Created
February 11, 2015 04:02
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| { | |
| "metadata": { | |
| "name": "", | |
| "signature": "sha256:58f4d4401af89fc3bd76e575df705b223c13e0e42e721323429046367ad5afca" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Homework - Feb.11" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import numpy as np\n", | |
| "from numpy.random import rand,choice\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from numpy.random import randn\n", | |
| "\n", | |
| "# get n values from a Normal distribution N(mu, sigma)\n", | |
| "# using randn function - value = sigma*rand() + mu\n", | |
| "# here sigma = 1 and mu = 1\n", | |
| "# get_sample - function to generate the numbers\n", | |
| "\n", | |
| "def get_sample(n,m,sig,y):\n", | |
| " y=np.zeros(n)\n", | |
| " for i in np.arange(n):\n", | |
| " y[i] = sig*randn() + m\n", | |
| " return y" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 3 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "xvals = np.arange(100)/100. - 0.5\n", | |
| "par1 = 0.8\n", | |
| "par2 = -0.1\n", | |
| "yvals = par1 * xvals + par2\n", | |
| "plt.plot(xvals, yvals)\n", | |
| "y = np.zeros(len(xvals))\n", | |
| "for i in np.arange(100): y[i] = get_sample(1, yvals[i], 0.07, y[i]) \n", | |
| "yerr = 0.075*np.ones(100)\n", | |
| "#print len(yerr), len(xvals), len(y)\n", | |
| "plt.errorbar(xvals, y, yerr, fmt='o')\n", | |
| "j = np.arange(100)\n", | |
| "np.random.shuffle(j)\n", | |
| "xj = xvals[j]\n", | |
| "yj = y[j]\n", | |
| "#plt.plot(xj, yj,'o')\n", | |
| "plt.plot(xj[:22],yj[:22],'o')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 5, | |
| "text": [ | |
| "[<matplotlib.lines.Line2D at 0x106993f90>]" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
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rR2cOk3dkas1IKrdp8FctIlYCMrcnEHvw67YFvlhSScHX8N3yo7h06i+o2rSq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\n7wnVAAAAAElFTkSuQmCC\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x1068ec110>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 5 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from astropy.io import ascii\n", | |
| "from astropy.table import Table\n", | |
| "data= Table([xj[:23], yj[:23]],names = ['xi','yi'])\n", | |
| "data['xi'].format = '%.3f'\n", | |
| "data['yi'].format = '%.3f'\n", | |
| "#data={'xi': xj[:23], 'yi':yj[:23]}\n", | |
| "#ascii.write(data, 'data.dat')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 6 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "plt.plot(data['xi'],data['yi'],'o')\n", | |
| "plt.plot(data['xi'],0.5*data['xi']+0)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 7, | |
| "text": [ | |
| "[<matplotlib.lines.Line2D at 0x1075a9190>]" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x106875d10>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 7 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "x = get_sample(2e4,2,0.1, y)\n", | |
| "num_bins = 40\n", | |
| "n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.5)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x1075b5210>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 8 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 8 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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