oh0BxKw7E27DPNc1GTVZ2TrHE3W6XbFaiFwArJZDmhzlu80COaBFRliy4KVBQR\nBJ47qIVBBhIZdrcwNc45C5zOC+O2L5hQ4Rlc03mpkZEkUD4msphc0tatu0WpIZqGhLOA/FBRPLps\nHNISnS4IY4/yWLsVRUnIVHpcLUOmiWLmCZapRxoookCSBIoASxRIQiWIfUXoCzzlbm/5UvbrPF0T\nP38KnqTVlkY72dbaUDcGrQ1lY8mbc0pNZxvgUBoOlSGvNLtjxV4bno5O7n7k2iRMBaYx3fLwCl1X\niCDCihYvid3tRqPxYtXZUTdWxbbOJmzLjPppS5Mf8U/OsK2iyhus9Mjv1wTLhZvg390Rtd2ibJcN\nt+52qDII627aysRd4Q+CEK0b2tYSTHyUD3XlMuSyAaE1rdFI6wI/NyVHgLYkCoJUMolDTmPJauKz\nSHyWkeQkViSBJAkkk1C523Kim4GsjcsOdwN48n1OYwWNNhS1Ias0h0PBIdfsypoaSa0EWa7ZZ0dy\nBc+HllYFVJOWyA/xu5u2ZdPQFhpP+thQUFQW1bo+sELX6IkLcFOrKIwhQFC2LoDRUmC0oG5cCVC7\nR4A04HnWtZAIqLU7U3JP4FU4m4vbhqHAXTiwILs+LmstWgZoKzBeyNn1D+iHtU6TmMD3qY0kCT2S\nKCIKfDwliX2fMJCcXZ8R+D6+F7gWGSlRQrqtFFKAcFk4YyymqvmPf/bPOe6Prrf35p9ykxcuoxYo\nmrqmrmou/0PKu7s7l3tQHsfdE09Pz0gEpQ24vb1ldXbGcVtw8+dvaNsWW7lLP8HpFM/3eHxa85e/\n+l/IuuSLL675+c//mkO54nB4xmpJEPj8iy/+jH/4+9/wJ1eX6Cp3e8S7RdQvfZbFYvFqH0hYZxW/\nE37961/z+PhIURT863/z7/ivf/NMXhl2eUvVGvJacygt26ImKwzbrOUprym0pmqsS9fbhm6eoSvR\nWdx8Gb+LWBrcMECruyGFLnVNLSAwhL4k8gTzxCOWijiQzBNF4rsD+GwacDr1OZ8FzGPFNFakgWKR\neJxO3SqUzx1j3IG/OTbktTMAu0LzlFVklWZfWjZ5y3pXkNWGrIX9rmZfNBxqTV5B29gu46cxysfz\nhZvQrA36mGOlIJhO3K0yU6OrBltXyCQhnoZYpFsem+eIwCexLW++WDGPPOJQMPEEU1OzSHwmSUAU\nekx8wSwwTJOQ2TRkEiji0Bm9NFJMo+/Uv/+obLOWx6xhW2judzXrY8v9tqZoNIdC81xodnnLU95y\nzFsOraDOG7d4VrbIyMOTHiqQlLsSwPWABQpTGfeB96Tb1WfceAEvCFxpGsN04hO0LdNpRBIKfGAe\neazmPmcTn9XU53IesEoU56nHLPZYJB6++vz0WxtLVmmy2vCcNayPLbtSsy0NrRSsdzUP64IKQSHh\n6as12wzqaco2b6lK1xOoAlwJL9Mov9vf17pMnm6brmkZVBSgC1d+8gMPg8ZmJaGniAKPNPBZnobM\nI0kqNUHbMIs90jRkMU1YTFxwdT5RLGKf06nHJFCfWoz/KFmtXaBWaY6Fc3AfDg3HrGZfwbYx1Bby\n0nIwLXkrKRtNURueC8N+X9IWBfXmgNU5Kpyj0pQ48vCnPvn+QLXNSS+7xnQsTd52Ixlcj6nsCtZC\nWAIpSQLwpWSWKpZTn5kQhL4gCRSLRJEGkkhK4sj1IU18SRIqTmKPNJKESpKGXYvGJ+RQtuSNYVdq\n9o1hk7dsipbGSBAK4wvqxnDUhryy2MBNxrcGVCAJFeBJfAS1cJ9jX3TtwdpVKbTrw6e7lEkrLKIE\n4zmny0eQeIIkcO2xXufSBqEglYLIFyRSEEqXBAgl+FIQSEH4GdqDlzRNQ9XU5GVB3TaUdcWxzBBK\nEQUh1X7PL//6v6FbzX6/oygKVmcrZhcz1D7nXVEQYPjyT6/IG4v2BKrW/MWP/z2Lsyt+8pOf8LOf\n/QxwN/5eO9zzO3WoXkvdGhptKWvnfFWtoW5d07SrJbvdfVKAr8TwSkLFJFBEvvxgh8hay+Fw4Pb2\nlvV6zdPTE3mes9/veX5+Hl5FUVDXtWtobBrKsuR4PFIUBWVZ0jSN86RfiFUI4ZbiSonv+wRBgO/7\neJ43fD+fzzk5OWEymbBYLJjNZkwmE1arFUmSEEURURQxm81YLBakacrJyQlKfZghr1pD1RjKxlAP\nWTNotHFRRtczYYwFY5ESlJT4vsT3JIESBAoCKZhErz+o67rm+fmZu7s7drsdWZZxPB45HA4URTHI\nt38mWZZRFAVZlg1y759N27bDc9BauzKEu1Iy4HkeQRCQJAlhGBLHMUmSEAQBYRgyn8+ZzWbDM+m/\nrlYrLi8vOT09Zblcuv6Db0jZybs1LvPQaEvVdJmHbtqQ7a7bC3o9hzj0iDzBJFTE3YTsb4u1lizL\nuLu74/7+nuPxSJ7nw9csy9hut+/J/Xg8kmUZVVUNut40zaD7L2WqlML3fcIwxPd90jRlNpsxnU5J\n03TQ6V6mvXyXyyWLxYL5fM5kMvmgBaVVa8iq38u1bg0tXcas7Wc7Of1VnstSBL3tCBRpKAm8b/4c\nv07bttzd3XF7e8u7d+/Y7/fc398PNiTPc6qq4ng8UpblYDN6WWqt/8BuuJU8ctDHXl97ezGZTIZX\nGIZMJhOWyyWz2WyQba+nk8mE2WxG3G1X+LYYa8lrQ9U5WZXpprPj+kpb7fRASekys10lwGXgXGYo\n6MYxhJ6T+Wuw1lLXNcfjkc1mQ5Zlg114+/btIPuHhwc2mw37/Z7D4UBZlrRtS13X7+mu53l4njfo\nbhRFzOfzQXdXqxXT6ZQkSZhOp5yenpKmKVdXV7x584bpdPrqxbq1tjTGVTHcQg1XqTEwZNZFfzMe\nBsc0kBB21ZIPkeN2u+Wrr77i7u6Op6en9+xqL9enpye22y1FUXA4HAZ7UFUVbesuu/R29qVMlVKD\nXMMwJIqi9/R3Op1ycnLCbDYjiiKSJOH09JSzszOWyyVpmpKm6WAbxAc6z87+u8+Y7IQopML3A370\nox/xq1/9ijAMKYrig3/G8O9+1Z/+Gj/+8Y+5u7sjiiImkwlnZ2eDMZ1MJsRxPDgOvWNwenrKdDod\n/n9vQE5Okm98WPWHxdO+GJQgy7L3Dubtdkue5zw/P/P27VseHx+5u7tjv98Pr6qqvktxfHSEEIPS\npWnKxcXFINflcsnFxcUfHGjL5ZLJZMJ8PieKIqZRRDSNPkiRjDHUdU1+dHJdr9c8Pj6y2+2GA/n+\n/p79fk+e5xRFwW63Y7vdst/vh2eSZRlt234ECX1cfN9nsVjw5s0bzs/Pub6+HnT99PR00O/+oDs5\nOXHPIwgIkuBbOWP9I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"text/plain": [ "<matplotlib.figure.Figure at 0x7f15a015b5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig_x = tm.plt.figure(figsize=(tm.cm2in([11, 6])))\n", "\n", "# Locations\n", "MDF = [19.433333, -99.133333] # Mexico City\n", "URI = [27.216667, -107.916667] # Urique\n", "CHI = [28.635278, -106.088889] # Chihuahua\n", "# CRE = [27.752258, -107.634608] # Creel\n", "# CUA = [28.405, -106.866667] # Cuathémoc \n", "\n", "# Create basemap\n", "m_x = tm.Basemap(width=3500000, height=2300000, resolution='c', projection='tmerc', lat_0=24, lon_0=-102)\n", "\n", "# Plot image\n", "m_x.warpimage('./data/TravelMap/HYP_HR_SR_OB_DR/HYP_HR_SR_OB_DR.tif')\n", "\n", "# Put a shade over non-Mexican countries\n", "countries = ['USA', 'BLZ', 'GTM', 'HND', 'SLV', 'NIC', 'CUB']\n", "tm.country(countries, m_x, fc='.8', ec='.3', lw=.5, alpha=.6)\n", "\n", "# Fill states\n", "fcs = 32*['none']\n", "ecs = 32*['k']\n", "lws = 32*[.2,]\n", "tm.country('MEX', bmap=m_x, fc=fcs, ec=ecs, lw=lws, adm=1)\n", "ecs = 32*['none']\n", "ecs[5] = 'r'\n", "lws = 32*[1,]\n", "tm.country('MEX', bmap=m_x, fc=fcs, ec=ecs, lw=lws, adm=1)\n", "\n", "# Add visited cities\n", "tm.city(URI, 'Urique', m_x, offs=[-.5, -1.5], halign=\"right\")\n", "tm.city(MDF, 'Mexiko-Stadt', m_x, offs=[.5, .5])\n", "tm.city(CHI, 'Chihuahua', m_x, offs=[.5, .5])\n", "#tm.city(CRE, 'Creel', m_x, offs=[.5, .5])\n", "#tm.city(CUA, 'Cuathemoc', m_x, offs=[.5, .5])\n", "\n", "# Save-path\n", "#fpath = '../mexico.werthmuller.org/content/images/barrancasdelcobre/'\n", "#tm.plt.savefig(fpath+'MapUrique.png', bbox_inches='tight')\n", "tm.plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maps with Natural Earth backgrounds\n", "\n", "I got the background image from [Natural Earth](http://www.naturalearthdata.com); it is the [10 m, Cross Blended Hypso with Relief, Water, Drains, and Ocean Bottom](http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/raster/HYP_HR_SR_OB_DR.zip). I changed the colour curves slightly in Gimp, to make the image darker.\n", "\n", "**Adjustment for Natural Earth:**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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PUNB12H9pD4jfCLRdasvD0njTSfDN3cwaD4dv/EGoW19YaLZT6RpnjR9Qub6y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\n8UfHL4pQ/BmOLT9MsotP+D/hHWIfAXhlYp9OsrSXVoN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/AGwv/QE8Z/8AhA+OP/mepP7c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\n28zKriK9fl9tXrVuW/L7WpOpy3tfl55O17K9t7InooorQxP/2Q==\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='./data/TravelMap/HYP_HR_SR_OB_DR/Adjustment.jpg') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Profile from viewpoint down to Urique\n", "Not used in blog, later added" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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MTxfHChqZiIiIlCWFBhG5JIZhEJuaQ2xyDuN/2MWZHyA3NwrihT6N2XA4mabB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\n/d1jFPCf0YUTucWas/EHcdNLw8DtSQAAAKg1xeU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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7b3c33e358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "fig_p,ax = plt.subplots(figsize=(tm.cm2in([10.8, 5])))\n", "\n", "# Switch off axis and ticks\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['left'].set_visible(False)\n", "ax.spines['bottom'].set_visible(False)\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('none')\n", "\n", "# Get data\n", "pdat = np.loadtxt('./data/Mexico/ProfileUrique.txt', skiprows=1)\n", "\n", "# Ticks, hlines, axis\n", "plt.yticks(np.arange(1,6)*500, ('500 m', '1000 m', '1500 m', '2000 m', '2500 m'))\n", "plt.hlines([1000, 2000], -.5, 17, colors='.8')\n", "plt.hlines([500, 1500, 2500], -.5, 17, colors='.8', lw=.5)\n", "plt.axis([-.5, 17, 200, 2500])\n", "\n", "# Sum up differences to get distance, distance starts now at every waypoint\n", "distance = np.cumsum(pdat[:,4])/1000\n", "\n", "# Plot data\n", "plt.plot(distance, pdat[:, 2])\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
cc0-1.0
nkmk/python-snippets
notebook/sklearn_load_iris.ipynb
1
12640
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.datasets import load_iris" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = load_iris()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'sklearn.utils.Bunch'>\n" ] } ], "source": [ "print(type(data))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(issubclass(type(data), dict))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n" ] } ], "source": [ "print(data.keys())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" ] } ], "source": [ "print(data['feature_names'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" ] } ], "source": [ "print(data.feature_names)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/site-packages/sklearn/datasets/data/iris.csv\n" ] } ], "source": [ "print(data.filename)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".. _iris_dataset:\n", "\n", "Iris plants dataset\n", "--------------------\n", "\n", "**Data Set Characteristics:**\n", "\n", " :Number of Instances: 150 (50 in each of three classes)\n", " :Number of Attributes: 4 numeric, predictive attributes and the class\n", " :Attribute Information:\n", " - sepal length in cm\n", " - sepal width in cm\n", " - petal length in cm\n", " - petal width in cm\n", " - class:\n", " - Iris-Setosa\n", " - Iris-Versicolour\n", " - Iris-Virginica\n", " \n", " :Summary Statistics:\n", "\n", " ============== ==== ==== ======= ===== ====================\n", " Min Max Mean SD Class Correlation\n", " ============== ==== ==== ======= ===== ====================\n", " sepal length: 4.3 7.9 5.84 0.83 0.7826\n", " sepal width: 2.0 4.4 3.05 0.43 -0.4194\n", " petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n", " petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n", " ============== ==== ==== ======= ===== ====================\n", "\n", " :Missing Attribute Values: None\n", " :Class Distribution: 33.3% for each of 3 classes.\n", " :Creator: R.A. Fisher\n", " :Donor: Michael Marshall (MARSHALL%[email protected])\n", " :Date: July, 1988\n", "\n", "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n", "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n", "Machine Learning Repository, which has two wrong data points.\n", "\n", "This is perhaps the best known database to be found in the\n", "pattern recognition literature. Fisher's paper is a classic in the field and\n", "is referenced frequently to this day. (See Duda & Hart, for example.) The\n", "data set contains 3 classes of 50 instances each, where each class refers to a\n", "type of iris plant. One class is linearly separable from the other 2; the\n", "latter are NOT linearly separable from each other.\n", "\n", ".. topic:: References\n", "\n", " - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n", " Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n", " Mathematical Statistics\" (John Wiley, NY, 1950).\n", " - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n", " (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n", " - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n", " Structure and Classification Rule for Recognition in Partially Exposed\n", " Environments\". IEEE Transactions on Pattern Analysis and Machine\n", " Intelligence, Vol. PAMI-2, No. 1, 67-71.\n", " - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n", " on Information Theory, May 1972, 431-433.\n", " - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n", " conceptual clustering system finds 3 classes in the data.\n", " - Many, many more ...\n" ] } ], "source": [ "print(data.DESCR)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "X = data.data\n", "y = data.target" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'numpy.ndarray'>\n" ] } ], "source": [ "print(type(X))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 4)\n" ] } ], "source": [ "print(X.shape)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[5.1 3.5 1.4 0.2]\n", " [4.9 3. 1.4 0.2]\n", " [4.7 3.2 1.3 0.2]\n", " [4.6 3.1 1.5 0.2]\n", " [5. 3.6 1.4 0.2]]\n" ] } ], "source": [ "print(X[:5])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'numpy.ndarray'>\n" ] } ], "source": [ "print(type(y))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150,)\n" ] } ], "source": [ "print(y.shape)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2]\n" ] } ], "source": [ "print(y)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "df_X = pd.DataFrame(data.data, columns=data.feature_names)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", "0 5.1 3.5 1.4 0.2\n", "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2\n" ] } ], "source": [ "print(df_X.head())" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "s_y = pd.Series(data.target)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", "dtype: int64\n" ] } ], "source": [ "print(s_y.head())" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " sepal length (cm) sepal width (cm) petal length (cm) \\\n", "count 150.000000 150.000000 150.000000 \n", "mean 5.843333 3.057333 3.758000 \n", "std 0.828066 0.435866 1.765298 \n", "min 4.300000 2.000000 1.000000 \n", "25% 5.100000 2.800000 1.600000 \n", "50% 5.800000 3.000000 4.350000 \n", "75% 6.400000 3.300000 5.100000 \n", "max 7.900000 4.400000 6.900000 \n", "\n", " petal width (cm) \n", "count 150.000000 \n", "mean 1.199333 \n", "std 0.762238 \n", "min 0.100000 \n", "25% 0.300000 \n", "50% 1.300000 \n", "75% 1.800000 \n", "max 2.500000 \n" ] } ], "source": [ "print(df_X.describe())" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 50\n", "1 50\n", "0 50\n", "dtype: int64\n" ] } ], "source": [ "print(s_y.value_counts())" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "X, y = load_iris(return_X_y=True)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'numpy.ndarray'>\n" ] } ], "source": [ "print(type(X))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 4)\n" ] } ], "source": [ "print(X.shape)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'numpy.ndarray'>\n" ] } ], "source": [ "print(type(y))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150,)\n" ] } ], "source": [ "print(y.shape)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
diego0020/tutorial-vtk-python
color_map_med.ipynb
1
605584
{ "metadata": { "name": "", "signature": "sha256:5e6d54894543038bc917d2d46b3591b989520083697b36302adf0ebb65cac835" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Processing example 1: Colormaps in 2d Data\n", "\n", "\n", "## Step 0: Setup environment" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib\n", "import seaborn as sns\n", "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "%cd C:\\Users\\da.angulo39\\Documents\\ipython_notebooks\\Assignment1\\Example1\\data\n", "\n", "#figure size\n", "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C:\\Users\\da.angulo39\\Documents\\ipython_notebooks\\Assignment1\\Example1\\data\n" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Load the data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "vortex_file = \"vorticity.asc\"\n", "in_file = open(vortex_file)\n", "#first line contains dimension and spacing of grid\n", "l = in_file.readline().split()\n", "nx, ny = int(l[0]), int(l[1])\n", "sx, sy = float(l[2]), float(l[3])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "#initialize data object for speed\n", "data = np.zeros((ny,nx))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "for j in xrange(ny):\n", " l = in_file.readline().split()\n", " values = [float(x) for x in l]\n", " data[j,:] = values\n", "in_file.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "print data[5,5]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.0026946405\n" ] } ], "prompt_number": 66 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Display as image\n", "\n", "We can simply use the [imshow](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow) method" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.imshow(data)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 67, "text": [ "<matplotlib.image.AxesImage at 0x1770cc18>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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IWq2Gvb09tFotVKvVSNwCeQkAePHiBZ49e4ajoyMXA2FVayX4bipxIdP0Omgf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KQprwcTAAEo2FUxYJhgM7extLTU/GVWgGIZOBPR5B5hQYi92WzWsu9QPI5l0AJ8XItcDLsPuD6V\nB/Hy8c3NzRwcHLQ2vnjxov1mZWUlm5ubrd8rL4LC8FtcKYDXCWA8i6o0Du/CNdRqW1bCWpEKs94+\n/+soZH2eHkfkUiRpRHYFBwOjuaUaCaNdb0sW4PCZ9Bj/mqDDrJlcL0JCEb0Qht+buPSMYZKTWdDx\nd68vQAw0kKKHh4dt8RDHAg4svPL1YO339/cb6LE6cTQatRmSNnNOZl3K3AFSDr/RXlssWA/sS0ES\nE2158eLFYD0I7bWrgrKMx+NmBdGfpFLbpbMVV5UZzsI7ajlaw7E1mmNCmL/9TO4znmw51PwZJpjK\nO2AtAf7VVepFsBaE5FuWeQ+xZg8mGcymZpaN/iS32PKwq8CqwGTIyJv4YlapWXEGAQMPKddJWm5A\nvTfuyXF1VvtxDw8ePLhlEbiKk4HB6wCqAjGrk7REURVmvv39/bx8+TL7+/tt8RN9Z0B25IfnQMIQ\nwICSWbH4Le6blcmhWPdrjQIYDD6PAno8mUvx2PKYcni0EpKOStRjvgj54MEh6StRL7bNRjWOi9e4\nv4uxGhz4m9mdcKV9d65FVKOGG526fX5+nuPj40ZoYb0ANrTBgwdwAhyYmTHtuQ/4Es+U8BV1dabD\ntb6WrYe1tbVsb28PzGKiFxQ1STJQeK5N9Ke2BwtkNpsNVmwCBuYqKjjQ1prHYXO/gsHrAoM5kV40\nx5zGvDFpMhQLwm2p7Xvb8sGDgxnuHlPO4E/SSEAAweYfvzWLjHimdyKVB4cZaTPh/h422/wA51lZ\nWWlhQQjUi4uLxhN4VuIe9/b2GjhcXl5mPB7fYv29pLuuJoUgrbNs7csHDx5kfX19ALj7+/uN34A3\nwBKxqQ9g+fzJMFGNvqiWR3UxAAcrv62GHgA4j6COmbukWqGApQvG1pyTHnFJCJZ2mlv4oiwG5IMH\nh+pWmC9wxmOSZoLPS5312ohqjlalsq/u8KRnO7fHCkubLi8vm9WAW+BwJa6DQ2b43EQ49vf326xE\n/oD5DocnrXyO6tSYfU1IIr9hMpk0a4ZkJrtPLNJKbjIQe1aJnxHndp/Z+qjg4JT2qmC+J8Qz831D\nhj2OoVoM7kdHIio/ZYD4MgDB8sGDQzXP/ECSm4KxSQaZdDV5qAKETVaUFsV1jYCaFefBy0C3P4of\nvr6+niSDHARmeGbis7OzFq5z4ZDT09N2zxcXFzk6OsrS0tIgHFqVn5nL1o3dBPcl7TdYQgZubGxk\neXm5ART9kFy7Lp5JAap5y+NRPO/1US2JmhtQrQDfaw01VkV8ExLSlqiBoYaAe2OxAtN92vA25YMH\nB4vNTM80XgvBQOslMTkGzrG2ClhwVENl9o9t5jJjkIvga3Is519eXm41CNmfAaIThWYhETM2Zr3Z\nf4cI7e/yW4CI89tNMsdBf+CmAIREeR4/ftwAEwIV7oH2GkABY2d64laYFHaImPtzFMkKyLG20qrZ\n/qbRCZPatKVGHWo0pJKQvu6XbTUkC3BIcnvDGC+2gYBMbkqa9ZDfZqfPZ5Bgsxv7xT1CylZIL2JR\nme3kZu9H+6VEFXA5iE5cXl7m4cOHgwU9SdqaBZTeLsDm5mY7DwVsyWnAbK6LkWqKcXLjLgCYS0tL\nDRR45zqVFDYZTIiVc3oVo92LqpQA/tLSUuNMesBggDZZeR/x+HCyG+f2c6P/a5SiWjtfhSzA4TOx\n2Uw6KzN3zeCrLPNdrHGdKew78l1dA1BDXL0ZxYQkGZCHh4eNOEyu14KQx8B9sRqSsKXdKFyenu9O\nijaJO8vLy20xVSUEe2FAi2f45eXl5gadnp62/rZieXZ3X6LkdstqijoL2ByJsNDf9v8NBq9jMdTz\n1TwZgKlKtR7eFfngwcEP35ZDcmOGekBZifl9JbUYIHW1pa/hRTQ2oesKQBNtKK/JQf6ntPnu7m7b\n2eng4KDlGOC2eMMdiEhetAdlY0GZZ/4eyed+qWa5+6aa1eYHkgyqafE7Zv8KFgZQ5zgYSCt5WgGG\nc3DcPICfJz3uopKR5myqi9DjG+q5eVZfhXzw4JAM1xKA7OYVesrPgO0RmV5dWUkouxvk+zu8BgfA\nOW2229xn8JC3cHh4mP39/ezu7ubg4CBJ2t4TuATmUAhp1plubW3t1pJ0iD0va7br4BnP91hdLis3\n98lniCMU/I4+BARdvs5g5khLVVCOJQLltpovMeD5mCqvAoZeFKf2jZW+/t6ZnwaIBSH5FYlNec/i\nfojV5PZgqGHQu7LiSECqNQUYDPwOHsBl2c3gs2bi+fPn+fTTTwcbxh4eHra0ZfINACb4E7+sNL4+\noVGK1zBoTdb6vJVv4HycCxLRRGByW9HNDfD7ukSe6I0rM9vl8jO08lXrhGfL+5so4TxgAAgNDj03\nsRfJWBCS74iYCLSi+HvPkr0MyF4c20BhHxrTnYFM2JFl4k51xlLxsl789qurq7ZuYnd3t7H/VGxG\nKStB2ONN/B2zNREJwIC+cfp10i/Am1wPfBdStfVkUELJ6/3VRCyXtQdkAQe4E55d7x6rtddzheYp\nIu29K7RY3Uh/XsebLR3ea2QKgOR8XxZILMChyF1+svmCBw8eNKW2ueschmqJoPgoe92wlpkScGCZ\nMynXuB9syMJs6MIkKGGSASAxqJ06zP36vj34bEFcXFwMFDq5KXPnlGXPirSB+6GPDAqOyFCAlmXL\nNrG5L++FgaXhJLBqpfHcnExV3SBzAncpn5XbLmePE/A46lktHFOtCdrO+d3eauF90bIAhyIV9c09\nJMMCHV78ZKWfzW5KfjmFF//54cOHzSrABWAWhhtwAVJcAvgJXBHM1fF43GZOrsc5zeDT7l62XTVh\ne6axeQ6ONT9QZ2UUGxCEL6mrJiGA61ZvzKC4Vryctg0Pwnsv+uD7RpEdwvQ9vspq4NnaFeGzanlg\n+dgqcNtqn1UXpEdscp4vAyAW4CCpZqZRPbkZCGTiERZk4NuHZvB6DQYkIDM7S7BZfMTszKBCsayM\nPi+5E+PxOFtbW9na2mop00laURNmYsBl3uzIvdv9MVnoPkHu8omdNmzrxbMhblKSwVZv5l0quVuJ\nvrr2wNaSeYwaRraCV5ex3p+V9eHDh+2Z+J5tfVS3w4qe3JQFMEBgQTEhuV1fhSzAoSOeUZLhA0Yc\nFqx5CgwcBj8mLw+ZmZJVmlgdzIgm4lAuyMdqdj58+DDr6+t58uRJq5BEMtGP/diPZXt7O5PJpHED\nNaXZVoL93boGpBeVeFX4ss54KI/PBWglaXwGv5uXcty7lt2Y3s5Rdc1IBYearOXjfL/0h6MJFoAB\ngKu/5XdMFDWnwiSuAWtBSL4DUhl8z5ROxuHBsaQYWV5ebpaBZy0/ZHME1F9wSM9JQrguuBrwCqen\np+2cLIm+urrK6upqu/4f+SN/JNvb21lfXx8oXnK7YIjvoTfDe4bmeK8ZMJhWJeO3XNduhWsw2PUy\nUFVLwX2EcG8Ov2JdOXuS+5uXtTjPoqpJTb0wt++9Why1f3uRk2q5GDB6QPhFywIc7hA/wCQDU9LA\nweDmQZLUVENyJuHY2JXCMDZFvcrQuQC4LwYHBj4VocbjcWPrP/roowYMDu+ZWE1ySzFMfiEemLYA\n+G0vTZlz4UdXN8Dv9LetlR6xa2Cp1gx9BDh4RaqTySroVJJy3jio0Qb+nwcU9fjeMTwPjycDzFdp\nPSzAocg8Yi25MSU5zg8UVwG3gMQhs+i2DKhwxB6NVTlrIlAl+Fg8NZvNBq4LoJOkFXCBF3EIEEBD\nrBxYLXYvHIWhbVY6IjHuk9qP1RSv/IUZ+uqLw/PwXV3HkWSQPVqfZQX1XmJaHQe9sdGTOolUS8x9\n2gOgGsXgHDWCsnArvkLx7F5fye19LeoAIMzIQEZ5HFJMbhYKjUajVlcRoot2uMgobkVVND63/04I\nMEnblNchQNpd+QPfPyDkLMkacnUdCUhE/OjK1/TM5WSYJ5LcthRM/tJGRzgcmpzNZgPyc555XxPV\nsELqs3Z/8Lf/N6dQpWd1Afg93qRaBtVq+CpcimQBDrekms8Wm391trA4IuBZt5qOZEiijIQxkxug\nWVtbG4CAB6ln98oL0A6u79Rhm9eVmPM5e5WLOKaCKMqK2Gzn3NUKQkldFr+a+FUh7Zo5nZtr9vII\nuG/4jLqZbu+aPoeJxTo+qhtV73fezF/7r0Zdeq7EwnL4CqTnW/dmOqccW0mcEVlnyloboa5PWFtb\nG4ADAx3LYn19fTDL0d6qZJ4h7YOvrKy02pe1DJvJvzrL0ge4NvAo9XMP3Ao8gE5l+vkMF8nZf1Z6\ngy8zdW/WrS6e+99cC+6VuZeeItZ+9jjogUEPBCp/MJvNBrkivd/ajfiqgSFZgENX6oBHPGgYrDaR\nHc6sDxZAcM4DTDrWgzkGchgmk0kuLy/bcuYkA0DwoEOZZrNZJpNJdnd323lcAt48gvMPvAqU+6wv\n94WzRXEpvB6k9oMFYHKkg+iFy8YbcOs9era12+F7AICw4nj5fisw9GZ4/vYY6QFjBQ0/m3ljbZ6V\n8VUCQ7IAh7nScx2qL20GfHl5eWCCo7SAAWa3STzM8frwAQf2lbi6ulnFmNxETbz4yLM0ZvoPf/jD\nLC0tZW1tLevr6xmPxy2z0sqb3N7duQ7QeYqBwE+Q0AX5CQjWGdoWF+I+svWD0jhb0O2wtcd9VNCu\n1ouzSWvat++5KrtBaN6Y8fF+rzIPXOo1vyr54MGhh+g1OmCltMnNoEI5vWmL/WKnNlcFcWq1Y+lL\nS9ekJLUil5eXB5vI1BnQbgcVnr7//e/n5cuXWV1dzfb2dp4+fZqrq6u2y5VNaM+481yFef0DKLp0\nvZOQkgwAwm4A104yyCa1aY4L59wEt4+/AeHa1goqXpdRLYfqFnDuOlYqmdsDrh7XYF6jvn/VlkKV\nDx4ceuKBj6mcDAugVsbcs11yM1i9p4S/t8k8T3AJkutBxRbyHog2sVEeAOTFixc5OjpqEQ9CqKur\nq7fCgGbfa7IXn9ksdwJUrf9gy4n3HtlndyZJt/o2x1dSl3MhdtPcL72oQa+gbw8QqnvhdwOcQb0C\nyDxrxMD8LgJDsgCHgTBDoQgMfGZili7bB8Y1cIIOVoVz+vksycAaqYPOA9SDi/O52Ancg2evqtCA\nCfUkub8KVABgva7P441tWAru8KjPd9eM7/t1tMEz+bw+qfdoIpI+4ji/uIaXedulcx/2FHWe9dQD\nCIR+7lljjJ8aJXmX5IMHhzo7GBi8CjBJUyA/WPx+RyJ6tQy9gMkKU3mMOkAIeXpQ1z0QOJddhCSD\nfSKoOkWbnHXpWRYQc5LU5eVlTk5OcnBwkP39/ezv7+fo6Khld3K8q1rNW4tR3QmDULW+uP95QGoS\nGK6C++1Ve8aaq9cxcTrP3+/xQj2i1haFv6tWAoBVv+9d66uSDx4ckuFgMzC46hDf15Vz1D20e0Hk\ngYfvUBqDvS6gMsFZZylMYZTQwNDLCmT2fPLkSQOW9fX1TCaTliTFGg3uD0UyuLnQK/UlPv300/zo\nRz/K0dHRIFSK5VTLxPfCntyTXZoktzJCeTbVSvBs7RL1vlYvbMi1fD5nvSJ38QG0c57LUtvol3mI\n5Hba9bsGEB88ONicNDBgOnuD22Q4qB0vZwCZ9QdAGBgcC2A47AnoOD6fZDDLmOew+4O/TxSDc21v\nbzfycTQaZTKZtOXG3inbIU0IVBQd6+jly5fNeqhWA7+Zt709bbLLVXMMkjTysloYKL6BE2Agf8O1\nKyq4cg6DSj1/j+ispKIVtmfx1fP1wKG+6vHvAiggHzQ4+GFa0VjYRI2FOoNwvP10P3APfmYn3q38\n1dXA3PdvkpvISI3L1zUORAm4xsbGRlMgV1c6OztrdSS8Ke5sNmtrMSiAW8181ovQprW1tVY3AnAg\nryIZRlWcAFYBgnOjID3roc74gC3WlDmL+mz5jRPWTCKaaPRzrBYdbajrbHoEZnWDOLaXS/EuAsQH\nCw4V5VFWrAaUhtkxGaZPE75DbNJamZOhb1yJwOT2zFMTlDjGilUrILF0nDBqkuA9c9gAACAASURB\nVLYIy66GcxF4d1VraiuwAY95lNFolK2trTx+/Li5FI8fP27l3XAnAKOVlZVBBagep1LFLkHPcuB/\nk7rsJMYybQM4II771avz6Wv3wpo1R4Ny/7aEOM7PtoJD7/57APKuAMQHCQ71QXgQmWvwmgUf75Ce\nZ3crQC/n39ZCz3Q1+VavhTjRx7s/4XJ4wNstoK3cG4Bwenqao6OjnJyc5OrqquVkcP9YHOPxOLPZ\nrG2Aa3fB5CvAhovisKgXWAEu9AHFdqu70TPHccu4DmBuwrXmYBDlcfq0nwX3UMeG+91jgYxVrJ0e\nQNRz1Gdb763HkXyV8kGCA1JNzxqrT27IQP72LAAr7j0mUVYGjs+fDENltjAMEPzGpmu1IlgGzkCH\neOQ+fH5HNTx7uj4E4ACA2HJ5+PBhxuNxWxLO5xVYbU05XdsEbAVF7n0ymTRy12HheULSGX0+Go3a\n7wFZF9yFP/IqUPelLQNbkrXfadPx8XFz6YgiVbC3S1NzUxgPBlZHwd4F+aDBAfFgqHUXCM3xf3K7\nIrDrK6yurt5inzHLmcWT4TLemldgf5wVmckwFbjmF9jdqAQqM7p/z31XRbALxIsIBElZ/I7+opKV\ny91BaHJtR1TcNtyWJK2QbuVeuJ8aGgWMHj58mJOTk5YaTsYqlpFrdKLANSfFXMWrrLckOTo6auSw\nl8knN/kaPZfBfWwwQd4FdwJZgMNnUpNYnMSEH+4NXTxDM0hdXZnZmRnEYT370D0zmu+91uDBgwcD\nk7i22VEPK5AFM7YqBP/XWcuAWd2HJM3tIvqBQkKCwkNwLucezPPV3dZeuLdGi8j8HI1GOTk5adaa\nozjwKrg6uEquQ1Fnd6TyHAhuCs8FhR+NRrc4CkuNSt117FctC3CQ1OiAE3uSGx+ewcTMSPYgYIEp\ny0DgnI73V0bcCo2iw8bPA4ieP54Mi6nUQQ/ooPTcI7O2K1Q7KtNj9PnbIMPxx8fHzfS2gln5rCDJ\nTXk8R3l6JrgViR2/jo+PB6X9zfHYYqLPHKqtESUfZ+CkT3l3non7l/bVlPmepWDX6l0DiA8aHHrM\nMQPHO0vhO9eHvbx8nXmI384gxNe10jpr0oBgxemFtMye81tzDSi7lcozu5OduAdzATaZTUCaTLVy\neRZ3/9kSIKPy6Ogoq6urcy0jWy3JMAmqErjVRfN9OLWbTYNru0z2midB8VF0g1cFb/fVysrKAHg4\nHmvPQGaS1H3Ab6r18K4AxAcNDlWsjC6DxmBjINXZj8VMnkXMFThe7gxLpM7uvRBYDyQMDgxI52Wc\nn5/n6Ogoh4eHLRIwHo9bTgKKCNuOS2TW32BlHoJ7t9Ji3UBqHh0dZTQaZTweN2ulWmd2g9wXNS28\nXscL2mqUiWdmsjAZ5iY4csL/FRh8XbctSSNAcSV55nX7ACewmXSs465af++CfJDgMC9sZJOPAei9\nIZOhH+7BxGD1LGOAwGx3roNnEQND9XtpW5LBTGZLx+Y7cnZ2lv39/fzoRz/K4eFhHjx4kGfPnuXh\nw4fZ2NjIaDQazOS81/0tLL53m930GSAFEXh0dDRYG5IMk5qscChvdZkMIFgOZGSS0OWICfkOAB19\nBNj0uJkeuVvBy/3BBkKAUrUK5lmB88KV7xIoIB8kOFSppp0JM+owJkNz1EuTGdx1RSCEXTI0Qxms\nXLuGVKv0fGCsGH9mpUquwWFvby/f//738+LFiywvL+f09LRVvmbHLga2ffvKX/TCdFZmvoPoI3yI\n9bC+vt7yMMxh8H+SgZnvawE+5kzW1tYG7pV5huq2wNvUsCTnrJaQOR5bbLYktra2mrWAOwMo9QhW\nrvUugsA8+eDBwQ+ewVCTlnqmZ7UgkpucCA9C3g0WPTPSMxft4vNq6fh3lbewXF1dL9Xe3d3NJ598\n0hRha2srT58+zXg8bjUskwwqTPf8Yn9eLR3ahMX14MGDNqtjPcCN1AxFrgOhWwk6FN997D7lOXjm\nBlTcf47qcF+VA7FbVfuU6yTXLhv5FbgYLv1fidOvo3yw4GCFqzNG3RwXqea+waNGHzyLm/CqM5CP\n7YGD21ln8Z7S2jXi+Kurm81o9/b28uLFixwfH7dVmy49j3Vjy8YWlWsg1OXeNRKAyY31gGtRU5gR\nr3A1r1LBCOIvGZKuJgDpA/IwnCJu3objzWPAm9A+2sr9fPTRR/nud7/bMkcnk0kL29acDAP51w0o\nPlhwsDBYGLwMRDgDzyJ1tp9HOtVZERO851LY1O5ZFXdxJLTD//OO+U2IDyuHXIQaerPb04uW1AG/\nvLw8yL6s+RYrK9e1KrEenF/g9GcU2RmSfGYAdp861Ly6uto+szsAMBweHubo6KjdM7/18vLV1dWW\nOj6b3RS/4dngIu3v7+eP/tE/mj/4gz/Iw4cPs7m5madPn+bJkyfNdUJsOXzdgCH5wMGhKoGz3Rj0\nDKgqvZneMfHKUyQ3PrUtDOf/oyj2dXttdRv4rjcI19bWsrm5mWfPnuX4+LgReZVk5X4BD8/c9dzV\nYrFl5AxTQJCFXsfHx1lfX28RoJ7b5gxGk6TVzaMt5gacrYh1wzJzVqCaA3JfAzCOaljgFk5OTrK/\nv58k+cEPftC4FXMtLFev/fV1lHuBw3Q6/bEk/3eS/yzJVZJf/uz93yb5uZ2dndl0Ov1rSf56kosk\nf3dnZ+effCEt/oLEBCT/OzEmGc7UBgfH6z0wkmFtguq+XF1dDfL+k5uaBibTuGZvkPUAit8ADt/8\n5jczm82yvr6elZWVbG5uDpKFHHnxrOoS8dyLLSQUl2Nww3wvy8vLg5WgAARtr24bIMm1OM7thHcw\nINNeIi1YfnVDnpr5StSD39W8ip5V6PbSLq9F4Vl/XUEBeSU4TKfTh0n+5yRHSZaS/HySv72zs/Mb\n0+n0F5L8pel0+ptJ/maSP5VkLcm/nE6nv7qzs3M+77zvinhGdkZjNbN9rK0BPq/gYMXxgHbGJf74\n6enpQMnsayfDBV8Ggx5ZyXmSZHV1NZubm+2cW1tbSa730GQdgmduLAaWYGMiM+gdsvTgZ0avRWds\nSbnq1PLyclZXV9u9YcIDMNyPCWKv9vSOYslNSjJWid0drgGY4GrxwpKqiVMGAto1Ho9bWz/66KOs\nrKxkfX29AZ7Hj8fG11XuYzn8gyS/kORvffb/z+7s7PzGZ3//0yR/Icllkn+1s7PzMsnL6XT67SR/\nIslvveX2fiFiM5b/GWA1MeauDDqb4lgcJtiYpWDyj46OcnR01JZAk6vASsOe22CQ6EU6PHOxijJJ\n84+vrq4aD2HFdF0IF2vxqlNMcIvNfa8xsbnfc59Y47C0tJSzs7NB/15eXg4UzVaD80+4Z+c+0G6H\nZeESDNCAgwsAG1jdxyabAcxvfetbWVpayurqasbjcUv0qtGor7PcCQ7T6fSvJPnDnZ2dfz6dTv9W\nri0H3/FBks0kG0n2Op9/bcQA4YdbrYTqThgYeDc4cOyjR48yGo1a1SIyF9llm8Fnl8WvXsjS7H0F\nCO6J5dbMmEQcUEzOjZKgYMvLy4Ml3VdXV4Paku4bcwdecOaVj8y+mPl8ZyW3gmKF1QjF6upqi3zg\nktjfNxmc3CyWw/Ih7RrAABSry4DYZeR3SfKNb3yjtQmwel9CmMirLIe/mmQ2nU7/fJL/KMk/TvKR\nvt9IsptkP8lEn0+SvHiL7fxSxO4DM69lY2PjrV1rfX093/zmN9/a+XrCLAcw9WRtbS3b29tzz7G5\nOR/jHz9+/Fb75Gd+5mde63gqU70t6Sl0bxwkyU/8xE+8teu+q7LUY+J7Mp1O/68kfyPXbsb/uLOz\n8+vT6fR/SvJrSX4jya8m+U+SrCb5zSR/8hWcw7tV9uYzMflmfmF1dTW7u7u3CoaYOGS2c8QCiwCC\nk7AeG9R4RnN4zSXXary8tjXJLeuBPANbQf6+Znomw1Lxs9ksx8fHrQw9PvfW1la2traysrLSisTw\nOjg4yMHBQauSROm6w8PDXF5eZjKZ5NmzZ3n69GnW19ebC/Dy5ctsb2/n937v93J1ddXyB0goc30I\nMh0d3bFb4IhJzwX0M6rJVvOkum8rKyuNdK2h56+ZxXBnY183lDlL8t8m+cXpdPooye8k+ZXPohX/\nMMm/SLKca8LynScj7xIrUzXj7Yt6kNVcAMzi5CYZ5/j4eFB4BNO27sI0z8xFatto8133U7M/rTyO\n7XNdEn8cBmTvThOhBsYKjuYjWMp+dnaW0WjUbS/95pWm5gWcTGaOh8+dmt3L0TCYVDettsN9XZW/\nZ018zYDhlXJvcNjZ2flz+vfPdr7/pSS/9Bba9JVKZf/rIKkAUfMSPADtZzO7GhhsMdhf5VpVCWs7\na5t6uQ5uez2+fu9wZnJTMMVLoR1OrOCA1eO0cpejW1paytHRUdbX12+FDJGaIelj6pJp93G1Fiog\n9P6f9/zv+v9Vn79P8kEnQc0TK1k1Fa1czIp1hyvHyLEY6u7TdXFPMtwmz0pSt4nrmbG0x9JzO+q9\noeQGD7P25GJQwJVVj163ADCYmCSkyWekLh8fH7fajz0z3KRuzadwyNUkp7M0sRh6L5/zVcr9ISj/\nq2QBDndIVUD77s6i65VRc84/MX4vbOKctUpRXbkJQ+4ogtvnQe/oSJJbSl4JV9+Tj6lWCWXgXHjW\nbgkhQp/bu23jzrjsv9PFzQv0QoEGPvMLPYuhPrfqWljpe1bCPFfnQ5QFOMyRCgjJcClz9dttTeAD\nOymopuR6ybdTj33s0tJSC91NJpOsr68PwnUm5O5jBVjqTMo9+Z49c7t9Jv7IPcDScRrz8fFxZrNZ\nywLl+F6xWa5bXSve7Ra4PJuzEnvn61l/PcDvHfOhywIcXiGeZU1mJTc+uddhkKHHjGs3gQFnEKh/\n2xwHHNbX1wdWBwDhBU8GLkckqrVQFcEZoLS7kq2uCO0oDteHNMTacWSB311eXu+36ZRsuzm1j3wf\nyQ0w14iErbnqVvkZ3gUSFSwWci0LcLhDrDTJTSYeCm3SzcRbXZNgnxcl8cY5nv1QJD6rHAQvMhVt\nVnONupDJiUi2GJJh6fWe5eDIQfX/mbmxZJwI5l26AZSTk5P2nd2d6lbwudvSs9jcrxx/n9B8BYUF\nMPRlAQ6vEA8Y1jy4enO1ANhHoVYEsu/OKsFKTlYz3sBweXnZzHMKpxBOw6rgeN69g5StgQpWTmu2\noqDIrFh0SjNtJPWZYyt5SEEU+m1paamFJrECDKKI7939UiNFtU11zcurXr3nvJBrWYBDR+bNPsyk\n5hds5layzmY15CSRC6f+onyu3Vg5Dcf5vYmO6w84ryBJy1sw6WdrgPPVRCh+Y3BgU11enN/1E1HM\nHmGJu5Wk/V1ne1sBBin3N585ZFyf231AoccpLWQoC3AocpdZalO8hgkh3FBUz3AotLkEwKDuTO0w\nXV23gMJi0jtHwu2nbQYLm+L+nYGF89udAhwoRusaD7TDtR+5bq3yZIvFNQ98TA1d0rd1hq/cCb9x\n293OHvnI/wuZLwtwKFJJvUqYJTf7K1TirgIACueEHy8RdqFXm+49PsDZf1ZMX9+8RXJTH8GVn1im\n7ciKZ/iqSKzLAAS8S9TS0lJrk/fpqCXgACRe1FEA6CoJ2SMWaR9tqzxFfV7VnVq4E68vHxw49JS+\nJ3XAmvTyDAubT1Ugb43GbzHlSRRiRmYNBSsEnSdRyT+ToIAOn/cyApMMshs5DmCrCVu93y8tXUcf\n1tbWBvtS1jUYuCacu1o7mP8slSbaglvibQVNnvr+XQSnuhoO5xrcagJUz4W473j4EOW9B4d5bsJ9\nWG0fWxlxm7dW/loVqM6C9fhKRnL+mpoMCEF6MttznQooyU0thlqfkmPq7Ovly7aKcH+Y9VHUmvdR\nk5p8boeBidiwjd3FxUV+/Md/vJWyq3URKpC5/Y7C1FDoq4BhIXfLew0OTqLpJcrMMy89M/FiUNff\nM3hRLs+aPW6izmjVz7Zy15wDb1xTCUjzAJCLdf2CAaBaDHUFqNuMxWPLIbnZxMbXr4lYBqSLi+s9\nNNnfksKvl5eX+eN//I9nd3e38S+ucm3ri/8dvu3xC9zvAhjeXN5bcKjKfVcWHe+Vb6gzrN2K3nkq\ng94Dh/pu87j3znGY1uYWekA0m80Gy4lR4HmgZHCos7NnZCwGjsVFIe/D56r3CTDQXpZ37+3tteXc\nSfL8+fNmCcFJmGz0szUwkmsxj3xcAMObyXsLDkiNu/dmcgZgzx+tM3n9XY/gqnxFvW49f+9VrQiU\nhpnbiuGkKf8GE71yC7121jb5f1tH5gRwjeAKfB33Pe2Ht8Bq2N/fz+HhYQOz58+fD85hK8XuWU1b\nr/3re53HMdS+WMhteS/BoedO9GZj3u8iqyo4mIyrM1Xv2vXvVwlWAu+87MuTD+HIg5cz0xbyCayw\ntMf3ZAB1f3Ge3orQJC3ignLzvQHEYVT3T7WwkuT4+LhFbQxAdyl4r/9qO22hLeT+8l6CQ5Xe4O/N\n/hUg6uw0b3ZE7pqJ73Jpep9bOeelUtd1HvbBkwxMc7fHJjnnXVpaGmzkQjo07an8SJJGUELCVh4D\ni8O8iJ8F1z8+Pm7tI1nMe2uQ6FVBrlo0vec4DxgW1sKr5YMAB4v5gzrY+L4OKAaSZ8S70nTvw3dU\nAKhEmo+jvTUy0atoBM+QZLA/hNtZ079toTg86sQmFNTtXlm5LkiLy+BrGEirxcVxLnaTpIU44SgA\nFsDLSj+PO+m5dfexNBZyW95LcPDsxv9VeZO7zdN531Vr4j4+PO/1nD1wqudDAWpiESFGl6ljdqW2\nwvLy8mAfTPIrXHWZY7Ao5qVEAzq+R9yK2WzWirlwbLWyvPrTAOd+oKp0DVn2rAGHRitIvOo5LgDh\nfvJeggMyz23wDHiXVJKyZwXM4yv8/TyA4Pw9spDPnBhl68EzsUuqQRIm1wrpXAJm+vX19ZaujWJ5\n8RaWgBW8N0Mz89NOk6I+Nrm9gxTRF5emp64kINQjgJF54OC+nfcc/WwWMl/ea3BIcmuQ8llvJp8X\nUbBpXwesB+e8c/SuawWyP10Bgu8ABywIlAPrwZu24FacnZ1lf38/e3t7rSK0d20ajUaDCk5LS0vt\n3MlNhIHkK6dp13uvCu12c/5qPSRDYhI3qPIHPb7jLtfqrufZc+MW0pf3Fhzm+fDJbQugWhLVbahu\nyDxgqOe+6zwVIDimmsdVMbx2w8k/3iQGcDg+Ps7u7m4++eSTHBwcZGVlpe16xXnx863cjoDgWngx\nVc8Ccji1uhVVIeuCMs7l+6lWAd9V3sV9dd+IxAIY7ifvLTgg1a9NbocZ/d7724OvmtrzSMp5bbF5\nXVOOqwleQSi5KRfP8U5QSjKoI3FycpLd3d388Ic/zP7+flZWrveaYCs6VkdyvDMk2VHK7ZpHtJI9\nCSkJ2FRT3/ft/AxHYQyGgALZm/y+unW1bf689v8CGO4v7zU4mF9Ibi/a6QHEXUpeldUZfK/iMTwo\nPeMZICw909quBUpri8Fmf5JBJuLe3l5T8vX19WxtbbWNbZPh3pLs5+nVnD2AcD/xP5xCz+S31VGT\nt7i2QQV3CfI0uVkO3gvRzgP1BSi8mbzX4JAMAaJyDT2QsJJXkJjHjNeZrM5e9f/q5jhJqNfuGssn\nOgDH4PJsnC9JqxxF5eilpaW2eS+VqByVqFW0WUKOAvcAgvtxxKQnBhQnRtkSIKxprsVl6vzbyiHN\nA9napwu5v7z34JAMldQgMY8HmGcJVJOX39XrOATI5/W4OpvVFOh6nJXW/ANuArMuSsQ5fT6UC7Aw\nOLiUvs9ll6ZyCAYHiE0v0+7xDQCEwZfvSKoy6cmy9uXl5UHNTbepZzFUq2EBDK8vHwQ4IBUk+MxA\n0CMTkUqy9WZJA8RdvnE1iSsZyG+4nreeJ/wHQLAbFXyDfXgUzrUk+J3BAYWkfB0mfd2erwIE7XQF\nK+7J/QZo+J59j0na0nADHPUyl5aWBkvh3af1em7XAhTeXD4ocEDuY3reBQ53Dco6U/bOX39TV3F6\njQSKBzisrq4Okpas6LgaCMQj4curq6vGTfh3s9lsYHWwyrKma/fAwYrKec2LeCXnPN7FGZ2AAdYF\nZKvL0fv39Rn0+n8eKC3kbvkgwaFKtSLq50iNeNS/ez5vDwx6AIOPjVK4OKyjEg5lMssDFKRD20wf\nj8fZ3NxsMy+WhHmGq6urZiEsLy8PwoXUmXQ7bDnZRcByYGerq6urQem7umiN6+GSUB0LcOAekptN\ngCA8fZ5qjfWIyHlcyELmywIciswDinmfWXouw11SwaYqna+Lib+2tjZQXBQGgHj58mWzRB49epT1\n9fUGCmQ9kh2Z3PAQ1LZ0PoGJUs/0tLMuCAPUqLBNX9XK1XZjnNHpfTloG9exS2GStvf8XuVOLKyG\n+8kCHO6Q3iCaBxD+rPrX9TzVH5/NZgOTvC6AYrDD5pughKTjur42xWGTNHckSdt/0wlOKCqRgbvI\n0XnKRb4DlgPnNK9hcKCNXgtS8xjoB+/41Yve9Pp5YTV8PlmAwxvKq0DiLrKS7zmHk59qyA7FRxzu\nW1lZaXtg4Iu7lB0ZkEtL16Xe7K97XQQzM+nYWCS9tveiAHxmUONaPXIVIMSlcKTD7hT94/M59wFg\n7UWC6vPoPYOF3C0LcHhLYrC4a6bq+cF8ZmKyZ0EgBgiUzTtXW8GcJOX21ZRkXA9mcl+T9rhUm9vt\nCIn5Cb8bGFydyhmdWAcOo5p45XgXwq1uT+95LOTNZAEOn1PmMeY9wrL+pprEKEwtVOscCMxulLhG\nV4g+JDfLpPHrrTzOR/BsD2HpEvsOH3Itt9efocCc35WkndTkzzj32dlZO8a1L+1OYGlgYc1ze15l\nuS3k1bIAh88h86Ibdhnucw67F/xtgPACJacge3MchxC9DwTK6BL41TK5urpquRJEDM7Pz3N2dtZm\nbl4GlKqQPSDi7x440Fe0F3CYVxfC95Lc3mz3VXKf57IAkBtZgMPnlB6P8Ca/tTXhNGhi/Jj+vVWR\nAAQJUXznhCaUy8SngcfZkS7/xnEXFxcNsKq1UglWXB5bDnxnghJQwg3i+rYmTGR6TwtbDXels1d5\nFWDfF9DvCnm/L7IAhzeQeWHOZDg73WfQ9I5x4RZKuhGmrMuck7REocePH+f09LSdE6CwCe7ZmPba\nenBGpRdeOW25AqItGsDNs30FB47j3FgOztHgPFg8/tvX64UzF/J2ZAEObyiVL3gVGMwLt9l/r+dm\nFsddYCY3r4ASkqewurqak5OTJBkkDdXScCgfboNdEu9paUulWi0GFh+TZAAQTpLimrPZrFkNjoxA\nsgKMbrfbW+//q5y931c3ZQEOn0PmxdgrUNT/ewOmHuNZlrTn3jJnlKOuo0huQotnZ2e3Vm/WytJ1\nsZaJQSIXPd6jR1rWkKXBoeYnuKS+15BwH263SdoaRn0XpGdNzpsUvg6yAIfPKXf5n/cdGPOIMjP0\n5h14t5uA2c3ipSRtJWNywzvACXB+/57fGBxwSbgHhyxrBWuE6/T6qefOIN6B24VwCbe6MEwlQl/V\nv1XuA9b3Peerjv26gsQCHN6C9ADiPpzDfQaYcwPgHbzjtn17V3hKrpUN0x2yDxDwblIoMtGG6vdb\ngWmriUoTpXZXqni5uYGBdqyurrZduEnlrlEV2sX7vD6e17eVG7rrd/O+I7x61/F1DLzquu+iLMDh\nLcldYc15n/WO6R2P0tZNZAALWxiY5EkaQUlY0iY91zL/4EpMboMJRX5XQ5wGB//WGZu9e7YlMx6P\nMxqNBkVm6vqNJANQ63E4bmPvGfVA5VWg4PPVpK96b/V6875/12UBDm9R7gKE3nEc23MpGIw2/W09\nELmgpqQThHArRqNRq/h0cXGR09PTgck/m81uRQPs39d8A3MF9eVjK2Fp8bX5n/Tp0WjU6lp6xWgv\nn8HvlSSt/7+pC9G7T/qsZoT671dxUV8X62EBDl+A3Cfe7mPnAYTPYfeCMKP5BxY7UQzmwYMHGY1G\nOT4+buXiABHPuLYMzCvU+gkmE2sole/NgVROoKdkyU3dyOTarWBnLW/iY2Wqadn0EedDaSsoVVCp\nz6d+ftdr3rP9Oij868gCHL5AmTcQe8fdNXtxDNwCGYxYEIDDyspKTk9PW9EU/PeHDx+2tRfn5+e3\nZmArvAvIMHNbIQESzH4+65GjBh7fT414uJ9q4pXBsVoAPdDpAZePr+3o9X+vjdVNqe7Jff/+OskC\nHL4keZWP+yoLgmMclYBLSNJIx0ePHuX09DSTySSPHz/OaDTK0dFRTk9PB9WiXJOSkCng4CXSSZqi\n28VxqNJtd5SjmthVACG+Pzk5GaRGz+MxrHDzFHne8f6Nj6t8yrzz+bzVAuu5L183QLAswOErkp5V\nYaKrSlW+x48ft/wFrAdSoAEMkqKwHk5PTxuIGCBqDgVWSU1cot2AFG2uS8Er8ZkMQ6AOjXK9JDk8\nPBxsylMBwOBkF6gmYrmP66t3bOUvnM/hvvexBr/eM63nnffs32VZgMNXKL0ZCRO9R+bNZrOB2Y4i\n8ZuXL1/m5ORkkATlvIGVlZXmikBEsjzby8M5ZjYbpl7TPsRgRdsqh4HC+b6Sm7wL2p0kR0dHLeHL\nUZN5ylSBocdPuB0c63wJn7/HYcwDpQoMPtbydQWGZAEOX4lUUqvHbDMY57HvgAP5CrgES0tLOT09\nTXI9K2M9eFcsIh5EP3pK5pJs1Y+vhKOl95kXUvmcABSWjsOtNXzaO38vY7OCmCMslWilLVgBtV6F\nr1cXd/m59X7Te55fN1mAw5csPWWvxNxds1OSudYDsz7gcH5+nrW1tUFVZ47r5Sj4mvPaWdtlTuEu\nJh9BCV03AssBgHN7ev3i9Rp1/87adyi1M0xtGQG0bts8juMuYLjLQvg6AkOyAIcvVeYx6z0Log7y\n3qD34iSXmwccHLmw9VALx1ixzBnY556nrPMIO0svs5FsTq+t6CVguV+SiXC01QAAHCFJREFUG4ug\nt+BrHjAYSOBlzs/PG6dC0Rw4GO79VSBd78nv74MswOFLkgoIdXD3lIwBXmP7FSBM4pngOz09zWg0\nagVlAQgvje6Z415w1ZstHaHoWQvV9K+WiVdeUk07yWBNiBW7p6S9tGqkugC2GsgHIbTLtn+OsgAW\nbvM8S2EeKfk+gMQCHL5EMUBUH/+udFyHDutqStwLl04DHE5OTlp4c3V1tYU1vd9kVXKv47iLqPP/\ndzH/PQ7D2ZiQp8k1OMBFzMtrMLFosHWf+FjaAGienZ210vmU8Sdfoy5r790f7zV7tFp39bl/HcFi\nAQ5fgtRBVl2L3mCvprRn9Kurm2Kvdds6r8Q8OTlp1gOrNUej0aDadAUI1jrwNyBy1731OAsrw9XV\nVUv3dnSD9rMWZDQatdoO84hIg8O86/a4BlyK09PTHB8f5+joqIEDgGSC14BYrTy4kh5ZafCu/fR1\nA4gFOHyFUsm+HsHWM/ddSZoBSdq0GXdmyfPz8+ZWjEajlgzlgU076gC/uLjoLnByu00k0tbZbDZY\nhelU7Br98HZ4XiLuGhFYSf59z5WZ53qYrD0+Ps7BwUGrm7m0tDTgZtyHPBOTt+PxuFXcqntxAKq9\n6MbXTRbg8BVK9akNEA4xWmmpDGUg8JqLR48eDUKDp6enA2IScKgKzPU5X40M9GZC3r2UHMVmdait\nk7s4llo4lr6gn+wm1Jl8HnlbiUj4hqOjowYOXJfl4gYM35vXsjx79ixHR0eDDFO/AxJ1SXx1vd51\nWYDDlyg907eCg8NtteqTV2aS4GSXw9WTkjRmvroWhA7hFXqcAorqqEUPIHAZyMzEOsBVYNdsQMIl\n6QwOa2trDQCxWJBKUFZgqMStFbuSkefn5wO3Aj6GrFAAg/PDU/Bb+oJUb9e2xD3hPUmzhNz+r4ss\nwOFLkGq2V/LKvrSJMxY/ecapG854ezxyB7yTFbPl+fl5m81XV1ebUnuGSzJwDWDtnR5t8cx6enra\nXJgkzTqBDyFFu7pOPbbfCt7jEO6yGKp1UfMbaCdrTZaWlm4V0DF42vqiVH9ybZXB/RiEHDlCvAal\nWovvsizA4UuSCggm+yrnAFcAQFhhHzx4MEjgATBQRFeCStJmw7Ozs6ytrQ3yC1C2nlIlGYBGjVIk\nGZjsZ2dnOTo6auBweXnZakt4Fy0rrS0izlc5EL6vprn7tUYoKk/gtSe2BJxjUS0N7gFwZV0K14dc\nrcTyvMgN9/F1AogFOHyJYkDAN/dnDGxmuMPDw2aqm3hk8NpcBxxcCQrltyXiytJJ2oB3dAAF8YpN\nV3Tq8Q24FfAdS0tL7X9IPvgSk5c9YtHXsPK/KkJhgLO1UCtnecVpBSNXw3Y2JW6eyVrawvOp7bO1\n4zHwdSEqF+DwBck839KzSHIzW8J28/nl5WWOj49zfHzczF94BWYtCEgsBxOTSQbuBbMmROajR4+a\nggBUHuAo17wNa6u147oSEJKeiQEa+Ia6+Im+qbxCj9Cs/VjdjkoizksXd55ITcnmXJUUrms+zAvB\nYfB9b6GW2/+uWw8LcHjLch/CqUYCnApNVAHGnCXW+LfMargQKCXHE+pMMtgty7MfDD3gYfKv+utO\nCDKYILYEUJK6eIrzmRsBfKq7ldzs+l0Vssf2V3IXywuCsVaU4jcGBsDBoOh21+skN8VuAET6CmBx\nH1RLgXO8y8CQLMDhrcmrQKHOEvztsCGz+Wg0yunpacbjcY6Pj3NycnJrG3rCbt4FC0WGEHOyEe4F\nQFKLvNQZzQDhYi9IDUtaiereE8z6KIqvXWdj8wWcr6dENepDHxsIiS7QPxUUakFd3ApvKVj7xsAO\nOMAPLS8vD6IudvsqEf11AIgFOHxOuW9oat4AT24TVePxeBCGZJD75dna6dAVHJx4hOIQAq2l3+t9\n2bzvmfwVIHAnSLaiYGytCWElRZEQh3FrDkYPYOfxDT2rwcBFclctl4/VQFttwfhz2nNxcZGjo6Mc\nHR0lSXOr4Hbsmph78HN/V2UBDp9D5gGDB1BPatSiJgOtrq5mPB632DozGXUie0rT88dZPMVxlQOo\n5r/bPy/RqDej2mVYXl7OaDTKeDzOeDxukREDUAUJux9OpuolaFVLwv/3chsc+oSnYfEZv6/Ka97B\nwMJ3yU2tSwrs7O3ttf+JCGE51XySu+7nXZIFOLyhzGPYe7Oqj5v38jGsNwAgiLufnp5mNpu1XIVK\nFtZkJZOAyTAV2Mf2CEffY41mzHMnIDpHo1HW19czGo2yvLzcZt9e+rTTonEFCIf6XurfVariVYvI\n/er1JckNx+EokgHcrofDnPTnyclJiyxRRIc9OGy5VMvBbtG7CBALcPgcUkHBg6DO5v5NJat6ipek\nlZcnr+H4+Diz2axlOhJ5YIZjhkK8irOSgyhBvRfa0GtP734dEjQwrK6uNmunhi5ryI93Qq4siPL1\nK2nZa6P7tnc/hHvX1tYa0UtUAgB2HzkRDFcNQHEK+suXL3N4eJiTk5NcXl42zqiGf3FDHA7tTRDv\niizA4Q2lmrKVB7Dy9MxoZkwv1qnhQUxrah6w4MrJTl6PUE1f+9TVfLdiVtN83oCtroyVDoJvNBq1\nlOlaccm/reCU3KwitStUr+W21O/NZThd25YUfWdrhsQzWxKABbkZ/N6WA0KBnePj4yS5lTBVn4vv\nwZbbuwYQC3D4HGJgcCy9xtV7RJzDaJ7hOadTp5ntCD16eXEv0aYqrcnK3sxrqcBQla8HGJjrWA6A\nmFd/OoejKglKCes/mw0L2/baWjkPtwUy0Htg8D0hXGpHOE2dZ4lrQ6QGHsS5IZUwJRrEMY5YeF2F\nLUvGg4HyXQKIBTi8oZjEsjI7P59BZUKMAcMAvrq6agt9GCiO1TMIyX9wTkSSwWADBBADTnVlelJ9\nbX7rhKQKEJjeXtlIPoaZ/nlgA4gkGYQEAbV6vMUA4f4lGmFgNkB4HUp9jldXVy1dmuQz0q1JP+d6\nnMthUYDHaeOOiHjC8DN5F3mHBTi8gVTz32m6rhnAYGdm8LJqM9lYB8yWAMvJyUmurobl5V3Uxey+\ni6Q4b4BBicLdBxw8y9H2an0w6zkHgv0xkqFr43ZUN8smPArpDE7P0D1uxmQrs7TTzA0wbhOKXLMm\nvcUgada2DillBzgAiuPxOEtLS9ne3s6TJ0+yubnZrCiDdCWHq4X0LgHEAhzeQKrpj4Ky+Gh3dzc/\n+tGP8uLFi5ycnAxIxPX19Uwmk4zH42back7yEs7Pz3NyctIISBSPHAJmL2c94ttDoCU3lgPKazOX\na1Z/HTHZaLPX4GALiL+dJVjXIdTkpuRm5WOSZuITgvUsW90Lf+YoCACI61VToO3SOFvTFoRT2flN\nb4UsgD+ZTPLs2bNcXFzko48+yscff5zt7e2Mx+PB8wBg6Y/arneNe1iAw2tKJR4ZxOfn5zk8PMwP\nf/jD/Lt/9+/y3e9+N8+fP29m8urqaiaTSba2tvL06dNsb29nY2NjMGswAKlUdHBwkOQm09HFZD2w\nAKYkA6WsG8PUmb4OdAanE5GS4b4T8Bw+r4HH5jbWjQGU8/lahC5dD8IzuhUHMd/gDEjSw1HceSHl\nHsFZrSrCsJyLF31L/z59+jSPHj3K8vJytre389FHH2VjYyOPHz9uQODnYnenR6q+K7IAh9eQOlt5\n3cLx8XE++eSTfPvb387v/u7vNnCApNrc3MzW1laOj48Hac8+3/Lycl6+fJm9vb08f/48BwcHjeSr\nSocw89pXZ4DVPSvr73tWA8x7XbnIADdvktxec2AlAxjw2QE4fuPvkwwyIys4zLMc+Ju+BKy9kS8K\nDRfC9wCJS/x7ZasBEo5iPB4nSXMLOf9kMmn5Devr623VK+1yvzutvTe+3hWAWIDDawomoE3Ns7Oz\n7O3t5Tvf+U5+7/d+L9/+9rfz+7//+3n+/Hlms+t06CdPnrQ06DrQbVqfnJzk+fPn+fTTT3N+fp7J\nZDJg4/1bfkchEhObSQZkWI9s5G9eWC1wHT6OGbQuVKrgZgaedQfE/23yc04nI/XqPfRIyZ5i+Tfw\nFhUgqn9Pu13ngVcte2dSOMkglEx74DoAHvoAy8NrXyoQVOB7FwBiAQ6vIZWIZGCfnZ1ld3c3P/jB\nD/KDH/wg3/nOd/K9732vuQXHx8ctA6+y07DjpBkfHBxkd3c3h4eHLW+g5ihgYpubIGSYpAsOvgfO\nY2vk6uqquTNYIYCNi8xCqNIGm/ReUAUw0DbuwXtiOATstrmNNZGsmuPJMI/A9SlMwjq3xKBhgHCV\nKFycGpXACoF8rRvxGDQBGFty3oDI0aV3AQyqLMDhNcTg4JfrEjLzMhPZV9/d3W1l3FDay8vLHB0d\ntXg655jNZplMJk0ZHUJLMkjbPTo6ymw2y+rq6mBmrtEJt70q1tnZWfb397O/v5+rq6vGymOdmBhl\nMZWXK5MhicKhaPwWU51wbCUcaYtdlB4Q1GdhV8eAw/FO0LI7RB/hRlC2n3azrwXkanWnejkj5hfM\ntSDeF8PtexeBIVmAw2uLAaKy6A8ePMh4PM729nYODg4acJCpd3Z21kAERbq4uMjh4WEz6/H1WbjD\n4iWHw5LrVOOjo6McHh7m+Ph4kObr4qb8rob0zCFcXFw0i4W22T0girC0tDRI58ZyIUrg87GaFGAg\nB4LoQC8C4VWaNTcCsVtRf2vA9bJrhzhrXgKzOQAMX/D48eNb3AvXpz9dwo/vKri5rykGw/WRdxUg\nFuDwGlKZ5eQmCWh9fT0fffRRTk5OsrS0lMlkkk8++SR7e3stn54EIUxOb3jrWQdQ2N7eztbWVtbW\n1gbmJy7A/v5+cwMcTjSjXttdrYbZbNZWFe7v798awIAD+zSgMLPZrH1OIheD3GnTDuG6NJ3dGhN1\njnz03KHe8+A5WOkACMTgAIjazTCAAdzLy8sN5JxLklznZDjcyYvxwDOgj3FdzL0gBsJ3SRbg8JpS\nZy2Ufnt7Oz/5kz+ZtbW1fPTRR3nx4kU+/fTT7O3ttTLoDBwnNVnp+Y64+Te+8Y1sbGzcOu7ly5cN\nGI6OjloCUC1Nzyxqko93BiTn2t3dzf7+fjN9nbxDQtbKysqgOCuFcDk/YASHgEuE9UM0xq7NPHBw\nPoWl/g+govDet8P5H4AASu8+4NpU1iJ56eHDh+33kJa23GoeBM9hbW0t4/F4AALVjXI4910EhuSe\n4DCdTn87yd5n//5/Sf5+kl9OcpXk3yb5uZ2dndl0Ov1rSf56koskf3dnZ+efvPUWv0PCwGZh1PLy\ncjY2NvKtb32r+fAABLUgGQzMck64IfV2a2srz549y/b2dlMqfnd1dZWjo6Ps7e1lb29vsBcmrohr\nRzIY61oPFP/k5KSBAynCruDENbF0HIYlzRgF5TfJzXJxCr7AqbgN9J/Bgd+aOLwLJDgX/QnAsggK\npcRFct9YuR2luLy8zPr6+oCnqNd3n7okHffg1GmOd0r3vIjRuySvBIfpdLqaJDs7O39On/3vSf72\nzs7Ob0yn019I8pem0+lvJvmbSf5UkrUk/3I6nf7qzs7Oee+874uYAIR9plTYN77xjfzUT/1UqxQE\nyeXQJOfgPKTirq2tDYABYabf399v1Ycg0/gN53Vo0BEWWw2Hh4fZ29tr7hDrAh4/ftyUzhYBvMjy\n8nJTKI7jvviN08Sd5l3TmE2g8t4LwSL+vxKsvu7JyUnrB46dzWaDkC/WFVaG14OMx+PB1oOurEWh\nX5PS7JbljYgBF1tt9fm/a6CA3Mdy+JNJRtPp9P/87Pj/PsnP7uzs/MZn3//TJH8hyWWSf7Wzs/My\nycvpdPrtJH8iyW+9/WZ/eVJZ/eoHMwuza5I3dsESGI1Gefr0abfYaSXbbEkkw9V/RAHInry8vN5E\nhZTs0Wg0iLsz0A0MVo7T09Ps7+/n8PAwV1dXrUAJYbrZ7KawjNdMOIZPmBMh1u91IM4ktTvB/dpi\nSIb8gF0Mi8lKhwtdeh/XyGFFKjU5CoRVgRVwdHTUABvQ9TqMJK0QD1aT+9kkpMGtpkxzXz0+5XXH\nZ+2XtyH3AYejJP9gZ2fnH02n059J8s/K9wdJNpNs5Mb18OfvtfCgHQP32n6v0JtnKvdCpAxEk1uE\nOr0J7MbGRra3tzOZTFr4sZdU5DRkh1DhQyBVx+PxwDWBpGMH7J6SeoaE+Xc8vweK/Ib7Tm4yBwEE\n5yZwnZ6Pzr1xXsKqtYq3XaqHDx+2cnaAn92e09PTRrICmFbk8XjcIjMGQXMlvchF7QfaXPu0J/cB\nkbcJFvcBh/83ybeTZGdn53en0+mnSf5jfb+RZDfJfpKJPp8kefHGLXtHxJ2LCby6upqNjY1bx04m\nk3z88cdfaHu++c1vZjqd3uvY7e3tO79/8uRJfuZnfubOY+BQPo9QKv9V8rM/+7Of6zpfpnz00Uf3\nPvYb3/jGW7nml+1+3Acc/mqu3YOfm06nH+da6f/5dDr9Mzs7O7+e5C8m+bUk/zrJ35tOp4+TrCb5\nY7kmK79U+TwmWhXP7Mw+JP7UOD6z9u7ubr7//e/ne9/7Xj799NMWAvQ+lfyNVTAvLg7Tv76+nocP\nH+b4+DgvXrzI8+fPc3V11aIa29vbbYUns9+3vvWt/MEf/MHgXh48eNAWA+3t7eWTTz7J8+fPc3l5\nmclkkidPnmRjY2NgPZyfn7d07rOzs4zH42xubmZ5ebnxHisrK5lMJs214Z58P5UQRXBPfvInfzK/\n8zu/03x1hxZJnPIz5pwkWlF7AXdhNrsuq7e3t5fd3d0WNSIsaR5hMpnk6dOn2drayng8Hqwb8V4i\nWH+j0Sj7+/ttFW7NUmXyYGEd/cS1JpNJI2krp2Kr422N5TcFlfuAwz9K8r9Mp1M4hr+a5NMkvzid\nTh8l+Z0kv/JZtOIfJvkXSZZzTVh+6WTk2+zUu2LrdgXwZQGJyWSSzc3NNnDIurPZzWBzNSjaT64D\nW9hDHuLj4qZsbW1la2urVVOG76DdpEEzyLkOIUjWIECCejm4Y/Yo6fLycouKoAS4GvafTbr1ckN8\nn9Uy67lYNS/Dx/hcybD+I1YeLhHXIHv18PAwp6en+cM//MP86Ec/yscff5xvfvOb2dzcbC6J+R8n\nPNEunik5LEkasBIy5djK/fSiFr2xN288f9GWxCvBYWdn5yLJX+589Wc7x/5Skl/6/M36fPImAHGf\nB+BBWfMHSHQiy25zc7MVGSVRycu7z87OBsDg3H3Aw+E6XpBp4/G45UCQvs3mNwjbyRsYWMtBWi+W\nDKRbnaFRfJY/0zYShCAukzSr6q6iMiZ2K0BYCYkeeD1CfQ7z/HbazayPVeU8DXJESFff3d3N8fFx\nTk9P8/HHH7diLdw3k8DS0nWCm9dewIsQyjU49BLQeu1/nTFZ7/OLkvc2CcouwaukHlNR22aeZzWH\n5TBrTXZtbm62QYnZ6bwD3r1gCdO4Riwgu0ajUSuv/uDBgzYDHh0dDeovJjcJQgAO4UfXRyR8aSuF\nl0OXDvHVGRVFJifi/2/v/EHkqqIw/suya8if3Q0Eoo3YiLfW+I+IiqBEbSK2VkGRdAaUICpJI2iT\nCELSCCKKKESMoIVYWAgpYhOwkWuwEywsUmR3iQSzFu99b7+5e2d3k0nyXrLng5CZN29mzpx773fP\nv3tWi9nrA8ZZDcoUwGpy0LXyNKc+rwxC+li5G+WHw0Q2V69e5dKlSyNWmUgQGmJVF22Rq+TYs2cP\nCwsLqywcWKnv8PR2OZ9c9toCr2WyXHflvWsRxM0OSN7WuBaSKO/zQartAm4+l9V/qljcuXNnZ4Ir\nF+7Re5dR5OJ5fhGI+9NayEpryjz2tCfQLQ7dL6tFO6DcGi0ATx8uL6+UR2uXXV5e7gq+pJsynuBZ\nlpoF4ZO59r9nN3xnrkX/XY7SCnGLR26X61wEu23bts7i8lSxDnKV7flEvKox8d/kZzbKTIzLXptr\nG7le6u9m444nB+F6LIn1LIqSKDzf7Sa5Fp+by6qq85y3/HuZ7e4e+USXBaB6Bx0J18JWdkCTX/0Y\nRQyw0tHJ+1pqx1Pw1asnFxcXO4tIroMWrz6zZip7BaSPQy3mIPi5CD8V6Tt7GY+owd0h17lXo87N\nzXVjIj3IavJj8BpXkYMIVrKVaVx9ho9fWRZeBrzdmij1VT6+Fdg05CDUgj61GMU4v1CDqoH2IJW/\nr4QsCc/919qxeU9GQSa1B7nKDsmeDVG3IlX4ASOmddn30Z9rUl++fLmrnrx48SJLS0vd3/B0Mqjp\ntDS1fbdb6wSiu29uPTg5uAvhRFyOgZOud3ryw0/bt29n165dnZUwPT3d1XTIfRNpumsBjBC4x4sU\nb3A3VOPq8ZPy7EhJDNJZ6daWultLl5Ni05GDw0lhI0FM92VnZmZGdg+v5tPkc99f7/eiJk89lucN\nShPZDw5pcSrTACttzBSdB7oybsUr3FXx+IgXHSmWooyGgnYKPvqOLV2Uu6N/D4z2XnBd1KwM/90i\nNK+6lMxlcE+fKYgg/HeqjFu6ViOdHTt2dLESuWFlNqnMKs3Pz4+QlkjI50A5nk4wbj24i1bbjPrC\nbU0O15qRcJT+bukLlxYBjLZ6Vy7da+i1s2tCyzT1AJsWtHYgr53wrkiwerKUfq16S5a7o6wF/yMs\n+m5fqD5x9X2++H3R+a7oMnhkvowN+OQvLapxwTYP6Hn5t/TkB6bWgv9Od8k0XnLhZmdnuyCurnl/\nB4/FuK52795dnUeuR323nxkRMei+kiDWwkashhtJJlsmWWCBQODOxdT6twQCgc2IIIdAIFBFkEMg\nEKgiyCEQCFQR5BAIBKoIcggEAlUEOQQCgSpueRFUSmkKOEXTQOZf4LWc85+3Wo71sNGO2/1I1yCl\n9BjwYc75mZTS/TX5htQRvJD3QeB74EL78qmc8+khyJtSmgE+Be4DtgLvA78zUP2Okfcv4AeaTm5w\nHfrtw3J4Cbgr57wPeBs43oMMa8I7brf/XgVO0DSweQrYAhzoWcYjwCc0kwEq8qWU7qHpCL4P2A98\n0DboGYK8e4ETpuPTA5L3FeCfVpfPAydp5ulQ9VuT9yHg+CT67aN8+gnaJrU553MppYd7kGE9bLTj\n9nc9yQdNX8+XgS/a50PvCF7Kuxd4IKV0gMZ6OAw8OhB5TwPftI+ngCsMW781efcCaRL99mE5zNE0\noxX+a12NIUEdt/cDh4Avi9cX6Lmzds75WxrTUPCi+sF1BK/Iew54K+f8NI3bdoymP2nv8uacF3PO\nCymlWZqF9x6ja2VQ+q3I+y5NT9eJ9NvHoiy7VE/lnFefcuoXf9ASQs75Ak3PTG8hPEvTcXtIcB3e\nDh3Bz+Scz+sxTUfzwcibUroX+Bn4POf8FQPXbyHv19wA/fZBDmeBFwFSSo8Dv/Ugw3o4SBsLKTtu\nt6+/APwy5r194XxFvl+BJ1NKW1NK8/TUEXwMfkwpPdI+fpbGtB2EvCmlu4GfgCM558/ay4PV7xh5\nJ9ZvHzGHM8BzKaWz7fODPciwHjbUcbsv4QooY/ImA+0IXkDyHgJOppSuAH8Dr7em8RDkfYfG3D6a\nUjraXnsD+Hig+q3Jexj4aBL9xpHtQCBQxdACgYFAYCAIcggEAlUEOQQCgSqCHAKBQBVBDoFAoIog\nh0AgUEWQQyAQqOJ/+qE1qwG8cFwAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x1877cd30>" ] } ], "prompt_number": 67 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Use colormaps\n", "\n", "We are going to use some of the colormaps defined in [colorbrewer](http://colorbrewer2.org/) via [seaborn](http://stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html)\n", "\n", "We will use 8 colors to illustrate the palettes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "grayscale = sns.color_palette(\"Greys\",8)\n", "sns.palplot(grayscale)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x178bca90>" ] } ], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "hot = sns.color_palette(\"YlOrRd\",8)\n", "sns.palplot(hot)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuAfkqSFrYSgiAAAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x177ba5f8>" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "hotcold = sns.color_palette(\"RdBu\",8)\n", "sns.palplot(hotcold)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIJgAEAgmAASCCQDBH2KtI9lo5ES9AAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x17799860>" ] } ], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "rainbow = sns.color_palette(\"husl\",8)\n", "sns.palplot(rainbow)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAcNJREFUeJzt2DFqFVEAhtE7khSSXbgBUUFwEzY2gqlSCHYRC9dgIdoJ\nKawi2Ni4CUEw4gayi0cKhbG3eHzNeDWcU85M8d/q486yrusAAPa7MXsAAPwPBBMAAsEEgEAwASAQ\nTAAIBBMAgoN9L9efv9blcO8nAHCt7E4ux9H7W8ufz/fWcDk8GFfPX223arKbb16O72/vzp6xmTun\nF+Pd+fU937Pji3Hv89PZMzbx7eHZuP/pw+wZm/n66Ml48PHH7Bmb+fL49jh/vZs9YzPHL47G7uRy\n9oy/zi9ZAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwAC\nwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgE\nEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBM\nAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwCCZV3X2RsA4J/nhgkAgWACQCCYABAI\nJgAEggkAgWACQPAbQxckyJWg960AAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x1884a550>" ] } ], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.imshow(data,cmap=sns.blend_palette(grayscale,as_cmap=True))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 72, "text": [ "<matplotlib.image.AxesImage at 0x1943eda0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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9SYeSBvb6QNKbdSfp9XrnJ9jausElvBt5aNe6s7Oz9v3f//3fv58LuSP5lV/5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\nQCQCb453uupYPgapwKFEmDTud8Zog/u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zl8tlci+YKDmzVipWerKt/OnpqabTaWEjXY6L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\n/evj4+OSKbm1tVXWWNgq4Izs6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kth+YFVb9JUWmMXiTEqQKvQ3rRYpOBI04VJWAQgTI5Uq2H1o5/6FLZoRRSrSJ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\nmG278rn7sRxzvy15HOlPoQb8cx5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DCzjrMXkCgLn3AcVgjzJojPDoYAnFnNA0sTO1VRT3YrX5O3m2dTn4DW4GG3J4y7BuOXjn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J+9YPPdj7n7rZdpC1GQL4pkGBOLtM4t3/mP/is+/OV/TaTbVYnJs4RCi/gupU91MholMSvC4PHd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\nIcKoCKPAca1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pRrQyJC1UJWWdfH3GuMVljKqwjctneha3qC7cSYHqShgj2WKYXZrZZ2GM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\ngOJ4TTDqTHtdo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3gJ4cXgpirUM3jMWvr5yKWE05kJZSWqz4FEbLgBykTFf/2Kbdy5iHapfH+Ryd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9pXOK2BZZ7kPTQTVrrgKGpas8nu3Qmva4yudqNg+RyyrZ4R8ZuEALdCJzT2UXfKzAgxLCKv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\naXvCtH6ZUXuH1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"text": [ "<matplotlib.figure.Figure at 0x1981af98>" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 75 } ], "metadata": {} } ] }
mit
mne-tools/mne-tools.github.io
0.22/_downloads/f781cba191074d5f4243e5933c1e870d/plot_find_ref_artifacts.ipynb
1
7888
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Find MEG reference channel artifacts\n\nUse ICA decompositions of MEG reference channels to remove intermittent noise.\n\nMany MEG systems have an array of reference channels which are used to detect\nexternal magnetic noise. However, standard techniques that use reference\nchannels to remove noise from standard channels often fail when noise is\nintermittent. The technique described here (using ICA on the reference\nchannels) often succeeds where the standard techniques do not.\n\nThere are two algorithms to choose from: separate and together (default). In\nthe \"separate\" algorithm, two ICA decompositions are made: one on the reference\nchannels, and one on reference + standard channels. The reference + standard\nchannel components which correlate with the reference channel components are\nremoved.\n\nIn the \"together\" algorithm, a single ICA decomposition is made on reference +\nstandard channels, and those components whose weights are particularly heavy\non the reference channels are removed.\n\nThis technique is fully described and validated in :footcite:`HannaEtAl2020`\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Jeff Hanna <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne import io\nfrom mne.datasets import refmeg_noise\nfrom mne.preprocessing import ICA\nimport numpy as np\n\nprint(__doc__)\n\ndata_path = refmeg_noise.data_path()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read raw data, cropping to 5 minutes to save memory\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_fname = data_path + '/sample_reference_MEG_noise-raw.fif'\nraw = io.read_raw_fif(raw_fname).crop(300, 600).load_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that even though standard noise removal has already\nbeen applied to these data, much of the noise in the reference channels\n(bottom of the plot) can still be seen in the standard channels.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "select_picks = np.concatenate(\n (mne.pick_types(raw.info, meg=True)[-32:],\n mne.pick_types(raw.info, meg=False, ref_meg=True)))\nplot_kwargs = dict(\n duration=100, order=select_picks, n_channels=len(select_picks),\n scalings={\"mag\": 8e-13, \"ref_meg\": 2e-11})\nraw.plot(**plot_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The PSD of these data show the noise as clear peaks.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw.plot_psd(fmax=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the \"together\" algorithm.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_tog = raw.copy()\nica_kwargs = dict(\n method='picard',\n fit_params=dict(tol=1e-4), # use a high tol here for speed\n)\nall_picks = mne.pick_types(raw_tog.info, meg=True, ref_meg=True)\nica_tog = ICA(n_components=60, allow_ref_meg=True, **ica_kwargs)\nica_tog.fit(raw_tog, picks=all_picks)\n# low threshold (2.0) here because of cropped data, entire recording can use\n# a higher threshold (2.5)\nbad_comps, scores = ica_tog.find_bads_ref(raw_tog, threshold=2.0)\n\n# Plot scores with bad components marked.\nica_tog.plot_scores(scores, bad_comps)\n\n# Examine the properties of removed components. It's clear from the time\n# courses and topographies that these components represent external,\n# intermittent noise.\nica_tog.plot_properties(raw_tog, picks=bad_comps)\n\n# Remove the components.\nraw_tog = ica_tog.apply(raw_tog, exclude=bad_comps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cleaned data:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_tog.plot_psd(fmax=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now try the \"separate\" algorithm.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_sep = raw.copy()\n\n# Do ICA only on the reference channels.\nref_picks = mne.pick_types(raw_sep.info, meg=False, ref_meg=True)\nica_ref = ICA(n_components=2, allow_ref_meg=True, **ica_kwargs)\nica_ref.fit(raw_sep, picks=ref_picks)\n\n# Do ICA on both reference and standard channels. Here, we can just reuse\n# ica_tog from the section above.\nica_sep = ica_tog.copy()\n\n# Extract the time courses of these components and add them as channels\n# to the raw data. Think of them the same way as EOG/EKG channels, but instead\n# of giving info about eye movements/cardiac activity, they give info about\n# external magnetic noise.\nref_comps = ica_ref.get_sources(raw_sep)\nfor c in ref_comps.ch_names: # they need to have REF_ prefix to be recognised\n ref_comps.rename_channels({c: \"REF_\" + c})\nraw_sep.add_channels([ref_comps])\n\n# Now that we have our noise channels, we run the separate algorithm.\nbad_comps, scores = ica_sep.find_bads_ref(raw_sep, method=\"separate\")\n\n# Plot scores with bad components marked.\nica_sep.plot_scores(scores, bad_comps)\n\n# Examine the properties of removed components.\nica_sep.plot_properties(raw_sep, picks=bad_comps)\n\n# Remove the components.\nraw_sep = ica_sep.apply(raw_sep, exclude=bad_comps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cleaned raw data traces:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_sep.plot(**plot_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cleaned raw data PSD:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_sep.plot_psd(fmax=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n\n.. footbibliography::\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
tcstewar/testing_notebooks
Accuracy-Integrator.ipynb
1
54031
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import numpy as np\n", "import scipy.optimize\n", "\n", "def accuracy(n_neurons=100, dimensions=25, low_rate=50, high_rate=100,\n", " synapse=0.1, time=2.0, dt=0.001, time_stim=0.1,\n", " show_plot=False,\n", " ):\n", " model = nengo.Network()\n", " with model:\n", " ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=dimensions, \n", " max_rates=nengo.dists.Uniform(low_rate, high_rate))\n", " conn = nengo.Connection(ens, ens, synapse=synapse)\n", " \n", " stim = nengo.Node(lambda t: synapse / time_stim if t < time_stim else 0.0)\n", " nengo.Connection(stim, ens[0], synapse=None)\n", " probe = nengo.Probe(ens, synapse=synapse)\n", " \n", " sim = nengo.Simulator(model, dt=dt)\n", " sim.run(time, progress_bar=False)\n", " \n", " data = sim.data[probe][:,0]\n", " start_index = int(time_stim / dt)\n", " \n", " def curve(t, a, tau, b):\n", " return a * np.exp(-t / tau) + b\n", " fit = scipy.optimize.curve_fit(curve, sim.trange()[start_index:], data[start_index:])\n", " \n", " fit_a, fit_tau, fit_b = fit[0]\n", " \n", " if show_plot:\n", " plot(sim.trange(), data, label='neurons')\n", " plot(sim.trange(), curve(sim.trange(), *fit[0]), label='best fit')\n", " legend(loc='best')\n", " \n", " return fit_tau\n", " \n", "tau = accuracy(show_plot=True)\n", "print 'decay time:', tau" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "decay time: 0.239261515427\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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XFr84fbp4p8bD2Ck8a5aIigE0EacGwZvl/umnwJYtNf48jzSEi4thmOAmaMTd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\nqAGexVr+nbJF780nanwSpTqTIP/znyJm34j8JBA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"text": [ "<matplotlib.figure.Figure at 0x10406c18>" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "neurons = [10, 20, 50, 100, 200]\n", "N = 10\n", "results_X = []\n", "results_Y = []\n", "\n", "for i in range(N):\n", " for j, n_neurons in enumerate(neurons):\n", " print i, n_neurons\n", " results_X.append(n_neurons)\n", " results_Y.append(accuracy(n_neurons=n_neurons))\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0 10\n", "0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 20\n", "0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 50\n", "0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 100\n", "0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 200\n", "1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 10\n", "1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 20\n", "1" ] 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"text": [ "<matplotlib.figure.Figure at 0x11120278>" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
caganze/wisps
notebooks/cands spt unc.ipynb
1
6640
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import splat\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import wisps\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "cands=pd.read_pickle('/users/caganze/research/wisps/db/true_spectra_cands.pkl')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "x=cands.spectra.iloc[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#save fluxses and noises as seperate files then read this over and over instead of reading\n", "cands['flux']=[x.flux for x in cands.spectra]\n", "cands['noise']=[x.noise for x in cands.spectra]\n", "cands['wave']=[x.wave for x in cands.spectra]\n", "cands['contam']=[x.contamination for x in cands.spectra]\n", "cands['spectrum_image']=[x.spectrum_image for x in cands.spectra]\n", "cands['image']=[x._image for x in cands.spectra]\n", "cands['pixels_per_image']=[x.pixels_per_image for x in cands.spectra]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cands.to_pickle(wisps.OUTPUT_FILES+'/true_spectra_cands.pkl')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import wisps" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'distance' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-42f646342a0b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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"outputs": [], "source": [ "s.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
DEIB-GECO/PyGMQL
examples/notebooks/02a_Mixing_Local_Remote_Processing_SIMPLE.ipynb
1
133044
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interfacing with an external GMQL service: Aggregating the Chip-Seq signal of histone marks on promotorial regions\n", "\n", "In this first application, genes' promoters are extracted from a local dataset and a large set of Chip-Seq experiments is selected from a remote repository. Then, for every promoter and for every Chip-seq experiment, the average signal of those Chip-Seq peaks intersecting the promoter is computed. The result is finally visualized as a heatmap, with rows representing promoters and columns representing Chip-Seq experiments. \n", "\n", "This example shows: \n", "1. the integration of local PyGMQL programs with remote repositories,\n", "2. the possibility to outsource the execution to an external deployment of (Py)GMQL, \n", "3. the interplay between PyGMQL data and Python libraries written by third parties. \n", "These features allow users to write arbitrary complex queries - whose execution and size of the inputs exceed the capabilities of the local environment - and, at the same time, analyze/visualize the output by means of well known Python libraries. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import gmql as gl\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code begins by loading a local dataset of gene annotations and extracting their promotorial regions (here defined as regions at $\\left[gene_{start}-2000;gene_{start}+2000\\right])$.\n", "Note that the `start` and `stop` attributes automatically consider the strand of the region." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "genes = gl.load_from_path(\"../data/genes/\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "promoters = genes.reg_project(new_field_dict={\n", " 'start':genes.start-2000, \n", " 'stop':genes.start + 2000})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `genes` and `promoters` variables are `GMQLDataset`; the former is loaded directly, the latter results from a projection operation. Region feature names can be accessed directly from variables to build expressions and predicates (e.g., `gene.start + 2000`). \n", "\n", "Next, we load the external dataset of Chip-Seq from a remote GMQL Web service; in order to do so, the user has to specify the remote address and login. If the user has already signed to the remote GMQL installation, he/she can use his/her own credentials (this will also grant the access to private datasets), otherwise a guest account is automatically created, without requiring the user to do it manually." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[PyGMQL] Logging using stored authentication token\n" ] } ], "source": [ "gl.set_remote_address(\"http://gmql.eu/gmql-rest/\")\n", "gl.login()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following snippet we show how to load the Chip-Seq data of the ENCODE dataset from the remote GMQL repository and select only the experiments of interest.\n", "First, the user sets the `remote` execution mode and imports remote datasets with the `load_from_remote` function; such loading is *lazy*, therefore no actual data is moved or read at this point.\n", "Then the user specifies the select condition; the `hms[\"experiment\\_target\"]` notation enables the user to build predicates on the given metadata attribute. The GMQL engine loads from the dataset only the samples whose metadata satisfy such condition; specifically, only experiments targeting the *human H3K9ac marker* will be selected." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "gl.set_mode(\"remote\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "hms = gl.load_from_remote(\"HG19_ENCODE_BROAD_AUG_2017\",\n", " owner=\"public\")\n", "hms_ac = hms[hms[\"experiment_target\"] == \"H3K9ac-human\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, the PyGMQL `map` operation is used to compute the average of the signal of `hms_ac` intersecting each promoter; iteration over all samples is implicit. Finally, the `materialize` method triggers the execution of the query.\n", "Since the mode is set to \\texttt{\"remote\"}, the dataset stored at `./genes` is sent to the remote service GMQL system that performs the specified operations. The result is loaded into the `mapping` `GDataframe` variable which resides on the local machine." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 3/3 [00:03<00:00, 1.10s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "DS_CREATION_RUNNING.." ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 16/16 [00:27<00:00, 1.33s/it]\n", "Collecting regions: 755392it [01:38, 24250.50it/s]\n", " 0%| | 0/44 [00:00<?, ?it/s]\u001b[A\n", " 50%|█████ | 22/44 [00:00<00:00, 209.23it/s]\u001b[A\n", "100%|██████████| 44/44 [00:00<00:00, 219.17it/s]\u001b[A" ] } ], "source": [ "mapping = promoters.map(\n", " hms_ac, \n", " refName='prom', \n", " expName='hm',\n", " new_reg_fields={\n", " 'avg_signal': gl.AVG('signal')})\n", "mapping = mapping.materialize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, Python libraries for data manipulation, visualization or analysis can be applied to the `GDataframe`. The following portion of code provides an example of data manipulation of a query result. The `to_matrix` method transforms the `GDataframe` into a *Pandas* matrix, where each row corresponds to a gene and each column to a cell line; values are the average signal on the promoter of the given gene in the given cell line. Finally, the matrix is visualized as a heatmap." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "heatmap=mapping.to_matrix(\n", " columns_meta=['hm.biosample_term_name'],\n", " index_regs=['gene_symbol'], \n", " values_regs=['avg_signal'],\n", " fill_value=0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Collecting regions: 755392it [01:51, 24250.50it/s]" ] }, { "data": { "image/png": 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X+S6QmSJ2BGkFH1KiuWauNpFNjUdr9HZa8u1oAeDuK8ysH9AfyDPQM7NLgWOBIz3/Z7wd8PO2Xqi7ryVm/V5hR/QGtRm4rartQlkpv3jQVZMHaF9Tu+5q4jJt26c9K+vWJS1Yq13PeXftTnDPrTJfvyXaEZMXqnWU+k7sW07qq9TvnYIPKsHcU1tckSFN1xkDYP7y86W+eCReAr1uBIkNsbxKENTF8gYwyMyye20daWaxq8jPAB4DfgO+CkfcXnP3O8L92Wv0Egh64nYvsjsoAHXgFe/9KFPba5MxUibGd79NZfC1ceqzMhfAjSfq1q8VB91WTtAK+2l1aakTpb7M37Wv9awv3iv4oCLkpt7aL1pyojV6EcVFi9pt/ZQyDWW+wYsnsKC5dtHtbcJ1bLcvnsDagRpf5cETaFdTl9k4adlcmSsb5YilerSybY0mDKOSzLfH3qsoVVqm4+Z5NTlqi07YbeUEafD106G9qHuEbh2iJZdm68/rJK7Se1Xi9jcqSFwAI1I/54hazWS+T5bOlK/R2/Ljl7KgqEzjtlELtIi/iHrdlmzUBZMTE7SD9QvXRSUXIiIA9qnyj4pcO5R5q3WljYoDeaA3f5Iu0Nu7XZSMocbMMoGZgAGZQC93/zLctzcwAmhMkEH7E0Eyxr7Am8AvQFlgvLv3CR/Tnb+6aZQmSMx4MsZ3IPAVcLa7v5Lftb1TpT3tR+i6R/yn38/0/v6Ogg8sIpJT2hd8UAnms3q1Za6j/ljJDz/k++dUpKifu54p7Rg++S6Z79mWt9K+oi5zs96NzUns0lPm69XmJsqiK5B+XvoWmnTRpSyMH1eN02fcKfOd1Ooqxk19ROY6v05jiQtg4OJPZWWpIChNFbH9RCN6MZjZBnevEP77WIIyKYeHNfhmAte7+1vh/o7ACoLEij7u3tnMkoGpBG3WvshRkqUmMBto6u5LYzp1bAaeKSjQU4/o1a1YTanjj/UrCz4oIqIYuFNcvuIWcfmKR2tq7++KOC+lFFG0yEf0fvhMN6LX5PBoRK+YqcRfnS/OAb7KDvIA3H0C/BnwZW9LM7Np5NJ9w92XmdnPQH1gKcFo4KvAgYW5mKVHa5MHan34k9SnXjD9WCtdFiVAVXGf7AtXRB+mRcUtiz+lW52DZb7179wicwE8ddEkqU+N+r3l+Rba95bLlkev9Yj8iQK9v5McBmplCbpfHBFubwp8X9CDzawKwdTu57ns2xPYE/jJzOoCpxC0aytUoFdpzKiCDypC4r0WVM+pumlpgEWddFNxAGkztB9uSjJmfiL1JTY7ouCDSjCXPqv9GOjZTPvaU3P2C0dJfeftF9+/z4jtJwr0/k6au7cEMLNDgWfNrGkhHtfezKYTBHkj3H1JzL7skixbgB7uvsrM/gfc5O5ZYQmXXIltgXZzleacVqHBv7qpf0OTu5rLXABrHtWOKlRoWkbqe3y9dir8wGbaUaGmFXWN1aevryJzAbRvOFbq+/UXbeJOgmmX77T+plDfbYsMT1sv9aVe+YLU99z6D6W+64/RtVcESHryA6mPqGDyroO7fxV206hBsLYuv2alE8M1eg2Br83sZXefFu57yd175Ti+DfBiGORVB04wswx3/1t7CHd/AngCgjV6fY7VfQBUvOgZcX9PbdsgvtXqLks5TOZ6MrUYKtfH8xLLlbC8i27Be4fJX3NDSgeZb1jq56wfc6nMV26vE/is6qEy3+GrvmLd8FNkvn3mz+KjKm0lrqNWf0k/YR/tz9KXcuYnZWW+5oumkRHfZSwlRMkYMeRIxmgCTAJqEWTMzgSudfe3w/0dgFXEJGOE23sDB7l7t9hkjHycowkydfPPuhX3um1YSVMHKpuqu2+U+r6fo+vkANCXX6S+h2gg9U0tkyRz9ey2SeYCeGGstpND+3K6vsEAi9ZWlPoOvWCr1JeRqu0dfNsXNaW+uq4dr1lh2gXH9/76gjYZY/bHumSM/Y+MkjGKgew1ehCUWLnQ3TOBNDPrDIwwsxFAOjADuJaYdmYhjwF9zKxBUV7YSavFa650M3EBv4l9aJNNapSrLPUduyl+F9j3G17cVxBfXJCiG10DOP5B8XC6nHlSW8PKutJNAL+sXVLwQUXIvVJbfBKN6JUQLmpwmvSJujozQ6mj2VTtp3fmvK+kvrQR2vmHGm/+KPUNr6Ur0aFOpJnZqrfUp34tvNf0ZqnvuFlDpD6ytCNQnzfT9iVv1VYbeCXV0Y5wVxz5rnZEb9aHuhG9pkdHnTEi/iLqjBERUXyo2/Mpeb2qbj0gwJF9ykt9F/5Hmzzw6H5rpL43Z9aT+r5K3Cz1PfXrK1Ggt51EgV4JIX3FAu0Tlb5FqiNJmwWr5uP9B0h9R84eKvXFMxnTP5L6Eltoy3P83DbPJcQ7hL2+fFjqi3eWnqhLpAGodktnqS+58/XaQG/G+7pAr/mxUaC3vZhZbYK2ZQcCawgKFQ8CHiQoiJwJDHH3l8LjxxJkxKYT5GX2cPf0bXS+AOwPjAJSgC7AVuBn4CJ3X2NmBxFm0xKsBRzk7q/nd171iN4zNbTV8i+O86Kfe1TSLtCOes9GRETEA/LOGFGgV3KwoHbJl8AYd38s3NYC2A1IdfcfzSyFoBDyvmEAdgLwbniK/wKfu/ujhfQlEiRmTHL3RuG2Y4BP3D3DzO4BcPebzKwcsDXcXgeYDqS4e54L49JXLPB1F160zb+Hf0v5QdfwbZcXZb5DZ91D+rO6/qVJF/Rny9DrJK4yA0Zw0QF9JC6Akcds4LO3dHX7jps1hMXHXSbzffNHbUoJ37e6zBrMzW1069iGTB7CC8LuCl2PWMx5nyXLfK9NeYitD+t+n6V7DWHi/v1kvvaz76ZXm5skrocn38M7TXVrAo/uXYZFo3RrAht+cj+lU/aXBnqbp78je3Mp2+KEKNDbHszsCIKRsnwXoISFjk939x9zbO8NVHf3m82sKvAMQWeLTcDl7j7DzAYBe4XbFxKM5DUmSLu62t0nxpzvlNBzbg5PQ+BroG5+gV60Rq9kU79SLanvt3VLpb4ja+kKbH+8dIbMVRwoa8xBUGcuouTSvuZ+Ut/EZXOkPvWIXjwGevFcXqXAtmXhFGppgmnV2O1JwPkE5VMAbgemuvvJYQD5LNAy3Lcf0C7sc9uAoCZeS/7JxcBLMY6DCYLH+sD5uQV5sZ0xRg4bzKUXdMvvdiJ2Yn7rcIXUV/+HfMsylngy3hwp9SV2vVLqU/KiuItK15l3Sn2ZP30n9WXN+Frqs/p7SX2JBzwu9cmJw84Y8Tyidw3Q0N1zrY0QTplOIKiV93WOfU8CG939uvDnqcBp7r4g/HkRwejd9YC7++3h9gYEgV7THOe7mWDt36me4xduZvsCY4AO7p5nOlM0oleyqZasLUq7Utz26cVqHWWus1dOkLkiIiKKF/mI3rTxuhG9lp2jEb3tZDZwem47zKwS8DZwcy5B3m0Ebc96FNKTb0uHsDtGZ+DInEEegLvPNbMNBCOQkwvp3OH80qKJ1Ndw+g9Sn5oBldtIfTekaZNb4jn4ujKlndQ3MjV+i11HROz0ZMXfiF48B3qfAEPN7PKwZyxm1hyoAtwKPJuz7ZiZXQocSxCUxT7bE4FzgTvNrCOwwt3Xhb1q88TMjgNuBA53900x2xsCi8JkjPpAE+DX/M51aI0mfHCvpn8iwP0DFtD3tTNkPg69irRUXfeP5JT2rL1J03+28j1fcO6+iyQugAMnr5H+Lnu36c/wybpEmgXtrqL2wbo2Wud9upGXnzpB5mtySRKXCYtCH9eyJ1dn5Gzws+P4pizc+rDui0/FU4fJ31s2Tn5G4irf5mLGVWkvcUHQoWlmfV0P9Ga/TZe54pm4nboFCLNqRwAHAJsJgqmvCQK92TGHdnf3aWaWQdCMK3ve6zV3v6OAZIwN7n5/6GtAzNStmf0ElOGvlu9fu3tPMzsf6EdQxiULuMPd38jvXtKXzpM+URlzv1DqSGzaUerL+HCM1LfuiQlSX4X22l6+iafrMsIzP3lN5gJIOk+TQVlcrL9E99wBlL9H29nExMsmslYslPo2DNR2UklboR0fqvfdx9qp2+/f0E3dHnBylHUb8Rcd6h7pzws/uz9ZXIdqmbrWQcfNGsLGa3WFP5MaVCPhcE2twKzPPqXyfV9KXACzGrSgz2bde2NpK8UVm3XlORYmJbFvZprM12Hl1wyto6srOWDxp9xTW+e7acmnnFHnQJlv9palnFNGl0AwcPGn3Cl8/m5ZrF028Vz1jjLX+SsmcHmKZiYE4InUL/Rr9KJAL6K4UCdj7F+1vlLH7FW/SX0REREBDwn7FANcszS+i6NHFC1RoLf9lPg1enl0v8iuhDuCoK7deuAn4GpgX+BNYAFQLjz+XncfH3POCwjW1jmQAYx19/vDqdrLgOzmiQPc/Z2wHMtTQGuC3+mz7n5XeK5MYGa4fS5Blu+mcF8pggSMP9w9374yyjUmAJvvuEbqq/KYNtAbJhwxATixgrbfZv3PC1Xnu8TydCtdQWGAC0fr+sGqW6C9Ji6vsv4drS+x5TFSX8bEl6W+hf0/k/rqdtWN3hcLWbqZLBUlekQvn+4XlQjW1F3v7m+F2zsCKwi6V/TJDqzMrCXwBnCJu39sZscDQ4DO7p5qZmWAC9z9yZxr8mKu4xzgJHc/O+x6MQfo6O6/mtkGd68QHjcW+N7dHwh/vp6g7EqlggK9qLxKyebVqodLfaet0r75K5lRL7cylTuOBzPLSX1Pp+qm+SMidnbkI3rfvaob0TvwtGhErxB0AtKzgzwAd59uZhcDX2UHeeH2CfBnwEfM9mlmdgfQC/gY6E8QCKaG+7cATxZwHQ6UD9ugJRP0tl2Xy3ETgebhdewOnEgQVF5f0I3OaqDLdAIoXSbPJh07hFfWaXvB1t+qjZsHZP0k9a254RCp793RZWWuStUXy1wALNUGejelaL8UzPd8K0QVOY80WiP1fTi3ntRXWjx48kBCqtT33fL5Up+cOCyYXNIDvby6XxTYFSMHU4C+hXxsr3BqdzJwg7uvBl4BugKLCaaDe7v7qtgHhUHg8cB74aYRBNPDeaaExXbGuLvhPlz/7TvbcEvbx2OtbuXHUuky37DvtZl4bzUdyPH3N5S43u3zC1+fVlXiAih95fUk1NZWyz/5Bp3rw/0HcPTsoTJf3w5XUKuDblCh7OCR+IZVBR9YRHQ/fAi3lN4i83mmMXduDZnvzNna95a0/j0p3f1CiWvr6DGMeV/3ReTZAzYw+ytdi75WF8Zf0FUclPRAr6go7Lv4o8CdBCN4dwLDCFqbHQRkAikEdfommtlHYSeNZDObFj5+IvC0mXUGlrn79zlHGGMJ6/89AZC+YoH0a+I5zXV134qDLrMG61zHwbIuuoxiLruLmm89pfMBa7rpSnQcPXuUzAVQsdZmNs3T+coCVkH3xeDQrPJ8tLm8zNfzhWHo8jYh/aUHhDZI6ngw/qumAHxSx4M5f/wCiQvgw69346TndXX7ioWoYPJOR17dL2YD2zL/0YogUSL7sQcQFFz+G+7+Z6f4sE1adgLHOcB77p4OLDOzLwjW3i0A0nL2vjWzw4CTzOwEgvf1Smb2vLuftw3XvEN5epZ2uiPXPnU7kM2Dekl9py/UTud8l6J9M1YWUV1xaC95IdULUnSjGIe11Caa/J6oXXyeXF+bbDJcnFXce+nrUl/bGtouRi9e/LbU9/rCnlJfPFLSA728ul/MB/qb2Ynu/na4vQPwj/mQ8PhbgOwhl7uA+8LHLjGz0gTJGE+ZWR13z14gdAowK/z3QuAI4DkzKw8cQjA1myvu3p9gLWD2msE+O1OQB9BbPJWq5ofXy0h9nzx1nNSXeGC+uT0lHl0VPfh8//5SYQfxVOPmwddKfYMHaisIqCktDtS7f6INhBKqpkh9cuJwjV6JzrqFPLtfXAeUCrfvRdCBYgZwLf8sr7KMoLzKWzHnvAi4gWBK14Fn3P0BM3sOaBlu+xXo4e6LzawCMArYL3zMKHe/LzzXn1m3eVx/R2KygPNCnXXbrua+Sh2Tls0t+KASzILm2m/de86I797BarrUbi1zvbVkisy1K6AuTaUmWTx6r0aedfvVC7qs20O7RQWTI/5CvUZv6+ODlJGqrm0AACAASURBVDpK99D6IiIiAjLeKaioQNGSeMJlUp+aLfcIM5OApIu1NU8TamiL6SdV31Mb6H0xVhfoHXZuFOhF/EVUR69ks/ToRlJfrQ+15VwiIiIidgTyEb04DPRK+hq9f0U+3TQcGE4wvbuGoBbebe7+uZl1B+4D/iBIoHjc3YeH5xvE3ztmAHQkmOZ9E/gl3LbC3Y8K1wuOIKipd7a7v1LQNW+c/AwJKY3//U1vI4s69WTm8uoy3wnCLNhsNlxxscRT4dFnpJ0cLpl6h3QNG8DKUzW/S4Bqrz1Dxvu6zNvEoy8ga4kus1H5Ogd4r+nNHNJMV4tttxdGSTvvlL31IZkLIOO9p0l7dZLElXxaO9648VeJC+D0GXeysOMVMl+9cXfKXH8Sh1m3u9yIXgHdNJ4mWC83LtzeFGjj7qPDQK+Nu/cys2rAPKCVuy/Kp2NGR3JZf2dmDUJfH2BcYQK9aI1eyeYq8TqaR+J4XVLHWk2lvnYJui88AB03b5X6FiZqE5O+StLe3+VZuhqBAP1ce38fL50h9amRj+hNfE43otf+/GhEbweRVzeNSwi6aYyL2T6LvzJridm+0sx+AuoA21xwzt1/BTCznfarw/AEXV0tCIZW45l4DrzUTFj6j5fkjvVJbaAf245vGon7Wn+89FOpL6JocY+/Xre7YqCXV+eL/Qk6ZBSIme1BMH0b+1Wqt5lll0hZ7e7Z7y7tYwom/8/dhxT2QmM7Y4wcNphLL+hW2IduNwvaXSVzAax/RVxJb/1aqW5qnzlS34Ez75P6Mt57WubKWvCrzAVgNbUjeonHnS/1bbn3Fqkv8WhtizcSEqS6c4f9LvVN+rGu1HfEQdr7i9h+dsVAr1CY2etAY2C+u58abj4rXF/XBOjl7ptjHjI859RtyMSCSqfkRWxnjMTSdf3qfiP/zWlKBqfH9/RDkyraAtQ/xHnJhfimwJUcJZun4vu1rudHre5drU7bdZ24XKO3KwZ6+XXT6JD9g7ufYmZtgNjg7aVwjV4b4AMzG+fuS3bs5QakpU6U1ktaP+pivrtBV4vt0Fn3aO/vv1dQ8ZxH484F8HrVDlxXSbdO6IcfXpE+d/LXwlv9qdjlLplPfX/rhh5PpQG6T+/142+mYudCT2xsN2mpE5ne8nqZr8W0BxhygGaU9Obv79S+Fl7pTcXTh8t86vqj8cqumozxNfB0jm4alQmKHl8fk4zRAbjD3TvGJmOE+x4ENrl7/21NxojZPxoYvzMmY8Q7y07Uljup+XZU7qSksl/VPaS+OasWSn0RETsz6mSMtE+fkn3WJne6NErG2BG4u5vZKcAIM7uJv3fT6Aw8YGYjCEqurCfvtdH3AFPMbGj4c+waPYCT87oGMzsQeB2oAnQxs9vdff+Crn3jrJcKOqTIKN/0LD6p2lbmO2LVl6y6SJdNWXXULMZX0XwT7rx6IntUqilxASxct4zVlzaX+ao8NYNFB+0t89X7dj7d6hws81WxJDps0b1Vno0+0LtGOCr0UJSYFBEhZZcb0SuppH38hPSJWnrD/5Q66n70uNS3dfhNUt9dY5OkvrZp2nUmjaqukblSji8tcwHMelH7ffiAGbkt9d1xjG2h7c167nRtL99vm94o9aW7dACKVaZ9bzniSMlqpT/Zbewn0YjedrLLjeiVVBJbHCX11TzvW6lPTene90h9A3bTFs1Iumig1Kdm7fkXyVyNWsDWtdrMTSX1MtKL+xJ2KAfNulfq23yrtmJB4oHNpD52080WFAtRMkbJJ5+uGEnAf4C6QALwLDA4nOq9Hmjq7heH5zgXOMfdTwx/PhMYRNBZY7q7nxMWRZ5LUFjZgI3ARe4+Lyy4/Ep4DaOz1/3tTCgXZwOg9ok5P+UQqe+5m6Os26IiTTzVuPXJ26W+Zk21IzR92gyQ+oZeINXhmdpAIbFLT6kvouSxS03dFtAVYzRwhbt/YGblgFcJEiUeMbNEYDJwFUF27lTgSHdfYGaNgZeBI9x9tZnVdPdlYaA33t2bhp4eQFt3v9DMygOtCGr6NS1MoJe+YoH0iVp+0iVKHTXG6eqwFQcZ378j9SUecILUF1FyyfxdW+Ox1O77SX3xjrKGJUDCwSdKfWUat9VO3X70mG7q9qie0dTtDiC/rhhfuPsH4bZNZtaLoCj+I+6eYWZXAiOBb4Fn3D27+eVl4TGrw8cuy8NdCcg+ZiMwycwKnfqpTKEHOK3OgUxYoyuvsjLO676lVKgqc6VuWAXoyoFERETsSoyW2jK2/iH1xSO7WqCXX1eMv21395/NrIKZVXL3de7+pZnNBY4CYhvB7g1gZl8ApYBB7v5euG+vsCtGRaAcsE2pgrGdMfpVbsnJ5Rpuy8O3i5afaBcwl9/3NKnvbnFbpPMaKKvJV2P3r7VFVJXTm8u6XCpzAaxcrG0H2PhB7ZeeiZcXqiFQkdFp9tCCDypCtgzrK/Wl/7RS6lv3s/ZjfPMmbfKHnGiN3q6LmVUA2hCs5asBZH9yJxJ00OgI7A58bmbZq2N/dveW4ePPIuhycVxhnTk7Y9y3Ka/Bwh2AOPBS02+Jth/lIxu1bbTUqEec45oztIGXnOhvJWIbkHfGiEN2tUAvr64Yc4jpigFgZnsSFEFeF266HXieIHljOHBGuP134Bt3Twd+MbP5BIHf8hyOcQQFmSN2QT6tp6ujB9BozgqpLyIiIiIu8GhEr6TzCTDUzC7P0RVjHjDAzI5y94/MLBl4CLg3PKYZcCLQEtgKXGJmR7v7h8AbQDdglJlVJ5jKXUAwXRtLO+Dnf3vh6sw/ZcsgCNoGKcmY9oHURyntSy2t2RFSn5qs1dpMUV/2i8xVap9DZS7QT4XXfEPXDhCAUtqpxvRXHpT6lj6qTaapeUp8z07EI7tUoFdAV4yuwH/M7BGCtXbPAQ+HmbqPAr3dfTOAmV0BPGtmLYH3gWPMbA6QCfR195VmVpG/1ugZQYD45zuqmf1KkKBR2sxOBo5x9zxfsT+31VZg2a1SptSXPkpbZ27rFN0HN8B9X9WR+gYO+U3m+mOItuZi/QkPk1C5hsy38Ajta6/22dpSQ6kLK0t9NcWBV8Y7T0p9D96lXaN38OYqUl/1VeulPjlxuEZvlyqvUpJRl1dRoy4RkHictnxMRERERMS2k1R9T215lXcf0pVXOf6aqLxKxF9816wvB868T+bb0ONiMtbpYsuKgy6XTllNa3EDLacPk7meS9K17dovM4m2pdbKfCn7rKPs/rpRoY/+W4EOB+tKLtw4vQbDOulavE1+pxqZ6D7bylsGP5YqK/M18U00719b5sv6fQlZazbKfKu/y6RCXU23kQ1/JPHZUt3vclpSBsekyXQcdtpakkaKi+lHI3q7HmaWCcwkyLbNIOiYMdzds8ysI/Am8AtBN41lBB0zlplZd+A+4A+gLPC4uw8Pz9mBoDtHc+Bsd3+loOvYeMe50ifqPnH94sGLJ2iFYg6ssbfU993y+VLfiFq6cjXXLdVmTHes1VTqm7B0ltQX71yZ0k7qG5k6SeqLdzK2/qEd0Xt7hG5E78TrohG9nYS0mBIpNYH/Eqytuy3cP9HdO4f77yLonpG97yV37xW2PJtnZq+4+yJgIdAd6FPYi6g8eEIR3EpEcVExoUxxX8IORR18KYkCr5JNFHhFbBNR1u2uTThSdznwnZkNit0XJm1UBH7K5XErzewnoA6wyN1/DR9T6L8oddZtRNGyefC1Ul/ZgdHfS0ThiNrzRUTEN1Ggt42E/W1LAdmF0dqHmbXVgI3APzp2m9keBNO3M7bFFdsZY+SwwVx6QbftufRtIvO3bbrU7Sb96celvsQux2t9Rxwu9cU16Vukuqw12lIuCTXqa32N2kh98U7GnM+1wpXav8/E9mdKfXLicI1eFOhtP7FTtzcR1N7rGe47K1yP1wTolV2epbDk7Ixx7jzdG8iV75XnxcXfyHxqLnunIk+mfqFxpRwmc2UzrspkmWtM2c28uvg7mU9N+5r7Me50XbJClZHazhjjq7Sn82rdCPD6j4ZQ8aibZb54590qujWIryU7SUyV+c7YnEn7JQUuYY8ogCjQ20bCjhmZBIkX++bYPQ54Nebn7DV6bYAPzGycu/+rr1/rH9WN5gGMGnJlXLfxyFr6CyPoIfN1OFpbO+zoGXfqXAQtY1RsGXqd0AZlBoyQ+tIGSnVsfexW1qJLpklschhpv0+Q+UgopXMVA2vPvUjmagUkH5oi8wU5jhHbSxTobQNmVgN4DHg4LL6c85Bcu1+4+2Qzew64Fuj/r+Rlk3n+Jl2R35QBN/NqsqZEAMBFm+GQWffKfMsvHkJydU1R6LQVpfgxSVcw+ZDNmWzqe7nMt/iL0uz15cMy36j/VeLo5FUy35jXBtIoQ/eB021gNeYM1hW8/iKhAjMTtmmyYbuo+tSt9GuRKvNVfHoU6f8bLvO9Mng16aI80SSHUaYr3ZThWRz3g+61cNviCWQMkukC4jAZIyqvUgC5lFd5Dnggl/IqBqwFLnX3+WF5lTbu3is8TwowhaAPbhPgdaAKQXeOJe6+f37XkTamn/SJqnjZc0pdRBEzroq2cfxJwqk/NSfWbiX1vb1ENzUG0DdFu370vtTPpD41l6ccJvXVc12gBzCHTVLfc7+9pi2v8ua9uvIqXW+MyqvsDLh7nuP+7j4ByLVSrLuPBkbH/JwKZFe2/A7YfZuuY6W2rc5FKW2lvoc/7C31TWqn7Ue5T4PlUl+1Aa2lvmWjF0t9/51cTydzqJWh+571UNOcK0J2LFN1g2sArLn2IKkv6eJrpL7NQ7XtHPtMrCr1dcgoJ/XJicNkjGhEr4TwcL3zpE9UsvjPosey+K3DBvBITd0aKID1wqUt/ZZon7sjajWT+tJd2/f5ysyaBR9UhCxJlA6YkFpK+0Gakqld51VRHCe8kKAdBPh4qbYig7xg8ut360b0Tuknube4DvS2oatFGeBFd789r+0x5xwBnAHUcw8m83M8BuA1d78jxzUkAnOBC919U7ivFDAZ+CM7czcvti6aLn2ifN0KpY6EWg2lPjWbB14p9ZUdPFLqSx+lG8VI6HiizBUItYv5S9VvLvVFlGwyf9JmvJu4/E+ZvQ7RBnqvDdUFeqcOiKZui4BCdbUws/LANDN7K6/t7j7FzBKAU4BFwOFA7FDGxDyCtdhrGEtQeuWBcN+1BMFfpYJuZPOgPox7t1bh73w7qZyVyTEfXyrzAdzQ5t/lqfwb7r6iHOlzFkpcSfvtwXtvVpO4AKaXce5+RrdG7+FanbjwNt0o1OJLH2P3j3V1F38/sgeN586R+TZ8/SgLz9UF6s+mVeVu4bq5tQM6UHmorlTUmmva8PD/Ksp8X/kaVmVqGsJWLZUsXdN5a52O3CFsVzl7z+bs84O4120cEu8jehvcvULMz3sSrI+rThCo9Ympgfci8BpB2ZR/bHf3l83sCIK2ZS8Bh7n75eExHWMfk9c1mFlPoLm7X2lmuwNjgCHA9QWN6CWWrhu/T1REiad+Jd2XkN/WLZW5dgXivQ9zRMlGPnX7ymDdiN7pA6MRvaIml64WAIS9aA8B7gRq5LEdoBvwAsE07VAzS3L37Bokh5rZdCCVIOibncORCBwPvBduGgHcSNA2LVdiO2NYqcokJJTf5nuOiFDg6L6H7FGpJgvXLZP54p14D7zWPXS61Hfm0H90wdyhvLdkmtQXUfLYpQK9XGhvZlOBLOBud58djs7ltr00cALB6Nt6M/sGOBYYT1A2pb67bzCzE4A3CMqoACSHLdIAJgJPm1lnYJm7fx/6ciW2M8bju2uTMZqJph6yaXnbNiUhbzdJp2t7z57eWpv598qUh6S+r5reJHNlOVClkcx36IfaJQwJKY0LPqgIyfj8Ranvpxu+kvqSTu0l9b3WWVeTECB99D1S36bPFkl9cuIw63aXCvRy6WqR17q63LYfC+wGzAwLJZcD0oDx7r4u+yB3f8fMRppZdXdfQcwavZjrOAw4KQwKywKVzOx5dz8vr2u/cPzZ23q720Wp3feT+tSkP699c3xxzMlSX9aSf9Tt3qEcOkv7+9z6YD+ZK+N/T1P62rtlPjUr7/lI6mvy3VNSnxpfqx1t3vyVNvBK3E2bpR2x/ewygV4huloURDeCYsgvhOcrD/xiZuUIkimWhuc9iKBvS5457+7en7BDRsz6vjyDPIDRXbTfujsKOw8AvLylitTXxTdIfU2nHCH1Pd3qVplL/beSlan9oKl/cTW2Pj5I5lvxqraw3X9XKltawbWjh0p9tsceUt+s3tqC11NMWFMSOHXfOB/Ri8O8hXgP9LKnTf/W1WJbTxIGc8cRZMwC4O4bzWwS0IVgXd8VZpZBMMp3thdxlsslU+8oytPtdPT9brzUl3hgvrkvRc53zfpKfZfMvE/qI32LzpVURucqBlJ0LZgBuOahAVJfUnetT03L6fl+Zy9yKrbVTk1vWKbtxFGj4EMiCiCus27jibR3H/LuPT+R+V5drK3NFO+kVNBVr7+hQgse2qQrB3JD8n5075Uk81W65QOZK5ubhG3Ceu/7B8/M0Y3SzLU0SqEbJV2atZl5aUtkvtlzX6ZHmxtlvscn38uDrTUj6tdOuYPkFF0ppZ4p7Xh7/Q8yX+Nydfhg0XvarNsXbtNl3Xa7PSqYHPEX6SsWaJ+ozPSCjylC1na/XOqr/NwoqS8iIiIiHkjr37Pgg4qQSk9+EAV620m8T90Cf+tOkc2L7n63mU0AKrh7m/C4NsD97t4xptvFAoLEi6XAve4+Pua8lwPXhz+uI8jInRTumwDUAbYApYGPgIHuvibc/wyQnX3btKB7SH9F25vVU7W1ypLb6OqwFQfq6vWbH3xM6iv/n6elvqxV2nVsv596Z8EHFRF7THhU5gJYftIlUl+Ncdq/FTVvNrtF6tvdtBUSypfRLp3QNjwkyrotwfwj8zWGmmZ2vLvnVn77z+xbM2sJvGFmae7+cVgipQfQzt1XmFnrcP9B7p49L3Guu08OS7PcRRA4Zs8BjQYeJmjLViCVrnmlMIdFFJZ7dNMduwSvRr/PIkM4FVcsxPn9NamiTY74YXV8J0dkFPcFxAG7SqCXH/cBNwP59llx92lmdgfQC/gYuAnoG5ZQIWyRNga4Crglx2O3mtmNwE9m1sLdp7v752bWoLAXmZY6cRtuafv5rcMVUt/Tm3QtwgDShQV+AQa/eb7Ul/74f6S+xK66/rMVu9wlcwFs+vkdqc+Sde26ANaee5HUV3lsfC+bWHGydoS0QnNtb+TELsdLfRHbz64S6MUWLQa4y91fCv/9FXCKmXUC1hdwnilAdvrk/sD3OfZPBi7M7YHunhl2zmgCTC/MRcd2xji16kH0TGxQmIcVCU+ULcvaLF0m5SdLdb02szmmdguJ54Ml0/niBN3i87NL1eXKb7Y5ufxfk1z/KHhqhswH8GpVXXLEde2HMD19hcx3TVYduo47VebrOqsUtRIrFHxgEXHQAbdyw/e6KgI92txIn1K66c3mi8SdRr7V6tSv9Yyt2mLzeDR1W1LJb+oWYDAwkGCULj+2d+HkNj0+tjNGYum6/ia/b6d+52XluftKfdXGzuWDJYWKt4sEZZup75jPnEN1QfolKW15OvVLmQ/gtFX6LwYqzmUeHDahuC9jhzEOGCievi3U+piIiDhlVwn08sXdPzGzwQR9bfOjFTA3/Pcc4AAgtubJAcDsnA8CCHvsNot5fEQM131amedSvy7uy4gL9qhUUx54KXm0ZieuWPZpcV9GXFG/ki4Z6rd12kQvgFbV95K5pq7QdqWJKGKiZIy4ZjBB54wFue00s+YEa++yG1/eC9xjZse5+8owWaM7cHAuj00ChgCL3F077v0v2fTD61Lf2PYP0aFGJ5nv6N21WZsNps6TuRau07ZgUrNX+lY+qHKYzHfM6i9kLoA5e2nzDPf7eWaxBF8qTqzdSurT9sXQ06xqg+K+hIhtZFcJ9HKu0XvP3f/WLDPsUbs8x+Pam9lUgvIqy4Br3P3j8PhxZlYX+NLMnGB933nuvjjm8WPNbAtQhqC8StfsHWb2AtARqG5mvwO3uXuedQcer6kLggCy1ujWlAGcNWA3qe+LO7R1Ahvvpm25NmPmc1LfK610a66WAhsSdKW1Hq3ZiTYJBS3fLTpqd9gscwEM3qR9b+nRRrsEpcLjD0l96mSMMtW0I1BHfhPnebBxWFs4KphcQhhS/1zpE3Xb4glKHcfVzm8JZdHz8vW7S32VbtS2eJuS0lrmap06ReYC+KL6PwbNdyhTS5WT+tTtDpWdFYqDP9o2lvrqfvmj1BfvZGz9Q1sweUw/XcHkC+/O997yqrdrZlcTVPjIBN5293xbv+wqI3olnhu/1xVsBbj+wzFSX+LRuSYrxw1p5xWU51NySQMypn8k8yW2OErmAqh74qUFH1SC+aVFE6mv9rtPSn1q1gy9TupbPXGj1Fd9mLZUlJyda43eaHLU2w0rhHQFWrj7FjOrWdBJdqkRvVw6ZJzs7r+G+0YAZwD13IP8ajPrDowCjnb3j8JtJwOvA2e4+ysx534IuNjdK8RsOxMYBDgw3d3PCWvnzQV+AMoSTPmOdPfR+V17Yum60ifq82qH0GFllBxRVOxRqcDXYpER72v0IiIiiocBKR0ZmjpB6pSP6I26UTeid9G9Bd5bGDOMzx7RM7OXgSeyY5LCsKuN6OVaZsXMEoBTgEUEnStiU/pmAmcTrLED6EaOOnhh67QqObY1BvoDh7n76hxR98/u3io8bk/gNTMzd8+zkui6ezsX7g6LiK3fzGcFe8t88hZaS7SZcX07P6WTVdibYZO1RYWVfNVUOzp6yGRNg/psrGx5qW/rk7drfZN/k/oqPP6M1Jchng3ZOHqC0PY7/Z+4WegrBoQjerG1ckOeCMuq5cfeBPkDQ4DNQB93z7fH5q4W6OVFR4KyKC8RBHKxgd5Egl9qEkFSRSPgz8SOsGzKfcA5BMFiNpcBj7j7agB3z3WYxd0XmNn1wDCC0cNcuWbEym2+qe3hwQu0vWfV64Q+r1ZQJZ2ipXVCstTXo02+SzaKnH7JumST2lWCTFEZex7HM8KM8G7TtWv07nh8q9R3VmZlqW9WC22gfvFybemfh2ppk2nevOQtqe/9RVdKfUpia+VuA4lAVYJycAcCL5vZnp7P9OyuFujFZt/+4u7ZgVk34AWCXrRDzSzJ3bPTMp1gNO9YoDJBvc+GMefsBYxz98VmfxuF3RvAzL4ASgGD3P29PK5rCkHHjL8RG+1bqcokJOi+6Y++W6YqFqJp6aLltCodZK6uqz+XubJRfnhfLP7S82nVQ6W+g1ZNlvrm7d204IOKkpy1G3Yw1yyNakoWKTt/Z4zfgdfCwO5bM8sCqpPPX96uFuj9Y+rWzEoDJwDXu/t6M/uGIKiLTZN8EbiGINC7ARgQPjaFYF1fx1xciUDjcN/uwOdmlleBrFzn6WOj/fQVC6Rr9Ga17q3U0XTKcKlPTcbXb0h9iYecLPUpSQMyZk2QOhdf/aLMVe/Tx2QugJ/b9pL61l7SVurb8KV2zeqSeo2kvudm15P6mm/RlqaK+AdvAJ2AT81sb6A0kG+Pxl0t0MuNY4HdgJnhiFw5gs+SPwM9d/82DNI2ufv8mJG7VgRTuT9lP9bMfnL3RgRR9zfhyOAvZjafIPDLLeqO7biRK+mvaZvU1228RurL/CnfJQZFzuo+2g/TlanadVf7fKsL9LY+pp0aS9hL1+UAwFetpHZP3XpV36zNovxxnXYqtV5yKamv6v/yXBGzQ/itwxVSX68p2qn+zQPjdyoVwLN2ngTV3OrtAs8Az5jZLGArcGF+07YQBXoQTNte6u4vAJhZeYLALGfxrH4ECx//xN3fBmpn/2xmG8IgD4KouxswysyqE0zlLgAqxp4jzKi5H8g3krNadbbppraXKi9p3xyzUrW1p6qO7FfwQUXIzKO1v8+6PS6W+srdrlug7Wu1IzTpE7SdUpPO0n4p2L30JqmvdE/t6L36S3LZitoRr0/3HyD1NWus66MNOT4wdzHcvVseu87blvPs0oFeGMwdB/TM3ubuG81sEtAl9lh3f3cbT/8+cIyZzSEoatg3bJVWEdgr7LiRXV7loQLLqxx2ujQYynjjYRJP1k3pJKQ0JuPL12S+xLancuEBN0hcY74fxmHXlZG4ANIm/EpCpSSZr9x927qWeDupvRcbhIGsOmtTzX6f9pcHz8r3sqRTr5a5AGqdCunP3yNxJZ13E9Xe1r3+Ek+8XP4+LWfnqqNXJOxSdfRKMp/XPkP6RD1XVmmDUalfaoURJZbfDthH6qv5lrA0DpAh/OAG+OzGX6W+2WW04wtX3q+bdgfY9NzHUl+lUdrZgsyFs6S+sq1PktbR2/TYtbLP2nI9H5TcWxTolRCO3P0Y6RP12bLZSl1ExE7NlSntZK6RqZNkLoATa7eS+mZsXCT1LVqf7zr1Es+L1TpKffuV067f3u/nt6NAbzvZpaduc5Jb5wygAUHZlV8IplrHu3uf8PjuBDX0/oh5zDnApvD4Ie4+MDy2OrAYeNzde5lZT/7qVbcBuNzd5+R1ba8fqw3Iqz4n1cU9l6UcJvU9mfqF1BfPzGrQgmCFhYZhqRNlLoDL2/SV+iY2qib11Xr7danv26baGpYHzdS2x8z4OM4/HHb+8irbTBTo/Z3cyq80ACa6e2czSwammtnr7p79SfqSu/fK5TG/ACcCA8PNZxAUZc7mv+7+WHj8ScADBOsFcyV9mbao6fgq2lpe45K1L66rk3QFfgEeSNcG6mniYEG55iohRdukfsnxl0l9aq437XtLrbe1U+FZq1Klvkb7agvp3X3ALVJfr4P+KPigIiT5rNukvngkCvS2AXdPCwsu1y3E4ZuAuWbWxt0nA2cBLwMp4bnWxRxbnqAwc56U63rAv7vof0m7pto2Re3LlJb6SKhS8DFFSO8x2umOazstpAAAIABJREFULff1kfqSLrhK5vrhwGtlLoCKlROkPjV7dEyT+jI+19UkBNjy0odSX5na2r+XHgm/S31J9XeT+uTsROVViooo0Ps7eXXOAMDMqhDUwostzX+WmcUu4IktM/8icLaZLSWYok0lDPTC810FXE9Q8PCInBcT2xnjxkqt6FquYc5DdhhR54ii5bvabaS+3YZ/I/Wh9gkZlqltMdVj3ldS36Of1C74oCJk4NhHpL6IIkY7WUDG/VpfPBIFen/nH1O3Ie3NbDpBkDfC3ZfE7Mtt6jb7n+8BdwJLCfro/g13fwR4xMzOIZjivTDH/mLrjPHm/trG1cfMHiL1qZmwf3+pTzl1+31z7ejhy6W1o79XTLhO6rMKVaW+nodop1JveEQbKWR88YrUt+ruvDpd7hi+/FVbY7XzaG3LPDlReZX4Jix4XCHHto5An3CNXkPga+BYd58WJmO0yWON3nh3b2pmzxC0WNsPOCmP4xOA1e6eZ4l6daAXEbEtbLpBt46tdEftMoa1o76V+qq9Ft91+yKKlozpH0l9iS2OkvqSqu+pzbr9z5W6rNurR0ZZtzsb7v6Lmd0N3ETQ9aIwDAM+c/dVMSN9mFljd89ewX4ikO9q9mRxo3M1d9bRTo/dsljbCLxWee26lqUbtWsCpbzwQ3FfwY5F/Fr/ovrBUl9fWyv1fbk8zv9e5NwutWVs1SZ/xOOIXhTobTuPAX3CUTv45xq9KwnW4gHg7rP5e7ZtNr3M7CggHVhNjmnbnKy7r0t+u4ucSn3fkvrUgddpdQ6U+g7PqlDwQUXIoRV15UD27KTNYD75Y+1i98uzakp9SeKx+3WZGVLf22fousQAkNhaqstYpHvtAVz/vbZczQGZyVJfxPYTTd2WENRTt+p1LYmHnS71ZS35Weqb1/khqW/fyQ9KfUqylmszwhNq1Jf61GRM0a4pS2ydZxWpuCD9f9pevquf1Ra3L1MpU+qr/v5n2qnbET10U7fXPR5N3Ub8RXJKe9Y/fq7MV7HHWOmC/uSU9lJf+dbd2Tj9eY2rxXn636XMVgzPXYvzeLNKB5mv6+rP4/q1ULHzENrX3E/mm7/xEX79UTdjkJzSnvVv6ZKhKl37GutHniVxVbzyJTZ8+7jEBVDhoB7y14J2vDk+iUb0CsDMagHDgUMIpli3AveG//4UuMzdnwqPbQlMBfq6+/1mNho4HFgLZAFXuftXZnYI8CBQJvzvJXcflN91JJauK32i1o/XZt2+0V3byaHL+draYV3Haqc3oxZ2JZd1d58g9VXq947UFxGxLWRs/UM7ovfAZboRveufjEb0ihsLsifeAMa4+znhtvoE2bOrgVnAmUB2fYJuwPQcp+nr7q+Y2THA40BzYAxwprtPN7NSQIFd2pd0alQEd1R4XrzoS6nvlMO11eu3zNIuuP1s2U9Sn5ppu2v7pd6dqS2x8sTVupInSRdoS/F8cI82OWJxKe1zN6qUtlPFuBsaSH3zhy+W+hr31Babj9h+okAvf44Atma3KgNw99+A/4RlV34DKoWjfssIWpjl9fX4cyA7WqtJ0PcWd88E8uxxm025o/b6l7fw7zj7gsI0/yg6Eo+9Q+rLXDhL6nt38lipr+Psu6Q+JfMOuoZ+pXRtuypVTWPFi7pgqE53mQqAVHHgddYM7Wv9TKkNlp90idS3crM2WWifmjWkPjlRZ4xdjv2BKQUc8wpBH9up4bFb8jiuCzAz/PdwYJ6ZTSAoqjzG3TfnfEBsZ4y2VVuxT8U9t/X6/zXHbsnrNnYM/xugLUr7xuLvpT45cV6O59GawnI8S3ZjZmK6TFdd3Lu0nklnxuSloq5L0a3nBLitszYr9djJk6Q+rtDqMi6JWmNsL9EavXwws2uAhu7eO/z5EaAdwTq9vkAf4FKCrhczgXFAW2BDLmv0lgO93X1WeK69gGOAswF39475XYt6jV5ERMSuQd+Uw6W++1I/k/oiSjbyNXr3Xaxbo9f3mWiN3k7AbOC07B/c/Sozqw5Mjtm2xMzSgaOBawkCvVj6uvs/apW4+8/Ao2b2JLDczKq5+8odcRP/htUXN5P6qjwzs+CDSjBjq3WU+s5dOUHqiyi51MvU1iWMiIjQEgV6+fMJMNTMrnD3R8Nt5XI57lagprtnWiGmQczsROAdD4ZTGwOZwE7VymDt1K00mDqvuC9jh7H02EbUel+TILH02Ea0/lIXyP6xfqf5vhBRArhmqbZYeUREhJYo0MsHd3czOxkYbmY3Eky/biRogRZ73LamqJ4fnnMTkAGcGyZl5Il0TRJw0sJfaV6tocw3Y+UvMhfAKVMSaVujicgF15VvLnEBUB4u76EdpSl1TFeZ6/TTtL1g31syTeqLKFqUdd8AUo+5XOrba9ZcqS/uicNkjGiNXglB3RnDN29U6rCy5aU+0rXJJiSJ2z5FFCmZP30nc5VqpG3Pt/mOa6S+xOZ7a30n95L6Mt4cKfWljdf9bQKUaaPtFFP+5me1a/TuuUi3Ru+mUZJ7iwK9EoI60Mtaqh1hS6ilGz0sDjLefkLqSzxRO6oQERFRPExrcYPU13L6MKkvqfqe0kBv410Xyj5ry/cfEyVj5IaZbXD3CjE/dwfauHuv8OcLgBsBJ5gWHZtLBizAM+7+kJlVAIYBRxGsk1sP3OTu35jZM0BnYJm7Ny3guk4GXgf2dfcfwm0JwAiCenwObCYolPyLmQ0BLgCqxN5PXqw9/6KCfzlFSIWB2kAhM0M7wlaqrmbaNpv2/bSdPybtub/U55m68iPfnPWuzAX6Ar+nz7hT6tvU+zKpr9zwJ6U+NVsfHyT1lS2tbRK2ocfFUl+VVydIffFIiQv08sPMjgeuA45x91QzK0MQTGWTWwbsU8AvQGN3zzKzhkB248fRwMPAs4XQdwMmhf+/Ldx2FpACNA/PvTvBGj+At8Jz/1iYe6v98U+se/DUwhxaJFTodGNc9/dU+pJT2rP6ytYSF0CVkVPwTboCvxW73MWGrx8t+MAi4qjVX8r/Vladr+sFq34tVHvpB2mWfXG81tcO7CjzVR48gW9rtZG4Dlo6mdU9dV1pqjw2lTlldX8r+/08U9/rNlqjV/zkN6JnZp8Dg9z9k1weNxoYHxvohbXsPgIa5ZUMYWYNwsflOaIXjgrOAzoBb7n7PuH26wnq8F1d2PvJi6iOXkREwMI22jVee0yeL/VFRET8hbqO3sYhF+imbkXrD0viiF6ymcWmwVUlKFQM0BTIr+XBfWY2MPz3+UBDYFpBGa+FoCvwnrvPN7OVZnaAu38PvAxMMrP2wMfA8+4+tbAnje2McUrVg+hTevftvMzC816ZsnRJ3yTzHbr8W4bX0mUW9176KbMatJC4mv46nRuE1fnf2/wbzydXk/la/TGFK1PayXx7TJ7EJSk5y1XuOM5PqYqyM/LIC5K4Q7j+fFjq51wj7FbxUOpE3qmi852weuL/2TvzOJvr748/D2Nn7NugopS9rGkhkhJ9kxZLu7T+aCESKRKtSvtG0a6URCnJlixFFKGyRBj7DGYYy5jz++PzubpNM3NnzJ0zM9f7+Xh8H9/mc9+fz+vzGTNzzz3vc86LueXOMdO7IG6h2YeRk5b8yQWV7Mo05u5YyZeG/3aXxdt2TAOgtj7oFuTHQC9JVc8KfBHI6GXy3AGpMnrh6gDoAbzg//cE/+ufVXWziJyBV6N3ITBTRK5R1ZmZuaiqvgm8CXBo+XTTjF6FbqPB0Mkn4bW+dmJAr7V2D7ePakQ/mJ4Fcs6gMXaB3tKYJgxNsftQsOf+lmAYeq1+1/YPf9S1dzH8Wju9PjfswJscZcPIETebaQEkFDccbQQkRF9kpwXMvm6Omd79ZVvlTvDlyBb5MdDLiJVAU7xBx5ldf6aIFMxsVk9EauDV1wG8jpe1uxBoKCIKFARURAaoxyHga+BrEdkOXIGX3csSJZrZFsCac3VkO2NUKVnWVK9JbCiL5vxLGdumP3saGUZ5ucEtv+f2HTgc6ROBNXqRFug9gbc928m3JisM3KiqY9NarKrrRGQJ8KiIPOwPSD4FqK+qX6VzziYgOKN4O/Ceqt4RdGwu0EpEEoFtfmNIAaARsPx4Hmxbu9OO57TjZuYvdtvEAF0W2mb0dl8/wFRv43rbQO/8xB/NtHpUPdtMC+DNkXVN9W586DdTvdcb2ZrkvLTc9nd9ixw21VNs37g3HbWdQTpz+3G9pThOICIq0FPVaSJSGfhOPC8yBUKN0b8Vb7zKWhFJAnYBAwBE5COgDVBBRDYDQ1X1rVTn9wCeSnXsM//4F8AYv/sX4Ce8TltE5GngWqC4f+2xqjosvZss/d64EI8RXuz6e3OHCpNT/zPmLGWsh6h2HmWql7wis0n07FPqkqGhF4WR+NttajkDFB1m+7s+YPTA0IvCSKHew0z1pLBhDQqQPPllU70vHy5nqjeriN0opVwhJfJq9PJd1+2Jyr5e7VWKFTLTK1AumsL3PG6mZz1yYWCzwTy1xOb5BjYbTLQWNNECeDthOWu+f85M78CjIyn5mp0t2aBmD/H4LLsM8Gfnv8AVo207fQs0sGtuGddhHF2bbDLTK/WWbSCbG+Ncdl9nk3Uu/8FqEhfbzSWcdsn7XNTZzkt7weSydNg+wbbrdlgPu67bYR85ZwzHP0T6eJV9L15tqhd9T+pxig6Hw+HIa5iPV3mku12gN9wmiI2ordtQZMJV43agn//yPqCfqv4gIgXxtl37qur3/tpvgTGqOlFENuA1QB3Fa8YYoqpf+OvSdNcQkcfwxrKkADuAm1U1NscePo/jAi+H48SgWim7jnCALQl2GagTgdplquX2LTiyyAkV6GWEiFwG3AGcr6q7RKQJMFlEWviNHf+HV2/XFLgaSFHViUGXaOufdwbwLV59HqTvrvGMqj7sa98DPALcmVPP58hdyhcrZaq3OynBVM/hyCwu8MrfrNmzJbdvIWdxc/QimoF4c/Z2AajqUhF5B+gNPOx73y4EhuE1UbRP5zrRQHzgC1X93u/k/Requi/oyxKQcWvYuga2nYan/rbaVC/SsQ68LGuSwKtLcuRP5lew7Zo+b5ddRzhAwtgbQy8KI6VuzYxjZvj4pbqdBRrA5v0hjZwceYwTLdDLyFWjPv911VgC3BT09SBgE/C8qq5NtXa23+lbC+iamZsRkZF4Xrx78ezTUr9+zBnj1WdHcOuNPTJz2bCwb+JoMy2AQtfYjlex5shbtkb11lgHlo7wcXTtYlO9pNNsO8KtSfiyhq3g1r9N5c7odLupniP7nFDNGCF8cuPwfGn3Br3eGbhJVa/0v74CeBVYrKqdg9Zt8K+zy/fPnQk0UNVE//VTyMAvV0QGAUVVNd25EZHejOFwOHKHR6q2MdUbvnWOqZ4jf2PejPHQNXbNGCMnumYMY1bxX1eNpnjuGYhICeBpPBeMcSLSUVX/42vlD2HeDtTDa+DIDB8A0wDbAWEOhyNPcn4lu1KNn3UvSZpspudwOGxxgd4/PA08JSIdVHW3iJwF3AwEClgeAT5R1d/9xowJIjJLVQ8GX0REKgE1gY0ZiYlIbVVd43/ZGcjQF8jaGaPKzNQ70478xIuV/1MJkKPcs322qZ4lCZPuz+1byFFG9f4ZDHMm9Y3rOW8vtC/0ojASn2g7oLnJJ1eY6hWse56pnjUagQOTXaDno6pTRKQasMD3rE0ArlfVrSJSH+gCnOmvXSYi0/EaOB71LzFbRI4ChYAHVXU7ZOiu8aTfoZuCFxRm2HFr7Yyx4pw+pnqnLbSdJp/81ZumessH/Wmq13jefaZ6PReeaqq3rJ+dLdmy2+bT+JWmZnpEFbbTAs47aOt00PyaRFO9osNs/7bsv/dWW72n03T4zDEK1/jQVK/Qq1+b6kUiJ1SNXn7G1eg5HI6c4OToyqZ6G/dtN9Vz5G+sa/QSB15p9l5b8qlJrkbP8Q+PV7Xdihu81XYr7rVKts931w7b57u4iq1f6rfbfjXVi2QurNzQVG/W9hWmereVqGeqt7ZkLVO9LUf3m+rN2L7cVG9wTBtTvS8O/mWq58g+ER3oBXfZikhtYDRQF9iD53wx1J9zdzNBDhn++veAOf42a+DY1XhduP/zZ+x1BLao6lkZ3MNmvLl6R4FDqnq2f7wbXvNFHaCJqv6S3jXAPvDKDayDL0tiChQ30xofu5AuVZuZ6X2+dYmZVoALKtU305q1fQWtKtkGQ3XK2o3oGHIC/G2JZB6PnWOqd03V5mZaE7fajv4BICXyNs8iOtALICJFga+A/qo6xT/WAGgGfJ/OaR8BfYG3go51948DvA28AmSm2KuVqu5JdWwFcIV/nTzH12XtTNUBWrTbSlfqmOnNnG67XdU1dq6pXm4EX5bM3bHSVG/ejlWmer/HbzLVc4SPCsWjTfV2HbBtNsmV4MuRLU6IQA+4DlgYCPIAVPU3IKOK7hnA2yJSSVV3iEgpvKaKm/zz54rIcbfCquoqAG/Gct6jWslE7j5kV6Q996MMm47Dzp6+pSkz2mZC/56+Z3u5ZIfDEfFYB16OMOMs0PIt9YGlWTlBVY+IyGTgGrzMXWfgO1XNasGHArP8Tt5Xg7eCQxHsjCEFS1OgQIksSh8/jTZluJOc77n/44L0ijnXSMtE5oShp9G/W4BxsQtM9ayJKVnOVC82Mc5Uz+E40TlRAr1/ISKfA7WBPwOuF+nwETACL9DrDow5DrmWqrpFRKoAM0Rktapm6p1DVd/E3xq27rqdW+4cSzneLWqb2Xx+aE1TvbfuiOxgwZJxsQvMgnSAXjHn8lYEB3su8HI4gnA1evmWlUDrwBeq2kVEmgGhTBfnASeLSCOgOZBRUBiwOpvsf/myqo5V1S2+5jYR+QJoAeT5d43CBY+a6u1WW739E2yN1d8w7iq+I4IbW6x9dc9r1JMmFewGli/d5YaVh5M997Yw1du/ON5Ur9qCNaEXOU5oTpRA70NgkIhcHlSnF7INUlVTRGQi8C6eV+3hEOs3AMc6cEWkpH880bdQaw88dHyPYMs5OzPr3pY/mbzVWtG9eYeLwy8ONtVzgVf+pswLkf23zBFe1GX08ieqmiQilwHPicjzwHY854sRQctuFpFgL5mWqroZb/u2H9A/+Jp+AHg+UN4foTJEVcenkq4KfOo3XEQB76nqd/751+CV6FcEpovIElXtlN4zHFj/DXtusnOr2L+rMBVaFTLTKzrsZQ6/McxMb/+sjRRvYlObdGBpHPeuLWuiBfByg3iKd7XbepfTGiKlK5np7es7wrReemPTM4huXMROsIAQ1bKxmdwrgzdySOze3HpE76TG7NfN9MDWCSdl02aS/7D5JBl1RlUmvmYiBUD7k2KZ+XeMmd7VQyuaaUUyzhkjn7Cg6lWm/1BfFLG1YXo2Nr0pN5HB3sGtQy8KI2e9Ymu59tfebaZ6lsyvcHboRWHkvF22ZQW9jb1nR/34aOhFYaRYjQtN9WqXqWaqNyTqdFO9YUdWm+qt2fmzaQF3wj2Xmb3XlnrxS+eM4fiH5iueMdWr1PouU70RxnVX1hz59AVTvVWrR5rqOcJHkrHe0b+W2er9bedTDPY1ndZoUoKpXtdipUz1HNnHBXo+qVw0OgLPA+1VdaOI3Ag8gDcqJRn4QFVHich4vLq7Wqp6SEQqAEtU9RS/MeNLVW0QpDEMSFTVUUHH7sdrCqmoqrsMHjVTFCmRbKo3rcEQU72WdWJN9eb/brfdAdCp/Q5Tvb23DzTT2vxnGTMtgDr3lDfVS9lq+2cg6qpupnq61dhCq6bdNnhuIIWLmupZltgAFHroXVO9SMQFeqkQkXbAi8AlfpB3KXAfcLGqxopIEeDGoFOOArcAWa6UEJEawMXA36HWFjPeXrHHdmCyfd+zcWdcXVsnDmumlLX7fdg26gCXx0dwVujln3P7DnIYwyI2R9hJtm5fTHEDkyMaEWmNNyuvo6qu8w8PwrNOiwVQ1UP8e57e80BfETmeGXuj8TKFXxz/XeccH5VvY6b1uG7g24Z2P44J24rwUoJNM8bdpeK4Oc5ufMyCncZBcy4Q0YFXLvBkFbvxPw9um83Qqm3M9B7dOsdMy+HIi7hA7x+K4M3Aa6Oqwe+UDYCMPvL+DfwA3ABMTfXaqSISbDFRBX92n4h0Brao6q+ZsUEbUdV2Dts99xYz1evx4AaqRmgS6iXggkr1zfQuqFTf3As2krkvxraRpl+MbWPLSUv+5MFttnMXXfDlyLO48SoRzRG8Db1ewL1ZPPcJvKzcV6mOr1PV4Ll6w/z/Lw4Mxtu2TZdgC7RXnx3BrTf2yOJtHT8JvXpStEMjM72k2Hkk3nGLmV6JZ0chRtZPmhjHrmsHmGgFKNGujplW8WfHsKntnWZ6NWa/DkftfJiTp74BlQxrLA8fJKrNtWZyCV+8SsqOnWZ6Bdt0QDfYZZ2j2t/E/t69zPRSDqdQoHABM63VC+1GkDR5/yKizsrwbSusWM/MjFTceBUfEUkEKgEzgamq+rh/fB4wVFVnpXHOeLyGi09F5EO8QLF/qGYMYLqvc8B/qToQC7RQ1TQ/zh/Ztd79Q4WRtefYzSQEePNItKne00seN9VLXmU3Hieqnm2GLdIZ0/gRU70qR2xroP7324jQixx5lkIVatmOV7mzg914lde/ceNVrFHVAyLSCZgnIttV9S28bN0zItLJtzErDNyoqmNTnT6S/2b00tNZgRdUAiAiG4BmGXXdRn4zhiOcvOR+XvItHxjWxgLcs3uOqZ457nchX5N8eEtu30K+xwV6qVDVOBHpAHwvIjtVdYqIVAa+E6+YToG30zhvpYgsBZrkxH2ta1A3Jy6bLkmJtgOTG2z41VTPGmuv27ML7zHVezUlpKNg2Hgzdr6ZFkDPmHNN9TpPsh138trlpgkTfoo6ZKr36pKnTPU2Gs8g3RFf0lSv5qlxpnrWROIup9u6zSdYb90mL/7SUo6o5peZ6iV/P8FUb9NDts4fNee9aqp35KNRoReFiZSNtp/wizw42lTPmt+a9DXVO6O3TW1sgEK9HjbVs+bI+7aBbMp22zmPJZ/4zPSTyL47LjF7r41+Y7rbunX8G8vt2z/PqM/pf9h1bu57bBnRD39rppcUO8/s+5kUO496614x0QL4rNwF1DP8WUmKnUf0/XYTgnb3qMMLc6qY6fWv8jjRg78207P82QQYWrUNC1LssjSPvRRPyx2LzfQOXHsfxU/taKYXd0M9yr23KuK0AHZ1OZ0Kn9vZKya8cZ2Z1jEisOvWZfTyCVGFq7l/qDDSz3hkxldJ6031bi9S21TvfuPxHA6HI3eoUaqCqd5fu3+1zejddrFdRm/Mty6jl1ukY4c2ErgTOFdVVUQKAkuA3qq6wF/7C/C7qnb3v34FOA8oDNQE/vAlRgBnAp2BFGAHcHNgKHNaWH/KP7D+G4rX6mCmZ/18I5eMNM3oPWf4bCtrNaL+ervAKyl2Hj269TTTK/vxONOfFeufTWu9fY9dHLHZ9NzQS3i1G6X+7+OI0wJI/OkNSra4w0wvaeN3ZlrHcBm9E4NAoOfbob2BZ4e2TkQmAN+p6lgRuQ+or6q3+efUBT4BygGnq+r+oOudwn9HrUSr6j7/v+8B6qlqusPIXEbP4XA4HKmxdBkB+2HXyYe32Gb0erW3y+i9NcNl9HKTdOzQ+gI/iMhCoA/QIuiUHsB7QF28TN2HGV0/EOT5lMDr5k2X9pXthhcDzNi+3FTv8qpNTfWmbI10f0+Hw3EiMCbB9m91pKMRmNFzgV7apGmHpqpbReR5YCFwj6oGVzB3A9oDdYC7CRHoAYjISOBGYC/wn/kbwc4YUrA0BQqUOO4HyirWNWwtDtlMkg8wxVQNLqzc0FRv1vYVpnqWnFbG0KUCeFZOM9XrHG/bob24SjNTveLFD5vqfZJk2+VbUm3H1dQ/lGyq16ilrUWfI/u4rds0EJEDwCw8C7N7U71WAEhQ1RJBx5oBL6jqeX7t3kagUSAQTGvrNtU1BwFFVXVoevdkvXV7TkU7Cy2AhTvtLJEcDseJw3UxLU31PohdZKoX6Vhv3e69qZ3Ze23pd2a6rdtcJAXoCswUkcEBOzQAVU0RkdQ/CD2AOr7DBUA0cBXe1m9m+ACYBqQb6FlzclRpJmz90UzvzpjzeT32BzO9bW1Po8rstRGnFeDxqnYDmgdvjeyO28YVTmXZrnWhF+ZTGpY7hRVxG3L7NnIMF3g5TnRcoJcO6dih/Qc/w9cVaBjomhWRtsDDZBDoiUhtVV3jf9kZyFMpLcsgDzAN8gDTwKvK7LXse9JujhdA9IPTTPUsmV3uHFO9trsWmupZE8lBnsPhcIFehqRlh5bGslbAllSjUb4H6olIVVXdms7lnxSRM/CyhxvxRrekywWV6h/HExw/c3fYDUs+EWjy5C+5fQsRQ9s4+8CrWqnyZlpbEnabaQGcFF0p9KIw8ve+HaZ6kU7N0nbDwwH+2hvhNXopuX0D4cfV6OUT3HiV/I31kNFNCbY2RQ6Hw5ETmNfo3WBYo/eeq9FzBLHlXFungzJvjjDVS1m9wFQPse3ybX7bp6Z6H5RvY6p3SZt0Z33nCFuXFDPV+/hwWTOtDkeSzLQAzt9lW6ZhWT8KsFdsUzQT99tZhAGs35veplHOMDDmAlM9ayJxvEq+zegFu1cEHRsGJKrqKBEZjzfupJaqHhKRCsASVT3FX3s6nuNFbSABWAvcrarb/S7YXsBRvDEq0/1zOgAvAAWBsar6pH/8QmAUngPGz0AvVU0WkeuAgYD4Gnep6q/+OWWAsUADvBl6t6hquntSLqPncDgcjtR0qWo3HudhSaFJ7FIzPbDP6O257kKz99oyH8xyGb0wcBS4BXgt+KCIFAW+Avqp6lT/WBugooiUB7oD9YEY4Ds/KAR4BS943AwsFpEpeE0U7wDtVPVPERkO3AS8BfwFXKCq8SJyKfAmcLZ/rReAb1T1ahEpDBTP6EHmGhegly4J8YCNAAAgAElEQVR+0FRv/uEypno3DLLVmzoi3lRvbWFTOQ4Z/ilumXTUTgyYYZs85BbZH3pRGJmbXNpUb0NB23+/C5NsPyOvK2z7ttq5om1Gb4i2MdUzJwIzepEe6D0P9BWR1N2v1wILA0EegKrOgWMz7Sao6iHgLxFZyz8OGGtVdb2/bgJet+xO4LCqBvL1M4BBwFsBD1yfRUB1/9zSQGvgZl/7MJDhFNEL4haSFDsv80+eTYrFtDLVO8tYr1hMKxLeu91Eq9QNb/Jd2XNNtAAuil8Q0T8r4896hO5X7zXTu/zVpaZd09EPzjP9fjaOacWua84w06sw8Q/T5/vozEfo8etwM73LY1qR+MPzJlolz7+PK6rYzTyt+evvxN9p98mn7OvLGGamFrlE+tbtl0BHYB4wFX/rVkSeAzaq6gtpXPdlYJGqvu9//Rbwtf9yB1W91T9+A1527m5gA3CVqi4RkReAC1W1Yarr9gfqqOqtInIWXnZvFXAm3nbvvcH+uP45wc4YTS2dMSKdhRVbhF4URs7Z+ZOpnsPhcEQC5lu33drabd1+PNtt3YaJJ4Av8LZqw46qqoh0B0aLSBHgW7wt42P4c/V6Aef7h6KAJng1gT/6weGDeLP3gq/9Jl5A6Gr0wowLvBxZoXIJu63+32+oaaYFMHRKydCLwsiLhtk8gN09bF1+yn+Up0aiOhyRH+ip6hoR+QVvqHGAlUB6rUNbgBpBX1f3j5Hecb+JohWAiFwMBGr6EJFGeE0Xl6pqYEDWZmCzqgba3T7FC/QcRvSMsdtKBRgXa9xVHMFYd20O3jqb7fv3mOmd94ntHL1VcctM9axxgZcjK0Ri123EB3o+I/l3Ru9DYJCIdFLVrwBEpDUQh+d3/6G/vRuD15X7E17nbG0RqYkX4HXHq/VDRCqp6g4/ozfQ10NETgImATcE1fChqttEZJOInKGqfwDt8LZxHUZYB14xJW2N1WMT40z1LIl0y7VVcX/n9i04HI4IIj8HesVFZHPQ18+lt1BVV4rIUrztUlQ1SUQuA54XkeeBI8ByvDq57SLyCV7glQz0VtWjACLSB5iON17lbVUN2EcM8K9XAHhNVWf5xx8BygOvighAsqoGeuHvBj7wO27XAz2z883ICT4rZzcv6aq4uWZauYF14PW/Kk3MtKZusx234HA4cocbY87h3Vg7Z5p9Iy8x0zqGc8Zw5BauRs+Rl3mmit126oBtkZ3Rczgc/2DdjBF/VRuz99qyn81xzRiOf3itUlvOKWxXJzT7SBn6bo/cN9RFlZrTcsfiiNPKDe6Lac2A2Mj9WWlfuRF+Rt6EW46UpfOKx8z0RjZ9mB9T7DLO4087QLX5a830ppRtxctF9pnpnVEgmpeMGk7ujmnF8I52o4bKjl3OH6c3MNPTXMiuRWKN3gmd0Qs1oiXV8aPACrzgeDVwk6oeSH0NEbkZaKaqffxr3YY3ay8KGKyqU0SkH3Ar3tbwTjxXjI0Z3at1Ru/rsueHXhRGHvjXLnzOsyJug6me5VYquO1Uh+NEoUmF00z1lu6yC9LBPqMX1+UCs/facp/PdRm9PEaSqp4FICIfAHeSQV1gEKP9uX51gXkiUglYhhcMHhCRu4CngW45dePHw6XxP+T2LUQUi/aty+1bcDgcEYh14BXxRGCNngv0jo95QKOsnKCqq0UkGaigqsH7XIuA60OdbzlJHmBrh9tM9ap+k9q8JGc5unq+qZ7u2hJ6URiJatU19KJ8yvrze5vqnTzmWlO9gnXPM9U7PHqgqV7hvk+Z6iUv/tJUL6r5ZaZ6a86+21Sv6lm2Fn2O7OMCvSwiIlHApcA3/qFi/py+AOXwRrSkPu9svM8KO1O91It/nDdSn3PMGeO8co2pU6pW9m4+C1SkspkWwLkNHjLV6xL3vane8hpnmer1PjTTVC+6QBEzrcZiO6pmStc3TPWW77YdqfmkYSMNwIPPtDLVa185S5/Js82M7baBrGUjFMCAqb+EXhRGkk3VIhMX6GWe4IBuHvCW/9/HtnThnxq9oPP6isj1QALQTYOKIv3jzUhneHOwM0ab6hfpeMO2doBl1ezqyhrH2deU/XbKmSY6DTb8SqNNdn8cT4quxN/7dpjpVSlZlm2J8WZ6OWJxcwLz4ZENLN/9l5lerdJVWb93q5neuoM7TfXqlzuZlXEZllyHVSuSu9CbVzw99KIwkxsNIDmNC/Qyz78CuiwwOnVjB4CIXAQ8BFygqodCXeSHHauPQ/r4qVGqApfvi+zBrQ02/Jrbt5AjWAZ5gGmQ5wg/lkEeYBp05YaeVZBnrZUbLN75Z+hFjpC4QC8XEJHGwBtAB1XN1Luy+fZKBH9KdISfTlUam+otS7R9gytUoJCZ1sZ92820AHrH2G6lzkzaYKrXP8q2K3VtIduUUL8zbet/K30d4c0fLqMXcaTnrjFERO4LHFTV6mHWfQYoCUz053P9raqXZ3SCdeAV6V6wJ0VXMtWrWqSsqV5BTCcS8NU2O7/UG2Ja0i7abkvnvdhFZlq5weQEW/fFLQm2Xr63sslUr01luzlzcAIEXo5sc0LP0ctPOGcMR16mdplqpnpr9thmMRwOR+5gPUdv16V2c/QqfO3m6DlymWqlyptpWX/Kd4QX68Dr/2LsBnq/Ghv5MyVrla5qpmVdMxfpWDpVdNm1j1VxkV27HYnkaEYvLecJ//jtQD//y31AP1X9wX+tEPAYcBVep+ohYLiqfi0iI4EbgbKp3CiKAO8CTYHdeN2tG/zXBuGNMDkK3KOq04POKwgsAbao6mX+sT7AfcCpQEVV3eUfLwu87R8/iOdm8Zv/Wl88pwvFc8/oqaoHReRCYBRQGPgZ6KWqySJyHTAQEP8Z71LVDDsDXEbP4XA4sk6dsjVM9X6Pt90qjnTMM3qXGGb0pkdoRk9ELgPuAM5X1V0i0gSYLCItVHUbXpBXFWigqodEpDL/jB+ZCrwMrEl12V5AvKqeJiLdgaeAbiJSD+gO1AdigO9E5HRVPeqfdy+enVl00LXmA18Cc1JpDAZ+UdUuIlIHeAVoJyLVgHuAeqqaJCKfAN1F5F3gHaCdqv4pIsOBm/DGsvyF120bLyKX4o1QOTvU9+5Sw4L3rw1rrhzhxzIb+3nxmrTYvsRMD+BOw4ze67E/cHK03VzJjfu2E39LQzO9sm+vMM/oWW7150bgdVqZGBOdtXtieb6yXaPefdtns6ZuPTO92qtt60cjFfOMnojMA4aq6qygYwEH7yeATUBNVU3XhToNf9npwDBVXegPNN4GVAQeBFDVJ9JYVx0vEBuJl1G8LJXGBjybskBG7yvgSVWd53+9DjgXL1heBJyJl52cDLyIZ3O2SFVP9de3AgapasdUOmWB31Q1w79871a73jSj17y4nck5QMONtqNO4nraFkyXG/ebqZ4jfIyoatvx3qP8NlO9b3ZUMdUrf9R2c+KyL68x1Tv43Iumev3m2w4Qv+OwbVvq2bGTTDN6O9vbZfQqzojQjB5edu3nVMeW4GW7TsPrQE03yEuHangBIv7W6F6gvH88uGVus38M4HngAaBUJjV+Ba7E86ttAZwMVFfVn0VkFPA3kAR8q6rfitdOGyUizVR1CXA1kNYeQqacMaRgaQoUKJHJW3WEwjrw2tyytqle9UWpk96O42XIVtuO9yHmJWy2MzrNOWdObt9BjlK/3Mmmelcc3GOq5yo6s88J2Yzhbx/v8IO0Npk87UngBd8dYwVexu6on5HrDNQE9uCNTLleVd/3t5FH+zWE3+LVCQbfR1u8QC/NfahgZ4xbT7na9GOwtQtHpOMCL4fDkRNE+tBka5wzRnhYhdc0MSvoWFNgJbAWOElEorOY1duCly3b7G/dlsZryggcD1DdP3Y5cLmIdASKAtEi8r6qXp+egH8/PQH8bN1fwHrgEuAvVd3pvzYJb0v3fVVdCLTyj18MHBv+JSKNgLHApaoasuXUBV4Oh8dow5okgL7b3fBwh8ORf8mNQO9p4CkR6aCqu0XkLOBm4GxVPSAib+Flzu5Q1cMiUhFoo6oTM7jmFLyt34V4W6SzVFVFZArwoYg8h9eMURv4yQ/ABgH4Gb3+GQV5/roywAFVPYzXYfu9qu4Tkb+BliJSHG/rth3eVjQiUklVd/gZvYF49YCIyEnAJOAGVc2THi+PV23LYMMtq/aVGzFj+3IzPUd4+V8VO1/kvs61xeFw5BB5KaMnIm8DgR3IBv6xZ4D/AYeBdXhTPjLcT8/pQO8/zhOq+pzfqbpARBRvvMj1qhrYih8CjABWichBYD/wCICIPA1cG3Tdsao6DK+T9T0RWQvE4XXaoqor/S7YVUAy0Duo4zZNROQevNq9KsByEZmmqrcCdYF3/Hteibfliqr+KCKfAkt9jWX4263AAH+buADwWlADyiN4NYSv+s4YyaraLJPfUxMsgzzABXlhJqakbYH21G1LTfUcjryK9e9ebKJt45zDlPF4k0beDTo2A6+xM1lEnsJLWg3M6CLOGSOfMPLk60w/aDy6dY6hWu6Q8PVQE51Slz7Ki4bbjS00kb+Ti5vpHRWhx+45ZnoAvQwt+o6gvLHkaTO9nZf3ouKUt8z0Hmg2mASSzfReeq4JGx+0G0L9xOFinHO0mJlekRQ4p4RN8LVwfzk61t4cemGYeGVDNe6pa6dXZeZa8zl629u0MQuKKs+ZE/LZROQU4MtARi/Va12Aq1X1ugyv4QK9/IEbmOxwOHKCnyrbbiZYz1x05G8iOdCrMnfuHfiTNXze9JswjxEi0JsKfKyq72ekk6Nbt7ntjCEi5YFPgebAeFXtE3RON+AhoCDeN3Ggf7wfXg1eMrATzwFjo98hOzroMeoA3VV1sojUBCbgbcf+jFd7d9i/XldgGJ5rxq+qeq1//Cmgk3+tx1T14yx8ax2OE5qehhk9gHGxC0z1LHGBl8OROwRP1sgqIvIQXpzyQai1Ee2MgWdV9jDQwP9f4B7KA88ATVV1p4i8IyLtVHUmXo1dM78x5C685pFuqjobOMs/vxxeh/C3/iWfAkar6gQRed2/n9dEpDbe/vl5vgtGJf/8TkAT/3pFgDki8vVxzA905BO2nGs7R6/agsge5xLJgZfD4cg98lIzRnqIyM14TRrtNBPbsrnRdTsQGBBwnFDVpSLyDtBbRJ4AbsNzxjjkv74d+MT/70UAfgNDMJ3xsmbgZfBeFhFR1f3ADyJyWqr1tYA1gZEowHd4GcSZfkAXYBGQVjfu1cDXfjAowIV4TSLguW0MA17zn+UVVY3373+Hv6YeXtduMpAsIsuBDoHndEQekR54ORyZ5aToSqZ6f+/bEXqRw5FPEJEOeA2jF6jqgcycE+nOGLvSWb8WOMPf+94MXAEUTmNdeq4V3YHn/P8uD+zxgzb4t/vG6QAiMh9vi3iYqn6D57IxVESeBYoDbfE6g/9FameMm6q3S+dxws97sYtCLwozSbHzzLSKxbQy03KEn0WVmptptdyxmEeqtjHTG751DvteuNJMr/bgWfQqfZaZ3uOxc8y0ALa1O42bV/6ngijH+KDdIZ6ebRPMPtB2B3fNyqy5U/b5ZOtP3BDT0kwvN96HNMW0JDBDROQjoA1QwZ80MhRvl7AIMMNPei1S1Tszus4J6Yzhb6PeBXwMpAALgFOD14jI9UAz/tk2DhyvCjQEpmdCKgpvdl8bvGHN34tIQ98irbmvuxNv/t9/xr4E79/PqtxVScrCQ2aTQafamaoDPHigMN2a3memt75RHTMtgFrLfzfVuz3mPFO9F5Y8aaZ1b7MHedtMzR/QbLidc2PMOfR5ys74ac3wC0IvCiMXjbStr6wy03ab//qZjfFKzy20ivJC6f0mWgAjouuTmHDYTK9vjN18zryIqvZI43CWW/Ij3RkjXVR1Kl7NXyBzdizQEpGL8Bo1LghsIQfRFfhcVY/4X+8GyohIlJ/VC7hvgJfd+9Ff+5eI/IkX+C1W1ZH8M0D5QyDDwckXx8/P6OXwE28rZ82UCDdQfDPW9uflTeMMqeW4GoB7Itgd4937c/sOIouvty0z1jOVM8du8I9HfqjRyyoR7YyR0U0EuVaUBf4PL4BDRBoDbwAdgmrqgumB76oB4DtwzPZ1J/j38YX/8mR//TgRqYC3lbteRAoCZfznbwQ04p/GDkcEUq/cSaZ6PYqcGnpRGPk+JaSLX9i4Wst7PexG3LEjcoM8h8MR+US0M4Z/zgYgGigsIlcAF6vqKrxg8kx/2fAgK7JngJLARH//+29Vvdy/1il4mcO5qZ5zIDBBREbgde0GUqvTgYtFZBVexnCAH9wVBeb519/nP3+GH1wO/P55Ri+HHYmuYKpnztEjodeEkdhLe5vqVexVxlSv9WMHDdUO0uhKu+2qrjQy0wIoOvwVU72Nre8y1asx8QFTvQKVa5rqrW52r6le+ap2vwsApRpEdsWXat6p0QsXbmByPsF6YPKYim15X+y61ebuWGmmlRtUK1XeTCtKoti4b7uZXqQzq9y5fFK0gJne67F2rhEAu646nQqf5UnL7bCR8KFdMFvq2tfMtACmlbUrm3ixSAJPFLT7XdhxoDjttn9sGnltOedCs/faagtnmTybC/TyCTMrdzP9h/qsmO3PxUUHI/tT4oBk22aMJRdEm+ot+KGqmVbyf8cr5SjTitpmf0d1TDTVGzKttKnehhTb5xtGQVO9qWLX4QuQKLZFZQn/7RvMUV7b8InpL/zms+0Cveo/RkCglwecMU4BVgN/+EuPtSGLyDd4g5mjgHlAb1U96r92N9Abb7v1K1V9QEQK49XuNcPrwbtXVef467PksuG/dhPeNjXACFV9J6Pv5aHVs7XEmWmN9MsZkmLnRfQIknUN6vJ8nM329H3ldnHqb6tNtMD7hN8x3m5Uzc7Otan4hd2cwISpgyj1vyfM9H475UwabPjVTM/6d292uXNoG7fQTO/A759TvE4XMz3r7+e6BnXNft8ttQD2PdmR6AenmekdWDOVwic3cYFeNjEP9HxnjEeBS4KdMYAWqrpNRJ7EC8BuD3bGUNVPRKQlsBFv2HFwoPd/QCNVvdN3xuiiqt1CeMRFq+o+f+Dxp8BE39miLV7Q1snXDzRt9MZzzOjpO1x8jWetVhavLu+YywbwrqrO9K/1Y5DLRhv/vsrhzQ5shldW/rN/frq9rs7rNn9Ts3QVU72/9kZuK97iKrberM23OYswR97lySq2HejtSDDVa/z3F6aB3qbm7czea2ssnmnybBHtjJHRTQSNb4nCG5Yc+Me9C3gySD/YzWJW4JiI7OGfQC2rLhuXADNUNc5/nhl4zhgfpXe/lsOEAZLn2Zp0RLXqaqpnzaHH7WYEAkT1GGyqV7BmY1M9S5KA5J/tshhRTTuaaQHMq/+gqd55c20bkwpUqGGqx5HUE7lylmmNHzPVqzv9DlM9R/aJdGcMgJoisgxvi3iIqh6LmERkOtACLzv3qX/4dKCVv018EOivqovx3Cwu9ydV18DbJq6BF/xl1WXj2P36BLtpHCPYGWNohQZ0jbYb0bE50W7aOkDH+JdM9T4v19pUb1dB25q5216+x0xrVrlz8SYL2XBhnO0A3F4xtgN+34q125bOFRrZznj8u9nppnr7dhUz1bsqzq6sAIDmqYdO5CzJh7eEXhRGIrFtIbIr4GErcJI/0qQpMFlE6gcCSVW9xB918gGeX+0MvO9JOaAl3tbsJyJSC3gbqIsXlG7Ec7U4mh2XjVAEO2Pcdso1OtqwBnacYY1XbtAl7ntTvZiS5Uz17o+xC2S/Ap6Ntf1+WlJd0/rclnMkjLnBVO+2R2w7bids/dFUr0xX20Cv1O8bTfVMbWIc+ZKIdsbwhyYHtmB/FpF1eBm7Y0U3qnpQRL7A2/6dgZddm+Sf+5OIpAAV/K3ZvoHzRGQBvpvFcbhsbMGzRQtQHZiT0QOOi7XNYjjCS5GCdsHCX3u38WyibeB1TVU779mJWxebaQE8unWOrd5tpnJ8UbY1neMjN1CPfuDL3L4FRz4iL3ndhouIdsbwz41T1aN+Vq42njNFSaCUqm71A8NOeJ234DWGtAVmi8jpeNuwu0SkOF7zyn4RaQ8k+4OXj8dlYzrwuL8e4GKC3DbS4h7jDtjWB+1mJQF0XJLh44edAwPstjYB+iwoG3pRmDi7+MmMnXSjmR7AwadfMNN6Nbm2mRbA0z/HmOrdVmqXqd4XewvyjGFB/y0X2PoPTp1pN/oHoO3JsaZ6SXsLmeqVr29bg+jIPjnddZsCBP/UB5wx7gLuw2tkSADuV9Xv/XMK4zljXIlXI7cfeERVpwc5Y8T41x2rqsP87df3gMb4zhiqul5ErgKGA0fwtlWHqupUv5P3S6AIUACYDfT16/sK4yXDzwIO49XozfJr8Kb719kC9AoalfIREOyyMcE//h3QEG8LGf7tsnELEKiYH6mq4zL6Xlp33Z5Rtjp/xG8OvTCMdK96tpmW9faRJZVLlGH7/j25fRsOR7rsfdDug2vpJyO7DCXSST68xTTFtuGs9mbvtaf8MiP/j1dxhA83XiV/c1vMeaZ60xJsBzRvSbDzurWmQ5WzTPW+2faLqd6IqrbjOT46tN5Ub2Wccc2cMd+VtW0WuijetozIBXrZJ9KbMSKGK6o2NdVrpbZdovdvi2zj+DGxtp2GjvBhHXhZU8T4I2RMIVsnjkVTbctCZt1o+7u+rpCt88eE8m1M9RzZJ98EejngslEYeBmvKSIFeEhVPwu67lV4I1eaq+qSEC4baTpj+K91xZvxp8Cvqnqtf/wosMJfdmxLNz3e7WPj4hBgzEuHTfUSF48x1dt202hTvcqP2GZNSnV/xUzLesajtWPLU8YDaY8a14J3jrbztAa49JDt2471XMImtT8LvSiMPLG5kqleHbGtCbQmEjc5883WbQ64bDwKFFTVISJSACgXGOIsIqXwpkYUBvoEBXr/cdkQkfKk74xRG2/Y84X+GJZKgaaM9ALX9HBbt46sYD3OJTYxzlTP4XCcGFhv3f51pt3Wbc1f3dZtZjhulw3gFqCOfzwFCG51ewx4ChiQiXuoRTrOGL7+KwFrs1Sdt1liQvk2/GK4x/Jk7FzTurIxsfN5pGobM73hxiMzXqlklxXqvWO2eeB1cZUzQy8KE99uMx4QC9wYc46Z1us/DKV4rQ5meudWrMOCnbY1ndacHF3ZTGvjvu1mWo7w48ar5D2Oy2VDRMr4//mYiLQB1uFl7rb7WcEaqvqViKQO9NJy2VhL+s4Yp/t68/G2dYep6jf+a0VFZAmQjGe5NjmN+zzmjNGs3JmcVviU0N+RMGJdV2YdfFnSe0dk1yBaBl/Tytpu3b5b9DCHNcVMb89NfYhtdZqZXsw82yDvA+Mar/lFkk31XnWBniOPETLQE5EE/vGBBRD/awFU1bhqPzxE4Q0pXqCq/USkHzBKRG4CnsOb65ea9Fw2MnLGiMKb3dfG1/teRBqq6h7gZFXd4s/3myUiK1R1XbBgsDPGqJOuVww3bzMaWujIOuMr2NZ53bwrcgPLjhHu2jLBdsycOdftnpPbt+BwpIvqCZjRU1Vb09OscbwuG7uBA8Ak/+uJeF60pYAGwBwRAagCTBGRy1V1Cem4bGTgjLEZ+FFVjwB/icifeIHfYlXd4l9rvYjMwZsB+K9AL5gHjbtSa5W2HTJ6KMW2+cN6HEgkB16O/I3lsGSAARHeYe9w5DWytHUrImcCgX2T71V1efhvKUsct8uGiEzFy7TNAtoBq1R1L3CsvdUPwPr7zRhpumz469J0xsBrDOkBjBORCniB4Xp/3QG/QaQCcJ7/LOmSOHdUtr9ZWWHvoDdM9cp9amvYmDToTlO9lAO220dR1cuEXhROvat6mOrphtWmegvuXmmm1eKqrLg/Zp9Zk+y2pQESF9p1hAMUqFTTVO/I6yNM9XZPt63HXbiliqmeNYZVGmZkOtATkXvxmgsCWbAPRORNVX0pR+7svxQXkWCrhoDLRjVggYgEXDauV9XA5scQPJeNVSJyzGXDf20g8J6IPA/sBHqG0G8NDBeRgMvGnaoa+A17wQ+CwRvfEnAJnw5cLCKr8LJ8A/yA9FzgDd85pABejd6qjMQL1j6b5EX/KePLMVKShVLn2AYLhx6/z0yr0DVXoKttPqdI3UZ8drPdkNGTUg5x9vz7zfQACpS2G/Fw6Kn7KTLwWTO95MVf0nrlE2Z61pw+ow81P+1jplewWh0zrdygQK2Tier8fyZayV+8SlJiookWwMb40vzvMbvfdSmR6cEUjgzI9HgVEVkOnKOq+/2vSwALVbVRDt6fw8d6vMrdxrPKXjKexeZwOByRwPOVbbfe79tuu/VuPV7lz7odzN5rT1/9TZ4bryL8U3uG/9+RV7WYR/mybCt+Kmo3Af3qgntotMnOESDuhnqUey/DpKYjk4yv0Jb2Z2wy01u4uhpXx80109vW7jSqzFxrpjeialvWykEzvVsOKWfeave7/to7hbGcKHHh0UTqPWK3nbrz9RXEfPummV6xmFZ8W9ZmNNXF8fNpV9Ru6/bbQ+VMx25dmeRCjHCQlYxeP7yxJZ/jBXidgfGq+nzO3d7x4W/jfqCq1/tfR+F1zf6oqpcFrZsMVFHVlkHHhuFtUe/EC4QHq+oUEbkT6I0X4CbiDWFe5Y9n6R+4roiMAJrhfX9uA+7D68KtGJj3F6TVHFgIdFfVTzN6Jjcw2eFwOBypec1wRifA+cVsawLrrplmGu39UedSs/faM37/Om9l9Px6uDnA+XjjVXqq6rKcurFssh9oICLFVDUJaA9sCV7gz9JrCiSKSC1VDXbaHq2qo0SkLjBPRCoBH6rq6/65l+ONYemQ6ppD8BorOvqNFvOBL4E5qW9QRAriDWX+NjMPlLRpVuhFYWTvjbeb6pW481JTPQoVDr0mjPx6+w+mek2X2zbvWJM8P8PPRWEn6ryrTfUsWdTgAVO9lr9l2HeW70leMO1t5DMAACAASURBVCn0orAK2k4siGrd3VTPkX2OZ2By8By9vMw0oBOeX20P4CP+6RgGuBJvJMp2oDvweOoLqOpqEUkGKqRytSjBv2cLIiL3A5fi2bEl+ecv819L6/7uBj4DmmfqaQra+guW/mCcqV6k802h1HO9c5ampmr2WAZeh57pz9EFi8z0Ct/xoJkWQIVSB0z1Ip2o5p1M9X5q/LCp3ll9bEtsCg1531TvhHbGEJFHgGvwghPBGxkyUVVte8kzzwTgERH5EmgEvM2/A70ewHC8QO8z0gj0RORsvA7bnf7XvYF+eM4XFwYtPQ84A8/vNmQLlN8p3AVoSwaBXrAzxgut6tGzXo1Qlw4bHaceMdMCmPXrGFO9o2t+NNVrftC2Zz/umlBN5OEl+mY7i7CuD/9mpgVwbXJpU73OJ71rqvfzfltf5MlNHwm9KIx0joo31av9o9UgCo965+0KvSiMXP+m7Wjdz4eYykUkWcnoXQecqaoHAUTkSeAXvPEleQ5VXe7bkvXAy+4dQ0Qq483B+0FVVUSOiEgDVQ28g/QVkevxxrV0U7+QUVVfAV4RkWvxRrfc5K9fC5TF2yL+LBO39zwwUFVT0sn2BZ7hmDPGkV3rtZhhJ2xS7Dws9QBTPcvnS4qdR+f4/iZaAPuXjqdEk5vN9JJi57H3OrvActLScaY/K5OMfxeSuhnrWT+fsV5/6+fD7m9ZUuw8Kk5dY6IV0Jtq/LNiTSbbFvIVWWnGmA108e27AjVuk1T1wozPtEdEElW1pJ+FvBdvMHJ5/KYJEbkbL0ANfNSLBl5T1Yf8ZoxEVU23yElECgDxqlo60IyBl+mbCdyoqrNTrd8ANAs0Y4jIX/yz9V0Bz6Xj9rT8bgMkTRll+uMXde6VlnLsv/dWU70SL4w11Xuiqe32yqCfHzPVs+TwmEdN9RKnbzTVsx4efmSSbQaq0JV3m+qZk3I09Jow8ktj2xrL+vdXCL0ojBTvN8Z0L3V17Y5m77VWjSaZ8bp9Ca8ebS+wUkRm+F+3B37K2dvLNm8De1R1hR+QBegBdFDVhQAiUhP4DngovQuJSG1VDXx06gT862OUqv4pIlfieeB2UtV0Z5Oo6rHZAiIyHvgyoyAP4OB7X1L49IoZLQkrCeOmUmqMXZ1ekc5tkDOamekd6HsbUriAiZYeTmHQz3Zb052b2A2/BUi86xYK1bCzvC7c/wmkcDEzvaSP76Dad3ZOMXHX9KTcRLvfvcSPl1L69cgdCJ28YJLpB9c5DYfQesYtJlrft3+bVpO7hl4YJvr3mMzoGweZ6W1qeye1+pnJRSwhM3oiclNGr6vqO2G9ozAQyOilOtYGL/PWB5gPVNeghxeRpcBdeA0V/8noicgLwEXAEbxMYB9VXZnGeJWLgbF49XedgAfwPHN3ANNU9dZU1x2PF+hl2EaY9Mlw04zekZnzLeUoNvI1U734brY1bAtWVjPVO7VYgqleldPsbLueX2P7vXywo22NV9HhthZh/ZsNNtUb9aNtRnZGo6GmeoWw3furUty2maZYMdsu39NWTTfN6K06tZPZP2C9dV+ZPFumt24ducuRXevdP5Qjz/L2WbYF9rf8MtxUz+Fw5A6FKtRygV42yUrX7WXAY8DJ/nkCqKra7dmcwFg3RjjCS9eqLUz1Ptma16sqskdv9/vgcOQKFYvbdqFv3WM7ziVFT+DxKnidolcCKzSPpwFDOWOIyM14zRF9UjlhFAYeU9WP/POuAYYBdYEWqrrEP94eeNJffxgYoKqz/Ne64dX6FcTbkh2Yzj2OBG4EyqbeZk4L8+6jI4ds9QoVMZVLnvyyqd5bj+4IvSiMjBnSxlSvwJlnmmmNvNfOmg/gllK24yuqfXC/qV7CgP9MlspRStzW3lQvqs21pnrz6tvOQdxZwHbG6mW32o7ecmSfrHbdtlNV24Fgx4GIJOKNPDlHVZNE5FLgCWBzOoFeou+EURv4GSivqkd8Z4wU4A28OrxAoNcY2K6qsSLSAJiuqtVEpDywDG+e3k4ReQd4V1VnpnGPLYGNwJrMBHrTKnc3Da6XFS3I0K1zLCUjmhqlbDvVyhe2TbT/snt96EVhYkDMBWzHrk4oPuUQU7ctNdOz5sLKDZm1fUVu34YjH9C1agvz3YLkw1tMU2wrav7P7L224V9T89bWLV5TwTQRmQscS/eo6nNhv6vwEMoZ4z+o6hoROYA3E2+Hqq6G/zpbpLJ+WwkUE5EiQC28wG2n/9p3wFV4Y1dSX2NRWtdOj8vjbTN6ewe35j5am+mVfvx7M63cYFOCbVZoE7Z6ljwTOze3byGicEGeI7NEeklIpJKVQG8kkAgUxduyzOuEcsb4DyLSBC9Qy8o+21XAUt/bdi1whj+oeTNwBdn4XgU7Y0jB0hQoUOJ4L5Vlmr661kzrROCj8m1M9QYn/26qt/Qqu9E/AIm/2ZYWjN9k1+n78NbZoReFkbEV25rqdT53s6lexS/sBgoDtKpUz1Rv3g7bGrZIJ28Xph0fWQn0YlS1QY7dSZjJyBkjDfqKSE/gdOB/mdUQkfrAU8DFvma8iNwFfIy35bsAODXLN+8T7Ixx7ym2W7cNko/HBvn4mVKlsqne19uWhV4URsZE7TbVa1jYdgTJwG+KmmlVwLYm6UpN4FLsxsf0N67HndTQdph3kTa2byN/bShoqjd6Z3lTvYuqtjHV2y7JpnqO7JOVd/NpInKxqn6bY3cTfqYAo/jHGSM9Rvs1epcDb4nIqQGrt/QQkerA53hOGOsCx1V1KjDVX3M7cFRECuLV/gFMUdUsz6J4JResYBzhw22PhZdnq9hloeYSzYYCdm9uL0V6R3Hf3L6BnMY2m25NheK29b8vmqq5rtu7gP4icghvaHB+GK+SnjNGmqjqFBHphedhm+4ofN/+7SvgQVWdn+q1Sqq6Q0TKAv8HdFXVo8BZ2XgORz5niPGn7hER3khz/za77c3KJcqYaeUG7Ss3MtWbsX25qZ4jvOw6YJfddoSHTAd6qloqJ28kJ1DVzWT9A8Fw4EMRGQN0Bl4CKgJficgvqnoJnrvGaXg1gIHs3MV+bd8LIhKYNTFcVf9MS0REngauBYqLyGZgrKoOS++mPit3QRYfI3ssK2L7qWaH2LbsP31lhgnbsFPm5Tmmes8YZrwAqh+xqywoZewl+lkxW72n2tiO4knaZlvvWKzJaaZ6v/9k2/He+LGTTfUmDrX9eVldKM8P3sgWGoEZvayMV/kMeAv4Jj+MWIk0juxar4l32PgnApR8w9ZYPdL5o8U9Zlpn/GS92RHZpMTFUqBcTG7fRo6xv3cvilx+vple1CW29oPWHJk4Gilv05yku3dSoFFLEy0AqXgyKcv+M0Qix9jy6AJzC7RlJ3U2+9Ta+O8v8pYFmohcBPQEWgITgXGq+kcO3psjiKjC1SKwF8iRU9Qrd5Kp3qq4v031HA7HiYH1HL2lNewCvSabbAK9rGzdfgd8JyKl8TpZvxORTcAY4H1VzbPjssPllBF0vfvxmjwqququoOPNgYVAd1X9VETaAqODTq3jvzZZRPoA9+F15f7rOmmxf+XE4/8GHAdrLrGdlm+dhTq6en7oRWEkYfhYUz2xbZqmxKCRpnq7B7xrqrd8QyUzrca1t5tpAYzdZJut7NNii6le0Stty14OTrKd8/h/P9nWkLYIPd/fkcfIdEYPwHd+uB64AYgFPgDOBxqqapucuMFwEC6nDP9aNYCxeEFb00CA5nfWzgAOAm+r6qep7qGcfw/VVfWA764RD8zxtTMM9FxGL39TrZTtyIUtCbbjXBwOhyMnsM7oLal+hdl7bbPNk/NWRk9EPgfOAN4D/qeqW/2XPhaRJTlxc2Em204Z/uHReC4hX6RafjfwGdA8nctdDXytqgf8ay+DzDtjOPI3LvDKv9wcc46p3vjYhaZ6DocjssnKBs+LqprmTANVbRam+8lJsu2UISKdgS2q+mtwgCYi1YAuQFvSD/S6A1myiwt2xnihVT3umjQ1K6dni55N+zPu51FmejPrD6bdSrvt4mIxrUgymk1YLKYV+5d/aKIFcFG74cz6dYyZ3i1N+/O24c/Krc0GMHbJM2Z6xWJasafv2WZ640dj9rMJ3sDkC2rbbade8udhlvz2vpme5e96QC/htR4mWqXu+oidnWubaAGUfO1tDvS9zUyv2Td7zLQimaw0Y1yD13GbICJDgCbACFXN827fIpKoqiX9zOMrQG3gW6B/BjV6e/CdMlT1GxEpDszGG6OyV0Q2+OfsEpGJwLOqukhExgNfBm/dikhVYDmeu8i/ahmDr5PRM1hv3d4ZY9eFB1DAVA1aH7ItYut3yHZ2WGxinKnexVXODL0oTLSnnJkWwADDmX25wapTG5rq1Vvnhoc7Mo/11u3ial3M3mubb/k8b23dAg+r6kQROR+4CHgGeA2w+6ibfY7bKQOvaaImEMjmVQeWikgLoBkwwT9eAegoIsmqOtm/Xlfg8+w0rOzp0/R4Tz0uCjY93VTPeuTCjv/daqr3xwPXmepFtepqqmeO4Sy9PgyFArY2WpHM/lhb79kCMXYZL4cjL5KVQC/wl7UT8KaqfiUiI3LgnnKS43bKUNU3gGOtd6kycTWDjo/Hy+hNDrpUD2BQdm68zMs/h14UVqz1InxuXzfrSUQvGes58isjqtoO1x6yNbIzpI7wknzYtkv7RLdA2yIibwDtgadEpAj2O27ZIrtOGcczKFpETgFqAHNTHb8Hr6mjCrBcRKaparpppn4xrbMqnS3mHdlmqtegkO30+m0pSaZ6vydtDb0ojPy11/bf74qqthnnaClsqqcYOn9k6c9y9nkjwW2lhhNrL9joQiVM9ZLV1inGkX2yUqNXHOgArPC7UavijVX51n+9rKrG59ytntj0OaWbaY3e5Um2n2o6xtsVSwO8Uck2i3HHDpfFCBeXGweVm47YFoR/166QqV75j3831XM4soJ1jd6imCvN3mtbxk7KWzV6/liQSUFfb8UbOhxgJl6DhiMH2K22fpQd4xeb6lljHXhZBydTtlpvvdth/WyPG29t9p1n68NsjWWXKEDFL2xrAh2OvEY49wjy5MZ2OF0xRORuoDdeveJXqvqAiFwHDAiSbAQ0UdVfRKQw8DJe80cK8JCqfuZrPgMEig9eVtUMrRMuPFqStVF2KfPZ5c6hbZzdPK8dnU6j2bx9Znr3Fq/P/UbdlM9Wacv9hnVJ71RoSyfDjGWSwH3b7Z7vvpjWPB/7vZlezBG4eZfd8205tzbVFtgFJ9fFtOSD2EVmekVvupwGN9uNV0mKnUexmAwnaYWVZdWa0HiLzTAKSy2AJ6u05UHDLvS4m+qbaQWIxBq9LDljZHghkaWqmucyeuFyxfDtzB4COqnqIRGpFJivF6TVEJisqqf6Xz8KFFTVISJSACjnj2M5ppnZ53DOGPmbk6LtLLQA/t63I/Qih8PhyONYb90uqHqV2XvtuVs/y1tbt/mccLhi3AU8qertoaYO8nx64A1mDnALnlUafiNHhrPyMsJy4CdAfDfbcSfRzw001StYrY6pXvLM90z1otrdYKrnyL8kfz8h9KIwEtW6u6lepPNHi3tM9ax9ya3RCMzoRfzWrU+2XTHwhie3EpGReH62/VU1dSFbN6Czf37Aafoxf5TLOqCPqgYcy68SkdbAn0BfVd2Uxj0cc8aQgqUpUMC2u8qU5nbT1k8M3sztG3A40uGV3L6BiKJeuZNM9VYZboOD/XiVSCRLgZ4/LLm2qo4TkYpASVX9y3+5XdjvLkyo6nJ/zEkPvOxeRvQVkZ74rhhBx6OAckBLPJuzT0Sklvp73yJyNnBAVX8LWl8dWKCq/USkH96w5huAqcBH/hbwHcA7wIVp3Peb+O/YN51il04GeLSMXb0cwKg9tm4Hr8f+YKo3OKaNqd6QBYNN9Y7OsrN4A1j3yK+mep8fLW2mdVNF29E4tZbbdt12qWrrmDm+Z3FTvVmvm8rRJc6uXhWgV8y5pnrWZHmGWj4g04GeiAzFc4A4AxgHFALeB84DUFVbz6Wsc9yuGKp6ENgMTPIDu59EJAXPBWOnf153vC3hALuB4E7liUAvAFUNdrgfCzwd6uY/iF1k7tdoqfd6BOsVi2lFq6RkEy2AS+N/YNCsU8z0BgxZy+glT5jpFYtpxZ77W5rplXnW9nfv2qZ9+fDn0WZ6xLQi4dVuZnKl/u/jiP5b1uVxO2/rEo2uNf9evrzkKVO9N8zUIpeszNH7BWgMLFXVxv6x5araKAfvL9sE+dxWB65U1Rf9rdT0fG4TVXWUf+4XwDRVfUNE7sTzqn1ERE7HGydzkqqq32ixCWilquuDtCfguYjM8nU6qeo1IlLVH0+DiHQBBqpqhu9c1s0YNUtXsZQzH/BrjWvGcDgcjqxj3YzxfZVrzN5rW2+bmOeaMQ77QU1gqzJfFYxl1xUDr67vbRH5DTiMZ4sW+IFoDWwKDvJ8BgLvicjzeJm/QIfDPX7GMBmIA24OdSNJm+c4v80wktCrJ6XeGmemdXCnnYlMVLEUyr5vt5Wa/MdCohq0MdP7vv4gWq+0yyBa/qwAHH75IQr3GWmmF3vx7ZSoeNw23Fmm+GX1KdSjv5meNb+e1Y/6w2qZaK0ctp66d9k5cUx+JYUrR8aY6f02xM1ADAdZyej1B2rjWaA9gddR+qGqOlNNAy47qZNpRu+bbb9YykU8zSuebqpXPcrWhunzrUtM9SypXaaaqd6aPa74PJysqVvPVG/BzsqmerP/n70zj7O5/v7484yZsS9jn6FsiUpla8/WIn6l0jekRWmj9FUqX6IkWkiSSouE9JVEiUT5JhMtKmsprbaYsY99LDNzfn98PpdrzO7OGXO9n49Hj9zP9vrcuXfmnnve55xXtO2A7fEJdvNVwT6jF1/FLqPXctMJltHz69auBHbh1ekNUNX/5dudOY5i8r0xpnqRHSeb6kWUt/uWCJC6eqmp3q83TDDVq9nY1rZrfFPbXqzUdZuyPyiEfP9xuewPChF16tlpASzbYusz3eY/totBUV0eM9W76hbb0VTnrStuqndzzCWmeo7jJ2QDkwuKHDpfBFwoigFvquoI/9iBHHHCiAT6qeoMEakHvAmUA4oCC1T1XhE5nyNzKwQYqKrT/GuNBa4BNqtqg6D7Gw+0AHbiNfT0UNXv0mnja2faEewGJhduGlWsY6q3dOvfpnoOh8ORH7iM3vGTm67bG4ChQGW8IEcAVVXbNaJj2Qs0EJHiqpqMt7Scfu1jst9sUQH4XUSmBs2tC3TZngEsEJHKeLV8I1R1Ohx2vABYgde4kSIiscByEflEVVOA8Xh2Zxmlbnqr6lQRaY0XQAYaWEYEGj+y452Ktn6bZdLs7NYALvu3rZF7kbY3muodGDnSVK/ofS+a6hFV1EwqechLZloARS8/11Qv8uo7TfWSbnvQVK/81LGmetZ81qC/qd7F5yWY6pW49ZhJYGFF2gk9Ejhv5KYZ43mgnaquzK+bOQ5y5HyhqttE5C8gFq9LNnjfShFJwRuZEos3TiWw72f///uCTikGaNAx8/1ZfVkxHzgtp08qmNsNvTYLhKds5TZ+vZaqc/+y0br8NDOtw0x62FYvnJluXBDec6qtnjXGA3fDns/tpC6tfAabvv/UTK9a0Ri+sJv8E7bkJtDbdIIGeZBD5wsRORUvQPspg30X4C2tbgFGAF+KyLfAHGCcqu4IOm4sUAO4zc/m5ZR2wM9Bjx8QkS7AIuARVU1Kd0+HnTEeKt2Ea4rbLf/VqpyU/UEhZNt227qdxN9SWFrNxpo58Te4N862ruXM1GhTvSopdpUFWyJtv3G3q5poqle+TVZjPkPPmHds3yvdZ91uqnfglRwtmoSMlM22zRHvLTrFVO+6GpZVRHbzRwNoGGb0ctN1OxKoCnwMHAhsV9WPMj3JgKA5eYvwvHXq4gVnwXPyhuHV7dXHsyEb7Z87kCN1crvx6uQW+PvigDZ4lmb1gHMDPrf+/jPwHC2a+wOV8TN6M7Oo0duCZ3e2QkSq4HnfKjAYiFXVTNdsXI2ew+HID9zMTMeJjHWN3twqncw+ay/fNPnEqtEDyuA5PbQO2qYccX4oaLJyvgjU6DUF5ojIDFUN/LXJsE5OVRM4enZeA2Bx0P6VIrLH357dbIneqnrUekyQ5y3+nL6ZOXiODofDEVIWtbHt8q0w2QV6jhOXk9oCTVVte8Zzz1hgh6r+7DtfHIOqLhKRd4EHgUx77kWkDTBXVQ+JSFW8wHGDiNTCG4ycIiI18DKEa/Jys8HuGEB7vEYPR5jyd4MzTPXqrDhRqywcJxoVJtt63TpCy5OxLU31nkqMN9VzHD+56bo9HXgdqKKqDUTkHOBaVX063+4uF+TC+WIosEREns3imNbASBEJFFv0VtWNInIb0FdEDuEF/ver6lYAEZmEl02sKCLrgSdV9e0sNJ4XkYZ4WdE1QLcc3LsZW9vbDvitOO0PUz1r2qzfVdC34HA4whAXeIWWk71G7yugN94cuoDX7YrgejRH/mFdo1ettG1B+Ibd20z1rKlRxnZa/qE0O0srgAal7ArC52xcbqblCD175g4x1St1eV9TPWusVws6bLT92/JDwlemkdecKjeZfda23vT+CVejV0JVfxA56r7sW2JOUnZ//hSlr3rSTG9ulVjq/2W3mpycsIDihmMXdg1rR5nen4SdFsDU8i24cftXZno7+zaj7JAFZnrW75XdU3tR+sYRZnrWz29nv+aUfXa+mV7asu8o88h0Mz3z98snj1G6nY0X8+5PHmPKnQtNtABaVNlIg+hKZnpvLnreTCtAONbo5SajNxt4AJiiqo1F5EbgLlVtm583mF/4jhovquoj/uNHgVKqOjBdN24xYB6eo0VaFk4XwdvBqxkcA0wB6gCpwCeq2tfXK4o3XLkJsA3opKprMrtf64xe7bKxlnKs2mk7wsKaU0rb2kz9s3urqZ7D4SgYJlZoaaq3sKjtMP0Ra2yyXgE+M8zotTkBM3o98Oy/6ovIBmA1cEu+3JUNB4AbROS5QJ1dOgKOGRF4g45b4AV8kLnTxVHdtSJSAnhBVeeJSDQwV0Taqups4C4gSVVPE5Gb8GoHMx0NuXum7bR1ttgGXpFX2fb6pMx/31Rv50tzTPUqfDTNVM+aQxNsMiYB9i+wqyEt/dY4My2AAU0fN9UbtOiEKOvONxaf86ipXrVqtl3M17SzHcdjTThm9HIT6F2P50AxD4jAsx67QkQWq+qy/Li5fCYFL3DtBWQVRUXjZfUymiCcpdOF76Qxz//3QRFZAlT3d18HDPT/PRV4VUREM0mxps79IotbDD0RsXbpeQBSbes+9k+0DbxW/Wlb8xiz3dYWKW3Bx2Za+z/LbppRaFmxoCJg9/vQZIrdMjHAxfttR3RaB+lRXTIdsJAvnDu4tqnemIG2gV7KGFM5eg2w1QtHchPoNfX/m4Hnc3srnsNEdxGZoqr2i+nHzyjgJxHJ6N57iciteA4YszMJZtM7XQwTkcDX49sC1mkAIlLOPz5geloN34bNH9eyE2+My9agcw47Y0iRskRE2LlH7B57h5kWQPFTbP0Te8e1MNX7OmJT9geFkFatXzfVezYh3lTPFmMLNFvrWXuMeyM+H2rb8X5V0temeuFOL2O9cOy6zU2gVx1orKp7AETkSeBToDneIOFCF+ip6i4RmQD0BJLT7Q4s3UYBU0XkJlUNrPcFAroteEuwAY4ZjAwgIpF4/rsvq+qqXNzfaLysI5HR1TQ5wa7gvXhcM/YunWCmB+Oxfn5WesXjmrHrWbtS1jL9ZvNlwltmehMaDgjb1w7grUYDuGfpIDM96+f333MHcHP8/WZ6Jc/qENbvF+KasWtEexOpMr2mhfXP0rKJJpzJTTPGb8DZqnrIf1wUWK6q9UVkaWDkSmEhyDqtPLAEGIf38wg0Y+wJOGaIyH14z/1+v+liZvqALrPt/r6x/vV6Bm37HBjoN3JEAhuBSpkt3R7ausp0fWX/07ZphWKPj8z+oELMxrb3mOpVnW0X6IHtclzaetvsaGT7DqZ6Rc6w9UXe18v2vRl1bi1bvTv6meol97/PVG/ODNtGr0vrbjDVqzo/3jTF9knVzmafte02TjrhmjEmAt+LSKAvvh3wnoiUBH4N+Z0ZoarbReQDvMzc2PT7xZsncwmwNC/XF5GngbLA3el2zQBuB74DbgS+zCzIA0hL+JN7rx2dl1vIE6NujKDcy3a1ULuvj6d06yfM9Da3PY3Ks/8y06q13EYLYPW59W3HSXz+FGX6zjLT2/qv06n4oV1zxKIPoWni4uwPDBG7Xr6RMj2P+b6Yb3xQvgUdDcfx7L7yfErfbbdasPv0+qbjceZXuJDm22xGnsyvcCE3bYs30QL4pfY5VF9oV8owJ+YSwrv1w4YcZ/QAfK/YwNfNb1TVtio6hAQyev6/q+B1ET+fwXiVKLxaxDtVNTk3GT0RqY5Xh/cbXpcvwKuqOkZEigHvAo2A7cBNWS3rWmf0fmjwH0s5Wmz/zlRvZoztksA7xfZnf1AIGV5tZ/YHhZCISLu3Z1JiCTMtgLcOlTHVe+b5M031xjxi60pT7ZBtX+OSYqZyXLX/oKnelGJFTPXK5So/dPwMWjPRZfSOk1y9Yn5gV2iDu2ACQZ7/701AiaDHAznSEZv+vDtyut23ZcvwhVTV/YDtmlAuOH+Fbcll+gLJ/KZPU9vlnBiiTfVm/FPNVO+2LgeyPyhElAYWZ2UuGGKuJ5VqFQ0L+suWt9MCtkXYdt12X2E7XmVVY9u2zaaT2pjqVX9osqneh0m2Lj/WpJ3kzRiOAsS6KHVptcamepGRtkM47y9hq2fpMgK2XcXDEr7igWFmcua0q9oYdtkF6u8Zz7C0zQfB3xc/YKp31wX7TPVm3jwv+4NCyOTo4qZ6PQ/afalzhAYX6AWRC7eMaGCwqk7yjxuGV7N4EPgb6KqqO0SkU6FN/gAAIABJREFUJTAdb1kYYKuqXuGf0xEva6h4TS03Z3VvraueG8Jnmj3dD+411audVs5Ub9La7031rDklNcJM6+Uqrei5yfbDzZJtqbaBwov915jqxaXaZjDGHSxrqtd8vq3eSuOl4uLGH+OREo4jhY9gm9+2wQV6R5NTt4y6wGIRmep3If8PeMyfhzcUeAzo45+zQFWvCb6If/5jwCWqmiQilbO7sXsPljP1L7UmvMMuqB9zip1Wscr0TAzfwMuaUdElaLRhiZle35hKXJtkN8Li3YotuW1rvJne6nPrU2v5b2Z6w82Uwp/pMc1pnmTnizzLuJY6XHGB3tHkyC1DVf8UkX1ADLBZVYNtFhbiddFmxT3AKFVN8q+3Obsba3beBjZlbsIRcoqUsMsIAaTssv2WWPZdW5updS0tRy4cYHC0bQZ48sEYM61bS9r6+M7dVZZRlVuZ6V25bJBpzer+gQ+QhN37JerW29ljpgaa8LehGuiG9aZ6f7/4j6HaHna/0s1Qz55wzFe6QO9YsnLLAEBEGgN/ZhKg3QkEV8c2E5GAq8YUVX0GON2/zjd4JTIDVfWzrG6qyud24zkAtt1yhqle1Wm2nX+4QZyFlvB2SoUe4f7eHP1wQd+B43i41S67DZDS0XmgHS8u0EtHNm4ZvUSkK16g1i79uSLSHy8rODFo8zFLt3g/97pASzzHkfkicraq7kh3vcMWaHeUPZ9WJevm+XnllgoT3dJfKNnxyIWmeqkbbcerpGy18yq2/tJjze73bAfu3tDrG1O9N6vYjh+p1P9KU7205ctN9co+a7eUejKQJq7r9mThJY64ZQQTqNG7FnhbROr4Y1IQkTuAa4DLsxp87LMe+N6v71stIn/gBX4/Bh8UbIFmPUfvjHMfsZTjjCxbUUJPyvod2R8UQt6YaNsZ191uFBQA0+fYzbYbW+kUOjxnV/MI8OIT68y0Nj8bb6YFcJ3xSNoSFSyXGmH/e1kuloScOV/bjjbqa+zb3Xq/3Zc6R2hwgV4GZOeWoaozROQuPGeLN0WkDfAfoIWq5qRF72OgMzBORCriZQhz7IFrQZTxuJOifV4y1Yv807b9447tb5jqFe1tW4PYubedVurvtsO1pWxlHv3UTi+iah07MeDmx7qb6hU5pbqpXvT9g0312vaxrWFrG2GbgSpSrYKpnjWu6/bkYjiQ1cCnQXgWcG8BrwJFgf95jmksVNWs/np+DrQWkV+BVKC3qm7L6mYOjuiT1e6Qc8YsW6/bzxpk2vuSL1zxjO237lkrbDNQnU3V4J9WdsFC3Du2c9i+uPyY73r5SqsBth+kv88qaarXcLlt4LWra1dTvRI3tzTVW/HoT6Z69drYNkM5jp9cWaA5Co7I6GqmL1RS90bEvJEne988kZywwHwodLhyWrk4/tqRUNC34XA4DOgYe76Z1geJP5hpBUg5uME0ZTk59hazz9pOiTb2bi6j58iQuR+UYWp5u9oPF+SFDhfkORwnDwURfDkKFy7Qc2SI9XDm1w3nlAHct9l1FTscDofjaNLCr+m28AR6IlIdb8bdmUAEMBN4As9erJaq7go69mNgkqpOFpG2wGCgBJ7zxZeq+ohvabZHVV8IOm8N0FRVt4rIWLwu2s2q2iCbe7semAacoaq/+dsi8Lp3L8Or79wPdFTV1SLyDNAFiFHVUsf5owkLXODlcDgcueem2AtM9d5PDHcfo/CjUAR64nU4fAS8rqrXiUgRvLEjT+A1NrQH3vGPLQtcCtwsIg3wGiWuVtXf/PPuzaHseP/cCTk4tjPwtf//J/1tnYA44BxVTfMD1YCB7Cf+tf/M4b2wtf3pOT00JETWLm+qV/SRYaZ61vx2nm1zS/0fR5rqWWPdnLTsbbsS2fNXZDqrPV94oYntQNqHBtiOc4m87n5TvZQltuNc9r0yOfuDQsgbHe1qAguCNMIvpVcomjFE5HLgSVVtHrStDF42706gu6q29bffAVypqrf4g4/jVfWYtrnsMnr+45rAzKwyeiJSCvgdaAV8oqr1/O0P42Ua/53FuXtymtGzbsbYNawdZXp/Yqb3cFxzXkywG/w5I6aZmZ/ojJhmjCmWk6k7IdJLXGymVRAMrdqKPhtdBtjhOBmwbsaYGHer2WftLQn/dc0YQZwFHPXp5TtYrMML9hqLSAV/RMlNeNkygAZk7WndS0RuDXocl4d7uw74TFX/EJFtItJEVRcDHwBfi0gzYC7wX1XNVRtrsDPGa8Of5u4udkMz9j9+P0l3nm2mN2p2EZ6OtavTO73kFn6rkOWKfAhJotK+MkZacFfcxbz0uO04l0PfrzDT+uqTVKaVb579gSEiIbKImRZAu1M3mOqN3pCXP3t5p2djWy/YNLvvWAB8t9z257m1iO37MyY1HN1gT0xEpBdwN175189A14BJQ24oLIFedswAbhSRD4FGeMu5OWFEBhm93NIZCKyTve8/Xqyq60WkHl6N3mXAXBHpoKpzc3rhYGeMV065VUe+ZLfEMjVlP2OK2ZUPPp44j6S7zzHTixmzgp2PtzTRKvt0PM8aBrGzUjfS+sntZnrdNJb23SuZ6e0TOKPoruwPDBHtE5bwr9jzzPR+SajMgDM2muk9vSieYVXt3p+710YR0yTCTK/8O7+YPr/e2+fRuuq5JlpzNi7nA8PpCB23f0UHw9+FKYk/kmKm5nGirHGKSDU8K9YzVTXZN3G4Ca+sLFcUlkDvV+DG4A3+0u2pwF/AJLx6PQGm+9ZiAL8ATYCQmA+KyCl49XUAb+Bl7S4DzhYRBYoAKiK91eMAMBuYLSKbgOvxsnu55u4R9Y/7/nPD//VJwvotHzPGbvDnrufT2w/nr9b44XuzPzBEdJSqdB1Y2UwP4PfBdv6zt2yzXZq+I+4iU71Hym9j76YoM71nY1thaWoVN2e0oRokP2cqx72m9aMXs3nmFjO1P6ucyXs77AZsN4htaaZ1ghIJFBeRQ3gNpXmanVVYAr25wBAR6aKqE/ymiuHAeFXdJyLxeE0TPfAi4ADDgI9E5Gt/aTUCuFdV8+RHpar/AA0Dj/2l1XdVtVvQtq+AZiKyB9ioqgm+7jlAniOZFx9ZmddT88STictM9awp85+ZpnqnlTNeHrvf1bCFivEJ37Gx1Wl2eittl91H7bV1VujnZmaGFMtsJQByouS88gfL8SrB5Vk+o/2VPFR1g4i8AKwDkoE5qjonLzqFItBTVRWR9sBrIvIE3niVWUA/f3+aiEwFOgJfBZ33k4g8BEwSkRJ4KaocfcKLyCSgJVBRRNbjNYO8ne6wzsDQdNs+9LdPB94SkaL+9h/wawdF5HngZqCEf+0xqjowq/t5MjE+J7ftOEFJVVfXUli5K+5i+v9up/e2azRx5ILeYf5+sTXHtCW4PCs9IhKD1wNQC9gBTBGRW1X1v7nVKRRdtw74pGpn0xeq5fVJlnIUe/o1Uz1rJ44njZcgnjL+YvC44fN72vi57ZlnO+4k7aeFpnqzBtnVcwJc/WYjU73Ii28w1Tv42hOmels+2myqV/GKEqZ6pV6Ybtp1O76aXdftHRsy77oVkQ5AG1W9y3/cBbhQVXM9L6hQZPQc0PCUzVT9NE8rznkiuX8Ptv9oF1tObPIED91lJgfAzt4Xm+iUHfYtt5az+2P87ZYq/HO+3dzFn/+sQuP6dp2UTyfCrlc7mum16TSONkXsZr89njiPbbecYaZXr1hxxqhd3VXvru/yfLRVxzt03jaCpO52wWXMG0sZYPTFZ1BiPEMMl277bpzH3xF27806K1aS8kL2x4Up64AL/dXIZOByYFFeLuQyej4ikorXvhwJrARu9+v/qgAjgAuBJOAg8LyqThORlnhLtKvxlpM3Azer6mZ/nt8wYD1QClgFPKWq3/p6A4F7gEAlbT9VnZXZ/VnP0XOElrhStgOoE/bYZmkcDocjP7CeozfOMKPXNYuMHoCIPIVnvpACLAXu9ps8c4XL6B0hWVUbAojIRKC7iIwAPgbeUdWb/X01gGuDzlugqtf4+57DawgJuGNMVtUH/H2t8BpDWqlqoLPiqPEuWd5cgs1w35OFlOVfmOodnGA7vb74U+NM9b68wPZr94UtN5nqRTetZaYV1fVxMy2AlE9tu2Ajr86pOZHjRCTlV7vB9ic7qvokR+KJPOMCvYxZgNclexlwMLhLV1XXAq+kP8G3aSuNN+7lGFR1noiMxuuw6ZXbG7KuKQt3ZsdcaqrXNuk3Uz0mXWerZ800a70/7LT653QMaGHl3YK+AUchIuXgHaZ6ll23VrhALx0iEgm0BT7Dc+RYks0pzURkGVABz8u2XxbHLgG6BT1+wC+wXAQ8oqpHdUAEt16/dHF9utavnpunclzsWpFqpgUwcJPt0mbLRbbDtU47s5Op3p3F65nq9UsM784/R+iIKW43iB0gKXmPqZ7DcaLhAr0jFPcDNvAyem8D3YMPEJFRwKV4Wb7AePDgpds+wPPpzwu+RNC/XwcG4418GYw3F/DO4IODW693dbvKtEYvcb3tH+PXl9t2Nv598QOmetERtr9qN1fN01zNPFGmQRG6c6aZ3tovi5lpFQT1fxyZ/UEh5L6m/zHVG3KG3YBfgLLv2pYx7B9o+7dl8RS7RhqAVOxcTQqCcByE5QK9Ixyu0QsgIr8A/wo8VtUeIlKRzDtfZuDN0cuMRniNHqjq4SIjEXmLbOb7lR9n5yVaIBgvTV9d1XbEw6/b15nq1bTsxciVg7MjW8K8TGO83XcQjzD/eYY71hZo4YgL9LLmS+BZEblPVV/3t2U1ROhS4O+MdohIC7xl2Fb+41hVTfR3tweyjOTer9CSm7bF5+LWj4+dA1pRdpDdclxywgLTOsQp8QMoUb+9ida+36aZaQEk3XsuMaND4vrnAHaNvIHOz60205u65GVeaGLna13zkFKbZDO9xj+9QKcmD5npvXXOTsqMs8vq7XvkHoZ8VcVEq2+LTaTusAuFSjx6N0XOuMRMryAIx4yeG6/iIyJ7VPWY9UoRicUbr3IB3iiUvcAbqjo53XgVAXbitT//ETReZQNecLgaGKSq3/jXfRfPTk2BNUC3oMDvGNx4lcJNpRJlTfWii9h+h9uwe5upnsPhODmwHq/yZnW78Srd1mc9XiVUuIyeT0ZBnr89Ebgpk33xQIaf4Ko6Hhifhd5tub1HR+Fly76dpnotq9gNpIXwD/Suj21ipvVx4mIzLYBH4pqb6g1PcOM5QsnECi1N9W4xXFkqCNR13Toc+YPVJPkAg4xttM4uX9NUL35T+NZ0jqhia+Lea9M88+DLEhd4FW7GRrrh6I6scYFeFmThllEdGAWcieeIMRPoraoH/eXcecA9qjrGv05DvJL13qr6goiMB2aq6lQRKQ/MBV5W1UwLSdrHNs2vp5kh0xLz5LSSZ6wDL2t2puwr6FvIV7rEXWSmtZT9NE6167x9qUorHtrkxsc4TkyqFbHtunUUPlyglzWZuWV8BLyuqteJSBG8ESjPAL3981YAHYEx/uPOwDHV8SJSFvgcGJ1VkAfw3uIRIXg6ueRQrp1W8k5UUdI2ZtjHki9EVK1jpgWw/cY7sz8oRJQ4pxTFBmTV/F34mXdWVuMqQ8sl3aBbz/B1ptly7V1UmvG2md6hqSOJuvFBMz1rDk1+kahOD5tp6Y5dJloAn7+Swqsj7BrL1o1cZaYVIBybMVygl3OC3TL2BwIzVU0VkV7AahEJWJWsBcr4PrmbgTZAeh/bUsBs4L2gjt4ThpTvZ5jqRV5wrXnwZcnSlVXtxFbC5XZNmwVCq1+eNdNK/fN7Uv/83kyvSN0LzLQAStaNYN8j95jplRj+lplWQZCy5HdSlnTL/sAQsWO5XWjStDqkrd5rplf92igzrXDGBXo5IAO3jKMKdlR1l4isA04L2jwV6IC3ZLsESJ8eexEYo6qZpuqCnTFeG/40HefOOc5nknNKDR/Euc0fNdP7+ddrTcerWI5zSU5YwAvRSdkfGCJmL32dNxvZRXrdlg5i8Tl275UmP71g/l4Z0NTOf3bQogu4rrHd0N3pS96y/XkOt7V0TE5YwN4ed5nplRz1NrMa2Lxf/m/F05QP07+bAT1rwjGj58arZEFQjR54Gb1H8Fwvaqlqr3THLgVuB8oDjwJ3A5P982cAFwN7gmr0SgFnA81UdXN293Jo6yr3QhViUpbZBekAkQ1bm+qFM8l97LIzAMWHvmmq53CcyERVrG3aB/vqKXbjVR74x41XORHIyC3jV+DGdNvKAKcCfwHnA6jqRhE5BFwJPIgX6AXzPvANMEtEWqnq7qxuxPJblCP0WM/R27JvsKmeI4S8637XHY4AKQc3mOqFY0bFBXq5Zy4wRES6qOoEvxljODDe78gNPnYAUNmv4zvmQqo6QkSqAh+JyNWqejAz0YJIYTtCh2UNFECFSXZz+6zfm6mrbEedFKltN0PP4cgtKfPfN9WLbJ7hWFnHCYwL9HKJqqqItAdeE5En8MarzAKOaQNU1W9zcL0+IjIOeFdEOqtqhiUCKZ+OPs47zx3/euInU713z7Ur8AUQ43f+sKXVTPVWn2untbHtPfy5roKZ3l4pYqYFsLLodFO9Bx4sbqr35kt29mcAXS+yzdA8971hIxRwebJtTmhXhO3vw4fRNh3FASat/dhULy0MBya7Gr1CgqvRK9zsvKWrmVapgd3NOzfDnYNv2DW3RHcfZKYFcOi/Qzm03G60UYlhtl9arTn4an8iGttkgdOWLKZIu04mWgBFapzDvt73mumVGDbavEZv5Kl2NXoPrnM1eo4gUr6Zaqr3zIPLTPX6P1fXVG9o379M9fq8c7uZlh6wzdAAbO9gF8iWPN8uewgQUak8UsxuQLM1q17eANg9v1P732emBfDXLNvXrs5le2D1bDO9v/9lNwMR4LSetcy0Dk14jqiHbcfxuK7bAiQLl4r+wM1AKt5r1E1VvxeReKA2UEP9JykiHwNXqGopEanpX+c3vL9yu4HXfI/agGZL4CUgCtiqqi2yuL+PgaqqemHQtnrAm0A5oCiwQFXvFZErgSFANHAQzzHjy6yef2R0NdMXamhVW5upPhttnQfuMHRyABif8J2pnsPhcIQDKQc3mGb0Rhhm9Hq5jN4xZORS8R1wDdBYVQ+ISEW84CnADuAS4GsRKQfEprvm36rayL9mbbymCFHVcf7xrwFtVHWdiFTO7Mb8Y5sAe0SktqoGxnm/DIxQ1en+cWf727cC7VQ1QUQa4LljZFnEZV3wvu0GOycHgJ5LbJ9fWtJGU70RI2y7YIsNGmWqZ0nq+l9N9VL+a5tRKNrX1gXnn1bdTfWqdMr0T2m+EH3PE6Z6u7vbLW0CHEqyLSordZltvbE14ZjRK0yBXjABl4o1eJm2AwCqujXdce8DNwFfAzfgWZedldEFVXWViDyM10E7Di9L+JGqrvP3ZzXr7gbgE2CTrxcY2x8LrA/S+Nn//9Kgc38BiotI0cDzyIj/GVo+ASwqZvvLXLqxrZVDb+MMojX3zOprqrc187duvmDtxdwh9jwzrSkv245XuaBSPVO9LSN/M9VbNegyU71wJ3nSCWfk5MiGQhfopXOpmAMMEJE/gC+Ayar6VdDhc4G3/BEoN+G5TGT19W4JUN//9+lAlL8EXBoYqaoTMjmvMzAIL9D7kCOB3gjgSxH51r/Xcaq6I925/wKWZBTkpXfGuLtL5yxuPbRctm6FmRZAkVMbmOrZ+Q54jG9oG8jel2AXyFpnmzU5y5GTISflY9sBxhM62/48PzFycQjQbuWY7A8qxOy5z3Y1JKKYbddt6/N6murNW/8/U73CUcyWOwpToFdcRAIdAguAt1X1oIg0AZoBrYDJItI3qM4uFS+bdxNQXFXXZDTPLojgnZF4y7GXA8WB70Rkoar+cdQJnp9tXeBrf/TKIRFpoKor/CXgz/G8bq8DuonIuYGgTkTOAoYCGdoYqOpoYDR4NXr/7vtatj8kh8MaN8w7tCR3trOTA1hi3GfS0b1fHA5TClOgd4xLBYCqpgLxQLyI/IxnQzY+6JD3gWnAwBxoNMJr0ABvyXWbqu4F9orIfOBcv5EiMP32//AycjHAaj+ILIOX4evv318CMBYYKyIrgAbAYhGp7t9XF1XNdrZBcsIC0hL+zMFTCA07ej5H+aljzfRS/vcOkVfadaYefGMASISNmKaZjsxo0uAWFj7Z1EwvqkOv7A8KIc81eYKbS6av0sg/okukmGkBpO3cTERZuzq2h+ptYMmy9OXL+ceT1hngXVvZP9SuTi/q8kv46t82daQtXjmTRT3tZp7GVdzNKTOfMdM7+LLtqCEIzzl6hSnQOwa/qzVNVQMRUENgbbrDFgDPAZOyuVZN4AXgFX/TdOBVf6k4GrgAr7FiCjAq6LzOeA0b3/mPa+EtI/cXkTbAXFU95DtgVAA2+M0bnwJ9VfWbnDzXx5r2z8lhIeO5RXZBHsC+9+LhvXgzveg6Zcy0AHbfYzd+JP6iaGYN2m6mx6AnuLhWgplc75euMtMCINo25ZW2eplpQfgPhkFeQfDXlU9h+VF3StRCLulgo5UyfyEVS9toARw8UAQpVd5Mr2i/l8y0wplCHegBpYBX/MApBc9r9qiWJ3+0yguZnF9HRJZyZLzKy4FlX1VdKSKfAT/hNeKMUdWjCtf84LAGsDBIb7WI7BSRC/CWZEeKyH5/d2/fA/dx4DS8+sJA8VbrrBo+upcw/OAGUuLfM9UrM26cqV7Kr/NN9RZ2sJurBXDNx9eY6umvhrZk27fy/gDbrumLStr9/p16b5yZFsAhq8y2z8E3B5rq1f3+lewPKsREx9vOJfysgW3Sod3GLHM0jhxQaObonexYz9FzhJZ3KtrOJbx9a3h3FYczf9TLcDBAvnH677+Y6jkcucF6jt6QGnZz9PqudXP0HCcRP9cwNGcFzl673FTPBV6OnGIdeF0ba2PXFWBGomH21xFy4gyXbh2hodAGeumcMlYDt6nqjiDHi9/xumj3Al1V9Xff6WIecI+qjvGv0xBYCvQGduI5Z3Ty95Xx912J14E7EDgDOF9VjxrkJSIvAR2AU1Q1zd92HTAYb+k3BXhIVb9Od4/RwHzg/sB5GXFL3IVMTFiY2e6Qk9jiNGK/srMJq9JgL5U/tdPrEncRE4zcKiy1AB6PbcnTifFmeu9WbMltW+30Xq3SipoH7RokBkdu5n61W06dH32Ah6L2mumdvXYxCyvbzQkc26Eeg76xazZ52bj5w5oVNe2+JKekRJC0z65mtdV2e0ehcFw6K7RLtyKyR1VL+f9+B/hDVZ/xg6iZqtrA39cNuFhVb/cDvVeARFVt7e8fClwF/BdvWPI3wABV/cIP3rb41z0DL2B7E3g0ONATkQi8YDMReExV5/nbSwF7/bEr5wAfqGr94Hv0mz2+BF5S1Y8ye76Htq4qnC9UDnncuNnkic4HTfUO/bXNVK9Yt5tM9SIbZjghKF+wrvGScraNO1GdHjbVOzDEtmva2vnDmpTPbeuNf+ptmwE+/RLbevGYKfGmS7fPGS7dPuaWbnPFd3hOGRlRBkgKerwWKOPPv9uMN+NuFniNGyLSHXhPRO7Am6HXxN+3EiCTOXwt8RwuJuONVpnnn7Mn6JiSZPBlQVVT/IHKp2X1BN2sstAyfHhB30E+M93Ocq1a6Qp4boE2bNhtGzSb02taQd9B/mLs/BHuWC+lype280fSj9HIb9LCMKdX6AM93/XicuDtoM11/OHKpYESeKNRgpmKt8y6FM8N47Arhar+5A85ngtcp6o5Sf10xhvfMh14VkSiVPWQf3/t8ca7VAauzuD+S/j3f4x1QkE6Y4Q7Kcu/MNWb3MW2y/eW5fbzp6zY++DdpnqRp8aY6hV9ZJipXkJrW2/WuDmjTfWs2XLtXaZ6aam2gVfpM227tB3HT2EO9AJOGdXw6t2CfVL+DgxXFpFOeO4SbYL2f4CXfauPF6BdnO7ao4C2qhqf3U2ISDTe4OSHVXW3iHyPtxQ8E0BVpwHTRKQ5Xr3eFf6pgWBUgemqesz8DeeMET5ULWUbLNztMsAhpWnFumZai4bbvnYXVaqf/UEh5Dv33gwpg2NtO/rnbNhkqmfdxmY5w9KKwhzoJatqQz8j9jnQA3g5g+NmAEcVTfiz7A7hNVk8yLGBXho5f72vAsoBP/vLuiWAZPxAL0hzvojUFpGK/qbDwagj/Nm4Jyn7gxwnLIu22rnS3BWX/s9R/jJv72pTPWvOKl/DVO+X7baLjU8kuo5+R9YU5kAPAFXdJyI9gY9FJKOU16VARhZjA4DKqpqajf9tdnQG7lbVSQAiUhLPDq0EEIcX0KmINAaKAtvwBj3ninvjLjmee8w1oxNyZNhRaHm1iu234Pan/WOqV+0buw5mR2h5O+Hbgr6FsOKrFkVN9SqGeYlluBN+FXphEOgBqOpSEfkJL+hawJFlUQEOAscU9ahqrv6a+rV2rwCVgE/967fHWxLuHnTdvSLyNdAOqAl08bOHyUAnP+jL9XMcuWiIaUPG7rF3UPrO8XZ6795L6dvsanduf+YUSt89wURr95gulL7b7lv3P+efbqYFsOvZtpTpZ+f8sbVDPSpO+d1MLzlhgenvnrXezn7NKfusXQ3pruHXUeaR6WZ6JUe9bfrzTOreiJg3lpppDZ1h1xXef0xzrrj9AzO9+OVjzLTCmUI7XuVkwzljhJaGFWqb6m05uNNUL+w7Ux2OE5R6MdVN9X5PWm+qZ421M8bAGreYfdYOXDvRjVdxHGFShZamep23xZvqWbNs2ypTvYolbGexORw5pW3VRqZ6szfaZLsKioERWU7KCjmdCe9Az3H8FKpALxs3jNVAT1V9xT/2VWCRqo4XkclAPf8y5YAdQY0cb+HN4BNgB9BGVfcED2QO0u+O1/SRCuwB7lXVX9M5XQAsVNXu/vWnAHX8cz5R1b7+tQYC9wBb/OfTT1VnZPbcwz3wCnfsTrqsAAAgAElEQVROKVHJVK9F2XrZHxRCPkz80VTPETrCPfCyxv2tLtyk2U6rMaFQBXr4nbZw2A2jB/CMv28z8KCIvJl+9l3A0sw/bzie1Rl4HbebVPVsf1894FAW+u+p6hv+sdcCL3JkbEtmXbQvqOo8fwzLXBFpGzRKZYSqvuC7biwQkcpZ2aA5Ci9Lt2bUD5SPehn2HzkcJx8tqzQw1YvftMJUz+HIjsIW6AWT3g1jC5592e14WbpjEK8LoiNwmb8plqDB26qaZYW3qu4Kepih00W64/dxxCXjoIgsAY4p4FDVlSKSAlTEC1gdDsdJyrLqtkupDdeHd0bPBV6O3OCcMU4QMnHDABgKzBaRsZmc2gwvgxcYijUWmCMiN+I5YbwTtC8z7R7Aw0A0RwJGgFoishTYBTyuqgvSnVcOrxN3ZAbXvABvbt+WdNsLzBnDWa45HAVDZKRL6hdm+sa1MNUbkvCVqZ6j8FHYAr2s3DBQ1VW+M8XNmZwfsCoLHL9MRGoDrfEcK34UkYsCvrYZoaqjgFEicjPwOF4GMRE4VVW3iUgTvJl+ZwUygCIS6eu+rKrBXQC9RORWYDf+6JV0Wkc5Y6x8+acsfzihZnbMpWZabZO+ZtU5dhP6a//0G0l3nm2iFTP2Z36s2tREC+C8jYt42XBOYM9N80znPI5O+IYOseeZ6U1J/JHkhAXZHxgiisc1M3tvAtT9qBotS9Q003sr4Rt2jWhvplem1zR2PHKhmV654V8xqrLN71+PzfPYPfnfJloApTu9wu5pve302tvaAYYrhWq8SqBBIsgNY4qqvuw3Q8xU1QYiUh/Py/Yr4EdVHe+fGwlsAJqoaoZtSn4Dx2pVHZ5RM0a6YyOAJFUtm8G+eOBRVV3kPx4L7FHVnkHHDPS3vZCT537gl7mmL1TJRl0s5RyOExZrpwrrgcnNKp9pqpecllUZdOixdDUpCNZfaGfPB7B3u+0A6jorPjdtj+hf82azz9pn1rznxqtkRlZuGKr6m4j8irdMGtwKeAXwW3CQJyKXAL+qapLfLHEmEJ+ZrojUDVravRr4099eCdjuu2zUBuoCq/x9TwNlyWBoc25wgZfDUTCEu1PFQym2HeH/2u6WGkNJ9YXhHcimFPQNhAGFMtCDDN0wgnkGSF9hfBNBy7Y+dYDX/SaNCOBT4EN/XwkRCc78vQjUEJEr8Dpzk/CWbQGaA4N8B4w0oLuqbheR6kB/4Ddgie+I8aqqnvDjvp81NsruF+Z+jRWKlzbV25a821TPUXhxgZfDcYRwrJAtVEu3JzPWzhgdY8+3lOMMSpjqPZUYb6rnCB1PxbY01SuC7WCtX9hnqnfdweKmeq8X2ZL9QSFkZHS0qd6pV9ouTUtUEVO96O69TPWKNrjS9BfwMcOl2+eMlm5doFdIsA70psc057okO/9Laza2Oo3fl9ssWdU7dwtV5/1louVwOBzhhLUFWp+anc0+a4eumeRq9PKL43DYGA+04MjA5bF+M8gzQBcgJriBIwsnjfPxu2nxHDkGquq0/HzOuSWcgzzAD7yMgq/wXpV2OHJF1VIxpnob9ySZ6jkcJxonZaBHHh02fHqr6tR02z4BXsVvzggiMyeNFUBTVU0RkVhguYh8oqqZ1p1WK12Bv37/OHfP8jgoHteMvSsmm+mVbNCJ3XMGm+mVbv0E+1Z9ZqJVonYbkrrbDcFt8+E+5v34ipleiZqtTcePvNp4AA8sGWSmVzyumfl4FWu9rf863Uyv4od/hP3P0+r3PeaNpez7e5aJFkCJOv9n/rO0JhzXOE/Kpdvg0Sl+1u0cVb0/MKYFz2Fjkaq+lUFGb2YGgd4x181gX2egi6q2Tbe9FrAQqJZVoPdllY6mL1SU2JakHtIIU70L/m1bt/Ph66ZyXFlrg6leuQEdzLQOTvrITAtg8DzbrtRBfSqa6n06aLupXuvWG031om+ze28C7B2evucvf0lYWcZUL67eruwPCiEVP//KdOn2P4ZLt8+7pdv8J48OG8NE5HH/37ep6s/ZaGTopOG7YYwFavjXOSbIC3bG6BhzPheXspuX1KlmhqMG8w3zEQF2CSEAGlaobarX+eN3TPX2dLvTVK//4sqGaqksPJBgpvbSg+FdNnH1PFuLtx6fxZvqzStezVTP9isyrPrNtvnDNmx2XbdhQ1CNXsBho5U/A68mRwYvT8Bz3riA0GT0bgauUtXb020/A3gHaK6q+zO7Z+tmDGvGV7Qd51LXuLOxe9omU71GxWLNtK48aDtA9Z8o2y7YncbZ7aHO0sqRC04rF2eqV6Oobcb5839mm/7CP2qY0XvBZfTylWRVbRjksNEDeDndMc9yxGEjFLwPHLOAp6orRWQP0ABYlOkNG9ZFAOzrfa+pXoWJth0Lu2c/aar3c9unbPVYY6Y1wUypYFjbpJ6p3hNT0v8pyl/atn/DVK9UhO0Xg3cv2muqF31eHVO9NiNWm+rNmP2wqZ41aWFYpXeyBnpAnh02ckwWThq1gH/8ZowaQH3I+pO5IIpSLf1ZrSltHHhZcnGl+ny75beCvo18o1HFOizd+reZXutVe/k9ybCU4ZKe2R8TYjrHXmCmNSnxezMtgIrW8wym/WEsaEuDVn3NtFbv3EjKQdt643DkpA70IE8OG8cgIs8DN3PETWOMqg4EHsjESeNSoG+Qk8b9qro1K41ZMbaB3qUDbNPzSX/aLo9FNm5gqpe2wa7GCyB1lW0djeXQ1uiH+ptpAaS8+zJeKa0N0Q8OMdMCODDczqQeYOwjtqsTKcvmmOpFNmxtqrera1dTvTLjxpnqWRN++byTtEavMHJbjRvC+oWy/pYf7jSqaLt8tGDmo6Z6pS68z1SvddVzzbTOjChrpgVw1iHbLwX3bAnvwZK941qY6g0L85pO64HJvWreZPZZO2LN+65Gz3EE60CoWukKpnqO0GK5tAn2gZc1czYut9MyUzo5sK6xfDXRVM4RYsKx67bQBXrH4WoRDzyqqov8fTXxO2z9x+cAbwJl8F7r89J3wYrIlcAQvFEpB/GGJ3/p7+sM9MPL/CYAt6rqVhEZCNwDBAwe+6nqLBFpCUz377ko8L6qnjCFYxt2byvoW3A4MmV4Vdsu7Uc2hm8W6rtKtr7WF235wVSvxuLfTfXAWs/hyJpCF+hxfK4WGSIikcB/8YLG5SJSAa+uLj1bgXaqmiAiDfA6dqv5548EzvSDu+eBB4CB/nkjVPWFDK63QFWvEZGSwDLfHWNJTu/b4ThZCefAyxrrwMua2mXtRg0BrNrpUnqOE4vCGOgF8x1wTtDjLXiuFrcDb+XiOq2Bn1R1OYCqZpjOUtXgxoxfgOIiUhQvAyhASRHZhpcVzLGRqqruFZHFwGmAC/QcjpMY61FK9evfaKq3dpftTMliRaJM9RyFGw3DdoxCG+jl0dUiM04HVEQ+ByrhLaM+n805/wKWqOoB/37uw1tS3os3RqVH0LEPiEgXvDl5j6jqUS7bfgbxQmBwuu2HnTGkSFkiIkrm4ik5HOHJLXEXMjFhoZme9biaghil9I7hwPLbsQ30ft2+zlQP7LKI1tnDMZVacbdhM02XuIvMtMKZwhjoFReRZRxxtfhf8E5VXSUi3+ONOzlqVwbXCmyLxBt5ch6wD5grIotVdW5GNyAiZ+EFlK39x1HAfUAjYBXwCvAY8DTekOTBvtZgYDgQ8ItqJiJL8TKCQ1T1l3TPZTQwGsLfGcNRuHkorrmZ1ksJthZh4TyTMMDtW91SeCgJ1+VbyyAPYELCd+QmYxMKXDPGiUFeXS22ATFBj8vj1dwBrAfmB2bZicgsoLGIlAECFgp3q+oiEakOTAO6qGqgtbEhQOCxiHwA9PW3Hf76KiJvATOD7mGBql6Ty+fvKITElSpvqteqzOmmetbBl8Ph8Bhm3JjU29XHFjoKY6AH5MnVIh64VUS+UG944O1A4B37OfAfP3g8CLTAa6D4FC+oA0BEygGfAn1V9ZsgyQ3AmSJSSVW3AFfiZRsRkVhVDXy9aw+sCM1PwFGYqFOiqqme5dKmw+EoOKwDr0olbOc8WuMs0E4wculqMRrPamy5iChevdxj/nWSRORFvKBQgVl+kJeeB/AaJgaIyAB/W2u/C/cpYL7vdrEWuMPf/7yINPSvuwbodhxP2VFIWbD514K+hbAhodlppnrj/zrFVK9fosuYhJJ74y4x1Wu733YAdfvtttn0Lft2muo5jh/njFFI+LRKZ70uye4XenBsK55wHzgOB80qn8nFkZXM9IaGudNBQbC9q53lYflxK7gl7kITrZMhc2/tjHFfzY5mQdHraz4weW4u0Csk3FjjWtMX6uPExZZy5t+6Ryd8k/1Bjhwzw9CL+b4U2+yoGx7ucBQcLtA7fgr10m1eyYu7BhAFXKGqnfztZfCWhq8EupAL9wtfZyVHRqgvVNXuWd2zdeBljQu8QkeVkuXYtHeHqea1Sbaz3xyOE5lwHa9yMuBq9MKHvLhrjAG6isgVqvoFMAgY649zgVy4XwDbgb8D9+BwhBLrIM/hOJEpCGcMF4A5TiRO1kAvmBy5a6iqikh34D0RuQNvWHOTnIqkc78Ib88hh8NRaBhTyXY8h/UsNuugq1ZZ2w771Ts3muqFO26OXpiRW3cNVf3Jd8+YC1yXLuOXW/eLWv6w5F3A46p6zNpXsDPGKze35M5L7QqKkQg7LSDqxgdN9VI32A7B1d8XmerJ6Y3NtIpUP9NMC+Dvix8w1Tulq10jBkD0PU9mf1AIWXVpj+wPCiHJy8N7mX/3XV1N9aJqNLLV69DJVM9x/JyUzRhBNXoBd41Wqprq187NVNUGIjIBz3XjAmCRqo73z63tH3Nm0PWq4A1fDrhfxKrqnUE1eqvwvii8papv+P64pVR1m4g0AT4GzlLVXZnd84Rqt5q+UPFR+y3lmJDwnanezj62zR8/v51iqnfp1u9N9frGtTDTKqW2X0Ied93njlxwh7Ft13jjv53WWDdj3F3zRrPP2jFrprpmjHwkr+4a4AVsR2V3c+t+4fvjHvD/vVhE/sbz28007XPnlnmsv7BuDp5aaLhz4Z9mWgANK9Rm2bZVZnplh7rmj1AyxI0EcTgA+8DrrPI1zLR+2b7WTMsROk7WQA/Ik7tGhuTW/UJEKgHb/SxibaAuXtYvS6obB1+WWAZ5jtBTL6a6mdbvSevNtByOEx0XfIUWV6MXhuTSXSMzcut+0RwY5LtopAHdVXV7rm48n7m4Un1Tvan1bZc25660C0wABqXYBul/7thgqvdwlF22mcp1qZ9iW1rQYnt4L4+FMzMNZzwCXONGDTlOME7KGr3CSGR0NfdChZC74i421Xs74VtTPYfD4QgHrGv0utb8l9ln7bg1H7oaPYcjv3CBV+Hm2VjbkSDOf9bhcBRWwirQy8bxIuBEEQ3MB+4HTsXvsk13ncHAdXjLqpuBO1Q1wd/XEngJzyljq6q2yEB7Jd4cvgrABKAK3rLuaFUd6R9fHpgM1MRb7u2YfiSLw1EYuCn2AlO99xO/d4GXw+Fw5JCwWroVkT2qWsr/9zvAH6r6TLqxKZHAl3jB2hIyDvTKBEad+M0aZ6pqdxEpB3wLtFHVdSJSWVU3Z6A9EVgMTMIbtbJEREr7265X1V9F5Hm8howhItIXiFHVPpk9t9oVG5m+UOt2bbaUczgcjnyhWWXbOY8LNtt6MYc71ku3txsu3b7jlm6Pm/SOFwCoaoqIfIvnULEkoxPTzbMrCYfN724GPlLVdf5xmUVDC4Bz/E7cRP/Y3SKyEm923694GcOW/vHvAPFApoHeul2b6RlnV1T88q7N9DGcjTY04St+rXO2mV633ak8k1rGRKt/kV30OVTBRAu8YnDr1+7Hqk3N9M7buIjZMZea6bVN+ponY1ua6U09sJqvbyhrphcz5if6xbU003s2Id501tz4hO/4u8EZZnp1NsO2W2z0KkxcSfc4u9+FNxK+Zlsnu0a9CpNtB9uHK2GZ0fMdL94H3lbVz9Jl9ErgzcYbgLfEekxGz7/WM0AXYCfeQOUtIhJYsj0LKA2MVNUJ6bQjgQ+Bz1T19aDr1cRbMm6gqrtEZIeqlvP3CZAUeBx0zmFnDClStklERMnQ/KAc5lQoXtpUb1vyblM9h8PhyA+sM3q31bjBLCh6d+1HLqOXB4qLyDKOOF78L2hfHX+fAtNVdbYffGWIqvYH+ovIY8ADwJN4P68meLZpxYHvRGShqv4RpA1eRu+wrZqIlMIL/h7KyP3C99E95s2lqqOB0eC6bgs7kRFFCvoWHA6Hw3ESEm6BXlaOF3+rasM8XHMiMAsv0FsPbFPVvcBeEZkPnAv8EdBOf7KIROEFeRNV9aOgXZsCg5ZFJBav6cMRpjQuXctUb/benIx/dOSE2+IuNNV7N2GhqZ51DdvM+2JN9d5/3XYEbrfNto1CX8TYjoq6Iim8JxaEY0Yl3AI9IGvHi5wgInVVNTDh9jogUCgwHXjVX56NxvPBHZHFdQQvs7dSVV9Mt3sGXmfuEP//03N7n/nJvXG2XrCjE8Lbkmz2xvAOvEZVtht30sP4g9Q68LLGunmg7FOuWSGUhHvg5Th+wjLQg2wdL4KpJyLBnkq9gJtEpB7eeJW1QHf/mitF5DPgJ3/fGFXNyu7sEuA24OegZd1+qjoLL8D7QETu8jU6Zvecdva1a8YoO2QBa5vUM9MbnQDJCXYT5YvHNWP3W7eZaJW+511W1DzXRAug1eY1rPtrZvYHhojicc24c9kgM70ecc3Y2dsui1F22Lfm701rvZ197L7YFYTPdLi+fsXjmrF3yXgTLYCSje9gz9whZnqlLu9rphUgLQxzemHVjBHOuBq9wo1loAfQYM1yUz2Hw3FyULVUjKne+u0rTJsxbq7R3uyz9r2101wzhuMIyQkLKG44XmX3mC680/8fM727lg4yfX6WP0/r1y45YQGE6c8SYM/8FynV/GEzvS3X1aXSdDuv4oJ4vzi90Opd3eh+E61Pl75Gz6Z2Wa9LDkZzbbstZnrFn3k9+4NCjLqM3olJHhwxwBuYfBle7eV+PGeK1f7SbKx/rQVAD1VN9XUi8ebiva2qh3+7RCTeP2c/sAe4U1V/9wcnNwUOAT8A3VT1kH9OSzJw2MgMl9Fz5Abrb93FixQ101q9c6OZ1slArbJVTfXc6+fIDdbjVTrXuN7ss3bS2o9dRi8XHO549R0xegDP+Pv+9jtxA44Y1wNFgTi8ocZpIlId2Osf39GfcyfAVKAD3kw+gCvxOmw7iMhjenSUfIuqLvJn3w0DrsXr2L3V3/8ecDfwuu+w8RpBDhuh/XE4sqN2WdvOv1U7E031Nu5xbnqOnOECL4fjCLY92jaES6AXTE4cMVKARFVN8/etDzouMOcu0FkbHMx1BkYC9wEX4dmhpWc+8JB/rVmBjSLyA1Ddf5hTh40CI65UeVO9hD3bTfVSvCStw3HSY539DfcvIeH+t9NR+AirQM93xLicoGHFQftK+PsG4C3zfi0izYC5wH9VdWnQsZ8D5wOz8bJ6iEgx4AqgG1AOL+jLKNBr518/WDsKr/v2QX/T6UCUv+R7lMNGuvMOO2O8Nvxp7u7SOSc/hpDwWYP+ZloAbRKeyf4ghwM4NDn9pKL8JaqTXT1gQfBPq+6menHjnzbVK1LjmO/9juMgZclnBX0L+Uo4dt2GS6CXK0cMAH98ymX+f3NFpIOqzgVQ1av8wG6iv/9/wDXAPFVNFpEPgSdE5KFA/R4wUUSSgTXAv9Pd32vAfFUN9Nxn5bBxmGBnjAdqdtJDRe2W//ZERHDb1ngzvS8b9OGiFUPN9PZ0u5NKn9gU2G9pV9dMC6BllQbMXmpXxHzB2V344nw754+5P1Tn6t4lzPRubNyTdy7Zm/2BIWLV12U4e2mm4zlDzj1J8MQhO2/dxddMod3MDmZ6f130AKd996qZ3v1N+9At5ZCJ1puRUdyXetBEC+D8TYvYNeT/zPTK9J1FykGbxpZwJlyaMQI+swFHjCmq+nKwx2025z8K1FDVf6fb3gU4X1Uf8IO7S4Fkf3dl4DpV/Z+fmXtUVRdlcO0ngUbADYGlYhHpCxRX1Sf9x2/jeeNOyeweD21dZfpCpSybYymHGC8fUczWN3jA9RNN9Z4adYGpHsl7zKS2D//STAug/L1NTfUi29lm2A48+5CpXpGz65vqWf88Uz47ZkEpX1nzlO0w9lpj7VaWAIo1u820GePGGteafdZOXTvD5LmFVaDn/7sR8DFQB68m7phAT0QaAxtVNUFEIoDxeEOQ3wBK+7ZkkXgZvQXABOAv4BRVPeBfoyvQTFXvzCzQE5G7gTuBy1U1OWj7GcCrwFV4dYA/ADdlNXw53Ltuu8ddaqr3RsLXpnoOh8ORH3SOtf1SNynxe1M9667bcAz0wmXp9jA5dMSoDLwlIoGZED/gBV5lgRn+9ghgHl7wdwvwZSDI85kOPB90jYx4A8/14juviZePVHVQHhw2zPk8xjbwuirMA69fatvWCb24v5Sp3tsJzobJcWKS2OI0U733fzvFVK9Xoq0loKPwERYZvZOBjc1bmr5QkSVt3xdl3x1nqrera1dTveLtGpvqrRn6R/YHhZA6M2wbFnb0GGyql7DKroat/mtZjtQMOUN7LDbV6/OGcVnBwf22eqkppnIje9l6B/d8INpUr0TvsaYZvRsMM3ofuYyeI5gKH40t6FsIK8qMsw0sralV166OLfLsy8y0AFIWz6LMYzea6UU2+T8sB2akJdnOtesU/YWpXuR515jqWbN/QA9TvXsb7zPVi7jUtubRcfycVBk9EemPN8MuFW/JtBswFM/VIlBD95eq3igiA4E9qvpCumsEXDgCvK+qQ/w6vVKq2tQ/rinwgqq29B9fCrwIlPHPe9HvqiUzrWDCvUbP4XB4/FrnbFO9M//+OfuDHCcsw6q2MtXrvdF2qdi6Rq/9qe3MPmunrfvEZfRCiYhchDcipbGqHhCRiniNEOC7WuTwUoddODKgsoi0DYxwCdKuiueMcb2qLvG1PxeRDar6aU5E36rUisVRNi37ADXTovhWdmV/YIiYkWi7fGRN3zi75bhTUiJILGL3vaD/4sGmXqIA71doaab1bdFUNh5Vnpu/lD91OwN+r2Kmt+erF5h//XQzvVa/PMuhCc+Z6S16fgcxxe2Wbx9LTeOzjctMtNpUbcgesftd73nZJlp92cRM77SOdmObwpmTJtDDy9ptDTRUqOpWAL9JIlQMA/rjDVoOpgcwXlWXBLRF5D/AQCBHgd6tywcd9lKz4sHsD3HkkJRPR5vqRV59r6leckJmfU+hJ23TajMtgOuq1DLVA89+x4pDE5/n0n7lDBUhqstjZloXdTGTAmDKtx8Bdlk23bjBTAsqU/9u23FD1pxoA5N9I4hFwAZVzVPdw8kU6M0BBojIH8AXwGRV/crfFxh2DPA/Ve2dxXUCw5kDPKeqk/1/fwe0F5FWwO6gY84C3kl3nUX+9kwJdsY4rWw9YktWy+rwkPJkSkUzLYCrkmy7bkdUsV3uSI4wlaPfPbYZNsshquANUrXksip2y6kfdcqqkT/0LP1vlKlezMiepnp1Xm9jqpc87hNTvXGLbbt852I7E3TWultM9U5AHsQzgiiT3YGZcdIEeqq6R0SaAM3wvm5N9gcXQ+iWbgGeBh4H+uT9bj2CnTH2PGzXCQRQtJ/d0gocKZAMV5I62Xb59lpsl2ErCJINM0Ipi22DyrRFP5jqFSuyO/uDQkiteyqY6kXE1jXVK/2WbaPXlU1t116utm26NSetoG8gCBGpDlwNPAPkebTBSRPoAfh2ZfFAvIj8DNyeDxpfisjTwIVBm3/FszwLLoRpAvyS0+sW7fdSaG4wh6Ql2Fl2AUTE2f4xtiZmcnh3+YYzkU1ss5UY652pA0z1ou950lQv5WdbJxXrv2Vn/GDr/UyEq5sLFcGrdj6jA02aPi8B/wFKH4/OSRPo+d62aaoaiGAa4g0zztIeLY88jTcseZX/eBTwvYh8pKrLRKQCXrfvoHzQDgkRVWpy8PWBZnrRDzxjphUg9U+bCe9F6l7AwKaPm2gBVEuN4NY2m830ij/3hpkWwKG3BxPZ4T4zvd8uG0wtw4R6ZOvLiLzwejO9MaOh5kG7PMaVUYNJ+cuurqz4oJEQZbccPv+sx6hU3GbkyZbkEqyMtlt6P+fQAb4tavez7FB6C3VWfG6mB6CGNXrBq3bpEZFrgM2qulhEWh6PzkkT6AGlgFdEpByQgmdpdi8wlaNr9Laq6hX+vx8XkcNGkKpanWNr9D5T1b5Bj1HVWSKyJehxoojciufGURoQ4CVVzXExx75e9+T4iYaCtH2ppnoFsRpQpK7d4Nbd2P08fyuSSnSX28z0Uld+Q8rMaWZ6Uq40hyaPMtMDWG0z1xSA2tFfkRr/VfYHhohTDpY0fHcCZUoR2biemVzKZ7bZ9LPqbTLTqsxuRv0/e2ceb0P9//Hn+7qWa9/jokIkJFulxZpKSloI7WhRob2UFqF+qSRpL9Kq0IJWRcpXKWtISpHt2mW/luu+f3985nAcd7HMvG/OnefjcR/OmfnMvGbMOWfe8/68lyWHHbp1yEzMD9fttDMs5+0sRVUztf8cZwEXi0hroABQVETeVdVDzsvMVXX0jmYS81VQy8zGpOTGbOxpl11V/PnpppmbScmN2fp9pmULfaVw03t4p3QzEy2Aa9ZNYm1bu+mjMmMWml67hxr2pv90Ow9wUnJjNvWyS24p9uRk8+/Cmgvt2oSV/fwv8/Oz1tvy1g0mWkWue4NFdWqYaAFUmbOATfeeaaZX7OkfzevotT62tZlR9MXSLw7q3DyP3j2Hm3UbGnpHCdsHdDa9UEUfHm8pxw+lGmU/yEdKFLZN/5iztYSpXrXEraZ6x9bcaKY1cr5tlmGJPba/kfnTbfVql9hgqle2jl1NQoDPJ9tVKwBoUNj2//Ombbb/n43ylpE19VQAACAASURBVDXVe+qfEaGhFxp6WZNFN4x7YjNtReQ04BngGGA7MAPoCVyKy6IVXNmUW1T1V2+b4sAbuFg/Bbqo6k/eukRgJTA0enpXXPG+/kB777heVtXnszoPy2rdOcG71xcy1Vv32TpTvac32GYavpJiW67msfLNTPVO3GUqRz+1q93324YlZloAjxpfu8dWTjLVCzm6sfboXVDpArN77ZfLvgw7Yxwp2XTDiB17DDAK6BhlqLXDZbssBpqq6r8icgEueDIS4DUYF6fXTkTyAQWjdnsu8CfQXkQe0H1W9fVAJaCGqqaLSLaPSONWzTyUUz/qKPZkTh9ByOHyVunmYNe0hevW2bZgindCwyskJL6Ja0OPQ+uGcRvwVsTI88aP9l5GR9dOBSp6+ykGNMEZbqjqLiDa19AJZwjeApwB/OgtvwW4UlXTve2yTZG0jDEJ8Z+0T18w1Uu8pLupniVXGOtZZWdHsEwSAth6SxdTvUIDnjLVk6K2xd/ZY/jUA8yoZ1dTEuCUgba9mK35L9XR84t4N/Sy6oYRS20O7F6REV3Z1+KsMrAWeFNETsFN9d6uqttEpADQEjdVXBxn9EUMvapABxG51Nu+Z1TZlwxJSm5MD8N+okNCw/KoZViZ5nS51e6z8nLZ5tyyJn69bDVLHsv8DUtz+jACo37pE5i57i87wTGX2mkBFYqUYsWW9aaa8cq3Jc6kyHVvmGqmdXrMVC8eiWtDL5tuGIeM19qsK3C2tygRqA/0UNWfRWQw0At4GDdl/J2qporIR8DDInKHV7Q5P7BDVRuKyGXAMO8YY/X2FlN8aWB/bri20+Ee+iFj+8wNu16xLdqacKpt8kf6D3blMmAnne60NdSvN9QaXedhQzVoN6efqZ41e36fYqqX56SzTPU01bbzR/o/c0z1NM02GWNbhbAL+tFG3CdjROPF3F2Hi7vbLxlDRPoBqppxmXgRqQN8Alygqn96y8oBU1X1eO99Y6CXql7oGXdns6+7V1mgrap+IyILvP0s9hIzNqpqsayO/ebj25teqIePsX0CPn7WH6Z61lQoYpuMYe3B6JxsV3IB4M2UH7Mf5COXlrcrNfTJyoPtxnh0svU728fIFzt9mf0gH1mSYDt1O3+PXcY7wKTV80z1rJMxzqvUyuxeO37ZV2EyxpFyiN0wXgB+EZHPVfVnb/vLgCk4D9zHwDURIw9AVVeJyDIROVFV/wDOAeaLSFGch65SJD5QRDrjpm+/AT7FeRgXA01xCRtZMtT4xvb4iXZ1tXID1obXlNK2cV5nGX8+Lbkq2db7a1kDEWDgtGRTvf5XfW6q9+QqS2+6PXcnNzHVy3NMHVO9kCMnrj163rTtEFyMXGw3jJPYlyv4k6q297J0n8J539KBH4A7geeBy3FGIkCaqjb0NOriyqvkw7U86wxcjPPYdYw6lpLAH7hEjiTgPeBYYCvQLVKuJTMS81WI3wsVEhISEnJYvFW6uamedda7tUevZaXzze613y772uTc4trQiyd2zBxre6Hy2Dp781Swq+4OsLlzZ1O9i2YmmOr98OtQM60VLW820wLYsL5g9oN8ZE+67bWr++tAU72lzez6BgOU7277Xc/T1K5vMEBKxydM9WatLmOq1+KcVaZ6xd+bGBp6R0hcT93GE3nK207njKtnG4DeZo5tZlXBHh1M9ZZ1fMlUz5IK375qWoJk2aUfmWkBnDbets+0NfkL2caU/fXkIlO9k9odZ6pnbXhd8F4zU73tz71nqmdNPDq/cszQy6JjReGoadGGwDOq2sx7fzbwLBDp4vysqr7mresD3IgrV5IP6KeqI7x1w3GxcJtx06ZTgQdVdbm3/nHgWqCEqhaOOsYmwHNAHVwh5Uhdvcj6osB84FNVPaBwmYicCzzpHc8u4F5VneitawAM947nC1xZlsw/YXnzZ7oqCFITEmhUcq2Z3p6VC029eon1W5lpASz+004vbdpnZloRLGu/NXz7X9Prt6NPdwr0sa2DaMnyZcVpMMem7zNAiZceJs+F7c30rGlxzioKDbYpQbLt9hvY/qyd4ZW2HUqOetNO78ePzbTimRwx9LLpWFFWRC5Q1S9jtikHvA9coqozvW2+FpEVqhqJ7h2kqs+ISDVghoiMVtXI4+q9qjray3K9A5goIrW9IsfjcMkYsbXsluIqR9yTyan0w8XxZcY6oI2qpohIbeBrINJ48WWcYfozztBrxb76fDlOmzZ2Rh7YT91a81Xt3qZ6reYdVkvEowJrIz2hXEnT8j95LuqY/SAfKVxgF3+c1tNMr/KNpdgz/hMzvTw32iYP5Du7FrtHDTLT+qKvbW/dC7+2M/RygnRCj55fZNWx4mmgNwcaPbcBw1V1ZmQbEbkP6APsl8alqgtFZDtQAlgTs06BQV6x4guAMao6NUo/euw/3vIDimV7HrljgK+ADGsvqOqsqLe/AUkikh8oCRSN0n0buCSDc95LkmGx5Bzhzfg+v9IFi2Y/yEfWxfvnxZhrDDNv3+lrG/NozqPWet8aC8Y5nSeZyqVdFd91LC3IKUMvq44VPwGXesWJoytd1uLAzhXTveX7ISL1gYXZtBabCdQAxhzqwYtIAjAQuBrX/eJguByY6XkwKwDLo9YtZ5+nLyQOKZSYZKq3js2mevHOOylTc/oQQkJCDNDQo+cPB9Gxoj/wEHD/Ie76Tq9eXXWgTTZjjyTb5VbgC1Vdnknf3P2FRGrh4g/POxSRnOyMYe1B3L7oK1O9tE9eNtX75nHboqatU0ZnP8hHLLsrWHdWWH6OrYft3w22WcWJeWy7e5avZvsQUnxEfE81jjnZtlPMuefZZt2GHDk5lozhtQKbBEwSkbm4jhWRdRNFpD8QPV8yH2jA/h64Brgp0QiRGL2LgaEiUlVVd2RyCPWACYd5+GcAjUXkVqAwkE9EtuLi7SITEzeo6nQRqYjrqHGtqv7trVuBq6cXoaK3bD+8RJPXAHavW6Rp87MKB/SX1BzodbvrBbs4tnzdHzfTAmhtZ6PnCNbGlyXlnszumdFfKp5qG1+5pattqaEiQ+Pb8LJuuXZhv2NM9RKa9zDVsyY9zLr1h4PsWNEfeAVXhBjgReBnEflYVWeLSCmcl6xv7P5VdayIdMUZj6/GaAvQAxcneFhuJFW9Kmp/1wMNVTXikfwkal1xXPxgL1WdErX9ShHZLCKNcMbhtbjCzpmSNnnk4RzqYbP+si6mesUH9iDPhe3M9HZ/+KyZFsCOb+ea6hXqb9s7eM9Eu5InP/W37TKyVfKY6rW88X+memOnVzLVuyr7Ib7ye0Pb3qzHNU/NfpCP9P7Otr3igMW2dR7zPz7KVC8eySmPXmFgiGcIxXasAEBVvxCRtVHvV4rI1cDrIlIEN/X6nKqOy0SjL/C+iLzuvX9aRB4GCuLKqzT3Mm4RkadwpV4Kishy4A1V7SMip+IMtxJAGxF5TFUPiAnMgu7ACbh4xMid9zwvdvBW9pVX+ZJsMm4TG19h+uRd6uM3ebyB3ZRA78r1WHT2bWZ6xw2/ngltPzXROmfMJTz7tF1mXPKeBKq1eM1M7+whtZjzf3bTOae138Hiz+yMr7PeuYIG7ezKq7S+fQTf1XrQTK/9HXl48NWdZnpXAcPq2j2IdJk9mJ0D7jbT270ojfQdNl6hhALClTvTTLQAKlfdwMB37Vrmda+7nEJmao748+eFnTGOGnavWxReqKOYtNnjTfUS6x5SOGhISEjIf5K8pauYdsZoXOEcs3vt5BUTws4YITnHPQ3tPAoAi9K3mup9uWpW9oN8pGFp284mlfPaJrdsVzuvAkCphAKmej2d89+E8pVtkxU++rti9oN85JJKB4QjB8rvf5c11RNjn9CeI8orPHQaNLJNxig55vvsB/lIWEcvJNfwzHTbfo3xjnU/0WMnGcYgpu+x0wJIsI2ZS9+QYqqXUNJuagygxZkHNPUJlDJj7fowA9g2JINpJ99rrGhLQkHb3s8hR05o6GVAFu3ZygM7cO3MblTV2d74f3A1/xRYhcuwXSUi+XAdN5p5++mtqh+JyLG4moDFgTy4ZI0vsjqmtDG2vVIXPBbbJCRYTl013VTvjuQmpnrbKWaq99zmdaZ6D7Ww6QQA8ND5tp0A8lSyNRXydTsgvyxQEhONDfU4p+5dtt913bLdVC9fT1tDPeTICQ29GLJpz3aVVzKlM66Dx7lRmzb3unU8ATwI9MR1+FijqtW9IsslvbEPASNV9WURqYlrgXZ8VseV2PZW09p2W797isLN7zPT2/LqVXR8dL6ZXr83mlKktU3F9S1fPGymBbD4lBoUrHGpmV5qymSeTbEr/dP/wXEUrGZX8iQ1ZbLpdy+1m20dyy3fPk6RlnaljVL37CapUgs7PevrZ6hnfW5b3r3ZVu/1a8h73ZNmehCfU7dhMkYMInIZ0FlV28QsnwTc4xl6NYCPVbWmt+4fXImVdSLSCuipqq1FZBlQQ1W3xezrVWCRqg7wDMuBqnpmVseVmK9CeKGOYhqXrWmqN3mNndEcEhISEhRpu1aYBiGeUaG52b32pxXfhckYOURW7dkitAIyq81xETDXKx0D0E9EmgF/A91VdTWuP+94EekBFCKTNmrRnTEkTzESEqwTzUP8okKeIjl9CHHFtpnDTfUK1b/eVC8k5L9KmYK2U9PWxKPzK/ToZYCI5GFfe7abgV7A9bgYvXy4OoB1VXWFN/4fXIzeHmAObto2EVgLtFfV0SJyF1BPVa/xXouqDvQ8ekOB2qqaaS8i6/Iq1uVALKc2wb7zx7bbbzDVKzT4DVM9S3a9ZNvyKd+t8d1U3bp4eN4Od5nqWaO7bAsmr2tnm0xT/No6pnoFuw029eg1Sm5mdq+dmjLJ5NxCQy8bRKQdrsNGEeAeYAYuPq+Kql7mjfkHb+o2ajsBtgJFVDVdRCoBX6lqLRH5DWilqsu8sYuARl4h5QyxNvSs+3tW+912qrF/+eamel2rLzPVKzrQtjxOQrJt+Zifah9qG+wjI1+CXcJCzQtsW2htmGEqxzEPNTXV2/7WN6Z6S38tnv0gHxlpPNNz4m7brNvrV7xrauidltzU7F77S8r34dRtTpBdezZVVa/Dxt8iUkNVF2S0H2/cOFzG7UTgHFy/XoCl3vvhInISUADn/csUywDY3MBDK78z1jOVg4a2LexCfOQdW7kzytQw1fvpypdN9eId66nUtds3mepdb6oWn4SG3oEcTHu2VBEZCNwLdM1iX/cD74jIczhDLtLD7G5cK7c7cSVZrtdsXKvflzzjME/n8KhQzvbLfML8+E4eaF/+VFO9USunmer9Ub129oN84sQ/55lpAXRLPttU75UU2163rRPLm+qNH9PWVO/BW3401RtiHBbyaJH6pno18tu1y8sJNMy6DckprjnuMh024xkzvaTkxqZxbPGsl5TcmJNLHm+iBTB3wz9x+38Z6gWjt+XDHmZ6RToMifv/zzUXnGCiVfbLv9g2620TLYBC9a5l8+DLzPSK3v6xedbtqclNzIyiaSk/hDF6Ifu47/hOphfKsi5abqBVubqmeiPvrmSq99ggOw/wQOPP5vDStvGc1izLa6tXZ4dtgeZX8tvGPN67K8lUb0r+/KZ69XbYtjtstfoDU0OvYfnGZvfa6SsnhzF6R4qIlAImeG/L4bJiI7FwpwGtgU+AkyKxdl5h4+eAFrhp1R3AFcBqYBRQ1dvPOFXt5W3TB7jR23c+oJ+qjvDWPQ20wXXT+BtXo2+jV3JlDLAYyA98oKqPZXYupdW27dP08g1M9RqutI0IX3OhzRN3hCY/rjfVK3rvbFO9LZ/YtX3qw+nceKudsfcV25mz0y7Icv6GpWZaOcGF5eqZ6n0883lTvbTJI031WnQYYqpnja1ZGZ/kGo+eZ4xtVdVnopZ9CCQDE1X1UW9ZJ+By4AovW7YisA3YCZyuqt95rc0mAE+o6pfR+xaRarjM3FKqultEzvP2nyYiAwBU9X7P0LtHVS8SkULAbKCDqs7M6PjDgskhITmDtWHy+apZpnohIf9lrKdu65c/2+xeO3Pl/0KPXpCISGHgbFytvHHAo96q8sDKSE07VV0etdl33rJdIjITqBi7X1VdKCLbgRK49mfRBemmAu0y2GabiMwATgAyNPSsiXePnjXJhUtmP8hHUrba9oONZ0LD6+hmTiXbsIk6y2y96SEh2ZFrDT2gLa6u3Z8isl5EGqjqDGAk8D8RaYzz2r2rqvv90nsZuW2AwbE7FZH6wMJMauJ1AT7MYJtSQCOgX8zyvZ0xXhrYnx69XjqM0zw8as14Lm77Q1rrWZ/bpoeaUaz/JDM96/PbOuFJCp/Ty05v2usUPvVGMz3zz8v9Z1FswBQzPevzq/bzENvP5w/PUriJTVFoSy2A7QvH0fD028z0Zsx7z0wrQjzOcubaqVsR+QwYrKrfiEhP4FhVvcdblx8Xo9cCVz6lvapO8NYl4jyAX6vqc1H7vhHYCFQH2qjqVzH6vYGGwGVejb1muBi9RUA68LqqvpLZ8VsXTP689kOWclw4r7+pXveGtgV3n2llGxBeoL/dQ4E1qffbFvNOGvCqqZ41u0fYZfMD5O10j6meNXt+tzOaAfKcdJapnjV5S1cxnbqtV+4ss3vtrFVTwqxbP4mJoysJLMclTyiQx/v3uNh6diJyj7e8h/d+mLefnpns+2LgZaCqqu7w1l+Pa6V2jqpu95Y1w4vRO5jjz4kYvY9K2lWwv3xDbDvhkJDci3U5kJCQg+Gx8s14dOUkU03rGL1Typ1pdq/9ddWPYYxegLQD3lHVva4BEfkeaCwiW4FVqpriZeDWwfWvRUT6A8WATBuXqupYEemKa5v2qoi0Au4DmkaMvMPhkfLNDnfTw6Lvykmh8RUSkkOExlfIfxFrIy/EH3KrodcJGBCz7CNv+Rhc14pIcaJfgBe87NvewAJgpmtlywuqmlH3+L7A+yLyOvACrnzKN942U1W126EecJXdpg81XJ9s24ljeMpPpnp3JTcx1btsl201+Z8SC5rqzU3YYaq3Ys82U73PZr1oqmfJ3Hp3murV+vxWUz3rPsxpM74w1Uts0NpUL+ToI9dM3R7tDK14temFunZ2X0s5tt2eqZM0EPJfa1fdHeDfxz4y1Tt2+p9mWhs627U/A/h6bGlTvZn5bX8ju5dfZao3fXE5U71zHy1lqrdl5BxTvTwFTOUYPc+2OPpOW58Dty9911SxTrkzzL7wc1b9FE7dhuzj2N27TfU2tOtiqle4cVlTvY39bA2vAmXSTfU2P93GVE+qVjfTuuhy2ztpuWsnmuqVaF/VVO+Pwammei1XrzPVK9rVtlfx272WmOqdVyKjAg/BUf4u23I1IUdOXHn0sumEcQrwnqpe7Y1NBFYCP3tFi68HngZWRO3ySmA7rnvF46r6kLdtaW/bV1W1u4g0wXXTqAN0VNXR3ri6uMSMot6xPK6qH3rrWgDP4DppzAC6qmqmRcDvOr6j1t1tZ5dvSoAbP77UTK/wWT05r9wpZno1EooyYPoTJlr3N3yQ540bnVtSt1QVWuQ/oKRkYOREe76zy55kpvW/Nb+zsWdDM73iz09nTAm7UIaee/5gyebVZno5wW1GMZYvpkw2DbOpkV6Amy/ZaKb32qfFudvYo1f7mEZmRtG81VPDrNsjIYNyKluBv4AzVDVVRC4A/g9YHmXoNVTV7jH7OR6YCGxS1XresltwWbT/8wy943HG3D3A2ChDrzqgXhHlZJxBdxKwGViCy8L9U0T6AktUdWhm52NdXmV8rd6Wcpz32+OmejPr2JZ4GJ/Xtv9li522MYFLE+z6bR6bbntuXxTIZ6rXZ7ptqSHrUkpbExJM9S57xHaq+Ikn12Y/yEdm7PnXVM92bgK+XvZlaOgdIblt6vYL4EJgNC7xYgRwMI9e24HfRaShqk4HOuAKKycDqOo/ACKy33dAVf+Mep0iImuAMkBeYFfU+m+AB4BMDb236z5yEIfpH1e+btsZY0VL29podZ46zVSvbfePTfVOKFjfVK/dnH7ZD/KRkXXsvg81d8HrCXZxc+kpC820AGoUtfPQABQpZZu4IyXtwgoAnkwZbapn2WcaYGUv22QTa5T4c37lNkPvA+ARr1hyHWAY+xt6HUQkOqDjjJhtO4rIatw0bAqeoXcwiMhpuGnav3E1+xKjDMd2wAERtdGdMQafU4cudY47WLkjZtdHX5ppAVT49nVTvbQptj/GTYudaKpXP8m2BZrlg8hVY6/g8s+uMNPrcdk7VKOEmV76X7btAAfssvU2vzbuBVO9ccYey81PHVRpVN/4susvpnopeW3jqbtnPyQkG3KVoaeqc7xp1k44714sH2YwdRt5+RWuRdlqMmhjlhUiUh54B7gu0kNXRDoCg7wyLuNxxmPs8b4GvAauYPJdP9gGMZsyIqwb5ie2qSa23NzoO3PNbsl2Af1FOtqWculpXLMv7msE3mcr96hxjdXi1nO3xqTHYThbrjL0PMbikiCaAQcdvKGqu0RkBnA3UBO4+GC2E5GiwOdAb1WdGrW/n/C8iSJyHq51WqZYVsoHSJs80lQvsbGdhyYn2P3xEFO9vJf1MNNK+/pNMy2A9H9ssxp1/UbALjN10IO23/XFjW3r2g0w/i1LX2v7eUnpaJPkFSH5Y7vvOkBCMVuPXsiRkxsNvWHARlWd67UhOxQGAt+r6oYoT1+miEg+4BPg7UiCRtS6sqq6xvPo3Q/YZiNkQ7wbXtasGvKbqV4lwzKBied3Jn3V32Z6//QZZKYFUL6+bfkRu7QWR4FCtqWbrEkoYxfyArAzNa+p3vzm/2eql7bHNpnm1BWfmOqFMXpxgKouB57PZHVsjN6tuFi8yLa/AQfcsUXkVJxBVwJoIyKPqWot4AqgCVDKy+oFuF5VZwP3ishFQALwsqpmWazrlob3mXaPeLVscyrszrTai++sSkzkvONWZD/QJwpWgkIvZpr74ivbbutK9T/sChiPLtmU32rbZU3Xr7ya42b8Yab3WPlmlEi3S8T79eedDE350Uyv58+25XgeLd+MsbuXmek9XutBWv9rd37/1DuR42fZfT5XnFmNK/+yydR+/4Rd1P7RLnlnYLnm3L3WLnRiennbpMB4JW7Lq8QbifkqhBcq5D9Ls2PsumNMWj3PTCskJCRnSdu1wrS8SvUyDc3utX+unR6WVwnZh3WMXkjIf5ndQ23LueTt+rCpXkjIfxXr+O2QIyfXe/QOopvGrziDeDFwjapujNr2DuBJ4BhV3eQtawbc4xVhbgaM8bYF+FhV+3rj7gRuwJVamQt0VtVMC0xZe/SOK3qMpVzcV8qvUqy8qd6OPbZFhVO22pVzWXPhCWZaAGU//8tUL+TopnHZmqZ6k9fMN9WzxtqjV61MA7N77cK1M0KPngWquh6oCxl301DVyLq3gNvYP2miEzANuAzILPVwsqruV1hJRCoAPYGaXpeOkUBHYLhPp3XExLvhNTPZtqBw/ZSZpnrxjLXh1bH86aZ6H6z82VQvxF9OSrSruQgw7CRbw7LsxbbnF3Lk5HpD7xD4CVdkGQARqQoUxiVs9CZzQy8zEoEkEdkNFCQq6SMjvixh25j7kyRbT+9rKVNM9UKOXixr2kUoiF2m4bpLbTs5lP7ELlEoJ7DsBQvQIM22Zd7UtbblTq4ZMMlUL802SiOso5dbEZE8wDns36KsI65bxmTgRBE5RlUzcoOdISK/4gy5e1T1N1VdISLPAEtxBbrGq+r4DHT3dsaQPMVISCjk63lliW37RHOsPWzXJDcy1XsnZWr2g45SXkn5X04fQqA8a1tNIu6xrFYA/6FpmZAQj9DQy5okEZkNVAB+x/WkjdAJuFRV00XkI6A9ENvbZyZwnKpuFZHWwKdANREpAbQFKgMbgVEicrWqvhu9cWxnDP9PL2ss48oWbVppWqHfsnwFwITNdl4Ty3i5EP/Z1KsxxZ6M3+SrXslNeTLl+5w+jLjBMrSgpOTlJcMHrTuSm5hpxTO5Phkjmkxi9AqLSEHga2CUqj4vIicD04GV3qb5gMWqelZ0MkYG+/8HaAg0B1qpaldv+bVAI1XNtER9WF7l6KZMwWKmemu3bzLVCwkJCQkC62SMKqXrmd1rF62bFSZj/FdQ1e0i0hP4VERewnnz+qjq3pLkIrJYRPYrwS4i5YDVqqoichquOPJ63JRtI8+ATMVNC0/P6hisy6vsfneAqV7eq+831bNmY6fOpnrFR3xmqhcSEuJIX704+0F+ksf2Np5QupKpXsiRExp6B4mqzhKROTgjryPQOmbIJ97yGUCktkU74BYRScMZdB3VuVB/FpHRuKndNGAW3hRtZqR985Zfp3JQFL3P2FCw1jPmKuMYvffivXF8SMhBMu/4U0z1av/zq6levJO2y65jEoBquqmeBeHUrc+IyO1ABVW9z8/97l63KLxQRzEPNLRrSQbwf9NtWyfr5nVmWlK0tJkWQGrvW0z1kh5/2VTvpob3muoVMfYvDJxu3Av260MtwHBkJJ5vO1tgTd7SVUynbiuXOsXsXrt4/a8m5xYaej4iIkOB2sAVqrrEz33vXv2H3n/6o37uMku6JW2k5t9zzfRSUyaTZOiF2vRQM4r1n2SmtXi4XczcgtRiFEzfY6Z37m9P8Gq9R8z02pRZxcfrypnpdXu7BRdd/YGZ3lezX2H3m/3N9KR6DfrePttMr+/0/qbf9dSUyay+8AYzvRLnlzVLptnUqzEL39puogVw4kstuKTb12Z6H7XPS+Fnx5oaeseVqmNmFC1ZPyc09A6Hw+l0ISLHe+97quoQbz8vANNVdbj3PhGXfDFUVXtF6eUDngIiyRcLgFtVdam3fpi3bo2q1o7ariTwIXA88A/OOMy0qIl1MsaJJSpayvHHv8tN9caWsJ3avHGXndEMsHrbxuwHhYSEhPzHsU7GiEdDL+5i9I6g08Ua4HYReVVVd2Ww63OBaEYf+wAAIABJREFUP4H2IvKA7rOQnwCKACeq6h4R6QyMEZEG6ib7h+PKrrwds79ewARVfVJEennv4zsj4T9E2byZdpsLhow+USEhIYGz0LhzRLXf47slWbwTb84viEND7xDYr9MFzus3BbgOeD2D8Z2AwcAtwBnAj17WbGegsqruAVDVN0WkC9ASVwj5B89jGEtboJn3+i1gElkYev92q3eQp+UPJV6ZZapnzYL0wqZ6U45LNtVLzG/bqzgxv10Ac8WpC820wN77e/G/8VtDLyeoOOFVU70tX2R0+wiOIjfE+hBCQvYnVxp6mXS6ABgAfOlNt0aPL4Az3G4GiuOMvh+BE4Clqro5Zj/TgZrAAd0uojhGVSN1+FYBB9yZoztjDGpYnetPsDUWLNn+5xhTvVcbP2eqt2KVbR29BYkFTPVSDSdXnjumIpYd+lYCBdPtBDc9aFskttgTP5jqVStewVRvW4+upnoYJ22+XLa5qd6ltZaZ6lmTTujRO9rJqtMFqrpIRH4GrozZ7iLgO1VN9bpgPCwid/h1UF6dvQM+XdGdMcaUu1K/N/x+re9k19sTYM8vX5jqdfvFNgv27jP6mOo91sAu5vH16bbxnNdUsi23kLopr6neig9tv3t/VK+d/SAfSR7S3lRvz+fjTPWGjCpiqnd5EbuMd4BfZtt1TIID65iFHDq5zdBLVdW6UZ0ubgOejxnzBDAaiO7R0wk42+tsAVAKaIHz6h0rIkVUdUvU+AbAR9kcy2oRKa+qK0WkPC5GMFMu32DcMmiErRwjFhgL2k7nJBcuaar30jjLNmi2U6kPrsx+TMh/mPPn5fQRxBUP5/QBBEyasV4YoxcnZNDpInrdAhGZD7QBpolIUaAxUElVdwJ4CRedVPUbL6njWRHp5iVjXAvswMX7ZcVYXDzgk96/Wc5drmx6AsVH2NVnGnHKI5xVYm32A32i0vhnKXj8eWZ6dyU3oU/3QiZafV7YxsOXbDXRArjv0yRqFcpnpldv9w4+K2CnV3+n0Gn9JDO9W5PPZjt25Wr+r9paCp1fzUyv65B1jFo5zUxv452nU3zQz2Z6mx5ryZBX7K5fuTS4Ye13JlpvlGlupgUwpkQT2v5rN9U/seSZZlrxTNyVV4kms961UevHASOBycBnkfInInIKrltFF0CBC1S1Y9R2JYE/gMic1dM4D3MSLqnjDFVN9caOwCVdlAZWA4+q6lCvDMxI4FhgCa68SqZumLDXbUhI7qBWyeOyH+Qjv23wteRnSIivWJdXKV+8ptm9duXG+WEdvaMNr7ftl8DLXnydb0w85grTC5VczM4DBVDlqz6menuWzDHV2/7MO6Z6F9r0yt7LxD62WeEvPG7nbQbYYRig3aWc7dz0bSsLmuqNnhkbLRMsm6+z7Rxx6s+xuXnB0qHwSaZ6bXbuzH6Qj5y58qPQ0DtCQkPvKCH06PlLmYK2WbBrt9t1xggJCQmJF6w9euWKn2R2r1218fewYLLfePXs9k7Resv6AFtxrcuaApuAAsAIVX3MGzMJKA+kepv9partROQu4AZcvOhaoEuk9ZmI1AKG4DJ8E4F3gcdUNV1ErsLVzBNgC3CLqv6nOmHfm9zUVK/vdLuWTwCfnmwbwjymWGr2g3zkg5V2MVDxzqXlG5rq7TEu7zB25QxTvTuSbcvH1Nqdx1Sv5B7b+iqt59n+du4ePdhUL+TIyVWG3kFwr6qO9urmzReRt1V1sbfuKlWdHjN+FtDQS+64BdcKrYOIJOGSLW5R1fFelu9HwO3AIFy7taaq+q+IXIAroXK6wfkdNE+n2Gb5lqr/CLfP7Gum12n9JEaWtDFmr7DOmA7xlU9WTueDUs3M9DoaJpoAvFi2OWvy2BmXj6VMMtOKYFlwvsQrs9g2y6aIcaF617Lx6XtMtADWfLGVHdvtyg0N21OUZ7qZycUtoaGXMZFqs9uyGqSq0elOU4GrvddXAlNUdbw3bruIdMclfQxS1R9jtsu2ENnmQZce5KH7w5ZRtiUQSo62M/IAtv5iV15lK1fS7uKXzfQAPpk5xFRv9/AnzLTmP2dbN6x40VTATnPLczeZaQEsfXSqqV7XhtVN9Up0sY0fbVp2F63PH2CkVYtfhlvexotzxrMnmqn1M1PaRzyGs+WqGL1DmLo9AXheVR/0xkxi/6nbb1T13ph9vwCsUtX+IvIssERVB8eM+RfXLm1j1LJ7gBqqekMGx7u3M8YL93RucPvgrw/73A+VrVOep/BZPc30Qo5e6pSqzJz1i7Mf6COW03/Ppdh2jggJCdmHdYzeMcVqmBlFqzctCGP0AiCzCxhZHpm6LQxMEJEzo7xvGU3dAiAiVwMNcYbiQSMizYGuwNkZHlRUZ4xzKp6nTcvWOpTdHxGhkRdysFgbeRAaXyEhIcEQtkA7+lkPlIhZVhIXM7cXVd3qefHOxnW/yBQRaQn0xsXcRfLO5wNNYsZVAdZHvHkiUgd4A1ejb312B/79mt+yGxLyH+a4oge0Mg6UJZtXm+r1L2/bb/OhlXZFYkNCQkKOZnKVoecZcCtFpIWqTvQKH7cCBgN771QikohLjsgy0ElE6uF6abVS1egWZu8BD4pIS1X91kvOeB541NvuWOBj4BpV/fNgjj11ybcHfZ5+kDb7m+wH+Uie2rZZvpJk24/yp9r3m+qdOuwCMy05+SwzLYDtfQZwEyeY6SV1bmOmBZB45mWmeu3q23rvRzxk1/UDIO9lPUz1/u1gW7dv3B+VTPXanmzYdD0HiMdwtlwVowcgIjWBF9nn2XtaVd8TkeHsi9HLB0wAeqqqZhCjt05VW4rIt8DJQKTC6VJVvdjTqc2+8ioVgP6q+n/eujeAy3EdMQDSVDXLGg7HljzZ9EKlbLXslRr/HFOouKne6m0bsx8UclCE185f4t27HeIv1jF6pYtWN7vXrtv8Z1gwOV4QkUuAZ4HmkTp7h8rudYtML9SGdl0oOXqYmd43tR7k3N/sMjcXnt6Daj/bZKYuPL0Hlfvb1WJLPPc6M62cYFKtB2j22/+Z6e35fQp5TrL1Wlqy9ZYu5D+tiple3s4PmWnlBGkT3iHxnGviTiunyFu6iqmhV7JINbN77YYtC0NDL2QfFx17oemF2qV2TcABJq6ea6oX73ROtm0Gnhe73+Iq6fnMtACmim1Lq0+NCxiHHN3ULWVnpAPMXr/IVM/aoxePhl6uitGLRUQG4cqgPOe9/xpYFil1IiIDgRXA48ACXH29LcBLqjrcG3M98LQ3rgDwqqoO8tZ1A24D9uBKuNykqvO9zhjR5VnqAPVVdXZmx/puY9vOCnMnlDTVG/u/50z1dKFtr9sdn0421Stw3RmmenumTDFUS+W9d5LM1M6lKB3OWmGmN+JF289K6gO2FWl1l+1DZMGBr5vq7X7bztsMsPZd26z30rfZJl5ZE4/Or1zt0RORdsAVqnqFiCQA04BdqnqGt/4n4E7gjUjtPS979mNgsKq+6Rl6DVW1u4iUAv4A6qnqMhEpqqqbve0uBm5V1VYxx3Ay8KmqVs3qWK2nbkP8RTfbFvmVoqVN9SxZe3FXU738JWxbWhV9601TveXn3GyqV3GCXbHyHCHd1pBNm/a5qV7i6Reb6llP3ZYofILZvfbfrX+FHj0DfsS1JAOoBcwDyotICWA7cBKwX1aCqi7yetwOBN6MWbdeRP7CJW4sixh5HoXIuI5fJ+ADH87FVzZdY5s5VuR526fg3a89bar38fuFTPU6/WrbacSSMmOHsuOR20w1815zrZ3Ynt12WsC9G+28owBvPf+gqV6+nnaxvwBp42wN2T/6HlThBt9IPuETU72yE2xbSIZ19OIMVU0RkTSv3MmZwE+4DNkzcNm3c4FdGWw6E6gRu9DbTwFgTtSy24C7cJm8LTLYVwegbUbHF90ZQ/IUY+eqmQd9bkdKuQl/kZpiN4WUlNzYVK/44F/M9JKSG3NbcmMTLYAXUybTyUzN/trlhN6WBnZxc0lN7zE9v09XzmB2Rbs2YcWenBzXn5cit4xgTqW6Jlp1ls1mfYcDbkWBcdX3BRjVOcP6/oFQpMtw0szU4pdcPXULICLvAeOAC3CZsRVwRt8moBTwCge2TSsBpKhqUlSM3kqc8dfd62gRq3MlcL6qXhe17HTctPDJ2R2n9dTtjj7dLeUo0OcFUz3r80vfsjP7QT5iHZekqVvMtCY2tPX+ntUpy5bXvlPgocHZD/KRefXvNNWrPXNQ9oOOYtI+tf0t2zbKNnlHbSMZKPPN96ZTt0ULVTG7127etijMurVARG7FGWhnA6cCxYBRwGbc1OwcDjT0WgDPqGr9mBi9hsB4oKaqrorRSQD+VdViUcsGAWtVNdu5hZyI0bOMhSozdqiZVoS0L2yMocTWN/JL7ftMtABOm/eUmVZOkfbjx2Za1gWMAX6o9YCZ1hmPlGHdm7+b6ZX/6nV2DrjbTC///QPNtCLseuURE5183fryVe3eJloAjZuuQtPtbkWFXx5mHqMXGnpxiIjUxSVXLFLVlt6yGTjPXm2gMFGGnogc740fEpuM4a0fDGxX1QdEpJqqLvSWtwEejRRG9gy/ZUBjVc02Xz1Mxji6SV/1t6leQrksc3tCQkJCjgqsDb3CBSub3Wu3bl8cJmMYMRcoDbwfs6ywqq4TkcJAVRGZxb7yKs9HyqtkwABgpog8AXT3euHuBv4FoivZNsElbBxUUaJdr/Y5+DPygXUfpZjqJY8/YLY7UH5veLup3obUAqZ6Lf/NskWz71jGQAFs7mybLFSwbQMzrcRLbMMKRtd52FSv3Zx+pnppk0ea6u14/ytTvfxNaprq7Zq6wFSv+HsTTfXikVzv0TtasPbopZx3k6WcuaG35UZbQ6HA+TbB2RESW9lWy0/fYPdgoCl/mWkB7HxnnKle/kuamOoNvc/W29zl4TKmeuy0jY9d/pJtQeGXU21b9FVMt/UP3b303dCjd4SEht5RQjh1e3Szo7+tB9E6oD/k6GXHQ7ea6hXo/5KpXryz+sIbTPXKvNvfVC9/tTNNDb1CBY83u9du2/5POHUbFCJyKfBozOI6uC4WLwE9VXWIN/YFYLqqDheR4UBTXEYuwDDgDVzyRlVcB4xxqtrL2/Yu4AYgDVgLdIn0uhWRAcCF3n76qeqHAZzqYZNkWA4E7Kf+rM+vV3JTM60Hbs4b19cvns8tJygxzLb9YGp/mHbyvdkP9Ikm66eaaeUE5qVqSpQz0wvxh9Cjx956dVcBnXG19LbgMmd3ZWDofaaqo6O2LQicrqrfiUg+YALwhKp+KSLNgZ9VdbuI3AI0U9UOInIhcAeupEt+YBJwTkyB5f0YUulq0wtVNs32c3HV+kmmetZ8UKqZqd6LedaY6jVItOvEcWJaHjMtgKq7MyqlGRyn35xgqjf7NdtKZUUK2E6lVnv0JFO9ib2Wmeqlm/q7oJBxH/QWq0eanmFS0nFmN7/U1CWhR88CEakOPIKrnZeA87xNwSVOZFt/Q1W3A995r3eJyEygovf+u6ihU4Grvdc1gR9UNQ1IE5E5QCsg06jh8mlKUrpdAaMmHbYw7FO72I/V55/ApTPtPo7vHCOUvszmyXTdx6totmSeiRbAB/mroobV3f+35ndqJJ9lpnfd1AeZf5ZdSYmU3QXZlMfOuMzXsx83NbTzeFXPn0Qlw2YcDfJu56uddr20K33xC0N/rmimd7rspkrlDdkP9IFFi0syKX9+Ey2AoiqUMnzQOiX/puwHhWRLrvboiUhenAfvaVX90Cud8hlwMfAlziAbTOZTt9eo6tyo/RXHdc1oGZtN63kGV6lqfxE5Dzd1fC5QEPgFeFFVB8Zss7czxksD+ze44Vq7fgc7B9rdaADy323bksyaxnW6mOq9kreoqV6tGc+ZaY052TZLtO1c2yxRa3b07WmqV+CR5031rLGOeUw8066rCcCeOfNN9Qo/M8bUo1egwLFmRtGOHUvDZIygEZEngfKRbhURQ09Va4vI28A3wOlkMXUbta9EXIeNr1X1uZh1VwPdgaaqutNb1htoj/MgrgGmxW4XTWK+Crn3QsUBZ5Sxa1ME8NNa2xII1rxTupmp3jXrJpnqhYSEONJ2rQgNvSMk107dikgz4HKgfiZDngBGAwfbUfk1YGEGRl5LoDdRRh6Aqj4OPO6NeR/IsjP11qkvH+Rh+ENCyWRTPSlsN5UDsO4Su64fAG8ssw1g/vwauziv6Z+VMNMCOOW01cByM73UNXn4p9KJZnqlX77DTAtgeFvbOnPXdLONQWSnbYzll6/bxpC2vregqd4nz6Sa6lljGfZiRa409LxetW8CV6pqhk06VXWBiMwH2gDTstlff1zrtBtiltcDXgVaqeqaqOV5gOKqul5E6uAyfsdnpXFR2xezPS8/aYdtrasSxskf2xMqmOqVMw6Y/vqLsnZiCbA8r90JLp5VyUwLYEIe2163N13wjqne70m2hsmytzLNOQuEpMK2hl7jGoYBj8Dbg2y/D3mN7fSQIydXGnpAN6As8LLIfjeoETHjHgdmZbUjEamI89gtwHXEAHhBVd8Ansa1UBvlLV+qqhcDeYHJ3rLNwNVeYkam/L51Oau3bTyok/ODs8s3o7bh7+MzsoIffnzGTK9g9bbMTM7Mmesv9VNmMqaEXRHc7mnzWbZlnZkewBcl7EqedE//i0WbVprpAfx7q81nBaDES7+YlsxomdyY5Y2qmelVnLrQvCTIukurm+mV/uQvRpa0Kad0xYbv2TK6jYkWQJF2g9jy7eN2ei17Yzv3AvEYzparY/SOJqxj9CwNE4BmN9mm7KctXW+qt+J/ti3Q6iybbapnyZY3bRNbFva2/b9cvKOIqV6TxrbtDjf8bpclCnDsJNuwlw4NbKfe62H7eWlqPBXeZNUo0/mQfPkrmt1rd+1cHiZjhOzDujPG7FPutpSj7q8Dsx/kI3uWzDHVW3RFtpV6fKX3btv5lZEzbTtxbL/zRlO9fK3ONNNKPN+2Pd+uVx4x1cvXra+pnjW61aa0SoSlre1KDQGI8dRt1Xlfmxp6eQ2dKruNEk1CQ+8oYVvfq+L6QuW71bjR+VzjRtkrFpvKJbaynfBIG2PX1iqxrW35ingnfbXtZzP9H9tOHImnX2yqZ43ldw+A5GNN5ZIu6JlrDT0RaYUr8ZYHeENVnzwcndDQO0qwnrr9toSdBwNgN7aPiY062AbYdx9rO131Xkp8t30KCQnJHViXV7G812Z1bl7S5p+4ervLcUmhnVT1kAsZ5tZkjKMOy16pAC1TDraqzFHKKzl9APFD+/Knmuq13FPYVO/mNd9lPygkJCTEX04D/oo0XxCRD4C2wKFXrFbV8C+O/4CbQr1QL9SLf714PrdQL9SLxz9c56vpUX83Ra1rh5uujby/BlfR45B1woo48c9NoV6oF+rlCr14PrdQL9SLO1T1NVVtGPX3WhA6oaEXEhISEhISEvLfYgUQXQ27orfskAkNvZCQkJCQkJCQ/xbTgGoiUllE8gEdgbGHs6MwGSP+CcQVHOqFeqHef04vns8t1Av1chWqmiYi3YGvceVVhqnqb4ezr7C8SkhISEhISEhInBJO3YaEhISEhISExCmhoRcSEhISEhISEqeEhl5ISEhISEhISJwSGnohR4yIHCMiQ0XkS+99TRGxbbYaEvIfQ0QKGunkEZFkETk28heg1u0iUlQcQ0VkpoicF6BeyQz+8galZ0Em57T3L6ePzy9E5PaDWRYSPKGhF+IHw3GZQcne+z+BOywPQERqBLTfxKjXhUWkYVA/xiJyvoi0y2B5OxE5NyDN00TkVO91TRG5S0RaB6GVE3j/p11F5PiY5V0C1DxTROYDC7z3p4hIIJ3nRaQHsBr4Bvjc+/ssCC2PLqq6GTgPKIGr1n9YjdYPkpnAWtxvykLv9T+egdnAbzERqS4iE0Rknve+jog85LPMDFwXhBkZ/E33WesARORsEensvS4jIpUDkroug2XXB6QVkgWhoRdHeIZIXxH5TUQ2ichaEZkqItcHLF1aVUcC6eDSwoE9AWvGMt7vHXr/b6tF5E8RuQCYAwwAfhWRTn7rAY8AGTUZngT09VtMRB4FngdeFpH/A14ACgG9RKR3AHqVROQDEZksIg9Ge2ZE5NMA9J4AegMnAxM8oyhCd7/1ohgEnA+sB1DVX4EmAWndDpyoqrVU9WTvr05AWgCRJuytgXe8cg9BNp3/BmitqqVVtRRwAc6QvRUIwnh+HXgA2A2gqnNw9ct8Q1Urq2oV79/Yvyp+asXifefvx50jQF7gXZ81OonIOKCyiIyN+psEbPBTK+TgCOvoxRfvAZ/gbjJX4G7aHwAPiUh1VX0wIN1tIlIKUAARaQRs8ltERJ7PbBVQ3G894G7gRKAI8CtQT1X/FpFjcDegET7r5VfVtbELVXWdiBTyWQtcL8W6QH5gFVBRVTeLyDPAz8DjPusNAz4CpgJdge9FpI2qrgeO81kLoA3umqWJSB/gfRGpoqp3EqxxgqouE9lPIqgHn2UE8F3LghkiMh6oDDwgIkXwHvACopGq3hh5o6rjReQZVb1ZRPIHoFdQVX+JuXZpfgqISP2s1qvqTD/1YrgUqIfzlKKqKd419JMfgZVAaWBg1PItuIflEGNCQy++OF5Vh3uvnxWRaaraz3PTzweCMvTuwlXsrioiU4AyQPsAdDrjjK+dGawLwsO2R1XXAetEZKuq/g2gqqtjbgR+UVREEj2P6F48z1dSAHppqroH2C4if3tTcqhqqogEcfMuo6qveK97iMjVwA8icjHeQ4LP7P2/VNWNItIGeE1ERgH5AtCLsExEzgTUu3a3A78HpLUImCQinxP1vVDVZwPS64p7OFikqtu9B7zOAWkBrBSR+3EPrAAdcF72PARjYK4Tkarse2hthzNa/GRgFusUaOGzXjS7VFVFJHJ+vj9AquoSYImItARSVTVdRKoDNYC5fuuFZE9o6MUX20TkbFX9n3fz3ADgfdGC9GD8BjTFeb8E+INgwgKmAfNU9cfYFZ7Hxm+WelOaRYAFIjIQ+Bhoif8//nj7fl1EuqvqNnDT8cBgb53f7BKRgqq6Hdgb7yQixQjmJppXRAqo6g4AVX1XRFbh4juD8Fj+LSJNVfV7T28P0FVE+gOXB6AXoRvumlXA9aYcj5tqDIKl3l8+AjReRaSGqi7AGXkAVYL9SdnLlcCjQGRqf4q3LA9u1sJvbsN1cKghIiuAxcBVfgqoanM/93eIjBSRV4HiInIj0AU3XR0EPwCNRaQE7jswDWeo+/r/GZI9YWeMOEJE6gBvANVwxlcXVf1TRMoAnVQ1s6nPI9Wdqar1s1vmg05JYIdnmASOiBTF/fArLn7tfJz3YgnQX1V9NfbEJX70B27wNATX1Hoo8LCq7vZZL7+qHuAdFZHSQHlV9fXpW0TuBGZGDK+o5fWAp1TV14QTEUkC56HMYF0FVT2sBuEHoXuWqk7JbtnRhIi8pqo3ich3GaxWVQ3SC2WGiFRW1cWepytBVbdElgWgVRA3G3Ks939bDRdvGWQyDeISuyKZ0uNV9ZuAdGaqan0vNjZJVZ8SkdmqWjfbjUN8JTT0Qg4bESmH81q8i3vKjjziFwVeUdVAMmFjjqF+wDEt5ngGygne278yMlR80ikI7I4YkCJyIi7IfomqBuFBNEVcI/Dd6v3IiUhzoD4wX1W/DFA38AcfEXlOVe/wgt4P+BFX1Yv90spJvCm/e4DjiZqBCsqwzOTazVDVIDJ8P8Rl2l6rqrW97+OPQRtC3u/2abjPzTRVXRWQziycJ3sQ0FVVfxORuap6chB6IZkTTt3GOSIyMcCn7fNx6fIVgeiYoM0EEA+YQRCzAGO82Cvx2+ATkY9xyQNjVHWrn/vORO9UYJn3wztXRK4F+ovIEqCPqvqdsfYVLuZqoYicAPyES+i5SEROVdUHstz6EBGRWkBVVR3rvR8EFPNWvxCAwT4NaAb8KyL34gLRvwDuEpEmAZzfGcCZQBkRuStqVVHcVKOfvOP9+4zP+80SLzbuQg40vIKKCRwFvIKbqQgsk19ceaZaQDERuSxqVVGgQECyVVW1g3gZ/F7MY6Dz4SJyAy67fyLu93OIiPRV1WEByN2Oy+79xDPyqgAZeYRDAib06MURIhKb0SRAdVzMHEGVXRCRy1X1oyD2HaOTjsvYjJ5ubOQt8336yIvR+QkXHP0tLsv2c1Xd5adOlN5MoKWqbhCRJrgA9B64uKiTVPWAGntHqLf36VpE+gElVfU2zxM2w+8nb8/79H+RGEtxteYeBgoCl6vqJT7rzVPV2t7r6UBjL9EkETeF7Ov3QUSa4gzLbjjjJMIWYJyqLvRTL0o3CTf990cQ+4/R+gLYgQuq3xvHqaqPBaQXiDctA522wCXAxbjEsghbgA8yigv2QfNH4BxgijfFWRUYoaqn+a0VpfkHcKaX6Y6XTPOjqp4YlGZIzhN69OKLf3DetP5AKs7Qm4wrMxEkT4srqTJMVYPKLgSXydsTF88V6cKxOMDg5jWq2s6L1WsL3IjL2vwM94Psd+2+PFFeuw7Aa54B/ZGIzPZZC/af8msBPA2gqrskmKzb8jE3zM2RBwQRuTkAvc0iUltV5wHrcJ6ZVNzvnu/JQl7s4fci8om6+muB43mzn8ElYlQWkbpA3wCnbisG9cCYCeNE5FZc2ajorGJfvduqOgY3O9BYVSf7ue8seBTnVa8kIu8BZxF8QeH1OOM1whZvme94seH34Tyle72i8RLPeTQRevTiDBG5FLgTeEZVx4rIIg2+CGcRXFHRzrgb6DDcU/DmALQKA/1w08V3A5OCOr9M4nVK4QzOKwLwIM4D6qqr+7YAuElVf4isi3infNR7F1c/bwXQC6jsTR8VB75X1VN81vsjM8+BiPypqtV91quDm+L81Vt0Fi4T8GTgWVV930+9KN3JuNqEw4H3VDWwOnciMgNnpE9S1XressDioERkADBxFlomAAAgAElEQVQhgIeczPQySoLQAL/zC4HZwJvAlxrwDdL7PWmEeyifqq6cUxA6kVCCurjP/xjcg15bYI6qXh+A5njgQ1yMZTdcp4y1qnq/31ohWRMaenGIlzHWD6gKNFDViobaTYH3cQWMRwP9VPWvAHTq4eICa6tqGb/372n8oKpBdTTISK83LhliHXAsUF9V1Yufe0tVz/JZLwkXR1Me54391Vt+Ji5+6J2stj8Mve+AXqr6c8zyRsCTqtrMTz1v33lwGYbVcZ685cDXqrrRb60Y3eq4B5/2wC/Am0FkN4rIVFVtJCKzogy9OQGGaVyKS75KwHWPEJzhVTQIPWu8GLmWuLIjpwIjgeGq+mcAWpcCEyMPAt4DVjNVDaJLzKNZrQ9i6j0y7R79eRRX2/VUv7VCsiY09OIYETkFOEP3FakNSicSoN0ZF6T9Di6ovzHwhN+emihdAYoE4TnMKTyjpzyu7EGkll51oHAAyQoZ6QeWxSwip+Ge8IfjVebH1e+7Duigqr8EoZtTeN+LS3Bt5jbjjKIH1ceMZhEZCkzAeWQvx4U25FXVbn5pxOgtxnmB5gbt7fL0rs1ouaq+baDdHGfUFsJ5hXup6k8+7v+AUiPRBvvRTtRDyNe470AKMFpVq+bwoeU6whi9+OZvII+IFA/Yg7EQl031dEwM1mgvqSAQPG/XJFzJDN/xkhI6Aimq+q2IXInLqvwdFz/nd127Fqo60XtdGVesFXW1EC9jn3EUJG8Q0P+nutZSp+P6zF7vLZ6Ha3O12m89EWmlql95r4vhPMCnepp3BqHpadXBPfRciGuV10ZVZ4pIMi65x8/SNT1w/Xx34jzpX+NidINiGa5ouZWHINr7UwCXvDATCMTQ86ZSrwauAVbj/n/H4qY8RwGVfZTLKE400Huy51XPqBxPEHFz/b3v3d3AEFwG850B6IRkQ+jRiyNE5CVVvdV7fTbuh/9vXE22m1X1i4B0C6tB+ZFMtAN7AvYCpBNxWaEbgcK4m/Q5uO/OdT7r7Y0JjI0PzCheMAisPAqeEX0SLnPzDw0gkznm//MNXDzi68BlQFP1Ocs3Svd7nME8WmNqIIrINX5OiQfpgc1EbzhQBfgSm5ZrsfrFcfG/rQLa/5+4GYk3VXV5zLr7VXWAj1rDcL8rL3qLbsNlvl/vl0YGmtEZzAVwXuA0Vb0vKM2QnCf06MUXjaJe9wMu8TwJVXCxJoEYesCLInJ7xGsoruXNQFXtEpBeNJ8HuO+TVbWOuHIcK4BkVd3jJTH8ms22h4Nk8jqj90ERSJmMaESkNfAq7iFEcNmiN2uARYyBhlHTZINExFcjPYYLcT0+9wCISAJQQFW3+x33CAwUVwB3NPChugzjIFns/QXaci0LtuGvVy2WEzPzVvpp5Hn0wJUX+hDnZfsGZ+wFhqrOiFk0RUR8DZkQkSFk0btaVXv6qReSPaGhF78UjTzpq+oi72YTFHWip4ZV9V8vWSIQvKf6at7bp4PSARI8z1MhnFevGK5/cH4gbwB6msnrjN77imecVwMiNfyIZPwGwLNA80iSjrj6YZ/jvER+UtbLNhSgqIhI1E08yO/Dt7iA/oiXuyCu1+eZfgupanPP0LsCeFVcKaAPVdX36Vsv5rCIqt7j976z0Izu/JEA1MQ9tAbFeBFpH/PQ+oGqnu+3kBeD28vv/WaFuDaSERJwMbLFMhl+uEz3eX8hR0ho6MUXNcQVTRbgeBEp4RldCQT79J0Q0YK9Pya+f7ZEJD/OE3QJzqsgwHEi8gnQLYDpv6HAAlxXg97AKBFZhPOcfuCzFrhG8WNx5xV5jfc+MC+GuGr5t+NK1szGnV+kUHQQbInJxF7E/rW9/OJ1oIj3+i2gNLDWM4yCqEsYoUB0KIOqbhXX3ioQ1HVSed6Lv7oP1/nAd0PP82b7mvl9EER3/kjDtedbntlgHyiTwUNr2QD1rJmBM5wF9/+5GNcdxzdU9a3o997Dh6pqEN/xkIMgjNGLI0TkuJhFK9UVvy0NNPEz2y9G91pcy7NRuB+QdsDjAZTn6IsrGdMt8qMhrobfi7gbwMN+6nn7TwZQ1RTPk9gSWBpEhqi40jSZoq4gr++IyFxc0PtUVa0rrh3UE6p6WTabHq7ey8BxOM+M4kqQLMV5wvDrcyoixTSTGnYi0lBVA/E8iMgUoEfEo+7FRb2gqmcEoHUSrrj25bjCtx8CH6nqGr+1PL2Xcf2tR+GmUQH/rllOI64u4aWqutR7fxyuhVfg8bEWiEgBVd0Rsyy/qu7MbJsj0GqIq0dYBHdf2Ah0yWD6OCRgQkMvFyAilYCOqhrYNKeI1MR5gBT4TlXnB6AxDzhNVbfHLC+MM1J8LSicWxCvtpW47hunq+pOEflNVWsFpPdmFqvVr9hOEZkGnBfxNEctPw8YqqqV/NDJQPdUnMc3BXeDK4crH+P7DU5EfvK0Rqlqit/7z0Avo2vn2zWL0tlCxuEKgdbtE5FWwGvA955WY1zh8q+D0LMmo6SuoBK9vNml29TrNOIlCL6ktp1VQginbuMWce1n2gOdgGRcC6Egycu+hIEg4tcA/r+9Mw+TpKrS/u8FWUWEkXFFFgFhUBahAUFUUHFjE8G1cRBX3MAdx40G/HQAURZFQYRBoMFBBAUGBLHZVxubRrbxA1xQv0EZQJQd3u+Pe7MrKzurqumKeyMr6/yep57MiKiMc6sy88aJc895zxO9Th4sWBpr/I4ly2QcQ4pgnAvs27U8fY0L9aTMy2OzSFGvpzBycSvV4eTOHK08E7hA0j3A7wrZwvaepc7dwzHAHEnb2f4LgJJEzv8hFUwUwfa1OSra6QJyqxuW4umy1XiUcAJ7Vd4720+b+LeK2D1P0iaMFLZ93A13q2ijWCGnKzwPWC7nT3fm6hVJOaQleNxd7eRsXybpsUK2gnEIR2+IyMuYbwbeSeoE8GNgTRfujCFpH1If2NNJE8hJko6xfWTDppyTo/tVoJbozXoUyeG6CngfcJmknWzfRjlnFlJu4CdI+TSPF7QDgO1d8tNZOc/r6TRfGLEAJQHo7wDPsv3i7FDv1HQBge3vSXoI+EWO4r2N1IppW9u/bdJWN5KWAj4EdDQkL5J0dClnrya13ruW2YqR9w7g7IbP30axwutI2pWrkoqhOtxPSrtpjOwoQ+r7fDRwCsmxfRtwUZO2gkUjlm6HCEkPktotfRG4zLZVp9ftfFIHjk4nh6cCVzYdopf0W5JD18/RazziJel6d/V7VVLKP4YkpnpUqbwdSVfb3qLEuZ/EGH5ve7VC574Y+AxwtEfadjXey7fL3ltIgq2/B97YdISmj71jSTcCnaT0d5GiG+8rabcGtd+72kj6d1K+6sl51zuAa2036gy1haRdbZ9e2MaccQ7bZcSZg3GIiN5w8W+kTg5HAadI+mElu2J05OlxCui+2V6j6XNORHdCv+05knYlRS7/afxXLpatjuM4R9IhpIhstyhtNWFcyur2Le/UJaN7X+NLOrnIpFNhuDzwDFJ0r7MUXipXaLPuG4Rss4TuYhvUeu+KFAgsAm8ENrb9RB7HCcCvaDjqlc/9z8C+JMmYZTv7CztCF0r6BiMRy4uBA8YqWlocbG/b1LmCZghHb4iwfRhwmJJA8ttJOVfPlbQvqXKs8cbcmeOBq7PMCST5k+83bUTSuBGmTqVcgxxE6t5wVZeN+ZJeTRI6bZpDe7ZndD035eRO+lEy1P9XJe08A0jaDfhzATs7FDjnovC4pLXyEj/5+9joErxG68sthO2dmrTXRa337kpgE0kn2n5XgfOPx0okvUxoXmOum5NJVdLbk1IK9gD+UtAepHn51yTdRUjR5uNJKT+NotT+bD8KOpXBohFLt0OOpBeTlh/eZnvtgnY2BToaW5fa/lUBG90Rmg4G/hl4pu0lm7Y5zCiJCfc9BHzBduNRy2z3BaQl8K2Ae0haXruXzJvrsr2D7aZzrnptvJp08bydrPUI7Gl7vCWtJ2ujLSmeKu9drrD/KqnDz2d6jxeUinoH8O+k3t0iOSmfs9346oikubY3lTS/E13uVMA3bavL5jyPdIgZc19Dtk4nOZXdKQwblZJtCsYmInrDzxq2v0AS/C3JPNKd/VMgRd+ajrDZ3qB7W9IapKWP15AuCtXIxSYfqGDnbNulIlPjVTYeXsgmtm8HXpNzOZdwXSHVA2g+uX4BSuLkD5K6jHRX3Ta6DFnKkVsEu7Xeu72AmaTo2o69wyClNTSO7VMkXUTK04NUaf//StgCOsU5f5a0PUmOp8jNVRcPStra9mWwoML/wQles7isZXvXru39s4RTUJmI6A05pTSSemx8jBSi/x9G8vOK5UBJWofkuG5BWu48oURFo0a3Cxp1CLi+dDVzHsOvOknvU51xIogA2P7GeMcbGkPx/2fN9yx/F77GwnleTRcmtfLeSXqv7cbTQCaw+TxGpI2AMu0AJe0AXAo8n1QstCKwv+2fjvvCydncmBRh6yxJ3wPsYXt+AVtXAp/pcSq/XlsSKIiI3nSgZFJ9h31IzcDvLmkkL0N/AXgRcDDwXufG8YX4C0lPrnepWECttkiNL4H3UlEyozuC+EFSO7vafLCCjQtz0c6PXf5O+njSTdY3gW2BPSnTx7fz3q1LinZ1nJEdSZX+pThR0t6MzvP6bimpGkkHkWRAbmREsslA445eVwrBfaT3rgY32N5IqS0Ztv9W0NaHgBNyrh5kp7KgvWAMIqI35Eja3AXadfXYmANsZ7uoGKakx4E/AOfQJ7m9aaFRSb8BXt1vCVrSH1yos0Jt2pDMqBRZGzcXqGCe1/3AU0nVqA9RsJtDV57XDZ3Uhs6+pm3lc18CbO/RLQjPsf2K8V+52PaqStVIuhXYsGTFr6TP2j5YYwgnNz2P9di+naQacJztm0vZybaWdOqPXMOpDMYhInpDxFgXNkmrQtF+lLeTRGHPYbQcSNPLOY22WVoEDgNWJumv9XJwzYFIOtf2GwqdvopkRg817jB7c7t67ZfK86rZ1eHhnBf4G0kfBf4IrFDQ3rOAR7q2H8n7SlFbquZ2kmNZUtql42C1IZy8EUmR4fv5c3MccGohJ+w3uSCjuFMZjE84esNFKxc2kiP0e2Dp/FME2ydM/FuN2vv2OMea7vrRraO30CGg8aq4LmpJZlTF9VqtjULShbZfPdG+htiHpBG4N6lCdVvKLo/9ALimR0rpPwraKy5V08MDwDxJFzL6prWxKJvts/Jj1fks27wf+B7wvVy5PRv4pqQfAQfa/r8NmqvpVAbjEEu3QWNIWgFS79mCNvYg5wTmXTcDR9j+QQFbVZf+8tJ0p5l6Ly+1vVyT9rrs1pLM6MjjAKwNdC4qpQWMyVWNL2J0wcIBDdtYluR0zQG2gVH9RM+zvV6T9toi35C8PG9eUkJKqctWcamaHnt9neQmnbIWNRCRtCRJt29PYA3gRJKe38uBr9p+YSG7HadyJaCEUxmMQzh6Q0qNC1uXrReTJoxOlepfgX+1fWPDdvYAPg58EriONPFvAhwCHGb7xIbtPUGSjelIAowqyrDd6FJy1g7bxfZv+hwrnhNYWjJD0urjHbf9u0J2v0tywLYFjgV2A66x/d6G7exD+nw+l7SE2vm8/A34nu1vNWkv27wAeIvte/P2yqSoyeuattUWkpahoFRNbdrSQMy2byfdiHzf9hU9x45oMnLZllMZLEw4ekNIrQtbl70rSAK7c/L2NqQv8lYN27kKeHtvpCnr6Z1q+6UN23sTaelhbeAnwCkl70LzkukNtm/tNxbbZzZsbxDkTmoIGM+3vWHX4wrAubZfPuGLF8/ex0os7Y9ha6GilmGS5KmFpP+0/daeqPMCCkpFLQes1u87X8jeCiVXXHpsVXMqg/GJHL3hZKuuC9v+kg4Fzi1o76ndSym2L8rRoaZZsd9you3fdiq7miQ7Vmfmv2Vn4FBJzyA5tY3fddv+0QRjaZpBkDspKmCc6QjCPiDpucDdwHMK2vt/kp5m+35JXyRFnb/iMr2Kn1CXOHmOmjZ+9672es/WYp/8WK1tnqQdga+T8prXzBp3B5RcugX+WdIBpAhbt05gCZsbjuVUhpNXl3D0hpPaF7bbJX2JFJoH2J2UU9M04ym4l1J3hySRcR9pCW51upbDm6R2hM32/l2239S9XZEaOo9nS1qJtMR/HckROragvS/ZPk3S1qSuLYeQdAq3KGDrC8BlWSJHpGWxEh1b2uw9WxzbneKjD9vet/tY1tbbd+FXTZpZwObARXkM8yStWcBON2eS+t2exYhOYClqOpXBOISjN5zUvrC9B9ifkareSykjhfIvkvopuAtotBMAgKRXkZZuNwd+Dhxuu6QkQpsRtrZyOIoLGNs+MD89XdLZwLIu21i9UxW6PXCM7XMkNS0+DYDt83JxRCdt4eO2/1rA1NKS3gls1a9IqaAmYc0KZoDtWNipe0OffU3wqO37emSNSn8PH7J9RGEbHWo6lcE4RI7ekJMTmUtf2KpQO5k/F2PMBy4jTcCjviyFhU2r5lmpTqu8VgSMs+2tWDiy0HildrZ1NqkYYzvSsu2DpBzZjcZ94ZOzsZ7tW8aS5Gl6mThHJ2cCb2WkK0aXucYLk6pWMEv6EPBh0g3jbV2HngZcbnv3Ju1lm98HLgQ+B+xKkshZyvZeTdvqsvlOUh/m8xktH9N4WoGkq22XiGIHT5Jw9IaUyhe2GcDn+9grJpfRZbtYMr+kdzO+DEIxHaxKjldVuRNJx49zuHFnocvuicBapOrpTrTNpRx1ScsDrycV1vxG0nOADWyf36CN79l+v1JXml5s+1VN2eqxW6X3bO0KZqU2XSuT+gZ/ruvQ/bb/t0lbXTaXJy29v5b09/2MJDvyUAl72ebXSN1FbqOrxVuJz0tNpzIYn3D0hpAWLmy3klpo3UBXiL6UXEaP7eIOURtUcvRakTupjaSbgfVdebKT9ExGyxv167AypZC0NLAX9XrPVqtg7rE7dO8dgKT/S/ouPDLhL0/eVjWnMhifyNEbTmZQ98L2F9u9yzm1KJbMn5erXtCJhCqpx3e0Ar9i+xcN2xsVYevKRywSYevnyNWQO8l2quk8Ar8Gnk2lbh+SdgIOJUWj7gJWA24h/b1N2WhrGfwoUouwo/L2u0iFJkV6z1K3grlTCfsNRt671Umi7I29d122Xgh8moVXQko6Qr8miRbfVdBGh7eQ5s/iTmUwPuHoDSdVL2zAfkrNx3vbBhXLueqiZDL//sDHurbXBd5Nalj/eaBRR4+K0g7jUFzuZCydx4ImVwFuknQNoz+fpar/DiQVR/zc9kskbUuqRG+Sttod1u49W7OCGeArlH/vOpwGfJf0HSjZ1q2blYBbJF1L+e9CTacyGIdw9IaT2he2PYH1SHf6C0L0NHyxGSuKIWlVKOJYrmj7pq7t39iem21+rWFbrUbYuk1WsFFb53FWwXP341Hbd0taQtIStudIOqxJA26pjy/1e89Wq2DOFH/vunjM9ncKnXss9qtoq6ZTGYxDOHrDyazK9jazve7EvzZpakcxVhplwO52NJ/VsK2xqCEo3E1xuRMq6zy6YEupMbhXqfvGJcDJku4C/tGkAUm72z5pLO3FpjUXu/gMMEep68GC3rOFbAH8UdLRpArmg7KKwBIF7dV47zrpH2dJ+jBwBqMdoSLFH/ncC74LFW4iazqVwTiEozeEtHBhu0LS+j3Rr8ZpIYpxi6TtbZ/TvVPSDkCVlkWUzUGsHSHtUFXnUdL9LFw9fR/wS+BTtpsW996Z5Mx+giRJ8nSSw94knc4zTxv3txrG9oWS1qFe79m3kiqYv2773lzB/JmC9mq8d3NJn8fOd7v77zEFNEHHoOhNZAvXoWAMoup2CKl9YctVjWsBd5DuTIsUD/TYLJ7ML2lt4BzgCpJDArApsBWwg+3/btLeGGPY3HaR/LW25E56xlBc51HSgcCdwGzSZ/PtpM/rdcCHbG9T0HbtpfehpEYVrKQlSbl52zZ97jHsLdsrpdJvX0H70RN5mhCO3hBS+8I2lkxHKXmOsZL5bb+3gK1lSHf2naq7G4HZJSbjNgWFa1NZ5/H6ngICJM2zvXG/Yw3bLiqTk3PkDicVEJjUquwTBaKUrTBWBbPtxqtgs70LgTeXvPHosrXQZ6OmXFTJm8hgsIil2+Fkp56L1zH5wravpM83bawFvbVqyfx5Weq4znaO0JS6426rkrKq3MlYOo9AEUePlAv4VuBHeXs3Uv/ijt2SlC5umQ18G9glb78dOIVyVam1qVHB3M3fgRskXUBXbl6TGqSSng08D1hO0ksY3fVj+abs9NhsJU1D0nLAarZrpboEfQhHbzhp88JWg6rJ/D0Uy2tpq5KyBbmT2jqPM0lRr6NIn/+rgN3zReijhW2XLm5Z3vaJXdsnSSqWw6b6vWdrVsFCupkqHTl/HUmmaVWSZl+H+0myTSWofhOZNQm/DiwNrClpY+CAqLqtTzh6w0mbF7YaVE3m76GG/EhtQeHacidVdR7zMuZYF7rLmrJTM2rSVbl5rqTPAaeSvgdvA/6rKTtd9jq9Z1eRtDKjo1DPa9peF8WrYLuxfULpKJRT68QTJO1q+/QSNvrYbOMmchawOXBRHsM8SWu2MI5pT+ToBY2Q8/TWsf3zPFE+xfb9FewWT+bvsVc8r6VmDmK2d7XtLSRdBbyZFCG90fbahezNATYmRQ2L6WtJ+qztgyUdSZ9IdpPLcdleteIWSXcwunKz11ajlZuq3Hu2y+5TSasRYqQK9mTbdxeytyAKZbtIFKpLGudT9P9clpLG6divchMp6SrbL+0u+sg3k8V7oAejiYjeEFH7wtZl9/3AB0jtwdYiLUl8Fyi1nLNQMr+kxpP5W5QfqR1hqx0hnVXw3N3cnB9/WcNYzaiJ7aqREduHA4ercu9Z293RuxMqmJzFwlGopuVOOtI4KzR83gmpnKZxo6R3AktmSZ69SQoGQWXC0Rsuql7YuvgIaXK8GsD2b7IcQhEqJvO3VRxRW1D4wPz0dElnUzhCWktfy/ZZ+XGBgyBpCWAF238rabti1GRJUteINRhdwVwqKlSl92wfiSgxEsG07RWbtNfFo7bvk0YFSp8Y65cXB9tH58f9mzzvIlLzJvJjwBdIUftTgJ+RimuCyoSjN0S0eGF72PYjnclR0lMoW/RRJZm/reIIWshBrBEh7bJVW+dxNrAX6abgWmBFSYfbPqRJO132akZNziItbd5Aww7JGFTpPWu7qhB0F9WiUC1J41S7ibT9AMnR+0KJ8weLTjh6Q0jtCxtwcZZtWU7SdsCHSRegUlRN5oe6xRG1I2wtyJ0cxtg6j8cB2zRsb33bf5M0kxS9+BypO0Gp70PNqMmqlXOeaveeRdImwNakz+Rltn9V0Fx3FGo2cD7lolBtSOMUv4mUdBbj3OhH1W19ohhjCNGIGOxM0tLK54C5pS4IOWr4XuC1pAv3z4BjS0XcaiXzd9mrWhyRbdYUFL6ZinInqixgLOlG0udlNvAt2xeXsNNlr1pxi6SDgAttn9/0ucewdzapGGM70tzyIOm7UOp/+WXgLYykSbwJOM12EedS0pq27+jZt5ntawvYWqgwoeTnso/9IoVskl453vFaqRvBCBHRG06WkrQUaVL8lu1HJZW8iL8J+IHt7xW00c2sSnY6VC2OaCHCVjtCWlvn8Wjgt8D1wCW5QrxkKkPNpfergDPyzdajlM9hq917diawkbNIuaR/J30vSkURT5e0o+0/ZnuvIEXdNihgq680jrJ0ju3/LWCzeJpGx5HLFdMP2n4iby8JLNOUnWDRiYjeECJpb2Bf0oVte1LboJNsv7yQveOBV5G0rn4InGf7sRK22qAF+ZHaEbbaEdJObtKWjOg8foIUKdrUdmPadmPYF7Bk5zMqaY/uvNaGbRWV/8kyKzsDN9T6vGS7xXvPZjtzgF1s35u3VwJ+bPtVhextRtIf3ZEUsfwaqa/1HwrYumOcw41L5GSbfW8iSygy5PnyNbb/nrdXAM63vVXTtoLxCUdvGlDjwpYjiG8g3ZVuDVxg+31N2uiyVTuZ/0vAkSS5mG9n28fa/lKTdrrsnQbsbbtKhG2spZbpssSiAv1Fay29S7oE2KYTNSmNKvWe1YhE1GrAZsAFeXs70lLxuH2hJ2l7S1IU+CFge9t/KWWrNjVvIjvpGBPtC8oTS7fTgPyl7o6w7UPDmlR5efhc0mS8HGk5t4ijR+Vk/trFEcAqwE2SqkTYajl0aknncRFotNtJ5aX324GL8nev+7NSSl6lVu/ZjkTUXOCMrv0XFbDVr4BgedLN4/fz0mbj3z1JywOfJHXh+ECu8l3XdpEWi5maaRr/kLSJs/SOpE0ZqfoNKhKO3vSk6QtbJ5K3DWkiPpaUy1OKnXoSlo/Jd4r75urfxqkpP0LlHMSKEdK2dB4nounoRs1evnfkn6XzT2mq9J71aImopYEX5s1bbT/atD1SN4zaHE9yZDtLmX8ETqNQL+1MzZvIjwOnSfoT6ZrzbNJ1IqhMOHrTk6YvQP9Kys37oO2HJ/rlBqiazF+7OKKFJdMqEVK3KGA8AU33L64WNXF90d2qvWclbUNaffgt6X16fk49uaRJO/2+c5J2KBxdW8v22yS9I4/hgZxmU5JZhc+/ANvXSloPWDfvKuWkBxMQOXrTEHX1HpyK1E7mb6E4onYOYm25k4V0HoGSOo8Tjedbtj/a4PmqFbdkW/2WwUsVK9TuPTsXeKftW/P2C4FTbG9awl6P7cZzN3vOfwUp7/dy25tIWov0t21eymZNJL2FVJhXtItKMDER0ZueXN7ESSRdZnvrPo5JUYmH7OiM1Z6sRMVmbfmR2oLCteVOqgoYS/pkn933kbQl5zXp5GVmNXy+8fh01/NlgV0ZnY/bKK7fe3apjpOX7f93LvyqQeno2n7AeaQo5cnAy4B3lzRY+Sayu4vKq0nL4413UQkmJiJ6QwWE0nUAACAASURBVMhEF7ba42mKtpL5W5AfqR1hqx0hrS1gPJuUN9fp1rIDMJ+Uc3ma7YNL2G0LSdc0HRUa62aOwjd1ko4jtXY7Ke+aSVIQeE8Jez22N7ddqnVdx8YzSMUtAq6y/dfC9g5k7JvID9nepkFbv8oFO18jyf/MnuqrSVOViOgNJzPof2HbS1LjFzZJJ9p+10T7GqCtZP5Zle1VjbC1ECGtLWC8KrBJl57XfsA5wCtIkcSmvw/VoibK4rqZJYBNScupjeL2es9+CPgIqecswKUknbtGkdRXrkXSqgC2f9zv+GTJS97nlDj3GNQsZPujpKNJkjgHZU3JJRq2ESwCEdEbQrK21ht7hCrPISnaz7W9fsP2RuWySHoKML9pO2PYHoRk/kapFWEbFLmT0jqPkm4BNugkgucLzvW21ysRYagcNbmDkcjaY6QK3AOajsL22KzSe1apk8IPbM8scf4eW8ePc9g1Iog1kHQl8E1G30R+0vZLm9a4y/IxrydF836j1EVlA1dq1xeMEBG94eSZdC0xklojPcv2g5Iaq4qV9G/A54HlJHUcLQGPAMc0ZaeP3YWS+SUVS+avXRxRMcI2EHInucilpM7jycDVkn6St3cEZufCgpsatNOhWtTE9ppNnm8itHDv2f/IqwSNtySz/bik1SUtbfuRps/fY2vPkucfIGaSbiKPYuQmcndJywFN56quQp5bJK2W993SsI1gEYiI3hCi1MlhF6D7wvZTkqL9MU3fIUv6mu1/a/KcE9jr5KvNJFVyfY4Uqdxwgpcurr0qEZpBiLANQoS0UJRtBinZHVKVYzHntmbUpDaSbmV079nlgHm21x3/lYtt7wfAv5DmrwWFIC4nCI2k7YEXMbrF2wEF7BwKHGf7xqbPPQhIuoGRaPOywJokiZVGu6gEExMRvSHE9oFKSvmdC9teXRe2xpw8SevZvoUkirmQDEHBMvqlcuXdm0jJ/I9KKnnHUitC00qErXaEdBFo9L2UdARwqu3DmzzvONSMmtTmT6SLdidndBlSSkEpbss/SwDF8wQlfZfUFWNbkvD7bqQirBLcTJpLnkISTz7F5XoiV7+JtL1Bzxg2AT7ctJ1gYsLRG0IqXtg+CXyAFCnsxUARLS/qJ/NXKY5we4LCVeVOFoGmZS3mAl+UtC6pndapJSN6NZbeJb3M9uWSlnEFkfIuB+E+4EZJo3rPlrLrLAgtacW06ftL2cpsZXtDSfNt75+jbueWMGT7WODY/LncE5gv6XLge7bnNGyu9TQN29dJCmmVFoil2yFE0h6kVjNVLmxtUyGZv7b8SFVB4dpyJ4swnkYFjLvO+08knbm3k/qLrtPw+atFTSTNtb1pbyFUKfKcMiZNft967M4gRbs60bz7gPfYnlvI3tW2t5B0FfBm4G7gRttrF7K3JEkVYU/g+cB/kgpd/mH77SVsdtkuehOp0TJfS5DSbJ5h+3Ul7AVjExG9ISRPuid0XdgOktT4ha2D+iugH1iqGq+X0sn8LciP1I6wVY2Qqr6AcYe1gfWA1RmJcDRJzajJo5KOAZ6XI/ijaHopzvV7z3Y4Dviw7Uuz7a1Jjl+RfFzgbEkrkb5r15Ec9mNLGJL0TZKT9wvgqx7R7Dso50KWsFkzTaN7qf0xkvLD6QXsBBMQEb0hRtLmpMjezsDNtsdyViZrZ35e7tga+Appkvyy7VbC9E0l87dVHNF2hK1ChLSqgLGkg0nFSbcBpwJn2r63SRvj2C4SNZG0CvAa4CDgy73HC0bYtqGn9yywhxvuPdtlb6HvcsUo5jLAsiXy5vJ37IvANzy620jn+NML2a1ayBYMBhHRG0L6XNgOLHxhezw/bk+q6j1HUuNyC0+Cpu5e2sprqZ2DOIoKcidVBYxJ34OtgBeQigc2lERB56R41MSpg8Kpkm62fX1T510EDgVe657esySh5sboKu66WEl09xTS9/ptwEVN2upjeyvSTcdT8ja2f9CkDduW9FbbB45xvEhRBhUL2fKy+xdIEfQFvkY4lfUJR284qXphY/AU0BtJ5m+rOML2EcCC5ThJvydVAXa2G42wLQJNF0dU0Xns4gnS8tiqwDxSy6krKVcsVHPp/W5JZzBSYX8psI/tOwvYgnq9Z3sLvPbrel5sGUrSiSTppHmM3MAaaNTRy1wnaTPb1xY491jUvIk8GfgMcAPpOxi0RDh6w0ntC9tbSQroX7d9r5IC+mcK2VoULm/yZG3Lj1SIsE04hIbPV1vAeG9gM1Iv0W0lrQd8tYCdDjXlf44nLfG/JW/vnvdtV8jeLyUdy+jes41HvG1vO/FvFWEGyVGvkdO0BTBT0u9IGoGdvsHFIl6VbyL/YvunDZ0rmASRozeEKAlVdi5sG3cubLb79nNsyOZGwMvz5qUll5MmSuYvYG+g8lqaykFs057qChhfa3szSfOALWw/LOlGFxJulbQ3sC8parI9sBpwku2Xj/vCxbO1UO6mCooy52j9R0iVoZB7z7phiRdJu9s+aYzvejHBZEmnAXvb/nOJ8/fYWr3fftu/K217LJrMf5T0auAdwIV0RfBdqG9wMDYR0RtOHrL9kCSUdLZuUdJqKoKkfYD3M9IW6SRJx9g+spDJGfRP5t9LqR1T0zletQWaJ6K27aYjpLUFjO/MlZRnAhdIugcodjGtHDX5q6TdSTlskC6sdzd07lFkKZDjnDrrFOtMkVk+PxYXSe5hFeAmSdcw2jnZqWlDHYdO0jPp6sLRMk2maexJqnJfipGlWzNynQgqEY7ecFL1wga8lxQp+QeApINIS8WlHL3ayfytFkf0odGcuRbkTmoLGO+Sn86SNAd4OnBeKXt97Jdcen8P6Xv2TdJF9ArSBbZxXLH3LClPDuAm26cVttXNrFqGJO1EykV8LnAXI7I/bbYIa/ImcjMXao0XPDli6XbIkfRK8oWt1OTcWSr2SP/LZYFr3dMCp0F7twAbdPS78nLS9bbXq7GsWVp+ZBHsNyooXFvupMtuUQHjQaX20nuTqFLv2TynbEi62SgupdIGkq4n5U3/3PZLJG0L7G77vS2OqbHPpqTjgUNsl8i7DZ4EEdEbcmxfXMHM8aTk+jNI0aadge8XtFc7mX8UpYsjWoiw1Y6QdigtYDyoTOW761q9Z88D7gFWkNQdPe8ULKxYwqik+1n4/bmPVHDyKSfx9KZ41PbdkpaQtITtOZIOa/D8i0OTaRovBeZJuoO0DF682CToT0T0gkbIuldbkybJy1y4K0bNZP5FGEujEZraEbbaEVK1KGA8CEzliF4HVeo9K+kntncuaaPH3oHAnaRKZpGizWuRumR8yPY2Ddr6OSnv92uk3MC7SCsjWzVlo4/NaoVsg1hsMl2JiF7QJCI5ek3rro02Uj+ZfyKavluqHWGrHSGtrfM4aDRa3FIT9fSelVS092y3kydpB9tnl7DTxU49VczH5CrmfSV9vmFbOwMPkfpmzySl2BzQsI1eqhWyhUM3OISjF0waSV8m6XidTnLyjs+TRqnuGFWT+ReBKS0obPtASecyEiHdq+v/ObNpe9TXeaxKjaX3sWRHOpSSH6F+79luDgBKO3oPSHor8KO8vRvJGYOGb+i6itdWZMTxKk1baRpBi4SjFzTBTGCjrmKMfyddwIs4ernw4YSuZP6DJLWZzN90hKZqhK2FCGltAePa1Iia1JYd6fB4x8kDsH2ZpMfGe0GDFF0pyMwEDgeOIjl2VwG7S1oOaDQ3VtIHgf1JjuQTjKyIvKBJOz3U7koTDADh6AVN8CeSDlTnzncZ4I8V7FZJ5q9dHNFChK12hLSqzmMLFI+a2N5/sud4MqjF3rNdfLC0gVxsseMYhy9r2NyngRc79S2uRauFbEE7RDFGMGkknUmK0FxAmvy3A64hJTVje++G7VVN5m+hOKITYbuiyfMugt0qcie5OntP4OOk5dp7SD1U31jCXm1qFLfkz8iYFPjOzRnfnBtddpc0bhcfN9xdQdJnbR8s6Uj6LNE2/f/MNs8D3mz7gabPPYHdooVsY1QuQ+GK6WBsIqIXNMEZ+afDRYXt1U7mr53X0lYOYpUIadsCxhWoETUpUvwwFq7fe3asqBqU6a7Q+bzXzPX9N+AKSVczugtH405lhxppGrbbSisIxiAiekGjSNrE9nWFbbyflOc1Kpm/6ahCl71WBJorRtimtdxJCWrL/0havmRkSC31nm0TSUsAK9gu0gVHqc3aZcANjLQI6+QgF0HSHqTl9mo3kepp8Wb79yXtBQsTEb2gaY4FSivZ107mbyuvpZag8HSXO2mUmsUtkrYkiZOvAKwmaSPgg7Y/3LCptnrPIml7UluwbmehiAxJTtPYC3gcuBZYUdLhtg8pYG4p2+NWTzdNzUI2DWaLt2lJOHpB09SojKuazF+7OKJPhO3AwhG2oZY7aYGaS++HAa8jtSTD9vWSXlHATiu9ZyV9l+Rkbku6idyNlP9bivVt/03STOBc4HOk97OEo3eupA+Qcn+7l27/t4CtXmrcRB5ImktGtXgrZCsYhyXaHkAwdNSoBrxT0krAmcAFOdJWTJwzR2iWtn14/imdx9OJsO0H3E6KsJW4eHfoREh/l3OxXgLE0u1iYvuEXFiyGXArKWrym4L2/tCz6/ECZt4oSaS8sppsZftfgXtypfGWwAsL2ltK0lKkjhU/zekapfKb3kHO0yM5k3MpnCMo6eD8WTyAtGQ8w/Z4+ZCT4VHbdwMLWryRitqCykREL2iazUkOWDFaSOavXRxRO8I27HInbVEjavIHSVsBzg7KPoVstdJ7FngwPz4g6bnA3cBzCtkCOBr4LXA9cIlSG68iOXq21yxx3gmomaZxr6QVgEuAkyXdBfyjgJ1gAqIYI1hs+kg8CHgX8AMoWz3WBhWLI25gJAdx404Oou1xJScmYW+o5U5qU7O4RdIqJIHf15C+f+cD++RISgl7tXvPfgk4Eng18G1SdO1Y21+qZF/AkrYfy9t7NFUsIWlJYHuSTNOCoEvJwpaahWw5h/lB0sphp8XbSZWWpoMuwtELFhtJfwAuJl1cOrl5XycJgRatHmsDSZuTKtZ2Bm4uteQh6Vrbm0maB2xh+2FJN9ounsQs6ZXkCKntR0rbG0Zyx4MfMxI1ARi64hbV6T3bbW8ZYFnb99Wy2WcM19lupNhM0n+RROZ7q26Lpb/UvImUdJDtfSfaF5Qnlm6DybA+KeH29cCnbf9J0n5D6ODVLo7ozUG8h4I5iN3YvriGnSGn2tK7pBNIEbx78/bKwKG239O0rT7U6D1LXppeg3y9ykuNPyhtd6zhNHiuVW3X6BHcTc00je2AXqfuDX32BYUJRy9YbGzfD3xc0qakHIxzGM4Cn6ryI9NAUHjYqSn/s2H3TYfteyQV0XXsQ/EKe0knkip+5zFSZGJyekgLNLkEdq6k19o+v8FzTkTxm0hJHwI+DLxA0vyuQ0+j+b7gwSIQjl4waWzPlfQq0pe76X6Qg0Br8iMRYZuS1IyaLCFpZdv3wII80lrzevHes6QqzfU9ODlGTTq3VwFnZGHmR6nQIqzSTeRskjTN10jyNB3uj/y8dghHL2iEPBF/O/8MG7UFmoOpTc2l90OBKyV1tO3eAvyfpo1ojN6zklaF5nvPdvFr4NnAnwud/8nSZETqGyS5mBvacGRL3UTmHMr7gHdI2hpYx/bxklaRtKbtO0rYDcYmijGCIki6wfYGbY+jCdosjgimNjWKWyStz0h0+Re2G+/WIun4cQ67VE5gjjptTBJJ7hYV3qmQvX6dKu4D5tqe17CtS4BtbD8x4S9PQZR6gs8A1rX9wiyPc5rtl03w0qBhIqIXLDZj3eWTliCeXXMshWmtOCKY2tRYes+OXclWfNjes+T5x2FWZXsz8s9ZeXsHYD6wl6TTbB/coK3bgYty151uJ3ZY+gbvQhJfvw4gF+tVb6EXhKMXTI4fkvrA9gsLL9tn35QkiiOCYARV7D3bQo7qqsAmtv8OC6JS5wCvIAmnN+no3ZF/ls4/w8Yjti3JsEBXL2iBcPSCyTAf+LrtX/cekPSaFsZTnCiOCKYztXvPSrqfhW8k7yO1CvuU7dsbNvlMuqJrpCKJZ9l+UNLDY7xmsSiplzcg/Keko4GVslDze4DvtTymaUk4esFk+DhjtwfaZYz9QRBMXbayvaGk+bb3l3QoqcKyFIcBd5IqOUXqSrMWaTnwOGCbhu2dDFyd+2cD7AjMztGoRpfHJf0z8FkWjo4Wr+avge2vS9qOdI1YF/iy7QtaHta0JIoxgiAIphhjRLqgsESHpKttbyHpKuDNpN6zN9peu5C9621v1LNvXu7qsNCxhmzOADoFA5eX6mst6XxS+sungb2APYC/ROeIoGkiohdMCknbAh8j3bFBaqj+LdsXtTaoIBhybLeV1H52Lkw6hBRVM2kJtxQPSHor8KO8vRupbRg0K14MLOjffartw5s+dx+eYfv7kvbJKSEXS7q2gt2itHUTEoxNRPSCxSYnZX+L1ArpOtIXeRPgi8BHbf9Xi8MLgmmDpGcyevnv9xVsFu89K+kFwOEkvTmTRIY/AfwR2NR2owLtkvYg9bNeFziD5PSViuhdZfulkn4GHAH8CfiR7bVK2AumL+HoBYuNpItIfTav79m/IXCk7Ve2MrAgmCZI2okkmvxc4C5gdeDmkhqPvb1ngTZ7zxYhdxjZlZQTuJrtdQrY2AG4FHg+cCSwIrC/7Z82bastegWTgaeFYHJ9Yuk2mAzP7nXyAGzPl/SsNgYUBNOMA0kt+X5u+yU5lWL3UsZq9Z6V9FnbB0s6kj7LgLb3btJeH9YG1iM7ziUM2D47P72PVMU8VHQLJgPHkyRkTmIk/zGoRDh6wWT4x2IeC4KgGR61fbekJSQtYXuOpMMK2qvVe7bjXBVZNh0LSQeTFANuA04FDrR9byFba5Lym9dgdHS0SNePFgjB5AEhHL1gMqwlqd8yg4AX1B5MEExD7pW0AnAJcLKkuyh7k1Wl96zts/LjCZ19kpYAVrA9lqRTE9wGbEWav5YBNpSE7UsK2DoT+D6pC8cwtkELweQBIRy9YDLsPM6xr1cbRRBMX3YmVaF+AphJ6tpSpEtFZhXgJkm1es/OJkmPPA5cC6wo6XDbh5SwR3K4fkHqkDGPtCx+JSO9hJvkIdtHFDjvoNBPMLlkhXYwBlGMESw2WfDzn3ubqOcm63+x/Zd2RhYEQQkk9S2wKtUxpkszbyapov9zwFzbGxaydwOwGXBVtrse8FXbY/X1noytdwLrAOcz2mm+rmlbbZEFk19LWuX5WQgmt0NE9ILJcCRwVJ/9zyBJrLyz7nCCYHog6TLbW/fRLCuqVdZCC8ClJC0FvImkz/loZymwEA/ZfkgSkpaxfYukdSd+2WKxAfAuUrSws3RrykQPqyPpDbbPBS7o2reX7e+2OKxpSTh6wWRYu1/uiu1LJX2njQEFwXTA9tb5sWpyewu9Z48GfgtcD1wiaXXGbrvYBHdmQegzgQsk3QP8rpCttwAvsP1IofO3zZckPWz7F5AqqUnVxeHoVSaWboPFRtKttvve7Y53LAiCyZF13sbE9v8WsnsgY/ee/ZDtbUrY7bIvYEnbj+XtPboLNhq29UpSzuN5JZwxSWcCH7B9V9PnHgSybt7ZwGeA15Pkat4xxI7twBKOXrDYSDoH+HZvBwxJbwD2tv2GdkYWBMONpDtIkTX1OWzbRare2+g9O8F4rrO9SU2bTZEF5zckFZkUL2xpg9yx5efAXOA9FWR5gj7E0m0wGT4OnJN7Uc7N+2aQ2hXt0NqogmDIsb1mS6ar9p5dBPo5ulOF/doeQAm6lveVH5cmydXsJil63bZARPSCSZH7Xb4TeHHedSMw2/ZDY78qCIImyEuZM4E1bR8oaTVSx5prCtmr2nt2EcYzZSN63UjaoatTRhA0Sjh6QaPEhBUE9chFT08Ar7L9L5JWBs63vVnLQ6uCpF/Zfknb45gsw+KwdiPpZcA82/+QtDtJHucw279veWjTjli6DZrmAFICbhAE5dnC9iaSfgVg+x5JSzdtZAB6z47F5S3ZbZqpvAQ9Ft8BNpK0EfApkljyiUBfLcagHOHoBU0zjBNWEAwqj0pakux8ZRHzEu202uo9+8k+u+8jiSbPs/3RmuMpyAfbHkABHsst0HYmaSB+X9J72x7UdCSWboNGkbR5qfygIAhGkztGvI20LHYCqTjii7ZPq2C7eO/Z3AJtBqkfLKQir/nAGsBptg8uZbsUksbtsmH7x7XGUhJJFwPnAXsCrwDuAq63vUGrA5uGhKMXLDbTZcIKgkEmt+l6NSmafqHtmyd4yWRsLdR7FijWe1bSJcAbbf89b68AnEPSZZtre/0Sdksi6fhxDtv2e6oNpiCSnk0q1Ls2i+ivBmxj+wctD23aEY5esNhMlwkrCAaZvHT7LLpScUolvLfQe/YWYAPbj+btZUhRofWGpRBjOhBFeu0SOXrBYmN7z7bHEATTGUkfI+mx/Q8pytbRLivieFG/9+zJwNWSfpK3dwRmS3oqcFNBu1WQtD3wImDZzj7bB7Q3omJEkV6LhKMXNMI0mrCCYJDYB1jX9t2V7FXtPZu1Ac8FXpZ37WW7UxAys5TdGkj6LrA8qf/rsaT8ymHNb44ivRaJpdtg0ow1YdmOCqsgKIikOcB2nd6vLdgv2ntW0hHAqbavaOqcg4Kk+bY37HpcATjX9svbHlvTRJFeu4SjF0ya6TRhBcEg0CU78iJgXVKBQne/1G+0NK5GhX8l7UGqKl4XOIPk9FWVeCmFpKttbyHpKuDNwN3AjbbXbnlokyKK9AaPWLoNmuDB/PiApOeSJqzntDieIBh2npYff59/ls4/0E7P2Q6NLtHl6OAJkv4J2BU4SNJqttdp0k5LnC1pJeAQ4DrS+3Zsu0NqhB3HOWYgHL3KhKMXNMGwTlhBMJDY3h9A0lt6NfMkvaWdUQHlnMy1gfWA1RkRb57S2D4wPz1d0tnAsrbva3NMTRBFeoNHLN0GjZLlD4ZiwgqCQaffUmmbfVObljyRdDCwC3Ab8EPgDNv3NnX+tpG0FUn8uVsaZ2h05qJIbzCIiF7QCL0TlqShmrCCYJCQ9AbgjcDzcsFChxWBVgozMk33nr0N2NL2Xxs+b+tIOhFYC5hHksaBFBEdinlzmlUVDzQR0QsmzVgTVouNzoNgqMmN4jcm6ZN9uevQ/cAc2/cUsjtu79lCNlcG1mF0VOiSErZqIulmYH0P6UU4ivQGh4joBU0wgyGesIJg0LB9PXB9bkkm4IX50K2dLhKFmEH/3rN7SWq896yk95G0Alcl3Ui+FLgSeFWTdlri18CzgT+3PZBCRJHegBCOXtAEwz5hBcGgshVpqe+3JIfv+VnLrlTEa1Vgk67es/uRpF1eAcwFGnX0SE7eZsBVtrfNfX2/2rCNtlgFuEnSNYyWxtmpvSE1ShTpDQjh6AVNMOwTVhAMKt8AXmv7VgBJLwROATYtZO+ZdH3HgUeBZ9l+UNLDY7xmMjxk+yFJSFrG9i2S1i1gpw1mtT2AkgxrVfFUJBy9oAlmtT2AIJimLNVx8gBs/3fuRVuK2r1n78xRoTOBCyTdA/yugJ3q2L647TGUJor0BoMoxgiCIJiiSDoOeAI4Ke+aSWpJ9p6CNmcw0nv28lqdKiS9Eng6cJ7tR2rYLImk+1lYd/A+4JfAp2zfXn9UzRFFeoNDOHrBpBn2CSsIBpWsW/kRYOu861LgKNslllGHuvdsbSQdCNwJdApq3k5yjK4DPmR7m/ZGN3mGvap4KhGOXjBphn3CCoIgMcy9Z2sj6XrbG/Xsm2d7437HphqSTgP2th1Fei0Tjl4waYZ9wgqCQUPSDYzTbsz2hoXtd3rPvh0Ylt6zVZF0JfBN4Ed5127AJ22/tDN/tje6ySNpDknrMYr0WiaKMYImeEDSWxk9YT2Un8edRBA0zw758SP58cT8uDt1vnND13u2BWYChwNHkd6zq4DdJS0HfLTNgTXErLYHECQiohdMGkkvIE1YWzIyYX0C+COwqe3LWhxeEAwt/XrLlux1O+y9Z4NgGImIXjBpcrHFjmMcDicvCMohSS+zfXne2ApYoqC9oe09WwtJn7V9sKQj6RN9HZaq1CjSGxzC0QsWm+kyYQXBAPNe4DhJTycVQt0DFJNWsX20pJUlbc6Q9Z6tSGepe9iLWA5j7CK944BtWhvZNCOWboPFRtKOts/KlXgLYfuE2mMKgulIdvQo3XlgrN6ztoeh92xrSFoCWMH239oeS1NEkd7gEBG9YLGxfVZ+XODQDeOEFQSDStbR25XcfUASALYPKGRymHvPVkXSbGAvkpjwtcCKkg63fUi7I2uMKNIbEErmcgTTBEmzJa2Y2yD9mtT39jNtjysIpgE/AXYGHgP+0fVTiodsPwQs6D1L0tQLnjzr5xviNwHnAmsC72p3SI0yk/T33AX8T34+TFXFU4aI6AVNsL7tv0maSZqwPgfMBYblzjQIBpVVbb++or2h7T3bAkvlvsRvAr5l+1FJQxPpiiK9wSEcvaAJhnrCCoIB5gpJG9i+oYYx27vkp7OyIO7TgfNq2B5CjgZ+C1wPXCJpdWDKp7xEkd7gEY5e0ARDOWEFwRRga+Ddku4gdR8QqXF80c4YJCMXl7YxzNg+Ajiisy3p98C2Xdt7TNGCtulSVTxliKrboHGUMsKXtP1Y3p6qE1YQDDT5pmohbMdy6hSnpPB1baJIr12iGCNoHCce69q1T2uDCYIhJjt0zwdelZ8/QMzrw4LaHsBkiCK9wSEmhKAGU3rCCoJBRdJ+wL7Av+VdSwEntTeioEGm+nLbsFcVTxnC0QtqMNUnrCAYVHYBdiJLqtj+E/C0VkcUNMVUv0HuLtL7qe1HiWtBK4SjF9Rgqk9YQTCoPOKUaG2AvEwWDAeXtz2ASdIp0nsqUaTXKlGMERRH0rdsh0BmEDSMpE8D6wDbAV8jJWqDdQAADG5JREFU9bmdbfvIVgcWTIikT/bZfR8w1/a82uMpTRTptUc4esGkmW4TVhAMEpK2A15Lipz/zPYFLQ8pWARyC7QZwFl51w7AfFI7u9NsH9zS0KowTFXFg044esGkme4TVhAMApJ2sH122+MIFg1JlwBvtP33vL0CcA7wetJN8vptjq80kn5l+yVtj2M6EDl6QROsCmxi+1O2PwVsCjwTeAXw7jYHFgTTiAPaHkDwpHgmSeS6w6PAs2w/2LN/WIkoUyWiM0bQBGNOWJKmw4QVBINAFD1NLU4Grpb0k7y9IzA7F9Tc1N6wqhGf10qEoxc0wXSfsIJgEPhg2wMIFh3bB0o6F3hZ3rWX7U7bsJktDasmU72qeMoQOXpBI0iawciEdXnXhBUEQcNIevN4x23/uNZYgsVD0hHAqbavaHssJYgivcEhHL1g0gz7hBUEg4ak48c5bNvvqTaYYLGQtAfwNmBd4AzSHDo0N8hRpDc4hKMXTJphn7CCIAhKIemfgF2BtwOr2V6n5SE1wnSvKh4kIkcvmDRZ9PKErgnrIElDM2EFwSAjaXvgRcCynX22owJ36rA2sB6wOnBzy2NpkijSGxDC0QuaZFgnrCAYSCR9F1ge2BY4FtgNuKbVQQWLhKSDSb2KbwN+CBxo+952R9UoUaQ3IMTSbTBp+kxYZwzZhBUEA4mk+bY37HpcATjX9svbHlswPpI+CJxu+69tj6UUUaQ3GEREL2iC24Ath3nCCoIB5cH8+ICk5wJ3A89pcTzBImL7aEkrS9qc0cvul7Q4rMboKtI7vO2xTHfC0QsmzbBPWEEwwJwtaSXgEOA6UreBY9sdUrAoSHofsA+ps9A84KXAlcCr2hxXg8wFvigpivRaJpZug0kz1oRle1gmrCAYeCQtAyxr+762xxJMjKQbgM2Aq2xvLGk94Ku2x9VInGoMa1XxVCIiekET7MPIhLVtZ8JqeUxBMC2QtBVJm+wpeRvbP2h1UMGi8JDthyQhaRnbt+To17ARRXotE45e0ATTZcIKgoFC0onAWqRI+uN5t4Fw9AafO/Oy+5nABZLuAX7X8pgaYxpUFU8ZwtELmmCoJ6wgGGBmAOs7cnCmHLZ3yU9nSZoDPB04r8UhNU0U6Q0IkaMXNIqkV5InLNuPtD2eIBhmJJ0G7G37z22PJQh6kbQysA5RpNcq4egFQRBMUXIkaGOSSPKCbgO2d2ptUEFAFOkNErF0GwRBMHWZ1fYAgmAMokhvQAhHLwiCYIpi++K2xxAEYxBFegNCOHpBEARTFEn3k6psu7kP+CXwKdu31x9VEABRpDcwRI5eEATBFEXSgcCdwGxAJFHatUhdMj5ke5v2RhcEiSjSa5dw9IIgCKYokq63vVHPvnm508JCx4IgmH4s0fYAgiAIgsXmAUlvlbRE/nkr8FA+FnfxQRBERC8IgmCqIukFwOHAliTH7irgE8AfgU1tX9bi8IIgGADC0QuCIAiCIBhSouo2CIJgiiHps7YPlnQkfZZobe/dwrCCIBhAwtELgiCYetycH3/Z6iiCIBh4Yuk2CIJgCJC0BLCC7b+1PZYgCAaHqLoNgiCYokiaLWlFSU8Ffg3cJOkzbY8rCILBIRy9IAiCqcv6OYL3JuBcYE3gXe0OKQiCQSIcvSAIgqnLUpKWIjl6P7X9KKGfFwRBF+HoBUEQTF2OBn4LPBW4RNLqQOToBUGwgCjGCIIgGBIkCVjS9mN5ew/bJ7Q8rCAIWiQcvSAIgiFF0nW2N2l7HEEQtEcs3QZBEAwvansAQRC0Szh6QRAEw0ss2QTBNCccvSAIguElInpBMM0JRy8IgmB4ubztAQRB0C5RjBEEQTBFkfTJPrvvA+banld7PEEQDB7h6AVBEExRJM0GZgBn5V07APOBNYDTbB/c0tCCIBgQwtELgiCYoki6BHij7b/n7RWAc4DXk6J667c5viAI2idy9IIgCKYuzwQe7tp+FHiW7Qd79gdBME15StsDCIIgCBabk4GrJf0kb+8IzJb0VOCm9oYVBMGgEEu3QRAEUxhJM4CX5c3Lbf+yzfEEQTBYhKMXBEEwRZF0BHCq7SvaHksQBINJ5OgFQRBMXeYCX5R0m6Sv5+heEATBAiKiFwRBMMWR9E/ArsDbgdVsr9PykIIgGBAiohcEQTD1WRtYD1gduKXlsQRBMEBERC8IgmCKIulgYBfgNuCHwBm27213VEEQDBIhrxIEQTB1uQ3Y0vZf2x5IEASDSUT0giAIpjCSVgbWAZbt7LN9SXsjCoJgkIiIXhAEwRRF0vuAfYBVgXnAS4ErgVe1Oa4gCAaHKMYIgiCYuuwDbAb8zva2wEuAyNELgmAB4egFQRBMXR6y/RCApGVs3wKs2/KYgiAYIGLpNgiCYOpyp6SVgDOBCyTdA/yu5TEFQTBARDFGEATBECDplcDTgfNsP9L2eIIgGAzC0QuCIAiCIBhSIkcvCIIgCIJgSAlHLwiCIAiCYEgJRy8IgiAIgmBICUcvCIYcSZZ0aNf2pyXNKmxzlqRPN3SubSSdPcax/8pVpwOBpIskzViM131c0vIlxhQEwfQmHL0gGH4eBt4saZW2B9I0tt9oexgEgj8OPClHT9KShcYSBMEQEY5eEAw/jwHHAJ/oPSBpDUm/kDRf0oWSVsv7/0PSEZKukHS7pN26XvMZSdfm1+w/jt31c4Trdkl7d9m7JZ//vyWdLOk1ki6X9BtJm49xrhUlnSPpVknflbREPt9vOw6spE9K+nX++Xje99T8uuvz/rfl/V/Of8OvJR0jSXn/RZK+KemXkm6WtJmkH+exfaXnbzg5/86P+kXjJL1W0pWSrpN0mqQV+v1h+X/zXGCOpDnjvTb/vQdJug54y6KMdwyba+Tf/56kGyWdL2m5fOz9+X9zvaTTO39bfs++I+mq/J5uI+m4fJ7/eLJ/dxAEdQhHLwimB98GZkp6es/+I4ETbG8InAwc0XXsOcDWwA7Av0O6iAPrAJsDGwObSnrFGDbXA16Xf3c/SUvl/WsDh+bj6wHvzHY+DXx+jHNtDnwMWB9YC3hz90FJmwJ7AluQ+r2+X9JLgNcDf7K9ke0XA+fll3zL9mZ533L5b+zwiO0ZwHeBnwAfAV4MvFvSM/LvrAscZftfgL8BH+4ZzyrAF4HX2N4E+CXwyX5/mO0jgD8B29redhFee7ftTWyf+iTG2491gG/bfhGpbdquef+P8/9mI+Bm4L1dr1kZ2JJ00/BT4JvAi4ANJG38ZP7uIAjqEI5eEEwDbP8N+AGwd8+hLYHZ+fmJJIerw5m2n7B9E/CsvO+1+edXwHUkR22dMcyeY/th238F7uo6xx22b7D9BHAjcKGToOcNwBpjnOsa27fbfhw4pWec5O0zbP/D9t+BHwMvz+fcLkfBXm77vvz720q6WtINwKtIzkqHn+bHG4Abbf/Z9sPA7cDz87E/2L48Pz+pz3heSnJKL5c0D9gDWH2Mv62XiV77w57fX5Tx9uMO2/Py87mM/O9fLOnS/L+Zyej/zVld79X/9LyPayzC2IMgqEy0QAuC6cNhJOfs+EX8/Ye7nqvr8Wu2j+7+RUkfAd6fN9/Y5/WPMzLfdO9/omv7Ccaek3qV3RdJ6d32f0vaJI/pK5IuBA4GjgJm2P6DUmHKsl0v6x5P71g745toPAIusP2ORRnnk3ztP3q2F2W8/eh9f5bLz/8DeJPt6yW9G9jmSdh6fIKxB0FQmYjoBcE0wfb/Av/J6KW4K4C35+czgUsnOM3PgPd05Yw9T9IzbX/b9sb5509Njx3YXNKaOTfvbcBlPccvBd4kaXlJTwV2AS6V9FzgAdsnAYcAmzDi1P01/x278eRZTdKW+fk7+4znKuBlktaGBbmCLxznfPcDT1vM1zbN04A/56X2mU/ytW2PPQiCHsLRC4LpxaFAd/Xtx4A9Jc0H3gXsM96LbZ9PWuq9Mi/t/YgRB6UxJM2QdGzXrmuBb5Fyxu4AzugZ13WkSNQ1wNXAsbZ/BWwAXJOXEfcDvpKrdL8H/JrkuF67GEO8FfiIpJtJeWvf6RnPX4B3A6fk/+2VpGXusTgGOE/SnMV4bdN8ifQ/vBy45cm8cADGHgRBD9HrNgiC4EkgaQ3g7FzIEQRBMNBERC8IgiAIgmBIiYheEARBJSSdAazZs3tf2z8rZO8ZwIV9Dr3a9t0lbAZBMFiEoxcEQRAEQTCkxNJtEARBEATBkBKOXhAEQRAEwZASjl4QBEEQBMGQEo5eEARBEATBkPL/AeYghozZapjnAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 720x720 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 10))\n", "sns.heatmap(heatmap,vmax = 20)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
theodoregoetz/wernher
sandbox/Flight.ipynb
1
4376
{ "metadata": { "name": "", "signature": "sha256:1c08f6dbea209f2529be2891629e479bbb4df265d8190897ad996b1a32936f2b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%run -i '../Common.ipynb'\n", "import krpc\n", "import wernher" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "con = krpc.connect(name='laptop0', address='192.168.1.2')\n", "ksc = con.space_center\n", "vessel = ksc.active_vessel" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "vessel.flight(vessel.orbit.body.reference_frame).mean_altitude\n", "vessel.flight(vessel.reference_frame).roll" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "-90.0" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "def dot_product(x, y):\n", " return x[0]*y[0] + x[1]*y[1] + x[2]*y[2]\n", "\n", "\n", "def magnitude(x):\n", " return math.sqrt(x[0]**2 + x[1]**2 + x[2]**2)\n", "\n", "def angle_between_vectors(x, y):\n", " \"\"\" Compute the angle between vector x and y \"\"\"\n", " dp = dot_product(x, y)\n", " if dp == 0:\n", " return 0\n", " xm = magnitude(x)\n", " ym = magnitude(y)\n", " return math.acos(dp / (xm*ym)) * (180. / math.pi)\n", "\n", "def vessel_pitch(vessel):\n", " vessel_direction = vessel.direction(vessel.surface_reference_frame)\n", "\n", " # Get the direction of the vessel in the horizon plane\n", " horizon_direction = (0, vessel_direction[1], vessel_direction[2])\n", "\n", " # Compute the pitch - the angle between the vessels direction and the direction in the horizon plane\n", " pitch = angle_between_vectors(vessel_direction, horizon_direction)\n", " if vessel_direction[0] < 0:\n", " pitch = -pitch\n", " return pitch" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "cont_alt = wernher.Controller(set_point=5000,kp=1/3,t0=ksc.ut)\n", "cont_alt.min = -15\n", "cont_alt.max = 15\n", "cont_alt.ziegler_nichols(ku=1/3,tu=6,control_type='no_overshoot')\n", "\n", "cont_pitch = wernher.Controller(set_point=5,kp=1/30,t0=ksc.ut)\n", "cont_pitch.min = -1\n", "cont_pitch.max = 1\n", "cont_pitch.ziegler_nichols(ku=1/25,tu=1,control_type='no_overshoot')\n", "\n", "while True:\n", " t = ksc.ut\n", " flight = vessel.flight(vessel.orbit.body.reference_frame)\n", " alt = flight.mean_altitude\n", " pitch = vessel_pitch(vessel)\n", " cont_pitch.set_point = cont_alt(alt,t)\n", " vessel.control.pitch = cont_pitch(pitch,t)\n", " time.sleep(0.1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "con_alt = wernher.Controller(set_point=5000)\n", "con_alt.ziegler_nichols(ku=1/2000,tu=33)\n", "con_alt.min = -1\n", "con_alt.max = 1\n", "\n", "#con_pitch = wernher.Controller(set_point=0,kp=15)\n", "#con_pitch.min = -1\n", "#con_pitch.max = 1\n", "\n", "while True:\n", " t = ksc.ut\n", " flight = vessel.flight(vessel.orbit.body.reference_frame)\n", " alt = flight.mean_altitude\n", " #pitch = vessel_pitch(vessel)\n", " #con_pitch.set_point = con_alt(alt,t)\n", " vessel.control.pitch = con_alt(alt,t)\n", " time.sleep(0.1)" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
changhoonhahn/centralMS
centralms/notebooks/notes_siglogMstar_tduty.ipynb
1
1156
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# plotting $\\sigma_{\\rm log \\mathcal{M}_*}(\\mathcal{M}_h = 10^{12} M_\\odot)$ as a function of $t_{duty}$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np \n", "\n", "import catalog as Cat\n", "import evolver as Evol\n", "import observables as Obvs\n", "import util as UT\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
grehujt/SmallPythonProjects
jupyterNotebooks/tutorials/kde.ipynb
1
107341
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt \n", "import seaborn as sns; sns.set() \n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11628ce10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def make_data(N, f=0.3, rseed=1):\n", " rand = np.random.RandomState(rseed)\n", " x = rand.randn(N) \n", " x[int(f * N):] += 5 \n", " return x\n", " \n", " \n", "x = make_data(1000)\n", "hist = plt.hist(x, bins=30, normed=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "density, bins, patches = hist\n", "widths = bins[1:] - bins[:-1]\n", "(density * widths).sum()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.39172292, 0.39172292, 0.39172292, 0.39172292, 0.39172292,\n", " 0.39172292, 0.39172292, 0.39172292, 0.39172292, 0.39172292,\n", " 0.39172292, 0.39172292, 0.39172292, 0.39172292, 0.39172292,\n", " 0.39172292, 0.39172292, 0.39172292, 0.39172292, 0.39172292,\n", " 0.39172292, 0.39172292, 0.39172292, 0.39172292, 0.39172292,\n", " 0.39172292, 0.39172292, 0.39172292, 0.39172292, 0.39172292])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "widths" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x118787fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = make_data(20)\n", "bins = np.linspace(-5, 10, 10)\n", "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharex=True, sharey=True,\n", " subplot_kw={'xlim':(-4, 9),'ylim':(-0.02, 0.3)})\n", "fig.subplots_adjust(wspace=0.05)\n", "for i, offset in enumerate([0.0, 0.6]): \n", " ax[i].hist(x, bins=bins + offset, normed=True) \n", " ax[i].plot(x, np.full_like(x, -0.01), '|k', markeredgewidth=1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.62434536, -0.61175641, -0.52817175, -1.07296862, 0.86540763,\n", " -2.3015387 , 6.74481176, 4.2387931 , 5.3190391 , 4.75062962,\n", " 6.46210794, 2.93985929, 4.6775828 , 4.61594565, 6.13376944,\n", " 3.90010873, 4.82757179, 4.12214158, 5.04221375, 5.58281521])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x119231cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "bins = np.arange(-3, 8)\n", "ax.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1)\n", "for count, edge in zip(*np.histogram(x, bins)): \n", " for i in range(count):\n", " ax.add_patch(plt.Rectangle((edge, i), 1, 1, alpha=0.5))\n", " ax.set_xlim(-4, 8)\n", " ax.set_ylim(-0.2, 8)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c364690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_d = np.linspace(-4, 8, 2000)\n", "density = sum((abs(xi - x_d) < 0.5) for xi in x)\n", "plt.fill_between(x_d, density, alpha=0.5)\n", "plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1)\n", "plt.axis([-4, 8, -0.2, 8]);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.62434536, -0.61175641, -0.52817175, -1.07296862, 0.86540763,\n", " -2.3015387 , 6.74481176, 4.2387931 , 5.3190391 , 4.75062962,\n", " 6.46210794, 2.93985929, 4.6775828 , 4.61594565, 6.13376944,\n", " 3.90010873, 4.82757179, 4.12214158, 5.04221375, 5.58281521])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kn89vOgZ2wLIsjc+uKpvNmo7iOhQuAFeanJrWSiZgOgYugzdSq7ZOznLfjsIF4ErtvcOK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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118931e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import norm\n", "x_d = np.linspace(-4, 8, 1000)\n", "density = sum(norm(xi).pdf(x_d) for xi in x)\n", "plt.fill_between(x_d, density, alpha=0.5)\n", "plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1)\n", "plt.axis([-4, 8, -0.2, 5]);" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.02, 0.22)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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y+FNLRGVvemYW0bRFdAz6iCRJmJxdQjabFR2lJLCoiajs9fSPQHXWiI5Bd5CVWvj6BkXH\nKAksaiIqa5qmIRBagiRJoqPQHaw2BYPjs6JjlAQWNRGVtbHxCaRNTtExaAWhKBCOhEXHMDwWNRGV\ntT7/FBTVJToGrcBRtRm3uvigjlxY1ERUtrLZLIKhmOgYtApJkjA+HYWm8f72e2FRE1HZ6u0fhKzU\nio5B96BZqzEw5Bcdw9BY1ERUtoYnZmG1KaJj0D3YFDv6RwKiYxgai5qIylIikcBsmA9/KAWzixks\nLS2JjmFYLGoiKkudvgEoLo/oGKSDWlWHjm5eVLYaFjURlaVxPimrZMiyjPHpMC8qWwWLmojKzsLC\nAhZ5sXdJSUpOjI5PiI5hSCxqIio7Xj4pq+Qodhd6B1nUK2FRE1FZ0TQNk7NRThlagoILCSQSCdEx\nDIdFTURlZXxiEimJU4aWIpurDp2+AdExDIdFTURlxTc0AdXhFh2D1sFkMmNkal50DMNhURNR2chk\nMgjOx0XHoA1YStsQCE6LjmEoLGoiKhs9fQMw23kRWSmzu2rQ1TciOoahsKiJqGyMTM7DYrGKjkEb\nNBWKIZPJiI5hGCxqIioLkWgUsxH+cS8HFrsHnT19omMYBouaiMpCR3cf7G5OGVoOzBYLRqYWRMcw\nDBY1EZWFyZkoZJl/0srFQlzGXGhOdAxD4E81EZW88YkJJOAQHYPyyO6qhbdnWHQMQ2BRE1HJ8w1N\n8t7pMiNJEibnlpDNZkVHEY5FTUQlLZ1OYyrEJ3CUI8lWi54+zlTGoiaiktbZ0wernReRlSOrTcHQ\nOD+nZlETUUkbmVqA2WIRHYMKZH4JWFxcFB1DKBY1EZWs2bk5LCZMomNQAdndm3Gru7JPf7Ooiahk\ndfqGYXfVio5BBSRJEiZmo9A0TXQUYXIWtaZpOHbsGBoaGvDCCy9gdHT0M+vEYjE8++yzGBoa0r0N\nEdFGZLNZTMwu8bnTlcBag76BYdEphMlZ1OfPn0cymcSZM2dw9OhRNDY23rXc6/Xiueeeu6uMc21D\nRLRRPX0DkBUeTVcCq03FwGhQdAxhchZ1S0sL9u/fDwDYu3cvvF7vXctTqRROnTqFXbt26d6GiGij\nhsbmYLUpomNQkcxFNYQjEdExhMhZ1JFIBC6Xa/lrs9l81w3ojz32GOrr6+/6/CDXNkREGxGaD2E+\nzlPelcTu9uBWV2U+qMOcawWn04loNLr8dTabzTmf7nq2AQCPx5VzHeI4rQXHSp9SG6f2rm54tmwr\n+ufTDoetqPsrZYUYq/nwHDZvdlbcdQk5i3rfvn1oamrCwYMH0dbWhj179uR80fVsAwDT02Fd61Uy\nj8fFcdKJY6VPqY1TJpNB1+As1KrilqbDYUM0mijqPktVocYqkbTjyjUvfmHXzry/tgh63yDnLOoD\nBw6gubkZDQ0NAIDGxkacPXsWsVgMhw8fXl7vznc4K21DRJQPXb39MNs3i45BAtgUFf0jgbIpar0k\nzUA3p5XSu3pRSu3oRySOlT6lNk4/PX8ZGVt90ffLI2r9CjlWSwtB/O+nHoHL6SzI6xeT3iNqTnhC\nRCUjEJxGOGUVHYMEUt0edHT3i45RVCxqIioZ3l4/7M4a0TFIIEmSMD4TqaiZyljURFQS4vE4AvM8\n9UxA1lxZM5WxqImoJLR39kJx1omOQQZgU1QMVtBMZSxqIjI8TdMwEliEbOKTsui22aiGcKR0LoLc\nCBY1ERleb/8gNCs/m6ZP3J6prDIuKmNRE5Hh9fqDsNpU0THIQCRJwvh0ZTz+kkVNRIY2FQgiwluy\naCVKLXr7B0WnKDgWNREZmtfnh8pbsmgFVquC/tEZ0TEKjkVNRIYVjkQQWMyIjkEGNh8DFhYWRMco\nKBY1ERnWzY5e2N0e0THIwOyuzWjvGhAdo6BY1ERkSIlEAmMzSxX3SENaG0mSMDG7hEymfM+8sKiJ\nyJBuen1QXMV/+AaVHpN9M7p6y/dWLRY1ERlOJpPB8CQnOCF9LBYrhsdDomMUDIuaiAynvdMHs52f\nTZN+4ZQFgeC06BgFwaImIkPJZrMYGJuD2WIRHYVKiN1Zg84+v+gYBcGiJiJD8fb0AbZNomNQCZoK\nJZBIlN8T1ljURGQYmqahb2QGFqtNdBQqQTanBx3dfaJj5B2LmogMo7OnD1lLregYVKJMJjNGphbK\nbv5vFjURGYKmaegZDvJomjYkKTvhHxkTHSOvWNREZAgdXT4eTdOGKaoLPUMTomPkFYuaiITLZrPo\n8c/AalNER6EyMBvREI6ERcfIGxY1EQl3s6MbssL7pik/7G4P2rzlc1EZi5qIhEomk+gbDfG+acqb\ncpv/m0VNREK1tHfB6uSc3pRfsroZnT3lcVTNoiYiYSLRKIamljinN+WdxWLF0ER5zP/NoiYiYa60\ndEKt4tE0FcZSRsX4xKToGBvGoiYiIQKBIKajMp83TQWjOtzo6i/9e6pZ1ERUdJqm4Wp7H1Qn75um\nwgospBGJRkXH2BAWNREVXaevD3GpWnQMqgCOqjq0d/aKjrEhLGoiKqpEIgFvfxBWRRUdhSqAJEkY\nm46W9K1aLGoiKqrm67dgdW0RHYMqiKxuRmd36R5Vs6iJqGjGJiYRCMuQZf7poeKxWKwYHC/dW7XM\nogNQZUmlUkgkEkinUwAAk8kEq9UGq9XKq3/LXDabxdX2AajObaKjUAVKSA4M+0exc8d20VHWjEVN\nBRGLxTDoH8XcfBTRRBpL8TTiyQwyWUAyWaBJJkgaoEEDsmlIWhpWiwTVZoXdZoJTtWBbfS22bd0K\nEyfDKAuXb7RD43zeJIjto6dqsaipogUCQfQOjWN2MYFwXIPqrIXZUgXIgGwH7Pbcr5EBENaA8BIw\n1BVCpnUYVQ4rat1W7H5gG+rqPDzyLkGTUwGMzGSgujifN4kzGwVC8yHUVNeIjrImLGrakFgshvau\nPkxMRxDXFNid1YAKuPJwQa+iOgDVgTSAYBwYbh2DRetFXY0dn9u5FVu3bGFpl4BUKoUPW/ugunjK\nm8SyuzbjpncAX/3KE6KjrAmLmtZldm4ObV2DmAoloLrrITuc0HHAvCF2ZzWAaoTSwHvtAVjbB7Ft\nkwOP/OIvwOV0FnjvtF4fXLkJk53ThJJ4kiRhaj6BeDwORSmdZ5+zqGlNZufmcKOjD7NRGXbXJjgE\nzVmhOtwA3JiKaRhs6sAml4zP7ajHrp07eJRtIL6+QQSXbFDsvM6AjEFx1eOmtwe/8sSjoqPoxqIm\nXWKxGC63eDG1kIXd7YHdJTrRbZIkwVFdjziAG31htPsuYcfWaux9+CHR0SreXCiEm70BKG4eTZNx\nyLKMkUAEv5zNlsxtgixquidN09DW0Y2e0RAU1xbY3cY9WrWpDgAODM2l0Pv2FTy8ezN273gADj1X\nsVFepVIpXLzkheK+T3QUos+Q1c3wdvfiiw9/XnQUXVjUtKpAcAaXb/YgZd4E1b1VdBzdzGYLzFXb\nMLVkRceFNmypsWDfI79Qcld6lipN03DhwxuQHZx9jIzJYrGif3QKX/glrSQ+KmNR02dks1lcbb2F\n4WASqmsbSvWGmtunxbdgMavhZx/2wuOSsPcXd6G+brPoaGXtSks7FrNVsFr5uTQZV0p2YXDYj90P\n7hQdJScWNd1ldm4O71/rRNrqgepyi46TF5IkwVHlwRKACy1+1NgG8MhD27H9Pt4ulG/enj6MzAI2\ne+lcUUuVyaY60DU4yaKm0tLR3Qvv4BxU930lexSdi91ZgwSADzsCcPb48fDubdi1c4foWGVhcNiP\njsEFqC4+Y5pKQyRlw+TUFLZuMfbHNCxqQiqVwruXWzAXt0N114mOUxSqowoZVOFa7zxu9Y7j8zvr\n8dDndpXE51VG5B8dx9WuKaguThFKpUN1VKO9x8+iJmObnZtD0+VOyI4tsKmV95mioroAuHBrJIbO\ngWbsvr8WX3z48yVz24YRjIyN43LHGEuaStJcVDL8tKL8a1TB+oeG8c5lH8zu+yBX+IMvrDYVJuc2\n9E3L+L8/v4TLN9oQj8dFxzK8oeFRNLePQWFJU4myuzejtaNfdIx74hF1BdI0DddvejEYTEHlZBR3\nsVisgGUbJiMZDP3PDWytVfDow7sN/W5blO7eAbT3z0F1s6SptE0tpBGJRuF0OERHWRGPqCtMJpPB\nO+9dhX9OhiJq/s8SIJtMsFdvw3ymBj9r7sXPm65iYGgYmqaJjiacpmm42nILbYMLUFybRMch2jC7\nuw6tt3yiY6yKR9QVJLq0hHfeb0FWqYfFxv/1ekiSBIfbgwSAlv4I2nyXcL/HhS/+0uegqnl4RFiJ\nSSaTuPDhDUSyVVAdfBAKlQdJkjA+GzPswzr417pCzM7N4cLlTlhc22Dilc3rYlXsAOwYj2TRf/4m\nPFUW7N7uqZgHgUxOBfBBSy/Mzq2wWHgyjsqLzVWPG+1d+Mov7xMd5TNY1BVgZGwcl9r9nHc5T2RZ\nhqN6C5YA3OiPoKW7GVtqHfj87u2o85TfrGefzFSXgsqfISpTsixjdHoJyWQSVqtVdJy7sKjLnK9/\nCDd7p6G4jX2fYKmyKXZAsWMuBfzPdT/spl7U19rx0O4HsKm29Cf+GPaPoqXLD81WPjPVEa3G5qxH\n661ufOmJvaKj3IVFXcZu3upC70Qciqv8jvKMyOG6fWV4IKZh+MogFLkHnhoVD95fh/u2bSup0+OB\n4Axavf1YSCpQnJxqlSqDbDJhOBDGE+k0zGbj1KNxklDeaJqGKy3tvLJbEEmSYP9oGs3ZJDDunQFu\nDmGTW8Hmajs+t+sBOJ3GvBBrZGwcXX1jCMVMUF11UMp1LlmiVVgcdWht78L/evyLoqMsy1nUmqbh\n+PHj8Pl8sFqtOHH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"text/plain": [ "<matplotlib.figure.Figure at 0x119ed9750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.neighbors import KernelDensity\n", "# instantiate and fit the KDE model\n", "kde = KernelDensity(bandwidth=1.0, kernel='gaussian')\n", "kde.fit(x[:, None])\n", "# score_samples returns the log of the probability density\n", "logprob = kde.score_samples(x_d[:, None])\n", "plt.fill_between(x_d, np.exp(logprob), alpha=0.5)\n", "plt.plot(x, np.full_like(x, -0.01), '|k', markeredgewidth=1)\n", "plt.ylim(-0.02, 0.22)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'bandwidth': 1.1233240329780276}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.grid_search import GridSearchCV \n", "from sklearn.model_selection import LeaveOneOut ## deprecated\n", "bandwidths = 10 ** np.linspace(-1, 1, 100)\n", "grid = GridSearchCV(KernelDensity(kernel='gaussian'),\n", " {'bandwidth': bandwidths},\n", " cv=LeaveOneOut().get_n_splits(x[:, None]))\n", "grid.fit(x[:, None]);\n", "grid.best_params_" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.base import BaseEstimator, ClassifierMixin \n", "class KDEClassifier(BaseEstimator, ClassifierMixin):\n", " \"\"\"Bayesian generative classification based on KDE\n", " Parameters\n", " ----------\n", " bandwidth : float\n", " the kernel bandwidth within each class\n", " kernel : str\n", " the kernel name, passed to KernelDensity\n", " \"\"\"\n", " def __init__(self, bandwidth=1.0, kernel='gaussian'): \n", " self.bandwidth = bandwidth\n", " self.kernel = kernel\n", " \n", " def fit(self, X, y):\n", " self.classes_ = np.sort(np.unique(y))\n", " training_sets = [X[y == yi] for yi in self.classes_] \n", " self.models_ = [KernelDensity(bandwidth=self.bandwidth,\n", " kernel=self.kernel).fit(Xi) for Xi in training_sets]\n", " self.logpriors_ = [np.log(Xi.shape[0] * 1.0 / X.shape[0]) for Xi in training_sets]\n", " return self\n", "\n", " def predict_proba(self, X):\n", " logprobs = np.array([model.score_samples(X) for model in self.models_]).T \n", " result = np.exp(logprobs + self.logpriors_)\n", " return result / result.sum(1, keepdims=True) \n", "\n", " def predict(self, X):\n", " return self.classes_[np.argmax(self.predict_proba(X), 1)]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'bandwidth': 7.0548023107186433}\n", "('accuracy =', 0.9666110183639399)\n" ] }, { "data": { "image/png": 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FQcDKlStRUFCA5cuXo6amps/xjRs3YsmSJcjPz8cHH3zgyVJoEO9uOYIv9tYg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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d2cdc90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_digits \n", "from sklearn.grid_search import GridSearchCV\n", "digits = load_digits()\n", "bandwidths = 10 ** np.linspace(0, 2, 100)\n", "grid = GridSearchCV(KDEClassifier(), {'bandwidth': bandwidths})\n", "grid.fit(digits.data, digits.target)\n", "scores = [val.mean_validation_score for val in grid.grid_scores_]\n", "plt.semilogx(bandwidths, scores)\n", "plt.xlabel('bandwidth')\n", "plt.ylabel('accuracy')\n", "plt.title('KDE Model Performance')\n", "print(grid.best_params_) \n", "print('accuracy =', grid.best_score_)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.81860038035501381" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.cross_validation import cross_val_score \n", "cross_val_score(GaussianNB(), digits.data, digits.target).mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
wesleybeckner/afm-miner
.ipynb_checkpoints/CEI-checkpoint.ipynb
1
6508566
null
mit
ajgpitch/qutip-notebooks
examples/qip-noisy-device-simulator.ipynb
1
412807
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Noisy quantum device simulation with QuTiP\n", "\n", "Author: Boxi Li ([email protected])\n", "\n", "This is the introduction notebook to the deliverable of one of the Google Summer of Code 2019 project (GSoC2019) \"Noise Models in QIP Module\", under the organization NumFocus. The final product of the project is a framework of noisy quantum device simulator based on QuTiP open system solvers.\n", "\n", "The simulation of quantum information processing (QIP) is usually achieved by gate matrix product. Many simulators such as the simulation backend of Qiskit and porjectQ are based on it. QuTiP offers this common way of simulation with the class `qutit.qip.QubitCircuit`. It simulates QIP in the circuit model. You can find the introduction notebook for this matrix gate representation [here](quantum-gates.ipynb).\n", "\n", "The simulation introduced here is different as it simulates the dynamics of the quantum device at the level of driving Hamiltonians. It is closer to the physical realization than the matrix product approach and is more convenient when simulating the noise of physical hardware. The simulator is based on QuTiP Lindbladian equation solvers and is defined as `qutip.qip.device.Processor`. The basic element is the control pulse characterized by the driving Hamiltonian, target qubits, time sequence and pulse strength. Our way of simulation offers a practical way to diagnostically add noise to each pulse or the whole device at the Hamiltonian level. Based on this pulse level control, different backends can be defined for different physical systems such as Cavity QED, Ion trap or Circuit QED. For each backend, a compiler needs to be defined. In the end, the `Processor` will be able to transfer a simple quantum circuit into the control pulse sequence, add noise automatically and perform the noisy simulation.\n", "\n", "This notebook contains the most basic part of this quantum device simulator, i.e. the noisy evolution under given control pulses. It demonstrates how to set up the parameters and introduce different kinds of noise into the evolution.\n", "\n", "### Note\n", "This module is still under active development. Be ready for some adventures and unexpected edges. Please do not hesitate to raise an issue on our GitHub website if you find any bugs. A new release might break some backwards compatibility on this module, therefore we recommend you to check our GitHub website if you are facing some unexpected errors after an update.\n", "\n", "## Links to other related notebook\n", "There is a series of notebooks on specialized subclasses and application of the simulator `Processor`, including finding pulses realizing certain quantum gate based on optimization algorithm or physical model and simulating simple quantum algorithms:\n", "\n", "The notebook [QuTiP example: Physical implementation of Spin Chain Qubit model](spin-chain-model.ipynb) shows the simulation of a spin-chain based quantum computing model both with `qutit.qip.QubitCircuit` and `qutip.qip.device.Processor`.\n", "\n", "The notebook [Examples for OptPulseProcessor](qip-optpulseprocessor.ipynb) describes the class `OptPulseProcessor`, which uses the optimal control module in QuTiP to find the control pulses for quantum gates.\n", "\n", "The notebook [Running the Deutsch–Jozsa algorithm on the noisy device simulator\n", "](qip-processor-DJ-algorithm.ipynb) gives an example of simulating simple quantum algorithms in the presence of noise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The pulse level control\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import copy\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "pi = np.pi\n", "\n", "from qutip.qip.device import Processor\n", "from qutip.operators import sigmaz, sigmay, sigmax, destroy\n", "from qutip.states import basis\n", "from qutip.metrics import fidelity\n", "from qutip.qip.operations import rx, ry, rz, hadamard_transform" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Controlling a single qubit\n", "The simulation of a unitary evolution with `Processor` is defiend by the control pulses. Each pulse is represented by a `Pulse` object consisting of the control Hamiltonian $H_j$, the target qubits, the pulse strength $c_j$ and the time sequence $t$. The evolution is given by \n", "\n", "\\begin{equation}\n", "U(t)=\\exp(-\\mathrm{i} \\sum_j c_j(t) H_j t)\n", "\\end{equation}\n", "\n", "In this example, we define a single-qubit quantum device with $\\sigma_z$ and $\\sigma_y$ pulses." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "processor = Processor(N=1)\n", "processor.add_control(0.5 * sigmaz(), targets=0, label=\"sigmaz\")\n", "processor.add_control(0.5 * sigmay(), targets=0, label=\"sigmay\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The list of defined pulses are saved in an attribute `Processor.pulses`. We can see the pulse that we just defined by" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------------------\n", "Pulse label: sigmaz\n", "The pulse contains: 0 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[ 0.5 0. ]\n", " [ 0. -0.5]], 'targets': [0], 'tlist': None, 'coeff': None}\n", "----------------------------------------------------------------------\n", "----------------------------------------------------------------------\n", "Pulse label: sigmay\n", "The pulse contains: 0 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[0.+0.j 0.-0.5j]\n", " [0.+0.5j 0.+0.j ]], 'targets': [0], 'tlist': None, 'coeff': None}\n", "----------------------------------------------------------------------\n" ] } ], "source": [ "for pulse in processor.pulses:\n", " pulse.print_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the pulse strength `coeff` and time sequence `tlist` still remain undefined. To fully characterize the evolution, we need to define them both.\n", "\n", "The pulse strength and time are both given as a NumPy array. For discrete pulses, `tlist` specifies the start and the end time of each pulse coefficient, and thus is one element longer than `coeff`. (This is different from the usual requirement in QuTiP solver where `tlist` and `coeff` needs to have the same length.) The definition below means that we turn on the $\\sigma_y$ pulse for $t=\\pi$ and with strength 1. (Notice that the Hamiltonian is $H=\\frac{1}{2} \\sigma_z$)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------------------\n", "Pulse label: sigmaz\n", "The pulse contains: 0 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[ 0.5 0. ]\n", " [ 0. -0.5]], 'targets': [0], 'tlist': None, 'coeff': None}\n", "----------------------------------------------------------------------\n", "----------------------------------------------------------------------\n", "Pulse label: sigmay\n", "The pulse contains: 0 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[0.+0.j 0.-0.5j]\n", " [0.+0.5j 0.+0.j ]], 'targets': [0], 'tlist': array([0. , 3.14159265]), 'coeff': array([1.])}\n", "----------------------------------------------------------------------\n" ] } ], "source": [ "processor.pulses[1].coeff = np.array([1.])\n", "processor.pulses[1].tlist = np.array([0., pi])\n", "for pulse in processor.pulses:\n", " pulse.print_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This pulse is a $\\pi$ pulse that flips the qubit from $\\left |0 \\right\\rangle$ to $\\left |1 \\right\\rangle$, equivalent to a rotation around y-axis of angle $\\pi$:\n", "\n", "$$R_y(\\theta) = \\begin{pmatrix} cos(\\theta/2) & -sin(\\theta/2) \\\\ sin(\\theta/2) & cos(\\theta/2) \\end{pmatrix}$$\n", "\n", "We can run the simulation to see the result of the evolution starting from $\\left |0 \\right\\rangle$:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0\\\\1.000\\\\\\end{array}\\right)\\end{equation*}" ], "text/plain": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\n", "Qobj data =\n", "[[0.]\n", " [1.]]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "basis0 = basis(2, 0)\n", "result = processor.run_state(init_state=basis0)\n", "result.states[-1].tidyup(1.e-5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As arbitrary single-qubit gate can be decomposed into $R_z(\\theta_1) \\cdot R_y(\\theta_2) \\cdot R_z(\\theta_3)$, it is enough to use three pulses. For demonstration purpose, we choose $\\theta_1=\\theta_2=\\theta_3=\\pi/2$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "processor.pulses[0].coeff = np.array([1., 0., 1.])\n", "processor.pulses[1].coeff = np.array([0., 1., 0.])\n", "processor.pulses[0].tlist = np.array([0., pi/2., 2*pi/2, 3*pi/2])\n", "processor.pulses[1].tlist = np.array([0., pi/2., 2*pi/2, 3*pi/2])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\\begin{equation*}\\left(\\begin{array}{*{11}c}-0.707\\\\0.707j\\\\\\end{array}\\right)\\end{equation*}" ], "text/plain": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\n", "Qobj data =\n", "[[-0.7071043+0.j ]\n", " [ 0. +0.70710926j]]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result = processor.run_state(init_state=basis(2, 1))\n", "result.states[-1].tidyup(1.0e-5) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pulse with continuous amplitude\n", "If your pulse strength is generated somewhere else and is a discretization of a continuous function, you can also tell the `Processor` to use them with the cubic spline interpolation. In this case `tlist` and `coeff` must have the same length." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tlist = np.linspace(0., 2*np.pi, 20)\n", "processor = Processor(N=1, spline_kind=\"step_func\")\n", "processor.add_control(sigmaz(), 0)\n", "processor.pulses[0].tlist = tlist\n", "processor.pulses[0].coeff = np.array([np.sin(t) for t in tlist])\n", "processor.plot_pulses();" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tlist = np.linspace(0., 2*np.pi, 20)\n", "processor = Processor(N=1, spline_kind=\"cubic\")\n", "processor.add_control(sigmaz())\n", "processor.pulses[0].tlist = tlist\n", "processor.pulses[0].coeff = np.array([np.sin(t) for t in tlist])\n", "processor.plot_pulses();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Noisy evolution\n", "\n", "In real quantum devices, noise affects the perfect execution of gate-based quantum circuits, limiting their depths. In general, we can divide quantum noise into two types: coherent and incoherent noise. The former one usually dues to the deviation of the control pulse. The noisy evolution is still unitary. Incoherent noise comes from the coupling of the quantum system with the environment. This type of noise leads to the loss of information. In QIP theory, we describe this type of noise with a noisy channel, corresponding to the collapse operators in the Lindblad equation.\n", "\n", "Although noise can, in general, be simulated with quantum channel representation, it will need some pre-analysis and approximation, which can be difficult in a large system. This simulator offers an easier, but computationally more demanding solution from the viewpoint of quantum control. `Processor`, as a circuit simulator, is different from the common simulator of QIP, as it simulates the evolution of the qubits under the driving Hamiltonian. The noise will be defined according to the control pulses and the evolution will be calculated using QuTiP solvers. This enables one to define more complicated noise such as cross-talk and leakage error, depending on the physical device and the problem one wants to study. On the one hand, the simulation can help one analyze the noise composition and identify the dominant noise source. On the other hand, together with a backend compiler, one can also use it to study if an algorithm is sensitive to a certain type of noise.\n", "\n", "### Decoherence\n", "\n", "In `Processor`, decoherence noise is simulated by adding collapse operator into the Lindbladian equation. For single-qubit decoherence, it is equivalent to applying random bit flip and phase flip error after applying the quantum gate. For qubit relaxation, one can simply specify the $t_1$ and $t_2$ time for the device or for each qubit. Here we assume the qubit system has a drift Hamiltonian $H_d=\\hbar \\omega \\sigma_z$, for simplicity, we let $\\hbar \\omega = 10$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "a = destroy(2)\n", "initial_state = basis(2,1)\n", "plus_state = (basis(2,1) + basis(2,0)).unit()\n", "tlist = np.arange(0.00, 2.02, 0.02)\n", "H_d = 10.*sigmaz()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Decay time $T_1$\n", "The $T_1$ relaxation time describes the strength of amplitude damping and can be described, in a two-level system, by a collapse operator $\\frac{1}{\\sqrt{T_1}}a$, where $a$ is the annihilation operator. This leads to an exponential decay of the population of excited states proportional to $\\exp({-t/T_1})$. This amplitude damping can be simulated by specifying the attribute `t1` of the processor" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from qutip.qip.pulse import Pulse\n", "t1 = 1.\n", "processor = Processor(1, t1=t1)\n", "# creat a dummpy pulse that has no Hamiltonian, but only a tlist.\n", "processor.add_pulse(Pulse(None, None, tlist=tlist, coeff=False))\n", "result = processor.run_state(init_state=initial_state, e_ops=[a.dag()*a])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(tlist[0: 100: 10], result.expect[0][0: 100: 10], 'o', label=\"simulation\")\n", "ax.plot(tlist, np.exp(-1./t1*tlist), label=\"theory\")\n", "ax.set_xlabel(\"t\")\n", "ax.set_ylabel(\"population in the excited state\")\n", "ax.legend()\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Decay time $T_2$\n", "The $T_2$ time describes the dephasing process. Here one has to be careful that the amplitude damping channel characterized by $T_1$ will also lead to a dephasing proportional to $\\exp(-t/2T_1)$. To make sure that the overall phase dampling is $exp(-t/T_2)$, the processor (internally) uses an collapse operator $\\frac{1}{\\sqrt{2*T'_2}} \\sigma_z$ with $\\frac{1}{T'_2}+\\frac{1}{2T_1}=\\frac{1}{T_2}$ to simulate the dephasing. (This also indicates that $T_2 \\leqslant 2T_1$)\n", "\n", "Usually, the $T_2$ time is measured by the Ramsey experiment, where the qubit starts from the excited state, undergoes a $\\pi/2$ pulse, proceeds for a time $t$, and measured after another $\\pi/2$ pulse. For simplicity, here we directly calculate the expectation value of $\\rm{H}\\circ a^\\dagger a \\circ\\rm{H}$, where $\\rm{H}$ denotes the Hadamard transformation. This is equivalent to measure the population of $\\frac{1}{\\sqrt{2}}(|0\\rangle+|1\\rangle)$. The envelope should follow an exponential decay characterized by $T_2$." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "t1 = 1. \n", "t2 = 0.5\n", "processor = Processor(1, t1=t1, t2=t2)\n", "processor.add_control(H_d, 0)\n", "processor.pulses[0].coeff = True\n", "processor.pulses[0].tlist = tlist\n", "Hadamard = hadamard_transform(1)\n", "result = processor.run_state(init_state=plus_state, e_ops=[Hadamard*a.dag()*a*Hadamard])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "# detail about lenght of tlist needs to be fixed\n", "ax.plot(tlist[:-1], result.expect[0][:-1], '.', label=\"simulation\")\n", "ax.plot(tlist[:-1], np.exp(-1./t2*tlist[:-1])*0.5 + 0.5, label=\"theory\")\n", "plt.xlabel(\"t\")\n", "plt.ylabel(\"Ramsey signal\")\n", "plt.legend()\n", "ax.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random noise in the pulse intensity\n", "\n", "Despite single-qubit decoherence, `Processor` can also simulate coherent control noise. For general types of noise, one can define a noise object and add it to the processor. An example of predefined noise is the random amplitude noise, where random value is added to the pulse every `dt`. `loc` and `scale` are key word arguments for the random number generator `np.random.normal`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from qutip.qip.noise import RandomNoise\n", "processor = Processor(N=1)\n", "processor.add_control(0.5 * sigmaz(), targets=0, label=\"sigmaz\")\n", "processor.add_control(0.5 * sigmay(), targets=0, label=\"sigmay\")\n", "processor.coeffs = np.array([[1., 0., 1.],\n", " [0., 1., 0.]])\n", "processor.set_all_tlist(np.array([0., pi/2., 2*pi/2, 3*pi/2]))\n", "processor_white = copy.deepcopy(processor)\n", "processor_white.add_noise(RandomNoise(rand_gen=np.random.normal, dt=0.1, loc=-0.05, scale=0.02)) # gausian white noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We again compare the result of the evolution with and without noise." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\\begin{equation*}\\left(\\begin{array}{*{11}c}-0.707\\\\0.707j\\\\\\end{array}\\right)\\end{equation*}" ], "text/plain": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\n", "Qobj data =\n", "[[-0.7071043+0.j ]\n", " [ 0. +0.70710926j]]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result = processor.run_state(init_state=basis(2, 1))\n", "result.states[-1].tidyup(1.0e-5)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\\begin{equation*}\\left(\\begin{array}{*{11}c}(-0.645-0.002j)\\\\(0.120+0.755j)\\\\\\end{array}\\right)\\end{equation*}" ], "text/plain": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\n", "Qobj data =\n", "[[-0.64493797-0.00204507j]\n", " [ 0.12023457+0.75471483j]]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result_white = processor_white.run_state(init_state=basis(2, 1))\n", "result_white.states[-1].tidyup(1.0e-4)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9932265373557044" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fidelity(result.states[-1], result_white.states[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the result of this this noise is still a pure state, we can visualize it on a Bloch sphere" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from qutip.bloch import Bloch\n", "b = Bloch()\n", "b.add_states([result.states[-1], result_white.states[-1]])\n", "b.make_sphere()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can print the pulse information to see the noise.\n", "\n", "The ideal pulses:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------------------\n", "Pulse label: sigmaz\n", "The pulse contains: 0 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[ 0.5 0. ]\n", " [ 0. -0.5]], 'targets': [0], 'tlist': array([0. , 1.57079633, 3.14159265, 4.71238898]), 'coeff': array([1., 0., 1.])}\n", "----------------------------------------------------------------------\n", "----------------------------------------------------------------------\n", "Pulse label: sigmay\n", "The pulse contains: 0 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[0.+0.j 0.-0.5j]\n", " [0.+0.5j 0.+0.j ]], 'targets': [0], 'tlist': array([0. , 1.57079633, 3.14159265, 4.71238898]), 'coeff': array([0., 1., 0.])}\n", "----------------------------------------------------------------------\n" ] } ], "source": [ "for pulse in processor_white.pulses:\n", " pulse.print_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the noisy pulses:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------------------\n", "Pulse label: sigmaz\n", "The pulse contains: 1 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[ 0.5 0. ]\n", " [ 0. -0.5]], 'targets': [0], 'tlist': array([0. , 1.57079633, 3.14159265, 4.71238898]), 'coeff': array([1., 0., 1.])}\n", "\n", "Coherent noise:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[ 0.5 0. ]\n", " [ 0. -0.5]], 'targets': [0], 'tlist': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n", " 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n", " 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n", " 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7]), 'coeff': array([-0.04111324, -0.06137941, -0.05426716, -0.04919859, -0.04264403,\n", " -0.02250504, -0.03375665, -0.05148854, -0.02148299, -0.06914471,\n", " -0.04056917, -0.08183237, -0.07305101, -0.0299176 , -0.01313447,\n", " -0.07059048, -0.01996858, -0.05178805, -0.04831687, -0.05601915,\n", " -0.06255294, -0.04576603, -0.06118154, -0.05631155, -0.06109015,\n", " -0.03506447, -0.03834254, -0.04466844, -0.00812871, -0.08938573,\n", " -0.02702706, -0.05005737, -0.05355745, -0.01683736, -0.09146077,\n", " -0.01140311, -0.02461833, -0.06842676, -0.01661333, -0.06730827,\n", " -0.06145252, -0.04804409, -0.06802508, -0.03654118, -0.04002454,\n", " -0.01915866, -0.04294549, -0.09000612])}\n", "----------------------------------------------------------------------\n", "----------------------------------------------------------------------\n", "Pulse label: sigmay\n", "The pulse contains: 1 coherent noise elements and 0 Lindblad noise elements.\n", "\n", "Ideal pulse:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[0.+0.j 0.-0.5j]\n", " [0.+0.5j 0.+0.j ]], 'targets': [0], 'tlist': array([0. , 1.57079633, 3.14159265, 4.71238898]), 'coeff': array([0., 1., 0.])}\n", "\n", "Coherent noise:\n", "{'qobj': Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", "Qobj data =\n", "[[0.+0.j 0.-0.5j]\n", " [0.+0.5j 0.+0.j ]], 'targets': [0], 'tlist': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n", " 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n", " 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n", " 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7]), 'coeff': array([-0.06078027, -0.05756695, -0.04474645, -0.10254234, -0.06073313,\n", " -0.04446161, -0.06643672, -0.02880216, -0.04554076, -0.03670151,\n", " -0.09613619, -0.01831891, -0.04388728, -0.07712125, -0.02209791,\n", " -0.05018907, -0.03550091, -0.01924158, -0.03945293, -0.02134434,\n", " -0.04928715, -0.03777599, -0.0269203 , -0.01067214, -0.04945603,\n", " -0.0600917 , -0.04800358, -0.04740925, -0.03920404, -0.04555261,\n", " -0.04992821, -0.04579555, -0.05915739, -0.05714142, -0.05466035,\n", " -0.06307616, -0.03623067, -0.04228734, -0.05886232, -0.06229366,\n", " -0.0272317 , -0.03896859, -0.06195115, -0.06732644, -0.06956238,\n", " -0.05670983, -0.05463061, -0.06188376])}\n", "----------------------------------------------------------------------\n" ] } ], "source": [ "for pulse in processor_white.get_noisy_pulses():\n", " pulse.print_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting a `Pulse` or `QobjEvo` representation\n", "If you define a complicate `Processor` but don't want to run the simulation right now, you can extract an ideal/noisy `Pulse` representation or `QobjEvo` representation. The later one can be feeded directly to QuTiP sovler for the evolution." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "ideal_pulses = processor_white.pulses" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "noisy_pulses = processor_white.get_noisy_pulses(device_noise=True, drift=True)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "qobjevo = processor_white.get_qobjevo(noisy=False)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "noisy_qobjevo, c_ops = processor_white.get_qobjevo(noisy=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Structure inside the simulator" ] }, { "attachments": { "processor-noise.png": { "image/png": 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x4ds4c/Yse7cSEREREXkhhq12KANXab3H8+fP43/LfpTNP3NsL0R26eTuzSYiAJFdOql6jH22ZJl4ei7gfNBq78cX4e93K35RLffagrGs00pEdvkafDTLCXzz/U+s3UpERERE5IWYAmjQGwRH2ott07YdquWumZDk7k0nIgmt9+S6TdsaFWDo/fgi9GpV/vgyMTWO5QOIyCmJ0UZMTI2TTftsyTKxdyvAoJWIiIiIyFswbNVhL3C12Wz47kd5L7aJqXHs1UrkYSK7dFIFGF99+2ODglZnfnz5bf1m1XJzpw5w9+4TkRfRajN+Wb2evVuJiIiIiLwMw1YFvdOFpSFLhcmsGgRnxpie7t50ItKgfG/mFxRpDpRlL8Bw1C6sXrdJNj9/fCGihtL6cejn1esYtBIREREReRlfd2+AJ7JXq9Vms2GzooRAaJA/+ieGuXuzXepsrQWb99YHyqMGRCPAz3UvldU7ClTTIkI7trpjSJ6hf2IYQoP8caqqRpy2ftNWXHXl5bDZbGjXrp34V8leyCr0as0vKFL9+HLDpGR373arJW2bpFJ6dGXATV7vhknJWJNZKN7OLyhCXn4BEnskiO2RvTaLiIiIiIjcj2GrhLTHiL2erasVpwyPHxLr7k13OVN1LZ79NAMAsPTpKS4NW4X1Sk1MjWPYSs1m/JBYLNtwSLz98+r1mD7lMvF9LYQWWgGGox9fMnbslM0fGuTvslqtZ2stLn3vtQbStknq8ZvSGLaS10uMNqp+HNqcsQM9usc7/HGIiIiIiIg8A7/Fa7DXi+3s2RoUKnqxTWhC2Fpy8jSufXIFgPqQ5vt/TVPNs2zDIbzxdX2g8/hNaUgfGu/uQ9Qkj9+UJl7fmlUs68WjZ/WOAs2AJTUpHMZAP8SHB2P66EQYg/zdvXvkgSYowtb8giKcPXsWHTt2VAWuAmd+fLHZbFi1ZqNsuemjm1ZSJPeYCYt/zcaug8fFwCU1KRyjBkRj5thezXaMPlm5Hx+tzMLSp6c4FVqOWbBUc7pwGvSki+MxtE8EfA2urVZjDPSTtSFfrM5GXrG52Y5LU7DdosaYPronPlqZJd5e8csa/PUvM536cYiIiIiIiNyPYauCsh6aMlgpPV6uWiY5oYtLHvtUVQ325Zarenj+uCXX3YfFpZRhsTNhq57MnDLx+kcrszBncgpuntzP3btIHkbrPVpcehw9eySIt/UCV3s/vlSYzDCZK2XLXDosAY0l/WFFKjOnDJk5ZTh6vArzZwxyeYAJAHtyy5u+Elx4P6/JLERokD+e/dsIl/ZaD/DzlbUhW7OKPTZs1cN2i+y5dFiCLGytMJlRYTIjtLNRVr+VQSsRERERkWdi2KrBXi82ZW3GHlEhLg0+1u4skgUTucdMXhckNLelT08Rr58wnUFmznGs312EvGIzPlqZheiwQK/v/Uuu5WvwQY+oENl7Kf9IIRL/PDXX2R5iyvaguKRMNU9jT2Xfl1suC1pnju2Fmy7rix1/lIm9I5dtOISYbkHN0sNVGgA2RGpSOP45e6h4+0ipGdsPlGLdziKcqqrB4x9uxZKnrmjz5RActVtJ8Z0xvF+UuzeTPIBWG3L0WAlCOxvdvWlEREREROSEtv3tV0JrpF+tsPVQbr5sucG9urnk8VOTwpGZU4ZlGw5h7tQBYjCxYmseAKiCIqV9ueVYu7MIR0orkZlThompcUjp0RUTh8TaPUXVVFWD7zflir3aRg2IxqCeYejo397hNpuqarBmZxGy8k5gTWYhUpPCMTAxDKMHRrusZqUW6RfRyC6d0D8xDJcOSxDLMazMyFeFrU9/vA1A/eAjidFGnK21YPfh49h+oBTLNhxyeJq29PjmF5sxuHc3p46vpc6KHX+U4tftBcgvqX/+xg2KxeiB0YiPCLYb1Dfl+JacPI2svBPYmlWMXQePY/yQWFzcNwLJcaEOt7egtBK7D5eLgxANTAxDalI39IzpbDcw09rehIhgTBnRQ3d7TVU1eOObXQCAJ28ZLlvPj1tykVdsxsTUOMwY07PJvSMH9+omew/9cSgXE8eNEm/r1UO09+PL3v3ZssdQjiTeEB+t3A+gvpzIO/dfIr7O04fGY9SAaDzy/mZk5pTh818OYNqoRNlrR3h93331YADAmp1F2Lz3mPhanXRxvGaQJxx/U3WtOO3lL3fAGOgnm+/uqwfrvm6MgX6q9+TwflGI6RaEN77eiVNVNdi895jsPSlsr97zumzDIWTlnXDJ8650ttaCw0crxLCze2QIRqREoUdUiMP3ZGPbAeG4SK8r262v1h5UPUdst1qm3fp+Uy4Kyio9qt2amBonO+tj194s9O/Xp0E/DhERERERkXswbJXQGnlcep/NZsPRYyWyZfq6qISAMdAPM8f2wrINh7D78HEM7xcFS51VrDN55chEzdOLLXVWLPp2t6weJVB/Cu+azEK88fVOvHTnaM2gZV9uOeYvXCubJvRumzM5xe72bttfjAff2aRaNjOnzC2nxUZ26SR+OdXrobcmsxApPboiqmsgrnvqJ9kAJJk5ZeJz0NDju+ieCZpfqE1VNfjHm+tVIbnQkw0AVr02UzMI0Kr16Ozx1ToVfdmGQ+I+2KvJ+beXflVtb/1j1gf+Hz44STNosbe9yzYcwsyxvXRPfxcChblTByAr74RqPWsyCzEiJQr9E515Jejrm9BF9jwWFh2TvdelQavyxxe9sPVYsfy1ltKja6O2zVRVI75up4/uqXp+Avx8MWdyP2TmlOFUVQ12/FEqe08Lx3BEShQWfbtb9toWXqtar5mz5+pUZTy03j9zpw6AsYH7NDmtu/g6PHDkpCxslW6v1vMqBHWueN6lpDWyBXnFZnF77p41RDO4dNQOfP7LAby2YGyDf2Rytt0akRLlMe1WaJC/bk9lR+2W3vEFLtQMlpK2Wz/8a5pu4Gqv3UpNCscr88Y0W7t199WDZb3Plc9bY1+/KT26yt6bRcdKZJ9LOFgWEREREZHnYtiqQxmyCPYdyJHN1yMqxCWPt+vgcTz7txFYtuGQ2MMp+8hJAPWnE1edPqe53LqdReKX0R5RIbjv2lQkJ3TBup1F4pe/B9/ZpArYLHVWPP7hVvH2nMkpuHZiEjbvPYZF3+5WfemVKjl5WgxapY9ZUFqJxb9mY01moVtOi40PD3Z4X1beCWTlncCpqhpMTI3DiJQopPToihOmM5rLLV6VrXl8l2/OFYOk+QvXaoam0sBiYmpcfWAV6Ifdh4/jpcU7cKqqBi9/uUPsGSU9vsJzl5oUjjmT+2ke39SkbqqwxFJnxee/HAAALLpngtirS+i99dHKLDz83ibN0HRfbjnyis3oERWCf90+GmHGAPgafFBy8jQefm8T8orNWL45VxWWSLc3NMgf82cMwvghscg+chKvLc1EXrEZyzYcQt+ELqoex9LwZGtWsXhM50xOQVJ8ZyREhCAr70SjQ0wp5Xt1977646Ss0yzQClyl99lsNmRly9uDqK6NKyFwsvJCgDZ6YLTmPNK6s8UnTmvOIzwPM8f2wtypA8T386mqGs3XTJgxAEufnoIjpWbxPf3SnaORECE/VmHGgAbvU3tfH3FUdWnPWXd6/4e94vG5ZkISIrt0gqXOiuwjJzF/4Vq88fVOjEiJUoXd0nZWWDbMGIAdf5TiveV7kVdsxi3/WqX744k9yl7EUkK7tTWrGFuzipvcbi1elS227e5ot974eid6xxg12y1hu/TarX+8ub7B7dadr/6GzJyyZm231uwsapZ2S9mW7M3KZtBKREREROQlGLYqKHuOKC9Kzpxu74xTVTVimJKZU4aztRas3VkEoH4k9W83HlYtI/ReAqDqtSacenzpfcsAAKt+PyK7P/vISbGH1McPXyr2yEofGo/xQ2LxwNsbdXtaCYEFANmX38RoI568ZTiMgX5YtuEQ3lu+t8XC1rO1FvHLempSuOr+6LBAABd6IymPl1ZPT+k6lfPPHNsLk9O6i8d36Zoc2f1CAABA1YNseL8oPHjDUDz4ziasySwUTxHWOr7SHlnC8TVV1yIzpwyvLc3EJ49cJttmoU5malK47DGNQf644dJk8VhUnzmn6iX22tJMAMDt0waoTnn+1+2jser3Iwju2EF1nITtVfZ87Z8Yhk8euQxPf7wNazILsejb3Rg/JFYVlgg9+4TAQnm8GlsDVUnrvap8r+sFF1o/vthsNpgrq2TzKUNKZ0l75em1KdLjlpV3QreHoPSHFaEduPO135BXbFbVhPY1+KiOb0JEiEuOubSNGZHi/lqkZ2stsl7UQsjoa/BB/8QwPH5TGirPnMMJ0xnVD1NCOztzbC/cPWuIeN/wflEY1LOb2A4oyyU4s01CKNoS7dbNk/vh2olJzdpuhQb5Y+Fd48TpzrZbwjFwZbv1zv2XNKrd+vDBSeL/QL12Syj901ztlrItqTDVPy96Pw4REREREZHncP2Q1q2E8guNXtjqSr4GHzFA2bz3GJZtOITQIH/dmm8FpZVimJGapK4dG+DnK9aQVPZUPXjUJF5Xnvrqa/DBQDt15oQv/jPH9tI8vfLivhEA6gOkkpOn0dxKTp6WBZSjBkTbnT81KdypEgeHj1ZIltE+vkJA8v0meRguDce1nr/h/aKw9OkpWPr0FER1DWzQ8b1mQm/x+JokpxRLZeaUIfeYSTbN1+CDmyf3Q/rQeM3TcbtH1n+5f2nxDljqrLL7Irt0EpdVErZ3cK9umtsrBG2nqmpQbjpr95i/dOdol9fodMTe+9rejy/NJaCDQfc+Rz3pQ4P8NUsQCM+t8rTy5mCps2JfbrkYggGNL6/QXKTthSB9aDxmju2lev2Vm86K7eyEIbGq5aTtwBers+GshrZbE1PjvKLdGq9xjIDma7c6/zntveV7XdZu+Rp8MDmtOwD9dkvaI7ml2i29M26IiIiIiMizsGcrtE8TFm5Lv9TU1dU1+7ZMGdEDyzYcEk9vnD66p+680t5wsd2CNOdR1n0TZOWdAKDdmwoAkuI7a06Xfpk1Vddi9Y4C1TzHyqub9RgJg8YA9eUXpDUMU5PCMW2UukieNKQSvkQ7Unrqwim6esd3YGKYWEdTi97xBfR7pQmcOb5nz9XJammmD43HF6uzkVdsxn1vbRAHmBnUs5vD05vnTh2ANZmFOFVVg7+99CvGDYpFalI3JCd00R0QRxqm69UvbkipjUE9XTPgnLMsFgs6dLjQ601Zs1U6XXrdZrPBYrE4/TgN4ddB/3mqD5b0B8ob3Fv7+Om1A66w6+Bx2XtS+TiP35Tmst7JTRHg54u7Zw3BG1/vxLINh3CktFIcENBerVWhrQTqf6SStgsCIXizN4iho3ZLq6dyRGhH8bqzvYPd0W5Jw9PGtFvjh8Q2ut365+yhuPbJFcgrNrf6dquurg4+PvyNnIiIiIjI0zFsVdDrMdJSPUkSo41inUMAmD7audE19AYOkdZ9M1XVqObTqxeodzq0tIePMNiKPUdKzS4PWrQeU1pzT+sLtvTUbGd72VWeuVAnV+/4Cqf5AvVBqRAMCNs4sIG9naS1LZ05vspTnoH6cOvZTzPEmoPS05SvmdAbQ/tEaB6jyC6dcPesIfj8lwPIKzb/OSBO/X1zJqc4HE1c77hKe8Bl5Z3QfT30iAppcL1LV1EOkiVMU/5tiTbAVF2rexz0SnsI9J4DrdOoXeVUVY3m61SoFdrSPZXtmTYqURx8SxgISdjWK0cmYnJad7uvQa1BCp3lqN3S0tV4IWx1tt2Shpot1W6dPXfhh8jGtFu+Bh+77ZZQ/9Veu/XG1zs1261LhyXY/R/kTe2WVpvE2q1ERERERJ6HYeuf7PVqFe4zGAyNWXWD/fWyvnjj653oERWi+2XZWdVnzovX7fWYU9IbeEVq5theur2CBI2tYWnP0qenyG4bA/0a9GXX2fC3oQFVe98LQYAQmBeUVTZ6P505vtIwRpAYbcQnj1yGkpOnser3I1i/uwh5xWYxXLI3IvjMsb0wbVQiso+cxLcbD4uhyUcrs/DRyizVQGtSZ2rOa06vPXehB2hgR/0ax8Kp7i3Jx8dH870vBBf2Bs9iD7N6qUnh+OfsobJpwiBFnsbX4IMnbxmOf84eis17j2FlRj4yc8qQV2zGG1/vxI9bcjUHYRI8flNaox+7pdqtoE4Na7ek2yC0W+bTjR/QrKntVu4xEzbtOeb2dkv6w5e0h7FSS7Zb0s8gDFiJiIiIiDwXw1YPNG1UIkakRNkdpRqQB1clJ09rfpk8cOSkeF3ri31+ifZpr1qnygLyL/whnfwaNBiMq7jjlGRnjq80oBncuxvWZBZi18Hjjd63mG5BTTq+Qr3Cmyf3w9laC17+coc4EJW9HnzCgEH9E8Pw5C3DkXvMhPve2oBTVTV4/4e9slHIpa/RvGKzZs/XouMXBpFqjvC9OTjz44srSXvXbc0q1gyVpKdq651SrjdwVnOW9jAG+rVI73VXCvDzRfrQeKQPjYelzop1O4vEXpXrdhbJ3nfSdjalR9dG72tLtVvSH4n02q2tWcWay4rt1qGGtVthxgDxelPbrcRoIxKjjWK79cj7m8WBqFzVbknrIuu1W0dKL/xv1AqH3UGr/WHoSkRERETkeTyv65EH0wpZ9HrFNIUwQrijXk/S4Er6xVBK+NKsrD8nhDt6NQb1vowD9b2fAGD97iLdeZQDlXgjaQDm6PgqaxwKIeSpqhpZHVbp8Xn64214+uNt2JdbLrtPOL6b9x7T3TZ7x1frvgA/Xzx64zBx3VoDBOktmxhtxGsLxgKoD8Gk9Q6lr1G914x0MDZ7gz81t+Z4r4YEy2ti6r1OHJEGYj9uydWcZ83OC++3oX20a2rqhftCD+uG1KFsTsJ2SH+sECgHSHI15Wvc1+CD9KHxmDM5BQCw6Nvdsvul7ay0fquU1nvcXaTtlt4ZCsLrRK/dyis2a7YFZ2stmu2Wr8Gn2dqtV+aNEde9dE2O08smRhvx7N9GAFC3W9IzRvTareITknbODe2Wsi0xhgS3+DYQEREREVHjMGx1khC0piT3lk23NyBKc4vs0kkMLX7dXqD6wr8vt1zcvuvTk2X39Y4xite37Zd/2cw9ZrLbI3P+jEHivmsFI8s2HMKEu7/GPW+u9+rQtSHHVzno1jUTksTrKzPyVcdh8apssbZhzxj5YGTC8c3MKZMFBIJPVu4Xj6+Upc6K6Q8vx4S7v1Y9p0rKUdVzj5kwZsFSXP3Yj3aDo9Agf1kvNgBiSLXr4HHV9pacPC2GhxNT45pcFqMplO/VgSnJjVxTPZvNhr595L1IpQFNQwmnqOcVm/H0x9vEnqyWOiuWbTgk1gu1dxxPVdWownvp+/nKkdo1oKU9lBsbGDeEsB3KHpSWOis27TnWmFU6tGzDIYxZsBTPf/a73fmUgxJK24EvVmer3h+WOiseeX8zxixYqjkwVEuTbu+3Gw9rtltCTfBrJsj/n0nbreWbc1Xt1tI1OWKP/eZot5SvXaXUJPlAVI7aLaFWt712K7/E7JHtlrItGdCvT4tvAxERERERNQ7LCDRQdGQEsrIPircPHDnpllPpBfddm4r5C9eKwd2cySlITeqG15ZmiuFSaJC/agCW/olhYn2+B9/ZJA5CItS7C7Xz5XL8kFgs+nY3TlXV4JZ/rRIHwgGAzJzj+GhlFgBgzuR+slPrS06eVvUME3oV5ZeYVUFFU07ZdffxjezSCRNT48TT9j//5QDmzxiEiNCOWLuzSBz85fGb0lQ9mKXH99onV9g9vlK+Bh/MnzEIz36agZcW78D8GYPQIyoEHf3b44TpDNbuLMKpqhr0iApRDVqUGG1EalI4MnPK8Mj7m3HNhN5ij76svBP4YnU2gPpARVnP8tqJSfhoZZa4vRNT4zBjTE9Z7UQAmDt1gFufS2UvytgY50Z3tycqQt4zUO80fmekD43HsfJqfLQyS3y9zRzbC+v+fN6A+p6Ij944zO565i9cK75mlM/BRJ2BmAL8fMXX60uLd2D66J7iIEoHjpyEqbpWdhp2Uw3qWf/6yys24+YXfsHt0wag+sx58XUvtE1bs4pl7atWmCmUQtHqoShdduKQWHz+ywGsySyEMdAPE4bEoquxI87UnMfuw+Xi++raiUmq9QjtQF6xGZfet0wceCkr74RY9zU0yB+jBkS77Bg1xfXpyXj20wyH7dbQPhGy5Zxtt+bPGKRqt0YNiBaft2ufXCH+TwGcb7ce/3Bri7dbecVmj2y3lP8rY6Ii3bIdRERERETUcAxbnSTUREvsEQ+svTC9obXtXK1/YhheunM0HnxnEwBhQJAL96cmhePJm9M0B3x5bcFYsaaddGTu0CB/fPLIpZj68HLNx/Q1+OCTRy7F059kiIPLzF+4VjbPnMkpqi/GWXkn8OynGZrrzCs2q+57/KY0t4etzhzfF+aO0jy+j944DPHhwWIQqdy/mWN7aY5C7szxvXvWEM1R3tOHxmNrVjHWZBZqHuvQIH/dQX5emDsK1z31k+y1INUjKkRzewP8fPHDv6bhH2+uR16xWTUaeWiQP15bMNbtz6XyvZrUs0eT1teuXTukJPfGsh9+FqetySxsUih58+R+MJ+uFUMt4S9Q/1p7Zd4YuwNP3T1rCDbvPYbMnDLVa2bRPRPs9tCbMaYn1mQW4lRVjRiMSbkybI2PCMbMsb2wbMMh5BWbxfeXsJ3KwEug134Ix165jDRsNQb548EbhuLBdzbJRruXmjm2l2b5lv6JYVh0zwQ8/uFW8fgoj9Enj1zaYqPSO5I+NB6BHds3ut0yBvph2YZDDWq3Avx8Ze2WVjviie3WzS+swqmqGo9rt5Sv5QH9m9YTn4iIiIiIWk47W3OM9uJlpAPfWK1WWK1W1NXVaV7yC4rw0FMvyZZf+8asRo+8fbbWIta4s9dDdl9uOUpPndHt7Xm21oLDRytw8KgJR49XoW9CF6d6hgqPf+DISYR08kNSfGcM6tkNAX6+Yi+yUQOiNUMES50V5aazyMo7gQNHTqJvQhf0iApBfESw5vHQ6tlqj3T7pcs2piexs8fZ3vKNOb5A/cBG2YWnUHziNKpOn8PogdG6x0h5fAtKK5FXbHbq+Cofs+h4FUpPncGBIydxcd8IJMeFOnU6bMnJ0zhSakZOQQWCOnVA7xgjesZ0dhgkSbd3a1YxRqREISK0o8Nlhdd2RGhHzSDGFSx1Vky4+2vZtP+8/DQSu8fDYDDAYDDAx8cHPj7y4+qoPagwmXHHPY/KlrE38rmz29qQ9xUAjFmwFED9DxTjh8TKXjcNee6F53DTnmOIDgtEYMf2SIgIgTHQT/UcCu1DU563s7UW7D58HDkFFUhN6ia+VoT3u3LdDT1NX+u9rnV8nX0vC+2A9H2VEBGiu2xra7e0BpLSOr7e3m71iApBVNdAt7VbJSdP49onV8imff7+vxHa2ahqr9q1aydeiIiIiIjIMzBsRcPC1tNnzmDO/H/Kll90z4RmC4mIqOn25Zarenp+9cnb6NSpo2bY2q5dO1l7oGwTLBaLeP3Oex+DubJKXO+cySm4WXGqdHOThq3uLGtCRE33ycr9sp7TxpBgfP7+v1VtlbLNIiIiIiIiz8ABsv6k/KIi7S0ivc/fzw+x0fLaaWslo4QTkedRvkfjY6MREOCv2SNMelsvwJC2DRPHjpTd9/2mw+7eXSLyYso25PJLxqnmYbhKREREROS5GLYq6J2OJw1Xxo+W1y5cx7CVyKMp36OTJoyR3Za+7+391Wobhg7uL7t9qqoGucdM7t5lIvJCucdM4oB4gmFDBzvVRhERERERkWdg2Ar9Lyx6tdDShg6W3T5VVYN9ueXu3g0i0rAvt1wVXowecbHdXq2OertKL3Gx0are7ot/zXb3bhORF1K2HfGx0egef2GALwarRERERESej2GrDnvBa0hwkCpc+XYjTx0m8kTK92Z8bDRCgoPE2/bCC2VvMr0fYMaNlo+UviazECUnT7fYPj5+UxoevykNKT26tthjEpFrlZw8jTWZhbJp6RNGq9odDohFREREROTZGLYq2AtXpNOnXDZBtlxLhytE5JhWeDHjyssaHF5ozSddx9iRw1TLvP/D3hbbz/Sh8UgfGu/UKPNE5Jm02oyJY0ZqlhAgIiIiIiLPxbBVh14II1xPu2iwapmv1ua4e7OJSELrPTky7SKnwgt7dRGVP8L4+/nhqimXypZfk1nI2q1E5JTcYybVD0PXzpiCgAB/8TZrtRIREREReQeGrRJaPdak90kvBoNBFa4s23CIvVuJPETJydNYtuGQbNq1M6bA19dXvK3Vw1Xvr6PL5EnjVNtw31sbYKmzuvtQEJEHs9RZcd9bG1TTp02eZPcMG+lfIiIiIiLyHAxb/+RM7zXl5ZJxI1XrefnLHe7eFSKC9nvxskvG6dZedWagPHuXAH9/3DHnetmyp6pqsOjb3e4+FETkwRZ9u1s1iN/dd9yCgAB/3faGiIiIiIg8F8NWDc6GKyHBQbhqyiTZspk5ZdiXW+7uXSBq0/blliMzp0w27dqrpsAYEtyk8MJRm5B20SDEKAbPW7bhEMsJEJGm3GMmVQ/8uJgoWbkTBqxERERERN6FYatCQ3u1Tb38EtnI5gDw+IdbcbbW4u5dIWqTztZa8PiHW2XTjCHBmDntcrvhhVaYofe+9/HxUV3atWsHX19f/OPOW1True+tDTApeq4RUdtmqqrRLB/w0D3z0L59e1nbwt6tRERERETeg2GrBnvhivS2EK5cN/NK2fKnqmrwyPub3b0bRG3SI+9vVp2Se+N1M+Hr62s3vFBqSEkR6aVb1y644drpsnWdqqrBzS+sYv1WIgJQX6f15hdWqdqqOTfMQni3rnbbGAEDVyIiIiIiz8SwVcKZcEUrdB0xLBV9+/SSrSszpwyrdxS4e5eI2pTVOwpU5QP6903C2JHDnOohZi90Vf7QotU2CH8njhmhahNOVdXggbc3MnAlauMsdVY88PZGVdDav28SJk0Yo9u2cFAsIiIiIiLvwLBVg7PhivRy55zrVeUEnv00g/VbiVrIvtxyPPtphmxaSHAQ7r5zTqPDC2VbYK+UgPQ+X19f/OOOmxEcHChbX2ZOGQNXojZMCFqVPwqFBAfhgbtvl5UPsNcLn4ErEREREZHnYtiqYC9c0QtYfHx8YAwJxoK5N6rWN3/hWpScPO3u3SJq1UpOnsb8hWtV0+9bcBs6G0Ocqn1oL7xw1C5o9Xr39/fHc4/ex8CViADYD1pfffYRdAwIcFg+gCErEREREZHnY9iqQytc0QprpNOSevbA9CsmqdZ156u/cXAcomZiqqrBna/+ppo+a/pkJCf1tPseBhyHF/baAq0fXqQXY0gwFtym/hGGgStR26IXtALAvfNvhdEYotumMHAlIiIiIvIuDFs1OBuuaH0Zmnr5RM1ajTe/sIqBK5GLmf58bylrH6b0TcKMKy9z+H5tSHihV15E+RjK9Sf17IFH7punWl9mThmufuxHtgtErZypqgZXP/ajZtD61EP/QHJSL4dtFcCQlYiIiIjIWzBstcNe7Va9Hm2+vr64f8Hf0Depp2xdQuCae8zk7t0iahVyj5m0g9bk3njk3nmy2odNHWhGr4yA8v2vF77qBa6nqmow9eHlrO1M1Ertyy3H1IeXq9opQAhae+q2IywjQERERETkndrZbDabuzfCU9lsNvFitVrFS11dHerq6jSvC/OcPVuDB578Fyorq1XrXXTPBPRPDHP37hF5rX255Zo1WkOCg/Cfl59Cx4AAGAwGGAwG+Pj4yK5r9T51htBUKtsDvXZBa57Co8V45T/vo7JK3S7MmZyCGy5Nhq+Bv4EReTtLnRWLV2Xjo5VZqvtCgoPwyH3zkRAXI7ZPeu2UtL0CGLYSEREREXkDhq122AtX9AJX6cVkrsTLb7yHo8WlqnXPmZyCmyf3c/cuEnmdT1bu1wwwYmOi8Pj9C2A0hjgMWhsbXkh/gJG2C9L3v7I9EK4L85srq/DY869p/hDTIyoEj9+UhsRoo7sPMxE1Uu4xE579NAN5xWbVfSHBQXjp6YdkA/cp2yjpda3erURERERE5NkYtjqg17tVGqLY69FmsVjw2lsf4kDOYdW6U5PC8cLcUQjw83X3bhJ5vLO1Fjzy/mbNuocpyb3x0D13okOHDqrwQi9obUx4If0Bxl4PV60AVhq4WiwWvP/p//D7/+3WfJyZY3th/oxB7OVK5EUsdVYs+nY3lm04pHn/8KGDMe9vfxXbKWWvVnv1nwH2aiUiIiIi8hYMWx2wF644E7Y6ClxDg/zx4A1DMbxflLt3lchjbdtfjJcW79CseygEre3bt9fszapXPgBoXHih7PFur13QCmOFi81mw+p1m/HF18s1Hyc0yB/zZwzC+CGxDF2JPJilzop1O4uw6Nvdmm0UANx03UxcdslYVZhqr51i+QAiIiIiIu/EsNUJTa3VKCxnL1hJTQrHkzenwRjk7+7dJfIYpqoaPP1JhmZvVkAdYLRU7UOtNkG4rtWjVS9stVqtKD9xEm+8+4lmuRGgvrTAfdemss4zkQfal1uO15ZmapYMAIDY6Ejct+BvCO8Wphu0Sq8rB8oCGLQSEREREXkbhq1OcrZWo6PA9eDhfLz1wWeaA+QA9bVcr52YxNIC1KadrbVg6ZoczdqsQH3dw7vvvAXJvXva7SHWnLUP9doEe6UFtNoEm80Gi8WCtRu36f4YA1wIXZMTurCnK5EbWeqsyD5y0m7ICgA3/mUGLhk3Eu3bt9cNWe2VDmCdViIiIiIi78Sw1UkNqdWoFa5IgxVzZRXe/egLzbICAoau1BY5ClkBoF9ybyy47UYYQ4Lt9hBr7tqHyjbBUeAqbRu0esTabDaHvVyB+vICf72sLyandWf7QNSCztZasDIjH5//ckC3XABQ35v13vl/Q3i3rmLb4yhg1asnzbCViIiIiMj7MGxtgIYGro6Cld37DuCjxV/r9nIF6kPX6aMTWV6AWjVTVQ2+35RrN2QNCQ7CbTf9BUMGpugGGC09yIy9wFWrrIC9cgLSdRw8nI/P/vet3dAVqB9Ia8KQWPZ2JWomQi/WtTuLdAe+EsRGR+Lm2VejT+9EWTkArYte2QAGrURERERE3o9hawM5GhzH2TqNQqhSU1OLr5evxG/rt9h93NSkcMyZ3I+hCrUaQojx0cr9ujVZBZMmjMa1M6YgwN/fYXjR0oPM6AWuzvZ01QtcbTYbtm7fif8t+9HuDzKCmWN7YcqIHoiPCGYbQdQEljorCkorsWJrnsOAFaj/IWj2rKkYOewiVYDqTE9WBq1ERERERK0Lw9ZGEIIQAE6FKsrerVrhSvmJk/hm+c/4PXOPw8efObYXLu4bgUE9u/E0YvIqZ2st2H34OLYfKHUqxEgbOhjXXjUF3cK6qAIKZ+oe+vjUh47NXfuwsYGrXq936cVisWDfgRx8s/xnhz1dBRNT4zAiJQopPboiskunFnhmibxbycnTyMo7ga1ZxViTWejUMrHRkbjmqiswoF8f+Pr6avZUZdBKRERERNT2MGxtBK1gpbG92JThSkNCV6C+x+vAxDCkJnVDbLcglhsgj2KqqkHR8Spk5hzHntxyhz1YBWlDB+Oa6VcgvFtXWSDRkN5iLR1gOGoX9NoIZwJX4VJ4tBg/rVrrdPsgmJgah5QeXRHVtRMSIkIYwFKbVnLyNI6UmlF8oj5gdTZcFaQNHYypl09EXEy02MY4G7TqlQ1g0EpERERE1HowbG0kabACQDc0aUzgKoSumzMysXzl6gZv28TUOADAiJQoAEBKj66qecKMATzVmBrFUmdFuemsanpW3gkAwNasYgBocIABAFdNuRSjhw8Ve7I6G7RqhRwAWjzA0GsXHLUNDQlchfIjm7Ztx4Yt253u7arUIyoE3SNDYAz0Q9+ELgC02wqA7QV5Lr32CLjQJh04chKm6lrkl5iRV2xu1OPERkdi3Kg0jBl5MQL8/WUhqTNBq1bI6q52ioiIiIiImhfD1iawF7jqlQ1oSOAqnEKclX2wQacQE3mT2OhIzJp+BQb0S4Kvr68qvHAUXGjNC7gvwJA2qcqSAvZCV632wFHoCgDmyipsz9zdpOCViNRioiMxbtQwpF00GCHBQaqeqFptj17bpAxZlaVNGLQSEREREbUeDFubyF6w4kzoqhXE2AtV8guK8OvaTTiQc9jdu07UaH379MJlE8egR0KcLMTQCiQaErJ6UoBhr6yAVvugvO6obVCuGwBqamrxx6FcZB04iO079zg1sBYR1QsJDsLFQwagf78+SO6dCH//+rI8WgGr1nW9Hq5atVnZm5WIiIiIqPVi2OoijgbIsReqONuLTRqqWCwW5B0pQkHRURzOK2hwDUeiljTsokHo1SMB8XHR6Nk9XuzBqnXR6yXmbIgBeE6AoRe46g2ipdceONM2aD1eZVU18guKUH7iJNsJIgWhXeoW1gXd42MREhwEALptk6MfhfTaKK2L8DhERERERNT6MGx1IXvBijOhirM9XLXCFQAoP3ESFaZKnDhVgePlJ1BSehzHSsp4ajG1iJioCERHRSAyvBvCu3VBl9DOCO1sRFiXUFXA0NDwwtkQQ1i/J9ELQvXe840NW/VCV+lfoL73a1V1NQ7lFQAA8vILUVVdjarq0+wxT61K36SeCAoKRFBgJyR2r69l3rNHAkKCAuHn56dqMxyFrA1prxz9EORp7RQREREREbmOR4Wt9jbFW76YaIUc9k4LbkywIqzXXqBi73ZdXR0qTGa781LbZvf91q4dQo0hMBgMmvPqBRjS6w0JMOydtutNAYbWe1f5Xne2hEBTAldH7YRyOtsLcreGtEd6y+i1FQ0NWh2VElDOJ123w31xMUfvU09uL4mIiIiIvJlHhK3KTThbU4PNW7cDf34RqKqqxoypl9dvsBd8OdCq4+psb7aGnjKsFaDoXde67Wg6tU167zO9AEPvurNBq6PeYo56s9rbZk/iqG3QawMaG7baC1qdaRvYXpAncLY90pqm1y4Jf5satjoqF+CONkr5/vz2h58RFNhJ2AiMGj4UAX/Wo23J7SIiIiIiaivcHrZqffkvKTuO626ZL5tv/cqvvTZYcXT6cGPCFen6tB7L3nV704iUHAUarggzHIUXre10XHttgzPha1PDVmd7wTuaRtTSGhqwSm83V9jqqKSJu3qzCtfHTZ4lm2fJx4sQGd7N6z5TERERERF5C193PrhuQGinN5XwhUB63VO1a9dOdzt9fHxk4YgwrxCsSG83R9hqLzhhqNI22Xs/NaZHq/C3MT1bnQkyHG2zJ9NrG/Te947aA722wVVhK9sLammNbY/s3bZXOkB5uzEX6Tqc2Q9X0/qff+bsWdV8/n5+4jze9JmKiIiIiMhbuC1stRcCOOpV5U1fCLS+yDgKThoTtGp9yWLYSg3R1LBVOa2pvcc8padYcx9v4T0vvO+F+xyFqI1pHxr7YwzbC2pprgpbnS1vIr3ubKDqKSEroP+ZyqSotwwAIcFBmp+pGLgSEREREbmGW3u2CpShgN+fvS6kKkxmdDaGqMIIb/lioHfqo6vCFK2/9q5r3SbS0pRercLfpoQZWutsTfRCV2n7INzvTHshnS5c1/pr77ozt4ncwRVBq/KvvfZJa7rWcnrb4w56P7BI7xe2VXqdiIiIiIhcwy1hq73eVzabDSHBQaplTp6qgDEkWBVMeFPgCmiHxNJQxZkebVrHTvpXb5ozt4mkGnp6rtY0RyGqN4UYzUmvd5kz4av0ttZ1rb/K647u05tG1FIc1WttSA/8hvRy1brP0WO3JHttwpHCo7J5pT9aS7fbWz9TERERERF5Io/p2aq8dI+PRX5BkThP3pFC9EiIE29785eBxoYqWte1/jp73d40ooYEG64INbSW0duO1sxe6RGtH2aEeR21C40pL8L2gjyFo/bI0W177ZLyrzPT7G2XO2i9d202G4pLy2TTB/Xvq/ke9pT9ICIiIiJqDdxas9XeJT4uRha2Hisu0f2C4K09MZwJVaTHSriu9Vdvmt48SgxQSErv/eRML7LGBK/21tdWSfdf64cZYXpD/tq77sxtR9OJmoMz7ZGj240NXh2ty92U72flZX/2Qdn8ffv01q2B762fpYiIiIiIPE2Lh632vrxLL8NSB2H9pm3i/Zu2bsdf/zITgGd90XEFR192lMGr8jiypxo1B1f1JJNe96YQw5M4GsBG+SONMoy1d11vGoNW8iSNDVyV05xto5xdtyewF7hKP0cBQER4mO6ynrp/RERERETexu1lBKS9NqXi42Jkt/MLinDm7Fl06thRNX9r+oLgbIkBZUArxYCVXK2pwav0tjPrIm2Oersqg9aGtBP2prO9IE/SkDaksT8QOVqvp9EqN1RSdlw1X6/E7q36MxQRERERkSdwa9hqrzdGbHSkav7cvAL079cHgPrLQWs7/U0rJFGGVcrpeqMKs4caNYWzIYbe9IYuT85x1AY2tJ3QWofyPiJ3s9duNOS+1tAu6dVpFa4fUJQQ6GwMQUhwkGab4E37TURERETk6dzesxXQHknXx8cHg/r3xe59B8T5NmzJQErfJNlp9d5cs9VZzoYiWqcOay1vL2wh0uPo9dLYEIRcw9n3ub3ernptjd59RO7izOvRVfN4MunnJuXtVWs3yuYdPeJi1bLevv9ERERERJ7II8JWPcOGDpaFrct/+hW333I92rdv3+Z7ZNjrfdbQUIw91khLY95XbfG96KnsPRcNCVDZXpAnaErb0lbaJWngajJXYvfe/bL7hwxMabU/nrD2LBERERF5Eo8JW7W+wKePG433PvpCNi1z9z4Mu2iw5heGthq8Sjm7//xiQg3B10jr0tDnk+0FeRK+BvVrMAt/N2zOUC0zZGCKbH5vPo72fvRhTVoiIiIicrcWD1u1SgBIr0sv/v5+GNi/L/ZIerd+9PlSMWyVLi+sg5zDY0VEzmJ7QeR5tEoICH//t+wH2bxXXn4JDAaDeNvRwGCeytmB/qT71Vp78xIRERGR5/KInq16AazNZsP0yZNkYWt+QRGyDuSgf78+Xt8zg4iIiKippJ+bsg7koMJklt0/evjFjVmtx1AOqKqcBgAmcyXWbdyKrOwc7N67H6cqTACADT9/I87Pz4xERERE1BLcHrbqDXYlTBs8sB+MIcEwmSvFZX5Y+atsoCyWEiAiIqK2wF4JAZvNhkUffCq7PyEuBn379FKtp127duLnJel1T2IvZLXZbKirq8O6jVvxzn8/VwXMyuXawoCqREREROQZfNz1wI4+5AvTDAYDbr3xL7L71m/OQN6RQlXZAQ7cQkRERK2dVgkBm82G7Zm7kV9QJJv3zlv/Krut/MzlqeGjct+kF6vVit/WbcI1N92JF157SzdoBYCS0jLV+oiIiIiImpNbwla9D/l6PStGpV0EY0iwbNpLC9+GxWIB0LAaXkRERETeSOvzjnCpq6vDwkUfyu43hgSjT+9E8bajkNVTgletXqxCyHr6zBk88NhzDkNWe8eNiIiIiKg5ub2MAHAhZNWq2woAvr6+uPXGv+C1N98Xp+UXFGH95gxcMm4UB8oiIiKiNkGvV+uSb5arwsdbb/wLDAaD7tlEnvh5Sblf0usVJjNuu+ufuiFrZ2MIrrz8EvTu2QMJcbFo1w7o2iVUtm6WEyAiIiKi5ubWsFUZsEpDV2Ud1lFpF2HZ8pU4UnhUnP7SwreROqg/QjsbxXVIl+EHaSIiImoN7PVqLT1ejs//963s/oH9+2LcqDTZNK2g1ZPqtdorHVBSehx/nXu35nLd42Pxz3/ciR4JcbJ90voxn4iIiIioubmtZqtAq3aY1odhg8GA++6aq5r+4BMvwGKxaNZu5WljRERE5O3shZAWiwX/ePAp1TJ3zb1ZFToqA1ZPqt1qbx/PnD2Lv//zCdUynY0heOW5R7HoteeQEBfDz31ERERE5BHcOkCW8rajLwHxsdEYM2KYbLn8giK8+9/FHCyLiIiIWi29H5Vf/vc7qtPqZ8+ahvBuXWXT9HqyujtkFfZDax+FMPnJ519T7WNCXAwWvfos+ib10lyOnweJiIiIyF08qmervd4Xgnvm36oaLOv7n1ZhzfrNqg/V/JBNRERE3kzvs43NZsPn/1uG9ZszZPMnxMXgmqumOPxM5SnlA+wFrTabDes2bcOuvftlywzq3xdvvPQUgoODHH7O84R9JCIiIqK2xa1hq1b5AL3gVbgYDAa8+txjqnW9uPBt7Dvwh2ZPBgauRERE5G30QlabzYbvfvxFVacVAJ57/H4YDAbZNK3PWMr7PYlYPuDMGby08G3ZfUZjMO7/+1z4+PgwaCUiIiIij+T2nq0AHIasSt3CumjWb73noaex78AfsFqtuqP1EhEREXkyrc8v0su+A3/g7Q8/Uy330tMPIyQ42GGdVnufsVp6P/X20Waz4ZvlK1XLPPvo/QgJDra7XmfHAyAiIiIiag5uD1v1erc6Ki8wblQaZs+aplqfXuDKQbOIiIjI09kLIK1WKz7/3zLc+/AzquVmz5qGvn16qaY7CljdHULqBa0Wi0XVc3fMiGGIj40Wj5FegKy3v3o9e4mIiIiIXMntYavA2Q/MwsVms+G6q6dhzMhhqnXd89DT+GzJN7LAFQADVyIiIvJYWp9XhJBVCFo/W7JMtdzsWdNw3dX1P0A7Ch09rVercppwyc45rLr/r3+ZIe6DVEMCVyIiIiKi5ubr7g0AIIan0tvCX+kHZOU8NpsN987/Gyorq7B73wHZOj9bsgxFR4vx0L3z4evrq/qiIe0VQUREROQu9mqz2mw2nDl7Fk8+/zp27c1SLets0Gqvp6e791urbMKGLeqBv8K7dXVYFsHHx4e9WomIiIjIrTymZysApz40K0NTg8GApx6+B9dfM121vnWbtuEvt8xHSelx1Wl4WqfpEREREbUErTJHWiWQ8vILcNPt97g0aPWkwFGvbMKmrdtl802aMEa8rvc5Ue8zoyfuNxERERG1Xh7RsxVQ9za12WyqD8fKD8nC/AaDAX+ZORUA8MVX38vmqTCZccNtf8eN183E9ddcJfZyFZbX6u2q9VhERERETaH8YVevbIDQm/X1tz7A+k3bNNd1+5zrMeXSiQD0T6t39IO1Jx4H4XqFySybZ+iQgXZ/kNfaV+l+8nMdEREREbUUjwlbBcqgVZgm/Ssl/cD+l5lT0bNHAp5+8d+q+T5bsgw//vwb7rj1rxg/ejgMBoPuB29l8EpE7scvyg3HdozI8yhLBgh/hesWiwXrN23Dux8tVgWOAGA0BuPhe+aLg2EpT6t31LtTuoy7j4NW+QAAKC07rt7vkGCnSwcwaCUiIiIid2pn87Bv41q9O6Sn1WmdYqfchcqqKjz6zCs4UnhU8zE6G0M0Q1d+KCfyDI15D7bF921jmm8Pa/KJ2hy9QTsdhawAMKh/X9z/97kICQ4GoA5aG9rb053HQOtznjAQWGnZcdwy737ZMj8u/Ui1X8q/nhosExEREVHb4nFhK+CawNVqteKr71aoygpIdTaGYOrkdIxMG4rE7vH1B4Qf0Ik8iqP3ob37W9N72F5T7agZ98BmnqjN0evRmnekEJu3bcdnS5bZXf72Odfj8kvGwWAwAPDuoFX4qxW02mw2lJSWYc78B2TLrfjqY6fDVk/YTyIiIiJquzwybAXUI9M6E7ZK5xc+ZB8vP4k33/sYu/cdsPt43eNjMWbkMAwekIKeiQkI8Pe/cJD4gZ3II9gr/eFoPm98H2s1z3q1Dp1ZlohanvK9eObsWeTmFWDnnn348effdHuxCsaMHIa5N18n9mYFvDdolR4PZdCq7NmqDFt/+voTp8JWT9lPIiIiImq7PDpsVf5tbODarl07HPjjEN7+8DPd0gJK3eNjkRAXg37JSQgKCkT3+Fh0DAhw92EhalP0vi/7+/ujszFEMa9+4OpMGOsp7IWpjoJWm82mWeuw/j537xlR23Pi5CmUlZ9AZVUVDmQfxO59BxyGq4KEuBjM+9uNSE7qKZvuzUErAM3PbdJerY7CVmnAam8AMCIiIiIid/HYsBXQLiegV+fLXkkBcWcbEboSkWfrbAzBoAH9EBcThejICKT0TUJEeDcA2nWYPbVUiF6oqvzhCQBKSsuwP/sgjpWUovBoMdZt3OruzSciFxkzchimXDpRFbIC0AwW7Q0UJV3GUziq11pfRuA4bl0gD1tXfvOpbD/tlRAgIiIiInInX3dvgD3t2rWT9VDV4uPjI35IVy6n1esrOakn3nr1WZQdP4G1G7dg5a/rnO5lQkSep8Jk1gwbr7ryMgwdMhAD+/dFgL+/Zu9WoY1w5xd0e8Gq8PfM2bPYm5WN1es2MVglaoU6G0NwzYwpGDPiYgQHBWnO09igtbVp7ftHRERERN7Po3u2CpQ9XAHYLSOgVVJAc+f//JBeUHgU+w7kIGPHToe1XYnI+1x15WUYN2o4+vbpBYPBoPtlvSW/uOuFrNKRybNzDuPTL7/Gzj1Z7j6ERORig/r3RdrQIUjp2xsJcbEOP6s4G7Iq2zZPCyQdlRCoLyNQrtmzVauEgPDXE/eViIiIiNomrwhbAe0wwtlyAs6GrkB9iHvi5Clk5xxGVfVpZOccAtq1w4bNGe4+BETURJ2NIbjz1r9i/JgRbgtdnQlZ123ahnf/+zl73RO1AmNHDgMAJCf1QlBgJyQn9USX0M4wGAwAHH820SoJoFer1BuCVuGvXgkBvbD152WfOazZSkRERETkCbwmbAW0T7F19qK1vOYBaSWjmBO1ZrW152CurAQAFB4tRvXpM9ieuRt7s7KdCiiloauvb301lZYIKfTaMKDhIWtnYwgGpCTj4tRBCOzUEXExUQCA4KAg+Pv7NevxJyLnaX3mcOZziKMerd4WtEr3u6lhK+u1EhEREZEn86qwFWhY4Kp1v3RZ5XXVweEHdyKPZO+9WVlZhYO5+fjux18clgXpnhCHR+5bgMTu8c0aVtjrzWqz2ZCbX4AHn3jBYcg6dXI6Ugf1R68eCQgJCXb4WETkOZz9vOEoZHV0n3J9nkTa/knPSBLCVmGaM2GrXvkEIiIiIiJ387qwFbDfO8yZ4FVrHcp1iweIH96JvIr0PVtTU4uM/9uFr7//CUcKinSXGT9mBB65b4GstICrggtl0Cq9nDl7Fq+/9YHdQa8S4mNx8+yr0b9vH7HHqhc220RtmqPPFnq9WaXXHQWvWuv1NFphq1bvVr2w1V4JAU/fdyIiIiJqO3zdvQGNoXeqv/TLjHBbGbgq5yMi7+Xovezn1wFjRw7DuFFpOPDHQSz68HPN0HXdxq3YvXc/3nr1WURGhMvuE9qMxnyRt9cT31Fv1kH9++L6a6YjOamX7vqU20lEnkkvXFXedhSyKqcrlyUiIiIiIvfzyp6tUno9Ve2VE7C3nPI6EXkOe+9NZ2sgHvjjIF54bZFuyPnIfQtwyfjRTQ4z7LVJ3/74Mxa9/6nmcglxMXjiwbvRLayr0/vV0PuIyH30SgYo/zYkYPWW97vWmUfs2UpERERErY1X9myVstfrTNq7VdnTVbqc9K+wnIDBK5Hn0OuZLm0DHA1Gk5zUC5+++zpW/roO7360WDXvC6+9hWMlpbjxuqvFL/PKtsIRvbImFosFX3z1HT5bskxzuQf+fjtGDR8Kg8HQ4PqOevcTkfvZe586G7Rqzau3bm/Dz1pERERE1Jp4fdgKQBayOCoxIL0uDV6F2wKt4JWIWp5erUNHX87t9Vb38fHBlMsmYsyIi/HIs6+oSgt8+uU3KDxajIfvnQ9fX19ZwGGvbbBXn9ViseChp17Erj1ZquUG9e+LB+6+HSHBwZqlT5T7rhe06LVXbMeIPIO9966juq32liciIiIiIs/RKsJWgaPQlYjaHnu9XoODg/Cfl57Ch5/9Dz+sXC27b93GrTCbK/Hi0w/D1/dCU+lM2NuQoPX6a6bjmqumwMfHx+mQlWErkXdqatiqvE5ERERERJ6nVYWtAr3eZ/ZKBygDGUe91oioZSjfi8r3tlYZkIaUGvDx8cHcm2cjqWcPvPKf92TL7NyThXc+/BwLbr/Z4XZJ16sMW/WC1pefeQTJST1V2+To9GK9+extGxG1LGd+/HBUg5XvbSIiIiIi79Mqw1ZAXXdV6wuLVh1GrXlZUoDIMyjfn1oBqjJ0VYavyhrOgrGj0tAtrAseePwF2WN+t+IXBAcH4sbrrhan6QW6wvqll8+WfON00OrsacTO9HZje0XkGRzVVW5sj3UiIiIiIvJMrTZslWpoTVdhXuXyROQeeoPXafVu1RoYT1jWXo9Xm82G5KReeOXZR1SB66dffoOoyAhcMm6UU9sqXH5bt0lzMCwhaFW2M3qBalNqOLL9IvIc9n4U4Q8mREREREStQ5sIWwXKIFWrxIByXun8ROQeWu9HrR9RlCGrXi9W6bqUIa1e4Pqv195Cvz69ERnRzWGpEZvNhuKSMry48G3VPMqg1V6o6kzoqjw+WreJyH0aWkuZ71/vxc+L1JzYNhAREXmPNhW2ChpaJsDZ0c+JqPnp/WiiVYdZOeCUcpoWIXB94O+3q2q43vXA41jy0VuyAbOUywoDYt394JOq+5986B92g1Z7IaujXq1at4nIfZx9P/J9650cfSbkZ0ZyBUedP9h+EBEReaY2GbYq6X1QUX6o4QcaIs+h1Rtdr3SAo4AVkL/fbTYbxo5KQ86hPPzw82pxeoXJjHf/uxjz595kt2brS/9+BxUms2z6lZdfgosGD5Btm6Ow1VHvVuXxICLPxfdo66Bs+5X/O4hcSe+sO3tn6xEREZH7MWy1gx9ciDyTM18utGq46l2U6xWu33rjtdi7PxtHCo+K07//aRWumnoZIsO7aW5X3pFCrN+0TTY9IS4Gt/71WlXQ6uxFuq96Yat0v4mIyLW0/lfY+/9B5Cp6ZZOknwcYuhIREXkWhq1E5HX06qUqv3ho9X61Wq121y39Eu3j44Pnn3gA1//tbtk8T73wOt5Z+IJqWYvFgpf//Y5q+nOP3w8fHx9xGxpz0dpvfqkiImp+yiDVZrNh/BXXuHuziEQbfv4GgP5gwERERNSyfNy9AURErmCv1qmPjw98fHxk16XTtIJNoP5LS3BQEG6/5XrZY+UXFGHNhi2wWq2yy4YtvyO/oEg27+23XI/goCDVNikvyu3S2j6t/SQiouajVQecPVfJ00hfl3x9EhERuR/DViJqdRpSD1UvcJWeqnfZJWNhDAmWPcYHn36J8+fPi0Hr+fPn8cGnX8rmSYiLwWWXjLW7HfZCX716rURE1PyU4RXDVvJUWq9VIiIich+GrUTUammFlVq9Rh0FrgaDAQ/dO0+2bpOpEjt3Z4lfvDdu3Q6TqVI2zx1zrofBYFBtj9bjam2Xch+IiMg9bDab7EwGIk9itVoZsBIREXkQ1mwlolZPWcNVCC99fHzEL9A+Pj6yL9DKGn3JvXsiPi4GBZLBsj5Z8g2GDEoBAPz3s//JHjM+LgbJSb3Ex9cLWvVKBUj/EhFRy9IrHyD8z1B67r9fuHuTqQ157FZ5eSNlj2vl5x0iIiJqWezZSkRtgjODUGn1cBXYbDbcoajdWlB4FPlHCrE/+yBMZkWvVsm8DSldwJqsRESeQS9oZc9W8jRar1UiIiJyH/ZsJaI2Rau3h7RXq7RXqbQ3LAD06Z2I+NhoFBQdE5ddvW6z6jHiY6ORnNRTtT5Hoat0fiIicg9lUKUsIcCwlTyN8JoUPlcI2LuViIjIPdizlYjaHOkXD2d6mErnTx8/Wraun35di59+XSublj5htOxxnF03vxAREXkOrd6C7DVInoivUSIiIs/CsJWI2iStoFOvt6lwPwCMTLvI4bonjhmpWq+jkJVBKxGR59Aa3d1ezVYid5IGrMq/RERE1PIYthJRm6YVgNoLRkOCg9C/Xx/d9Q3o1wf+/n5O95hlyEpE5Nmk4RVHfSdPxICViIjIszBsJaI2y9nT/JXB6ITRw3XXOWGsc71aWaeViMizsYwAeQu+RomIiDwLw1YiatOUQadWAKvUp3ei7vr69Oqh20vW3uMREZH76fUQZJBFnkzrdcnerkRERO7DsJWICLDb41R5X7ewrrrrCe8WphvUsnwAEZH3UQZZDK+a5nRVJb567y1krPnV3ZvSqvCHACIiIs/BsJWI2jy9UFR6XTmg1SiNgbJGDR+qWoe9gJWBKxGR59IKWRloNd3R/Dzs3b4NhYcPuntTWg2t1ykRERG5D8NWIqI/2RskS3l/dFSEavmYyIgGrYOIiDyP3unYerepYY4dyXP3JrRafK0SERF5Bl93bwARkSdo166d+KVEuG4vOI2MCFetIzKim91wVdk7loiIvENr79VaV1eHw/v3oebMaQDAyeNliE7oge5JfdDBz193uXO1NSgpLEBJYQEKDx9E18godOkWjrievdG5a5hs3ooT5Sg8fBD7M7cDAMqKj2JPxhYAQPLgVHTw80fBoRyYTp6AsUtXxPdK0nxMYRnlPMJ04bFPV1XiaH4eDmXtBQD0ShngcH+8VWt+bRIREXkjhq1ERBqU4avykhAbrVomPi5Gt2wAw1UiIvJEezK24OelX6C60qx5/4CLh+Oa2xeoppceLcQnr72ou9yEaTMx9oppMBgMAIDCwwfx9Qdvi/eXHS0Sb9/30r/Rwc8fv69djb3bt2HAxcN1w1ZhGeU8wvRZt81Dzp5dWPHlp7LlMtasAgDc9tATuusmIhLwB4y2h9/XyJUYthIRSQj/ZKU9W7UuAR0DVMt27Bhgt1YrSwgQEZEnOV1VKYaU4TGxGDpmAhKS+qDsaBF2bt6I3Ows7N2+DQOHj0LSgEHichUnyvHWkw+Lt6fMvgkBnToBgBjcrl2+DAUHc3DL/fXzxfXsjVm3zZM93pjLrwQAdAoKctk+CdsNAInJKUgenCoLXj948Rk8/O930Cko2N2Hn4g8hKNglcFr6yX9bqb1PPO7GzUWw1Yioj/ZKyXgaLArAGgH+0Gr9HGIiIjcbd/2DPH6nPsfEQPIiJg4DEwbiRVffoaMNavw3cfv494XXxdPwV+9bKm4nLKnaMrQNPy89AtkrFmF3OwsVJwoR+euYeJFDFujYjAwbaTL9yk3OwvhMbGYesMt4nYNGTUG33/yIfZu3ybud9rESe4+/ETkJlqhGmset03S51lrMGPl64Df48hZDFuJiHQo/8HKQlS005xfK4jlP2UiIvJEJ8pKAQCBwSGa919+7fUYOelyWf3V01WVYmg5ZfZNqlPyDQYDLr/2evG0/S2//owps29s0f2SBscA0MHPH9fcvkDc7vUrvmfYStQGST/XO3Nd6za1PtIzG5W3lR1mlPMQ6WHYSkRkhzRAlfZ0Desaig/+85I4DwCEdQ3VHFCLiIjIE42cdDky1qxCdaUZbz7xEO565kVZSGkwGFQDXR3NzxOvJyT10VyvwWBAYnIKcrOzkJ9zoEX3KTwmVrdEgLBNenVmiaj10QtP7f01mStx9FgJyo6XI2PHThwpPIr8giJ37wo1o+4JcUiIi0Ha0CGI6BaGmOhIdDaGqAJXQLtDDpESw1YiIgVl7R6tkgC+vr6IjOimWs5erVb+IyYiIk/SuWsYBlw8HHu3b0N1pRn/+sedYp3ThKQ+iIiJUy1Tc+a0eP3n/32hW2+17Fh9MFF21PmA4nRVVZP3KTwqRvc+6bbW1dWJg3cRUeuj1VtV+ld63WKxIPvgYezak4Uff/4NFSb+INPW5B8pRP6RQqzbuFWc1tkYgqmT0zFkYH8kJ/WEr6+vrAMOwN6upI9hKxGRhN4vle3atYOPjw+sVit8fHxkH9KUoaqPjw9LChARkVe4YvaN6BoZhe3rfkN1pRm52VniAFPCIFYpQ9PEYDJnzy5xWWE+V3H1+pSSBg4WSwlUVpxS9dolIu+nF7Iqr9tsNuTlF+Dn1evx/U+r3L3Z5IEqTGZ8+uU3+PTLbwAAV025DJMvnYDE7vEsMUAOMWwlItKhVa9Hc2AsjR6symlERESeqFNQMCZMnYGxV0zD0bzDyM3eLwavZUeLxAGthMGs4nr2FgPLv979ALpFRbt7F5x28niZZL+DmrAmIvJEej1YlWHrvgN/4M13P2ZpAGqQ71b8gu9W/ILuCXG4+4456N+vj+q7nrLnK7VdDFuJiDTo/Tqpd124rRW4as1LRETkSQwGA+J7JSG+VxImTJ2BgkM5+ODFZwAAPy/9QgxbAzp1ki3Xkr1Dz9XWNGn5EyXF4vUOfv4ttt1E1LyUvVn1LlkHcvDme86HrIPS4hEWGYyu4UGIiDHCz5/xSWtUW2NB6VETTpRVobykErszCuzOn3+kEP946Cl0j4/F3Xfeqgpd2cuVAIatREQq9v5BSksI6JURYOBKRETeLr5XEiZMm4m1y5ehutKMgkM5iO+VhPCYWHGeQ1l7kTRgkMseUxjAau/2bbjm9gWq+/Nz/nC4jr3bt2Hm3+7UrMea90f9YF2BwSFuOKJE1Bzs9Wa1Wq2w2WwoKTuOp1543WHI2rNvBPoNiUbyoGiEhgW6e9eoBUXGGsXrs+8ciVPl1cjefQz7dx7D4QOlmsvkFxSJoetzjz+AyIhw2f3s5dq2MWwlItLh6FdJRz1b9eYjIiJyt9NVlfjo1RdQdrQItz30BOJ7JanmiU7oIV4P7hwKAAiLjEZgcAiqK83I2pGBy6+9XhVsnqutwesP3Yvw6FhMmDZDc91lxUdV04aMGiPWbT1dVYlOQcGy+7f++rNT+5a1I0PsiSuoOFGO6sr6QW8uHn+Juw8/EbmAXrkAIWi1WCxY8ctvePvDz3XXERjij4lX9sPAYfEIDGaPd6oXGhaIkelJGJmehOrKGuz5vQBrftyParP6DIv8giJc/7e/Y8HcmzF1cro4kBbAwLUt83H3BhAReSKtmqvSIFU6CJaPj4/moFjK60RERJ6iU1Aw+qVeDAD44MVnkLN3t3iafl1dHUqPFuK7j98HAAy4eLhYLsBgMODya68HAFRXmvHk3Bux9odvca62RlzuizcX1td8PVaErhGRssdNTE4BAJQdLULBoRxUnCgXH7dnv/7ifF+9twgFh3JwrrYGezK24MV75jk9gNbXH7yNN598SFx+xZef4bUH/yHe33fIRe4+/ETUBHplAqxWK6xWK+rq6nCqwoR59z2mG7RGxBox79F0PPGfGRiZnsSglXQFBvtjZHoSnvjPDMx7NB0Rkl6wUm+9/wluv/shnKow6b5Gqe1gz1YiIgf0glK93q4MWImIyBuMvWIa9mduR9nRInz+xiu686XPvFZ2O2VoGk4eL8Pa5csAAGuXLxOvS91830Oq3qnJg1PF0FSoCTvrtnkYmDYSnYKCER4Ti7KjRcjNzlKFq7c99AR+X7taHKBLy5TZNyF7VyZys7PE9UtNmDYTETFx7j70RNRIyrIBQqkAadmAffv/wPOvvokKk1m1fGCIP2bdOgzJA71ncD/yHAm9w3Dvc5ORvecYvv7v76qervkFRbjtrn/iyYfuEWu5CmXo2Mu1bWHYSkSkQ1lGQPjnKJ2unN/RdSIiIk9hMBhw+yNPIXtXJnL27ELeHwfEU+0Tk1OQPDgVSQMHqwbBMhgMmDB1BvoOuQgbf/rB6eUAYOi4iQCAwsMHxWnGLl3F67c/8hS+/+RDWaCaNvFS9EoZgPheSSgprB+4JK5nb819CujUCTfe809s+Gk5Cg7miIHtgIuHY+DwUS6tMUtELUuvbIAQslqtVixe+i0WL/1Oc/mR6b0x5bohMBh4gi81TfLAaDy6cDpWLNmJLasPyu6rMJnxj4eewo3XzcRNs2fBarWyrEAbxLCViMgO4R+h9J+ivX+QDFmJiMibdPDzx8C0kWKN09NVlfDv2ElzgCmliJg4cSCrc7U16ODn+DRcg8GAtImTkDZxku72XHP7Alxz+wLNddpbVvoYE6bOAFBfEsGZfSEiz2ZvACyhfMDipd/ii6++Vy0bGOKPBY9P4qBX5FIGgw+m3XARRl/aB289+6uql+tnS+rP+Ljxuqvh43Mh4Gfg2jbwJx0iIicoQ1S9i9b8RERE3qJTUHCjwklnglZ3rJNBK5H30wtapRe9oLVn3wg8unA6g1ZqNqFhgXh04XT07Buhuu+zJcvw2ZJvZOUulKUwqHVi2EpE5CStULUh9xMRERERkfMcBa11dXW6QWv6Vf0x98EJLBtAzc5g8MHcBycg/ar+qvsYuLZNLCNARNQIDFSJiIg8z8njZe7eBCJyEWd6tH7x1XeaQet1d4zA4OEJ7t4FamPSp/dH1/AgLHl3q2w6Swq0PfyJh4iIiIiIWoUTJcXu3gQicgFngtasAzmaQeu8R9MZtJLbDB6egHmPpqumf7ZkGfbuz2YP1zaCYSsREREREXm1WbfNw6zb5mHYhPSmr4yIPIK9oLWk9Dj++cQLqmVuuXcsEnqHuXvTqY1L6B2GW+4dq5p+78PPoLikTDNwpdaFZQSIPBwbX6Km4Wk5RESt38C0ke7eBCJyEen3HyFolQau58+fx7Mvv6FaLv2q/kgeGO3uzScCACQPjEb6Vf2x+rt9sulPvvAa3ln4Anx9feHj4yN+V2E5gdaFYSuRB3A2UGXwSuQc6QcVe+8bfqAhIiIi8hzS06qlvf+kgesHny7BkcKjsuVGpvdG+vT+jXlIomaTPr0/zlTXYsvqg+K0/IIivPPfz7Fg7s3ia1oIXRm4th4MW4ncQC/80ZrOgJWo4aTvG60PLNJfkLWmExEREVHLcqZOa96RQqz4ZY1suYhYI6ZcN8Tdm0+kacp1Q5D7x3GUFpnEact/+hWXp49HYvd4+Pj4iK99Bq6tB8NWohbgKERV3s/QlajptAJV5TTpBxnhw43WOoiIiIio+UmDVmXgeu7cObz25vuqZW6+ewwMBg5HQ57JYPDBzXePwYv3/yCb/tLCt/H268+jffv2DjuKkPdh2ErUTOwFqFqjDiqvn62pgdlchYKio6iuPo3sg4dhNlfCVFmFXXuy3L17RB5r8MAUGIODEBISjOSkXgjs1BHxsTEICQlCx4AAcT5puKr1K7IyfOUHHyIiIqLmoVWnVbgItzdu3Y6ComOy5abdkIrQsEB3bz6RXaFhgZh2QyqWL84Up+UXFGH9pm2YOG4UALB+ayvDsJXIhRoSsEr/msyVKDpWjMN5R7A/+yDWb9rm7l0h8lrSHyO+X7FKdf/40cPRN7k3evXojpjoSHQ2hogfaPQ+2DB4JSIiImpe9nq1njlzFgsXfSibPyLWiLQJvdy92UROSZvQC79vyJWVE3jp3+9gxLBUdOzYUXzNA/yu0RowbCVyAXs9VLXC1TNnz2JvVjb+b+debNiSgQqT2d27QNRmrNu0DeskP2h0NoZg7Kg0XDR4AAb27yv2fhU+5Cg/7Ch7xBIRERFR42nValUOivXdil9Uy113+wiWDyCvYTD44LrbR2DhYytl07/+/ifceN3VqrCVvVu9G8NWoiZw1HNVer3CZMb6Tduw8te1yC8ocvemE9GfKkxmfL9ildgLtnt8LCZfOgHjR4+Q9Xpl6EpERETUPJTfoaRB6/nz5/G/ZT/K5h+UFo/IWKO7N5uoQSJjjRiUFo/dGQXitMVLv8N1V09Dhw4dHJ5tR96DYStRA2mVCtALWM+cPYtVv21oUsAaGuSPwb27wRjoh74JXQAAPaJC0NG/vbsPBZHHOVNzHnnF9T3FDxw5CVN1LXYdPI5TVTVOryO/oAiL3v8Ui97/VAxeL7tknKzHq/RDEEsMEBERETWOVq9WadBqtVqxadsO1XKXXT3Q3ZtO1CiXXT1QFrYCwIbNGZg4bpTDMSXIezBsJWoAeyUCpJes7Bws/+nXBtdeTU0Kx6gB0Yjq2gkJESGI7NLJ3btM5HUSo40AgPSh8bLpJSdP40ipGcUnTmPz3mPIzClzuC5p8Dp+9HBMvWIS+vftI+vpquz5yg9FRERERM6zF7jabDZ88sXXsvkHpcVzUCzyWqFhgarerR98ugQTxo4UX/MMWr0fw1YiJzgqEWCz2WCxWLB+0za89/EXTtdgTU0Kx+S07kjp0ZXBKlEzi+zSSXyfzRxbP5hCycnTyMo7gZUZ+Q7DV6HWa2djCO6YcwPGjR4OX19fVS9XKX5AIiIiItLmKGS1Wq3ILyiCyVwpW27ExN7u3nSiJhkxsbcsbK0wmZF3pBCJ3ePZu7WVYNhKZIdWT9amhKyhQf4YPyQWE4bEIjmhC3xZ0J3IrYQANn1oPCx1VmQfOYm1O4uwbmeRbumBCpMZ/3p9Ed79aLEsdAXkvVpZ05WIyP0KDuXAdPIEBqaNdPemEJEGR4Hr6nWbZPNHxBqR0DvM3Zvd5tTVWWE+dQYAEBLa0eMGJjtXa0F1Zf1nd2/o9ZzQOwwRsUaUFpnEab+sXo95t93I3q2tBMNWIg3OlAs4f/481m/OwPtOhKwzx/ZiwErk4XwNPuifGIb+iWGYP2OQGLwu23BIc35p6Hr7Lddj/JgRYugKyMsLMHQlInKPD158BgAYthJ5GL1ybNLA9fz581j56zrZcsPGJjbL9lRX1qDKXAM///rPct4Q2LUk86kzePH+HwAAD706VXZ8TpVXi9edOW7NEYzu33kUS97dCgB4+dPZ7j5cThk2NhHLF2eKt3/4eTXm3jIbPj4+sgyCoat3YthKpKBXMkD6jz/rQA6effkNuyFrj6gQXJ+ejFEDohHgx7cakTeRBq9zpw7A5r3H8MXqbHHwLakKkxkvLnwb7338BR5/8G4M6JesW1KAH5SIiFrO6arKpq+EiJqNo8A153CeapmBw+Ib+jCaztVakPtHGQ7uK8Ge7YWoNsvPaAoM8cfwCb2QOrI7g1cHhBAWAK67YwQGD0+wO783BqPNYeCweFnYCgDZBw9jQL9k9m5tBZgAEf1Jr2SA9HKqwoR3PvwM6zdn6K6nR1QI7rs2Ff0TeXoLUWsQ4OeL9KHxSB8aj3255XhtaaZu6Hrvw89g3OjhmH/bTehsDBFDV5YWICKpihPlAIBOQUHo4OcvTis8fBD+HTuhW1Q0Ond1/DniXG0NSgoLYDp5AgAQHhOLsMhoGAwGzXlPV1UBgLjuuro6VFackk2TOl1ViaP5eag5cxr+HTshpnsPdAoK1tyW01WVOFdbiw5+fuI8p6sqcXj/Phi7dEVkXLy4r8r1+wcEIL5XklP7e7qqCoWHD4r7GxrWTbVeYb9y9uyye8yV8x8vPoaaM6dh7NIVXSMidfdV2A7pvp6rrcGp8uOq7RHWXVtzFmVHi3SPBVFbpfe9y2azYV/WH7J5I2KNCAxu+nunurIGrz+2UhWwyuYx12D1d/uw+rt9uOXesUgeGO3uQwUA2LXtCJa8u9WpUNMdlry7Ff2GxKBDC3c26jckBg+9OtXdu98ggcH+qlICu/fuR/++fWTvA35n8E4MW4mg35tVqBdksVjw48+r8faHn+uugyErUevXPzEMnzxymd3Qdf2mbVi/aRvm33YTrrz8Evj6+sLHR14+hL9SE7Vtrz34DwDArNvmwdilq3iqu1RicgquuX2+ZuC3J2MLNv78I8qOFqnuCwwOwXXz7laFl9m7MvH1B28DAJ777xd488mHxOVn3TZPFsD+vPQLZKxZpbntAy4ejitm36jarp++/Ax7t28T73/ziYdQXSlvI9MmXoops2/E6apKfPTqC7LtDwwOweXXXo+UoWmaYXHGml+x4stPdbdp5t/uFJerrDglHmOtYy4tKZCzdze++/h91bYC9WHunPsfUe2r9Fg+8fZ/8fpD94rL3/fSv9HBzx91dXXI2pGBn5d+oblue88vUWun/O4lXFdetv3fLtlyrighoAxap92QiuRB0WId0urKGhTln8TBfSXYsrr+h52PX9/gMYFrYe4Jd2+CQ9989Dtm39mypVs6+Pl6ZQ9kZSmBLRn/hxuunaF6b/C7g/dh8Uhq87QCVunlVIUJDz/9km7Q2iMqBIvumYBPHrmMQStRGyGErovumYAeUSGa8yz64FM8/NSLOFVhkrUpyjrQRNR27dy8UQxaA4NDkJicIt6Xm52FN594CHV1dbJlTldV4usP3kbZ0SIEBodgwMXDMWX2TQgMrm+LqivN+ODFZ8SenFpWfPmZZlALAFk7MsSgNTwmFhOmzUTaxEvF+/du34av3luku+7TVVX46NUXUF1pRnhMrGyfMtasQsGhHLz5RH3QGx4TiwEXDxe3++sP3sbh/ftU6/z41X+JQauwzwMuHo7wmFhxm95+5lGcq60PTzr4+cnuByAuY+zSVbI9v+LzN15BdaVZc71lR4vw5hMP2T2Wm1et1AxTd6xfg68/eFs8DvVB803i8cjNzrJ7HIlaO0clBCwWCwqLjsmW6dEnvEmPKQ1aA0P8cc9zkzEyPQmhYYHigE+Bwf5IHhiNaTdchPSr+ovLfv3f31FXZ3W4/qY4V2tx+Bi5fxxv2oFvhu0W9OwbAQDYnVGAIwfLm7g212+fJ26H8jV9pPAoLBaL5uDc5F3Ys5XaNEejX2bs2Iknnn9Nd/nHb0rD+CGxHPSKqI3qnxiGDx+chHU7i/Dsp+ryIrv27sc1N92J5x57AMOGDka7du1kvVz5KzVR25abnaXq0VlwKAdL3n4D1ZVmVFeacXj/PiQNGCQu89OXn4nX733xdfF09LSJk5Czdzc+f+MVAMDqZUtxze0LNB83Y80qpE28FP2HDpOdLn+utkbssTng4uGy5SfNvAa/LvsKGWtWITc7C6VHCxERE6e7T3+9+wFxuytOlIs9Sz948RkEBodg1m3zxH2+YvaNYk/XQ1l7ZftbcCgHudlZAOqD1rueeVHc3rq6OuxYvwYrvvwUZUeLsHPzRqRNnIROQcG45vYF2JOxRdwf5bE4V1sjC3Cl6wXkPV7tHcu1y5dhwrSZSEzuJysPsGPjWgD1PVhvuf9hcf60iZNQerQQbz35MHKzs5Cx5lekTZzU0i89Io+gFSgJl6JjJar5u0U1rSf4l+9sFXu03rhgNCJjjXbnn3BlP+TnlOPwgVIMvDgOdRarGMoKdm07gh0b83D4QKk4LTDEHwMvjsPEqSmqsgfVlTX44Yv6noxTr08FAOz5vQC/b8iVnU6uLBOwZXUOCg6fEOdZ99MBZO+uD6OFXqRfvrMFADBiYm90jQjCtrWHUF5Sid0ZBbLaqHV1VmSsPYT9O4/Jtjsi1ojEPt1w+axBjSoDcOXsIVjy3laUFpnw2Vub8OjC6arj5Yy6OivW/rgf+/6vSHZMevaNQPekMEy4sp9qvUcOlmPrmoOy4yFd397thfhxyU5Z6YhBafEYPCIBvVMiNbdT2A7hNSBdLr5nV4xMd1z+xhGt13Rh0TEk9khgKQEvx7CV2iStU1aEkNVqtcJiseDdjxbjh5WrNZefmBqHf84eyoGviAi+Bh+kD43HqAHRePnLHViTWaia57HnXsG0Kybhjjk3oH379rLQlXVcido2ZcgX3ysJD7z6Jp6ceyMAYOuvP4vh47naGsT17A0ASBo4WFX3UxpSlhUftfu4k2Zeo1FDtQppEy/FmapKpM+8VnZfBz9/9B86TOz1Wna0SDNsBYCrbpkr25bOXcOQmJwihqbjpkyXncrfKSgY4VExKDtahKwdGZgy+0bxvh8WfwxAOxA1GAxImzgJhYcPYu/2bVi/4nsMHTdRswyB0uZVK3WfA+FYXn7t9fj6g7exd/s2zdIJwnZNmDpDNq3iRLnYczi+t/rLeERMHG576An4BQQgKMTocFuJWjutnq0Fil6tEbHGRgV3gnO1FjEwi4g1IqG34zMSDQYf3Hr/OPG6VF2dFUvf34bdGQWq5arNNdiy+iC2rD6IeY+myx7rXK1FXGb8lH5iOKm05N2t8O/YXixdUHD4hOyxSotM4nJCuCjcnzwoGlvXHNTetsoafPnOVll4qFznnu2FuO2BCQ7DaCU/f19cd/sILPyz9/DaH/cjfXr/Bq3DXj3dwwdKcfhAKfb9XxHm/nOCLMiuOHla3F9p2HqqvBpvPfur5vp2ZxRgd0YBBqXFqwLa6soavP/yWs3nRlhu/85juPX+cU16XRoMPqq6rfmFRejRPZ69Wb0ckyJqc5Rd8aUhqzAI1kNPvYgjBerT60KD/PHagrFIjDa6ezeIyMME+PniyVuG44ZJybjvrQ04VSX/ULf8p1+xNysbLz3zCDob60/31QpdGbgStR3hMbGaAZ40LBQCSqA+8EybOMluT0gh1NQrEwDU91rVGqCpc9cwWdCp1DUi0qn9iuneQzUtLCpa3JeEpD6q+5MGDsbe7dtQXWlGxYlysYassB89+vTVrW86cPgocdnKilNODS52oqQYQH1YqrdeIdgGgMP798kCYsG4KdM1j2N4TCzKjhZh7fJlSEzup6qh68yAYEStkVYvVuVtm82G0lL56fIR0SENfiyp/Tsv/AA1/oq+Ti+nF6Tt3V4ohnsj03vLerGWFJnwwStrUW2uwdvPr8Zz71+j2VN04WP1P/oMSovHZVcPRAc/X+z5vQBrftyPanMNdm09Ioats+8cicuuHogX7/8BAOwOkLXupwMoLTKJPVXjEi+UT/nhi0wxaE2/qj/GXp6MDn6+qKuz4mBWCT5+fQOqzTVY8t5W3Pvc5AYf58hYI0am98aW1Qex+rt9SB3ZvUG1VN9/ea0YjN5y71gk9glHBz9fnKu1IPePMnz8+gaUFpnwwxeZTtWFzdySL65v3qPpiE3sItbm3fN7AZYvzsTujAIMHpEgq8krDVrTr+ov7ofQM3l3RgEOHyjFiiU7Me2Gixp8nKQiokNkYWtxSZnm+4LfFbwLw1ZqU/T+mQu1FCtMZtx5zyOoMKlrb7E3KxE5IzHaiCVPXaHZyzW/oAi33/0Q3v33vxDa2QgfHx9YrVbxQxM/RBG1LeFRMbr3Dbh4OPZu36Z5X11dHcpLjqHsaBGK8nJxpqpSvK/smH7IKhg2Id3u/edqa1BSWADTyRPI2bPL4fqk9MLL2B6JyFhTfz0s0rlBZk5L9guoHxhMy8njZQ3aRgDise0UHKy7XmckDRysOX3qDbeI9Xg/ePEZJCanYMioMYjr2dupMJioNVP22NPq2Xq0RN7zMnmQ6waniogxNmn5c7UW/LhkJ4D6U9uVYVtkrBELHp8kBqP7dx7VDUaVPV9HpifhRFkVtqyu75l67dzhDe45WVpkwsj03qrtOlVeLQuIpb1ODQYfJA+Mxi33jhUDzSMHy53qAax0+axB2LO9ENXmGnzyxkanQ9sjB8vF0PG6O0bIws8Ofr5/1tJNFQPSy64e6DDI3bb2EID6wFS6L4HB/mIZgILDJ3DqeLXmdqRf1V92nAKD/TH7zpEIiwzG6u/2Ycvqg40uuyBIHhQt64V89FiJ7IcIAb8jeBemRtRm2KvParVasW//H7j/sec0l338pjSkD4139y4QkZcQermOSIlS1XKtMJlx7c3z8PoLT6B/vz6s40rUhnWNjHLqvnO1NWJP1IoT5Vj85mt2e646Etw5VPc+aZ3TxujRx3GPMWdO86/f71rx+t7t23TDZ6njxccaFGaWHS1yan/1At1OQUGa0+N7JWHCtJnYvu43VFeakZudJas9O27KdAwZNUazhzFRW6A38I/wPW1/9kHZdP+O7Zv0eCfKqsTrfv5Ni0GKCyrE3pKTrtI+TT40LFA8PXzHxjzdsFUrzIxL7Iotq+v3vyj3ZKMCz4vH9lRNKzh8wu79ANA75cIZDFvXHGzUY3fw88WsW4eJoe2ubUd0919qz/YLgWO/Ido/Rg4cFo/lizPF/XEUtgYG+6PaXINtaw9h+IReqhq6I9OTVLVXpdsx9vJkzfWmjuyO1d/VD+hoL0x3hvK1vXf/H+J16fuE3w28C8NWahMaG7SGBvnjnfsvQWSXTu7eBSLyQulD45HSoyvufPU3VVmBex95RgxcrVYrA1cikjlTfaGXjRDIna6qFAeaAoAJ02ai75CL4OcfAKA+RF324TuaoaR/R8efZdb+8C3WLl8GAOLAXdJT6Tt3DcNjt17vluMx4OLhur1IpbpFNaz3W3hMLMZcfqXD+YxdumpOtxeWTpg6AxOmzkDBoRzs2/E7snZkiAOfrfjyU6xf8T0eePVNp8NnotZGr6QAAJgrq2Tzhkc1rYxAx04dXLbdFSdPi9e7RgTpziecHq5VHxWo712qJb7nhfam4uRpJKBhgWdgiL9mvVVp4KxXj9Vg8MGgtHjNeq8NkTwwGj37RuDwgVIseXcr+g2Jcdj783RV/Q9sPftG6M4rDUuzdx9zGHLOuHEo3n5+NarN9bVgZ906DLHdu6hCV63tCAzxR3VlDRw5U13rcB57lK9tk7lSVWKD3wm8D8NWavX0glbhsu/AH3jgsedVy6UmhePJm9NgDGKPAyJqvMgunfDJI5fi6U8ykJkj7xl17yPP4LXnH0f/fvX1Cxm4ErUtQt1QLWcUp9AD9XVDBbc99ESD6n46E0Luz9wOoD5o1QoBT2tsU3NS9hrVqpnaWIHBIaiuNKPsaJFL16slvlcS4nslYcrsG1FxohzvPf+kGLoezTvM+q3UpmkNXNwcAwOFdnO+bqgj0nDNXmgX37Or3dBSWkvVlXomh2tOLy9pWBu+O6PAqbqoembfOQLP3PUtAOCbj34X19VZpyPT4ez6z8mBwX4uOxYJvcPEHsbV5hp8/PoGAPWDpPW/KFazt6uwHdXmGrEUhD0Fh0+oese6CgfJ8l6NHzaNyAs0JWh9Zd4YBq1E5BLGIH+8Mm8MUpPUH37ve/RZ7Nv/h9guaQ0YQUStU1nxUd37tHqn7ty8UbyuFdDV1dUh748DjdqW01WVYmmCi8dfotnb8kRpSYseH2mvUWdKCDSEtNxBxYnyFtunzl3DcNczL4q3f1j8cYs9NpG7OSod0JykvQelp9M3hrPLdwy8EBo600OypQSGtMx33MBgf0y7IRVAfXBb8mcd1ODOAZrzC6UZHOnZN6JB23Hvc5Mx79F0WU/i0iITVn+3D8/c9S2+fEdet9vZ7WhOejVbpX/Js7FnK7VajQ1aZ47thfkzBsG3gYXIiYjs8TX44JV5Y7Do291YtuGQ7L77Hn2WPVyJ2qCyo0WoOFGuqjF6rvbCF70J02aK16U9Pevq6lSB6I71a1BdabY7j572HeyfYltXVycLBovycpu9RyggHyhM61gBQM7e3TiUtRf9hw7T7SUqrXsL1A9sJaxXr85rxYlyrF62FAOHj0LPfv2dPpY5e3djz7bNOF1VhVvuf1h1f6egYCQmp4g1XInaIr2A1WazwWKxuPzxQkI7itedOf1cqqTIJDvtXjqgUV2dVXcAq8LcC6GsvR6wLc3ZMDFCp9RAQ6RN6IXfN+SitMiED15Zi0cXTtedVyg74Igz8ygl9A5DQu8wTLluCI4XVyIrs0isubo7owAjJvYW69NKt+PlT2c3+Rg0hvT/t/Be4XcB78I0iVo1vaC1osKE5199UzX/xNQ43D1rCINWImoWvgYf3D1rCCamxqnue+6V/+BUhUm3hysRtU6vPfgPrP3hW5yrrUFdXR32ZGzB6w/dK97fd8iF0aSHTUgXr2ftuDD4XsGhHLz55ENY8eWnCI+JFaeXlxxzejs6+PljwMXDAQDb1/0mBr7namuw9odv8cr9d+F0ZaW4funjN6f0mdeK1997/klkrPlVFkbn7N2Nz994BRlrViE3e7/uejavWim7nTI0DYHB9T3dPn/jFaz94VtZmQThdP+927dh668/N2ibu0VFY+/2bcjNzhKfW6mCQzli0Dr1hlta5DgSeQp7vfWa8zOPUIsUqA/XTpVXO7Xcrm1HsPCxlXj9sZXiMtIBjcynzuguK9T+9BTJgy6Uk6mrs+rOV3qs/ke7iOim1ckF6o/7dbePAFAf8masPaQ7r1A+QHh8LdLtDosMbtT2RMYakT69P+Y9euF/6ref7VBtBwCnXyeuZq8XOHkH9mylVklZ+0c6GNb58+dx532PwmSS16xJTQrHozcOc/emE1Eb8OiNw2CqrpXVcK0wmXHnPY9g8QdvoEOHDuKgWcKv2PxFm6j1SZt4KbJ2ZGDt8mVYu3wZwmNixVP5gfperRExF36cienRU6w1+vUHb+PnpV8AgNibdcK0mRh7xTS8cv9dqK40460nH0Z4TCzuevpFp7Zn2IR07N2+DdWVZjwz71ZV78vbHnoCAPDBi8+gutKMx269HlNm34S0iZOa7Rh17hqGWbfNw9cfvC0OLLXiy09lPV4BIDE5BWOvmCZbVjq4l3CMB1w8HNfcvgAGgwHXzbsbS95+A9WVZvH+xOQUlB0rEo9pYHAIrrl9foMGsZJus3S9nYKCkPfHAXHd4TGxrNdKXqUxQY8zn12U622uQeMuu3qg2CP1kzc2YsHjk+wO2lRSZMKSd7eKt4XesbHdu4jTsncf06zXWVdnFR9LCHndTVortSj3pNiTU6q6sgalf57u35gwU0tkrBEj03tjy+qDWL44U1ZeQUqocVtaZEJ1ZY1mb+Ci3JPi9ZjuoXYft7qyBlXmGphOnUbyQHXd8oTeYUi/qj9Wf7cPpUUmnKu1oIOfr2y/y4rNCA1T1/utq7OiKPckouI7Oxz4qzEMBgODVS/H7nvU6kh/BZL2DhN6i73yn/c0g9ZX5o1hj1YiahFCSQFlDdcKkxkvv/Guqu1ijSai1im2RyJuf/RpJCanAIAYtAYGh2DK7JswYeoM2fwGgwG3P/q0WFpAGGQpMTlFnN9gMOCqW+aKvTal4a0jMT164q93PyBujxC0Drh4OBY8/S9xoKe0iZeKyxQePtjsx2lg2kjc99K/Zb12haBV2Pcb7/mnKqDp3DUMU2bfpLve+F5JuOuZF8UevcI+V1eaER4TiwnTZuKuZ15Ep6CGBw4D00Zi1m3zxHXnZmeJQbaw7jn3P9Lsx47IWdIzavQuzs6nN39DtqU5hIYFIv2q/gDqa3a++MAPYh1RqerKGmxZnYMPXlnTDcOuAACAAElEQVQLoL7G6dx/ThDLBQQG+4sB6pof92PXtiOqnqJrf7zQ0/6yqwe6ZPulod66nxpenzuhd5hYr/WztzbhyEF5veq6Oit++CJTvD328mTXHHgAl88aJD72j0t2as4zdEyieP39l9dqbt+vf576Hxjij94pkXYfsyj/JBY+thIfv75BtS5hfdv+7Gk7KC1ePL7S/d70S47ma2Ttj/vx9vOr8djcr9zW+5U8G3u2UqviqE7r2o1bsHHL77JlekSFMGglohYnBK5/e+lX5BVfOF1qw+YMDEsdhInjRgGor98qtG2s30rU+nTuGoZb7n8YdXV1qKw4hQ5+fnbDvc5dwzBh6gyMvWIaKitOoVNQkKwWKQAkDRiEhxa+rVruuf9+YXdbDAYDkgYMQtKAQThXW4PTVVWatUynzL4RU2bfKJt2ze0LcM3tC3TXPTBtpN0ar47u79w1DHc9/aJ4nAAguHOowx5waRMn2e152ykoWNx2YaAsrWPakG1Vzies+3jxMXRP6mN33UTNwRVBp7PT9T6jCJ9hHG1Lc/+wPOHKfigvqcTujAJUm2uw8LH6EiNCeFp6zCz27BTc9sAEVS9LoZdstbkGS97dih+X7MSsW4eh5sx5ZO8+JuvVqtUzsjECg/0RGOKPanN979Mtq3NQcPgEpl6f6nRN2CuvG4Il725FtbkGbz+/GhGxRsy4cSgqTp7Gjo15Yq3S9Kv6u7THZgc/X8y6dRg+fn2Dbs3YDn6+mHZDKpYvzkRpkQlvP78ag9LiMWJib1ScPI11Px0Qn5srrxuiWytXkDwwWuxRK6xr8IgEGEM7ofSoCet+OiBuizQQl27H4QOlWPjYSkTEGnH5rIGoOXNedpyuu2OEy55fal0YtlKro+wNJgStefkFePU/76vm//dd4xi0EpFb+Bp88O+7xmHqw8tl019+410kxMcisXs82rVrJ5YUAJw7HY+IvI/BYNAMNl01f0N18PP3yFCwOfe7OY9n565hzbp+artaMkjVmia9LQSq0uvSzy0NOVMnJDgI5soq8bbe6dwNZTD4YPadIzF4RAK+/u/vYtgmhKNSEbFGXHf7CNngWILQsEDc89xkfPDKWlSba1BtrsHHr2+QzTMyvTemXDekydssNfHKfli+uL73qfC3IT1nBw9PQOcunfD286sBQAw1pa67Y0SDBhBzVvLAaIeDYI1MT0LHQD+xfMPujALZcxMY4o9Ztw7TLAugZcp1Q5D7x3GUFplU6xKkX9Vf9doamZ6E0G6B4nNaWmRSPb/pV/V3yXEqK5bXqDWGuKZ8A7kXw1ZqNbROXREC1/Pnz+PRZ19RLfPSnaNhDPK8LxJE1HYYg/zx0p2j8eA7m2TTH33mZSz+4A20a9dO1rtVwNCViIiodfOkINXePML3rXbt2uHMmfoBo86ePQuLxYLa2lr4+/sjISFBtryjfevbpxe2bb9wunnNmfMuPbbJA6Px6MLpMJ86g7JiM47mn0J5SSXCIoPRNTwInbt00qxpKhUZa8RDr0xFcUEFjhWcQsHhE+LyvfpFaPY2DQz2x3V31A8YFd+zq+Z6pfP0GxIju29kehJ69AlH6VGTbH4A4jLS2qxaEnqH4bn3r0HuH2U4dbwaBYdPIL5nV4R2C0Rs9y4Ot1t5v950LbPvHIFD+0vtzjN4eAJ69YtAUf5J8XkRti+xT7hmj9v4nl3F7ZAyGHxw73OTUV1ZgxOlVTh0oFR8nlNSY9EtKli3h2zywGjN49SjT7jd5RpK+dru35e1vFsDhq3UKigHxFKWEPhp1VqYzPI6rXMmp2B4vyh3bzoREYb3i8KcySn4aOWFgWgqTGb8+MtvmH7FpaqeIgAHzCIiIvJ2zoap7gxSAeDcuXMAgJqaGlitVpw7dw61tbWwWCxiuGqPn58frFarw8eTioroJrudvfuYy3tbGgw+CA0LRGhYoNM9JZU6+PkioXcYEnqHaQ6UpTW/o/1wNE9krFGzt21Djk8HP19xn5u63Q153MBgf6fmDwz2R/LAaKefF+F5tLe+wGB/hwF6U49TY2TvPia7HR0V0SyPQy2LYSu1Glohq81mQ4XJhPc+ltco6xEVghsudV3BbyKiprrh0mSs310kq9/67n8XY+zINIR2NgJQ128lIiIiz+QtQarFYoHVasX58+dx/vx51NXV4cyZM04Hqc4wm826j6935k54N3koVnrMDKLWSPnajgzv1sg1kSdh2EpeT29kTCFwfe8j9WAQj9+UxjqtRORRfA0+ePymNNzyr1Wy6e/+93M8dO981cASHCyLyHvNum0eACCuZ293bwoRNUJznd7v6iDVarXCYrGoglQAqKysREuyWq3iZxbpWTpa2rVrh7gY+RmIpUUm1NVZXXbqNpEnqKuzqgZki49tXC9r8iwMW8mrKX8RVdZqzTqQg41bt8uWmTm2FxKjje7edCIilcRoI2aO7YVlGw6J0zZs+R1TLr8E/fv2kQWsyr9E5D2cGcmeiFpeQ0JUe/M3NmBtTJBaV1eH2tpaAEBVVf2AUqdPn0ZdXZ1bjmFAQAD8/f1RUVEhm37u3Dm0b99eVhrJnujIcNW048WVmqfPE3mr48XqHz1ioiMB8Cw2b8ewlbye3qBYVqsVb//3c9m8oUH+mDt1gLs3mYhI19ypA7BuZxFOVdWI0xa9/yneWfiC2CtEOfIvERGRK+mFYefPnxcDM2/jit6ozs7n7HS9oNVms4l1UrUGnBLC1Zbm6+uLoKAgAIDRaAQAdOpUPxhThw4dZK+LzMxM2bJ1dXXw9fWVfY5Rfp6RLm8wGBAbHYmiYyXitLw/yhi2UquS90eZ7HZ8bLT4PgHUn/O9se1tqxi2ktfSGhRLGrSWlB5HQeFR2TLzZwxCgB9f9kTkuQL8fDF/xiA8+2mGOO1I4VGUlJYhKjJC9zQ8fvgiIqKmshcGnjp1Cu3atUNoaKjH1Q93dZBqb57GBKnK20JYWltbi7q6Oo8IUgGgc+fOAC4EqQEBAfDx8YGvry8MBkOT1i0E9QIhXNWq2Spchg4ZKAtbf9+Q22yDFBG5w+8bcmW304YOVs2j/CGCvANTJ/Jqej1abTYbPvvfMtm8oUH+GD8k1t2bTETk0PghsVj07W5Z79ZPvvwGD90zD+3atYPVauVgWURE5DL2elgC9aFgQUEBOnfuLAZy0qCsuf8PNbWnqTPzuGpAqvPnz8NqtYpB6rlz58Trp0+fbtbjZI/wvAUGBsLX1xcdOnRA+/bt4ePjIwtBXfl40lIC586dQ8eOHWVlkAR6r5+U5F749sdfxNulRSZUV9YgMNjfbceRyFWqK2tU9Vr79+sDQDtg1evtSp6JYSt5Ja1erdJLSelxbFLUav3rZX05KBYReQVfgw/+ellfvPH1TnHaxi2/4+bZVyMqMkLW3rF3a8M4+iLOY0hEbY1WuKocD+HQoUOy28qgtSmhqyt7pbrytH6tY2OxWMRaqefOnXPrgFNSQUFB8PX1hb+/P/z9/WEwGODvXx9I+vn5uW27pOrq6sQfi5Wkrx/ppWePBNW8e34vYO9WahX2/F6gmpbUs4esHeXnUu/FsJW8llbIKvRqXbtxi2r+yWnd3b3JREROm5zWXRa2AsCaDVtw/TVXiV9shQvAD2OO2PuiLT12rIfrftkHD8luJ8TzrBSi5qIXrkqnFRYWwmKxAACqq6tlgatyoMaGDjCltS0Nvd9VQaoQolqtVtTU1J9ZIgw45c4gVRhwqn379ujUqZNHBqlaAgMDZT1bz549C6PRqApWpa8l5f9dg8GASRNG49e1m8RpLCVArYWyhMDl6eNgMBhU7wd+HvVODFvJqyk/GAoflH5evV4235zJKazVSkReJcDPF3Mmp+CjlVnitJ9WrcXsWdNVX4Y98UOYM1+4W2K7tb5cW+rqUH7iJAAgJDgIHQMCAECc3q5dO4R17QLfP+vTeeLxbUviY6PdvQlErZJe0Cr9H1NRUQGTySQuI4SRjtpF5f2uPr2/oUGqzWbD+fPnAVwYcEoIUk+fPo26urqWOOQqwoBTQpAK6A845W18fdXfvZQ/Ems9t8rereNGDZOFraVFJhw5WI6E3mHu3kWiRjtysFxVQuCScaNYOqAVYfpEXsfRB8P8giKYzPJfny8dluDuzSYiarBLhyXIwlaTuRL5BYVI7J6gOpVT2bvIXfS+8JrMZpytqZUFmU3pAWVvPx196S4vP4Hr5iwAADxy/124ZNwoAJBNX/LxIkSGd2vRmoRUr6T0uO59HJWXyLX0Pk/bbDbU1tbi6NGjqmXq6upg+LMN1+rdqvcYDbnPUTsuZbVaVUGqJww4JQSpQPMMOOXpAv78IVNw+vRp8bUl1J3XOl1aGbbGxUQjJDgI5soqcd6taw4ybCWvtnXNQdltY0gwEuJiVK9/KX7m8S4MW8kr6fVotdls2LY9UzZvaJA/Irt0cvcmExE1WGSXTggN8pcNlLX190x0j4+TlRLwtKBV+UX4zNmzmH7d38Tbf79jDmZMvVy83Vw1/rR+nAPqv5hLZnI4XdhGV4WujQ2Z3f38NifpMfnp17Wy+y5OHeTuzdP94kPkCRrz2lSeISH9LC1cz8vL01z23Llz8PPzU9UMd7acQEN7qp47d04Mf71hwClXBqm5+QVY8ctvWLdxKwYPTIExJBhzb7keAf6eP0CUvdqsQuAqpQyZpJfrrp6Kdz/6Qpx3d0YBLrt6IELDAt29m0QNdqq8Grsz5PVab7xupmbQypIC3othK3kVRwNjWa1W/PLbBtky00f3dPdmExE12vTRPWW9W1f+uk5WSsATBspyNLjK5m07ZPN/vvRbXHXlZQ7XO2fefcg7Uoj1K79W3ffbuk147pX/4LEH/o5Lxo+2u13Kv127hOLz998AAISEBIkhq/S7vhA6AOqgtTGhq6Nw4dmX/o01G7aIPWq11t+UnsBSnvJBXWt/SsvKYTLJz05JiIvR3P7m2g9Ho2RrPe5jt17fMgeNyA5ne0Fp1SpVBq5Hjx7VPbX+3LlzaN++vSwE0BphXo90HovFgrq6OtWAU+4OUvfn5OKXNRvxz7tvR1xMtBik+vj4oH379s3ymJ988RU++nwpJo4bhUcf+DuuvmEuTlWYxPvXrN8MAFi3cSu+X/Jftx0bZ2mVETh//rwYREs/r2jVbpVe0i4aJAtbAeCXb/Zg9p0j3b2bRA32yzd7VNNGDkvVfO2T92LYSl5HK2QVLqXHy1UlBEYPZK03IvJeowdGq0oJlJaVIyoyXLO+njs/mGkNrGKz2fC/b5YDAIYMTMHOPVmoqDBh3/5spPTtI86j3O66ujrkHSkEIO9xKsx3IOfPkbHh+DRUrRq34d26ynq0tmvXTvY4NhtUy+iFrg09Rlq312zYIj5wY+rxNjb4ddfrRe+5UQ5w2dkYgu7xsW7ZTuUXHX75IU+l9TrVY69sgNVqhdVqhdlsFuuZaqmtrYW/vz98fHxUva+klANOCUEq4N4Bp4KCguDr6wt/f3/4+/trDji1YvVGHD1WAoPBF6GhoY16nH37s5G5ex++X7FKFppOHDcKd98xB0ZjiGz+gqJjAIBde7Kw6P1PcKrChNDORjx4zzwUl5bhx59/Q15+AU79+T+0f79ktx1DZ2j17lX+SKwcoFIvbPX19cVVUy7FdytWifPvzijA+Cn9EBlrdPeuEjmtpMik6tV6zfQr4Ovr67BXK3kXhq3k1ZQfEouOFqvmSYw2unsziYgaTasNKzx6DJER3WRBlbvohWbCJe9IoRia3jHnBvzr9UXILyjCdytWoV9ykmo9grLj5Zr3Cdd3792vely97VJuk3K6EJxabRfCViF0ED7klpYdR8SfNVylnPkArFdr8EI920rZNK15lI8jbI/e8XN28JqWCunt1VsUppnNlfjiq+9l0ydPGm93+1ti27VCVyJP05Av5FpluIQ2r7a2FiUlJXaXF3qiCsueO3cOAFBdXQ0AYs9UdwgICIC/v7/LBpwqPV7eqFBT6KWqZc36zVizfjMWvfacbN0pfZOwZv1mnKowYdnylZjz12txw19mioM1TrviUky44hoAwGtvfYBP3nndLcdYi/R1IPRI1uqZfP78eTHYVp6loFezVbhMnjROFrYCwJL3tuLupy+DweADIk9XV2fFkve2qqZfOfkSu2U0AH728EYMW8lraJUQkF632Ww4nHtEtszE1Dh3b3abMGaB+sNkaJA/BveuDwJmjOmJ/oksYk/UWBNT47Ams1C8fSg3HxenDlK1gy1dSsBeDylh+spV9fU3u8fHIj4uBtfOuBIvLnwb6zZuxT3z/4YAf3/ZtpaWleOG2/4u3/8r/wIAGD96OABg3aZt4n0vvPomXnj1TQDAbz8sAQBcMvU6AMDD987HkEH98ePPq7Fv/x/YtXc/fvn2c5SWHcfNd94HAHjg7jvE9dqkPVthQ25+Af637AeslzzekEH9cfPsq1VfvoWyBgBUZQ+E4/Hb+s14/s951vz4P9m+CWbfelf9vo4ZgUfvv0s8NhaLBV989R02bd0uhtfC9gzo1wfXX3OVrBeR8Jh1dXVYt2kb3vnwM1SYzOL9qYP6Y2TaUEwYOwKdjcZmDV0dBfKC9z7+QrXsxLEj7facczW906F5Wh95MnsDqSh7DtoLWuvq6lBYWKhaf0BAAM6ePSvePnHiBE6cONHi+2lvwKnGBKlaSkqPIyv7D+zaU39GyRdffY9F73+KUxUmLP3kHURGdHO4jtXrNopB68Rxo3DDtTOQ2D0eQH1v18efexWnKkx4/LlXseTjRWL91eCgC/VHQzsbcfP118j332DAzGmTsWz5SuTlF8BkMqt6xzYXYZAxoYdyTU2NeJG+NhxR9mh1ZoAs4RLg7487bpmNdz/+UlymtMiEjLWHMDI9yeltIHKXjLWHUFpkkk27a+5N4mdhvc8aDFy9E8NW8jr2yggIp5UKUnp0dffmtlmnqmrEcGhNZiFCg/zxySOXwhjk+QX9iTxNSo+usrB1/x+HdNtBd9drlV7Onj2L73+q74Vyefo42Gw2pA0dLM7/y+r1mD7lUtk6ZINUKR+rAdsi3F70waeysFQIFS7MY5XUbL2w/P4DOXj5jXdVj7Fz9z7s3L0Pb7z0NPr363MhmHOwHYAizHXQK1l6HE3mSvzz8eeRX1Ckuz17s7Lx9KP3iV/a27Vrh7M1NfjrbXfLQtbOxhBUmMzI3L0Pmbv3YcUvv+H9N19G+z9r67n6NaQXyCvv+2HlamzY8rts2SsvvwTh3dQ/1LXUa9xeL5NvF78nhlPSwEpv/4icpdfLTzhl38fHBwaDQfbXmTp/em210CZarVaUlJSoeqR269YNnTp1Qn5+frPvuzDglDJIddWAU5a6OpSXn0RW9h84VlyKgqJjsiAUALZu/z+88faFeqh5+QUNfpxF738KAEgdPACPPvB3sWcqAPTvl4xnH7sf8+97rD7AXfaDGKr2SLiwHYMHpmiue8KYEVi2fCUA4GSFySVh6/nz52G1WnH+/HmcO3dODFEtFovdchKNfSwAdnvs2Wt7h100CCt+XYejxy70vl6+OBPJg6I5WBZ5tFPl1Vi+WD6Qd1xMFEbo1GrlD7zej2EreRWtL7DSy979f8ju7x1jdPcmtylzJqfg0mEJ4u2svBPYmlWMNZmFOFVVg3+8uR4fPjgJvjzVh6hBlG3Z3qxszZ6BQMv3ahWua122/n7hQ+Xo4RfDarWiQ4cOGDsqDRs2Z2DJN8sxdXK6OI9QS3XVd4uxdsMWvPTvdwAAv3z7ueyxb75+Fm6+414AwIP3zMOEMSMAqIPapd/+iPyCInSPj0X/fn0wZGDKn4GYZD+sNnE5q/XCHULQOnZUGm654Rq0a9cOT73wuhh4PvWv1/HN5+/pHg/N65JtE4K5Vd8txtqNW/HSwrcBAJ+8+zoiuoXJasgu+uBT8XEHD+iHpx6+p76nWU0NFi76EOs3bcPOPVl47c338ej9d4mPu3vvfjFoffje+Rg3ejgMBgOsViuycw7j7gefRN6RQvzw06+YMfVy8Tlo6mvI0f9q5SUrOwfvfrRYtZ6bZ8/yiN4c0m3w8fERR9GWjqYt7Q2r994kagi9L93S0FX6V1hGi7N1WoUyAIKAgADExMS4ZKAqIUgNDAyEr6+vSwacKik9Ll531OPUUleHBx57Dpm79sqmr1m/GRPHjcLcm69HZEQ3jLj4IgQHBeKLr75HXn4BUgcPwCvPPSYLTO3J/bOmKgBcc9UUzeX690tGj+7xyMsvwPrNGWLY2jEgQJwnpa92T82uXbqI1/OOFMiCYi3KINVisYjPc0VFRaOOe1OcP3/eYY89e6GTr68v7rhlNh577jXZej95YyPLCZDHqquz4pM3NqqmL5h7k6pWK0sItB4MW8nr6H1ZUw6MBQCx3YLcvbltSnRYICK7dBJvR3bphPSh9R8C12QWIq/YjHLTWdk8+3LL8e3Gw4gPD8bNk/sBAHKPmbBpzzGs310EU1UtBvfuhruvHqzZK/ZsrQUrM/KRlXcCuw4eR/eoECREBOPivhEY3i/K7vaWnDyNVb8fQUFZJdZkFmJiahwmXRyP5LhQuz1wLXVW7PijFL9uL0B+SX2Q0T0yBCNSojB+SKzdMPlsrQWHj1YgM+c41u8uQucgf4waEI1BPcMQHxFsd1lTVQ2yC09h+4FS7Dp0XHzMlB5dZcfU0fYKx9TR9i7bcAhZeSfEMhCWOiuyj5zE2p1FWLbhEHpEhaB7ZAievGW4619MJKPVlpnMlQjtbHRrz1a99lgIEpd+twJA/WjyQUGBYg3UKZdOwIbNGagwmbHvwB9I+bN2q15IqQxRZT1EFT1VpfILijB1cjpuv+V6Wdggnd/65+127drJarYCwH/fegUR4WHiB953Fr6AdZu24aWFb6PCZMbe/dno37ePbp1VVSBtldeE1ZoOyfYA9bVrhZ65Y0el4YG/3y4Gpn4dOuChe+ZhWOogvPTvd7B+0zbcM+9WsR6e0Ptp8IB+GP9nIC28Rvol98YLTz6I1es2obKqChaLBb6+vqpT6B29nhz1ztV7nQivkbUbtmj2IH7yoX/A399Ptg1ag0Y0F2U5AWXYJQ1ahWBcr+euo+NEJHB0SrVe0Kr1pVz5/tALWW02G2pra1FWVqbanl69eqFdu3YOw1BnBpxyFZPJjO9/WqUadCq0sxHz596E9PFjVMsog1bh1P7d+/bj8yXLxBqqQpmAyIhu2Pp7JvLyC5C5a6/TQStQH4AKEuJidecbNyoNefkFyMsvwNmaGgT4+8NoDBbvl5YUkJLOY7XWP3dWq1U8ld9kqj8mVX+26+5gMBjg6+sLHx8f+Pn51ZcACAhAu3bt4C85XVr6+gUutKXKtlZ68fHxQVxMFNLHjcTq9RcGVCwtMmHFkp2YdsNFbtlnIntWLNmpKh9w2cQxSIiLgY+Pj3hh4Nq6MGwlr2Lvi0tNTY1qfp6y7hlumJQsngKdlXdCFgx29G8v3nfz5H544+udWLZBXg5iTWYh5k4dAKNivftyy/H4h1txqurCc38qpwaZOWVYtuEQJqbG4dEbh2mGics2HMIbX+9UPY6wLXfPGoKZY3upljNV1eDpTzKQmSP/UpJXbMaazEJ8sTob/75rnG4wfOl9yxRTzeK6Zo7thbtnDdE8htv2F+PBdzZpPiYAfPzwpZoDKeltr7Cvi77djXfuv0QzrM3KO4E1mYUwBvohOaELrn7sR9mxzis2I6/YzLC1BWi9nmr+rJ8GqNvGlhzsSLit1VvxyJ+9MW+67mpZAJWc1AtGYzBMpkqs+Pk39E3qZfcxpOGospamox6EkyaMVodg0j6mOj0Rp1w2Ed3CuqjWndw7Uby+c/c+9OvTu/542wuHxcdVHzOtfZXel7Fjl3jf7TfPlvWeFAwa0E+8viXj/8RgtV9yb+zaux+79u7Hvv1/oF9yb/GDu81mw9AhA3Fx6iDZNCmtaQ15TWjtp7B/Z8+exUeLv8IPK1er1jV71jQMHTJQtS2OrruavfqByqDVUa9Whq3kDGdOp9YLWvVOO1W+F7XqtGoNiNWzZ08xZPXx8RHLCQAXBpxydZDqiHLQqdDORjFwPVVhwrMvvYHKqmrMnDpZtpwQtIZ2NuLBe+Zh+MWpAIDE7vEY1L8fbrmz/kyJr777EXffeSsAID42uln3JanXhf8lJlMlAiL8xTIwAHAg+xDGjBgGQD7glHAaPgCsXPUbuhpb9tR5oXcpUD/gGADxdaIsayENkYSyF/bqUQrXpUGr3ntg9qxpyD6UJysnsGX1QXQM9EP69P4tekyI7Fn9/T5sWX1QNi02Jgp//csM3fadIWvrwLCVvIIzg2OR5+oS7O/Ufcs2HMKyDYcQGuSPv17WF71jjOjo3x55xWYYA+Uf6M/WWjB/4Vrx9pzJKbh2YhIOH63Aa0szxSBS2mNWsC+3XAxaQ4P8MX/GIIwfEouC0kqs2JonBrETh8SqQq43vtmFzJwyhAb5Y/ronpg+OhF+HXyxMiMfn/9yAHnFZvzjzfX45JHLVPu6dE0OgPpQ9ZoJSYjs0gmWOisKSitxy79WYdmGQ5gwJFZzMLH3ltf3xnj8pjQM7RMOY5C/2Kv3ja934r63NmDJU1cgwM9Xc3uB+kGW5k4dAGOgH97/YS+WbTiEU1U1uPPV3/D9/7N373FR1OsfwD+wKKDALgiIInIVBLxT3u+GlZVZlmZ2sTzHMuvY3SzLY3e7+zul5TmZleWxstTMUo73u0YqoihyRxREYRdQFmWX3x84w8zsd2ZnYWF34Xm/XrzYnZ2ZnR2GvXz2mef7zp0W9zm0V1dsTS3AkTMX8NnPR1FWaURUVy2mJ8cjqqsWV4zXUFx2xUFHFWFV0bXkIFnWqlo3/v4/AIBO64d+fRL4AJILpSYkj8H3P67Hzr0H8dRjj/BVjPz6IX9avvB0/zrUidYtpNP5ITysm3LFofC6YHqP6EhRhQ330ynAn5+n8Ow5UdAmt738/pJUtrLCX7NZXHl78lTDl09Xqo24Um1kBDINl09lZmPU8MFwc3PD3XfcilVrfgEAPPfKG5g980EMuqEfQjoHyx4bcgNEqTkeWI9ZeqxUV1fjj//txLIvv2Wup2+veEy7p+H5qCWrWaX7QHidC1el88kFrfS+hNiDmlYC0qooFqXAtbS0lB9FnhMcHAyttqEXaLt27RAWJl+l2RJqTSbRoFNzH38UOp0WtSYTMk5l8oNOLVn6pShs1esNfEXrnFkP80ErJzoyHJ99+CYCO3UStSII7RoiWoe9B6LqGtK5Yf0GPWqv1eDatWsI7dIZRedLkJOXh/T0dOayvj4dUVnV9NYOUqwglQtIud/CeYWXlXoMK1XtSY9baWsWVnUrt51Pz34Ezy94W/QYUn45jm6RAYjv27xhOSFqZBwrQsovxy2mv/DU3/k2KtL/DwpcWw8KW4nLsFZJdalML7o9gKpanca63dn85aiu4jerwjBzyY9/MQfSYlVscsElAGz+cDIfMvaODsLKl2/Byk0nsGJTOlZsSsfUcXH87bUmMz5cU99HMimuM95/YiRf+RodqsOcu/th+1+FKLteEfrxU6P5+zl/6TJfSTrvgRtFbQomj+qBfjFBeOSdzcg5Z0B2kV603dU1tVixqf5NMxe0AoCHxh3RoTp89sxYZJ7VM/ff8exS5JwzIMDXi2/LAADenh6YPKoHKi9fRWiQD/RVNaKwVbi9nz0zVhTizr13AMYOCMOcj7ehrNKI49mlFiEv97fiKlil+6t+fzfvsUMaBPh6iSqLL10qR5fOwS3et9VaBWN9oGbkBzvqk9gTpZfKLE5Lj4mK4NexZdsu3HHrTZL1Wr9Pbj65gYj6JMSzQ1aFL/A4QZ0CrO5XYVCr2PaAUdkqbiMgWLZO3BYh/2wRf/lvT71o9e+jN1Twj6l9+3aYPmUSvvthHQBg2Zff8iHnnbeNx63JYxAV0V1xfdbCG7nLXHuf6mojrly5gpz8QmzZuhNH0k7I3tf999yJe++6jb9fax/mmxurylAYunLHAw2MRZqDtYGyhEGWtcpW6QBuXFVrZWUlDAaD6H49PDzQrVs3Rz98C9t3NZwyzgWtAOCh0aB3Yjwm3X4zH8Zm5zb0Mj185JjVdfdOjLeY5tOx4YyfamONxdlVcoSDXJ3MOA3P9h7MAaeELRDOFhYiwL/+HoKDOqHofAmycuQH5oqJCseRtJOK87BwFajt2rVjhvUcpbMHlPqtyoWprC8FWGGSXMsWVlsBNzc3BAd2wsvPPYG3P1wq2savPtqJJ15JRkSsZfECIS0lL7MUX32002L6wnlz0Tk4yOJ/gULW1ofCVuKSWB9gLly8JLrePzZY7epIM6muqcXRrAt8yBjg64WugcqnOy17/iZV7R/W7c4CUB+YSqs5AWBE31D+frPOlvNBYqm+Gjnn6j9YTBgcadFiwEPjjtX/vA36qhqLdabnXOQv94uxPL6iQ3V8ILZxX46oJUA7D3f+tuUb0ixOve8dHcSsaBUqqzRi/4lzFr1opZW7nH3p5/jLMd38LW4X3t/Pu7Is7r+DV0OPtqiuWouglbSs/rHBfHgOAKWS57yWDnaUzjLYsr1hEIBd+w5h175Diuv64ZeNuO3msZKAwGxxX3zVLqMNgMxWWg/BZMLWAH+t7HojundDXsFZUX9VpTY3ciEv/9jADn8BIL/grK1/GVGl732TJ+LW5NHYtGU7NqVsh15f3998/W9bsP63LYgMD8Mni/8pGpjFWlVrfYhajSvVRuTkFQB1dcjIzILeUAGDoQJ/HUuHrZ598m8YPdyyJYmjPniwBgrjLgsrr6QDY3HThb8JaQy1QZZS0Mph9Wm9du0aiouLLebt2bOnU37ADwkOwqvz5gIAs8o0qV9vPmyVGzgqJFh9+Cbst3rx0iW+6tXagFMmU8Nr18E/U6Hz68Bcv7HmKnN656D6AbDUVK4K5+EG2dFoNBbVqBpBz1lbg1Q1l5WOUVsCV+F6pYMRCnu2Cp9be/aIxqTbxmPdb1tE61j6VgqmPT4U/YdEqP6bE2IvR/bnYfXn+yymT554K+LjYmQrvylwbV0obCUuRe7DPX2gcQ7fpWTwAZ++qsaiT+iHT45iBqOcAF8vxYGehLgKv74yAaWwPUFx2RW+AvOKsaHXVa+oQOay3p4ezO08mXeJ3849aUXMZXW+niirNFqEtR4ad8y5ux/e+PoAtqYWQF9Vww+MxarcFeodHYRxSd2xNbUA85btxrik7hjaqyvfTkAOFw4H+HrJ7veorlo+fFZyx7BoClqdDFdVqdRKoNnum1EZKvxZs/ZXm9anN1Tg5KkzSOjZQ7BeWNwH8/4h/xrAqjy1WBeEI8k3LFtw9hyCg9jPEXnXA1C5qlq5aafOZDPnEW2+5G86fMiN2LP/MADg51VfqPoALFy3m5sb/Hx9cd/kibhv8kRcKL2EU2ey8OU3a1CuNyA3vxA/rfsND943GdVGIyoqquDmBpzIyATc3HDufDEKCougr6jEX0ctT4VrqhFDBuKB++5CSHCQqIKJe0xqg4HmJq2qavjbybc1ovcmpClYld1KVa5KIav0x2QyoajI8n1MZGRki/dhVat3YryoApVrH1B8oRQnT53hn5cBoKKyirmOwE6dFO/DZDKhtrYWZrMZeoOen56WfgJXjeraJmk07ujSORjnSy5g78G/cNOoYdAw3j9l5zZ8edqxY0MgG9ip4cvxqsuX4dOxI9/vlOuJmtS3F46knQQAaLVaPkxlhZ+c5rysJmxlVWVLj1l3d3fmAFlygSs33123jwcAi8B19ef7cLGkknq4khaVsu44s3XA5Im34J47b1UVtAIUsrYGFLaS1oM+1Dgcd7q5FNcrVC5ITYrrjNTTJaqrkfWCU6njwv2Z88iFkMLtC9J5wxZcgFpWacQbXx+wef+MGRCGk3mXsHbnGaSeLuHDaGH/V7ntnntPf+SeN/C9aLkKx6iuWjx2Zx/c2DNENgxV2q/9ewQj55wBRzIvKG57bDedzY+XtD5y7VyETmRkQm+or558Zs7fMHLoQItluTeQRmMNps18CgCwact2xMfFKN4vu0K0YbrFG9M6y+Vlg1nJbZevVDPnNVRU8pf9/HyYfVfL9Xpo/fws1v/r9T62Fo9JJrwWPgYAKLlQis42VGaxBAd1QufgQPTvk4gnn38N5XoDNv6xFdPuuVNwv26oA+DGbQsArZ9vk+5XnjgYZnFECwHuvoSVq9K+gnKhq/A3IU0hF2YJL7M+nCtVp9fV1TH7tPr7+yMgIMDRD9mq/YdS8cMvG/k+rCzpJ0/zfVuFp/UfTUuHTjsE1dXVAAC9Xg8AqKysRG1trez6SksvoWdMpOptHDdqMFb9sAGVVZfxv517MXr4IHhe74EK1IeoKTvq2yIM6JuIjh068IGq8LXD09MLOp0OgPj1jeurCgDl+goEBQbIBq1NDV5tnVftj5RS2wxp4ApYPsfKBa4pvxxH7ulSzHx+NDP0JsReTCYzvvxgB7JOWp4xUB+0TpCt8lZqr0FcF4WtxCXJDZhFHOvRCb1w86AI0TQ1larc4FdDe3W1Oi8A+HQQvGG9ck3VMixVV66qalkgxQ0SZSsPjTvm3jsAsyb2wdGsC9hyKB9bUwtQVmnEik3pWLc7Cz+9eQczNNX5emHly7dAX2nEut3Z2HG0kA+3uWpXaWsCNbgAWeerXMkSqOugZnWkBTmyio5139zPxs1b+emDkvrJLu/m5gYvL0/cdvNY/LZ5G3bvP4Qn/vZgw0BZgvXX1taKToOUrExxQKfGhl/bdu3DyKEDLZarNjZ82RMbE8nfHiSomDpbVAw/X3E4mZtfKLtddZLpwvscOWwg9hyor2w9dSaHWW1rNptx9PhJJPaM5fdfTc1VFF8oRX5hEYYPvoE/nZRbt5+vL2Y+NBUf/N9ylOsNyC8sQlREd3gHe8HNzQ2dgwMt3uS/8nx9MF6uN8BorMGVK1eQm1+IOgCnMrNgMFRAX1GJowp9WaV27z+M3fsP45k5f8Po4YP56awP5Y740MEKWoXTWccevSch9qQUakkvc9flzgKrq6uT7dMaGak+THSUld/9wLcKCPDXYczIoUjo2QOJ8XHQuLvjxVffQm5+Ia7WXEVhYSGuXbuGK5cbTrVfu/43BHdiD3RVVq6H/vqXaVHh4sHAcgvOYsSQG1RvZ1LfXjiUmobM7Dz8sXU3/ti6G2NGDEZSv17IzMrFjj0HUVl1GX6+Pnh42mT4+NS32HJzc0OE4L4NlVXoHBxk8fdN6t8b74S9AC8vL3QK0IkGr7IlbLVnAKvmCwG5thfWgltp6xbuMocLY+UC16yTxXjrmXV48tXxCAhSbmdGSGOUlVbh0ze2oMpgtLjt7jvkg1Y1fbeJ66KwlbgMaxVVgZ3E38Zbq9Qj9hca5KO6DUBTCMPIk3mXRINGcc5fYve6ErYOKLxQaVPYGt65vtog55yBeZ9qeXt6YEhiVwxJ7IpXHhqEw6eKMW/ZbpRVGrFqc4ZsD1agPnSdMSERMyYkorqmFmu2nsaKTenYmlpgUT3MhdhbUwtkg1ju/ySyi31H2SX2J31OCwqsf85z5ABZ0vs1GCqw6/rAWLffMg7e3l5WWxskjx6O3zZvAwBs3bUXt40fazFPZnYu4mMbql59fRo+LP386x8YOWyQ1e2WC82ETmfl8JePnziFH37ZiIm3JvOPo/TiJbz94Wf8PIOS+vP7IFZQ9XTs+ElRlW7JhYv48pv/yu7Ljh0aquxPnclBUGBDcNu3Vzx0Wj/oDRX4+vufENQpwKIC+Kd1m/Dfn+tbN3zyzmsI794NxpoaPP3SIgDA+eIS3Dd5okUwmLJ9DwAgIjwMEd27qQ4JtX6+0Pr5oq6uDhHhYairq8OYEUMsQh29wYDq6hpcqa5GXsFZbN25F2npGcx1fvzZf1BccgFT7rpdNFAL97dzdAsBueppvo8wha2kmTS28pAVuJpMJpw7d455P5cvX+ZDP2dTU1OD9IzTfNA6dFASHr7vbmg07qisrMT5ovoWAjpt/Zdcu/cfwt23Nwy62L9PAo6knURmdh5qrl4VVZkC9ZWmb3xQP8jSU7MetFjuSNpJ3D5+ND+IFYcbcIoLO7mKU3d3d8z5+0P4/qcNOPjnUQDA9t0HsH13w1lRWj9fvLngOfj5NgStAKDz88Xbrz0PL08v+Pn5WLQIAICOHTqgY/cOsgGnmt9yl+3RSkC4TWqqXoXLCZ9rpQMSSs8oEN5mNpsVA9cqgxHvPr8Bw5Jjcfu0AVTlSuzCZDJj4+q/sDclk3l7fdB6q2LIKr3OocDV9VHYSpye0ijUQv7+4rBIOGo3cW7CQX/U4loPHDnDDtXzihuqNoQBq3f7huq4zLN65qBUKYfz+d6zwpAyVPBteHaRntlrtdZkVuxtWl1TK+qf6qFxx5DErph77wAs+fEvrNiUjgdujrdYB2u93p4eeODmeBzLLkXq6RL8sO20aFCuhIhOWLvzDID61gvSYLm6ppb/P+GCZOK8pM9pnSQf+gA0a+iqdEoqZ1PKdv7y+LEjAbA//AuFd++G8LBQ5BcW4ad1m/iwNTwslJ/n/SVfoFdCHHr2iMaE8WPg7e3FB5D5hUVY+M7H0Pr64m8P3wedVnwsCz/AsQJXN8E8wj3VJ7En1vy8EWt+3oj77r4DZ88X871TAWDEkBvh6dmeX7e7uztGDh2IXfsO4fuf1mNTynZMuet2nMrM4gcIu/2Wcdj4x1ZIxURF8Je//v4npB45DqAOTz8xExqNBg9Pm4wln38FvaECC978AOFhoZg+ZRIulF7CodSjSDtxCgDwt4fvQ3j3+lHEtX6+WPDCU3jz/X/hv2t/xcnTWRiU1A9REd1ReukStu7Yi2PXg885f3tQPACZQqWw9G+p1Evdz9cXvj71rRa6d+uKEUNuRHW1Edt27cOX10MTodU/bcDJU2fw1msvNvFotT/pvuGmCYMB2XYWhNhBY/oXs/5Pw8LCcO7cOZhMJn6+2tpanD59GjExMdBqW/bLV+mAU0ajEUajEbW1taisbGjbkioYeO/WccNx5Yrll+pZOfnM+xg+OInvcXooNQ1DB4rDtiNpDV8ChXerf+3x8PBATGQ4v9yK739GaEhnwA2Y/eh0q6GITuuHJ2Y+gJtGD0N+YRGysvMANzf0iIpAz9hodO/W1WJ5Nzc3dOjQAREdOzJvk5smV+ncHFWtai5bq3KV2065dUqDVynu2BYGrgk9Y/Dpv79BRYW4f+/elEwcO1SAe2cOQnzfUBDSWBnHivDjlweZ1axaP1/MfXwGesbGiMJUru8y95uqWls3CltJq0BVJG3PhMGRSD1dgpxzBhzPLhWFptU1tdhyqP4Nd1RXrbja09eLH2zq173ZGNqrq+j26ppavh+rMLgEgOF9Gt6UbdyXgzl39xMFoPpKI2a8vRlA/WBgwjB27c4zWPLjX0iK64z3nxhpEZz6XW+NMC6pu8Vtz/xrB1JPl+DVhwdbVNR6aNxRfj2ES4gQD/wg3N6tfxXizuENg1zVmszYdCCXv13a/oE4P0c/7Umfd81mM1+hqtP5IfJ6xaOQXIA3fuxI/Pvr1dAbKpBxOgvxcTHoFtoFfXrFIy09A3pDBfbsPww3ALfdXB/GPjL9Xny89EsA4KslH5x2t3jFbuoqWgUbyF+cMf1erPzuR6SdOMVXjXL69IrH00/M5PcDt+7Jd05A2olT0BsqoDdUYPnK7/ll3v3nPJSUXrLYH0D9m/I+iT35Zbm2Ac/M+RsAYOSwQQgK6oQP/m85HzALK2wB4L7Jd+C28WNFj/OG/n34Ng1p6RnMqtKRwwYhoWes6G9qbV8pBa3WlvHy8sStyaMxZuQQfP/DelFADwDH0jOw+qf1mD7lLpk/kWM/hLCqr1jbRu9LiD1YO95Zt8v1D+Z+t2/fHl27dkVRURHMZrNo2aysLHTp0gVdu6pr62QNN+AUF6TW1taiqqo+/CovL7dpXSdOZfGXfTpankWVk1+IyqqGAPZc8QV0DanvWR8VHsZXqf68cQtSduzFzWNHIK5HFLZs243DR+oHs5l4603odP1MOTc3N9w0eji2bN8DQ0Ulis4Vo+hcMbR+vsxqU7m/S3xsTP2ZGeNGWdzWmFP+WUGk9HZ792215TY1bS+s7Tvpl1rSoFWpPRAXuMbFROHNV57De0u+wNlz4h6aVQYjvvpoJ0LCdLj7oRsREdu0XuikbcnLLMXP3xxGcaGeeXtYaBfMf/YJ6LR+FsGqmjYCgOPf6xD7cKujd4PEyQm/reROgTKbzTCZTPxPbW0tysv1+Pvc+aJlN7xzZ6N6chLbjHyyvkKJFQY21/K1JjPuWfArX+0X1VWLVx8ejN3HirBiU0P1w2fPjLWoXj1/6TKmLtzIXx+X1B13j4xB5lk9ft2bjZxzBgT4ejH7p67cdIJff4CvF+bc3Q9RXbXIOWfAZz8fRVmlkRmoCrd38qgeGDsgDIG6DrhivIaccwY+4F2z6HaLVgzZRXo88k59iDv33gHoFxOEDl7tcFF/Bdv+KsTanWds2l4A/LZyj5/VZkC4n1jbRVqOvtKIifPXi6Z9+el76BTgz49QLPy2XPjhxN6VrVyoxj0nc5erq6ux72Aq6gBEhIUiXHJautJp1kZjDQ78eQQA0DM2Gp2v9yU1mUx8NWl4WCh0Oq2ocjWv4CzyC4vg07EDwrp1RVCnAGg0GuzcU///FBTUCQlxPSzuu7raiEOpR1EHoGePKP60/QulF3E6q/5LiIED+sLLyxOlFy/hVGY2/jyShp6xMUiMj0XE9epR1v41VFTi6PETyMzKha9PR/TtFY/YmChoNBqUlF5Exun6wGDU9dYHwm1LPXocly9fQWBgADr56yz6sxqNNcgtKETpxTKkHklDXGw0esXHISy0i3xP2+v7sbDoPE6eykRGZjbCunZB394JiOsRBQ8PD5vf6FsLW1l9IrlQRxrK7t5/GEuWrbC4j0UvP4tBN/Rnnm5nbfschd5Sk+ak9piXvm/m3jOz3juzBsoC6gfLioyMVLxPbh3c8z+gfsCpxkg9lo5VP2wAUH+qP9dX1WQyY9+hv/Dzxi2IjY5AZnYeACCuRxSGDRyAvr3i0bFjB1RWVuHDz760CN44k24bj3Gjhlqc1l9z9Sr+OpaOwIAABPjr4OvbEV6enqqeK62FsU3ps9qYkLWpQWpjrqutrpUSvp6YzWaLz4Hcj/A6d1m4TG1tLbbt2o/vflwve18+Wi+MuyMRfQeFw8ePPjcSS1UVRhw7mI+tv55gVrJyHrzvLiSPHs6/txIGrdLQVfh+XdhCwBnf35DGobCVOD01YSv3hm/qI0+KlmUFbcT+HBG2AvUB1KKVB5B6uoR5++LZIzAkkV2dkV2kx3Of7mS2m+CCW1abAKChSpVFLvTk7pMLTVkmj+phUU3LEYamtjzWWpMZqzZnyC776IRezLYFAIWtzuR4dinmfLxNNG3tt5+LgtaWDlulgSvrR7ic9LK12+Sm2/IBUel+1A6uqGb9tuxjpUG8rE2T3p+tpxIr9dSzNliJmv2odCywjgtu3afOZOOV19+32O513/8bHa6P0i03Ui8hREz6HC0NoqTvnSsrK3H5suUp+b6+voiOjhZ9kVNYWIiysjK7B6lqmExmLHrvX6isugxfn46YkDwagZ388e2adaiorIKfrw8W/3MejqSdwPKvG3pkz5oxDUMHDri+DhO27z6ArJw8VFRdRtG5YowdORQTkkfBy6s+ZLOlqlQ6TXobi60Bqi3zNjVkVXO9sdMa03eb9aUd60cawAp/uGUvXLyEJcu+kg3bOTEJIUgcEIr4fqE0mFYbV1ZahYyjRTjxVxGyTiofN2GhXfDMEzPROTiQWbmqVN1K721aLwpbiUsQfqhnBa3cz+uLlyA9o6FB9dx7B2DyqB6O3vxWL+Vw/Sn7vaICGxXINWX5WpMZ+cUVyDlnwMm8S0iI6ISQgA6I6eYv6o2qZtmBCSHoFxNsdTmgPujNKCjDuYuXUXn5KpLighEW7Gu1krrWZEapvhrpORdxMu8SugX7ol9MEMJD/BR7vQL1LQ7OXawSbW9EiFbVPuO293R+/Wl7ceH+iO8eoLi91TW12JNWBKC+JYGa/UKahzTg750Qh3/Of8YibOXezHFhqz3ftNkaqFHYqrwfGzNNen/NFbZae0xKpynbelxw97lpy3b8++vVovt58L678eB9k+kDCSE2YH0hxgpahdcvX77Mn94v5OHhgYSEBH4gqJycHJtP/7cFdz/CgacA8P//+w8fwfKVqy2W6xbaBY/NmIaw0C4AgAOHj/C39YiO5AeUbGyQakvYqrb1Q1NaBzTmt9pttfV1zl7TWeS+5LUlcJV+MZyZlYtv/vuz1dCV029wOIK6+CGwsy9Cuung6dXwXlgb0IEG2nJRJpMZhrIr/PUaYy2Kz+pxsaQSpecrcPRAvqr1hIV2wcPTJqNnbLSoSrUxQStAVa2tDYWtxCVIX2CFbxJra2v5N4s//LIRP63/nV9O7vRoQghxJYu+2i8aSG7KpNswdfId0Gg08PDwUGy4by9KgRprunAZ6WW525QCV1urZqw9BuE2y7F30Kr0GNXeLr3fxvyNWzJsBSD6oMva79z9fvjpv7H7+mBinG+++ARdQoKZxzV9KCHEEquNgFxlq3B6TU0NDAYDc51xcXHw8fFpUtjKBajc6bWs9iBqVFRWITe/EMdPngYAjB4+mA9ZbelD2pQg09o0OU2pmm1qKKy0fU15fbPlMTeGLYGrUnWr9EwcW0NXQoS4kDWuR5ToecyWHwpaWz8qUyIuSfrGgbseFdFdNN/W1AIKWwkhLk8YtAJAVGR3xdO9m5v0vqRhqHSAC24e1vzcZek0pfuztbpVOmK89Lctj7Wp+1lt9amt61JT1SucT/oG355v+rn9yv24u7tbBLDCfc/d14P33W0RtqZs34WHpt3DPxb6MEKIOtLnN+n/Ofd/yV1v3749dDodDAaDxfPG6dOnERkZCZ1OxwxbPTw84ObmJlvBxd2/cFuk26qWv04Lf50WA/r2srouewSqzRG0qt02NZdtmdfWfd7Y1yN7Yr2fsCWcd3Nzs+gZXldXh7geUXhzwXMoOHsOu/Ydwv927G2W7SetS/KY4Rg9fDC6d+sqei61NXCloLVtoLCVtBpubm7odv2bbaHsIr1s701CCHF22UV6i2lhofYZKboxWCEZ63Zp0CoNV4WXpcGn2jedaj/oyt23XCBs74DV1sci3Z7GPGaleawFrrasm/U3lF6W+5H+zTsHBeL+e+7E9z81DGSy8Y+tePC+yY0+RloSnSxGmpMtz4vSNh1KQSsXXNXV1cHDwwM6nQ4VFRUwmUyi9ebm5sLDQ/zR0d3dHX5+fqL7Yl1mPQZ7Pc+6Stiqdl+oaU+g9jY1r4uN2cctzVrgKnecc0Erd5lV4RoeFooHpkzCtMl3IDu3ACdPn8G2XftRUVnVmE0lrYzWzxdjRgxBYnwPxESG818scc+d0t9K4SoFrW0Pha3EpVirxAkO7AStny8MFZX8MruPFVHYSghxWbuPFYmua/18ERzUSbYasbmrS6TXlQJX4XUOa4AkuTYC1sJG1jSlCk9WyMu63Zb1Nie58M4e22UtZLX2Id1atbDawFW63sEDB4jC1nK9AcUlpejapbNiiO8sfxu1txOihtzZAazbldbBClq536z1a7VaVFRUWAyEJb0uDLKUKuTVhJPOFraq3famfgHWlDDalrM+1G6Ps1IKXFmvJazAlfstDV25dcX1iEJsTCQm3TYehopKlFy4iItl5Th2/CSKzpdQy4FWrlvXEIR2DUG/3vHoFOCPLp2DofXztXi/JAxM5YJW6TTpchS0tg0UthKXJPeGw83NDTeNHo61Gxr6tq7bnYUZExIdvcmEENIo63Znia6PHzui0acENpVcsKZme1jhHDedNc3aY7L1Q6Vc4Cud1pT7YK2rsX8XpSBR7XQ1lbFqggW1WFXKciGr8EOvcDsjuneDTusHvaGCn3biVCa6hAQrHm8tpTGDvBHSWHLPJWqeK1n/68KAQLp+6X35+fmhuroa1dXVitsordiS3p9wW5oSUirdv9rLTalwber22zMstfU9QGsJdOQCV2klN9c2QHgcCl+bpK8/rH7z/jotdFo/xAEYOnCAaDu4QefK9QaL6cS5yB77bm4I0Gn5QfiUngfkzg5gha1qQlYKWtsOCluJy2BVSrF+Bib1FYWtZZVGnL902eZR7gkhxNHOX7qMskqjaNqgpH6yVUSclg5e1c4PqOvX2pTTG5WWtRb4qll/Y7bJ1tvV7IOmViw1pvJMbvvUVroKg1ZpVR23XgCYMH4Mvv+xobr14J9HMG7UMIe2ErA2oBuFrqS5KD13crdL/ydY75lZfS6tHcMdO3aEm5sbrly5AiVqwlZ7B62NqRC1JVC1d9iqNF9Tvjy05bbWQPr6zYWrcqErK2y1dtYFID8QJHfZ3d0dwUGBom2j537no+Z/ztagVS5sZYWu0i+4KGhtWyhsJS5B+kZS7s0cAHTv1tWilcDmg3lU3UoIcTmbD+aJrmv9fNE9LBSAbR9m7Yn1wd6W8EsumOPWY68P3krz2HqfLf2muClhM4u1XrT2qNCS3s4KRqVBK3ed20ZuvV1COksfAD+PIypbpR+y6+rqMOa2KS26DYQo2fn7TwDYX4QIyYWuwtuFlYF1dXXo2LEj2rVrB4PBwFzG3d0dGo1GMZgQbou15xt7vAY0pcJV7bba40vOpn7J11YDG+lrjPA1BRC/5rFaCKgNW9WcBUMBq+tQ8wWNXM4gF7ZaC16tPQeS1o3CVuJSlCpahE+AN40ehrUb/uBvW7EpHVPHxcHbkw55QohrqK6pxYpN6aJpyWNGWK1kbamqVsC2kFVpXXLhrdrtaMr227oONZWz9txWVi+6xj5epWmNDQ6UXpOlH2qBhlM+uTDH3d1dFOyw5BcWWR1krbmwqpvowzVxNqwvsFhnH3D/b2pGc+cCqrq6Onh6eqJTp04oKyuzOP5ra2vh6enJPFXWWcNWucuNeV5siS8IiSW5fSMc/E0uYLVX2Kr0WkCvE46jtvLb3mGr9LLccyBpOyh5Ii5H6YmP+xk5dKAobAWATQdyMXlUD0dvPiGEqLLpQK7FtFHDB1l9/mtJwvtTEwqqDWdb6nGoDXcdWWXUnBXK9rivxv6dhSGr9DiKj40RzZubX8jf1pKtBOSCVvoQTZyNXC9tWwJXafAg7Wvp7u6OoKAg6PV6XL16VbQ+rrJVTdDQEmGr0v3YO1Bt6nMQBTBNIz3GpaEq94WB8OwKCltbt5YIW1nPd0oDYNH/edtEYStxKcIPWNx11k9wUCCG3Ngf+w8f4ef99o+TuHN4NDw01r/NJ4QQR6o1mfHtHydF04YOHIDOQYFWA1ZHvaFrTJWOI3pvym2X3AcjR29bSzz25lynmn64SvtfzXpaAmv0akKcBReeygWFtlS4StsICH/c3d35CteamhoAwLVr12waDMaer1mNqdq3ZZrabWztrxPOjnWMc4QtBppS1coKXlnXifOSe25g/VYbuKopfqDnh7aLwlbictQ+wU29+3ZR2FpWacT2vwqRfGO4ox8CIYQo2v5XocXAWPfdM9GmN3euQC58tdf65LDux9X2XWM44jHKfQhmVeCp1RLBK6uaVdpnVujNL79rgb1JSL0FM6eLrku/BJCr9pMGrnLPhcKqWO5LBu5+3NzcEBwcjIqKChgMBpjNZmZlq3BAGLVVrWpuU8PaFzyNvc+28DrhqoRnqrCqXFlhamPCVull1nXifNRUvtsatrJuZ62PtF0UthKXIT1t0NqTYHBQIMJCu6Cw6Dy/js9+PorhfUKpdyshxGlV19Tis5+PiqaFdeuqWNXaWr5Fb+n2AaRlSIMeuZHUrWnpAbLkglZW2EqII7EqrlnhAuv/RxqKCtt7SE+5Fq4rICAAnp6e0Ov1NlW2Km1jU9grLKXXB9fGOs6lx7CagNXWFgIUuDovW1uMyL2/tvb+uzW8Dyf2RYkTcWnWKrweeeBevL74//j5yyqNWL4hDXPvHeDoTSeEEKblG9IsqlpnPjCl1VSzkrZJ+oUpKxiypa1Ecw6SxfoQzYWsFLYSZyQccE6pjYdc4CoMC7jwValXMffa4+fnBy8vL1VnXDhD2Noc90ucj9xrg/D4t7VtgJpwlQJX52NLqxFb2gqw5pO7P9J2UdhKXI4tPVR69ojG4Bv744CgncDanWdw+9AoRIfqHP1QCCFEJLtIj7U7z4imDRk4AD1jo23uEUWIM5O2EJAOfCXliJ6t1k41JcRZsI5PpVPplVoKSFsGSNctfR/u7e0tqmgVVrYK55NuQ3Oi10ICKLcWEL7eSJ/XGzMwFr0uOC9rgatSSwHusrUWAfScQ1gobCUuhdVKQPjDNfznLtfV1eGh++4Wha0A8MbXB/CfeeNpsCxCiNOoNZnxxtcHLKbPuH+yRR88pZCV3vARVyFX4WqNtN9rc2AFT0o9WwlxJGllnrX/D1ZPV27ALKW+ltJ1SN+DO6qqlRAlStXe3O3S/x/ushC1DnB91kJSNcGr3LKESFHYSlyStCJGGK4KR510c3OD1s8XD953F7797y/88jnnDFi1OQMzJiQ6+qEQQggAYNXmDOScM4imPTxtMrR+fvxznFxPPIDe9BHXZkvv1pYm/BAuHCyIEGfBCoqskQauwvfP3LpYfS2Fy9tytgW9RhFnoKbFAOt2bpoUvR64DrVf+lh73qLnMqIWha3E5UhP/7D2Js/d3R03jRqGDZv+B0NFJb+eFZvSERfujyGJXR39kAghbdz+E+ewYlO6aJrWzxfJY4bb1AuP3gCS1k76Abi5q1upjQBxBaxj1JbBoOTaCjQ2bBWum16XiDNiBamskE16zAvnZ62HOD+lin9bphNiDZ1DTVwaq38rV/0lrALz8PDAvKcfs1h+3rLd0EsGoiGEkJakrzRi3rLdFtNfemY2PDw8mM9pVNFKSPNQGgyFwlbirFjHpdyxzKLm/TTrh4JW0looVWTLFfNY+1+gH+f4Ufu3snYsEGIrqmwlLkvujSGr2tXd3R3du4Xi8Uen4/MV34nW8/S/dlD/VkKIQ9SazHj6Xzsspj8x8wFEdO8m+6ZR6U0iIcS+5AZPIS3ncmUFfvv+G6Qd2g8A6DNwCKY89iQAoPhsAX7891KUnC20uK0taeoXAdLTqKXT5W7jflPQSloTNcevXNUrcV70dyIticJW4pKEvd3kwghpFYq7uzuGD74BR4+fFA2YlXPOgBeW7sL7T4ykwJUQ0mJqTWa8sHSXRZ/WITf2x/AhN6r+Np5DbyAJsS9WyEqVrY6x4oO3+TBVyGQyYeWH76KqwtCItbYerOO0Ma8JcoErd5vwvlihKgWtpC2h45wQooTCVuLShG/qpANksUJXNzc3PP7I/cg4nSXq35p6uoQCV0JIi+GC1tTTJaLpWj9fzJ75gEXQKq1qZbUTIITYB41A7Vzyz5wWVa0OGpuMwJAuAICsE8f5oHXsnZMRHZ/I39YWsY5VW18jlAYIEt4uN41ekwghhBDq2UpcGKu3ijCIkAYU3I+HhwfeevV5aP18RetLPV2Ct7456OiHRQhpA9765iAzaH1n4Yto166d7HOYNGjl0IdbQpoXVbU6jv7SRf7yyNsmIrxHHDr6+gEAjFcu87f1HzpCdFtb0hzHprQ1gLUBsbj5CCGEEEJhK2kF5PoZyjXyd3d3h07rh6dmPWyxrq2pBVjy41+oNZkd/bAIIa1QrcmMJT/+ha2pBRa3zX18BnRaP6vPX9SrlRDiLEwmE67WNG6g0as1RphMJpuW8fTylr3Nzz/A0bujVbL2ekOvR4QQQoglaiNAXJqwt5RwYCxpSwHhZaC+AiCuRxReeX4O3vrgM9E61+48g7ziCmopQAixK7nWAQDw6gtPIa5HNDNclatopd54hJCmulxZge2/rsOVyorr1ysR1DUUt06dDo1Gw1ym/GIpUtauQc6pk6Jeqdwp/uE94mTvL//MaRzclsIPdAUAnbuFITIuAWPumCSqSuXmLTl3lp+WsnYNAMBHq0OVQS+6be1/lgEAusfEYvC48QDqA909mzdh2/q1ou0YPO5m9OjVBzGJvWUfJ7FErzeEEEKIOhS2EpcnbeYvDFwB8alV0t89e0QzA1euh+vCGYOh8/Vy9EMkhLg4faURi1YekA1ae8ZGM6tZlQbHAuiDLyGk8covluKLtxZaDC6VnZGO3NMn8cBTz8E/MEh024GtW7Dx+6+Z60s7tB9ph/Zj7J2TMXbi3Ra3b9vws0XoCQAlZwtRcrYQ6YcPYNoTc/mwVn/poiiU5e4DAMKiYlCYk8W8DQAGjxuP4rMF+HThfOa2Hti6GQe2bsbt9z/MB7OEEEIIIfZCYStpNaSDZQFgVrQKfwPKgeuMtzdj2fM3oUunjo5+eIQQF3X+0mXM/uB/KKu0PNVWTdDK6kNNISshpCnyz5zGv999HQDg46fFrVOnI75/Es4X5GPb+p+RnZGOD+c9jScXvYOQbt0B1IezXNDq46fFjOde4m+7XFmBH774DNkZ6di2fi2i4xNFFa75Z07zQSu3bFCXUGg0Ghw7sBc//nspqioM+Pe7r+O1pV+ivacX+g4ehr6Dh/G3A8CbX34nehxKt/3+34brTy56h7+/y5UVOLj9f9i2fi02fv814vr2twiVCSGEEEKags6RJq0Ca7Asub6HrEoxLnCVKqs0YurCjUg5nO/oh0gIcUEph/MxdeFG2aA1rkeUTc9VNBAJIaSprtYY+aA1Or4Xnn33I/QdPAztPb0Q3iMOk2b8jZ9XGFhyp/ADwFOvv8sHrQDQ0dcPDz3zIjp3CwMAbFj1leg+ues+flp+We70/b6Dh2H+J8v4ef/as8sujzE7Ix0AcPv9D4vur6OvH8ZOvBuDx92MPgOHoCAr09F/EkIIIYS0MlTZSloNafAg7d0KQLbC1Ww2o2ePaPzrvX9iwZsfwlBRKVrXG18fwL70c3jx/hvh7Un/NoQQZdU1tXjv+8PMgbC0fr54+7UXoPXzVRWysloIUNBKCGms3NOn+MsDho9Ee09xuyT/wCA8OPcFGK9c5sNToOE0/cHjbhb1VuVoNBokJg3k2wKUXyyFf2AQyi+WouRsIQDg1qnTmct29PVDdHwvZGekY8fGdU0+tV/j0Q4+flpUVRiwY+M69B442OJ+b7//IQf/JQghhBDSWlFlK2l1hFWt1irGNBqNKNTQaf3w5oLn0C20i8V6t6YWYNo/f0N2kd7RD5EQ4sSyi/SY9s/fmEFrWGgXi6BV+DykJnAlhJCmOJOexl/uHhPLnCeuTz/0HTxM1EKAExYVLbvuhAE3WEyrMVaLrpdfLGX+BHUNBQCLHrKNodFocOvU6fz6/vXaS8g/cxpXa4xNXDMhhBBCiHVUokdaFdZgWVxVKyDu1cpiNpuh0/rh9flPY/VPG5CyY6/o9rJKIx55ZzPGJXWnKldCiIhSNSsAJI8Zjun33gkPDw9m0CoXutKgWIQQe7pSWcFf7ujra/Pyuk6Bsrd5ennzlyvKy+AfGMRXtQLg+6taYzKZ+NP+G6vXjYPx+5rvUFVh4PvBAkDnbmG4ceRYZlUvIYQQQog9UGUraXWkQatc/1alkKNdu3Z4YOpdeOaJR5n3sTW1ADc/txYph/NRazI7+iETQhyo1mRGyuF83PzcWtmg9dk5M/HQfXejXbt2qoNWaZU+QEErIcS+1IaNFeVl/GU//wBVy+gvXWz0dgnvr7E0Gg1e+OBf+PtLr6HPwCH89JKzhdj4/dd4/YmZ2Pj9N02+H0IIIYQQKSrLI60SV+HKXXZ3d4fZbBZVucotZzY3hKf9+yTi/xYvxOcrvsPJ01kW87/x9QF8l5KB56YmoXc0jWRLSFtzPLsUH65JRc459mmvCT174ImZDyj2Z5ULWoUhK0BBKyHEPuL69uf7r3J9Va0RBqwFWZmqlhH2e+X8/aXXEN4jrsUeq0ajQXiPOIT3iMPkv83G2ZwsZGecwLb1awEAB7ZuxrDxt6p6PIQQQgghalFlK2nVhKff2hJ0CH90Wj88/9Tf8cCUScz7yDlnwJyPt2HG23/geHapbRtICHFJx7NLMePtPzDn422yQesDUyfhhaf+Dp3Wr1HPPzQYFiGkOQhD0AvnipjzXK0xwmQy8deF7QaqL1+WXbdwfVxLAWFf2OyMEw573FzwOnbi3bj370/w0/du+d1h20QIIYSQ1onCVtJqCU+7VRO4cj+sEKRdu3ZIHjMc/7d4IQbd0I95fxS6EtL6qQlZB93QD/9675+4eexIvm2A9LlF+nxDfVoJIS0lICiYv1xeeoE5zxdv/xMLZz2Ed5+pDyXbe3rBx08LADi8a5vsuoWDb3EBrTCoPZF6SHbZ4rMFuCzoJ9sUlysrkH/mNIrPslu79B08jG8tcGDrZvvtXEIIIYQQUNhKWjlbAldpECJX5frEzAfw8nNPwM/Ph3mfwtA15XA+qmtqHb0bCCFNUF1Ti5TD+VZDVq2fL155fg7m/O1BZjWrtYCVglZCSEto7+mFweNuBgBs/P5rHDuwV1TFmn/mND+o1V2PzOKn3zp1OoD6nqfbNvyMqzVG0Xrzz5zmg8uxd07m+8G29/TC2Dsn88tK7w+ob2fw6cL5eOfp2Xbpo3r80AH8+93X8enC+czA9WqNkW+lcPv9Dzvwr0EIIYSQ1oh6tpJWj+vfKgwsuN6twlBD6Yfr41pXV4e6ujrExUTh47cW4GDqMfx37a+oqKyyuN+ccwa88fUBAMDkUT0wdkAY4iM6wUND33EQ4uxqTWZk5F3Ctr8KsXbnGcV5/fx8MG3yHRiU1A/t2rVT/cWOXI9WCloJIc1t2Phb+WD0x38vxe9rvsOtU6fjrz27kJ2RDqC+3UBcn378Mr1uHIzf13yHqgoDtq1fi23r12LsnZMRGhGF8tIL2Pj91/y8w2+eILq/4TdP4Pukcvc3cMxNCI2Iwpn0NKQfrn+/FB3fiw91m2LwuPHIOJKK7Ix0rPzwXfS6cTB69OoDL29v6C9dxK7ffwUA+PhpcePocY7+cxBCCCGklaGwlbQJcoGrmkGzuOW50JUbeKtdu3YYNigJg5L6KoauALB25xms3XkGAb5eGDMgjIJXQpyQMGDd/lchyiqNivMLQ1YPDw9RYCoNUSloJYQ4E//AIDy3+BN88dZCVFUYUFVhwI//XsrfHh3fC5Nm/E20jEajwbPvfoTv/vUxH8hyAapwuSmPzeGrWjntPb0w/5Nl+OGLz5Cdkc4HtkI+flpMeWwONBqNXR7j9KeewUcvPYuqCgMObN3MbBdw1yOz7HZ/hJC2jfuMSIgros8d9udWR88KpA3hDneuQpX7MZvNfJDKXWb9cLcLf3M/tbW1VkNXqaS4zpgwOBK9ogLRpVNHR+8eQtqc85cuIz3nIjYdyEXq6RJVy7BCVmnQai1klQayFLSStkD62st6/a2rq0NxSSlmPvmCaNnf136j6n/HntvIbZvJZLL4ueeh2aLl3vzyO0fv3ka5WmPE+YJ8nC/IR0FWJvoOGY7IuJ4WYalU8dkClJwtRGFONgAgLCoanbuFIahLqGJ4aTKZUHq+SLRsj1590C0yCh19/ZjLlF8sRUFWJoD6Xqtqb+NcrqzA2dwcFOXl4OL5c+geE4uIuJ4I6dbd0bu/0RbMFFf/bvjvl8x+4DTQIiH2pTY6oYiFuAK1rw30GtI4FLaSNkcYuAJghqhywav0dlboWldXh8ysXGzdtQ8H/zxq07YlxXXG8D6h6BrYEREhWgpgCbGj85cuI6/YgHMXL2NPWpHqcJUz6IZ+GDdqKOJioiz6QEtbB7DCVtbt3G8AFLSSVo/CVkLsg8JWQlqGXFTCmk6xCnFlrNcJudcOek1Rh9oIkDZH2lLA3d3dIoAV9mmV9m7lLnMfxISX+Z6uPaIQ1yMKj06/F7v2HcLOvQdx9lyx1W1LPV1iEQAF+Hqhf2wwdD6eSIjoBACI6qpFB692FssH6bypNQFpE2pNZpTqqy2mXzFe4wewOpl3CfqqGhzJvGC1JYCcbl1DMHr4YIwceiM8PT0VQ1a5qlalkFX6IZjevBBCCCGEOI40NBVeV7pNaRohzor77CE8bqXTpJ9VWPMSSxS2kjZJ+AQiF7qyAlZhUCsNWrnLwh9vby+MHzsCyWOGo6KyCgf/PKo6eOWUVRqxNbV+JF1rA/UQQpqOC1gHJvWF1s9XcfA8a1WtrCo86s9KCCGEEOIclAJTVtDKmlZcUoor1dXIzStARVUVTmbUtzfZvnu/ox8eIYrGjBgCuLkhsWcsfH19EBkeho4dvBHSORiAZaGa8LI0eOWmkXoUtpI2TW7gLC4s5eYRBq5qq1ulPzqtHx+81tRcRUZmFtIzMnEo9ZjqHq+EEPvz8/PBwAF90TshDj17RMPLy1P0JkJNyKo0OBYrYKWglRBCCCHEceSqVK39LtcbcPpMNjKzcnD8xGkcSUt39EMhpNG4LwS279pncVv/vr3QJ7EnYmOi0DM2Bjqtnyg/kQavALsatq2isJW0eawnCmHYKq1kZT3BcPOzqltZP97eXujfJxH9+yTigSmTYKioRMmFi8gvLEJWTh4Oph5z9G4hpNUadEM/xESGIzwsFCGdg6D18wUAqwGrLWGr3DzSNyT0RoQQQgghpOUoVatKPwMCwJXqamTl5OFo2gn8+vv/UK43OPohENIijhxLx5FjDV8m+Ou0mDghGf379EJMdAS8vbws8hFAXPHalj/rUNhKCNgVroC4ylXaOqApYav0hdxfp4VO64e4HlFIHjMcs2c+gJqaq6iorETR+RJcqTYiJ68AlZVVqKy6jJOnsxy9ywhxWglxMfD19YGvT0dERXRHB28vhHYNgZ+PDzw928sGnmpCVjVhq1IlK/VnJYQQQghpedKQVS5oraurg8lkwp9/pWHthk04knbC0ZtOiFMo1xvw9fc/4evvfwJQX/l6z50TcOOAvvDw8LDISNp66EphKyHXKfUhkQau9gpblU5T8fb2gpeXJ4KDAgEAwwYlKTZcr6urw8VLZbK3EeKKlF6YgwI7qV5OKWDlfjc1bFUKWln3SQghhBBCmpdSyCr9THb85Cns3HMA63/b0qj7ikkIgY+fJ8JjAtHBxxP+nTrCz9/b0buAEKaK8mqUX7qMK1U1yM+6iKqKGmSdVD+2jLDyddLtN2P08CHondjTIldpq6Erha2ESMhVuUrDVWno2tSwVS54lV5WmsYFs2rmJcQVyL0gs6ZbmyYXsAovNyZstTbwFVWzEkIIIYS0LLUha21tLXbsOYDlX31nU4uAfoPDER4TiNDwAASG+MLHz8vRD5kQmwQE+SACQQCAYclx/PSqCiMuFleiKL8M+VkXcfRAvtV1rdu4Ges2boa/TovHZz6IMSOGwMOjPm5sq6Erha2EMCh9G8PdLg1WhS0G7Bm2yjVvb8w0QlxZYwJW4XV7hq3WKlgpZCWEEEIIaXlqQ9Yr1dXY/L8dWP3TBlUha0xCCBIHhCK+XygCgnwc/TAJaTY+fl7w8fNCRGwQhiXH4f7Zw1BWWoWMo0U48VeRYvVrud6Adz78FJ9/+S2mT7kLtySPRgfv+urutha6UthKiAKl1gLCy/aoam1s2Gpr0EohLHF2Si+6toSr0utKrQOk1239ES7Pui9CCCGEENK8lPqwcj/Xrl3Dzj0HsPiTZVbXF5MQghG3xCG6Z2e096TohLRdAUE+GJYch2HJcbhaU4vsUyXY/cdp2eC1XG/Ap8tX4tPlKzH/uSdFla4cuTOKWwt6xiBEBbmWAnIhKwCbw1bWZY6tgSuFrcSV2RK22nJdqaJVeFltqErtAgghhBBCHI9VzSr9MZvNOPjnEXz82X8UK1lDwnQYNCoaN46MpoCVEIb2nh6I7xuK+L6huFpTi8O7snFwZzaKC/XM+blK1+f/8RgG3zjA4jNUa61ypWcPQmwgfTKQq3IFoCqAFU7jLrN+K11mXZebRogrs7Wq1Vq/Vu63mhDVWkBrbTsJIYQQQoj9sT5TcWNrcD9l5XrM/+e7yM0vlF1PSJgOdz90IyJigxz9kAhxGe09PfiK17zMUvz8zWFm6FquN+CV199DZHgY3n9zAfx1Wri7u1u0FmhNn6MobCXERsInANaTg1LFqtpqVntUt8pNI8QVNaV9gPCymrBVeFlti4DW9MaAEEIIIcTZKVWzCsPWrTv24L0ln8uup9/gcNxyT1/qw0pIE0XEBuHZNyegrLQKf/x0jDmwVm5+Ie558DHMf3YObhozwuLzVGuqcqWwlZAmYPV0Fd4mbTXAzWstXG1sv1YKXElr1NSgVXhdzW+ladJ1t4Y3AoQQQgghroT1mUkYsJrNZpTrDXj3o89w9PhJ5jpCwnSY9thQdAnTOfrhENKqBAT54P7ZwzDm9kSs/mIfs9L1nY8+wx9bd+KV55/iq1w5raXKlcJWQuzAWujK3WbLb6XLctPkglUKXImrknuRVXPqfmNaCrB+q70/QgghpK1hvRc1Go3o0KGDozeNtFJy7dmEYevxE6fw1gf/YvZm9dF64d6ZgxDfN9TRD4WQVq1LmA7PvjkBGceK8OOXB1FlMIpuP3IsHX9/6kW89tLT6JMYz+zl6sqfuShsJcSOWC0GWPMIq12lYaz0sprrctOUphPiKtQGrqxpje3pqmZdhJC2Q9pahBBn0tLHptJ7zqysLHTq1Ane3t78thFiL6yglQtZud/rftuMz79cxVx+WHIsbp82ABqNuy13Swhpgvi+oXjl40nYuPov7E3JFN1WrjfgmZcWYc6sh3HX7be0qgpXClsJaSZKwSsraJXOL2RrkEoBK2mtbAleWdPVhq/W1ksIab2ErX+469LbpRbMnO7ozSakRb4kVCoOyMrKQmVlJQICAkTz0GspsQdp0CoNWa9du4blK7/Hhk0pFsv6aL3w0JMjaPArQhxEo3HHnQ/cgL4Dw/HNp7stqlw/W/41ioqKMftvD8LDw4MPXV05cKWwlZAWIH1yYIWv3HTp/EpPLkqhKgWupLVRepG19TZbQ1tCSNvD6tdM1a3EGbGOU3tjhazCacXFxaisrLSYx5U/KBPnIR3zQhi0ms1m1NbWYsEb7zP7s8YkhGDG0yPR3pOiD0IcLSI2CC+9PxErP9mFrJPFotvW/bYZ+YVFeHfRS6LAleNqryP0jEOIA7DCV9Z04TRWeMqavzWN4EcIi9pjW8189H9CCLFGaZA8QpxFc34RoDTOQF1dHUpKSlBSUiKaX9oyiwJX0ljCoFVazWo2m3Ht2jW8+uYHzKA1+a7eSJ7U29EPgRAi0N7TA7PmjUXKuuNI+eW46LYjael4aeG7eOef89CuXTu4ubnB3d3dJTMOClsJcQJKTxq2PrHIBbmEuKqmvqi60osyIcSxpOGQcDpVthJnxTouWb3IG0MuYOV+Ll++LApapfNIBzuxxzaRtkP4PCytZuVaB8gFrY88O4oGwSLEiSVP6o1ukQH46qOdoulH0tIx/5/v4p1/voR27drBbDbzVa6u9MUdha2EOLnGPJko9ZojxJXR8UwIaSnSgFX48/OqL2A2m2EymSwqreSqAAlRQxqScsecu7s7/1uj0Yh+s45Re5ALWIVBa25uLnM5s9msOFCs8DESwiI3GJbw5/0lXzCD1ideSab+rIS4gPi+oXjilWQsfUvca/lI2gm8+/FSvPL8UxaVra4SuFLYSkgr5ApPPoQQQogrEIZe3Bt+4W8AompY4Q8hjSUX9AtDV+Fvbhl7sRa01tTUMINW4TKsxyQdIJY4D2f6/MAaDEv6s2FTCnbtO2ixLAWthLiWiNggZuC6Y/d+JMT1wF133AIALjdoFoWthBBCCCGECLB6TQrDLmHQ6ubmJqpqZVW0UqhE1GD1BlYTtAqrWe0RvMpVFHKXr127hpycHMXlucpW4f8Md90VPiS3FdJBeZVubymsoFUauqafPI0vvvrOYlkKWglxTXKB69L/fIOY6Aj0SYwXtRNwhdcSClsJIYQQQgiRYIVdwvCIu40LlZSqWilsJWqw+q5aq2yV+2kspaCVa52RnZ0Nk8mkuA7u/8Ld3V3UUoAGzHIucq3HpNXHLf23kquorqurg95gwLyF71gsQ0ErIa5NLnB9dv7r+PGbz+Gv07pUu0QKWwkhhBBCCLmONTgWF65K55MLWilcJfagppWA8EdukCy11ASthYWFikErAL76kPWFhXC7nP2DclshDcKll1uyz670jABWG4H3PvncYrnku3pT0EpIKxARG4Tku3oj5Zfjoulvf/AvvPfGKxbPRc78xR2FrYQQQgghhEiwgith6Mq9waeBsUhzsDZQljBstXdlqzTgqqur44PWK1euiOb38PBAu3btUF1dzU8rKSlBSUmJo3chsQMPDw/4+voiMjISQPMGG2oGxNq+ax+OpWeIlotJCEHypN6O3lWEEDtJntQbuadLkXWymJ92JO0E/rdjD24aPdxlBsyisJUQQgghhBAB1pt37rJwUCzpwFjcdOFvQhpDLmxlVbiygtbGVLUqDYpVUlLCDFoTEhJQWFgoCltJ61FbW4vy8nJEREQAaP5gQ651gNlsRrlejw8//bdofh+tF2Y+P9rRu4kQYmcznx+Nt55ZhyqDkZ+2+OOlSOrXGwH+OpdoJ0BhKyGEEEIIIQqEg/xIAynhZRoUi9iLNOiXBqlyA2Q1BuvUbWHF9oULF6DX6y2WS0hIQLt27Ry9q0gLkPbatXfgaq19QF1dHb746nuL5R56cgQ0Gndb7ooQ4gI0Gnc89OQIywGz/v01XnnhHy7RToDCVkIIIYQQQiSUggXhKWxU1Uqai1x1q/ByU3uhWhv5Xa/X49KlSxbLxcXFtZqg1WQyw1BRgY4dO8CzfXtHb45TaomerXIhq9lsxvniC9i975Bo/n6Dw6lPKyGtWERsEPoNDsfRA/n8tB17DuCRB6eia0hni3YCzobCVkIIIYQQQhjkKriErQOkA2pR0ErsidXKghW6sua3Rq5tABd0VVVV4fz58xbLxcXFwcfHh78eFRVlMU9NTY2jd51q23buxTsfLcX8Z+dg7KgBjt4chzMajcjKyhJNM5vNop7V9qxuVWpfwR2L36xea7HcPY8OcvSucilVFUZcralFe08P+Ph5OXpziINwxwEABAT5NHFtze+eRweJwlYA+OrbNXj5+adEX3Y7Y+9WClsJIYQQQgiRoTQSt/D0NQ6FrcSe5IJUVhVrYz5kyg1GVF1djcLCQov5u3TpIgpa5Xh6ejp616mWmZ0LAPBo5+FS292SpG1SAPtUkrHOCpAej8UXSrF7v7iqNfmu3mjv2bgo42pNLaoqjMzbXCF8aqwN36Xi6IF89BscjvtnD3P05rQ5JpMZhrKGvtdqjrXmCEa54wAA3vv6fkfvFqvae3og+a7eSPnlOD9NWN3a3C1OmoLCVkIIIYQQQhSweoJJQ1hnPpWNuD6lULWxIavwsvDn2rVrskFr165dW/yxny++gPSMU+gV3xPeXp7Q6bR2Xf+RtBN2WU+tyYTS0vqWCzqdH7y9bK8erDYaoddXNHr55iKtIBNOt1foKg1aucvrf9tiMf+oW+MbfV8n/jqL1Z/vk73dR+uFmPjO6D80AtE9Ozc61CVEyFB2Be8+v4G//sybE9AlTKe4jKsFo81l1K3xorAVANau34QnZ82QfW5yBvTMQQghhBBCiArSSla5AIyqW4k9WPvg2NgPlkpVhFevXkVubi5MJpNoGX9//xYNWlO278KmLduReiTN4rZxo4dj7uOPyoautSYTXljwpsWyi19/GUMGJvHXF737Mbbu2MNff2PxEryxeAkAYNcfa6FWdm4+nnv5dZSV6y1ue/TBqXjgvsnw0GhE07n7Hjd6OF554R9Y9d+12LHnAHJyG06XTerfBwvnPW33cLkxzGYzADRLj0S5vsFmsxnXrl3Db5u3ieZvSlWrGlUGI44eyOdDrmmPD0X/IRHNdn+kbVr9xT7MXXSL4gBvQV387H6/E6cn4ZZ7+jr64duEVd26YVMKHntkOtq3b++UVa0Aha2EEEIIIYTYhPVmXhiwOtObfdI62PuYkuuPWVhYaBG0+vr6IjIyssUe6/niC3zoCdSHjgD48HTrjj3IzS/Efz593yLE1OsNmDH7WT74jIoMh15vQFm5HvNee5sPNz00GvRKiOPXx80bGR4GvaFC9bau/O4HrPh2DX+dWwe3zhXfrsGx9Ay8vXCebKXqqv+u5dcR4K/jtz31SBqenr8IK5d91GL7Xo50IEB7BBtqerWePpNjsdyQsT3s9riE1YJce4H8rIs4vCsHWSeLAQCrP9+HiyWVGHtHomIwRogtigv1OLDtDIYlx8nOE9jZ1+7366r9eoeM7WFR3ZpxOgt9esU7be9WClsJIYQQQghpImd4Y0+IrYQBl9FoZA5s1blz5xY9vucvehdAffD406rlfKBabTRi+VffYe36TcjJzcfh1KOiSlVhhWmAvw5zZj2MMSOHwUOjQXZuPh6Z/Swf1K5c9hEmT5yAyRMn8MHo9CmTkDxmpOrtPF98gQ9JpVWoC196hg9iU4+kYc3aDZgxfYrFOrj7Hjd6OGbNmI4uIcHIzs3HqjU/Y+uOPcjJzUe10ejwlgLSoLU51ittH1BXV4c9Bw6L5g8J0zVbWNTe0wMBQT4ICPJB/yERuFpTi5Wf7ELWyWKk/HIcuadLMfP50VYD16s1tdB4uLdYMGsymWGqNTe62rexy1dVGJs1uGvq+pt7+4S4gcfU6jc4HEcP5GP9qlT0HRRut+1sycdsz/2hho+fF0LCdCgu1PPTdu07iF4JcdSzlRBCCCGEEEKI4yhVE7Zv3x6BgYG4ePGiaJmsrCwkJCTA29u72bcvOzcfkeFhiAwPw9BBSaLKVW8vL0y56w6sXb8JAHD6TLYobF215me+KlQY0gJAdGQ4Hn1wKlZ8uwY5ufnQ6w1NPj3/h19+5S+zKldnTJ+CY+kZSD2ShnUbNzPbCQD1rQaEQWx0ZDhmzZjOB7FH006IHqcjsI4Xe1S1yq2b+9m0ZbtouUGjolvsMbf39MDM50fjyw92IOtkMbJOFstWIp4v1GP7xhPIyihBlaF+8K2QMB1CQrWYOD1JMQCrqjBi64Z0XK6suX69Bp1D/XD7tAGygW1ZaRX++OmY6P6A+hBv6LhYRMQGWX18R/bnIeNoEd8uwUfrhXF3JGLw2B6y9ytdBgBiEkJkt7eqwogN36UCqD99vdJgxKGdWSgpqoCPn6dooC6TyYyNq/9C9qkLokCt3+BwxPcLZbZy2JtyGvlZFxHUxQ/Jk3rjfKEeh3Zm4dihAtHfYdaLY2X/Btz9Cvc/ANw/e6jsMldravH7j0dt2lahex4dxO/D75ftw6x5Y63+vWzZjpiEECQOCGUeq9w+q3+MwyzWt/P3DIsK0mHJsYjt3QXxfUObZX+oNWhUNNavSuWvb9iUgtkzH2Q+LzlD6EphKyGEEEIIIU1EfVpJc2rOakLhj5+fH2pra6HX60XLZGZmIiEhAe3atWvWxxkdGY6FLz0je3uXkGD+cn5hEX9Zrzfw4WRS/z7MUHPq5Im4edxo0SBb1UYjGosLfZP695GtPJ1y1+1IPZKGsnI9SksvibafM2LoIItpQUGd+Mtbtu1ymrBVOq0plWRyASvXQqC45ILFMn0Hhbfo49Zo3DHj6ZFYMOsHAMDWX09YBFh7U06LAiBOcaEexYV6HD2QjydeSWYGoGWlVfj0jS2iwBQAsk4WI/vUBcyYO9JiFHq5+wPA95pNvqs3kif1ln1cKeuOWwRqVQYj1q9KxcWSStz5wA2i20wmM9Ys3y8KWYXbmnWyGMcOFeDZNyeIAsqrNbX8MkPHxeKbT3fzj7Xf4Ia/5flCPf79/jaL/SB8TAXZFy0C3fysi/z6eySEYOlbKcy/w+tP/Yx3VtzHDIO/X7aPbxkh9NGCTbh35iCLgFHNtlprO9He0wPTHh+K1Z/X33fGsSLZIFNOXmapaH+y/iYn/iqyqMYW7jNh2JqXWcrcfwCwNyUTe1MycecDSRbHvz32h1p9B4VbHPvFJRfQtUuI6DkEcI6zjShsJYQQQgghxAbWglUKXok9WBt0zd4BLPej1WpRXV0tailQW1uLkydPtkjgCtQPclVaegl5BYWounwZALDvYKrs/NXGhm2N6N6NOY+3lxe8Q8ShqF6vvj+rnAnjx8jeFtE9jL+cV1DIDFvDGdvrodEgqX8f5gBhjtRcz22s0LXw7HmL+RxxinR7Tw/+9OUqgxFlpVV8AJqXWcqHPz5aL/z9hbHoFOyD9p4eOLI/D7+u/gtVBiOWvpWCN5dPEZ1aLQy3fLReuGPaACQO6IZz+eXY8stxZJ0sxrvPbxCNWl9WWmVxf9xtwuAw5Zfj6JEQwgx4uaAtJiEE4+/qjbDoTijMvoSfvzmM4kI99qZkWoSa2349IVrunkcHQhvQgb8t5ZfjqDIY8dGCTXjt/+5m7sefvzmMKoMRMQkhiIwLQo+EEAD1Qa4wrEu+qzeGjO0BHz8vlJVW4acVh5B1shh7UzIR2NlXFPYJB5Ba+lYKvx/7DOyOwuxL2Lc1k9/uC+cq+H3F7a+PFmwS3e+oW+Nx6UIVDu3Mwt6UTHz10U5RwGhtW1cu2YXiQj1SfjmOwM6+ihWd/YdE8L2Bf/zyIF56f6LqU+9NJrMoaH3k2VGI7dUFGo27qDo162Qxtv16QjF452y5Hr5zx1VwVz9oNO6oqjBi/7YzSPnlONavSkV8v1D++Lfn/lCD9f+fX1iELiGdnfJ9F4WtTsYZDxJCCCHE1TnDN9zE9bEqu+RuI6Sp5AZdE54mKb2tMetmHdedOnXCxYsXcfXqVX56bW0tcnNz0aNHj2Z9Tj1ffAHzF72LnNz8Ri2f0NN+AygpbaOtuNBYaNzo4cwqXABOFbRKB8iy1ym6rPVyP1m5eaJ5hZWQLS26Z7Do9GjOz9/U95T10XpZVHX2HxKBxAHd+KrYnb9n8KGXyWTmg9aYhBDMeHokH7RFxAaJqml//f4v/jTzP346xq9fen8+fl6Y+fxoLFn4B4oL9fj5m8N49s0JzMcjrVCMiA3CtMeG4uMF9dXawmDyak0tXwUbkxBiUSmZPKk3X1VaZTAiL7OUGfIWF+qZFb5pglP+pRW5AUE+mDVvLNav+hN7UzKx9dcTojYHwgGkQsJ0mLvoFv62iNggdA3358PW9NRCUdi6/L36kFDaZqBLmA63TxvAtyIQVjNb29Zn35yA75ftxdED+fh19V8q2gkMxLvPb0CVwYjffzxqUVEsR7gdjzw7SlQV297TA8mTeqP0fAWOHshHyi/HMerWeMUg92pNLV/de8e0AaL95OPnhbF3JOJKVQ0uV9ag5JyBD1vtvT/U4Prdcs5k52LQDf0B2Pe5yR4obHUgqooghBBCmk9LVoWR1o0VTMmFVfT+jdgTq/+c8DJ3GrdwXluwjmduXf7+/igtLYXZbOanV1ZW4syZM6LAtayszG6PN6/gLJ5+aRF/fdrkiQgJCUbP2IZenY/NnQ8AuFpzlb9vvUHP315VdVn1NjX3cnLzXa25yv9mDUomZDaZrM5jT0YbWis0JtiQHnNy7QSKzpWIlguPCWyxfSDVPToQe1MyAYAPm8pKq/gAdtwdicyqu/aeHohJCEHWyWLs33aGD6My0xuqdm8cGWURhLX39MATrySj/NJlhHTT8dO5kGlYcizz/jQad/S+IYxvYSCswhWK6tnZYlqn4Ib5hMFk9qmGv8P4u3ozTwUXBqj7tmbK9owNi+5kMS3jaEM7ELkKzL4Dw7E3JRNVBiMKsy8x1z9oVLTFtgmrkoX7X/i3CwnVWuxLjcYdf39hLIrP6uHfqSNMJjM0Gnd+W320XrLb2n9oBI4eyEeVwYjzhXpRcCkVEOSDOx9IwvpVqdibkomBo2IU5+cc3pXDb4dc+4Gh42L54yX7VIlimwKNhzt8tF6oMhjx6+q/0CMxRLRPNBp3ZhBs7/2hRnhMoChsLTx73qKFgLMMlkVhqwNQVQQhhBDS/JqzKoy0HayBXMbcNqUJayTEvnb+/hMA24Iv6QdR1nLu7u4ICAjApUuXRP8HlZWVOH/+PLp27QoAyM3Ntdtj+XnjFv7yC0/9DV2vn3Z/parSYt6qy1X8fZtMDYHwn38dRfeuwVCDG1ALAEpLL6h+LML7U1pOuP6qygp+vqrLVfzv9PR0xfvSGwxW53GUxr5+qglc9x78U7RMaHiAox8uAMDbuz1zellpFXN651A/ZJ0sFvW0PJvbEM4Lw1ShiNggRKAhVKyqaFi+e7R88NwrKcyiH6sUK/ASBr6l5xvaaxivXFP1OLlQmRtgSipZJqjNyijhl5dbt1D5pcui/cJhBchAfZjKtYDgcANEAUB8P3YI2SVMZ7GfuJAvJFSnaluLz7LDxas1tfz+Hjy2B7b+egJVBiNWf7FPVJ0rh6tCjYnvLDtPYEhD1a/0byil0bjjjmkDsPrzfXw7iIeeHIGu4f6KFbH22h+2kD4P7Np3EPOeme10QStAYWuLoqoIQgghpGU1d1UYad3kAgFCnInwOczeHzDd3d3h7+9vUbl5/nx9ZR4XuNrL0eMZAIAunYP5oFWoRtDWQEijcYevT0dUVl1GyYWLzHlqrl5Fdm4BQoID0bFjB3i2Z4dmaogGnCk8h6S+vZjz6SsaQuKQYMdVZjojuapquedZP39vh21rQXbDMcVthzCwW78qVXbQKiGu0lQYZgorSpVcranlL3t1kO+b7OnVEPEIT/nm2NqOQfjY5QZQEmINNgWIT/kX4kJQrkdtYzU2xJMLu6WEX7A0dVurKoz834Wrov14wSYUF+pxYNsZi0Go5ChVewsrU69UWa+M7zOwO99jmOszDNS3ZxhzWwISB3QTBa/23B+2UHoekAaujkZhawthVUUAoMoIQgghxAns/P0np3lzRpyDUuUVIc5EWslj63OZ8Msn4Q83TaPRwNfXF5WV4urS8+fPo2PHjs3ymHx9OjCn/7ZlB3/58uUrotuGDRqAP7buRmZ2Hn/ar9COPQfxx9bdAIA3Xp5rEbZeuaL+9HkA6N8nAUfSTmL3/j9x5603MavRuPAYADp27GDL6p2K9Jiw12ulqzy/Zp9q6NHLOi2/KTQeto/Q3rmrVtV81ioa1bhc2XItLNQoyL5ol96fHGE4rcRUa1Y1X2N0CdPxvUjXr0pF30HhsoPBCSucO/h4qlp/ftZFqwGuRuOOVz6ehMLsS/wAbUB9r93Vn+8DIO7LWn35KpwFq4WAM6CwtQXQm3VCCCHEuTnTaUfE+dTV1cFsNtP7N+KUzGYz3N3dG9VCQOnH3d2dn8/Ts/5DvTRwzcrKsli/RqNp9PPoiCE3YFPKTmRm56HaWANfn/owNzuvAD/8sgnnii+ga0gwzhVfQGZ2Htzc3PmQ86bRw/gwddF7/8L0eyci8fpgWZfK9PxtE5JHQaetD6s6BTSckpqyYy9GDh1o9RRezp233oQjaScBAB8tXYEpd01AdER3/vbsvALs3v8nf58dOzSEre7X94+7mxs8PDxgNptFvXGdlb1DVu6y9Ke2trYJa7cvk8nM9/eMSQjhp/t3aviiQTpIkTXx/UL5U7ANZVdsDnDzsy6qWkZt1aYSYY/Mlz6YaPewmd/WMJ3sgF72JqwIVbsvhVWdMQkh/KBl9nLPo4OQlVGCKoMR3y/bJ7t+YQirFDwLK0/lWiVIaTTuiIgNwqx5Y3G1phbn8stx5mQx35Yi5ZfjGDK2B3z8vETb0Rz7wxa1tbX86xXHGd7PU9jazJSCVld4QSWEEELaCgpcCUepjyDr/dubX37n6E0mbciCmdNF16VfAqh5HlMKXLkPrXV1daLAtX379vDy8rI6gJK3tzfc3d35+7HFqGGDsCllJwDg5Tc+RHxsDIrOF6Oisr4f4MRbb8LQgQPw0qL3AAAffPofjBh6I8aNHApvb29MvPUmbPj9f6isuozPv1qNbl3rw7Gz5xpObb795nHw9Gyoao2Pi0HG6SxUVl3GF1//F5WVVXhq1kMICuwku511dXXo0KEDxo0aiq079+Fc8QV8smwlunUNwYihNyI7pwCH/qofOd7P18fiPjUe9R/DPTw8rFYHt2/XDlqt1m6vS9I+vcK/O/dbo9GIfrOqWZX6/SrtN6XrzsZkMuPLD3bw128cGcVfFp7OnHn8vE1hqzAEZZ3qD9S3DdB4NHyZoA1oCOuVTgsvOWfgL6ut2lQirJ6U29am4AawKi7UMyvSm4MwKLxYUsmcx2Qyw1RrFoWs3CBSWSeL7b6t7T09+L6pWSeLkXGsSHZebp8pVR0byq6gKdp7etT3DY4NQoeO7fk2GcKBxppzfzQW6znGUe/pHb832hDuDbrwhxBCCCGO5+wf+EjLkwta6f0bcTasY1UOa7BAadDKCt+4n44dO8LLy0txe6TLcD8ajcbiRzpPcFAgnp79CBLiYgAAGZlZqKisQreuIZh+70RMui0ZwUGdMCipL4D6EDUnt4BfftJtyZj18H38tpw9V4yz54rh5+uDOyck471F8+Dl5SnaxrtuGw8/v/oAKeN0Fs6eK4Y7Y9tYPw9MmYRZM6aJ7m/1T7/yQeugpL748M2X4e3tJVqO/zvI7CsurOb2p0Zhe1j7VelHblnp9kl/s9pMNPW4lTuWhY/fkdYs38+fTh0SphNVEQoDu2OHCmTXcb5QLzr1GxD3aS27wB5Y6NM3tmD+o//F6//4GUB91aGPtv4+D+7Mlr2/zOPnmdvYWMIKXuG6pfIySy0epxrRPRt6M184V8Gcx2QyIy+zVFSt2RTtPT0Qcr3Ha+7pUuY82349gQWzfsCLD3/P98vtO7Chcl1pW89fD45t1X9IBF89/eOXB2WDYG6fHT2QL3s/GUcbwlprFc5VFUbkZZbi/PUKbqlhyXH8du3fdoafrmZ/XK2pbfT+UEuj0YiuO8t7eqpsbUbWqiJYb9Z3/bHW0ZtNCCGEtHojb5ksum5rVRhpvVhVEfRlOXFm3DEpbSWg9DwmrUrkwi1hNSt3XbguLnCtra2VPdWbm491P3KE99O/TyL690lERWUVcvIK0LNHtKgqFABmz3wAs2c+wFzX0EFJGDooCaWXynDufAkC/HUIC+3CfPwAENcjCh+8Ph8VVZfrb4MbAvy1VgM/bpuHDUrCoKS+KNMbkJWTBwDo1rULggM7wdOzPfOxPzj1LkyZdBs8Pdsz78fNzQ0fvbUAAETzNPV1SW3Yzgpa7XH/wv0mvO5MLVqqKow4djAfB3dm8+0DAGDG3JGi+dp7eiD5rt5I+eU4qgxGHNmfhz4Du4uq+6oqjPh4wSYA9QNT3T97mMWy61elooOPp2jZ89crPQHg3pmD+PVxlY/FhXqkrDuOUbfGiyov8zJLsTclE0B9f02lkeTViogN4ispjx0qQN+B4QiL7iR6nHmZpfyAStMeH2pTT9URN/fkt/nX7//CPY8OtKie/fKDHXzo/ebyKXZ5XGNuS+CrSNev+hO33tuPX+/Vmlr+1HnhfrRlW320Xnjl40k2V3ve8+hAvPv8BlQZjPw2KO2zNcv345Z7+oq2o6rCiK2/ngBQ/yWBtcHDjh3M5ytXWa0iqiqM/P6/Y9oA1X87k8mMlZ/satL+UEPuOcTR7+UpbG1mSkErvVknhBBCnIP0DRoFrYT1Hs6ZAgFCOKzjU00LAXesYHkAAIAASURBVOF8wrCNWyfrN9cfVqvVoqKiAteuWQ7AI63etBbUSfvscb91Wj8M6Nur0fslJDgIIcFBqubt0KEDOnQQD2CltA+l29y+fXuEBAehc1Cg7PLC6zqtn9X76BwcyLy9Ka9PrG1itZGQq2gVrqOlXycryqvtfgr798v2iq4XFxlEAStQf6r0318Yy7zvUbfGY/+2M6gyGLH6831Y/fk+JN/VG90iA5B5/Dxf8RoSpsPUWUNEyyYNi+TDtNWf78Ovq//CHdMG4PCuHFE1rbA9gXDE+JRfjiPll+P8/ZVdqOIDM27b7GXaY0Px8YJN/Cj1PlovPnQryL7IB279BofbPHhVQJAPhiXHYm9KJj+qfb/B4Rg6LhZF+WU48VcRvz+eeCXZLkGrdF/uTcnEsUMFGHdHIi6WVPKPR7ofA4J8+IGsrG3rQ0+OaFSwGBDkgzsfSBL9LVnzcNvB/fQbHI7+QyNgvHKNf1zc386aYclx/LZ/+sYW9B3YHbG9u8Dbuz2K8sv4SmofrRf6CKpZW2J/SFWUVzd5HS2FwtZmoqYqgsJWQgghxDmwBphx9DfixHGo537jmEwmVJSXAQD8/AP4U/uu1hhx+frASsLpxD5YIzGref6SVjlKq1u5y6z7c3Nzg1arxaVLlyzmEYataqoipf9vSpdZ285al7XHq+Z2W8Jhtdtpa3hqy/yNeb1SGhyN1Q7Cnm0ElNTV1WHIwAHYf+gvflpRfhkiYtWF52pxAz/JSb6rN8bekSgbErX39MCzb07A98v28aGStBrRR+uFWS+OtVhHQJAPXvpgIj59YwuqDEY+sOXEJITgnkcHipbRaNzx0vsT+WpB1v3FJITg/tlD7RZKAkCXMJ3itnL3Kw2U1brzgRsQ2NmXDxi5AFE8T5Jd//4ajbvob1dlMIoCzpAwHW69t6/Ffrx/9jCExwQqbuu0x4c2aVsHj+2Brb+e4ANTlqmzhiCoix//95duB/clgbWqVs6Mp0fi3Rc28OGzMHDm3DtzkMVx3BL7Q6gov0x0fcSQG+2y3uZAYWszU6qKoDfrhBBCiHOwpSqMtC3CIMhsNlNlq4KK8jJ8OO9pAMBziz+Bf2D9h6uMI6n48d9LLaYT+5AGrWpIA1nhb+Fp7ayqf+7/wM3NDZ06dbIIXLleoNKglcJW5w1bhZelfXuVQtbmfK3sGhIsup6fdRHDkuOavN7wmEBMe3yo4u0+fl6qw0ofPy/Mmje2/tT/s3oUZF8EAMT27oKwyE6KfVMDgnzw0vsTcS6/HEX5ZcjPuoj+QyMQ3bOz7P239/Rg3l/36ECEdNMhuKsfMxweOi4W8f1CRT1Ypbj9wponIMgHr3w8CYXZl1B+6TIyjhYhqIsfeiSEoGu4P3N7ffy8+HWGxwRCybDkOPQdFI7C3Es4m1uG0vMViO8XqviYrP0thY9b7m838/nRuHCuAsVn9cg4WoTwmED0HRSu+HeT29bwmEDZ6mvhvrDWS1ejcceTr45HftZFxXmSJ/VG0rBI5GddxMWSSn47/Dt1tGj1YG1/tPf0wGv/dzeqKoyixxUeE4ionp0VQ9vG7I/Gku6T0OuDIDojClubkbWqCHqzTgghhDiHxlSFkdaN2ggQV8E6RtU8f7HaCagdmEh4P506dcLFiw0fgFkBnZqwVXjZmcPWpmxnU8NWe7YVYAXtchWuzTFAlrVti4roLpp29EA+3/O0KQKCfOweAAH11Z9dJINoqSEc9d2WMNnW+4uIDUIElL/osrYujcadX4+a+23v6WHT/vDx80J831BR6wQlav6W1h63RuPeqL+drdtq675Qe5zaejxb2x+2Pq6mLmcradVsTFREs95fU1DY2kJYVRFU2UoIIYQ4DwpYCStA4a5T2Nrghy8+Rdqh/aqrVOP7J+G5xZ8AqG8j0FTFZwvw6cL5uPfvT6Dv4KaHL65OaWAQW9sJcNeVqluF91lXV4d27drxFa6A7WGr8D6shZfNxdaKTTXVrdbW2dTw116DZTUmbLXH/VvTjVGxVlVhtFoVSAhpnaoqLNsqSAc/dCYUtjYzpaoIerNOCCGEOIfGVIWRtkH6no3evwFph/bbNH97Ty+097RfQFJyttDRu8DpNPazBSuQdXd3h9ls5sM2pVYC3G3e3t584CptI+DsYWtjq0WtbbNSOGpr0GptfU193KwgtaWrWaWCAjtZTDt2MN8urQQIIa7n2EHLHsvBQYGNWFPLoLC1GaitiqA364QQQohzaEpVGGmdWCErvX8DLldWOHoTcPrYEUdvglNhHae2PG/JBa5Aw+CBwnm5IFb6P+Hj4wOTyaQ4oFJj+7Y2J3uGrdx1a+t0hrBVuA65KlfW5abet7B9hXD9rHWOHzsCW7bt5q8f3JlNYSshbdTBndmi6xOSxwCQfw5x9Pt3CltbiNwbdkIIIYQ4Hr0uEw4rOFG63pb88MWnuFxZyV9ft/I/6Ojri+4xsRg8brzscvlnTuPgthQAwG33P4SOvn78bSaTCemHD+D3Nd+hqsLAT4+O74UBw0civn8SXxV7YOsWFGRlIufUSQDArt9/5YNX6XrbIrWhHwsrcBW2E+A+yHJt0OS+fAgIqG8TIaxsla5DadtdvY2A0n5vahsBpWWaGn7KXWaFq/YKMOQen/B+B9/QTxS2FhfqqZUAIW1QVYURxYV60bShg5Ps2sfa3ihsbWZKVRFt+c06IYQQ4oyojQBhofdu9aTtA7Iz0vnLSmGr/tJFftnkyVPR0bd++uXKCvzrtZdEIatw3dkZ6YiO74VHnp8PACjIyhRtQ8nZQr6lgHC9bY29jk1p4Mq1ExD+5uYTDvYrHGRLqecnN4/S42BdbilqP7RbC1qVlrdlMCw1t9nztUopdLXX/bGqWpXmjY4Mt5i+f9sZJE/qbbfHTQhxfvu3nbGYFhsdaTHNmapbKWxtJmqqItr6G3ZCCCHEmTSlKoyQtuDNL7/DsQN78eO/lwKA6gGy5Bw/dIAPWh+c+wIi43qivacXrtYYkXv6FL5d8j6yM9Jx7MBe9B08DFMeexJTHnsSC2ZOBwAaIKsZSANXud9cL1dWNaxSv09W6CoMa4WXpbcL70OJ2s9YTQk+pdvJ2m4161PzGtPY7bSVUqhq79dC6T6U+/Hw8EDymOFI2b6HXzbll+MYdWs82ntSlEFIW3C1phYpvxwXTbs1eTQ8PDxk+0o7w/t3eoZqQVQVQQghhDgnem0mpOUd3rUNANBn4BDE9enHT2/v6YW4Pv1w+/0PoyArE4U52RSqtiBhECasahVWssp9rpGrbBXexro/Duu52BFhq7XbbW0vYo/AtDnCg6bsA3vev1Lf1ltvGi0KWwFg5+8ZVN1KSBux8/cMi2m33zwOAGS/0HMGFLYSQgghhBBCWlznrt1QcrYQaYf2Y9DYZIT3EA98M3jceMX2BKT5CCs2uaBVGLqqDVvVVh0pVbTa+uG5sR+2bR1UTLidTa1QbY7lnPV+hPfH2nfSYyYoMACDb+iHA38e5eeh6lZC2gZWVevQQUnoHBwoW4HvLIGre9NXQQghhBBCCCG2SZ48lb/873dfx7EDe3G5ssLRm0UEhB9e3d3dbf5hVR1ZGxhJWBXbkj/WgmK5H2vLN3X9SvvInj+OOq7UPLZ7J91msY6fVhxs0W0mhLQ81v/5/ffcaVOrGkehr4IciE5ZJIQQQgghbZV/YBD6DBzCD3rF9YL18dNi4JibMGjMTejo6+fozWyz5CoPhbfJneKvdGqn2g/BLf1ZqbEfztVW4DrDh39nIDyuuN/WfoKDOmHQDf1wUFDdevRAPoaOi0VEbOP7RhNCnFdeZimOHsgXTRNWtTp74EqVrYQQQgghhBCHmPLYk3hy0TsYPO5mflpVhQHb1q/FO0/PxlcfvAOTyeTozWyzWFWpwipNVnWo8HZuOem6bLnvlvpp6j5ypspRV8E6vuR+HpgyyWL5bz7dDZPJ7OiHQQixM5PJjG8+3W0x/ZH771EMWZ3puZbCVkIIIYQQQojDhHTrjtvvfwiLln+DJxe9g9vvfxg+floAQHZGOtIPH3D0JrZ5rOBU7anxwuUIYVFzHGn9fPHYI9NEy1UZjPjygx2O3nxCiJ19+cEOVBmMomlP/v0haLV+LvOFFoWthBBCCCGEEIfTaDQI6dYdg8eNx4znXuKn7/r9V0dvGoH6/prWBsMiBJA/jpT66Q4dmISEuBjRerJOFiNl3fFGbgUhxNmkrDuOrJPFomm94mMxYuhA1b22nQGFrYQQQgghhJAWdbXGiOKzBTiddpR5e0i37rj3708AAErOFjIHzqq+fNnRD6PNUTNok9y8hHAaM/AXF6w8/uh0i/Wl/HIceZmljn5YhJAmysssRcovll+ePPXYwy73JR+FrYQQQgghhBBVvDp05C8XZGU2ej1lpRfw6cL5+HbJ+zh2YK/F7SaTia9o7TNwCHOgrB0b11E/VwexFrA6wwdd4vyU+gFLq1y56zqtH15+7gmLdS19K4UCV0JcWF5mKZa+lWIxfeG8udBptcxqVrke4c6AwlZCCCGEEOIwzvTGmFjXLTKKv/z7mu9wYOsW/PDFpzavp75P68MAgB//vRQ/fPEpjh3Yi+KzBTh2YC/W/mcZSs4WAgCSJ08VLdtn4BAA9QNp/b7mO2z8/hvknznt6F3TplHASmyl1JbCWqgSFxOF6ffeabFOClwJcU1yQevD0yYjPi7G6vODMwauHo7eAEIIIYQQ0jZwb4Dr6uocvSmkkTr6+iE6vheyM9JRVWHAxu+/BgBMeexJm9c1eNx4ZBxJRXZGOtIO7Ufaof2MeW6Gf2CQaNrI2yby8x7YuhkAEBYVjfAecY7ePYQQG7m5uaGurk4UpNTV1fG/hT/cNDc3NySPGY6s3Hwc/POoaH1L30rBE68kIyI2qHEbRAhpUXJB65Ab++OWm0Yxezk7e1UrQGErIYQQQgghxAYPPfMisk4ch/GKuGdqR19fvs9qR19ffnr3mFjmdAB45Pn5uFpjxPmCfJwvyEdBViYCu3RFdHwiukXFQKPRWNx/SLfueHLRO3zlK3cfhBDXxGonwIWq0tDV3b3h5NxZD9+HysoqnDydJVrf0rdS8MizoxDfN9TRD40QoiDjWBG++minxfTE+Fg88bcHme1EWC1HnC1oBShsJYQQQgghDsJVNFljNNbA29vLKd9Mt0UajQZxffpZTG/v6YW+g4dZTPcPDLKoTpUuF94jDuE94jB43HhV2xDSrTtCunV39K4ghDSBsKpVWN0qDVmFlwHw83p4eOC5J/+GDz/9j0Xg+tVHO5F8V28kT+rt6IdJCGFIWXecORhWYs8emDf3MbRr145ZzSoNWgE4ZXUr9WwlhBBCCCEtivVmmJvWOTjQ4jZDhXgkempDQAghrYOa3q1KPx4eHnh2zkwkxMVYrDvll+NYvngbrtbUOvphEkKuu1pTi+WLt8kHrU8/bhG0sqpbnbVXK4fCVkIIIYQQ4tSEPfkoaCWEkNZHbgAcNYFru3bt8OycmbhplGVlfdbJYrz7wgYaOIsQJ5CXWYp3X9iArJPFFreNHztCVdAqV93qbChsJYQQQgghzU76Zlh4XXrbHbfeJLq+/1AqAHHQKneZEEKIa5G+HtgSunK3tWvXDtOn3Inp9060WH+VwYilb6Vg/ao/YTKZHf1wCWlzTCYz1q/6E0vfSkGVwWhx+4P33YWH7rtbNmi1FrI6Y+BKPVsJIYS4rFqTCdt37QUA9IrviS4hwY7eJNIIKdt3AQBCgoPQOzHe0ZtDWpiwbyt3ecSQgfj19//x8xw9fhLFJRfQJaQzAIh6/HHLEUIIcW1KoSsAUc9WTl1dHcxmMz9P8pgR6N4tFJ/951tUVFaJ1r83JRPHDhXg3pmDaPAsQlpIxrEi/PjlQWbIqvXzxT8em4G4HlFWK9hZ7QOc+f0fha2EEEJc1vZde/HG4iUAgM3rvnP05pDrqo1GeHt52bQM93dcs3IZheYEcT2iLKZ99d2PmP/sHIvBVAghhLg+6fO6MGTlyIWtwt9msxlxPaLwxivP4ouvvrcYOKvKYMRXH+1ESJgO0x4bii5hOkc/dEJapfOFeqz+Yh+KC/XM2xN69sCcvz0IndZPdcsQpcGxnA2FrYQQp3S++AKmzphtMT3AX4f+fXtBp/XD7bfchOjIcEdvKnEQvd7AB3SvzpsrG+4tevdjbN2xBwDw2YdvMisnR94yGYB9gj65Y1fJuNHDsfClZ5i31ZpMeOv9/+Mfw64/1tp1P6Zs38Xvx6Zsr15vQEZmFn74ZSNSj6SJ5p01Y7rifk0eMxKbtmxH6pE0vLdkGT5+Z6FdHyNxXqyqVjc3N2g0Gtx/7534/sf1/Lw79xzAIw9MQZfOwRaVrcL1EUIIcU2sMxZYla0s0gBW6+eLF/4xC/sP/YXlX//XYv7iQj0+XrAJ/QaH45Z7+iIgyMfRD5+QVqGstAp//HQMRw/ky87z+KPTMWxQkihA1Wg0/HXhZVdrH8Chnq2EEJdSVq7H1h17sHb9Jjwy+1k8M38Rak0mR28WcYBFiz8BAERFhiN5zEhVy3z46b8dvdk2qTWZ8MKCN/mg1VkdP5GBifc9inmvvS0KWgFg6449mDpjttX/0xfn1gfUqUfS+LYCrs5gMODEiROO3gynwzrti/Vm+a7bb7GY9sxLi6A3VKCuro7/EaLerYQQ4vqkLQSkPVylYQwrmOF+hg5KwifvvIpuXUOY93X0QD7efX4DPlqwiQbRIqQJ8jJL8dGCTXj3+Q2yQWtYaBd8+v4iDB98g+r/Z9ZzgTOHrByqbCWEOL3Fr7+MiO5hAIAr1dU4evwE9uw/jNQjaUg9koa33v8/2apA0jrtP5TKh3rvLHxJ9XI5ufk4fiKjWfuCBgV1wpqVyyymc9Wujz44FTePGy26zdvLk7+s1xtw+MgxnDx1Btt37UNZuR4B/jqUleubZXuHDxnYpO0FGkLspP598OgDUxDfMxbXrl1DVnYu5jy3AEB9ywelULxLSDDmPjETS5Z+ic+Wf43hQwba3IrAGRgMBuzcuRN//PEH8vLyMGrUKCQmJjp6s5ySsJJVWt0KAF5ennjuqVn48F/L+WXK9Qa8+9FneOu1F9GuXTsA4oCV2gsQQohrYz2PS9sJWCNsLWA2m6HT+uGNV57FsfQMrFj1o0UvV6C+0nXpWykICdNh0Kho3DgyGu09KS4hRMnVmloc3pWNgzuzZdsFAPW9WWc+OAX9+ySKglPplyNKX5y4SvsADj17tHKL3v0YADD38UcBAFt37cWe/YeRm1eA/n17YfzYkRgyMIm57PETGfj51z/QKyEOkydOAABk5+Zj976DWLdxMwCgf99emPv4o9DptBbL6/UGbN21F+knT2Prjj1I6t8HEd27YezIoVaDDu5+8guLsHXHHky+cwIGJvVDvz6Jih++ucFy9h1MRW5+Yf029knEwKR+uDGpHzw0Gtllq41GZGXnIvXocezYcwCR4WEYOigJURHhCO/eTXHZ88UXkJ5xCvsOpuLIsXSMGTkUA5P6IT42hrlvlLY3MjwM48eOVNxe7u/KnZpbbTTiaNoJbNm2i9/Xw4fcyP/dXF1E9zDRKcjRkeGYPHECJk2byVe6vvLCP0T7K2X7Luw7mMofv7UmE/ILzqo+fs8XX8DmrTv4YzCpfx/07RWPEUMHWW1dcPxEBrbt2oe8grPIzSvAmJFDMXbkUMRER6o+fo8cS4dOp+WPX7n/Uw53Cveh1KPYvmsf+vftxR+/1rZXePzm5hfy92nL8cttry3HL7fvueOXO/1czfG7+OOlAOqDQFtP+1+x6odmPU3dQ6NR3KbQriGKty/5fIWokvXVeXMBwOZT/dXy9vKCd4j8cWlte4+fyEBObv231y/Onc3P66HRoHdiPB59cCpWfLsGny3/2moF8p233YxvV69FWbkey7/6DnNnz2yWx2xv0oCVyJOe+i/8UM0KX4cPvgH/27EHx46f5Jc5knYCc55bgMWvvwx/nZbZSoAGziKEENfFClxZPVyFtwl/zGYzAHHoWldXh369E/DRW6/gUOoxZmsBoD50Xb8qFetXpSImIQQjbolDdM/OFLwSct3VmlpknyrB7j9OI+tksdX5H3/kfgy6oR88PDxkQ1ZbBsYCXOP9HT1jtHLcB/ahg5Lw2fKvRZVRW3fswdYde/Dog1MxY/oUi2WLL5Ri6449OHIsHZMnThD1PRSuY9aM6dBJlt1/KBXzXntbNI2rQly7fhMm3zkBc2bNsAhjak0mfLZ8Jdau3ySavnb9Jn7a4tdfZgZPer0BT89fxH/o5+Tk5mPt+k1I6t8Hby+cxwy7WD0Wc3Lz+cc794mZssHP2g2bsGTpl7LbK9cDUml7t+7Yg6jIcHzyzkJ2kG2oQOqRNAwdlASdzg/THpkj+tumHkmDTutnl7BVrzdgyecrbFqmpapM58x6mA+gSksvWeznrTv2QG+owOSJE/DCgjeZpzezjl9WD0vu+F3x7Ro8+uBUPHDf5EYdv3I9Q88XX8DsZ+aL/o5l5XrR8fv+mwuYASbr+OX+v4H6sE4u5Fr53Q9Y8e0a0TTuPgFgw39XyIbRctvLhdML5z3NXDY3vxA5ufkYOigJN/bvi4n3PWqxr5WO3+zcfP5+RwwdBLW40C/1SBrOF19w2kGYeiXEITwsFHE9ovnA29lPq3/0wakAwNynSf16Y8W3a1BWrodeb1AM8D00Gky6/Was+HYNtu/a59RhKwWs9iPXt7Wurg4eHh54/qlZeOqF16A3VPDL5OYX4rG5L+HVeXPRR+YLXHtWubrSaWuk7aFjk7RGrAGzuN9KwauQ2WyGu7s7H74CgIeHB4YMHID+fRKxe/9hbNy8jVnpCgBZJ4v5MCkmIQSJA0IR3y+U+ruSNqestAoZR4tw4q8iVQGrn58PJt5yE0YOvRFeXl6K1axqAlZXC1oBClvbDC44mnznBMx6ZDr27D/Eh68rvl2DpH69LQKgXvE9AdQHKCnb6ysmA/x1eHDaZMRGR6JDhw7IycuHTucnWu588QU+aA3w12HOrIcxZuQwZJzKxIef/psPchJ69rAIgLbv2suHPNyyw4cMxLnzJVi15mds3bEHiz9eitVffWYRmnLBJbeNE8aPBQCsWbsB6zZurh945ZNlzCBw+cr6Ucy502W7hASj1mRCxqlMzHluAZYs/RJDB95gESTUmkz4dnX9YDWfffgmX7mo1xuw7rfNWPHtGsxf9C7+8+n7FiGZMGgdN3o45j7+KKqNNVi+8jts3bEHObn5eHr+Iqxc9pHF9vbtFY/UI2nYdzAVm7ZsR1m5HuNGD8fQQUnoFd8TFy9dstuxU22ssblfZEuFrSHBQbK3RUXUV3Ny/R9Tj6QhKjIc06dMQkhwkOLxy/2/SI/fV9/8gP+fCe0aYnH8rvrvWv74jYoMx2OPTEe/PonIys7FilU/IPVIGl598wP8tGq56HioNZn44FJ43NcYa/jjKPVIGj5bvpIZPnHH79wnZmLcyGGiatF5r72NNxYvwY39+1qEXNVGIx+0frXsI3Tt0pk/fr9e/RPWrt+ERYs/sQh5hdsL1P/fTJ08EXp9BX/8ph5Jw5LPVzCPhcjwMOTk5uPkqTPYtGU7AIiO37yCQvh07CD7t9297yD/97FlgLTQriF84Lp85XdO23rC1SrSeyfGK56t8POvfwCobzGgFLRyRgwdxIez2bn5TjUIXm5uLvbv3489e/ZQwNpEwjfKcpWt3G+tny/ef+MV/P0f80TrKNcb8Oz81xEZHob5zz0pOlZY67Jl26TVt3Lbzlkwc7qjdykhqo5VQlyJ8PmYC02tDZbFqnLlfnNtBYD6VjU3jR6GMSMG49BfaVjz80bZ0BVoCF7Xr0oFAPQbHI7wmECEhgcgMMQXPn6u1/qIEJaqCiMuFleiKL8M+VkXFQe6kvLz9cF9k+/AoKS+FpWsrKBVLnyVLsNxpdc1ClvbEGGFZfKYkRg+ZCBmP/MycnLzsW3XPosPy0FBnfjLbyxeggB/HVYu+0j0YZn1IZgLfgL8daJQqXdiPFYu+4ivkP1s+dcYM3IYf3t9VeDXAGBRbRsdGY4Xn56NI8fSmaeXCk9jfWPB86LHMmP6FMT1iMa8197G1h17LE4b1+sNfJg4dfJEPsTlToF9dd5cVFRW4eIly8rJ7bv2oqxcj6T+fUT3qdNp8cB99aObh3YNQVVlleg+hdsrrHTUoT6ovPuOWzDnuQXIyc1nVuCFXm/wzm23dH/Zs2JPrv+kM1ix6gf+sjQ07eDtzV9+Y/ESZmWo0vErnb93Yjx+WrWcr5D97od1orBVGFxK/x69E+Px9sJ5uHnSdJSV67Hqv2tFt3PHEQB8+PZr/HZ5e3mJjt+16zdh1iPTRV80nC++wB8HE8aP5W/z9vLCkIFJmPtE/f9JYdE5i6BrzdoNAOq/hBHuC51OizmzZkDr54vQriG4du2aaL8Jt1f4vOId4oWFLz2DoYOS8MbiJXzlsPR4HDooiR/kjLW/rB2/O/YcAABMuv1mm4+Zm8eNxopv1zCfC4j9HT+R0fA89cAUVcsIj8WcPMeHrXl5edi/fz9+++03lJeXO3RbWgtbWwkAQOfgQHy7/BOLClegvsp11j/mwV+nxcQJyYjrEY3I8DDAzQ1BgZ3QzsOjURWurBFvqYKQOCPWcUpIa8Edz3V1dXB3dxe1BhDOI20hIJwuDFyFwSv32jDkxv4YcmN/ZGbl4vCRNGzduc/qdh09kG8RQsUkhMDHzxPhMYHo4OMJ/04d4efvbbGsNqADNBoaq5y0DJPJDEPZFYvpFeXVKL90GVeqapCfdRFVFTWqqlZZbho1DAOT+iI2JtJiQCtpqGotYJUuB7jmaxuFrW1EgL/OIsDw9vLiK8zWrt9kUTEnrcSUBq1yuA/W/fv2Yp7yzAUtZeV60anf+QVn+QAnqV9vi+W8vbywctlHqDbWWNy2bVfDC2J8z1iL2/v1aRic5PCRY6KQzFMw0AurR6CaUc5Tj6RZVGB5aDTM9gxAQ6UXAGZFWFhoV/5yesYpxfBp3OjhsvdjD9b6TzoCN4AQ1xYgqX8fi0pnafgqdwq+UK3JxB+/fXvFW8zvodFgwvgxSD2SZhGEH01rGHGcdWq7t5cXNvx3BaqNNRaDC+07WP8NuVylZnxsDH95z/5DomNSuK41azdYHAtKVZJcaL92/SZMuesO0d9Z6fjlthdgB6NcVTxg/fidfOcEm49f7ouKUJlRZZV0CQnGuNHDsXXHHqz7bXOz/u+0dbUmEz9wVlRkuE2DkkVFhiMnNx9F5xr3hq+p8vLycPDgQWzatAl6vb5J6yopKcGECS1Xrbxp06amr6QFsU4TZZ0mxlW4/vv/FuPND/4l6uHKKdcb8PX3P4mmrV7xKbqEdG5SSwFW6EqIs3HFD6KEqCU9tuWqXOWqWlmBK1flyoWudXV1iOsRhdiYSEy96zacOHUGW7bvQcbpLNXbyQVVtlQCEuKqEuJiMH7sCPSKj4WHh4dFyKo2aJULZl2xdYAQha1tRP++vZjTeyXEqTpFXO3pn3q9gb88dBB7QB9hEHOlupq/nJPX8KIkDBuFdDqtRX9NAHyVS1RkOLbv2stclhvNW/rh3dvLiz+1eO36TcgrOIvhQ25Ev96JViuqkseMxHc/rENObj6ee/l1fmAsawN5cdub1L+P7OPk7DuYahH4+nTsaHU/tybvLVkGnbY+POX6fXIC/HVYOO9pi2WE+3/c6OFWg1agvu8rJ65HNHMerj2BVNXly/zlrl06M+exdvw2pjenTqflj98V367BsfQMTBg/htk2QGrMyGF8O5HZz8xXffweOZbO71cWYbiqdDoWAAxM6mfT47WHWTOmY+uOPVjx7RpRNTuxH73egEWLP+H/V199ca5Ny3NfBOYXFrXQFtehoKAAf/75JzZv3tzkgJWoIw1AhdWt3O1CdXV18PRsjzcXPI8dew7gy2//C72+QtV9qW0pIFdZy+oZRoizYB2XrvwBlRAW6XM4q8qVFbrKnTkhDF2lP+3atUO/3gno2yseRmMNCs6eQ0ZmFrbvPmD1vS0hrZWfrw/GjBiCxJ490L1bV3h5ecr2VlVT1WotZHX11zEKW9uIXglxzOl+vuqae/ftpa4iSVh1KhdKCdsTCE8RFb5w2XpqLxf+5OTmWx2xm/Xh/YH7JvOjznMDIQH14e0dt94kOj1b6tUX5+KN95bwFcLcqdFJ/ftgyl23M0dl59avtF+56juWiO5h/GVheN1aSQe24nC9Qq2FZXLHvxLhPhbq5K/jL+cVFPLB4slTZwDUh7+2hnfc41Nz/J48dcYifJ86eSKOpWfwx67a49dDo8EbC54X9VIWHr+PPjAF8T1jLY5frgJdab8m9e+D1CNpSD95WrG6Vli1q4bwCx255xhruoQE85WTrGpg0jR6vQEzZj/L9yBWe1YEaVukYSY3jfVbiPsgPHr4YIwcOhB79h/Gf775L8oFzw1CnYODmlzRyvr5edUXMJvNMJlMoj6A3PZx2yr8TYgaSoMBcb81Go3ot9wgIoS0NqwvzaShq1xFq1xfcFaFq3B93t5efMXrnROSUVFRidyCs8grOIvMrDxkZKqvfCXElcTHxSA2OhKR4d0QGR4GP18fixCUFbAqhaisoJUVsrp60ApQ2EpUaszpuleuXGFOr5L5NlBt8MsSGdEdZeV6fgAkJaxBlTw0Gix86Rm8+PRs7Nl/CJu2bOdPFV+y9Ev8+vv/mINcAfX9BVcu+wjniy9g89Yd2LHnAHJy8/nQa+4TMy3CJq7K1lBRKbud0p50cpr7FH+93oAln6+waRl7Dzy0+PWXReGnt5enTeFNv96JquflCKuuhYRfKAgHcdL6+QJoCCJtwR0PSf37YML4MYrzso5fby8vfPzOQlQbjdi0ZRv27D9scfyyBloDGnopZ+fmY/e+g1i3cTPKyvX88fvqvLmyrTQqFb7Zz80rUPXYHRXCPffk3zHnuQVY8e0avr8yabrjJzIw57kFAOBiQasbunfvjsjISNx3330oKCiwWxsBooz1JlrtG2x3d3eMGj4Yo0cMQUVFJXbtO4SM02dwLD2DD19ZlbONGTCL+819qBf+lq5b+mGdkMawpVqoNXwoJUQtVvsZYTgqDV6F88u1EpCrcGWtV6v146teOaUXL8FYcxVnzxXj8uUryLp+Zs+h1GOO3l2EKBqY1BcAEBMZDp+OHdCtawi8vDwRFFhfICfX5snaa5StASvrtczVX9MobG0j5KrL7N0PT1i1mpmdy+zRV1h0jr8sV5XJGhRKCXeKuV5vUNVjVY63lxeSx4xE8piRqDWZsH3XXryxuL5qdfuuvYrr7hISjBnTp2DG9CmoNhrx3ifLsHXHHixZ+qVFZWH/vr2wdcceHBH0+ZTiqhPDw0Ib/XjsodpYo6rVhJC9w9aI7mFNCpWFg2UpEfY/lRuYJ6+gkL8c2KnheBd+IaHXG2wKmLjjodwOx+/kiRMweeIE1JpMWP/bZixZ+iVycvOx/1AqhgyUbzkRHRmO6MhwzJg+hT8FPPVIGj5b/jWGDxkoOn65qtVj6RnMdVUbjXzobO82F8L92pTBk3onxvPVrdt37eUDb9J454sv8EEra0A6VxIREYGoqCjcf//9yM/Pb9IAWYGBgfjqq6+aZTsLCgqwcOFCR++uRpMLPq29yWaFmFqtHyZOSMadt40XvXEXzm9rwCoNZ4XrFQat0sFWWBWtFLwSNVi9gdUErawqIFf/kEqIEqUKV2lAqlTdKvdFmVLoyvodHBQIAOjerb4V3k2jhwEAHn/kftF219XVwWQyyZ6NQUhzcHNzg79OC43kfbm1919yZ1o0JmxlvWbJVbOyts0VUdjaRnCn2Utxp9RH2Wm0Z+EHa7mANzM7l78sDLeEwavw9Gyhld/9gPzCIui0fqKBrLhAsqxcLxt0VRuNiqd315pMou330GiQPGYkis4VY8W3a/DZ8q+ZQZh0ufrH5YVXXvgHjhxLR1m53mLgLS4czsnNZy5/vvgCf7kxVcX2FBTUCWtWLnPoNrQU4XHDOl0fAE6fyRbM3zAIl7DitLDoHPMYlDt+hceD3HHKOk6UbvfQaDB54gScLTqPtes3YfHHS7Fu9ZcWy7HuT6fT4u2F8zDtkTkoK9dj05Ztov9lbnvl2jucO1/CXxb2F3Y2jz0yHfNeexufLf+ar45XIvy/1On8nL7Xa0tub7XRiNnPzAdQP+jZnFkzmhS05ubXf6nh6C+bgPrgNTIyEvfffz9yc3Oxf/9+7NmzB3l5eaqW12g06Ny5s6p5ST1hPz7hqNK2amz7AOl2yH2Y4G6Tq4qSbgsh1rA++FqrbJX7IaS1k561wJrOClylASv3WqM2bFVqFWPtSzbu/rhwVmleQmwl99yvdBaR3GVbK1qthams1yzhe6nW+EUhha1tRFm5HsdPZIgqTbNz8/kQ9o5bb7LbfXGD9Rw5lo7sXHHl2fniC/j19/8BqO9JKgykhH0Uf/hlo0U14/niC1jx7RoA9aeVC908bjR/27rfNuOB+yaLPuifL76AqTNmIyoyHK++OFe0TWs3bMKSpV9i3OjhihWZk26/WXS91mTCPQ/MQlm5Hotff1mxanDsyKGi61PuuoPvjbn+t82487ab+e2tNZmweesOft7hQwba7W/TGB4aTbO3KnAm/PGbdsKiwvp88QXs2HMAQP3xKwyv4nvG8tWRK1b9gBfnzpY9fj/78E3RfQqPhz37D2HMyGGi4zc7Nx+PzH4WUZHheGfhS6L1Lln2Jdau34TJd04QBbgcrh2F9PitNhr5MPWrZR9ZVIi2a9eOvzx04A2i2+6+4xa+2jll+y5RKF1tNGLVmp8B1J9GfmMzDIBlr5Hqb0zqx//NrAWttSYTps6YzV9fs3IZvEOcN2xtye2tNZnw8qLFKCvXY9zo4czj0FbcwFqO/rJJKjIykg9eDQYDdu7ciT/++EN18EosWTutnxW4svq9ymlM4MoaHIv7QCCdT+n0U0KaSk0rAemIztLlCWntWINgCatclcJSuevNGbay0GsGsQdXC1tbYzWrEIWtbcic5xYgKjIczz35d/z86x+iU8PHjRxmt/uZOnkiVny7BmXlejwy+1mMGz0cd99xi8V9zpox3WJZro9i6pE0TJ0xm1829ehxPuSKigy3CDa7hATzA0qt+HYN1m3cjDmzHkZURDiOHj+Bb1evBQD075NoESqNGzkM365ei6079kCn9cPYkUMR2KkTrlRX4+jxE3xANnXyRNFyHhoN5sx6GG8sXoLFHy/l76+DtzcuXrqEbbv28X1kpe0UhNvL9dR87sm/o/hCKT86PFAf/Dl79Vxrwx2/Obn5omPQ2vErPB5Yx++6jZsB1Ie0SsfDG4uX4LPlXzOP39HDB1sE3w9Pu4cf2Err54ukfr1Fxy+3zdLj19vLi9/e515+HXNmPcxXl+cVFGLLtl18eCa9T+Ep+G8sXoLvfliH5578O1KPHuf/XwBgzqyHm+U08tHDByMnNx/rNm5u0uBWwr+ZNfkFZ/nLSf37NOsXEMdPZKD4Qqlo2r6DqfzllO27RLexKrCF28v6G9rTqv+uFbU9kW6fUK/4nla3Jft60Ao0fhC0lqDVajFx4kRMnDiRgtcmUhO4Wgsym6PCjxVcCUNXYbUUDYxF7E3uA6/0wysNkEUIu8pV+kWYUo9Wa60EuHWraSkgvcy6LjeNEHuxFrBKr6tpHSC9bi1sVQpZWffV2lDY2kbMfWImP2gO11OP89mHb9p1ABNvLy9s+O8KPD1/EXJy87F1xx5RSBXgr8OHb7/G/MDdOzEen334Jl598wOUlestlk3q3wcvzp3NvN9XXvgHwsNC+aBXGqAk9e+DObNmWCyn02kx75knMO+1t0WjsUv3Hyv0TB4zEvsOpvIhmVSAvw6vvjhXdnt1Wj+sXb8JObn5Fn8X1sBapPl5e3lhzcplmP3MfOYxqHT8Jo8ZCZ+OHTHvtbcBgHn8zn38Ueb9Co8HueOXNZCTTqfFq/Pm4o3FS7Di2zWisJPz6ry5sscvNxic3PHL+lIEAP7z6ft46/3/w9Yde5jHr9LAWk01Yugg/v9cWj1vqzEjh6kKW48eb+iv/OgDjQ941ZAG+1LS7WXtZ+H23n3HLc26vcJjjnX8Cb06b67VsHX3voMA6o+/pvxtWxIFr01nLXCVm1943Z5v1lnbI2xvAID5IZ2bLvxNSGOo6ZWn5kMsIW2JUi9w1nM292WZUtgKyFfBcpc5aqtclV4f6LWD2ELpuV5NuCp3WS5glV5vzGtTa65mFe3vOvpvtjvhk7HZbIbZbIbJZLL4qa2txX2PPiVadtcfa+26LSNvqQ9nXp03F2NGDkN+wVnk5OXj5KkzGJjUD/GxMYpBK1ehpKYaSarWZOLvb9/BVAwdlISQ4CDEREdardasNhqRlZ2L4gul/Lb265OoqspTrzcgIzOL762Z1K+3qvusNZlQWnoJ6RmncPLUGST07KH6cev1BhQWnRNtr7V9yzlffAHpGadQdK4Yvr4+6BrS2eqy1UYj9uw/BADNFmg5mvAxSgdoUos7fhuzPOv4jYoIR3j3blarNaXH79iRQ1Udg0D98ZBXUIjTZ7Lh6+uD2OhI1f8zen1Fk47fzOxcnC0636jj9+SpM+gW2kXV8cstAzT++J00bSbKyvV49MGpVqtbuUpRuf0hrCSVO1aemb8IqUfSEBUZjpXLPmrUNqt9PmVVtiph7cOW3F6lSlYpa+sStmdR87dtCu71kfPbjyuh0Wig0WiY1WKNYTAYcPbsWSQmJjbLYygpKcEjjzwimrZp06ZGrs05WDvV01p1K/fbHlUUrPBU+gFd7oO4cDlCGkP6YZT1YZUGyCJEnvT5WG27gKZUtdoSsNJrBGlO1trKqK1q5X7bEra29ZCVf4wUttqfs4atrTWUI4S0PfsPpfIVxGtWLmvW0+RrTSaMva0+9Pvswzct2kA4m2qjETdPmu4y2yvE9dAO8Ndh9VefNWsblZYIW5tbawxbAeWQU+mDrZoqDO6yvbeFqlpJc7Cldx5rfkKI+tCVNa2pLQQa28OVkKZoTI9W4eXGhK2s6cJpStvWWlEbAUIIIS5nyMAkJPXvg9QjaZi/6N1GV2+qwfU/ZfVfdkZZ2bkutb2c88UXsGTplwDq+/1Sv+q2S3gaqFz/PaVTRbnf9giebDkllUMfnok9qT29kzU/IcSyn6vwOVt4uTGtAyhsJc6oucJW4WWl69Jl5LaptaOwlRBCiEtaOO9pTLzvUeTk5iNl+65mq97n+p9OnzLJ0Q9ZldSjx11qeznvLVkGoL4/MZ2JQZR6uErDWOF06WV7VPxJP5hLbwOsf6AmpLHUfiiWXiaEiMmFrtLfcmc0SKexfitdZl2Xm0ZIYzV2YCzhZbWhq9I0pe1pKyhsJYQQ4pKEg4O9sXhJo3v7qjFu9HCMGTnM0Q9ZFV9fH5faXqC+72vqkTQAkB0EkbQ90g/GSvPITbPXG37WtshVR7XlDxak+Sgdy3TMEaKe0vO5tNJVzW+1l1nXrU0nxBZq3ys1tcpV6be1bWlLKGxt5V6dNxdA/aAkhBDS2ggDRb2+At4h9g9bJ0+cgMkTJzj6obba7QUAn44d8eq8uQgJDmrW/rvENVkLOpWWkbtuj22Rrld4mT44E3uwdtzSh1lCGk/u+VyulU1Tq1gpaCUtoTGBq/S62rMo1FTRtmUUtrZydComIaQ189Bo6HmuFRgyMMnRm0BcgFLQaW2Z5toWIbmWBoTYAx1ThDQP6f+WtTMYlL5YU9smgAJW0hLUhqGNDWLl1kfqUdhKCCGEEEJcBuuDsdxtjtwuQgghrkcaprKCJumXfnJfAiq1waHAlTQnpfckja1+VbNu0oDCVkIIIYQQ4rLoTT8hhJDmIPflnlIoZe0LQOrvTVqSPdrR0LHaOBS2EkIIIYQQQgghhChQCp3UhqhKZ2cQ0lSNDUYpULU/ClsJIYQQQgghhBBCGsmWsMpZ2t+Q1o2OLceisJUQQgghhBBCCCGkBVAIRkjr5+7oDSCEEEIIIYQQQgghhJDWgMJWQgghhBBCCCGEEEIIsQMKWwkhhBBCCCGEEEIIIcQOKGwlhBBCCCGEEEIIIYQQO6CwlRBCCCGEEEIIIYQQQuyAwlZCCCGEEEIIIYQQQgixAwpbCSGEEEIIIYQQQgghxA4obCWEEEIIIYQQQgghhBA7oLCVEEIIIYQQQgghhBBC7MDD0RtACCGEEEIIIYQQwlJXV8ec7ubm5uhNI4Q0g9bwP09hKyGEEEIIIYQQQpyKXOCi1xtQXVMDndYP3l5eLhXAEELkSf/nq41G6A0V8Pb0hE6n5W93hf95ClsJIYQQQgghhBDiNIShC3f5jcWfYOvOvfz0BS/ORfKYEairq3OJ8IUQIo/1P79n3yG8+f7/8dPHjRqO11562iX+56lnKyGEEEIIIYQQQhyurq6OD1osLkvmraioFN1OCHFNcv/zFZVV0jmZ8zojClsJIYQQQgghhBDiNKSBSl1dHRJ7xormSc84bTEvIcS1SP9/hf/z6SdPieZNjI8TzevMqI0AIYQQQgghhBBCHEoudOEu+/p0FM1/5Fi6KHRxc3NzidOLCSH1ZKvYr18+knZCNL+PT0f+f1z629lQZasDOeMBQQghhBBCCCGEOAKruo37SZBUtpbrDThfXCKaX7gOQojzUgpa6+rqcL74Asr1BtEyiT17uMz/OoWthBBCCCGEEEIIcRjp4Disn+CgTvDXaUXLbd66kzmwjjOHMIS0daz/U+n/+5ZtO0XL+Ou06BwcJJpHuj5nQmErIYQQQgghhBBCnJIwXLn9lnGi277+/idcqa52mWo3Qkg9VjWr2WyG2WzGlepqfLN6rWj+O269ydGbbBMKW1uQm5sb/0MIIYQQ50GvzYQQQohjyVW0Cn/GjR5usdx/vl5tUe3m7COVE9JWybUNMJvN/OUV366xWO6m0SNknxecEQ2Q1Uy4Rr3C69LbWR/sRt4y2dGbTgghhLRJrNdqQgghhDgGK1TpHBSIUcMHY+eeA/x86zZuxqjhg9EnMZ6fZu3zOCGkZckNgCcNWtNPnsb637aIlh09fDA6Bwfyy7rC/zNVtjYzaagqrG51hQOEEEIIaUvo9ZkQQghxPKWBsmbcf4/F/M+8tAhl5Xpmhat0PYSQlqHm/1EYtJbrDXjulTcs1jNj+r0u1yqEwtYWIhe6EkIIIcTx6HWZEEIIcX71A2UF4vFHH7C4bdY/5iHtRAbf91EY4qgZgIt+mv/HUccM/Tj27y0NV1n/o8dPnMLsZ162+Ps9PvMBdA4Ocsix0xTURqAZcKcssE5doMpWQgghxPnItRCg12pCCCHE8aRVbbffMg4Zp89g596D/DzlegOeeWkR5j87B6NHDIGHhwc/v/AzOmk50v3NClzt+TdRE+i6QlVka6JU1VpXV4fa2lrs2L0f7y353GLZUcMG4Y5bbnLJ/123OjrSmoVccm8ymWAymfjLwt/SEmpuPcLfhBDn8eSTT4quf/rpp47eJELaPGlIyn256e7uzv/WaDQWv7nL3I8rfDFaUlKCRx55RDRt06ZNjt4sQgghxGasqjhh5Rvrc7LZbMbcef9EXsFZi/X567R47qlZuHFAX3h4eFicZUpaltw+Z0235e/DyknkshPKVFqeUkV5bW0t/jySho8/+w/K9QaLZSO6d8OSxf/k/3+F7+Wl79Wd8T07Vba2EOGHPnd3d9TV1Yl+A+Jv3Bxdak8IsZ1Go3H0JhBCrmO9AZO+URP+5pYhhBBCiGNwr8PSs0OlZ41yv93d3fHJuwvxz3c+xtHjJ0XrKtcbsOCN9wEAk26/GfFxPZDYMxZuboBW6wdvLy/RfZLmJf2bsrD+/o1Zv9xl0jKk+7yurg7VRiP0+goAwImM08jIzLIYBEuob+8ELJr/DP/5Wu69ujOGrBwKW5uR9IVB+mFPGLS6ubkxe8rQEwUhroP7fyaEOAarakVN0Cp8o0bBKyGEENLyhGGq8LVYrgUA95lZo9Hgn/OfwY/rfsN3P6xjrnvdxs1Yt3Gzox8ikRg7cii6h4VixNBBiIrobvE3VnovZi1czc7Nx579h1Bw9hy279rn6IdKbHD/PXdiyt238xWtcmetOXu1OoWtzYx1QAg/9HG3mc1mq1WtFLYS4tyospUQx5L7tlspcJX7IYQQQohjsYIWVtWru7s77ps8EX17JWDpf75hthUgzmfb9RB05Xc/wl+nxZ23jce9d92ODt7eVpdlFaddqa7Gj79sxIZNKczT0olzi+jeDY/PfACJPWNF0131fTqFrc2ENTgWq+pNKWilcJUQ18I14SeEOAc1rQSkfVqlyxNCCCGk5VgbsFJa3Sr8HB0fF4N/vf86Mk5nUejqYsr1Bqz87kes/20LZs98EGNGDrVayMLlJbW1tdix5wA+//JbClldEBeyJsT1UCxWdLUCCUoGmhnrxUIYunIvEDQwFiGujypbCXEe1gbKcqUG+4QQQkhbIG0lIDytXK7NjzRwdXNzQ3xcDD794A0YKipwJjsP23ftQ/7Zc8jLL3T0QyRWlOsNePvDT/HH/3bg5eefgr9OK7pdmo9w8x85lu7oTScqRXTvhu5hoRgzfAh6xETAz9cXAFQHrcJ5pZedCYWtzYjVW4a7LBwUS/oCwU0X/iaEOD8KWwlxHkr9nVjBq9KbOEIIIYS0HGufo81ms2h+1udpAPDz9cUN/fvghv59ROsquVBKr/MOdqlMj6PHT+C3zduY1ah/HUvH3596Ef/33usI6RxkcXtdXR2KS0oxd95C2WpWf50Wt908Fn17JyAwwN/RD7nNkWZZwUGBoumsQdNa03t1CltbkHBgLOEBJr1Mg2IR4poobCXEeUg/oEnfnMkNkEUIIYQQx2EFrdx0jjRwlbbwE2JNDw4KpNd9BwsOCkR8XAzuv3cSSi6UYuX3P2HnngOiecr1Bjw4ay6+/uJjhASLA9fzJRcw4/FnmeseNWwQHr7/HnQODgJxHGtjELHeqwsvqxlfwZn/jylsbWZKIyhywSsAqmolpBWgsJUQ5yJX3Sq8LDfKKSGEEEIcQ9pOALAMWIVFTGoGlpaujz5rO4e6ujoEBwXixbmP4/abx+LtDz+zqFR9et4/sfSjt6DT+qGurg4GQwWeeWmRxbr8dVrMf3YOEnr24NdNHE/p76C2dYAweJUu66wobG0BSt/OSdsIcOiJgRDXQ2ErIc6H9aaMFbqy5ieEEEKI40g/R3MBq7SNAGt++jztPKz9LRp67fbApx+8jveXfIGjx0/yt5frDVj88VIserm+knXxJ8ssAtl+vRPw/D9mQevnpyrcIy1Hbp83puWX0vqcDYWtLYT17ZzwNgAUthLi4oSD3xFCnINckOpKDfYJIYSQtkbNGaKsH6XAlT5jO4ZSNbE0A/H18cHCl57GP9/5GMfSM/jbjh4/iV17D/KXhfr2isc/5z/DHxes93P0Hs+x5DIwtZWtrngWGoWtLUj4JCM8SKQjLgrnJYS4DgpbCXFeSqEqveYSQgghzkcpcGXNZy1wpYpXxxH+/VhtE4XtINzd3fHcU7PwjxcXQm+o4Of58ts1FuvVaf3w3FOzRJ/DpHkLh97vOYYtQav0uqsGrQCFrQ4hfZKRewKgFwJCXIurPPET0lZY+5+k/1lCCCHEuSkFrsKQVXpdOr9cURN95nYsuXFr/Hx98OLcx/Hy6+/x8wqDV868p2dD6+drEbBS2Oo41toGCC/Lhays25TW7YwobHUg1oEi/eaNEOI66H+WEOdF/5+EEEKIa7KlslVYvSr8LVfURO8PmhdrYDK58Wqkv3vGRqNPYk+knTjFXHefXvGIj4thBq1KPT7pb95y1FS1cr+VprHW5ewobHUyrnYAEUIa0P8vIYQQQggh9sdqyceah9VGQBq0Kr1np0pX+2Lte6V9LAxc6+rqMGP6vXj25TeY8z46/V7+PtT2+iT2Z+uZZEqBq9JvV0NhKyGEEEKICwoMDMRXX33l6M0ghBBCWozaKldbglaqdG1+rBCc+zGbzaL5hLp36wqd1s+ihYBO64fw7t0AiMNWpdHr6W/b/NS0EBBeb82D11LYSgghhJA2qdZkQmnpJQBAUFAneGg0qpY7X3wBAODt5QmdTuuw7ddoNOjcubPD7p8QQghxBDVVrtx8rCBVGui5cqDjCqS9c6X7393dnQ9cWX+jm8eOxJpfNoqWuWXcKIsKVmnQKg1dhesnLYO1v+X+Hq3t70RhKyGEENJGZefm40LpRUR0DwPg+PCwpZWWXsLUGbMBAGtWLkOXkGBVy3HLjBs9HAtfesbRD4MQQghpk2xpLcBajluWNC9Wv1ZpZSsXuLKqG6Mjwy3WGRMdwQxaWYGrdH2keVn7AsSW666MwlZCCCGkDdHrDdi6ay9+/f1/yMnNZ87z6ry5SB4z0tGbSgghhBBilS09Wa0tS5qPMPh2c3Pjq1mFQSsrHA3r1sViXWHduipWtrq7u/PraU0BnrNSu4+tVbq2JhS2EkIIIW1Edm4+Hpn9rGhaVGQ4IsPDcORYOsrK9QCANxYvwXc/rMM7C19SXe3ZlqxZuQxAfSUwIYQQQpwHKzxtrWGOq+JCV2n7AC4gFVa4crex1sEtI61mFV6XW57Yl637uC38TShsJYQQQtqIVWt+5i8vfv1l9OuTCG8vL36aXm/Aut82Y8W3a5CTm4+pM2Zj87rvRPMQUABNCCGEuAClQIdVxdoWAiBnwwWuwupWLnTlWgwEBXbCF0veAXA9ZIUbggIDLCpZrQ2QRVpWW9/vFLYSQgghbYBeb8DWHXsA1PcaHTIwyWIenU6LGdOnIL+wiJ93z/5DVlsK6PWGFu31Wm00NioAbuntFG5vu3btVA/ARQghhJDm1daDIEeQa/EgDVqF4StXudqlc/0X3dzyrHBVrl+rcDlCWgqFrYQQQkgbsHXXXv7yA1PvVpz3lRf+gfFjRyKiexh0Oj+L26uNRuzZfwgnT53B9l37+PYDAPDog1MxdfJEURi66N2PAQDhYaGYMX2K7P3uP5SKLdt2AQBmzZguqiDlbuNCYKC+BUL/PomY9ch02fBVrzdgyecr+OUC/HUYM3Iobr/lJnTw9m7UvuQeT6+EOEyeOEF0P9y2BwV1wvrfNmPP/sNIPZLGLzv3iZn8MoQQQgghbYXSgGZyQSs3iBY3j7BaVWlQLKpqJY5GYSshhBDSBnQN6cxffu7l17H6q89kA0oPjYZZ+cqZ/czLsoNrrfh2DdZt3IyfVi3nKznDw0Kx4ts1AGARxAp98dV3yMnNR4C/jg9aa00mfLZ8Jdau32Qxf05uPnJy87F91z4s+/gdi9P7j5/IwJznFoimlZXrsXb9Jqxdvwlzn5jZqH0pDHy54LTaWMNP75UQZxGycpYs/VK0nBrHT2Tg51//4MPd/YdScSj1KLbv2ofIiO4YPuRGjBs5jFm1W2004r1P6nvMzn38Ueh0WlQbjTiadgI//LIRqUfSMG70cFFwLMXd35G0EwCAyPAwDB2UhDEjhylW69aaTNi+ay/2HUxFbn4h/HVaDB9yI4YOvMFqK4bs3Hzs3neQr7LmtlHucQqdL76A9IxT/P2OHj4YSf16IyY6UrEiuvr/2bv7uKjqvH/8Lx3ahjIZaDXMdATNRAVhWTWVZUHyZsm7okts1Q1xc5dsl9QKK82v6VrUqnH9UvZrK9GlruE3Sq1IM5QINXW5UBAxU3A0AyFhMItpY/D3x3Q+njNzzjDcOdy8no/HPBhm5sx8zs1w85r3eX8sFpw9V4b840XIyfsCfsZ+GD70PgQHDoOx/z1O11Ua74mTJYpt6mxd7fer/Hl2frgXABAyYrjYb0RERJ2B/URZ8q9qQatW2MpqVmrPul3nlHtERM0SHa0MJrKyspr5TERtr85iwaQZs8X3oSFBeG31siaf2r7vQC5WJacAAJYnJSJszCh46vU4V2bC1oz3ROAYPzdWVLHKJ+ZKful51SDXbK7FtFnxDsvKX8/H24C1a16Esf89AIBdH+0V4aWPt0ER8AJAXMJiEQr7+xnx8oqlMBh6IiNztwh/JRnpqS73Yg2fHAPA1o5hxdJFqttXun9B3GwYDD2R9cl+bNmeieoaM0JDgrD+5RVN3uZREWGK4FrOx9uA9NR1qqGcNN63UtcBgMMkafbrIt8nK5NfVw2NpddUC7mlfb7k+ZcUVc9y/n5GpP88Hnvp23aorqNkw9rVCBwWoHrfylfWK8Jw+/HaHyMStf0nFzM9GokJjuF8vdWKre9kOh3v8qRE1VYc8g8DcvdkIiV1s+qHCk05NomIiDoKeZja2AVQD1udXYjcqbu7B0BERERtz1OvR/zcWPF9fkEhxj84EytfWY99B3JRXlHZ6HPUWSzYsOltALawbEJkuKgUHOhnVIR1OXlfiOsD/Yzw8TYAAHa8/6Hqc8vbHMx4cBIAqarV9npSmDjQzwgPnQ4eOh1ipkWLALG6xoyS02fEcxQVl4igNX5uLNJT16GPb2946vWImz0Te3dua/XtK5f80vNYsXSReM2YadGYMWWS2PZ1FkuTX6PgxEmkbcmAv58RG9auRkZ6KkJDgsT6r0x+XXU56THHi4qx5PmXANjCw+VJichIT8XypEQ8PHWyw3Ip/0gTQWtURBgy0lOxd+c2REWEiddMWPQc6q1WxXJ1FgvmJSwWQWv83FhkpKdi9ztpopq4tMyEfQdyHV5z34FcEVxGRYRhw9rV2P/RDmxYuxr+fkYAwMIly2A21zosW15RieycPPh4G/BW6jrs3bkNuXsyxXaqrjFjw6Z01W2UkblbbJeM9FTk7snE/o92iOMrc1cWzqlUcx/IPSjGK+2X/R/twPKkRHHMr0pOUX1//fLOOxXrnbkrCz7eBiQ+MR8b1q7GW6nrsDwpUbWVBxERUWfQWGAq9WyVLgxZqaNgGwEiIqIuIm72TPS92xfbduwUQWR2Tp6iEjB+bix+M3Y0Bv4cbMl56vXYud1WSaoVFoaGBCG/oNChzcDcR2OQsnGzCBrtw8kPPv4UgC2wkqozTRe+FoFd0qInVKs2pSC3usaM9z7YIyoez5wrE4+ZFBWhui7SWNvCyNBgx20THCiCueOFxU5bNaiprjEjZno0Fi6IE9WZ619eISpBtbatwcsW1klVwPaVlmpVk1JwCThWI69YuggPT52MhUuWiZBbXmkqBZcAsHfnNsV4YqZF47vvriFtSwZWJaeIymhAGa6HhgQpwvvAYQFIT12HGY/OR3WNGTs/2uvQ/3dTui1AnzFlkuL47ePbGyuSnsLb29/F0CH3OmyjOotF7JfHHn1EHGceOh0G+hmR/NLz+PKrc/jhhx8Urycfr31l8ITIcESGj8MjcxagusaMTenbHCqH5dt9VXKKanWy2vuQiIioM5DaCWgFpGr3219nn1Zqr1jZSkRE1IVMiAxHeuo6UWUoVT1K0rZkYF7CYsx4dD6Kiks0n8dTr0e91YryikpxOVdmQo1KxSEARIWPE9eP/9z7U2I214pw9k/zbpzKXXpeGdjKX0t+8RvQH4Cyl+rJU1+K61qVgSOGq5+K3lKhIUGqp6rLKxmvff99s557fPhYh+eWh8nflF92WMbYr6+4nvjEfNVT2u0dOvpvcT1g8CCH++Xh6nsf7FHcJ/UbDQ0JUu2ROmdWDDLSU5GRnopbbrlF3F5VdUWE6zMfmqI6rsjwsQCgetq+FCrv/HCvw4cBBoMXEhPmK6qxJbfccouoQpUmOZMbMyoUcbNnOrQukI937GjH4NxDp0PIiOEAoNnaQE6rDQQREVFXYF/Rat+TVes6w1Vqj1jZSkRE1AUZDF6ImRaNmGnRqLdaYbrwNUrPm0TVa3WNGQuXLMNbP5+6L7fvQC42bHpbsx+n1uv5+xlRWmbCjvc/VFRKylsIBAcNE9evfndNXE96cU2z11VrUqS+d/u2ybaNnhjZJs8LAP363q2yfreK66XnTQ77S76eY0f92qXXUYbV6gGgVmWwdFxohdkeOp1qNe0PdXXi+pdfnVMNpM21VzXHvGDebGTuyvq5vcHzYmKsgCGDnfYm9tDpsHDBY1iVnILsnDyYa68ibMxIBAcOc1pZev7CRXF9eMAQ1ceMHR0qgtZ6q1VzHD7eBgatRETU5cirV7WqXJ3dpvWVyN0YthIREXVx0unSA/2MilOfAeDDPZ+KiYHqrVY8s2y1ImCTJm2SAr1DR/I1q/j+NG82kl5c43C6u9RCIGZ6tCIY/fpSeYvXzd9JWKYVkLVnaoFcU0K6Xr3udPmxAER/VjUD+t+D/IJCFJw4KW6T91K9796BTXoteSWzswmn5K8lX3dPvR7LkxKxYdPbKC0zobTMJJ4nZno0xoeP1ZxYKzJ8HE6d/gqZu7KQX1AojnEfbwPmPhqDqPBxDttZHgZrbVf/ATeOv6qqK5oTXUkVu0RERF1NY+0EtJaRf7W/TuRuDFuJiIhI8NDpsHbNi2LG+gO5h0TYarrwtWLCpGefSnCoGj10JF/zueVVq1LPUnkLgfF2gdPQIfcic5ftenNnZC9VmdRIcrLktLs2s1totTdoLqnKVB5C3iqrsm1qq4Qet98uric+MR897+jh9PHy15JMiAzHhMhwFBWXYH/uIRzIPYTqGjMyd2Uhc1cWNqxdrRq4euh0SEyYjwXzZiPv8FFkfXIA+QWFqK4xI2XjZmzZnol3t27S3H4//fST6n2VVd+6tO5Dh9zb4v1BRETUUUmBq3Tdlcc7+57I3dizlYiIqJOrs1hQVFyCzN3qM6rbkyadAqBoFfD5oSPieuKf41VPz3d2mrenXi8qJXe8/yGAGy0EfLwNmlWHQMuCUbNGH1l5m4KOQm1d5DPdywNLe1JPU1dIj3XWa1SqaPUz9hO3yY+JU6e/atK6Deh/43kGD/QTwanWRas9BGDrKZuYMB87t2/G7nfSxPG89o03nY7BU6/HhMhwrH95BfZ/tAPJLz0PwPY+2PXRXsVj5VWrar1yAVs7BElzPiwgIiLqKuz7r6pNgKX1GKL2hmErERFRJ2c2X8XCJcuQsnEzljz/ksPkQfYOH80XIWvM9Ghxu+niJXFd7dT18opK1R6ecg9PnQwAopWA1EJgxpRJDo8dGTJCXHdWMXuuzOQQQsonLLqi0VtWeu2O5OKlbxxuk/c6lQeWLSGvtFQLeOssFnGMyCfgAm60biiwmwhNUl5RiZWvrMfKV9Yrwn9579n840Wqy9ZbrU1eF4PBC+mp6wDYKp0PH813aTkPnQ5jRoUi8QlbZXfKxs2K17/N01Nct5/MTXLipG2SOSnsJSIiIuecTXylFboStTcMW4mIiDq5Pr69sTwpEYCtQu/5lcmagWudxYLk9RvF91MmPyCuTxx/YxZ7eUhWb7WiqLgEz618RfFc8opLScCQweJ6RuZucZr/jAcdw1aDwUtUwmbn5GHfgVyHsK2ouATzEhZj2qx47DuQK26XB7WrXk1RjNdsrsXKV9Y7bTHQXu3PPaTYBnUWC7ZmvAfAFug1tSerlrAxo8T17NyDitest1qR9cl+8f2kqAjFsrNnzgBgCzbV9tmm9G3IzslDmemiYgIq+f7OyftC9Rh9ZtlqhE+OQfq2HYrbyysqMePR+QifHKNZySyRt7MAgMzdWQifHINFz61UfbzUziAqIkzRKqCPb28RLGd9csBhvEXFJeLDh4ULHmuV/UJERNSVMFyljoo9W4mIiLqACZHhog9lfkEhJs2YDX8/IyLC7kffu31x6vRXKCgsVgSQ/j9PmiWRh1RLnn8Jcx+NQc87emDDprdRXWOGj7cBb6WuE/1eX01JRfTESEyIvBHSeuh0iJ8bi7QtGWLyIn8/o+YkTwviZotT2Vclp2DDprexcMFj6HH77TiafxwHcg8BsPUjlb+OFNxl5+ShtMyEeQmLxWRe0uuGhgQ1Wonbnvh4G5C5KwsHcg+JbZC8fqOoMJ0xZVKr9WT11OvFfpJ6lkqBobS/AVsAaX96fGT4OPEYaZ+tWvY0rn3/A3a8/6HY5sufTXR4XWl/l5aZMGnGbERFhGFO7MOorPoWn+zPRX5BIXy8DQ7hfB/f3ogMH4vMXVl46rmV+NO82aLK9/yFi6JtRfzcWIf2A9ETx2PL9kzkFxQiJXUzxoePxS/vtIXWJ0tOY1VyihibvSVPPo6FS5aJ91T83FiEBgdi7RtviveSj7cBkeHjbsIRQkRERETtAcNWIiKiLmLNiiRsemubmDRImrHdnr+fEUuefFxRhQrYArjEJ+YjZeNmMXGQxMfbgPTUdTAYvERIJwW78hAUAH4zdrRitvk/zXMMsSR9fHsjIz0Vz618BaVlJhHgyYWGBOG11cscln3hmb/CXHtVhHvy/qOhIUF4ckGcCIa/vXKl3ffUDBkxHA9PnYyFS5apboM5s2Ja9fWk50vbkqG63WOmR2PhgjiH5Tx0Omx/awOeX5ksJplauOTG/vHxNmDGlEmKIF/Sx7c33kpdhyXPv4TqGjOyc/Ic+sauWva0aji/cEEcDuQeQmmZCUkvrnG439/PiNiYaQ63e+r1YlI4aSItezHTo1WPj8BhAdiwdjWWr/47qmvMig8RpP2yIumpVp2YjIiIiIjat27XpSnfiIioSaKjoxXfZ2VlNfOZiG6+c2UmfH7oCEwXLyn6m/r27uV0oirAdhp5yekzeO+DPRg+9D6MHfVrRRBVb7WiquoKAFs/UbVQTX7Kf9iYUU4nO5Ke03Tha5SeN+HQkXwY+/VFaHAgBg30c7qsNNYz58pw8tSXGDs6FP4DjDD2vwcAcODnCbqGBwxxOWyVxm6/raTbtZ6rzmJB3uGjzXq9Vckp8PczIj11HcorKnH+wkUczT+Oe/r2QXDgMBj736MZ6JVXVOJkyWmX9q0as7kWJWfOisme+t7t6/L4pde+9E0F7rijh8OxoqXOYsHZc2WoqKzCoSP5GDs6tEmvef7CRXxTcRnffXfNpeMEuHHcniw5jVOnv3Jp29qP98y5Mnx9qRxDh9wrjjNnyzZ2zBARERFRx8OwlYiomRi2EtHNIIWtAJC7J9PdwyEiIiIiIic4QRYRERERERERERFRK2DYSkRERERERERERNQKGLYSERERERERERERtQIPdw+AiIiIiLQNDxiC5UmJ7h4GERERERG5gGErERERUTvWx7c3Z6onIiIiIuog2EaAiIiIiIiIiIiIqBUwbCUiIiIiIiIiIiJqBQxbiYiIiIiIiIiIiFoBw1YiIiIiIiIiIiKiVsAJsoiIiIg6oMuXL2PevHmK27Kystw9LCIiIiKiLo2VrUREREREREREREStgGErERERERERERERUStg2EpERERERERERETUChi2EhEREREREREREbUCTpBFRERERO3K9evX3T0EIqIOo1u3bu4eAhERyTBsJSJqRFlZGXbs2OFw+29/+1vF98nJyQ6PmTlzJvz8/Ny9CkRE7VZjwSqDVyIiR/KAVe3nJANYIiL3YdhKRNSIPn364LPPPmvWsomJie4ePhFRu2QfDsi/Z8BKROSc/OekPFiVrkv3M3QlIrr5GLYSETVCr9cjODgYx48fb9JywcHB0Ov17h4+EVG7ohaqagWtDF2JiLTJg1W16926dWPoSkTkBgxbiYhc8NBDDzU5bH3ooYfcPWwionbFPkiVvo98cKa7h0ZE1Cl99vG7AJQhLBERtS2GrURELggMDLwpyxARdVb2VaxS2MrqVSKitiOvbGXgSkR0c3R39wCIiDoCqZWAq9hCgIjoBmdBa0NDg7uHR0TUaan9/CUiorbFsJWIyEX3339/mzyWiKgrkQJW+YWIiNpGQ0MDA1YiopuMbQSIiFz029/+Fv/4xz9cfiwREalXs8qrWtXC1tw9me4eNhFRhxQ+OUbxvX27FrYTICJqe6xsJSJykZeXFwYMGNDo4wYMGAAvLy93D5eIqN1wFrSyspWIqO2o/fwlIqK2xbCViKgJJk+e3CqPISLqCuz/qVdrIcCwlYio7Ug/Z9V+HhMRUdtg2EpE1ASutAdgCwEiIiW1yipOkEVE1PZY2UpEdPMxbCUiaoLGWgmwhQARkZLaTNjyoJX/+BMRtR15wGr/lYiI2gbDViKiJnLWJoAtBIiItMn/0ZeCVla2EhG1HQasREQ3H8NWIqImctYmgC0EiIgcOWsjwACAiKjt8GcuEdHNx7CViKiJvLy84O3t7XC7t7c3WwgQEf1Mq5qK//gTEd08aj9nWe1KRNS2GLYSETXDgw8+6NJtRER0g/0//QxbiYjaHn/WEhHdXAxbiYiaYcyYMS7dRkRE2iErAwAiorbFD7iIiG4+hq1ERM3g5+enaCXg7e0NPz8/dw+LiKhd0Tp1Vf49//EnImp7jf08JiKi1sOwlYiomeRtA9hCgIjINaxoJSK6efjzlojo5vNw9wDo5uAvWKLWN2bMGGzdulVc5/uMyHXdunVz9xCIiIiIiIhaHcPWTqixwIeBEFHrMBqNMBgM4jrfW0TOyQNWtfcLA1giIiIiIuroGLZ2Is768DAEImob0dHRAPgeI3KF/H0iD1al69L9DF27Fv78JCIiIqLOhGFrJ6AWqmoFrfyHhqh1jR49GgDQ0NDg7qEQdQjyYFXterdu3Ri6EhERERFRh8WwtYOzD1Ijov/L3UMiIiJqkc8+fheAMoQlIiIiIiLqCLq7ewDUfPIqVs4ySUREnYX8dxp/txERERERUUfCsLWDsv8nlGErERF1Fmq/44iIiIiIiDoChq2dwPXr19HQ0MCekURE1Ck0NDQwYCUiIiIiog6JPVs7ILX2AVqVrbl7Mt09XCIiIqfCJ8covrf/nSZNmsX+rURERERE1N4xbO2g1IJWVrYSEVFnoPYBIoNWIiIiIiLqCNhGoIOx/+dT3kKAYSsREXUG0u80td95RERERERE7RnD1g5KrbKV/4QSEVFnwN9vRERERETUUTFs7YDUZmnmP6NERNRZyH+n2X8lIiIiIiJqzxi2dnDyf0LZRoCIiDoDBqxERERERNRRMWztoNhGgIiIOiv+fiMiIiIioo6KYWsHolXpw39GiYioM1H7ncZqVyIiIiIi6gg83D0AahmGrERE1Bnx9xsRUePCJ8cAAKIiwrBi6aKbvnxH09XWl4iI3IOVrR2U/T+h/KeUiIg6C/5+IyIiIiKijoqVrR2M1mmVREREnY3a77xu3bq5e1hERNRBRUWEAQCGD73P3UMhIqJOjGFrJ8CerURE1JnwdxoREbUFtg4gIqKbgW0EiIiIiIiIiIiIiFoBK1uJiIiIqFPK3J2Fk6e+xPCh9yFmWjQOH83H0fzjOJB7CNU1ZkRFhGHi+HCMGRWq+RwrX1kPAHh46mQEDgsAAJwrM+HzQ0ew88O9qK4xw8fbgLmPxiBmWrTqc5jNtcjOPYiTp75Edk4eANvpzMZ+fTHjwUkwGLycrkedxYKsT/bj5KkvUXDiJAAgMnwsxoePxaCBfvDU650ub//6Pt4GhIwYjuFD70NU+DiXXv/suTLkHy9CTt4XMJtrERk+FqNCgxEweFCbL9+S7dca+6+lyisqcbLkNA4dyVeMf+zoUESGj4OHTud0+aLiEuzPPYQDuYdgMHghIux+3HfvQKfHbWsu31z27z9pu+fkfYHSMhNCQ4IQNmYkpj84yek2kB//zd3/9qQxuaI1jn+18bv6/iMioo6HYSsRERERdUrycKPnHT2wKjlFcX92Th6yc/IQPzcWcbNnqj6HufYq8gsKAQCBwwKw8pX14jkl1TVmnDz1pWp4U1RcguWr/47qGrPDawPAzg/3ImnRE5rB17kyE5Y8/5LD8pm7spC5KwsAsDwpERMiw1WXV3v96hqzWPct2zOxds2LGOhnVF2+zmLBpBmzHW6Xv35Geir6+PZuk+Ub235pWzKQ/NLzmtuvpfuvpfYdyHU47qTxZ+fkYcOmt/Hu1k2aYWP6th1I25KhGGtpmQkAkPjE/EZfv6XLt4T8/Td4oB8WLlmmuD+/oBD5BYXIO3wM619eofocLd3/9vtazpX93ZbHryvvPyIi6pgYthIRERFRp1Zw4qQIXeLnxmJSVAQ2pW9TBDahwYGi8lFuQP97kF9QiIITJ3H4qGNlojPlFZWKgCkqIgwL4mzBjfT61TVmJL24RjWwqbNYMC9hMQDAx9uApEVPYED/fvDU34pjBSewbcdOlJaZsGHT2wgbM8qhwtX+9aV1B4C92TlI25KB6hoz5iUsxt6d21QrZDMydwMA/P2MWPLk4+jX927cqr8VZvNVvJqSivyCQjy38hX8843XVAPDlizvyvYDoLn9AGDE8ACX9l+P229vzUNO7L8Nm94GAISGBCF+zkz063s3Ll76BmvfeBOlZSZU15ix9Z1M1bD/XJlJEZRK6y/tu5SNm52+fkuXby3S+8/H24CFCx7D8IAhOHT039iyPRPVNWbkFxSiqLjE4f1nv//9/Yx4ecVSAK7v/4z0VMX30jHnqpYcv3UWS4vff9T5sCc9uYITwnZ87NlKRERERJ1adY0ZoSFB2LtzG+Jmz0Qf395YsXQR3kpdJx6TtnWH6rJDh9wrniN5/UYAwIa1q7Fi6SJMiAxXXOxtSt8mrr+Vug4rli5CH9/e4vU3rF2t+ljJq6/fCIrSU9dhzKhQ9PHtDYPBCxMiw0XwVF1jVjxW7Tl3v5Mm1r2Pb2/EzZ6pCKKkUEmuzmIRYd3yZxMROCwABoMXPPV69PHtjTUrkuDvZ4SfsR9KTp9p9eXl45e2eVO2HwD0vdvXpf3XFqfUZ2TuFhWNzyYmiPUPHBaA9NR18PE2ALBVN6vZmvGeuC4/fuJmz8TendsQFRHm9PVbunxrkVp2vLt1EyZEhqOPb2/ETIvG2jUvisecOVfmsJz9/k9PXafY//L3r9b+lx4vXQxePV0ed0uPX/l7qjnvP+qY5JNXq12a8lheutbF1WODOgaGrURERETU6T25IM6hcmygnxGhIUEA4FK1W3WNGRvWrlatgLVXZ7GIyruY6dGqpwkHDgsQr29/unO91apYXq2vYx/f3kh+6XksT0rE2NGhqLdaVZf39zNqLu//87jUAr+zsgDseFGxw/2eej3Sfw7x1LZJS5a3335qzx84LEAEhs5OF2/O/msNUqCmVXUZMmK4GJd830nkVbj2x4+nXo+J49VbR7TW8q1p4vhwh8pPY/97xPWTp75U3Gd//Kvts4F+RsRMjxbrqrYNW+Kb8svi+ueHjjjc39jxLwW1zX3/UcegFYSphWQNDQ2Ki7vDPV7a18WV40Pr2KL2h2ErEREREXV6Wj0RB8gCH7O51uF+3969xPXQkCCXgzqz+aq4LlXHqomeGCmul1dUiutVVVdcWn7MqFBRnSkPs+TLT/3dA5rLS/dV15hRZ7Eo7pOHmSkbN2PfgVzVbaSlJcu7uv3krQDk20/iP+DGfm/K/mstUiUjYAsQyysqxUVeZSnfX7b1v7GdjP36qj73gP79nGy/li3f2oKDhjnc5qHTiQ8b7Ll6/Mvvs9+GLTXQzyiO37QtGU06fuXHoqvvv6a8t8h9Ggu/7IMytfDMPlTjhZfGjhP5bY0dg9Q+sGcrEREREXVZQ4fci8xdtut1lh9hsLv/l3feKa6PGO56UPftlRvBz/CAIU0e1w91da22fMrGzS715zSbr8LTV1n9uyButqgwlCZ68vE2YMaUSZgUFaE5MVBLl3d1/RvrtXqbp6e43pT911rKKyqx4/0PxGRKrqqz/CiuS60Q7PXqdWebLd+a/P2Mmv1ItU7rl+9/eWBuT/5hSFto6fEPuP7+U/v5Q+2HfdWq/fXG7gdsE/bV1VlA5Ix9v1ZPT734WdmtWzdxTEmPu379urguv1/tuejmYdhKREREROSC0OBAlx9bUVnl0uPkQZI8YCo9b2rRWCurvm2Vde7j2xsZ6anYm52DnR/uRXWNGdU1ZqRtyUDalgz4+xmx/NlEzcrh5i7v6vrLqzO/vXLFafjVlP3XGoqKSxQTJAFQ9EktM11EaVnj66kVKKpNSKZGK5B2dfmW8jM2vYJWvv/lgbk9+Ych5y9cdCn8bIrmHr/yD1uo41Lro9nYV6vVisqqKzh1+gwulVfgwtffIOfzw+5eFeoEvA1eCA4cimEB96GPb28MGTwIBq+eImCVfwXUg1m6eRi2EhERERG5QB7sNMZZNZ6cVqikFcK6Sh5CLk9KVJ3Ay1XShD5zZsXAdOFrHC8qxgcff4rSMhNKy0xY9WoK0mWTFbXG8q6u/8mS0+J6Y/unKfuvpeqtVhG0+ngbsGrZ0w4tDDJ3Z7lU8VhRWdWi9gfXvv/+pq13a5FXMzsL0c9fuCiut1VbBLXjN+/wMeQXFGoev/JjraXvP7r5nFWuqp26ba69ii/PnMOnn+UxWKU2U2OuxYHPD+OA7BjzNnhhWvQEhAQNx9Ah98LDw0MRuAIMXd2FPVuJiIiIqNNT6+cJAIeO5IvrBoPrM5U3Rh6cygNBe1e/u6b6+vLlm1Pl6qm/VXUdW8JDp7NNSjQtGump6xA/N9Y2vjIT9h3IbdXlm7P+8nV2N3n/0BlTJqmGpfaTQmmty6nTX6k+xr7Hbmsu3544qxKXB8ltvf/lx+/6l1c4PX7b4v1HN4dWH1b7Hpo//fQTvjj6v1jw1yT81x/+jGWrX2PQSjddjbkWb//rXTy19P9g4ozZ+P/+8RbOlZkUvV6lrwDY1/UmYthKRERERJ2evAJOzlx7YyImrb6SzSHvh5n1yQHNx33w8acAbNWP8td3dfm4hMUInxyD8MkxitnYDQYv+HgbANyYlV5NncXiNHQzm2s1758zK0bMpq41xuYuL69kdLb+23bsFNtPbcb31lRUXIJ9B3Kx70AuiopLnD5WHrCrtS8wm2tRcOKk5vLydZEfo3Jnz5W12fLu5ur+l9/XFvvf2fEbN3umOH6l41A+Flfff5wYq33Qmh3efrKib8ov43+2v4vJD8/FstWvocx0seUvTtRK3v9wDx7/y7P445PP4PDRfPz0008OxzPA0PVmYBsBIiIiIur0drz/IQIGD1IEMvsO5CK/oBAARJVaa/HQ6RA/NxZpWzKQX1CIfQdyERk+TvTJrLdacSD3oOjZuXDBY06XLyoucaiOLCouEcsvT0p06ME599EYcZp65u4sTH9wkuIxdRYLHp23ENU1ZkRFhGHF0kWK5Rc9txL5BYXw9zPin2+85vD8P/30kwiKZj40xWEbNGX56ImRDsu3ZPu1hfc+2COCs6iIMKen9o8MGSGu5x8vcnjsyuTXUV1jdvp6URFhyM7JQ3ZOHiaOD8eYUaHivnNlJqx94802Xd7dXNn/bfX+BRo/fuutVnH8zp45w2H5hQseE5Nqqb3/6q1W8f4LDQnC+pdXuHuTd0n2LQPU2gQ0NDSgrq4O6zf8Ezl5X7j0vL79DPDt64WA4L7wvvN29PT2RI+eevziVkYw1DTXrlrwnx/rcfmbWlRXXoPp7Lc4W3IZ12q1PygtM13ECy+9Cm+DF/48fy4ifzMGHh7KY8++xyu1Lr7TiYiIiKhT8/E2IL+gENNmxSN+bix+M3Y0Vr2aopicaFJURKu/bmzMNKRtyQBgm8l8w6a3sWrZ06iorMKGTW+LsM3H24DI8HFOl1+4ZBkSn5iPwQP9AAD7cw/hQO4hALbZ3tWWj544Hlu2Z6K6xoyUjZuxZXsmFi54DP4DjCg9b8K2HTtRXWOGj7cBiX+Od1h+RdJTmDYrHqVlJvzttf/GxPHhoi/m+QsX8cn+XLH8yNDgFi3f2Po7234AVJd3J4PBC/5+RpSWmbDzw724444eCA4chs8PHUFO3hcoLTMh+aXnkfTiGgDAjvc/wMyHpqJXrztFIPfw1Mki3E16cQ1CQ4IQP2emIvSVXkNNS5d3t6bs/9iYaYpl6ywW5B0+6vCcUhVimemiw6n/vr17KULxFUlPIS5hcbOP38jwcWKc8vff8IAhOFlyWvH+W5H0lLs3d5ek1Y/VPmR9d1cWtrzzXqPPF3y/ESFjB6Cf353o0bP1zpSgrk06lnx69QAAjJtwHwCguuoaSo5fQvH/XsLZUxWqy9aYa/Hy2jfwzru7kJgwH4HDhqBbt26cROsmYNhKRERERJ1ayIjheHjqZCxcskzMIi63Ye3qVp/FHLC1Jdj9ThriEhaLWcztZ6cPDQnCiqSnVGeGl5Zfmfw68gsKVSdT8vE2YPmziZrLp6euE8tX15hFpZ3c2jUvqp6CbTB4iUBQqpBUs3DBY6qv39LlpfV/6rmVKC0zNXn7uduSJx/HwiXLRNgmt2HtagQOC4CPtwHVNWZk7spC5q4sZKSnimMxcFiAqO4EgPyCQlHJKT3Hex/s0QxLW7q8u7my//39jHj95RUOLUDM5quqx7qktMzkcL99tbLB4IWkRU80+/j10Olcev+tWvZ0m7fAICWtalb7062P/LsA6zf8EzVOWj0MGuqLiQ8Fot/AO6HTsUsj3Tw+vXpg3IT7MG7CffjPj/U4lnsO2R8Uq1a8lpku4qml/wchI4bjhaf/Am+DF7p3v3G8ssq19XW7zkYNHYb9L4CGhgZYrVbF5aHZCxTL5O7JdPewiZpEbYIN+0qDjkJal7Axo1q1D2BrKSouQUVlVYfdvq6SV7cMDxjSJoGKFuk0R3e8dlO3zciQEW77Zy98cozi+93vbIZOpxOX7t27o3v37uKTeP4haHP58mXMmzdPcVtWVpa7hyW48neL1WpFfX09ZsX/RbFsa/39svKV9cjOyROnyJ8rM6H0vElMWDN86H0YO+rXTt+b8vdJc3+e11utOJZ/HF9+dQ6mi5fEa9/texdGhgY3GhRKy39TcVkxqdLE8eHNXn740PsQHDgMxv73NLq82VyLi5e+wZlzZYrXHz70PkRPHN/oNmnp8i3Zfq2x/yTS703A9b9NpLF/sj8XBq+eGBUajOCgYWIc8vu1tsfho/k4mn8cBYXF8DZ4IWzMSHHcuvK7vKXLt3R7OXtuVx7TnP2vVdnqjNYYWuP4LTl9RrF8U95/TcXfqc5pVbPKJxOqq6vD+o2b8ZmTlgETHgrEmPH3soKV2hWrtQEXz13Be/9zDBUXzZqPe27Jk3ggIkzxc0Be7Uotx7C1A2HYSl2B/R+IAFT7yHUE0rrIq1TaE/sQorMqr6hEbFwCAFtPwwmR4TfttdO37UDalgz4eBvw7tZN7bLySjpF0p394viPYfMwbG1cV/k5R0Qk4e9UbVrtAuRha1Hxafzt7/+fZjXruAmD8bv/CmbvVWr3zp+pchq6howYjpdeWILbb7tN9edBV/rZ0Bb4E4KI2pXlSYni+rYdO9vtqW3UuZnNtZg2K75FAU15RaU4dTNp0RPtMmgFgJdXLEVsXALyCwpx+Gi+YgIVIiLqWuQfUDZXe/2Qmbo2taBVXs16/fp1bM14D1sz3lddPvh+IyY/MkL0zSRq7wYM7oXFq6NRcPg8Ptj+vw7tBQpOnMQfFjyF/++1Vejj2xvdunUTrQXYVqDlGLYSUbsirzo8dCSfYWsbWrF0UZeo9Orj27vJVXIXL33Totest1rx3MpXANgqs9tzgNnHt7fo6Ze8fiO2v7WhXba9ICKitmcw9FR88N3c5yBqL9T6s9qHrD/99BNeS/kHPjt4xGH5Hl56/Nf80QgY0dfdq0LULCFjBmDYr+7Bu2lHcPwL5f/WNeZazHn8r1j/ygoEDQtAQ0ODw+RZDFybh2ErERGRnTPnylq0/IHcg+KDArUZvtubObNisPPDvaiuMWPTW9uQmDDf3UMiIiI38NTrb2q7HaK2ZN+f1T5kbWhoQE2NGcmvp+J40SmH5QcN9cXvE8ayLyt1eL+41QO/TxiHkLED8P82H3Gocl20dCX+8GgM/vDoI5w4q5UwbKUWy9ydhZOnvsTDUycjYMhgHMg9iENH8kWPtOFD78P0ByepnkJbXlGJTenbAEBU2JnNtcjOPYgPPv4UpWUmREWE4eGpk1Ub1kuTzxw6ko+CEydhMHjBz9jPpQkjpNc5eepLFJw4iZARwzF2dKhLk8RIEw2cv/A1ys5fEMs2NvFCvdUK04WvcbyoGHmHjwEAwsaMxOCBfggYMrjR8ZacOYuj+cdxIPeQeE1XJt2RT4xgNtciZMTwRpv6S/t14vhwjBkVKpr77889hAO5h8S2bk+VkXUWC7I+2S/2qcHghZCgYRgVGuy0stB+v5Sdv4AZUybhN2NHNzpxgXQc5R0+hhpzLSLC7sd99w5s80rG8opK7M3OgeniJfFemzg+HAGDB2kev0XFJXjvgz0Otw8feh9ipkU7fT35+zxwWIDieMjclQV/P6PT46Gp4zWba5HyjzQAtrBS7TErX1kPAKo/H6Tx2tP6WWK/XMGJkwBsp9dIr+Pq9qqzWMRsw8uTEl2edOpcmQkf7vkUBYXF8DP2Ez+PAIht0Vrb156HTodVy57GwiXLkLkrCzMfmspTQImIiKjDcha0StdrasxIWPICzOarDstPeCgQE2YEuns1iFpVwIi+WLw6Gv9KPYSzpyoU9/3PdtuZgAxcWwfDVmqxk6e+RHZOHoYPvQ9pW3cgv6BQ3Jedk4fsnDzkHT6G11YvUw2tsnPyAADPPpWAvMNHRUghv3/s6FCHgKS8ohIJi55DdY1Z3FZdY0ZpmQnZOXkIDQnCiqSnVIOGfQdyVV9HGkvM9GjVyq56qxV/e+2/xePsl/XxNiA9dZ3qa9ZbrXhkzgLFeAGI7eVscpqi4hIsXLJMc7xak/40Nt4t2zOxatnTmuGTtNzI0GD88clnFKf0S9u6vYSt58pMWPL8S6rHQ+auLKfHw4ZN6cjcpZxUJm1Lhui3uXfnNtVQ2myuRVzCYsVrimrGJ9quMrCx4zd+biziZs90WK6issrhWJA0FrZ+9901sWzAkMEOx0NpmUnzeMjcnYWUjZubNN46y4/i/gVxs2FQGZN0v9rPB+nnkj21xzpbrrrGrPo8zrbXWVlVrBSWNsZ+G0k/x3y8DUha9IQYQ3O2b+IT8xvdv4Btv0oOHf23S8sQtXddpV0KERHdoDURlhS0ShWtTyxZBnOtY9D6xAsTMGBwL3evBlGb6NFTj/lPRyBj02GHtgIMXFsPw1ZqNdI/+6EhQXg2MQE/1NVh1aspKC0zIb+gELs+2uvwz7u8ckoetCY+MR93+96FAf374WTJaQwPGKJYrt5qVQSt8XNjERszDWbzVWxK34bsnDzkFxQi5R9pDv9klVdUKoIq+bJ7s3OQtiUDmbuyMGXyAxjoZ1Qsu/WdTEVANCkqAr163YkDuQfFZE5xCYtVZx0/kHsQ1TVmsX169boTHjqdCAnzCwqx70CuamgqVSMmPjEfUeHjYDB4oc5iwfHCYiS9uAarklNUK3Ll4/X3M+LlFUthMPRERuZupG3JQHWNGQuXLMP+j3Y4jPdu37sAQATppWUm+PsZMXvmDPgPsG2X0vPto59qvdWqCFqbejxIQevypESEjRkFwHY8btj0NqprzDheWKxaqboy+XXxmlERYXj2qQScPVeGtW+86RB+tZai4hJx/Pp4G7BwwWMIGzMK35Rfxod7PkXmriykbcnApKgIh8rEsDGjkJGeKr6XjndX9L3bF4Ct0nPXR3sdjocffvgBFZVVquOVtkVTx9sSiX+Ox4K42eJ7Vyf7kJZ7NSUV+QWF4v0q56m/1elz7M89BMD2nnOlqtRsrlUcL8uTEjEyZAR2frQXaVsykPTiGs1l1bZvZPg4mC58LbZvysbNGDvq141uXw+dDjHTo5G5KwsffPwpw1YiIiLqsOwDV1eC1h5eejy5fCInwaJOT6frjt8njEOvPj2x7/0ixX0MXFtH95Y/BdENy5MSsf7lFejj2xsD/Yz45xuvISoiDACwZbv6BDX+PweaUoC0Ye1qxEyLxphRoejj2xsTIsMdQgIpuASA3e+kIW72THjq9ejj2xsrli4Sjf2zc/JQXlGpWPbVFFvYFBoShP0f7VAsGzd7JkJDgmzjeTUF9VarWK7OYhHBlFSJ18e3Nzx0OkyIDMfrP1elVteYUXL6jMN6btj0tm3ZOTeWA4CBfkasXfMi4ufGqm6f8opKEZhOf3CSCG889XqMGRWKxCfmY3lSIq7YVczKZ0KPnxuL9NR16OPbG556PeJmz8Tud9IU29PegP79xPWUjZsRGhKEf77xGiZEhmOgnxED/YztpqdXY8eDtG2zc/JgNtcqlt2bnQMfbwP8f14fT71e9Cub+2gMAOD/vrXN4TXN5lpRlZz80vNYsXQRPPV6BA4LQHrqOsRMb5ugau0bbwKwHb/vbt0kxjzQz4jEhPni/SRNziQnbRPpIgWorpA+8KiuMaseD4HDAlSPB1fHK70vW4vB4KVY16YuZ/CyTe5h8OqpeJ4+vr0bDVCl8H7q7x5w6TV3frRXXN+7cxsmRIbDYPBC3OyZ2Ltzm9Nl1bavh06HgX5GLFwQ1+TtOz58LABbZW2dxeLSMkRERETthXxCLPvWAQ0NDfjpp5+Q/HqqatC6eHU0g1bqUibMCMSEhxzbZfzP9ky8/8Ee8R6SLoDyPUbaWNlKrcr+lFkPnQ5jR4ciOycP1TVmlFdUOgQffsZ+4nTk5Jeed3qKr+TQkXwAtkouteBDXgl7suS0eM16q1UEZCOGB6i2NVizIkm1b0/e4aPi+m/Gjna432Dwgr+fEaVlJrz3wR6H9TAYvFBdY0ba1h0OLRWk8LIxatXBWtVnJ0tOi+uToiJUx+vjbUB1jRmHjuQ7DU59vA2abSDag8aOh0lRESJ4PlZwQrGucbNnqp5yD9yo7i0tM8FsrlU897GCE+J6wOBBDssOHXIvMne17nqazbXivRI2ZqTq/khdv0b1+G1N/n5Gl46H9jJed+l5h2t/rJsuXgJgq462b1fhqdcjNCRI0Z5FUl5RKbZv9MRIh+3rodM1efv+8s47xXWz+So8fTkhBBEREXUM8jBILWhtaGjApvR/4cTJEsVyUtDKibCoK5J6E9tXuG54820M9DciaFgAK1ybgWErtSq1oEs65dwVI0ODXXqcNHlNyIjhqvfLA92r310T16uqrojr9907UHVZT72+0YCh9LxJ9RR675/Xv8x00eG+JU8+joVLliG/oBB/fPIZRITdj9DgwEYnxurj21uc2puycTNOnvrSpYm85Ovdq9edqo8JGTEc2Tl5YntqiQwf226DVuBG706t40Fr/e2VV1TiZMlpEd7KP/Gus/yo2jcUQKOBf2ups/worg8e6Kf6GFeO35YKCRrm0vEgH29w4DC3jfdmkldOu/qzTzp+jf36qt4/oP89qmHrD3V1jb5WZ9u+RO2dNMmewaunau93d5FPkKg16WB7oDbxoXzCRK3b2/M6tRX5PpW3SJKOQVcmv+xquG06N7U+rfZB666PPsGHe7IVyzFoJdIOXBc/9xJ2vJ0KH2+DQ+BKzjFspVYjnX5v7zZPT5eXdzXQk04ZHztae8Z3qcr05KkvxR9U8nBCfpq8K06d/kpct5+cyJ584iBJ4LAAJD4xHykbN4vJhNK2ZMDH24AZUyY57Vm5YN5snL/wNfILChUT3/j7GTH1dw8geuJ4h4o4aSZ2fz+j5naVVx3bk49l6JB7m7St3EXrePDQ6TSreKV/1rQmjtIiHQ/+GhXJrga8TXH+wo0Qf5BG2HozjHLxQxF5dfXdfe5y23hvJnnA7OrPPolWWwetKmn5Bz6ttX3l7/vzFy62ah9dos7u8NF80UP5rdR14nYpQASAObEPN3omS1uEiPIJErUmHWwP1CY+lE+YqHV7e16ntiLfp/KwVT7RIwNFJW6bzstZf1bpUnTqNDal/8th2SeXT2TQSgTtwPVPiUvx9v9dj9s8PdG9e3cRtLK61TmGrdRqpB6HN3P5S99UaN5n35sTaHr4IefV8w5xXeoJ21Qx06Ix/cFJKDl9Bu99sEcEnWlbMpC2JUNz1ntPvR7rX14Bs7kW2bkHkXf4GPILClFaZkLKxs3IO3wM63/uGevKdnBl+8m1RZVmW5BX89qTAmX76sGVya+LqkFp0jPJD3V1mJewWPX5pONBa/teczKW5upx+23iujtP8Xb1g4oet9/eLsbb0V114Vj60fKj6s8OIro5yisqxWR2y5MSFYGq/MO8MtNF/PON15x+uNwWIaJv716ih35jk/y1N576W8XYfXtzdnBqvuFD71N8pc5FXtFqX936ww91eGXdRodl5i3+LXu0EslMmBGIsi+rcPbUjZygxlyLdW+8iRee/ot4T0mhKwNXbQxbqdVk5+Q5zPQOKKvxWovUw1DqdWivzmJptPpV3svVFfKqs7Axo5odbHjodAgcFoDAYQFYsXQRzpWZRKD36uupqttQYjB4IWZaNGKmRaPeasXWdzKRtiUD+QWFKCouUfSJlVet1lksquOV+hVpVSV3FFLVqryKWU4eiMr3Y3lFpWKSqzGjlMfK4aP5mq8pPY9aVTAAXLz0Tauvp7yfZkeoOpSHsq09XmcfIriTPMSQV9K74tTpr1R7J0tV6vbkH4JcvPRNq1S/yScUbGr1P1FXJk1K6N/I5JGlZSbV/uttTfq7oyMyGLyc/m1E5Crpb2jqXOST9WhVtf73/33LYUKsCQ8FImBE36a+HFGnN//pCKSs2IOKi2ZxW87nhxH123G4f+Sv0L17d/G+Y+CqrXvLn4LoBrUA5Nr334vrBkPLql/F8/xcBat16vc35ZfFdXl1nTzskbcFkDt8NB8rX1mPla+sV8zGLa+mOHuuTHXZxmbvrrdaHW4b6GfEhrWrxfpohUj2y3rodIibPVNUe0izkqutt9YEOVLQqFZV3FozkZdXVIpLW5F6tWr1npUHn/L9KP8gQC1YOpp/3OV1tFdRWdXq6yl//3z51TnVx2gdv+4gDx5dGa+cvA2DvB2BpOTMWbeumxZ54KnW11mN9GGH/T8BEq3jWr59z2j8TNp3IFd1+xJR6ykqLhHtg15esbTRx6ds3NxuPzAiIupI1Pq02rcSOJp/HJ8fOqpYbtBQX3HKNBEp6XTdseDZ8Q63r3vjTfxQV+fwXiN1DFupVWXnHlSEgmZzLbbt2AnAFii01mmuD0+dLK7bVx/WWSzYmvEeAFvFo/2kW/FzYwEAB3IPoahYORNlvdWK5PUbkZ2TB4NXT8V4A4YMho+3AQCQtnWHQ5BVb7Xi0XkLMePR+dh3IFdx37kyE8Inx+CROQtUA1eJv5/RoTpt5SvrET45BlvfyXS6TSLC7ld8PzI0WIx3U/o2h/HKxyjfnpLWmiE+Ni5BXNqKNP7qGrPDPq2zWMQEEj7eBgQMGSzuCxg8SFz/9soVxXJFxSXI3JWl+ZojQ0aI63uzcxT7tbyiUhz3as/dXJ56vQjXd3641+nxa+zX1+2nlRsMXk7HW2exKMYr56HTiWXtPxipt1qx4/0P23Ts0ngamzzOGVdO/weAsDEjAdg+bDln1+/58NF8zepp+fb94ONPHUL/OosFq5JTkJ2T5/Ipk/JjtbU+HCPq7Jav/jsAICoizGkFv/T3B2BrYdMSdRYLiopLxAcq+w7k4vDRfM0Qt7yiEvsO5GLfgVzVD+LqrVYcPpovHrPvQC7OlZmc/s2iNY5zZaZWDZPrLBYxpqZ+cCtfJ7W/286VmbDvQC5SUjcjc3cWiopLWmXsdRaL+KC5qLjE4fefGrO5FoeP5iNzd5bYlkXFJW3ywan0WunbdiB92w6n21Zt+0vLS9vtXJmp0XFKx4l00bZB3QAAgABJREFUcWXd6q1WcexKr3X4aH6zP8CXxqC1P8zmWsUY9x3I5QcjHYA8XLWvbP3hhzr8f/833WGZ3yeMdfewidq1Hj31ePTPyvdJjbkW/2fNOvH+sq8qJyW2EaBWlbJxM7Zsz8TCBY/h6nfXxEQRADDzoSmt9jqBwwLEBFhJL66Bv58RS558HPnHi5C2JUM8buGCxxz6osXGTEPalgxU15ixcMkyseyZc2XIO3xMBBsL5s1WLOeh02HhgsewKjkF+QWFmDRjtujxebLkNLbt2InqGjP8/YyIDB+nWNbY/x7R+uBvr/03Jo4PF5WUJ0tOY8OmtwEAS5583GFdF8TNRnZOHtK2ZOCOO3pg8EA//PLOO/FDXR2OFxWL6t7YmGma483OyUPBiZNYuOAx+PbuhbVvvCmqcPz9jKqnFt7snm5FxSUO1aBlpoviq32ALW/lID8e5Pu0sePBYPASy6194038ad5s9Lj9NuzPPYTMXVmInxsrlt+Uvg0L4maLf6QNBi+xT9O2ZCAn7wssefJxsawUdAO2KtfWOn1TOh7Ujt8PPv4U1TVm+HgbMGdWTKPb99CRfJe2b2uONyoiDHNiH8bxomKn4wVutMLI3JWF8xe+RvycmWKf+ngbxL6zZ78uaussp3bK72/GjhY/JxY9txLREyMB2Pocmy5ecjppTcz0aGTuysIHH3/q0umKUeHjxM/KeQmLERURhoenThbvU631lG/f0jITYuMSHLYvYPuQYfqDk1zaX/tzDwGw/Vxwd1hP1BGcKzOJvxvUPriU63u3L5YnJYq/Iw4fzXdoX+OKw0fzkbx+o+KDGPmZPolPzHf42XOy5LSY3DMjPVX00K63WnEg9yA2bHpb84MdtTY7gO33yvLVf9ccx4a1q1vld5/ZfFWMfXlSosstadK37RC/w+Pnxip+ppVXVCJh0XOa6xw/NxZxs2c2a7xFxSVYuGSZw+0x06OxcEGcw9+l9VYrdn20V/E3s3xb+ngbsGrZ062yLc+VmbDk+Zc01zsqIgwvPPNXxRjtt//e7BzF31YSH28D0lPXOfxuPFdmwqpXUzR/j8VMj0ZiwnyH283mWjz13ErN5eLnxmLOrBiXJ9cFIOZLiIoIU2zPOosFGZm7VdfL2bqRe9kHPfaB6/Xr1/H+B3sczhx69M9jOSEWkQtCxgxAyfFLOP7FjZ/DBYXFKCo+jaDhAYrKVrYTcMSwlVpNVEQYjP36Im1LhvijTBI/N7ZZ/1A48883XsPfXvtvETTY/2G7PClRNUTx1OuRkZ6K51a+gtIyk8Oy/n5G/GnebNWgYUJkOHrcfruYBEOa2Eri423A6y+vcPjDz0Onw4qkpzBtVjyyc/JU2x+EhgSp/iHdx7e3+OfM/g9x+bpqjRcAViWnoLrG7LBfpD+q1dzsPyilP4DVlJaZHMYu/2cRAFLXr8HzK5PFxGGuHg9/mjcbSS+uEcG9fNvEzZ6J2qvfIXNXlthvuXtuVBivSHoKcQmLUV1jdnjNtWte1JxcqyX6+PZGRnqq+CdR7fhd8uTjDsdgS7dva43X/vjXGi9gC3ylQFu6SFLXv4xN6dtU/wmzXxc5tfef2nEx0M8oQk771wacT1ozPnwsMndlofTn6q7G3ksGgxc2rF0t9qN8jKEhQYieGKm5Tn18e+Ot1HXin+embF979VarqOae+rsHXNzDRF3b8aJicX3QQL9GHz8hMhzbduxEaZkJyes3YvtbG5r0wca+A7ni54GPtwEzpkzCffcOxDcVl/HBx5+KiTO/++6aS2Fhyekz4vn8/YyY+rsH0POOHrj0TQVy8r4Qvxt3v5Om+FkmHwdg+zuv792+ig/aFy5Zphr83gz2Qat8W5RXVCrOtomKCMPY0aG4+t01bNmeKSYuNV281OResZm7sxR/q8XPjRV/R2TuysKB3ENIXf+yIjCW/paV74O7fe/CNxWXxXgWLlmm+XeMq+qtVqx6NUV8yBkZPhZDh9yLU6e/Ej/7pTO71MJPAMj65ID4fejvZ0RI0DCxbHWNGXEJi/Hu1k3id06dxSL+FpKOV+k4kSZ7zdyVhaFD7lWsm9Y+AiA+GEjbkoETJ0s0J4htik1vbRPrIb1Wj9tvx9H84ziQewjVNWasTH69VV6LWpez9gE15lpsz9ytePygob4IGTPA3cMm6jAeiR+NsyWXca32xpkIb2xKx4a1q3HLLbcAALp3t50wz6BViWErtaq42TNFpeep019h6JB7MTxgiNMqhIenTsbY0aFNnmHWQ6fDiqWLsCButni9e/r2wd2+dyFg8CCnAUcf39745xuvwXTha5SeN4mxjgwZ0WgwMmZUKPbu3Iaz58pw5lwZvr5UjlGhwY2+psHghf0f7UBV1RWcv3ARX351Dnfc0QPBgcNg7H+P0zBkQmQ4wsaMwjfllxXjbWzbSsuODBmBkjNn8U3FZXz33TX0vdvXpWWXJyX+PPbmn04sPUdjpOPAVfZj8tTrsf7lFSivqFQcD4MH+mHQQD/Nf2bHjArF/o92oOT0GeQfL8J99w5U7MvEhPmYMvkB1f6bBoMX3t26CQdyD4pT3eXHQvJLz+Pa998rJjJqDX18e+PdrZsUx29jx2BLt6/B0LPZx0Nzxivfp3UWC7I+2Y/vvruG0OBABAwZDA+dTqyT/fZ19ZhrTHrqOpjNtSg5cxZffnUOfe/2hf8AI27z9HT63pEHLscKTrj0z3HgsADsficNxwpO4NCRfAwfep/42XAg96DTZQf6GVW3b3DQsCaFOCWnz4jrUXbV+USkTqogb0qrpJdXLEVsXAKqa8zY9NY2zWDLXr3VKs6EUau0m/7gJDyzbLU44yI2ZlqjY5Kq2X28DfjnG68p/haZMysGf3zyGZSWmRRBk3wcgGMFa1T4OKxMfh35BYXYsj0T0x+c1KTqw5YqKi7RDFoB25kqEvv7oyeOF8Fbdk6e4oyWxsiD1qiIMDz7VILY/gvmzcakGbNRXWNGwqLnsHO77XHlFZWKD9deW71Msa2iwseJD3W37djZorD1QO5B8QGlvFJ2QmQ4FsybLT6wztyVpXlM5hcUig/xpOVnPjRVFDBU15hxLP+4KLA4Xnjjw4i1a17EQD+j+D5mWrStNcCuLKxKTlGcUeNsH0WGj8OGTenI3JWF/IJCnCszKZ63qeQfNNpX2Y4ZFYopkx/AvITFyC8oxL4DuS3aB9R6nIWsUlXrm+n/cljukfhR7h46UYfyi1s9MPXRX2H7Pw6J28pMF/FZ3heIiggT7z8paGV16w0MW6nV9fHtjT6+vV3+Y6SlM+Q29fUkHjodBvoZMbCRmYPVeOr1zRq3h04nxtvUSl9Pvb7Z4zUYvJpVWdwaf1DerONA0pzjwUOnc/r60nbXWnZCZLjq69lvc7O5FnWWH5u1Xr163an4B6ypx29Lt6+nXt+i46Gl7ze1yiitdWrNf4Sk905T3j+eer2oRl+VnOLShzjSa2kdS41pyfYFbP9sSn0nY6ZH81RJIhdJ4ZXUasQVfXx7izY1mbuyMGXyAy6FRcfyj4vTv1cte9rhfSo/iwYAMjJ3N1rdeuDnsNVvQH+H+zx0OqSuXwOz+ariQzb5OJJfet7h57DB4IVnExNEoCwP39qa/BT+mOnRDutvNteKcFPtfk+9HgsXxInwbW92jksVwvVWK7Zst5354u9ndKiI9dTrxT73G9AfdRYLPPV67Hj/A/GYFUlPOYTSBoMXkhY9Ic7AKSouadHv8uVJibj63TWH5/DU6zFieICoWi2vqNQMmVPXr1GE+H18eyN1/RpMmmFrv/XJ/lyxv+UTjd4pa68kWbggDjMfmgpP/a3iORvbRx46HRIT5ot9tDXjvSZXIMtVVd3oVe7V8w6H+wf6GbH7nTTUWX50OXintuVK+4Dyy5X4/PAxxXLjJgyGT68e7h4+UYcTMmYADnx0ChUXzeK2Ten/wtjRobjtttsc2gmQDSfIIiK6SVL+kaaYMKwpF/k/A9T+RYaPg//P4UnKP9LcPZxGbX0nU5xaat+vmojUtWTinDmzYkRv71WvpjQ6ERUAfPnVOXFdPtmjnMHgJZ7XdPFSo8+5cMFjAGwVi88sW+0wDk+9Hn18eyvCtW8qLovrwUHDVJ9XHkp9sl+7j3ZrKq+oFEFraEgQFi6Ic3hMyZmz4vqUyertUjx0OoSGBAEAcvK+cOm1q6quiAA6RGObxM2eidw9mVj/8oobweLPvSTVJkhV28b2vdebQvowT6utQ9+7fRt9Dq0Kbvlt8lY2Mx+aemP9ExY7vGekIgT5ul+R9ZMdH649iZE0QaRWiyRX9fHtLX5fp23JcJh4F7C9rxi0ti/2Fa3232955z2HZX73X8HuHjZRh/XwH0Yqvq8x12LPp585VJgDnCxLwspWIqKbZMXSRS2qvqCOw0OnE6cKZ+fkYeL48JtW2dVU5RWV4pTbpEVPcGIsIhfJz1TocfvtTVrWQ6fDqmVPY+GSZSgtM2HXR3sb7W0qD0+dnZYfMmI4snPyHCaFUWM7JdvWAzO/oBDjH5yJmOnRTluRnDz1pbj+6uupms/t423QnIiptZ2/cBHJ6zcCUD8dX3Lt++/F9Tc2pcPgpd4Wp+z8BQDQnJzJ3g91deL60CH3ujxuKSj0M/bTfIx8H1z6pqLF20qaMFM+Saar6zmg/z2a90VFhDkEn318e4vbq2vMmDYrHqEhQQgbMxJR4eNUA2Z526a0rTu099HPk6i2hiVPPi6Cemni3Yiw+xWti6h9UKtqVevVmmdX1TrhoUD84lZGH0TNNWBwLwTfb1RMlvVO5m5MmRyFX/ziF6J9ANsI3MCfOERERG1Afqpw8vqNiklD2pPnVr4CwBZQtNdAmKi9G9C/X5OXCRwWIIKolI2bNcMniRSeSlWXjbGf2E+Nh06HtWtexNaM90RQJk3mBNgCtIenTlacdi4PcVtaVdha5BNclp2/gJ9++kn1563UX93V7eMqeUDoP6D5/UMb40q1shazuVb00m2upgTJksQ/x4sJdAGISSdTNm4Wk9KODA1W3V+ujrXeam3R79fAYQFYnpQoJq+TLmlbMsTEXq70QKabw1m/1uvXr2PPvhyHZX77u5a3KiPq6iKnDFOErTXmWvy7oBD3j/yVCFoBMHT9GcNWarHmTnBFRNTZzZkVI07NrKq60qzTEIcHDGm1Sb/s1VksmD1zBgBgZMgIt20noq7q2acSRGDZ2GznUoVfjYvtC3xUemSqGfhzj9Fnn0rA8cJifLI/V4wpOycP2Tl5yEhPFT+/5JWGbfWzqTn8/YxikqbnVyarVrfe07ePuB4/N9alU+ddIZ+osfR8yyZsaivSRFuALURfEDcbBkNPESDK+922JoPBC3GzZ2LOrBiUnD6D/bmHcCD3EKprzCgtMyHpxTVYnpSo2m/c1X2kFa43hdRm4VyZCceLivHBx5+K4yltSwZy8r5Aeuq6Vt8+5DpnVa3S5aeffsI7732gWI5VrUSto08/A3z7GRS9W9/augOjfx2imCiLQasNf+pQi7XWxEZERJ2NNIFaS0iTvrWFlk58RkQ2J0tON+t9Kp9QL7+gEIeP5mtWyUohZ2OnfEuVpyEjhjd5LNKEgC8881dsfSdTVCNuSt+m2gZneMCQdtHLMmZ6NBYuiMOB3INiW259J9NhcqWed9yYHOe+ewe2WjW/fAKxppzqLwXErho7unnjNZtrRdAaPzdWddIvrX6w8nU7dCRf83dGY6f1yycjTUyYj8NH80VF8oZNbyMyfBw8dDpFS46+d/ve9N9R0mSTMdOiUWex4NF5C0Uw3NIJyqjlnFW1NjQ0oKCw2GGZ0HF+7h42Uafx8B9GYuPf9onvz1/4GidPfYnAYUNY3WqHE2QRERERETWRPIRqiQmR4WKCnuT1G/HtFfUJEeWncGtNzlVnsbTK6fEeOh3mzIoRLQuyc/LEa8oDv/MXWq9vZkvMfGiq+HBLGnPalgwUFZcoHic/xf9o/vFWe3356eVap/qfKzNh5Svrkbk7S2xLqVdrwYmTms99rglhrJZjBSfE9d+MHa36GKmHq7N1cxaoSqGxq20uxowKReIT8wEA1TVmHPt5f8g/bNAa083iqdcrqlnXvvGmW8dDNvJJeOwvuz/+VPHYQUN94dOrR3NehohUDBjcCz28lC1Vcg8e4SRZKhi2EhERERE1kTyEuvrdtRY918srlgKwhU5agY78VPW3t7+r+pjjsqquh6dOdvqa58pMWPTcSsx4dL5qeOuh0yFsjG32YR9vA3r8XBUqH8cn+3NVn9tsrkVcwmKkb9uB8orKFm7pplmzIkm0UFi++u+KdTP2v0fcdyD3EOqtVofl661WxCUsRkrqZoew1pn4ubEAbMG02jpLfXG3bM8U21LaR9U1Zuw7oL4tP9xzIzwKGzOqWdtE3urrhx9+cLj/XJlJ0X9XK0QvLTOphr/y26Rjps5iwcpX1iN8cgwOH1UPTYMDh4nrPW6/DQDQq9edYh9l5+Rp7qNFz61s8j5Sk5K6GTMenY9Fz61Uvb/HHT3EhyHOJjKjtmUf4KgFrXV1FhQVn1YsN/GhQHcPnajTiZo6TPH97o/3ob6+3uE9CXTt0JVhKxERERFRM0hVfB/YVVM1lTShHqDdJkCa2R2wTWIVl7BYETTJT8v29zM2erqz8eeZ5atrzHjquZU4V2ZSBFtFxSVI2bgZALBwwWOiJ6Z8HNk5eWIc0rLlFZV46rmVKC0zYeeHe9Gr1503dZ946vVYu+ZFsW4rk18XY/PQ6bBwwWPivkfmLMC+A7mKsT+zbDVKy0zI3JWFfn3vdvl1Y2Om3bgel4CU1M2os1hgNtcic3eWCDPl2zJwWIAI8lYlp2DRcytFUFtvtSJ92w4xWVn83NhmT9AUMGSwCDDTtu4QAXRRcQniEhZjXsJiRUWqVogOAPMSFmPlK+tRXlGJOosF6dt2YF7CYnF/VPg4sR+M/foCsE1gVlRcgjqLRTzObK7FqldTACiPVw+dDquWPS0e98icBcjcnSWWNZtrsWFTOvILCpG5Kwu33XZbi46XmQ9NRXWNGfkFhUjftkMRztdbrdj6TqZ4Ty6Im92i16KWUQtapUmxGhoacPLUlw7L9Bt4c3/+EHUFI0Y79iUvOXOWQasd9mwlIiIiImqGsDEjkV9QiNIyE+oslhbNVj5nVgx2frhX9NZUk/jneJhrr4rXXLhkGXy8DYplfLwNeN3JRFsSD50OK5KewrRZ8SgtM4nALDQkSNGKwMfbgMifAzTJs08lOIzDno+3Aemp61o8cVFzDPQzIvGJ+UjZuNmhf2tk+DicOv0VMndlobrGjFXJKdiw6W2H7b5h7WoYDF4uv6anXo8Na1eLbZG5K0sEpRJ/P6PDtlz+bCKWPP+SCPxi4xIc9mloSJAizG0qD50OM6ZMQtqWDOQXFGLarHjFa4SGBOG11ctEn15pYjT7iatipkfjQO4hcb+9+Lmxim02Z1YMTpwsQX5Bodgu9scXACx58nHF94HDAhA/NxZpWzJQXWNGysbNIviXS37p+RZPRtbHt7fom5y2JQNpWzJEMC3fBzHTo9tFf+KuqLGJsaTA9YM92Yrlgu83QqdjbRlRa+vRU+8wUdbnB48icOgQxXuzK/drBRi2EhERERE1S1T4OBECHS8sbtGES1JFn7MZ4Q0GLxGK5eR9IWZLB2xBXkTY/ZgzK8blgNNg8MJbqevw+aEjIhSTgrDQkCCEjRmJqJ8nLpLz1Oux/uUVyNydJWZtl0jLjR316yaFla0tZlo08g4fQ35BIdK2ZOA3Y0djoJ8RHjodEhPmY1RoMHa8/yHyCwodtqH02KYKHBaADWtX470P9ijCyJjp0RgfPhYBQwY7bMuBfkZsf2sDNr21DQdyD6G6xqwYz9TfPYCYadEt3h5zZsUgNDgQaVt3iHWW9tX0ByfBQ6dDbMw0Rc9ZefsBwNY3+LFHH8Hb298VYwVs+zx6YqTDZFZSoJ+dexAnT32J7Jw8cXxJ2zo0OFC1Cjtu9kz8Zuxo0X5B0thyzTEhMhw9br8dR/OPK9ZL2nejQoMxMjS4VV6LmkctZJVf6uvrUXRK2UIgZOwAdw+bqNMa/duB2LX1RouY3R/vQ8If5zp8ONKVJ8rqdr2r1/Z2IGqzLlqtVsXlodkLFMvk7sl097CJiIicCp8co/h+9zubodPpxKV79+7o3r07unXrJi4EXL58GfPmzVPclpWV1cxna32u/N1itVpRX1+PWfF/USzbkf5+iUtYjNIyE/z9jIrJdG6GeqsVVVVX0KvXna1SQSo9n8HQs8lVuuUVla02jpvNbK7FrfpbW1SZrPW8Pe7o0aRtYms9cLVNqyjrLBbccsstLo9L+hltX+nanPUrr6iEp/7WJgfxbbWPtLZPW++DttTZfqfa/y6xWq0Ov09Kz1/AkhdWK5ZbvWkmfnEra8s6mmtXLfjPj/X4xa0e6NGz7d/v1DzXrlrw0l/eU9z21sa/o4/vXR3y50xbYF09EREREVEzSZNblZaZWjxZT1N56HTo49u71QJO6fmaE2i15jhuNoPBq01CPIPBq8nbxFOvb/OQz1Ovb5V91Zz16+Pbu1kVz221j7S2T0cNWjsbtRYC8uvSpbjkjGI5334GBq1N9J8f61FddU1cXHHtqgXVVddw7arFpce7Yve2fLzy9G7s3pbf8iejNtOjpx49vJQ/k0+d/kp1kqyuimErEREREVEzySeMWvvGm6qzpxMRUfM11kbgy69KFY8P/HU/dw+5wyn+36/xytO7xcWVwJXBaNc2YlR/xfdH8o8DcPyQxP62roJhKxERERFRCyT+2TbhUGmZCVvf6TgtEIiI2jv7kEYtbD145N+Kx9w71Nfdw+7w0lNyYbU23PTXnTY7FEv/Pg3TZje/BzrdHIMD+yi+zz14RLWytSsGrQDDViIiIiKiFjEYvLB2zYsAgLQtGTh8lFU+REStxVlV6+XKbx0e/0vfO9w95A6v4qIZhUcv3PTX7dFTD59ePdivtQO4627HljBmcy2Arl3RKmEjEyIiIiKiFhroZ8RbqetQet6Ea99/7+7hELWa5UmJAIDhAUPcPRTqgtROSZZ/X1dX57AMg7qWCb7fiONfmLD9H4dw7zDfFm/P//xYL/q6tsbEV/Ln69FT73J/3uYuR+p8evVwuK3abIa3twGA7f3ZFSfGkvDoIiIiIiJqBQP9jBjoZ3T3MIha1YTIcHcPgbogVybHAoALX3+jWG4QWwi02LTZoTj+hQmArS/r7xPGNet5Sk5cwv/bfATXah0n0JrwUCDGTx0GnU55svW/Ug/i+BcmBN9vdHjd82eq8N7/HEPFRbPidt9+Boz+7UCMm3Cf6jjOn6nC/7zxucM4enjpETV1mOZy1DjffgbF/igzfQ3/AUaH92+3bt26XPjKNgJERERERERE1Kja2lrs3r37pryWK/1ayy9XKh7To+et7t5EHV6Pnno8+uexAIDjX5hQcuJSk59j19Z/4611n4mAc9BQXwTff+PDyH3vF2Hz33Nc7gu7a+u/sfFv+0SwN2iorwjWKy6asWtrPgoOn3dYbt/OImz82z5F0Cotd63Wgl1b8/Gv1INu6U/bGfj2VbYSKK+4DADs2wpWthIRERERERGRhsuXL+PIkSPYs2cPzp8/DwCYNm3aTR2DWlhz/fp1XCq/rLgtILiv27ZTZxI0qj8OfHQKFRfN+H+bj+CF9TMcqlC1VFddw8F9ZwDYgs35T0eIZX+fMA77dhZh3/tFOHuqAoVHLyBkzACnz2e1Nojns694tVobsP+DYux7v8ih7UF11TXse78IgK2KdfHqaHHftasW7N6Wj+NfmHD8CxNCxg5AwAgeO01lHPRLUQUNAF9fKu+Swaoahq1EREREREREJJSVleHw4cP46KOPUFNT47ZxaLUQkG4rLjmjeLz+tlvct9E6EZ2uO+ISw/HK07txrdaCD7f/L6bP+bVLy+5594S4Lg9aJRNmBKLsyyqcPVWBD7b/L4JG9Xca5F48d0VcHxs12GGc46cOAwD88q478J8f6x3GYR+0Arbq3d8njMO1qz/i7KkKfPz/TjBsbYbbeigryQuLT6tWoHel9gESthEgIiIiIiIi6uLKysrwr3/9C7Nnz8bChQuxdetWtwatzkghTu3V7xS3q82QTs3j06sHxk2whZsH951BddU1l5aTKh3HTRisGaL+ZrKtT+q1Wgtqq39w+nx3G73F9f9543MxyZVEp+uOCTMCETJmgGLSJmkcgwLu0pyUa9ivbAFrxUWzy+tHNxgH/VLxvbn2qruH1G6wspWIiIiIiIioC2ovFaxq1CbJkle1Utub8uivcOLoBVyrtSA9JReJKyc7rUKVB6H9B/5S83HyUPxHSz2c+cWtHpjwUCD2vV+Ea7UWrFuWhaipwxAQ3FcRrmqNo1efnppBqn1lJrWerv4+ZdhKRERE1El89dVX+M9//uPuYQBQ/mPc0NAgvlqtVjQ0NKChoQGDBg1y9zCJiLqc8+fP44svvkBWVla7C1gB9YmxJFarFVVVVbBarbBare4eaqen03XHf80fjbfWfYaKi+ZGe6zKT+N3VcXXZvTpZ3D6mN/+LgBF/76IiotmMbHVrq35AIAJDwUidJyfIniVj2Pf+0Wid6szjYW+5Bq1D0SkVgJdqaUAw1YiIiKiTiIxMdHdQ2gmT3cPgIioUzOZTDh27Bj27NkDs9nc4ueLjo529yr9jL8/2lrAiL4Ivt+I41+YHCahull+casHFq+ORnXVNeQfLMPh/V/hWq2telUKUyc8FIgJMwIdlvXtZ4Bv38bbS/xY99NNXaeuoCv3bGXYSkRERERERNTJdEMDMjMz8emnn6K2ttbdw6EObNrsUNEDdfe2fPw+YZzq435xa9MjJu87b3f5sT69emDCDFuoeu2qBSeOmESF6773i/Db3wU4jGHgkN4uT+5FLWe1WqHT6dw9DLfjBFlERERERC4qr6jEvgO52Hcgt8nLFhWXNHvZlpBes7yi8qa+LjVfS46zmzEOdx3LLSWNuai4xN1DuSmuoztGjhyJiRMnwmAwuHs41IH16KnHo38eC8A28VTJiUuaj5N8e/k7zee7/M2N8L+nd/Oqk3v01GPchPswfU6ouO2zj23vbXlLgRNHL7h783Up3bt312wl0JWwspWIiIiIyEUnS05jVXIKAGBCZHiTln3vgz3Izslr1rItIY13eVIi+vj2vmmvS83XkuPsZozDXcdyS0nrEhURhsBhAe4ezk3Rv39/+Pn5ITY2FleuXEF+fj4++eQTnD9/3t1Dow4maFR/HPjoFCoumvH/Nh+Bb1+D6uN8+xlQcdGMw/u/Uj2tHwA+3/OluN5YS4KSE5dQXXkNfY0+GDC4l8P94ybch+wPinGt1oIfrv3oMI5rtRZcu2pRfZ3yi2aYq79HP787b3prBOrcGLYSERERdUB33XUXsrKyFLclJyfjs88+E98/88wziIyMdMv47CfIkibHsr/U19djVvxf3Lotqf2ot1pxIPcgfHv36hJhmFQZ2pECS+q4evfujQcffBBTp07F1atX8fnnn2PPnj3NCl7tf/+0Jv7+aJ90uu6ISwzHK0/vxrVaC87WVqg+7uE/jMTGv+3DtVoL1i3LwsN/GKkISc+fqcLZU7ZlJzwU2Gjrga/LqsUEV0v/Pk1RtQoABYfPi/6tv5k0RNz+6J/GYv0y23G66dX9DuO4dtWCN1/bj2u1Fgwa6osFSePdvYmpE2HY2gl069ZNXIiIiDo6/k4j6rqqqq5gVXJKl6g8rLdaNatXJ0SGM4ClNuXl5YWpU6di2rRpqK2txWeffdbs4JW6Dp9ePTBuwmAc3HdG8zEDBvfCoKG+OHuqAhUXzdj4t30YNNQXw37VF6az34rerz289Pjt7xr/OT9+6jCUfWkLaF95ejd8+xkw+rcDAUDxfMH3GxVBbJ9+BjFWaRzSssX/e0kEvj289Jj/dIS7Ny11MgxbO5hu3bopel009s9o+OQYdw+ZiIioWex/xzGAJer8TpacdvcQbhrTha/dPQQiALbgddq0aZ0ieP3RUu/uIXR6Ux79FU4cvSCqSdUsSBqPg/u+FJNXnT1VIcJNABg3YTCmPPor6HSNTyOk03XH7xPG4l+ph0SAKz2vJPh+I6bNDnVYdvqcX6P/wF9i+z8OAYDDsoOG+uL3CWNdGgc54vtNG8PWDsq+6odVQERE1Fnw9xu1pnqrFVVVV3Cy5DR8e/fCL++806W+pWZzLS5e+gbXvv8BAYMHwWDwcvk1yysqcf7CRQBo8rLSeKXlB/Tv59J45evpP8AIY/974NGKswGbzbUoOXMW177/HsMDhsBg6AlPvXp/O2kirqY8ps5igdl8FYeO2P4JNtdeFY/p1etOh3WRb6fmjklaJ61tXGex4Hhhscv7wNXtJI3980NHGt0eABp9ben5PPW3unSs1VutMF34GqXnTU16TzgjH6/a/rLfRnWWHxtdN2mbaK2XK6/Z2HNIz3P2XBmuff8DBvTv1+j4pWW+Kb+M0vMm+A8w4k5vg+bzS/vHfpxmcy2OFZxAj9tvb9Ix1tY6WvA6PGAwTpbcqLKs+NqMPv0M7h5WhzPsV/dg6d+nufRYna47Fq+Oxn9+tAVtWm0Axk24DyPDB+LaVQtMZ78FAPjeY8CdvXtoLjNtdigmPzLC4f4ePfXiNP/qqmswnf0WP1z7Ef5D7kLvu3s6DUtDxgxA0Kj+qK3+QYzDOOiXDu0IqOkqvjYrvg8a3rnPSGkKhq0dHP8JJSKizoi/36iliopLkLZ1B/ILCh3u8/czYvmziRjoZ3S4r95qxd9e+28x+Y/chrWrnb5mncWC51cmO7ymj7cB6anrnC5bb7Vi6zuZSNuSoXp/4hPzETMtWnNdFy5Z5nB7VEQYXnjmr83ehnUWC159PRUFJ06iusbscH/83FjMmRXjEEzFxiUAsE3IpXUqvP1j8g4fFafUA0B+QaF4TEZ6qgii6q1W7PpoL1I2blZ93pjp0Vi4IE5zTIlPzMfYUb/GcytfQWmZSfEYaSxmcy2eem6l4v7QkCDEz5mp2tqgqdupquqKGI8r2yN3T6bDc6qNUVo/reMkc3cWPvj4U4dlANt74uUVS5sd+J09VyaOweSXnseYUaGqj6u3WjFtVrzYV4kJ8zWfU9pHoSFBWP/yCof7X309VbxPN6xd7bBv6q1WxX633y5a2zA0JAgrkp5SDU/N5lqsTH5d9eeKj7cBa9e86PBzRb6/M9JTcf7CRSSv3+hwrEjv19b8kKSlOkLwescdysDs28vfuXtIHdIvbvVoUvjo6mRS0vO6+tyuPG9Tnk+i03Vv1nLknP37zesObl8Ja6U7EOmfTrXTKvlPKRERdRZqv9O0fgcSqamzWLBwyTIRiERFhCFmejR8vA0AgNIyE+YlLIbZXOuw7DPLViuC1tCQIPj/HJ4sXLIMWZ8cUH3NeqsVj85bqAhhQkOC4ONtQHWNGXEJi1Fmuqi57DPLVoug1d/PiKiIMERFhIkxp2zcjJWvrHdY9lyZSRG0+ngbEDPdFipl5+ThmWXOA2JnMjJ3IzsnD9U1Zvj7GREzPRqhIUHi/rQtGdj6Tmazn1/Ot3cvREWEKdZD2gae+lsV+0cKWqV1jZ8bK/ZR5q4sPDJnAeqtVtXXyTt8DAmLnkNpmQn+fkaxfQHbTPXnykyIS1iM0jITfLwN4nnzCwqxcMkynFMJKje9tc1hO/nLAjf77eSpv1WxbwGIdfXt7TjTtj35GAHb8SK9XsrGzVj03ErUWZSn95ZXVCJl42axTFREGOLnxor7S8tMSFj0nMNyrgocFiDW5/++tU3zcSWnb1Qgjg8f6/Q5p/7uAbHt1cjfp/nHixzul7dpGDvq14r7zLVXFftZflznFxQi5R9pDs9XXlGJuITFYjyhIUGIiggTy1bXmDEvYTGKiks012lvdg6SXlwjjhX5cZKdk4dj+cebtf1vBil43bhxI9577z13D0cYPNBP8X1V+VV3D4moy7B/v/1a9rO0q2NlawclD1jll13b/wmr1SpmbGxoaBCzOEozOgJw+EpEzj355JOK79944w13D4moQ7APSaXfV927dxdfdTqd4qva7zeipnj19VRxfe/ObeI07oUL4hRVkW9vf1dRWVdeUSmCFB9vA5IWPSEq9IqKS7D2jTc1g59j+cdFpZq8crbeasWB3IPYsOlt1YpCADiQe1AR4Ly2epmobpOqJrNz8pCdk4eHp05WVPCtevVGNWjM9GgsmDcbnno9Hnv0EaT8I021QtcV5RWVIvyNnxuLuNkzFfdJVYdpWzIw48FJTWqVoCZwWAAChwWI8YaMGI4VSxcpHlNUXCK2k/2Y4mbPRPq2HUjbkoHqGjMO5B5UrarNLyiEj7dBUX15+Gg+kl5cAwCYl7AYPt4GLE9KRGT4OHjodIrK4eNFxYrKxfKKSmTuymrSdjIYvLBi6SKsfGW9WF/7ddVy7ucPCgDbcbbkycfF8VBeUYnYuATkFxQiYdHzimrqV1Ns7wkfbwPe3bpJHF9zZsWI90R1jRl5h482e2KuuY/GiEDXbK5VPSb25x4S42hsArTgwGGKbSmvupXaA0gfZuTkfaHY9tK+kh5jX7GbX1AIfz8jkhY9gZGhwfDQ6VBnsWDTW9uQuSsL2Tl5DlWmm9K3ife4/b4+V2bCkudfQnWNGctX/12xjeXStmQgKiJM8T6WL3s0/7hmVXB7ote7VtXY1rp164Yet9+muK3iUm0zn42ImupsyWXF97fbvR+7Mla2dnDyf17l/7hKX6WLTqcTF63beeGFF+2LPXePhxdeOsrFld9Far/D5L/jiJpCqmiMigjD8qRERb9MD51OcSrxebsJina8/4G4np66ThF6BA4LwOsqpzJL5NV86anrRCDnodNhQmQ41q55UXPZDZveBmALz+RBKwB46vVYsXSRqBpM27pD3Gc214oAN/GJ+UhMmC/WVwr05NWiTXH+wkVRbRkbo+zj18e3N2bPnCG+v6Jy6nxbWPvGmwBs4Zl9sAbYAlepynDDprc1q1vXrnlRsW/tw62kRU9gQmS42A+BwwLE8+YdPubW7SQP1//5xmuKwLKPb29RrSoFnoDtODF49URURBiSFj2hOL48dDpETxwvvpd65jaH/Hmycw863F9nsYhgeu6jjU/ia+x/j7huP3Ga9P3cR2Pg421AaZnJoSpX2lczpkxSff4/zZuNMaNCxfbw1OsxZfID4n55FW55RaUIxu2DVgAY6GdE6vqXAdgqXJ1VqL7wzF8V+22gnxEhI4YDsFVmax23pG7wIGVla8VFs7uHRNQlWK0NDpOk9evbx93DajdY2doBdevWDdevX1d8lVcKXb9+Hd27dxePlVe1qlW0srqVqHEjR47EsWM3/sH697//jVGjRrl7WETtmv1EV9JXtepW+Vd5NSuDV2oqD52u0SrB0JAg5BcUOlSpysNXtao8Z9Wb8tOz1dwpO2VczmyuFdVyU3/3gGa/xhlTJiFtS4ZizCVnzorrPTX6pI0dHdqs6tYxo0KdVtjJT3cvPW9S7X/bmuqtVrGNtcIzAIieGIn8gkJU15hRVXVFtQepPMSTREWEie0UMHiQw/0D+t+jeszczO0kD9ejIsJUj5U5s2IcKo2l4F2LZytVKXrq9eK9tWV7pkOP1OOFxeK6PJjV4qHTif1y6Ei+ouJWaucRHDgMX18qR+auLJw9VyZCzHqr9Ua1eHCg6vMHBw1zuO3uPneJ6/tzD4nnk4e9k6IiVJ9Pfqx9sj9X9bgIDQlS3W/y96nWcdvVSP/ryr9XO9vlDpWffdVV19ibk6iNVX7j2LKj5x13aL5Xu9rf8gxbOyi1f1rl/7xK9zU0NIhfVPaBq4RhK1Hj1H5Z6HTtZwIDovbIWY9xrcBV60LUHOUVlTh/4SK+qbiMk6e+FLeXnb+g+nj5qfxa5KGcdGqzvKLO4NVTdTntmdB/FNdPnvpSMzQ1XbykWK8+vr1x7fvvxW3DA4aoLudKD1Bn5LOunzr9Fcy1tn+upK83izSbOwD0vdtX83Fa20GiFXZJfDRmlR865F5k7nLvdpIfK8Z+fVUf46HTaR5r9VYrqqquqL4npNPxWyp+zkwRdp8rU4bLO97/EIBtH7ga8E4cHy5aaEiBsTxINfa/B6NCg5G5Kwv5x4tEOCqvSg0YMtjhebXGIL9Nvu9Onf5KXD9Zctqh0lbi72fUbBcCADMfmqK+3IC2/bCis5H/XaC/9VZ49bwDtVdvTNRjOvstw1aiNlZ6WtlCwNivLzw920eLkfaAYWsHo/YJnxSu2j9OK2hluErUdPZhj06ng4cHf4QSNYVWkKrW/kbeTkC+PJErnM0Y7gqtwBRQD7nM5huhzNAh92ouKw9qJfLQRgqVXHX1u2s3xmxQH3O/vnc3axtIvWZXJac0a/nW9kNdnbjuajB1suS0qBCUQjBn+xaAOJ27vW+n++4d2KTHy/vHtqWAIYNFcPvhnk9FT+Q6i+VGv905M11/PlmVsdQHVpr4SgrOpcfI+7ZKE2ZpheuNHQf25MGrK/taazK8Af37qd5+m6dnSzZ7p6b1oav874jRvw7GJ/s/F/eVHL+EkDED3D10ok6t+H8vKb6/f2SIw2O6ctEEk4IOSu0fUHnoKrUX4MRYRK3D/j0n9Z8kItc0NlGWPGxlZSu1hNlci2mz4sX3URFhmDg+XBFyvJqS2uwg1llVZXP0uP12cT1merTTsFYiBavfycLWHy0/ttrp4ACwYVO66K/p423A3EdjEBw4TIRC5y9cFJNKtVfybetn7NcmQaO7tpO8qrkx0sRZkvi5sbjv3oGt9p6Q89DpRMuLzF1ZWLggDh46nWgh4ONtUK001WIweImgvOTMWYwZFSomvgobM1I8Rt631VOvR07eFwBsbSVa2/KkxFZ/TlKyb5snv13tb4JBfkZ8ghthq/2kPUTU+s6eqlB8HzjMdmaJsxYCXelveoatHZD8l4/8NgAicJUCVnllq3S7/CsRuYZhK1HLaIWtahWuakFrV/rjjFrmWMEJcV0+47xcY1Vtzk79VptAyFN/q7h+6vRXTZrNXR543dO3T5OWlQe/dZYfYVB5jPzU86aQAsTQkCCHSbtcJa+8lZMmbmoKeeWfs96n8gpYrSrC1lJvtbbKdnKVvHq5KcfZ3uwccf0t2eRtclqtNZpjxoO2sBWwnc4fOCxAtBCYMWVSk7dRRNj9KC0z4Wj+cYwZFSomvgoOvNFzNTJ8rOjbOmignwjWR4aMaJV1kle0Dw8Ywp6qbmD/v6/8+3sHKifJulZrwbWrFvToyVOaidpCucpEdP7G/qoZVVfFsLUTkE+MJQ9T7a9zUiyi5mPYStQyav8kOaty7ep/oFHzSRPnAMDI0GCH++utVhScOKm6rNbEWXJqpwfLe2TKJ9myf121FgHyoDbv8DGHSYVcJT9lXu78hYtNfq5zsgrQ6ImRquGYVs9KxWNOfam6Pmoz1TemV687xfVL31RoPk6qerwZTLJ9PfOhKc3eTq7y1OvFKfpax1mdxYK8w0cBAGFjRimqPP39jKpBa53F0ir9WiUGg5d4L6Vt3YE1K5LEe2rGg5Oa/Hy/GTsaaVsycCD3EBbMm63o1yqR922VaPXfbQ75Bxta7zW6+aS/F3r3utOhb+uJIyaMm3Cfu4dI1CmdzFf+bdG/X1/o9beyWEKGYWsHZX9qhfwgloJXAKxqJWol9r2RGbYSNZ1Wdav8utofaF35DzVqPh9vg2r4dSD3oGawNGJ4gAhypEmo5MorKjVPRZdOdc4vKES91erw2sfyj6suJz9NWmtZAMjcbaugDA4cJgIzedWeVgD5yf7cFm1H+an4knqrFRs2va25TMz0aGTuylJMaiQnVSY6Y19d7KHTiQBv54d7MWdWjOp2+uDjTwHY9r+7A7HGtlNzSKfo5xcUilPm5TIyd4uq0r07t7n0nJveuvE4rV6jTSVNlJVfUCiePzQkqFnhpxSqVteYkfXJfvFc8v0vVTFLwbK0rVqLfOK1Q0fyVauKbfs7HaNCgxEcNKxV23p0Zc4mz5T/nRD127F474O9Yrkjn51j2ErUBqzWBhze/5XitgkRYWwdYKd7y5+C3EXrH1R5zzv5RCM6nU585YUXXpp20aps5YUXXly/yH8Xqf2uYvsAag3S5DvVNWYcPnrjlP+i4hKsfGU9ViWnwF9W3VdeUSmu/2bsaHE9Ni4B+w7kot5qRZ3FgvRtOxR9L+39ad5scf2ROQvEa5dXVGLlK+ud9u1c8uTj4vofn3wGRcUlqLdaxW2Hj+YjZeNmpGzcjM8PHRG3S0EtAKRtycDKV9aL9SkqLkFcwuImTbglGehnhI+3AYBtBnlpLNJ2eGTOAsXj7VsrjJJVFEvbUL4tnFUOh4YEAQDyCwoV+8Z+3z4yZwH2HVAGyenbdogwfOGCx5q83jd7O8lPTS8qLnHpNWNjponrj85bqNi+58pMImiNnxsrwr6pv3sAAFBaZlJULUvHSOauLLEeUt/TlpImygJutKSY+dCUZj2Xh06HqIgwADfCdKlfq0QK1kvLTCJwDQ0ObPF6yJ9fGkN2Tp7ivQbYgta/vfbfyNyVhaQX1ygmzaPWZX/2i3T91yHK/V1x0YxrV1t+LBOR0sVzV3CtVvneGnf/rznfgh1WtnZw8qbhWp8ksH0AUeuzn5SOiFyj1ctJLVzlH2rUHPLJd5JeXCPCSCmEi5kejYUL4vDInAWorjEjNi4BURFhWLF0EQb6GUX1JGCbdXzbjp1iWR9vg+gNaS84aJg4xbu6xoykF9eI7wFb5aufsZ9q+Bk4LABREWHIzslDaZkJC5csg4+3ASEjhiseHxURJmZblyx/NhHzEhYDsIVA2Tl5olIWsIVuUgDXFHMfjUHKxs3ILyjE+AdnivFJ2yE9dR2OFZzAquQUZOfkocx0EUuefByBwwIQHHSjl+aq5BSsSk5RbNflSYmaM7qHjRkpHieF28uTEjEhMlyxnaprzGL/+Bn7oeDESbGtQ0OCEBk+ri0Ps1bZTqHBgWLfLFyyTOxjtWpgiadeL/apfBuYzbWKim15KBs9cTxSNm4GAMxLWIzQkCDUmGsVx0hszDRMmmH7wGDSjNmNjqMx8omyJGptPVw1cXy4eH8Ayn6tEvl7CECTJuJSM3yosioy8c/x4jiT3mvyDwckG9audntVdWchP4tTLWSVX/r2cZy8kK0EiFrfiaPKM3z633M3vHreAUC7t3JX/JueSUEn0FjfO7WZnnnhhZemXdQ+zHD3mHjhpSNeGvs9Zf97jaipPHQ6vJW6DjHTbb1CS8tMKC0zwd/PiPi5sWJ2dHnlozzQfG31MhGgSMsDtuBs1bKnMT58rOrreur12P7WBsWy8qD19ZdXYOzoUGhZsXQRkl96XrGsNC5p7C8881eH5Qb6GbFh7WrFbfJg2T6cddX0Bych8Yn5ojpRGktURBjWrnkRBoMXIsPHKSoiKyqrxLaQrwtgC6N8vA1IfGK+04mdoieOV2xDte0knw2+tMwkwlfAFsyuf3lFm05U1VrbKWDIYHGcNkXc7JmK7VtaZkJ1jRk+3gbEz41FRnqq4hR2T70eG9auVgSD0nsi8Yn5iJs9U4S4kuZURNuT92eNnxvbon0SMHiQ4nt5v1aJ/P0VFRHW4mOg5x09FN8bDF54d+smxT6T93gODQnC8qREBA4LaPG2I+0PYbUuHh4emBAZpniO7A+KYbU2uHtViDqN//xYj4P7zihue+DnFgJaf8d31cC123WWOnY6je1S7nKipnv11VeRm3vjdMWnn34aERER7h4WUYfS2B9ZXe2PsLaQnJyMzz77THz/zDPPIDIy0i1jkU/Q2dDQgIaGBlitVodLfX09ZsX/RbFs7p7MVhlDncWCs+fK0K/v3U3uFVlvtaKq6gq+vXIFgwb6ifBKuh2wTdqkFujUWSz4pvwyAODuPneJZessFnF6sbPKN7O5FldqzPjhhx8Ur90Ys7kWJWfOYkD/fornN5trUWf5EQZDz2b1kZROPTf2v6dJAZa0vucvXETA4EGKfSCdgt3cMUnrdfHSN7jttttwZyOTIUnbwFN/q+rjGrvflX3X3O3kbNs5ez35NnD1GJfeE64eV1rjkLaXs/EVFZeIit2M9NQWV3vKT9tXey75e1PruJKeQ2s/yx/T2LFZXlGJ8xcuOrzfnI1L62eGK49xVfjkGMX3u9/Z7NDSR/4hZ3v+3Sv/HXL9+nVYrVaH3yX19fXi9vMXLuL5l/6ueI4nXpiAAYN7uXtViDqFfTuLsO/9IsVtW998Hbffdptq+7CO8rOmLbCNQCekdhDLA9audpATtQa1yla+l4iaj+8fuhk89fpmV5l56HTo49vbIUSRbm/sddVmfffU6+Hp23jAZTB4NWsiIYPBC2NGhao/Xwu2o9q6uEJaX7Xt1RqnWTdlOzW2DRq735V919zt1NzXa+o2kJ63Ke8JrXE0tr3qrVasfeNNALbK7NbY3409hyvvTVfG4epY1X4+NHdcrjymK7OfFFproqz+9/RFv759cPFSuVj2vf85hsWrm15BTkRKahNjjRv9a3jq9ezXqoJhaxfBA56o9fF9RURERKRUZ7Eg65P9op2FfPI4oqZS69uqdsqydPmvGQ9i3YZ/iuUrLppRXXUNPr16NHcIRASg8OgFh4mxfv9f05229+jK4SvDViIiIiIiImqRw0fzseP9DxWTRcXPjVWttiZyhTxotZ8UWivUCRp2H7x63oHaq9+J59nz7gn8PuHmTJpH1BlZrQ34YPv/Km7rf8/duKv3L51+CCLpioErJ8giIiIiIiKiFvlkf64iaA0NCWr2BG1E9tSCVft+kNJEWfYTGR7/woTzZ6rcvQpEHdYX+79yqGr94x9msZLVCVa2EhERERERUYs8PHUyxo62VbH6DzC2ag9b6tqctRLo3r07GhoaFLdN+10U9uceUlS3sncrUfP858d67Nqar7it/z13I+C+QWwh4AQrW4mIiIiIiKhFAocFYEJkOCZEhjNopVYjPy1Zfpt94CqvcvXw8MCjMVMVz1Nx0YySE5fcvTpEHc67aUccbnsm8U8uh6xdNXBl2EpERERERERE7ZorVXRS+Dr618Hw6nmHYvn/t/kI/vNjvbtXg6jDOH+mCse/MCluGzs6FL69e4kPOew/7FD7gKQrYthKRERERERERO1WYxNjqfVufXLBHxTPca3WolqlR0SO/vNjPf7njc8dbv/TvN83WtHKwJVhKxERERERERG1U40FrVoVdvcN8sfQIfcqnuv4Fya2EyBywbtpRxwmxXpywWO4zdNTdXI6BqxKDFuJiIiIiIiIqF1TC121Qh/p9oT42artBK5dtTRnCERdQsmJSw7tA4YHDMZvxoxU/ZCDE2M5YthKRERERERERO2WK0GrffjTvXt3GLx6Yv7cmYrnulZrwaZX98NqbXD3ahG1O+UXzXhr3WcOt//1T3GNBqycGOsGhq1ERERERERE1O5pha5qwat0PThwKEb/OljxPBUXzcjYdNjdq0PUrly7asGbr+13uP3Jx/8Ag8Gr0fcdq1pvYNhKRERERERERO2as+pWtb6t8iBo/pz/cmgncPwLE/btLHL3ahG1C1ZrAza9ut+hT+ukqHD8ZuyoJrUOYODKsJWIiIiIiIiIOgj70FUe/GgFrnq9HquXLXF4rn3vFzFwpS7Pam3A5r/noOKiWXH78IDB+MOsh52+t+yDV7Jh2EpERERERC1Sb7WivKIS5RWVqLNw4pnOjvub3EWtutX+lGa1UKh79+7w6nkHnl/yhMNzMnClrkwKWs+eqlDc7tXzDiz5y+Pw8PBwKWiVMHC18XD3AIiIiIiIqGOrqrqC2LgEAMDypERMiAx395BaTb3ViqqqK81evlevO+Gh06neV15RCQDw1N8Kg8Gryc9tNteizvJjs8al9Zpmcy0uXvoGFZVV8B9ghLH/PQ7j99DpsCl9G7Jz8uDjbcC7WzdpriNRW9BqKXD9+nVcv35dcR0Arl+/jm7dumHIvQMx48GJ2PnRJ4rn2/e+LWydMCPQ3atGdNM4C1qTVy6Fp17vckUrQ1Ylhq1EREREREQa5EFyc2Skp6KPb2/FbfVWK/722n8jOycPABAVEYYVSxc1+blT/pEmnqOp7F+zvKJSBKj2QkOC8OSCOAz0M4rbXnjmrygzXURpmQnPLFuN9S+vaNmGJnJRt27dRIiqVuEqD1jl1wGgoaEBD02ZCAAMXKlLu3bVgnXLshx6tHr1vAOv/J8kGLx6alaJawWtDFxvYBsBIiIiIiJqkV697kRGeioy0lMRNmaUu4fTrp0rM+GROQuaHZK2hTqLBbFxCYoxRUWEiev5BYVY8vxLqLdaxW0eOh1eXrFU3L/vQK67V4O6mMb6tNpPmiW/PDRlImY8ONHhOfe9X4RNyfthtTa4e/WI2sz5M1VNClobC1kBBq32WNlKREREREQt4qHTOVRvdhZ9fHsjd0+m6n3hk2MAuF6Zmr5tB9K2ZACwVYvWmGtRWmZq9thWLF2k+rr7DuRiVXIKAPXKWnub3tomrr+Vuk60Dnjhmb+i5PQZLFyyDNU1Zuz6aC9ipkUrtk383FikbcnAhk1vI2zMKHjq9W21K4gEqbpVbbIsAA4VrfbtBKTAFXCscD17qgIpK/ZgwbPj0aMnj2fqXAoOn8f2fxxyuN1Z0Kr1IQbbB2hjZSsREREREakym2sV1YzuIk3IZDbXNvs5pAmd3EkKWhOfmI/XVi+Dn7GfW8cjbZfMXVkAgPi5sRjoZxT9Vz10OgQOC0BoSBAA4IOPP3VYPjZmGgCgusaMjMzd7l4d6kLsq+qcVbJqVec9NGUi5syc4fDcFRfNeOkv76Hg8Hl3ryZRq7h21YJNyfubHLS6UtnKwNURK1uJiIiIiEgwm2sdeoFGRYRhTuzDGOhnFNWc8omwyisqHSbImvHofFTXmOHvZ0R66jrN18vcnYWUjZsB2Koq5X1BM3dnYcv2TFTXmBXLREWEIfHP8Q4TPMnHkZGeCk/9rXh7+7siTJTEz43FnFkxN31SJ38/I15esbTdVQEvT0rEoSP5Iji1N/OhKcgvKFQNhz31esRMj0bmriykbclwy3alrs0+dHVW4WqvoaEBEyLD0L/f3VizdqPD/dv/cQjHckvx+4SxrHKlDkurmhUAhg25F88m/gm33HJLkz6skDBoVcfKViIiIiIiAmALWuMSFiuCVh9vA7Jz8jAvYTGKiktcfq4ZUyYBAErLTE4rUqVqSX8/owha6ywWLHpuJVI2bnYIWgEgOycP02bFOx3PyZLTiEtY7BC0ArYK02P5x2/69v3nG6+1u6C1j29vTIgMx4qlizRbAHz51TmnzzE+fKy4brrwtbtXiboQ+zYCWhWuOp3OaXh03yB//HfyCvTs2cPhNc6eqsBLf3kP+3YW4T8/1rt7lYlcVn7RjHXLsjSD1oenTkLSU392CFql94v8fWNf0cqQ1TmGrUREREREBABYmfy6CDf9/YzISE/Fu1s3YXlSIny8DVi4ZJnLzzUpKkJcz849qPoYs6xn6dTfPSBuz8jcjfyCQgC23qb7P9qB3D2ZyN2TieSXnoePtwEAsHDJMs02B6uSU1BdY0b83Fjs3bkN+z/aoVh2x/sf3vTt2xErPuutVuz8cC8AwODVU/UxAUMGi+ufHzri7iFTFyMPXOWhkFbgqtPpHEKk7t27w6vnHVj/t2V4IGKc6uvse78IyxbsQMHh85xAi9q16qpr+FfqQaxfloWKi2aH+7163oElT/4RMdN+h1tuucXhPeFKCwGAVa3OMGwlIiIiIiKYzbUi4IyZHo301HXo49sbHjodJkSG492tm0RQ6Yo+vr3h/3OlqlqvT0AZwkZPHA/AVtUqn0TqtdXLFCHlmFGhSF3/svj+gEaQC9haCcTNnglPvR4eOh3GjAoVFbf5BYWos1hAzpWcPoPqGjN8vA1YMG+26mM8dDrR1zUn7wt3D5m6ILX+rY31cVWr3rvlllswZ+YMvPT8IvS8o4fqa23/xyE8F/8O9u0swrWr/BlC7cf5M1XYlLwfrzy9G8e/UJ988f6RIfj76ufxqxHDXa4Cl3+AwaDVNQxbiYiIiIgIxwpOiOtDh9zrcL+HToeQEcOb9JxStapWKwEphA0NCRKnsJ89Vybuj58zU7UatI9vbxH8HjqSr/raPt4G1VP277t3oLgufy1yVG+1Yu0bbwIAFi54TLPNAHCj6lWqVCZyB1faCcjDJPtgSVrO2K8vXnvpOYz+dbDma+17vwgv/eU9bErej5ITl9higNyiuuoaDu77Ei/99T1s/Ns+nD1Vofo4r553YPHCP+LJx/+A2zw9G31vaIWsAINWV3CCLCIiIiIiUhgeMET19rGjQxX9XBsTPXG8mPwqO/cgYqZFi/vOlZlEMBc/Z6a4vaKySly/9v0PKK+oVH1uvwH9UV1jRnZOHlYsXeRwf6Ssj6jcgP43JnmqqKxC4LCAm759OwKzuRZPPbcSpWUmhIYEicnQtDT12CBqbd26dcP169cVQZA0UZbW49Vua2howPXr16HX34qE+Nl4ZNpkvLvrYxzJP6H6PGdPVYiAy7efAaN/OxD+Q+7Cnb174Be3MnKh1nXtqgXfVnyHr05V4PD+r3Ct1nl1tVfPO/DoI9Nw/6+D4eHh4fIkWM7aB1Dj+M4nIiIiIiKcOv2VuG4wqPfm9B9gbNJzeur1CA0JQn5BIT74+FNF2Crv7Snv+XnpmxtVOUkvrmn2+qhV55JriopLsHz130XP27jZM5u0vNlcC4PBy92rQV1QUwNXaRnp0tBg68V6/fp18bV3r18iYf4cPDL9d0j5Rzq+/qZC87kqLpqxa6uy2j74fiN69emJX951h7jN9x4DbtUzjiF1P1rqUfG1WXz/7eXvUPZllWbVqpaHpkxE9MRIeOr1jbbXUOtjzD6tzcd3NxERERERwVx7VVy/5ZZbVB9zm6dnk5935kNTkF9QKFoJSCGcNOlS/NxYRauA2qvfieuhIUGakzJR28jcnSWqkZsTtAJAneVHGNy9ItRluRK4alXpyUNXeZWrFLquXrYEVd9eQd4X+diVtc+l8Wj1ziRqC/f07YMpEyMxWlbJ6mofY/uwlUFr8zFsJSIiIiIixWngVVVXVPudnr9wscnPOzI0WFwvOXMWY0aF4lyZCdU1ZgDAb8aOVjx+6JB7kbnLdv3JBXEY6Ne0alpqnnqrFVvfyRSTk21Yu7rZLRa0KqOJbhZngau8glX63r6yVStwlULXh6ZMxNTJ43Gy5Aw+2f85Tn151t2rTF2YV887MCp0BH47bjT633O3Q1jqLGi1v51Ba+tg2EpERERERArfXlEPW4/mH2/yc3nodIiZHo3MXVnY8f6HGDMqVLQQ8PE2OISpPW6/XVw/XlTMsPUmqLda8cyy1cgvKATQvKD16nfXxHVnE2kR3SxagWtDQ4Oi0lUeKkkXaTkpcL1+/bpD6HrLLbcgJGgYggOHor6+HqXnL+LUl1/h3wVFTlsNELWGoUPuxa+ChiE4aCh6//JO1eNYK2hVC17VQlYGrc3HsJWIiDoVs7kWdZYfAUA1KFBTZ7HAbLadPtur152qM18TEXV28n6sWhNHFRQWN+u5x4ePReauLOQXFKLOYhEtBOY+GuPw2IDBg8T1vMPHFH1e5Q4fzUfvXr+Esf89/LndQlvfyRRB6/KkxGZVtJ489aW7V4PIgVbgKvVkta9mtW8jIA9apetql1/84he4715/DB7khxkPTgQAVH17BZfKL6Pq22qc/XkyQPz8ulqTbRFJRv86WPF9cGAA7vTxho+3Ab3u9HEIQ9UCVrWwtbFKVnkFuPwrNQ3DViKiTqTeasWx/OPo3euXoq9eVwsPU/6RJk6Dzd2T6dIyeYePYlVyCgAgIz3V5ZCWiKgzGehnhI+3AdU1ZqxKTsGlbyoQGzMNnno9yisq8dzKV1Ba1rzeg4HDAsRzv/p6qmghEBU+zuGxBoMXoiLCkJ2Th/yCQqRv2yHGITl8NF9MnpX4xHzNQJYaZzbXitYBMdOjMSEyvFnPU2aytZgIDQly9yoRKdgHrlKY2ljoKi0j/ypdpCpXAIrbpe8B4K7evdC71y8BABPxGzGe69evI2H+HHdvlnbhySefVHz/xhtvuHtIbqfVS1jrqysVrc5CV622AVpjIdcwbCUi6gSKikuwP/cQMndlqd7v72fEyyuWMkQkIiKn5j4aIyZHStuSgZ0f7oXfgP6i6jF+bqwI5ppqxpRJSNuSIT4QCw0J0pyxPvHP8Sg4cRLVNWakbclA2pYMxM+NxR139EDe4WNiPDHToztM0LrvQK74YM9edk6e2C6Sm/Xh37GCGxV2mbuyNP+WkERFhGHF0kWK28zmWhHER0+MbPMxEzWVfeAqBa3ygNRZyCoPW+2rXKXl1QJX++t0g9VqdbhN14UKRFyhFny6ErQ2FraqVcCybUDrYthKRNTBpaRudvjHKCoiDObaq+Kf0dIyE2LjEhA/NxZzZsV0qUpXV4SNGYWM9FQAtkpgIqKuKmZaNHre0UOEgtU1ZlTXmOHvZ8TU3z2AsaN+3eywdVJUhGLZmQ9N0XysweCF7W9twPMrk8XvMvvXjZkejYUL4ty9yTq8U6e/avFzlJy5MTnQyJAR7l4lIlVSgKTVVsA+eJVPkCUPV+2rXKVlmhq2dvUQtrq62uE2eS/drkYr4GxOVauzVgJqj1F7fmoZhq1ERB1YncUiglYfbwNS17/sUAUjP/UzbUsGaq9+h8SE+e4eerviqdfD05eTeRARAcCEyHBMiAxHeUUlvr1yBf363i0qUMsrKsXjfHv3Etd79bpTfGilNRN9H9/e4jHSMs546vVY//IKlFdU4vyFi7j2/fcAgOEBQzQrPl0ZhyuPcZX0PJ76Wxt9rPyDPVe05MO/pnyI+Nijj2DmQ1Ndfm61dd3x/ocAbGfSaFUrE7UXam0F5EGq1CJAq4WAPJxVuwAMW12hVsXalStbmxK2NqWNgLMqVrYNaDsMW4mIOrCMzN3i+to1L6r+89nHtzdef3kF4hIWo7rGjMxdWVgwb3ajMwWbzbU39R+m5rxe/c+nH7mjUvdmbx8iopul3mrFTz/9hD6+vR1+r5wsOS2uBwwZLK576HQunfLenNPi1cahxZVxuDpWV8fmqpv5wV5TXstg8IKhBa9VVFwiqo+XPPn4TVk/opZSmzhLYh+mOpsYy1nQqha80g1qwaqHByMqOa02As2pbnXWLoAha+vjkUxE1IFJp1T6eBsw0M+o+TiDwQtr17wIALjN01M1aD1XZsLxomJFLzzAVqWy5MnHFTMTl1dUYlP6NgDAw1MnO521OCV1M8y1V2Hw6qmoqK23WrFhUzoKCosVE65ERYRh7OhQpxN0ZO7OwgcffyqWCw0JwojhAZgzKwbNUVRcgvc+2APA1idQClHlt69Yugjnykz4cM+nOJB7SEzuwn64RNSZzHh0PqprzPDxNiA9dZ3iQ6U6iwVZnxwAYPu5y5Y0BABr33gTgO33obO/B4jaG7W2Aq4Gq9JyroSt9tfJhpWtzrnSr1V+3ZWL/bL2r0Oth2ErEVEHFhoShPyCQlTXmJG+bQfiZs/UfKyzMLa8ohLzEhar3ldaZsLCJcsUsz336nWnmMjDXHsV619eobrsuTKTaHOQ+MSNoNVsrhWVtvakSUIOHcnHC8/81eGfebUetfkFhcgvKMSJkyXN2o4VlVVifRbEzRYVPvLb58Q+rLqNpH64e3dua7RaWG7lK+tvvJ6hJ7I+2Y+8w8dQdv4CQkYMx8Tx4RgzKlR1WSkEHj70PrFPzpWZ8PmhI9j54V4YDF7wM/ZTBMdyZnMtsnMP4uSpL1Fw4iT8BvTHgP73YHz42Eb/UZZex3TxErJz8hAzPRqjQoMRHDTM6frXW604ln8cn+zPFTNWhwQNw9Ah9yIyfJzT0KbOYsHZc2XIP16EnLwv4Gfsh7GjQ+E/wAhj/3ucLms216LkzFkczT+OgsJihAQNw6jQYAQMHuS0MlltvNLrOhuvtF/nxD6MgX5GMXZpArvQkCAYvHo6TC5D1F4sXPAYViWnoLrGjLiExVi44DH4DzCisupbJK/fKH5uP8leqQTbpF/SB5/Ln01093CImkVr9vemhKuNha1q33d1DFudayxslb5qhakMWd2LYSsRUQcWNmakYuKQvnf7Oq0I1fLcylcAKPu+1lksyDt8FNt27ERpmQkpGzcjKnwcDAYveOh0iJkejcxdWcgvKESdxaIatH1+6Ii4HhU+Tlx/6rmV4h/2+LmxmPHgJBgMXjCba7Ey+XXkFxQiOycPxn59FQGyPLyVlo2NmYaz58qw9o03FRW5rW3J8y/Bx9uAhQseQ9iYUfim/DK2ZrwnwtjjhcWa4agaabnhQ+/Dlu2ZiuBZCpzj58aqBujXvv8B2Tl5KDhxEjHTorHylfWKWayra8woLTMpgmNJUXEJlq/+u+L1qmvMyC8oROauLDHhjH2YWG+1Yus7mQ4T1Mhnrl6elKh6/JnNtXjquZWKCmbAFlRn7gKyPjmANSuSVI+hOosFk2bMdlhOWl/5hwD21Gb+LpUdQ1ozfTsbb3ZOHrbt2InXX16hGdZm5+Rh+ND7cHefu/DovIWKbS0dowxbqb2aEBmOQ0fykZ2Th+oas8N7CLC975x9gEddQ3lFpTg+licl8pigDk+ttYDWxFiAayErA1dtapNhMWy1sQ9CGwtbXb2u9tzUNhi2EhF1YNMfnKQ47X9Vcgq27diJiLD7cd+9Axut3gOAw0fzRai0cMFjInzy1OsxITIcPW6/HUkvrgEAHCs4IcK0KZMfEKHV2XNlqhWROz/cC8BWgSs/NV96Pfsw0WDwwvqXV4jq1Z0f7sWcWTEi+Nua8Z54rDwoCxwWgPTUdcjcnYWUjZvbbHu/u3WTGMtAPyNWLF0kQr+j+cebFLZKpPFGRYTh2acSkHf4KDZsehvVNWakbclAaHCgw7Yd0L8fAFtImrk7C9k5efDxNmDuozEYPNAPt912G0rPmxwmfjGba7FwyTLxffzcWMyZFYOS02ew9o03RRA5dMi9DqHpgdyDirYVaqHzhk1vI2zMKIfQVAoufbwNmDFlEmJjptn2YeZu7PxwL/ILCvH8ymTVCmmpL3HM9GjMfGgq+vj2Rr3VipLTZ7BwyTKkbNyM4MBhDv/k29pUvA0ASH7pefFeMJtrsfOjvUjbkoHnVr6Cf77xmkOwLA9aoyLCsCDOFvZuSt+G7Jw8lJaZ8NRzK5Geus5hvMOH3ofsnDycPPUlTp76EtU1ZtEaQ6oOlCb5IWqvVixdhAVxs3Ho6L9x8tSX4nZjv77iwzHq2uosFiQseg6A7edzcz7oJWqP1FoLSLfLA1fpMQxam4+Vrc41JXCVvjrrxcqQ9eZi2EpE1IF56HRYkfQU3t7+rgg+S8tMioo8fz8jpv7uAVGVam/MqFDk7slEncWCW265xeF+KdgDgEvfVIjrA/2M8PE2oLrGjP25hxwCwXNlJlHRN/OhKeJ2qQcqAM22B+PDxyJzVxaqa8w4JgsxpWDT38+oWpE4dtSv2yxsjQwfq3rqeFREGLJz8pC5K0vRk7Yp5MHxhMhwhI0ZhYRFz6O0zKS6beUzS6ds3KzaW1GtwijlH2niurztgRRWSyH3hk1vK06VlweX9pWvA/2MeOGZv6LgxElU15jx6uupiqpNebi+atnTinWJmz0TocGBWLhkGfILClFeUanYr3UWiwh4H3v0EbF+HjodAocFIPml5/FNxWX88MMPDut6IPcgqmvM8PczKkJwg8ELc2bFoPbqdxg65F5UVV1RvKZ8vBvWrlaMd8XSRXh46mQsXLIMpWUmFBWXOOybnnf0AHDjWLX/QIGVX9RR9PHtjZhp0ZqV49S1eer12Lm97T7cJHI3Z/1ctYJXCUNW1zBsdY1WYOpqsMqQ1T0YthIRdXAGgxcSE+Zj4YI4mC58Lfp2SkGn1AIgZeNmhIYE4bXVy1RDQyl4q7NYYDZfFbefv3BRXDddvKRYZsaUSUjbkoHMXVkOp57LWwiMDA0W16X+l/5+RpRXVDa6flIVYL3VKm7zM/ZTfWxbTlI1Pnys6u1SJWNL2I/bU6+Hn7GfqDS1D3Ht95990KpFGmdURJjqKfujQoNFyC0PIauqrojjaVRosMPre+h0SE9dhzrLjw7PmX+8SFyXz1yudtvJktOa+/Dt7e86bAdnlcS+vXsBgGoo6qHTaQbj8g8D1MY7aKCfuH5Go6JbEhoS5LSPMhEREbVv8qBKK3iV7pM/zh4DV0dqIaBaa4GuTKufcGPXtZalm4dhKxFRJ+Gh02GgnxED/YyImz0TZnMtLl76BvnHi0T4ml9QiGeWrXYIXOssFmx6a5vDxFONmfHgJFF5WHL6jCJ4kloIxEyPVryWVDUoTSzlqqqqK+L62NHaIZu/n9Gh12ZrUAvegBuVjM0VFRGmevvY0aEuhbj+fkaXglZ5WD186H2qj5FXMZ+/cFEEnydLTqs+Rs5g8HLoDwvcCOj9/Yw4kHtQcx1Ky0w4dCRfcSqqp16P5UmJWJWcgsxdWTh/4WuEjRmp2jbAXuCwAPG8y1f/HZHhY12ayEs+Jq0PJaSK7pOnvnSo+hseMERcj54Y2ejrEBERUcegFbxK90mBqv3j7G8jG7VglWGrusZCV2e3kXswbCUicsGpU6cU37/22mt47bXX3DaerKzGQ1GDwQsGgxcChwXgN2NHY17CYgC2CXpMF74WYdW5MhOWPP+SqFz09zPCz9gPw4feJ4JEtQlSpNeQAi356e7yFgJTJj/QonW1D+AaI1WEtjaPNjqtydivr+rtPW6/XVyvt1o1Xz8i7H6XXkceVg+WVWfKydsTaPUVbWr1sFTJXFpm0jyOnIkMHycm68kvKBT9iaX2GNETx2uGp8ufTcSqV1NEhbD0YUJoSBBmPjQFI1WqdAtOnAQAhAQN0xxTyIjhLgXh8uBVS3Q0T9EmIiLqaOxDLfvwVe1xrG5VYljonCvbgtur/WLYSkTUBQz0MyJmerQIm44XFYuw9cM9n4pg1L5HpcRZSDZ75gxReSi1EpBaCPh4GzQrEP39jKoTDLlC3jv2ZnEWeLbEHRqVsfKw09nr9r3bt8mvee37H1Rv/+mnn1p9O0jht7+fEbNnznD6WOnUfzkPnQ4rli4Sk4dlfXIA+QWFoj3Glu2ZionL5Ab+fIyVV1Rib3YOcvK+QGmZSYS29v1UAdsHCNU1Zphrr2qO09l9cvLwmoiIiDovrdBLHrAyGFNi2Oo6bpeOh2ErEVEHVV5RiZMlp3HpmwrMmRXTaAA2ZfIDImz9+lK5uF1e7acWtJrNtU6fN2zMKHFdaiUgtRCY+2iMw+OlCaVKy0yos1hcOqUbUFZU2veOlZMqE1ub/URKrUXtVHQAuPrdtVZ9HfnYv6m4rPqYb8pv3O4/4EZILq+ybe52MJtrWzRbtadejwmR4ZgQGY56qxXH8o8j6cU1qK4x40DuQafP3ce3N+Jmz0Tc7Jmos1jw6uupyM7JQ9qWDIeZ1aVw2NlxJFXXGrx6Oh1zW1VDExERUcfAkEwbw1bqzNgQg4iog9qbnYNVySlI25KBre9kNvr4Nzali+vSqf3yIDVszEjV5XZ+tNfp83rq9QgNCQIA7M89pGghEBU+zuHx8oDq7Lky1eest1pRVFyi6DMK2CplAe1AVf7aHYXW6egnT32pWOfWID2X9Nz2Ss/faL9wm6enuC7v0yrv3yq370AuVr6yHimpytmppf661TVmzeC+zmJp0np46HQYMyoU8XNjAQAbNr3t8rKeej1eeOav8P+54vrt7e8q7pfaOlTXmB2OPwCKSd2GDrm3SeMmIiIiIqLOj5WtRETNMG7cOPzxj3906xjmzIoRp0WnbcnAffcOVO1BCQCHj+Yrel1Kp/bLe65+8PGnigrLOosFGZm7xQRYwI3+m/bi58xEfkGhYoKt0JAg1YmbFsybLR6XtnUHnk1McKiU/Ntr/y1CyN3vpInnmTHFNiFXdY0ZKambsWDebFEZW1RcgrVvvOnWfdJch4/mY8yoG5N+nZNVVs6YMqnVXmfhgsewKjkF2Tl5mBP7sKLFQ53Fgm07dgKwHSPyfdLHt7c4TrI+OYDhAUMU95dXVIpWE8uTEhWvKa983vnRXocqbLO5FtNmxcPfz4jlzyYqxpS5OwspGzcjKiIMK5YuclgfqQVDyIjhitvrrVb88clnUFpmQvJLzyu2rb3x4WMV30+KihDH/K6P9mL6g5PEeOutVhw6+m/VdWuut956q8XP4YyXV+OTpxERERERUeth2EpE1AwGgwF33XWXW8fgodPh9ZdXYNqseABA0otrANhCzrAxI9Hzjh5iYiE5+76ZUs/V0jITFj23EjMfmoKj+cdFIBo/NxZ97/YVj8ncneUwG3zAkMHiurTczIemqI7bU69H/NxYpG3JQH5BIWLjEhAVEYaJ48PxTcVl5B0+JoLhDWtXKwLbGQ9OEkFY5q4sHMg9hLmPxuCDjz8Vk2JJM8V3JEkvroG/nxFLnnwc+3MPKULrSVERrfY6keHjRCg6L2ExoiLCMCf2YXx+6IgiVF/y5OMOyy558nEsXLJMsc8enjoZZ86V4YOPPwVgC2ntT+eX7++0LRnY+eFeLFzwGPwHGFF63iSqUv2M/Rz6+0ZPHI8t2zORnZMHg1dPjA8fi1/eeSd+qKtD6Xlbz1YAWBA3W7Gch06HP82bjaQX1yDpxTVYnpQoJqv69soV7M89JPrI2rfO6OPbW7S6kHrCrlr2NCoqq7Bh09vi2IqfG+tyCwxn3P1zhIiIiIiIWhfDViKiDsxg8MJbqevwxqZ0EVDKZ2yXi4oIw4K42Q5VpGFjRiE0JEgsJ182NCQIc2bZ+q5KQZNapaGHTqeYgAsARoYGa447bvZMEeACtlPp7UPh+LmxDkGYweCFDWtXY+GSZQAgxiPZsHY1KiqrmjXrvbskPjFfBMzSesnXpzX7xHrodNj9Thqeem4lSstMDtvdx9uAVcueVu3dGzgsABvWrsby1X9HdY3ZYVmpMlWNdAxJVcn2+yc0JAgvPPNXh+U89XqsWvY0Fi5ZhsxdWYrjSxIzPVp1G40ZFSqOSa3jQWu8LzzzVxi8eiJzVxaqa8wO+yV+bqxYJyIiIiIiIrlu1+XT4xERkarHHnsMVVVV4vsHH3wQCxcudPewFOosFhwvLMYn+3Nh8Oqp6Cc5MmSE6in9cmZzLbJzD+LrS+UYFRqM4KBhiso9s7kWdZYfAQA/1NU5VCGazbU4VnACgG1WebXATu01S86cxTcVl3Hy1JcYPvQ+BAcOg7H/PU4nF5KWO5p/HAAwKjQYAYMHwWDwEhOHAXB5Qib5MmFjRon11rpda9mmTAAVPtkW1i1PSkRk+DiYLnyN0vMmnDr9lWJ9tOw7kAsADqf0u6LeahWvd+hIPsaODoVv714YNNCv0WrNOosFZ8+VoaKySozV/lhpbL99+dU5AEBocCD69b270WOzzmKB2XwVJ0tO49Tpr3BP3z4uHSfSa1689I1ivI1tW4l8EjoAuO/egY0uW2exIO/wUQBNOx46o+vXr4tLQ0MDGhoaYLVaHS719fWYFf8XxbK5exrvQ01ERI6kvy8ku9/ZDJ1OJy7du3dH9+7d0a1bN3EhcofLly9j3rx5ituysrKa+WxE7QvDViIiF3SEsJU6FnnY2tVDOeqcGLYSEd18DFupo2DYSp1Zd3cPgIiIiIiIiIiIiKgzYNhKRERERERERERE1AoYthIRERERERERERG1Ag93D4CIiKgrWp6UCMA2wRURERERERF1DgxbiYiI3ICTYhEREREREXU+bCNARERERERERERE1AoYthIRERERERERERG1AoatRERERERERERERK2APVuJiGTMZjMSExMdbq+rq4NerxffHzx4EEePHlU8ZvDgwXjhhRfcvQpERERERERE5CYMW4mIZAwGA8xmM3766Senj7NYLA63TZw40d3DJyIiIiIiIiI3YhsBIiI7o0aNatZyDz74oLuHTkRERP9/e/ceH0Wd5/v/HRKOiRfSQWVAxBDwQoAg+WVAwZgFUWEQBc0c0IOMiDusiGdRGUUdlB/CqugiZn9iXPZwmUVW4DFxuCgDKpKNEUbcnGC4BC8Q4gUQlHRQJ3FJk98fmSqqu6v6EjrpdOf1fDz6kU51V/W3qqsv9e5vfb4AAABRRNgKAD5+85vfhD3P+eefL5fLFe2mAwAAAACAKCJsBQAfPXr0UMeOHcOaZ+zYsdFuNgAAAAAAiDLCVgCwEW4pgby8vGg3GQAAAAAARBlhKwDYCKeUQMeOHdWjR49oNxkAAAAAAEQZYSsA2OjRo4cSEhJCum9zB9QCACjk91oAAAAgFhC2AoCDrKyskO7XnAG1AAAAAABA/CFsBQAHY8aMCXofSggAAAAAAAADYSsAOMjNzQ16eislBAAgPAkJCeYFANCyeL8FgNaXFO0GAEBblpWVpYqKCsfbKSEAAM4SEhLU2Njo9b/v7XYhQN6o/Gg3HQDiit37LwCgZdCzFQACCFRKgBICABAa31DV2ruVA34AaDl2770AgJZF2AoAAQQqJUAJAQAIj1PoCgBoObzXAkDrImwFgCCysrJsp1NCAACcGQf2TqUD6NkKAC3P7n3W6f0ZABAZ1GwFgCDGjBnjV7eVEgIAEDq7gNV6+eO/F8rj8ej06dNqbGw0/xr1Xn3/AkB75BuSGu+hHTp0MP8mJiZ6/eUHLgBofYStABCEUUrAepCfmZkZ7WYBQMyxBgQdOnRQY2Oj11+pKVA13nOtFwBAE6cfrqyhq/WvMQ8AoHUQtgJACLKysrx6twYaOAsAcIYRnFr/WoMBa9CakJDg1avVrkcrwSuA9si33rXxN1jQau3NSvAKAK2DsBUAQmAtJZCQkKDc3NxoNwkAYoZdMGANCIzbTp8+HbRXK2ErgPYolPrXTkErZQQAoHURtsY5DkiAyBg0aJB5PSsri9cWcJY42GsffEuwWMNV3/s5Ba283wKAv1BKCVgvToNkAQAij7A1jgQ7GOFgBWi+jh07KiMjQ1VVVbrlllt4PQHNYD2ws3sNceAXv+wO8q2hq1FegIGxACC4YANlWcNWerYCQOsjbI0DgU6v4+AEiJxRo0bptdde09ChQ3ltAc3g28PR97pxOweC8cVaq9U6TZLXoFi+A2MZ061/AQDOYatdD1e7oJXPWQBoWYStMcwuVHUKWjlIAc7eDTfcoNLSUp0+fTraTQFiljVYtbtuPe2cg8H4ZB0Yy/r9xfc632MAwJ7vj1e+QarTAFkAgNZB2BqjfA9AjP+H3zI+2k0D4txpjbj1zmg3Aohr//nnP0qSX29IxC5r71bf59UIXiXRqxUAQuTUu9V63a43K5+rANDyCFtjkN1BCANIAK2lw9kvAkBA1p6tBK7xw+n5tJYO8B1Qi+82AODMrjyLXehqd38AQMshbI0xgYJWTm0GAMQD3x6QBK7xI1CZCN/avb7XAQDenIJUu16sfI4CQOshbI1hRsBK2AoAiCenT582a80h/oRSq5e6vQAQukChKu+jAND6CFtjiF1vVmvQahe2lmwuinazAQAIKG9Uvtf/vqVx6N0an3x7sjqFBfRuBQB/wT4T+cwEgOghbI0xgYJWerYCAOKBXR1yDhrjl91z6xu2AwCc8T4JAG0LYWuM8D3o9A1ZCVsBAPHC+DzzLSVA79b2g+cZAAAAsYphtWOMXc9WarYCAOKJ3WccAAAAAMQCwtYYYq3Zavy1Bq0cjAIA4oE1YPX9CwAAAABtGWFrjLIefBpBKz1bAQDxgIAVAAAAQKwibI0xgcoIcFAKAIgHfL4BAAAAiFWErTHAqYcPB6MAgHhk95lGb1cAAAAAsYCwNUb5HogStgIA4gmfawAAAABiEWFrjHEKWTkoBQDEC35MBAAAABCrCFtjhNPplNb/ORgFAMSTYJ99AAAAANDWELbGMHq0AgDiEZ9tAAAAAGIVYSsAAAAAAAAARABhKwAAAAAAAABEAGFrHOGUSwAAAAAAACB6CFsBAAAAAAAAIAIIWwEAAAAAAAAgAghbAQAAAAAAACACCFsBAAAAAAAAIAIIWwEAAAAAAAAgAghbAQAAAAAAACACCFsBAAAAAAAAIAIIWwEAAAAAAAAgApKi3QAAAAAAsaXB49Hx499LklyuTkpJTvabfvHFFyopMfGsH6uuvl5u90lJUreuXaK96jHH7a5VXf3Pktr+9jPampJ8jlyu1LDmPXL0mCTv/REAgGigZysAAACAsBw//r0mTJ6mCZOnqXTHTtvpRuh6tkp37DSXifAVvLYsZraf0daC15aFPa/d/ggAQDQQtgIAAAAAAABABFBGAAAAAEBUud21uu3OKRoxLFdzHn842s2JKzPun6KpkydGuxktbs2KQklNZQSiqa6+XiPHTWRfBoB2jLAVABAX6urrzVMH+2f2iXhdune3lbTYsluS0e5ePdPVOyM92s0BAFtffXM42k2IWy5XqlzRbkQraCufzYePfBvtJgAAooywFQAQF5YsX6Wi9ZvUOc2lP76+JOLLn7egQJL01KwZUT2gq6uvD2vgj28OH9WylWvUOc2lN5YvZtAQAEHV1derY8eOERncKlSfHahq1nxud23YAym19Pq1xTZFux0NHo9OnTrV7M+gBo9HkiLWruY8R6E6eKi6RZYLAIgdhK0AEGeMUzGfmjVDNw3Pa7XHnTztER2sqlbJ5qJWX+cDVdUqWr9JkrTw2aejfrAaaUeOHtOSFau0tbhUktQ5zaVxY0bq+qHXBO2teved+Vr31hadqHHrhZcLOaURaAd2763Umxs3m/+Xf7JHsx5+QEMG59jev8Hj0cdlu7SzbJe2lWzXiRq3eduUSRM0If+2Fvuhxmhr+Sd7zLbOfX6RpKbT3+0CsR07y7SzbJf5vi9J+WNHa/rUyY7v/weqqvX6mjdV/skec/16ZaQre0A/3XPXr8MO3oo2bNKefZ+qf9+rlH/baO3eW6n3S7Z7bb9eGel6+bk5jss22mS8t0tN7+/ZV/fX3RPucHx/9/1MMOYbN2akcgZmKatfpm1bJdl+Bjhtm4z0Ho7PQSjc7loVvLbMa7mSNGJYrm6+Ic9xfzQ0eDx6fXWRikv/ooNV1V7Pmd1zbew3d9w6ym8bGNuhdMfHKiuvMKflZA/Q1f0zdfed+QG/O4Tymgq0Lzu1CQAQnwhbASDOVH72Ras/ZoPHYx4IReOx573Q1Ot0xLDcuDtVfsfOMs16+lmvaSdq3Fq2co2WrVyj/LGjNWPafY7zJyUmauGzT+veaY9oa3FpwAP4eFZVVaVzzz1Xv/jFL6LdFKBFvbutxOyJbzXr6Wc1Yliufv/oP/qFSv/04r94BXdWxnvNhtXLWqQn4NFjx70e+0SN2/x/6uSJfqe/795b6feeKElF6zdpW8l2rXtjqf9tGzap4FX/6QerqnWwqlrbSrZr3uzfhRWG7dn3qbYWl2prcamu7J2h6TNn2y5/8rRHbNu0YtVaLVu5xm+6sf5bi0s144H7lH/b6JDWxfq5sLzwJa/3eaOtkn/YGmzbbC0u1eKF88MOCu0+uwzG+gULyO32S6Ndh778Wi/On+01r3HfodfkeLW3wePRo7Pne4WshrLyCpWVV6i49C+OwXior6lA+7JvmwAA8a1DtBsAAIisTz8/0OqPefz491Fb3/Vvb9HBqmp1TnPpsYemRa0dLaHB49GCRa9KagqSN6xeppLNRdqwepmmTJogqSlgOHL0WMDl9M5I14hhuZKkmU8+Y56OGe+qqqr0H//xH5o4caKmT5+uffv2RbtJQItasWqtGQr1ykjX4oXztWXdKi145kl1TnNpa3Gpfn33VNXV15vzHPhboCZJMx64z3yfWbOi0HyfkaSC15a1SJtvGp6nks1F5nvUiGG5KtlcpJLNRbYlW6bPnK1eGel6atYMvf/2Wi1eON+c90SNW253rdf9d++tNMPEzmkuLS98Se+/vVYlm4v01KwZ5nzTZ8722i7hmD5ztjqnuRzbdMDnx8jdeyvNoDUne4DWrCg013nNikLlZA9o2uavLvV6f2/weLTyjSLz+XWab+aTz4S0LnbbZsu6Vea26ZzmMtfPd7sGUldfbwatxnKNdm5YvcxsZ1NA/qHtMoxA1tiuW9at8tofy8or9HHZrpDa8/rqIjNotW7v999eq/yxTWH2wapqPfTEXK/5GjyesF5Tgfbl1jzTCAAQffRsRYsyTlu649ZRyuxzpbaVfKjtH5Vpa3GpRgzL1dBrcjQ87zrbX7SNU3FcqZ3MXlsHqqq1a/debfzze3K7a5V9dX/H03Lq6uu16Z33tWffpyr/ZI8yel6mnpddqsE5A4OetnTk6DFt2Vqs6q++Mdt68w15yrzy8qC9OoxT28or9pptDLSe1vZ+caBKZbt2q7j0L0pzpSp3yCBd2TtDmX2uDDiv212rys++MB83I72Hhl6TE9JAPnbt7d/3Ko2++QbHUwatz2tWv0w1eDyq3P+Z3i/ZrqL1m8xTzyJ5urKxP6T36K7JE8eb+8MH2z9ScelfzLb7nu5mPe3Lrj3GKW52txunft094Q6lX3ap1ymW48aM1MgRw1qsdqfT/jAwq5/SL7vUdn8wnhfj9LVVa9dp+0dlXvcxTne0Y+xHn35+wHzM0TcP16Dsqx33e2P7VlV/5bfdrALtC8bz+MmeSpWVV2jEsFz173uVRuRdF/T1Vldfbx4oTp96T0inuUZi/zUG49r+UZnKP9mjcWNGatwtIwO21zhN9533S8ztZbxWnd4ftpV8qBM1bnVOc3n1RnO5UjV54njVnvxBRes36YWCQi16bk7A9X7soWnaWlyqEzVubSv5MC4P/Dwej/bv368PPvhAGzZsiHZzgFZlDfB8e0QOGZyj6VPv0bwFBTpR49auir3mdyHjzIDOaS6NvWWk+T7TrWsXTZ443nxv3lpc2ibKkORkD/B6v8vql6nLe2eYgfHWkg+91n3hK/9mrt+Kwpe83qdvGp6n3CGDNXLcREnSmqIN5neMcPTKSNf/eeVFc9v5tumD7R959TS1tsm3d2a3rl304vzZuuGWpnYsWbHK3O6V+z8zT8d/6jHv+uHdunbRs3Nm6YWXCyU1DdIU7CyGcLbNure3hLxtlixfZV73Xa7LlaoX58/W3z/4qA5WVWvV2nWOn0e+PV8nTxyvkSOGadrDT+hEjVtr//RW0O/0dfX1XsG2dXsnJSZqxrT7NDhnoGY9/awOVlXryNFj5nat3P+ZOa9vaaZArykAAAhb0aKM05b6971Ky15f63X6jvGL9aZ3tvl90ZSkiy680PySOn3qZK1/e4vfaU5bi0ttT8s5UFWtmU8+41Uf6kSNW2XlFSpav0k52QP07JxZtsGM3elURlulptpldl826+rr9eTcBX6nKBnzLl7yB78vnIYGj0d33Tvdq72SzGWNGJbreICze2+l36lrBy29VBY886Ttlz+3u1ZzF7zs2N6VbxRp4bNP235R/+GHH83lZ/a50vzCbH38g1XVET0oO/fcc83HnDxxvAoKl3rVajPa7nvKofWULrv21NX/7Hi7Mb1/36v0ypIVXtvKOFUv0HPTXHX19ebBjZXx+E6njVtPE7Q+D77swta6+npNnvaI4z7otN/7njJn3W5WdtvIqMXmexqlsQ8WvLo06KmLuyr2mtdzhwwOafsa28mV2kmZfa7Ur++e6rXewfbfkz/8qGkPP+m1bYOd0u/0ejNP0XR4f1i85A+SpEl32deSGzPqRhWt36Sy8gqvA0Q7KcnJyh87WkXrN2nTO9viJmwlYAWaLHt9rXl9RN51frcPt0zLvPJy8/qKwpckNb1P2b3P9LzsUvO9qyUHFQrV6JuH+01LSU5W5zSXTtS4tfHP75mfc0eOHjPfqyfdlW/b9pTkZOVkD1BZeYXWvRV6oJjeo7t5/dZf3ei37VKSk9UrI10Hq6q9lmtt07gxI223eVJiovl+bQ25L7rwQvM+ryxZ4ff9OSU5OeTvJG53rVc7nLbNiGG52lpcGta2Mb6f5WQPsF1uUmKibv3VjSp4dalfwGl1z12/9ts+3bp2UfbV/bW1uFRl5RVq8HgCdkgo3bHTvP7YjGm29x04oJ95fe2fNpqf49bX1KDsq/3mc3pNAQBA2IpWsfKNIp2ocWvEsFxNnTxR333/vZ6a/89mAGrXy+rii898odxW8qEKXl2qzmkuTborX1f2ztBFF16oPZX71T+zj9d8DR6PV9BqDOzgdp/UE3Of18GqapWVV9j2XvA9nWr61HuUO2SwDh/5Vm9tfk9F6zdp2co1tj0aX3i5UGXlFeYABeNuGalzks9R6Y6dWrzkDzpR49ZDT8w1D2qsjN5rxvbp1rWLGjweVX/5tWY++Yy2Fpc6DiRg9Ep4atYMswei0etu3oICLVj0qu0I5AWvLfMKc6dOniiXq5PWFG3QspVrdKLGrZlPPqM/vr7E74tp90u6Smoq/P/66iIdrKpWr4x0TRw/Tr16puuvf/2rjh47HtF96JJuZ+osFm3YZI4677s/uFydIvaYxkGSsU8Y+9LhI9+aA1psLS7VYw9Ni+jAIWuKNng9L9b94d5pj6ho/SbbHtoz7p+iqZMn6oWCpn1xyqQJGjlimNd9UpLPsX3MJctXmT0oCxc9p4svvlDHj39vDsCxbOUa28EjcocM1poVhdqytdgMTdesKAxpPbeVfGjOM2JYrtmDuHL/Z1r4yr/pYFW1ps+cHbBO4No/vSWp6YAu1Odg6DU52lpcqkNffq3FS1boRI07rP3XeC+y2x+K1m+yrT/30BNzzYNa6+vthZcLzd6mdu8PxvvYlb0zbNti/THk0JdfBe1pPThnoBnO1tXXt9iANy2NgBXwZ/0xxyngCvQjizGPb1kSd+1J83pd/c9+NVRbm13oJckM4ALVD3cquWIEyr4/OAZifBeSpKGDf2l7n4z0HjpYVe243OuHXuO4/L59rlDR+qbrRsjdrWsXM/wsK6/Qo7Pn68Gpkx3PeAmkrv5n8/oFF5zvuG2MUDnUbWMtX2AXjFu3mfH96q91deZ06/7m9NlvDbqPH/8+4GffN4ePmtf/WlfnuJ7Gdz7r45/tawoA0H4RtqJVnKhxe/Ww7Na1i/74+hKzWL3dKUTWL43zFhTYnuJk9+Xq9dVF5hfCLetWmWFCStdkrSh8yRyQYNnKNX6j6xrBpe9pRr0z0jVj2n0qr9irg1XVfqfsHjl6zOzNN+mufK+egzcNz1Ovnum6928jte/eW+lXtN/ovXb3hDvMdUpKTFTvjHTNm/07le3abbtdd++tNGtVWrdfSnKybhqep28OH1X3S7rK7T6plK7Jtu317flqnKI1YfI0x9ONjYDbGIzBd3tJivggANbnyQi7QtkfzoZxkCTJa7CJ3hnp5inZkvTFgaqIra/1dDcjaJXO7A+LF87XZweqdP555/rN63KlyiXJldoUOHe/pGtI26Suvl6HvvxakjTr4QfMebp17aI5jz9srufHZbv8At6U5GSldE32OugM5TGt+31O9gCvnjhZ/TK1ovAljbvrPp2ocQc8ddE4EAp0QOerV890c96y8opm7b/Wfc93f6jc/5nX/MbrVJJfT905jz+sO24dpekzZ/u9P1gPWI3eTLv3VppBcO6QwV6vix9/+inoult778TaKY/19fWqqqpSaWkpASsQgFEPMxy791aaP3K1deH0rN1Tud+8XvDqUtuBoHw1p/duON8/Dn15puzOuSkpIc1jDbmnTp5oft6UlVfo3mmPSGp63sffPkaDcgaGFLxa2xHqtgl2BkXT9jsZdDm+Dh6qNr9jWWurOrF+5wim+qtvzOvGtgpXc15TAID2jbAVrWZQzkCv/5MSE5U7ZJDKyitC+nK/8NmnQ/ry+8meSklNv1Db9doaOWKYGWaV7thpBonW06lyhwyy/aJauOhZ2y+R1i/zdqfu9c5IN09ve79ku2OQ88qSFX51F7P6ZTre3whgTtS49e62Er9Q1CmgsrbX7rQn6xfp7R+VBfzVvldGum0ZiJZWuOi5VjuVsXOay6+cgvX0wEDPabg6duxo7it2dTgD7Q/NlZKcHLDep3F65Tvvl0QsnDt+/HvzR5Hxt4+xvc/wvKFmb3Kn0h2G8887L+THth7cNmf/7Zzm8tv3rPtD2a7dXs+R9ceSy216qGb2udK8bt2X7N5r3ty42TzIXrOiUCldz5ziGYpww9loq6+v1/79+7Vx40Z98sknZ728jRs36sUXX4z2agERt2nTmbI6xg9uoZr7/CKv95ARw3LlSu2kvn2ukCSz1n570dK9d0N97+3a5WLb6d26dtGWdau0q2Kv/nX5KvO7q/EDouRcQqo57YiWcPfjSLGrTRyJtnz77be69957o7JOAIDWR9iKVtE5zWUbZlzS9cyp4Xa/lltDhGBF/g3GF81sSw8uK6fTzK2nUw3Msp/X6MXny3qK0sfl9oGAy5XaNEpurXeAkpSYqHmzf6fpM2errLxCk6c9olt/daMGZvULus7W08nmLSjQ9o/KNPSanIADGknSvv2fe7XLjrFc3/b6GpZ7basHrZ3TXC02MJWd4XlDbacbPV+DbaNwJCUmmgMulJVX6OEn5poDY4X6Gjgb1oG5jN4gVYe+jPjjWE8Z/PTzA7YHfcG2qzWM7HlZj2a1w67OXjDjxoy0nW7sD9ZeNNKZXjWd01y2PwAlJSbanr7YfjWqvLxcmzdv1u7du89+cUA7YfwwZgySGAq3u9b8npWTPUBzZj1k+70gVsNW6w9xoQSQrcEpRPUVqJxNSnKyhgzO0ZDBOaqrr9euir165/2SM2ctLXrVtgyUUzuivW3sfjC1DrwZKdYz3kLRnNcUAAASYStaSfbV/W2nhxqQjBiWG/ZjGj0yfFkHUdi3/3Oz16a1t6e1PmgorOHKvAUFAe9r94Utq1+mORCCtUaoUf810Kj3M+6fInftSXOkYOOLdq+MdP3DvRM1cEA/vy+WRqATaLsa9bB8B/TxddUVvcPaVmfDCICd9qeW4rQvtZSbhudp3/7PzdqaxnNgrQcc6V69O3aWefWQaWkHD3kPLhVMSw3K4lQLNZBwTl+UzrzeAu23RlAb7OCyf9+rzOtO9XeDMV5HwXqtA4gtV/fPNOuOOtVk9n0vrfzsC/P6lLvH277PWn9QjjXW75k7bUrhRIN1kKvvvneuN3ryhx/N69ZxDHxZg1dJZh1wuzJQTu2I5Laxrk+gfee77783r9sdDwT6PrL9ozLzerBa/Uaddin8sk/WWr6hvqYAAJAIWxFl1t5tgVgDhkgwTl++tHs3c5r1V3XfGqehMgbVao4Z0+7T9KmT9XHZLrN3glETddnKNY6/xrtcqVr03Jym3iklH2rjn98zR1Of9fSzGjEs13Fk2kC96GpP/mCuUyDN7VF4NoZeE7mDpVD3wdY2Y9p9mnrvRK/eKsb+sO6tLUF7rITjwN/2FakppJ/54G/Ng7CU5HNU8NqyFu3VNOOB+9TpgvMD3uecZgaLwVgPNltKOKcfplkO2OwOrvNvG+1VE1qK3R5nzhKUnZ2tX/7ylzp16lREywgA8cz646fd95i6+nrdducUSU0DPk6eOF47y3aZt/fofonfMq11xGORNYjbVrL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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "The figures below help one understanding the workflow inside the simulator. The first figure shows how the noise is processed in the circuit processor. The noise is defined separately in a class object. When called, it takes parameters and the unitary noiseless `qutip.QobjEvo` from the processor, generates the noisy version and sends the noisy `qutip.QobjEvo` together with the collapse operators to the processor.\n", "\n", "![processor-workflow.png](attachment:processor-workflow.png)\n", "\n", "When calculating the evolution, the processor first creates its own `qutip.QobjEvo` of the noiseless evolution. It will then find all the noise objects saved in the attributes `qutip.qip.device.Processor.noise` and call the corresponding methods to get the `qutip.QobjEvo` and a list of collapse operators representing the noise. (For collapse operators, we don't want to add all the constant collapse into one time-independent operator, so we use a list). The processor then combines its own `qutip.QobjEvo` with those from the noise objects and give them to the solver. The figure below shows how the noiseless part and the noisy part are combined.\n", "\n", "![processor-noise.png](attachment:processor-noise.png)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th>Software</th><th>Version</th></tr><tr><td>QuTiP</td><td>4.5.0.dev0+4ad874f6</td></tr><tr><td>Numpy</td><td>1.17.5</td></tr><tr><td>SciPy</td><td>1.2.1</td></tr><tr><td>matplotlib</td><td>2.2.4</td></tr><tr><td>Cython</td><td>0.29.14</td></tr><tr><td>Number of CPUs</td><td>12</td></tr><tr><td>BLAS Info</td><td>Generic</td></tr><tr><td>IPython</td><td>7.11.1</td></tr><tr><td>Python</td><td>3.6.7 (default, Dec 6 2019, 07:03:06) [MSC v.1900 64 bit (AMD64)]</td></tr><tr><td>OS</td><td>nt [win32]</td></tr><tr><td colspan='2'>Tue Jan 28 22:01:08 2020 W. Europe Standard Time</td></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from qutip.ipynbtools import version_table\n", "version_table()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }
lgpl-3.0
cesarcontre/Simulacion2017
Modulo3/.ipynb_checkpoints/Clase20_ProgramaciónLineal-checkpoint.ipynb
2
14613
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Programación lineal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Problemas de programación lineal\n", "\n", "De acuerdo a lo visto la clase pasada, un problema de programación lineal puede escribirse en la siguiente forma:\n", "\n", "\\begin{equation}\n", "\\begin{array}{ll}\n", "\\min_{x_1,\\dots,x_n} & f_1x_1+\\dots+f_nx_n \\\\\n", "\\text{s. a. } & a^{eq}_{j,1}x_1+\\dots+a^{eq}_{j,n}x_n=b^{eq}_j \\text{ para } 1\\leq j\\leq m_1 \\\\\n", " & a_{k,1}x_1+\\dots+a_{k,n}x_n\\leq b_k \\text{ para } 1\\leq k\\leq m_2,\n", "\\end{array}\n", "\\end{equation}\n", "donde:\n", "- $x_i$ para $i=1,\\dots,n$ son las incógnitas o variables de decisión,\n", "- $f_i$ para $i=1,\\dots,n$ son los coeficientes de la función a optimizar,\n", "- $a^{eq}_{j,i}$ para $j=1,\\dots,m_1$ e $i=1,\\dots,n$, son los coeficientes de la restricción de igualdad,\n", "- $a_{k,i}$ para $k=1,\\dots,m_2$ e $i=1,\\dots,n$, son los coeficientes de la restricción de desigualdad,\n", "- $b^{eq}_j$ para $j=1,\\dots,m_1$ son valores conocidos que deben ser respetados estrictamente, y\n", "- $b_k$ para $k=1,\\dots,m_2$ son valores conocidos que no deben ser superados.\n", "\n", "Equivalentemente, el problema puede escribirse como\n", "\n", "\\begin{equation}\n", "\\begin{array}{ll}\n", "\\min_{\\boldsymbol{x}} & \\boldsymbol{f}^T\\boldsymbol{x} \\\\\n", "\\text{s. a. } & \\boldsymbol{A}_{eq}\\boldsymbol{x}=\\boldsymbol{b}_{eq} \\\\\n", " & \\boldsymbol{A}\\boldsymbol{x}\\leq\\boldsymbol{b},\n", "\\end{array}\n", "\\end{equation}\n", "donde:\n", "- $\\boldsymbol{x}=\\left[x_1\\quad\\dots\\quad x_n\\right]^T$,\n", "- $\\boldsymbol{f}=\\left[f_1\\quad\\dots\\quad f_n\\right]^T$,\n", "- $\\boldsymbol{A}_{eq}=\\left[\\begin{array}{ccc}a^{eq}_{1,1} & \\dots & a^{eq}_{1,n}\\\\ \\vdots & \\ddots & \\vdots\\\\ a^{eq}_{m_1,1} & \\dots & a^{eq}_{m_1,n}\\end{array}\\right]$,\n", "- $\\boldsymbol{A}=\\left[\\begin{array}{ccc}a_{1,1} & \\dots & a_{1,n}\\\\ \\vdots & \\ddots & \\vdots\\\\ a_{m_2,1} & \\dots & a_{m_2,n}\\end{array}\\right]$,\n", "- $\\boldsymbol{b}_{eq}=\\left[b^{eq}_1\\quad\\dots\\quad b^{eq}_{m_1}\\right]^T$, y\n", "- $\\boldsymbol{b}=\\left[b_1\\quad\\dots\\quad b_{m_2}\\right]^T$.\n", "\n", "**Nota:** el problema $\\max_{\\boldsymbol{x}}\\boldsymbol{g}(\\boldsymbol{x})$ es equivalente a $\\min_{\\boldsymbol{x}}-\\boldsymbol{g}(\\boldsymbol{x})$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Explicar la función pyomo_utilities.py\n", "\n", "Para trabajar con `pyomo`:\n", "1. `pyomo` a parte de contener funciones para optimización, es en sí un lenguaje de programación. No tiene solucionadores instalados, utiliza solucionadores externos (glpk, ipopt).\n", "2. Modelos concretos y modelos abstractos.\n", "3. Forma de ingresarle los parámetros para un modelo abstracto.\n", "4. Resultados." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Ejemplos de la clase pasada\n", "\n", "### 3.1 \n", "Una compañía produce dos productos ($X_1$ y $X_2$) usando dos máquinas ($A$ y $B$). Cada unidad de $X_1$ que se produce requiere 50 minutos en la máquina $A$ y 30 minutos en la máquina $B$. Cada unidad de $X_2$ que se produce requiere 24 minutos en la máquina $A$ y 33 minutos en la máquina $B$.\n", "\n", "Al comienzo de la semana hay 30 unidades de $X_1$ y 90 unidades de $X_2$ en inventario. El tiempo de uso disponible de la máquina $A$ es de 40 horas y el de la máquina $B$ es de 35 horas.\n", "\n", "La demanda para $X_1$ en la semana actual es de 75 unidades y de $X_2$ es de 95 unidades. La política de la compañía es maximizar la suma combinada de unidades de $X_1$ e $X_2$ en inventario al finalizar la semana.\n", "\n", "Formular el problema de decidir cuánto hacer de cada producto en la semana como un problema de programación lineal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solución\n", "\n", "Sean:\n", "- $x_1$ la cantidad de unidades de $X_1$ a ser producidas en la semana, y\n", "- $x_2$ la cantidad de unidades de $X_2$ a ser producidas en la semana.\n", "\n", "\\begin{equation}\n", "\\begin{array}{ll}\n", "\\min_{\\boldsymbol{x}} & \\boldsymbol{f}^T\\boldsymbol{x} \\\\\n", "\\text{s. a. } & \\boldsymbol{A}_{eq}\\boldsymbol{x}=\\boldsymbol{b}_{eq} \\\\\n", " & \\boldsymbol{A}\\boldsymbol{x}\\leq\\boldsymbol{b},\n", "\\end{array}\n", "\\end{equation}\n", "con\n", "- $\\boldsymbol{f}=\\left[-1 \\quad -1\\right]^T$,\n", "- $\\boldsymbol{A}_{eq}=\\left[0\\quad 0\\right]$,\n", "- $\\boldsymbol{A}=\\left[\\begin{array}{cc}50 & 24 \\\\ 30 & 33\\\\ -1 & 0\\\\ 0 & -1\\end{array}\\right]$,\n", "- $\\boldsymbol{b}_{eq}=0$, y\n", "- $\\boldsymbol{b}=\\left[2400\\quad 2100\\quad -45\\quad -5\\right]^T$." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "f = np.array([-1, -1])\n", "A = np.array([[50, 24], [30, 33], [-1, 0], [0, -1]])\n", "b = np.array([2400, 2100, -45, -5])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pyomo_utilities" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# ==========================================================\n", "# = Solver Results =\n", "# ==========================================================\n", "# ----------------------------------------------------------\n", "# Problem Information\n", "# ----------------------------------------------------------\n", "Problem: \n", "- Name: unknown\n", " Lower bound: -51.25\n", " Upper bound: -51.25\n", " Number of objectives: 1\n", " Number of constraints: 5\n", " Number of variables: 3\n", " Number of nonzeros: 7\n", " Sense: minimize\n", "# ----------------------------------------------------------\n", "# Solver Information\n", "# ----------------------------------------------------------\n", "Solver: \n", "- Status: ok\n", " Termination condition: optimal\n", " Statistics: \n", " Branch and bound: \n", " Number of bounded subproblems: 0\n", " Number of created subproblems: 0\n", " Error rc: 0\n", " Time: 0.32701873779296875\n", "# ----------------------------------------------------------\n", "# Solution Information\n", "# ----------------------------------------------------------\n", "Solution: \n", "- number of solutions: 0\n", " number of solutions displayed: 0\n" ] } ], "source": [ "x, obj = pyomo_utilities.linprog(f, A, b)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-51.25000148248087" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2\n", "Mónica hace aretes y cadenitas de joyería. Es tan buena, que todo lo que hace lo vende.\n", "\n", "Le toma 30 minutos hacer un par de aretes y una hora hacer una cadenita, y como Mónica también es estudihambre, solo dispone de 10 horas a la semana para hacer las joyas. Por otra parte, el material que compra solo le alcanza para hacer 15 unidades (el par de aretes cuenta como unidad) de joyas por semana.\n", "\n", "La utilidad que le deja la venta de las joyas es \\$15 en cada par de aretes y \\$20 en cada cadenita.\n", "\n", "¿Cuántos pares de aretes y cuántas cadenitas debería hacer Mónica para maximizar su utilidad?\n", "\n", "Formular el problema en la forma explicada y obtener la solución gráfica (puede ser a mano)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solución\n", "\n", "Sean:\n", "- $x_1$ la cantidad de pares de aretes que hace Mónica.\n", "- $x_2$ la cantidad de cadenitas que hace Mónica.\n", "\n", "\n", "\\begin{equation}\n", "\\begin{array}{ll}\n", "\\min_{\\boldsymbol{x}} & \\boldsymbol{f}^T\\boldsymbol{x} \\\\\n", "\\text{s. a. } & \\boldsymbol{A}_{eq}\\boldsymbol{x}=\\boldsymbol{b}_{eq} \\\\\n", " & \\boldsymbol{A}\\boldsymbol{x}\\leq\\boldsymbol{b},\n", "\\end{array}\n", "\\end{equation}\n", "con\n", "- $\\boldsymbol{f}=\\left[-15 \\quad -20\\right]^T$,\n", "- $\\boldsymbol{A}_{eq}=\\left[0\\quad 0\\right]$,\n", "- $\\boldsymbol{A}=\\left[\\begin{array}{cc}0.5 & 1 \\\\ 1 & 1\\\\ -1 & 0\\\\ 0 & -1\\end{array}\\right]$,\n", "- $\\boldsymbol{b}_{eq}=0$, y\n", "- $\\boldsymbol{b}=\\left[10\\quad 15\\quad 0\\quad 0\\right]^T$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Problema de transporte\n", "\n", "- **Referencia**: https://es.wikipedia.org/wiki/Programaci%C3%B3n_lineal\n", "\n", "<img style=\"float: right; margin: 0px 0px 15px 15px;\" src=\"https://upload.wikimedia.org/wikipedia/commons/a/a0/Progr_Lineal.PNG\" width=\"400px\" height=\"125px\" />\n", "\n", "Este es un caso curioso, con solo 6 variables (un caso real de problema de transporte puede tener fácilmente más de 1.000 variables) en el cual se aprecia la utilidad de este procedimiento de cálculo.\n", "\n", "Existen tres minas de carbón cuya producción diaria es:\n", "- la mina \"a\" produce 40 toneladas de carbón por día;\n", "- la mina \"b\" produce 40 t/día; y,\n", "- la mina \"c\" produce 20 t/día.\n", "\n", "En la zona hay dos centrales termoeléctricas que consumen:\n", "- la central \"d\" consume 40 t/día de carbón; y,\n", "- la central \"e\" consume 60 t/día.\n", "\n", "Los costos de mercado, de transporte por tonelada son:\n", "- de \"a\" a \"d\" = 2 monedas;\n", "- de \"a\" a \"e\" = 11 monedas;\n", "- de \"b\" a \"d\" = 12 monedas;\n", "- de \"b\" a \"e\" = 24 monedas;\n", "- de \"c\" a \"d\" = 13 monedas; y,\n", "- de \"c\" a \"e\" = 18 monedas.\n", "\n", "Si se preguntase a los pobladores de la zona cómo organizar el transporte, tal vez la mayoría opinaría que debe aprovecharse el precio ofrecido por el transportista que va de \"a\" a \"d\", porque es más conveniente que los otros, debido a que es el de más bajo precio.\n", "\n", "En este caso, el costo total del transporte es:\n", "- transporte de 40 t de \"a\" a \"d\" = 80 monedas;\n", "- transporte de 20 t de \"c\" a \"e\" = 360 monedas; y,\n", "- transporte de 40 t de \"b\" a \"e\" = 960 monedas,\n", " \n", "Para un total 1.400 monedas.\n", "\n", "Sin embargo, formulando el problema para ser resuelto por la programación lineal se tienen las siguientes ecuaciones:\n", "\n", "Restricciones de la producción:\n", "\n", "- $x_1 + x_2 \\leq 40$\n", "- $x_3 + x_4 \\leq 40$\n", "- $x_5 + x_6 \\leq 20$\n", "\n", "Restricciones del consumo:\n", "\n", "- $x_1 + x_3 + x_5 \\geq 40$\n", "- $x_2 + x_4 + x_6 \\geq 60$\n", "\n", "La función objetivo será:\n", "\n", "$$\\min_{x_1,\\dots,x_6}2x_1 + 11x_2 + 12x_3 + 24x_4 + 13x_5 + 18x_6$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La solución de costo mínimo de transporte diario resulta ser:\n", "\n", "- $x_2 = 40$ resultando un costo de $11(40) = 480$ monedas\n", "- $x_3 = 40$ resultando un costo de $12(40) = 440$ monedas\n", "- $x_6 = 20$ resultando un costo de $18(20) = 360$ monedas\n", " \n", "para un total de $1280$ monedas, $120$ monedas menos que antes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<script>\n", " $(document).ready(function(){\n", " $('div.prompt').hide();\n", " $('div.back-to-top').hide();\n", " $('nav#menubar').hide();\n", " $('.breadcrumb').hide();\n", " $('.hidden-print').hide();\n", " });\n", "</script>\n", "\n", "<footer id=\"attribution\" style=\"float:right; color:#808080; background:#fff;\">\n", "Created with Jupyter by Esteban Jiménez Rodríguez.\n", "</footer>" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
awjuliani/DeepRL-Agents
Simple-Policy.ipynb
1
8300
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Simple Reinforcement Learning in Tensorflow Part 1: \n", "## The Multi-armed bandit\n", "This tutorial contains a simple example of how to build a policy-gradient based agent that can solve the multi-armed bandit problem. For more information, see this [Medium post](https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149).\n", "\n", "For more Reinforcement Learning algorithms, including DQN and Model-based learning in Tensorflow, see my Github repo, [DeepRL-Agents](https://github.com/awjuliani/DeepRL-Agents). " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "import tensorflow.contrib.slim as slim\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### The Bandit\n", "Here we define our bandit. For this example we are using a four-armed bandit. The pullBandit function generates a random number from a normal distribution with a mean of 0. The lower the bandit number, the more likely a positive reward will be returned. We want our agent to learn to always choose the arm that will give that positive reward." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "#List out our bandit arms. \n", "#Currently arm 4 (index #3) is set to most often provide a positive reward.\n", "bandit_arms = [0.2,0,-0.2,-2]\n", "num_arms = len(bandit_arms)\n", "def pullBandit(bandit):\n", " #Get a random number.\n", " result = np.random.randn(1)\n", " if result > bandit:\n", " #return a positive reward.\n", " return 1\n", " else:\n", " #return a negative reward.\n", " return -1" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### The Agent\n", "The code below established our simple neural agent. It consists of a set of values for each of the bandit arms. Each value is an estimate of the value of the return from choosing the bandit. We use a policy gradient method to update the agent by moving the value for the selected action toward the recieved reward." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "#These two lines established the feed-forward part of the network. \n", "weights = tf.Variable(tf.ones([num_arms]))\n", "output = tf.nn.softmax(weights)\n", "\n", "#The next six lines establish the training proceedure. We feed the reward and chosen action into the network\n", "#to compute the loss, and use it to update the network.\n", "reward_holder = tf.placeholder(shape=[1],dtype=tf.float32)\n", "action_holder = tf.placeholder(shape=[1],dtype=tf.int32)\n", "\n", "responsible_output = tf.slice(output,action_holder,[1])\n", "loss = -(tf.log(responsible_output)*reward_holder)\n", "optimizer = tf.train.AdamOptimizer(learning_rate=1e-3)\n", "update = optimizer.minimize(loss)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Training the Agent" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We will train our agent by taking actions in our environment, and recieving rewards. Using the rewards and actions, we can know how to properly update our network in order to more often choose actions that will yield the highest rewards over time." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running reward for the 4 arms of the bandit: [ 1. 0. 0. 0.]\n", "Running reward for the 4 arms of the bandit: [ -5. 3. -3. 12.]\n", "Running reward for the 4 arms of the bandit: [ -4. -2. 0. 19.]\n", "Running reward for the 4 arms of the bandit: [ 0. 0. 2. 25.]\n", "Running reward for the 4 arms of the bandit: [ -3. -1. 2. 37.]\n", "Running reward for the 4 arms of the bandit: [ -4. -4. 6. 53.]\n", "Running reward for the 4 arms of the bandit: [-14. -9. 6. 70.]\n", "Running reward for the 4 arms of the bandit: [-17. -12. 11. 87.]\n", "Running reward for the 4 arms of the bandit: [-20. -19. 14. 98.]\n", "Running reward for the 4 arms of the bandit: [ -23. -17. 14. 113.]\n", "Running reward for the 4 arms of the bandit: [ -28. -22. 20. 131.]\n", "Running reward for the 4 arms of the bandit: [ -26. -22. 20. 141.]\n", "Running reward for the 4 arms of the bandit: [ -27. -24. 28. 160.]\n", "Running reward for the 4 arms of the bandit: [ -29. -24. 34. 168.]\n", "Running reward for the 4 arms of the bandit: [ -32. -25. 37. 181.]\n", "Running reward for the 4 arms of the bandit: [ -36. -20. 37. 190.]\n", "Running reward for the 4 arms of the bandit: [ -34. -17. 40. 204.]\n", "Running reward for the 4 arms of the bandit: [ -37. -14. 48. 222.]\n", "Running reward for the 4 arms of the bandit: [ -37. -15. 56. 233.]\n", "Running reward for the 4 arms of the bandit: [ -40. -13. 64. 246.]\n", "\n", "The agent thinks arm 4 is the most promising....\n", "...and it was right!\n" ] } ], "source": [ "total_episodes = 1000 #Set total number of episodes to train agent on.\n", "total_reward = np.zeros(num_arms) #Set scoreboard for bandit arms to 0.\n", "\n", "init = tf.global_variables_initializer()\n", "\n", "# Launch the tensorflow graph\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " i = 0\n", " while i < total_episodes:\n", " \n", " #Choose action according to Boltzmann distribution.\n", " actions = sess.run(output)\n", " a = np.random.choice(actions,p=actions)\n", " action = np.argmax(actions == a)\n", "\n", " reward = pullBandit(bandit_arms[action]) #Get our reward from picking one of the bandit arms.\n", " \n", " #Update the network.\n", " _,resp,ww = sess.run([update,responsible_output,weights], feed_dict={reward_holder:[reward],action_holder:[action]})\n", " \n", " #Update our running tally of scores.\n", " total_reward[action] += reward\n", " if i % 50 == 0:\n", " print(\"Running reward for the \" + str(num_arms) + \" arms of the bandit: \" + str(total_reward))\n", " i+=1\n", "print(\"\\nThe agent thinks arm \" + str(np.argmax(ww)+1) + \" is the most promising....\")\n", "if np.argmax(ww) == np.argmax(-np.array(bandit_arms)):\n", " print(\"...and it was right!\")\n", "else:\n", " print(\"...and it was wrong!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py2]", "language": "python", "name": "conda-env-py2-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
agile-geoscience/striplog
docs/tutorial/10_Extract_curves_into_striplogs.ipynb
1
63145
{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Extract curves into striplogs\n", "\n", "Sometimes you'd like to summarize or otherwise extract curve data (e.g. wireline log data) into a striplog (e.g. one that represents formations).\n", "\n", "We'll start by making some fake CSV text \u2014\u00a0we'll make 5 formations called A, B, C, D and E:"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["data = \"\"\"Comp Formation,Depth\n", "A,100\n", "B,200\n", "C,250\n", "D,400\n", "E,600\"\"\""]}, {"cell_type": "markdown", "metadata": {}, "source": ["If you have a CSV file, you can do:\n", "\n", " s = Striplog.from_csv(filename=filename)\n", " \n", "But we have text, so we do something slightly different, passing the `text` argument instead. We also pass a `stop` argument to tell Striplog to make the last unit (E) 50 m thick. (If you don't do this, it will be 1 m thick)."]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/matt/miniconda3/envs/welly/lib/python3.9/site-packages/striplog/striplog.py:512: UserWarning: No lexicon provided, using the default.\n", " warnings.warn(w)\n"]}], "source": ["from striplog import Striplog\n", "\n", "s = Striplog.from_csv(text=data, stop=650)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Each element of the striplog is an `Interval` object, which has a top, base and one or more `Component`s, which represent whatever is in the interval (maybe a rock type, or in this case a formation). There is also a `data` field, which we will use later."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><td style=\"width:2em; background-color:#DDDDDD\" rowspan=\"6\"></td><td><strong>top</strong></td><td>100.0</td></tr><tr><td><strong>primary</strong></td><td><table><tr><td><strong>formation</strong></td><td>A</td></tr></table></td></tr><tr><td><strong>summary</strong></td><td>100.00 m of A</td></tr><tr><td><strong>description</strong></td><td></td></tr><tr><td><strong>data</strong></td><td><table></table></td></tr><tr><td><strong>base</strong></td><td>200.0</td></tr></table>"], "text/plain": ["Interval({'top': Position({'middle': 100.0, 'units': 'm'}), 'base': Position({'middle': 200.0, 'units': 'm'}), 'description': '', 'data': {}, 'components': [Component({'formation': 'A'})]})"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["s[0]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["We can plot the striplog. By default, it will use a random legend for the colours:"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 108x324 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["s.plot(aspect=3)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Or we can plot in the 'tops' style:"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 216x216 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["s.plot(style='tops', field='formation', aspect=1)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Random curve data"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Make some fake data:"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["from welly import Curve\n", "import numpy as np\n", "\n", "depth = np.linspace(0, 699, 700)\n", "data = np.sin(depth/10)\n", "curve = Curve(data=data, index=depth)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Plot it:"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "\n", "fig, axs = plt.subplots(ncols=2, sharey=True)\n", "\n", "axs[0] = s.plot(ax=axs[0])\n", "axs[1] = curve.plot(ax=axs[1])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Extract data from the curve into the striplog"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": ["s = s.extract(curve.values, basis=depth, name='GR')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now we have some the GR data from each unit stored in that unit:"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><td style=\"width:2em; background-color:#DDDDDD\" rowspan=\"6\"></td><td><strong>top</strong></td><td>200.0</td></tr><tr><td><strong>primary</strong></td><td><table><tr><td><strong>formation</strong></td><td>B</td></tr></table></td></tr><tr><td><strong>summary</strong></td><td>50.00 m of B</td></tr><tr><td><strong>description</strong></td><td></td></tr><tr><td><strong>data</strong></td><td><table><tr><td><strong>GR</strong></td><td>[ 0.94912455 0.97582052 0.99276641 0.9997929 0.99682979 0.98390669\n", " 0.96115272 0.92879523 0.88715753 0.83665564 0.77779416 0.71116122\n", " 0.6374226 0.55731505 0.471639 0.38125049 0.28705265 0.18998668\n", " 0.09102242 -0.00885131 -0.1086366 -0.20733642 -0.30396461 -0.39755568\n", " -0.48717451 -0.57192566 -0.65096231 -0.72349476 -0.78879829 -0.8462204\n", " -0.89518737 -0.93520992 -0.96588815 -0.98691556 -0.99808203 -0.99927599\n", " -0.99048552 -0.97179845 -0.94340148 -0.90557836 -0.858707 -0.80325573\n", " -0.73977859 -0.66890982 -0.59135753 -0.50789659 -0.41936092 -0.32663513\n", " -0.23064571 -0.13235175]</td></tr></table></td></tr><tr><td><strong>base</strong></td><td>250.0</td></tr></table>"], "text/plain": ["Interval({'top': Position({'middle': 200.0, 'units': 'm'}), 'base': Position({'middle': 250.0, 'units': 'm'}), 'description': '', 'data': {'GR': array([ 0.94912455, 0.97582052, 0.99276641, 0.9997929 , 0.99682979,\n", " 0.98390669, 0.96115272, 0.92879523, 0.88715753, 0.83665564,\n", " 0.77779416, 0.71116122, 0.6374226 , 0.55731505, 0.471639 ,\n", " 0.38125049, 0.28705265, 0.18998668, 0.09102242, -0.00885131,\n", " -0.1086366 , -0.20733642, -0.30396461, -0.39755568, -0.48717451,\n", " -0.57192566, -0.65096231, -0.72349476, -0.78879829, -0.8462204 ,\n", " -0.89518737, -0.93520992, -0.96588815, -0.98691556, -0.99808203,\n", " -0.99927599, -0.99048552, -0.97179845, -0.94340148, -0.90557836,\n", " -0.858707 , -0.80325573, -0.73977859, -0.66890982, -0.59135753,\n", " -0.50789659, -0.41936092, -0.32663513, -0.23064571, -0.13235175])}, 'components': [Component({'formation': 'B'})]})"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["s[1]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["So we could plot a segment of curve, say:"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"text/plain": ["[<matplotlib.lines.Line2D at 0x7f9d161b3520>]"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.plot(s[1].data['GR'])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Extract and reduce data\n", "\n", "We don't have to store all the data points. We can optionaly pass a function to produce anything we like, and store the result of that:"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><td style=\"width:2em; background-color:#DDDDDD\" rowspan=\"6\"></td><td><strong>top</strong></td><td>200.0</td></tr><tr><td><strong>primary</strong></td><td><table><tr><td><strong>formation</strong></td><td>B</td></tr></table></td></tr><tr><td><strong>summary</strong></td><td>50.00 m of B</td></tr><tr><td><strong>description</strong></td><td></td></tr><tr><td><strong>data</strong></td><td><table><tr><td><strong>GR</strong></td><td>[ 0.94912455 0.97582052 0.99276641 0.9997929 0.99682979 0.98390669\n", " 0.96115272 0.92879523 0.88715753 0.83665564 0.77779416 0.71116122\n", " 0.6374226 0.55731505 0.471639 0.38125049 0.28705265 0.18998668\n", " 0.09102242 -0.00885131 -0.1086366 -0.20733642 -0.30396461 -0.39755568\n", " -0.48717451 -0.57192566 -0.65096231 -0.72349476 -0.78879829 -0.8462204\n", " -0.89518737 -0.93520992 -0.96588815 -0.98691556 -0.99808203 -0.99927599\n", " -0.99048552 -0.97179845 -0.94340148 -0.90557836 -0.858707 -0.80325573\n", " -0.73977859 -0.66890982 -0.59135753 -0.50789659 -0.41936092 -0.32663513\n", " -0.23064571 -0.13235175]</td></tr><tr><td><strong>GRmean</strong></td><td>-0.12697991702493913</td></tr></table></td></tr><tr><td><strong>base</strong></td><td>250.0</td></tr></table>"], "text/plain": ["Interval({'top': Position({'middle': 200.0, 'units': 'm'}), 'base': Position({'middle': 250.0, 'units': 'm'}), 'description': '', 'data': {'GR': array([ 0.94912455, 0.97582052, 0.99276641, 0.9997929 , 0.99682979,\n", " 0.98390669, 0.96115272, 0.92879523, 0.88715753, 0.83665564,\n", " 0.77779416, 0.71116122, 0.6374226 , 0.55731505, 0.471639 ,\n", " 0.38125049, 0.28705265, 0.18998668, 0.09102242, -0.00885131,\n", " -0.1086366 , -0.20733642, -0.30396461, -0.39755568, -0.48717451,\n", " -0.57192566, -0.65096231, -0.72349476, -0.78879829, -0.8462204 ,\n", " -0.89518737, -0.93520992, -0.96588815, -0.98691556, -0.99808203,\n", " -0.99927599, -0.99048552, -0.97179845, -0.94340148, -0.90557836,\n", " -0.858707 , -0.80325573, -0.73977859, -0.66890982, -0.59135753,\n", " -0.50789659, -0.41936092, -0.32663513, -0.23064571, -0.13235175]), 'GRmean': -0.12697991702493913}, 'components': [Component({'formation': 'B'})]})"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["s = s.extract(curve, basis=depth, name='GRmean', function=np.nanmean)\n", "\n", "s[1]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Other helpful reducing functions:\n", "\n", "- `np.nanmedian` &mdash; median average (ignoring nans)\n", "- `np.product` &mdash; product\n", "- `np.nansum` &mdash; sum (ignoring nans)\n", "- `np.nanmin` &mdash; minimum (ignoring nans)\n", "- `np.nanmax` &mdash; maximum (ignoring nans)\n", "- `scipy.stats.mstats.mode` &mdash; mode average\n", "- `scipy.stats.mstats.hmean` &mdash; harmonic mean\n", "- `scipy.stats.mstats.gmean` &mdash; geometric mean\n", "\n", "Or you can write your own, for example:\n", "\n", " def trim_mean(a):\n", " \"\"\"Compute trimmed mean, trimming min and max\"\"\"\n", " return (np.nansum(a) - np.nanmin(a) - np.nanmax(a)) / a.size\n", " \n", "Then do:\n", "\n", " s.extract(curve, basis=basis, name='GRtrim', function=trim_mean)\n", " \n", "The function doesn't have to return a single number like this, it could return anything you like, including a dictionary."]}, {"cell_type": "markdown", "metadata": {}, "source": ["We can also add bits to the `data` dictionary manually:"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><td style=\"width:2em; background-color:#DDDDDD\" rowspan=\"6\"></td><td><strong>top</strong></td><td>200.0</td></tr><tr><td><strong>primary</strong></td><td><table><tr><td><strong>formation</strong></td><td>B</td></tr></table></td></tr><tr><td><strong>summary</strong></td><td>50.00 m of B</td></tr><tr><td><strong>description</strong></td><td></td></tr><tr><td><strong>data</strong></td><td><table><tr><td><strong>GR</strong></td><td>[ 0.94912455 0.97582052 0.99276641 0.9997929 0.99682979 0.98390669\n", " 0.96115272 0.92879523 0.88715753 0.83665564 0.77779416 0.71116122\n", " 0.6374226 0.55731505 0.471639 0.38125049 0.28705265 0.18998668\n", " 0.09102242 -0.00885131 -0.1086366 -0.20733642 -0.30396461 -0.39755568\n", " -0.48717451 -0.57192566 -0.65096231 -0.72349476 -0.78879829 -0.8462204\n", " -0.89518737 -0.93520992 -0.96588815 -0.98691556 -0.99808203 -0.99927599\n", " -0.99048552 -0.97179845 -0.94340148 -0.90557836 -0.858707 -0.80325573\n", " -0.73977859 -0.66890982 -0.59135753 -0.50789659 -0.41936092 -0.32663513\n", " -0.23064571 -0.13235175]</td></tr><tr><td><strong>GRmean</strong></td><td>-0.12697991702493913</td></tr><tr><td><strong>foo</strong></td><td>bar</td></tr></table></td></tr><tr><td><strong>base</strong></td><td>250.0</td></tr></table>"], "text/plain": ["Interval({'top': Position({'middle': 200.0, 'units': 'm'}), 'base': Position({'middle': 250.0, 'units': 'm'}), 'description': '', 'data': {'GR': array([ 0.94912455, 0.97582052, 0.99276641, 0.9997929 , 0.99682979,\n", " 0.98390669, 0.96115272, 0.92879523, 0.88715753, 0.83665564,\n", " 0.77779416, 0.71116122, 0.6374226 , 0.55731505, 0.471639 ,\n", " 0.38125049, 0.28705265, 0.18998668, 0.09102242, -0.00885131,\n", " -0.1086366 , -0.20733642, -0.30396461, -0.39755568, -0.48717451,\n", " -0.57192566, -0.65096231, -0.72349476, -0.78879829, -0.8462204 ,\n", " -0.89518737, -0.93520992, -0.96588815, -0.98691556, -0.99808203,\n", " -0.99927599, -0.99048552, -0.97179845, -0.94340148, -0.90557836,\n", " -0.858707 , -0.80325573, -0.73977859, -0.66890982, -0.59135753,\n", " -0.50789659, -0.41936092, -0.32663513, -0.23064571, -0.13235175]), 'GRmean': -0.12697991702493913, 'foo': 'bar'}, 'components': [Component({'formation': 'B'})]})"]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["s[1].data['foo'] = 'bar'\n", "s[1]"]}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7"}}, "nbformat": 4, "nbformat_minor": 2}
apache-2.0
Kyubyong/numpy_exercises
1_Array_creation_routines.ipynb
1
16400
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Array creation routines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ones and zeros" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a new array of 2*2 integers, without initializing entries." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0, 0],\n", " [0, 0]])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let X = np.array([1,2,3], [4,5,6], np.int32). \n", "Create a new array with the same shape and type as X." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.array([[1,2,3], [4,5,6]], np.int32)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a 3-D array with ones on the diagonal and zeros elsewhere." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 0., 0.],\n", " [ 0., 1., 0.],\n", " [ 0., 0., 1.]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 0., 0.],\n", " [ 0., 1., 0.],\n", " [ 0., 0., 1.]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a new array of 3*2 float numbers, filled with ones." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 1.],\n", " [ 1., 1.],\n", " [ 1., 1.]])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let x = np.arange(4, dtype=np.int64). Create an array of ones with the same shape and type as X." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1], dtype=int64)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(4, dtype=np.int64)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a new array of 3*2 float numbers, filled with zeros." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0.],\n", " [ 0., 0.],\n", " [ 0., 0.]])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let x = np.arange(4, dtype=np.int64). Create an array of zeros with the same shape and type as X." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 0], dtype=int64)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(4, dtype=np.int64)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a new array of 2*5 uints, filled with 6." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[6, 6, 6, 6, 6],\n", " [6, 6, 6, 6, 6]], dtype=uint32)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let x = np.arange(4, dtype=np.int64). Create an array of 6's with the same shape and type as X." ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([6, 6, 6, 6], dtype=int64)" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(4, dtype=np.int64)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## From existing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an array of [1, 2, 3]." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let x = [1, 2]. Convert it into an array." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 2])" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = [1,2]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let X = np.array([[1, 2], [3, 4]]). Convert it into a matrix." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[1, 2],\n", " [3, 4]])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.array([[1, 2], [3, 4]])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let x = [1, 2]. Conver it into an array of `float`." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1., 2.])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = [1, 2]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let x = np.array([30]). Convert it into scalar of its single element, i.e. 30." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.array([30])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let x = np.array([1, 2, 3]). Create a array copy of x, which has a different id from x." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70140352 [1 2 3]\n", "70140752 [1 2 3]\n" ] } ], "source": [ "x = np.array([1, 2, 3])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numerical ranges" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an array of 2, 4, 6, 8, ..., 100." ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26,\n", " 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52,\n", " 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78,\n", " 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100])" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a 1-D array of 50 evenly spaced elements between 3. and 10., inclusive." ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3. , 3.14285714, 3.28571429, 3.42857143,\n", " 3.57142857, 3.71428571, 3.85714286, 4. ,\n", " 4.14285714, 4.28571429, 4.42857143, 4.57142857,\n", " 4.71428571, 4.85714286, 5. , 5.14285714,\n", " 5.28571429, 5.42857143, 5.57142857, 5.71428571,\n", " 5.85714286, 6. , 6.14285714, 6.28571429,\n", " 6.42857143, 6.57142857, 6.71428571, 6.85714286,\n", " 7. , 7.14285714, 7.28571429, 7.42857143,\n", " 7.57142857, 7.71428571, 7.85714286, 8. ,\n", " 8.14285714, 8.28571429, 8.42857143, 8.57142857,\n", " 8.71428571, 8.85714286, 9. , 9.14285714,\n", " 9.28571429, 9.42857143, 9.57142857, 9.71428571,\n", " 9.85714286, 10. ])" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a 1-D array of 50 element spaced evenly on a log scale between 3. and 10., exclusive." ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.00000000e+03, 1.38038426e+03, 1.90546072e+03,\n", " 2.63026799e+03, 3.63078055e+03, 5.01187234e+03,\n", " 6.91830971e+03, 9.54992586e+03, 1.31825674e+04,\n", " 1.81970086e+04, 2.51188643e+04, 3.46736850e+04,\n", " 4.78630092e+04, 6.60693448e+04, 9.12010839e+04,\n", " 1.25892541e+05, 1.73780083e+05, 2.39883292e+05,\n", " 3.31131121e+05, 4.57088190e+05, 6.30957344e+05,\n", " 8.70963590e+05, 1.20226443e+06, 1.65958691e+06,\n", " 2.29086765e+06, 3.16227766e+06, 4.36515832e+06,\n", " 6.02559586e+06, 8.31763771e+06, 1.14815362e+07,\n", " 1.58489319e+07, 2.18776162e+07, 3.01995172e+07,\n", " 4.16869383e+07, 5.75439937e+07, 7.94328235e+07,\n", " 1.09647820e+08, 1.51356125e+08, 2.08929613e+08,\n", " 2.88403150e+08, 3.98107171e+08, 5.49540874e+08,\n", " 7.58577575e+08, 1.04712855e+09, 1.44543977e+09,\n", " 1.99526231e+09, 2.75422870e+09, 3.80189396e+09,\n", " 5.24807460e+09, 7.24435960e+09])" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building matrices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let X = np.array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]]).\n", " Get the diagonal of X, that is, [0, 5, 10]." ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 5, 10])" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a 2-D array whose diagonal equals [1, 2, 3, 4] and 0's elsewhere." ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 0, 0, 0],\n", " [0, 2, 0, 0],\n", " [0, 0, 3, 0],\n", " [0, 0, 0, 4]])" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an array which looks like below.\n", "array([[ 0., 0., 0., 0., 0.],\n", " [ 1., 0., 0., 0., 0.],\n", " [ 1., 1., 0., 0., 0.]])" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., 0., 0.],\n", " [ 1., 0., 0., 0., 0.],\n", " [ 1., 1., 0., 0., 0.]])" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an array which looks like below.\n", "array([[ 0, 0, 0],\n", " [ 4, 0, 0],\n", " [ 7, 8, 0],\n", " [10, 11, 12]])" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 0, 0],\n", " [ 4, 0, 0],\n", " [ 7, 8, 0],\n", " [10, 11, 12]])" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an array which looks like below. array([[ 1, 2, 3],\n", " [ 4, 5, 6],\n", " [ 0, 8, 9],\n", " [ 0, 0, 12]])" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 3],\n", " [ 4, 5, 6],\n", " [ 0, 8, 9],\n", " [ 0, 0, 12]])" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
Roc-J/Python_data_science
Data_Mining/Local_outlier_factor.ipynb
2
14496
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 局部异常因子方法发现异常点\n", "\n", "局部异常因子(Local Outlier Factor,LOF)也是一种异常检测算法,它对数据实例的局部密度和邻居进行比较,判断这个数据是否属于相似的密度的区域,它适合从那些簇个数未知,簇的密度和大小各不相同的数据中筛选出异常点。 \n", "\n", "从k近邻算法启发来" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import defaultdict\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "instance = np.matrix([[0,0],[0,1],[1,1],[1,0],[5,0]])" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x66279f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.squeeze(np.asarray(instance[:,0]))\n", "y = np.squeeze(np.asarray(instance[:,1]))\n", "plt.cla()\n", "plt.figure(1)\n", "plt.scatter(x,y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "局部异常因子计算出每个点的局部密度,通过它与K最近邻的点的距离来评估点的局部密度,并与邻居的密度进行比较,以此找出异常点--异常点比邻居的密度要低得多\n", "\n", "为了理解LOF,先了解一些术语的定义\n", "* 对象P的K距离:对象P与它第K个最近邻的距离,K是算法的参数 \n", "* P的K距离邻居:到P的距离小于或等于P到第K个最邻近的距离的所有对象的集合Q\n", "* 从P到Q的可达距离:P与它的第K个最近邻的距离和P和Q之间的距离中的最大者。 \n", "* P的局部可达密度(local Reachability Density of P):K距离邻居和K与其邻居的可达距离之和的比值 \n", "* P的局部异常因子(Local Outlier Factor of P):P与它的K最近邻的局部可达性的比值的平均值" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# 获取点两两之间的距离pairwise_distance\n", "distance = 'manhattan'\n", "from sklearn.metrics import pairwise_distances\n", "dist = pairwise_distances(instance,metric=distance)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 1. 2. 1. 5.]\n", " [ 1. 0. 1. 2. 6.]\n", " [ 2. 1. 0. 1. 5.]\n", " [ 1. 2. 1. 0. 4.]\n", " [ 5. 6. 5. 4. 0.]]\n" ] } ], "source": [ "print dist" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 计算K距离,使用heapq来获得K最近邻\n", "k = 2\n", "# 计算K距离\n", "import heapq\n", "# k_distance的值是tuple\n", "k_distance = defaultdict(tuple)\n", "# 对每个点计算\n", "for i in range(instance.shape[0]):\n", " # 获取它与所有其点之间的距离\n", " distances = dist[i].tolist()\n", " # 获得K最近邻\n", " ksmallest = heapq.nsmallest(k+1,distances)[1:][k-1]\n", " # 获取索引号\n", " ksmallest_idx = distances.index(ksmallest)\n", " # 记录下每个点到第K个最近邻以及到它的距离\n", " k_distance[i]=(ksmallest,ksmallest_idx)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 计算K距离邻居\n", "def all_indices(value,inlist):\n", " out_indices = []\n", " idx = -1\n", " while True:\n", " try:\n", " idx = inlist.index(value,idx+1)\n", " out_indices.append(idx)\n", " except ValueError:\n", " break\n", " return out_indices" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "k distance neighbourhood 0\n", "[0.0, 1.0, 2.0, 1.0, 5.0]\n", "[1.0, 1.0]\n", "set([1.0])\n", "k distance neighbourhood 1\n", "[1.0, 0.0, 1.0, 2.0, 6.0]\n", "[1.0, 1.0]\n", "set([1.0])\n", "k distance neighbourhood 2\n", "[2.0, 1.0, 0.0, 1.0, 5.0]\n", "[1.0, 1.0]\n", "set([1.0])\n", "k distance neighbourhood 3\n", "[1.0, 2.0, 1.0, 0.0, 4.0]\n", "[1.0, 1.0]\n", "set([1.0])\n", "k distance neighbourhood 4\n", "[5.0, 6.0, 5.0, 4.0, 0.0]\n", "[4.0, 5.0]\n", "set([4.0, 5.0])\n" ] } ], "source": [ "# 计算K距离邻居\n", "k_distance_neig = defaultdict(list)\n", "for i in range(instance.shape[0]):\n", " # 获得它到所有邻居点的距离\n", " distances = dist[i].tolist()\n", " print 'k distance neighbourhood',i\n", " print distances\n", " # 获得从第1到第k的最近邻\n", " ksmallest = heapq.nsmallest(k+1,distances)[1:]\n", " print ksmallest\n", " ksmallest_set = set(ksmallest)\n", " print ksmallest_set\n", " ksmallest_idx = []\n", " # 获取k里最小的元素的索引号\n", " for x in ksmallest_set:\n", " ksmallest_idx.append(all_indices(x,distances))\n", " # 将列表的列表转换为列表\n", " ksmallest_idx = [item for sublist in ksmallest_idx for item in sublist]\n", " # 对每个点保存\n", " k_distance_neig[i].extend(zip(ksmallest,ksmallest_idx))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "defaultdict(<type 'list'>, {0: [(1.0, 1), (1.0, 3)], 1: [(1.0, 0), (1.0, 2)], 2: [(1.0, 1), (1.0, 3)], 3: [(1.0, 0), (1.0, 2)], 4: [(4.0, 3), (5.0, 0)]})\n" ] } ], "source": [ "print k_distance_neig" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 计算可达距离和LRD\n", "# 局部可达密度\n", "local_reach_density = defaultdict(float)\n", "for i in range(instance.shape[0]):\n", " # LRD分子,k距离邻居的个数\n", " no_neighbours = len(k_distance_neig[i])\n", " denom_sum = 0\n", " # 可达距离求和\n", " for neigh in k_distance_neig[i]:\n", " denom_sum += max(k_distance[neigh[1]][0],neigh[0])\n", " local_reach_density[i] = no_neighbours/(1.0*denom_sum)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(0, 4.0), (1, 4.0), (2, 4.0), (3, 4.0), (4, 18.0)]\n" ] } ], "source": [ "# 计算LOF\n", "lof_list = []\n", "for i in range(instance.shape[0]):\n", " lrd_sum = 0\n", " rdist_sum = 0\n", " for neigh in k_distance_neig[i]:\n", " lrd_sum +=local_reach_density[neigh[1]]\n", " rdist_sum += max(k_distance[neigh[1]][0],neigh[0])\n", " lof_list.append((i,lrd_sum*rdist_sum))\n", "print lof_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "一个点的LOF很高,则认为它是一个异常点" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
mayankjohri/LetsExplorePython
Section 3 - Machine Learning/Supervised Learning Algorithm/Classification/5. Perceptron.ipynb
2
4859
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Perceptron" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Perceptron is another simple algorithm suitable for large scale learning. By default:\n", "\n", "- It does not require a learning rate.\n", "- It is not regularized (penalized).\n", "- It updates its model only on mistakes.\n", "\n", "The last characteristic implies that the Perceptron is slightly faster to train than SGD with the hinge loss and that the resulting models are sparser." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The perceptron can be used for supervised learning and can be used to solve binary linear classification problems." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.72\n", "0.753333333333\n", "[ 6.8 -8.9 8.8 -3.5] [ 6.8 -8.9 8.8 -3.5]\n" ] } ], "source": [ "import numpy as np\n", "import scipy.sparse as sp\n", "\n", "from sklearn.utils.testing import assert_array_almost_equal\n", "from sklearn.utils.testing import assert_greater\n", "from sklearn.utils.testing import assert_raises\n", "\n", "from sklearn.utils import check_random_state\n", "from sklearn.datasets import load_iris\n", "from sklearn.linear_model import Perceptron\n", "\n", "iris = load_iris()\n", "random_state = check_random_state(12)\n", "indices = np.arange(iris.data.shape[0])\n", "random_state.shuffle(indices)\n", "X = iris.data[indices]\n", "y = iris.target[indices]\n", "X_csr = sp.csr_matrix(X)\n", "X_csr.sort_indices()\n", "\n", "\n", "class MyPerceptron(object):\n", "\n", " def __init__(self, n_iter=1):\n", " self.n_iter = n_iter\n", "\n", " def fit(self, X, y):\n", " n_samples, n_features = X.shape\n", " self.w = np.zeros(n_features, dtype=np.float64)\n", " self.b = 0.0\n", "\n", " for t in range(self.n_iter):\n", " for i in range(n_samples):\n", " if self.predict(X[i])[0] != y[i]:\n", " self.w += y[i] * X[i]\n", " self.b += y[i]\n", "\n", " def project(self, X):\n", " return np.dot(X, self.w) + self.b\n", "\n", " def predict(self, X):\n", " X = np.atleast_2d(X)\n", " return np.sign(self.project(X))\n", "\n", "\n", "def test_perceptron_accuracy():\n", " for data in (X, X_csr):\n", " clf = Perceptron(max_iter=100, tol=None, shuffle=False)\n", " clf.fit(data, y)\n", " score = clf.score(data, y)\n", " print(score)\n", " assert_greater(score, 0.7)\n", "\n", "\n", "def test_perceptron_correctness():\n", " y_bin = y.copy()\n", " y_bin[y != 1] = -1\n", "\n", " clf1 = MyPerceptron(n_iter=2)\n", " clf1.fit(X, y_bin)\n", "\n", " clf2 = Perceptron(max_iter=2, shuffle=False, tol=None)\n", " clf2.fit(X, y_bin)\n", "\n", " assert_array_almost_equal(clf1.w, clf2.coef_.ravel())\n", " print(clf1.w, clf2.coef_.ravel())\n", "\n", "\n", "def test_undefined_methods():\n", " clf = Perceptron(max_iter=100)\n", " for meth in (\"predict_proba\", \"predict_log_proba\"):\n", " assert_raises(AttributeError, lambda x: getattr(clf, x), meth)\n", " \n", " \n", "test_perceptron_accuracy()\n", "test_perceptron_correctness()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reference \n", "- https://maviccprp.github.io/a-perceptron-in-just-a-few-lines-of-python-code/\n", "- https://datasciencelab.wordpress.com/2014/01/10/machine-learning-classics-the-perceptron\n", "- http://scikit-learn.org/stable/modules/linear_model.html#perceptron\n", "- https://en.wikipedia.org/wiki/Perceptron\n", "- \"Online Passive-Aggressive Algorithms\" K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR 7" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
zhongdai/learn-scikit
plot_dataframe/scatter_df.ipynb
1
44662
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "A simple example of an animated plot... In 3D!\n", "\"\"\"\n", "%matplotlib inline\n", "from sklearn.datasets import load_iris\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = load_iris()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pandas import DataFrame, Series\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = DataFrame(data.data)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 5.1</td>\n", " <td> 3.5</td>\n", " <td> 1.4</td>\n", " <td> 0.2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 4.9</td>\n", " <td> 3.0</td>\n", " <td> 1.4</td>\n", " <td> 0.2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 4.7</td>\n", " <td> 3.2</td>\n", " <td> 1.3</td>\n", " <td> 0.2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 4.6</td>\n", " <td> 3.1</td>\n", " <td> 1.5</td>\n", " <td> 0.2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 5.0</td>\n", " <td> 3.6</td>\n", " <td> 1.4</td>\n", " <td> 0.2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3\n", "0 5.1 3.5 1.4 0.2\n", "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2\n", "\n", "[5 rows x 4 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.columns = ['a','b','c','d']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "target = Series(data.target)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", "5 0\n", "6 0\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 0\n", "14 0\n", "...\n", "135 2\n", "136 2\n", "137 2\n", "138 2\n", "139 2\n", "140 2\n", "141 2\n", "142 2\n", "143 2\n", "144 2\n", "145 2\n", "146 2\n", "147 2\n", "148 2\n", "149 2\n", "Length: 150, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sXbs2RFGe3ebNmycJzRpLbOMoufWWqbXW7p133inRTaMlLilOXnvttVprV6kf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\nBvnko486NJF9Zzl48CB/3bOHHwWD9AHKgVE+H9u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"text/plain": [ "<matplotlib.figure.Figure at 0x7f47f5523c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1)\n", "colors = {1:'r',0:'b',2:'g'}\n", "for i in target.unique():\n", " mask = target == i\n", " plt.scatter(df['a'][mask], df['b'][mask], c = colors[i], label = i)\n", "plt.xlabel(\"X\")\n", "plt.ylabel(\"Y\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def scatter_df(df, x_name, y_name, groupby, colour_dict, size=(10,10)):\n", " \"\"\"\n", " df - DataFrame with columns name\n", " x_name - the x column name\n", " y_name - the y column name\n", " groupby - the categorical column Series\n", " size - the plot size\n", " \"\"\"\n", " plt.figure(1, figsize=size)\n", " for item in groupby.unique():\n", " mask_s = groupby == item\n", " plt.scatter(df[x_name][mask], df[y_name][mask], c = colour_dict[item], label = item)\n", " plt.xlabel(x_name)\n", " plt.ylabel(y_name)\n", " plt.legend()\n", " plt.show" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_df = DataFrame(np.random.rand(500,2))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0.769329</td>\n", " <td> 0.986802</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 0.116339</td>\n", " <td> 0.473210</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 0.122275</td>\n", " <td> 0.049640</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 0.936482</td>\n", " <td> 0.804029</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 0.350899</td>\n", " <td> 0.594228</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " 0 1\n", "0 0.769329 0.986802\n", "1 0.116339 0.473210\n", "2 0.122275 0.049640\n", "3 0.936482 0.804029\n", "4 0.350899 0.594228\n", "\n", "[5 rows x 2 columns]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.head()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "t = np.random.random_integers(5, size=(500))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "color = {1:'r',2:'b',3:'g',4:'k',5:'y'}" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "group = Series(t)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_df.columns = ['x_value','y_value']" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndexingError", "evalue": "Unalignable boolean Series key provided", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexingError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-45-2e09f31af1ee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscatter_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'x_value'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'y_value'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-29-1475de3bee7e>\u001b[0m in \u001b[0;36mscatter_df\u001b[1;34m(df, x_name, y_name, groupby, colour_dict, size)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgroupby\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mmask_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroupby\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolour_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 13\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 519\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m_is_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 520\u001b[1;33m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 522\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_check_bool_indexer\u001b[1;34m(ax, key)\u001b[0m\n\u001b[0;32m 1379\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1380\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1381\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unalignable boolean Series key provided'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1382\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1383\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIndexingError\u001b[0m: Unalignable boolean Series key provided" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f47f53ff1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scatter_df(test_df, 'x_value','y_value',group,color)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(500,)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "group.shape" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(500, 2)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.shape" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "5 False\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 False\n", "11 False\n", "12 False\n", "13 True\n", "14 False\n", "...\n", "485 False\n", "486 False\n", "487 False\n", "488 False\n", "489 False\n", "490 False\n", "491 False\n", "492 False\n", "493 False\n", "494 False\n", "495 False\n", "496 False\n", "497 False\n", "498 False\n", "499 False\n", "Length: 500, dtype: bool" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "group == 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
springcoil/Data-Science-45min-Intros
networks-201/network_analysis.ipynb
19
21325
{ "metadata": { "name": "", "signature": "sha256:4a881f8f2271b5d812c8227bcc208ffa4dac518b854efdf599a9466c78d8a377" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Network Analysis--Using Null Models" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Adapted from Professor Clauset's lectures and homeworks for Network Analysis and Modeling //\n", "Course page: http://tuvalu.santafe.edu/~aaronc/courses/5352/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#relatively fast networks package (pip install python-igraph) that I used for these homeworks\n", "import igraph \n", "# slow-and-steady networks package. fewer bugs, easier drawing\n", "import networkx as nx\n", "# plots!\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# other packages\n", "from __future__ import division\n", "from random import random, shuffle\n", "from numpy import percentile\n", "from operator import itemgetter\n", "from tabulate import tabulate" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Graphs!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A good example of a real-world graph (because it happens to be one). For now it's just important to know that this is a graph of social interactions between 34 individuals involved in the same karate club. Drawing it less because it's informative, and more because plotting is fun." ] }, { "cell_type": "code", "collapsed": false, "input": [ "real_graph = nx.karate_club_graph()\n", "positions = nx.spring_layout(real_graph)\n", "nx.draw(real_graph, node_color = 'blue', pos = positions)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now. What's the difference between that (^) drawing of nodes and edges and a completely random assembly of dots and lines? How can we quantify the difference between a social network, which we think probably has important structure, and a completely random network, whose structure contains very little useful information? Which aspects of a network can be explained by simple statistics like average degree, the number of nodes, or the degree distribution? Which characteristics of a network depend on a structure or generative process that could reveal an underlying truth about the way the network came about?\n", "\n", "The question to ask is: how likely is a specific network characteristic to have been generated by a random process?" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Random Graph Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Erd\u00f6s-R\u00e9nyi Random Graph\n", "\n", "The simplest random graph you can think of. For a graph $G$ with $n$ nodes, each pair of nodes gets an (undirected) edge with probability $p$. There are ${n \\choose 2}$ pairs of nodes, so ${n \\choose 2}$ possible edges. Then the average degree of a node in this random graph is $(n-1)p$, where $(n-1)$ is the number of possible connections for a node $i$ and $p$ is the probability of that connection existing. Call the expected average degree $\\bar k = (n-1)p$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Giant Components\n", "\n", "One property that we see all the time in social graphs (and many other graphs) is the emergence of a \"giant\" connected component. The Erd\u00f6s-R\u00e9nyi also develops a giant component for certain parameter spaces. In fact, when the average degree is more than 1 we see a giant component emerging, and when it is more that 3 that giant component is all or almost all of the graph. That means that for a random graph with $p > \\frac{1}{n-1}$ we will always start to see a giant component. \n", "\n", "To demonstrate why this is true, consider $u$ to be the fraction of vertices not in the giant component. Then where $u$ is also the probability that a randomly chosen vertex $i$ does not belong to the giant component of the graph. For $i$ to not be a part of the giant component, for every other vertex $j$ ($n-1$ vertices), $i$ is either not connected to $j$ (with probability $1-p$), or $j$ is not connected to the giant component (with probability $pu$). Then:\n", "$$ u = ((1-p) + (pu))^{n-1} $$\n", "We can use $ p = \\frac{\\bar k}{n-1} $ to rewrite the expression as:\n", "$$ u = (1 - \\frac{\\bar k(1-u)}{n-1})^{n-1} $$\n", "And then taking the limit for large $n$ and using the fact that $\\lim_{x\\rightarrow\\infty}(1-\\frac{x}{n})^n = e^{-x}$:\n", "$$ u = e^{-k(1-u)} $$\n", "Now if $u$ is the fraction of vertices not in the giant component, call $S = 1-u$ the fraction of vertices in the giant component. Then:\n", "$$ S = e^{-\\bar kS} $$\n", "\n", "There is no closed-form solution to this equation, but below we can show a simulation of random graphs and the size of the largest connected component in each one." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# list of the sizes of the largest components\n", "big_comp = []\n", "# number of nodes in the graph\n", "num_nodes = 500\n", "# vector of edge probabilities\n", "p_values = [(1-x*.0001) for x in xrange(9900,10000)]\n", "# try it a few times to get a smoother curve\n", "iterations = 5\n", "for p in p_values:\n", " size_comps = []\n", " for h in xrange(0, iterations):\n", " edge_list = []\n", " for i in xrange(0,num_nodes):\n", " for j in xrange(i,num_nodes):\n", " if (random() < p):\n", " edge_list.append((i,j))\n", " G = igraph.Graph(directed = False)\n", " G.add_vertices(num_nodes)\n", " G.add_edges(edge_list)\n", " comps = [len(x) for x in G.clusters()]\n", " size_comps.append(comps)\n", " big_comp.append(sum([max(x) for x in size_comps])/len(size_comps))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot([x*(num_nodes-1) for x in p_values], big_comp, '.')\n", "plt.title(\"Phase transitions in connectedness\")\n", "plt.ylabel(\"Fraction of nodes in the largest component\")\n", "plt.xlabel(\"Average degree (k = p(n-1)), {} < p < {}\".format(p_values[99],p_values[0]))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clustering coefficient\n", "\n", "The clustering coefficient is a measure of how many trianges (completely connected triples) there are in a graph. You can think about it as the probability that if Alice knows Bob and Charlie, Bob also knows Charlie. The clustering coefficient of a graph is equal to $$ C = \\frac{\\text{(number of closed triples)}}{\\text{number of connected triples}} $$\n", "\n", "Finding the expected value of $C$ for a random graph is simple. For any 3 vertices, the probability that they are all connected is $p^3$ and the probability that at least 2 of them are connected is $p^2$. Then the expected values of closed triples (triangles) and connected triples respectiely are ${n \\choose 3}p^3 $ and ${n \\choose 3}p^2 $, and the expected value for $C$ is then $\\frac{p^3}{p^2} = p$. Notice in the above plot that the values for $p$ are very small, even when the graph is fully connected. In a randomly generated sparse graph (a graph where a small fraction of the total possible ${n \\choose 2}$ edges exist), the clustering coefficient $C$ is very low." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# number of nodes in the graph\n", "num_nodes = 500\n", "# vector of edge probabilities\n", "p_values_clustering = [x*.01 for x in xrange(0,100)]\n", "# try it a few times to get a smoother curve\n", "iterations = 1\n", "# store the clustering coefficient\n", "clustering = []\n", "for p in p_values_clustering:\n", " size_comps = []\n", " for h in xrange(0, iterations):\n", " edge_list = []\n", " for i in xrange(0,num_nodes):\n", " for j in xrange(i,num_nodes):\n", " if (random() < p):\n", " edge_list.append((i,j))\n", " G = igraph.Graph(directed = False)\n", " G.add_vertices(num_nodes)\n", " G.add_edges(edge_list)\n", " clustering.append((p, G.transitivity_undirected(mode=\"zero\")))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot([x[0]*(num_nodes-1) for x in clustering], [x[1] for x in clustering], '.')\n", "plt.title(\"Clustering coeff vs avg degree in a random graph\")\n", "plt.ylabel(\"Clustering coefficient\")\n", "plt.xlabel(\"Average degree (k = (n-1)p), 0 < p < 1\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Small diameter graphs\n", "\n", "So we know that the giant component is very likely, even for sparse graphs, and also that the clustering coefficient is very low, even for relatively dense graphs. This means that the graph is almost completely connected, and that it is, at least locally, pretty similar to a tree graph (acyclic). \n", "\n", "Consider that a graph has a mean degree $\\bar k$. Now consider the number of vertices reachable from some vertex in the graph, $i$, call the number of vertices that $i$ can reach $l$. Because the clustering coefficient is very low (the graph is locally tree-like), it is likely that any neighbor of $i$'s has a completely new set of neightbors ($k$ neighbors, less $i$, $k-1$ total new neighbors). Then for each step, you reach $k-1$ new vertices. Thus the number of vertices reachable in $l$ steps from some vertex $i$ is $(k-1)^l$.\n", "\n", "The diameter of a graph is the maximum number of steps $l$ one would have to take to reach any vetex from any other vertex, or the number of steps needed to make any vertex reachable.\n", "$$ (k-1)^l = n $$\n", "$$ l = \\frac{1}{log(k-1)}log(n) \\approx O(log(n))$$\n", "\n", "Thus the diamater of the graph grows as $O(log(n))$, or shows \"small world\" characteristics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A comparison with a real social graph:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"The number of nodes in the graph (all are connected): {}\".format(len(real_graph.nodes())))\n", "print(\"The number of edges in the graph: {}\".format(len(real_graph.edges())))\n", "print(\"The average degree: {}\".format(sum(nx.degree(real_graph).values())/len(real_graph.nodes())))\n", "print(\"The clustering coefficient: {}\".format(nx.average_clustering(real_graph)))\n", "print(\"The clustering coefficient that a random graph with the same degree would predict (k/(n-1)): {}\"\n", " .format(sum(nx.degree(real_graph).values())/len(real_graph.nodes())/(len(real_graph.nodes())-1)))\n", "print(\"The diameter of the graph: {}\".format(nx.diameter(real_graph)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Configuration Model\n", "\n", "Another random graph model: the configuration model. Instead of generating our own degree sequence, we use a specified degree sequence (say, use the degree sequence of a social graph that we have) and change how the edges are connected. This allows us to ask the question: \"how much of this characteristic is completely explained by degree?\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an example of using the configuration model to create a null model of our \"real graph.\" Note that the algorithm that I am using works well for creating configuration models for large graphs, but produces more error on this smaller graph." ] }, { "cell_type": "code", "collapsed": false, "input": [ "A = []\n", "for v in real_graph.nodes():\n", " for x in range(0, real_graph.degree(v)):\n", " A.append(v)\n", " shuffle(A)\n", " # make the edge list\n", " _E = [(A[2*x], A[2*x+1]) for x in range(0,int(len(A)/2))]\n", " E = set([x for x in _E if x[0]!=x[1]])\n", "# add the edges to a new graph with the name node list\n", "C = real_graph.copy()\n", "C.remove_edges_from(real_graph.edges())\n", "C.add_edges_from(E)\n", "nx.draw(C, node_color = 'blue', pos = positions)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"The number of nodes in the graph (all are connected): {}\".format(len(C.nodes())))\n", "print(\"The number of edges in the graph: {}\".format(len(C.edges())))\n", "print(\"The average degree: {}\".format(sum(nx.degree(C).values())/len(C.nodes())))\n", "print(\"The clustering coefficient: {}\".format(nx.average_clustering(C)))\n", "print(\"The clustering coefficient that a random graph with the same degree would predict (k/(n-1)): {}\"\n", " .format(sum(nx.degree(real_graph).values())/len(C.nodes())/(len(C.nodes())-1)))\n", "print(\"The diameter of the graph: {}\".format(nx.diameter(C)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Asking questions using a null model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A famous example of centrality measuring on a social network is the Florentine Families graph. Padgett's reseach on this graph claims that the Medicci family's rise to power can be explained by their high centrality on the graph of business interactions between families in Italy during that time. We will use a null model (configuration model) of the graph to rearrange how edges are places without altering any node's degree to discover how much of the Medicci's power is determined by thier degree (ranther than other structural components of the graph)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# get the graph\n", "florentine_families = igraph.Nexus.get(\"padgett\")[\"PADGB\"]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's show the relative rankings of the families with respect to vertex degree in the network and with respect to our chosen centrality measure, harmonic centrality. I won't go into various centrality measures here, beyond to say that harmonic centrality is formulated: \n", "$$ c_i = \\frac{1}{n-1}\\sum_{i,i\\neq j}^{n-1}\\frac{1}{d_{ij}} $$\n", "where $d_{ij}$ is the geodesic distance between vertices $i$ and $j$. Basically, harmonic centrality is a measure of how close a vertes is to every other vertex." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# degree centrality\n", "d = florentine_families.degree()\n", "d_rank = [(x, florentine_families.vs[x]['name'], d[x]) for x in range(0,len(florentine_families.vs()))]\n", "d_rank.sort(key = itemgetter(2), reverse = True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# harmonic centrality\n", "distances = florentine_families.shortest_paths_dijkstra()\n", "h = [sum([1/x for x in dist if x != 0])/(len(distances)-1) for dist in distances]\n", "h_rank = [(x, florentine_families.vs[x]['name'], h[x]) for x in range(0,len(florentine_families.vs()))]\n", "h_rank.sort(key = itemgetter(2), reverse = True)\n", "# make the table\n", "d_table = []\n", "d_table.append([\"Rank (by degree)\", \"degree\", \"Rank (h centrality)\", \"harmonic\"])\n", "for n in xrange(0,len(florentine_families.vs())):\n", " table_row = []\n", " table_row.extend([d_rank[n][1], str(d_rank[n][2])[0:5]])\n", " table_row.extend([h_rank[n][1], str(h_rank[n][2])[0:5]])\n", " #table_row.extend([e_rank[n][1], str(e_rank[n][2])[0:5]])\n", " #table_row.extend([b_rank[n][1], str(b_rank[n][2])[0:5]])\n", " d_table.append(table_row)\n", "print tabulate(d_table)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the fun (?) part. Create a bunch of different random configuration models based on the florentine families graph, then measure the harmonic centrality on those graphs. The harmonic centality of a node on the null model will deend only on its degree (as the graph structure is now ranom)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "config_model_centrality = [[] for x in florentine_families.vs()]\n", "config_model_means = []\n", "hc_differences = [[] for x in range(0,16)]\n", "for i in xrange(0,1000):\n", " # build a random graph based on the configuration model\n", " C = florentine_families.copy()\n", " # graph with the same edge list as G\n", " C.delete_edges(None)\n", " # print C.summary()\n", " # Add random edges\n", " # vertex list A\n", " A = []\n", " for v in florentine_families.vs().indices:\n", " for x in range(0,florentine_families.degree(v)):\n", " A.append(v)\n", " shuffle(A)\n", " # print A\n", " # make the edge list\n", " _E = [(A[2*x], A[2*x+1]) for x in range(0,int(len(A)/2))]\n", " E = set([x for x in _E if x[0]!=x[1]])\n", " # add the edges to C\n", " # print E\n", " C.add_edges(E)\n", "\n", " # rank the vertices by harmonic centrality\n", " C_distances = C.shortest_paths_dijkstra()\n", " C_h = [sum([1/x for x in dist if x != 0])/(len(C_distances)-1) for dist in C_distances]\n", " del C\n", " for vertex in range(0,16):\n", " hc_differences[vertex].append(h[vertex] - C_h[vertex])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot([percentile(diff, 50) for diff in hc_differences], '--')\n", "plt.plot([percentile(diff, 25) for diff in hc_differences], 'r--')\n", "plt.plot([percentile(diff, 75) for diff in hc_differences], 'g--')\n", "\n", "plt.xticks(range(0,16))\n", "plt.gca().set_xticklabels(florentine_families.vs()['name'])\n", "plt.xticks(rotation = 90)\n", "\n", "plt.gca().grid(True)\n", "\n", "plt.ylabel(\"(centrality) - (centrality on the null model)\")\n", "plt.title(\"How much of harmonic centrality is explained by degree?\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dashed red, blue and green lines repectively represent the 25th, 50th and 75th percentile of the difference in centralities between the real graph and the pool of null models. We can see that for the Mediccis, that difference is basically always high--their centrality on the real graph is higher than their centrality on this null model. This shows that there is in fact something important about their place structurally in the graph, as well as thier high degree." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "That's all folks!" ] } ], "metadata": {} } ] }
unlicense
DOREMUS-ANR/recommender
training/training_unneural.ipynb
1
18905
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pasquale/anaconda3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5\n", " return f(*args, **kwds)\n", "/Users/pasquale/anaconda3/lib/python3.5/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import codecs\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Load data in Tensorflow." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>seed</th>\n", " <th>target</th>\n", " <th>score</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>http://data.doremus.org/artist/d33ebb23-7b8d-3...</td>\n", " <td>http://data.doremus.org/artist/6329cd86-d47a-3...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>http://data.doremus.org/artist/01915146-b964-3...</td>\n", " <td>http://data.doremus.org/artist/6329cd86-d47a-3...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>http://data.doremus.org/artist/01915146-b964-3...</td>\n", " <td>http://data.doremus.org/artist/d33ebb23-7b8d-3...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>http://data.doremus.org/artist/72b3b303-5c15-3...</td>\n", " <td>http://data.doremus.org/artist/6329cd86-d47a-3...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>http://data.doremus.org/artist/72b3b303-5c15-3...</td>\n", " <td>http://data.doremus.org/artist/d33ebb23-7b8d-3...</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " seed \\\n", "0 http://data.doremus.org/artist/d33ebb23-7b8d-3... \n", "1 http://data.doremus.org/artist/01915146-b964-3... \n", "2 http://data.doremus.org/artist/01915146-b964-3... \n", "3 http://data.doremus.org/artist/72b3b303-5c15-3... \n", "4 http://data.doremus.org/artist/72b3b303-5c15-3... \n", "\n", " target score \n", "0 http://data.doremus.org/artist/6329cd86-d47a-3... 1 \n", "1 http://data.doremus.org/artist/6329cd86-d47a-3... 1 \n", "2 http://data.doremus.org/artist/d33ebb23-7b8d-3... 1 \n", "3 http://data.doremus.org/artist/6329cd86-d47a-3... 1 \n", "4 http://data.doremus.org/artist/d33ebb23-7b8d-3... 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "root = \"../\"\n", "training_data_folder = '%straining_data/web-radio/output/rec' % root\n", "embDir = '%sembeddings' % root\n", "what = 'artist'\n", "\n", "uri_file = '%s/%s.emb.u' % (embDir, what)\n", "vector_file = '%s/%s.emb.v' % (embDir, what)\n", "# header_file = '%s/%s.emb.h' % (embDir, what)\n", "training_file = '%s/%s.dat' % (training_data_folder, what)\n", "\n", "vectors = np.array([line.strip().split(' ') for line in codecs.open(vector_file, 'r', 'utf-8')])\n", "# heads = np.array([line.strip() for line in codecs.open(header_file, 'r', 'utf-8')])\n", "uris = np.array([line.strip() for line in codecs.open(uri_file, 'r', 'utf-8')])\n", "\n", "train_array = np.array([line.strip().split(' ') for line in codecs.open(training_file, 'r', 'utf-8')])\n", "pd.DataFrame(train_array, columns=['seed', 'target', 'score']).head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data pre-processing: I want to substitute the seed and target with their embeddings" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def get_embs(x):\n", " # uri to embedding\n", " v = vectors[np.argwhere(uris == x)]\n", " if v.size == 0:\n", " result = -2. * np.ones(vectors[0].size)\n", " else:\n", " result = v[0][0]\n", " return result.astype('float32')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "col1 = np.array([get_embs(xi) for xi in train_array[:, 0]])\n", "col2 = np.array([get_embs(xi) for xi in train_array[:, 1]])\n", "col1 = np.concatenate((col1, [12., 45., 73.] * np.ones((train_array.shape[0], 3))), axis=1)\n", "col2 = np.concatenate((col2, [12., 45., 73.] * np.ones((train_array.shape[0], 3))), axis=1)\n", "col3 = np.array(train_array[:, 2]).astype('float32')\n", "col3 = col3.reshape((col3.size, 1))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def next_batch(num, data, labels):\n", " \"\"\"\n", " Return a total of `num` random samples and labels. \n", " \"\"\"\n", " idx = np.arange(0, len(data))\n", " np.random.shuffle(idx)\n", " idx = idx[:num]\n", " data_shuffle = data[idx]\n", " labels_shuffle = labels[idx]\n", " return data_shuffle, labels_shuffle" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(12333, 35)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "training_vector = np.concatenate((col1, col2, col3), axis=1)\n", "training_vector.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split test and train" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pasquale/anaconda3/lib/python3.5/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n", " FutureWarning)\n" ] } ], "source": [ "train, test = train_test_split(training_vector, train_size=0.7)\n", "\n", "train_vector = train[:, : -1]\n", "train_label = train[:, -1]\n", "train_label = train_label.reshape((len(train_label), 1))\n", "\n", "test_vector = test[:, :-1]\n", "test_label = test[:, -1]\n", "test_label = test_label.reshape((len(test_label), 1))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train\n", "(8633, 34)\n", "(8633, 1)\n", "Test\n", "(3700, 34)\n", "(3700, 1)\n" ] } ], "source": [ "print('Train')\n", "print(train_vector.shape)\n", "print(train_label.shape)\n", "print('Test')\n", "print(test_vector.shape)\n", "print(test_label.shape)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Parameters\n", "learning_rate = 0.1\n", "num_steps = 1000\n", "batch_size = 64\n", "display_step = 100" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Network Parameters\n", "n_hidden_1 = 256 # 1st layer number of neurons\n", "n_hidden_2 = 256 # 2nd layer number of neurons\n", "num_input = train_vector[0].size\n", "num_output = col1[0].size\n", "num_output_wrap = train_label[0].size\n", "\n", "# tf Graph input\n", "X = tf.placeholder(tf.float32, [None, num_input], name=\"X\")\n", "Y = tf.placeholder(tf.float32, [None, num_output_wrap], name=\"Y\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Network" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "def weighted_l2(a, b, w):\n", " with tf.name_scope('weighted_l2') as scope:\n", " # https://stackoverflow.com/a/8861999/1218213\n", " q = tf.subtract(a, b, name=\"q\")\n", " # return np.sqrt((w * q * q).sum())\n", " pow_q = tf.cast(tf.pow(q, 2), tf.float32, name=\"q-power\")\n", " return tf.reduce_sum(tf.multiply(w, pow_q), name=\"o\", keepdims=True)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "def compute_penalty(expected, taken, total):\n", " with tf.name_scope('penalty') as scope:\n", " penalty = tf.divide(tf.subtract(expected, taken), total)\n", " return tf.cast(penalty, tf.float32)\n", "\n", "w = tf.Variable(tf.random_normal([1, num_output]), name='w')\n", "\n", "def neural_net_wrap(x): \n", " seed, target = tf.split(x, [num_output, num_output], axis=1)\n", " \n", " bs = tf.equal(seed, -2.)\n", " bt = tf.equal(target, -2.)\n", "\n", " _ones = tf.ones_like(w, tf.float32)\n", " max_distance = weighted_l2(_ones, _ones * -1., w)\n", "\n", " bad_mask = tf.logical_or(bs, bt)\n", " good_mask = tf.logical_not(bad_mask)\n", "\n", " bs_count = tf.count_nonzero(tf.logical_not(bs), axis=1, keepdims=True)\n", " good_count = tf.count_nonzero(good_mask, axis=1, keepdims=True)\n", "\n", " _zeros = tf.zeros_like(seed, tf.float32)\n", " _seed = tf.where(good_mask, seed, _zeros)\n", " _target = tf.where(good_mask, target, _zeros)\n", "\n", " # distance\n", " d = weighted_l2(_seed, _target, w)\n", "\n", " # how much info I am not finding\n", " penalty = compute_penalty(bs_count, good_count, num_output)\n", " multiplier = tf.subtract(1., penalty)\n", " \n", " # score\n", " s = tf.divide(tf.subtract(max_distance, d), max_distance)\n", " return tf.multiply(s, multiplier)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 17)\n", "(1, 17)\n", "(1, 17)\n", "(?, 17)\n" ] } ], "source": [ "# Construct model\n", "logits = neural_net_wrap(X)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "TensorShape([Dimension(None), Dimension(1)])" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y.shape" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "TensorShape([Dimension(None), Dimension(1)])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logits.shape" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "# Define loss and optimizer\n", "# loss_op = MSE\n", "loss_op = tf.reduce_mean(tf.square(tf.subtract(logits, Y)))\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", "train_op = optimizer.minimize(loss_op)\n", "\n", "# Evaluate model (with test logits, for dropout to be disabled)\n", "correct_pred = tf.less(tf.subtract(logits, Y), 0.1)\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start learning\n", "Step 1, Minibatch Loss= 0.0880, Training Accuracy= 0.953\n", "My weights [-0.26 3.04 -0.96 -0.5 0.17 0.24 1.49 -1.61 -0.63 -0.48 1.69 2.2\n", " -2.43 -0.67 0.72 0.02 0.54]\n", "Step 100, Minibatch Loss= 0.0820, Training Accuracy= 0.969\n", "My weights [-0.31 2.99 -0.94 -0.51 0.2 0.01 1.55 -1.57 -0.61 -0.39 1.77 2.3\n", " -2.33 -0.57 0.82 0.12 0.64]\n", "Step 200, Minibatch Loss= 0.0588, Training Accuracy= 0.953\n", "My weights [-0.37 2.94 -0.94 -0.52 0.21 -0.14 1.58 -1.56 -0.61 -0.35 1.8 2.36\n", " -2.27 -0.51 0.87 0.17 0.69]\n", "Step 300, Minibatch Loss= 0.0503, Training Accuracy= 0.984\n", "My weights [-0.42 2.89 -0.95 -0.53 0.2 -0.26 1.59 -1.56 -0.62 -0.33 1.81 2.38\n", " -2.25 -0.49 0.9 0.2 0.71]\n", "Step 400, Minibatch Loss= 0.0633, Training Accuracy= 0.953\n", "My weights [-0.45 2.86 -0.96 -0.55 0.19 -0.34 1.59 -1.56 -0.63 -0.32 1.81 2.39\n", " -2.24 -0.48 0.91 0.21 0.73]\n", "Step 500, Minibatch Loss= 0.0634, Training Accuracy= 0.969\n", "My weights [-0.48 2.83 -0.95 -0.55 0.2 -0.4 1.6 -1.56 -0.63 -0.31 1.82 2.41\n", " -2.22 -0.46 0.93 0.23 0.74]\n", "Step 600, Minibatch Loss= 0.0592, Training Accuracy= 0.969\n", "My weights [-0.51 2.8 -0.95 -0.55 0.2 -0.46 1.6 -1.56 -0.63 -0.3 1.83 2.43\n", " -2.21 -0.45 0.94 0.24 0.76]\n", "Step 700, Minibatch Loss= 0.0511, Training Accuracy= 0.406\n", "My weights [-0.53 2.78 -0.94 -0.55 0.2 -0.48 1.61 -1.56 -0.62 -0.29 1.84 2.44\n", " -2.19 -0.43 0.95 0.25 0.77]\n", "Step 800, Minibatch Loss= 0.0685, Training Accuracy= 0.953\n", "My weights [-0.54 2.78 -0.92 -0.53 0.21 -0.48 1.62 -1.55 -0.61 -0.27 1.86 2.45\n", " -2.18 -0.42 0.97 0.27 0.79]\n", "Step 900, Minibatch Loss= 0.0441, Training Accuracy= 0.969\n", "My weights [-0.55 2.76 -0.92 -0.52 0.22 -0.5 1.62 -1.54 -0.61 -0.27 1.86 2.46\n", " -2.17 -0.41 0.98 0.28 0.79]\n", "Step 1000, Minibatch Loss= 0.0945, Training Accuracy= 0.922\n", "My weights [-0.56 2.75 -0.91 -0.52 0.22 -0.52 1.63 -1.54 -0.61 -0.26 1.87 2.47\n", " -2.16 -0.4 0.98 0.28 0.8 ]\n", "Optimization Finished!\n", "Testing Accuracy: 0.038108107\n" ] } ], "source": [ "with tf.Session() as sess:\n", " writer = tf.summary.FileWriter(\"output\", sess.graph)\n", "\n", " # Run the initializer\n", " sess.run(init)\n", "\n", " print(\"Start learning\")\n", " for step in range(1, num_steps + 1):\n", " batch_x, batch_y = next_batch(batch_size, train_vector, train_label)\n", "\n", " # Run optimization op (backprop)\n", " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", " if step % display_step == 0 or step == 1:\n", " # Calculate batch loss and accuracy\n", " preds, my_weights, loss, acc = sess.run([logits, w, loss_op, accuracy],\n", " feed_dict={X: batch_x, Y: batch_y})\n", " \n", " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", " \"{:.3f}\".format(acc))\n", " # print(\"Predictions %s VS %s\" % (preds[0], batch_y[0]))\n", " np.set_printoptions(precision=2)\n", " print(\"My weights %s\" % np.mean(my_weights, axis=0))\n", "\n", " print(\"Optimization Finished!\")\n", "\n", " print(\"Testing Accuracy:\",\n", " sess.run(accuracy, feed_dict={X: test_vector, Y: test_label}))\n", " writer.close()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
napjon/ds-nd
p2-introds/nyc_subway/project.ipynb
1
354727
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NYC Subway contains regular number of ridership across different conditions. It also contains time series. In this analysis, I investigate whether there is difference between raining vs not raining, and other statistical method to build the model, predicting number of ridership." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import scipy.stats as sp\n", "# %matplotlib notebook\n", "%matplotlib inline\n", "import seaborn as sns; sns; sns.set_style('dark')\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('turnstile_data_master_with_weather.csv')\n", "df.index = pd.to_datetime(df.pop('DATEn') +' '+ df.pop('TIMEn'))\n", "df.sort_index(inplace=True)\n", "del df['Unnamed: 0']" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>UNIT</th>\n", " <th>Hour</th>\n", " <th>DESCn</th>\n", " <th>ENTRIESn_hourly</th>\n", " <th>EXITSn_hourly</th>\n", " <th>maxpressurei</th>\n", " <th>maxdewpti</th>\n", " <th>mindewpti</th>\n", " <th>minpressurei</th>\n", " <th>meandewpti</th>\n", " <th>meanpressurei</th>\n", " <th>fog</th>\n", " <th>rain</th>\n", " <th>meanwindspdi</th>\n", " <th>mintempi</th>\n", " <th>meantempi</th>\n", " <th>maxtempi</th>\n", " <th>precipi</th>\n", " <th>thunder</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2011-05-01</th>\n", " <td>R114</td>\n", " <td>0</td>\n", " <td>REGULAR</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.31</td>\n", " <td>42</td>\n", " <td>35</td>\n", " <td>30.23</td>\n", " <td>39</td>\n", " <td>30.27</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>50</td>\n", " <td>60</td>\n", " <td>69</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2011-05-01</th>\n", " <td>R123</td>\n", " <td>0</td>\n", " <td>REGULAR</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.31</td>\n", " <td>42</td>\n", " <td>35</td>\n", " <td>30.23</td>\n", " <td>39</td>\n", " <td>30.27</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>50</td>\n", " <td>60</td>\n", " <td>69</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2011-05-01</th>\n", " <td>R429</td>\n", " <td>0</td>\n", " <td>REGULAR</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.31</td>\n", " <td>42</td>\n", " <td>35</td>\n", " <td>30.23</td>\n", " <td>39</td>\n", " <td>30.27</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>50</td>\n", " <td>60</td>\n", " <td>69</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2011-05-01</th>\n", " <td>R081</td>\n", " <td>0</td>\n", " <td>REGULAR</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.31</td>\n", " <td>42</td>\n", " <td>35</td>\n", " <td>30.23</td>\n", " <td>39</td>\n", " <td>30.27</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>50</td>\n", " <td>60</td>\n", " <td>69</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2011-05-01</th>\n", " <td>R029</td>\n", " <td>0</td>\n", " <td>REGULAR</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.31</td>\n", " <td>42</td>\n", " <td>35</td>\n", " <td>30.23</td>\n", " <td>39</td>\n", " <td>30.27</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>50</td>\n", " <td>60</td>\n", " <td>69</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " UNIT Hour DESCn ENTRIESn_hourly EXITSn_hourly maxpressurei \\\n", "2011-05-01 R114 0 REGULAR 0 0 30.31 \n", "2011-05-01 R123 0 REGULAR 0 0 30.31 \n", "2011-05-01 R429 0 REGULAR 0 0 30.31 \n", "2011-05-01 R081 0 REGULAR 0 0 30.31 \n", "2011-05-01 R029 0 REGULAR 0 0 30.31 \n", "\n", " maxdewpti mindewpti minpressurei meandewpti meanpressurei \\\n", "2011-05-01 42 35 30.23 39 30.27 \n", "2011-05-01 42 35 30.23 39 30.27 \n", "2011-05-01 42 35 30.23 39 30.27 \n", "2011-05-01 42 35 30.23 39 30.27 \n", "2011-05-01 42 35 30.23 39 30.27 \n", "\n", " fog rain meanwindspdi mintempi meantempi maxtempi precipi \\\n", "2011-05-01 0 0 5 50 60 69 0 \n", "2011-05-01 0 0 5 50 60 69 0 \n", "2011-05-01 0 0 5 50 60 69 0 \n", "2011-05-01 0 0 5 50 60 69 0 \n", "2011-05-01 0 0 5 50 60 69 0 \n", "\n", " thunder \n", "2011-05-01 0 \n", "2011-05-01 0 \n", "2011-05-01 0 \n", "2011-05-01 0 \n", "2011-05-01 0 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* https://class.coursera.org/statistics-003\n", "* https://www.udacity.com/course/intro-to-data-science--ud359\n", "* http://blog.minitab.com/blog/adventures-in-statistics/multiple-regession-analysis-use-adjusted-r-squared-and-predicted-r-squared-to-include-the-correct-number-of-variables\n", "* https://en.wikipedia.org/wiki/Coefficient_of_determination\n", "* http://napitupulu-jon.appspot.com/posts/inference-diagnostic-mlr-coursera-statistics.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Statistical Test" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rain</th>\n", " <th>ENTRIESn_hourly</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>1090.278780</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1105.446377</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rain ENTRIESn_hourly\n", "0 0 1090.278780\n", "1 1 1105.446377" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('rain',as_index=False).ENTRIESn_hourly.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this data, we can see summary statistic of number of ridership hourly, represented by `ENTRIESn_hourly` variable between rainy days and non-rainy days. So the independent variable is `rain` that represented as non-rainy day in control group, and `non-rainy` in experiment group. How rainy days affect the number of ridership, so the dependent variable is `ENTRIESn_hourly`. \n", "\n", "We can see that means of number ridership hourly of non-rainy days is 1090, where the means with rainy days is 1105. Such small difference, and we're going to test whether the difference is significantly higher, using independence test with one-tail p-value. I'm using 0.05 as p-critical value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* H0 $ P_\\mathbf{(rain > non-rain)} = 0.5$ : Population number of ridership in rainy days and non-rainy days is equal.\n", "* HA $ P_\\mathbf{(rain > non-rain)} \\gt 0.5$ : Population number of ridership in rainy days is higher than non-rainy days.\n", "\n", "\n", "The conditions within groups have validated. The sample size in this data is more than 30, and less than 10% population." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Non-parametric test used as statistical test that doesn't assume any underlying probability distribution. Mann Whittney U test is one of non-parametric test that I will be using in this case. Since we see that the distribution of both rainy and non-rainy is very right skewed in the Visualization section, we can't use any statistical test that assume normal distribution. So instead we can use non-parametric test." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rain</th>\n", " <th>ENTRIESn_hourly</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>1090.278780</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1105.446377</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rain ENTRIESn_hourly\n", "0 0 1090.278780\n", "1 1 1105.446377" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('rain',as_index=False).ENTRIESn_hourly.mean()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1924409167.0, 0.024999912793489721)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sp.mannwhitneyu(df.ix[df.rain==0,'ENTRIESn_hourly'],\n", " df.ix[df.rain==1,'ENTRIESn_hourly'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're using Mann-Whitney U test with average 1090 hourly ridership on non-rainy days and 1105 hourly ridership on rainy days. Because p-value is 0.025 less than 0.05 p-critical, we reject the null hypothesis, and conclude that the data provide convincing evidence that average number of hourly ridership in rainy days is higher than those of non-rainy days." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* OLS using Statsmodels or Scikit Learn\n", "* Gradient descent using Scikit Learn\n", "* Or something different?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm going to use linear regression with multiple predictor, hence multiple linear regression with OLS." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I use all numerical variables in my data plus additional variable isBusinessDay, except exits, since it will be expected that number of ridership between entries and exits will be similar. I use UNIT and Hour as dummy variables. I don't test dummy features, since it's computationally expensive. I also subset the data since it's also computationally expensive learn from dummy features. Moreover I know that UNIT and Hour features improve the model when I try it at the Udacity website. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "length = df.shape[0]\n", "subset = df.take(np.random.permutation(length)[:int(length*0.1)]).reset_index()\n", "\n", "dummy_hours = pd.get_dummies(subset['Hour'], prefix='hour')\n", "dummy_units = pd.get_dummies(subset['UNIT'], prefix='unit')\n", "\n", "# features = subset.join(dummy_units).join(dummy_hours)\n", "features = subset\n", "banned = ['ENTRIESn_hourly','UNIT','Hour','DESCn','EXITSn_hourly','index']\n", "candidates = [e for e in features.columns if e not in banned]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " R squared is not a significant measures for testing our model. Since every time we're adding a variable, R-squared will keep increasing. We're going to use adjusted R-squared, since it will incorporate penalty everytime we're adding a variable." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def test_adjusted_R_squared(col):\n", " \"\"\"Testing one variable with already approved predictors\"\"\"\n", " \n", " reg = sm.OLS(features['ENTRIESn_hourly'],features[predictors + [col]])\n", " result = reg.fit()\n", " return result.rsquared_adj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm going to choose forward selection, where I add one variable at a time based on highest adjusted R squared. And I will stop adding a variable if there's isnt anymore increase compared to previous adjusted R squared." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step 0: Adjusted R-squared = 0.1838127827 + maxpressurei\n", "Step 1: Adjusted R-squared = 0.1932579575 + BusinessDay\n", "Step 2: Adjusted R-squared = 0.1939087238 + mintempi\n", "Step 3: Adjusted R-squared = 0.1939172251 + precipi\n", "Step 4: Adjusted R-squared = 0.194064459 + rain\n", "Adjusted R Squared can't go any higher. Stopping\n" ] } ], "source": [ "predictors = []\n", "topr2 = 0\n", "for i in xrange(len(candidates)):\n", " \n", " filtered = filter(lambda x: x not in predictors, candidates)\n", " list_r2 = map(test_adjusted_R_squared,filtered)\n", " highest,curr_topr2 = max(zip(filtered,list_r2),key=lambda x: x[1])\n", " \n", " if curr_topr2 > topr2:\n", " topr2 = round(curr_topr2,10)\n", " else:\n", " print(\"Adjusted R Squared can't go any higher. Stopping\")\n", " break\n", " \n", " predictors.append(highest)\n", " print('Step {}: Adjusted R-squared = {} + {}'.format(i,topr2,highest))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are non dummy features after I perform forward selection" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['maxpressurei', 'BusinessDay', 'mintempi', 'precipi', 'rain']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test collinearity that may happen in my numerical features, I use scatter matrix." ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scatter Matrix of features and predictors to test collinearity\n" ] }, { "data": { "image/png": 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s58Aq9jxhaz0eSxW+poBvAW8BrqQKYVc3WVNX6ffrl3X6+c3sr99fW6lZ27dv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\nYDvVrg1XAv8buJjqFDi3AU8G3gv8LtX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"text/plain": [ "<matplotlib.figure.Figure at 0x125702110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Scatter Matrix of features and predictors to test collinearity');\n", "pd.scatter_matrix(features[numerics],figsize=(10,10));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I can see that there are no collinearity among the predictors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next I join non-dummy features and dummy features to `features_dummy` and create the model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features_dummy = features[predictors].join(dummy_units).join(dummy_hours)\n", "model = sm.OLS(features['ENTRIESn_hourly'],features_dummy).fit()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "maxpressurei -398.519202\n", "BusinessDay -547.164804\n", "mintempi -9.653110\n", "precipi 39.689893\n", "rain -117.832918\n", "dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filter_cols = lambda col: not col.startswith('unit') and not col.startswith('hour')\n", "model.params[model.params.index.map(filter_cols)]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.62666677409655058" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.rsquared" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*R2 is often interpreted as the proportion of response variation \"explained\" by the regressors in the model.* So we can say 61.67% of the variability in the % number of ridership subway hourly can be explained by the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the time of this writing, pandas has grown mature, and ggplot for python,which relies on pandas, is not being updated. So I will not use ggplot in this section, and use pandas plotting." ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a359f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axes = plt.subplots(nrows=1,ncols=2,sharex=True,sharey=True,squeeze=False)\n", "\n", "filtered = df.ix[df.ENTRIESn_hourly < 10000]\n", "\n", "for i in xrange(1):\n", " axes[0][i].set_xlabel('Number of ridership hourly')\n", " axes[0][i].set_ylabel('Frequency')\n", "\n", "filtered.ix[filtered.rain == 0,'ENTRIESn_hourly'].hist(ax=axes[0][0],bins=50)\n", "axes[0][0].set_title('Non-rainy days')\n", "filtered.ix[filtered.rain == 1,'ENTRIESn_hourly'].hist(ax=axes[0][1],bins=50)\n", "axes[0][1].set_title('Rainy days')\n", "\n", "fig.set_size_inches((15,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this plot, we can see that more people is riding the subway. But we want to know whether the difference is significance, using hypothesis test. The frequency is indeed higher for non-rainy days compared to non-rainy days." ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QUBHxVLprMTfIlXLpIPq2VziZTMZgp/683qg/ieokVviu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ZXFZ599RmBgII2NjQwYMAAHBwccHBxU7b5mzRpWrFiBv78/oaGhDB8+nA4dOqj+WLke1SX3qqoq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\neHg4CQkJj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"text/plain": [ "<matplotlib.figure.Figure at 0x10d410e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(df\n", " .resample('1D',how='mean')\n", " .groupby(lambda x : 1 if pd.datetools.isBusinessDay(x) else 0)\n", " .ENTRIESn_hourly\n", " .plot(legend=True))\n", "plt.legend(['Not Business Day', 'Business Day'])\n", "plt.xlabel('By day in May 2011')\n", "plt.ylabel('Average number of ridership hourly')\n", "plt.title('Average number of ridership every day at in May 2011');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the difference is likely siginificant of ridership from the time of day. We can create a new variable to turn this into categorical variable." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['BusinessDay'] = df.index.map(lambda x : 0 if pd.datetools.isBusinessDay(x) else 1)" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 20\n", "1 10\n", "Name: rain, dtype: int64" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.resample('1D').rain.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the data is observation and not controlled experiment, we can't make causation. However there is likely to be no different for average number of ridership hourly of non-rainy days and rainy days. We know that the dataset is taken from NYC data subway, but because the data is not random sampled in this observation, we can't generalize to all people who use subway in NYC. So pretty much we can't make any causal statement that whether or not there is a difference of average number ridership hourly between rainy days and non rainy days. Moreover, the data also doesn't provide convincing evidence that the number people ride NYC subway is significantly different between rainy days and not rainy days." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using Statistical Test, If in fact there's no different of average number of ridership hourly of non-rainy days and rainy days, the probability of getting a sample with size 44104 for rainy days and 87847 sample size for non-rainy days with average difference of 15 ridership, is 0.025. Such a small probability could means that rain is a significant predictor, and the difference it's not due to chance.\n", "\n", "Using Linear Regression, we can say that all else held constant, the model predicts number of ridership in one hour for non-rainy days is 117 people higher than rainy days, on average." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reflection" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "So where will this lead us? Well we could see that in average day, number of ridership still following some pattern. But it's not clear how this affect through season since we only have limited data. The data itself could expand to through one year. As we can see that this data only include May 2011, and we have no idea how winter, autumn, summer, and spring affecting the number of ridership.\n", "\n", "That's more analysis that can be done. With statistical test, I just analyze how rain is not significant different. The different is just due to chance, or it could be other factor than the rain. Fog may be significantly different, or you also that in Visualization section, the number of ridership is different between business day and non business day.\n", "\n", "We also have seen that the distribution of number in hour (ENTRIESn_hourly) is right skewed, so we could do some tranformation to make it more normal. The number of ridership between business day and non business day also not linear, it follows what seems to be cyclical.\n", "\n", "\n", "The model predict not really linear. To test the performance of our model we can do following things:\n", "\n", "* linear relationship between every numerical explanatory and response\n", "* Nearly normal residuals wih mean 0\n", "* Constant variability of residuals\n", "* Independent residuals\n", "\n", "Our model is not a good fit if at least one this diagnostics failed, which it does." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### linear relationship between every numerical explanatory and response" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test if the model is good we can plot all the numerical features again residuals, see whether every plot is random scatter around zero. This is to check whether there is a linear relationship between residuals and numerical features, to make sure that it doesn't containy any other dependent variables." ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xTCo1WmG5jAP58v5e/Ttdj26cVxh8ttOyee3RX2nZastylNUGi+ul0yuUSiUC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\nPUTlRT/IVPT/anWfVssiEonQb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TQtw+5u/cJzh+O4lMvyTI1Dx5nnxXDAzRMo6a8po2wh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\nZOL1NcD7cZVx7j7bzC4HjgLWAee5+2/N7HPAvUAv4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"text/plain": [ "<matplotlib.figure.Figure at 0x1145d1d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axes = plt.subplots(nrows=1,ncols=3,sharey=True,squeeze=False)\n", "numerics = ['maxpressurei', 'mintempi', 'precipi']\n", "for i in xrange(len(numerics)):\n", " axes[0][i].scatter(x=features[numerics[i]],y=model.resid,alpha=0.1)\n", " axes[0][i].set_xlabel(numerics[i])\n", " \n", "\n", "axes[0][0].set_ylabel('final model residuals')\n", "axes[0][1].set_title('linear relationships between features and residual, alpha 0.1')\n", "fig.set_size_inches(12,5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that eventhough seems categorical `maxpressurei` and `mintempi` is random scatter. But `precipi` is not a good candidate for linear relationship of the model. It seems it's not randomly scattered." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nearly normal residuals wih mean 0" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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D9iI4bW8ywWl8jyazUyJdwerV8TeXOu201G0ecckll/PMMwtYsuRVCgsLOf30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\nCBHmz9+oXAjJVdcRTEHcaGY3hmU/BO4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"text/plain": [ "<matplotlib.figure.Figure at 0x1151fcbd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axes = plt.subplots(nrows=1,ncols=2,squeeze=False)\n", "\n", "sp.probplot(model.resid,plot=axes[0][0])\n", "model.resid.hist(bins=20,ax=axes[0][1]);\n", "\n", "axes[0][1].set_title('Histogram of residuals')\n", "axes[0][1].set_xlabel('Residuals')\n", "axes[0][1].set_ylabel('Frequency');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we're checking by histogram that the residuals is normally distributed. The histogram shown that it's pretty normal and distributed around zero. Quantile plot checking if the residuals randomly scattered around zero. We can see that our model failed in this test. The residuals is very skewed, explained by large number of points deviated from mean line at tails area. This means that our linear regression is not a good model for this case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constant variability of residuals" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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K5QkqlRSlUmnjnFKpRKMxor1cRESOodZeKel0jWw2ta9rHHRKWJQ0xt/4PhqJ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\nXm7OIb4T+OMulOUHANcY82C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/j2jjKznmNldMe9n989qC71amLJ90Otp7ZLcKJJVKkEoF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lAAAg\nAElEQVTCiSwuc0lziVwueq77XtwQg4OmKdy5M6TbFYgiidHIolabsblZxvcHnJxMGA5z+L6KogR0\nOl2gQr8fE8c+GxvJXBHbVjk6mnJ2lqPX62KaCmGo0O0OAZVy2cIwxkjSEroeIQhjrl/X0LQAQXCJ\nIgXPK+H7XZpNGdt2keUCslzk4OAD3nzzBooCw+FNWq0raJo2H+arsZAMXtBsanjeCaIosbGxTBAM\nqFRUNK2K4wwfK7O6bL7MRYfn4rl7e0OiaAnbhoODPpVKInnvuia1WqKIdXENf9RuXLbWO45PsZgl\nm33yun+ZHXrWa09JWZA6KCmfKZubdTY36/zyLwfcvNnmH//jHQ4PaxwfZ5lMksZAiBCECNP8EEG4\niiAs0+3exTCuYlkWrhvQaLxKEJxQLOZJnJNdlpaaTKdVHOeYTCZLv9/CMCJqtUNeeaVKFOkcHExY\nXl7i8NBjMgnRtCnD4Zg339wmDAN2d4d0u3kcZ9FIKeK6wrwe2QIUHKeC5/VR1QquqxIEg3m5go/j\nBLTb3ry/RGVvr3duABbyxfDAIXnaorwwzL3eFMcpzye+SwiCy0Lf4rLo1WfhOKRGJiUl5aPyoNTV\nf+I5i6b4hSriIpjTapV4++0Tjo8znJ7mEYQQURRxHJl22yOORRSlhCi2KRbz+L6MZTnkcgVGozGa\nlgE0DCNDr+cyHIb4vsH+vstopDKdHgA6KytNTk5+RLX6dWR5hqKcsrJiEIag6+uY5n0ajSKyLKDr\nFqVSxNJSHV2P8bw2b755A1FMyra++c1XMM02mlZ+bCjvom9G0ypUq0vE8RlxPKBWS0rAfP+5VLDP\nHwseVoV0HAnHqcyDWgJRVMXzAkQxJIrq58GuJ63hT1vrDUN5oopXGsBK+bikDkrKzwTDUM5Lv37n\nd97jxz8+ZjB4GccxEMXbWNYR2ewaw+EM3x+haW8yHB5g2y6G8TLd7gGGkUeSMsTxTRRlnek0w3Q6\nRtNKrK3lUNUphuGwslIHIlw3aYosl1V03ef27Q5xvM5sVuSnPz3k5Zc1kib9hDg2MAwfwwjmSmF5\n4ljBcRQUpYQgJFG2SkWdq3y5QB7bVnEcF0GY4jhFdD04NwAvOgE3l8tQq0mAOa93hlyuT7Op8ehw\nR0gdh5SUlM8PFzfMhqHQ6fQfakx/UmlQu+3S6Qi4roYgeNRq3nzNSwbhalqBfN5iNIqQZRVZljg9\ntQiCiHI5D8RUqw1GozNMs0irVaLTOSaOa/g+9HomnidychJz/75Cr+dwcCAxHpvouoGqqkynfdbX\nX8Z1HQQhIJdrMBqN5yXGNpK0hecNMAwTVc1hGB6aplCrabhugG0zHybp47o+jYYKeBhGdP6+Ewci\ni21Xse1k6nut1pqrkPlzVUkB29bOS4gX1/Np/Yq2nTT9J7cDNE3BdV08LwLU8yyO/bAy8yf6uT/N\nqfms+lxSvtikDkrKzxTDUPgrf+Xn5hmVY/b3D9nYuMLNm1m+970qZ2cRw2ENz/MRhDxhWMNxhqhq\nAc+bIklnXL2aQRDK9PtHqKpOFK0yGJzwxhs1FEXDcVRsO3FQDCODIDhkMhIrK68gim2q1QLV6hrd\n7i663kDXPVw3Io61eRZlRhSVsO1kiOOiVKteD9H16XmZQhTVzqex93oigiAQRQqC0Gd9/aM7CLqu\nsL6eGDrgvHb4Z0lqZFJSUp7GoxF0AE2rzEtWBw81iV8kyZxI9HoxnicwGAj0ehJHRyNUVaZWMzg+\n7qAoMtVq0hA/HncRRY9sdhXbdhGEPuVyhXJZQ1UDfH9MHLcYjaaMRj18fwLk6HajeSYmJAxbSJKF\nILhkMgGK4pHNrqDrZxQKBfr9GYIAzWYVyCCKFoIwJY41ZLmOLPscH/cQhCm1Wh7X7XJwIBBFdYbD\nAUdHPteulYnjCRDMN/FFXNdiMAhQVYVeL8m4NJsajuPT64GqlrBt6PVcBGF8LsZyMat0sV8RFg7B\nCFDRdQXHcahWDURxjOcdsra2AoDrdtH1Bxn+Z83Jetp5L8KzZnOlpEDqoKR8TkgyKld4660rOE7A\nxsYWnc4JH3yQQRSLWNYQXZ/RbgtMpzM8r8ZodEwc6yhKhTC8zdLSdXz/DEH4U9544yqTiYnvywhC\nhOMouG4V3x+gaTKiOCKTCalUHizqSZbDoV43MIyQfn+fWk1FVRu4bogguHiehGH4aJpJoyHSaCRG\nNpFeTB6j1xsSx2V0XcDzuqhq4rgIgv1UBZUnXZeFcXg06nhZfe9n6TikRiYlJeUyFhH0RcAm2WoI\n6PqivEt+ambXdQP6fRnfF/C8Mt3uIRsbJYrFHP3+CXFcRJZBEE7xvJB8XqPRMBiN+mSzy4ThEM/r\nsLycJ4pm9Ps+t24dYhgGUaSwvx9SKmVQlADLspBlBU2ziWORTMYgl5PIZDJ43h1yOZUgEJGkGYIw\n5ezMRpJaTKddRDGiVKoSBAOCABRlFc/r4roDGo0sjqPjeRZxrGGaBnfuDFEUkfX1HKBxcNAnjjX6\n/QyjkcfSEqyuRth2hCCAqiZS9KbpEscVOp0Rtj08zybBg/6eR7loC+p1Hc8z5wN4ly+Uy1Wfaw1/\n0bX+WXYoFVdJeR5SByXlc8ciDfw3/obEn/7pKX/8xx+iadc4OGgzGgVIUp3x+IhMpoTjHHD/fol8\nvsTe3ocsLV2hVIoJgi7lsoEsNzg+HuJ5Ia3WEkGg4jgChiEzGo2YzV5GEDzi+B6vv16cL/Y+pjmg\nUmkSRYl2f62mzXs/8njeBF33MYzKQ0osntdH0yrouojvn2IYGoaRBUZ4nke12sSyeEhB5Xm4zDg8\nrb73s3QcUgOTkvLV5kmbzW73wRrl+wNqtedbiwxDYTg8ZDyuIMtFHMdEEAziOGY4dBCEAsvLEkFw\nRq+noOsapmkyneqsrW3R7d6nWNQol8sMBiEffDCg3XY4PCwQhn1EsQI0ODs7o1pdxvNMJpMpiiIT\nhhL5fES1OuDatXVMc58omhEEM2w7AgpMJiFhOGBlJUO5HDMYnCDLy7iuiuf12dzMo+syrtsBNHw/\nYDjMEscKpimgKAa1WlL2mwitSGgaBEHyv2GIxLGG53Xw/QFxrBPHiQKY60ZEUYVeb4CmKdTr2vn6\nf5lDcFEV8mJw7LK5MM/zubwIT7JDaW9KyvOSOigpn1uaTZ1f/MVVXn+9wnvvWdRqBpYVcHx8hCgu\n47oBirI1j5bp5PObWJbN+nqN09NjlpY02u0ulqUymxlY1m1efvnaXJoyZH39JbrdXYIgolK5Rrtt\nYxgujhMymy1j29Dv9+aRwOm85GuAqhr0ell++lOP9fXyeSO8pqkcHp6gaTlcN6bXiyiVKoThKSsr\neeCBXHDSP+I+c3H+qCooqeOQkpLyafP0zeaDuW2apiAINpDMHnlaZveDD0yy2Q1keYJpHlEo5Iii\nIp1Om0KhSBxr7O7us7S0hOMojMdtVHWLft/Btu9TrZYJggnvvdfBsnTefVdnOOwQRXkmkxKyLJHN\nuhhGFcvqk887iKJKsQhBICLLY27cqCCKI2q1Eqqq0m6PsG0NKJHNOoBLuVygWpXRNAXP88/f5yJr\n5LoR/f4Qz8tjWR6FgkupVMDzHgzHrlQ0PC85X1ULaFoARHS7HoKQCMYEQR/DqAABqlqan68Tx8p8\nllay/j/JITAM5Ymf06edyXiRhvuUlEdJHZSUzzWGobC1pdBo6Ny/30WWp/z4xwb37vUYDmU0TSaT\naRHHAwxDolwuYJr3qVY3+OM/7lAotICk7villxqcnQ3QtABdz7C/P0AQGsxmOd5++4Br17K4rozn\nzVAUGAzGuG6BajVGFB0cRwVUXFfBdRWgj66P50ooyUJv2xVcd0IUVRmNHHQ9qYVOHBaLOH4wzPFZ\ni/PTjIrriheUcD470tR8SkoKPHuzWa9r5+uirmvk8wIL1a4nOSedTrKeahpUKh6wRrVq4XmnKMoS\nltXB8wY0GhucnfUIggy2LQEShYLEZDJDVUPGY4fTU4333rtJv7+F520ym3UoFGrMZjaiOEEUywSB\nSa0msr6+zmzWoVIRCUODvT2ffF6mUqkyHJ5Sr+cJQ4mzsz3K5SUUZZmzs2OqVYn19RauG9DrJTbC\ncVSOjkwUpcTKisbp6RGbmyrlsoyqxnieja7n0HUZw+ih60mG5Pj4jFxOATRM06VSqaGqAEMMY4Cm\nlbHtxLlT1cql1++ydflJn9PHGfi7sAP1ev6575OS8qKkDkrKFwLDULh+fZVq1WVry+QHP+jS76/j\n+0PiuDJvmj+mWKwTxyWGww6g0ulEKIqHphUAgW63y7VrdTxvwnjsU68b7O62abVWeffdI1TV49VX\nW9y7d4Ki1HBdlclkn9dfb86HJCbTjwXBQVHKeF4bZW4TXDeY/37EcBgwm1VxXQtFiTAMj4vzUHT9\n2ZmTJxuVIo7j4jgz6nXtU+0zuUiamk9J+WrxUQMSix4EXf/ovXCVikYcRxiGy+pqmZ/8pA3oNBp5\nLKtNHPuYZkSvdwXPO2NtLaLVqmPbx/T7M46PVbpdA8tygRxRVGI83qNWq5DPN+h2/y1LS98gimL2\n93dYXr5Cr2cjyxFhqCLLMoLgUq+3sO09wjCLpq3T71uUShKVSlJClaznBomavIeuZ/D9HI4j0e/b\nlEoNSiUXwwBdT65NLhfhOCO2tnIA7O+fsL2dOB39/imVyub5dVDVEvX6EMOIEASTRqNCt+uQ9PR8\ntPV/0aC/4EUyGRftwOmpi/QCu8hUXCXlRUgdlJQvFM2mzre+leFrXyvy4x93iKIWZ2cuEFIs1rl1\na4/Z7BUsy2cy0Tk+3iGXq1AqVZhMDnj55U1qtRGeFxJFJcLwmFqtRhCETKclslmJ3d1DFKVBGPaY\nTidUKpt0uwG6Hs4bKQ1cVwAcqtUCpnlELldD1xVEsYeq5lGUGb7fR1V1YEqtFtNsZul0TDTt2WUO\nl3HRqNTrOq4bnA+X/LRJU/MpKV8tnhWQeNZm86P0wjUaBTqd7jyLojAafUi1epXDQ4/xWECScrTb\nHopSQVVdRHGIogzI5eoEwRG7u+9zfJzBMK5xcnIHz6vPRVVuomktCoVlSiUbz7tLrfZNJpMOoxGU\ny0Xa7R65XI5KRaTZrKIoLqIYkMl4LC9n8f2ITsdDEAw0bcLKSp44jsjlYsBHFAN0vcTBwYjZrMzZ\n2QBBmNFsytTrDltbFSCRGE6ubRHLAtcdnNsESBrjHWeIppXm19U978vc2kp6JPN5cX7241Lzj3L5\n56RgWQ/Ocd0QUQyeuZ4/bgd0HKf3QnYgFVdJeV5SByXlC8disf72twt0OmMWs0tcN2B1dYvvf99C\n19fY2bnPbFZjOtU5OTlFUbIEwYg4liiVavR6Q4IgRlVFZjMLTcuhKAonJxGyPEUQklKvTGaEKAas\nrS1jmm3iWKJel/G8MZ4Ha2sr82FVDq++qtDt+qyuKqytRei6dy4bedHAJO/jyYvz8xgVXVcwjPgJ\nj5CSkpLy0XhSQOLRkp5nbTY/SgDjtdeqdDpDbt3qcuXKVTwvxPcD1tYaHBwMkeUqqhpgWT6atkUm\nY2HbI27dcghDCdvemGfQFSyrz2zWRJK+ThT9mEIhTz5fQBAMplMN25YxzQynpy6lksfVqxUEISIM\n2wRBgUKhSq93i9XVCpJUI47PkOUccSzj+wN0vUG320HXFRqNAru7A1S1gmkOgZhstkAQJEpd3e6Y\ner3w2LXt9UTiWMB1I5L5LUUE4YRcTpv3mEwxjAeSzM/jRDx63mWf08K+dLseEKPrRdrtzyYznga2\nUp6H1EFJ+UJzMXvgOAFRJLK9HbC/36XVEsjlkkFV2WwFz5M4PLxNqfQKqgqVyhRdL3BwcIfl5Rsc\nH5vs7/dpNm+wu/shmtYgCBS63QP+/J9/lVu3TNbXE0NxfHxCuVwliop0uy71ugbI5HI+uVyGTsc+\n1/wH54UMzIKnGRX4dNLjF6c3XyRNzaekpFzG09azj1oi5jgzomgJ0xQQhIhiscVodEazmQiQJEGj\nmMPDHtmswc2bt2i3W8hyHd9XGQxULKsHrCMIAoIwwjDWgTtAiVyuTLf7PnFcxbJiZFlHVZe4d6+N\nLMP6ugY4hOEZm5tvcHp6n9HIJJ9fwrbPUJQWADs7R5TLa7TbIfv7Z9y4UQZ8arWAOC4BMYKgcHio\n4LoylhVjGD66nsjbu27S+D4YtInj5DF9v8/6+hK93hm6XkbX88/tODwt6/XoZ9Bs6nQ6IwwjmZUC\nz86MP24H3Cf2vVz2nGkPY8qLID77lJSULwZJZmXC6qpEqzVjaUmk2ZzRaFQolQyKRY9r19YRxZDR\n6AjPk5HlGmtrW9y+fcR4PCWOc9y69T612vV5dG1MEFT40Y/2iGMd0xxz506byeQqpilimh5xrM8d\nkQevY2urjOf1SSJTZdptF3jgALzYe3o4EpbP+4jiiFwu89yP8zzP2267jEYKlqWev96LLJ47n/fT\n/pOUlC8xhqHMh9QmLDK4L7J+tdsulqU+cT15Ep3OmNu3HVxXwfNiXFdhPO5SqcDKSoAoHpHNlul0\nQg4P3+PevQ/xfZHxuMdwqNLrHTGdgiRVyGQAAgRhgCxHRBFo2jGO82NkuUociyiKR7FYQpJkDKOA\nKNpMp6c0GiVyuVV2d7sYRg1FERiNjmk2I1ZWfEQxRFWbmKaP4yjY9gr7+wMMY0q1WkDTZnMJe53F\nVst1JVxXotc7u3BtXdbWShhGgGEE55Podb3yiOPw9Ot+edbr6fdZ2JYH82qezUU7sLT0uB140uf+\nUb8PKV9dPhcZlO3tbQE4Am7PD/1gZ2fnb29vb/888L8BU+B7Ozs7/+P8/L8D/IX58b+1s7Pz9vb2\ndg34vwAVOAH+052dHe8zfispP2OaTZ1cLqDZlOl2I27fDhgMInzfodXKsLxc4uTkGEFQcZwSZ2dH\nqKpBPn8FxzlGkkr0eiNOTs4IgqRJ03Fk9vY8ut09VldbzGbLjMenXL9uADGeF5DLPZxVcJzgobri\nODbY3R2g68ngrY/TZL5olLes53uc52luf94ekzTylZLy1eDRDG677VKp5LGsCZ1On0bjydH5xTR4\nCOaTzCU6nfFT++UcJ6DTcTk8DLGsTcbjPsWiRhBMMYwh1Wod3x8gSWN+7/d2CIJXsKxfYH//nfm0\ndY/x+D1kuUgUheRyGkFwiuetkcnIBMFd4rjFYHBAsfg1LAsU5ZR8foPptIskyZTLEo1GjUqlxOHh\nCdnsJrbtIctdbNtnMMhSLDYwTZ9mM4vnhcTxg7I3180iCFOazQzg4boSphkwHAa4bhHTjND1iO3t\nJICVzC+JiGMNXV+UWunnM7Uuu0aXXe+LXHQ2RPHBz5fdx7ZnOI447yVxaDTi58qMP+n5n2RHFj8/\nejy1JylP43PhoABXgXd3dnb+0iPH/w/gP9rZ2dnb3t7+f7a3t79GEor493Z2dr65vb29Bvw28HPA\nfw/85s7Ozv+5vb393wH/JYlzk/IVY5F1aDQKbG4G3LzZwbY1KpUGvj9gZaWMIEz4yU9OkOU63e6Y\n6dSl2axxdDQkn7/CYLDHdGowmagMhxMkaYnd3Q6WNcQwJshyjn4fwCGbHdJsrj5xYXfd4FyvfpFp\nWWjXP+8CfTES9iILfdrcnpKS8lFZrBMX15FkAGOVbneIpskPDQtcsL8/wrYraJrMwUGPSqUGCMTx\n5bOf2m2XbjfCNLO4bgZBcCkUKljWGZbVZ2VlmcPDGd/97ofs78/wvH+XMOwzm90kCF7CcSwcp04c\njwmCA1R1FUnKIIp5BGGIIAwoFF7H90cMh1UKBZlcLqDRqHF8vEsuVySX0xCEMS+/3CKOVTRtiXv3\nblIuX+HsrM90mqdYLOM4PUqlbeAugmACiYMyGh1hGDU6HZ96fcaVKyUcJ8A0k5KwwQCCwKdSSeTh\nEwfkwfySRIY5kWJutcq02w+X1Nr2g7X/smG/hqGwt9cjiuqYpocguFSrGhBRr+uPfUaLz7ReXzg1\nErlc9El8bVJSPhE+Lw7KN4CV7e3t3wc84DvAGaDs7Ozszc/5XeCXgAD4HsDOzs7h9va2NM+efAv4\ne/Nzfwf4dVIH5SuPYSi89dbaPKI3BASiSMN1M7RaWTwvRhRdHEciCCQKBQVBGHD9+go3bw4ZDmdo\nmsr9+21EcYV220UUJ2xvT+h2R2SzOSqVJnt7Nq+99rCxsG2HTkcgjg1836PXc6hUkqiY47hzg/Rs\nLmZAkrrkyic+A2XxeiGRvfysekzSmuSUlC8Oi03tg2GB8kPDApNN8oDZrIXrevT7IxSlju/3aTRK\nxLH8WIAkyZyIuK6M44Dnxei6x2AwYTwOyWSW+OEPO/zBH+xycvIavd6AMLyDKK4jijGiOCGbnSCK\nReJYRVUlJMlGkiw0rYwsD5Cka8TxDN8fkc9/nePjewCsr1d4+eUWsjwklwsolV5lNOqiKMkMrKWl\nV9jfP0QQihSLJbLZCYVCiyDosbSks7VVYn//eJ45qdDve4BGHE/J5ZK1bW1tCVUVGA6HzGZFEoX5\n6KH3v+j7u3hdBGGK53Wo1wtABstK+laeNOzXcQJqtRq93nje45LHdeP58Ej/qUGxB/bE/1jfj6f1\nKqY9jCkvymfuoGxvb/8q8LceOfxfA7++s7Pz29vb298CfhP4y8D4wjkWcIXkL8h85HgRKACj+TF7\nfiwlBXi4l6PddoAs1aqO7/d55ZU606nI/ftHzGYlFKWCrouAw82bM3o9C1m+guvauG5AtbpGp3OT\nRqNApdLAcXyiSGJ/v0u9Xjh/nlwuQ7c7wfd76LqM40h4XoCmKUAMCM/coD8cufSI4yV6vQGa9nwz\nUF6kub3Z1NH1gDB8tnTlJ0E6VyUl5YvBgwDGA+lbVS0/dt7F9apa1ej3p4BFraY98bGTwbPJRPXD\nwxN8P8v2dglNC1EUi/ffv8f+vsj+/kv0ekNgGXCIolOiqAAckcm0kOUJguAQBC6SpBPHNqJoYxhN\nxuMp0KZWU7HtQ2azMbq+Rq+3w/LyVdbW/gy63uH4eB/DyNNq5RmPj3HdAoaRQ5J0XPeQQqGGLMuI\n4imGoWMYeW7caLG/36XX81GUCo4DruvQbCZOh64nQa9SqYDrzhCEAFDxvD6CoMyDWCUEwaXRSByO\nDz4wiaI6ALbdZWsrh+uG81lbTx/2q+sKUSTjeSFP46MInzxPQOlJym6pvHDKi/KZOyg7Ozv/CPhH\nF49tb29rJP0k7OzsfH97e3uZxPG4qGlYAIZA+Mjx/Pz4eH5O98Kxp5JOQU34ql2Hej2P4wScnrro\n+hqQGNyvfe0aZ2cu47FKHGtUKgLZ7Cl37yqcnYVUKiq5nIphnLK5ucRkUkdVXRRFwjRD1teLTKc5\nxmOXq1fLjEZTJhODTEZjPB5SKik0mxN0XcIwGnheMh0YYDp1L2041HUZWVZw3YBSKclu5PPJn22x\nGNBoNJ/7/QIYxrPP/yyyGY4TUKlc/N7l0PVn6/B/lnzV/i5SUi7y6GY0CWBAv9+n0ShfOizQcRY9\nJ8nGV9NkfP8UXd8Akk2w40xxnIBGozDPnngcH0d8//s9wnAJw5gwnf6UIBB5+22VbvcqJyc/wbYH\nQAvokGR6ReCMTKaEqsbMZreRpAKzmUEUiWjaBoNBMilelvvEscJ4PGM6tRGEBo4zoFDYZmdnhOPc\noV6XEYQiUQRBMESWK7TbU0Cl2z3i6tVlSqWI0ehdvvWtP4vnTc7VtXRdQVVV4nPVd/H82n344Qlx\nXAMgl+vPy66SzPr9+965o5f0gSQKlFH0YJ2Oojr7+ydEURHXTbIhtdrssWG/F4djOo6LridBMEgG\nA1/mgFx0GiBzqYLjghcJKD1NBSwl5Xn5vJR4/R2SrMj/sr29/WeAg52dnfH29na4vb19BdgDvg38\nD8AM+J+3t7f/PrAGCDs7O+b29vb3SRrn/wnwy8AfPutJu13rWad86anX81/Z61AsSjhOD4DNzRrd\nrkWhIJHJODhOH0WB9fU8mYxMJtPFtiUgi2GMWVnJcHR0zOGhQBSVmUxiptM216+rgMzt2x+iqmWG\nwylxHAMy4/ExxWIZURQZDNpoWoVerw8kjkgQXD7wqt8f4jhZbHuKIDhks4lhyGbDF/7sLqqNXcZn\n9X1wnADLmpzfdt2AwcD/TIZOPg9f5b+Li6RO2leTvb0BcWyg68pDm1HDUOa9FZcPC1xskut1g4OD\nPoIgsL29gesmPRN7ex5RVMfzQn7wgztUqzkcR+O99+4yHN5AknQmkw47Ozo7O11cV8V1b5OY+h6w\nAlSBHaAMXCGT6ZPJ3KbVatHpdBHFV6hWY8Kwx2yWzEKpVrcYjY4RxSnZbInxOEsmk2c8VtH1mPH4\nJrq+gmGUiGOFwaBHpzMlCGTCMAJUptP7rK5WuHLlBr3eGEEQHyqbqtWi8wZ1XY/Oy90UZWWezXDR\ntKTsqlaT59kQHc8L0bTLS3Y9L8DzJtRqxvlw3l7PxTAeZD0uOhaP97PAoq/laVmLdtt7qoDL887F\nSUn5JPm8OCj/E/Cb29vbfxGYAP/J/Ph/BfwWkAF+d2dn522A7e3tPwL+hCRM8Tfn5/494J9sb2//\nFyRZlP/4M3v1KV9YLnMIFuVgiRJXTKkE3/jGFf7kT24SxxLV6lVMs8PKyhRVreH7FpBhNlvh5GSA\nomiIYhnTDOn3R1QqDqVSgc3NMrmcj2HE5PM6u7sLCcqn96QkRidAED76FPrPGxfLC7pdlyQS+9kN\nCktJSbmc3d0htl0FwHESZ+PRMqKLzfOPspivUasZ570Nul6m2+3gOGVMc8xwGHHv3iqKYhGGPp1O\nGUHIYpr3aLcH3L07Zja7StKrUSfZFuRJxD5lkmrvO0DIdJpjOi1gmi6a1mAyCbFtFUlS8H0XUJGk\ngOlUZHVVp9/vMJnM0LQ3gD6ZTDIHRZZrgEgQeEhSFtvuM5kU6HQCstl1RqMzfq7M468AACAASURB\nVPSjLvm8xsZGEd93qNV8btxYiLK4OE48vz4RkDnf1GuajGlGeN4UTcsSxy71uo4gDBgMbKCJrs8w\njIhms84Pf3iCaeZQlCJBMDhXCtN1hfV1BVEcYRgRts15b8rCsXiRLEW7nfRf2nYFx0nmeaVCKimf\nFz4XDsrOzs4Q+A8vOf6nwC9ccvzvAn/3kWMdksxJSsonRr2uYRgh9+7dY2PjKkmPewzUMYwDVFUC\nJG7ebNNuJ45GrQblssre3oDRqEG77bKx0WVzs3SeIUgM+8Up8It0/OW8yBT6LwoPBoVp5xuZ1Dim\npHw0PgnBieQxHo6Uu65P/pJA+aLkJ8nKDrly5eFhtFH0cFag1/PY3VUYjcA0PYKgiCxPAJFsNsO7\n7/4ug8Eyo9GirTRLUsqVB34w/7kFHJNsXZokhRdNfH8Dy9qnWj1BklQmkzxxPEZRrpBMlL9LPq/h\nOMdks1fRdR3HuTmf96IyndYIggiYMZ06gMVsJuD7JYIgxDTfJ59fxbavIAhnTKd5JhOBIAip12ds\nbSnngaSEzLzcrUivN8R1JTxPwTBG6HqeODY4OBjgujGKUmE47FGvazSbJdptl1qtgueFCMIZ1683\nOTgY0euNqdUKCIJzXh73cRQaO50xjqORSOWHgITrhpcKsKSDelN+FnwuHJSUlM8jF2t6S6Uc43GE\nqqrnv6/V8tj2mMPDLMNhEccZoSganc6EMPTIZpfI50UyGQlQEITpQ4+fpOwThyOpJ36ygsqXVe3q\nso1MSkrKi/FJCk4kgh7ueXY32Yw+aIjvdMa4bkAUleh2R2haCVDY2zPZ2krOe3RD67oDfvpTm729\nIbPZywwGU2T5hHy+ws7OHX7/93+XyeQtQCEp4coC7vz22/NjU+CUpEl+DAyAJaJoxHSqI4o/j2n+\nAZVKlijqIwgRuZzCeCwgSRuE4RGZTIFCISKKdslm18lkJKbTQ+r1LcLQZjKRyedlut0BjcYGZ2e3\nGI+XyGQ2OTo6Y3l5iUqlhmX5iGIW359xcODTaDzIJCVzqgxA5b339iiVVhgMhniezepqC3ARxTGC\nMCWO88SxRjab5+CgjSh2UdUGEFAuF/A8hZ2dDuVyExjjuoOHHMGPSpI5UbFthX6/TxyrxLGBIAzY\n2Jhc6nxc1q+SlnilfJqkDkpKylNYLMrXr+dx3Q6z2ToAmcwBm5srOE7A8XGbYrHB6uomvh9g220m\nk4A4VudTissoio/rDs4f96LzA0+PSF22+XiSNOUXjTQyl5Ly8Xg0kt7tzjDNIzY26i+8NlzsIUn6\nKZxzp8NxAu7cGeN5JY6PR7TbPRqNOprmU62qDw1jdJyAXC7DIujyT//pTU5PXyebVWm3f0Sr1UBR\nPP7hP/y/cZwzYAPYIinpypEIcp6RtJyGJNMHyvOfk2h/cmwCaEynEnCCKF7DticoikMcF+h07pLN\nbhDHHqqaZTot4rpZarXX8DwXRZlRLG5xdHRCPl+g0chydHSMql7h7CxgMNDwfYt8vkg228R1T6nV\nCsRxC9vuzfsYK7zzzsm81ybEcSLqdbh/f0Q2e5XhsIOqypRKyrzfREcQTtE0A8tK1M0GAx9NSxw+\nVU3Kv27fPsHzCihKhTju8cori7I7/3zdv2ztfLTs7rIhmkl/EfR6Q6KoimH4CIJJraaTy8U8jYv9\nKqenLlK6i0z5lEi/Wikpz2CxwP+5P7fC/v4ZAJubK+e/X1mpcHLi0u36FAp5Njby5PMeh4dnZLMt\nBMFB1925nv0Dnkd28bI0/t5eH9vW5tKUzrk05ReVVH4yJeWT4dYtE9PMo2kaBwd9rl2TXzjinsij\nd8jlFBqN5L6LQYphuEK7fYzrargunJ1NKBYlRqP7LC21sG3odEx0vTCfjzLl3Xfv026/immqOE4H\nUbzB3bs/5g//8F8D28Avkihz3Sdphj8iaS/VSRwViaTMSwIawD6JY7IJLBroq0CXKFphNisjSeZc\nuGTGdLqPpl0hiiRmswnZbITnjQnDAEUJEYSXmEwGnJyM8DyHfH6Tw0OXIDjCdd9EUVw07YBarUm9\nXkSWTbJZgelUJopyDAY+glCm10tENeK4QK83nr9+UFWF2ayIKI4Jwz6GkWdrq0S77SEIDp6XBWIE\nwUPTFOJY4PbtNlFUIwhMVHVGpVKbzzJ52Nl4dO1MMiNZul0PXc8+NKDxsn6hWk0jjn0Mw6dWW2TJ\n/Esz9pf1q9h2hjAcPCZu8mXN+Kd8tqQOSkrKC7C5WX/otmEomGaP8VjH80pY1iHVasTXv77KyopL\nr9dG0xTq9cszHS+6gCda+Nnz8otF3fHT5CG/CHyRX3tKys+SRSS9253hukUgwvNifL/Kd77zG2ha\nyD/4B//Z+d/YQgBk8fNFdneHdLsimtbEshy63Q6eZxPHRTxvQhhaDIcwm4k0GmUGgw/Z3c1QKGjE\n8QhBkHCcPKPRmDjW6HT69PsGw2GWfv+IyaTCnTs/wrL+Fcl85pdIMiNLJMpceyTTAhySbMoqSX/e\nmCRjMiDZ+PskPXs14B6JwlcNGBFFHkEgMpmcIEklplOYzTpEUR5JiqlUQkzzFEXZIghETPPfUq2K\nOE6JwSDH8bFHGPbY2GgShkcoikK1uomm3afRCLhypYwsy9y9m2zKk6GSGoIwY3m5TL/fp1aT0DQZ\nz+tRLle5e3dAEPg0m2v0eub5pHnPO6PfjxCEPKoqE0U6vj/AdSeAQKOxzHA4wPMCcrnLM8wXBQuS\ngZfgOGVc18MwkiGau7uD86yHIDgIgjPPosgYRo9arXb+u4sT6y86N8mx5HsTxzoHB0OWllaYTDTi\n+EGQLJ1vlfJJkTooKSkfg05njKo2efllGI1GBEEeTZNotwUMQ+H69Qzw0Tfgj6bxE017A9v+hN5A\nSkrKF55mU8fzuuh6BlA4PY349V//HrPZr+C6d3nttf+Vt94q853v/CWWlgrnG9KLG8h33jmg18sT\nRTqCMMLzYtptkSAo026P5gpTLq4bc3b2AZ43ZmnpJfb2HHK5LJJUot0+AgIsSyCKQlw3x+7ufU5P\np8Aa77+/AxwAv0TiYCQTzxMHpEzibCyGMa6QNOwfkWRX1uf3UUga5PdIBkduzG/vIggqotglDFeA\nNVz3LobxCtmshyRNEYRESWx19U16vZtYVgZRXAFsLEsGptj2BN/XgD4bG3UUZcZkckIul0GWG0yn\nBZrNDJWKTRwb+L7IcOiiaTlM06dWU8jlbGzbYX29gmn2qNWmRJEBjKlUKjhOwE9/2kHTqpTLAY5j\nU60uA+D7AeVyk37fwfdjSqUyQXCbXK5+Xj4HD9sUxwm4f7+L67ZYiK/EsYbrLmZXP5yFz+d9FuV3\n+XwOxxnNH+/BxPrFuQ/6TjifceO6EqAjCB66LhPH8vl5H6dxPyXlIqmDkpLyCaCqCiDgugqLGaHJ\n5PjRY6VdC563j+RiGr/VKs1T7TPieDF8K760NCpNs6ekfHXY3Kxj2ya3bwf8s3/2r4miX8J1b5GU\nQP1N3nnnt/lrf+03+cY3rvGd7/wCb7yxcb6B/L3fu8dwWGc0yjAe9ykWs4zHSa9HtztgNKoThg6D\nwQnjsY9piojiDXZ3jyiXV9C0HO+/36FUMuh09sjn32Iy8bh79xDHKdDrCZyd/RHJpvjfJ2mA90nm\nMS8yIneAr5FE6R0Sp8VmoZqYlH2JgEaSXbFJHJY8SXblGnF8kyjKI4odZjNx3hzfR9NahOGd+f3g\n+Pguspwjk1GZThVcd4jjZHBdDzCIY4XRaIzjuLRaAmE4olZbpdls4Xkj4jiPrvfxfQtBUGg2y3ie\ng+8P0PU8hlEkjiN6PQddlzHNAFkuzJ97gCjalEp1ZjOFwWBMuVwlCDqAQKWic3TUR1GW8P2AdnuX\nb35zjShS+OCD3mPOZbvt0umImGaZ01OTVquMILhAPJ9ib5/L01/EMJQL2Q4V23bmfUOPswiUOY6E\nYWQQRRNNq9Jsapjm5ydaltq8Lxepg5KS8jFoNAp0OiaumwcyiGKXSkXFcWb0eiK6XsayJhjGkGZT\nO184E6Mi4LoaguCysTF77sm8zaZOLhfQ7bbRdeXS4YZpmj0l5avHa69VyeW66PoBjnOLpE9jSrKx\n/zXge7z77gF//a//v/zVv3qFX/mVN/mjP3qP4fAGw+GUbtckjvNks8mQxWw2S6dTptu1MIwcBwdn\nzGZjlpe/jutG2HYSJGm32whCTD4/IpORyGa7TKcC43GWmzc7JA6HRhLJd0lGlRVJGt4/IHFC8vPz\nACrzc5T5/YL5e3iDRMnrPkkz/eLfopE+w2zmIootZrMJi2Z6x9llefl1LOuEycTEdXOEoYQsW4ii\nhWGU8DwXx5miKCK6HlIsXmE4POT42GJ19efZ2+sTx31u3DAIghGiOEUUp6hqkbOzPp5XJww1guCI\nen2dONZR1aSPRlGq89ItlfF4hqqCLMe4ro/nFdnb6yDLEqVShTA8RlVVMpkRhiGgaS1cN5grp9Xn\nvT0ycWzQ6YxwHI1eLyKOC8iyzHA45OWXNQxjQqOhYxhl2u3Lm+kfzXaAf17+dfFcYP4aInRdoVYr\nkPQB8dh5PyvRk9TmfflIHZSUlI/Ja69Vz6U341gijjN0uwIQo2kyvV6E6xpAjGG48ybUGUdHMYqS\nA1Tu3x+QyyXRn+fJqtj2DFVtEEU8Ntzw4+rjp6SkfHHZ3KzzG7/xn/PGG/87cfzLJJv9CnAC/DzJ\nkMN3+e53b/Hd7/6Qr3/9l5CkDN1un9FoRibTpVgMabWq2PYxw2EVz8thmrdR1VUmk3iejcgSRUXa\n7XvIcpk4rjOb9Wm1NrFtk+PjHY6O2iRlWNdJHJJDkn6Rl0hKvRYZEYvEmRqQOBw2SflXlaSBvkiS\nZflwfv8CycBGYX5/iUT1K0Mc+0ynGTKZMrPZMWAgCAqTyTHFYpUgcJhMYmazLL4fAHlmMxffHzGZ\nyEjSGN8XyecD8vkymlZCFPtIUgHLchkM2th2jiBo4vsB9+4dEUWlecN9QDZbxzQtFCVZbz0vQNdV\ncrkJR0cBxaKOqhY4OzPxfZk4VhEEhdFohijaeF4eRZFYXhYRhCSj4zhJ2ZXruo9lORK7UwSS+Vth\nmEEUh2xtrZ6f8yJCJJed++jwTl1PysSKxYAwfHhK/c9C9CS1eV9OUgclJeUT4GIWI3FWRDStgOeF\nxLHGot43jg3299uYpo7jlOn3B5TLGoahs7dnEsfFZ6pzpYtxSkrK02g2dXZ3/xu2tv4+iUqWAGRI\not4GSQ9IB/gjfvSj/2/+8zILh6DXszBNm0Ihg2W9RxQtEcdruO6IKCrgeRNEccJs5iBJa/i+TRxb\n6Pq/w+npT+j3/zlJ6dYNkh4RmaT0tUySLTkjcTqGJI5IiyRjIpE4KNb8PHN+WybJkFgk5V4Lxaki\niTNzQpJlmSCKCnF8RhRNkKRNptMOYegAAr4/JAyLSJKIqmrMZjGuO8ayTlGU68xmPkGwi6atIctD\nSqUcul6lUplSKERUqx65nIXvb+C6MBpFtNtZJEmlXI6p15nPyrLw/UQZbGlpCc9zEIQZxeLS3OmY\n0Olk8P0MqppkjXy/zGjk4XkGk8kZKysVVLXAYDCgWm0B4Hl9XDdDEuya0mgUsO0hguARxxrDoUm5\nnENVc7TbD9uPR+3D0yTeH+1vedLwTsNQ5oM6H+YyaePLjqekPA3xZ/0CUlK+bDQaBep1cV4HzFxm\neIquK7hugKaV0bQZ43EfzyszHAYMBn2S2ueL6lzSpdKQzyKZjuyc304MT2oYUlK+ShiGQqfzt6lW\nfwj8C5IyqC4PHAIL+MvAf0CSzZCBPsm2oMxwGHFwoGNZCqPRLo4TEkV1RDHHbAZBEJLJQBgKeF4R\n3/fo9z+g3/8XwOvAXwRenT/PItsRkmx27fn/Psmsk8z835TEeYEk6yPNj8nz17VJ0hBvzd/HGYkj\nIwIyglBHkiJEUQc04tgjkxEQxQJhKOM4HeK4x2xm0e2OGY8n+L5JGOYAH0mCTEYjm3VYWzMoFmXC\ncMZ4LBGGPZaXI1ZXq8QxBEFAEGSo1Zrk831kWcCypsxmJ+j6DM8LkeUV+n2PalWlWtWZTO6j6wK6\nnqFUglZrwsZGA9+f4jhDul0f05wwm9U4PT3FMHyuXSuTywXkcgHVqjK/jgKOE+I4AVeulNjcdMhm\nj6hUZAwjUThLSsDGT7UhzaZOPu+Tz/tPLYnSdfncnsGL2ZR228WyVCxLpd12n32Hj0Bq876cpA5K\nSsqnQLOpc+WKwNaWy+amR73+YCqzrsvUahpLSyKVis3yskC5rOJ5AZ73wJiY5phud3zp43te//zn\nyxbji4Ynl8t8JEcnJSXli8/Nm/8tv/qrNeA3SDb2IfDHwGskGZVtEiflRyTOwCLL8gowYDqdkZRB\n7RCGJ4ThkDAcMJ0aOI5CGM7mzyQAPwZ+DvgmSUZGJpkKPyRxOBygTTKU8T5JI7wx/311fs4pSWbE\nISkP0+f3yZFkUUYkvSmD+e2FsyMgCBGgI0k9MhmH2WwfAFGcYVm3EMUKkKfdbuM4MaORzWSSQ5Ja\nWNYHhKFEJvM1plMR30/6+156ycEwdmm1RKrVGlGUpVZz0PUATQtpNKZsbRk4jo1lxfz/7d15cGRn\nee/xb7eWXo5ae0sjzYxmPF4eM97wgodgYmNiDJgirLkBQxUQwGzFvSa5oQIEm+ISmwRMkaTAVIEJ\nJgbuZYdcgu3kwrWNIY4Jjrm2x+/YeMaezdrX7lZL6u77x3t61CNrJM0iqSX9PlUqqV+93X3O0dF5\n+znPuwwPF3jmmQjxeDPxeD2xWAvZbA2ZTJGWlhQQIZ8fobu7js2ba8nnR9m0KUU83s/U1BQNDVFi\nsRqKxQ4ikRxBME0yWY8PxCK0t6fIZApMTLTS2xuhtzdLMhmjpaWBXK5AJhNjYiLG448PMzGxeGCw\nWHfi8of/dDoRBkpDRxbvXMz82f7laYuWGmzJ2qEuXiLLpPLCPzsLlx+wCLVs3tzI5OQI7e0JMpko\nuVyRbLZENptjZGSQ5uZ2YrE4jzzSz7nntoXz3GdJJFpJJOJks8PhwPv5L8blWVoymbrw8dpe0FFE\nTszNN7+OD33oDzj33L8GLsQ3/Vvxa4cM4jMdffiZtDqBJ/FjVVLAQfwCiVmmp4fx4z/68R+Ya/AB\nxG/C1zwfH1zE8NmRPnwXr6fx3bAuBHbjg6HO8KscpNQyG2xM4LtsNeKDlEH84PoUs0FWCT+Wpfw4\nTrH4LFNT5xOJ5CmVngS2USgMUigM0NLye4yNZZmeHiQaNfL5Qerrm6iri5DP76ampptSaQbIEoud\nTl/fU+zdexizzTQ1XUpv72GeeKKfCy7YTFvbNHV1UwwNTZDNQi7XQLE4RDxeR11dN88+O0kiMcXY\n2Bi5XC2JRI6Wlhq2bm1icHCUUikKlMIP7zkSiXG2bGll374OYrFhOjpiNDcnCYL8kTEd0WgeSDAw\nME6p1Egulw/L4vgZu+qIx/3sjocODVIqRcPuVxGSyZPrBlzeBt+ta/6FP6uhG5eyJuuLAhSRFTB3\nFi5/MR+io6MlbERK9PS0kM3mOXhwkJaWZlpb/cDIYjHNI48cJplsZWKijYmJDOl0Mlx4a3Le94PZ\nhbtKpXKQVKChYW0v6CgiJ8bPOPhX7Nr1KfbuLXf3+kN8APFv+HVGTsN3m8rgA4nywrQDwAX4aXqH\n8ZkOgEfxwcM2/LolLcxmN2rxA9h/hw+EEvhAJRm+TwkfdNTisyfj+IAnjQ+UdjKbPdkaft+PD2rK\nXb76wvpPh2VbgGcplbbgu7EdDLfzIoaHn6WmJk6h0IIPaHwAVChEqa+vJxKJUFs7TUNDLblcH/F4\nienpRoaHYWjoWVpaWjl4cIpc7mmuvfYMamsLnHZaPZlMnp///Blise3U1jZw4EA/iUSG6eleampO\nZ2BgjHg8Q7HYyuDgJPF4E7lcga1b/QruIyNTxGINnHaan4ErFgtobq4lmZw4MkW9Hy9SIJOZIZuN\ncfDgCIkEQJJsNkt7u28r2tqSHDw4wtRUjsHBOKOjGU47LU4ymSOVipzAWTNroXbjWDNoLTTORWQx\nClBEVkEQxNixI3bkzlgy6Wdh8an6xiNZD/CzwATBcwcpJpMLBxo+vT57t6tUSpLJjChAEdnAHnjg\nL3nssQO85CVfAH4MXI4PKmrwHwlOw3/ovwc/PiSKvxESxwcM4LtW3YsPTHbiA4V6fPewGD7j8m/4\n7l2bwuc34rMlSXxwUghfsxXfbWsmrPPv4XNm8EFLHT5AGgu3rRhuQzd+iuKnwu0+gA+y2sPXLobb\nEQ+3bwQ4j5qaAoXCXmATU1N7qaurpaPjDHK5w0QiU0xO9lIqJUgmIzQ1+X2ORGYYGppgaipCJJLg\nqaeG2bRp9prc1dXG4cMzHDo0wsxMQDY7RiwWob09y5YtLUAzfX0DpFLt4TGKhtfjHMViPdlsjFKp\nwNlnR4EiQTBKT0+iIgPvu0oFwRSTk2NMThaJx1vIZGJEoxNEIhkSiVb6+0fI5UpMTgYcPDhNQ0MH\nxWKG1lafPT/Ra/9C2ZHFJm1ZjVm9ZH3QGBSRVRQEvp9z5QC/dDpKEMyOPYlGB2hvTx0ZqJjL+Tnx\n5449KS/8WPnak5NDR8a1aOCgiADs3LmFvr6b8R/8f4ofHJ/BBxklfLbjhfjuVeA/VD+DDxiKwMP4\nQCSFDwi6w3qR8LWewAcT2/EZj2ngAXyw0YrPhtTjg5X94XvmwudtDV97NPwq4LM0MWbXO0niA5Nm\nZlei78QHQsP4LNA4vrsZYXk3cIBodIBY7BxghGh0mFSqlampKYIgQTRaAA7S3BylpWUTkKSuLgfA\n6OgMExOTFItN9PVFj7rWtrYmaG4eIh6fobU1Snt7glKpk3w+T6nkV3ZvbIyRTOZpbS3S2lpieHic\nXK6WLVtqqanpJZ/P0dMT0NMzxc6dDc/pjtvfn2NiIkY2GyMWayaZzBONjpFIBARBTTj2Ik9bWw2R\nCHR1dVJfH2FiIkex2E5fX/SosShz24tjOdYg96U+HxYf5yIyHwUoIlVg7gC/c89tY9OmETZtGuEF\nL+iaM0NJjmQyftTz52tEentzlP/FJyeH6egoqZEQkSP6+j7C8543BPwz8Ct8wFLEBwbNwO/jsynD\nwB7gceDn+CDjGnz25AA+85HFZ2Hi+O5TLfjMxiA+yOjAZ0UG8MHLCLAXf43K4QfBD+EDoSI+izId\nlm3Cj02ZCusdCut044Oi8/BjZhrC8kT4VV49vhE4RCw2Fa4fNUBLS5zm5kuYmHiGaLSWQqGZ+voI\nra09NDXNEI+3kcnMkEw+zciIo1SCIEgzMTFOLNZEf/8YfX1j9PbmKJVqiMVqCYI6GhrG6epqAArk\n8wXy+RJTU/10dxdpaZmitbWBfH4UP/akjkcfHSCTaWNoKMITT+xjxw4fFD73w78PdOJxvzp8Llei\nWGwkmy0wMVEkCGKk040kEjM0NQVEIjkikSxB0E4k4gfSlwepL3VmrWMNci8/f3TUdz3TDFqyHNTF\nS6RKzL2oV66t0tmZpK9vlCCIk077u4KlUv2R1PncRuTRRw9TKHQTj0MuN0J7e5KGhiIiAmYWBb6I\nH9WdB97lnPvd6m7V6rjnnk8C0NHxEXzwcCnQhQ8InsIHIecA38NnJs4Nf5/ABySn4WfeSuHHm8Tx\n41HKq70n8IPeS/iMRj6sX74/WmJ2CuRd+GBlPz5j0hKWF/FBUQkfnIyF7/F4WCcZ1msNt6k82H8H\nsJe6uiZSqc0UCvuoqXmS2tot1Na2MDMzTH19FJgmCBooFMZIJDrC7MV+8vkxfve7KMXiTsbHJwiC\nQ2zd2sVTT+2ltTUFJMhmC7S3w3nnpejr66excRNTUxCL5bj44gQwRiLRQEeHDyJ2795HPN5GIlHP\n7t0HGB7uIZ/PE49HiMW2c++9z3DWWduAo8dzpNNJstlJGhogEomQy0WIxydJJiGZbDnSrWrbtgIw\nQSQSZXR0jFgsx5YtqXAmsHLQUc4sHf86WvM9P5WapDweUt245FRRBkVkjQiC2KLjTgCy2amjApZE\nYv5ZV0Q2sNcC9c65FwF/Adyyytuz6nyXr8PAnfhAYw9+8Hp5muA/xgcqBfxYjzp8wNDC7IKLXfgA\n5AA+WOjGZ0AIfy5nM1rxQUUP/oPtFH4QfgyfsTk73Jba8H0KYXl5TZRmfJCSxndN289scDIG9FBX\ntwV4iiBoo6kpTRDU0NPTydatB9m+vUAqNU0sNkFLyxnANIVChKamHdTWjhKNPgsUicfTjI72MDQ0\nQxC0MTgY49ChJ6mvbyWb9VlsPyawBohw/vmbiMeHiMdzpNNpisU6tm5to709dSQAKBYbKRYbGRyM\nMDkZMD2dIxaL0tDQyujoDNls6sjih+WMRXmq32TStwHpdImtW+tJpyGdThz1d+zsTHLOOXF6enKc\ne24T3d0xytmX481uHM/6IurGJaeaMigia8RCM6JUlkOG9vYW+vuPLjvW9JAiG9Bl+E/iOOceMLNL\nVnl7qkJf32f47nd/xfvffyc+g/Jq/Af/OD4oaGM2s9KK7161m9ngo4jPqOTxwcZBfEAyHdZNM9sl\nrHynvRk/iL0WH3z0h89tDX9uwGdeHmM2q3KImppuCoUhfMYlhe9iNg4Mk0icQ339U8Tj9TQ0BCQS\nOdrapujubmPLllacO0wQdBKNbieX208q1Ug0GqW7u0RtbYyamhmi0TSDgwcJgrOZmSkSjR4mCBpp\nbh5ny5YmDh4sAPlwAUN/rzcIJtmxw898ls0W8LNsTREE0wRBkr6+MeLxVjIZP9V8KtVOJrOHVOpc\nwI83bGnpohxQVKocbO6nq88uODtWW1snAO3t/qZVNDp6JCt/PDNrzTfIXTNzyUqIlAdwbUCl/v7x\n1d6GVZdOp9BxWFvH4VgzqpQHLZanpPR39vxg+qUurJVM1tPfP77h74StAI3NwQAAE/FJREFUpfNh\nOaXTJzk3aZUysy8D33PO3Rk+fho4zTl3rH6QG66hjETejQ8MXo3vCfc4sxmV3fjMSBwflJwRfh/C\nBxlN+ICjvF5JEZ/paMdnQfL4MSmH8dmRQvjcC8Ln/yZ8jalwa1Lh6+WBHK2tcRoaWhgff5ZsdguF\nQpZCYZy6uhhdXZM0NhZJp2HnzgvZv/8Btm49n+3b2xkf3822bdsYHi6xb18fdXUR6upamJgo0dg4\nSU9Pia6uLkZHDzE0FCWXa+TRR0epqemgqWmI1tY8r399mkKhlv7+CEEQJZkskErl6epKcvhwlrGx\nlvD45WhoiNLUlD8SGGQyefbsKVIqJchm8wwNDdLWluTAAT+Y3qyJSCRDR0d7+BpZurqO/eF/obZg\ndPTosqamo6eYP9l1S6ph3ROpKqe8rVAGRWSNOVaD4IOSJsbH/V0tP+gegmBpwUlvb5bW1hTj49NH\n9X0WWYfG8J96y6ILBCcAGy5g7ev7HFdccQO7d/8v4EHgpfjDtgcfKEzix6zswAcUJeAsfACyG5/t\naAifM4PPiOzBZ2Bm8FmTWPga5ezKQ8xOI/wI7e1byefzwAi1tZ00N0NPzwRnnumnPp6Y6OSRR/aQ\nz28mGk2TTA5y+ukNdHcnOeecgMnJPbz1rbt44ol+YIyrrz6DwcFeNm3Kk07Xk8m0MjmZY3q6l02b\nmuju3sTQUC9btvisSy6Xo7Y2z9jYI5h1EI9PE4lE6exMMj39DEFQc6RrUzY7RVNTLYOD+4GAZLKe\nfH6MSCR51LlTV5clk8lRVwc7dtTQ0DBDc/MMAEHgu1BlMgPh49iSzrtyl7BKQ0MjR2U56uqS89ab\nr2ypdCPH03Hwx+BUU4Aisg7MN9sKHO/Ax2PPZS+yztyPTw18x8xeCPx2lbenKs0OoH8j8C+L1G4B\nfgCUaGnpprExxuTkDLW1UYrFEsViiYaGGPX1tbS2NhAEccbG/E2Rrq4WBgYOMjAwRixWxznnbOPC\nC323p+bmBkZGJujtHaWzs5FLL90VfnAfI5vNA2fx2GP7Adi+vZNksj4cq1FHR8c5ZDJ5Ojvbwm2c\nZNOmDsBf8/r7RwBIp3vCshFOP93fmNmxIwizBA3hF3R0tJHJTNLUVEdd3fw3fnbsaA6fNzlv16fZ\nhXpnu0zNvc6eiuuu1h+RtU4Bisg6t29fPwDbt6cXqbn81C1AqsQPgJeZ2f3h43es5sZUu76+7wLV\ndac4CGavZzt3blmg3kKDutML1p3vuZUZk+N9z6X+/lTRdVbWMgUoIuvAsQbQ33PPQTKZTUxOTvP4\n40/zildsW/D55TuFyzHwsXJQp7qQyWpyzpWA9632doiIyPwUoIisE3NT+vv29TMw0MHISIlSqYHh\n4Vp+/etnuOSScneGo7MZnZ1Jksk8U1Pzd004GepCJiIiIkulAEVkHan8wJ/N5pmcrKFU8gt0lUpx\ncrkaMpn8kVm+4OhsxmJdF0RERESWmxZqFFmntm1LU1Nz8Mjjmpr9bN6cPkY2I7+s23I8C36JiIjI\nxrYqGRQzex3wRufcW8LHLwQ+j5978G7n3CfD8huBa8Ly651zD5pZO/BN/ATsh4B3OOdyZvZq4ONh\n3a86576y0vslUk2CIMaVV7bx8MN7KZWSdHc3EwQzBEGM8VUY56pZZURERGQpVjyDYmZ/C9zE0Yu6\n3Aq82Tn3YmCXmT3fzC4CLnfO7QLeBHwhrHsDcIdz7nL8pOnvMbM64HPAy4ArgOvMrGNl9kikenV2\nJrnssg4uvLCOHTv8/P2rmc0oz4AjIiIiciyr0cXrfvzsKREAM2sEYs65veHv7wKuAi4D7gZwzu0H\nasPsyWXAnWHdn4Z1zwaedM6NOuemgV8Al6/M7ohUtyCI0dHReFRg0NmZDBdynNRsWiIiIlJVlq2L\nl5m9E7h+TvHbnXPfNrOXVJQ14peaLRvHL007CQzOKW8K64+GZRPzlFXWFZFj2AiZDK27IiIisvYs\nW4DinLsNuG0JVceAVMXjRmAEmJpTngrLx8I6/XPK5tYdXuyN0+nUYlU2BB0HT8fBWy/H4fDhLPX1\n7QDMzGTp6jq+TNF6OQ4iIiJrzapPM+ycGzOzKTPbAewFrgY+ARSAvzGzzwJbgYhzbjBc+fca4Hbg\nlcC9wG7gTDNrATL47l2fWey9q2VF3NVUTSsDryYdB2+9HIdMJs/4eByfZPXy+YElZ1LWy3E4WQrS\nRERkNaxWgFIKv8reC3wDqAHucs49CGBm9wG/wo+V+UBY91PA7Wb2bnwW5Vrn3IyZ/Sl+/EoUuM05\nd3hF9kRERERERE6ZSKlUWrzW+lTSHVLdKS7TcfBW+zicyjEjvb3ZI+u9RCKZ45oMYLWPQ7VIp1OR\nxWttCGov0P9FmY6Dp+Pg6TgsT1ux6l28RGT9OZFAozKgqFzd/kRp3RUREZG1SQGKiJxSJxJozL+6\n/eRJZ1I0e5eIiMjasxrroIjIOjU30Mhka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"text/plain": [ "<matplotlib.figure.Figure at 0x115292f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axes = plt.subplots(nrows=1,ncols=2,squeeze=False)\n", "axes[0][0].scatter(x=model.fittedvalues, y=model.resid, alpha=0.1)\n", "axes[0][1].scatter(x=model.fittedvalues, y=abs(model.resid), alpha=0.1);\n", "\n", "axes[0][0].set_xlabel('fitted_values')\n", "axes[0][1].set_xlabel('fitted_values')\n", "\n", "axes[0][0].set_ylabel('Abs(residuals)')\n", "axes[0][1].set_ylabel('residuals');\n", "\n", "fig.set_size_inches(13,5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model also failed in this diagnostic. The first plot, the fitted values and residuals should be randomly scattered around zero, and not performing some kind of fan shape. For the plot in the left, we're seeing that there's some kind of boundary that limit the plot to be randomly scattered, and it's performing fan shape. This could means there's another dependent variables that we don't yet find. Some fan shape also ocurring where we plot in the right with absolute value of residuals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Independent residuals" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hjfsAXLruD68pDYq+9LGDYysFk2Fa0CjFb5jx2sCbuGzBYWXjN5VBz4qv/Q3a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\n/H4A7wdwJIAHAEBK+UcAre5s/0gA97nn/sw9lxBCCCEWpLb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QuwKnApsl/QZ4P6DGzGz/\nkFiJf5GI7LZM0uJy+3zg6+X5dcTuYy1Wx+ONeZX07yZ2F7YQ8dxPJwKwfFDSGiIgy41De/Uk6R1y\nqz9JkkYO1H8o0cjqGQfcaXsBgKSJNPQlkk4BLrd9I7AWWCtpCRHvfWHJY47tF0r6NwD/Ilbw+yo0\njQOW2F5c0r8G6LO9V9JpwIXARUS8+LcP9cWTpBfIFX+SJK2yAbhY0qRyaO824LMNaZ4FriuhSmu8\ng4hvD/AgMB+gmA62EfHVB5w/qEv/sRLKdTwRPvZSSfMJu/5dJb8Ti90/SZJByIE/SXqTwUKBNoYE\nHZDO9jZgETEYby/Xv1GfqKzk5wE3SHqqnPC/Cri8JLkWmCXpCeJw3pW2X6oov1bmz4DVwGbgd8DW\nYkpYDkjSNuBh4iDgnibrIEl6kvw5X5IkSZL0ELniT5IkSZIeIgf+JEmSJOkhcuBPkiRJkh4iB/4k\nSZIk6SFy4E+SJEmSHiIH/iRJkiTpIXLgT5IkSZIeIgf+JEmSJOkh/gsj5iYSd1k4SwAAAABJRU5E\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112f0c490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "resids = pd.DataFrame(model.resid.copy())\n", "resids.columns = ['residuals']\n", "resids.index = pd.to_datetime(features['index'])\n", "resids.sort_index(inplace=True)\n", "\n", "plt.plot_date(x=resids.resample('1H',how='mean').index,\n", " y=resids.resample('1H',how='mean').residuals);\n", "plt.xlabel('Time Series')\n", "plt.ylabel('residuals')\n", "plt.title('Residuals Variability across time');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, our model should be independent across time. We can plot this by residuals through time series, checking whether the residuals is constant variability, randomly scatter around zero. In this plot, it's pretty constant across May 2011. But since we only have limited data, 1 month and 1 year, we can't be sure whether the model predict accurately in any other month and year.\n", "\n", "As linear regression is not a good model, there's could be another model, and some additional dependent variables, that can be used to better fit for this problem." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
cggh/scikit-allel
notebooks/profiling/tables.ipynb
1
43587
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "import numpy as np\n", "import bcolz\n", "import numexpr\n", "import humanize\n", "import h5py\n", "import tempfile\n", "def binarysize(n):\n", " return humanize.naturalsize(n, binary=True)\n", "\n", "%reload_ext memory_profiler\n", "sys.path.insert(0, '../..')\n", "%reload_ext autoreload\n", "%autoreload 1\n", "%aimport allel.model\n", "%aimport allel.bcolz\n", "%aimport allel.io" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(9516386, '72.6 MiB')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pos = np.unique(np.random.randint(0, 100000000, size=10000000))\n", "pos.size, binarysize(pos.nbytes)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'72.6 MiB'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.random.randint(0, 100, size=pos.size)\n", "binarysize(x.nbytes)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'72.6 MiB'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = np.random.random(size=pos.size)\n", "binarysize(y.nbytes)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'9.1 MiB'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z = np.random.choice([b'a', b'b', b'c'], size=pos.size)\n", "binarysize(z.nbytes)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "query = \"(x > 50) & (y < .5) & (z == b'a')\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantTable(9516386, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>5</td>\n", "<td>17</td>\n", "<td style='text-align: right'>0.941178328631</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>39</td>\n", "<td>78</td>\n", "<td style='text-align: right'>0.954006026102</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>58</td>\n", "<td>93</td>\n", "<td style='text-align: right'>0.369881354724</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>64</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.636430366687</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>84</td>\n", "<td>41</td>\n", "<td style='text-align: right'>0.311720778772</td>\n", "<td>b'b'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "VariantTable(9516386, dtype=[('pos', '<i8'), ('x', '<i8'), ('y', '<f8'), ('z', 'S1')])\n", "[(5, 17, 0.9411783286306781, b'c') (39, 78, 0.9540060261020432, b'c')\n", " (58, 93, 0.3698813547240103, b'b') ...,\n", " (99999971, 11, 0.9627122072169108, b'b')\n", " (99999973, 13, 0.9434966815056626, b'a')\n", " (99999978, 5, 0.17309121393683735, b'c')]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.rec.fromarrays([pos, x, y, z], names=['pos', 'x', 'y', 'z'])\n", "vt = allel.model.VariantTable(a, index='pos')\n", "vt" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([17, 78, 93, ..., 11, 13, 5])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vt.x" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantTable(9516386, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>5</td>\n", "<td>17</td>\n", "</tr>\n", "<tr>\n", "<td>39</td>\n", "<td>78</td>\n", "</tr>\n", "<tr>\n", "<td>58</td>\n", "<td>93</td>\n", "</tr>\n", "<tr>\n", "<td>64</td>\n", "<td>63</td>\n", "</tr>\n", "<tr>\n", "<td>84</td>\n", "<td>41</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "VariantTable(9516386, dtype=[('pos', '<i8'), ('x', '<i8')])\n", "[(5, 17) (39, 78) (58, 93) ..., (99999971, 11) (99999973, 13) (99999978, 5)]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vt[['pos', 'x']]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantTable(9516386, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>5</td>\n", "<td>17</td>\n", "<td style='text-align: right'>0.941178328631</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>39</td>\n", "<td>78</td>\n", "<td style='text-align: right'>0.954006026102</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>58</td>\n", "<td>93</td>\n", "<td style='text-align: right'>0.369881354724</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>64</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.636430366687</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>84</td>\n", "<td>41</td>\n", "<td style='text-align: right'>0.311720778772</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>101</td>\n", "<td>56</td>\n", "<td style='text-align: right'>0.702506983122</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>121</td>\n", "<td>3</td>\n", "<td style='text-align: right'>0.512401263151</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>134</td>\n", "<td>20</td>\n", "<td style='text-align: right'>0.549689464029</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>138</td>\n", "<td>83</td>\n", "<td style='text-align: right'>0.978512936125</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>188</td>\n", "<td>79</td>\n", "<td style='text-align: right'>0.176854785614</td>\n", "<td>b'a'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vt.display(10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantCTable(9516386, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>5</td>\n", "<td>17</td>\n", "<td style='text-align: right'>0.941178328631</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>39</td>\n", "<td>78</td>\n", "<td style='text-align: right'>0.954006026102</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>58</td>\n", "<td>93</td>\n", "<td style='text-align: right'>0.369881354724</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>64</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.636430366687</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>84</td>\n", "<td>41</td>\n", "<td style='text-align: right'>0.311720778772</td>\n", "<td>b'b'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "VariantCTable((9516386,), [('pos', '<i8'), ('x', '<i8'), ('y', '<f8'), ('z', 'S1')])\n", " nbytes: 226.89 MB; cbytes: 91.50 MB; ratio: 2.48\n", " cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n", "[(5, 17, 0.9411783286306781, b'c') (39, 78, 0.9540060261020432, b'c')\n", " (58, 93, 0.3698813547240103, b'b') ...,\n", " (99999971, 11, 0.9627122072169108, b'b')\n", " (99999973, 13, 0.9434966815056626, b'a')\n", " (99999978, 5, 0.17309121393683735, b'c')]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtc = allel.bcolz.VariantCTable(a, index='pos')\n", "vtc" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ctable((9516386,), [('pos', '<i8'), ('x', '<i8'), ('y', '<f8'), ('z', 'S1')])\n", " nbytes: 226.89 MB; cbytes: 91.50 MB; ratio: 2.48\n", " cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n", "[(5, 17, 0.9411783286306781, b'c') (39, 78, 0.9540060261020432, b'c')\n", " (58, 93, 0.3698813547240103, b'b') ...,\n", " (99999971, 11, 0.9627122072169108, b'b')\n", " (99999973, 13, 0.9434966815056626, b'a')\n", " (99999978, 5, 0.17309121393683735, b'c')]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtc.ctbl" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "carray((9516386,), int64)\n", " nbytes: 72.60 MB; cbytes: 10.08 MB; ratio: 7.20\n", " cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n", "[17 78 93 ..., 11 13 5]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtc.x" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantCTable(9516386, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>5</td>\n", "<td>17</td>\n", "<td style='text-align: right'>0.941178328631</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>39</td>\n", "<td>78</td>\n", "<td style='text-align: right'>0.954006026102</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>58</td>\n", "<td>93</td>\n", "<td style='text-align: right'>0.369881354724</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>64</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.636430366687</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>84</td>\n", "<td>41</td>\n", "<td style='text-align: right'>0.311720778772</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>101</td>\n", "<td>56</td>\n", "<td style='text-align: right'>0.702506983122</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>121</td>\n", "<td>3</td>\n", "<td style='text-align: right'>0.512401263151</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>134</td>\n", "<td>20</td>\n", "<td style='text-align: right'>0.549689464029</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>138</td>\n", "<td>83</td>\n", "<td style='text-align: right'>0.978512936125</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>188</td>\n", "<td>79</td>\n", "<td style='text-align: right'>0.176854785614</td>\n", "<td>b'a'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vtc.display(10)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantCTable(9516386, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>5</td>\n", "<td>17</td>\n", "<td style='text-align: right'>0.941178328631</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>39</td>\n", "<td>78</td>\n", "<td style='text-align: right'>0.954006026102</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>58</td>\n", "<td>93</td>\n", "<td style='text-align: right'>0.369881354724</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>64</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.636430366687</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>84</td>\n", "<td>41</td>\n", "<td style='text-align: right'>0.311720778772</td>\n", "<td>b'b'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "VariantCTable((9516386,), [('pos', '<i8'), ('x', '<i8'), ('y', '<f8'), ('z', 'S1')])\n", " nbytes: 226.89 MB; cbytes: 91.50 MB; ratio: 2.48\n", " cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n", " rootdir := '/tmp/tmpadwqs9ve'\n", "[(5, 17, 0.9411783286306781, b'c') (39, 78, 0.9540060261020432, b'c')\n", " (58, 93, 0.3698813547240103, b'b') ...,\n", " (99999971, 11, 0.9627122072169108, b'b')\n", " (99999973, 13, 0.9434966815056626, b'a')\n", " (99999978, 5, 0.17309121393683735, b'c')]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rootdir = tempfile.mkdtemp()\n", "vtcp = allel.bcolz.VariantCTable(a, index='pos', rootdir=rootdir, mode='w')\n", "vtcp" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantCTable(9516386, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>5</td>\n", "<td>17</td>\n", "<td style='text-align: right'>0.941178328631</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>39</td>\n", "<td>78</td>\n", "<td style='text-align: right'>0.954006026102</td>\n", "<td>b'c'</td>\n", "</tr>\n", "<tr>\n", "<td>58</td>\n", "<td>93</td>\n", "<td style='text-align: right'>0.369881354724</td>\n", "<td>b'b'</td>\n", "</tr>\n", "<tr>\n", "<td>64</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.636430366687</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>84</td>\n", "<td>41</td>\n", "<td style='text-align: right'>0.311720778772</td>\n", "<td>b'b'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "VariantCTable((9516386,), [('pos', '<i8'), ('x', '<i8'), ('y', '<f8'), ('z', 'S1')])\n", " nbytes: 226.89 MB; cbytes: 91.50 MB; ratio: 2.48\n", " cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n", " rootdir := '/tmp/tmpadwqs9ve'\n", "[(5, 17, 0.9411783286306781, b'c') (39, 78, 0.9540060261020432, b'c')\n", " (58, 93, 0.3698813547240103, b'b') ...,\n", " (99999971, 11, 0.9627122072169108, b'b')\n", " (99999973, 13, 0.9434966815056626, b'a')\n", " (99999978, 5, 0.17309121393683735, b'c')]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtcp2 = allel.bcolz.VariantCTable.open(rootdir)\n", "vtcp2" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantTable(776406, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>188</td>\n", "<td>79</td>\n", "<td style='text-align: right'>0.176854785614</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>213</td>\n", "<td>90</td>\n", "<td style='text-align: right'>0.367455877413</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>289</td>\n", "<td>82</td>\n", "<td style='text-align: right'>0.171532726593</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>400</td>\n", "<td>83</td>\n", "<td style='text-align: right'>0.113906927424</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>700</td>\n", "<td>51</td>\n", "<td style='text-align: right'>0.473933866061</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>796</td>\n", "<td>66</td>\n", "<td style='text-align: right'>0.406920706967</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>810</td>\n", "<td>74</td>\n", "<td style='text-align: right'>0.303002118461</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>825</td>\n", "<td>95</td>\n", "<td style='text-align: right'>0.235118323645</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>840</td>\n", "<td>88</td>\n", "<td style='text-align: right'>0.347419256774</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>873</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.153837759783</td>\n", "<td>b'a'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vt.query(query).display(10)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>VariantCTable(776406, dtype=[('pos', '&lt;i8'), ('x', '&lt;i8'), ('y', '&lt;f8'), ('z', 'S1')])</caption>\n", "<thead>\n", "<tr>\n", "<th>pos</th>\n", "<th>x</th>\n", "<th>y</th>\n", "<th>z</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>188</td>\n", "<td>79</td>\n", "<td style='text-align: right'>0.176854785614</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>213</td>\n", "<td>90</td>\n", "<td style='text-align: right'>0.367455877413</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>289</td>\n", "<td>82</td>\n", "<td style='text-align: right'>0.171532726593</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>400</td>\n", "<td>83</td>\n", "<td style='text-align: right'>0.113906927424</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>700</td>\n", "<td>51</td>\n", "<td style='text-align: right'>0.473933866061</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>796</td>\n", "<td>66</td>\n", "<td style='text-align: right'>0.406920706967</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>810</td>\n", "<td>74</td>\n", "<td style='text-align: right'>0.303002118461</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>825</td>\n", "<td>95</td>\n", "<td style='text-align: right'>0.235118323645</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>840</td>\n", "<td>88</td>\n", "<td style='text-align: right'>0.347419256774</td>\n", "<td>b'a'</td>\n", "</tr>\n", "<tr>\n", "<td>873</td>\n", "<td>63</td>\n", "<td style='text-align: right'>0.153837759783</td>\n", "<td>b'a'</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vtc.query(query).display(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Query profiling" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 154 ms per loop\n", "peak memory: 1170.03 MiB, increment: 0.11 MiB\n", "10 loops, best of 3: 48.5 ms per loop\n", "peak memory: 1170.08 MiB, increment: 0.05 MiB\n" ] } ], "source": [ "# baselines with plain numpy arrays\n", "%timeit eval(query)\n", "%memit eval(query)\n", "%timeit numexpr.evaluate(query)\n", "%memit numexpr.evaluate(query)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 252 ms per loop\n", "peak memory: 1170.20 MiB, increment: 0.00 MiB\n", "1 loops, best of 3: 304 ms per loop\n", "peak memory: 1170.20 MiB, increment: 0.00 MiB\n", "1 loops, best of 3: 343 ms per loop\n", "peak memory: 1170.48 MiB, increment: 0.28 MiB\n" ] } ], "source": [ "vm = 'python'\n", "%timeit vt.eval(query, vm=vm)\n", "%memit vt.eval(query, vm=vm)\n", "%timeit vtc.eval(query, vm=vm)\n", "%memit vtc.eval(query, vm=vm)\n", "%timeit vtcp.eval(query, vm=vm)\n", "%memit vtcp.eval(query, vm=vm)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 61 ms per loop\n", "peak memory: 1170.48 MiB, increment: 0.00 MiB\n", "1 loops, best of 3: 168 ms per loop\n", "peak memory: 1170.63 MiB, increment: 0.00 MiB\n", "1 loops, best of 3: 227 ms per loop\n", "peak memory: 1170.63 MiB, increment: 0.00 MiB\n" ] } ], "source": [ "vm = 'numexpr'\n", "%timeit vt.eval(query, vm=vm)\n", "%memit vt.eval(query, vm=vm)\n", "%timeit vtc.eval(query, vm=vm)\n", "%memit vtc.eval(query, vm=vm)\n", "%timeit vtcp.eval(query, vm=vm)\n", "%memit vtcp.eval(query, vm=vm)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/tmp/tmpsoyqr5mx'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fn1 = tempfile.mktemp()\n", "fn1" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with h5py.File(fn1, mode='w') as h5f:\n", " h5g = h5f.create_group('table')\n", " h5g.create_dataset('pos', data=pos, chunks=(1000,))\n", " h5g.create_dataset('x', data=x, chunks=(1000,))\n", " h5g.create_dataset('y', data=y, chunks=(1000,))\n", " h5g.create_dataset('z', data=z, chunks=(1000,))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pos</th>\n", " <th>x</th>\n", " <th>y</th>\n", " <th>z</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 9</td>\n", " <td> 94</td>\n", " <td> 0.048534</td>\n", " <td> b'c'</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 10</td>\n", " <td> 87</td>\n", " <td> 0.622883</td>\n", " <td> b'b'</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 25</td>\n", " <td> 29</td>\n", " <td> 0.625948</td>\n", " <td> b'b'</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 42</td>\n", " <td> 34</td>\n", " <td> 0.838398</td>\n", " <td> b'a'</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 67</td>\n", " <td> 64</td>\n", " <td> 0.094999</td>\n", " <td> b'c'</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "VariantCTable((9515340,), [('pos', '<i8'), ('x', '<i8'), ('y', '<f8'), ('z', 'S1')])\n", " nbytes: 226.86 MB; cbytes: 92.26 MB; ratio: 2.46\n", " cparams := cparams(clevel=4, shuffle=True, cname='lz4')\n", "[(9, 94, 0.048534332330125385, b'c') (10, 87, 0.622882512425196, b'b')\n", " (25, 29, 0.625947574251329, b'b') ...,\n", " (99999966, 62, 0.4685614407421974, b'a')\n", " (99999973, 46, 0.8942343318112237, b'c')\n", " (99999995, 97, 0.3411698223021882, b'a')]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtc2 = allel.bcolz.VariantCTable.from_hdf5_group(fn1, 'table', names=['pos', 'x', 'y', 'z'], cparams=bcolz.cparams(cname='lz4', clevel=4))\n", "vtc2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "carray((9515340,), int64)\n", " nbytes: 72.60 MB; cbytes: 11.84 MB; ratio: 6.13\n", " cparams := cparams(clevel=4, shuffle=True, cname='lz4')\n", "[ 9 10 25 ..., 99999966 99999973 99999995]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtc2.pos" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 192 ms per loop\n" ] } ], "source": [ "%timeit vtc2.eval(query)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak memory: 1306.82 MiB, increment: 0.76 MiB\n" ] } ], "source": [ "%memit vtc2.eval(query)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 400 ms per loop\n", "peak memory: 1463.86 MiB, increment: 145.38 MiB\n" ] } ], "source": [ "h5g = h5py.File(fn1, mode='r')['table']\n", "%timeit numexpr.evaluate(query, local_dict=h5g)\n", "%memit numexpr.evaluate(query, local_dict=h5g)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## VCF" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>CHROM</th>\n", " <th>POS</th>\n", " <th>REF</th>\n", " <th>DP</th>\n", " <th>MQ</th>\n", " <th>ZZ</th>\n", " <th>FLG</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> b'chr1'</td>\n", " <td> 961</td>\n", " <td> b'A'</td>\n", " <td> 4</td>\n", " <td> 0.922264</td>\n", " <td> b'a'</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> b'chr1'</td>\n", " <td> 1812</td>\n", " <td> b'A'</td>\n", " <td> 70</td>\n", " <td> 0.970685</td>\n", " <td> b'c'</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> b'chr1'</td>\n", " <td> 1937</td>\n", " <td> b'G'</td>\n", " <td> 71</td>\n", " <td> 0.925726</td>\n", " <td> b'b'</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> b'chr1'</td>\n", " <td> 2324</td>\n", " <td> b'A'</td>\n", " <td> 71</td>\n", " <td> 0.190789</td>\n", " <td> b'b'</td>\n", " <td> True</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> b'chr1'</td>\n", " <td> 2383</td>\n", " <td> b'T'</td>\n", " <td> 50</td>\n", " <td> 0.214158</td>\n", " <td> b'b'</td>\n", " <td> True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "VariantTable((99939,), dtype=[('CHROM', 'S4'), ('POS', '<i8'), ('REF', 'S1'), ('DP', '<i8'), ('MQ', '<f8'), ('ZZ', 'S1'), ('FLG', '?')])\n", "[(b'chr1', 961, b'A', 4, 0.9222638986796139, b'a', False)\n", " (b'chr1', 1812, b'A', 70, 0.9706849895478908, b'c', False)\n", " (b'chr1', 1937, b'G', 71, 0.9257257271415631, b'b', False) ...,\n", " (b'chr1', 99997122, b'A', 94, 0.17218578661826123, b'b', True)\n", " (b'chr1', 99997355, b'G', 43, 0.1816417733322946, b'c', False)\n", " (b'chr1', 99997830, b'C', 38, 0.6657626339540229, b'b', False)]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "size = 100000\n", "pos = np.unique(np.random.randint(0, 100000000, size=size))\n", "chrom = np.array([b'chr1'] * pos.size)\n", "ref = np.random.choice([b'A', b'C', b'T', b'G'], size=pos.size)\n", "x = np.random.randint(0, 100, size=pos.size)\n", "y = np.random.random(size=pos.size)\n", "z = np.random.choice([b'a', b'b', b'c'], size=pos.size)\n", "flag = np.random.randint(0, 2, size=pos.size).astype(bool)\n", "columns = [chrom, pos, ref, x, y, z, flag]\n", "names = ['CHROM', 'POS', 'REF', 'DP', 'MQ', 'ZZ', 'FLG']\n", "a = np.rec.fromarrays(columns, names=names)\n", "vt = allel.model.VariantTable(a)\n", "vt" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.08 s, sys: 16 ms, total: 3.09 s\n", "Wall time: 3.09 s\n" ] } ], "source": [ "fn = tempfile.mktemp()\n", "%time vt.to_vcf(fn)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>CHROM</th>\n", " <th>POS</th>\n", " <th>REF</th>\n", " <th>DP</th>\n", " <th>MQ</th>\n", " <th>ZZ</th>\n", " <th>FLG</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> b'chr1'</td>\n", " <td> 961</td>\n", " <td> b'A'</td>\n", " <td> 4</td>\n", " <td> 0.922264</td>\n", " <td> b'a'</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> b'chr1'</td>\n", " <td> 1812</td>\n", " <td> b'A'</td>\n", " <td> 70</td>\n", " <td> 0.970685</td>\n", " <td> b'c'</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> b'chr1'</td>\n", " <td> 1937</td>\n", " <td> b'G'</td>\n", " <td> 71</td>\n", " <td> 0.925726</td>\n", " <td> b'b'</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> b'chr1'</td>\n", " <td> 2324</td>\n", " <td> b'A'</td>\n", " <td> 71</td>\n", " <td> 0.190789</td>\n", " <td> b'b'</td>\n", " <td> True</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> b'chr1'</td>\n", " <td> 2383</td>\n", " <td> b'T'</td>\n", " <td> 50</td>\n", " <td> 0.214158</td>\n", " <td> b'b'</td>\n", " <td> True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "VariantCTable((99939,), [('CHROM', 'S4'), ('POS', '<i8'), ('REF', 'S1'), ('DP', '<i8'), ('MQ', '<f8'), ('ZZ', 'S1'), ('FLG', '?')])\n", " nbytes: 2.95 MB; cbytes: 2.33 MB; ratio: 1.27\n", " cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n", "[(b'chr1', 961, b'A', 4, 0.9222638986796139, b'a', False)\n", " (b'chr1', 1812, b'A', 70, 0.9706849895478908, b'c', False)\n", " (b'chr1', 1937, b'G', 71, 0.9257257271415631, b'b', False) ...,\n", " (b'chr1', 99997122, b'A', 94, 0.17218578661826123, b'b', True)\n", " (b'chr1', 99997355, b'G', 43, 0.1816417733322946, b'c', False)\n", " (b'chr1', 99997830, b'C', 38, 0.6657626339540229, b'b', False)]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vct = allel.bcolz.VariantCTable(columns, names=names)\n", "vct" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.09 s, sys: 12 ms, total: 3.1 s\n", "Wall time: 3.09 s\n" ] } ], "source": [ "%time vct.to_vcf(fn)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "##fileformat=VCFv4.1\r\n", "##fileDate=20150225\r\n", "##source=scikit-allel-0.6.0.dev2\r\n", "##INFO=<ID=DP,Number=1,Type=Integer,Description=\"\">\r\n", "##INFO=<ID=FLG,Number=0,Type=Flag,Description=\"\">\r\n", "##INFO=<ID=MQ,Number=1,Type=Float,Description=\"\">\r\n", "##INFO=<ID=ZZ,Number=1,Type=String,Description=\"\">\r\n", "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\r\n", "chr1\t961\t.\tA\t.\t.\t.\tDP=4;MQ=0.92226389868;ZZ=a\r", "\r\n", "chr1\t1812\t.\tA\t.\t.\t.\tDP=70;MQ=0.970684989548;ZZ=c\r", "\r\n", "chr1\t1937\t.\tG\t.\t.\t.\tDP=71;MQ=0.925725727142;ZZ=b\r", "\r\n", "chr1\t2324\t.\tA\t.\t.\t.\tDP=71;FLG;MQ=0.190789251089;ZZ=b\r", "\r\n", "chr1\t2383\t.\tT\t.\t.\t.\tDP=50;FLG;MQ=0.214157901464;ZZ=b\r", "\r\n", "chr1\t3740\t.\tA\t.\t.\t.\tDP=23;MQ=0.0477399302848;ZZ=b\r", "\r\n", "chr1\t3978\t.\tT\t.\t.\t.\tDP=20;FLG;MQ=0.489855469117;ZZ=c\r", "\r\n", "chr1\t5397\t.\tG\t.\t.\t.\tDP=65;FLG;MQ=0.292203259144;ZZ=c\r", "\r\n", "chr1\t5646\t.\tC\t.\t.\t.\tDP=7;MQ=0.940430619662;ZZ=c\r", "\r\n", "chr1\t6761\t.\tA\t.\t.\t.\tDP=54;FLG;MQ=0.459540675249;ZZ=a\r", "\r\n", "chr1\t6839\t.\tC\t.\t.\t.\tDP=29;MQ=0.961988545598;ZZ=b\r", "\r\n", "chr1\t7801\t.\tC\t.\t.\t.\tDP=78;MQ=0.0522209535111;ZZ=c\r", "\r\n" ] } ], "source": [ "!head -n20 {fn}" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chr1\t99975348\t.\tT\t.\t.\t.\tDP=20;FLG;MQ=0.648477263337;ZZ=c\r", "\r\n", "chr1\t99978373\t.\tC\t.\t.\t.\tDP=60;FLG;MQ=0.378684269053;ZZ=c\r", "\r\n", "chr1\t99978911\t.\tG\t.\t.\t.\tDP=84;FLG;MQ=0.880819844242;ZZ=a\r", "\r\n", "chr1\t99980404\t.\tC\t.\t.\t.\tDP=70;FLG;MQ=0.0227780525978;ZZ=b\r", "\r\n", "chr1\t99980473\t.\tA\t.\t.\t.\tDP=36;MQ=0.789241246483;ZZ=c\r", "\r\n", "chr1\t99980767\t.\tT\t.\t.\t.\tDP=79;MQ=0.146154489605;ZZ=b\r", "\r\n", "chr1\t99981663\t.\tC\t.\t.\t.\tDP=96;FLG;MQ=0.634845144778;ZZ=c\r", "\r\n", "chr1\t99983640\t.\tC\t.\t.\t.\tDP=34;FLG;MQ=0.807069869529;ZZ=a\r", "\r\n", "chr1\t99984076\t.\tA\t.\t.\t.\tDP=50;MQ=0.77312365257;ZZ=b\r", "\r\n", "chr1\t99984504\t.\tG\t.\t.\t.\tDP=22;MQ=0.46349080245;ZZ=c\r", "\r\n", "chr1\t99985202\t.\tG\t.\t.\t.\tDP=44;FLG;MQ=0.752395855687;ZZ=a\r", "\r\n", "chr1\t99985548\t.\tA\t.\t.\t.\tDP=36;MQ=0.330695408744;ZZ=b\r", "\r\n", "chr1\t99986566\t.\tA\t.\t.\t.\tDP=35;MQ=0.795041710901;ZZ=c\r", "\r\n", "chr1\t99987058\t.\tA\t.\t.\t.\tDP=82;FLG;MQ=0.77352409433;ZZ=b\r", "\r\n", "chr1\t99991192\t.\tT\t.\t.\t.\tDP=29;MQ=0.990447613724;ZZ=b\r", "\r\n", "chr1\t99991731\t.\tG\t.\t.\t.\tDP=34;MQ=0.425923765829;ZZ=a\r", "\r\n", "chr1\t99993549\t.\tA\t.\t.\t.\tDP=8;MQ=0.130474513454;ZZ=c\r", "\r\n", "chr1\t99997122\t.\tA\t.\t.\t.\tDP=94;FLG;MQ=0.172185786618;ZZ=b\r", "\r\n", "chr1\t99997355\t.\tG\t.\t.\t.\tDP=43;MQ=0.181641773332;ZZ=c\r", "\r\n", "chr1\t99997830\t.\tC\t.\t.\t.\tDP=38;MQ=0.665762633954;ZZ=b\r", "\r\n" ] } ], "source": [ "!tail -n20 {fn}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
KatiRG/MethaneEmissions
notebooks/regionalStats.ipynb
1
35280
{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Script to read regional stats files for BU and TD approach. \n", "#Values for SumSources mean, min, max written manually into index.html\n", "\n", "#Created: 16.09.2016\n", "#Last modified: " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import collections\n", "import os\n", "import xlrd" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>stats</th>\n", " <th>Bor_NAme</th>\n", " <th>contUSA</th>\n", " <th>Cent_NAme</th>\n", " <th>Trop_SAme</th>\n", " <th>Temp_SAme</th>\n", " <th>NAfr</th>\n", " <th>SAfr</th>\n", " <th>Russia</th>\n", " <th>Oceania</th>\n", " <th>Europe</th>\n", " <th>China</th>\n", " <th>India</th>\n", " <th>SE_Asia</th>\n", " <th>Temp_Eurasia_Japan</th>\n", " <th>GLO</th>\n", " </tr>\n", " <tr>\n", " <th>proc</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td> mean</td>\n", " <td> 32</td>\n", " <td> 13</td>\n", " <td> 2</td>\n", " <td> 42</td>\n", " <td> 4</td>\n", " <td> 8</td>\n", " <td> 19</td>\n", " <td> 14</td>\n", " <td> 3</td>\n", " <td> 4</td>\n", " <td> 5</td>\n", " <td> 6</td>\n", " <td> 29</td>\n", " <td> 3</td>\n", " <td> 185</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td> min</td>\n", " <td> 15</td>\n", " <td> 6</td>\n", " <td> 1</td>\n", " <td> 19</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 15</td>\n", " <td> 5</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 18</td>\n", " <td> 1</td>\n", " <td> 153</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td> max</td>\n", " <td> 61</td>\n", " <td> 23</td>\n", " <td> 4</td>\n", " <td> 59</td>\n", " <td> 7</td>\n", " <td> 16</td>\n", " <td> 22</td>\n", " <td> 26</td>\n", " <td> 6</td>\n", " <td> 7</td>\n", " <td> 9</td>\n", " <td> 13</td>\n", " <td> 35</td>\n", " <td> 6</td>\n", " <td> 227</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNatural</th>\n", " <td> mean</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td> -99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td> 199</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNatural</th>\n", " <td> min</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td> -99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td> 104</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNatural</th>\n", " <td> max</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td> -99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td>-99</td>\n", " <td> 297</td>\n", " </tr>\n", " <tr>\n", " <th>AgriWaste</th>\n", " <td> mean</td>\n", " <td> 3</td>\n", " <td> 17</td>\n", " <td> 5</td>\n", " <td> 21</td>\n", " <td> 6</td>\n", " <td> 14</td>\n", " <td> 6</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 17</td>\n", " <td> 30</td>\n", " <td> 21</td>\n", " <td> 22</td>\n", " <td> 20</td>\n", " <td> 195</td>\n", " </tr>\n", " <tr>\n", " <th>AgriWaste</th>\n", " <td> min</td>\n", " <td> 2</td>\n", " <td> 15</td>\n", " <td> 2</td>\n", " <td> 18</td>\n", " <td> 5</td>\n", " <td> 12</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 15</td>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 18</td>\n", " <td> 17</td>\n", " <td> 178</td>\n", " </tr>\n", " <tr>\n", " <th>AgriWaste</th>\n", " <td> max</td>\n", " <td> 3</td>\n", " <td> 23</td>\n", " <td> 6</td>\n", " <td> 23</td>\n", " <td> 6</td>\n", " <td> 18</td>\n", " <td> 7</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 18</td>\n", " <td> 36</td>\n", " <td> 24</td>\n", " <td> 25</td>\n", " <td> 22</td>\n", " <td> 206</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td> mean</td>\n", " <td> 2</td>\n", " <td> 11</td>\n", " <td> 2</td>\n", " <td> 8</td>\n", " <td> 1</td>\n", " <td> 9</td>\n", " <td> 4</td>\n", " <td> 20</td>\n", " <td> 2</td>\n", " <td> 6</td>\n", " <td> 24</td>\n", " <td> 3</td>\n", " <td> 6</td>\n", " <td> 22</td>\n", " <td> 121</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td> min</td>\n", " <td> 1</td>\n", " <td> 9</td>\n", " <td> 0</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 7</td>\n", " <td> 3</td>\n", " <td> 18</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 15</td>\n", " <td> 2</td>\n", " <td> 5</td>\n", " <td> 20</td>\n", " <td> 114</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td> max</td>\n", " <td> 3</td>\n", " <td> 16</td>\n", " <td> 3</td>\n", " <td> 19</td>\n", " <td> 1</td>\n", " <td> 14</td>\n", " <td> 5</td>\n", " <td> 26</td>\n", " <td> 2</td>\n", " <td> 8</td>\n", " <td> 31</td>\n", " <td> 4</td>\n", " <td> 7</td>\n", " <td> 26</td>\n", " <td> 133</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td> mean</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 0</td>\n", " <td> 4</td>\n", " <td> 6</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 5</td>\n", " <td> 1</td>\n", " <td> 30</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td> min</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 3</td>\n", " <td> 4</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " <td> 4</td>\n", " <td> 1</td>\n", " <td> 27</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td> max</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 6</td>\n", " <td> 1</td>\n", " <td> 5</td>\n", " <td> 10</td>\n", " <td> 4</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 4</td>\n", " <td> 3</td>\n", " <td> 5</td>\n", " <td> 2</td>\n", " <td> 35</td>\n", " </tr>\n", " <tr>\n", " <th>SumSources</th>\n", " <td> mean</td>\n", " <td> 37</td>\n", " <td> 42</td>\n", " <td> 9</td>\n", " <td> 75</td>\n", " <td> 11</td>\n", " <td> 36</td>\n", " <td> 35</td>\n", " <td> 41</td>\n", " <td> 10</td>\n", " <td> 28</td>\n", " <td> 61</td>\n", " <td> 32</td>\n", " <td> 63</td>\n", " <td> 47</td>\n", " <td> 537</td>\n", " </tr>\n", " <tr>\n", " <th>SumSources</th>\n", " <td> min</td>\n", " <td> 19</td>\n", " <td> 31</td>\n", " <td> 3</td>\n", " <td> 47</td>\n", " <td> 7</td>\n", " <td> 25</td>\n", " <td> 27</td>\n", " <td> 28</td>\n", " <td> 7</td>\n", " <td> 22</td>\n", " <td> 43</td>\n", " <td> 23</td>\n", " <td> 44</td>\n", " <td> 42</td>\n", " <td> 492</td>\n", " </tr>\n", " <tr>\n", " <th>SumSources</th>\n", " <td> max</td>\n", " <td> 66</td>\n", " <td> 63</td>\n", " <td> 13</td>\n", " <td> 103</td>\n", " <td> 14</td>\n", " <td> 53</td>\n", " <td> 47</td>\n", " <td> 60</td>\n", " <td> 14</td>\n", " <td> 34</td>\n", " <td> 79</td>\n", " <td> 42</td>\n", " <td> 73</td>\n", " <td> 54</td>\n", " <td> 587</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " stats Bor_NAme contUSA Cent_NAme Trop_SAme Temp_SAme NAfr \\\n", "proc \n", "Wetlands mean 32 13 2 42 4 8 \n", "Wetlands min 15 6 1 19 1 3 \n", "Wetlands max 61 23 4 59 7 16 \n", "OtherNatural mean -99 -99 -99 -99 -99 -99 \n", "OtherNatural min -99 -99 -99 -99 -99 -99 \n", "OtherNatural max -99 -99 -99 -99 -99 -99 \n", "AgriWaste mean 3 17 5 21 6 14 \n", "AgriWaste min 2 15 2 18 5 12 \n", "AgriWaste max 3 23 6 23 6 18 \n", "Fossil mean 2 11 2 8 1 9 \n", "Fossil min 1 9 0 3 1 7 \n", "Fossil max 3 16 3 19 1 14 \n", "BioBurBiof mean 1 1 1 3 0 4 \n", "BioBurBiof min 0 0 0 1 0 3 \n", "BioBurBiof max 1 2 1 6 1 5 \n", "SumSources mean 37 42 9 75 11 36 \n", "SumSources min 19 31 3 47 7 25 \n", "SumSources max 66 63 13 103 14 53 \n", "\n", " SAfr Russia Oceania Europe China India SE_Asia \\\n", "proc \n", "Wetlands 19 14 3 4 5 6 29 \n", "Wetlands 15 5 1 1 1 1 18 \n", "Wetlands 22 26 6 7 9 13 35 \n", "OtherNatural -99 -99 -99 -99 -99 -99 -99 \n", "OtherNatural -99 -99 -99 -99 -99 -99 -99 \n", "OtherNatural -99 -99 -99 -99 -99 -99 -99 \n", "AgriWaste 6 5 5 17 30 21 22 \n", "AgriWaste 5 5 5 15 23 16 18 \n", "AgriWaste 7 5 5 18 36 24 25 \n", "Fossil 4 20 2 6 24 3 6 \n", "Fossil 3 18 1 3 15 2 5 \n", "Fossil 5 26 2 8 31 4 7 \n", "BioBurBiof 6 2 1 1 3 2 5 \n", "BioBurBiof 4 0 0 0 2 2 4 \n", "BioBurBiof 10 4 1 1 4 3 5 \n", "SumSources 35 41 10 28 61 32 63 \n", "SumSources 27 28 7 22 43 23 44 \n", "SumSources 47 60 14 34 79 42 73 \n", "\n", " Temp_Eurasia_Japan GLO \n", "proc \n", "Wetlands 3 185 \n", "Wetlands 1 153 \n", "Wetlands 6 227 \n", "OtherNatural -99 199 \n", "OtherNatural -99 104 \n", "OtherNatural -99 297 \n", "AgriWaste 20 195 \n", "AgriWaste 17 178 \n", "AgriWaste 22 206 \n", "Fossil 22 121 \n", "Fossil 20 114 \n", "Fossil 26 133 \n", "BioBurBiof 1 30 \n", "BioBurBiof 1 27 \n", "BioBurBiof 2 35 \n", "SumSources 47 537 \n", "SumSources 42 492 \n", "SumSources 54 587 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#df_BU = pd.read_csv(\"../data/Sankey_BU_2003-2012_25MAy2016.txt\", header=1, delim_whitespace=True)\n", "df_BU = pd.read_csv(\"../data/Sankey_BU_2003-2012_sept2016.txt\", header=1, delim_whitespace=True)\n", "df_BU.rename(columns = {'proc':'stats'}, inplace = True)\n", "df_BU.index.name = 'proc'\n", "df_BU" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>stats</th>\n", " <th>Bor_NAme</th>\n", " <th>contUSA</th>\n", " <th>Cent_NAme</th>\n", " <th>Trop_SAme</th>\n", " <th>Temp_SAme</th>\n", " <th>NAfr</th>\n", " <th>SAfr</th>\n", " <th>Russia</th>\n", " <th>Oceania</th>\n", " <th>Europe</th>\n", " <th>China</th>\n", " <th>India</th>\n", " <th>SE_Asia</th>\n", " <th>Temp_Eurasia_Japan</th>\n", " <th>GLO</th>\n", " </tr>\n", " <tr>\n", " <th>proc</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td> mean</td>\n", " <td> 13</td>\n", " <td> 9</td>\n", " <td> 2</td>\n", " <td> 44</td>\n", " <td> 8</td>\n", " <td> 12</td>\n", " <td> 20</td>\n", " <td> 13</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 5</td>\n", " <td> 6</td>\n", " <td> 27</td>\n", " <td> 3</td>\n", " <td> 167</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td> min</td>\n", " <td> 7</td>\n", " <td> 6</td>\n", " <td> 1</td>\n", " <td> 30</td>\n", " <td> 4</td>\n", " <td> 8</td>\n", " <td> 11</td>\n", " <td> 10</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 0</td>\n", " <td> 8</td>\n", " <td> 1</td>\n", " <td> 127</td>\n", " </tr>\n", " <tr>\n", " <th>Wetlands</th>\n", " <td> max</td>\n", " <td> 21</td>\n", " <td> 13</td>\n", " <td> 3</td>\n", " <td> 61</td>\n", " <td> 10</td>\n", " <td> 14</td>\n", " <td> 29</td>\n", " <td> 16</td>\n", " <td> 2</td>\n", " <td> 4</td>\n", " <td> 7</td>\n", " <td> 10</td>\n", " <td> 41</td>\n", " <td> 5</td>\n", " <td> 202</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNatural</th>\n", " <td> mean</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 9</td>\n", " <td> 2</td>\n", " <td> 6</td>\n", " <td> 6</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 6</td>\n", " <td> 4</td>\n", " <td> 9</td>\n", " <td> 4</td>\n", " <td> 64</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNatural</th>\n", " <td> min</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 5</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 4</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 21</td>\n", " </tr>\n", " <tr>\n", " <th>OtherNatural</th>\n", " <td> max</td>\n", " <td> 4</td>\n", " <td> 9</td>\n", " <td> 2</td>\n", " <td> 22</td>\n", " <td> 2</td>\n", " <td> 11</td>\n", " <td> 11</td>\n", " <td> 4</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 16</td>\n", " <td> 12</td>\n", " <td> 26</td>\n", " <td> 13</td>\n", " <td> 132</td>\n", " </tr>\n", " <tr>\n", " <th>AgriWaste</th>\n", " <td> mean</td>\n", " <td> 3</td>\n", " <td> 18</td>\n", " <td> 5</td>\n", " <td> 21</td>\n", " <td> 6</td>\n", " <td> 12</td>\n", " <td> 7</td>\n", " <td> 5</td>\n", " <td> 4</td>\n", " <td> 15</td>\n", " <td> 27</td>\n", " <td> 25</td>\n", " <td> 24</td>\n", " <td> 19</td>\n", " <td> 188</td>\n", " </tr>\n", " <tr>\n", " <th>AgriWaste</th>\n", " <td> min</td>\n", " <td> 2</td>\n", " <td> 16</td>\n", " <td> 2</td>\n", " <td> 19</td>\n", " <td> 5</td>\n", " <td> 11</td>\n", " <td> 7</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 9</td>\n", " <td> 16</td>\n", " <td> 14</td>\n", " <td> 12</td>\n", " <td> 9</td>\n", " <td> 115</td>\n", " </tr>\n", " <tr>\n", " <th>AgriWaste</th>\n", " <td> max</td>\n", " <td> 4</td>\n", " <td> 23</td>\n", " <td> 8</td>\n", " <td> 27</td>\n", " <td> 8</td>\n", " <td> 15</td>\n", " <td> 9</td>\n", " <td> 7</td>\n", " <td> 5</td>\n", " <td> 19</td>\n", " <td> 37</td>\n", " <td> 43</td>\n", " <td> 32</td>\n", " <td> 24</td>\n", " <td> 243</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td> mean</td>\n", " <td> 2</td>\n", " <td> 12</td>\n", " <td> 2</td>\n", " <td> 4</td>\n", " <td> 1</td>\n", " <td> 7</td>\n", " <td> 4</td>\n", " <td> 17</td>\n", " <td> 1</td>\n", " <td> 7</td>\n", " <td> 18</td>\n", " <td> 3</td>\n", " <td> 8</td>\n", " <td> 20</td>\n", " <td> 105</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td> min</td>\n", " <td> 1</td>\n", " <td> 7</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 11</td>\n", " <td> 0</td>\n", " <td> 5</td>\n", " <td> 9</td>\n", " <td> 1</td>\n", " <td> 5</td>\n", " <td> 14</td>\n", " <td> 77</td>\n", " </tr>\n", " <tr>\n", " <th>Fossil</th>\n", " <td> max</td>\n", " <td> 4</td>\n", " <td> 18</td>\n", " <td> 3</td>\n", " <td> 8</td>\n", " <td> 1</td>\n", " <td> 8</td>\n", " <td> 5</td>\n", " <td> 24</td>\n", " <td> 2</td>\n", " <td> 9</td>\n", " <td> 30</td>\n", " <td> 4</td>\n", " <td> 14</td>\n", " <td> 27</td>\n", " <td> 133</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td> mean</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " <td> 7</td>\n", " <td> 0</td>\n", " <td> 5</td>\n", " <td> 6</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 6</td>\n", " <td> 1</td>\n", " <td> 34</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td> min</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 3</td>\n", " <td> 0</td>\n", " <td> 2</td>\n", " <td> 4</td>\n", " <td> 1</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 3</td>\n", " <td> 0</td>\n", " <td> 15</td>\n", " </tr>\n", " <tr>\n", " <th>BioBurBiof</th>\n", " <td> max</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 15</td>\n", " <td> 1</td>\n", " <td> 6</td>\n", " <td> 9</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 4</td>\n", " <td> 3</td>\n", " <td> 8</td>\n", " <td> 1</td>\n", " <td> 53</td>\n", " </tr>\n", " <tr>\n", " <th>SumSources</th>\n", " <td> mean</td>\n", " <td> 20</td>\n", " <td> 41</td>\n", " <td> 11</td>\n", " <td> 84</td>\n", " <td> 17</td>\n", " <td> 42</td>\n", " <td> 44</td>\n", " <td> 38</td>\n", " <td> 11</td>\n", " <td> 28</td>\n", " <td> 58</td>\n", " <td> 39</td>\n", " <td> 74</td>\n", " <td> 46</td>\n", " <td> 558</td>\n", " </tr>\n", " <tr>\n", " <th>SumSources</th>\n", " <td> min</td>\n", " <td> 13</td>\n", " <td> 34</td>\n", " <td> 5</td>\n", " <td> 65</td>\n", " <td> 12</td>\n", " <td> 36</td>\n", " <td> 38</td>\n", " <td> 31</td>\n", " <td> 7</td>\n", " <td> 21</td>\n", " <td> 51</td>\n", " <td> 28</td>\n", " <td> 55</td>\n", " <td> 38</td>\n", " <td> 540</td>\n", " </tr>\n", " <tr>\n", " <th>SumSources</th>\n", " <td> max</td>\n", " <td> 27</td>\n", " <td> 49</td>\n", " <td> 15</td>\n", " <td> 101</td>\n", " <td> 27</td>\n", " <td> 55</td>\n", " <td> 53</td>\n", " <td> 45</td>\n", " <td> 19</td>\n", " <td> 34</td>\n", " <td> 72</td>\n", " <td> 46</td>\n", " <td> 84</td>\n", " <td> 54</td>\n", " <td> 568</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " stats Bor_NAme contUSA Cent_NAme Trop_SAme Temp_SAme NAfr \\\n", "proc \n", "Wetlands mean 13 9 2 44 8 12 \n", "Wetlands min 7 6 1 30 4 8 \n", "Wetlands max 21 13 3 61 10 14 \n", "OtherNatural mean 2 3 1 9 2 6 \n", "OtherNatural min 0 1 0 5 1 3 \n", "OtherNatural max 4 9 2 22 2 11 \n", "AgriWaste mean 3 18 5 21 6 12 \n", "AgriWaste min 2 16 2 19 5 11 \n", "AgriWaste max 4 23 8 27 8 15 \n", "Fossil mean 2 12 2 4 1 7 \n", "Fossil min 1 7 0 1 0 3 \n", "Fossil max 4 18 3 8 1 8 \n", "BioBurBiof mean 1 0 1 7 0 5 \n", "BioBurBiof min 0 0 0 3 0 2 \n", "BioBurBiof max 1 1 1 15 1 6 \n", "SumSources mean 20 41 11 84 17 42 \n", "SumSources min 13 34 5 65 12 36 \n", "SumSources max 27 49 15 101 27 55 \n", "\n", " SAfr Russia Oceania Europe China India SE_Asia \\\n", "proc \n", "Wetlands 20 13 3 2 5 6 27 \n", "Wetlands 11 10 1 1 3 0 8 \n", "Wetlands 29 16 2 4 7 10 41 \n", "OtherNatural 6 2 3 3 6 4 9 \n", "OtherNatural 4 1 1 0 1 1 2 \n", "OtherNatural 11 4 3 2 16 12 26 \n", "AgriWaste 7 5 4 15 27 25 24 \n", "AgriWaste 7 3 3 9 16 14 12 \n", "AgriWaste 9 7 5 19 37 43 32 \n", "Fossil 4 17 1 7 18 3 8 \n", "Fossil 2 11 0 5 9 1 5 \n", "Fossil 5 24 2 9 30 4 14 \n", "BioBurBiof 6 1 1 1 3 2 6 \n", "BioBurBiof 4 1 0 0 0 0 3 \n", "BioBurBiof 9 2 1 1 4 3 8 \n", "SumSources 44 38 11 28 58 39 74 \n", "SumSources 38 31 7 21 51 28 55 \n", "SumSources 53 45 19 34 72 46 84 \n", "\n", " Temp_Eurasia_Japan GLO \n", "proc \n", "Wetlands 3 167 \n", "Wetlands 1 127 \n", "Wetlands 5 202 \n", "OtherNatural 4 64 \n", "OtherNatural 1 21 \n", "OtherNatural 13 132 \n", "AgriWaste 19 188 \n", "AgriWaste 9 115 \n", "AgriWaste 24 243 \n", "Fossil 20 105 \n", "Fossil 14 77 \n", "Fossil 27 133 \n", "BioBurBiof 1 34 \n", "BioBurBiof 0 15 \n", "BioBurBiof 1 53 \n", "SumSources 46 558 \n", "SumSources 38 540 \n", "SumSources 54 568 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_TD = pd.read_csv(\"../data/Sankey_TD_2003-2012_sept2016.txt\", header=1, delim_whitespace=True)\n", "df_TD.rename(columns = {'proc':'stats'}, inplace = True)\n", "df_TD.index.name = 'proc'\n", "df_TD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
rubensfernando/mba-analytics-big-data
Python/2016-07-22/aula2-parte1-funcoes.ipynb
1
13845
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Funções\n", "\n", "* Até agora, vimos diversos tipos de dados, atribuições, comparações e estruturas de controle.\n", "* A ideia da função é dividir para conquistar, onde:\n", " * Um problema é dividido em diversos subproblemas\n", " * As soluções dos subproblemas são combinadas numa solução do problema maior.\n", "* Esses subproblemas têm o nome de funções.\n", "\n", "* Funções possibilitam abstrair, ou seja permite capturar a computação realiza e tratá-la como primitiva.\n", "* Suponha que queremos que a variável z seja o máximo de dois números (x e y).\n", "* Um programa simples seria:\n", "```\n", "if x > y:\n", " z = x\n", "else:\n", " z = y \n", "```\n", "\n", "* A ideia é encapsular essa computação dentro de um escopo que pode ser tratado como primitiva.\n", " * É utilizado simplesmente chamando o nome e fornecendo uma entrada.\n", " * Os detalhes internos sendo escondidos dos usuários.\n", "\n", "\n", "* Uma função tem 3 partes importantes:\n", "```\n", "def <nome> ( <parametros> ):\n", " <corpo da função>\n", "```\n", "\n", "* ```def``` é uma palavra chave\n", "* ```<nome>``` é qualquer nome aceito pelo Python\n", "* ```<parametros>``` é a quantidade de parâmetros que será passado para a função (pode ser nenhum).\n", "* ```<corpo da função>``` contém o código da função.\n", "\n", "Voltando ao exemplo:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def maximo(x, y):\n", " if x > y:\n", " z = x\n", " else:\n", " z = y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ótimo temos uma função e podemos reaproveita-la. Porém, para de fato reaproveita-la temos que utilizar o comando ```return```." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def maximo(x, y):\n", " if x > y:\n", " return x\n", " else:\n", " return y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pronto agora sim! Já podemos reaproveitar nossa função!\n", "\n", "**E como fazer isso?**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = maximo(3, 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quando chamamos a função ```maximo(3, 4)``` estamos definindo que ```x = 3``` e ```y = 4```. Após, as expressões são avaliadas até que não se tenha mais expressões, e nesse caso é retornado ```None```. Ou até que encontre a palavra especial ```return```, retornando como valor da chamada da função.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Já entendemos o que é e como criar funções. \n", "\n", "Para testar vamos criar uma função que irá realizar uma conta." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def economias (dinheiro, conta, gastos):\n", " total = (dinheiro + conta) - gastos\n", " return (total)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "eco = economias(10, 20, 10)\n", "print(eco)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Também podemos definir um valor padrão para um ou mais argumentos**\n", "\n", "Vamos reescrever a função economias para que os gastos sejam fixados em 150, caso não seja passado nenhum valor por padrão." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def economias(dinheiro, conta, gastos=150):\n", " total = (dinheiro + conta) - gastos\n", " return(total)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(economias(100, 60))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150\n" ] } ], "source": [ "print(economias(100, 60, 10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "É importante notar que uma variável que está dentro de uma função, não pode ser utilizada novamente enquanto a função não terminar de ser executada. \n", "\n", "No mundo da programação, isso é chamado de escopo. Vamos tentar imprimir o valor da variável dinheiro." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'dinheiro' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-10-7f3f8da2b4c1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdinheiro\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'dinheiro' is not defined" ] } ], "source": [ "print(dinheiro)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color:blue;\">Por que isso aconteceu?</span>**\n", "\n", "Esse erro acontece pois a variável dinheiro somente existe dentro da função economias, ou seja, ela existe apenas no contexto local dessa função.\n", "\n", "Vamos modificar novamente a função economias:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def economias(dinheiro, conta, gastos=150):\n", " total = (dinheiro + conta) - gastos\n", " total = total + eco\n", " return(total)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n" ] } ], "source": [ "print(economias(100,60))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color:blue;\">Por que não deu problema?</span>**\n", "\n", "Quando utilizamos uma variável que está fora da função dentro de uma função estamos utilizando a ideia de variáveis globais, onde dentro do contexto geral essa variável existe e pode ser utilizada dentro da função.\n", "\n", "<span style=\"color:red;\">Isso não é recomendado! O correto seria ter um novo argumento!</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercício de Funções\n", "\n", "Crie uma função que receba dois argumentos.\n", "* O primeiro argumento é o valor de um determinado serviço\n", "* O segundo é a porcentagem da multa por atraso do pagamento. O valor padrão da porcentagem, se não passado, é de 7%. A função deve retornar o valor final da conta com o juros. Lembre-se de converter 7%." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def conta(valor, multa=7):\n", " # Seu código aqui" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Funções embutidas\n", "Python tem um número de funções embutidas que sempre estão presentes. Uma lista completa pode ser encontrada em https://docs.python.org/3/library/functions.html.\n", "\n", "<span style=\"color:blue;\">Já utilizamos algumas delas! Quais?</span>\n", "\n", "### input\n", "\n", "Uma outra função que é bem interessante, é a ```input```. Essa função permite que o usuário digite uma entrada, por exemplo:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "idade = input('Digite sua idade:')\n", "print(idade)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nome = input('Digite seu nome:')\n", "print(nome)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(type(idade))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(type(nome))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note que ambas as variáveis são strings. Portanto precisamos converter para inteiro a idade." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "idade = int(input(\"Digite sua idade:\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(type(idade))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### open" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A função ```open```, permite abrir um arquivo para leitura e escrita.\n", "\n", " open(nome_do_arquivo, modo)\n", " \n", "Modos:\n", " * r - abre o arquivo para leitura.\n", " * w - abre o arquivo para escrita.\n", " * a - abre o arquivo para escrita acrescentando os dados no final do arquivo.\n", " * + - pode ser lido e escrito simultaneamente." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "os.remove(\"arquivo.txt\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arq = open(\"arquivo.txt\", \"w\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(1, 5):\n", " arq.write('{}. Escrevendo em arquivo\\n'.format(i))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arq.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Métodos\n", "\n", "* ```read()``` - retorna uma string única com todo o conteúdo do arquivo.\n", "* ```readlines()``` - todo o conteúdo do arquivo é salvo em uma lista, onde cada linha do arquivo será um elemento da lista." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = open(\"arquivo.txt\", \"r\")\n", "print(f, '\\n')\n", "texto = f.read()\n", "print(texto)\n", "f.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = open(\"arquivo.txt\", \"r\")\n", "texto = f.readlines()\n", "print(texto)\n", "f.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#help(f.readlines)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para remover o ```\\n``` podemos utilizar o método ```read``` que irá gerar uma única string e depois aplicamos o método ```splitlines```." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "f = open(\"arquivo.txt\", \"r\")\n", "texto = f.read().splitlines()\n", "print(texto)\n", "f.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
jlapeyre/SJulia.jl
TutorialNotebooks/StackExchange Examples.ipynb
1
10114
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples from Mathematica Stack Exchange\n", "\n", "This notebook contains examples from [Mathematica Stack Exchange](http://mathematica.stackexchange.com/) applied to Symata.\n", "\n", "Disclaimer to avoid any possible confusion.\n", "\n", "Neither Symata nor the Symata language are affiliated in any way with [Mathematica and/or the Wolfram language](http://wolfram.com). Symata is an open source project. Mathematica and Wolfram language are software products developed and licensed by [WRI](http://wolfram.com)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "using Symata" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "FloatFormat(Short);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Determining all possible traversals of a tree](http://mathematica.stackexchange.com/questions/5753/determining-all-possible-traversals-of-a-tree)\n", "\n", "The [following example](http://mathematica.stackexchange.com/questions/5753/determining-all-possible-traversals-of-a-tree/5756#5756) example is from L. Shifrin.\n", "\n", "`C` is a tree:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "C = [a,[[a1,[a12,b12,c12]],[b2,[a22,b22,c22]],[c3,[a32,b32,c32,d32]]]];" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "trav(tree_List) := Flatten(trav([], tree), 1)\n", "trav(accum_List, [x_, y_List]) := Map(yy -> trav([accum, x], yy), y)\n", "trav(x_,y_) := Flatten([x,y])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "$$ \\left[ \\left[ a,a1,a12 \\right] , \\left[ a,a1,b12 \\right] , \\left[ a,a1,c12 \\right] , \\left[ a,b2,a22 \\right] , \\left[ a,b2,b22 \\right] , \\left[ a,b2,c22 \\right] , \\left[ a,c3,a32 \\right] , \\left[ a,c3,b32 \\right] , \\left[ a,c3,c32 \\right] , \\left[ a,c3,d32 \\right] \\right] $$" ], "text/plain": [ "L\"$$ \\left[ \\left[ a,a1,a12 \\right] , \\left[ a,a1,b12 \\right] , \\left[ a,a1,c12 \\right] , \\left[ a,b2,a22 \\right] , \\left[ a,b2,b22 \\right] , \\left[ a,b2,c22 \\right] , \\left[ a,c3,a32 \\right] , \\left[ a,c3,b32 \\right] , \\left[ a,c3,c32 \\right] , \\left[ a,c3,d32 \\right] \\right] $$\"" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trav(C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [What are the use cases for different scoping constructs?](http://mathematica.stackexchange.com/questions/559/what-are-the-use-cases-for-different-scoping-constructs/)\n", "\n", " In this example, `Module` creates a *closure*. We want to use big integers, so we use `big\"1\"` for one of the values.\n", "\n", "[This example](http://mathematica.stackexchange.com/questions/559/what-are-the-use-cases-for-different-scoping-constructs/569#569) is also by L. Shifrin\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "Module([prev, prevprev, this],\n", "begin \n", " reset() := (prev = big\"1\"; prevprev = 1); \n", " reset(); \n", " nextFib() := (this = prev + prevprev; prevprev = prev; prev = this)\n", "end\n", ");" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$ 113796925398360272257523782552224175572745930353730513145086634176691092536145985470146129334641866902783673042322088625863396052888690096969577173696370562180400527049497109023054114771394568040040412172632376 $$" ], "text/plain": [ "L\"$$ 113796925398360272257523782552224175572745930353730513145086634176691092536145985470146129334641866902783673042322088625863396052888690096969577173696370562180400527049497109023054114771394568040040412172632376 $$\"" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reset()\n", "\n", "a = Table(nextFib(),[1000]);\n", "\n", "a[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Return all free symbols in an expression\n", "\n", "Below is another example from L. Shifrin. `allsyms` returns all free symbols in `expr`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "ClearAll(a,b)\n", "\n", "allsyms(expr_) := Cases(expr , s_Symbol => HoldComplete(s),[0,Infinity])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "$$ \\left[ \\text{HoldComplete} \\! \\left( a \\right) ,\\text{HoldComplete} \\! \\left( b \\right) ,\\text{HoldComplete} \\! \\left( x \\right) \\right] $$" ], "text/plain": [ "L\"$$ \\left[ \\text{HoldComplete} \\! \\left( a \\right) ,\\text{HoldComplete} \\! \\left( b \\right) ,\\text{HoldComplete} \\! \\left( x \\right) \\right] $$\"" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "allsyms(a + b * (1 - x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Finding real roots of negative numbers (for example $\\sqrt[3]{-8}$)](http://mathematica.stackexchange.com/questions/3886/finding-real-roots-of-negative-numbers-for-example-sqrt3-8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Power` function returns the principal root, not necessarily a real root." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/latex": [ "$$ \\left[ 2 \\ \\left( \\left( -1 \\right) ^{\\frac{1}{3}} \\right) ,1. + 1.73205\\mathbb{i} \\right] $$" ], "text/plain": [ "L\"$$ \\left[ 2 \\ \\left( \\left( -1 \\right) ^{\\frac{1}{3}} \\right) ,1. + 1.73205\\mathbb{i} \\right] $$\"" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[(-8)^(1/3), (-8.0)^(1/3)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`CubeRoot` and `Surd` give real roots" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$ \\left[ -2,-2 \\right] $$" ], "text/plain": [ "L\"$$ \\left[ -2,-2 \\right] $$\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[CubeRoot(-8), Surd(-32,5)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Surd` returns unevaluated if the root is even." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "┌ Warning: Surd::noneg: Surd is not defined for even roots of negative values.\n", "└ @ Symata /home/lapeyre/.julia/dev/Symata/src/wrapout.jl:29\n" ] }, { "data": { "text/latex": [ "$$ \\text{Surd} \\! \\left( \\left( -8 \\right) ,4 \\right) $$" ], "text/plain": [ "L\"$$ \\text{Surd} \\! \\left( \\left( -8 \\right) ,4 \\right) $$\"" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Surd(-8,4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or complex" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "┌ Warning: Surd::preal: The parameter I should be real valued\n", "└ @ Symata /home/lapeyre/.julia/dev/Symata/src/wrapout.jl:29\n" ] }, { "data": { "text/latex": [ "$$ \\text{Surd} \\! \\left( \\mathbb{i},3 \\right) $$" ], "text/plain": [ "L\"$$ \\text{Surd} \\! \\left( \\mathbb{i},3 \\right) $$\"" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Surd(I,3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Version and date" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Symata version 0.4.6\n", "Julia version 1.6.0-DEV.58\n", "Python version 3.8.3\n", "SymPy version 1.5.1\n" ] } ], "source": [ "VersionInfo()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020-05-29T22:44:02.976" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "InputForm(Now())" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.6.0-DEV", "language": "julia", "name": "julia-1.6" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
jasonding1354/ScalaFAQ
type_system/type_bound.ipynb
1
14125
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 类型边界\n", "类型边界是与类型相关的规则,一个变量要匹配一个类型时必须符合这些规则。\n", "\n", "类型边界的两种形式:\n", "- 类型上界(超类型约束,也称为一致性关系)\n", "- 类型下界(子类型约束)\n", "\n", "类型上界是指,某一类型必须是另一种类型的子类型。类型下界表示某类型必须是另一个类型的父类(或该类型本身)。\n", "\n", "**类型边界与型变标记是两个不相干的问题。类型边界对参数化类型所允许采用的类型做了限制,如T <: AnyRef。型变标记表示参数化类型的子类实例是否可以替换父类实例。**" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "实际场景中,常常使用型变标记和类型边界配合的工作方式,这主要是为了解决在错误的位置使用型变参数的问题,下面以Option的getOrElse方法作为例子进行解释:\n", "``` scala\n", "sealed abstract class Option[+A] extends Product with Serializable {\n", " ...\n", " @inline final def getOrElse[B >: A](default: => B): B = {...}\n", " ...\n", "}\n", "```\n", "可以看到,为何getOrElse方法返回B(A的父类型)呢?这里解释原因。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "defined \u001b[32mclass \u001b[36mParent\u001b[0m\n", "defined \u001b[32mclass \u001b[36mChild\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class Parent(val value: Int) { \n", " override def toString = s\"${this.getClass.getName}($value)\"\n", "}\n", "\n", "class Child(value: Int) extends Parent(value)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mop1\u001b[0m: \u001b[32mOption\u001b[0m[\u001b[32mParent\u001b[0m] = Some(cmd0$$user$Child(1))\n", "\u001b[36mp1\u001b[0m: \u001b[32mParent\u001b[0m = cmd0$$user$Child(1)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val op1: Option[Parent] = Option(new Child(1))\n", "val p1: Parent = op1.getOrElse(new Parent(10))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mop2\u001b[0m: \u001b[32mOption\u001b[0m[\u001b[32mParent\u001b[0m] = None\n", "\u001b[36mp2a\u001b[0m: \u001b[32mParent\u001b[0m = cmd0$$user$Parent(10)\n", "\u001b[36mp2b\u001b[0m: \u001b[32mParent\u001b[0m = cmd0$$user$Child(100)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val op2: Option[Parent] = Option[Parent](null) // None\n", "val p2a: Parent = op2.getOrElse(new Parent(10)) // Result: Parent(10)\n", "val p2b: Parent = op2.getOrElse(new Child(100)) // Result: Child(100)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mop3\u001b[0m: \u001b[32mOption\u001b[0m[\u001b[32mParent\u001b[0m] = None\n", "\u001b[36mp3a\u001b[0m: \u001b[32mParent\u001b[0m = cmd0$$user$Parent(20)\n", "\u001b[36mp3b\u001b[0m: \u001b[32mParent\u001b[0m = cmd0$$user$Child(200)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val op3: Option[Parent] = Option[Child](null) // None\n", "val p3a: Parent = op3.getOrElse(new Parent(20)) // Result: Parent(20)\n", "val p3b: Parent = op3.getOrElse(new Child(200)) // Result: Child(200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "关键在这里:\n", "``` scala\n", "val op3: Option[Parent] = Option[Child](null)\n", "val p3a: Parent = op3.getOrElse(new Parent(20))\n", "```\n", "op3显式地将`Option[Child](null)`(即None)赋给了`Option[Parent]`。\n", "\n", "但从调用者的角度,我们并不知道真实类型到底是什么?如果调用者持有对`Option[Parent]`的引用,那么将自然认为它可以从Option[Parent]中提取一个Parent值。故如果是None的话,调用者将返回默认的Parent参数;如果是Some[Parent],则返回Some中的值。**所有情况都认为返回一个Parent类型的值。但实际返回的是Child子类的实例。**如果不适用类型下界说明,那么`val p3a: Parent = op3.getOrElse(new Parent(20))`语句将无法通过类型检查。\n", "\n", "这就是编译器不允许简单的方法签名,而采用[B >: A]边界标记的签名的原因。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**同时使用类型上下界的例子**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "defined \u001b[32mclass \u001b[36mUpper\u001b[0m\n", "defined \u001b[32mclass \u001b[36mMiddle1\u001b[0m\n", "defined \u001b[32mclass \u001b[36mMiddle2\u001b[0m\n", "defined \u001b[32mclass \u001b[36mLower\u001b[0m\n", "defined \u001b[32mclass \u001b[36mC\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class Upper\n", "class Middle1 extends Upper\n", "class Middle2 extends Middle1\n", "class Lower extends Middle2\n", "case class C[A >: Lower <: Upper](a: A)\n", "// case class C2[A <: Upper >: Lower](a: A) // Does not compile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. 通过引入新的类型参数来解决协变和逆变故障\n", "这里的实例中,我们实现一个List中简化的++版本,将两个集合类型组合起来。\n", "\n", "我们希望能有自动转换功能,比如把字符串列表转换为Any列表,所以把参数类型标注为协变。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "Compilation Failed", "\u001b[31mMain.scala:78: covariant type ItemType occurs in contravariant position in type $user.this.List[ItemType] of value other\r", " def ++(other: List[ItemType]): List[ItemType]\r", " ^\u001b[0m" ] } ], "source": [ "// ++方法定义为接受另一个ItemType类型的里诶包作为参数\n", "// 返回新列表\n", "trait List[+ItemType] {\n", " def ++(other: List[ItemType]): List[ItemType]\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上面由于ItemType出现在了逆变位置上,出现了编译报错。\n", "\n", "为了绕开编译器限制,我们可以用新类型参数来避免把ItemType放在逆变位置上。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "defined \u001b[32mtrait \u001b[36mList\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "// 简单绕开型变约束\n", "trait List[+ItemType] {\n", " def ++[OtherItemType](other: List[OtherItemType]): List[ItemType]\n", "}" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "Compilation Failed", "\u001b[31mMain.scala:81: type mismatch;", " found : cmd6.this.$ref$cmd5.List[OtherItemType]", " required: cmd6.this.$ref$cmd5.List[ItemType]\r", " def ++[OtherItemType](other: List[OtherItemType]) = other\r", " ^\u001b[0m" ] } ], "source": [ "// 实现空List类\n", "class EmptyList[ItemType] extends List[ItemType] {\n", " def ++[OtherItemType](other: List[OtherItemType]) = other\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "由于上面定义的方法得到的结果类型不匹配,OtherItemType和ItemType类型不兼容,造成编译失败。\n", "\n", "可以通过对OtherItemType做某种类型约束,使得OtherItemType和ItemType类型建立联系。\n", "\n", "我们希望OtherItemType是能和当前列表很好的组合的类型,因为ItemType是协变的,那么可以把当前列表向ItemType层级上方转换。**因此,我们用ItemType作为OtherItemType的下界约束,我们修正++方法,返回OtherItemType类型。**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "defined \u001b[32mtrait \u001b[36mList\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trait List[+ItemType] {\n", " def ++[OtherItemType >: ItemType](\n", " other: List[OtherItemType]): List[OtherItemType]\n", "}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "defined \u001b[32mclass \u001b[36mEmptyList\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class EmptyList[ItemType] extends List[ItemType] {\n", " def ++[OtherItemType >: ItemType](\n", " other: List[OtherItemType]) = other\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "// 确认把各类型的空list组合是否返回我们期望的类型" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mstrings\u001b[0m: \u001b[32mEmptyList\u001b[0m[\u001b[32mString\u001b[0m] = cmd7$$user$EmptyList@fb8491" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val strings = new EmptyList[String]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mints\u001b[0m: \u001b[32mEmptyList\u001b[0m[\u001b[32mInt\u001b[0m] = cmd7$$user$EmptyList@1cc1a6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val ints = new EmptyList[Int]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36manys\u001b[0m: \u001b[32mEmptyList\u001b[0m[\u001b[32mAny\u001b[0m] = cmd7$$user$EmptyList@1b7cf4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val anys = new EmptyList[Any]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres11\u001b[0m: \u001b[32mList\u001b[0m[\u001b[32mString\u001b[0m] = cmd7$$user$EmptyList@fb8491" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "strings ++ strings" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres12\u001b[0m: \u001b[32mList\u001b[0m[\u001b[32mAny\u001b[0m] = cmd7$$user$EmptyList@1cc1a6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "strings ++ ints" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres13\u001b[0m: \u001b[32mList\u001b[0m[\u001b[32mAny\u001b[0m] = cmd7$$user$EmptyList@1b7cf4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "strings ++ anys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看到,编译器推断出Any是String和Int的共同超类,于是得到了Any列表,这正是我们期望的结果。\n", "\n", "**一般来说,当在类方法里碰到协变和逆变故障时,通常的解决办法是引入一个新的类型参数,在方法签名里用新引入的类型参数。**\n", "\n", "所以,当我们向一个不可变集合添加新元素以构成一个新的集合时(包括上面这个例子),**其类型参数必须具有逆变的行为,但传入的是协变的参数化类型。**\n", "\n", "**总的来说,那些类型参数为协变的参数化类型,与方法参数的类型下界关系密切。**" ] } ], "metadata": { "kernelspec": { "display_name": "Scala 2.11", "language": "scala211", "name": "scala211" }, "language_info": { "codemirror_mode": "text/x-scala", "file_extension": ".scala", "mimetype": "text/x-scala", "name": "scala211", "pygments_lexer": "scala", "version": "2.11.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
jsharpna/DavisSML
lectures/lecture5/lecture5.ipynb
1
298706
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# The Lasso\n", "\n", "## StatML: Lecture 5\n", "\n", "### Prof. James Sharpnack\n", "\n", "- Some content and images are from \"The Elements of Statistical Learning\" by Hastie, Tibshirani, Friedman\n", "- Reading ESL Chapter 3" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Recall Convex Optimization\n", "\n", "**Def** A function $f : \\mathbb R^p \\to \\mathbb R$ is convex if for any $0 \\le \\alpha \\le 1$, $x_0, x_1 \\in \\mathbb R^p$,\n", "$$\n", "f(\\alpha x_0 + (1 - \\alpha) x_1) \\le \\alpha f(x_0) + (1 - \\alpha) f(x_1).\n", "$$\n", "\n", "> For convex functions, local minima are global minima" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Recall **1st Order Condition**. If f is differentiable then it is convex if \n", "$$\n", "f(x) \\ge f(x_0) + \\nabla f(x_0)^\\top (x - x_0), \\forall x,x_0\n", "$$\n", "and when $\\nabla f(x_0) = 0$ then \n", "$$\n", "f(x) \\ge f(x_0), \\forall x\n", "$$\n", "so any fixed point of gradient descent is a global min (for convex, differentiable f)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Subdifferential\n", "\n", "**Def.** $g(x_0) \\in \\mathbb R^p$ is a *subgradient* of $f$ at $x_0$ if \n", "$$\n", "f(x) \\ge f(x_0) + g(x_0)^\\top (x - x_0), \\forall x.\n", "$$\n", "The set of all subgradients at $x_0$ is call the *subdifferential*, denoted $\\partial f(x_0)$.\n", "\n", "> For any global optima, $0 \\in \\partial f(x_0)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Wavelet denoising\n", "\n", "Soft thresholding is commonly used for orthonormal bases. \n", "- Suppose that we have a vector $y_1,\\ldots, y_T$ (like a time series).\n", "- And we want to reconstruct $y$ with $W \\beta$ where $\\beta$ has a small sum of absolute values $\\sum_i |\\beta_i|$ \n", "- $W$ is $T \\times T$ and $W W^\\top = W^\\top W = I$ (orthonormal full rank design)\n", "\n", "Want to minimize \n", "$$\n", "\\frac 12 \\sum_{i=1}^T (y - W \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|.\n", "$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "## Explore Turkish stock exchange dataset\n", "\n", "tse = pd.read_excel('../../data/data_akbilgic.xlsx',skiprows=1)\n", "\n", "tse = tse.rename(columns={'ISE':'TLISE','ISE.1':'USDISE'})" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def const_wave(T,a,b):\n", " wave = np.zeros(T)\n", " s1 = (b-a) // 2\n", " s2 = (b-a) - s1\n", " norm_C = (s1*s2 / (s1+s2))**0.5\n", " wave[a:a+s1] = norm_C / s1\n", " wave[a+s1:b] = -norm_C / s2\n", " return wave" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def _const_wave_basis(T,a,b):\n", " if b-a < 2:\n", " return []\n", " wave_basis = []\n", " wave_basis.append(const_wave(T,a,b))\n", " mid_pt = a + (b-a)//2\n", " wave_basis += _const_wave_basis(T,a,mid_pt)\n", " wave_basis += _const_wave_basis(T,mid_pt,b)\n", " return wave_basis" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def const_wave_basis(T,a,b):\n", " father = np.ones(T) / T**0.5\n", " return [father] + _const_wave_basis(T,a,b)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Construct discrete Haar wavelet basis\n", "T,p = tse.shape\n", "wave_basis = const_wave_basis(T,0,T)\n", "W = np.array(wave_basis).T" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = plt.plot(W[:,:3])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def soft(y,lamb):\n", " pos_part = (y - lamb) * (y > lamb)\n", " neg_part = (y + lamb) * (y < -lamb)\n", " return pos_part + neg_part" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "## Volatility seems most interesting\n", "## will construct local measure of volatility\n", "## remove rolling window estimate (local centering)\n", "## square the residuals\n", "\n", "tse = tse.set_index('date')\n", "tse_trem = tse - tse.rolling(\"7D\").mean()\n", "tse_vol = tse_trem**2.\n", "\n", "## Make wavelet transformation and soft threshold\n", "\n", "tse_wave = W.T @ tse_vol.values\n", "lamb = .001\n", "tse_soft = soft(tse_wave,lamb)\n", "tse_rec = W @ tse_soft\n", "tse_den = tse_vol.copy()\n", "tse_den.iloc[:,:] = tse_rec" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 9 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = tse_vol.plot(subplots=True,figsize=(10,10))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 9 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = tse_den.plot(subplots=True,figsize=(10,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Wavelet reconstruction\n", "\n", "Can reconstruct the sequence by\n", "$$\n", "\\hat y = W \\hat \\beta.\n", "$$\n", "The objective is likelihood term + L1 penalty term,\n", "$$\n", "\\frac 12 \\sum_{i=1}^T (y - W \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|.\n", "$$\n", "> The L1 penalty \"forces\" some $\\beta_i = 0$, inducing sparsity" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 2 48 63 344 345]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(tse_soft[:,4])\n", "high_idx = np.where(np.abs(tse_soft[:,4]) > .0001)[0]\n", "print(high_idx)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f17b67b3090>]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 7 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(len(high_idx) + 1,1)\n", "for i, idx in enumerate(high_idx):\n", " axs[i].plot(W[:,idx])\n", "plt.plot(tse_den['FTSE'],c='r')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Non-orthogonal design\n", "\n", "The objective is likelihood term + L1 penalty term,\n", "$$\n", "\\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|.\n", "$$\n", "does not have closed form for $X$ that is non-orthogonal.\n", "\n", "- it is convex\n", "- it is non-smooth (recall $|x|$)\n", "- has tuning parameter $\\lambda$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Compare to best subset selection (NP-hard):\n", "$$\n", "\\min \\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2.\n", "$$\n", "for\n", "$$\n", "\\| \\beta \\|_0 = |{\\rm supp}(\\beta)| < s.\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Image of Lasso solution\n", "\n", "<img src=\"lasso_soln.PNG\" width=100%>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Solving the Lasso\n", "\n", "The lasso can be written in *regularized form*,\n", "$$\n", "\\min \\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|,\n", "$$\n", "or in *constrained form*,\n", "$$\n", "\\min \\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2, \\quad \\textrm{s.t.} \\sum_{i=1}^T |\\beta_i| \\le C,\n", "$$\n", "\n", "- For every $\\lambda$ there is a $C$ such that the regularized form and constrained form have the same argmin\n", "- This correspondence is data dependent" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Exercise 5.1. Solving the Lasso\n", "\n", "A quadratic program (QP) is any convex optimization of the form \n", "$$\n", "\\min \\beta^\\top Q \\beta + \\beta^\\top a \\quad \\textrm{ s.t. } A\\beta \\le c\n", "$$\n", "where $Q$ is positive semi-definite.\n", "\n", "Show that the lasso in constrained form is a QP. (Hint: write $\\beta = \\beta_+ - \\beta_-$ where $\\beta_{+,j} = \\beta_{j} 1\\{ \\beta_j > 0\\}$ and $\\beta_{-,j} = - \\beta_{j} 1\\{ \\beta_j < 0\\}$). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Solution to 5.1**\n", "\n", "The objective is certainly quadratic...\n", "$$\n", "\\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2 = \\frac 12 \\beta^\\top (X^\\top X) \\beta - \\beta^\\top (X^\\top y) + C\n", "$$\n", "and we know that $X^\\top X$ is PSD because $a^\\top X^\\top X a = \\| X a\\|^2 \\ge 0$.\n", "\n", "What about $\\| \\beta \\|_1$?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](lasso_QP.PNG)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Solving the lasso\n", "\n", "For a single $\\lambda$ (or $C$ in constrained form) can solve the lasso with many specialized methods\n", "- quadratic program solver\n", "- proximal gradient\n", "- alternating direction method of multipliers\n", "\n", "but $\\lambda$ is a tuning parameter. Options\n", "1. Construct a grid of $\\lambda$ and solve each lasso\n", "2. Solve for all $\\lambda$ values - path algorithm" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Active sets and why lasso works better\n", "\n", "- Let $\\hat \\beta_\\lambda$ be the $\\hat \\beta$ at tuning parameter $\\lambda$.\n", "- Define $\\mathcal A_\\lambda = {\\rm supp}(\\hat \\beta_\\lambda)$ the non-zero elements of $\\hat \\beta_\\lambda$.\n", " 1. For large $\\lambda \\rightarrow \\infty$, $|\\mathcal A_\\lambda| = 0$\n", " 2. For small $\\lambda = 0$, $|\\mathcal A_\\lambda| = p$ (when OLS solution has full support)\n", " \n", "Forward greedy selection only adds elements to the active set, does not remove elements.\n", "\n", "### Exercise 5.2.1\n", "Verify 1 and 2 above." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](active.PNG)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Lasso Path\n", "\n", "1. Start at $\\lambda = +\\infty, \\hat \\beta = 0$.\n", "2. Decrease $\\lambda$ until $\\hat \\beta_{j_1} \\ne 0$, $\\mathcal A \\gets \\{j_1\\}$. (Hitting event)\n", "3. Continue decreasing $\\lambda$ updating $\\mathcal A$ with hitting and leaving events\n", "\n", "\n", "- $x_{j_1}$ is the predictor variable most correlated with $y$\n", "- Hitting events are when element is added to $\\mathcal A$\n", "- Leaving events are when element is removed from $\\mathcal A$\n", "- $\\hat \\beta_{\\lambda,j}$ is piecewise linear, continuous, as a function of $\\lambda$\n", "- knots are at \"hitting\" and \"leaving\" events" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](lasso_path.PNG)\n", "from sklearn.org" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Least Angle Regression (LAR)\n", "\n", "1. Standardize predictors and start with residual $r = y - \\bar y$, $\\hat \\beta = 0$\n", "2. Find $x_j$ most correlated with $r$\n", "3. Move $\\beta_j$ in the direction of $x_j^\\top r$ until the residual is more correlated with another $x_k$\n", "4. Move $\\beta_j,\\beta_k$ in the direction of their joint OLS coefficients of $r$ on $(x_j,x_k)$ until some other competitor $x_l$ has as much correlation with the current residual\n", "5. Continue until all predictors have been entered.\n", "\n", "### Exercise 5.2.2 \n", "How do we know that LAR does not give us the Lasso solution?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Lasso modification\n", "\n", "4.5 If a non-zero coefficient drops to 0 then remove it from the active set and recompute the restricted OLS." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](lars_lasso.png)\n", "from ESL" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# %load ../standard_import.txt\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn import preprocessing, model_selection, linear_model\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Index: 263 entries, -Alan Ashby to -Willie Wilson\n", "Data columns (total 20 columns):\n", "AtBat 263 non-null int64\n", "Hits 263 non-null int64\n", "HmRun 263 non-null int64\n", "Runs 263 non-null int64\n", "RBI 263 non-null int64\n", "Walks 263 non-null int64\n", "Years 263 non-null int64\n", "CAtBat 263 non-null int64\n", "CHits 263 non-null int64\n", "CHmRun 263 non-null int64\n", "CRuns 263 non-null int64\n", "CRBI 263 non-null int64\n", "CWalks 263 non-null int64\n", "League 263 non-null object\n", "Division 263 non-null object\n", "PutOuts 263 non-null int64\n", "Assists 263 non-null int64\n", "Errors 263 non-null int64\n", "Salary 263 non-null float64\n", "NewLeague 263 non-null object\n", "dtypes: float64(1), int64(16), object(3)\n", "memory usage: 43.1+ KB\n" ] } ], "source": [ "## Modified from the github repo: https://github.com/JWarmenhoven/ISLR-python \n", "## which is based on the book by James et al. Intro to Statistical Learning.\n", "\n", "df = pd.read_csv('../../data/Hitters.csv', index_col=0).dropna()\n", "df.index.name = 'Player'\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "## Simulate a dataset for lasso\n", "\n", "n=100\n", "p=1000\n", "X = np.random.randn(n,p)\n", "X = preprocessing.scale(X)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of non-zero's: 20\n" ] } ], "source": [ "## Subselect true active set\n", "\n", "sprob = 0.02\n", "Sbool = np.random.rand(p) < sprob\n", "s = np.sum(Sbool)\n", "print(\"Number of non-zero's: {}\".format(s))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "## Construct beta and y\n", "\n", "mu = 100.\n", "beta = np.zeros(p)\n", "beta[Sbool] = mu * np.random.randn(s)\n", "\n", "eps = np.random.randn(n)\n", "y = X.dot(beta) + eps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 5.3\n", "\n", "- Run the lasso using `linear_model.lars_path` with the lasso modification (see docstring with ?linear_model.lars_path) \n", "- Plot the lasso coefficients that are learned as a function of lambda. You should have a plot with the x-axis being lambda and the y-axis being the coefficient value, with $p=1000$ lines plotted. Highlight the $s$ coefficients that are truly non-zero by plotting them in red." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "?linear_model.lars_path" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "## Answer to exercise 5.3\n", "## Run lars with lasso mod, find active set\n", "\n", "larper = linear_model.lars_path(X,y,method=\"lasso\")\n", "S = set(np.where(Sbool)[0])\n", "\n", "def plot_it():\n", " for j in S:\n", " _ = plt.plot(larper[0],larper[2][j,:],'r')\n", " for j in set(range(p)) - S:\n", " _ = plt.plot(larper[0],larper[2][j,:],'k',linewidth=.75)\n", " _ = plt.title('Lasso path for simulated data')\n", " _ = plt.xlabel('lambda')\n", " _ = plt.ylabel('Coef')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_it()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Index: 263 entries, -Alan Ashby to -Willie Wilson\n", "Data columns (total 20 columns):\n", "AtBat 263 non-null int64\n", "Hits 263 non-null int64\n", "HmRun 263 non-null int64\n", "Runs 263 non-null int64\n", "RBI 263 non-null int64\n", "Walks 263 non-null int64\n", "Years 263 non-null int64\n", "CAtBat 263 non-null int64\n", "CHits 263 non-null int64\n", "CHmRun 263 non-null int64\n", "CRuns 263 non-null int64\n", "CRBI 263 non-null int64\n", "CWalks 263 non-null int64\n", "League 263 non-null object\n", "Division 263 non-null object\n", "PutOuts 263 non-null int64\n", "Assists 263 non-null int64\n", "Errors 263 non-null int64\n", "Salary 263 non-null float64\n", "NewLeague 263 non-null object\n", "dtypes: float64(1), int64(16), object(3)\n", "memory usage: 43.1+ KB\n" ] } ], "source": [ "## Hitters dataset\n", "\n", "df = pd.read_csv('../../data/Hitters.csv', index_col=0).dropna()\n", "df.index.name = 'Player'\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>AtBat</th>\n", " <th>Hits</th>\n", " <th>HmRun</th>\n", " <th>Runs</th>\n", " <th>RBI</th>\n", " <th>Walks</th>\n", " <th>Years</th>\n", " <th>CAtBat</th>\n", " <th>CHits</th>\n", " <th>CHmRun</th>\n", " <th>CRuns</th>\n", " <th>CRBI</th>\n", " <th>CWalks</th>\n", " <th>League</th>\n", " <th>Division</th>\n", " <th>PutOuts</th>\n", " <th>Assists</th>\n", " <th>Errors</th>\n", " <th>Salary</th>\n", " <th>NewLeague</th>\n", " </tr>\n", " <tr>\n", " <th>Player</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>-Alan Ashby</td>\n", " <td>315</td>\n", " <td>81</td>\n", " <td>7</td>\n", " <td>24</td>\n", " <td>38</td>\n", " <td>39</td>\n", " <td>14</td>\n", " <td>3449</td>\n", " <td>835</td>\n", " <td>69</td>\n", " <td>321</td>\n", " <td>414</td>\n", " <td>375</td>\n", " <td>N</td>\n", " <td>W</td>\n", " <td>632</td>\n", " <td>43</td>\n", " <td>10</td>\n", " <td>475.0</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <td>-Alvin Davis</td>\n", " <td>479</td>\n", " <td>130</td>\n", " <td>18</td>\n", " <td>66</td>\n", " <td>72</td>\n", " <td>76</td>\n", " <td>3</td>\n", " <td>1624</td>\n", " <td>457</td>\n", " <td>63</td>\n", " <td>224</td>\n", " <td>266</td>\n", " <td>263</td>\n", " <td>A</td>\n", " <td>W</td>\n", " <td>880</td>\n", " <td>82</td>\n", " <td>14</td>\n", " <td>480.0</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <td>-Andre Dawson</td>\n", " <td>496</td>\n", " <td>141</td>\n", " <td>20</td>\n", " <td>65</td>\n", " <td>78</td>\n", " <td>37</td>\n", " <td>11</td>\n", " <td>5628</td>\n", " <td>1575</td>\n", " <td>225</td>\n", " <td>828</td>\n", " <td>838</td>\n", " <td>354</td>\n", " <td>N</td>\n", " <td>E</td>\n", " <td>200</td>\n", " <td>11</td>\n", " <td>3</td>\n", " <td>500.0</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <td>-Andres Galarraga</td>\n", " <td>321</td>\n", " <td>87</td>\n", " <td>10</td>\n", " <td>39</td>\n", " <td>42</td>\n", " <td>30</td>\n", " <td>2</td>\n", " <td>396</td>\n", " <td>101</td>\n", " <td>12</td>\n", " <td>48</td>\n", " <td>46</td>\n", " <td>33</td>\n", " <td>N</td>\n", " <td>E</td>\n", " <td>805</td>\n", " <td>40</td>\n", " <td>4</td>\n", " <td>91.5</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <td>-Alfredo Griffin</td>\n", " <td>594</td>\n", " <td>169</td>\n", " <td>4</td>\n", " <td>74</td>\n", " <td>51</td>\n", " <td>35</td>\n", " <td>11</td>\n", " <td>4408</td>\n", " <td>1133</td>\n", " <td>19</td>\n", " <td>501</td>\n", " <td>336</td>\n", " <td>194</td>\n", " <td>A</td>\n", " <td>W</td>\n", " <td>282</td>\n", " <td>421</td>\n", " <td>25</td>\n", " <td>750.0</td>\n", " <td>A</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits \\\n", "Player \n", "-Alan Ashby 315 81 7 24 38 39 14 3449 835 \n", "-Alvin Davis 479 130 18 66 72 76 3 1624 457 \n", "-Andre Dawson 496 141 20 65 78 37 11 5628 1575 \n", "-Andres Galarraga 321 87 10 39 42 30 2 396 101 \n", "-Alfredo Griffin 594 169 4 74 51 35 11 4408 1133 \n", "\n", " CHmRun CRuns CRBI CWalks League Division PutOuts \\\n", "Player \n", "-Alan Ashby 69 321 414 375 N W 632 \n", "-Alvin Davis 63 224 266 263 A W 880 \n", "-Andre Dawson 225 828 838 354 N E 200 \n", "-Andres Galarraga 12 48 46 33 N E 805 \n", "-Alfredo Griffin 19 501 336 194 A W 282 \n", "\n", " Assists Errors Salary NewLeague \n", "Player \n", "-Alan Ashby 43 10 475.0 N \n", "-Alvin Davis 82 14 480.0 A \n", "-Andre Dawson 11 3 500.0 N \n", "-Andres Galarraga 40 4 91.5 N \n", "-Alfredo Griffin 421 25 750.0 A " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Index: 263 entries, -Alan Ashby to -Willie Wilson\n", "Data columns (total 6 columns):\n", "League_A 263 non-null uint8\n", "League_N 263 non-null uint8\n", "Division_E 263 non-null uint8\n", "Division_W 263 non-null uint8\n", "NewLeague_A 263 non-null uint8\n", "NewLeague_N 263 non-null uint8\n", "dtypes: uint8(6)\n", "memory usage: 3.6+ KB\n", " League_A League_N Division_E Division_W NewLeague_A \\\n", "Player \n", "-Alan Ashby 0 1 0 1 0 \n", "-Alvin Davis 1 0 0 1 1 \n", "-Andre Dawson 0 1 1 0 0 \n", "-Andres Galarraga 0 1 1 0 0 \n", "-Alfredo Griffin 1 0 0 1 1 \n", "\n", " NewLeague_N \n", "Player \n", "-Alan Ashby 1 \n", "-Alvin Davis 0 \n", "-Andre Dawson 1 \n", "-Andres Galarraga 1 \n", "-Alfredo Griffin 0 \n" ] } ], "source": [ "dummies = pd.get_dummies(df[['League', 'Division', 'NewLeague']])\n", "dummies.info()\n", "print(dummies.head())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Index: 263 entries, -Alan Ashby to -Willie Wilson\n", "Data columns (total 19 columns):\n", "AtBat 263 non-null float64\n", "Hits 263 non-null float64\n", "HmRun 263 non-null float64\n", "Runs 263 non-null float64\n", "RBI 263 non-null float64\n", "Walks 263 non-null float64\n", "Years 263 non-null float64\n", "CAtBat 263 non-null float64\n", "CHits 263 non-null float64\n", "CHmRun 263 non-null float64\n", "CRuns 263 non-null float64\n", "CRBI 263 non-null float64\n", "CWalks 263 non-null float64\n", "PutOuts 263 non-null float64\n", "Assists 263 non-null float64\n", "Errors 263 non-null float64\n", "League_N 263 non-null uint8\n", "Division_W 263 non-null uint8\n", "NewLeague_N 263 non-null uint8\n", "dtypes: float64(16), uint8(3)\n", "memory usage: 35.7+ KB\n" ] } ], "source": [ "y = df.Salary\n", "\n", "# Drop the column with the independent variable (Salary), and columns for which we created dummy variables\n", "X_ = df.drop(['Salary', 'League', 'Division', 'NewLeague'], axis=1).astype('float64')\n", "# Define the feature set X.\n", "X = pd.concat([X_, dummies[['League_N', 'Division_W', 'NewLeague_N']]], axis=1)\n", "X.info()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>AtBat</th>\n", " <th>Hits</th>\n", " <th>HmRun</th>\n", " <th>Runs</th>\n", " <th>RBI</th>\n", " <th>Walks</th>\n", " <th>Years</th>\n", " <th>CAtBat</th>\n", " <th>CHits</th>\n", " <th>CHmRun</th>\n", " <th>CRuns</th>\n", " <th>CRBI</th>\n", " <th>CWalks</th>\n", " <th>PutOuts</th>\n", " <th>Assists</th>\n", " <th>Errors</th>\n", " <th>League_N</th>\n", " <th>Division_W</th>\n", " <th>NewLeague_N</th>\n", " </tr>\n", " <tr>\n", " <th>Player</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>-Alan Ashby</td>\n", " <td>315.0</td>\n", " <td>81.0</td>\n", " <td>7.0</td>\n", " <td>24.0</td>\n", " <td>38.0</td>\n", " <td>39.0</td>\n", " <td>14.0</td>\n", " <td>3449.0</td>\n", " <td>835.0</td>\n", " <td>69.0</td>\n", " <td>321.0</td>\n", " <td>414.0</td>\n", " <td>375.0</td>\n", " <td>632.0</td>\n", " <td>43.0</td>\n", " <td>10.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>-Alvin Davis</td>\n", " <td>479.0</td>\n", " <td>130.0</td>\n", " <td>18.0</td>\n", " <td>66.0</td>\n", " <td>72.0</td>\n", " <td>76.0</td>\n", " <td>3.0</td>\n", " <td>1624.0</td>\n", " <td>457.0</td>\n", " <td>63.0</td>\n", " <td>224.0</td>\n", " <td>266.0</td>\n", " <td>263.0</td>\n", " <td>880.0</td>\n", " <td>82.0</td>\n", " <td>14.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <td>-Andre Dawson</td>\n", " <td>496.0</td>\n", " <td>141.0</td>\n", " <td>20.0</td>\n", " <td>65.0</td>\n", " <td>78.0</td>\n", " <td>37.0</td>\n", " <td>11.0</td>\n", " <td>5628.0</td>\n", " <td>1575.0</td>\n", " <td>225.0</td>\n", " <td>828.0</td>\n", " <td>838.0</td>\n", " <td>354.0</td>\n", " <td>200.0</td>\n", " <td>11.0</td>\n", " <td>3.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>-Andres Galarraga</td>\n", " <td>321.0</td>\n", " <td>87.0</td>\n", " <td>10.0</td>\n", " <td>39.0</td>\n", " <td>42.0</td>\n", " <td>30.0</td>\n", " <td>2.0</td>\n", " <td>396.0</td>\n", " <td>101.0</td>\n", " <td>12.0</td>\n", " <td>48.0</td>\n", " <td>46.0</td>\n", " <td>33.0</td>\n", " <td>805.0</td>\n", " <td>40.0</td>\n", " <td>4.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>-Alfredo Griffin</td>\n", " <td>594.0</td>\n", " <td>169.0</td>\n", " <td>4.0</td>\n", " <td>74.0</td>\n", " <td>51.0</td>\n", " <td>35.0</td>\n", " <td>11.0</td>\n", " <td>4408.0</td>\n", " <td>1133.0</td>\n", " <td>19.0</td>\n", " <td>501.0</td>\n", " <td>336.0</td>\n", " <td>194.0</td>\n", " <td>282.0</td>\n", " <td>421.0</td>\n", " <td>25.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AtBat Hits HmRun Runs RBI Walks Years CAtBat \\\n", "Player \n", "-Alan Ashby 315.0 81.0 7.0 24.0 38.0 39.0 14.0 3449.0 \n", "-Alvin Davis 479.0 130.0 18.0 66.0 72.0 76.0 3.0 1624.0 \n", "-Andre Dawson 496.0 141.0 20.0 65.0 78.0 37.0 11.0 5628.0 \n", "-Andres Galarraga 321.0 87.0 10.0 39.0 42.0 30.0 2.0 396.0 \n", "-Alfredo Griffin 594.0 169.0 4.0 74.0 51.0 35.0 11.0 4408.0 \n", "\n", " CHits CHmRun CRuns CRBI CWalks PutOuts Assists \\\n", "Player \n", "-Alan Ashby 835.0 69.0 321.0 414.0 375.0 632.0 43.0 \n", "-Alvin Davis 457.0 63.0 224.0 266.0 263.0 880.0 82.0 \n", "-Andre Dawson 1575.0 225.0 828.0 838.0 354.0 200.0 11.0 \n", "-Andres Galarraga 101.0 12.0 48.0 46.0 33.0 805.0 40.0 \n", "-Alfredo Griffin 1133.0 19.0 501.0 336.0 194.0 282.0 421.0 \n", "\n", " Errors League_N Division_W NewLeague_N \n", "Player \n", "-Alan Ashby 10.0 1 1 1 \n", "-Alvin Davis 14.0 0 1 0 \n", "-Andre Dawson 3.0 1 0 1 \n", "-Andres Galarraga 4.0 1 0 1 \n", "-Alfredo Griffin 25.0 0 1 0 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 5.4\n", "\n", "You should cross-validate to select the lambda just like any other tuning parameter. Sklearn gives you the option of using their fast cross-validation script via `linear_model.LassoCV`, see the documentation. You can create a leave-one-out cross validator with `model_selection.LeaveOneOut` then pass this to `LassoCV` with the `cv` argument. Do this, and see what the returned fit and selected lambda are." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "LassoCV(alphas=None, copy_X=True,\n", " cv=<generator object BaseCrossValidator.split at 0x7f17afebdcd0>,\n", " eps=0.001, fit_intercept=True, max_iter=2000, n_alphas=100, n_jobs=None,\n", " normalize=False, positive=False, precompute='auto', random_state=None,\n", " selection='cyclic', tol=0.0001, verbose=False)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Answer to 5.4\n", "## Fit the lasso and cross-validate, increased max_iter to achieve convergence\n", "loo = model_selection.LeaveOneOut()\n", "looiter = loo.split(X)\n", "hitlasso = linear_model.LassoCV(cv=looiter,max_iter=2000) \n", "hitlasso.fit(X,y)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The selected lambda value is 540.66\n" ] } ], "source": [ "print(\"The selected lambda value is {:.2f}\".format(hitlasso.alpha_))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "array([-0. , 1.49586273, 0. , 0. , 0. ,\n", " 1.13979587, -0. , -0.33734233, 0.82306877, 0. ,\n", " 0.79947238, 0.65458696, -0.03331169, 0.27575248, 0.13166923,\n", " -0. , 0. , -0. , 0. ])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hitlasso.coef_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We can also compare this to the selected model from forward stagewise regression:\n", "\n", "```\n", "[-0.21830515, 0.38154135, 0. , 0. , 0. ,\n", " 0.16139123, 0. , 0. , 0. , 0. ,\n", " 0.09994524, 0.56696569, -0.16872682, 0.16924078, 0. ,\n", " 0. , 0. , -0.19429699, 0. ]\n", "```\n", "\n", "This is not exactly the same model with differences in the inclusion or exclusion of AtBat, HmRun, Runs, RBI, Years, CHmRun, Errors, League_N, Division_W, NewLeague_N" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "bforw = [-0.21830515, 0.38154135, 0. , 0. , 0. ,\n", " 0.16139123, 0. , 0. , 0. , 0. ,\n", " 0.09994524, 0.56696569, -0.16872682, 0.16924078, 0. ,\n", " 0. , 0. , -0.19429699, 0. ]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AtBat, HmRun, Runs, RBI, Years, CHmRun, Errors, League_N, Division_W, NewLeague_N\n" ] } ], "source": [ "print(\", \".join(X.columns[(hitlasso.coef_ != 0.) != (bforw != 0.)]))" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
allentran/reinforcement-learning
DP/Policy Evaluation.ipynb
1
8343
{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "if \"../\" not in sys.path:\n", " sys.path.append(\"../\") \n", "from lib.envs.gridworld import GridworldEnv" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "env = GridworldEnv()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\n", " \"\"\"\n", " Evaluate a policy given an environment and a full description of the environment's dynamics.\n", " \n", " Args:\n", " policy: [S, A] shaped matrix representing the policy.\n", " env: OpenAI env. env.P represents the transition probabilities of the environment.\n", " env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\n", " env.nS is a number of states in the environment. \n", " env.nA is a number of actions in the environment.\n", " theta: We stop evaluation once our value function change is less than theta for all states.\n", " discount_factor: Gamma discount factor.\n", " \n", " Returns:\n", " Vector of length env.nS representing the value function.\n", " \"\"\"\n", " # Start with a random (all 0) value function\n", " V = np.zeros(env.nS)\n", " \n", " while True:\n", " V_ = np.zeros(env.nS)\n", " for state in xrange(env.nS):\n", " v_s = 0\n", " for action, p_action in enumerate(policy[state]):\n", " for prob, next_state, reward, _ in env.P[state][action]:\n", " v_s += p_action * prob * (reward + discount_factor * V[next_state])\n", " V_[state] = v_s\n", " max_diff = np.abs(V - V_).max()\n", " if max_diff < theta:\n", " break\n", " V = V_\n", " return np.array(V)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "random_policy = np.ones([env.nS, env.nA]) / env.nA\n", "v = policy_eval(random_policy, env)\n" ] }, { "cell_type": "code", <<<<<<< HEAD "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], ======= "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "AssertionError", "evalue": "\nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22,\n -20, -14, 0])", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-22-235f39fb115c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Test: Make sure the evaluated policy is what we expected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mexpected_v\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m22\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m22\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_array_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_v\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_almost_equal\u001b[0;34m(x, y, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 914\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\n\u001b[1;32m 915\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Arrays are not almost equal to %d decimals'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m precision=decimal)\n\u001b[0m\u001b[1;32m 917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision)\u001b[0m\n\u001b[1;32m 735\u001b[0m names=('x', 'y'), precision=precision)\n\u001b[1;32m 736\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcond\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 738\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22,\n -20, -14, 0])" ] } ], >>>>>>> f45bcbf23daddbb7cfd9d08103609053ba4f92c6 "source": [ "# Test: Make sure the evaluated policy is what we expected\n", "expected_v = np.array([0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22, -20, -14, 0])\n", "np.testing.assert_array_almost_equal(v, expected_v, decimal=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, <<<<<<< HEAD "nbformat_minor": 0 } ======= "nbformat_minor": 1 } >>>>>>> f45bcbf23daddbb7cfd9d08103609053ba4f92c6
mit
hh-italian-group/hh-bbtautau
Studies/notebooks/SignalSelectionNNTraining.ipynb
1
18581
{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import re \n", "import os\n", "import tensorflow as tf\n", "import ROOT\n", "\n", "from tensorflow.keras.callbacks import CSVLogger\n", "\n", "import sys\n", "sys.path.insert(0, \"../python\")\n", "import InputsProducer\n", "from SignalSelectionModel import HHModel" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def ListToVector(l, elem_type):\n", " v = ROOT.std.vector('string')()\n", " for x in l:\n", " v.push_back(x)\n", " return v" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "files = []\n", "for r, d, f in os.walk('/data/dido/NN_samples'):\n", " for file in f:\n", " if re.match(r'.*2018.*ggHH_NonRes\\.root', file):\n", " files.append(os.path.join(r, file))\n", "# print(files)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "file_name = '/data/dido/new_samples/NN_samples/VBFToBulkGravitonToHHTo2B2Tau_M-400_narrow_eTau_2018_VBFHH_Res.root'\n", "# file_name = '/data/dido/new_samples/NN_samples/*.root'" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10.2 s, sys: 170 ms, total: 10.3 s\n", "Wall time: 10.3 s\n" ] } ], "source": [ "%%time\n", "data = InputsProducer.CreateRootDF(file_name, 0, True)\n", "X, Y, var_pos = InputsProducer.CreateXY(data)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def sel_acc(y_true, y_pred, n_positions, n_exp):\n", " pred_sorted = tf.argsort(y_pred, axis=1, direction='DESCENDING')\n", " #return pred_sorted[:, 0]\n", " n_evt = tf.shape(y_true)[0]\n", " evt_id = tf.range(n_evt)\n", " matches_vec = []\n", " for n in range(n_positions):\n", " index = tf.transpose(tf.stack([evt_id, tf.reshape(pred_sorted[:, n], shape=(n_evt,))]))\n", " matches_vec.append(tf.gather_nd(y_true, index))\n", " matches_sum = tf.add_n(matches_vec) \n", " valid = tf.cast(tf.equal(matches_sum, n_exp), tf.float32)\n", " n_valid = tf.reduce_sum(valid)\n", " return n_valid / tf.cast(n_evt, tf.float32)\n", "\n", "def sel_acc_2(y_true, y_pred):\n", " return sel_acc(y_true, y_pred, 2, 2)\n", "def sel_acc_3(y_true, y_pred):\n", " return sel_acc(y_true, y_pred, 3, 2)\n", "def sel_acc_4(y_true, y_pred):\n", " return sel_acc(y_true, y_pred, 4, 2)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-10-17 15:03:45.987294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n", "2019-10-17 15:03:46.343299: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.344220: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n", "name: GeForce GTX 1060 3GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845\n", "pciBusID: 0000:01:00.0\n", "2019-10-17 15:03:46.344553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n", "2019-10-17 15:03:46.346772: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n", "2019-10-17 15:03:46.348679: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n", "2019-10-17 15:03:46.349091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n", "2019-10-17 15:03:46.351115: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n", "2019-10-17 15:03:46.352602: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n", "2019-10-17 15:03:46.356525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n", "2019-10-17 15:03:46.356667: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.358025: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.358552: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n", "2019-10-17 15:03:46.359116: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n", "2019-10-17 15:03:46.365833: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3000000000 Hz\n", "2019-10-17 15:03:46.366158: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1a6e0120 executing computations on platform Host. Devices:\n", "2019-10-17 15:03:46.366173: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version\n", "2019-10-17 15:03:46.433562: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.471677: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1ace26a0 executing computations on platform CUDA. Devices:\n", "2019-10-17 15:03:46.471692: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): GeForce GTX 1060 3GB, Compute Capability 6.1\n", "2019-10-17 15:03:46.471850: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.472381: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n", "name: GeForce GTX 1060 3GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845\n", "pciBusID: 0000:01:00.0\n", "2019-10-17 15:03:46.472420: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n", "2019-10-17 15:03:46.472434: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n", "2019-10-17 15:03:46.472446: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n", "2019-10-17 15:03:46.472458: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n", "2019-10-17 15:03:46.472470: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n", "2019-10-17 15:03:46.472482: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n", "2019-10-17 15:03:46.472506: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n", "2019-10-17 15:03:46.472572: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.473063: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.473522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n", "2019-10-17 15:03:46.473550: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n", "2019-10-17 15:03:46.474550: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n", "2019-10-17 15:03:46.474561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 \n", "2019-10-17 15:03:46.474566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N \n", "2019-10-17 15:03:46.474739: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.475219: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2019-10-17 15:03:46.475710: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2650 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 3GB, pci bus id: 0000:01:00.0, compute capability: 6.1)\n" ] } ], "source": [ "model = HHModel(var_pos, '../config/mean_std_red.json', '../config/min_max_red.json')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.25 s, sys: 377 ms, total: 1.62 s\n", "Wall time: 1.61 s\n" ] }, { "data": { "text/plain": [ "<tf.Tensor: id=478, shape=(1, 10), dtype=float32, numpy=\n", "array([[0.76205575, 0.7586758 , 0.75255466, 0.75118256, 0.7507615 ,\n", " 0.7470724 , 0.7442271 , 0.777784 , 0.777784 , 0.777784 ]],\n", " dtype=float32)>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "2019-10-17 15:03:47.079428: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n", "2019-10-17 15:03:47.918418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n" ] } ], "source": [ "%time model.call(X[0:1,:,:])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "model.compile(loss='binary_crossentropy',\n", " optimizer='adam',\n", " metrics=[sel_acc_2, sel_acc_3, sel_acc_4])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"hh_model\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "std_layer (StdLayer) multiple 0 \n", "_________________________________________________________________\n", "scale_layer (ScaleLayer) multiple 0 \n", "_________________________________________________________________\n", "lstm (LSTM) multiple 1984 \n", "_________________________________________________________________\n", "dropout (Dropout) multiple 0 \n", "_________________________________________________________________\n", "concatenate (Concatenate) multiple 0 \n", "_________________________________________________________________\n", "lstm_1 (LSTM) multiple 3072 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) multiple 0 \n", "_________________________________________________________________\n", "time_distributed (TimeDistri multiple 170 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) multiple 0 \n", "_________________________________________________________________\n", "time_distributed_1 (TimeDist multiple 11 \n", "=================================================================\n", "Total params: 5,237\n", "Trainable params: 5,237\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.build(X.shape)\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 1888 samples, validate on 473 samples\n", "Epoch 1/10\n", "1888/1888 [==============================] - 10s 5ms/sample - loss: 1.2160 - sel_acc_2: 0.0175 - sel_acc_3: 0.0607 - sel_acc_4: 0.1195 - val_loss: 1.1947 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0486 - val_sel_acc_4: 0.1205\n", "Epoch 2/10\n", "1888/1888 [==============================] - 0s 29us/sample - loss: 1.2010 - sel_acc_2: 0.0169 - sel_acc_3: 0.0545 - sel_acc_4: 0.1094 - val_loss: 1.1784 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0486 - val_sel_acc_4: 0.1226\n", "Epoch 3/10\n", "1888/1888 [==============================] - 0s 30us/sample - loss: 1.1835 - sel_acc_2: 0.0156 - sel_acc_3: 0.0544 - sel_acc_4: 0.1131 - val_loss: 1.1624 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0486 - val_sel_acc_4: 0.1247\n", "Epoch 4/10\n", "1888/1888 [==============================] - 0s 31us/sample - loss: 1.1672 - sel_acc_2: 0.0184 - sel_acc_3: 0.0545 - sel_acc_4: 0.1194 - val_loss: 1.1468 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0486 - val_sel_acc_4: 0.1247\n", "Epoch 5/10\n", "1888/1888 [==============================] - 0s 31us/sample - loss: 1.1538 - sel_acc_2: 0.0122 - sel_acc_3: 0.0525 - sel_acc_4: 0.1060 - val_loss: 1.1314 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0486 - val_sel_acc_4: 0.1247\n", "Epoch 6/10\n", "1888/1888 [==============================] - 0s 30us/sample - loss: 1.1376 - sel_acc_2: 0.0135 - sel_acc_3: 0.0562 - sel_acc_4: 0.1163 - val_loss: 1.1163 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0507 - val_sel_acc_4: 0.1247\n", "Epoch 7/10\n", "1888/1888 [==============================] - 0s 31us/sample - loss: 1.1226 - sel_acc_2: 0.0080 - sel_acc_3: 0.0396 - sel_acc_4: 0.1055 - val_loss: 1.1015 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0507 - val_sel_acc_4: 0.1247\n", "Epoch 8/10\n", "1888/1888 [==============================] - 0s 30us/sample - loss: 1.1063 - sel_acc_2: 0.0097 - sel_acc_3: 0.0490 - sel_acc_4: 0.1033 - val_loss: 1.0871 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0507 - val_sel_acc_4: 0.1247\n", "Epoch 9/10\n", "1888/1888 [==============================] - 0s 30us/sample - loss: 1.0941 - sel_acc_2: 0.0116 - sel_acc_3: 0.0464 - sel_acc_4: 0.1096 - val_loss: 1.0729 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0507 - val_sel_acc_4: 0.1247\n", "Epoch 10/10\n", "1888/1888 [==============================] - 0s 30us/sample - loss: 1.0807 - sel_acc_2: 0.0091 - sel_acc_3: 0.0391 - sel_acc_4: 0.1020 - val_loss: 1.0591 - val_sel_acc_2: 0.0085 - val_sel_acc_3: 0.0507 - val_sel_acc_4: 0.1247\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2019-10-17 15:03:57.904935: W tensorflow/core/grappler/optimizers/implementation_selector.cc:310] Skipping optimization due to error while loading function libraries: Invalid argument: Functions '__inference___backward_standard_lstm_19615_20214' and '__inference___backward_cudnn_lstm_with_fallback_17895_19354_specialized_for_StatefulPartitionedCall_at___inference_distributed_function_21201' both implement 'lstm_507d93a1-51d6-4df5-85cb-6f9b2c346321' but their signatures do not match.\n", "2019-10-17 15:04:01.370787: W tensorflow/core/grappler/optimizers/implementation_selector.cc:310] Skipping optimization due to error while loading function libraries: Invalid argument: Functions '__inference_cudnn_lstm_with_fallback_23835_specialized_for_hh_model_lstm_1_StatefulPartitionedCall_at___inference_distributed_function_25587' and '__inference_cudnn_lstm_with_fallback_23835' both implement 'lstm_e6afa1f0-51ad-42d2-a064-c45b83d884fe' but their signatures do not match.\n" ] } ], "source": [ "csv_logger = CSVLogger('history.csv', append=True, separator=';')\n", "history_callback = model.fit(X, Y[:,:,:], validation_split=0.2, epochs=10, batch_size=1000, callbacks=[csv_logger])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "model.save_weights('./checkpoints/my_checkpoint')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-2.0
dr-guangtou/hs_galphot
fake/GalSimSersicTest.ipynb
1
175051
{ "metadata": { "name": "", "signature": "sha256:3c54fca562ebf4d83759b61011f7ed2f2da04b3be4fb847a9bebbee7879e53c6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import galsim\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the Sersic model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "gal_flux = 1.234e5\n", "gal_rpix = 24.0 \n", "gal_ns = 2.5 \n", "gal_ba = 0.8 \n", "gal_pa = 45.0 " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Use the Sersic function to create a galaxy model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "serModel = galsim.Sersic(gal_ns, half_light_radius=gal_rpix, flux=gal_flux)\n", "print serModel.getFlux()\n", "print serModel.centroid() \n", "print serModel.hasHardEdges() #Returns True if there are any hard edges in the profile, which would require very small k spacing when working in the Fourier domain" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "123400.0\n", "(0.0, 0.0)\n", "False\n" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Use the shear function to apply axis ratio (q=b/a) to the Sersic model " ] }, { "cell_type": "code", "collapsed": false, "input": [ "serSBP = serModel.SBProfile\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "serModelAxis=serModel.shear(q=gal_ba, beta=0.0*galsim.degrees)\n", "print serModelAxis.getFlux()\n", "print serModelAxis.centroid()\n", "print serModel.hasHardEdges()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "123400.0\n", "(0.0, 0.0)\n", "False\n" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "serModelRotate=serModelAxis.rotate(gal_pa*galsim.degrees)\n", "print serModelAxis.getFlux()\n", "print serModelAxis.centroid()\n", "print serModel.hasHardEdges()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "123400.0\n", "(0.0, 0.0)\n", "False\n" ] } ], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "serImg = serModel.drawImage()\n", "serImgAxis = serModelAxis.drawImage()\n", "serImgRotate = serModelRotate.drawImage()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.subplot(131)\n", "plt.imshow(numpy.arcsinh(serImg.array))\n", "plt.subplot(132)\n", "plt.imshow(numpy.arcsinh(serImgAxis.array))\n", "plt.subplot(133)\n", "plt.imshow(numpy.arcsinh(serImgRotate.array))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 71, "text": [ "<matplotlib.image.AxesImage at 0x11da569d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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6DlHyidMkhj7zBHR0w76YnF/xFnh0A0zeAs/GIfMYHHsCrN4J2w7CgrvhpfPhuS/CzD8T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\nJouyHuHhicODNzpKfZK4ZFQ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"text": [ "<matplotlib.figure.Figure at 0x11c2a0510>" ] } ], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "serModel = galsim.Sersic(gal_ns, half_light_radius=gal_rpix, flux=gal_flux)\n", "serModel = serModel.shear(q=gal_ba, beta=0.0*galsim.degrees)\n", "serModel = serModel.rotate(gal_pa*galsim.degrees)\n", "serImg = serModel.drawImage()\n", "serImg.addNoise(galsim.PoissonNoise())\n", "plt.figure(1, figsize=(5,5))\n", "plt.imshow(numpy.arcsinh(serImg.array))\n", "plt.savefig('/Users/songhuang/Downloads/temp.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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JV5rb1su2vkVIHM+Q73b+VaBGOMIbgcR+GlFz+ZuPgz8o2/iWyVjw2eKrXtUy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\nyf8BISFCiPM/wT4AAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x11fb676d0>" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "serModel1 = galsim.Sersic(3.0, half_light_radius=8.00, flux=1.000e5)\n", "serModel1 = serModel1.shear(q=0.9, beta=0.0*galsim.degrees)\n", "serModel2 = galsim.Sersic(1.0, half_light_radius=45.0, flux=1.345e5)\n", "serModel2 = serModel2.shear(q=0.4, beta=0.0*galsim.degrees)\n", "serModel2 = serModel2.rotate(45.0*galsim.degrees)\n", "serDouble = serModel1 + serModel2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, axes = plt.subplots(1, 3, figsize=(15,5))\n", "imgs = serModel1.drawImage().array, serModel2.drawImage().array, serDouble.drawImage().array\n", "titles = \"SerComp1\", \"SerComp2\", \"SerDouble\"\n", "for i in range(3):\n", " axes[i].imshow(numpy.arcsinh(imgs[i]))\n", " axes[i].set_title(titles[i])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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UxCpSfSHMY6supb8o1olLseu8bxI9Z1/i47uIcxzHeYi4eHMmKG/w2NqpOvZE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\nucgYh02KA6UQJyHp1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"text": [ "<matplotlib.figure.Figure at 0x11d1425d0>" ] } ], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "psfGauss = galsim.Gaussian(fwhm=2.0)\n", "psfImage = psfGauss.drawImage().array \n", "plt.imshow(numpy.arcsinh(psfImage))\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 85, "text": [ "<matplotlib.image.AxesImage at 0x10a79e710>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10a646510>" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "psfIm = galsim.image.Image(psfImage)\n", "psfObj = galsim.InterpolatedImage(psfIm, scale=1.0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "gal = fits.open(\"galaxy_test.fits\")[1].data[0]\n", "double(gal[\"mag\"])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "19.5" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "from makeFakeGalaxy import * " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "testMakeFake(\"galaxy_test.fits\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/songhuang/anaconda/lib/python2.7/site-packages/galsim/image.py:237: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if array != None:\n", "/Users/songhuang/anaconda/lib/python2.7/site-packages/galsim/image.py:278: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " elif array != None:\n", "/Users/songhuang/anaconda/lib/python2.7/site-packages/galsim/image.py:329: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " def array(self): return self.image.array\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "---------------------------------\n", " Input Flux : 1000.0\n", " Input Parameters : 2.5 10.0 0.8 40.0\n", " With Flux : 1000.0\n", " Output Flux : " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 963.422\n", " Shape of the Output Array : (100, 100)\n", "---------------------------------\n", "---------------------------------\n", " Input Flux : 1905.46205692\n", " Input Parameters : 1.0 35.0 0.35 80.0\n", " With Flux : 1905.46205692\n", " Output Flux : " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 1905.46\n", " Shape of the Output Array : (350, 350)\n", "---------------------------------\n", "---------------------------------\n", " Input Flux : 1000.0\n", " Input Parameters : 4.5 6.0 0.75 60.0\n", " With Flux : 1000.0\n", " Output Flux : " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 925.595\n", " Shape of the Output Array : (60, 60)\n", "---------------------------------\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 0 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
sdpython/ensae_teaching_cs
_doc/notebooks/td2a_ml/seasonal_timeseries.ipynb
1
457104
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Timeseries\n", "\n", "Ce notebook pr\u00e9sente quelques \u00e9tapes simples pour une s\u00e9rie temporelle. 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parseInt(child.tagName.substring(1,2));\n", "\n", " text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", " if ((level < lfirst) || (level > llast)) {\n", " continue ;\n", " }\n", " if (title.endsWith('\u00b6')) {\n", " title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", " .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", " }\n", " if (title.length == 0) {\n", " continue;\n", " }\n", "\n", " while (level < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " format_open -= 1;\n", " memo_level -= 1;\n", " }\n", " if (level == lfirst) {\n", " main_item += 1;\n", " }\n", " if (keep_item != -1 && main_item != keep_item + 1) {\n", " // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", " continue;\n", " }\n", " while (level > memo_level) {\n", " text_menu += \"<ul>\\n\";\n", " memo_level += 1;\n", " }\n", " text_menu += repeat_indent_string(level-2);\n", " text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", " format_open += 1;\n", " }\n", " while (1 < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " memo_level -= 1;\n", " format_open -= 1;\n", " }\n", " text_menu += send;\n", " //text_menu += \"\\n\" + text_memo;\n", "\n", " while (format_open > 0) {\n", " text_menu += end_format;\n", " format_open -= 1;\n", " }\n", " return text_menu;\n", "};\n", "var update_menu = function() {\n", " var sbegin = \"\";\n", " var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", " var send = \"\";\n", " var begin_format = '<li>';\n", " var end_format = '</li>';\n", " var keep_item = -1;\n", " var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", " begin_format, end_format);\n", " var menu = document.getElementById(\"my_id_menu_nb\");\n", " menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", " </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Donn\u00e9es\n", "\n", "Les donn\u00e9es sont artificielles mais simulent ce que pourraient \u00eatre le chiffre d'affaires d'un magasin de quartier, des samedi tr\u00e8s forts, une semaine morne, un No\u00ebl charg\u00e9, un \u00e9t\u00e9 plat." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-02-13 18:54:23.461489</td>\n", " <td>0.005357</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-02-14 18:54:23.461489</td>\n", " <td>0.009562</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-02-15 18:54:23.461489</td>\n", " <td>0.014353</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2020-02-16 18:54:23.461489</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020-02-17 18:54:23.461489</td>\n", " <td>0.003475</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date value\n", "0 2020-02-13 18:54:23.461489 0.005357\n", "1 2020-02-14 18:54:23.461489 0.009562\n", "2 2020-02-15 18:54:23.461489 0.014353\n", "3 2020-02-16 18:54:23.461489 0.000000\n", "4 2020-02-17 18:54:23.461489 0.003475" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from ensae_teaching_cs.data import generate_sells\n", "import pandas\n", "df = pandas.DataFrame(generate_sells())\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Premiers graphiques\n", "\n", "La s\u00e9rie a deux saisonnalit\u00e9s, hebdomadaire, mensuelle." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n", "df.iloc[-30:].set_index('date').plot(ax=ax[0])\n", "df.set_index('date').plot(ax=ax[1])\n", "ax[0].set_title(\"chiffre d'affaire sur le dernier mois\")\n", "ax[1].set_title(\"chiffre d'affaire sur deux ans\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Elle a une vague tendance, on peut calculer un tendance \u00e0 l'ordre 1, 2, ..." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.tsa.tsatools import detrend\n", "notrend = detrend(df.value, order=1)\n", "df[\"notrend\"] = notrend\n", "df[\"trend\"] = df['value'] - notrend\n", "ax = df.plot(x=\"date\", y=[\"value\", \"trend\"], figsize=(14,4))\n", "ax.set_title('tendance');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Autocorr\u00e9lations..." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", " from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n", "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", " from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n", "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:657: FutureWarning: The default number of lags is changing from 40 tomin(int(10 * np.log10(nobs)), nobs - 1) after 0.12is released. Set the number of lags to an integer to silence this warning.\n", " warnings.warn(\n", "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:667: FutureWarning: fft=True will become the default after the release of the 0.12 release of statsmodels. To suppress this warning, explicitly set fft=False.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([ 1. , 0.03577944, -0.06235687, -0.02029818, -0.02898255,\n", " -0.06825401, 0.0250769 , 0.93062748, 0.0120951 , -0.08157127,\n", " -0.04537123, -0.05365516, -0.08887674, 0.00289459, 0.88395645,\n", " -0.01531838, -0.1028712 , -0.06616495, -0.07120575, -0.10659382,\n", " -0.01690792, 0.84848022, -0.0335295 , -0.12382299, -0.08744705,\n", " -0.09339856, -0.12657065, -0.04305763, 0.80550906, -0.05483815,\n", " -0.14409999, -0.10895806, -0.10812254, -0.14125818, -0.05846692,\n", " 0.79099037, -0.05773434, -0.14731918, -0.10789494, -0.10483253,\n", " -0.14058412])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.tsa.stattools import acf\n", "cor = acf(df.value)\n", "cor" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(14,2))\n", "ax.plot(cor)\n", "ax.set_title(\"Autocorr\u00e9logramme\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La premi\u00e8re saisonalit\u00e9 appara\u00eet, 7, 14, 21... Les autocorr\u00e9lations partielles confirment cela, plut\u00f4t 7 jours." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.tsa.stattools import pacf\n", "from statsmodels.graphics.tsaplots import plot_pacf\n", "plot_pacf(df.value, lags=50);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme il n'y a rien le dimanche, il vaut mieux les enlever. Garder des z\u00e9ros nous priverait de mod\u00e8les multiplicatifs." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>value</th>\n", " <th>notrend</th>\n", " <th>trend</th>\n", " <th>weekday</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-02-13 18:54:23.461489</td>\n", " <td>0.005357</td>\n", " <td>-0.000507</td>\n", " <td>0.005864</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-02-14 18:54:23.461489</td>\n", " <td>0.009562</td>\n", " <td>0.003694</td>\n", " <td>0.005868</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-02-15 18:54:23.461489</td>\n", " <td>0.014353</td>\n", " <td>0.008481</td>\n", " <td>0.005873</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2020-02-16 18:54:23.461489</td>\n", " <td>0.000000</td>\n", " <td>-0.005877</td>\n", " <td>0.005877</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020-02-17 18:54:23.461489</td>\n", " <td>0.003475</td>\n", " <td>-0.002407</td>\n", " <td>0.005882</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date value notrend trend weekday\n", "0 2020-02-13 18:54:23.461489 0.005357 -0.000507 0.005864 3\n", "1 2020-02-14 18:54:23.461489 0.009562 0.003694 0.005868 4\n", "2 2020-02-15 18:54:23.461489 0.014353 0.008481 0.005873 5\n", "3 2020-02-16 18:54:23.461489 0.000000 -0.005877 0.005877 6\n", "4 2020-02-17 18:54:23.461489 0.003475 -0.002407 0.005882 0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"weekday\"] = df.date.dt.weekday\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>value</th>\n", " <th>notrend</th>\n", " <th>trend</th>\n", " <th>weekday</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-02-13 18:54:23.461489</td>\n", " <td>0.005357</td>\n", " <td>-0.000507</td>\n", " <td>0.005864</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-02-14 18:54:23.461489</td>\n", " <td>0.009562</td>\n", " <td>0.003694</td>\n", " <td>0.005868</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-02-15 18:54:23.461489</td>\n", " <td>0.014353</td>\n", " <td>0.008481</td>\n", " <td>0.005873</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020-02-17 18:54:23.461489</td>\n", " <td>0.003475</td>\n", " <td>-0.002407</td>\n", " <td>0.005882</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2020-02-18 18:54:23.461489</td>\n", " <td>0.005454</td>\n", " <td>-0.000432</td>\n", " <td>0.005886</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2020-02-19 18:54:23.461489</td>\n", " <td>0.005075</td>\n", " <td>-0.000816</td>\n", " <td>0.005891</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2020-02-20 18:54:23.461489</td>\n", " <td>0.006801</td>\n", " <td>0.000906</td>\n", " <td>0.005896</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2020-02-21 18:54:23.461489</td>\n", " <td>0.009831</td>\n", " <td>0.003931</td>\n", " <td>0.005900</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2020-02-22 18:54:23.461489</td>\n", " <td>0.014204</td>\n", " <td>0.008299</td>\n", " <td>0.005905</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2020-02-24 18:54:23.461489</td>\n", " <td>0.003875</td>\n", " <td>-0.002039</td>\n", " <td>0.005914</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date value notrend trend weekday\n", "0 2020-02-13 18:54:23.461489 0.005357 -0.000507 0.005864 3\n", "1 2020-02-14 18:54:23.461489 0.009562 0.003694 0.005868 4\n", "2 2020-02-15 18:54:23.461489 0.014353 0.008481 0.005873 5\n", "4 2020-02-17 18:54:23.461489 0.003475 -0.002407 0.005882 0\n", "5 2020-02-18 18:54:23.461489 0.005454 -0.000432 0.005886 1\n", "6 2020-02-19 18:54:23.461489 0.005075 -0.000816 0.005891 2\n", "7 2020-02-20 18:54:23.461489 0.006801 0.000906 0.005896 3\n", "8 2020-02-21 18:54:23.461489 0.009831 0.003931 0.005900 4\n", "9 2020-02-22 18:54:23.461489 0.014204 0.008299 0.005905 5\n", "11 2020-02-24 18:54:23.461489 0.003875 -0.002039 0.005914 0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_nosunday = df[df.weekday != 6]\n", "df_nosunday.head(n=10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:657: FutureWarning: The default number of lags is changing from 40 tomin(int(10 * np.log10(nobs)), nobs - 1) after 0.12is released. Set the number of lags to an integer to silence this warning.\n", " warnings.warn(\n", "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:667: FutureWarning: fft=True will become the default after the release of the 0.12 release of statsmodels. To suppress this warning, explicitly set fft=False.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(14,2))\n", "cor = acf(df_nosunday.value)\n", "ax.plot(cor)\n", "ax.set_title(\"Autocorr\u00e9logramme\");" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_pacf(df_nosunday.value, lags=50);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On d\u00e9compose la s\u00e9rie en tendance + saisonnalit\u00e9. Les \u00e9t\u00e9s et No\u00ebl apparaissent." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<ipython-input-13-a9c8eeeaae9a>:2: FutureWarning: the 'freq'' keyword is deprecated, use 'period' instead.\n", " res = seasonal_decompose(df_nosunday.value, freq=7)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.tsa.seasonal import seasonal_decompose\n", "res = seasonal_decompose(df_nosunday.value, freq=7)\n", "res.plot();" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(res.seasonal[-30:])\n", "plt.title(\"Saisonnalit\u00e9\");" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:657: FutureWarning: The default number of lags is changing from 40 tomin(int(10 * np.log10(nobs)), nobs - 1) after 0.12is released. Set the number of lags to an integer to silence this warning.\n", " warnings.warn(\n", "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:667: FutureWarning: fft=True will become the default after the release of the 0.12 release of statsmodels. To suppress this warning, explicitly set fft=False.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cor = acf(res.trend[5:-5]);\n", "plt.plot(cor);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On cherche maintenant la saisonnalit\u00e9 de la s\u00e9rie d\u00e9barrass\u00e9e de sa tendance herbdomadaire. On retrouve la saisonnalit\u00e9 mensuelle." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<ipython-input-16-285cbf1eb085>:1: FutureWarning: the 'freq'' keyword is deprecated, use 'period' instead.\n", " res_year = seasonal_decompose(res.trend[5:-5], freq=25)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "res_year = seasonal_decompose(res.trend[5:-5], freq=25)\n", "res_year.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test de stationnarit\u00e9\n", "\n", "Le test [KPSS](https://en.wikipedia.org/wiki/KPSS_test) permet de tester la stationnarit\u00e9 d'une s\u00e9rie." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1875: FutureWarning: The behavior of using nlags=None will change in release 0.13.Currently nlags=None is the same as nlags=\"legacy\", and so a sample-size lag length is used. After the next release, the default will change to be the same as nlags=\"auto\" which uses an automatic lag length selection method. To silence this warning, either use \"auto\" or \"legacy\"\n", " warnings.warn(msg, FutureWarning)\n", "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1906: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is smaller than the p-value returned.\n", "\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "(0.980161988153884,\n", " 0.01,\n", " 19,\n", " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.tsa.stattools import kpss\n", "kpss(res.trend[5:-5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme ce n'est pas toujours facile \u00e0 interpr\u00e9ter, on simule une variable al\u00e9atoire gaussienne donc sans tendance." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.4813396167770415,\n", " 0.04586945568084651,\n", " 22,\n", " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy.random import randn\n", "bruit = randn(1000)\n", "kpss(bruit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et puis une s\u00e9rie avec une tendance forte." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1906: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is smaller than the p-value returned.\n", "\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "(2.9761535894770517,\n", " 0.01,\n", " 22,\n", " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy.random import randn\n", "from numpy import arange\n", "bruit = randn(1000) * 100 + arange(1000) / 10\n", "kpss(bruit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une valeur forte indique une tendance et la s\u00e9rie en a clairement une." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pr\u00e9diction\n", "\n", "Les mod\u00e8les *AR*, *ARMA*, *ARIMA* se concentrent sur une s\u00e9rie \u00e0 une dimension. En machine learning, il y a la s\u00e9rie et plein d'autres informations. On construit une matrice avec des s\u00e9ries d\u00e9cal\u00e9es." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>value</th>\n", " <th>notrend</th>\n", " <th>trend</th>\n", " <th>weekday</th>\n", " <th>lag1</th>\n", " <th>lag2</th>\n", " <th>lag3</th>\n", " <th>lag4</th>\n", " <th>lag5</th>\n", " <th>lag6</th>\n", " <th>lag7</th>\n", " <th>lag8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>726</th>\n", " <td>2022-02-08 18:54:23.461489</td>\n", " <td>0.008707</td>\n", " <td>-0.000466</td>\n", " <td>0.009173</td>\n", " <td>1</td>\n", " <td>0.005806</td>\n", " <td>0.020552</td>\n", " <td>0.013143</td>\n", " <td>0.009727</td>\n", " <td>0.007346</td>\n", " <td>0.008504</td>\n", " <td>0.004580</td>\n", " <td>0.013786</td>\n", " </tr>\n", " <tr>\n", " <th>727</th>\n", " <td>2022-02-09 18:54:23.461489</td>\n", " <td>0.008582</td>\n", " <td>-0.000596</td>\n", " <td>0.009178</td>\n", " <td>2</td>\n", " <td>0.008707</td>\n", " <td>0.005806</td>\n", " <td>0.020552</td>\n", " <td>0.013143</td>\n", " <td>0.009727</td>\n", " <td>0.007346</td>\n", " <td>0.008504</td>\n", " <td>0.004580</td>\n", " </tr>\n", " <tr>\n", " <th>728</th>\n", " <td>2022-02-10 18:54:23.461489</td>\n", " <td>0.009646</td>\n", " <td>0.000464</td>\n", " <td>0.009182</td>\n", " <td>3</td>\n", " <td>0.008582</td>\n", " <td>0.008707</td>\n", " <td>0.005806</td>\n", " <td>0.020552</td>\n", " <td>0.013143</td>\n", " <td>0.009727</td>\n", " <td>0.007346</td>\n", " <td>0.008504</td>\n", " </tr>\n", " <tr>\n", " <th>729</th>\n", " <td>2022-02-11 18:54:23.461489</td>\n", " <td>0.014557</td>\n", " <td>0.005370</td>\n", " <td>0.009187</td>\n", " <td>4</td>\n", " <td>0.009646</td>\n", " <td>0.008582</td>\n", " <td>0.008707</td>\n", " <td>0.005806</td>\n", " <td>0.020552</td>\n", " <td>0.013143</td>\n", " <td>0.009727</td>\n", " <td>0.007346</td>\n", " </tr>\n", " <tr>\n", " <th>730</th>\n", " <td>2022-02-12 18:54:23.461489</td>\n", " <td>0.019851</td>\n", " <td>0.010660</td>\n", " <td>0.009191</td>\n", " <td>5</td>\n", " <td>0.014557</td>\n", " <td>0.009646</td>\n", " <td>0.008582</td>\n", " <td>0.008707</td>\n", " <td>0.005806</td>\n", " <td>0.020552</td>\n", " <td>0.013143</td>\n", " <td>0.009727</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date value notrend trend weekday \\\n", "726 2022-02-08 18:54:23.461489 0.008707 -0.000466 0.009173 1 \n", "727 2022-02-09 18:54:23.461489 0.008582 -0.000596 0.009178 2 \n", "728 2022-02-10 18:54:23.461489 0.009646 0.000464 0.009182 3 \n", "729 2022-02-11 18:54:23.461489 0.014557 0.005370 0.009187 4 \n", "730 2022-02-12 18:54:23.461489 0.019851 0.010660 0.009191 5 \n", "\n", " lag1 lag2 lag3 lag4 lag5 lag6 lag7 \\\n", "726 0.005806 0.020552 0.013143 0.009727 0.007346 0.008504 0.004580 \n", "727 0.008707 0.005806 0.020552 0.013143 0.009727 0.007346 0.008504 \n", "728 0.008582 0.008707 0.005806 0.020552 0.013143 0.009727 0.007346 \n", "729 0.009646 0.008582 0.008707 0.005806 0.020552 0.013143 0.009727 \n", "730 0.014557 0.009646 0.008582 0.008707 0.005806 0.020552 0.013143 \n", "\n", " lag8 \n", "726 0.013786 \n", "727 0.004580 \n", "728 0.008504 \n", "729 0.007346 \n", "730 0.009727 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.tsa.tsatools import lagmat\n", "lag = 8\n", "X = lagmat(df_nosunday[\"value\"], lag)\n", "lagged = df_nosunday.copy()\n", "for c in range(1,lag+1):\n", " lagged[\"lag%d\" % c] = X[:, c-1]\n", "lagged.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On ajoute ou on r\u00e9\u00e9crit le jour de la semaine qu'on utilise comme variable suppl\u00e9mentaire." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "lagged[\"weekday\"] = lagged.date.dt.weekday" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((627, 9), (627,))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = lagged.drop([\"date\", \"value\", \"notrend\", \"trend\"], axis=1)\n", "Y = lagged[\"value\"]\n", "X.shape, Y.shape" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1. , 0.99999912, 0.99999792, ..., 0.99999049, 0.99999476,\n", " 0.99999663],\n", " [0.99999912, 1. , 0.99999936, ..., 0.99998874, 0.99999358,\n", " 0.99999672],\n", " [0.99999792, 0.99999936, 1. , ..., 0.99998653, 0.99999169,\n", " 0.99999553],\n", " ...,\n", " [0.99999049, 0.99998874, 0.99998653, ..., 1. , 0.9999852 ,\n", " 0.99998403],\n", " [0.99999476, 0.99999358, 0.99999169, ..., 0.9999852 , 1. ,\n", " 0.99999084],\n", " [0.99999663, 0.99999672, 0.99999553, ..., 0.99998403, 0.99999084,\n", " 1. ]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy import corrcoef\n", "corrcoef(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etrange autant de grandes valeurs, cela veut dire que la tendance est trop forte pour calculer des corr\u00e9lations, il vaudrait mieux tout recommencer avec la s\u00e9rie $\\Delta Y_t = Y_t - Y_{t-1}$. Bref, passons..." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['weekday', 'lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7',\n", " 'lag8'],\n", " dtype='object')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une r\u00e9gression lin\u00e9aire car les mod\u00e8les lin\u00e9aires sont toujours de bonnes baseline et pour conna\u00eetre le mod\u00e8le simul\u00e9, on ne fera pas beaucoup mieux." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "clr = LinearRegression()\n", "clr.fit(X, Y)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8789132280236231" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import r2_score\n", "r2_score(Y, clr.predict(X))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.0016414 , 0.34593324, 0.26436686, 0.08632379, 0.01356802,\n", " -0.03755237, 0.40831821, -0.12106444, -0.07772163])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clr.coef_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On retrouve la saisonnalit\u00e9, $Y_t$ et $Y_{t-6}$ sont de m\u00e8ches." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X(t-1) -0.48934404097448847\n", "X(t-2) -1.0143639444424148\n", "X(t-3) -1.228186547024929\n", "X(t-4) -1.0378510803717922\n", "X(t-5) -0.5496246593771723\n", "X(t-6) 0.7876799792178883\n", "X(t-7) -0.5843479675288277\n", "X(t-8) -1.1145521360143462\n" ] } ], "source": [ "for i in range(1, X.shape[1]):\n", " print(\"X(t-%d)\" % (i), r2_score(Y, X.iloc[:, i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Auparavant (l'ann\u00e9e derni\u00e8re en fait), je construisais deux bases, apprentissage et tests, comme ceci :" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "n = X.shape[0]\n", "X_train = X.iloc[:n * 2//3]\n", "X_test = X.iloc[n * 2//3:]\n", "Y_train = Y[:n * 2//3]\n", "Y_test = Y[n * 2//3:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et puis *scikit-learn* est arriv\u00e9e avec [TimeSeriesSplit](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html)." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAIN: (107, 13) TEST: (104, 13)\n", "TRAIN: (211, 13) TEST: (104, 13)\n", "TRAIN: (315, 13) TEST: (104, 13)\n", "TRAIN: (419, 13) TEST: (104, 13)\n", "TRAIN: (523, 13) TEST: (104, 13)\n" ] } ], "source": [ "from sklearn.model_selection import TimeSeriesSplit\n", "tscv = TimeSeriesSplit(n_splits=5)\n", "for train_index, test_index in tscv.split(lagged):\n", " data_train, data_test = lagged.iloc[train_index, :], lagged.iloc[test_index, :]\n", " print(\"TRAIN:\", data_train.shape, \"TEST:\", data_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et on cal\u00e9 une for\u00eat al\u00e9atoire..." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8074141052461008\n", "0.740621811076557 0.6980805419883442\n", "0.9382338424157237 0.938776218559058\n", "0.8763703927657517 0.7726502689480175\n", "0.6429356690810921 0.6615420727005181\n" ] } ], "source": [ "import warnings\n", "from sklearn.ensemble import RandomForestRegressor\n", "clr = RandomForestRegressor()\n", "\n", "def train_test(clr, train_index, test_index):\n", " data_train = lagged.iloc[train_index, :]\n", " data_test = lagged.iloc[test_index, :]\n", " clr.fit(data_train.drop([\"value\", \"date\", \"notrend\", \"trend\"], \n", " axis=1), \n", " data_train.value)\n", " r2 = r2_score(data_test.value,\n", " clr.predict(data_test.drop([\"value\", \"date\", \"notrend\",\n", " \"trend\"], axis=1).values))\n", " return r2\n", "\n", "warnings.simplefilter(\"ignore\")\n", "last_test_index = None\n", "for train_index, test_index in tscv.split(lagged):\n", " r2 = train_test(clr, train_index, test_index) \n", " if last_test_index is not None:\n", " r2_prime = train_test(clr, last_test_index, test_index) \n", " print(r2, r2_prime)\n", " else:\n", " print(r2)\n", " last_test_index = test_index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2 ans coup\u00e9 en 5, soit tous les 5 mois, \u00e7a veut dire que ce d\u00e9coupage inclut parfois No\u00ebl, parfois l'\u00e9t\u00e9 et que les performances y seront tr\u00e8s sensibles." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6615420727005181" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import r2_score\n", "r2 = r2_score(data_test.value,\n", " clr.predict(data_test.drop([\"value\", \"date\", \"notrend\",\n", " \"trend\"], axis=1).values))\n", "r2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On compare avec le $r_2$ avec le m\u00eame $r_2$ obtenu en utilisant $Y_{t-1}$, $Y_{t-2}$, ... $Y_{t-d}$ comme pr\u00e9diction." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 : -0.5322711277436745\n", "2 : -1.0330346880701402\n", "3 : -1.2289501631550408\n", "4 : -1.0866973927813812\n", "5 : -0.6533003518045957\n", "6 : 0.683558097073121\n", "7 : -0.6863597347439538\n", "8 : -1.2181386641893033\n" ] } ], "source": [ "for i in range(1, 9):\n", " print(i, \":\", r2_score(data_test.value, data_test[\"lag%d\" % i]))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>value</th>\n", " <th>notrend</th>\n", " <th>trend</th>\n", " <th>weekday</th>\n", " <th>lag1</th>\n", " <th>lag2</th>\n", " <th>lag3</th>\n", " <th>lag4</th>\n", " <th>lag5</th>\n", " <th>lag6</th>\n", " <th>lag7</th>\n", " <th>lag8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-02-13 18:54:23.461489</td>\n", " <td>0.005357</td>\n", " <td>-0.000507</td>\n", " <td>0.005864</td>\n", " <td>3</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-02-14 18:54:23.461489</td>\n", " <td>0.009562</td>\n", " <td>0.003694</td>\n", " <td>0.005868</td>\n", " <td>4</td>\n", " <td>0.005357</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-02-15 18:54:23.461489</td>\n", " <td>0.014353</td>\n", " <td>0.008481</td>\n", " <td>0.005873</td>\n", " <td>5</td>\n", " <td>0.009562</td>\n", " <td>0.005357</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020-02-17 18:54:23.461489</td>\n", " <td>0.003475</td>\n", " <td>-0.002407</td>\n", " <td>0.005882</td>\n", " <td>0</td>\n", " <td>0.014353</td>\n", " <td>0.009562</td>\n", " <td>0.005357</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2020-02-18 18:54:23.461489</td>\n", " <td>0.005454</td>\n", " <td>-0.000432</td>\n", " <td>0.005886</td>\n", " <td>1</td>\n", " <td>0.003475</td>\n", " <td>0.014353</td>\n", " <td>0.009562</td>\n", " <td>0.005357</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date value notrend trend weekday lag1 \\\n", "0 2020-02-13 18:54:23.461489 0.005357 -0.000507 0.005864 3 0.000000 \n", "1 2020-02-14 18:54:23.461489 0.009562 0.003694 0.005868 4 0.005357 \n", "2 2020-02-15 18:54:23.461489 0.014353 0.008481 0.005873 5 0.009562 \n", "4 2020-02-17 18:54:23.461489 0.003475 -0.002407 0.005882 0 0.014353 \n", "5 2020-02-18 18:54:23.461489 0.005454 -0.000432 0.005886 1 0.003475 \n", "\n", " lag2 lag3 lag4 lag5 lag6 lag7 lag8 \n", "0 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 \n", "1 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 \n", "2 0.005357 0.000000 0.000000 0.0 0.0 0.0 0.0 \n", "4 0.009562 0.005357 0.000000 0.0 0.0 0.0 0.0 \n", "5 0.014353 0.009562 0.005357 0.0 0.0 0.0 0.0 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lagged[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En fait le jour de la semaine est une variable cat\u00e9gorielle, on cr\u00e9e une colonne par jour." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import OneHotEncoder" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 1. , 0. , 0. ],\n", " [0.0053571 , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 1. , 0. ],\n", " [0.00956219, 0.0053571 , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 0. , 0. ,\n", " 0. , 0. , 0. , 1. ],\n", " [0.01435337, 0.00956219, 0.0053571 , 0. , 0. ,\n", " 0. , 0. , 0. , 1. , 0. ,\n", " 0. , 0. , 0. , 0. ],\n", " [0.00347454, 0.01435337, 0.00956219, 0.0053571 , 0. ,\n", " 0. , 0. , 0. , 0. , 1. ,\n", " 0. , 0. , 0. , 0. ]])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = ['lag1', 'lag2', 'lag3',\n", " 'lag4', 'lag5', 'lag6', 'lag7', 'lag8']\n", "ct = ColumnTransformer(\n", " [('pass', \"passthrough\", cols),\n", " (\"dummies\", OneHotEncoder(), [\"weekday\"])])\n", "pred = ct.fit(lagged).transform(lagged[:5])\n", "pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On met tout dans un pipeline parce que c'est plus joli, plus pratique aussi." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('pipeline',\n", " Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(transformers=[('pass',\n", " 'passthrough',\n", " ['lag1',\n", " 'lag2',\n", " 'lag3',\n", " 'lag4',\n", " 'lag5',\n", " 'lag6',\n", " 'lag7',\n", " 'lag8']),\n", " ('dummies',\n", " Pipeline(steps=[('onehotencoder',\n", " OneHotEncoder()),\n", " ('truncatedsvd',\n", " TruncatedSVD())]),\n", " ['weekday'])])),\n", " ('linearregression', LinearRegression())]))])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.pipeline import make_pipeline\n", "from sklearn.decomposition import PCA, TruncatedSVD \n", "cols = ['lag1', 'lag2', 'lag3',\n", " 'lag4', 'lag5', 'lag6', 'lag7', 'lag8']\n", "model = make_pipeline(\n", " make_pipeline(\n", " ColumnTransformer(\n", " [('pass', \"passthrough\", cols),\n", " (\"dummies\", make_pipeline(OneHotEncoder(), \n", " TruncatedSVD(n_components=2)), [\"weekday\"])]),\n", " LinearRegression()))\n", "model.fit(lagged, lagged[\"value\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "C'est plus facile \u00e0 voir visuellement." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div id=\"Mb360f02fcd8f4b539fa5939edfe75045-cont\"><div id=\"Mb360f02fcd8f4b539fa5939edfe75045\" style=\"width:100%;height:100%;\"></div></div>\n", "<script>\n", "\n", "require(['http://www.xavierdupre.fr/js/vizjs/viz.js'], function() { var svgGraph = Viz(\"digraph{\\n orientation=portrait;\\n nodesep=0.05;\\n ranksep=0.25;\\n sch0[label=\\\"<f0> date|<f1> value|<f2> notrend|<f3> trend|<f4> weekday|<f5> lag1|<f6> lag2|<f7> lag3|<f8> lag4|<f9> lag5|<f10> lag6|<f11> lag7|<f12> lag8\\\",shape=record,fontsize=8];\\n\\n node1[label=\\\"Identity\\\",shape=box,style=\\\"filled,rounded\\\",color=cyan,fontsize=12];\\n sch0:f5 -> node1;\\n sch0:f6 -> node1;\\n sch0:f7 -> node1;\\n sch0:f8 -> node1;\\n sch0:f9 -> node1;\\n sch0:f10 -> node1;\\n sch0:f11 -> node1;\\n sch0:f12 -> node1;\\n sch1[label=\\\"<f0> -v-0|<f1> -v-1|<f2> -v-2|<f3> -v-3|<f4> -v-4|<f5> -v-5|<f6> -v-6|<f7> -v-7\\\",shape=record,fontsize=8];\\n node1 -> sch1:f0;\\n node1 -> sch1:f1;\\n node1 -> sch1:f2;\\n node1 -> sch1:f3;\\n node1 -> sch1:f4;\\n node1 -> sch1:f5;\\n node1 -> sch1:f6;\\n node1 -> sch1:f7;\\n\\n node2[label=\\\"OneHotEncoder\\\",shape=box,style=\\\"filled,rounded\\\",color=cyan,fontsize=12];\\n sch0:f4 -> node2;\\n sch2[label=\\\"<f0> weekday\\\",shape=record,fontsize=8];\\n node2 -> sch2:f0;\\n\\n node3[label=\\\"TruncatedSVD\\\",shape=box,style=\\\"filled,rounded\\\",color=cyan,fontsize=12];\\n sch2:f0 -> node3;\\n sch3[label=\\\"<f0> weekday\\\",shape=record,fontsize=8];\\n node3 -> sch3:f0;\\n\\n node4[label=\\\"union\\\",shape=box,style=\\\"filled,rounded\\\",color=cyan,fontsize=12];\\n sch1:f0 -> node4;\\n sch1:f1 -> node4;\\n sch1:f2 -> node4;\\n sch1:f3 -> node4;\\n sch1:f4 -> node4;\\n sch1:f5 -> node4;\\n sch1:f6 -> node4;\\n sch1:f7 -> node4;\\n sch3:f0 -> node4;\\n sch4[label=\\\"<f0> -v-8\\\",shape=record,fontsize=8];\\n node4 -> sch4:f0;\\n\\n node5[label=\\\"LinearRegression\\\",shape=box,style=\\\"filled,rounded\\\",color=yellow,fontsize=12];\\n sch4:f0 -> node5;\\n sch5[label=\\\"<f0> Prediction\\\",shape=record,fontsize=8];\\n node5 -> sch5:f0;\\n}\");\n", "document.getElementById('Mb360f02fcd8f4b539fa5939edfe75045').innerHTML = svgGraph; });\n", "\n", "</script>" ], "text/plain": [ "<jyquickhelper.jspy.render_nb_js_dot.RenderJsDot at 0x206b8aeca30>" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from mlinsights.plotting import pipeline2dot\n", "dot = pipeline2dot(model, lagged)\n", "from jyquickhelper import RenderJsDot\n", "RenderJsDot(dot)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8800492232136333" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2_score(lagged['value'], model.predict(lagged))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Templating\n", "\n", "Compl\u00e8tement hors sujet mais utile." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Hello John Doe!'" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from jinja2 import Template\n", "template = Template('Hello {{ name }}!')\n", "template.render(name='John Doe')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "John Doe Doe\n", "------------\n", "Poss\u00e8de :\n", "\n", "- table\n", "- tabouret\n" ] } ], "source": [ "template = Template(\"\"\"\n", "{{ name }}\n", "{{ \"-\" * len(name) }}\n", "Poss\u00e8de :\n", "{% for i in range(len(meubles)) %}\n", "- {{meubles[i]}}{% endfor %}\n", "\"\"\")\n", "meubles = ['table', \"tabouret\"]\n", "print(template.render(name='John Doe Doe', len=len,\n", " meubles=meubles))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
bbalasub1/glmnet_python
docs/glmnet_vignette.ipynb
1
583123
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Glmnet Vignette (for python)\n", "July 12, 2017\n", "\n", "## Authors\n", "Trevor Hastie, B. J. Balakumar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "`Glmnet` is a package that fits a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix `x`. It fits linear, logistic and multinomial, poisson, and Cox regression models. A variety of predictions can be made from the fitted models. It can also fit multi-response linear regression.\n", "\n", "The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon. The Python package is maintained by B. J. Balakumar. The R package is maintained by Trevor Hastie. The matlab version of glmnet is maintained by Junyang Qian. This vignette describes the usage of glmnet in Python.\n", "\n", "`glmnet` solves the following problem:\n", "$$\n", " \\min_{\\beta_0, \\beta}\\frac{1}{N} \\sum_{i=1}^N w_i l(y_i, \\beta_0+ \\beta^T x_i)^2+\\lambda \\left[ (1-\\alpha)||\\beta||_2^2/2 + \\alpha||\\beta||_1\\right],\n", "$$\n", "\n", "over a grid of values of $\\lambda$ covering the entire range. Here $l(y, \\eta)$ is the negative log-likelihood contribution for observation $i$; e.g. for the Gaussian case it is $\\frac{1}{2} l(y-\\eta)^2$. The elastic-net penalty is controlled by $\\alpha$, and bridges the gap between lasso ($\\alpha=1$, the default) and ridge ($\\alpha=0$). The tuning parameter $\\lambda$ controls the overall strength of the penalty." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is known that the ridge penalty shrinks the coefficients of correlated predictors towards each other while the lasso tends to pick one of them and discard the others. The elastic-net penalty mixes these two; if predictors are correlated in groups, an $\\alpha=0.5$ tends to select the groups in or out together. This is a higher level parameter, and users might pick a value upfront, else experiment with a few different values. One use of $\\alpha$ is for numerical stability; for example, the elastic net with $\\alpha = 1-\\varepsilon$ for some small $\\varepsilon>0$ performs much like the lasso, but removes any degeneracies and wild behavior caused by extreme correlations.\n", "\n", "The glmnet algorithms use cyclical coordinate descent, which successively optimizes the objective function over each parameter with others fixed, and cycles repeatedly until convergence. The package also makes use of the strong rules for efficient restriction of the active set. Due to highly efficient updates and techniques such as warm starts and active-set convergence, our algorithms can compute the solution path very fast.\n", "\n", "The code can handle sparse input-matrix formats, as well as range constraints on coefficients. The core of glmnet is a set of fortran subroutines, which make for very fast execution.\n", "\n", "The package also includes methods for prediction and plotting, and a function that performs K-fold cross-validation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installation\n", "\n", "### Using pip (recommended, courtesy: Han Fan)\n", "\n", " pip install glmnet_py\n", "\n", "### Complied from source\n", "\n", " git clone https://github.com/bbalasub1/glmnet_python.git\n", " cd glmnet_python\n", " python setup.py install\n", "\n", "### Requirement\n", "Python 3, Linux\n", "\n", "Currently, the checked-in version of GLMnet.so is compiled for the following config:\n", "\n", " **Linux:** Linux version 2.6.32-573.26.1.el6.x86_64 (gcc version 4.4.7 20120313 (Red Hat 4.4.7-16) (GCC) ) \n", " **OS:** CentOS 6.7 (Final) \n", " **Hardware:** 8-core Intel(R) Core(TM) i7-2630QM \n", " **gfortran:** version 4.4.7 20120313 (Red Hat 4.4.7-17) (GCC)\n", "\n", "\n", "## Usage\n", " import glmnet_python\n", " from glmnet import glmnet\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Regression\n", "\n", "Linear regression here refers to two families of models. One is `gaussian`, the Gaussian family, and the other is `mgaussian`, the multiresponse Gaussian family. We first discuss the ordinary Gaussian and the multiresponse one after that.\n", "\n", "### Linear Regression - Gaussian family\n", "\n", "`gaussian ` is the default family option in the function `glmnet`. Suppose we have observations $x_i \\in \\mathbb{R}^p$ and the responses $y_i \\in \\mathbb{R}, i = 1, \\ldots, N$. The objective function for the Gaussian family is\n", "\n", "$$\n", "\\min_{(\\beta_0, \\beta) \\in \\mathbb{R}^{p+1}}\\frac{1}{2N} \\sum_{i=1}^N (y_i -\\beta_0-x_i^T \\beta)^2+\\lambda \\left[ (1-\\alpha)||\\beta||_2^2/2 + \\alpha||\\beta||_1\\right],\n", "$$\n", "\n", "where \n", "\n", "$\\lambda \\geq 0$ is a complexity parameter and $0 \\leq \\alpha \\leq 1$ is a compromise between ridge ($\\alpha = 0$) and lasso ($\\alpha = 1$).\n", "\n", "Coordinate descent is applied to solve the problem. Specifically, suppose we have current estimates $\\tilde{\\beta_0}$ and $\\tilde{\\beta}_\\ell$ $\\forall j\\in 1,\\ldots,p$. By computing the gradient at $\\beta_j = \\tilde{\\beta}_j$ and simple calculus, the update is\n", "$$\n", "\\tilde{\\beta}_j \\leftarrow \\frac{S(\\frac{1}{N}\\sum_{i=1}^N x_{ij}(y_i-\\tilde{y}_i^{(j)}),\\lambda \\alpha)}{1+\\lambda(1-\\alpha)},\n", "$$\n", "\n", "where \n", "\n", "$\\tilde{y}_i^{(j)} = \\tilde{\\beta}_0 + \\sum_{\\ell \\neq j} x_{i\\ell} \\tilde{\\beta}_\\ell$, and $S(z, \\gamma)$ is the soft-thresholding operator with value $\\text{sign}(z)(|z|-\\gamma)_+$.\n", "\n", "This formula above applies when the `x` variables are standardized to have unit variance (the default); it is slightly more complicated when they are not. Note that for \"family=gaussian\", `glmnet` standardizes $y$ to have unit variance before computing its lambda sequence (and then unstandardizes the resulting coefficients); if you wish to reproduce/compare results with other software, best to supply a standardized $y$ first (Using the \"1/N\" variance formula)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`glmnet` provides various options for users to customize the fit. We introduce some commonly used options here and they can be specified in the `glmnet` function.\n", "\n", "* `alpha` is for the elastic-net mixing parameter $\\alpha$, with range $\\alpha \\in [0,1]$. $\\alpha = 1$ is the lasso (default) and $\\alpha = 0$ is the ridge.\n", "\n", "* `weights` is for the observation weights. Default is 1 for each observation. (Note: `glmnet` rescales the weights to sum to N, the sample size.)\n", "\n", "* `nlambda` is the number of $\\lambda$ values in the sequence. Default is 100.\n", "\n", "* `lambda` can be provided, but is typically not and the program constructs a sequence. When automatically generated, the $\\lambda$ sequence is determined by `lambda.max` and `lambda.min.ratio`. The latter is the ratio of smallest value of the generated $\\lambda$ sequence (say `lambda.min`) to `lambda.max`. The program then generated `nlambda` values linear on the log scale from `lambda.max` down to `lambda.min`. `lambda.max` is not given, but easily computed from the input $x$ and $y$; it is the smallest value for `lambda` such that all the coefficients are zero. For `alpha=0` (ridge) `lambda.max` would be $\\infty$; hence for this case we pick a value corresponding to a small value for `alpha` close to zero.)\n", "\n", "* `standardize` is a logical flag for `x` variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is `standardize=TRUE`.\n", "\n", "For more information, type `help(glmnet)` or simply `?glmnet`. Let us start by loading the data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>.container { width:100% !important; }</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Jupyter setup to expand cell display to 100% width on your screen (optional)\n", "from IPython.core.display import display, HTML\n", "display(HTML(\"<style>.container { width:100% !important; }</style>\"))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import relevant modules and setup for calling glmnet\n", "%reset -f\n", "%matplotlib inline\n", "\n", "import sys\n", "sys.path.append('../test')\n", "sys.path.append('../lib')\n", "import scipy, importlib, pprint, matplotlib.pyplot as plt, warnings\n", "from glmnet import glmnet; from glmnetPlot import glmnetPlot \n", "from glmnetPrint import glmnetPrint; from glmnetCoef import glmnetCoef; from glmnetPredict import glmnetPredict\n", "from cvglmnet import cvglmnet; from cvglmnetCoef import cvglmnetCoef\n", "from cvglmnetPlot import cvglmnetPlot; from cvglmnetPredict import cvglmnetPredict\n", "\n", "# parameters\n", "baseDataDir= '../data/'\n", "\n", "# load data\n", "x = scipy.loadtxt(baseDataDir + 'QuickStartExampleX.dat', dtype = scipy.float64)\n", "y = scipy.loadtxt(baseDataDir + 'QuickStartExampleY.dat', dtype = scipy.float64)\n", "\n", "# create weights\n", "t = scipy.ones((50, 1), dtype = scipy.float64)\n", "wts = scipy.row_stack((t, 2*t))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example, we set $\\alpha = 0.2$ (more like a ridge regression), and give double weights to the latter half of the observations. To avoid too long a display here, we set `nlambda` to 20. In practice, however, the number of values of $\\lambda$ is recommended to be 100 (default) or more. In most cases, it does not come with extra cost because of the warm-starts used in the algorithm, and for nonlinear models leads to better convergence properties." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# call glmnet\n", "fit = glmnet(x = x.copy(), y = y.copy(), family = 'gaussian', \\\n", " weights = wts, \\\n", " alpha = 0.2, nlambda = 20\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then print the `glmnet` object." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\t df \t %dev \t lambdau\n", "\n", "0 \t 0.000000 \t 0.000000 \t 7.939020\n", "1 \t 4.000000 \t 0.178852 \t 4.889231\n", "2 \t 7.000000 \t 0.444488 \t 3.011024\n", "3 \t 7.000000 \t 0.656716 \t 1.854334\n", "4 \t 8.000000 \t 0.784984 \t 1.141988\n", "5 \t 9.000000 \t 0.853935 \t 0.703291\n", "6 \t 10.000000 \t 0.886693 \t 0.433121\n", "7 \t 11.000000 \t 0.902462 \t 0.266737\n", "8 \t 14.000000 \t 0.910135 \t 0.164269\n", "9 \t 17.000000 \t 0.913833 \t 0.101165\n", "10 \t 17.000000 \t 0.915417 \t 0.062302\n", "11 \t 17.000000 \t 0.916037 \t 0.038369\n", "12 \t 19.000000 \t 0.916299 \t 0.023629\n", "13 \t 20.000000 \t 0.916405 \t 0.014552\n", "14 \t 20.000000 \t 0.916447 \t 0.008962\n", "15 \t 20.000000 \t 0.916463 \t 0.005519\n", "16 \t 20.000000 \t 0.916469 \t 0.003399\n" ] } ], "source": [ "glmnetPrint(fit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This displays the call that produced the object `fit` and a three-column matrix with columns `Df` (the number of nonzero coefficients), `%dev` (the percent deviance explained) and `Lambda` (the corresponding value of $\\lambda$).\n", "\n", "(Note that the `digits` option can used to specify significant digits in the printout.)\n", "\n", "Here the actual number of $\\lambda$'s here is less than specified in the call. The reason lies in the stopping criteria of the algorithm. According to the default internal settings, the computations stop if either the fractional change in deviance down the path is less than $10^{-5}$ or the fraction of explained deviance reaches $0.999$. From the last few lines , we see the fraction of deviance does not change much and therefore the computation ends when meeting the stopping criteria. We can change such internal parameters. For details, see the Appendix section or type `help(glmnet.control)`.\n", "\n", "We can plot the fitted object as in the previous section. There are more options in the `plot` function.\n", "\n", "Users can decide what is on the X-axis. `xvar` allows three measures: \"norm\" for the $\\ell_1$-norm of the coefficients (default), \"lambda\" for the log-lambda value and \"dev\" for %deviance explained.\n", "\n", "Users can also label the curves with variable sequence numbers simply by setting `label = TRUE`. Let's plot \"fit\" against the log-lambda value and with each curve labeled." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3723bpk/ci3MHwb1CnxALCC+dfYimpbO/VUpdi+5hPKGUugG4DvAD9cCPRGRx\nG9fq9WNVDU3iFE1MGhubgs/XFCLznalraNAnrtbW6hCZjgxKtS8moePAMzIgM1PHodAyn5LSt4f0\nbTtAbe0qRzw+p7r6MwKBKtLTZzniMZu0tJnN5z8qK+Hhh/XOtrPP1qLRz9y5hlYXHzgAf/vb4f0D\nA/qQWHQn5gzu9unvtopocWlPTGpqdM9y//6mUFXVPB8q8/ubhKWlmGRmNnkxHzAASku1vdnZOp+R\nEZ++AH2+nbz99pMcdVQ11dWfUVOzguTkvIjex2ySkkbrm/Doo/Dgg1BQAHfcEf3sjx6mp76zfr/u\nQHk82rN8dzj97av/v/rKaiiDodtQqmn4qjtWvPj9bYtKVZX291dcDHv3ao8cr7yif5jv3atFKTOT\nsHi0jEPp7Gy9YSw3VwtMT/dkEhKGkpl5MkccUQCAbfs4cGAZ1dWfUV7+Ghs3/gi3O4ucnG+Sc8M3\nSb/pRtSfn9CnDB13nBaNY47pWSN7AY8HXn4ZLrgArrgCnnsuPsU9njA9C4OhBwgEtJiExKNlHEpX\nVMDOndp3oIh2TZGbq+NQiMzn5PTsQ03E5sCBr6ioeIOKitcJBPaSnX0+OelnkfVaCdbv/qBdqN9x\nB5xwQs8Z0kvU1enRtrw8vdu7Lw87HixmGMpg6GNUV8OOHfp4ix07Wqd37NDj7MOGRReV0Cmv3Tlp\nW1e3ISwctbWrGZA5l5zVGQy49308g8dp0Tj11D79lD1wQJ+2d9xx8Ic/9OmPclAYsegl+uo4ZbzT\nl2yF3rO3vh7KyloLyfbtekispESLyMSJrUNm5qHZ2ti4m4qKt6isfIOqqo9JrxlJzuvlZG8bQeLN\n98CZZ3b7k7a37mtVld7kftZZesf3wdCXvrNmzsJg6OckJekVrW2tavX7YdMmWLdOh4UL4U9/gqIi\nSEvTopGeDqtXN4nI0KGde8Z7vYMZNuxqhg27mkCghn373qVi1N/ZvOsNkrZfSM5tA8g+4VZSzrkR\n5XJ17wfvYTIz9erhU07Rq+Zuvz3WFsUfnepZKKVOAJaLSK1S6jJgOvCQiGztaQO7ghmGMhiiY9u6\nBxISkcjg9+ujwCdOhCOP1F5AZs7s/JJS2/azf9/HVHz5ByrqPkAFISd5LkNOuofU9KN79oN1Mzt3\nasG4/npwNo73e7p1GEoptRKYCkwBngWeAr4tIqccop3dihELg6HrVFQ0CceaNfDvf+vjwWfN0qtm\nTz1VL4AB0FAqAAAgAElEQVTyeDq+ltg2Ne8+Svln97F7RiXenDyGjVvAwIHf6difVZywbZsWjNtu\ng2uvjbU1PU9nxQIR6TAAy5z4V8BVkWXxFPTHic6VV14pgwYNksmTJ4fL9u7dK3PnzpUJEybIN77x\nDamqqmrz9W2xcOHCLr8mVhhbe46+ZG9nbN27V+SNN0RuuUVk2jSRtDSRM84Que8+kS++EPH7O7hA\nMCjBxx+V8m+ky8qXj5BFn2RKUdG1Ul29tNtt7Qk2bhQZPlzkuec6/5q++h1wnpsdPl87uwjvgFLq\nF8BlwD+dcyg68Tsjfrjiiit49913m5X99re/5fTTT6e4uJg5c+Zw3333xcg6gyG+yMqC88/Xq4O+\n/hq2bIEf/lAP01xzjd4fcvbZ8F//BV9+qZcKN8OysK69npznipn892OYuSCNhG31rF59IV99NYOy\nsj8TCFTH4qN1inHj9BzGbbfp/TOGzg9DDQEuBb4UkUVKqZFAgYg839MGdoWOhqG2bt3Keeedx8qV\nKwHIz8/n448/ZvDgwezatYuCggKKilqeu2QwGFpSUaFPoFu4ULv93rEDTjxRD1kVFMC0adohZZh3\n3oHrr0eOn8neuy9gZ8NrVFV9RE7OPIYN+wFpaTPj8gz0FSv0stqnn9be3vsjnR2G6mzP4kci8oCI\nLAIQkW3ApEMxMB7Ys2cPg53TUIYMGcKePXtibJHB0DfIyYF58+CRR/TKqvXr4fvf1yuxLr9c119z\nDSxe7DjePPNMWL0aNXoc2bNv4ah/z2XmMWtISjqCtWsv4auvjqa09FH8/qpYf7RmTJ0Kb70FV14J\nH3wQa2tiTGfGqogyPwGs7MxrezPQzpzFgw8+KBMmTJCEhAR56KGHREQkKyurWZsBAwa0+fq26Kvj\nlPFOX7JVpG/Z2xu27tih5zfGjRM56iiRBx8UqahwKletEpk9W2TWLJGVK8W2g1JZ+b6sXv1t+eST\nDFm3br5UVX0mtm3HzX395BORgQN13BbxYmtn6PY5C6XUdUqpVUCeUmplRNgMrOphHes21qxZw9NP\nP81bb73FhAkT+Mc//kFJSQmDBw9m9+7dAOzatYtBgwbF2FKDoX+Qm6vH+9evhz/+Uc9rjBsHl1wC\nH+4+CvvjRdop02mnoW77BQMSZjFp0sscd9x6kpOPpKjo+3z55WTKy1+Li7mNk06C//1f3Zv68stY\nWxMb2p2zUEplAFnAfcBtEVUHJMrhQ7GmrTmLV199lXfffZfbb7+d8847j3nf/jYNLhd7KyrIyMri\n2p/8hCceeIDqqipuveceQoN3Sl8znA6XNb1fs7JQe9WyfURZZJ4WZZaTtpx6K/J1cTieazB0hX37\n9FnYTz6pXWxcdRXMP6ec3P+6BT77TO8ePOssQI94VFV9TFnZY1RVLWT48AXk5t6E250e08/w1lt6\neO2jj/SelP5At7v7cA4qGkzErm/RcxdxQ1tiUVRUxMyZM/H7/TQ2NiJAQn4+Qx98kN23305gzx5c\nQ4Yw6N57sdLSAGd4znl9ZBy6frOyiPYSWdeiLDJPG6+znXw4jvgcbYmJRZPYWBGxKxQ7Zc3iTtS7\nneACHYfyLWOnPlpddwavUiRYFl7LakorhdeySLAsPI7dhvhGRB/S99RT8Ne/6k2AVx/zNec8/108\nM6dpt+hDh4bb19UVs3Xrvezd+w65uTczfPjNuN0ZMbP/L3/Rx3x88gmMGRMzM7qN7t6UdyNwF7Cb\npueXiEjvO7hvh/ZWQ51wwgmUlpYyevRoJk6ciMvl4pFHHjnk9+wNfzCRghJNTEREx0DQKQ+KYIsQ\ndNoGRfjsk0+YedJJ2G3Uh8udOChCwAmhdBCa5zto42/RprNh95IlZBxzTDjvF6HRtmkUwWfbzdMR\ndW6lSHAEpC1xSXK5SLYskl0ukiwrnE62rDbrkiLahOIcjweP4wK2r/oFijW1tXpp6tNPw8YNwvfH\nfsJVRbcy/tfz4dprKfzkk7CtdXXrHdF4m9zcmxg+fEHMROORR7SmLVrUpGvxdF87oid9Q90C5IlI\n5cGbFzuqq6vZtWsXW7ZsAeD2229nxIgRsTWqC0QOW7kO4ZfzloQExicnd49RPUxhZSUFU6d26TUS\nISq+CAFptO2wqPhsm3rbpi4Y1HFkOhikNhikwu+nLhikzrapd+KW7WuDQfYGAmS43QzxevFu3MiR\ngwczxOuNGga43WYoMQopKTB/vg5FRYqnnz6FE4s/ZeJtq7j6/rsZ+OMRei0ukJw8gYkTn6eubgNb\nt/6axYuPIDf3RnJzF+DxZPaq3TfeqIfVzjhDLyHOyurVt48Jne1ZLATmikjLrTdxRVs9ixUrVjBn\nzhzq6uoQEZISEyl69FEWPPEE60tLQSn21dSQlZrKsief1F7VLKspjkxHq7Msvaj8UOLQNQ19BluE\nSr+fXY2N7GxsZFc7oSYYZHAUERnq9TImMZHxSUmMSkwM91QOZxob4a03bJ66u5Qla1K44aRV/ORv\nJ5CR3fy3bV3dRrZt+zUVFW+Rm3sDw4ffgsfTe09tEfjJT+CLL+D997Xw9UW6exjqaSAP+CfgC5WL\nyAOHYmR305ZYfP7558yePRu3200wGCR/7FguBv7ziCO0hzURflpcTKbLxR2jRoXLmsXRykT0gb6h\n+lD6YGIRLRxud+eDxxO93OvVISHh0NLJyU0hdIh1crKuM8LWJXy2ze4oIlLm81HS0MCG+np2+nyM\nSEzkiKQkxiclNYtHH6ZCUvL5bu65aAX/3HMMP/2RzY135dCyc1xfv4mtW39NRcWb5OZez/DhP+o1\n0RDRE/U7dujJ74SEXnnbbqW7xeLOaOUi8p8HYVuP0ZZY7N69m+OPP57CwkLOO+88gsEgAwYMYNGi\nReE2I0eOZOHChYxry/dzG3TbOGVIeAKB9oPf33F9Y2NT8PnC6cK1aykYMSJqXau0z6cPTwgdaB0K\ntbX6PVoKSFvCkpKi/T9nZbUdorg37Uvjv9A99vpsm8319Wysr2dDi7jU52NEQoIWkOTkZkIypotC\n0pfubWFhIQUnn8y6O17kV/dn8Fnyadx+bzJXX6NafW3q60vYuvU3VFS8Tm7udY5odMP5uh0QCMB3\nvgMVFYV8+GFBt5zn3dP02JxFSBSUUskiUncoRsaCwYMHM3LkSEpKSrBtm3379jFlyhT279+PUorP\nP/+cQYMGMXjwYGpra/UcQWhZrJNuK2/bNiJy6OPRSjX1DHqKwsLw+O8hEQhoIYkUkEhBiSyrqdEn\ny6xdqwd5W4aqKi0WLQXE54M33mjKDxigZxJzc3VI7RseTLtCgmWRn5JCfpTxjEbbZnNDgxaQujo2\n1NXxr8pKNtbXs8PnY3hCAlNTU5memsqMtDSmp6UxuLM+xuMdy2Liby7nlUtXs/RbV3HHPbfy3/81\nlbvudvG97zW5FUlKGkt+/lPU19/Otm2/YfHi8Qwb9kNGjPgxHk92j5nndus9GLNnay+1Tz3VPzve\nne1ZzAKeBlJFZKRSaipwrYhc39MGdoX2VkN9/fXXzJo1C5/Px6hRo9i7V28TEREaGhqwLAu32x25\nG7zl7vA28xHv32GwLKvN8lBdKN1W6KiNy+VqFdxud7v5lmVutxu3243H4+l03JW24djlwtvYiKem\nBk9NDe4DB3AfOICruhpVVdUkKpWV2otdaakOHk+TcAwb1pSOzA8Z0rPiGyc02jYl9fWsqK1l6YED\nLDtwgGU1NSRZFtPT0poEJDWV3ISEvj3R3tAAt9/OJ89v4ZeDn2avncm998K3vtX6AV1fv4Vt2+6j\nvPxVhg27lhEjftqjPY2aGpg7V4vGf/933xGM7h6GWgxcBLwpIkc7ZatF5KhDtrQb6YwjwXPOOYdB\ngwbxyCOPcOSRRxIMBsnNzWXZsmUMGzbsoN87JByhnkZ7IVqbUFlk3F5oq00wGMS2bQKBAMFgsFlo\nWdZem0AgEA5+v7/DuDNtuhIHAoGwaIXEJSEhQQevl4EeDyMsi2EiDLFthgSDDAwEGOjzke3zkVVf\nT1pDA7VJSRxIS6MmM5O6zEzqs7PxjRpF8IgjsPLySBs4kPT09HBITk7u2w9TBxFha0MDy2pqWHbg\nAEtralh64ABAs97H9NRURicm9r3P/MEHyPwreGfmf3D7lquxXBa/+Y1+WLf8KA0NW505jdcZOfIX\n5ObegGX1TK9r7159FsZ3v9t3TtvrdrEQkeOUUl9HiMUKEena2sYepiOx2LJlC+eddx5nz5vHikCA\n2VdfzcaPP+bfjz/Olf/3f612Vofilju1I8s3fv4542fNar3jmug7t6026iI32rXcXBfaeNeyLtpG\nvMi45Sa7ZYsWcdzJJ3dqY16zTXlOHHrP3mDhwoWcdNJJzUTE5/OFQ0NDQ7N8tLrGujrcFRUkVFaS\nWFlJ8r59pO7bR1ZlJYP37WNwbS173G42ut0UAav8ftYGg5SmpdGYkUFGZmZYRDIyMpqJSkZGBgMG\nDCA7O5vs7Gw2bNjA2WefTUZGRtw+eEWEssZGnnvnHfxTp2oROXCAWttmemoq09PSmJGaysmZmQyL\nk5nadudXKivh2muxizfw2vy3+I8nRzJkiD5D+4QTWjevrV3Lpk23UldXxNixv2XgwHnd+rcK2bpz\np3YP8qMfwQ03dNvlu5We3GexXSk1GxCllAdYAKw7WEN7m4qKCq677jo+/fRTKisrKb7vPrLmzWNv\nZSWbX3uN1DlzeKuyksi7pUD/RBFpSresB6qrqlhVXh4uk4jXtrz7oby0UdayfTTZ68wucmjaOWlH\nbLI7sGULSdnZzTfgRdmYZ0dsrovcfGdDq93cbe3ajtzV7XF2V3ujpL1K6bxTHkqX7dzJJzt2hNt6\nLYtEyyIxMZGk5GSSnHySy0WWZZHkhMRQ2uXCGzG3FBW/n+GbNzO8qIiCoiIoKsJetw6KipD9+2nI\nzORAWhr7Bg1id1YWZenp7PB6qaqpYefOnezdu5fKykoqKyspLS3lhz/8IfX19WRlZYVFJFqIFJns\n7GwGDhyIpzPH0B0iSilyExKYnZFBwejR4fLdjY187Qxd/bW8nBs2bGCI18vpWVmcnpXFKZmZpMfj\ncF52NrzyCtazz3LxrTP41i/+gxcybuTSSy0mT4Z779Wu0kOkpBzJlCn/YN++D9m06afs2PEA48bd\nT0bGrG41a+hQvZT25JP12o7vfa9bLx8zOtuzyAEeAk5HP9PeAxbE2ya9tnoWq1atYubMmfh8PpRS\n3HbXXQy+8kp2rFnDK7/8JX6fD5fbzTfvvZfcKVOi7phumQ7lI3dQ2xFxsEW+rbjljumWD+hARHk4\nHWXHdOQu6dDGNL+TjnwIeyMezN5oZRH50AM4tIM5USkSXa7wDunEUFul8LpczUTADXiUwm1ZKKBR\nBL+zMa6lfaF0OHbaRqZ9ERvq6oNBGkJp225KtygPiITFI1JE0l0u0t1u0l0uMpy4Vb62lozt20kv\nKSF9/XrS164lZdUqVGkpjB2rD62eNg1mzNBhyBAAGhsbm4lIZWVlq3y0kJ6eHl5k0VGckpLSo72X\noAjLa2r4YN8+Pti3jy+qq5mSksLpWVnMzcriuPT0+FvGu2kTXHYZpKfj+/OzPPHWUH7zG/3Avvtu\nyMtr3lzEZvfuF9i8+Q7S02czdux9JCWN7VaT1qyB007TvrDOO69bL92tdLtvqL5Ae8NQgwYNotzp\nAYTajBkzhn379oUnuLOzs9m+fXuv2dsbRO5qbmyRjnxIN7Z4WEc+mOs6SId2N0dL19k2FpDudpPm\nPKjTIh7QaREP77R2ykIP8q74fgqKaPGIEJE62+ZAIEB1MEi1E++Plo8o2+/EPtvW9oiQ6fMxZP9+\nhpWWkrt+PcNqasgdMIBhI0aQm5fH4KOPxtXJOTDbttm7dy+7d+9mz5494TgyHRmLSFQRGT16NGPH\njmXs2LGMGDECdzf1BuqDQT7dvz8sHhvq6zkpIyPc85jUw+LVaQIBPQb12GPw2GPUfuNb/PGPcP/9\n+tS/a66BmTObH8oUDNaxY8cf2L79DwwZ8n1GjbqjW/doLFkC55yjXZrE62rlbhELpdStIvJ7pdQf\niTIqIiI3H5qZ3Ut7YjFv3jwWLVpEeXk5l156Kc888wxTpkzB6/WybNkyLrzwQtavX9/lk/L63Jr1\nXrZVHOGpDgY54Dx0D0Q8mJulIx7QW7/4Avf06eH2VYEA9bZNjsfDQI+HQR4PA71eHXs8DHImvUPx\nQI+HzG52sRFwPkd1IEBVIMCuxkZKGxspbWjgq/feQ8aMobShgTLLojIxkUHV1eT6fAzzeMhNTyd3\n6FCG5eSQm5DAsIQEcr1eMg7Cxtra2lYiEnJnU1JSQklJCbt27WL48OGMGzcuLCChUFZWxrmHcOxb\npd/Pwn37eN8Rjzrb5rTMzLB4DE9MPOhrt+SgvrOff657GaeeCg8+SFUglQcfhNde0wvq5s7VZzGd\ncUa4Q4jPt4stW+6iouJvjBz5S3Jzr+/yJHhbtn70kZ7w/te/4JhjuvZReoqemLMIzUt8dWimtY1S\n6kzgQfQc7tMi8rsobR4GzgJqgfkisrwr71FWVsabb75JwDkoOBAI8NJLL7F//3527dqFx+PB5XJx\n3HHHHfLnMTRHOUNXiS4XXTktpLCigoLp05uV+WybCr+f8sZG9vj9lPv97GlspNzvZ0l1NeUtykLi\nEiksg7xeRicmMjYxkbHOhrbkZud/to3bshhgWQyIMr9QmJfX7EHhDwbZuXkzZatXU7plC6V79lBW\nW8u6nBxKR42iLDub0uRkbMviiORk8pwwISkpnM5oo2eQkpISfvC3hc/nY9u2bWzatCksIF988QUl\nJSUUFxeTnJzcSkRCwjJixAhc7dyTbI+HiwYN4iLn/JeS+no+3LePt/fu5aebNjHQ6+W0zExmpqcz\nJSWFicnJJHbyHncLs2bB8uWwYAEcfTSZL77IXXcdy1136Z3W77yjd1vfcov2GnvWWXDmmUM4/vjH\nGT78JjZtupXS0ke6bRJ8zpymoaiPPoKJE7vnY/Y2MR2GUkpZwHrgNKAM+BL4rogURbQ5C7hRRM5R\nSh0HPCQix7dxvag9i7KyMiZNmsS5557LX/7yF84991wWLFjAeeedx+jRoykrKyMQCDB06FA2btzY\nEx/VEAMaHKeAIWEpd1xsbGlooKShgc0NDWxpaCDT7Q6LR8t4qNfbfW7PRWDrVu2f2wn7i4rYkJPD\n+hkzKM7Pp3jECIqzsljvdpPm8WgBSU4mL0JEurpju7UZQkVFRVhESkpKwqKyadMmKisrOeKII8jL\nyyM/P5/8/Hzy8vLIy8sjzXHh3xa2CCtqavhw3z6+rqlhZW0tG+vrGZ2YyJSUFCanpDAlNZXJKSmM\nSkzseZfyr70G11+vPf9deSUMHhzee+P3a79O77yjQ0mJnmM480w47rhFNDTchMuV4kyCR33kdInn\nn4c77tCuzSPWF8Sc7l46+z5wsYhUOfks4CUROeMQjTweuFNEznLyt6Fdn/8uos3jwEIRednJrwMK\nRGR3lOu1OQz18MMP85Of/IRAIMBll13GCy+8gGVZYdfl2dnZLF26FNu2o77e0D+xRdjZ2EhJfT0l\nDQ2t4qpAgFEJCa2EZHRiIlkeD6kuF2kuF95DmfCtrITiYigq0nFxMVJURGlNDcVTp7J+yhSKjziC\n4sGDKU5JocyyGJWYGBaPPMcFSLbbTZozz5Pmch20oNTW1oaHZIuLiykqKqKoqIgNGzaQlZXVSkTy\n8/MZPnw4Vhvv12jbFNXVsaq2lpU1NayqrWVVbS37AwGOaiEgk1NSyOrulWGlpXoN6+LFUFEBAwc2\n38jppHcljeG9kiN4Z9kg3iv0MHQonHjiGo488m5OPNFDfv69JCUd2gEWDz+sTw5ctKhpCCzWdLdY\nLBeRaS3KwnsuDhal1DzgDBH5gZO/DDg2ci5EKfUWcJ+IfObkPwBuFZFlUa4XVSyqqqqYN28e+fn5\n/OlPf+Kb3/wmF198MVdccQUej4f6+nqOPfZYli1bRllZGdnZnXcNcLDzACKCLTa22Ag6HbSDBCXY\nZjpoO3kn3VZbQW/0C9pBxBYkICDw9edfM3XGVMQW7KCt64ISjrEJl+vlXKBEhdMIEBCUP4jlC6Aa\nbazGgJMPYvmDqMagLvcHUH4b5T848V1WuoHpueMP6rXaWBsIgtggfgLBRvx2A/5gAMGOWH5sg4RW\nuIXSoTq9tNgn4AsKfr9NwC/Yfhs7KNqfpPOOG/btZHzWkKZ9NEjzPTYR+dBC6YDPS6MvgTZPNxbB\nFRRcwSCuoI0rEMRl22Ar9qelsW9AJvuyMqnKyKA6PR2f10PA7cHvcRNwe1AiuP1+PIEA7oAT+/3s\n27qS3EHjcQcCeAJ+3H4d63YBrHZ+MIkI2BGeDESwbWd9oLKwLGe5cpSl462uhSJoWYhSiGVhWy5s\nLBQ2lm2jRNhRsYmRA0Z39Ac/eET/8Zv/bTS2s8NJxEJQKBXEm1RHUloVKP0ipQSl9OffvKuMscOG\n6LxThxIUThzOQ1ACHLAH8esnHu65z9YOPbnPIqiUGinOyXhKqVFE3wYQc+bPn89op4+XmZnJtGnT\nqKioYMmSJXz00UcAvPHGGyz+xxeMUMMpqd2MV3lYtXgVttj8d/5vmZiQhxJY5ysGgSO9+QCs8xUB\niiO9eShRrG0sYqt/GzuTS0AU6xp1/SR3Pgis9ReBwFGeI1ECa/x6dO0olz6PcW1gHYhisnsSSmB1\ncB1KFFNcR2GJxcrgaiyxmGJNRqFYGVyNwmKqmowSixX2SsBimpqCEovlsgIlFtPUNJS4WM5yBJtp\nagogbJQiUH6OVkfhUjbLZSVgc7SahEL4WlahsJmujgRslstqlAgzyENhs0zWYiHMYDyihKUUg7KZ\nbo1HLGGpFANwtGccKJul9kZEwXSPHlv/OrAJEKa7x0XkiZIfixVoZDlrwnkBlgVKEBGOduvrLQuU\nADDNNRZBdF5gmnssNvC1Uz/VNRZFEsuDO1FOe4AVwRJAhfPLg03XUy3yTfWKaa4jWrWXoEcfOhWl\n/VRrHIJieWATIoqpriNAFCvsjYDNNNcYp/0m53rjwnl9vSParE9nDxe4xgF7W9V/LSXYysWkhAnY\nlosVwY364WxZ5CdZrGrcgm1ZTEg5iqDLxbqGdfiUxfhUvdd2fe1qACakHNVredUiH2xQjKmdEjN7\ndH4SAFtS3mHAyS9y1JhsxLZYubYBRDElPxkRi63v7sPKTmRKXioiipVFdfr//4R0EMWq4lqdH58B\nonjln+XNHtqFhYUAPZ4HePbZZ3n22WfpCp3tWZwJPAF8jBbgk4AfiMi7XXq31tc9HrhLRM508p0Z\nhioCTunKMNSSJUs48cQTCQQC2ukfMMNKosIOsIcA9QgeFF4Um7MmE9ZBJYR/9DX7mRixHU4pxHJ+\nSVggzq8HlHK2Zoeu4cSWfr04vz6aforaiEu3F0v061yCuAQsGxSIyw7nxdJ5ve1al4tl69d7gthu\nAY+N7QJxC7ZbX0vcgu3E4opIuwVxg+1S2C4IuhWNbheNbosGl0WjW+FzWTS4FI1YBGxFoyj8NjTa\nCr+T9os0ldlCow0+W2gISlPspH1B52CioNAYdPZh2KE9FvrXrUdZuLHw4MIrbhKCbhKCHhL8XryN\nCST6EklqSCW1IZ20ugzSarPIqBlAZs0AMmszyarJIv1AOomNiQQ8AQKJAfypfoJpQewMG9JBZSpc\nmS48Azx4B3hJzE4kKTuJlEEppOakkj4onZSMFFxW9EnaujrYsEGPIK1f3zx2ufQa/wkTmsfjxkFS\nUmf+lxjigb17P2DdukvJy3uanJyubZpo2NpA2eNlLHtyGfcF7yNpeBIvvvkio8aM6iFru0ZPnMGd\nA4Rmeb4QkYpDsC90TRdQjJ7g3gksAS4RkXURbc4GbnAmuI8HHuzqBDfA9OnTWbFiBbZtc+aFcymb\n/RmBWsGTplj36zoaK4X0yS5GfT9RDxiIOAMHAEKopx0mNHThxHo8Qr/G6Z3rfDjtdHJFCP1dBAFR\nRJrc9DdzlEmIsEOF31fpzm3IFH0N1WS3OIMponDSoaNXbUQJdqhMCXaoTNnY2NiWHo5xixuX7cIt\nbty2E0LpoBuP7cFtu/EGvbhtN56gJxy8Qa9OBzwkNSaR0JhAQmMCSb4kEhsSwyEhkIAXLwlKx4lW\nIglWAgmuBDweDypBYSVYqASFK8WFO8WNJ82DJ9WDN81LQnoCnlQPVoqFK9WFKyUipLp0eYoLV7IL\n5Tr4ydTqati8WU+ClpToPWDr1+tQXq4f/tFEoQsjmoY4Zc+eV9iw4UYmTXqVzMyTOvUasYV9H+yj\n9NFSqhZVsfjYxfxuye/4+S9/zo9//OM253diQbcMQyml8kWkSCkVWsNY5sQjnWGpVvMGXUFEgkqf\n7/0eTUtn1ymlrtXV8oSI/EspdbZSaiN66ewVB/NeZWVl4fR7r39IzrIcfLU+qiuqmzzH7kxh4JJj\nUKIIjbhaYqFoyofSyrm3e8v3MnDgQCyxsJxuiOX8C7UNp8VJKxVur/076de5Iv5ZSteH0i1jN3p9\nvgsXLqVD6DUe5cGt3Lgtt46d9Lpd6zh6+NF4LI+uCwWXjj0uTzjtcrlQHoVy62B5rHBauVWzumbp\nKHkrwQqH0MM/nG/jAd7be0ICAb2sMiQGLUN9vd64HQoTJ8IFF2hRGDkSFi0y+216gljbWlr6OFu3\n3sPUqe+Rmtq+K7zCwkJOmHYCu5/bTemfSrGSLJLnJ/NH9x9ZW7yW9z96n2nTprV7jd7iYO5rR3MW\nPwZ+ANwfpU6AOV16t2gXEXkHfQpfZNmfW+RvPNT32bVrF1u3buW8885j5cqV4fKLL76YX/3qV5x/\n/vksXbqUAQO65sI41l/mrtCXbO1ORPRQUUUF7NkDW7ZoAYjsKWzfrldVRgrC+ec3pQcObOUezNCP\nEbpRFSgAACAASURBVBG2br2XXbue4+ijF3XoCqRmZQ3b7t+G+99uBpw1gPz/yefL+i+58IoLmTdv\nHs/95TmS+vi4Y0c7uC8WkVeUUmNFpKQX7TooOuOiPFIs3nzzTQoLC3nggQcYM2bMQYmFoXeJfPBX\nVOhVp9HSLfNK6Qd+To5e4x4pCmPHwqhRffNITEP3I2KzceMtVFV9wpQp75CQEH2Nq91oU/63csoe\nLaN+cz3Drh3G0GuGIpnCL3/5S/7617/yzDPPMHfu3F7+BF2ju1ZD/QJ4BXgVmN5B2z5FfX09v/nN\nb3j//ffDZf3JT1a80dgIBw7oUFPTPO5MWXW1fvhXOq4rc3KaQnZ2U3rixOb5UH3Lc5sNhmjYdiNF\nRfPx+XYwbVohHk9mqza+Uh9lT5Sx88mdJOcnM/yW4WRfkI3ltli5ciXfm/s98vPzWbFiRZeW4cc7\nHYnFXqXUe8BYpdSbLStF5PyeMavn2bRpE1u2bGHq1KmICDt27GDGjBksWbKEQYM675gi3od2gsGm\nY7kXLizk2GMLwvm24lC6vr4pNDR0nG6r7sAB3SNIS9MhNTV6HErn5EBpaSFnnFHQrC4kAvH44I/3\n70EkxtboBIO1rF49D8tKYMqUd3G5mg8bBRuCbLx5I+WvljPo0kFM/WAqKUfqI3Bt2+b666/nlVde\n4f777+fyyy+PD+eKbdATcxZno3sULxB93qJPEdpItGcP/POfR3HLLbv0xiob/vu/x3Dppct47LGs\n8AqmUF20dCi/bRu8/rpOH2oIBvVEazDYFFrmO9Mm8sEvok8g9Xr1UExysk6HytqLk5KaQmJiUzor\nK3p5y3wonZbW9P6dpbuOCzcYOoPfv5dVq84hOTmfCROexLKaPxp9u3ys/uZqksYkcfyW43GnN9Vv\n376d+fPns2fPHpYsWcKYMYe2yzte6WjO4gURuTzkfbYX7Too2puzuPTSSyksLKSyspKcnMFMmfKf\nTJlyBZalH2KPPTaWq676iuTkATgbUMN1LdMt8y6Xzh9KUEq7rHG5mkLLfGfLvN6mB77LZSZmDYb2\naGjYwcqVZ5CdfTZjx/6+VY+gZkUNq85fxdCrhjLqP0Y1q3/55Ze56aabuOWWW/j5z3/ergPGeKW7\nXJSvRR949DZQADS7oIjsPTQzu5eOJrgNBoMhkrq6YlasOIPc3BsYOfJnreor3qig+Jpixj8ynkHf\nbhqe3r9/PzfeeCNffvklf/nLXzgmXnyPHwSdFYuOdoY8DnwI5ANLW4Qec1vel4jcQh/vGFt7jr5k\nr7FVU139FcuXFzB69J2thEJE2Pa7bay/YT2T/zm5mVB8/PHHTJ06lfT0dJYtWxYWiv5+X9udsxCR\nh4GHlVKPich1B2mXwWAwxBXafccljvuO5ut0bJ9N8bXF1K6qZcbiGSTk6jXVtm1z11138dRTT/Hk\nk09yzjnnxML0mNEVdx8nAuNF5BnH9UeaiGzuUeu6iBmGMhgMHbFnz6ts2HC9477j5GZ1jeWNrP7W\narxDvEx8biKuFD0H0djYyJVXXklJSQmvv/56l1ZMxjvdNQwVutidwM/R+y4AvMBfDt48g8Fg6H3K\nyv7Mxo0LmDLlvVZCUbO6hmXHLSOzIJNJf50UFor9+/dz1llnUVtby4cfftivhKIrdNab1beA89G+\nmRCRMqD9I7MOE/r7OGWs6Eu2Qt+y93C0VUTYsuVetm37PUcf/Qlpac19NFX+q5IVc1Yw5p4xjL13\nLMrSP7RLS0s5+eSTmThxIq+++mq7Ljv6+33trFg0OuM72n+qUildfieDwWCIASJBNm68hfLyVzj6\n6H+TlDQuou7/t3fn4VGV5//H389MErIvbAFkB0EQLItsiohb7aIgVakGq7ijv6+tiLRavbSu1Vpx\nb/1+rRVRI3VpAS0uoAFBQCCCLLLvGCBk3yaZ7f79MZOQhKySyZmZ3K/rOtdZ5szkM0M4d87znHmO\ncOi5Q+y4ZQdDFg4hdVpq1WNbt27lnHPOIS0tjZdeeikkL4ttSU29n8W9wOnAJcCfgZuAdBF5KbDx\nmkf7LJRS1Xk8DrZtuw6XK5chQxbUGL7D6/Sy6392UfRNEUMXDSW6V3TVY8uXL2fq1KnMmTOHadOm\nWRG91QTifhaXAD/F912Lz0RkSSNPaXVaLJRSlZzOHLZsmUx0dC/OOOMNbLYTI0W6cl1svWor9kQ7\ng94ZRET8iQtDK79o9+6773LRRRdZEb1VtWgHt98mfHfKWwZ89yNzhZ1wb6e0SihlhdDK2xayOhx7\n2bDhXJKSzmPQoLdrFIrS7aVkjskkYVQCQ/49pKpQiAjPPvss9957L0uXLm12oQj3z7VJ9+A2xkwF\nnsFXKAzwkjFmtoh80OyfqJRSAVRUtI4tWybTq9eDnHbanTUey/s8j23XbaPv033pemPXqu0ej4dZ\ns2axdOlSVq1aRY8ePVo7dtBrap/Fd8AlIpLtX+8ELBWRhm8d1cq0GUqpti0n52N27Lixzi/b/fDK\nDxx4/ACD3xtM8nkn+i7Ky8u57rrryMnJYcGCBSQnnzwseThrqftZVLJVFgq/XJrXhKWUUgHluwXq\nIwwd+jGJiWOqtnscHnbP3E3hikKGfz2cmL4nLn/Ny8tj8uTJnHbaaXz22We00ztg1aupB/xPjTGf\nGWOmG2OmA/8FFgcuVugI93ZKq4RSVgitvOGWVUTYu/ePHD78LMOGrahRKEq3lvLt6G9xF7gZsWpE\njUJx4MABxo8fz5gxY0hPTz/lQhFun2ttDZ5ZGGP6A6kiMtsY8ytgvP+h1cA7zf5pSinVgnx3truJ\n8vI9DB++iqioToCvgGS9msX+h/bT9+m+dLmxS42hxTdu3Mhll13G7Nmz+d3vfmdV/JDS2BDlHwP3\ni8jmWtuHAk+KyOUBztcs2mehVNvhdheyZcuvsNsTGDw4HbvddwtFV66LHbfsoPxAOYPfHUzswJq3\nVlyyZAnTpk3jb3/7G1dddZUV0YNKS106m1q7UAD4t/X+kdmUUuqUlJcfYsOG8cTFDWbIkA+rCkX+\nsnzWD1tPdN9oRqwecVKhmDdvHtdddx0ffvihFopmaqxYNHRZQP2DpLQh4d5OaZVQygqhlTfUs5aU\nbGLDhnNITb2B/v1fxBg7XpeXvQ/sZVvaNga8NoD+z/bH1u7E4U1EePLJJ3nooYdYtmwZ5513Xqtk\nDVaB+J7FemPMrSLyWvWNxphb8N0ASSmlWo3vPhRpnH76S3Tu/GsAHPscbEvbRkRyBGdvOJuo1Kga\nz6moqOCuu+5i7dq1rFq1im7dulkRPeQ11meRCvwHcHKiOJyNb4jyKSJyNOAJm0H7LJQKX0ePzmPP\nntmceeb7VcOLH3v3GLt/u5uef+xJ9991rxotttKBAwe4+uqr6dGjB2+88QaJiYlWRA9qLTo2lDHm\nAmCIf3WriHx5ivkCQouFUuFHRDhw4AmOHPkHZ521mLi4wbiL3ey6axdFq4sY/O5gEkacfMeETz/9\nlOnTpzN79mzuueeeGldDqRNadGwoEckQkZf8U1AWCquEezulVUIpK4RW3lDK+uWXX7Bz5+3k5Pyb\nESNWExc3mOLMYjJHZGLshpGZI08qFB6Ph4cffphbbrmF999/n1mzZrVKoQilzzVgY0MppVRrc7ny\n2bfvfjp0aM+wYcux2+I5+NeDHPrLIU5/+XQ6Tz35jnU5OTlMmzaNiooK1q9fT5cuXSxIHp6aPER5\ni/9gY1KAfwG9gP3AVBEprGO//UAh4AVcIjK6gdfUZiilwkBx8Ua2br2Sjh0n0bfvX3Ble9l+w3Y8\nxR4GpQ8ipvfJF2N+8803TJ06lWuvvZbHH3+ciAj9W7gpAjFEeUu7D99ghAOBLzlxf+/avMBEERne\nUKFQSoWHo0fnsWnTJfTp8wT9+z9H/qdFZA7PJHFMIsO+GnZSoRARXnnlFS6//HJefPFFnnrqKS0U\nAWBlsZgMvOlffhO4op79DEE8aGG4t1NaJZSyQmjlDdasXq+TnTv/HwcOPM5PfpJB+6greWvKW+yc\nsZPB/xpMn0f7YIuoeSgoKSlh2rRpvPbaa6xevZrJkydblD54P9e6BPIe3IHQWUSOAfgvwT25AdJH\ngCXGmHXGmFtbLZ1SqtWUlx9m48bzqaj4geFnfUPBG8msHbgWcQtnbzyb5Aknfz94+/btjBkzhujo\naFavXk2/fv3qeGXVUgJ6rmaMWQKkVt+E7+D/YB2719fZcK6IHPHfQ2OJMWabiKys72dOnz6d3r17\nA5CcnMywYcOYOHEicKKatvR6pUC9fkutV24LljwNrU+cODGo8oRb3mBa/8lPYNu2NA4e/AXR+35J\n2Zs7ie4ZTdFTRfTs25PI9pEnPf+9997j1ltv5bbbbuOZZ54JivdTuc3qz7Ox9crluXPn0hxWdnBv\nw9cXccwY0wXIEJFBjTznYaBYRObU87h2cCsVIkSEQ4ee5dChv9I7+v/Iua8XFYcr6PdsP9r/vH2d\nl7s6nU5+//vfs2jRIj744ANGjBhhQfLwEgod3IuA6f7lG4CFtXcwxsQaY+L9y3HAT4EtrRWwKapX\n62CnWQMnlPIGQ1a3u5jvv/81x7Lmk/x+Ovt/0YEOkztw9qaz6fCLDlWFonrWw4cPM3HiRPbs2UNm\nZmbQFYpg+Fyb6sdktbJYPA1cYozZAVwEPAVgjOnqHxodfE1YK40xG4A1wEci8rklaZVSLaK0dDuZ\nmWNwbLFTPuVp2tl7Mnr7aLr/T3dskXUfkr744gtGjRrFZZddxsKFC0lJSWnl1MqyZqhA0GYopYJb\ndvaH7NhyO2burSSXTKPv032JPT223v29Xi9//vOfefnll3n77be56KKLWjFt29DS9+BWSqkfzet1\ns2PVvWQf/xfRc59lwMxJpExs+Ozg0KFD3HbbbRQVFbF+/XpOO+20Vkqr6hK0318IFeHeTmmVUMoK\noZW3tbMW7zvI6nfHk73ha/oWL2H0v69vsFB4vV5eeeUVhg8fTteuXcnIyAiJQhHuvwN6ZqGUCgh3\niZtdf1vEsT53kOi6kqHTnyMyoV2Dz9m2bRu33HILAF999RXZ2dlERUU1+BzVOrTPQinVojylHo6+\nfZS9K1/Am/Y6p/f5O93OmNrgc5xOJ0899RQvvvgijzzyCHfccQc2mzZ8tAbts1BKtarib4s58toR\njv13N/Y//J3IW/Zw1qjVxMYOaPB533zzDbfccgu9evViw4YN9OjRo5USq+bQ0n2Kwr2d0iqhlBVC\nK29LZnUXucn63yzWn72ezVM24/zJYmzv3Eynn/bm7HPXNlgoSkpKuPvuu7niiit44IEH+Oijj04q\nFG31cw007bNQSgWciFC8rpis/8si58Mcki9MptsTcDz1Tzhc2QwduJDExIYHiP7ss8+YMWMGEyZM\nYMuWLXTo0KGV0qsfS/sslFJN4ipwkf1ONln/l4WnxEPXW7vS+Yb2ZDtf4tChOfTseR/du9+NzVb/\n36A5OTncc889rFixgldffZVLL720Fd+Bqov2WSilTpmIULSqiKzXsshZkEP7S9vT79l+pFyYQlHx\nKjbv+CXR0b0YOXI9MTG9G3yd+fPnM3PmTK699lo2b95MfHx8670Rdcq0z+IUhXs7pVVCKSuEVt6m\nZHXlujj0/CHWDVnH9pu2EzckjjG7xnDmv84k4XzDzl0z2Lp1Kr17P8zQoR83WCgOHjzI5ZdfzpNP\nPsnChQt57rnnmlwowu1zDRahNjaUUiqIeCu85C3N4/tp37Om3xqK1xcz4G8DGL19ND3v7Ulkx0iO\nHZvPunVnYoyd0aO/p3Pnq+scHRZ8X657+eWXGTFiBGPHjiUzM5MxY8a08rtSLUX7LJRqo8QrlHxX\nQv7SfPKX5lO0qojYQbGkTksl9TepVfeRAHA49rJz5504nVkMGPC/JCWNa/C1v//+e2699VaMMbz2\n2msMGtTg3QeUhZraZ6HFQqk2xLHX4SsOX+RT8GUBER0iSLkohZSLU0iemExkSmSN/b1eF4cPz+Hg\nwWfo2XM23bvfg80WWc+rw44dO3jiiSf45JNPeOSRR5gxY4Z+uS7IhcL9LMJCuLdTWiWUskLw5nXm\nOMl+L5sdt+1gTd81bDh3A5+//zntf96ekd+OZMz2MQx4ZQCdpnQ6qVAUFq4mM3Mk+fkZjBy5lp49\n/1Bvodi2bRvTpk3jvPPOY+DAgezevZs777zzlAtFsH6udQn3rHo1lFJhxFPqoXBlYVXTkmOvg+QJ\nyaRcnEL333UndnAszuVOuk7sWu9ruFwF7Nv3R3JyFtCv3xw6d/51vf0SW7Zs4fHHHycjI4OZM2fy\n6quvkpCQEKi3pyykzVBKhSjxCo69Dkq3lFL6XSn5GfmUZJYQPzyelIt9TUsJoxLqvaHQSa8nHrKz\n32PPnnvp0OEy+vZ9isjIukeH3bRpE4899hgrVqxg1qxZ3HHHHXopbIjSPgulwoSI4Dzm9BWFzf5p\nSyml35cS2SGSuKFxxA+NJ2lCEknnJRER37wGA4+nnGPH5nHo0F+JiGhPv35/JTl5fJ37btiwgcce\ne4zVq1cze/Zsbr/9duLi4lribSqLaJ9FHbxeLyNGjGDSpEkt9prh3k5plVDKCi2X113spnBNIVmv\nZbHrt7vYeOFGVnVexboz13Hg8QOU7y0ncUwi/Z/vzzk/nMO4A+M46+Oz6PvnvnT4eYcmFYrKrC5X\nPgcOPMk33/QhJ2cRAwf+gxEjVtdZKDIzM5k8eTKXXXYZ559/Pnv27OGee+4JeKEIpd+DcM/apvos\nXnjhBQYPHkxRUZHVUVQbJSK48904jzpxHnNS8UMFZdvKqs4YnNlOYgfFEjfEd7bQ4bIOxA2JI6pr\nVL39Bs3ldGaze/csjh59gw4dJnHWWUuIjx9S575r167l0UcfZePGjdx3333Mnz+fmJiYFsmhQkub\naYY6fPgwN954Iw888ABz5sxh0aJFrZxOhTNPqcdXAKpPx5x1brPF2IjqElU1xQ2KI25IHHFD44jp\nF4Oxt0xRqK2kZAuHDj1Dbu5HdOlyI9273010dN3Dga9evZpHH32UrVu3ct9993HTTTcRHR0dkFzK\nWjo2VC0zZ87kmWeeobCw0OooKsh5HB7ceW5cuS5ceS7fcu15rgtXtquqCIhbahSAyilhZEKN9cjU\nSOzR9lZ7LyJCYeEKDh78C8XF6+ne/bf07/98nR3XXq+Xr776iieffJKdO3dy//33s2DBAtq1a/ju\ndqptaBPF4r///S+pqakMGzaMZcuW0ZJnU8uWLWPixIkt9nqB1Fayel1ePMUe3IVuPEUe3EUn5u5C\nN+58d90FwD8XESI7RBLZPpKI9hFEpERULUe2jyS6dzSR7SOJSj1RBFZ8u4KxF4xt2Q/hFIh4yMlZ\nyMGDf8HtzqNHj3s588wPsNuja3y2IsLmzZtJT09n/vz5xMXFMXPmTK6//vqguJ1pW/mdbW0/Jmub\nKBZff/01ixYtYvHixTgcDoqLi7n++uuZN2+e1dHaLPEK3nKvb3J48Tg8Vcteh5eidUXkFOac2F7m\nLwBFtQpAofukbV6nl4jECCKSIrAn2olIrDmPbB9JZOdIYs+IrSoA1ef2mOb/5d9S/Qmnyndl01v+\nK5uS6dnzD3TsOBljar6nvXv38u677/Luu+9SXFzMtddey6JFixg6dGjQvBcVXNpMn0Wl5cuX8+yz\nzwZln4V4BfEKePzLHgEvNebirWdbteeI27/uPrFee46Hhh9zC16nF3EJ4jqxXLXNKXhdtZadde/r\ndZxcFMQp2NrZsMX4p2jf3B5jr1quvf2kA3/SyYUgIjECW4ytzR3wXK4CsrJe5YcfXiQ+fjg9e/6e\npKQJNT6HY8eO8d5775Gens6ePXu4+uqrSUtLY9y4cTokRxumfRYN8JR4WDtkrW9FTkwicmKdOrZV\nrtd+nldqzPH69/PW8Xhd2/zPAcAOxmZ8nZw23/JJ26rN63w8wrdcfY697u0NPh5lsEXaMFEGE2mw\nx9uJiIzARBpsUbYa89r71nhejL3Ggd8WY8PWru0d0Fuax1NKbu5ijh9/j7y8z+nYcTJnnfUZ8fFD\nq/YpLCzkP//5D+np6axdu5ZJkybx0EMPcfHFFxMZWf8YT0rV1ubOLMDXgenY7QBT9Tzfsn+qWqeO\nbZWT/3lfrfmKCedO8B20DVVzbP7n2Dixva5ttecBFO5tqlZqrbweT5m/QLxPXt6nJCaOoVOnqXTs\neAVRUR0BcDgcLF68mPT0dJYuXcqFF15IWloav/zlL4mNjQ2pz1azBkb1rHpm0QB7jJ34oS0zNEHU\nviiiu+slhSpwPB4HeXmfkJ39Hnl5n5CYOJpOnaZy+umvVBUIt9vN559/Tnp6OgsXLmTkyJGkpaXx\n+uuvk5ycbPE7UOGgTZ5ZKBXsfAXiU44ff5/c3MUkJJxN585T6dhxClFRnRARtm/fTkZGBl9++SXL\nli2jX79+pKWlMXXqVLp2rX+gQKWqC/qxoYwxVwF/AgYBo0Tk23r2+xnwPL7Gm9dF5OkGXlOLhQpZ\nHk85+fmfkZ39Hrm5/yUhYQSdOk2lU6dfERnZid27d5ORkUFGRgbLli0jOjqaCy64oGrq3r271W9B\nhaBQGBtqMzAFWF7fDsYYG/AycClwJnCtMeaM1onXNOE+HoxVQikr/Pi8Xm8FOTmL2LbtN6xe3ZXD\nh58nKWk8Y8bsIDn5n3z+eTtuvnkWPXr04IILLmDFihVccsklrFq1in379vHPf/6T3/zmN80qFKH0\n2WrWwAipsaFEZAeAafiSmNHALhE54N93PjAZ2B74hEq1PK+3guLi9RQULKegYDlFRWuIjx9O585X\nExMzkxUrtvrPHv5CWVkZF1xwARdeeCEPPfQQ/fv31yvIlGUs77MwxmQAs+pqhjLGXAlcKiK3+dev\nA0aLyG/reS1thlJBxeNxUFS0hoKC5RQWfkVx8TpiYgaSlHQeLtdgNm0SMjIyycjIIC8vj4kTJ1Y1\nKw0aNEiLgwq4oLgayhizBEitvgnftwoeEJGPAvmzlbKC211MUdEqCgq+oqBgOSUlG4mPH0ps7DkU\nFFzK+vXn8/XXG1mzZj4AY8eO5fzzz+fOO+9k6NCh+uU4FbQCWixE5JJTfIkfgJ7V1rv7t9Vr+vTp\n9O7dG4Dk5GSGDRtWdT1xZTtdS65v3LiRu+++O2Cv35Lrzz//fMA/j5Zar96mGgx56lt3u0sYNgw+\n++wtSkq+o7z8ABMmjMbjOZNFi3qweXMS+/dnsXPnq/Tq1YvBgwdzzTXX8Pzzz7N3716MMa2ev3Jb\nMHx+ja3r/6/A/PvPnTuX5gqWZqh7RSSzjsfswA7gIuAIsBa4VkS21fNa9TZD3XzzzXz88cekpqay\nadMmwHdryBkzZlBaWkrv3r155513mn1ryGUh+kWcYBdsWb1eNxUVB3A4dlNWtouysu0UFX2Nw7Gb\nmJiRLF/ejoiI01i69AdWrVpPQkICY8eOrZqGDx8eNKO3Bttn2xDNGhjVs4bCpbNXAC8BHYECYKOI\n/NwY0xV4TUQu8+/3M+AFTlw6+1QDr1lvsVi5ciXx8fFcf/31VcVi9OjRzJkzh/HjxzN37lz27t3L\no48+2pJvU4WQyoJQVrYLh2M3DseJeXn5AaKiumC396KsLIXs7CgyM8v55JM97N17kJEjRzJu3DjG\njh3LmDFj9HsOKmQEfbEIhMbOLBYuXEhpaSkOhwOAmJgYBgwYgM1mIykpiSNHjrBjx47WjKxamdfr\nprx8/0nFwOHYTXn5QaKiuhAd3Y+Kio7k5kZz8KCHbduKyMw8wtatu7Db7QwcOJAzzjiDUaNGMXbs\nWIYOHUpERJscDEGFAS0WtaxcuZKioiKuvPLKqmIxbtw47r//fiZNmsSUKVNYvHgxFRUVzfqZoXrq\nGeyak1XEi8uVi9N5FKfzGC7XsarlE3PfstudR7t2pxEd3R+RbuTnx/LDD7BjRykbNx7j++93kZWV\nRZ8+fRg4cOBJU8eOHU85r9U0a2CEataguBoqmIwfP56vv/66xrY333yTu+66i8cee4zk5GS9EiUI\niHhwu4txOo9SUrIJt7sQj6cIt7ug2kHfd+CvLAouVw52exJRUalERXUhKioVaI/DEU1xcTfy8rpx\n7JiTw4fL2L8/n+3bd7Nz5xpiY2OrFYKzuP1233KfPn10RFalamkzZxbguwnSxRdfXHVmAfDggw8y\nb948YmJiSEhIYP369a0RNSyIePF6y/F6HXi95Xg8Dv+yo9q2Mv/BvrBqqr7u8RTidhdVLXs8Zdjt\n8UREJBERkYTdnlS1HBnZGUihpCSSwkIbx497OHKkgoMHi8jKyiYrK4sjR46QlZWFx+OhW7dudO3a\ntca8W7dunH766QwcOFAH2FMKbYaq08qVK7n44ospLy8H4Pjx43Tq1Amv18uoUaPo0aMHCxYsaLE8\nviyCiBfwNmHuQcSNiAfw1FhveNuJdd/kRMSF1+uqtuyb17dc9zbfQb9mESiv2ibixGZrh80Wjc0W\nUzXZ7TE1tp048CcSEZEExFJeHoHDYaO0VCguFgoLPeTnV5CX56CgoIiCggIKCgrIz8+vmh85cgQR\nqTro1y4E1eeJiYn6hTalmkCboWpJS0vjiy++wOl00qNHV264wUNZmYcPPywCYNSoaDZs2MSqVd2p\nfmcj34G8crlyu7dqecMGF8OG2ajr4O/b13dzC98wVw3NDcZEAHaMqZwi/LfDbN42my0KYyIxJgqb\nLbJqee3aI4wb1x9jIrHb4zDGt59vn8rlyudGAlE4nYaKCqiogPJyD+XlXioqfJ9daamTsrJySktL\nKSsrq3NeWlpKQcGuqoN/QUEBHo+HlJQUUlJSSE5OPmlq3749LpeLCy+8sGpbSkoKXbp0ISEhISiL\nQKi2Vwc7zRoYPyZrmykW6enp7N+/n8svv5zvvltPefkh9u49wBNP9AEMf//7PETWMWLE3wEQ6HvG\nXgAACcFJREFUOTF5vb5CUXObb56bu5qBA8fi9UrVNo/Ht+zb5q2aPB5Pg8sulxu3u/7J4/E0+Hjl\nVFFRgdPppKKiwr/soKKigqysLN56q7Dqsdrz2ster5e4uDhiY2OJjY2tWm5o3qVLl5O2Vx7sKw/8\n0dHRjR7wQ+k/nlJtQZtphkpLS2PZsmXk5ubSvn17cnNz8Xg8/iJw8nN8f+kbbDYbNputarmuud1u\nx2aznTSvb7m+xyMjI4mIiCAiIgK73V61XNdU3+N2u5127drRrl07oqKiTlpuzraoqKig/CteKdVy\ntM+iAR6Ph/Ly8hoH/doFQCml2oJQuJ+FZex2O3FxccTExBAdHU1UVFTVX+XNLRTVx1sJdpo1cEIp\nr2YNjHDP2iaLhVJKqeZpk81QSimlfLQZSimlVIvRYnGKwr2d0iqhlBVCK69mDYxwz6rFQimlVKO0\nz0Ippdow7bNQSinVYrRYnKJwb6e0SihlhdDKq1kDI9yzarFQSinVKO2zUEqpNkz7LJRSSrUYLRan\nKNzbKa0SSlkhtPJq1sAI96xaLJRSSjVK+yyUUqoN0z4LpZRSLUaLxSkK93ZKq4RSVgitvJo1MMI9\nqxYLpZRSjdI+C6WUasO0z0IppVSLsaxYGGOuMsZsMcZ4jDEjGthvvzHmO2PMBmPM2tbM2BTh3k5p\nlVDKCqGVV7MGRrhntfLMYjMwBVjeyH5eYKKIDBeR0YGP1bKC8RdIMzVNMGaC4MylmZomGDM1lWXF\nQkR2iMguoLG2MkMQN5dNnDixwceD6ZejMmswZapUO1Njn2traM7n1Jp5T/XfLxBZA/U7dSpZW/v3\nvClZg+X/3o/5XIP2IFyNAEuMMeuMMbdaHUYppdqigBYLY8wSY8ymatNm//zyZrzMuSIyAvgF8P+M\nMeMDFPdHCZa/FJpCswZOKOXVrIER7lktv3TWGJMBzBKRb5uw78NAsYjMqedxvW5WKaWaqSmXzka0\nRpAmqDOoMSYWsIlIiTEmDvgp8Eh9L9KUN6yUUqr5rLx09gpjzCFgLPCxMeYT//auxpiP/bulAiuN\nMRuANcBHIvK5NYmVUqrtsrwZSimlVPALhauhmsUYc5cxZpu/M/0pq/OAr6/FGHPYGPOtf/qZ1Zkq\nGWNmGWO8xpj2QZDl0WpfwPzUGNMlCDL9xf/7tNEY86ExJjEIMjXpC62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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff9c4900518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glmnetPlot(fit, xvar = 'lambda', label = True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now when we plot against %deviance we get a very different picture. This is percent deviance explained on the training data. What we see here is that toward the end of the path this value are not changing much, but the coefficients are \"blowing up\" a bit. This lets us focus attention on the parts of the fit that matter. This will especially be true for other models, such as logistic regression." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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bv1+le8ydq7bbboMnn1T/EwPlC9OZM2dISkoCIDk5mTNnzvjkvhcUA2l3rkBK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\nCXbcQfxcxACfAp3cx3HBjjuIufg98OjpPAAlQFSwY2+AXKQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff9a273aef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glmnetPlot(fit, xvar = 'dev', label = True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can extract the coefficients and make predictions at certain values of $\\lambda$. Two commonly used options are:\n", "\n", "* `s` specifies the value(s) of $\\lambda$ at which extraction is made.\n", "\n", "* `exact` indicates whether the exact values of coefficients are desired or not. That is, if `exact = TRUE`, and predictions are to be made at values of s not included in the original fit, these values of s are merged with `object$lambda`, and the model is refit before predictions are made. If `exact=FALSE` (default), then the predict function uses linear interpolation to make predictions for values of s that do not coincide with lambdas used in the fitting algorithm.\n", "\n", "A simple example is:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "any(fit['lambdau'] == 0.5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.19909875],\n", " [ 1.17465045],\n", " [ 0. ],\n", " [ 0.53193465],\n", " [ 0. ],\n", " [-0.76095948],\n", " [ 0.46820941],\n", " [ 0.06192676],\n", " [ 0.38030149],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0.14326099],\n", " [ 0. ],\n", " [ 0. ],\n", " [-0.91120737],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0.00919663],\n", " [ 0. ],\n", " [-0.86311705]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glmnetCoef(fit, s = scipy.float64([0.5]), exact = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output is for `False`.*(TBD) The exact = 'True' option is not yet implemented*. \n", "\n", "Users can make predictions from the fitted object. In addition to the options in `coef`, the primary argument is `newx`, a matrix of new values for `x`. The `type` option allows users to choose the type of prediction:\n", "* \"link\" gives the fitted values\n", "\n", "* \"response\" the sames as \"link\" for \"gaussian\" family.\n", "\n", "* \"coefficients\" computes the coefficients at values of `s`\n", "\n", "* \"nonzero\" retuns a list of the indices of the nonzero coefficients for each value of `s`.\n", "\n", "For example," ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.98025907]\n", " [ 2.29924528]\n", " [ 0.60108862]\n", " [ 2.35726679]\n", " [ 1.75204208]]\n" ] } ], "source": [ "fc = glmnetPredict(fit, x[0:5,:], ptype = 'response', \\\n", " s = scipy.float64([0.05]))\n", "print(fc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "gives the fitted values for the first 5 observations at $\\lambda = 0.05$. If multiple values of `s` are supplied, a matrix of predictions is produced.\n", "\n", "Users can customize K-fold cross-validation. In addition to all the `glmnet` parameters, `cvglmnet` has its special parameters including `nfolds` (the number of folds), `foldid` (user-supplied folds), `ptype`(the loss used for cross-validation):\n", "\n", "* \"deviance\" or \"mse\" uses squared loss\n", "\n", "* \"mae\" uses mean absolute error\n", "\n", "As an example," ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "warnings.filterwarnings('ignore') \n", "cvfit = cvglmnet(x = x.copy(), y = y.copy(), ptype = 'mse', nfolds = 20)\n", "warnings.filterwarnings('default')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "does 20-fold cross-validation, based on mean squared error criterion (default though).\n", "\n", "Parallel computing is also supported by `cvglmnet`. Parallel processing is turned off by default. It can be turned on using `parallel=True` in the `cvglmnet` call. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parallel computing can significantly speed up the computation process, especially for large-scale problems. But for smaller problems, it could result in a reduction in speed due to the additional overhead. User discretion is advised.\n", "\n", "Functions `coef` and `predict` on cv.glmnet object are similar to those for a `glmnet` object, except that two special strings are also supported by `s` (the values of $\\lambda$ requested):\n", "\n", "* \"lambda.1se\": the largest $\\lambda$ at which the MSE is within one standard error of the minimal MSE.\n", "\n", "* \"lambda.min\": the $\\lambda$ at which the minimal MSE is achieved." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.07569327])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvfit['lambda_min']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.14867414],\n", " [ 1.33377821],\n", " [ 0. ],\n", " [ 0.69787701],\n", " [ 0. ],\n", " [-0.83726751],\n", " [ 0.54334327],\n", " [ 0.02668633],\n", " [ 0.33741131],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0.17105029],\n", " [ 0. ],\n", " [ 0. ],\n", " [-1.0755268 ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [-1.05278699]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvglmnetCoef(cvfit, s = 'lambda_min')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-1.36388479],\n", " [ 2.57134278],\n", " [ 0.57297855],\n", " [ 1.98814222],\n", " [ 1.51798822]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvglmnetPredict(cvfit, newx = x[0:5,], s='lambda_min')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Users can control the folds used. Here we use the same folds so we can also select a value for $\\alpha$." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "foldid = scipy.random.choice(10, size = y.shape[0], replace = True)\n", "cv1=cvglmnet(x = x.copy(),y = y.copy(),foldid=foldid,alpha=1)\n", "cv0p5=cvglmnet(x = x.copy(),y = y.copy(),foldid=foldid,alpha=0.5)\n", "cv0=cvglmnet(x = x.copy(),y = y.copy(),foldid=foldid,alpha=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are no built-in plot functions to put them all on the same plot, so we are on our own here:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AHwIHY96ud1JnJz0qrYJFxgG/Bb4B5qrqJVH2sXZSS0xKTz2Vsig9ML82U+ctoAar25Ys\nctmYMWx89lkG7NxJIXA+8IfOnSl9772UH/5B5gI6M6UaxEFEOgEjMWMcW4FnReQiVX0y6LIszZe8\nAw5gJ4TfACAlm2mfxLv4x+q2JRmqQiFqZ83i6Z07wwPA17ZvT96IERnt+bvEbABE5DQ1+VGqRKSX\nqoY820ZRN96WCsOANepMvCEizwMnAA1ukjFjxoRD+zt16sTAgQPD0Xeu/S3ZZXddqsdHW46Una48\nd7miooIJEyYEJq8p1jeyzu72vmedxdnPzGH2nv/SDngJ+FP79jzo2Ewj97/nnnuoqKhAVelVd3Pl\nNXXdDvK/jJSZK7pwzz33BHLv59r1mv/XvzLt6695F6gAJgDTduzgBwsWUF5entT1qaioSD5/UKwA\nAWBJtN/RlpP9AMcCHwBtMK/d04FrouznP/IhCZpbIFhzlxtNZuWaNTrl4ov1tILB+v96FOuoAw7Q\nKRdfrBPGjUsob9CgQapqgmWag27namCTlZWY35SUmMg10HnOt4JeUlycllx8BoLFHAMQkaWqOijy\nd7TlVBCRUox3xF5gKXCZRuRht3ZSixd34GzDF1/w+vjxTN20qe612bGZTn/88YR51AcNGsTSpUvd\nMYAKq9uWxiIyhuWdJ57gD6+91sCMeemRR/LcsmUplxNEHIDG+B1tOWlUtUxVD1PVo9Sk5M34JBzN\nlZaSC8j1/1/94ovhhz+YMYBpX3/tO2IywgXU6nYO09xyAUXGsLRr145LaM1k6jwRSvv04bunntoo\n9YnXAPQWkVkiMtvz211u0tPoee13uSzTr9yystTkpnKcH7mZlun6TXtpB/x35UqKioood5JrxZoX\nYM2aNYwYMcJdbPK6HeQ1zzVZro4mIyuRXufCOboxLFNmzeJ5dnMTcFleHj869FCOvu02BgwalFCP\ngyCeF9BIz++7IrZFLlssjUYs75/v9O/vax7VmTNnAjB79myor+dgddvSCESLYbm+tpa/tm7NBRde\n2Gj1SBgHkE2sndQfLS0XUFUoxA0DBvK37dsajAEk4zrn106aCaxu+6O55gKKFcNyRb9+XPTAA4Ax\nF6U6K1iQcQAWS85QFQoxffJkNraBHxR0oVvbfejerx/9zziDUFUVoaqqtG4ciyUTeAd/F7/zDotX\nrWISZrL1MZigkZ3AAd/7Xti1s1Hw4yqUrQ/WDdTOB6CqTz31lM6bN0+fevJJvbxbt3r50sd27pxS\nvnRV/65ymfgEqdu55NYYtKzmNh9A1LkrQFeCDm/fPmVdjsSvbvuZD8CS4zT3XEA9evQIxPvH0vRo\nbrmAYuWvmlRcTLszzmiU6F8v8eIAZhPHJU5VR8TaFhTWTmrxkm7uH5ezzz4bEXEHgWdHbre6bckU\nsXR43KBBDB43Lmy6TNeMGcQYgOsNMQroATzhLF8IbEy5ZhZLigSU+4eJEycCYS+gXVjdtjQS6Xqw\nBU4iGxGw2M+6THywYwBWrkdm5Zo1enG77hkdA2iKup1rdnsrqz6hUCg8hnX+wIH6o7xCnQRaGaHD\nQdYrmm5H+/jxAmonIr1VdQ2AiPSCBnE4FkvguJ4TFRUVLH7nHdbMncummh6c3aGAzvsSiPeP1W1L\npikuLkZU+fdll/E3z7SP/1NQQNezzqJjURFFvXoRqkonB2Fq+JkPYDjwELAGk9yqCLhSVV/OeOWs\nndSCcf0s+973woNnqfr9R+KkhFiH1W1Lhin76U+Z+I9/NDD93HXxxXDIIQnzVyVLIHMCA6jqHOAQ\nYDwwDujbGDeIxT/NPRdQNM+JAL1/rG7nMM0lF1C66UsyRcIGQETaAr8CfqGq7wPfFZEfpluwiHQU\nkWdEZJWIrBCR49KV6RebC6hp5QJas3x51Jun9vNAZlps8rqdC7ltMiWrueQC2taxI5OBUozbp5v4\nzR38dYO/GjuA0c8YwKPAe8DxzvJ64Bng32mWfS/wf6r6YxEpwMydarE0QLp1C8T7JwZ7sLptyQDe\n9OXb33iD2yFswpwMrO/Qgd9ncMJ3P/gZA1isqkdHzA/wvqoOSLlQkQ7AUlWNOx2ftZP6o7nlAooM\nm18xcyZb336bw2truQzoSnBjAKoqVrdzl+aQCyiW/f+Cvn2Z/eGHGSkzyFxAe0RkH5ygMBHpA+xO\ns369gK9E5FFgALAYGK+qu9KUa2kGuN48VaEQj48axX2ewd//KShAjjmGo845J5DcP1a3LZkmlv1/\nf5GwCSlb+av8NAClwBzgIBH5B3AiJn9RuuUOxkyVt1hE7gFucsqqh50TOPG8qpB6fcvLc3dO4NIr\nruAnznypJcC7wEXV1bzTuzcTb7ih3s3jR16MeVObvG4HqXuRMrOtC65uN+U5gfMOOICXgH3CZ2Pm\nr95z4IH19p8xY0bK1yvwOYGd11MBDgK+A5wF/BDo6ifAIIHc7piJs93l7wOzo+yXahxEXJpbIFhp\naWpyUznOj9ygcOdL9c6VqqC/OfXUtGXX1ta6cwI3ed1uykFSiXB1NBlZifS6Mc+xcs0anThihJ4r\n+fWCv37Zp0+DAMZsBIL5GQP4QFWPTK5ZSYyIzAcuV9WPnDlU26rqjRH7aKL6WZonVaEQ1592GodV\nVjZImXvXxRdT+sQTcY/3Q6bmA7C63bKJN3f1/xQU8FXv3jw4Z05GE78FOQawRESOUdV3A6iXl3HA\nP0SkEBOIMzZg+ZYmRuSN87jnxikFfg78oXNnSgP0nLC6bQka155f9tOfNshe+2B1NVd16ZIzc1f4\nSQd9HLBQRD4VkWUi8oGIpD5dvYOqvq+qx6jqQFUdpapb05XpF6/9LpdltjS5sSZ9fxfjOz2xe3f6\n33QToaoqyoMLmmnyuh3kf2llBScr1uBvr332ocSZGN778M/UPRkPP28AZ2a8FhaLh1g3ztH9+zPx\nhhuCLi6uu6bFkipBZa/NJL7nBBaR/YA27rKqrs1UpTxlWjtpCyRe3pQgbP8unjgAq9uWQKkKhbhv\nwgTWzJ5Df90TaPyKHwLLBSQiI0TkYyAEzAcqMV5MlhyhOeUCqgqF2LF9O5fmFTCZupD5azt3ZkwG\noiatbuc2TSkXUGVlJeXl5Tz91FP89rjjmDJrFs/pHm4CJhUUcNXxx2fChJkeidyEgPcxrnJLneVT\ngUf8uBil+8G6gbaIOYG9+dIj5/z9aUGBnt6/v/7hd7/TefPm6bx58zQUCgVSV+rcQJu0buea62aQ\nsprinMBTLr44rMPq0eUpF1/caPUiwPkA9qrqf0UkT0TyVHWeE9xisQRCIq+Ja3r2zITtHwCr25ag\niTWGFVDywkDxEwfwKnAO8L8YM9aXwDGqekLGK2ftpL5oLrmAgprz1y/OfAD7YnU7Z2lquYAaI37F\nD4GNAQAjMfOmXocJm/8UODu96lks9akKhVheWckk6tLlQqN4TVjdtgRCVSjEtNNP5/HKSm4HJgLT\ngFXAdd260fess3LH9u/ix06UrQ92DKBFjAFUrlmjv+zTp57t/5egK0Ev79ZNJ0+aFKjt3wWfdtJM\nfILU7Vyz2wcpqymMAbhjWFcNHRrV9j+quNjXvNU5OQYgIttxsiUCrYBCYKeqdshMk2RJllRnkwt4\nFrqkcSN//3X77fzBmSsVjL20DLi0uJi7X3+dUFVVOOlV0IjINuen1e0cJBUdbWy9dsew5pWVRbX9\nH9GrV8bdPlPFdxwAgBij6UhgiKrelLFa1ZWnydTP0jRpbNu/i9dOanXbki6NFb/ihyDHAMI4bxcz\nsNHBlgBxIya9NHbEpNVtSzq48StjGil+JSj8BIKN8nx+JCJ3At8GUbjjfrdERGYFIc8vNhdQ7sgN\nB37RKuaNk8kcKc1Bt5tinpzmIuvpp5/m6aee4vff/z5TZs3imdrqlAO/cjUXkNcrohoTLTkyoPLH\nAysBa3NtQSRKlxs549eGDRsyWR1Xv61uW5KmR48ezP/rX/n95583iF+5y5m4KKfxM1KciQ9wIPAK\nZpKcWTH2SXcw3JLDpBoxGRRkyAvI6nbLwp24KPITxMRFqeJXt/14Af0pQQMyLsW2ZyrwK6Bjisdb\nHKZMST1nSjbzAeVCxGQ8/ba6nX1S0dHG1uttHTsyGWNPz8MEf3Ult7J+xsKPCagN0B/4p7P8Y8yr\n7cJUCxWRs4CNqlohIiWYqSejYucETjyvallZCVOmJF/fsjIoKcnOnMBVoRDzVq1iDXAw5qYJYaKy\n3Bsn6P8rxrypg2niuh2k7kXKbAxdiLfs6nYycwIbvY69PYjr9fTTT7NhwwY2//e/bH/jDc7AzPl7\nDDAZeL99e07s1Yvy8nKKi4vD9v9MXa+MzAls3iR4GyjwLBcCb/t5vYgj8w5gLWa2pC+AHcDjUfbL\nyOuRDQTLbiBYosCvp558sl7gV6auLSa+pcnrdq4FbwUpK9cDwUYPGxbVjDlxxIikZeXqnMCrgeNV\ndbOz3Nm5Sfom19TElH8K8EtVHRFlmyaqn6Xp5AKqF/j12msN/KXdwK/GCppxcgF9x+p27pLLuYCq\nQiGuGzKEI7/8Mmz6KXK2ZTqGJRFBzgl8J7BUROZhXmdPBqakVz1LSyRHIyatbluSxs378/cvv6w3\nb/W1GPv/prw8Kisrszrfrx8SxgGo6qOYeYFfAJ7DvA08FlQFVHV+tB5SJsmEv20mZDZHuakkfctU\nXR2avG4HeX2srPi4k7787vLLKfv0U9511rvpS/4KlPbpw40PP5z0wz/Deh6VmA2AiBSJSEcAVd0A\nbAOGAheJSKtGqp/FB00lF1CuZEusqqpi61YzT7vV7dwm13IBFRcXU1JSQreamqhvsVXdu3PtK6/k\nbO6fSGKOAYjIIuBcVf1cRAYCr2Lyph+FmSTmsoxXztpJmwW5Zvs/7rjjeOGFFzjggAMABmF125IE\nuZLzPx5BjAHso6quQ/ZPgb+p6h9FJA+oCKKSlpZBcXExosqGDz7g91BvwCwbtv9du3bRs87cZHXb\n4hvvW6zX9v9zYGq3bpzmvMW64125TrwxAG/rcRrwGoCq1ma0Ro2AHQNoXLneAbMy6kw/bt6fRAEz\nQdc1oufd5HU7V+zjzVlWpO3ffYt9F2P7n1RczC2LFnHBhRdSUlKS0sM/G2MA8d4AXheRf2F8mTsD\nrwOIyP7Ankaom6WZMH3y5Ho3jTtgdiewMQu9ptNOO43zzz/fXbS6bUlIrLdYyP2c//GINwYgwE+A\n/YF/qep6Z/0gYD9VfTnjlbN20iZNZWUlby9cyBPXXMP3vv66ga/0pd27c9vChY1+46gq//znP7nw\nwgsBDrS6bUmE+xbrdmQi3T5/NXQo50+alDOmH79jAMlGOf4wmf3T/WATZvmitLRxj/NLrIjfykZO\n+hYLPNGSVrdzk1R0NGi9DoVCesnxx+sk0N+ATvHo8CTQX/bp42vKx8YEn5HAySrtkmT2T/eTqZvE\npoLIbCqIRHOkToqR8iGezEwQ0QA0Wd3OtfQNQcrKhVQQlWvW6Ng2baJ2ZE7v3Dmwh39Ozgkc+WaR\n5P6WFkgir5+qLJl+EmB121IPr/vytG+/jTqGtU+vXoSqqlCRnDD9JEuycwIfq6rvZLA+keVpMvVr\nqeRaLqBE9tJc9JW2up2bZDsXULx8Pxe3acMdK1fmWkcG8D8G4KsBEJETgGI8XkOq+ng6FfSDvUn8\nkSsNgDvo+/frrmPQxo0NgmTCXj/33kuP/ffP+oCZkwzuRKxu5yzZaABcPX75vvv45p13OLS6mssw\nnRdvR2bKiBH8YebM1AvKIIFNCi8ifwfuAr6PSXd9DHB0mpU7UEReF5EVIvKBiKQ68UZK2DiAzMgV\nVV6YOJF/bdxYL9VDFXWmn1R8pTPsH93kdTvbPvLNTZaosnjyZO576y3+WW3m+J0GfEVdvp8bevbk\nexdcwNNPP91o9coEfsYAjgb6B9xdqQauVzNpRnvgPRGZq6ofBlhGiyHbuYC8Pf8uGzdyF3U9/zLM\nE3Yi0P6II3LRXnqi1e3cpbFzAXnTPETT41KcMaw336TImfClSZNolBh4Btjfz4hyqh9gBjA0yvp0\nBsItjUQ8d08FvSWHXeWsbltc3liwQK/u2TOmHv/GWXfV0KG+vNeyCQFOCDMPGAi8A+z2NByBpLkV\nkWKgHDhN6keOAAAgAElEQVRCVXdEbNNE9bNkj0Q2f7fnf3737lwydSpDjj8+l3r+7hjAFqxut3gq\nKyv5zUUXUbRwYYO5fV09vhPY1adPk8j2GVggGHBKtI+f1sWH7PbAYmBkjO0Bt4uG5hYHkA25lWvW\n6MQRI/SiNm10kicw5vwM9PwzPCVkk9ftXPPdb2qyXF3+sUg9XXZ7/7eAXlJYqJccf7w+9eST9Xr+\nuXqOBBUHoKrzfTY6SSEiBcCzwN9VNeZQeqYmhU/n+MZcrqioyKn6bPjiC97429/YvGABBXv2cDZw\nNsY2ejRmhvXpmB7Tq507c8aFF6Kmp531/yvaxNmZ0O9s6nYu3RtB6m5FRUXg5xupy6Oor8tDMQO+\nS7t353uXX07/ww/nggsuyMnrleqk8H5MQEMwg+CHAa2AfGCnqnZIqqSGch8HvlLV6+Pso4nqZ2kc\nqkIhfn/FFWxesICD9+yJ6hbnDpJNAr5tAq/KjgloMVa3WxRVoRD3TZjA53Pn0vvbb+Pqci77+scj\nMDdQ4D7gQuBjYB/gMuDPaVbuROBi4DQRWSoiS0RkeDoyWzJTpmTuuKpQiF+NHMmv+/eny6uvcuee\nPQ3c4qZj3DxrMbb/pd27c/Rtt4V7/jmO1e0cJhXdjnWMV5fbzJrFHd9+m1CXWw0e3FT0ODUS2YiA\nxc73Ms+6pX7sS+l+sGMAWcsFFMvGXy+ZW4R3xCTQkd/5jr6xYEHyFUpQ10zg6FeT1+1ctUPnUi6g\nN+fP17Pbt9dbQEeDrvShy1f37JlQl3Pterm4up3o4ycO4BtnntQKEfk9Zn4AP28OliZCVSjE9MmT\nqV2/nm86dGDbzp3s+M9/6P3tt9xB/ddjrz+020uqBa5u1Yo2J5/MBT/7Gd8/6aRsnUrSWN1unrg6\n/c2nn/LhunXUrF/PIMwr3krgEcwsXtOJrcu/fuihJmf6SRY/YwBFwEaMjfQ6oCNwv6p+kvHKxbCT\nev/cTzZsYL9Onfhyy5asfx/UvTudDz6YMbfd1qiKk2zou3v9pvzjCUYccCCyaRNHeuz6k4HxxLaL\nlgI3YNzi1jbhm8UZA9iHHNJtv7h27I1vv83Wmhr2tGlDcbduYX38fNMmCnbtolqkwTY/+ySzre+B\nB2ZM7/3oduTDftb6zxiWl89htbX8kjo9/jnmwe/V51qahy5HEnQuoH2A76rq6iAq55doN0msRGPu\nn5sL379r3ZqNXbtGveky8f18ZYhRxb187f/5pk3hB/7tKDuQmDdHKXX+/M3pwe/iNABtyRHd9ktV\nKMTdJSXcsXZt+B6YDOzAtGKR+vhwnG1+9vG7zav3QTUuc9Z/1kC3vfvsys+n7ZYt9Tow7VFWIuG6\n3UJ9f36vs4LQPHQ5kiDjAM4GVgMhZ3kgMMuPfSndD1GMeRNHjoyaY35KEt//l+T+fr5HRyxPcmyM\nK9P8nu5jP9CU5ILWs4FG2kLV89s9p0tbtdIrhg2L6dvflMZXAM0l3fZL5D0wz/P/xNLPeNu8+0Tq\nsZ/jJxFd7893vq8DvTyGLiba5ur2+T6Pr3SOiaybV6cvdbadIRJXl/3Q1McA/Ng7pwDHYiImUdUK\nIGvNZNutW8N5uV3cUXu/3/skub+fb41YzsPYy/+V5vc8H/vdzJSU5N7MlHpeD17vB1cxXLvotfvs\nw46RI7n1ww/5S467dyZJzui2X2LdA3nE1s9427z7ROqxn+PziK73Y5zv24DuRNfFRNtOcnR7jM/j\npwOlTGlQN1enw/rcvj2/KS9vbrqcNH4agL2qujViXdYcmPMOOICdEeu8f66f72OS3N/Pd+8o64No\nWIp87HcHZSnJdY+LvDkmY264ndQ9+EtXrGDqjBkJbxY3MCVIMiHTJZd02y+R90AJdf9dLP2Mt827\nTzQ9Tla2q2c/IPkGJPL7VEdHf5DE8b+irF7dajEmn/Mx+tx15EhKly3jxJNPTvLKNyRI3cyknsfC\nTwOwQkQuAvJF5BARmQa8leF6xWTolVdyQ8+e4RvAHQM4P8e+xxBsA5Op78ib48uhQ9GRI/nbqaea\niVt8PvibKrmk234ZeuWVTOjatd49MBnjqRFNH+Nt87NPMtvGkH4DkkrDFU2fJwMfFhay+IADqB4y\nhH+1AH1OFj9eQG0x4yhnYMZMXgZuU9VvM165BF5AOz/9lE83bKBbp05s2rLF9/fe/HwKa2qSPi7e\n9weffUa3bdvqDUYFMTh9JPBBAHKiyZ3nDNr1PeggOvfpE4gXR3l5eeA9mUzIhPAg8B3kkG77JewF\ntGgRn+7aRYd996WoW7ewPn6xaRMF335LNbCnTZt62+Lts/KLL+i///5xj/duizYI6+r9FOcTxACz\nKyvRIPTnHTuyb00NWlBA0ZAhTJg6tYFOB6lPuSorUC+gbJGpcPlMPaR6FRWl1TBF+3ZvyKAaqk1b\ntnBQjx5sad+esgx4PTS1BsDPTZIJgtTtbD+EonXIvti0ia+3b6dzYWHcBsTvNm+nLXKfZDsw2b5e\njSEr7QZARGbFO1ADSpkbD5svxZIJRowwqjt79myA2ZHbrW5bmjpB5AI6HjgQeAPjOvvHiE+zIT8/\nn8GDB3PEEUcwaNAg7r77bprSzenNffKnP/2J/v37c8kll9TbZ/78+XTq1InBgwczaNAgzjjjjJRz\nCMVj7NixPP/888ELDpCFCxfy2WefuYvNWrebOkHmArJEIZZ/KCYz4nDgMWApcDtwuB/f0qA+pOEr\nHY9If9t99903/HvTpk06bNgwLS0tTUumS01NTZK18yfXi/cy9evXT9evX99gn/Lycj377LPryY28\nvNXV1alWM8zw4cP1ueeeS1uOl6DjAKqrq/Wll15y4wCavG7nqi96LuUC8pJr55gJWaQbB6CqNao6\nR1VHA0OAT4ByEflFEA2PiAwXkQ9F5CMRuTEImX5xc4tHo2vXrjz00EPcd999ANTW1nLDDTdw3HHH\nMXDgQB5++GHANJxXX301/fv358wzz+Tqq68O93x79erFTTfdxNFHH82zzz7LmjVr+MEPfsAxxxzD\nKaecwkcffQTAV199xY9+9COOO+44jjvuOBYuXAiY3vqgQYMYPHgwl1xyCTt3Rjq+wt13382RRx7J\nUUcdBfwJgKuuuipc1r333tvgGPW81bjXYOzYsVx11VUMGTKEG2+8kW+++Yaf//znDBkyhO9973vM\nmjUr7nUA+MUvfsFhhx3GGWecQVVVVXj9a6+9xuDBgxkwYACXXXYZe/fuDV+fX//61wwaNIhjjz2W\npUuXMnz4cA455BD+8pe/JPV/pUJ+fj7Dh4cTdDZ53Q7y+lhZzUOWX+ImgxOR1sBZmJS5xZgnzQvp\nFioieZg000OBz4F3RWSmNtLE2Vu2bIm7vVevXtTW1rJp0yZmzJhBp06dWLRoEXv27OHEE0/kjDPO\nYPHixaxdu5aVK1eycePGBhMxdO3alcWLFwMwbNgw/vKXv9CnTx/eeecdrrrqKl577TXGjx/P9ddf\nzwknnMC6des488wzWblyJX/84x+5//77Of7447nlllvYZ5996slesmQJjz32GO+++y41NTW0b38c\n779/Cg888AAvv/wy5eXldO7cucF5vfHGGwwePBjA2T4BgPXr1/P2228DcMsttzB06FAeeeQRtm7d\nyrHHHsvpp5/OE088EfU6LFmyhI8//phVq1bxxRdf0Lt3bwB2797N2LFjmTdvHn369GH06NE88MAD\njBs3DoDi4mKWLl3K9ddfz9ixY3nrrbf45ptvOOKII7jyyiuT+r9SYffu8AyQT9DEdTvI62NlNQ9Z\nfonZADiTWhwB/B9QpqrLAyz3WOBjVa1yynoaGAk0SgOQDHPnzuWDDz7gmWeeAWDbtm18/PHHvPnm\nm/z4xz8GoHv37vSK8D74yU9+AsDOnTt56623+PGPfxzugbs94VdffZVVq1aF1+/YsYNvvvmGE088\nkeuuu46LL76YXbt2kZdX/0XtzTff5Nxzz6VNmzbOmlG88cYbDBgwwGtiaMDJJ58c7tFPmTKF1183\n693zcM939uzZ/OEPfwBgz549rF27NuZ1WLBgARdeeCEA+++/f/g6rF69mt69e9OnTx8ARo8ezf33\n3x9uAM4++2wAjjzySHbu3Enbtm1p27Ytbdq0Ydu2bXTokNacLHG59NJLWb48rM4tVrctlnhvAD/F\nxFaMB8ZJ3aQIgrEvpXOHHgCs8yx/hrlxGoXKysq429esWUN+fj7dunVDVZk2bRqnn356vX1efPHF\nesuRZpp27Uywfm1tLZ07d2bJkiUNylFVFi1aRGFhYb31N954Iz/84Q958cUXeeihh/if//kfDj30\nUL+n5wvvNXDr6vLcc89xyCGHNKhrstchVkME0Lp1awDy8vLCv8F4L1RXV8esaxA88cQT3nN+q6nr\ndpDXx8pqHrL8kpU4ABE5DzhTVa9wln8KHKuq4yL2azquOJYmiQYcB2B125Ir+NHtbE1+sR74rmf5\nQGddPVRVGuMDVGO8QVYAFcD1nm15mGjRD4DlwOtAB2f9A8AqYC7wCjDMOSYEfMcjoxiYA7zvlDHZ\nWd8V+CewzFn/gLN+mlPW+8CTQKsodb7O2ecDYJxnfb2yPetLgNlR1j8KnOdZ3gf4i+d8Z8e7Ds62\n+zAmjrnAi6484DTnui7DBG+2iqwjJnvAtET1z+B/HzQ5pdv203I/fpRVNDtvAPmYNLxDMbMwvQNc\nqKqrGr0yaSAi7VR1p4h0ARYBJ6rql9mulyV7NBfdtrQM/EwJGTiqWuO43M3F9CwfaaI3yL9FpBNQ\nCNxqH/6WZqTblhZAVt4ALBaLxZJ9msQE2CJyrYisEpEPROTOAOSVishnIrLE+QxPfFRS8n8pIrWO\naSgIebeKyPsislRE5ohIj4Dk/t65rhUi8pyIpO17KSI/EpHlIlIjIoMDkBd4UJWIPCIiG0VkWRDy\n0qhHIHqdCX0OQoeD1NsgdTUIHQ1SL4PSRxE5UEReF5EVjk6NS3iQNmL4eyofzODlXKDAWe4agMxS\nzEBvJup7IGbANwR0CUhme8/vazGDxUHIHQbkOb/vBP43AJl9gUMwg8SD05SVh4nSLcKY2SqAfgHU\n8fuY6R+XZUIHfNYhML0OWp+D0uEg9TZIXU1XR4PWy6D0EegBDHSvPWYsKm69cuINIFbLJSKdgaeB\nw4AXRaSjqn4VVLGJyhaRuSKyWkReFpGOPuVOBX6VsPAkylXVHZ5D3YmPUsYtG7gXeN8p+22gd4rn\nHEZVV6vqxzjXN1q5SVzrcFCVqu7F6MLIdM5ZRFZgvLdGJig701wF3Kmq1QAB6HWQHk2+dDgRQeqt\nqr6qqu7xb2MaqVRlxdRRnwSml0593gS+TvV4j5wNaqY1da/9KkxcSkxyogHAuGFer6qHY7KQXiMi\n/YCbgBqMC+FhwDIROTqgMn8hIhXA74DfxCj7VVXti+kp3JxIoIiMANap6gc+yo93zg3KFZHbRWQt\ncBHwmyTPNWHZwDiMC2hS55xuuQmudbSgqrgKnUTZlwKt45SdaQ4FThaRt0VkXgB6/QvHPPLXdBqx\nJHXYj7wg9dblZ8BLAclKhSD1MiOISDHmrWJRvP2y4gUUhb8D3aUuIrMHJgVFK2Ab0BnT6r6Nmf+5\ndyKBIvIKZr7o8CpM9sdbgPsxXjsqIrcDY4E3VXWHiKzC9C5GAqc4xz4GlAM3xZE7Cfg1cHrEtqio\n6gZgg/N7h/O2839AT2CNiFyCycjaAbhJVScBkxx747WYCZISkuA6VDhl12BeZQ8FLo8852RkqmqD\n/PpxzjnutfZzfn6JUvYnGDNAxspOoCsFQGdVHSIix5BAr5PU57sxE2alUi/fOpyoXqo6Oxm99aNX\nInILZp7yJ9OpV7xjmzoi0h54Fhgf8RbWkHRsTpn4YIKmKjE2rK8xD8VTnG2bMba37wRYXhGO7S2y\n7Ij9NieQcwTmAbMGYzvd68jaL9lzjlcucBDwQYDnPxH4FuiS7DknkDuPOPZVP+eMydQ5x7P+JuDG\ngHRsHSagLbBzTrIOYb12lgPRa68+p3BsyjrsQ3baeosJGvwP0Dqg/yCujsY5LnC9TOd/i5BTgBm/\nGe9n/1wxAQFRWy4FZmAiSsG04oWq+t80y/F6I4wClsco20tcf1lVXa6qPVS1t6r2wrwWDtIEsQF+\nyhWRgz3L52Bse2kjIucAtwGXqermaGWnW0SMcv1e63eBg0WkSERaARcAcWeqS1ihurJvdcoJ+pz9\nEtZrETmUNPQ6mj6nIidVHY5Tr8D0Voxn06+AEaq6O9H+yYhO4ZjA9dKpRxDjOH8DVqpqw3zw0Qii\nJQ2oNW7QclE3iPF35/cuPL2mNMp6HJOeoAJzI/aMUXZ353cPYFWSZawhgQdFnHOuVy7mgeXWdyaw\nf0DXeyfmrWqJ8/k6nXN2jjsH07vehYmEfSmVc/ZsG47xZvgYYwoLQsfew6Rq3o3p5Y5P55xTrEuh\no9cfAIvT0eso+tw9oDom1OEExwemt87/X+XR1fvTkBVXR33KCFIvn/To41pgbIpyTsSMmVZgUrAs\nAYbHOyZnAsHEpJ/+SlWv96z7HeaV/HeODbGzqgZqG85m2facG6/cbJdtseQiOdEAiMiJwAJMb8h9\nLf81Jo/KvzD2wyrgfFUNdNaEbJVtz7llnLPFksvkRANgsVgslsYno4PAEiXEOYuBNxZLoyAi451A\nN3/h+BZLlsi0F9CjwJkR67IVeGOxZBwRORzjg380JhDnhyKSMG7FYskGGW0ANHqI80hMwA3O9zmZ\nrIPF0sgcBixS1d2qWoMZdxiV5TpZLFHJRhzAfqq6EcLRmftloQ4WS6ZYDpzkmDrbAv8PM8BsseQc\nuZAKIuYotNh5Uy0ZRn1OnZeEvA8d19JXgB0Yf+yayP2sblsyjR/dzsYbwEYR6Q7hCMa4UYZ1QQ5B\nBLb422/06NFpl5XqJ1tlt8RzzhSq+qiqHq2qJcAW4KN4ut2Yn9LS0hZVbks9Z7/EbQBEJE9Ezk9G\n+aOJoX6I8yxMTg+A0ZgIQYslJwhC50Wkm/P9XeBcTKSnxZJzxG0A1OTfviFV4SLyJPAWcKiIrBWR\nsZjJHE4XEXfi7LRn+Aqa4uLiFld2SzznaKSr8w7PichyTOfmalXdFm/n4uJiRKRRPmVlZY1WlvvJ\npf+3Htu3w8KF5jvRrru3s3DdQrbvTrxvU8LPGMCrIjIR+CcmdwwAapKHxUVVL4qxaZi/6mWHkpKS\nFld2SzznOKSs885+JydTWFVVVVKv7U0NEck9/dq+HU46CVasgMMPhzfegH33jXr89t3bOenRk1ix\naQWHdzucN8a+wb6to++bsNwcw08D8BPn+xrPOsVHTn6LpYlidT5gcq4BWL7cPPyrq2HlSvN7yJCo\nxy//cjkrNq2guraalZtWsmLTCoYcGH3fhOXmGAkbADVpYbNOaWluyLAEz4YNG5gxYwYbNmxgy5Yt\n1NbWUlNTQ35+PrW1ZhbAvLw8ampqEBFUtd62goICqqurw79ra2tp1aoVXbt2pVOnTgwcODApM0RW\ndX77dvNwOuKI6D3SRNst/jjiCNPzX7kS+vc3v6Owffd2du7dSb/v9GP1f1fTv1t/Du8Wfd+mSMJc\nQCJSiJm/1H2tLQf+omYuzNQLFhkPXOYsPqyqf4qyjzbnV+OWQGVlJRUVFWzZsoWvvvqKPXv2kJeX\nF/OBXVVVFT62tLSUsrKyBr/jbYv8HQ+nMYk2d3FaOi8i12GigWsxyefGquqeiH3Cuu02agnNEkmY\nLVyuu+46pk6dWm/dY489xsCBAxkwYICf02nA3r17ueOOO9i6dSt33313wv3D55dLbN8OixaBCBx7\nbNTr6DX99PtOP+4Zfg/HHnCsL/NPtoml25H4MQE9gMldfr+zfImz7rKYRySunDdcvhp4SUT+rapr\nUpVpyS6VlZVUVlayZcsWPvnkEwoKCvjmm28AaNu2LdXV1ezcaczpo0eP5rHHTDD4pEmTKCsrY+/e\nvUycOLHeQz6LpKzzItITM/VhP1XdIyL/xEwY8njCUhOZJXyYLZYtW8aCBQv4+OOPmTp1KiJCVVUV\nEyZMYNSoUezdu5eCggKef/55HnzwQcaNG8fmzZt57733+OKLL/jf//3fsKxp06aF37IOPPBAzjvv\nPAAKCwspLS3l+uuvp0kSrSGNgtf0s/q/q2nXql2TePgng584gGNUdbSqvu58xgLHpFluTofLl5eX\nt7iyky23srKS8vJyZsyYwV133cWMGTN4++23+eSTT9i5cyfFxcXs3buXvXv3MmHChPDDHxp6/YRC\noQDOIFDS1fl8oJ2IFABtMZN9JMY1SxQWRjdLJNoO7Ny5k9atW7NlyxY+//zzcM974MCBXHLJJaxY\nsQKAc889lyuvvJLFixezfft22rVrx/Ll9ScS83ryRCPnevV+idaQRuGI/Y7g8G6HU5hX2OxMPy5+\n3gBqRKSPqn4K4CS2ahDZmCTLgdvFTIS+GxMu/26aMi0ZJNKUU11dTZs2bcI9+1NOOYX58+eze7eZ\nre+cc87h/fffz3KtUyZlnVfVz0Xkj5iZnb4B5qrqq75K3Xdf0xt1e6aRZolE2zFvAB07dqSmpoba\n2trww3vJkiVMmzaNvn37AmZMxQ0aWrFiBT179mTv3voWrl/84hcxq/rnP/+ZiooKFi5cyPHHH+/r\n9HIGn/b/fVvvyxtj3wh7/zS33j/4awB+BcwTkTWYgK4iYGw6harPcPlskXMeC1kq1/vQ37LFzJHS\nqVOncG/+pptuCptsSkpKmD9/fkpl9+qVE34GXlLWeRHphEl4WARsBZ4VkYtU1V8w2L77xvRG8bP9\nyiuvBOCCCy4A4O6776aqqoqhQ4dy7bXXNtjfOw5w4YUX+qoiwDXXXMM111yTeMdcxEdDCmYMYPmX\nyzlivyOa1MPfR1hDmLgNgIjkYebNPATo66xerQFMyqyqj2LSRSMiv8XM0dmAMWPGUFxcTHk5nHOO\n8ehwH1au2cLv8pgx5YwZ43//lri8YcMGevTowZYtW3j11Vdp3749PXr0YOvWrYRCIYYPH45LeXk5\noVAo/AB3TTmRyy7Rlr2mp0h5qciPlOc9v3vuuYeKioq4HkEB6PwwYI0bMyAizwMnECUaeMqUKT5F\npkdRURHjxtlpCeqRoCFN1fc/W5SXl1NeXs727fDoo/6P8+MFtFRVB6VZv2hyu6nqJidcfg4wJDJi\nsr6nBKRrcvQro7y8PGs98WyVXV5eTnFxMZWVlYDp/VdVVYVNOy5+vG+S3TZmzBh69eqVkowtmzdT\nUFnJp6+9xvbaWmTffcnfto2a/HwOHzaMCVOnUhTjDSOOF1DKOi8ixwKPYMYMdmM6Oe+q6p8j9mvo\nBdRMaYrnt3DdQk6efjLVtdUU5hWyYOwCX77/2cD1DP7Od+C002D9eoDgvIBeE5HzgOcD9sl8TkS6\nAHvxES5vyRyuqcf9XVxcHHbHTMe0E4uqUIhlzz1H682bGfnww3zx5ZesLyjgjPvvJ2/rVvZUV7O3\noID/3H8/+du2US0SdVve1q1U79lDf1WmAl2Bybt2sQO4Dnh45kx+u2QJt8yfH7MRiEHKOq+q74jI\nsxiz5l7n+yG/xycyOzRVs0TO4DOOwh0AXrlpZU4PAH/+OZxyCoRCkJ8Pe/YkPsaLnwbgSuB6oFpE\nvsXYRFVVOyRdWw/Jhss3Ji1lDMBr4weoqKhg69atdOrUKS25WzZv5lcjR4Z75d6H9578fF6+9Va+\nV1tLV+B8THf553v38rD34V1Tw/Yvv0Tc5XjbgN8CtwC3YZJL/cv9vW4d0ydPpvSJJ5I5hbR0XlXL\ngKT9WROZHVIxS2QiDmDHjh385je/oX379px33nlhOTNnzmTevHn07t07N01OScRR5PoAsBvGcOWV\nsMZxnq9JYRQ10RiAAIer6toU6mjJUVyffYAtW7ZQXFxcr5fv14Mnsie/+6uvzEO+pobBEL1XXlPD\ndoxtJPzwx/PAjvhNEtumA6UY3+ZaoJ37+3N/XpiQXZ1PlHLAT0qCxogDePXVVxk1ahQnnHACN998\nc7gBaNeuHR06dGDv3r2oakz30ayRRPqHXH7T8vb6vQ/9Vq2gthaKi+GTT/zJSpQNVIEXU69q06Q5\nxwF4H/6unR/8++K7D/2VDzzAmEMPpecHH9B3/XruXL+eI3bv5s81NRyLGUX9LfAV5gHdnbqHdQ/P\nchnwOyIe2J7fDR7mCbbtdL7zvL979vR9fbKp84n8zv34pTdWHIC7zrtt2LBh3HrrrfTu3Ttws2Eg\n+IijgLo3rZOnn8xJj56UUxlAP//cBC5/8kndwz8/Hw4+GD74wLzULFniX54fE9ASETlGVQP10/cT\nLu/F5gJKj8gHf6Sdf/r06VGPqwqFWPzUUxR+9hnbamrqmW+mYHrwsXryELtXjme5hvoP7ALPbyKW\n420DmAzht43JwI6DDuKW225L5lJBGjovIodisogqxnTUG5gcLdVJJInMDn7MEo0RBzBs2DBKS0uZ\nO3cuF110EatXr2b9+vXk5+ezaNEiKisr+e1vf5vwWjU6Pt0/U03+lmm2bzc9fzPIa+jdGx56KGY2\ni4T48QL6EDgYqMLca6499KjkiwvL7Am8Sf1w+RdV9fGI/WwuoACI1uv3evdEetiMufRSrh85koIv\nv2TjV19xZE1N2F7/MHUPWO9DfyJwF+ZBXIZ56LvcgGkQCpz93MbBXb4Q8xbglf0wsB3q2fnjbVsD\n/Ld1a/bp2DEIL6BAdN5xKf0MOE5V10VsazQvoKqqKmbOnJk1u3xT8wJy3wDcAeBccAHdvh2eegqu\nvrqu53/ggWYcINoLbpC5gM5Msq5+ccPla0kmXN6SNMXFxWHf90SBW+tCIa496iiO2rGDQhr28i/H\nOLS75hv3od8O4/JSSP0eOTTslXsf3lfk57M9P59x+fnIvvuSt3Ur1ziePu7DfJxIzG3jnQd9r6Ii\nBvm25iQAACAASURBVHXpklQyuDgEpfPDgE8jH/6NjY0DSI5cGwD22vwLC407e3ExzJ8f/eGfDDEb\nABE5zcmDUiUivVQ15Nk2CtM7Som0wuUbgeYSBxDN7BONqlCIOY88Qo+vvmLbrl0cjnnQd8X05N1e\nvvvA95pzvA/9ZZievPuQ30Rdr9z78G7ftSttgUldunD3zJmUlpWlHAcQbVuqZEDnfwI8lXbFLMHg\nwwXUO/ibbbNPNE8fEXjgAfjJT4LJBh5vEPguz+/nIrZNSqfQiHD5nkB7EYk1e5glBWIN9kayLhTi\nF0ccwcHr1jFw1y7uw/QK3AHcMszD3/vA9w6yLsO8HVyRn8+2wkKuyc9ncZs2tB85kl7jxjHs5puZ\nu3Ejx998M6dMnsywm2/mpc8+46jLL+eo885L1j8/0wSm805K6RHAM8kcl2iWwiRmMbR4cV1ATz7Z\nfEe5gLk0+OtW9wc/qHv4g+n5B/Xwh/gmIInxO9pysvgOl3dTQQDhyT1yKXVCJpZdUj3ejeitqKhg\nw4YN7LPPPoCZeMVNreD2+lutW8fJmDzHKzEmnymYP6IUGENdL/8l4HXgCIz5ZmReHts6dGDSQQeF\ne/JAuDc/ZswYvt66NXw+sVI3xEr9kIVUEEHq/A+A91R1U6wdIlNBZGA6gEaNA8hpfLiA5tLg76JF\npspeT59evYzZJ9p/7qaCSJZ4DYDG+B1tOVnWAkNEpA3GJXwoMbKBut4pU6bAhAn1t0WaShItl5eX\n4L3nkj2+KS0XFxdTUlJSzzRywQUXsHr16rCdf6hj5z8fmIZJYv836sw8RZh8BndjTDu3Aa3324//\nFhYyqUsXHpk5k+mPm3H7ol69GiR187PsHYuIHJeIXE5FnpcJEQoUxWwUpM5fSALzj9sAuPUIYDqA\nrMYB5DQ+MoDmQvSva/YZN67u4X/YYTBtWnxPn5KSknr67tckGq8B6C0is3Bc2ZzfOMtpvbenEi5f\nVgYRHaak8SujqY4BJLL5uy6dbVavZgCm1+/a+Y+kzmVzL3Xd3SsKCtjbpQuTunfnu6eeypP33htW\nrqDMN95efpYJROdFpC3mLfeKZApP9Izyk8U4URzAL3/5SwYMGMC5554LwOLFi+nWrVvcOIAY51jv\nO+fx4QKa7cFf7xueM2EeBQXm4T90aGbKjNcAjPT8vitiW+Ry0qQaLm+JTjSbv7cBcHv9A6L0+ssw\ns58XUWfXL8jLY9fBB/PgnDnhXn4LIBCdV9VvgG7JFh7AdABZiQNoMvjIAJrN6N9oZp/DDzc9/0wR\nswFQ1RwM5WscmmIuoFiunjNfeIFPXn45aq//59T1+g/EDO5eLsJ+Q4eyb//+dOrSpVEGaf30/h98\n8EFat24dnjD+jjvuID8/H4A777yz3u/CwkJatWrF9OnTk5oUPhd0Ps3pABplPoD27dvzxz/+0de+\nTYVsp3/evh2uuy45s08Q+IkDsOQw8cw+/1mwgFUPPsjRe/ZE7fW7Pvw7gXdEyD/kEPoNH85Uj5kn\nCGbMmAGYXDF79uxp8MB2f99xxx3k5eWFA4fuuOOO8MO8TZs29OvXz/fD3FKHjQMgoQtotgeAFy2C\nVavM70ybfbzYBiAKTWkMILLnP2bMGObPn8+6UIjbbr+do6urww9/N3J3OvVt/de2b0/hiSdybBpT\n+8V6yOfl5VFZWUm7du04+OCD6dSpU706p3LOFktS+HCfytYAcLRB3379Mmv28ZKVBiCVfCk2F1BD\nInv/AE8/9VR4oPcYGpp8XJ9+b6//wTlzwi6ciXBdzdq1a8c999xDYWFhuPx4D/mWhIh0BP6K8Zit\nBX6mqoti7V9UVNR0BlNToKioKLsV8OE+lY0B4FiDvvfck1mzj5d4kcCzieP6pqojUi1UVT8CBjnl\nuPlSXoh3TBCz5/mV0RTGAKIN+m7ZvJmFN93E02vX0g7zkC+lvsnH2+v/7nnncVCvXlFdOL3luHTs\n2JHKyko6duxIcXFxYA/6XOn9B6jz9wL/p6o/FpECTKqTmHivsSUDJHCfylb0bzYGfSOJ9wbgej2M\nwmTwdWfUuBDYGGAdciJfSlMj2qDvh3PmcPTatfwe48c/hroHv2vy8fb6Y3n3eHv5M2bMoHXr1hQU\nFNCpUyc6depESUlJc+3dp63zItIBOElVxwCoajVgZ7vLJnHcp7I1+JutQd9IEnoBicgfVfVoz6bZ\nIrI4wDrkXL6UXB4DiDbou2XzZj55+WXaf/QRAD+jzuxzLXUmn8UFBXz34ovDvX6vTNcXP1O9/Hjk\nyhhAQDrfC/hKRB4FBgCLgfGquivY2lp84R38jeI+lY3BXzezZzYGfSPxMwbQTkR6q+oaABHphUkL\nkzaefCk3xdonG6kgXLKRCqKiosL3/mPGjGH4mWey9e9/5+mvv+ZdzEQsrqfPUEwCm2LgisJCzvrD\nH3hv6dLw+bmpFNwGZdeuXeEHv3eCePf6N/Xr7SMVhEs6Ol8ADAauUdXFInIPRr8bjEB5U0FERnJa\nAiAHB3+9VXIzewZh9kk1FYSf+QCGY6J012DMx0XAlar6cvLVbCB7BGZC+OExttv5ADxEs/t/+vLL\nFC1cGJ4hawym9++afX4M7Dj00Abune58AB07dqRTp07hqSFb0gBunPkAUtZ5EekOLFTV3s7y94Eb\nVfXsiP2sbmeahQtN8rfqavO0XbAg6lvA9t3bG23w99VXYfhwY/opLIT77w82uZtLYPMBqOocETkE\n6Oes+lBVd6dbQYeE+VJcpkxJfyA4CBnZJNLuvy4U4stFi/gu5uHv9fN3zT7f9u3LkBgBPkVFRWF5\nLeWh74d0dF5VN4rIOhE51HF2GIrJs2dpbPzkzsB4ADXG4G+k3b9v38w8/JPBzxtAW+B6oEhVL3du\njL6q+u+0CjZyq4Deqho172r9WZMg3Q6TXxm5NgYQze4/84UX+PKBB3ho7956Hj8/py6l6q4+fag5\n6yw6dekC1PX6oz34c+2cG4M4bwBp6byIDMC4gRZi3iLGqurWiH3sG0BjsH173NwZjZn+wdv7LyiA\nOXMyZ/f3+wYQbz4Al0eBPYAbJbQeuD2NugEmX4qqdov18LfU4Wb2LCkpoaqqil5FRSx77DF67d3L\nXZhWtB11ufs/BBYfeijXvvJK+OHvYnv9vkhL51X1fVU9RlUHquqoyIe/pZFwB4DjPPwbK/9/ZO+/\nMYO94uGnAeijqr/HeBG6ia6ab9QKuR0HsGXzZqadfjqztm7ldsycutOoawT24ph9LroI9QQXucE4\n7kM/0pUzl885C7Q4nW92+JgAJpoHUKaqEun105jBXvHw4wW0R0T2wQmQEZE+mBz+lkYg0vzz4Zw5\nHP3pp1F9/ScCy9q35+AzzwzvD7bXnwJW55s6PqJ/G8MDKFNeP0Hh5w2gFJgDHCQi/wBeA25It2AR\n6Sgiz4jIKhFZISLHpSszKFJxp8pU2W5vvVdREc/deWc9X3+39/8Vpqt6RWEhN774Yj2zj9+Hfy6d\ncw6Qls6LSKWIvC8iS0XknUxV0hIHdwC4sDDmALCb/mHB2AUZCwBzo32rq43554EH/M3k1ljEfQMQ\nk6DkQ0xk5BDMa/B4Vf0qgLKTCpdvabmAvD3/xe+8Q+jee3n688+jpni4E1jYsSNHjR7NXmcGp0iT\nj+31+yMgna8FSlT16wxU0eIHH5MnZHoAOBe9fiLx4wX0gaoeGWihJlx+qar2SbCf9ZQAftC3L0d/\n9FFMX//z8/M54Jpr6NSlC6ecckq9tND24R+bOF5Aaem8iISAo1X1v3H2sbqdKRKkfobGSQHhDUPI\ntNdPJIHFAQBLROQYVY06Z2+K2HD5OET2/ruuWcNN0KD37/r67zr4YDN5i+31B0W6Oq/AKyJSAzyk\nqg8HWDdLPHxE/0LmU0Bs3w47dxpvn9WrjRUqV+z+Xvw0AMcBF4tIFeZ5I4Cq6lFplusrXD5bqSBK\nSkqylgpiwoQJFBcX8/RTT/Hkrbfy/6qruQvoi8lQ5pp9KoGf9ewZHvR1r1Mq5Ueee2Odr7fMHEoF\nka7On6iqX4hIN0xDsEpV34zcyaaCyAA+Bn8hswPA3jaoXz946aXMJ3nLZCqIqMm8VbUq6dLqZOZ0\nuHw2g6KefvppevTowYYvvuD18eOZumlTg55/EXUpHkZPmcLqjz5K29PHBoLVWx+YzotIKbBdVe+O\nWG9NQJnAffq60b9xRlwzlQIiMt1DjAwUGcWvCShhA+ARuB/Qxl1W1bWpVw9EZD5wuap+5NwkbVX1\nxoh9WuxNct0559B+5syodv+JwAV9+3L0hRdam38aJLpJUtF5J4o4T1V3iEg7YC5QpqpzI/Zrsbqd\ncbIY/bt9O5xwgnkRATMM8dZbjT/wG1gksIiMEJGPgRAwH2N5eCntGsI44B8iUoEZB7gj3s6NOSFM\ntqkKhdj68svchDH3RLp73uCYfRIFd1lSI02d7w68KSJLgbeB2ZEPf0sGyWL0by4HfMVEVeN+gPeB\n72C8dgBOBR5JdFwQH1M9g+dnyviVMW/evPQLS5JQKKTz5s3TqVOn6iVDhugOk7Yo/NkBOgn0tI4d\ntXLNGp0yZUqg5WfjnLNdtqNfWdF5glBoS322bVMdMEC1oMB8b9vWYJe31r6lBbcWKFPQwlsLdeG6\nhYEX3aZN3Co0CrF0O/LjJxBsrxp3tjwRyVPVecDRiQ6yJIfbgx84cOD/b+/M46Oszj3+fbKwBFkU\nkEUkAakSwCpeRVAUtNdbBJde7624VqnX5boh1d5alQra24teqyCtvcVakFZwobVuoEIFihWCCyhb\nrchMQoGAgCGBQCDhuX+c900mk5nJJPO+M5PJ+X4+88m7zXvOwFmf85zfw4GtW2tdPKdSJ/PweXY2\n37zhBgLFxeTn59cu/NiQgp5jy3xLJNICcBju4m9uVq6ni7/pvuErGvF4AZWJyDHAXzAmm12YNcmM\nJdkLkuFun+127eI+aKDy6bp7+pE/qwVUj1ZX5jOCOOWfn/iXJxCEYScM82QNoCVs+IpGPF5AHYBD\nGFe4a4HOwAsaY5NLXAmLBIF9GHf2I6rawEs2VXLQqaI4EOD7Q4cyYt8+cjELv/mYlufK9u05+eab\nOW3oULvg6xExvIASLvMikoXZ3/IPjRBM3i4C+0SMBWC/Nn8lU+Y5XjxbBFbVA6pao6rVqvq8qj6d\naOPv4G6XHxqp8U8lqdCmKQ4EmHnRRdwXReXzxH79fG38rRZQHR6V+YnYQDDJpZEFYD/UP9NV5jle\nGjUBiUgFjioi0AYT5OKAqnZKMG0hPjE6IDO1gEJNPzMnTWLQl1/yIrCahiqfvYYO5cYbb0xNRlsZ\niZZ5EekDjAX+GxNYxuI3KYr/u349/O1v5rhFeP2EEfc+AKgVyrocGK6qUQO5x/muLUAZEHW7fGuZ\nJhcHAjx08sn8X3V1g01fzwK7unfnwhkz6NmrlzX7eEg80+TmlHkReQXT+HcG7rUmoCSQgvi/FRVm\n8XfSpDq5h3RZ+PV8I1jYy9eo6tBm5azuHb00ZLs8cKeGbZfP5Erijv5Ld+zgt3fdxbA9exrY/acB\nH3TuzG/XrCG/X79UZjcjibeSOM/GVeZFZBxwsareKSKjMR3ApRGey9iynRIa2QHs9eavcLmH6dP9\nl3toCp6JwYnIFSGnWRh3uEMJ5A0AVd3h/P1KRF4FhgEN9FIyVQuooKCAVStX8up99/Hqnj10wOw0\nuhGYg+kEVmRlUTh+PIHiYgLFxZSWltKzZ09f8mO1gOpIsMyfC1wmImOB9kBHEZmrqt8Lf9BqAXmE\na/tfuBBKShqsAfix+Ou6fdbUmNF/hw6pbfz91AKaHXJajdkV+ayq7mpyanXvTOvt8snSpokk97AR\nswbgyj284RoYfcZqAdW77kmZF5FRWBOQv8Rh+1+5dSXnzzmf6qPV5Gbl8pcJf0lI+TNd5B5i4asJ\nKFFEpB/wKmahLQfjYjctwnMZWUmCwSCrVq5k0Y038szhwxHt/l/37k2/iRM503EpsLZ/72mKCaiZ\n77cdgN/EYft3ZwDu4m8iMwBX7uH229PL7TMczzoAEXk61n1VvbuJeYub0EoyZUriWj5evMMrpl53\nHfe98AIdQq65dv+irl35/syZdtHXZ2LMAHwv87YD8Ig41D8rqioo2laU8Oav0MlGTo7pc2KEHEgp\nXnYAs4BBwEvOpe9iLBUrAVT1+cSyGjPtlGwE88skEer2OeummzhlyxaOUmf+yQdGZ2Xx/ObNSV/0\ntSagetd9L/O2A/CQJG3+Cpd5fuaZ9N3x62VEsG8CI1W12nnx/wErVPW2BPPY6nBH88WBALJ1a0S5\nB+nXr3bR147+U4Yt8y2FRsI/ehX5qyXLPcQinhnA58AIVd3rnB8LrFLVU3zPXAZKQRQHAvzgwgsp\nDAYbuH1efcwxzPzsM+vymSRizAB8L/N2BuABcSwAe2X/T0e5h1h4OQOYBqwRkaWY3bvnA1MSy56h\nMb2UTMJd+F1x333M3b49YpSv3n37EiguRkXsyD+1NLvMi0hbjIhcG0z9WqCqU33KZ+umkfCPru//\nwmsWUlJe0uzNXy1d7iEW8WgBzcbESH0V+ANmZOSV3T8t9VL80KYpKCig6KWXOG77dh7HyDzsdv7O\nwXQGvYYOrX022VgtoDoSKfOqWgVc4GwaOx24WEQypLlIM1z1z9zcBuqfoYFfxs4bm1Dj3+KCvDSB\nqDMAJy5qmaruU9VSESkHvgMMEJFfqOrhRBJuql5KS9UCCt3xu2fhQn4KDUb/bpSv88aNo7S0NPmZ\ntADelXlVrXQO22LqmLX1eE0jm7+8sP2HWphyc40JefDgzBn9A9EjggFFQG/n+HTMgPVe4HngN9G+\nF+8HeMV57yjg9SjPaCYQ3LJFrygo0AdBp4AGw6J8XVFQoMEtW1KdzVYHYVGTvCrzmJn1GqAc+J8o\nzyT3x2YScUT+2rZvmw6YMUBzpuboab86TcsPNT001+LFqtnZJihfbq7qs8+mLsJXUwkv29E+sdYA\n2qvqduf4OuC3qvpzx26/NpFOx9FL2amqax29lKiLFamQgvDyvHTHDj6aPJm5wSAfAgcxMs93YQLO\nvp+Vxa0/+xmB4mJWFhX5JvVgz+OSgvCkzKvqUWCoiHQC/iQig1S1ganTSkE0kzhs/2PnjSW4L0jB\nsQUsvGZhk80/Lc3rp7lSELFGMetCjj8Bvh1y/lk8vUuMd/8MKAG2ADuA/cDcCM/50Tk2ilcxagOB\ngF4/YoQ+BPqTkNH/fud4P+h9l13mS9pNxcYE9qfMA5OBH0S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ZWCYid3qU9mzg24099LOS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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff99a9df320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f = plt.figure()\n", "f.add_subplot(2,2,1)\n", "cvglmnetPlot(cv1)\n", "f.add_subplot(2,2,2)\n", "cvglmnetPlot(cv0p5)\n", "f.add_subplot(2,2,3)\n", "cvglmnetPlot(cv0)\n", "f.add_subplot(2,2,4)\n", "plt.plot( scipy.log(cv1['lambdau']), cv1['cvm'], 'r.')\n", "plt.hold(True)\n", "plt.plot( scipy.log(cv0p5['lambdau']), cv0p5['cvm'], 'g.')\n", "plt.plot( scipy.log(cv0['lambdau']), cv0['cvm'], 'b.')\n", "plt.xlabel('log(Lambda)')\n", "plt.ylabel(cv1['name'])\n", "plt.xlim(-6, 4)\n", "plt.ylim(0, 9)\n", "plt.legend( ('alpha = 1', 'alpha = 0.5', 'alpha = 0'), loc = 'upper left', prop={'size':6});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that lasso (`alpha=1`) does about the best here. We also see that the range of lambdas used differs with alpha.\n", "\n", "### Coefficient upper and lower bounds\n", "\n", "These are recently added features that enhance the scope of the models. Suppose we want to fit our model, but limit the coefficients to be bigger than -0.7 and less than 0.5. This is easily achieved via the `upper.limits` and `lower.limits` arguments:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+C50721oaTVVVEamp/yE1dR7+/jczdOhu2rfv0fwdZ2Ya0+Dy8vRK7EZQXAxX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\ny96Hvwm298OVsDFwudpxcCxZqOpRVR2pqomqOkp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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99a9f94a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cl = scipy.array([[-0.7], [0.5]], dtype = scipy.float64)\n", "tfit=glmnet(x = x.copy(),y= y.copy(), cl = cl)\n", "glmnetPlot(tfit);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are rather arbitrary limits; often we want the coefficients to be positive, so we can set only `lower.limit` to be 0.\n", "(Note, the lower limit must be no bigger than zero, and the upper limit no smaller than zero.)\n", "These bounds can be a vector, with different values for each coefficient. If given as a scalar, the same number gets recycled for all." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Penalty factors\n", "\n", "This argument allows users to apply separate penalty factors to each coefficient. Its default is 1 for each parameter, but other values can be specified. In particular, any variable with `penalty.factor` equal to zero is not penalized at all! Let $v_j$ denote the penalty factor for $j$ th variable. The penalty term becomes\n", "\n", "$$\n", "\\lambda \\sum_{j=1}^p \\boldsymbol{v_j} P_\\alpha(\\beta_j) = \\lambda \\sum_{j=1}^p \\boldsymbol{v_j} \\left[ (1-\\alpha)\\frac{1}{2} \\beta_j^2 + \\alpha |\\beta_j| \\right].\n", "$$\n", "\n", "Note the penalty factors are internally rescaled to sum to nvars.\n", "\n", "This is very useful when people have prior knowledge or preference over the variables. In many cases, some variables may be so important that one wants to keep them all the time, which can be achieved by setting corresponding penalty factors to 0:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NWw4nTsAll7jiDmPG+KyHgtNWq7XKMfp5a6M4WCxlxMVNcBOGczvcZdVf8NXv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\n0afPzRw58hDbto2nuPhd7HZzl6/fEsHynULw2Bo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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99a97f6a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pfac = scipy.ones([1, 20])\n", "pfac[0, 4] = 0; pfac[0, 9] = 0; pfac[0, 14] = 0\n", "pfit = glmnet(x = x.copy(), y = y.copy(), penalty_factor = pfac)\n", "glmnetPlot(pfit, label = True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see from the labels that the three variables with 0 penalty factors always stay in the model, while the others follow typical regularization paths and shrunken to 0 eventually.\n", "\n", "Some other useful arguments. `exclude` allows one to block certain variables from being the model at all. Of course, one could simply subset these out of `x`, but sometimes `exclude` is more useful, since it returns a full vector of coefficients, just with the excluded ones set to zero. There is also an `intercept` argument which defaults to `True`; if `False` the intercept is forced to be zero.\n", "\n", "### Customizing plots\n", "\n", "Sometimes, especially when the number of variables is small, we want to add variable labels to a plot. Since `glmnet` is intended primarily for wide data, this is not supprted in `plot.glmnet`. However, it is easy to do, as the following little toy example shows.\n", "\n", "We first generate some data, with 10 variables, and for lack of imagination and ease we give them simple character names. We then fit a glmnet model, and make the standard plot." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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I93ohu1xU7K7AfdRNzJgY4rLiiJ+uvZHYzFhCo6zv1usRDydKTvgFxCciA2MHkpWSxYzB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\nmtmz+0ZNOLthQmEGg4XU1OgQkC9stn+//uL0Cc2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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99a5c2128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scipy.random.seed(101)\n", "x = scipy.random.rand(100,10)\n", "y = scipy.random.rand(100,1)\n", "fit = glmnet(x = x, y = y)\n", "glmnetPlot(fit);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We wish to label the curves with the variable names. Here's a simple way to do this, using the `matplotlib` library in python (and a little research into how to customize it). We need to have the positions of the coefficients at the end of the path. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%capture\n", "# Output from this sample code has been suppressed due to (possible) Jupyter limitations\n", "# The code works just fine from ipython (tested on spyder)\n", "c = glmnetCoef(fit)\n", "c = c[1:, -1] # remove intercept and get the coefficients at the end of the path \n", "h = glmnetPlot(fit)\n", "ax1 = h['ax1']\n", "xloc = plt.xlim()\n", "xloc = xloc[1]\n", "for i in range(len(c)):\n", " ax1.text(xloc, c[i], 'var' + str(i)); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have done nothing here to avoid overwriting of labels, in the event that they are close together. This would be a bit more work, but perhaps best left alone, anyway." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear Regression - Multiresponse Gaussian Family" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The multiresponse Gaussian family is obtained using `family = \"mgaussian\"` option in `glmnet`. It is very similar to the single-response case above. This is useful when there are a number of (correlated) responses - the so-called \"multi-task learning\" problem. Here the sharing involves which variables are selected, since when a variable is selected, a coefficient is fit for each response. Most of the options are the same, so we focus here on the differences with the single response model.\n", "\n", "Obviously, as the name suggests, $y$ is not a vector, but a matrix of quantitative responses in this section. The coefficients at each value of lambda are also a matrix as a result.\n", "\n", "Here we solve the following problem:\n", "\n", "$$\n", "\\min_{(\\beta_0, \\beta) \\in \\mathbb{R}^{(p+1)\\times K}}\\frac{1}{2N} \\sum_{i=1}^N ||y_i -\\beta_0-\\beta^T x_i||^2_F+\\lambda \\left[ (1-\\alpha)||\\beta||_F^2/2 + \\alpha\\sum_{j=1}^p||\\beta_j||_2\\right].\n", "$$\n", "\n", "Here, $\\beta_j$ is the jth row of the $p\\times K$ coefficient matrix $\\beta$, and we replace the absolute penalty on each single coefficient by a group-lasso penalty on each coefficient K-vector $\\beta_j$ for a single predictor $x_j$.\n", "\n", "We use a set of data generated beforehand for illustration." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import relevant modules and setup for calling glmnet\n", "%reset -f\n", "%matplotlib inline\n", "\n", "import sys\n", "sys.path.append('../test')\n", "sys.path.append('../lib')\n", "import scipy, importlib, pprint, matplotlib.pyplot as plt, warnings\n", "from glmnet import glmnet; from glmnetPlot import glmnetPlot \n", "from glmnetPrint import glmnetPrint; from glmnetCoef import glmnetCoef; from glmnetPredict import glmnetPredict\n", "from cvglmnet import cvglmnet; from cvglmnetCoef import cvglmnetCoef\n", "from cvglmnetPlot import cvglmnetPlot; from cvglmnetPredict import cvglmnetPredict\n", "\n", "# parameters\n", "baseDataDir= '../data/'\n", "\n", "# load data\n", "x = scipy.loadtxt(baseDataDir + 'MultiGaussianExampleX.dat', dtype = scipy.float64, delimiter = ',')\n", "y = scipy.loadtxt(baseDataDir + 'MultiGaussianExampleY.dat', dtype = scipy.float64, delimiter = ',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We fit the data, with an object \"mfit\" returned." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mfit = glmnet(x = x.copy(), y = y.copy(), family = 'mgaussian')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For multiresponse Gaussian, the options in `glmnet` are almost the same as the single-response case, such as `alpha`, `weights`, `nlambda`, `standardize`. A exception to be noticed is that `standardize.response` is only for `mgaussian` family. The default value is `FALSE`. If `standardize.response = TRUE`, it standardizes the response variables.\n", "\n", "To visualize the coefficients, we use the `plot` function." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ppKSk0KZNG9q0aUPbtm1p165dw0xWdDh0P8P8+docDpg8Ga65Ro9Wqq9j2LQJ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\nQ0EfJtL5r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RktVtKVPIVvEpHOItIe+BV4tcR7jYCeaHH2fwbW2hKe5AN9StTLsB3/yqal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\n99uo441lAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff9a3ef3358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glmnetPlot(mfit, xvar = 'lambda', label = True, ptype = '2norm');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we set `type.coef = \"2norm\"`. Under this setting, a single curve is plotted per variable, with value equal to the $\\ell_2$ norm. The default setting is `type.coef = \"coef\"`, where a coefficient plot is created for each response (multiple figures).\n", "\n", "`xvar` and `label` are two other options besides ordinary graphical parameters. They are the same as the single-response case.\n", "\n", "We can extract the coefficients at requested values of $\\lambda$ by using the function `coef` and make predictions by `predict`. The usage is similar and we only provide an example of `predict` here." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-4.71062632 -1.16345744 0.60276341 3.74098912]\n", " [ 4.13017346 -3.05079679 -1.21226299 4.97014084]\n", " [ 3.15952287 -0.57596208 0.2607981 2.05397555]\n", " [ 0.64592424 2.12056049 -0.22520497 3.14628582]\n", " [-1.17918903 0.10562619 -7.33529649 3.24836992]] \n", "\n", "[[-4.6415158 -1.22902821 0.61182888 3.77952124]\n", " [ 4.47128428 -3.25296583 -1.25725829 5.2660386 ]\n", " [ 3.47352281 -0.69292309 0.46840369 2.05557354]\n", " [ 0.73533106 2.29650827 -0.21902966 2.98937089]\n", " [-1.27599301 0.28925358 -7.82592058 3.20521075]]\n" ] } ], "source": [ "f = glmnetPredict(mfit, x[0:5,:], s = scipy.float64([0.1, 0.01]))\n", "print(f[:,:,0], '\\n')\n", "print(f[:,:,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The prediction result is saved in a three-dimensional array with the first two dimensions being the prediction matrix for each response variable and the third indicating the response variables.\n", "\n", "We can also do k-fold cross-validation. The options are almost the same as the ordinary Gaussian family and we do not expand here." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "warnings.filterwarnings('ignore')\n", "cvmfit = cvglmnet(x = x.copy(), y = y.copy(), family = \"mgaussian\")\n", "warnings.filterwarnings('default')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the resulting `cv.glmnet` object \"cvmfit\"." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff9a3ef3860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cvglmnetPlot(cvmfit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To show explicitly the selected optimal values of $\\lambda$, type" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.04731812])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvmfit['lambda_min']" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.1445027])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvmfit['lambda_1se']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, the first one is the value at which the minimal mean squared error is achieved and the second is for the most regularized model whose mean squared error is within one standard error of the minimal.\n", "\n", "Prediction for `cvglmnet` object works almost the same as for `glmnet` object. We omit the details here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic Regression\n", "\n", "Logistic regression is another widely-used model when the response is categorical. If there are two possible outcomes, we use the binomial distribution, else we use the multinomial.\n", "\n", "### Logistic Regression: Binomial Models\n", "\n", "For the binomial model, suppose the response variable takes value in $\\mathcal{G}=\\{1,2\\}$. Denote $y_i = I(g_i=1)$. We model\n", "\n", "$$\n", "\\mbox{Pr}(G=2|X=x)+\\frac{e^{\\beta_0+\\beta^Tx}}{1+e^{\\beta_0+\\beta^Tx}},\n", "$$\n", "\n", "which can be written in the following form\n", "\n", "$$\n", "\\log\\frac{\\mbox{Pr}(G=2|X=x)}{\\mbox{Pr}(G=1|X=x)}=\\beta_0+\\beta^Tx,\n", "$$\n", "\n", "the so-called \"logistic\" or log-odds transformation.\n", "\n", "The objective function for the penalized logistic regression uses the negative binomial log-likelihood, and is\n", "\n", "$$\n", "\\min_{(\\beta_0, \\beta) \\in \\mathbb{R}^{p+1}} -\\left[\\frac{1}{N} \\sum_{i=1}^N y_i \\cdot (\\beta_0 + x_i^T \\beta) - \\log (1+e^{(\\beta_0+x_i^T \\beta)})\\right] + \\lambda \\big[ (1-\\alpha)||\\beta||_2^2/2 + \\alpha||\\beta||_1\\big].\n", "$$\n", "\n", "Logistic regression is often plagued with degeneracies when $p > N$ and exhibits wild behavior even when $N$ is close to $p$;\n", "the elastic-net penalty alleviates these issues, and regularizes and selects variables as well.\n", "\n", "Our algorithm uses a quadratic approximation to the log-likelihood, and then coordinate descent on the resulting penalized weighted least-squares problem. These constitute an outer and inner loop.\n", "\n", "For illustration purpose, we load pre-generated input matrix `x` and the response vector `y` from the data file." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import relevant modules and setup for calling glmnet\n", "%reset -f\n", "%matplotlib inline\n", "\n", "import sys\n", "sys.path.append('../test')\n", "sys.path.append('../lib')\n", "import scipy, importlib, pprint, matplotlib.pyplot as plt, warnings\n", "from glmnet import glmnet; from glmnetPlot import glmnetPlot \n", "from glmnetPrint import glmnetPrint; from glmnetCoef import glmnetCoef; from glmnetPredict import glmnetPredict\n", "from cvglmnet import cvglmnet; from cvglmnetCoef import cvglmnetCoef\n", "from cvglmnetPlot import cvglmnetPlot; from cvglmnetPredict import cvglmnetPredict\n", "\n", "# parameters\n", "baseDataDir= '../data/'\n", "\n", "# load data\n", "x = scipy.loadtxt(baseDataDir + 'BinomialExampleX.dat', dtype = scipy.float64, delimiter = ',')\n", "y = scipy.loadtxt(baseDataDir + 'BinomialExampleY.dat', dtype = scipy.float64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The input matrix $x$ is the same as other families. For binomial logistic regression, the response variable $y$ should be either a factor with two levels, or a two-column matrix of counts or proportions.\n", "\n", "Other optional arguments of `glmnet` for binomial regression are almost same as those for Gaussian family. Don't forget to set `family` option to \"binomial\"." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fit = glmnet(x = x.copy(), y = y.copy(), family = 'binomial')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like before, we can print and plot the fitted object, extract the coefficients at specific $\\lambda$'s and also make predictions. For plotting, the optional arguments such as `xvar` and `label` are similar to the Gaussian. We plot against the deviance explained and show the labels." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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l31oOgKr+mchT73DuAb490s8/0pHImcCNQE5Vp8cu4T+kqh8Z6YMOBGYkYjAY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\n+mBvW5OuvfZaMjMzAejXrx9ZWVmHfnE0tIH2hfXg9t6eUJ/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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99aa56be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glmnetPlot(fit, xvar = 'dev', label = True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prediction is a little different for logistic from Gaussian, mainly in the option `type`. \"link\" and \"response\" are never equivalent and \"class\" is only available for logistic regression. In summary,\n", "* \"link\" gives the linear predictors\n", "\n", "* \"response\" gives the fitted probabilities\n", "\n", "* \"class\" produces the class label corresponding to the maximum probability.\n", "\n", "* \"coefficients\" computes the coefficients at values of `s`\n", "\n", "* \"nonzero\" retuns a list of the indices of the nonzero coefficients for each value of `s`.\n", "\n", "For \"binomial\" models, results (\"link\", \"response\", \"coefficients\", \"nonzero\") are returned only for the class corresponding to the second level of the factor response.\n", "\n", "In the following example, we make prediction of the class labels at $\\lambda = 0.05, 0.01$." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0.],\n", " [ 1., 1.],\n", " [ 1., 1.],\n", " [ 0., 0.],\n", " [ 1., 1.]])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glmnetPredict(fit, newx = x[0:5,], ptype='class', s = scipy.array([0.05, 0.01]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For logistic regression, `cvglmnet` has similar arguments and usage as Gaussian. `nfolds`, `weights`, `lambda`, `parallel` are all available to users. There are some differences in `ptype`: \"deviance\" and \"mse\" do not both mean squared loss and \"class\" is enabled. Hence,\n", "* \"mse\" uses squared loss.\n", "\n", "* \"deviance\" uses actual deviance.\n", "\n", "* \"mae\" uses mean absolute error.\n", "\n", "* \"class\" gives misclassification error.\n", "\n", "* \"auc\" (for two-class logistic regression ONLY) gives area under the ROC curve.\n", "\n", "For example," ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "warnings.filterwarnings('ignore')\n", "cvfit = cvglmnet(x = x.copy(), y = y.copy(), family = 'binomial', ptype = 'class')\n", "warnings.filterwarnings('default')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It uses misclassification error as the criterion for 10-fold cross-validation.\n", "\n", "We plot the object and show the optimal values of $\\lambda$." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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aPZvGewPPKY40WbubvIlrroEYD6xzP+PdPMMwjLLDMwoNaTOve0PV9u0ZFxva\ns25dX0SMFLl8El92v/8zw+emkOUyXOLe52v6RZu465cUSa0h4dEFDBg7thTilIRc3U0L3O8vqKoF\n9DMMIxZk647yR4L1OPjEE7miYwd3bml3fBLAzJEjaWxu7k+RS0ouI/F14H7gN4B1L5WIcuzzLSam\nX7SJon7+7ih/WPBMHHf88Tyx9ia+fOBs3k4+xYRJkxgxalTFOK0hd3fTJhGZD9SLyCPpn/4S0DDi\nTNQd13Gnu3sAL71cz40P3cuERILGe++luqam1GL1K7mMxDk4K9JtJLNfwugH4t7nW+n6Weym4ISx\nXkQ+nntuK0ccASNGhHaIsifXynTvAItF5IOq+lY/ymQYhrEP2VaPC5O33jqYSZPyl4szWY2EiNyi\nqrOAn4vIPjOsVXVqqJIZQDT7fAvB9Is2cdDPexvyO7G97c7OT3L66aWTrRzI5bi+x/224a6GYcQW\nz9B5byeLFi1CVJlz0UUctPavPP+rUxl/+L8CjkGpra1NGZZMI6LiRq7upufc79RK4CIyEjhcVV/s\nB9kM6BGSOI6YftGm1Polk8mUT6Kvs6s9OjZvZu6ZZ9K0ahUNwCmPrKLx5WfoPuecWJ/LbOQNyyEi\nrcBUt+xzwAYReUpVv5yzomEYebHYTbnJZwSy+SkWLVpEb1mzcCF3p4XiaFq1iksWLux1m1EmSBTY\nEaq6Dfg0cLeq/iPw0XDFMjzi/uRS6fpFfQhs2Oevrq4uFVajryE2guIPxeEdqcrNr0SCGIn9RORQ\nnBDej4Usj2EYRknpGjYsYyiOrmHDSiFOyQliJL4FPAH8n6ouEZEjgJXhimV4VPo8gqhj+kWP8VOm\ncMmIIXQBrTgGonHCBMZPmVJawUpEkFDh9+OE5/DSrwHnhSmUYRhGPnINXe0NHZs30zR9Om8uXMIr\n3afytU8M5ZXnljDpox9lZnMzLXffXRzBI0YQx/WNwLeBt4HHgeOBL6lqZQRTLzGV3mcfdUy/3pHN\nYe3fzjR0tbcGYnVbG1vvuYdvb9niBvJ7lcZXJvD+z36Wxltv7ZMuUSdId9NZruP6k0ASOBK4Pkyh\nDKNSiLrjOiwyOay9/DBomT2bua6BgL0jmtZU6IgmP4Ec1+73OcD9qro1RHmMNOLY5+un0vWz2E3l\nwZ61azMuLrR9w4ZSiFNWBDESj4nICuBk4E8ichCwM+gBRORsEVkhIq+KyA0Z9v+LiCxzP0+KyPG+\nfUk3f6mKfZ5wAAAgAElEQVSIPBP0mIZhGIUwYNy4jCOadg4ZUgpxyoogjuuvuX6JraraLSJdwLlB\nGheRAcBtwBk4S58uEZGHVXWFr9hrwIdVdauInA38hL3rae8BGlR1S3CV4oX1aUcb069/Wd3WRsvs\n2ax66imaVq6kY9SoHnnXP/8874qwadmy1H6ARHMzM3//+1SXkzei6eRzzimpPuVAXiPhMhb4qIgM\n9uUFcfWfCqxU1dUAInIfjoFJGQlVXewrvxgY50sLwd52DMOocPzhNKqArmSSK4cP5zsPPsjNr7+e\nypsNNAOjk0lmjhzJ6lmzqK2vZ8TFF/OBJ37L4dvg1I9MrugRTX7y3oBFpBGY636mADfihOkIwjjg\ndV/6DXoagXS+CPzBl1ZggYgsEZHLAh4zVsSlzzcbpl+0KSf91ixcmDIQ4PgUfrxtG2NcA+HlNQMt\n7vbcLVtomT0bgOqaGl75xDCGnn8pjffeS219PW1tbf2sRfkR5E3ifOAEYKmqfk5ExgBFH/4qIlOA\nzwH+wLyTVPVN1w+yQESWq+qTmeonEonUyIfq6momTpyYehX2LmRLW7rc0o2NxWnv0kvBCyJRTvoV\nI93W1tbDGLW2tva4eXv7vXAaXskGHEOwxs1rcPOX4PRx4+5/7eWXaW1tZXe38k7VawxIvk6rL3Ch\n/9jl8Hv0Nu1tF7pIk6jus1REzwIiz6jqqSLyHM6bxHZguaoenbdxkdOAOap6tpv+GqCq+r20cscD\nDwBnq+qqLG01AttV9QcZ9mk+PQzDyE+2+QleID3//hUrVjB48GB27txJZ2cno0ePpqOjg7q6Oqqr\nq4sWRrupqYnGxsbUtz/Pv33e8cdz90sv9Ril1AV8F+ftwZ93E9DobV90EY333svF187mvv3n8c1h\nl+Y8TlwQEVRV8pUL8ibxrIhUA/+FEwW2E/hLQDmWAEeKSC3wJnABcGGaoONxDMTFfgMhIkOAAara\nKSJVwFlAiQb6GUZlkG/1N//+RYsW9biBXnfddTQ1NTFt2rSCjukZno6ODpLJJNXV1T2MTVDGT5nC\n7K4dNL+2KuV8vnL4cDrfHUHXjtdTebOBa93tmSNH0tjsmJA1O95m5NC8z74VR16fhKpeqaodqnoH\ncCZwqap+LkjjqtoNXA3MB14G7lPV5SJyhYhc7habDdQAP04b6joGeFJEluI4tB9V1fkFaRcD0l91\n44bpF22KoZ83cW7atGls3bqVRCLB1q1bmTZtWqrLJAjVNTV8/McL+PCwizjvkHF8ePgANg8ewYp3\nTuSGs8/lkro65kydip57LrNq3sNHB9WxdcgQWmbPZnVbG5u7BnHk4NN6tGk+idzLl56Ua5+qPh/k\nAKr6OHBUWt6dvu3LgH2c0qraBkwMcgzDMAyA7V311JzSzIilJ/O/2/ZQte11unidxpUT2H/qVL5/\n662sbmuj6eSTufedLVStha5582hcvBg94BYunfxJ1q+3Dgs/ubqb/jPHPgU+UmRZjAwU8iQVRUy/\naFNu+q1YAUPezBxiw1s0KFsIjj8O+SlHH/1J1q/f2159fX0/Sl+e5Fq+tDLj4hpGPzJnTnHiLs2a\n1cG0aS8AmR3KGzduZOjQoQD75A0ePJht27YxfPhwAHbu3MnRRzt9872Jqtra2trDv+A/jr/tMNaH\nfuUVGLMnc4gNb9GgbCE4Rry9haOPhpj3EBZMkHkSV7mOay89UkSuDFcsw8P6tKNNPv2KFXPp1lur\naXAD4rW3t5NIJJgxYwZdXV0kEgm6urqYMWNGxrxEIsGWLVtIJBIkEgna29tTbwj5buKZ9Ev3L/iP\n47Xd0NAQSrC+P+w3g3fHDc+5aFC2EBztMpaDD+6Z7w2vbW1tpba2NrVd6DDSKBNkdNNlqvojL6Gq\nW9yJbT8OTyzDMIzC2KPKW2N+ySVfWMjMpX/eJ8SGt2hQphAcVxwwhNHdr/Cti6enQnV4lFuXWn8T\nxEgMFN9EBBEZCAwKVyzDI+4XqOlX/mSbOwHlpV971x5k4CAaPngSD198MTdt2sSqp55iwqRJPUJs\neCE4btq0iVcWLWLDhnbm7trBMSyla97SHqE6zCcRLC7S48CvROQMETkD+KWbZxhGBZBpbYdyMg4e\nye3vMGyn4++orqmh8d57mZBIpEJs+PH27xo5koff2c0xbn56qA4j2JvEDcDlwL+66QXAT0OTyOiB\nPzxAHDH9yhvP55Btdbje6Jfu2O7NxLlMrN21nUOGHZW/oA8vlEePPGDPunWAzZOAYJPp9qjqHap6\nPo6x+Is7Sc4wjD5SrCgPYUWLSH+DgL6vDleMiXOZ2Mgm3lNT2IzprmHDMjqxB4wd2ydZ4kSQ0U2t\nIjJcRGpwwnL8l4jcHL5oBpRXn28YVLp+UV++tJzO3wFLZ/DZo6cXVGf8lCk0TpiQMhReqI6EG6rD\nfBLBuptGqOo2EfkicLeqNorIi2ELZhiGkU76okKJ5mY6Nm+mafp06v+2mhd+UcuHJzbnb8iluqaG\nxIIF3OS2OWHSJEaMGrWPD6OSCbTGtYgcCnwGeCxkeYw0Kn0eQdQx/YqHt6jQdfPmcXcyyXXz5vGD\nhgbWtbRw3bx5LOx+km89Oo+5Z55Jx+bNgdutra/v4eSurqlJ7TOfRLA3iW8BTwBPquoSETkCWBmu\nWIZh+CkkRHdcWbNwIXenLSr072vW8F1328vzh+Aw+k4Qx/X9qnq8ql7ppl9T1fPCF82A8urzDQPT\nLxj+YaiZZlQXy/lbKP15vGwjkdJvYv4QHH3FfBI5jISIfNX9nisiP0z/9J+IhhFfiuVwXrhwcnEa\nKmOyjUTakyHPC8Fh9J1cbxLL3e9ncUY1pX+MfsD6tKNNf8VuWrSooTgNFUh/nD/PMX3A5s1M3X9g\n6sbUBVw1ZAjP4ixKs5p9Q3D0FfNJ5I4C+6j7fVf/iWMYhrGX1W1tbL3nHr7ti7N0ddVQtgytonPj\nVubu2MExbv6M/fZj9DnnMOvmm1MhOHqLZ/wOOeSQ1HYYUWujQK5Fhx7JVVFVpxZfnPiQz9GYKUxz\nttDOzz77bMF1enOcoHWKuY6x+SSiTdj6ZVr74bauTqbuN5BHunf2yL/j3Xe5aejQogxf9fSK+/kL\nQq7upg8AhwF/xlk3/D/TPkYO8jkaM4Vp7k1o51LU8TtJK/HJyug/sq39ULNzZ85wGkbxyGUkDgG+\nARwL3IqzvvVGVV2kqov6QzjD+uyjjunXN7Kt/bB58OB+CacR9/MXhFw+iW6caK+Pi8gBwIVAq4g0\nqeptQQ8gImcDt+AYpJ+p6vfS9v8LThBBgO3Alar6YpC6RunItfpYId1a69atI5lMZl0ZLe79wMWK\nuTR5civQUJzGyohEczNX/e4P/Khjc4+1IepPP53GJ5+kyZ034YXTaGwOPtvaj9c17F9cCGC9fy3T\nCiXnZDrXOJyDYyDqgB8CDwZtXEQGALcBZwDrgCUi8rCqrvAVew34sKpudY3CT4DTAtaNPQ0NDSxa\nVH4vbl5fbVNTE7NmzaKpqYnrrrsulXfNNdektq+77rp99nt5P/jBDzLWmTFjRj9rFA69id2UyZ8F\nuZcVnTJlEaUwEmH22XshODZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99a6b25f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cvglmnetPlot(cvfit)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00333032])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvfit['lambda_min']" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00638726])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvfit['lambda_1se']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`coef` and `predict` are simliar to the Gaussian case and we omit the details. We review by some examples." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.1834094 ],\n", " [ 0.63979413],\n", " [ 1.75552224],\n", " [-1.01816297],\n", " [-2.04021446],\n", " [-0.3708456 ],\n", " [-2.17833787],\n", " [ 0.37214969],\n", " [-1.11649964],\n", " [ 1.59942098],\n", " [-3.00907083],\n", " [-0.3709413 ],\n", " [-0.50788757],\n", " [-0.54759695],\n", " [ 0.37853469],\n", " [ 0. ],\n", " [ 1.22026778],\n", " [-0.00760482],\n", " [-0.8171956 ],\n", " [-0.4683986 ],\n", " [-0.44077522],\n", " [ 0. ],\n", " [ 0.51053862],\n", " [ 1.06639664],\n", " [-0.57196411],\n", " [ 1.10470005],\n", " [-0.529917 ],\n", " [-0.67932357],\n", " [ 1.02441643],\n", " [-0.49368737],\n", " [ 0.41948873]])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvglmnetCoef(cvfit, s = 'lambda_min')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned previously, the results returned here are only for the second level of the factor response." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.],\n", " [ 1.],\n", " [ 1.],\n", " [ 0.],\n", " [ 1.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 1.],\n", " [ 1.]])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvglmnetPredict(cvfit, newx = x[0:10, ], s = 'lambda_min', ptype = 'class')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like other GLMs, glmnet allows for an \"offset\". This is a fixed vector of N numbers that is added into the linear predictor.\n", "For example, you may have fitted some other logistic regression using other variables (and data), and now you want to see if the present variables can add anything. So you use the predicted logit from the other model as an offset in." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like other GLMs, glmnet allows for an \"offset\". This is a fixed vector of N numbers that is added into the linear predictor.\n", "For example, you may have fitted some other logistic regression using other variables (and data), and now you want to see if the present variables can add anything. So you use the predicted logit from the other model as an offset in." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Logistic Regression - Multinomial Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the multinomial model, suppose the response variable has $K$ levels ${\\cal G}=\\{1,2,\\ldots,K\\}$. Here we model\n", "$$\\mbox{Pr}(G=k|X=x)=\\frac{e^{\\beta_{0k}+\\beta_k^Tx}}{\\sum_{\\ell=1}^Ke^{\\beta_{0\\ell}+\\beta_\\ell^Tx}}.$$\n", "\n", "Let ${Y}$ be the $N \\times K$ indicator response matrix, with elements $y_{i\\ell} = I(g_i=\\ell)$. Then the elastic-net penalized negative log-likelihood function becomes\n", "$$\n", "\\ell(\\{\\beta_{0k},\\beta_{k}\\}_1^K) = -\\left[\\frac{1}{N} \\sum_{i=1}^N \\Big(\\sum_{k=1}^Ky_{il} (\\beta_{0k} + x_i^T \\beta_k)- \\log \\big(\\sum_{k=1}^K e^{\\beta_{0k}+x_i^T \\beta_k}\\big)\\Big)\\right] +\\lambda \\left[ (1-\\alpha)||\\beta||_F^2/2 + \\alpha\\sum_{j=1}^p||\\beta_j||_q\\right].\n", "$$\n", "\n", "\n", "Here we really abuse notation! $\\beta$ is a $p\\times K$ matrix of coefficients. $\\beta_k$ refers to the kth column (for outcome category k), and $\\beta_j$ the jth row (vector of K coefficients for variable j).\n", "The last penalty term is $||\\beta_j||_q$, we have two options for q: $q\\in \\{1,2\\}$.\n", "When q=1, this is a lasso penalty on each of the parameters. When q=2, this is a grouped-lasso penalty on all the K coefficients for a particular variables, which makes them all be zero or nonzero together.\n", "\n", "The standard Newton algorithm can be tedious here. Instead, we use a so-called partial Newton algorithm by making a partial quadratic approximation to the log-likelihood, allowing only $(\\beta_{0k}, \\beta_k)$ to vary for a single class at a time.\n", "For each value of $\\lambda$, we first cycle over all classes indexed by $k$, computing each time a partial quadratic approximation about the parameters of the current class. Then the inner procedure is almost the same as for the binomial case.\n", "This is the case for lasso (q=1). When q=2, we use a different approach, which we wont dwell on here.\n", "\n", "For the multinomial case, the usage is similar to logistic regression, and we mainly illustrate by examples and address any differences. We load a set of generated data." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import relevant modules and setup for calling glmnet\n", "%reset -f\n", "%matplotlib inline\n", "\n", "import sys\n", "sys.path.append('../test')\n", "sys.path.append('../lib')\n", "import scipy, importlib, pprint, matplotlib.pyplot as plt, warnings\n", "from glmnet import glmnet; from glmnetPlot import glmnetPlot \n", "from glmnetPrint import glmnetPrint; from glmnetCoef import glmnetCoef; from glmnetPredict import glmnetPredict\n", "from cvglmnet import cvglmnet; from cvglmnetCoef import cvglmnetCoef\n", "from cvglmnetPlot import cvglmnetPlot; from cvglmnetPredict import cvglmnetPredict\n", "\n", "# parameters\n", "baseDataDir= '../data/'\n", "\n", "# load data\n", "x = scipy.loadtxt(baseDataDir + 'MultinomialExampleX.dat', dtype = scipy.float64, delimiter = ',')\n", "y = scipy.loadtxt(baseDataDir + 'MultinomialExampleY.dat', dtype = scipy.float64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optional arguments in `glmnet` for multinomial logistic regression are mostly similar to binomial regression except for a few cases.\n", "\n", "The response variable can be a `nc >= 2` level factor, or a `nc`-column matrix of counts or proportions.\n", "Internally glmnet will make the rows of this matrix sum to 1, and absorb the total mass into the weight for that observation.\n", "\n", "`offset` should be a `nobs x nc` matrix if there is one.\n", "\n", "A special option for multinomial regression is `mtype`, which allows the usage of a grouped lasso penalty if `mtype = 'grouped'`. This will ensure that the multinomial coefficients for a variable are all in or out together, just like for the multi-response Gaussian." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fit = glmnet(x = x.copy(), y = y.copy(), family = 'multinomial', mtype = 'grouped')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the resulting object \"fit\"." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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J9VKio6FePWjQAOrXV22xxMRAw4Zq29vbfjZoNGWwSisPL3+Y9SfWs2LSCuoF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Rj08fno6/N11H19PvrlamOnO3gpXeexebP7u9+4cRetP0IhGDjQ7Rc/6igYPHhHnXccjpOlvf1j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\n/ogYNsTt2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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff9cf005ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glmnetPlot(fit, xvar = 'lambda', label = True, ptype = '2norm');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The options are `xvar`, `label` and `ptype`, in addition to other ordinary graphical parameters.\n", "\n", "`xvar` and `label` are the same as other families while `ptype` is only for multinomial regression and multiresponse Gaussian model. It can produce a figure of coefficients for each response variable if `ptype = \"coef\"` or a figure showing the $\\ell_2$-norm in one figure if `ptype = \"2norm\"`\n", "\n", "We can also do cross-validation and plot the returned object." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99aa1c0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "warnings.filterwarnings('ignore')\n", "cvfit=cvglmnet(x = x.copy(), y = y.copy(), family='multinomial', mtype = 'grouped');\n", "warnings.filterwarnings('default')\n", "cvglmnetPlot(cvfit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that although `mtype` is not a typical argument in `cvglmnet`, in fact any argument that can be passed to `glmnet` is valid in the argument list of `cvglmnet`. We also use parallel computing to accelerate the calculation.\n", "\n", "Users may wish to predict at the optimally selected $\\lambda$:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3., 2., 2., 1., 1., 3., 3., 1., 1., 2.])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cvglmnetPredict(cvfit, newx = x[0:10, :], s = 'lambda_min', ptype = 'class')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Poisson Models" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Poisson regression is used to model count data under the assumption of Poisson error, or otherwise non-negative data where the mean and variance are proportional. Like the Gaussian and binomial model, the Poisson is a member of the exponential family of distributions. We usually model its positive mean on the log scale: $\\log \\mu(x) = \\beta_0+\\beta' x$.\n", "The log-likelihood for observations $\\{x_i,y_i\\}_1^N$ is given my\n", "$$\n", "l(\\beta|X, Y) = \\sum_{i=1}^N (y_i (\\beta_0+\\beta' x_i) - e^{\\beta_0+\\beta^Tx_i}.\n", "$$\n", "\n", "\n", "As before, we optimize the penalized log-likelihood:\n", "\n", "$$\n", "\\min_{\\beta_0,\\beta} -\\frac1N l(\\beta|X, Y) + \\lambda \\left((1-\\alpha) \\sum_{i=1}^N \\beta_i^2/2) +\\alpha \\sum_{i=1}^N |\\beta_i|\\right).\n", "$$\n", "\n", "Glmnet uses an outer Newton loop, and an inner weighted least-squares loop (as in logistic regression) to optimize this criterion.\n", "\n", "First, we load a pre-generated set of Poisson data." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import relevant modules and setup for calling glmnet\n", "%reset -f\n", "%matplotlib inline\n", "\n", "import sys\n", "sys.path.append('../test')\n", "sys.path.append('../lib')\n", "import scipy, importlib, pprint, matplotlib.pyplot as plt, warnings\n", "from glmnet import glmnet; from glmnetPlot import glmnetPlot \n", "from glmnetPrint import glmnetPrint; from glmnetCoef import glmnetCoef; from glmnetPredict import glmnetPredict\n", "from cvglmnet import cvglmnet; from cvglmnetCoef import cvglmnetCoef\n", "from cvglmnetPlot import cvglmnetPlot; from cvglmnetPredict import cvglmnetPredict\n", "\n", "# parameters\n", "baseDataDir= '../data/'\n", "\n", "# load data\n", "x = scipy.loadtxt(baseDataDir + 'PoissonExampleX.dat', dtype = scipy.float64, delimiter = ',')\n", "y = scipy.loadtxt(baseDataDir + 'PoissonExampleY.dat', dtype = scipy.float64, delimiter = ',')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "We apply the function `glmnet` with the `\"poisson\"` option." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fit = glmnet(x = x.copy(), y = y.copy(), family = 'poisson')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The optional input arguments of `glmnet` for `\"poisson\"` family are similar to those for others.\n", "\n", "`offset` is a useful argument particularly in Poisson models.\n", "\n", "When dealing with rate data in Poisson models, the counts collected are often based on different exposures, such as length of time observed, area and years. A poisson rate $\\mu(x)$ is relative to a unit exposure time, so if an observation $y_i$ was exposed for $E_i$ units of time, then the expected count would be $E_i\\mu(x)$, and the log mean would be $\\log(E_i)+\\log(\\mu(x)$. In a case like this, we would supply an *offset* $\\log(E_i)$ for each observation.\n", "Hence `offset` is a vector of length `nobs` that is included in the linear predictor. Other families can also use options, typically for different reasons.\n", "\n", "(Warning: if `offset` is supplied in `glmnet`, offsets must also also be supplied to `predict` to make reasonable predictions.)\n", "\n", "Again, we plot the coefficients to have a first sense of the result." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zLVthfQW4CfhRA+cUeF9LHnrKTVT/BYyqd+xXCds/A352ts9pd6yPh9GJOHIE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\njbDEQsOiE8Kjtb10pialMmPwDBbMXsB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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99aa9dfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glmnetPlot(fit);" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Like before, we can extract the coefficients and make predictions at certain $\\lambda$'s by using `coef` and `predict` respectively. The optional input arguments are similar to those for other families. In function `predict`, the option `type`, which is the type of prediction required, has its own specialties for Poisson family. That is,\n", "* \"link\" (default) gives the linear predictors like others\n", "* \"response\" gives the fitted mean\n", "* \"coefficients\" computes the coefficients at the requested values for `s`, which can also be realized by `coef` function\n", "* \"nonzero\" returns a a list of the indices of the nonzero coefficients for each value of `s`.\n", "\n", "For example, we can do as follows:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.61123371],\n", " [ 0.45819758],\n", " [-0.77060709],\n", " [ 1.34015128],\n", " [ 0.043505 ],\n", " [-0.20325967],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0.01816309],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ]])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glmnetCoef(fit, s = scipy.float64([1.0]))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2.49442322, 2.54623385],\n", " [ 10.35131198, 10.33773624],\n", " [ 0.11797039, 0.10639897],\n", " [ 0.97134115, 0.92329512],\n", " [ 1.11334721, 1.07256799]])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glmnetPredict(fit, x[0:5,:], ptype = 'response', s = scipy.float64([0.1, 0.01]))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "We may also use cross-validation to find the optimal $\\lambda$'s and thus make inferences." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "warnings.filterwarnings('ignore')\n", "cvfit = cvglmnet(x.copy(), y.copy(), family = 'poisson')\n", "warnings.filterwarnings('default')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Options are almost the same as the Gaussian family except that for `type.measure`,\n", "* \"deviance\" (default) gives the deviance\n", "* \"mse\" stands for mean squared error\n", "* \"mae\" is for mean absolute error.\n", "\n", "We can plot the `cvglmnet` object." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99a5bd588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cvglmnetPlot(cvfit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also show the optimal $\\lambda$'s and the corresponding coefficients." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2.72128916e-02, 1.85696196e-01],\n", " [ 6.20006263e-01, 5.75373801e-01],\n", " [ -9.85744959e-01, -9.32121975e-01],\n", " [ 1.52693390e+00, 1.47056730e+00],\n", " [ 2.32156777e-01, 1.96923579e-01],\n", " [ -3.37405607e-01, -3.04694503e-01],\n", " [ 1.22308275e-03, 0.00000000e+00],\n", " [ -1.35769399e-02, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00],\n", " [ 1.69722836e-02, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00],\n", " [ 3.10187944e-02, 2.58501705e-02],\n", " [ -2.92817638e-02, 0.00000000e+00],\n", " [ 3.38822516e-02, 0.00000000e+00],\n", " [ -6.66067519e-03, 0.00000000e+00],\n", " [ 1.83937264e-02, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00],\n", " [ 4.54888769e-03, 0.00000000e+00],\n", " [ -3.45423073e-02, 0.00000000e+00],\n", " [ 1.20550886e-02, 9.92954798e-03]])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "optlam = scipy.array([cvfit['lambda_min'], cvfit['lambda_1se']]).reshape([2,])\n", "cvglmnetCoef(cvfit, s = optlam)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `predict` method is similar and we do not repeat it here." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Cox Models" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The Cox proportional hazards model is commonly used for the study of the relationship beteween predictor variables and survival time. In the usual survival analysis framework, we have data of the form $(y_1, x_1, \\delta_1), \\ldots, (y_n, x_n, \\delta_n)$ where $y_i$, the observed time, is a time of failure if $\\delta_i$ is 1 or right-censoring if $\\delta_i$ is 0. We also let $t_1 < t_2 < \\ldots < t_m$ be the increasing list of unique failure times, and $j(i)$ denote the index of the observation failing at time $t_i$.\n", "\n", "The Cox model assumes a semi-parametric form for the hazard\n", "\n", "\n", "$$\n", "h_i(t) = h_0(t) e^{x_i^T \\beta},\n", "$$\n", "\n", "\n", "where $h_i(t)$ is the hazard for patient $i$ at time $t$, $h_0(t)$ is a shared baseline hazard, and $\\beta$ is a fixed, length $p$ vector. In the classic setting $n \\geq p$, inference is made via the partial likelihood\n", "\n", "\n", "$$\n", "L(\\beta) = \\prod_{i=1}^m \\frac{e^{x_{j(i)}^T \\beta}}{\\sum_{j \\in R_i} e^{x_j^T \\beta}},\n", "$$\n", "\n", "\n", "where $R_i$ is the set of indices $j$ with $y_j \\geq t_i$ (those at risk at time $t_i$).\n", "\n", "Note there is no intercept in the Cox mode (its built into the baseline hazard, and like it, would cancel in the partial likelihood.)\n", "\n", "We penalize the negative log of the partial likelihood, just like the other models, with an elastic-net penalty.\n", "\n", "We use a pre-generated set of sample data and response. Users can load their own data and follow a similar procedure. In this case $x$ must be an $n\\times p$ matrix of covariate values — each row corresponds to a patient and each column a covariate. $y$ is an $n \\times 2$ matrix, with a column \"time\" of failure/censoring times, and \"status\" a 0/1 indicator, with 1 meaning the time is a failure time, and zero a censoring time." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import relevant modules and setup for calling glmnet\n", "%reset -f\n", "%matplotlib inline\n", "\n", "import sys\n", "sys.path.append('../test')\n", "sys.path.append('../lib')\n", "import scipy, importlib, pprint, matplotlib.pyplot as plt, warnings\n", "from glmnet import glmnet; from glmnetPlot import glmnetPlot \n", "from glmnetPrint import glmnetPrint; from glmnetCoef import glmnetCoef; from glmnetPredict import glmnetPredict\n", "from cvglmnet import cvglmnet; from cvglmnetCoef import cvglmnetCoef\n", "from cvglmnetPlot import cvglmnetPlot; from cvglmnetPredict import cvglmnetPredict\n", "\n", "# parameters\n", "baseDataDir= '../data/'\n", "\n", "# load data\n", "x = scipy.loadtxt(baseDataDir + 'CoxExampleX.dat', dtype = scipy.float64, delimiter = ',')\n", "y = scipy.loadtxt(baseDataDir + 'CoxExampleY.dat', dtype = scipy.float64, delimiter = ',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Surv` function in the package `survival` can create such a matrix. Note, however, that the `coxph` and related linear models can handle interval and other fors of censoring, while glmnet can only handle right censoring in its present form.\n", "\n", "We apply the `glmnet` function to compute the solution path under default settings." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Cox model has no intercept!\n" ] } ], "source": [ "fit = glmnet(x = x.copy(), y = y.copy(), family = 'cox')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the standard options are available such as `alpha`, `weights`, `nlambda` and `standardize`. Their usage is similar as in the Gaussian case and we omit the details here. Users can also refer to the help file `help(glmnet)`.\n", "\n", "We can plot the coefficients." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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P9yvfZ1vVNjJiM7wexsrJK4k06RDKxejt7aW4uHiAWNTX1zN//vwBeYvMzEw9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\ndDIyPkKxvTgsKCGhKcssCwtPjiUnIXqPJaqAnAPuklL2B4W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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff9a2729898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glmnetPlot(fit);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, we can extract the coefficients at certain values of $\\lambda$." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.37693638],\n", " [-0.09547797],\n", " [-0.13595972],\n", " [ 0.09814146],\n", " [-0.11437545],\n", " [-0.38898545],\n", " [ 0.242914 ],\n", " [ 0.03647596],\n", " [ 0.34739813],\n", " [ 0.03865115],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ],\n", " [ 0. ]])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glmnetCoef(fit, s = scipy.float64([0.05]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the Cox Model is not commonly used for prediction, we do not give an illustrative example on prediction. If needed, users can refer to the help file by typing `help(predict.glmnet)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Currently, cross-validation is not implemented for cox case. But this is not difficult to do using the existing `glmnet` calls that work perfectly well for this case. (TBD: `cvglmnet` to be implemented for cox)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "<p>Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2008). <br>\n", "<a href=\"http://www.jstatsoft.org/v33/i01/\">Regularization Paths for Generalized Linear Models via Coordinate Descent</a><br>\n", "<em>Journal of Statistical Software</em>, Vol. 33(1), 1-22 Feb 2010.</p>\n", "<p>Noah Simon, Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2011).<br>\n", "<a href=\"http://www.jstatsoft.org/v39/i05/\">Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent</a><br>\n", "<em>Journal of Statistical Software</em>, Vol. 39(5) 1-13.</p>\n", "<p>Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, Ryan J. Tibshirani. (2010).<br>\n", "<a href=\"http://www-stat.stanford.edu/~tibs/ftp/strong.pdf\">Strong Rules for Discarding Predictors in Lasso-type Problems</a><br>\n", "<em>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</em>, 74(2), 245-266.</p>\n", "<p> Noah Simon, Jerome Friedman and Trevor Hastie (2013). <br>\n", "<a href=\"http://www.stanford.edu/~hastie/Papers/multi_response.pdf\">A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression </a><br>" ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.5" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
guangtunbenzhu/BGT-Cosmology
Spectroscopy/archetype/HST-NFL.ipynb
1
1032097
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from importlib import reload\n", "import mathutils\n", "import fitsio\n", "import setcover\n", "import scipy.optimize as op\n", "import cosmology as cosmo\n", "import datapath\n", "import ebossspec\n", "import archespec\n", "from scipy.ndimage import gaussian_filter1d\n", "from matplotlib.colors import LogNorm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams[\"figure.figsize\"]=(10,8)\n", "plt.rcParams['axes.linewidth'] = 2\n", "plt.rcParams['xtick.major.size'] = 15\n", "plt.rcParams['xtick.major.width'] = 2\n", "plt.rcParams['xtick.minor.size'] = 10\n", "plt.rcParams['xtick.minor.width'] = 2\n", "plt.rcParams['xtick.labelsize'] = 25\n", "plt.rcParams['ytick.major.size'] = 15\n", "plt.rcParams['ytick.major.width'] = 2\n", "plt.rcParams['ytick.minor.size'] = 10\n", "plt.rcParams['ytick.minor.width'] = 2\n", "plt.rcParams['ytick.labelsize'] = 25" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def color_legend_texts(leg):\n", " \"\"\"Color legend texts based on color of corresponding lines\"\"\"\n", " for line, txt in zip(leg.get_lines(), leg.get_texts()):\n", " txt.set_color(line.get_color()) " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "elg_composite = fitsio.read('../ELG_composite.fits', ext=1)\n", "feii_composite = fitsio.read('../FeII_MgII_Composite.fits', ext=1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nfl_sdss = fitsio.read('/Users/Benjamin/AstroData/Garching/hstnfl_sample.fits', ext=1)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(13659,)\n" ] } ], "source": [ "index_of_index = np.array([0,5,4,1])\n", "nfl_composite = np.mean(nfl_sdss['ALLFLUX'][0], axis=1)\n", "print(nfl_composite.shape)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading /Users/Benjamin/AstroData/AllInOne/AIO_ELG_eBOSS_SDSSRestFrame_Wave01800_03600A.fits.\n", "Reading /Users/Benjamin/AstroData/AllInOne/AIO_ELG_eBOSS_SDSSRestFrame_Wave03600_07200A.fits.\n", "Reading /Users/Benjamin/AstroData/AllInOne/AIO_ELG_eBOSS_SDSSRestFrame_Wave07200_10400A.fits.\n" ] } ], "source": [ "masterwave, allflux, allivar = ebossspec.rest_allspec_readin()\n", "objs_ori = ebossspec.elg_readin()\n", "nobj = objs_ori.size\n", "galaxytype = objs_ori['CLASS']\n", "zgood = objs_ori['zGOOD']\n", "z = objs_ori['Z']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "index_wave_all = np.searchsorted(masterwave, [2000., 5199.])\n", "tmpflux = allflux[index_wave_all[0]:index_wave_all[1],:]\n", "tmpivar = allivar[index_wave_all[0]:index_wave_all[1],:]\n", "tmpwave = masterwave[index_wave_all[0]:index_wave_all[1]]\n", "tmploglam = np.log10(tmpwave)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "median_sn = np.zeros(objs_ori.size)\n", "for i in np.arange(objs_ori.size):\n", " iuse = (np.where(tmpivar[:,i]>0))[0]\n", " if iuse.size>0:\n", " median_sn[i] = np.median(tmpflux[iuse,i]*np.sqrt(tmpivar[iuse,i]))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.858337689539 3719.99614856 3737.16687061\n", "0.604586134328 2622.0591844 2630.52534306\n" ] } ], "source": [ "# Calculate oii_ew and oii_luminosity\n", "# 1Mpc = 3.08568025x10^24 cm\n", "Mpc_cm = 3.08568025E24\n", "oiilum = np.zeros(nobj)\n", "oii_ew = np.zeros(nobj)\n", "index_oii = np.searchsorted(tmpwave, 3728.48)\n", "dnoiiwave = 10\n", "dwave = np.median(tmpwave[index_oii-dnoiiwave:index_oii+dnoiiwave]-tmpwave[index_oii-dnoiiwave-1:index_oii+dnoiiwave-1])\n", "print(dwave, tmpwave[index_oii-dnoiiwave], tmpwave[index_oii+dnoiiwave])\n", "oiisum = np.sum(tmpflux[index_oii-dnoiiwave:index_oii+dnoiiwave, :]*\n", " (tmpivar[index_oii-dnoiiwave:index_oii+dnoiiwave, :]>0), axis=0)*dwave # Need to subtract the negligible continuum\n", "oii_left = np.sum(tmpflux[index_oii-25:index_oii-15, :]*(tmpivar[index_oii-25:index_oii-15, :]>0), axis=0)/(25.-15.)\n", "oii_right = np.sum(tmpflux[index_oii+15:index_oii+25, :]*(tmpivar[index_oii+15:index_oii+25, :]>0), axis=0)/(25.-15.)\n", "oii_cont = (oii_left+oii_right)/2.\n", "oii_ew = (oiisum-oii_cont*dwave)/oii_cont\n", "oiilum = (oiisum-oii_cont*dwave)*np.power(cosmo.luminosity_distance(z), 2)*4.*np.pi*np.power(Mpc_cm,2)*1E-17\n", "\n", "\n", "# Calculate feii_ew and feii_luminosity\n", "feiilum = np.zeros(nobj)\n", "feii_ew = np.zeros(nobj)\n", "index_feii = np.searchsorted(tmpwave, 2626.)\n", "dnfeiiwave = 7\n", "dwave = np.median(tmpwave[index_feii-dnfeiiwave:index_feii+dnfeiiwave]-tmpwave[index_feii-dnfeiiwave-1:index_feii+dnfeiiwave-1])\n", "print(dwave, tmpwave[index_feii-dnfeiiwave], tmpwave[index_feii+dnfeiiwave])\n", "feiisum = np.sum(tmpflux[index_feii-dnfeiiwave:index_feii+dnfeiiwave, :]*\n", " (tmpivar[index_feii-dnfeiiwave:index_feii+dnfeiiwave, :]>0), axis=0)*dwave # Need to subtract the negligible continuum\n", "feii_left = np.sum(tmpflux[index_feii-15:index_feii-8, :]*(tmpivar[index_feii-15:index_feii-8, :]>0), axis=0)/(15-8.)\n", "feii_right = np.sum(tmpflux[index_feii+8:index_feii+15, :]*(tmpivar[index_feii+8:index_feii+15, :]>0), axis=0)/(15.-8)\n", "feii_cont = (feii_left+feii_right)/2.\n", "feii_ew = (feiisum-feii_cont)/feii_cont\n", "feiilum = (feiisum-feii_cont*dwave)*np.power(cosmo.luminosity_distance(z), 2)*4.*np.pi*np.power(Mpc_cm,2)*1E-17" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(87,)\n" ] } ], "source": [ "inuv = (np.where(np.logical_and(np.logical_and(\\\n", " np.logical_and(np.logical_and(zgood==1, galaxytype==b'GALAXY'), median_sn>0.),\\\n", " z<0.4798), z>0.4702)))[0]\n", "print(inuv.shape)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x22ac28898>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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aiNiry/77AC8B3jTylkqSJM24sQW9OqgdU/944hK7vRl4DvDutu+tAm4Dbo+IcyLicfXn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\n5iY5R9wkQ6YkSZp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"text/plain": [ "<matplotlib.figure.Figure at 0x10af13ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(oiilum[inuv], feiilum[inuv], '.')\n", "#plt.xlim(0,100)\n", "#plt.ylim(0,20)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x22a9fb438>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x22a962080>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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JbXuJVXUOzajaocCfTqqCi5XkfsBfAEfPEZhtBxwE/HjI6acAt0qy/aj5xldz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\n2j/g1wCPGLiXfmWW9r03PPnHIXn6A99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"text/plain": [ "<matplotlib.figure.Figure at 0x22a96cb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9,7))\n", "ax = fig.add_subplot(111)\n", "n, bins, patches = ax.hist(oii_ew[inuv], \n", " bins=np.arange(0, 200, 5), edgecolor='black')\n", "ax.tick_params(axis='x', which='major', length=8, width=2, labelsize=22, pad=8)\n", "ax.tick_params(axis='x', which='minor', length=4, width=2, labelsize=22, pad=8)\n", "ax.tick_params(axis='y', which='major', length=6, width=2, labelsize=20, pad=8)\n", "ax.tick_params(axis='y', which='minor', length=3, width=2, labelsize=20, pad=8)\n", "this_ylim = [0, 800]\n", "ax.set_ylim(this_ylim)\n", "#ax.set_xscale('log')\n", "#ax.set_xlim(3680, 3780)\n", "ax.set_title(r'[OII] EW Distribution', fontsize=20)\n", "ax.set_xlabel(r'$W_{\\rm O\\,II}^{\\lambda3728}$ [$\\AA$]', fontsize=20)\n", "ax.set_ylabel(r'$\\Delta N (\\Delta W_{\\rm O\\,II}^{\\lambda3728} = 5\\, {\\rm \\AA}))$', fontsize=20)\n", "#fig.savefig('/Users/Benjamin/Dropbox/Zhu_Projects/Fine Structure Emission/Version 1/EW_distribution.eps')\n", "\n", "# Luminosity\n", "fig = plt.figure(figsize=(9,7))\n", "ax = fig.add_subplot(111)\n", "n, bins, patches = ax.hist(np.log10(oiilum[inuv]), \n", " bins=np.arange(40., 43.5, 0.1), edgecolor='black')\n", "ax.tick_params(axis='x', which='major', length=8, width=2, labelsize=22, pad=8)\n", "ax.tick_params(axis='x', which='minor', length=4, width=2, labelsize=22, pad=8)\n", "ax.tick_params(axis='y', which='major', length=6, width=2, labelsize=20, pad=8)\n", "ax.tick_params(axis='y', which='minor', length=3, width=2, labelsize=20, pad=8)\n", " \n", "this_ylim = [0, 1100]\n", "ax.set_ylim(this_ylim)\n", "#ax.set_xscale('log')\n", "#ax.set_xlim(3680, 3780)\n", "ax.set_title(r'[OII] Luminosity Distribution', fontsize=22)\n", "ax.set_xlabel(r'$\\log_{10}\\,L_{\\rm O\\,II}^{\\lambda3728}$ [${\\rm erg\\,s}^{-1}$]', fontsize=22)\n", "ax.set_ylabel(r'$\\Delta N (\\Delta \\log_{10}\\,L_{\\rm O\\,II}^{\\lambda3728} = 0.1\\, {\\rm dex})$', fontsize=22)\n", "#fig.savefig('/Users/Benjamin/Dropbox/Zhu_Projects/Fine Structure Emission/Version 1/Lum_distribution.eps')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(40,)\n" ] } ], "source": [ "istrong = inuv[(np.nonzero(np.logical_and(oii_ew[inuv]>50., oiilum[inuv]>1.E41))[0])]\n", "print(istrong.shape)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x227d96470>]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IvExEXpKm6bzzf/uKyKdF5NA2lg3U1WYoVLVSqGj4mDsNo28D3VWjaTfsKsNJ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\nPdb8/4IFZh1sBdCPfmQu9cG+LsvV9InQ1q3mZOLOO83f/+rV5jXa9b7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dRg2AfFHIFxCm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vNbb+3uTKsoFKorpAKLfk+dGTrFNCBB2pgx4tS56KLkfvGDmyxs+ljSJVRv8xwYSN5ZV+bNK+YU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\nmCikRarTagoRQgghhBBCCCG9C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"text/plain": [ "<matplotlib.figure.Figure at 0x22b629240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "i=0\n", "i = i+1\n", "fig = plt.figure(figsize=(20,8))\n", "ax = fig.add_subplot(111)\n", "x = gaussian_filter1d(tmpwave, 3)\n", "y = gaussian_filter1d(tmpflux[:,istrong[i]], 3)\n", "ax.plot(x,y)\n", "ax.set_xlim(2000, 5200)\n", "ax.set_ylim(-1,4)\n", "ax.plot([2586, 2586], [0, 5])\n", "ax.plot([2600, 2600], [0, 5])\n", "ax.plot([2626, 2626], [0, 5])\n", "ax.plot([2800, 2800], [0, 5])\n", "ax.plot([3728, 3728.5], [0, 5])\n", "ax.plot([5577./1.475, 5577./1.475], [0, 5])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(40,)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "istrong.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ 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kVTqV00g8bXjdhRdq9cfQoSLf/GZ5y2iVTsV6C1RS6dTe7n8Ph9eJ+IqjzhzA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\nMpszmbkjWVuYOtWyq9qS9fy1r0XZbl272nfv8OHZ2WsHH2xtsKnJXvv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"text/plain": [ "<matplotlib.figure.Figure at 0x227dbaac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fullsum = np.mean(tmpflux[:, istrong], axis=1)\n", "fig = plt.figure(figsize=(20,8))\n", "ax = fig.add_subplot(111)\n", "x = gaussian_filter1d(tmpwave, 1)\n", "y = gaussian_filter1d(fullsum, 1)\n", "ax.plot(x,y)\n", "ax.set_xlim(2400, 3000)\n", "ax.set_ylim(-1,4)\n", "ax.plot([2586, 2586], [0, 5])\n", "ax.plot([2600, 2600], [0, 5])\n", "ax.plot([2612.65, 2612.65], [0, 5])\n", "ax.plot([2626, 2626], [0, 5])\n", "ax.plot([2803, 2803], [0, 5])\n", "ax.plot([3728, 3728.5], [0, 5])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0714828897338402\n", "1.0624521072796935\n", "2798.5229999999997\n", "3946.34\n", "7679.245283018868\n" ] } ], "source": [ "print(2818./2630.)\n", "print(2773./2610.)\n", "print(2613*1.071)\n", "print(3730*1.058)\n", "print(8140./1.06)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(i)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ -4.3198748 -4.0618974 18.2561 19.756447 19.280708 19.473042\n", " 19.786367 19.704497 19.715439 18.478122 19.833328 20.633017\n", " 20.616442 21.286183 20.879819 22.445864 21.465633 22.538039\n", " 22.519314 21.088039 20.859942 21.679197 22.612669 21.684489\n", " 22.330972 21.793461 21.962758 22.304883 21.862958 22.300603\n", " 21.618336 21.351994 21.296242 22.341172 52.518806 53.58374\n", " 52.976055 52.514118 -6.2660589 -6.7720394]\n" ] } ], "source": [ "print(objs_ori['PLUG_DEC'][istrong])" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f280n = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/f280n.UVIS2.tab')\n", "f343n = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/f343n.UVIS2.tab')\n", "f395n = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/f395n.UVIS2.tab')\n", "f390m = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/f390m.UVIS2.tab')\n", "f680n = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/f680n.UVIS2.tab')\n", "f625w = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/f625w.UVIS2.tab')\n", "f621m = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/f621m.UVIS2.tab')" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JwM+u9Y2C3muSoA9svPetDtCHA4tj2Nb8jC2XiYiIiAIlSrBZRPjJwQ/00EueF/c95fmc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\nhxHbtwfXU0SDi+ecA7Rpo4M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1RXo9/8ABHVgM0MCcHRAFtBXcu+9q0LlvX/9+PwHtnqF/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\nIH8jMbhM+Ubnzp3x/PPP46GHHgIANGzYEOvWrTv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8PQARERG5sKkshsi5qW3b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g9+3FpbDvwnmbVFRETE+26++WZWrFhBx44dgdo4iwEDBkT9GGstkydP5v7772fFihU8++yzAPTt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\n8LSZHR/LVuh7Xu1KylY729RW1Fe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"text/plain": [ "<matplotlib.figure.Figure at 0x134d54f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(24,8))\n", "ax = fig.add_subplot(111)\n", "i3650 = np.searchsorted(nfl_sdss['WAVE'][0], 3650.)\n", "ax.plot(nfl_sdss['WAVE'][0][i3650:]*(1.+0.061), nfl_composite[i3650:]*0.0008, color='magenta')\n", "ax.plot(elg_composite[0]['WAVE']*(1.+0.065), elg_composite[0]['FLUXMEDIAN']/10, 'b')\n", "ax.plot(f280n[:,1], f280n[:,2], '-k')\n", "ax.plot(f343n[:,1], f343n[:,2], ':k')\n", "ax.plot(f395n[:,1], f395n[:,2]*1.05, '--k')\n", "#ax.plot(f621m[:,1], f621m[:,2]*1.05, '-.k')\n", "#p680n, = ax.plot(f680n[:,1], f680n[:,2]*1.05, '-k')\n", "#p680n.set_dashes([8, 4, 2, 4, 2, 4])\n", "#leg = ax.legend(['HST-pNFL target composite (z=0.065, offset for clarity)', 'eBOSS ELG composite (shifted to z=0.065)', \\\n", "# 'F280N throughput curve', 'F343N', 'F395N', 'F621M', 'F680N'], \\\n", "leg = ax.legend(['HST-pNFL target composite (z=0.065, offset for clarity)', 'eBOSS ELG composite (shifted to z=0.065)', \\\n", " 'F280N throughput curve', 'F343N', 'F395N'], \\\n", " bbox_to_anchor=(0.01, 0.90, 1., .102), loc=2, frameon=False, fontsize=24, )\n", "color_legend_texts(leg)\n", "ax.set_xlim(2270, 5400)\n", "#ax.set_xlim(2100, 3200)\n", "ax.set_ylim(0.0004,0.36)\n", "#ax.plot([2773, 2773], [0, 0.07], 'g')\n", "#ax.plot([2818, 2818], [0, 0.07], 'g')\n", "ax.set_xlabel(r'observer-frame $\\lambda$ ($\\AA$)', fontsize=22)\n", "ax.set_ylabel(r'$f(\\lambda)$ [Arbitrary Unit]', fontsize=22)\n", "\n", "#ax.text(6563.*(1.+0.065)+10, 0.30, r'H$\\alpha$', fontsize=20)\n", "ax.text(5007.*(1.+0.065)-150, 0.28, r'[O III]', fontsize=20)\n", "ax.text(4861.*(1.+0.065)-60, 0.235, r'H$\\beta$', fontsize=20)\n", "ax.text(4341.*(1.+0.065)-50, 0.13, r'H$\\gamma$', fontsize=20)\n", "#ax.text(3889.*(1.+0.065)-100, 0.09, r'[Ne III]', fontsize=20)\n", "ax.text(3730.*(1.+0.065)+2, 0.26, r'[O II]', fontsize=20)\n", "ax.text(2626.*(1.+0.065)-40, 0.07, r'Fe II*', fontsize=20)\n", "fig.savefig('/Users/Benjamin/Desktop/UVIS/composite_filters.eps')" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10b9c6668>" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZQsfe2lss6K41dmzDoEOx4AjARx+loTd7791w/zXXpDlAvv51eOKJ1NvjvPPS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sWvUhskVwMjgZ5R/oUwCOgCbATtndW2KTapoEEStEFi/+oaT1CDpEqW+U3wDQSva1NsjJQgqBS5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\nSZIWvP8DnbOiIWt/6rwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f9e8b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "fig = plt.figure(figsize=(18,8))\n", "ax = fig.add_subplot(111)\n", "i3650 = np.searchsorted(nfl_sdss['WAVE'][0], 3650.)\n", "ax.plot(nfl_sdss['WAVE'][0][i3650:]*(1.+0.0655), nfl_composite[i3650:]*0.0008, color='magenta')\n", "ax.plot(elg_composite[0]['WAVE']*(1.+0.065), elg_composite[0]['FLUXMEDIAN']/15, 'b')\n", "ax.plot(f280n[:,1], f280n[:,2], '--k')\n", "ax.plot(f343n[:,1], f343n[:,2], '-k')\n", "ax.plot(f395n[:,1], f395n[:,2], ':k')\n", "leg = ax.legend(['ELG composite ((shifted to z=0.065)', 'F280N', 'F343N', 'F395N'], bbox_to_anchor=(0.01, 0.90, 1., .102), loc=2, frameon=False, fontsize=22)\n", "color_legend_texts(leg)\n", "ax.set_xlim(2000, 7160)\n", "ax.set_ylim(0.0004,0.35)\n", "#ax.plot([2773, 2773], [0, 0.07], 'g')\n", "#ax.plot([2818, 2818], [0, 0.07], 'g')\n", "ax.set_xlabel(r'observer-frame $\\lambda$ ($\\AA$)', fontsize=22)\n", "ax.set_ylabel(r'$f(\\lambda)$ [Arbitrary Unit]', fontsize=22)" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kyg3q1NGpfZMmubsnRO6nzFeii4jdGGHMySciolQVK+oLaz/6yN09IbK3bp2+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\nRu3atd3dJaJs5yonP6tn8j8CsAPAGgBXYTu6DgC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67Ph3wD8TmbO8b1jgrWEYJXDPPYEA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\ncZxzfYET0DShhmEYRhBjxowhPz+fE044gV69eqVbHMNIKmbJNyoDcSv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orNmjVr2LZtG/Xq1aNBg4hhSYZhFMPh/iISeXl5FBYWkhEtQMcwyoiYv4XOuWzn3H3AEuAyVGm+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\nxwi/iH9PNGw3ad+N0TE7p2x/gPAr+WaNM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"text/plain": [ "<matplotlib.figure.Figure at 0x10ba7d8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(elg_composite[0]['WAVE']*(1.+0.065), elg_composite[0]['FLUXMEDIAN']/10, drawstyle='steps')\n", "ax.plot(f280n[:,1], f280n[:,2], '-k')\n", "#ax.plot(f395n[:,1], f395n[:,2], 'k')\n", "leg = ax.legend(['eBOSS ELG composite (shifted to z=0.065)', 'F280N throughput curve'], \\\n", " bbox_to_anchor=(0.01, 0.90, 1., .102), loc=2, frameon=False, fontsize=22, )\n", "color_legend_texts(leg)\n", "ax.set_xlim(2710, 2850)\n", "ax.set_ylim(0,0.08)\n", "#ax.plot([2773, 2773], [0, 0.07], 'g')\n", "#ax.plot([2818, 2818], [0, 0.07], 'g')\n", "ax.set_xlabel(r'observer-frame $\\lambda$ ($\\AA$)', fontsize=22)\n", "ax.set_ylabel(r'$f(\\lambda)$ [Arbitrary Unit]', fontsize=22)\n", "ax.text(2613.*(1.+0.065)-18, 0.045, r'Fe II* 2613', color='blue', fontsize=22)\n", "ax.text(2626.*(1.+0.065)-5, 0.045, r'2626', color='blue', fontsize=22)\n", "ax.text(2587.*(1.+0.065)-16, 0.010, r'Fe II 2587', color='blue', fontsize=22)\n", "ax.text(2600.*(1.+0.065)-5, 0.010, r'2600', color='blue', fontsize=22)\n", "fig.savefig('/Users/Benjamin/Desktop/UVIS/composite_filters_f280n.eps')" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0, 0.27)" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gBmSzbJm0fLlUWSntvXfUrQF2wRi19AvV9UlQKwkENSAbJhIgpryn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\nmuM4juM4ToXijprjOI7jOE6F8v9D5PTuTLvHZgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x241e40320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(f395n[:,1], f395n[:,2], 'b')\n", "plt.plot(f390m[:,1], f390m[:,2], 'r')\n", "plt.xlim(3700, 4080)\n", "plt.ylim(0,0.27)\n", "#plt.plot([2773, 2773], [0, 0.07], 'g')\n", "#plt.plot([2818, 2818], [0, 0.07], 'g')" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10240,)\n" ] } ], "source": [ "iz = np.nonzero(np.logical_and(np.logical_and(np.logical_and(np.logical_and(np.logical_and(np.logical_and(\n", " dr7_info['Z']>0.064, dr7_info['Z']<0.066), \n", " dr7_mass['MEDIAN']>7.), dr7_mass['MEDIAN']<12.), \n", " dr7_ssfr['MEDIAN']<-1), dr7_ssfr['MEDIAN']>-20),\n", " dr7_info['SN_MEDIAN']>0.))[0]\n", "print(iz.shape)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "844\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x22ad6c860>]" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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G1KB//vPS4sXZrw8AAKAhNOfIe/fcI82du+fr3/8+Gm3/r/+StmzJXV0AAAD10Zwj782a\nte+5bduiNdKTjbQDAADkCq0J8t4DD0gvvCAddtje52+/Pb92FQUAAC0fzTkKwsknS2++Kd13n3TA\nAdH89HPPTR67c2d2awMAAKhlzvpysWRmLkn8fDJv40Zp7Vpp0KB9r23dKo0eLZ1zjvTNb0pdumS9\nPElSRUVFcOyGDRsyfv+SkpKguPLy8ozfGwCAODEzSZK7Wzbux8g5Ck7nzskbcylaN33uXOm226Rh\nw6Rf/UravTur5QEAgAJGcw4kLF8ebWJUa9Uq6bLLpCOPlKZPz11dAACgcNCcAwkLFyafxvLOO9L6\n9dmvBwAAFB6acyDh+OOjKS033rj3Ki6nnCKddlru6gIAAIWD5hyoo1Mn6eabpTlzop1EW7WSfvhD\nyZI8ArJtm7RrV/ZrBAAA+YvmHEhiwADp4YelBQukQw9NHvO970Uruzz7bHZrAwAA+YvmHNiPgQOT\nn1+8WLrzTun996WPf1w64wxp9uzs1gYAAPIPzTnQBFOmRNNaaj35pDRypHTllSy9CAAAmo7mHGik\n3buldu32Pb9rl1RZGc1TBwAAaAqac6CRWrWSHnxQev11ady4PefbtJHuuCN3dQEAgJbP2B4+nszM\nJYmfT2ZVVFQEx4ZsTe8uTZ0qXXeddO650Tz0ZNaskXr0CL61qqqqgmPXBy7C/vzzzwfnnDhxYlDc\nhg0bgnMOSrUtaxpKS0sznhMAgLossWSbuydZuy3zGDkH0mAmnXde9DDod76TPGbuXKl/f+nyy6XV\nq7NaHgAAaGFozoEMaN9eKi5Ofu2666KHR++9Vxo2TPqf/5F27MhufQAAoGWgOQea0fPPS48/vufr\nDRuka6+VRoyQ/vWv3NUFAADiieYcaEZm0pAh+55ftkzq0yf79QAAgHijOQea0YQJ0UZFd94plZTs\nOX/ttdE8dAAAgLpozoFm1q6ddM010rx50le/Kg0YIF1/ffLY5cv33twIAAAUFppzIEt69pTuuUf6\n4IPkD4+6SxddJB18sPTYY23EKpoAABQemnMgyzp1Sn7+ySel556TFi2SvvjFTjr99GK9/TbbjQIA\nUEhozoEY2LkzmvpS16uvttaECcW6/voOuSkKAABkHc05EAPbt0uTJkmt6g2Uu5t6967JTVEAACDr\naM6BGCgulu6+W3r3Xem00/ac79+/Rpdfvj13hQEAgKxqnesCgGwqLy/PdQn7dfDB0dzzO+98Vz/7\n2WBdfHGl5s5ds0+cu7RwYScNGbJZXbp0Ccq9du3a4Dref//9oLhNmzYF5wxVUnfNyRwoLS3N6f0B\nAIWN5hyIoaOPXq8jjnhzn2kutV58sYduvvkQnXLKSl199Rr17r0zuwUCAIBmwbQWIKZat452GK1v\n+3bTL34xWJL07LMH6JxzDta99/bW1q38cQYAoKXj/+ZAC+IuPfpoP61Y0f7f57ZtK9Ivf9lbZ501\nXAsXtsthdQAAIF0050ALYiYdemi1hgzZd653p0416t+fh0cBAGjJaM6BFubwwzfovvtm6tpr56pr\n1x3/Pn/11R+pTZscFgYAANLGA6FAC9SqlXTmmSs0YcJqPfroUC1c2F7jxlUnjf3ggw4aOJARdQAA\nWgKac6AF69Rpt668crnckz88umVLka68crDcpZNOWqYjj3xXRUWe/UIBAEAQprUAeSBZYy5JDz7Y\nS2vWtNHatW30pz+dqp/85GItWNA/u8UBAIBgNOdAnlq+vI1++9te9c4doPvu+7yefvrEHFUFAAD2\nh+YcyFMlJbt14YWr1K5dzT7XDjywIgcVAQCAhpg780/jyMxckvj5FKaKisw1z8uWtdJtt3XTE08U\nS5LKyv6l4467Y7+v6dGjR1DuysrK4Do+9alPBcUtW7YsOGeom266KTh22rRpQXGjRo0Kzrl+/fqg\nuPLy8uCczaGqqioorrS0NGf3bq77A0Aqlpg76u4pJpFmFiPnQJ7r23e37r57tU466Vvq3v1DjRz5\n26RxO3Z01MqVI7NcHQAAqIvVWoAC0aPHXJ188o0pr3/44ac1b94n1afPGzrxxCdUWroyi9UBAACJ\nkXMAkjZu7K3580+TJC1fPkaPPvpfeuWV87RtW8ccVwYAQGGhOQegd9+9UO57fpFWU9NK7747QY88\ncrO2beuUw8oAACgsNOcANHz4X9S9++x9zvfr96Hat9+cg4oAAChMNOcA1K3bfJ100n/pmGPuUseO\nqyRJrVrt0DHH/CXHlQEAUFh4IBSApGiX0f79p6us7A0tW3aezFydOydfAnD+/CPVt+++I+0AACA9\njJzvh5kdamaPmtkCM1thZq+b2RfSyHeVmdUk+bg1k3UD6WjVaoeOOOIZHX7435NeX7euTM8/f4l+\n//tbVFFxtmpq+Dc+AACZQnOegpl9QtK/JP1T0lBJfSQ9JOm3Zrb/HVyS52sr6RpJXu9jh6QfZ6hs\noFm5S9Onf1ruRdqxo6Nmz/5/evnle7Vy5TFivywAANJHc56EmZVJekTSY+5+t+/xY0m/knSNmYVt\nd7jHRZKmSTq43sdB7r48g+UDzWbp0oO1dOkhe53bsqWvZs68SRUVjf0jAQAA6jO2h9+XmX1f0hRJ\nF7v7b+tdO0TSe5LmSjrYA95AMyuS9L6ks919TmANLkn8fApTRUVFcOySJUuC4u69997gnG3atEl6\nfvfu1po9+xS9++5Z2rlzzxrorVpt0fHHf0nt2qXepr5jx7A10ydMmBBc58qVYRsllZeXB+c899xz\ng+IWLVoUnDNUSUlJxnM2Ruj7VFVVFZyztLQ04zkzfW8A2B8zkyS5u2XjfoycJzcxcfyo/gV3/0DS\nSknDJB0RmO88SSWSjjWzgRmpEMiBVq12acSIp3XWWddp2LAXJO2WJJWXP7LfxhwAAIShOU+uW+KY\nahirUpJJOiow3xRJvSU9KKnCzJ4zszHplQjkTocOG3Xssb/Wscd+VX36PKsBA/6UNK66eohWrhzH\nfHQAAALRnCe3NHE8OsX12l9rdG8okZkNl7RN0nxJNYnTEyS9ZmZXplMkkGudOy/SoYfeoVatdu5z\nzV2aM+cKzZp1o954405VVQ3OQYUAALQsNOfJ/TlxvMjMOie53jdx3NFQInef7e5j3f1ARSu+XC1p\ng6L3/i4zuzgTBQNxs3LleFVVHSpJqqoapX/840699dZ/aNu2rjmuDACA+Mrr5tzMbk2xrvj+Ph6Q\ndK+kFxRNRXnMzPon8g0zs18oarKlaHpLMHdf7e4/knSIpNoHQ39oZsUZ+YaBmHA3zZ//pXpni7R4\n8cc0bdptqqnJ6796AABosrz+P6S7T3H3okZ+XOLuuySdKumbiuafv2JmT0n6rKLpKaZojfLXmljX\nckkfk7RRUldJk1LFmlmTPoBcMnONHn2junf/1z7Xhgx5XEVFNUleBQBA82kpPVVeN+fpcPed7n6r\nu4929wHufpq7f1fSuETIDHdfnEb+ZZLuTnzJZFzkneLiJTriiG/r8MO/pU6dKhPnlqq8/OkcVwYA\nQHyx73YjmNnBkk5TNGp+ewZSPi/pW5I2pQpgnXO0dD16vK5u3WZq1aqzVVy8XEVFu/eJcTfNnv05\nDRr0TA4qBAAUgqb2VNkePac5b5y7JLWS9LS7/18G8tXuDNqk6TFAS1FUtFuDB6ceMV+y5CTNnfsZ\nLVhwloqK3taECW+qXbtdWawQAIB4YFpLIDO7XNLHJS2UdFGG0h4q6RV3fytD+YAWZ9eu9vrggwsl\nSbt3t9Pf/naMvve9i/T66wephqnpAIACQ3MewMwuUDQ/fI6kk919bZKYkWb2ipl9v975tmbWJUl8\nG0lfkfTlZiobaBEqK0/R9u3d9jq3YUOxHnpokt55Z2iOqgIAIDeY1pKCRROMjpP0NUnnSXpI0hXu\nnmp++GRJYyWNNbM73L12L/M3JA0zs+9K+qG7bzWzgYp2Db3e3eekyIcCVl5envHYuXPnBud84okn\nguLef//94Jyp6uzS5TcaMmStFi++TDt37lkDvWvXxdq58w96443UcwQXLlwYdO/t27cH1zl16tSg\nuC1btgTn7NixY8Zzfv3rXw+Kq6ioCM4Zav369Q0HJZSWlmb8/s2REwDigpHzJMzs24o2CvqNooc1\nj3X3i/fTmEvSVEnrJD1WpzGXpFskzVP04GeFmf1B0pmSrmI6CyCZ1ah378d1xBHnq2/fh1VUFO02\neuSRD6uoiAeiAQCFhZHzJBJLJn63ka95SVKPJOenKmrcAexH69abNWjQvTrqqLe0ePEY9enzYdK4\nhQvHqqamtYYM+WeWKwQAoPnRnAOIlc6dV2nEiKeSXtuxo6PeeOML2r69i+bM+ZjKyu5USck7Wa4Q\nAIDmw7QWAC3Gu+9+Utu3R89Xr1tXrvfe+5lmz/5vbdtWluPKAADIDJpzAC3Cjh3tNW/eSfucX7v2\nZM2bd0P2CwIAoBnQnANoEdq23aYzzvi2Bg7cd8+ugQN/noOKAADIPJpzAC1GcfEajR9/jyZN+m91\n775AktSjx7Pq0iV8SUcAAOKMB0IBtDi9es3TqafeohkzhqR8IHTnzlItXHilBgy4P8vVAQDQdDTn\nAFokM1evXn9PeX3x4ku1Zs0pWrv2ZJm9omOPfU7t22/NYoUAADQe01oA5J3Nm8u1YsUnJUnurfXm\nmyfq/vun6O23j1NNDX/tAQDii5FzoEAceOCBwbFDhw4NituwYUNwzsWLFwfFvfNO+LrlAwYMSHp+\nxYovS2q117lt2zrp+efPVps2M1RauixlzmXLUl+ra9euXcF19unTJzg21G233RYUN3ny5OCcFRUV\nQXFLliwJzhmqMf8tVVdXB8WNHz8+OOe0adOC4kaNGhWcs7S0NDgWAGoxhAQg7xxwwB3q2/datWnz\n0V7nBw9+SaWlYf9IAAAgF2jOAeQdM6mk5GkNGXKGevb8sVq33qbWrbdq5MhHc10aAAD7xbQWAHmr\nqGi7evb8pY47bo7Wrx+kDh2ST52YOfMideq0SsOGPauiot1ZrhIAgD1ozgHkvQ4d1qtDh/VJr61b\nN1hz554qSZo//2MaPfphlZW9JbNsVggAQIRpLQAKlrs0c+aF//5648a+evnl6/XSSzeoqqp/DisD\nABQqmnMABWvjxt6qqtp3xZeVK0dpwYKJOagIAFDoaM4BFKwuXVbojDOu1pAhz8ms5t/n27TZpEMP\nnZrDygAAhYrmHEBBa99+g4466n5NmnSDevV6T5J06KF/Urt2m3JcGQCgEPFAKABIKi1drJNP/p6W\nLz9MvXu/lzRm9eqD9O675+nwwx9S587zs1whAKAQMHIOAAlmUlnZO0mXU3Q3zZx5kVatGqFnnvme\n3njjq9q2jR0gAQCZZe6e6xqQhJm5JPHzQUOqqqoynvO73/1uUFxlZWVwzpkzZwbFtW4d/gu9efPm\nBcU1Zsv1I488MsW9jtPLL1+617nWrbfq4IMf0/DhT6lVq50pc86aNSvjde7atSsobtKkScE5Q+s8\n7rjjgnOGKisrC44NfZ8WLVoUnHP06NFBcRUVFcE5Q5WXl2c8J4DMscTauu6elUV2GTkHgAArVhy0\nz7lduzpo7txTVVPDDEEAQGbQnANAgBNOeFCnnHKXSko+2uv8yJGPqk2brTmqCgCQbxjuAYBA/fu/\np759P9Ts2Sdq5sxPqmPHdRo8+MWkse6mxOw0AACC0ZwDQCMUFe3WIYe8oD59XtS2baUqKtq3Aa+p\nKdJzz92ssrK3NHz4EzmoEgDQUtGcA0ATtGu3We3abU56bcGCCVq3bqjWrRuqBQsmqFu329Sly1OM\npAMAGsSccwDIoB07Ouq9987799dbt3bXsmW3a9Gih7V168gcVgYAaAlozgEgg6qr+yY9v3XrYdqx\nY0CWqwEAtDQ05wCQQT16zNPpp1+l4cP/qqKiPWuRt28/S126PJXDygAALQHNOQBkWNu2WzR69O90\n6qnXql+/f0mSeve+Nemcc/dWytK+FgCAFoDmHACaSefOKzVu3F0aMuST6tjxnaQx69ZdoIqKR7Rl\nyxFZrg4AEEes1gIAzaxduwVJz+/a1VWrV39FNTVdtGjRb7V792s67LDfqbh4dZYrBADEhbmztFcc\nWeL33/zVCmfpAAAgAElEQVR80JCqqqqguNLS0ozf+6KLLgqOfemll4LievXqFZyzoqIiKG737t3B\nOXfu3BkU15g627Ztm/T8ypU3acOGz+91zmynysv/omHDfquiotR1l5SUBN17165dDQcldOnSJTg2\n1DnnnBMU169fv+Cc06ZNC4obP358cM6lS5cGx4YaN25cUFxz/NkEkDlm0dRDz9IcRKa1AEAOuJtq\najonOd9GVVXDZRb+DwoAQP6gOQeAHDBz9elzrfr1+7zatXuvzpUaDR/+SxnPiAJAQaI5B4Ac6tjx\nTQ0YcK4OOOA/1a7dWvXr96xKShYmjd21q0OWqwMAZBsPhAJAjpm5Skr+T6NGzVNNTfK/lrdt66Z/\n/vNe9e37rIYO/X2WKwQAZAvNOQDEROvW21Jemzv3Yu3c2VmLFn1Ky5ZN1KhRUzVkyAt69NGHk8af\nf/7nkp6fOvWPSc+fe+55Sc8/8MD9Sc9fcsmlQfEPPBAd//rXJ5LGn3nmGUnPv/XW20nPH374aEmj\n9zn/4x/fvZ/48Pyp6mls/aniu3ZN/vBnqmf/U01vIp544rMfny005wAQcxs2DNWyZaf8++udO0v0\n5puXav78j+ewKgBAc2DOOQDE3M6dxWrfftU+56ury3JQDQCgOdGcA0DM9ejxtsaPn6xhw/5XrVrt\nmfoydOhzOawKANAc2IQoptiECKHYhKhhcd2EqL4hQ4Y0GLNtWzfNnXuxVq8+VqeffrXatdu033g2\nIQrDJkQAUsn2JkTMOQeAFqR9+3UaNeoutW9fpnbtNue6HABAhjFyHlOMnCPfhI6yL1yYfI3vZKZP\nnx4UV1QUPoOvpqYmKK45Rpm7du0aHNu9e/eguPLy8uCcM2fODIo74ogjgnP26dMnKG7+/PnBORvz\nG5tQc+bMCYpr3759xu89YsSI4Ngzzki+OgyA5pPtkXPmnAMAAAAxQXMOAAAAxATNOQAAABATNOcA\nAABATNCcAwAAADFBcw4AAADEBM05AAAAEBM05wAAAEBM0JwDAAAAMUFzDgAAAMQEzTkAAAAQE61z\nXQCAlquqqio4dsSIERm/f48ePYLiHnvsseCcbdq0CYrr0qVLcM7du3cHxVVXVwfnbNu2bVDc9OnT\ng3P26dMnKG758uXBOUN/Rp06dQrOGfrz7NixY3DOsWPHBsUdeOCBwTlDf579+vULztkcpk2bFhQ3\nfvz4Zq4EgMTIOQAAABAbNOcAAABATNCcAwAAADFRcM25mY0zs6fN7MaA2KFm9oiZLTSzBWb2czPr\n2sT7TjCzfyRyfWhm15tZwb3/AAAASK1gmkMzO8XMnpE0TdKkgPijJL0haZmkIZIOkdRN0mtm1quR\n9/6KpKcl/cTdB0s6WdIlkv5Igw4AAIBahdQYzpd0qqQXGwo0s86S/iSp0t2v8ch2SZdJ6ivpgdCb\nmtmRkn4q6T53nypJ7r5C0tcknSPpmsZ+IwAAAMhPBdOcu3uFu9coGg1vyJWS+kn633o5Nkh6QtJp\nZtbg6HvCbYre5wfr5XpW0ipJ3zGz0sBcAAAAyGMF05zXsS0g5guJY7IFgl9LHC9rKImZ9ZY0QdJW\nSTNT5Oog6YKAmgAAAJDnCrE59/1dNLPBkg5KxC1MEvJe4nhiwL1OSxwXu3uy+9bmOjkgFwAAAPJc\nITbnDTk8cdzl7iuTXK/dErGbmfUPzLU0xfXaXIc1oj4AAADkqda5LiCGeiaOqfZd3lDn8+6SlgTk\nSrXHeW2usP2tgZgpLQ1/XOIzn/lMUNzzzz8fnDN02/Fzzz03OOdTTz0VFLdixYrgnP37N/Tv+EjP\nnj0bDkooKysLitu6dWtwzlAlJSXBsTt27AiKe+ONkMeBIieccEJQXHV1qr/G9zVr1qyguC1btgTn\n7NixY1DcwoXJfkmb3NKlqcZ69jZixIjgnOPHjw+OBdD8WszIuZndamY1jfwIXlWlju6JY6q/gWvq\nfN4+Q7kaygMAAIAC0GJGzt19iqQpWbhV7TCPpbjets7n6zKUq6E8AAAAKAAtpjnPouWJY6cU1+v+\nHn9NA7lqf+/dUK6UecxS9fX7l/z5UwAAgMLU1J4q21rMtJYsqp14WGpmyaabHJA4LnP3hka830kc\nU00OPaBeHAAAAAoYzXk97j5Le0a8kz1RMzRxfDog3TOJ48EprtfmSvkEmrs36QMAAAB7tJSeiuY8\nufsUzRNP9gj72MTxdw0lcfc5kl6W1MXMDq97zcyKJB2taFWYJ9OqFgAAAHmhEJvz2nn2rfYTc5ei\nuecX1j1pZj0UbSz0jLv/o96128zsZTM7pF6uKYpWZbmo3vnTFC2h+AN3D1/vCwAAAHmroJrzxGj1\nUYkvj7YUTwa4e5WkCyQdZGY3JF7bXdLvFe0amqxpv07S8ZIm18v1qqRvS/qKmU1IxI+QdI+kP7r7\nbZn57gAAANDSFUxzbmZ3KFqy8GOSXNIkSevN7JZk8e7+oqJpLSeZ2UJJ/5Q0XdLR7r6mXuwaSY9L\nWivpz0ly3Srpi5LuNLP5kv5X0o3u/tnMfHcAAADIBwWzlKK7X6dodLsxr3lTURMfEnt2A9f/IOkP\njbk/AAAACouxskc8mZlLrFeO/FFVVRUUt379+uCcd911V1Dc8uXLGw5K2Lx5c1Dchx9+GJyzbdu2\nDQcpfGt2Serdu3dQXPv24RsQh25Nf8ABBzQclLBx48aguF69egXnLCkpCYrr0KFDcM7S0tKGgyR1\n69YtOGfoz/2ss84KzlldnflHlPr37x8UV15envF7Ay1B7Sxod8/KQukFM60FAAAAiDuacwAAACAm\naM4BAACAmKA5BwAAAGKC5hwAAACICZpzAAAAICZozgEAAICYoDkHAAAAYoLmHAAAAIgJmnMAAAAg\nJmjOAQAAgJhonesCAKCu8vLy4Ni+ffsGxQ0dOjQ454cffhgUd8wxxwTnvP/++4PiBg0aFJyza9eu\nQXHz588Pztm2bduguI0bNwbnHDVqVFDciy++GJyzd+/eQXG9evUKzjl48OCguMZ876Hv57Rp04Jz\nbt26NShu7NixwTlD/8xVVVUF5wxVWlqa8ZxAS8fIOQAAABATNOcAAABATNCcAwAAADFBcw4AAADE\nRJMfCDWzmyR5Jopw91sykQcAAABoydJZreWmDNXgkmjOAQAAUPDSac5nSDpfkqVZw+/TfD0AAACQ\nF9Jpzre5e2W6BZjZtnRzAAAAAPmAB0IBAACAmEinOf9ahmrIVB4AAACgRTP3jCy4ggwzM5ckfj5A\nao888khQXHFxcXDOLl26BMW9+uqrwTmXLVsWFDdz5szgnKHbzc+aNSs4Z6hBgwYFx4Z+T2effXZw\nzsWLFwfF7dixIzhnSUlJUFzfvn2Dc/bs2TMobtKkScE5Q+ucMWNGcM6ysrKguPHjxwfnBPKJWfR4\npbun+5xlEKa1AAAAADFBcw4AAADEBM05AAAAEBM05wAAAEBMZKQ5N7MzMpEHAAAAKGSZGjk/J0N5\nAAAAgIK13x1CzexnkjpISrV0jCeunSXp0syWBgAAABSWhkbOX5V0aOLzZA26pTgPAAAAoJH2O3Iu\n6WFJo9392v0Fmdn9mSsJAAAAKEz7HTn3aHvKfwXk+UtmygEAAAAKl7E9fDyZmUsSPx8gfVVVVcGx\nU6dOzfj9165dGxS3bNmy4Jyh39PChQuDc5555plBcatXrw7O+eijjwbFde7cOThnr169guJGjhwZ\nnDP0/RwzZkxwzvnz5wfFffzjHw/OuWnTpqC44uLi4JwjRowIjg3RtWvXjOaTpNLS0oznBEKZRTO4\n3T0rU7lZ5xwAAACIiSY352Z2QSYLAQAAAApdOiPnJ2asCgAAAABMawEAAADiguYcAAAAiAmacwAA\nACAmaM4BAACAmKA5BwAAAGKC5hwAAACICZpzAAAAICbSac6nZ6wKAAAAADJ3z0wisyMktXX31zKS\nsMCZmUtSpn4+QEtRVVUVHFtaWhoU98QTTwTnHDduXFDc+vXrg3Nu2LAhKG7atGnBOUPv3759++Cc\nixYtCopbs2ZNcM4ePXoExQ0aNCg4Z3V1dVDckiVLgnP2798/KG7FihXBObt27RoUN2bMmOCcxcXF\nQXFnnHFGcM633347KC70Z9SYPxuh71Hon3WgOZiZJMndLRv3a53BXE9IWitpZAZzAgAAAAUjk3PO\nt0p6YH8BZjYig/cDAAAA8komm/MrJKWcg2HR7wT+ksH7AQAAAHklk9NajpZ0jJmdKOmtetdaSTpW\n0tAM3g8AAADIK5lszj+tPfPNz0oRw9ONAAAAQAqZbM7/LOl5SU9J2pXkermkezN4PwAAACCvZLI5\nnyqpjbunWpPppcSUFwAAAABJZPKB0B/tpzGXJLn7FzN4PwAAACCvZLI5n2hmL5jZ6Va7WjsAAACA\nYJlsztdK+pOkSyXNM7NvmdkBGcwPAAAA5DXL1PbwZnaKuz+b+LyPpEskXSzpHUk/d/fnM3KjAmFm\nLkmZ+vkACPPEE08ExTVmG/dQb775ZnBsmzZtguLatm0bnLOysjIo7sADDwzOuXDhwqC4JUuWBOfs\n0aNHUNzQoeGr937wwQdBcZ06dQrOefHFFwfFhb5HktSrV6+guE2bNgXnHDNmTHBsiOrq6uDY8ePH\nZ/TeQHOonRDi7lmZGZKxkfPaxjzx+XJ3/56k4Yp2Db3TzOaY2TVm1i1T9wQAAADySSantSQzRtJX\nJI2SNEzS+ZJeNLPfmtlhzXzvlMxsnJk9bWY3BsQONbNHzGyhmS0ws5+bWdcm3re9ma0ws5p6H5v5\nRwsAAAAy1pyb2e11Pp9gZs9Jek3SmYrWP/+Yux/l7odJelDST8zsh5m6f2CNp5jZM5KmSZoUEH+U\npDckLZM0RNIhkrpJes3Mwn63uLdLJPVStBlT3Y8H3X1dE/IBAAAgj2RynfNvmFk7ScdJOlJSjaK1\nz29195l1A939BTObJmmOma1x9+9nsI79mS/pVEnPSjp5f4Fm1lnRA66V7n5N4vR2M7tMUbP+gKQz\nQm9sZq0lXSXpREkr613+KDQPAAAA8lcmp7W0lvQ1SSMl/UrScHf/TP3GvN69eyVekxXuXuHuNYpG\nwxtypaR+kv63Xo4Nkp6QdJqZNTj6XsfnJL3u7i+7+9x6H+FP7gAAACBvZXrO+VOShrr7/3P3+Q3E\ndpfUSdLGDNcQYltAzBcSx+lJrr2WOF4WcrPEuu9TJG01s4mJUXQAAABgL5lszmdI+qS7Lw0Jdvfl\nkiZKOiWDNYTa7/qEZjZY0kGJuGRrXL2XOJ4YeL+zJB0s6UuKptR8ZGbfNrP2ga8HAABAAWhyc25m\nF9T92t3HJqaMBHP3FyWNa2oNzejwxHGXu9efHy5JVYljNzPrH5DvOEmzEq9zST0k3aLowdK+6RYL\nAACA/JDOyPmlGarhyxnKk0k9E8dUOylsqPN594aSufv17j46EXu8otFzKVpi8m9m1rGphQIAACB/\nNPc65xllZrcmWSO8oY8HmnCr2oZ7S4rrdX9DEDw1xSOvufskSbUrwIyQ9I3GlwgAAIB8k86Dib3N\n7KI072+SDggNdvcpih6sbG47EsdU27TW3Q+7SeuTu/tdiSkx31C0kkvS5SRrt4xtQv4mvQ4AACAf\nNbWnyrZ0mvODJP06Q3XEzfLEsVOK66V1Pl+Txn1uUTStZ3AaOQBk0BlnhG1fUFVV1XBQwtSpU4Pi\nevfuHZyzb9+wx1Vmz54dnPOEE04Iilu8eHFwzokTJwbFFRcXB+fctCls9dkDDzwwOGe7du2C4iZN\nCl9Bt7o61czIvfXqFb6n3apVq4JjQ02bNi0obvz48RmNA5BcOs35hAzVEMch3lmJY6mZtXf3+ksv\n1o72L0tnZ093rzKzmYpWckkV09T0AAAASGhqT5XtEfcmN+fu/lIG64gVd59lZisUNeEjJL1ZL2Ro\n4vh0Bm63XHs/YAoAAIAC1aIeCM2y+xTNOU/2+7mxiePvMnCfEZLuzkAeAAAAtHCF2pzX/sag1X5i\n7lI0qn1h3ZNm1kPSaZKecfd/1Lt2m5m9bGaH1DvfLdkNzOwcSdPd/flG1g8AAIA8VHDNuZkVSToq\n8eXRlmIikbtXSbpA0kFmdkPitd0l/V7RrqHJmvbrFK1jPrnO+U9JWmNmr5nZUYlzrczsQklHSro8\ng98eAAAAWrCCas7N7A5FSx9+TNGDqJMkrTezW5LFJ3YwHS/pJDNbKOmfkqZLOtrd19SLXSPpcUlr\nJf25zqXnFU1/GSzpZTObIelOSR+6+7cbu6sqAAAA8lc6q7W0OO5+naLR7ca85k1FTXxI7NlJzm1Q\nNAIPAAAA7FdWR87N7G/ZvB8AAADQkmStOTezfpLCdqMAAAAAClBWmnMzayPp/mzdDwAAAGiJ0ppz\nbmZ9JX1TUh9FSwv+IklMH0l/lHRcOvcCgJaqoqIiKK4xW8O/+uqrQXEDBgwIzhm67Xrodu+StGXL\nlozGSdLKlSuD4tauXRucM/S9f/vtt4NzDh48OChuzpw5wTnbt28fFDdw4MDgnOeff35QXOh/xwDS\n0+Tm3Mx6SZohqSxx6iwzO9jdv1En5gxJD0jqIWm9pCvSqBUAAADIa+lMM7lGUWM+R9JURRv2/IeZ\nDTaz9mb2U0VLC/aQ9JykUe7+SLoFAwAAAPkqnWktkyT9WtKX3b3GzIolvSjpeknjJB0iaZukKe7O\n9vQAAABAA9JpzvtIurp2Ex1332Rm1ypq0CVppqQL3H12mjUCAAAABSGdaS1rE1vc1/VPRaPl35d0\nbP3G3MyuT+N+AAAAQF5LpzlvU/+Eu++W9GZiW/pdda+ZWZGkr6RxPwAAACCvpTOtpb+Z3VjvnEka\nYGb155i3knSYpPC1nQAAAIACk05z3lbSd1Jc+48U5z2N+wEAAAB5LZ3mfJOkhyStVsNNd2tJoyWd\nlsb9AAAAgLyWTnN+p7vfEhpsZiZpbhr3AwAAAPKauTdtpomZHefu0xv5mqvc/a4m3bDAmJlLUlN/\nPgCaJnSL8hkzZgTnHD58eFDc7NnhK8+uWrUqKG79+vXBOTdu3JjxnAceeGBQXHV1dXDO8847Lyhu\n2rRpwTm3bt0aFDd27NjgnI8//nhQXLt27YJzHnDAAUFxgwcPDs45YsSIoLjy8vKguLfffjv43oMG\nDQqKKy0tDc4JZFo0viy5u2Xjfk1eraWxjXniNTTmAAAAQApNntZiZmcpmmu+xd2fy1xJAAAAQGFK\nZ53zv0j6hqSFGaoFAAAAKGjpPBC6Q9L57r5KkszsakmdFY2mm6RV7n5v+iUCAAAAhSGdkfMFtY15\nwl2SKhWtfT6LxhwAAABonHSa8y11v/DIrxU15n9JqyoAAACgAKXTnKeSco0tM7uhGe4HAAAA5IXm\naM7356tZvh8AAADQYqTzQGiZmd1Y75xJGpTkfCtJh0vql8b9AAAAgLyWTnPeR9HDn8mkOs92lwAA\nAEAK6TTnWyXdJ2mDGm6620g6VNKZadwPAAAAyGvm3rTBbDO7yd1vbuRr5rr7gU26YYExM5ekpv58\nAMTH22+/HRRXUlISnHPJkiUZvbckDR48OCju/fffD845efLkoLhFixYF51y6dGlQ3KZNm4Jzvvvu\nu0Fx5513XnDOUM8880xw7NixY4PiunTpEpwz9P0MzTl+/Pjge1dVVQXFrV+fcq2JfZSXlwfHAiHM\nTJLk7paN+6XzQGj43yZ7/DSN+wEAAAB5rcnNubu/1oTX3N3U+wEAAAD5rsnNuZl1MrOOdT461bt+\nuZktMLPNZvaMmQ1Pv1wAAAAgf6UzreV+SZskVUn6naT/V3shsdnQTyXVTvwaJ+llM+ufxv0AAACA\nvJZOc/53SUslHeHuZ7v7XZJkZgdpz1KKv5fUU1JXSX+VdFMa9wMAAADyWjpLKZ4u6RJ3f6/e+R8o\nWjpxjqQvuvtOSTKzr0l6PY37AQAAAHktnZHzvu7+XN0TZjZG0tmJL2+obcwlyd03K1oTHQAAAEAS\n6TTnu5KcuzVxfN3d/y/J9c5p3A8AAADIa+k058VmVlr7hZmdL2lC4sv/rB9sZgMlHZzG/QAAAIC8\nlk5z/rSkR83sCDP7jKRfJM5PdfeX6gaaWZGkn6RxLwAAACDvWVO3h0+sa/4PSUfUOf2epBPdfX0i\npljSyZKuU7Scotw9nX8QFAwzc0lq6s8HQHxUVFQExb3//vvBOf/+978HxXXuHD6bcOTIkUFxZWVl\nwTlfffXVoLjQbeklae7cuUFxxcXFwTlDY1esWJHxnI2pM1S/fv2CY0ePHh0UN23atKC4UaNGBd87\nVGlpacNBQDMxM0mSu1s27tfk1VrcfbOZjZN0maQRkj6UdJ+7b6kTdkXiHn9PfNBpAgAAACmks5Si\n3H2b9jNdxd1vSyc/AAAAUEgyNsXEzAYkPgbWOTfCzJ42s1lmNiVT9wIAAADyUSbnfy+S9Iakz0iS\nmXWV9JykSYpG6K82s8kZvB8AAACQVzLZnO9W9DDoHYmvp0g6QNLt7n6IpEMlfTGD9wMAAADySiab\n81nu/qEkmVlnSZMlVUr6L0ly91WKGngAAAAASaT1QGg91XU+v0ZSF0nXuftOSTKztpKGZ/B+AAAA\nQF7JZHP+ppn9VNJ6RTuEvifpwTrXp0jqlsH7AQAAAHklk9NaviVpk6SzJP1N0lnuvkuSzOwuSedJ\nCt9hAwAAACgwGRs5d/ftikbH91ky0d2vytR9AAAAgHyVyWktAFAwQrcyl6Tx48cHxb3/fvgvF4cO\nHRoU16tXr+CclZWVQXGf+MQngnM+88wzQXGvvvpqcM7Vq1cHxY0ZMyY4Z+h7P3bs2OCc1dXVDQdJ\n6tKlS3DOF198MShuxIgRwTlzqbS0NNclALGTyWktAAAAANJAcw4AAADEBM05AAAAEBM05wAAAEBM\n0JwDAAAAMUFzDgAAAMQEzTkAAAAQEzTnAAAAQEzQnAMAAAAxQXMOAAAAxERBNudmNs7MnjazGwNi\n25vZ5WZWYWYD0rjnEWb2pJktNLN5ZnarmbVvaj4AAADkH3P3XNeQNWZ2iqRrJZ2SOPUdd78lRWwH\nSZdL+rqk/pJcUrm7L27Cfc+U9KikG9z9R2bWRdKTiv5xdIq7b0nyGpekQvr5AIWuoqIiOHbGjBlB\ncZWVlcE5Bw4cmPGcY8eODYqrrq4Ozrlp06aguGOOOSY454YNG4Li3njjjeCcvXv3DoobMWJEcM7Q\nOhvzfn700UdBcZ/4xCeC4hYtWhR879GjRwfHArliZpIkd7ds3K/QRs7nSzpV0osBsa0k/UbSyYoa\n8yYxs/6SHpb0vLv/SJLcvVrSpZKOlXRHU3MDAAAgvxRUc+7uFe5eI6nBoQ933+Tua9x9oaQ1adz2\nJknFkh6sl3+upNclfcXMhqeRHwAAAHmioJrzOrY1c7wkyczaSDpP0cj79CQhr0kySV9uSn4AAADk\nl0Jtzhs7TaWp01pOkNRZ0nZ3X57k+nuJ48lNzA8AAIA8UqjNebYcnjguS3G9KnE81GqfNgAAAEDB\nojlvXj0Tx6oU12sfu28tqaT5ywEAAECctajmPLE2eE0jPx7IYcndE8d9lkpMqKnzOWueAwAAFLjW\nuS6gMdx9iqQpua6jEXYkjqmmrLSt8/m6ZAFNne3C+ugAAAB7tJQZxC1q5LwFWpE4dkpxvTRx3Ozu\nO1LEAAAAoEC0qJHzFuidxLEsxfUD6sXtgxFwAACA9DW1p8r2iDvNefN6UdJOSb3MrLu7r613fWji\n+FR2ywKQTRUVFUFxM2bMyPi9t20L36ahrCzVOMLeKisrg3OGbg0/fXqyrSDSs2nTpuDY4uLioLiJ\nEycG59ywYUPDQZJ+9rOfBee84oorguJmz54dnHPVqlVBcevXrw+Kq66uDr53qKqqVOsq7Ku0tLTh\noGbKCWQC01qakbtvlPSIojnn45OEjJW0W9Kj2awLAAAA8VSozXntbwxaZSrezL5hZq+ZWf0m/GZJ\nmyVdVC/+UEXroP/K3RcE1gEAAIA8VnDNuZkVSToq8eXRDW3+Y2blknopGv0eu5/Q70o6WtI1dU+6\n+0JJkyWdbmZfSOQcIOkhSf+UdFUTvg0AAADkoYJqzs3sDkVLFn5MkkuaJGm9md2SIr5S0hxFI+Yu\n6SEzW2pmo5KEPyipWtE0lr24++8knSbpcjNbIOlJSb+RNMHdwyeEAgAAIK8V1AOh7n6dpOsaET+w\nEbFfk/S1/Vx/TtJzofkAAABQeApq5BwAAACIM5pzAAAAICZozgEAAICYoDkHAAAAYoLmHAAAAIgJ\nmnMAAAAgJgpqKUUAyIWuXbtmPGdZWVlQ3P9v797j5KrLw49/HggBEgjhFiLIJRFtIMpFboJRwRt4\noVariK3a/qi32mK9YbFVbNWqVK2I1Z/2or8iWC+tVqEgqAWDYrBegohAgIQAYoxcQkwIQsjz++Oc\ngXGZ2f3u7uzO2Z3P+/Wa18mc88xzvvPdk91nznzP91x//fXFOdevX18Ut3jx4uKcl1xySVHc/vvv\nX5yz1A477NDznLfeemvPc+6zzz493/9o3ntp7DXXXFMU9+hHP7p430uXLi2Ke+pTh958u7tVq1YV\nxS1YsKA4pzSZPHMuSZIkNYTFuSRJktQQFueSJElSQ1icS5IkSQ1hcS5JkiQ1hMW5JEmS1BAW55Ik\nSVJDWJxLkiRJDWFxLkmSJDWExbkkSZLUEDP63QBJUmU0t1y//fbbe77/DRs2FMVdffXVxTl33XXX\noriFCxcW57zsssuK4mbNmlWc88UvfnFR3DnnnFOcs/Q9nXjiicU5v/WtbxXFHX744cU5169fXxRX\neswdcsghxfsutXz58uLYidi/NJk8cy5JkiQ1hMW5JEmS1BAW55IkSVJDWJxLkiRJDWFxLkmSJDWE\nxbkkSZLUEBbnkiRJUkNYnEuSJEkNYXEuSZIkNYTFuSRJktQQkZn9boM6iIgE8OcjDY7R3KL8uuuu\nKwaiXkIAACAASURBVIpbu3Ztcc4f/OAHRXEnnXRScc41a9YUxZW+H4BFixYVxd15553FOUv9/Oc/\nL4495ZRTiuJK+x3g3nvvLY4tNW/evKK4o446qijunnvuKd73+vXri+L23nvv4pylFixY0POcmp4i\nAoDMjMnYn2fOJUmSpIawOJckSZIawuJckiRJagiLc0mSJKkhLM4lSZKkhrA4lyRJkhrC4lySJElq\nCItzSZIkqSEsziVJkqSGsDiXJEmSGsLiXJIkSWqIGf1ugCRNd0uXLi2KmzNnTnHOq6++uihuxYoV\nxTkfeOCBorjRtPOSSy4pijvmmGOKc1544YVFcXvvvXdxzpUrVxbF/emf/mlxztKf+8KFC4tzXnbZ\nZUVxixYtKs551FFHFcVdc801RXGLFy8u3vf69euL4hYsWFCcc926dcWxUhN55lySJElqCItzSZIk\nqSEsziVJkqSGsDiXJEmSGsLiXJIkSWoIi3NJkiSpISzOJUmSpIawOJckSZIawuJckiRJagiLc0mS\nJKkhIjP73QZ1EBEJ4M9HmvqWL19eFHfxxRcX59x+++2L4m688cbinP287fljHvOY4tg777yzKG7m\nzJnFOdeuXVsUN2/evOKcxx57bFHcNddcU5zz5ptvLop72cteVpxzxYoVxbElXvWqVxXHrlq1qqf7\nBliwYEFRXOn/S4BDDjlkrM3RNBARAGRmTMb+PHMuSZIkNYTFuSRJktQQFueSJElSQ1icS5IkSQ1h\ncS5JkiQ1hMW5JEmS1BAW55IkSVJDDGxxHhFLIuKiiDijIHa7iHh9RKyKiH3Gud/lEbFlyOPBiDhg\nPHklSZI09c3odwMmW0Q8C3gr8Kx61feGid0eeD3wBmBvYFx3BIqI5wIHdchzUWZeO57ckiRJmvoG\nrjgHbgSeA3wDOG6E2K2BfwO+AtzQg32/HXgpcNWQ9b/qQW5JkiRNcQNXnGfmKoCI+AEjFOeZuQHY\nANwREXcAu411vxHxFGBGZn5prDkkTU0Tcevvc889tyjugQceKM5ZGrvNNtsU5yyNveWWW4pzzpw5\nsyhu48aNxTlnz55dFHf//fcX5/ze97p+MftbnvCEJxTnPP7444vi9ttvv+KcK1asKIq79957i+LW\nrVtXvO8rr7yyKO6EE04ozllqIv5fSr0wsGPOgfsmOH6ovwLWRsRz6+EykiRJ0m8Z5OJ8tOPHxzze\nPCIOAY4HTgQuANZExD9ExNyx5pQkSdL0M8jF+WQ6Dvgx1djyBHYE3ggsj4jF/WyYJEmSmsPifBJk\n5kcy87DM3AM4FPhCvWkf4JKImN+/1kmSJKkpplxxHhEf6DBP+EiPT/e73S2Z+ZPMfBnVrC0PAo8C\n3t3fVkmSJKkJplxxnpmnZ+ZWo3yc0u92D1XP2vKW+ulLusVFxJgekiRJethUqammXHE+zXwCWA3M\niYjd+90YSZIk9ZfFeR9l5mbg2/XTDV1ixvSQJEnSw6ZKTWVx3n+/AH6amZv63RBJkiT1l8V5/z0e\nOLvfjZAkSVL/DXJxPqNebt2r+Ih4Y0Qsi4inDlk/NyIe0dcRcTiQmfmvhW2QJEnSNDZj5JDppy6U\nj6ifHhkRkcMMKoqIBcA8IICjgVVdQt8LzKKahWVp/drDgSuBGyLi1Mz8RlSX/j6X6q6hL+3BW5I0\nDaxfv744dscddyyK27hxY3HOuXPLblq8evXq4pybNpWN2Js/v/x2D/vss09R3EUXXVScc8GCBUVx\ne+21V3HOUtdff33PY4877rixNqerE088sSju5ptvLs558sknF8WtW7euOGep0eQs/b8h9cLAnTmP\niA8CdwHPpLpb5/HA3RHRca7xiFgNXE91xjyBcyPitog4qEP4Z4D1wOfb1l1FNSvLHOCCiLgK+Diw\nKTPf4FhzSZIktQzcmfPMPA04bRTx+44i9lTg1CHrHqjXndrxRZIkSVJt4M6cS5IkSU1lcS5JkiQ1\nhMW5JEmS1BAW55IkSVJDWJxLkiRJDWFxLkmSJDWExbkkSZLUEBbnkiRJUkMM3E2IJKmpVqxYURy7\n3XbbFcXNnj27OOfq1auL4g488MDinMuWLSuKmzVrVnHONWvWFMXtvffexTl33333ori1a9cW59xm\nm22K4g4++ODinHfffXdR3Pe+973inEcffXRR3DnnnFMU9653vat432effXZR3Ctf+crinKtWrSqO\nLTV37tye55S68cy5JEmS1BAW55IkSVJDWJxLkiRJDWFxLkmSJDWExbkkSZLUEBbnkiRJUkNYnEuS\nJEkNYXEuSZIkNYTFuSRJktQQFueSJElSQ1icS5IkSQ0RmdnvNqiDiEgAfz6SOlm1alVR3Pnnn1+c\n85ZbbimKW7t2bXHOW2+9taf7BrjvvvuK4g4++ODinJs3by6KW716dXHOJUuWFMXNnj27OGep/fff\nvzj27rvvLoo77rjjxtqcrm6//faiuJNPPrnn+5ZKRQQAmRmTsT/PnEuSJEkNYXEuSZIkNYTFuSRJ\nktQQFueSJElSQ1icS5IkSQ1hcS5JkiQ1hMW5JEmS1BAW55IkSVJDWJxLkiRJDWFxLkmSJDXEjH43\nQJI0cRYuXFgcW3ob90WLFhXnXLp0aVHcTjvtVJzzqquuKopbvXp1cc6dd965KG7Tpk3FOS+77LKi\nuCc+8YnFOW+44YaiuAceeKA45+zZs4viLr744qK4BQsWFO97/vz5RXGrVq0qzln6s5w7d25xTmky\neeZckiRJagiLc0mSJKkhLM4lSZKkhrA4lyRJkhrC4lySJElqCItzSZIkqSEsziVJkqSGsDiXJEmS\nGsLiXJIkSWoIi3NJkiSpIWb0uwGSpNEbzS3SSy1ZsqQobjS3Pd9hhx2K4i688MLinE960pOK4tas\nWVOc86abbiqK23777Ytzbt68uTi21H777VcUt3HjxuKcX/va14ri/viP/7go7rrrrived+nxcc89\n9xTnnAilx/y6det6nlODxzPnkiRJUkNYnEuSJEkNYXEuSZIkNYTFuSRJktQQFueSJElSQ1icS5Ik\nSQ1hcS5JkiQ1hMW5JEmS1BAW55IkSVJDWJxLkiRJDWFxLkmSJDVEZGa/26AOIiIB/PlImizLly8v\nittvv/2Kc379618fY2u6u/rqq4vi7rrrruKc8+fPL4o755xzinNu2bKlKG79+vXFOWfPnl0Ut+OO\nOxbn3HfffYvidtttt6K4efPmFe9748aNRXG77LJLcc6jjz66KO75z39+cc5169YVxc2dO7c4p6aO\niAAgM2My9ueZc0mSJKkhLM4lSZKkhrA4lyRJkhpi4IrziFgSERdFxBkjxM2LiE9GxO0RcX9E3BQR\nZ0bEnDHu9+kR8e2IWBkR10bE2yJi4PpfkiRJ3Q1McRgRz4qIi4GlwPEjxD4K+D7wGmA3YGtgAXAa\nsCwiyq6KeTjf64CLgI9l5kLgOOAU4EsW6JIkSWoZpMLwRuA5wKUFsWfV8UcA21IV6B+qty0CPlq6\n04g4DPhH4J8z8z8AMnMNcCrwQuAtpbkkSZI0vQ1McZ6ZqzJzC/CD4eIiYldgHnBCZv4wK3dn5tuA\nz9ZhL4qImYW7PpOqnz8zpD3fANYCfxMRzr0kSZKkwSnO29w3wvZ9gb/MzM0dtrXOns8EdhppRxEx\nH3g6sAn4UYeQZcD2wMtHyiVJkqTpbxCL82Hv6pOZP8rM73fZfGO93JSZvyrY13Pr5S3Z+W5CP62X\nxxXkkiRJ0jQ3iMX5eLQuBD2/MP7Qenlbl+2tW44dPOYWSZIkadqY0e8GTDHPrJcfGjbqYbvXy273\n/b2nXo5q9hdJmgiHHHJIz3PuueeeRXFz5pTPUnvCCScUxX3qU58qzll6C/uTTz65OGeplStXFseu\nWrWqKG40t7ufObPsEqoVK1YUxc2bN69436NpZ6kNGzYUxS1durQ4Z+nxORH/hzR4psyZ84j4QERs\nGeXj0z1uxp9Tzbryw8L4XevlvV22b6mX242rVZIkSZoWpsyZ88w8HTi9X/uPiD8CZgFvHsXL7m+9\nvMv21umKu4bZ7yh297DOQ9wlSZIG01hrqsk2ZYrzfoqIBcBfA8/LzI2jeOmaejm7y/bWFIp3jLVt\nkiRJmj4szkcQEbOBc4BXZuYNo3z5VfWy26DLPYbEPYJnwCVJksZvrDXVZJ9xnzJjzvshImYA5wHv\nzcxlY0hxcb08oMv2/evlhWPILUmSpGnG4ryLiNga+DfgvMy8uMP2vSJim+FyZOb1wOXAnIg4tH1b\nRGwFHAmsB/67Zw2XJEnSlDWIxXlrKM/W3QLqM+bnAJdk5pc6bD8G+GxmPtC27syIuDwiDhwSfjrV\nrCyvHLL+uVRTKL4/M9eP/m1IkiRpuhmo4rw+W31E/fTI6DCIKCK2Bf4DOAn4cETc0fa4MyLuA75T\nP1qv2Q04DXgy8Nr2fJn5PeAdwOsi4ul1/GLgE8CXMvPMXr9PSZIkTU0Dc0FoRHwQeDWwI5DA8cDd\nEXF2Zp7RFnoO8Lt1zM5d0iXVWPTqSeYdEfE1quL8y48IzvxARKwCPhQRc6huPnRGZv6/cb8xSZIk\nTRsDU5xn5mlUZ7dHinsp8NIx5P+9EbZ/AfjCaPNKkiRpcIRT9TVTRCQ4laIkjdW6deuKY+fOnTty\nEHDBBRcU5yy95fuKFSuKc86fP78o7pJLLinOOXPmzJGDgLVr1xbFzZ7d7dYej1T6M1q4cGFxzj32\n2GPkIGDevHnFOTds2FAU96pXvao4p6aO1ijozJyUORUHasy5JEmS1GQW55IkSVJDWJxLkiRJDWFx\nLkmSJDWExbkkSZLUEBbnkiRJUkNYnEuSJEkNYXEuSZIkNYTFuSRJktQQFueSJElSQ1icS5IkSQ0R\nmdnvNqiDiEgAfz6SNPHWrVtXFDd37ty+7RvgJz/5SVHcnDlzinOuX7++KG758uVFcddee23xvnfZ\nZZeiuGuuuaY456GHHloUd9hhhxXnLLVmzZri2Pnz5xfFLVmypDjnRByfgogAIDNjMvbnmXNJkiSp\nISzOJUmSpIawOJckSZIawuJckiRJagiLc0mSJKkhLM4lSZKkhrA4lyRJkhrC4lySJElqCItzSZIk\nqSEsziVJkqSGCG8P30wRkQD+fCRJo7Vu3bri2NJbvpfmvPnmm4v3fdtttxXFzZkzpzjn7bffXhR3\nxRVXFOd89rOfXRS3ePHi4pw777xzUVzpz0cTJyIAyMyYjP155lySJElqCItzSZIkqSEsziVJkqSG\nsDiXJEmSGsLiXJIkSWoIi3NJkiSpISzOJUmSpIawOJckSZIawuJckiRJagiLc0mSJKkhLM4lSZKk\nhojM7Hcb1EFEJIA/H0mSem/VqlV93f/OO+9cFDd37twJbolGEhEAZGZMxv48cy5JkiQ1hMW5JEmS\n1BAW55IkSVJDWJxLkiRJDWFxLkmSJDWExbkkSZLUEBbnkiRJUkNYnEuSJEkNYXEuSZIkNYTFuSRJ\nktQQ4e3hmykiEsCfjyRJUv9EBACZGZOxP8+cS5IkSQ1hcS5JkiQ1hMW5JEmS1BAW55IkSVJDWJxL\nkiRJDWFxLkmSJDWExbkkSZLUEBbnGjgR8dCcpeo9+3di2b8Tx76dWPbvxLJ/pw+Lc0mSJKkhLM4l\nSZKkhrA4lyRJkhrC4lySJElqiIEsziNiSURcFBFnjBA3LyI+GRG3R8T9EXFTRJwZEXPGuN/tImJN\nRGwZ8tgYEbuM7d1IkiRpuhio4jwinhURFwNLgeNHiH0U8H3gNcBuwNbAAuA0YFlE7DaGJpwCzANy\nyOMzmXnXGPJJkiRpGhmo4hy4EXgOcGlB7Fl1/BHAtlQF+ofqbYuAj45mxxExA3gT8DTggCGP00eT\nS5IkSdPTjH43YDJl5iqAiPgBcFy3uIjYleoM97Myc3O9+m7gbRGxB/AK4EURMTMz7y/c/cuA/83M\ny8f8BiRJkjStDdqZ85b7Rti+L/CXbYV5u9bZ85nATiU7i+quAKcDmyLiGfVZdEmSJOm3DGpxnsNu\nzPxRZn6/y+Yb6+WmzPxV4f5eQDV85f8A3wBuj4h3RMR2ha+XJEnSABjU4nw8WheCnj+K1xwD/ARY\nR/XBYDfg3VQXlu7V2+ZJkiRpqrI4H71n1ssPDRvVJjPflpmHALsCT6Y6ew5wEPD1iJjV2yZKkiRp\nKppSxXlEfKDDHOEjPT7d42b8OfDPmfnD0b4wK8sy83jgLfXqxcAbe9lASZIkTU1T6sLEzDydPk47\nGBF/BMwC3jzeXJn5kYjYm6owfxnwvi77HO+u1IV9O7Hs34ll/04c+3Zi2b8Ty/6d+qbUmfN+iogF\nwF8Dv5uZG3uU9t3ABmBhj/JJkiRpCptSZ877JSJmA+cAr8zMG3qVNzPXRcSPqGZyGbrNj76SJEkD\nxuJ8BPWc5OcB783MZROwi18A90xAXkmSJE0xDmsZRkRsDfwbcF5mXtxh+14Rsc04d7MYOHucOSRJ\nkjQNDGpx3vrGYOtuAfUZ83OASzLzSx22HwN8NjMfaFt3ZkRcHhEHDondpcs+XghckZnfGsN7kCRJ\n0jQzcMV5RGwFHFE/PTI6XNYcEdsC/wGcBHw4Iu5oe9wZEfcB36kfrdfsBpxGNY/5a9vWvwi4IyKW\nRcQR9bqtI+IVwGHA6yfkjUqSJGnKGajiPCI+CNxFdSOhBI4H7o6Idw8JPQf4Xar+2XnIYy6wTf36\n81ovyMw7gK8BdwJfbsv1LeBzVDOyfCci1lGNMf8g8GLgjPrDwGjfy0e7zOv+utHmmg4iYklE/HdE\n3BQRv4qIb0fE8WPMNT8iPlXnWhkRn6+nvRxYvezfOt/AHr8R8byIuKKemnW4uCfWfb4yIm6o7/Ow\n3Rj3+eKI+H7987sqIv5kbK1vvj717/yIuK/D8XxrPTxyWhhF386JiLdHxC/Hub+n179rVkbEtRHx\ntvoE27TVhz7eLiLWdDh2N3b71n8qKunXiHhMRJxX/437TUT8LCL+KiJmjnGfY/+9m5k+JuEBvATY\nDLwT2LpedwiwmuoM/DajyLUbsBF4cMhjLbBdv99rH/r2T+r+eGn9fBuqDz9bgD8fZa4FwG3AvwPb\nUn1A+wfgl8Dj+v1ep3r/1q8fyOOX6pu4ZXW/baGa/alb7InAJuCN9fM5wOXAd4FZo9zv+4D1wFPq\n579TH88f7XefTIf+rV//9/U+hx7Tb+13v0xm31KdwPob4I5Wf4xjn68DfgO8uH4+H7gO+E9gq373\nyXTo4zrf67scu//Y7z6Z5H5dTHXydkt93D3Y9pqlo/3bNN7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"text/plain": [ "<matplotlib.figure.Figure at 0x2374e2ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(11,11))\n", "ax = fig.add_subplot(111)\n", "ax.hist2d(dr7_mass['MEDIAN'][iz], dr7_ssfr['MEDIAN'][iz], bins=80, cmin=2, cmap='Greys', norm=LogNorm())\n", "#ax.plot(dr7_mass['MEDIAN'][iz], dr7_ssfr['MEDIAN'][iz], '.')\n", "#ax.plot(dr7_mass[1000:11000]['MEDIAN'], dr7_sfr[1000:11000]['MEDIAN'], '.')\n", "#ax.plot(mass[iarchetype], sfr[iarchetype]-mass[iarchetype], '*', ms=15)\n", "ax.set_ylim(-12.5, -8.5)\n", "ax.set_xlim(8.5, 12.)\n", "ax.set_xlabel(r'$\\log_{10}$ $M_*$ [M$_\\odot$]', fontsize=22)\n", "ax.set_ylabel(r'sSFR [yr$^{-1}$]', fontsize=22)\n", "xmass = np.linspace(8.5, 12.0, num=100)\n", "yssfr = -0.65*(xmass-10.)-9.8\n", "ilow = np.searchsorted(xmass, 9.0)\n", "isort = xmass.size - np.searchsorted(yssfr[::-1], -9.8)\n", "iselect = np.nonzero(np.logical_and(dr7_ssfr['MEDIAN'][iz]>-9.8, dr7_ssfr['MEDIAN'][iz]>-0.65*(dr7_mass['MEDIAN'][iz]-10.)-9.8))[0]\n", "print(iselect.size)\n", "ax.plot(xmass[:isort+1], yssfr[:isort+1], '--b', lw=4)\n", "ax.plot(xmass[isort:], xmass[isort:]/xmass[isort:]*(-9.80), '--b', lw=4)\n", "#ax.plot([9.0, 9.0], [yssfr[ilow], -8.5], '--b', lw=3)\n", "#print(iarchetype.size)" ] }, { "cell_type": "code", "execution_count": 223, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "177\n", "112\n", "46\n", " 10425 178.950943 -0.548661\n", " 10931 178.950943 -0.548661\n", " 23846 214.843231 -0.080412\n", " 24317 214.843231 -0.080412\n", " 113800 169.752884 68.033020\n", " 155733 215.384262 3.239949\n", " 168857 225.886475 61.289570\n", " 185016 351.637756 -10.503380\n", " 294032 155.610886 53.296211\n", " 319005 204.200027 59.064163\n", " 333827 324.627686 0.683894\n", " 337330 155.484619 5.613559\n", " 362736 221.510727 50.077003\n", " 367224 248.734726 32.998070\n", " 388825 127.758049 4.055246\n", " 398157 127.377419 31.163918\n", " 400641 140.534836 38.131268\n", " 404196 168.902740 9.839472\n", " 436111 179.446442 58.584145\n", " 461645 179.236435 44.832783\n", " 462137 182.560287 44.665779\n", " 462820 186.585510 45.622383\n", " 485914 151.452576 37.749313\n", " 486209 154.635056 38.168709\n", " 498887 186.071823 42.472801\n", " 501136 192.090317 47.010868\n", " 529402 244.636063 27.731224\n", " 581231 196.305161 12.788695\n", " 585419 213.581482 10.403776\n", " 601872 143.569946 10.677863\n", " 607034 165.841064 13.806926\n", " 660315 121.050789 52.778694\n", " 690439 191.202042 33.909134\n", " 725005 200.003235 30.821304\n", " 810038 154.061234 21.994684\n", " 814822 157.390854 21.073999\n", " 825435 159.858459 21.688000\n", " 826851 161.513412 15.418530\n", " 827600 162.264664 23.972656\n", " 842667 176.526855 20.918373\n", " 866734 187.590179 17.738111\n", " 873203 189.826111 18.871485\n", " 873826 190.639328 20.935989\n", " 888511 198.907471 24.649128\n", " 891206 210.637543 15.695745\n", " 910407 207.824570 24.111452\n" ] }, { "data": { "image/png": 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RBwDFuNULILXKzbc7apTp2Welgw+Ou4UAkCwUfgBSaXeeb1QwPt/RvT167MFg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\n4e17JEuHgvEX75R23/7tUeXra5eCebE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"text/plain": [ "<matplotlib.figure.Figure at 0x25c10a6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ioii = np.nonzero((dr7_line['OII_3729_REQW'][iz[iselect]]+dr7_line['OII_3726_REQW'][iz[iselect]])<-80.)[0]\n", "icont = np.nonzero((dr7_line['OII_3729_FLUX'][iz[iselect]]/dr7_line['OII_3729_CONT'][iz[iselect]] \\\n", " +dr7_line['OII_3726_FLUX'][iz[iselect]]/dr7_line['OII_3726_CONT'][iz[iselect]])>50.)[0]\n", "igalex = np.nonzero(np.logical_and(dr7_galex['NUV_MAG'][iz[iselect[ioii]]]<19.2, dr7_galex['NUV_MAG'][iz[iselect[ioii]]]>15.))[0]\n", "print(ioii.size)\n", "print(icont.size)\n", "print(igalex.size)\n", "plt.plot(dr7_line['OII_3729_REQW'][iz[[iselect]]], dr7_line['OII_3726_REQW'][iz[iselect]], '.')\n", "plt.plot([-200, 50], [-200, 50])\n", "for i in np.arange(igalex.size):\n", " print(\"{0:7d}\".format(iz[iselect[ioii[igalex]]][i]),\\\n", " \"{0:.6f}\".format(dr7_info['RA'][iz[iselect[ioii[igalex]]]][i]), \\\n", " \"{0:.6f}\".format(dr7_info['DEC'][iz[iselect[ioii[igalex]]]][i]))" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0, 80)" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6V/P9Snlrn9WtKeXS8OnckiRJPc0Y0DJzdWYenJlPAnakub5s6ujYWyPilI7mO5VyDb3d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\npw/pUXdgKT+TmQ9sui5tWhH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"text/plain": [ "<matplotlib.figure.Figure at 0x237a00a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = plt.hist(-dr7_line['OII_3729_EQW'][iz[iselect]], bins=50, range=(0, 150))#, normed=True)\n", "y = plt.hist(-dr7_line['OII_3729_EQW'][iz[iselect[ioii]]], bins=50, range=(0,150))#, normed=True)\n", "plt.ylim(0,80)\n", "#print(dr7_galex['FUV_MAG_APER_2'][iz][iselect])" ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lf_z = np.linspace(0.1, 2.5, num=40)\n", "lf_oii = np.power(10, np.linspace(39, 44, num=40))\n", "#lf_L = np.power(10, 0.341*lf_z + 40.773)\n", "lf_L = np.power(10, 0.30*lf_z + 40.773)\n", "#lf_logphi = 0.475*lf_z - 2.522\n", "lf_logphi = 0.40*lf_z - 2.522\n", "lf_alpha = 0.25*lf_z - 1.6 + 1.\n", "#lf_alpha = 0*lf_z - 1.257+1.\n", "lf_lf = np.zeros((40, 40))\n", "lf_sigma = -0.0*(lf_z-1.) + 0.46\n", "for i in np.arange(40):\n", " lf_lf[:,i] = np.power(10, lf_logphi)*np.power(lf_oii[i]/lf_L, lf_alpha)\\\n", " *np.exp(-np.power(np.log10(1.+lf_oii[i]/lf_L)/np.sqrt(2.)/lf_sigma, 2))" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YEAjlkiiNwGg/eOW93zfVDpm54LYBLlQDGB2GvqehZ5MKPm3EcmDuSpi1AlId4BvQuws2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qwt+5V//c8Dn+p59lmSBx06dOgAX9OmP8PBNxEoKOhQZQnttbZF86efWlV5Q9tXUVXVx8WL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IQIksAtq7oUFaJJiLB98o/Ac/9hyA14twswxSGQiHhVHUdASq5S7UEYH7dnNIlpi06CWm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\n3+7BwyYPFovFUgScP1+/pbeFqV9QZlAqkXj5coW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"text/plain": [ "<matplotlib.figure.Figure at 0x24790ce80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "CM = plt.get_cmap('jet_r')\n", "\n", "fig = plt.figure(figsize=(8,10))\n", "ax = fig.add_subplot(111)\n", "for i in np.arange(40):\n", " color = np.fmin(np.fmax((2.8-lf_z[i])/2, 0), 1.)\n", " thiscolor = CM(color)\n", " ax.loglog(lf_oii, lf_lf[i,:], color=thiscolor)\n", "ax.set_ylim(1E-7, 1E-1)\n", "ax.grid()\n", "#plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 270, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-1.575 , -1.55961538, -1.54423077, -1.52884615, -1.51346154,\n", " -1.49807692, -1.48269231, -1.46730769, -1.45192308, -1.43653846,\n", " -1.42115385, -1.40576923, -1.39038462, -1.375 , -1.35961538,\n", " -1.34423077, -1.32884615, -1.31346154, -1.29807692, -1.28269231,\n", " -1.26730769, -1.25192308, -1.23653846, -1.22115385, -1.20576923,\n", " -1.19038462, -1.175 , -1.15961538, -1.14423077, -1.12884615,\n", " -1.11346154, -1.09807692, -1.08269231, -1.06730769, -1.05192308,\n", " -1.03653846, -1.02115385, -1.00576923, -0.99038462, -0.975 ])" ] }, "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lf_alpha-1." ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr7_info = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_info_dr7_v5_2.fit.gz', ext=1)\n", "dr7_sfr = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_totsfr_dr7_v5_2.fits.gz', ext=1)\n", "dr7_ssfr = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_totspecsfr_dr7_v5_2.fits.gz', ext=1)\n", "dr7_mass = fitsio.read('/Users/Benjamin/AstroData/Garching/totlgm_dr7_v5_2.fit.gz', ext=1)\n", "dr7_phi = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_phi_dr7_v5_2.fits', ext=1)\n", "dr7_uniq = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_uniq_dr7_v5_2.fits', ext=1)\n", "dr7_line = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_line_dr7_v5_2.fit.gz', ext=1)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr7_galex = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_galex_dr7_v5_2.fits', ext=1)\n", "dr7_fibersfr = fitsio.read('/Users/Benjamin/AstroData/Garching/gal_fibsfr_dr7_v5_2.fits.gz', ext=1)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RA, DEC, MASS, SFR, fiber SFR, sSFR, R_EXP, SFR/A, fiber SFR/A, u, u fiber, nuv, nuv fiber\n", "179.446 58.584 9.421 4.766 1.492 -8.816 2.323 0.178 0.134 17.722 18.692 18.306 19.277 18.638 19.608\n", "244.636 27.731 9.280 15.679 13.077 -8.143 3.056 0.339 1.173 17.751 18.046 17.691 17.986 18.120 18.415\n", "182.560 44.666 9.880 12.266 10.629 -8.798 1.347 1.364 0.953 18.486 18.622 18.140 18.277 18.589 18.726\n", "127.758 4.055 9.756 10.134 7.275 -8.778 2.523 0.321 0.652 17.532 18.454 17.827 18.748 18.431 19.353\n", "[-63.53063583 -76.31105042 -55.51638794 -42.38479996 -61.41056061\n", " -75.7973175 -55.92394257 -61.03907013]\n" ] } ], "source": [ "print('RA, DEC, MASS, SFR, fiber SFR, sSFR, R_EXP, SFR/A, fiber SFR/A, u, u fiber, nuv, nuv fiber')\n", "for i, j in zip(nfl_sdss['INDEX'][0][index_of_index], np.arange(4)):\n", " sfrarea = np.power(10, dr7_sfr['MEDIAN'][i])/np.pi/(dr7_phi['R_EXP'][i][0]*1.256)**2\n", " fibersfrarea = np.power(10, dr7_fibersfr['MEDIAN'][i])/np.pi/(1.5*1.256)**2\n", " fiber_nuv = dr7_info['PLUG_MAG'][i][0] - data_skyserver[j][4] + dr7_galex['NUV_MAG'][i]\n", " fiber_fuv = dr7_info['PLUG_MAG'][i][0] - data_skyserver[j][4] + dr7_galex['FUV_MAG'][i]\n", " print(\"{0:.3f} {1:6.3f} {2:.3f} {3:6.3f} {4:6.3f} {5:6.3f} {6:6.3f} {7:6.3f} {8:6.3f} {9:6.3f} {10:6.3f} {11:6.3f} {12:6.3f} {13:6.3f} {14:6.3f}\".format(dr7_info['RA'][i], dr7_info['DEC'][i], \\\n", " dr7_mass['MEDIAN'][i], np.power(10, dr7_sfr['MEDIAN'][i]), np.power(10, dr7_fibersfr['MEDIAN'][i]), dr7_ssfr['MEDIAN'][i], dr7_phi['R_EXP'][i][0], \\\n", " sfrarea , fibersfrarea, data_skyserver[j][4], dr7_info['PLUG_MAG'][i][0], dr7_galex['NUV_MAG'][i], fiber_nuv, dr7_galex['FUV_MAG'][i], fiber_fuv))\n", "print(dr7_line['OII_3726_EQW'][nfl_sdss['INDEX'][0]])" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INDEX, ra, ra fiber, u u totaa\n", "436111 179.446451 18.692499 17.722150 18.306219 19.276568\n", "388825 127.758047 18.454000 17.751260 17.826565 18.529305\n", "601872 143.569934 20.357599 18.486000 18.948124 20.819723\n", "461645 179.236432 19.093700 17.532270 17.668858 19.230288\n", "462137 182.560285 18.622499 17.767190 18.140104 18.995414\n", "529402 244.636076 18.046000 17.065990 17.690878 18.670887\n", "826851 161.513414 19.159500 17.707580 18.509342 19.961262\n", "866734 187.590171 20.332001 18.275890 18.189762 20.245873\n" ] } ], "source": [ "print('INDEX, ra, ra fiber, u u totaa')\n", "for i, j in zip(nfl_sdss['INDEX'][0], np.arange(8)):\n", " #print(\"{0:6d} {1:.6f} {2:.6f} {3:.6f} {4:.6f}\".format(i, dr7_info['RA'][i][0], data_skyserver[j][2], dr7_info['PLUG_MAG'][i][0], data_skyserver[j][4]))\n", " fiber_nuv = dr7_info['PLUG_MAG'][i][0] - data_skyserver[j][4] + dr7_galex['NUV_MAG'][i]\n", " print(\"{0:6d} {1:.6f} {2:.6f} {3:.6f} {4:.6f} {5:.6f}\".format(i, data_skyserver[j][2], dr7_info['PLUG_MAG'][i][0], data_skyserver[j][4], dr7_galex['NUV_MAG'][i], fiber_nuv))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "179.446451060553,58.5841325235255,17.72215,16.95618,16.86512,16.59029,16.59573,0.064461\n", "127.758047177716,4.05524750834444,17.75126,16.96656,16.74418,16.31073,16.35811,0.06475648\n", "143.569934270161,10.6778559344543,18.486,17.60747,17.35559,17.08135,17.07806,0.06525452\n", "179.236432162099,44.8327830805518,17.53227,16.96531,16.93552,16.76346,16.78894,0.06439935\n", "182.560285457813,44.6657831628324,17.76719,16.99928,16.86522,16.50574,16.61898,0.06594051\n", "244.636075514965,27.7312286348228,17.06599,16.52032,16.73591,16.24394,16.43703,0.06422318\n", "161.513414086334,15.4185175577052,17.70758,16.86378,16.67677,16.3776,16.3641,0.06442506\n", "187.590171299233,17.7381113893822,18.27589,17.39026,17.20262,17.01104,17.03539,0.06586163\n" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_skyserver = np.genfromtxt('/Users/Benjamin/Desktop/UVIS/notebook.csv',delimiter=\",\",skip_header=1)" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "17.722149999999999" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_skyserver[0][4]" ] }, { "cell_type": "code", "execution_count": 349, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10000,)\n" ] } ], "source": [ "itmp = np.nonzero(np.logical_and(np.logical_and(np.logical_and(np.logical_and(np.logical_and(np.logical_and(\n", " dr7_info['Z']>0.02, dr7_info['Z']<0.30), \n", " dr7_mass['MEDIAN']>7.), dr7_mass['MEDIAN']<13.), \n", " dr7_ssfr['MEDIAN']<-1), dr7_ssfr['MEDIAN']>-20),\n", " dr7_info['SN_MEDIAN']>3.))[0]\n", "iarche = np.random.choice(itmp, 10000)\n", "print(iarche.shape)" ] }, { "cell_type": "code", "execution_count": 350, "metadata": { "collapsed": false }, "outputs": [], "source": [ "objs_dtype = [('PLATE', 'i4'),\n", " ('MJD', 'i4'),\n", " ('FIBER', 'i4'),\n", " ('RA', 'f8'),\n", " ('DEC', 'f8'),\n", " ('Z', 'f8'),\n", " ('MASS', 'f8'), \n", " ('SFR', 'f8')]\n", "objs = np.zeros(iarche.size, dtype=objs_dtype)\n", "objs['PLATE'] = dr7_info['PLATEID'][iarche]\n", "objs['MJD'] = dr7_info['MJD'][iarche]\n", "objs['FIBER'] = dr7_info['FIBERID'][iarche]\n", "objs['RA'] = dr7_info['RA'][iarche]\n", "objs['DEC'] = dr7_info['DEC'][iarche]\n", "objs['Z'] = dr7_info['Z'][iarche]\n", "objs['MASS'] = dr7_mass['MEDIAN'][iarche]\n", "objs['SFR'] = dr7_sfr['MEDIAN'][iarche]" ] }, { "cell_type": "code", "execution_count": 351, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fits = fitsio.FITS('/Users/Benjamin/AstroData/Garching/Archetype_sample.fits', 'rw', clobber=True)\n", "fits.write(objs)\n", "fits.close()" ] }, { "cell_type": "code", "execution_count": 352, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test = fitsio.read('/Users/Benjamin/AstroData/Garching/Archetype_sample.fits')" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype([('PLATEID', '>i2'), ('MJD', '>i4'), ('FIBERID', '>i2'), ('PHOTOID', '>i2', (5,)), ('RA', '>f4'), ('DEC', '>f4'), ('PLUG_MAG', '>f4', (5,)), ('PRIMTARGET', '>i2'), ('SECTARGET', '>i2'), ('TARGETTYPE', 'S19'), ('SPECTROTYPE', 'S6'), ('SUBCLASS', 'S21'), ('Z', '>f4'), ('Z_ERR', '>f4'), ('Z_WARNING', '>i2'), ('V_DISP', '>f4'), ('V_DISP_ERR', '>f4'), ('SN_MEDIAN', '>f4'), ('E_BV_SFD', '>f4'), ('ZTWEAK', '>f4'), ('ZTWEAK_ERR', '>f4'), ('SPECTRO_MAG', '>f4', (3,)), ('KCOR_MAG', '>f4', (3,)), ('KCOR_MODEL_MAG', '>f4', (5,)), ('RELEASE', 'S12')])" ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 354, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Now I am writing everything out...\n" ] } ], "source": [ "archespec.rest_allspec(overwrite=True)" ] }, { "cell_type": "code", "execution_count": 326, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<module 'datapath' from '/Users/Benjamin/Code/BGT-Cosmology/Spectroscopy/archetype/datapath.py'>" ] }, "execution_count": 326, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reload(datapath)" ] }, { "cell_type": "code", "execution_count": 327, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/Users/Benjamin/AstroData/Garching'" ] }, "execution_count": 327, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datapath.garching_path()" ] }, { "cell_type": "code", "execution_count": 361, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 356, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype([('WAVE', '>f4', (7382,)), ('FLUXMEDIAN', '>f4', (7382,))])" ] }, "execution_count": 356, "metadata": {}, "output_type": "execute_result" } ], "source": [ "elg_composite.dtype" ] }, { "cell_type": "code", "execution_count": 367, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x2767da5f8>]" ] }, "execution_count": 367, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x2777061d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(elg_composite[0]['WAVE'], elg_composite[0]['FLUXMEDIAN'])" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "112" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ioii.size" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0, 50)" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GAAA6EsYAADoSxgAAOhLGAAA6EsYAADoSxgAAOhLGAAA6EsYAADoSxgAAOhLGAAA6EsYA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\nCgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x237441860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xx = plt.hist(dr7_galex['FUV_MAG'][iz], bins=50, range=(17,24))\n", "yy = plt.hist(dr7_galex['FUV_MAG'][iz[iselect]], bins=50, range=(17,24))\n", "zz = plt.hist(dr7_galex['FUV_MAG'][iz[iselect[ioii]]], bins=50, range=(17,24))\n", "plt.ylim(0,50)" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200000.0 2222.222222222222\n" ] }, { "data": { "image/png": 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9RQaOnWUfsC9evKAiRYoonXQzMzPy8/PLkuOxnEXfSWNOTyJjY2OpYsWK1KFDh1TbOnfu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\n6j1aFWylPxeyBpwvdJ7eB3vjOdCTAdMHE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"text/plain": [ "<matplotlib.figure.Figure at 0x136be1e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "r = np.linspace(0, 50, num=200)\n", "r_uv = 3.\n", "f0_uv = 200000.\n", "f0_sky = 10000. #2700./100. #f0_uv/1000.\n", "f_uv = f0_uv*np.exp(-r/r_uv)\n", "\n", "r_fe = r_uv*3.\n", "f0_fe = f0_uv*r_uv**2/10./r_fe**2\n", "f_fe = f0_fe*np.exp(-r/r_fe)\n", "\n", "#mu0_uv = 20.\n", "#mu_uv = mu0_uv + 2.5/np.log(10.)*r/r_uv\n", "#mu0_feii = \n", "\n", "fig = plt.figure(figsize=(10,8))\n", "ax = fig.add_subplot(111)\n", "ax.semilogy(r, f_uv+f_fe, 'k', lw=4)\n", "ax.semilogy(r, f_uv, 'b-.', lw=4)\n", "ax.semilogy(r, f_fe, 'g--', lw=4)\n", "#ax.semilogy(np.r_[[0],r[10:]], np.r_[f_fe_iso[10], f_fe_iso[10:]], '-.', color='orange', lw=4)\n", "#ax.semilogy(np.r_[r[10],r[10:]], np.r_[[1E-10], f_fe_iso[10:]], '-.', color='orange', lw=4)\n", "ax.semilogy(r, np.sqrt(f_uv+f_fe+f0_sky), '--', color='dimgrey', lw=4)\n", "leg = ax.legend(['Total counts (UV+Fluorescent)', 'UV continuum, $\\sim \\exp(-r/r_s)$', \\\n", " 'Fluorescent Fe II*, $\\sim \\exp(-r/3r_s)$', 'Poisson uncertainties'], \\\n", " loc=1, frameon=False, fontsize=23)\n", "# bbox_to_anchor=(0.91, 0.90, 1., .102), loc=2, frameon=False, fontsize=20)\n", "color_legend_texts(leg)\n", "ax.set_ylim(5E-0, 2E5)\n", "ax.set_xlabel('$r$ [arcsec]', fontsize=22)\n", "ax.set_ylabel(r\"$f$ [counts per arcsec$^2$]\", fontsize=22)\n", "\n", "print(f0_uv, f0_fe)\n", "fig.savefig('/Users/Benjamin/Desktop/UVIS/exponential_profile.eps')" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200000.0 2222.222222222222\n" ] }, { "data": { "image/png": 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0pAEDBtCDBw8yvc5FixaRq6srWVtbU9OmTeno0aM635NDhw5Rs2bNqESJElSh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\nH/4HsAu4IE8QXl3fC4CXytzPzAycRJqZdQFP1pns1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"text/plain": [ "<matplotlib.figure.Figure at 0x1354609e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "r = np.linspace(0, 50, num=200)\n", "r_uv = 3.\n", "f0_uv = 200000.\n", "f0_sky = 10000. #2700./100. #f0_uv/1000.\n", "f_uv = f0_uv*np.exp(-r/r_uv)\n", "\n", "r_fe = 9.\n", "f0_fe = f0_uv*r_uv**2/10./r_fe**2\n", "f_fe = f0_fe*np.exp(-r/r_fe)\n", "\n", "r_fe_iso = r_uv*3.\n", "f0_fe_iso = f0_uv*r_uv**2/20./(np.log(15./r_uv))\n", "f_fe_iso = f0_fe_iso*(1./r)**2\n", "\n", "#mu0_uv = 20.\n", "#mu_uv = mu0_uv + 2.5/np.log(10.)*r/r_uv\n", "#mu0_feii = \n", "\n", "fig = plt.figure(figsize=(10,8))\n", "ax = fig.add_subplot(111)\n", "ax.semilogy(r, np.r_[f_uv[:10],f_uv[10:]+f_fe_iso[10:]], 'k', lw=4)\n", "ax.semilogy(r, f_uv, 'b-.', lw=4)\n", "ax.semilogy(np.r_[r[10],r[10:]], np.r_[[1E-10], f_fe_iso[10:]], '--', color='brown', lw=4)\n", "ax.semilogy(r, np.sqrt(f_uv+f_fe+f0_sky), '--', color='dimgrey', lw=4)\n", "leg = ax.legend(['Total counts (UV+Fluorescent)', 'UV continuum, $\\sim \\exp(-r/r_s)$', \\\n", " r'Fluorescent Fe II*, $\\sim r^{-2}$', 'Poisson uncertainties'], \\\n", " loc=1, frameon=False, fontsize=23)\n", "# bbox_to_anchor=(0.91, 0.90, 1., .102), loc=2, frameon=False, fontsize=20)\n", "color_legend_texts(leg)\n", "\n", "ax.set_ylim(5E-0, 2E5)\n", "ax.set_xlabel('$r$ [arcsec]', fontsize=22)\n", "ax.set_ylabel(r\"$f$ [counts per arcsec$^2$]\", fontsize=22)\n", "\n", "print(f0_uv, f0_fe)\n", "fig.savefig('/Users/Benjamin/Desktop/UVIS/powerlaw_profile.eps')" ] }, { "cell_type": "code", "execution_count": 215, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "19.042275032520141" ] }, "execution_count": 215, "metadata": {}, "output_type": "execute_result" } ], "source": [ "18.8+2.5*np.log10(1./0.8)#-2.5*np.log10(1.25*1.25/(3.75*3.75))#-2.5*np.log10(2.)" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2])" ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.r_[[0], [1, 2]]" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "21.788024660055239" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "19.04+2.5*np.log10(2*2*np.pi)#-2.5*np.log10(2.)" ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1, 13659, 8)" ] }, "execution_count": 229, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nfl_sdss['ALLFLUX'].shape" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "114.27884932551329" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "15367./np.sqrt(15367.+2715.)" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.99944691562985222" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(1.-np.exp(-15./2.))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.56433264798310034" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(1./3.14)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.1304" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "0.6*0.6*3.14" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9847040000000001" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "0.56*0.56*3.14" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0650508\n", "127.757\n", "4.05415\n", "9.8806\n", "7.82132919995\n", "-9.02692\n", "7.34821896085\n", "0.0763304432555\n", "0.658977246395\n", "0.0\n", "58.7676\n" ] } ], "source": [ "imin = np.argmin(np.abs(dr7_info['RA']-127.75746))\n", "print(dr7_info['Z'][imin])\n", "print(dr7_info['RA'][imin])\n", "print(dr7_info['DEC'][imin])\n", "print(dr7_mass['MEDIAN'][imin])\n", "print(np.power(10, dr7_sfr['MEDIAN'][imin]))\n", "print(dr7_ssfr['MEDIAN'][imin])\n", "print(np.power(10, dr7_fibersfr['MEDIAN'][imin]))\n", "print(np.power(10., dr7_sfr['MEDIAN'][imin])/np.pi/(dr7_phi['R_EXP'][imin][0]*1.256)**2)\n", "print(np.power(10., dr7_fibersfr['MEDIAN'][imin])/np.pi/(1.5*1.256)**2)\n", "print(dr7_galex['NUV_MAG'][imin])\n", "print(dr7_line['OII_3729_FLUX'][imin]/dr7_line['OII_3729_CONT'][imin]+dr7_line['OII_3726_FLUX'][imin]/dr7_line['OII_3726_CONT'][imin])" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "19.084621874040891" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "17.827+2.5*np.log10(5./3.) + (18.454-17.751)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.064461 0.06422318 0.06594051 0.06475648]\n" ] } ], "source": [ "print(dr7_info['Z'][nfl_sdss['INDEX']][0][index_of_index])" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 61.84864044, 73.18855286, 60.36682129, 74.24503326], dtype=float32)" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xx = dr7_line['OII_3729_FLUX'][nfl_sdss['INDEX'][0]]/dr7_line['OII_3729_CONT'][nfl_sdss['INDEX'][0]] \\\n", " +dr7_line['OII_3726_FLUX'][nfl_sdss['INDEX'][0]]/dr7_line['OII_3726_CONT'][nfl_sdss['INDEX'][0]]\n", "xx[index_of_index]" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9993280567362987" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "0.564*0.564*np.pi" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = fitsio.read('/Users/Benjamin/Desktop/UVIS/h2templat_J3.fits', ext=0)" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "54.598150033144243" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.exp(10./2.)/np.exp(2./2)" ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.4722222222222223" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7500./36./60." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
waltervh/BornAgain-tutorial
old/python/tutorial.ipynb
2
822124
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Python\n", "\n", "## Useful links\n", "\n", " * BornAgain: http://bornagainproject.org\n", " * BornAgain tutorial: https://github.com/scgmlz/BornAgain-tutorial\n", " * Python official tutorial: https://docs.python.org/3/tutorial/\n", " * Anaconda Python: https://www.continuum.io/\n", " * PyCharm IDE: https://www.jetbrains.com/pycharm/\n", " \n", "*Note that BornAgain Win/Mac requires Python 2.7*\n", "\n", "\n", "## Clone the BornAgain Tutorial Repository\n", "\n", "From command line:\n", "```bash\n", "git clone https://github.com/scgmlz/BornAgain-tutorial.git\n", "```\n", "\n", "Windows/Mac: can also use [Github Desktop](https://desktop.github.com/).\n", "\n", "## Power Users: Build BornAgain with Python 3 Support\n", "\n", "```bash\n", "git clone https://github.com/scgmlz/BornAgain.git\n", "mkdir build; cd build\n", "cmake .. -DCMAKE_BUILD_TYPE=Release -DBORNAGAIN_USE_PYTHON3=ON\n", "make && make install\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Verify your Python Environment\n", "\n", "Check the Python version:\n", "```\n", "$ python --version\n", "Python 3.5.2\n", "```\n", "\n", "Check for numpy and matplotlib\n", "```\n", "$ python -c \"import numpy\"\n", "$ python -c \"import matplotlib\"\n", "```\n", "\n", "Check for BornAgain Python module\n", "```\n", "$ python -c \"import bornagain\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running Python\n", "\n", "* Direct from command line:\n", "```\n", "$ python -c \"print 'hello, world'\"\n", "hello, world\n", "```\n", "\n", "* Run script from command line:\n", "```\n", "$ echo \"print 'hello, world'\" > hello.py\n", "$ python hello.py\n", "hello, world\n", "```\n", "\n", "* Default interactive interpreter:\n", "```\n", "$ python\n", ">>> x = 5\n", ">>> x * x\n", "25\n", "```\n", "\n", "* IPython interactive interpreter:\n", "```\n", "$ ipython\n", "In [1]: x = 5\n", "In [2]: x * x\n", "Out [2]: 25\n", "```\n", "\n", "* IPython interactive notebook:\n", "```\n", "$ ipython notebook\n", "```\n", "\n", "* Jupyter interactive notebook:\n", "```\n", "$ jupyter notebook\n", "```\n", "* Run within PyCharm IDE\n", "\n", "*Notebook support is included by default with Anaconda, and can be installed as an optional package on most Linux distros.*\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python 2.7 vs. 3.5 Compatibility\n", "\n", "There are a few important differences between Python 2.7 and 3.5. The line below is used to ensure that this notebook remains compatible with both. There is a list of differences between Python 2.7 and 3.5 near the end of this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Data Types\n", "\n", "Python has many data types, e.g.\n", "* numeric: int, float, complex\n", "* string\n", "* boolean values, i.e. true and false\n", "* sequences: list, tuple\n", "* dict\n", "\n", "Variables are declared via assignment:\n", "```python\n", "x = 5\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numeric Types\n", "\n", "Python numeric types are similar to those in other languages such as C/C++.\n", "\n", "```python\n", "x = 5 # int\n", "x = 10**100 # long (2.7) or int (3.5)\n", "x = 3.141592 # float\n", "x = 1.0j # complex\n", "```\n", "\n", "*Note: ordinary machine types can be accessed/manipulated through the ctypes module.*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arithmetic Operations\n", "\n", "```python\n", "3 + 2 # addition\n", "3 - 2 # subtraction\n", "3 * 2 # multiplication\n", "3 ** 2 # exponentiation\n", "3 / 2 # division (warning: int (2.7) or float (3.5))\n", "3 % 2 # modulus\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "Use the Python interpreter to perform some basic arithemetic." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strings\n", "\n", "```python\n", "x = \"hello\" # string enclosed with double quotes\n", "y = 'world' # string enclosed with single quotes\n", "x + ' ' + y # string concatenation via +\n", "\"{} + {} = {}\".format(5 , 6, 5+6) # string formatting\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lists\n", "\n", "```python\n", "x = [1, 2, 3] # initialize list\n", "x[1] = 0 # modify element\n", "x.append(4) # append to end\n", "x.extend([5, 6]) # extend\n", "x[3:5] # slice\n", "```\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuples\n", "\n", "Tuples are similar to lists, but are immutable:\n", "\n", "```python\n", "x = (1, 2, 3) # initialize a tuple with ()\n", "x[0] = 4 # will result in error\n", "```" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List Comprehension\n", "Comprehension provides a convenient way to create new lists:\n", "```python\n", "[ i for i in range (5) ] # result: [0, 1, 2, 3, 4]\n", "[ i**2 for i in range (5) ] # result: [0, 1, 4, 9, 16]\n", "the_list = [5, 2, 6, 1] \n", "[ i**2 for i in the_list ] # result [25, 4, 36, 1]\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise \n", "\n", "Create a list of floating point numbers and then create a second list which contains the squares of the entries of teh fist list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boolean Values and Comparisons\n", "\n", "Boolean types take the values ```True``` or ```False```. The result of a comparison operator is boolean.\n", "\n", "```python\n", "5 < 6 # evalutes to True\n", "5 >= 6 # evaluates to False\n", "5 == 6 # evaluates to False\n", "```\n", "\n", "Logical operations:\n", "\n", "```python\n", "True and False # False\n", "True or False # True\n", "not True # False\n", "True ^ False # True (exclusive or)\n", "```" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Functions\n", "\n", "Functions are defined with ```def```:\n", "```python\n", "def hello():\n", " print 'hello, world'\n", "```\n", "*Note: Python uses indentation to denote blocks of code, rather than braces {} as in many other languages. It is common to use either 4 spaces or 2 spaces to indent. It doesn't matter, as long as you are consistent.*\n", "\n", "Use the return keyword for a function which returns a value:\n", "```python\n", "def square(x):\n", " return x**2\n", "```" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Loops and Flow Control\n", "\n", "For loop:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "4\n", "9\n", "16\n", "25\n", "36\n", "49\n", "64\n", "81\n" ] } ], "source": [ "for i in range(10):\n", " print(i**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to use `for..in` to iterate through elements of a list:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n", "world\n" ] } ], "source": [ "for i in ['hello', 'world']:\n", " print(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While loops have the form `while condition`:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n", "world\n" ] } ], "source": [ "i = 0\n", "while i < 10:\n", " print(i**2)\n", " i = i + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The keywords `break` and `continue` can be used for flow control inside a loop\n", "* `continue`: skip to the next iteration of the loop\n", "* `break`: jump out of the loop entirely" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "4\n", "5\n", "6\n" ] } ], "source": [ "for i in range(10):\n", " if i == 3:\n", " continue\n", " if i == 7:\n", " break\n", " print(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the keywords ```if, elif, else``` for branching\n", "```python\n", "if 5 > 6:\n", " # never reached\n", " pass\n", "elif 1 > 2:\n", " # reached\n", " pass\n", "else:\n", " # never reached\n", " pass\n", "```" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "Write a function `fib(n)` which returns the `n`th Fibonacci number. The Fibonacci numbers are defined by \n", "* `fib(0) = fib(1) = 1`\n", "* `fib(n) = fib(n-1) + fib(n-2)` for `n >= 2`." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Exercise\n", "\n", "> ”Write a program that prints the numbers from 1 to 100. But for\n", "multiples of three print Fizz instead of the number and for the\n", "multiples of five print Buzz. For numbers which are multiples of\n", "both three and five print FizzBuzz.”\n", "\n", "http://wiki.c2.com/?FizzBuzzTest" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Modules\n", "\n", "Load external modules (built-in or user-defined) via ```import```:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.141592653589793\n", "1.0\n" ] } ], "source": [ "import math\n", "print(math.pi)\n", "print(math.sin(math.pi/2.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rename modules with ```as```:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.141592653589793\n" ] } ], "source": [ "import math as m\n", "print(m.pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load specific functions or submodules:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "from math import pi, sin\n", "print(sin(pi/2.0))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## User-defined Modules\n", "\n", "Any code written in a separate file (with `.py` extension) can be imported as a module. Suppose we have a script [my_module.py](my_module.py) which defines a function `do_something()`. Then we can call it as" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "doing something from my_module!\n" ] } ], "source": [ "import my_module\n", "my_module.do_something()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Exercise\n", "Implement your FizzBuzz solution as a function called `FizzBuzz()` in a module called `fizzbuzz`. Check that it works by importing it and calling `FizzBuzz()` in a separate script." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## numpy\n", "\n", "`numpy` is a module used for numerical calculation. The main data type is `numpy.array`, which is a multidimensional array of numbers (integer, float, complex)." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "2.5\n" ] } ], "source": [ "import numpy as np\n", "x = np.array([1, 2, 3, 4])\n", "print(x.sum())\n", "print(x.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The basic arithmetic operations work elementwise on `numpy` arrays:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 6 8 10 12]\n", "[ 5 12 21 32]\n", "[ 0.2 0.33333333 0.42857143 0.5 ]\n" ] } ], "source": [ "x = np.array([1, 2, 3, 4])\n", "y = np.array([5, 6, 7, 8])\n", "\n", "print(x + y)\n", "print(x * y)\n", "print(x / y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to call functions on numpy arrays:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.84147098 0.90929743 0.14112001 -0.7568025 ]\n", "[ 0. 0.69314718 1.09861229 1.38629436]\n" ] } ], "source": [ "x = np.array([1, 2, 3, 4])\n", "print(np.sin(x))\n", "print(np.log(x))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Generating `numpy` Arrays\n", "\n", "`numpy` arrays can be generated with `zeros`, `ones`, `linspace`, and `rand`:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0. 0.]\n", "[ 1. 1. 1.]\n", "[-1. -0.33333333 0.33333333 1. ]\n", "[ 0.34741091 0.66844901]\n" ] } ], "source": [ "print(np.zeros(4))\n", "print(np.ones(3))\n", "print(np.linspace(-1, 1, num=4))\n", "print(np.random.rand(2))" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Plotting with `matplotlib`\n", "\n", "We use `matplotlib.pyplot` for plotting:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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55miNk8rLyyMvL2+L2/Lz8+N+PnOVqGRmtjPQEz/AdBfgqdiffweucM7dE/eT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\nZisc33PNNaGTiEimUimR4C6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81f8123710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "\n", "x = np.linspace(-3.14, 3.14, num=100)\n", "y = np.sin(x)\n", "\n", "plt.plot(x, y)\n", "plt.xlabel('x values')\n", "plt.ylabel('y')\n", "plt.title('y=sin(x)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Exercise\n", "\n", "Create plots the following functions\n", "* `f(x) = log(x)`\n", "* `f(x) = sqrt(x)`\n", "* `f(x) = x**2`\n", "* `f(x) = log(1 + x**2)`\n", "* anything else you might find interesting or challenging" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Combining Plots\n", "\n", "Plots can be combined using addition:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ZxzNa3Le0plTMeHuG8Pudn/jkm09cZtNDDwnh4yPEc88J0fTBv+Xt7sMPr7vv2rVy30WL\nhGhqcplJmlaoaagRt753q/D7nZ/46mDLN4Dq+mpx32f3CZ5G/HzNz4XdbneJTf/1X3LoLFokH3Za\nwiEqZs7sOqKiKwmKGy4Iiaartjcv/P4t4OvrfCYbqAEOAve1cg4tKDpIaU2pCP9juLjx7RvF+Zrz\nbfrM2aqzIuEfCaL3//YW2Sed/52vWydH5V//2vbPvPpq+z+jcR7nqs+JmD/HiNgXY8Xe03vb9Jna\nhlrxnU++I4ynDbF031Kn27R+vRwTL78shCgqEqJ3byG+9a0WP/PRR0IYhhS0LpqbNC1QVV8lZr87\nWwQ8GyDWHl7bps/Y7Xbxp8w/CZ6mWa9YZ0hLk+Po2WfbPia6mqjoMoLCHZsWFB3nia+eEEG/DxIF\nZQXt+ty56nNi8muTRdQLUaKq3nlXTGmpELGxQsyY0f6nxJ/9TLq2v/jCaeZo2sgTXz0hgp8LFvml\nzcSmmqHJ3iTmfDBHxPw5xqnjqLpaiMGDhUhJEaKp0S7E/PlC9OsnxJkzrX7WIU5/+1unmaNpI49/\n+bgI/H2gWH90fbs+Z7fbxe3v3y5i/hwjKutacSG0g7o6IYYPF2LatPbfjxyi4v77nWaOy9CCQguK\nTrOjaIew/NYi/pDxhw59/lDJIeHzjI94Lv05p9m0eLEQISFCHD3a/s82Nsp5IzhYiL1te0jWOIF9\nZ/YJ72e8xfObnu/Q5w+WHBQ+z/iIZzY84zSbfvELIXx9hdi/Xwjx7rvyNvdZ259en35afmSH66J6\nmqs4WHKwU+PoUMkh4fc7P/HLdb90mk3PPy/Dqbt2dezzL78sPV65uU4zySVoQaEFRadosjeJ5DeS\nxcj/GynqG+s7fJzHv3xchDwXIk5Xnu60TStXytH4z392/BiVlfLJdP78TpujaSO3vX+biPtLnKhp\nqOnwMX6+5uci8PeBorCssNP25OTISeDZZ4UQhYVChIYK8d3vtusY9fVCDBokxNy5nTZH00a+9fG3\nRPSfo0V1fXWHj/H0+qeFzzM+Yv+Z/Z2259gxmcf11FMdP0Z9vRBDhwpx882dNselaEGhBUWneDv3\nbcHTiK+PfN2p45yuPC16PN9DPP7l4506ztmzQkRECHHbbZ2PXb/zjhzVOTmdO46mdb46+JXgaTqd\nWFlWWyb6/bGfuPezezt1nIYGIRIShBgz5sJqje98R4jwcCFKStp9rPfek+MoK6tTJmnawOaCzYKn\nEW/lvtWp41TXV4v4l+LF7HdndzpBc/58ISIjhSgv79RhxCefyHG0bl3njuNKtKDQgqLDnK85L/r+\noa9Y9MkipxzvufTnhPcz3uJgycEOH+P++4Xo1UuIEyc6b09Dg/RSLFjQ+WNpmqe+sV6M+PsIccNb\nNzglu/617a8JnkZkFXR8Bv/DH2QezZYtQojNm+Xt7Y03OnSsxkYhRo0SYtasDpujaQN2u11Mf2u6\nGPPyGNHY1Njp431+4HPB04iP9nzU4WOsWCGHzkcdP8RF7HYhpk6VQlfV1UNaUGhB0WF+8sVPRPBz\nweJEuRNmbyEzs6NeiBLf+rjlDPrmOHpUTgJ/+5tTzBFCCPH223Jkqx677Mr8dfNfhfG0IXKLnPMl\nNzY1inGvjBOJryeKJnv777wFBUL4+wvx5JNC3sWnTZOuisaOT1KffSbH0fr1HT6EphVW7F8heJpW\nl4i2h3lL5omoF6JERV1Fuz9bVSXEwIFC3HST81b6pKfLcbRkiXOO52y0oNCCokPsLN4pLL+1iBds\nLzj1uG/mvCl4GrG5YHO7P/uzn8kwd2vru9tDQ4OMgS9c6Lxjai5xtuqs6PU/vcQPlv/AqcfdcHSD\n4GnEezvfa/dnf/lLWeSsvFwIsXSpvLWtXt0pe+x2ISZNEsJq1ctIXUFDU4MY/vfhIvWdVKfWkDhy\n7ojwf9Zf/L+1/6/dn33mGZnQ21zxqo4yd64Q8fFqFk7TgkILig7xwNIHxMC/DOxUIub1aGxqFKNf\nHi2mvzW9XTeG8nIhevYU4j//06nmCCGEePNNoTP1XcQTXz0hQp4LEcUVxa3v3E7u/PBOEfVCVLuS\n82pqhAgLE+KJJ4TMhBsyxGmZcKtWyXGklyM7n39s/4fgaVxSz+YXa38hejzfo13LSOvrZS7Xo486\n3RyxZ4/0xL70kvOP3VmcKSh0z8Zuwrmac3z4zYc8PPFhfLx8nHpsL4sXf5j1B9KPp/PFwS/a/Ll3\n3oHKStmjw9ncey/Ex8Mzzzj/2N2ZiroK/pnzT55KeorwYOe3/X4+9XlOVJzgk72ftPkzH34IZ8/C\nj38M/OMfcPgw/PGPTrHnpptkh9v//m/ZX0bjHKobqvnNht/w3THfJaG/83swPTLpESrqKvj3nn+3\n+TPLlsk+HT/6kdPNYdQo2VTsd7+TvYk8FS0ougnv7HiHJnsTiycsdsnxbxl8C0nRSby05aU27W+3\nw0svwV13QUyM8+3x8YH/+i/47DPYtcv5x++uLNmzhJrGGn6Q8AOXHH9InyHMjJvJ6zmvt2l/IeBv\nf5NdZ4f0K5Otab//fRgzxin2GAY8+yzk5sqxpHEOH33zEcWVxfz2xt+65PgDQwdy65BbeWX7K23+\nzCuvwLRpMHq0S0zit7+FsjJ46y3XHF8FtKDoBggheDX7Ve4ceSf9gvq55ByGYfDDhB+SdiSNY6XH\nWt3/iy/g0CHZNtpV3HcfxMVpL4UzeS37NW4bchvRPaJddo6HEh5iU/4m9p/d3+q+W7ZAdjY89hjw\n/PNQU+P0P/j06TBrFvzhD049bLfmzdw3SY1LZVDvQS47x6OTHiW7KJvtJ7e3uu++fbB+PTz6qMvM\nIToa5s6FN9/0XG+XFhTdgA3HNpBXkscjEx9x6XnuHnU3Qb5BvL3j7Vb3/ctfIDERpk51nT0+PvCr\nX8Gnn0ovuKZz5Bblkl2UzQ8TfujS8ywYvoA+AX14Pbt1L8Xf/iZDW7eMLpSD6uc/h8hIp9v08MOw\ndauceDSdI68kj035m3hwwoMuPc+tg28ltmcsr25/tdV9X30V+vaFO+90qUksXiw9prm5rj2PWWhB\n0Q14ZfsrjAgbwfQB0116nmDfYL496tu8teMt7MLe7H67dsHXX8OTT7rUHADuuQeCg+H9911/Lk/n\n9ZzXiQyJ5LYht7n0PH7efjww7gHe2fkOdY11ze5XXAwffyxzJyx/ewkCAuBnP3OJTXPnQq9eMu9H\n0zneyn2LUP9QFgxf4NLzeFm8eCjhIT7Y/QGltaXN7ldVJf+uixeDn59LTeLmm6F/f+ml8ES0oPBw\niiuLWbp/KY9MegTDMFx+vsUTFpNfls/XR79udp+//EW6/+64w+XmEBgonzr+9S/PdTO6g6r6Kt7b\n9R6Lxy/G2+Lt8vP9IOEHlNSUsGz/smb3ee016YX6/p1lMhnz0UchJMQl9vj5waJFchw1NbnkFN2C\nRnsj7+x8h3tG30OAT4DLz/dgwoM02Bv4185/NbvPv/8tEyUfftjl5uDtDQ88IB9wamtdfz53owWF\nh/NGzhv4WHy4b+x9bjlfUnQSw8OG80buG9f9/enT8mL6yU/kZOAO7rtP5mts3eqe83kiH33zEZX1\nlTyY4Fo3tYMRfUeQEpvSbHJmfb10U997L/T6+HV5d37sMZfa9MADcPIkpKW59DQezepDqymqLHLb\nOIoIjmDh8IW8sv0VR5mBa3jlFbj1Vplv5Q6+/30oLZWrSjwNLSg8mCZ7E6/lvMZ3Rn+HXgG93HJO\nwzBYPH4xS/ct5VzNuWt+/8Yb4OUFP3RtGP4KbrxRhtX/1fxDiqYVXs95nZsG3cTA0IFuO+cPE35I\n2tE0Dp+7NgFm6VIoKoLHHq6XLq9775W+ZBcyeTIMH67DHp3hjdw3GBc+jgkRE9x2zkcmPcK+s/vY\nlL/pmt9t2yaTel2ZjHk1Q4dCcrJnrvbQgsKDWXVoFfll+Tw6yY1XC3D/uPtptDfywe4Prvndv/8N\n8+dD797us8fLC777XXnu+nr3nddT2HN6D1mFWS5Pxryau0beRah/KP/M+ec1v/u//4MbboDRez+C\nEydcljtxOYYhvRRLl3p2LQFXcbrqNCvzVvLghAfdEn51MGPgDIb2GXrd5MxXXoEBA6SHwp0sXgxr\n10J+vnvP62q0oPBgXtn+Cgn9E5gUOcmt5w0PDmfO0Dm8mXtl5lFenkzIvPtut5oDyLBHSQmsXu3+\nc3d1Xs9+nX5B/Zg7bK5bzxvgE8C9Y+7lrR1v0dDUcPH9ggLYtAkeXCzgT3+Ss8GoUW6x6d57ZXTl\n44/dcjqP4r1d72ExLNwz5h63ntcwDB6Z+Aif7P2E01WnL75//rx8yHjoIfnQ4U7uvlvmd3mat0sL\nCg+lqKKILw9+ycMTH3br04CDxRMWk1ucS27RpfVRH38MQUHufxoAWedo7Fgd9mgvtY21/GvXv/j+\n+O/j6+Xr9vM/NPEhTlWd4vO8zy++99lnMv9mYc802LkT/uM/3GZPdLSsSfH22247pUcghOCN3Dfk\nkuDAPm4//wPjH8BiWHhv13sX3/v0U6irk94CdxMSAt/6lgx72JtfENfl0ILCQ1m2fxkWw8JdI+8y\n5fy3DbmNiOCIK7wUH30kl98FuD65+7rcdx+sWCETojRt47N9n3G+9rzLKmO2xpjwMSRGJfLP3Eth\nj08+gdmzIfiVP8GECTBjhltteuAByMjQtU3aw9YTW9l7Zq/La080R++A3tw8+GY+23ep3Olnn8mi\nZRERppjE4sVw9Chs3GjO+V2BFhQeytL9S7lx4I30DnBjssJleFu8uX/s/by/+31qG2tNDXc4uOce\nmUPx6afm2dDV+GTvJ0yNnsrg3oNNs+G+sfex5vAaSmtLKSqCzEz4YeIuGb/6j/+QyQ1uZOFC+YT5\n7rtuPW2X5s3cN4npEUNqXKppNiwcvhBbgY3iymLKymDdOvcsXW+O5GQYMsSzalJoQeGBnK85z/pj\n61k4fKGpdnxv/Pc4X3ue1YdWmxrucBAZCampOuzRVqobqll1aJXp42jesHk02hv56uBXLF0KFgvc\nsvcF2QTGBIUaGChP++67nuWudhUNTQ18tPcj7h93P14WNycrXMbcoXOxGBaW71/OF19AQwMscG1t\nrRYxDLmE9NNPZXEtT0ALCg/k87zPabQ3urwSXWuM6DuCYX2GseLACtPDHQ7uu0+6GI8fN9eOrsC6\nI+uoaawxfRzF9IxhUuQklh1YxqefwryU8/gv/bd7i5lcxfe+B8eOyeRQTctk5GdQWltqujDtE9iH\nGwbewNL9S1m6VC4DdkVjwvZw112y/cy6deba4Sy0oPBAlu5fSmJUIlE9osw2hXnD5rFs30p27W4y\nNdzh4I475BPmB9euaNVcxbL9yxgRNoKhfYaabQoLhi3gi7wvWb+pjif7/1uWq7z/ftPsmTZNJmjq\nDqSts+LACqJColzSpry9LBy+kLSjaXyRVmpquMPBkCGytsmKFWZb4hy0oPAwHG7qO0YocLUA84fN\n51zdGfwHbzU13OEgOFi6OZcsMdsStWmyN7EybyXzh8032xQA5g+fT1VDJcR9TeKBd2RTBLOy6ZDu\n6jlzYOVKXdK9JYQQLD+wnHnD5pmy2uxqFgxfQKO9kZroL5QQFADz5slx5Akl3bWg8DBWH1pNTWON\n6e5FB1Ojp+JVF0bcLctND3c4mD8fdu/2vKIyzsRWYONs9VnTwx0ORvUdRUDtIJLHvYNvzha51MJk\n5s6VWfq6A2nzfHPmG46WHmXesHlmmwJAdI9owmqnEDJlKUPNd7wBUlCcOeMZrQG0oPAwlu5fyuh+\noxnSZ4jZpgBw6KAXTXvnUBGpjk/vpptkIZsvvzTbEnVZtn8Z/YP7MzlqstmmAFBaalCbu4AFxZ8j\nQkPlXdhkZs6U4bOVK822RF1WHFhBsG8wMwa6d2lvczQ0QNX2O6iN/oqahhqzzQFg6lQIC/OMsIcW\nFB5EQ1MDK/NWKuOdAFnMyu/YfArr9nGw5KDZ5gAQGipj4J9/3vq+3REhBMsOLGP+sPlYDDVuEStW\ngLF3Lt/aWcXpOTPA399sk/D3l0Wu9DhqnuUHlnPzoJvx83ZxX/A2snEj1OQupMGoZs3hNWabA8iH\nmzlztKAQcAhHAAAgAElEQVTQKMaGYxuUyKa+nI8/hrkjZ+Pn5cfKPHUe5ebMga+/lhnWmivZc3oP\nR84fYf5wNfInQC6t+3FILTHl8MnkILPNucjcuWCzybLumispqihi64mtyuThgEyiHRgylJF9R/LZ\nfnUyaufNg717ZVfkrowWFB7EZ/s+Y2DoQMZHjDfbFAAOHpTFrBbdFcSs+FksP7DcbJMucvvtUkys\nX2+2JeqxbP8yQnxDlHFTl5fLGlaPBrxHcWQP/maoE2y+/XZZi0KHz67l87zPsRgWbhtym9mmAPLv\ntHSpLEx2x/A7WHlg5RU9Ysxk9mzw8+v64TMtKDwEu7Cz/MByFg5fqEQ2NcBXX8kyATfdJFd7ZORn\nUFKtxqPc8OEQFwdffGG2Jeqx7MAybhtymzJu6i++AL/6coZ98ynnvzWPA+fy2H92v9lmAbJj+qRJ\nOuxxPZYfWM602Gmm9O64Hps3Q3GxXDq+cMRCzteeZ+NxNepeBwfLontdPeyhBYWHsKVwC0WVRcos\nFwX5VJmSIi+WOUPnYBd2vjyoxqOcYciny88/18v+LqegrICcohxlVneAvMn+LPYTLHW1xD32awJ9\nAlm+Xx1v19y5sGqVLOuukVTVV7HuyDrmDTU/edbB0qUQHg5JSTAhYgIDeg5g6b6lZpt1kXnzZKG0\nc+fMtqTjaEHhIXy27zP6BfUjKTrJbFMA2eJ5wwZZLgCgf0h/pkRNUSrsMWeOXDr6zTdmW6IOyw8s\nx8fiw62DFSgaglybv3Yt3M87kJqKf/wQbh50M8sOLDPbtIvMmSPDMhkZZluiDmuPrKWuqU6Z5aJC\nyPyJBQtkEqRhGCwcvpCl+5diF2rUT58zR473r74y25KOowWFh/D5wc+ZO3SuqbXyLycjA6qrLwkK\nkGGPVYdWUdtYa55hl3HDDXLZnw57XGLZ/mXMiJtBT/+eZpsCQG4uhJQcJS4//WLtiQXDF7C5cDNF\nFUUmWyeZMEH2ienq8W9nsvzAckaEjVBm+frBg3DkiJy0HSwYvoCiyiJyi3LNM+wyoqJk+Kwrhz20\noPAACssL2X92PzcPurn1nd3E6tWykOHYsZfemzdsHlUNVWw4tsE0uy7HsexPCwrJ+ZrzbDi2gQXD\n1Al3rFkDD/r+CxEcLLPpgNuH3I6X4cWKA2rceXXVzCtpsjfxed7nyngnQHq5fHzgxhsvvZcUk0SQ\nTxBrj6w1za6rmTdPeii6avhMCwoPYO3htRgYzIybabYpF1m9WnonLs8PHdV3FHGhccpMBCDzKDIz\nu3bc0ll8ffRrmkQTtw+93WxTLrJmDdzj/xnGvHmyXS2yyVNSTBKrD6822bpLzJ0Lhw/DgQNmW2I+\nmws3c7b6rHKCIilJ5nM58PXy5caBNyonKCoqZL2MrogWFB7A2iNrmRg5UZls6pMnZWnrm69ymBiG\nwfxh81mZtxKhyKOcY9nfanXmJtNYd2QdQ/sMJbZnrNmmAPLGeiLzGPHlO2W99MuYHT9bCiC7Gg0Q\nUlNlJ10d9oCVeSvpG9iXxKhEs00BZHXM9evl0syrmR0/m4z8DKobqt1v2HUYOxZiY2G5Oqlm7UIL\nii6OXdhZd2Qds+Ovc7WYxOrV0jNxvQv4pkE3UVheyIESNR7loqJg/Hgd9gBYd3Qds+JmmW3GRTZs\ngFsaVyJ8fOCWW6743az4WZTVlZFdlG2OcVcRECBFhV4+Kh9wZg+arUw+19atMmn2uoJi0Gzqm+rZ\ndFyNPvSO1Wdr1Cji2W60oOji7Dq1izPVZ5QTFBMnyvr0V5MyIAUfiw9pR9Lcb1gz3H67jFt6Qre/\njnKs9BiHzh1iVrw6gmLNGviO/3KYMQN69Ljid5MjJxPiG8Law+q4q+fOleGz8+fNtsQ8ztWcI7co\nVylhunatLLc/adK1vxsRNoLIkEilwh6pqTKJtKDAbEvajxYUXZy1h9cS6BOINcZqtinApWV+Vz1Q\nXiTYN5ip0VNJO6qWoDh3Tha+6a6kHUnDYli4ceCNZptykayvSplavxFj/rWlm328fLhx4I2sO7rO\nBMuuz003yfHfVePfzmD90fUIBKnxqWabcpG1a+Uk7XUdh4lhGMyOn62UoJgxQ3oq0tS5RbYZLSi6\nOGuOrGH6gOnKVDXMzpaT89X5E5eTGpfK+mPrlYl/T5kCffp07fXfnWXd0XVMipxEr4BeZpsCwLFj\nMPTwl3jZG5vtLDorfha2AhtV9VXuNa4ZBg6E+PiuORE4i7SjaQzuPViZPJyyMtiy5frhDgez42ez\n69QuiiuL3WdYC/TuLZcid8Vx5BZBYRjGjw3DOGoYRo1hGJsNw2i2J7JhGA8YhmE3DKPpwqvdMAw1\nMmYUo6ahhk3HNykX7ujRAxJbyMdKjU+ltLaUnKIc9xnWAl5eshV1d+3roWIeztq1MN9YQeO4BIiO\nvu4+s+Nl/DsjX52KUjNnyqZz3ZW0o2lKhTvWr5deo5YEhSPMt+6IOt6u1FQpKBTJXW8zLhcUhmF8\nG3gB+A0wAdgJrDYM4zoR9ouUARGXbQNaO09jY+dt7Wpk5GdQ11THTYNuMtuUi6xaJWs7+Pg0v09i\nVCLBvsFKhT1mzJDJW5WVZlvifnad2sXZ6rNK5U98vaqe2y1f4X1H850qh4cNJzIkUqmJYOZM2TWy\nWI2HXbdSUFZAXkmecuGO+Hi5NUd4cDhjw8cqFfZITYWiItivRsuaNuMOD8WTwD+EEO8KIfYDjwDV\nwOIWPiOEEGeEEKcvbGdaO8m+fU6ytgux9sha+gf3Z1TfUWabAkBpqXQvthTuABn/nj5gulKCYuZM\nKUq7Y/nkdUfWEeAdoEzZ9sZGqFu9geCm8muWi16OYRjMip+lVB7FjAsNWrujtyvtaBoGhjJdakEK\nipva8Lw1O342aw+vVWY5+7Rp8qFsnTpDu024VFAYhuEDTAQuzhxC/sXWAS3dvYINwzhmGEa+YRjL\nDMMY2dq5tm3rtLldjrVH1jIrfpYy3UXT0qR7sTVBATKPIiM/Q5ky3EOHys6R3dFdve7IOqXycLZv\nh9Sq5dRGDLiy1Op1mBU3ix3FOzhT1eozh1uIiIBRo7pm/LuzpB1NY0L/CcrUwzl+XK6WaCnc4WB2\n/GyKKovYe2av6w1rA0FBshBXVxtHrvZQhAFewKmr3j+FDGVcjwNI78U84LtIG22GYUS1dKLt2ztn\naFfjdNVpdhTvUCruvXo1DBsGA1oNUMm4ZW1jLbYCm+sNawOG0T3zKOoa60g/nq5UuGPNasF8YwW+\nd867stTqdXC4178+qo4S7I55FEII0o6kkRqnVrjDYpF/j9ZIGZCCr5evcmGPDRu6VjjfrFUeBnBd\n35IQYrMQ4j0hxC4hxCbgDuAM8FBLB9y5s+vWP+8IjjoOSk0Ea9rmnQAY3W80fQP7KlWPYsYMyMmR\noZvuQlZhFjWNNUqNo2NLc4kWhVgWNh/ucBAZEsnIviOVmghmzoSjR+XWXdh3dh9FlUVKCYo1a2Dy\nZFmDojUCfQKZFjtNqXGUmipXqeSokbveJrxdfPyzQBMQftX7/bjWa3FdhBCNhmHkAoNb2q+29klm\nzuxJ796X3lu0aBGLFi1ql8FdhbVH1jK632j6h/Q32xRALvM7frxtTwMAFsPCzLiZpB1N4/f83qW2\ntZWZM2UZ7vT0ZlcqehzrjqwjLDCMseEthxbcRVkZxO1aTl1gKH7Tp7fpM7PiZrHswDKEEEqE/268\nUT4Zf/01PPig2da4h7Qjafh6+TItdprZpgAy9JqWBj/6Uds/Mzt+Ns+mP0t9Uz2+Xr6uM66NTJki\ne4+kpcmfncGSJUtYsmTJFe+VlZU55+C42EMhhGgAsoGLstWQV3wq0CZft2EYFmA00GKv4qCgF7nl\nlhWsWHFp81QxIYSQ5W0VCnds3Ci90ykpbf9Malwq205uo7RWDZdAXJwM13Qnd/W6I+tIjUvFYqhR\nkmbDBpgrltMw67aWlwpdxqz4WeSX5XP4/GHXGtdGQkMhIaF7jaO0o2kkRScR5BtktimAbHt/7lzb\nEjIdzI6fTVVDFVkFWa4zrB34+MANNzg3j2LRokVXzJErVqzgxRdfdNrx3XEX+TPwkGEY9xuGMRx4\nFQgE3gYwDONdwzCec+xsGMb/ZxjGbMMw4gzDmAC8j1w2+s+WTpKQ0H3i3wdKDlBYXqiUoNiwQebP\nXe4hao1Z8bOwCzsbj6lTWrA75VGU1pay7eQ2pcbRrpXHGc9Ogu9pu4voxoE34mV4Kbd89Ouvu14d\ngY7QaG9kw7ENSoU71q6VT/dTp7b9MxP6T6BPQB/lwh6ZmVCrRu56q7hcUAghPgJ+BjwD5AJjgZsv\nWwoazZUJmr2A14C9wBdAMJB0Yclps0yaBDYb1NU5+T+gIOuOrMPHIpdeqsLGjVJNt4e4XnHEhcYp\ntXx0xgzYtQvOqLFowKVsOLYBu7ArlT9hrF1Nk+HVfO326xDiF8LU6KlKCYrUVFmLojssZ88pyqGs\nrkypcZSWJu9HbXRyATIMmxqfqtw4qq2Vc1tXwC1+TiHEy0KIgUKIACFEkhBi+2W/mymEWHzZv58S\nQsRd2DdSCDFXCLGrtXNMmiS/+O7Qj2Hj8Y0kRicq417Mz5cJaO0VFCDDHipdwI46At2hH8O6I+sY\n3HswA0LbsCzHDZSVwbD8dZTETYaePdv12Vnxs5RqZ56cLCez7hD2WHdkHSG+IUyOarYAslupr5cT\ncEfuRzMGzmD7ye1U1qtR4W70aOjbt+ssH1UjcOoEhg6FXr2k692TEUKQfjyd6bFqeScA2phDdwWp\n8ansO7uPkxUnnWtUB4mOlmOpu0wEKrmpMzfZmcHXeN/cfptmxc/ifO15cotzXWBZ+wkKku727jCO\n0o6mccPAG/C2uDrHv23k5EBNTccExfQB02kSTcrkUTiWvWpB4WYsFjmAPD3+nVeSx+mq08qFO0aP\nvn678taYGSeXhahUR2DGDM8fR6cqT3Gg5IBS3UXzPtlFGCX0uqv9gmJK1BT8vf1JP57uAss6Rmrq\npV4SnkpNQw2Z+ZlK9e9IT5eCbsKE9n92RNgIwgLDlBtH27Z1jeXsHiMoQE4EWVlSnXoq6cfT8TK8\nlGlXDh3Ln3DQL6gfY8PHKiUoZs6UNfRPquE0cQmOhlopse1YluNiLOvTqPfyx7C2vwS4r5cvU6On\nsil/kwss6xgzZ8pJYMcOsy1xHVmFWdQ11V18MFCBjRvBam1f/oQDwzBIiU0hPV8tQWG3d40wrMcJ\nivp6KSo8lfT8dBL6JxDiF2K2KQCcOAGHDnVcUABMj52u1BPBjTfKV08On6UfTye+VzxRPVosQOs2\nqqpgSEEap4dMA3//Dh1jeux0Nh3fhF3YnWxdx0hMhIAAzw57pB9Pp3dAb0b1U6OfUFOT7MfTkfCr\ngxsG3MCWwi3KtAWIj4fYWOl5UR2PEhSjRkm3uye7q9OPpysX7oDOCYqUASkcPn+YoooWS424jX79\nZAjHoyeCfLXGUdbGelJEOj63dDynI2VACiU1Jew/q0aLRl9fWZelq8S/O8Km/E1Mi52mTB2TXbug\nvLyTDzgDplPXVMe2E+o0iEpJgU3qON+aRY1R4CQ8PY/iWOkx8svylZoINm6EESPkJNxRHG53ldzV\nM2Z4rqAoqy1jZ/FOpRJ7j364lWCq6HdPx2PxSdFJeFu8lfN2ZWZ2rX4MbaWhqYHNhZuVCpulp4Of\nnyy53VHGho+lh18PpcZRSopMNq1UY/FJs3iUoAA5EWzdKl2onoZjgKtS3hY6lz/hoH9Ifwb3Hsym\n4+oICkc/huPHzbbE+WQWZCIQpAxQZyLw2pBGpU8oRkIHMukuEOQbxMT+E5WaCKZNk5PArlYXvnc9\ncopyqG6oVuoBJz1dhpo6GDUDwMvixbTYaUrlUaSkyHCO6mURPFJQNDR0nUIg7SH9eDpj+o2hd0A7\nylG6kKIiOHDgUs5BZ0iJTVHKQ+EoId4V3IztJf14Ov2D+zOo1yCzTQFkEvWQgjSKR8wAL69OHSsl\nNoX04+kIRUpUTp4sQx+eOI425W8i0CeQCREdF4HORAgpKDqTP+Fgeux0MvMzabSr4VoaMQL69FE/\nj8LjBMWIERAe7plhD9XyJxyDu7MeCpATwa5Tu5Tp69GnD4wcKRO8PI1N+ZtIGZCiRCMtgG0bqkgU\nm/G/rfM1MaYPmM6JihMcKz3WecOcgL+/bOzkiYIi/Xg6SdFJ+Hh1YDmFC9i3D86edc79aPqA6VQ1\nVJFTpEarT8OQ3i7Vx5HHCQpHgypPmwiKKoo4eO6gUoJi40ZZBCoiovV9WyNlQAoCQWZ+ZucP5iSm\nTfO8cVTdUM22E9uUyp/If38TvjQQeV/nBUVybDKAcmGPjAzP6uthF3Yy8jOUy5/w9oak9q86voaJ\nkRMJ8A5QahylpMiQR3292ZY0j8cJCpAX8NatntXXwxEOUElQbNjgnKcBgEG9BhERHKFU2GPaNPjm\nGygpMdsS57GlcAsN9galxpFv+jpK/COxjBjW6WP1DujNmH5jlBpHKSlw6pRcXu0p7D2zl/O155XK\nw0lPh4kTZVGrzuLr5Ys1xqqcoKithexssy1pHo8VFHV1an/x7SX9eDpD+wwlItgJ7gAncPq0dDE6\nI38CZEGZ6QOmKzcRgGfl42zK30Qv/17K1A2or4dhhWkUj0qV7kUnMH2AWnVNrFb5X/Mkb9em45vw\ntngzNbod7TxdiDPzJxw47keq1DWZMAECA9UOe3ikoBg3TqpUT7qANx7fqJSb2pn5Ew5SYlPYdmIb\nNQ1qlDodMACiojxrHKUfT1eqbkDu2rOMEzsInOO8niIpsSkcPHeQ4spipx2zM4SGwpgxak8E7SU9\nP52J/ScS6BNotikAHDkii+w5W1CU1pay5/Qe5x20E/j4yHCOyuNIjbuKk3HE0TxlIiipLmHP6T1K\nuak3boRBg+SE6yxSYlNosDew9cRW5x20EzgSoTxlHDU0NZBVmKVU3PvEezJ7OuZ7ThQUF9zwKi1D\n7iqFidqCEIJNxzcpNY7S0y9dr84iMSoRH4uPUt6ulBRZ18SuhtPkGjxSUIAcWCp/8e3B0XdBJUGR\nmencixdgdL/R9PTrqVTYY9o02ZjHE/rDqFg3wC8jjYKgYXgPjHbaMSNDIhnce7ByE8GhQ1CshtOk\nUxwrPcaJihPK5U+MGye9Qc4iwCeAKVFTlBtH58/L3C4V8WhBce6cbPLU1Uk/nk5sz1gGhA4w2xQA\nKipg507nCwovixfJsclKCYqUFFnXZJs6VXg7TPrxdAJ9Aknon2C2KYCsHjnsRBqnxzi/hbpqdU0c\n14oneLsc36tKBfacnT/hwJGPo0pdk6lTpQdeVW+XxwqKxERZIydTnVWIHUa1vgubN0vPT3Ky84+d\nEpuCrcCmTEGZ0aOhRw/PmAjS8xWrG5B2ksHiEIG33ej0Y08fMJ1dp3Zxvua804/dEaKiIC5O3Ymg\nPWw6vonR/UYrU2CvsFDmULhKUJyqOsXBcwedf/AOEBgoV7KoOo48VlAEB8us2K4+EVTUVZBTlMMN\nA5yY/dhJMjOhd28Y1vlVftcwfcB0Kusr2VGsRs9nLy+Zpd/Vx5GjboBKwrTwQ6n24+51/pPu9AHT\nZV2TAnWeKDylPk56frpS+ROOyTXFBSYlxyRjMSxsPKZO73BHPo4iTpMr8FhBAZ6RUJdVmIVd2JVy\nL2ZmyknW4oLRMylyEv7e/sol1GVmylr6XZU9p/dQWluq1ERAxiYK/QbhH9ff6YeOC40jMiRSqXE0\nbRrs2CG7YXZVTlWeIq8kT6lxlJkpC+x1pkFhc4T4hTA+YjwZBepMJCkpckXLsWNmW3ItHi8ojhyB\nkyfNtqTj2Aps9Anow7A+LnAHdIDGRhnycEW4A2RBmcSoROXi3+XlsEeN1WMdIv14Oj4WHxKjE802\n5SLRxzIoHuwaoeyoa6Jagye7HbKyzLak4zgSxFVKyMzMdN39CKSXwlagTjEax/9VxbCHRwsKxxff\nlfMoMgsyscZYlem7sHu37J7oygs4JTaFjPwMZRKhJk+Wa8C7srcrIz+DiZHq1A0o3FvOyIadeN3g\nOs9bSmwK2SezlalrMmwYhIV17XG0KX8TA0MHEt3DeatyOkNFhezkarW67hzJMckcOneIU5WnXHeS\ndtCnD4wapQWF24mIkLUSuuoF3GRvYnPhZqwxLrxa2klGhuyeOHmy686RMiCFM9VnOFBywHUnaQcB\nATBpUtcdRyA9XckxLlSB7eTQe5vxwk7sPa4TFNYYKw32Braf3O6yc7SHrtLgqSU25atVf2LLFtcl\niDtw3H9V8lKoWtfEowUFdO08it2nd1NZX6nURJCZKbOM/f1dd46k6CQshkWp+LfKiVCtUVBWQEF5\ngVLjqGZdBucsYfSxui6UN6bfGIJ9g5VLzNyypWv2Gaqoq2BH8Q7l8rlclSDuIKZnDDE9YpQTFAcO\nyBYIKtEtBMWOHdI11tXIzM/Ex+LDpMhJZptyEVfHK0EmQo0NH0tWoTrB5mnTZCLU8eNmW9J+HDdC\nlTxdYfsyOBo1zWn9O66Hl8WLqdFTlZsIamshR42u2O1i64mt2IVdKWGamSmrIrsiQfxykmOTlRKm\njromquXjdAtBYbfLRMKuhq3QRkL/BAJ8Asw2BYD8fLnm29WCAtRLhHLEaLuit8tWYGNQr0GEB4eb\nbQoAFecaGFW5mYZE1z/pWqOt2ApsyuTjjB+vfoOn5rAV2Aj1D2VE3xFmmwLIVVeuTBC/HGu0leyi\nbGoba11/sjYQEyNrm6jWuNDjBcWwYTKJpStOBJn5mco9DYBrE6AcWGOsHCg5wNnqs64/WRvo0wdG\njuyi4+hCYq8q7Psgl0BqCL/T9YIiOTaZkpoS8kryXH6utuDjI4vuqTYRtAVboe1iOFIF9uyRnme3\nPODEJlPfVE/2STVaWBuGvA+rNo7UGBkupKs2eDpRfoLjZceVmggyM2HIENes974ax/87q0Adn56q\niVAtUVVfxY7iHUqNo/MrM6gmgAELJrj8XIlRiRgYSnm7kpPlRKCI06RN2IWdrIIspcZRZqYsQz3J\nDRHhseFjCfIJUirsYbXKlgAq5eN4vKAAKSi2bJE9GboKjhtgcqw6HoqMDOf372iOAT0H0D+4v3IT\nwd69sjlPV2HbyW00iSalPF1BOzI41DsRi7+vy8/V078nY8LHKDcRnDkDhw+bbUnb2XtmL2V1ZcoJ\nioQEGUJyNd4WbxKjE5W7H9XVQW6u2ZZcotsIiqoqmZzZVbAV2IjvFU9EcITZpgCysNPu3e5xL4Is\nTJQcm4ytUJ0L2BHq6Ur5OJn5mfTw68HIviPNNgWApkbBsDMZVIxz30oBRx6FKkydKl9Vc1e3hK3A\nhpfhxZSoKWabchGbzX33I7iU16VSPk5AgFrjqFsIioQE8PNT64tvDdXi3q5sCNYc1mgrW09spb6p\n3n0nbYH4eOjbt2uNI0fc28viZbYpAOR9nkdfcYYet7lRUMRY2Xd2H+dqzrntnC3Rq5fMx+lS46jA\nxriIcQT7BpttCiCrHx875p58LgfWGCtnqs8o0yjMx0fWA1JpHHULQeHrK+Nsqi2xaY7qhmpyi3OV\nclNnZsrERFeu974aa4yV2sZaZRqFqZoI1Rwqxr2LPs6gCQuD70ty2zkdYUOV8nG60jgCKSis0eqM\nI0eCuDsfcKZGT1UuH8dqld+FIk6T7iEooGtdwNtObKPR3qjUROBoCObOCuAT+k/A39tfuQt4yxbZ\n00R19p/dz/na80oJU6/NGRwKHEtAeA+3nTMuNI7woHDlxtGePVBWZrYlrXO66jQHzx1U6n5ks8l2\n8P2d31euWUL9QxnVbxSZ+erk4yQnQ3GxOo3CupWgKCiQm+pkFsi496i+o8w2BXB9Q7Dm8PXyZXLk\nZOUmgqoqmU+iOrYCGxbDolTcOzY/g9ND3Vtp0ZGPo1piphBSnKqOw7OjUoK44wHH3STHqDWOVMvH\n6TaCIumCh7UrhD1sBWrFvXfulJOou1Z4XI41xkpmQaYyiVATJ8rYpSoXcEtkFmQyLnwcIX4hZpsC\nQFFuMXGNh/Cd4f6B5MjHaWhSY6nX0KGyZHRXGEe2AhtRIVHE9Igx2xQAqqvlygZ3P+CAFBQq5eOE\nhckwtCrjqNsIivBw2ShMlS++OezCLuOVCrkXs7LkJDpxovvPbY2xcrLiJPll+e4/+XUICJBJvqqP\nI0C5cXTkPflkF3ev+2cCa4yVmsYanY/TAWyFNqU6Hm/bJr2mZggKFevjqDSOuo2gAOmlUOWLbw4V\n4942m+sbgjVHUrR0LakW9lB9HJ2tPkteSZ5SgqLh6wwKvAfSL8H9ra8T+ifg5+Wn3DjavFmWkFaV\n+qZ6tp3YptQ4ysyEHj1kC293E98rXrl8nORk2cJdhX5V3UpQWK3SVVZdbbYlzaNi3NtmMydeCdA3\nqC9Deg9R6gK2WmUSVFGR2ZY0z8XCaAoJ0755meRHm2OPn7cfkyInKVfXpKICvvnGbEuaJ6coh7qm\nOuUExdSp4GVCRNgwjIthWFWwWuWS/q1bzbakGwqKxkbYvt1sS5pHtbj3yZOyw6ZZggLU6/Tn+C5U\nzsexFdiIDIkktmes2aYAUH22mqHVuTRNNU/gJMckk5mvTj7O5MlyUlTZ22UrsBHgHcCECNeXSW8L\ndru87swIdzhIjklWKh9n2DBZ2yRTgVtktxIUo0dDcLD6E4FKTwOO7yrJfWUDrsEabWXnqZ1U1lea\nZ8RlREbCgAHqTwTJMcnKxL0PLtmOD42ELzBvIFljrJyoOEFBuRpLvQIDYcIE9cfR5KjJ+Hj5mG0K\nAAcOyNL3Zj/g1DTWkFusRs1ri0WdcH63EhReXtJVpsIXfz0ccW+V3NQ2m5w8IyPNs8EaY8Uu7Gw9\noYBP7wIq51HUN9Wz7aRace9zX2ZRQTCDF4w2zYakGClmVKojoPI4EkLIir0KFbSy2eQEOsXEiPCE\niF34p9sAACAASURBVAn4efkplZiZnCwf/ux2c+3oVoICLl3Aing9r8AxQB03PhUwM3/CwYi+Iwj1\nD1UujyI7G2przbbkWnKLcqltrFVKUATm2jjYOxEvP2/TbOgX1I8hvYcoFz47fBhOnTLbkms5Xnac\n4spipcaRzQZjxsikTLNQNR+nvFw2LzQTtwgKwzB+bBjGUcMwagzD2GwYxuRW9r/bMIx9F/bfaRjG\nrc6yJSkJzp6FQ4ecdUTnkVWYRf/g/gzoOcBsUwA5WebkmC8oLIaFpOgk5SaC+nr5/aiGanFvYRcM\nOm2jfJT5E5M1xkpWoTpPlirn4zg8OSo94GRlmX8/Arn6TCUPhSr5OC4XFIZhfBt4AfgNMAHYCaw2\nDCOsmf2TgA+A14HxwDJgmWEYTmmXqFplsctx5E+oEvfOyZGTppn5Ew6sMVayCrKwC5N9ehcYO1bG\nwJUcR4Vqxb2Ppx0iTJwl+CbzZwJrjJWdxTupqq8y2xQAYmIgOlrRcVRgY2ifoYQFXvdW7XbOnYN9\n+9S5HxWUF1BQpkY+TlCQ7D5q9jhyh4fiSeAfQoh3hRD7gUeAamBxM/s/AXwlhPizEOKAEOI3QA7w\nE2cYExoq1y+b/cVfTUNTA1tPbL1Yd0EFbDY5aY4da7Yl8gIuqytj35l9ZpsCgLe3jOOqNo6EEBcr\nrapCwcfySW7IfVNNtkQ+WTaJJrad3Ga2KRdRNY/CVmhTKp9r82b5qoSH4oLXRiVvV3Ky+Ss9XCoo\nDMPwASYCaY73hFyztQ5o7o6XdOH3l7O6hf3bjYoX8M5TO6lprFEqXpmVJSdNHwUedKdETcFiWJTL\no1AtH6egvICTFSeVGkciw8Yhv5H0HBBqtimM7DuSHn49lBtH27dDXZ3Zllyioq6CXad2KTWObDbo\n1w/i4822BCKCI4gLjVMq7JGUJEP5Z86YZ4OrPRRhgBdwdcrRKSCimc9EtHP/dmO1ymIyKnX6yyrI\nwtfLl4T+CWabAshJUoWETAfBvsGMDR+r1BOB1SqT6Y4eNduSSzgmSpU8FBFHbZwcqMZA8rJ4MTV6\nqnKCoq5OrXycbSe3YRd2pQRFVpacNBWJCGONsSqXmAnm5uOYlXJtAO15rmt1/yeffJKePXte8d6i\nRYtYtGjRNfsmJV3q9HfTTe2wwoXYCm1MipyEn7ef2aYAshJkcbEa8UoH1mgraUfTWt/RTVyej6PC\nUxNIQTG492D6BvU12xQAyvLLGFy7h1PWJ8025SLWaCt/3fpXhBBK5CuNHy97xDgmTBWwFdgI9Q9l\neNhws00BZEHCLVvg178225JLWGOsfPjNh9Q01BDgE2C2OcTEQFSUvB/Nm3f9fZYsWcKSJUuueK/M\niU/WrhYUZ4EmIPyq9/txrRfCQXE79wfgxRdfJCGhbU/3l3f6U0ZQFNi4e+TdZptxEYfKnWp+2Psi\n1hgrL29/mbPVZ5VIFOvTB4YPl3HLe+812xqJaoXRDr2/hYkIou5WxyZrjJWnNz5NXkkew8KGmW0O\nPj4yS99mg6eeMtsaia3AxtToqVgMNSoL7N4tOx6r4jEFOY4a7Y1kF2UzLdaEVsxX0ZaGc9d7yM7J\nyWGikzo/unS0CCEagGwg1fGeIR8JUoHm/ttZl+9/gdkX3ncKqnX6O1F+gvyyfKUmAptNlnQNM3/e\nvojj+9lcuNlkSy5htaqz5K+qvoodxTuUKkRUsSaLc0Zv4m4earYpF0mMTsTAUC7skZmpRj6OXdjJ\nKsxSahyZ2fG4OUb3G02QT5By42jbNrk6zwzcIT//DDxkGMb9hmEMB14FAoG3AQzDeNcwjOcu2/8l\n4FbDMJ4yDGOYYRhPIxM7/+5Mo1Tq9OfIC1Ap7m2zqeN+dTAwdCARwRHKXcC7d8uiMmaz/eR2mkST\nUsI0eLeNQ32tGBbzQwsOevj1YHS/0cqNo+Ji2TfHbPaf3U9pbalS48hmk2XKA8yPLFzE2+JNYnSi\nUuMoKUnWD9qxw5zzu1xQCCE+An4GPAPkAmOBm4UQjlzUaC5LuBRCZAGLgIeAHcAdwHwhhFNrgKnU\n6c9WYGNg6ED6h/Q32xQAKitlO1yV3IsgO/0lRScpl5ipSqc/W4GNEN8QRvZ1SsmWTmNvaGJIyWaq\nxymmTFEvoc4h3lXwmmYVZOmOx23EGi0LpanScG7CBPDzM28cuSVAJoR4WQgxUAgRIIRIEkJsv+x3\nM4UQi6/a/1MhxPAL+48VQqx2tk2qVBYD9eLe27ZJz42SF3CMVclOf0qMo0IZ9/aymNDX+TocXrmX\nnpQTept6A8kaY2Xvmb2U1paabQogQ4tDhyoyjgpsjA0fq0zH4+JiuZJK1fvR6arTHDl/xGxTAPD1\nlXObWWFYNTJuTMDR6c/sQiC1jbXkFOUoFa+02aBnTxgxwmxLrsUaY6W6oZpdp3aZbQogGxWpkI8j\nhCCrIEspYVr0WRaNeDHknhYr7ZuCI7yoWj6O2eMIpDBV6X6kQsfj5pgaLbPWVQp7mDmOuq2gAFlZ\nzOwLOPtkNg32BqXq5dtscnWHRcHRkdA/AV8vX+UuYLM7/R08d5CSmhKlBIVls428wPEE9Qsy25Rr\nGNx7MGGBYcqNo507ZcjRLEqqS9h/dr9y9yNHiXLV6BXQixFhI5QLwxYWQoEJVcEVnDLch9UKR45I\nl5pZ2ApsBPoEMjZcgfrWyElx82Y1nwYA/L39mdh/olLxbxU6/dkKbBgYJEYlmmfEVUTl2zg9WB2B\nczmGYcg8CsUEhd0uQ45m4fDYqCRMVWkI1hyqjSMz83G6vaAAc5f92QptJEYl4m0xr63z5Rw4IJvw\nJKtTwv8akqKTlLqAVcjHsRXYGNVvFD39e7a+sxso2X+GuIaD+E5XVJkiE+q2nNhCk12BpV7IEGPP\nnuaPo/CgcOJC48wz4jLq6mRZctUFxe7TuymvU2CpF7I8+aBBWlC4neho6Uoz6wJ2xL1VWi6amSlD\nHYnqPOhegzXGSn5ZPifKT5htCqBGpz9bgVpx78PvSZUe8211bLoaa4yVyvpK9pzeY7YpgLzukpLM\nHUdZhVlKdTzOzZWiQmVBkRSdhF3Y2XpCgaVeFzArj6JbCwowN4/iaOlRTlWdUsq9aLPBuHEQokaC\n93VRsdOfozCRGZTWlvLNmW+UGkc16zIpskQSbY0125RmmRQ5CW+Lt1LeLjPzcRrtjWw5sUWpcZSV\nJWtPjBtntiXNMyxsGL38eynVKMxqlWKsqsq95+32gsLR6a+21v3ndtzIHJnCKpCZqfbTAEBkSCQD\nQwcqNxEcOgSnT7v/3FsKtwAolUjXe18GRyJTlCpodTUBPgFMiJigXD7O+fMy9Ohudp3aRXVDtVKC\nwmaTIUUVOh43h8WwkBSTpNw4amqSc5s70YLCKsuUmtHpz1ZgY1ifYfQJ7OP+k1+HM2cgL0/t/AkH\nqiVCmZmPYyuw0SegD0N6D3H/ya9DfVkNw8q3UT/F/P4GrWGNsSr1ZDlligx9mOE1tRXY8LH4KNXx\nuCs84MCFAlcFWdiFiUu9LmPUKOlldvf9qNsLirFjZU0KMy7gzIJMkmPUmb0d30FXEBRJ0UnkFOVQ\n22iCa+k6XN7pz93YCm1Kxb0PfrANXxroe0eK2aa0ijXGyuHzhzlV2WLvQbcREiLvSWYJiomRE/H3\n9nf/ya/DsWNQVNQ17kfWGCtldWXsPWPiUq/L8PKSeXDuHkfdXlD4+JjzxZfWlrL71G4lutQ5sNnk\npBgTY7YlrWONsdJgbyD7ZLbZpgDmNZxrsjexpXCLUom951ZmUEYPht052mxTWsXh3lfN22WGoFCt\nIZgjJ6kreCimRE3B2+JNRn6G2aZcxDGO3FkVvNsLCjDni99cuBmBIDlWHfmdmSmfBhR50G2RseFj\nCfQJVG4icHenv12ndlFRX6GUMA3MyWB/bys+/mqUAG+J6B7RxPSIIbPA5JK5l2G1wv79UFLivnOe\nrDjJsdJjSuVPZGTIpbS9e5ttSesE+QYxIWKCcuOopAQOHnTfObWgQH7xp07JIlfuIjM/k76BfZWJ\nezvWe3cF9yJc6PQXlajcBVxXJ7Or3UVmQSY+Fh8mRU5y30lbQDQ2Mfi0jfIx6gic1pgWO025cQSy\nwJy7cAhzlRJ7HQ84XYXkmGQy89UZR4mJ8uHQnavPtKBAlpkG97oZMwsylYp75+Sov977apJjksks\nyFSm09/48eDv795xlJGfwaTISQT4qNHXuWDVN/QUZYTc2nUERXJMMtkns6lpqDHbFAAGDoSICDff\nj/IziQuNIzIk0n0nbYHSUtkJelrXGUYkxyZztPQoJytOmm0KAKGhMjnTneNICwqkS23ECPd98Q1N\nDWwu3KyUmzozUyanqrze+2qmxU7jbPVZ8kryzDYFuNTpz13jSAhBRn6GUom9Jz/KoB4fhv3/7Z15\nXFT31f8/X3YRBUQEXAARjYj7FhWQzdQ1xmYxaBazPG2etE1a+2uaPm2fJ2Zv0iSmzdo0NTEmajRx\njQKugOC+4y7IIgiKguzrzPn98WUUkW1m7sy9A+f9et0XMHPv9x5m7vK555zvOY9rryFYa4T7h6Ne\nX49DV1Ssed0EQz6ONZ8sUy+naup6tG+fDEHbmocCgKa8FOHhMnRkLVhQNGLNAlfHC4+juqFaUzeC\ntDTpItPyfO/mTB4wGXbCTlOJUIbjyBpOk9zSXOSX52vqRmC3NxVnuo2DZz9XtU3pMMP7DEdP556a\nOo6mTAEOHgTq6y2/r4q6ChwrOKap4ygt7XYJaVvBr4cfgjyDNBU+Cw+X+ThFRdbZHwuKRqZMAdLT\nZZMnS5N2OQ3O9s4839tMejr3xEifkZo6gadMAa5cAXJzLb8vw/+tmUQ6Ivjn7MG1wdq5MXUEezt7\nTO4/WVPHUXg4UF1tnXycg/kHoSOd5gSFrSSIN8UQhtUKhpCRtR6WWVA0MmWKvLFaIxEq7XIaJvSb\nAGcHZ8vvrANkZkoFa0vuRQNhA8I09WRp6PRnDTdjam4q7vG6B97dvS2/sw5Qmp4L34Y8OERrv/5E\nc8IGhGHv5b2aKUw0ZowsOW2t48jTxRNDew+1/M46QH09cOCA7V6PjhUcQ2WdlWtet4K/v+xZZa2w\nBwuKRoYMAby8LK/kiAhpudoqaGWI1Wq1ZXlbhPuH42LxRc0UJurdGxg61DonsNYKo2WtkP/0wMc0\n4jExgnD/cNysuamZwkROTjJZfM8ey+8rNTcVYf5hsBPauB0cOya9MzYpKPzDoCOdZhqFCWHdPApt\nHEEawJAIZekPPutmFgoqCjTnXgwNlVnBtobhc9SSmzEiwvI3Ai0WRqvdmYoL9iEIHN9bbVOMZmK/\nibAX9prydhluBJbMx2nQN2Bf3j6ED9DOcZSWJmdLjdVGRNgohnkPg4eLh+aOoyNHpEizNCwomhAe\nLkMelkyEMmQAaybuDemVscWnAUAWJgpwD9DUCRwRIae8FRdbbh9aLIzmfSEV2QPCbS7uDcjCRGP9\nxmpKmIaHA9evy/46luLk1ZOoqKvQlDBNS5OzpZyc1LbEeOyEHaYMmKK546i+XhbdszQsKJoQESHb\nvVoyESrtchpCeoegVzdtlH8rKZE3P1sVFID0UmhJUBgSoSw57S81N1VThdHqr5UgqPIUdJO0c2My\nFq3l40yaJBuFWdLblZqbCmd7Z+0URiPbK2jVnLABYdiXtw86vU5tUwAAw4cDPXtaJ+zBgqIJ48bJ\nRChLnsBai3sbutHZ2gyPpoT7h+NYoXYSoQIDZU8Uix9H/mGaKYx26VuZfOT7sO0KinD/cGTfzEZ+\nWb7apgCQN4HRoy17I9BagnhWFlBYaPuCoqy2DKeLTqttCgDZKGzyZBYUVsfSiVAl1SU4de2UptyL\ne/fa3nzv5oQNCEODvkFTiVAREZY7get19TiQd0BTce/SLakogB9C5wxU2xSTMYSPtOauttRxpMXC\naLbUEKw1JvSbAEc7R015u8LD5bVeZ2GnCQuKZhhuBHoLzB7blyfdAVqKe6em2uZ876aE9gmFu7O7\n5k7gw4ctkwh1rPCYLIymoeOo54k9ONs7HE7Otnsg+br5YpDnIM1VOszMlG28lSb7ZjaulF/R1ANO\naiowbJhtNARrDVdHV03m45SWyvC2JWFB0YyICNmh7dw55cdOy01Dn+59MMhTG+6AmhqZhDp1qtqW\nmIedsEOYfxhSL2tHUERE3J5PrzSpualwcXDRTmG06hoMvHEIlaO1c2MyFa0dR4Z8HEt4KQwCXEsJ\n4raeP2FAa43CJk4EHBwsH/ZgQdGMSZNkzMkSYQ9D/oRW4t6HDsmGYJGRaltiPuEDwrH38l406BvU\nNgWATIRyd7fMCZx2OQ0T+02Ek7020uALftwLZ9Sh51zbP5DCB4TjeOFxlNeWq20KAMDPT4YjLSUo\nQr1DOUHcAoT5hyGnNEcz+TiurjJHkAWFlXFzk/OflRYUdbo6HMw/qKl4ZUqKTPwaOVJtS8wn3D8c\nFXUVSL+arrYpAGR2fliY8seRIe6tpfyJotU7cQ3eGPXYCLVNMZsw/zDoSY8D+RZwLZmIpfIotNgQ\nDOgkgmJA18rHMcCCogUsUZjoWIH24t4pKfIgs7dX2xLzmdBvApzsnTR1AkdEyESoBgWdJpklmbhW\neU1Tx5HbgZ045hEDj162fzkZ2nsoenXrpSl3dXg4cPw4UK6g06S4uhhnis5oSlDs2QP4+Nh2grgB\nHzcfBPcKxp4cK5Q67SDh4cDly5btM2T7VwALEBEhP3QlP/jknGS4OrpinN845QY1g4YGGa+09fwJ\nAy4OLhjnN05TiZkREUBFBXDypHJjpuamQkBgcn+N1EkvLUXg9UMoGRertiWKYChMpKU8iogImSRu\neIJXgr2X5TRfLXlMk5Nl+FUjEWGzmeo/FSm5KWqbcQuD58eS9XFYULSAIRFKSS9Fck4ywgaEwdFe\nG/3Bjx2TRbw6i6AAZNhjT+4ekDV6h3eA8eMBZ2dlj6PU3FSE9gmFZzdP5QY1g6IfkmEPPTwf6hyC\nApB5FPvz9msmH2fIENkjRkl3dWpuKvr26ItAj0DlBjWDykqZ09UZ8rkMRAZGIv1qOoqrLVgy1wi8\nvYF77rFs2IMFRQv07g2EhCh3I2jQN2BPzh5EBmjnbElOvp2o01kI9w/HlfIryCnNUdsUAFJMTJyo\nvDDV0nF0fc1OZCEQ4x4JUtsUxZgaMBUVdRU4WnBUbVMAWKbBU2quzJ/QSoK4ITTYqQRFQCQIhJQc\n7XgpLJ1HwYKiFSIiZI6BEhwvPI7yunJEBUYpM6ACpKTI6mm2WC+/NQzuW63FLZVq8JRXloeM4gxN\nHUfuB3fgqGcsetteP7BWGd93PFwdXZGUnaS2KbdQss9QTUMNDl05pLlwR+/esgZFZyHAIwCBHoFI\nzk5W25RbhIUB6enAzZuWGZ8FRStERABnz8rmPOaSnJ2Mbg7dMKHfBPMHUwC9Xj41d6ZwBwB4uXph\neJ/hmroRREQAV68CGRnmj2W4ME0N0MgXV1CAvjfPoHR85wl3AICjvSPC/cM1dxxVVwNHFXCa7Lu8\nD3W6Ok0J086WP2EgMiASSTlJaptxi4gI+XBjKS8FC4pWiIiQP5X44JNykjBlwBTN1A04dUoq1M4m\nKAAgKiBKUyfwlCnyIqlE2CMpOwmh3qHo072P+YMpQMm6XQAAr4djVLZEeaICorAnd49m8ijGjFGu\nz1BSdhK8uknxrQWqqmQBuKgotS1RnqjAKJwoPIGS6hK1TQEgZ9D07w/s3m2Z8VlQtEJAADBggPkn\nsE6v01z+REqKDHXce6/alihP9MBoXCq5hNxSC86NMgJ3d1nnQylhqqWnypK1O5GO4bh3ro/apihO\nVGCUpvIoHB1liFKJMOzu7N2IDIyEndDG5d8QyulM+RMGDHkUe3K1EYYVAoiOZkGhCkrUozhx9QRK\na0s1dSNITpbJgt26qW2J8kQGREJAYHeWhc4YE1DiONJc/gQR3I/sxFHPWPj6qm2M8mgxjyImRp67\n5tQ1qaqvwv68/YgOjFbOMDNJTpa9O0JD1bZEeQI9AuHv7q+pPIroaFnXpNgCk09YULRBRISMWVZU\nmD5GcnYyXBxcMLHfROUMMwMi+ZTTGcMdgMyjGOkzUlNhj8hImUORl2f6GJrLn8jMhFdFLsomTFPb\nEougxTyKmBigrMy8PIq9l/eiXl+vHWEKIClJniN2nfBuJITQXB5FdLS8D1iivUQn/AqVIyJCtnvd\nv9/0MZJykjCp/yQ4OzgrZ5gZXLgAXLvWeQUFIN3VWvJQGGLD5rgZtZY/Ub5hJxpgjz4Pd94DKTow\nWlN5FOPHA927m38cebt6I9RbG+6AmhqZP9EZwx0GIgMicbzwOEprStU2BQAQGChD+pYIe7CgaIOQ\nEFkMxNQPXk967MnZg6iAKEXtMoeUFPkkMEU7DQYVJzowGjmlOcgqyVLbFAByOtyoUcDOnaaPobX8\nidJ1O3EQExE2s6faplgMLeZRTJ0K7Npl+hi7s3cjKjBKM/UnDhzoPA0KWyMqMAp60muqiq+l8ihY\nULSBnZ10M5p6I0i/mo6SmhJEBmrnbElJkc3PevRQ2xLLMTVgKgSE5tzVu3aZVo9Cc/kTej08ju3C\nUY9Y9O+vtjGWY5zfOHR37K6p4yg6Wib41tUZv21FXQUO5h/UznEEGe7w9OwcDQpbI8gzCP169NPc\ncXTypDJlEZrCgqIdYmNlSVhTCoEkZSfB2d4Zk/pPUt4wEyC6Pd+7M+PZzRNj/MZgd7Z2wh6xsbIx\nT2am8dtqLn/i5Em41dxA5aTOVX+iOVrNo6iqAg4eNH7btNw0NOgbNJeQGRHROfMnDAghEBUYheQc\nbSVmAvLzVxKLfo1CCE8hxHdCiFIhRIkQ4kshRPd2tkkSQuibLDohxKeWtLMtYmNlIShTPvjknGTc\n2/9euDi4KG+YCeTkyJtaZ86fMBAVEIXd2bs109cjIkJ2dTXF26W1/ImqzTtRhW7we1AjDcosSFSg\ntupRjB4NeHiYFvZIyk6CT3cfDO09VHnDTKC2VjY86+wPOIDMozhScARltWVqmwJAlkQYNEj5sIel\ndeFKACEAYgHMBjAVwL/a2YYAfAHAB4AvAD8Af7SgjW0SFCSTWIy9EehJr7m+C8nJt/sCdHaiB0Yj\nrywPmSUmuAQsQM+ewIQJJt4INJY/Ub5hB1IRjohp2kg0tiRay6Owt5c3YFNuBFrLnzh4UCZldsaC\nVs2JDIyEnvRIy7Vgq08jiY6WISclsZigEEIMBTAdwLNEdJiI9gJ4AUCcEKK9metVRFRERNcaFzMm\nbppPbKzxguL0tdMori7W1I1g+3ZZca9XL7UtsTwR/hGwE3aaclfHxsobgV7f8W00lz9RUYFeJ3Zj\nn8csBAaqbYzl0WIeRUyMbKZVXd3xbcpry3H4ymHNhTvc3WXCcmdncK/B8HPz09RxFB0NnD6tbD0K\nS3ooJgMoIaJjTV7bAemBaK9G42NCiCIhRLoQ4i0hhKolmGJjgTNngIKCjm+TnJMMRztHzeRP6PXA\njh3Az36mtiXWwd3FHWP9xmoqjyImBigqkqXPO4rm8icSE+Goq0Vp5NxO13ehJbSYRxEdLZMy9+3r\n+DZ7cvdARzpED9SOoEhKkt5Se3u1LbE8QghEBkZqKo/C4Bk6fFi5MS0pKHwBXGv6AhHpABQ3vtca\n3wF4HEAUgLcAPAFghWVM7Bgxja0KjHFXJ2Un4d7+98LV0dUyRhlJerpsUnXffWpbYj2iA6OxO0s7\neRSTJ8uW5sYeR1rKn6j5fiPSMRyjH+w87crbQ2t5FKGhcjq7sceRn5sfBvcabDnDjKCuTnpZukK4\nw0BUQBQOXzmM8tpytU0BAPTtCwwZorKgEEK83SxpsvmiE0IMaWsISC9FixDRl0S0nYhOE9EqAE8C\n+LkQYmBbdi1evBhz5869Y1m1apWx/16L+PgAI0Z0POyhxfyJ7dtlqe0w7XQstjjRgdEoqCjAxeKL\napsC4Pbnb9SNQEv5Ew0NEPFbsBEPdClhqrU8Cjs7eSM25jjanb0b0QOjNZM/ceCADNl0JUERGRgJ\nHelUrUexatWqO+6RtbVzsXXrYsXGdzBhm/cAfNXOOpcAFAK447FKCGEPwBPAVSP2dwBShAQDaLVS\n0dKlSzF27FgjhjWO2Fhg3To59bK9c/JYwTFcr7qO+4K0c9Xdvl3O7nDu/Hl0twj3D4e9sMfurN0Y\n4tWWxrUeMTHAu+/KfgwO7Zx9hvyJt2Pfto5x7ZGWBueKYpwJfgB+fmobYz2a5lFopYR+TAzwwgtA\neXn7NWVKa0pxtOAonhv3nHWM6wAJCbLgmwUv2ZrjHq970L9nf2zL3IaZg2eqYsOCBQuwYMGCW39/\n/z0QF3cUwDhFxjfaQ0FEN4joQjtLA4B9ADyEEGOabB4LKQ4OGLHLMZAeDSMyGJQnNhbIze1YHYHE\nzES4Oblh8gBtTKurqZEFrbrSUyUA9HDugfF9x2suj6KsDDhypP11d2XJR1Ct5E/Qho0otPND/weU\nufjYCoY8ip1ZZpQ6VZiYGClKO9LFNiUnBXrSayohMzFRXo86c/2J5gghMH3QdCRmJqptyi2U9hBZ\n7OskonMAEgH8WwgxQQgRBuAjAKuIqBAAhBB9hRBnhRDjG/8OEkL8VQgxVggRIISYC2A5gGQiMiKV\nTXmmTu14HYGEjATEDoyFk72T5Q3rAKmpUlR0lYTMpkQHRiMpO0kzeRQTJsgnyo64qxMyEjDWb6w2\n8ieIUP/DRmzQz8V907vQXaCR6YOmIzk7GVX1VWqbAgAYPFjGwDtyHCVlJ6F/z/4I8tRG3ktRkWxw\nNn262pZYnxnBM3D2+lnkluaqbQoAGc4f2GYygXFY+sqwEMA5yNkdPwFIAdDU7+YIYAgAQ+ZiHYBp\nkELkLIC/A1gLYK6F7WyXnj1ly+8dO9per7SmFPvy9mH6IO2cLdu3A76+wPDhaltifWIGxuBqnHMQ\nMgAAIABJREFU5VWcuqaqHr2Fg0PH+jHo9DrpGg1WxzV6F6dPwynvEuIdH+gSdUyaM3PwTNTqajUz\n20MI6aXoSD2K7Ze2I2ZgjGbyJ7Zvl6HjrviAEzswFnbCDokZ2vFSTJig3FgWFRREdJOIHicidyLy\nJKJfEFFVk/dziMieiFIa/84joigi8iYiVyK6h4j+R+06FAY6UkdgV9YuNOgbMD1YW4Ji2rT2cz86\nIxEBEXB1dEV8RrzaptwiJua216g1Dl85jBvVNzAjeIb1DGuLTZtQbd8d+shodFN1Erc63ON1DwLc\nA5CQkaC2KbeIjpZP+iUlra+TV5aH9Gvp2hGmkOGOkSPRpfJwDHh288Sk/pM0Ffb4+c+VG6vr+S7N\nIDYWuHEDOHGi9XUSMxMxuNdgTbkXjx3revkTBlwcXBA7MBZbL25V25RbxMZKMbF/f+vrxGfEw8PF\nQzt1TNZvRALNQPRMbZSRtzZCCMwMnqk5YUrUtrcr/mI87IQdfjZIG+4AvV4Kihka0clqMH3QdOy4\ntEMz05CHKJivzoLCCCZPllP/WsujICIkZCRo56kSt23tqoICAGYNnoXU3FSU1pSqbQoAOQXZy6vt\nfJyEjATcF3QfHOxMmYilMFeuwO7wQazTP9Al3dQGZgTPQEZxBjKKM9Q2BYBsCRASAmxtQytvzdiK\nyf0no1c3bZTHPXlS1sPpivkTBqYPmo7S2lIcyDNmboJtwILCCJydZZOn1m4EF25cQE5pjqbyJ7Zt\nk7kTXdG9aGBm8EzoSIcdl9pJgLESdnbSS5HYitfzetV1HMw/qB1hunkzdMIeR3xmIzRUbWPUI2Zg\nDBztHDUV9pg9WwqKlsKwdbo67Li0Q3PhDlfXrlUPpznj+45Hr269NHUcKQULCiOJjZVTMGtr734v\nISMBTvZOmilERCTzJ7qydwIAAjwCMMx7mKbCHnPmAIcOtVzOfXvmdhBIO4Ji0yYcdQ3HxBm9umQe\njoEezj0Q7h+uqbDH7NlAYaHMpWhOam4qKuoqMGvwLOsb1gqJiTL3oyvVw2mOvZ097gu6T1N5FErB\ngsJIZswAqqqkqGhOYmYiIvwj0N2pzQ7tVuP8eSAvjwUFAMwKnoX4jHjNTB+dOVN6KlpyV8dnxGOk\nz0j07dHX+oY1p6ICtHMnVlY+0KXd1AZmBs/E7qzdqGloI6PWioSFyQZbW7bc/d7Wi1vh5+aH0b6j\nrW9YC1RUyGRkPo5k+OzwlcO4XnVdbVMUhQWFkYwYAQQEAJs23fl6TUMNkrKTtPNUCemdcHKS0xS7\nOrMGz0JBRQFOXG0jo9aK9O4tc3I2b77zdT3pkZiZqB03dWIiRG0tNuIBTJumtjHqMyN4BqobqpGS\n08IThQo4Osrply0JiviMeMwMnqmZ6aK7dwP19V07IdPAzwb9DATC9sztapuiKCwojEQIYO5cKSia\nPuzuydmD6oZqzeVPhIUB3bXhMFGVMP8wuDm5aSrscf/9UvQ1nT56rOAYrlVe046gWLkS2Z6j4Tk2\nCN7eahujPsP7DEe/Hv00Ff82hM+uNmlokH0zG2eKzqhW4rklEhNlEaXgYLUtUZ++PfpipM/IThf2\nYEFhAnPnyjLcJ0/efi0xMxF9e/TF8D7aqB5VVyfbA3O4Q+Jk74T7gu7TnKCoqrqzOFFCRgJ6OPXA\nlAFT1DPMwI0boM2bsaz+yS49u6MpWpw+OnOmfNBpGj6LvxgPe2GvqX5CiYky3KERh4nqGMpwayUM\nqwQsKExg6lRZObNp2CMhIwHTB03XjHsxKUnGLGdpJx9LdWYNnoV9eftQUt1GJSArEhIin9h++un2\na/EZ8ZgWNA2O9o7qGWZg9WpAr8e/KhayoGjCjOAZOHf9HLJvZqttCgDZynzixDvDHvEZ8Qj3D4e7\ni7t6hjXh0iUgI4PzJ5oyfdB0FFYU4uTVk+2vbCOwoDABJyf5VGAQFHlleThddFpT+RPr1gFBQbIi\nHSOZGTwTetJjW+Y2tU0BIJ/U7r9f5lEQASXVJdiXt087x9Hy5cgYPAsVrj6YogGHiVaYFjQN9sJe\nU2GP2bNliLOuTuZz7czaqbnZHQ4OshgXIwn3D4ero2unCnuwoDCRuXOBw4eB/HwgMSMRdsIO04K0\nkbWm0wEbNgAPPsjuxab069kPI31GYmuGdsIec+YAly/L8NmOSzugJ702BMXZs8ChQ/h33SLMmtW1\np/k1x93FHVMGTNFU2GP2bNnKPDVVdhetqq/STh4OZLvyKVOkZ5eRODs4IzowWlPC1FxYUJjIzJmy\n++hPPwFbLm7BxH4TNVONbt8+maClZI32zsKs4FmIvxgPPbXRkMWKREYCbm7yOErISMAw72Hwd/dX\n2yxg+XLo3D3xj0tz8PDDahujPWYGz8TOSztRp6tT2xQAwJgxsvvoTz/J6aL9e/bXTD5XZaVsqsjh\n17uZETwDqbmpuFlzU21TFIEFhYl4esqqmet+Kkd8RjweCnlIbZNusX697C46SRttIDTFrMGzUFRV\nhKMFLVQCUgEnJxlX3rSZkJCZoI2nSp0OWLEC6aELIJyd+UbQAjOCZ6CyvhKpualqmwJAeiJnzZJ5\nFPEZ8ZgVPEsz+Vxbt8rkYxamd/PzoT9Hvb4em85van9lG4AFhRnMnQvsyv8JNQ01eGTYI2qbA0DG\n4tetk94JO/5272LygMlwd3bX3GyPg/kHcKX8CuYMmaO2ObK2/JUr+KhsEWbMAHr0UNsg7THadzT6\n9+yPdWfXqW3KLWbPBi5cz8CFGxc0lT+xdi0wdiwwaJDalmiPfj37Idw/HGtOr1HbFEXgW44ZzJ0L\nNAxZi8HdJiLAI0BtcwAAx48D2dkyf4K5Gwc7B0wPno7NFza3v7KVmDkTwPDVcLf3Q4R/hNrmAMuX\noz54KJadmoCHtON40xRCCMwfNh9rz6zVTNfIadMA+5CfYA9HxAzURvZjZaX0mjyijectTfLIsEew\nLXNbpwh7sKAwgz79yyGGbIV7/ny1TbnFunUyHBMZqbYl2uXhkIdx+MphzXSN9Oqtg+PoNehdOB/2\ndvbqGlNWBqxfj0Mhi+DoKHD//eqao2XihsfhWuU1JGUnqW0KAJmL4zZpNTxvzEQPZ224lQzhDhYU\nrfNQyENo0Ddg47mNaptiNiwozGDzhc0g+1pkbn4YOp3a1kjWr5cudEcNlDHQKnOGzIGbkxtWpa9S\n2xQAsolTvUsBrmyLa7HpnFVZuxaoqcEH1x7HtGmAh4fK9miY8X3HI8gzCN+f+l5tUwAAmcWZKO1x\nACXJC1FcrLY1Eg53tM+tsMcZ2w97sKAwgzWn1yDUfRJKsgKwf7/a1shmYKdPc7ijPbo5dsODIQ/i\nu/TvNFGlbvWp1ejrGoDqi/feUTVTFZYvR+3UaVh3sD8n0bWDEAKPhj6KH8/+qInZHqtOrYKrQ3fo\nz92PNRq4N1VWylkn7J1on/mh87Etc5tmiu6ZCgsKEymrLUNCRgKemvAI+vS5u1mYGqxfD7i6gqsa\ndoCFwxfi/I3zOF54XFU76nX1+OHsD3hs9KO45x6B775T0ZgLF4A9e5AatAh2dsADD6hoi40QNzwO\nJTUlqjd5IiJ8l/4dHhz2c0yPccW336pqDgAZ7qiuZkHRER4KeQg6vQ4bzm1Q2xSzYEFhIpvPb0at\nrhbzhz+MuXOla0+vcmmDdevk1LFu3dS1wxaIDYqFt6s3VqavVNWOXVm7cL3qOhYMj8OTT8rvsLxc\nJWOWLgV8fPB+9kOIjga8vFSyw4YY0WcEQnqHYPXp1araceLqCZy7fg4Lhy/E448DaWmy3LWacLij\n4/j18ENEQITNhz1YUJjImjNrMLn/ZPi7+2PRIiArC0hOVs+ey5dlx0EOd3QMBzsHPBr6KFadWqVq\nkavvT3+PIV5DMNp3NJ54Qj7R/fCDCoYUFQFff42qZ1/AthQXnt3RQYQQiBseh43nNqK6vlo1O1am\nr0Rv196YFjQN8+bJDsNqeik43GE884fNx45LO1BcrZEEGBNgQWECpTWlSMhIwPxQObsjLAwYPBj4\n6iv1bNqwQSZichGijrNwxELkl+djT84eVfZf21CLdWfXIS40DkIIDBggex0sX66CMZ9+CtjZYb3P\nf0Ov5yqrxvBo6KMorytXrRS3nvRYdWoV5g+bD0d7R3TvDjz0kBQUaqUIcbjDeB4aZvthDxYUJrD5\nwmbU6erw8DCZtSYE8Mwz8smytFQdm9asAWJjAXdtNBe0CSb1n4RAj0DVwh6JmYkorS3Fo8MfvfXa\nokXS05WVZUVDqquBjz8GnnkGKxO9EBEB+PhYcf82zj2978Fo39FYfUqdsEdqbiryyvKwcMTCW689\n8QRw8SJw8KAqJmHNGg53GIuvmy8iAyNtusgVCwoTWHN6DaYMmIL+Pfvfeu3JJ4HaWuB7FWaQnT4t\nmwI9/bT1923LCCGwcPhCrD2zVpUs/dWnVmNEnxEY5j3s1msPPijrCXzzjRUNWb4cKC5G2TO/w/bt\nXCLZFOJC4/DThZ9QUVdh9X2vTF+JAPcATB4w+dZr0dGyt8eKFVY3h4tZmcEjwx7Bjks7cKPqhtqm\nmAQLCiO5WXMTiZmJmD/szmJWffvKiofLllnfps8+k0+U8+ZZf9+2zoIRC1BSU4LEDOu2EK6qr8Km\n85sQNzzujte7d5cX4m++sZK7WqcDPvgAePBBLEseBCK+EZjCo8MfRXVDNTaft24F1jpdHdaeWYu4\n4XGwE7cv5/b2wMKFwOrVQH29VU3icIcZPBjyIAiE9efWq22KSbCgMJK1p9eiXlePh4bdnbX29NPA\ngQPAmTPWs6eiQt58/uu/ZKMpxjiG9xmOEX1GYOUp64Y9tlzYgsr6Sjwa+uhd7y1aJDP0U63Rd2rz\nZuDiReh//wd88om8Cfj6WmG/nYxAj0BM6j/J6rM9tmVuQ3F18R3hDgNPPAHcuCFbh1uT//wHmDCB\nwx2m4Ovmi8iASHxzwpouSuVgQWEERIQPD3yIuffMvSPcYeD++4Heva2bnLlypXQx/vKX1ttnZ2Ph\niIXYeG6jVd3V36Z/i/F9x2NQr7uvuhERQGCglZIz33sPiIhA4s17kZEBvPCCFfbZSYkLjUP8xXhc\nr7putX2uTF+JUO9QjOgz4q73Ro6UizXDHmfOAImJwIsvWm+fnY3nxz+PPbl7cKzgmNqmGA0LCiPY\nlrkNZ4rOYPGkxS2+7+QEPP649BhYw81IJMMds2cD/v6W319nJW54HKobqrH+rHXcjBdvXMTm85vx\ni7G/aPF9OzuZk7NmjeyDYDH27ZMFC/7wB3z8sUyi45b3pvPYyMfgYOeATw99apX9ldeWY+P5jVg4\nYmGrrcoff1wW3bNWsvg//yk9XPO1097I5vh5yM8R4B6ApfuXqm2K0bCgMIKl+5dijO8YTA2Y2uo6\nzzwDXLsm44iW5sAB2V30+ectv6/OTKBHIKYFTcPS/UutUor7g30fwLu7N54c9WSr6zz5pCxwtd6S\nGufvfweGDEHG0DmIjwd+8xs5Y4kxjd6uvfH06Kfx8cGPrVKT4osjX6BOV4cnRj7R6joLFwJ1ddap\nbXLjhnyY+vWvOfxqDg52Dnhh4gtYfWo1CsoLLL6/Q/mHFBur0wiK3Ju5Fh3/TNEZJGYmYvGkxa0+\nDQDAiBHA+PHWSc787DNg4EBg+nTL76uz86ewP+FY4TFsy9xm0f1cq7yGr098jRcnvggXB5dW1xs0\nCAgPt2DY48ABqVb+9Cd8+rkdevUC4uLa34xpm99P/j1uVN/A8hOWjVfVNtTi/X3v4/GRj2OA+4BW\n1+vXD7jvPllmxNJa+d//ltWCn3vOsvvpCvzX2P+Cs4MzPjn0iUX3Q0SK7qPTCIplxy17B/9w/4fw\nc/O7o2ZAazzzjJw2VVhoOXtu3JBTVJ97TrrIGfOIGRiDif0m4q3Utyy6n48Pfgx7YY/nJ7TvVnrq\nKWDHDtn0TVGIgD/8ARgxAhUPPolly2RSL5dsN59BvQbhwZAH8f6+96HTW64F8fITy1FYUYiXw15u\nd90//Qk4elRekyxFfb0sZfL444C3t+X201Vwd3HHM6OfweeHP7eot2tn1k6kX01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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81f819cfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-10, 10, num=100)\n", "\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "y3 = np.arctan(x)\n", "\n", "\n", "plt.plot(x, y1, x, y2, x, y3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "*todo*\n", "array manipulation routines\n", "numpy.flipud, fliplr, transpose, rot90, flatten, ravel" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Colormap Plots\n", "\n", "Plot color maps with `pcolormesh`:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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A7SjAeaN0ioIDtUttoNxfwOqL/FpULwmfZXuqRJ0x/wevr4qe1uXRCi2rmIzKaVm0M5qe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3AAAg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uT8b1UiyqdAlJYOnZD5bAHEmHNMED71UGicioIeThiq/C4Tw0vAgsR54ERfGQWRPlsd9IKp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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f81ef6ce0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace (-1, 1, num =100)\n", "y = np.linspace (-1, 1, num =100)\n", "xx, yy = np.meshgrid (x, y)\n", "z = np.sin(xx**2 + yy**2 + yy)\n", "plt.pcolormesh(x, y, z, shading = 'gouraud')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or with `imshow`:" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WrOCtr79cf3rZLP9UYZ/RuvilV0YqYhbAdbh+9q16MwN/y8XhyOMAP/KA9RYJas9y1+HR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ULuu7Bf2d2APAf/h7xf33XQO1UYPzgFq9aDT1X4RPSPA/gPAfxaAD8B4G8D+LeZ+a8q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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81f81506d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(z, aspect='auto')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the image is flipped because images start from top left and go to bottom right. We can fix this with `flipud`:" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Mry32kx69DPlfsIU9bz8pfU6NdjkV8ATseNHeKwL+x5iB37v47sgjeNZcvGYQ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\nWTE8U46t9dur8fnav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rjPA3xvqp12UULq4bCfPHB5wJrbkQYH7PV0ugTpw96XVBnoBfB1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\n9TF0vmcw6Kb41tJ6XC6N7t9VrN+4pWMtW6D31jWoW/mt1At8cnR6wf+KD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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81ef642b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(np.flipud(z), aspect='auto')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 3D Plots" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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J42I6v87ouIBluUO5bkxs0wfnry+guMPHjeNHsr3dw+Y2N6Udvq4qW3bZRKpk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\nB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igypjgY6umir6eDVCLF6+K/Jn2mpalJ/+52iMUSEpPTVPJqf8ex81fR0FBn6lRZq9SQ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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f81f9404c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib import cm\n", "\n", "%matplotlib inline\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "ax.plot_surface(xx, yy, z, rstride=5, cstride=5, cmap=cm.coolwarm, linewidth=1, antialiased=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D Wireframe Plot" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jx6FxYwcVKpjcdZfGyJFKVBQ1a6qMHRtP/foFMQwj2rQCrBP9mTM6rVsXp3Rpk5kzfbRu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mTJ1HH3WRliasHPv3N4mIsHnxRY25c3Xat09fcNeulWnTRmP8eIO+fY0MhOz1Qq1awmFp06YL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\netGWK6CWvfTw8ECv19t501n1xBfHh7P8ohwjwJbmALhy5QqDBw/mxo0brFy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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81ef603550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "ax.plot_wireframe(xx, yy, z, rstride=5, cstride=5, antialiased=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gallery of `matplotlib` Plots\n", "See http://matplotlib.org/gallery.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting Exercise\n", "\n", "Consider the function `f(x, y) = exp(x + 1.0j*y)` for −4 ≤ x, y ≤ 4. Create colormap and 3d plots of the magnitude, real,\n", "and imaginary parts of `f`." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting Images" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DlrRospQAlMdSt2GWBCAtOwAtCWvC9u6EBfW7LezSEgagHsaQZahhp92Qt+nJctKHlnho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\nXTMNYmNVdpc+IzcCCI7E3wiEjPRJL6Z70yBZ/4c00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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81f818c5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-2, 2, num=100)\n", "y = np.linspace(-2, 2, num=100)\n", "\n", "result = np.flipud(np.array([[u*v for u in x] for v in y]))\n", "\n", "fig = plt.figure()\n", "plt.imshow(result, extent=[x.min(), x.max(), y.min(), y.max()], aspect='auto')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classes\n", "\n", "Classes can be used to package data and methods together:" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "my x value is 5\n" ] } ], "source": [ "class SomeClass:\n", " def __init__ (self, x):\n", " self.x = x\n", "\n", " def doSomething(self):\n", " print(\"my x value is {}\".format(self.x))\n", " \n", "obj = SomeClass(5)\n", "obj.doSomething()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inheritance\n", "\n", "Classes can be derived from others:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "my x value is 5\n", "my y value is 6\n" ] } ], "source": [ "class SomeOtherClass (SomeClass):\n", " def __init__ (self, x, y):\n", " SomeClass.__init__ (self, x) \n", " self.y = y\n", "\n", " def doSomethingElse(self):\n", " print(\"my y value is {}\".format(self.y))\n", " \n", "other_obj = SomeOtherClass(5, 6)\n", "other_obj.doSomething()\n", "other_obj.doSomethingElse()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Polymorphism\n", "\n", "An instance of a derived class is automatically an instance of its base class:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The type of obj is <class '__main__.SomeClass'>\n", "The type of other_obj is <class '__main__.SomeOtherClass'>\n", "obj is instance of SomeClass? True\n", "obj is instance of SomeOtherClass? False\n", "other_obj is instance of SomeClass? True\n", "other_obj is instance of SomeOtherClass? False\n" ] } ], "source": [ "print('The type of obj is {}'.format(type(obj)))\n", "print('The type of other_obj is {}'.format(type(other_obj)))\n", "\n", "print('obj is instance of SomeClass? {}'.format(isinstance(obj, SomeClass)))\n", "print('obj is instance of SomeOtherClass? {}'.format(isinstance(obj, SomeOtherClass)))\n", "\n", "print('other_obj is instance of SomeClass? {}'.format(isinstance(obj, SomeClass)))\n", "print('other_obj is instance of SomeOtherClass? {}'.format( isinstance(obj, SomeOtherClass)))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# scratch area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "*todo*" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# todo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*todo*\n", "Python 2 vs. 3\n", "Key differences include:\n", "print statement\n", "integer division\n", "int vs. long\n", "new style classes\n", "some standard modules/functions have been moved/renamed\n", "The “ future ” module can be used to write code compatible\n", "with both Python 2 and 3." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
PYPIT/PYPIT
doc/nb/LRIS_blue_notes.ipynb
1
140661
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notes on the LRIS Blue reduction" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# imports\n", "sys.path.append(os.path.abspath('/Users/xavier/local/Python/PYPIT/src'))\n", "import arload as pyp_arload\n", "import ario as pyp_ario" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Detectors\n", "\n", "Note: LRISb has employed different detectors. We may need to\n", "make PYPIT backwards compatible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FITS file" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/xavier/PYPIT/LRIS_blue/Raw/b150910_2033.fits.gz\n", "No. Name Type Cards Dimensions Format\n", "0 PRIMARY PrimaryHDU 720 () \n", "1 VidInp1 ImageHDU 360 (577, 2048) int16 (rescales to uint16) \n", "2 VidInp2 ImageHDU 360 (577, 2048) int16 (rescales to uint16) \n", "3 VidInp3 ImageHDU 360 (577, 2048) int16 (rescales to uint16) \n", "4 VidInp4 ImageHDU 360 (577, 2048) int16 (rescales to uint16) \n" ] } ], "source": [ "fil = '/Users/xavier/PYPIT/LRIS_blue/Raw/b150910_2033.fits.gz'\n", "hdu = fits.open(fil)\n", "hdu.info()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'dark'" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head0['OBSTYPE']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SIMPLE = T / file does conform to FITS standard \n", "BITPIX = 16 / number of bits per data pixel \n", "NAXIS = 0 / number of data axes \n", "EXTEND = T / FITS dataset may contain extensions \n", "COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy\n", "COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n", "BZERO = 32768 / offset data range to that of unsigned short \n", "BSCALE = 1 / default scaling factor \n", "DATE = '2015-09-10T01:58:26' / file creation date (YYYY-MM-DDThh:mm:ss UT) \n", "COMMENT BEGIN observation-specific keywords written by write_image \n", "COMMENT BEGIN keywords that came from KTL via watch_ccd \n", "TEMPDET = 60.19650650 \n", "UTBTEMP = -1375.50000000 \n", "UTBP30V = -57.37888718 \n", "UTBP15V = -22.86830330 \n", "UTBM15V = -22.86830330 \n", "UTBP5V = -7.35938454 \n", "SLITGRAB= 'UNKNOWN ' \n", "SLITMASK= 3 \n", "SLITNAME= 'Slug_nig' \n", "BLUFILT = 'clear ' \n", "BLUFNUM = 1 \n", "BLUFTRAN= 'stowed ' \n", "BTRAN = 0 \n", "BLUFOCUS= -3450.21655273 \n", "DICHNAME= 'clear ' \n", "DICHROIC= 4 \n", "DICHTRAN= 'stowed ' \n", "DTRAN = 0 \n", "GRANAME = 'mirror ' \n", "GRANGLE = 13.87923241 \n", "GRATING = 1 \n", "GRISM = 5 \n", "GRISNAME= 'clear ' \n", "GRISTRAN= 'stowed ' \n", "GTRAN = 0 \n", "INSTRUME= 'LRISBLUE' \n", "REDFILT = 'Clear ' \n", "REDFNUM = 1 \n", "REDFOCUS= -0.60640621 \n", "TRAPDOOR= 'closed ' \n", "TV1FILT = -999999999 \n", "TV2FILT = -999999999 \n", "TV1FPOS = 218.70066833 \n", "MSWAVE = 0.00000000 \n", "WAVELEN = 0.00000000 \n", "MERCURY = 'off ' \n", "NEON = 'off ' \n", "ARGON = 'off ' \n", "CADMIUM = 'off ' \n", "ZINC = 'off ' \n", "HALOGEN = 'off ' \n", "KRYPTON = 'off ' \n", "XENON = 'off ' \n", "FEARGON = 'off ' \n", "DEUTERI = 'off ' \n", "FLAMP1 = 'off ' \n", "FLAMP2 = 'off ' \n", "UTC = '01:58:22.51' \n", "MJD-OBS = '57275.082206' \n", "ST = '14:51:39.22' \n", "DATE-OBS= '2015-09-10' \n", "AIRMASS = 1.41290614 \n", "AZ = 229.99715402 \n", "DEC = '+45:00:00.0' \n", "EL = 44.99169167 \n", "EQUINOX = 2000.00000000 \n", "HA = '+00:00:00.00' \n", "PONAME = 'Bedge ' \n", "RA = '15:20:00.00' \n", "ROTMODE = 'stationary' \n", "ROTPOSN = 89.99988480 \n", "ROTPPOSN= 89.37988479 \n", "SECFOCUS= -0.00159533 \n", "SECTHETX= -0.00196931 \n", "SECTHETY= -0.00022058 \n", "SIMULATE= F \n", "TARGNAME= 'unknown ' \n", "TELESCOP= 'Keck I ' \n", "TELFOCUS= -0.00169979 \n", "TUBETEMP= 5.07289000 \n", "DOMEPOSN= 140.50558119 \n", "FLIMAGIN= 'off ' \n", "FLSPECTR= 'off ' \n", "DRA = '0.000000' \n", "DDEC = '0.0000000' \n", "DTRACK = 'disabled' \n", "PMFM = 0.00000000 \n", "ADCNEED = T \n", "CURRINST= 'LRISADC ' \n", "ADCPRENA= 1 \n", "ADCBYP = F \n", "ADCCAL = T \n", "ADCCMT = ' ' \n", "ADCCON = F \n", "ADCDCC = 'hand paddle' \n", "ADCENC = 306331 \n", "ADCERR = 0 \n", "ADCGTE = -3 \n", "ADCLCK = 'unlocked' \n", "ADCLIM = 0 \n", "ADCMOD = 0 \n", "ADCRAW = -6126772 \n", "ADCRCD = ' ' \n", "ADCSTA = 3 \n", "ADCSVA = -0.00004355 \n", "ADCSVR = 0 \n", "ADCSVX = -0.00000002 \n", "ADCTVA = 1550.71997070 \n", "ADCTVX = 1.00305426 \n", "ADCVAL = 19.02700043 \n", "ADCVAX = 0.01926695 \n", "ADCVEL = 0.00000000 \n", "ADCVEX = 0.00000000 \n", "ADCWAVE0= 3400.00000000 \n", "ADCWAVE1= 9000.00000000 \n", "CTRL0ERR= -5414 \n", "CTRL0MSG= 'Software deadman switch activated (sent ST to axes), and has been cl'\n", "DISP0ERR= 0 \n", "DISP0STA= 0 \n", "POW24V = 26.02556801 \n", "TEMP1C = -100.00000000 \n", "TEMP2C = -100.00000000 \n", "TEMPELC = 21.94996452 \n", "OBJECT = ' ' \n", "OBSERVER= 'ProchaskaLeiblerCai' \n", "AMPMODE = 'SINGLE:A' \n", "AMPLIST = '1,4,0,0 ' \n", "AUTOERAS= T \n", "AUTOSHUT= F \n", "CCDGAIN = 'low ' \n", "CCDSPEED= 'fast ' \n", "ERASECNT= 1 \n", "FRAMENO = 2001 \n", "MPPMODE = T \n", "NSUBINT = 0 \n", "OUTDIR = '/sdata243/lris8/2015sep10' \n", "OUTFILE = 'b150910_' \n", "TODISK = T \n", "ROWSHFT = 0 \n", "TTIME = 0 \n", "ELAPTIME= 0 \n", "NUMAMPS = 4 \n", "VOFFSET0= 1799 \n", "VOFFSET1= 1842 \n", "VOFFSET2= 1933 \n", "VOFFSET3= 1762 \n", "OBSTYPE = 'dark ' \n", "ANTIBLM = 'off ' \n", "ABFREQ = 20 \n", "POSTPIX = 80 \n", "WINDOW = '1,0,0,1024,2048' \n", "PANEFITS= '1HDU/pane/amp' \n", "PANELIST= 'PANE ' \n", "PANERROR= 'NO_ERROR' \n", "PANE = '0,0,4096,4096' \n", "PANE1 = '0,0,0,0 ' \n", "PANE2 = '0,0,0,0 ' \n", "PANE3 = '0,0,0,0 ' \n", "PANE4 = '0,0,0,0 ' \n", "PANE5 = '0,0,0,0 ' \n", "PANE6 = '0,0,0,0 ' \n", "PANE7 = '0,0,0,0 ' \n", "PANE8 = '0,0,0,0 ' \n", "PANE9 = '0,0,0,0 ' \n", "PANE10 = '0,0,0,0 ' \n", "PANE11 = '0,0,0,0 ' \n", "PANE12 = '0,0,0,0 ' \n", "PANE13 = '0,0,0,0 ' \n", "PANE14 = '0,0,0,0 ' \n", "PANE15 = '0,0,0,0 ' \n", "PANE16 = '0,0,0,0 ' \n", "DATE_BEG= '2015-09-10T01:58:21' \n", "DATE_END= '2015-09-10T01:58:23' \n", "UTC-END = '01:58:26.01' \n", "DATE-END= '2015-09-10' \n", "COMMENT END keywords that came from KTL via watch_ccd \n", "COMMENT BEGIN keywords that came from CCD crate imparm structure \n", "IMTYPE = 'n MOSAIC' \n", "PRECOL = 50 \n", "PREROW = 0 \n", "PRELINE = 0 \n", "POSTLINE= 0 \n", "ERASLINE= 0 \n", "KEEPPREP= 1 \n", "PREFLUSH= 0 \n", "OVRFLUSH= 0 \n", "DETCNFID= 201 \n", "VIDINACT= 15 \n", "BINNING = '2,2 ' \n", "CCDSUM = '2 2 ' \n", "AMPPSIZE= '[1:1024,1:4096]' \n", "DETLSIZE= '[1:4096,1:4096]' \n", "COMMENT END keywords that came from CCD crate imparm structure \n", "COMMENT ...Generic comments for this instrument follow... \n", "COMMENT LRIS Blue CCD \n", "COMMENT ...Comments specific to this run follow... \n", "COMMENT This is a test run comment \n", "COMMENT ...Comments specific to this frame follow... \n", "COMMENT \n", "COMMENT END observation-specific keywords written by write_image \n", "CHECKSUM= 'NYg8PXe5NXe5NXe5' / HDU checksum updated 2015-09-10T01:58:51 \n", "DATASUM = ' 0' / data unit checksum updated 2015-09-10T01:58:51 \n", "KOAID = 'LB.20150910.07102.fits' / KOA data file 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\n", " \n", " \n", " \n", " \n", " \n", " " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head0 = hdu[0].header\n", "head0\n", "#head0['DATE']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.clf()\n", "plt.imshow(hdu[1].data)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Display Raw LRIS image in Ginga" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Need to port readmhdufits" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SIMPLE = T / file does conform to FITS standard \n", "BITPIX = 16 / number of bits per data pixel \n", "NAXIS = 0 / number of data axes \n", "EXTEND = T / FITS dataset may contain extensions \n", "COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy\n", "COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n", "BZERO = 32768 / offset data range to that of unsigned short \n", "BSCALE = 1 / default scaling factor \n", "DATE = '2015-09-10T01:58:26' / file creation date (YYYY-MM-DDThh:mm:ss UT) \n", "COMMENT BEGIN observation-specific keywords written by write_image \n", "COMMENT BEGIN keywords that came from KTL via watch_ccd \n", "TEMPDET = 60.19650650 \n", "UTBTEMP = -1375.50000000 \n", "UTBP30V = -57.37888718 \n", "UTBP15V = -22.86830330 \n", "UTBM15V = -22.86830330 \n", "UTBP5V = -7.35938454 \n", "SLITGRAB= 'UNKNOWN ' \n", "SLITMASK= 3 \n", "SLITNAME= 'Slug_nig' \n", "BLUFILT = 'clear ' \n", "BLUFNUM = 1 \n", "BLUFTRAN= 'stowed ' \n", "BTRAN = 0 \n", "BLUFOCUS= -3450.21655273 \n", "DICHNAME= 'clear ' \n", "DICHROIC= 4 \n", "DICHTRAN= 'stowed ' \n", "DTRAN = 0 \n", "GRANAME = 'mirror ' \n", "GRANGLE = 13.87923241 \n", "GRATING = 1 \n", "GRISM = 5 \n", "GRISNAME= 'clear ' \n", "GRISTRAN= 'stowed ' \n", "GTRAN = 0 \n", "INSTRUME= 'LRISBLUE' \n", "REDFILT = 'Clear ' \n", "REDFNUM = 1 \n", "REDFOCUS= -0.60640621 \n", "TRAPDOOR= 'closed ' \n", "TV1FILT = -999999999 \n", "TV2FILT = -999999999 \n", "TV1FPOS = 218.70066833 \n", "MSWAVE = 0.00000000 \n", "WAVELEN = 0.00000000 \n", "MERCURY = 'off ' \n", "NEON = 'off ' \n", "ARGON = 'off ' \n", "CADMIUM = 'off ' \n", "ZINC = 'off ' \n", "HALOGEN = 'off ' \n", "KRYPTON = 'off ' \n", "XENON = 'off ' \n", "FEARGON = 'off ' \n", "DEUTERI = 'off ' \n", "FLAMP1 = 'off ' \n", "FLAMP2 = 'off ' \n", "UTC = '01:58:22.51' \n", "MJD-OBS = '57275.082206' \n", "ST = '14:51:39.22' \n", "DATE-OBS= '2015-09-10' \n", "AIRMASS = 1.41290614 \n", "AZ = 229.99715402 \n", "DEC = '+45:00:00.0' \n", "EL = 44.99169167 \n", "EQUINOX = 2000.00000000 \n", "HA = '+00:00:00.00' \n", "PONAME = 'Bedge ' \n", "RA = '15:20:00.00' \n", "ROTMODE = 'stationary' \n", "ROTPOSN = 89.99988480 \n", "ROTPPOSN= 89.37988479 \n", "SECFOCUS= -0.00159533 \n", "SECTHETX= -0.00196931 \n", "SECTHETY= -0.00022058 \n", "SIMULATE= F \n", "TARGNAME= 'unknown ' \n", "TELESCOP= 'Keck I ' \n", "TELFOCUS= -0.00169979 \n", "TUBETEMP= 5.07289000 \n", "DOMEPOSN= 140.50558119 \n", "FLIMAGIN= 'off ' \n", "FLSPECTR= 'off ' \n", "DRA = '0.000000' \n", "DDEC = '0.0000000' \n", "DTRACK = 'disabled' \n", "PMFM = 0.00000000 \n", "ADCNEED = T \n", "CURRINST= 'LRISADC ' \n", "ADCPRENA= 1 \n", "ADCBYP = F \n", "ADCCAL = T \n", "ADCCMT = ' ' \n", "ADCCON = F \n", "ADCDCC = 'hand paddle' \n", "ADCENC = 306331 \n", "ADCERR = 0 \n", "ADCGTE = -3 \n", "ADCLCK = 'unlocked' \n", "ADCLIM = 0 \n", "ADCMOD = 0 \n", "ADCRAW = -6126772 \n", "ADCRCD = ' ' \n", "ADCSTA = 3 \n", "ADCSVA = -0.00004355 \n", "ADCSVR = 0 \n", "ADCSVX = -0.00000002 \n", "ADCTVA = 1550.71997070 \n", "ADCTVX = 1.00305426 \n", "ADCVAL = 19.02700043 \n", "ADCVAX = 0.01926695 \n", "ADCVEL = 0.00000000 \n", "ADCVEX = 0.00000000 \n", "ADCWAVE0= 3400.00000000 \n", "ADCWAVE1= 9000.00000000 \n", "CTRL0ERR= -5414 \n", "CTRL0MSG= 'Software deadman switch activated (sent ST to axes), and has been cl'\n", "DISP0ERR= 0 \n", "DISP0STA= 0 \n", "POW24V = 26.02556801 \n", "TEMP1C = -100.00000000 \n", "TEMP2C = -100.00000000 \n", "TEMPELC = 21.94996452 \n", "OBJECT = ' ' \n", "OBSERVER= 'ProchaskaLeiblerCai' \n", "AMPMODE = 'SINGLE:A' \n", "AMPLIST = '1,4,0,0 ' \n", "AUTOERAS= T \n", "AUTOSHUT= F \n", "CCDGAIN = 'low ' \n", "CCDSPEED= 'fast ' \n", "ERASECNT= 1 \n", "FRAMENO = 2001 \n", "MPPMODE = T \n", "NSUBINT = 0 \n", "OUTDIR = '/sdata243/lris8/2015sep10' \n", "OUTFILE = 'b150910_' \n", "TODISK = T \n", "ROWSHFT = 0 \n", "TTIME = 0 \n", "ELAPTIME= 0 \n", "NUMAMPS = 4 \n", "VOFFSET0= 1799 \n", "VOFFSET1= 1842 \n", "VOFFSET2= 1933 \n", "VOFFSET3= 1762 \n", "OBSTYPE = 'dark ' \n", "ANTIBLM = 'off ' \n", "ABFREQ = 20 \n", "POSTPIX = 80 \n", "WINDOW = '1,0,0,1024,2048' \n", "PANEFITS= '1HDU/pane/amp' \n", "PANELIST= 'PANE ' \n", "PANERROR= 'NO_ERROR' \n", "PANE = '0,0,4096,4096' \n", "PANE1 = '0,0,0,0 ' \n", "PANE2 = '0,0,0,0 ' \n", "PANE3 = '0,0,0,0 ' \n", "PANE4 = '0,0,0,0 ' \n", "PANE5 = '0,0,0,0 ' \n", "PANE6 = '0,0,0,0 ' \n", "PANE7 = '0,0,0,0 ' \n", "PANE8 = '0,0,0,0 ' \n", "PANE9 = '0,0,0,0 ' \n", "PANE10 = '0,0,0,0 ' \n", "PANE11 = '0,0,0,0 ' \n", "PANE12 = '0,0,0,0 ' \n", "PANE13 = '0,0,0,0 ' \n", "PANE14 = '0,0,0,0 ' \n", "PANE15 = '0,0,0,0 ' \n", "PANE16 = '0,0,0,0 ' \n", "DATE_BEG= '2015-09-10T01:58:21' \n", "DATE_END= '2015-09-10T01:58:23' \n", "UTC-END = '01:58:26.01' \n", "DATE-END= '2015-09-10' \n", "COMMENT END keywords that came from KTL via watch_ccd \n", "COMMENT BEGIN keywords that came from CCD crate imparm structure \n", "IMTYPE = 'n MOSAIC' \n", "PRECOL = 50 \n", "PREROW = 0 \n", "PRELINE = 0 \n", "POSTLINE= 0 \n", "ERASLINE= 0 \n", "KEEPPREP= 1 \n", "PREFLUSH= 0 \n", "OVRFLUSH= 0 \n", "DETCNFID= 201 \n", "VIDINACT= 15 \n", "BINNING = '2,2 ' \n", "CCDSUM = '2 2 ' \n", "AMPPSIZE= '[1:1024,1:4096]' \n", "DETLSIZE= '[1:4096,1:4096]' \n", "COMMENT END keywords that came from CCD crate imparm structure \n", "COMMENT ...Generic comments for this instrument follow... \n", "COMMENT LRIS Blue CCD \n", "COMMENT ...Comments specific to this run follow... \n", "COMMENT This is a test run comment \n", "COMMENT ...Comments specific to this frame follow... \n", "COMMENT \n", "COMMENT END observation-specific keywords written by write_image \n", "CHECKSUM= 'NYg8PXe5NXe5NXe5' / HDU checksum updated 2015-09-10T01:58:51 \n", "DATASUM = ' 0' / data unit checksum updated 2015-09-10T01:58:51 \n", "KOAID = 'LB.20150910.07102.fits' / KOA data file name \n", "KOAIMTYP= 'bias ' / KOA data file name \n", "PROGID = 'U044LA ' / WMKO ID for observing program \n", "PROGINST= 'UCSC ' / Program institution \n", "PROGPI = 'Prochaska' / Program principal investigator \n", "PROGTL1 = 'Resolving the Small-Scale Structure of the CGM at z~3 ' / Program tit\n", "PROGTL2 = ' ' / Program title 2 \n", "PROGTL3 = ' ' / Program title 3 \n", "SEMESTER= '2015B ' / WMKO observing schedule semester \n", "UT = 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pyp_ario.read_lris('/Users/xavier/PYPIT/LRIS_blue/Raw/b150910_2070.fits',TRIM=True)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: Writing kludge file to /Users/xavier/local/Python/PYPIT/doc/nb/./tmp_ginga.fits\n" ] } ], "source": [ "xdb.ximshow(img)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import subprocess" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.call([\"touch\", \"dum.fil\"])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = 'as'" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "tuple index out of range", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-ec1388c43a85>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'{1:s}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mIndexError\u001b[0m: tuple index out of range" ] } ], "source": [ "'{1:s}'.format(b)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(1,5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tmp = np.ones((10,20))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 20)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp[0:1,:].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
ozorich/phys202-2015-work
assignments/assignment02/ProjectEuler6.ipynb
1
2532
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 6" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "https://projecteuler.net/problem=6\n", "\n", "The sum of the squares of the first ten natural numbers is,\n", "\n", "$$1^2 + 2^2 + ... + 10^2 = 385$$\n", "\n", "The square of the sum of the first ten natural numbers is,\n", "\n", "$$(1 + 2 + ... + 10)^2 = 552 = 3025$$\n", "\n", "Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640.\n", "Find the difference between the sum of the squares of the first one hundred natural numbers and the square of the sum." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "def fn(r):\n", " s=sum([x**2 for x in range(r+1)]) #sums the square of the first r natural numbers (r+1 in order to be inclusive)\n", " s2=sum([x for x in range(r+1)]) #sums firt r natural numbers\n", " difference=s2**2-s # squares the sum and subtracts the sum of the squares\n", " return difference" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25164150\n" ] } ], "source": [ "print (fn(100))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "4a8ce9efca8c824de365eec816018842", "grade": true, "grade_id": "projecteuler6", "points": 10 } }, "outputs": [], "source": [ "# This cell will be used for grading, leave it at the end of the notebook." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
madsenmj/ml-introduction-course
Class03/Class03.ipynb
1
112482
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Class 03\n", "## Big Data Cleaning: Data Transformations\n", "\n", "Although machine learning is the exciting part of this course, most data scientists spend the vast majority of their time doing data clearning and data wrangling. Some put the figure at as high as 90% of their time! There is a good reason for this: most of the data out there is not in a format needed for the machine learning algorithms. So, in order to do machine learning, the data must be reorganized, cleaned, rearranged, normalized, enriched, and filtered. We'll begin this process today and continue working on it through the course." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature Types\n", "\n", "We start with an overview of some of the types of features we could potentially use. In the end, all of the data are represented as bits in the computer (ones and zeros), but we can organize those bits in a bunch of different ways in the pandas dataframes. We'll build a \"fake\" dataframe with the different types in them.\n", "\n", "#### Integers\n", "\n", "Integers are counting numbers and other whole numbers (including negatives): ...,-4,-3,-2,-1,0,1,2,3,4,... They are somewhat special because they can be stored very efficiently and the computer can operate on them very efficiently (positive integers especially). Pandas stores these using a data type called **int64** where the 64 means they are 64-bit integers (capable of storing any number between -9,223,372,036,854,775,807 and 9,223,372,036,854,775,807)\n", "\n", "We'll use a sample dataset to look at the different types of data as we go." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IntCol int64\n", "FloatCol float64\n", "TextCol object\n", "CatCol object\n", "DateCol object\n", "LatCol float64\n", "LonCol float64\n", "dtype: object\n", "\n", "Integer Values\n", "[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]\n" ] } ], "source": [ "import pandas as pd\n", "\n", "sampledata = pd.read_csv('Class03_sample_dataframe.csv')\n", "\n", "# This will let us look at the data type of each column. Note that the first column is an \"int64\".\n", "print(sampledata.dtypes)\n", "\n", "# These are the values stored in this column.\n", "print(\"\\nInteger Values\")\n", "print(sampledata['IntCol'].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Floating point numbers\n", "\n", "Floating point numbers, or decimal numbers are just that: any number with a decimal place in it such as 4.566642 and -156.986714. Pandas stores these as a **float64**. They could also be stored in scientific notation like this: 4.509013e+14. This means \"4.509013 times 10 raised to the +14\". These are still floating point numbers and are treated like any other decimal number." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Float Values\n", "[ 1.34846527 1.65852321 1.99091463 2.15807893 2.50018684 2.60270486\n", " 2.79540154 3.01384446 3.1916722 3.25200862 3.46085057 3.6586824\n", " 3.79061854 3.83351212 3.94635944 4.15475126 4.27976091 4.27608798\n", " 4.44673513 4.59670764]\n" ] } ], "source": [ "print(\"Float Values\")\n", "print(sampledata['FloatCol'].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we move on, I'd like to take a quick look at the data graphically." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2ac455cd8d0>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata.plot(kind='scatter', x='IntCol',y='FloatCol')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because this is \"fake\" data, I put in a functional dependence here. The float column looks like it is some function of the integer column. It is almost always a good idea to visualize your data early on to see what it looks like graphically!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Text\n", "\n", "Pandas can store text in its columns. Because there are a number of different types of text objects, by default pandas will store text as an **object** which just means it doesn't know which of the types it really is. Text can, in principle, be anything you want it to be, so it is both the most flexible and the most challenging data type." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Text Values\n", "['cat' 'dog' 'horse' 'cow' 'elephant' 'fish' 'bird' 'dinosaur' 'giraffe'\n", " 'wolf' 'prairie dog' 'whale' 'dolphin' 'clam' 'lizard' 'snake' 'fly'\n", " 'beetle' 'spider' 'worm']\n" ] } ], "source": [ "print(\"Text Values\")\n", "print(sampledata['TextCol'].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Categorical\n", "\n", "A categorical data type is a finite set of different objects. These objects are represented internally as integers but may be displayed as text or other generic objects. To make things simple, we'll start with a categorical object that has three possible values: \"yes\", \"no\", and \"maybe\". Internally, pandas will represent these as integers 0,1, and 2. But it knows that this is a categorical data type, so it keeps track of the text value associated with the integer and displays that for the user." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Categorical Values\n", "['no' 'no' 'yes' 'no' 'yes' 'no' 'yes' 'yes' 'maybe' 'no' 'no' 'no' 'no'\n", " 'no' 'yes' 'maybe' 'no' 'yes' 'yes' 'yes']\n" ] } ], "source": [ "print(\"Categorical Values\")\n", "print(sampledata['CatCol'].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we loaded the data, it actually loaded this column as an **object**, which means it doesn't know that it is supposed to be a categorical column. We will tell pandas to do that. We will use the `astype()` command that will tell pandas to change the data type of that column. We check to make sure it worked, too. Note that the \"CatCol2\" column is now a 'category' type.\n", "\n", "_**Data Processing Tip**_\n", "\n", "A quick aside here: there are a couple of ways of doing this kind of transformation on the data. We'll see this a little later when we do more column-wise processing. We could either change the original column or we could create a new column. The second method doesn't overwrite the original data and will be what we typically do. That way if something goes wrong or we want to change how we are processing the data, we still have the original data column to work with." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "IntCol int64\n", "FloatCol float64\n", "TextCol object\n", "CatCol object\n", "DateCol object\n", "LatCol float64\n", "LonCol float64\n", "CatCol2 category\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata[\"CatCol2\"] = sampledata[\"CatCol\"].astype('category')\n", "sampledata.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now look at how the data are stored as categorical data. We can get thi internal codes for each of the entries like this:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 1\n", "2 2\n", "3 1\n", "4 2\n", "5 1\n", "6 2\n", "7 2\n", "8 0\n", "9 1\n", "10 1\n", "11 1\n", "12 1\n", "13 1\n", "14 2\n", "15 0\n", "16 1\n", "17 2\n", "18 2\n", "19 2\n", "dtype: int8" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata[\"CatCol2\"].cat.codes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also get a list of the categories that pandas found when converting the column. These are in order- the first entry corresponds to 0, the second to 1, etc." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['maybe', 'no', 'yes'], dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata[\"CatCol2\"].cat.categories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We may encounter situations where we want to plot the data and visualize each category as its own color. We saw how to do this back in Class01." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x2ac493ab4a8>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import seaborn as sns\n", "sns.set_style('white')\n", "sns.lmplot(x='IntCol', y='FloatCol', data=sampledata, hue='CatCol2', fit_reg=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Date/Times\n", "\n", "We will frequently encounter date/time values in working with data. There are many different ways that these values get stored, but mostly we'll find that they start as a text object. We need to know how they are stored (in what order are the year-month-day-hour-minute-second values are stored). There are utilities to convert any type of date/time string to a datetime object in pandas. We will start with the ISO 8601 datetime standard, since it is both the most logical and the easiest to work with. Dates are stored like this: **2017-01-23** where we use a four-digit year, then a two-digit month and a two-digit day, all separated by dashes. If we want to add a time, it is appended to the date like this: **2017-01-23T03:13:42**. The \"T\" tells the computer that we've added a time. Then it is followed by a two-digit hour (using 00 as midnight and 23 as 11pm) a colon, a two-digit minute, a colon, and a two-digit second. There are other variations of this that can include a time-zone, but we will leave those for later. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date/Time Values\n", "['2017-01-25T11:10:15' '2017-01-26T00:06:43' '2017-01-26T21:11:33'\n", " '2017-01-27T06:44:09' '2017-01-27T20:55:49' '2017-01-28T03:26:11'\n", " '2017-01-28T17:50:22' '2017-01-29T04:02:27' '2017-01-29T15:19:37'\n", " '2017-01-29T18:03:21' '2017-01-30T00:06:48' '2017-01-30T14:10:13'\n", " '2017-01-31T10:56:47' '2017-01-31T12:03:30' '2017-01-31T18:25:47'\n", " '2017-02-01T16:11:57' '2017-02-02T10:41:38' '2017-02-02T23:53:35'\n", " '2017-02-03T22:16:36' '2017-02-04T06:41:42']\n" ] } ], "source": [ "print(\"Date/Time Values\")\n", "print(sampledata['DateCol'].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "They are currently stored as **objects**, not as datetimes. We need to convert this column as well, but we'll use a special pandas function to do that. Take a quick look at the [reference page](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) for this function to see what else it can do. Note that the new column has type **datetime64[ns]**. That means that the date format is capable of counting nanoseconds. We won't use all of that capability, but pandas used that format because our dates are accurate to the second." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "IntCol int64\n", "FloatCol float64\n", "TextCol object\n", "CatCol object\n", "DateCol object\n", "LatCol float64\n", "LonCol float64\n", "CatCol2 category\n", "DateCol2 datetime64[ns]\n", "dtype: object" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata[\"DateCol2\"] = pd.to_datetime(sampledata[\"DateCol\"])\n", "sampledata.dtypes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 2017-01-25 11:10:15\n", "1 2017-01-26 00:06:43\n", "2 2017-01-26 21:11:33\n", "3 2017-01-27 06:44:09\n", "4 2017-01-27 20:55:49\n", "5 2017-01-28 03:26:11\n", "6 2017-01-28 17:50:22\n", "7 2017-01-29 04:02:27\n", "8 2017-01-29 15:19:37\n", "9 2017-01-29 18:03:21\n", "10 2017-01-30 00:06:48\n", "11 2017-01-30 14:10:13\n", "12 2017-01-31 10:56:47\n", "13 2017-01-31 12:03:30\n", "14 2017-01-31 18:25:47\n", "15 2017-02-01 16:11:57\n", "16 2017-02-02 10:41:38\n", "17 2017-02-02 23:53:35\n", "18 2017-02-03 22:16:36\n", "19 2017-02-04 06:41:42\n", "Name: DateCol2, dtype: datetime64[ns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We print out the column to see what it looks like\n", "sampledata[\"DateCol2\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the datetime column, I'd like to plot the data as a function of date. This is often a useful thing to do with time series data. We'll need to import the matplotlib library and use a trick to format the data by date. Here's the code that makes it work." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x2ac483266d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "# We will plot the data values and set the linestyle to 'None' which will not plot the line. We also want to show the individual data points, so we set the marker.\n", "plt.plot(sampledata['DateCol2'].values, sampledata['FloatCol'].values, linestyle='None', marker='o')\n", "# autofmt_xdate() tells the computer that it should treat the x-values as dates and format them appropriately. This is a figure function, so we use gcf() to \"get current figure\"\n", "plt.gcf().autofmt_xdate()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Geographical\n", "\n", "Although this is not typically a single data type, you may encounter geographical data. These are typically in a Latitude-Longitude format where both Latitude and Longitude are floating point numbers like this: (32.1545, -138.5532). There are a number of tools we can use to work with and plot this type of data, so I wanted to cover it now. For now, we will treat these as separate entities and work with geographical data as we encounter it." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Latitude Values\n", "[-39.49065628 -70.12948911 -16.13173975 87.89217686 70.44943608\n", " 58.05434711 -2.53972797 -62.7995723 -82.55162128 -70.17320869\n", " -25.36587139 -75.60602277 -7.53093654 -42.9814863 66.79396011\n", " 25.04139344 1.83167685 8.00208991 33.87311843 37.14139842]\n", "Longitude Values\n", "[ 155.028043 -38.5430472 29.02027996 -88.75756107 -83.95066221\n", " -115.3764318 -68.05589951 -29.09891648 -45.48397025 79.36955726\n", " -154.2330359 8.00061215 -173.3395272 -75.00614985 -113.6252933\n", " -1.75222417 -33.07273958 138.9382069 102.8687652 69.72581269]\n" ] } ], "source": [ "print(\"Latitude Values\")\n", "print(sampledata['LatCol'].values)\n", "print(\"Longitude Values\")\n", "print(sampledata['LonCol'].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also useful to plot the geographical data. There are python libraries that make this easy to do." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\mpl_toolkits\\basemap\\__init__.py:3274: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\n", " b = ax.ishold()\n", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\mpl_toolkits\\basemap\\__init__.py:3283: MatplotlibDeprecationWarning: axes.hold is deprecated.\n", " See the API Changes document (http://matplotlib.org/api/api_changes.html)\n", " for more details.\n", " ax.hold(b)\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x2ac49165898>]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jTqdLUSgUbeijTspPCn4FgDparTaxe/fufjt7yrer5hccL+gKN0J1GWJsWbnp\nYSrGZRvxevPB7SEt+kT4oQCmK5Xs2rYtSTImMtJj5Cxv9BncL6qFChx1+gpgGYOBb44dyzDJdDRE\nOnf8+HHn9RcuXOBIN7sPe0v81SmVfE+h4Cbp9+eCpNzSABYH8iXfXF5oj8QXT+5xDuoP0bbsS5l3\nkXUzBwCG6XS0SDv9NnqIzeHA77//zuIREZyhUtEOsLtGQ5PRmCs5nTt3LrVarU2r1Q6hH/opPyio\nD1coFE8KgpD09ddf+83EgoJREpCZboSqD9yHtPPFB5cQV6YBUBkd7dd2zlomE9euXcukpCQatdpc\n2dpeBVgfok+y44XI7wSOvkJu1jkFMcJXPSkmbT+Vindldd66dSu3bt3qPK6kVPItwLnbjBDdnP4O\nAcXmoECHaQwENZC2kffSar1etwFgTfi3kSgFYrwKG8SFXLkS/uWXX7zKwqVLl1izfHkO1OlYVDIb\n5bS24QkHDx5kdHR0ssFgmIwgbqQIykNJQqVSddHpdGk7duzIFQMLAvKX/6AbYUpE3uM3OMr3NfI/\nIX4AVJITf19ByNVzj0L0aZ0jq0OoQL6l16jR8CnJy8BBlyFuTQWQJQrab0FUWv6QHeI6QKAicOWF\n5MHl/+PjPbndfUZk3z4OeI5G50BCQgLbNW3K8hoNi4eH50m2rl69ysjIyDSj0bgEgJL8H1G6arW6\np16vT/vuu+/yxMD8hqtwOMwIeyEGaQmE0Julsv2JNfonwArStDknW9s1iKEN5bQMkgN7RATNUkjF\n9rKV/VBA3759+eGHHzIhIYEJCQksGR3t7Idalvsp5fXSx+ebEFBgvpIdYHmA+0KgLpkQtyV/BP8i\nluX2HXCMVisBLCzrx9kzZniNu2Gz2Th0wAA2e/TRPMvWuXPnGBUVlW40Gr8CoCL/5UpXrVb3DA8P\nTw51N6WMjAy6Kt15EKeGVeD/Sq4n+h6iPc3di5nXkdBuqd5FCxViywYN+GyzZny2WTM2q1ePVSpW\n5LOyNNuCIPDMmTPBZrtbfL92LSsUK8bm9etz8uTJbPXEExQEgeF6fa7s2aFAswF+GwL1yC0dgRjQ\n3l9fYYe82QF2NRjY/qmnCIB6hYKVy5fPcZ0mUHE+EhIS+OijjyYbjcblBT3iLbAHkYRKpeqq1Wpt\nnnKXhRpu3bpFudJdKgnLz8i/qWEmxFGb45mxuTAfuC5eWNRqDujZ09mu5ORk5znX3WqhirfGj6dW\no2H5UqV2Bw9xAAAgAElEQVSoUypZQqHIccPKQ8pf2g7/7NPyeNOEGOg/xmDgC127ZpHBvKZ19xUJ\nCQmMjIy0SZsoCszGWyAPIQmlUtnKYDCkrlmzJrCcy0fY7XaWLlLEKQz7kPuUI77QFoCVjEaqTCai\nSBFi924qVSqO9MN5/zWZ8HaT/f/D99+TFOPldu/enQBYTJqaPwhK11E/E8BVQVY2mRBt4r6s4qfA\ns1/uFmTPXPGgUSbErc3ernFNC3VOdq6F1cr//ve/znOLFy8uULmKi4tjTExMmiAIs8h/kdIFUF8Q\nhCR/tuGGAn777TenMNQA2AzgonwSXnniQgBURkbS0LkzATHoeByyjq5TXO7fiPsBxYfgvnlCrkwd\nqdTLly/P5cuXZ8nwC4BhFkuQOe4ZSUlJXLt2LR+rVYvjIS5s5rcXwDyJL2aIo7KxANvKApvDw33t\nIS5KPS0I1Gq1VCgUbCwLJemg7gArKBRs5aO74HrZcwsDrA3RhFSQStaVfocY3CbRyzWOEa4J2WeI\nT1ss/P7777P4ZU+ZPLlAZevGjRssWbJksk6ne535pAPllP8PAMpqtdqk999/P+DMyk/IbbqEuCOm\nJwKzm8gdHYYYTHs9RN9U1xGoSq1muF7PMxCTCQLi/n+d7BoB93NbJUIMCO0497gsR9yQIUOc7UxO\nTub169c5f/78oIVp9AfHjh0jIAYB98UNbA/Aflotf3TzwnujX9z0gSt5WsR8SaFguFpNfdOmxM2b\nxLffEsi6jdY1LU6qD3VyXFtd9n8gthXnlUYhe+YNOaVDjDbm7lxri4Xr1q0jSfbv29fZroLGwYMH\nqdPpbEqlshvzWyfma+FAhNFovNyvX7+Cj3KdR7w7aRKbCEKB54dyfRnrKpXUdukiHisUfFWlYjqy\nmg6A+9PU7QAf96Io+vfvH/BdewUJu91Oi17PphD35Lvj4TkZP1qaTCxfrhwB8N0cHP/dKTi73c6G\njz6ajY8j4VnpPmKxEHPmEHfuEG3aOO/ZK7tmFUChdm3i/HmqIiLYx4e69ZHMQQUpj/lJdoAROp1z\ny77dbmftypVZ1Grl0s8/Z0ZGBuPj4wtMtr7++msaDIYUAI8zgHrQlfKvYEBrMpkOxMbG5j7aTJCw\nZ88eFjYYeFESjpkQR6AFIYgzHcG0NZpse/sfValYThYPOAaibZGSAugns9E+JkXVB8Dezz/PkydP\n8vjx45w3b16w2esTMjMz+eUXX3DKW29x9erVXLt2LdevX89vvvmGRQ2GLDzbC3EbazLAmUolIwWB\nkUYjR+v1fF+lYpnoaE6bNo1RJhOXwbeg4ADocGm02+0cM2IERw0fzqtXr3L37t18rHp11jEaucPN\nvT0dmY1v36ZW2tThmprnPECdXk9N06YEfHd5y81GmIKgYxAXZX251pEk4ATAEhERWfp93iefiLIu\nM+P4G/wqL/jpp59oMBjuACjL/NKN+VIooDAajSvLlSuX7ktIwlBCWloayxYt6syjdh1idKsLBSC4\ndoCCTkdFoUJcoFLxKu4nCnRNGNhGr88yCh+vVrN+1arO8JVXr17lwD59eP78eWfbpk+fzldeeSVI\nnPUPQ/r1YyW9noN1Ona0WPi0xcJ2FgtbW62cotPxMESb7k6ARcxmNq5RgzqVik3q1uXZs2d58+ZN\nFjIaeRLiRoqShQpx2ddfO/m3zks/LAdo1Om8mlvsdjtXLF/OUlFR7CwIWWzsCY5+WrmSyMykULMm\nK5vN3ODynJ8BNlOpWFipdH7gH1RKBVgOolnG23WnJN40MBpZwmDgu2+9lYWv0UWLEhDt6R9J1zZt\n0iS/xS0LevXqZTcYDFcBWJkf+jE/CtVqtSOioqLSCnJqECjs3LmTdcxZt+2eLSDB/UcSssKCwLsA\nB8sy/DrIXRJAR0LBrh07em3brl27+McffxQQJ/OG77//no9ZLB55FQ3wIsRU983r1iUphvOT+3F+\n8sEHLGow8EuAL+l0LBoezlcGDGCHDh1YISaGMzx4hfSX+H779m2S5NmzZ9m9QwdWKFqUj1Wrxt9/\n/935jHv37rFZgwZc4FLG9wANYWHEyZNEejrRsycBsK2Lgr4KMSh5sJVmIOgCct6hOUSajX27ahW3\nbd2ard+HukSU69ShQz5Lmns0bNjQZjKZtiEffHgDWhhJAGhiNptTzp49m38cySekp6ezRYMGfE+y\nr11B7jK25paSAY7DfReclQDr6fXspdfzsMu1CQD7Gwws4pjKApw6aVKwWRgwfP/99+zgRek2BVjO\nYGCj6tXpmhm6b7dujLJaeebMGe7Zs4e1K1ZkY0FgrMSnlwcO5NKlS9lZKv86xF1ZLQ0GPm80sh6y\nfugi9Hq+o1LxJMRwjpE6HauWKMGurVqxc8uWDNPrudFNHRcrFDRERhJ79xJXrjjL+4+f8XvvwMLF\n6McpGM/FUnbpYCtYT5QKcK5CwWeMRp52c36CSsU+3bp57Pf09HQ2a9zYyaubN2/mt6h5rEf9+vWT\nBUF4l4HWkQEtDCis1WoTv/jii3xkR/7h+++/ZwOTyfm1fh7guyEgyHLKgLiTaRLAp554gpcuXfLJ\nmXz37t2cMmVKAXAxMJgxbRpf0unc8sAOsJkgcOL48W7bXqV4cTZQKvmelMg0IyODH33wAQu5uHtV\nEgTehhi0yNPC4xyI3iLy56dBdFlbDjH6nDfTQDPJRo/ERGeZB1yuWQHwSw/3+xu1Ltj0HcT8cu3a\ntOHzajVHazScqFBwM8RZWjGDgQs/+cRr3//www8EwMJKJXt17VpgmyVccfHiRep0OptCoXiKgdST\nASsIUJjN5v9r2bLlg2XElWH6tGl8Va0mIbqGdYd3/8OCJof5wUHbXUZ43vDmm2/yjTfeyEfuBQ6Z\nmZkEPPtE35Taf/XqVbf316tWjQD47bffZvn9wIEDBMCSMTFctXIlhw0cSAD8WCpv48aNXChlU/41\nQH32mFpNxbx54ksiLRJNdfHLXQXRt9edwvVWfKgp3lUANQoFK5Qpw8ckj49ezz3H12JjCYjxcMOM\nRm7atMlr/2dkZLD5E09wEsAyRiMPHDgQKNHyGzNnzqRer08AUISB0pWBKkij0QytVq1aUnp6ev5y\nIR8xZ/ZsAlnTl4cSOdLVAODChQv9atvJkyezLKqFMhzR3ap64MNqiQeeAq//9ddf/OWXX2i322mz\n2dijY0e2qFuXpaOiCICLFy0i6T6+xsmTJwmI6XcC0WedBYGKadPuvyjLl7Omi9nkDrLH37iTD5lI\n/CV/zRrbJR4+2bAhV61axQoVKvDMmTO8e/euk7+jABYTBC5asMBj/9tsNs6aOZN9JdPZIqm/goXR\no0enm83mLQjQVuE8F0ASAMpptdrU3Ma5DBXs3r2bgLiq/FYIKFlXcghuocjIYLMq39GlVStW1etZ\n12TiAYhT080QA8WE6fWsWbMm4+LiuG/fPn766af84osv+NmSJXylf39OnTKFjWvUYJmSJfn5kiUU\n9Hon73r95z9Zpqtz3n8/i9Il6QyG3hxiGMPPAGdWjqMQQ0ie97HP/oDoCWFs0YLYuJHYuJEatdpr\nzj0CXJwPOff8odyaNZIAdjQYqFEqWUHKVff6sGGcNWsW20o+y98ALGkw8HM3W37Pnz/v7IttAJ/U\n6/nOxIkFJnfukJaWxsKFC6epVKr+DAWlK7mH/d6tW7cHbgOEKxISEmjSajkOOWdnDQYVloTRX6+Q\nEydO8IUXXsgnruUPdu3axTVr1nD+J5/QolYzUhDYok4dtn7sMf73v//lB7NnM8xgYC2LhS8KArub\nTGxrMvF9gLFqtXPL7PPduzMjI4Pr169n7+eey8a71NRUFi9WjAC4a9cukmJGEseLX0Kno0KhYKRe\nz54GAyOk0dcYP/otCeBCgI+YzaxktfI5gyHbDrR3AKebIgFOyYfs0v4o3Lw8MxFiIlAArFalCjdJ\n8XIdI141RL/eQkYjb9y4QVKcdVy+fJkHDx4kAM6TzHxfAaxTqZJP2bDzE8uXL6dGo7kHoCiDrXRV\nKtWLlStXfqDNCnI0rVWLHyL0ApHI06YnJyf71abPPvvsgVO6DjiyQhSLiGCJyEg2b9yYhQSBLQ0G\nr1tPCfBdpZJtGjf26TnudullZGTwhx9+4JUrV3jy5EnOnz+fp06dYv/nn+fcAPfvTIBDZcfBGukG\nyqyxV5LVixcvZuHpihUrWEyn47PS+R07djA5OZlN69VjIWlGEqVWczDEka5D5uPi4gIiT3nBiBEj\n0i0WyzrmUWfm7WYgTK/XJ+zbt68AmlwwqF+9OotpNFwbAoqWEL0VvgJolPwbc+NCc/v27ZAQWn+R\nkZHBNk88wQUQs1xslZTTMYh+ut5iL9gBltXrOScfYn7MmzePL3gJubkA/keju+HSnmDZdAOl7PcC\n1KlU7NW5cxbf6Tt37jgVqcMXumvbtgRABUCtSkVBqcxyTezw4QHvw9wgOTmZERERKQBaMFhK12g0\nLqxVq9aDu5HfDbp06cKJEycy2mr1OQFfftF1gC0EgQ2rVeOmTZuC5joTDJw/f55P1KnDZoLgVoG1\ngufYC4S4LbquwcBGNWrwxx9/5O+//+5xinrixAleuHDB57p1a9cu22YIOQFgwwD0fzC8FwJp1jgD\n0KhUcs7MmVn4d/bsWV6/fp0k+cnHH2dRsAAYLrn2+dMnBYXRo0dT2q2mZi71Zq5uIgkA5fV6/T1/\n0yGHMuLj4xkREcHExESOHT2aUyC6JwUjl9UCSQDfGDkyT/asjIwMVq5cmffu3Qsgp/IXFy5cYJTZ\nzBkqVZ4CDmVADDzfwGxmZZOJRSwWfvnZZ87nZGZmcmDPnixiMNCq1bJT+/bcsGGDV34nJCTQotM5\no7m5o/EyBTLAj/q2hTiKd1W8BemnG0izRn8ZHz6YM8ctP5cvX+68xmow8Ol27XjixAleu3Yt4HIV\nCGRmZrJ8+fL3VCrVYBa00rVYLOsnT578wPrkeoIjI+7s2bM5QqtltFbLBkajM7BMQdBRiFOt0aNG\n5bk9f/31F0uVKpXncgoSY0aO5Gs57NpyxF7wh697IC7ezJ41i8eOHWN8fDyNGg3vQgyJOUyrZWm9\nnuViYtihSRN+NHcu9+3bx/4vvMAyhQuze9eu7NapE9ubTDk+6zZEG+0sP+rXGeJGCdfffc0uHQgK\npFkDEGPtOpTqzz//7La/v5VCXzq8R0IdBw4coMFguA1AYC50Z64ULoDqOp3O5rDlpqWl8dChQ3Tk\nPXtQj7dv3+48njJlChvrdDTrdBwyeDB7q1ROxZsGMX6oP8e/SorCLv1tBTHXmkPYXoEYmCUD4i6o\nZ9u2DUj7MjIyeP78+ZDgr6/H/Z9/nmNz4GchiAst/vbHTwAH6HQspNezXrVqrGgwZDmfJJU7A2B7\nvZ6ljUaOVyo5TeqnqRCjY/n6PDvEOAz9cX/Xmafrf4G4jTY38hXI40k5mDUmYZzb+/+UeBQBsAHu\nx/v9RPq/nZT81LW/ExISWLdWLY4YNiwk5M+X49KlS6drNJoxLCilazKZfgwLC2PLli1JkteuXWPN\nmjUf6ONHHnmEGo2GNpuN165dY7ly5ahWKChoNIyNjWWkQsGWkpBdA1gT8OlYnr0BEHfsyI8VCgXN\nZrN4Tq3mGxoNFwO0mkwBaZ/dbg8J/vp6fO/ePdaqUIGlcuCvCWCTXPSH47g6wMoQF7B8vR8AJ0CM\nj1Hex+fFu/T/SR/kJTftCfRxUYyjymXEq5LMGt7u/1zW1nLS+fIQZ26OzSehJG+5PS5VqhQ1Gk0K\nAL0/upO5UboAygiCcC9YgSjyCxs2bOAw6UtLiruiOrVuTQDU63SsbzLlmO5cTnaI24gdAvjOO+9w\n586d/PLLL1mlQgXK/ULtdjvj4+N5/vx5alUq/gKwYeXKAWlXr169nJH5QxmZmZncv38/m9Sty+4G\ng1teBzvwS0edzpnyfbiP9yRCTHc/Z+ZMzpk5ky3cpO1x0DmAtXIobx/yP02Rg3Jr1kiWZD5WqSQh\nrk+8jIKPjZvfaNasWZJSqfTbtuu30hUEYe7QoUP/HU65XuDIJxajUDAmPJwlihblkx4CsLijNwDq\ntVo+88wzPi+EfffddwTEUJKlArTrrH79+vy///u/gJSVnxg+aBDLGo2cqla7VbihEPjlJsBCWi0B\n0KpWM8HH+/YDLBYeznPnzvHRqlVZy2TiFumcHeJuOzvE4Oo6eA6P+LqkuP6vgNqbF8pA1tRWjrqH\n6gJZbrBlyxaaTKZL/m4P9kvhAtBrNJpUT3veH1Skp6dz586d2X6LkWUCHj50KCMNBu7yUegAsFev\nXn7V49y5cwTE7a6CRhOQtiUmJjLUN65s3bqVEXq9M6OAO4Xrjd0FqXiXAWxYrRr7v/ACB/jxEZ6p\nUrGo1cpTp05x/fr1jLZa2dNgYEOTicD9zCS3c5CpkgFWjFcKgGc3pLoPffnlYItaQJGZmUmj0ZgO\noAnzUek+V6ZMmQfH98hH7Ny5k3Xq1Mn2+7179zhlyhQC4HOtW7N3jx7s6UMuqzSAZqWSX375pd91\n6dC8OQWAz7Roked22e32kPftTUhIoEWv5yYPvAyFwC9ysgEsazTyp59+YunChflfP+5tJynYoQMH\n8urVq1ywYAHbSRsDovV6jlKpuBJiQtFMSSFuhzg9H6tQsIekvALVFgS4PG/vAwAOHDiQ586dC3mZ\n9Afdu3e3m83mVfRDjyrhB8LDwwe/+eaben/ueRCQkpKC3r17Z/tdr9cjOjoaWgD/XL2KvoMGYb1G\ngx9zKG8mgES7HZ06dfK7LqMmTkQKgGdeeMHve11x9OhRNGjQIM/l5Cd++OEH1Ndq0crD+dXoimSY\nvJaRDBNWo2vgK+cGagCjk5PxwdSpmL90KUabTKCP976WlAQA+OvkSRiNRlSrVg37duwAAAx/+21Y\n3noLY2Ji0MBohF6pRA2TCW9Wq4b9zz+Pn6tUwV6VKiBtOAlAIf0/OiAlekY6AJ30v1arRdmyZfH5\n55/n81MLDrNmzVKkp6d3UCgUBp9v8lU7AzBpNJo0RwqT/xVcv36d0ZGRBMDZM2ZwyaefsoogeLXn\n/cdo5Keffpqr52VmZjIlJSUgdf/hhx/Ypk2bgJSVH3DYsL3l1Qpm4BdPdE8auU2aNImPVq3KdoKQ\nxX7pjd4HqFQoqFYqKajV/AHgIqWSxSMiOG7UKA4fPpyTJk3KJgM3b96kVkoj5GsSS0/kzOEGcEiA\nePJfiIH15fGn7QAHSc9ZvGgRO7dqJf7vJrrYg4wKFSokAXiavupSny8EOsfExDxwmX19wcqVK5mW\n5rlpt2/fJgAO6t2bJDmoVy/+x8ve+5oWC/fv3+/z8//55x92bNfO+SLEhIVx7KuvMjU1NU/tstvt\nIb0TrX2LFuzqEtDblYId4tATDdJo+FynTrx37x4b16rlMfODO7oGZMmTRoC7IeYPq1qmjMeEmKdO\nnXLKSHGViqmSAs3tjsnzEH1oO6tU/CeP/DAjq2tcCYDFJffIqVLGkmLS4MXTJokHFe3bt7ebTCaf\nTQw+K12LxbJ85MiRBduaAkB8fDwtFkuWoBzucPz4cadiTkhIoFmn4x0PAlhcEOhpe7TdbueJEye4\nZs0a/vPPP7x+/XoWYQXAqlKa9ZhChXj48OFcty2UbWcHDx5kjMGQY0rxULPpOug6wKaCwEH/+Q9X\nr17N1l7yuflKdoBN9XrO85LO5tatW6LSVSp5TZKXD/x8jnyk66BeLmntfaUzADtBDE15zEX5Pvv0\n01kWqKtXr04AfBDzJ3rD9u3bqdfr78BHLwbfNLMYM/fmyZMnC7o9+Y7z58/zzTff9OueRCnflacM\nEy8aDJwza1a2+86cOcMKMTEsJgisYzCweOHCTElJ4ejRo3no0CHa7XaeOXOGy5cv5zNSwGcA1Gm1\nuQr+MXz4cH788cd+31cQ6Cllx/VllBZK3gtycqzK79mzh8UEwWs8Bl/oZ4iRuRo0aOCVd5t++omF\nBIFdpCDhb/j5nP7SfQDYtWtXnjx5ksXCw7nUjzIEgHo3yrt4dDTneIizsHjxYm7YsCE/xCmosNvt\nLFKkSBKA6gyg0q1sNpvvBXrkdOrUKTasXZt///13QMvNb3zzzTcE4HGzxCaAj7rZ3DBm5EgOl/xQ\nH9HpONeDcJLZQ+AB4LChQznwxRc5Y/p0fvfddzmOznv06MFly5blub35gSNHjhAQnf19eclDwU/X\nlRy7Da9evcoxsbF8RBC8hpv0Ro4NBUMHDWKFChVy5N/vv//OdyZP5uuvvcbhWq3PzzkKsIogcLVL\n/rgvly4lAH7sg3cOAe6U6tusWTMmJyezVEwMDTqdc8PP/xqaN2+eplAoXmMAle7Q6tWrB9zZc/78\n+QTABfPmBbpon7Fu3Tq/c4clJSWxTqVKfEeKbu9KNoBFDAb+8MMPJMm4uDju2LGDhc1mHoGYSRbI\nHuDZFXNmz+aYMWM4a9YsfvHFF07l+6q0oAKAd+7c8Xh/Zmam2+DcoQC73U6dWu3X6FC+Q8qKvjxZ\nwCYFd/SExeIcvY19/XV2EIRc2Vg3QFxgs9lsfvXZwgULvMb2ldMvACMFgbNnzHBrepo8eTIBMRZu\nTmUdBliuSBFmZmbyww8+YAOtluUUCk5++22PdU1MTOTixYt57dq1kDZ95QaxsbG0WCy/M1BKNzw8\nfKunKUNe4JhOBxN169bl7t27/b7v77//ZsmoKLY1m7nGjVB+rlazuMHAaqVLM1KvZylB4DxpW6RD\nYXrDpLffdl73zz//UD7ijZYp3YVeEvwFO8VJTqhTrhx/y6WyyymebkHRKoWCUUYjf/vtN6alpbFy\niRJ+BcC3Q/RGCNPruUjyeMnMzPRZKV28eJGRen2OITAvACznZoQrh81mY3h4OGOlCG/uPh4/S3JX\nEWAJg4Fr16zh0aNHadbpWNRq9RosPyUlhRqNhkqpDEeqnn8Dzp49S41GkwpAw7wqXQBKnU6X/KCZ\nAHzFhAkTnOEc/cXt27e5cuVKxoSHc6RGw3MuAnoFYrYD1ySEFQWBa9as8Vp2XFwcxwwfzuc6duSt\nW7e4e/duLlu2jJPeeotD+vTh9evXeeTIEa8mhkKFCoW0YLd+7DF+BHHbsy8K6gbAlyAGulkeAgrX\nQZ8BbCulBdq2bRtLCEIW1ylPdBmiC1nJQoW4avlyJ1/KlCnj1+yrWqlS3O3lOecBxhgM/MAlmLg7\n7N+/n1XMZsZJivGSS1l3AC6Szk2GGCrzzz//5Pnz532StU6dOrGTycTaJpNzJvhvQfny5e8CaMQA\nKN3qVqs1b75L/3JcunSJrw8bxkijkZ0EgdukEcFbCkW27BOO9OFbt27lihUr+MUXXwTML1eO9PR0\n6nS6HO2+wYJr+vNnc7AlpsiuLS79dQ34HSxKBhih1ztHeS3q1eO6HO55RRAYaTSya+vW3PnLL1l4\nU79+ffqTAmt0bCwnSLMoVzoM0KJWs/uzz/pU1q5du7L0CzzU33HuK4CRBgPnffihT+XXrFqVFqWS\nTwuCc2T/b0Hz5s1tSqVyLAOgdF/KD3tuKODw4cNcv359wMpLTEzkJx99xBqlS7Nx9ep8dfhwRhmN\nXC0fFalUNKpUbGC1sqvZzHZGI4tHRPDYsWMBq4cDoWxemDV9OmtKbkpLpBfYdaYgp70QQyo6jrUA\nXw0BheugVpLJ58aNG3ymadMsmX0dZIcYV7mXwcAwQeDly5fd8sbffjt8+DCLunG/+1vi64ypU302\nVzhMWQaDgYmJiTRoNG5H7eekssdDnKkUNRi4Z8+eHMuvU6UKKwA063R+J1gNdcTGxtJqte5jXpWu\n1WpdOWPGjIJvQQHgww8/5Mv5HITj4MGDjLZaOVehyJZ220Ev6vWcP39+QJ+bmZkZsqPczMxMRlmt\nnC/jgTcTw3yAlQB+JPutCUS3JVfTjSe6DM8ufoGgKwBrqNUMNxjY+4UXOFKW+cIRSewFvZ4VYmI4\na8YMr1Nxu93ut+Lt2rYt33cZ7V4EGGUy5WnRqmrp0vzUQ5sdLmMEOE2h4Mv9+uVY3qhRoygfRb/3\n3nshu9jrL44fP069Xp+Qk79ujko3LCws7sCBA0FoQv5j27Zt3Lx5c74/5/Tp02xSuzbDpU0PYUol\nf5YJbwOLhVu3bg3oM/fs2cP69esHtMxA4qOPPiIAvuhFkf0seznbuDnvuqvLGz0lxcGdiPzNebcN\nYITBwEI6HW0Ax+t0LGk0MiYsjAN79vRpdDdkyBDO89Oj58iRIyxsMGQblZY0Gnnq1KncdhNbNG9O\nAPxSqWQzQeBfsrLnSX2TBtHUUzIyMkcFf+zYMTZr3DiL4h03blyu6xdKsNvttFqtKQBKMLdKF4BW\nqVRmhvJW0gcJf//9N7t3704ArCltf42HONXyZcvvoUOH+HyXLtyxY4fHqakDW7ZscUa6D0WMHj2a\ngJiJwd1odbb0QnYA3O5a8zdHmiOVTLSPfqh5oe8AqpVKKgA+UacOjx8/nkUZnT592utIdtSoUczN\n7LLH00/zPdloNxNgpF7PS5cu5aqPSNHUUbpwYdaTzCdGtZozpUD7DqWZjvt+xleuXPGpXJvNximS\nixoAtm/RwueZ2Z075OLF5JQp4l8vXpMFjuLFi98D0J55ULrVzGbzvzLeAknOnTvXmQq6IHH16lWW\njIriEoArAbZ/4gmP19psNn7xxReMHTYsy+gAADdt2uT1OaHsCynf/uwaHvEocg7GEg04NyJMB/i2\nD8rwNAoms/N8qf6//vor7XY7U1JS+NVXX/Hdd95hy4YNCcDjri0y9/124sQJRun1vCvV4yzE4Ol5\nRc2KFQmA8z75hGfOnGGYwcDpMjm8APAIwHBByLGsPXv2sFOnTtyzZw8BsE716nxa2n3pS5zuqVNJ\nozEry41G8fdQQK1atTIBjGYelG7nJk2a5M6f6gFAkSJFgrYbbtq0aQRE16cyRYp4DEwzuHdvFtPp\n2F9NCJQAACAASURBVEdSLAA4RKHgawCNGg2b167NVo89xldffdX5EhQpUoQ2my2klS55P5BQa7U6\nizJMBtgrB8Um99NNBPK8BTeQ9A9EG2e01coyhQtTo1KxjdHIMSoVvwCoAdixY0ePfLHb7bm2c3Zu\n1YqLpHockuQhr3JgkMxi8fHxJMnYV17JNgAoZDDwQx9c0hzX//HHH87/16xZwxaNG/O3337zeu/U\nqd5ZHwqKd9asWbRYLMuYW6WrUChGDR8+/F/puUCS7733ntfoYvmJM2fOOIWujlbLlwYN4rVr19ju\nqafYrnlz/vbbb0xMTKRereY1SaqGSKlitkrHWyFmmVgC8EmTiR1kL0G3rl35IAQoOn/+PAGwTx4V\n3Q2Ai0NA4cppB8Bdbj4IOY10v/76a77wwgu54ueKFSvYxmxmJsCKkrzUcxOg3x84ZOr48ePO39au\nXcviUVHOc0eOHMmxnLVr1xIAO7Rvz/j4eL7YqxcBcMuWLWzy2GPs/fzzHu+9cyf7CNeVjEYyly73\nAcOWLVsYGRl5iLlVukaj8csBAwYEo+7/Ezh06BC7demSbdQgpwF6vXMU+BPAxw0GXvUgdbtd7u3c\nuXOwm+gTVq1aRcB7qhpXcrXpLpTaXAzwaWNCsOgUwEImExMTEz3y47vvvmOXLl1yxcuEhARGGI1s\n5yILW/KwYFyxVCkC4FMNG2b5/VEpathXX32VYxnx8fEEwBrVqjl/i4uLY5GIiCz19JTwdvFi31i8\nZEmumxkQbNu2jXq9/i5zq3TDwsL2Tp48ORh1z3dcv36ds2fPDnY1nCNehRR7tGLJkqxft65TCHOS\nsoFuFPUUgF8CrCAInB4Kc64c4DAzvOOH8pLbdB10z4UPG0JAybpSX72ekyZMyFd+7tq1i3UqV+bu\n3bsJiMHFn8ohcpk3vDJwoJOn8t2bN2/e9KgkXXH58mUOGjiQGRkZXLlyJQ8dOsT2Tz3lLHcZwGoa\nDT/77DO390+Z4huLgy3u169fp0KhyGRulW54ePhfv/76azDqnu/Yv3+/27xowUJ8fDwfKVeOk8aN\nY1xcHAsZjTnuuPpeEtihEJ3Ub8nO2SDuFgLgMbZvKOGXHTtYyGTiIL3eJ9/bVhAXcHZCdCd7GeIq\nOgA+AtF1K6d4BAVN5wFGCAJv3brllRd5sem6ok2jRpwNUKtS5clv++2xYwmIo/RlX3/t9/0qyWXv\n+vXrHDBgABcsWMByJUuySuXKBMCqUt8t8ZBV4kEZ6ZKkwWBIZW6VrtFovOlvBK4HBXFxcVy6dGmw\nq+ERHZ54wm0gHTltAfiMh8wLwwCOgTjdLmKx8PGqVfloxYps98QTfmW18IT8cNtJSEhghxYt2Nol\ndoEd4uKUY3NJPMC3VSoW0uv5aKVKVCqVlI9wvwgBBeuOXtLpOPa113Lkw86dO/n444/nnaEklyxa\nRAAc/+qrft136tQpzp8/n9u3b2dCQgIzMjJYxGLh9xB3n62UxYrICb/88kuW/pk1axb37t3LtLQ0\nZmRkcNIbbzjPeYob/aDYdEmyRIkSCcyt0lUqlZlJSUnBqPf/PGqULs2DeXjBX4aYRZYQ7Z+/AtwD\n8FOA0QYDhw0ZkuvU7PnptmOz2dj/hRdYSxC4FGAHo5FGjYYRgkCzVssnzWaG6/Xs2KYNjx496rzP\nEVgeENOUQxpZBlvROuhvgOEGg08uivv27csxkLm/PPUXy5Yto1ah4KNmMysUK8b4+Hh2bNGCKyR5\nCjMYcgwUlZmZydnvv08A7A44fXufc+O58fvvv3NvDtuIHwTvBZIsXLhw7ke6SqUyNPeRBgD79+/n\n6tWrg10NjygSHs7P8kkBXJKE/20/M2aQBSP4drudXy5dyjaNGvHjDz+kIxnqzZs3uXbtWsbFxTE6\nOjqbu99aKdGlnOJCQOGmA2wuCHxz9Oi8M6eAkJyczGb167ONIPBxrZbtnnqK0997jy9I8TIaWCw5\nungtX7aM1QwGroM4S3H0yfr16/n999/nql6h7qdLkiVKlEhjbpWuxWL5d0WkkGH+/PkcPHhwsKvh\nFsePH2d0eLjXLLk5kQ3eNwJ8BrBrq1Z+1SuUpnitWrXyOmq8d+8eAdHu7amyd3PgYSDIDvBlnY7t\nmjb1OZ6C3W7P9SwkkLDZbGzXrBlfVSrZW69nxeLFGS4I3AewmdXqMcHkrVu3+FLfvowwGLhN4oNj\nx9qkiRO5cOFC5sUr6u5d0XY7dar4NxRMCnJ07949iblVutHR0e7Tkv4LsH///hx3dAUDjsSDU2QB\nU3JDnYAs0c1c6TTERR154sCc8CAtZtjtdsaEhWWJFSAnx8jrY2QdGb8fQIWbCnCATsca5cr5FbP5\n/PnzLFmyZD5yx3ecPXuWkUYj31ep+JK0ScKq0zFaoWDVSpWyXHv37l126tCBsbGxbK7T8bQLP17F\n/Y0R69evz7Kwd/z4cV68eJEpKSkEwBMnThR0UwOGV155Jfcj3bCwsIdxdAsQV65cYcP69RmlUtHm\n5WW+AwsXox+nYDwXox/vIHsm2q5AjgtxOoB1atb0uX6h5LZz+PBhrxtbHK54nnx2N8kUrQbgCIln\nAFjXB2WaExOuAmwgCOzStq3HlOqecOHCBZYtWzavLAoYzp07R7NOx6sAB0P0YHC4OMoV5xsjRxIA\nK5tMzvUEOTlsuq8OH86I8HACYO3y5fl8584EwMWLF3ORtPAHIIgtzhsaN25spxe9qoQX2Gw25aBB\ngxAXFwcAmDp1Kv4tx9u3b8eLL74YMvUBgCFDhuDQgQNYmZkJNYCpAAYBiJP6YyqA+hiHGPyNAViC\nNzEVA7AEUfgbLTAO3WXXfwvgmJv75eeh0SATwOeffw6SOdbv9983eBMXJ7Ta+HznV5s2bTBhwgSP\n548fPw4A6OGh/V9Iv7UAcBrABwBqAmgHoKub6wFgIoBiajX0AJQARnrg7xkAXQUB2lq1EF6sGG7d\nuuVX+wDg7NmzQZdHx7FSqcSjNWtiIAA7gJZJSSCJNq1bY8iQIc7rt27fjhoKBX5KSsJgN/x7Uymq\nm9379uHW7dsAgENnzmD5mjWIKVwYV65cwfLly+GA4/k7duzArVu3QoYfOR2npqYq4AVqbyetVmta\nvXr1NIIgAACqVKmCwoUL499wvHfvXiQkJKBt27YhUR+bzYYf//tftCFRTeJ/FQCFAQjS8XGMwwG8\nk62fbDBhu/P3d9EFQHEAlV3ud5SnAdANAGw2aI4exYB+/dCvXz8AQN++faHX693Wt3NnOzZutCEt\nTZOtDg7odDZ06YJ851f16tX/v73zjo+iWv//Z7bPloSSBELvPUiREhFFlKpcLChcEL8UAQWV6wVR\nAioICCjlB5eqoohKB+WKCIr0IooikBiDQCghIbSQtptsdvfz+2M3SxKS7G62zIab9+t1XsnMnjnn\nmbMzz5555jnPg+bNmyMvL6/Yz6dNnQrArkSLnr8GQP6t3b+Yz58qYfyVAG7o9cCYMeDcuVADaF7M\n8UsUCujbtcOL48fj1q1bHp+fKIqwWCxBdb8s+fxzRLdrh2EmE6YBiFercT0lBU/06+esX7tBA2z9\n4w9kFjN+lwD8BWDQoEFYv349AODh6GhcSkzEw488gs/WrsXWrVuh0+mwZ88ejBg+HM2bN8fGL7/E\nxtWrYRME9OjZE70c/dlsNjRv3jxoxqfo91cqpU2D69WrF2Qmat9x9OhRn8ew9YZz586xmkbDs6WY\nFIqmIC9atMgkYCBgXzywHvYXOekAIwo8OqOYMqHA/88PGsSSXAXLi9vOjGnT+IRGU6yQ9VE2l7J/\nA5TVrElFdDQfKaHtl2UyVg8NZWpqapllP3PmDBs2bOjD0fANG9av54N6PQm7B0x1lYpymYwDHn+c\n5J2Xl8uKGZfOosiqVao4lwNXd/hWh+n1/LHIC7mLFy/ywoULzpWKTTUajgUYqtPRZrNx0aJF1KvV\nbFW3LpcsXlxqRmwpmDhxooVltenqdLp7NqxjsJGenn7HllVMmYcRbumGVRjubOeZEhRsdJHtfOXM\nAvs0CgUH9+/PkYMGccIrrxRSIsHgtuPKppuRkcHqoaH8tphBAlCszbGkku8J8p8C47O0mHqrHIrE\n28D4Z86cYbNmzbxqwx/k5eWxXng4f3acb0SB8fjuu+9IkjPee4+d5HKOVyq5EvYwnVZHnWVLl/KL\nNWvYRhTZSxQJgGOKyTbx0cqVfKp3b/7rlVcYFxdX6Frt2aMHa4kiVwsC9wB8TqtlJY2GLw4ZwmBJ\nttCmTZuyK12lUnmXV3UwBxD2hGPHjgWVn67ZbOZTjz/OdiXMoIApbuqImLuU7BnYg4XnK/Q0gPNh\nj/ZfVMnPFQQ+qFDwIuxZXz8COF6pZLhez53ff++UV2q3neL8dIvy888/M0yr5eEi5zgNYIYHShcF\njtkIcFMxdUywLzo5ceJEgEZAGlZ99BGbabW8CnvAofkAWzdtykuXLpG0r2Sb/f77BMAm9eoVug4v\nXLjAlStXFtoXFhJSqH2bzVbo82eefJIhBkOhfSeLjH0KwFkyGetqtby/adNi/YdtNhtPnToVkFlx\no0aNzCyr0hUEwVowFmcwzHB8xZo1azhkyBCpxXCyfft2NhZF3i7hxv/YzZkuMJzNYH90Lhh7IAd3\nR/GywR5+0J2GDwIM02q5atWqoFAsrvx08/l+xw5GaDQepfYpTumGlrDc2jnLBdinlGD0nmC1WoM6\nb9i7kyczSqvlDYBvCwLfLibdTr6L3K5du9i/Vy9nLN68vLy7JgXnz58vdGxmZiatVitPnTrFgwcP\n8siRI+zXp4+z/hxBuCs1PB3X+0aA4Votjx454mwvKSmJx44dIwCOHTXKjyNjp1u3bmWPMqZQKHLy\nVwOVF1ueu8THx3Pr1q1Si+HkwoULlAlCiQsa3LHp6pDJdBhI2KOPefII7U7ZA/Apg4FhGg137twp\n9ZC5TYfmzQslwSyL0i3J7JNf+hgM3LBhg0/kDbZgTEWx2WycNH68c1zGjRvH+Ph4txZ/5M9kRZWK\ny5cudSuDcMFjDxw4wFHPP08AfK+E72KiILBz27bcvn37XQo+EPd8q1atbrOsSlej0aT/9ttvQbUS\n6V4lJSWFADi1lEURMxFT+g8fYpwb4wEu9rHSzS8LAY4eOlTS8XJl0y3I0KFDKbqYqZZWwh35wa6W\nUqezXs/PfLQq5NChQ3zooYd80pa/sFqt7PfEEwRAGcD6Oh2jW7fmunXr2KtLF44eOrTEhJgHDx6k\nt9ELBw4cyLAi2Y/zSzrA5x1KtrcjfGRlvZ4L588PSIZsx7uwsildg8GQuHbt2nK1EsldUlJSOH/+\nfKnFKMS1a9fYKDKS20oZ5JmIuWvGq0NmIYXr73IEYPtGjSQdK3dsuvmkpKSwkkbDm2U837OOG3hI\nKT+Iy2G3Yf4vkZaWxto1axIA33C8RDTIZJwtl/MNgK0aNOCKFStoNBpJ2hW11WrlunXr+Ntvv5Fk\nmTO3DH72WQJ2s1f+dzDWIcMy2GMz14yM5PXr1wOetkoul5c9nm6VKlV+WL9+fVCtRPIVZ8+eZb0g\nvEn27t3LOlptqTFl02HgKgznTMRwFYY7TQoFixUodVWbN2UTvAuK7Qvctenm80zv3sW6MrlbRMcN\nXdLnAwD+o29fn5ybxWJxO06D1NhsNu7du5fT3nmH702fzvubNmWISsU2ISHMf6Tfv38/N65d69yu\nXasWAVDtWFacmZnJD+bMYVSzZlSr1S7jTuTbZwFQIQj8HYVNCA1r1GAvjYbdNRpOfP31gMxu88nI\nyKBcLi/7izRRFJfMnj37npzpZmVlcfHixVKLUSzd2rfnOi8V4xyAb/hB4eYCbKrVclsZo0RJRafm\nzYt1H3O3nAA41WGi+BngF0XMFV8BfKZHD5/Ium7dOj733HM+aUsKbty4wWPHjnHdunUc9NxzTEpK\n4sbVq51KUcCdTCmzZs0qlGttght5/cxmMy9fvkyTycQ1q1dT4fhBfMwRg9hoNLJh9ep8x9Fmt4cf\nDths9+TJkwwJCbnCsipdAOM7dOiQV2HTDSxff/01Ozmc0MtaFgP8lx+U7vtyOft17y71EHlk0yXJ\nd2Ni+IpK5ZMxmOm4ma8V2JcI2KNq+WDBzZo1azh8+HCv2wlGvt66lQCoE0WS5LgxYwiAIwcPdno4\neEp+yM+CHh+7du2i3pGYEwDfnDjRJ/K74u2332ZoaOgf9ELp9q1du7aJvPe8F0jygw8+YHJystRi\n3IXFYmH9iAivQjv6o5wHWFUU73LxkQJPbLokefr0adZzNXNws3zvuJGLBhQaL5fz1dGjS5Uj0PbF\nYOS1117jxYsXSdq9dvL/L4rVamVoSAj79u7NM2fOkGSxpofFixYRAFNSUgrtT0tL4y+//MLRI0Yw\nLi7Ox2dRPM8++yx1Ot1n9ELpNqpatapzPei95KdLkg888AD3798vtRjFsmnjRjbRapldRsVgg3vR\nsNwtfwN8SKPhrGnTpB4akp7bdL/++mt2Cbk7GltppaRobqtRfOLLKYLAd6ZMKVGGTu3bs4cbTwm5\nubkBtUMGkszMTGo0Gpc/Pn/99RcL2mkLlgUffuisZ7FYGKLTEQA/++wzP0vvmpEjR5oAvEYvlK5c\noVCY8311SelXIvmSLVu2BMWsrST+2b8/J5bxkXg3wO4+VLr5F3xOTvmL9nnhwgVWCwnhLg/OtzQv\nkbWOsfipmJnuv8aNK1aGxMRE5xi6uuYmTpzIuXPn+mMoJCczM5MrVqwo9rP09HTabDbm5uayfp06\nzh+2K7AnIR3mGL99+/Y5j7l06RIB+4pLqd0YSTIqKuo2gO4sq9IlCb1ef/HLMmT/rMB7fvjhB0bp\ndKVmgCipHAfY2YdKNzo0tNDFLjWe2HTfiYnheIXCI4VbWpU3HEut3yrywVyAr5egdPP9WXsaDC5T\n1YwdO5Yff/yxx2NSXrly5YrzB+mFAQM4Z8YM1lYoCtnMCXCQWs0+vXoVOnbRokX8P42GSx3HSxl/\nIS8vj3K53AIgjN4oXZ1O97k3qTWCmRMnTnDlypVSi1EieXl57BwVxfFKJXM9VJJlUdSulO7hw4el\nHhInnth02zZs6LZ93N2Vf5/BwCNFPlgMcFC/foX6tlqtToWSDLCNTMZ5BR6PS+Jetf3u2LGD27Zt\nc27fvHnTOT6LcOeJKqrIj+QFgDJB4OXLlwu1N2XyZE4WBMbDngG7Zd26AT6jO+zZs4cajabU1Wik\niyDmAJCdnb376tWrWa7qlUcyMzOxevVqqcUoEYVCge/27cOZzp3RUqvF17Bfke5i8qEsNgCCUGps\n5oASFRUFpbLkuL75pKWl4ezly3jAzXY3YwCyoS+1Tjb0sGIAogvsuwVgjihifEwMACAvLw+vjhwJ\nuVwOwB7w/AqAP2w2576SMJl8+c0FFzt27EBiYiIAYPPmzahatSoAYCuAUY46LwLYabEUOq4OgHFK\nJZ545BHs2bMHAJCRkYFPli7FcySaAXgegMrF2PqTixcvQqPRHHJZ0ZVWBlDfYDAY78Vf3rS0NK5d\nu1ZqMdxi165djKpfn111Op52Y8ZmBSiH7xZIdAwN9WidfLCQnJzMao4Mtu6UGR5Ec/sT9uhi82Qy\nhms0jCngY7plyxber9Pxb9ijYBF2+2RklSouZ7GtW7fm77//7u+hkYSffvqJCQkJ/H+O1OwAKHrw\n9LYWYCOdjkOefJJv/vvfHFogKl887Hn/MjMzA3pOiYmJTEhI4JNPPmkCMI6udKrLCoCgUqkyjxSI\n2lOBNFgsFi5fupQRosgTblykNQHe8IHCNQGspdXy9OnTUg+BE3dtuqmpqaysVrttbvnEg2hukQDr\niiL/8eijjI2NLdTva2PGcG6Rg15VqThnzhyXMjdp0oQXLlwo89iUB9o2bEgBYKsyXI85AB/T6dhA\nFHmhyGc6pdLjnHTecOjQIQJg9H33URRFM4AoutCpLs0LJKlWq3/atm2bFxPv4GX+/Pk4ceKE1GK4\nhVwux0tjx2L24sUYbzC4rH8ZQFUf9HsYQM26ddGqVSuXdQNF7969cePGDZf1wsPDUadWLXzrZrsD\nsBk6lG5N0yELZ7EZLVQqDBg5Ett270bLli0L1anVoAFSFXeyYRHAXpUKnTt3dilDQkIC6tSp46bE\n5YeMjAyMHTsWH8yaBVNKCm4DOF2GdtQAfszOxjmTCXUL7LcAMFksSE5O9om87tC0aVOMGz0aI155\nBVar1QIg1tUxLpUuAGRmZm6NjY29J+26Z8+excGDB6UWwyOeeuYZ/J6TA6uLenkAcn3Q32UAkTVq\n+KAl3+GuTVcQBLwzdy7e0+vdsoeHIgOTMbvUOiMxG42QiWp9+2L6nDnF1qldpw4SC+TKOgEgSxTR\ntWvXUtu2Wq0wGo1BZT/3Bb/88gsG9e+PTz/+GJ/MmoXdRiNCfNyHAsAKAA+2a4cv16zx+HiLxYIN\nGzbkP+G7RVhYGJasXAmZTAatVruH7hzsairsaKOmKIq55SUIhyf88ccfdz0algdqV6nC8y4ew/4J\n8AsvTQsWgM10unIVP7coVquVUfXr8zsPzrs0P90DsMcOKMk2m5eXxy5t2nBhgdCDr6lUfKeYYN9F\niYuLY9OmTX09BJKQkpLCf/bvT51azVpaLd8F+Cns7xu8NXmVVk4CFJVKjz1AcnJyCIBjXawqLI5u\n3boZAbxId/SpO5VIQqvVpvorAPC9kgIokES3aMEDLi6+N1B8Li9PymaArevXDzoXJk9jL2zcsIEd\n9XqPXOlKiuZmBlhDFHnq1Kli+9q8eTOj9XqncskFGK7R8OzZsy7lPHz4MHv27On2eQUj2dnZHDRw\nIMP1er6pVPI4fLs60p0iFwT+8ccfHsv+wqBBBMDxr77q9jFms5lKpTIPQF36UumKorh01KhRPl+b\nKPXSYovFwqeeeqrchNLLp25YWImZg70t12GPD9vbEQlqsRt+pYHG09gLVquVzWrVuitfWlnLixoN\nlyxZUmxfb73xBt8tUPcbgA+2bu2rUw96PnLkQdtTZMxeAXguQEoXjpKYmOiR7DabjVNiYtisSRO3\nj9m1axd1Ol0q3dSlbtl0AcBkMm05dOhQttvGDjeYNQuYOhXILtJqdrZ9/6xZvuyteORyOU6dOoWE\nhAT/d+YjSOLKrVuIdFHPCiDDg3YtAD4C0EOrxdYuXRAbGYnpU6bg1YkTyyyrv3DXppuPTCZDm9at\nccFH/WcqlahSpUqxnzVu1gzndTrn9uc6Hf7vlVfcajcrKwu5ub6wxEvH6iVLsBnAIwX2WQCsBlA5\nQDKkOP5evHjRo+MEQcDMWbMQ74E+OHjwoJXkOrcPcFc7A1CqVCqjr+yfwRQu8siRIwHJEupLmtao\ncVdW1KJlJ8BH3ZwZ5AHsodWyW4cO/GjlyqAzJ/iCcSNGeJ3CyAK7X26YTldi5Krjx4+zlcFAG8AE\ngKEajdvX1xtvvMH333/fl6cdUK5cucJKavVd/uFm2OOBBGKWm182AmxYvbpfr2WbzcZq1aoZAXSl\nr2e6JPNUKtWOGTNm0G2NXgqbN989wy1Kdra9nr+Jjo5GaGio/zvyIW3btsUfLurUB1w4P91BCeBn\nQcDuo0cxavTooH97furUKZjNZo+OqVqjBm560edlAJ1EEdvatMGxU6fQokWLYuu1atUK8urVUVmp\nxIMGA96aNMnt6yszMxMNGjTwQkpp+fPPP3G7mJm6EsCjAZZlAABZZiZ+/fVXv/Wxbds2pKWl2QAc\ncfcYhesqd8jKylp2+PDhPiS13t6UKSmu63hSzxuSk5MxatQofPfdd/7vzEcYs7LgylO3CYCf3WxP\nLgj45r//dblENVjo3bs3jh8/jhoeuLJVr1EDR0URKOMy25kAfjOZYPnll1LHSa1W49e4OFy8eBEN\nGjSATOb23AbLly8vk2zBQo8ePQAANwBUL7D/dQBPAng4gLIIACIEASl+VCLbt2+3KBSK1bm5ua48\nOJ24fzXY2Xf79u1MX/xyRLoySHpYzxuqVauGY8eO4dKlS/7vzEekpKS4tOkCwE3YL7788mQJ9Yaq\n1YiNdenXHTR4atMFgGeeeQbbSVwtY591BAGvjBrl1g+TUqlEo0aNPFK4gP17JX3yMCkJw4YMwTMo\nrHBtANbCHj8hkJwDcFYmQ9++ff3SvtlsxubNm/OMRuNST47z6IogacvJyVk0ZsyYPM/Eu5sBA4AC\n7xqKRaez1/M3crkcR44c8WjWJDXJ16/DHWnzlew0ADMAFO/Kb3/pplarfSBZYNi1axfCw8M9OiYi\nIgJDnn8eCz1U1oD9RdB/dTr0feopj491l4yMDDRu3Nhv7QeCJ556CqaQwsseBAD7YDd3BZJsAOGV\nKnn84+wukyZNAskzJOM9OtBd429+AVBZLpebT5065UydYbPZaDabPd6ePt1Sqi18+nSLV+17um0y\nmQLanzfb1UNCnIFvbI4XFeZitp9wuM6U9DkdLzgiQ0OdfqTBcH6uto8fP86srCyPj79w4QIrq9W8\nVsp4FN22AOyv0fDRzp2Zk5Pjt/OLi4vjQw89FBTjW9bts2fPsoYoFhq/PBfj66/tSQoFhz79tF/O\nNzc3l6GhoWYA/eipDvX0AJLQarXLVCoV69SpQ5JMSkqiQqEo0/bMmaRWay2kbLVaK2WyqT5p391t\nuVxOuVzOrKysgPTn7bYgCKzqGLAkgAqAdYrZPuhQuiV9/qDjc7lMFlTn52obAGvUqFGm4wc8/jhl\nJYxHcdvTYc9gG0znH6zbu3fvZke9vtD4tYE94p274+2rbZkgMDk52W/6QqvVXgEgYyCULoBIjUZj\nSkhIoC8IlhRAffr04fbt26Xp3EN6durEbwr+UpVQJjmUanErsXJwx4m8vLnMeZojrSBxcXGMEEW3\n8s/9CnsyTl9d66Vx6dIlZmdn+70ff7Jjxw4+ViAX3WWHMrSUML7ZjjH+FOAG+CYUaQbADlotqwqM\niQAAHsZJREFUFy9c6JdztFqtbNasWZYgCINYFv1ZloNon+0ubd++/Z2cx/cA+eaF8sB7777LiW6k\noIFjllHcZ9sdn08oEAf2f4X7mzZ1mTNtI8AwrZbffP11QGTq3bt3oawK5ZEjR46wpcFQaBxNRf7/\nHODTej0b6/XUKBRsXa8eh/Tvz65t2rCOKPKrAvV3eqhwLQAfEkWOGDzYb/65MTExFEUxGYCcgVS6\nAKr4crFEsJCUlMSCiTiDlf3797OjG9ltGzgU62yAHxT5bCnALvffL/WplAlPYy8UJDs7mwBKXEZt\nBDhNoWCdsLCABhPv3bu3x8tWg4nU1FT27taNM+VyEvbANn8WGNd9AKtpNOwZHc0vvviCsbGxd6VU\nf/PNNwmH8nzfsQy9pFly0ZIAsLNOx4fat/fbBMpsNrNy5co5APqwrLqzrAeShEql+nfXrl2z7qXV\nSy+88ALnz58vtRguuXbtGiup1S4vRBPsEfXbOJTvJcf+m7AHbQmmvGee4GnshaIIgsBhCgXPApwm\nl7NDSAh7h4Twab2eoWo1H3/4YaakpPhQ4nufsSNGsJ9czkzY43c8qVBQKQisq9Oxrk7HKlotd//4\nY6lt5OeU66xWO01f7ijcywBriSIXLVjgdfr6vLw8bt26lXl5dz/Iz58/3xoSEnIYgEAplC4ApU6n\nuzR9+nSvTjKYOHz4MCdMmCC1GC45efIkW7ox0yXs2SO24Y79tqZWS41CwUkeRFIKNryx6ZLk9m+/\n5SPR0QwVRY4bOZL79u3jt99+yy+++IJXr171oaTuceXKlXKfLeLTFSsYIYp8Tq9nmEbDR7p25YwZ\nM5iYmMjExETeunXLZRvJycnO6xQAGzlmzaWVbIBNlWHs9dg6n0QqjI2NJQAOHDiw0P4TJ05QrVbn\nAmhGb/SmNwfTrngf1mg0ZncGtALf8e2337KPm0p3PcAnHbMPANy0adNdj3X/q3g7K/IV7733HidN\nmiS1GF4TGxvLr776in/++WeZ27h06RJffuklt2a6NoANle9SITcW+sibSIWHDx8mABoMhkL769ev\nn6fVav8fvdSZnq5IK87Pd79CodgwadKk8h0aqQA3btwI+uWYvxw7hjpuxh7oDEALIAzAfADL5871\nm8N4oChL7IXi8HTFmL+QyWR45JFHXFcMclq2bInBgwcjLi4O8fGerRn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"text/plain": [ "<matplotlib.figure.Figure at 0x2ac49227ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.basemap import Basemap\n", "import numpy as np\n", "\n", "# Draw the base map of the world\n", "m = Basemap(projection='robin',lon_0=0,resolution='c')\n", "# Draw the continent coast lines\n", "m.drawcoastlines()\n", "# Color in the water and the land masses\n", "m.fillcontinents(color='red',lake_color='aqua')\n", "# draw parallels and meridians.\n", "m.drawparallels(np.arange(-90.,120.,30.))\n", "m.drawmeridians(np.arange(0.,360.,60.))\n", "#m.drawmapboundary(fill_color='aqua')\n", "\n", "# Prep the data for plotting on the map\n", "x,y = m(sampledata['LonCol'].values, sampledata['LatCol'].values)\n", "# Plot the data points on the map\n", "m.plot(x,y, 'bo', markersize=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Column-wise processing\n", "\n", "Now that we have data columns, we've already seen a couple of examples of column-wise processing. When we created the categorical column and the datetime column we took the data from one column and operated on it all at the same time creating the new columns with the different data types. There are other ways to manipulate the columns.\n", "\n", "#### apply\n", "\n", "The `apply` function takes each entry in a column and *applies* whatever function you want to the entry. For example, we are interested in whether the entry is greater than 4. We will simplify the code by using what is called a **`lambda`** function. So, inside the `apply()` function we have: `lambda x: x>4`. This is shorthand notation for the following:\n", "\n", "\"Treat `x` as if it were each entry in the column. Apply whatever follows the colon (:) to each entry and create a new column based on the output\". The use of `x` was arbitrary: we could choose any variable. For example if we chose `w`, the code would read: `lambda w: w>4`. This would do exactly the same thing." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " FloatCol GTfour\n", "0 1.348465 False\n", "1 1.658523 False\n", "2 1.990915 False\n", "3 2.158079 False\n", "4 2.500187 False\n", "5 2.602705 False\n", "6 2.795402 False\n", "7 3.013844 False\n", "8 3.191672 False\n", "9 3.252009 False\n", "10 3.460851 False\n", "11 3.658682 False\n", "12 3.790619 False\n", "13 3.833512 False\n", "14 3.946359 False\n", "15 4.154751 True\n", "16 4.279761 True\n", "17 4.276088 True\n", "18 4.446735 True\n", "19 4.596708 True\n" ] } ], "source": [ "sampledata['GTfour'] = sampledata['FloatCol'].apply(lambda x: x > 4.0)\n", "print(sampledata[['FloatCol','GTfour']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Common functions\n", "\n", "There are a number of common functions that we could use inside the `apply`. For example, if we wanted to get the square root of each entry, this is what it would look like. We are using the function `np.sqrt` from the `numpy` library. We already imported this library, but if we didn't, we'd need to `import numpy as np` before running this function." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " FloatCol FloatSQRT\n", "0 1.348465 1.161234\n", "1 1.658523 1.287837\n", "2 1.990915 1.410998\n", "3 2.158079 1.469040\n", "4 2.500187 1.581198\n", "5 2.602705 1.613290\n", "6 2.795402 1.671945\n", "7 3.013844 1.736043\n", "8 3.191672 1.786525\n", "9 3.252009 1.803333\n", "10 3.460851 1.860336\n", "11 3.658682 1.912768\n", "12 3.790619 1.946951\n", "13 3.833512 1.957936\n", "14 3.946359 1.986545\n", "15 4.154751 2.038321\n", "16 4.279761 2.068758\n", "17 4.276088 2.067870\n", "18 4.446735 2.108728\n", "19 4.596708 2.143993\n" ] } ], "source": [ "sampledata['FloatSQRT'] = sampledata['FloatCol'].apply(np.sqrt)\n", "print(sampledata[['FloatCol','FloatSQRT']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another useful function is adding up columns. Note that we need to tell pandas to run through each row by adding the argument `axis=1` to the `apply` function. Otherwise it tries to add up each column. This might be something you might want to do, too, though the easiest way to do that is to use the pandas `sum` function for the column." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " IntCol FloatCol IntSUM\n", "0 1 1.348465 2.348465\n", "1 2 1.658523 3.658523\n", "2 3 1.990915 4.990915\n", "3 4 2.158079 6.158079\n", "4 5 2.500187 7.500187\n", "5 6 2.602705 8.602705\n", "6 7 2.795402 9.795402\n", "7 8 3.013844 11.013844\n", "8 9 3.191672 12.191672\n", "9 10 3.252009 13.252009\n", "10 11 3.460851 14.460851\n", "11 12 3.658682 15.658682\n", "12 13 3.790619 16.790619\n", "13 14 3.833512 17.833512\n", "14 15 3.946359 18.946359\n", "15 16 4.154751 20.154751\n", "16 17 4.279761 21.279761\n", "17 18 4.276088 22.276088\n", "18 19 4.446735 23.446735\n", "19 20 4.596708 24.596708\n" ] } ], "source": [ "sampledata['IntSUM'] = sampledata[['IntCol','FloatCol']].apply(np.sum,axis=1)\n", "print(sampledata[['IntCol','FloatCol','IntSUM']])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "210" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata['IntCol'].sum()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Custom functions\n", "\n", "We will now create our first custom function and use it to process the data. We will make a short function that will look to see if a value in the TextCol feature matches an item on a list we create." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TextCol IsMammal\n", "0 cat mammal\n", "1 dog mammal\n", "2 horse mammal\n", "3 cow mammal\n", "4 elephant mammal\n", "5 fish notmammal\n", "6 bird notmammal\n", "7 dinosaur notmammal\n", "8 giraffe mammal\n", "9 wolf mammal\n", "10 prairie dog mammal\n", "11 whale mammal\n", "12 dolphin mammal\n", "13 clam notmammal\n", "14 lizard notmammal\n", "15 snake notmammal\n", "16 fly notmammal\n", "17 beetle notmammal\n", "18 spider notmammal\n", "19 worm notmammal\n" ] } ], "source": [ "\n", "# We first tell the computer that we are writing a function by starting with \"def\"\n", "# The next text is the name of the function. We name this one \"isMammal\" meaning it will tell us if an animal is in our list of mammals\n", "# The final text in the parenthesis is an input to the function. This is another \"dummy\" variable - we could give it any name we want. \n", "# In this case we call it \"animal\" to remind ourselves that we expect an animal type in text form.\n", "def isMammal(animal):\n", " # We create a list of text objects that will be our \"inclusive\" list. If the item is on this list, the function will return True. Otherwise it returns false.\n", " mammallist = ['cat','dog','horse','cow','elephant','giraffe','wolf','prairie dog', 'whale', 'dolphin']\n", " # This is our first \"if\" statement. What this particular version does is look at the list \"mammallist\". \n", " # If the text passed into the variable \"animal\" matches any item in the list, it jumps into this next block of code\n", " # Otherwise it jumps into block of code following the \"else\" statement\n", " if animal in mammallist:\n", " # the \"return\" code word tells the computer we are done and to send back to the apply function the value following \"return\". In this case, send back \"True\"\n", " return 'mammal'\n", " else:\n", " # The other case will send back \"false\".\n", " return 'notmammal'\n", " \n", "sampledata['IsMammal'] = sampledata['TextCol'].apply(isMammal)\n", "print(sampledata[['TextCol', 'IsMammal']])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TextCol FloatCol IsSmallMammal\n", "0 cat 1.348465 True\n", "1 dog 1.658523 True\n", "2 horse 1.990915 True\n", "3 cow 2.158079 False\n", "4 elephant 2.500187 False\n", "5 fish 2.602705 False\n", "6 bird 2.795402 False\n", "7 dinosaur 3.013844 False\n", "8 giraffe 3.191672 False\n", "9 wolf 3.252009 False\n", "10 prairie dog 3.460851 False\n", "11 whale 3.658682 False\n", "12 dolphin 3.790619 False\n", "13 clam 3.833512 False\n", "14 lizard 3.946359 False\n", "15 snake 4.154751 False\n", "16 fly 4.279761 False\n", "17 beetle 4.276088 False\n", "18 spider 4.446735 False\n", "19 worm 4.596708 False\n" ] } ], "source": [ "# We'll now operate on an entire row of data at once and do a more complicated operation. We'll return only mammals where the 'FloatCol' is smaller than 2.\n", "\n", "def isMammalFloat(row):\n", " # We create a list of text objects that will be our \"inclusive\" list. If the item is on this list, the function will return True. Otherwise it returns false.\n", " mammallist = ['cat','dog','horse','cow','elephant','giraffe','wolf','prairie dog', 'whale', 'dolphin']\n", " \n", " # We need to identify the animal from the row - it can be addressed using the column name\n", " animal = row['TextCol']\n", " \n", " if animal in mammallist:\n", " # the \"return\" code word tells the computer we are done and to send back to the apply function the value following \"return\". \n", " # In this case it returns True if the float value is less than 2 and false otherwise.\n", " return row['FloatCol'] < 2\n", " else:\n", " # If it isn't a mammal, return false\n", " return False\n", "\n", "# Note that we need to tell `apply` to send one row at a time by adding the `axis=1` argument\n", "sampledata['IsSmallMammal'] = sampledata.apply(isMammalFloat, axis=1)\n", "print(sampledata[['TextCol', 'FloatCol','IsSmallMammal']])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 cat\n", "1 dog\n", "2 horse\n", "Name: TextCol, dtype: object" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata['TextCol'][ sampledata['FloatCol']<2 ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature extraction\n", "\n", "We can often pull additional features from what we currently have. This involves doing a column-wise processing step, but with the additional component of doing a transformation or extraction from the data. We'll look at a couple of techniques to do this.\n", "\n", "#### Date/day/week features\n", "\n", "We already saw how to take a text column that is a date and turn it into a datetime data type. The `to_datetime()` function has the capability of parsing many different string formats. I recommend looking at the [documentation for the function](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) to learn how to do parsing of more specific date time formats. \n", "\n", "Once we have a datetime data type, we can use other functions to get, for example, the day of the week or the week of the year for any given date. This may be useful for looking at weekly patterns or yearly patterns. The full list of features we can easily extract is [found in the documentation](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-date-components). We use the `apply` function with the simple in-line `lambda` function to get the date or time features. Another use for this might be to identify holidays- for example, Memorial day is always on the same relative day of the year (last Monday in May). We could use these functions to identify which days are national or bank holidays." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " DayofWeek WeekofYear\n", "0 Wednesday 4\n", "1 Thursday 4\n", "2 Thursday 4\n", "3 Friday 4\n", "4 Friday 4\n", "5 Saturday 4\n", "6 Saturday 4\n", "7 Sunday 4\n", "8 Sunday 4\n", "9 Sunday 4\n", "10 Monday 5\n", "11 Monday 5\n", "12 Tuesday 5\n", "13 Tuesday 5\n", "14 Tuesday 5\n", "15 Wednesday 5\n", "16 Thursday 5\n", "17 Thursday 5\n", "18 Friday 5\n", "19 Saturday 5\n" ] } ], "source": [ "# Get the day of the week for each of the data features. We can get either a numerical value (0-6) or the names\n", "sampledata['DayofWeek'] = sampledata['DateCol2'].apply(lambda x: x.weekday_name)\n", "# Or the week number in the year\n", "sampledata['WeekofYear'] = sampledata['DateCol2'].apply(lambda x: x.week)\n", "\n", "print(sampledata[['DayofWeek', 'WeekofYear']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Unique values\n", "\n", "Sometimes it is helpful to know what unique values are in a column. Especially when there are many rows (millions), it is impractical to manually scan through the columns to look for unique values. However, we can use a pandas function `unique()` to do just that. We will see this is particularly helpful in doing data cleaning to identify rows with problems in the data." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['no', 'yes', 'maybe'], dtype=object)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sampledata['CatCol'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Text regex features\n", "\n", "Another type of text feature extraction using a `regex` or *regular expression* pattern recognition code. The date/time conversion uses one form of this, but we can be more general in identifying patterns. There are some very useful tools for testing your pattern. I like the tester at https://regex101.com/. I use it whenever I build a pattern recognition string. " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>hors</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>el</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>giraff</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>prairi</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>whal</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>snak</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>beetl</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>spid</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "0 NaN\n", "1 NaN\n", "2 hors\n", "3 NaN\n", "4 el\n", "5 NaN\n", "6 NaN\n", "7 NaN\n", "8 giraff\n", "9 NaN\n", "10 prairi\n", "11 whal\n", "12 NaN\n", "13 NaN\n", "14 NaN\n", "15 snak\n", "16 NaN\n", "17 beetl\n", "18 spid\n", "19 NaN" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This simple text pattern gathers all the letters up to (but not including) the last 'e' in the text entry. There are lots of other pattern recognition tools to extract features from text.\n", "# Note that it returns \"NaN\" if there are no 'e's in the text string. We could use that to find all the strings without an 'e' in them.\n", "sampledata['TextCol'].str.extract(\"(.*)e\", expand=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Converting to categorical\n", "\n", "We already saw how to convert text columns to categorical columns. We can also covert other data types to categorical columns. For example, we could bin a float column into regularly sized bins, then create a categorical column from those bins.\n", "\n", "### Word/Text cleaning\n", "\n", "Finally, it is often useful to clean up text entries before trying to turn them into features. For example, we may want to remove all punctuation, capital letters, or other special characters. We may also want to consider all of the forms of a word as the same word. For example, we may want to have both \"dog\" and \"dogs\" as the same feature. Or we may want \"wonder\" and \"wonderful\" as the same feature. There are a couple of text processing tools in python that simplify this work considerably.\n", "\n", "I created a small dataset to work with. We'll use one of the rows to test our text cleaning process." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"I went to see this film with a great deal of excitement as I was at school with the director, he was even a good friend of mine for a while. But sorry mate, this film stinks.<br /><br />I can only talk about what was wrong with the first half because that's when I walked out and went to the pub for a much needed drink:<br /><br />1) someone's standing on a balcony about to jump and so you send a helicopter to shine a searchlight on them??? I don't think so - nothing would make them more likely to jump.<br /><br />2) local radio doesn't send reporters to cover people about to attempt suicide - again for fear of pressuring them into jumping - or for fear of encouraging copy-cat instances.<br /><br />3) whatever the circumstances, radio reporters don't do live broadcasts from the 10th floor of a tower block. Radio cars don't carry leads long enough to connect the microphone and headphones to the transmitter.<br /><br />4) the stuck in the lift scene was utterly derivative<br /><br />5) the acting and direction was almost non existent.<br /><br />I could go on, but I won't.\"" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "textDF = pd.read_csv('Class03_text.tsv',sep='\\t')\n", "testcase = textDF['review'][3]\n", "testcase" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing we notice is that there are hypertext bits in the text (the `<br />` items). We want to clean all of those out. The BeautifulSoup function does this for us." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"I went to see this film with a great deal of excitement as I was at school with the director, he was even a good friend of mine for a while. But sorry mate, this film stinks.I can only talk about what was wrong with the first half because that's when I walked out and went to the pub for a much needed drink:1) someone's standing on a balcony about to jump and so you send a helicopter to shine a searchlight on them??? I don't think so - nothing would make them more likely to jump.2) local radio doesn't send reporters to cover people about to attempt suicide - again for fear of pressuring them into jumping - or for fear of encouraging copy-cat instances.3) whatever the circumstances, radio reporters don't do live broadcasts from the 10th floor of a tower block. Radio cars don't carry leads long enough to connect the microphone and headphones to the transmitter.4) the stuck in the lift scene was utterly derivative5) the acting and direction was almost non existent.I could go on, but I won't.\"" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bs4 import BeautifulSoup\n", "cleantext = BeautifulSoup(testcase,\"html5lib\").text\n", "cleantext" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now want to get rid of everything that isn't an alphabetical letter. That will clean up all punctuation and get rid of all numbers. We'll use a regex substitution function to do this. It looks for everything that is not an alphabetical character and replaces it with a blank space." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'I went to see this film with a great deal of excitement as I was at school with the director he was even a good friend of mine for a while But sorry mate this film stinks I can only talk about what was wrong with the first half because that s when I walked out and went to the pub for a much needed drink someone s standing on a balcony about to jump and so you send a helicopter to shine a searchlight on them I don t think so nothing would make them more likely to jump local radio doesn t send reporters to cover people about to attempt suicide again for fear of pressuring them into jumping or for fear of encouraging copy cat instances whatever the circumstances radio reporters don t do live broadcasts from the th floor of a tower block Radio cars don t carry leads long enough to connect the microphone and headphones to the transmitter the stuck in the lift scene was utterly derivative the acting and direction was almost non existent I could go on but I won t '" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "onlyletters = re.sub(\"[^a-zA-Z]\",\" \",cleantext)\n", "onlyletters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll get rid of upper-case letters to only look at the words themselves." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'i went to see this film with a great deal of excitement as i was at school with the director he was even a good friend of mine for a while but sorry mate this film stinks i can only talk about what was wrong with the first half because that s when i walked out and went to the pub for a much needed drink someone s standing on a balcony about to jump and so you send a helicopter to shine a searchlight on them i don t think so nothing would make them more likely to jump local radio doesn t send reporters to cover people about to attempt suicide again for fear of pressuring them into jumping or for fear of encouraging copy cat instances whatever the circumstances radio reporters don t do live broadcasts from the th floor of a tower block radio cars don t carry leads long enough to connect the microphone and headphones to the transmitter the stuck in the lift scene was utterly derivative the acting and direction was almost non existent i could go on but i won t '" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lowercase = onlyletters.lower()\n", "lowercase" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next two steps we'll do at once because we need to split up the text into individual words to do them. The `split()` function breaks up the string into an array of words. We will then eliminate any words that are **stopwords** in English. These are words like \"and\", \"or\", \"the\" that don't communciate any information but are necessary for language.\n", "\n", "The other thing we'll do is cut the words down to their root stems. This will get rid of plurals or other modifications of words." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "went see film great deal excitement school director even good friend mine sorry mate film stinks talk wrong first half walked went pub much needed drink someone standing balcony jump send helicopter shine searchlight think nothing would make likely jump local radio send reporters cover people attempt suicide fear pressuring jumping fear encouraging copy cat instances whatever circumstances radio reporters live broadcasts th floor tower block radio cars carry leads long enough connect microphone headphones transmitter stuck lift scene utterly derivative acting direction almost non existent could go\n", "\n", "\n", "went see film great deal excit school director even good friend mine sorri mate film stink talk wrong first half walk went pub much need drink someon stand balconi jump send helicopt shine searchlight think noth would make like jump local radio send report cover peopl attempt suicid fear pressur jump fear encourag copi cat instanc whatev circumst radio report live broadcast th floor tower block radio car carri lead long enough connect microphon headphon transmitt stuck lift scene utter deriv act direct almost non exist could go\n" ] } ], "source": [ "import nltk\n", "from nltk.corpus import stopwords # Import the stop word list\n", "\n", "words = lowercase.split() \n", "meaningfulwords = [w for w in words if not w in stopwords.words(\"english\")]\n", "\n", "from nltk.stem import SnowballStemmer\n", "snowball_stemmer = SnowballStemmer(\"english\")\n", "\n", "stemmedwords = [snowball_stemmer.stem(w) for w in meaningfulwords ]\n", "\n", "print(\" \".join(meaningfulwords))\n", "print(\"\\n\")\n", "print(\" \".join(stemmedwords))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>review</th>\n", " <th>cleaned</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9999_0</td>\n", " <td>Watching Time Chasers, it obvious that it was ...</td>\n", " <td>watch time chaser obvious made bunch friend ma...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>45057_0</td>\n", " <td>I saw this film about 20 years ago and remembe...</td>\n", " <td>saw film year ago rememb particular nasti beli...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>15561_0</td>\n", " <td>Minor Spoilers&lt;br /&gt;&lt;br /&gt;In New York, Joan Ba...</td>\n", " <td>minor spoilersin new york joan barnard elvir a...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>7161_0</td>\n", " <td>I went to see this film with a great deal of e...</td>\n", " <td>went see film great deal excit school director...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>43971_0</td>\n", " <td>Yes, I agree with everyone on this site this m...</td>\n", " <td>yes agre everyon site movi bad even call movi ...</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>36495_0</td>\n", " <td>Jennifer Ehle was sparkling in \\Pride and Prej...</td>\n", " <td>jennif ehl sparkl pride prejudic jeremi northa...</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>49472_0</td>\n", " <td>Amy Poehler is a terrific comedian on Saturday...</td>\n", " <td>ami poehler terrif comedian saturday night liv...</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>36693_0</td>\n", " <td>A plane carrying employees of a large biotech ...</td>\n", " <td>plane carri employe larg biotech firm includ c...</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>316_0</td>\n", " <td>A well made, gritty science fiction movie, it ...</td>\n", " <td>well made gritti scienc fiction movi could los...</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>32454_0</td>\n", " <td>Incredibly dumb and utterly predictable story ...</td>\n", " <td>incred dumb utter predict stori rich teen girl...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id review \\\n", "0 9999_0 Watching Time Chasers, it obvious that it was ... \n", "1 45057_0 I saw this film about 20 years ago and remembe... \n", "2 15561_0 Minor Spoilers<br /><br />In New York, Joan Ba... \n", "3 7161_0 I went to see this film with a great deal of e... \n", "4 43971_0 Yes, I agree with everyone on this site this m... \n", "5 36495_0 Jennifer Ehle was sparkling in \\Pride and Prej... \n", "6 49472_0 Amy Poehler is a terrific comedian on Saturday... \n", "7 36693_0 A plane carrying employees of a large biotech ... \n", "8 316_0 A well made, gritty science fiction movie, it ... \n", "9 32454_0 Incredibly dumb and utterly predictable story ... \n", "\n", " cleaned \n", "0 watch time chaser obvious made bunch friend ma... \n", "1 saw film year ago rememb particular nasti beli... \n", "2 minor spoilersin new york joan barnard elvir a... \n", "3 went see film great deal excit school director... \n", "4 yes agre everyon site movi bad even call movi ... \n", "5 jennif ehl sparkl pride prejudic jeremi northa... \n", "6 ami poehler terrif comedian saturday night liv... \n", "7 plane carri employe larg biotech firm includ c... \n", "8 well made gritti scienc fiction movi could los... \n", "9 incred dumb utter predict stori rich teen girl... " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now we make a function that we can apply to every entry in the dataframe\n", "\n", "def cleantext(textinput):\n", " \n", " # First Pass: remove any html tags\n", " from bs4 import BeautifulSoup\n", " cleantext = BeautifulSoup(textinput,\"html5lib\").text\n", " \n", " # Second pass: remove non-letters and make everything lower case\n", " import re\n", " testcase = re.sub(\"[^a-zA-Z]\",\" \",cleantext)\n", " lowercase = testcase.lower()\n", " \n", " # Third pass: remove all stop words (non-essential words)\n", " from nltk.corpus import stopwords # Import the stop word list\n", " words = lowercase.split() \n", " meaningfulwords = [w for w in words if not w in stopwords.words(\"english\")]\n", "\n", " # Fourth pass: get the word stems so that plurals, etc. are reduced\n", " from nltk.stem import SnowballStemmer\n", " snowball_stemmer = SnowballStemmer(\"english\")\n", " stemmedwords = [snowball_stemmer.stem(w) for w in meaningfulwords ]\n", "\n", " # Put the words back together again with a single space beteen them\n", " return \" \".join(stemmedwords)\n", "\n", "textDF['cleaned'] = textDF['review'].apply(cleantext)\n", "textDF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Cleaning Example In-class Activity\n", "\n", "The tutorial on cleaning messy data is located here: http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb\n", "\n", "Follow the tutorial, looking at the data and how to do a preliminary clean to eliminate entries that aren't correct or don't help. The data file can be loaded from the SageMath folder. I've reduced the number of column features in the data set to make it a bit easier to work with." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "requests = pd.read_csv(\"Class03_311_data.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment\n", "\n", "Your assignment is to do data processing and cleaning on your own dataset. I want documentation of what you've done and why you chose to do those things to your data. \n", "\n", "I would also like you to try redoing your regression from last week, using the new features that you create through the data processing steps. See if you can improve the quality of your regression.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
Hvass-Labs/TensorFlow-Tutorials
13B_Visual_Analysis_MNIST.ipynb
1
296865
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Tutorial #13-B\n", "# Visual Analysis (MNIST)\n", "\n", "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "Tutorial #13 showed how to find input images that maximized the response of individual neurons inside the Inception model, so as to find the images that the neuron *liked to see*. But because the Inception model is so large and complex the images were just complex wavy patterns.\n", "\n", "This tutorial uses a much simpler Convolutional Neural Network with the MNIST data-set for recognizing hand-written digits. The code is spliced together from Tutorial #03-B for constructing the neural network and Tutorial #13 for finding input images that maximize individual neuron responses inside the neural network, so a lot of this code may look familiar to you." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Flowchart" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. Note that there are two separate optimization loops here:\n", "\n", "First the weights of the neural network are optimized by inputting images and their true classes to the network so as to improve the classification accuracy.\n", "\n", "Afterwards a second optimization is performed which finds the input image that maximizes a given feature or neuron inside the network. This finds an image that the network *likes to see*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Flowchart](images/13b_visual_analysis_flowchart.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TensorFlow 2\n", "\n", "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "import math" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "non-resource variables are not supported in the long term\n" ] } ], "source": [ "# Use TensorFlow v.2 with this old v.1 code.\n", "# E.g. placeholder variables and sessions have changed in TF2.\n", "import tensorflow.compat.v1 as tf\n", "tf.disable_v2_behavior()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.1.0'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from mnist import MNIST\n", "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Size of:\n", "- Training-set:\t\t55000\n", "- Validation-set:\t5000\n", "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# The number of pixels in each dimension of an image.\n", "img_size = data.img_size\n", "\n", "# The images are stored in one-dimensional arrays of this length.\n", "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", "img_shape = data.img_shape\n", "\n", "# Number of classes, one class for each of 10 digits.\n", "num_classes = data.num_classes\n", "\n", "# Number of colour channels for the images: 1 channel for gray-scale.\n", "num_channels = data.num_channels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-functions for plotting images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None):\n", " assert len(images) == len(cls_true) == 9\n", " \n", " # Create figure with 3x3 sub-plots.\n", " fig, axes = plt.subplots(3, 3)\n", " fig.subplots_adjust(hspace=0.3, wspace=0.3)\n", "\n", " for i, ax in enumerate(axes.flat):\n", " # Plot image.\n", " ax.imshow(images[i].reshape(img_shape), cmap='binary')\n", "\n", " # Show true and predicted classes.\n", " if cls_pred is None:\n", " xlabel = \"True: {0}\".format(cls_true[i])\n", " else:\n", " xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n", "\n", " # Show the classes as the label on the x-axis.\n", " ax.set_xlabel(xlabel)\n", " \n", " # Remove ticks from the plot.\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " \n", " # Ensure the plot is shown correctly with multiple plots\n", " # in a single Notebook cell.\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function used to plot 10 images in a 2x5 grid." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def plot_images10(images, smooth=True):\n", " # Interpolation type.\n", " if smooth:\n", " interpolation = 'spline16'\n", " else:\n", " interpolation = 'nearest'\n", "\n", " # Create figure with sub-plots.\n", " fig, axes = plt.subplots(2, 5)\n", "\n", " # Adjust vertical spacing.\n", " fig.subplots_adjust(hspace=0.1, wspace=0.1)\n", "\n", " # For each entry in the grid.\n", " for i, ax in enumerate(axes.flat):\n", " # Get the i'th image and only use the desired pixels.\n", " img = images[i, :, :]\n", " \n", " # Plot the image.\n", " ax.imshow(img, interpolation=interpolation, cmap='binary')\n", "\n", " # Remove ticks.\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", "\n", " # Ensure the plot is shown correctly with multiple plots\n", " # in a single Notebook cell.\n", " plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function used to plot a single image." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def plot_image(image):\n", " plt.imshow(image, interpolation='nearest', cmap='binary')\n", " plt.xticks([])\n", " plt.yticks([])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 9 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get the first images from the test-set.\n", "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TensorFlow Graph\n", "\n", "The neural network is constructed as a computational graph in TensorFlow using the `tf.layers` API, which is described in detail in Tutorial #03-B." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Placeholder variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph.\n", "\n", "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The convolutional layers expect `x` to be encoded as a 4-rank tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "y_true_cls = tf.argmax(y_true, axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neural Network\n", "\n", "We now implement the Convolutional Neural Network using the Layers API. We use the `net`-variable to refer to the last layer while building the neural network. This makes it easy to add or remove layers in the code if you want to experiment. First we set the `net`-variable to the reshaped input image." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "net = x_image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From <ipython-input-16-df6771052e21>:2: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.keras.layers.Conv2D` instead.\n", "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `layer.__call__` method instead.\n" ] } ], "source": [ "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n", " filters=16, kernel_size=5, activation=tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the convolution we do a max-pooling which is also described in Tutorial #02." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From <ipython-input-17-b75c98dafe2c>:1: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use keras.layers.MaxPooling2D instead.\n" ] } ], "source": [ "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we make a second convolutional layer, also with max-pooling." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n", " filters=36, kernel_size=5, activation=tf.nn.relu)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output then needs to be flattened so it can be used in fully-connected (aka. dense) layers." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From <ipython-input-20-f3e227e0e1ce>:1: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use keras.layers.Flatten instead.\n" ] } ], "source": [ "net = tf.layers.flatten(net)\n", "\n", "# This should eventually be replaced by:\n", "# net = tf.layers.flatten(net)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now add fully-connected (or dense) layers to the neural network." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From <ipython-input-21-33fd5057adca>:2: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use keras.layers.Dense instead.\n" ] } ], "source": [ "net = tf.layers.dense(inputs=net, name='layer_fc1',\n", " units=128, activation=tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n", " units=num_classes, activation=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The outputs of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name which we will also use further below." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "logits = net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "y_pred = tf.nn.softmax(logits=logits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "y_pred_cls = tf.argmax(y_pred, axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loss-Function to be Optimized" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make the model better at classifying the input images, we must somehow change the variables of the neural network.\n", "\n", "The cross-entropy is a performance measure used in classification. The cross-entropy is a continuous function that is always positive and if the predicted output of the model exactly matches the desired output then the cross-entropy equals zero. The goal of optimization is therefore to minimize the cross-entropy so it gets as close to zero as possible by changing the variables of the model.\n", "\n", "TensorFlow has a function for calculating the cross-entropy, which uses the values of the `logits`-layer because it also calculates the softmax internally, so as to to improve numerical stability." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true, logits=logits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "loss = tf.reduce_mean(cross_entropy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimization Method\n", "\n", "Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the Adam optimizer with a learning-rate of 1e-4.\n", "\n", "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Classification Accuracy\n", "\n", "We need to calculate the classification accuracy so we can report progress to the user.\n", "\n", "First we create a vector of booleans telling us whether the predicted class equals the true class of each image." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimize the Neural Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create TensorFlow session\n", "\n", "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "session = tf.Session()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize variables\n", "\n", "The variables for the TensorFlow graph must be initialized before we start optimizing them." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function to perform optimization iterations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", "\n", "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to do more optimization iterations." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "train_batch_size = 64" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", "total_iterations = 0\n", "\n", "def optimize(num_iterations):\n", " # Ensure we update the global variable rather than a local copy.\n", " global total_iterations\n", "\n", " for i in range(total_iterations,\n", " total_iterations + num_iterations):\n", "\n", " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", " x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n", "\n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", " feed_dict_train = {x: x_batch,\n", " y_true: y_true_batch}\n", "\n", " # Run the optimizer using this batch of training data.\n", " # TensorFlow assigns the variables in feed_dict_train\n", " # to the placeholder variables and then runs the optimizer.\n", " session.run(optimizer, feed_dict=feed_dict_train)\n", "\n", " # Print status every 100 iterations.\n", " if i % 100 == 0:\n", " # Calculate the accuracy on the training-set.\n", " acc = session.run(accuracy, feed_dict=feed_dict_train)\n", "\n", " # Message for printing.\n", " msg = \"Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}\"\n", "\n", " # Print it.\n", " print(msg.format(i + 1, acc))\n", "\n", " # Update the total number of iterations performed.\n", " total_iterations += num_iterations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function to plot example errors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for plotting examples of images from the test-set that have been mis-classified." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", " # This function is called from print_test_accuracy() below.\n", "\n", " # cls_pred is an array of the predicted class-number for\n", " # all images in the test-set.\n", "\n", " # correct is a boolean array whether the predicted class\n", " # is equal to the true class for each image in the test-set.\n", "\n", " # Negate the boolean array.\n", " incorrect = (correct == False)\n", " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", " cls_true=cls_true[0:9],\n", " cls_pred=cls_pred[0:9])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function to plot confusion matrix" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def plot_confusion_matrix(cls_pred):\n", " # This is called from print_test_accuracy() below.\n", "\n", " # cls_pred is an array of the predicted class-number for\n", " # all images in the test-set.\n", "\n", " # Get the true classifications for the test-set.\n", " cls_true = data.y_test_cls\n", " \n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_true,\n", " y_pred=cls_pred)\n", "\n", " # Print the confusion matrix as text.\n", " print(cm)\n", "\n", " # Plot the confusion matrix as an image.\n", " plt.matshow(cm)\n", "\n", " # Make various adjustments to the plot.\n", " plt.colorbar()\n", " tick_marks = np.arange(num_classes)\n", " plt.xticks(tick_marks, range(num_classes))\n", " plt.yticks(tick_marks, range(num_classes))\n", " plt.xlabel('Predicted')\n", " plt.ylabel('True')\n", "\n", " # Ensure the plot is shown correctly with multiple plots\n", " # in a single Notebook cell.\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function for showing the performance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a function for printing the classification accuracy on the test-set.\n", "\n", "It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function.\n", "\n", "Note that this function can use a lot of computer memory, which is why the test-set is split into smaller batches. If you have little RAM in your computer and it crashes, then you can try and lower the batch-size." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", "test_batch_size = 256\n", "\n", "def print_test_accuracy(show_example_errors=False,\n", " show_confusion_matrix=False):\n", "\n", " # Number of images in the test-set.\n", " num_test = data.num_test\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", " cls_pred = np.zeros(shape=num_test, dtype=np.int)\n", "\n", " # Now calculate the predicted classes for the batches.\n", " # We will just iterate through all the batches.\n", " # There might be a more clever and Pythonic way of doing this.\n", "\n", " # The starting index for the next batch is denoted i.\n", " i = 0\n", "\n", " while i < num_test:\n", " # The ending index for the next batch is denoted j.\n", " j = min(i + test_batch_size, num_test)\n", "\n", " # Get the images from the test-set between index i and j.\n", " images = data.x_test[i:j, :]\n", "\n", " # Get the associated labels.\n", " labels = data.y_test[i:j, :]\n", "\n", " # Create a feed-dict with these images and labels.\n", " feed_dict = {x: images,\n", " y_true: labels}\n", "\n", " # Calculate the predicted class using TensorFlow.\n", " cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n", "\n", " # Set the start-index for the next batch to the\n", " # end-index of the current batch.\n", " i = j\n", "\n", " # Convenience variable for the true class-numbers of the test-set.\n", " cls_true = data.y_test_cls\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", "\n", " # Calculate the number of correctly classified images.\n", " # When summing a boolean array, False means 0 and True means 1.\n", " correct_sum = correct.sum()\n", "\n", " # Classification accuracy is the number of correctly classified\n", " # images divided by the total number of images in the test-set.\n", " acc = float(correct_sum) / num_test\n", "\n", " # Print the accuracy.\n", " msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n", " print(msg.format(acc, correct_sum, num_test))\n", "\n", " # Plot some examples of mis-classifications, if desired.\n", " if show_example_errors:\n", " print(\"Example errors:\")\n", " plot_example_errors(cls_pred=cls_pred, correct=correct)\n", "\n", " # Plot the confusion matrix, if desired.\n", " if show_confusion_matrix:\n", " print(\"Confusion Matrix:\")\n", " plot_confusion_matrix(cls_pred=cls_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance before any optimization\n", "\n", "The accuracy on the test-set is very low because the variables for the neural network have only been initialized and not optimized at all, so it just classifies the images randomly." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test-Set: 8.7% (871 / 10000)\n" ] } ], "source": [ "print_test_accuracy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance after 10,000 optimization iterations\n", "\n", "After 10,000 optimization iterations, the model has a classification accuracy on the test-set of about 99%." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization Iteration: 1, Training Accuracy: 10.9%\n", "Optimization Iteration: 101, Training Accuracy: 82.8%\n", "Optimization Iteration: 201, Training Accuracy: 89.1%\n", "Optimization Iteration: 301, Training Accuracy: 90.6%\n", "Optimization Iteration: 401, Training Accuracy: 89.1%\n", "Optimization Iteration: 501, Training Accuracy: 93.8%\n", "Optimization Iteration: 601, Training Accuracy: 87.5%\n", 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Accuracy: 100.0%\n", "Optimization Iteration: 9001, Training Accuracy: 100.0%\n", "Optimization Iteration: 9101, Training Accuracy: 100.0%\n", "Optimization Iteration: 9201, Training Accuracy: 100.0%\n", "Optimization Iteration: 9301, Training Accuracy: 100.0%\n", "Optimization Iteration: 9401, Training Accuracy: 98.4%\n", "Optimization Iteration: 9501, Training Accuracy: 98.4%\n", "Optimization Iteration: 9601, Training Accuracy: 98.4%\n", "Optimization Iteration: 9701, Training Accuracy: 100.0%\n", "Optimization Iteration: 9801, Training Accuracy: 96.9%\n", "Optimization Iteration: 9901, Training Accuracy: 98.4%\n", "CPU times: user 25.6 s, sys: 2.81 s, total: 28.4 s\n", "Wall time: 24.7 s\n" ] } ], "source": [ "%%time\n", "optimize(num_iterations=10000)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test-Set: 98.8% (9881 / 10000)\n", "Example errors:\n" ] }, { "data": { "image/png": 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wq9XK7OysjN4xGAx0d3cTCATo6emhv78fi8WC0+nccuFsGbFU89eNj49z/fp1XnrpJebn50mlUtJwrIrl0NAQsViMsbExZmdnpVgCcpSi0mw2ZeJZbcfHzUUVy6tXr5JMJqnX64RCIfbv38+uXbvo6Oigq6tLdnYbieoRsbCwwOjoqJyJaGK5uQgh5J486ihwenqaarVKJpOhWCzeVSzVGchq3nnnHflar9dz5MgRBgYGOHbsGA6HA7fbjcPh+GyJper2MTU1xZUrV1hcXOTSpUtSJEul0hqjsaIonD9/nr/+67+WKaOmpqbWOBkrikKlUkEIwUcffUS9Xsdut+NwOGhvb2doaAir1YrH49kxLgutjrpPy9zcHFevXiUajWIwGAgGg/T19TE8PEwoFNrwH7eiKLKzjEajjI+PMzk5SX9/P2azmb6+PlwuF3v27MHn82lJhjcJs9mM0+mks7OTRqNBV1cXgUBARvao4aqrF/VUL4ZSqUQ2m71j5I/qaqYOgtLpNIFAgF27dmE2m6XfrBpPvplsq1jWajWq1SqffPIJf/VXf8XCwgJXrlyhWq1Sr9dv+wBfffVVfvGLXwC/8s+7WSxzuRz5fJ6//du/5aWXXpIG/iNHjvCd73yHUCiEw+HQxHKDSCaTvPHGG0SjUU6cOEE8HicSidDV1cXRo0d55plnNiVcTVEULl68yMmTJ/nkk09455136O3t5dixY/T19fEP/sE/wOv1YjAYpF1MY+Ox2+3YbDY8Hg/Dw8NrcjQoikK5XOb06dPE43Gi0SiJRILp6WlmZ2dZWFi4bbYoFUVRiEajTE1NcerUKXQ6HV1dXTz66KO43W76+/vx+/0899xzD6dYqlPjxcVF4vE4U1NTLCwsyF5IDWkzmUxyIUBtbGqPpEYP3AlVRGu1GiaTSTrQZrNZGTGisTGUy2WuX79ONBqV0y7VPqz6T26kUKq/nVwux/T0NPPz8zSbTYLBIKFQiM7OToLBIE6nU+4cqNmrN5e7rVTrdDra29vlIo7P58PtdhMMBkkmk3R1dVEsFonH49TrdemKVCgUqFarUnTVNpvL5ZidnZXuZKqNVN2DfrNEc1vEslKpUK1W+eijj/jggw84d+4co6OjaxIlmEwmXC4XTz/9NF1dXTKr9uXLl7l8+TLJZPK24W23Qx3Cl8tlpqamEEJovpcbSCKR4Gc/+xkzMzNymw+Vm23IG0GlUuGdd97h6tWrjI6OMjU1RWdnJ1/5ylfo7OzkwIEDeL1e7Ha75gnRAphMJoaGhuRia7PZpFqtysFMpVJhZmaG1157jXQ6LUOWR0dH5YaEq0mlUpw8eRKDwSBz2ppMJgYGBjh+/Dh9fX2b8j22XCwbjYZMCDs7O8v09LQcUarCZzKZ8Pl8eDweent76e7ulqPL1am7VqMKrBACs9ksk4eqMciqS4Lql9dqDq87mUajQaFQkDsAGo3GNRmDPq0nghoap94nn88zPT0tfzuFQgGTyURXVxft7e14PB5cLpdmZmkhbu60Vgun+m8kEiGdTmMwGKRt0+12S5NcPp+X9k11wFWr1dDpdExNTW36hmZbKpaNRoNyucybb77J2NgYJ06c4Ny5c2uEEsDj8fDcc8/R29vLl7/8Zbq7u+VDvXHjhvSpXH2OyWSSoXMDAwN4PB65Atve3i7/T+2FtCiOzUU1e+TzedLpNBaL5YGfubplQSqV4oMPPmBhYYE333yTqakpud90IBDg7/29v4fb7aajo0NO/zVak9U+k4qi0NPTwwsvvEC9XqdQKMjAlHK5TC6Xo1Qq8dZbb/Gzn/2MQqEgRbNWq5FOp3nllVfwer0cOHBgTfTXRrJlYqmmc6pWq9J/cm5u7pYwKKPRiM1mo6enh3A4TEdHB8FgkFKpRLValdNp1V6p7v5mtVplYtGenh78fj+7du2iu7tbiqUae6yxuag/4kqlQj6fJ5VK3deIXrVPqS5fuVyOhYUFEomEzLqeSqXkfj6q353f78fhcGC327WZww5ArSMhhAwuUH87jUaDYDAoRTOfzzM2NobZbL5lQbdWqxGPx+VuCpvFlollo9GQmYNOnz7NO++8I30jVXw+H3v27GFgYIAvfvGLdHV14fV6aTabTExMEIvFuHHjBoVCQTqgO51O+vv76erq4rvf/S6dnZ0y2avT6cRqtWIymbBYLJqBf4toNBrE43HS6TSvvfYas7OzPPbYY/zar/3autLlZTIZ0uk0CwsLXLt2jVgsxltvvUWlUqGtrQ2j0cjnPvc5DAYDPT09BAIB9u/fT3t7+4YvJmlsLUKINSGOzWZTOrar4a3NZhOdTreuvX42ki0Ty2azSaFQIJfLMTc3x/T09C3HWK1Wuru7CYfD9PX1EQwG5UNR/SozmYzsWdQeKRgM0tvby2OPPaZllNkmVo8SAJnM4vr16zKSR+31Vduyurp58yKdmvl8bm5O+k6ePHkSgMOHD+PxeOjq6sLj8TAwMEBnZ6eMDtLYOtRVavVv9W/gQQYm6u9g9cq6+jtRZ5Kq4/vt2OzY8S0Ty1KpxCeffMLk5CSJRGLN/1ksFhwOBwMDA3zpS1+iq6trjYe+6quVz+flQwsEAkQiEfr7+3nhhRcIhUKa0/E2YTQa8Xq90tVDjQduNBosLCxQKpWwWq00Gg3C4TDHjx+nWCzyySefkMlkyOVya9zAcrkc2WxW+u7t2rWLf/JP/glms5menh4cDgd+vx+bzUZbWxt2u10Tyi1GTZyRSCSYnJwkFovJ+ujp6eHYsWP35ddaKpVYWFiQv5tarcaVK1dIJBKMj4/LxeDJyUnK5fIa1z+9Xo/D4ZDJpDeLLRPLSqXC5cuXuXr16i3Tb7PZjMPhoLu7m0ceeQS/34/Val3TS6ibVakuP21tbQwODrJv3z6+9KUv4Xa7tenXNqFm9nG5XGSzWZncVd0OJJPJIISgVCpx4MABhoeHSaVSvPnmm8zPz0tBValUKlQqFYaHh3nmmWcIBoNyZ0a1QWhuQdtLo9GQybY/+OADLl26hNfrJRQKceTIER577LH7Eq5qtcrCwgKVSkW29RMnTnDjxg2uXLlCLBaTv4ubUbfPbWtr29SMUpsulqrbR6FQYG5ubs2WAeqwua+vj+PHjzMyMoLP58Nut68Zxq8eWarZlT0eD4ODg/T29mp2qm0mFArx4osvMj8/z4cffkgqlZIRHGo0FSBDU9X6vHDhAtlsVtarWofqDoy7d+8mHA7j9/sJBAJYLBasVusdd4fU2HzUDOmFQoHR0VHOnj3LtWvXmJ+fl2Y2u93OxYsXZUo8ta6azSZLS0vSBSiTyQDLOpBOp7l8+bKcmVQqFSYmJkgmkzI/xM02StW+6XK5ePbZZ+nr6yMcDm/ad98SsVQjZ6amppicnJSNR01sMTw8zDe/+U0CgQCBQOC2vYN6DVUs/X4/+/fvp7OzUxthbDPd3d18//vfZ3FxEb1eTzQalb6u0WiU+fl54vE44+PjjI+Pc+LECeBX4aoqalji7t27OXjwIP39/ezatQu32y33WtLYXtSOLpvNcvbsWd566y1SqRSZTEYu3ul0Ok6fPi19m9W0eI1GQyaEjkajRKNR4FdiefXqVcrlMo1G45ZtrW+Huq2yx+Ph+eef58iRI/j9/k377lsyDVfdQFYncRVC0N/fT3d3NyMjIwQCAVwu120Nw0IIQqEQQ0NDALhcLh555BG5Wq6NMrYfnU6HxWKhr68Pm82Gw+HAaDTS3t5OIpFgbGxMbveg/gZW15sQgu7ubrxeLwcPHuTgwYMEg0EZiaN5MrQG6mLr0tKS9H9UTWPqLDIej3Pq1CkcDgc+n08OfprNJjMzMzKxt7p2IYSQGdfVKL7VrA6dNZvN8r3D4SAcDtPV1UUwGMThcOzsabjqX6k2ErXXEELw5JNP8vzzzxOJRBgYGLhjaJxer+fAgQMyTVM2myUQCNDX16dNyVoIm83GE088QaVSkQsw6qjj1VdfZW5ujmKxKDeUW43JZOL48eMcOHCARx99lKNHj66pW00sW4NarcbU1JTcf2d1Hlk1fHFiYkJmyb95hVqNxlF9aNVjVJ24HeqMo62tDbfbLf2lOzs7+epXv0pnZyeDg4N4vd6dvRquOo3b7XaGh4cxm80y2cLg4CAdHR243e679giqi5Dao1gsFulvp9kqWwe1t1d337RYLDKPaG9vL4cOHaJUKlEsFm+ZXhkMBoaGhgiHw/h8Pi14oEVR92x3uVwy21C1WpUuPurI8G65F1bXvdpprk52oibNUXeDVDPce71egsGgjM4KBoMyAEUdcW4mmy6WqgHWZrPxR3/0R1SrVfmAnE7nujNlq9sCqE6pm5HyS+PTodqS1Sm2EAKXy4XD4eC5557j+PHja7LHrEYIgc1mw2QyaVs/tDBms5mRkRG6u7sZHh4mmUzKAIRKpUKxWHyg6+r1eimCVqsVm83G5z//eXp6egiFQng8HsLhMLt3714jqna7XeaB2Gy2xGapplsLhUKf6hoarc/NJhHVtOJyuXC5XNtUKo2NQp3lNZtNOjo6CIfDWK1WHA6HnJKrU2wVNYxZjcxRt3xZfU1VJNUoHbvdTiQSoaenh2AwuCapznbREttKaGho7CwsFgvf+MY3+PVf/3Vpl37vvff4yU9+QrFYJJVKSUE0Go0MDQ3h8Xik2Q3WRuxYrVba29txOBzs3r0bh8NBW1sbZrMZk8mE0Wjc9hmHJpYaGhr3jbrFMSzH8ufzeebn5wkGgzIjkGpuMZvNdHZ2EggEpN/szVitVjo6OnC5XIyMjLRkRJYmlhoaGp8K1db89NNPEw6HZcYpdeSo+kKuXvi7Gb1eL6fh2z2CvBOaWGpoaHwqjEYjRqORXbt2sWvXru0uzqahrZpoaGhorANNLDU0NDTWgSaWGhoaGutAE0sNDQ2NdaCJpYaGhsY60MRSQ0NDYx1oYqmhoaGxDsTdkmve9UQh4kB0Y4vT8oQVRQlsdyG2Cq2OH360Ol4/DyyWGhoaGp8ltGm4hoaGxjrQxFJDQ0NjHWhiqaGhobEO7iqWQgifEOLsyt+8EGJ21ftNS00shPiaEEIRQhxdx7GNlfJcFEL8UAhh+xT3/UshxNfvccxvCSHOCyEuCCHeF0IcfND7tQLbUcdCiG8IIUaFEJeEED9Yx/GTK8/7vBDiF0KIB84iLYT4YyHEH67juH8phBgXQlwRQnzpQe/XCmx1HQshvr9SX2eFEO8JIUbWcc5Wt+OnhRCZVc/hj+55YUVR1vUH/DHwhzd9Zljv+fdxHyfwDvAhcHQdx+dXvf4b4A8etIzAXwJfv8cxxwDPyuvngI82+hls199W1DGwGziz6hm2r+OcScC/8vrfAf/xpv8XgO5Bv+NtjhkBzgFmoA+YAPTbXT87qI5dq14/D/x8HedsdTt+Gvj/7ud73fc0fEW1/0wI8RHwJzf31Cs9Q2Tl9XeEEB+vKPefCyHWsw3j/wr870D5fssGvAvsWuk13hVC/BQYFULohRD/hxDik5XRyT9aKZ8QQvynldHDL4H2e91AUZT3FUVJrbz9ENi+PPebxCbX8e8B/1l9hoqiLN5n8d5huY4jK/X2V8BFoEcI8S9W1fG/WVXefyWEuCqEeA8YWsc9/gfgvyqKUlEU5QYwDjx2n+VsaTazjhVFya56awfu1+Vm09vxg/CgNstu4JiiKH9wpwOEEMPAN4EnFEU5BDSA31r5v78Qt5liCyGOAD2KorxyvwUSQhhYHuldWPnoCPDPFUUZBL4HZBRFeRR4FPg9IUQf8JssN54R4LssjxrV6/1bIcTz97jt94BX77esO4RNqWNgEBgUQpwQQnwohPj1+yzXb/CrOt4N/KmiKHtZrsfdLIvaIeARIcSTQohHgG+tfPZllutfLf/3hRDfv809uoDpVe9nVj572NisOkYI8U+FEBPAnwD/83oLtMXt+PNCiHNCiFeFEHvvVbYHTf77Q0VRbr/J7694FngE+EQs78JoBRYBFEX5n24+WAihA/5P4H+8z7JYhRBnV16/C/w/LD+sj1dGBQB/Hziwyo7RxnLDehL4LyvfJSaEeEO9qKIod7VhCCGeYbnyjt9neXcKG17HKxhYfvZPs9xY3xFC7FcUJRojXQIAACAASURBVH2Pe70phGgA54F/DbiBqKIoH678/99f+Tuz8t6xch8n8CNFUYoAK6MUVsr4Z/e458POZtUxiqL8Z+A/CyG+zXJ9/c497rPV7fg0y87peSHEl4Efr1zrjjyoWBZWva6zdoSq5owXwP+rKMq/XOc1ncA+4K2VSgkBPxVCPK8oysm7nFda6fEkK+evLqMAfl9RlNduOu7L6yzbGoQQB4C/AJ5TFGXpQa6xA9iMOoblUdpHiqLUgBtCiKss/0g/ucd5zyiKklDfCCHc3FrH/5uiKH+++iQhxP9yH2VTmQV6Vr3vXvnsYWOz6ng1/xX4v9Zx3Ja249WmAkVRfiaE+FMhhH/1b+xmNsJ1aJLlobI6je5b+fx14OtCiPaV//MKIcJ3KXxGURS/oigRRVEiLNsDn1cU5aQQoksI8fqnKONrwD8WQhhXyjIohLCzbP/65ootpAN45l4XEkL0Ai8Dv60oytVPUaadxCQbUMcr/JjlUSVCCD/L0/LrK+8vf4oyvgb8rhDCsXKtrpVyvQO8IISwCiGcwFfXca2fAt8SQphXpnm7gY8/Rdl2ApNsUB0LIVaP0L4CXFv5vJXacUisqLEQ4jGWtfCuA5+N2IPnJeC7QohLwEfAVQBFUUaFEP8a+MXKFLsG/FMgKoT4C+DP7jFiXE0Hyz3fg/IXQAQ4vfKA4sALwI+ALwCjwBTwgXqCEOLfAicVRfnpTdf6I8AH/OnKs64rinJPF6cdzkbW8WvA3xdCjLJs//oXiqIsrQineNACKoryixX72gcr9ZIHvqMoymkhxH9jeXV7kVUjWNVeefN0XFGUS0KIv2X5d1EH/uk6pqs7nY2s438mhPjiyrEpfjUFb6V2/HWWhbcOlIBvKSvL5HdiR8SGCyH+GTB1my+s8ZAghPgNoF9RlP+43WXR2Bx2ejveEWKpoaGhsd1o4Y4aGhoa60ATSw0NDY11oImlhoaGxjp44NVwv9+vRCKRDSxK63Pq1KmE8hnKoq3V8cOPVsfr54HFMhKJcPLkej1/Hg6EEJ+p9PtaHT/8aHW8frRpuIaGhsY60MRSQ0NDYx1oYqmhoaGxDjYi3HFbKJVKZLNZ5ubmeP/996lUKhiNRiwWC48//jhdXV3YbDbMZvN2F1VDQ+MhYMeKZbFYJBaLcfr0af79v//3ZDIZbDYbHo8Hi8WC2Wymvb1dE0sNDY0NYceIZbPZRFEUZmZmiMViLCwsMDExwcTEBPl8nnK5jKIoCCE4f/489Xqdz33uc7S1tW130TXukwsXLnDhwgXsdjsej0dN1YUQArvdjtFoJBwO43Q6t7mkGp8ldoxY1ut16vU6J0+e5Oc//znT09NcunSJYrFILpej0WhQKpXI5/P89Kc/5b333sPpdDI8PLzdRde4DxRF4Sc/+Ql/8id/Qnd3NwcPHpRiaTab6erqwu1288ILL2hiqbGltLRYKopCpVKhXq8zOztLKpXi6tWrTE9Ps7CwQC6Xo1qtqhsQydGnarvU69ez5Y9Gq1Gv18nn86RSKWKxmBRLo9FIpVLB7XYzPz+Px+PBbrdjsVjucUWNrabZbNJsNqlWq9TrddLpNNVqdc3/NxoNKpUK+Xwek8mE2+1Gp1tec9bpdDidTsxmMxaLBZNp0zaTXTctLZaNRoPFxUUymQx/93d/xyeffMLU1BRTU1PUajXq9TrNZnPNOTqdjp6eHiKRCB6PZ5tKrrERLC0tcerUKfleCIFer8flctHT00OhUGB4eJje3t5tLKXGzTQaDarVKpVKhfn5ebLZLO+//z4LCwvymFKpRKlUYnZ2lkuXLtHe3s6xY8dkx2cymXjkkUfo6OggHA7T3r4pe5DdFy0rlo1Gg1qtRjqdJpFIEIvFmJmZIZFIUCgUuDm1nDr6UBSFer1OrVajWCzKXqsVeiaN9WG329c0jmq1SqFQkKMVRVFYWFhgbm5OE8oWoFQqUS6XqdfrciRZqVQol8vMzs6SyWSYnJxkcXFxzTmlUkm263K5TFdX1xqxDAaDNJtNvF4vfr8fIYRs59tBS4plvV4nl8uRyWQ4ceIEk5OTnDt3jsnJSWq12l3PbTQaXLhwgRs3bhAIBBBC0NfXx9DQenZA1dhuhBAcOXKE3/3d35WfxWIxfvnLX5LL5SgWi1QqFT788EOmpqZob2/X7NLbiKIoXLhwgUuXLjEzM8PExMQtYlkul8nlcmvabrPZlIOaZrNJOp3mvffek9Nwo9FINBrF7/fz7W9/G7/fL6fk20VLimWz2aRQKJDJZJibm2N6eppUKkWhULjnuY1Gg3Q6LXut6elpHA4Hvb296HQ6hBDodDoMhpb86hqAz+djaGhIzh4MBgN2u51arUa5XKbZbJJMJhFCUCwWt7m0nz1U81etVqNWqxGLxbh+/To3btxgdHRUTsPL5TILCwvU63U5ItTr9ej1elm3Op0Ol8sFIIWzXC6j0+mwWCwUCgVSqRTVanXb22xLKkYmk+Gtt95iZmaGt99+m6mpKZLJ5LrPbzQalMtl3nnnHcbGxjh06BDXrl3D4XDg9/tpa2tjZGREWxhoUSKRCG63W77v7OxkfHyc+fl5zpw5ownkNlIulxkdHSWZTPLee+8RjUaJRqPMz89TLBbJZrMoikKj0UCv1+P1ejEYDDidTiwWC319ffj9fnm9QCBAX18fOp0ORVGIx+P89V//NXNzcywtLZHJZJifn2dpaQlFUbDb7dv23VtSLMvlsuypbty4wczMzD3PWW3DrNeX90SanJwkGo2iKApWqxWPx0NPTw+hUIjBwcFNK7/Gp8Ptdq8Ry0qlQnd3N81mE6PRCLCttqvPMvV6nYWFBWZmZnjnnXe4ePEi+XyeUql0i03RarXicDiwWq14vV4cDgd79uxZY2fu7e3l0KFDUiyj0SivvfYayWSSXC4nTXLFYnHbXcVaSizL5TLJZJLJyUnOnj3L9PQ0uVzutseuFse7NRxFUZienuadd94hFApJB/bPf/7zG15+jc1HURR0Oh3BYJDu7m45hdPYGprNJqVSiUKhQLFYpFQq0d3djc/nk+3Q6/UyMDCA3W6nu7sbi8WCw+HAZDLR3t6+RvRcLhcOh0O6FxUKBfmnerq0SsfYUmJZqVSIx+PMzs5y7do1pqen1zXlUiN37sT8/Dzz8/Oy4sxmsxx9auw8hBD4fD4Z/6+xdai+z+oKeLlcpr29nf3798tjwuEwTz31FG1tbXR1dWE2mzGZTHLx5naoC3fqKnmlUqHRaNz1nK2mpcQyk8lw6dIlxsfHyefzaxzOVex2Ozabje7ubvbv30+j0ZDTgMnJSQqFAktLS5TL5VuuXygUmJ6exmKxMDY2RiaToaurC6vVulVfUeMBKJVKRKNRpqenpaG/o6OD/v7+NdN1jc3HbDYzODiI3+9Hp9MxNzdHJBKhs7NTHuP1euno6JDO5Hq9/p6jQ9Xlb/WfwWDAZDJhsViwWq3SBLNdtJRYJpNJ6RKSTqepVCpr/l8IgdPpJBQK8YUvfIF/+A//IZVKhenpaeLxOK+++ipzc3PSbWH1eYqikM/nuXz5MpVKhZMnT9Lb20tbW5smli1OPp9nbGyMubk5yuUyNpuNSCTCvn378Pl82128zxQWi4UjR46gKApf+MIX5KzuZjG8X59IdXW9Wq3KVXan04nVasVut2O327fdV7qlxLJYLDI9Pc38/PyaafLqBAqDg4OMjIwwNDSEy+WSvZDa4zkcDqLRKEtLS/L81RWq0+nQ6XQYjUaMRmPL2EM07ozJZMLv90vndCEELpcLv9+veTRsA5vhHF6tVllcXCQej68Ji1Tv1wq0lFiqI8tUKrXGgVWv1xMIBPD7/Xz5y1/mN3/zN2VjEUIQDAYpFos4HA7m5ua4ePEiU1NTt1xfp9PJYb3T6cTpdGrx4zsAl8vFwYMH8Xg8xONxdDodXV1d7N69W6u/h4RcLsf58+eZnJykVCpJn+hWEUpoMbFUA+/VnmX1g3K5XASDQbxeLy6XS44oFEVBURS5SlcsFm+JF1cxGAw4HA5cLhdutxuXy7Xtjq4at6KGNZbLZbLZLAsLCxQKBSqVCiaTSdqytLp7eFBdhPL5/JpgBHVxSLV9rka1c6rtX1EU9Ho9Op1uXXbS+2VH/Nr0ej179+7lkUceYWBgQNouyuWyTM0Wj8f54IMPmJqaIpFI3PY6bW1tDA8Ps2fPHg4dOkQwGNSSA7cgqs358uXLvPvuu0xPT/P+++/L2YOa4Fnj4aFcLkuvlUqlItcnfD4ffr9fLiitpl6vk0wmqdVqFAoFGo0Gbrcbq9WK1Wrd8N/IjhDL1ZRKJZLJpLQ91mo1stksS0tLzM3NsbCwcIvNQ8VoNOL1eqWDrLaw0xqoowK1MVSrVXK5HIlEgsnJSebn58lkMiiKQmdnp5YB/yGk0WiQy+VkkhwhBBaLBZvNhhBiTZtWk+xUKhUWFxepVCoyp22xWJRJo91uNwaDYcNmIDtCLOv1OidOnGBsbIyOjg7a29sxGo2YTCZyuRyTk5MUi0Xi8Tjlcpl0On3b63g8Hg4dOkQ4HN72lTWNZZrNJplMhlqthtVqxWQycf36dcbGxmSiZ9X3LhgM8u1vf5tdu3YRiUS2u+gaG0gul+PcuXPMzs5Km2VPTw+9vb0sLCzwxhtvyGPj8TjXrl0jl8sxNTUlzTVqhiKbzcbx48c5evQoHR0dDAwMbMiUvCXEUk0EqtofVFYn9V1YWCCVSpFMJonFYhiNRsxmM5lMhqtXr97iZrQag8GAXq/H6XQSCATwer3awkCL0Gw2ZVSVSiqVYnZ2Vv6p4apWq5Vdu3YxPDy87aFvGhuDGkdeKpVIJBJyoGOxWOTIMpvNMjk5Kc+JxWKcP3+eTCZDNBqlXC5LW6fP58PpdNLb28vAwMCGbivTEmI5Pj7O6dOn+eSTT6Qj+uqeQDXkNptNlpaWyOfz0g1IzZ93Nw4ePMgTTzzB4OAgn//852UGZo3tJ5/P84Mf/IDx8XGOHTvGwMAA7733Hm+99Rbz8/M0m038fj+PP/44kUiE/v5+bRr+EDEzM8O5c+c4e/YslUpFtv16vc7Zs2e5du2anHGoqAs69Xodh8OBw+Ggv78fq9XKo48+Sm9vLyMjI+zevRuHw7FhCz0tIZYLCwucOnWK8fFxKXw3C6b6ebVaXbNipnK3B9LT08NTTz1FT08P/f392ipqC1Eqlfjggw84efKk9KW9du0aly5dkunY1L2UIpEIfr9fG1U+RCSTSS5dusT169fXtP1arbbG/W91cu+2tjY6Ojrk9jFms5nu7m7cbjePP/44IyMjBAKBDQ9Y2DbVUBSFS5cuMTk5yenTpzl16hTxePyBgufvdazP52PPnj20tbW1VKzpZxk1MezMzAzJZJJ8Ps/o6CilUonx8XEKhQIGgwG/309PTw/Hjh2jo6NjW1N0aWwcY2NjjI2NceXKFd59910SiYT0rVa3D/H5fGsyF4VCIbq6unC5XHR0dKxxIVNTwO3evRu/378pOQO2VSwvX77M66+/zpUrVzhz5ozcV2f11qfr5W7Her1eBgcHNTtlC1Eul5mYmJC5SrPZLKOjo2u2DnG73Xi9XsLhMI8//viaPIgaO5uxsTF+/OMfE41GOXfuHJVKRboMwbK7YHt7O16vl1AohMfj4cCBAzz66KNyZKn6YW6V4/qWi6WiKBSLRcrlMrFYjMnJSdmr3M72qC7OhMNhfD6fjB9NJpPMzMzcMh2/HQsLC5w+fRqv10tvby8Gg6GlIgM+C1SrVRk0sLS0RDwe58SJE8RiMTKZDHq9Hr/fT2dnp4wFbjab5HI55ufnuXTpEqFQiHA4rPlY7hCq1SqJRIJiscjk5CS5XI5SqUS1WpVmt2QyKW2VBoMBm83G8PAwHo+HvXv3ymTdNpuNnp4eAoGATKqxGY7nd2NbxDKZTJJKpbh8+TJnzpyhXC5TKpVuET41PNFqtXLs2DEOHz5MNpslk8lw4cIF5ubm1pVq7erVq/zkJz9h7969BINBhBCa3XKLKRQKLC4uMjc3x5kzZ5ienubHP/4xyWSSYrGITqcjHA6zd+9eEokE8XicqakpLl68yMTEBL/85S8Jh8N4vV5NLHcIpVKJsbExYrEYL7/8MtevX5e7taoeMKsjbwwGAz6fj69//ev09/dz9OhR2V4B6VsN2xMvvuWK0Ww2SSQSzM3NycxCNzuRqz2M2Wymq6sLt9vN4OAgkUhEJgYtFotcvnyZYrFIKpW6q2im02kmJiawWCxMTk7idrsJhUKaYG4BaqKTRCLBxMQEsViMq1evkkgk0Ov1WCwWms0mOp0On89HZ2cnNpsNh8OBwWAgl8vhcDjkZnWzs7PAcjSW5ivbWqhJgVUn8WQyyYULF1hcXGR+fp50Oi2ToKjHwbLwGY1G/H4/7e3tdHR00NnZKRMGtwpbrhb1ep1z585x/vx5mbfy5lhus9lMJBLB5/Px/PPP09fXx8jICF1dXbInCofD1Ot1pqeneffddymVSjQajdve8/r168zMzDA5OYnJZCIcDvPcc89pWba3gEKhQC6X4+zZs7z66qvMzMxw6tQpDAaDzLAdj8cB2LdvH8ePH5f2q2g0Sm9vL7Ozs/ziF7/A7XZLJ+OjR48SCoW2+dtprGZhYYGxsTHm5+c5f/488XickydPkslkZITN8PAwXV1dXL16lWvXrslz29raOHToEH19fdL9Z7vzV97Mloml6itZKpVIpVLE4/HbCiUsjyy9Xi/BYJDOzk46OztlzKdKIBAgEonI9Gyqf+btrqcm51B7O4/Hc8dkGxobSzqdZm5ujtnZWebm5ojH4zLGOxgMygxQQgja29tpa2uTOQ3L5TLhcBhFUQgEApjNZrLZLPF4nEwmg8PhwGw2t1yj+qygDlwKhQKlUomZmRmi0ajco2d1TtpAIIDJZKKvr4/u7m7ZQaoYDAa595LVam1JP9otE8t6vU4sFmNpaYkLFy5w8uTJOya8cLlcHD9+nEgkwpEjRwiFQrc8vF27dvHd736Xjz/+mI8++gghhLSF3IlCocDMzIzc80Njc2k2m7z77rv8/Oc/Z3JyktHRURmN09PTw9e+9jU6Ojqk6PX29uL1emXy10AgwNDQEJlMhuPHj5NOpzl//jyxWEyOTAcHB+nq6trur/qZQ1EUuf3DiRMnOHfuHBMTE5w/f55SqUQ2m8VqtTI4OIjX6+Wpp56is7OTnp4ePB4P/+E//Ac++ugjYHkabrPZ2LVrV0sv4G2ZWKop1PL5POl0WvY6N288ptqx2tvbCYVCtLW1Sd86NTRKjeZZvbeHavi90+q4+nm9XqfRaKxrFV3jwVFH+UtLS0xMTLC4uEipVMJiseD3+wmFQnR3d9PV1YXP58Nischs2Ore0haLBZfLhdPpxGAwsLi4yNjYmFxRF0IQCoUIhUIyoktj81EXZ7LZLLlcjpmZGcbHx4lGo8RiMdm29Ho9wWBQ7qba29tLIBDAbrffYgLT6/W0tbXhcrla1sVvy8Sy0WiQSCRYWFggmUySTqdvsTHabDYCgQD9/f3s37+fcDgshVJ1LRofH2diYoKrV69y4sQJ4vG4jA9dnTB4NWrlORwOdu/e3ZL2kIcNdUOrmZkZrly5gtPpZPfu3ezbt48XX3wRv9/PwMDAGjcQtZGoOQlVDAYDRqOR9vZ2/H4/6XSaH/zgB7zyyisyx6Xb7da2mNgC6vU6S0tLZLNZfvjDH3Lx4kVu3LjB3NwcQggcDgfd3d08/vjjBINBjh49KhdULRYLxWJR2jBXr2irNsvOzs6WzQa2pTbLcrks7Ru3S3xhMBhwuVy0tbXh9Xpxu90IIWQ6JjX1/Pj4OKdOneJnP/uZnE7fzZVAHXlarVZ8Ph9ut7tle6+HATVcTc0Gk0qlsNvteL1e+vv7eeaZZ3A4HHc8/+YM2eo2IE6nE7/fTyqVIpfLcfnyZWKxGOl0umWnbg8bauKTpaUlzp8/z4cffkgmk6FQKMh1hmAwyMGDB+no6ODw4cNrZoaZTIZsNrtmJVyv12O1WmlvbycQCLTsQGZbfGfuJGyVSkXmMHzjjTdoa2sjFotRLBbl9HlmZobZ2VkWFhbWHRp5/Phxfu3Xfo3u7m4OHDhAW1ub1rg2kUajIfNQqnZpv9/P/v37iUQin7qjMpvNPPPMM3R2dhIKhVhYWNDCILeIXC7HT3/6UyYmJhgdHSWdTjM8PExfX5/M9NPe3s7w8DB2u33NWkOj0eDixYuMjo5y/fp1FEWhq6uLvXv3cujQITweD1artWXNKVsulncLZVTtIAsLC5w5cwaj0cjJkydZWlqS9spCobAmkcZ6QiP37t3L7/zO72Cz2WR8eKtWyMOAmlLvxo0bZLNZhBC0tbXR19dHMBj81M/eaDRy6NAhurq6yOfzJJNJQqHQPfeP1/j0FItF3n//fc6fP8/8/DzFYpFwOMzTTz/Nrl272L9/PxaL5bbZfprNJpOTk5w6dYq5uTlgOW/D4cOH5WaDrbgKrtJSXtnqdpiZTIbR0VH0ej2JRIJ8Pi+PuVMW9NWoNq++vj5CoRB79uzB6XTKxSCtQW0uQgjMZvOa9P5q1FWlUiESiazJHLNearWaDGZ47bXXuH79unQz0ul0HDp0SKvbTUYd0KRSKdkWjUajrGvVBr26HhqNhqy3q1evyjBHWLZV7tq1i+7u7pYPEmmp0jUaDRqNBpVKhVOnTqHT6R7IH1K1cY2MjPDoo4+yf/9+2tratIa0hZjNZux2OzabDbvdTjKZ5KOPPiKbzRIOh+no6MDn892XWFYqFcbHx5mZmeHll1/m1KlTchW8t7eXb37zm5v4jTRgeUCTSqWkeUVdfFPj+e12+y3trFarcePGDWKxGKOjo1y6dIlarYYQArfbzcjICH6/XxNLFZ1Oh9vtplQq0dbWhtPpvG2oo8qdHMxV1H2/Ye0UXK/X09HRgcvlYmRkhOHh4TXxpRqbjxq6qCgK/f39xGIxmc06lUpx8uRJmW7N6/XKEMc7USwWmZ2dJZlMcvLkSWKxGMlkEkVRCAaD+Hw+AoGAVsdbgMVi4ZFHHsFms0n3v3K5zMWLF2UYs9VqlZvKtbe3U61WicViRKNRMpkM9Xodj8eDy+WSvrVqYEIrs2ViaTQa6e7uxmazyU2nEonEuqbVt8NkMkn74+qHbDabOXbsGJFIhC9+8Ys88sgj2sr3FqPX6+nv7yccDlMoFGhra+Py5cucPHmS6elpLl26RHt7O6lUinA4zFe+8pW7imUikeCXv/ylTL6hbo1rMBg4cOAAhw4dYnh4uOUb28OA2+3mt3/7t1laWuLcuXMyc9iPfvQjdDodBoOBYDDIoUOHCIVCPPnkkwCcO3eOa9euMTc3R7VaJRwOc+TIER577DEikQhGo7Hl1xG2TCxVO5bNZpMuBqVSiUwms67zXS6XDG0zGAx4PB66u7tvEUuTycTQ0BAdHR14vd6WCsT/LKHajX0+H+FwmGq1SiaTIR6Pk06nURQFl8t1173bc7kc8XicmZkZuXNnuVxGURQ6OjqwWCwMDAwwMDCg+VhuEWpCZpPJJENOTSaTjIqr1+tYrVYWFxep1WqMjY3RbDaZmZlhcXFR1p/b7aa/v59AIIDBYGh5oYQtFEvVQ99kMnH48GHMZrMMgbwXBoOBoaEhmdPS7/czODjI8ePH5ahxtWBarVYMBkPLOrd+VhBCMDw8zMDAAPF4nKeeeopTp07x53/+57S3t/PEE08QiURwu923Pf/y5cv83d/9HclkkuvXr1MsFhFCEAgE+I3f+A2GhoY4fPgwu3fv3tIksJ9ljEYjkUiEZrPJ7t275faz5XKZTCbD0tISo6OjvPzyy5RKJX7+85/TaDSYn5+XuSz1ej3Dw8O88MILuFyuHSGUsMULPOrCSyAQoLu7m56eHubn5+95ntFoJBwOEw6H5Ybr4XCYnp4ebYrd4qi79KmuXrFYjGAwSHt7Oy6XS6ZiW02z2ZRb5E5NTVEoFGg2mzKKx2q1EolEiEQihEKhO4qtxsYjhJCzNdVX2eVyyc3DzGYzyWRS1oka5prL5WS0ldlsxul0ytykO6WT2/LlJ6PRyKOPPsq+fft48sknyeVy9zxHDaNS3RKMRiM2m00Tyh2EGvd99OhRfu/3fg+bzXZHkVMTPF+7do2TJ0/idrv53Oc+J33y1Iz3LpdLc0ZvAVSTi7pQ097ezsjICHNzc7zyyivMzs5y4sQJFhcXCYVCclHP6XTuqDa8LU7pXq8XQMsW8xnCYDDITNiDg4Ny/5TbobqPqan1dDodgUCAzs5O9u3bh8/nw+FwtGxY3GcNNTzVZDJhMpmw2+20t7fj8/k4e/Ys1WpV1pXNZsPn88mdPHcSre3YpPHQYbfbGRgYkGm5dDrdLaMLVQi/9KUv0dvbi9VqpaurC6vVSjAYxGw276gRyWcVNft5oVCQuzBGIhH27dtHR0fHdhfvvtHEUmNLMZlM91y5NpvNmM1mXC4Xe/bs2aKSaWw0Op0Op9Mpo+dWb228E3cp0MRSQ0NjU7Db7TLVotvtJpPJsGfPHrn/905DE0sNDY1NwWq1Mjw8DMDnPve5bS7Np2dnODhpaGhobDOaWGpoaGisA00sNTQ0NNaBJpYaGhoa60ATSw0NDY11IB50S1ghRByIbmxxWp6woiiB7S7EVqHV8cOPVsfr54HFUkNDQ+OzhDYN19DQ0FgHmlhqaGhorANNLDU0NDTWwV3FUgjhE0KcXfmbF0LMrnq/4fs1CCF6hRBvCiHOCCHOCyG+vI5zGivluSiE+KEQ4s6budz7Wn8phPj6Oo57euWel4QQbz/o/VoBrY5ve8xvrZTtghDifSHEwQe9XyuwDXX8B0KI0ZVn+LoQIryOcyZXnvd5IcQvhBChT3H/PxZC/OE9jvmtVc/grBCiKYQ4dNcLK4qyrj/gj4E/vOkz4bpNaQAAIABJREFUw3rPX+c9/m/gH6+8HgEm13FOftXrvwH+4EHLCPwl8PV7HOMGRoHelfftG/kMtvNPq2N5zDHAs/L6OeCj7a6bHVbHzwC2ldf/GPhv6zhnEvCvvP53wH+86f8FoHvQ73iP4/cDE/c67r6n4Ss9858JIT4C/uRmFV/p/SMrr78jhPh4Rbn/XAhxrySECqDmbmoD7r1Bz1reBXatjPzeFUL8FBgVQuiFEP+HEOKTlZ7rH62UTwgh/pMQ4ooQ4pdA+zru8W3gZUVRpgAURVm8zzK2PJ/1OlYU5X1FUVIrbz8Euu+zjC3PZtaxoihvKopSXHn7IM/vHZbrOLJSb38FXAR6hBD/YlUd/5tV5f1XQoirQoj3gKH7vN+LwH+910EParPsBo4pivIHdzpACDEMfBN4QlGUQ0AD+K2V//sLIcTR25z2x8B3hBAzwM+A319vgYQQBpZHARdWPjoC/HNFUQaB7wEZRVEeBR4Ffk8I0Qf8JssPdgT4LssjCvV6/1YI8fxtbjUIeIQQbwkhTgkhvrveMu4wPst1vJrvAa+ut4w7jM2q49U8yPP7DX5Vx7uBP1UUZS/L9bgbeAw4BDwihHhSCPEI8K2Vz77Mcv2r5f++EOL797jfN4H/cq9CPWiKth8qitK4xzHPAo8An4jlDYms/P/tnXtwXOd12H9nse9dLBaL3cX7wQdI8CGRFB8iVY0iUR6NHSUaZ5I0Th17MnaaKq3dpk39R5qMJ/UfzUyTtDOZ1rU6ztRWplFTJ/GUndSWbckZiZYoUpJJgiBBgHgtiAexeC32gX1//WP3XoMkAIIv7IL8fjMYLLD3cfaeveee75zznQ9mAJRSv7XGPr8OfEsp9WcicgL4SxHZr5QqrnMel4icL79+F/gLSjfEWaXUSPn/LwFPys9iVXWULvpzwBvlzzIpIm8bB1VKfXWN81nLn+vF8md6X0TOKKUG1pFxK/I46xgAEXmB0s3+7HrbbWEelo6BkkcKHAF+boPy/FhECsBF4A8phbzGlFJnyu+/VP75aflvLyUd1wLfNbzZ8miDsozfuIOMTwMppdSlOwl3r8YyueJ1nps9VKchB/BtpdTv38Vxvwh8EkAp9b6IOIEgZeWswXL5iWdSVupKGQX4slLqzVu2u2NyYRWuA3NKqSSQFJF3gAPAo2YsH2cdIyJPAt8EPqWUmruXY2wBHpaOEZFPAH8A/JxSKrPB3V5QSs2uOIaf23X8x0qp12451+/ejWy38Bk24FXCgykdGqU0HEJEngK2lf//FvArIhIuvxeQO2fFIpSeZIb77wSiItIqIm/dh4xvAr8jIrbysXeJiIdSbOTXyvGuZkqB6Tvxf4BnRcQqpazs08CV+5BtKzDKY6RjEekA/g743CM4YliLUR6QjkXkEPAa8MqtMX0R6b8PGd8EviAi3vKxWstyvQN8WkRcIlIL/OJGDiYiFuAfs4F4JTwYY/m3QEBE+oAvUfawlFKXKbnSPxCRi8APgeaykGvFOn6PUqzpAiVr/5uqlK5qpvTku1e+SSmD/bGIXKKkSCvwXWCw/N7rwPvGDmvFs5RSV4DvUxoqnAW+uREXfovzWOkY+CrQAHy9nNT48D7k2io8SB3/CaUh8nfK1+9UefsgJe/wnlBK/QD4K0qhr17gb4BapdTHwF8DFyjFR88Z+9whZvkcMK6UGt7I+bfE3HAR+RIQUUqduuPGmi2J1vGjj4j8ArBdKfXnlZblXtgSxlKj0WgqjZ7uqNFoNBtAG0uNRqPZANpYajQazQa453XDg8Gg6urqeoCiVD8fffTRrHqMumhrHT/6aB1vnHs2ll1dXXz44eNQUfEzROSxar+vdfzoo3W8cfQwXKPRaDaANpYajUazAbSx1Gg0mg2gjaVGo9FsAG0sNRqNZgNoY6nRaDQb4J5Lhx42xroX2WyWfD5PNBolHo+zsLDA4uIiTqeT2tpaamtr6ejowG6343A4sFi0/a8m5ufnWVpaYmpqirGxMWPNEzweD7t27cLr9RIKhXA4HBWWVKNZn6o1loVCgUKhQCwWI5VK8f7773Pt2jUuXbrEpUuXaGxsZPv27ezYsYNPf/rT1NfXEwwGtbGsIpRSjI2NcfXqVd5++22+853vUCyWGqJ3dnby27/923R2dnL8+HFCocemDlyzRakaY6mUolgsks/nWV5eZmxsjFQqxcLCAqlUiqtXrzI+Ps7i4iIAyWSSiYkJLBYLvb29hEIhDhw4gM/nw2q1Gp20NRVmenqay5cvMz4+TjKZpFAorWIwPz/PwMAAy8vLtLa2UigUsNvtWK1WHA6H9jSrAGN0Zzgu2WyWYrFIoVCgWCySTqfN+zWdTpvb2+12vF6v6bhYLBYCgYA58hMR82crURXG0lBGJpMhHo8zPDzM17/+dSYmJpifnyedTpPJZMjlcjQ2NtLW1kY0GuWDDz7gwoULnD9/no6ODr7yla+wfft26urq9M1WBRSLRd577z1ee+01MpnSygLGDTQ3N8cbb7xBIBBgeXmZnTt30tjYiM/no7W1lZaWlkqK/thTLBYpFotks1lSqRSpVIrp6WkymQzJZJJMJmM6L9euXWN8fNw0qE1NTRw4cACbzQaA2+3m5MmTtLS04HQ6sdlsWK1WamrutBBodVFRY5nNZsnlciSTSeLxuGksp6enSaVSZLNZrFYrbrcbt9sNQHNzM83NzeTzeYaHh0mn00xPT+N0OlleXiaXy6F7dFYPyWSSubnSEjaGVwGYIZZCocD4+DhWq5VMJoPf78flchEIBKipqTFvOM3mUCwWUUqZBjKZTDI7O2say2w2e5uxjEQiXL9+nVwuZzo9dXV1pu48Ho95r3q9XjPf4PV6sVqtW0bHFTOWRjwrEolw6dIlPvjgA3K5HJlMBo/Hw6FDh/D7/ezfv5/6+npzP+Op9A//8A/EYjEzeeDxeFhcXGRpaQmfz4fT6Vzn7JpqYXl5mb//+7/H5XLR0tKC3+/nxRdfJJ/P09DQQFtb25Ybrm1lUqkU6XSavr4+ent7GRoa4r333iObzZLJZMxwWbFYNEd76XSaXC5nGtpYLMbk5KSpN6vVyg9/+EMcDgednZ00NDRw5MgRDh8+TDAYpLOzc0vouKKeZSqVYnFxkenpaYaHh81YSGNjI4FAgObmZnbt2kU4HMZqtWKxWMhms2SzWUKhEG63m1QqZRrZXC5HPp/XnmUVYbfb8Xg8ptdxK/l8nhs3bpi6raurY2pqilgshtvtRim1JW6kRwGlFOl0mng8zo0bNxgaGuLq1atcvHiRfD5/08jA2H4lK4fuiUTitu1qamqIx+MEg0GCwSDt7e3Y7XaKxeKWGJJX1Fg6nU58Ph+7d+/GbrcTCATYtWsXhUKBhYUFEokEp0+fxm63s2fPHpqbm7ly5Qr9/f18+OGHDA4OmkNvTfUhIpw4cYJsNsv58+f5yU9+suaDrFgssri4SDKZZHp6munpaVwu1yZL/HhTLBa5fPky/f39nDlzhvfff5/l5WWcTidOp5NwOHyTUTN0mc/nyefzLC4uMjOz9orGhUKBGzduEIvFsFgsTE9Pc+LECbZt24bD4cButz/0z3g/VMxYigg2m81UgtVqpb29nRMnThCPx3n77beZn59namqKQqFAIBCgtraW8fFxent7uXbtGnNzc+Tz97MgoOZhIiK0t7dz9OhRFhYWeO+998zSodUwEnlLS0ssLi6SSqUoFotbMnO6FVFKEY1GGRoaYmRkhOHhYTOz7fV6aWxsNA3aSj1mMhny+TyFQoH5+XkzK76aruPxOIlEArvdTiaToa2tjUwmY8anq1nPFfUsGxoacDgcZLNZ0uk0tbW1ZtmPoZQLFy5w48YN+vr68Pl8TE5OMjk5yfz8/KrDOk114fV6CQaDeDwegFVvIOPmglISqK+vj1QqxeHDh3G73dTX17Nt2zas1qoo3nhksVgsdHd343A46Orq4tixY6ZD4/V6aWlpuSkZY+jMmDgyMTHB0NAQ0WiUK1eukEwmiUajZLPZ2861tLREPp9nZGSE/v5+gsEgO3bsqOpkT0W/fXV1ddTV1d32f8PrVEpx9epV+vv7TVdfs3UQEVwuF/X19bhcrlWH4CsNpeGNDA0NMTQ0RDabZffu3bS3t9Pe3q6N5UPGYrHQ2dlJKBRi7969JJNJbDYbDodj1WG4gZErGB8fZ2BggMHBQdNQLiwsrGosE4kEiUSCyclJRkZGyOVydHV1aWN5tzidTrq6unC73Zw4cYJwOExfXx8TExO3bSsiZvlBTU0NNTU1Ve3KP24Ynkl9fT0tLS0kk0kWFxfNUYGhq9USOVNTU7z11ls88cQTHD58WFc4bAIOh8Mc2blcLiwWi3l/rXVfGQbU7/eb921NTQ2Tk5NAaQJCNBolnU7ftu/U1BQ//vGP2b59O6FQiEAgQCgUqkqjWZXG0uVysWfPHjo7O0mlUoyNjRGPx9c1lsbsD20sqwuHw0FtbS3hcJjt27ebc/xXeplGXPLWqaojIyOMjo4SjUb5/Oc/v+ooRPNgMZI5d4PFYsFisRAOhwmFQvT09PD0008zPDxMNBpldHTULEm6lZGRESYmJti3bx87d+6kra2N2tpabSzvBhGhpqYGn89HQ0MDLpcLq9VKoVC46Uaz2+00NDQQCATw+XxmoaumOrDZbLjdbnNWx9jYmJn1Xjn90eDW0pS1EgWa6sS4b+12O36/n0OHDhEKhVhYWKCmpoalpaWbjGahUCCXy7GwsMBHH33E7Ows27dvN73TaqKqrYrVaqWlpQWHw0EgEMDpdJp1lgZut5vu7m527NhBR0cHzc3NuplGFeHxeHC73Rw9epTW1lZ6e3tJJBJEo1GuXbvG8vJypUXUPGCMnENraytf+MIXmJ2dJZPJcOHCBfr7+5menja3NaY6Dw8P89prr7Fz506OHz9OfX09dru9qgxmVVsVEcHhcOByucwpj7e658VikVwuZ06d1EXp1YUxvDamMPr9frxeL263W4dLHnEsFgtutxufz0dHRwc7duzA5/Otum0+nyeRSLC4uMjY2BgjIyOkUqlNlnh9qtqztFgs+P1+8ynV2dnJ9evXSSaT5jbpdJrR0VGzyNXn85leqKZ68Hq9uFwuotEoLS0tW2bWhub+8Xq9vPLKKzzzzDPMzMwwMDCw5rYLCwt861vfoq2tjVdffZUDBw5soqTrU9XGErgtZrWyxZNxsxlTtBYXF5mfn8fr9ZpZPU11YCQB3G43oVCIZDKJw+FgeXnZLGiG26fQQamOLxqNYrVazVpcHWrZOlgsFhoaGkz9ORwOsx3jrRj1mkop4vE4+Xy+apK2VW0sc7kcw8PDzMzM0N/fz8DAAMVi0czY+Xw+MpkMi4uLTE1N8aMf/Yhr167x8ssvs3379i3ZBupRp7Ozk89+9rMMDg4yOjrK+Pg4U1NTZrJntZticHCQr33ta7S3t/O5z32O1tZW6urqqn56nKaEiOB0OikUCnR1dbF//36mpqaYmpq6bdtsNsvExATLy8uMj4/T3t5uzt6rNFX7eFZKmfNNjeLWWCxGPp/H5XJRW1tLfX29eREzmQzXr19ndHSUWCxGJpPRM3yqEI/Hw44dO9i2bRsNDQ14vd47PtBisRg//elPOX/+PLOzsySTST1BYQuxMkNeW1trVresRrFYNFvDJZNJc+RRDVSlZ2mUEhjziQcHB80C13379vHUU0/R1NREd3c34+PjnDp1ikQiQX9/P6Ojo4TDYSKRCE899RQ7d+6s8KfRrIbb7Wbv3r1mHDMej69aawml70M0GkVEeOedd4hEInziE5+go6OjApJr7gWjsL21tZXdu3cTi8UYHh5edVsjzGa1Wqsq5FKVxrJQKLC0tMTc3BwDAwP09/cTi8UAaGlp4ciRI3R2dnLo0CH6+/u5ePEiExMTXLhwgVwuR19fH9lslo6ODm0sqxS73U5LSwu5XM6ML9/aAsygUCgQj8epqalhYGCAdDrNsWPHKiC15l4xjJ/f76e5uRmv17vmdsZvI85dDfFKqFJjmUgkOHv2LNevX2dwcJDr16+TyWTMaXPNzc0Eg0Hcbjft7e28/PLLjI+Ps7CwQDQaJRKJEI/H6e7uprm5GZ/Pd1MDYU3l8Xq9HDlyhMbGRr7//e9XWhzNJmA06vD5fCwvL5PJZJiZmWFoaMhM4hpLUywtLXH69Gnm5uZ44YUXOHjwYIWlr1JjGY/HOXfuHMPDwwwMDDAzM4PL5bppjnFDQwNut5vW1lZefvllRkZGePfdd0mn04yNjTE2NsbBgwfZtm0b7e3t2lhWGR6Ph8OHD9PU1ITf76+0OJpNwGKxsHPnTrZt22ZOf+zt7WVkZMQ0lsby10opTp8+zcDAAF1dXdpY3srKzPbk5CQ3btwws989PT20tbWxe/duMzEAJQU4HA58Ph/79u3D5XJx6dIlFhYW6O/vx+FwcOzYMRobG8055Jrq4W6GWPl8nqmpKZRSRCIR6urqqK+vX3NIp6kejDZu8/PzJBIJbty4QTweN6c+riwZW7mqpNErsxr6mlaVsYzH4wwODppxysnJSfL5PF6vl+eff54TJ06wZ88eM7BvZNncbjfhcJiTJ0+ya9cuJicnmZqa4vTp05w5c4Z0Os3BgwfNGUDVEgPR3B3pdNqcLnfw4EGKxSL79+/XxrLKKRaLZmbbuK8HBweZm5sjmUzeVEdtGE3DUBoNoY16y0qWAlaFsTQKVOfm5rh06RIjIyMsLS2Ry+Ww2+243W6CwSDNzc3U1dWtauxsNhvhcJhCoUA4HGZycpJ0Om2WIRjZ1traWm0sqxBj8sFqmU9jISxjkSxjnWpdHlZdGE0xcrmcWfJjrNo6MTFBIpFgdHSU2dlZxsfHmZmZYWlpadVjGesBJZNJRkdHuXDhAoFAgHA4bLb922yqwlgaM3B6e3v5xje+wdzcHNFoFIBQKERDQwN79uzh6NGja3YUcjqdPPHEE3R2dnLu3DkKhQJ9fX1Eo1Ez6dPU1EQoFNrMj6bZACsbAK/XST2fz5NMJhER4vE48Xhcr79URaRSKRYWFsz53YuLi/T19TE3N8cHH3zAzMyMuQqk0dPBeG2w0pGZn58nFotx6tQpent7OXHiBCdPnsTv99PW1rbpJUVVYSzz+TypVIp4PG5eoGKxiNVqJRAI0NjYiM/nw+FwrHmMlQ1L/X4/DQ0NOJ1OlFIkEglmZmbMWQTVVI7wuFIsFkmn0yQSibvyDo0uNcvLyyQSCdO71DqtHMbDbGlpiUgkwsLCAsPDw8RiMSKRCPPz80xPTzM7OwusHqde2QTawGiKMzc3h91uJxKJMDIyQmtrK01NTZteg1kVxnJpaYnR0VEmJibMoK/dbqe+vp4XX3yRnp4eurq6NnQsq9VKT08PNpuNsbEx+vv7uXbtGqdOneLYsWPs378fj8dTlc1FHyeSySSXLl1ifHzcDJGsVZQOmAa1pqYGpRTXr1/HZrPR3d1NZ2fnPTWt1TwY8vk8uVyOc+fO8cYbbzA7O0skEiGbzZqrr95rB6FiscjU1BRzc3NMTU1x7tw5nn32Wdra2vD5fLhcrk17SFaFsUyn0+bSt8bUJmO96ebmZtrb23G73Rs6lojg8Xjw+/2mJ2osr7qwsGCuTa7Xo64suVyO2dlZc2i2XlH6rboyRgvz8/Mkk0my2axu+FxBCoUC+Xze9CTn5uaIRCI3TVOsqalZtVeDkfVer8GzEQetqanBYrGwuLhoNl/ZzPu4ot8w4yJfu3aN733vewwPD5PL5XC5XPT09NDe3s6BAwfYu3fvfU2kT6fTzM/Ps7S0RDab1UmBKiAWi/H2228zMjLC3NzcXe2by+W4cuUKkUiEgwcPsnfvXgCdFa8Q2WyWZDKJUgqv12vGlQ2sVivhcBi3200gELhJT9FolLGxMXK53JoJO5fLhcPhYO/evRw6dIj9+/djt9s3PfRSUWNpuO+zs7MMDQ0xMzNj9jkMhUI0NjYSDocJBoMbPqaRNV0ZOM7n82ZzYON97VlWFqO7zPj4+Kprs6yHUsqMbS8uLlZVs4XHEaOaxegu5HQ6bwpzGUtM1NbWmhUtBiLCjRs3gFKd9WpYrVZcLhfhcJidO3ea8crNvn8rZiyNZW4HBwc5e/Ys165dM4fhbrebXbt20dnZaa43fSeKxSLxeJylpSXOnDljxsMAOjo6OHbsGE8++aTZ67JaJuc/rgQCAV555RUikQivv/76miUkmurHWB/r6NGj+P1+4vG4OaEESkPw+vp6nE4ntbW1N4XUhoaGzKbe77777m2xTYvFQl1dHeFwmD179vDcc89RV1eH2+3edINZUWMZiUT48MMPuXLlChMTE+bFtdvttLW10dHRseGgfaFQMJdZvXLlCh999JH5xAqFQjz55JNs27YNl8ulkztVgM/n45lnnqGjo4NTp05VWhzNfeBwOHA4HOzevZudO3eaCR0js23MsjPatK2ML7e2tmKxWOjr6+PMmTOrHt/j8RAIBOjs7GT//v0Vc3QqHrM0qvOVUuZKgMFgkK6uLrq6utbse7e0tEQ0GjVjHfF43KyrHBgYYH5+HqvVSn19Pe3t7fT09NDU1KQ9yiqhpqYGv99PKpUy1+YxMqe3YoRUROS2wnW98mP1sFIvtyZkjTKfW+8/j8dDS0sL0WgUp9NJKpUyw2WGvpPJpBl2SSaT2O32dcsIHxYVj1mm02nTWBrGrbGxkd27d9PV1bXmXO7FxUUuX75MKpVicXGRubk53nzzTWZmZszZAuFwmIaGBrq6ujhw4IC5aLym8hg1tLlcjlAoRCgU4saNG6vGrQwP5dbCdb0wXXVhTD82PMiN4PV66ezsNOugbTab+cA09JtIJADMJK3H48Futz8+MctbMZ4iRnH6zMwMbreb+vp6bDabWSYSi8WIxWJMTExw5coVMpkMiUTC3CeRSFBfX2/O+mlvb6e7uxun01mRoLBmfRwOB3v27KFQKHD27Nk7xi6NG9Lr9eLxeMw1XfTyIVsTu91OXV0dPp8Pj8dDIpEwk7EGRiLYWL21UtUsVWMsoZQhNar9+/v7WV5eZufOnbjdbj7++GNGR0e5fPkyfX19zM/Pc/36dXM+KpQuqtVq5fjx47S2tvLJT36So0ePmuUK2lBWH263m5dffpkjR44wPz/PyMjIqtsZ3qTFYqGmpoa2tjaCwSBNTU1m0k6z9XC73TidTpqbmwmFQmSzWRKJhFl7KyLmaMPogelwOCpSzVIxYyki1NXV0d7ezvT0NF6v11z/O5VKMTQ0ZDbBcDqdZhIoEokwMzNjxi+MrkM2mw2Xy4XL5aK7u5uOjg5aW1upr6/f1Cp/zd1hsVjwer1kMhmCwSDBYJBUKsXy8rK5za3DbSO7akyDNUYNmq2JkQAKBoOk02mmp6dvCsfYbDYcDgf5fJ6lpaWKJWgraiyN5rxOp5OBgQFisZjZx/L111/HbrebxadG9xLDoBoT8evr6zl8+DB+v59du3YRCATM6VAejwen06kNZRVjlIbU1NTwxBNPkEwmuXr16rprS1utVvbs2cO+ffvYsWMHDQ0NWsdbHL/fz9NPP01jYyOjo6NmnBKgrq6OhoYGCoUC/f39dHV1PX6NNDweD1arlaamJjo6OpifnzdjEsVikeXlZbOphjFZ32j0a8SuQqEQXV1d+P1+tm3bht/vN7sL6Ruo+hER03Pw+XxmB/w77eN0OvF4PDpe+Yhgt9sJh8PmSNJms5lVEMa9b9Rv1tXVUSgUHp86SygF9202Gy+88AI9PT1MT0/T29trdglKJBL09fURi8VobGyktraWp556ymzkW1dXh8PhwO/3m2VHNpvNNKaa6kdEzALj9vZ24vE4kUik0mJpNhm/38/x48dpamrinXfeAWB2dpZUKsXs7CxLS0ssLi7S39/P888/z5EjR/D5fLjd7sdjbrhRd9XY2EgoFDKb98bjcWpra4nFYszOzpreZ319PT09PRw+fBiv10tDQ8NNK8BpA7k1uXVNab/fj9frXbM0yEjo2Gw2XQr2iGC32814dSAQYG5uzkz0KKXIZDLMz8+bxjOXyz1ejTQMjI4zgUCAQ4cOmTMAcrkcJ0+eJJPJ4Ha7cTgcplE11hTWRvLRwGKxsHv3blpaWuju7uaXf/mX1zSWVqvVDLk0NDRssqSah4HVasXr9dLY2MhLL73E/v37uXTpErOzs2bDHaPyJRgMYrPZqKmpeXyG4SsREVwuF62trZUWRVMBRIRwOEw4HGbHjh2VFkezyRjNu30+Hz09PQSDQTKZDLW1tWad5fLyMslkktra2k1v/AtVZCw1Go3GbrfT1dVFY2MjgUCAZDJpJnwNo9na2mo28H4sPUuNRqOx2Wy0t7cDsGvXrgpLczM6Oq7RaDQbQBtLjUaj2QDaWGo0Gs0G0MZSo9FoNoA2lhqNRrMB5F4bqIpIFBh7sOJUPZ1KqVClhdgstI4ffbSON849G0uNRqN5nNDDcI1Go9kA2lhqNBrNBtDGUqPRaDbAusZSRBpE5Hz5Z1pEJlb8vbHl2+4CEekQkR+LyE9F5KKI/PwG9imU5bkkIt8RkfU7x65/rG+JyK/cYZvPlmXrFZH3ROTAvZ6vGqiAjp8TkY9FJH+na71in9Hy9b4oIj8Qkab7OP8fici/vcM2NhH5dvmcV0Tk9+/1fNVABXT8n1ccf0BEFjewz2br+NgKGS+IyC/d6bjrzg1XSs0BBw0BgIRS6k9XnNCqlMpv6BNsjD8E/rdS6r+JyF7g/wFdd9hnWSllyPg/gVeB//QQZRwBfk4ptSAinwL+O/D0Azz+plIBHUeA3wTW/TKvwgtKqVkR+Q/AvwP+pfGGlLopiFLqQS0i/quAQyn1RPnhe1lE3lBKjT6g428qm61jpdS/XnHsLwOHNrjrZur4EnBEKZUXkWbggoj83/Wuw10Pw8ve1zdWdUYXAAAEe0lEQVRE5APgP95qxcseXlf59W+IyNmy9X5NRO7U/18BvvLrOmDyLsV7F9gpIs+LyLsicorSF71GRP5ERM6Vn1z/rCyfiMh/EZGrIvIjIHynEyil3lNKLZT/PAO03aWMVc/D1LFSalQpdRG41y/9O5R03FXW2+uUvvjtIvKVFTr+9yvk/YOyh3Ma2L2BcyjAIyJWwAVkgfXX6N1iPOT7eCW/Drxxl+I9dB0rpVIrDKOTks7X5V5jlm3AM0qpf7PWBiKyB/g14B+VPb8C8Nnye98UkSOr7PZHwG+IyHVKXuWXNypQ+Yv9KaC3/K+ngH+llNoFfBGIKaWOAkeBfyoi24BfonRh9wKfB55ZcbyvicgrdzjtF4HvbVTGLcbD0vH98gv8TMfdwNeVUvso6bEbOEbJizospSH/YeAz5f/9PCX9G/K/KiKvrnKOvwGSwBQlT/hPlVLzD+GzVJqHqmMR6QS2AW/fpVyboWNE5GkR6Suf69U7edf32qLtO0qpO610/iJwGDhX8qBxATMASqnfWmOfXwe+pZT6MxE5AfyliOy/g+vtEpHz5dfvAn9ByeidVUoZi1C/BDwpP4uR1VG66M8Bb5Q/y6SImEpVSn11vQ8nIi9QMpbPrrfdFuZh6fhe+bGIFICLlMI1fmBMKXWm/P5L5Z+flv/2UtJxLfBdpVQKoDzaoCzjN9Y41zFKRqEFqAfeFZEfKaWGH+xHqjgPW8efAf5mA+cw2Ewdo5T6ANhXfiB8W0S+p5RKr7X9vRrL5IrXeW72UJ3l3wJ8Wyl1N8HxLwKfBFBKvS8iTiBIWTlrYMYsDcpKXSmjAF9WSr15y3Z3TCCthog8CXwT+FQ5HvQo8rB0fK+8oJSaNf4QET+36/iPlVKvrdxJRH73Hs71T4DvK6VywIyI/AQ4AjxqxvJh6/gzwL+4i+03U8cmSqkrIpIA9gMfrrXdgygdGqU05EVEnqLkdgO8BfyKiITL7wXKbvl6RCg9yQz33wlERaRVRN66DxnfBH5HRGzlY+8SEQ+l2MivlWOazcALdzqQiHQAfwd8Tim19uLWjxajPDgdr4mI9N+HjG8CXxARb/lYrWW53gE+LSIuEakFfnEDx4oAJ8vH8QDHgfuRbSswygPUsYj0UPLK37/l/1WhYxHZVg7dGeGCHkrXYE0ehLH8WyBQHvt/CRgAUEpdpuRK/0BELgI/BJrLwq0V6/g9SvHEC5SCwr+pSvMxmyk9+e6VbwKXgY9F5BLwGiWv+rvAYPm911mh2HVill8FGoCvlwPeaz6JHiEemI5F5Gg5Jv2rwGvlYyIiQUqewz2hlPoB8FfA+yLSSynuWKuU+hj4a+ACpfjyuRWyrBXP+q+AtyzbOeB/lJNSjzIP8j6Gklf5v9SK+dRVpuNnKWXAz1OyA/98pVe7GltibriIfAmIKKVO3XFjzZZERH4B2K6U+vNKy6J5OGx1HW8JY6nRaDSVRk931Gg0mg2gjaVGo9FsAG0sNRqNZgNoY6nRaDQbQBtLjUaj2QDaWGo0Gs0G+P+yEraVMkZn9QAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 9 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix:\n", "[[ 977 0 1 0 0 0 0 1 1 0]\n", " [ 0 1125 4 0 0 1 1 3 1 0]\n", " [ 1 0 1029 0 0 0 0 2 0 0]\n", " [ 0 0 3 1000 0 4 0 0 2 1]\n", " [ 0 0 3 0 973 0 1 1 0 4]\n", " [ 2 1 0 3 0 883 3 0 0 0]\n", " [ 6 2 0 1 1 4 943 0 1 0]\n", " [ 1 0 8 1 0 0 0 1016 1 1]\n", " [ 5 0 11 2 1 4 1 2 945 3]\n", " [ 3 3 1 0 4 4 0 3 1 990]]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 288x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print_test_accuracy(show_example_errors=True,\n", " show_confusion_matrix=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizing the Input Images\n", "\n", "Now that the neural network has been optimized so it can recognize hand-written digits with about 99% accuracy, we will then find the input images that maximize certain features inside the neural network. This will show us what images the neural network *likes to see* the most.\n", "\n", "We will do this by creating another form of optimization for the neural network, and we need several helper functions for doing this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function for getting the names of convolutional layers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for getting the names of all the convolutional layers in the neural network. We could have made this list manually, but for larger neural networks it is easier to do this with a function." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "def get_conv_layer_names():\n", " graph = tf.get_default_graph()\n", " \n", " # Create a list of names for the operations in the graph\n", " # for the Inception model where the operator-type is 'Conv2D'.\n", " names = [op.name for op in graph.get_operations() if op.type=='Conv2D']\n", "\n", " return names" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "conv_names = get_conv_layer_names()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['layer_conv1/Conv2D', 'layer_conv2/Conv2D']" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conv_names" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(conv_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function for finding the input image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function finds the input image that maximizes a given feature in the network. It essentially just performs optimization with gradient ascent. The image is initialized with small random values and is then iteratively updated using the gradient for the given feature with regard to the image." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "def optimize_image(conv_id=None, feature=0,\n", " num_iterations=30, show_progress=True):\n", " \"\"\"\n", " Find an image that maximizes the feature\n", " given by the conv_id and feature number.\n", "\n", " Parameters:\n", " conv_id: Integer identifying the convolutional layer to\n", " maximize. It is an index into conv_names.\n", " If None then use the last fully-connected layer\n", " before the softmax output.\n", " feature: Index into the layer for the feature to maximize.\n", " num_iteration: Number of optimization iterations to perform.\n", " show_progress: Boolean whether to show the progress.\n", " \"\"\"\n", "\n", " # Create the loss-function that must be maximized.\n", " if conv_id is None:\n", " # If we want to maximize a feature on the last layer,\n", " # then we use the fully-connected layer prior to the\n", " # softmax-classifier. The feature no. is the class-number\n", " # and must be an integer between 1 and 1000.\n", " # The loss-function is just the value of that feature.\n", " loss = tf.reduce_mean(logits[:, feature])\n", " else:\n", " # If instead we want to maximize a feature of a\n", " # convolutional layer inside the neural network.\n", "\n", " # Get the name of the convolutional operator.\n", " conv_name = conv_names[conv_id]\n", " \n", " # Get the default TensorFlow graph.\n", " graph = tf.get_default_graph()\n", " \n", " # Get a reference to the tensor that is output by the\n", " # operator. Note that \":0\" is added to the name for this.\n", " tensor = graph.get_tensor_by_name(conv_name + \":0\")\n", "\n", " # The loss-function is the average of all the\n", " # tensor-values for the given feature. This\n", " # ensures that we generate the whole input image.\n", " # You can try and modify this so it only uses\n", " # a part of the tensor.\n", " loss = tf.reduce_mean(tensor[:,:,:,feature])\n", "\n", " # Get the gradient for the loss-function with regard to\n", " # the input image. This creates a mathematical\n", " # function for calculating the gradient.\n", " gradient = tf.gradients(loss, x_image)\n", "\n", " # Generate a random image of the same size as the raw input.\n", " # Each pixel is a small random value between 0.45 and 0.55,\n", " # which is the middle of the valid range between 0 and 1.\n", " image = 0.1 * np.random.uniform(size=img_shape) + 0.45\n", "\n", " # Perform a number of optimization iterations to find\n", " # the image that maximizes the loss-function.\n", " for i in range(num_iterations):\n", " # Reshape the array so it is a 4-rank tensor.\n", " img_reshaped = image[np.newaxis,:,:,np.newaxis]\n", "\n", " # Create a feed-dict for inputting the image to the graph.\n", " feed_dict = {x_image: img_reshaped}\n", "\n", " # Calculate the predicted class-scores,\n", " # as well as the gradient and the loss-value.\n", " pred, grad, loss_value = session.run([y_pred, gradient, loss],\n", " feed_dict=feed_dict)\n", " \n", " # Squeeze the dimensionality for the gradient-array.\n", " grad = np.array(grad).squeeze()\n", "\n", " # The gradient now tells us how much we need to change the\n", " # input image in order to maximize the given feature.\n", "\n", " # Calculate the step-size for updating the image.\n", " # This step-size was found to give fast convergence.\n", " # The addition of 1e-8 is to protect from div-by-zero.\n", " step_size = 1.0 / (grad.std() + 1e-8)\n", "\n", " # Update the image by adding the scaled gradient\n", " # This is called gradient ascent.\n", " image += step_size * grad\n", "\n", " # Ensure all pixel-values in the image are between 0 and 1.\n", " image = np.clip(image, 0.0, 1.0)\n", "\n", " if show_progress:\n", " print(\"Iteration:\", i)\n", "\n", " # Convert the predicted class-scores to a one-dim array.\n", " pred = np.squeeze(pred)\n", "\n", " # The predicted class for the Inception model.\n", " pred_cls = np.argmax(pred)\n", "\n", " # The score (probability) for the predicted class.\n", " cls_score = pred[pred_cls]\n", "\n", " # Print the predicted score etc.\n", " msg = \"Predicted class: {0}, score: {1:>7.2%}\"\n", " print(msg.format(pred_cls, cls_score))\n", "\n", " # Print statistics for the gradient.\n", " msg = \"Gradient min: {0:>9.6f}, max: {1:>9.6f}, stepsize: {2:>9.2f}\"\n", " print(msg.format(grad.min(), grad.max(), step_size))\n", "\n", " # Print the loss-value.\n", " print(\"Loss:\", loss_value)\n", "\n", " # Newline.\n", " print()\n", "\n", " return image.squeeze()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This next function finds the images that maximize the first 10 features of a layer, by calling the above function 10 times." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "def optimize_images(conv_id=None, num_iterations=30):\n", " \"\"\"\n", " Find 10 images that maximize the 10 first features in the layer\n", " given by the conv_id.\n", " \n", " Parameters:\n", " conv_id: Integer identifying the convolutional layer to\n", " maximize. It is an index into conv_names.\n", " If None then use the last layer before the softmax output.\n", " num_iterations: Number of optimization iterations to perform.\n", " \"\"\"\n", "\n", " # Which layer are we using?\n", " if conv_id is None:\n", " print(\"Final fully-connected layer before softmax.\")\n", " else:\n", " print(\"Layer:\", conv_names[conv_id])\n", "\n", " # Initialize the array of images.\n", " images = []\n", "\n", " # For each feature do the following.\n", " for feature in range(0,10):\n", " print(\"Optimizing image for feature no.\", feature)\n", " \n", " # Find the image that maximizes the given feature\n", " # for the network layer identified by conv_id (or None).\n", " image = optimize_image(conv_id=conv_id, feature=feature,\n", " show_progress=False,\n", " num_iterations=num_iterations)\n", "\n", " # Squeeze the dim of the array.\n", " image = image.squeeze()\n", "\n", " # Append to the list of images.\n", " images.append(image)\n", "\n", " # Convert to numpy-array so we can index all dimensions easily.\n", " images = np.array(images)\n", "\n", " # Plot the images.\n", " plot_images10(images=images)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First Convolutional Layer\n", "\n", "These are the input images that maximize the features in the first convolutional layer, so these are the images that it *likes to see*." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Layer: layer_conv1/Conv2D\n", "Optimizing image for feature no. 0\n", "Optimizing image for feature no. 1\n", "Optimizing image for feature no. 2\n", "Optimizing image for feature no. 3\n", "Optimizing image for feature no. 4\n", "Optimizing image for feature no. 5\n", "Optimizing image for feature no. 6\n", "Optimizing image for feature no. 7\n", "Optimizing image for feature no. 8\n", "Optimizing image for feature no. 9\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 10 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "optimize_images(conv_id=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how these are very simple shapes such as lines and angles. Some of these images may be completely white, which suggests that those features of the neural network are perhaps unused, so the number of features could be reduced in this layer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Second Convolutional Layer\n", "\n", "This shows the images that maximize the features or neurons in the second convolutional layer, so these are the input images it *likes to see*. Note how these are more complex lines and patterns compared to the first convolutional layer." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Layer: layer_conv2/Conv2D\n", "Optimizing image for feature no. 0\n", "Optimizing image for feature no. 1\n", "Optimizing image for feature no. 2\n", "Optimizing image for feature no. 3\n", "Optimizing image for feature no. 4\n", "Optimizing image for feature no. 5\n", "Optimizing image for feature no. 6\n", "Optimizing image for feature no. 7\n", "Optimizing image for feature no. 8\n", "Optimizing image for feature no. 9\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 10 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "optimize_images(conv_id=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final output layer\n", "\n", "Now find the image for the 2nd feature of the final output of the neural network. That is, we want to find an image that makes the neural network classify that image as the digit 2. This is the image that the neural network *likes to see the most* for the digit 2." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 0\n", "Predicted class: 8, score: 89.26%\n", "Gradient min: -0.846559, max: 0.558144, stepsize: 5.45\n", "Loss: 0.3713841\n", "\n", "Iteration: 1\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.535254, max: 0.621156, stepsize: 5.34\n", "Loss: 34.539898\n", "\n", "Iteration: 2\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.664117, max: 0.673569, stepsize: 5.53\n", "Loss: 45.18827\n", "\n", "Iteration: 3\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.549961, max: 0.498956, stepsize: 5.67\n", "Loss: 48.934826\n", "\n", "Iteration: 4\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.540532, max: 0.564252, stepsize: 5.45\n", "Loss: 50.952587\n", "\n", "Iteration: 5\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.581356, max: 0.486933, stepsize: 5.69\n", "Loss: 51.000446\n", "\n", "Iteration: 6\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.578246, max: 0.520858, stepsize: 5.55\n", "Loss: 51.367252\n", "\n", "Iteration: 7\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.592440, max: 0.511202, stepsize: 5.60\n", "Loss: 51.47485\n", "\n", "Iteration: 8\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.589151, max: 0.507705, stepsize: 5.53\n", "Loss: 51.796883\n", "\n", "Iteration: 9\n", "Predicted class: 2, score: 100.00%\n", "Gradient min: -0.614109, max: 0.527479, stepsize: 5.59\n", "Loss: 51.947083\n", "\n" ] } ], "source": [ "image = optimize_image(conv_id=None, feature=2,\n", " num_iterations=10, show_progress=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how the predicted class indeed becomes 2 already within the first few iterations so the optimization is working as intended. Also note how the loss-measure is increasing rapidly until it apparently converges. This is because the loss-measure is actually just the value of the feature or neuron that we are trying to maximize. Because this is the logits-layer prior to the softmax, these values can potentially be infinitely high, but they are limited because we limit the image-values between 0 and 1.\n", "\n", "Now plot the image that was found. This is the image that the neural network believes looks most like the digit 2." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOsAAADrCAYAAACICmHVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAJXElEQVR4nO3dS0iUbR/H8XueToopaGmYyqOBQm5aOFASdKJNJNRKioKKoE2ZFC1a5aZF5EKsXQfadYDoQEFJRFZ0VCtCMltUL2ZWRpJDZWnMu3gI3pfH6397O3OP89PvZ/t7rpnr0flxR/+uayLxeNwDkP7+mugNABgbygqIoKyACMoKiKCsgAjKCoiYHuQ/zsrKiufm5jrz3t7ehDc0GVVVVZl5R0fHuNeGbWRkxJlNnx7o4/Mvg4ODZt7X1+fMvn37Zq4tLi4284KCAjMP09DQkDPr7e31BgYGIqNlkSBz1uLi4nhdXZ0z379//5hfayrx+xlHIqP+bsa0NmyfPn1yZol+4G/cuGHmBw8edGaPHz821x4+fNjMrc9x2F68eOHMamtrvc7OzlE/EPwxGBBBWQERlBUQQVkBEZQVEBHo796/f//uPXv2LKy9TFrW3/b6sUYnnud5w8PDZp6ZmWnmfuOTMEccq1evNvOenh5n1tDQYK5duXLluPaUCpWVlc4sIyPDmfFkBURQVkAEZQVEUFZABGUFRFBWQARlBUQEOnUTiUS4CnGSeffunZkXFRWF9t7Pnz838+XLlzuzzs5Oc601o/U8z+vu7jbzLVu2mHlYotGo197ezqkbQBllBURQVkAEZQVEUFZABGUFRCR2PV0S+Y2QTp8+beabNm1K5namDL9bAGfNmuXMzp8/b671u4Hw8uXLZt7c3OzM/Pbt93m6e/eumfuJxWLOLDs7O6HXduHJCoigrIAIygqIoKyACMoKiKCsgAjKCohI6RG5M2fOOLPy8nJzbWFhoZn7Xblpfftdot6/f2/mra2tZs6MeHQbN250ZgMDA+ba69evm3k0GjXztrY2M7fmrKWlpebaL1++mHk8HueIHKCMsgIiKCsggrICIigrIIKyAiIoKyAi0Jy1vLw83tTU5MzXrVtnrv/9+/eY3wv/uHPnjplb13Wqq6mpcWZlZWXm2qNHj5r5xYsXzXz9+vVmbr3/27dvzbVr1qxxZvfu3fO+fv3KnBVQRlkBEZQVEEFZARGUFRBBWQERlBUQEeje4L6+Pu/QoUPOnDlq8i1btszMg8zJRxOJjDrSSwvW56m2ttZcW1lZaeZZWVnj2tMffrNUy+vXr53Zz58/nRlPVkAEZQVEUFZABGUFRFBWQARlBUQEGt1kZGR4FRUVYe0FIRgZGTFzv9HPRI52rKNkVVVV5tqlS5ea+fbt2838wIEDZp6Ily9fOjPrilSerIAIygqIoKyACMoKiKCsgAjKCoigrICIlH7lY6LHuSw7duww82PHjjkzv68PzMvLM/OioiIz9ztOdfbsWWe2efNmc63fHHX69ECj9CnDOormef/8m4KwWD2IRqNee3s7V5ECyigrIIKyAiIoKyCCsgIiKCsggrICIlI6Z7XMmzfPzD9+/GjmM2bMMPPh4eHAe0oHfr8fv/8vv5/LVPXmzRszX7BgQYp28m/xeJw5K6CMsgIiKCsggrICIigrIIKyAiIoKyAibeasfjZs2GDm1plQZWGeAQ7bhw8fzLy6utrMFy1a5MwuXbo0rj398fTpUzO/cuWKmTc0NCT0/hbmrIA4ygqIoKyACMoKiKCsgAjKCoigrICISXNvsLKuri5ntnDhwlDfu6+vz8wLCwud2bVr18y1jY2NZn7r1i0z37VrlzMbGhoy1x4/ftzMHzx4YObl5eVmnp+fb+aJYM4KiKOsgAjKCoigrIAIygqIoKyAiKR+HyCjmfEJezxjaWtrM3NrBBKLxcy1t2/fHtee/qirq3NmFRUVCb2239d4+o2VJgJPVkAEZQVEUFZABGUFRFBWQARlBURQVkBEoCNyJSUl8fr6eme+b9++ZOwJAfgd9dq5c6eZt7S0mHlBQYEzGxwcNNfm5OSYeTrP5SORUU+pJYV1vG5gYMAbHh7miBygjLICIigrIIKyAiIoKyCCsgIiKCsgItB51h8/fpjXZmJ89u7d68yamprMtStWrDDzJ0+emHki50L95qizZ88e92tPZv39/eNax5MVEEFZARGUFRBBWQERlBUQQVkBEZQVEBFozlpaWuqdPHkyrL1MGOurBT3P89auXWvmFy5cMPMTJ04E3tNYtba2mnlmZqaZ+311YiL8fm4IhicrIIKyAiIoKyCCsgIiKCsggrICIigrICLQvcGRSCR9L3qFHL/PXph393Z0dJh5VVVVaO9t3fW8bds2r6uri3uDAWWUFRBBWQERlBUQQVkBEZQVEMHoZgxisZiZZ2dnp2gnmAri8TijG0AZZQVEUFZABGUFRFBWQARlBURQVkBEoKtIJ6sgs+Z0k+je/Y6hWa/vt3bPnj1m7vd1lvh/PFkBEZQVEEFZARGUFRBBWQERlBUQQVkBETLnWfv7+808Pz/fzJcsWeLMrKshxyLMKzP9KM+Iw+T3Oy0tLTXz+fPnJ3E3wXCeFRBHWQERlBUQQVkBEZQVEEFZARGUFRAhc5517ty5Zh7mvHEi56iNjY0T9t7KqqurE1q/detWM8/JyXFmzc3N5trdu3c7s3PnzjkznqyACMoKiKCsgAjKCoigrIAIygqIkBndhKmsrMzMKyoqzPzVq1cJvX9ubq4zy8jISOi1MT6nTp0K7bWPHDnizO7fv+/MeLICIigrIIKyAiIoKyCCsgIiKCsggrICImSuIp2sR+DSHVedBnf16lUzr6mpcWbRaNRrb2/nKlJAGWUFRFBWQARlBURQVkAEZQVEUFZARNqcZ2Wel57CnEHfvHnTzFetWhXae/tJx9k7T1ZABGUFRFBWQARlBURQVkAEZQVEUFZARErPs6brLLWlpcXMu7u7zby+vj6Z28EUF4/HOc8KKKOsgAjKCoigrIAIygqIoKyACMoKiEjpnPXz58/ObM6cOYm8tK+enh5nVlJSYq71O9u4ePFiM3/06JGZA/+LOSsgjrICIigrIIKyAiIoKyCCsgIiknoVaV5enpmHPZ6xTJs2zZnFYjFzbboe7Ut31dXVZv7w4cMU7WRy4MkKiKCsgAjKCoigrIAIygqIoKyACMoKiEjqEbmJnEf++vXLzGfOnJminSAV0vErGZOFI3KAOMoKiKCsgAjKCoigrIAIygqIoKyAiKDnWT97nvcfVziZZ19AivztCgL9owgAE4c/BgMiKCsggrICIigrIIKyAiIoKyCCsgIiKCsggrICIv4LEoI3VDUtKtEAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_image(image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although some of the curves do hint somewhat at the digit 2, it is hard for a human to see why the neural network believes this is the *optimal* image for the digit 2. This can only be understood when the optimal images for the remaining digits are also shown." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final fully-connected layer before softmax.\n", "Optimizing image for feature no. 0\n", "Optimizing image for feature no. 1\n", "Optimizing image for feature no. 2\n", "Optimizing image for feature no. 3\n", "Optimizing image for feature no. 4\n", "Optimizing image for feature no. 5\n", "Optimizing image for feature no. 6\n", "Optimizing image for feature no. 7\n", "Optimizing image for feature no. 8\n", "Optimizing image for feature no. 9\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 10 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "optimize_images(conv_id=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These images may vary each time you run the optimization. Some of the images can be seen to somewhat resemble the hand-written digits. But the other images are often impossible to recognize and it is hard to understand why the neural network thinks these are the *optimal* input images for those digits.\n", "\n", "The reason is perhaps that the neural network tries to recognize all digits simultaneously, and it has found that certain pixels often determine whether the image shows one digit or another. So the neural network has learned to differentiate those pixels that it has found to be important, but not the underlying curves and shapes of the digits, in the same way that a human recognizes the digits.\n", "\n", "Another possibility is that the data-set contains mis-classified digits which may confuse the neural network during training. We have previously seen how some of the digits in the data-set are very hard to read even for humans, and this may cause the neural network to become distorted and trying to recognize strange artifacts in the images.\n", "\n", "Yet another possibility is that the optimization process has stagnated in a local optimum. One way to test this, would be to run the optimization 50 times for the digits that are unclear, and see if some of the resulting images become more clear." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Close TensorFlow Session" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now done using TensorFlow, so we close the session to release its resources." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", "# with the Notebook without having to restart it.\n", "# session.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "This tutorial showed how to find the input images that maximize certain features inside a neural network. These are the images that the neural network *likes to see the most* in order to activate a certain feature or neuron inside the network.\n", "\n", "This was tested on a simple convolutional neural network using the MNIST data-set. The neural network had clearly learned to recognize the general shape of some of the digits, while it was impossible to see how it recognized other digits." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", "\n", "You may want to backup this Notebook before making any changes.\n", "\n", "* Plot the images for all features in each convolutional layer instead of just the first 10 features. How many of them appear to be unused or redundant? What happens if you lower the number of features in that layer and train the network again, does it still perform just as well?\n", "\n", "* Try adding more convolutional layers and find the input images that maximize their features. What do the images show? Do you think it is useful to add more convolutional layers than two?\n", "\n", "* Try adding more fully-connected layers and modify the code so it can find input images that maximize the features of the fully-connected / dense layers as well. Currently the code can only maximize the features of the convolutional layers and the final fully-connected layer.\n", "\n", "* For the input images that are unclear, run the optimization e.g. 50 times for each of those digits, to see if it produces more clear input images. It is possible that the optimization has simply become stuck in a local optimum.\n", "\n", "* Explain to a friend how the program works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## License (MIT)\n", "\n", "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
speed-of-light/pyslider
docs/nb/ground_truth/grouping_pairs.ipynb
1
240685
{ "metadata": { "name": "", "signature": "sha256:79d88803ccd51376dce851fb34a42cacba19bc5387418efc54b9d23a4e1ad702" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from lib.exp.evaluator.base import Evaluator as EVA\n", "from lib.plotter.ground_truth_plotter import GroundTruthPlotter as GTP\n", "roots=['univ_07']*3; names=['chaves', 'coates', 'rozenblit']" ], "language": "python", "metadata": { "run_control": { "breakpoint": false, "read_only": false } }, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "gtp = GTP()\n", "eva = EVA()\n", "keg = eva.pack_gnd(roots, names, ['absp', 'relp', 'seg'])" ], "language": "python", "metadata": { "run_control": { "breakpoint": false, "read_only": false } }, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(18, 6))\n", "for key, item in keg.iteritems():\n", " print key\n", "gtp.segmentize_fig(fig, [ii['seg'] for kk, ii in keg.iteritems()])" ], "language": "python", "metadata": { "run_control": { "breakpoint": false, "read_only": false } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "univ_07_coates\n", "univ_07_chaves\n", "univ_07_rozenblit\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x505e9d0>" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot grouped dataset pairs result\n", "\n", "- `fig`: figure handle\n", "- `keg`: `dict` contained with datasets information, like:\n", "\n", " + `info`: summary info of dataset\n", " + `absp`: (optional) use for plotting absolute pair result\n", " + `relp`: (optional) use for plotting relative pair result\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(18, 6))\n", "fig = gtp.group_pair(fig, keg, 'absp')" ], "language": "python", "metadata": { "run_control": { "breakpoint": false, "read_only": false } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ZefPmud3v1q1b239PTU2lTZs29ucmk4nWrVuTkpJiL/vzn/9Mbm4u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\nc2yr/4LHKYxfzNyp/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8ma8IdW/JzqTVxDQbzI+pmzGZDGjUWvqFjARgd3I8fYJiCXKLYGfSagwmA1q1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AelOCGEEEJ0MVKgEEJ0O27FRUbmYY6f3IJB72zzHI2qYWLv83vkV5taN1qE7hP72Z4GzZwEMyeR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\nOCJicSQMxWXxxam4OF2RRJyvm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fU1bM5Mpy4pgeaYTPz2zvjUVgi0QsOOw76GP+jjzZ1P\nkxNXyGcNH/dYX1+7rNf9PYEOzs25VMUJEREREZEIpQKF9Km+YS8lG9/CGeXr9XkDW4/1oZLUEWxo\nwLN0KZ7FSwhUVJA+/Yt8dONtvOJ2ccPukq7UjMsPc56+LFi++ZRI1zCCfnOuiLBChNFUgdFWhyU2\nDUtSDpakHGoyclgWW40regLjqmqIOetrJJY8w9hx3+gxJ0V/dQ7bmFf4VVZXLWJqxhxWVy/qWu9r\nQsu+nhcRERERkcigAoUcpKqmlJKNb1Jbt4szxl/I+efdwAvPPt9jDoq/P72G2df/qGt9sJM6gi2t\neJcvx7NkCf4dO4gqLibmphtxTJxIszfAg08s4pHrJlL5aMmgXeNQZQSDsL+WYFMFRnNYIaK1GmKS\nsSaahQhr3llYzrgcS0ImFpuDBncN7+95mSZPLTPz52H/9G2yv/h9XDFpB6Vn9NeBc0xMzZjTPQeF\nM/WgBI3+pnKIiIiIiMjQN/T65HfTHBQnkGEYVFRupWTjv2huqWHihLmMGTUdu92cGPLB3z7Ai68+\njyvKSjAIEyZMAUdsV1LHVZfNPeETZAbb2/GuWoVn8RL8mzbhOOssnLNmEnX22Vic3cMu7l9Ygsth\n4/vnf6FfKR6Hs25PLWdFUMJGJ8MwoK3+4EJEcyW44rEm5WAJFSMsSTlYErOw2A8evuINeFi1byEb\n61YyNfMCJmfMomr7W/1O8TiU8FSOzvU0V1a/Ujy6XjfseREREZHe/PmJ2wD45q2PD/KViAwMz9O3\nAOD88pP9PqbuoosBSHtn4WH31SSZclyEx4X6/Qbz513L1ddcQMnGN2loqqNsZ4Btn7dQ19BIVlY2\nyYnxjMzPZceuPdS0B0iPsXHN5Zee0GJEeFHB8HjwfrwGz5LF+NaVYJ8wwSxKFBdjjTn4E/NX1pfy\nxMptPP/1C4hz9jGZRoQ4XJRn5x/rFa2l5NhSidrfgLdhF+1124hp34/RVIE1KtbsDdGjGJGNxXHw\npJUH/vFvGAaf1K5kSfnrFCadzszhVxAflXTC7l9ERERERIYGTZIpx6wzLvSmr3QP1XjtpWeobVjD\nOcWXsGRVLSMmzSCwewl3/OqXAGxZ9xHvvPNPbvnJ/V3HPLXgQYATVqRo+mgN3kQXnsVL8K5ejX3k\nSJyzZhL33e9hTYjv87igYfDEym18c+a4iC9OAKTmTjsoyrNq+UNkjppHYOsichp301LzODkdHnz4\nCSblUeXwMSxzMhsCFZw569s4j2C4RU5cYddwiRZvA+/ufoEGdw2XFX6FwqRxhz+BiIiIiIjIEVAP\nilNIX3Ghf3tqI4Xjz+Wy2+/hxT/+hmu//YOu5w5c73QiIkX9ZWWsf+xpcjasoSkjh51jv8CuUWfg\njuu7KBGuvs3Npn31vHbHXKyWofyj3j+Gz0NT2WI8n7yKxR5FXNt+DIuV9rgE2mPjaI+JpyU6hlLq\niY/PZWfLFkYkjKXOXUl27Ajs1iOvR/qDfva0fk6Hv5306Czmj/w6MY64Abg7ERERERGJFOpBIcfM\nbu/958fusGC3mz8KVlvPH4kD1zsNVKSoEQzi+3gNtY89gbWqktbYVKICfrZm5EFDI4kN1aTlZh7y\nHHsb91PeuB+AmlY3j6/YAsCkvPSImjvCMAyMliqCFZ8QqNhIoHY7TU4L3tRMCir28tlZswmm5EJY\n8cUO5PnbWLnvbSYNm0lJzRKmZc8l2h571NeR4ExmWcW/uLTwyypOiIiIiIjIgFGB4hRRUbkVZ5S3\n9yeDFmJCxYtgwN/zqQPWOx3vSFHD7cb93nu4X3sNXNGk3nAttunT+fffv8X/enfypVA06JGKtEhQ\nw+chWL2FYMWnBCs+ASNAS2oWa5w1BCdNZkrGbIx1f8dz6U9IKnmG7IJLes5JEUq1uGXcj3ij7K/c\ncvqP2FC3grMzZh9VqkXn+e444xeK8BQRERERkQFlHewLkIHl9/tY+fELvL9kAeeeO5cXnu0Zs/n8\nsyXmRJnzLuGtBQ8yoXgGL/yhe+jGhOIZPPk/P+5xzJuPPsBVl809LtcXqKml7bHHafjyV/CtX0/c\nXXeR9IdHcJ1/PkGbHZs18odmHIphGASbK/FvfgfvogfwvnQXgc3vYIlLpWnq1bw4Jps304NMmHgH\nc/KuYv+6v5M97U4SUk4jOxTl6W6vA7qLCVMz5rChdgVXj7yDDXUrmJoxh6UVb+D2tx/RtYVHeIZH\nfB7peURERESGmj8/cVtXkofIycjz9C1dSR79VXfRxV1JHoNFPShOYnX1e3h/6WMkJWZyzfz/JNoV\nT7Qzlb8+9TxRDitGEObPu4l77r6365iX//U2Vncr//ez75CZmUVKUgIXTZ/Csmce6YoUveXa+cc8\nQaZv61Y6Xn0V39p1OOfMIen3D2PLyuqxjz8QxGa1kDxl8lG/zqQhOKSjt14S1uwJ2EbNwjrj27Qa\nbpaUv86e2hXMGD6P8alTsFisVGx7vWuCTABXTBrZ0+7sSvGo2F/GjJx5XY8ue0yP9SON3gw/D9Dj\nfIrwFBERERGR400FipPEgfGhc84vJr/IyrQp1zGq6JzOSUw4c+Jk1nxeQYzTgdNmrnc6f/asAUnm\n6IwJNQIBvCtW0PHKqwQb6om+4grivvtdrLG9z48QMAwM6IoYPRoncs6JPmNA96wgK2sKwYpPCO77\nFKO2FEtqAY0p6STO+AbO1JFYLBa8AQ/L9r1NSc0yJmfM4vaC/yDK5uw6V87oyw96TVdMWtf2zqJB\nePHAZY/pdXt/9LZ/+PlERERERESOJxUoTgK9xYe++o+lRLuuZvTIaV3b3v9wMU/9459c8W/3dG07\nEZGhLSs/or25Fvfr/8Salkb0VVcRNe0cLDbbIY8LBA0iKcklPAbU6YjHs2c1bRv+QSoOfJ8tCfWS\nmI11xrexREWTGBpCcW5iFmXNm1m89zUcNic3j72bYTE5g307IiIiIietb976+GBfgsiAcn75ySM+\nJu2dhQNwJUdmKA/wV8xoP/UVH/rUkxv56V/e6Fr/28P/y1XfvO+g/QYiMtQwDPzbPqf+n28QXPwh\nJdkjWTRyIruSD53AES5oGLR5fLx/18E9B4YSIxjAaN6H0bAbX/VWPHtWY/X78dosbEuJY09iDI0u\nR4+0ja5jjSAd/jaSXcNIcqZxyYibNAmliIiIiIgMaYoZlT71FR/qirJy3sjuIQ7/inX1ut/xjAwN\ntrTgef8Dml56CWN/G+VJwygMBojOy2Wet47o7OEUzDznkOfYtK+BTfsaAHhxXSkLlm8GhkZMqBH0\nYzTtw2jYRbB+N0bDboymciwxyVhS8nHHJ7NsWDQXVrTSMvtbjEsewbjDnLPF28SzW3/HVad9Q8UJ\nERERERE5ZalAcRLw+3vvaRIMQpyzu4ltlt73O9bIUCMYxLdhA+6F7+Bbu5aoqVNIve8+HGdM4OfP\nLuXKbau55Of3Hv5EIdmJsVw4NheAeKdj0GJCjYAfo6kco2E3wYbdGPW7MJoqsMSlYUnJw5JSgK1g\nCpbkPCxR0fgCXl795FcU+5x4Lr0TT8kzpIZNatkbt7+dLQ2LFOMpIiIiIiKnPBUoTgJzzp/Gay8t\nZv7V3RNemvGhN/XY7+p5l/DUgge55PbuOSjefPQBbrl2/lG9bqC2Fs+77+J+510ssbG45l5M3J3f\nwRofD0BDm5ud9a3EOR1Hdf4TyQj4MBr3msWI+t0YDbswmiuxxKVjSc3HkpKPbUSxWYxw9N4T5YPS\nZ5hU2UjBzH8nOjadqFAMaHYfRYrwGM/OhIzwdRERERERkVOJ5qCIQOGJHcFgkClTcuhoz2TFqmU4\nouz4fQHOKZ7Nn37/h4OOff/Dxbz8r7e7IkOvumzuISfI7Ezg6GT4fHhXr8a98B38W7bgnDUL18UX\nYRs5sisppNMfFm+iomk/N8V6jzqJY92e2sMO6+gtPWNb1UqcjZUUjL2qa5vb307F/jIK40Z1FyMa\nduGu2YqjrQlrQiaWlHysKfn4kjLZZ/dRmDax19csbdpETlxhVyFha8N6tm76G6eNvIJx2bO6X7O9\nrisG9HDnCL9GJWWIiIiIDJw/P3EboMky5eTlefoW4Mgmy6y76GKgf5NlDtQcFCpQRJjOxI7rbuxO\n7HjxubVYnPnc/t9/6tr21oIH+do1VxxzOseyX/2Wc396N/49e/G8sxD3+x9gGz4c10UX4Tx3OhZX\n770JAK5/7F2+XDyaS8fnH9M1HI67va5HTwV3ex0VKx+mLL+Ic3PnE9VSi7duO9XlK8lwG1j212FJ\nyMSSmo81pQBfYgbL2zcyPW8+LnvMQT0ben3NsH08ATd//ezX5CacxtyCG9X7QURERERETmoqUAjQ\nd2LH2+83c8O9D/XYdqzpHJ79bWz6wc/IthnYqivxTp+JZ+ZsglnZhz3WMAxue2Yx/7zjYlL6mJzz\neHK317FvxcO4ciZi+XwJ0QnDcbTUYmmrJxCfTrXTQmLWRKypBQQShoGt57ATT8BNSfUSxqdNZVPd\naiZlzMRpO/R1dx7T6KnFZrFxzahvqTghIiIiIiInPaV4CNB3YoeFwEHbjiadw/D72fHY37AsW0ps\nfQ35RpCPskZSNew0drQ42FtSAVT0eXyH10+Hzw+ALxDk5fVlwMAlcBgBP959G2n6fCFpNRU4q8op\nTYrlc2sdDdkuGqLyqPPVkuIchs26Axp3QGPv5woE/by0vYQU5zDe2/1Cv14/EPTT4Knh3yb8XMUJ\nERERERGRY6ACRYTpK7HDwHbQtv6mcxiGgX/zFjwffoBn6TISMzJ5cfjpRN98B6NKVnLZT+8+qmtd\nsHzzgCRwGAE/warP8O5cSWDvBhocBg0pGcSnpJE+9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"prompt_number": 19, "text": [ "<matplotlib.figure.Figure at 0x5053a90>" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(18, 6))\n", "fig = gtp.group_pair(fig, keg, 'relp')" ], "language": "python", "metadata": { "run_control": { "breakpoint": false, "read_only": false } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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eHR3N/v37v1WdWVlZxMfHt9nu5OTEoUOHGDx48Leq90LRX58lERER6T42u43M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agpl-3.0
y2ee201/Deep-Learning-Nanodegree
sentiment_network/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Lesson 5).ipynb
2
419025
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- **Twitter**: @iamtrask\n", "- **Blog**: http://iamtrask.github.io" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What You Should Already Know\n", "\n", "- neural networks, forward and back-propagation\n", "- stochastic gradient descent\n", "- mean squared error\n", "- and train/test splits\n", "\n", "### Where to Get Help if You Need it\n", "- Re-watch previous Udacity Lectures\n", "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (40% Off: **traskud17**)\n", "- Shoot me a tweet @iamtrask\n", "\n", "\n", "### Tutorial Outline:\n", "\n", "- Intro: The Importance of \"Framing a Problem\"\n", "\n", "\n", "- Curate a Dataset\n", "- Developing a \"Predictive Theory\"\n", "- **PROJECT 1**: Quick Theory Validation\n", "\n", "\n", "- Transforming Text to Numbers\n", "- **PROJECT 2**: Creating the Input/Output Data\n", "\n", "\n", "- Putting it all together in a Neural Network\n", "- **PROJECT 3**: Building our Neural Network\n", "\n", "\n", "- Understanding Neural Noise\n", "- **PROJECT 4**: Making Learning Faster by Reducing Noise\n", "\n", "\n", "- Analyzing Inefficiencies in our Network\n", "- **PROJECT 5**: Making our Network Train and Run Faster\n", "\n", "\n", "- Further Noise Reduction\n", "- **PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary\n", "\n", "\n", "- Analysis: What's going on in the weights?" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" } }, "source": [ "# Lesson: Curate a Dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbpresent": { "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" } }, "outputs": [], "source": [ "def pretty_print_review_and_label(i):\n", " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", "\n", "g = open('reviews.txt','r') # What we know!\n", "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", "g.close()\n", "\n", "g = open('labels.txt','r') # What we WANT to know!\n", "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", "g.close()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "25000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(reviews)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "nbpresent": { "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" } }, "outputs": [ { "data": { "text/plain": [ "'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn t '" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews[0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "nbpresent": { "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" } }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lesson: Develop a Predictive Theory" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "nbpresent": { "id": "e67a709f-234f-4493-bae6-4fb192141ee0" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels.txt \t : \t reviews.txt\n", "\n", "NEGATIVE\t:\tthis movie is terrible but it has some good effects . ...\n", "POSITIVE\t:\tadrian pasdar is excellent is this film . he makes a fascinating woman . ...\n", "NEGATIVE\t:\tcomment this movie is impossible . is terrible very improbable bad interpretat...\n", "POSITIVE\t:\texcellent episode movie ala pulp fiction . days suicides . it doesnt get more...\n", "NEGATIVE\t:\tif you haven t seen this it s terrible . it is pure trash . i saw this about ...\n", "POSITIVE\t:\tthis schiffer guy is a real genius the movie is of excellent quality and both e...\n" ] } ], "source": [ "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", "pretty_print_review_and_label(2137)\n", "pretty_print_review_and_label(12816)\n", "pretty_print_review_and_label(6267)\n", "pretty_print_review_and_label(21934)\n", "pretty_print_review_and_label(5297)\n", "pretty_print_review_and_label(4998)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 1: Quick Theory Validation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from collections import Counter\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "positive_counts = Counter()\n", "negative_counts = Counter()\n", "total_counts = Counter()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(len(reviews)):\n", " if(labels[i] == 'POSITIVE'):\n", " for word in reviews[i].split(\" \"):\n", " positive_counts[word] += 1\n", " total_counts[word] += 1\n", " else:\n", " for word in reviews[i].split(\" \"):\n", " negative_counts[word] += 1\n", " total_counts[word] += 1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('', 550468),\n", " ('the', 173324),\n", " ('.', 159654),\n", " ('and', 89722),\n", " ('a', 83688),\n", " ('of', 76855),\n", " ('to', 66746),\n", " ('is', 57245),\n", " ('in', 50215),\n", " ('br', 49235),\n", " ('it', 48025),\n", " ('i', 40743),\n", " ('that', 35630),\n", " ('this', 35080),\n", " ('s', 33815),\n", " ('as', 26308),\n", " ('with', 23247),\n", " ('for', 22416),\n", " ('was', 21917),\n", " ('film', 20937),\n", " ('but', 20822),\n", " ('movie', 19074),\n", " ('his', 17227),\n", " ('on', 17008),\n", " ('you', 16681),\n", " ('he', 16282),\n", " ('are', 14807),\n", " ('not', 14272),\n", " ('t', 13720),\n", " ('one', 13655),\n", " ('have', 12587),\n", " ('be', 12416),\n", " ('by', 11997),\n", " ('all', 11942),\n", " ('who', 11464),\n", " ('an', 11294),\n", " ('at', 11234),\n", " ('from', 10767),\n", " ('her', 10474),\n", " ('they', 9895),\n", " 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('especially', 1572),\n", " ('played', 1571),\n", " ('almost', 1566),\n", " ('want', 1562),\n", " ('yet', 1556),\n", " ('give', 1553),\n", " ('pretty', 1549),\n", " ('last', 1543),\n", " ('since', 1519),\n", " ('different', 1504),\n", " ('although', 1501),\n", " ('gets', 1490),\n", " ('true', 1487),\n", " ('interesting', 1481),\n", " ('job', 1470),\n", " ('enough', 1455),\n", " ('our', 1454),\n", " ('shows', 1447),\n", " ('horror', 1441),\n", " ('woman', 1439),\n", " ('tv', 1400),\n", " ('probably', 1398),\n", " ('father', 1395),\n", " ('original', 1393),\n", " ('girl', 1390),\n", " ('point', 1379),\n", " ('plays', 1378),\n", " ('wonderful', 1372),\n", " ('far', 1358),\n", " ('course', 1358),\n", " ('john', 1350),\n", " ('rather', 1340),\n", " ('isn', 1328),\n", " ('ll', 1326),\n", " ('later', 1324),\n", " ('dvd', 1324),\n", " ('war', 1310),\n", " ('whole', 1310),\n", " ('d', 1307),\n", " ('away', 1306),\n", " ('found', 1306),\n", " ('screen', 1305),\n", " ('nothing', 1300),\n", " 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937),\n", " ('today', 936),\n", " ('live', 934),\n", " ('less', 934),\n", " ('moments', 933),\n", " ('others', 929),\n", " ('brilliant', 926),\n", " ('shot', 925),\n", " ('liked', 923),\n", " ('become', 916),\n", " ('won', 915),\n", " ('used', 910),\n", " ('style', 907),\n", " ('mother', 895),\n", " ('lives', 894),\n", " ('came', 893),\n", " ('stars', 890),\n", " ('cinema', 889),\n", " ('looks', 885),\n", " ('perhaps', 884),\n", " ('read', 882),\n", " ('enjoyed', 879),\n", " ('boy', 875),\n", " ('drama', 873),\n", " ('highly', 871),\n", " ('given', 870),\n", " ('playing', 867),\n", " ('use', 864),\n", " ('next', 859),\n", " ('women', 858),\n", " ('fine', 857),\n", " ('effects', 856),\n", " ('kids', 854),\n", " ('entertaining', 853),\n", " ('need', 852),\n", " ('line', 850),\n", " ('works', 848),\n", " ('someone', 847),\n", " ('mr', 836),\n", " ('simply', 835),\n", " ('children', 833),\n", " ('picture', 833),\n", " ('face', 831),\n", " ('friend', 831),\n", " ('keep', 831),\n", " 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('idea', 731),\n", " ('late', 730),\n", " ('becomes', 729),\n", " ('despite', 729),\n", " ('able', 726),\n", " ('case', 726),\n", " ('money', 723),\n", " ('child', 721),\n", " ('completely', 721),\n", " ('side', 719),\n", " ('camera', 716),\n", " ('getting', 714),\n", " ('instead', 712),\n", " ('soon', 702),\n", " ('under', 700),\n", " ('viewer', 699),\n", " ('age', 697),\n", " ('days', 696),\n", " ('stories', 696),\n", " ('felt', 694),\n", " ('simple', 694),\n", " ('roles', 693),\n", " ('video', 688),\n", " ('name', 683),\n", " ('either', 683),\n", " ('doing', 677),\n", " ('turns', 674),\n", " ('wants', 671),\n", " ('close', 671),\n", " ('title', 669),\n", " ('wrong', 668),\n", " ('went', 666),\n", " ('james', 665),\n", " ('evil', 659),\n", " ('budget', 657),\n", " ('episodes', 657),\n", " ('relationship', 655),\n", " ('piece', 653),\n", " ('fantastic', 653),\n", " ('david', 651),\n", " ('turn', 648),\n", " ('murder', 646),\n", " ('parts', 645),\n", " ('brother', 644),\n", " ('head', 643),\n", " ('absolutely', 643),\n", " ('experience', 642),\n", " ('eyes', 641),\n", " ('sex', 638),\n", " ('direction', 637),\n", " ('called', 637),\n", " ('directed', 636),\n", " ('lines', 634),\n", " ('behind', 633),\n", " ('sort', 632),\n", " ('actress', 631),\n", " ('lead', 630),\n", " ('oscar', 628),\n", " ('example', 627),\n", " ('including', 627),\n", " ('known', 625),\n", " ('musical', 625),\n", " ('chance', 621),\n", " ('score', 620),\n", " ('feeling', 619),\n", " ('already', 619),\n", " ('hit', 619),\n", " ('voice', 615),\n", " ('moment', 612),\n", " ('living', 612),\n", " ('low', 610),\n", " ('supporting', 610),\n", " ('ago', 609),\n", " ('themselves', 608),\n", " ('hilarious', 605),\n", " ('reality', 605),\n", " ('jack', 604),\n", " ('told', 603),\n", " ('hand', 601),\n", " ('moving', 600),\n", " ('dialogue', 600),\n", " ('quality', 600),\n", " ('song', 599),\n", " ('happy', 599),\n", " ('paul', 598),\n", " ('matter', 598),\n", " ('light', 594),\n", " ('future', 593),\n", " ('entire', 592),\n", " ('finds', 591),\n", " ('gave', 589),\n", " ('laugh', 587),\n", " ('released', 586),\n", " ('expect', 584),\n", " ('fight', 581),\n", " ('particularly', 580),\n", " ('cinematography', 579),\n", " ('police', 579),\n", " ('whose', 578),\n", " ('type', 578),\n", " ('sound', 578),\n", " ('enjoyable', 573),\n", " ('view', 573),\n", " ('husband', 572),\n", " ('romantic', 572),\n", " ('number', 572),\n", " ('daughter', 572),\n", " ('documentary', 571),\n", " ('self', 570),\n", " ('modern', 569),\n", " ('robert', 569),\n", " ('took', 569),\n", " ('superb', 569),\n", " ('mean', 566),\n", " ('shown', 563),\n", " ('coming', 561),\n", " ('important', 560),\n", " ('king', 559),\n", " ('leave', 559),\n", " ('change', 558),\n", " ('wanted', 555),\n", " ('somewhat', 555),\n", " ('tells', 554),\n", " ('run', 552),\n", " ('events', 552),\n", " ('country', 552),\n", " ('career', 552),\n", " ('heard', 550),\n", " ('season', 550),\n", " ('girls', 549),\n", " ('greatest', 549),\n", " 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('complex', 311),\n", " ('studio', 310),\n", " ('successful', 310),\n", " ('quickly', 310),\n", " ('male', 309),\n", " ('plus', 309),\n", " ('co', 307),\n", " ('minute', 306),\n", " ('images', 306),\n", " ('casting', 306),\n", " ('exciting', 306),\n", " ('following', 306),\n", " ('members', 305),\n", " ('german', 305),\n", " ('e', 305),\n", " ('reasons', 305),\n", " ('follows', 305),\n", " ('themes', 305),\n", " ('touch', 304),\n", " ('genius', 304),\n", " ('free', 304),\n", " ('edge', 304),\n", " ('cute', 304),\n", " ('outside', 303),\n", " ('ok', 302),\n", " ('admit', 302),\n", " ('younger', 302),\n", " ('reviews', 302),\n", " ('odd', 301),\n", " ('fighting', 301),\n", " ('master', 301),\n", " ('break', 300),\n", " ('thanks', 300),\n", " ('recent', 300),\n", " ('comment', 300),\n", " ('apart', 299),\n", " ('lovely', 298),\n", " ('begin', 298),\n", " ('emotions', 298),\n", " ('doctor', 297),\n", " ('italian', 297),\n", " ('party', 297),\n", " ('la', 296),\n", " ('missed', 296),\n", " ...]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "positive_counts.most_common()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pos_neg_ratios = Counter()\n", "\n", "for term,cnt in list(total_counts.most_common()):\n", " if(cnt > 100):\n", " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", " pos_neg_ratios[term] = pos_neg_ratio\n", "\n", "for word,ratio in pos_neg_ratios.most_common():\n", " if(ratio > 1):\n", " pos_neg_ratios[word] = np.log(ratio)\n", " else:\n", " pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('edie', 4.6913478822291435),\n", " ('paulie', 4.0775374439057197),\n", " ('felix', 3.1527360223636558),\n", " ('polanski', 2.8233610476132043),\n", " ('matthau', 2.8067217286092401),\n", " ('victoria', 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" ('morality', 0.58525821854876026),\n", " ('growing', 0.58466653756587539),\n", " ('experiences', 0.58314628534561685),\n", " ('stood', 0.58314628534561685),\n", " ('touch', 0.58122926435596001),\n", " ('lives', 0.5810976767513224),\n", " ('kubrick', 0.58066919713325493),\n", " ('timing', 0.58047401805583243),\n", " ('struggles', 0.57981849525294216),\n", " ('expressions', 0.57981849525294216),\n", " ('authentic', 0.57848427223980559),\n", " ('helen', 0.57763429343810091),\n", " ('pre', 0.57700753064729182),\n", " ('quirky', 0.5753641449035618),\n", " ('young', 0.57531672344534313),\n", " ('inner', 0.57454143815209846),\n", " ('mexico', 0.57443087372056334),\n", " ('clint', 0.57380042292737909),\n", " ('sisters', 0.57286101468544337),\n", " ('realism', 0.57226528899949558),\n", " ('personalities', 0.5720692490067093),\n", " ('french', 0.5720692490067093),\n", " ('surprises', 0.57113222999698177),\n", " ('adventures', 0.57113222999698177),\n", " ('overcome', 0.5697681593994407),\n", " 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0.53329847961804933),\n", " ('explore', 0.53329847961804933),\n", " ('series', 0.5325809226575603),\n", " ('fellow', 0.5323318289869543),\n", " ('loves', 0.53062825106217038),\n", " ('olivier', 0.53062825106217038),\n", " ('revolution', 0.53062825106217038),\n", " ('roman', 0.53062825106217038),\n", " ('century', 0.53002783074992665),\n", " ('musical', 0.52966871156747064),\n", " ('heroic', 0.52925932545482868),\n", " ('ironically', 0.52806743020049673),\n", " ('approach', 0.52806743020049673),\n", " ('temple', 0.52806743020049673),\n", " ('moves', 0.5279372642387119),\n", " ('gift', 0.52702030968597136),\n", " ('julie', 0.52609309589677911),\n", " ('tells', 0.52415107836314001),\n", " ('radio', 0.52394671172868779),\n", " ('uncle', 0.52354439617376536),\n", " ('union', 0.52324814376454787),\n", " ('deep', 0.52309571635780505),\n", " ('reminds', 0.52157841554225237),\n", " ('famous', 0.52118841080153722),\n", " ('jazz', 0.52053443789295151),\n", " ('dennis', 0.51987545928590861),\n", " 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" ('viewing', 0.50302139827440906),\n", " ('shining', 0.50262885656181222),\n", " ('investigation', 0.50262885656181222),\n", " ('duo', 0.5020919437972361),\n", " ('cameron', 0.5020919437972361),\n", " ('finds', 0.50128303100539795),\n", " ('contemporary', 0.50077528791248915),\n", " ('genuine', 0.50046283673044401),\n", " ('frightening', 0.49995595152908684),\n", " ('plays', 0.49975983848890226),\n", " ('age', 0.49941323171424595),\n", " ('position', 0.49899116611898781),\n", " ('continues', 0.49863035067217237),\n", " ('roles', 0.49839716550752178),\n", " ('james', 0.49837216269470402),\n", " ('individuals', 0.49824684155913052),\n", " ('brought', 0.49783842823917956),\n", " ('hilarious', 0.49714551986191058),\n", " ('brutal', 0.49681488669639234),\n", " ('appropriate', 0.49643688631389105),\n", " ('dance', 0.49581998314812048),\n", " ('league', 0.49578774640145024),\n", " ('helping', 0.49578774640145024),\n", " ('answers', 0.49578774640145024),\n", " ('stunts', 0.49561620510246196),\n", " ('traveling', 0.49532143723002542),\n", " ('thoroughly', 0.49414593456733524),\n", " ('depicted', 0.49317068852726992),\n", " ('honor', 0.49247648509779424),\n", " ('combination', 0.49247648509779424),\n", " ('differences', 0.49247648509779424),\n", " ('fully', 0.49213349075383811),\n", " ('tracy', 0.49159426183810306),\n", " ('battles', 0.49140753790888908),\n", " ('possibility', 0.49112055268665822),\n", " ('romance', 0.4901589869574316),\n", " ('initially', 0.49002249613622745),\n", " ('happy', 0.4898997500608791),\n", " ('crime', 0.48977221456815834),\n", " ('singing', 0.4893852925281213),\n", " ('especially', 0.48901267837860624),\n", " ('shakespeare', 0.48754793889664511),\n", " ('hugh', 0.48729512635579658),\n", " ('detail', 0.48609484250827351),\n", " ('guide', 0.48550781578170082),\n", " ('companion', 0.48550781578170082),\n", " ('julia', 0.48550781578170082),\n", " ('san', 0.48550781578170082),\n", " ('desperation', 0.48550781578170082),\n", " ('strongly', 0.48460242866688824),\n", " ('necessary', 0.48302334245403883),\n", " ('humanity', 0.48265474679929443),\n", " ('drama', 0.48221998493060503),\n", " ('warming', 0.48183808689273838),\n", " ('intrigue', 0.48183808689273838),\n", " ('nonetheless', 0.48183808689273838),\n", " ('cuba', 0.48183808689273838),\n", " ('planned', 0.47957308026188628),\n", " ('pictures', 0.47929937011921681),\n", " ('broadcast', 0.47849024312305422),\n", " ('nine', 0.47803580094299974),\n", " ('settings', 0.47743860773325364),\n", " ('history', 0.47732966933780852),\n", " ('ordinary', 0.47725880012690741),\n", " ('trade', 0.47692407209030935),\n", " ('primary', 0.47608267532211779),\n", " ('official', 0.47608267532211779),\n", " ('episode', 0.47529620261150429),\n", " ('role', 0.47520268270188676),\n", " ('spirit', 0.47477690799839323),\n", " ('grey', 0.47409361449726067),\n", " ('ways', 0.47323464982718205),\n", " ('cup', 0.47260441094579297),\n", " ('piano', 0.47260441094579297),\n", " ('familiar', 0.47241617565111949),\n", " ('sinister', 0.47198579044972683),\n", " ('reveal', 0.47171449364936496),\n", " ('max', 0.47150852042515579),\n", " ('dated', 0.47121648567094482),\n", " ('discovery', 0.47000362924573563),\n", " ('vicious', 0.47000362924573563),\n", " ('losing', 0.47000362924573563),\n", " ('genuinely', 0.46871413841586385),\n", " ('hatred', 0.46734051182625186),\n", " ('mistaken', 0.46702300110759781),\n", " ('dream', 0.46608972992459924),\n", " ('challenge', 0.46608972992459924),\n", " ('crisis', 0.46575733836428446),\n", " ('photographed', 0.46488852857896512),\n", " ('machines', 0.46430560813109778),\n", " ('critics', 0.46430560813109778),\n", " ('bird', 0.46430560813109778),\n", " ('born', 0.46411383518967209),\n", " ('detective', 0.4636633473511525),\n", " ('higher', 0.46328467899699055),\n", " ('remains', 0.46262352194811296),\n", " ('inevitable', 0.46262352194811296),\n", " ('soviet', 0.4618180446592961),\n", " ('ryan', 0.46134556650262099),\n", " ('african', 0.46112595521371813),\n", " ('smaller', 0.46081520319132935),\n", " ('techniques', 0.46052488529119184),\n", " ('information', 0.46034171833399862),\n", " ('deserved', 0.45999798712841444),\n", " ('cynical', 0.45953232937844013),\n", " ('lynch', 0.45953232937844013),\n", " ('francisco', 0.45953232937844013),\n", " ('tour', 0.45953232937844013),\n", " ('spielberg', 0.45953232937844013),\n", " ('struggle', 0.45911782160048453),\n", " ('language', 0.45902121257712653),\n", " ('visual', 0.45823514408822852),\n", " ('warner', 0.45724137763188427),\n", " ('social', 0.45720078250735313),\n", " ('reality', 0.45719346885019546),\n", " ('hidden', 0.45675840249571492),\n", " ('breaking', 0.45601738727099561),\n", " ('sometimes', 0.45563021171182794),\n", " ('modern', 0.45500247579345005),\n", " ('surfing', 0.45425527227759638),\n", " ('popular', 0.45410691533051023),\n", " ('surprised', 0.4534409399850382),\n", " ('follows', 0.45245361754408348),\n", " ('keeps', 0.45234869400701483),\n", " ('john', 0.4520909494482197),\n", " ('defeat', 0.45198512374305722),\n", " ('mixed', 0.45198512374305722),\n", " ('justice', 0.45142724367280018),\n", " ('treasure', 0.45083371313801535),\n", " ('presents', 0.44973793178615257),\n", " ('years', 0.44919197032104968),\n", " ('chief', 0.44895022004790319),\n", " ('shadows', 0.44802472252696035),\n", " ('closely', 0.44701411102103689),\n", " ('segments', 0.44701411102103689),\n", " ('lose', 0.44658335503763702),\n", " ('caine', 0.44628710262841953),\n", " ('caught', 0.44610275383999071),\n", " ('hamlet', 0.44558510189758965),\n", " ('chinese', 0.44507424620321018),\n", " ('welcome', 0.44438052435783792),\n", " ('birth', 0.44368632092836219),\n", " ('represents', 0.44320543609101143),\n", " ('puts', 0.44279106572085081),\n", " ('fame', 0.44183275227903923),\n", " ('closer', 0.44183275227903923),\n", " ('visuals', 0.44183275227903923),\n", " ('web', 0.44183275227903923),\n", " ('criminal', 0.4412745608048752),\n", " ('minor', 0.4409224199448939),\n", " ('jon', 0.44086703515908027),\n", " ('liked', 0.44074991514020723),\n", " ('restaurant', 0.44031183943833246),\n", " ('flaws', 0.43983275161237217),\n", " ('de', 0.43983275161237217),\n", " ('searching', 0.4393666597838457),\n", " ('rap', 0.43891304217570443),\n", " ('light', 0.43884433018199892),\n", " ('elizabeth', 0.43872232986464677),\n", " ('marry', 0.43861731542506488),\n", " ('oz', 0.43825493093115531),\n", " ('controversial', 0.43825493093115531),\n", " ('learned', 0.43825493093115531),\n", " ('slowly', 0.43785660389939979),\n", " ('bridge', 0.43721380642274466),\n", " ('thrilling', 0.43721380642274466),\n", " ('wayne', 0.43721380642274466),\n", " ('comedic', 0.43721380642274466),\n", " ('married', 0.43658501682196887),\n", " ('nazi', 0.4361020775700542),\n", " ('murder', 0.4353180712578455),\n", " ('physical', 0.4353180712578455),\n", " ('johnny', 0.43483971678806865),\n", " ('michelle', 0.43445264498141672),\n", " ('wallace', 0.43403848055222038),\n", " ('silent', 0.43395706390247063),\n", " ('comedies', 0.43395706390247063),\n", " ('played', 0.43387244114515305),\n", " ('international', 0.43363598507486073),\n", " ('vision', 0.43286408229627887),\n", " ('intelligent', 0.43196704885367099),\n", " ('shop', 0.43078291609245434),\n", " ('also', 0.43036720209769169),\n", " ('levels', 0.4302451371066513),\n", " ('miss', 0.43006426712153217),\n", " ('ocean', 0.4295626596872249),\n", " ...]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('boll', -4.0778152602708904),\n", " ('uwe', -3.9218753018711578),\n", " ('seagal', -3.3202501058581921),\n", " ('unwatchable', -3.0269848170580955),\n", " ('stinker', -2.9876839403711624),\n", " ('mst', -2.7753833211707968),\n", " ('incoherent', -2.7641396677532537),\n", " ('unfunny', -2.5545257844967644),\n", " ('waste', -2.4907515123361046),\n", " ('blah', -2.4475792789485005),\n", " ('horrid', -2.3715779644809971),\n", " ('pointless', -2.3451073877136341),\n", " ('atrocious', -2.3187369339642556),\n", " ('redeeming', -2.2667790015910296),\n", " ('prom', -2.2601040980178784),\n", " ('drivel', -2.2476029585766928),\n", " ('lousy', -2.2118080125207054),\n", " ('worst', -2.1930856334332267),\n", " ('laughable', -2.172468615469592),\n", " ('awful', -2.1385076866397488),\n", " ('poorly', -2.1326133844207011),\n", " ('wasting', -2.1178155545614512),\n", " ('remotely', -2.111046881095167),\n", " ('existent', -2.0024805005437076),\n", " ('boredom', -1.9241486572738005),\n", " ('miserably', -1.9216610938019989),\n", " ('sucks', -1.9166645809588516),\n", " ('uninspired', -1.9131499212248517),\n", " ('lame', -1.9117232884159072),\n", " ('insult', -1.9085323769376259)]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Transforming Text into Numbers" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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id8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "review = \"This was a horrible, terrible movie.\"\n", "\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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4nb799ttmyZIlLeslHQN9WpN91nx5boSslh0hMlL33XffbX19Jk2aJKcq/U1f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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review = \"The movie was excellent\"\n", "\n", "Image(filename='sentiment_network_pos.png')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Project 2: Creating the Input/Output Data" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "74074\n" ] } ], "source": [ "vocab = set(total_counts.keys())\n", "vocab_size = len(vocab)\n", "print(vocab_size)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['',\n", " 'inhabitants',\n", " 'goku',\n", " 'stunts',\n", " 'catepillar',\n", " 'kristensen',\n", " 'senegal',\n", " 'goddess',\n", " 'distroy',\n", " 'unexplainably',\n", " 'concoctions',\n", " 'petite',\n", " 'scribe',\n", " 'stevson',\n", " 'sctv',\n", " 'soundscape',\n", " 'rana',\n", " 'metamorphose',\n", " 'immortalizer',\n", " 'henstridge',\n", " 'planning',\n", " 'akiva',\n", " 'plod',\n", " 'eko',\n", " 'orderly',\n", " 'zeleznice',\n", " 'verbose',\n", " 'amplify',\n", " 'resonation',\n", " 'critize',\n", " 'jefferies',\n", " 'mountainbillies',\n", " 'steinbichler',\n", " 'vowel',\n", " 'rafe',\n", " 'bonbons',\n", " 'tulipe',\n", " 'clot',\n", " 'distended',\n", " 'his',\n", " 'impatiently',\n", " 'unfortuntly',\n", " 'lung',\n", " 'scapegoats',\n", " 'muzzle',\n", " 'pscychosexual',\n", " 'outbid',\n", " 'obit',\n", " 'sideshows',\n", " 'jugde',\n", " 'particolare',\n", " 'kevloun',\n", " 'masterful',\n", " 'quartier',\n", " 'unravelling',\n", " 'necessarily',\n", " 'antiques',\n", " 'strutts',\n", " 'tilts',\n", " 'disconcert',\n", " 'dossiers',\n", " 'sorriest',\n", " 'blart',\n", " 'iberia',\n", " 'situations',\n", " 'frmann',\n", " 'daniell',\n", " 'rays',\n", " 'pried',\n", " 'khoobsurat',\n", " 'leavitt',\n", " 'caiano',\n", " 'sagan',\n", " 'attractiveness',\n", " 'kitaparaporn',\n", " 'hamilton',\n", " 'massages',\n", " 'reasonably',\n", " 'horgan',\n", " 'chemist',\n", " 'audrey',\n", " 'jana',\n", " 'dutch',\n", " 'override',\n", " 'spasms',\n", " 'resumed',\n", " 'stinson',\n", " 'widows',\n", " 'stonewall',\n", " 'palatial',\n", " 'neuman',\n", " 'abandon',\n", " 'anglophile',\n", " 'marathon',\n", " 'chevette',\n", " 'unscary',\n", " 'eponymously',\n", " 'spoilerific',\n", " 'fleashens',\n", " 'brigand',\n", " 'politeness',\n", " 'clued',\n", " 'dermatonecrotic',\n", " 'grady',\n", " 'mulligan',\n", " 'ol',\n", " 'bertolucci',\n", " 'incubation',\n", " 'oldboy',\n", " 'snden',\n", " 'plaintiffs',\n", " 'fk',\n", " 'deply',\n", " 'franchot',\n", " 'cyhper',\n", " 'glorifying',\n", " 'mazovia',\n", " 'elizabeth',\n", " 'palestine',\n", " 'robby',\n", " 'wongo',\n", " 'moshing',\n", " 'eeeee',\n", " 'doltish',\n", " 'bree',\n", " 'postponed',\n", " 'gunslinger',\n", " 'debacles',\n", " 'kamm',\n", " 'herman',\n", " 'rapture',\n", " 'rolando',\n", " 'tetsuothe',\n", " 'premises',\n", " 'bruck',\n", " 'loosely',\n", " 'boylen',\n", " 'proportions',\n", " 'grecianized',\n", " 'wodehousian',\n", " 'encapsuling',\n", " 'partly',\n", " 'posative',\n", " 'calms',\n", " 'stadling',\n", " 'austrailia',\n", " 'shortland',\n", " 'wheeling',\n", " 'darkie',\n", " 'mckellar',\n", " 'cushy',\n", " 'ooookkkk',\n", " 'milky',\n", " 'unfolded',\n", " 'degrades',\n", " 'authenticating',\n", " 'rotheroe',\n", " 'beart',\n", " 'neath',\n", " 'grispin',\n", " 'intoxicants',\n", " 'nnette',\n", " 'slinging',\n", " 'tsukamoto',\n", " 'stows',\n", " 'suddenness',\n", " 'waqt',\n", " 'degrading',\n", " 'camazotz',\n", " 'blarney',\n", " 'shakher',\n", " 'delinquency',\n", " 'tomreynolds',\n", " 'insecticide',\n", " 'charlton',\n", " 'hare',\n", " 'wayland',\n", " 'nakada',\n", " 'urbane',\n", " 'sadomasochistic',\n", " 'larnia',\n", " 'hyping',\n", " 'yr',\n", " 'hebert',\n", " 'accentuating',\n", " 'deathrow',\n", " 'galligan',\n", " 'unmediated',\n", " 'treble',\n", " 'alphabet',\n", " 'soad',\n", " 'donen',\n", " 'lord',\n", " 'recess',\n", " 'handsome',\n", " 'center',\n", " 'vignettes',\n", " 'rescuers',\n", " 'pairings',\n", " 'uselful',\n", " 'sanders',\n", " 'nots',\n", " 'hatsumomo',\n", " 'appleby',\n", " 'tampax',\n", " 'sprinkling',\n", " 'defacing',\n", " 'lofty',\n", " 'opaque',\n", " 'tlc',\n", " 'romagna',\n", " 'tablespoons',\n", " 'bernhard',\n", " 'verger',\n", " 'acumen',\n", " 'percentages',\n", " 'wendingo',\n", " 'resonating',\n", " 'vntoarea',\n", " 'redundancies',\n", " 'red',\n", " 'pitied',\n", " 'belying',\n", " 'gleefulness',\n", " 'bibbidi',\n", " 'heiligt',\n", " 'gitane',\n", " 'journalist',\n", " 'focusing',\n", " 'plethora',\n", " 'citizen',\n", " 'coster',\n", " 'clunkers',\n", " 'deplorable',\n", " 'forgive',\n", " 'proplems',\n", " 'magwood',\n", " 'bankers',\n", " 'aqua',\n", " 'donated',\n", " 'disbelieving',\n", " 'acomplication',\n", " 'immediately',\n", " 'contrasted',\n", " 'reidelsheimer',\n", " 'fox',\n", " 'springs',\n", " 'toolbox',\n", " 'contacting',\n", " 'ace',\n", " 'washrooms',\n", " 'raving',\n", " 'dynamism',\n", " 'mae',\n", " 'sky',\n", " 'disharmony',\n", " 'untutored',\n", " 'icarus',\n", " 'taint',\n", " 'kargil',\n", " 'captain',\n", " 'paucity',\n", " 'fits',\n", " 'tumbles',\n", " 'amer',\n", " 'bueller',\n", " 'redubbed',\n", " 'cleansed',\n", " 'kollos',\n", " 'shara',\n", " 'humma',\n", " 'felichy',\n", " 'outa',\n", " 'piglets',\n", " 'gombell',\n", " 'supermen',\n", " 'superlow',\n", " 'enhance',\n", " 'goode',\n", " 'shalt',\n", " 'kubanskie',\n", " 'zenith',\n", " 'ananda',\n", " 'ocd',\n", " 'matlin',\n", " 'nosed',\n", " 'presumptuous',\n", " 'rerun',\n", " 'toyko',\n", " 'mazar',\n", " 'sundry',\n", " 'bilb',\n", " 'fugly',\n", " 'orchestrating',\n", " 'prosaically',\n", " 'maricarmen',\n", " 'moveis',\n", " 'conelly',\n", " 'estrange',\n", " 'lusciously',\n", " 'seasonings',\n", " 'sums',\n", " 'delirious',\n", " 'quincey',\n", " 'flesh',\n", " 'tootsie',\n", " 'ai',\n", " 'tenma',\n", " 'appropriations',\n", " 'chainsaw',\n", " 'ides',\n", " 'surrogacy',\n", " 'pungent',\n", " 'gallon',\n", " 'damaso',\n", " 'caribou',\n", " 'perico',\n", " 'supplying',\n", " 'ro',\n", " 'yuy',\n", " 'valium',\n", " 'debuted',\n", " 'robbin',\n", " 'mounts',\n", " 'interpolated',\n", " 'aetv',\n", " 'plummer',\n", " 'competence',\n", " 'toadies',\n", " 'dubiel',\n", " 'clavichord',\n", " 'asunder',\n", " 'sublety',\n", " 'airfix',\n", " 'stoltzfus',\n", " 'ruth',\n", " 'fluorescent',\n", " 'improves',\n", " 'rebenga',\n", " 'russells',\n", " 'deliberation',\n", " 'zsa',\n", " 'dardino',\n", " 'macs',\n", " 'servile',\n", " 'jlb',\n", " 'apallonia',\n", " 'crossbows',\n", " 'locus',\n", " 'mislead',\n", " 'corey',\n", " 'blundered',\n", " 'jeopardizes',\n", " 'disorganized',\n", " 'discuss',\n", " 'longish',\n", " 'tieing',\n", " 'ledger',\n", " 'speechifying',\n", " 'amitabhz',\n", " 'bbc',\n", " 'chimayo',\n", " 'pranked',\n", " 'superman',\n", " 'aggravated',\n", " 'rifleman',\n", " 'yvone',\n", " 'radiant',\n", " 'galico',\n", " 'debris',\n", " 'waking',\n", " 'btw',\n", " 'havnt',\n", " 'francen',\n", " 'chattered',\n", " 'scathed',\n", " 'pic',\n", " 'ceremonies',\n", " 'watergate',\n", " 'betsy',\n", " 'majorca',\n", " 'meercat',\n", " 'noirs',\n", " 'grunts',\n", " 'drecky',\n", " 'tribulations',\n", " 'avery',\n", " 'talladega',\n", " 'eights',\n", " 'dumbing',\n", " 'alloimono',\n", " 'scrutinising',\n", " 'geta',\n", " 'beltrami',\n", " 'pvc',\n", " 'horse',\n", " 'tiburon',\n", " 'huitime',\n", " 'ripple',\n", " 'loitering',\n", " 'forensics',\n", " 'nearly',\n", " 'elizabethan',\n", " 'ellington',\n", " 'uzi',\n", " 'sicily',\n", " 'camion',\n", " 'motivated',\n", " 'rung',\n", " 'gao',\n", " 'licitates',\n", " 'protocol',\n", " 'smirker',\n", " 'torin',\n", " 'newlywed',\n", " 'rich',\n", " 'dismay',\n", " 'skyler',\n", " 'moonwalks',\n", " 'haranguing',\n", " 'sunburst',\n", " 'grifter',\n", " 'undersold',\n", " 'chearator',\n", " 'marino',\n", " 'scala',\n", " 'conditioner',\n", " 'ulysses',\n", " 'lamarre',\n", " 'figueroa',\n", " 'flane',\n", " 'allllllll',\n", " 'slide',\n", " 'lateness',\n", " 'selbst',\n", " 'gandhis',\n", " 'dramatizing',\n", " 'catchphrase',\n", " 'doable',\n", " 'stadiums',\n", " 'alexanderplatz',\n", " 'pandemonium',\n", " 'misrepresents',\n", " 'earth',\n", " 'mounties',\n", " 'seeker',\n", " 'cheat',\n", " 'outbreaks',\n", " 'snowstorm',\n", " 'baur',\n", " 'schedules',\n", " 'bathetic',\n", " 'incorrect',\n", " 'johnathon',\n", " 'rosanne',\n", " 'mundanely',\n", " 'cauldrons',\n", " 'forrest',\n", " 'poky',\n", " 'legislation',\n", " 'womanness',\n", " 'spender',\n", " 'crazy',\n", " 'rational',\n", " 'terrell',\n", " 'zero',\n", " 'coincides',\n", " 'thoughout',\n", " 'mathew',\n", " 'narnia',\n", " 'naseeruddin',\n", " 'bucks',\n", " 'affronts',\n", " 'topple',\n", " 'degree',\n", " 'preyed',\n", " 'passionately',\n", " 'defeats',\n", " 'torchwood',\n", " 'sources',\n", " 'botticelli',\n", " 'compactor',\n", " 'kosturica',\n", " 'waiving',\n", " 'gunnar',\n", " 'stiffler',\n", " 'fwd',\n", " 'kawajiri',\n", " 'eleanor',\n", " 'sistahs',\n", " 'soulhunter',\n", " 'belies',\n", " 'wrathful',\n", " 'americans',\n", " 'ferdinandvongalitzien',\n", " 'kendra',\n", " 'weirdy',\n", " 'unforgivably',\n", " 'chepart',\n", " 'tatta',\n", " 'departmentthe',\n", " 'dig',\n", " 'blatty',\n", " 'marionettes',\n", " 'atop',\n", " 'chim',\n", " 'saurian',\n", " 'woes',\n", " 'cloudscape',\n", " 'resignedly',\n", " 'unrooted',\n", " 'keuck',\n", " 'hitlerian',\n", " 'stylings',\n", " 'crewed',\n", " 'bedeviled',\n", " 'unfurnished',\n", " 'reedus',\n", " 'circumstances',\n", " 'grasped',\n", " 'smurfettes',\n", " 'fn',\n", " 'dishwashers',\n", " 'roadie',\n", " 'ruthlessness',\n", " 'refrains',\n", " 'lampooning',\n", " 'semblance',\n", " 'richart',\n", " 'legions',\n", " 'gwenneth',\n", " 'enmity',\n", " 'assess',\n", " 'manufacturer',\n", " 'bullosa',\n", " 'outrun',\n", " 'hogan',\n", " 'chekov',\n", " 'blithe',\n", " 'code',\n", " 'drillings',\n", " 'revolvers',\n", " 'aredavid',\n", " 'robespierre',\n", " 'achcha',\n", " 'boyfriendhe',\n", " 'wallow',\n", " 'toga',\n", " 'graphed',\n", " 'tonking',\n", " 'going',\n", " 'bosnians',\n", " 'willy',\n", " 'rohauer',\n", " 'fim',\n", " 'forbidding',\n", " 'yew',\n", " 'rationalised',\n", " 'shimomo',\n", " 'opposition',\n", " 'landis',\n", " 'minded',\n", " 'despicableness',\n", " 'easting',\n", " 'arghhhhh',\n", " 'ebb',\n", " 'trialat',\n", " 'protected',\n", " 'negras',\n", " 'rick',\n", " 'muti',\n", " 'tracker',\n", " 'shawl',\n", " 'differentiates',\n", " 'sweetheart',\n", " 'deepened',\n", " 'manmohan',\n", " 'trevethyn',\n", " 'brain',\n", " 'incomprehensibly',\n", " 'piercing',\n", " 'pasadena',\n", " 'shtick',\n", " 'ute',\n", " 'viggo',\n", " 'supersedes',\n", " 'ack',\n", " 'cites',\n", " 'taurus',\n", " 'relevent',\n", " 'minidress',\n", " 'philosopher',\n", " 'bel',\n", " 'mahattan',\n", " 'moden',\n", " 'compiling',\n", " 'advertising',\n", " 'rogues',\n", " 'unimaginative',\n", " 'subpaar',\n", " 'ademir',\n", " 'darkly',\n", " 'saturate',\n", " 'fledgling',\n", " 'breaths',\n", " 'padre',\n", " 'aszombi',\n", " 'pachabel',\n", " 'incalculable',\n", " 'ozone',\n", " 'sped',\n", " 'mpho',\n", " 'rawail',\n", " 'forbid',\n", " 'synth',\n", " 'guttersnipe',\n", " 'reputedly',\n", " 'holiness',\n", " 'unessential',\n", " 'hampden',\n", " 'asylum',\n", " 'bolye',\n", " 'strangers',\n", " 'rantzen',\n", " 'farrellys',\n", " 'vigourous',\n", " 'cantinflas',\n", " 'enshrined',\n", " 'boris',\n", " 'expetations',\n", " 'replaying',\n", " 'prestige',\n", " 'bukater',\n", " 'overpaid',\n", " 'exhude',\n", " 'backsides',\n", " 'topless',\n", " 'sufferings',\n", " 'nitwits',\n", " 'cordova',\n", " 'incensed',\n", " 'danira',\n", " 'unrelenting',\n", " 'disabling',\n", " 'ferdy',\n", " 'gerard',\n", " 'drewitt',\n", " 'mero',\n", " 'monsters',\n", " 'precautions',\n", " 'lamping',\n", " 'relinquish',\n", " 'demy',\n", " 'drink',\n", " 'chamberlin',\n", " 'unjustifiably',\n", " 'cove',\n", " 'floodwaters',\n", " 'searing',\n", " 'isral',\n", " 'ling',\n", " 'grossness',\n", " 'pickier',\n", " 'pax',\n", " 'wierd',\n", " 'tereasa',\n", " 'smog',\n", " 'girotti',\n", " 'spat',\n", " 'sera',\n", " 'noxious',\n", " 'misbehaving',\n", " 'scouts',\n", " 'refreshments',\n", " 'autobiographic',\n", " 'shi',\n", " 'toyomichi',\n", " 'bits',\n", " 'psychotics',\n", " 'barzell',\n", " 'colt',\n", " 'shivering',\n", " 'pugilist',\n", " 'gladiator',\n", " 'dryer',\n", " 'reissues',\n", " 'scrivener',\n", " 'predicable',\n", " 'objection',\n", " 'marmalade',\n", " 'seems',\n", " 'spellbind',\n", " 'trifecta',\n", " 'innovator',\n", " 'shriekfest',\n", " 'inthused',\n", " 'contestants',\n", " 'goody',\n", " 'samotri',\n", " 'serviced',\n", " 'nozires',\n", " 'ins',\n", " 'mutilating',\n", " 'dupes',\n", " 'launius',\n", " 'widescreen',\n", " 'joo',\n", " 'discretionary',\n", " 'enlivens',\n", " 'bushes',\n", " 'chills',\n", " 'header',\n", " 'activist',\n", " 'gethsemane',\n", " 'phoenixs',\n", " 'wreathed',\n", " 'sacrine',\n", " 'electrifyingly',\n", " 'basely',\n", " 'ghidora',\n", " 'binder',\n", " 'dogfights',\n", " 'sugar',\n", " 'doddsville',\n", " 'porkys',\n", " 'scattershot',\n", " 'refunded',\n", " 'rudely',\n", " 'insteadit',\n", " 'zatichi',\n", " 'eurotrash',\n", " 'radioraptus',\n", " 'hurls',\n", " 'boogeman',\n", " 'weighs',\n", " 'danniele',\n", " 'converging',\n", " 'hypothermia',\n", " 'glorfindel',\n", " 'birthdays',\n", " 'attentive',\n", " 'mallepa',\n", " 'spacewalk',\n", " 'manoy',\n", " 'bombshells',\n", " 'farts',\n", " 'lyoko',\n", " 'southron',\n", " 'destruction',\n", " 'flemming',\n", " 'manhole',\n", " 'elainor',\n", " 'bowersock',\n", " 'lowly',\n", " 'wfst',\n", " 'limousines',\n", " 'skolimowski',\n", " 'saban',\n", " 'koen',\n", " 'malaysia',\n", " 'uwi',\n", " 'cyd',\n", " 'apeing',\n", " 'bonecrushing',\n", " 'dini',\n", " 'merest',\n", " 'janina',\n", " 'chemotrodes',\n", " 'trials',\n", " 'authorize',\n", " 'whilhelm',\n", " 'asthmatic',\n", " 'broads',\n", " 'missteps',\n", " 'embittered',\n", " 'chandeliers',\n", " 'seeming',\n", " 'miscalculate',\n", " 'recommeded',\n", " 'schoolwork',\n", " 'coy',\n", " 'mcconaughey',\n", " 'philosophically',\n", " 'waver',\n", " 'fanny',\n", " 'mestressat',\n", " 'unwatchably',\n", " 'saggy',\n", " 'topness',\n", " 'dwellings',\n", " 'breakup',\n", " 'hasselhoff',\n", " 'superstars',\n", " 'replay',\n", " 'aggravates',\n", " 'balances',\n", " 'urging',\n", " 'snidely',\n", " 'aleksandar',\n", " 'hildy',\n", " 'kazuhiro',\n", " 'slayer',\n", " 'tangy',\n", " 'brussels',\n", " 'horne',\n", " 'masayuki',\n", " 'molden',\n", " 'unravel',\n", " 'goodtime',\n", " 'interrogates',\n", " 'bismillahhirrahmannirrahim',\n", " 'rowboat',\n", " 'dumann',\n", " 'datedness',\n", " 'astrotheology',\n", " 'dekhiye',\n", " 'valga',\n", " 'kata',\n", " 'wipes',\n", " 'hostilities',\n", " 'sentimentalising',\n", " 'documentary',\n", " 'salesman',\n", " 'virtue',\n", " 'unreasonably',\n", " 'haver',\n", " 'cei',\n", " 'unglamorised',\n", " 'balky',\n", " 'complementary',\n", " 'paychecks',\n", " 'mnica',\n", " 'wada',\n", " 'ily',\n", " 'prc',\n", " 'ennobling',\n", " 'functionality',\n", " 'dissociated',\n", " 'elk',\n", " 'throbbing',\n", " 'tempe',\n", " 'linoleum',\n", " 'photogrsphed',\n", " 'bottacin',\n", " 'hipper',\n", " 'titillating',\n", " 'barging',\n", " 'untie',\n", " 'sacchetti',\n", " 'gnat',\n", " 'roedel',\n", " 'cohabitation',\n", " 'performs',\n", " 'sales',\n", " 'migrs',\n", " 'teachs',\n", " 'nanavati',\n", " 'fresco',\n", " 'davison',\n", " 'obstinate',\n", " 'burglar',\n", " 'masue',\n", " 'dickory',\n", " 'grills',\n", " 'appelagate',\n", " 'linkage',\n", " 'enables',\n", " 'loesser',\n", " 'patties',\n", " 'prudent',\n", " 'mallorquins',\n", " 'nativetex',\n", " 'suprise',\n", " 'drippy',\n", " 'quill',\n", " 'speeded',\n", " 'farscape',\n", " 'saddening',\n", " 'centuries',\n", " 'mos',\n", " 'improvisationally',\n", " 'neccessarily',\n", " 'transmitter',\n", " 'tankers',\n", " 'latte',\n", " 'mechanisation',\n", " 'faracy',\n", " 'synthetically',\n", " 'thoughtless',\n", " 'rake',\n", " 'ropes',\n", " 'desirable',\n", " 'whitewashed',\n", " 'donal',\n", " 'crabby',\n", " 'lifeless',\n", " 'perfidy',\n", " 'teresa',\n", " 'bulldog',\n", " 'cockamamie',\n", " 'rasberries',\n", " 'notethe',\n", " 'captivity',\n", " 'chiseling',\n", " 'smaller',\n", " 'clampets',\n", " 'alerts',\n", " 'tough',\n", " 'wellingtonian',\n", " 'aaaahhhhhhh',\n", " 'dither',\n", " 'incertitude',\n", " 'florentine',\n", " 'imperioli',\n", " 'licking',\n", " 'disparagement',\n", " 'artfully',\n", " 'feds',\n", " 'fumiya',\n", " 'tearfully',\n", " 'lanchester',\n", " 'undertaken',\n", " 'longlost',\n", " 'netted',\n", " 'carrell',\n", " 'uncompelling',\n", " 'reliefs',\n", " 'leona',\n", " 'autorenfilm',\n", " 'unfriendly',\n", " 'typewriter',\n", " 'shifted',\n", " 'bertrand',\n", " 'blesses',\n", " 'tricking',\n", " 'fireflies',\n", " 'zanes',\n", " 'unknowingly',\n", " 'unnerve',\n", " 'caning',\n", " 'flat',\n", " 'recluse',\n", " 'dcreasy',\n", " 'chipmunk',\n", " 'dipper',\n", " 'musee',\n", " 'cousin',\n", " 'shys',\n", " 'berserkers',\n", " 'eve',\n", " 'conflagration',\n", " 'irks',\n", " 'restricts',\n", " 'parsing',\n", " 'positronic',\n", " 'copout',\n", " 'khala',\n", " 'swiftness',\n", " 'higginson',\n", " 'imprint',\n", " 'walter',\n", " 'sundance',\n", " 'whispering',\n", " 'thematically',\n", " 'underimpressed',\n", " 'uno',\n", " 'expressly',\n", " 'russkies',\n", " 'discos',\n", " 'shaping',\n", " 'verson',\n", " 'prototype',\n", " 'chapman',\n", " 'trafficker',\n", " 'semetary',\n", " 'unrealistically',\n", " 'lifewell',\n", " 'rivas',\n", " 'consequent',\n", " 'katsu',\n", " 'titantic',\n", " 'jalees',\n", " 'ranee',\n", " 'shipbuilding',\n", " 'gambles',\n", " 'dispenses',\n", " 'disfigurement',\n", " 'bright',\n", " 'cristian',\n", " 'puertorricans',\n", " 'constituent',\n", " 'capta',\n", " 'jewel',\n", " 'erect',\n", " 'farah',\n", " 'despondently',\n", " 'avoide',\n", " 'inconnu',\n", " 'headquarters',\n", " 'sanguisga',\n", " ...]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(vocab)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "layer_0 = np.zeros((1,vocab_size))\n", "layer_0" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih\nyGJVVsUIiIhU3AF1qJ43aqJvHnTQQTb2xL/8y7/UQS3pkAAB+u355583//RP/5Qgdb2SQJxwlL7m\nmmvqpZi0qRwBNlaEbJRJaipvpBToIKCpmQ4Ug3vgpmUgJE5uuukmw5u2pN4IELWUnW+bOr3BPYej\nNFOCmgqs973ma4f147/+67/MunXr/NOFHVPuySefbF5//XW7G3RhBaugWiIgi0gtu6W/SjElw4oZ\nSbMQcNaQppIQ0IZ8XH755eYrX/lKs8CXtkJACBSGgIhIYVA2uyDIiIKaNasP77jjDjN37txmKR2h\n7WWXXWZX/MhXJAIcnRICA4CAiMgAdHLSJhJw6p577kmaXOkqRIBBe9myZXZDuQrVKKRqrCIQ4fXr\n1xdSXpMKcc6XOO5yjAMozpjuw2+uRQnX+OBH4RxFx4wZMyLpgw8+aH1xXJl8f+ELX7D1jUj8hxOU\niY+Gnwd/njhdyIYOUfVzLao80ob9QEhHnUzLIOPHj7e/nXOqj5dNEPqDfocffvgwvWkrPidhYfqH\nuigTjMLYuzrD+fS7eARERIrHtNElYubHVC6pNwIM2oTmb4tfBffdo48+Wm/QS9Tue9/7nh10ia8y\nNDTU+UyaNMlccMEFlkjEVf+pT33KXtq1a5fZvn37sGSQAwLdsTTflbtjxw7zi1/8wtYX5ePBoH3s\nscdaYvjAAw908n3mM5+xU7iUmUYY6HGEJ9gePh9Oj3nz5plbbrnF1gUBQXbbbTd7/fHHH7e/Xfor\nr7zS/o77Q36IxO23326thK4O19Z99tkn1p+FjT8h9fw/uXzUz/8YZUr6gEAAvEQIjEDgBz/4wYhz\nOlEfBIKH5lBgvaqPQjk1CZxVh4LHXc5Smpc9GGhtu2n7nXfeGdmAYFWUTXP99dcPu37iiScOBQPs\nULCZ4LDz7gflcT0YjN2pYd/k43pAYIadp9xgr6IR510iVy/fvlx00UW2PP8cZVPWWWed5Z/uHLu2\nhdsQEAHbZvDxxeEVxoryu+ns2upj4eqIyxdXl6+PjotBQBaRPpC9JlaBqVxSXwQee+wxc+SRR9ZX\nwZSaYdnhLRwH3EGUYDA0s2bNimw6/RwM8tZ6EE7AGz/XooR4QLNnzzZ777131GWbj/xYPZxgIXni\niSdsXBqsE1HCcmvyJRHKxhKC9SNKXNvuu+++qMuJzm3atMncf//9XXUGI3RevHjxiDKJwRPV1nHj\nxhksKdu2bRuRRyeKRUBEpFg8VZoQKB0Bt8y6bWSRwZiYKIMowRt912Yfc8wxdiBl0PUFzKKIBj4P\nDLxEr40T8pHfXzH3zDPP2OSTJ0+Oy2YJMPmSCGWTlkE9Tm677bYRU0pxaaPOv/rqq/Z0N51dW92U\nj1/OwQcf7P8cccw0lqRcBEREysVXpQuBwhEg5sbEiRMLL7fqAhkQwj4OVevUr/r32GOPrlXtvvvu\n9jp+IL7stdde/s/OsUvHfeI7nIaPsVb8/Oc/7+Rj0MUKEEVuOomCgwMOOMD/GXv87LPPJk4bW0iP\nC/jXIFFWDT/rEUccYXbu3Omfsse98o3IoBOFIyAiUjikKlAIlIsAVoOxY8eWW0kFpfPWLDN4d+Ad\nweie6o9XAz+HjgNmMJsfeRzlsPrHEnQkBMpHQESkfIxVgxAoHIG4ZZKFV6QC+4LA22+/3bUeZylK\n2u/OgvLaa691LTd88S/+4i/slI5bxRK+7n7/6Ec/coddv0ePHm16pWVVTXgZb9dCQxc/8IEP2DO9\ndH7hhRcM+kjqh4CISP36RBoJgYFE4P3vf79ZtWrVQLYdZ8tugq8FUyZJHZQ/8pGP2OK2bNkSWyzL\ndJmq8ZfjHnLIITZ9N18diANTOkkE3xfSkidOVqxYYR1xs06ROB8PHLjjxOncyxcnLr/Ol4uAiEi5\n+Kp0ISAEEiLAjryDKgzWccHCcDyFqMStPInCDB8PVopcd911Juzg6tK7FSSXXHKJO2XOOOMM61xK\nyP04CwOrcZIKQdAgUHF5IEOsmPnEJz6RtMgR6SBnEAxiiHTTGT0+97nPjcivE9UjICJSfR9IAyEg\nBAYcgSBGiB1IsU74gylTFmeeeaYlFXHLe+Og+7d/+zcTxPqw5MInOQz+EARIShCPY8SKFojB97//\nfUOgNEiQE6wK5MO5NW7JsEvrviFE1A2RIi91O6HsKVOm2OkSdPXFTS3h7JpE2O4Ax12WgPs6u7ZS\nDnpktbok0UFpsiMgIpIdO+UUAkKgQATYBbotkWLTwsKqmZdeesnsu+++NgqpW91CdE/IAktc0wqD\nLo6oWFIeeuihzuoZLAMIMT6iyA1Ow88995whqiskyOlCuHV8SNI6txKdlKXE++23n7WOuPKI+Iol\ng3aHCYKLL0JU2fD0URQOrq3EBCFKqquDtrJ8mPYoSmoUcvU4N4q4aPVQRVoIASHQCwH2mPnqV79q\neGO89NJLeyVv1HWCmS1YsMAOmo1SPIeyWBkY4CEbUaQgR9HKKgQag8A7GqOpFK09AgwkPFgJMMQy\nzDfeeMOEneV44yW2AW+AEyZMsMcf+tCHat+2KhWEfAQh960KvPmxb0cb92XxpySqxFt1CwEh0F8E\nRET6i3fratuwYYPdwh2PdZbGYc7Fix2TLoNmOPonUUEJyMXcLUsL2cb+6aefthtOnXLKKTb/IDst\nuhvE4cRvcPTJGgP2m2++6ZK25puYF0cffXRr2qOGCAEhkAwBTc0kw0mpPAR4Q7/77rutGR3ywe6V\nJ5xwQub5fcpjLpxlfCwbxKntsssuy1yep2qjDn3y8d73vrdr+5kDh5C0ibSdfvrp5uyzzzannXZa\no/otj7KamsmDnvK2BQE5q7alJ/vQDgjDzTffbIj3wJ4Ua9asMZs3bzZs4Z7HyZDBlMEHhzq36RkD\nMc5s1NlmwUGTNrt2Y/ng0wtPVgd85zvfaRU0kFDCcEuEgBAYLARERAarvzO3loGSDbQcAYE0+NMF\nmQsOZWQAvvHGG+30DdEmIT3Lly8PpWr2T0c8+Ka9ScmH32qICCsB2iJggXWtFwFrS3tdO1ihwnqB\ntjmqYt079NBDXTP1LQS6IqCpma7w6CIIEIyIYEHEHcD60U9hgOIh/cEPftAsWrSosVMRtMNJEQQO\nSwp+OFik2iDcYz/72c/MTTfd1IbmDHwb6E/2xeGlQiIEeiEgItILoQG+zrQIAYeQr3/965W9raIH\nfig4aBINMuwAW8cuQme30gX9iiAf4Xbyxrlw4UJz/PHHhy817jdTcY888oh5z3ve04j+bRzAfVaY\ne5MAYmXc931uiqrrAwKamukDyE2swpEQggGtXbu2MhICdviQLF261EZNPO644wzWgDoK5mgsH3w4\ndlMuZT2MP/vZz9oVS3XEIo1ODz/8sCUf3GtMzfjWozTlKG09EHD9V9Z9X49WSosiEZBFpEg0W1KW\nT0LqZlpl0GJvDDYBq4NlJM1Kl6JvD/qJpb0sh26ybwVvz/Pnzx+2WobBTANZ0XdMf8rDyZyAe2n2\nxumPZqqlrgiIiNS1ZyrSq84kxEFSNRnBIuOCb/VaZut0LuMbEkQk0t/85jfWYlRGHWWXSV9ec801\nkb4uIiNlo198+Tw/cDCn75pMjotHRiV2Q0BEpBs6A3ht5syZhtUqrIqps+AMRyA0po36EUvDmZvB\nhAdtP+rshj9kCB34oA9LqZtmQWDQYiVW2Britxvc64C3r5OO4xGAWN5yyy3WYhmfSleEwHAERESG\n4zHQv4gRctddd5mNGzdWPtAm6QgCYBEqHv+RMsQnH3Ua5MODM8ubWVHUlH5zfQWZZAuAXqQX0sXb\nddXkz+mt73gEeJEhQvIgBaWLR0NXkiIgIpIUqZan42HPmycrPerge5EEbmcGvvfeewtZOUJ5Za90\nSdKubmkgIVGkCFJ2yCGHNGZennZMnTo1sQlfZKTbXVGPa0wVMlXZtoi/9UC33VqIiLS7fxO3joGM\nfT6atqMre92ce+65lkBkeWP2yUfU3jiJASw5odMzioRQtVulc+utt9b+bTSrriIjJd9kOYvHModV\nriwLZU71lL3GCIiI1Lhz+qWacxhsmmnf4ZOWRDEQstIEqTP5cO1DX4hIL0uVI2V1WVHk9Pe/aQex\naViqm2VFFmQEwilHSB/VehyztP4LX/hCIdbJerRIWvQLARGRfiFd43qilk/WWN0RqjE48RBkWiXO\nKkKaOqx0GaF8jxOQECTJwEvab33rW+bKK6+szfJmv3mOhOy333653prTYOLXr+PyEHD/g47gl1eT\nSm4jAu9oY6PUpuQIYA1BmuxchqWAHXvZEdifWvLJB/4vvSwKyVHrT0r0T/P2zyDwD//wD+Zd73qX\nJWZ1sow4EgJyONbmEUgZZIRPEoKWpy7lTYbAgw8+aP8Hk6VWKiEQQiDYcKmv8sADDwwFKtjPWWed\n1de6i6jM1592+OLaxXewk6h/qedxYKru4HLnnXf2TF9UgmnTpg2tXr26qOIqKyfYiXYoGJSG+Haf\nwAJSmT55K6YNafQnvS/0KfdhHfo2sFQNBZv0DV1++eW+irmPA+I1RNmS6hHgf099UX0/NFUDhXgP\nntaDKrxRrlq1ykyaNKkVELBPCdMvOHTyiZumqXtj3cqYpPrTj6xW8AULV0BObBRaIl1ikahCsLgx\nbcYKmSw+Id10xhrCB8uRpDoE8E1i5+SmWRyrQ0w1hxEQEQkjMkC/n3zySTNjxozGDth+V0E8cJR7\n7LHH/NONOoYsOBKSRnGmZBiQwwImlLdt2zYbOIzjfgnkiJgShOMn2Jo/ZVakDm7qSmSkSFTTlcX/\nHJtSSoRAVgRERFIid8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ\nVhiVSUggUwTGox0Em8OBOG2b0rSftCIjaRErLj39zQ7QJ5xwQnGFqqSBQ6AWRITtokeNGtX58Gb7\n1ltvxXbGpk2b7Nuvn2fMmDF222m3MiKc2U9Lfj4nnXSSrZP6kSRpcMry04Xr8X+vW7euUwd5qI9z\nWSSqzThook9WYVrmiCOOyJo9dz6HI+0oQpxpuGlvxxAQxOmfFAvyhadk4vKed955lhQQK8YREkzq\nRQmYMwWEU/AzzzxjV+0wFZN0eimvHo6MlEmy8urYxvzr1683gZ9ZpEWuje1Vm0pCoN/OLb6zJ86q\nfIKmjfjss88+Qzt27Bih3vXXXz8irZ+ffK+//vqIfH6acBnOOTRJGl9/0vvi57/66qtj9XT1+Xm7\nOauS3i87fExdaQXHsiASZ9pshaZ37TjxxBMLKzeYaqqFg2bSBtEPOF1mkbCDatIycIK95557bP8H\nFhPrRBoMKKn1oP5rr712WDlZ25JU9yTpsuKSpGylGY5AW5zdh7dKv/qNQKXLd++///5gLIqWgITY\neAgrV67sJMBycdVVV3V+Rx2Q7+STTzavvfaaDVYVlaZXGeRJkiaqbHfuuuuuc4cjvi+44AJz8MEH\nG6YSegkWFNJ3E+oaO3asmTVrVrdkw65t3brV7L///sPOVfXjiSeeKKzqCRMmGO6BJghv71k3dOs1\nJdOt/VgPsJDwwZLBPbZ48WLruEyYeO4L7iesjGHB2vHzn//cbjgYrIQxfDDNH3/88eGklf12vjFl\nTwlV1sCaVIxFDqsqy+YlQiAPApVPzQRvwyawYFifi127dhl+O/GJClMubJLlhMiMwRLZjq9GYBVw\nl+xAdMcdd3R+Rx2QPmB99hM3gCdJE1W2OxdYMjp1BJYUd9p+sz9KEvHb5WPFYBtYkzpFgE3ctFQn\nkXdAfsz0dRGmnoqQ8ePH26mBIsoqswxHJLJMXaSZkunVBqaDcCTFj4T/B+7TuXPnRpIQyrrooovM\nggULbFrilMybN69WJMS115GRqlYLOT3a/M09EyzJ7tv0W5uxHPi29dsEE57aCAbEYSoQfyPolM4n\nICf2ejhfVJwOf5qHKRpf/DJJFyVJ0oT18Mvx8wcEwr9kj8NTLK5tXIyammGKyS8zjBX5aadLg25J\n5aabbhriU6U4vflmesbHI6temOUxF9dVmBbJO3WQN39dsSlDL6a+wFxSPAJM7TKlJxECeRGo1CKC\nVWPvvfcOxqE/Svi3e8tnu3AnweAbOa3xmc98xiWxVhGmH6KEuAa9JEmabmWceuqpIy5/9KMfHXau\nm0MuCZlecoI1JIwN+6Scc845Lon5yU9+0jnudYCJHetBXsHR1Dmdpv3262Z6BjM/02+9cPHzNekY\nSwafPFMGzpLSpHZXqSsWHzCXZaTYXnAO4XWakiu2hSqtnwhUSkQOOOCAxG198803O2nDA7q7sPvu\nu7tD++1IzLCTwY9wuvB1fidJE5XPnQuTBs5DHHyJ08+lCSwE7tAwUEcN9L4vyttvv91Jn+QgrE+S\nPGWmefnll60/TJNjgcThw2CIpF0Z45dHGUlXyfj5Bv1YZKT4O4AXBlbLSIRAEQhUSkSKaIDKEAJ1\nR8C9PUYFHUuje1zgsjRlDGpaR0YcIRxUHIpqN4sIiKkkEQJFINAYIsKOnU6effZZdzjs27cgcKHK\nN34ccMMStoBEWU38PL5VBsfUYB6u6+fLX/6yn73rMasi4qauumbUxVQIMJUCAclLQjQlkwr2yMTO\nGiUyEglP4pPEnwFLh2fijEooBGIQqHT5boxOkadZVuiEFR+ssggvfw1iI7gkBj+ScePGdX73+wAf\nDAKY+eITKPTrRUQOOuigTnbyQmSKIlcszQwTt05lKQ5YNfH5z38+RY7eSXvh0ruEeqQoijz0Y0qG\nOl544QXrW8W9i7hluhxDpCZOnMih4X+R+4f/v6YNRrSDtvLJSw4tGAP4B2sIS78lQqAoBBpDRIgN\nwuANCUH+8R//0Xzta1/rkBGisfrLfX0nzqLASlNOOLYHEVD9eCBJ9INI4dCL7wTt/tSnPmW3UHcE\nizKJZukwCVbNpDKXFkFEnC5psCkzLVYerD1VCo6RRYY2Z0omj4NrHBZMGXEPEQti586dlmiwpPvs\ns8/ukGRXLwM3eiAsm9+4caO9F8k3efJku1UAG+01QRwZof1NI1JV48u9vWzZMhMEsqtaFdXfJgTy\nLrtJm99f/hq1jDYYVDvLUQOch0VXDS9/5XrUJyAsI5aC+unilrkmSePrT3pf/Py9jmmnL1HLd7ke\nri+uXPKnkbovc03TFj8tkT6rXJZMZFGWjBYlZSzVXb16dScaKnjlqYP2EqU1WPE0FAzwFnvONUEC\nC2OhfdWENufVkb4merFECBSJQGN8RIIB2EYO9QN8cS4sWE0ef/zxwqYwwuUn/R2EkY9NSqCzpNMP\nOISRvptgNVm7dm23JCOusfqCN+G2Ccu83RRCv9uG1QAp6i2b8opcJfPwww+bQw891FxzzTVm/vz5\n1sLB1JqzemTBC+sCZnqCm7HL7vbt263ObHxX9yWzbn8a50ycpf2Dlmf58uWt2ihz0Pqvru1tFBEB\nRBwyIRphQgIBYcAm9kYdpguIrxFYM+zUiut8YoGge1wkV5cu/E16nF/D5AYCQptfeumlxMTGlc0A\nwlx/mx7CDHyQK8Km91scjuBalDAVUkR56Mauu46AbN682ZQxjQKhYaM79MbPhH4ocmO9onD1yxEZ\n8dHofgwx5l4q497pXrOuth2BUZhX2t5ItS8aAfxLcDokxHcbhEEPosrbeT8Fp9Sse8bE6VmUoytv\nsJB2wraff/75fQ3HTX+ce+651odk0aJFfa07Dte480X79cTV0+TzbCOBXxlkUyIEikSgcRaRIhs/\n6GXhZIg5vQ3CoLdixYq+e/M7wpBlz5g43IuakoFoQkLoY8hmkTrG6e6fJ+omTrsE2psyZUqtrW9g\ng0WH/pREI4C18cwzz4y+qLNCIAcCIiI5wGt6VgYKTK1uWqHJ7fnwhz9sp71Gjx5tBxMGlDIHFd6g\nHQkpGre8UzLoxhYFrLYqcvVOlnYywLM5GtOI6FT3e01kJLqX3f9SHn+i6JJ1VggYo6mZAb8L2mJu\nZQrikUcesYOe36XuAerOFTGFgsWCwb4op1SnG995yQ16YX1AcGDutxXEVhzzB2fZSy65xE6dlYFd\nTLWZTufth0yV1jgT1jUCLOLcLBECRSMgIlI0og0rz00D5H0Lr7rZrAZZuHBhzy3peSP3I9yyKiWN\nQyh4IWnyJMWmiLJxSiUQWd1IiMOgaWSkCOLq2t7Ub8gtOEDOyrjvm4qL9C4OARGR4rBsbEm87SBN\ndULDGoIzJKtB0gqDPyTMCZFr497WITFYGMp6GOd9C8e69fTTT9eWhDiMm6In+tLn9HedLEsOx359\nQx5vueWWvjuB96t9qqd6BEREqu+DyjVwVhH8CeIG4cqVjFHAva3hkFnE/DXlgYMvzm+gzLfjvCTE\nrVBpylsrlps99tjDLF261Ie6lseDTkZmzpzZqMi5tbyJpFRXBEREusIzOBcJQMVg3u+lr3kR5iGJ\nlDmgYXHxY9MUQXj8duedknFk7N577+05NeXXW+Vx03Qu2xpWZV90q9u9pDCdOchWoW4Y6Vp+BERE\n8mPYmhJY1TB16tTGxBUp2wrgrCNh4oHVwZe8lpK81pB+kDG/vUUdu/7DAtWEQS4vYSwKt36WAwln\nX6EyiX4/26O66omAiEg9+6USrXjrg4w04c26bF0ZdCAiSaaq0CWrA2xeEkJ+yGNTBvPwjc0UDRF+\nm7IaY9DICM8DNhRlqb9ECJSFgIhIWcg2tNwmrGqAILBENdhorZQBLO9gQ/4kDrB56+EWYyBnx9ym\nRscFA1YuNWnVVhH91oTHgyP7/r3cBL2lY/MQEBFpXp+VrjHmWCJy4i+SxCJQukJeBZCQE044wXzs\nYx8rZZUPD9+iV8a4KR6vGZ0onuFpHz9Nr+OmW0Nc+5oYowIyktRi5trZtO+2xBhqGu6DqK+IyCD2\neoI2MzisXLmyVmTEWUIOOuggc8455xSySsaHgoE9r7+HX16347ADbJZ66aM27BXU1Ddv7kcISd3I\nerf7Ls01LFV1fBlJ0walbQYCIiLN6KdKtHTTNHXwGWGwYp+LSZMmdSwhRRIHyspjnUjTQVGmfdqX\nxs+EQZCYJ02a0uiGEb4Is2fPbtzOrm0lIzgSX3755Zli83TrZ10TAlEI/On/CyTqgs4JgQMPPNDs\nt99+ZtasWea3v/2t+fjHP14JKFgP2MX1i1/8ornqqqs6OvDGtm3bNvN///d/mVddMJC88sorfSMh\nKI9jKRYQX/baay/rK0Gb+KAX6SAtfH79618b0jj55je/af7nf/7Hhkx355r+vX79evP3f//3jWrG\nO9/5TsOHe4h+a4vcdttt1g+LiMUSIVA2ArKIlI1wC8rnbf2iiy6yLVmwYEHfBm0GYJww33jjDbNk\nyZLYeknHwJ3WRJ41X54uzWp5ccTE1X3TTTdZX5nzzjvPnWr0N32BRarJjpFZ+7ZuHce91iZrW93w\nlT4jEdDuuyMx0ZkQAgzwzBWzTJQPvgkMHGUJD0Ic5XjDZGkncQy6TZsQgpsPA0FScfqnJS9Jy49K\nR51Z35pxoAUD93nttdfMe97zntrvZhuFQ9Q5+s/tnBx1vQnn6Js092Dd2sT/HYJlatq0aaVtZVC3\ndkuf6hEQEam+DxqjAdYJpgsQBlQCaTGXXJRgeYHk8Da2a9cu+3ZMfIkkwa7cQM1A4B6ocXpRD8Lg\n108pyp8DQrNlyxa7dLefRKpsrPD/2bp1a9nVlFp+k8nInDlz7P/zihUrzNlnn10qTipcCPgIiIj4\naOi4JwIM+GyOh2PlhAkTrEMb88gQCEhJLxIQrgDigPWDMnBYZKvxZ555xsyfPz8TUWAgYKB2Fo+o\n+pwFJXytzN+0E92KEAgNb6xtE1YAvfrqq41vliMjaf8Xqm7422+/bZ3BV61aZadD2fYh7v+oal1V\nf7sQeEe7mqPW9AsBCAkWEj5YGB588EGzePFi+yBjOmX//fe30yoQi7BANNiqnp1iCUrGZ+HChcOi\nN+YZuLES8ABFL99ikKfMcBvS/EaXrFMyUfVgNRg7dmzUpUafmzhxoiWhjW7EH5SHjHD/QXqTWPTq\n1macwlk102+rYd1wkD79QUBEpD84t7oWBns/RDcPYCwmzz//fGS7cXxl+qWbhcC9VXZLE1n4H07y\nAOWNFPLBChWmlLKW1a2eJNewYBRZN9NWWA8k9UaA/4umkhFeDrB8SoRAPxAQEekHygNWh7NC5B18\nsSJgTcj6VsabKGWsW7fOnHTSSZX0QlVWmEoam7NSCCPTAm0SR0a4F7Pex/3GAxKydu3afler+gYY\nAfmIDHDn173pzqqRda7dzW+zHwvHWcvJilPRUzJZ9WhKviZOYSTB1hFzdz8myVNVGv7nmGJta19U\nhavq7Y6AiEh3fHS1YgR4iLuVOmlUwSSOuLdQymEg6OdgUNQqmTTtVtp6IuDuw37ef1mQWLNmzTC/\nqixlKI8QSIuAiEhaxJS+7whgsnfEIknlTIfw4HcPf5fHvZmmKcvlTfutKZm0iJlc03Dpa+t/Dnc/\n1pWMfPnLXy7Ul6n/CKvGpiIgItLUnhsgvTET80nyAHcEIM607AgK6coS9CxylUxZetatXCxIrJxp\nszgy0g8ynBZHR9TT5lN6IZAXATmr5kVQ+TsIMAC/8MILZseOHZ1lmG6ZLoncsl53zMqPI488MpEp\nmAe4s3R0KvQO8P9IujIGkoIjLeVhbYkjLV7xqQ6LXiUTrnyfffYxjz76aPh043/7m/41vjFdGsC9\nzP0KGSli8Of/7vvf/755/fXX7Z43xAPx/++ozxE8/gf5vxs3bpysH136SJf6i4D2mukv3q2rjYcp\nMURY7bBz5077wDv66KPN+PHj7RJdGuxWz5CWwYaPe2i++OKLNt/06dPN5MmTh8USiQLLWTz8azyI\nebBneaijE0TEvan65WY5jtIvSznd8lDH3Llzbdj9bumado0AWgixaQZBuGe5d7Pet+7/jii7BLjj\n/w6Suvfee1v4wv93nGRJ/fbt220Yd/5f+Z875ZRTbPyfogn5IPSh2lgMAiIixeA4UKXwAGU/imuu\nuca2m4fgGWeckemBSgGQgU2bNplFixZZUsIge/7550daKsIPbx7kSB4ikYfI2Mr/8KcIXfzy4o7B\ngDgsELqsA1lc2VWeZ8sABlJ2e87Tn1W2IW3d9GVSSx5lP/zww+aWW26x/zNJyXucTtw7Tz75pGF3\na/4HL7zwQjNjxoyBwT4OF52vAIEhiRBIgcDq1auHgkFiKCAfQxwXLd/5znds2dQR7DA7FAy2I6oI\nHtz2PN/BNMiI61lOUA9155G8+ZPUTR18Dj/88KFvfOMbSbI0Jg197vrUtbMfmNYBoF7tDIj/UDCt\nYj9l/N+BexBJdSgYgux31P9dHXCSDu1EwLSzWWpV0QjwoAoCHdkHYa+HZhF1Uwdkh4cvD+Gw3HPP\nPZEkJZwu7W/qzfIQLgsTyvU/rj3XXnvtEJ+2CPcXfR0lfvuLIp5R9VR9Luoeor38H0DSyiAg4TZT\nX2AVsfXxPyYRAv1AQESkHyg3vA4eSLwpYaHotzgLDIOuIwg8sDlm8CpDKDfNgEfaNOm76Uzd/sAb\nl9a9Icddb9p5+pc38l4Czknw6VVOXa/7ZCTq3u+X3ugBMYSUuP+7ftWtegYPAfmIVDAd1pQqmb/G\nDwR/kAceeCCzD0je9jKX/elPf9r87ne/M8GAZT7+8Y/bIsv0yaBs2p/EkTCPgyr1BINrB6I0q3hY\nIkwAKueU2CmkgQfsvrxkyZLUbQF7J+ARWA7cz8Z+06b77rvPOoBX2b/c/3PmzDE4lFf5/9/YjpTi\niREQEUkM1WAl5CE0ZcoU22j2najao54B+4tf/KLdN+app57qEIQ8JKBXj4JBYKHoOjimrd+V6erO\nM3h+6UtfMmyA1/TNyXDAhPBu3rzZwZLp2yd1OPMmIZGZKio50xVXXGG+/e1vm3vvvdcccMABJdfW\nu3hWMy1YsMCu0moqpr1bqRRVIiAiUiX6Na3bkZDDDjusNoMcOkGGeEivXLly2EMxLRlICzvlR1kq\nGPiQXm/h5HdS5ABJ/RAZLCq9dHD11/GbvYDOPvtsc9pppxWmXpjwRfVfYZUVWBD398svv2w3naMN\ndelXyOIll1wy7P+uwGarqAFHQERkwG+AcPPrSELCOjoygrUCcoLODMplvq1FxRuJI0A+8UD3MqdO\nwAJpqlUErKZOnWotT2Va3ei/wNfBYlUkGbQFFvTHJyFlYpFVXZGRrMgpXy8ERER6ITRg1+v+MHTd\n4fRkmgZhoOHtscwHOGQH0gPh8UmIP8ihS5nEg/J9QSfq86er/Ot1P8Y3ZP78+YVaQ3q1mT6ExDqp\ng7WE6Y+77rrLbNy4sdR72LU56zcxR4j3U3c9s7ZP+apBQESkGtxrWSsPmauvvrr0t9OiGn/ccceZ\nYEmxmTdvni3SJwdF1REuh0EMB8K//Mu/NKNHj7aXqx7IGMTQyZGysM51/V0XvX0iWYW1xFmFmkIm\nCTxHGPmHHnqorreW9GoYAiIiDeuwstR1b9ZVeumnbVuUzmWQkfAbNKGxISFVExAfL0gZUxxNCY/O\n4A9+WCbKnFLzMUpyHO7rsvuY+qjjuuuuM+edd14SFStPg85HHXWUXVHTFJ0rB00KdEVARKQrPINz\nEYfBIG5Ax7rQlJaHTcWQEySvkx+Exon/luwTnX5MBzkden2jC2QEsznh9ussTRrIfGtJnhVOcf3B\nyif2immadcFZcfjO+78Wh43ODw4CIiKD09exLXUPFef8GZuwphcgUWz45awBPllIqrI/4JAnys8j\niuSQD7+UOjyMb731VvOv//qv5j//8z9rZWXw+wASwrLwOq3I8vXrdkz/+zFfou6RbvnD1yivyaue\nmu4oHe4P/a4QgcGL4aYWhxEggmI/wkeH6y3qN1EgAyIwLAKkH6Eyqp6AdA2L0JkkemRcmUT7pLwq\nBd1oA1FwwaJqfeKwIHoqWwUkwTuujLqcB3P34R5IK2BRRbTitHrGpafNwdCVO6pwsCOwLYey7rzz\nzrjqBup8EECugwm49FvoB+rlE+yUXnr1/W9h6U0qpoLgja3TEeF/jm7Xiqm9f6W0JVQ4+3H4D3UG\nOn8w5qHpBg03aKdBmTzdhPp6pemWP+u1qHoZ4OpGRtCTcOFtISHh/grfX+Hr4d9uEAeXJgv3Gp88\n4p6nfA+SuIGeb4iHL1UTEXRx/XLiiSf6qpVy/CcBCJIGIzBq1CjjPg8++GDqlhAcjDDOTZe5c+fa\n5Y9+O1599VU7TcFUDYIp3X3SLPN1JnS/7PAx5VE2dTH90A9BLz7hKQJiiuD8iM/Ihg0b+qFK1zrc\ndMybb75pA3Wlwb5rwTW6yNScu7fcfcC9wIc+CstXvvIVEwzgtV6qG9Y56vdll11mFi5cmPmeJ6w/\nAdwQ/ocl9UHA9ccTTzxhsowtqVpSCr1pQaGODQZgjjAXdrvW76ajn/uEWXUvXdryVuba+bd/+7dD\nX/va16xlwllDirBSpC2DusG2TElSh9s0zbcUlalTVNlgh3Um71tzVNlNOedbS9x9WTeLVR4ssUZm\nmdoNticY2meffezzi+9BE/fc5jvts7sfWIX7h99liSwiwV0wqPLkk0+awFze+Lcy+o83T1aL8NbN\nG6lbEureTrP2MeVSRhpxdePIWoagE2/gfLoJIdOJTcGSbKwjZekTpQNWEFaEsKQYJ9qmRn6Nalva\nc/QT9xAfjm+77Tbjr8RKW17d0hOef8WKFanVCgZfs2PHDptv9uzZw/LzBu4svV/4whfstRtuuKFz\njmsXX3yx2bp167B8/g+sLYcffviwPJx76623/GTDjimPcl3dY8aMsdYA8rhzfIfL4HdYP9JxLqzj\n9OnTbVl+xWeeeaY95ywPfvspBwmm8YbpgJ5hCadZt27dsCSbNm0y4Om3BX1cvX5i7tFzzjnHnqKf\nHn/8cf9yscdFMhx8KXxrQaCptSYEjRhWTRA0q/MWDxMOX/fL4DgsMDPfmYZ64ury8ybVjzy+Dml9\nRHC+8tuIbmeddVYs6/XrAgvyMy/n2gVGYR0oz10Pfydl18zZZ3mT8TGt0zFvmzjehoU30iwWiqz5\nXP3M/6e1pri8Ud95ysMqEgyC1jKRBYsofaLOoaOri/urzLqi6m/CuWAH6SE+bRH6nGcQ32nEf+7x\nzPOFZ5h7rvEs9Z+H7rz7DuflGdotPc/TKAdMynFlhr+vv/76Ydf8MYuywunDv/3nd5Jnt99+ynJy\n0UUXdeqKsiJRj6ub674Vw7/m0vjf4ByWgHx0yivTV+SPLQxrkOJ32o4nPSA5EHwAwh0QvsnodD+v\nK8P/DudJqx9N9/9J/Juo17UsnR2uy2+Lf8wN7CTJzezSxn1TdtEDBVjTh/QpOkb1Fee4RhrSkqco\nYbCNahMkJe2DsigSQTlp6w7jQZucWT98LelvymCKhH7nO295fr2U7QgIpvqisPPraMsxDrtF41P1\n/x1twvE9qYQH73C+8DjgPwfDx+EBshsJcXl5BvmDNMdRzyqXPvztP7P853c4nf/b1Zfk2R1uv8Mn\nfD5MqHyiwrETn1D4OoWPw2MdOvtpwvW58vN+F0JEsnR8eMB2Het3qg8kDU16s1CGL1n08/UId07c\ntayd7Zfnd3rUMXUgSW5mH4PwcZz1IJwu6W/6z/8niNK92znyunsgaZ1R6brNV6d5+KdJG6VH+Bx4\nRxGkcLqo33nyRpWHHryRQ9qwIHGcpb3oxXJhLB/0Ld9ZyonSsc3nwKooqcv/HfdQGl8k//kfJhJg\nEx5wSeMGQcaB8PPPDfLhfP6zu9s1Xx/6x88XtoZw3T2rwlYUP1/4mnt2u76nHPdBN1/CurprYWLg\n10can0z59fljjI8l7fCxDBM0yvTzhuvjehGS+z8iDJivaLdrKO830L0du44BENfZrqGU7a7z7dcV\nvllcJ3TTods1Xze/nrDe/jU/T5rO9vOF20U7/DbTTl/8a7QnqfD2wqBdhKCj/w/g65TmmDJcv2XV\ni4dh3AMRqwSDZy9hoM5KGrqVTZlJ6vfLIH1ea4pfXviY+4BBBEJCX/FmC6FwOIa/Sct9A4nhQ1qm\n98rUMaxzk39D1MC4CKnT/x33APdCUvFfWsIvnJQRfjaHx4KwRcVd98v1Le1OL38M4bnrhOe1e1ZF\n5eOcu863q88vj+dXWPxne/j57JcXvhZuv1+u30YfOx8TdHHkzD/v6+7KJJ3//A7r4hOVKGxcOXm+\nczurfutb3wra9nsJlDSzZs1yP63zYNBRnd+3335755iDYIVD5zfLDRcsWND5zUZme++9d+c3B34Y\n5HBdV155pY3W6DK88sor9jCPfq6sJN84JLllaKRftmyZGTdunM1KO+644w4TdLb9HdzEsY4/4Xad\ndNJJJrjZbD7+sNlUERLcnDYaad6ycNKiz2lTXqEMygo7gqUpF4yfeeaZyCxu2Wiv5bUBYejpCBpZ\nQY+TwcBty8XZNIk4p1Snd5I8adMcf/zxNqz/5s2brTMc/4PsIxInu+++u11miW7gtHTpUrtzbpk6\nxunSxPMBYTN77rlnbtXr9n/HMy7Ns+mFF17oYLDvvvt2jqMOgsF8xFjgnq3h9H65RFsOy+TJkzun\neF7TH3xYouokKl/UOdLzvAoGYPvZvn27LQKHUJxicSb1xwRXft7vY445plPEf/zHf3SOn3322c7x\nJz7xCesQzYnXXnutcz4gXCOw9J1SSfiTn/ykk56D/fbbr/P7xRdf7BwXefCOvIVl6Xgajhx55JF2\nt1dICOI6jRvPJzT2YvDHv1mCNzh3uvP90ksvdY7dQR79XBlJvpN2tmtruLNdHVHt+sAHPuAu1+6b\nOCRhEkL/BSza7LHHHubggw+O1JkYHzy4gjfyYf1KWZQJscwiYfIaLoMVLQyicSthul0Ll5XlNwM2\ndVNP3IZqEKXAEhKrY5Z6k+RxusVhk6QMpemOQFEvAHX7vyNU/apVq7o33rvKxpFOeE50kwMOOKDb\n5WHX3BjCyZNPPnnYtagfkJCwjB8/Pnyq8xI54kJwgjICK4K54IILoi4Xfs5vF89LXoIhZt/73vc6\ndbGNgpPA4uEO7bPWrcLpnAwddCOUP/vZz0Kpi/mZm4hk6XhHRGgCb/v33XffsMHMt5S4ZobfkllW\nlUTy6pekDtIU1dlJ25VUr7h0WA1YdpdXIBK+8A+ZZNM1SCgC4eANYuLEiZ1iKDMrEekU0uXAEYHw\ngJskcFmXYlNdou6ofWrQASIS1i1V4UrcegTq9n+HtS+NhF9e0uStU1pISDDV1nmJdrph2R47dqxh\nFsAfg9z1PN+Mn7zo3X///bYYLCG8gC1evNj+xirsP0/z1NWvvH/Sr4ri6qEjwzclb8uS8hHoZT1I\nooFvpeKfLwkJCZfrLGPuvF+mO1f0N29w4YiXZU3JxOkejjfi4ny483H5dF4I+P8jTfq/cz2H1bQM\n8csNnEU70yZu+iT8HfUMxGoVlvAY5a7z4uWIBnWTjjq+/OUvR1r1Xb6838QFcoIlhLY68adlOMd0\nqhMITBiD8G9077fkJiJ5O56gR2HhXNgC4ltRSO/m48J5w7/z6hcuL+53Ezo7TveizvMGkFXy5M1S\nJ29wWB6cv0jZUzJxOjq/keXLl1v/kbRvlnHl6vzgIJDnfydP3jwI77XXXp3s3aYCOokSHhxxxBGd\nlElfaCEjzn+PzFE+ZlHnSEvAQCcXXnjhMP8LXrIdSXFpivomAJoTLCG+fv60DGkOOuggl9RgPUGv\nrJJmmixNHbmJSJaOdwoSzc2Zl7gRcKRBYJXOzOTSQkT8myXKx4JpDRcxDmchJI9+ru4k30V2dpL6\n8qZhXjYc8S9vmf4/Zdqy8uRNW5dLj+UBX4x+Tsm4ut238wc577zzrC6OGLnr+hYCvRDI87+TJ28v\nvbpdf9/73te5/OMf/7hznPfAd+TEZ8ONA5TLy60fNZWoq05cBFF+48fn5+PY+fa59FHfLKZwgzzT\nzZ/61KeikkWe86f2IxOETrrpGXfa6Rc1LYP/iHshZ2xFL//Zzzjsj53hKKtEq3biynG/C/sOzDK5\nhKU+gTKdj7+cNWj0sNgSQSM6dYWXDJEvvO6a374EJshOPdTpL/UML99lyRKSVT90de3y20SZcdf8\n8/7yXadHcJN0ykQvJ36+cJtJQ/1OFzDwxZ3nO6ynny587JZlhs+n/e0v7UIH+oG+TSqk9dtHGZSZ\nVVgemWZZcvDgGPrGN76Rtbpc+aKW87Jcl/P9FnAAO+4Lt0QXHMMfAqGRJvBRqETPfuNSdH0O37zl\n1u3/jvs2sOYlbpb/P8+zMiz+czvueeA/+xhrnPjPUz9N+Nh/BpM/fL3bb1dfeNzplsevD12j0ro0\nfvtJFyU+hq4sfzmvnydcnksf/ga7sPjjlj/mhtPl+R3dwpQlZul4n1TQUDd4+f9gYVCS3izhzsii\nn58nPMDHXcva2X55eYiIu6nczdytG4t6IEb9M6AHDxf6kutRH66Rxunsf4fx7taO8DUCbKXZYI3B\nl4G/34N/N8LRL32oB7yIawH+fDuiAS5RH9Jz70BQyJMnIFq47wbhN5imIcpxmNTt/y5tu8LPcvf8\nd+31n6VpiQhlxz1b3HMm6hnj1+nSue8w4XBEhG9/oHbp+WaMYyxy5yjDlygd3bM7rIufzx2DmSvb\nfXcjCnH3jMvLOOTaFVdHuJ9curzfhRCRtB2PtcI1nm//puh2jVopbAYAACaLSURBVMaGO8gvh2M6\nNwxWWv2oxycHvn69rmXpbL+utESk282MrnGS9sERVw5YR+kQ7pekv6P6L67uqPNpIjz6Az549Euo\nCwtEN0E3yEoZgjXDEQmIB7976ROnB21xAdEgJRCVrGXF1dGm8/QpOOWVuv3fpX0BoP3+cyM8gPrP\n+bRExGHLs9ivg2cQ5CDqGevycI363POKZzO6cN6d49sfYxizfMLh8lBmHIHhWjgf5VIX4ref83Hi\nt89/oY9LT51hndA3PMa5/PSLa3dcP7i0eb7jW5ih1KQd74MHCGEJW0vCLA0w/TQA1Q1MV35S/UhP\nea4Dwp3U7Rp503a2X17UPwn1O11oty/dbmY/XfiYgS6NKTWc3/+NDn6fOl3TflMGZeURBlgG1iQS\nJh/h30nKSJPGTX8kzZM2fa9yaR+DYFmEwREc7iusJpJoBPi/KIKs1en/DkILGUkj/mAbfq6lKacf\naf1nMAP+oIhPsBxJKqPthRKRMhRUmeUhwIBU5Fs3/6w+qUpKRMgTJntZW530IR9FOnwLSdb64/Ll\nsXCga56Bi7oJvw1BSDtYxLWn23n0hRByf0Xh3C3vIFxLQ5aT4FGH/7ssfY1VwU1rJHmbT4JF1jT+\nixTPI//ll5dDpyfPF9IOgtA/7hkOJmXKKAoPKpMMIAJXXHGFjXzKio0iBe/05557zgZ5Y437L37x\ni2HF/8Vf/IUhWixLnj/ykY8MW/I2LGHKHxs2bLDr93utBHDxQ4KBeUQNxPLgfJEhy6MCl42ouMeJ\nrGWAybnnnmumT59u5s+fX2i7eqhsWJIcvOkaljWyZYPk9wjcfPPNNvzAjTfeWCgkVf3f8f9EAL6A\n8KZuDytSXETSgFCVGnujm3K+Ht3ScS2wDGSKl9Sr3Lpd9zEpvc1lshyVXW8E2KiqqA24qm4p0wKz\nZ8+2/gq9dOn1lt7req/y/etYnPJYM/yy0lpV8N3ACpJ0qsqvq6hjdOYe41MUDkXpVkU5YMAqrdGj\nR7cGjyz+IT72zhpR9lu3X2fUse8bEpCCjjXAP677FFJUu7Kc861V/bAAaWomSy+1JA8PRQYqBoum\nC235q7/6K/uQh0jEkYm48377KauIKSvqoqwihfKStIE5ewb/ItpRhP7oU/RUYBF69aMM+oA+4+P6\nAyyqJIhFtjtvW/B1cYN9UVO0WduHH4TvF+H0wsEzyn8vaz11z0c/0HampPxpqrL01tRMgPYgC9Mz\nSNFm4n5j+vDDD5tbbrllWKRDoqU6IQCQm26JmpJx6dx3t+kblybu2wUpK3O/mG6b5tGnRHRcu3Zt\np81xuvbzPHqxWRtTZ20OY8+9409TRG1uyLTVI488MmxH8X72RVF1cR9OnTp1WHuLKlvlDA4CIiKD\n09eRLXXzu8GbWq0GrUhlu5w89NBDrQ/EaaedFpkKchBMRdldKknAXjO9CEmWsO/gSV39GGij/Ebq\nSkJcpzgy0vT7zbXHffukN8m9xT0CQSFfr/vQ1VHH79NPP90cffTR5tJLL62jetKpIQiIiDSko8pU\nk8GBEL9NfZhgDbnmmmvM5s2bY2EKk4okb60UFs4XW0FwIYoYdEtfxDWf+OAEedddd5mNGzfWmlTW\nnSwl6Rf6Opgm6yTNYv2iv9gjhNDgTRT+N7CGtI1UNrEvmq6ziEjTe7AA/RnMeIvDnNy0tzPeLI86\n6iizcOFCc/zxx0eiQfuQbm2LG1gon/y9LBw8lKNM8JEKFXwSHbH2/PM//7N5+umne+pacPWZimP3\n0MCHpTGracCYAddJEX1NmZSzZs0au+rEld2Ub1lDmtJT9ddTRKT+fdQXDdnxeMuWLY17O0uidxqr\nhgObPE7+93//13z0ox+NtDK4ASrLG7ErP++3G9BuvfVWEzc1lbeOovND7sDs3nvvjSWQRdeZtjz/\nHsDHqBcZTVs+6fEVWbRoUe2tWOG2Ob27WSHDefRbCMQhICISh8yAnWcww7IwZ84cU3RckbKgZKDA\nNMx3nLWDa3lJAthE+ZcwmHKtjAEqDWZMdbCV+tKlS9Nkqzytm1Kry1QS/ek7mea9b5ICjGUhWHnS\nGOsQ1kMsWk215CTtF6XrHwIiIv3DuvY18YDBVIwJuurBtRdYSawADCxIHEnpVYd/nfooD1z4JmDb\nu9/9bhPEg6hsSgb93KDQjYz57ajbcdXmfXBzksTJ1KUt8pv7CdLTBIsW/wdTpkyxLwBN9Skrsu9U\nVjEIiIgUg2NrSuEt9ZJLLqn1Ekv3MAwCIHVddlyENcTvWEdseGv2fQTi/Ev8vGUdVz2Q520XfdRP\nh8cq+6obVi4CLkt6ua/rKjNnzrTWt6Y62NYV10HXS0Rk0O+AiPbXeVWDIyF77rlnV3+WokkIMFE3\nUzS9pq78t+yyfAvQx1lDmr5qoUwyRZ8V7WQK9mXIv//7v9upUVbS1NEiWefnQhn9oTL7h4CISP+w\nblRN7qGzePHi2jwUHQkByG7BupzloogpGddplEn9DBBpSE54ICzS/E8fsV9P0/dxcVYR3z/D4Z7l\nu19EMItucXnc/bV169ZaWiTd86Db/11c23ReCPRCQESkF0IDfJ2HT10iYfKg/vSnP232228/u8rA\nRUmN6p40RCEqf/gclgfqc8QGcoE+Wd5ayecPuP4UT7jebr/Rgby01enVLX3drxGQrtsS7G76hzHt\nl5NpN53SXEN/xPWjmx6tg88I9xk+IYhIiIVBf0pA4E//XyAllKsiW4BAsNmRHfhZTbPvvvsaBosq\n5MEHH7R+BGeccYYdrN75znfGqlEGCWGA2GuvvTp1Uj9Levnupksng3cAocEq4j7btm0zP/7xjy2x\n+fWvfz2sHi/biMNvf/vbxr09j7jYwBPvete7zLPPPmu453oJg+Mrr7xiMWMQZ/oLUuYw7ZW/TtfD\nJATdDjzwQPu/NmvWLPPb3/7WfPzjH69EZf6XWA7O0vXbb789cvl6JYqp0tYhIItI67q0+AZhETjz\nzDPN/vvvb4gG6d7ciq9peIkMOCwnfuyxx6wVhCWO3awQUQ/14SWm+8WDuJvFomjSQ3t9f4Zu0zhY\nq5ocDTfcE/QdlgzfWuSn8Z1My/S78ess+7jX/cp1rIDIggULci9DT9oe7sOvfvWrlnxcd911PX2i\nkpardEIgFoGydtNTue1CgF1fb7rpJrsjIzupBgNGaQ10dQWEZ2jGjBmdHWw5HwzUsfUm2ZU2NrN3\ngXqSlpU0nVd84kMwpnz3QS8n7HjaDQuXrknffptcH0S1vUltitOVvk36P8T/Hf8LZf/foWvgjG3r\nmjZtWmL94tqo80IgKQImaUKlEwIgwMOTB2LAbO13kYMhZbuHLg/CqEHeDVDh3ohKG06T5Dc6pGkT\n6fn0Q9CLdr700ksW/37U2c86zj333KGrrrrKtjFNH/RTxyLqynLPcN/7/3dF3e+0h7L5v4MI8lm/\nfn0RzVQZQiAxAn8SayrRBSEQgQDTMjfeeGPHhE6ERT5M2TBVkVYwuRMumjKYiti+fbuN2Eicgiin\nQ3wsOE9dmJARTNjkzSvognSb/gnXAR7o4XQJXy/yN3rR9t/97ncmIGpFFl2Lsg4//HDzZ3/2Z7aN\nafqgFsonVKLXdExcMdz37v+OKTn8R/DZYouDLP936IFTLHFBmOrC52bJkiV248i4PZvidNN5IZAX\nAfmI5EVQ+Q3BmPDjCN6k7H41DJJjx461PgzAwxJTHnY7duywaO3atcume/755+3vyZMnm1NOOcVM\nmjQplUMcxIEHdPCGGUlabOEJ//Aw7+YP0qsY8kcRp175slyHuL366qtdg7llKbfqPGCIL0Rbg2Vl\nJSFx/cL/HRF+2eiQD5sIsqpswoQJnSzjx483r7/+uv0d9393xBFH9M3vq6OYDoSAh4CIiAeGDvMj\ngGUgMKvbFR08+BCsHOyF4j8gJ06caK0YWBTyyDe/+U3zvve9L5UVw6/P6ZuXRFAOA00/3uSxPiFt\nC7HdZiJSNAnx72GO3X3MSqpu/3cQk7333rsv92lYR/0WAnEIiIjEIaPztUfAPdyxikB+0pIJ8vMA\nL4o8YKGBWKFPmdJWIkJfYDkLJpbLhK/vZbv7NC/p7rviqlAI9AkB+Yj0CWhVUzwCTMm4gR8Swhs1\ng1kSyeIP0qtcCA2ESJINgbIJXDat8uVy95lISD4clbvdCIiItLt/W9u6KJ8MyAhvn+4NNK7x5GVg\nKGNwcIQorm6dHxwEnIWsjPtscFBUSwcBARGRQejllrURohG3SsZNs7g3Ub/pWEscgSnz7RvdepEh\nXy8d/x4BMGvLoO1ISJn3me4bIdAWBERE2tKTA9QONyUT12Rn7YB0OGGQ45PWj8TlT/NN/ZCepNNE\nacpuc1r6FSfmpotISNN7UPr3G4F39LtC1ddeBBh48ZFgWa5bKhjVWre0Fw9+9tVI8xbsLBpR5frn\neBN10yR/+qd/av76r/+6MKdUv564YywzSXWNKyPuPMuhN27cGHe5seeDwFqN1d0pLhLikNC3EEiO\ngIhIcqyUMgIBrAxPPvmkDUrmYhkcdthhNobI3LlzI3IYu7SXJb2LFy82q1atMkE0Rxugi03t3NRK\nVEbqipuSiUrPOVZh/OY3v4m7XOp54pIwMHVrUxYFxo0bZ/HOkrfOeYh3wb3QVBEJaWrPSe+qEdDy\n3ap7oKH1E5VxxYoV1voxffp0Q1CyD3/4w5mWrrrATOzwOXr0aDN//vzI4GZpLQykd0HKIDFYbIom\nBb26j3qRNFafXmXSjjYucyXKJ4Ht2PG1aSIS0rQek751QkBEpE690QBdIA3BnhdW0zjCkKcZPsG5\n9dZbO4NSGhLipojC/iBx5/PomyRvGt2TlEcawnsTkjvcxqT565gOa9dTTz3Vd7KYFwuRkLwIKv+g\nIyAiMuh3QML282ZPJE/8P3yCkDB76mQM3uynseeeexq2vGfgTWJVSGL5KIMY9Gpg0XWCCb4i8+bN\n61V1I64zmLPfEA6rTRKRkCb1lnStKwJaNVPXnqmRXlgpePNm/h5n1H6Yzqlv8+bNZurUqdZcn2T/\nEQYFpNf0C2VDDLCQ9EuKXNIL2WJPkfvuu8+2o19tKLOeBx980DDF1yThHoIca4luk3pNutYRAVlE\n6tgrNdKJN++VK1faHXGrmgaAYFx00UXWOnL33XdHPvgZFJw/SFL4KJdBJImlJWmZ3dJlfXsmn7+i\nBFKDzk2dyojCCIvXwoULTVN2fi3awhWFic4JgUFBQERkUHo6ZTuxFpx//vnm5z//ufn617/et8E6\nTk30mTNnjnnzzTfN2rVrO2Qkr99HkqmcOJ2ynE8ygIWJRxzBYgt4lkmzPXyTxfkdYQFrgiTpwya0\nQzoKgbogICJSl56okR4M7lOmTLEa+YN+HVTEQvPyyy9bMoKefHpNxfTSOy+Z6VW+f526ID++zgxs\nvsQRDz8Nx5SDVaRXgLdwvrr9Pv30083RRx/diN2ERULqdvdInzYgICLShl4suA0sowxbHgquIldx\nkJFvf/vb5t577zUHHHBArrL8zAwySUmAny/t8Te/+U2bhaXKSJ4pL7BAmmoVAXP8gPA9qruvhUiI\nvdX0RwgUjoCISOGQNrtAzP0EJqubJcRHFVM+JIRAZUmcWP28vY6L9htx1ha/Xucsm4eAuPKabhVh\npQxEhBVZdRaRkDr3jnRrOgIiIk3vwQL1Z4A/99xz7UqMfjlwZlWfAZ7pozIGsTx+I2HiQeAxfxrG\nb29Rg9vNN99snnnmmcJJma9rGcfLly83ixYtsqujyii/qDKL6qei9FE5QqBtCIiItK1HM7aHAZRp\nCSwNTVm5gPWCN+o1a9bkmt6IgswRil5WC5fOldGNeLg07pu8YX8Rdy3NN+UcddRR1pn3vPPOS5O1\nsrRl9l2RjRIJKRJNlSUEohEQEYnGZeDO4heCLF26tFFtL/utmoHI9xuBOPhBt9IQjyhgGZCLiEVB\nOeiJr0WcBSaq/irOQZzKsmYV2R6RkCLRVFlCIB4BEZF4bAbmCg/cpjgMRnUKVhEsAWVYAyAezz33\nnHn3u99t98FxMTyi9Mh6rqgBj8Bzl1xySe3DpEN633777VpPJRXVJ1nvCeUTAoOEgIjIIPV2TFub\ntHwyqglFEiksC1HBw/L4jUTp7J8raoqGMv3lzXVchVJ3/egLrEq9puT8/tOxEBAC+RAQEcmHX+Nz\nFzmIVwkGZOrUU09NbRUJEw9/GibcnjIHKYgOUoSTsBvs6xCIzsfQ6VXXFVlFEkK/3ToWAkKgOwLa\na6Y7PrFXDz/8cDNq1Cj7YRdUX9x5vjdt2uRf6nrMfht+3q6JC7p4xx13mLlz59Y+hkOv5hICnhUY\nvQTi5X8Y+Hn7dZ9uVgSukY78DFpFCnr4vid5yiamyGGHHWZ1hWhVLWAFUXSB6LphXJWuIiFVIa96\nhYAxtSUiDO5uUGbQlxSPAA/fZcuW2UGi+NL7W6Jb6QNJ8MUnHRw7wuG+swyK5MWC4awYfn15jh3J\nyVOGywsZYZdkLDw49FYlECFW9Oyxxx61jU0jElLV3aF6hcDvEXiHgBhcBNavX29mzJhRyHRAHVD8\nxCc+YYmVrwuDexnCyhRHRoqYTnE6QhwYvItY+cIuyd/5znfMrFmzzCOPPGKIN1Kkrk7nqG8GdzYo\n/PznP2/uueee1FNmUWWWcU4kpAxUVaYQSIdAbS0i6ZrR/9QvvfSSGRoash8e9E2URx991L6tNlH3\nsM4EY/vYxz5mp8KctaMsEuLqdoN6kdMfzkLDAFmEgMHGjRvNIYccYvelgYwUVXacfqzewQpCkDUc\nP8tYzRRXd5rzIiFp0FJaIVAiAsFgWit5/vnnh4LmRn6Cee8Rut55551DnPfzcG7Hjh2RaV26s846\ny16//vrrO3ldBpeGb/Thc+KJJ9p0lI34dbpzcfkff/zxTn7KpCzOheWBBx7o6EK6KEnT3qj8/rlg\nIB0K/BL8U40/rqJNwSqbocDyUCh2RZeHcgEpGJo2bdoQGF177bWF9j0YrF69eiggPPbDcZ0FfcFD\nIgSEQPUIRI92FeqVlIhANBw58ImDO95nn32GXn/99WEtYRB31yEi4fwusUvDt09U+O1IR1IicvXV\nV3fq9Mv1y3L1diMiWdrryo365iHMgNQ2YaANppwqaRbkgQGuKCmDjKAbfX/55Zfb+/LYY48dCqZO\nMg3KjnxQFvcS2NedgNB+kRBQkAiB+iDQWB8RfBueeOKJYDyPlmDgNieffLJ57bXXDNEvw3L//feH\nT0X+vuqqqyLPJz153XXXxSa94IILzMEHH2yOPPLI2DTuQt72unLc91tvvWUmTpzoflbyPX36dOP6\nIfiXKEQHtpMPCGglYeqZBmGahumVYGDO3R6Cp+GHUkRZvjL4n+DMOn/+fIOfEFN0AWG2SbgnwBAZ\nP378sP+drVu3ml27dplXXnnFvPjii2bLli0mIB922fRll11WuJ5WiYL/aDqmYEBVnBAoAIHa+Ygw\nKDMoBZaHTvMC64M9h18GwjJXn4SQljx8AqtCJx9kxP/dufCHA8oNLDCdvOHr7jcPaVd+Fn+QOP0o\nn71deklR7fXrYbB2A45/vqrj4C21kKoDS5gdKAspLEMhzsm0CL8RCEhRS3qjmgJhwqGVsP7U89RT\nTxmWQSMQjsWLF5sFCxZ0Pq+++qq9dsoppxhWtfE/we7H+IAUTZZsRQX/cc7Fro8KLl7FCQEhkBWB\n4GFSS2EKJGiT/TAN4kvwsOxcY+ojLHF5/fOUzTRQlLh6+Xa+JOF0aaZmwnnDegQPfZskbmoma3vD\n9fq/b7rppiE+VQrYOqzj+iKtfkxnMEVQtWD+L2pqpahyqsakyvrxhWqbP1SVeKpuIVAkArWziAQD\nU0954YUXOmmi3uonT57cuU4Qpbi37SRTIuxjkkeI9hmWj370o8NOMU3STYpqr18H5nWsB3UR97Zd\nF33y6oG1gamaIoKfuSW9eXUa1Pwu3ksTrDaD2kdq92Aj0EgiArlwgh+IC3zmvsMDbBQRYVomiey+\n++5JksWm2XvvvUdcC/usROnnZyqivX55HLPpWJRu4XT9+v2lL33J4IPQNoGMuCmBrG0reklvVj2a\nmE8kpIm9Jp0HDYFGEpFB66Q6t9ePgOuIYNJv56hK+5xz8Q033NA6QuJ8EvL4jVBGsNqlzrdC7XQT\nCaldl0ghIRCJQCOJiG/N8J1NgzmrjlOpf1zlmz9OoWEJW0B66VdGewm53WtKKKx32b8hI6xSwjrS\nNmFagE84BH2adrqpnjR5BjWtSMig9rza3UQEGrl894gjjrAbaAE4vgVJfD2q6hyiS5500knDqn/2\n2Wc7v5lG6kVEymjvhAkTrBWio4gOSkfA9xvptstvN0XKWtJLnVhsmB6DEG7fvt1s27ZtmCqQV+4b\npivHjRtnfWCGJajJD5GQmnSE1BACCRFohEVk586dw5pzzDHHdH4Ti8Pf/Za3/IsvvrjjN1L1hnnE\nEfH1YykuOjs555xz3GHsd1ntZYlm24SBlAGzzpLHbwSrCrEwigjTThmEY585c6YN/37mmWeaFStW\nWOjGjBljd2VmZ2b3YdkuAvnnHFNwOHMTNt4N/jZBhX+cHnJMrbATVLUQSIlAIywivKHx0GOKglgi\nZ5xxho1t4Jw4Gdj9wd3HgAdm1dJNPxe3oZuOZbSXYFXEicgrrFBieqxICTvzpikbcsVbe90Fnw8G\nTawQzockqc6kdzsJJ83jpyMv8XUWLlzYCUgWhHzvGQsEAuULRCZYWmwee+wxax1Br9mzZ9vYJH66\nfh2LhPQLadUjBApGoMi1wEWWxV4sQVOHfQIi0qkiICcjQrSH0xOvwxc/fodflp+GY78cYntECfld\nunA97jzf4RDx/rVwvrg4ItSfpb1RertzxFQI3hrdz9Z8VxniPQuIgb9Q5vDqafdKcTFW6HdiyBQZ\nV4N2VLnXjOKEZLn7lEcI1AOB2k7N4FcRDNSxsS7wq1i3bp1NE+wZE4zvfxQiofKWniUK6h9LKeaI\nMOa8fWLNcYK+AdFKpV/R7XWm6zwrOVx76vS9atUqc+CBB9ZJpa664DdCX6R1YiUfH2cF6FYJlgsc\ngKdOnWqj6bL65tJLL+1pAelWZvgauhCldfPmzTZ0/DXXXGOnbfpxf7k63D0d1k2/hYAQqDcCo+BD\n9VZR2pWFAL4BbNde123a07Z7w4YNJtiAzQ6GafPWIT1kJK0Ta68pGq5DyPfff3/ry9HPwRrfkc9/\n/vMmsL5Y4lMGxpAQ2gQRkggBIdBMBGprEWkmnM3SGufD5cuXN0vpLto+99xz1uehS5JaX8rixNpt\nSS99ixVkzpw5dk+YfpIQgMbqgvVlzZo11iG2CAdbvwNFQnw0dCwEmouALCLN7bvcmjMw4BgazK8X\naqbPrVjGAljaysZtaZ0/M1ZXWjamW+ibpO1w0zM+0bjiiivMypUra4EHbYEMvfnmm2bt2rWFWC9E\nQkq7/VSwEOg7ArKI9B3y+lSIOZupjGXLltVHqYyasAx19OjRiQfvjNX0JRuEgk9SvxHSMtjzQSAh\nrCjDGpGUzJTZMO4zdvjFT2rKlCkdPbPWKRKSFTnlEwL1REAWkXr2S9+0YrDDfM+g1eR59g984APm\nkksuMTNmzOgbdv2oiP5J6jdCWhyjISFFWR6KbqMjSVn1EwkpukdUnhCoHgFZRKrvg0o1wMdg4sSJ\n5u67765UjzyVYw3B55qYJgzG/idPuXXIm8ZvhGBuTMd8/etfry2pvPHGG81+++1nzj///NTwioSk\nhkwZhEAjEJBFpBHdVK6SDNxYRfj2/QzKrbW40g899FC7ZJTlo2GhTb7gE1OH6QpfpyTHvfxGGKSx\nnNRlOqZbm5hCYorm2GOPNfPmzeuWtHNNJKQDhQ6EQOsQEBFpXZdmaxAm87ffftvO5WcroZpcLBFl\nVQZOqkmEQZDB2pekUx9+niqOne5YSXzhPMuwcQhtylJsiAXh4em7cHv8tnEsEhJGRL+FQLsQEBFp\nV39mbg2DGQPyrbfeWlmI7rTKF2UFoJzwjsi9Bse0uhaZHiuPT54IVrZlyxa7RLfIesoui+XFixYt\n6hr3JdzWsnVS+UJACPQfARGR/mNe2xp56DNF04QlsGVbAcJTOiwNrtO0FeTJORejW1OXYGMV4Z4j\n5khY6IM6E8KwvvotBIRANgRERLLh1tpcTHXcddddZuPGjZ2Bro6NZQBjOSjOj/0QfDQY7H3xrRL+\n+X4do9MXv/hFs++++yb2teiXbknrceQ3vGpLJCQpgkonBJqPgIhI8/uw8BbkXWJZuEKhAuuiX3hK\np9+OsBARrCHoccABB4RQas7P008/3e6B46wiIiHN6TtpKgSKQEBEpAgUW1hGXQZ7H1qmY9hMra5x\nMtAv7Ahb5pQOfYT0yyrk90WRxxAP9sNhwzyRkCKRVVlCoBkIiIg0o58q0ZKBbv369TZIVtVLXhnk\nWfKJZA2GVQWIUVM6Rfk9QHKa4M+TBHeWYF9wwQXm4osvTpJcaYSAEGgRAu9oUVvUlIIR4E0bnxH8\nMapcTcNbMg6N06dPt/FCnJNmwc0tpTgcXMNOrrTHlyxTOuw0DDmsmiD67chzPG3aNLsXTZ4ylFcI\nCIFmIiCLSDP7ra9aOyJA5NJrr712xMBaljJYQb761a+a22+/3Vx33XWNiZGRFo+oKZ1ejrBYq3bf\nfffGOqmGMcLPBcIbdggOp9NvISAE2oeAiEj7+rSUFjFY4p9BCPG5c+faEN1lWiYI2059+++/v7XK\nhK0KpTSyRoWGHWFRzZ/SYSpjyZIlw87VSP1MqrRpqikTAMokBAYUARGRAe34rM3GOrJgwQLz/PPP\nW0LCioeiSAJkZ/Xq1TbIFfotXLjQHH/88VlVbV0+N6Xz29/+1hxzzDGNjR0S1zEzZ860EWKbEh02\nrh06LwSEQDoEtOldOrwGPjVv5Q899JANzb19+3a7fJQBhCiZOGamFcgH1g/KwFfikUcesQSEFRQi\nIcPRBHs+7373uw0+FQiWk7bIhAkTDPeURAgIgcFCQBaRwervwlsLkWBlzaOPPmoee+wxM3r0aDud\ncvTRR9u62NnXF3aI3bVrl3nllVfMiy++aEOTM6ieeuqp5oQTTijMuuLX2bZjSB8B55YuXdqqpuGA\nu3jx4saFqm9VJ6gxQqACBLRqpgLQ21QlfiLseut2vuUNHbKxY8cOSziYxvFl7NixZsyYMeaUU04x\nn/vc51rl4+C3s8xjiBzWg7YJFjGJEBACg4eAiMjg9XmpLW7TktJSgVLhIxDAWRXfI4kQEAKDhYB8\nRAarv9VaIVBbBHB6zuJnVNsGSTEhIAQSISAikggmJRICQkAICAEhIATKQEBEpAxUVaYQEAKpEZA1\nJDVkyiAEWoGAiEgrulGNEALNR4Coqm5ZcvNboxYIASGQFAERkaRIKZ0QqAkChHZXvI2adIbUEAJC\nIDcCIiK5IVQBQqC/CIwbN85s27atv5X2oTZWzBxyyCF9qElVCAEhUCcERETq1BvSRQgkQIAN8Vat\nWpUgZbOSEOSOGDMSISAEBgsBEZHB6m+1tgUIEEQOy0GbwrvTLUTaPfLII1vQQ2qCEBACaRAQEUmD\nltIKgZogMGnSJLNu3bqaaJNfDUjVzp07DQHxJEJACAwWAiIig9Xfam1LEJg8ebLdeLAlzTGbNm0y\n06dPb0tz1A4hIARSICAikgIsJRUCdUGAnYmxIrQl9saiRYsM5EoiBITA4CEgIjJ4fa4WtwQBLAhf\n+cpXGt+a7373u3ZaBnIlEQJCYPAQGDUUyOA1Wy0WAs1HAGvIhz70IfODH/zA4MDaVDn99NPN0Ucf\nbS699NKmNkF6CwEhkAMBWURygKesQqBKBNgkbuLEiebuu++uUo1cdWMNIX7I+eefn6scZRYCQqC5\nCMgi0ty+k+ZCwPqJEFeE8OgQk6aJrCFN6zHpKwSKR0AWkeIxVYlCoG8IsNz12muvbeS0xvLly80b\nb7wha0jf7hZVJATqiYAsIvXsF2klBBIj8Ktf/cpgFbnuuuvMeeedlzhflQmdf8uaNWusn0uVuqhu\nISAEqkVARKRa/FW7ECgEAXwtpk6dap566qlGBAU77rjjzLHHHmvmzZtXSPtViBAQAs1FQFMzze07\naS4EOgiwembu3LnmzDPPNFhI6ixXXHGFVU8kpM69JN2EQP8QkEWkf1irJiFQOgIM8i+//LJZu3Zt\nLZf0ot/69evNxo0ba6lf6R2kCoSAEBiBgCwiIyDRCSHQXARuvPFGc9hhh5kpU6bUzjICCVm5cqV5\n4IEHREKae4tJcyFQOAIiIoVDqgKFQLUI+GSkDjv0MlXkLDVN8WGptgdVuxAYLARERAarv9XaAUEA\nMoIzKE6hGzZsqKzVrI7BOuOmi7S7bmVdoYqFQG0REBGpbddIMSGQDwGcQe+9915z7rnnmpkzZ/Z9\nqoY4ITjRQoiwhDQ5DH2+nlBuISAEuiEgItINHV0TAg1HgI3k2IsGIdbIzTffXHqLWEqMJYYddYkT\notUxpUOuCoRAoxHQqplGd5+UFwLJEYAgLFiwwEYz/exnP2sjmhZppXj44YfNihUr7N4xTQqulhxB\npRQCQqAMBEREykBVZQqBGiMAIbnjjjvMsmXLzIwZM8wpp5xiJk2alGnqhLIef/xxc/vtt5vRo0eb\nOXPmmE9+8pOZyqoxZFJNCAiBEhEQESkRXBUtBOqMAI6kTz75pHnkkUfMqlWrrC8HS3/HjBljxo8f\nb3bbbbcR6rNT7q5du8yWLVtsnkMOOcRMmzbNnHHGGY2I6DqiQTohBIRA5QiIiFTeBVJACNQDAawb\nW7dutUTjmWeeiVRq7NixHaJy4IEHNnLH38iG6aQQEAKVISAiUhn0qlgICAEhIASEgBDQqhndA0JA\nCAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUhICJSGfSqWAgIASEgBISA\nEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJCQERE94AQEAJCQAgIASFQGQIiIpVBr4qF\ngBAQAkJACAgBERHdA0JACAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUh\nICJSGfSqWAgIASEgBISAEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJC4P8Di13nEo+f\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'': 0,\n", " 'inhabitants': 1,\n", " 'goku': 2,\n", " 'stunts': 3,\n", " 'catepillar': 4,\n", " 'kristensen': 5,\n", " 'goddess': 7,\n", " 'offing': 49797,\n", " 'distroy': 8,\n", " 'unexplainably': 9,\n", " 'concoctions': 10,\n", " 'petite': 11,\n", " 'paramilitary': 24759,\n", " 'scribe': 12,\n", " 'stevson': 13,\n", " 'senegal': 6,\n", " 'sctv': 14,\n", " 'soundscape': 15,\n", " 'rana': 16,\n", " 'immortalizer': 18,\n", " 'rene': 67354,\n", " 'eko': 23,\n", " 'planning': 20,\n", " 'akiva': 21,\n", " 'plod': 22,\n", " 'orderly': 24,\n", " 'zeleznice': 25,\n", " 'critize': 29,\n", " 'baguettes': 25649,\n", " 'jefferies': 30,\n", " 'uncertainties': 61695,\n", " 'mountainbillies': 31,\n", " 'steinbichler': 32,\n", " 'vowel': 33,\n", " 'rafe': 34,\n", " 'donig': 68719,\n", " 'tulipe': 36,\n", " 'clot': 37,\n", " 'hack': 12526,\n", " 'distended': 38,\n", " 'cornered': 37116,\n", " 'impatiently': 40,\n", " 'batrice': 12525,\n", " 'unfortuntly': 41,\n", " 'lung': 42,\n", " 'scapegoats': 43,\n", " 'pscychosexual': 45,\n", " 'outbid': 46,\n", " 'obit': 47,\n", " 'sideshows': 48,\n", " 'jugde': 49,\n", " 'kevloun': 51,\n", " 'quartier': 53,\n", " 'harp': 61948,\n", " 'unravelling': 54,\n", " 'antiques': 56,\n", " 'strutts': 57,\n", " 'tilts': 58,\n", " 'disconcert': 59,\n", " 'dossiers': 60,\n", " 'sorriest': 61,\n", " 'craftsman': 49412,\n", " 'blart': 62,\n", " 'dependence': 37120,\n", " 'sated': 61698,\n", " 'iberia': 63,\n", " 'sagan': 72,\n", " 'frmann': 65,\n", " 'daniell': 66,\n", " 'rays': 67,\n", " 'pried': 68,\n", " 'khoobsurat': 69,\n", " 'leavitt': 70,\n", " 'caiano': 71,\n", " 'attractiveness': 73,\n", " 'kitaparaporn': 74,\n", " 'hamilton': 75,\n", " 'massages': 76,\n", " 'horgan': 78,\n", " 'chemist': 79,\n", " 'audrey': 80,\n", " 'yeow': 55655,\n", " 'jana': 81,\n", " 'dutch': 82,\n", " 'pinchot': 24773,\n", " 'override': 83,\n", " 'dwervick': 63223,\n", " 'spasms': 84,\n", " 'resumed': 85,\n", " 'tamale': 66259,\n", " 'calibanian': 49636,\n", " 'stinson': 86,\n", " 'widows': 87,\n", " 'stonewall': 88,\n", " 'palatial': 89,\n", " 'neuman': 90,\n", " 'abandon': 91,\n", " 'lemmings': 65314,\n", " 'anglophile': 92,\n", " 'ertha': 61706,\n", " 'chevette': 94,\n", " 'unscary': 95,\n", " 'spoilerific': 97,\n", " 'neworleans': 67639,\n", " 'metamorphose': 17,\n", " 'brigand': 99,\n", " 'cheating': 41603,\n", " 'clued': 101,\n", " 'dermatonecrotic': 102,\n", " 'grady': 103,\n", " 'mulligan': 104,\n", " 'ol': 105,\n", " 'incubation': 107,\n", " 'plaintiffs': 110,\n", " 'snden': 109,\n", " 'fk': 111,\n", " 'deply': 112,\n", " 'franchot': 113,\n", " 'henstridge': 19,\n", " 'cyhper': 114,\n", " 'verbose': 26,\n", " 'mazovia': 116,\n", " 'elizabeth': 117,\n", " 'palestine': 118,\n", " 'robby': 119,\n", " 'wongo': 120,\n", " 'moshing': 121,\n", " 'mstified': 12543,\n", " 'eeeee': 122,\n", " 'doltish': 123,\n", " 'bree': 124,\n", " 'postponed': 125,\n", " 'debacles': 127,\n", " 'amplify': 27,\n", " 'kamm': 128,\n", " 'phantom': 18893,\n", " 'boylen': 136,\n", " 'rolando': 131,\n", " 'premises': 133,\n", " 'bruck': 134,\n", " 'loosely': 135,\n", " 'wodehousian': 139,\n", " 'onishi': 70389,\n", " 'encapsuling': 140,\n", " 'partly': 141,\n", " 'stadling': 144,\n", " 'calms': 143,\n", " 'darkie': 148,\n", " 'wheeling': 147,\n", " 'ursla': 15875,\n", " 'subsidized': 49420,\n", " 'mckellar': 149,\n", " 'ooookkkk': 151,\n", " 'milky': 152,\n", " 'unfolded': 153,\n", " 'degrades': 154,\n", " 'authenticating': 155,\n", " 'writeup': 12548,\n", " 'rotheroe': 156,\n", " 'beart': 157,\n", " 'intoxicants': 160,\n", " 'grispin': 159,\n", " 'cannes': 61718,\n", " 'antithetical': 70398,\n", " 'nnette': 161,\n", " 'tsukamoto': 163,\n", " 'antwones': 44205,\n", " 'stows': 164,\n", " 'suddenness': 165,\n", " 'vol': 61720,\n", " 'waqt': 166,\n", " 'camazotz': 168,\n", " 'paps': 55042,\n", " 'shakher': 170,\n", " 'terminate': 63868,\n", " 'kotex': 56419,\n", " 'delinquency': 171,\n", " 'bromwell': 25214,\n", " 'insecticide': 173,\n", " 'charlton': 174,\n", " 'nakada': 177,\n", " 'titted': 24791,\n", " 'urbane': 178,\n", " 'depicted': 54491,\n", " 'sadomasochistic': 179,\n", " 'hyping': 181,\n", " 'yr': 182,\n", " 'hebert': 183,\n", " 'waxwork': 12990,\n", " 'deathrow': 185,\n", " 'nourishes': 24792,\n", " 'unmediated': 187,\n", " 'tamper': 37143,\n", " 'soad': 190,\n", " 'alphabet': 189,\n", " 'donen': 191,\n", " 'lord': 192,\n", " 'recess': 193,\n", " 'watchably': 61023,\n", " 'handsome': 194,\n", " 'vignettes': 196,\n", " 'pairings': 198,\n", " 'uselful': 199,\n", " 'sanders': 200,\n", " 'outbursts': 72891,\n", " 'nots': 201,\n", " 'hatsumomo': 202,\n", " 'actioned': 18292,\n", " 'krimi': 24797,\n", " 'appleby': 203,\n", " 'tampax': 204,\n", " 'sprinkling': 205,\n", " 'defacing': 206,\n", " 'lofty': 207,\n", " 'verger': 213,\n", " 'tablespoons': 211,\n", " 'bernhard': 212,\n", " 'goosebump': 64565,\n", " 'acumen': 214,\n", " 'percentages': 215,\n", " 'wendingo': 216,\n", " 'resonating': 217,\n", " 'vntoarea': 218,\n", " 'redundancies': 219,\n", " 'strictly': 57081,\n", " 'pitied': 221,\n", " 'belying': 222,\n", " 'michelangelo': 53153,\n", " 'gleefulness': 223,\n", " 'environmentalist': 24803,\n", " 'gitane': 226,\n", " 'corrected': 66547,\n", " 'journalist': 227,\n", " 'focusing': 228,\n", " 'plethora': 229,\n", " 'his': 39,\n", " 'citizen': 230,\n", " 'south': 55579,\n", " 'clunkers': 232,\n", " 'pendulous': 55991,\n", " 'mounds': 24805,\n", " 'deplorable': 233,\n", " 'forgive': 234,\n", " 'proplems': 235,\n", " 'bankers': 237,\n", " 'aqua': 238,\n", " 'donated': 239,\n", " 'disbelieving': 240,\n", " 'acomplication': 241,\n", " 'contrasted': 243,\n", " 'muzzle': 44,\n", " 'amphibians': 72141,\n", " 'springs': 246,\n", " 'reformatted': 49443,\n", " 'toolbox': 247,\n", " 'contacting': 248,\n", " 'washrooms': 250,\n", " 'raving': 251,\n", " 'dynamism': 252,\n", " 'mae': 253,\n", " 'disharmony': 255,\n", " 'molls': 72979,\n", " 'dewaere': 12569,\n", " 'untutored': 256,\n", " 'icarus': 257,\n", " 'taint': 258,\n", " 'kargil': 259,\n", " 'captain': 260,\n", " 'paucity': 261,\n", " 'fits': 262,\n", " 'tumbles': 263,\n", " 'amer': 264,\n", " 'bueller': 265,\n", " 'cleansed': 267,\n", " 'shara': 269,\n", " 'humma': 270,\n", " 'outa': 272,\n", " 'piglets': 273,\n", " 'gombell': 274,\n", " 'supermen': 275,\n", " 'superlow': 276,\n", " 'kubanskie': 280,\n", " 'goode': 278,\n", " 'disorganised': 45570,\n", " 'zenith': 281,\n", " 'ananda': 282,\n", " 'matlin': 284,\n", " 'particolare': 50,\n", " 'presumptuous': 286,\n", " 'rerun': 287,\n", " 'toyko': 288,\n", " 'bilb': 291,\n", " 'sundry': 290,\n", " 'fugly': 292,\n", " 'orchestrating': 293,\n", " 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'unknowingly': 937,\n", " 'flatmates': 51897,\n", " 'unnerve': 938,\n", " 'caning': 939,\n", " 'shortland': 146,\n", " 'recluse': 941,\n", " 'dcreasy': 942,\n", " 'scratchiness': 24911,\n", " 'pms': 30930,\n", " 'chipmunk': 943,\n", " 'tkachenko': 49537,\n", " 'dipper': 944,\n", " 'europeans': 61601,\n", " 'berserkers': 948,\n", " 'shys': 947,\n", " 'monte': 68505,\n", " 'eve': 949,\n", " 'luxury': 61828,\n", " 'conflagration': 950,\n", " 'water': 46389,\n", " 'irks': 951,\n", " 'positronic': 954,\n", " 'cushy': 150,\n", " 'swiftness': 957,\n", " 'underimpressed': 964,\n", " 'imprint': 959,\n", " 'sundance': 961,\n", " 'aida': 31951,\n", " 'thematically': 963,\n", " 'uno': 965,\n", " 'expressly': 966,\n", " 'russkies': 967,\n", " 'discos': 968,\n", " 'shaping': 969,\n", " 'verson': 970,\n", " 'blushed': 61831,\n", " 'prototype': 971,\n", " 'lifewell': 976,\n", " 'trafficker': 973,\n", " 'crucifixions': 62188,\n", " 'unrealistically': 975,\n", " 'rivas': 977,\n", " 'consequent': 978,\n", " 'katsu': 979,\n", " 'titantic': 980,\n", " 'jalees': 981,\n", " 'ranee': 982,\n", " 'gambles': 984,\n", " 'dispenses': 985,\n", " 'disfigurement': 986,\n", " 'bright': 987,\n", " 'cristian': 988,\n", " 'subculture': 37268,\n", " 'capta': 991,\n", " 'jewel': 992,\n", " 'erect': 993,\n", " 'avoide': 996,\n", " 'inconnu': 997,\n", " 'headquarters': 998,\n", " 'babbling': 1000,\n", " 'pac': 1001,\n", " 'performace': 1003,\n", " 'dorrit': 1004,\n", " 'runners': 1005,\n", " 'sentimentality': 1006,\n", " 'marred': 1007,\n", " 'commemorative': 1008,\n", " 'helpers': 1012,\n", " 'chiles': 1011,\n", " 'snowy': 1013,\n", " 'cheddar': 1014,\n", " 'neath': 158,\n", " 'outshine': 1016,\n", " 'nadu': 1019,\n", " 'wellbeing': 1020,\n", " 'envisioned': 43779,\n", " 'fanaticism': 1021,\n", " 'morrisette': 12687,\n", " 'sesame': 1024,\n", " 'gran': 1023,\n", " 'marlina': 1025,\n", " 'artificiality': 1030,\n", " 'coinsidence': 1027,\n", " 'founders': 1028,\n", " 'dismissably': 1029,\n", " 'dracht': 66299,\n", " 'scavengers': 1031,\n", " 'neese': 12685,\n", " 'pangborn': 1034,\n", " 'elmore': 1039,\n", " 'bristol': 71162,\n", " 'lillies': 1035,\n", " 'parkers': 1036,\n", " 'skipped': 1038,\n", " 'clipboard': 1042,\n", " 'jucier': 1041,\n", " 'haifa': 1043,\n", " ...}" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word2index = {}\n", "\n", "for i,word in enumerate(vocab):\n", " word2index[word] = i\n", "word2index" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 *= 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_target_for_label(label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[0])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'NEGATIVE'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[1]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 3: Building a Neural Network" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "- Start with your neural network from the last chapter\n", "- 3 layer neural network\n", "- no non-linearity in hidden layer\n", "- use our functions to create the training data\n", "- create a \"pre_process_data\" function to create vocabulary for our training data generating functions\n", "- modify \"train\" to train over the entire corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Where to Get Help if You Need it\n", "- Re-watch previous week's Udacity Lectures\n", "- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (40% Off: **traskud17**)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] += 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):587.5% #Correct:500 #Tested:1000 Testing Accuracy:50.0%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):89.58 #Correct:1250 #Trained:2501 Training Accuracy:49.9%\n", "Progress:20.8% Speed(reviews/sec):95.03 #Correct:2500 #Trained:5001 Training Accuracy:49.9%\n", "Progress:27.4% Speed(reviews/sec):95.46 #Correct:3295 #Trained:6592 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-62-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):96.39 #Correct:1247 #Trained:2501 Training Accuracy:49.8%\n", "Progress:20.8% Speed(reviews/sec):99.31 #Correct:2497 #Trained:5001 Training Accuracy:49.9%\n", "Progress:22.8% Speed(reviews/sec):99.02 #Correct:2735 #Trained:5476 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-64-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m 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120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):98.77 #Correct:1267 #Trained:2501 Training Accuracy:50.6%\n", "Progress:20.8% Speed(reviews/sec):98.79 #Correct:2640 #Trained:5001 Training Accuracy:52.7%\n", "Progress:31.2% Speed(reviews/sec):98.58 #Correct:4109 #Trained:7501 Training Accuracy:54.7%\n", "Progress:41.6% Speed(reviews/sec):93.78 #Correct:5638 #Trained:10001 Training Accuracy:56.3%\n", "Progress:52.0% Speed(reviews/sec):91.76 #Correct:7246 #Trained:12501 Training Accuracy:57.9%\n", "Progress:62.5% Speed(reviews/sec):92.42 #Correct:8841 #Trained:15001 Training Accuracy:58.9%\n", "Progress:69.4% Speed(reviews/sec):92.58 #Correct:9934 #Trained:16668 Training Accuracy:59.5%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-66-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m 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120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Neural Noise" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "image/png": 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id8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 *= 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "review_counter = Counter()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for word in reviews[0].split(\" \"):\n", " review_counter[word] += 1" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('.', 27),\n", " ('', 18),\n", " ('the', 9),\n", " ('to', 6),\n", " ('i', 5),\n", " ('high', 5),\n", " ('is', 4),\n", " ('of', 4),\n", " ('a', 4),\n", " ('bromwell', 4),\n", " ('teachers', 4),\n", " ('that', 4),\n", " ('their', 2),\n", " ('my', 2),\n", " ('at', 2),\n", " ('as', 2),\n", " ('me', 2),\n", " ('in', 2),\n", " ('students', 2),\n", " ('it', 2),\n", " ('student', 2),\n", " ('school', 2),\n", " ('through', 1),\n", " ('insightful', 1),\n", " ('ran', 1),\n", " ('years', 1),\n", " ('here', 1),\n", " ('episode', 1),\n", " ('reality', 1),\n", " ('what', 1),\n", " ('far', 1),\n", " ('t', 1),\n", " ('saw', 1),\n", " ('s', 1),\n", " ('repeatedly', 1),\n", " ('isn', 1),\n", " ('closer', 1),\n", " ('and', 1),\n", " ('fetched', 1),\n", " ('remind', 1),\n", " ('can', 1),\n", " ('welcome', 1),\n", " ('line', 1),\n", " ('your', 1),\n", " ('survive', 1),\n", " ('teaching', 1),\n", " ('satire', 1),\n", " ('classic', 1),\n", " ('who', 1),\n", " ('age', 1),\n", " ('knew', 1),\n", " ('schools', 1),\n", " ('inspector', 1),\n", " ('comedy', 1),\n", " ('down', 1),\n", " ('about', 1),\n", " ('pity', 1),\n", " ('m', 1),\n", " ('all', 1),\n", " ('adults', 1),\n", " ('see', 1),\n", " ('think', 1),\n", " ('situation', 1),\n", " ('time', 1),\n", " ('pomp', 1),\n", " ('lead', 1),\n", " ('other', 1),\n", " ('much', 1),\n", " ('many', 1),\n", " ('which', 1),\n", " ('one', 1),\n", " ('profession', 1),\n", " ('programs', 1),\n", " ('same', 1),\n", " ('some', 1),\n", " ('such', 1),\n", " ('pettiness', 1),\n", " ('immediately', 1),\n", " ('expect', 1),\n", " ('financially', 1),\n", " ('recalled', 1),\n", " ('tried', 1),\n", " ('whole', 1),\n", " ('right', 1),\n", " ('life', 1),\n", " ('cartoon', 1),\n", " ('scramble', 1),\n", " ('sack', 1),\n", " ('believe', 1),\n", " ('when', 1),\n", " ('than', 1),\n", " ('burn', 1),\n", " ('pathetic', 1)]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_counter.most_common()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 4: Reducing Noise in our Input Data" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] = 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):91.50 #Correct:1795 #Trained:2501 Training Accuracy:71.7%\n", "Progress:20.8% Speed(reviews/sec):95.25 #Correct:3811 #Trained:5001 Training Accuracy:76.2%\n", "Progress:31.2% Speed(reviews/sec):93.74 #Correct:5898 #Trained:7501 Training Accuracy:78.6%\n", "Progress:41.6% Speed(reviews/sec):93.69 #Correct:8042 #Trained:10001 Training Accuracy:80.4%\n", "Progress:52.0% Speed(reviews/sec):95.27 #Correct:10186 #Trained:12501 Training Accuracy:81.4%\n", "Progress:62.5% Speed(reviews/sec):98.19 #Correct:12317 #Trained:15001 Training Accuracy:82.1%\n", "Progress:72.9% Speed(reviews/sec):98.56 #Correct:14440 #Trained:17501 Training Accuracy:82.5%\n", "Progress:83.3% Speed(reviews/sec):99.74 #Correct:16613 #Trained:20001 Training Accuracy:83.0%\n", "Progress:93.7% Speed(reviews/sec):100.7 #Correct:18794 #Trained:22501 Training Accuracy:83.5%\n", "Progress:99.9% Speed(reviews/sec):101.9 #Correct:20115 #Trained:24000 Training Accuracy:83.8%" ] } ], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):832.7% #Correct:851 #Tested:1000 Testing Accuracy:85.1%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyzing Inefficiencies in our Network" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { 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kXFTbwvXm0k/K0NchI8Tp5BOdPl86uRb3F2dRbZa6Cu3Jq/u4UPq0XIfglrLm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\nAghYEyB4WesY9SKAAAIIIICAWYFnu/zIiInRwogAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0 = np.zeros(10)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0[4] = 1\n", "layer_0[9] = 1" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.])" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "weights_0_1 = np.random.randn(10,5)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.10503756, 0.44222989, 0.24392938, -0.55961832, 0.21389503])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0.dot(weights_0_1)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true }, "outputs": [], "source": [ "indices = [4,9]" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1 = np.zeros(5)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for index in indices:\n", " layer_1 += (weights_0_1[index])" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.10503756, 0.44222989, 0.24392938, -0.55961832, 0.21389503])" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_1" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "image/png": 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apXPHaKqJjr744WSiQNCj0ezqd865210y9hI/u3m/vfdKvdUE4BGUGvC09FTb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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='sentiment_network_sparse_2.png')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
tavallaie/pypot
samples/notebooks/Controlling a Poppy Creature using SNAP.ipynb
3
17983
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How-To: Control a PoppyCreature using the visual programming language [Snap!](http://snap.berkeley.edu) *(a variant of Scratch)*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![alt text](image/snap-header.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook will describe how:\n", "* you can **connect the visual programming language [Snap!](http://snap.berkeley.edu)** to a Poppy Creature \n", "* and how you can **control it in real time** using poppy's custom blocks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Snap!](http://snap.berkeley.edu/) is a *\"very powerful visual, drag-and-drop programming language. It is an extended reimplementation of [Scratch](http://scratch.mit.edu) (a project of the Lifelong Kindergarten Group at the MIT Media Lab) that allows you to Build Your Own Blocks\"*. It is an extremely efficient tool to learn how to program for kids or even college students and also a powerful prototyping method for artists." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Snap!](http://snap.berkeley.edu/) is open-source and it is entirelly written in javascript, you only need a browser connected to the Poppy Creature webserver. **No installation is required on your computer!**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note: We assume in this tutorial that you are familiar with the basic of Snap! or Scratch. If it's not the case you can find a lot of documentation online. We especially recommand the very good [Snap! reference manual](http://snap.berkeley.edu/SnapManual.pdf).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example of the [Snap!](http://snap.berkeley.edu/) interface:\n", "\n", "![alt text](image/snap.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comments, issues, improvements and updates can be sent directly on the dedicated section of the [github issue tracker](https://github.com/poppy-project/pypot/labels/Notebooks).**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What's needed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**First, if you do not know how to run an IPython Notebook please refer to [our readme](https://github.com/poppy-project/pypot/blob/master/samples/notebooks/readme.md#notebooks-everywhere).**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To follow this tutorial you will need:\n", "\n", "* a poppy creature (real or simulated)\n", "* the python [pypot](https://github.com/poppy-project/pypot) library version >= 2.1 (only if you are working with a simulated creature, otherwise you can [directly connect to your poppy creature](https://github.com/poppy-project/pypot/blob/master/samples/notebooks/readme.md#connecting-to-a-remote-notebook))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note: for this notebook we will use a simulated poppy humanoid in V-REP (see [this v-rep notebook](http://nbviewer.ipython.org/github/poppy-project/pypot/blob/master/samples/notebooks/Controlling%20a%20Poppy%20humanoid%20in%20V-REP%20using%20pypot.ipynb) for details on how they can be installed and connected) but you can use any other creature (e.g. a real poppy ergo for instance). Only the configuration of the robot host will change (see details below).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Please refer to [the install section](https://github.com/poppy-project/pypot#installation) if you don't know how to install these libraries or how to connect to your Poppy Creature.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Connect [Snap!](http://snap.berkeley.edu) to a Poppy Creature" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before being able to control a simulated Poppy Creature with [Snap!](http://snap.berkeley.edu) blocks a few steps are required:\n", "* First, we need to connect to a Poppy Creature (real or simulated).\n", "* Then, we need to tell [pypot](https://github.com/poppy-project/pypot) that we wan't to control it through Snap!.\n", "* Finally, connect to [Snap!](http://snap.berkeley.edu) web interface (locally or online) and import the Poppy specific blocks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note: If you want to control a real Poppy Creature this is even simpler. As Poppy Creatures come with an embedded board with everything configured, you only need to connect to their webserver. Assuming that you are working with a Poppy-ErgoJr and that you are on the same network that your creature, you only have to connect to http://poppy-ergojr:8080/snap (see [here](https://github.com/poppy-project/pypot#pypot-a-python-lib-for-dynamixel-motors-control) for details).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a simulated Poppy Creature and connect it to [Snap!](http://poppy-project.github.io/pypot/pypot.server.html#pypot.server.snap.SnapRobotServer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, first we will create and launch the Robot Snap Server. In more details, we will:\n", "* instantiate a [Poppy Creature](https://github.com/poppy-project/Poppy-Creature)\n", "* connect it the [SnapRobotServer](http://poppy-project.github.io/pypot/pypot.server.html#pypot.server.snap.SnapRobotServer) and run it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create a [Poppy Humanoid](https://github.com/poppy-project/Poppy-Humanoid) using the approach discribed in [here](http://nbviewer.ipython.org/github/poppy-project/pypot/blob/master/samples/notebooks/Controlling%20a%20Poppy%20humanoid%20in%20V-REP%20using%20pypot.ipynb) and specifies that we want to use [Snap!](http://snap.berkeley.edu) to control it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from poppy.creatures import PoppyHumanoid\n", "\n", "poppy = PoppyHumanoid(simulator='vrep', use_snap=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note for advanced users: setting the use_snap arg to True basically creates a webserver which allows [Snap!](http://snap.berkeley.edu) to get/post values from/to a Poppy Creature through [pypot REST API](https://github.com/poppy-project/pypot/blob/master/REST-APIs.md). Then we use the *http* block to connect Snap! with the robot (you can refer to the section [The Outside World](http://snap.berkeley.edu/SnapManual.pdf) from Snap! reference manual for more details). You can manually specify the host and port to which the server will be attached using snap_host and snap_port args. Here, we use the default values which bind the server to the *localhost*. Yet, this will not allow for external connections. You can use *snap_host='0.0.0.0'* to automatically attach the webserver to the IP of your machine. Hostnames can also be used, for instance Poppy Creatures usually provide an hostname such as *host='poppy-humanoid.local'*.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "poppy.snap.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note for advanced user: the run method will run the server forever and thus block the main thread. This is here not a problem as you do not need to execute extra code for this tutorial. If you need to run other python code after, you can run this method inside a thread.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can create a poppy creature from python with the above code, but you can also use a simple command on your terminal: \n", "``` \n", "poppy-snap --vrep poppy-humanoid \n", "```\n", "If you want to try other configurations, look at the help of poppy-snap:\n", "``` \n", "poppy-snap --help \n", "``` " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"image/vrep-poppy.png\" alt=\"V-REP Poppy Humanoid Scene\" style=\"height: 500px;\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use [Snap!](http://snap.berkeley.edu/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have anything we need to control our Poppy Creature, we just need to run [Snap!](http://snap.berkeley.edu/) on a web browser." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As stated above, [Snap!](http://snap.berkeley.edu/) is entirelly written in javascript and thus the only things needed to run it is a (not too old) web browser! You can run [Snap!](http://snap.berkeley.edu/) in two modes:\n", "* **online**: you just need to go to http://snap.berkeley.edu/snapsource/snap.html\n", "* **locally**: you have to first download [Snap! sources](https://github.com/jmoenig/Snap--Build-Your-Own-Blocks) and then open the *snap.html* file. No internet connection is required after downloading the source.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now detail on you can control your Poppy Creature via the two approaches:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note that the online one is more straightforward and should thus be privileged except if you do not have an internet connection.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Alternative 1: Run [Snap!](http://snap.berkeley.edu/snapsource/snap.html) Online" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last step required before actually making your robot moves in [Snap!](http://snap.berkeley.edu/snapsource/snap.html) is to import our predefined blocks. [Snap!](http://snap.berkeley.edu/snapsource/snap.html) provides a really simple way to do that: you just have to go to this url: http://snap.berkeley.edu/snapsource/snap.html#open:http://127.0.0.1:6969/snap-blocks.xml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note the **#open:http://...** at the end of the url. It tells [Snap!](http://snap.berkeley.edu) to automatically loads the blocks that can be found at the url: http://127.0.0.1:6969/snap-blocks.xml." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "*Note: if you changed the web server host, you need to change it in here as well. For instance if you use the Poppy Ergo default hostname you need to go to http://snap.berkeley.edu/snapsource/snap.html#open:http://poppy-ergojr.local:6969/snap-blocks.xml instead.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should now see something like this in your browser (note importing the blocks may take a few seconds):\n", "\n", "![alt text](image/snap-ready.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Alternative 2: Run [Snap!](http://snap.berkeley.edu/snapsource/snap.html) locally" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you use [Snap!](http://snap.berkeley.edu) locally instead, you will have to first:\n", "* launch [Snap!](http://snap.berkeley.edu/) by opening the **snap.html** file from you snap local folder\n", "* import the project with our specific blocks via the snap menu\n", "\n", "![alt text](image/snap-import.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Poppy's specific blocks can be found on pypot/server/snap_projects/pypot-snap-blocks.xml directory in the pypot installation folder (its location will depend on your operating system and how you installed it). You can use the explorer/finder to find it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively you can directly download it from the [github repository](https://raw.githubusercontent.com/poppy-project/pypot/master/pypot/server/snap_projects/pypot-snap-blocks.xml). For instance:\n", "\n", "```bash\n", "wget https://raw.githubusercontent.com/poppy-project/pypot/master/pypot/server/snap_projects/pypot-snap-blocks.xml\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once imported you should see something like:\n", "\n", "![alt text](image/snap-ready.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using [Snap!](http://snap.berkeley.edu) to make your Poppy Creature moves" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that our base project comes with a few specific blocks such as:\n", "\n", "<img src=\"image/snap-basic-blocks.png\" alt=\"Drawing\" style=\"height: 700px;\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those blocks can be used to respectively:\n", "* test if connection with poppy robot is working well\n", "* get a list of all motors name\n", "* get a list of all motors refered by an alias\n", "* get the value of a register motor (e.g. get motor \"head_z\" register \"present_load\")\n", "* get the index of a motor\n", "* get all alias avaible for the current robot\n", "* set a motor position in a specified time\n", "* turn a motor compliant or not\n", "* set a register of a motor (e.g. set motor \"head_z\" register \"present_load\" to 10)\n", "* create/attach a move to some motors (you have to create a move before to record or replay it)\n", "* stop the record of a move\n", "* start the record of a move\n", "* play a move at a defined speed\n", "* play a move in reverse at a defined speed\n", "* play concurently many moves\n", "* play sequentialy many moves\n", "\n", "Other blocks are also available. Their behavior should be easily deduced from their name." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can easily see all blocks relative to poppy in Snap! with the \"find blocks\" feature.\n", "You have to right-click in the left part of Snap! page and select \"find blocks\":\n", "<img src=\"image/snap-right-click.png\" alt=\"Drawing\" style=\"height: 250px;\"/>\n", "\n", "If you type **robot** on the search input, you will select all poppy blocks:\n", "<img src=\"image/snap-find-pypot-blocks.png\" alt=\"Drawing\" style=\"height: 700px;\" text-align=\"left\"/>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To control a motor via a slider you need to:\n", "* first, make a variable - we will call it **head position**\n", "* right click on it and use the slider option\n", "* change the slider min/max to (-50, 50)\n", "\n", "![alt text](image/snap-slider.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, connect it to a motor:\n", "* use the *motor(s) goto position* block\n", "* put it inside a forever loop\n", "* add a wait for performance issue\n", "\n", "<img src=\"image/snap-slider-example.png\" alt=\"Drawing\" style=\"height: 100px;\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project example: orchestration of move records" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the pypot install directory pypot/server/pypot-snap-record-orchestration-demo.xml (its location will depend on your operating system and how you installed it), you can find a project tutorial of how to make orchestration of move record.\n", "Alternatively you can directly download it from the [github repository](https://raw.githubusercontent.com/poppy-project/pypot/master/pypot/server/snap_projects/pypot-snap-blocks.xml):\n", "```bash\n", "wget https://raw.githubusercontent.com/poppy-project/pypot/master/pypot-snap-record-orchestration-demo.xml\n", "```\n", "or getting it from [Snap! cloud](http://snap.berkeley.edu/snapsource/snap.html#cloud:Username=showok&ProjectName=pypot_orchestration_demo).\n", "\n", "\n", "This project will guide you step by step on how to use Snap! to record and play many nested move.\n", "\n", "<img src=\"image/snap-orchestration-demo.png\" alt=\"Drawing\" style=\"height: 700px;\" text-align=\"left\"/>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project example: apply a sinus on a few motors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![alt text](image/snap-sinus.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration Video" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
Startupsci/data-science-notebooks
titanic-data-science-solutions.ipynb
1
261787
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Titanic Data Science Solutions\n", "\n", "This notebook is companion to the book [Data Science Solutions](https://startupsci.com). The notebook walks us through a typical workflow for solving data science competitions at sites like Kaggle.\n", "\n", "There are several excellent notebooks to study data science competition entries. However many will skip some of the explanation on how the solution is developed as these notebooks are developed by experts for experts. The objective of this notebook is to follow a step-by-step workflow, explaining each step and rationale for every decision we take during solution development.\n", "\n", "## Workflow stages\n", "\n", "The competition solution workflow goes through seven stages described in the Data Science Solutions book's [sample chapter online here](https://leanpub.com/data-science-solutions/read_sample).\n", "\n", "1. Question or problem definition.\n", "2. Acquire training and testing data.\n", "3. Wrangle, prepare, cleanse the data.\n", "4. Analyze, identify patterns, and explore the data.\n", "5. Model, predict and solve the problem.\n", "6. Visualize, report, and present the problem solving steps and final solution.\n", "7. Supply or submit the results.\n", "\n", "The workflow indicates general sequence of how each stage may follow the other. However there are use cases with exceptions.\n", "\n", "- We may combine mulitple workflow stages. We may analyze by visualizing data.\n", "- Perform a stage earlier than indicated. We may analyze data before and after wrangling.\n", "- Perform a stage multiple times in our workflow. Visualize stage may be used multiple times.\n", "- Drop a stage altogether. We may not need supply stage to productize or service enable our dataset for a competition.\n", "\n", "\n", "## Question and problem definition\n", "\n", "Competition sites like Kaggle define the problem to solve or questions to ask while providing the datasets for training your data science model and testing the model results against a test dataset. The question or problem definition for Titanic Survival competition is [described here at Kaggle](https://www.kaggle.com/c/titanic).\n", "\n", "> Knowing from a training set of samples listing passengers who survived or did not survive the Titanic disaster, can our model determine based on a given test dataset not containing the survival information, if these passengers in the test dataset survived or not.\n", "\n", "We may also want to develop some early understanding about the domain of our problem. This is described on the [Kaggle competition description page here](https://www.kaggle.com/c/titanic). Here are the highlights to note.\n", "\n", "- On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. Translated 32% survival rate.\n", "- One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew.\n", "- Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n", "\n", "## Workflow goals\n", "\n", "The data science solutions workflow solves for seven major goals.\n", "\n", "**Classifying.** We may want to classify or categorize our samples. We may also want to understand the implications or correlation of different classes with our solution goal.\n", "\n", "**Correlating.** One can approach the problem based on available features within the training dataset. Which features within the dataset contribute significantly to our solution goal? Statistically speaking is there a [correlation](https://en.wikiversity.org/wiki/Correlation) among a feature and solution goal? As the feature values change does the solution state change as well, and visa-versa? This can be tested both for numerical and categorical features in the given dataset. We may also want to determine correlation among features other than survival for subsequent goals and workflow stages. Correlating certain features may help in creating, completing, or correcting features.\n", "\n", "**Converting.** For modeling stage, one needs to prepare the data. Depending on the choice of model algorithm one may require all features to be converted to numerical equivalent values. So for instance converting text categorical values to numeric values.\n", "\n", "**Completing.** Data preparation may also require us to estimate any missing values within a feature. Model algorithms may work best when there are no missing values.\n", "\n", "**Correcting.** We may also analyze the given training dataset for errors or possibly innacurate values within features and try to corrent these values or exclude the samples containing the errors. One way to do this is to detect any outliers among our samples or features. We may also completely discard a feature if it is not contribting to the analysis or may significantly skew the results.\n", "\n", "**Creating.** Can we create new features based on an existing feature or a set of features, such that the new feature follows the correlation, conversion, completeness goals.\n", "\n", "**Charting.** How to select the right visualization plots and charts depending on nature of the data and the solution goals. A good start is to read the Tableau paper on [Which chart or graph is right for you?](http://www.tableau.com/learn/whitepapers/which-chart-or-graph-is-right-for-you#ERAcoH5sEG5CFlek.99)." ] }, { "cell_type": "code", "execution_count": 371, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# data analysis and wrangling\n", "import pandas as pd\n", "import numpy as np\n", "import random as rnd\n", "\n", "# visualization\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# machine learning\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC, LinearSVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.linear_model import Perceptron\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.tree import DecisionTreeClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Acquire data\n", "\n", "The Python Pandas packages helps us work with our datasets. We start by acquiring the training and testing datasets into Pandas DataFrames." ] }, { "cell_type": "code", "execution_count": 372, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# read titanic training & test csv files as a pandas DataFrame\n", "train_df = pd.read_csv('data/titanic-kaggle/train.csv')\n", "test_df = pd.read_csv('data/titanic-kaggle/test.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze by describing data\n", "\n", "Pandas also helps describe the datasets answering following questions early in our project.\n", "\n", "**Which features are available in the dataset?**\n", "\n", "Noting the feature names for directly manipulating or analyzing these. These feature names are described on the [Kaggle data page here](https://www.kaggle.com/c/titanic/data)." ] }, { "cell_type": "code", "execution_count": 373, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n", " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n" ] } ], "source": [ "print train_df.columns.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Which features are categorical?**\n", "\n", "These values classify the samples into sets of similar samples. Within categorical features are the values nominal, ordinal, ratio, or interval based? Among other things this helps us select the appropriate plots for visualization.\n", "\n", "- Categorical: Survived, Sex, and Embarked. Ordinal: Pclass.\n", "\n", "**Which features are numerical?**\n", "\n", "Which features are numerical? These values change from sample to sample. Within numerical features are the values discrete, continuous, or timeseries based? Among other things this helps us select the appropriate plots for visualization.\n", "\n", "- Continous: Age, Fare. Discrete: SibSp, Parch." ] }, { "cell_type": "code", "execution_count": 374, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 374, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# preview the data\n", "train_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Which features are mixed data types?**\n", "\n", "Numerical, alphanumeric data within same feature. These are candidates for correcting goal.\n", "\n", "- Ticket is a mix of numeric and alphanumeric data types. Cabin is alphanumeric.\n", "\n", "**Which features may contain errors or typos?**\n", "\n", "This is harder to review for a large dataset, however reviewing a few samples from a smaller dataset may just tell us outright, which features may require correcting.\n", "\n", "- Name feature may contain errors or typos as there are several ways used to describe a name including titles, round brackets, and quotes used for alternative or short names." ] }, { "cell_type": "code", "execution_count": 375, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>886</th>\n", " <td>887</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>Montvila, Rev. Juozas</td>\n", " <td>male</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>211536</td>\n", " <td>13.00</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>887</th>\n", " <td>888</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Graham, Miss. Margaret Edith</td>\n", " <td>female</td>\n", " <td>19.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>112053</td>\n", " <td>30.00</td>\n", " <td>B42</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>888</th>\n", " <td>889</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n", " <td>female</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>W./C. 6607</td>\n", " <td>23.45</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>889</th>\n", " <td>890</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Behr, Mr. Karl Howell</td>\n", " <td>male</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>111369</td>\n", " <td>30.00</td>\n", " <td>C148</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>890</th>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Dooley, Mr. Patrick</td>\n", " <td>male</td>\n", " <td>32.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>370376</td>\n", " <td>7.75</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Name \\\n", "886 887 0 2 Montvila, Rev. Juozas \n", "887 888 1 1 Graham, Miss. Margaret Edith \n", "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "889 890 1 1 Behr, Mr. Karl Howell \n", "890 891 0 3 Dooley, Mr. Patrick \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "886 male 27.0 0 0 211536 13.00 NaN S \n", "887 female 19.0 0 0 112053 30.00 B42 S \n", "888 female NaN 1 2 W./C. 6607 23.45 NaN S \n", "889 male 26.0 0 0 111369 30.00 C148 C \n", "890 male 32.0 0 0 370376 7.75 NaN Q " ] }, "execution_count": 375, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Which features contain blank, null or empty values?**\n", "\n", "These will require correcting.\n", "\n", "- Cabin > Age > Embarked features contain a number of null values in that order for the training dataset.\n", "- Cabin > Age are incomplete in case of test dataset.\n", "\n", "**What are the data types for various features?**\n", "\n", "Helping us during converting goal.\n", "\n", "- Seven features are integer or floats. Six in case of test dataset.\n", "- Five features are strings (object)." ] }, { "cell_type": "code", "execution_count": 376, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.6+ KB\n", "________________________________________\n", "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 418 entries, 0 to 417\n", "Data columns (total 11 columns):\n", "PassengerId 418 non-null int64\n", "Pclass 418 non-null int64\n", "Name 418 non-null object\n", "Sex 418 non-null object\n", "Age 332 non-null float64\n", "SibSp 418 non-null int64\n", "Parch 418 non-null int64\n", "Ticket 418 non-null object\n", "Fare 417 non-null float64\n", "Cabin 91 non-null object\n", "Embarked 418 non-null object\n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 36.0+ KB\n" ] } ], "source": [ "train_df.info()\n", "print('_'*40)\n", "test_df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What is the distribution of numerical feature values across the samples?**\n", "\n", "This helps us determine, among other early insights, how representative is the training dataset of the actual problem domain.\n", "\n", "- Total samples are 891 or 40% of the actual number of passengers on board the Titanic (2,224).\n", "- Survived is a categorical feature with 0 or 1 values.\n", "- Around 38% samples survived representative of the actual survival rate.\n", "- Most passengers (> 75%) did not travel with parents or children.\n", "- More than 35% passengers had a sibling on board.\n", "- Fares varied significantly with few passengers (<1%) paying as high as $512.\n", "- Few elderly passengers (<1%) within age range 65-80." ] }, { "cell_type": "code", "execution_count": 377, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>714.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>446.000000</td>\n", " <td>0.383838</td>\n", " <td>2.308642</td>\n", " <td>29.699118</td>\n", " <td>0.523008</td>\n", " <td>0.381594</td>\n", " <td>32.204208</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>257.353842</td>\n", " <td>0.486592</td>\n", " <td>0.836071</td>\n", " <td>14.526497</td>\n", " <td>1.102743</td>\n", " <td>0.806057</td>\n", " <td>49.693429</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.420000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>223.500000</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>20.125000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>7.910400</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>446.000000</td>\n", " <td>0.000000</td>\n", " <td>3.000000</td>\n", " <td>28.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>14.454200</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>668.500000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>38.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>31.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>891.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>80.000000</td>\n", " <td>8.000000</td>\n", " <td>6.000000</td>\n", " <td>512.329200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp \\\n", "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", "\n", " Parch Fare \n", "count 891.000000 891.000000 \n", "mean 0.381594 32.204208 \n", "std 0.806057 49.693429 \n", "min 0.000000 0.000000 \n", "25% 0.000000 7.910400 \n", "50% 0.000000 14.454200 \n", "75% 0.000000 31.000000 \n", "max 6.000000 512.329200 " ] }, "execution_count": 377, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.describe(percentiles=[.25, .5, .75])\n", "# Review survived rate using `percentiles=[.61, .62]` knowing our problem description mentions 38% survival rate.\n", "# Review Parch distribution using `percentiles=[.75, .8]`\n", "# Sibling distribution `[.65, .7]`\n", "# Age and Fare `[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What is the distribution of categorical features?**\n", "\n", "- Names are unique across the dataset (count=unique=891)\n", "- Sex variable as two possible values with 65% male (top=male, freq=577/count=891).\n", "- Cabin values have several dupicates across samples. Alternatively several passengers shared a cabin.\n", "- Embarked takes three possible values. S port used by most passengers (top=S)\n", "- Ticket feature has high ratio (22%) of duplicate values (unique=681). Possibly an error as two passengers may not travel on the same ticket." ] }, { "cell_type": "code", "execution_count": 378, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Ticket</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>891</td>\n", " <td>891</td>\n", " <td>891</td>\n", " <td>204</td>\n", " <td>889</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>891</td>\n", " <td>2</td>\n", " <td>681</td>\n", " <td>147</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>top</th>\n", " <td>Graham, Mr. George Edward</td>\n", " <td>male</td>\n", " <td>CA. 2343</td>\n", " <td>C23 C25 C27</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>freq</th>\n", " <td>1</td>\n", " <td>577</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>644</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Sex Ticket Cabin Embarked\n", "count 891 891 891 204 889\n", "unique 891 2 681 147 3\n", "top Graham, Mr. George Edward male CA. 2343 C23 C25 C27 S\n", "freq 1 577 7 4 644" ] }, "execution_count": 378, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.describe(include=['O'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assumtions based on data analysis\n", "\n", "We arrive at following assumptions based on data analysis done so far. We may validate these assumptions further before taking appropriate actions.\n", "\n", "**Completing.**\n", "\n", "1. We may want to complete Age feature as it is definitely correlated to survival.\n", "2. We may want to complete the Embarked feature as it may also correlate with survival or another important feature.\n", "\n", "**Correcting.**\n", "\n", "1. Ticket feature may be dropped from our analysis as it contains high ratio of duplicates (22%) and there may not be a correlation between Ticket and survival.\n", "2. Cabin feature may be dropped as it is highly incomplete or contains many null values both in training and test dataset.\n", "3. PassengerId may be dropped from training dataset as it does not contribute to survival.\n", "4. Name feature is relatively non-standard, may not contribute directly to survival, so maybe dropped.\n", "\n", "**Creating.**\n", "\n", "1. We may want to create a new feature called Family based on Parch and SibSp to get total count of family members on board.\n", "2. We may want to engineer the Name feature to extract Title as a new feature.\n", "3. We may want to create new feature for Age bands. This turns a continous numerical feature into an ordinal categorical feature.\n", "4. We may also want to create a Fare range feature if it helps our analysis.\n", "\n", "**Correlating.**\n", "\n", "1. Does port of embarkation (Embarked) correlate with survival?\n", "2. Does fare paid (range) correlate with survival?\n", "\n", "We may also add to our assumptions based on the problem description noted earlier.\n", "\n", "**Classifying.**\n", "\n", "1. Women (Sex=female) were more likely to have survived.\n", "2. Children (Age<?) were more likely to have survived. \n", "3. The upper-class passengers (Pclass=1) were more likely to have survived." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze by visualizing data\n", "\n", "Now we can start confirming some of our assumptions using visualizations for analyzing the data.\n", "\n", "### Correlating numerical features\n", "\n", "Let us start by understanding correlations between numerical features and our solution goal (Survived).\n", "\n", "A histogram chart is useful for analyzing continous numerical variables like Age where banding or ranges will help identify useful patterns. The histogram can indicate distribution of samples using automatically defined bins or equally ranged bands. This helps us answer questions relating to specific bands (Did infants have better survival rate?)\n", "\n", "Note that x-axis in historgram visualizations represents the count of samples or passengers.\n", "\n", "**Observations.**\n", "\n", "- Infants (Age <=4) had high survival rate.\n", "- Oldest passengers (Age = 80) survived.\n", "- Large number of 15-25 year olds did not survive.\n", "- Most passengers are in 15-35 age range.\n", "\n", "**Decisions.**\n", "\n", "This simple analysis confirms our assumptions as decisions for subsequent workflow stages.\n", "\n", "- We should consider Age (our assumption classifying #2) in our model training.\n", "- Complete the Age feature for null values (completing #1)." ] }, { "cell_type": "code", "execution_count": 379, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x11fd279d0>" ] }, "execution_count": 379, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11e742990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.FacetGrid(train_df, col='Survived')\n", "g.map(plt.hist, 'Age', bins=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can combine multiple features for identifying correlations using a single plot. This can be done with numerical and categorical features which have numeric values.\n", "\n", "**Observations.**\n", "\n", "- Pclass=3 had most passengers, however most did not survive. Confirms our classifying assumption #2.\n", "- Infant passengers in Pclass=2 mostly survived. Further qualifies our classifying assumption #2.\n", "- Most passengers in Pclass=1 survived. Confirms our classifying assumption #3.\n", "- Pclass varies in terms of Age distribution of passengers.\n", "\n", "**Decisions.**\n", "\n", "- Consider Pclass for model training.\n" ] }, { "cell_type": "code", "execution_count": 380, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dXyA/sydyfYvzC7x4+gUcOXJkIBmGJQkZIBk5kpBhmJKwvcqgDMqQzAzDlITt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\nfo5w6hw76DoIgoDyUnHTcb+u61ApLXA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"text/plain": [ "<matplotlib.figure.Figure at 0x11fcffc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n", "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n", "grid.add_legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlating categorical features\n", "\n", "Now we can correlate categorical features with our solution goal.\n", "\n", "**Observations.**\n", "\n", "- Female passengers had much better survival rate than males. Confirms classifying (#1).\n", "- Exception in Embarked=C where males had higher survival rate.\n", "- Males had better survival rate in Pclass=3 when compared with Pclass=2 for C and Q ports. Completing (#2).\n", "- Ports of embarkation have varying survival rates for Pclass=3 and among male passengers. Correlating (#1).\n", "\n", "**Decisions.**\n", "\n", "- Add Sex feature to model training.\n", "- Complete and add Embarked feature to model training." ] }, { "cell_type": "code", "execution_count": 381, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x11e71b0d0>" ] }, "execution_count": 381, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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h4Q1JdYnEmvySApYcWM5n+74gqyi72nIuXAxpM5BJ3S6iW7MuDRhh9ZRYS6ho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\nKqxFREREREREIqDCWkRERERERCQCKqxFREREREREIqDCWkRERERERCQ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"text/plain": [ "<matplotlib.figure.Figure at 0x11fbc82d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid = sns.FacetGrid(train_df, col='Embarked')\n", "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n", "grid.add_legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlating categorical and numerical features\n", "\n", "We may also want to correlate categorical features (with non-numeric values) and numeric features. We can consider correlating Embarked (Categorical non-numeric), Sex (Categorical non-numeric), Fare (Numeric continuous), with Survived (Categorical numeric).\n", "\n", "**Observations.**\n", "\n", "- Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.\n", "- Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).\n", "\n", "**Decisions.**\n", "\n", "- Consider banding Fare feature." ] }, { "cell_type": "code", "execution_count": 382, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x120aa3410>" ] }, "execution_count": 382, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x121b107d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n", "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n", "grid.add_legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wrangle data\n", "\n", "We have collected several assumptions and decisions regarding our datasets and solution requirements. So far we did not have to change a single feature or value to arrive at these. Let us now execute our decisions and assumptions for correcting, creating, and completing goals.\n", "\n", "### Correcting by dropping features\n", "\n", "This is a good starting goal to execute. By dropping features we are dealing with fewer data points. Speeds up our notebook and eases the analysis.\n", "\n", "Based on our assumptions and decisions we want to drop the Cabin (correcting #2) and Ticket (correcting #1) features.\n", "\n", "Note that where applicable we perform operations on both training and testing datasets together to stay consistent." ] }, { "cell_type": "code", "execution_count": 383, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n", "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating new feature extracting from existing\n", "\n", "We want to analyze if Name feature can be engineered to extract titles and test correlation between titles and survival, before dropping Name and PassengerId features.\n", "\n", "In the following code we extract Title feature using regular expressions. The RegEx pattern `(\\w+\\.)` matches the first word which ends with a dot character within Name feature. The `expand=False` flag returns a DataFrame.\n", "\n", "**Observations.**\n", "\n", "When we plot Title, Age, and Survived, we note the following observations.\n", "\n", "- Most titles band Age groups accurately. For example: Master title has Age mean of 5 years.\n", "- Survival among Title Age bands varies slightly.\n", "- Certain titles mostly survived (Mme, Lady, Sir) or did not (Don, Rev, Jonkheer).\n", "\n", "**Decision.**\n", "\n", "- We decide to retain the new Title feature for model training." ] }, { "cell_type": "code", "execution_count": 384, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x12084ac10>" ] }, "execution_count": 384, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cCTzd+NRbNJap1t2/TCxsERERSYdc7FGK7d2ZQygWZ8Rpb/X53H0tYQqBPOAf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\nti+whnV7g9bnpag4mgrcamYFGzogum4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"text/plain": [ "<matplotlib.figure.Figure at 0x121daec50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_df['Title'] = train_df.Name.str.extract('(\\w+\\.)', expand=False)\n", "sns.barplot(hue=\"Survived\", x=\"Age\", y=\"Title\", data=train_df, ci=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us extract the Title feature for the training dataset as well.\n", "\n", "Then we can safely drop the Name feature from training and testing datasets and the PassengerId feature from the training dataset." ] }, { "cell_type": "code", "execution_count": 385, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Sex</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>418</td>\n", " <td>418</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>top</th>\n", " <td>male</td>\n", " <td>S</td>\n", " <td>Mr.</td>\n", " </tr>\n", " <tr>\n", " <th>freq</th>\n", " <td>266</td>\n", " <td>270</td>\n", " <td>240</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Sex Embarked Title\n", "count 418 418 418\n", "unique 2 3 9\n", "top male S Mr.\n", "freq 266 270 240" ] }, "execution_count": 385, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df['Title'] = test_df.Name.str.extract('(\\w+\\.)', expand=False)\n", "\n", "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n", "test_df = test_df.drop(['Name'], axis=1)\n", "test_df.describe(include=['O'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Converting a categorical feature\n", "\n", "Now we can convert features which contain strings to numerical values. This is required by most model algorithms. Doing so will also help us in achieving the feature completing goal.\n", "\n", "Let us start by converting Sex feature to a new feature called Gender where female=1 and male=0." ] }, { "cell_type": "code", "execution_count": 386, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Gender</th>\n", " <th>Sex</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>male</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>female</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>female</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>female</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>male</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Sex\n", "0 0 male\n", "1 1 female\n", "2 1 female\n", "3 1 female\n", "4 0 male" ] }, "execution_count": 386, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df['Gender'] = train_df['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n", "train_df.loc[:, ['Gender', 'Sex']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We do this both for training and test datasets." ] }, { "cell_type": "code", "execution_count": 387, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Gender</th>\n", " <th>Sex</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>male</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>female</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>male</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>male</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>female</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Sex\n", "0 0 male\n", "1 1 female\n", "2 0 male\n", "3 0 male\n", "4 1 female" ] }, "execution_count": 387, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df['Gender'] = test_df['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n", "test_df.loc[:, ['Gender', 'Sex']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now drop the Sex feature from our datasets." ] }, { "cell_type": "code", "execution_count": 388, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>Gender</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>S</td>\n", " <td>Mr.</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>71.2833</td>\n", " <td>C</td>\n", " <td>Mrs.</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.9250</td>\n", " <td>S</td>\n", " <td>Miss.</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>53.1000</td>\n", " <td>S</td>\n", " <td>Mrs.</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8.0500</td>\n", " <td>S</td>\n", " <td>Mr.</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Age SibSp Parch Fare Embarked Title Gender\n", "0 0 3 22.0 1 0 7.2500 S Mr. 0\n", "1 1 1 38.0 1 0 71.2833 C Mrs. 1\n", "2 1 3 26.0 0 0 7.9250 S Miss. 1\n", "3 1 1 35.0 1 0 53.1000 S Mrs. 1\n", "4 0 3 35.0 0 0 8.0500 S Mr. 0" ] }, "execution_count": 388, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = train_df.drop(['Sex'], axis=1)\n", "test_df = test_df.drop(['Sex'], axis=1)\n", "train_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Completing a numerical continuous feature\n", "\n", "Now we should start estimating and completing features with missing or null values. We will first do this for the Age feature.\n", "\n", "We can consider three methods to complete a numerical continuous feature.\n", "\n", "1. A simple way is to generate random numbers between mean and [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation).\n", "\n", "2. More accurate way of guessing missing values is to use other correlated features. In our case we note correlation among Age, Gender, and Pclass. Guess Age values using [median](https://en.wikipedia.org/wiki/Median) values for Age across sets of Pclass and Gender feature combinations. So, median Age for Pclass=1 and Gender=0, Pclass=1 and Gender=1, and so on...\n", "\n", "3. Combine methods 1 and 2. So instead of guessing age values based on median, use random numbers between mean and standard deviation, based on sets of Pclass and Gender combinations.\n", "\n", "Method 1 and 3 will introduce random noise into our models. The results from multiple executions might vary. We will prefer method 2." ] }, { "cell_type": "code", "execution_count": 389, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SJEmStsMhuSRJkiRJPcMiV5IkSZLUMyxyJUmSJEk9wyJXkiRJktQzLHIlSZIk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\nMFR3OqCBwSHyg33r1i8vLnQ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"text/plain": [ "<matplotlib.figure.Figure at 0x11d2eb950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n", "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n", "grid.add_legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations." ] }, { "cell_type": "code", "execution_count": 390, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0.],\n", " [ 0., 0., 0.]])" ] }, "execution_count": 390, "metadata": {}, "output_type": "execute_result" } ], "source": [ "guess_ages = np.zeros((2,3))\n", "guess_ages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we iterate over Gender (0 or 1) and Pclass (1, 2, 3) to calculate guessed values of Age for the six combinations.\n", "\n", "Note that we also tried creating the AgeFill feature using method 3 and realized during model stage that the correlation coeffficient of AgeFill is better when compared with the method 2." ] }, { "cell_type": "code", "execution_count": 391, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 40. , 30. , 25. ],\n", " [ 35. , 28. , 21.5]])" ] }, "execution_count": 391, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i in range(0, 2):\n", " for j in range(0, 3):\n", " guess_df = train_df[(train_df['Gender'] == i) & \\\n", " (train_df['Pclass'] == j+1)]['Age'].dropna()\n", " \n", " # Correlation of AgeFill is -0.014850\n", " # age_mean = guess_df.mean()\n", " # age_std = guess_df.std()\n", " # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n", " \n", " # Correlation of AgeFill is -0.011304\n", " age_guess = guess_df.median()\n", "\n", " # Convert random age float to nearest .5 age\n", " guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n", " \n", "guess_ages" ] }, { "cell_type": "code", "execution_count": 392, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Gender</th>\n", " <th>Pclass</th>\n", " <th>Age</th>\n", " <th>AgeFill</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>21.5</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>21.5</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>21.5</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>25.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Pclass Age AgeFill\n", "5 0 3 NaN 25.0\n", "17 0 2 NaN 30.0\n", "19 1 3 NaN 21.5\n", "26 0 3 NaN 25.0\n", "28 1 3 NaN 21.5\n", "29 0 3 NaN 25.0\n", "31 1 1 NaN 35.0\n", "32 1 3 NaN 21.5\n", "36 0 3 NaN 25.0\n", "42 0 3 NaN 25.0" ] }, "execution_count": 392, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df['AgeFill'] = train_df['Age']\n", "\n", "for i in range(0, 2):\n", " for j in range(0, 3):\n", " train_df.loc[ (train_df.Age.isnull()) & (train_df.Gender == i) & (train_df.Pclass == j+1),\\\n", " 'AgeFill'] = guess_ages[i,j]\n", "\n", "train_df[train_df['Age'].isnull()][['Gender','Pclass','Age','AgeFill']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We repeat the feature completing goal for the test dataset." ] }, { "cell_type": "code", "execution_count": 393, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Gender</th>\n", " <th>Pclass</th>\n", " <th>Age</th>\n", " <th>AgeFill</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>24.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>41.0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>24.0</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>24.0</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>42.0</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>24.0</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>28.0</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>24.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Pclass Age AgeFill\n", "10 0 3 NaN 24.0\n", "22 1 1 NaN 41.0\n", "29 0 3 NaN 24.0\n", "33 1 3 NaN 22.0\n", "36 1 3 NaN 22.0\n", "39 0 3 NaN 24.0\n", "41 0 1 NaN 42.0\n", "47 0 3 NaN 24.0\n", "54 0 2 NaN 28.0\n", "58 0 3 NaN 24.0" ] }, "execution_count": 393, "metadata": {}, "output_type": "execute_result" } ], "source": [ "guess_ages = np.zeros((2,3))\n", "\n", "for i in range(0, 2):\n", " for j in range(0, 3):\n", " guess_df = test_df[(test_df['Gender'] == i) & \\\n", " (test_df['Pclass'] == j+1)]['Age'].dropna()\n", "\n", " # Correlation of AgeFill is -0.014850\n", " # age_mean = guess_df.mean()\n", " # age_std = guess_df.std()\n", " # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n", "\n", " # Correlation of AgeFill is -0.011304\n", " age_guess = guess_df.median()\n", "\n", " guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n", "\n", "test_df['AgeFill'] = test_df['Age']\n", "\n", "for i in range(0, 2):\n", " for j in range(0, 3):\n", " test_df.loc[ (test_df.Age.isnull()) & (test_df.Gender == i) & (test_df.Pclass == j+1),\\\n", " 'AgeFill'] = guess_ages[i,j]\n", "\n", "test_df[test_df['Age'].isnull()][['Gender','Pclass','Age','AgeFill']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now drop the Age feature from our datasets." ] }, { "cell_type": "code", "execution_count": 394, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>Gender</th>\n", " <th>AgeFill</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>S</td>\n", " <td>Mr.</td>\n", " <td>0</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>71.2833</td>\n", " <td>C</td>\n", " <td>Mrs.</td>\n", " <td>1</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.9250</td>\n", " <td>S</td>\n", " <td>Miss.</td>\n", " <td>1</td>\n", " <td>26.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>53.1000</td>\n", " <td>S</td>\n", " <td>Mrs.</td>\n", " <td>1</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8.0500</td>\n", " <td>S</td>\n", " <td>Mr.</td>\n", " <td>0</td>\n", " <td>35.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass SibSp Parch Fare Embarked Title Gender AgeFill\n", "0 0 3 1 0 7.2500 S Mr. 0 22.0\n", "1 1 1 1 0 71.2833 C Mrs. 1 38.0\n", "2 1 3 0 0 7.9250 S Miss. 1 26.0\n", "3 1 1 1 0 53.1000 S Mrs. 1 35.0\n", "4 0 3 0 0 8.0500 S Mr. 0 35.0" ] }, "execution_count": 394, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = train_df.drop(['Age'], axis=1)\n", "test_df = test_df.drop(['Age'], axis=1)\n", "train_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create new feature combining existing features\n", "\n", "We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets.\n", "\n", "Note that we commented out this code as we realized during model stage that the combined feature is reducing the confidence score of our dataset instead of improving it. The correlation score of separate Parch feature is also better than combined FamilySize feature." ] }, { "cell_type": "code", "execution_count": 395, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Logistic Regression Score is 0.81032547699214363\n", "# Parch correlation is -0.065878 and SibSp correlation is -0.370618\n", "\n", "# Decision: Retain Parch and SibSp as separate features\n", "\n", "# Logistic Regression Score is 0.80808080808080807\n", "# FamilySize correlation is -0.233974\n", "\n", "# train_df['FamilySize'] = train_df['SibSp'] + train_df['Parch']\n", "# test_df['FamilySize'] = test_df['SibSp'] + test_df['Parch']\n", "# train_df.loc[:, ['Parch', 'SibSp', 'FamilySize']].head(10)" ] }, { "cell_type": "code", "execution_count": 396, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# train_df = train_df.drop(['Parch', 'SibSp'], axis=1)\n", "# test_df = test_df.drop(['Parch', 'SibSp'], axis=1)\n", "# train_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also create an artificial feature combining Pclass and AgeFill." ] }, { "cell_type": "code", "execution_count": 397, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Age*Class</th>\n", " <th>AgeFill</th>\n", " <th>Pclass</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>66.0</td>\n", " <td>22.0</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>38.0</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>78.0</td>\n", " <td>26.0</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>35.0</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>105.0</td>\n", " <td>35.0</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>75.0</td>\n", " <td>25.0</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>54.0</td>\n", " <td>54.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>6.0</td>\n", " <td>2.0</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>81.0</td>\n", " <td>27.0</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>28.0</td>\n", " <td>14.0</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age*Class AgeFill Pclass\n", "0 66.0 22.0 3\n", "1 38.0 38.0 1\n", "2 78.0 26.0 3\n", "3 35.0 35.0 1\n", "4 105.0 35.0 3\n", "5 75.0 25.0 3\n", "6 54.0 54.0 1\n", "7 6.0 2.0 3\n", "8 81.0 27.0 3\n", "9 28.0 14.0 2" ] }, "execution_count": 397, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df['Age*Class'] = test_df.AgeFill * test_df.Pclass\n", "train_df['Age*Class'] = train_df.AgeFill * train_df.Pclass\n", "train_df.loc[:, ['Age*Class', 'AgeFill', 'Pclass']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Completing a categorical feature\n", "\n", "Embarked feature takes S, Q, C values based on port of embarkation. Our training dataset has two missing values. We simply fill these with the most common occurance." ] }, { "cell_type": "code", "execution_count": 398, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'S'" ] }, "execution_count": 398, "metadata": {}, "output_type": "execute_result" } ], "source": [ "freq_port = train_df.Embarked.dropna().mode()[0]\n", "freq_port" ] }, { "cell_type": "code", "execution_count": 399, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Embarked</th>\n", " <th>EmbarkedFill</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>61</th>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>829</th>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Embarked EmbarkedFill\n", "61 NaN S\n", "829 NaN S" ] }, "execution_count": 399, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df['EmbarkedFill'] = train_df['Embarked']\n", "train_df.loc[train_df['Embarked'].isnull(), 'EmbarkedFill'] = freq_port\n", "train_df[train_df['Embarked'].isnull()][['Embarked','EmbarkedFill']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now drop the Embarked feature from our datasets." ] }, { "cell_type": "code", "execution_count": 400, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Title</th>\n", " <th>Gender</th>\n", " <th>AgeFill</th>\n", " <th>Age*Class</th>\n", " <th>EmbarkedFill</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>Mr.</td>\n", " <td>0</td>\n", " <td>22.0</td>\n", " <td>66.0</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>71.2833</td>\n", " <td>Mrs.</td>\n", " <td>1</td>\n", " <td>38.0</td>\n", " <td>38.0</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.9250</td>\n", " <td>Miss.</td>\n", " <td>1</td>\n", " <td>26.0</td>\n", " <td>78.0</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>53.1000</td>\n", " <td>Mrs.</td>\n", " <td>1</td>\n", " <td>35.0</td>\n", " <td>35.0</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8.0500</td>\n", " <td>Mr.</td>\n", " <td>0</td>\n", " <td>35.0</td>\n", " <td>105.0</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass SibSp Parch Fare Title Gender AgeFill Age*Class \\\n", "0 0 3 1 0 7.2500 Mr. 0 22.0 66.0 \n", "1 1 1 1 0 71.2833 Mrs. 1 38.0 38.0 \n", "2 1 3 0 0 7.9250 Miss. 1 26.0 78.0 \n", "3 1 1 1 0 53.1000 Mrs. 1 35.0 35.0 \n", "4 0 3 0 0 8.0500 Mr. 0 35.0 105.0 \n", "\n", " EmbarkedFill \n", "0 S \n", "1 C \n", "2 S \n", "3 S \n", "4 S " ] }, "execution_count": 400, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df['EmbarkedFill'] = test_df['Embarked']\n", "train_df = train_df.drop(['Embarked'], axis=1)\n", "test_df = test_df.drop(['Embarked'], axis=1)\n", "train_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Converting categorical feature to numeric\n", "\n", "We can now convert the EmbarkedFill feature by creating a new numeric Port feature." ] }, { "cell_type": "code", "execution_count": 401, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>EmbarkedFill</th>\n", " <th>Port</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>S</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>C</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>S</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>S</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>S</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Q</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>S</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>S</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>S</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>C</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " EmbarkedFill Port\n", "0 S 2\n", "1 C 0\n", "2 S 2\n", "3 S 2\n", "4 S 2\n", "5 Q 1\n", "6 S 2\n", "7 S 2\n", "8 S 2\n", "9 C 0" ] }, "execution_count": 401, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ports = list(enumerate(np.unique(train_df['EmbarkedFill'])))\n", "Ports_dict = { name : i for i, name in Ports } \n", "train_df['Port'] = train_df.EmbarkedFill.map( lambda x: Ports_dict[x]).astype(int)\n", "\n", "Ports = list(enumerate(np.unique(test_df['EmbarkedFill'])))\n", "Ports_dict = { name : i for i, name in Ports }\n", "test_df['Port'] = test_df.EmbarkedFill.map( lambda x: Ports_dict[x]).astype(int)\n", "\n", "train_df[['EmbarkedFill', 'Port']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly we can convert the Title feature to numeric enumeration TitleBand banding age groups with titles." ] }, { "cell_type": "code", "execution_count": 402, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Title</th>\n", " <th>TitleBand</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Mr.</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Mrs.</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Miss.</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Mrs.</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Mr.</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Mr.</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Mr.</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Master.</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Mrs.</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Mrs.</td>\n", " <td>13</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Title TitleBand\n", "0 Mr. 12\n", "1 Mrs. 13\n", "2 Miss. 9\n", "3 Mrs. 13\n", "4 Mr. 12\n", "5 Mr. 12\n", "6 Mr. 12\n", "7 Master. 8\n", "8 Mrs. 13\n", "9 Mrs. 13" ] }, "execution_count": 402, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Titles = list(enumerate(np.unique(train_df['Title'])))\n", "Titles_dict = { name : i for i, name in Titles } \n", "train_df['TitleBand'] = train_df.Title.map( lambda x: Titles_dict[x]).astype(int)\n", "\n", "Titles = list(enumerate(np.unique(test_df['Title'])))\n", "Titles_dict = { name : i for i, name in Titles } \n", "test_df['TitleBand'] = test_df.Title.map( lambda x: Titles_dict[x]).astype(int)\n", "\n", "train_df[['Title', 'TitleBand']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can safely drop the EmbarkedFill and Title features. We this we now have a dataset that only contains numerical values, a requirement for the model stage in our workflow." ] }, { "cell_type": "code", "execution_count": 403, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Gender</th>\n", " <th>AgeFill</th>\n", " <th>Age*Class</th>\n", " <th>Port</th>\n", " <th>TitleBand</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>0</td>\n", " <td>22.0</td>\n", " <td>66.0</td>\n", " <td>2</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>71.2833</td>\n", " <td>1</td>\n", " <td>38.0</td>\n", " <td>38.0</td>\n", " <td>0</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.9250</td>\n", " <td>1</td>\n", " <td>26.0</td>\n", " <td>78.0</td>\n", " <td>2</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>53.1000</td>\n", " <td>1</td>\n", " <td>35.0</td>\n", " <td>35.0</td>\n", " <td>2</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8.0500</td>\n", " <td>0</td>\n", " <td>35.0</td>\n", " <td>105.0</td>\n", " <td>2</td>\n", " <td>12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass SibSp Parch Fare Gender AgeFill Age*Class Port \\\n", "0 0 3 1 0 7.2500 0 22.0 66.0 2 \n", "1 1 1 1 0 71.2833 1 38.0 38.0 0 \n", "2 1 3 0 0 7.9250 1 26.0 78.0 2 \n", "3 1 1 1 0 53.1000 1 35.0 35.0 2 \n", "4 0 3 0 0 8.0500 0 35.0 105.0 2 \n", "\n", " TitleBand \n", "0 12 \n", "1 13 \n", "2 9 \n", "3 13 \n", "4 12 " ] }, "execution_count": 403, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = train_df.drop(['EmbarkedFill', 'Title'], axis=1)\n", "test_df = test_df.drop(['EmbarkedFill', 'Title'], axis=1)\n", "train_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quick completing and converting a numeric feature\n", "\n", "We can now complete the Fare feature for single missing value in test dataset using mode to get the value that occurs most frequently for this feature. We do this in a single line of code.\n", "\n", "Note that we are not creating an intermediate new feature or doing any further analysis for correlation to guess missing feature as we are replacing only a single value. The completion goal achieves desired requirement for model algorithm to operate on non-null values.\n", "\n", "We may also want round off the fare to two decimals as it represents currency." ] }, { "cell_type": "code", "execution_count": 404, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Pclass</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Gender</th>\n", " <th>AgeFill</th>\n", " <th>Age*Class</th>\n", " <th>Port</th>\n", " <th>TitleBand</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>892</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.83</td>\n", " <td>0</td>\n", " <td>34.5</td>\n", " <td>103.5</td>\n", " <td>1</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>893</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7.00</td>\n", " <td>1</td>\n", " <td>47.0</td>\n", " <td>141.0</td>\n", " <td>2</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>894</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>9.69</td>\n", " <td>0</td>\n", " <td>62.0</td>\n", " <td>124.0</td>\n", " <td>1</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>895</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8.66</td>\n", " <td>0</td>\n", " <td>27.0</td>\n", " <td>81.0</td>\n", " <td>2</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>896</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>12.29</td>\n", " <td>1</td>\n", " <td>22.0</td>\n", " <td>66.0</td>\n", " <td>2</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>897</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>9.22</td>\n", " <td>0</td>\n", " <td>14.0</td>\n", " <td>42.0</td>\n", " <td>2</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>898</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.63</td>\n", " <td>1</td>\n", " <td>30.0</td>\n", " <td>90.0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>899</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>29.00</td>\n", " <td>0</td>\n", " <td>26.0</td>\n", " <td>52.0</td>\n", " <td>2</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>900</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.23</td>\n", " <td>1</td>\n", " <td>18.0</td>\n", " <td>54.0</td>\n", " <td>0</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>901</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>24.15</td>\n", " <td>0</td>\n", " <td>21.0</td>\n", " <td>63.0</td>\n", " <td>2</td>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Pclass SibSp Parch Fare Gender AgeFill Age*Class Port \\\n", "0 892 3 0 0 7.83 0 34.5 103.5 1 \n", "1 893 3 1 0 7.00 1 47.0 141.0 2 \n", "2 894 2 0 0 9.69 0 62.0 124.0 1 \n", "3 895 3 0 0 8.66 0 27.0 81.0 2 \n", "4 896 3 1 1 12.29 1 22.0 66.0 2 \n", "5 897 3 0 0 9.22 0 14.0 42.0 2 \n", "6 898 3 0 0 7.63 1 30.0 90.0 1 \n", "7 899 2 1 1 29.00 0 26.0 52.0 2 \n", "8 900 3 0 0 7.23 1 18.0 54.0 0 \n", "9 901 3 2 0 24.15 0 21.0 63.0 2 \n", "\n", " TitleBand \n", "0 5 \n", "1 6 \n", "2 5 \n", "3 5 \n", "4 6 \n", "5 5 \n", "6 4 \n", "7 5 \n", "8 6 \n", "9 5 " ] }, "execution_count": 404, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n", "\n", "train_df['Fare'] = train_df['Fare'].round(2)\n", "test_df['Fare'] = test_df['Fare'].round(2)\n", "\n", "test_df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model, predict and solve\n", "\n", "Now we are ready to train a model and predict the required solution. There are 60+ predictive modelling algorithms to choose from. We must understand the type of problem and solution requirement to narrow down to a select few models which we can evaluate. Our problem is a classification and regression problem. We want to identify relationship between output (Survived or not) with other variables or features (Gender, Age, Port...). We are also perfoming a category of machine learning which is called supervised learning as we are training our model with a given dataset. With these two criteria - Supervised Learning plus Classification and Regression, we can narrow down our choice of models to a few. These include:\n", "\n", "- Logistic Regression\n", "- KNN or k-Nearest Neighbors\n", "- Support Vector Machines\n", "- Naive Bayes classifier\n", "- Decision Tree\n", "- Random Forrest\n", "- Perceptron\n", "- Artificial neural network\n", "- RVM or Relevance Vector Machine" ] }, { "cell_type": "code", "execution_count": 405, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((891, 9), (891,), (418, 9))" ] }, "execution_count": 405, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train = train_df.drop(\"Survived\", axis=1)\n", "Y_train = train_df[\"Survived\"]\n", "X_test = test_df.drop(\"PassengerId\", axis=1).copy()\n", "X_train.shape, Y_train.shape, X_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Logistic Regression is a useful model to run early in the workflow. Logistic regression measures the relationship between the categorical dependent variable (feature) and one or more independent variables (features) by estimating probabilities using a logistic function, which is the cumulative logistic distribution. Reference [Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).\n", "\n", "Note the confidence score generated by the model based on our training dataset." ] }, { "cell_type": "code", "execution_count": 406, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "81.03" ] }, "execution_count": 406, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Logistic Regression\n", "\n", "logreg = LogisticRegression()\n", "logreg.fit(X_train, Y_train)\n", "Y_pred = logreg.predict(X_test)\n", "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n", "acc_log" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use Logistic Regression to validate our assumptions and decisions for feature creating and completing goals. This can be done by calculating the correlation coefficient for all features as these relate to survival.\n", "\n", "- Gender as expected has the highest corrlation with Survived.\n", "- Surprisingly Fare ranks higher than Age.\n", "- Our decision to extract TitleBand feature from name is a good one.\n", "- The artificial feature Age*Class scores well against existing features.\n", "- We tried creating a feature combining Parch and SibSp into FamilySize. Parch ended up with better correlation coefficient and FamilySize reduced our LogisticRegression confidence score.\n", "- Another surprise is that Pclass contributes least to our model, even worse than Port of embarkation, or the artificial feature Age*Class." ] }, { "cell_type": "code", "execution_count": 407, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Feature</th>\n", " <th>Correlation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>Gender</td>\n", " <td>2.603587</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Fare</td>\n", " <td>0.003058</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>TitleBand</td>\n", " <td>-0.000969</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>AgeFill</td>\n", " <td>-0.011226</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Age*Class</td>\n", " <td>-0.014132</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Parch</td>\n", " <td>-0.065878</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Port</td>\n", " <td>-0.138667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>SibSp</td>\n", " <td>-0.370618</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>Pclass</td>\n", " <td>-0.630987</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Feature Correlation\n", "4 Gender 2.603587\n", "3 Fare 0.003058\n", "8 TitleBand -0.000969\n", "5 AgeFill -0.011226\n", "6 Age*Class -0.014132\n", "2 Parch -0.065878\n", "7 Port -0.138667\n", "1 SibSp -0.370618\n", "0 Pclass -0.630987" ] }, "execution_count": 407, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n", "coeff_df.columns = ['Feature']\n", "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n", "\n", "coeff_df.sort_values(by='Correlation', ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we model using Support Vector Machines which are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training samples, each marked as belonging to one or the other of **two categories**, an SVM training algorithm builds a model that assigns new test samples to one category or the other, making it a non-probabilistic binary linear classifier. Reference [Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).\n", "\n", "Note that the model generates a confidence score which is higher than Logistics Regression model." ] }, { "cell_type": "code", "execution_count": 408, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "92.93" ] }, "execution_count": 408, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Support Vector Machines\n", "\n", "svc = SVC()\n", "svc.fit(X_train, Y_train)\n", "Y_pred = svc.predict(X_test)\n", "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n", "acc_svc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. A sample is classified by a majority vote of its neighbors, with the sample being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. Reference [Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\n", "\n", "KNN confidence score is better than Logistics Regression but worse than SVM." ] }, { "cell_type": "code", "execution_count": 409, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "84.74" ] }, "execution_count": 409, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn = KNeighborsClassifier(n_neighbors = 3)\n", "knn.fit(X_train, Y_train)\n", "Y_pred = knn.predict(X_test)\n", "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n", "acc_knn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features) in a learning problem. Reference [Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).\n", "\n", "The model generated confidence score is the lowest among the models evaluated so far." ] }, { "cell_type": "code", "execution_count": 410, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "79.46" ] }, "execution_count": 410, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Gaussian Naive Bayes\n", "\n", "gaussian = GaussianNB()\n", "gaussian.fit(X_train, Y_train)\n", "Y_pred = gaussian.predict(X_test)\n", "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n", "acc_gaussian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not). It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for online learning, in that it processes elements in the training set one at a time. Reference [Wikipedia](https://en.wikipedia.org/wiki/Perceptron)." ] }, { "cell_type": "code", "execution_count": 411, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "70.93" ] }, "execution_count": 411, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Perceptron\n", "\n", "perceptron = Perceptron()\n", "perceptron.fit(X_train, Y_train)\n", "Y_pred = perceptron.predict(X_test)\n", "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n", "acc_perceptron" ] }, { "cell_type": "code", "execution_count": 412, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "77.44" ] }, "execution_count": 412, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Linear SVC\n", "\n", "linear_svc = LinearSVC()\n", "linear_svc.fit(X_train, Y_train)\n", "Y_pred = linear_svc.predict(X_test)\n", "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n", "acc_linear_svc" ] }, { "cell_type": "code", "execution_count": 413, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "59.93" ] }, "execution_count": 413, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Stochastic Gradient Descent\n", "\n", "sgd = SGDClassifier()\n", "sgd.fit(X_train, Y_train)\n", "Y_pred = sgd.predict(X_test)\n", "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n", "acc_sgd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model uses a decision tree as a predictive model which maps features (tree branches) to conclusions about the target value (tree leaves). Tree models where the target variable can take a finite set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Reference [Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).\n", "\n", "The model confidence score is the highest among models evaluated so far." ] }, { "cell_type": "code", "execution_count": 414, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "98.2" ] }, "execution_count": 414, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Decision Tree\n", "\n", "decision_tree = DecisionTreeClassifier()\n", "decision_tree.fit(X_train, Y_train)\n", "Y_pred = decision_tree.predict(X_test)\n", "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n", "acc_decision_tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next model Random Forests is one of the most popular. Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees (n_estimators=100) at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Reference [Wikipedia](https://en.wikipedia.org/wiki/Random_forest).\n", "\n", "The model confidence score is the highest among models evaluated so far. We decide to use this model's output (Y_pred) for creating our competition submission of results." ] }, { "cell_type": "code", "execution_count": 415, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "98.2" ] }, "execution_count": 415, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Random Forest\n", "\n", "random_forest = RandomForestClassifier(n_estimators=100)\n", "random_forest.fit(X_train, Y_train)\n", "Y_pred = random_forest.predict(X_test)\n", "random_forest.score(X_train, Y_train)\n", "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n", "acc_random_forest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model evaluation\n", "\n", "We can now rank our evaluation of all the models to choose the best one for our problem. While both Decision Tree and Random Forest score the same, we choose to use Random Forest as they correct for decision trees' habit of overfitting to their training set. " ] }, { "cell_type": "code", "execution_count": 416, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Model</th>\n", " <th>Score</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3</th>\n", " <td>Random Forest</td>\n", " <td>98.20</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Decision Tree</td>\n", " <td>98.20</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>Support Vector Machines</td>\n", " <td>92.93</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>KNN</td>\n", " <td>84.74</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Logistic Regression</td>\n", " <td>81.03</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Naive Bayes</td>\n", " <td>79.46</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Linear SVC</td>\n", " <td>77.44</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Perceptron</td>\n", " <td>70.93</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Stochastic Gradient Decent</td>\n", " <td>59.93</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Model Score\n", "3 Random Forest 98.20\n", "8 Decision Tree 98.20\n", "0 Support Vector Machines 92.93\n", "1 KNN 84.74\n", "2 Logistic Regression 81.03\n", "4 Naive Bayes 79.46\n", "7 Linear SVC 77.44\n", "5 Perceptron 70.93\n", "6 Stochastic Gradient Decent 59.93" ] }, "execution_count": 416, "metadata": {}, "output_type": "execute_result" } ], "source": [ "models = pd.DataFrame({\n", " 'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n", " 'Random Forest', 'Naive Bayes', 'Perceptron', \n", " 'Stochastic Gradient Decent', 'Linear SVC', \n", " 'Decision Tree'],\n", " 'Score': [acc_svc, acc_knn, acc_log, \n", " acc_random_forest, acc_gaussian, acc_perceptron, \n", " acc_sgd, acc_linear_svc, acc_decision_tree]})\n", "models.sort_values(by='Score', ascending=False)" ] }, { "cell_type": "code", "execution_count": 417, "metadata": { "collapsed": true }, "outputs": [], "source": [ "submission = pd.DataFrame({\n", " \"PassengerId\": test_df[\"PassengerId\"],\n", " \"Survived\": Y_pred\n", " })\n", "submission.to_csv('data/titanic-kaggle/submission.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our submission to the competition site Kaggle results in scoring 3,883 of 6,082 competition entries. This result is indicative while the competition is running. This result only accounts for part of the submission dataset. Not bad for our first attempt. Any suggestions to improve our score are most welcome." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "This notebook has been created based on great work done solving the Titanic competition and other sources.\n", "\n", "- [A journey through Titanic](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)\n", "- [Getting Started with Pandas: Kaggle's Titanic Competition](https://www.kaggle.com/c/titanic/details/getting-started-with-random-forests)\n", "- [Titanic Best Working Classifier](https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
mannyfin/IRAS
Type C calibrations/TypeC calcs corrected.ipynb
1
262654
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# The situation\n", "\n", "Type C thermocouples are not NIST calibrated to below 273.15 K. For my research specific scenario, I need to cool my sample (Molybdenum) to cryogenic temperatures and also anneal to very high ~2000 K. There is no thermocouple with these properties. \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The solution\n", "\n", "We know that Type K thermocouples are accurate down to cryogenic temperatures. So what I've done here is to read the Type K temperature and record the corresponding Type C mV to create a calibration table. Both thermocouples were spot welded to a large mass very close to one another to ensure the temperature readings will be accurate.\n", "\n", "Then I will use a polynomial fit to get the low T calibration for the Type C thermocouple." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import a few packages\n", "%matplotlib notebook\n", "from thermocouples_reference import thermocouples\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import sympy as sp\n", "from scipy import optimize, interpolate, signal\n", "\n", "\n", "typeC=thermocouples['C']" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>T</th>\n", " <th>TypeKmV</th>\n", " <th>CJC</th>\n", " <th>TypeCmV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>295.68</td>\n", " <td>0.90</td>\n", " <td>25.27</td>\n", " <td>0.021</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>292.79</td>\n", " <td>0.78</td>\n", " <td>25.27</td>\n", " <td>-0.017</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>292.04</td>\n", " <td>0.74</td>\n", " <td>25.26</td>\n", " <td>-0.028</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>291.22</td>\n", " <td>0.72</td>\n", " <td>25.26</td>\n", " <td>-0.037</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>290.56</td>\n", " <td>0.69</td>\n", " <td>25.26</td>\n", " <td>-0.045</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " T TypeKmV CJC TypeCmV\n", "0 295.68 0.90 25.27 0.021\n", "1 292.79 0.78 25.27 -0.017\n", "2 292.04 0.74 25.26 -0.028\n", "3 291.22 0.72 25.26 -0.037\n", "4 290.56 0.69 25.26 -0.045" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# make sure you are in the same dir as the file\n", "# read in the file and drop Na cols\n", "df = pd.read_excel('Type C Table 4-2-18.xlsx')\n", "df.dropna(axis=1, inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>T</th>\n", " <th>TypeKmV</th>\n", " <th>CJC</th>\n", " <th>TypeCmV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>295.68</td>\n", " <td>0.90</td>\n", " <td>25.27</td>\n", " <td>0.021</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>292.79</td>\n", " <td>0.78</td>\n", " <td>25.27</td>\n", " <td>-0.017</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>292.04</td>\n", " <td>0.74</td>\n", " <td>25.26</td>\n", " <td>-0.028</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>291.22</td>\n", " <td>0.72</td>\n", " <td>25.26</td>\n", " <td>-0.037</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>290.56</td>\n", " <td>0.69</td>\n", " <td>25.26</td>\n", " <td>-0.045</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " T TypeKmV CJC TypeCmV\n", "0 295.68 0.90 25.27 0.021\n", "1 292.79 0.78 25.27 -0.017\n", "2 292.04 0.74 25.26 -0.028\n", "3 291.22 0.72 25.26 -0.037\n", "4 290.56 0.69 25.26 -0.045" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NIST has values calibrated for T > 273.15 K, lets find the Tref based on these points\n", "# I am using Kelvin for all T. The CJC is quoted in deg C.\n", "tempdf = df.query('T>273.15')\n", "tempdf.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let's find the T_ref by using this function to take the TypeC mV and the T to find the Tref\n", "def find_Tref(mV, T):\n", " x = np.arange(290, 301, 0.01)\n", " x = x[::-1] # lets reverse x\n", " i = 1\n", " while typeC.inverse_KmV(mV, Tref=x[i]) - T >= 0:\n", " i += 1\n", "# print(x[i])\n", " return x[i]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# This isn't the fastest way to do things, but since its just a short amount of rows, lets iterate over the mV and T\n", "# to find Tref\n", "Treflist=[]\n", "for idx in tempdf.index:\n", " # print(idx)\n", " Treflist.append(find_Tref(mV=tempdf['TypeCmV'][idx], T=tempdf['T'][idx]))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['294.16', '294.01', '294.05', '293.89', '293.81', '293.93', '293.96', '294.15', '294.08', '294.21', '294.18', '294.30', '294.32', '294.24', '294.37', '294.28', '294.38', '294.37', '294.42', '294.42', '294.41', '294.42', '294.54', '294.47', '294.58', '294.48', '294.58', '294.55', '294.65', '294.71', '294.72', '294.60', '294.70', '294.71', '294.68', '294.72', '294.73', '294.81', '294.86', '294.78', '294.83', '294.74', '295.78', '294.98', '295.06', '294.99', '294.99', '294.92', '294.89', '294.95', '295.08', '294.91', '295.03', '295.05', '295.17', '295.11', '295.10', '295.12', '295.16', '295.20', '295.13', '295.19', '295.19', '295.25', '295.26', '295.24']\n" ] } ], "source": [ "print( ['%0.2f'% x for x in Treflist])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "294.6749999999957\n", "0.2937438416187858\n" ] } ], "source": [ "# now average the Trefs:\n", "avg_Tref = np.mean(Treflist)\n", "print(avg_Tref)\n", "# I will use this Tref for further calcs\n", "Tref_emf = typeC.emf_mVK(avg_Tref)\n", "print(Tref_emf)\n", "# The Tref_emf value is very close to the value in the table at 273.15 K, so we'll use this value to correct the new values\n", "# The value taken at 273.15 K was during the cooling process and is likely to be less accurate than the room temperature value\n", "# across these multiple observations" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>T</th>\n", " <th>TypeKmV</th>\n", " <th>CJC</th>\n", " <th>TypeCmV</th>\n", " <th>TypeC_calib_mV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>295.68</td>\n", " <td>0.90</td>\n", " <td>25.27</td>\n", " <td>0.021</td>\n", " <td>0.314744</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>292.79</td>\n", " <td>0.78</td>\n", " <td>25.27</td>\n", " <td>-0.017</td>\n", " <td>0.276744</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>292.04</td>\n", " <td>0.74</td>\n", " <td>25.26</td>\n", " <td>-0.028</td>\n", " <td>0.265744</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>291.22</td>\n", " <td>0.72</td>\n", " <td>25.26</td>\n", " <td>-0.037</td>\n", " <td>0.256744</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>290.56</td>\n", " <td>0.69</td>\n", " <td>25.26</td>\n", " <td>-0.045</td>\n", " <td>0.248744</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " T TypeKmV CJC TypeCmV TypeC_calib_mV\n", "0 295.68 0.90 25.27 0.021 0.314744\n", "1 292.79 0.78 25.27 -0.017 0.276744\n", "2 292.04 0.74 25.26 -0.028 0.265744\n", "3 291.22 0.72 25.26 -0.037 0.256744\n", "4 290.56 0.69 25.26 -0.045 0.248744" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The emf correction for 273.15 K is then: calibrated_emf = raw_emf + Tref_emf\n", "\n", "# Let's add this to the df we initially imported...\n", "\n", "df['TypeC_calib_mV'] = df['TypeCmV'] + Tref_emf\n", "\n", "df.head()\n", "# Compared to the NIST table, we appear to be off at most a little less than 1 deg K\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.09996884322359076\n", "-0.0478884150234353\n" ] } ], "source": [ "# Had we used the CJC temperature as a proxy for room temp, we would've been even more off.\n", "# compare the TypeCmV using Tref = CJC vs using Tref = 294.67:\n", "print(typeC.emf_mVK(291.22, Tref =(25.26+273.15)))\n", "print(typeC.emf_mVK(291.22, Tref =avg_Tref))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Text(0,0.5,'Type C calibrated emf (mV)')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's visualize these results\n", "plt.plot(df['T'], df['TypeC_calib_mV'], 'o', ms=0.5 )\n", "plt.xlabel('Temperature (K)')\n", "plt.ylabel('Type C calibrated emf (mV)')\n", "\n", "# Interesting. I cooled first to LN2 temperatures and then allowed the sample to heat up slowly by evaporating LN2\n", "# The data agrees fairly well (within ~3 K) between the heating and cooling curves. I didn't heat all the way back up." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 10 9 8 7 6\n", "-2.468e-21 x + 4.853e-18 x - 4.224e-15 x + 2.138e-12 x - 6.955e-10 x\n", " 5 4 3 2\n", " + 1.516e-07 x - 2.234e-05 x + 0.002193 x - 0.1365 x + 4.852 x - 76.48\n" ] } ], "source": [ "# Now lets fit the data to a polynowmial using least squares\n", "fit_coeffs = np.polyfit(df['T'],df['TypeC_calib_mV'], deg = 10 , full=True)\n", "# print(fit_coeffs)\n", "fit_poly = np.poly1d(fit_coeffs[0])\n", "print(fit_poly)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x199bafc6828>]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(df['T'], df['TypeC_calib_mV'],'o',ms='0.5')\n", "ax.plot(df['T'], fit_poly(df['T']) , 'o', ms='0.5')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 10th degree polynomial appears to give the best fit overall. \n", "The lower order polynomials dont fit the curve exceedingly well below 100 K\n", "\n", "Also, the polynomial tracks the heating curve (the slightly higher mV points from 80-150K) a little more closely than the cooling curve (295 to 80 K). Heating occurred much more slowly than cooling, so I expect it to me more accurate anyways." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.008672368463692237\n", "0.0\n" ] } ], "source": [ "# These mV values are also close ~0.5 degrees K of one another\n", "print(fit_poly(273.15)) # fit\n", "print(typeC.emf_mVK(273.15)) # NIST value " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's also a good idea to check that the polynomial does not have any inflection points, at least in the area we are interested in using the polynomial (77 K - 273.15 K). We can use the second derivative test to see if this will be important for our case." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Poly(-2.220789193755e-19*x**8 + 3.49447952674968e-16*x**7 - 2.36535708742166e-13*x**6 + 8.97950152217577e-11*x**5 - 2.08660828978412e-8*x**4 + 3.03157355808982e-6*x**3 - 0.00026813508993774*x**2 + 0.0131554746779969*x - 0.273020054051992, x, domain='RR')\n" ] }, { "data": { "text/plain": [ "[79.5291555364771,\n", " 123.504171677505,\n", " 139.821834537708,\n", " 287.451235817243,\n", " 209.472872466926 - 34.4469269589835*I,\n", " 209.472872466926 + 34.4469269589835*I,\n", " 262.139177916871 - 19.6448808864128*I,\n", " 262.139177916871 + 19.6448808864128*I]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = sp.symbols('x')\n", "polynom = sp.Poly(fit_coeffs[0],x)\n", "# print(fit_coeffs[0])\n", "# find the second derivative of the polynomial\n", "second_derivative = polynom.diff(x,x)\n", "print(second_derivative)\n", "sp.solve(second_derivative,x, domain= sp.S.Reals)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.000224266702337661\n", "3.40080073257809e-5\n", "\n", "\n", "1.97843428458855e-5\n", "-5.80942005079521e-6\n", "\n", "\n", "-1.15999729153327e-5\n", "1.73786045589708e-5\n", "\n", "\n", "2.48646079961112e-5\n", "-4.02928122955615e-5\n" ] } ], "source": [ "print(second_derivative.evalf(subs={x:77}))\n", "print(second_derivative.evalf(subs={x:80}))\n", "print('\\n')\n", "print(second_derivative.evalf(subs={x:120}))\n", "print(second_derivative.evalf(subs={x:125}))\n", "print('\\n')\n", "print(second_derivative.evalf(subs={x:135}))\n", "print(second_derivative.evalf(subs={x:145}))\n", "print('\\n')\n", "print(second_derivative.evalf(subs={x:283}))\n", "print(second_derivative.evalf(subs={x:291}))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Poly(-2.46754354861667e-20*x**9 + 4.3680994084371e-17*x**8 - 3.37908155345951e-14*x**7 + 1.4965835870293e-11*x**6 - 4.17321657956824e-9*x**5 + 7.57893389522456e-7*x**4 - 8.93783633125802e-5*x**3 + 0.00657773733899845*x**2 - 0.273020054051992*x + 4.85219278621867, x, domain='RR')\n" ] }, { "data": { "text/plain": [ "[75.9199175625991,\n", " 83.8674958793105,\n", " 314.778606875682,\n", " 141.153509360179 - 43.2042440876864*I,\n", " 141.153509360179 + 43.2042440876864*I,\n", " 219.436756251011 - 70.4230792350175*I,\n", " 219.436756251011 + 70.4230792350175*I,\n", " 287.237629544313 - 47.6057277244884*I,\n", " 287.237629544313 + 47.6057277244884*I]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_deriv = polynom.diff(x)\n", "print(first_deriv)\n", "sp.solve(first_deriv,x, domain= sp.S.Reals)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.000565456502618744\n", "3.17758165184756e-5\n" ] } ], "source": [ "print(first_deriv.evalf(subs={x:80}))\n", "print(first_deriv.evalf(subs={x:84}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well this is not optimal-- there exists a local minimum at 83.86 K in our polynomial fit. We can attempt to fit an exponential curve to this very low temperature data and append this to the polynomial function." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([16.41152512, 0.1313597 , 19.07371315, -1.87311107]), array([[1.91104025e+11, 1.71274981e+02, 1.16371755e+10, 1.36039738e+01],\n", " [1.71275015e+02, 1.17634328e-05, 1.04308994e+01, 1.53406689e-06],\n", " [1.16371755e+10, 1.04308973e+01, 7.08639458e+08, 8.28565598e-01],\n", " [1.36039785e+01, 1.53406690e-06, 8.28565885e-01, 2.64521791e-07]]))\n" ] } ], "source": [ "lowT_df = df.query('T<103')\n", "# Now lets fit the data to an exponential\n", "# print(np.min(lowT_df['TypeC_calib_mV']))\n", "def func(x, a, b, c, d):\n", " return a * np.exp(b * x - c) + d\n", "\n", "fit_coeffs = optimize.curve_fit(func, lowT_df['T'],lowT_df['TypeC_calib_mV'], p0=(1, 1, 90, -3))\n", "print(fit_coeffs)\n", "a = fit_coeffs[0][0]\n", "b = fit_coeffs[0][1]\n", "c = fit_coeffs[0][2]\n", "d = fit_coeffs[0][3]\n", "expfunc = func(lowT_df['T'],a,b,c,d)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x199bb4a60f0>]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig3, ax3 = plt.subplots()\n", "# ax3.plot(lowT_df['T'], a*np.exp(b*lowT_df['TypeC_calib_mV']), 'o',ms='0.5')\n", "ax3.plot(lowT_df['T'], lowT_df['TypeC_calib_mV'], 'o',ms='0.5')\n", "ax3.plot(lowT_df['T'], expfunc, 'o',ms='0.5',color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This appears to be a better fit than the polynomial in this regime. Now lets concatenate these two functions and interpolate near the points around 100 K to smooth things out if necessary. Recall that the two functions are fit_poly and expfunc" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x199bb4eecc0>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select data from 103 to 120 K just so we can see the point of intersection a little better\n", "checkT_df = df.query('77<=T<=120') \n", "fig4, ax4 = plt.subplots()\n", "ax4.plot(checkT_df['T'], fit_poly(checkT_df['T']), 'o', ms=0.5, label='polyfit', color='g')\n", "ax4.plot(lowT_df['T'], expfunc, 'o', ms=0.5, label='expfunc', color='r')\n", "ax4.plot(df['T'], df['TypeC_calib_mV'],'o',ms='0.5', label='Data', color='b')\n", "ax4.set_xlim([80,110])\n", "ax4.set_ylim([-1.88,-1.75])\n", "ax4.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two fitted plots almost match near 103 K, but there is a little 'cusp'-like shape near the point of intersection. Let's smooth it out. Also, notice that the expfunc fit is a little better than the polyfit." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x199bb4a6e10>]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def combined(switch, low_f1, high_f2):\n", " comb = (1-switch)*low_f1 + switch*high_f2\n", " return comb\n", "comb_fcn = combined(sw, expfunc2,fit_poly(df['T']))\n", "fig, ax = plt.subplots()\n", "ax.plot(df['T'], comb_fcn, 'o', ms=0.5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x199bb5f3d68>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig5, ax5 = plt.subplots()\n", "ax5.plot(df['T'],comb_fcn, 'o', ms=2, label='combined')\n", "ax5.plot(checkT_df['T'], fit_poly(checkT_df['T']), 'o', ms=0.5, label='polyfit', color='g')\n", "ax5.plot(lowT_df['T'], expfunc, 'o', ms=0.5, label='expfunc2', color='r')\n", "ax5.set_xlim([80,110])\n", "ax5.set_ylim([-1.88,-1.75])\n", "ax5.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I will take the polynomial and take the values from 77 K to 273 K for calibration and append them to the NIST values" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Type C (mV)\n", "Temperature (Kelvin) \n", "77.15 -1.870959\n", "77.25 -1.870931\n", "77.35 -1.870902\n", "77.45 -1.870872\n", "77.55 -1.870843\n", " Type C (mV)\n", "Temperature (Kelvin) \n", "2587.65 37.056717\n", "2587.75 37.057632\n", "2587.85 37.058548\n", "2587.95 37.059464\n", "2588.05 37.060379\n" ] } ], "source": [ "# low temperature array\n", "low_temp = np.arange(77.15,273.15, 0.1)\n", "# low_temp_calib = fit_poly(low_temp)\n", "low_temp_calib = combined(switch_fcn(low_temp, 103, 3), func(low_temp,a,b,c,d), fit_poly(low_temp))\n", "\n", "# high temperature array\n", "high_temp = np.arange(273.15,2588.15, 0.1)\n", "high_temp_nist = typeC.emf_mVK(high_temp)\n", "\n", "# concatentate and put into a dataframe and output to excel\n", "Temperature = np.concatenate([low_temp, high_temp])\n", "TypeC_mV = np.concatenate([low_temp_calib, high_temp_nist])\n", "\n", "typeC_calibration = pd.DataFrame(data=TypeC_mV, index=Temperature, dtype='float32', columns = ['Type C (mV)'])\n", "typeC_calibration.index.name = 'Temperature (Kelvin)'\n", "\n", "print(typeC_calibration.head())\n", "print(typeC_calibration.tail())" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Uncomment these lines and run the cell to output a calibration table\n", "# write to excel\n", "\n", "# xlwrite = pd.ExcelWriter('Type C calibration_low_res.xlsx')\n", "# typeC_calibration.to_excel(xlwrite)\n", "# xlwrite.save()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "But wait! Suppose we also want to fix that discontinuity at 273.15 K? We can apply the same procudure as before.\n", "1. Apply a tanh(x) function: $switch = 0.5 + 0.5*np.tanh((x - switchpoint)/smooth)$\n", "2. Combine both functions: $comb = (1-switch)*f1 + (switch)*f2 $" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mV\n", "272.95 0.000000\n", "273.05 0.000000\n", "273.15 0.000000\n", "273.25 0.001339\n", "273.35 0.002678\n", "273.45 0.004017\n" ] } ], "source": [ "\n", "low_calib = combined(switch_fcn(Temperature, 103, 3), func(Temperature,a,b,c,d), fit_poly(Temperature))\n", "\n", "high_calib = pd.DataFrame(index=high_temp, data=high_temp_nist,columns=['mV'])\n", "dummy_df = pd.DataFrame(index=low_temp, data=np.zeros(len(low_temp)),columns=['mV'])\n", "concat_high_calib = dummy_df.append(high_calib)\n", "print(concat_high_calib.loc[272.9:273.5])\n", "\n", "freezept_calib = combined(switch_fcn(Temperature, 273.15, 0.45), low_calib, concat_high_calib['mV'] )\n", "freezept_calib.index.name = 'T'" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "T\n", "272.95 -0.008172\n", "273.05 -0.006156\n", "273.15 -0.004336\n", "273.25 -0.002013\n", "273.35 0.000205\n", "273.45 0.002266\n", "Name: mV, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "freezept_calib.loc[272.9:273.5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The prior value at 273.15 K was -0.00867, when the actual value is 0. After the smoothing, the new value is -0.004336, about half of the prior value. Some of the values a little after 273.15 do not match exactly with the NIST table, but it is much better than the jump that we had before." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x199bba899b0>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig, ax = plt.subplots()\n", "freezept_calib.plot(ax=ax, label ='combined')\n", "ax.plot(Temperature,low_calib, label = 'low calib')\n", "ax.plot(Temperature,concat_high_calib, label= 'high_calib')\n", "ax.set_ylim([-.04,0.04])\n", "ax.set_xlim([268,277])\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([], dtype=int64),)\n" ] } ], "source": [ "print(signal.argrelmin(freezept_calib.values))\n", "# print(signal.argrelextrema(freezept_calib.values,np.less))\n", "# print(signal.argrelextrema(freezept_calib.values,np.greater))\n", "# No local maxima or minima!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "array([1.88131088e-05, 2.83440425e-05, 3.81253795e-05, 3.86294970e-05,\n", " 3.91402800e-05])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# How about candidates for inflection points?\n", "\n", "df = np.gradient(freezept_calib,0.1,)\n", "fig, ax = plt.subplots()\n", "ax.plot(Temperature, df)\n", "\n", "d2f = np.gradient(df, 0.1)\n", "ax.plot(Temperature, d2f)\n", "\n", "d2f[:5]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Uncomment these lines and run the cell to output a calibration table\n", "# write to excel\n", "\n", "xlwrite = pd.ExcelWriter('Type C calibration_corrected_temp.xlsx')\n", "# freezept_calib is a Series, not a Dataframe, so use the line below\n", "freezept_calib.to_frame().to_excel(xlwrite)\n", "xlwrite.save()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "array([ 4.62013919e+05 +0. j, -1.20818784e+04+173688.3611543 j,\n", " -2.00578272e+02 +81799.54158636j, ...,\n", " 1.47125374e+02 -53298.88110465j, -2.00578272e+02 -81799.54158636j,\n", " -1.20818784e+04-173688.3611543 j])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4212018\n", "fig, ax = plt.subplots()\n", "ax.plot(Temperature,TypeC_mV)\n", "\n", "# np.fft.fft(TypeC_mV)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max: = 1.0\n", "min: = 0.0\n", "max: = 1.0\n", "min: = 0.0\n", "mean: -3.124011072398051e-16\n", "std: 1.0\n", "max: = 1.5307623001406279\n", "min: = -1.6628138862714015\n", "mean: 2.1732250938421223e-16\n", "std: 0.9999999999999999\n", "max: = 1.731981830415568\n", "min: = -1.7319818304155097\n" ] } ], "source": [ "def scaled_data(data, low_range=0, high_range=1, standardize=False, print_=False):\n", " \"\"\"\n", " scale data from input range to (low_range,high_range)\n", " assumes data is a np 1d array\n", " also allows capability for standardization (mean=0 and variance = 1)\n", " \"\"\"\n", " _min_ = np.min(data)\n", " _max_ = np.max(data)\n", " \n", " if standardize is True:\n", " data = data - np.mean(data) #remove mean\n", " scaled_data = data/np.std(data) #unit std\n", " if print_ is True:\n", " print('mean: '+str(np.mean(scaled_data)))\n", " print('std: '+str(np.std(scaled_data)))\n", " else:\n", " scaled_data = (high_range - low_range)*(data - _min_)/(_max_ - _min_) + low_range\n", " if print_ is True:\n", " print('max: = '+str(np.max(scaled_data)))\n", " print('min: = '+str(np.min(scaled_data)))\n", " \n", " return scaled_data\n", "\n", "def revert_to_unscaled(scaled, original):\n", " \"\"\"\n", " reverts normzlied data back to original scaling\n", " \"\"\"\n", " scaled_min = np.min(scaled)\n", " scaled_max = np.max(scaled)\n", " orig_min = np.min(original)\n", " orig_max = np.max(original)\n", " \n", " data = (scaled - scaled_min)*(orig_max - orig_min)/(scaled_max - scaled_min) + orig_min\n", " \n", " return data\n", "\n", "normmV = scaled_data(TypeC_mV, standardize=False, print_=True)\n", "normT = scaled_data(Temperature, standardize=False, print_=True)\n", "stdmV = scaled_data(TypeC_mV, standardize=True, print_=True)\n", "stdT = scaled_data(Temperature, standardize=True, print_=True)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Text(0,0.5,'normT')" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,ax = plt.subplots()\n", "ax.plot(normmV, normT, label ='norm')\n", "plt.xlabel('normmV')\n", "plt.ylabel('normT')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div id='0e03aba6-7b66-4f50-8d0a-6838709f7d24'></div>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fit_fcn(mV, param):\n", " return param[0]*np.tanh(param[1]*mV+param[2]) +param[3]\n", "fig, ax = plt.subplots()\n", "\n", "ax.plot(normmV,normT, label='norm')\n", "ax.plot(normmV,fit_fcn(normmV, param=[1,0.1,-1, 1]), label='start')\n", "try:\n", "# popt, pcov = optimize.differential_evolution(fit_fcn, normmV, normT, p0=(2,1,-1,2), method='lm')\n", " bounds = [slice(0,100.,1), slice(-10.,100.,1), slice(-10.,100.,1),slice(-10.,100.,1)]\n", "# result = optimize.differential_evolution(lambda param:np.sum(fit_fcn(normmV, param) - normT)**2,bounds)\n", "# result = optimize.basinhopping(lambda param:np.sum(fit_fcn(normmV, param) - normT)**2,bounds)\n", " result = optimize.brute(lambda param:np.sum(fit_fcn(normmV, param) - normT)**2,bounds)\n", "# result = optimize.least_squares(lambda param:np.sum(fit_fcn(normmV, param) - normT)**2,[1,1,-1, 1])\n", "\n", " print(result)\n", " ax.plot(normmV,fit_fcn(normmV, result), label='fitted')\n", "except RuntimeError:\n", " print(\"curve fit failed\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
sdpython/pymyinstall
_doc/notebooks/example_xgboost.ipynb
1
16622
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# example with xgboost\n", "\n", "Test XGBoost after it was compiled, pickle, unpickle." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", " var a = \"\" ;\n", " for ( ; n > 0 ; --n)\n", " a += \" \";\n", " return a;\n", "}\n", "// look up into all sections and builds an automated menu //\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", " var anchors = document.getElementsByClassName(\"section\");\n", " if (anchors.length == 0) {\n", " anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", " }\n", " var i,t;\n", " var text_menu = begin;\n", " var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", " var ind = \"\";\n", " var memo_level = 1;\n", " var href;\n", " var tags = [];\n", " var main_item = 0;\n", " var format_open = 0;\n", " for (i = 0; i <= llast; i++)\n", " tags.push(\"h\" + i);\n", "\n", " for (i = 0; i < anchors.length; i++) {\n", " text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", " var child = null;\n", " for(t = 0; t < tags.length; t++) {\n", " var r = anchors[i].getElementsByTagName(tags[t]);\n", " if (r.length > 0) {\n", "child = r[0];\n", "break;\n", " }\n", " }\n", " if (child == null) {\n", " text_memo += \"null\\n\";\n", " continue;\n", " }\n", " if (anchors[i].hasAttribute(\"id\")) {\n", " // when converted in RST\n", " href = anchors[i].id;\n", " text_memo += \"#1-\" + href;\n", " // passer \u00e0 child suivant (le chercher)\n", " }\n", " else if (child.hasAttribute(\"id\")) {\n", " // in a notebook\n", " href = child.id;\n", " text_memo += \"#2-\" + href;\n", " }\n", " else {\n", " text_memo += \"#3-\" + \"*\" + \"\\n\";\n", " continue;\n", " }\n", " var title = child.textContent;\n", " var level = parseInt(child.tagName.substring(1,2));\n", "\n", " text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", " if ((level < lfirst) || (level > llast)) {\n", " continue ;\n", " }\n", " if (title.endsWith('\u00b6')) {\n", " title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", " .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", " }\n", " if (title.length == 0) {\n", " continue;\n", " }\n", "\n", " while (level < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " format_open -= 1;\n", " memo_level -= 1;\n", " }\n", " if (level == lfirst) {\n", " main_item += 1;\n", " }\n", " if (keep_item != -1 && main_item != keep_item + 1) {\n", " // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", " continue;\n", " }\n", " while (level > memo_level) {\n", " text_menu += \"<ul>\\n\";\n", " memo_level += 1;\n", " }\n", " text_menu += repeat_indent_string(level-2);\n", " text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", " format_open += 1;\n", " }\n", " while (1 < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " memo_level -= 1;\n", " format_open -= 1;\n", " }\n", " text_menu += send;\n", " //text_menu += \"\\n\" + text_memo;\n", "\n", " while (format_open > 0) {\n", " text_menu += end_format;\n", " format_open -= 1;\n", " }\n", " return text_menu;\n", "};\n", "var update_menu = function() {\n", " var sbegin = \"\";\n", " var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", " var send = \"\";\n", " var begin_format = '<li>';\n", " var end_format = '</li>';\n", " var keep_item = -1;\n", " var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", " begin_format, end_format);\n", " var menu = document.getElementById(\"my_id_menu_nb\");\n", " menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", " </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an example taken from [xgboost website](https://github.com/dmlc/xgboost)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "\n", "import numpy as np\n", "from sklearn.model_selection import KFold, train_test_split\n", "from sklearn.metrics import confusion_matrix, mean_squared_error\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.datasets import load_iris, load_digits, load_boston" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import xgboost as xgb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Zeros and Ones from the Digits dataset: binary classification" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([[87, 0],\n", " [ 1, 92]], dtype=int64), array([[91, 0],\n", " [ 3, 86]], dtype=int64)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng = np.random.RandomState(31337)\n", "\n", "digits = load_digits(2)\n", "y = digits['target']\n", "X = digits['data']\n", "conf = []\n", "kf = KFold(n_splits=2, shuffle=True, random_state=rng)\n", "for train_index, test_index in kf.split(X, y):\n", " xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])\n", " predictions = xgb_model.predict(X[test_index])\n", " actuals = y[test_index]\n", " conf.append(confusion_matrix(actuals, predictions))\n", "conf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iris: multiclass classification" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([[19, 0, 0],\n", " [ 0, 31, 3],\n", " [ 0, 1, 21]], dtype=int64), array([[31, 0, 0],\n", " [ 0, 16, 0],\n", " [ 0, 3, 25]], dtype=int64)]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iris = load_iris()\n", "y = iris['target']\n", "X = iris['data']\n", "kf = KFold(n_splits=2, shuffle=True, random_state=rng)\n", "conf = []\n", "for train_index, test_index in kf.split(X, y):\n", " xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])\n", " predictions = xgb_model.predict(X[test_index])\n", " actuals = y[test_index]\n", " conf.append(confusion_matrix(actuals, predictions))\n", "conf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Boston Housing: regression" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[9.860776812557337, 15.942418468446029]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boston = load_boston()\n", "y = boston['target']\n", "X = boston['data']\n", "err = []\n", "kf = KFold(n_splits=2, shuffle=True, random_state=rng)\n", "for train_index, test_index in kf.split(X, y):\n", " xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])\n", " predictions = xgb_model.predict(X[test_index])\n", " actuals = y[test_index]\n", " err.append(mean_squared_error(actuals, predictions))\n", "err" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameter optimization" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", "[Parallel(n_jobs=1)]: Done 45 out of 45 | elapsed: 2.3s finished\n", "c:\\python370_x64\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", " DeprecationWarning)\n" ] }, { "data": { "text/plain": [ "(0.6699572097100618, {'max_depth': 2, 'n_estimators': 100})" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import joblib # to check you can parallelize GridSearchCV\n", "y = boston['target']\n", "X = boston['data']\n", "xgb_model = xgb.XGBRegressor()\n", "clf = GridSearchCV(xgb_model,\n", " {'max_depth': [2,4,6],\n", " 'n_estimators': [50,100,200]}, verbose=1, n_jobs=1, pre_dispatch=1, cv=5)\n", "clf.fit(X,y)\n", "clf.best_score_, clf.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pickling sklearn API models" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The sklearn API models are picklable\n", "# must open in binary format to pickle\n", "pickle.dump(clf, open(\"best_boston.pkl\", \"wb\"))\n", "clf2 = pickle.load(open(\"best_boston.pkl\", \"rb\"))\n", "np.allclose(clf.predict(X), clf2.predict(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Early stopping" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-auc:0.999497\n", "Will train until validation_0-auc hasn't improved in 10 rounds.\n", "[1]\tvalidation_0-auc:0.999497\n", "[2]\tvalidation_0-auc:0.999497\n", "[3]\tvalidation_0-auc:0.999749\n", "[4]\tvalidation_0-auc:0.999749\n", "[5]\tvalidation_0-auc:0.999749\n", "[6]\tvalidation_0-auc:0.999749\n", "[7]\tvalidation_0-auc:0.999749\n", "[8]\tvalidation_0-auc:0.999749\n", "[9]\tvalidation_0-auc:0.999749\n", "[10]\tvalidation_0-auc:1\n", "[11]\tvalidation_0-auc:1\n", "[12]\tvalidation_0-auc:1\n", "[13]\tvalidation_0-auc:1\n", "[14]\tvalidation_0-auc:1\n", "[15]\tvalidation_0-auc:1\n", "[16]\tvalidation_0-auc:1\n", "[17]\tvalidation_0-auc:1\n", "[18]\tvalidation_0-auc:1\n", "[19]\tvalidation_0-auc:1\n", "[20]\tvalidation_0-auc:1\n", "Stopping. Best iteration:\n", "[10]\tvalidation_0-auc:1\n", "\n" ] }, { "data": { "text/plain": [ "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", " n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n", " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", " silent=True, subsample=1)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = digits['data']\n", "y = digits['target']\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n", "clf = xgb.XGBClassifier()\n", "clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=\"auc\",\n", " eval_set=[(X_test, y_test)])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
leosartaj/scipy-2016-tutorial
tutorial_exercises/Advanced-Simplification.ipynb
1
13297
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simplification" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import *\n", "x, y, z = symbols('x y z')\n", "init_printing()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each exercise, fill in the function according to its docstring." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Polynomial/Rational Function Simplification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In each exercise, apply specific simplification functions to get the desired result." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def polysimp1(expr):\n", " \"\"\"\n", " >>> polysimp1(cos(x)*sin(x) + cos(x))\n", " (sin(x) + 1)*cos(x)\n", " >>> polysimp1(cos(x)*sin(x) + cos(x) + 1)\n", " (sin(x) + 1)*cos(x) + 1\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "polysimp1(cos(x)*sin(x) + cos(x))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "polysimp1(cos(x)*sin(x) + cos(x) + 1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def polysimp2(expr):\n", " \"\"\"\n", " >>> polysimp2((2*x + 1)/(x**2 + x))\n", " 1/(x + 1) + 1/x\n", " >>> polysimp2((x**2 + 3*x + 1)/(x**3 + 2*x**2 + x))\n", " 1/(x**2 + 2*x + 1) + 1/x\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "polysimp2((2*x + 1)/(x**2 + x))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "polysimp2((x**2 + 3*x + 1)/(x**3 + 2*x**2 + x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Powers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In each exercise, apply specific simplification functions to get the desired result. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def powersimp1(expr):\n", " \"\"\"\n", " >>> powersimp1(exp(x)*(exp(y) + 1))\n", " exp(x) + exp(x + y)\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "powersimp1(exp(x)*(exp(y) + 1))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def powersimp2(expr):\n", " \"\"\"\n", " >>> powersimp2(2**x*x**x)\n", " (2*x)**x\n", " >>> powersimp2(x**x*x**x)\n", " (x**2)**x\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "powersimp2(2**x*x**x)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "powersimp2(x**x*x**x)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def powersimp3(expr):\n", " \"\"\"\n", " >>> a, b, c = symbols('a b c')\n", " >>> powersimp3((a**b)**c)\n", " a**(b*c)\n", " >>> powersimp3((a**b)**(c + 1))\n", " a**(b*c + b)\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a, b, c = symbols('a b c')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "powersimp3((a**b)**c)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "powersimp3((a**b)**(c + 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logs" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def logsimp1(expr):\n", " \"\"\"\n", " >>> a, b = symbols('a b', positive=True)\n", " >>> logsimp1(log(x**y*a**b))\n", " y*log(x) + log(a**b)\n", " >>> logsimp1(log(x*y*a*b))\n", " log(x) + log(y) + log(a*b)\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a, b = symbols('a b', positive=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "logsimp1(log(x**y*a**b))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "logsimp1(log(x*y*a*b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Miscellaneous " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def miscsimp1(expr):\n", " \"\"\"\n", " >>> miscsimp1(sin(x + y))\n", " 2*(-tan(x/2)**2 + 1)*tan(y/2)/((tan(x/2)**2 + 1)*(tan(y/2)**2 + 1)) + 2*(-tan(y/2)**2 + 1)*tan(x/2)/((tan(x/2)**2 + 1)*(tan(y/2)**2 + 1))\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "miscsimp1(sin(x + y))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def miscsimp2(expr):\n", " \"\"\"\n", " >>> miscsimp2(gamma(x + 4))\n", " x**4*gamma(x) + 6*x**3*gamma(x) + 11*x**2*gamma(x) + 6*x*gamma(x)\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "miscsimp2(gamma(x + 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Continued Fractions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we do not cover this, see http://asmeurer.github.io/scipy-2014-tutorial/html/tutorial/simplification.html#example-continued-fractions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def list_to_frac(l):\n", " expr = Integer(0)\n", " for i in reversed(l[1:]):\n", " expr += i\n", " expr = 1/expr\n", " return l[0] + expr" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a0, a1, a2, a3, a4 = symbols('a0:5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Determine the list used to create the continued fraction $$\\frac{a_{0} a_{1} a_{2} a_{3} a_{4} + a_{0} a_{1} a_{2} + a_{0} a_{3} a_{4} + a_{0} + a_{1} a_{2} a_{3} + a_{1} a_{3} a_{4} + a_{1} + a_{3}}{a_{0} a_{1} a_{2} a_{4} + a_{0} a_{4} + a_{1} a_{2} + a_{1} a_{4} + 1}.$$" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def continued_frac():\n", " \"\"\"\n", " Determine the original list used to create the fraction. \n", "\n", " Return the original list from this function.\n", "\n", " >>> orig_frac = (a0*a1*a2*a3*a4 + a0*a1*a2 + a0*a3*a4 + a0 + a1*a2*a3 + a1*a3*a4 + a1 + a3)/(a0*a1*a2*a4 + a0*a4 + a1*a2 + a1*a4 + 1)\n", " >>> pprint(orig_frac)\n", " a₀⋅a₁⋅a₂⋅a₃⋅a₄ + a₀⋅a₁⋅a₂ + a₀⋅a₃⋅a₄ + a₀ + a₁⋅a₂⋅a₃ + a₁⋅a₃⋅a₄ + a₁ + a₃\n", " ─────────────────────────────────────────────────────────────────────────\n", " a₀⋅a₁⋅a₂⋅a₄ + a₀⋅a₄ + a₁⋅a₂ + a₁⋅a₄ + 1\n", " >>> cancel(list_to_frac(continued_frac())) == orig_frac\n", " True\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "orig_frac = (a0*a1*a2*a3*a4 + a0*a1*a2 + a0*a3*a4 + a0 + a1*a2*a3 + a1*a3*a4 + a1 + a3)/(a0*a1*a2*a4 + a0*a4 + a1*a2 + a1*a4 + 1)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAArBAMAAABxz2pBAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "text/latex": [ "$$\\frac{a_{0} a_{1} a_{2} a_{3} a_{4} + a_{0} a_{1} a_{2} + a_{0} a_{3} a_{4} + a_{0} + a_{1} a_{2} a_{3} + a_{1} a_{3} a_{4} + a_{1} + a_{3}}{a_{0} a_{1} a_{2} a_{4} + a_{0} a_{4} + a_{1} a_{2} + a_{1} a_{4} + 1}$$" ], "text/plain": [ "a₀⋅a₁⋅a₂⋅a₃⋅a₄ + a₀⋅a₁⋅a₂ + a₀⋅a₃⋅a₄ + a₀ + a₁⋅a₂⋅a₃ + a₁⋅a₃⋅a₄ + a₁ + a₃\n", "─────────────────────────────────────────────────────────────────────────\n", " a₀⋅a₁⋅a₂⋅a₄ + a₀⋅a₄ + a₁⋅a₂ + a₁⋅a₄ + 1 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "orig_frac" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cancel(list_to_frac(continued_frac())) == orig_frac" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
pxcandeias/py-notebooks
FRF_plots.ipynb
1
186155
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='top'></a>\n", "\n", "# Frequency Response Functions (FRFs) plots\n", "\n", "This notebook is about [frequency response functions](http://www.vibrationdata.com/tutorials/frf.pdf) (FRFs) and the various ways they can be plotted.\n", "\n", "## Table of contents\n", "\n", "[Preamble](#Preamble)\n", "\n", "[Dynamic system setup](#Dynamic-system-setup)\n", "\n", "[Frequency response function](#Frequency-response_function)\n", "\n", "[Nyquist plot](#Nyquist-plot)\n", "\n", "[Bode plot](#Bode-plot)\n", "\n", "[Nichols plot](#Nichols-plot)\n", "\n", "[Odds and ends](#Odds-and-ends)\n", "\n", "## Preamble\n", "\n", "We will start by setting up the computational environment for this notebook. Since it was created with Python 2.7, we will import a few things from the \"future\". Furthermore, we will need numpy and scipy for the numerical simulations and matplotlib for the plots:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "System: 3.5.2 |Anaconda custom (64-bit)| (default, Jul 5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]\n", "numpy version: 1.11.2\n", "scipy version: 0.18.1\n", "matplotlib version: 1.5.3\n" ] } ], "source": [ "from __future__ import division, print_function\n", "\n", "import sys\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib as mpl\n", "\n", "print('System: {}'.format(sys.version))\n", "print('numpy version: {}'.format(np.__version__))\n", "print('scipy version: {}'.format(sp.__version__))\n", "print('matplotlib version: {}'.format(mpl.__version__))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also need some specific modules and a litle \"IPython magic\" to show the plots:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import linalg as LA\n", "from scipy import signal\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#top)\n", "\n", "## Dynamic system setup\n", "\n", "In this example we will simulate a two degree of freedom system (2DOF) as a [LTI system](http://en.wikipedia.org/wiki/LTI_system_theory). For that purpose, we will define a mass and a stiffness matrix and use proportional damping:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 0.]\n", " [ 0. 2.]]\n" ] } ], "source": [ "MM = np.asmatrix(np.diag([1., 2.]))\n", "print(MM)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 20. -10.]\n", " [-10. 10.]]\n" ] } ], "source": [ "KK = np.asmatrix([[20., -10.],[-10., 10.]])\n", "print(KK)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.5 -0.2]\n", " [-0.2 0.4]]\n" ] } ], "source": [ "C1 = 0.1*MM+0.02*KK\n", "print(C1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the LTI system we will use a [state space formulation](http://en.wikipedia.org/wiki/State-space_representation). For that we will need the four matrices describing the system (A), the input (B), the output (C) and the feedthrough (D):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 1. 0. ]\n", " [ 0. 0. 0. 1. ]\n", " [-20. 10. -0.5 0.2]\n", " [ 5. -5. 0.1 -0.2]]\n" ] } ], "source": [ "A = np.bmat([[np.zeros_like(MM), np.identity(MM.shape[0])], [LA.solve(-MM,KK), LA.solve(-MM,C1)]])\n", "print(A)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.]\n", " [ 0.]\n", " [ 10.]\n", " [ 0.]]\n" ] } ], "source": [ "Bf = KK*np.asmatrix(np.ones((2, 1)))\n", "B = np.bmat([[np.zeros_like(Bf)],[LA.solve(MM,Bf)]])\n", "print(B)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 0. 0. 0.]]\n" ] } ], "source": [ "Cd = np.matrix((1,0))\n", "Cv = np.asmatrix(np.zeros((1,MM.shape[1])))\n", "Ca = np.asmatrix(np.zeros((1,MM.shape[1])))\n", "C = np.bmat([Cd-Ca*LA.solve(MM,KK),Cv-Ca*LA.solve(MM,C1)])\n", "print(C)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.]]\n" ] } ], "source": [ "D = Ca*LA.solve(MM,Bf)\n", "print(D)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The LTI system is simply defined as:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "system = signal.lti(A, B, C, D)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the results presented ahead we will need the angular frequencies and damping coefficients of this system. The eigenanalysis of the system matrix yields them after some computations:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Angular frequency: [ 1.48062012 4.77574749]\n", "Damping coefficient: [ 0.04857584 0.05822704]\n" ] } ], "source": [ "w1, v1 = LA.eig(A)\n", "ix = np.argsort(np.absolute(w1)) # order of ascending eigenvalues\n", "w1 = w1[ix] # sorted eigenvalues\n", "v1 = v1[:,ix] # sorted eigenvectors\n", "zw = -w1.real # damping coefficient time angular frequency\n", "wD = w1.imag # damped angular frequency\n", "zn = 1./np.sqrt(1.+(wD/-zw)**2) # the minus sign is formally correct!\n", "wn = zw/zn # undamped angular frequency\n", "print('Angular frequency: {}'.format(wn[[0,2]]))\n", "print('Damping coefficient: {}'.format(zn[[0,2]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#top)\n", "\n", "## Frequency response function\n", "\n", "A frequency response function is a complex valued function of frequency. Let us see how it looks when we plot the real and imaginary parts in separate:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Paulo\\Anaconda3\\lib\\site-packages\\scipy\\signal\\filter_design.py:1092: BadCoefficients: Badly conditioned filter coefficients (numerator): the results may be meaningless\n", " \"results may be meaningless\", BadCoefficients)\n" ] }, { "data": { "image/png": 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dh8fj4ayzzmLIkCHs3LmTWbNm0a5dO5+EqXfv3gwfPpy7776b1NRUzj33XMqX\nL893333Hq6++ymOPPVbobbVoEnGDf0XkbBFZKCI/i0imiETMb2rdOrjlFmjaFPr2hbQ015y9ZYtb\n9TUYrW2WcMQWVViwwM2dkZTkkoQ1a0qXbGT55z/d8VNSSn8sExn8ub2R31oi+b2vWrVqXHPNNaSm\nppKcnMz48ePZsmULTz75JKNHj87eb8yYMVx66aU8/fTTDBo0iLFjxxa7/KK25zR8+HDmzZvHoUOH\nmDhxInPmzOHiiy/ms88+y56DA9wK5C+//DJHjhzhlltu4e233+a5556jY8eOeY751FNP8fjjj/PL\nL79w2223cfvtt/PZZ58xZMgQunXrVuj5RJXSjKkF1gINSzs2t5hl9gfuwq0WewzwFLF/yObhOHBA\n9f33VW+4QbVVK1VQPfFE1auvVl26VDUzM+gh6aJFqhD4+QI2bNgQsGOb/OWu8zVrVPv0cdddv36q\n69eXfZl/+5tqly55t0f7nBRZctZ5rJyziS7BnIejVC0cqtpeVX8szTFKUOZ7qnqnqr4JhNWSfL/+\nCm++6dY86dkTatSAfv3gtdfcKICFC12rxmOPuenJQ7GgYLBaOG666abgFGSyZdX55s2uX0aHDvDj\nj65f0LvvQtu2ZV/mpZfCihWunFhk17kx/vOrD4eInAqsVdXMInd2+7cDvlXVo0XuHGEyM+GXX+CH\nH1yHz7Vr/3xkTbV/0klw5plwzz3u1km7dqFJLvLjEo62vPnmWtq2bRawch599NGAHdvk75ZbHmXE\nCHjmGdfpeMYMGDECjjsucGX27+9Gurz1lpvLI0vbtm1Zu3YtzZoF7hoLB3adG+M/fzuNrgLqATv9\n3P9LoCOwpSRBBZOqm2Xxjz9g/3747TfYudO1Vuzc+ee/t21zo0i2bftzWKkItGgB7du7P+zt27vJ\nkBo2DJ8EIzeXcFSiUaN2+U7uVFZsWGzwrF7tZgZNSWlE9epudtprrsl/8q6yVr26myvmzTd9E45K\nlSrRrl27wAcQYnadG+M/fxMOASaLSIaf+wfwO1Xp7NsHp532Z4Kxf79LOvJTpQrUru0ejRrB3/4G\nTZq4R+PGLtmItFmbs26pBGMBNxM4mZmweLFLND74wF2f06a5xDdHv7aguOACN4fM/v2+U6EbY0xO\n/vbh+AxoDZzm5+NL/F93JSgSExPxeDxcfrkHEQ/Vq3uoWbM7V1+9gBdecD35lyyBRx9dzLnnesjI\ncEnJDz9yRWXEAAAgAElEQVTA119D3bqjadt2DmPGwIABbrKjDRtS8Xg8pKen+5SVlJTE1FwTFGzb\ntg2Px8PGjRt9ts+cOZMJEyb4bMvIyMDj8bB0qe/8aSkpKQwdOjTPuQ0cOJAFCxb4bFu8eHG+Q62m\nTh0NzPFJOFJTI+88Ro8ezZw5c3y2xcJ53HrrVO65xyW7558PO3Zso1MnD4sWbWTcuD+TjWCex7nn\nwpEjo7njjtj7feQ+D2MiWUpKCh6Ph+7du1OvXj08Hg/jxo0ruwJK2+s0lA8gkzAepRKOfvjBjVpY\nvDiw5dx3332BLSCGHDig+vrrqhdcoBoX51ZxHTJE9YsvfEc6harOMzNV69Rxq8rGmpx1bqNUTCQK\nx9Viw4aIVAFa8OcIlWYi0gH4TYM8YiYSBeuWSkaGv3ffTH4OH3Ytbi+/7Frf9u2DTp3g0Ufh8svB\nuxaVj1DVuYibvfTjj0NSfEjZdW6M/yIu4QC6AB/jMi4FHvRunwsMC1VQkSJYCUdhK2Ca/O3Z4/pl\nvP22G/Wxe7dbTO3GG2HgwKIXVgtlnf/lL/DKK7B3r1trJVbYdW6M/yIu4VC3kFzEzZAaLqzTaPhQ\ndWubLFrkkozPP4ejR13/oGuucUnGKaeE74innHr1ch1Zly93Q8GNMSY3++COMS7hSOPll5NJS0sL\ndTgxZ+tWN0/GlVe64dNt2sCtt0J8vFuT5H//g//+183hcuqpkZFsALRqBSeeCF9+6Z6npaWRnGzX\nmDHmT5ZwxJjy5UEkjYULJwX0wyB3j/5YlNWCMXcuDB8OzZq5IdXDh7t1dS691LVs7NoF77zj5rHw\nrnpdIqGscxE3B83y5e55WloakyYF9hoLB3adG+M/f2ca9XuBNFVdWPJwTDAcd1zgb6kMGzaMhQtj\n61LYu9cNoV6+3H3TX77cTSQHblK4Cy5wfR169nStAWUt1HXerRtMn+5urcSKUNe5MZHE3z4cC4re\nBXCdOMuVMBYTJBUqBD7hSE5ODmwBIbZ/v7v1kZoKq1a5RGPtWteqccIJ7sP3+uvdzzPOcDNyBlqo\n67x7d/j9d9eqEytCXeembMXFxZGcnMydd94Z6lAK1aRJE3r37s3TTz8d6lCKxa+EQ1Xt1ksUCeTa\nGlk6deoU+EKCZPdul1RkJRepqe5DNTPTJW/t27ukYuxY96HbujXEheB/TKjrvGtXd2tl+XLX/yQW\nhLrOg23NmjVMmjSJFStWsGPHDmrWrElCQgIej4cxY8aEOryYIZHSuSuXiBulYkovGAlHJFJ1q/nm\nTC5WrXIdOQEqV4aOHaFPHxg/3s2L0a6d1WeW44939fHll7GTcMSSZcuW0bt3bxo3bszIkSOpV68e\nP/74I8uXL2fGjBmWcJgilSjh8E6+1QtoRK51U1R1RhnEZQKoQoVQRxB6mZnw/fcuofjmG/dYtQp2\n7HCv16jh1tz5xz/cz06doGVLKGc3DAvVrRt89RWMGhXqSExZu+eee6hevTorVqygWrVqPq9Z51nj\nj2I3/IrIacD3QArwKHA78DBwLzC2TKMzAZE1F0cg5V6PIpQOHoQVK2D2bBg9Gs46y30bb93ajRR5\n4QU3emfkSHj9ddeisWsXfPgh3H+/m9mzTZvwTzbCoc67dnV9WQ6E1UpKgRMOdR4sW7ZsoV27dnmS\nDYBatWrl2fbCCy/QpUsXKleuTM2aNbnsssv46aef8uz31VdfkZiYyIknnkjVqlXp0KEDM2b4fm/9\n6KOPOPvss6latSo1atTgwgsvzLPuTnJyMnFxcWzevJkhQ4ZQo0YNqlevzrBhwzh48KDPvocPH2bc\nuHHUqVOH448/ngsvvJCff/7Zr3r49NNPiYuLY/78+UyaNIkGDRpw/PHHc/HFF7Nv3z4OHz7M2LFj\nqVu3LtWqVWPYsGEcOXLE5xjHjh1j8uTJtGjRgvj4eJo2bcptt93G4aylyHO4++67adiwIVWqVKFP\nnz6sX78+37j27NnD2LFjadSoEfHx8bRs2ZJp06ZlLfERFkrSwvEQ8BZwNbAH6AYcAV4AHim70Eyg\nVKwYT/XqCcTHxwesjNTUVIYPHx6w4xfk4EHXWvH11y7JWLUKNmyAY8dcv4rWrV2LxYUXup8dOrjV\ngKNBqOo8p65dXV1v2RJPQkJgr7FwEA51HiyNGzdm+fLlrFu3jnbt2hW67z333MOdd97JpZdeyogR\nI9i5cyczZsygV69erFq1iuO909EuWbKECy64gJNOOomxY8dSr149NmzYwKJFi7j++usB+OCDD0hM\nTKR58+ZMmjSJAwcOMGPGDHr06EFqaiqNGjUC/uzXcMkll9CsWTPuu+8+UlNTmT17NnXr1mXKlCnZ\n8Q0fPpx58+YxaNAgunfvzkcffcRf//rXYvWNmDJlCpUrV+aWW27h+++/Z+bMmVSoUIG4uDh+//13\nJk2axPLly5k7dy7NmjXj9ttv9yn/ueee45JLLmH8+PF89dVXTJkyhY0bN/Laa69l73fHHXdwzz33\nMGDAAM4//3xSU1Pp27dvngTmwIED9OzZk7S0NK6++moaNmzIsmXLuOWWW/jll1+YPn263+cVUMVd\nfAX4HWid499tvf8+A9hY2sVdyvqBLd6Wx1lnqV55ZaijKL1jx1TXr1d99lnVa69V7dJFtUIFtzjd\nccepnn666qhRqo8/rrp8uer+/aGOOPodOeIWl3vwwVBHEnwlWbxt+/btunLlygIf69atK/IY69at\ny/e927dvL83p5LFkyRKtUKGCli9fXs8880ydOHGiLl68WI8cOeKz39atW7V8+fJ5FhNct26dVqhQ\nQadMmaKqqseOHdOmTZtqs2bNdO/evQWW27FjR61Xr57+/vvv2dv++9//arly5XTIkCHZ25KTk1VE\ndMSIET7vv+iii7R27drZz1evXq0iotddd53PfoMGDdK4uDidNGlSofXwySefqIjoqaeeqkePHs3e\nfvnll2tcXJz+9a9/9dn/zDPP1KZNm+Ypf9SoUT77TZgwQePi4vSTTz5RVdWdO3dqxYoV1ePx+Ox3\n2223qYjo0KFDs7dNnjxZq1Wrpps3b/bZ95ZbbtEKFSroTz/9VOD5BHPxtpJ8gO8EWnr//R3Qz/vv\nNsD+0gbkZwyjgR+AA8By4PRC9rWEI5c+fVQvuSTUURTf4cOqX32lOm2a6oABqtWruysYVNu2VR08\nWPXf/1b9z39UDx0KdbSxq0cP1YEDQx1F8JUk4UhKSsr6Y57vIyEhochjJCQk5PvepKSkUpxN/las\nWKH/93//p1WrVtW4uDgVEa1Tp44uXLgwe5/p06druXLldPPmzZqenp792LlzpyYkJGjfvn1VVfU/\n//mPiojOmDGjwPLS0tJURPSWW27J81r//v21Tp062c+Tk5M1Li5OV6xY4bPfQw89pHFxcbpv3z5V\nVZ0yZYrGxcXpd99957NfVjz+JhwP5sqqH3nkEY2Li9PXXnvNZ/u4ceO0fPnyeuzYMZ/yN27c6LPf\nL7/8oiKiEyZMUFXVefPmaVxcnC5ZssRnv507d+ZJODp06KCJiYk+9Z2enq4ffPCBiojOmzevwPMJ\n99ViVwGnA5uAT4G7RKQWcAWwtgTHKxYRGYhbsG0k8DUwDnhfRFqpqvVc8kPFipGxlooqrFkD777r\n+lMsW+bmv6hcGc48E/71LzcM9fTT81891YRG167wxhuhjiIyjBo1Co+n4HkV/bklNX/+/Dx9FADq\n169fqtjy07lzZ1599VWOHj3K6tWreeONN3jooYe4+OKL+eabb2jTpg3ff/89mZmZtGjRIs/7RYTj\nvMO6tmzZgogUentm69atALRq1SrPa23btmXx4sUcOHCASpUqZW/PusWSpUaNGgDs3r2bqlWrsnXr\nVuLi4mjevLnPfq1bt/azFpyGDRv6PD/B+0cov+2ZmZns2bOHGjVqZJefu37q1q1L9erVs89527Zt\nAHn2q1WrVvY5Zdm0aRNr1qyhdj73h0WEX3/9tVjnFiglSThuBbJ6Dd0GPAc8hktAgrFa6zjgCVV9\nDkBErgb+6i17WhDKj3jx8e6DOxzt3w/vv++SjHffhZ9/dglGr15wxx3uZ6dONhQ1nHXt6mYc3bkz\nevrHBEr9+vVLnRgkJCSUUTT+K1++PJ07d6Zz5860bNmSoUOHMn/+fO644w4yMzOJi4vjvffeIy6f\nCWmqVq0a0NjKFdC7W7VsO08WVI6/5ZflXBqZmZmcd955TJw4Md/zzC9hC4ViJxyquiLHv38F+pdp\nRIUQkQpAZ9yImKwYVEQ+ALoHK45IV7Hin1NuB4rH4/F7yueMDJdcvPKKW1skIwPatoVLLoHzz4ez\nz3ZJkilcceo8kLp2dT//8x9ITAxtLIEWLnUeSl26dAHIXjenefPmqCpNmjTJt5UjS9Z+a9eupXfv\n3vnu09i7uNC3336b57WNGzdSq1Ytn9YNfzRu3JjMzEw2b95My5YtfY4XDFnlb9q0yadV5ddff+X3\n33/PPuesn5s2baJJkybZ+6Wnp7N7926fYzZv3pw//viDv/zlL4E/gVIo0XyIIlJeRM4VkVEiUs27\n7SQRCWzqCrVwU6fvyLV9B1AvwGVHjfj4wN9SKWoSIFX3gTRiBNSt6+a72LQJ7rzTzY+xfr37lnze\neZZs+CtcJl5q0gRq1XIjhaJduNR5MHzyySf5bl+0aBEAbdq0AeCiiy4iLi6OSZMm5bv/b95vO506\ndaJp06Y8/PDD7NmzJ99969WrR8eOHZk7dy579+7N3r527VoWL17MX//612Kfx/nnn4+q5hl6+/DD\nDwdlBs/ExERUlYcffthn+4MPPoiIZJ/TueeeS/ny5Zk5c6bPfg899FCeY15yySV8+eWXLF68OM9r\ne/bs4dixY2V4BiVXknk4GgNrgDeBfwNZjaYTgQfKLrSylZiYiMfj8Xl0796dBQt8l4lZvHhxvvdU\nR48enWfMfWpqKh6PJ8+kN0lJSUydOtVn27Zt2/B4PHmy6JkzZzJhwgSfbRkZGXg8HpYuXeqzPSUl\nhaFDh+aJbeDAgcU6jy1b5pDzlm8gzqNv3775nsehQzBiRAq1ag2la1d3+2T8eDdVeMuWA2ndegE5\nb63Gwu+jrM6jb9++YXEeIq6VY8mS6P99rFq1Ks8xotV1111H8+bNGT9+PLNnz2bWrFkMGjSI2267\njWbNmjFkyBAAmjVrxt133828efPo0aMHDzzwAE888QQTJ06kdevWPPvss4C7pfDYY4+xfft2Onbs\nyF133cVTTz3FjTfeyPnnn59d7v3338+uXbvo1q0bDz74IJMnT6ZPnz7UqFGDpKSkYp9Hhw4duOyy\ny5g1axZXXHEFjz32GP/4xz9Yv359qW+7+PP+U089lcGDB/Pkk09y6aWX8thjjzFkyBDuv/9+/v73\nv9OrVy/A9dUYP348ixYtYsCAAcyaNYsRI0bw3HPP5emrMWHCBE477TQGDBjAyJEjeeKJJ5g+fTpD\nhgyhYcOGBSZ0uaWkpGR/NtarVw+Px8O4ceOKXxEFKW4vU9xCbs/jZhjdBzTzbj8H2FTaXqxFlF0B\nN+eHJ9f2Z4E3CniPjVLJ5YYbVP3o/F6m9u1TfeAB1ZNOUhVRveAC1UWLVHOMKjNRJDlZtWZN1czM\nUEcSPCUZpRJJ3n//fb3qqqs0ISFBjz/+eI2Pj9dWrVrp2LFjdefOnXn2f+ONN7Rnz55arVo1rVat\nmiYkJOj111+vmzZt8tlv2bJl2q9fPz3hhBO0WrVq2rFjR501a5bPPh999JGeffbZWqVKFa1evbpe\neOGFeUZ5ZI1S2bVrl8/2Z599VuPi4nTr1q3Z2w4dOqRjx47V2rVra7Vq1fTCCy/Un3/+WePi4vSu\nu+4qtB4++eSTfEejZJWT+/efX1zHjh3TyZMna/PmzbVixYrauHFjvf322/Xw4cN5yps8ebKefPLJ\nWqVKFe3Tp4+uX79emzZtqsOGDfPZb//+/Xrbbbdpq1atND4+XuvUqaM9evTQhx56yGf4bm5Z1+2n\nn67UXbtUd+xQ/fln1a1bVTdvVn399dAOi93Fn/Nw5Ew4mgAZpQ3Ij/KXA4/keC7Aj8CEAva3hCOX\n4cPXaYUKCX6N8S+tI0dUn3hCtW5d1fLlVYcOVd2wIeDFmhB7/PF1Cgn63nuBv8bCRbQnHCY6/Tns\ndWX2NAO+j7JLOErShyOO/Jegb+BNQAJtOjBCRK4UkTbA40BlXCuH8UNc3EGOHFmf71C6srJgwQI+\n/9wtdjZqlOuL8f338PTTbppwU/Zy3zYIpZYtDwLrWbkycNdYOAinOjemNKZMgddegzffhEWL3O3u\nDz+Ep54quzJKMix2MW7NlJHe5+rtLDoJeKesAiuIqr7inffjLqAu8A1u8rGdgS47WgR6SOn+/TBh\nQgqbN19It26uc6i3I7sJoJSUFC688MJQhwFA9eru57p1oY0j0MKpzo0pjb593ZQDuWX9Xy4LJUk4\nbsRNtLUeiAfmAS2BdOCysgutYKo6C5gVjLKiUSATjvXr4aKL4OefX2b6dLjuuvBf9CxavPzyy6EO\nIY+1AZ8KMLTCsc6NCVclmYfjJxHpAAwEOgBVgTnAi6oaI2tERrZAJRzz58PQoW5Y5KpVbqE0E9s2\nboQjR6BChVBHYowJtZK0cKCqR4EXvY9sIlLJko7wl5VwZGaW3TFnzIAbboCBA2HOHKhSpeyObSLX\n4cOuleO000IdiTEm1Eo08VduIlJRRG7ELahmwlxWwnH4cNkc7667XLIxfjykpFiyYf4UFxcbE4AZ\nY4rmd8LhTSqmiMgKEVkmIhd6tw/FJRpjgbxToJmwU5YJx/TpkJQE99wD99/vJn0C8p2AyQRWONZ5\ny5bRnXCEY50bE66Kc0vlLmAUsAQ4C5gvIs8A3YB/AfNVNTzmTzWFqlu3PpBEjRqlWzTqxRfhxhvh\n5pvh1lt9X8s566UJjnCq8/r165OUlMTmzfWjOuEIpzo3JtwVJ+G4GLhSVReKSHvgv973d1At5Xyw\nJqhOOqk+kFyqJd1XrYKrroLBg+Hee/O+ftllQRmwZHIIpzqvX78+ycnJPP20S0z37YNq1Yp+X6TJ\nqvMDB+C//3XbNmzYEMKIjCmeYF6vxUk4GgArAVR1rYgcAh6yZCPyVKzofpZ0Abfdu+H//g8SEuDx\nx/+8jWJMbl27urkKU1PBu0RExFN1k9gtWwZffeVuGa1eDUeP1iIurjL//Oc/Qx2iMcVSuXJlatWq\nFfByipNwlANy3vU/CvxRtuGYYMhafbWkE42OHeuWt//wQ1vJ1RSubVvXifjrryM34VCFzZvh44/h\nk0/cY/t291rbti6pGj4cunZtRPXqG9izJ72wwxkTdmrVqkWjRo0CXk5xEg4BnvW2bICb9OtxEdmf\ncydVvaisgjOBUZoWjvfeg+eec0NfmzYteL+lS5fSo0ePkgVoSiQc67xcOTfLbKT148jIcAn122/D\nu+/Cjz+6ETedO8OgQfCXv8CZZ8KaNbnrvJH3YQIlHK9z45/iDIudC/wK7PE+XgC253ie9QgYEblV\nRL4Qkf0i8lsgy4pmJU04DhyAq69266IU1Tl/2rRpJQvOlFi41nnXrpGRcOzc6W4RJiZCzZrg8cBH\nH7mZc99+291K/PprmDYNzj8fTjghfOs8mlmdRy6/WzhUNRzGf1UAXgG+BIaFOJaIVdJbKjNmwM8/\nw5IlRffbeOmll0oWnCmxcK3zrl3dkOlffoF69UIdja/ff4cFC9z8MR9+6Lb17OmGeQ8YAK1aFf7+\ncK3zaGZ1HrlKNNNoqKjqJAARGRzqWCJZSVo40tPdaJRrrnFzKxSlcuXKJQvOlFi41vkZZ7ify5dD\nOKxzpur6YTz5JLz+upt6vWdP+Pe/XWtG7dr+Hytc6zyaWZ1HrjKZadREFjf7/Dr27PF/FvoHHnBT\nod9xR+DiMtHjwIEDrFu3jgMHDtCwITRvDosXhzamXbtcS0vr1tC7N3zzjUuif/zRJSCjRhUv2TDG\nFI8lHDHohx82AO29P4u2dy889phr3bA/yMYfGzZsoH379tlj/M8/33W+DMUg+h9+cKsWN2rkEuYz\nzoDPPnMrG994I5x8cvBjMiYWhTzh8E6XnlnI45iIFHEntWiJiYl4PB6fR/fu3VmwYIHPfosXL8bj\n8eR5/+jRo5kzZ47PttTUVDweD+npvsPgkpKSmDp1qs+2bdu24fF42Lhxo8/2mTNnMmHCBJ9tGRkZ\neDweli5d6rM9JSUl36mUBw4cWKzzePddt2/W1OZFnceTT7oOozfc4P95TJgwIeDnES2/j7I6j5xx\nh8N5AIwbN4709HTOPx/+9z+3emywfh9r1rjFBJs1G8jcuQuYMMG1Zjz/PBw4sJi//a30v4+ePXsG\n/Dyy2P8P58orr4yK8wjH30dKSkr2Z2O9evXweDyMGzcuz3tKTFVD+gBqAq2KeJTP9Z7BwG9+Hr8T\noCtXrlTjrFy5UgGdOLHoOjl6VLVhQ9XBg4tXxowZM0oWnCmxcKrzrGss6//d/v2qFSuqPvBA4Mve\ntEn18stVRVSbNlX9979d+YEQTnUeK6zOgyvr/zLQSUv5eR/yTqOqugvYFeo4YtGRI0Xvs2SJ+1Z4\n7bXFO/Z1111XsqBMiYVznVeu7IZTv/qqu40RCL/+CrffDk8/DXXrutuAw4ZBhQqBKQ/Cu86jldV5\n5Ar5LZXiEJGGItIBaAyUE5EO3octiF4C/oxSmTMH2reH008PfDwmul1+uRupsnlz2R43M9PNn9G6\nNbz2mpsn4/vvXSfQQCYbxpjiiaiEA7dibSqQBFT1/jsV6BzKoCJVUS0c6enw5ptu2mZbL8WUlsfj\npjmfN6/sjrlqFXTv7jo0X3QRfPst/OtfUKlS2ZVhjCkbEZVwqOpQVS2Xz+OzUMcWiYpq4Xj9dTh2\nzE3lXFy5OzeZwAv3Oq9SxS36N2cOHD1aumPt2QPXX++mTc/IgM8/d8cNwvpTPsK9zqOR1XnkiqiE\nw5Stolo4XnvNLbhVkqGwN910U8mCMiUWCXV+ww2wdatLZktCFV56Cdq0cX01pk51K9GGammNSKjz\naGN1Hrks4YhBbdu2pW3btVSq1LbAfXbvdutI/N//layMRx99tITRmZIKpzpv27Yta9eupW1b32us\nUyc36da997rWs+L47jvo2xcuu8wtnLZhA4wfH9p+GuFU57HC6jxyWcIRgypVqkStWu04dKjgG91v\nv+2avUs6FXUwljo2vsKpzitVqkS7du2olE9ninvugdWr4amn/DtWRoabsOuUU1yH00WLXOtbw4Zl\nHHQJhFOdxwqr88hlCUeMqloV/vij4NffesstumWzMJqy1q2b64h8441ucq6CqLphtG3auJEnN90E\n69a51VyNMZHHEo4YVaUK7N+f/2vHjrmVM/v1C25MJnY88ohbBPC88+CLL3xfO3LEreB6xhlw8cXQ\nsaObhnzyZBt9Ykwks4QjRhWWcKxaBb/95j4MSir31Lwm8CKpzqtUcZPKNWvmOnz26uXmzfj736F+\nffezcmX44ANYuNAt/haOIqnOo4XVeeSyhCNGFXZLZckS93q3biU/fkZGRsnfbEok0uq8dm03nPW5\n56BGDVi50l2TI0e6pPeTT6BPn1BHWbhIq/NoYHUeuURDsXxjEIlIJ2DlypUr6dSpU6jDCRsTJ7qO\nd99/n/e1Pn3ct8u33gp+XMYYY8JHamoqnTt3BuisqqmlOZa1cMSogm6pHDrk7qmfe27wYzLGGBO9\nIibhEJHGIjJbRLaISIaIbBKRZBGx1RKKKS0tjS++SGbv3rQ8r6WmuqQjVBMpmeiQlpZGcnIyaWl5\nrzFjTGyKmIQDaAMIMAJIAMYBVwP3hDKoSJSWlsbixZPIyEgj9x21Zcvc7ZRTTy1dGenp6aU7gCm2\ncKrztLQ0Jk2aFPUJRzjVeaywOo9cEZNwqOr7qjpcVT9U1f+p6tvAA8BFoY4tkh086Pt82TI3/0Zp\nZ28cNmxY6Q5gis3qPPiszoPP6jxyRUzCUYDqwG+hDiKS5RypouoSju7dS3/c5OTk0h/EFIvVefBZ\nnQef1XnkitiEQ0RaAGOAx0MdSyTL2XF061b45Re3TkVp2Yig4LM6Dz6r8+CzOo9cIU84RGSKiGQW\n8jgmIq1yvedk4F3gZVV92p9yEhMT8Xg8Po/u3buzYMECn/0WL16Mx+PJ8/7Ro0czZ84cn22pqal4\nPJ489xSTkpLyTE6zbds2PB5PnqWVZ86cyYQJE3y2ZWRk4PF4WLp0qc/2lJQUhg4dmie2gQMHFus8\ncu67f/+f5/H+++48subfCPfziJbfR7SeB8C4ceMi/jyi5fdh52HnUdR5pKSkZH821qtXD4/Hw7hx\n4/K8p6RCPg+HiNQEahax2xZVPerd/yTgY2CZquatvbzHt3k4cvlzXPVKvviiU3aLxpgxbtKvb78N\naXgmCmRdY/b/zpjIFlXzcKjqLlX9rohHVrJxMi7Z+A9gPYfKwK5df/77669LN7toTvl92zWBZXUe\nfFbnwWd1HrlCnnD4y9uy8QmwFbgJqCMidUWkbkgDi0Dx8fG0bZsAxGcnHIcOwTffuBEqZSE1tVSJ\nsCmBcKrz+Ph4EhISiI+PD3UoARVOdR4rrM4jV8hvqfhLRAYDuftrCKCqWq6Q99ktlQJUqwbJyW6Z\n8K++cq0b//kPdOkS6siMMcaEg6i6peIvVZ2rquVyPeIKSzZM4WrWdKvCgrudctxxpZ/wyxhjjMlP\nxCQcpuzVrPlnH46vv4bTTnNJhzHGGFPWLOGIYSee6JtwlFX/DWOMMSY3SzhiWL16sH077N4N330H\np59edsfObyy6CSyr8+CzOg8+q/PIZQlHDGvWDH74AT77zD0/++yyO/aYMWPK7mDGL1bnwWd1HnxW\n55HLEo4Y1qwZpKXBokXQpIl7lJW+ffuW3cGMX6zOg8/qPPisziOXJRwxrE0b9/Opp+Dcc0MbizHG\nmE3qkOYAACAASURBVOhmCUcMWr9+Pe3ataNSpfVUrOi2XXFFaGMy0SXrGlu/fn2oQzHGhAlLOGLQ\nwYMHWb9+PUePHmThQnjsMejZs2zLyL2okQm8cKrzrGvs4MGDoQ4loMKpzmOF1XnksoQjxvXtC1df\nXfbHzb3aoQk8q/PgszoPPqvzyBVRCYeIvCkiW0XkgIhsF5HnRKR+qOMyedWuXTvUIcQcq/PgszoP\nPqvzyBVRCQfwEXAx0Aq4CGgOzA9pRMYYY4wpUvlQB1AcqvpIjqc/ish9wBsiUk5Vj4UqLmOMMcYU\nLtJaOLKJyInAIOALSzaMMcaY8BZRLRwA3laNMUBl4EtgQBFviQfYsGFDgCOLHFl1Ecg6+frrr0lN\nLdVKxqaYwqnOg3GNhYNwqvNYYXUeXDn+D8eX9liiqqU9RukCEJkCTCxkFwXaqup33v1PBE4EGgNJ\nwF5VLTDpEJHLgRfLLmJjjDEm5gxS1XmlOUA4JBw1gZpF7LZFVY/m896TgR+B7qr6VSHH7wf8D4ju\nSQGMMcaYshUPNAHeV9VdpTlQyBOO0hCRRrhE4hxV/SzE4RhjjDGmABGTcIhIV+B0YCmwG2gB3AXU\nBtqr6pEQhmeMMcaYQkTSKJUM3NwbHwAbgaeAb3CtG5ZsGGOMMWEsYlo4jDHGGBO5IqmFwxhjjDER\nKqoTDhEZLSI/eNdeWS4ip4c6pmgmIreIyNcisldEdojIGyLSKtRxxQoRuVlEMkVkeqhjiXYicpKI\nPC8i6SKSISKrRaRTqOOKViISJyKTRWSLt76/F5HbQx1XNBGRs0VkoYj87P074slnn7u865hliMgS\nEWlRnDKiNuEQkYHAg7i5Ok4DVgPvi0itkAYW3c4GZgJnAOcCFYDFIlIppFHFAG8yPRJ3nZsAEpHq\nwBfAIdyQ+7bAjbjO7CYwbgZGAdcCbYCbgJtEZExIo4ouVXD9Iq/FzX/lQ0Qm4ibdHAl0BfbjPlOP\n87eAqO3DISLLga9U9Qbvc8HN2TFDVaeFNLgY4U3ufgV6qurSUMcTrUSkKrASuAa4A1ilqv8KbVTR\nyzvbcXdV7RXqWGKFiLwF/KKqI3JsexXIUNUrQxdZdBKRTOBCVV2YY9t24H5Vfcj7/HhgBzBYVV/x\n57hR2cIhIhWAzsCHWdvUZVYfAN1DFVcMqo7LlH8LdSBR7t/AW6r6UagDiREXACtE5BXvrcNUEbkq\n1EFFuWVAHxFpCSAiHYCzgHdCGlWMEJGmQD18P1P3Al9RjM/UiFtLxU+1gHK47CunHUDr4IcTe7wt\nSg8DS1V1fajjiVYicinQEegS6lhiSDNca9KDwD245uUZInJIVZ8PaWTR6z7geGCjiBzDfVm+TVVf\nCm1YMaMe7stjfp+p9fw9SLQmHCb0ZgEJuG8hJgBEpAEuqTvX5qIJqjjga1W9w/t8tYi0B64GLOEI\njIHA5cClwHpckv2IiGy3JC9yROUtFSAdOAbUzbW9LvBL8MOJLSLyKJCIm5QtLdTxRLHOuJl2U0Xk\niIgcAXoBN4jIYW8rkyl7aUDuZXA3AI1CEEusmAbcp6rzVXWdqr4IPATcEuK4YsUvgFDKz9SoTDi8\n3/ZWAn2ytnn/+PbB3Qs0AeJNNv4G/EVVt4U6nij3AXAK7tteB+9jBfAC0EGjtUd46H1B3luzrYGt\nIYglVlTGfYnMKZMo/QwLN6r6Ay6xyPmZejxuRKLfn6nRfEtlOvCsiKwEvgbG4S7aZ0MZVDQTkVnA\nZYAH2C8iWdnwHlW1lXrLmKruxzUvZxOR/cAuVc39DdyUnYeAL0TkFuAV3B/dq4ARhb7LlMZbwO0i\n8hOwDuiE+5s+O6RRRRERqYJboyyrZbSZt3Pub6r6I+727e0i8j1u0dTJwE/Am36XEc1fgkTkWtx4\n7bq48cXXqeqK0EYVvbxDqfK7oIaq6nPBjicWichHwDc2LDawRCQR15GxBfAD8KCqPh3aqKKX98Nw\nMvB3oA6wHZgHTFbVo6GMLVqISC/gY/L+DZ+rqsO8+yTj5uGoDnwOjFbV7/0uI5oTDmOMMcaEB7v/\nZYwxxpiAs4TDGGOMMQFnCYcxxhhjAs4SDmOMMcYEnCUcxhhjjAm4iEw4RGS0iPwgIgdEZLl3aW5j\njDHGhKmISzhEZCBu0aQk4DRgNfC+dyl0Y4wxxoShiJuHQ0SWA1+p6g3e5wL8CMxQ1WkhDc4YY4wx\n+YqoFg4RqYBbsOrDrG3e9SI+ALqHKi5jjDHGFC6iEg6gFlAO2JFr+w6gXvDDMcYYY4w/onnxNgBE\npCbQD7fYjC0gZowxxvx/e/cdHlWZPXD8ewIIAUKRIigdpAQUBCzsoig2RHesiKirlBVURGEFXWtA\nVBClCIprBwtRUQHLKuBafqLrqom60pSyCyJBCQICoSbn98ebCTOpk8n0nM/z3CeZO3fuPffNzcyZ\n974lcDWAVsBiVd1WkR3FW8KRjZui+KhC64/CTZ1bnHOBl8MZlDHGGJPgrsJNmBe0uEo4VPVg/nTz\nZwJvQUGj0TOBmSW87H8AL730Ep06dQpbbIcOwd69sGePW3JyDv/cvRt27oTffju8bN8O2dmwdevh\nfVStCq1bQ2oqdO4Mxx8P7dqBSMnHDcaqVau4+uqrw1Imqu68rr76Qq68chGbN0NWllt+/dWVRUmS\nkiA5GapXd0u1alCliluSkvx/en8HyMtzx/X+9P09L+/w74cOueXgQbccOOAeHzjgtglU1aouzuRk\nqFnT/UxJgbp13VKnDtSr535619WvD40auW3D5cILL2TRooBnig6rcF5jscTjuZDnn1/E1q3uf3n7\ndve/7l127PB/7H1PKE1SEtSocfj/INClWjV3bVatWvLvpT3n/b3w/1vh/z3vusKPIyWWrvPKwPu/\nTP5naUXEVcKRbxowJz/x+BIYA9QE5pSw/T6ATp060b1794gEWB7798P//gfr1sHatfD99/DVV/DO\nO5CbC8ccA/37w1VXwWmnhTb5CFWZbN4M774LH3wAH3/sEguox/PPd6d1a2jZEk44AZo1g4YNoUGD\nw0u9elCrlvvgrlatwqEELTfXJR4HDri/yYEDsG+f+4DYvfvwUtzjXbvcB8tvv7m/oTepLC65qlvX\n/U2PPtotxxzjksx27eDYY926YN+869WrF3PXeKz+3wUqNxd++sn9Xb3/o//9L/z8s7vuf/65Huec\n439+tWv7X+Pt27ufRx7p/v4pKW6pU+fw776Pq1cP/ZeMRBKL13klUeEmCXGXcKjqa/ljbtyHu5Xy\nLXCuqm4t/ZWxqXp16NDBLb727oXPPnMf5G+9BU8/DR07wm23wTXXuG8W0ZSX5+J64gmXaACcdBL8\n5S9w4onw2GOtWbo0ft44q1Q5XGsRKvv3u2+8v/0Gv/zi/YA6/HPtWpegbdp0uIYlORnatnXXQ7du\nLlE74QRo2rTssmzdunXogq+EtmyBb7+F775zy3/+Az/+6GrDwF0jrVq5BLFDB+jbF956qzUTJx5O\nIBs1cv/TJnzsOo9fcZdwAKjqbGB2tOMIp+RkOOsst0yb5j6YHnsMhg6FqVPh+efdB3s0fPIJ3Hyz\ne0P+wx/gySfh0kvdbQOv556Ln2QjXKpXhyZN3JKaWvJ2Bw64b81r1rhl7VpYudL9nXfscNs0bgyn\nnAKnnw59+kDXrtFPOuOZKqxeDZ9+CsuWueW//3XP1a7tbmeedhpcf72rfWrXztXUFa6F+/57uOii\nyMdvTDyKy4SjshGBM85wy1dfwY03ug/6hx+G0aMjF8ehQzB2LDz6qDv+//0fnHpq5I6fqI44ovha\nLlXYsAG++QYyM92H4h13uJqTunXh3HPdh13//u6xKd2BA/DRR+525TvvuFuZVaq4GqQLL3TXdPfu\nrgYjkm0SjKksLOGIMyeeCJ9/DnfeCWPGuPYSDzwQ/tqEnBy4+GL48EOYMQNGjSr9TblPnz7hDagS\nEHFV+K1aubIHl2x8+aX7O7z9Nlx5pfvWfe650KBBHw4ejG5bmFijCl9/DXPnQnq6u73VsiVccAGc\nfz707u3aTQTLrvPIszKPX5ZwxKFq1VztRtOmcOutrsr+5pvDd7yDB+Hyy12bkvffhzPPLPs1n3zy\nCbfeemv4gqqkqld3tUqnngppabBxIyxcCC+9BO+88wnvv38rw4e766FhJZ5d6OBBeP11eOQRVzt0\n9NGufdFVV8Fxx4UuQS98nW/cuJHs7OzQ7NwUa9GiRZxxxhnRDiOhNGzYkBYtWoT/QKqa0AvQHdCM\njAxNRLfeqpqUpPqvfwX+mpycHF2+fLnm5OQEtP0dd6hWraq6eHHgx9iwYUPgG5uQeO+9DTpypGrN\nmm75619Vf/01OrGU9xoLlUOHVOfMUW3Z0nWQPvts1X/8w60PB9/rfMOGDVqzZk0FbLElrpaaNWuW\n+J6dkZHh3a67VvDzOO4mbysvEekOZGRkZCRkV6pDh+CPf3R9/L/91vXfD6Vly1zjuQcecO0HTOzb\nutW1s5k1y932mjABbrgh8W+1fPIJ3HKL62Fy6aVwzz2ucW2kZGZm0qNHj4Qfe8QkFu84GyV9Rnqv\na6CHqmZW5Fh2SyXOVa3qeoR07ep6i9xyS+j2nZfn9tezp+uOa+JDo0Zw//3ub3f33a5h8fPPu9su\nnTtHO7rQ270b/vY3ePxx15Pns89cA9BoifexR4wJF2uLnQA6d4bBg10txJ49odvva6+5+9/TplkX\nzHjUqJFLQr/80jU27dHDda1OpErN5ctdL5Pnn3e1OtFONowxJbOEI0HcfTds2wbzKjTSvb9p09w4\nIL17l/+1Dz30UOgCMQEpqcx79oSMDBg+3PUuGj7cdRGNd6+/7mo0kpPd7cSbb458d1a7zo0JnCUc\nCaJVKzcew+zZofkG++9/uzE/gr1Fk1PWhBEm5Eor8+RkmDkT5sxxXUTPO6/0eW1i3ezZMGCA6976\nr3+5YeGjwa5zYwIXVwmHiNwpIp+JyB4R+S3a8cSaESPcN73lyyu+r7lzoXlz98EUjAkTJlQ8CFMu\ngZT5tde6oei/+solqLt2RSCwEJsyBUaOdG1T5s1zc/FEi13nxgQurhIOoBrwGvBEtAOJRWef7SaA\nevPNiu0nN9ftY8AAa7uRiE47DRYvdsnpn/7k2nfEi6efhttvh7vucrf8bERQY+JHXP27quoEVX0U\n+D7ascSi6tVdFXNZCUdWVhbjx48nKyur2Oc/+8xNNnbZZWEI0sSEXr3gvffgiy/cgFihbkha1jUW\njHfecXOb3HgjTJxoc/VE2ty5c0lKSip2ufPOOwFo1aqV3/ratWtz8skn8+KLLxbZ3yeffFLi/q68\n8spIn56JAOsWm2AuuMBVM2/Z4kYgLU5WVhYTJkzA4/HQtGnTIs8vXux6OJx8cvBxZGdn07AyD3UZ\nBeUt8z/+0bXpGDTI9XT6299CF0tZ11h5rVnjRgn1eFxblFhJNirbdS4iTJw4kVatWvmt79KlS8Hz\nJ5xwAmPHjkVVycrK4plnnuHaa6/lwIEDDBs2rMg+R48eTc+ePf3WFd6/SQyWcCSY0093Pz/5BAYO\nDG4fH33k9lOR6uqhQ4fy1ltvBb8DU27BlPkVV7g2P3ff7W61xGKX0r17XW3bUUe5tkWxdJuvMl7n\n/fr1K3WckWOOOYZBgwYVPL722mtp06YN06dPLzbh6N27N5dccklYYjWxJeq3VERkkojklbLkikj7\naMcZL5o2hY4d3XT2wdi92zUorOhUBePHj6/YDky5BVvm48e77qWDBsH27SENKSTuvBN+/BHeeMO1\nUYoldp2XrWHDhnTs2JF169ZFOxQTZVFPOIBHgI6lLJ2A9RU9SP/+/fF4PH5Lr169WLhwod92S5Ys\nwePxFHn9yJEjefbZZ/3WZWZm4vF4ikzWlJaWVqR//saNG/F4PKxevdpv/axZsxg3bpzfupycHDwe\nD8uWLfNbn56ezpAhQ4rENnDgQL/zOO00eO+9ks+j8Dn7nse//uWGS+/Tp2Ln0b179wqfByTG3yNS\n5+H7rbM85zF/fjpHHz2EHTv8b6tU9DwAxowZU6G/x5gxs5gxYxwPPOAmXSvtPKLx91i6dGmRfSS6\nnTt3sm3bNr+lNLm5uWzatIn69esX+/yuXbuK7C/Rp9yIVenp6QWfjU2aNMHj8TBmzJjQHaCik7FE\nYwGuBX4LcNuEnrytOE895SZ027On+Oe9k/EUVyaTJqmmpKjm5oY5SBNzHn/cTXi2bFnF91XaNRao\nfftUO3ZUPfnk8E2+FkqhOOdYNmfOHBWRIktSUlLBNq1atdJ+/fppdna2Zmdn6/Lly/XPf/6zJiUl\n6c033+y3v48//rjg9YX3Z5M/Rk5Z120oJ2+LqzYcItIcOBJoCVQREe/UTGtVNYSDese37t3dPCjf\nf1/+hp/ffAPdull3w8ro+uvhhRcOj+dSNcrvDo895hqLfvttbLXbCJWcHChUoRNyHTtCzZqh25+I\nMHv2bI4tZaS1xYsX06hRI791Q4cOZcqUKcVun5aWRu9Cwxk3KanFu4lrcZVwAPcB1/g89s5cdwbw\nf5EPJzZ16eI+LL75JriEI9jBvnw9++yzxTYQM+FT0TJPSoInnnAJ6/PPw3XXhTC4ctq2zU1AN3y4\nu55jVUXKfPVqN79NOGVkuL9nKJ144omlNho95ZRTeOCBBzh06BDLly/n/vvvZ/v27RxxxBHFbt+l\nSxf69u0b2iBNTIqrhENVhwBFb9IaP9WrQ2qqSx6KU6NGDVJTU6lRaC77Xbtg7Vo3GVZFZWZmWsIR\nYaEo8xNOgCuvhLQ01w012G/HJV1jgbrvPjcAXay3yaxImXfs6BKCcOrYMbz7L07Dhg05I7/V+dln\nn02HDh244IILePTRRxk9enTkAzIxI64SDhO41FT44YeSnktlxYoVRdavWuUGgDr++Iof//HHH6/4\nTky5hKrMJ050H1QzZwY/NkdJ11ggfv7Z1bSkpUHjxsEdP1IqUuY1a4a+9iEW9e/fnz59+vDggw8y\nYsQIkpOTox2SiRK7U5+g2rd3XQnLY80a9zNaE2GZ2NCmDQwb5oYO37s38sefOtV9GI8aFfljm/C4\n/fbbyc7O5umnn452KCaKAqrhEJGi/cfKtlRVo/B2ZcAlHFlZ8PvvgY9dsGaNG1wpJSW8sZnYN3Ys\nPPWUG4n0hhsid9zsbHjySfjrX2NvzA1D0N1V+/XrR5cuXZg2bRojR46kSiK2AjZlCvSWysKyN/Gj\nwLGEYPwME5wOHdzPNWsCb5i2Zo3VbhinbVu4/HJ4+GHXeDRSPVZmzXI/b7klMscz5SNljCkvIiVu\nM3bsWIYMGcLLL7/MNddcE9D+TGIpzy2VJqqaFMgC5IQrYBMYb+JQUjuO4oQy4ShuUCUTXqEu89tu\ng//+FxYtCuluS3TgAPz97zBkCMTL9CSV6Tq/9tpryc3NLbWHyvr161lUwgVzzTXXkJubW5Bs9OnT\nh9zcXBvWvBIJNOGYC5Tn9shLwO/lD8eESt260KAB/O9/gW2vGtqE46abbgrNjkzAQl3mJ5zgZpV9\n8smQ7rZEb74Jv/7qZoONF3adGxO4gBIOVR2iqrsC3amq3qCq2WVvacKpeXP46afAtt25E3bscA0G\nQ+Gcc84JzY5MwMJR5tdfD0uXuu7S4fbEE25I/dTU8B8rVOw6NyZwFeqlIiKDRKRWqIIxoVWehOPn\nn93PZs3CF4+JPwMGQP36rgFpOK1YAf/3f/FVu2GMKZ+Kdot9EjgqFIGY0GveHDZuLLp+5cqVdO7c\nmZUrVxas27TJ/bSEw/hKTobBg93IowcPBv664q6x0jz/vGu3cdFFwcVpjIl9FU04ItbEWERaisgz\nIrJeRHJEZI2IjBeRapGKId6UVMOxb98+Vq5cyb59+wrWeWs4mjYNzbELz85pwi9cZT54sOuuunhx\n4K8p7horSW4uzJsHgwZBCaNfxyy7zo0JXDwN/NURl+BcB6QCY4DrgQeiGVQsa97ctcvYvbvsbTdt\ncmNwhOoNPz09PTQ7MgELV5kff7ybz+Tll8Oyez76yI0Zc/XV4dl/ONl1bkzgKppwnAf8HIpAyqKq\ni1V1mKr+U1X/p6rvAI8A1qeqBC1auJ+BtOP4+Wc45pjQHfvVV18N3c5MQMJZ5ldf7brH7gq46Xjg\nXnrJ9Y468cTQ7zvc7Do3JnAVSjhUdZmq7g9VMEGoB/wWxePHNO/tkS1byt520yZrv2FKNmiQG+Z8\nwYLQ7jcnB954wyU0NgaUMYktoIRDRDJFpH6gOxWRZSISwu/LxR6jHXAT8PdwHieeeSe++vXXsrcN\ndQ2HSSwtWrguq/PmhXa/77zjbvlddVVo92uMiT2BDljcDegqIoHWJnQDqgeyoYhMAm4vZRMFOqlq\nwVRk+cnMe8CrqvpcgDFVOikpbqr6X34pe9tffnFtOIwpyeWXuyHHt293XWVDYcEC6NbNDaVujEls\n5bml8k/g2wCX8sw//AiuQWhJSyd85mQRkaOBD4Flqjoi0IP0798fj8fjt/Tq1atIK/MlS5YUO1zx\nyJEjefbZZ/3WZWZm4vF4yM72H+MsLS2Nhx56yG/dxo0b8Xg8rF692m/9rFmzGDdunN+6nJwcPB4P\ny5Yt81ufnp7OkCFDisQ2cODAYs/jwgs9NG7sX8MxcuTIIttmZGTyyy8ekpNDdx5DhgwJ2Xkkyt8j\n3OfhG0s4zuOii+DQoSWceWZg5wEwZsyYEs9j/35491245JL4/Xv07NmzyD6MiVfp6ekFn41NmjTB\n4/EwZsyY0B1AVctcgJZBLFUC2Xd5FuAY4Afc0OkS4Gu6A5qRkaGVUc+eqtdd579u8+bNmpaWpps3\nb1ZV1e3bVUH1tddCd9x58+aFbmcmIJEo8169VC+6qOztCl9jxXn3XXfdff99CAOMMN8yz8jI0Mr8\nXuN1zDHH6HWF33QqkauuukrbtWsX7TACVtZ1630e6K4V/AwPdGjzDUEsuSHJiPLl12x8DGwAbgMa\ni8hRImI3AkpRuIYDoGnTpowfP56m+a1Kt25160M5YdagQYNCtzMTkEiU+SWXwPvvw549pW9X+Bor\nzoIF0K4ddO4c4iAjqDJd53PnziUpKanY5c477yzYLikpKaSzwL788svM8k4jHAdKmzG3sovQpNMh\ncTbQJn/xdvQUXOZVJVpBxbrGjaFQLXUR3priRo3CH4+JbxdfDOPGuaTj0kuD309urutmO3iw9U6J\nJyLCxIkTadWqld/6Ll26FPy+bt06qlQJ3VvySy+9xLp16xg1alTI9mmiI24SDlWdi5u11pRD48Zu\njorSeGs4LOEwZWnb1g0E9uabFUs4Pv/cXXcXXxy62Exk9OvXr9Qp6qtVK3vw55ycHGrWrBnKsEwc\niKeRRk0QirulUpi3hqNBg9Adt3CDPhN+kSrzSy5x3VkPHAh+HwsWuHFiTj45dHFFg13nRTVr1ozh\nw4cXPH7mmWdISkris88+4/rrr6dx48a0bt0agN9//52bb76ZVq1aUaNGDY466ijOPfdcvv/+ewBO\nPfVUFi9ezNq1awtu37Rv377EY+fm5pKUlMRf//pXXnvtNVJTU6lZsyZ//OMfC+b1mT17Nu3atSM5\nOZkzzzyTTd6JpHy88sordO/eneTkZBo3bsy1117LlmIGNHrjjTfo0qULycnJdO3albfeeqvYuFSV\nadOm0blzZ2rUqEHTpk258cYb+f333wMv2AQQNzUcJjhHHunGOTh4EEr64rF1q+vmWDWEV8OUKVPo\n3bt36HZoyhSpMr/4Yhg/3g1Jfu655X+9qks4LrwQkuL8K09lvM537tzJtm3b/NY18Pm2Urj9gvfx\niBEjaNKkCePHjy+YY+e6667j7bffZtSoUXTs2JHs7GyWLVvGqlWrOO6440hLS2Ps2LH8+uuvTJ06\nFVUlJSWlzBg/+ugjFi5cyA033EBeXh6TJk3iT3/6E6NHj+aZZ55h1KhRbNu2jYceeoi//OUvvP/+\n+wWvfeaZZxg+fDinnHIKU6ZMISsrixkzZvD555/zzTffULt2bQDee+89Bg4cyHHHHcfkyZPJzs7m\nmmuuoVkxIygOHTqU9PR0hg4dyujRo1m/fj2zZs3iu+++49NPPyUp3v8RAlXeVqa42xqnVbS1aqQW\nKnkvlQULXE+AX34peZtbb1Vt3z60x92zZ09od2jKFKkyz8tTbd1adcSI4F7/7bfumly8OLRxRYNv\nmSd6L5U5c+aoiBRZkpKS/LZr1qyZXy+VZ555RkVE+/btW2SfKSkpOmbMmFKP269fPz322GMDivHQ\noUMqIlqzZk39+eefC9bPnj1bRUSbNWumOTk5Betvu+02TUpKKth2//792rBhQ+3evbseOHCgYLtF\nixapiOj9999fsO64447TFi1a+F0D77//voqIX7wfffSRioi+/vrrfrH+4x//UBHR+fPnB3Ru4RLJ\nXirBfKetC3wgIhuA54G5qhqR+VRM+XkHaNq+/fDIo4VlZ4e+/Ybdn428SJW5iKvlmDcPZs8ufy3F\nwoVQpw6cfnpYwouoipZ5VlYWWVlZJT5fo0YNUlNTS91HSbPyNm3atNReQsEQEWbPns2xxx5b7tf5\n3mbxqlu3Ll988QVbtmyhSZMmoQqTc889l6OPPrrg8cn59+4uv/xykpOTi6xfv349Rx99NF9++WVB\nzYdvWxSPx0O7du149913ueuuu9i0aRPLly/n3nvv9bsGzj33XNq3b09eXl7Butdff50GDRpw+umn\n+9UM9ezZk+TkZD766CMuu+yykJ17LCt3wqGqF4lII+DPwLXABBH5AHgWWKSqB0Mco6kA34TDa+/e\nvaxfv542bdqQnJzMtm2hbb9hEt/FF8O0afDFF/CHPxR9vvA15mvRIujfP/6mog+HJ598kgkTJpT4\nfGpqKitWrCh1HwMGDChon+ArLS2N8ePHVzTEIk488cRSG42WpHDPFoCHH36YoUOH0qxZM3r21gko\nnQAAIABJREFU7En//v255pprit22PJo3b+73uG7dugBFbnfUrVsXVWV7/hvkhg0bEJFi24l07NiR\njIyMgu0A2rVrV2S7Dh06sGrVqoLHa9asYdu2bTQq5ludiPBrIHNPJIig7tqr6lZgGjBNRLoDQ4AX\ngd0i8hIwW1XXhC5ME6ziEo5Vq1bRo0cPMjIy6N69Ozt2QJs20YnPxKdevVyN2YIFxSccha8xr40b\n4Ztv4LbbIhhsDBsxYkSxI5561ahRo8x9zJ8/v8QajlhSOPEEuOKKK+jTpw8LFixg6dKlPPzwwzz0\n0EMsWrSIs846K+hjldQtt6T16m6/h0VeXh5HH300L774YrHHaVxS1XMCqlAzQRFpihsf42wgF/gH\ncBywUkRuU9XpFQ/RVERxCUdhO3dC/heAkBk3bhwPP/xwaHdqShXJMq9SxTX6XLAApkwJfCyNt95y\njZfPOy+88UVKRcs8FLc9yrrlEuu8PTZuvPFGtm7dSteuXXnwwQcLEo5IDqLVsmVLVJUffvihSGPg\nH374gZYtWxZsB672orAffvjB73Hbtm359NNP6d27d0BdhhNZuZvGikg1EblURN7Bjfo5AJgBHK2q\n16rqWcDlwL2hDdUEo1Yt1/uktIRjx47QJxwtWrQI7Q5NmSJd5hdfDOvWQRk1/n4WLoQzzgj99RYt\ndp0HLzc3l127dvmta9SoEU2bNmX//v0F62rVqsWOHTvCFodvQnPSSSfRoEEDnnjiCQ4dOlSw/u23\n32bNmjVccMEFgLs106VLF+bMmcMen2F333vvPX788Ud8XX755Rw4cID777+/yLEPHTpUqbrGBlPD\nkYVLVNKBk1T122K2+QgI3xViAibiajnKquGoVy+0x7VRASMv0mXet6+bkXjBAvAZaLJEO3bAJ5/A\no4+GP7ZIqWzXebC3Hop73Y4dO2jdujUDBgzguOOOo1atWixZsoRvv/2WmTNnFmzXo0cP3nzzTcaN\nG0ePHj2oU6cO/fv3D/ocSovtiCOOYPLkyQwfPpzTTjuNQYMGsXnzZmbOnEm7du24+eabC7adPHky\nHo+HP/7xjwwePJitW7cye/ZsOnfu7Jcw9e3bl2HDhnH//feTmZnJWWedRdWqVfnxxx95/fXXeeKJ\nJ0q9rZZIgkk4xgDzVbXoTcN8qroDaB10VCUQkUVAN6AxsB34ALhdVUtu5m1KTTjy8mDXrsT5xmki\np3p11/hzwQK4556yt//HP+DQIagk760JKZDbG8XNJVLc61JSUrjhhhtYsmQJb7zxBqpKu3bteOqp\npxg2bFjBdjfddBPff/89zz33HNOmTaNt27alJhwlzWVS2npfw4YNo3bt2kyZMoXbb7+d2rVrM2DA\nACZPnlwwBge4GchfffVV7r33Xu644w7at2/PCy+8wGuvvcaXX37pt8+nn36ak046iaeeeoq77rqL\natWq0apVKwYPHswpp5xS4rkknPL0oQWqAYeALhXtjxvMAtwCnAQ0B04BPsNNU2/jcJTi5JNVhww5\n/Ni333U4Zoo1lccrr7jr57//9V9fXN/+Sy91sxcnqkQfh8MkppibLdYnOTkIbCRKk6Wp6qOq+qWq\n/qSqXwCTgVNExCZvK0VpNRw7d7qfob6lsrqsGeNMyEWjzM87z3VvXbSo9O1+/90Nhz5wYGTiihS7\nzo0JXDDjqT4APCgiR4Y6mPLIP/5VwGeqmhvNWGJd/fru/nlxvOtDfUvlNuv3GHHRKPM6deCss9xt\nldIsXAj79ydewmHXuTGBCybhuAk4DdgsIj+ISKbvEuL4ihCRySKyG8jG3Vq5KNzHjHcpKa6dhlen\nTp1Yvnw5nTp1KqjhCHXC8dhjj4V2h6ZM0SrzSy+FTz+Fn346vM73GgNIT4dTT4VC4zHFPbvOjQlc\nMAnHQuARYBIwD1hUaCkXEZkkInmlLLki4jvs2xRcw1Hv2B8vBnEOlUrhhCM5OZnOnTuTnJwctlsq\n1l0w8qJV5gMGQHIyPPfc4XW+19imTbB0KVx1VVTCCyu7zo0JXLkTDlWdUNoSRAyPAB1LWToB632O\n/5uqrlXVfwKDgP4iUuYk1/3798fj8fgtvXr1YuHChX7bLVmypNguSiNHjuTZZ5/1W5eZmYnH4yHb\nO797vrS0NB566CG/dRs3bsTj8RS55ztr1izGjRvnty4nJwePx1Nk6uv09HSGDBlSJLaBAweWeh6+\nCUfh83C3VDIZNiz2z8NXPP89Eu08UlKgZcuRPPros+T63Nz0nsf06dnUrAlXXhnb5wEV/3sYE8/S\n09MLPhubNGmCx+NhzJgxoTtARVudRnMBWgB5lDJ7LdZLRadOVa1du/jnZs1SPeKIyMZjEs/XX7ve\nKunp/uv37FFt3Fh15MjoxBVJ1kvFxKOY7aUCICJVRGSsiHwpIltE5DffJWSZUNHjniQiI0Wkq4i0\nEJG+uFs6a4B/heu4iaBOHdi92425UVg4Bv0C7JtfFESzzHv0cGNypKW5sTa8HnsMfvsNQvklKZbY\ndW5M4IJpw5EG/BV4FTdV/TTgTVxNw/iQRVZUDnAJbrCv1cDTwLfA6Woz1JYqJcX93L276HPhGNYc\nXLW3iaxol/nEibB2LXhHcP7hB7jvPrjhBmjbNqqhhU20y9yYeBLMSKNXAdep6rsiMh5IV9V1IvIf\n3GBcM0t9dZBUdTlwZjj2nei8CceuXa62w9fu3YefD6XSptw24RHtMu/eHcaPh3vvdfOrfPqp65Xy\n4INRDSusol3mxsSTYGo4mgDf5/++G1fLAfAOcH4ogjKh5ZtwFLZ7N/iM1mtMhdx9N8yYAevXu7lW\nPv7Yri9jjBNMwrEJ8M6nvA44J//3E4H9xb7CRJU34fBOSpiVlcX48ePJyspizx43o6wxoSACt9wC\n77yTRfv248nLs2mOjDFOMAnHAg7f2pgFTBSRNcALwHMlvspEjfc2ireGIysriwkTJpCVlRW2Go7C\nXQhN+MVSmfteY4kslsrcmFgXzDgcf1PVB/N/fxU36ugTwGWq+rcQx2dCoKxbKuGo4Rg6dGjod2pK\nZWUeeVbmiSUpKYn77rsv2mGUqVWrVnF57QVTw+FHVf+lqtNU9e1QBGRCr7SEY8+e8NRwjB8/PvQ7\nNaWyMo+8ylbm33//PZdddhmtWrUiOTmZZs2acc4559gQ7xEmItEOISjB9FJBRI4FzgAaUyhpUdXY\nTw8rmerVoWrVw204fIXrlkr37t1Dv1NTKivzyKtMZf7555/Tt29fWrZsyfDhw2nSpAk//fQTX3zx\nBTNnzuSmm26KdogmxpU74RCR63C3ULKBLbgRyLwUsIQjxogUnU/FyxqNGmMC8cADD1CvXj2+/vpr\nUgr1pbe2LCYQwdxSuRu4S1WbqGo3VT3BZ6k86X6cKSnhsG6xxphArF+/ns6dOxdJNgAaNmxYZN1L\nL71Ez549qVmzJg0aNGDQoEFs2rSpyHb//ve/6d+/P0ceeSS1a9ema9euzJzpP5zThx9+yKmnnkrt\n2rWpX78+F110UZF5d8aPH09SUhLr1q1j8ODB1K9fn3r16jF06FD27dvnt+2BAwcYM2YMjRs3pk6d\nOlx00UX8/PPPAZXDJ598QlJSEvPnz2fChAk0a9aMOnXqMGDAAHbt2sWBAwcYPXo0Rx11FCkpKQwd\nOpSDB/3HpszNzWXixIm0a9eOGjVq0Lp1a+666y4OHDhQ5Hj3338/zZs3p1atWpx55pmsXLmy2Lh2\n7tzJ6NGjadGiBTVq1ODYY49lypQp3ik+YkIwCUd9YH6oAzHhlZJSdKTRvLzw1XAUngDLhJ+VeeRV\npjJv2bIlGRkZrFixosxtH3jgAa699lo6dOjA9OnTGTNmDP/85z/p06cPv/vc2126dCl9+vRh9erV\njB49mmnTptG3b1/efffdgm0++OAD+vXrR3Z2NhMmTODWW2/l888/p3fv3mzcuLFgO2+7hssvv5w9\ne/YwefJkBg4cyNy5c4sM0DZs2DBmzpxJv379eOihh6hWrRrnn39+udpGTJo0iaVLl3LHHXcwbNgw\nFixYwIgRIxg6dChr165lwoQJXHrppcydO7fIEPjDhg0jLS2Nnj17MmPGDE4//XQmTZrEoEGD/La7\n5557uPfeeznhhBN45JFHaNOmDeecc06REW737t3Laaedxrx58xg8eDCzZs2id+/e3HHHHdx6660B\nn1PYlXfyFeBZ4PqKTuISqQWbvE1VVU88UXXYMPf7ihUrNDU1Vb/6aoWC6rx5oT/ejTfeGPqdmlLF\nUpl7r7EVK1ZEO5Sw8i3zYCZv27x5s2ZkZJS4BFJ+K1asKPa1mzdvDuqcSrJ06VKtVq2aVq1aVf/w\nhz/o7bffrkuWLNGDBw/6bbdhwwatWrWqTp48uUic1apV00mTJqmqam5urrZu3VrbtGmjv//+e4nH\n7datmzZp0kR37NhRsO4///mPVqlSRQcPHlywbvz48Soiet111/m9/pJLLtFGjRoVPP7uu+9URHTU\nqFF+21111VWalJSkEyZMKLUcPv74YxURPf744/XQoUMF66+88kpNSkrS888/32/7P/zhD9q6desi\nxx8xYoTfduPGjdOkpCT9+OOPVVV169atWr16dfV4PH7b3XXXXSoiOmTIkIJ1EydO1JSUFF23bp3f\ntnfccYdWq1ZNN23aVOL5RHLytmA+wO8AtgJzgFuBm32XigYUYAxH4OZRyQOOL2NbSzhU9fTTVQcN\n8l+3ZYu7At56KzoxGZNIgkk40tLSvG/mxS6pqall7iM1NbXY16alpVXgbIr39ddf66WXXqq1a9fW\npKQkFRFt3LixvuXzJjJt2jStUqWKrlu3TrOzswuWrVu3ampqqp5zzjmqqvrVV1+piOjMmTNLPF5W\nVpaKiN5xxx1FnuvXr582bty44PH48eM1KSlJv/76a7/tpk+frklJSbpr1y5VVZ00aZImJSXpjz/+\n6LedN55AE46pU6f6rX/00Uc1KSlJ33jjDb/1Y8aM0apVq2pubq7f8VevXu233ZYtW1REdNy4caqq\nOm/ePE1KStKlS5f6bbd169YiCUfXrl21f//+fuWdnZ2tH3zwgYqIzivlW2UkE45geqkMxw1p3id/\n8aswIUxzqRQyBTfi6XEROFZCqFXL3T7x5X1sjUaNiY4RI0bg8XhKfL5GjRpl7mP+/PlF2igANG3a\ntJitK6ZHjx68/vrrHDp0iO+++44FCxYwffp0BgwYwLfffkvHjh1Zu3YteXl5tGvXrsjrRYQjjjgC\ncG1CRITOnTuXeLwNGzYA0L59+yLPderUiSVLlrB3716Sk5ML1rdo0cJvu/r16wOwfft2ateuzYYN\nG0hKSqJtoRkFO3ToEGApOM2bN/d7XDd/Fszi1ufl5bFz507q169fcPzC5XPUUUdRr169gnP23i4q\nvF3Dhg0LzslrzZo1fP/99zRq1KhInCLCr7/+Wq5zC5dyJxyq2jocgQRKRM4DzgYuBfpHM5Z4UrOm\nmybcl7dNhzUaNSY6mjZtWuHEIDU1NUTRBK5q1ar06NGDHj16cOyxxzJkyBDmz5/PPffcQ15eHklJ\nSbz//vskJRVtJlg7zG84VapUKXa9amgbT5Z0nECPH8qxNPLy8jj77LO5/fbbiz3P4hK2aAhqHI5o\nEZGjgKcAD7A3yuHElVq1oHADcavhMMZUVM+ePQEKhrFv27YtqkqrVq2KreXw8m63fPly+vbtW+w2\nLVu2BOCHH34o8tzq1atp2LChX+1GIFq2bEleXh7r1q3j2GOP9dtfJHiPv2bNGr9alV9//ZUdO3YU\nnLP355o1a2jVqlXBdtnZ2Wzfvt1vn23btmX37t2cccYZ4T+BCgiol4qITBORWj6/l7iEN1yeB2ar\n6jdhPk7CKe6WSjhrOEqrJjbhYWUeeZWpzD/++ONi13t7lHTs2BGASy65hKSkpCI9Q7x+y69q7d69\nO61bt2bGjBns3Lmz2G2bNGlCt27dmDt3rl/vluXLl7NkyRLOP7/8E5Sfd955qGqRrrczZsyIyAie\n/fv3R1WZMWOG3/qpU6ciIgXndNZZZ1G1alVmzZrlt9306dOL7PPyyy/nX//6F0u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"text/plain": [ "<matplotlib.figure.Figure at 0x1ea2c6c3908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w, H = system.freqresp()\n", "fig, ax = plt.subplots(2, 1)\n", "fig.suptitle('Real and imaginary plots')\n", "# Real part plot\n", "ax[0].plot(w, H.real, label='FRF')\n", "ax[0].axvline(wn[0], color='k', label='First mode', linestyle='--')\n", "ax[0].axvline(wn[2], color='k', label='Second mode', linestyle='--')\n", "ax[0].set_ylabel('Real [-]')\n", "ax[0].grid(True)\n", "ax[0].legend()\n", "# Imaginary part plot\n", "ax[1].plot(w, H.imag, label='FRF')\n", "ax[1].axvline(wn[0], color='k', label='First mode', linestyle='--')\n", "ax[1].axvline(wn[2], color='k', label='Second mode', linestyle='--')\n", "ax[1].set_ylabel('Imaginary [-]')\n", "ax[1].set_xlabel('Frequency [rad/s]')\n", "ax[1].grid(True)\n", "ax[1].legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#top)\n", "\n", "## Nyquist plot\n", "\n", "A [Nyquist plot](http://en.wikipedia.org/wiki/Nyquist_plot) represents the real and imaginary parts of the complex FRF in a single plot:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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A69sACxYsWECbNm18jVWpUunWTZa3/vTTxGxr9Wr46qvw3e7I6ddfpdHVE0/I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\naQ4UZmZmVpoDhZmZmZX2P9o5B24FoNEsAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1ea2c6a0e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.title('Nyquist plot')\n", "plt.plot(H.real, H.imag, 'b')\n", "plt.plot(H.real, -H.imag, 'r')\n", "plt.xlabel('Real [-]')\n", "plt.ylabel('Imaginary[-]')\n", "plt.grid(True)\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#top)\n", "\n", "## Bode plot\n", "\n", "A [Bode plot](http://en.wikipedia.org/wiki/Bode_plot) represents the complex FRF in magnitude-phase versus frequency:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Paulo\\Anaconda3\\lib\\site-packages\\scipy\\signal\\filter_design.py:1092: BadCoefficients: Badly conditioned filter coefficients (numerator): the results may be meaningless\n", " \"results may be meaningless\", BadCoefficients)\n" ] }, { "data": { "image/png": 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JZwFKwf/9H9SsqUPt33KLjmTryxW//N7nQ4YMwW63Z3o9KAdHr5YsWZLlioq/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"text/plain": [ "<matplotlib.figure.Figure at 0x1ea2cb37cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w, mag, phase = system.bode()\n", "fig, ax = plt.subplots(2, 1)\n", "fig.suptitle('Bode plot')\n", "# Magnitude plot\n", "ax[0].plot(w, mag, label='FRF')\n", "ax[0].axvline(wn[0], color='k', label='First mode', linestyle='--')\n", "ax[0].axvline(wn[2], color='k', label='Second mode', linestyle='--')\n", "ax[0].set_ylabel('Magnitude [dB]')\n", "ax[0].grid(True)\n", "ax[0].legend()\n", "# Phase plot\n", "ax[1].plot(w, phase*np.pi/180., label='FRF')\n", "ax[1].axvline(wn[0], color='k', label='First mode', linestyle='--')\n", "ax[1].axvline(wn[2], color='k', label='Second mode', linestyle='--')\n", "ax[1].set_ylabel('Phase [rad]')\n", "ax[1].set_xlabel('Frequency [rad/s]')\n", "ax[1].grid(True)\n", "ax[1].legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#top)\n", "\n", "## Nichols plot\n", "\n", "A [Nichols plot](http://en.wikipedia.org/wiki/Nichols_plot) combines the Bode plot in a single plot of magnitude versus phase:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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K7gPy/feQl6fHVKyol1uK9gOpUWMlJ5zQ0ObiIL3+PvLztWWsaOfbZcv09kqV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\nReJu1y7f6iHdKvLEE74myb77egvi17/uycjw4d5tI8mg5ER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FwmPAghDC1wp9syTViQJj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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1ea2cbac198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.title('Nichols plot')\n", "plt.plot(phase*np.pi/180., mag)\n", "plt.xlabel('Phase [rad/s]')\n", "plt.ylabel('Magnitude [dB]')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#top)\n", "\n", "## Odds and ends\n", "\n", "This notebook was created by Paulo Xavier Candeias.\n", "\n", "[Back to top](#top)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
lithiumdenis/MLSchool
2. Бостон.ipynb
1
21381
{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:06.359703", "start_time": "2017-07-19T12:05:06.349392" }, "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Загрузим данные" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:14.966064", "start_time": "2017-07-19T12:05:14.461429" }, "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import load_boston" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:15.778886", "start_time": "2017-07-19T12:05:15.761514" }, "collapsed": true }, "outputs": [], "source": [ "bunch = load_boston()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:16.965402", "start_time": "2017-07-19T12:05:16.960434" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Boston House Prices dataset\n", "===========================\n", "\n", "Notes\n", "------\n", "Data Set Characteristics: \n", "\n", " :Number of Instances: 506 \n", "\n", " :Number of Attributes: 13 numeric/categorical predictive\n", " \n", " :Median Value (attribute 14) is usually the target\n", "\n", " :Attribute Information (in order):\n", " - CRIM per capita crime rate by town\n", " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", " - INDUS proportion of non-retail business acres per town\n", " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", " - NOX nitric oxides concentration (parts per 10 million)\n", " - RM average number of rooms per dwelling\n", " - AGE proportion of owner-occupied units built prior to 1940\n", " - DIS weighted distances to five Boston employment centres\n", " - RAD index of accessibility to radial highways\n", " - TAX full-value property-tax rate per $10,000\n", " - PTRATIO pupil-teacher ratio by town\n", " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", " - LSTAT % lower status of the population\n", " - MEDV Median value of owner-occupied homes in $1000's\n", "\n", " :Missing Attribute Values: None\n", "\n", " :Creator: Harrison, D. and Rubinfeld, D.L.\n", "\n", "This is a copy of UCI ML housing dataset.\n", "http://archive.ics.uci.edu/ml/datasets/Housing\n", "\n", "\n", "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", "\n", "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", "prices and the demand for clean air', J. Environ. Economics & Management,\n", "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", "pages 244-261 of the latter.\n", "\n", "The Boston house-price data has been used in many machine learning papers that address regression\n", "problems. \n", " \n", "**References**\n", "\n", " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", "\n" ] } ], "source": [ "print(bunch.DESCR)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:39.336807", "start_time": "2017-07-19T12:05:39.330614" } }, "outputs": [], "source": [ "X, y = pd.DataFrame(data=bunch.data, columns=bunch.feature_names.astype(str)), bunch.target" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:41.249948", "start_time": "2017-07-19T12:05:41.221702" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>CRIM</th>\n", " <th>ZN</th>\n", " <th>INDUS</th>\n", " <th>CHAS</th>\n", " <th>NOX</th>\n", " <th>RM</th>\n", " <th>AGE</th>\n", " <th>DIS</th>\n", " <th>RAD</th>\n", " <th>TAX</th>\n", " <th>PTRATIO</th>\n", " <th>B</th>\n", " <th>LSTAT</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.00632</td>\n", " <td>18.0</td>\n", " <td>2.31</td>\n", " <td>0.0</td>\n", " <td>0.538</td>\n", " <td>6.575</td>\n", " <td>65.2</td>\n", " <td>4.0900</td>\n", " <td>1.0</td>\n", " <td>296.0</td>\n", " <td>15.3</td>\n", " <td>396.90</td>\n", " <td>4.98</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.02731</td>\n", " <td>0.0</td>\n", " <td>7.07</td>\n", " <td>0.0</td>\n", " <td>0.469</td>\n", " <td>6.421</td>\n", " <td>78.9</td>\n", " <td>4.9671</td>\n", " <td>2.0</td>\n", " <td>242.0</td>\n", " <td>17.8</td>\n", " <td>396.90</td>\n", " <td>9.14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.02729</td>\n", " <td>0.0</td>\n", " <td>7.07</td>\n", " <td>0.0</td>\n", " <td>0.469</td>\n", " <td>7.185</td>\n", " <td>61.1</td>\n", " <td>4.9671</td>\n", " <td>2.0</td>\n", " <td>242.0</td>\n", " <td>17.8</td>\n", " <td>392.83</td>\n", " <td>4.03</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.03237</td>\n", " <td>0.0</td>\n", " <td>2.18</td>\n", " <td>0.0</td>\n", " <td>0.458</td>\n", " <td>6.998</td>\n", " <td>45.8</td>\n", " <td>6.0622</td>\n", " <td>3.0</td>\n", " <td>222.0</td>\n", " <td>18.7</td>\n", " <td>394.63</td>\n", " <td>2.94</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.06905</td>\n", " <td>0.0</td>\n", " <td>2.18</td>\n", " <td>0.0</td>\n", " <td>0.458</td>\n", " <td>7.147</td>\n", " <td>54.2</td>\n", " <td>6.0622</td>\n", " <td>3.0</td>\n", " <td>222.0</td>\n", " <td>18.7</td>\n", " <td>396.90</td>\n", " <td>5.33</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", "0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \n", "1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \n", "2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \n", "3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \n", "4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \n", "\n", " PTRATIO B LSTAT \n", "0 15.3 396.90 4.98 \n", "1 17.8 396.90 9.14 \n", "2 17.8 392.83 4.03 \n", "3 18.7 394.63 2.94 \n", "4 18.7 396.90 5.33 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Зафиксируем генератор случайных чисел для воспроизводимости:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:54.565451", "start_time": "2017-07-19T12:05:54.562199" } }, "outputs": [], "source": [ "SEED = 22\n", "np.random.seed = SEED" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Домашка!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Разделим данные на условно обучающую и отложенную выборки:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:06:06.969114", "start_time": "2017-07-19T12:06:06.937099" }, "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:06:07.153370", "start_time": "2017-07-19T12:06:07.147084" }, "collapsed": true }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:06:07.443579", "start_time": "2017-07-19T12:06:07.438465" } }, "outputs": [ { "data": { "text/plain": [ "((404, 13), (404,), (102, 13), (102,))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, y_train.shape, X_test.shape, y_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Измерять качество будем с помощью метрики среднеквадратичной ошибки:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:06:47.313356", "start_time": "2017-07-19T12:06:47.310497" }, "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"panel panel-info\" style=\"margin: 50px 0 0 0\">\n", " <div class=\"panel-heading\">\n", " <h3 class=\"panel-title\">Задача 1.</h3> \n", " </div>\n", " <div class=\"panel\">\n", " Обучите <b>LinearRegression</b> из пакета <b>sklearn.linear_model</b> на обучающей выборке (<i>X_train, y_train</i>) и измерьте качество на <i>X_test</i>.\n", " <br>\n", " <br>\n", " <i>P.s. Ошибка должна быть в районе 20. </i>\n", " </div>\n", "</div>" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Вышла средняя ошибка, равная 21.7029\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import cross_val_score\n", "\n", "clf = LinearRegression()\n", "clf.fit(X_train, y_train);\n", "\n", "print('Вышла средняя ошибка, равная %5.4f' % \\\n", " (-np.mean(cross_val_score(clf, X_test, y_test, cv=5, scoring='neg_mean_squared_error'))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"panel panel-info\" style=\"margin: 50px 0 0 0\">\n", " <div class=\"panel-heading\">\n", " <h3 class=\"panel-title\">Задача 2. (с подвохом)</h3> \n", " </div>\n", " <div class=\"panel\">\n", " Обучите <b>SGDRegressor</b> из пакета <b>sklearn.linear_model</b> на обучающей выборке (<i>X_train, y_train</i>) и измерьте качество на <i>X_test</i>.\n", " </div>\n", "</div>" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Вышла средняя ошибка, равная 0.3137\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] } ], "source": [ "from sklearn.linear_model import SGDRegressor\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "ss = StandardScaler()\n", "X_scaled = ss.fit_transform(X_train)\n", "y_scaled = ss.fit_transform(y_train)\n", "\n", "sgd = SGDRegressor()\n", "sgd.fit(X_scaled, y_scaled);\n", "\n", "print('Вышла средняя ошибка, равная %5.4f' % \\\n", " (-np.mean(cross_val_score(sgd, X_scaled, y_scaled, cv=5, scoring='neg_mean_squared_error'))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"panel panel-info\" style=\"margin: 50px 0 0 0\">\n", " <div class=\"panel-heading\">\n", " <h3 class=\"panel-title\">Задача 3.</h3>\n", " </div>\n", " <div class=\"panel\">\n", " Попробуйте все остальные классы:\n", " <ul>\n", " <li>Ridge\n", " <li>Lasso\n", " <li>ElasticNet\n", " </ul>\n", "\n", " <br>\n", "\n", " В них, как вам уже известно, используются параметры регуляризации <b>alpha</b>. Настройте его как с помощью <b>GridSearchCV</b>, так и с помощью готовых <b>-CV</b> классов (<b>RidgeCV</b>, <b>LassoCV</b> и т.д.).\n", " \n", " <br><br>\n", "\n", " Найдите уже, в конце-концов, самую точную линейную модель!\n", "\n", " </div>\n", "</div>\n" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Вышла средняя ошибка, равная 21.3110\n" ] } ], "source": [ "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", "from sklearn.pipeline import Pipeline, make_pipeline\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.linear_model import RidgeCV\n", "\n", "############Ridge\n", "params = { \n", " 'alpha': [10**x for x in range(-2,3)]\n", "}\n", "\n", "from sklearn.linear_model import Ridge\n", "\n", "gsR = RidgeCV() #GridSearchCV(Ridge(), param_grid=params)\n", "gsR.fit(X_train, y_train);\n", "\n", "print('Вышла средняя ошибка, равная %5.4f' % \\\n", " (-np.mean(cross_val_score(gsR, X_test, y_test, cv=5, scoring='neg_mean_squared_error'))))\n", "\n" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Вышла средняя ошибка, равная 21.2454\n" ] } ], "source": [ "############Lasso\n", "from sklearn.linear_model import Lasso\n", "from sklearn.linear_model import LassoCV\n", "\n", "gsL = GridSearchCV(Lasso(), param_grid=params) #LassoCV() - медленнее\n", "gsL.fit(X_train, y_train);\n", "\n", "print('Вышла средняя ошибка, равная %5.4f' % \\\n", " (-np.mean(cross_val_score(gsL, X_test, y_test, cv=5, scoring='neg_mean_squared_error'))))\n" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Вышла средняя ошибка, равная 21.3403\n" ] } ], "source": [ "from sklearn.linear_model import ElasticNet\n", "from sklearn.linear_model import ElasticNetCV\n", "\n", "gsE = GridSearchCV(ElasticNet(), param_grid=params) #ElasticNetCV() - просто заменить, не слишком точен\n", "gsE.fit(X_train, y_train);\n", "\n", "print('Вышла средняя ошибка, равная %5.4f' % \\\n", " (-np.mean(cross_val_score(gsE, X_test, y_test, cv=5, scoring='neg_mean_squared_error'))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итого самый точный среди этих трёх - GridSearchCV + Lasso" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"panel panel-info\" style=\"margin: 50px 0 0 0\">\n", " <div class=\"panel-heading\">\n", " <h3 class=\"panel-title\">Задача 4.</h3>\n", " </div>\n", " <div class=\"panel\">\n", " Проверять качество правильно на кросс-валидации, как известно. Вы знаете, что делать: подключаем <b>cross_val_score</b> из <b>sklearn.model_selection</b>. Параметр <b>cv</b> установите равным 5.\n", " <br><br>\n", " Вспомните про все штуки, которым мы с вами научились.\n", " <br><br>\n", " Добейтесь <b>MSE < 27</b>.\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Oops! Все случаи уже были рассмотрены для cross_val_score" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
mit
balouf/INF674
01-Galton-Watson-TP.ipynb
1
21238
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ACN 903 S1: Galton-Watson Process\n", "\n", "## Céline Comte & Fabien Mathieu" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pylab inline" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "For the first session, we propose to investigate the **Galton-Watson process**.\n", "This process was first introduced to analyse the propagation of a feature (family surname, DNA) through generations, and in particular to estimate its probability of extinction.\n", "\n", "A Galton-Watson process can be depicted as a (directed) random tree. The nodes represent individuals and the arrows family relations. The tree is built generation by generation as follows. At generation $0$, there is a single individual, the population ancestor, represented by the tree *root*. Then, each individual from a given generation $ i $ gives birth to a random number of individuals at generation $ i+1 $, represented by its direct successors in the tree.\n", "\n", "The unique parameter of the process is the distribution of the number of children per individual.\n", "Specifically, we assume that the numbers of children are independent between individuals and\n", "drawn from the same distribution $(p_k)_{k\\in \\mathbb{N}}$ (with $\\sum_{k=0}^{\\infty} p_k = 1$).\n", "We let $\\mu$ denote the mean number of children per individual, which we assume to be finite:\n", "$$\n", "\\mu = \\sum_{k=0}^{\\infty} k p_k <+\\infty.\n", "$$\n", "\n", "Note that, in practice, there are multiple ways to explore the tree. We will consider two of them in the pratical:\n", "- **Generation by generation**. This is the method we described above. For each $i \\in \\mathbb{N}$, we let $ G_i $ denote the random variable that counts the number of nodes at generation $ i $.\n", "- **Active node by active node**. The nodes are visited one by one to decide on their number of children. We keep track of the number of nodes that are *active* in the sense that we have discovered them but we haven't drawn their number of children yet. As long as there is at least one active node, we can perform a *termination* that consists in desactivating a node, drawing its number of children according to the distribution $(p_k)_{k \\in \\mathbb{N}}$, and adding these children (if any) to the set of active nodes. For each $t \\in \\mathbb{N}$, we let $ X_t $ denote the number of nodes that are active after $ t $ terminations, with the convention that $X_0 = 1$. Observe that $(X_t)_{t \\in \\mathbb{N}}$ defines a Markov process that is similar to a birth-and-death process, except that state $0$ is absorbing.\n", "\n", "The goal of the practical is to play with these two viewpoints in order to better understand the Galton-Watson process. We will focus on two metrics: the **mean population size $\\chi$** and the **extinction probability $P_{ext}$** (i.e., the probability that the population is finite).\n", "\n", "If you want to deepen your theoretical knowledge of this process, you can read Chapter 1 from the book [Epidemics and Rumours in Complex Networks][massoulie] (which is **not** mandatory).\n", "\n", "[massoulie]: https://www.cambridge.org/core/books/epidemics-and-rumours-in-complex-networks/8C1D162F44C2C09F2B913038A7FA8BF6 \"Epidemics and Rumours in Complex Networks by Moez Draief and Laurent Massoulié\"" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "# 1. Bimodal distribution" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Throughout the practical, we will first gain insight into the Galton-Watson process by running simulations with a specific distribution of the number of children, and then generalize our observations to any distribution.\n", "\n", "For the simulations, we will focus on the **bimodal** distribution, in which a node can only have 0 or 2 children: $p_0 = 1-\\frac{\\mu}{2}$, $p_2 = \\frac{\\mu}{2}$, and $p_k = 0$ for $k \\notin \\{0,2\\}$. Note that the mean $\\mu$ can range from $0$ to $2$. The objective of this first exercice is to build a realization of the Galton-Watson process with the bimodal distribution, using either the *generation by generation* traversal or the *active node by active node* traversal.\n", "\n", "Note: Try to write a flexible code, as the parameters and distributions will change later." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 1\n", "\n", "We consider the *generation by generation* traversal.\n", "\n", "First, complete the function ``generation_growth(μ, imax)`` that returns the values of $G_i$ observed during a realization of the process, up to generation ``imax``. The function ``random.rand`` from ``numpy`` package may be handy.\n", "\n", "Second, complete the function ``display_generation_growth(μ, imax = 20, n = 20)`` using the function ``plot`` from ``matplotlib`` package. You should plot the number of nodes as a function of the generation. The additional argument ``n`` gives the number of realizations of the process to plot on the same graph." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def generation_growth(μ, imax):\n", " g = ones(imax + 1, dtype = int)\n", " # to be completed\n", " return g" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "def display_generation_growth(μ, imax = 20, n = 20):\n", " figure()\n", " for i in range(n):\n", " # to be completed\n", " \n", " # just to make the plot prettier\n", " xlim([0, imax])\n", " axes = gca(); yl = axes.get_ylim(); ylim([0, yl[1]])\n", " xlabel('Generation'); ylabel('Number of nodes')\n", " title(\"μ = %.1f\" % μ)\n", " show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "for μ in arange(.4, 1.5, .2):\n", " display_generation_growth(μ)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 2\n", "\n", "We now consider the *active node by active node* traversal.\n", "\n", "First, complete the function ``active_growth(μ, tmax)`` that returns the values of $X_t$ observed during a realization of the process, up to ``tmax`` terminations. Look out! Your function shouldn't return negative values.\n", "\n", "Second, complete the function ``display_active_growth(μ, tmax = 50, n = 10)`` using the function ``plot`` from ``matplotlib`` package. You should plot the number of active nodes as a function of the termination step." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def active_growth(μ, tmax):\n", " x = zeros(tmax + 1, dtype = int)\n", " # to be completed\n", " return x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "def display_active_growth(μ, tmax = 50, n = 10):\n", " figure()\n", " \n", " for i in range(n):\n", " # to be completed\n", " \n", " xlim([0, tmax])\n", " axes = gca(); yl = axes.get_ylim(); ylim([0, yl[1]])\n", " xlabel('Termination step'); ylabel('Number of active nodes')\n", " title(\"μ = %.1f\" % μ)\n", " show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "for μ in arange(.4, 1.5, .2):\n", " display_active_growth(μ)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "# 2. Mean population size" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The first quantity we evaluate is the mean population size. The results obtained by simulation under the bimodal distribution will prove representative of what we obtain under any distribution." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 1\n", "\n", "Complete the function ``chi_bim_generation(μ, imax, n = 1000)`` that estimates $\\mathbb{E}(G_i)$ for $i$ up to ``imax`` by averaging the results of ``generation_growth`` over ``n`` independent runs.\n", "\n", "For $\\mu = \\frac12$ and $\\mu = \\frac43$, plot the results and comment them. We advise you to start with $\\mu = \\frac43$ and use a semi-log plot." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def chi_bim_generation(μ, imax, n = 1000):\n", " # to be completed" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Discussion:" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Your turn!" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 2\n", "\n", "Write $\\mathbb{E}(G_{i+1})$ as a function of $\\mathbb{E}(G_i)$ for each $i \\in \\mathbb{N}$. Derive an explicit expression of $\\mathbb{E}(G_i)$ for each $i \\in \\mathbb{N}$." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 3\n", "\n", "Use the result of the previous question to derive the value of the mean population size $\\chi$." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 4 (Optional)\n", "\n", "Redo Question 2.1 with the *active node by active node* traversal.\n", "Specifically, write a function ``chi_bim_active(μ, tmax, n = 1000)`` that estimates $\\mathbb{E}(X_t)$ for $t$ up to ``tmax``. Plot the result. What do you observe? Can you explain it?" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 5 (Optional)\n", "\n", "Write a function ``chi_bim_conditional(μ, imax, n = 1000)`` that evaluates the mean value of $G_i$ for $i$ up to ``imax``, **given that the run has lead to extinction**. Discuss the results." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "# 3. Extinction probability" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We now focus on the extinction probability $P_{ext}$, that is, the probability that the total population is finite." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 1\n", "\n", "What do the results of Exercice 2 suggest about the extinction probability?" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 2\n", "\n", "Complete the function ``pext_bim_trials(range_μ, tmax, n)`` that estimates the probability that the population extincts after no more than ``tmax`` termination steps by averaging the results of ``active_growth`` over ``n`` independent runs, for several values of $\\mu$.\n", "\n", "Plot the results and comment them. Suggested values: ``range_μ = (np.)linspace(2, 0, 40, endpoint = False)``, ``tmax = 10, 100, 1000``, ``n = 1000``." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def pext_bim_trials(range_μ, tmax, n):\n", " # to be completed" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Discussion:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 3\n", "\n", "Give an equality that relates the extinction probability $P_{ext}$ and the distribution $(p_k)_{k\\in \\mathbb{N}}$ of the number of children per node. In the sequel, we will admit that $P_{ext}$ is the **smallest** solution of this equation in the interval $[0,1]$." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 4\n", "\n", "In the special case of the bimodal distribution, use this equation to write $P_{ext}$ as a function of $\\mu$.\n", "Write a (very) small function ``pext_bim_exact(range_μ)`` that computes $P_{ext}$ for a list of values of $\\mu$ and display the results against the empirical values obtained in Question 3.2." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 5 (Optional)\n", "\n", "Evaluating the results by simulation has an inherent lack of accuracy. Write a function ``pext_bim_distrib(range_μ, tmax)`` that computes **exactly** the probability of an extinction after no more than ``tmax`` terminations. Display the results and compare them with those obtained by simulation.\n", "\n", "Hint: write a function ``pop_after_t(p, tmax)`` that computes the **distribution** of the number of active nodes after ``tmax`` termination steps as a function of $ p = (p_k)_{k \\in \\mathbb{N}}$. The function ``convolve`` from ``numpy`` package may be handy." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "# 4. Other Distributions " ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We again focus on the probability $P_{ext}$ of an extinction. For the bimodal distribution, we observed the following phase transition:\n", "- If $\\mu < 1$, then $P_{ext} = 1$.\n", "- If $\\mu > 1$, then $P_{ext} < 1$.\n", "\n", "This result is valid for any distribution of the number of children per node, as long as its mean $\\mu$ is finite. The proof will be given orally during the practical. You can also find it in [Epidemics and Rumours in Complex Networks][massoulie], along with more details (in particular, what happens when $\\mu = 1$). We now consider two other distributions of the number of children per node and look at the extinction probability when $\\mu > 1$.\n", "\n", "[massoulie]: https://www.cambridge.org/core/books/epidemics-and-rumours-in-complex-networks/8C1D162F44C2C09F2B913038A7FA8BF6 \"Epidemics and Rumours in Complex Networks by Moez Draief and Laurent Massoulié\"" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 1\n", "\n", "We now consider a geometric distribution $p_k=(1-a)a^k$, $k \\in \\mathbb{N}$, for some $0\\leq a<1$. Relate $a$ and $\\mu$ and study the extinction probability as you did for the bimodal case. In particular,\n", "- Give the equation that $ P_{ext} $ should verify and write $ P_{ext} $ as a function of $ \\mu $, for $ \\mu\\in [0, 2] $.\n", "- Validate the results experimentally. You should, for instance:\n", " - Run multiple simulations using a geometric generator. The function ``random.geometric`` from ``numpy`` package may be handy.\n", " - Adapt the function ``pop_after_t`` of Question 3.5 to compute the probability of an extinction after ``tmax = 1000`` termination steps for a truncated geometric distribution." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 2\n", "\n", "We finally consider a Poisson distribution of parameter $\\mu$:\n", "$$\n", " p_k = e^{-\\mu}\\frac{\\mu^k}{k!},\n", " \\quad \\forall k \\in \\mathbb{N}.\n", "$$\n", "Study the extinction probability.\n", "In particular,\n", "- Give the equation that $ P_{ext} $ should verify and compute $ P_{ext} $ as a function of $ \\mu $ for $ \\mu\\in [0, 2] $. When there is no explicit expression of $P_{ext}$ as a function of $\\mu$, you can use the fixed-point equation to compute the solution iteratively.\n", "- Validate the results experimentally. You should, for instance:\n", " - Run multiple simulations using a Poisson generator. The function ``random.poisson`` from ``numpy`` package may be handy.\n", " - Adapt ``pop_after_t`` of Question 3.5 to compute the probability of an extinction after ``t = 1000`` termination steps for a truncated Poisson distribution." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "## Question 3\n", "\n", "Plot the three theoretical $ P_{ext} $ you obtained on the same figure, as a function of mean number $\\mu$ of children per node. Discuss the differences (informally)." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Answer:" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "# 5. Generalization (Optional)\n", "\n", "Redo the exercices of this Notebook for a Galton-Watson process that starts with two nodes (you can re-use results from above)." ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
eecs445-f16/umich-eecs445-f16
handsOn_lecture04_linear-regression-part1/lecture04_linear-regression-part-1.ipynb
1
14699
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "$$ \\LaTeX \\text{ command declarations here.}\n", "\\newcommand{\\R}{\\mathbb{R}}\n", "\\renewcommand{\\vec}[1]{\\mathbf{#1}}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# EECS 545: Machine Learning\n", "## Lecture 04: Linear Regression I\n", "* Instructor: **Jacob Abernethy, Benjamin Bray, Jia Deng and Chansoo Lee**\n", "* Date: 9/21/2016" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Notation\n", "\n", "- In this lecture, we will use\n", " - Let vector $\\vec{x}_n \\in \\R^D$ denote the $n\\text{th}$ data. $D$ denotes number of attributes in dataset.\n", " - Let vector $\\phi(\\vec{x}_n) \\in \\R^M$ denote features for data $\\vec{x}_n$. $\\phi_j(\\vec{x}_n)$ denotes the $j\\text{th}$ feature for data $x_n$.\n", " - Feature $\\phi(\\vec{x}_n)$ is the *artificial* features which represents the preprocessing step. $\\phi(\\vec{x}_n)$ is usually some combination of transformations of $\\vec{x}_n$. For example, $\\phi(\\vec{x})$ could be vector constructed by $[\\vec{x}_n^\\top, \\cos(\\vec{x}_n)^\\top, \\exp(\\vec{x}_n)^\\top]^\\top$. If we do nothing to $\\vec{x}_n$, then $\\phi(\\vec{x}_n)=\\vec{x}_n$.\n", " - Continuous-valued label vector $t \\in \\R^D$ (target values). $t_n \\in \\R$ denotes the target value for $i\\text{th}$ data." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Linear Regression " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Linear Regression (General Case)\n", "- The function $y(\\vec{x}_n, \\vec{w})$ is linear in parameters $\\vec{w}$.\n", " - **Goal:** Find the best value for the weights $\\vec{w}$.\n", " - For simplicity, add a **bias term** $\\phi_0(\\vec{x}_n) = 1$.\n", "$$\n", "\\begin{align}\n", "y(\\vec{x}_n, \\vec{w})\n", "&= w_0 \\phi_0(\\vec{x}_n)+w_1 \\phi_1(\\vec{x}_n)+ w_2 \\phi_2(\\vec{x}_n)+\\dots +w_{M-1} \\phi_{M-1}(\\vec{x}_n) \\\\\n", "&= \\sum_{j=0}^{M-1} w_j \\phi_j(\\vec{x}_n) \\\\\n", "&= \\vec{w}^\\top \\phi(\\vec{x}_n)\n", "\\end{align}\n", "$$\n", "of which $\\phi(\\vec{x}_n) = [\\phi_0(\\vec{x}_n),\\phi_1(\\vec{x}_n),\\phi_2(\\vec{x}_n), \\dots, \\phi_{M-1}(\\vec{x}_n)]^\\top$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Method I: Batch Gradient Descent\n", "- To minimize the objective function, take derivative w.r.t coefficient vector $\\vec{w}$ and descend: initialize $\\vec{w}^0$ to be any vector, and at each step $s$,\n", "$$\n", "\\vec{w}^{s+1} \\gets \\vec{w}^{s} - \\nabla_{\\vec{w}}E(\\vec{w}^s)\n", "$$\n", "\n", "<b>Exercise</b>: Compute the partial derivative\n", "$$\n", "(\\nabla_{\\vec{w}}E)_j = \\frac{\\partial E}{\\partial w_j}\n", "$$\n", "where\n", "$$\n", "E(\\vec{w}) = \\frac{1}{2} \\sum_{n=1}^N \\sum_{i=1}^{M} \\left( w_i \\phi_i(\\vec{x}_n) - t_n \\right)^2\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Solution\n", "In the summation over $i$, only $i = j$ term is a function of $w_j$. So, \n", "$$\n", "\\frac{\\partial E}{\\partial w_j}\n", "= \\frac{1}{2} \\sum_{n=1}^N \\frac{\\partial}{\\partial w_j} \\left( w_j \\phi_j(\\vec{x}_n) - t_n \\right)^2\n", "= \\sum_{n=1}^{N} (w_j \\phi_j(\\vec{x}_n) - t_n)\n", "$$\n", "\n", "*Tip*: If you find subscript notations confusing, just plug in $j = 1$, differentiate, and get $\\sum_{n=1}^{N} (w_1 \\phi_1(\\vec{x}_n) - t_n)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Linear Regression: Matrix Notations\n", "The matrix $\\Phi \\in \\R^{N \\times M}$ is called **design matrix**. Each row represents one sample. Each column represents one feature\n", "$$\\Phi = \\begin{bmatrix}\n", "\\phi(\\vec{x}_1)^\\top\\\\ \n", "\\phi(\\vec{x}_2)^\\top\\\\ \n", "\\vdots\\\\\n", "\\phi(\\vec{x}_N)^\\top\n", "\\end{bmatrix}\n", "= \\begin{bmatrix}\n", "\\phi_0(\\vec{x}_1) & \\phi_1(\\vec{x}_1) & \\cdots & \\phi_{M-1}(\\vec{x}_1) \\\\\n", "\\phi_0(\\vec{x}_2) & \\phi_1(\\vec{x}_2) & \\cdots & \\phi_{M-1}(\\vec{x}_2) \\\\\n", "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", "\\phi_0(\\vec{x}_N) & \\phi_1(\\vec{x}_N) & \\cdots & \\phi_{M-1}(\\vec{x}_N) \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "Target value vector is $\\vec{t} \\in \\mathbb{R}^M$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "$$\n", "E(\\vec{w}) \n", "= \\frac{1}{2} \\sum_{n=1}^N (y(\\vec{x}_n, \\vec{w}) - t_n)^2\n", "= \\frac{1}{2} \\sum_{n=1}^N \\left( \\sum_{j=0}^{M-1} w_j\\phi_j(\\vec{x}_n) - t_n \\right)^2\n", "= \\frac{1}{2} \\sum_{n=1}^N \\left( \\vec{w}^\\top \\phi(\\vec{x}_n) - t_n \\right)^2\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Batch Gradient Descent with Matrix Calculus\n", "Write the objective function in matrix-vector form:\n", "$\n", "\\begin{align*}\n", "E(\\vec{w}) &= \\frac{1}{2} \\sum_{n=1}^N \\sum_{i=1}^{M} \\left( w_i \\phi_i(\\vec{x}_n) - t_n \\right)^2 \\\\ \n", "&= \\frac{1}{2} \\sum_{n=1}^N \\left( \\phi(\\vec{x}_n)^\\top \\vec{w} - t_n \\right)^2 = \\frac{1}{2} \\|\\Phi \\vec{w} - \\vec{t}\\|_2^2\n", "\\end{align*}\n", "$\n", "\n", "Rewrite $E$ as a sum of three matrix-vector products. <i>Hints:</i>\n", "* $ \\vec{x}^\\top \\vec{x} = (x_1,\\ldots,x_M)^\\top (x_1,\\ldots,x_M) = x_1^2 + \\cdots + x_M^2 = \\left(\\sqrt{x_1^2 + \\cdots + x_M^2}\\right)^2 = \\|\\vec{x}\\|_2^2$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Distributive law: $(\\vec{a} + \\vec{b})^\\top(\\vec{c} + \\vec{d}) = \\vec{a}^\\top \\vec{c} + \\vec{a}^\\top \\vec{d} + \\vec{b}^\\top \\vec{c} + \\vec{b}^\\top\\vec{d}$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Transpose of a product: $(AB)^\\top = B^\\top A^\\top$ for matrix-vector multiplication." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Batch Gradient Descent with Matrix Calculus\n", "Treat $\\Phi \\vec{w}$ as a vector and we get from the distributive law\n", "$$\n", "E(\\vec{w}) = \\frac{1}{2} \\|\\Phi \\vec{w} - \\vec{t}\\|_2^2 = \\frac{1}{2} \\left(\\vec{w}^\\top \\Phi^\\top \\Phi \\vec{w} - \\vec{w}^\\top \\Phi^\\top \\vec{t} - \\vec{t}^\\top \\Phi \\vec{w} + \\vec{t}^\\top \\vec{t}\\right).\n", "$$\n", "\n", "Note that $\\vec{w}^\\top (\\Phi^\\top \\vec{t}) = (\\Phi^\\top\\vec{t})^\\top \\vec{w} = \\vec{t}^\\top \\Phi \\vec{w}$. So, the above simplifies to\n", "$$ \\frac{1}{2} \\left(\\vec{w}^\\top \\Phi^\\top \\Phi \\vec{w} - 2\\vec{w}^\\top \\Phi^\\top \\vec{t} + \\vec{t}^\\top \\vec{t}\\right).$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Batch Gradient Descent with Matrix Calculus\n", "Write the objective function in matrix-vector form:\n", "$$\n", "E(\\vec{w}) = \\frac{1}{2} \\left(\\vec{w}^\\top \\Phi^\\top \\Phi \\vec{w} - 2\\vec{w}^\\top \\Phi^\\top \\vec{t} + \\vec{t}^\\top \\vec{t}\\right).\n", "$$\n", "\n", "Compute the gradient $\\nabla_\\vec{w} E(\\vec{w})$ with matrix calculus. <i>Hints</i>:\n", "* $\\nabla_\\vec{x} (\\vec{x}^\\top A \\vec{x}) = (A + A^\\top) \\vec{x}$ <i> (Challenge: prove this!) </i>\n", "* $\\nabla_\\vec{x} (\\vec{x}^\\top \\vec{y}) = \\nabla_\\vec{x} (\\vec{y}^\\top\\vec{x}) = \\vec{y}$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* $\\Phi^\\top \\Phi$ has a special property.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Batch Gradient Descent with Matrix Calculus\n", "\n", "\n", "* Since $\\Phi^\\top\\Phi$ is symmetric, $\\nabla_{\\vec{w}} \\vec{w}^\\top(\\Phi^\\top\\Phi)\\vec{w} = 2(\\Phi^\\top\\Phi)\\vec{w}$.\n", "* Treating $\\Phi^\\top t$ as a vector, $\\nabla_{\\vec{w}} \\vec{w}^\\top(\\Phi^\\top t) = \\Phi^\\top t$.\n", "* Finally, $t^\\top t$ is constant with respect to $\\vec{w}$.\n", "\n", "So,\n", "$\\nabla_{\\vec{w}} E(\\vec{w}) = (\\Phi^\\top\\Phi)\\vec{w} - \\Phi^\\top t$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Method I-2: Gradient Descent—Stochastic Gradient Descent\n", "**Main Idea:** Instead of computing batch gradient (over entire training data), just compute gradient for individual (or a small subset of) training sample and update." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "<b> Exercise </b>: How do you implement the update rule for a minibatch gradient descent (of size, let's say, 5% of the whole dataset)?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "You randomly choose 5% of indices between 0 and $M$. Take the corresponding rows of $\\Phi$ and $t$. Compute the gradient on this subset of data and desend along." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Method II: Closed-Form solution, invertible case\n", "\n", "**Main Idea**, also **Exercise:** Solve $\\nabla_\\vec{w} E(\\vec{w}) = 0$, assuming $\\Phi^\\top\\Phi$ is invertible. Discuss why it is sufficent to solve this equation to find optimal $\\vec{w}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "*Answer*: It is sufficent to find a local minimum because $E(\\vec{w})$ is convex. The solution is $\\vec{w} = (\\Phi^\\top\\Phi)^{-1}\\Phi^\\top t$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "<b> Exercise </b>: Show that $\\Phi^\\top \\Phi$ is invertible if $\\Phi$ has *linearly independent columns*. Interpret its implications about our features." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "*Answer*: It implies that our features are linearly dependent" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "<i> Challenge: </i> Similarly, we can show $\\Phi\\Phi^\\top$ is invertible if $\\Phi$ has linearly independent rows. Why do we care/not care about this case?\n", "\n", "<i> Challenge: </i> Show that $\\vec{b}$ is in the column space of $A$ if and only if there exists a vector $\\vec{x}$ such that $A\\vec{x} = \\vec{b}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Digression: Moore-Penrose Pseudoinverse\n", "- When we have a matrix $A$ that is non-invertible or *not even square*, we might want to invert anyway\n", "- For these situations we use $A^\\dagger$, the **Moore-Penrose Pseudoinverse** of $A$\n", "- In general, we can get $A^\\dagger$ by SVD: if we write $A \\in \\R^{m \\times n} = U_{m \\times m} \\Sigma_{m \\times n} V_{n \\times n}^\\top$ then $A^\\dagger \\in \\R^{n \\times m} = V \\Sigma^\\dagger U^\\top$, where $\\Sigma^\\dagger \\in \\R^{n \\times m}$ is obtained by taking reciprocals of *non-zero entries* of $\\Sigma^\\top$.\n", "- Particularly, when $A$ has linearly independent columns then $A^\\dagger = (A^\\top A)^{-1} A^\\top$. When $A$ is invertible, then $A^\\dagger = A^{-1}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "** Exercise **: One property of Psuedoinverse is that $A A^\\dagger A = A$. \n", "Show that $$(A^{\\top} A)^{-1}A^\\top$$ satisfies this property (assuming linearly independent columns of $A$)\n", "\n", "*Challenge: * Show that $$\\hat{\\vec{w}} = (\\Phi^\\top\\Phi)^\\dagger \\Phi^\\top \\vec{t} = \\Phi^\\dagger \\vec{t}$$\n", "satisfies $\\nabla_\\vec{w} E(\\vec{w}) = \\Phi^\\top\\Phi \\vec{w} - \\Phi^\\top \\vec{t} = 0$.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "** Discuss **: What are the advantages and disadvtanges of each method we learned today (stochastic gradient descent, batch gradient descent, and closed-form solution)?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "*Answer*: There are no right answers, but you can say things like: matrix inversion is a cubic-time operation (technically $O(n^{2.37...})$). Performing better on your training data $\\Phi$ doesn't necessarily mean performing better on unseen test data." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
nicolasm/lastfm-notebooks
lastfm-plays.ipynb
1
3063
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import MySQLdb\n", "import netrc\n", "import pandas\n", "import lastfmDb as lf\n", "import datetime\n", "\n", "from plotly import __version__\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", "import plotly.graph_objs as go\n", "\n", "init_notebook_mode(connected=True)\n", "\n", "login = netrc.netrc().authenticators('lastfm.mysql')\n", "if not login:\n", " raise netrc.NetrcParseError('No authenticators for lastfm.mysql')\n", " \n", "mysql = MySQLdb.connect(user=login[0],passwd=login[2],db=login[1], charset='utf8')\n", "cursor = mysql.cursor()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Last execution date and time" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "today = datetime.datetime.now()\n", "print today.strftime('Generated on the %d %b %Y at %H:%M:%S')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Play count by month" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = lf.retrieve_play_count_by_month_as_dataframe(cursor)\n", "df.head(15).T" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['Month'] = pandas.to_datetime(df['Month'], format='%Y-%m')\n", "df = df.sort_values(by='Month', ascending=[1])\n", "\n", "data = [go.Scatter(x=df.Month, y=df.PlayCount)]\n", "iplot(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Play count by day" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df = lf.retrieve_play_count_by_day_as_dataframe(cursor)\n", "\n", "data = [go.Scatter(x=df.Day, y=df.PlayCount)]\n", "iplot(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recent plays" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df = lf.retrieve_recent_plays_as_dataframe(cursor)\n", "df" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
mlmurray/TensorFlow-Experimentation
notebooks/3 - Neural Networks/convolutional_network.ipynb
1
11286
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# A Convolutional Network implementation example using TensorFlow library.\n", "# This example is using the MNIST database of handwritten digits\n", "# (http://yann.lecun.com/exdb/mnist/)\n", "\n", "# Author: Aymeric Damien\n", "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import MINST data\n", "import input_data\n", "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Parameters\n", "learning_rate = 0.001\n", "training_iters = 100000\n", "batch_size = 128\n", "display_step = 20" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Network Parameters\n", "n_input = 784 # MNIST data input (img shape: 28*28)\n", "n_classes = 10 # MNIST total classes (0-9 digits)\n", "dropout = 0.75 # Dropout, probability to keep units" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# tf Graph input\n", "x = tf.placeholder(tf.types.float32, [None, n_input])\n", "y = tf.placeholder(tf.types.float32, [None, n_classes])\n", "keep_prob = tf.placeholder(tf.types.float32) #dropout (keep probability)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create model\n", "def conv2d(img, w, b):\n", " return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], \n", " padding='SAME'),b))\n", "\n", "def max_pool(img, k):\n", " return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')\n", "\n", "def conv_net(_X, _weights, _biases, _dropout):\n", " # Reshape input picture\n", " _X = tf.reshape(_X, shape=[-1, 28, 28, 1])\n", "\n", " # Convolution Layer\n", " conv1 = conv2d(_X, _weights['wc1'], _biases['bc1'])\n", " # Max Pooling (down-sampling)\n", " conv1 = max_pool(conv1, k=2)\n", " # Apply Dropout\n", " conv1 = tf.nn.dropout(conv1, _dropout)\n", "\n", " # Convolution Layer\n", " conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2'])\n", " # Max Pooling (down-sampling)\n", " conv2 = max_pool(conv2, k=2)\n", " # Apply Dropout\n", " conv2 = tf.nn.dropout(conv2, _dropout)\n", "\n", " # Fully connected layer\n", " # Reshape conv2 output to fit dense layer input\n", " dense1 = tf.reshape(conv2, [-1, _weights['wd1'].get_shape().as_list()[0]]) \n", " # Relu activation\n", " dense1 = tf.nn.relu(tf.add(tf.matmul(dense1, _weights['wd1']), _biases['bd1']))\n", " # Apply Dropout\n", " dense1 = tf.nn.dropout(dense1, _dropout) # Apply Dropout\n", "\n", " # Output, class prediction\n", " out = tf.add(tf.matmul(dense1, _weights['out']), _biases['out'])\n", " return out" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Store layers weight & bias\n", "weights = {\n", " # 5x5 conv, 1 input, 32 outputs\n", " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), \n", " # 5x5 conv, 32 inputs, 64 outputs\n", " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), \n", " # fully connected, 7*7*64 inputs, 1024 outputs\n", " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), \n", " # 1024 inputs, 10 outputs (class prediction)\n", " 'out': tf.Variable(tf.random_normal([1024, n_classes])) \n", "}\n", "\n", "biases = {\n", " 'bc1': tf.Variable(tf.random_normal([32])),\n", " 'bc2': tf.Variable(tf.random_normal([64])),\n", " 'bd1': tf.Variable(tf.random_normal([1024])),\n", " 'out': tf.Variable(tf.random_normal([n_classes]))\n", "}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Construct model\n", "pred = conv_net(x, weights, biases, keep_prob)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define loss and optimizer\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Evaluate model\n", "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Initializing the variables\n", "init = tf.initialize_all_variables()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter 2560, Minibatch Loss= 26046.011719, Training Accuracy= 0.21094\n", "Iter 5120, Minibatch Loss= 10456.769531, Training Accuracy= 0.52344\n", "Iter 7680, Minibatch Loss= 6273.207520, Training Accuracy= 0.71875\n", "Iter 10240, Minibatch Loss= 6276.231445, Training Accuracy= 0.64062\n", "Iter 12800, Minibatch Loss= 4188.221680, Training Accuracy= 0.77344\n", "Iter 15360, Minibatch Loss= 2717.077637, Training Accuracy= 0.80469\n", "Iter 17920, Minibatch Loss= 4057.120361, Training Accuracy= 0.81250\n", "Iter 20480, Minibatch Loss= 1696.550415, Training Accuracy= 0.87500\n", "Iter 23040, Minibatch Loss= 2525.317627, Training Accuracy= 0.85938\n", "Iter 25600, Minibatch Loss= 2341.906738, Training Accuracy= 0.87500\n", "Iter 28160, Minibatch Loss= 4200.535156, Training Accuracy= 0.79688\n", "Iter 30720, Minibatch Loss= 1888.964355, Training Accuracy= 0.89062\n", "Iter 33280, Minibatch Loss= 2167.645996, Training Accuracy= 0.84375\n", "Iter 35840, Minibatch Loss= 1932.107544, Training Accuracy= 0.89844\n", "Iter 38400, Minibatch Loss= 1562.430054, Training Accuracy= 0.90625\n", "Iter 40960, Minibatch Loss= 1676.755249, Training Accuracy= 0.84375\n", "Iter 43520, Minibatch Loss= 1003.626099, Training Accuracy= 0.93750\n", "Iter 46080, Minibatch Loss= 1176.615479, Training Accuracy= 0.86719\n", "Iter 48640, Minibatch Loss= 1260.592651, Training Accuracy= 0.88281\n", "Iter 51200, Minibatch Loss= 1399.667969, Training Accuracy= 0.86719\n", "Iter 53760, Minibatch Loss= 1259.961426, Training Accuracy= 0.89844\n", "Iter 56320, Minibatch Loss= 1415.800781, Training Accuracy= 0.89062\n", "Iter 58880, Minibatch Loss= 1835.365967, Training Accuracy= 0.85156\n", "Iter 61440, Minibatch Loss= 1395.168823, Training Accuracy= 0.90625\n", "Iter 64000, Minibatch Loss= 973.283569, Training Accuracy= 0.88281\n", "Iter 66560, Minibatch Loss= 818.093811, Training Accuracy= 0.92969\n", "Iter 69120, Minibatch Loss= 1178.744263, Training Accuracy= 0.92188\n", "Iter 71680, Minibatch Loss= 845.889709, Training Accuracy= 0.89844\n", "Iter 74240, Minibatch Loss= 1259.505615, Training Accuracy= 0.90625\n", "Iter 76800, Minibatch Loss= 738.037109, Training Accuracy= 0.89844\n", "Iter 79360, Minibatch Loss= 862.499146, Training Accuracy= 0.93750\n", "Iter 81920, Minibatch Loss= 739.704041, Training Accuracy= 0.90625\n", "Iter 84480, Minibatch Loss= 652.880310, Training Accuracy= 0.95312\n", "Iter 87040, Minibatch Loss= 635.464600, Training Accuracy= 0.92969\n", "Iter 89600, Minibatch Loss= 933.166626, Training Accuracy= 0.90625\n", "Iter 92160, Minibatch Loss= 213.874893, Training Accuracy= 0.96094\n", "Iter 94720, Minibatch Loss= 609.575684, Training Accuracy= 0.91406\n", "Iter 97280, Minibatch Loss= 560.208008, Training Accuracy= 0.93750\n", "Iter 99840, Minibatch Loss= 963.577148, Training Accuracy= 0.90625\n", "Optimization Finished!\n", "Testing Accuracy: 0.960938\n" ] } ], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " step = 1\n", " # Keep training until reach max iterations\n", " while step * batch_size < training_iters:\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", " # Fit training using batch data\n", " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})\n", " if step % display_step == 0:\n", " # Calculate batch accuracy\n", " acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n", " # Calculate batch loss\n", " loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n", " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \"{:.5f}\".format(acc)\n", " step += 1\n", " print \"Optimization Finished!\"\n", " # Calculate accuracy for 256 mnist test images\n", " print \"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], \n", " y: mnist.test.labels[:256], \n", " keep_prob: 1.})" ] } ], "metadata": { "kernelspec": { "display_name": "IPython (Python 2.7)", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
nkmk/python-snippets
notebook/str_removeprefix_removesuffix.ipynb
1
3748
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "s = 'abc-abcxyz'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcxyz\n" ] } ], "source": [ "print(s.removeprefix('abc-'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abc-abcxyz\n" ] } ], "source": [ "print(s.removeprefix('aabc-'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xyz\n" ] } ], "source": [ "print(s.lstrip('abc-'))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def my_removeprefix(s, prefix):\n", " if s.startswith(prefix):\n", " return s[len(prefix):]\n", " else:\n", " return s" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcxyz\n" ] } ], "source": [ "print(my_removeprefix(s, 'abc-'))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "s = 'abcxyz-xyz'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcxyz\n" ] } ], "source": [ "print(s.removesuffix('-xyz'))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcxyz-xyz\n" ] } ], "source": [ "print(s.removesuffix('-xyzz'))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def my_removesuffix(s, suffix):\n", " return s[:-len(suffix)] if s.endswith(suffix) else s" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcxyz\n" ] } ], "source": [ "print(my_removesuffix(s,'-xyz'))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "s = 'abc-abcxyz-xyz'" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcxyz\n" ] } ], "source": [ "print(s.removeprefix('abc-').removesuffix('-xyz'))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcxyz\n" ] } ], "source": [ "print(my_removeprefix(my_removesuffix(s, '-xyz'), 'abc-'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
PaulSoderlind/FinancialTheoryMSc
Ch17_Bonds2.ipynb
1
59028
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bonds 2\n", "\n", "This notebook discusses *duration hedging* as a way to immunize a bond portfolio." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Packages and Extra Functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "printyellow (generic function with 1 method)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using Printf, Roots\n", "\n", "include(\"jlFiles/printmat.jl\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "using Plots\n", "\n", "#pyplot(size=(600,400))\n", "gr(size=(480,320))\n", "default(fmt = :png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## From the Notebook on Bonds 1\n", "\n", "The file included below contains the function `BondPrice3()` which calculates the present value of a cash flow stream." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BondPrice3" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "include(\"jlFiles/BondCalculations.jl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Value of a Bond Portfolio after a Sudden Interest Rate Change\n", "\n", "The calculations below assume that the yield curve is flat, but that it can shift up or down in parallel. This assumption is similar to the classical literature on duration hedging.\n", "\n", "The initial values are indicated by the subscript $_0$ and the values after the interest rate change by the subscript $_1$. It is assumed that the change is very sudden, so the time to the different cash flows is (virtually) the same before and after.\n", "\n", "The next cell sets up the cash flow for a bond portolio $L$ that pays 0.2 each year for the next 10 years. The value is calculated at an initial interest rate ($\\theta_0$) and at a new interest rate ($\\theta_1$)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mValue of bond portfolio at θ₀=0.05 and θ₁=0.03:\u001b[22m\u001b[39m\n", "PL₀ (at θ₀=0.05) 1.544\n", "PL₁ (at θ₁=0.03) 1.706\n", "ΔPL/PL₀ 0.105\n", "\n", "\u001b[31m\u001b[1mNotice that the bond is worth more at the lower interest rate\u001b[22m\u001b[39m\n" ] } ], "source": [ "θ₀ = 0.05 #initial interest rate\n", "θ₁ = 0.03 #interest rate after sudden change\n", "\n", "cf = ones(10)*0.2 #cash flow of liability\n", "m = 1:10 #time periods of the cash flows\n", "\n", "PL₀ = BondPrice3(θ₀,cf,m) #value of bond L at initial interest rate\n", "PL₁ = BondPrice3(θ₁,cf,m) #and after sudden change in interest rate\n", "R = (PL₁ - PL₀)/PL₀ #relative change of the value\n", "\n", "printblue(\"Value of bond portfolio at θ₀=$θ₀ and θ₁=$θ₁:\")\n", "xy = [PL₀, PL₁, R]\n", "printmat(xy,rowNames=[\"PL₀ (at θ₀=$θ₀)\";\"PL₁ (at θ₁=$θ₁)\";\"ΔPL/PL₀\"],width=15)\n", "\n", "printred(\"Notice that the bond is worth more at the lower interest rate\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Macaulay's Duration\n", "\n", "A first-order Taylor approximation gives the (approximate) return\n", "\n", "$\\frac{\\Delta P}{P} = -D^M \\times \\frac{\\Delta \\theta}{1+\\theta}$, \n", "\n", "where $D^M$ is Macaulay's duration\n", "\n", "$D^M = \\sum_{k=1}^{K} m_{k}\\frac{cf_{k}}{\\left( 1+\\theta\\right) ^{m_{k}}P}$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BondDuration" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", " BondDuration(P,cf,m,ytm)\n", "\n", "Calculate Macaulays (bond) duration measure.\n", "\n", "P: scalar, bond price\n", "cf: scalar or K vector of cash flows\n", "m: K vector of times of cash flows\n", "ytm: scalar, yield to maturity\n", "\"\"\"\n", "function BondDuration(P,cf,m,ytm)\n", " (length(cf) != length(m)) && error(\"BondPrice3: cf and m must have the same lengths\")\n", " cdisc = cf.*m./((1+ytm).^m) #cf1/(1+y) + 2*cf2/(1+y)^2 + ...\n", " Dmac = sum(cdisc)/P #Macaulays duration\n", " return Dmac\n", "end" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Macaulay's duration of the bond portfolio 5.099\n", "\n", "\u001b[34m\u001b[1mRelative price change (return): \u001b[22m\u001b[39m\n", "Exact 0.105\n", "Approximate (from duration) 0.097\n", "\n" ] } ], "source": [ "Dmac = BondDuration(PL₀,cf,m,θ₀)\n", "printlnPs(\"Macaulay's duration of the bond portfolio\",Dmac)\n", "\n", "Δθ = θ₁ - θ₀\n", "R_approx = -Dmac*Δθ/(1+θ₀)\n", "\n", "printblue(\"\\nRelative price change (return): \")\n", "printmat([R,R_approx],rowNames=[\"Exact\",\"Approximate (from duration)\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hedging a Liability Stream\n", "\n", "Suppose we are short one bond portfolio $L$ (we have a liability) as discussed above, which is worth $P_L$. To hedge we buy $v$ units of bond (portfolio) $H$. The balance is put on a money market account $M$ to make the initial value of the portfolio zero ($V=0$)\n", "\n", "$V=vP_{H}+M-P_{L}$.\n", "\n", "Over a short time interval, the change in the overall portfolio value is\n", "\n", "$\\Delta V=v\\Delta P_{H}-\\Delta P_{L}$.\n", "\n", "In the cells below, we assume that the yield curve is flat and shifts in parallel. This means that the ytm of both $L$ and $H$ change from one common value ($\\theta_0$) to another common value ($\\theta_1$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Duration Matching\n", "\n", "Choose a hedge bond with the *same duration* as the liability and invest same amount in each. Clearly, this gives $\\frac{\\Delta V}{P_{L}}\\approx 0$.\n", "\n", "The code below uses a zero coupon bond as bond $H$, but that is not important." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mHedge bond: a zero coupon bond with m=5.1 and face value of 1.\u001b[22m\u001b[39m\n", "\n", "\u001b[34m\u001b[1mRecall that the interest rates change from θ₀=0.05 to θ₁=0.03.\u001b[22m\u001b[39m\n", "\n", "\u001b[34m\u001b[1mResults of the calculations:\u001b[22m\u001b[39m\n", "PL₀ 1.544\n", "PH₀ 0.780\n", "v 1.981\n", "v*PH₀/PL₀ 1.000\n", "Duration(liability) 5.099\n", "Duration(hedge) 5.099\n", "Return -0.002\n", "\n", "\u001b[31m\u001b[1mNotice, the duration matching gives a return of -0.2%. Close to zero.\u001b[22m\u001b[39m\n" ] } ], "source": [ "PH₀ = BondPrice3(θ₀,1,Dmac) #value of hedge bond, here a zero coupon bond\n", "PH₁ = BondPrice3(θ₁,1,Dmac)\n", "v = PL₀/PH₀ #so v*PH₀ = PL₀\n", "\n", "ΔV = v*(PH₁-PH₀) - (PL₁-PL₀) #value change of total portfolio\n", "R = ΔV/PL₀ #relative value change \n", "\n", "txt = \"\"\"Hedge bond: a zero coupon bond with m=$(round(Dmac,digits=2)) and face value of 1.\\n\n", "Recall that the interest rates change from θ₀=$θ₀ to θ₁=$θ₁.\\n\n", "Results of the calculations:\"\"\"\n", "printblue(txt)\n", "\n", "xy = [PL₀,PH₀,v,v*PH₀/PL₀,Dmac,Dmac,R]\n", "printmat(xy,rowNames=[\"PL₀\",\"PH₀\",\"v\",\"v*PH₀/PL₀\",\"Duration(liability)\",\"Duration(hedge)\",\"Return\"])\n", "\n", "printred(\"Notice, the duration matching gives a return of $(round(R*100,digits=1))%. Close to zero.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Naive Hedging\n", "\n", "The \"naive\" hedging invests the *same amount in the hedge bond* as the value of the liability, that is, $v P_{H}=P_{L}$ so $v=P_{L}/P_{H}$.\n", "\n", "The effectiveness of this approach depends on the interest rate sensitivities of $L$ and $H$. In particular, it can be shown that the relative value change of the overall portfolio is\n", "\n", "$\\frac{\\Delta V}{P_{L}}\\approx\\left( D_{L}^{M}-D_{H}^{M}\\right) \\times\n", "\\frac{\\Delta\\theta}{1+\\theta}$.\n", "\n", "If $D_{L}>D_{H}$, and $\\Delta\\theta<0$ (as in the example below), then we will lose money. \n", "\n", "In contrast, with $D_{L}=D_{H}$, then we are doing duration matching (see above)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mHedge bond: zero coupon bond with m=3 and face value of 1\u001b[22m\u001b[39m\n", "\n", "PL₀ 1.544\n", "PH₀ 0.864\n", "v 1.788\n", "v*PH₀/PL₀ 1.000\n", "Dur(liability) 5.099\n", "Dur(hedge) 3.000\n", "Return -0.045\n", "\n", "\u001b[31m\u001b[1mThe naive hedge gives a return of -4.5%, which is a bad hedge\u001b[22m\u001b[39m\n" ] } ], "source": [ "mH = 3 #a mH-year zero coupon bond is used as hedge bond\n", "\n", "PH₀ = BondPrice3(θ₀,1,mH)\n", "PH₁ = BondPrice3(θ₁,1,mH)\n", "\n", "v = PL₀/PH₀ #so v*PH₀ = PL₀\n", "\n", "ΔV = v*(PH₁-PH₀) - (PL₁-PL₀)\n", "R = ΔV/PL₀ #relative value change\n", "\n", "printblue(\"Hedge bond: zero coupon bond with m=$mH and face value of 1\\n\")\n", "xy = [PL₀,PH₀,v,v*PH₀/PL₀,Dmac,mH,R]\n", "printmat(xy,rowNames=[\"PL₀\",\"PH₀\",\"v\",\"v*PH₀/PL₀\",\"Dur(liability)\",\"Dur(hedge)\",\"Return\"])\n", "\n", "printred(\"The naive hedge gives a return of $(round(R*100,digits=1))%, which is a bad hedge\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Illustrating the Problem with the Naive Hedging\n", "\n", "by plotting the value of the liability ($P_L$) and of the hedge bond position ($v P_H$) at different interest rates." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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ICJSUlGAYdv/+fTU1NTKZ3L0eUVHRhoYG+udqamrCMAyLjsZkZTEfH6bEP8J0dnYSiUR2RzHUtba2kkikgdczf/78wMDAgdeDDDsFBQXKysoYJSkN2GA9Uq1du7alpWX16tUqKioqKipbtmzBt3t5eSUkJACAkpLS5s2bLSwsXFxcNmzY8NtvvxEIhAGdcupUiImBa9fA2xs6Ogb+FRAEQdhrsJo4Xrx40UGVJYWEhPAf4uLiJCUl8Z+PHz/u6en5+fPna9euSUlJMeGsKirw9i14eoKtLQQFgbQ0E+pEEARhk8FK0D02KAPAhAkTqD9OnDhx4sSJzDyxkBAEBcGpU2BmBkFBYGLCzMoRBEFYaCS+NSIQYPduuHQJZs8GPz92R4MgCNJPg94Pmm2cnODVK3B1hawsOH4cUAcGBEGGmxGdtnR14f17SEwEJydoaGB3NAiCIH0zohM0AEhIQGgo6OmBmRl8/MjuaBCE+T58+HD06FEA6OrqWrBgQcf3uzDdv3//9u3b3bcvX768trYWAM6fPx8eHg4AMTExJ0+eHKyIEYaN9AQNAFxccOIEHDoENjbw9Cm7o0EQJistLY2KigIADMM6OzvplMzKykpLS+u+vbOzE8MwAHj79m1+fj5eFYlEAoDq6mrKRAsI643cNmgaixeDsjLMnw/JyXDoEAywzzWCDD1cXFynT5/G8ymGYR8/fiwvL58wYYKqqip1sdra2rS0NE1NTVlZWXzLzz//TDMZg5GRkZqaGgCUl5djGFZYWAgA0tLS1dXVSkpKeBkMw4qKisaPH4+m5hg8o+AJmsLCAj58gNBQ8PCA1lZ2R4MgTNbR0aGmpoaPw/by8tq0aZOvr+/s2bPXr19PKZOUlDRjxozr168bGBjcu3cP32hgYFBeXk5dVXBw8Jo1awDg0qVLZDJ5z549e/bsqaystLKySkpKwstERkba29sPdHwZQteoeYLGjRsHr1/D2rVgaQnBwTB+PLsDQkaBiAhITGRynZs20Z++8dKlSzw8PADQ1tamoaGRk5ODDzjIy8v79OmTsLBwQkLC7NmzXVxcBAUF6dTzyy+/3L59+8GDB/jHlStXXrt2zdjYGACuXr3q7e2NJngZVKMsQQMALy/cugVXr4KFBdy7BzY27A4IGelaW6G+nsl19rYAeWlp6e+//56fn9/Z2dnc3Jybm4snaAcHB3xZjEmTJgkKCmZnZ/c4l9n3eHl56erqnjp1qqOj46+//rpw4cJAvgTSq9GXoHFeXjBhAixYAAcOwI8/sjsaZERzdgZnZ1aekEwm29ra7ty5c8uWLeLi4rNmzaKs6Y4/VlN+7uta7/Ly8jY2NgEBAQ0NDbNmzWLODA3I943WBA0ADg7w9i24uEBmJly4AFS/uAgyrNXU1FRVVa1bt46Dg6O+vj47O5uyKy4uDsMwAoFQUlJSUVHR42rO1Pj5+bu6ujo7Oyl9OdatW7dv376mpqbLly8P4ndAAGB0vSTsTkUFEhKgpgamT4eqKnZHgyDMISUlpaqqumTJEh8fH2dnZwkJCcqurq4uNze3c+fOzZ49e+vWrdS7eiQsLGxoaDh//nz8JSEA2Nvbf/v2jUwmT0Nzrw++UfwEjUOTKyHDnJmZ2aFDhwCAi4srICCAl5eXQCC8efPm3r17RCLx9u3b5eXleN+4hQsXzp07t7m5OTY29uTJk7NmzcJruH37Nr7+3ObNm/HJJq2srMb/8wo9JiYmNja2sbGRn58fAAgEwsSJE62srFD/DRYgYL29bWAvMTGxz58/019R5du3b0xYDjwkBFavhjNnYOnSgVY19KAlrxhBJBKZsuSVu7v7woUL58+fz5SohpTW1tbIyMilS5cWFhaKi4uzO5whp7Cw0N7evqCggDlJCT1B/8vZGcLDwc0NTa6EIN9z7ty5ly9f+vn5oezMGigNUdHTQ5MrIQgdP/3005s3b5ycnNgdyGiBEvR/ocmVkBHq8+fPqamp3bdXVVXNmTOnHxWSyWR7e/vWAQzKzc3NXbZsWb8P7+7MmTNBQUH9ODA/P3/16tVMjIRZUBNHN/jkSjo6YGMD165Bv353EWToyMnJERQULCgoyMvLGz9+fEFBgQnVy/C2trbo6Oh+VIth2KtXr/A5lfqnsbExPj6+34d3l52d3b+RjVxcXExpMmY6lKC/Y8kSUFFBkyshQ19jY2Npaam2tjb+kUgk5uTkGBoaUgoICAgcO3bs27dvzc3NcXFxO3bs6F5JV1fX69evMQyzsrKiDGZpa2tLSUn59u2bubk55UV9e3t7QkICmUy2tLSkrqG4uDg9PV1fX5+Hh4dEIikoKODbc3Jy8vPztbS0lJWVe4yfSCRGR0ePGTPGzMyM0jOkvr7+w4cPwsLCxsbGeDzV1dWtra2ioqIxMTETJkzQ09Oj1JCbm/vp06dJkyb1WH9xcbGAgEBzc3NWVpaxsfG4ceMAoKOjIysrS1dXNzY2lp+fX09Pz9vbm3JITU1NSkoKAFhYWOCJm0wmp6amVlZWGhoaUiaZYgGUoL8Pn1zJzQ1ycuDmTRAQYHdACNIDEolkZmZWWFgoLS0NAP7+/g8ePAgLC6MUUFRUXLZs2datW+vr60+fPk2d2nBdXV2enp4EAiEvL09AQCAyMpJAIBQVFTk7O6uqqgoLC3t5eQUHBxsYGLS0tNja2vLz848fP/7s2bOUGu7du7dt2zZnZ+dTp05xcHDo6+tfuHABw7CVK1empKSYmppu27Zt7dq127Ztozl1S0uLvb39xIkTExMT1dTUHjx4QCAQ4uLi5s+fP23atIqKiqamprCwMAkJiXv37t2/fx8A1NXVX716hY+TBIBz58799ttvM2fOPHr0KIFA0NHRoTnFwYMHv3z5QiKRVFVVV61adefOHUdHx6qqKktLy6lTpwoLC+vr63Nxcbm7u+OT9uHfxc7ODsOwK1euBAUFNTc3u7i4dHV1qaiorFu3zsfHx9XVlWn/fnShBE3XuHHw5g2aXAkZiJBPIXElcd23K4kreRv/+9RW31Z/MrbnOfJ/NP9RTliO8tE3zXe+1nwB7r+fGCQkJBwcHAICAjZv3gwAd+7cWbt2LfXhYWFhiYmJR44cKS4u/vr1661bt1asWEFdoLW1dfXq1dOmTevs7FRQUEhLSzMwMNi2bduKFSu2b98OAP7+/rt27QoLC7ty5YqYmFhoaCiBQLhx48bz588BoKOjY8uWLU+ePLGwsCCRSEZGRni19+/fz8nJSUpK4uTkrK2t1dTUXLx4Mf5XhKKiouLhw4eTJ09ub2/X0tIKDQ11dHTcsGHDkSNHvLy8MAybO3fuqVOn8NUDvnz5kpOTIyQk9Pr16+XLl2/ZsqW6uvrQoUMpKSkqKioNDQ3fe0hvaWl5+/YtJyfn48ePf/zxR3zOayKRuH79emdnZwCgTNFXXV29bt26iIgIvBUI74V8+vRpeXn5O3fuAEBGRsaMGTNcXFxY0w0cJejeUCZXmjQJ/vwTTa6E9JUAt4A4fw+d0oR5/tPoyUHg6LEYAHBx/Oc+FeIRoskOy5YtO3r06ObNm4uKijIyMtzc3Kj3Ojg4ODg4xMTE8PHxLV++vHv9vLy8NjY2AMDNza2qqlpaWmpgYBAVFSUhIbFnzx4AaGhowFNYQkLCnDlz8LO7uLjgL9aKioo6OzstLCwAgIuLy8HBAZ/iIyoqioOD46effvr7W3BxZWRk0CRoSUnJyZMn4zHMmDEjISHBxsYmPT193rx5AEAgEObNm3ft2jW8sKWlpZCQEABoamqWlpYCQGpqqqqqqoqKCgCIiYlZW1v3eAGdnJzw7u1OTk7u7u7V1dUAwMnJ6ejoSFMyKSlJUVGR0kaPf9OoqCgxMTH8UgBAbW1teXm5nJwcDD6UoBmDJldC+mu60vTpStN7LSbKK7rbcjcjFc7VnEuzxcnJae3atWlpaUFBQQsWLBDoqTnOysrKysqqxwp5eHgoGZ+Tk7OrqwtfnEVXVxdvsQUAe3t7ACCRSJSBPFxcf2cPAoFAJpMptVHGvrW3tysoKOBzkwKAsbFx96k/qN/pcXFxdXZ2kkgkDMOoz0JZJoYy0oqTkxM/I4lEoqmhxy9IqY2Dg4ODgwN/scnHx9e9PIlE6r6xvb1dTU2N8kXu3r0rIiLS44mYDiVohjk4QHw8uLqiyZWQoYaLi8vDw+POnTvBwcF+fn4Dr5BAIJiZmWEY5u7uTr3d0NAwOjoab0LB19kCACUlJQEBgaioqGnTpuHTkE6fPh0AJk2a5OfnN3/+fDqtAdXV1R8/ftTU1MQw7PXr1/v37xcWFlZVVY2MjJw7dy4AvHr1ivqFJw1dXd3c3NyamhpJScn29vb4+Hj8eZxGdHQ0/iAfGxsrISEhKyuLP4B3Z2homJ+fX1hYSN1aMmnSpPr6eppLwRooQfeFqiokJICnJ0yfDg8fwn//s4YgbLRs2TJzc/Px48f3mKH6AZ9oKS0tTV1dvbi4uLm52d/ff/369WZmZsuWLRs/fnxMTAxekpub++LFiwsXLpw+fXpBQYGMjAz+ELp69erg4GBra2sHB4eWlpbQ0NCoqCiaIYhjx45dvXq1o6NjfHy8sLAwnpTPnj3r5eWVmJhYXl4eFRUVGxv7vSAVFBS8vb3t7OwWLFgQERFBs3AXRVVV1YIFC7S1ta9evXry5Ek6fzDk5eUPHTpkY2ODvzWtra39448/Dh48OG3aNFdXVzMzs+rq6vfv3zO3dyAdaC6OviOT4eBB8PeHgAAwN2fdeQcAzcXBiOE+F0dwcLC8vLxJHyf8am1tffnyJaXZOjo6Wl1dHW/ZaGxsDA8PLysrU1BQsLW1xdNfXV1dSEgImUyeN2/ey5cvXV1d8ZlIKysrs7OztbW1d+/eraenh3fYwDAsOjo6MzNTWFjY0tISX+eQoq6u7t27d4aGho8fPx4zZoyLiwvlVzQ/Pz8yMlJERGTmzJn4efPy8urr683MzACgo6Pj6dOnlCscFhaWk5Nja2tLJBJFRUXV1dWpz+Lp6WlsbGxgYJCenj558mS8pYLmWzc2NiYlJdna2uIf09PT4+PjOTk5p0+fjj9Kd3Z2vnz5sqCgYOzYsdOmTaO0/NBg+lwcKEH3Fz650rZtsGvX0O8ljRI0I4Z7gmaj+Pj4lpYWRUXFuLi4bdu2paenKyoqsjuov+EJGu/iMtjQZElDhrMzJCbCokUQHw+3bsGYMewOCEHYhpub+/bt29XV1QoKClFRUUMnOwOAtrb2kIqnT1CCHgAFBXj9GvbvB0NDCAgACwt2B4Qg7GFqaurv78/uKHq2ezdDfWOGJjRZ0sDgE3dcuACurnDyZK9LeSIIgjAOJWhmmDMHPnyAJ0/A1ZX56zcjCDJaoQTNJIqK8OYNaGqCoSG8fcvuaBAEGQlQgmYevLnj/HnU3IEgCFOgl4TMNmcOGBjAokXw9i3cugVoZaDRh4+Pz9/fPzExkd2BIKzWwOyVmFCCHgR4cweld8d3pqlFRqpz587duHGD3VEMlvb2dl5eXnZHMUSJi4ufPn2aiRWigSqD6ckT8PIaCoNZ0EAVRjBroMrINnRvt6GEWVcJtUEPJhcX+PABgoPBzQ317kAQpK9Qgh5keHPHxIlgaAgJCeyOBkGQ4QQl6MHHzQ0nToCPD7i5gY8Pu6NBEGTYQAmaVVxcICEBAgJQcweCIAxCCZqFxo+H6GhQVAQjI3j3jt3RIAgy1KEEzVq8vODjA+fOgasrau5AEIS+3vtBd3R0lJaW1v/3f+WKioqSkpKDFtVI5+oKhoawcCFER8OtW/CdZSAQBBnl6D1Bd3V17d27V1xcXEVFxeS/Hj16xLIQR6bx4+H1a1BUBEND1NyBIEiP6D1BX7ly5cSJE8uXL581axbNSmLdV+dF+gxv7pg2DVxdYc8eYMmKDwiCDCP0EnRsbOyMGTNu3brFsmhGI0pzx+vXcPMmau5AEISCXhMHhmHDd6mY4QRv7lBQAENDeP+e3dEgCDJU0EvQCxYsiJuHsiQAACAASURBVIyMJBKJLItm9MKbO377DVxcUO8OBEFw9Jo4zM3N9fX1bW1tt2zZoqSkRD2JDOrFMSjc3P5u7njzBm7cQM0dCDLK0UvQly9fxntrLFq0qPsub29v+lUXFRUlJiaSyeSFCxf2WKChoeHBgweVlZXq6urz5s3j5ubuS+Qj1IQJ8OYN7NoF5uZw/z4YGLA7IARB2IbedKMFBQVFRUU97tLU1JSTk6NT7507d7Zs2TJu3LjOzs5Pnz51L1BfX29oaGhpaWlmZvbw4UM+Pr7w8PDuxYb3dKMD8egRrF8Pe/cypXcHmm6UEWi6UUaMzNuN2Zh1lQZrPui2tjY+Pr6QkJDt27f3mKCfPHmybdu2/Px8AoHw9etXSUnJ8vJyWVlZmmKjN0EDQF4eLFgAyspw8ybQvQK9QgmaEShBM2LE3m5Mxbr5oMlk8sePH589exYaGpqXl8dgQu81FygpKdXX19fV1QFAXl7emDFjxFCTKw01NXj7FuTlwcwM0tLYHQ2CIKzWy1Dv169fr1q1qqCggLJFT0/v9u3bhoaGAzyxnp7e6dOntbW1FRUVS0tLQ0JC+Pn5uxcjkUj79++nLLGzbNkyNTU1mjJtbW0juf365EnOyZO5HBxIO3d2bdzYvzrwJ2jmxjXytLW1kclk9ARN3wi/3ZiEkavEw8PDwdHLIzK9BP3p06dZs2bJy8tfunRJR0enq6srNTX1999/t7e3z87OlpKS6nPUVPLy8g4cOPDzzz/r6+s/evRo7dq17969656jCQSCuLg45Xmcj4+v+/3Dyck5wm+quXO7dHW5PDw4k5K6Ll4EEZG+VoBhGIZhI/wqDRjnP9gdyJCGLhEjGLlKBEaWwcO+b+vWrcrKynV1ddQbS0tLJSQkTp8+TedAiqdPn6qpqfW468CBA/Pnz6d8VFRUDAkJ6V5MVFS0oaGB/lmampoYCWbYa23F1q/HJkzAoqP7emhnZyeRSByMoEaS1tZWEonE7iiGutFyuw0Ms64SvQfsvLw8R0dHmlk45OTkpk6d2uN7P0ZkZmbiE+OJiIiUl5fjG5ubm5uamkT6/mA4uvDzw6VLcOUKLFkCmzdDezu7A0IQZHDRS9DCwsIlJSXdt3/58qXXZJqbm2tvb3/w4MGysjJ7e/utW7fi252dnUNDQwFg2bJlZWVljo6Oe/futbKyMjExsbS07N93OBJ75ML7Cw1tDf07fJhxcICMDPj6FYyNISWF3dEgCDKI6HWz8/f3X7p06ZkzZzZu3MjDwwMARCLx+PHjx44di4iIsLW1pVNvU1PTe6ppJcTExExMTAAgNjZWTU1NWloary0sLKyqqkpFRWXatGk9tpcz0s3uZc7L21m3Q/ND3Sa6eZt4m8uZ9/atR4TAQNi4EdatgwMHoLfWLtTNjhGomx0jUDc7RrCiHzQ+CPDhw4cSEhJqampdXV2fPn1qbGzcsGHDxYsXB35uRjDeD7qhreFB1gOfdz4Yhi0zWOZl7CXOJ07nqJGgpASWL4f2dvD1BRUVOgVRgmYEStCMQAmaEazoB83BwfHgwYMHDx7Y29uTyWQODo45c+aEhoayLDv3iRifmJexV+a6TB9Hnw9lH9QvqDe1N7E7qEGmqAgREbBgAVhYwNWr7I4GQRAmG6yRhMzS75GEDW0NYnyjZuRLVhYsXQpKSnDlCowd230/eoJmBHqCZgR6gmYE60YSDlM02TmjOmPji40Z1RnsimdwaWvDu3egqwt6ehASwu5oEARhjh4GqkyfPl1SUjIgIODmzZt37tzp8bAdO3Y4OzsPcmzMJC0oPVZgrONdx/Fi472NvRdoL+DjGlmPk9zccPgwzJgBS5dCYCD88QcICbE7JgRBBqSHJ2hBQUF8RB83N7fgd3Bx9b4c+JAiJSh12OZwydaSIzZHnn16Jv+bvPcz7xH4QG1hAcnJwM8P+voQG8vuaBAEGZAR2wZNX3FD8bXka7dSbu203Ll10taBxTgkPXoEGzbAsmVw9Cjw8KA2aEagNmhGoDZoRrCim11WVlZra6upqSnN9rCwMFVVVWVl5YGfvleDOt1oJ7mzpaNlxL5LrKqCNWugshL8/EgqKihB9wolaEagBM0IVrwkvH///h9//NF9+44dO3qcXH/Y4ebgpsnOh6IPBX0M6iR3siskZpKWhqdPYeNGsLLiOH0ayGR2B4QgSN/0pxdHS0uLoKAg00MZCrQktS68uzD+3Pj9kfs/N35mdzjM4OkJ794Rnj/nmTMHysrYHQ2CIH3Qw7s+Eon0+PFjAMjOzq6trQ0MDKTs6uzszM7OLiwsVFdXZ12MLLRQe+FC7YWfaj/dTLlpds1MT1rPy9jLbaIbF8cweyn6H0pKXa9ewdmzHIaGcO4cLF7M7oAQBGFID23QLS0tQt/vocXJyenl5dVj08dgYOOSV0QS8UHWgyuJV6pbqj9u/MjNMYwnKf/7JWFGBixdCnp6cOUKiI/0cfB9h9qgGYHaoBkxiC8JMQzD14o9d+5cRUXFqVOnKLu4ubllZWVZ2cduKKxJWNpUKi8iP3j1s8C/vTiIRDhyBO7fh5s3Ydo0dsc1tKAEzQiUoBnBil4c5eXlHR0dEyZMGPhp+m0oJGgaieWJ4YXhKw1XSgtKs+ykA0TbzS48HFatAjc3OHUK/llODEEJmhEoQTOCFb04xo0bx97sPDSJ8okW1hdqXtSc92Dei7wXXVgXuyPqO3t7SE+H2lo0qTSCDGU9PEHr6+tLSUmFh4dfuHDhf//7X4+HHT58eMGCBYMf3lB8gsYRScRnn55dTbqaXZO9VG/pGuM1KuL0Jvxkr+8OVOnLpNIjHnqCZgR6gmYEs65SD63JJiYm+DJXcnJy3Uep4Aa4YuwIwM/F767l7q7lnlWTdSP5xuQbk4/ZHltjtIbdcfWRuzuYm8Py5WBlBb6+oKrK7oAQBPnXKB3qzXQdXR1tpDYR3iG6rGIvQ70xDK5dg0OH4MgR8PJibWhDCHqCZsRQuN2GPvZMN0pGo9G+g4eThyY7r3++/nLi5cb2RnaF1AcEAnh5QUQEXL4M8+bB16/sDghBEAD6CTojI2Pfvn2dnX+Pe967d6+wsLCUlNTly5dZEtvw5qbpFlUcpfS70rLgZTElMUP8fyoAAFpa/04q/fQpu6NBEIRuE8fevXvfvn0bHR0NABEREXZ2dh4eHnx8fLdv387IyNDW1mZBfMOlieN78JUS/5f4v5aOlh90f1hpuFJRVJH1YfRtNru3b8HTEywsRtuk0qiJgxFD+XYbOljRxFFYWKivr4///ODBAw0NDX9//5s3b1pYWAQFBQ383KMBvlJiineK31y/8m/lVres2rva2R1UbywsICkJ+PlBTw9NKo0gbERvTGBbWxs+cz+GYWFhYa6urgQCAQDU1dVLS0tZFOBIYS5nbi5n3oV1cRKGwwOaiAhcuQKPH4O7O6xZAwcOAPcwHumOIMMUvSfo8ePHv337FsOwhISE4uJie3t7fHt1dbWIyBDtrjDE0WTnsIIw2zu2/hn+baQ2doVEj5sbpKRASgoYG8OHD+yOBkFGHXoJevny5fHx8YaGhq6urkpKStOnTweAzs7OxMREDQ0NVkU4kk1Xnr7Pat+TnCf4ElxJFUnsjqgbGRkICYEDB8DZGby9obmZ3QEhyChCL0EbGRmFhYXhCTo0NJSXlxcA3r9/r6CggCdrZIA4CZx2ynYP3B+kr0tXFldeGLhQ+w/tk3Ena4m17A7tv9zd4eNHAAA9PQgLY3c0CDJaoIEqQwgZI0cWRd5IuWE6znSbxTYm1sy0NQlDQ2HtWjAzgz/+gLFjmRHaEIJ6cTBixNxug2oQh3rTaGlpeffuXUFBARcXl6qqqrm5OQ8Pz8BPjHTHQeCwU7azU7aj2U4ik4bKigEzZ0J2Nhw9Cvr68Ouv4OnJ7oAQZCTr5bZ/9OjRmjVr6urqKFvGjRvn5+dna2s7yIEh/5pxd4YQj9Aqw1Wz1GaxP1MLCMCJE+DiAmvWQGAg/PEHKCiwOSQEGaHotUGnpqYuXLhQTU0tKCjo48ePWVlZ9+7dGzNmzJw5c0pKSlgWIvLU4+lczbmn408rnlPcGb4zqyaL3REBWFhASgpMmQImJuDjg1akRZDBQK8N+scff4yMjExMTMR7Q+Pq6up0dHQ2bty4b98+FsQ3qtqge5Vbm3sn9Y5fup+MkMwy/WWrjFbxc/H3fhgT26C7y8yENWuAiwuuXYOJE5lfPwuhNmhGjJ7bbSBYMZKwqKho+vTp1NkZAMaMGWNpaYmviYWwmIaExvHpxz9v+fyL7S+plakchP4sys5kOjoQFwdLl4K1NRw+DB0d7A4IQUYOenf4mDFjPuKdq6hgGJabmyshITGYUSH0cBA4HFQcrs+5zsv572pV7V3taVVpbAqIA7y84P17SEgAU1NITGRPGAgy4tBL0HPnzn316tWuXbsaG/+eM7Ompmbt2rWZmZmurq4sCQ9hVGF9ocufLoZXDH3e+dS01rAhggkTIDQU9u8HZ2fYvBlaWtgQA4KMLPQStIuLy8aNG0+fPi0pKamkpKSgoCArK3v9+vWjR49OmjSJZSEijNAcq1m4ufCC44Xsmmy182r2fvaB2YEdXSxvcHB3h7Q0qK8HfX2IiGD12RFkZOl9oEpsbGxQUFBhYSEHB4e6uvqiRYsMDQ1ZExygl4T98q3jW1B20K3UWzlfc/ZM2bN10tZBfEn4Pc+fw4YNYG0Nv/8OY8aw7rwDgF4SMgLdboxg1lVCIwlHssL6wuqW6knyk9iQoAGgqQkOHICgIPDxgXnzWHrqfkEJmhHodmME6xJ0U1NTTExMUVERJyensrKytbU1Tb+OQYUSNFNQEnTQxyAMw5w1nKlfMA6uuDhYvRpUVeHyZZCTY9FJ+wUlaEag240RLFqT8NatW/Ly8k5OTj/++OP69etnzpypqKj45MmTgZ8YYQteTt7/Jf5P/jf5jS82fihnyQyilpZ/T1hqZARXr8LQ/h8bggwp9BJ0eHj4qlWrpkyZEh0dXVFRUV5e/vLlS01Nzfnz56elsalHFzIwTupOEZ4RqWtTFUQVFgct1rykeTj68OfGz4N7Vj4+OHwYwsPh+nWYNg3y8gb3dAgyUtBr4liyZEleXl5cXBwX17/zPxCJRF1dXTc3t9OnT7MgPtTEwRQ9tkFjGBb3Je5O2p2g7KDQJaFmcmYsiAMuXYJffoHt22HHDhhijQmoiYMR6HZjBCuaOJqbm6dMmUKdnQGAn5/f3Ny8GU3cPvwRCIQpilOuOV8r215mPM6YFafk4oLNmyEhAV69AlNTSE5mxUkRZNiil6AtLCzevXtH/u88OB0dHUlJSagf9EjCz8VPvRZXS2eL2TWzX2N/LW0anJUnlZUhLAy2bIFZs2DPHmgf8qvoIgib0EvQmzZt4uXl9fDwSEpKIhKJzc3N8fHxLi4u+vr6S5YsYVmICIsJcgv6OPp8bvhscNlgxt0Z9zLuEUlEJp+DQABPT0hNhYIC0NGB6Ggm148gIwK9NuiDBw/+/PPP9I83MzN79+4ds6P6F2qDZor+9YNuI7UF5wT7pvkmlCbM0ZizRG9J98UEmCAkBDZsAEdHOHMG2PrviNqgGYFuN0awYkUVe3t7QUFB+sfLysoOPAhkaOLj4luks2iRzqLqluqAzIDA7MBBSdDOzmBlBbt3g6YmXLwIaJoXBPkHGkk4KgzGSMKqlqp2UruiqCLTanzzBtasAX19uHQJJCWZVi3D0BM0I9DtxggWDVRBkO/5UPbB5KqJzW2b68nXG9oamFCjtTWkpoKyMmhrw9WrTKgQQYY5lKCRfnJSd6rYUbHfev+bz29Uzqs4/+nsm+bb2tk6oEr5+eHECXj+HC5dAgcHyM1lUrAIMiyhBI30HyeB007ZztfNt3Bz4VzNuXfS7iieU/RL9xtovaamkJQEs2fDlCmweTN8+8aMYBFk+BnENui2trb09HQAMDP77hC1ioqK6OhoAoFgbm6upKTUvQBqg2YKls1mV9pUSiKTJohNYE51lZWwaxdERsLx47B0KRAIzKn2O1AbNCPQ7caIQWyDLioqamtrG2C9N2/eFBUVdXR03Lhx4/fKPHz4UEdHJzAwMCQkZMuWLQM8IzIUyIvI02Tn/ZH7zyWcq2iu6E91MjLg6wuPH8PFi2BrC5mZTAkSQYaLHhK0hobGhw8fAODy5cuHDx/uX71OTk41NTW///779wpUVlauXLkyODj40aNH/v7+aIa8kcpBxSGzOlPnD50Zd2f4pfs1d/R9kgBTU0hIgBUrwN4eNm+GpqZBCBNBhqIeErSAgAD+BF1eXv75cz/nOZOSkhIREaFTICQkRF9fX0dHJzIysrCwsH9nQYY+6/HWN+bcKN9evtFsY0huiNxvcs5/OgdmB3aSO/tQCwcHeHr+/QStpQW+vmjaUmQ06GGgiq6u7pEjR758+ZKdnV1bWxsYGNi9jLGxsbKy8kBOXFBQ0NLSMmXKFF1d3devX69evbrHUYtdXV03btygLBEwc+ZMeXl5mjKdnZ2dnX2520cfvA2aja2rHMAxU2nmTKWZtcTahx8f+iT4mMqYygn3cf5+ERE4c4bg4cG5aRPcuNH1+++Yjg4Tg+zs7CQQCDSTzyA00O3GCEauEhcXF6G31yo9JOjff/998eLFq1atwj9G9zRPwuXLl729vRmK9Dva2to+ffqUn58vIyNTVFSkqam5dOlSdXV1mmIYhqWmpvLw8OAfzc3Nu49d7Orq6urqGkgwI17XP9gdCIjxiK3WX71afzUeFWV7SVOJoghjY14MDEjR0Zx//snl6EieP7/z4EGg+381xg2RSzTEoavECEauEicnZ38StLGxcU5OTmNj4/79+ysqKk6dOtW9zNixYxmPtUeysrIaGhoyMjIAoKSkpKiomJWV1T1Bc3FxXbhwgX4vjs7OTlavtjfcsGdNQoZVtVRN9ZuqLK68WHfxQp2FEvwSvR+zahW4uXEeOcJpZMSsPh4YhqFeHL1CtxsjmHWVvjsXh6ioqLOzc2Nj4wCbMmg0NDQICgpyc3NPnz797Nmz7e3tvLy8zc3NFRUV3dsukFFCWlD6y9Yv4YXh/un+P0X+ZKVotURvibOGMz8X3dUvx4wBHx/w9IQNG+DGDbhwAfT0WBUygrACQ/2g29vbCwsLubi4xo8fT2ltoC87O9vHxycvLy8zM9PNzU1XVxfvbycmJvbnn386OjoCgLOzM5lMdnJyCgoK4uPje/bsWfd6UD9ophjiT9DUmjuag3OC76bffVf2zlPf02emT+/HkMlw9y7s2QPu7nD0KND9baED9YNmBLrdGMGiVb1zcnI2btwYFRWFvznh5OR0dnY+f/68goIC/XpLS0tfvHhB+aioqDhz5kwAuHPnjq2tLX54Z2enr69vYWGhlpbWokWLerwxUIJmimGUoCmqW6o/fv04dfxURg+oq4MjRyAoqN8tHihBMwLdboxgRYKuqKjQ19cnkUgrVqzQ0dHp6upKS0u7ffu2lJRUWlqakJDQwE/fK5SgmWI4Jujunuc9DysIW6SzaJLcpO++XUlOhg0boKsLLl0CU9M+1Y8SNCPQ7cYIVswH/ccff3BwcKSmpioq/vt63dvb28LCwtfXd/369QM/PYIwTldKN7kiefXT1a2drQu1F3roeuhL69MWMjKC+Hjw8wNnZ3BwgHPnQIKBV44IMiTRmywpPT3dxcWFOjsDgI6Ojq2tLT7JBoKwkqKo4gHrA1nrs54sesJB4HALcNO6pHX09dHC+v8OdMLX0/r4EcTFQUsLfHwAdW1Ghid6CZqHh6epp2G1TU1NvLy8gxYSgvRCT1rv+PTjBZsKbrrcrCPWxZbE9lBIXBx8fCA0FO7fB3NzeP+e5WEiyEDRS9DTpk0LCgqimSXj9u3br1+/trGxGdy4EKQ3BAJhkvyk32f+7qnvSb39Q/mH6pbqvz8YGkJcHPz4I7i4gKcnfP3KhkARpL/oJeiVK1eamJi4uroaGhouXbr0hx9+0NbWXrFixezZs11cXFgWIoL0SXBOsMZFjRl3Z9xKvdXQ1oBaPJDhq5dudm1tbT4+PkFBQYWFhRwcHGpqaosXL/by8uLiovd2kYlQLw6mGBm9OBjXRmoLLwwPzAp8nvd8ssJkdy13N003YR5hSE2FDRugvR0uXQJzc5qjUC8ORqDbjREs6gfNdihBM8VoS9AUTe1NwTnBAZkB8V/iY1fG6kjpAIaBnx/s3g329vDbb0A1aQFK0IxAtxsj0KKxCNI7EV4RT33PF4tfFGwu0JTUBOipjwea+gcZqlCCRkYFCX4JTsK/j8ZEIV5l5ZAfz06PC7+BmZlBQgIbY0OQ70EJGhmN+Ln4o5dHq6pP2j1XRHFu8eazdrFeM6Gmht1xIch/oDboUWHUtkEzIrM6MyDxdkDCdc5vzR7jZhzaEkzg5mZ3UEMXut0YgdqgEYQ5dKR0js06k3+04ebsGxIZBQQ9PXj6lN1BIQgA4wm6sbERrXODjGxGlgvW38qCCxfg4EGYPBnevs35mnMw6mBGdQa7Q0NGqV4SdF5enrOzs7i4uJiY2MuXLwHgw4cPDg4OPQ4BR5CRwM4OkpNh7VqYP19g84622iqXP100L2keiDqQXoWmoEFYil6CLisrmzJlSm5u7u7du6WlpfGN+vr6KSkpf/31F0vCQxB2wBcR//RJUc/q1NqgwkJnP/v/dXR1uAa4Trw48UDUgayaLHaHiIwK9BL0pUuXhISEkpOT9+zZQ3lNx8PDY2homJaWxpLwEIR9BAVh925IT4e2NhOrhSeTxhSuy7k7925nV2dQdhC7g0NGBXoJOjMzc/bs2fjE/NTzo0tKStagDknIKDFuHFy5AlFREBMDenomcUUn7E4cnHqQukh6VXpaFXpkQZiPXoLm5+fvsa25oKBgzJgxgxYSggw9Wlrw7BlcvAjHjoGtLSQnU+/Mqsmad3+e+gX1nyJ/Sq1MZVeMyMhDL0Hb2NgEBQUVFBQA1RP0s2fP3r17Z2dnx4roEGRIsbODlBRYvhycnWHBAiguxjd76Hjkb8p/vOgxNwf3ooeLlHyUNoduji2JHeKDDJChj16CXrFihaqqqrGx8aZNm+rr6588ebJ48WJXV1dHR0eUoJFR6p/3h2BsDObmsGcPNDbie7QltQ/bHM7ZmPNo4SNBbsEVT1bcTL3J3mCR4a6XkYQNDQ179uzx9/dvbm4GABERkdWrVx87doyfn5818aGRhEyBRhIyos+z2ZWVwdGjEBwM+/bBhg3QbQ5eEpnExfHvxuqWailBKWZFyy7odmMES6cbJZFIZWVlACAnJ8eymaBxKEEzBUrQjOjndKPZ2bBzJxQUwM8/g7s7nYKz783+VPvJXct9vtZ8I1mjAcXKPuh2YwSaD/pf6DemVyhBM2JA80G/egXbt8PYsXDmDBgafq9UVk1WYFbg/az7xE6iy0QXdy13SwVL6i5SQx+63RjBigQdExMTHx/f464ZM2YYGBgM/PS9QgmaKVCCZsRAJ+wnk+HuXdi7Fywt4dQpmDCBTtnUytTA7MDArEAyRg73DFcSU+rnSVkO3W6MYNZVotdeER4e/vPPP/e4S0xMjDUJGkGGDfz94bx5cPEimJvDihWwbx+IiPRY1kDGwEDG4BfbX1IrUxVEFKh3YRg2vJ6pkcFDrxfH0aNHMSqdnZ3Z2dlubm7e3t5eXl4sCxFBhhN8/GFyMtTXg5oa+PgAiUSnuIGMAfWLxDZS2wSfCauernqe97y9q33ww0WGtD5MN8rFxaWpqRkQEBAaGhoUhIa6Isj3ycn9Pf4wLAx0dCAwkMHj+Lj4YlfGmsuZX068LHVayvlPZ98036Z2NDfZKNXn+aDxuTgiIyMHIxoEGVG0tOD587/HH06fDikpjBykIKLgZewV4hGS92PeHI05f2b+qXBOwe2+G4lM70kcGZH6nKCJRGJaWpq4uPhgRIMgIxA+/nDZMpg9m3r8Ya+kBKXWGK35a/Ffn7d89jL2om4JQUYJev/k3Xtx1NXVPX36tLS0dO7cuYMcGIKMIH15f9idGJ+Yo6oj9ZbM6sy1z9a6abrN1Zw7jHqAIH3Vt14cEhIS2traPj4+xsbGgxwYgow4+PvDJUvg6FFQU/ve+ENGaEpqnrA7EZgdaHnDUpxf3F3L3VnD2VgW3ZUjDRqoMiqgftCMGGg/6D7JyoJdu6CwEI4epT/+kL4urCuuJO5xzuPHHx/zcfGtMlq1c/JOJobZHbrdGIFGEv4L/cb0CiVoRrA0QeOePYNdu0BGBo4dg8mTB1hZYnnil6YvbhPdmBLa96DbjRGDOFDl69evvWZtYWFhdLcjyEA5OcGsWRAUBJ6eoKQEv/4KJib9rsxknInJuP8cHvIpJCQ3ZK7mXFslWx5OngGHi7BaD704ZGVlpXpz584d1seKICMQBwe4u8PHj+DuDi4uYG8PqUyb8t9I1mji2InH3hyTPSu75NGSRx8ftXa2MqtyhAV6aOK4ffs2mUymf5ilpaWGhsagRfUv1MTBFKiJgxFsaOKg0dEBt2/D4cMwZQocOwbq6syquLK5Mjgn+NHHR+/K3k1Xmr7OdJ29sn3/qkK3GyNQG/S/0G9Mr1CCZgT7EzSupQUuXoSzZ2HmTDh8GJSVmVh3fVt9SG6IhIDEbLXZ/asB3W6MYNZV6vNAFQRBBhfeG6+gALS1wdwcvL2hvJxZdYvziXvqe9Jk5ytJV468PpJSydBAR4SVeumD2dnZGRISkpmZWV5eTv2svXjxYmtr60GODUFGMWFh2L0bVq2CM2dATw8WL4Z9+0BaejBOZTLOJCAzYGHgwo6ujjkac1wmuliPt+bm4B6McyF9Qq+Jo7W11d7ePj4+fsyYMY2NjVJSUhUVFQCgoKBw8uRJDw8PFsSHmjiYAjVxMGKoNHF0V10Nv/0G16/D6tWwZw+IiQ3SVsOunAAAIABJREFUebJrsp/mPg3OCc6ry5upOnO7xfbuK7+g240RrGjiuHXrVlJSUlxcXEhIiLi4eHl5eXV19ZIlS9TU1ObPnz/wcyMIwhApKThx4u8pTDU04PBhaBqU+e20JLX2TNmTsDohc33m1PFTB+MUSJ/QS9DJyclz586dPHkyAJBIJACQlJS8detWeXn53bt3WRQggiA4RUW4cgVev4bCQlBXh5MngUgcpFPJCsl6GXvRPD7vjdh77M2xzJrMQTop0h29BF1bWztu3DgAEBcXb2pq6urqAgAuLi4TE5MPHz6wKEAEQahNnAi+vhARAUlJoKEBPj7QzqJ5/Z3UneqIdT88/UHZR3nry61RxVFoBtTBRi9By8vL44t5KygocHBwvH79GgAwDMvIyBAUFGRRgAiCdKetDQ8ewMOH8OoVaGjA1avQ1TXY57RUsPxtxm/pq9KfeDwZKzB2d/humTMyno89C+oLBvvUoxa9BD116tSIiIiOjg4hISF3d/dFixZ5e3tbWVmlp6e7uLiwLESEKYqKivA/twORmZnZ0tLS16OamprS0tIGeGqkB2ZmEBIC9+5BQADo6oKvL/Q2xIwpdKV0f7L66f2a92nr0iYrTBbkRo9rg6WHBN3Q0ID/MHv27Hv37rW3twPAhQsXZs+eHRER0dbW5ufnN2XKFJaGOTiKi4uVlZVJdJeMGzHOnTvn6+vb16MiIyOfPHlC+ejq6pqdnd3XSpKSkpYtW0anwOvXrx89etTXahkRExPz8OHDfh9+/PjxqqoqJsYzKCZPhshIOH8eLl4EfX0IDARWjT6TE5Zba7JWRkiGeuOCwAUnYk9k1/T59wTprocEfenSJQ0NjcOHD9fU1NjZ2eGdRSQkJG7dupWfn5+YmLh48WKWxzkoOjo6ioqKhvhYSvaKi4sLDw8fYCWTJ08ODQ2lUyAhIYF+gX57//79ixcv+n34pUuXampqmBjPILKzg/fv4dw5+PVXsLCAkBC2RIFhmLeJd/m38ln+s9QvqO8M3xlTEtOFDXrzy4iFdRMREaGvrw8AnJycs2bNCgwMbG9v716MNURFRRsaGuiXaWpq6l/lubm5ANDR0YFh2I4dOwICAtzd3XV0dDZu3EgkEruXv379urW1tamp6c8//9zZ2Ylh2Pv37zdu3EgpMG/evC9fvmAYtn//fl9f30WLFk2cOBGfHZCioqJi5cqVBgYGjo6Ob9++xTAsPT3d1dW1sbERw7CvX786Ozvn5uZiGHbq1KkpU6Zoa2t7eHgUFhbih+/fv9/Pz2/BggVaWlrbt29vaWnZtGmTjo7O0qVL8Rq+fPkyb948Pz8/ExOTqVOnvnz5EsOwzs7ONWvWHD9+HK/k7du3zs7Oenp6y5cvr6ys/N71SUpKUlFRkZeXt7Oz27RpE4ZhKioqfn5+s2bN0tPT279/f1dXF14yLi7OyclJT09v5cqVVVVVNPWkp6dTrpKdnd1ff/1lY2NjZGR08eJFDMPS0tJUVVXl5OTs7OzWr1+PYRiRSPzpp59MTU2tra3v3r2LH3j58uULFy6sW7dOU1MzKSmpsbFx8+bNRkZG06dPf/78OV4mJibGwcFh4sSJU6ZMefLkSWZmppqa2rhx4+zs7Ly9valDam5utrOze/78uZWV1fLly4lE4vr1642MjAwMDDZv3oxfycOHD/Py8k6aNMnOzi48PBzDsJSUFDc3N11d3cWLF5eUlOBVnT171tTUVEtLy8nJqays7HsXk3W6urAHDzB1dczSEouKYnr1jN9uyRXJh6MPG142lDwluSJ4RR2xjunBDFn9Tko0ekjQuMzMzN27d0tJSQGAmJiYl5dXUlISU07ZJyxL0La2thMnToyLiyssLDQ1NfXx8aEp7O/vP2HChMTExLy8vMmTJ+/ZswfDsL/++svc3JxSRlJSEs+tzs7OCgoKYWFh+fn5bW1tlALt7e0aGhrnzp2rqqp6+fKltLR0aWkphmHe3t4LFiwgk8kuLi7btm3DCwcEBBQWFlZVVZ06dcrIyAjf6OzsrKqqGhsbm5ubq6amZmxsHBQU9OXLl5kzZx44cADDsJycHG5u7mXLllVUVLx48UJUVDQ/P586QWdkZMjIyISFhVVXV//666+TJk0ik8k9Xp/W1tatW7cuWbKkoKAATz0qKiqTJ09OS0vLyspSUlJ68uQJhmFpaWkyMjKvXr2qrq7++eefp0yZQlNhZGSkvr4+/jMAuLq65uXlvX37VlRUNCMjg0gk7tq1a+HChQUFBfjVWLhw4cqVK798+ZKenq6urh4aGoph2Pbt20VFRf/888+ioqL6+nobG5udO3dWVFQkJCTIy8snJydjGCYlJfX8+fOGhoacnJwPHz4QicR9+/bNnz+/oKAA/8NJgbfjzZ07Nycnp7CwsKWl5c8//ywoKCguLl68ePGGDRswDKusrJSSkvrrr78KCgq+fftWWFgoKSkZEhJSXV198eJFHR2dzs7OqKioiRMnFhQU1NXVvX79uvsfJ7bB07SKCmZnh334wMSK+3G7fW74/L8P/yORSUwMY4gb9ASNI5FI4eHh7u7u3NzcAKClpXXixInq6moGa6+srKyoqKBfpqmpqaCgAH8g7Y6VCZqSlC9cuLBkyRKawra2tpcvX8Z/jomJkZSUxOgm6EOHDnU/4+PHj42Njev+4eHh8ccff2AYRiQS9fX17e3tzczMqP+/kpqa+uTJk/v37/Pw8NTW1uI1//rrr/jeHTt2LFiwAP/53r17M2fOxDAsJyeHQCDU1NTg25cvX3748GHqBO3t7b1r1y5KDDIyMpTH8+6OHj2KZyuciooKnpQxDNu+fftPP/2EYdjq1av37t2L11ZbWzt27FjK0yWOJkFnZGTgP7u6ut68eRPDsBMnTqxZswbfWF5ezsvLW1FRgVd45syZZcuW4afz8PDAyyQnJ0tLS9fW1uJldu3atWfPHjKZLCYmdvPmzdbWVsqpz5w5s2LFiu7fC0/Qnz59omxpb2+Piop6+PDhqVOnNDQ08I3jxo2jRLt7925vb2/KdVNTU0tOTg4NDVVTU4uPj//eHzk26+jArlzBxo3D7OywlBSmVMmU1POt/ZvJVZN9EfvefnnbRe4aeIVDDbMSdC9zcXByctrZ2dnZ2VVWVvr7+9+6dWvPnj2HDh0KCAhwdXWlc+C1a9cOHjxYVVXl6Oj4/PlzOg0srq6ukZGRRUVFEyZM6EvbDPPJysriPwgJCTU3N9PsLS0tVVFRwX9WVVWtqalpa2ujU9v48eO7bywuLi4qKrK3/3emR/x1Kx8f348//rh69eqQkBAeHh4A6OjocHR0xDDMyMiIi4uLk5Pz69evY8aMoY5TUFBQRubv9zMCAgKU/hXCwsJjx47Ff1ZWVv78+TNNDLm5uREREfhHOTk5ymthRlBfpcbGRgAoKiqKiIgICwujfPH6+noFBYXv1YB3rsfj7H6di4qKAMDJyYmyxczMDP+B8htSVFTU0tLi4OBAKTNr1iwCgRAYGHj06NFNmzZZW1ufPn1aS0uL/nehVPjly5dp06bp6empqanV19d//fq1e+Hi4uL4+PjExET8o4iIyLdv3xwcHDw9PT09Pb99++bh4XH8+HF+fn76J2Upbm7w8oLly+H2bZg1C6ZMgV9+ATU1docFAtwCl50uh+SGbHyx8XPj52kTpjmpO7lMdBHlpTepwyjE6IKVMjIyq1evFhYWPnLkSHl5ea+vtqdMmRIZGfn8+fOoqCg6xa5cucL2vMwgWVnZkpIS/OeSkpIxY8bw8fEJCAi0tv49A3p7ezt1puPg6OEFrJycnIqKyvv372m2l5WV7d+//8cff9y5c6eNjY2QkFBcXFxNTU16ejoANDU1nT59useoCARC943fvn1rbGzEJzD58uULJaVSYsCb0Rn51hwcHL1ODi4nJ2dlZXXo0CFGKuz1LPLy8gAQExPTPdNRLqmcnJywsPD79+9pLjL+MFFbW3vs2DFvb++YmBj68VMOv3v37vTp03///XceHp7IyEhKlxIODg7sn3fIcnJyrq6u58+fp6lk//79+/fvz8jIWLFixbVr1zZt2tSPKzC4eHjAywsWL4aLF+H/7Z1nQFNJ14AnoYqAtNB774oBu9ICIqIofq4Kil1wV1xdEdS1YFt1VxS7Yl1RXpHFgrsiAlJUwAIqyCKiIIgQUCkhkJCQzPdj9L55AyIWIMA8v+ZOzp17ZnJz7s3MnHNGj+6KKKZfCplEpmpQqRrUMKewsoayf57/85+n/wlKCBqpPfIfv3/ESKIXDqWH+Hy4UT6fn5SU5Ofnp6GhERAQICcnt2PHDh8fn47PsrCwsLCwaNdIEbx58+bAgQPbtm37MpV7iNmzZ4eHh7969aq2tnbDhg2zZ88GAFhYWJSUlNy9e7epqWn9+vXwcxtCPD09a2pq9uzZw2Kx2Gx2SkrKixcv+Hz+3Llzf/zxx/37948YMWLx4sUAADk5uerq6oqKiubm5uDg4HYN8acgk8nr169vaWm5f/9+bGzszJkzBT8NCAg4cuTIrVu3eDxebW1tVFQUqt+6dWtcXJxQU7q6uo8fP37+/HnlpyNeBgYGHjx4MC0tTajBzqOrq5uXl/f8+fM3b97o6urSaLSlS5fW1tZyudzHjx/fvXtXSN7e3l5bW3v16tUMBoPD4WRnZz969KipqSk2NpbJZCopKWlpaaE/Irq6uvn5+UVFRRUVFR0oICsrW1hY2NTUVFVVJXhD6ujo3Lp1q6SkhMlkLly48Ny5c9evX29tbW1oaLhw4QKbzc7JyXn48GFra6uBgYGsrKyUlNSX9r37QFFMi4qAjg4YNgwEBYGPLxw9i94gvR8dfkzwS6hcVbl27FpB6wwh7OfOih0Z0PLy8l27dhkbG7u7u1++fNnLyyspKamwsHDNmjUUCuXbr41mRTuOVMfn81NTU5M+8vbtW/73Q0pKauTIkegqJiYmCgoKqF5JScnQ0FBIeN68eXPnzvXy8ho2bJiFhcVvv/3G5/OVlZUPHTo0f/78wYMHm5ubOzo6SkpK8vl8Q0NDJSWltlccMGBASkrKvXv3rKyszM3NDxw4ACGMiYmhUChr167l8/n79+9Hy01Dhw4NDAwcNWqUvb29vb29k5NT25bV1dW1tLRQWV5e3tTUlNDfxMTE0tJywYIFR44csbCw4PP5BgYGSNje3v4///nPli1btLW1x4wZc/v2bXRWfHx8c3OzkMI+Pj7omfHrr7/y+XwrK6sBAwagjzQ0NFCDw4cPP3fuXFhYGGoQTcgKIicnN3jwYFSmUqnorZbP5+vp6VEoFD6fP3ny5HHjxi1ZsiQ0NJTP5587d05BQWH06NEGBgYrV65ks9l8Pl9LS0tDQwOdSCKR4uPjGxoaqFSqsbFxWFhYa2srn8+Pjo62sbHR19dPT08/fPgwn8/38vJycXEJCAhYvXq1oEokEolKpUII0eH8+fO1tbVtbW29vb0XL15sa2uL6vfs2ZORkeHr65uenm5ubn716tW9e/fq6ekNGzYMbY9pbGxcsWKFoaGhnZ0dlUqdP39+2y9dtBg0iL91K//pUygjA4YOhQsX8p8/72mdPjBAbMBYnbGCNYVvC9V2q838a+b5vPPvmt71lGJdRGeMZDvhRhkMxpUrV6KiolJSUiCEVCp1yZIlvr6+srKyX2qC9+zZk5KS0u4c9JkzZ27cuHHhwoXm5uaBAwd+ag564MCBVCpVXPzDVMymTZuoVKqQDJPJ/Ard+irFxcXu7u5oJpfgs+FGmUymk5NTdnY2MdT9ENENN9o1kBobJU6ckNi3j+fgwPn1V/6QIZ05q5t/bu9Y726W3LxRcuNW2S1DBUMPQ48JRhPs1Oy6TYGvozOjJCMj0/EcAwDt7YNGf/E0NTVDQ0MFl7m/gvDwcE9Pz3Y/0tLSWrly5c6dO9FkaGho6NOnT9uKdekujj7Js2fP0A4TQbhcbrs7uwlaWlqIjR/9lubm5tbWfrQV7AONjTAiAmprQy8vmJ39WfGe+rmxuKyE4oQf//lRb6/e2FNje0SHztOFuzgcHByuX7/u7u7epa8SwcHBLd0VhatfoaOj8xXOzZKSksTGD0z/QlYW/PwzCAwEMTHAzw+oq4OwMECj9bRawkiLS3sYe3gYexzyPERn0gU/YnKYzdxm1YGqPaVbF/JdzHxbCgoKdu7cOXHiRDMzs507dyJfr9bWVjk5OeRTQIA2hyGX67b0ojdoFotFeNa1C4/HE3Ra6SQtLS2ZmZkQQg6Hg/zZOgmPx8vJyUlKSuJwOJ99g24XPp+flpb2pWd1zKtXr/79999PXU5wC3O7NDU1ta1EU+cdnJWbm9vxXcRms7OyspKSkr7iDfqzjfcmOBz455/QzAyOHg3j42F7oyoiPzdB0l+lK+5UHH58+Nb0rblVuZ8/oev5XqPUVUljORxOXV2dtbX1lClT6urq0F40Eom0bNky5J1IICEhERoa2vFSoYjDYDC8vLzU1dWVlJR27NjRrszWrVsVFRXV1NS8vb0bGxtRpbOzs9JHaJ94Z9m3bx+KJtHY2Ci4gfqzzJgxY8WKFbGxsVwu9ws79AESiXTgwIEOtrF/BRcvXty7d2/b+n379ikrK2toaLi5ub1//76twP37901MTLS1tQ0MDG7fvo0qjx49SqFQ1NTUBg4cOHPmTGJgBamoqPDz8+tgezKXyx02bNjOnTtv3LiRkpJy5syZL+rRgwcPNm7c+EWniC4SEsDfH/z7LwgNBWFhwM4OnD3bDYFMv5FxeuPehrzd7b6byWHOvjRbbbea/2X/2H9jGznt3A+9jO9i5ruOXvEGvWrVKi8vLxR6iUKhoBdeQTIyMlRVVcvKypD7CXIThxBSqdRLly4hz7R2e8FisdTV1ZEDMTJbnVSJy+WKi4ujmBKwE3PQn+Lu3bsODg5fceKn+P333wmnQYL8/PxBgwYVFha2trbOmjVryZIlQgI8Hs/ExOTIkSMQwvPnz2tpaSHX06KiIjR1/v79+5EjR65du7btFZctWxYeHt6BSvn5+bq6uhDC5uZmwnGx87DZbHV19Q5CmvRW+HwYHw+HD4dWVvDPP+FHX98e/7l9lqJ3RbszdzufcZ78n8k9pUM3uXr3OF1toM+cOTNq1Ch9fX0nJ6f09HRUefLkyfDw8IULF+rr69++fbuuri4wMNDCwsLBwSE6OrptIyoqKqkfo9IEBQUJxeWBEC5cuHDFihWonJSUpKGhgcpUKvXmzZsdqHfx4kVXV1dURgY6JibGxsZmyJAhZ8+eJcSOHz8+YsQIc3Pz5cuXM5lMJpPp7OwMAHBxcQkKCoIQPnz40N3d3djYePLkyc+ePSO0io6OdnJysrCwgBDm5uZ6enoaGxt7enoWFBQgGT6fr62tnZeXJ6RYVlaWh4eHoaEhlUolPOAfPnz4448/Hjt2zNTU1MHBAe0CQpr7+voaGRn5+PiEhIS0NdCrV6/29/dH5ZycHFlZWSHX/9u3bysrK6P5Bz6fr6OjgwJ0CDUyb948oUoul6ugoEC4nl++fNnR0VFfX3/UqFHXrl2DEObn5zs4OMjIyNBotF9++cXU1FRDQ4NGo6GHRFNT06pVq6ytrYcOHYqc8iGEJ06c2LNnD7o97t69CyGcP39+x8+A3s3t25BGgwYGMCICsliib6AJhJzIX9S+uFV6i8PjdMOlsYH+L98yFjdv3iwpKWGxWAkJCerq6nV1dRDCdevWycrKxsXF1dTUMBgMR0fHtWvXNjQ0PH361NDQ8Pbt24ItIO9B4gXq8OHDNBpN6CqOjo7Hjh1DZRQ1n8lkQgipVCqFQlFUVBw7dmzb924I4dKlS5H/C/xooGfOnFlXV/fgwQMKhYKsw4kTJxwcHIqKihoaGubPnx8YGMjn85FfRlVVFYPBqK2tVVZWPn78OIPB2Lt3r7a2NprqtbKysrOze/LkSUVFRUVFBYo01NTUFBMTo6enR0wHT5s2bf/+/UKK5ebmPnr0iMViFRQUmJiYoKnqxMREaWnpnTt3vnv37vTp0xoaGmhSHm0ubmhoSEtLU1RUbGugp06dSgQYYbFYAACh2EanT58eOXIkceju7o4i4aHxjI6O3rx5s6Wl5ZMnT4Ravn//vpaWFnGYnp5eVFTEYrHu3LmjpqZWVlbG5XKTkpIMDQ1ra2vpdPrWrVtnzpxZW1uL/nz88MMPS5Ysef/+/cuXLwcPHhwXFwchXLt2rZyc3KVLl9DtASE8duyYl5dX26+vT3H7NvTygrq67F27YHsrAaJPSknK8OPDFXYqTL0wNTIn8nXD68+f87WI+hx0b8HNza2xsTEhIQGlSX/8+DGqHz9+vI+PD4VCKSsrKygoCA4O5vF4mpqac+bMiY2NFWwBxaMgcoDJysrW1tYKXaWhoYHYFIkKyKxHRESgaGoeHh6enp50Ol3oxNLSUi0tLcGaLVu2KCgo2NvbI8c2AMDBgwdDQkIoFAqPxwsODo6NjSWRSAoKCgAARUVFOTm5a9eumZqazp49W05ObsWKFbKyssnJyai1X375xdbWVktL68yZM+7u7iNHjmxpaXFzc1NVVc3OzkYy2traQruqAQB2dnZKSkpJSUkFBQWWlpaEQz+FQgkNDVVWVp43b15jY2NlZSWDwbh27drvv/8uLy/v6Og4bdq0tt9CfX09MT7S0tLi4uJ1dXVCAyiYZU1OTo4QePv2bVpaWkZGhqysLPIeFBpA5DuOGDduHJ/Pv3HjRmVlpa6ubmZmpri4uJycnJiYmKKiory8/IABA6SkpFC5pqbmypUrYWFhJBIJPVeIr97Dw2Pq1KkUCgVFS9fS0mo7RH2NMWPAtWsgPp786BEwMgJhYaChoad1+jJcDFyyF2UXBxVPs5yW9irN7pjd4KODQ5ND778RDr0gOvRfrwTE3Llzi4qKXFxcBg0axOPxiOUpXV1dVCgrK2tpaZkxYwZxyrhx4wRbQLvTGAwGYXnV1NSErqKiotLw8W6ur68nkUjoLCIxzbp166Kjo9PS0oTcskkkYU8iQjE9Pb2EhAQAQHl5+Z49e44dO4bq7ezsOByO4CmVlZWCTkAGBgZE7iuitfLy8szMzB9++AEdDho0iPB0ghC2dTSPjIz8/fffJ0+erKqqymQyiWeSYN8HDhzY1NTU1NQkJyeHHhhI7fI2HsYUCoUYHyaT2draKrSSrKKiwmAwiMO6ujpCYPDgwajvmzZtWrZsGfHsQQgNYEhISGJiooeHh5KSUktLS7urkQTl5eUkEsnf35+osbGxQQVi3AQv1EFTfYfBg9mRkRLl5WDXLmBoCBYvBiEhQEmpp9X6AlRkVPxs/Pxs/PiQ/6DywfXi69kV2cO0hvW0Xu3Trw30u3fvLl682NDQICkpCSE8evQo8RHh4aOrqyshIXH9+nUUcLUtMjIy+vr6ubm5KEjbo0ePLCwshGQsLS1zc3NROTc319jYWChoA4SQw+G09eLT09OrqqoSrCkrKzM1NQUAvHr1Cr1c6+johISECAUXFNy5oampGR8fTxwKvlQSZkVHR2fkyJHolVyIysrKthnO9u3bd/r06bFjxwIACgsL255FoKGh0djYWFdXp6ioiNRu6z0lND4UCkUoloClpeW///7LZrOlpaV5PF5eXt7mzZvbNtI2b5auri4RRaS1tXX//v2vX79GjbebZEtMTIx4Muno6PB4vL/++qvtFiOhLlRWVrYbvLDPYmUFzp4FpaUgIgKYmQFfXxAaCj4GKewtkEnk4VrDh2sNF6q/8uzKvTf3JhhPGKUzSpzcwxayX09xDBgwgEQi3b17t7m5efv27e3GA7K2th48ePCPP/5YVVXV2NiYnp6elZUlJBMQELB58+bS0tLk5OS4uLhFixYBAGpqalxdXdE72uLFiy9evJiSklJaWrp169aAgAAAQGVl5alTp4qKioqLi1etWsVgMNDKniBOTk737t0TrNm4cSOagz516tScOXMAAMuXL1+7di2KT//8+fPo6GihRiZNmvT8+fNz584xGIyIiAgmk+nq6iokM2/evISEhKioKCaTSafTz507R8wh3L9/38nJSUheSUkpIyODzWb//fffV65c6WCQ5eXlJ0+eHBISwmAw0tLS2jWL8+fPT0pKio+PLy8v37hx4+LFi5EFDA4ORt2xs7OztLRcv349nU7fsmWLurr66NGjAQDHjx9/8ODB69evU1NTt23b1jaX8dChQxkMBvpmxcTE5OXlU1NTWSzW0aNHieksQfT19R8/fozm5dXU1KZNm7Zw4cKysrLm5uasrKxbt26128d79+45Ojp2MAh9EwMDsG8fyMkBAAArKxAQAF6/7mmdvgNmKmYSZIlVN1ep/qE6PXb6qUenqphVnz+ti/guM9ldR1cvEsbGxg4ePNjMzGzLli3BwcEZGRkQwpMnTxI7EyCE9fX1K1euHDJkiJmZmbe398OHD4Ua4XK5ISEhRH4TVFldXe3q6krku4qJiRk6dKipqenatWvRbgQ6ne7t7W1mZmZubj5jxgxi44Qgzc3NampqaCcZg8Gg0WgXL160tbW1s7OLiooixM6dO+fo6GhgYDB69OhDhw5BCFksFo1GQ7kIIIQ5OTkeHh6mpqZTpkwR3MUhuD0jPz9/2rRppqamNjY2AQEBjY2NEMLs7Gw7O7u2ij158sTR0dHQ0HDu3LkHDx5ES3ZoFwchM336dLTW9/79ez8/P2Nj42nTph0+fHjPnj1tG/znn3+GDRtmbGy8fPlywp0nODj4/PnzqPzq1Stvb28DAwNPT8/i4mJUuWvXLgcHBwMDgxEjRuzevZvoryBLly6NiIhA5Zs3b9rb2xsbGwcHB2/ZsgUlHygsLERb65qbm1taWjZt2jRx4kS0i6O5uXnDhg1UKtXExGTChAloLfTEiRPEki+EkMPhaGhoiESyq26h/Z9bTQ3ctAlSKHD6k25TAAAZhElEQVTOHFhU1O1KdQl0Jv3M4zMzYmco71K2O2oXnd/ODq5P8b0WCdsJliRSKCgolJWVdezGgtb3uk2lbmbHjh0cDudboi2DTgRL+hSzZs2aOXNm2zfTXsTr168nTpyYm5v72ThQXxcs6fTp0w8fPjx06NA36Nib6OjnxmCAI0fA7t3A2RmEhYHPJUzoLfAgL7siW1JM0kHTgajkQz6Z9MkZiO9llLCBFnXYbPbjx49HjBjxLY18tYFOT0/vA3/ec3NzTUxMPnuTfJ2Bfvz4sb6+PrEK2uf5/M+NyQQnT4I//gB2dmDDBjBMRNffvpHVSatvld6aYDzB08RzuPZwoSQD2ED/l75toL8LX22g+xX9Ldzo19HZn1tLC/jzT7BtG9DVBVu2ABeXrletW+FB3mP64+SS5GtF1/Kq8xz1HSeZTppoOlFLTgtgAy0INtCfBRvozoANdGf4sp8bhwMuXADbtwMKBYSGgkmTulK1HqOKWZVQnJDwIiGlJGWL85Zlw5ZhA/1fsIH+LNhAdwZsoDvD1/zcWlvBhQtgxw4gKwtWrQLTpoE+Osit/NZmbrO8lPz3Mkr9epsdBoPpDsTFwezZID8frFsHDh4ExsYgIgK0F3qwtyNOFpeXkv+ODWIDjcFgugUyGXh7g4wMcOkSyM0F+vrg559FJHGtyIINNAaD6V5QmOmnT4GiInBwAD/8AP7XGwtDgA00BoPpCTQ0QFgYePECjB4NfvgBjBkDYmNFPzlAN4MNNAaD6Tnk5MDPP4PSUhAaCvbsAebmYN8+0Nzc02qJCthAYzCYnoZMBpMmgawscPo0uHsXGBmBNWtAe7Fx+hvYQGMwGJFhzBhw8SK4fRuwWMDa+kOCxH4MNtAYDEbEMDYG+/aBoiJgZQXc3YGbG7h2rad16hmwgcZgMCIJcj58+RLMmQPWrv2w9+NrU9T3UrCBxmAwIoyUFPD3B/n54I8/QGws0NMDYWHgfzOi9WGwgcZgMCIPiQRoNHDtGrh+HZSUAGNj8PPPoKysp9XqcrCBxmAwvYchQ8DZs6CgACgqAnt7MGkS+JjduE+CDTQGg+ltqKuDsDBQUgJoNDBzZh92csEGGoPB9E6Qk0tJCQgNBXv3AjOzvufkgg00BoPpzSAnl8xMcObMf51c3rzpabW+D9hAYzCYPgFycrlzB7BYwMYG+PuDgoKe1ulbwQYag8H0IYyMwL594PlzYGICaDTg5QVSUoBopyXpAGygMRhMn0NFBWzYAF69AlOmgBUrgLU1OHy4N6YIwAYag8H0UaSkwKJFID8fnD0LsrOBvj4ICAD5+T2t1heADTQGg+nrUKkfdk8bGgJPzw/b8lpbe1qtz4MNNAaD6R+oq38I7vHzzyAyEujpif5+D2ygMRhMf0JSEkyfDpKSQErKh/0eP/wAkpN7Wq32wQYag8H0S1D2llevAI0Gli8HVCqIjBQ1PxdsoDEYTD9GXh4sWQKePgW7doHkZKCr+yEFl2iADTQGg+n3kMmARgMXL4L798GAAWDECODmJgrxPbCBxmAwmI8YGoKdO0F5OViyBOzdC8zNwa5d4P37nlIHG2gMBoP5X6SkwPTpIDMTXLjwIfy0vz948qT7FcEGGoPBYD4BlQqOHfuQHdHbG9jbd3PaLWygMRgMpkNUVUFoKCgpAZs2gagooKsL1qwBr193w5WxgcZgMJhOgOKaJiWB9HQAALCzA5MmgeTkLo3EhA00BoPBfAmmpmDnTlBaCiZNAitXAgsLsG8fYDK74lLYQGMwGMyXIycHliwB+fngxAlw9y7Q0wMBAd89AjU20BgMBvMNoEQB+flAQwO4un7YQP2dIjFhA43BYDDfjKYmCAsDZWXA3x+Eh0seOPBdWhX/Lq1gMBgMBkhJgTlzwJw5nPp6qe/RXq9/g25paamsrOxpLUQdBoPxvue8oXoL7969a+yFSTe6mTdv3nA4nJ7WQqTh8/nl3ymKaa830Hl5eb6+vj2thahz/vz5bdu29bQWos6vv/566dKlntZC1Jk6deqLFy96WguRprm5efjw4d+lqV5voDEYDKavgg00BoPBiCjYQGMwGIyIQoJd6af47bi4uDx69IhEIn1KgM/nt7S0DBgwoDu16nW0trby+XxJScmeVkSk4XA4ZDJZXBxvbeoIFoslJSVFJuN3u45obm6WkZHpWCY9Pd3GxqZjGVE30Gw2m8Vi9bQWGAwG852Rl5cXExPrWEbUDTQGg8H0W/D/FAwGgxFRsIHGYDAYEQUbaAwGgxFRRHTB+vXr11FRUWw2e9q0aYMHD24r0NLScubMmdLS0mHDhvn4+BD1BQUFFy9elJCQmD17tr6+Pqrk8Xjnzp0rLCy0trb29fXtMwvQL168iI6OhhDOmjXL1NS0rUBTU9OpU6cqKysdHR09PDxQZXFxcWJiYmVlpaGh4axZswYOHAgAYLFYUVFRxIlUKpVKpXZPL7qahISEjIwMTU3NBQsWoM4KUVRUdOHCBRKJ5Ovra2xsjCpPnTrV+jEgmampqZOTEyqXlZVFRUVxudzp06dbW1t3Sw+6HAjhpUuXHjx4YGhoOG/evHZ3+zx+/PjSpUvS0tL+/v7a2toAgBcvXty6dUtQZurUqRQKJScnJycnh6j09/eXlpbu6i50Aw0NDadOnXr79q2bm5uzs3Nbgdra2pycnNLSUhqNZmhoSNRXVFScPXuWzWb7+PgMGTKEqL927VpmZqa2tvaCBQs+tQ9NFE0VnU63t7en0+mSkpLjxo3LyspqK/N///d/MTEx6urq69ev37RpE6p89OjRqFGjSCRSfX29vb19eXk5ql+8ePHhw4c1NDQiIiKWLVvWfT3pSoqLix0cHFgsFpfLHTZsWGFhoZAAhJBGo928eVNVVTUwMPDgwYOofsyYMfn5+YqKijExMfb29ij6RGNj49KlS3M+0mfCm+zfv3/p0qWqqqo3b950c3NruyT+77//Dhs2jMvlslgsBwcHwok5KCgoIyMDjcarV69QZUVFBZVKra2tJZPJo0ePFjRDvZqNGzdu2LBBXV39woUL06dPbyuQmZnp6OgoKSmJfpvV1dXgoz1CXLlyZfny5ejV59q1awcOHCA+4nZjBr+ug8vljhkzJisrS1lZ2c/PT/BthoBKpa5bty4kJOThw4dEZXV1tb29fWVlpZSUlJOT0927d1H9H3/8sXLlSlVV1b///tvT0/OTF4aiR1hYmI+PDypv3bp1ypQpQgKPHj2Sl5dnMpkQwry8PHl5eQaDASH09fVds2YNkpkzZ05oaCiEsKysTEpKqrq6GkL45s0baWnpqqqqbutL1/HTTz8FBgai8vLly5csWSIkkJiYqKOjw+FwIITJyclaWlpcLhdC2NTUhAS4XK6Ojk5cXByEsLq6WlxcvPu07xa4XK6mpmZycjKEkMPh6OjoJCUlCcksWrRo+fLlqBwYGBgUFITKMjIyr1+/FhJeu3atr68vKq9fv37mzJldqH13wWAw5OTk8vLyIIRMJlNeXv7JkydCMt7e3tu2bUNlHx+fLVu2CAmsWLFixowZqLxp06aff/65i7XubmJiYqysrHg8HoQwLi7O3Nycz+cLyaDfl62tbUxMDFG5ZcsWb29vVP7tt98mT54MIWSz2RQK5c6dOxDClpYWNTW127dvt3tdUXyDTktLc3d3R+Xx48enpqa2FRgzZgz6u2pjYyMrK4veZVJTU8ePHy90YkZGhq2traqqKgBAU1PTzMzszp073daXriM1NfWzo+Ti4iIhIQEAcHJyevfu3fPnzwEAxP55MpnM5XKJQwjh4cOHIyMjnz171k196GKKiopqa2vR7ISEhISLi0vbURK8Z9zd3QUFzp07d+jQodzcXEHhjse8N5KTkyMnJ4c8JgYOHDh69OjPjlJaWprgpxwO5/z58wsWLCBqnj17Fh4eHhsb22ecGNLS0mg0GvqLMH78+GfPnlVVVQnJtOvilJaW1tYoFRQUtLS0jBo1CgAgKSnp5OT0qXtJFA00nU5H9hQAoKam1tDQ0NTUJChQVVVFCCCZyspKHo9XU1NDoVAEK9sKq6qq9o3/73Q6vW1nBRHsuJiYmIqKipDMb7/9pqGhQaPRAABkMtnZ2bmkpCQzM9PBweHUqVNd34Mup6qqSklJifAFUFNTa/uj+tQwjhgxorq6Oj8/39XVdfPmzYSw4J359u3bPvD/ve2vSWiUmpqaGAxGBzfb1atXpaSkXF1d0aGysrK6unpVVdXevXutra1ramq6uAfdgeAoDRw4UFZWtpNmROieaWxsbGxsrKqqUlFRIRyk270zEaK4SCgmJsbj8VC5tbWVRCIJ+duIi4vz+XzikMvlSkhIkMlkMTExor61tRW9PAoJE/W9nXY7K0i7o0Qcnjt37tixY2lpaeixr6KikpSUhD7y9vaeN2/e3LlzP+vmJOJ05qv/1DCmpKSgQkBAgIODQ2BgoJqamtCdSSaT+8CC82dHSVxcnEQidXCznTp1auHChcTdEhQUhAoQQnd39/Dw8F27dnVhB7oFcXFx4qsHX2JGhO4Z1FTnjZIo3l6amprE06myslJZWVloFVhTU/PNx3jYEEI6na6pqUkikdTV1QVP1NTUBABoaWm9EQieTdT3doRGqW2nBEeJzWbX1dURMrGxsaGhoYmJiUZGRm1bHjt2LIPB6AMvPpqamu/fv2ez2ejwU6PU8TDa2dnJyMiUlpa2FVZXV+/tzzDwsVPw4/JpZWWlhoaGoICUlJSSkhJxLwmNUkVFRXJy8pw5c9q2TCKRxo4d+/Llyy7TvfsQ/Opra2vZbHYnzYjQPaOkpDRgwABNTc2amhri71cHRkkUDbSXl9elS5fQHRMXF+fl5YXqc3Jy3r59CwCYMGFCZmYmnU4HAKB3QAcHBwDApEmT4uLikDBxoqura3FxcVFREQDg6dOnb968aXeLTK+j3c4CALKzs+vr6wEAEydOTE5OZjAYAID4+HgjIyO0h+zy5cs//fTTtWvXLC0tidYEc2QkJCQoKSmpq6t3W1+6CBMTE0NDw/j4eAAAg8FITk6eOHEiAKC+vj47OxvJTJo06a+//kJlYhgFR+P27dtsNhsNnZeXV7tj3qtxcHAgk8np6ekAADqdnp2djTYV1NTUENtUiI5DCC9duiTY8dOnTzs5OQk+6YnRa21tTUxMFLzNei9eXl7Xr19HU+pxcXHDhw9Hcz6FhYVlZWUdn9jWmllZWamqqiYkJAAA6urqUlNT0Z3ZDt9zpfM70dDQYG1t7ebm5ufnp6qq+uzZM1RvZGQUFRWFysuWLTMxMQkICFBTUztx4gSqLCkp0dDQmDFjhqenp6mp6fv371H95s2bdXV1AwICdHR0du3a1f096grodLq+vr63t7ePj4+uru6bN29QvZycXGJiIirPmDHD1tZ28eLFKioq6C5hsViSkpJGRka0j8TGxkIIw8PDhw8fPm/evIkTJ8rLy6PKPkBcXJyysvLixYttbW1nzZqFKhMSEuTl5VG5oqJCW1vbx8fH29vbwMCATqdDCP/66y9ra2t/f/9p06bJyspGREQg4draWjMzswkTJsycOVNdXf3ly5c90qnvTmRkpJqaWkBAgImJCbGn5c8//zQ2NkblwsJCCoXi5+fn5uZmY2ODNk1BCPl8vpGREdqMT2Brazt58uT58+ebmZnZ2dnV1dV1Z1+6CD6f7+HhYW9vv3DhQmVlZWI7kJeX17p161A5JCSERqPJysra2NjQaDS0GYbBYKDD2bNnUyiUwsJCJBwdHU2hUJYsWWJpaTlv3rxPXVdEgyWxWKzExEQWi+Xu7q6srIwqc3JydHV1icWKO3fuvHjxwsHBwcrKijixrq4uMTFRUlJy/Pjxgl4JDx48KCgosLW1HTp0aHd2pEtpbGy8ceMGhNDDw0NeXh5VZmdnm5ubKygoAAAghKmpqeXl5WPGjEHvgDweT2i92MTERE9Pr7m5+d69e+Xl5QoKCiNHjhRcNertvHjx4s6dO3p6ek5OTmhZpr6+/tmzZyNGjEACDQ0NiYmJZDJ5/PjxcnJyAAAOh/Pw4cOXL1/KyMg4ODjo6uoSrTU1NSUmJnI4nPHjxysqKvZIj7qCp0+fPnz40MTEZPTo0aimpqbm9evXhL/Su3fvbt68KSMjM378eMKrgs1m37lzZ+zYsVJS/02RWl5enpOT09DQYGhoOGbMmD4wTY/g8XgpKSlVVVVOTk56enqosrCwUEZGBh3m5ubW1tYS8lQqFd0hyJo1Nze7u7urqKgQAkVFRVlZWQYGBuPGjftURGURNdAYDAaD6SMPNwwGg+l7YAONwWAwIgo20BgMBiOiYAONwWAwIgo20BgMBiOiYAONwWAwIgo20Jie4cyZM4aGhsjD87MUFRVFRkb2SGg0JpMZGRlZXFz8fZuNiYlBznsYTAdgA43pGRoaGkpLSwWdqjvgzp07AQEBKLdAN1NXVxcQEHDv3r3v2+yGDRvOnj37fdvE9D2wgcaIFjwe7+3bt533n2ptbaXT6UIBaQXhcDh0Op3IXwUAqK+vRzlB2sJms6urq78liOjbt28FHyRsNvvdu3df1AKTyaTT6YKx0zD9l67zXsdgOiAiIgIAgBJ5QAgnTZrk4+MTFRWlpqYGAJCVlf3111/RR/v27UNZBRQUFBQVFRUVFVFOnJaWluDgYOTUTiaTXVxciOAYfD5fUVHx999/DwoKQn7J9+7dgxDGx8cTsXt0dHQEg0jcu3fP3t4efUQmk1Fqq7y8vEGDBgEABg4ciC4tFHcCERISYmFhcfPmTRQzaPbs2aiDJiYmqMFBgwYtWrQI5QCCEBoaGpLJZCkpKdQmkRnn3r17KGcbAEBJSWnXrl1t03Zg+hWiGA8a0w9hMpkFBQUvX76MjIxUV1ePjIzcvn27s7Ozq6urt7d3eXl5eHj48ePHkTlGIQ5mz5598+bN7du3Ozo6VlZWrl69mkaj5efnoxgsdXV1u3fvHjp0aFxcnLS0tK6u7tWrV318fGbPnn3ixAlpaemTJ0/6+fkpKChMmDCBx+N5e3tbW1tnZWVRKJTKysobN27w+Xx9ff0jR474+voGBQWhgPTtxmZjsVjl5eWLFi1at26dra0tqnz79m1oaKitra2MjExqauqaNWvIZPKxY8cAAKdOnfLz87OxsVm1ahUAAIX3zMvLc3Z2HjVqVHJysqqqanx8/K+//iohIbFy5cpu+g4wIkhPPyEw/RShN2hnZ2dpaeny8nJ0yOFwVFVVf/rpJ3R44sQJAABKLInIzMwEABDRDSGEJSUlYmJip0+fhhCiaOhGRkYoTRzCxMSERqMJ6uDo6Oji4gIhRGlhr1692lZPlHpY8EJtQSHq2z2dYOvWrbKyssQbsYmJyYIFCwQFJk+ebGRkxGKxiJqlS5dqaGh00Camz4PfoDGigrW1tY6ODipLSEgYGxu/fv36U8KJiYkAADk5ueTkZKJSRUXl6dOnxKGXlxeRJq6kpKS4uNjd3V1QXkdHB7WjoaGhrq7+yy+/vHjxYsqUKYaGhl+qvLi4eNvczOnp6enp6WhCubS0lMlkVldXtxtom8/nJycnu7q6CibMRJM57969EwyBhulXYAONERWUlJQED6WkpDrY41FdXU0ikebPny9U39DQQJTRdDYhDwD4888/o6OjhU5pbW2VlJRMSEgIDQ1ds2bNqlWrTExMgoODlyxZ0nnlVVRUBHOGQgh9fHyuX7/u5uamr68vLy+Pck00NDS0a6AbGxubm5uTk5OFMhorKirW1NRgA91vwQYa0ysZNGgQiUQqKytDEZzbRTDGLoqX/ccffwQGBrYrPGTIkMTExMbGxoyMjGPHjqFcEN7e3p3URyjqcU5OzpUrVy5cuDBjxgxUc/To0cuXL3/qdBkZGXFx8cWLF+/bt6+TV8T0B/A2O0wvAFlhQUcVR0dHPp9PJKD6LObm5mpqaihTTMcXmjhx4uXLl6WkpLKysgAAsrKyAAAisWEnQZPaRLR7AMD169cFBWRlZQW7IyEhMWrUqL///rtHnHEwIgs20JhegKWlJYlEioiIyMzMzMnJ4XK5Hh4eTk5OK1asOHHiRHV1dWNj4+PHjzdu3Cg0RUAgJia2ffv2W7duLVy4sKioiMVilZaWRkVF7dy5EwCQl5cXGhr66NGj5ubmpqam48ePt7S0IPOqqKioqakZFRWVlpaWk5Pz/v37zihsY2NDJpN37NhRX19fU1OzYcMGoUQ2VlZWqamp8fHxOTk5yJpv3bq1vLx8ypQpOTk5LBaroqLi6tWraJsHpv/S06uUmH5K210c7u7uggLOzs4eHh7E4c6dO3V0dFAWbZSAsaGhYf78+YL56ocMGfLo0SP4cRfHjh07hC568uRJwSlgCoUSHh4OISwoKDAwMCDqZWVlN27cSJyVmJhoa2uLUsujXSJCBAUFaWpqClWGh4cTiaBGjhx56NAhAACRYLOkpIRGo6GJF39/f1R548YNMzMzQg05Oblly5Z96cBi+hI45RWmZ4AQ8vl8ZHC/BSaTWVRUJCYmpq2t3ZnFND6fX1RU1NjYqKampq2tLagAnU6vqKgYMGCAgYEBco35Rurr61++fKmoqPhF20Jevnz5/v17RUVFPT09SUnJb1cD03vBBhqDwWBEFDwHjcFgMCIKNtAYDAYjomADjcFgMCIKNtAYDAYjomADjcFgMCIKNtAYDAYjomADjcFgMCLK/wO2AjPROIje6QAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "θ_range = 0:0.01:0.1 #possible ytm values\n", "\n", "#(PL,PH) = (fill(NaN,length(θ_range)),fill(NaN,length(θ_range)))\n", "#for i = 1:length(θ_range)\n", "# PL[i] = BondPrice3(θ_range[i],cf,m)\n", "# PH[i] = BondPrice3(θ_range[i],1,mH)\n", "#end\n", "PL = [BondPrice3(θ_range[i],cf,m) for i=1:length(θ_range)] #an alternative to a loop\n", "PH = [BondPrice3(θ_range[i],1,mH) for i=1:length(θ_range)]\n", "\n", "txt = \"In our example, the interest rates \\nare $θ₀ (before) and $θ₁ (after)\"\n", "\n", "p1 = plot( θ_range,[PL v*PH],\n", " linecolor = [:red :green],\n", " linestyle = [:solid :dash],\n", " label = [\"liability\" \"v * hedge bond price\"],\n", " title = \"Naive hedging\",\n", " xlabel = \"Interest rate\",\n", " ylabel = \"Value of position\",\n", " annotation = (0.005,1.4,text(txt,8,:left)) )\n", "display(p1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Duration Hedging\n", "\n", "With $D_{L}^{M}\\neq D_{H}$, we could *adjust the hedge ratio* $v$ to compensate for the difference in interest rate sensitivity (duration). In particular, we could set \n", "\n", "$v =\\frac{D_{L}^{M}}{D_{H}^{M}}\\times\\frac{P_{L}}{P_{H}}$. \n", "\n", "The balance $(P_L-v P_H)$ is kept on a money market account ($M$).\n", "\n", "It can be shown that this gives an (approximate) hedge." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mHedge bond: zero coupon bond with m=3 and face value of 1\u001b[22m\u001b[39m\n", "\n", "PL₀ 1.544\n", "PH₀ 0.864\n", "v 3.039\n", "v*PH₀/PL₀ 1.700\n", "Dur(liability) 5.099\n", "Dur(hedge) 3.000\n", "M -1.081\n", "Return -0.004\n", "\n", "\u001b[31m\u001b[1mNotice, the duration hedging gives a return of -0.4%. Close to zero.\u001b[22m\u001b[39m\n" ] } ], "source": [ "PH₀ = BondPrice3(θ₀,1,mH)\n", "PH₁ = BondPrice3(θ₁,1,mH)\n", "v = Dmac/mH * PL₀/PH₀\n", "M = PL₀ - v*PH₀ #on money market account\n", "\n", "ΔV = v*(PH₁-PH₀) - (PL₁-PL₀)\n", "R = ΔV/PL₀ #relative value change\n", "\n", "printblue(\"Hedge bond: zero coupon bond with m=$mH and face value of 1\\n\")\n", "xy = [PL₀,PH₀,v,v*PH₀/PL₀,Dmac,mH,M,R]\n", "printmat(xy,rowNames=[\"PL₀\",\"PH₀\",\"v\",\"v*PH₀/PL₀\",\"Dur(liability)\",\"Dur(hedge)\",\"M\",\"Return\"])\n", "\n", "printred(\"Notice, the duration hedging gives a return of $(round(R*100,digits=1))%. Close to zero.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convexity (extra)\n", "\n", "A second-order Taylor approximation gives that \n", "\n", "$\n", "\\frac{\\Delta P}{P}\\approx-D^{M}\\times\\frac{\\Delta\\theta}{1+\\theta}+\\frac{1}{2}C\\times(\\Delta\\theta)^{2}, \n", "$\n", "\n", "where \n", "$\n", "C = \\frac{1}{P} \\frac{d^{2}P}{d\\theta^{2}}\n", "$. \n", "\n", "The function below calculates $C$." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BondConvexity (generic function with 1 method)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function BondConvexity(P,cf,m,ytm)\n", " cdisc = cf.*m.*(1.0.+m)./((1+ytm).^(m.+2)) \n", " C = sum(cdisc)/P\n", " return C\n", "end" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mConvexity\u001b[22m\u001b[39m\n", "C 35.602\n", "Δθ -0.020\n", "0.5*C*Δθ^2 0.007\n", "\n", "\u001b[31m\u001b[1mCompare the magnitude to ΔPH/PH: \u001b[22m\u001b[39m\u001b[31m\u001b[1m0.059\u001b[22m\u001b[39m\u001b[31m\u001b[1m. It seems convexity is not important in this case.\u001b[22m\u001b[39m\n" ] } ], "source": [ "C = BondConvexity(PL₀,cf,m,θ₀)\n", "\n", "printblue(\"Convexity\")\n", "xy = [C;Δθ;0.5*C*Δθ^2]\n", "printmat(xy,rowNames=[\"C\";\"Δθ\";\"0.5*C*Δθ^2\"])\n", "\n", "printred(\"Compare the magnitude to ΔPH/PH: \",round((PH₁-PH₀)/PH₀,digits=3),\". It seems convexity is not important in this case.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "anaconda-cloud": {}, "kernelspec": { "display_name": "Julia 1.7.2", "language": "julia", "name": "julia-1.7" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.7.2" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
ES-DOC/esdoc-jupyterhub
notebooks/bnu/cmip6/models/sandbox-3/land.ipynb
1
173496
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "**MIP Era**: CMIP6 \n", "**Institute**: BNU \n", "**Source ID**: SANDBOX-3 \n", "**Topic**: Land \n", "**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "**Properties**: 154 (96 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:53:41" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-3', 'land')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Conservation Properties](#2.-Key-Properties---&gt;-Conservation-Properties) \n", "[3. Key Properties --&gt; Timestepping Framework](#3.-Key-Properties---&gt;-Timestepping-Framework) \n", "[4. Key Properties --&gt; Software Properties](#4.-Key-Properties---&gt;-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --&gt; Horizontal](#6.-Grid---&gt;-Horizontal) \n", "[7. Grid --&gt; Vertical](#7.-Grid---&gt;-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --&gt; Soil Map](#9.-Soil---&gt;-Soil-Map) \n", "[10. Soil --&gt; Snow Free Albedo](#10.-Soil---&gt;-Snow-Free-Albedo) \n", "[11. Soil --&gt; Hydrology](#11.-Soil---&gt;-Hydrology) \n", "[12. Soil --&gt; Hydrology --&gt; Freezing](#12.-Soil---&gt;-Hydrology---&gt;-Freezing) \n", "[13. Soil --&gt; Hydrology --&gt; Drainage](#13.-Soil---&gt;-Hydrology---&gt;-Drainage) \n", "[14. Soil --&gt; Heat Treatment](#14.-Soil---&gt;-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --&gt; Snow Albedo](#16.-Snow---&gt;-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --&gt; Vegetation](#20.-Carbon-Cycle---&gt;-Vegetation) \n", "[21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis](#21.-Carbon-Cycle---&gt;-Vegetation---&gt;-Photosynthesis) \n", "[22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration](#22.-Carbon-Cycle---&gt;-Vegetation---&gt;-Autotrophic-Respiration) \n", "[23. Carbon Cycle --&gt; Vegetation --&gt; Allocation](#23.-Carbon-Cycle---&gt;-Vegetation---&gt;-Allocation) \n", "[24. Carbon Cycle --&gt; Vegetation --&gt; Phenology](#24.-Carbon-Cycle---&gt;-Vegetation---&gt;-Phenology) \n", "[25. Carbon Cycle --&gt; Vegetation --&gt; Mortality](#25.-Carbon-Cycle---&gt;-Vegetation---&gt;-Mortality) \n", "[26. Carbon Cycle --&gt; Litter](#26.-Carbon-Cycle---&gt;-Litter) \n", "[27. Carbon Cycle --&gt; Soil](#27.-Carbon-Cycle---&gt;-Soil) \n", "[28. Carbon Cycle --&gt; Permafrost Carbon](#28.-Carbon-Cycle---&gt;-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --&gt; Oceanic Discharge](#31.-River-Routing---&gt;-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --&gt; Method](#33.-Lakes---&gt;-Method) \n", "[34. Lakes --&gt; Wetlands](#34.-Lakes---&gt;-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land surface key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of land surface model code (e.g. MOSES2.2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Fluxes exchanged with the atmopshere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Types of land cover defined in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Conservation Properties \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Water\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestepping Framework \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a time step dependent on the frequency of atmosphere coupling?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overall timestep of land surface model (i.e. time between calls)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestepping Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of time stepping method and associated time step(s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Software Properties \n", "*Software properties of land surface code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Grid \n", "*Land surface grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --&gt; Horizontal \n", "*The horizontal grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the horizontal grid (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the horizontal grid match the atmosphere?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Vertical \n", "*The vertical grid in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the vertical grid in the soil (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Total Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The total depth of the soil (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Soil \n", "*Land surface soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of soil in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Heat Water Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the coupling between heat and water in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the soil scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Soil --&gt; Soil Map \n", "*Key properties of the land surface soil map*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of soil map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Structure\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil structure map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Texture\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil texture map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Organic Matter\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil organic matter map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Albedo\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil albedo map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil water table map, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the soil properties vary continuously with depth?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.8. Soil Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil depth map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Soil --&gt; Snow Free Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow free albedo prognostic?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, describe the dependancies on snow free albedo calculations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe the distinction between direct and diffuse albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, enter the number of wavelength bands used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Soil --&gt; Hydrology \n", "*Key properties of the land surface soil hydrology*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the soil hydrological model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river soil hydrology in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil hydrology tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers that may contain water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe the lateral connectivity between tiles*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The hydrological dynamics scheme in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --&gt; Hydrology --&gt; Freezing \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How many soil layers may contain ground ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of ice storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Permafrost\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of permafrost, if any, within the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Soil --&gt; Hydrology --&gt; Drainage \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General describe how drainage is included in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Different types of runoff represented by the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --&gt; Heat Treatment \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of how heat treatment properties are defined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of soil heat scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil heat treatment tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the method of heat storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe processes included in the treatment of soil heat*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "*Land surface snow*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of snow in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of snow levels used in the land surface scheme/model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow density*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the snow water equivalent*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the heat content of snow*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow temperature*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow liquid water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify cover fractions used in the surface snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Snow related processes in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Snow --&gt; Snow Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of snow-covered land albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "*Land surface vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of vegetation in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of vegetation scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Dynamic Vegetation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there dynamic evolution of vegetation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.4. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vegetation tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Vegetation classification used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of vegetation types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of biome types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How the vegetation fractions in each tile are varying with time*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is vegetation interception of rainwater represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.11. Phenology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.13. Leaf Area Index\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.15. Biomass\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biomass *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biomass*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.17. Biogeography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.19. Stomatal Resistance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify what the vegetation stomatal resistance depends on*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation stomatal resistance*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.21. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the vegetation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Energy Balance \n", "*Land surface energy balance*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of energy balance in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the energy balance tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Number Of Surface Temperatures\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify the formulation method for land surface evaporation, from soil and vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe which processes are included in the energy balance scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "*Land surface carbon cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of carbon cycle in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the carbon cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of carbon cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Anthropogenic Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Describe the treament of the anthropogenic carbon pool*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the carbon scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Carbon Cycle --&gt; Vegetation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of forest stand dyanmics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for maintainence respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for growth respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Carbon Cycle --&gt; Vegetation --&gt; Allocation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the allocation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Allocation Bins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify distinct carbon bins used in allocation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the fractions of allocation are calculated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --&gt; Vegetation --&gt; Phenology \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the phenology scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Carbon Cycle --&gt; Vegetation --&gt; Mortality \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the mortality scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Carbon Cycle --&gt; Litter \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Carbon Cycle --&gt; Soil \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Carbon Cycle --&gt; Permafrost Carbon \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is permafrost included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.2. Emitted Greenhouse Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the GHGs emitted*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the impact of permafrost on soil properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Nitrogen Cycle \n", "*Land surface nitrogen cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the nitrogen cycle in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the notrogen cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of nitrogen cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the nitrogen scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. River Routing \n", "*Land surface river routing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of river routing in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the river routing, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river routing scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Grid Inherited From Land Surface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the grid inherited from land surface?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.5. Grid Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of grid, if not inherited from land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.6. Number Of Reservoirs\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of reservoirs*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *TODO*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is river routing coupled to the atmosphere model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.9. Coupled To Land\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the coupling between land and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of basin flow direction map is being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the representation of flooding, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the river routing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. River Routing --&gt; Oceanic Discharge \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify how rivers are discharged to the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Quantities that are exchanged from river-routing to the ocean model component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "*Land surface lakes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lakes in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Coupling With Rivers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are lakes coupled to the river routing model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of lake scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Quantities Exchanged With Rivers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vertical grid of lakes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the lake scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Lakes --&gt; Method \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is lake ice included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of lake albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which dynamics of lakes are treated? horizontal, vertical, etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a dynamic lake extent scheme included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.5. Endorheic Basins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basins not flowing to ocean included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Lakes --&gt; Wetlands \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of wetlands, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
liuhanfei0615/liupengyuan.github.io
chapter1/homework/computer/笔记+201611680927.md.ipynb
27
2246
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 如何使用jupyter notebook与Github\n", "### jupyter notebook的使用 \n", "\n", "**1. python的安装**<br> \n", "* 进入http://www.python.org/ftp/python/3.6.0/python-3.6.0-amd64-webinstall.exe<br>\n", "- 安装到D:\\python下(安装过程中勾选 Install for all users以及add python to Path选项)<br>\n", "- win+R回车<br>\n", "- 键入power shell 回车<br>\n", "- 键入python 回车<br>\n", " \n", "**2. jupyter notebook的安装**<br>\n", "* 键入win+R回车<br>\n", "* 键入powershell回车<br>\n", "* 键入pip install jupyter,回车<br>\n", "* 键入jupyter notebook,回车<br>\n", "\n", "**3. jupyter notebook的使用**<br>\n", "* 点new<br>\n", "* 点python3<br>\n", "* 点击Untitled页面<br>\n", "* 敲下面代码<br>\n", "```\n", "print('hello world')\n", "print(100)\n", "print(123.7)\n", "```\n", "* 点击执行图标或按CTRL+ENTER编译代码<br>\n", "* 键入h,查看快捷键帮助<br>\n", "* 点击help下面的markdown中的基本语法<br>\n", "* 回到notebook,进入Markdown模式,输入一些基本语法例子<br>\n", "\n", "### Github的使用\n", "* 注册一个github账户<br>\n", "* 点击头像进入your profile<br>\n", "* Create a new repository<br>\n", "* 尝试操作<br>\n", " * fork一个项目<br>\n", " * 提请pull request<br>\n", " * upload files<br>\n", " ![github](http://wanglu.info/wp-content/uploads/2015/04/github.png)\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
DS-100/sp17-materials
sp17/disc/disc11/disc11_solution.ipynb
1
17308
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discussion 11: Logistic Regression and Gradient Descent" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from matplotlib import patches, cm\n", "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", "from mpl_toolkits.mplot3d import Axes3D\n", "%matplotlib inline\n", "\n", "from IPython.display import display, Latex, Markdown\n", "from ipywidgets import interact, interactive, fixed\n", "import ipywidgets as widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Understanding Gradient Descent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to better understand gradient descent, let's implement it to solve a familiar problem - least-squares linear regression. While we are able to find the solution to ordinary least-squares linear regression analytically (recall its value as $\\theta = (X^TX)^{−1}X^TY$), we can also find it using gradient descent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 1:\n", "First, let's consider the gradient function for ordinary least squares regression. Recall the OLS loss function as\n", "\n", "$$Loss(\\theta) = \\frac{1}{n} \\sum_{i=1}^n \\left(y_i - f_\\theta(x_i)\\right)^2$$\n", "\n", "And the function $f_\\theta(x_i)$, for input data with $p$ dimensions, as\n", "\n", "$$f_\\theta(x_i) = \\sum_{j=1}^p \\theta_j x_{i,j} $$\n", "\n", "Given these functions, what is the gradient function for OLS regression? First, state it in terms of a single component of $\\theta$, $\\theta_j$, using a sum over each data point $i$ in $X$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "q1_answer = r\"\"\"\n", "\n", "Put your answer here, replacing this text.\n", "\n", "$$\\frac{\\partial}{\\partial \\theta_j} Loss(\\theta) = \\frac{1}{n} \\sum_{i=1}^n \\dots$$\n", "\n", "\"\"\"\n", "\n", "display(Markdown(q1_answer))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "q1_answer = r\"\"\"\n", "\n", "**SOLUTION:** \n", "\n", "$$\\frac{\\partial}{\\partial \\theta_j} Loss(\\theta) = \\frac{2}{n} \\sum_{i=1}^n -x_{i,j} \\left(y_i - f_\\theta(x_i)\\right)$$\n", "\n", "\"\"\"\n", "\n", "display(Markdown(q1_answer))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 2:\n", "\n", "Now, try to write that formula in terms of the matricies $X$, $y$, and $\\theta$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "q2_answer = r\"\"\"\n", "\n", "Put your answer here, replacing this text.\n", "\n", "$$\\frac{\\partial}{\\partial \\theta} Loss(X) = \\dots$$\n", "\n", "\"\"\"\n", "\n", "display(Markdown(q2_answer))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "q2_answer = r\"\"\"\n", "\n", "**SOLUTION:** \n", "\n", "$$\\frac{\\partial}{\\partial \\theta} Loss(X) = -\\frac{2}{n} X^T (y - X^T \\theta)$$\n", "\n", "\"\"\"\n", "\n", "display(Markdown(q2_answer))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 3:\n", "Using this gradient function, complete the python function below which calculates the gradient for inputs $X$, $y$, and $\\theta$. You should get a gradient of $[7, 48]$ on the simple data below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def linear_regression_grad(X, y, theta):\n", " grad = -2/X.shape[0] * X.T @ (y - X @ theta) #SOLUTION\n", " return grad\n", "\n", "theta = [1, 4]\n", "simple_X = np.vstack([np.ones(10), np.arange(10)]).T \n", "simple_y = np.arange(10) * 3 + 2\n", "linear_regression_grad(simple_X, simple_y, theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 4:\n", "\n", "Before we perform gradient descent, let's visualize the surface we're attempting to descend over. Run the next few cells to plot the loss surface as a function of $\\theta_0$ and $\\theta_1$, for some toy data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_surface_3d(X, Y, Z, angle):\n", " highest_Z = max(Z.reshape(-1,1))\n", " lowest_Z = min(Z.reshape(-1,1))\n", " fig = plt.figure()\n", " ax = fig.gca(projection='3d')\n", " surf = ax.plot_surface(X, Y, Z, \n", " cmap=cm.coolwarm, \n", " linewidth=0, \n", " antialiased=False, \n", " rstride=5, cstride=5)\n", " ax.zaxis.set_major_locator(LinearLocator(5))\n", " ax.zaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", " ax.view_init(45, angle)\n", " fig.colorbar(surf, shrink=0.5, aspect=5)\n", " plt.title(\"Regression Loss Function\")\n", " plt.xlabel(\"Theta_0\")\n", " plt.ylabel(\"Theta_1\")\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create some toy data in two dimensions to perform our regressions on:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(100)\n", "X_1 = np.arange(50)/5 + 5\n", "X = np.vstack([np.ones(50), X_1]).T \n", "y = (X_1 * 2 + 3) + np.random.normal(0, 2.5, size=50)\n", "plt.plot(X_1, y, \".\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And plot our loss:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "angle_slider = widgets.FloatSlider(min=0, max=360, step=15, value=45)\n", "\n", "def plot_regression_loss(angle):\n", "\n", " t0_vals = np.linspace(-10,10,100)\n", " t1_vals = np.linspace(-2,5,100)\n", " theta_0,theta_1 = np.meshgrid(t0_vals, t1_vals)\n", " thetas = np.vstack((theta_0.flatten(), theta_1.flatten()))\n", " loss_vals = 2/X.shape[0] * sum(((y - (X @ thetas).T)**2).T)\n", " loss_vals = loss_vals.reshape(100, -100)\n", " plot_surface_3d(theta_0, theta_1, loss_vals, angle)\n", " \n", "interact(plot_regression_loss, angle=angle_slider);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider: \n", "- What do you notice about the loss surface for this simple regression example? \n", "- Where are the optimal values $(\\theta_0, \\theta_1)$? \n", "- Do you think that the shape of this surface will make gradient descent a viable solution to find these optimal values? \n", "- What other loss surface shapes could you imagine?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 5:\n", "Now, let's implement a general function to perform batch gradient descent. Given data X and y, initial weights $\\theta_0$, a learning rate $\\rho$, and a function `gradient_function` that has the same function signature as `linear_regression_grad`, implement a general gradient descent algorithm for finding optimal $\\theta$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gradient_descent(X, y, theta0, gradient_function, learning_rate = 0.001, max_iter=1000000, epsilon=0.001):\n", " \n", " theta_hat = theta0 # Initial guess\n", " for t in range(1, max_iter):\n", " \n", " grad = gradient_function(X, y, theta_hat)\n", " \n", " # Now for the update step\n", " theta_hat = theta_hat - learning_rate * grad #SOLUTION\n", " \n", " # When our gradient is small enough, we have converged\n", " if np.linalg.norm(grad) < epsilon:\n", " print(\"converged after {} steps\".format(t))\n", " return theta_hat\n", " \n", " # If we hit max_iter iterations\n", " print(\"Warning - Failed to converge\")\n", " return theta_hat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta_0 = [10, -1]\n", "gradient_descent(X, y, theta_0, linear_regression_grad)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's visualize how our regression estimates change as we perform gradient descent:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta_0s = []\n", "theta_1s = []\n", "plot_idx = [1, 5, 20, 100, 500, 2000, 10000]\n", "\n", "def plot_gradient_wrapper(X, y, theta):\n", " grad = linear_regression_grad(X, y, theta)\n", " theta_0s.append(theta[0])\n", " theta_1s.append(theta[1])\n", " t = len(theta_0s)\n", " if t in plot_idx:\n", " plt.subplot(121)\n", " plt.xlim([4, 12])\n", " plt.ylim([-2, 3])\n", " plt.plot(theta_0s, theta_1s)\n", " plt.plot(theta[0], theta[1], \".\", color=\"b\")\n", " plt.title('theta(s) over time, t={}'.format(t))\n", " plt.subplot(122)\n", " plt.xlim([0, 20])\n", " plt.ylim([-10, 40])\n", " plt.plot(np.arange(50)/2.5, y, \".\")\n", " plt.plot(np.arange(50)/2.5, X @ theta)\n", " plt.title('Regression line vs. data, t={}'.format(t))\n", " plt.show()\n", " return grad\n", "\n", "gradient_descent(X, y, theta_0, plot_gradient_wrapper)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 6:\n", "\n", "In Prof. Gonzalez's lecture, instead of using a constant learning rate, he used a learning rate that decreased over time, according to a function:\n", "$$\\rho(t) = \\frac{r}{t}$$\n", "Where $r$ represents some initial learning rate. This has the feature of decreasing the learning rate as we get closer to the optimal solution.\n", "- Why might this be useful, compared to a constant learning rate? \n", "- What problems might be caused by using too high of a learning rate? \n", "- What about too low?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extending to Logistic Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 7\n", "\n", "As discussed in lecture, while ordinary least squares has a simple analytical solution, logistic regression must be fitted using gradient descent. Using the tools we've constructed, we can do just that. First, create a new function, `logistic_regression_grad`, which functions similarly to its counterpart `linear_regression_grad`. In the case of logistic regression, this should be the gradient of the logistic regression log-likelihood function - you may wish to refer to the lecture slides to find this gradient equation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we define the sigmoid function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid(t):\n", " return 1/(1 + np.e**-t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then complete the gradient function. You should get a gradient of about $[0.65, 0.61]$ for the given values $\\theta$ on this example dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def logistic_regression_grad(X, y, theta):\n", " grad = (sigmoid(X @ theta) - y) @ X #SOLUTION\n", " return grad\n", "\n", "theta = [0, 1]\n", "simple_X_1 = np.hstack([np.arange(10)/10, np.arange(10)/10 + 0.75])\n", "simple_X = np.vstack([np.ones(20), simple_X_1]).T\n", "simple_y = np.hstack([np.zeros(10), np.ones(10)])\n", "linear_regression_grad(simple_X, simple_y, theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see how we can use our gradient descent tools to fit a regression on some real data! First, let's load the breast cancer dataset from lecture, and plot breast mass radius versus category - malignant or benign. As in lecture, we jitter the response variable to avoid overplotting." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sklearn.datasets\n", "data_dict = sklearn.datasets.load_breast_cancer()\n", "data = pd.DataFrame(data_dict['data'], columns=data_dict['feature_names'])\n", "data['malignant'] = (data_dict['target'] == 0)\n", "data['malignant'] = data['malignant'] + 0.1*np.random.rand(len(data['malignant'])) - 0.05\n", "\n", "X_log_1 = data['mean radius']\n", "X_log = np.vstack([np.ones(len(X_log_1)), X_log_1.values]).T\n", "y_log = data['malignant'].values\n", "plt.plot(X_log_1, y_log, \".\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 8:\n", "\n", "Now, using our earlier defined `gradient_descent` function, find optimal parameters $(\\theta_0, \\theta_1)$ to fit the breast cancer data. You will have to tune the learning rate beyond the default of the function, and think of what a good initial guess for $\\theta$ would be, in both dimensions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta_log = gradient_descent(X_log, y_log, [0, 1], logistic_regression_grad, learning_rate=0.0001) #SOLUTION\n", "theta_log" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With optimal $\\theta$ chosen, we can now plot our logistic curve and our decision boundary, and look at how our model categorizes our data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y_lowX = X_log_1[sigmoid(X_log @ theta_log) < 0.5]\n", "y_lowy = y_log[sigmoid(X_log @ theta_log) < 0.5]\n", "y_highX = X_log_1[sigmoid(X_log @ theta_log) > 0.5]\n", "y_highy = y_log[sigmoid(X_log @ theta_log) > 0.5]\n", "\n", "sigrange = np.arange(5, 30, 0.05)\n", "sigrange_X = np.vstack([np.ones(500), sigrange]).T\n", "d_boundary = -theta_log[0]/theta_log[1]\n", "\n", "plt.plot(sigrange, sigmoid(sigrange_X @ theta_log), \".\", color=\"g\")\n", "plt.hlines(0.5, 5, 30, \"g\")\n", "plt.vlines(d_boundary, -0.2, 1.2, \"g\")\n", "plt.plot(y_lowX, y_lowy, \".\", color=\"b\")\n", "plt.plot(y_highX, y_highy, \".\", color=\"r\")\n", "plt.title(\"Classification (blue=benign, red=malignant), assuming a P=0.5 decision boundary\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, we can calculate our classification accuracy." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_errors = sum(y_lowy > 0.5) + sum(y_highy < 0.5)\n", "accuracy = round((len(y_log)-n_errors)/len(y_log) * 1000)/10\n", "print(\"Classification Accuracy - {}%\".format(accuracy))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
KatiRG/flyingpigeon
notebooks/modules_sdm.ipynb
1
6510
{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/nils\n", "['/home/nils/data/sdm/FD_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_May_20010101-20051231.nc', '/home/nils/data/sdm/TG_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_AMJJAS_20010101-20051231.nc', '/home/nils/data/sdm/TG_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_AMJJAS_20010101-20051231.nc', '/home/nils/data/sdm/FD_EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1_May_20010101-20051231.nc', '/home/nils/data/sdm/TG_EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_AMJJAS_20010101-20051231.nc', '/home/nils/data/sdm/ID_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_yr_20010101-20051231.nc', '/home/nils/data/sdm/FD_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_May_20010101-20051231.nc', '/home/nils/data/sdm/ID_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_yr_20010101-20051231.nc', '/home/nils/data/sdm/ID_EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_yr_20010101-20051231.nc', '/home/nils/data/sdm/ID_EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1_yr_20010101-20051231.nc', '/home/nils/data/sdm/TG_EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1_AMJJAS_20010101-20051231.nc', '/home/nils/data/sdm/FD_EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_May_20010101-20051231.nc']\n" ] } ], "source": [ "from flyingpigeon import sdm \n", "reload(sdm)\n", "from datetime import datetime as dt\n", "from os.path import join\n", "from os import getenv\n", "\n", "tic = dt.now()\n", "\n", "names = ['FD_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_May_20010101-20051231.nc',\n", "'TG_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_AMJJAS_20010101-20051231.nc',\n", "'TG_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_AMJJAS_20010101-20051231.nc',\n", "'FD_EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1_May_20010101-20051231.nc',\n", "'TG_EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_AMJJAS_20010101-20051231.nc',\n", "'ID_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_yr_20010101-20051231.nc',\n", "'FD_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_May_20010101-20051231.nc',\n", "'ID_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_yr_20010101-20051231.nc',\n", "'ID_EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_yr_20010101-20051231.nc',\n", "'ID_EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1_yr_20010101-20051231.nc',\n", "'TG_EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1_AMJJAS_20010101-20051231.nc',\n", "'FD_EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1_May_20010101-20051231.nc']\n", "HOME = getenv('HOME')\n", "\n", "ncs = [join(HOME+'/data/sdm/', nc )for nc in names]\n", "\n", "print HOME\n", "print ncs\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "length of decimalLongitude: 244289\n" ] } ], "source": [ "reload(sdm)\n", "csv = join(HOME+'/data/sdm/','output_csv-246e16ee-ba7f-11e6-8713-142d277ef1f3.csv')\n", "latlon = sdm.latlon_gbifcsv(csv)\n", "\n", "PApoints = sdm.get_PAmask(coordinates=latlon, domain='EUR-44')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1', 'EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1', 'EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1', 'EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1']\n" ] } ], "source": [ "PApoints.shape\n", "\n", "ncs_dic = sdm.sort_indices(ncs)\n", "print ncs_dic.keys()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "failed for EUR-44_ICHEC-EC-EARTH_historical_r1i1p1_KNMI-RACMO22E_v1 : cannot import name constants\n", "failed for EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1 : cannot import name constants\n", "failed for EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1 : cannot import name constants\n", "failed for EUR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_HMS-ALADIN52_v1 : cannot import name constants\n" ] } ], "source": [ "#dataf.sample\n", "reload(sdm)\n", "for key in ncs_dic.keys():\n", " try:\n", " gam_model, predict_ref, gam_info = sdm.get_gam(ncs_dic[key], PApoints)\n", " print gam_model.names\n", " from IPython.display import Image\n", " Image(filename=gam_info)\n", " except Exception as e: \n", " print 'failed for %s : %s' % (key, e)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prediction = sdm.get_prediction(gam_model, ncs_indices)\n", "\n", "from numpy import invert, isnan, nan, broadcast_arrays, array, zeros, linspace, meshgrid\n", "mask = invert(isnan(PApoints))\n", "mask = broadcast_arrays(prediction, mask)[1]\n", "prediction[mask==False] = nan\n", "\n", "species_file = sdm.write_to_file(ncs_indices[0], prediction)\n", "\n", "tac = dt.now()\n", "\n", "print 'prediction finished in %s minutes' % (tac - tic)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
girpas-ulg/nb_geoschem
Extract_GCprof_station_dev.ipynb
1
1164322
null
lgpl-3.0
tridesclous/tridesclous_examples
Brochier_motor_cortex_96_channels/Brochier_motor_cortex_96_channels.ipynb
1
110874
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Massively parallel recordings in macaque motor cortex\n", "\n", "Brochier and collaborator release public dataset recorded with 10-by-10 Utah electrode arrays on macaque motor cortex.\n", "\n", "See https://www.nature.com/articles/sdata201855 for description.\n", "Files can be downloded with gin here https://web.gin.g-node.org/INT/multielectrode_grasp\n", "\n", "\n", "Here an example of tridesclous on one of this files : **i140703-001**.\n", "\n", "of course the spike sorting will be done on the raw signal. this is the ns6 file in the blackrock world.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# suposing the datset is downloaded here\n", "basedir = '/media/samuel/dataspikesorting/DataSpikeSortingHD2/ThomasBrochier/multielectrode_grasp/datasets/'\n", "filename = basedir + 'i140703-001.ns6'\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tridesclous as tdc\n", "from tridesclous import DataIO, CatalogueConstructor, Peeler\n", "import os, shutil" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## create a DataIO (and remove if already exists)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DataIO <id: 139763945949056> \n", " workdir: /media/samuel/dataspikesorting/DataSpikeSortingHD2/ThomasBrochier/multielectrode_grasp/datasets/tdc_i140703-001\n", " sample_rate: 30000.0\n", " total_channel: 96\n", " channel_groups: 0 [chan1 chan2 chan3 chan4 ... chan93 chan94 chan95 chan96]\n", " nb_segment: 1\n", " length: 30096285\n", " durations: 1003.2 s.\n" ] } ], "source": [ "dirname = basedir + 'tdc_i140703-001'\n", "\n", "if os.path.exists(dirname):\n", " #remove is already exists\n", " shutil.rmtree(dirname)\n", " \n", "dataio = DataIO(dirname=dirname)\n", "\n", "# feed DataIO with one file\n", "dataio.set_data_source(type='Blackrock', filenames=[filename])\n", "print(dataio)\n", "\n", "# set the probe file\n", "dataio.set_probe_file('mea_utah_96.prb')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = tdc.plot_probe_geometry(dataio, chan_grp=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
probml/pyprobml
deprecated/simulated_annealing_2d_demo.ipynb
1
543381
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "simulated-annealing-2d.pynb", "provenance": [], "toc_visible": true, "authorship_tag": "ABX9TyP9S1O4x5pDb8rKiz1Xcsaw", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/simulated_annealing_2d_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "dCJMeGbhe0LM" }, "source": [ "# Simulated annealing on a 2d surface\n", "\n", "Code is based on\n", "\n", "https://krischer.github.io/seismo_live_build/html/Seismic%20Inverse%20Problems/Probabilistic%20Inversion/pi_simann_wrapper.html\n", "\n", "and modified by murphyk@ and Neoanarika@\n" ] }, { "cell_type": "code", "metadata": { "id": "kUEiOsuTeven" }, "source": [ "import numpy as np\n", "import matplotlib\n", "\n", "matplotlib.use(\"nbagg\")\n", "import matplotlib.pyplot as plt\n", "from IPython import display\n", "\n", "\n", "from mpl_toolkits.mplot3d import Axes3D" ], "execution_count": 1, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "v5n8yJr0e3xn", "outputId": "c9cf301a-7ce8-4c18-9d33-9882e7cf1518" }, "source": [ "\n", "!mkdir figures\n", "!mkdir scripts\n", "%cd /content/scripts\n", "!wget -q https://raw.githubusercontent.com/probml/pyprobml/master/scripts/pyprobml_utils.py\n", "import pyprobml_utils as pml\n" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "/content/scripts\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "qrY9Z9FHUZJn" }, "source": [ "# Target distribution\n", "\n", "We use the [peaks](https://www.mathworks.com/help/matlab/ref/peaks.html) function from matlab, modified so it is positive:\n", "$$\n", "p(x,y) \\propto |3 (1-x)^2 e^{-x^2 - (y+1)^2} \n", " - 10 (\\frac{x}{5} - x^3 - y^5) e^{-x^2 -y^2} \n", " - \\frac{1}{3} e^{-(x+1)^2 - y^2} |\n", "$$\n" ] }, { "cell_type": "code", "metadata": { "id": "fYVrOMUte59A" }, "source": [ "# Generate a pdf\n", "\n", "# the following steps generate a pdf; this is equivalent to the function \"peaks(n)\" in matlab\n", "n = 100 # number of dimension\n", "pdf = np.zeros([n, n])\n", "sigma = np.zeros([n, n])\n", "s = np.zeros([n, n])\n", "x = -3.0\n", "for i in range(0, n):\n", " y = -3.0\n", " for j in range(0, n):\n", " pdf[j, i] = (\n", " 3.0 * (1 - x) ** 2 * np.exp(-(x**2) - (y + 1) ** 2)\n", " - 10.0 * (x / 5 - x**3 - y**5) * np.exp(-(x**2) - y**2)\n", " - 1.0 / 3 * np.exp(-((x + 1) ** 2) - y**2)\n", " )\n", " if pdf[j, i] < 0:\n", " pdf[j, i] = pdf[j, i] * (\n", " -1\n", " ) # in contrast to the peaks function: all negative values are multiplied by (-1)\n", " y = y + 6.0 / (n - 1)\n", " x = x + 6.0 / (n - 1)\n", "\n", "pdf = pdf / pdf.max()\n", "energy = -np.log(pdf)" ], "execution_count": 3, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 314 }, "id": "nVsNGdGRe8Ca", "outputId": "9620a327-1f99-4a74-d11d-ecdc8a818f0c" }, "source": [ "# Plot the 3D plot of pdf\n", "# --------------------------\n", "\n", "X = np.arange(0, 100 + 100.0 / (n - 1), 100.0 / (n - 1))\n", "Y = np.arange(0, 100 + 100.0 / (n - 1), 100.0 / (n - 1))\n", "fig0 = plt.figure()\n", "ax = fig0.gca(projection=\"3d\")\n", "X, Y = np.meshgrid(X, Y)\n", "surf = ax.plot_surface(Y, X, pdf, rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0.1)\n", "# plt.gca().invert_xaxis()\n", "plt.tight_layout()\n", "pml.savefig(\"sim_anneal_2d_peaks.pdf\")\n", "plt.show()" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_peaks.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 314 }, "id": "Dow4ARLnfHFk", "outputId": "4ab2a660-8207-4999-a6ce-4e1ca1264239" }, "source": [ "# Plot the 3D plot of Energy function\n", "# --------------------------\n", "\n", "X = np.arange(0, 100 + 100.0 / (n - 1), 100.0 / (n - 1))\n", "Y = np.arange(0, 100 + 100.0 / (n - 1), 100.0 / (n - 1))\n", "fig0 = plt.figure()\n", "ax = fig0.gca(projection=\"3d\")\n", "X, Y = np.meshgrid(X, Y)\n", "surf = ax.plot_surface(Y, X, energy / energy.max(), rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0.1)\n", "# plt.gca().invert_xaxis()\n", "plt.tight_layout()\n", "pml.savefig(\"sim_anneal_2d_energy.pdf\")\n", "plt.show()" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_energy.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "Z8gyFXUsWABz" }, "source": [ "# Heat bath\n", "\n", "The \"heat bath\" refers to a modified version of the distribution in which we vary the temperature. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "5UHUdBBEfHUB", "outputId": "99647351-632f-4e57-e05a-98507a375b84" }, "source": [ "Tplots = 10 # initial temperature for the plots\n", "stepT = 4 # how many steps should the Temperature be *0.2 for\n", "\n", "for i in range(0, stepT):\n", " sigma = np.exp(-(energy) / Tplots)\n", " sigma = sigma / sigma.max()\n", " ttl = \"T={:0.2f}\".format(Tplots)\n", " Tplots = Tplots * 0.2\n", " X = np.arange(0, 100 + 100.0 / (n - 1), 100.0 / (n - 1))\n", " Y = np.arange(0, 100 + 100.0 / (n - 1), 100.0 / (n - 1))\n", " fig = plt.figure()\n", " ax = fig.gca(projection=\"3d\")\n", " X, Y = np.meshgrid(X, Y)\n", " ax.set_title(ttl)\n", " ax.plot_surface(Y, X, sigma, rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0, antialiased=False)\n", " # plt.gca().invert_xaxis()\n", " plt.tight_layout()\n", " pml.savefig(f\"sim_anneal_2d_cooled{i}.pdf\")\n", "\n", "plt.show()" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_cooled0.pdf\n", "saving image to ../figures/sim_anneal_2d_cooled1.pdf\n", "saving image to ../figures/sim_anneal_2d_cooled2.pdf\n", "saving image to ../figures/sim_anneal_2d_cooled3.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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8wEUWzHw+j7W1NRQKBQAwIi5SfOx0pU8fp1sWQr/7llslRVGMz82r7VOhUEChUGi67VNQhOyuAFBXqNyKa5sZqd5usSyLYrGI6elpbG5u4sCBAy1Fbp2IoK500cWvTl0brO2GANgirkgkYlisu0HEXdotUhSlLjBZlvXsV0gPlHRq+xSPx10jLsBcy5VKpQAA/f39Vw24AkBdQaKvtkiE0o6R6u1UoVBAJpPBhQsXMDo62pZ+fe3eg7oSTRd+5QYuusFrPp/HysoKisUiVFWFJEmYnJw0getyQGsr96D8yA+g3ESbLRpt++Rmksnn84bD1akImWEYm6uQ47grGlwBoK4AOU2uJX909Ej1a6+91nYl5/f4rf4R5/N5TExMIJ/PIxKJ4PDhw21rjbRdU3xbKbcGr/l8HhcvXkQsFjOBCzDvrRBwdXKx6zZAdSLl6Lft0+bmpq2eLp/Po6+vz3G8jFsR8tLSEv7iL/4CX/jCF9r6PrZKAaC6WMQqrigKnn/+eVxzzTUQBAGaprVtpHojNUtOymazmJiYQLlcxqFDhzAwMICXXnqpI6YGJ6mqitXVVbAsi0QiAUEQ6i6iV/IVZbvFMAwEQcDQ0JDpdjriymazWFpacjQFtBNc3QiorTofa9snIroQPJ1OY3NzE6urq77bPqXTaaTT6S15D51QAKgulFMNkyRJkCQJS0tLmJ2dtXVbaFYcxzWVykin05iYmDDtdRG1OyXndDxVVU0dMDRNw+zsLCqVCnieNwwC5If+4m7nFF+jcoue6YiLhhcxBdDgKhaLRmRAmzPC4XDD3US6CVDA5b+YoQvBl5aWsG/fPsTjcRO4CoWCY9unH/7wh2BZtunOMPfddx/++Z//GUNDQzh37pztfk3T8J73vAff/e53EY1G8dBDDxlt09qlAFBdJLfiWlmWUSqV8PTTT2P37t245ZZb2jaIr1GY0B3ODx8+bBtt0cwx64kGCg2moaEh3HLLLcbtZDGh+7UtLy/bupSXy2VkMhn09fV1lWvscqjR9K6XKYA42bzGxnuBqxsB1U2iLyTdOpgAeklHLpdDuVzGk08+iQsXLuDGG29Eb28vTp8+jY9//OO+Xu/ee+/Fu971Ltx9992O93/ve9/DpUuXcOnSJTz55JO4//778eSTT7b2Ji0KANUFcqthomuHWJbFtddea/tjbFUkgqp3frQJo16H805FUDMzM5idnTXARCBt3Sx2mgtFdynf2NjA0tISpqenoShKxwthndQtEVy7bOZu4KILX53ARUe6rZgSrgbJsuyrCTFpdvwHf/AHePTRR3Hbbbfhgx/8IDY3NzE3N+f79W677TZMTU253v/YY4/h7rvvBsMwOH36NFKpFBYXF7Fz507fr1FPAaAuo5zAxDCM40j1l19+uSPn4AUTTdOMIYGNmDDaCShVVTE7O4tcLodKpdJ09Eh3KY9EIhgdHUU4HHYthKXtwARc7TYJXO70EdD5Oii68JUWDa5UKoX5+XlkMhlwHId0Om2KuEKhUFd8Vpdbsiw3lYonF2p9fX1tnek2Pz+PvXv3Gv/es2cP5ufnA0Bd6XIrrvUaqe4n0mlGTjDRNA0rKyuYnJxEPB7HiRMnGhq94dXc1a/oJrIjIyOIxWIYHR1t6Zj0+ZEIpl4hLJkLRbvbrE1GG91r6SZdrkJdJ3BNTU0hHA4jGo0aTra5uTmUy2VwHGdKFcbjcaNPXifUbTVZRI1G9plMBnv27OnQ2XReAaC2UG7FtX5GqncKUPRxNU0zZjElk0mcPHkSkUik4WO2EkEpioLZ2VnMz8+bmsguLy83dTwn+TFJ0HZgWm57LWQBpVNWnVxA26luOUfiJnXqkUc3d7WCy6kreavvabukG+kIqt3avXs3ZmdnjX/Pzc1h9+7dbX2NAFAdlltxLcM0NlKd4zjTrJp2iZgw5ufnMT09jf7+/pZnQpFjNiIaTDt37rQ3kX3He/Ezj+dzIf3KkuUYnPzf/6vu6zW7B+S110LShPRYBzKPiAZXuwwu7VA3RQpeXS3cmrsScOVyOc9xGvF43FcJAlEAqPq688478bnPfQ5vfOMb8eSTTyKZTLY1vQcEgOqYvIprmxmp3okISlVV5HI5LC0tYefOnW2bCeXVzdwqMnZjfn7e1N38qVvOQKnUojCWry0sqqzZbtcUDQzHQFU0PHPmNrCcfrsTrDqxILtd+dOOwpWVFeRyOVNql8wbulzDDLutF1+jKSwvcHldMFgjLqvoPnzdoGYvJDKZTNOAetOb3oTHH38ca2tr2LNnDz7ykY8YhqTf//3fxx133IHvfve7GB0dRTQaxd/8zd809TpeCgDVZrkNCARaG6lOBty1Q/T+jiiKOHjwIPbt29eWYwP+UnxWMJ0+fRo8z+M/T79SP8eK+/NZngEnsrbHaYoOLiFWi1DOv+bXAADX/cu/GrdtZR2Um6NweXkZa2trUBQFCwsLxvj4rXYUXumAchPP845zoMgAw1wu5zp5NxaLQdO0roqgmo3oyEDSZvTII4943s8wDD7/+c83dWy/CgDVJhEwLS8vGw1ayUK4sLDQ8kh1Mp69FdFDAkkabX5+vu0LoBegZFnGzMwMFhYWsGfPHmMu1fO/+kuo5CUwHAtVUgwA0SIwou+jQRXqqV0Jq1VYKZIKTmBx/jW/Bj7M4+g/fR/A5bV5k+4NkUjE5IIiwwyJMcPNUdjOfnnbFVBuchtgSEe6q6urSKfTKJVK+PnPf+5Z9L1V8msxt6qTKb6tUACoFmUdEHjx4kW88pWvhCzLmJubw/z8PIaGhlpOn7WS4iNTdMmQQHoWUydSh06Asg4qpMEklaqmkSqc3MSJLPiw/icrl2TbfaZzqKb7gBqk5JKMi3e+Vv+jf/jvW3yXrckJkPQUXrfRGrlcztYvj15AG3UUXm2AcpM10l1fX0cqlcK+ffuQy+VQKBRMKVrSrWGrwNUsoEiB+pWqAFBNyq24VtM0jI+PY3Fx0ffEWD9qBiR+hgSSMfDtFA0oGkx79uwxncMLr/llA04AwAksOKG2QEkF83kROJH/t0FKYKFI1L6VC6T4MI/Ft/wOEtVo6nLJLxi8OpS7FcFajRluzrYAUM4ie1CCIDjWD5E2Q9ZuJTS4yOffju9/M4DqlmLwVhQAqkG5FdeSwXz5fB6CIBgRQrvUCKDK5TImJyexsbFhFPq6ffFZlu1IBCXLMsbGxrC8vOwIxxde88vgQ7U/P6c9JyFauyK1wgqAKZoKJfSrRCdIhZO1foUMqy/GclnBzFtfj31ff6zZt3nZRTsKh4eHjdtpR+HGxoajQYAsngGgnFVvz0cURYii6Akuuj+etbFro+BqxVXYLb/fZhQAyqfcimvJSPVUKoUDBw4gkUhg9+7dbd9g9QOoYrGIyclJ41yOHTtW94+T47i2tiWSJAnz8/NYWVnBkSNHbHC8+F9eC6Uigw/xkMv+rejRQb0eqZKr2O6LD8dMAKMhFenT9/s0VTP+y7AM+JD++7nSIeWkRhyFxWLRaKtFGzPacdXfqLoJUM12+HcCF93YNZ/PY3Fx0TDF0ODy+uybiaBKpVJL5SLdoABQdeRWXEtGqheLRRw8eNAYqb6wsNBUS5J68gJUoVDAxMSE0YGikfHu7YqgJEnC1NQUVlZWMDw8jIGBAZszkMAJgAlOfIgHHwLKWTt8AHMkJcZFE6RIdCREBRukxHgt986wjA1SclkHZTdCavXdbzIgq8rm3w95H0Qsry/qO7/kva/m5ChcXFxEqVRCMplEPp93dRSSn04CpJsAReDRDrk1drWCi/7syQwo8kMi4EaUSqUu2+DSdikAlIO8imtTqZRtzAQNA57nIctyy2MwrHICFA3JQ4cOOXag8HPcViIoep+LpBNLpZKtd+DEm19nwIlhGQiRGnTIghtKiEYKDgBK6bLja1ohRURDKjqgR1w0CGlIRfqiKG4WDEgtvuN36i7wndbqu98EhmUNILEcA0VSjM+EnDv9b/o9LfzeXab7/bwfTdMgCAL6+/tti2e5XDaMGfTkV7p2a7s6CreiULceuIibc35+HhsbG2AYBqurq7aLBrfzvNIdfEAAKJO8imv9jlRvhx3cSTSgstksxsfHUalUcPjwYRskmz1uI6pUKpiamsLq6ir2799vSuVZC3Un3vw6lLMliDH9ilQq1iIdazRAi0RH9J4SkRgXjWJcqwicAHimEqMDtW4QlVzpskBq84/uMcCtqRo0tfa7UChHo9PnRMPc6bbFd/wOmOrvZOQLf+f4+m5QoB2F1gF6Xo7CVuZBeXWS2Gpdzk4SNLjIZz8+Po5kMol4PG5EXPPz88jn847j4nmeRzqdDiKo7SCv4tpGR6p3qiURwzBQFAU///nPoapq20aqN9o3r1KpYHJyEuvr664GDLpZ7MSbXwc+LECr/tsLTk4LLgBEeiMopoq220OJMMrZkuk2Oh3opJ5dSQNYmqoaC3ikT4faVkGK/8T70Q9ArsKJE3nT+1dlxTCROMHJCl2WNy+mmqqCE2q3rb77Tcbr9H/ya7XHNQiFZh2FNLiuhB6F3dbqiDgE3S4a6I786+vruP/++5FOp8HzPP7kT/4Ex48fxw033IATJ074er3vf//7eM973gNFUfB7v/d7+OAHP2i6f3p6Gvfddx9WV1fR39+Pr3/96x1pSntVA8ptQKCqqkbT1EZHqpMUXzu1ubmJ8fFxlMtl3HDDDW29KvIbQRGX4vr6Ovbv348jR454OgNVVTXgJBVqqbpQorZpSy+8VtBYZYVUpDdiHI9+bmyH3h27kqvdRqKoxEiP6d/6OeiQIjZ0AEYX93g83pFO5akP3Ft9bQ2cqH8F6X0m656TVdYUKWCPMmmHpB6ZVe32FRmr736TAapOzINq1FFI4NVN6rZWR14mCaeO/P/+7/+Ov/u7v8OFCxdw5swZnD9/Hi+99JIvQCmKggceeAA//OEPsWfPHpw6dQp33nknrrvuOuMx73//+3H33Xfjnnvuwb/927/hQx/6EL72ta95HLU5XZWAshbXEjApioL5+fmWRqq3C1BkSOD4+DhEUcSxY8fwwgsvtD1krxdB0Zb1AwcOeIKJPubIFz4GVOEkRKupukrtc7FGBdGBWtun4mbB+H8xVvv860VSBE6tiA/x6P/S/4X1t/+fhoHAWldERj00qvxH74eUL4ILiZCLNWh7AalelEkiQL7aLFcuy6ZaMvo51ihr4w/fhh5RQPE9/7PBd+JfXo5CuuXQ5OQk8vk8nnnmGVvnhsvlKOy2CKrRzyGbzeLQoUN43eteh9e97nW+n3f27FmMjo7i0KFDAIA3vvGNeOyxx0yAOn/+PD71qU8BAH7lV34Fb3jDGxo6N7+6qgBFwDQxMYG9e/eCZVmjZoe0ABoZGWlppHqrKT7rkMDrrruuo1eXbhFUuVzGxMQENjc3fVvWidbf/zYAMPacGhUxMDje1+tv/IcYD5uiqIHRYVRyNbh5RVGVXBG7/v4vEP7AZwDUogDSMXt6ehqSJEEQBBO0vDas8x+9H6qsgAvZPxM+Ql0EWYorNerigY5EXd839ZmTCwIrmPTjkohKgviJP0JaFJD82Fddj/uT4VOmf/8fy0/VPRcvObUceuqpp3D8+HHDHEDbsUOhkAlc0Wi0owDpthRfM+eTTqdx9OjRhl/LaRChdZT7yZMn8a1vfQvvec978I//+I/IZrNYX183pR7boasCUNbi2uXlZezZsweSJBkONGuXg2bF8zzK5foLidM50kMCndKKZG+nnakHawRFT/O1Dk30o5X3vRmldBFiTEQlX3PaeUVPnGj/zCN9USiV1swmBFLJvQPVf0dMkHJSZKAHxfUMyqkcSDLSLQqwOq3oDWsaXPjk+8GFBCNSYlgGQkw/Ov25eMEJgBGJEsklc/EysZsTkfSh0x4WuY9W9sPvQOKjX8JPhk9Bk7y7EPx7/83gIlRPxAiH02NPeD7Hj8jU40YchfQFQrschdsFUJ1y8f35n/853vWud+Ghhx7Cbbfd1pHaT2CbA8qthollWVy4cAHZbLZup4VGxfM88vm878drmobFxUVjv8trSCCJdtoJKAKfUqmEiYkJpNPppsAE6HDiIyGILimrcFJ/X27RkR9FB4GQccoAACAASURBVGIorNs/32bSeySK6tmj5+1pIIiJCNIfus8zqnBbTEulkgGu+BcfBBcSIOVLBpTo9F6zYljWBCyn6MoaOSmUIcOq57/6gv4/X6wPJzf9x+gZA1qnXvhpU8dw0uVwFHbbHhTQeEeIZgHlZxDhrl278K1vfQuAXu7yD//wDx2B4bYDlFcNExmpnslkMDg4iBMnTrR9A9zvHpSqqkaX8/7+fl/7XQRQ7WxKWSqVUCwW8cwzzzRc5EuLwKmS1eFTyVcMICkV2bap70duMHK6PTqYQGEta3ssiZ6IrFEUgROtyED9+Vxuojes+x75BOSQ/r6d4GSKniyyRk/1JCYituNb5QSmS9+9hMqGBJZnoBTtr8kItb8FAi46ciJSigq4CGcc46nrfxF8nMMrnvh3X+ffzLwjP47CTCbj6Cj0Mza+2wDVqJq1mZ86dQqXLl3C5OQkdu/ejUcffRTf+MY3TI9ZW1tDf38/WJbFxz72Mdx3333tOm2Tth2gAD31QtvFrSPVWZZFMpnsiNW1Xh2UqqqYm5vD7Oxsw13O22lhLxaLmJiYQCaTAc/zOH36dNOfR/rBt5vgJCaijvsegJ7eog0RnCigsG4HC+32I6KfR6tnT7/j7fWUPDACAJDypeq5sbYoSv7Mfwf/3/7vho8tf+a/Qy56OxNNalNjT7KfpUr2vxOGSsFc/M5LqGxI4CJcFSyKDTz0YEhAhxUX0Y+hOdSmKUUFoWHRGCLJcgyee9UvQVW0uqBqZ+raa+oxMWZYx8bT+1vd5ihstulrJpNpqhSF53l87nOfw+233w5FUXDffffh+PHj+PCHP4ybb74Zd955Jx5//HF86EMfAsMwuO222zo2F2rbAYruKu42Uj2VSnWkVglwhwg90rxZI0Y7ioBpMB06dAjXXXcdnnjiiZbgRCIBMVHt3uAzSiCKDiQcIaXf13wU1X9kF4pr6fpvwkFKRXaMOPyKi0VsgKI/FyEWgVJxfs9iMobypvN9QM25Z/ybsy/stPFCLpZNcLr0/ZehFFUDNkrR+W+KnmLMWJ2BAmtASuwXqrfpj1dlTY/IKio4kQXLMXjx134VQlQw5nFZtRVdJDiOQyKRQCJhTgeT6bv0EMNucxQ2A+9W9qDuuOMO3HHHHabbPvrRjxr/f9ddd+Guu+5q6tiNaNsBCgBWV1cxMTHhOlKd5/m2j5igj00DigzoW1xcxK5du1oav9HK7CbSry+XyxlganVBSD/4dnCRMJRKztfjrRZpTqwBmoaUNXpyi5yaVTmrg2L1xRnbfb0HhszOuVwe8p/9N8PR50faV/4U5bXNuo8L9+kLpTWdp2kaQn3mRdQLWFZZo1c+EgLLczj/recBAHKm9jfECAx4odoVPuN8McH3UF3nqTQgI7AQ+3kj9adJmiOkdOnHfvHXfhXH//VHtte4nH34nKbvEkchAZfVUUjvb3XaUdjsLKhKpeK6n32laFsCqlKpeI5UFwShYxEUARRxCBLHYDscgs0AqlAoYHx8HPl83rNfXzNXsFwkjEo6ByEWgZTX93UajZ5oRQcS1ef5u3iIDsTMFm0HRQaTWLugb/iqsgUEqmZyvmmqhtTUivHv/lE9BchH/HeE5r75Cbi9axo6aoOfDXku+R2VU/pFgVP0ZBUNJyHKmUwQdBqPBhHgDCySBqTTgYzAmCBFbiOQAmrzu8K9IcdoqpsaxZKUmltncuIopKced9JReLXOggK2KaD27NnjWXzarBXcj2RZRqFQwNmzZ9vuEGwEUPl8HhMTEygUCjh06BAGBwddAUSO28iXIPdn77bByU3hvgRKDUQAToqNDCC/tF73cSTN139kF9ZfngNQi9xoO7tSUWy2bGtBa3pmrVr0uoEd8Cc5m4MmSRCTcVTStciSC1Pj6L3MET4XllCvvk9C9v2IrNETgZMQ1W+vpGuvbd1jsorv4Q0Q0ZGT2Kv/nShlOppiTOALJUWU0xUIMcHomUjmd/FhDpzA4uKdrzUg1U2A8nLw+XEUknEmVkchgVczjsKrcRYUsE0BVe+XIghC21N8pVIJU1NT2NjYAMuybQUTkR9A5fN5jI+Po1gs4vDhwxgYGKj7eTTajy/3Z+8GoO+jOCmU1CNXOmKhnXGlzawpvWd6bnXhLazYU2ROkIrvGUJubsV0GyewBpzcFEpQFu1qf0DaaUhaB5H/rr77Tdjx2Uc8j+kUPYX69bSRXHCGeKNuPavCVFRmvQiY/t9j4MOcCU5istpaSTHDqZJyj5bI/ytF1YATAHAhti6kyGsRSMklBXyYs4066SZANdNFwo+jMJvNYmlpyZjB5ddR2EwEVS6Xr+hR70TbElD11M5+eWRIYDqdNjouPPHEEx35snkBih694RdMRI0AqvKFPwYAaFTRqXF+Ik/1lnM/HllUiXuunmIjztXpvUf22m6T8iW9RihS+3JKRfNoDrpPHQBHCzxpF0SDavOP7gEA9H3i/3U8Hzlr3ourB6dGZf19WqOlyA59Q7y8mTXgVEqVjTEkBE5OosEDmKMjIi7C2iYWc9UWS+Tx0eGwDX6AO6QAfWhk8vN/2zWAameRbquOwlgs1hSg0um0be/9StS2BNRWRFC06aCV+qFGxHGcLTWZy+WMRrLNjt7wmzosfeb9kItl8NEIpEwOQk8cci5vuPeUsvPAQTdFhvpQdIiUACA61OcYRbmJwM6pdx3dz6+Sbyy1SxZkMSYa0c7mH93jCimt+nfFuCxwje49NaO1lxfBhzkoFdWxu7sTQGhxAmsAupKrna8Y5437rc1po4Nh47NnOaYupMRYyPhdSEUJ6QfuBvunf+73LXZUW9FFohFHYaFQAMdxqFQqJnh5QSuTyVzxs6CAbQqoemolgvIzJLATLYkAM0hyuRzGxsYgSRIOHTrUUg8svxEUK4rgGdaAEy0vOLmN0QDMkCLpPSInSDml+fxGYoC+MNI1W7RIZwZrV4ZIX8xk3NBU1QYpL3MErdAOS82Wx+deXKm/5+YkffZWCMWKHrnRk4brwckqAiWrnCBFD0+0QiraXzOakMiP9FWUihI4kUfoI+8HumCy8eVsc+TkKCRdHcgsKCdHIT0ynuO4bTELCggA5Vuk2JcAwStSIcdvdw6Y4zgUi0U8++yzkCTJiJjacdx6EVTlC39sFH8SOMk555ZOXuk9Ip7av6oXSbkpN7digpPf+VKAXrNFQ4puGyREQwakxLh+npwomCDFR0KuKT9hoB9yqlZ/JQ5SrZDoAto6FwWRkdpeBsMyKC6uGv+2pveY6r8nfvi8DqdNHU7hZAicWLtQqtm+7SpnK7ZO6AAgxvQorJI3Zx0IpEKJ2t+5E6RoOAF693aW54zmvCTFyok8Vh54IziRx8Cnv+56np1Wt7U5kmUZ8XgcfX19no7C2dlZFAoFvPe97zUchN/85jdx4sQJHDlyxHfdZb1ZUDMzM7jnnnuQSqWgKAo+/vGP22qm2iWmjmvoivQqqqpaN4X3s5/9DK985SvrHouMeNc0zVTs66VnnnkGx44d8z1Dyo+y2SwuXLiAQqGAkydPtmVYIdGFCxcwNDTkGoWp3/wE1FIJcjoLLh6FktMXdhpQJIIK9SdtoChv2ItleQeDhepgL7cWpRIJiSiyM8su70iXW9sf2ppOIGVtxOr22gRSVoNIfP9O/dg9esqGAIqN1h6nWbs7eAHKWoxrgW15dcN8P89h7mcvAwCKm0VjGjE9INILTkScyKKcMUfDBFCAHVLh3jDkkv1ij/wNRPrCjs1qgRpkCRQ5kTfB6nJBam1tzShk7wZdunQJg4ODvr/ziqLgy1/+Mp599lkcP34c586dA8MwePjhh3099+jRo6ZZUI888ohp1MY73vEOvOIVr8D999+P8+fP44477sDU1FSzb4/I8Wryqoyg/IiMeOc4znPEu5PaacLIZDIYHx+HoijYvXs31tfX2wonQI+g3FJ8lUoFbB048bGII3AAfS8mXE1rlSyLqh9FD+1DYcJcUCsXy3UbrvJh0fEx1ropMRGF5hI9iokIKlmzwSEy0GPrIg4AuelF9F6vjzYgcBJ2DEJpoHFwIwoN6T0EyytrAIDNizXXohOc/IhEWqEePSIqZyomOAHmaCrcq0dGpMaJVqSvFjXRURUtayRF6uaEiACGZbC0tGSkrbYyoum2TuaNmiQ4joMoijh9+jTe/e53N/RafmZBkfZxgG7G2LVrV0Ov0Yi2JaD8mgSsxamapmF9fd3oQnHs2DHbJqYftaMlUSaTwdjYGFRVxejoKHp7e1EoFLCyslL/yQ3KKcUnSRKmpqZw8IlH9cfEzdGg2FcDtlqpXXG7XS0DQHhHvyukQtU0WHHBX1Qkl+obMlqpvyJWeSfxYcERUqkXLqL3+qMQdtgb0NrUor2cKDQ0iOX/eAH5tTxCiRBKaWfHoBARIFSvIUppO7jpNCBRfFj/DCo5+2ed2JkwAZCGVKRPfyHaQu8GqUhf7e+KE1hU8hXjuOIn/gjT930QhYJ+UdRqh3K/6jZANTtq4+DBgw2/lp9ZUA8++CBe85rX4LOf/Szy+Tz+9V//teHX8attCSg/IsYAjuOgaZox1TMajbY8JLCVpq7pdBrj4+PQNA2HDx82OXFaaXXkJdokIcsypqamsLy8jDPzP4MKGBEGiZ5aUXhHf8O2axJF+YUTTxXF0pBy6joRSsZQ2si4HouOokjHcP01zJAie1qFqTnEjugLQ7uiJ9teGlODyeyPnoYYE011XYCePgslaosaPbWXRFi0vKItMS46QkqICDZI0ZZ9a/NdGlKkfRUZFmm8Vkw0ShXkkoSdX/04+j/5NVM9UTqdNjqUcxxnm3bcarf/RovWO61mbeadcvE98sgjuPfee/GHf/iHeOKJJ/C2t70N586d60iU2z2/hS2WIAioVCpIp9OYnJxET0+P45DAZtRMii+dTmNsbAwAMDo66phS7BSgiIV1fHwci4uL2LdvH16ZuwClVAIbi0HJmBdwVqj92dDRk1VuVuvw8A6Ullcd74vsGjaiqNjoAeN2uVg2AMOFzAtQOe0NAlJ35Qa2cH+PCVLW6Mkp1QfUIEVs9uS/+UuTBqSIbPtPXmrgiy7GRKOeqJQuGiNO6GGRXiPlATPMylnddGKtDaMhFeqppe9skApxkMtUrz8LpEjEpKmaAV7rRGPSqJf8HvIfvR+xD/+VUU80PDxsHM9qy56cnDQMSjS4vKYdW0Xccd2iZgDVrM3czyyor3zlK/j+9/UOIGfOnEGpVMLa2hqGhoYafr162paAqhf2ExPF008/jYGBAbziFa9AOOy/31o9NQKoVCqF8fFxMAzjCiaiTgBKlmWsr68jlUrh0KFDOHPmjB5VPlM0wYlhWPAJPapUS862bq/0nlVekLKqvLhswMnaeYHlOWMKbiuyQsoqOnoy3+58QZO/NInwrtoXlhF48P2UCYVhIC0sNHeyVS3/xwsI9UQhr+rnTeDUikijXieo1YukImRPygVSNJzIf63RoXU/igvp0TCZ9muVky1b0zRUKhUDXPS0Y7pfXjweRyQSsa0X3ebi0zSt4fNpNoLyMwtq3759+NGPfoR7770XFy5cQKlUMnXPaKe2JaAAHVJWhyI9JFDTNBw9erQz1PfR629zcxPj4+PgOM6x47qT2plvVxQFMzMzmJ+fRyKRwL59+7B//34AgPbYZ6HJjW2wCwM1K3Vl1b1+h0COVmjQ3SpfXlyG4mMCLWml5AYqMRmDmIyhsFyzs3vtM/kVH4tAdulFWFpYQfzkCf0fDhcWAtlcphYfac67RRNRbnIOoZ4o8qv299to9GQ7r4gAQDCiKVrx4YRjOpDAiYiGVGwwBkWigcXYIGVN9RERdygfFlH55Psg/uGnPd8PoH9PQqEQQqGQbdoxPYF3eXnZaDsUjUaNaEuSpK4CVDNKp9MdmwX1yU9+Em9/+9vx6U9/GgzD4KGHHupYk4JtaTMHakMLAX0xnp+fN4YE7t+/H1NTU+jr6+sI+ZeXl5HNZjE6Omq7jwbT4cOHG25H4tce7yYyl4qE7vv27cPa2ppxvtoP/hpgWWiZlP74TAZcPAG1up9CR0+cA2xoSRsp221WQJWWV10BJaUzNjg5RVC25zkU7ooUjAiknADl5ugLDfShvG6u1RJ7a787K6RoV2N49LAZUNYvs3UxrKaipGqqxRRlMCw2z11CJVfyBSgx5l6LV0oXPQCli4YU3ZGDhlQ4WZ0W7GAcoUen0JCio23y/qIDCeOzIecl5YsI9yUgFcrgwyKEWNgXpBoR3XYon89jeXkZLMsiFAqZ9rYu1zyop556CqdOnWroOb/+67+OH/3oR64THbpQV5fNnGEYSJKEubk5xyGB7bSCW+V07I2NDYyPj0MQhKbdga2IBtOuXbtw+vRp48vGsmwtdVgq1qInVTPBCQC43iR9UM/XFPp7HSFFKzy8wxEKUtq+8PqBEwBEdw6isLjm+prR4T5TJAUAuQXvrg35Zf190LCgAUVHUqSQmbyv0tg4wgcPeB7fScLevQCrv0d5drp2eywChmVbghMARAf086Sdf9a9JwIYazRl3XsC3N2NRJzAGZAyGyao74KmAQxjWNDpejM+EoKUL4H/wh+D/f3/6fneGpG17VC5XMaBAwcgiqIBraWlJeRyOWN/igZXJ23wzY7NKJVKV/wsKGAbA2pmZgbT09OuQwK3amghDaZrrrlmy8FERszPzMxg586dJjARkToo7fGvA9EYkHGGCtebdN1/chIjhiCODKOy5G0dt0pKZ8BFwtBkxYighJ44GJZxLPp1Uj1IKeUKimv670iVFdeuE9Z9NfrfqUu1zeTeI3vBxyImYwjDcQakpMVFCDt31j9xl418fq+efk395AnPGrBof21PjJgn6onsXblZ1AEgMdJjDHokEiKCaYQJYIZUpI90ta9dgNCQig0lfY+6pwup5XwRnezTTfagBEFw7d5AwLW+vm7Y4CORiLG31S4bfDMWc03TtmRC8VZo2wKqr68PO3fudP3lCoJgzGtpt1iWRT6fx9mzZxEKhXDttde2ZFun5bfPHwHT7OwsRkZGHMFExHEcrpn5qTnVRC3EJGoywakBswaBlNP+k1UETkQkGiEAIR3C3UBF9wgkkLKm91RJNvY76u/R1D4TuoWTdRx8ZlI3PCRHzR3WCaSUUhmO5ucGrrzzzz5n+jcBQHFTj3CtXdq95BR9xoeqEYTD3hOgjyihIUXAZusWHxZM3eRZnrN9ztFBj9S2JYoirj6lXNGnA4f870c1Iy8o0POgBgdr9W6qqqJYLCKXyzna4Ok0YSMt0JqdpkvO9UrXtgVUT0+Pp+OtExGUpmnY2NjApUuXUCwWcerUqbaBiYg4+dwApaoq5ufnMTMzg+HhYVNa000sy0JNDoDNbhp7T0SM0J4/EXFk2JQqNF77mG4iUM7riy+Bk1YHHARUUsZ73DyBk5HS0zRjcdZU1QYaoDYJ2BpVsTxLPdf5yj89NgtW4JHYX4uWhN4kNEWBvLYGNiSC7WmuiaeULRjRk3XBj/TFTE1u/UZPTgolwpa9J5G6L+QQSYkmSBFwuqX7YkNUOyyG8YyirJACAKVYBhcJQfvKn4L5rx9p7M35UDPzoOj5Tk42+Hw+b3QnlyQJgiDY9recXrMZQLUCtW7T9ngXTaidY99JB4rx8XFEIhFce+21eOmll9oOJ6AGKCt0iENxenoaQ0NDvsBEFH/6MUDSFxgmpl9Fa9nWbNtO4vbuh0LtpVjlVVPlJDYaQSgaQXnJ3a5eWN4Ey3P6VTxVi+Q1KJAshAzLmhrE0lFHrYZHM9WFkdtzs0vgRAGRYd1aTiIptVxBM7sV1uiJFgGC1/0kynKTNfqyQsp8X8jmuLNCyioSRYX7ahGxJ6SqUZRVSrliWM8BQG3zfhTQ3um+TjZ4QDdxkTShmw2eOAqbKdLdDp3MgW0MqHrhbTtMEpqmYW1tDRMTE4hEIjhx4gRisRg0TetIQS1gr4VSVRWLi4uYmprCjh07cOrUqYa7qKvhGFipUkvbMQwYcpVftLv3AIAdoNyPHAd1Zcl0PyM2VujICDzgAiivruQAEBrZ4Qip3OyyARW1kUJZ1BrF0pN/naDGR0KmaIau7VEqEorL60hYUpvy6ip4P+5RYpBYmINEdV53SkvS0ZMYD0OkXlJTNVM60M1cYlUoEYYQDaGSs6fC6Y7vtGhYWk0TseE+E/C9FBms/v1Vfw9sNbpgqxcP5PPgImEwX/sf0N72J76O61edTo+Jooj+/n5PG/zKygqy2SwURTE6mtebvgt0tovEVmvbAqqeWhlaSIMpGo3aOlB08o+bAErTNANMAwMDTYEJAJT/+EewWZfBgMXaVbcJSA7wZYdGAMAGKtv5O0RR6qUXoeRrCzCfiEPa9GeGIAqN7IBabaFUWq+lKfmwAFVWTYuyE2ho44FTPQ4dJZHaHHIlb0CwCg4rpLJj00iM7jeZJhwh5ZJWKlfnQnmZI0hkAnjDONIXM86NmCLq7V2J8YgJUmQEiRVS8ZE+21wwAilSp0aPLXGKogwwWaQqigEpABCrKV42HIJaKjt7lK8wOY2NX15eRqFQwODgIPL5vGn6Ls/ztjZPPM8HEdSVoE5EUKRn38TEBOLxeNtaIzUilmWxsrKCc+fOob+/HzfddFPTbVnkn3/P+GJr0TiYrAsUegeMFGDd8xsagZayA4/pd26gql56EWw4bE8Z9Zm/YLKD7dxJpfUUOKG6oR62pzjdUnukUwU5DxoGrGUfjk4x0aLNAJqqQYyHIRXKYEXeBik2GoGaz4FNeNfByQtUl/J+/bGFlU2EB3pQ8tE9w2tGFjE5kPdjbRlFO+eskHITFxIbmqxMIEUAZpOqmowkakUGK/LQJAkMlcJmv/FxqG/+oNMRrmiRtk1O03clSTIVHY+Pj+N973sfOI4Dx3F4+OGHcf311+Oaa67xffFabxbU+973Pvz4xz8GAKN5dSrlXUrSirYtoOrJ7xRZwA6mG264YcvBpGkalpeXsby8jN7e3pbARMRUSmAUyQwnGuy9Pqb0Olz1M719jpAynlKNosisJM3H3pMwNASJ6uROz1kCAGk9BVWSDDgR+Rme6CQCLDeXn7FhbxnhTiAlxnWzB1nkWZFHaXkV4eEd4HqTxntWsxlXSMkLcyivrIPlOIR6E1DKFaiKgrBlMad7EzaayqRhSwqX3XobksiJFomi6KJnK6RiO/tNo+6twx9d4WQRiaIIpABALZXBhkNgQmJbINVs3VGnJMuy6/dcEAT09vaa0nmPP/44HnroIZw9exYLCwv4wQ9+AJZl8dBDD9V9LUVR8MADD5hmQd15552mURuf/nTNOfnZz34WzzzzTPNvzoeuWkD5ScNpmoaVlRVMTEygp6cHJ0+e9F381q6x7/Q5JJNJ7N69G8lksmU4lc89bv7lx6uLRL46niJEta7xGT0Z5xyNA9E4sDDr+hgrYPzICikiJZuD6iNd62WMAJxTe3wVMDKVyqKdf5zI2yAl9kQdx2ko5QpKy6uIJuJgRNEbUuVq09Z4FEqxbItKGIYx0mFO4+uBaqf2zZrLsd5eHv08N9A5Nc6N7ew3fT5ADVKhaqNeVuRNkCKK7NAXV0/XpiWKIqKjKLVUAhsOg//2/wP5De9xP1YddVv9UKOd1VmWBcuyOHXqFN773vc29Fp+ZkHReuSRR/CRj7TfRUlr2wKqlT8yEq1MTk4imUzixhtvbLgqu9Wx7yRqGx8fRzKZNBraTk1NtWzAkH/+PfCVmumBsVw1aj39YMouozXabP7wEz0xFCyFoSEoudqMJyWbM7UZYngOPF+NzGQFHMVxN2u45NJLj6VAREDltJDS0RR5HFi2BimXBZah/ja0MvX74DjIm5vGnpqXnOBEoABVNfamaFAZ5+2SqgScQeTWMBfQPx8rpIzzcBAnCqb6tHoS+5KobKYh9CYgpbLgEnEo2Rz4RBxyNgc2XE3R8q2P2uimWVDNjtqgZzr5lZ9ZUETT09OYnJzEq1/96oZfpxFtW0D5kTXKsYKplS7nzXYet6YTrXBsR0dzRYyAr6b3TK/NCUCPe+PWhrVrr2MUpa3rURATjfkClFVcPAEll4WS1RddUjvlFUV5dVrXWwfpFzROffxokdSfk2EhlIxBoSMPB0gp5QoKY5OIjurjOAh8tXIJmiSBtfy9+WmUa7wPhxQcUbgvbkCyXGeII9m7cxszYpqR5QEZa6qPjqJC1f00YhpheM4R/uJArYtDaECPtgTy3/7afWwkArVaeC/881+ifMfvN5W92A6AanbURiN69NFHcdddd3X8s9q2gPITQZEoRxAELC0tYWpqCr29vW0Zv9GoCYM4A8fHxz33uTiOq9sp3Uvlc4+DlStQwzFw+RSUaBJ83mOT0yu95+ePc9deMCVqkSsVwURri5qpaFUQoK67tyciYnYMQ1lcBCuKUErOn0W9Ql83CTH99644pLg4yizBR0ImSBmzqgTe9Fyxt8eUWiSOPxpSgA4q0iZJ3nTev1PJYk79bYcHap+fKQ1oTTFSizWJbKwRj5MIjKzRkxO86CiKvIYTpIS48/6tFVI0nADKHakoemmDJIEVBOcO+Y9/DT9LXGd0KSc/XvZsoPtGbWzlsEI/s6CIHn30UXz+859v+DUa1bYFFOA8coMWx3FYWFjAwsIC+vr62joXyi+g6CLfaDRa14DRSgSVf/kstFACIXkd0DQTnOR4H7iiHpE4pfeU/mHbbdxGYz32oCqARi2cDvs07IDu9iOgotN7ROXnn7XBqdnoCXDemxGqe2RSNc3GOXTUIJCyTuq1Qso6tM9RHuNNGnHF+RUn8OCSvM0Q4eR8dFNs56Dx+RjPd0j1eYm23tOywskmC6QAPYpixRDUcgksgFcqL2Dz2t9FLpfD5uYmZmdnUalUwPO8CVp0F4dmukh0Us2Oe+/ULCgAeOmlLdWl8QAAIABJREFUl7C5uYkzZ840/BqNalsDyk2khmhzcxMcx7XFEWeVn7Hv6+vrGBsbMxX5+jlus4DipQKkkH1fgIaT6fbeIVsakJbSP2yDlBZ16Z6hNnbO7MCgYzSllUtgHfb1+Hjts7NFT5oZDpKLqcB4bcq+TEDlBkCnMfIAEOpLOi68pHUPHwlB2dwEP2JpIFsd527dfwqP7EBhfgmRoQGUVjf086SKiJuFWD3nHqCDCACknP0xQjRig1Rs16ApuqSjqFC1fID+PK2Q8oKT05BDAIAsA6Q7fyism3w4zoAQLUmSkMvlHIcZ8jyPSqWCQqHgOMxwq7WVEZSfWVCAHj298Y1v3JLPZtvOgwL0P0TaSk66LkxPT2NgYACyLGNkZAQDAz7s1A1qfHwcsVgMIyMjtvs2NjYwNjaGUCiEw4cPN9QSaXNzE4uLi67OGjelx18Ap1YQylfHSmhUgSRgApQSqZ2PF6AAQBWqduplvfjWCVBMoXpsyghgi55c2jJpudp5aeUSlOoiqZTK4KupIqvjzAQozTtycRo2yFrOhTZLWB9PN7ZVirX3R4BJL7zWVB8XjdQARSKoKqAqc3MQRoZRmpwxasI0STLgFN7Rj0qaMov4TO8BztGg7RiUBAr+BFL0bUAt0gz1VtOHDntnvMW5SUOKfE5if5/rTC4iE6BIhwlBqAFKDOmAKpeAaAzSq+/xPB5Q6+KwuLiIVCoFURSNYYZ0IWw8HvfdQqwdamYW1O23347vf//7Dc+au8y6uuZB0bKC6eabb4YoihgfH9+SkRtEm5ubGBsbgyAIuO6665rq1ddsBMWp1OJjgRMtGk7tkAGnZrV7PzA/bYITG42AcWnX0+jeE939XMrkbHCyymuCLhcJQymWTNEcLWuqTykUgaVF8IP2ImZhZBhaqYTQziGopTK06t9peIe3iSU80Gtb4Mspb1OE8Zo9cVvzXSuIhHjMMZKyyrpH50di1fTglvYjqhdFqZWy3u8wFAYEEcIT/x+kM3d5vjbp4pBIJMBxHA4cOABAT7GRYtjV1VVMTk4atUk0tDo5E6pRFQqFK2lQoae2NaDoWUiDg4MGmIg6PbSQwC+VSmFsbEwfa9HiTKhGAaWqKrKTL4IDwCoSpHAPhKJzxwipZxCsRNmd60RPpucO7wefdRn6F4kBKe+BgF4iFmynwt5GC1O9JPTEdYt3rpYCZB26nRNI0dETkTjQB5WKRNwWW743CTnVWDsnWnT0JMSjrsYDoBbVAPVh5QQpqyIjO0yfEaCn+qyfFQ0pkaT2aMOEIBhRVGjHoOlz8oIUP6BDmqlGm4ZZh+wjUWUIKBWh9fovEbHu+XAch56eHlM0omma0ew1l8uZZkI1aspot0hGrFtg2aq2NaAmJiYgy7INTESCILTkiPMSz/NIpVJ4+umnwTAMjh492paQ2y+g6O7mo3t3IkYtDFJEXyyEkt4qRxXCRqquFcmJAROkGE3T4QTUCoEBowkqAE9wackBaBMv6//vo+tHvb0nqxjL5jP5N0kdWhdhWmSfRHGoVWJDoiOkBDKNmLr655LVQtVhvf6EWZk3HUt1cSkShQb7TY1866XHwjv6IaXsLZJYqiaKQMoaPdUTF404fh4i1bbK+tkA/iMnAqZ6YvuqKXsxBBTyYEpF8E99G/KpN9R9rh8XH8MwCIVCCIVCpu0BVVWN0fHWnnnNjI5XVbVpuF3uvbN2aVsD6siRI3VnQuUdZhS1qnQ6jYmJCUiShJMnT7a1cWO9qE/TNCwtLWFychIDAwM4MdIDruzedqgVeUFN40NgSDTm4VAz2inl7YsmszJvdLTQijosmqmbalZ8PAomJBr1Vk6iF2XOozuG0N9bGwKpagDLgK8CS15bAze814CT5vV5VRUabL5eTaiOqncClfGYnrhpaCURgTcfj5oALvQ6ZwXcTCREoeEd0FyiYBpYTnDSNFWPoqqOPuO/UgUQqsCNxgBBBFMqgn3hh1Cv/3XP81FVtenierJf5WbKyOfzWFxcRD6fh6IoptEa8XjcZspoxiDRbTb5VrWtAbUVIzdoZTIZjI2NQdM07N27tyNdhd0iKLolEt2rb3lmArFyBawlXUeip1JsEGKlxX2iqnKJXUBiF5IrF02pQj9ShvaAW5kz3UYWLrXoHMmw4TBYipF0BKVZoieGYSFnaguyNXpyElONKkjXAvp1aTlFDnSkwCebT+kCMPafjPOi9smsY1AakdDb4wkpK4T83G/9LHhjGjPVfLf62Qj9evTICLzxu7ZGUX6jJkdVynoURV63lEe9OLwThbpuo+Pp0RrLy8uGKYPuTN4obDKZTEtbCN2mbQ2oempl5AatbDaLsbExKIqC0dFR9Pb2GrnpdssKXbrAN5FImDpPLM9MgFed318ppm/M03BqFCpOSq5c9H4A6/7lpyFFu/dYasNXo00MtEPOhzmCp1KsiiVyrgcsrloM6hZNiYP9npDSb2BsUZRaLBhpPlpaE+Cp635zMICEhqoWcku3eOK4oyHEO+xz8fGozbDilurzKwIpAidPw4SfKEqqAKEwNCFcN4raqk4STqM1yOsTaG1sbCCTyeDs2bMQRdFUtxWLxRzhlUqlts0sKOAqB1SrEVQul8PY2BgkScLo6KjpCqmTBgwiYlcPh8OeBb5lIY6I0liar5Aw1+fInIjk+pjncwicTKDzka7yFLVfY0rv1Vm0aBmb6dQx6wGHcelTJwz0m2ZX0XJamIXBflsE5Ph6y7OGxbxZkWiFqJ4Jg64nE5I9NkgZx61CyhpJNnI+ZG4TkTgyDK1CzeCioiigxciJlkMUBQ9IXe5WR7QpIxKJQBRFjI6OmibwbmxsGFsT0WjUiLjK5TI2NzcDQF0pqpfiazaCyuVyGB8fR7lcxujoqGkqJlE7eua5SZZlPPXUU5529eWZCeP/GU1FKZREuKwvWArnvPjme3ZRz7HvP6QHRgHAEVRcMQc1pF91E0BZLeuc22BEi7RczmiHpJW8i2qB5tsaAXpkxHBcQwsvF4uaIEXvPZn2pKoQZAShBimHKMpNTgYJYWQY8nq1UDcagVoqga+aDKwgpAHhxzFIIGWtVwJq0ZMVUlxSj0pVS0Qq7txpS80SSPHVCzlGDJkgZRyTfJ+qxd31zBPoG6iVTFQhr1X3bphcFbpSBSwAVYxA5d33xbppD4fsQdUzZeTzeaTTaXzwgx/ExYsXIUkS3vWud+GGG27ATTfdhJtuusnX69WbBQUA3/zmN/Hggw+CYRicPHnSsdNEO7WtAVVPjUIkn89jfHwcpVIJhw8f9izw7UQERfa4KpUKbrzxxrquQKf0Xj4ygHDFbjXOxkfAK84GBNkCtPTAKBIZ834RgROjSK61VEpCX5g4iyFCiVWnrVbTe05w6rQ5go6o+IEBU8d0x8dbIGW6r16Ki4YU9D02FlEwoojK4eshvPxz16cSOBG5wckqYYeeyqsHKiHZ43gszqHfHS02FrNBio1EXfcPnWSNonw9p98ylVhTAYYFI8vQeB5qUv+OstVCdLai/140lyiqm1od1TNJ0KaM4eFh/P3f/z3+6Z/+Cc899xzuvPNOPP/88/jxj3/sC1B+ZkFdunQJH/vYx/DTn/4UfX19WHEYfdNubWtA1Yug/FoxC4UCxsbGUCwWDTC169h+RFKJsixjdHQUpVKpqY3QfMQO1Gxc73ThBqd60jih4b0rqW8EwqbLaPg6ffMAveea23NIek+pGiKc0nte8lqIrS2WuFi0VvRc5zimKAoAN6z3NlRWvb/kfPVxJFVKAMXv2AE137i5hdRfObWLMs4t2QPFY4IxiaJI9EREIOW0p0YkjIxAo0o7rFEUR2cjWM41imKswzQ1zbHwnFEkaJwANRKHJohGxxSVD0G7+DMwR19pevzlTvHRarYP38jICF71qlfhVa96le/n+ZkF9eUvfxkPPPCAsZUxNDTU0Lk1o20NqFZVKBQwPj6OfD6Pw4cPY3BwcEvrCwgYS6WSKZVIIj+3qytreg8AFNb+2LLovBg7pfesmus/id3p8w0X9qqiniqyQopbmQPCUaDgbPs3DfSj97VcgMZVo0sCKCXjvOC6mSPISI96YqMxqJZzZqsRoFp2BjfXP2Bs5nM7hmqGEE31jJ6IGoWT1RzB9ybB8ILjZ0KKoWlIOUG7XkRlHK+BKIrtr0KnTjTI9O/wnrVliaJoSQn9NbhyAUooisr0CxD3X2/c302AkmW54cndqVQKO3furP9Ai/zMgrp4Ud9j/sVf/EUoioIHH3wQr33taxt+rUa0rQHlFybWKZrFYhHj4+PI5XKXBUylUgnj4+PIZrMYHR21RWwkfegGqBIbAwMNZRaIS+Z9H5Ley0SGEFL8p1+s2p0+3/RzreIyGzY4kfQeE+8xj/xowHRBR08EWGAZV1gB+hW98Zy4HqUquaxjxMFG9MXDCVKA3rSUhhQjCI7j3Zkdw9BW7Z3h+SPXABnz74/fscP2OD8mDCdxPT2en4VXJMVWPxvr++ZHdkIrWtyMVUhxvdW9p1DIFkUx9D6qINQgRUdRA0O1iJWetUVUJ4piyD4UH4IU6wMrl8FZov9uA1Qzs6COHTvWsfO5dOkSHn/8cczNzeG2227DCy+80FFTxrYGlB/RM6EIGDKZDA4fPozjx4+3DKZGRkiXy2VMTEwglUrh8OHDuO666xyf67V3Nj1nX+iyQj+iir7QZCKth+XxvP4ardrSvVJ9TLzxrhtW955N1fSeAStegJr2mIVVFd8/YG6f43ToKqTYqLn7Ag0ptrpAGw5EYoku6ou8JsnOKUiZWqzbIHrqLA0p1sEcIezcWfe9244fidggReBkPMYCqboiKT2GsadVG4yiWFl/XZUPQRHCkGbOI7yvlsrqli4MjY57B/QUH+0m9is/s6D27NmDW2+9FYIg4ODBgzh69CguXbrUcDPbRtQddpUOyk+xbj6fx/nz5/HMM89gcHAQp0+fxtDQUMt/qH6NEpVKBS+//DKefvpp9Pb21n19v+YODQyyQhsn5AIISTnIQgSyoC9mshgzfqRIr/FTjtuv9B2l6J+PFq4u7LG40ZUagPfARB/7VfXEUvsldPRUu02PnEjEYDwvYk+9WOFkuq/Xe9FgdtTmbcmjN3g+1iquf6D209tn/Ph+fk+PI5yIrO/ddB89fNIhOjTkMNeLiOnrr9UsEdFpyX6XiyoaSiSacklPk/Qz4/D3JFTySC9Oo7KFXUr8SJblyzILqlKp4NFHHzXGaxC94Q1vwOOPPw4AWFtbw8WLF409q07pqo6gyuUy8vk8zp07h9HRUVx77bVtvXrieR6Kori255dlGVNTU1heXsaBAwdw5MgRXxbXeoBiLFNSSPSUZgcQgZ46aya9F5Jyxp4WK1cgi9692gikQrlVALX9JyKuOixRC8f0IYmxOvsaTab39BN2/70SSFmv+m2Piye8owmyWLvsoxkiBaVud5fywOhxwGFwJABj/4ntH/QEONfbB3D6V1zN6J81HT3RYnjBV4slwAHWDilOOopiyNRkMgKDPCYUqn1mgA4prwsS44kOURQlNUo1dq0u8Fp1D1bjeLBSCSofAiuXwQOQxSh4VcKLL76IfD6PZ5991tTw9XJ1Km82xddMBOVnFtTtt9+Of/mXf8F1110HjuPwiU98oiOjimht63lQgH0mFKCDaXJyEhsbGxBFEQcPHuzIB/3cc885znuSZRkzMzNYXFzE3r17sWfPnoa+AC+//DIGBgYwaBnTQNJ7BFBadcSKCVCMHVBWBx9tkiAWc16pgFdqiwsruy8kKufQsSC3agIUl0+BLeaMyMk0xZc2F5AFK9FrBpQ1nUeds5ZJ1QeU00JdvU3LVh2AXj3ZnABDL7b0gk0WaMml0JjjjKa62voKtD2HwZLPI7NpT/HR0Yh1Qbfuy3C1BU7NpNwBVXVGqtnanpM1IiRwdoyo4j1GqpKWVizWAAWYf7eA+TMDzO9HkmqpPcA89JJet6rvWY1TkUP1QtMJUACgciJYuWzURBWjA+CUCl5YzuPkyZNGp/JsNmt0Kqf75pFO5Z3U008/jRtvvLGhKOqOO+7Ad77znSuxWPfqnQdFVKlUMDk5ifX1dRw4cADHjh0zWhR1QtYUn6IomJ2dxfz8PHbv3o3Tp083tSHrFkHlVR0AcTaPrBJHnNMXjDSrf8kJnNw0zx0AAOyRJ83vo0kLOq1yfAeEin4+XD4FNRwzQOgKp3BU/2lQTE+vebM842OsBbVwM4keA1KOIoCgIWRdaKMx/f4el16M1iiqmAciMcijN+gRFGA2SPjZf/KAEwCwPb0Az0Pz+DzYRI8JUqb7PFJ9bmKs75+OopLV9LNE7UVRUZQ2OAKGviihDBN0FGUCk/X1FcWAlFUqH4LCh8DJZQhyERIfwdE9MYiiiP7+flMBPimKzWaz2NjYwPT0NCRJgiAISCQSiMViSCQSbY22VFVt+Fi5XK6pOXPdqm0PKIZhUKlUMDU1hbW1Nezfv9+USmtXPz4nEUCpqor5+XnMzMxg586duPXWWxsO3Wl5pfg0TV+YCZz8iICJaI4/iLIi4LB20YATr5RQEfRFOFxsrTu6GnZPDWqUOYKh9++80k/WLIA1TZukUh7Z+qYIQIcUBBHIeYCKQMivfKSwhOy6uUu85X1rO3aByWygKZHuCj1JE6QYS10Zm+hxT0HG4oCbxT0Ss0dR4SjgoxuIVVq1wFbjBTOkLFJjVQBa/waqjr7HY29ASNDfS4jT/3sy/79MD1X4EFSWhyAXUXa5IHLrVE7PhZqZmTFaELUr2mp0y6GbCo3boW0PqLm5OUxMTGD//v04ffq07Yqkkz3zOI7DysoKXn75ZQwNDeGWW25py7hojuNs53x+prY3QkdPkub+ekvQXTrWPauyImAvNwNUX0LiQ1CpK/hKqAYRcjuBllN6DwA2YnuAGLBr7kmo4ZhpxDwAqJEEGNmnq6ueW89LiV49WqoHKrJpH+8xQ8q62U8iJ6fFeXAYqHg7HZWd+21d3L1kgpOf/Rq341ggZZMTiMgeofU+2nFJfw6JamRjhVQobI6MhZApilIHdoJxSyFTUZSa6LNHjQD+Lf7bkGQGIV6/ryxxCAkKyor+t/pc7JcAACIrY7TyvOm5IbmA9OI0kjv3///svXt4HHd9N/qZy87edyXLluS7Letix4ltbDkJNM+L277lPadcWwgkJ5SWnLRcUhrogSRQeAscroUHCIRbXyAhUGihnLR50hBo3zSBQOw4sRMabF1sWbIlS7Ksy+7szu7czx+zv9nfzM7MXrSyZMuf59EjaXd2bvub3+f3+V69j+/CSlFbQLlZ4ZWEK56g1q5di46ODt+BwPN805sWmqaJ6elpjI+PI5FI4ODBg021V9d6zgtqGnHee/VKyMkLm7mzACxiAgDOKJMha3ivrItRS6VUa93hVk8my8GMVpqOmCUutGtPnuKCtz+Khpuk3IgGBIsIESdJlVSUsabTfsmr1QgNY9MOsOdHg9VTqtXZ/JGr/mgzXr4xGkFqaREwU6WOuI774iQpx/YeKqrCrMcweDz8RsgqAxSASMiArFnPfJg3bJICAFEWIPAWYb3E7ccu5rfWPlkeOsuDN1RMX7iIjnanj7dWNENtNUI2pMHhSgmTbwaueIKKRqOBCqmZJj7TNDEzM4PTp0+jpaUFW7duBcuyTXem1hJmvqD696Ga1/2jfNoxCY0VEDIaI21FSPiSVOfFlwDAVk96NAFWrqEtQ5CJJ9ECli5C6344q1UIT7ZUKh93yDNgkVQ1xUKrhxg1OblIiiYngsL2vYhOVRbhNTbtAGCZ9twwW8sTKKPrzoAClgUy1H3xMymHI9YPTcARyuTnR1Lkda98tWi8kvRrNPUZKWLaE3xVlJ5qA0MWSqWEXX3f/4H8cRY8Z03sRZVFJGQpqGyRRyqiQSyGbFWlaBwEXodqcDjJ7gbPGtjKjdrHCJnNDzkPUlukvcbZs2ehKIrd7XtycrJmtSWKYlO6dq8kXPEEVQ3NMPGZponZ2VmcOnUKiUTC7sk0NTW1JB17aYIyDAMD46VEz5L/STO8v9YpxconCXPlCZ82761hZ6HBSU60enLD8HHcZ2MdSEnOhOG23Fn7b1JMliYn2rxnROLgchnoCYtkGZqMPRScUSpCy9ZSLd1DLZnJ0nHEKsEUXkrKrZ68TH1uUAESJJFUyJfUj6YCrk7FXrk7Zi2JzMT3lqnhvgSpRL/w/4C0ACMSt1pbuEDUEwCYQsShorS2DWCrqCjdIyBC32eV23nDy0T807OWGo9HTBRV1l6vZIt8haqiSQoAxrANW7lR6CwPg+EweWEe69vrD9muB35qSxRFDAwMQFVVh9qKxWJIJpOevq2laJC63LjiCWqpWm4Q0D2ZrrvuOsSp5npevqJmgOx3amrK6qC76Tpk5DhSgv8KNaMGR/YkuHzVpAI/854XaJJiTMPu4FsNerzFqoieqP9BI0RFwOaqB0TQVQZsoioGqLpq5j4AxpqOysm5pKKMlhoTmD1AzHtGW6cz8tEN90o73Qrka6gGQUjPQ7GasSQYSax8zYOMfaPq6ozIdKsompxMlgNj6ND3vMrxmbdcb53jPz2bBN1HMSKYDlUlFjkkIzqyhRBSURU5WUAirGAM27BesMatCQaj0xK2ddQfSbpYsCyLWCyGLVu22K8Fqa1HH30ULMvaQWH1Wm2qtdp48MEH8YEPfMCuLvGXf/mXuOOOOxZ/oVVwxRNUNTSqoDKZDIaHh8HzvG9PJpKo20yYpglRFDE9bT1E+/fvx/Fz3oPRz/9EgwRTCKa1kjUYDjJXqjFn6lDZyuoKMcV/gi7ywcm7XtDiad+8KmYR94+Yi2pSViWYfBhmIlxJbnRwBJnIA87NS0E4yMkjWbfQ2e1p5rM/31ZpGgSq3yOTCwHE70P7sDyqOxiReMW1mzH/8HIzGvckKXtf1D0g7VZo5UxUlFb6roxQ2KGiCElpcYucWKogsZucaLzletFBUkWFsUlKURmkYjrEIocQZyJbsEx/OVkAz5kY0TahK2b5BAVGAXDpCcorSTfIt3X+/Hn8/Oc/x6lTp3DTTTfBNE0cOnQIn/vc56oeq5ZWGwDwlre8Bffff//iL64OXPEEVUupo3oUlCiKGB4ehmma6O3tDbT5NjtCcGFhAcPDw3bXzd27d5feKUcyzRYSSIedkWPn82sQFyoJIMlZvgXBLEJhrMmKR/V7IQnWNUe0YFNWNtaBdH4SjKHbVSf4Ui6UwQsw+FIZIYqcAiui16HgHB8rTYwmx4HL1ZAThZJvqwYFVvE5n15YgBXhyHpcn1e9OAAwOrdUvLZYmKk1VcPU/a7dS0UBTpJyqyc/U189IOQElO+hce3vV/3cW64X8f1fWeQajQCixCBcsvBmJQ4RwYCqMwhxJmSNRZg3oOkMABYj0iZsilkVUM5MFbC9078U1FKgnqK1giDg1a9+NQzDwPr16/HJT34SiqJgcnKyps/X0mpjuXDF1+KrhlrDPHO5HF544QUMDAygq6sLBw4cqN4wsEkEJYoijh07hpGREezcudNRRPaZYYucVJ3FbME5Oea1GM7nvWvxEXJSzZAnObFmdTIQQ2sCa/2l85NQQ2VFRchpOaEn0rb50IsUaAQlgALBZAQ4Ixa1pMd9ck1Ath8KgNy2yXe/dZn3PGCm1viqp6qfLampIFXlBZ0yv5LmlgRKmzMAxAg5Vbvpikg8KrdgcHAQExMTyGazgVaKt/6OiLf+johCac0mU+uDomLdK1W3niXin8rJPHSDxbi0DrJhncvodOOV/xtBo2WOSAUJQRCwdWttofJerTYmJiYqtvvJT36CPXv24E1vepOjsOxS4opXUIsF6QklSZLd+qJWLJagJEnC8PAwFEVBT0+PPfhUVfV9KGn1lJErJ6FUyJkfFWIWH8EohtYgqVauytVQHIJcNgeqkTKhc1r1SuhBpist0Qo+F2C6q1J5QU+kK1b2pkcrcCPRYjW6k5xmTUMolQaKJuxurV5wKwg/FeUHEiBhekUWwvLZ8dlZz/eAknnP67xK5OunEsn7jIdq9SInMxq3lQ5Xh2JSS8TtNu0RaDHXInDXf8NeTbPDtScmJpDP52GaJqLRKBKJhB1EIAiCvZB76+9YaornLZKSFROpOINMnkU4BBQBCCFLSQm8iZzMg2dNACmsi2TtAKRLhUYIamFhoaL8WbPw2te+FrfeeivC4TC++c1v4k//9E/xxBNPLMmxaFzxBNVoTyi6J1OjPaHqbSnvdWyvflC17HdkLoW2uNOslwrloZo8BA9SqsW0F3jOLt+T209lUoRBkxNt3tOEOEIFa8JUoy1OFbeY5FzAs9yNTibUfHVTnh5LVZAUASEpP0XlqZ4ISr4o2synJmqrQK+l1oHRVdt/QxBI3C40YsoMuhdeUNo2gFOcgSdGOBqYYmCEwg4lZXK83f2W53m0tLQ46s0ZhoFCoQBRFDE/P49z587ZAQSEsN6wN4d/OtqJeMx6lrJ5E2GBgawC4RCgqAyEkAlFYyDwVsRQXg6BY5KI8QpOThjYtfHSGJ00Tas70IG0CaoXtbTaoBfmd9xxB+6+++66j9MIrniCAiySCkp8Y1nWLhFC92Tq6ury7clUC1iWrSvhjq4VGHTseve7JppHmFUqyCnEqJBNaxLgqddlROzSjVHTezWsMuGK/0Nm4wnPSrQFrK5CjdZe5FJLOKP26pmYaWWhx1vqIiminmgE+p6EKFhqgq6mokh9OM/3wnEgHGyKo++Lu2KH5/klWjwj94jJz4uMgkhKj8QrVJQuRCtIqti6wbFYcasogwuD1WsbUyzLIh6PO6JoAeuZEkURuVwOs7Oz2JM4id/kfheAlRomK/4kBR7gWRM5JQRJ4bE2Jl0ykmqkcWKjlczpVhsbN27EP/7jP+IHP/iBY5vJyUm7U+8jjzyCXbt21X2VdYMpAAAgAElEQVScRrAqCKoaQqEQJEnC5OQkLl68iO3bt2Pnzp2XLCNb0zSMjY1hamoKW7duRW9vb13HnspGHGppZK5sFlkTzUM3OKiM86uWDQEG5a8omuVJl2HK5Fdg4jBMa7s4gkOVp7QOdPLTDvVEyiKFVGvC0viIPSnJkRZwPoVoa/GBuUEmZpPhEMrXVy9QTVs5YnzeGUThNq3psZRnbTi9VA2DKzjvETF7uUmKIN+6GYVQEm0zJwFYkXxuiO09SMyPVbweGFACi4S9TKFe5sJAwqHe0ymTG/06HcgAlElKi5fTBbxIyg9axGnaI+qpXgiCgLa2NocC2K9nkM/n8Q+H1yMWZRwkRf4O8VbkH8+ZSEWNS+6tb8TE12gvqFpabXz5y1/GI488Ap7nsWbNGjz44IN1H6cRXPHtNgDvlhsEmqbh8OHDME0T27dvx4YNG5paH+vXv/41XvEK74fLMAycPXsW4+Pj2Lx5MzZv3lzzsX/9619jOnzI/p8mqNm8gC2tOfCMdc2hUmIubUcPsWXfGAcnGdAERciJgJCUW0EBwKzaUkFQQJmcAIug7ONS5ORWFIsx75mMc+XJu3KwvPwyJKIQcJKU52QuRMG7JnOdKtdEkxQ9cbsJqpAqh40TklLibTBL1S8i4gUwquI4B51ST7UQFA1CVH7XBDjVkjtogpOyDoKit3cTFIAKggJgE5QSo1Seyx9pcM6xxe04ULHvZuHrP4+D48rPRVggvy2SAgCeM8GxQDKqIyFY9/zA1qUtxXXy5Els2rQJyWTtwSi33XYbvvCFL6C7u3KRcxnAc0W+aqP4dF3HmTNncOTIEYRCIVxzzTV192WqFe5FgGmaGB8fxzPPPANd13HjjTfaZZEaAU1OPGtUkJOshyDrtRWppcnJC3kkfckJqJIjFV6eLHc1tgZqrPbOwlo8XTGxEpCJnHbe6x61BK39uEKuKdNgIdnh3hyAFcnH+BCylK4sd1QP5HRHIDkBZYXkFdHnJifymhc5AZYJL+hYKwHvelUeul4e87JCfptQSxyk6Qx0AxALHC6I1th/+lQEo6OjmJ2dhSzLTS/UeikV1ErGqjPxGYaBc+fO4dy5c3ZPptOnT/sqrMWCBDTwPG8XkR0ZGUFbW1vzi8iyBjjWgGaw4DkDRZ23M6QESjEFqadasKCl0cJX5hN18tOAh8WOEBNfqz+hAfMegVs90SAkxctOMxytnmgoqXY7aMMLWixVoaSAMmGpQtzugUXDJqdSS4ggEPOelN6AcLG2HC7AP3pPibVCkILNn3os5Rm9p4WTFffOej0BXvb2dWlCvCK9gFZPAKBTZl9iEuZLPrilVE8E73qVdX5fejSKWJSDmDOQTLDI5kykEpaSsvKjTAAMLohhJCM6IpGIb0DGYjvxNuqDulrq6DIEwzCOnkydnZ248cYb7RXKUrbcIInACwsLOHXqFJLJJPbv349IpDIEvB7Qg5cvkRLHlkm2qPMQuPquSdKjjuoTbvMeDTdJdfLT4EwNcsjKug+r1n501nuI0eY9RUh49pgqRC1CiUoX67gKJ0yP49cySROo0ZaqJMV4rJ6ViHeOnFeABcHsul22L4qGl3KqZt4LQj3X74abpEjqgJuk1Kj/RKmFouDV6r6oS0FONF658dd4auIV4DkGYs6AIFgkJYSAEM8AYMBxQKL0FZ7KbcNN3WXzpDsgQ5IkMAxTUa28lpY7jSgowzAW1WduJeLKuhofzMzM4OTJk749mZayaaGu63jhhRcQi8WwZ88exGLNKZvCcRzSUQ0RXoNmsAjz/mQksBqKeql1O2MgxGooaBZBsoxTOeY16/xqKZNEk5TMRBEzrYnLYDgUBEtFCFrwRFSItIAzNLtdBwCHiYs1dchR78ikcAONE41S0idZxftN1LQfhJCUl3mKVMgI+SgIt4oqRNeApQvwllRUVBVRCCWhxC1nfkS8EHgdxbgz34UU/Q3nvXOidM6pEsn1e+Wj2ddE+e60cNLxt5eS8gOtosh36SYpnY84zlHjw7jUbfd0Xcd7/s8cvvLTBHiOgaJYJKWoAGAixDPQdSBXYMCxHFgW+NmJOP7HNaVyYR4BGbquI5/PI5fLYWZmBmfOnIGmaYhEIjZhJZNJRCIRR2CUaZp1qa8rsRcUsEoIKhaLBZrTeJ5HoVBbdFGtICWRJElCb28vNmxYnP+Axk9/wyMcux6awdjkpOjWV0lUk8Bptt/JrYT8yIlGXotBNTg7sZdA0j1CrH1cmTQ5KXzUNvFJ4Rbwhk/tvToCIopRZ/5PpOCfsOqHQqJU4b0YHGauRltqDnl2qyc3SRks7ySpALBG5cIpn+gATxELXZFejrfBYLiaux4r0RYIPgpRjaRqLvILlFVULrURYdVJ2F6mPhqybdqzriu89dqaj9ssEFJ472sK+OxPwojHOJukiNmPkFQmz4LngGQseLySsmR01RnTNFEsFu1k4+npaRQKBXAcZ5OWrut1mflIHueV1AsKWCUElUgkAk149dbjC4IkSTh16hRkWUZ3dzemp6cRDlcGFTSKn/6GB8ea0AwGEV6HqrNgGCc5yRoP0zsopmaQFgRZNV5BUjQWtDRSvIiYUVZPfpDClgPXj5wWi0KsrCoihXlP854f5EhLVZIK2kYNJ3xVlH1+0doDNdzQ+Ai0RO1mYaJIayEqmqSIeiIgJjy3GVMLJ2F6TIZa2D8fzK2E/Ux9y0FOBGSCv+eN1mLksz8JQ1EMhEIWSfE8A563tmlJMjAM4OFjSfzR/toVJcMwiEajiEajWLeuXEBYoypkqKqK48ePwzAMxGIx3woZBPl83rNg9eWOVUFQtbTcWKwPyq/6w+zsbFP8Ww8/LyASMsCx1kTBMSZUnYXAl1dwslYiKr7s4A6x5b95SjEFqSc3gkgqxZcfzAKbQNh0TjiksCzvoQSA4H5TiwmWKEZbYTAcohShGFW6zBICcoc5A4Aaijq2ATwm84DJ2Q0/FSXG2pGULPNernWrp4LiaygTRVCMtvqSlE71nQpSUoqQQNjDpBfUnFIOJSpUlMzHENYqTcdEPa1E3PNGGf/vP/CIJ8jYsawFPM9AzJsALCVVL0l5gVTISKfTmJycRH9/f00VMkRRBMuyV1yzQmAVh5nTWEyQhKIoGBwcxLFjx9DW1oYbbrjBURZpsQEYDz8v4OHnBXCs6SAnGkWNw1KboLNqvMK8R0xLDEwUWOfkrLGhquTkRj3mvVpRiLSgEPEOvdXZSme1HLA9vY3ss40c8iapTHKj5+sAQL68qLq4Cc4P+USHw8fnB6WOKh5y2Du03i+VgKhnN7SQc0xlYx0oFAoryqfykds05HPWM5zNqtA0E5pmWqHneROZ3NKFmJMKGZ2dneju7sa+fftw/fXXY/fu3WhtbYUsy7jvvvtw88034/nnn8ftt9+O++67D7/61a9qPt7jjz+Ovr4+dHd34zOf+Yzvdj/5yU/AMAyee+65RV9jrbhKUGgsSELTNJw+fRpHjx5FPB7HjTfeiM7Ozgq11mg9vh8dCePh5wWoGmMTk6qzUPXyV2aAQVFzVcT2UU+yxiOvCvZPTo3YP6LiNB0R854bk6JzUkryzhUy8UUVGP9SPM0y75muoes2N7lNjYVIi+8k6QWapNRQpd9N5evP51FcnzHqMEH6gXHl0geZWGmS0kOV5kKF9w/g8SMkRQgw6XmQtew6RiGUhMKVzyWXy2F4eBhHjx7FsWPHMDQ0hMnJSYiiuGSpIED1IAM3SeVyGnI5HboBGAYgSsBDT9dX4d0PtUTwkYCMrVu34v7778eXv/xlvO51r8Odd96JZDJZM0GRXlA//elPceLECfzwhz/EiRMnKrYTRRH33XcfbrjhhoauqVFcNfGhPpVD51Ft2rQJL3/5ywOjbRoJwPjhM2FEBBOqxlg9azSr2RoARHjdJiVi3gtz3gSYV6jqCFQIutftICSVFGozH7knRoIgcqrYlkuADtViqb5W7v0n5OAeRtVAwt2lcAtickDYOGXeK0RaHCZCN4pCChEqMZlMyAofg0CZsmhiVPho1chGN6Yj29CmTQGoz7wHVEbvFaOtCAUcvxhOIyKX0wdoApLDyUBTn5d6kkOJilQDP1NffGM3tlP/q6qKXC4HURRx7tw5u+15PB63zVvJZLIpodXuYtFe+MhtGj78HSAaC0FRrLGaywEcxyARZ8HzDB56Oom33bQ4JdxIDlQmk8HatWtx4MABHDhQe3h+rb2gPvKRj+Cee+6pqQFiM7EqCKoaOI6rujozTRMTExMYGxuryKMKQr0mvh8+E4aqARxrPSyq5nxo3OREUFB5hDgDAnRISshBSPTf1UCIKsJ7K8pJMYn1SRECq0I2woiwlRNmiKFynMywXSldYhLgKDJlQf8dfI65cGWAQTyAaIIghVugs6FAoiIoRFoCE4zdJEXgJqkgnAt1Y7N22vEa7X8i5OR4P2JFMBIiTxRrj2BU+WhdJEWjESXlh0IoWHGEQiG0trY6CqAahoF8Pg9RFB1h2+5WG+FwuK6ItlpJ4RO3A4CKT/5jCKKoIJkUALDI5Q2wLJCIL94odSmrSHj1gjpy5Ihjm2PHjuHcuXN49atffZWglgKLCb1cbPWHegiKkFNEQOl3STUJpt2ymkZBYRHmTRSM8tcoKbWVNCLws2zMF6JojXpPYorOI8yWSUgyooiw/pO4xFiTVyNVK4KQLykTg2GRkMuBAEFmLsd5VVFTAKByYahcGFGKhNzmvaJAIt2cJKvwMUhCCoJedL1eVlEz4c2AAZzjd1SQlBcIMQFOlZmLtMEEg7hcPWqPXBN97W7zXjGcritIpRBKeqoi4od0v+c29cU31lY/jmVZJJNJR4060zRRKBSQy+WQzWYxMTEBWZYRCoUcpBVU2aFe1fI3t6j48HcAUVQQ4jnwIRbxOI9C0cTXfx63q1M0gsU2K2wmDMPAX//1X1+y4rBurAqCagSmaWJ2dnbR1R/q8UF5kRNgVVWmoRsWOTWCWri6oFYOi5l82XSXDFmTjQEGkuHti5GMUot3xpuc2QCy8jMfBiEXtlbZCY8J2q+aBVA2v8XkBYd5z42CkHKQlBtBAR4KF6kgKQCYDa333L4YTtvRjbR5byG0DhEzWJXlS/chLs9XmPfcqEbQhVCyInCDqB6vgA4/053Xe0UuDsGoz1zpB4ZhEIvFEIvF0N7ebr+uKIptInRXdiAkF4/HwfN8Q2a1T9wOfOBrKlB6NPJ5DQwDpNMhfP7hCN7/R41dX6MKasuWLXUfq1ovKFEU8dJLL+HQoUMAgKmpKbzuda/DI488gv7+/rqPVy9WDUFV6wlFyiGxLIuFhQUMDw9DEIRFV3+oVUE99MswFBXgKN6hVVNEMCHJFruEQ6WqAXz5ekKct0mvqHFICI05l71UVFss2H+imiG7W68fOblRzbxXD3LhVhimNdEkVJ/wao/ovWx0XWChW8AiKb+IRK/JnKgHL3iR07SwBSmTUoLUedZCTjTy4VboLI+4Ely/Twq3gPdpeQJ4X1fQ60Ek5YbCRiAYRcxLCurvYlQdgiBgzZo1WLOmbB4mlR1EUcTU1BRyuRx0XbdTTWZnZ20TYS343Lt5B0nxIRaZjAqeb9zU16gPqhEFVa0XVDqdxsWL5VJjhw4dwuc///lLQk7AKiKoauB5HgsLCxgdHQUA7Ny5s65S90H7rUZQhJyi1DNBk1NRYWCUuIiQUxDySvlr5UoN17yg6QxaosFRdTRJtcUKKGghJENAXosgyltmPb1ECHk9BoH1yXdqsnmPwGCcEwEhJwDIhaxpL6oHO621EhFIQspBUqqHopL4JGKac3+km7DfpA1UqigTjK0UI6yMohFGQY8g5ZrXOMb7vrlVpl9idl5IO0jK85pc1w1UmuC8QK7Xz5fkJmhCXkXOGUizFB0E/OBX2WFqagrT09OOXCNBECpMhF7uAkJSRVYDH2KRTFr3uFEVRUoh1YNGTXy19IJaTqyKflBAcE8oSZLw7LPPIhwOY+fOnQ11pfSDYRg4cuQIXv7yl1e8pygKRkZG8PT5vYiGgULJjUOIivieBIqUvNSTopcfGp51fmUc6/8VatTnaKLyMvFpBouYoCEesk6SkBNQJigADoKiFRRNUJIRRYIth6jTCipvJpBgao+CCiIooBz6HtfLk7RbQWkeiiqmZD0nc4W1Jg6apNzt7qOq6KueBL2ILGONL5pkioZ1rLXMBYR02TbxzYXLPaNoBVWNoLzMmnEl431NpTBvmqTcBBVERH7wOgc3QZmGBkVRGjJPNROzs7NYWFhwtEyXZdk2EeZyOUiS5ChHREyERO3c9YUCwjEBfIgFx7GIx0OQZR1/+9b6FmenT59GS0uLo6ZfNfzJn/wJPvvZz6Kvr6+uY60geK6wVrWCKhaLGBkZQTabRSKRQHd3d9MdjV7t2XVdx9jYGCYnJ3FS/j0AleQEVPqe3OopL1uTb4j3JqEgcnJjoSCgJar4khNRT27UQk4AkNPLkxLLGMgZ5agvzlXVImeWJ0IyEceZ6q3Lg5DnrBBomqiCIAkphAKi97yUFEHQRE7ICXCqKBoqFwZnaJgRNjZVeeaFtKcvjMBLSRH4XVOBiSNqVgYESEgABhB2RXlm2DaEQbV5N4wVUT/Oy6wWDocRDocdRKFpmm0iPH/+PHK5HEzTRCwWw1+/0SKtz/zIGmv5vGXq+9j3ubpIirTnqQeZTKapC+uVglVDUPRDoCgKzpw5g9nZWXR1dWHXrl0YHBxsKKG2HtAtPzZs2ICT8u+hKJuIhK1z81JRRD0VZMYmqILMQis1WfMjp2qg1RPBQkFAmK9UmZNzHGJCyFZPACCVCs6GOW+TXk4rE5Kfmcp6rzb/U95MOFRCPSqLRpa1/BGkM7CXegIA1RSgsoJdYxAoqycCiU/W7T9TTR6hGn1zNOrxP/lBM0PQ2JDzmjjXNQkpz/JTRSbmex5+JAUAshGpICnZjCDMFNHRvhbj4+OX1MTnh1r9PjzPI51OO/ouGYYBSZLsNhu33jCG7z61CyzHIp4Mg+dZSJKEaDRaExlrmtZQL6grrVkhsIoICrC++LGxMUxNTWHr1q3o6emxH46lbLlBbNwjIyNYu3at3fKjOOAKG/dQUQU7MMIiJsCflGjzniSzSEabQ7g7Op1KolohWlGNOapY0KinBmBQNF/OTNot7OOcpa78zHteyCNpk1QQJDbpmNDdKJgxRBmp4jWYQIR1BpSIur+yIn6oalgwWysIjnwfCVjqJyhqEah+TRISiMFbsRaZWFWylOCdE1X0iPg0DKOm/khLjUYCEwhYlrXNfp2dlkl23z4T77tPRl6UwYc4fPShMN60/yg4jnMkGcfj8QqCbiSKjwR6XGlYNQQ1PT2NgYEB3+oPS9W0kIS3zs7OOkLV7/+pFQIcCTMOFUUgFU2EhfpMH7li+ZpYxmpRTaMaYS3kOQAcOtKVRE1awUuagGgpiZdWT3RbeT9yWirkdWtCjLLBEYa6i8DySELTeZvg/CCxSTvZmIZsWt+lF0kB1oTsJinAqaLcZr4s02pH8tHKc8Gsbr7JwfJ7RVFdbUlsEjzjvyALIikCumpIvSqqo92qPE8iZ5cbzW72xzAMvvTeCN77pSI0AHyIw0NP7cYX3yvYFcvd1TEIaSmKUte5mKa5omoXNhOrhqDS6XRg9YdQKARZrq3fTy3IZrMYGhoCz/OIxWLYuXNnxQqtWDTgLodomiakgMAfWj3JKgNZLRWl5aoPUEJYisYgHfMnkelMyCYpumIFTU4AICrlFbFf995mmPdqRa6Ue5Vga0uS1ExrLOT1hIOkVLMyf0gy4ojVsN+C6R39FqSemg339QCWec8Nr2uSjbLJj5AUMe8RuP8nsAmrjrmy3sZ8S4XFKKggfOm9EbzzU1nEktZ9DaqOQZoa5vN5HDt2DJFIxNFC3t3UkICQ00rw5TUbq4agotFo1Z5QZDWzGEiShOHhYSiKgt7eXqTTaTz33HMOu/L9PxVQLBqIRKwHMxJmUChYk3UkUjLpUeopTM0t+SIVsefzPLE1jNOMxAWSFABIKldRUmkxWIx5z21WJOY9Ap0i+pwRh2GySHC1f59ek7obQSTlR0x+Kqoe0KY/P/MeAWlOSVTlYq4JsEjKy88m6VFEucqVlPt7IVjQWuxqI+vXhKHruiP3cLmxVAQFAN/4UApv//AM4ukY/vJzedz/gcowe5I4vH79emQyGfT399tNDUVRxOTkJIrFInied5gIo9EoisVi0zp1rzQs/8i4RKilJ9RifFCyLOPEiRN48cUXsXHjRhw8eNB2pNLmw88/zJeUUxlucvJCvmD9EPiRUz3ISME7aYla94NxtffIKWG7gy/gVE9B5j3DZBFjCwgxGmLmPLZ3xrFjQ9L+aTZyetyOHnSb97yQ1xOe6kkxyq+RChnEvFcLZpU2KLpzv6pZvn80yShGc/0IC1qLp3pSTI9rMuqvlOJGIWAfhGhJZKssy8hkrKhKRVGg6zoMw1jSquV+WEqCAoAHPrEO+YwETdVx+/+8WHV70tRw3bp16Orqwp49e3D99dfjuuuuQ1tbG1RVxdjYGN7+9rfj0KFDmJmZwVe+8hU8/fTTyGZr74JcrdXGN77xDVx33XXYt28fbrrpJs9K50uJVZMHZRhGIAGJoogzZ85gz549de1X0zSMjo5ienoaXV1dni03XnrpJWzevBnpdBqff9iamCIR1iYqopYIQeUlE2ta2NL+y/uhfaA0QbnNe0EKStEq30zH9JL/qYxkzEBLVIWk8IiHrfumG+X1jEAVffUiqN2bnRMyKRu1ccsOdK5rqcvGPnI+W5eCAirb3KsGhxTvVAqaWXkOhCAS1LY0QRF4mS4LujU5u5VFXiuvbgWunG9GqyGiGItGGGuZC4ioecyFOzEptaE1kvP8DOCvoAhoIqQVpeJBxF4h7YRw4pzTr0V6g9HXSvcLi1I+J5q0dm2K2EFDo6Oj2LJlC9rb220/ins+YhjGVlhLqbROnDiBLVu2LHlX2rd/eAZM6QH9zsfXem5z9OhRHDx4sOZ9Pv/88/j0pz+Nm2++GcePH8eFCxfw4x//uOrndF1Hb28v/v3f/x2bNm3CwYMH8cMf/tBRyTybzdpJzY888gi+9rWv4fHHH6/53OrA6s6DambLDcDZdmPz5s2BbTc4joOmaTY5FYrliYA25eUl036N/rse5CQGqXh96wovJSVwJiSFtytYiMUQYkKwSXDfVivIwnE+pf4+PM9jz549mF3I1+2M7tpQTno9fV6sSk5+yGpxm6S8yMlx3lrcQVJuSHoUMW5xpjvV5DEuWr6IrtQF+/WIWptpsho5uZHT44Fmz7weqyAir/doIiroEU9Tn+8x8nkMDg4iEomgv7/fM/LMMAyYpmn/Jukf5DfDMPZPs0hL1/VLYmp84BPr8Na7z0OIeNdJbCQvTFVVbNu2DXfccUddn6ul1QZdcSOfz19yP9eqIahqqNXEZ5omJicncebMGXR0dNTUdoMUowQscopGnJO4VDAQDlc+HDQ50c9xUXYSEM85B002X/7fNIF0oj7Ccoexi0XnJEKrp+u7TLhJCbBMNqdPn0Yul0NPT085R2NhcX4+2hR4asLbv+KlngiypfysGFc9ICanxT1LN8klRUWTFFFP5G8yadPqCQAUXbBV1IxUfvhHsu3YkCgnEZMKErR6IrlnbqRC3vdU9SDhnO59TUXdMr/RROQ21/kRWEGPVJBlwYggyhYd+xDkCfz2t7Po7e0NzNkhREGb3NykRcyAuq7bgRaLIS3DMJbUxEfj+3+3wfe9RkLMFxYWHHlZtaKWVhsA8NWvfhVf+MIXoCgKnnjiibqPsxisGh9UNVSrOm6aJmZmZnD48GFkMhkcPHgQ3d3dNfeE+ub/7nAoJwDQdRNSwWlv91NMubxp/zj2zfmvaIi1JJNjkMkFr3wuzAbb/Wn1pOgcFgpCiZyc0HUdo6OjOHbsGFpbW9Hf379kCYTdGxPo3phA38b6HcSiWvkZL/9PTgvunEurCRoFPVL6acynNCm1YVIqVzCYL/qbnrJqHFm19kaROTX4mvK6//30u14vuAmOLFYaqXHJsiw4jkMoFIIgCIhEIhAEAaFQCDzP2wEXuq5DVVX7h/i1qmGpfVC14lL2gqoVd955J06fPo3Pfvaz+MQnPrFkx/HCqlFQ1aRp0Pukunk4HMbevXvrjpihB340wkGSLFNiOMyVfnuvE8S89WAJoebI6kyOQdRjET59sfwAh3gTqaj1v+EhvBTd+1xJ36zR0VF0dnbi4MGDng/8pvXedvfFgpDU4ET1HCC9pKgISZH2IX7IaVEkeH9znqRHPZOK85p34q2iC8jIpBKHDll33qcLwiaAOlwQOREYJmuTlJ+iopFTo0iE/K8pr8c8oy4lLYIYX2nSK2hhR31GAChqAiK8pRbbhCzkNWswOTmJ4eFhGIZhR6KRn3oTTWtRWuRvsvg0TRMcx9nPO9nHaiSoaq023Ljlllvwrne9q+7jLAarhqCA6i033Mjn8xgaGoJhGIuqbv7dX221/3aTEw1NM0v+JwMhH1Kix26QevLDXMbEmrT350K8iYLMgCs9tFGqTYekcFhvPIeZkNVO+vf68iDDhxB4IpHA/v3762ro2GwQojo5XrtfZLaY9Gx1r1CNIAlJyR4BE5ImIM77mwwLeghRKql5rhgFx1SOw/O5NDYkMg6yq4Wc3MiqcWgGi6RQSUB09CUhKWLec5yz5ixtRcNNUsT06EdSANC+qd3xOsn9yWazuHDhAk6fPg1d1xGLxWzCSqVSdY8lP9Iiv71MhADs15rp12oEjbba2LDB32zoh2qtNgBgeHgYPT09AIB/+7d/s/++VFhVBFULSPjrqVOnkM/n0dPT4+gn0ygKkopIxKO6s0s95aXGQmyzOQOpRHlfQTzsR1IFmUEiWvnBsbgHUx0AACAASURBVClrv6+4YTtmxq3Xjh8/bptQOI7D1q1b0dHR0dRs/MVg1yZr0vzN2dpSB0Ql4klSNHJa1DeMPq+FHSTlp54IdJPxJCk/hPn6q5yIStSTpGjk1KijfxiNvBp2kFSBuiY/JeWFl22rnPD9OuNKkgRRFDE3N4exsTEoimInrKZSKSSTSd+EVT/4RQEahoFisYihoSEkEokK0lqKYIxquJQKqpZWG/fffz/+4z/+w04w/u53v1v3cRaDVRNmDgS33ACAw4cPI51OY35+Hjt27EB7e/uio1Y+9O3yxEIIKhyuNPMBgCCQuoDlY9LmvULRQDLBQsxZ18D5KKhUgvUkqIIruGJNmrHNexs7GAdBEfVEyAkA/vz3rcnuiQEBm7nfYnZ2Fps2bQLLsshmsxBFEbqu2x1LSd+d5SYtmqR0wyOggzKx0SRFKygCWeOREJwqQdLKq3xCUl4EFeVUzBXLPhyaoIiZb0MigzCr4ELBOeFUIyh3YIhGpQTQJEUrKIKiFqq4JrpyPSGpguuaYnzRM3Ajysu2cgK8CapWmKaJYrEIURQhiiKy2SyKxSIEQXAoLb9eTUH7nZiYwPj4OLq7u7F2bbn0ElFT5G8aS01ak5OT0DTNEbxQDe95z3vwjne8w7Olz2WE1R1mHgRd13H27Fnkcjm0t7fjxhtvbPrgI+QkSSr0UiVyL3Jyg1ZULIuq5ARYakrXTbSkgk0Fc5nyw+dWT2NTLP789wv4X1PlCdUwDJw/fx6GsQPRRBQ33HCDPSkQEwOp7Ow23SwXaWUyGcjTQ1aNs9iOivcVl/9HVEq+IQ9CkDXrnHNK2J7QaXICqiunWuAmp2oIIifAUlJ+VeeLJSKir8ndViWvlhJsXUPOL6qwoIUDC/3WA5KwGo1GK9q5E8KamZmxezXRpOVViBWwTPcnT55EMplEf3+/YyzWE0GoaZrDl+X2azWCRhTUlVrJHFjlBEVWUWNjY1i/fj3a29uxdu3appETrZ4kqVRgNVz9lufzlonBj7SqgRDgQtbaTzWiIknBBBfmvLc7evQo2tracKg3D57f5LkNXdl5OUmLmGllWcauXbscCZjHR6ubUekJ2+99AGDr6Lk1lUtA4MsmwlrNfHk1ZBPmXMHyX6Uj9XVqlfUQFJ0LNGMGXbOkhpAQKkkup1S+XlB5xELWa4tRT0EQBAFtbW0VvZqI0hobG7NLlyUSCaRSKSQSCVy8eBHz8/PYuXOnI8cnCEF+LRLm7hf6Xm+Ssa7rNbebJ7hKUFcIyArHNE17omxra7PbX5w8ebLpFc2lvIpINPg2q6oBQWCRz2sIhbwH8mI4cyGr+0YK0sgVGMiK/4S5d+/eultRA5eWtAzDwNmzZzE1NYWuri6sW7euwvTzsm1s3SRF1BONglquskFDUkKIeUzmAKBonIOkasVcwRnenSmWv4dkWHFv7otqvracEgbn45Oq3DZk/3aTlKQSFXbpKtvzPF9RiFXXdeRyOUxPT+P06dN2qPrY2JjDr9VoBCFNPHQwBh09aJqmPQarmQhXYpj5cmJVERQAzM/PY2hoCPF43NH+Amhuy433frnSOU2rJ2Ley+dVCAKHfL724waZ9/xw8aKCtWsrI6Jo9eQmJ6vu1gH7/0bIyQ/NJi2SpzYyMoKOjg5cf/31gavWIJKiE3urKam8HHKQlFSatN0klSkEr4q9ws294FZcpglki9b3mor4ExVtyiQkVfTokFwsEXFcKO+LkI0XEblBd2T+nZ5L23bFC8QsXSgUcMMNNyAajTrGGRkzqqpWRBDWq2SCgjH8kowBJ2k1QlAkkORKxKoiqPHxcUxNTWH37t2eNbeWommhn3rK50vtLITyxEGrJ9q8J0kaEgkqPDhX/j8rakgly+8R814tWFhQkYxbkxshJzpXua2tDZiseXeLhhdpmabpGY5MkxbLshgZGUE4HMbLXvaymicWy/xk4sjpYMLPKeHAIrhukiLwU1K0iqpm5pvMRJCqUnWeIFsUoBlsIFERiEqkob5dhKSIeqJfByoJdLlArCQjIyPYtm0bdu7c6fARkXFGb18oFCCKIhYWFnDu3DnIsoxwOGyrLFI9vN7AqWp+LfJDTJRA2b9VLRjjSu0DRbCqCGrTpk1Yv3697/vNUlBu9STlrAlD00rFYWvwQ4lieWLjOAa5nGb/DcD+H7BICoCDqLzgVlGE5KSCCXfqxf/9u3mwbAfwUtVTXVIwDONLWvPz8xgcHEShUEA4HAbP85icnKzbbHPDjmCSKqociuAcpjRaKVTDZCaKmFCb2ex8Lo14qPzd10pONGpRVF7XVKTMmHlFcKgoGm5y8sJyqqdisYiBgQHwPI8DBw7UlEvFMAxisRhisRg6OjoAlFNOiF9rcnIShUIBoVCoIoKwXr+1m7RmZmZw6tQpu2BtPUnG5PyvRKwqgmJZNrCcUSgUQrFYn/OZhmmauHjxIlBqeW3ohk1OgkcOFA1F0aEo5XPj+fqdTllRg6YaSKedE4go+pOuVDBRlE3EY84BvhJ69PjBNE3Mz89jfHwc27Zts9ts+yV+EtNgEGkRklI9wtAJRFnw9ffk5RC85gjJZzKvxRflVk9e5r0gZIsCUhGlIlKRRuA1KUJFqxUCSeERE5zjqqhyiAvN9eHWA9M0MT4+jomJCfT09DgCKBoBwzCIRCKIRCJYt26d/TqJICQdEPL5vCOvi/RrqiXhVlEUDA4OwjRNTzKtFoyRy+WWpT3JpcKqIqhqWIyCymQyGBoaQjgcRiG/BQAQjlROTrR6UhUdaomUQpSpjyanIH+TuxSRphqlc1ErSIqAqChNsz48O2et1m9/ZQY/OLw0ZYiaibm5OQwPD6OtrQ0HDx502OtrNQ/6kdYNO0wAGp4e9n8sRNmaQLySW3Myh0TYm3QkhfVUUWemrP11r3f6uSYz9fsU3OHlAHAxH0YqEjymRVlAiPOe5HIyj0TY+XlJ8b8/efu9S6ugRFHEwMAAWlpafMtsNQt+EYS5XA7ZbBbj4+PI5awiv/F43GEiJOOVNkGSnEsvBPm1Tp48iTvvvBM7dlSmT1wpuEpQFBohKNJBV1VV9PX14QNfrXzQvdRTPqc4/E+1oJ7giCCSIuREzIT33qwBqL3Y6HKA3GcA2LNnD6LR6kVLg8yDQaR1w7Ykjoxa+y+qld9RUWWQcLm58oq1nZukMoXyd+8mKUJOAGoKkKgXim6Nl2yRd5CU1zWJRQ7JiJNUytdUSVKAU0XR+3zV7uqV4psFXddx5swZO3S80XJkiwXP82hpaXFE0xmGYXfEnZ6exqlTp6DrOiKRCCRJQjgcxp49exCP1/fsqaqK++67D48++ii+9rWvob+/v9mXs2KwqgiqmV11FUXByMgI5ufn0dPTY2eiAwsAyuqpkJdtgtJUA5paMvnRwRE+6mmxyGRUsD7dC8+OiVjTVntl6uUCaQg5OzvblLJTtZIWr+vQ2m7y3U+QWqoVPF9uSDk1z6Oz1UkCtHnvYpaHwFea21pitS2oskVrDAapKS+SIiAk5VZPXqa+gYEBWzX4Jcs2A3NzcxgaGsKGDRvQ39+/4vwwLMvaix7AGmfnz5/H6OiobTIcGBiAoiiIRqMVEYRe1/Pb3/4W73nPe/CqV70Kv/zlL+uONLzcsKoIqhpqUVCk6sT58+exfft29PX12QPpHZ9esLcr5K1VpBAJoZC3SCkk1Ha7P/Y2d/fYcrQPUA5L/dSPyvsj5j03ZqZzWNdRjlbKZopoaXEqq2KxiEgkYlWO+N9O0vJ67VKA9N0aGxuzu30u1UTnT1oLOHzW34/hR1I5uaQ8CiyiYSepEBU1vVBdMV3Mlr9fL3ICgAXJ2iYRqc0PkS3yEDh/51U1kvJa78zlQoiFreP/Xl8eotiBbDaLsbEx5HI5h3+GJMwu5rtUVRVDQ0NQFAV79+6tSU0vN4rFIk6ePIlIJIIbbrjBYZqmyzlls1lMTEw4yjn96le/wt69e/Hzn/8cP/vZz/D1r38d+/fvX8aruXRYVQS1mK669Opnw4YNuPHGGyvs3IWcFWARjtZXgfmTt3tPVn7ERPChN5fP9eP/UPsDf3ZMdPx/4sQJyLJcyqV4BQBLIS5XVXLiz0ulUr5dV5cahLT++zUy/uNEeZVaVJ1jKCdznsERBAWZqSCpWsipXpCuyOmS6iLmPTeKCoMiGLulClB5TWKRazgx3C9Zlky+586ds/0zpMIDIa1qfiO6VXxXV1dTamUuNeiaf729vZ4WAL9yTrIsY25uDseOHcPXv/51zM7Ooru7Gw8++CCy2SwOHTp0Ca9kebCqCAoIbrnBsqznexcvXsTw8DBaW1tx8OBBz4n7bX8zVfGaQAVJuNXT373Tf/J397NxE5MX/udthi9JERWVzZQjFGnz3v79++1V3HO/Bg5t+Q3+67+yUBSl1PvqRgBLT1rFYhGnTp2CoigV5YmWE26SciNfZBF3KZhcwX+G17Ry2xRi5iOWZTrEvBFkJM4mqSBkC6yDpGqBVLSuKeHxOUlm8Yb93pXTOY6r8M+QCDRRFDExMYFcLgfTNH2DCgqFAgYGBhAOh5dt0VIvJEnCyZMnkUgkGgrcYBgGDzzwAE6cOIHvf//72LdvHzKZDF544YW6e9Jdrlh1BFUPstkshoaGIAhCzY0K/dSTqmj46J8WHeGqXjBN01EepR5TiJukZmdqb69OVnEA7J4vJHnx2YvWNr/5zW+gqqo9iTRaJsYNYjadnp7Gjh07sHbt2hW3Mv7v18h49MXGsvVpFTWf9b+uqXkeO9orCcrPvEegG859ZiQOqgak4sEElC2wvvvOFVhPInK/V1Qa+544jkM6nXa0Kqd7RE1NTdmNDQFrcbR161Zs2LBhxZOTaZo4d+4czp8/j507dzZUhujFF1/EXXfdhde+9rX4xS9+YV9zOp3GK1/5ymaf8orFVYLygCRJdqHR3t5ex0PkBS/1RPDV91tRRSMjI9AC2odXM+dVAzF//EHXKP595Heqbn/vzRo+8+PKr5+01CDnQJNyf3+/3bOHlImhKzvQpFVLuRa6PFFnZ2fV8kTLjdfsLVaQlCRb5+ulomi4TX20inJjMhOBojVO0GrJ8pvNsw6S8iKTrMQiFXOeN7kmmoiIeiIIIrBGQfuqNm7ciGw2i5MnT9rjKpfL4YUXXnA0NiTjbTmbZNLI5/M4ceKEbW2pVzXJsozPfe5zePLJJ/Gtb30Le/bsWaIzvTyw6ggqyMSnqipkWcaLL75o94hpdCX/vz7stDX7+bcWS0yAVV9weHgY6XQa/f39ePnL/c19iwXDMIjH44jH43ZVDlLbLJPJOFa+tI8hmUw6HlZRFDE0NIRIJFJXeaLlRKFQwGa8iHO4wfN9QlJ+5r0g9bRUcJOU5zYeJEUQREQ04fmZ9xqBrus4ffo0stmsZ1kyepE0OzuLM2fOOGrpkfF2KceUYRgYGxvDhQsXsGvXrporpdM4fvw43vve9+KP/uiP8NRTT614pXgpsOoIygt0ZJ4gCNi3b1/NkUG0enrok52+2/E8D1ku54c0g5hIbpBpmti9e7cjn8Jt3qP9T3/3zuYOfLq22caNGwGUc0Cy2SzOnz9v1xiLxWKQZRmapjVs/rjU0HXdnnx6enqwt81SUkRp0MgX/RcGsmIiLFR+x7QfyitBd170HhetSbPCvOeFbN46JyHkXJjRBENIyuuacgXWM3pPlBgkY82tBTc7O4vh4WFs3LgRPT09ns+E1yKJrqU3Pz+Ps2fP2oE/hLCCwrcXA1EUcfLkSaxdu7ahaFNZlvGZz3wGTz/9NB544AFce+21TT2/yxmrjqDowUlCmc+cOYP169fjxhtvxEsvvRRYDsmNIFKiwXEcNE1rKADCDVVVcebMGSwsLKC7u7spLembDXcOCGmDMT4+bkd4DQ4OAoA9eaTT6SXNm2kExIzpNkG+Zm8RP3q20ieZLwmJmItnMmLlRB5k5nPvzwvzomUNSCfK40f1yZKQFUBWGCTj/oSSlVjwHhapQmltE/dYs4kSgz+5SfI/yRqhKAqGhoag6zr27dtXd3Vuv1p6XuHbdAHYVCpVdwt5AsMwcObMGczNzeGaa65pKKjn+eefx/ve9z686U1vwlNPPbXs3adXGlbt3SCReaQ0CrFhL0VFc6Bs4ms0AAKwHghSa2zr1q2+K0wAuO+9Udz1pepmF6uKxNJidnYWp06dwtq1a/Hyl7/cYeqjQ5DpvBlCbqQz6qUOmpAkCYODgwiFQr4myDdfL3mSVBD8VBQN4n+6MGsgHqs+RjI5i3RoovKDmA8mqZxkIhGrvp9Ck4pF0PluQSV/GkFQ+HY2m0U2m7ULwAqC4IgerNZCPpPJYGBgAB0dHThw4EDdz3KxWMSnP/1pHD58GA899BCuueaahq/zSsaqIyhRFHHixAmEQiHPyLxm9oQiME0TgiDgwoULKBQKtlqotSEfKUJ7+vRprFu3Dtdff31Tao0tNTlJkoShoSGwLOtbnsgrBJm0Hchms3YxTo7jHKRVbQJpFKR0zuzsLHp7ex35PF6gSYpWO1KxrKK81FM1XJitHoDg9qVmciZikdpIysu9UZRLJbAokipQtZPzBW8V1SgkScLAwABisVhFXcWlRDgcxrp16yoKwGazWbssUaFQAM/zDp9WPB6HYRi2f+zaa6+tu0wRADz33HN43/veh1tuuQX/+Z//eVU1BYCp0k/kims2cvHiRRiG4RuZd+bMGYTDYbuiwGLg9jMBlqM9k8nYKzg6mCCdTlckLNLBBDt27Kjb9HHXlwoIR9w5WPX5oL73dAxF2XRE+AVB0zS7PlpPT0/VSb4WqKoKURTteydJEkKhkIO0GunVQ0AiCk+fPo2NGzdi06ZNda2Kf/RszNMcF4t4ExStosj8pGlAOmFifLq8fZCCcj+79LoqRakp2aNYuayYSMSd94oQFABPgiKgb0sj5j06oKCvr2/F+iHJmCPPqiiKkGUZqVQK69evt9V9reOkUCjgU5/6FJ577jl885vfxM6dO5f4Ci4reD64q46gdF0PVEjnzp2DaZrYsmVLw8eoJwCCDiYgDwFgBRMUCgWYptlwVBDB3d9wmiwbCZL43tOxqpMRba7ZvHkzNm7cuKSmObLqJT+0qYb81OJfyOfzGBwcRDgcRnd3d8PRXw88VWnuy4qWPzMacU5ihKBmZjWs7yjVanQRVDXzXhBBAWWS8iMoAA6SogmKgPOIjijIJuJR6/V6CYqYxtatW4dt27atKH+jHzRNw6lTpyBJEnp6eqBpmv2s1lrK6ciRI3j/+9+P2267DXfdddeSVlu/THGVoACLEIJ8TMQm3dXVVfe+mxEAoes6RkZGMD09jdbWVjt5cbEmLkJSzY7gI1hYWMDQ0BDS6TS6urqWLUSW9i9ks1mHU5wmLcCp9Hp7exe9kg8iKKCSpM6fLyKRtO7TujaLpPwIKpPVkU6VJzWv59ZNULJiIpWoJABCTgSJOONJTlLBRDJe+flCadt3/kHtoeVkks/n89i5c2dDprHlAIkq3Lx5MzZs2OD5zNF+VEJaAPDwww8jlUphdHQUo6Oj+Pa3v42+vr5LfQmXC64SFFCdoC5evIjZ2dm6B5K7AkS9xESrDy8Tk6qqjolXkiRbLRB/VlAI7VIRVLFYxPDwMDRNQ29v74qbeEhXVPreybIMhmEgyzLa29vR1dXVtJwZmqRocgKcBHVhRoGmGhUEJebL5mBCULNzGnje+b2mkk7i8DIK0EREE5WboADvaEKpYG1Hk1SBIrKb948jlUpVXYzQ3WL9JvmVBlVVMTw8DEVRsHPnzrpN67qu43vf+x5+8IMfwDRNO0Dqd3/3d/G5z32uKed4++2349FHH0V7ezteeslqfT03N4e3vOUtGB0dxbZt2/CjH/0Ira2tME0Td911Fx577DHEYjE8+OCDK63g7FWCAqwJS1H8W2EvLCxgYmICu3fvrnl/i81nmpubw6lTp9DS0oLt27fXrD7IxEv8MiTvgxBWKpWyoxObTVB0bhBJar4ckMvlMDAwYDedIwmfpO4grbQaVYGEpNwEBZRJqhpBFYvOIAk3QZHHNp2y9uelntxIJVjP1wulYyUpEiPkREBIihDU668dsQlf0zRHDT1y72RZxuDgIBiGQW9v72WRjA2UCZV0a24kP/HjH/84/uu//gt///d/b5cOUxQFExMT2L59e1PO8xe/+AUSiQTe9ra32QR19913Y82aNbj33nvxmc98BvPz8/jsZz+Lxx57DF/5ylfw2GOP4ciRI7jrrrtw5MiRppxHk3CVoIDqBJXL5XDq1Cns27ev6n4WS0z5fB7Dw8NgGAY9PT2LLgBJ8j7oIAxSO+/bT/QgHOHxqTuYRUUN0Z1AN2zYgM2bN182fgQSfeVVvoquTuA18ZKfWu/dfY96T8bRCIsLM+XxR0gqiKD8yImG21/lRUQAPEPcC9SxCEm5Ccp9HrR5z+veFQoF6LqO9vZ2dHZ2LorwLxXo9us7d+6su3ySaZr49a9/jbvvvhu333473v3udy+5r2l0dBSvec1rbILq6+vDk08+ifXr12NychKHDh3C4OAg3vGOd+DQoUO49dZbK7ZbIfCcPK/GN7oQCoUCgyiaQUyqqmJkZASZTKZpUW7kXEjeR2dnp32++Xwe96ydRzabxbFjVuQgWemSVW8tJEMiCqPRKA4cOLBi6p8FgW7RsGXLFvT29tZVncCr+66btLwmobteI3uS1ORUERxXea9nZjWbpOqFphnIZK3xmE4F7yMr6kgl/SdNMWftx6t7c17SEY9Vfpa+d6lUCidPnkR7ezvWr18PSZJw8eLFUi1Ki/DpsbdSSGt6erpq+/Ug5PN5fPSjH8XAwAD++Z//ednasE9PT9vjt7OzE9PT0wCAiYkJbN682d5u06ZNmJiYWEkE5YlVR1CN9oRqRgAEnWi7bds238mymfBqxkciBzOZDMbHx5HL5cAwTEVFB3JuiqLYUUy9vb2Liii8lBBFEYODg0gkEg21aPC7d14Vt4NSBWrFzKyGSGRxajST1XxJSpYt8qFJqlCszLcqFHQkEt77yEs6/p/XV1ogSFUF4r8lCjWdTjsInyitlUJasixjYGAAHMc1NEZM08TTTz+Ne+65B3/+53+Or3zlKyvGotDIHLXSsOoICgguGMtxnCNvCVhcCwzyeVK1u729vWmJto3CXYYIsHxKxDxDJ8cyDANJkrBt2zbs3LlzxTx8QVBVFadPn0Yul0NfXx+SyWTT9u2uuA04UwUmJibsVIE/6Eri30deZn92YcGa2HXdsFUUH6q8n27/E41gi7yFi3Mqkj4EQ5AVdYRC/pNXLqc5SEpW/M9pYWEBg4OD6OjoQH9/v+8Y8VOpy0FadFBST09PQz7UXC6Hv/3bv8Xw8DAefvjhpvmWFoOOjg5MTk7aJj6iBjdu3Ihz587Z242Pj9vjdyVjVRJUrWiGOS+bzWJ4eBjRaHRFV+3mOM7RCZWUgkomk2htbcXc3BwmJiZ8Q7ZXAkjX47Nnz2Lr1q3o6+u7JCtIP8LP5XL44+Qp/H8vdgd+PieqSCRDnurJ7X9yQ9O8iUPMWVaAIKJSVbMukgLgUE+qquLUqVMoFAq47rrrGvKhLgdpkfbr4XC4oQoWpmnil7/8Je699168853vxFe/+tUVs3B73eteh+9+97u499578d3vfhevf/3r7dfvv/9+3HLLLThy5IhD2a5krEqCClJQBLquL4qY6O6wvb29TV3FLyXy+TyGhobA8zxe9rKXVRBQsVi0ldb4+DhkWUY0GnWEuy+HXyGbzWJwcHBZ28TToBvyLTxVaRKjVVQQFuYrP5tu8ff9qZpzXIs5DckEb5v3aMiyDlmGg4QKBWfkYS6nISQ4z5MOlCHKupkLgVpIi26xUWvkZS3t16tBFEV85CMfwejoKP7lX/4F27Zta/QyF41bb70VTz75JC5evIhNmzbhYx/7GO699168+c1vxre//W1s3boVP/rRjwAAf/iHf4jHHnsM3d3diMVieOCBB5btvOvBqoviA6yVn9uMB5QV029+8xvIsmxPMOl0uuYy/ZqmYWxsDDMzMyu2O6wX6Arp9SStkjYHdLh7rYEEzTpv4h/r6+tbMW3i3fjY9yvHACEoTbXG4tp1YUyM59G2NorZi1aUXDjsv4ZMtwgVCspNUASChylRlstkREjKTVAAHAR15//IYmBgADzPo7e3d1kDZdzRg6Io+pIW3X69u7u77vFomiaefPJJfOhDH8Kdd96JO+64Y8WopisEV8PMCdwE5RUAQRJjyaRbLBYDlQJtXtq0aRM2btx4WQxg+ryblUhJot/IvRNFEaZpVgQSLOb+kNXwuXPnsH37dnR0dKz4hUAtJCXL5QCdIHICAL00hlOpMkn4EZRS1O2cK+s4lUTkFblHb/f2/zaG8+fPo6enB21tbYHntlwICnnv7OxEe3t73So/m83iwx/+MCYmJvDNb35zUWXQrsIXVwmKgGR1A7VXgKBzjGilkEgkwPM85ubm0NbWtqxlfuoF6cTb0tKCrq6uJa2qbBhGReHNRttqZDIZDA4OorW1Fdu3b79sqkEvFUERRGPe404pOskokQx5ElSxqCEed+6D3u7/umEIXV1dl00dOdJ+PZ1Oo7Oz046+DFJaNEzTxBNPPIEPf/jD+Ku/+iu8/e1vvywWnZcprhIUgaZp0DRt0QEQpCqBpml2cVcSrk1U1nL0MqqGQqFgh0f39PQsW3ki0laDrlDO87w9YaTTaUexV0VRMDw8DFmW0dfXt+LKKvmB1KHL5XL4t4GDjvcaJSg3OZF9JNOVQThuggJQ4VcCLIIC4CApQlB//frsZeNHJdXSZ2ZmsHPnTs+0CD/zoCAIePjhh7F792488cQTWFhYwDe+8Q1HDtFVLAmuEhTBAw88gFQqhQMHDmDt2rV1r4oURcHIyAhEUURPT4/D4kDy+wAAH0hJREFUX0PCtcmkm8/nK2rmLVfkm67rGB0dxczMzIotT+RVoZxEPubzeWzfvn3Jq6Q3C6ZpYnp6GmfOnMHWrVuxfv16fPwfymNtYc7yM7Wti1cQVL3qiXyegBCVFzmRYySSZdMgISeCeNypsv72rZfHVEC3X6+3Wrppmpibm8MXv/hFPPXUU8jn84hEIujp6cEtt9yCN77xjUtyzl/84hfxrW99CwzD4LrrrsMDDzyAyclJ3HLLLZidncWBAwfwve9977JIjF8ErhIUwaOPPoonn3wSR48etYMCDhw4gIMHD2Lv3r2+fYUMw8C5c+dw/vz5uup0KYpiE1Ymk7Ej3+iaeUtpFqQnykZ6HS0n5ufnMTg4aFfIEEWxqXXzlgqkGV8kEkF3d7djcvnY9xlMjM0jnrQWKumWchfAZhFU0D5olUZIyk1QAMDz1hi5HMiJbr++a9euhoJlFhYW8MEPfhBzc3P4xje+gY0bN8IwDLto7HXXXdf0856YmMBNN92EEydOIBqN4s1vfrMdcffHf/zHuOWWW/DOd74Te/fuxbve9a6mH38F4SpBeUHTNJw4cQKHDx/G0aNH8cILLwAA9u3bhwMHDqC/vx89PT34wQ9+gI6ODvT19WHLli2LssOTyDe6Zh7xZxHSqrX8UDVks1kMDQ0hHo9jx44dl80qTJZlDA8PQ1VV9PX1OXJsvBzhJHKQvn/L4SshKvXixYu+zfj+4pML9t/xZMQmKJo4pHw5vJx19WRKt0SrEpSilNVPMlk2+9HHIPBKFpYpwlqqFi3NAt1+fcuWLQ0l0v/sZz/DRz/6Ubz//e/HW9/61ku2gJuYmMCNN96IF198EalUCm94wxvwnve8B7fddhumpqbA8zyeeeYZfPSjH8XPfvazS3JOy4Srtfi8wPM89uzZgz179uAv/uIv7Ai0559/HkeOHME999yD48ePY8eOHXjFK15hVwxvpMoxAcMwiMViiMVidp4HXY1gfHy8IoggnU7X1QOKlCcqFApNr6awlCAqdXJy0jdM3ytPhi5BdP78eeRyOZim6UjsXGzkYDWQ3kHr16/HwYMHfY/193/T4iApNy5MzCPR4u9fm5+zmgSmWkp9rTzUEw1RlB0k5UYuKyOR8n//7Nmzy0r6fiC90zKZTMPt1+fn53HvvfdCFEU8/vjjTemkXQ82btyI97///diyZQui0She9apX4cCBA2hpabGDf0jdvNWIVU9QbpD6a6985Stx+PBhhEIhPPnkk2hvb8ezzz6Lw4cP4zvf+Q6mpqbQ1dWF/v5+9Pf3Y//+/UgkEg2TFk1GmzZtAgC7c2c2m7VzfQRBsFUCyc+iQU/w27dvR3t7+2XhrwGstiPDw8NYu3YtDh48WNdk6FWCiFRzyGQyOHv2rN39tFHS9wNpK2GaJvbt27doH2MQOdHILlj92GPx6qpYFGUIgv/99COpj9xWRDbLYXJyEkNDQxWFhhutO7hYENPvhg0bcODAgYb6r/30pz/Fxz/+cdxzzz249dZbl8XsPT8/j3/913/FmTNn0NLSgptvvhmPP/74JT+PlYpVb+ILgiiKvqRjGAaGhoZw5MgRHDlyBMePH4csy7j22mtt0tq9e3fTfSNePaBisRjS6TQMw8D09DQ6OzsXbYa8lCgWi/bk19fXh2g0Wv1DDYImfRLEEgqFHP7AWtrEA9YkR3ySO3bswLp162o+D6KgaD8UMb8FmfcMo/KR1FXLnJcsmQpp8x4NpagikXJVBik4m3cmUuFA8x6t9En0G4BLplTp9uu7du1qaKzMzc3h7rvvRrFYxNe+9jW78v9y4Mc//jEef/xxfPvb3wYAPPTQQ3jmmWfw4x//+KqJD1cJqqmQZRnHjx/HkSNH8Oyzz+K3v/0t4vE49u/fb5PW1q1bm/rwmqZpm5YMwwDP8w7TVjOSYpcKhmHg7NmzmJqaWtaoQhI5SCdlRyIRh9Jy++5ILtaaNWuwffv2hhYDNEkRgnKb96oRFCEngmRLNJCgADhIyk1QABAqKa1afU9Eqbpz3GjSisfjix6DtbRfD4Jpmnj00UfxyU9+Eh/84Adxyy23LLt14ciRI7j99ttx9OhRRKNR/Nmf/Rn6+/vxi1/8Am984xvtIIk9e/bg3e9+97Ke6xLjKkFdapimifn5eTz77LM2aZFKE4SwDhw4gNbW1oYeFNJXKpvNOsLd6XYaZMLgOM4x4fpFKl4qkMmmo6Oj6aS9WJA28XQQC4kcTCQSds5Mo9FiBG6COjcyU2Heq5egNM36P55yKgtCTjT4UCWpqlQQxf0faPza/v/2zj0oyvNs479VwFRETqJBPCByFlFOkVaxZKzjF5sxGbXGmfSTfoZxahpBbWxMzIwmbfBYogSb6IfRRsdT7ERTos7XpLVJmTErGtTVcBJQDovIaTnLsvt+f5D3zS5HgYVd9PnNOAMrwrPr8l7v89z3fV3yjJvpTnXkyJGdROtR3oNy/PrDhw8JCgrq1xFqZWUlmzZtwmg0kpqayoQJE/rztAaFrVu3curUKcX/Mi0tjdLSUlauXEl1dTVhYWEcO3bMZo2mLYQQKFtAHiK8fPkyarWazMxM6uvrCQwMVFrdQ0NDe/wlNLX5kedrevtF1+v1nYZiZWdy+XhrKH4Bmpubyc3NVVKEB/M4z5LIu7179+7h6OiomAnL9k39aSIwbZQw6H+YTTIRqH3rzV+bDSktZgLVUZzgR4ECc5HqSqAetuiVI0YZSwlUV8jvQVm0mpqazG6cxo4d26kmOND4dUmS+Pzzz0lKSuLtt99mxYoVVt81CbpECJStotfr0Wg0imjduHFDuZuSjwf9/PwYOXIkX3/9NaNGjVKOlgZi8yM7k8ui1draqpi8Ojs74+TkZDEbIYPBwN27d6moqLBpL7euaGxsJDs7m9GjR+Pr66vUFeXOQdOdKtAp+LG73WFXAgVwaFvvR52Je5t7FSj4UaS6EyjATKRkgbK0OHWH7HlpKloODg44OjpSX1+PnZ0dwcHB/bp5evDgAa+//jojR44kJSWlX0m5giFDCNRwQZIk6uvruXr1qjKfdfv2bVpbW3FxcWH9+vXMmzfP4gap8nyR6dGW3LUlu7r3p5bw4MED7ty5ozRv2NJxXk8YDAZl+NM0Jba3f2O6S2hoaOhxlyCLVF8FSua13Q3Kxx3FCUD/sF2ERjuZ78YedhAsR6enBnX31BdKS0spKChQGn9kNxHT17CndAFJkvjss8/YuXMnW7duZdmyZWLXZPsIgRqu7N69m9OnT7Nx40aeeuoppZ5VWVmJr6+vUssKCwuzuPefbPIqi5bpBVc+GuyuntXU1ERubi4jR47Ez8/PpsINe6OyspL8/HwmTpw4YOeNro5XZfur3afbO/9kgeqLOMnIItVRoGRxkjEVqY4CBeDg0L5btpY4mcavBwQEmHXAtrS0KMIvu7GYNrKoVCrGjRtHRUUFv//97xk1ahQpKSk2aecl6BIhUMOVrKwsQkJCOh23GQwGsrOzFcH67rvvMBgMzJw5U2nCCAoKsrjbt+mxjE6nU+5wZcEaM2YMZWVlVFZW4u/vr6T0DgfklncAf3//QRNVeVxgy8H2i7AsUG/9t055Hfvq+vHb7TqzzzsKFLSLVFfiZPq1h9999HZ5S9Cf+HU5XUB+H7711ltoNBqamppYuHAhq1atIjIyclAFqra2lvj4eDQaDSqVio8//piAgABeeuklioqK8Pb25vTp08Pq/W9FhEA97sgWSteuXUOtVqNWq8nOzsbJyUmxbYqKirJ4VpVp15tWq6W6uho7OzucnZ1xcXEZ9NBCS2A0GikpKaGsrGxIW97XvFeriFPqG45m9RjTmqD8p6ubDXk2qLGxkf/9vx/j5bsSKICR9p2/h/y1Qy1OpvHr/v7+/bqZKi8vZ+PGjTg6OrJ+/XoKCwvJzMzk7t27nDp1ahBW3U5cXBwxMTHEx8fT2tpKU1MTSUlJuLm5sXnzZnbs2EFNTQ07d+4ctDU8RgiBehKRJInKykql1f3KlSuUlJQwdepURbQiIiJwdnYe0NGgHBVvb2+Pn58fDg4OivWQTqdTQgsftYFgKJFnmtzd3fH29h5yIX1lWyXQ+XivY02wvr6+k2djS0sLBQUFncImf7td16VAPWz5cQh4tFO7v6E1dk+WiF83Go18+umnJCcn86c//YklS5YMWa1Jp9Mxe/ZsCgoKzH5mQEAAly5dwtPTE61WS2xsLDk5OUOypmGOEChBO0ajkYKCAsUFIzMzk+bmZoKDgxXRmjlz5iN1Tsl+aDU1Nb1GxZs2EOh0OmU2xtS66VFdHCyBPF/T3NxMYGCg1fKluhOorpBn3KqqqigpKaGtra2TM77sfiJ/X1NMBQraRWqoBWqg8evQvmtav349Li4u7N27t18CNxCysrJYs2YNwcHBXL9+nYiICPbt24eXlxe1te2NL5Ik4erqqnwu6BEhUILuaW1t5caNG4poaTQaHBwcCAsLU+pZ06dPV3Y8RqMRrVarDB5PmjSp38PGsmDpdDrFxcFUtCxtF2Va8+jvfI0l6YtAmc7A+fr64uHhoQi/aQaZaefg2wdMAgg7CBSAvUP73w+2OJlaQwUGBvZ4M9MdRqORkydPsm/fPpKSknj++eet8n+XmZlJdHQ0GRkZzJkzh8TERMaOHcsHH3xgJkiurq7U1NQM+fqGIUKguuPixYskJiZiMBiIj49n8+bN1l6S1ZEkibq6Oq5cuaI0YRQUFODp6Ym3tzdqtZoVK1awdu1aiw74mha/5QtuW1ubxaI0GhoayMnJUeJHbCVHas17tRzc0vMFu7Gxke+//x4nJyemT5/eY72m43xRymdeXYqT6e7p1PtT+/8EekGOX3dxcel3bLxWqyUxMREPDw+Sk5Ot2nxQXl5OdHQ0RUVFAHzzzTfs2LGD/Px8ccTXP4RAdYXBYMDf359//OMfTJo0iaioKE6cOEFwcLC1l2Zz1NXVsWnTJv79738zd+5c7ty5Q3V1tVng4+zZsy1uoyRHoMi7LHkg1tS6qbf2etOjyEedabIV5DC+qqqqAa3915vLOj0mC9T+LY6DsluVnVMqKioICgrqMn79Ub7H8ePHSU1NZfv27SxevNgm5ppiYmJIS0sjICCAbdu20djYCIC7u7vSJFFdXc2uXbusvNJhgRCorujoFLx9+3YA3nzzTWsuyya5fPkyGo2G1atXK0d9bW1tfP/992aBj5IkMWvWLKWeFRAQYPFW966OteTOQdMoEpVKpQwKy2nCtnBxe1TkWAlLDjnLQmW6e/rj2va2d71eb+YmMpDuy4HEr8uUlZWRkJCAp6cnf/7zn/t1LDhYZGVlKR18Pj4+HD58GKPRyIoVK7h37x5Tp07l9OnTQ14fG6YIgeqKM2fOcPHiRdLS0gA4evQo3377LampqVZe2fBE7jyTAx/VajW5ubm4uroSERGh7LT640bdGx1dyZubm9Hr9Tg4ODBt2jTc3d1t5kivN/R6vRI4GRgYaJYobAl+vblMESjToz15t2p6PChJUifPwZ7ExnTHFxwc3C9DXaPRyLFjx/jwww/ZuXMnixYtGlY3FoI+IxJ1BYOPnHY7f/585s+fD7Rf9CoqKpRW9yNHjiiBiqaBj05OTgO6CDk4ODBu3Djc3Ny4d+8eLS0t+Pn5MWLECOrq6iguLlbatE2jSGxpPkt+rQoKCvD29iYwMHBQLszHdkzkpQ13Oz0uB3aOGTNGSZc1dccvKSmhoaEBlUqlWGCZOpObxq9HRkb2a9dUUlJCQkICU6ZM4euvvx5Wx7ECy/LE76DEEZ91MBqN5OXlKbusa9eu0dLSwowZM5Rd1owZM/rsplBbW0tOTo5yrNRRfLoyeO3uYjvUtLS0kJ2djZ2dHf7+/n1+7kOJwWAwcxNpbGxEr2/fkU2dOhUPD48+1yKNRiOffPIJBw4cYPfu3SxcuFDsmp4cxBFfV7S1teHv789XX32Fl5cXUVFRHD9+nBkzZlh7aU8cDx8+JCsrSxko1mg0/OQnPyE8PFwRre5qGa2treTn59PS0tLnIzH5Ymtaz5K98mTRGkwfQdP26+Hm9A7tNwXyrsnJyUkRro4mr3JdsCuKi4tZt24dPj4+7N69GycnpyF+FgIrIwSqO86fP8/69esxGAysXr2aLVu2DPrPLC4uZtWqVdy/fx+VSsWaNWtITEykurpaeHn9gCRJ1NbWcuXKFaUJo6ioCC8vL6UBIzw8nBMnTuDm5sYvfvELizm8t7a2KoIlm5N2HIi1RD2rvr6e7OzsAbVfW4tHiV839curq6tTXsfRo0dz69Ytfvazn5Gens6hQ4fYs2cPCxYsGPRdk8FgIDIyEi8vL9LT0yksLGTlypVUVVURERHB0aNHbXr3+pgiBMqW0Gq1aLVawsPDqa+vJyIigrNnz3LkyBHh5dUDcnDgt99+yxdffEF6ejrTpk1j+vTpSj1r1qxZFg9ClH0OTaNIOtoO9dY8YIoc5VFTU0NgYOCw2zH0N35dfh1LS0tJSkri6tWr6HQ6nn32WX76058SGxtLWFjYoK49OTmZzMxM6urqSE9PZ8WKFSxdulSJV581axZr164d1DUIOiEEypZ54YUXeO2113jttdfEoN8jcOHCBd577z1SUlIIDQ1Fo9Eo9azr16+jUqkICwtTdlr+/v4W353IzQOmfoMjRowwOxrsmBALUF1dTW5uLhMnTmTy5MnDqs5iifh1o9HIoUOHOHz4MO+//z7z5s0jOzubK1euMGrUKF5++eVBWHk7JSUlxMXFsWXLFpKTk/n73/+Oh4cH5eXl2NnZdapJC4YMIVC2SlFREfPnz0ej0TBlyhTh5fUIPHz4EDs7uy5FR5IkGhoazFrd8/LyGDdunJmru6UDH6H92Mu0eUDOfpKHiR88eIDBYCAwMHDYxN3LDDR+HaCwsJB169YREhLC9u3bh9z/cPny5bz55pvU19ezZ88ejhw5QnR0NPn5+UD70ftzzz2HRqMZ0nUJRJu5TdLQ0MCyZcvYu3dvpyl7lUo1rO6uh5Ke7JXkrrzY2FhiY2OBdtEqLy9XvAYPHjxIRUUFvr6+imiFhYUpRqv9xc7ODjc3N7PhzJaWFu7evUtOTo6y7tzcXLN6lqUHmS1Ja2srubm5GI1GwsPD+2VtZTAYSEtL45NPPmHv3r3Mnz9/yN/b6enpjB8/noiICC5dujSkP1vQP2z3t+IJQK/Xs2zZMl5++WWWLl0KwIQJE9BqtcoR3/jx4628yscDlUqFp6cnL774Ii+++CLQftHMzc3l8uXLnDt3jq1bt6LX6wkNDVW6BoOCggbUDNHc3Ex2djajRo1i7ty52NvbK8PMdXV1VFRUkJ+fj9FoNIsiGTNmjE1Ekdy/f5+CggJ8fHyYMGFCv77HnTt3SEhIYNasWWRkZFh86PhRycjI4PPPP+f8+fNK80ZiYiK1tbW0tbVhZ2dHSUkJXl5eVlmfoDPiiM9KSJJEXFwcbm5u7N27V3l806ZNwsvLijQ3N/Pdd98pre63b99mzJgxyi4rMjKSyZMn9yoeRqOR4uJitFrtI+UdmQ7DyvNZpo7kzs7OFvc47Ime4tcfFYPBwIEDBzh+/Dj79u0jJiZmEFbaPy5dusSePXtIT0/nV7/6FcuWLVOaJEJDQ3n11VetvcQnDVGDsiX+85//EBMTw8yZM5WLXVJSEnPmzBFeXjaEJElUVVWZtboXFxczefJkoqKiFPsmFxcXRTzKy8u5d+8ebm5uTJs2rd/NGXI9SxatpqYmZa7I2dkZZ2dni7dDy0ehRUVFSpxHf8jLyyMhIYHIyEj++Mc/Wm3X1B2mAlVQUMDKlSuprq4mLCyMY8eOWdShX/BICIESdI+YDXl0ZK8508DHxsZG/Pz8lFmfY8eODcqNhelckU6nM4uFd3Z2xsnJqd/1LEvErxsMBj788ENOnTpFSkoKc+fO7ddaBE8cQqAE3SNmQwbG+fPn2bBhAxEREdjb26PRaLCzszMLfPT19bV4XaljLLypuau8y3J0dOzx51oifh0gJyeHhIQEoqOjeffdd4ddl6LAqgiBEnSNmA0ZGJIk8e677xIfH68U2OXAx8zMTKWedefOHSZMmEB4eDhRUVFERkbi4eFh8bqS0Wikvr5e2WU1NDQo9Sy5c1CuZzU3Nyt1tv7Gr7e1tbF//37OnDnD/v37iY6OtujzETwRiDZzQdesX7+eXbt2KUGAVVVVuLi4KEc8kyZNorS01JpLtGlUKhVbt27t9JizszMLFixgwYIFwI87FflocP/+/VRVVeHn50dkZCQRERGEhYV1OdzbF0aMGKHsniZPngyYJ+yWl5fT3NyMJEno9XqmTp3KxIkT+yVO2dnZJCQkMG/ePDIyMgbVs1Dw5CEE6glHzIYMHSqVikmTJjFp0iSWLVsGtNds5MDHv/3tb7z99ttIkqS0ukdGRhIYGDjgOSl7e3vc3d1xd3dXouMdHR1xdXWlvr6emzdvotfrGT16tCJuTk5O3YpWW1sbKSkpnD17lr/85S8888wzA1qfQNAVQqCecMRsiHUZOXIkISEhhISEEB8fr9SUrl27hlqtZs+ePeTk5ODi4mIW+Ojl5dXnXVZ38etPP/00YB5WqNVqyc3NRZIkJYqkqqqKoKAgpUMvNjaWjIyMQe94E8bKTy6iBiVQELMhtokkSVRWVipHg2q1mrKyMry9vRXBCg8PZ+zYsd2Klhy/7u7uzrRp0/pkaivXs/7whz9w48YNdDodv/zlL1myZAnPPPMM3t7eFny2nRHGyk8EoklC0DO2MhtSW1tLfHw8Go0GlUrFxx9/TEBAgLhbNsFoNHLnzh0uX76MWq3m6tWrtLS0EBwcrIhWSEgIRqORkydPEhwcTFBQUL9d02/dusW6detYuHAhr776Kjdv3kStVlNUVERaWpqFn13PCGPlxxIhUILhQVxcHDExMcTHx9Pa2kpTUxNJSUnibrkXWltbuX79uiJaV65coba2lsjISJYuXUpUVFSfdk/Q3lyRnJzMhQsX+OijjwgPDx/EZ9A7wlj5sUUIlMD20el0zJ49m4KCArPjqoCAAHG33Afeeecd/vWvf7Fr1y50Op3iglFYWMjEiRPNrJvc3d27PBq8efMmCQkJPPfcc7z11ltWH9RuaGjg5z//OVu2bGHp0qW4uLiYCZKrqys1NTVWXKFgAAiBEtg+WVlZrFmzhuDgYK5fv05ERAT79u3Dy8tL3C33gaysLEJDQzvtliRJUgIf5fksnU6Hv7+/IlgzZswgNTWVL7/8ko8++ojZs2db6Vn8iF6v5/nnn2fRokVs3LgREDctjxlCoAS2T2ZmJtHR0WRkZDBnzhwSExMZO3YsH3zwgbhbHiTa2tq4deuWIlhffPEFy5cvZ8+ePVbfNYEwVn5CEAIlsH3Ky8uJjo6mqKgIgG+++YYdO3aQn58v7paHCEmSbCqHTBgrPxEIgRIMD2JiYkhLSyMgIIBt27bR2NgIIO6WBYLHFyFQguFBVlaW0sHn4+PD4cOHMRqN4m5ZIHh8EQIlEPSV999/n7S0NFQqFTNnzuTw4cNotVoRRSIQWJYuBcr6mdICgY1SWlpKSkoKmZmZaDQaDAYDJ0+e5I033mDDhg3k5+fj6urKoUOHrL1UgeCxRAiUQNADbW1tNDc309bWRlNTE56envzzn/9k+fLlQPtQ8dmzZ628SoHg8UQIlEDQDV5eXrz++utMmTIFT09PnJ2dlXh3EUUiEAw+QqAEgm6oqanh3LlzFBYWUlZWRmNjIxcvXrT2soYdFy9eJCAgAF9fX3bs2GHt5QiGEUKgBIJu+PLLL5k2bRoeHh7Y29uzdOlSMjIylCgSQESR9ILBYOB3v/sdFy5c4Pbt25w4cYLbt29be1mCYYIQKIGgG6ZMmcLly5dpampCkiS++uorgoODefbZZzlz5gwAf/3rX3nhhResvFLbRa1W4+vri4+PDw4ODqxcuZJz585Ze1mCYYIQKIGgG+bMmcPy5csJDw9n5syZGI1G1qxZw86dO0lOTsbX15eqqipeeeUVay/VZiktLVVi50HU7AR9QwiUQNAD77zzDtnZ2Wg0Go4ePcqoUaPw8fFBrVaTn5/Pp59+Oqg5WatXr2b8+PGEhIQoj1VXV7Nw4UL8/PxYuHCh4kkoSRIJCQn4+voSGhrKtWvXBm1dAsFQIARKILBhfvOb33RqzNixYwcLFiwgLy+PBQsWKI0HFy5cIC8vj7y8PA4ePMjatWutsWQzvLy8KC4uVj4XNTtBXxACJRDYMPPnz+9k6XTu3Dni4uIA8zmsc+fOsWrVKlQqFdHR0dTW1qLVaod8zaZERUWRl5dHYWEhra2tnDx5kiVLllh1TYLhg521FyAQCPrG/fv38fT0BODpp5/m/v37QPf1HvlrrYGdnR2pqaksWrQIg8HA6tWrmTFjhtXWIxheCIESCIYxKpXKpqIxumLx4sUsXrzY2ssQDEN6M4sVCARWRqVSeQPpkiSF/PB5DhArSZJWpVJ5ApckSQpQqVQHfvj4RMevs9LSBYIBIWpQAsHw43Mg7oeP44BzJo+vUrUTDeiEOAmGM2IHJRDYMCqV6gQQC4wD7gNbgbPAaWAKcBdYIUlStar9rC8V+C+gCfgfSZIyrbFugcASCIESCAQCgU0ijvgEAoFAYJMIgRIIBAKBTSIESiAQCAQ2yf8DfB7cPHsvWZIAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "display_data", "data": { "image/png": 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mRPSRSAQtLS1gGEbU9im7cnd+fh7f/OY38Z3vfEfW51EtqECpGFIqzjAMXn31VQwPD8NgMIDjONlGqstdEi48Zrmsrq7C4XBAr9djaGiIr7Aie0xilOs0IWVBFtryZC8swtETi4uLOXsQQuFSixgQaAQlTjXPJ9v2iUAiejJM0ufzYWlpqWTbp0AggEAgUJXnoARUoFRIvh6mVCqFVCqF+fl5TE9Po729XZaR6jqdrqJURj7KESiO43hhMhqNGB4ezhmcp1TRhVzHyufgLZyZFAqFMD8/j1gsBo1GA6vVCo1Gg3Q6jVgsBpPJVDORoAJVmFq/NsKIfn5+Htu2bYPNZssQrmg0mtf26dlnn4VWq5XsDHPHHXfg5z//OTo7O/H666/n/JzjOJw5cwZPPfUULBYLHn30Ud42TS6oQKkIsebadDqNeDyOF198EV1dXbjuuutyrpSkIvfiX+oxOY7DysoKHA4HzGYzdu/eLZqelNuLrxrefsI9iM7OTv520mezuLiISCSCiYkJ3rA3Xw+X0gskFaj1g/BCUsyvEAC/h5pIJHDhwgW8+eabOHDgAJqbm3H06FHcd999JT3e7bffjk984hO47bbb8v78P/7jPzAxMYGJiQlcuHABH//4x3HhwoXKnmQWVKBUgFgPk3Ckularxa5du3LejJVCIig5KSRQHMdhaWkJTqcTVqsVe/bsKWpKK3fjby3NZ0mfDUlbDgwMAMjs4fL5fJiZmUEikchoPiapQrksqQAqUOuJdDpdUhsEKf759Kc/jXPnzuH666/HXXfdxb+vSuX666+H2+0W/fmTTz6J2267DRqNBkePHoXf78fc3By2bNlS8mMUgwpUDcknTBqNJu9I9WvXrilyDtWKoDiOw+LiIpxOJ+x2O/bt21fWvplaGlvlJNtlIJ+zAXGdj0QiWF5ezjFBFfZxSYmq1SZQ1exDW2+k0+myU/GBQIBvr2hpaZF1ppvX60VPTw//fXd3N7xeLxWo9Y5Yc22hkepKRDqAMgIldJLgOI4f49HU1IQDBw7AbDaXfTy5z2+9CF6x5mPSHCrcfyin+VhtAqWWCEpN1Y1Cyn19gsEguru7FTob5aECVUXEmmtLGamulEApleJjGAZzc3NwuVyyjvGQg/UkUGJkNx8Db89LKrf5WC0LsZpEQe7CoVohjKDkpqurC9PT0/z3MzMz6OrqkvUxqEApjFhzbbkj1XU6XcasGrkgYiIXLMsiGAxiaWkJW7ZswaFDh1QjTELWu0Dlg8xLKqf5WKfTgWVZzM/P17z5WE2uFlSginPy5Ek8+OCDOHXqFC5cuICmpiZZ03sAFSjFKNRcK2WkupIRlBwpPpZlMTs7C4/Hg4aGBnR3d8s+xkOMcq+61XKVXi0KNR/Pz89jeXkZiUQCKysrOXY81Ww+VlOKT+jDpwakRpfBYFCyQN1yyy14/vnnsby8jO7ubnz5y1/m3Ss+9rGP4cSJE3jqqacwMDAAi8WC73//+5IepxBUoGRGbEAgUNlIdZ1Ol2NtIgeVCh/LsvxE3o6ODoyOjvIuC2qlHlJ8ckAiLovFgu3bt/O3F2s+Fu5xydl8rCaBUqNxrZTzIQNJpfCTn/yk4M81Gg3+4R/+QdKxS4UKlEwQYVpYWOANWslCSCKLSvZiyHh2uZFaJMGyLGZmZjA9PY3Ozs6M3iwlCi/khgrUGvmKJIo1H4fD4bzNx8IvKc3HahIotaX4Si0xz0bJFF81oAJVIdkDAsfHx/GOd7wD6XQaMzMz8Hq96OzsrHikulqKJBiGwfT0NLxeLzZv3py3abgWAlVONZoaUnxqEchyXjdhM7EQ4ZC/QCCA2dlZSc3HVKDEkSpQ6XRa1r65akMFSiJizbUcx8HhcGBubg5dXV04cuSILL0dSpaZl5I6TKfTvDBt3bq14PNSewSllhSfWoSy0vMQDvkTUqz5WJgqNBqNqhMotZwLIE2g1PAerxQqUGUi1lxLRqpHIhEYDAZ+pLpcKClQhY6bTqfh8XgwOzuLrq4uHD16tOgHhQrU+kHJPqhSmo+Xlpb45uN4PA63243GxsaKmo/lQG0RVCXno4YLIalQgSoRseZaMlLd7/djx44dsNvt6Orqkv3NXe0qvlQqBY/Hg7m5OXR3d5cluEoJlFyLKRWot6lFo65Y8/GLL76Ijo4OxOPxipqP5UBtRRJSIqh4PK7KFo9yoAJVBLHmWjJSPRaLobe3lx+pPjs7K8mSpBjViqBSqRSmpqawsLCAnp4eSZGgUu4UFPlRk5MEsGbHI3y/ldp8THq45PrckXEWakGKQPn9/poOLpUDKlB5KNRc6/f74XQ6RUeq6/V6pNPpisdgZKN0BCU0piX+f1Jz8EraJ+VbgKT0QdEIag01CZRYRaFY83EikeBThTMzM7JOPlZbii+dTpc98229V/ABVKAyKNRcW+pIdaXKwZUSKJZl4fP5cOnSJWzfvr0iYSLI7T4OyD/DiQrUGmoTqFLfexqNBiaTCSaTSXTycTgcltx8rDaBknI+gUCARlD1QKHm2nJHqitlSST3IpJIJOByubC0tAS9Xo8jR47IVrUkNIuVCzHRSyaTcLlcSCQSsNls/OJTbHOdCtQaarIXkgMpk48tFkvG/pbZbFadQElJ8VGBWueIDQgk3mRSRqqTFJ9aEY7y2LFjB7Zv346rV6/Kukgp7ZAOrO2Vud1uLC4uoqenB83NzfwgwHA4zPd/EMEi/2q1WlVEDGqJXNRyHkpTbvNxIpFAPB5Hc3NzRc3HciFVoGiKbx2S3VxLhIlhGHi93opGqqtVoGKxGFwuFwKBQMYoj2QyWZOJuuVC0nLCIg6yV0ZuF1q6ZBukTk9P86kehmEQj8fBsqzsdj3rjY0iUGKINR+/9tpr2Lx5MxiGEW0+Fg6QVPo1pAK1ASDC5HQ60dPTA61Wy49Un56exuzsrKg7QqkoleKTSjQahdPpRCgUQl9fH19tSKj2RN1KcLvdWFlZ4asLSdSX7/wLGaSOj49Dp9NlXDGThUcYcampikspNrpAicGyLBobG3MuUIXNx6urq5ieni7YfCwXUveghoaGZDuHWrAhBCq7uXZhYQHd3d381fji4iK6u7tx9OjRivPOer0eiURCjtPOgaS5SknHRSIROJ1ORCIR9PX15Z0xBai/JJw0Cvt8PjQ2Nlb8NyJVYTabLSPVQxYesrE+NTWFVCqV0YND/lXT3kSlUIHKj5gglNt8rNfrc3q4pFz8ShUoGkGpGLEeJq1WizfffBOhUKjikups9Hq9Yk7eJNopdK7C/qy+vj60t7cXXIDUujgxDAOPxwOv14vu7m60t7djy5YtsohDvio+sYVHbI5SdkWYxWJR7WtZiFIE6petowCA61cvV+OUVEG5VkeFJh+T94+w+dhoNOYY7BZL4ZX7/qICpUIK9TCRkerBYBDt7e3Ys2eP7IuKkntQRKDyXYGFQiE4nU4kEgn09/fn9GetF4gZLZnOSayVXn31VdHKOyX7oMR6cOLxOMLhcE5FWL40oZr/DmqJoNQ0TZcgx0WrwWBAS0tLzv5oNZqPaRWfSkkmkxnl4tkj1bVaLZqamhT5QCjVBwXk398KhUKYnJxEOp3mhWk9IhzfsWXLlhzPPzlTkZX2QQlLmbPThNFoFOFwOMMcVZjmIf+qBTUJVD2VuxdCSvNxPB6Hw+Eoq/k4GAxKngWlFupOoISu4mIj1f1+v6JRjlLHFopfIBCAw+EAy7Lo7+9ft29EoTBt3rxZ1CV9PTTXirl6p1Ipfn9rfn4e4XAYiUSC31OsxPGgUkoVqKEP9WP+Yzdj83ceq+l51DNizcfpdBovv/wympqa8jYfZw+QJO8hmuJTKUtLS3A6naIj1fV6vSLTacmxlRS/QCCAyclJAEB/f/+6fQOSEfFTU1M5Aw/zIac7RbXFzmAwoLm5OeNvtbCwgFAohKamJkQiEX7RAd5uHCULj5L9N8WEgew/Kc1GiqDKhfgCtre3l9R8/O///u947bXXEI1G8eyzz2LPnj185qgUzp8/jzNnzoBhGNx555246667Mn7u8Xjwp3/6p/D7/WAYBvfddx9OnDgh63Mm1KVAJZPJgiPVDQaDolGOEsf2+XxYXFxEQ0MDdu3aJXtuuVpXsCzLYm5uDm63Gx0dHRgbGyupHFdOdwo1RGMajQZ6vT5n0WFZNu/wv+wyZpvNJssoCrVELmoSqFq/N7IR64ESaz7eu3cv3njjDXziE5/Aiy++iB/+8Idwu9347ne/i8OHDxd8LIZhcPr0aTz77LPo7u7G2NgYTp48id27d/P3+epXv4oPfehD+PjHP46rV6/ixIkTcLvdsj1fIXUpUN3d3QUXMyVLweUu215dXYXD4YBer0dHRwdaW1tlFydSfCH3CAPh4sdxHC9MbW1tJQsTQW5RUdsiRNBqtbxl06ZNm/jbSRlzOBzG0tISXC5XXreMcjfVqUDlorZhheWWmJtMJhw6dAh6vR733ntvWY918eJFDAwMoK+vDwBw6tQpPPnkkxkCRfb1gbU04tatW8t6jHKoS4Eq9oEzGAyKpfjk+LBzHMcLU0NDA4aHh2G32+F2uxUbuaGUm4RWq8X8/DxcLhdaW1sluXOQ48mZ4ltv5CtjFnPLYFk2J00o5pZBBSqXepgFlUgkJDUKe71e9PT08N93d3fjwoULGfe555578N73vhd///d/j0gkgueee67sxymVuhSoYqjVjojjOCwvL8PpdMJsNmP37t0Z5rRKzoRSoll3fn4eU1NTaGlpkSxMwuMVOsdyFlklzGxrQSG3jHz+cvncMqhA5VIvRrHZe+9y8ZOf/AS33347/vIv/xK/+c1v8OEPfxivv/66In+/uhSoWkZQUuA4ji/ssFqt2LNnT979M51Op0hqUk7h4zgOi4uLCIVCWF1dxaFDh2SZ6rlRUnxyINyb6Ozs5G/P55YRCoUQiUTQ1NQk6pYx9KF+xc+ZCpQ4UgQqGAxKKqDq6urC9PQ0/z3pRxTy8MMP4/z58wCAY8eOIR6PY3l5OeO9Jhd1KVDFUDqCKtWSiCzmTqcTdrsd+/btK+iaruYISiiydrsdTU1NGBgYkG3kdLFzLCcSUEPEUAvyuWW88cYb6OrqAsdxCIfDGW4Z1RwXTgVKHCn7w1KbdMfGxjAxMQGXy4Wuri6cO3cOZ8+ezbjPtm3b8Itf/AK333473nzzTcTj8YwiDTmhAqXg8cVywBzHYWFhAS6XC01NTThw4ADMZnPR4yo5VVfqcUla0uFwwGaz8SJ75coVWdNoxSKoclN89RxBlQPHcTAajbBYLDluB79qG8u5/6VLlzKcDuRyy1CbQKnlXIC1CKrciwW/3y8pgtLr9XjwwQdx/PhxMAyDO+64AyMjI7j77rsxOjqKkydP4utf/zr+7M/+DN/4xjeg0Wjw6KOPKnbRV5cCVezFUmLiqxAxgRJWsrW0tODgwYNlvfGUagImo0bKgRRyTE5OwmKx5ER/cu9rFRKV9TjyXS17P2LnkU+cAODw4cO800G2W4ZQtErxlhOiJqujeiiSqKRJ98SJEzl9TcJqwN27d+PXv/61pGOXS10KVK3JjtBI78/U1FRFlWxKjpMvR0yIMJlMJtH9MiUESs7j1Vqg1EK5QqnVakt2yyDectll8PmiEzWJgtpSfBt1FhRQpwJV6gdOqatYIiRCtwSpAxCF1DrF5/P5MDk5CYPBkFNhmI3cAiUlyhNDLVfqakCuz0A+twziLUfK4Au5ZagpglKbQEkdtdHb26vQGVWPuhSoUiALqBJvRK1Wi7m5OVy9ehWdnZ1lN6WKUasiCb/fj8nJSeh0Or4nqxhyRzxypuXUkOJTC8UEqpIKPqG3XLZbBimDJ24ZoVAIWq0W4XBYdreMclGiab0SaAS1ASGl5nIKFBkZ7/V60dLSUtFk3nxUO4Iivn8ajQZDQ0Nl9VXIvc9HBUoZigkUm2ag1b/9GZHDMFY4Np24ZXg8Huh0OthstrxuGcL9rUpGUJQCSU2qhWqWmauNuhSoUlIFclbykRlGXq8XmzdvRm9vL/R6vexXf0pGUMLjBoNBTE5OguM4DAwMSCpXVSLFR/eg5EctxRqkrF3MLYPsbwndMoiTdzG3jHJRWxUfxxNp3UUAACAASURBVHFlnw+NoFROsatkOQQqnU5jenoas7Oz2LJlCz8qYnZ2VpGGWqUWEq1Wi1Qqxc+WIiM8KnmDV7OKT8qxao1ahEEt5yFWZi50y8ienUTShKQwQ+iWkV0GX+65qGkPSgqBQGDdjuARUrcCVYxK3CTS6TQ8Hg/m5uawdevWnBlGSkU6SpFMJuH1erG8vIyBgQFZ3thKRFD5BIrjOHi9XszPz2csSsK5ONnQFN/bqF2gxBC6ZQghbhmkKGNqagqpVAoGgyGjKKNQGbzaiiSkQAVK5SgRQaVSKXg8HszPz6O7uxtHjx7N+0ZWq9dfNuFwGA6HA+FwGHa7Hfv375ft2EqXmZNmZ6fTiba2NvT39yMejyMUCmF5eRnRaJRfxOx2e8YIdvL7lPwCVa0ZUELkatTN55YBICNNKByxbjKZMooyzGazqgRK6vs0Ho+X1PyvdupWoIpRztDCVCoFt9uNxcVF9PT0iAqT8NhqFqhIJAKHw4F4PI7+/n5oNBosLCzI+hhKpfiEzhWNjY04dOgQGhoakEwmYbVaM0xThSPYhVfTZEGen5+v2SRbNbEeI6hyMRqNMBqNOW4Z8XicL4NfWlpCLBZDLBYDwzBoamrixauhoaEmr5MUsSSfEzX8XSulbgWqFMPYWCxW8D7JZBJutxtLS0vYtm0bjh07VtKHSMmx76X6/OUjGo3C4XAgGo2iv78fbW1t0Gg0CAQCsqckyb6WnMeLxWK4dOkSTCZThnOF2FVmvhHsRODm5uaQSCQyenOEKUIpexcU6dTC6kij0cBsNsNsNmd4yb300kvo6elBIpEQdcsg/ypdji6lgo9ABWodUyiCSiQScLvdWF5exvbt20sWJuGxldqDIvtb5ZxPLBbjU3n9/f1ob2/PePMqOQ9KDoLBIK5du4ZEIoHDhw8XbBAuhkajgdFoRENDA7Zv387fTibZhsNhrK6uwuPxIJlMwmAwZIiW1Wrd0NGWUqipUZfjODQ2NuZELsQtIxKJ8OnldDqNhoaGnDJ4ud4jUgSqElFTG/XxLCSQb+x7PB6H2+3G6uoqduzYgcHBQUlvNCVTfESgSilhj8fjcDgcCAaD6O/vx8jISN5FQImiDjkEKhwOY3JyEul0Gj09PfD5fBWJUyGEk2yFkIGApMQ5EokAWHNCyI621LLArkeklFIrhVg0V8gtQzjGhETkpAyeiJfJZCr7PSK1SVfuqdu1om4FqtgbQSgi8XgcLpcLPp8Pvb292LlzZ0WLjZJVfKUcOx6Pw+l0IhAIoK+vD7t37y74fJSKoKRu8MZiMUxOTiIajWJwcBCtra0IhUJYWVmR5dzKqeIzGo1obW3NKHEWRlvCFFB2tFWooXQ97BEM/NGOqj2WmtzMgdLTY0K3DOH+p9AtIxQKYW5uDvF4HDqdLqcMvtDFplSbo3rogQLqWKCKYTAYEI/HcfXqVd63anh4WJZFQ8mFp5BAJRIJOJ1O+Hw+9PX1YdeuXSWdi1pSfOT8/X4/BgYGMlKRanKSKBRtkSvpmZkZRCIRcByX0VBqs9lU5VKgFtQmUJUi7McSkk6n+TSh0C1DeHFDfo/sZdMIqg4ptDCTYoFgMIgdO3aUvJCrgXwClUwm4XK5sLKyIklolYj4yvHiS6VScLlcWF5eFj3/9TAPKl+lWLbvnNfrRSKRAMuyMBqNvL1P9hTbjUa9CZQYer0+r1tGKpXiU8nCoZEA0NDQwF/klOKW4ff7qUCtRyKRCJxOJyKRCPr6+hAMBnkvsPWCsEKQVBkuLy9jx44dGBoakiS0tYqghA3P27dvx9GjR6vWXFutPqh8vnPA2ihtYtkjXJCIyzfp3ZKybyEHwvReth+fEqhFoGrRH0cKd7JTyRzH8ZZjpDCDuGUI90CtVmvGHihN8a0zwuEwnE4nYrFYRnn15OSkYo9ZSTl4IfR6PRKJBCYmJrC4uFh0YS8FJRbAQgLFsiymp6cxMzODrq6ukqok5RRRNUTLOp0OZrMZ3d3d/G1C+55gMIjZ2Vl+3yK7krBeqrQIatmTU8t5AGvvU9J4LHSDz+7vIxWnLMvikUceQUNDA5qbm3ln+HI4f/48zpw5A4ZhcOedd+Kuu+7Kuc9Pf/pT3HPPPdBoNNi/f3/OSHg5qa93uQCNRoNQKASHw4FkMon+/n60trZW7c1XbOy7FFKpFHw+H18uXm75ezXJJyjC+VibN2/OsYgqRLEIqpyFRa1WR0L7ns7OTv72dDrNp3+ELgjZe1tyRFu1cJFQE2pykQDyV/Hl6+8D1rYubrjhBjzxxBNwu904fvw4IpEIbrrpJnzhC18o+lgMw+D06dN49tln0d3djbGxMZw8eRK7d+/m7zMxMYGvfe1r+PWvf42WlhYsLi7K80RFqFuBSqfTGB8fR29vb0bYLESpKAeQd18nnU5jamqKdz7o7e1FT0+PLMdWCqFACW2J2tvbJY0hkXt8hxoFSgy9Xp+3vDkWi2VMsY3FYhl7WkS4pERbmw4NYuGlCTmfRkHUErWsB4ESw2Kx4MSJE3jxxRdx66234g//8A/BcRxCoVBJv3/x4kUMDAygr68PAHDq1Ck8+eSTGQL1ve99D6dPn+b3WYUXUkpQtwJlMBhw+PDhgvdRIsrJPnYlCPdouru7cezYMczPzyvilC43ZITH0tISJicn0dTUVNFE4VKKLkqNotSyGFaCMNoSuiCQKrFwOIyFhQU4HI4Mzzmhma7Y6/DOr76nWk9Ddaht1Ealwwo1Gk3Jc9y8Xm/GhW93dzcuXLiQcZ/x8XEAwDvf+U4wDIN77rkH73vf+8o6v3KoW4ECSjeMVZtAMQwDj8eD2dlZdHV1ZXj/rRen9GAwCL/fj4aGBuzfvz/HdbpcCv0tNRpNWWk7NaT4lNrrEKsSI55zRLjIZns8HsfMzAwfdW101DZqQ219UOl0GhMTE3j++ecxMzOD66+/Hq+99ppij1fXAlWMSkZuFEOKHx8ZfEiKB/KZ0iolUHKlO4VTeC0WC/bu3SvL+cmZ4lODQFUTMc+5dDqNy5cvQ6vVYnFxEeFweENHT8D6TvERpApUV1cXpqen+e/JOiSku7sbR44cgcFgQG9vL4aGhjAxMYGxsbGyH68U1BPLKkA5bhJyU44fH8uymJqawm9/+1uwLIujR4+it7c37wdFyam6lVTJhcNhXLlyBZOTkxgYGMCBAwdkjRA2mqhUA61WC71ej61bt2JoaAiHDh0q6fd8Pp+sF3Zq+ruqTaCA8lPSUmdBjY2NYWJiAi6XC8lkEufOncPJkycz7nPDDTfg+eefBwAsLy9jfHyc37NSAhpBKRRBlSJ+LMtiZmYG09PTJVe1qU2giC1RLBbDwMAAX5BCLP/VCBW7NaSmGZeXl3kHhIaGhoyiDClGqWoq7VajQJVLNBqVlK7V6/V48MEHcfz4cTAMgzvuuAMjIyO4++67MTo6ipMnT+L48eN45plnsHv3buh0Otx///0ZFk9ys6EFSukISkz8hOXWmzZtKquqTSmBKve4iUQCDocDgUAgx5YIUHchAhWoNaQKw+DgIP/7QjNdqaNL1NKkC6x/gSLva6mv54kTJ3DixImM2+69917+/xqNBg888AAeeOAB6SdZBnUtUKXMhFKqIk6v1yMej2fcxrIs5ubm4Ha70dnZKancWskIqpTjCm2JyvH7UxNUoNaQKlDzH7sZm7/zGDQaDRoaGtDQ0JBjlCp09yaDIo1GY46ZLonc1SRQajmXSkaQrLfPpBh1LVDF0Ov1/PgEuREWSXAcxwtTW1sbxsbGJFcOKhX16XS6gik+YS+WHO4VtYYKlHKpNa1Wm7eRNJFI8NNrPR4P/9kzmUyIx+NYWVmpuZku8UhUA1IKJNQksHJQ1wJV6yKJVCqFubk5uFwutLa2VtQHRKh2io9lWXg8Hni9Xr4Xa71/AOrl6rJSqr33IxZtra6uwu12w+fzYXp6uqaDItWU4pMiUMFgMOfCYD1T1wJVDKWKJDiOg9/vx8LCAnQ6nSzCRFBqQckukhDuk23ZsqUsW6JaQPugKj8H5h8/X/Vz0Gq1MJlMsFgsGBgY4G+v1aBINQmUlHPx+/11YxQLbHCBkjuC4jgOi4uLcDqdsNlssNvt2LVrl2zHVxISQXEch/n5ebhcLsm2RLUgGo1Cq9WWfCFQa4FSA2oQSSD/XovYoMhYLIZQKAS/388PitTr9TnRllSRUZNAVbMHSq3UtUCVUiQhRwTFcRyWlpbgdDpht9tx4MAB6PV6vPzyyxUfu1poNBr4/X44HA40NzfLGvUpSSQSwcTEBF/skkql+NJn4Wb8eqkwrCZqESiO40oSBbEhgPlmKYkNiiz2fNW0hyM1xVcvs6CAOheoYlS6n8NxHJaXl+FwOGCz2bB//36YzWb+Z0rtb8nN6uoqvF4vTCYTDhw4wD+HSlHSjJeUuQeDQQwODqKxsZFPUSaTSYRCIYTDYSwtLfHRFYlqbTab7POv1iNqESiGYSo6D4PBgJaWlozmVI7j+JEUgUCAH11SLNpSk9URjaDqXKCKvemlfig4jsPKygocDgcsFgv27duX4zWn9AdfjsUlEAhgYmKCdxMwmUyyiRMARUqI0+k0XC4XlpaWMsrcSSQsLH0WztARmqjOzc0hFovh4sWLGXsadrtd9j0NNaMWgeI4TvaLGI1Gk3dQZCqV4t8Hs7OzCIfDYFmWj7ZisRhSqZQqXhu1+fDVgroWKCVYXV3F5OQkTCYT9uzZUxODTRL5SS1aCIfDmJiYAMuyGBoaQmNjI2ZmZmSP+OQcMsiyLJLJJH77299i27ZtZZe5Z5uoBoNBjI2NZVxlk3HspIKMRFtS3BHWA2pYhIHqNuoaDAbR0SVEtNxuNxKJRM0HRabT6bJNlv1+P7Zs2aLQGVWfuhaoUj98pXxQfT4fJicnYTQasXv37rInVcoJKe4o98MSjUYxOTmJeDyOwcHBjJSITqdDMpmU9TzlMHglPWQulwvAml+YHHtjxAE931W2sIJsamoK0WiUv69wwVoPxSOFKPa+337LBzD1k3/H9ls+gOl/Pq/YedS6UVc4umRqagr79u2DRqPho+5QKIT5+XmEw2F+UKTVaoXdbofVai04uqQSpO5B7dy5U/ZzqRV1LVClQBZ7scWGCJPBYMDw8HDZPQZKXKWWu3cWj8fhdDoRDAbR39+fY0sk5ZilUGkEtby8jImJCTQ3N2NsbAwvv/xyVfYH8lWQMQzDp4ZIQYxwzhKJtuSYalstNmIEVQrkNSlldIlwUGT2BUyl0ZaULIlUo1i1UvcCVepMqGyBImMjtFqtJGEqdOxKKVVMkskkXC4XVlZWitoSyZmOI5QyZDAfgUAA4+PjMBqNGbOkCkVkSi+0Op0OjY2NGcPfhItVKBTC3Nxczka83W6HxWLJEVYl9l3KpRyB6rnpfYpFUWoTqEIUGl1S6aDIbNLpNN2DqvUJ1JrsXqhgMIiJibVR1wMDAxWVbJKRG9UWKKEt0Y4dOzA4OFh0AVBDBBWNRjE+Po50Oo2dO3fmTAKVKnj5kKMPSmyxEpY9z8zMIBwOA1hrMiWRllKDMstBTRGUmpvAS6FQtEXShIuLi4jFYiWni6Wm+GgEtY4oFkGRXqhQKITJyUmwLFuxMBGkDC0s9bj5xIQMPJRiS6REBFXqMbNLxsXs+9XgAFEK+cqeWZZFNBpFKBTCysoKVlZWwDAMlpaWMqItpfYz8lGJQKUf/Bz0n/hfspzHeoqgykF4ASOsKM1OF2ePLiFfqVSKpvhqfQK1hmVZjI+PQ6fTYWBgQNY/rpLGrkKBksOWqBYClU6n4Xa7sbCwUJIzerHjlbPgVjtyIH1YpLiGCFFbWxsfbZEr7OzqMZvNpsjem5oiKDUIVLUufsTSxcRMlwhXOBzGyy+/XFZxTjgcrmkBl9zUvUCJfQDD4TA/z2jz5s0YGhqS/bGVFiihLVFHR0dFtkTVTPGxLMuPtu/p6Sk50lsvEVSpaDQamEwmmEymvD1bZF+L9OqQni2SJqy0Z6sUgdp+ywdybmvfs0PyY+ajkrESclJLF4l874VLly7h4MGDfCvE8vIy3G4375YiFC7h6BK1NBrLQd0LVDaRSAQOhwPxeBz9/f1oa2tTxB0cUE6gtFotfD4fpqamZLMlqkYERQTV6XRi06ZNZUd69SZQYojtZ5CFyu/3Z7h+E8Eqt2dLKFDZRrGmnfJfsImhlghKjYu7TqfLGV2Sb1DkI488gl/+8pdIJBL4+te/jv3792P//v3o7Ows6XHOnz+PM2fOgGEY3Hnnnbjrrrvy3u+JJ57AjTfeiEuXLmF0dFSW51iIDSNQ0WgUDocD0WiUFyaNRoP5+XnFhhYqEZWsrq7C4/FAr9fLakukdAS1srKCiYkJNDY2YnR0VJKgyilQ603oCvVsEVsnMtFWuAlPxCvfhYBaUnxqqGgE1GUUW+j9mW9Q5Ne//nWEw2G85z3vQWdnJ55++mncf//9OHv2bEZ0ng+GYXD69Gk8++yz6O7uxtjYGE6ePIndu3dn3C8UCuFb3/oWjhw5UvkTLJG6F6h4PI5r164hHA7n7QFSauQGIG8EJbQl2rZtG1KplKy2REoJVCQSweXLl6HX6/NaQpV7vHry0JNDHIxGI9ra2jIKS4Sb8IuLi3zJc7Z5qlhqrVD01Pa5zwP//pOKz1uIWiIoNQmUlNcknU6jtbUVt912W1m/d/HiRQwMDKCvrw8AcOrUKTz55JM5AvWlL30Jn/vc53D//feXdfxK2BAC1dnZiZGRkbwfRqWHFlYaneWzJVpeXsbKyopMZ7mG3OmzaDSK+fl5sCyLvXv3ylIVWegc1RAJlIOSEVyhnq1QKMTvbYVCIWg0GiQSCfT/7n7a9/wh3rAdxsjVH4keP3DTJ9H0z9+U7XypQOUi1ShWyufM6/Wip6eH/767uxsXLlzIuM9LL72E6elpfOADH6ACJSetra0FIyQlI6hKysyL2RLJHe3ItcAnk0k4HA5+cFpjY6Ns9v9yWCcR1pugVYqw5JnsS8zNzSGRSNR8PAMVqFyqKVDFYFkWn/70p/Hoo4/Kfuxi1L1AFUPpCKpcIRHaEg0MDPB7ZUKUGvteCcLm4L6+PgwPD8Pr9cqakpOzUZeyFlXp9Xq0tLRAyrtJrj0sNQmUGs4DkCZQUqfpdnV1YXp6mv9+ZmYGXV1d/PehUAivv/463vWudwEA5ufncfLkSfzsZz9TvFCi7gWq2AdIr9erYg+qHFsiNQkUy7KYmZnB9PR0TnOw3IKykYsklKBcgem56X2ICr6/dOmSLD1bahIotURQUkdtSImgxsbGMDExAZfLha6uLpw7dw5nz57lf97U1ITl5WX++3e96134u7/7O1rFVw2U/GCUIlDCZtUdO3ZgaGio6KKhBoHiOA4LCwtwOp3o6OjIWzIud1GDnCk+QD1VbLWi0uq56667Dul0mi93ltqzpRaBUlOZuVSbIykRlF6vx4MPPojjx4+DYRjccccdGBkZwd13343R0VGcPHmy7GPKxYYXKCUpJFBCW6JymlWB2guUsGS8UA+W3AIlZ0S2kYWJIIdA6/X6nPlKLMvy85V8Ph/fs2U0GjMiLWFzqRoEqpIZa3IjNcVXrKRcjBMnTuDEiRMZt91777157/v8889LegwpqOOvoSByzoQql3xCwrIsvF4vPB4PtmzZgqNHj5Z91aaUQBUb0U6MdHU6Hfbu3Vt0WKMSAiVnkQSNoDKfv/Y9f1jS7zVGFwr+XKvV5u3ZElr5CHu2YrEYvF5vwZ6tasAwjCyzxuRAipkwGadTT9S9QAHFFzalLEKEKSk5bYnkTnUJj5tPoEhFYSKRwNDQUMl5biVSfHIer9b7ULUWx0IXI0sj/yc63ngu47Zo38GKHi+7uRRYE4WLFy9Cq9VmjKkQ9mzZ7XY0NDQo/nqpqUhCyh5UvTmZAxtEoIpBSs2VMuQkzZJy2RIpRfYoeWHJ+MDAQN5Bh4WoZgRFRsKbTKaSj1VLai2O5ByyX4cJ2+GqnoNOp4NOp8uoGhOOYM+esyW0dbJarbIKynrfg6q3WVDABhGoUocWyg1JYywuLspqS6QURKCEJeO9vb0YHh6WtKBXQ6A4joPX64Xb7eb/jqVMud0ovn6FUGuKUziCXeglR+ZshUIhTE9PIxKJAECGaardbpecmVBTFR8VqDU2hEAVQ+5mXeFEWJPJJOpioTY0Gg1mZ2exsLCArq6usgo38qF0kQQZCd/S0oKxsTF+wc035dZgMGSIFsdxG16g1htic7aIrdPKygrcbjc/W0kYbZUyZ4sKlPqgAgX5Iijh0EMyEfbChQuKVQfJdQVMSsZXVlag0+kq2h8TosQeFMMwCIVCGB8fh16vx/79+2E2m5FOp3lbKYPBgNbWVrS1tfECS9yfQ6EQlpeXEQ6HceXKFTQ2NvLCZbVaVbNAVQPy/uEmfivp9+UaWljJe1ir1eZ1+yYFGaFQCAsLCzlztvL9vdUkUFL3oGrtCiI3G0KgSmnWrUSghEUE2UMPybHlFqjs/SKprK6uYmJiAjabDZ2dneju7pZtRL3cabR0Oo25uTksLCzwxRosyyKdToNlWRiNRrAsC47jeGEk1Y4ajQZNTU1obm6GVqvFlStXsHPnTt4N3Ov1IhKJgOO4HCdwuV4PtSG8wCm1go8gpx+f3JFsoTlbpIpQ+Pc2m82w2+2Ix+M17y8kSFkzWJZVTZm8XNTXs5GI1BRfPB6Hw+FAKBQqaEuk5NBCqW9IEoVotVrs2bMHVqsVb731liqr7kgzs9frRUtLC/bs2QOO4/jXVaPR8K+D8KpTKFYkpUeKKZLJJBiG4YVIeL75xnGTqjJytV7psEA1oIY9qGqeQ6GerVAohGQyifHxcaRSqYyeLbvdDrPZXNUKv3KbqOs1Xb0hBKqUCCoWi5V8PKEtUX9/P3bv3i36GFL8+EpBai9ULBbDxMQE4vE4hoaGMj6scvdXVSpQpABiamoK3d3dGBwcRCwW4wVHo9EUHREPvC1a5HjT09PYsmULTCZT3mjLbDbzPTykpJ9UlQUCAczMzGQ0nhLRKmWfQ02ILYKdmvmSfj9w0yfRVvxuBal1k66wZ2t6ehoHDhzgHd5JtLW8vIxYLAaNRpNj66SWiKWUz8N6RB2vbo0p1Y9PaEvU29tbki2R0mPfSyWZTMLpdMLn84mWjKupb4kUQLS2tuK6666DXq+Hz+fDxMQEQqEQ7HY7GhsbRau23nvzpbzHffArTRgdHc35nexoi7y25F8yd6m9vZ1fUBOJBD8scHFxkd/nIKlBss+RbwHeaNGLGGoZ904g5yLWsxWJRPh9reyeLfJ3r0bPVjaRSAQ2m62qj1kNNoRAFXuzGAyGgiLCMAw8Hg9mZ2fLtiVSSqBKPS7DMJiamsLc3Bx27NiBnTt3Vs2EVopAhUIhXLt2DQaDAQcOHIDJZOL3mex2O44dO4ZYLIZgMIilpSU4nU6kUilYLBbY7XZ87POF52R94ksBAFcybnvmsbGcaAvIFS1hpMVxHHQ6HVpaWtDa2sr/fjqd5kVLWAotTBepZSFRgzioqfeoGGJztkh0HQwG4fV6kUgkKurZkpKu8/v9GedVL2wIgSqG2GIvtCXaunWrJFuiWkVQwnPv6uoq6dxJlZxclFMkEY/HMTk5iWg0yldAEmEixyKLKfnQE8giccP/fEPSeeaLtqSIFoHMwCK/n+/KOxaLwWQyIRaLZexrVZNiEdR/9X0U/8P5/wKo3EVCDDWIZCUU6tnKd6FitVozhCtf9E9LzN+GChRyiyQ4jsPc3BzcbnfFtkRK+eaJHVfoXNHe3l7Wuet0Oln7wUpZeNLpNFwuF5aWlvjUI0mxlZpXP37qslynzFOJaJH/k7+PsDIQWLsQcLvd0Gq1MBqN8Pl88Hg8SKVSGf07drs9b5OxXKglxacGeyG5iwxIq0Nrayt/m1gBDmksF154STGKrbcSc2CDCFSp+0Qcx2FpaQkOhwMtLS0YHR2t+Kq23AKMUsknUD6fD+Pj47BarTh06FDJtj/CY8bjcTlPUxSO4zAzMwOPx4Oenh4cOXKEj+DIwlls4RLbZ1KKckWL/Jsv2kqn07Barejo6EBnZydfjEH2tUKhEObn53Msfux2O+8CXilqEKhKR37IeR5KvxbCnq0tW7bwj0say8PhMBYWFhCJRJBKpXDt2jX+716sR49GUHWMTqdDMpnExYsXYbVacfDgwbIXdzGqkeILhUKYmJiARqPByMiI5D0OuYsk8sFxHJaXlzE5OYm2tja+AIKk80jEVGixqLYwFULsXJ55bAxA7rwx0poQjUaxdevWnBShXq/PaTIm6aJQKISpqSneBVy4pyVlUKDURTlo2VT8TiWilgiqVk26Go0GZrMZZrMZHR0dANaiofn5eWzatCmnZ4vM2SJ/e3IBTQVqHVPoQ+j3+zExMYFkMonDhw8XHSFRLkoKVDQaxWuvvYZYLIbBwcGKnYyVnjMVDAZx7do1NDQ05BRAAFhXwlSMfOf6vf/didnZWfT19aGzs5N/rsIIi3wB4k3G5GfEKWF2djZjARNGW4XSuxzHYSWUArFpFRrFXovlH9twzbAfW1BaGXopbHSBygcZtZGvZysajfJztjweD0KhEL7yla+gtbUVW7duxVtvvYXBwcGSn8v58+dx5swZMAyDO++8E3fddVfGzx944AE89NBD0Ov16OjowCOPPILt27fL+nwLsSEEKh8k6gCA4eFhvPbaa7KLE6DMop9KpbCwsIBAIICRkRF0dHTIkp5QKoKKx+MZvVeFCiDEWE/iJMaffXYRax85z+++yi/GIFEmgLxNxtFoFKFQKMOXzmQy8amlWpVBi0EFKhexIgmtVptTIMSyLB566CE88MADiEajuOeeezA+Po5bbrkFn/nMZwo+DsMwOH36NJ599ll0d3djbGwMJ0+exO7du/n7HDx4EJcvX4bFDz5RXQAAIABJREFUYsG3v/1tfPazn8Vjjz0m35MtwoYRKFJRFo1G+YhpcHCQv0IpNqxPKnJGUMJy97a2tpzKoUqRW6DS6TTi8TheeuklyQUQ9SBMhZCyr0Vev3yl7yaTCRaLJaPJOB6PIxQKIRAI8GXQBoMBsVgMewNXSz7X5yLvRE9zGHOpzdhikCeKUpNAqeE8gPLEUqvVYvv27bBarbj55ptx/PhxAKUVfVy8eBEDAwPo6+sDAJw6dQpPPvlkhkC9+93v5v9/9OhR/OhHPyrnqVTMhhEoUsYcCoUwODiY0YAHvC0kcpf6yiFQQkcFUu4eDAYxNzcn01muIVe0x7IsZmZmMD09Da1Wi+uuu44/tloLINREMdESvnbCYgxh9aBwf8lgMOQ0GSeTSVy5cgWAsnuOxVCTQKkpgip3DzwYDGakA0uJkL1eL3p6evjvu7u7ceHCBdH7P/zww3j/+99f1nlVyoYRKLfbjfb2dlFbIlJqriaBIlWFwoICsqegROpQDmsicr4dHR04cuQILl26xPvmrbcCCDVRjmgBxfu1tFrt79JISWVPvAhqESg1NQyrsQ/qRz/6ES5fvowXXnhBscfIx4YRqF27dhVcfJUqZpA6nr1YybgSAlXJMUkBhMlkwqFDh9DQ0ACWZdHa2ooLFy7AaDRm2BNZLJYMoaLCVD6FKggL7Wul02l4PB5Z3u8rKysVNRmrpVFXTRGUFBPoQCBQdpFUV1cXpqen+e9nZmYyJhsTnnvuOfzN3/wNXnjhhapPA98wAlUrz7xyCYfDGB8fB4CCJeNqEShiPptIJLBz507Y7faMAoj+/n4MDAwgmUwiGAxmzOch/T1/cXdQ1uex0SkUbZES/02bNuHIkSNgx38j6THIPlR2k7HQPLeUJmO1RFBqEqh0Oi1pFlS5EdTY2BgmJibgcrnQ1dWFc+fO4ezZsxn3efnll/HRj34U58+fl3W/u1Q2jEAVQ+6puuVC9sgikQiGhoaKXg3VOsWXTqfhdDqxvLzM7+kVKoAwGo1ob2/PmM+ztpBScaoGmaKlA7CMZx7rLXkH6rnIO/PevqzdjaMHkzlDAkttMmZZVhXzttQmUOVGUAzDlP066vV6PPjggzh+/DgYhsEdd9yBkZER3H333RgdHcXJkyfxmc98BuFwGDfddBMAYNu2bfjZz35W1uNUAhWo36F0BCXWFJlKpeB0OrGysoKBgYGSS8aVEqhi6UhSAOHxeLB9+/Z14QBByc97b76Ep75c+D7/1fdRHMJFIJL/54PWKaRSaxcder0+xzxX6EmX3WRss9kQjUZVYdGjpmF/5QoUqeiUwokTJ3DixImM2+69917+/88995yk48qFOv4iVaAUR3MyMlxuSGSSPV6alIxv374dg4ODZaU6pO5tSSW7AIKYz65XBwgK8L1vDgBZZeadmvmcJl2x6IlgMBhEHd+1Wq1okzGJtnw+H2ZmZspqMpYbNUVQ5Z4LWQfUsJcnNxtGoIqh1+t5x2Eljk3yyhzHYXZ2Fm63G1u2bJHkkF5tAoEArl27BrPZnFEAsdEabSn50el0ZTu+ExGKx+Nobm5GS0tLwSZjcn+lmozVJFBAeWITi8VgsVgUPJvasWEEqpQISqk9KDIQMRAIwOFwoLW1FWNjY1Ufr1AusViMH4G9a9cu2Gw2KkyUHIR/43Ic35PJJF8enU6nYTabRZuMiaUTaTLO3teqVLTUJlDlUK9O5sAGEqhiKLkHxTAMXn31Vdjtdhw4cABms1mRx5ELsi+2urrKO0BkN4BSYaofhB58lVKKMwbHcZifn4fb7ca2bdvQ0tKS1xkDWLtwJMU1wiZjIlrLy8uIRqPQ6XQZ5rnF3L+zWc8CVa9GscAGEqhalJmHw2FMTEwgHA6jr68vo2tbLuQcE8CyLJLJJC5cuIAdO3ZgcHAQGo2GFkBQKiLfe+Kbf23F6Oho3n2m7GiLiJWwyZikBYWTjMmeltfrRTgcBpA5INBut4sWH6jF6khKX1gwGKQRVL0jZ4qPjFQIh8MYHBzEysqKIg1upJKv0uoj4ZBDjuN4xwoSNdECCEo+KvHk++SXIwCuZNyWPaKk1H0tUoxBGsHJ75MBgaFQCEtLS3A6nWAYBmazOWdfSy1OElJKzGmKbwMgR9l2KpWCy+XC8vIy+vv7eVulQCCg6EyoSgTK7/djfHwcFosFhw4dwquvvkr3mTYQ5ab3pv029DSHFTkXqc4YhYoxLBYLrFYr//scxyEWiyEUCsHv92NmZgaJRAKJRAIejwdNTU2w2Wwwm801qYpTo81RLdkwAlXszVbJm5FlWXg8Hni9Xmzbtg1Hjx7NSBfodDrFhxaWC3F1T6fTGQUQdrsdly9fhs1m422JGhsb835oqDBRqkG5ju/ZxRgkEwCsCVRDQwNMJhM6Ojr4YoyLFy+iubmZn2pLnE6y97WUTgNSgcpkwwgU8PbIDbkotWRcr9cr0mMlRaCEBRDEAUJYADE4OIiBgQE+NUJSfwzDwGq1orGx8XdzjSjrme99c6DWp1ARUsaUkGGQ+aItjUaTd5Ix2dfyeDx8kzHZ1yLCJWdqUEqxRiAQwNatW2U7BzWxoQSqFEopOhCOLW9paSlaMq5Uj1U5AiWM8khjsFgBhEaj4T+A5I3Psized8uLAOKyPw/K+mUish2AvGNfpCLF8T0ej2N8fJzPIACZotXY2IimpqaMJmNy8TY3N4dwOAyWZfkmYyJaUltIaASVyYYSqGIRFKnkK9TBHggEMD4+zo8tL6VkXMmx78UEiuM4LCwswOl0YtOmTXz6cT0XQLT3bMl7+/K0OhbK9UKUyX3vZrtIPPPfLEb3V+uM5EdMtEj2Y2Zmhm+lADJThOQLyBQtq9UKm83GO38LR7FnNxkLU4SlmOdSgcpkQwlUMQoJVCQSwcTEBBiGwfDwMD9qu5zjyk0xgfL7/bh27RpsNhsOHz4Mo9G47gsg7G0tSMbfTpcaTW9XR1pbm2A0NcA3S1OQFHFyjXNdAFxlF2MQiy8AfJNxZ2dnRpMxSREKm4yFju/ZTcZSBEqKk/l6gQqUAFJqLoyKEokEJicn+ZLx1tbWso+rhLErIC580WgU4+PjYBgGIyMjsFqt616YgDVx0mgzzzsZT2SIFLAmVJHVQDVPbd3zT08b8X/9Xv7MweVXEhjdL94m8ddfPYovf/G3Sp1a1ZCyr0VS5PlK38kkY+G+FmkyDofDfJMxKZG32WyIRCJliw0VqDqhnGbddDoNl8uFpaUl9PX1iU7iLYVqRVDJZBJOpxM+nw9DQ0NobW2tGwcIe5v4+JHQqh/6hrWcP4muqEiVzj89rW7LrVpSzr6WsBhDWD0o3Ncu1GQcDocRCATg9/sxPT3NpxKJeIltPdAU3wZBr9cjmUzC7XaLloxLPa5SApVKpTIKIHbs2IGdO3cCQN4CCDHUKkzA2+KUHT0Vw9rahGQsgXRibaw5V8E4+43AE0/H8H8fV7cNlxqQUoxRqF+LFCU1NjYiHA5j69atfDSVr8mYiJbJZILFYkEymcyZuF0vbCiBKhQ9cByHaDQKr9eLnp4eHDlyRLb5MOUMAiz3uH6/H16vF5s3b84ogCCPt96iJo3gw61vMMJkeXvBjIWjMNvyuzanE0k+ihJiNDfwAkWhKIWYaAHFnTHIVzqdRigU4n9eqMk4EAjgO9/5Dh5//HHE43F8/vOfx6FDh3Dw4EH09/eXdFF9/vx5nDlzBgzD4M4778Rdd92V8fNEIoHbbrsNL774Itra2vDYY49hx44dkl4fqWwogcoHx3FYWVnBxMQE9Ho9enp60N/fX/wXy0CJjnSfzweHwwGtVovR0dG6KIDIFicmlUI8mnmfWHjtBiJUiWiM/5mYSGl1OrAMwx+fRlJvQ9N7ylGOMwaZtbZt2zbYbLaSmoy/+MUv4gtf+ALe8Y534Prrr8fLL7+Mn/70p3j00Udhs9kKnhvDMDh9+jSeffZZdHd3Y2xsDCdPnsTu3bv5+zz88MNoaWnB5OQkzp07h8997nN47LHH5HhpSmZDCVT2gi0sGd+/fz/vkKxmotEorl27Bo7j0Nvbi0AgkJFCXI/CBLwtTkSYmFQKugLl/mFfALaWXP8xIlKx4Jodj0bzu7TL7xYCRqGRKusJ0qT73xMtEB2VWyI2YwLhpPw+k/UM+fw989gYkskk/3kmlbZCihVjhMNhaDSavJNxC3Hx4kUMDAygr68PAHDq1Ck8+eSTGQL15JNP4p577gEA3HjjjfjEJz4hqzl1KWwogSIIS8Z37tyJxsZGAGvzj2ox9r0UkskkHA4HAoEAX00YCAT4/bKmpiZYrdZ11c8khGNZaPU6XkAKiRMh7FsrgjBkRU1Rf4jfr+K4321cp+WvolyvPPeWuOtAuftQVJyk8fS5Ub4/sb+/H52dnXnvV2hf680338Tp06clZXzIVgahu7sbFy5cEL2PXq9HU1MTVlZW+J6xarChBCqZTOLNN99EMBjkbX6EKDkTSqPRSHJMZlkWU1NTmJ2dRW9vL4aHhwGsXT1ZLBYMDg4iFArB6XQiEonAYDCgsbGR/zKbzTh+6rIST0l2iIhotNqcfSNh6i47CkolkrxIJWNrVXwcy0Gj1VBhKoDDWVn09O2zEXz4RipQ5fK/P2/Ab37zGzQ0NGDfvn2wWq1l/X4qlcK3vvUt/PznP8c//uM/YnR0VKEzrT0bSqC0Wi1aW1uxa9euvJGG0lN1y/HZIkPdnE4n7/OXXQCh1Wr5PgsC6bMIBoNYWFjAZ/52fRUIaEQ2d4WCla+aLxGJocGaeeVPxakynvnv4nt1//T4Kj58Yytu+J//B/6/7/+qCme1fnn63Cjv3dnR0QEAeOutt5BMJvkRIKSaT2y0/RtvvIG/+Iu/wHvf+1786le/kjzGp6urC9PT0/z3MzMzvDNG9n26u7uRTqcRCARyLuqVZkMJlMFgwObNm0V/rmQERY5dikeXz+fD+Pg47HY7xsbG+NlMpewzGY1GtLW14ZY/d8p6/tVCWMCQT6w4lsXvsnbQ6tfEnghRIhLLuT9Ffqb9ZANefn/JeuVnP9iLK1euwGQy5VQIC0fbB4NBeL1exONxGI1G2O12/PrXv8b+/fvxzDPP4Omnn8a3v/1tHDp0qKLzGRsbw8TEBFwuF7q6unDu3DmcPXs24z4nT57ED37wAxw7dgyPP/44fv/3f7/qI0g2lEDVYqpuOceORCIYHx8Hx3F15QAhlWLVdjRCUoZ8+1DZbhKPP+6ByULTe8V4+twovF4vrly5wjfPZ6PRaGA2m2E2mzP2ohKJBFZXV/HSSy/h29/+NlZWVjAwMIBHH30UwWAQ73rXuySfl16vx4MPPojjx4+DYRjccccdGBkZwd13343R0VGcPHkSH/nIR/DhD38YAwMDaG1txblz5yQ/nuTzrPoj1phChrHEQ0sJCglUMpnE5OQkgsEghoaG0NLSUjcOEBR1sed/7ANQ+f6TkJcmDNDVflq6qnjmsTFEo1G89NJLsNlsGBsbK3v/WaPR4Pvf/z6uXr2KH/3oRzhw4AACgQCuXLkCiyV/P2A55Kv8u/fee/n/m0wm/PM//3PFj1MJG06gakU+Pz6GYeDxeDA7O4u+vj7s2rWLv70eHCAo6mNkT31a4qiJp8+N8p/r4eFhSTZEr7zyCs6cOYM/+IM/wC9/+Uve5qipqQm/93u/J/cpqxYqUHlQotZfGEFxHIe5uTm4XC5s3bq1LhwgKPVFoQKJxx/35L19oxdKPPPYGCKRCC5fvszPiSs3akokErj//vvx/PPP46GHHsK+ffsUOtv1wYYTqGIzoYhQyDklE3hboFZXVzE+Po6mpqayCyAIVJgocpOIS997feOVBew7uEnGs1l/nP/JYbhcLiwuLmLXrl18b2U5vPzyy/jkJz+JD37wg3jhhRcKzqXbKGw4gSoGKTWXW6DS6TRmZ2dhtVqxd+9eWCwWKkyUmvCbX3nR2VU47RQKJWC3l14E8dLFWRy6rj7HjhfimcfGEAqFcPnyZbS3t2NsbKxsc+lEIoH77rsP//Vf/4Xvf//72LNnj0Jnu/7YcAJV7Uo+UgCxsrKCpqYm7N27lxZAUOqCeDSxoSv5zv/kMBwOB1ZXV7F79+6i/nf5ePHFF/GpT30KN954I1544QXZDKrrBfpqZCFXsy7DMJiamsLc3Bz6+vrQ2dmJpaWlsgogACpOFPkgFXylMOdahH1fT/E7bkCeeWwMgUAAly5dwqZNm3D48OGyo6Z4PI6vfe1r+O1vf4sf/vCHGR54lLehApVFpRFUdgHEsWPHoNFoEA6Hsbi4iFgshsbGRjQ1NaGxsVH0iokKE0VuSAVfsfSeGJdfScA9sSD685cuzqL/4E44Xr4m6fjrgf84ewjj4+MIBoPYs2dP2TZFAHD58mV86lOfwqlTp/Cf//mfNGoqwIZ7ZYql0yqJoMjYjuwCCIZhYDKZ8I53vAOxWAyBQACLi4uYnJwEy7Kw2Wy8aN30/4xLemwKpRRK2X+i5PJ3X2hAKBTCCy+8gMbGRmzZsoWvui01eorFYvjbv/1bXL58GT/+8Y95X02KOJoijanKdK3WEIZhCkZI09PT4DgO27ZtK/mY4XAY165dg06nw9DQEMxmc8kl4yzLIhwO48Y/q9+rTkrt2fM/9iHoW2vOzSdQ2VV8c65FAMBQVpovO4IS7kGlUwwMxrVr3nqKop768UFMTk4iGo1icHAQ6XQawWAQoVAI4XAYWq02w0fPZrPliNaFCxfwV3/1V7j11ltx5swZ2Yuw6oC8CySNoLLQ6/WIxUrzdEskEpicnEQ4HMbQ0BCam5vLLoB43y0vlnzuFIraSSXTMBj1sDQ3Ih5eE8T1akn1zGNjWFlZweXLl9HT04OdO3fyn+eWlhb+fgzD8D5609PTCIfXZpH967/+KxobG+F2u+F2u3H27Fns3LmzJs9lvUINSrIoJcXH/P/tnXtU1HX+/x8jCHIHUQFREAJRUBRmULpodlzrW9upjppLxz3auq5nrURrNTXdo7WbabnesrKOZqk/73vSlry01ZLFxk1FRUQHAUUcULkOw2VgPp/fH+x8YgQUEJxB3o9zOEc+4sxrPMw8P6/36/I0mbh8+TLp6el4e3szZswYPD09MZlMFpvGRXeeoDvQ1hmozNScZtdqq+uaXSu/WYHUYKKPa/vrM7bCN7tGk5WVRUFBAaNHj8bf37/V97OdnR2enp4EBAQQERHB2LFj0Wg0DBs2jMTERPLy8qivr2f69OksWrSo02KcNWsWAwYMsGhLLy0tZdKkSYSGhjJp0iTKysqAxtp4fHw8ISEhREZGcurUqU6LoyvpcQJ1L23msixTWFhIcnIydnZ2PPzww/j4+CjzTO1pGxfiJLhf+AYPVo73mnLxZC5Zqdp2PVZVWdscp1083ZTMybx1vjvw7b4Y/t/mIco2iFGjRtGnT592PUZ1dTXLli3jwIEDbN++naSkJFJSUvjvf//LK6+80mmxvvzyyxw7dszi2urVq5k4cSJarZaJEyeyevVqAI4ePYpWq0Wr1fLZZ58xd+7cToujK+lxAnU37O3tW8ygSkpKSE5ORq/XM2bMGAIDA5FlmYaGBiRJUtrG7+ZoK4RJcD9pSRxuFJZz8eSvdiyXz+ZZ/L25/gRw6WwBbcW+d+NzuXj8mjk59OmDLHWPUnbCzlGcO3cOnU6HWq3Gz8+vXSvPZFkmKSlJyWC+++47QkNDlb93cHAgKCio0+IdP358s+3ohw8fZubMmQDMnDmTQ4cOKddnzJiBSqUiNjaW8vJydDpdp8XSVfS4GtTd6N27t0UGpdfruXTpEnZ2dowaNUppgBAbIAS2jL2jA5LJxICA5tsdyopKu/S5DRWN2Zqxtla5Zvb2upuFijX4dl8MxcXFnDx58o7263fCYDCwcuVKsrOzOXjwYIds2DuD4uJi/Pz8APD19aW4uLGppSWL98LCQuVnbZUeJ1BtPeKrq6tDq9ViMBgICwvDw8NDbIAQdAt62dshmUxK5uLg9KtJZmvidPlsHg9Ftn53n/pdBs7uLW9KMG+UuHohH6BZxmR2QLbFZol/7YjkzJkz2NnZodFo2r3/TpZlfv75ZxYvXsyf/vQnPvzww3YP7XYVbfmMsnV6nEDBnRfGyrJMTU2NcjcVEREBtM8CA4Q4Ce4/v2Ypjb/bjs5OlN8oZUBgo4t0cf51HJrUU263f7l8Ng9nt+aNDbcf81WV6XH1crO4ZhYni3hUjfGYOmEzS2dzfK8GnU7HqVOnCA0NpV+/fu1+jKqqKlasWIFWq+Wrr77q1OO7juLj44NOp8PPzw+dTqdkg22xeLdFbEPqbQBZlrl27RopKSnKOe2AAQNEA4Sg29D0+MzR2Ym66hrqqmsozr/eTJxao1rfupFhdWVVi9eL865bZE2qXipUvVSY6uttTpy+3Rej2K+Xl5cTExPTbnGSZZkTJ07w5JNPKlbstiBO8KtNO8CXX37J888/r1zfsWMHsiyTnJyMh4eHzR/vgcigALh16xZarZa+ffsyZswY0tLSkGVZucMUx3mC7oIsSah69aKuunGWz8GpfctcjTW1zbKo8hslzX7OnEUV511v8twyql4qGuqMHYi86zm+V8O1a9e4du1aq/brd0Ov1/PXv/6V/Px8Dh06xJAhQzo/0Dby0ksvkZiYyK1btxg0aBBvv/02S5YsYdq0aWzbto3AwED2798PNLrnHjlyhJCQEJydndm+fbvV4m4PPW6TBEB9fT2SJKHX67l48SK9e/cmNDRUaYA4e/YsdXV1eHh4KF+Ojo6tipQQJ4Et4ujipPzZWFOHq5eH8v3tx3u//lxjY4PnAG/l2u0CZa5F6UvKAejVZCuCrWVM8Kv9+oULF3B1dSUkJKTdmxxkWSYxMZG33nqLV199ldmzZ9tMrekBocUP1x4pUObOvOrqasLCwnB3d0eSJGRZVo7y6uvrqayspKKigsrKSmpra3FycrJY9Nq0oCrLMk/FpVvxVQkEv6K6w4enq5fHXQUKfhWppgJlzo5uf3xb7M4DeHdh4w48k8mEr68vAwYMaPbevRuVlZUsX76cwsJCPv3003atQRO0GSFQZnJycnBwcFAKiG3pzJNlmdraWioqKhTRMplMuLq6Ym9vT2lpKd7e3gQHB1v88ovsSmBN7iRUAE7urlSXVyrf2zs2dvyZ6psPq9uqCLWE2X49KysLDw8PfH19MRgMyg69+vp6nJ2dcXd3V75uFy1Zlvnhhx9Yvnw58fHx/OEPfxBZU9chBMpMQ0ODMmDbloWurVFVVUV2djYNDQ04OztTU1ODSqXCzc1NybJcXFyaPa4QLYE1aUv2010ypJY4tkfNlStXuHnzJsOGDWvRfl2WZaqrq6msrLQQLQcHB7766isiIiL44YcfKC8vZ8uWLRYzRIIuQQiUme3bt+Pu7o5araZfv37tvisyGo3k5uai1+sJDQ3F0/PX7dAmk8niaNBgMODg4GBxNNjS6hQhWgLBvWG2X79w4QL9+vVjyJAh7Xpvy7JMaWkp69ev58cff8RgMNCnTx9CQ0OJi4tjypQpXRL3+vXr2bp1KyqVipEjR7J9+3Z0Oh1xcXGUlJSgVqvZuXMnDg4Od3+w7osQKDMJCQkkJiaSlpZGeXk5Q4cORa1WExMTo2yLaCmbkiSJgoICrl+/zpAhQ/D19W1T1mU0GhXBqqiooK6uDicnJ0WwWjpeEIIlELSdY3vU5OXlUVpayvDhwztkv15eXs7SpUspLS1ly5Yt+Pv7I0kSWq0Wo9HIyJEjOz3uwsJCHnvsMbKysnBycmLatGlKx93kyZOJi4vjz3/+M6NGjeo2+/M6iBColmhoaCArK4vk5GTS0tLIyMgAYPTo0ajVajQaDaGhoezevRsfHx/CwsIICAi4Jz8X8zCwWbSa1rPMouXm5tbs7k+IlkBgidl+PTs7Gx8fHwICAtp9IiLLMsePH2flypUsXLiQ3//+9/et1lRYWEhsbCxnzpzB3d2dF154gXnz5jF9+nSKioqwt7fnl19+YeXKlRw/fvy+xGQlhB9US9jb2xMZGUlkZCRz5sxBlmUMBgMnT54kJSWFxYsXc/r0aR566CEeeeQR6urq6NOnT5uzp5ZQqVQ4Ozvj7OysDMuZjQsrKyu5du0aer2eXr16KRmWh4cHx/dqRD1LIPgfR3dHo9Vqqaio6LD9ellZGUuWLEGv13Ps2DEGDmy+u7Ar8ff3Z+HChQQEBODk5MSTTz6JWq3G09NTsYI3783rifR4gbodlUqFq6srjz/+OMnJyfTu3ZvExEQGDBhAamoqycnJfP755xQVFREcHIxGo0Gj0RAdHY2rq2uHRaupGA0aNAhAce6srKxUHD0dHByULMvDw4Nv98VYPI4kScIEUfBA8+2+GMrKykhLS2PgwIGo1ep2v+9kWebo0aO88847LF68mJdeeskqHXplZWUcPnyYvLw8PD09efHFF5tZaPRkevwR353Q6/Wtio4kSVy6dImUlBRSUlI4ffo0dXV1jBgxQhGtiIiIdi+fvBt1dXUWTRh1dXU4Ozsry2yLi4vx9fW1OIYUWZbgQaGp/frw4cNxcnK6+z+6jdLSUt58801qa2v5+OOP8fX17YJI28aBAwc4duwY27ZtA2DHjh388ssvHDhwQBzxIQSqU6mrq+P06dOkpKSQmprK+fPncXFxITo6WhGtwMDATr1Tk2WZkpIStFotkiRhb2+PLMu4ubkpWZarq6uoZwm6NWb7da1Wy+DBgxk4cGCHsqaEhATeffddli5dSlxcnNW3faekpDBr1izS0tJwcnLi5ZdfRqPRcOLECaZMmaI0SURGRnaq2aENIgTqfiPLMmVlZaSmpiqidfXqVQYNGqQIllqtxsvLq0NvlPr6enJzc6msrLRodzfXs8xZll6vx87OzqKe1VKnohAtgS3yza7RaLVa6upjelc6AAAWDUlEQVTqGD58eLsdbqFx3+aiRYuQJInNmzfj4+PTBZF2jBUrVrBv3z7s7e2Jiopi69atFBYWEhcXR2lpKVFRUezatQtHx/btVexmCIGyBSRJ4sqVKyQnJ5Oamkp6ejp6vZ5hw4Ypre6RkZF3fBOarecLCgoIDAxsk/NnfX09er1eEa3q6mocHR0t5rNaegMI0RJYi2/3xXDz5k1ycnLaNdbRFFmW+frrr1m1ahXLly9n2rRpVs+aBC0iBMpWqa+vJzMzUxGts2fPKndT5uPB0NBQ7OzsOHHiBI6OjvTt25egoCCl06cj1NbWWtSzjEYjLi4uimi5ubk1e3whWIL7waa33dDr9djb2xMeHt6h7OHmzZssXLgQOzs7Nm3a1CGnXMF9QwhUd0GWZfR6PSdPnlTms7KysjAajXh6erJgwQIee+wxfHx8OvVu0Lz+pel8liRJyuomDw8PXFxcRD1L0GV8uy+GwsJCcnNzlcafmpoaJds3f93JXUCWZb766ivWrFnDihUrmDJlisiabB8hUN2VDz74gP379/PGG2/Qp08fpZ5169YtQkJClFpWVFRUi7v/7gWzLYlZtKqqqpR6lvloUNSzBPfKt/tiqKurIzs7Gzs7O8LCwiw6YGtra9Hr9RbbWPr06aMIlkqlol+/fty4cYO//OUvODo6smnTpg455QqsghCo7kpGRgYjRoxodtxmMpnIzs5WBOv06dOYTCZGjhypNGEMHz78no4BW8JsRWL+sDDf4ZoFy9XVlevXr3Pr1i2GDh2Kl5cXIERL0DJm+/UrV6602X7d7C5g/j186623yMzMpLq6mkmTJjFjxgw0Gk2XClR5eTmzZ88mMzMTlUrF559/TlhYGL/73e/Iz89nyJAh7N+/X/n9F9wRIVAPOuYVSqdOnSI1NZXU1FSys7Nxc3NT1jbFxMTg7+/f6a3udXV1VFRUoNPpKC0txd7eHg8PDzw9PZW73NvXQwnB6tl8uy+G2tpaLly4gKOjI0OHDu3QzVRRURFvvPEGLi4uLFiwgLy8PNLT07ly5Qr79u3rgsgbmTlzJuPGjWP27NkYjUaqq6tZtWoVffv2ZcmSJaxevZqysjLWrFnTZTE8QAiB6onIssytW7eUVve0tDSuXbtGYGCgIlpqtRoPD497Oho0GAxcunRJcSd2cHBQ/HcqKirQ6/XN5rNEPavncnyvhsLCwnuyX5ckiQMHDrBu3Tr+/ve/89xzz923WlNFRQWjR48mNzfX4jnDwsJITEzEz88PnU7HhAkTuHjx4n2JqZsjBErQiCRJ5ObmKlsw0tPTqampITw8XBGtkSNHtqlzymQykZubS1lZGUOHDrWwHmnpZ5vWEQwGA3Z2dharm/r06SPqWQ8wnWG/Do1Z04IFC/D09GTDhg0dErh7ISMjgzlz5hAeHs6ZM2dQq9Vs3LgRf39/ysvLgcabQy8vL+V7wR0RAiVoHaPRyNmzZxXRyszMxMHBgaioKKWe9dBDDykZjyRJ6HQ6ZfB40KBBHR42NgtWRUUFtbW19OnTx0K0WloXJUSr+3F8r0axqxk2bNgdb2ZaQ5Ik9u7dy8aNG1m1ahXPPvusVTr00tPTiY2NJSkpibFjxzJ//nzc3d358MMPLQTJy8uLsrKy+x5fN0QIVGscO3aM+fPnYzKZmD17NkuWLLF2SFZHlmUqKytJS0tTmjByc3Px8/NjyJAhpKamMm3aNObOndupE+5Ni9/mzsGGhgZcXFwsrEhEPav70NR+3dPTk+Dg4A5lTTqdjvnz59O/f3/WrVtn1eaDoqIiYmNjyc/PB+Cnn35i9erV5OTkiCO+jiEEqiVMJhNDhw7l3//+N4MGDSImJoY9e/YQHh5u7dBsjsrKShYtWsSPP/7Io48+yuXLlyktLbUwfBw9enSrho8dxWyBYs6y9Ho9gMXqppba64VoWR+z/fqNGzcYPnx4i/brd0OSJHbv3s3mzZt57733eOaZZ2xirmncuHFs3bqVsLAwVq5cicFgAMDb21tpkigtLeX999+3cqTdAiFQLXH7puD33nsPgKVLl1ozLJskOTmZzMxMZs2apRz1NTQ0cOHCBQvDR1mWGTVqlFLPCgsL6/RWd3M9y5xlGQwGpXPQLFrmYc6bN29y+fJl/P39+ePCok6NQ9Ay92q/bub69evEx8fj5+fHP/7xjw4dC3YVGRkZSgdfcHAw27dvR5Ikpk2bxtWrVwkMDGT//v33vT7WTREC1RIHDx7k2LFjbN26FYCdO3eSkpLC5s2brRxZ98S8jcJs+JiamsqlS5fw8vJCrVYrmVZHtlHfDaPRaHE0WFNTQ319PQ4ODgQFBeHt7S3qWfcBs/16SUkJ4eHhHbJflySJXbt28cknn7BmzRqeeuopm8iaBF2GcNQVdD0qlQoXFxfGjx/P+PHjgUbRunHjhtLq/sUXX6DT6QgKCrIwfHRzc7unDyEHBwf69etH3759uXr1KrW1tYSGhtKrVy8qKyspKCjAZDLh6upqYUVyu+mjEKyOYbZfT0tLw8fHB41G06Gs6dq1a8THxxMQEMCJEyfw8PDogmgF3YEen0GJIz7rIEkSWq1WybJOnTpFbW0tERERSpYVERGBg4NDux63vLycixcvKsdKtxfjJUlS6llmKxKVSqXsG3R3dxf1rA7w9oIG6uvrAQgMDKR///7trkVKksSOHTv49NNP+eCDD5g0aZLImnoO4oivJRoaGhg6dCjff/89/v7+xMTEsHv3biIiIqwdWo+jrq6OjIwMZaA4MzMTJycnoqOjFdFqrZZhNBrJycmhtraWYcOG4ezs3ObnNZlMFkeDBoMBBwcHi32DLdmfCNFqzJrKy8vJzs7Gx8cHNzc3Zf3Q7UtezXXBligoKGDevHkEBwfzwQcf4Obmdp9ficDKCIFqjSNHjrBgwQJMJhOzZs1i2bJlXf6cBQUFzJgxg+LiYlQqFXPmzGH+/PmUlpaKXV7/Q5ZlysvLSUtLU5ow8vPz8ff3VxowoqOj2bNnD3379uU3v/lNp214NxqNimCZl5M6OTkpguXu7t7j61ltsV9vui+vsrJS+X90dnbm/PnzPPLIIyQkJLBt2zbWrl3LxIkTuzxrMplMaDQa/P39SUhIIC8vj7i4OEpKSlCr1ezcubPdmbvgnhECZUvodDp0Oh3R0dHo9XrUajWHDh3iiy++ELu87oAkSVy9epWUlBS++eYbEhISCAoK4qGHHlLqWaNGjWrxw/JeMO85bGpFYq5nNZ3P6gmrm+7Fft38/1hYWMiqVas4efIkFRUVPPHEEzz88MNMmDCBqKioLo1/3bp1pKenU1lZSUJCAtOmTWPy5MmKvfqoUaOYO3dul8YgaIYQKFvm+eef57XXXuO1114Tg35t4OjRo7z77rts2rSJyMhIMjMzlXrWmTNnUKlUREVFKZnW0KFDOzQceickSaKqqspi32CvXr0sjgadnZ0fqHpWZ9ivS5LEtm3b2L59O+vXr+exxx4jOzubtLQ0HB0dmT59ehdE3si1a9eYOXMmy5YtY926dfzrX/+if//+FBUVYW9v36wmLbhvCIGyVfLz8xk/fjyZmZkEBASIXV5toK6uDnt7+xZFR5ZlqqqqLFrdtVot/fr1s9jq3tmGj9BY02xqRVJdXY2Dg4MyTHzz5k1MJhPDhg2zyPJsXbQ6w34dIC8vj3nz5jFixAjee+89XFxcuiDa1pk6dSpLly5Fr9ezdu1avvjiC2JjY8nJyQEaj96ffvppMjMz72tcAtFmbpNUVVUxZcoUNmzY0GzKXqVSiS6mVrjTeiVzV96ECROYMGEC0ChaRUVFyq7Bzz77jBs3bhASEqKIVlRUFK6urvf0f25vb0/fvn0thjNra2u5cuUKFy9eVOK+dOmSRT3r9lZ3sA3R+nZfDEajkczMTCRJIjo6ukOrrUwmE1u3bmXHjh1s2LCB8ePH3/ff7YSEBAYMGIBarSYxMfG+PregYwiBsiL19fVMmTKF6dOnM3nyZAB8fHzQ6XTKEd+AAQOsHOWDgUqlws/PjxdeeIEXXngBaPzQvHTpEsnJyRw+fJgVK1ZQX19PZGSk0jU4fPjwFpsh2kpNTQ3Z2dk4Ojry6KOP0rt3b2WYubKykhs3bpCTk4MkSRZWJLYwn/XtvhiKi4vJzc0lODgYHx+fDj3O5cuXiY+PZ9SoUSQlJbWrw7IzSUpK4uuvv+bIkSNK88b8+fMpLy+noaEBe3t7rl27hr+/v1XiEzRHHPFZCVmWmTlzJn379mXDhg3K9UWLFoldXlakpqaG06dPK63uWVlZuLq6KlmWRqNh8ODBdx1AlSSJgoICdDpdm/yOzPWspvNZdnZ2Fi3aLc0VdYVo3c1+va2YTCY+/fRTdu/ezcaNGxk3blynx9pREhMTWbt2LQkJCbz44otMmTJFaZKIjIzklVdesXaIPQ1Rg7Ilfv75Z8aNG8fIkSOVD7tVq1YxduxYscvLhpBlmZKSEotW94KCAgYPHkxMTIyyvsnT01MRj6KiIq5evUrfvn0JCgrqcHOGuZ5lFq3q6mplrsjDwwMPD48W26HvRbSO79VQVFREfn4+ISEh9O/fv0OPo9VqiY+PR6PR8Le//c1qWVNrNBWo3Nxc4uLiKC0tJSoqil27dnXqhn5BmxACJWgdMRvSdiRJIi8vz8Lw0WAwEBoaqsz67Nq1q0tuLJrOFVVUVGA0GnFxcVFEy83NrcXFvHcTrc6yXzeZTHzyySfs27ePTZs28eijj7b7MQQ9EiFQgtYRsyH3xpEjR3j99ddRq9X07t2bzMxM7O3tLQwfQ0JCOrSb7k6Y61lN57NkWVbms8zdg3eaz+oM+3WAixcvEh8fT2xsLO+8806nz6IJHmiEQAlaRsyG3BuyLPPOO+8we/ZspcBuNnxMT09X6lmXL1/Gx8eH6OhoYmJi0Gg09O/fv9O72SRJQq/XK1lWVVWVUs8ydw6a61k1NTVKna2j9usNDQ189NFHHDx4kI8++ojY2NhOfT2CHoFoMxe0zIIFC3j//fcVI8CSkhI8PT2VI55BgwZRWFhozRBtGpVKxYoVK5pd8/DwYOLEiUycOBFoFK3CwkLlaPCjjz6ipKSE0NBQNBoNarWaqKioFod720OvXr2U7Gnw4MFAY8eoOcMqKiqipqYGWZapr68nMDCQgQMHdkicsrOziY+P57HHHiMpKalDg7sCQWsIgerhiNmQ+4dKpWLQoEEMGjSIKVOmAI01G7Ph4z//+U+WL1+OLMtKq7tGo2HYsGH3bPjYu3dvvL298fb2xmAwcOHCBVxcXPDy8kKv13Pu3Dnq6+txdnZWxM3Nza1V0WpoaGDTpk0cOnSIjz/+mDFjxtxTfAJBSwiB6uGI2RDrYmdnx4gRIxgxYgSzZ89WakqnTp0iNTWVtWvXcvHiRTw9PS0MH/39/dudZUmS1KL9uq+vL9CY4RkMBiorK9HpdFy6dAlZlhUrkpKSEoYPH6506E2YMIGkpKQu73gTi5V7LqIGJVAQsyG2iSzL3Lp1SzkaTE1N5fr16wwZMkQRrOjoaNzd3VsVLbP9ure3N0FBQW1u1jCZTEo968033+Ts2bNUVFTw29/+lueee44xY8YwZMiQTny1zRGLlXsEoklCcGdsZTakvLyc2bNnk5mZiUql4vPPPycsLEzcLTdBkiQuX75McnIyqampnDx5ktraWsLDwxXRGjFiBJIksXfvXsLDwxk+fHiHfZbOnz/PvHnzmDRpEq+88grnzp0jNTWV/Px8tm7d2smv7s6IxcoPJEKgBN2DmTNnMm7cOGbPno3RaKS6uppVq1aJu+W7YDQaOXPmjCJaaWlplJeXo9FomDx5MjExMe3KnqCxuWLdunUcPXqULVu2EB0d3YWv4O6IxcoPLEKgBLZPRUUFo0ePJjc31+K4KiwsTNwtt4O3336b//znP7z//vtUVFQoWzDy8vIYOHCgxeomb2/vFo8Gz507R3x8PE8//TRvvfWW1Qe1q6qqePzxx1m2bBmTJ0/G09PTQpC8vLwoKyuzYoSCe0AIlMD2ycjIYM6cOYSHh3PmzBnUajUbN27E399f3C23g4yMDCIjI5tlS7IsK4aP5vmsiooKhg4dqghWREQEmzdv5rvvvmPLli2MHj3aSq/iV+rr63n22Wd56qmneOONNwBx0/KAIQRKYPukp6cTGxtLUlISY8eOZf78+bi7u/Phhx+Ku+UuoqGhgfPnzyuC9c033zB16lTWrl1r9awJxGLlHoIQKIHtU1RURGxsLPn5+QD89NNPrF69mpycHHG3fJ+QZdmmfMjEYuUegRAoQfdg3LhxbN26lbCwMFauXInBYAAQd8sCwYOLEChB9yAjI0Pp4AsODmb79u1IkiTulgWCBxchUAJBe1m/fj1bt25FpVIxcuRItm/fjk6nE1YkAkHn0qJAde7uf4HgAaKwsJBNmzaRnp5OZmYmJpOJvXv3snjxYl5//XVycnLw8vJi27Zt1g5VIHggEQIlENyBhoYGampqaGhooLq6Gj8/P3744QemTp0KNA4VHzp0yMpRCgQPJkKgBIJW8Pf3Z+HChQQEBODn54eHh4di7y6sSASCrkcIlEDQCmVlZRw+fJi8vDyuX7+OwWDg2LFj1g6r23Hs2DHCwsIICQlh9erV1g5H0I0QAiUQtMJ3331HUFAQ/fv3p3fv3kyePJmkpCTFigQQViR3wWQy8eqrr3L06FGysrLYs2cPWVlZ1g5L0E0QAiUQtEJAQADJyclUV1cjyzLff/894eHhPPHEExw8eBCAL7/8kueff97KkdouqamphISEEBwcjIODA3FxcRw+fNjaYQm6CUKgBIJWGDt2LFOnTiU6OpqRI0ciSRJz5sxhzZo1rFu3jpCQEEpKSvjjH/9o7VBtlsLCQsV2HkTNTtA+hEAJBHfg7bffJjs7m8zMTHbu3ImjoyPBwcGkpqaSk5PDgQMHutQna9asWQwYMIARI0Yo10pLS5k0aRKhoaFMmjRJ2UkoyzLx8fGEhIQQGRnJqVOnuiwugeB+IARKILBhXn755WaNGatXr2bixIlotVomTpyoNB4cPXoUrVaLVqvls88+Y+7cudYI2QJ/f38KCgqU70XNTtAehEAJBDbM+PHjm610Onz4MDNnzgQs57AOHz7MjBkzUKlUxMbGUl5ejk6nu+8xNyUmJgatVkteXh5Go5G9e/fy3HPPWTUmQffB3toBCASC9lFcXIyfnx8Avr6+FBcXA63Xe8w/aw3s7e3ZvHkzTz31FCaTiVmzZhEREWG1eATdCyFQAkE3RqVS2ZQ1Rks888wzPPPMM9YOQ9ANuduyWIFAYGVUKtUQIEGW5RH/+/4iMEGWZZ1KpfIDEmVZDlOpVJ/+7897bv85K4UuENwTogYlEHQ/vgZm/u/PM4HDTa7PUDUSC1QIcRJ0Z0QGJRDYMCqVag8wAegHFAMrgEPAfiAAuAJMk2W5VNV41rcZ+D+gGviDLMvp1ohbIOgMhEAJBAKBwCYRR3wCgUAgsEmEQAkEAoHAJhECJRAIBAKb5P8DY/S3IS6WgtoAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "ZCJBJsmTWQaD" }, "source": [ "# SA algorithm" ] }, { "cell_type": "code", "metadata": { "id": "YoIKUWRvfdhr" }, "source": [ "def sim_anneal(proposal=\"gaussian\", sigma=10):\n", " np.random.seed(42)\n", " xcur = np.array([np.floor(np.random.uniform(0, 100)), np.floor(np.random.uniform(0, 100))])\n", " xcur = xcur.astype(int)\n", " ns = 300 # number of samples to keep\n", " T = 1 # start temperature\n", " alpha = 0.99999 # cooling schedule\n", " alpha = 0.99 # cooling schedule\n", "\n", " # list of visited points, temperatures, probabilities\n", " x_hist = xcur # will be (N,2) array\n", " prob_hist = []\n", " temp_hist = []\n", "\n", " nreject = 0\n", " iis = 0 # number of accepted points\n", " npp = 0 # num proposed points\n", " while npp < ns:\n", " npp = npp + 1\n", " if proposal == \"uniform\":\n", " xnew = np.array([np.floor(np.random.uniform(0, 100)), np.floor(np.random.uniform(0, 100))])\n", " elif proposal == \"gaussian\":\n", " xnew = xcur + np.random.normal(size=2) * sigma\n", " xnew = np.maximum(xnew, 0)\n", " xnew = np.minimum(xnew, 99)\n", " else:\n", " raise ValueError(\"Unknown proposal\")\n", " xnew = xnew.astype(int)\n", "\n", " # compare energies\n", " Ecur = energy[xcur[0], xcur[1]]\n", " Enew = energy[xnew[0], xnew[1]]\n", " deltaE = Enew - Ecur\n", " # print([npp, xcur, xnew, Ecur, Enew, deltaE])\n", "\n", " temp_hist.append(T)\n", " T = alpha * T\n", " P = np.exp(-1.0 * deltaE / T)\n", " P = min(1, P)\n", " test = np.random.uniform(0, 1)\n", " if test <= P:\n", " xcur = xnew\n", " iis = iis + 1\n", " else:\n", " nreject += 1\n", "\n", " x_hist = np.vstack((x_hist, xcur))\n", " prob_hist.append(pdf[xcur[0], xcur[1]])\n", "\n", " npp = npp + 1\n", " print(f\"nproposed {npp}, naccepted {iis}, nreject {nreject}\")\n", " return x_hist, prob_hist, temp_hist" ], "execution_count": 54, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "YQS9aAbv8Bn3" }, "source": [ "# Run experiments" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kwbmsETk20Pw", "outputId": "ce2a4f56-992e-414a-818d-3b0726294180" }, "source": [ "proposals = {\"gaussian\", \"uniform\"}\n", "x_hist = {}\n", "prob_hist = {}\n", "temp_hist = {}\n", "for proposal in proposals:\n", " print(proposal)\n", " x_hist[proposal], prob_hist[proposal], temp_hist[proposal] = sim_anneal(proposal=proposal)" ], "execution_count": 55, "outputs": [ { "output_type": "stream", "text": [ "uniform\n", "nproposed 301, naccepted 33, nreject 267\n", "gaussian\n", "nproposed 301, naccepted 104, nreject 196\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 611 }, "id": "Os6Sn4Nonfnm", "outputId": "4f966e11-94e7-40a8-cca9-f34b31e2c61b" }, "source": [ "for proposal in proposals:\n", " plt.figure()\n", " plt.plot(temp_hist[proposal])\n", " plt.title(\"temperature vs time\")\n", " plt.tight_layout()\n", " pml.savefig(f\"sim_anneal_2d_temp_vs_time_{proposal}.pdf\")\n", " plt.show()" ], "execution_count": 56, "outputs": [ { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_temp_vs_time_uniform.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_temp_vs_time_gaussian.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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rn1xE9d59fpckIhLUFFDdKL1nPPf823i2Ve3lR8/qfn0iIoejgOpmxw1K45apI3lr5Q7+94MNfpcjIhK0FFA+uPqkXM4/th+3v7GaD9bu9LscEZGgpIDygZlx+/QxDOuTzHeeWsymij1+lyQiEnQUUD5Jio/h/m8VAHDtYwvZ06CbyoqItKSA8tHA9ETu/uZ41pXV8kMNmhAR+QIFlM9Ozs/klvNG8vry7fztvfV+lyMiEjQUUEHgmpMH89Vx/fmft9bwr9U7/C5HRCQoKKCCgJnxh6+P4Zj+KXz3qSW687mICAqooJEQG83/fquAuJgoZj6ygF17Gv0uSUTEVwqoIDIgtQf3X3Ec26rque6JhTQ2NftdkoiIbxRQQea4QWncPn0M84or+ekLejyHiESuGL8LkC/76vgBbCjfw13vrGNoVk+uO2Wo3yWJiHQ7BVSQ+t6Z+RSX7+G2N1aTm57EuaP7+l2SiEi3alMXn5mda2ZrzKzIzH58iGW+YWYrzWyFmf29c8uMPGbGHdPHMC4nlZufWcynW/QMKRGJLEcMKDOLBu4BzgNGAZeZ2ahWy+QDtwAnOeeOAW7uglojTkJsNPd/q4D0pHhmPrqA0uq9fpckItJt2nIENREocs5tcM41Ak8D01ot8x/APc65XQDOubLOLTNyZSbH89BVBdQ17ueqWQv0oEMRiRhtCagBQEmL91u8aS0NA4aZ2Rwzm2tm5x5sRWZ2rZkVmlnhzp16zERbjeibwv9+6zg2lO/m2scKaWja73dJIiJdrrOGmccA+cCpwGXAA2aW2noh59z9zrkC51xBZmZmJ206MpyUl8Ed08cyr7iS7/9jKc3NGn4uIuGtLaP4tgI5Ld5ne9Na2gLMc87tA4rNbC2BwFrQKVUKEBh+vqOmnt+/vpp+KQn87IJRR/6QiEiIassR1AIg38wGm1kcMAOY3WqZFwkcPWFmGQS6/PQ88y5w7VeGcNXkXB78qJgHP9SPWETC1xGPoJxzTWZ2I/AmEA3Mcs6tMLNfA4XOudnevLPNbCWwH/iRc66iKwuPVGbGzy8YRVltPb95dRVZKQlcNLa/32WJiHQ68+tWOgUFBa6wsNCXbYeD+n37ueKh+SwpqWLWVcczJT/D75JERNrFzBY65wpaT9e9+EJUQmw0D1xRwOCMJK59vJBFm3f5XZKISKdSQIWwXomxPD5zIpnJ8Vw1az6rt9f4XZKISKdRQIW4rJQEnph5AolxMXzroflsLN/jd0kiIp1CARUGctISeeKaiTTtb+bfHpzH9up6v0sSEekwBVSYyMtK5tGrJ1K9dx+XPzSPSj2RV0RCnAIqjIzJTuXBKwsoqazjylnzqanXfftEJHQpoMLMiUPSuffyCazeXsOVs+ZTq5ASkRClgApDp4/ow18vm8CnW6q56uEF7G5o8rskEZGjpoAKU+eO7stdl41nSUkVVz+8gLpGhZSIhBYFVBibemw/7rx0HIWbKrn6kQXsbdRjOkQkdCigwtyFY/vzp2+MY15xJdc8toD6fQopEQkNCqgI8NXxA7hj+lg+Xl/BfzxWqJASkZCggIoQ04/L5raLx/BRUTlXP6JzUiIS/BRQEeQbx+fwp2+MZe6GCq54SEPQRSS4KaAizNfGZ/PXyyawpKSKyx+aT3WdQkpEgpMCKgKdP6Yf915+HKu21XDZA3Op2N3gd0kiIl+igIpQZ43qwwNXFrB+524ue2AuZbW6wayIBBcFVAQ7ZVgmD//78WzZtZdL/3cuW3bV+V2SiMgBCqgIN3loBo/PnEjF7gam3/sJa3fU+l2SiAiggBLguEFp/OO6STQ7xyX3faLHx4tIUFBACQAj+qbwz29PJjUxln97YB7vrSnzuyQRiXAKKDkgJy2R566bzOCMJK55tJCXlmz1uyQRiWAKKPmCzOR4nv7PE5kwqDc3P7OERz/e6HdJIhKhFFDyJSkJsTx29UTOHNmHX85ewe9eW0Vzs/O7LBGJMAooOaiE2Gjuu/w4rpg0iPs/2MB3nlqsm8yKSLeK8bsACV7RUcavLjqG7N49+N1rq9lRU88DVxTQOynO79JEJALoCEoOy8y49itDufub41m2tZqv3/sxmyr2+F2WiEQABZS0yQVj+vPkNSdQWdfIxX/7mMW6VkpEupgCStrs+Nw0/vntySTFxzDj/rkahi4iXUoBJUdlaGZPXrh+MmOzU/nu00v441trNMJPRLqEAkqOWnrPeJ645gQuLcjhr/8q4vonF+kJvSLS6RRQ0i5xMVH84evH8rPzR/LWyu1Mv/cTtlXt9bssEQkjCihpNzPjmpOH8NBVx1NSWcdFd89h4aZKv8sSkTChgJIOO214Fs9fP5mk+Ghm3D+Xx+duwjmdlxKRjlFASafI75PM7BumcFJeBj9/cTk/fHaZ7jwhIh2igJJO0ysxlllXHs9NZ+Tzz0Vb+Pq9H1NSqaf0ikj7tCmgzOxcM1tjZkVm9uPDLPd1M3NmVtB5JUooiYoyvn/WMB66soDNlXVcePdHfLB2p99liUgIOmJAmVk0cA9wHjAKuMzMRh1kuWTgu8C8zi5SQs8ZI/vw8o1T6JOcwJUPz+eed4t0vZSIHJW2HEFNBIqccxucc43A08C0gyz3/4DbgPpOrE9CWG5GEi/cMJkLx/TnjjfXcO3jheza0+h3WSISItoSUAOAkhbvt3jTDjCzCUCOc+7Vw63IzK41s0IzK9y5U90+kSAxLoa/zBjHLy8cxftrdzL1rg9ZsFFD0UXkyDo8SMLMooA/AT840rLOufudcwXOuYLMzMyOblpChJnx7ycN5p/fnkxcTBQz7p+rLj8ROaK2BNRWIKfF+2xv2meSgdHAe2a2ETgRmK2BEtLamOxUXvnOFKYe24873lzDlQ/PZ2dtg99liUiQaktALQDyzWywmcUBM4DZn810zlU75zKcc7nOuVxgLnCRc66wSyqWkJacEMtdM8bxh4uPZX5xJef95UM+Wlfud1kiEoSOGFDOuSbgRuBNYBXwD+fcCjP7tZld1NUFSvgxM2ZMHMjsG6fQOzGWb82ax+1vrKaxqdnv0kQkiJhft6QpKChwhYU6yIp0dY1N/Gr2Sp4pLGH0gBTuvHQceVnJfpclIt3IzBY65750Wkh3khBfJcbFcNv0Mdx3+XFsq6rn/Ls+4pE5xRpAISIKKAkO547uyxs3n8zkoenc+vJKrnx4PturdUmdSCRTQEnQyEpOYNZVx/Obr46mcOMuzrnzA15dVup3WSLiEwWUBBUz4/ITB/HqTVPIzUjihr8v4rtPL9YdKEQikAJKgtKQzJ48d90kbj4zn1eXlXLWn9/n9U91NCUSSRRQErRio6O4+cxhzL5xCn17JfDtJxfx7ScW6uJekQihgJKgN6p/Ci9efxL/de5w3lldxll/fp8XFm/RU3tFwpwCSkJCTHQU15+ax2s3TWFIRhLfe2YpMx8tpLR6r9+liUgXUUBJSMnLSubZ6ybz8wtG8fH6cs760wc8MqeY/bpuSiTsKKAk5ERHGTOnDObNm7/C+IGp3PrySr56zxyWbanyuzQR6UQKKAlZg9KTeOzqidz9zfFsr6ln2j1z+OVLy6mp3+d3aSLSCRRQEtLMjAvG9OedH5zCFScO4rG5mzjjj+/z8tJtGkQhEuIUUBIWUhJi+dW00bx0w0n0SYnnO08t5opZ8ykq2+13aSLSTgooCStjslN56YYp/PLCUSwpqeLcOz/g/72yUt1+IiFIASVhJzoq8Ij5d394KpcUZDNrTjGn3fEeT8/frNF+IiFEASVhK6NnPL+/eAwv3ziFwRlJ/Pj5T5l2z0cUbqz0uzQRaQMFlIS90QN68ex1k/jLjHGU1zYy/b5PuOmpxZRU1vldmogcRozfBYh0BzNj2rgBnDWqD/e+t577P9jAG8u3c9VJudxwah69EmP9LlFEWtEj3yUilVbv5U9vreW5RVtISYjlxtPy+NakQSTERvtdmkjE0SPfRVro16sHd1wyltduOplxOan89rVVnPHH93lx8VY9bl4kSCigJKKN7JfCo1dP5ImZJ5CaGMvNzyzhwrs/4t01ZbrQV8RnCigRYEp+Bi/fOIU/XzqW6r37+PeHFzD9vk/4eH2536WJRCydgxJppbGpmWcXlvDXd4rYXlPP5KHp/ODs4Rw3qLffpYmEpUOdg1JAiRxC/b79/H3eZv72XhHluxs5bXgmPzh7OKMH9PK7NJGwooASaae6xiYe/XgT972/nuq9+zhjRBY3nJ7HhIE6ohLpDAookQ6qqd/Ho3M2MmtOMbvq9nFSXjo3npbPiUPSMDO/yxMJWQookU6yp6GJv8/bzP0fbmBnbQMFg3pzw+l5nDosU0El0g4KKJFOVr9vP88WlnDf+xvYWrWX0QNSuPG0PM4e1ZeoKAWVSFspoES6SGNTMy8u3srf3itiY0UdQzKSmHnyYL4+IVt3phBpAwWUSBdr2t/Ma8u38+CHG1i2pZq0pDguP3EQV0waREbPeL/LEwlaCiiRbuKcY35xJQ98uIG3V5URFxPFxeMHcM3Jg8nLSva7PJGgc6iA0t3MRTqZmXHCkHROGJLO+p27eeijYv65cAtPLyjhtOGZzJwyhJPy0jWgQuQIdAQl0g0qdjfw+NxNPP7JJir2NDI0M4krJuVy8YQBJCfoUR8S2dTFJxIE6vft59VlpTz2yUaWbqkmKS6aiydkc8WkQeT3UfefRCYFlEiQWVJSxWOfbOSVZaU0NjUzaUg6V0waxFmj+hATrfs4S+ToUECZ2bnAX4Bo4EHn3B9azf8+cA3QBOwErnbObTrcOhVQIgGVexp5ZkEJT8zdxNaqvfRJieeS43K49PgcctIS/S5PpMu1O6DMLBpYC5wFbAEWAJc551a2WOY0YJ5zrs7Mvg2c6py79HDrVUCJfNH+Zsc7q3bw1PzNvL92J80OpuRlcOnxOZx9TB/iY3RNlYSnjozimwgUOec2eCt6GpgGHAgo59y7LZafC1zesXJFIk90lHH2MX05+5i+bKvay3MLt/DMghK+89RieifG8rXx2cyYmMMwnauSCNGWgBoAlLR4vwU44TDLzwRe70hRIpGuf2oPbjojnxtPy2PO+nKeXlDC43MDN6odPzCViydkc+GYfqQmxvldqkiX6dTroMzscqAAOOUQ868FrgUYOHBgZ25aJCxFRRkn52dycn4mFbsbeGHxVv5RWMLPX1zOr19ewekjsvja+GxOG5GpLkAJO205BzUJuNU5d473/hYA59zvWy13JvBX4BTnXNmRNqxzUCLt45xjxbYaXli8lZeWbKV8dyOpibFcMKYfXxufzYSBqboIWEJKRwZJxBAYJHEGsJXAIIlvOudWtFhmPPAccK5zbl1bClJAiXRc0/5mPiwq54VFW3lzxXYamprJTU/korH9OX9Mf4b31fkqCX4dHWY+FbiTwDDzWc6535rZr4FC59xsM3sbOBYo9T6y2Tl30eHWqYAS6Vy19ft4ffl2Xli0lXnFFTQ7yM/qyQVj+nP+mH7kZfX0u0SRg9KFuiIRpKy2njeWb+eVZaUs2FiJczCibzIXjOnHBWP6k5uR5HeJIgcooEQi1Pbqel5fXsory0pZuGkXAKMHpHDe6H6cc0xfHVmJ7xRQIsK2qr289mkpLy8rZWlJFQBDMpM4a1Qfzh7Vl/E5qXoasHQ7BZSIfEFp9V7eXrmDt1bu4JP1FTQ1OzKT4zlzZB/OPqYPk4ema+i6dAsFlIgcUvXefby3poy3VuzgvTVl7GncT1JcNKcOz+K0EVmcMiyTzGQ9FVi6hgJKRNqkoWk/H6+v4K0VO3h71Q521jYAMCa7F6cOz+LU4ZmMzU4lWl2B0kkUUCJy1JqbHStLa3hvTRnvrtnJ4s27aHbQOzGWU4ZlctqILE7OzyQtSbdckvZTQIlIh1XVNfLBunLeW13G+2t3UrGnETMYM6AXJ+VlMCUvgwmDepMQq3NX0nYKKBHpVM3Njk+3VvPumjI+WlfO4pIq9jc74mOiOD43jZPyMjgpL51j+vdSd6AclgJKRLrU7oYm5hdX8NG6CuYUlbNmRy0AvXrEMnloOpPzMjhpaDqDM5J0r0D5go48D0pE5Ih6xsdw+og+nD6iDxC4m8Un61grAKQAAAmESURBVCv4aF05c4rKeX35dgCykuOZODiNEwanMXFwOvlZPXXtlRyUjqBEpMs559hYUceconLmF1cyv7iS7TX1AKQmxlIw6LPASuOY/inEREf5XLF0Jx1BiYhvzIzBGUkMzkji8hMH4Zxjy669zCuuZH5xBfOLK3l71Q4AkuKimTCoN8fnpjFhYG/G5vQiOSHW5xaIHxRQItLtzIyctERy0hKZflw2AGU19czfWHngCOvPb6/FOTCDYVnJjB+YyoSBvRk/MJWhmeoWjATq4hORoFRTv4+lJVUs2lTF4pJdLN5cRfXefQAkJ8QwLieV8QN7M2FgKmOzU+mta7FClrr4RCSkpCTEHnjcPQSGtRdX7GHRpl0sLqli0aZd3P2vdTR7f2MPSO3BmOxejB7Qi2O9L4VWaFNAiUhIiIoyhmb2ZGhmTy4pyAECQ9uXlVSxbGs1n26tZvnW6gOjBQGye/cIhFX256GVmqjQChUKKBEJWT3jY5icl8HkvIwD06rr9rF8WyCwPt1azadbvhxao/qlMKJfCqP6JTOibwoD0xJ1TisIKaBEJKz0Soz17mJx6NBaXVrD26t2HOgeTIyLZnjfZEb2S2Gk931432SNHvSZBkmISETa27ifdWW1rCqtYVVp4Pvq7bUHBmIA5KT1YHifFIb16UleVk/ys5IZmpVEYpz+tu9MGiQhItJCj7hoxmSnMiY79cA05xyl1fWs3v55aK3ZXsv7a8vYt//zP+aze/cgP+vz0MrzAixFR1ydSgElIuIxM/qn9qB/ao8Dt2wC2Le/mU0VdRSV1bJux27Wle2mqGw3H6+voKGp+cByfVLiA4GV1ZPBGUnkZiQxOD2JAb176Ia57aCAEhE5gtjoKPK8I6ZzR38+fX+zY8uuui+EVlFZLc8WlrCncf+B5eKio8hJ63Hgbhq53vfBGUn0SU7QAI1DUECJiLRTdJQxKD2JQelJnDnq8yMu5xw7dzewsbyO4vLdFJfXsbF8D8Xle/hwXfkXjroSYqPITU8iNz2JgemJ5PTuQXZaIgPTEhmQ2iOin62lgBIR6WRmRlZyAlnJCUwcnPaFec3NjtKa+gOBVVy+h43le1hXVsu7a8q+EF4Q6DYcmJZITu/EA7eHyundg4HpiWF/9KWAEhHpRlFRxoDUHgxI7fGFofAQCK+duxsoqayjZFcdmyv2UrKrjpLKOuZuqOCFJVtpOfA6LjqKAb170K9XQuDcmfe9n/e6X2oPesaH7n/zoVu5iEiYiYoy+qQk0CclgYLctC/Nb2jaz7aq+s8DrLKOLbv2Ulq1l4/WlVNWW3/g2q7PpCTEHBj4cSDIUhPo1ysQklkp8cTHBGc3ogJKRCRExMdEHxhccTD79jezo6ae0up6tlXtZVtVPaXVew+8XrR5F1V1+770ud6JsYEuyZR4LyDjyUr2vnuBmdkznriY7n1OlwJKRCRMxEZHkd07kezeiYdcpq6x6UCAlVbVs6Omnh219eyoaaCstoGisnLKahvY3/pQDEhLiiMr+fMQOzY7lW+dOKjL2qOAEhGJIIlxMQduunsozc2Oij2N7KipZ2dtQyDEahrYUVtPWU0DZbWBi5kr9zQqoEREpPtERRmZyfFkJscfdrmuvlVe93YoiohI2DDr2iHuCigREQlKCigREQlKCigREQlKCigREQlKCigREQlKCigREQlKCigREQlKCigREQlK1tVXAh9yw2Y7gU2dsKoMoLwT1hMK1NbwEyntBLU1XHVGWwc55zJbT/QtoDqLmRU65wr8rqM7qK3hJ1LaCWpruOrKtqqLT0REgpICSkREglI4BNT9fhfQjdTW8BMp7QS1NVx1WVtD/hyUiIiEp3A4ghIRkTCkgBIRkaAUsgFlZuea2RozKzKzH/tdT2czs41m9qmZLTGzQm9ampn9n5mt87739rvO9jCzWWZWZmbLW0w7aNss4C5vPy8zswn+VX70DtHWW81sq7dvl5jZ1BbzbvHausbMzvGn6vYxsxwze9fMVprZCjP7rjc97PbtYdoadvvWzBLMbL6ZLfXa+itv+mAzm+e16Rkzi/Omx3vvi7z5ue3euHMu5L6AaGA9MASIA5YCo/yuq5PbuBHIaDXtduDH3usfA7f5XWc72/YVYAKw/EhtA6YCrwMGnAjM87v+TmjrrcAPD7LsKO93OR4Y7P2OR/vdhqNoaz9ggvc6GVjrtSns9u1h2hp2+9bbPz2917HAPG9//QOY4U2/D/i29/p64D7v9QzgmfZuO1SPoCYCRc65Dc65RuBpYJrPNXWHacCj3utHga/6WEu7Oec+ACpbTT5U26YBj7mAuUCqmfXrnko77hBtPZRpwNPOuQbnXDFQROB3PSQ450qdc4u817XAKmAAYbhvD9PWQwnZfevtn93e21jvywGnA89501vv18/293PAGdbOZ8OHakANAEpavN/C4X85QpED3jKzhWZ2rTetj3Ou1Hu9HejjT2ld4lBtC9d9faPXrTWrRVdt2LTV69YZT+Cv7bDet63aCmG4b80s2syWAGXA/xE4AqxyzjV5i7Rsz4G2evOrgfT2bDdUAyoSTHHOTQDOA24ws6+0nOkCx89heY1AOLfNcy8wFBgHlAJ/9LeczmVmPYF/Ajc752pazgu3fXuQtoblvnXO7XfOjQOyCRz5jeiO7YZqQG0Fclq8z/amhQ3n3FbvexnwAoFfih2fdYF438v8q7DTHaptYbevnXM7vH/wzcADfN7VE/JtNbNYAv9hP+mce96bHJb79mBtDed9C+CcqwLeBSYR6JKN8Wa1bM+BtnrzewEV7dleqAbUAiDfG0USR+BE3Gyfa+o0ZpZkZsmfvQbOBpYTaOOV3mJXAi/5U2GXOFTbZgNXeCO+TgSqW3QXhaRW51m+RmDfQqCtM7xRUIOBfGB+d9fXXt55hoeAVc65P7WYFXb79lBtDcd9a2aZZpbqve4BnEXgnNu7wHRvsdb79bP9PR34l3fkfPT8HiHSgZElUwmMnFkP/NTvejq5bUMIjPhZCqz4rH0E+nHfAdYBbwNpftfazvY9RaD7Yx+BvuuZh2obgRFE93j7+VOgwO/6O6Gtj3ttWeb9Y+7XYvmfem1dA5znd/1H2dYpBLrvlgFLvK+p4bhvD9PWsNu3wBhgsdem5cAvvOlDCIRsEfAsEO9NT/DeF3nzh7R327rVkYiIBKVQ7eITEZEwp4ASEZGgpIASEZGgpIASEZGgpIASEZGgpIASEZGgpIASEZGg9P8B6b8CQqNPm38AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 611 }, "id": "NicNcHfB67VA", "outputId": "f68e34e7-b0eb-43e2-81aa-cfc0db8323f4" }, "source": [ "for proposal in proposals:\n", " plt.figure()\n", " plt.plot(prob_hist[proposal])\n", " plt.xlabel(\"iteration\")\n", " plt.ylabel(\"probability\")\n", " plt.tight_layout()\n", " pml.savefig(f\"sim_anneal_2d_prob_vs_time_{proposal}.pdf\")\n", " plt.show()" ], "execution_count": 57, "outputs": [ { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_prob_vs_time_uniform.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_prob_vs_time_gaussian.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 611 }, "id": "aBvRSqRgfjG6", "outputId": "83128f27-b919-4f40-9047-0231e15fddde" }, "source": [ "# Plot points visited\n", "for proposal in proposals:\n", " probs = prob_hist[proposal]\n", " xa = x_hist[proposal]\n", "\n", " f1, ax = plt.subplots()\n", " ax.imshow(pdf.transpose(), aspect=\"auto\", extent=[0, 100, 100, 0], interpolation=\"none\")\n", "\n", " # Maximum value achieved ploted with white cirlce\n", " # maxi = np.argmax(probs) # index of best model\n", " # ax.plot(xa[maxi,0],xa[maxi,1],'wo', markersize=10)\n", "\n", " # Starting point with white cirlce\n", " ax.plot(xa[0, 0], xa[0, 1], \"wo\", markersize=10)\n", "\n", " # Global maximm with red cirlce\n", " ind = np.unravel_index(np.argmax(pdf, axis=None), pdf.shape)\n", " ax.plot(ind[0], ind[1], \"ro\", markersize=10)\n", "\n", " ax.plot(xa[:, 0], xa[:, 1], \"w+\") # Plot the steps with white +\n", "\n", " ax.set_ylabel(\"y\")\n", " ax.set_xlabel(\"x\")\n", " plt.tight_layout()\n", " pml.savefig(f\"sim_anneal_2d_samples_{proposal}.pdf\")\n", " plt.show()" ], "execution_count": 58, "outputs": [ { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_samples_uniform.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "saving image to ../figures/sim_anneal_2d_samples_gaussian.pdf\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "XDM1SEc9dCKk" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }
mit
mne-tools/mne-tools.github.io
0.20/_downloads/bf3ad991f7c7776e245520709f49cb04/plot_cwt_sensor_connectivity.ipynb
1
4057
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Compute seed-based time-frequency connectivity in sensor space\n\nComputes the connectivity between a seed-gradiometer close to the visual cortex\nand all other gradiometers. The connectivity is computed in the time-frequency\ndomain using Morlet wavelets and the debiased squared weighted phase lag index\n[1]_ is used as connectivity metric.\n\n.. [1] Vinck et al. \"An improved index of phase-synchronization for electro-\n physiological data in the presence of volume-conduction, noise and\n sample-size bias\" NeuroImage, vol. 55, no. 4, pp. 1548-1565, Apr. 2011.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Martin Luessi <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\nimport mne\nfrom mne import io\nfrom mne.connectivity import spectral_connectivity, seed_target_indices\nfrom mne.datasets import sample\nfrom mne.time_frequency import AverageTFR\n\nprint(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set parameters\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname)\nevents = mne.read_events(event_fname)\n\n# Add a bad channel\nraw.info['bads'] += ['MEG 2443']\n\n# Pick MEG gradiometers\npicks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True,\n exclude='bads')\n\n# Create epochs for left-visual condition\nevent_id, tmin, tmax = 3, -0.2, 0.5\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),\n preload=True)\n\n# Use 'MEG 2343' as seed\nseed_ch = 'MEG 2343'\npicks_ch_names = [raw.ch_names[i] for i in picks]\n\n# Create seed-target indices for connectivity computation\nseed = picks_ch_names.index(seed_ch)\ntargets = np.arange(len(picks))\nindices = seed_target_indices(seed, targets)\n\n# Define wavelet frequencies and number of cycles\ncwt_freqs = np.arange(7, 30, 2)\ncwt_n_cycles = cwt_freqs / 7.\n\n# Run the connectivity analysis using 2 parallel jobs\nsfreq = raw.info['sfreq'] # the sampling frequency\ncon, freqs, times, _, _ = spectral_connectivity(\n epochs, indices=indices,\n method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq,\n cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles, n_jobs=1)\n\n# Mark the seed channel with a value of 1.0, so we can see it in the plot\ncon[np.where(indices[1] == seed)] = 1.0\n\n# Show topography of connectivity from seed\ntitle = 'WPLI2 - Visual - Seed %s' % seed_ch\n\nlayout = mne.find_layout(epochs.info, 'meg') # use full layout\n\ntfr = AverageTFR(epochs.info, con, times, freqs, len(epochs))\ntfr.plot_topo(fig_facecolor='w', font_color='k', border='k')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
balarsen/pymc_learning
Mixture/Gaussian Mixture.ipynb
1
3040946
null
bsd-3-clause
Tatiana-Krivosheev/ipython-notebooks-physics
Phys2212L.Capacitors.ipynb
1
19576
{ "metadata": { "name": "", "signature": "sha256:c8f331dfcac9cece8daec4ac0ecedd6e63b792cc34a2911afa1d88f646373747" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "PHYS 2212L - Principles of Physics Laboratory II\n" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Capacitors" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Objectives." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The objectives of this laboratory are\n", "a. to verify the functional dependaence of capacitance on plate spacing for a parallel plate capacitor, and\n", "b. to measure the dialectric constant of paper." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Theory." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ " a. General. " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", " a. The capacitance of a parallel plate capacitor with no material between its plates is given by\n", "\n", "where C0 is he capacitance without any material between its plates,\n", "epsilon0 is the permittivity of free space (8.8542 x 10-12 C2/(Nm2),\n", "A is the surface area of a plate of the capacitor, and\n", "d is the separation distance between the plates.\n", "\n", " b. The capacitance of a parallel plate capacitor with a dialectric material filling the region between its plates is given by \n", "C=kappaC0 = kappaepsilon0A/d\n", "\n", "where kappa is the dialectric constant for the material.\n", "c. In this experiment the plate area, A, is constant, while the plate separation distance, d, will be varied by inserting different thickness of dialectric material between the plates. From these measurements of A and d, the capacitance without dielectric present, C0, can be calculatedfrom euation (1). The capacitance, C, for each thicknesswill be measured. To accomplish the objectives of the experiment, two graphs will be constructed: \n", "1. A graph of capacitance, c, versus the reciprocal of the plate spacing, 1/d, should be a straight line (objective 1a).\n", "2. A graph of the capacitance with dielectric, C, versus capacitance without dielectric, C0, should be linear and have a slope of kappa, the dielectric constant (objective 1b).\n", "\n", " " ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ " b. Model. " ] }, { "cell_type": "raw", "metadata": {}, "source": [ " The general expressions for the position of an object undergoing two-dimensional motion with constant acceleration are\n", "\n", " x = x0 + v0xt + (1/2)axt2\n", "\n", " y = y0 + v0yt + (1/2)ayt2\n", "\n", " where\n", " (x,y) is the position of the object at time t,\n", " (x0,y0) is the initial position of the object (at time t = 0),\n", " v0x and v0y are the x and y components of initial velocity of the object, and\n", " ax and ay are the x and y components of the constant acceleration of the object.\n", "\n", " For the case of a object set in motion near the surface of the Earth over short ranges and neglecting the effects of air resistance, the general expressions become\n", "\n", " x = x0 + v0xt\n", "\n", " y = y0 + v0yt - (1/2)gt2\n", "\n", "\n", "\n", " where\n", " x is the position measured along an axis horizontal to the ground in the plane of the trajectory,\n", " y is the position measured along an vertical axis in the plane of the trajectory, and\n", " g is the local value of the acceleration due to gravity. \n", "\n", " The minus sign in the equation reflects the selection of the direction away from the center of the Earth as positive. The origin of the coordinate system is at the surface of the Earth.\n", "\n", " Since the position of the object along the vertical axis at the time the object strikes the ground is zero (y = 0), the y-equation above can be solved for the time of flight of the projectile. This time can then be substituted into the x-equation to find the range of the projectile (the displacement along the x-axis to the point where the object strikes the ground).\n", "\n", " The initial position of the projectile can be measured using a meter stick. The initial velocity will be determined by measuring the range when the projectile is given a purely horizontal initial velocity (vy0 = 0) by first solving for the time of flight in the y-equation and then substituting into the x-equation. If the projectile is then launched at some angle above the horizontal, the initial velocity of the projectile measured with the launcher horizontal can be used with the angle of inclination to predict the new range of the projectile using the same equations, but with\n", "\n", " v0x = v0 cosq\n", "\n", " v0y = v0 sinq\n", "\n", " where\n", " v0 is the initial speed measured with the launcher horizontal, and\n", " q is the angle of inclination.\n", "\n", " " ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "c. Testing the model. " ] }, { "cell_type": "raw", "metadata": {}, "source": [ " The predicted range will be compared to the actual range measured with a meter stick." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ " Apparatus and experimental procedures." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ " a. Equipment." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", " 1) Parallel plate capacitor with leads.\n", "\n", " 2) Caed stock paper.\n", "\n", " 3) Clothes pins.\n", "\n", " 4) Multimeter.micrometer and vernier caliper.\n", "\n", " 5) Ruler.\n" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "b. Experimental setup. To be provided by a stident." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "Figure 1. Experimental appartaus." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "c. Capabilities. To be provided by a student." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "\n", "d. Procedures. Detailed instructions are provided in paragraph 4 below.\n", " " ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Requirements." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "a. In the laboratory." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", " \n", "\n", " 1) Your instructor will introduce you to the equipment to be used in the experiment.\n", "\n", " 2) Measurements to determine the plate area and thickness of a single sheet of the card stock will be made. \n", "\n", " 3) measurements of capacitance versus plate separation will be made.\n", "\n", " 4) Your instructor will discuss methods to be used to prepare your data.\n", "\n", " \n", "\n", " " ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "b. After the laboratory. " ] }, { "cell_type": "raw", "metadata": {}, "source": [ " The items listed below will be turned in at the beginning of the next laboratory period. A complete laboratory report is not required for this laboratory. " ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Para. 4. Data and Calculations." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "\n", " 1) Provide your original data tables.\n", "\n", " 2) In your spreadsheet, provide data from your measurements.\n", "\n", " 3) In your spreadsheet, provide the following calculations:\n", "\n", " a) The initial velocity of the projectile from the horizontally projected data.\n", "\n", " b) The angle of the initial velocity when the projectile is set in motion at an angle.\n", "\n", " c) The predicted range of the projectile when set in motion at the angle of your experiment.\n", "\n", " d) The percent discrepancy between your measured results and your predictions for the case when the projectile is set in motion at an angle." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Annex A\n", "Data Tables\n", "\n", "1. Projectile with horizontal initial velocity.\n", "\n", " a. Initial position of bottom of projectile at release:\n", "\n", " x0 = ________________________ m\n", "\n", " y0 = ________________________ m\n", "\n", " Assuming y = 0 is at the top of the lab table.\n", "\n", " b. Range:\n", "\n", "trial\n", "\t\n", "\n", "range (m)\n", "\n", "1\n", "\t \n", "\n", "2\n", "\t \n", "\n", "3\n", "\t \n", "\n", "4\n", "\t \n", "\n", "5\n", "\t \n", "\n", "average\n", "\t \n", "\n", " \n", "\n", "2. Projectile with initial velocity at an angle.\n", "\n", " a. Angle determination.\n", "\n", " rise = ________________________ m\n", "\n", " hypotenuse = ________________________ m\n", "\n", " b. Initial position of bottom of projectile at release:\n", "\n", " x0 = ________________________ m\n", "\n", " y0 = ________________________ m\n", "\n", " c. Range:\n", "\n", "trial\n", "\t\n", "\n", "range (m)\n", "\n", "1\n", "\t \n", "\n", "2\n", "\t \n", "\n", "3\n", "\t \n", "\n", "4\n", "\t \n", "\n", "5\n", "\t \n", "\n", "average\n", "\t \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import \n", "import math\n", "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline " ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ImportError", "evalue": "No module named Markup", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-abf6f5086397>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mMarkup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: No module named Markup" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "class ListTable(list):\n", " \"\"\" Overridden list class which takes a 2-dimensional list of \n", " the form [[1,2,3],[4,5,6]], and renders an HTML Table in \n", " IPython Notebook. \"\"\"\n", " \n", " def _repr_html_(self):\n", " html = [\"<table>\"]\n", " for row in self:\n", " html.append(\"<tr>\")\n", " \n", " for col in row:\n", " html.append(\"<td>{0}</td>\".format(col))\n", " \n", " html.append(\"</tr>\")\n", " html.append(\"</table>\")\n", " return ''.join(html)\n", "table = ListTable()\n", "table.append(['trial', 'Range'])\n", "table.append([' ', '(m)'])\n", "trial=[1,2,3,4,5]\n", "x = [0.95,0.96,0.95,0.96,0.99]\n", "y=0.0\n", "for i in range(0,len(x)):\n", " xx = x[i]\n", " ttrial = trial[i]\n", " table.append([ttrial,xx])\n", "table " ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table><tr><td>trial</td><td>Range</td></tr><tr><td> </td><td>(m)</td></tr><tr><td>1</td><td>0.95</td></tr><tr><td>2</td><td>0.96</td></tr><tr><td>3</td><td>0.95</td></tr><tr><td>4</td><td>0.96</td></tr><tr><td>5</td><td>0.99</td></tr></table>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "[['trial', 'Range'],\n", " [' ', '(m)'],\n", " [1, 0.95],\n", " [2, 0.96],\n", " [3, 0.95],\n", " [4, 0.96],\n", " [5, 0.99]]" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "x0 = 0\n", "y0 = 0.09\n", "g=9.8\n", "x = [0.90,0.902,0.89,0.895,0.85]\n", "y = 0\n", "for i in range(0,len(x)):\n", " y = y+x[i] \n", "xaverage = y/len(x)\n", "print 'xaverage =', xaverage, 'm'\n", "t=((2*y0)/g)**0.5\n", "print 't = ', t , 's'\n", "v0=xaverage/t\n", "print 'v0 = ', v0, 'm/s'\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "xaverage = 0.8874 m\n", "t = 0.135526185436 s\n", "v0 = 6.54781212314 m/s\n" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "rise = 0.035\n", "hypotenuse = 0.4\n", "theta = math.atan(rise/hypotenuse)\n", "print 'theta=', theta" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "theta= 0.0872777129495\n" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "table = ListTable()\n", "able = ListTable()\n", "table.append(['trial', 'Range'])\n", "table.append([' ', '(m)'])\n", "trial=[1,2,3,4,5]\n", "x = [1.10,1.08,1.07,1.09,1.10]\n", "for i in range(0,len(x)):\n", " xx = x[i] \n", " ttrial = trial[i]\n", " table.append([ttrial, xx])\n", "table " ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table><tr><td>trial</td><td>Range</td></tr><tr><td> </td><td>(m)</td></tr><tr><td>1</td><td>1.1</td></tr><tr><td>2</td><td>1.08</td></tr><tr><td>3</td><td>1.07</td></tr><tr><td>4</td><td>1.09</td></tr><tr><td>5</td><td>1.1</td></tr></table>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "[['trial', 'Range'],\n", " [' ', '(m)'],\n", " [1, 1.1],\n", " [2, 1.08],\n", " [3, 1.07],\n", " [4, 1.09],\n", " [5, 1.1]]" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "x0 = 0 \n", "y0 = 0.11\n", "y = 0\n", "for i in range(0,len(x)):\n", " y = y+x[i] \n", "xaverage = y/len(x)\n", "print 'xaverage =', xaverage, 'm'\n", "t=(v0*math.sin(theta)+(v0*math.sin(theta)+2*g*y0)**0.5)/g\n", "print 't = ', t , 's'\n", "r = v0*math.cos(theta)*t \n", "print 'range = ', r, 's'\n", "Disc= (xaverage-r)/r*100\n", "print '%Disc = ', Disc" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5.44\n", "5\n", "xaverage = 1.088 m\n", "t = 0.233110895711 s\n", "range = 1.64838338729 s\n", "%Disc = -33.9959375719\n" ] } ], "prompt_number": 43 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "\n", "Para. 5. Results and Conclusions." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "a. Results." ] }, { "cell_type": "raw", "metadata": {}, "source": [ " 1) A statement regarding the agreement or disagreement between the predicted and measured dependence of capacitance on plate separation distance for a parallel plate capacitor.\n", "\n", " 2) A statement of the measured value for the dialectric cosntant of paper and its uncertainty.\n", " " ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "\n", "b. Conclusions.\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ " 1) An assessment of the accuracy and precision of your experiment. Use the value of the dielectric constant of paper given in your text for comparison purposes. Do not calculate a percent discrepancy. The actual value of the dielectric constant depends on the specific type of paper being considered.\n", "\n", " 3) Description of the sources of random and systematic error in the experiment." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
cc0-1.0
jorgedominguezchavez/dlnd_first_neural_network
Your_first_neural_network.ipynb
1
312379
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Your first neural network\n", "\n", "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and prepare the data\n", "\n", "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "data_path = 'Bike-Sharing-Dataset/hour.csv'\n", "\n", "rides = pd.read_csv(data_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>instant</th>\n", " <th>dteday</th>\n", " <th>season</th>\n", " <th>yr</th>\n", " <th>mnth</th>\n", " <th>hr</th>\n", " <th>holiday</th>\n", " <th>weekday</th>\n", " <th>workingday</th>\n", " <th>weathersit</th>\n", " <th>temp</th>\n", " <th>atemp</th>\n", " <th>hum</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>cnt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.81</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.22</td>\n", " <td>0.2727</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>32</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.22</td>\n", " <td>0.2727</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>27</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " instant dteday season yr mnth hr holiday weekday workingday \\\n", "0 1 2011-01-01 1 0 1 0 0 6 0 \n", "1 2 2011-01-01 1 0 1 1 0 6 0 \n", "2 3 2011-01-01 1 0 1 2 0 6 0 \n", "3 4 2011-01-01 1 0 1 3 0 6 0 \n", "4 5 2011-01-01 1 0 1 4 0 6 0 \n", "\n", " weathersit temp atemp hum windspeed casual registered cnt \n", "0 1 0.24 0.2879 0.81 0.0 3 13 16 \n", "1 1 0.22 0.2727 0.80 0.0 8 32 40 \n", "2 1 0.22 0.2727 0.80 0.0 5 27 32 \n", "3 1 0.24 0.2879 0.75 0.0 3 10 13 \n", "4 1 0.24 0.2879 0.75 0.0 0 1 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rides.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking out the data\n", "\n", "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n", "\n", "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10b0687b8>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ffapq86tvD37PeA3MPoSUuDz4PvcDevBJ3Ykx6Co7Dz6bbAkhdcUsPOLk4I8q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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu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\ndm5i60+ePo1b7rwPT9d4rZRMvmgMfErEI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/nu3u/efPfc55xjJeIC38zMzMysRFzgm5mZ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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112744978>" ] }, "metadata": { "image/png": { "height": 263, "width": 380 } }, "output_type": "display_data" } ], "source": [ "rides[:24*10].plot(x='dteday', y='cnt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dummy variables\n", "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>yr</th>\n", " <th>holiday</th>\n", " <th>temp</th>\n", " <th>hum</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>cnt</th>\n", " <th>season_1</th>\n", " <th>season_2</th>\n", " <th>...</th>\n", " <th>hr_21</th>\n", " <th>hr_22</th>\n", " <th>hr_23</th>\n", " <th>weekday_0</th>\n", " <th>weekday_1</th>\n", " <th>weekday_2</th>\n", " <th>weekday_3</th>\n", " <th>weekday_4</th>\n", " <th>weekday_5</th>\n", " <th>weekday_6</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.81</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.22</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>32</td>\n", " <td>40</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.22</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>27</td>\n", " <td>32</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>13</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 59 columns</p>\n", "</div>" ], "text/plain": [ " yr holiday temp hum windspeed casual registered cnt season_1 \\\n", "0 0 0 0.24 0.81 0.0 3 13 16 1 \n", "1 0 0 0.22 0.80 0.0 8 32 40 1 \n", "2 0 0 0.22 0.80 0.0 5 27 32 1 \n", "3 0 0 0.24 0.75 0.0 3 10 13 1 \n", "4 0 0 0.24 0.75 0.0 0 1 1 1 \n", "\n", " season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\n", "0 0 ... 0 0 0 0 0 0 \n", "1 0 ... 0 0 0 0 0 0 \n", "2 0 ... 0 0 0 0 0 0 \n", "3 0 ... 0 0 0 0 0 0 \n", "4 0 ... 0 0 0 0 0 0 \n", "\n", " weekday_3 weekday_4 weekday_5 weekday_6 \n", "0 0 0 0 1 \n", "1 0 0 0 1 \n", "2 0 0 0 1 \n", "3 0 0 0 1 \n", "4 0 0 0 1 \n", "\n", "[5 rows x 59 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n", "for each in dummy_fields:\n", " dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n", " rides = pd.concat([rides, dummies], axis=1)\n", "\n", "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n", " 'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n", "data = rides.drop(fields_to_drop, axis=1)\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scaling target variables\n", "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n", "\n", "The scaling factors are saved so we can go backwards when we use the network for predictions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n", "# Store scalings in a dictionary so we can convert back later\n", "scaled_features = {}\n", "for each in quant_features:\n", " mean, std = data[each].mean(), data[each].std()\n", " scaled_features[each] = [mean, std]\n", " data.loc[:, each] = (data[each] - mean)/std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Splitting the data into training, testing, and validation sets\n", "\n", "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Save data for approximately the last 21 days \n", "test_data = data[-21*24:]\n", "\n", "# Now remove the test data from the data set \n", "data = data[:-21*24]\n", "\n", "# Separate the data into features and targets\n", "target_fields = ['cnt', 'casual', 'registered']\n", "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n", "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hold out the last 60 days or so of the remaining data as a validation set\n", "train_features, train_targets = features[:-60*24], targets[:-60*24]\n", "val_features, val_targets = features[-60*24:], targets[-60*24:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time to build the network\n", "\n", "Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n", "\n", "<img src=\"assets/neural_network.png\" width=300px>\n", "\n", "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n", "\n", "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n", "\n", "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n", "\n", "Below, you have these tasks:\n", "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n", "2. Implement the forward pass in the `train` method.\n", "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n", "4. Implement the forward pass in the `run` method.\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class NeuralNetwork(object):\n", " def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n", " (self.input_nodes, self.hidden_nodes))\n", "\n", " self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " self.lr = learning_rate\n", " \n", " #### TODO: Set self.activation_function to your implemented sigmoid function ####\n", " #\n", " # Note: in Python, you can define a function with a lambda expression,\n", " # as shown below.\n", " self.activation_function = lambda x : 1/(1+np.exp(-x)) # Replace 0 with sigmoid calculation. DONE \n", " ### If the lambda code above is not something you're familiar with,\n", " # You can uncomment out the following three lines and put your \n", " # implementation there instead.\n", " #\n", " #def sigmoid(x):\n", " # return 0 # Replace 0 with your sigmoid calculation here\n", " #self.activation_function = sigmoid\n", " \n", " \n", " def train(self, features, targets):\n", " ''' Train the network on batch of features and targets. \n", " \n", " Arguments\n", " ---------\n", " \n", " features: 2D array, each row is one data record, each column is a feature\n", " targets: 1D array of target values\n", " \n", " '''\n", " n_records = features.shape[0]\n", " delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n", " delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n", " for X, y in zip(features, targets):\n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", " # TODO: Hidden layer - Replace these values with your calculations.\n", " hidden_inputs = np.dot(X, self.weights_input_to_hidden ) # signals into hidden layer DONE\n", " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer DONE\n", "\n", " # TODO: Output layer - Replace these values with your calculations.\n", " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", " final_outputs = final_inputs # signals from final output layer\n", " \n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error - Replace this value with your calculations.\n", " error = y - final_outputs # Output layer error is the difference between desired target and actual output.\n", " \n", " \n", " # TODO: Backpropagated error terms - Replace these values with your calculations.\n", " output_error_term = error \n", " \n", " # TODO: Calculate the hidden layer's contribution to the error\n", " hidden_error = np.dot(self.weights_hidden_to_output, output_error_term)\n", " \n", " # TODO: Backpropagated error terms - Replace these values with your calculations.\n", " hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)\n", "\n", " # Weight step (input to hidden)\n", " delta_weights_i_h += hidden_error_term * X[:, None]\n", " # Weight step (hidden to output)\n", " delta_weights_h_o += output_error_term * hidden_outputs[:, None] \n", "\n", " # TODO: Update the weights - Replace these values with your calculations.\n", " self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step\n", " self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step\n", " \n", " def run(self, features):\n", " ''' Run a forward pass through the network with input features \n", " \n", " Arguments\n", " ---------\n", " features: 1D array of feature values\n", " '''\n", " \n", " #### Implement the forward pass here ####\n", " # TODO: Hidden layer - replace these values with the appropriate calculations.\n", " hidden_inputs = np.dot(features, self.weights_input_to_hidden ) # signals into hidden layer\n", " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n", " \n", " # TODO: Output layer - Replace these values with the appropriate calculations.\n", " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", " final_outputs = final_inputs # signals from final output layer \n", " \n", " return final_outputs" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def MSE(y, Y):\n", " return np.mean((y-Y)**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit tests\n", "\n", "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ".....\n", "----------------------------------------------------------------------\n", "Ran 5 tests in 0.010s\n", "\n", "OK\n" ] }, { "data": { "text/plain": [ "<unittest.runner.TextTestResult run=5 errors=0 failures=0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import unittest\n", "\n", "inputs = np.array([[0.5, -0.2, 0.1]])\n", "targets = np.array([[0.4]])\n", "test_w_i_h = np.array([[0.1, -0.2],\n", " [0.4, 0.5],\n", " [-0.3, 0.2]])\n", "test_w_h_o = np.array([[0.3],\n", " [-0.1]])\n", "\n", "class TestMethods(unittest.TestCase):\n", " \n", " ##########\n", " # Unit tests for data loading\n", " ##########\n", " \n", " def test_data_path(self):\n", " # Test that file path to dataset has been unaltered\n", " self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n", " \n", " def test_data_loaded(self):\n", " # Test that data frame loaded\n", " self.assertTrue(isinstance(rides, pd.DataFrame))\n", " \n", " ##########\n", " # Unit tests for network functionality\n", " ##########\n", "\n", " def test_activation(self):\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " # Test that the activation function is a sigmoid\n", " self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n", "\n", " def test_train(self):\n", " # Test that weights are updated correctly on training\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " network.weights_input_to_hidden = test_w_i_h.copy()\n", " network.weights_hidden_to_output = test_w_h_o.copy()\n", " \n", " network.train(inputs, targets)\n", " self.assertTrue(np.allclose(network.weights_hidden_to_output, \n", " np.array([[ 0.37275328], \n", " [-0.03172939]])))\n", " self.assertTrue(np.allclose(network.weights_input_to_hidden,\n", " np.array([[ 0.10562014, -0.20185996], \n", " [0.39775194, 0.50074398], \n", " [-0.29887597, 0.19962801]])))\n", "\n", " def test_run(self):\n", " # Test correctness of run method\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " network.weights_input_to_hidden = test_w_i_h.copy()\n", " network.weights_hidden_to_output = test_w_h_o.copy() \n", " self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n", "\n", "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n", "unittest.TextTestRunner().run(suite)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the network\n", "\n", "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n", "\n", "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n", "\n", "### Choose the number of iterations\n", "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.\n", "\n", "### Choose the learning rate\n", "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n", "\n", "### Choose the number of hidden nodes\n", "The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress: 0.0% ... Training loss: 4.932 ... Validation loss: 1.332" ] }, { "name": "stderr", "output_type": "stream", "text": [ "//anaconda/envs/dlnd/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " app.launch_new_instance()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Progress: 100.0% ... Training loss: 0.047 ... Validation loss: 0.131" ] } ], "source": [ "import sys\n", "\n", "### Set the hyperparameters here ###\n", "iterations = 40000\n", "learning_rate = 0.5\n", "hidden_nodes = 35\n", "output_nodes = 1\n", "\n", "N_i = train_features.shape[1]\n", "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n", "\n", "losses = {'train':[], 'validation':[]}\n", "for ii in range(iterations):\n", " # Go through a random batch of 128 records from the training data set\n", " batch = np.random.choice(train_features.index, size=128)\n", " X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n", " \n", " network.train(X, y)\n", " \n", " # Printing out the training progress\n", " train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n", " val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n", " sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n", " + \"% ... Training loss: \" + str(train_loss)[:5] \\\n", " + \" ... Validation loss: \" + str(val_loss)[:5])\n", " sys.stdout.flush()\n", " \n", " losses['train'].append(train_loss)\n", " losses['validation'].append(val_loss)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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gmmuu4brrrqNVq1bccccdjB49mj59+vDss8+y/fbbN3suw4cP59e//jVbbLEF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\nqhaFVUoSVQQVNVJqBaleZq3Kt6i5Cld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"text/plain": [ "<matplotlib.figure.Figure at 0x11358b8d0>" ] }, "metadata": { "image/png": { "height": 250, "width": 377 } }, "output_type": "display_data" } ], "source": [ "plt.plot(losses['train'], label='Training loss')\n", "plt.plot(losses['validation'], label='Validation loss')\n", "plt.legend()\n", "_ = plt.ylim()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check out your predictions\n", "\n", "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "//anaconda/envs/dlnd/lib/python3.6/site-packages/ipykernel_launcher.py:10: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " # Remove the CWD from sys.path while we load stuff.\n" ] }, { "data": { "image/png": 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/XwJctQ/wi9nsD9UGeAVtYtdUTPw57eYt33QF9oZL9u8T+Rgt33QLEe9usr+w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jtpL8ephexpF3Sck3LPR758Y6aRdjhGvg3d0z7VQuga+tkBXWnPZ1rv2dBK+41Zrs2TZ/EE2LnQ\n8Iww5NcKbRShTjR95sq2K+0BguiEWH5GFRh4TNoeWrYxF1ILGtR72vWxb30InZAKEaOfcZqg72GP\nzyUiOkqtPBZkXwYJI9Su6cZ1yOd40yINiwYLonPOaRf+c9pt+2pMj/fDfU/oYyevDkTaqSoZc2aQ\nuUHecsQJgZKjSURFiw6vQa4vu+Q7O+R3o31O+2r2eH0bxxN6EzHa40dsDZQxHogu8OAxXz0h9vhI\nI+2kTzNAEB21wUeEjBU9lu22VE1ppwFxtL89eBAd+dJqaTWQJKFbDWyPp9+gghOlHe7jffn6bBmE\nV2wXJe3V9k5CbaMFaS6XRYHifbsp7TtrVNpvHGdGkWPiZ4/PB/gSUasTTQBGT3udtE8CzJenRS3J\nInCiYrdla6RCGgUSao+vrkk7rF9Pu1BGD3akj3xr2wf183dxLMh5utMzLK/YUPIevNsUC8AIHrQq\n7f02b7mZUiGyFV372uNPUGm/fH228Wrb4TzHYzdmNdJ+5WgY0k7JUMQYIsLax7727ULuIO2+iqZS\n+hSOnYFGvvXvaXdY+Df8s39a4c5aWPOGDISGZKcRWwfD0q2NU/Pof0yI0h5NCWk3gqoOe6Y2c23U\nkq6051ItQ8ZsmOcC5229pAAkscfTsWsrQeqLZUra25R2SXvaHUp7qCA6SoKEZxDd5euG0u4Y+TYZ\nUmnPpVE46Ka004r40Grc9eOs3n/fZo9fR087tccz6UcwaDAZ6onnCXKkfYsN5D0UixBpxcOWqQZZ\nQ9sEIcR9lXZZ62nX0+N9etr1z9BObRt3WADSrhqUdo9CLBOGPd5Q2kOmx3NW/1v72uNPirT/6jvu\nxw++7v147jP28Ybv/EKNcGwKnnh6jr/5k/8VV4/qboah7PEaaY8YeM6ARbFGSIUN3E0jVoTWChFz\nYLGs8VU06bnCGTAh49T65qZQ9FXaafF3JO2bD9d389jTPuKmgzKslJrCJdttirSXVAsnM+3xvXpJ\noSkyPO6Y2pxLfR4wXcSShbfIei5atAKIEerXUgBRghQM6BgzOvJtseDva+3WtiVKIFVR8OBQzqTx\nmj2+bU77QPZ4bUZ707g3oLmnPR32y/X6cYZEmzvuUNpJYacIdBvaHk+JZnt4Wu355fYax7x3T7s0\nlXZK2j2UdlfbhNbT3o8QN82S555K+w7INaY8PxPdTZOKdvdQI2jx0CTtHsepKT0+DjmnXSrdAbXc\nxp7p8Sdkj//B170fQNEr/p8Xqeybht9854NWwg6sh7THnNGhKsEmEYzYDNDvEyqk+F4zqCIfc44p\nUevTvmIFfR8bae8yls458m08oTcRwnFctsXpM5L2UwTa46gY11VsD6V9Qu3x0/3qDmNO+7yPLVUq\nvRfUSG1u68ud526yp4jSnGc9e9qpIshjMN7BHk9UfqWR9sqeGrOi+NB7pBHNB+ARMhCC5OgnvXx9\nZijHhBDzCFikkkdMBUnAtqEeRNdRaV+nPX6W1bfVRtovPmf542Aj35SdaEaQreFpgN5CI1i9DzsE\nadfadMA1pZ21kfamgEJij+878k3Ugui6hfplWYYJvY6V52esFxaAfmoxzaxQZraGh/VcK17yuDa+\nM1xPu31Oe5f0eNt1JpeeowwHxPsfro903AS89yH3dg3V066rp7o9fluCoEYUoOQo0ezxns+nrgzO\nCrV+gZD94rbv2U72ePK9qfW0b4lyu20YlfYR2wOlK9BUaecNwWTFcxUmoEo7Ie00EZn1S0SupTYb\nYVptRMdpS4WuPIqePe3M2Jesy6gl0tNOiRWAWl977xA1si2cRchoR4ywL9wu35hhyhzEiLHgJM6G\nuqLa0tNOtzGfazNfhybt148zrc8asYO0X6pIe8KGscczLSBRt3R7LYS0ILr6nPZJiONthOVRpZ21\n2eNzUd/XJYiKvcvmvcaB1dLOa0p7876UWTUCUERkXKFlQkQftZgJXWlHxzGeOUmPN+e8c8hOoVJN\nEI4guk72eMeiaxDHSgc8/nTPVquB0DSLfaiedkpwYs7Ax572rQUtwmhBdJ7HOTdI+0RT2odNj+9m\njx9+LN2IcBjT40dsDegCSbFIs56zNsVDpIWlGkCqIkynhGwagVdhbal6AFSbqpKl6XIUmQTTSTF5\nLZn3tccb6fG01aBlIaroe5tklByTKdL+hJMq7VGEnCrtjmPe2NMO1PraQ/aflUhz6S4c2KDZ43Wl\nfTZwenytpz1y9LRfvGv5Y4wcuVThE02Vy6XiG0RnpIkbrxNiTrs2Wxxcuw5FSjTuk1pRzqG0T5Fi\n1mPyQk1pNwogrepuTnIrInvCfXl+9/qMU9cCjw17fDshzjWlPakF0YVa6OTCrrR3DaL7FPYQvpS/\nE+cm1Xb1Oc6rwCxaPXLt2PHIk8ONWYb7jDFvFIONfKMfGc7AOSHtW6J0jSigpcfHND3e7zhLU2mP\nqNI+bHp8lwISvdZr9vhQFc0RQeEq7m7LyMmRtJ8mmOOBaBBd23zk7Gj54wxTrSpq2uN7kfaaLdVQ\nC9tsqWm1gMoYUbiM1xJ9SbuRHq8VB9ryAUhPOzNt34R8TpD3V9rJdnJuKu317ZRS4bEbDfZ4oD63\nezB7/Koj3/Se9qPUnxisghvHed0V0GKPX+YBhP7i72mP15R21El7zET/403fg0daAnzC8sZ9Mjft\n8Vp6PB35luKol9Luvg5FzMOSnVfXIeUIyyvt8b0+49p1iINz//F5gHEdjAylnfXst6fvI5Vdafck\n7Uop3CKexOsn/wyvnvwEvi1+/fK+3mGdHXHN6BP/6BOHGxdKdc+DzZb9oUh7XWmv7tsWe+qIAkLr\naefW25tQU9rjoZT2+vZ0aU3T0uPJnPZx5NtmwlU02rBL9MoYSftpgrHAiyghbls8kR7wGSaYJuTU\n0dKl+80eluZoIKOnvW2xLMj89ZwZRI/2twck7YxHYJzmA7S5FhpUbGqfZVkApZ30GEZmT3t9Hzx5\nOEculaG0u/fjUIFqhT1+daVdm9M+sBJXKO3GeK9dY047i4Dzdy5/LYPrQn/xaz3hhqXba6FijgAz\nXid0T3th6a6uH4X93r2d9WKOg7SzOY7mqxdraiSTJtwDyNrGqRGlXdFz00ba+9jjaSuOqbR7pMcL\nYo8H14PoIshgC51aMXaBtkkbJXKp8PL4dZguWiO+Tf768r75mpX268f6dTMVEvc/6Va1TwLveeha\n4/1Pz4Zpa6IKJucMEVXaR46zVdBJe/fjbPa0a/b4DVLa9fT4YdwAI8Jh7GkfsTXQ0uN5BE6D6NoW\neERpP1YTXWk3wouC2uPJazOPXtJ8Xm1nznWySRU9mfcd+WaQ9pgWQJqVdkaUdt6gtE/Rn7QzTWnn\nyBW1x9e387HrxbbpfcOG0k4t/CwbbORb0kNpX+uc9loQ3VQjkACAnXPafiyTtIO7FJrs8V2Vdtuc\n9iCkXU+PN4tATYW5xiA6LeQtw1EPBbtuj48hyNelaFF3WV4VDzXSnlDSXvwdvezdZiAmTeL3ON6S\nkvYo0YoT6wii87XHZ0LiIrP3aM/WrLTbEtk/9Ji7f/wk8O4HK9L+v3/Zp+Mrnncbvv7z/grO71bf\nU0OE0dEFc8wZ2NjTvrVw2uN90+ONc2WdSvuqQXR7RGkf0+M3E+457dtxvEbSfppgWLopaedtpD03\nlXayODSUuLD2+G7EQxB7vKiR9nA97ZqiySPN5t5aACFKO0ua+sX7B9HRMCrOY6c9/sphCiUFnnjy\ncQBVSBYAixvAGFs1gD2+PgWgY087VdoHt8dn7TPlJ2cK+3H5kJK0B7bHa20u5LhFnkF0TLb3tPd2\nB5hKO7XfQ1SvLyVwrCuGaZoiYsX9Elzbp7o9ft7bHh8z/XopiUtFOCYvLB9Oi4Ka0k572gME0Wk9\n7RyM2uM9lHZJr0WR2dMuwo18E3Z7vD9pV9iF/Zq9bqXdRnY/dHlzSLtSSiPtf+Mznol//w9eiH/x\nd/4annG2uiYMEUanq6dcS4/flkXziAJ5T3u8OWmAqvVhe9otQXQrjnzT0+NHpX0TMSrtI7YGmpWS\nRYiMAKhGGPZ4l9Ie97bHo3E+clt1U5LtFFwnepS0I6Q93iyAtCjtnNjSmUnwAqfHa0p7xK2k/Yd/\n9/343H/xRjz0is/BS37nc/H3ore4w75q29hvtJYLaS51Itw28i1KUI6ig8yxG1XnSZ+RWj6oz2m3\nFBgm+1qYWelkCG2Pp20uLDLD07rOaS9Ju1GUC6i0KyMPIkFeLLJEDrz6JcC/fi7wrl9b3p9r7S/G\n5AUSRLfD0n72eFW3xwtCaDVbuQVMVNtJHT60kFIq7f0+46bS3qHlCXpPO4uS+si3QAudXCrEPZX2\nXdjdUUN/vk2YPe0A8MENIu03ZjmeWCTa7yQcn/SMM8v7Lu1X19Eh+tp10o6xp70nHnjqCL/5zgdx\n/bglJ+cEIAOmx8fR5irtmWaPj6y3j9gcjOnxI7YHSrfHRzEd+da8eBJp1bM3w0Srimqkncleo5Zs\nttTlj1CtF0pJFvUiaiDtjnFnvqgr7f6tBkxW7x0lpvV8wJ52Hus97Yviwmvefj++mL8Hn5DeBw6J\nH09+vnncmlFYyPIB0uNrI99aSDtj2nbtRtV+66Nk+uBGLT3esq3JXk1RBsLb4znsSjuHWsEevzin\nKakOMapOU4d1pX3C8qK48MHfAx59T3HNet3Ll/fnZJRanbST4495L3t8beQbj6sef7Q7dajSzmir\nRFKf094vPd5dPGRtgZgAFFHauRlEB+k9c7kNQipwtnoQXSYkdpl9nw/9+TZx1aJQf/DyjbVuQxNo\nEejcTqL1lV/aG5a054bSTtPjt2TNvDZkQuLv/Ye343+7+x5832/dc9KbU4NLaV/FHl9Pj9+cOe10\nW7T0+NEev5EY0+M9wBj7V4yxNzHGHmSMHTPGrjDG3sUY+yHG2C2O57yYMfaGxWOPGWP3MMa+i9GB\n1/Xn/EPG2J8yxg4YY9cZY29hjH1liL/hVEALLeqmtNNe8RRTrVcNjGkqVJ6tXhUWQiGiiztqO2fK\n+YEsoYiNXxqknf69fYPomBFEp42talPayf3ctMcbc5z7Lkipu4JHEXJDaS+/vC6yA+15t7Er1m0C\noPflsnSgnnbRrPbbQLZzj1fPHXokVG3km63AcPb22pQFIHy1nlMSp6W+y9bPDgAt50DyxdeD2dPe\nOz2+WWnPhAQOHrc+NZ9XZNhsf9GU9p7jEqVZPGQRBCkSiKz5+hFJorQn9iC68vzuF0SnFxY0pd3D\nHk+vgzya1ILoQi10cil79bTnQmFnQ5R2W0/7g1eOcd8TB5ZHrx/0O4NmewDARaK0D9HTro3xYtDm\ntI9Kezc8cu0Yj90ozvl3PdAcLHgSoMc6ifvZ4yNWD6ILObnCRBcHEc1Y0ezxI2nfSDiV9i0xRoRS\n2r8bwD6APwLwUwD+E4AcwA8DuIcx9gn0wYyxrwbwVgBfBOC3AbwKwATATwJ4re0NGGOvBPAaALcD\neDWAXwPwPAC/xxj79kB/x3bD6GmnJFZT6CzIZ5XSnpqLZQCKqFBZtnrIW+vItzalnShxJmmnBNlH\nhWqE0gsglLS3LZa5prQ3qdh5L7Ww2M5qf0W19PhsucAzraefyh+ufiFkqL6NwwXRdVLaAU3F3Cd/\nz5AjoZRSuDHLl6nWAKoCw5e9ovifceC//6HalAUgbEouADBQ0q4r0XneTpCYRqhd6fE9FysN4xKT\nxfx6GNuOhdNHkKKcqCntND0+xWHP9Hhdaeea0q5aetq58FDaQ/S002sNj8Cpu8KDENNZ7iw2g+hU\nMKJV25/le3qS9lRIZ0/7upX2a45e8Ne/59G1bocLNJhvJ9GXeJf2q8/MlcPwlms9iI4b6fEjyekC\nep31KriuGbrS3j09njq/Is7AOUPMaV97uNYcn9tcoGOGddK+JSxwy0CP7WSFYtKmIxRpP6eU+jyl\n1Dcqpf6pUuo7lFKfA+DHADwbwPeVD2SMnUNBugWAlyilvkkp9U8APB/AOwB8Lfn08FAAACAASURB\nVGPspfTFGWMvBvC9AO4D8FlKqe9WSr0cwAsBXAHwSsbYXYH+lu2F2dOekP7FFqKZz+n8853a/YoQ\nkvl8VrvfF7XFXceedpX5Ke2srz2efOFwHoFrClfzIpIq7XXSTpX2/vZ4rvW0x8gUVdorNfISHD2Z\n03PAsz7T2EZ9bNUQSlfnkW8AsFeZevZFNad4SKX9MBUQUtlnh3/ONwMv/b+Al70JeMan1cgpELZa\nr5TSjjeL4ipMDoDwGa+l2eNL0l4f8ddLBTEcP7XXz+sBdLheFJFEk9KupcfPMc/lyl/UteIhiyA4\ntce7C5NKKcSSTIiYUKWd9rT3J+3cTI8n9vhItZMyGkQXxXoQHYcMNqYrd8xp186FBmRCYp8da7eV\nr7d+pb367viK5922/Pn19zyy1u1wgV7vTKX90j4JojvsOUHFAqpgcl50LS3vG0l7J1BSuImEgx5r\nrafdc1upSShekH6trz0QKbYq7R3ORXoc9hKaHj+S9k0ETY9fJWth0xGEtCulXCztNxf/fwq57WsB\nPAPAa5VS7zRe4wcWv36b8Trfuvj/R5VSV8lz7gfwMwCmAL5hpY0/TdAWeBwxVWVaiKZIK3t8xusE\niirt83R1QlzYUul2GqS9rS+XLqZN0k4Jck+lvcke36ZwRURpj2vp8bSnPcWsbxAdKGlPkGs97fmS\nMFxijp7MT/0f2nva1xFE56O079+6/PFMvrxMDKrElQFBiS08MYqBT/9bwB2fvfjd0tMecN9Jpfdh\nMx5D0TFlwkNpN3IvAFi3u5fyQ3vaayPfRPHax1f051x/YPHU6vMta/Z4feQbsLr1XNbmtJs97e7r\nRy6V1trBqdJOCguTZRBdmJFvrGPxEEAR+Fc+Pp4MNvJN9hz5lucSF6Hbz0t30JBOGhtoEN3ffcGd\ny3Cqjzx+gA9vwOg3zR4fm6SdKO0Wm39f1JR2Ns5pXxX0u2ETCaIzPd47iI4o7YvzROtrD1SMs6bH\nd7LHu9LjxxN6E+EaRbiJha9VMHQQ3d9e/E9TNL5k8f8fWB7/VgBHAF7MGKNMoek5bzQeM8IFQ2mP\nE5r63hJEN6/s8TbSDk1pX520NyntzGdslWs+MoB4Qq2jPUm7Ro4i8IQoXC37kroa6DYVNwyotMcx\nUqOnvVzg3eKYgYzP+Kr6bWvpaZftiewm9p+5/HE3rUjfkErc9cXC18sVYLHHh9x3xWdHV14VowqI\nTxAdHflWt8dPQvTi15R2wx4vJHB8VX/O9YcAAII6aRpIe0noVk2QF1ItCxTldkpix2+yx89zuVTR\nAegj34zPDtC3p10/3rQw6aO00+NtKu0RE0HT4/uMfBPH1xAbQXa7S6fCyaXHP/vCLr7kM6rrzuvf\nc/JqOyXtU8Mef3Fv2J52StgizjR7/LYsmteFTbfHu+e0d39+eZ6sS2nvZI939rRvXiFlhHFebqHS\nHrc/xB+MsX8M4AyA8wBeBOALUBD2V5CHfdri/w+bz1dK5YyxjwH4TADPBfABxtg+gDsAHCilbE1j\nH1n8/6me2/jnjrs+3ef5NzWMxPNIm+PcvHCkveI5t9jjycI7TXv0tPe0x8M1Hxm6qs1ECqWUHqjX\nAbrSHuv7smWxHCuitNdIe7XNk0V/b5pL7cusCyhpj0ylXaRLle+iyx7/yX+jfpuhtF8bxB7vsJw3\nYf8Zyx8L0v5sAMMq7TdmFtLucgXQIDpWkvZwXyRSGWO1DNIuPHraOSGjymaPL0fV5apIIVlpQ42+\neeKmSdiiZ/7IIO3XHiyeqrW/uIPoyqTxVTMhaiPfWATJaU+7m/CkudRDFGN7EF0IezyrZWt0C6KL\nxHxZuo+SaW3kWx4wDMra0+7jBgCgDp+s3bbLZoA6v3alndrjL+4n+PK/dht+/55iafKuB08+MKzJ\nHn8Lscc/NQRpFzoRY2MQ3crQlPYN3HfCobT7FvqE4coAdJIVauybbd91KUbS4zCNORgrJiEotbiu\n8dXWkCOGgaunfSTtdvxjAM8iv/8BgH+klHqC3HZ+8f912FHefmHFx49wgBtEM6E97RCNJFamFWk3\nR6kVL169VtrbHk+JB1WwZas9no5agjFOjRLreBGoNYlXJe1EaY94oVKV79OyENWUdmqdBWpKO1Ao\ncauSdvpeUTKp2+PzBnv8+U8s5oubMLZxiBTVNBeGep24H1yC2OOn61LaF/b4iS2IzgS39LQHnkcb\nGeowVU6lhz2ek/nieemoMXrOgZ4qiDKVdv31cykt9vhCaVfk861qpJ0S4oXSviJpr498i6DI8VMN\n9vg0l5gyh9JuS4/v0QJDg+hqUyw8SPuuqhxU0d4FgFwOI0ikGzKnnR8/VbutVNrna1TalVKa0n5h\nd4JPfmY1B/3hq8e2p60VtIgxNb43zu1W39M3Bpj9nUtTaa/u25I189qw6T3tGjmKuhdn6OPKQSXr\nUtp9LfxSKs05EHOGhPPltmVCIuLOgVcjTgBue/xJbE14BLXHK6VuU0oxALcB+Lso1PJ3McY+O+T7\n9IFS6oW2fwA+eNLbNjgMWyozekmbyJeipJ3v1u5nUaD0eNNGGdGEe9VKECnpYKY6a9hw+y2W9QKI\nZo9vWSzHRIlPpg32+ADp0lRtmySJYY9Pl/vAao//6p+2v2isz5oOnYAOFF/Y1kT2JhClfTKrlLn5\nkEp7SdppS4RtTjvgCKILSNpblHafOe3087Nsgwm93ZSoGfb4CRZz2o900v7ev3wfHr8xayHt+sg3\nADhKV7THK2P0JI8gNdLuvsbVlHZaPAw8p10rHvIYcQfSngmJM5S0714YLIhOSGm1xzPPIDp2VCft\ne4vCzGyNSvtRKpbXu2nMsTuJcMeF6pg+dO04WEvBqmga+bY/ra7/q342mkDVrIgxfeTbyNo7gboK\nhVQbN2eaHutV5rSb+QfF69D0+EBKe4857Rn5zkyiwjkyxDaOCAd6vFdxgGw6BulpV0o9ppT6bQB/\nE8AtAH6F3F0q4+drT9RvL31mXR8/wgHTSqmr2KJxUa/oKDWLVZmOl8p6Ku2xLdQLAINqDWTRRy0Z\nNn46b5qJYItlziPEEbXH5+4vWKWWxAcAEnMbyb4tiWCf4gJdKCeTKXKl97QX+0Dp9viXvRn4tncA\nz32J/UW1wkIWzMZGUYx862iPP1P1liazapE/W4fS7jOeTpvTHn7km7T0tNO5235Ke/X5WSrtvN7T\n3ueYs9rINz3oLrP0tF9IH8Pdf/GQTpbNQg6ZMz5hAnGPkYm14DQWaRMyVINCPM+Fu6edHJOYScTI\ng6bHU6U9gWhcnM5ziXM0kX3nfC2ILhTRyp32+B5KO1sE0a1Rades8Yv+8LM7Cc7vFp+RNJd4coBU\n9i6gziJz5NsZQtoP5+GLHZrSHumkfVvsqeuC+d2waRZ5LYhuBRuymX8AGEp7oO9tq9LuS9pFvbAQ\nEyLY2q45Yu1wKu1bcv0ZNIhOKfVxAH8J4DMZY6V39UOL/2s96IyxGMBzUMx4/+jiNQ4BPAzgDGPs\ndsvblMn0tR75EQaoOswibYEWtyjtyKg9vq60c7LwTrPVSXueS0SMbIc5p73lYktJB28g7QnyXkqD\nprRHEXii9/06vxRIL2yqIkwMJcRmn+0zq50q7UlipscXQXTncIRk0WONyRngzhcCz/qr7hc11MK1\nzGl3qdcUxB4fHVdK+6A97QvSfo5ViiUmZ+wP5nXSHrK1QEi17JUv3i8u0tlLeKiaVGkXZXaFxR7f\nS900wtPM18/yuj3+NnYFT904BvLq81NT2hmruUBW7mmvzWmPtAkZTT3t86aedmMbe4dNam06sVEY\nyhs/m8Vnn5y3O+f1ILqA6fEil+DM8lqeSns0u1K7rUqPXx9p16zxe9W+vvMiUdtP2CLflB4/javZ\n6amQwQuuVM0qlHb7fSPaYX52N80iT4+nrrT7Pd/MPzBfJ5jSbhGjpIKXc4GKRKXCrintPuGuI9YK\nerynWxhEN3R6PFCmQWEpAb158f+XWR77RQD2ALxdKUXL1U3P+XLjMSNcMILodNuraL5I5tVCxExl\nB6DNB857kHZB50SDG3bNdqU9ovORiV0WgKaAJsh7LZa5kR6v9ys3FECIUpgiqfUcUgIzDWCfpa6F\nuj2+IO1aP/veJY8XNXvaN0RpJ/Z4fkTs8QMu6m/MivNVm3NPtkMD+bwV1v/287kLarPFDaU995jT\nHtGe9vJc5BHKZueIFe0rhyumsgOm44fr9ngmgPRGrdc5YQLs4LI2HaLW/gLUxr71scebI9+0IkFD\nenwq5DIZHkAtEFPvvU+Dtb8U1yE6EUQ0qnPzXOIsVdqn57RCLocMRrSEw5ngq7THFtJe2uOHbH8x\nYVPagU0j7e4gOsYY9kkCdp/PsQ265VlPj98wzrnxML9XN09pJ/Ow4+42ZKvSTkhWqO9tV7HDpwiS\nWazWemFhs47JCCMgkWRWbUvRsDdpZ4x9KmOsZl1njHHG2I8CeCYKEl76He8G8CSAlzLGXkQevwPg\nRxa//qzxcj+3+P/7GWMXyXPuAvByAHMAv9T3b9l21O3xehBdk9WHNYxSAwBOXovLfOUKviLEoiDt\n1Snqkx7PyQz0aNKstPfraaf2+LiYyb1ADOGuwJLFfoq4HjBnUdpD2WeTiR5Ep0SG41TohHPvVrSC\nqpksRTpEEJ1YRWknZPnwiaWyJ6QarPfscJ5jFzPsLay6iKbA9Kz9wTzSzucIMuycdgljTJle9FJd\n7fHR4lxkrPbZ6WWtVWZhgUGQ8zI6esLyJGB68AgYVbhtbQi0r53Ne9njtX3JuGaPbyLt86yhpx2o\nJcj3KcpFxpx2vQUobywKza1Ku35+BlvnOBR13552G2lf2uNPSGm/SGae33GhOu9OOoxO72mvL/Go\nRf4gMGnXlPYxPb4XTEIoNowg0iWOFkTna483CjyATv5DEWJXscNnO+n3c7z4G2Pyt4Ysuo8IA3pc\naRFoW64/IdLjvwLAv2SM/QmAjwF4CkWC/BejCKK7DOCbywcrpW4wxr4ZBXl/C2PstQCuAPgqFOPg\n7gbwG/QNlFJvZ4z9BIDvAXAPY+xuFAOH/j6ASwC+Qyl1f4C/Zauhhxbp6vCECWQNlldGlHZK2paI\naH+8xFGaY+Lq7W2AJIqMYBFisohkkK12pJgq7TXSrvfm9lPadXu8aX12FhcIKcpaSHuZSN6nuEB7\nnJN4gpxYfGU+x7GSuERD6PZ9SLuutA9hscyEQhLRPnEPpT3ZBSZngfRpMJnjmckMl9PiXJ1lQquQ\nh8JhmuMW6lTYf0ZBcl3gyfIciCGCFjysSjsnX1ptSrvIlkUeoRgUmUuOaLLc7gnyfko7VV0XRQXB\nkiUBTY4etz5vd/aI1l7SprTvIl35s1Mo7eTY8EhT2lmb0u7qaTd+n7Ks1+cb1PFTs8c3tzzNMoln\nsKPqhh1daY8ggy10XOeer9I+mTfZ49entF8jSvsFp9J+hJNEk9IO6GF0h4HD6Grp8eRauGlBapsO\ns6C7aVbsXAtp6xdExxeknToPQzloXNcwn91pCzVL+Ki0bzKEI4huWw5VCNL+XwB8MoqZ7C9AMXrt\nEEWP+a8C+HdKKe0bVyn1O4yxLwbw/QC+BsAOgHtRkPJ/pyxXd6XU9zLG3otCWf8WFKuVvwDw40qp\n1wf4O7Ye2gJpsaAX4MuFftYwx5kTpR0TC2mntkwmcJgKXNirP6wNVA1cRWmnpD02t9NQCw969Yrr\n6fFmwrazAqv1tCc4F5s97dQev+hp76vELdZNLI4hCWkXeYaZNO3xt7S/qOEGSAMvmssAHn3kmwdp\nB4qiQ1oUIW6PD5akfZ5LOPTvXjiYC9wCStpb9l9Ukfakpee4K2rqMI/1kW9tqibJrZhhslxIAaj1\nSvdR6JgRngYAgsfLBqpkVp/JDQDnZo9q1xk7addV7FVJSb2nPdbeW8nmkW97rp52oJYJcb1HkFpk\nXofM4mHDynSeC5wFJe3na3PaQxEtV3Cf75z2yfxq7bbd5Zz79ZGZK4dEad/UnnY68q2NtAdW2gU5\n3yLOaM1wa4Kg1gVzHOimKYV0Kabb4/2eLy1KOy0y9cr6IHAp7cW1sXlcW2YpTAzRdz8iHJxz2jfs\n87MqepN2pdT7AHz7Cs97GwqVvstzXgPgNV3fa0QBbirtgEbaRe7uRacBVValnSz2EuQ4WnExIKk9\nnul24phJ5C0EMVHUHu8m7RPWT2lnSlZkOKqT9qxIOgE++Hrg6Cng+V9XWOjJPk4R13vaNUK8WJT2\nSo+vnhtFE83iK/MMM2Ha4z1Ie6JvY+iRb8uRSpo93mNOO1Ao3Vc/BgC4LboBoLDMDxVGdzi3KO1N\nMM+TwHPazT5s+vlRbaSdFOaOMdWSn2GMhwzV0768DhFVf3psV9r3siuYJReWv9eCJgHNHr+L+coq\ntjRdC0x30zTa43OBS0097aTYsIMUl3tdh6jjR2/TSVpanuazY+yw4u/IESFO9vQgOhYuPV46CsLM\nc077NK2T9j22fqX9kWsVIb/tXHVc7yCk/eFrG2SPN79fYNrjw+47YSjtfLTHrwyTbG5aT7twKO2+\nx1lzZSzOk11K2ns5kCr0Udo1ezy32eM365iM2P70+BBK+4ibBbbFMmJgQY7y3L0QjWgAlE1pr9nj\nV+0lNZR2ziERLcln3rLIi1W2JNPJ1JD6A85pp+QoMu3xbBHq9+B/A37j64obj64AX/g9UGJebl7R\n025atm0j31Zc1JvKK48iSJagdP3KbI5jIXAn60jaa/bewKQ9tyjtPvZ4QBv79syo+ruGUuMO5zme\n24W0c9O+HG678prSrk+IkG32eENp11z+RsHrsM+CykyPh0HaidI+w2RpNd/Lr2POq8+0nbQTezzr\nkx4PY3xeBEXHT7Yo7c097SQ9nmX9Mivo59voaW9zcojj68ufj/k+zjJWD6ILtM7pq7RPs/pE15NI\nj6eE/M6Le9afH7p6BKWU1s+9Tsxb7fHrCaIzSfuWrJnXBrMYvmk97XTzJivY401XBgDskZDEPlNz\nKFxuIx8SZ7PH05Fvm9ayMEK/Bk1XGEW46VhHevyIDUGtpx1F33iJPGsg7ZKQ9sSmtOuEeFVbqmaP\nX2ybpLbUlmR6qrTH0walvccMZ8AWRGdJj3/v3dUT3vR/AACylKRzI9YtyECNEAOrk3ahFGKt53UC\nRY6TXKTH60pxxyC6nunXNlSkvWMQHaBt/zPI3zWUGncwzw17fMv+M86TkD3tNXXYsHSbiew1kMLc\nXCWG0m58vnss9rmleEg/4zvzKoju4+yO5c9nxTVEJGiSJ5ZCjnZuzle+DkmpEDNdaWeUtDeE+qV5\nS0+74VQ5zsRKNnRpOCtYlBhFobxRnRNHFRE+5mfKF1neFoVMj3cUjLgPaZ/dwETU+8RL0j7kSEcT\ntF+dquvndxOc3YkX2yPx1OHqE1T6Qg+ia7bHBw+iI+exmR4/Ku3dkOX6/to0guhS2v1Je/VzqV7v\nTqpzM5Q93lXsaGodKpFZRr7R0L2QTrkRYaAp7dH22eNH0n6KYLWlkp4ekc1rzykRE3s8n1ia1Y2Z\n70cr2u4oaVeL01MynWi6kAu5tJQDQNQyp33QIDop6wTu2oPIj4i6xWyOhfrIt36zpo253ZS05ymO\nM4mLndPj9SC6UF+uJZakna2gtBOl+1ZC2odU2rWiR9v+M7Ifgo58s/W0R36WbgD1nnZNaQ9H2mHp\naafn5e78qeXP98lnL3++iBtQZGQibx351iOIjrgBJNhiNB0JolMrzmk3ft9BBqVWU4vNohwY1+3x\nrNker2bVtWgW7Rc/cJohEm5Ou6s1g/sE0T3+l9ab99acHi+lwiPXqu/BOy7o12/6+0kmyNOe9rb0\n+CGVdm4o7duidK0LJqnctKIHPdZ0drnvdlLSX54nVGkPZY93FS69guho332ptJNr5Ka1LGwqMiG1\nEM8h4QpI3Jb4gZG0nyLYlHZJlfaGIDo9lb3ZHh9D9lC4WpR24S4smItlViPtw9jj60p7XiyWmfHx\n+sj/g/zpSkW8xi+ghoAj3+okLtJ62pUoSE3nIDpCjKZIg49Uq4LoqNLuS9ore/wlVKRkqFnOh6np\nVGjrafe3L3eFkAoRM5V2aulu+dIkSntB2t1Ke59eWG6OnoRemNtLK3v8h8Tty58vMZ20x+Z0CCCY\nPd5WPOyktDf2tOthecBqn3FrhoFxfjWqSYS0z6NFTKOhtIc6PaVjf3mNfHvs/csf54o4MpZz2tez\nGnviYL68Nl3cSzTFGjAt8idI2rukxw848i3mTCv8jaS9G0x7/Kb1T9NjnWg2ZL/n58a5Aug97aHs\n8S7l1WvkW15X2mlP+xhE144PXr6Bl/z4W/CiH/kveON7Hx38/ba9p30k7acIWhBdVPaS0jRx96Ke\nkvbI7BUHarbMVQmx3R5P1OHMvciYt9lS6YK2RxCdUkrblzyKLPZ4qSmXAIAP/yHUQUXar7Pz9Re3\n9bT3GFsVM7fSrvKsCM3C6iPfdnpa+G2w97R3t8dfVISUDKDGZUIizSVuJcWBbkF04e3xNaWd7Dfe\nZo+nSrua6D25tdaSPko7LR4W1x/6Gd9LqdJekfZbcAOJqs6JmpMGMEa+zVfeTmUpHmqkXTWPfGvs\naU/CtMDUj3fd8dM4koiQ9jQ5U73G8vkB0+MdpN1LaSek/d3qk5c/75X2+DUF0VEiTq3xJZ59oTqu\nl2/MavevC3oQXZ20DxlEZ4aLUXv8KEp2g2mP32SlfRUbsh5aWDx/lyrtDWu9LnCliftkBGSam6B4\n7kRLj9+MY/If//ij+PxXvBm//Pb7T3pTNNz/5CG+7N/+MR6+doxcKvzGOx8c/D1zyzEDRnv8iJsQ\ndOTbcrGsze12LERFtrRZ54ojsfWSWka+rQIalqUWSrXyVNrTXC4XwcWGGNsZyB5vjoMyZ94ve0lz\nY+H2sbcC16qL1o3IprQT6/nib1nZHi8M+6zhCKiUdmqPv9T+woET7k3MrT3t3e3xF1TVsztE32up\nUulz7lucCvRzMoTSbpA4SjTREJ4GoMUeb4xL7NXTTq5DUb0wt59XSeGPqFtwrIr3nrIcF8m+jqfN\npH0H2eqBmOYUC0C3xzfsy3kmtDYdH6V9lcKcdSxdLT3efX7xeeUQSeOz1WuU9yNgerzLHo9uSvtf\nyE9Z/ry7cDOsS2mn/ex3WuaZXtqvzo91WUFt0Hva60s8akEOP/JNJ2JjevzqMF0ym9bTLnsqmvq5\nUvw/hD2+j/JaXj9vwXW89PovAu+920iPP/ljkguJV/7hh/DwtWP86z/4YLBCa1/kQuJlv/JO7baP\nPnE46HtKqbTAy8kYRDfiZgbTLN11e7xTaTcW87bZr3SxGEOuPPJNab2kxfsobdRSk9IujF5SM4iu\nep1JDzdAbtqQmZ7SHTFVzLw3lfb8GHv3vWH564GVtFsI8YqEMzdJXJRoCdhKZJinKc4wUlyYWtT/\nhm3cQXilPVtkEySlS4An/iPfzlbK7LOyh1FG5Q+hxpXEdfWRb2HT46XZ48xjjbTzNtKe0895QxAd\n69fTzixKu/YZJ7iGM3gK55a/386uLH+OW0a+7bAeI99ksz2+ybUwz3NMGbnfyx7f/TyQEo1j6Yps\nDfdChZL2PDlXvcYCUdD0ePtxaE2PV0oj7e+SldJepcevFuTXFTQ53qa0U9J+5USD6E7OHm8SMc7H\nnvZVYX43bFrRw6loeh7n3KK0D50eT9PEffZneQx+IPk1/K0brwX+72/CHeKh6v4NOCYH83z5mT9M\nVxfMQuOP/vIx3Pv4gXbb0O0EwgjC1IqGW3L9GUn7KYJujy9Ju4fSTsjnMSb12eJATWle9cJBbamK\n2Uh7i9LeNCbMmDXdZ4az3ksaAYwhJxMURZYCeX1bk4Pqgn8QNyvtvUe+tdhnlcjAsqryKZN9LYjK\niVphQQUNektziXMgadE75wHf8UmXngNMCtXwnLiKO1D0Rw+hxh3OBQCFW6g9vi2IbtCedmNMGdNH\ngLWS9szsaSf31ea0h+lpL7M1XEWZq+oMrqizy99vI6Tdmh6v2ePTlbM1oF2His8E9yyASLIfcz6p\nn7taJsTqRS9h+3x3OL94VrkWxKRU2o0gukCLUnpdp9fJ1vT4aw8AabGdV9QZ3K9uW961vwiik2o9\nNlVqj7/TQtov7lX7/uoJKu10Usa0LYiuT5uLBXWlvbpvJO3dkBr2+I3raVeUtJPijOdXmpl/AOhF\npmDp8Q6l3ed8LK8r/2P0tuVtf/3gzdX9G5Ae//QsN35v+Z5fE37xbR+r3fbUQTpogVW//hiZGhtQ\nYAmBkbSfIujp8aU9nirtjg97WlXLjtSOduFbguu2zOOVe0nrtlRlEE0XWlObI5p6nq7c71qzpS62\nM9cKIHNNubRhNrFY0SM9mR1Y3SZWn9tdTxPnhLSryRm/F46qUWIRU8XxDtzTfpYZpN0XPALueMHy\n1/+O3wdgGHv8wTzHWRxjUjoCJmcA22QFbfv0NpKQZKMIHmxS2ttGvlXn67yhp73PSEcAYNrkhXpP\nO8UhdvGUqpT2W2grgm0MYGz2tK+arVHtx7J4yGg+gNGL/ao3fwQv+fH/F3f/+UOQZKyj4LaxdDQT\nInQQHXVECOQNi8o4rZR2MXEp7eHT43NyrKO2nnaisn9QfiKOUO27XdKCMNRIRwqaCG8mxwM3q9Ie\ndr8Jg4hFrDuZG1HgZlLaV7HHm5MGAGCPjHwLobQrpbTvWNqP7lMEyaXUC+EAjonY4jM2bmiYpP3G\ncdhC3Cq456Fr+LP7izY3WtBJhcSN2XDbZ4Yb6iMnB3vbtWIk7acI+kzfcpwaDaJrJ+2H2LUr7cQe\nH6GHRUfYlHaS0ttE2rO8pac9huTFwipiCiJdLSxIStSVdpihfpmmXNoQn31m/cYoAVBcaBImEEHg\naFWlvYXEQWSIsurYepN2oKa2h+o/A4oLu660n3M/2IY7XrT88fkL0j5E4FKXdwAAIABJREFUEF0x\n7o2q7B7J+wPb42s97RrR7Kq0DzSnXVqUdgtpVyxCihhX4Dj+1pFv+vSF43Q167QtiI7HpABCyOZH\nnzjAK//ww7j/qSP82Bs+ADGvzl1py2KI9bF0wGqFOamMWfILx48gX+tCuMljQj/701Jpp20+MhhR\noBkBOav2SWsQHSXt6hNxrKrnliPfgOFGOlJoPe0X68U5TWk/PDm1qz2Irrot9Jx2k4ixLbSnrgub\n3tPunIe9Qk97zIcZ+UYvX4x1t/FnucInsMe128hLbEQQnfkZ3gSl/Rf/pFLZv/Kzno1PvFRdL586\ncLtl+4KGC0ZbOnJyJO2nCFRp52UvKRm1pFxKXFqpsYeYetjjRY/UZmrvXbyPZwBUNq+UkBSJ1VIt\nk/3qvdKna/f7QChTabcUQLKsVWmfnn9W/UbGNEI8QbZyyFutp50nujopUiR5dWzZ9Cy8YYymC6lk\n91LaAeBOStrvBTDMov5wnuMWdOhnB2ptJOGD6PQiDY/pZydvJrC5fxBdH4XOprTbpgOIaAcA05R2\nDVZCrCez51LVRid5wdKmw0iRICLXod+/pxpjc+UwxcNPVgGItVwNoFZYAFa0x9uUdgCCXNNF5ibt\nk7y6/skyy4Io7TxgTzvIdT2Lqn0StxWSHnvf8scPKF1pL0e+AcMr7Uqpbj3tJ2SPz4VcEmfOdJWr\nxKAj35SpdJH7Nkwp3nSY9niftPN1QiPd5EArBa9CKS3iRAPZ4+n3a8K5obw6tlFK4JF3A/kcmZR4\nLtPHlO2ras20CSPfDub6NdRU3teNx27M8HrynfiNn/8c3HKmujY+NaALiRa64sjzeN9kGEn7KQIl\ncFUAVHWRlK4gujlR2tUuppbqvakgrmxtsihcZh+2C1laLaoyZk8bp2oyS1dLsizIEbkAWJR2KdJG\npT1TEc5fdJA8o6995ZR7IWtKnJ4mniMmpJ3vrEbad5AGJ+2a0j5dXWl/HvsYYuSDBdHd2iWEDqgr\n7Xm4LxKh6nPamREg1/jFRZV2NdFCpOhncMJypItxd6tAy9ZoUNrzhbWc9rRrsI0BjMO0l9DiYdnT\nHjmU9t83Zs8+9ETVdx/ZZsnTz87CHr8qaY/NDANYrkMOTPLqus5KN4s28k0EUyekZo+v/v5W0n69\nmrbxUXk7ZqiO+S7my3DVoZX2pw7T5XucncY4v1s/Xy/sVbddPRy2d9OFWa5b45mlcL0/GY60m0q7\nrnQFfauth0kIm0IlTwKmUq7nF3R7fmRR2nuNFXW8B/1Oc+7P3/9u4Oe/GPiFL0WeS3wSe0S7e0dW\na5NNyBmo2eNPWGn/1Xd8fLlvP/euS3jenedxy371vTyo0t5wvEelfcRNB9ucdkms58KVzK7Z46et\nPe0REzhaUYmzBdFRosMaSHtOlPaMO+Z6TyqlnacH9se0oKZw2RbLWQqRHplPXeIKzuKZ5ywLeqBG\nPFYn7dW+FOAAY5pdWokM+6j2WTelXR9NF7KnfS56Ku1nnwWc/wQAxVioT2MPDRREl+OSRtq72uPz\n1VRgB4oRf2aGgT5fvXHhl1N7fKIbVYxiA7D6osqWraEsQXTZgtw95bLHtyntC+v5KgVEW7YGLYCU\nvdgfeexpfPCy7tiJZUWU46kl48CyjfMV57Tb2nRqjh8HdkS13Wx30adpjHwLttChpD2q/v5EpUDT\nexxX4/+u4iwU+MKBUaBv7ocvHmlR2YGCJO8vSEcuFZ4OTIh9oI97sxTXYc5pDxxEJwwix0d7/Kow\n7fGbphTWQ7+6qZoV4VXYlcVaLLQ9XutxjtjShg80kLi/fF3x/6Pvwc7BAzWlfVdVa5NNaFmok/aT\nU9pnmcB/+m8fX/7+jV9wFwDgVqK0P3kwpNJOzklmFpI26/OzKkbSfoqgjXxbpMcrssCDS2lPTaW9\nPYhu1aAqfU67JVm6wR5Pe9Rz5iDt00ppj3I3qW5CzR5vWyznKQ4P3UWBp9R5PPOcY/a4RohX7xen\nI/zEYnwe13raU5wBHffWgbQnel9u6CC6cyAuiK6kHQDueOHyx+fzewdR2g9T0dMeH7anXdR62nXS\n3jYCrD6n3R1EB6y+4KezufmieMgsoXIlaV9VaS/HEa5SXNDbdIrPTpTUe7F/7x59QVe8b/W549ax\ndIHS42uZFWXxkLqnmkh7dX3iu2UQXXVtjxCup12RQo3kE+RqkcgPpbmrajiqXAtXVXHtlqTlYG9h\nkQ+hyjWBBsvdesZx3QZwcZ/2ta/fIk9Ju/V7GoY9fsXMBxc0yzPTg+hWfZ9MSLzsl/8Mn/djb8Lb\n7n2y9zbeLDBdWJsQekZRI0gdVc3iMQq/krwCX/9fXwK8499jdxLWHk/nqJvBiNZrm8i0QiE7egLP\n5QZpJ0p7SKfcqtiknvY3vPdRXD0q3v/Oi7v40r9aTPvQ7PEDknazkNR6vG9CjKT9FIEq7WVPOyXb\nkqrYUgDv+Q3gfb+l2+P/f/beNFyWrKwSXjsicj7zufOteaIGKAtQZCwKUNrhs1AcikYRW7SFFmwE\n7NbPdmrRp0Uf6Kah8UNasdUGbFoQEEQUkEKGgmKowqqy6tZ4b9Wdz5wnp4jY34/IiHjfHTsyY0dE\n5rm36rzPA3XOuXky40Rm7NjrXetdCynu8Qo7XIY8PpJqZoyt8og83kth2i0C2qk03OgQU5h2Ctql\nyyPfupIziWflLPanMu10pj2Qx+cy03KTMUsMHHkDtASZuzcyolPUAGUa0bk+Zulx5QHth26IvrxC\nPDoR9/h2z8WCII2ZhiYNQC3SNAnc40s0otOBONZMc9kmJlFu1si34HOVd65dK4/XgPb+sPG2YjTT\nTgCxKMC0M0PMIcB0kkz7rfedTvwuN8McI4+PjOhy5LTrmjQAfDLT7o+IyGz48fpnNxaHz0Hd42V5\n17WyrvdB1kNNNGbwOx7QjY0e16EB7UMzurxmnVmLslc6aXxYO+0gP845HgicvkPjMM+XpZp0jopc\nyrtpfs+tD+Lv7z6FExtdvPVT9xY9xPOmVBXWuSDFDov6EwgRjEJUskjPSbm+j6eKI7jRvjPYT33y\nV1C145jAgScL3x/VuXuaaKv9PJImIQDY7dO4TJXHk2bnudBIUUH6Ts6030dy2X/oqYejsQcmj29P\nRx7v2IrSZ+ffqlLKGf+Q3Xq8lM49XjLQTi72O/4S+PCrg68veEb04zbq+pl2Mnc8J7bzG9FJyrSH\nRnRUHp/+vBy069kQi7DJjtcJALiVnPsbVUnDryFop6MGrgvLi0HQg/IArhHxfOZZzOEpsymMjc3Z\nQs8PYkuqjuFxkiaMPzyXVB4Pb6Aw7SagnTDtol/qxq/v+lgoyrSTvPRZ0ZmYe/wBkM+jDqCpRT7L\nVbilbsTGMe2BW31Gpl2mu8dXQ9Ce8xrnTPvweZ0kEOoNfSlS5fE693jNTHuuBqJMKn5s8tzOELSf\n2khuQCjTPha0F5pphzZ6kq5DqUy77zOZp9NMRr5Z8MtT0BAFlbCCVICQJYfb0689nTVg6B2yJVrw\nhxyDZO77wXNMWh6/3onP49wI0L7TWe1ZmHYAaNVs9LeDz85Wz00F+KY1atNsgtkfW+vgHZ85gmsP\nzuG9X4idqG9/eHXEbz2+6lyWx6ssOxCa0QWfv4HrA+mCFADB+sWa3gCEEGhWnYg93u57mG/k5xYH\nyty9Q1C7HrRzJcfM1oPYS0fgAFQ9akS38+/JViLybeeYdqouoqTUtJh2dwTT/niRx++C9idQ0c2y\nHRnRpcSphYAdAI7dFn2ZmtPeiLMr59DOPdPOI99CdpiAdpl+wfsZmHZBNoczooOu67Fs0EyHKCWq\nGvd47sTfh01YrofkAVyDGLSvivl0xoYBj3hTrz3vI0oSVUIkjyfgSPgDzFBGO+9Me9lMu+dhThQw\nogNYTNwstnPNDI+rrZ6HKiirmjKSQUuRx5c6066J+FNn6EcyA4Rp72F0TjuQz8RKSqll2i0N094d\n7vrS3eN18vjkvHNReXwE2iuEaR+eAx0wq9HPhFYen2Ta8+a0JzwMwBU/qaC9vxlI0wFsyTpq1eH1\nzJh2v7TrWkoO2nuUaU9TAxCZ6oaIPwOyEvsExPL4CTPtDLSn3y840z79zTN10R8FxFs1J5Kxtnvu\nSMm/SVEgZAnFiM4AdL7tU/fi/9x+LPHzeuWJIw5NyuPPHdDha5zfaZxaFobc8324SH5GG1U7Au3d\ngTdS2TL2NbwRzKsOxLU5aL9g85uJh9Q8Io8/B+hb1TtjJ5l2qi6iayFdX85MyYhOzWl/vID2J84K\n+ASvYLNMbqi2zpl9/MW+hbq+g0/Y0HnRRrs/Jl4q9UCTTLtQYqvSyiegg5oVsSIS8Ca6uTZ7vi9h\nqfnIAHx6Lt0+HMlBO61+dUnr7AsgEVsF5GOSqAmVH7GF3AGbGtGhmm+mvYhZnq76ro9ZFDCiA1gD\nYgadiUW+VQT5POpApFpUHl9y5JtWLq24x49k9hMz7eTf6Gy8CF4jD2hXgWZoiCk0DY8wk7uNOnpS\ns3Ebw7SHLPZGJ8cmhjUPw2uHnAPpojvQp2SMZ9o1M+05c9q1RnQW963QFpGdb6AZr+mTYtqpR4Bl\noy8zyOM7sVR1HfH1TEF7IxovmOxGlYL2USCCZ7VPn2nvMXl8+vZuUmZ0ZW2adYAdAK7ab3CPOs9L\nvTd454AUOyyV0QSAKokXHGSSx0sIKI9z+4qDfLH1hxrFOZYFmoCojdBTmPbLt7+ReEiVyuPPBdDe\nVUH7DjLtpIlN18KdiHyzLYuZ6Z5LSpUitQvanyDly8DVPSwRbuwIsyJHzIuH1UGdOXBGVadM+zZ8\niVySZEkuulAFQJn2cKb99GYP7/j0fbjtwXhjJ4kRna+bdwWYe/wMuvnAcCKn3WbHCwBw+6iQOCMV\ntHuNPUgtAg5CKXIettCn8XkR007MtPwBZkReeTxXA5RuRMfc43Mw7bUY6M+K7YnkOLf7LmowBO2K\nYWOZ8jrPh8K8WmZGdK4S+ZYij4+N6PJdO6zhFTmz60B7+JoCZ6HZrOvOt8bkbS2PTJk0DxFFvpGc\ndriMVaAbzfEz7XT8Zci05/h8er5MRDoCqrdG3BRY3ybHRUD7pmzGrKzCtJfGYJOIvFAeH1VaY4HM\nl24IsjZR0D5cvybNtDN5fH3UTHv8bzuR1d41YNrDyutNoSs18s02nHMO65JlTeoCzq257kmX6kx+\nLkixw9LFtVUImTPIsPfzfckbnADQ30KjQkF7sYZSwpiMyuMzMO11mYztZfL4cwAIJuTxO8i0r5J7\nDGXadyLybZdp363zutLmsJkzeyiPH/Hh7llNPUNM2NA5ESxqeTZSjEkPN/Qapv3//dCd+IO/uxc/\n8Z4v4/7TQedTEtAh00A7AaZN0c0FNl1PZTSToN0exN3YrqzguOQmZWJmFGhPRkLl2VhJIo0N4+gs\nci5t6WEGeeXxXA1Qak67VwLTrsjjJ8G0b/XcCMAC0DO/aiky8yybm6zl+8mcdpavPs6IjjDtnRFM\ne7FGEpISfuiZ9m1JuvOqRN6uArp1SDPTvpZjxo9FvlnhtcNj705vxpuPi5aamK0HjxvLtNPkhSJK\nmgTTHo48UfXUAGe3enjeWz6Np/723+Ez95wK/mHrVPSYs3KOMO3xlsARXokz7Xwkokfy1mmziBVh\n2tdkCmgvEOtnUjT7eCTTvuPu8YRp13nPDIuD9jKZdu7WTSXTfYO1br6pb4CeC8Zf0ypVHn8uMYU6\n0E7JnCwKMlcH2nubzEG+6L5ioLrHE8SjHddoj08nqLhthF4bZd6/89ZmTzWiOzdm2hdJA3OxGUfI\nrm4PJqZQGDXTfi5dP0VqF7Q/QcpPYYcp8xdlpHfSzV4Gtr4DzmfaA8CVa+ZVM0tqsXzkYEH61F0n\nAQQA7xc/EEiY5CAL0x5v/lo5mfa0c0k3ywy0o4rGnovZc1Tm9qe/gAZ45JEwUmPBSB5foczrQJHH\nmzDtfC63TNDeU5n2PDPt5HdmRWdi7vFspt1UHi9KnmmXupx2zpAPPAnfl/jcvadx30meL97txAxC\nNzHTrmPac8jjE3GJoTN78txt+fFrfth7Dv/HuUP6F9DMtK9t59jEMKY96XBfgYtTBLQvNqs4vNBg\nrxs8cLQRHfWsMC1fnWkXmjEdr4/3f+Uozmz14Uvg37z3K8E/tGPX+zOYR62SvB9Y8NF3y4l9U2fa\nGdOeFjXKMtrjtUlUkzPtZSp9dLWeUR6/1KQz7TtrRDdaHh8Do1Ll8eSjYlmCS6YN1ro0MPSEYtpV\n9/ic16HvSxxd2TZqmowrldEE+Ex7lvua50s0RBK0lymPV40Rxyo/tseDdkt6aAzXnXPBZ0Bl2ndq\npt33JZPHLzTIXtO2mFx+Uiok9XP5eHSP3wXtT5Dy1JiyUCbE5h+HF/u6fp4MAPw0YFedBRBcILOi\nAxv6ec+xpYl8E0zSnVyQ7ji2jruPb3DGJo31VEF7TgMonWqBgnZHAe37Dl/KnqM2vy/9BZTINyDf\nxkp6lGkPjtGpKEy7yJnTroCjMo3oBp4sdaZ9FpNyj/ei9weAsTy+Chc918cPv+sL+Og3HxvxS9kq\neY1z0B7I4338yRcewk/+8W34vrffiofPBkD9yw+cxamVteixyZz2JGjPO9OuiymzNNdrm4D2/+l9\nP36g92a8070Zdy3cBNz8Dv0LkGZdIFOXDHBlP1Cq+EmmWDjwcGozvnaWWlUcnA+uiboYN9NOG17B\nseU5Ru37Da74ke6AKQLC38PWyej703IhZtoVeTxQDiAWpkZ0vs/k8SuSgvZ4xKkhppPTntk9njLt\nO+IePz7yDQBa1ekw7dQ81QS0pzHqZTY5z/VS/1bTmXbfl/iLLz+M73rrP+J5b/kMfvQPv5DPZ0hT\nOqadvtdZmiueLyPwG1VvU5HHF2XaKYizmDxeK5fOwLQDiBSK54IRXTKnfWdA+0Z3ECVEzNachHHy\nMlkbJ+UgTz93KtNe1md/p2sXtD9BKm0OOzSCAhAD5o1HU5+nNZMCoCyLgatZbOeLhKKGRWE+Mp1p\nHzLt6oLw9n+4D4IYGsm0+C2y4WuJTgEDKEopJOXxFZfK46u46qJDOCUXht9XMLN8OP0FCPgLAUCe\njRVl2iWSZloVocjjTZj2igLaS2S6BoM+5vK62odVaUTvR00M4PVTJLgFaiuXER2fMQeCGKPXve/r\nhW8qvtS5xxNZu3Dh+hK//bG7AAQbmt//5L8AAG6970wk1QbCmXb9cYd/c56RDV9teIVqGk3k26bH\nf3anvAy/774Mf3vtW4BLn6d/AcviwB2DXOCJeWtoVElV4bK4t6VWFYd0TPsY0F4TAwj4uTYxXuL9\n1jRi/QHm6tzt/J4TG/A3Y3n8aTmfakQHlASIyfkUdgV9SZn2Ln/cB34CeMulwO3vjX686lPQnnTf\nn7x7/PmS024+0z4pIzpbkcebzGTTx/7vn/3O6OtpM+1lstOmpf6tpjPtf3HbI/jVD30LD5wJGrPf\nPLaOh85uj/mtbEXnwUNgZO4eL6Pxlqh6m2iQhlJRMoB+Hq/yjuBZ659Acxhzq498O6t/Hingz18U\nfT8rzh3Qrs6wb/XcHZGCrzBpfHIvNI3YNzXVgPRo9B4G52HtgvYnSPkJVibpHh/NtI9g2mfmFlL/\nTXWQzxX7RmWpw2OzGDs8wMDzhzdTiV9z/gxfrL0Wy0c+qDDtKaCdAMAWeqXmI9NzKQjT3hNVXLl/\nFr85+El8078Mv+2+AsuLi+kv0Ij/bRlBRqga65GlONMe3Agrjo2BjDdz8zQnNSfTXhf9UmfGrT5x\nZ63MMPYvcwkBSSTyFXdzxIPNS0qZlMfnmGmn1S5hg5LwWlCN6JSN38PDTdxapx9lXgNAFxWFaU/O\ntOdi2lPmsHXy+DO9+H1/EnGNfvF1BxKPZaXIzwvL462kPD5g2ok8noH2MUy7ZSXGS/IAPN+HVrXA\nmHZvkNjUffWhVfibMdO+Zi3EoxAapr3bL+HaJgopS2XaqTz+6JeAuz8KdNeYVPWMHzdbbcq070RO\nu9IEocVz2qc/W0qN6GoZ3ePLbHgw0C44aDdRO1GwTJnXac60v+uz9+PJv/FJ/Mpf3TG116SVdI83\nAx13PbaR+FlZqgrqvG7bIWiP7xdZFBGuL1EXCtPe30STvN9FyYDw87KMdfzOyhvx8hO/h190Phi9\nfqJSmPbjWIZoxr5EIdmx0+MaPdfTNpZUyfw0iq53etBOzOjakzGjU2fard2Z9t06Xysh6Y6YdsrK\njJfHz86NkCorc+1bvRybFhYNFDLtXNId3nheaH0dr3I+gYNiBW+U/wud7RiYVWqx2RMrhWnPs2HJ\nZOrXi4/FFVVcvNzEx/1n4iX9N+MvvO/C/rmUpgIAzF8QfXlQBJ3fwkz78P2u2IJloy6CgvZ8M+01\nlGtEJ7rxZsOr5phnD4uB9vaIB5pXz/Xh+hJVCpoMZ9qrCmjP5XJOSpvTztzqk0Z0IQu9tj1gpkBB\nTrv+uCvDvzmPksZPHGPotZBseHQQ/+zt//qp+P7rD+K3br4OTz48ZlxC8YTIJY/XeGuo8vjTm118\nh7gHH67+Gr7r0XfhgkUNaNfNtAPMTK05bB6aAs/UFAv6OfQGCbnkVx5agSRGdOs2MckkRnThc28P\nStgAkiaIZdvop8njH/2a9tfPevG6bVWpkd/kmXbfl8zcaZQ8fqEZ/9vqdn/qG8WdN6LjM8RVQ/Y1\nLArOm4R5naaD+u/97T3oez7ed9tRHF0ph6HOWlLKBKg0nZ/WmX2VpYgbz7SPP1ZfpjHt5cnjQ1B9\ni/1ZVIYN9p91Ph68vsFM+6NyHwQduRt67uy0e3ya2m1jB8zoqAndUjO5Rk5DHq+O5+y6x+/WeVtJ\nA6ghw0Xk8SIDaF9cWEr9N5VpzxU9wQygkixcBS7Wtgew4OM/OB+Ij0tsYd/mXdH3M614k8eKgvbc\nTLvCFmoi3+x+DNo9u4b9s3XGGBzICNoPi+AmkqtzSiPfhueyalsYEBMoFhllktOuMIVlyuNFLwbt\nMo8JXfg89fjvqXlbpc40hZsJcyO6dKY9FyNMSjsvrrrVK5uM8DU3truoDiMhfSnQG8G0F45804B2\nqqYJK2RjGxUbTzowi3e+/Gl45bMvGf8iivw8X+Rb0ltD9SM4tdnDO6tvxw3W/bj+oT/Gv5o/iusv\nmMfFDjHybKSsl1WeYgGYsw+JUYPw+Gj0pN9PbOC++tAqm2nfdIjqx6Ly+OCzUgYgFgy0O4oRHfm7\njydzkYF4pl0IwKLu+wVy7rPWVt+NZjVbVZuBE7UqthUx8VLm8yooUr2M8viJGdHRyDeRf6adgj5q\nTDYtObIaERom1EyrdKDXdKZdd67Kuk48loetAe0ZVBWulzLTTt7vTkmRbwMkr4WEXNr3mI8GrRP2\nfra/DZn2SRjcmlSaU/xOgPaVlIz2sGhU5qTm7vlMu7XrHr9b52/5qqRbww6L4by4nwLaXWlhz8II\nEMWy2tvYyLFhoZFvUnOMDlysdQZ4ifVPuNo6yn73uSKWsc3OphnmUSO6Tq6bQrIBopHPEnm8Z9Vh\nWQL/7qbLUbEFfuKZF2Fe04mMai6edz8ogptILvd4EvnmD4+x6ljaG5hr1QE7XfaZKIXNLHPTbA2I\nrC+PCd2wBMtqL9eMLmSneOSbIWgX/D0tusEf7x7vwfV87McKfs95N37e/jC2egN4vmTO8QFYFspM\ne3lGdDrHc1szWtCRwc9atXTwoa0KbyitdwZ6VmVU+cnmYUIev9HDPhGb99WP/RM+8trn4tlzhK3Z\ne7X++TUO6KYS+VTFj2Iuqq7DJza6LPJtq0KZ9vhch+9Tt+C1LaUEZHycluWgR+L8GGh/7Ova51gb\nusfXHCvx/gL5VB9Zi+bbj2LZw1ogG9ai6hnTyuoePymmnbLBjmUZzzlHj6XyeALipiVHVq/FI6em\nDdqT58pUZaB7fFmKFFWGDHB5fJYxhlT3+BLl8eF5pKot+vqstlcA6M/xaecgN7cdzrSXee3kqTTw\nuxNmdKtjZtrnGvGaM6mmwij3+MdLWqTBLn23zudKxpSF0nM6/xgskO7KUejgRxt17J9PkZ0DCaY9\n18IhRzcWAqa9jx93/iHxqzUCgur1NKadgHbRRSfHvKbv+bAEWdyH55KOGlS9dmimD3/I/L3uRVfi\n555/ecJEL1GEaT8kzgCQ+dzjaQMkksdbTB4f1sBpmi0GFS5RLbPjXOnHoF3UC8jjye/OYRu9gT+S\nfTKp8P2omhrROelMe1G3ad/zYKufS+XaWe+5+PfOX+EW57MAgPvkYTy29sJE3BsAdsPjTPtQHp+n\nkeQjmSUPbpAYVmd4HGdMpXRKQ8mXgSfEKAOx5IEmx3TUc3lmqwu2UPa2gO46xMaw6WlVgOXL9c9f\nSYL2s6agXUo4Qjd7Txqxfh8birzdgQunF6gBfCnQcQhoVyLfgOIbfV/yhrGwlcg3b/h3dzeAs0e0\nzxHmtM/WK4CTlMdPkmnPmtEeVpmRVaaV1T2eSs7zKGbSijHtljLnbNA0HTB5vK39+SRLle/ed3Ln\nQbspU6ibK++UMeqiHEvoyM4j3zK4x0vJfFQAAL0tNFrlR751EztaqQHt6c7xK5WDQC1WT4ZM+06D\n9rR94U6Adsq0L2lA+yxj2icD2lkzSYn42zWi263zqjxfwhJJQExdmy3pAr4HZ/uE9jnaqGP/3Aiz\nLWWmPc/CoUYDAUgAhvXOAAeEXsYUVaoRXfHIN5Z/DgvR8C9huJoynoGjTvZjATsQGNENN/Ut0cMc\n2vluDhp5fEWRx4flOSlNjrRSgFGZRnQOcd63myOMD8cV6YzPoMNMmopW+H5webyZER2bh0dxebwk\nmzRP2MHnkrLDwsPZrT5e7nw6+tnPOB/Hw2e30dtOgnaRJo8XZea0hzPtyZt8cqOVsbRZ7WaAWOhy\n2sn1XREemr7ik7B1Ejj9L/H3e67kPhe0NLFlK4bNiaS5aKgIGM198FEbAAAgAElEQVS0L2ETYsgo\nrWAW8zOkETuByDfX99lx2rbDZ9pDA9Hj39T+vrQcbCE4xtmaoyRXTH6mPWvcW1iUxZ50frxadI0b\nndM+hZl2y0LFMQNyYVGWuGpbkepHyunIXM9scTB576lyjUzHlY4lN51p18vjy7lPc9Ae/LdqKo/X\nusdvsIZS0WZcOA4mpWA/b6Gb/ByNiHtbrx1SYmSD/V2ZDa88lc60T18ev9YmRnQ7JI9XmXbHouqP\nXdC+W+dRJZn2pBGd8F1g8wQsqV+I2rIx2kAtwbQXNKLTbpaDmXYWVaarNCfvShNySIE3RB+9nnkU\nmEeO0SeXkHDimw09PpHWQEgrIRS2fSWfPJ4Z0Q1n2h3B3OPD8iqGsWqE7aqhj77na41vTMv1fDQ8\nAtobRUB7zLTPiu1S1QBbZcjjFaa9qDxe+vHvhxF/9Nqpwk1sRvdiDQ+c2YLbj5tMXTlMbaD7HCsp\nj88z/pKUdCel52GF8vgXXb3P7EWUmXYgR0PE1zDYlgWPXO+JxuHKA8Cp2FcD+65Jf37mrRGsQcby\neDXyTbumDxLr8F6xHn19Ws7jin1klIjK44dN3qIbZ9fj77ljO4p7/PAzmTLP7lYXEMqWZuoOUyk0\nItA+OWaJxr3RjWdaUWZ42kwck8ePNKIjx1jiufOUyCVTIAcEzahw8y1E8DxOTpl93lKZ9iMny/VE\nGVd6pr34THtZ14mOaXeIqiJb5JufMtMev9fFmfbgOKqCr4HzaBsx7ZvNw5wEOEfk8Wlmz3nuzUWL\nM+3JdXK2Pnl5vDq2wdQfOxjfWGbtgvYnSKXNP1pspt0dmdHeFXXGIiRKnWnPcWEKZgCVZI4ceFht\n9xgo/pT3NPYcHmzgkuemvICA68SbPq9n7iouKdNO3JYFAR4zJGdcVEeMFKQVmWs/JM7kAu2CRL6F\n/gBpTLus5mfaQzOobgmLYrvnYQ4xgBQFZtqpPH4W26XOtIdApkaBt6F7fNKIrpg8XjJlhX60RN2M\n7hXruPPYOmoy/nkkj2dMe9L1fqNrngcbNA9pYy4pPafHceNVe/GL332V0WuoJokAsGa6iZGaxgIA\nl1w7B7Wg/Z74+70jQDsDnjnl8RmYdukPEnGRe8kc/hk5jyv2EtBu8S2BgF8C087vPbbD5fEyBO0p\n8+yDanxfmak52vd3kow23QBnkcdTw9FJR9GplVUePzOlnPY8RnRUAl+xLQiR34U+b6mmkJs9N/CC\nmFLp/kbTeX4dW19W45pH+wX/Zf4FGe4Lng/tTHujUp5SJTwH6r12XrSTcmnKtCsGol5jHycBhvvP\nzsDbUYOzNIPinZ5pX9Ax7Y1pMO3cPT51/fGm39Qoq3ZB+xOkkptljTzedyFHSIQG4yTUBLTnnWkX\nY2baq3Cx1W5HLNAAFXzOv549x2eufTNjqtXyCGj3u+azan4a006OkzYVLLJBz1zzFLSfzeUe76fO\ntGsaLyYZ7QCbaa+VOFe61XejOBUAhYzo6E12RnRKZdqDTHXJNwOm7vFCBe1Fmfbk+62ap6lM+4zo\n4pvH1hJxb4DCtJPnqVnxeTRV05gw7T9907X4Xz/9jPERb2opoxtAQXk8kYx7goL2s/yXtk4Cj94e\nfz+SaScz7aE83tQ9PmE8GDLt8bl0+32oe9M9lGnHAmfagyeIvrThlzJXSu89wnLgCXKMgxC0pzDt\nROUxU3MSfhpAsDmfFJjj8vjxzh/M5G3qoD1bTvtUIt9UpisraCdgszJchCiLOw0zOl0k1b1TnGuf\nlDy+rDESdQwCMHeP93w/OdPe32JKlaJ7ivA41XjVedHmzOuZI8Ctb42/P/x09vhW3WH7iQU7buBM\n0gRzXNGEJjq/rTZqp1HjZ9oJ0z4hJQD9yKtMe3Q9+D6wcv9EXn8atQvanyDlpbjHq0z7ytnTqc8h\nx4HPEmbaqSxVaDb0Djz02jFT1Hda+KB3I77sX40H/f34qf5/QPfqHxz5EnR+W/ZygHZN/jnAN8vU\nFM+pGcrjAWD+wujLQ+Jsro2VoMc53PgGTLuGgTEF7TqmvQRQ3O65jGlHESM6ZQatzLn77b4LB15s\nSChsBu5Si820K6C96I3MI80kkWRdK3Bxpt3HquQg7dGTpyMZOZA20x4/D/1smzYaMjmeD2tuLud7\n7yRnnk1HD2iKhRBpoF3jq3HstvjrkaCde2sAedzjxyeC9PvxpvjgfB17ZqrYCy6Pv1wF7cpcexkz\n7TZ1ZbZsSDJK4vWHDc7N49rfd7rxeZ6pc6a9ST63k5prNzWiKzOyyrSo2mkU085B+6SM6EQuIzoK\n+MKZeMeiLO7kmXad+eV9J6c3116GEZ12pn0CTHv41piqKvQz7WpOe7HrJxzZS4B2bMXKu9WHgHc+\nA9h8LH7AFd+Few69FCfkIl7df31wvZD9xJxFQPsOSuSpSmb/bLwn2wl5PN0L6GbaKWifBtNuK+tP\ntK50VmMflfOwdt3jnyAVSCn5xglIGtGtrJzBctqTjAN2Jcy0U3m80OQjV+BiQEC767Tg1GZwS+/X\no5/9+4XRcnRJNsxud2PEI1N+P3WmXb+hc2o5mHYW+3Y2V+dUx7xWbQtrmsveruefaQ+75WWA9q1e\niUw7+d050Sl17nW77/GNQJqHgloj5PHrZTLtKckLK+1eYo7wEnGCMe1dqZPHk2YDcSw3BcMJbw2d\nedqwFudzvvdlzLQTxY8kIJaqVA5ghBmmUwcWL0n/9xLk8Ymcds1MuzuIn3OuXsF8s4I9R2PQvmEt\n4tC80lRUmPYyspIt5TilXUP48Xf73aBZOwivewEavVTtx+t9YEQX/31UXtvpe2YJARlr3VAe39pB\n93gKIGZGjLLRY2z3XUgpeZMuZ7kK055H1q7K44P/Tplp16hepukgr5XHm860u8nzVFpOu0wy7dT0\nK8t77fsyUspE1dvk4yUFG+3h5zEx0y7a8X7lwc8Bqo/ToRvwt1svxH994EcAAK9TQPs8GX/cSdBO\n99gHFxp4bL07/Pl0j8nzJVOzLWgijakfyEZ3UNqaQ0uNnKzqjDD7002CKLt2mfYnSKUZ0VHXZkt6\n6G6uRt8fF9wAyqqlZJ+HReTxs9hm0p2spZfHU0mxh0En7nh7lRkcXOCbzsOLo0G7VY//jsG2efec\nM+3xJWSlOEVX8oB2ZkQXMO2mRji6zPuKI7SRb3bDELSzzNJgs11GF7/dc7nJYNXwuGjV+Ez70dUx\n5oUGtd1zzaXxyuMato+Ll+PPxlqnvJl2meJ4vra5jbqygblUBe1j5PG0WWGqDlCl0tDI+MNaWsjJ\ntGtyvE3j9LQpFhgjj6e156rRygutPN7ciI6dy2EDxCIstiDmhBfUtvDG3rvwKucT8a/M7U9unEiT\ntBSm3dOoK0jSgj/oAT2yDldngIM3RN+emnlS9LXKtNcZ0z6ZjSqTx2cwomtUd04eTxk2ymyp5dhW\n5C4vZXnNBZ8ysEKdKc12/xonj98JIzoA+Ogdj+HW+9KViGVWOTPtk5PHu4qiAjCPfAuYdtWIbkOR\nxxdl2lNm2tGOlXfbSvP1pX8EXPRMBsZbNYep/qhn0U46yNOxyQvJvvf4enl7nSy10QmiVQFgru6w\nz0JY9YodNfEGnizVYyishKeGrmnYN/exOpdqF7Q/QSqLEZ3lcxa73TjEnqPSGLOJ1jDtxkCTMe16\nia/XidlxrzKDgyQ7vupY2NMazXo6hFV2u+agnTLtVB5vaWKrAKDaMDR5Azhox1n40hwUcxBHIt80\n7vGVpiGrqYxCALKULn675zKpNgVgxqW4vd5/qrwOa7vvKXFv5qD90gUHH/i3z4q+Xy3ItAtmkKiL\nS3RhDZI3rEvFcQW0h+7xenk8naMuzrSng/blMpj24XtkqmLQKn5Axg6QIo+P/vHbRr9AhUS+IV/k\nW5o/gGDrZfheSbxp4y34zpW/Zs/RXDyYfGIiRbbKmmkXGqZ9WP6gy9mP2izwQ/9f8D46dXzywjdE\n/zRTq2hVCsAE5fEFmPZpy+MpwzY7psEwidg3lWnXzpSOKZ08nj/PNGbak0z7dt/DT7/3K7jj2Jrm\nN8qtMmba9Tnt5TdnQoadNmiyJMm4nmT3HQBAbwvNSnlKFXfETHsvjEfskHX8hb8GXP9jADgYV+Xx\nM2SE71yRxz/j0lgj+63HNqbqlj5unj0s6gkyCQf5TOvPLmjfrfOhPJmUKAKATeXx8OB34huSP38x\ne45aK7s8fg5tDDzfuJsmfN2GXpEU92LQLquzOETk8Ifm61HnN60cyir328ZRZb6XIo9PAW61ejF5\n/H6xAgu+uRkdm2kfndPumMrjK42ILasJF3X0S3GP3+p5EdAaHlj+J1Pc44+UCNrLkMcLb8BkZOvb\n5k0uWlImmzTqtaOLSrzEOsFkiqE8XqQw7Q75u9cNGWzPBwdwmmscADqyinnNXFym0hnRFXKPJ0w7\nUS6kMu12FXjWa0c/P0lrmBFDSWPPjTeSGcr3/ciQkx4nHXkK36sfsz+La7pJd/aFfRrDTkUeX3Ts\nRc1ph2Wz90i6PYB6i9RmgH1XA790BHjD3bjPuSL6pyDyLV4TqmTTPykHedOcdhb5NkWmfeDFqghL\n8OaBrloTcJCnYM6280UuuTp5PGkkmcrETUtKiTNE9fKR1z4HB4cjJANP4k+/8PBEXz94neIz7Tpm\nvix5vBqtBfARhiwNmsGgz/xRAADSS4y8FKlwxlnHtPd0THszdo3noyY2U+41JWXadw60n9qMm0uX\n723hwqVgL9x3fdx13Hz0M29Rafyo+zZtJNIozbJKZdr1oH163hSTqF3Q/gSpxPzjcIPnkA2eLRVA\nvBCboQFAc2ZMZnalHoGsqvDQQM+4m2YpLsPBDzlzJBRWhs5kjpPGA1zmPyO6xgynlJRpjy8hO2Wm\nvd4cM1agq2ozih2pCg97sG5+c9DE51VtSyuPF6ZGdABj2+fRLo9pZ6A9IxjWFctp75QM2l1U6YYj\nZTQiUbSx4/VRr9ioDRmKvldQisyaNLqGl8dkfWFdliqP18+0OzJ+f0yZ9gQ7rImmA4AOqvnn3bQz\n7aby+PgYBZGLU6Y9BNuJet6bAtA5qghon3fic7jazn4+E94aw/NF5fEVeFjCBn7V+YvE7w+kjb2H\nL0s+sWJEVwbbxd9zCxYF7Tp5PBAwW80l1qycrTnMTyOIKgw2apMzootfPwvT3iTy+GlGvtHzNFNz\nxl4/LSrjL0niy8CcEtWW1T2+7yZZ3Gm6x2/13KjB0KjYuP6CBbzj5U+N/v3W+05PPLO9lJn2KRnR\n2SIpj8+ihpADvYS7IWMWu6zIN6aKgzLT3olHQtFYjL5k8vgqZ9obfhvhurNTTLuUkqkHL9s7g6dd\nFB//1x5e1f3aRCrrGjnHzOgmwLR7KtNOG0kyuG53mfbdmmbdfXwD77vtEbzn1gfw5QdGzFQq5XnE\n6RqI8pEtBbQ7/Ri0V5YvYc9xcO+e8S/E2PYcDvJ0ltTWS3xbNE6tPosr98eL6VX7M4BPMifdRNd4\n3tV39e7xdLNMq9HMIY8HgNbe6MsFsWUM2rkxWci0C5aRHNU4vwJdKRF/ZRnRVSfAtM+ggxMb3dJu\nFO2eKo/PyrRz0A5w05YisW+6cYjktZMEmoERHXWPD/6WNHm8LV2EGxbT403GlOkj38LGQa7SyONN\nmfYskW+sws3egacAz/3F8S9AJN7zdrz+6Ayw0sr39c1DS3nPX2R/DfND3wm5eAl+w/tp3OFfit91\nX45LLogVPVHRNQ1+YeDpemqevA2rQpn2Lmc/lAbilmquZjvx/D78aARgUlJ0UyM6xrRPcUNvIo0H\nJpPVTg3Kcue0k8eFv+/kAP95i86zL88E19INFy5G7/2pzR7uOTFZtk4rjzdsVujO0yQi32w7bKxk\nf4+klLBSHLyrbjtq1ri+NFIfpR0nNU8FgAXqHs9Ae8y0J9edSnSfsuBH95adAu3H17uRkmehWcGe\nmSoH7Y9MEbRn9NJgTPsEzPK4e7wFIZLA/QkP2oUQy0KInxFCfEgIcUQI0RFCrAshPi+EeJUQQvsa\nQohnCyE+LoRYGf7OHUKI1wuar5P8nVcKIW4TQmwNX+OzQoj/p+jfcD7VP957Gr/yV3fizX9zN/7h\nnlOZf49mdnuElXGYe7yPqhvfjJr7LmXPYWWRUCtAzjR6ghvRJSW+Djwm8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q9j2rM3K+h5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Xe7jGgDVDV5UQwtfSZ0+TzbTBpSycGDK1jtDGWL4w5CtdTAcu8Z8F51PYPNRns+O7MDQBrt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\nx3HeDc7MwwZHsMNTHLOHDZ42OIIdn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"text/plain": [ "<matplotlib.figure.Figure at 0x1135a7a58>" ] }, "metadata": { "image/png": { "height": 272, "width": 502 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,4))\n", "\n", "mean, std = scaled_features['cnt']\n", "predictions = network.run(test_features).T*std + mean\n", "ax.plot(predictions[0], label='Prediction')\n", "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n", "ax.set_xlim(right=len(predictions))\n", "ax.legend()\n", "\n", "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n", "dates = dates.apply(lambda d: d.strftime('%b %d'))\n", "ax.set_xticks(np.arange(len(dates))[12::24])\n", "_ = ax.set_xticklabels(dates[12::24], rotation=45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n", " \n", "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n", "\n", "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n", "\n", "#### Your answer below" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
pysal/pPysal
weights/EvenFasterSerialContiguity.ipynb
1
86431
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import time\n", "import collections\n", "import sys\n", "sys.path.append('/Users/jay/github/pysal/')\n", "import pysal as ps\n", "\n", "%load_ext line_profiler\n", "\n", "#Just to confirm that I am on the newest version\n", "print ps.version" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.7.0dev\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###List based contiguity" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def queen(fname):\n", " #ta = time.time()\n", " #t1 = time.time()\n", " #fname = '10000_poly.shp'\n", " shpFileObject = ps.open(fname)\n", " if shpFileObject.type != ps.cg.Polygon:\n", " print \"FAILED\"\n", " numPoly = len(shpFileObject)\n", " #t2 = time.time()\n", " #print \"Opening took {} seconds.\".format(t2-t1)\n", " \n", " #t1 = time.time()\n", " \n", " w = collections.defaultdict(set) \n", " #w = {}\n", " #for i in range(numPoly):\n", " #w[i] = set()\n", " #t2 = time.time()\n", " #print \"Preallocating the W took {} seconds.\".format(t2-t1)\n", " \n", " #t1 = time.time()\n", " geoms = collections.deque()\n", " offsets = collections.deque() #len(offsets) == len(geoms)\n", " c = 0 #PolyID Counter\n", " for n in range(numPoly):\n", " verts = shpFileObject.get(n).vertices\n", " offsets.extend( [c] * len(verts) )\n", " geoms.extend(verts)\n", " c += 1\n", " #t2 = time.time()\n", " #print \"Extending took {} seconds.\".format(t2-t1)\n", " \n", " #t1 = time.time()\n", " items = collections.defaultdict(set)\n", " for i, vertex in enumerate(geoms):\n", " items[vertex].add(offsets[i])\n", " shared_vertices = []\n", " for item, location in items.iteritems():\n", " if len(location) > 1:\n", " shared_vertices.append(location)\n", " #t2 = time.time()\n", " #print \"Shared vertex identification took {} seconds\".format(t2-t1)\n", " #Shared Vertices is a list, by index of those polys that share a vertex.\n", " #t1 = time.time()\n", " #print shared_vertices\n", " for vert_set in shared_vertices:\n", " for v in vert_set:\n", " w[v] = w[v] | vert_set\n", " #try:\n", " #w[v].remove(v)\n", " #except:\n", " #pass\n", " #t2 = time.time()\n", " #tb = time.time()\n", " #print \"Total processing time was {} seconds.\".format(tb-ta)\n", " return w\n", "w = queen('2500_poly.shp')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Line profiler for original Queen" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%lprun -f queen queen('2500_poly.shp')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Opening took 0.00869393348694 seconds.\n", "Preallocating the W took 1.19209289551e-05 seconds.\n", "Extending took 0.710283041 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Shared vertex identification took 0.288301944733 seconds" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Total processing time was 1.22213506699 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Two pass algorithm\n", "\n", "This is what Serge and I chatted about last week. We make two passes over the data.\n", "\n", "1. Read the vertices from the shapefile. As they are read, parse them into a dictionary of vertices with {(vertex):set[idA, idB, ..., idC]}\n", "2. Loop over the values in the vertex dictionary. Loop over each entry in the set and union any existing neighbors with the new entries.\n", "\n", "Questions:\n", "1. Can the double loop in #2 be converted into a single using a set operation? I do not *think* so, because we need the keys.\n", "2. Self neighbors - how do we want to handle this?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def onestep(fname):\n", " shpFileObject = ps.open(fname)\n", " if shpFileObject.type != ps.cg.Polygon:\n", " print \"FAILED\"\n", " numPoly = len(shpFileObject)\n", " \n", " vertices = collections.defaultdict(set)\n", " for i, s in enumerate(shpFileObject):\n", " newvertices = s.vertices[:-1]\n", " for v in newvertices:\n", " vertices[v].add(i)\n", " \n", " w = collections.defaultdict(set)\n", " for neighbors in vertices.itervalues():\n", " for neighbor in neighbors:\n", " w[neighbor] = w[neighbor] | neighbors\n", " \n", " return w\n", " \n", "neww = onestep('2500_poly.shp')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "assert w == neww\n", "print w[0]\n", "print neww[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([0, 1447, 1001, 2287, 272, 755, 180, 2326])\n", "set([0, 1447, 1001, 2287, 272, 755, 180, 2326])\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "%lprun -f onestep onestep('2500_poly.shp')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "import pysal as ps\n", "t1 = time.time()\n", "w = ps.queen_from_shapefile(fname)\n", "t2 = time.time()\n", "print t2-t1\n" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'fname' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-964c2e86945a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpysal\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueen_from_shapefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mt2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mt1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'fname' is not defined" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#Check to see that these two dicts are identical\n", "for k, v in w.neighbors.iteritems():\n", " if sorted(improved_w[k]) != sorted(v):\n", " print improved_w[k], v" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rook" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def rook(fname):\n", " ta = time.time()\n", " t1 = time.time()\n", " #fname = '2500_poly.shp'\n", " shpFileObject = ps.open(fname)\n", " if shpFileObject.type != ps.cg.Polygon:\n", " print \"FAILED\"\n", " numPoly = len(shpFileObject)\n", " t2 = time.time()\n", " #print \"Opening took {} seconds.\".format(t2-t1)\n", " \n", " t1 = time.time()\n", " \n", " w = {}\n", " for i in range(numPoly):\n", " w[i] = set()\n", " t2 = time.time()\n", " #print \"Preallocating the W took {} seconds.\".format(t2-t1)\n", " \n", " t1 = time.time()\n", " geoms = []\n", " offsets = [] #len(offsets) == len(geoms)\n", " c = 0 #PolyID Counter\n", " for n in range(numPoly):\n", " verts = shpFileObject.get(n).vertices\n", " for v in range(len(verts)-1):\n", " geoms.append(tuple(sorted([verts[v], verts[v+1]])))\n", " offsets += [c] * (len(verts)-1)\n", " c += 1\n", " t2 = time.time()\n", " #print \"Reading took {} seconds.\".format(t2-t1)\n", " \n", " t1 = time.time()\n", " items = collections.defaultdict(set)\n", " for i, item in enumerate(geoms):\n", " items[item].add(offsets[i])\n", " shared_vertices = []\n", " for item, location in items.iteritems():\n", " if len(location) > 1:\n", " shared_vertices.append(location)\n", " t2 = time.time()\n", " #print \"Shared vertex identification took {} seconds\".format(t2-t1)\n", " #Shared Vertices is a list, by index of those polys that share a vertex.\n", " t1 = time.time()\n", " for vert_set in shared_vertices:\n", " \n", " for v in vert_set:\n", " w[v] = w[v] | vert_set\n", " try:\n", " w[v].remove(v)\n", " except:\n", " pass\n", " t2 = time.time()\n", " \n", " t1 = time.time()\n", " for k, v in w.iteritems():\n", " w[k] = list(v)\n", " t2 = time.time()\n", " \n", " tb = time.time()\n", " print \"Total processing time was {} seconds.\".format(tb-ta)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "import pysal as ps\n", "t1 = time.time()\n", "w = ps.rook_from_shapefile(fname)\n", "t2 = time.time()\n", "print t2-t1\n", "print w[2499]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2.30595707893\n", "{2262: 1.0, 3080: 1.0, 2353: 1.0, 2514: 1.0, 2326: 1.0, 2615: 1.0}\n" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "#Check to see that these two dicts are identical\n", "for k, v in w.neighbors.iteritems():\n", " if sorted(improved_w[k]) != sorted(v):\n", " print improved_w[k], v" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "fnames = ['TestData/62500_poly.shp']\n", "\n", "for f in fnames:\n", " #Open the data source once to get the file pointer\n", " touch = open(f)\n", " touch.close()\n", " print f\n", " queen(f)\n", " t1 = time.time()\n", " ps.queen_from_shapefile(f)\n", " t2 = time.time()\n", " print \"Original queen took {} seconds\".format(t2 - t1)\n", " rook(f)\n", " t1 = time.time()\n", " ps.rook_from_shapefile(f)\n", " t2 = time.time()\n", " print \"Original rook took {} seconds\".format(t2 - t1)\n", " print" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "TestData/62500_poly.shp\n", "Opening took 0.148154973984 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Preallocating the W took 0.082967042923 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Reading took 5.90046095848 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Shared vertex identification took 11.0241189003 seconds" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Converting from shared points to W took 1.01461482048 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Total processing time was 18.1916129589 seconds.\n", "Original queen took 9.17589497566 seconds" ] }, { "ename": "NameError", "evalue": "name 'rook' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-18-3709db48eeac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"Original queen took {} seconds\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mrook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrook_from_shapefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'rook' is not defined" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "queen_fast = np.array([0.45458817482,\n", " 1.27110695839,\n", " 3.097905159,\n", " 5.1691391468,\n", " 6.72036194801,\n", " 7.85625100136,\n", " 11.6823840141,\n", " 12.812087059])\n", "queen_slow = np.array([1.07005596161,\n", " 4.87641596794,\n", " 16.385130167,\n", " 37.7573301792,\n", " 60.615033865,\n", " 80.6158120632,\n", " 158.111407995,\n", " 191.939512968])\n", "rook_fast = np.array([0.727967023849,\n", " 1.79104304314,\n", " 3.43500900269,\n", " 6.94867897034,\n", " 8.01557397842,\n", " 9.91741108894,\n", " 14.2835550308,\n", " 16.4100689888])\n", "rook_slow = np.array([1.16798591614,\n", " 6.33092308044,\n", " 18.7772500515,\n", " 44.9458069801,\n", " 60.5921809673,\n", " 88.5189139843,\n", " 162.452570915,\n", " 203.032716036])\n", "x = np.array([2500,10000,22500,40000,50000,62500,90000,100000])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(x, queen_fast, label='Fast')\n", "plot(x, queen_slow, label='Slow')\n", "xlabel('Num. Polys')\n", "ylabel('Time (sec.)')\n", "title('Queen')\n", "legend(loc=2)\n", "grid()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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vROol9lIsPkj8AMemHkOXNl1E+1xey4uISIsczDiIdxLeQVJEkqjFRCwc8iKtoK5j5VJg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\nBQsKEREpBQsKEREpxf8DbnegJBcTgGUAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x104daee10>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(x, rook_fast, label='Fast')\n", "plot(x, rook_slow, label='Slow')\n", "xlabel('Num. Polys')\n", "ylabel('Time (sec.)')\n", "title('Rook')\n", "legend(loc=2)\n", "grid()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Lzy7HlktbcGL8iUqLiabw0is6zND6wy/CXCgxF0qGkIstF7fg81Of47cxv8HKzErSWDhC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\nbOW/2dmaeX+2vHREclYyDt0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"text": [ "<matplotlib.figure.Figure at 0x105485410>" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(x, queen_slow / queen_fast, label='Queen Speedup')\n", "plot(x, rook_slow / rook_fast, label='Rook Speedup')\n", "xlabel('Num. Polys')\n", "ylabel('Speedup')\n", "title('Rook')\n", "legend(loc=4)\n", "grid()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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nzP7/YSnbJ06csKh4TLmt0+kIDAwE0H9eGtsL+zRq1arFzp07ad68OcePHwe0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\ngeTCtEw+TmPDhg0UK1aMmjV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"text": [ "<matplotlib.figure.Figure at 0x104dc7890>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
shreyasva/tensorflow
tensorflow/tools/docker/notebooks/2_getting_started.ipynb
3
143937
{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6TuWv0Y0sY8n" }, "source": [ "# Getting Started in TensorFlow\n", "## A look at a very simple neural network in TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "u9J5e2mQsYsQ" }, "source": [ "This is an introduction to working with TensorFlow. It works through an example of a very simple neural network, walking through the steps of setting up the input, adding operators, setting up gradient descent, and running the computation graph. \n", "\n", "This tutorial presumes some familiarity with the TensorFlow computational model, which is introduced in the [Hello, TensorFlow](../notebooks/1_hello_tensorflow.ipynb) notebook, also available in this bundle." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Dr2Sv0vD8rT-" }, "source": [ "## A simple neural network\n", "\n", "Let's start with code. We're going to construct a very simple neural network computing a linear regression between two variables, y and x. The function it tries to compute is the best $w_1$ and $w_2$ it can find for the function $y = w_2 x + w_1$ for the data. The data we're going to give it is toy data, linear perturbed with random noise.\n", "\n", "This is what the network looks like:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VCGqEpmtDScZZLK20wEh7LKpst9YBKQUf1A5mhovWCefMuU9eM9JbWnEQMAIY/DQOXLr+\nmqmHXkfIdj18YpSRByuFa6+2F1f+cgXkuWb3zfZdN6Twt/DCQuKpsgmVDQIXy9vPAmMB1krPRf9e\nryot/TpsXL4fG7BSuNa7Ssu4gNOvnmMFVtqY9azS0q/DxuX7sQRrXIy7kevJwNrIvV9i2xlYJRp3\nIxfNwNrIvV9i2xlYJRp3IxfNwNrIvV9i2xlYJRp3IxfNwNrIvV9i2xlYJRp3Ixf9d0NIelzdt4X5\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "import base64\n", "Image(data=base64.decodestring(\"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\"), embed=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fBQq_R8B8rRf" }, "source": [ "Here is the TensorFlow code for this simple neural network and the results of running this code:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 665, "status": "ok", "timestamp": 1446658971218, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "4896c353dcc58d9f", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "Dy8pFefa_Ho_", "outputId": "5a95f8c8-0c32-411d-956d-bb81aeed8e50" }, "outputs": [ { "data": { "image/png": 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VaElYNprZ98wsO7p9D9gYdlBNlZsLN9wAd90VdiQiUh8pUfpB6taUUgvJFIlE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\nYy/e0jlzwJlk78lmSO4Qim4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"text/plain": [ "<matplotlib.figure.Figure at 0x7f804039d090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "\n", "# Set up the data with a noisy linear relationship between X and Y.\n", "num_examples = 50\n", "X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])\n", "X += np.random.randn(2, num_examples)\n", "x, y = X\n", "x_with_bias = np.array([(1., a) for a in x]).astype(np.float32)\n", "\n", "losses = []\n", "training_steps = 50\n", "learning_rate = 0.002\n", "\n", "with tf.Session() as sess:\n", " # Set up all the tensors, variables, and operations.\n", " input = tf.constant(x_with_bias)\n", " target = tf.constant(np.transpose([y]).astype(np.float32))\n", " weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))\n", " \n", " tf.initialize_all_variables().run()\n", " \n", " yhat = tf.matmul(input, weights)\n", " yerror = tf.sub(yhat, target)\n", " loss = tf.reduce_mean(tf.nn.l2_loss(yerror))\n", " \n", " update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n", " \n", " for _ in range(training_steps):\n", " # Repeatedly run the operations, updating the TensorFlow variable.\n", " update_weights.run()\n", " losses.append(loss.eval())\n", "\n", " # Training is done, get the final values for the graphs\n", " betas = weights.eval()\n", " yhat = yhat.eval()\n", "\n", "# Show the fit and the loss over time.\n", "fig, (ax1, ax2) = plt.subplots(1, 2)\n", "plt.subplots_adjust(wspace=.3)\n", "fig.set_size_inches(10, 4)\n", "ax1.scatter(x, y, alpha=.7)\n", "ax1.scatter(x, np.transpose(yhat)[0], c=\"g\", alpha=.6)\n", "line_x_range = (-4, 6)\n", "ax1.plot(line_x_range, [betas[0] + a * betas[1] for a in line_x_range], \"g\", alpha=0.6)\n", "ax2.plot(range(0, training_steps), losses)\n", "ax2.set_ylabel(\"Loss\")\n", "ax2.set_xlabel(\"Training steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vNtkU8h18rOv" }, "source": [ "In the remainder of this notebook, we'll go through this example in more detail." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "r6rsv-q5gnn-" }, "source": [ "## From the beginning\n", "\n", "Let's walk through exactly what this is doing from the beginning. We'll start with what the data looks like, then we'll look at this neural network, what is executed when, what gradient descent is doing, and how it all works together." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UgtkJKqAjuDj" }, "source": [ "## The data\n", "\n", "This is a toy data set here. We have 50 (x,y) data points. At first, the data is perfectly linear." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 398, "status": "ok", "timestamp": 1446659128547, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "4896c353dcc58d9f", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "-uoBWol3klhA", "outputId": "efef4adf-42de-4e6f-e0c3-07ddd3083d85" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8030785b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "num_examples = 50\n", "X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(X[0], X[1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AId3xHBNlcnk" }, "source": [ "Then we perturb it with noise:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 327, "status": "ok", "timestamp": 1446659134929, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8040158790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "X += np.random.randn(2, num_examples)\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(X[0], X[1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3dc1cl5imNLM" }, "source": [ "## What we want to do\n", "\n", "What we're trying to do is calculate the green line below:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 414, "status": "ok", "timestamp": 1446659137254, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "4896c353dcc58d9f", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "P0m-3Mf8sQaA", "outputId": "74e74f19-6ff8-4a8c-81c7-9021a08b78b5", "scrolled": false }, "outputs": [ { "data": { "image/png": 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TUK71MzDG+JsnFYgikiciIRGp58X1YyUiT4jIOhFZJSKzRcS3u7mKSCcR+VhE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\nwBgTYcnAGANYMjDGRFgyMMYA8P8BB4YEUxpBpuwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f80640172d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "weights = np.polyfit(X[0], X[1], 1)\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(X[0], X[1])\n", "line_x_range = (-3, 5)\n", "plt.plot(line_x_range, [weights[1] + a * weights[0] for a in line_x_range], \"g\", alpha=0.8)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VYUr2uPA9ah8" }, "source": [ "Remember that our simple network looks like this:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 170, "status": "ok", "timestamp": 1446659140755, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "4896c353dcc58d9f", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "gt8UuSQA9frA", "outputId": "080025d5-d110-4975-e105-7635afaa3ce9" }, "outputs": [ { "data": { "image/png": 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VCGqEpmtDScZZLK20wEh7LKpst9YBKQUf1A5mhovWCefMuU9eM9JbWnEQMAIY/DQOXLr+\nmqmHXkfIdj18YpSRByuFa6+2F1f+cgXkuWb3zfZdN6Twt/DCQuKpsgmVDQIXy9vPAmMB1krPRf9e\nryot/TpsXL4fG7BSuNa7Ssu4gNOvnmMFVtqY9azS0q/DxuX7sQRrXIy7kevJwNrIvV9i2xlYJRp3\nIxfNwNrIvV9i2xlYJRp3IxfNwNrIvV9i2xlYJRp3IxfNwNrIvV9i2xlYJRp3Ixf9d0NIelzdt4X5\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "import base64\n", "Image(data=base64.decodestring(\"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\"), embed=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Ft95NDUZy4Rr" }, "source": [ "That's equivalent to the function $\\hat{y} = w_2 x + w_1$. What we're trying to do is find the \"best\" weights $w_1$ and $w_2$. That will give us that green regression line above.\n", "\n", "What are the best weights? They're the weights that minimize the difference between our estimate $\\hat{y}$ and the actual y. Specifically, we want to minimize the sum of the squared errors, so minimize $\\sum{(\\hat{y} - y)^2}$, which is known as the *L2 loss*. So, the best weights are the weights that minimize the L2 loss." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RHDGz_14vGNg" }, "source": [ "## Gradient descent\n", "\n", "What gradient descent does is start with random weights for $\\hat{y} = w_2 x + w_1$ and gradually moves those weights toward better values.\n", "\n", "It does that by following the downward slope of the error curves. Imagine that the possible errors we could get with different weights as a landscape. From whatever weights we have, moving in some directions will increase the error, like going uphill, and some directions will decrease the error, like going downhill. We want to roll downhill, always moving the weights toward lower error.\n", "\n", "How does gradient descent know which way is downhill? It follows the partial derivatives of the L2 loss. The partial derivative is like a velocity, saying which way the error will change if we change the weight. We want to move in the direction of lower error. The partial derivative points the way.\n", "\n", "So, what gradient descent does is start with random weights and gradually walk those weights toward lower error, using the partial derivatives to know which direction to go." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "W7SgnPAWBX2M" }, "source": [ "## The code again\n", "\n", "Let's go back to the code now, walking through it with many more comments in the code this time:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 718, "status": "ok", "timestamp": 1446659172854, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8047207c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Set up the data with a noisy linear relationship between X and Y.\n", "num_examples = 50\n", "X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])\n", "# Add random noise (gaussian, mean 0, stdev 1)\n", "X += np.random.randn(2, num_examples)\n", "# Split into x and y\n", "x, y = X\n", "# Add the bias node which always has a value of 1\n", "x_with_bias = np.array([(1., a) for a in x]).astype(np.float32)\n", "\n", "# Keep track of the loss at each iteration so we can chart it later\n", "losses = []\n", "# How many iterations to run our training\n", "training_steps = 50\n", "# The learning rate. Also known has the step size. This changes how far\n", "# we move down the gradient toward lower error at each step. Too large\n", "# jumps risk inaccuracy, too small slow the learning.\n", "mu = 0.002\n", "\n", "# In TensorFlow, we need to run everything in the context of a session.\n", "with tf.Session() as sess:\n", " # Set up all the tensors.\n", " # Our input layer is the x value and the bias node.\n", " input = tf.constant(x_with_bias)\n", " # Our target is the y values. They need to be massaged to the right shape.\n", " target = tf.constant(np.transpose([y]).astype(np.float32))\n", " # Weights are a variable. They change every time through the loop.\n", " # Weights are initialized to random values (gaussian, mean 0, stdev 1)\n", " weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))\n", "\n", " # Initialize all the variables defined above.\n", " tf.initialize_all_variables().run()\n", " \n", " # Set up all operations that will run in the loop.\n", " # For all x values, generate our estimate on all y given our current\n", " # weights. So, this is computing y = w2 * x + w1 * bias\n", " yhat = tf.matmul(input, weights)\n", " # Compute the error, which is just the difference between our \n", " # estimate of y and what y actually is.\n", " yerror = tf.sub(yhat, target)\n", " # We are going to minimize the L2 loss. The L2 loss is the sum of the\n", " # squared error for all our estimates of y. This penalizes large errors\n", " # a lot, but small errors only a little.\n", " loss = tf.reduce_mean(tf.nn.l2_loss(yerror))\n", "\n", " # Perform gradient descent. \n", " # This essentially just updates weights, like weights += grads * mu\n", " # using the partial derivative of the loss with respect to the\n", " # weights. It's the direction we want to go to move toward lower error.\n", " update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n", " \n", " # At this point, we've defined all our tensors and run our initialization\n", " # operations. We've also set up the operations that will repeatedly be run\n", " # inside the training loop. All the training loop is going to do is \n", " # repeatedly call run, inducing the gradient descent operation, which has the effect of\n", " # repeatedly changing weights by a small amount in the direction (the\n", " # partial derivative or gradient) that will reduce the error (the L2 loss).\n", " for _ in range(training_steps):\n", " # Repeatedly run the operations, updating the TensorFlow variable.\n", " sess.run(update_weights)\n", " \n", " # Here, we're keeping a history of the losses to plot later\n", " # so we can see the change in loss as training progresses.\n", " losses.append(loss.eval())\n", "\n", " # Training is done, get the final values for the charts\n", " betas = weights.eval()\n", " yhat = yhat.eval()\n", "\n", "# Show the results.\n", "fig, (ax1, ax2) = plt.subplots(1, 2)\n", "plt.subplots_adjust(wspace=.3)\n", "fig.set_size_inches(10, 4)\n", "ax1.scatter(x, y, alpha=.7)\n", "ax1.scatter(x, np.transpose(yhat)[0], c=\"g\", alpha=.6)\n", "line_x_range = (-4, 6)\n", "ax1.plot(line_x_range, [betas[0] + a * betas[1] for a in line_x_range], \"g\", alpha=0.6)\n", "ax2.plot(range(0, training_steps), losses)\n", "ax2.set_ylabel(\"Loss\")\n", "ax2.set_xlabel(\"Training steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lSWT9YsLP1de" }, "source": [ "This version of the code has a lot more comments at each step. Read through the code and the comments.\n", "\n", "The core piece is the loop, which contains a single `run` call. `run` executes the operations necessary for the `GradientDescentOptimizer` operation. That includes several other operations, all of which are also executed each time through the loop. The `GradientDescentOptimizer` execution has a side effect of assigning to weights, so the variable weights changes each time in the loop.\n", "\n", "The result is that, in each iteration of the loop, the code processes the entire input data set, generates all the estimates $\\hat{y}$ for each $x$ given the current weights $w_i$, finds all the errors and L2 losses $(\\hat{y} - y)^2$, and then changes the weights $w_i$ by a small amount in the direction of that will reduce the L2 loss.\n", "\n", "After many iterations of the loop, the amount we are changing the weights gets smaller and smaller, and the loss gets smaller and smaller, as we narrow in on near optimal values for the weights. By the end of the loop, we should be near the lowest possible values for the L2 loss, and near the best possible weights we could have." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dFOk7ERATLk2" }, "source": [ "## The details\n", "\n", "This code works, but there are still a few black boxes that are worth diving into here. `l2_loss`? `GradientDescentOptimizer`? What exactly are those doing?\n", "\n", "One way to understand exactly what those are doing is to do the same thing without using those functions. Here is equivalent code that calculates the gradients (derivatives), L2 loss (sum squared error), and `GradientDescentOptimizer` from scratch without using those functions." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 657, "status": "ok", "timestamp": 1446499870301, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "_geHN4sPTeRk", "outputId": "85c49bf6-8d07-401a-ae08-79c6933adff5" }, "outputs": [ { "data": { "image/png": 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2mJn1DTqu+gweDB99BFVVQUciIq0hLmOq3P0pYHA8riXJq7xLOT1O6oG/5nTd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\nP0xoL9GECeNYtGgmZWXh19X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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8040158550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "\n", "# Use the same input data and parameters as the examples above.\n", "# We're going to build up a list of the errors over time as we train to display later.\n", "losses = []\n", "\n", "with tf.Session() as sess:\n", " # Set up all the tensors.\n", " # The input is the x values with the bias appended on to each x.\n", " input = tf.constant(x_with_bias)\n", " # We're trying to find the best fit for the target y values.\n", " target = tf.constant(np.transpose([y]).astype(np.float32))\n", " # Let's set up the weights randomly\n", " weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))\n", "\n", " tf.initialize_all_variables().run()\n", " \n", " # mu is the learning rate (step size), so how much we jump from the current spot\n", " mu = 0.002\n", " \n", " # The operations in the operation graph.\n", " # Compute the predicted y values given our current weights\n", " yhat = tf.matmul(input, weights)\n", " # How much does this differ from the actual y?\n", " yerror = tf.sub(yhat, target)\n", " # Change the weights by subtracting derivative with respect to that weight\n", " loss = 0.5 * tf.reduce_sum(tf.mul(yerror, yerror))\n", " gradient = tf.reduce_sum(tf.transpose(tf.mul(input, yerror)), 1, keep_dims=True)\n", " update_weights = tf.assign_sub(weights, mu * gradient)\n", " \n", " # Repeatedly run the operation graph over the training data and weights.\n", " for _ in range(training_steps):\n", " sess.run(update_weights)\n", "\n", " # Here, we're keeping a history of the losses to plot later\n", " # so we can see the change in loss as training progresses.\n", " losses.append(loss.eval())\n", "\n", " # Training is done, compute final values for the graph.\n", " betas = weights.eval()\n", " yhat = yhat.eval()\n", "\n", "# Show the results.\n", "fig, (ax1, ax2) = plt.subplots(1, 2)\n", "plt.subplots_adjust(wspace=.3)\n", "fig.set_size_inches(10, 4)\n", "ax1.scatter(x, y, alpha=.7)\n", "ax1.scatter(x, np.transpose(yhat)[0], c=\"g\", alpha=.6)\n", "line_x_range = (-4, 6)\n", "ax1.plot(line_x_range, [betas[0] + a * betas[1] for a in line_x_range], \"g\", alpha=0.6)\n", "ax2.plot(range(0, training_steps), losses)\n", "ax2.set_ylabel(\"Loss\")\n", "ax2.set_xlabel(\"Training steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "TzIETgHwTexL" }, "source": [ "This code looks very similar to the code above, but without using `l2_loss` or `GradientDescentOptimizer`. Let's look at exactly what it is doing instead.\n", "\n", "This code is the key difference:\n", "\n", ">`loss = 0.5 * tf.reduce_sum(tf.mul(yerror, yerror))`\n", "\n", ">`gradient = tf.reduce_sum(tf.transpose(tf.mul(input, yerror)), 1, keep_dims=True)`\n", "\n", ">`update_weights = tf.assign_sub(weights, mu * gradient)`\n", "\n", "The first line calculates the L2 loss manually. It's the same as `l2_loss(yerror)`, which is half of the sum of the squared error, so $\\frac{1}{2} \\sum (\\hat{y} - y)^2$. With this code, you can see exactly what the `l2_loss` operation does. It's the total of all the squared differences between the target and our estimates. And minimizing the L2 loss will minimize how much our estimates of $y$ differ from the true values of $y$.\n", "\n", "The second line calculates $\\sum{x_i (\\hat{y} - y)}$. What is that? It's the partial derivative of the L2 loss, the same thing as what `gradients(loss, weights)` does in the earlier code. Not sure about that? Let's look at it in more detail. The gradient calculation is going to get the partial derivatives of loss with respect to each of the weights so we can change those weights in the direction that will reduce the loss. L2 loss is $\\frac{1}{2} \\sum (\\hat{y} - y)^2$, where $\\hat{y} = w_2 x + w_1$. So, using the chain rule and substituting in for $\\hat{y}$ in the derivative, $\\frac{\\partial}{\\partial w_i} = \\sum{(\\hat{y} - y)\\, x_i}$. `GradientDescentOptimizer` does these calculations automatically for you based on the graph structure.\n", "\n", "The third line is equivalent to `weights -= mu * gradient`, so it subtracts a constant the gradient after scaling by the learning rate (to avoid jumping too far each time, which risks moving in the wrong direction). It's also the same thing that `GradientDescentOptimizer(learning_rate).minimize(loss)` does in the earlier code. Gradient descient updates its first parameter based on the values in the second after scaling by the third, so it's equivalent to the `assign_sub(weights, mu * gradient)`.\n", "\n", "Hopefully, this other code gives you a better understanding of what the operations we used previously are actually doing. In practice, you'll want to use those high level operators most of the time rather than calculating things yourself. For this toy example and simple network, it's not too bad to compute and apply the gradients yourself from scratch, but things get more complicated with larger networks." ] } ], "metadata": { "colabVersion": "0.3.2", "colab_default_view": {}, "colab_views": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
DawesLab/LabNotebooks
Pu Zhang QuTiP List.ipynb
1
23461
{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import *\n", "from scipy import *\n", "from qutip import *" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define atomic states\n", "# Use ordering from paper\n", "state2 = basis(3,0)\n", "excited = basis(3,1)\n", "ground = basis(3,2)\n", "\n", "# Set where to truncate Fock state for cavity\n", "N = 30\n", "\n", "# Create the atomic operators needed for the Hamiltonian\n", "# |g><e|\n", "sigma_ge = tensor(qeye(N), ground * excited.dag())\n", "# |e><2|\n", "sigma_e2 = tensor(qeye(N), excited * state2.dag())\n", "# |g><2|\n", "sigma_g2 = tensor(qeye(N), ground * state2.dag())\n", "\n", "# Create the photon operator\n", "a = tensor(destroy(N), qeye(3))\n", "ada = tensor(num(N), qeye(3))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define collapse operators\n", "c_ops = []\n", "# Cavity decay rate\n", "kappa = 0.04\n", "c_ops.append(sqrt(kappa) * a)\n", "\n", "# Atomic decay rate\n", "gamma1 = 1.3164239028e-6\n", "gamma2 = 1e7 * gamma1\n", "c_ops.append(sqrt(gamma1) * sigma_ge)\n", "c_ops.append(sqrt(gamma2) * sigma_e2)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rho0 = tensor(basis(N,2),excited) #AMCD\n", "\n", "g = 0.2\n", "Omega = 10 * kappa\n", "\n", "# Hamiltonian\n", "H = g * (sigma_ge.dag() * a + a.dag() * sigma_ge) + 0.5 * Omega * (sigma_g2 + sigma_g2.dag())\n", "\n", "taus = linspace(0, 2e2, 2e2)\n", "\n", "g2, G2 = coherence_function_g2(H, rho0, taus, c_ops, a) #AMCD" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x113188860>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11340c6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "\n", "#ax.plot(taus, real(g2), label='g='+str(g)+', amplitude='+str(real(max(g2)-min(g2))), lw=2)\n", "ax.plot(taus, real(g2))\n", "\n", "#ax.legend(loc=0)\n", "ax.set_xlabel(r'$\\tau$')\n", "ax.set_ylabel(r'$g^{(2)}(\\tau)$')\n", "#plt.savefig('g2tau.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wlist = linspace(-8, 8, 200000) * kappa\n", "spec = spectrum(H, wlist, c_ops, a.dag(), a)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[30, 3], [1, 1]], shape = [90, 1], type = ket\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0\\\\0.0\\\\1.0\\\\0.0\\\\0.0\\\\\\vdots\\\\0.0\\\\0.0\\\\0.0\\\\0.0\\\\0.0\\\\\\end{array}\\right)\\end{equation*}" ], "text/plain": [ "Quantum object: dims = [[30, 3], [1, 1]], shape = [90, 1], type = ket\n", "Qobj data =\n", "[[ 0.]\n", " [ 0.]\n", " [ 1.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(spec)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(qutip.__version__)\n", "\n", "import sys\n", "print(sys.version)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [qutip]", "language": "python", "name": "Python [qutip]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
jhconning/Dev-II
notebooks/InsecureRights.ipynb
1
20065
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causes and consequences of insecure Property Rights to land\n", "\n", "Insecure property rights to land can affect investement decisions and the nature and efficiency of agrarian production organization by affecting the market for tenancies and land sales. \n", "\n", "The relationship between property rights insecurity and investment is not as obvious as it might appear at first. While it might seem obvious that property rights insecurity (e.g. the possibility of expropriation) could only reduce incentives to invest and trade this is less obvious once you consider that informal investments might be undertaken to strengthen property rights claims in environments where they might be contested. Think of the homesteader or the informal slum dweller who builds a solid foundation for their home, fences what they consider their property, and builds other 'defences' in the hope that this will exclude others and dissuade a would-be-evicter.\n", "\n", "There's a rich literature on this topic, including the readings by Besley (1995), de Soto's *Mystery of Capital*, and *The Other Path* books, the survey by Conning and Deb (2008) and many many others. In time I'll try to find time to fill out this introduction, in the meantime the rest of the notebook focuses on a short synthesis of Conning and Robinson (2007)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conning & Robinson (2007): Property Rights and the Political Organization of Agriculture\n", "\n", "This is a simplified version of Conning and Robinson's 2007 paper in the Journal of Development Economics, a \"general equilibrium model where the organization of agriculture and the political equilibrium determining the security of property rights are jointly determined.\n", "\n", "In its present form this notebook focuses primarily on the economic side of the model which demonstrates how insecure property rights to land (e.g. the risk of expropriation) might affect the equilibrium level of land leasing and equilibrium factor prices. The dull details of the probabilistic voting model that determines the equilibrium political risk of expropriation are left for another time.\n", "\n", "The model is another in the family of general equilibrium models of the size distribution of farms that we've already analyzed. In all cases we have an economy with an agricultural technology that is linear homogenous in farming skill $S$, land $T$ and labor $L$. We showed that even though there was no market for farm management skills (so $S$ is a non-traded asset) efficient resource allocations could be achieved if we had competitive land and labor markets that allowed households to achieve their efficient scale. This model is similar to Lucas' 1978 size distribution of business firms and would explain the size distribution of farms as determined by an underlying distribution of farming skills across households. In this model efficiency of allocation is independent of the initial distribution of factors $S, T,$ and $L$ in the population.\n", "\n", "If, in addition to the imperfection in the market for non-traded farming skills, we add a distortion to either the market for land or labor, efficiency can no longer be achieved.\n", "\n", "Eswaran and Kotwal 1996 paper on \"Access to Capital and Production Organization\" can be viewed as a variant on this type of benchmark model. They introduce two simultaneous distortions. They require that supervisory labor be used at increasing cost to supervise any hired labor. This is loosely analogous to our shutting down the market for farming skill (or supervision) ability. The second constraint is a distortion in the market for working capital which determines that households with low initial wealth (principally ownership of land) will be constrained in the market for working capital (in our benchmark model we implicitly assumed working capital could always be financed). The interaction of these two types of distortion determines that allocations cannot be efficient and also the shape of the equilibrium agrarian 'class structure' or occupational choice hiearchy). Even though in effect all farms have the same farming skill, households with less ownership of land are more likely to be constrained in access to working capital and hence have to drop out of production entirely (to become 'pure laborers') or may be a constrained 'labor-cultivator' who runs a small farm and then sells labor on the market. In addition households may be constrained or unconstrained 'capitalist' farms that hire other workers. \n", "\n", "Conning and Robinson's paper sets itself up against a similar benchmark model, but in a two period world. In a world with complete security of property rights land and labor markets will allocate land efficiently across farms in agriculture and labor efficiently across farms in agriculture and manufacturing activities in urban areas. The size distribution of farms in agriculture will be efficient and completely determined by the initial distribution of non-traded farming skills. Land tenancy markets will be efficient and active. Take the case of all households having the same farming skill. If landlords have relatively large amounts of land they will lease it out to tenants to bring the distribution of operational farm sizes in line with the distribution of farming skills. \n", "\n", "Starting with this benchmark the model asks what would happen if we introduce property rights insecurity in the form of the possibility of a 'land to the tiller' land or tenancy reform. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### The Model with secure property rights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To simplify greatly assume there are just two population groups: landlords and peasant households. They have access to the follwowing production technologies:\n", "\n", "_Peasants_: $F(T,L)$ \n", "_Landlords_:$G(T,L)=A_l F(T,L)$\n", "\n", "When $A_l = 1$ landlord and peasant production technologies are identical, when $A_l > 1$ landlords are more skilled farmers and we associate that with higher productivity of labor and capital. For the moment assume $A_l = 1$. \n", "\n", "Landlord and peasant housholds are each endowed with 1 unit of labor. There are $\\bar L$ households overall of which proportion $n_p$ are peasant households and proportion $n_l$ are landlord households. For example if $\\bar L = 100$ and $n_p = 0.90$ there would be 90 peasant households and 10 landlord households.\n", "\n", "We will assume there is an urban sector where production takes place using production function $H(L)$ which is subject to decreasing returns. In equilibrium $L_u$ units of labor will leave agriculture to work in the city." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only difference between landlord and peasant households then is their ownership of land. Landlords as a group own fraction $\\theta$ of the overall land endowment. That means that each landlord household owns\n", "\n", "$$\\frac{\\theta}{n_l} \\bar t $$\n", "\n", "units of land where $\\bar t = \\frac{\\bar T}{\\bar L}$\n", "\n", "Since by definition landlords own more land then peasants $\\theta > n_l$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a world of secure property rights the first period allocations will be identical to the second period allocations, and in any given period efficient allocation will be characterized by:\n", "\n", "$$w = F_L(T_p,L_p) = G_L(T_l,L_l) = H(L_u)$$\n", "\n", "$$r = F_T(T_p,L_p) = G_T(T_l,L_l)$$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "In the special case where landlord and peasant farming skill is identical efficient production allocations will be identical on all $\\bar L$ farms and equal to \n", "\n", "$$T^e = \\frac{\\bar T}{\\bar L}$$\n", "\n", "$$L^e = \\frac{\\bar L -L^e_u} {\\bar L}$$\n", "\n", "where $L^e_u$ is pinned down by $F_L(T^e,L^e) = w = H_L(L^e_u)$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's easy to show the efficient share of cultivated land under tenancy should be \n", "\n", "$$\\tau_e = \\theta - n_l$$\n", "\n", "Intuitively, the higher the concentration of land $\\theta$ the more land needs to be under tenancy to equalize farm sizes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Model with insecure property rights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now extend the setting to allow the initial production period to be followed by a political contest that will decide the likelihood of future property rights reforms that may affect ownership claims in a final production period.\n", "\n", "Households will choose factors of production as before except that there is now the threat that a tenancy reform may occur with (possibly zero) probability $\\alpha$. \n", "\n", "If a reform takes place all sitting tenants obtain protection from eviction on fraction (1-κ) of the land they leased in the pre-reform period and they will only have to pay a new capped rental rate v for those leases in the following period, where v will generally be set at or below the post-reform market equilibrium rental rate $v^e$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One interpretation of κ is that the reform beneficiary might have to pay monetary expenses $κ(v^e-\\bar v)$ per unit land transferred to cover such things as property registration paperwork, new taxes, excess loan financing costs, or other transaction or setup costs. The value of κ will be a key parameter establishing the size of the gap between the value of property rights lost by the landlord and the value of benefits transferred to the tenant or squatter.\n", "\n", "In a post-reform competitive equilibrium this transaction cost will be reflected in land rent and sale prices and how its burden is shared between landlord and tenant it would not matter if we had instead imposed these transaction costs directly on the landlord." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To simplify matters it is assumed that no further alteration of property rights can take place after the reform. This implies that in the post-reform production period efficient resource allocation $(T_{e},L_{e},L_{ue})$ will be achieved at market factor prices $v^{e}=F_{T}(T_{e},L_{e})$ and $w^{e}=F_{L}(T_{e},L_{e})$, except of course that reform beneficiaries now earn additional incomes generated by the transfer of property rights. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can model agrarian reforms of different type and depth by varying the three parameters $α,v$ and $κ$. For example, one may think of $v =0$ as expropriation without compensation to the landlord while $0<v <v^{e}$ could be thought of as a tenancy reform with a rent ceiling, or as a land reform with partial compensation. From a landlord's perspective a property rights reform means facing the prospect of being forced to cede the $(θt/n^{l}-T_{l})$ units of land they put under lease in the pre-reform phase at below-market rental rate v in the post-reform period. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Anticipating this the landlord will now choose $L_{l}$ and $T_{l}$ (and hence also how much land to lease out) in a preemptive manner, considering the risk that tenants may become squatters or agrarian reform beneficiaries. \n", "\n", "(Each individual landlord and peasant is assumed too small to internalize how their own production decisions might affect the subsequent political equilibrium and takes α as given.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Taking their conjecture of α as given, each landlord now chooses pre-reform factor inputs $T_{l}$ and $L_{l}$ to maximize the expected discounted value of farm profits plus factor sales taking into account that property rights over land leased out will be challenged in the event of reform:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "$$V^{l} = [Π^{l}(T_{l},L_{l})+w+vθt/n^{l}]$$\n", "$$+\\alpha [Π^{l}(T_{e},L_{e})+w+v^{e}θt/n^{l}-(v^{e}-v)[θt/n^{l}-T_{l}]]$$\n", "$$+(1-α)[Π^{l}(T_{e},L_{e})+w^{e}+v^{e}θt/n^{l}]$$\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Stated more compactly:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ V^{l}=[Π^{l}(T_{l},L_{l})+w+vθt/n^{l}]+[Π^{l}(T_{e},L_{e})+w+v_{e}θt/n^{l}]$$\n", "$$-\\alpha (v^{e}-\\bar v)(θt/n^{l}-T_{l})$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first two bracketed terms measure expected returns over the two periods under secure property rights (albeit with distortions in the first period) while the last term captures the expected loss of income from having to possibly cede property rights to a squatter or reform beneficiary in the second period." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The peasant household will similarly choose $T_{p}$ and $L_{p}$ to maximize earnings from farm profits plus factor sales taking into account the prospect (summarized by α) that they may acquire squatter rights over any land that they leased in the first period. We assume that in a post-reform period a tenant has to pay only the regulated rate v but can lease that land back out onto the market, to earn a windfall rent of $(v^{e}-\\bar v)$ per unit land. The tenant's discounted expected payoff is therefore:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$V^{p}=[Π^{p}(T_{p},L_{p})+w+v(1-θ)t/n^{p}]+[Π^{p}(T_{e},L_{e})+w^{e}+v^{e}(1-θ)t/n^{p}]$$\n", "$$+α(1-κ)(v^{e}-\\bar v)(T_{p}-(1-θ)t/n^{p})$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second line captures the expected windfall gain the peasant/tenant from a transfer of rights toward the tenant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first-order conditions for a competitive equilibrium with respect to first period land input choices are now given by:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$G_{T}(T_{l},L_{l})=F_{T}(T_{p},L_{p})-ακ(v^{e}-v)$$\n", "\n", "$$G_{L}(T_{l},L_{l})=F_{T}(T_{p},L_{p})=H_{L}(L_{u})$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**REMARK** *Even with a positive threat of property rights reform α>0, equilibrium allocations will remain efficient as long as κ=0*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first blush this might seem slightly surprising. When there are no transaction costs involved in transferring land under a reform market allocations will be efficient even though there may be a positive probability (or even a certainty) of a property rights reforms.\n", "\n", "Yes a reform will compel a landlord to surrender property rights to tenant-cum-squatters in the following period but this possibility is fully anticipated and causes no distortion because we have in effect left open a market for \"squatter rights\". Tenants understand that they may capture a windfall rent from the reform and in effect pay the going market rate for the right to be a sitting tenant/squatter. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now this might sound somewhat incredible but in fact if you read *The Other Path: the invisible revolution in the third world* the book that made Hernando de Soto world famous and that describes in great detail the organization of land squatting invasions in Lima Peru you will find de Soto dedicating several pages to describing precisely such arrangements. He argues in effect that many land-invasions take place with the explicit cooperation of owners who often in fact demand payments in advance for allowing the squatters into their properties ahead of staged invasions. This happens in particular in situations where land is not zoned for residential occupancy.\n", "\n", "But even more striking evidence of this happening comes from work of Jeon and Kim (2000) who analyze what happened in the land market in Korea following when the Japanese were forced to suddently and completely abandon the Korean peninsula and Korea came under the US military administration in August 1945. Tenancy under the Japanese colonial administration 1919-45 had been a widespread phenomenon as by one measure over 56 percent of farmer households were tenants and 58 percent of farmland was under tenancy in 1939. Although tenant protests demanding lower rents were not uncommon, the Japanese colonial military presence had strictly enforced landlord's property rights. With the Japanese military suddenly gone the eventuality of land reform became a near certainty. The interesting thing is that the authors document that in the period just prior to this anticipated reform 60 percent of landlords -- mostly the larger ones -- sold their land to tenants via the market at reduced prices before 1950. More than twice as much land was sold by landlords in anticipation of the reform than was eventually transferred directly via the land reform process.\n", "\n", "Most land reforms are however messy and somewhat uncertain affairs. A more realistic assumption is that the transfer of property rights will not be costless and without leakage, and therefore that $\\kappa >0$. When this is the case:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Proposition:** When κ>0, and as long as non-traded input S remains an essential input, then the expectation of reform (α>0) leads landlords to defensively *suppress* tenancy, or $T_l<T^e$. Landlord farms will become larger and more land-intensive, and peasant farms smaller and more labor-intensive than the first best efficient scale. Peasant off-farm labor supply and rural to urban migration will increase. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Anythin that increases the size of the wedge between the effective rental rate the tenant pays and the landlord can expect to recover $\\alpha \\kappa(v^{e}-\\bar v)$ will strengthen tenancy suppression. So a higher probability of reform $\\alpha$ a higher leakage rate $\\kappa$ or a lower cap $\\bar v$ on the rental rate in the post-reform period all contribute to a landlord trying to protect a larger fraction of his land from tenancy-would-be-reform-beneficiaries in the first period. It can be shown that in equilibrium this witholding of land makes landlord farms bigger, peasant farms smaller and leads peasant off-farm labor supply to increase which would drive down equilibrium wages." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
jbarratt/ipython_notebook_presentation
Command Line Demo.ipynb
1
3850
{ "metadata": { "name": "", "signature": "sha256:6cf257f44568ee9ffbdcd690881700f54d7b3728e6148a92ae40a2ad8355e55f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython Command Line examples\n", "\n", "IPython can be used to work with the system it's running on in pretty creative ways.\n", "\n", "* `!command`: run (and capture output) of a command\n", "* `$` variables" ] }, { "cell_type": "code", "collapsed": false, "input": [ "files = !ls -1\n", "for file in files.grep('ipynb'):\n", " !echo $file" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also some interesting 'magics' for working on shells, for example if you want to run some bash-specific code:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "\n", "echo \"Running under $BASH\"\n", "echo \"Only Bash could do this...\"\n", "echo {a,b,c}{d,e,f}" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or even ``perl``:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%perl\n", "\n", "use DateTime;\n", "\n", "print \"The first of next month: \";\n", "print DateTime->today()->add(months => 1)->set(day => 1)->ymd;" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Runbooks\n", "\n", "The basic capabilities of\n", "* Running commands\n", "* Displaying output\n", "* Being able to put comments around things\n", "\n", "makes IPython Notebook great for quickly documenting \"devops\" processes.\n", "\n", "For example, anything you can run via ssh you can quickly demonstrate, like finding the top recent account numbers referenced in one of our system logs:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ssh ho \"sudo head -2000 /var/log/mediatemple.log | awk '{print \\$29}' | grep '[0-9]' | sort | uniq -c | sort -rn | head -10\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 180 60\r\n", " 42 253084\r\n", " 24 77458\r\n", " 23 70\r\n", " 18 65\r\n", " 16 80\r\n", " 16 152904\r\n", " 15 258882\r\n", " 14 252005\r\n", " 12 84480\r\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even in a more analytics space than 'runbooks', that particular capability is useful.\n", "\n", "Or, showing off a fabric task:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!fab share:'Command Line Demo.ipynb'" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<blink>awesome idea</blink>" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
cc0-1.0
jskDr/jamespy_py3
wireless/polar_nb/Sage - NPolar Transform-Copy5.ipynb
2
17235
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Symbolic Polar Transformation using Sagemath" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from sage import *\n", "import numpy as np\n", "from wireless import sagepolar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Library Test\n", "- 여기는 라이브러리로 구현되어 패키지를 가지고 와서 실행함." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AE: [U0 + U4, U2 + U6, U1 + U5, U3 + U7]\n", "bit_forward: [U0 + U4, U1 + U5, U2 + U6, U3 + U7]\n", "AE: [U4, U6, U5, U7]\n", "bit_forward: [U4, U5, U6, U7]\n", "x_polar, x_polar_idx: [[U0 + U4, U2 + U6, U1 + U5, U3 + U7], [U4, U6, U5, U7]] [[0, 2, 4, 6], [1, 3, 5, 7]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7,\n", " U4 + U5 + U6 + U7,\n", " U2 + U3 + U6 + U7,\n", " U6 + U7,\n", " U1 + U3 + U5 + U7,\n", " U5 + U7,\n", " U3 + U7,\n", " U7]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sagepolar.npolar_coding(N=8, P=4)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=8, P=4\n", "AE: [U0 + U4, U2 + U6, U1 + U5, U3 + U7]\n", "bit_forward: [U0 + U4, U1 + U5, U2 + U6, U3 + U7]\n", "AE: [U4, U6, U5, U7]\n", "bit_forward: [U4, U5, U6, U7]\n", "x_polar, x_polar_idx: [[U0 + U4, U2 + U6, U1 + U5, U3 + U7], [U4, U6, U5, U7]] [[0, 2, 4, 6], [1, 3, 5, 7]]\n" ] }, { "data": { "text/html": [ "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\verb|X:| \\left[U_{0} + U_{1} + U_{2} + U_{3} + U_{4} + U_{5} + U_{6} + U_{7}, U_{4} + U_{5} + U_{6} + U_{7}, U_{2} + U_{3} + U_{6} + U_{7}, U_{6} + U_{7}, U_{1} + U_{3} + U_{5} + U_{7}, U_{5} + U_{7}, U_{3} + U_{7}, U_{7}\\right]</script></html>" ], "text/plain": [ "'X:' [U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7,\n", " U4 + U5 + U6 + U7,\n", " U2 + U3 + U6 + U7,\n", " U6 + U7,\n", " U1 + U3 + U5 + U7,\n", " U5 + U7,\n", " U3 + U7,\n", " U7]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\verb|UD:| \\left[U_{0}, U_{1}, U_{2}, U_{3}, U_{4}, U_{5}, U_{6}, U_{7}\\right]</script></html>" ], "text/plain": [ "'UD:' [U0, U1, U2, U3, U4, U5, U6, U7]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sagepolar.test_npolar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation code" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "def npolar_transform(u,N_P=4/1):\n", " u = list(u)\n", " print(len(u), u)\n", " if len(u) == 1:\n", " x = u\n", " elif len(u) > N_P:\n", " # 처음이 1이고 갈수록 2, 4, 8이 된다.\n", " # N_P = N/P 값에 따라 AE 담당할 앞부분은 polar 변환하지 않고 뒤집기만 한다.\n", " u1 = u[0::2]\n", " u2 = u[1::2]\n", " x = npolar_transform(u1,N_P) + npolar_transform(u2,N_P) \n", " else:\n", " u1 = u[0::2]\n", " u2 = u[1::2]\n", " u1u2 = []\n", " for u1_i, u2_i in zip(u1, u2):\n", " u1u2.append(u1_i + u2_i)\n", " x = npolar_transform(u1u2,N_P) + npolar_transform(u2,N_P)\n", " return x\n", "\n", "def npolar_coding(N=8, P=1):\n", " \"\"\"\n", " Input:\n", " P=1: AE 입력의 크기임. NPolar의 경우, P=N을 제외하고는 AE를 one-hot vector로 처리하지 않음.\n", " \"\"\" \n", " N_P = N / P\n", " u = var('U',n=N)\n", " y_polar = npolar_transform(u, N_P=N_P)\n", " #print('y_polar:', y_polar)\n", " x_polar = [] \n", " x_polar_idx = []\n", " ae_polar = y_polar.copy()\n", " idx = list(range(N))\n", " for i in range(N_P):\n", " y_polar_l = y_polar[i::N_P]\n", " idx_l = idx[i::N_P]\n", " x_polar.append(y_polar_l)\n", " x_polar_idx.append(idx_l)\n", " ae_polar_l = ae_coding_emul(x_polar[-1])\n", " for i, j in enumerate(x_polar_idx[-1]):\n", " ae_polar[j] = ae_polar_l[i]\n", " #print('x_polar:', x_polar)\n", " print(\"x_polar, x_polar_idx:\", x_polar, x_polar_idx)\n", " return ae_polar\n", "\n", "def bit_forward(x):\n", " \"\"\"\n", " polar encoding에서 사용한 방법을 역으로 수행함.\n", " bit_reverse를 역으로 복원함. 이 방법은 디코딩에서도 내부적으로 사용되고 있음.\n", " Polar encoding: x = encoding(x[0::2]) + encoding(x[1::2]) if len(x) > 1\n", " \"\"\"\n", " LN = len(x)\n", " if LN == 1:\n", " return x\n", " else:\n", " y = x.copy()\n", " #print(x, y)\n", " y[0::2] = bit_forward(x[:LN/2])\n", " y[1::2] = bit_forward(x[LN/2:])\n", " return y\n", "\n", "def ae_coding_emul(x):\n", " \"\"\"\n", " AE가 해야할 일을 Polar로 emulation시킴\n", " Polar로 emulation시키기 위해서는 bit reverse 되어 있는걸 복원해야 함.\n", " \"\"\"\n", " print('AE:', x)\n", " u = bit_forward(x)\n", " print('bit_forward:', u)\n", " x = sagepolar.polar_transform(u)\n", " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## n = 4" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 [U0, U1, U2, U3]\n", "2 [U0 + U1, U2 + U3]\n", "1 [U0 + U1 + U2 + U3]\n", "1 [U2 + U3]\n", "2 [U1, U3]\n", "1 [U1 + U3]\n", "1 [U3]\n", "AE: [U0 + U1 + U2 + U3]\n", "bit_forward: [U0 + U1 + U2 + U3]\n", "AE: [U2 + U3]\n", "bit_forward: [U2 + U3]\n", "AE: [U1 + U3]\n", "bit_forward: [U1 + U3]\n", "AE: [U3]\n", "bit_forward: [U3]\n", "x_polar, x_polar_idx: [[U0 + U1 + U2 + U3], [U2 + U3], [U1 + U3], [U3]] [[0], [1], [2], [3]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3, U2 + U3, U1 + U3, U3]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "npolar_coding(N=4, P=1)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[U0 + U1 + U2 + U3]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bit_forward([U0 + U1 + U2 + U3])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 [U0, U1, U2, U3]\n", "2 [U0, U2]\n", "1 [U0 + U2]\n", "1 [U2]\n", "2 [U1, U3]\n", "1 [U1 + U3]\n", "1 [U3]\n", "AE: [U0 + U2, U1 + U3]\n", "bit_forward: [U0 + U2, U1 + U3]\n", "AE: [U2, U3]\n", "bit_forward: [U2, U3]\n", "x_polar, x_polar_idx: [[U0 + U2, U1 + U3], [U2, U3]] [[0, 2], [1, 3]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3, U2 + U3, U1 + U3, U3]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "npolar_coding(N=4, P=2)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[U0 + U2, U1 + U3]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bit_forward([U0 + U2, U1 + U3])" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 [U0, U1, U2, U3]\n", "2 [U0, U2]\n", "1 [U0]\n", "1 [U2]\n", "2 [U1, U3]\n", "1 [U1]\n", "1 [U3]\n", "AE: [U0, U2, U1, U3]\n", "bit_forward: [U0, U1, U2, U3]\n", "x_polar, x_polar_idx: [[U0, U2, U1, U3]] [[0, 1, 2, 3]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3, U2 + U3, U1 + U3, U3]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "npolar_coding(N=4, P=4)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[U0, U1, U2, U3]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bit_forward([U0, U2, U1, U3])" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AE: [U0, U2, U1, U3]\n", "bit_forward: [U0, U1, U2, U3]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3, U2 + U3, U1 + U3, U3]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ae_coding_emul([U0, U2, U1, U3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## n = 8" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 [U0, U1, U2, U3, U4, U5, U6, U7]\n", "4 [U0 + U1, U2 + U3, U4 + U5, U6 + U7]\n", "2 [U0 + U1 + U2 + U3, U4 + U5 + U6 + U7]\n", "1 [U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7]\n", "1 [U4 + U5 + U6 + U7]\n", "2 [U2 + U3, U6 + U7]\n", "1 [U2 + U3 + U6 + U7]\n", "1 [U6 + U7]\n", "4 [U1, U3, U5, U7]\n", "2 [U1 + U3, U5 + U7]\n", "1 [U1 + U3 + U5 + U7]\n", "1 [U5 + U7]\n", "2 [U3, U7]\n", "1 [U3 + U7]\n", "1 [U7]\n", "AE: [U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7]\n", "bit_forward: [U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7]\n", "AE: [U4 + U5 + U6 + U7]\n", "bit_forward: [U4 + U5 + U6 + U7]\n", "AE: [U2 + U3 + U6 + U7]\n", "bit_forward: [U2 + U3 + U6 + U7]\n", "AE: [U6 + U7]\n", "bit_forward: [U6 + U7]\n", "AE: [U1 + U3 + U5 + U7]\n", "bit_forward: [U1 + U3 + U5 + U7]\n", "AE: [U5 + U7]\n", "bit_forward: [U5 + U7]\n", "AE: [U3 + U7]\n", "bit_forward: [U3 + U7]\n", "AE: [U7]\n", "bit_forward: [U7]\n", "x_polar, x_polar_idx: [[U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7], [U4 + U5 + U6 + U7], [U2 + U3 + U6 + U7], [U6 + U7], [U1 + U3 + U5 + U7], [U5 + U7], [U3 + U7], [U7]] [[0], [1], [2], [3], [4], [5], [6], [7]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7,\n", " U4 + U5 + U6 + U7,\n", " U2 + U3 + U6 + U7,\n", " U6 + U7,\n", " U1 + U3 + U5 + U7,\n", " U5 + U7,\n", " U3 + U7,\n", " U7]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "npolar_coding(N=8, P=1)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 [U0, U1, U2, U3, U4, U5, U6, U7]\n", "4 [U0, U2, U4, U6]\n", "2 [U0 + U2, U4 + U6]\n", "1 [U0 + U2 + U4 + U6]\n", "1 [U4 + U6]\n", "2 [U2, U6]\n", "1 [U2 + U6]\n", "1 [U6]\n", "4 [U1, U3, U5, U7]\n", "2 [U1 + U3, U5 + U7]\n", "1 [U1 + U3 + U5 + U7]\n", "1 [U5 + U7]\n", "2 [U3, U7]\n", "1 [U3 + U7]\n", "1 [U7]\n", "AE: [U0 + U2 + U4 + U6, U1 + U3 + U5 + U7]\n", "bit_forward: [U0 + U2 + U4 + U6, U1 + U3 + U5 + U7]\n", "AE: [U4 + U6, U5 + U7]\n", "bit_forward: [U4 + U6, U5 + U7]\n", "AE: [U2 + U6, U3 + U7]\n", "bit_forward: [U2 + U6, U3 + U7]\n", "AE: [U6, U7]\n", "bit_forward: [U6, U7]\n", "x_polar, x_polar_idx: [[U0 + U2 + U4 + U6, U1 + U3 + U5 + U7], [U4 + U6, U5 + U7], [U2 + U6, U3 + U7], [U6, U7]] [[0, 4], [1, 5], [2, 6], [3, 7]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7,\n", " U4 + U5 + U6 + U7,\n", " U2 + U3 + U6 + U7,\n", " U6 + U7,\n", " U1 + U3 + U5 + U7,\n", " U5 + U7,\n", " U3 + U7,\n", " U7]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "npolar_coding(N=8, P=2)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 [U0, U1, U2, U3, U4, U5, U6, U7]\n", "4 [U0, U2, U4, U6]\n", "2 [U0, U4]\n", "1 [U0 + U4]\n", "1 [U4]\n", "2 [U2, U6]\n", "1 [U2 + U6]\n", "1 [U6]\n", "4 [U1, U3, U5, U7]\n", "2 [U1, U5]\n", "1 [U1 + U5]\n", "1 [U5]\n", "2 [U3, U7]\n", "1 [U3 + U7]\n", "1 [U7]\n", "AE: [U0 + U4, U2 + U6, U1 + U5, U3 + U7]\n", "bit_forward: [U0 + U4, U1 + U5, U2 + U6, U3 + U7]\n", "AE: [U4, U6, U5, U7]\n", "bit_forward: [U4, U5, U6, U7]\n", "x_polar, x_polar_idx: [[U0 + U4, U2 + U6, U1 + U5, U3 + U7], [U4, U6, U5, U7]] [[0, 2, 4, 6], [1, 3, 5, 7]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7,\n", " U4 + U5 + U6 + U7,\n", " U2 + U3 + U6 + U7,\n", " U6 + U7,\n", " U1 + U3 + U5 + U7,\n", " U5 + U7,\n", " U3 + U7,\n", " U7]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "npolar_coding(N=8, P=4)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 [U0, U1, U2, U3, U4, U5, U6, U7]\n", "4 [U0, U2, U4, U6]\n", "2 [U0, U4]\n", "1 [U0]\n", "1 [U4]\n", "2 [U2, U6]\n", "1 [U2]\n", "1 [U6]\n", "4 [U1, U3, U5, U7]\n", "2 [U1, U5]\n", "1 [U1]\n", "1 [U5]\n", "2 [U3, U7]\n", "1 [U3]\n", "1 [U7]\n", "AE: [U0, U4, U2, U6, U1, U5, U3, U7]\n", "bit_forward: [U0, U1, U2, U3, U4, U5, U6, U7]\n", "x_polar, x_polar_idx: [[U0, U4, U2, U6, U1, U5, U3, U7]] [[0, 1, 2, 3, 4, 5, 6, 7]]\n" ] }, { "data": { "text/plain": [ "[U0 + U1 + U2 + U3 + U4 + U5 + U6 + U7,\n", " U4 + U5 + U6 + U7,\n", " U2 + U3 + U6 + U7,\n", " U6 + U7,\n", " U1 + U3 + U5 + U7,\n", " U5 + U7,\n", " U3 + U7,\n", " U7]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "npolar_coding(N=8, P=8)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[U0, U1, U2, U3, U4, U5, U6, U7]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bit_forward([U0, U4, U2, U6, U1, U5, U3, U7])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "SageMath 9.0", "language": "sage", "name": "sagemath" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
kkaiser/kkaiser.github.io
social_data/nb/project_assignmentB.ipynb
1
30831883
null
gpl-3.0
jfconavarrete/spts-uoe
build_full_dataset.ipynb
1
30569
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Merge Datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set Up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import logging\n", "import pickle\n", "import pandas as pd\n", "import numpy as np\n", "import math\n", "\n", "logger = logging.getLogger()\n", "logger.setLevel(logging.INFO)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stations = pickle.load(open('data/parsed/stations_dataset_final.p', 'rb'))\n", "readings = pickle.load(open('data/parsed/readings_dataset_utc.p', 'rb'))\n", "weather = pickle.load(open('data/parsed/weather_dataset_utc.p', 'rb'))\n", "readings_dataset = pickle.load(open('data/parsed/readings_dataset_final.p', 'rb'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1483149, 6)\n", "(779, 13)\n", "(3008, 16)\n" ] } ], "source": [ "print readings.shape\n", "print stations.shape\n", "print weather.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill Gaps" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_gaps = True" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def find_next(df, start_loc):\n", " if start_loc + 1 == len(df):\n", " return None\n", " else: \n", " return df.loc[start_loc + 1]\n", "\n", "def get_fillings(df):\n", " fillings=[]\n", " for idx, start in df.iterrows():\n", " end = find_next(df, idx)\n", " if end is None:\n", " break\n", " \n", " big_gap = (end.Timestamp - start.Timestamp).seconds > (60 * 5)\n", " if big_gap:\n", " gap_fillings = pd.date_range(start=start.Timestamp, end=end.Timestamp, freq='5min', tz='UTC')[1:]\n", " if (end.Timestamp - gap_fillings[-1]).seconds < (60 * 2 + 30):\n", " gap_fillings = gap_fillings[:-1]\n", " \n", " for timestamp in gap_fillings: \n", " fillings.append({'Id': start.Id, 'Timestamp': timestamp, 'Source': 'ARTIFICIAL'})\n", " \n", " return fillings" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if fill_gaps:\n", " # prepare to find gaps\n", " readings['Source'] = 'REAL'\n", " readings.sort_values(by=['Timestamp'], inplace=True)\n", " \n", " stations_ids = stations.Id.unique()\n", " \n", " # find the gaps for each station\n", " fillings = []\n", " for station_id in stations_ids:\n", " station_df = readings[readings.Id == station_id].reset_index(drop=True)\n", " station_fillings = get_fillings(station_df)\n", " fillings.append(station_fillings)\n", " \n", " # add the gaps to the original dataset\n", " readings = pd.concat([readings, pd.DataFrame(sum(fillings, []))])\n", " \n", " # fill the missing values using a fill forward strategy\n", " readings.sort_values(by=['Id', 'Timestamp'], inplace=True)\n", " readings.fillna(method='ffill', inplace=True)\n", " readings.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Merge Readings and Weather" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use binary search to look for the closest date to the given reading." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def binarySearch(data, val):\n", " \"\"\"Find the closest val in data\"\"\"\n", " \n", " lo, hi = 0, len(data) - 1\n", " best_ind = lo\n", " while lo <= hi:\n", " mid = lo + (hi - lo) / 2\n", " if data.iat[mid] < val:\n", " lo = mid + 1\n", " elif data.iat[mid] > val:\n", " hi = mid - 1\n", " else:\n", " best_ind = mid\n", " break\n", " # check if data[mid] is closer to val than data[best_ind] \n", " if abs(data.iat[mid] - val) < abs(data.iat[best_ind] - val):\n", " best_ind = mid\n", " return best_ind" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>NbBikes</th>\n", " <th>NbDocks</th>\n", " <th>NbEmptyDocks</th>\n", " <th>NbUnusableDocks</th>\n", " <th>Source</th>\n", " <th>Timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>REAL</td>\n", " <td>2016-05-16 05:41:16.870000128+00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 05:46:16.870000128+00:00</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 05:51:16.870000128+00:00</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 05:56:16.870000128+00:00</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 06:01:16.870000128+00:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id NbBikes NbDocks NbEmptyDocks NbUnusableDocks Source \\\n", "0 BikePoints_1 12.0 19.0 6.0 1.0 REAL \n", "1 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "2 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "3 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "4 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "\n", " Timestamp \n", "0 2016-05-16 05:41:16.870000128+00:00 \n", "1 2016-05-16 05:46:16.870000128+00:00 \n", "2 2016-05-16 05:51:16.870000128+00:00 \n", "3 2016-05-16 05:56:16.870000128+00:00 \n", "4 2016-05-16 06:01:16.870000128+00:00 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "readings.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Condition</th>\n", " <th>DewPt</th>\n", " <th>Fog</th>\n", " <th>Hail</th>\n", " <th>Humidity</th>\n", " <th>Pressure</th>\n", " <th>Rain</th>\n", " <th>Snow</th>\n", " <th>Temp</th>\n", " <th>Thunder</th>\n", " <th>Timestamp</th>\n", " <th>Tornado</th>\n", " <th>Visibility</th>\n", " <th>WindDirD</th>\n", " <th>WindDirE</th>\n", " <th>WindSpeed</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>15</th>\n", " <td>NaN</td>\n", " <td>8.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>92.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>9.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 04:00:00+00:00</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>30.0</td>\n", " <td>NNE</td>\n", " <td>3.7</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Unknown</td>\n", " <td>7.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>93.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>8.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 04:20:00+00:00</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>350.0</td>\n", " <td>North</td>\n", " <td>7.4</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Unknown</td>\n", " <td>7.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>93.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>8.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 04:50:00+00:00</td>\n", " <td>True</td>\n", " <td>9.0</td>\n", " <td>360.0</td>\n", " <td>North</td>\n", " <td>7.4</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>NaN</td>\n", " <td>7.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>92.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>9.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 05:00:00+00:00</td>\n", " <td>True</td>\n", " <td>11.0</td>\n", " <td>10.0</td>\n", " <td>North</td>\n", " <td>9.3</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Partly Cloudy</td>\n", " <td>7.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>93.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>8.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 05:20:00+00:00</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>360.0</td>\n", " <td>North</td>\n", " <td>7.4</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Unknown</td>\n", " <td>7.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>87.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>9.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 05:50:00+00:00</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>350.0</td>\n", " <td>North</td>\n", " <td>9.3</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>NaN</td>\n", " <td>8.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>83.0</td>\n", " <td>1022.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 06:00:00+00:00</td>\n", " <td>True</td>\n", " <td>11.0</td>\n", " <td>10.0</td>\n", " <td>North</td>\n", " <td>9.3</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Partly Cloudy</td>\n", " <td>7.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>82.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 06:20:00+00:00</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>360.0</td>\n", " <td>North</td>\n", " <td>13.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Partly Cloudy</td>\n", " <td>7.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>82.0</td>\n", " <td>1021.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 06:50:00+00:00</td>\n", " <td>True</td>\n", " <td>10.0</td>\n", " <td>20.0</td>\n", " <td>NNE</td>\n", " <td>11.1</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Scattered Clouds</td>\n", " <td>8.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>77.0</td>\n", " <td>1022.0</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>11.0</td>\n", " <td>True</td>\n", " <td>2016-05-16 07:00:00+00:00</td>\n", " <td>True</td>\n", " <td>13.0</td>\n", " <td>20.0</td>\n", " <td>NNE</td>\n", " <td>9.3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Condition DewPt Fog Hail Humidity Pressure Rain Snow Temp \\\n", "15 NaN 8.0 True True 92.0 1021.0 True True 9.0 \n", "16 Unknown 7.0 True True 93.0 1021.0 True True 8.0 \n", "17 Unknown 7.0 True True 93.0 1021.0 True True 8.0 \n", "18 NaN 7.0 True True 92.0 1021.0 True True 9.0 \n", "19 Partly Cloudy 7.0 True True 93.0 1021.0 True True 8.0 \n", "20 Unknown 7.0 True True 87.0 1021.0 True True 9.0 \n", "21 NaN 8.0 True True 83.0 1022.0 True True 10.0 \n", "22 Partly Cloudy 7.0 True True 82.0 1021.0 True True 10.0 \n", "23 Partly Cloudy 7.0 True True 82.0 1021.0 True True 10.0 \n", "24 Scattered Clouds 8.0 True True 77.0 1022.0 True True 11.0 \n", "\n", " Thunder Timestamp Tornado Visibility WindDirD WindDirE \\\n", "15 True 2016-05-16 04:00:00+00:00 True 10.0 30.0 NNE \n", "16 True 2016-05-16 04:20:00+00:00 True 10.0 350.0 North \n", "17 True 2016-05-16 04:50:00+00:00 True 9.0 360.0 North \n", "18 True 2016-05-16 05:00:00+00:00 True 11.0 10.0 North \n", "19 True 2016-05-16 05:20:00+00:00 True 10.0 360.0 North \n", "20 True 2016-05-16 05:50:00+00:00 True 10.0 350.0 North \n", "21 True 2016-05-16 06:00:00+00:00 True 11.0 10.0 North \n", "22 True 2016-05-16 06:20:00+00:00 True 10.0 360.0 North \n", "23 True 2016-05-16 06:50:00+00:00 True 10.0 20.0 NNE \n", "24 True 2016-05-16 07:00:00+00:00 True 13.0 20.0 NNE \n", "\n", " WindSpeed \n", "15 3.7 \n", "16 7.4 \n", "17 7.4 \n", "18 9.3 \n", "19 7.4 \n", "20 9.3 \n", "21 9.3 \n", "22 13.0 \n", "23 11.1 \n", "24 9.3 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather[15:25]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>NbBikes</th>\n", " <th>NbDocks</th>\n", " <th>NbEmptyDocks</th>\n", " <th>NbUnusableDocks</th>\n", " <th>Source</th>\n", " <th>Timestamp</th>\n", " <th>WeatherIdx</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>REAL</td>\n", " <td>2016-05-16 05:41:16.870000128+00:00</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 05:46:16.870000128+00:00</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 05:51:16.870000128+00:00</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 05:56:16.870000128+00:00</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>BikePoints_1</td>\n", " <td>12.0</td>\n", " <td>19.0</td>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>ARTIFICIAL</td>\n", " <td>2016-05-16 06:01:16.870000128+00:00</td>\n", " <td>21</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id NbBikes NbDocks NbEmptyDocks NbUnusableDocks Source \\\n", "0 BikePoints_1 12.0 19.0 6.0 1.0 REAL \n", "1 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "2 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "3 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "4 BikePoints_1 12.0 19.0 6.0 1.0 ARTIFICIAL \n", "\n", " Timestamp WeatherIdx \n", "0 2016-05-16 05:41:16.870000128+00:00 20 \n", "1 2016-05-16 05:46:16.870000128+00:00 20 \n", "2 2016-05-16 05:51:16.870000128+00:00 20 \n", "3 2016-05-16 05:56:16.870000128+00:00 21 \n", "4 2016-05-16 06:01:16.870000128+00:00 21 " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "readings.head()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 20\n", "1 20\n", "2 20\n", "3 21\n", "4 21\n", "Name: Timestamp, dtype: int64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "readings['Timestamp'][0:5].apply(lambda val: weather['Timestamp'].index[binarySearch(weather['Timestamp'], val.tz_localize('UTC'))])" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "readings['WeatherIdx'] = readings['Timestamp'].apply(lambda val: weather['Timestamp'].index[binarySearch(weather['Timestamp'], val.tz_localize('UTC'))])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "readings_weather = pd.merge(readings, weather, right_index=True, left_on='WeatherIdx')\n", "readings_weather['DifferenceS'] = (readings_weather['Timestamp_x'] - readings_weather['Timestamp_y']) / pd.np.timedelta64(1, 's')\n", "readings_weather['DifferenceS'] = readings_weather['DifferenceS'].apply(math.fabs)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "readings_weather_view = readings_weather[['Timestamp_x', 'Timestamp_y', 'DifferenceS']]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Timestamp_x</th>\n", " <th>Timestamp_y</th>\n", " <th>DifferenceS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>7076497</th>\n", " <td>2016-06-26 23:56:49.023000064+00:00</td>\n", " <td>2016-06-26 22:50:00+00:00</td>\n", " <td>4009.023</td>\n", " </tr>\n", " <tr>\n", " <th>4700828</th>\n", " <td>2016-06-26 23:56:49.023000064+00:00</td>\n", " <td>2016-06-26 22:50:00+00:00</td>\n", " <td>4009.023</td>\n", " </tr>\n", " <tr>\n", " <th>5160747</th>\n", " <td>2016-06-26 23:56:49.023000064+00:00</td>\n", " <td>2016-06-26 22:50:00+00:00</td>\n", " <td>4009.023</td>\n", " </tr>\n", " <tr>\n", " <th>3048556</th>\n", " <td>2016-06-26 23:56:49.023000064+00:00</td>\n", " <td>2016-06-26 22:50:00+00:00</td>\n", " <td>4009.023</td>\n", " </tr>\n", " <tr>\n", " <th>1016565</th>\n", " <td>2016-06-26 23:56:49.023000064+00:00</td>\n", " <td>2016-06-26 22:50:00+00:00</td>\n", " <td>4009.023</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Timestamp_x Timestamp_y \\\n", "7076497 2016-06-26 23:56:49.023000064+00:00 2016-06-26 22:50:00+00:00 \n", "4700828 2016-06-26 23:56:49.023000064+00:00 2016-06-26 22:50:00+00:00 \n", "5160747 2016-06-26 23:56:49.023000064+00:00 2016-06-26 22:50:00+00:00 \n", "3048556 2016-06-26 23:56:49.023000064+00:00 2016-06-26 22:50:00+00:00 \n", "1016565 2016-06-26 23:56:49.023000064+00:00 2016-06-26 22:50:00+00:00 \n", "\n", " DifferenceS \n", "7076497 4009.023 \n", "4700828 4009.023 \n", "5160747 4009.023 \n", "3048556 4009.023 \n", "1016565 4009.023 " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "readings_weather_view.sort_values(by=['DifferenceS'], ascending=False).head()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>DifferenceS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>9.221613e+06</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>3.518722e+02</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2.386331e+02</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>9.999872e-03</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.507070e+02</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>3.048800e+02</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>5.156200e+02</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>4.009023e+03</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " DifferenceS\n", "count 9.221613e+06\n", "mean 3.518722e+02\n", "std 2.386331e+02\n", "min 9.999872e-03\n", "25% 1.507070e+02\n", "50% 3.048800e+02\n", "75% 5.156200e+02\n", "max 4.009023e+03" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "readings_weather_view.describe()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "readings_weather.rename(columns={'Timestamp_x': 'Timestamp'}, inplace=True)\n", "readings_weather.drop(['Timestamp_y', 'WeatherIdx', 'DifferenceS'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 9221613 entries, 0 to 8696776\n", "Data columns (total 22 columns):\n", "Id object\n", "NbBikes float64\n", "NbDocks float64\n", "NbEmptyDocks float64\n", "NbUnusableDocks float64\n", "Source object\n", "Timestamp datetime64[ns, UTC]\n", "Condition object\n", "DewPt float32\n", "Fog bool\n", "Hail bool\n", "Humidity float32\n", "Pressure float32\n", "Rain bool\n", "Snow bool\n", "Temp float32\n", "Thunder bool\n", "Tornado bool\n", "Visibility float32\n", "WindDirD float32\n", "WindDirE object\n", "WindSpeed float32\n", "dtypes: bool(6), datetime64[ns, UTC](1), float32(7), float64(4), object(4)\n", "memory usage: 1002.6+ MB\n" ] } ], "source": [ "readings_weather.info()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pickle.dump(readings_weather, open(\"data/parsed/readings_weather_filled_dataset.p\", \"wb\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
anodos-ru/catalog
updaters/comptek.ipynb
1
5030
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comptek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Инициализация" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "from django.utils import timezone\n", "\n", "sys.path.append('/home/ubuntu/anodos.ru/anodos/')\n", "os.environ['DJANGO_SETTINGS_MODULE'] = 'anodos.settings'\n", "\n", "from django.core.wsgi import get_wsgi_application\n", "application = get_wsgi_application()\n", "\n", "\n", "import re\n", "import catalog.runner\n", "from catalog.models import *\n", "\n", "\n", "class Runner(catalog.runner.Runner):\n", "\n", " name = 'Comptek'\n", " alias = 'comptek'\n", " url = {\n", " 'start' : 'http://comptek.ru/',\n", " 'login' : 'http://comptek.ru/personal/auth.xhtml',\n", " 'price' : 'http://comptek.ru/',\n", " 'filter' : 'catalog/',\n", " 'unfilter' : 'item/',\n", " 'base' : 'http://comptek.ru'}\n", "\n", " def __init__(self):\n", "\n", " super().__init__()\n", "\n", " self.stock = self.take_stock('stock', 'склад', 3, 10)\n", " self.transit = self.take_stock('transit', 'транзит', 10, 60)\n", " self.on_order = self.take_stock('on-order', 'на заказ', 40, 80)\n", "\n", " self.count = {'product': 0, 'party': 0}\n", "\n", " def run(self):\n", "\n", " import time\n", "\n", " payload = {\n", " 'login' : self.updater.login,\n", " 'password' : self.updater.password}\n", " if self.login(payload):\n", " print('Авторизован.')\n", " else:\n", " print('Не удалось авторизоваться')\n", " return False\n", "\n", " # Заходим на начальную страницу каталога\n", " tree = self.load_html(self.url['price'])\n", "\n", " # TODO Проходим по всем ссылкам\n", " urls = []\n", " for u in tree.xpath('//a/@href'):\n", " if self.url['base'] not in u:\n", " u = self.url['base'] + urls[i]\n", "\n", " \n", " if u not in urls:\n", " urls.append(u)\n", "\n", " i = 0\n", " while i < len(urls):\n", "\n", " # Сслыка на категорию\n", " if self.url['filter'] in urls[i]:\n", " print('Загружаю:', urls[i])\n", "\n", " vendor = Vendor.objects.get_by_key(updater = self.updater, key = url.split('/')[4])\n", " print('Vendor: {}.'.format(vendor))\n", "\n", " if vendor:\n", " tree = self.load_html(url)\n", " print(\"Загружена: {}.\".format(url))\n", "\n", " for u in tree.xpath('//a/@href'):\n", " if not u in urls:\n", " urls.append(u)\n", "\n", " # Парсим таблицу с товарами\n", "# self.parse(tree, vendor)\n", "\n", " # Ждем, чтобы не получить отбой сервера\n", " time.sleep(1)\n", "\n", " else:\n", " print(\"Пропущена: {}.\".format(url))\n", "\n", " else:\n", " url = self.url['base'] + urls[i]\n", " print(\"Пропущена: {}.\".format(url))\n", "\n", " i += 1\n", "\n", " # Чистим партии\n", " Party.objects.clear(stock = self.stock, time = self.start_time)\n", " Party.objects.clear(stock = self.transit, time = self.start_time)\n", " Party.objects.clear(stock = self.on_order, time = self.start_time)\n", "\n", " self.log()\n", "\n", "s = Runner()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.run()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
GLiCom/CorpusAnalysis2016
notebooks/Practica 8.ipynb
1
3657
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Práctica 8: corpora and collocations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ejercicio: completar el siguiente Notebook. Añadir celdas adicionales para pasos intermedios y **mostrar los resultados intermedios**. Podéis añadir celdas de tipo \"Markdown\" para dejar comentarios." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import nltk\n", "#nltk.download('book')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Distribución de part-of-speech " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cuando trabajamos con el corpus `Brown` podemos usar `brown.tagged_words()` para obtener el texto anotado con etiquetas morfosintácticas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. ¿Cuáles son las 30 etiquetas más frecuentes en el corpus?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Haz un gráfico con las frecuencias de las 20 etiquetas más frecuentes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Crea la lista de bigramas de etiquetas del corpus Brown" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. ¿Cuáles son las secuencias de etiquetas más frecuentes (lista y gráfico)?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ranking y métricas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. ¿Cuáles son las palabras que más frecuentemente preceden la palabra \"water\"?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. ¿Y cuáles tienen la asociación más fuerte según la métrica `likelihood_ratio` (y aparecen un mínimo de 5 veces delante de \"water\")?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7. ¿Y qué adjetivos tienen la asociación más fuerte?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
SiggyF/notebooks
sealevelexample.ipynb
1
497117
{ "metadata": { "name": "", "signature": "sha256:2be34651646d672442140d1fc4158c3fe65f4deb31b9a4c3a74a2136aad3fcdc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Sea-level rise \n", "================" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# We need a few libraries\n", "import io\n", "\n", "import numpy as np\n", "import pandas\n", "import requests\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.style\n", "matplotlib.style.use('ggplot')\n", "%matplotlib inline\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with downloading the data from the different tidal guages. We use the 6 main Dutch stations.\n", "\n", "- http://www.psmsl.org/data/obtaining/stations/23.php Den Helder\n", "- http://www.psmsl.org/data/obtaining/stations/32.php IJmuiden\n", "- http://www.psmsl.org/data/obtaining/stations/20.php Vlissingen\n", "- http://www.psmsl.org/data/obtaining/stations/25.php Harlingen\n", "- http://www.psmsl.org/data/obtaining/stations/22.php Hoek van Holland\n", "- http://www.psmsl.org/data/obtaining/stations/24.php Delfzijl" ] }, { "cell_type": "code", "collapsed": false, "input": [ "stations = [\n", " ['Vlissingen', 20, lambda x: x - (6976-46)],\n", " ['Hoek van Holland', 22, lambda x:x - (6994 - 121)],\n", " ['Den Helder', 23, lambda x: x - (6988-42)],\n", " ['Delfzijl', 24, lambda x: x - (6978-155)],\n", " ['Harlingen', 25, lambda x: x - (7036-122)],\n", " ['IJmuiden', 32, lambda x: x - (7033-83)],\n", "]\n", "\n", "# you could use the monthly data\n", "monthly_url = 'http://www.psmsl.org/data/obtaining/rlr.monthly.data/%d.rlrdata'\n", "# and you can use the annual dat\n", "annual_url = 'http://www.psmsl.org/data/obtaining/rlr.annual.data/%d.rlrdata'\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's download them both\n", "monthly_dfs = []\n", "annual_dfs = []\n", "\n", "for station, id, to_local in stations:\n", " f = io.BytesIO(requests.get(monthly_url % (id,)).content)\n", " df = pandas.read_csv(f, sep=';', names=('year', 'waterlevel', 'code', 'another code'))\n", " # add a column with the station name\n", " df['station'] = station\n", " # convert to local coordinate system\n", " df['nap'] = df['waterlevel'].apply(to_local)\n", " # ignore the part before 1890\n", " df = df[df.year >= 1890]\n", " monthly_dfs.append(df)\n", " \n", " f = io.BytesIO(requests.get(annual_url % (id,)).content)\n", " df = pandas.read_csv(f, sep=';', names=('year', 'waterlevel', 'code', 'another code'))\n", " df['station'] = station\n", " df['nap'] = df['waterlevel'].apply(to_local)\n", " df = df[df.year >= 1890]\n", " annual_dfs.append(df) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have downloaded the stations, we can create a new station with the mean. You can use many other techniques, but the mean works just fine here." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# compute the monthly mean\n", "monthly_df = pandas.concat(monthly_dfs)\n", "mean = monthly_df.groupby('year').mean().reset_index()\n", "mean['station'] = 'mean'\n", "monthly_df = pandas.concat([monthly_df, mean[['year', 'waterlevel', 'nap', 'station']]])\n", "monthly_df = monthly_df.reset_index()\n", "# add the year number \n", "monthly_df['year_floor'] = np.floor(monthly_df['year'])\n", "\n", "annual_df = pandas.concat(annual_dfs)\n", "mean = annual_df.groupby('year').mean().reset_index()\n", "mean['station'] = 'mean'\n", "annual_df = pandas.concat([annual_df, mean[['year', 'waterlevel', 'nap', 'station']]])\n", "annual_df = annual_df.reset_index()\n", "\n", "# save the files to json\n", "# add the annual mean also to the monthly means\n", "monthly_df = monthly_df.merge(annual_df[['year', 'nap']], left_on='year_floor', right_on='year', suffixes=['', '_annual'])\n", "monthly_df[['year', 'nap', 'nap_annual', 'station']].to_json('monthly.json', orient='records')\n", "annual_df[['year', 'nap', 'station']].to_json('annual.json', orient='records')\n", "\n", "df = annual_df" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "# in memory file\n", "grouped = df.groupby(['station'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "fig, ax = plt.subplots(figsize=(10,7))\n", "for station, df in grouped:\n", " annual_jitter = np.random.uniform(-0.5, 0.5, size=len(df))\n", " monthly_jitter = np.random.uniform(-0.5*1/12.0, 0.5*1/12.0, size=len(df))\n", " if station == 'mean':\n", " pch = '-'\n", " alpha = 1.0\n", " ax.plot(df['year'] + annual_jitter, df['nap']/10 , pch, alpha=alpha, label=station)\n", " else:\n", " pch = '.'\n", " alpha = 0.5\n", " ax.plot(df['year'] + annual_jitter, df['nap']/10 , pch, alpha=alpha, label=station)\n", " ax.set_ylabel('waterlevel [cm NAP]')\n", " ax.set_xlabel('year')\n", "legend = ax.legend(loc='best')\n", "legend.legendPatch.set_alpha(0.5)\n", "legend.legendPatch.set_facecolor('white')\n", "\n", "fig.savefig('stations.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IyJ0clt/4ctO5dlQVarnStn3rtQnbUDJKgX4QUGSOIAiCeOXpJ1o1bpGtccDvAK6U3aFY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YqLi8nLyyM9PT3g5VdMMmqzMPH5lERFEX72LBaX8/w7QPhQdeZqvcoIUBdioLpry3d5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\ngoICALKzs0lMTKx0rn379t7nG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"text": [ "<matplotlib.figure.Figure at 0x113e00690>" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.api as sm\n", "df = grouped.get_group('mean')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "y = df['waterlevel']\n", "X = df['year']\n", "X = sm.add_constant(X)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "model = sm.OLS(y, X)\n", "results = model.fit()\n", "from IPython.display import display\n", "display(results.summary())" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table class=\"simpletable\">\n", "<caption>OLS Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>waterlevel</td> <th> R-squared: </th> <td> 0.828</td>\n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.827</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 589.0</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Sun, 16 Nov 2014</td> <th> Prob (F-statistic):</th> <td>1.60e-48</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>23:49:03</td> <th> Log-Likelihood: </th> <td> -598.81</td>\n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 124</td> <th> AIC: </th> <td> 1202.</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 122</td> <th> BIC: </th> <td> 1207.</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 3219.4017</td> <td> 149.443</td> <td> 21.543</td> <td> 0.000</td> <td> 2923.564 3515.239</td>\n", "</tr>\n", "<tr>\n", " <th>year</th> <td> 1.8581</td> <td> 0.077</td> <td> 24.269</td> <td> 0.000</td> <td> 1.707 2.010</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td> 3.101</td> <th> Durbin-Watson: </th> <td> 1.544</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> 0.212</td> <th> Jarque-Bera (JB): </th> <td> 2.704</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td>-0.357</td> <th> Prob(JB): </th> <td> 0.259</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 3.120</td> <th> Cond. No. </th> <td>1.06e+05</td>\n", "</tr>\n", "</table>" ], "metadata": {}, "output_type": "display_data", "text": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: waterlevel R-squared: 0.828\n", "Model: OLS Adj. R-squared: 0.827\n", "Method: Least Squares F-statistic: 589.0\n", "Date: Sun, 16 Nov 2014 Prob (F-statistic): 1.60e-48\n", "Time: 23:49:03 Log-Likelihood: -598.81\n", "No. Observations: 124 AIC: 1202.\n", "Df Residuals: 122 BIC: 1207.\n", "Df Model: 1 \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 3219.4017 149.443 21.543 0.000 2923.564 3515.239\n", "year 1.8581 0.077 24.269 0.000 1.707 2.010\n", "==============================================================================\n", "Omnibus: 3.101 Durbin-Watson: 1.544\n", "Prob(Omnibus): 0.212 Jarque-Bera (JB): 2.704\n", "Skew: -0.357 Prob(JB): 0.259\n", "Kurtosis: 3.120 Cond. No. 1.06e+05\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] The condition number is large, 1.06e+05. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "def fit(startyear):\n", " df_selected = df[df['year'] > startyear]\n", " y = df_selected['waterlevel']\n", " X = df_selected['year']\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " display(results.summary())" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html.widgets import interactive" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(fit, startyear=(1890, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table class=\"simpletable\">\n", "<caption>OLS Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>waterlevel</td> <th> R-squared: </th> <td> 0.681</td>\n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.677</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 172.6</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Sun, 16 Nov 2014</td> <th> Prob (F-statistic):</th> <td>8.98e-22</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>23:49:06</td> <th> Log-Likelihood: </th> <td> -403.92</td>\n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 83</td> <th> AIC: </th> <td> 811.8</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 81</td> <th> BIC: </th> <td> 816.7</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 3107.2138</td> <td> 287.403</td> <td> 10.811</td> <td> 0.000</td> <td> 2535.372 3679.056</td>\n", "</tr>\n", "<tr>\n", " <th>year</th> <td> 1.9146</td> <td> 0.146</td> <td> 13.138</td> <td> 0.000</td> <td> 1.625 2.205</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td> 3.114</td> <th> Durbin-Watson: </th> <td> 1.547</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> 0.211</td> <th> Jarque-Bera (JB): </th> <td> 2.643</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td>-0.434</td> <th> Prob(JB): </th> <td> 0.267</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 3.101</td> <th> Cond. No. </th> <td>1.62e+05</td>\n", "</tr>\n", "</table>" ], "metadata": {}, "output_type": "display_data", "text": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: waterlevel R-squared: 0.681\n", "Model: OLS Adj. R-squared: 0.677\n", "Method: Least Squares F-statistic: 172.6\n", "Date: Sun, 16 Nov 2014 Prob (F-statistic): 8.98e-22\n", "Time: 23:49:06 Log-Likelihood: -403.92\n", "No. Observations: 83 AIC: 811.8\n", "Df Residuals: 81 BIC: 816.7\n", "Df Model: 1 \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 3107.2138 287.403 10.811 0.000 2535.372 3679.056\n", "year 1.9146 0.146 13.138 0.000 1.625 2.205\n", "==============================================================================\n", "Omnibus: 3.114 Durbin-Watson: 1.547\n", "Prob(Omnibus): 0.211 Jarque-Bera (JB): 2.643\n", "Skew: -0.434 Prob(JB): 0.267\n", "Kurtosis: 3.101 Cond. No. 1.62e+05\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] The condition number is large, 1.62e+05. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(startyear):\n", " df_selected = df[df['year'] > startyear]\n", " y = df_selected['waterlevel']\n", " X = df_selected['year']\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " fig, ax = plt.subplots()\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.1)\n", " ax.plot(df_selected['year'], results.fittedvalues)\n", " \n", " ax.set_ylim(6400,7300)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x113dd2310>" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "\n", "def plot(startyear):\n", " df_selected = df[df['year'] > startyear]\n", " y = df_selected['waterlevel']\n", " X = df_selected['year']\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " fig, ax = plt.subplots()\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.1)\n", " ax.plot(df_selected['year'], results.fittedvalues)\n", " fit, lower, upper = wls_prediction_std(results)\n", "\n", " plt.fill_between(df_selected['year'], lower, upper, alpha=0.3)\n", " \n", " ax.set_ylim(6400,7300)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.sandbox.regression.predstd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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MQa2zHkMDXIe9Ve16HUVRxPxqLWkojhPBXHmNSriQ9BgqcN3lo7z7zSUuH8uR2UrS3M7f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\nRKSBkoOIiDRQchARkQb/HUC43LhJjxfSAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10c696fd0>" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext rpy2.ipython" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The rpy2.ipython extension is already loaded. To reload it, use:\n", " %reload_ext rpy2.ipython\n" ] } ], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "%Rpush df\n", "%R library('ggplot2')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ "<StrVector - Python:0x114c18998 / R:0x7ff2298824a8>\n", "[str, str, str, ..., str, str, str]" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "def test(startyear):\n", " df_selected = df[df['year']>startyear]\n", " %Rpush df_selected\n", " %R print(ggplot(df, aes(year, waterlevel)) + geom_point(alpha=0.3) + stat_smooth(data=df_selected, method='loess'))\n", "interactive(test, startyear=(1860, 1980, 10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IkZFbhyXt+5KJbmYGo+YK1ndL6zS3fZJ+kwWcpLOhtoiACIhASgiMHj3a6urqDAFu3Tq3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\nQmCC\n" } ], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(startyear):\n", " df_selected = df[(df['year'] >= startyear) & (df['year'] < (startyear + 18.6*2))]\n", " \n", " y = df_selected['waterlevel']\n", " X = np.c_[df_selected['year']-1970, (df_selected['year']-1970)**2]\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " fig, ax = plt.subplots()\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.3)\n", " ax.plot(df_selected['year'], results.fittedvalues, linewidth=5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 5))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x11398bdd0>" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(startyear, n_periods):\n", "\n", " df_selected = df[(df['year'] >= startyear) & (df['year'] < (startyear + 18.6*n_periods))]\n", " \n", " y = df_selected['waterlevel']\n", " X = np.c_[\n", " df_selected['year']-1970, \n", " np.cos(2*np.pi*(df_selected['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df_selected['year']-1970)/18.613)\n", "\n", " ]\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " # e^{i\\theta} = \\cos\\theta + i\\sin\\theta\n", " phase = np.angle(complex(results.params['x2'], results.params['x3']))\n", " ampl = np.sqrt(results.params['x2']**2 + results.params['x3']**2)\n", " \n", " # plot it\n", " fig, ax = plt.subplots()\n", " ax.set_title(\"Phase (rad): %.1f, amplitude: %.1f\\nA(cos): %.1f, B(sin): %.1f\" % (phase, ampl, results.params['x2'], results.params['x3']))\n", " ax.plot(df['year'], df['waterlevel'], '.', alpha=0.3)\n", " ax.plot(df_selected['year'], results.fittedvalues, linewidth=5)\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "interactive(plot, startyear=(1860, 1980, 5), n_periods=(2,5))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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RFGXQMUDWg1QiuVT8dGCbtfY24HhgGfAx4Dlr7Xki8ivgAmBKyN8APB4p3wRM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\niOwCPgx8WUQ6Q/q2HPVMBOIisjQk3QG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"text": [ "<matplotlib.figure.Figure at 0x11408fb10>" ] } ], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "df = annual_df[annual_df['station']=='mean']\n", "hkv = pandas.read_csv('full_output_station_gemiddeld.csv', sep=';')\n", "df = df.merge(hkv[['year', 'U2sin', 'U2cos']], on='year', suffixes=('', '_hkv'))\n", "df = df.reset_index()\n", "y = df['nap']\n", "X = np.c_[\n", " df['year']-1970, \n", " np.cos(2*np.pi*(df['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df['year']-1970)/18.613),\n", " df['U2cos'], \n", " df['U2sin']\n", "]\n", "X = sm.add_constant(X)\n", "model = sm.OLS(y, X)\n", "results = model.fit()\n", "linear = results.params['const'] + results.params['x1'] * (df['year']-1970)\n", "nodal = results.params['x2'] * np.cos(2*np.pi*(df['year']-1970)/18.613) + results.params['x3'] * np.sin(2*np.pi*(df['year']-1970)/18.613)\n", "wind = results.params['x4'] * df['U2cos'] + results.params['x5'] * df['U2sin']\n", "df['linear'] = linear\n", "df['nodal'] = nodal\n", "df['wind'] = wind\n", "df['residual'] = results.resid\n", "df['loess'] = sm.nonparametric.lowess(df['residual'], df['year'], frac=60.0/len(df))[:,1]\n", "df['residual_loess'] = df['residual'] - df['loess']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "df[['year', 'nap', 'station', 'linear', 'nodal', 'residual', 'wind', 'loess', 'residual_loess']].to_json('fit.json', orient='records')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "y = df['nap']\n", "X = np.c_[\n", " np.ones(shape=(len(df),))\n", "]\n", "# compute semipartial correlations\n", "rsquares = []\n", "terms = [df['year']-1970, \n", " np.c_[np.cos(2*np.pi*(df['year']-1970)/18.613), np.sin(2*np.pi*(df['year']-1970)/18.613)],\n", " np.c_[df['U2cos'], df['U2sin']]]\n", "names = ['linear', 'nodal', 'wind']\n", "rsquare = 0\n", "for name, term in zip(names, terms):\n", " X = np.c_[X, term]\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " rsquares.append((name, results.rsquared - rsquare))\n", " rsquare = results.rsquared\n", "rsquares.append(('loess', 1- np.var(df['residual_loess'])/np.var(df['nap'])- rsquare))\n", "rsquare = results.rsquared\n", "rsquares.append(('residual', np.var(df['residual_loess'])/np.var(df['nap'])))\n", "\n", "\n", "import json\n", "json.dump(dict(rsquares), open('rsquares.json', 'w'))\n", "rsquares" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 74, "text": [ "[('linear', 0.82769308510876782),\n", " ('nodal', 0.019216129938318116),\n", " ('wind', 0.043763073036397149),\n", " ('loess', 0.0036103727985814515),\n", " ('residual', 0.10571733911793549)]" ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "a = results.summary(yname='waterlevel', xname=['constant', 'linear', 'nodal cos', 'nodal sin', 'wind cos', 'wind sin'])\n", "#print(a.as_latex())\n", "a" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table class=\"simpletable\">\n", "<caption>OLS Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>waterlevel</td> <th> R-squared: </th> <td> 0.891</td>\n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.886</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 190.6</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Sun, 16 Nov 2014</td> <th> Prob (F-statistic):</th> <td>1.71e-54</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>23:49:24</td> <th> Log-Likelihood: </th> <td> -566.06</td>\n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 123</td> <th> AIC: </th> <td> 1144.</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 117</td> <th> BIC: </th> <td> 1161.</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 5</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>constant</th> <td> -44.6608</td> <td> 6.507</td> <td> -6.863</td> <td> 0.000</td> <td> -57.548 -31.774</td>\n", "</tr>\n", "<tr>\n", " <th>linear</th> <td> 1.7577</td> <td> 0.067</td> <td> 26.278</td> <td> 0.000</td> <td> 1.625 1.890</td>\n", "</tr>\n", "<tr>\n", " <th>nodal cos</th> <td> 3.7984</td> <td> 3.233</td> <td> 1.175</td> <td> 0.242</td> <td> -2.604 10.201</td>\n", "</tr>\n", "<tr>\n", " <th>nodal sin</th> <td> -11.8578</td> <td> 3.138</td> <td> -3.778</td> <td> 0.000</td> <td> -18.073 -5.642</td>\n", "</tr>\n", "<tr>\n", " <th>wind cos</th> <td> 1.2649</td> <td> 0.189</td> <td> 6.694</td> <td> 0.000</td> <td> 0.891 1.639</td>\n", "</tr>\n", "<tr>\n", " <th>wind sin</th> <td> -0.4672</td> <td> 0.266</td> <td> -1.753</td> <td> 0.082</td> <td> -0.995 0.060</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td> 0.139</td> <th> Durbin-Watson: </th> <td> 1.435</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> 0.933</td> <th> Jarque-Bera (JB): </th> <td> 0.311</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td> 0.013</td> <th> Prob(JB): </th> <td> 0.856</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 2.755</td> <th> Cond. No. </th> <td> 122.</td>\n", "</tr>\n", "</table>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 75, "text": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: waterlevel R-squared: 0.891\n", "Model: OLS Adj. R-squared: 0.886\n", "Method: Least Squares F-statistic: 190.6\n", "Date: Sun, 16 Nov 2014 Prob (F-statistic): 1.71e-54\n", "Time: 23:49:24 Log-Likelihood: -566.06\n", "No. Observations: 123 AIC: 1144.\n", "Df Residuals: 117 BIC: 1161.\n", "Df Model: 5 \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "constant -44.6608 6.507 -6.863 0.000 -57.548 -31.774\n", "linear 1.7577 0.067 26.278 0.000 1.625 1.890\n", "nodal cos 3.7984 3.233 1.175 0.242 -2.604 10.201\n", "nodal sin -11.8578 3.138 -3.778 0.000 -18.073 -5.642\n", "wind cos 1.2649 0.189 6.694 0.000 0.891 1.639\n", "wind sin -0.4672 0.266 -1.753 0.082 -0.995 0.060\n", "==============================================================================\n", "Omnibus: 0.139 Durbin-Watson: 1.435\n", "Prob(Omnibus): 0.933 Jarque-Bera (JB): 0.311\n", "Skew: 0.013 Prob(JB): 0.856\n", "Kurtosis: 2.755 Cond. No. 122.\n", "==============================================================================\n", "\"\"\"" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot(degree):\n", " fitted = pandas.DataFrame(dict(fit=results.resid, year=df['year']))\n", " %Rpush fitted\n", " %Rpush degree\n", " %R l = predict(loess(fitted, degree=degree, control=loess.control(surface='direct')), se=TRUE, newdata=data.frame(year=seq(1890,2100)))\n", " l = %Rget l\n", " import pandas.rpy.common as com\n", " l = com.load_data('l')\n", " plt.fill_between(np.arange(1890, 2101), l['fit'] - l['se.fit']*1.96, l['fit'] + l['se.fit']*1.96, alpha=0.5)\n", " plt.plot(np.arange(1890, 2101), l['fit'])\n", " plt.ylim(-200,400)\n", " plt.title('Loess model with degree %d' % degree)\n", " plt.ylabel(\"sea surface height residual ['mm']\")\n", "interactive(plot, degree=(1,2))\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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jEBGRAZX3DpuY2UHAKcB+tN2fwN2v7PgiERFZ9XIlGjN7LvAp4L+A/wF8Pz1/ndZtnkVE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\n0p58ioVAqVhgzXC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"text": [ "<matplotlib.figure.Figure at 0x114d40790>" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "# The broken linear model\n", "y = df['nap']\n", "X = np.c_[\n", " df['year']-1970, \n", " np.cos(2*np.pi*(df['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df['year']-1970)/18.613),\n", " df['U2cos'], \n", " df['U2sin'],\n", " (df['year']-1990 > 0)*(df['year']-1990)\n", "]\n", "X = sm.add_constant(X)\n", "model = sm.OLS(y, X)\n", "fit = model.fit()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "%Rpush df \n", "%R fit <- lm(nap ~ year + I((year > 1990)*(year-1990)) + I(cos((2*pi*year-1970)/18.613)) + I(sin((2*pi*year-1970)/18.613) + U2cos + U2sin), data=df)\n", "%R years <- seq(1890, 2100)\n", "%R pred <- predict(fit, interval=\"confidence\", newdata=data.frame(year=years, U2cos=rep(mean(df$U2cos), length(years)), U2sin=rep(mean(df$U2sin), length(years))))\n", "pred = %Rget pred\n", "%R print(summary(fit))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "\n", "Call:\n", "lm(formula = nap ~ year + I((year > 1990) * (year - 1990)) + \n", " I(cos((2 * pi * year - 1970)/18.613)) + I(sin((2 * pi * year - \n", " 1970)/18.613) + U2cos + U2sin), data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-66.682 -19.736 2.081 19.674 63.102 \n", "\n", "Coefficients:\n", " Estimate Std. Error\n", "(Intercept) -3.382e+03 1.867e+02\n", "year 1.688e+00 9.715e-02\n", "I((year > 1990) * (year - 1990)) 8.555e-01 6.372e-01\n", "I(cos((2 * pi * year - 1970)/18.613)) 5.595e+00 3.709e+00\n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 7.036e-01 1.751e-01\n", " t value Pr(>|t|) \n", "(Intercept) -18.112 < 2e-16 ***\n", "year 17.378 < 2e-16 ***\n", "I((year > 1990) * (year - 1990)) 1.343 0.181947 \n", "I(cos((2 * pi * year - 1970)/18.613)) 1.508 0.134162 \n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 4.018 0.000104 ***\n", "---\n", "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n", "\n", "Residual standard error: 28.58 on 118 degrees of freedom\n", "Multiple R-squared: 0.8528,\tAdjusted R-squared: 0.8478 \n", "F-statistic: 170.9 on 4 and 118 DF, p-value: < 2.2e-16\n", "\n" ] } ], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [ "def compute(split):\n", " %Rpush split\n", " %Rpush df \n", " %R fit <- lm(nap ~ year + I((year > split)*(year-split)) + I(cos((2*pi*year-1970)/18.613)) + I(sin((2*pi*year-1970)/18.613) + U2cos + U2sin), data=df)\n", " %R years <- seq(1890, 2100)\n", " %R pred <- predict(fit, interval=\"confidence\", newdata=data.frame(year=years, U2cos=rep(mean(df$U2cos), length(years)), U2sin=rep(mean(df$U2sin), length(years))))\n", " pred = %Rget pred\n", " %R print(summary(fit))\n", "interactive(compute, split=(1980, 2000))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "\n", "Call:\n", "lm(formula = nap ~ year + I((year > split) * (year - split)) + \n", " I(cos((2 * pi * year - 1970)/18.613)) + I(sin((2 * pi * year - \n", " 1970)/18.613) + U2cos + U2sin), data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-66.682 -19.736 2.081 19.674 63.102 \n", "\n", "Coefficients:\n", " Estimate Std. Error\n", "(Intercept) -3.382e+03 1.867e+02\n", "year 1.688e+00 9.715e-02\n", "I((year > split) * (year - split)) 8.555e-01 6.372e-01\n", "I(cos((2 * pi * year - 1970)/18.613)) 5.595e+00 3.709e+00\n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 7.036e-01 1.751e-01\n", " t value Pr(>|t|) \n", "(Intercept) -18.112 < 2e-16 ***\n", "year 17.378 < 2e-16 ***\n", "I((year > split) * (year - split)) 1.343 0.181947 \n", "I(cos((2 * pi * year - 1970)/18.613)) 1.508 0.134162 \n", "I(sin((2 * pi * year - 1970)/18.613) + U2cos + U2sin) 4.018 0.000104 ***\n", "---\n", "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n", "\n", "Residual standard error: 28.58 on 118 degrees of freedom\n", "Multiple R-squared: 0.8528,\tAdjusted R-squared: 0.8478 \n", "F-statistic: 170.9 on 4 and 118 DF, p-value: < 2.2e-16\n", "\n" ] } ], "prompt_number": 79 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.fill_between(np.arange(1890, 2101), np.array(pred)[:,1], np.array(pred)[:,2], alpha=0.5)\n", "plt.plot(np.arange(1890, 2101), np.array(pred)[:,0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 80, "text": [ "[<matplotlib.lines.Line2D at 0x1140ae490>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1140ae690>" ] } ], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "# The linear model\n", "%Rpush df \n", "%R fit <- lm(nap ~ year + I(cos((2*pi*year-1970)/18.613)) + I(sin((2*pi*year-1970)/18.613)) + U2cos + U2sin, data=df)\n", "%R years <- seq(1890, 2100)\n", "# Padd with means\n", "%R U2cos = df$U2cos\n", "%R U2cos[years>2012] = mean(df$U2cos)\n", "%R U2sin = df$U2cos\n", "%R U2sin[years>2012] = mean(df$U2sin)\n", "%R pred <- predict(fit, interval=\"confidence\", newdata=data.frame(year=years, U2cos=U2cos, U2sin=U2sin))\n", "pred = %Rget pred\n", "%R print(summary(fit))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "\n", "Call:\n", "lm(formula = nap ~ year + I(cos((2 * pi * year - 1970)/18.613)) + \n", " I(sin((2 * pi * year - 1970)/18.613)) + U2cos + U2sin, data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-59.919 -17.607 1.216 15.672 59.747 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|)\n", "(Intercept) -3.507e+03 1.289e+02 -27.207 < 2e-16\n", "year 1.758e+00 6.689e-02 26.278 < 2e-16\n", "I(cos((2 * pi * year - 1970)/18.613)) 3.427e+00 3.233e+00 1.060 0.29120\n", "I(sin((2 * pi * year - 1970)/18.613)) -1.197e+01 3.139e+00 -3.814 0.00022\n", "U2cos 1.265e+00 1.890e-01 6.694 7.94e-10\n", "U2sin -4.672e-01 2.665e-01 -1.753 0.08214\n", " \n", "(Intercept) ***\n", "year ***\n", "I(cos((2 * pi * year - 1970)/18.613)) \n", "I(sin((2 * pi * year - 1970)/18.613)) ***\n", "U2cos ***\n", "U2sin . \n", "---\n", "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n", "\n", "Residual standard error: 24.73 on 117 degrees of freedom\n", "Multiple R-squared: 0.8907,\tAdjusted R-squared: 0.886 \n", "F-statistic: 190.6 on 5 and 117 DF, p-value: < 2.2e-16\n", "\n" ] } ], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "fitted = pandas.DataFrame(dict(fit=results.resid, year=df['year']))\n", "degree = 1\n", "%Rpush fitted\n", "%Rpush degree\n", "%R l = predict(loess(fitted, degree=degree, control=loess.control(surface='direct')), se=TRUE, newdata=data.frame(year=seq(1890,2100)))\n", "l = %Rget l\n", "import pandas.rpy.common as com\n", "l = com.load_data('l')\n", "ci_loess = pandas.DataFrame(data=dict(year=np.arange(1890, 2101), se=l['se.fit'], fit=l['fit'], lwr=l['fit'] - l['se.fit']*1.96, upr=l['fit'] + l['se.fit']*1.96))\n", "\n", "# compute angle\n", "beta = (ci_loess[ci_loess['year'] == 2100].fit.item() - ci_loess[ci_loess['year'] == 2013].fit.item())/(2100-2013)\n", "# compute std error (1.96 -> z 5%, 2 two-tailed)\n", "se = ((ci_loess[ci_loess['year'] == 2100].upr.item() - ci_loess[ci_loess['year'] == 2100].lwr.item()))/1.96/2.0/(2100-2013)\n", "beta, se" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 124, "text": [ "(0.27802282630813135, 0.23436251419913748)" ] } ], "prompt_number": 124 }, { "cell_type": "code", "collapsed": false, "input": [ "fitted = pandas.DataFrame(dict(fit=results.resid, year=df['year']))\n", "degree = 2\n", "%Rpush fitted\n", "%Rpush degree\n", "%R fit <- loess(fitted, degree=degree, control=loess.control(surface='direct'))\n", "%R l <- predict(fit, se=TRUE, newdata=data.frame(year=seq(1890,2100)))\n", "\n", "import pandas.rpy.common as com\n", "l = com.load_data('l')\n", "fit = %Rget fit\n", "\n", "ci_loess2 = pandas.DataFrame(data=dict(year=np.arange(1890, 2101), fit=l['fit'], lwr=l['fit'] - l['se.fit']*1.96, upr=l['fit'] + l['se.fit']*1.96))\n", "#ci_loess = ci_loess.set_index('year')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 114 }, { "cell_type": "code", "collapsed": false, "input": [ "ci_linear = com.load_data(\"pred\")\n", "ci_linear['year'] = np.arange(1890, 2101)\n", "#ci_linear= ci_linear.set_index('year')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "ci = pandas.merge(ci_loess, ci_loess2, on='year', suffixes=('_loess', '_loess2'))\n", "ci = pandas.merge(ci_linear, ci, on='year', suffixes=('_linear', ''))\n", "#ci['fit'].name('fit_linear')\n", "#ci['lwr'].rename('lwr_linear')\n", "#ci['upr'].rename('upr_linear')\n", "ci.rename(columns={'fit':'fit_linear', 'lwr': 'lwr_linear', 'upr': 'upr_linear'}, inplace=True)\n", "ci.to_csv('ci.csv')\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.fill_between(np.arange(1890,2101), ci_linear['lwr'] + ci_loess['lwr'], ci_linear['upr'] + ci_loess['upr'])\n", "plt.plot(np.arange(1890,2101), ci_linear['fit'] + ci_loess['fit'])\n", "plt.plot(np.arange(1890,2101), ci_linear['fit'], color='black')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 86, "text": [ "[<matplotlib.lines.Line2D at 0x114c1b7d0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SWyl2V7E1dRLPFF1EvTltwH3Prd2ANeJnRc6pncuaN76NISWLxOK5w/Ex+nTzohxSjKM7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\n3HhKDi6vB3tKNgBvL/4mv4640bibeWL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"text": [ "<matplotlib.figure.Figure at 0x114c1b110>" ] } ], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.fill_between(np.arange(1890,2101), ci_linear['lwr'] , ci_linear['upr'] )\n", "plt.plot(np.arange(1890,2101), ci_linear['fit'])\n", "plt.fill_between(np.arange(1890,2101), ci_loess['lwr'] , ci_loess['upr'] )\n", "plt.plot(np.arange(1890,2101), ci_loess['fit'])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "[<matplotlib.lines.Line2D at 0x11390aad0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8cYPeqE5nOMnO1hBbGgLpnNz0MRk5iOcVRdGBn6qq+jOgRFXVvm9xO1AywWkQ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\nUkBkgXA0RU80/cHbPZzin19v4l92NnP0QB09zgD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"text": [ "<matplotlib.figure.Figure at 0x110d43ad0>" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "df = annual_df[annual_df['station']=='mean']\n", "hkv = pandas.read_csv('full_output_station_gemiddeld.csv', sep=';')\n", "df = df.merge(hkv[['year', 'U2sin', 'U2cos']], on='year', suffixes=('', '_hkv'))\n", "df = df.reset_index()\n", "y = df['nap']\n", "plt.subplots(figsize=(10,6))\n", "for split in np.arange(1990, 2000):\n", " X = np.c_[\n", " df['year']-1970, \n", " (df['year']-split)*(df['year']>split),\n", " np.cos(2*np.pi*(df['year']-1970)/18.613),\n", " np.sin(2*np.pi*(df['year']-1970)/18.613),\n", " df['U2cos'], \n", " df['U2sin']\n", " ]\n", " X = sm.add_constant(X)\n", " model = sm.OLS(y, X)\n", " results = model.fit()\n", " plt.plot(df['year'], results.fittedvalues, label=str(split), alpha=0.3)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Bd69+DVrougC4fQj+TuP7Ifn00UDs9rt3sZ3jAxZDS/aDyrXXa2QcneJk93o1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\nZaKxxght4vQJql6Id3/4tx3VcXUpTzTiceqRY1sGZwCZvl6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"text": [ "<matplotlib.figure.Figure at 0x110e61d90>" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "# Based on the records of Amsterdam/Den Helder\n", "df = pandas.read_csv('/Users/baart_f/src/oetpython/applications/sealevel//sealevel/static/data/extra/9000.csv')\n", "\n", "# Create a plot\n", "fig, ax1 = plt.subplots(1,1, figsize=(10,6))\n", "# compute a trend\n", "def trend(index):\n", " # Select based on index\n", " df_selected = df.ix[index]\n", " # Create a linear model\n", " y = df_selected['waterlevel']\n", " X = np.c_[\n", " df_selected['year.month'],\n", " np.cos(2*np.pi*(df_selected['year.month']-1970)/18.613),\n", " np.sin(2*np.pi*(df_selected['year.month']-1970)/18.613)\n", " ]\n", " X = statsmodels.regression.linear_model.add_constant(X)\n", " model= statsmodels.regression.linear_model.OLS(y, X)\n", " # fit the model\n", " result = model.fit()\n", " # ignore if we have missings\n", " if np.isnan(df_selected['year.month']).any():\n", " return np.nan\n", " # create a plot\n", " ax1.plot(df_selected['year.month'], result.fittedvalues, 'k-', alpha=0.2, linewidth=3)\n", " # return the trend\n", " return result.params['x1']\n", "df['waterlevel'][df['waterlevel'] == -99999] = np.nan\n", "ax1.plot(df['year.month'], df['waterlevel'], '.')\n", "ax1.set_xlabel('tijd [jaar]')\n", "ax1.set_ylabel('zeespiegelniveau [m boven NAP]')\n", "df = df[df['year.month']<1890]\n", "betas = pandas.rolling_apply(np.array(df.index, dtype='int'),20, trend)\n", "fig.savefig('amsterdam.pdf')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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dxpgWy7JeAlxpjLmnmgMUkZ2p4qUwypBKpQCYm5tjdHSUyclJEokE/qufwvzA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5mPlddrgC81HP8WVRAPl+0xe8v+QTYxdtB86bSXYuwabQMeiogPUf1H6GfKfnOvPa3iB+ZfO74W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\nSZJUY/8PQ9q0y5hMjtcAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x110dbd5d0>" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# standard error of beta_1 and confidence range \n", "np.std(betas[~np.isnan(betas)]), 1.96 * 2 * np.std(betas[~np.isnan(betas)]) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "(0.17157195469765205, 0.67256206241479599)" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# the same for the whole of amsterdam\n", "resp = requests.get('http://www.psmsl.org/data/longrecords/amsterdam.sea.level')\n", "f = io.BytesIO(resp.content)\n", "df = pandas.read_fwf(f, widths=(10,20), names=('year.month', 'waterlevel'), skiprows=1)\n", "df['waterlevel']/=10.0\n", "# Create a plot\n", "fig, ax1 = plt.subplots(1,1, figsize=(10,6))\n", "ax1.plot(df['year.month'], df['waterlevel'], '.')\n", "ax1.set_xlabel('tijd [jaar]')\n", "ax1.set_ylabel('zeespiegelniveau [m boven NAP]')\n", "\n", "betas = pandas.rolling_apply(np.array(df.index, dtype='int'),20, trend)\n", "np.std(betas[~np.isnan(betas)]), 1.96 * 2 * np.std(betas[~np.isnan(betas)]) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "(0.17411785038363373, 0.68254197350384416)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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40jbrRVRVvR14H/DvAT/wxY0ISgghhBDXXrFYJJlMsri4SLNUwmUZhGoO9u/cAbx61Ncr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pVoNIpOp5P1XkJdK5VKxGIxXnvtNTQaDW63m3g8LgOB4Y0IATGC7HQ6aLVanE4nbre7p6wn4Umy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\nZW5ujlKpRK1Wk7mFq6urpFIpq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"text": [ "<matplotlib.figure.Figure at 0x110dbd390>" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 } ], "metadata": {} } ] }
gpl-3.0