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1 | 1 | { |
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2 | 2 | "metadata": { |
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3 | 3 | "name": "example_nb_tour" |
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4 | 4 | }, |
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5 | 5 | "nbformat": 3, |
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6 | 6 | "nbformat_minor": 0, |
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7 | 7 | "worksheets": [ |
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8 | 8 | { |
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9 | 9 | "cells": [ |
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10 | 10 | { |
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11 | 11 | "cell_type": "markdown", |
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12 | 12 | "metadata": { |
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13 | 13 | "slideshow": { |
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14 | 14 | "slide_type": "-" |
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15 | 15 | } |
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16 | 16 | }, |
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17 | 17 | "source": [ |
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18 | 18 | "# A brief tour of the IPython notebook" |
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19 | 19 | ] |
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20 | 20 | }, |
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21 | 21 | { |
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22 | 22 | "cell_type": "markdown", |
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23 | 23 | "metadata": { |
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24 | 24 | "slideshow": { |
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25 | 25 | "slide_type": "fragment" |
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26 | 26 | } |
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27 | 27 | }, |
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28 | 28 | "source": [ |
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29 | 29 | "Rendered by nbconvert using [Reveal.js](http://lab.hakim.se/reveal-js)!\n", |
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30 | 30 | "\n", |
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31 | 31 | "by Dami\u00e1n Avila" |
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32 | 32 | ] |
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33 | 33 | }, |
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34 | 34 | { |
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35 | 35 | "cell_type": "markdown", |
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36 | 36 | "metadata": { |
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37 | 37 | "slideshow": { |
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38 | 38 | "slide_type": "slide" |
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39 | 39 | } |
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40 | 40 | }, |
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41 | 41 | "source": [ |
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42 | 42 | "This document will give you a brief tour of the capabilities of the IPython notebook. \n", |
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43 | 43 | "You can view its contents by scrolling around, or execute each cell by typing `Shift-Enter`.\n", |
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44 | 44 | "After you conclude this brief high-level tour, you should read the accompanying notebook \n", |
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45 | 45 | "titled `01_notebook_introduction`, which takes a more step-by-step approach to the features of the\n", |
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46 | 46 | "system. \n", |
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47 | 47 | "The rest of the notebooks in this directory illustrate various other aspects and \n", |
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48 | 48 | "capabilities of the IPython notebook; some of them may require additional libraries to be executed." |
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49 | 49 | ] |
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50 | 50 | }, |
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51 | 51 | { |
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52 | 52 | "cell_type": "markdown", |
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53 | 53 | "metadata": { |
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54 | 54 | "slideshow": { |
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55 | 55 | "slide_type": "notes" |
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56 | 56 | } |
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57 | 57 | }, |
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58 | 58 | "source": [ |
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59 | 59 | "**NOTE:** This notebook *must* be run from its own directory, so you must ``cd``\n", |
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60 | 60 | "to this directory and then start the notebook, but do *not* use the ``--notebook-dir``\n", |
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61 | 61 | "option to run it from another location.\n", |
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62 | 62 | "\n", |
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63 | 63 | "The first thing you need to know is that you are still controlling the same old IPython you're used to,\n", |
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64 | 64 | "so things like shell aliases and magic commands still work:" |
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65 | 65 | ] |
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66 | 66 | }, |
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67 | 67 | { |
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68 | 68 | "cell_type": "code", |
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69 | 69 | "collapsed": false, |
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70 | 70 | "input": [ |
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71 | 71 | "pwd" |
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72 | 72 | ], |
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73 | 73 | "language": "python", |
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74 | 74 | "metadata": { |
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75 | 75 | "slideshow": { |
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76 | 76 | "slide_type": "header_slide" |
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77 | 77 | } |
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78 | 78 | }, |
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79 | 79 | "outputs": [ |
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80 | 80 | { |
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81 | 81 | "output_type": "pyout", |
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82 | 82 | "prompt_number": 1, |
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83 | 83 | "text": [ |
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84 | 84 | "u'/home/damian/Desarrollos/ipython_mtaui_slide'" |
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85 | 85 | ] |
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86 | 86 | } |
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87 | 87 | ], |
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88 | 88 | "prompt_number": 1 |
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89 | 89 | }, |
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90 | 90 | { |
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91 | 91 | "cell_type": "code", |
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92 | 92 | "collapsed": false, |
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93 | 93 | "input": [ |
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94 | 94 | "ls" |
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95 | 95 | ], |
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96 | 96 | "language": "python", |
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97 | 97 | "metadata": { |
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98 | 98 | "slideshow": { |
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99 | 99 | "slide_type": "slide" |
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100 | 100 | } |
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101 | 101 | }, |
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102 | 102 | "outputs": [ |
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103 | 103 | { |
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104 | 104 | "output_type": "stream", |
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105 | 105 | "stream": "stdout", |
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106 | 106 | "text": [ |
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107 | 107 | "COPYING.txt \u001b[0m\u001b[01;34mIPython\u001b[0m/ \u001b[01;35mpython-logo.svg\u001b[0m setupbase.py \u001b[01;32msetup.py\u001b[0m*\r\n", |
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108 | 108 | "\u001b[01;34mdocs\u001b[0m/ \u001b[01;32mipython.py\u001b[0m* README.rst \u001b[01;32msetupegg.py\u001b[0m* \u001b[01;34mtools\u001b[0m/\r\n", |
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109 | 109 | "example_nb_tour.ipynb MANIFEST.in \u001b[01;34mscripts\u001b[0m/ \u001b[01;34msetupext\u001b[0m/ tox.ini\r\n" |
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110 | 110 | ] |
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111 | 111 | } |
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112 | 112 | ], |
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113 | 113 | "prompt_number": 2 |
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114 | 114 | }, |
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115 | 115 | { |
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116 | 116 | "cell_type": "code", |
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117 | 117 | "collapsed": false, |
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118 | 118 | "input": [ |
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119 | 119 | "message = 'The IPython notebook is great!'\n", |
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120 | 120 | "# note: the echo command does not run on Windows, it's a unix command.\n", |
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121 | 121 | "!echo $message" |
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122 | 122 | ], |
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123 | 123 | "language": "python", |
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124 | 124 | "metadata": { |
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125 | 125 | "slideshow": { |
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126 | 126 | "slide_type": "slide" |
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127 | 127 | } |
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128 | 128 | }, |
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129 | 129 | "outputs": [ |
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130 | 130 | { |
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131 | 131 | "output_type": "stream", |
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132 | 132 | "stream": "stdout", |
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133 | 133 | "text": [ |
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134 | 134 | "The IPython notebook is great!\r\n" |
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135 | 135 | ] |
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136 | 136 | } |
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137 | 137 | ], |
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138 | 138 | "prompt_number": 3 |
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139 | 139 | }, |
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140 | 140 | { |
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141 | 141 | "cell_type": "heading", |
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142 | 142 | "level": 2, |
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143 | 143 | "metadata": { |
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144 | 144 | "slideshow": { |
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145 | 145 | "slide_type": "header_slide" |
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146 | 146 | } |
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147 | 147 | }, |
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148 | 148 | "source": [ |
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149 | 149 | "Plots with matplotlib" |
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150 | 150 | ] |
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151 | 151 | }, |
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152 | 152 | { |
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153 | 153 | "cell_type": "markdown", |
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154 | 154 | "metadata": { |
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155 | 155 | "slideshow": { |
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156 | 156 | "slide_type": "notes" |
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157 | 157 | } |
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158 | 158 | }, |
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159 | 159 | "source": [ |
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160 | 160 | "This is a speaker note in the middle of the slide..." |
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161 | 161 | ] |
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162 | 162 | }, |
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163 | 163 | { |
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164 | 164 | "cell_type": "markdown", |
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165 | 165 | "metadata": { |
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166 | 166 | "slideshow": { |
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167 | 167 | "slide_type": "notes" |
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168 | 168 | } |
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169 | 169 | }, |
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170 | 170 | "source": [ |
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171 | 171 | "And also another speaker note..." |
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172 | 172 | ] |
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173 | 173 | }, |
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174 | 174 | { |
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175 | 175 | "cell_type": "markdown", |
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176 | 176 | "metadata": {}, |
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177 | 177 | "source": [ |
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178 | 178 | "IPython adds an 'inline' matplotlib backend,\n", |
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179 | 179 | "which embeds any matplotlib figures into the notebook." |
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180 | 180 | ] |
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181 | 181 | }, |
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182 | 182 | { |
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183 | 183 | "cell_type": "code", |
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184 | 184 | "collapsed": false, |
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185 | 185 | "input": [ |
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186 | 186 | "%pylab inline" |
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187 | 187 | ], |
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188 | 188 | "language": "python", |
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189 | 189 | "metadata": { |
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190 | 190 | "slideshow": { |
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191 | 191 | "slide_type": "-" |
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192 | 192 | } |
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193 | 193 | }, |
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194 | 194 | "outputs": [ |
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195 | 195 | { |
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196 | 196 | "output_type": "stream", |
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197 | 197 | "stream": "stdout", |
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198 | 198 | "text": [ |
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199 | 199 | "\n", |
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200 | 200 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", |
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201 | 201 | "For more information, type 'help(pylab)'.\n" |
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202 | 202 | ] |
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203 | 203 | } |
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204 | 204 | ], |
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205 | 205 | "prompt_number": 4 |
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206 | 206 | }, |
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207 | 207 | { |
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208 | 208 | "cell_type": "code", |
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209 | 209 | "collapsed": false, |
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210 | 210 | "input": [ |
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211 | 211 | "x = linspace(0, 3*pi, 500)\n", |
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212 | 212 | "plot(x, sin(x**2))\n", |
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213 | 213 | "title('A simple chirp');" |
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214 | 214 | ], |
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215 | 215 | "language": "python", |
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216 | 216 | "metadata": { |
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217 | 217 | "slideshow": { |
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218 | 218 | "slide_type": "slide" |
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219 | 219 | } |
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220 | 220 | }, |
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221 | 221 | "outputs": [ |
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222 | 222 | { |
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223 | 223 | "output_type": "display_data", |
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224 | 224 | "png": 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WNd1TNijL0o53HN4nA9n+o4AU6c+bNw979uxBbW0tTp48ifXr16O6unrQNY2N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\nMILw/wFa1cBzw0Gh4AAAAABJRU5ErkJggg==\n", |
|
|
225 | 225 | "text": [ |
|
|
226 | 226 | "<matplotlib.figure.Figure at 0xa70a0cc>" |
|
|
227 | 227 | ] |
|
|
228 | 228 | } |
|
|
229 | 229 | ], |
|
|
230 | 230 | "prompt_number": 5 |
|
|
231 | 231 | }, |
|
|
232 | 232 | { |
|
|
233 | 233 | "cell_type": "markdown", |
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|
234 | 234 | "metadata": { |
|
|
235 | 235 | "slideshow": { |
|
|
236 | 236 | "slide_type": "slide" |
|
|
237 | 237 | } |
|
|
238 | 238 | }, |
|
|
239 | 239 | "source": [ |
|
|
240 | 240 | "You can paste blocks of input with prompt markers, such as those from\n", |
|
|
241 | 241 | "[the official Python tutorial](http://docs.python.org/tutorial/interpreter.html#interactive-mode)" |
|
|
242 | 242 | ] |
|
|
243 | 243 | }, |
|
|
244 | 244 | { |
|
|
245 | 245 | "cell_type": "code", |
|
|
246 | 246 | "collapsed": false, |
|
|
247 | 247 | "input": [ |
|
|
248 | 248 | ">>> the_world_is_flat = 1\n", |
|
|
249 | 249 | ">>> if the_world_is_flat:\n", |
|
|
250 | 250 | "... print \"Be careful not to fall off!\"" |
|
|
251 | 251 | ], |
|
|
252 | 252 | "language": "python", |
|
|
253 | 253 | "metadata": {}, |
|
|
254 | 254 | "outputs": [ |
|
|
255 | 255 | { |
|
|
256 | 256 | "output_type": "stream", |
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|
257 | 257 | "stream": "stdout", |
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|
258 | 258 | "text": [ |
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|
259 | 259 | "Be careful not to fall off!\n" |
|
|
260 | 260 | ] |
|
|
261 | 261 | } |
|
|
262 | 262 | ], |
|
|
263 | 263 | "prompt_number": 6 |
|
|
264 | 264 | }, |
|
|
265 | 265 | { |
|
|
266 | 266 | "cell_type": "markdown", |
|
|
267 | 267 | "metadata": { |
|
|
268 | 268 | "slideshow": { |
|
|
269 | 269 | "slide_type": "slide" |
|
|
270 | 270 | } |
|
|
271 | 271 | }, |
|
|
272 | 272 | "source": [ |
|
|
273 | 273 | "Errors are shown in informative ways:" |
|
|
274 | 274 | ] |
|
|
275 | 275 | }, |
|
|
276 | 276 | { |
|
|
277 | 277 | "cell_type": "code", |
|
|
278 | 278 | "collapsed": false, |
|
|
279 | 279 | "input": [ |
|
|
280 | 280 | "%run non_existent_file" |
|
|
281 | 281 | ], |
|
|
282 | 282 | "language": "python", |
|
|
283 | 283 | "metadata": { |
|
|
284 | 284 | "slideshow": { |
|
|
285 | 285 | "slide_type": "fragment" |
|
|
286 | 286 | } |
|
|
287 | 287 | }, |
|
|
288 | 288 | "outputs": [ |
|
|
289 | 289 | { |
|
|
290 | 290 | "output_type": "stream", |
|
|
291 | 291 | "stream": "stderr", |
|
|
292 | 292 | "text": [ |
|
|
293 | 293 | "ERROR: File `u'non_existent_file.py'` not found.\n" |
|
|
294 | 294 | ] |
|
|
295 | 295 | } |
|
|
296 | 296 | ], |
|
|
297 | 297 | "prompt_number": 7 |
|
|
298 | 298 | }, |
|
|
299 | 299 | { |
|
|
300 | 300 | "cell_type": "code", |
|
|
301 | 301 | "collapsed": false, |
|
|
302 | 302 | "input": [ |
|
|
303 | 303 | "x = 1\n", |
|
|
304 | 304 | "y = 4\n", |
|
|
305 | 305 | "z = y/(1-x)" |
|
|
306 | 306 | ], |
|
|
307 | 307 | "language": "python", |
|
|
308 | 308 | "metadata": { |
|
|
309 | 309 | "slideshow": { |
|
|
310 | 310 | "slide_type": "slide" |
|
|
311 | 311 | } |
|
|
312 | 312 | }, |
|
|
313 | 313 | "outputs": [ |
|
|
314 | 314 | { |
|
|
315 | 315 | "ename": "ZeroDivisionError", |
|
|
316 | 316 | "evalue": "integer division or modulo by zero", |
|
|
317 | 317 | "output_type": "pyerr", |
|
|
318 | 318 | "traceback": [ |
|
|
319 | 319 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", |
|
|
320 | 320 | "\u001b[1;32m<ipython-input-8-dc39888fd1d2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", |
|
|
321 | 321 | "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" |
|
|
322 | 322 | ] |
|
|
323 | 323 | } |
|
|
324 | 324 | ], |
|
|
325 | 325 | "prompt_number": 8 |
|
|
326 | 326 | }, |
|
|
327 | 327 | { |
|
|
328 | 328 | "cell_type": "markdown", |
|
|
329 | 329 | "metadata": { |
|
|
330 | 330 | "slideshow": { |
|
|
331 | 331 | "slide_type": "slide" |
|
|
332 | 332 | } |
|
|
333 | 333 | }, |
|
|
334 | 334 | "source": [ |
|
|
335 | 335 | "When IPython needs to display additional information (such as providing details on an object via `x?`\n", |
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336 | 336 | "it will automatically invoke a pager at the bottom of the screen:" |
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337 | 337 | ] |
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338 | 338 | }, |
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339 | 339 | { |
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340 | 340 | "cell_type": "code", |
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341 | 341 | "collapsed": false, |
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342 | 342 | "input": [ |
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343 | 343 | "magic" |
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344 | 344 | ], |
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345 | 345 | "language": "python", |
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346 | 346 | "metadata": { |
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347 | 347 | "slideshow": { |
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348 | 348 | "slide_type": "fragment" |
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349 | 349 | } |
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350 | 350 | }, |
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351 | 351 | "outputs": [], |
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352 | 352 | "prompt_number": 9 |
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353 | 353 | }, |
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354 | 354 | { |
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355 | 355 | "cell_type": "markdown", |
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356 | 356 | "metadata": { |
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357 | 357 | "slideshow": { |
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358 | 358 | "slide_type": "header_slide" |
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359 | 359 | } |
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360 | 360 | }, |
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361 | 361 | "source": [ |
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362 | 362 | "## Non-blocking output of kernel\n", |
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363 | 363 | "\n", |
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364 | 364 | "If you execute the next cell, you will see the output arriving as it is generated, not all at the end." |
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365 | 365 | ] |
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366 | 366 | }, |
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367 | 367 | { |
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368 | 368 | "cell_type": "code", |
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369 | 369 | "collapsed": false, |
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370 | 370 | "input": [ |
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371 | 371 | "import time, sys\n", |
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372 | 372 | "for i in range(8):\n", |
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373 | 373 | " print i,\n", |
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374 | 374 | " time.sleep(0.5)" |
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375 | 375 | ], |
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376 | 376 | "language": "python", |
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377 | 377 | "metadata": {}, |
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378 | 378 | "outputs": [ |
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379 | 379 | { |
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380 | 380 | "output_type": "stream", |
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381 | 381 | "stream": "stdout", |
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382 | 382 | "text": [ |
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383 | 383 | "0 " |
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384 | 384 | ] |
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385 | 385 | }, |
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386 | 386 | { |
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387 | 387 | "output_type": "stream", |
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388 | 388 | "stream": "stdout", |
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389 | 389 | "text": [ |
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390 | 390 | "1 " |
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391 | 391 | ] |
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392 | 392 | }, |
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393 | 393 | { |
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394 | 394 | "output_type": "stream", |
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395 | 395 | "stream": "stdout", |
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396 | 396 | "text": [ |
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397 | 397 | "2 " |
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398 | 398 | ] |
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399 | 399 | }, |
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400 | 400 | { |
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401 | 401 | "output_type": "stream", |
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402 | 402 | "stream": "stdout", |
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403 | 403 | "text": [ |
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404 | 404 | "3 " |
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405 | 405 | ] |
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406 | 406 | }, |
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407 | 407 | { |
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408 | 408 | "output_type": "stream", |
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409 | 409 | "stream": "stdout", |
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410 | 410 | "text": [ |
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411 | 411 | "4 " |
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412 | 412 | ] |
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413 | 413 | }, |
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414 | 414 | { |
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415 | 415 | "output_type": "stream", |
|
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416 | 416 | "stream": "stdout", |
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417 | 417 | "text": [ |
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418 | 418 | "5 " |
|
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419 | 419 | ] |
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420 | 420 | }, |
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421 | 421 | { |
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422 | 422 | "output_type": "stream", |
|
|
423 | 423 | "stream": "stdout", |
|
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424 | 424 | "text": [ |
|
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425 | 425 | "6 " |
|
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426 | 426 | ] |
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|
427 | 427 | }, |
|
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428 | 428 | { |
|
|
429 | 429 | "output_type": "stream", |
|
|
430 | 430 | "stream": "stdout", |
|
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431 | 431 | "text": [ |
|
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432 | 432 | "7\n" |
|
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433 | 433 | ] |
|
|
434 | 434 | } |
|
|
435 | 435 | ], |
|
|
436 | 436 | "prompt_number": 10 |
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437 | 437 | }, |
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438 | 438 | { |
|
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439 | 439 | "cell_type": "markdown", |
|
|
440 | 440 | "metadata": { |
|
|
441 | 441 | "slideshow": { |
|
|
442 | 442 | "slide_type": "slide" |
|
|
443 | 443 | } |
|
|
444 | 444 | }, |
|
|
445 | 445 | "source": [ |
|
|
446 | 446 | "## Clean crash and restart\n", |
|
|
447 | 447 | "\n", |
|
|
448 | 448 | "We call the low-level system libc.time routine with the wrong argument via\n", |
|
|
449 | 449 | "ctypes to segfault the Python interpreter:" |
|
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450 | 450 | ] |
|
|
451 | 451 | }, |
|
|
452 | 452 | { |
|
|
453 | 453 | "cell_type": "code", |
|
|
454 | 454 | "collapsed": false, |
|
|
455 | 455 | "input": [ |
|
|
456 | 456 | "import sys\n", |
|
|
457 | 457 | "from ctypes import CDLL\n", |
|
|
458 | 458 | "# This will crash a Linux or Mac system; equivalent calls can be made on Windows\n", |
|
|
459 | 459 | "dll = 'dylib' if sys.platform == 'darwin' else '.so.6'\n", |
|
|
460 | 460 | "libc = CDLL(\"libc.%s\" % dll) \n", |
|
|
461 | 461 | "libc.time(-1) # BOOM!!" |
|
|
462 | 462 | ], |
|
|
463 | 463 | "language": "python", |
|
|
464 | 464 | "metadata": {}, |
|
|
465 | 465 | "outputs": [], |
|
|
466 | 466 | "prompt_number": "*" |
|
|
467 | 467 | }, |
|
|
468 | 468 | { |
|
|
469 | 469 | "cell_type": "markdown", |
|
|
470 | 470 | "metadata": { |
|
|
471 | 471 | "slideshow": { |
|
|
472 | 472 | "slide_type": "header_slide" |
|
|
473 | 473 | } |
|
|
474 | 474 | }, |
|
|
475 | 475 | "source": [ |
|
|
476 | 476 | "## Markdown cells can contain formatted text and code\n", |
|
|
477 | 477 | "\n", |
|
|
478 | 478 | "You can *italicize*, **boldface**\n", |
|
|
479 | 479 | "\n", |
|
|
480 | 480 | "* build\n", |
|
|
481 | 481 | "* lists" |
|
|
482 | 482 | ] |
|
|
483 | 483 | }, |
|
|
484 | 484 | { |
|
|
485 | 485 | "cell_type": "markdown", |
|
|
486 | 486 | "metadata": { |
|
|
487 | 487 | "slideshow": { |
|
|
488 | 488 | "slide_type": "slide" |
|
|
489 | 489 | } |
|
|
490 | 490 | }, |
|
|
491 | 491 | "source": [ |
|
|
492 | 492 | "and embed code meant for illustration instead of execution in Python:\n", |
|
|
493 | 493 | "\n", |
|
|
494 | 494 | " def f(x):\n", |
|
|
495 | 495 | " \"\"\"a docstring\"\"\"\n", |
|
|
496 | 496 | " return x**2\n", |
|
|
497 | 497 | "\n", |
|
|
498 | 498 | "or other languages:\n", |
|
|
499 | 499 | "\n", |
|
|
500 | 500 | " if (i=0; i<n; i++) {\n", |
|
|
501 | 501 | " printf(\"hello %d\\n\", i);\n", |
|
|
502 | 502 | " x += 4;\n", |
|
|
503 | 503 | " }" |
|
|
504 | 504 | ] |
|
|
505 | 505 | }, |
|
|
506 | 506 | { |
|
|
507 | 507 | "cell_type": "markdown", |
|
|
508 | 508 | "metadata": { |
|
|
509 | 509 | "slideshow": { |
|
|
510 | 510 | "slide_type": "slide" |
|
|
511 | 511 | } |
|
|
512 | 512 | }, |
|
|
513 | 513 | "source": [ |
|
|
514 | 514 | "Courtesy of MathJax, you can include mathematical expressions both inline: \n", |
|
|
515 | 515 | "$e^{i\\pi} + 1 = 0$ and displayed:\n", |
|
|
516 | 516 | "\n", |
|
|
517 | 517 | "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$" |
|
|
518 | 518 | ] |
|
|
519 | 519 | }, |
|
|
520 | 520 | { |
|
|
521 | 521 | "cell_type": "markdown", |
|
|
522 | 522 | "metadata": { |
|
|
523 | 523 | "slideshow": { |
|
|
524 | 524 | "slide_type": "header_slide" |
|
|
525 | 525 | } |
|
|
526 | 526 | }, |
|
|
527 | 527 | "source": [ |
|
|
528 | 528 | "## Rich displays: include anyting a browser can show\n", |
|
|
529 | 529 | "\n", |
|
|
530 | 530 | "Note that we have an actual protocol for this, see the `display_protocol` notebook for further details." |
|
|
531 | 531 | ] |
|
|
532 | 532 | }, |
|
|
533 | 533 | { |
|
|
534 | 534 | "cell_type": "markdown", |
|
|
535 | 535 | "metadata": { |
|
|
536 | 536 | "slideshow": { |
|
|
537 | 537 | "slide_type": "slide" |
|
|
538 | 538 | } |
|
|
539 | 539 | }, |
|
|
540 | 540 | "source": [ |
|
|
541 | 541 | "### Images" |
|
|
542 | 542 | ] |
|
|
543 | 543 | }, |
|
|
544 | 544 | { |
|
|
545 | 545 | "cell_type": "code", |
|
|
546 | 546 | "collapsed": false, |
|
|
547 | 547 | "input": [ |
|
|
548 | 548 | "from IPython.display import Image\n", |
|
|
549 | 549 | "Image(filename='../../source/_static/logo.png')" |
|
|
550 | 550 | ], |
|
|
551 | 551 | "language": "python", |
|
|
552 | 552 | "metadata": { |
|
|
553 | 553 | "slideshow": { |
|
|
554 | 554 | "slide_type": "skip" |
|
|
555 | 555 | } |
|
|
556 | 556 | }, |
|
|
557 | 557 | "outputs": [ |
|
|
558 | 558 | { |
|
|
559 | 559 | "ename": "IOError", |
|
|
560 | 560 | "evalue": "[Errno 2] No such file or directory: u'../../source/_static/logo.png'", |
|
|
561 | 561 | "output_type": "pyerr", |
|
|
562 | 562 | "traceback": [ |
|
|
563 | 563 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)", |
|
|
564 | 564 | "\u001b[1;32m<ipython-input-11-d52b796e1601>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mImage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'../../source/_static/logo.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", |
|
|
565 | 565 | "\u001b[1;32m/home/damian/Desarrollos/ipython_mtaui_slide/IPython/core/display.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, url, filename, format, embed, width, height)\u001b[0m\n\u001b[0;32m 495\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 496\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 497\u001b[1;33m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 499\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", |
|
|
566 | 566 | "\u001b[1;32m/home/damian/Desarrollos/ipython_mtaui_slide/IPython/core/display.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, url, filename)\u001b[0m\n\u001b[0;32m 263\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 264\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0municode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreload\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 266\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", |
|
|
567 | 567 | "\u001b[1;32m/home/damian/Desarrollos/ipython_mtaui_slide/IPython/core/display.pyc\u001b[0m in \u001b[0;36mreload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[1;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 501\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreload\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 503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 504\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_repr_html_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", |
|
|
568 | 568 | "\u001b[1;32m/home/damian/Desarrollos/ipython_mtaui_slide/IPython/core/display.pyc\u001b[0m in \u001b[0;36mreload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 268\u001b[0m \u001b[1;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 269\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 270\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 271\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 272\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murl\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", |
|
|
569 | 569 | "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: u'../../source/_static/logo.png'" |
|
|
570 | 570 | ] |
|
|
571 | 571 | } |
|
|
572 | 572 | ], |
|
|
573 | 573 | "prompt_number": 11 |
|
|
574 | 574 | }, |
|
|
575 | 575 | { |
|
|
576 | 576 | "cell_type": "markdown", |
|
|
577 | 577 | "metadata": { |
|
|
578 | 578 | "slideshow": { |
|
|
579 | 579 | "slide_type": "slide" |
|
|
580 | 580 | } |
|
|
581 | 581 | }, |
|
|
582 | 582 | "source": [ |
|
|
583 | 583 | "An image can also be displayed from raw data or a url" |
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584 | 584 | ] |
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585 | 585 | }, |
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586 | 586 | { |
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587 | 587 | "cell_type": "code", |
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588 | 588 | "collapsed": false, |
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589 | 589 | "input": [ |
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590 | 590 | "Image(url='http://python.org/images/python-logo.gif')" |
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591 | 591 | ], |
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592 | 592 | "language": "python", |
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593 | 593 | "metadata": { |
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594 | 594 | "slideshow": { |
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595 | 595 | "slide_type": "-" |
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596 | 596 | } |
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597 | 597 | }, |
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598 | 598 | "outputs": [ |
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599 | 599 | { |
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600 | 600 | "html": [ |
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601 | 601 | "<img src=\"http://python.org/images/python-logo.gif\"/>" |
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602 | 602 | ], |
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603 | 603 | "output_type": "pyout", |
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604 | 604 | "prompt_number": 12, |
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605 | 605 | "text": [ |
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606 | 606 | "<IPython.core.display.Image at 0xa7e68ac>" |
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607 | 607 | ] |
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608 | 608 | } |
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609 | 609 | ], |
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610 | 610 | "prompt_number": 12 |
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611 | 611 | }, |
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612 | 612 | { |
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613 | 613 | "cell_type": "markdown", |
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614 | 614 | "metadata": { |
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615 | 615 | "slideshow": { |
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616 | 616 | "slide_type": "skip" |
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617 | 617 | } |
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618 | 618 | }, |
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619 | 619 | "source": [ |
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620 | 620 | "SVG images are also supported out of the box (since modern browsers do a good job of rendering them):" |
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621 | 621 | ] |
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622 | 622 | }, |
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623 | 623 | { |
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624 | 624 | "cell_type": "code", |
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625 | 625 | "collapsed": false, |
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626 | 626 | "input": [ |
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627 | 627 | "from IPython.display import SVG\n", |
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628 | 628 | "SVG(filename='python-logo.svg')" |
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629 | 629 | ], |
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630 | 630 | "language": "python", |
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631 | 631 | "metadata": { |
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632 | 632 | "slideshow": { |
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633 | 633 | "slide_type": "skip" |
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634 | 634 | } |
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635 | 635 | }, |
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636 | 636 | "outputs": [ |
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637 | 637 | { |
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638 | 638 | "output_type": "pyout", |
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639 | 639 | "prompt_number": 13, |
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640 | 640 | "svg": [ |
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642 | 642 | " <metadata id=\"metadata2193\">\n", |
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643 | 643 | " <rdf:RDF>\n", |
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644 | 644 | " <cc:Work rdf:about=\"\">\n", |
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645 | 645 | " <dc:format>image/svg+xml</dc:format>\n", |
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646 | 646 | " <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n", |
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647 | 647 | " </cc:Work>\n", |
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704 | 704 | "<IPython.core.display.SVG at 0xa7e6e6c>" |
|
|
705 | 705 | ] |
|
|
706 | 706 | } |
|
|
707 | 707 | ], |
|
|
708 | 708 | "prompt_number": 13 |
|
|
709 | 709 | }, |
|
|
710 | 710 | { |
|
|
711 | 711 | "cell_type": "markdown", |
|
|
712 | 712 | "metadata": { |
|
|
713 | 713 | "slideshow": { |
|
|
714 | 714 | "slide_type": "header_slide" |
|
|
715 | 715 | } |
|
|
716 | 716 | }, |
|
|
717 | 717 | "source": [ |
|
|
718 | 718 | "#### Embedded vs Non-embedded Images" |
|
|
719 | 719 | ] |
|
|
720 | 720 | }, |
|
|
721 | 721 | { |
|
|
722 | 722 | "cell_type": "markdown", |
|
|
723 | 723 | "metadata": {}, |
|
|
724 | 724 | "source": [ |
|
|
725 | 725 | "As of IPython 0.13, images are embedded by default for compatibility with QtConsole, and the ability to still be displayed offline.\n", |
|
|
726 | 726 | "\n", |
|
|
727 | 727 | "Let's look at the differences:" |
|
|
728 | 728 | ] |
|
|
729 | 729 | }, |
|
|
730 | 730 | { |
|
|
731 | 731 | "cell_type": "code", |
|
|
732 | 732 | "collapsed": false, |
|
|
733 | 733 | "input": [ |
|
|
734 | 734 | "# by default Image data are embedded\n", |
|
|
735 | 735 | "Embed = Image( 'http://scienceview.berkeley.edu/view/images/newview.jpg')\n", |
|
|
736 | 736 | "\n", |
|
|
737 | 737 | "# if kwarg `url` is given, the embedding is assumed to be false\n", |
|
|
738 | 738 | "SoftLinked = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg')\n", |
|
|
739 | 739 | "\n", |
|
|
740 | 740 | "# In each case, embed can be specified explicitly with the `embed` kwarg\n", |
|
|
741 | 741 | "# ForceEmbed = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg', embed=True)" |
|
|
742 | 742 | ], |
|
|
743 | 743 | "language": "python", |
|
|
744 | 744 | "metadata": { |
|
|
745 | 745 | "slideshow": { |
|
|
746 | 746 | "slide_type": "slide" |
|
|
747 | 747 | } |
|
|
748 | 748 | }, |
|
|
749 | 749 | "outputs": [], |
|
|
750 | 750 | "prompt_number": 14 |
|
|
751 | 751 | }, |
|
|
752 | 752 | { |
|
|
753 | 753 | "cell_type": "markdown", |
|
|
754 | 754 | "metadata": { |
|
|
755 | 755 | "slideshow": { |
|
|
756 | 756 | "slide_type": "skip" |
|
|
757 | 757 | } |
|
|
758 | 758 | }, |
|
|
759 | 759 | "source": [ |
|
|
760 | 760 | "Today's image from a webcam at Berkeley, (at the time I created this notebook). This should also work in the Qtconsole.\n", |
|
|
761 | 761 | "Drawback is that the saved notebook will be larger, but the image will still be present offline." |
|
|
762 | 762 | ] |
|
|
763 | 763 | }, |
|
|
764 | 764 | { |
|
|
765 | 765 | "cell_type": "code", |
|
|
766 | 766 | "collapsed": false, |
|
|
767 | 767 | "input": [ |
|
|
768 | 768 | "Embed" |
|
|
769 | 769 | ], |
|
|
770 | 770 | "language": "python", |
|
|
771 | 771 | "metadata": { |
|
|
772 | 772 | "slideshow": { |
|
|
773 | 773 | "slide_type": "slide" |
|
|
774 | 774 | } |
|
|
775 | 775 | }, |
|
|
776 | 776 | "outputs": [ |
|
|
777 | 777 | { |
|
|
778 | 778 | "jpeg": 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M0AiO6uO2eamEEtKc3pABA2lWTM5A+3+KAiDHeSaFCYAHP9qAiTBn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779 | 779 | "output_type": "pyout", |
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780 | 780 | "prompt_number": 15, |
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781 | 781 | "text": [ |
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782 | 782 | "<IPython.core.display.Image at 0xa81892c>" |
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783 | 783 | ] |
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784 | 784 | } |
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785 | 785 | ], |
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786 | 786 | "prompt_number": 15 |
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787 | 787 | }, |
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788 | 788 | { |
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789 | 789 | "cell_type": "markdown", |
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790 | 790 | "metadata": { |
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791 | 791 | "slideshow": { |
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792 | 792 | "slide_type": "skip" |
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793 | 793 | } |
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794 | 794 | }, |
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|
795 | 795 | "source": [ |
|
|
796 | 796 | "Today's image from same webcam at Berkeley, (refreshed every minutes, if you reload the notebook), visible only with an active internet connexion, that should be different from the previous one. This will not work on Qtconsole.\n", |
|
|
797 | 797 | "Notebook saved with this kind of image will be lighter and always reflect the current version of the source, but the image won't display offline." |
|
|
798 | 798 | ] |
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|
799 | 799 | }, |
|
|
800 | 800 | { |
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|
801 | 801 | "cell_type": "code", |
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|
802 | 802 | "collapsed": false, |
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803 | 803 | "input": [ |
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804 | 804 | "SoftLinked" |
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805 | 805 | ], |
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|
806 | 806 | "language": "python", |
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|
807 | 807 | "metadata": { |
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808 | 808 | "slideshow": { |
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|
809 | 809 | "slide_type": "slide" |
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|
810 | 810 | } |
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|
811 | 811 | }, |
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|
812 | 812 | "outputs": [ |
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813 | 813 | { |
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|
814 | 814 | "html": [ |
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|
815 | 815 | "<img src=\"http://scienceview.berkeley.edu/view/images/newview.jpg\"/>" |
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816 | 816 | ], |
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817 | 817 | "output_type": "pyout", |
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818 | 818 | "prompt_number": 16, |
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819 | 819 | "text": [ |
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820 | 820 | "<IPython.core.display.Image at 0xa818b4c>" |
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821 | 821 | ] |
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822 | 822 | } |
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823 | 823 | ], |
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824 | 824 | "prompt_number": 16 |
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825 | 825 | }, |
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826 | 826 | { |
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827 | 827 | "cell_type": "markdown", |
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828 | 828 | "metadata": { |
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829 | 829 | "slideshow": { |
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830 | 830 | "slide_type": "skip" |
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|
831 | 831 | } |
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|
832 | 832 | }, |
|
|
833 | 833 | "source": [ |
|
|
834 | 834 | "Of course, if you re-run the all notebook, the two images will be the same again." |
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|
835 | 835 | ] |
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|
836 | 836 | }, |
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837 | 837 | { |
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838 | 838 | "cell_type": "markdown", |
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|
839 | 839 | "metadata": { |
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840 | 840 | "slideshow": { |
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841 | 841 | "slide_type": "header_slide" |
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|
842 | 842 | } |
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843 | 843 | }, |
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844 | 844 | "source": [ |
|
|
845 | 845 | "### Video" |
|
|
846 | 846 | ] |
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|
847 | 847 | }, |
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848 | 848 | { |
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849 | 849 | "cell_type": "markdown", |
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850 | 850 | "metadata": { |
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851 | 851 | "slideshow": { |
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852 | 852 | "slide_type": "-" |
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|
853 | 853 | } |
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|
854 | 854 | }, |
|
|
855 | 855 | "source": [ |
|
|
856 | 856 | "And more exotic objects can also be displayed, as long as their representation supports \n", |
|
|
857 | 857 | "the IPython display protocol.\n", |
|
|
858 | 858 | "\n", |
|
|
859 | 859 | "For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other\n", |
|
|
860 | 860 | "hosted content is trivial):" |
|
|
861 | 861 | ] |
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|
862 | 862 | }, |
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|
863 | 863 | { |
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|
864 | 864 | "cell_type": "code", |
|
|
865 | 865 | "collapsed": false, |
|
|
866 | 866 | "input": [ |
|
|
867 | 867 | "from IPython.display import YouTubeVideo\n", |
|
|
868 | 868 | "# a talk about IPython at Sage Days at U. Washington, Seattle.\n", |
|
|
869 | 869 | "# Video credit: William Stein.\n", |
|
|
870 | 870 | "YouTubeVideo('1j_HxD4iLn8')" |
|
|
871 | 871 | ], |
|
|
872 | 872 | "language": "python", |
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|
873 | 873 | "metadata": { |
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|
874 | 874 | "slideshow": { |
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|
875 | 875 | "slide_type": "slide" |
|
|
876 | 876 | } |
|
|
877 | 877 | }, |
|
|
878 | 878 | "outputs": [ |
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|
879 | 879 | { |
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|
880 | 880 | "html": [ |
|
|
881 | 881 | "\n", |
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|
882 | 882 | " <iframe\n", |
|
|
883 | 883 | " width=\"400\"\n", |
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|
884 | 884 | " height=\"300\"\n", |
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|
885 | 885 | " src=\"http://www.youtube.com/embed/1j_HxD4iLn8\"\n", |
|
|
886 | 886 | " frameborder=\"0\"\n", |
|
|
887 | 887 | " allowfullscreen\n", |
|
|
888 | 888 | " ></iframe>\n", |
|
|
889 | 889 | " " |
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|
890 | 890 | ], |
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891 | 891 | "output_type": "pyout", |
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|
892 | 892 | "prompt_number": 17, |
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|
893 | 893 | "text": [ |
|
|
894 | 894 | "<IPython.lib.display.YouTubeVideo at 0xa81856c>" |
|
|
895 | 895 | ] |
|
|
896 | 896 | } |
|
|
897 | 897 | ], |
|
|
898 | 898 | "prompt_number": 17 |
|
|
899 | 899 | }, |
|
|
900 | 900 | { |
|
|
901 | 901 | "cell_type": "markdown", |
|
|
902 | 902 | "metadata": { |
|
|
903 | 903 | "slideshow": { |
|
|
904 | 904 | "slide_type": "skip" |
|
|
905 | 905 | } |
|
|
906 | 906 | }, |
|
|
907 | 907 | "source": [ |
|
|
908 | 908 | "Using the nascent video capabilities of modern browsers, you may also be able to display local\n", |
|
|
909 | 909 | "videos. At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n", |
|
|
910 | 910 | "we will continue testing this and looking for ways to make it more robust. \n", |
|
|
911 | 911 | "\n", |
|
|
912 | 912 | "The following cell loads a local file called `animation.m4v`, encodes the raw video as base64 for http\n", |
|
|
913 | 913 | "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control\n", |
|
|
914 | 914 | "bar at the bottom with a play/pause button and a location slider." |
|
|
915 | 915 | ] |
|
|
916 | 916 | }, |
|
|
917 | 917 | { |
|
|
918 | 918 | "cell_type": "code", |
|
|
919 | 919 | "collapsed": false, |
|
|
920 | 920 | "input": [ |
|
|
921 | 921 | "from IPython.display import HTML\n", |
|
|
922 | 922 | "video = open(\"animation.m4v\", \"rb\").read()\n", |
|
|
923 | 923 | "video_encoded = video.encode(\"base64\")\n", |
|
|
924 | 924 | "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n", |
|
|
925 | 925 | "HTML(data=video_tag)" |
|
|
926 | 926 | ], |
|
|
927 | 927 | "language": "python", |
|
|
928 | 928 | "metadata": { |
|
|
929 | 929 | "slideshow": { |
|
|
930 | 930 | "slide_type": "skip" |
|
|
931 | 931 | } |
|
|
932 | 932 | }, |
|
|
933 | 933 | "outputs": [ |
|
|
934 | 934 | { |
|
|
935 | 935 | "ename": "IOError", |
|
|
936 | 936 | "evalue": "[Errno 2] No such file or directory: 'animation.m4v'", |
|
|
937 | 937 | "output_type": "pyerr", |
|
|
938 | 938 | "traceback": [ |
|
|
939 | 939 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)", |
|
|
940 | 940 | "\u001b[1;32m<ipython-input-18-8b8f5414a141>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mHTML\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mvideo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"animation.m4v\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\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 3\u001b[0m \u001b[0mvideo_encoded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvideo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"base64\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mvideo_tag\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvideo_encoded\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mHTML\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvideo_tag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", |
|
|
941 | 941 | "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'animation.m4v'" |
|
|
942 | 942 | ] |
|
|
943 | 943 | } |
|
|
944 | 944 | ], |
|
|
945 | 945 | "prompt_number": 18 |
|
|
946 | 946 | }, |
|
|
947 | 947 | { |
|
|
948 | 948 | "cell_type": "markdown", |
|
|
949 | 949 | "metadata": { |
|
|
950 | 950 | "slideshow": { |
|
|
951 | 951 | "slide_type": "header_slide" |
|
|
952 | 952 | } |
|
|
953 | 953 | }, |
|
|
954 | 954 | "source": [ |
|
|
955 | 955 | "## Local Files\n", |
|
|
956 | 956 | "\n", |
|
|
957 | 957 | "The above examples embed images and video from the notebook filesystem in the output\n", |
|
|
958 | 958 | "areas of code cells. It is also possible to request these files directly in markdown cells\n", |
|
|
959 | 959 | "if they reside in the notebook directory via relative urls prefixed with `files/`:\n", |
|
|
960 | 960 | "\n", |
|
|
961 | 961 | " files/[subdirectory/]<filename>" |
|
|
962 | 962 | ] |
|
|
963 | 963 | }, |
|
|
964 | 964 | { |
|
|
965 | 965 | "cell_type": "markdown", |
|
|
966 | 966 | "metadata": { |
|
|
967 | 967 | "slideshow": { |
|
|
968 |
"slide_type": "s |
|
|
|
968 | "slide_type": "skip" | |
|
|
969 | 969 | } |
|
|
970 | 970 | }, |
|
|
971 | 971 | "source": [ |
|
|
972 | 972 | "For example, in the example notebook folder, we have the Python logo, addressed as:\n", |
|
|
973 | 973 | "\n", |
|
|
974 | 974 | " <img src=\"files/python-logo.svg\" />\n", |
|
|
975 | 975 | "\n", |
|
|
976 | 976 | "<img src=\"/files/python-logo.svg\" />" |
|
|
977 | 977 | ] |
|
|
978 | 978 | }, |
|
|
979 | 979 | { |
|
|
980 | 980 | "cell_type": "markdown", |
|
|
981 | 981 | "metadata": { |
|
|
982 | 982 | "slideshow": { |
|
|
983 | 983 | "slide_type": "skip" |
|
|
984 | 984 | } |
|
|
985 | 985 | }, |
|
|
986 | 986 | "source": [ |
|
|
987 | 987 | "and a video with the HTML5 video tag:\n", |
|
|
988 | 988 | "\n", |
|
|
989 | 989 | " <video controls src=\"files/animation.m4v\" />\n", |
|
|
990 | 990 | "\n", |
|
|
991 | 991 | "<video controls src=\"/files/animation.m4v\" />\n", |
|
|
992 | 992 | "\n", |
|
|
993 | 993 | "These do not embed the data into the notebook file,\n", |
|
|
994 | 994 | "and require that the files exist when you are viewing the notebook.\n", |
|
|
995 | 995 | "\n", |
|
|
996 | 996 | "### Security of local files\n", |
|
|
997 | 997 | "\n", |
|
|
998 | 998 | "Note that this means that the IPython notebook server also acts as a generic file server\n", |
|
|
999 | 999 | "for files inside the same tree as your notebooks. Access is not granted outside the\n", |
|
|
1000 | 1000 | "notebook folder so you have strict control over what files are visible, but for this\n", |
|
|
1001 | 1001 | "reason it is highly recommended that you do not run the notebook server with a notebook\n", |
|
|
1002 | 1002 | "directory at a high level in your filesystem (e.g. your home directory).\n", |
|
|
1003 | 1003 | "\n", |
|
|
1004 | 1004 | "When you run the notebook in a password-protected manner, local file access is restricted\n", |
|
|
1005 | 1005 | "to authenticated users unless read-only views are active." |
|
|
1006 | 1006 | ] |
|
|
1007 | 1007 | }, |
|
|
1008 | 1008 | { |
|
|
1009 | 1009 | "cell_type": "markdown", |
|
|
1010 | 1010 | "metadata": { |
|
|
1011 | 1011 | "slideshow": { |
|
|
1012 | 1012 | "slide_type": "header_slide" |
|
|
1013 | 1013 | } |
|
|
1014 | 1014 | }, |
|
|
1015 | 1015 | "source": [ |
|
|
1016 | 1016 | "### External sites\n", |
|
|
1017 | 1017 | "\n", |
|
|
1018 | 1018 | "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n", |
|
|
1019 | 1019 | "page for mobile users:" |
|
|
1020 | 1020 | ] |
|
|
1021 | 1021 | }, |
|
|
1022 | 1022 | { |
|
|
1023 | 1023 | "cell_type": "code", |
|
|
1024 | 1024 | "collapsed": false, |
|
|
1025 | 1025 | "input": [ |
|
|
1026 | 1026 | "HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')" |
|
|
1027 | 1027 | ], |
|
|
1028 | 1028 | "language": "python", |
|
|
1029 | 1029 | "metadata": { |
|
|
1030 | 1030 | "slideshow": { |
|
|
1031 | 1031 | "slide_type": "slide" |
|
|
1032 | 1032 | } |
|
|
1033 | 1033 | }, |
|
|
1034 | 1034 | "outputs": [ |
|
|
1035 | 1035 | { |
|
|
1036 | 1036 | "html": [ |
|
|
1037 | 1037 | "<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>" |
|
|
1038 | 1038 | ], |
|
|
1039 | 1039 | "output_type": "pyout", |
|
|
1040 | 1040 | "prompt_number": 19, |
|
|
1041 | 1041 | "text": [ |
|
|
1042 | 1042 | "<IPython.core.display.HTML at 0xa71412c>" |
|
|
1043 | 1043 | ] |
|
|
1044 | 1044 | } |
|
|
1045 | 1045 | ], |
|
|
1046 | 1046 | "prompt_number": 19 |
|
|
1047 | 1047 | }, |
|
|
1048 | 1048 | { |
|
|
1049 | 1049 | "cell_type": "markdown", |
|
|
1050 | 1050 | "metadata": { |
|
|
1051 | 1051 | "slideshow": { |
|
|
1052 | 1052 | "slide_type": "header_slide" |
|
|
1053 | 1053 | } |
|
|
1054 | 1054 | }, |
|
|
1055 | 1055 | "source": [ |
|
|
1056 | 1056 | "### Mathematics\n", |
|
|
1057 | 1057 | "\n", |
|
|
1058 | 1058 | "And we also support the display of mathematical expressions typeset in LaTeX, which is rendered\n", |
|
|
1059 | 1059 | "in the browser thanks to the [MathJax library](http://mathjax.org). \n", |
|
|
1060 | 1060 | "\n", |
|
|
1061 | 1061 | "Note that this is *different* from the above examples. Above we were typing mathematical expressions\n", |
|
|
1062 | 1062 | "in Markdown cells (along with normal text) and letting the browser render them; now we are displaying\n", |
|
|
1063 | 1063 | "the output of a Python computation as a LaTeX expression wrapped by the `Math()` object so the browser\n", |
|
|
1064 | 1064 | "renders it. The `Math` object will add the needed LaTeX delimiters (`$$`) if they are not provided:" |
|
|
1065 | 1065 | ] |
|
|
1066 | 1066 | }, |
|
|
1067 | 1067 | { |
|
|
1068 | 1068 | "cell_type": "code", |
|
|
1069 | 1069 | "collapsed": false, |
|
|
1070 | 1070 | "input": [ |
|
|
1071 | 1071 | "from IPython.display import Math\n", |
|
|
1072 | 1072 | "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')" |
|
|
1073 | 1073 | ], |
|
|
1074 | 1074 | "language": "python", |
|
|
1075 | 1075 | "metadata": { |
|
|
1076 | 1076 | "slideshow": { |
|
|
1077 | 1077 | "slide_type": "slide" |
|
|
1078 | 1078 | } |
|
|
1079 | 1079 | }, |
|
|
1080 | 1080 | "outputs": [ |
|
|
1081 | 1081 | { |
|
|
1082 | 1082 | "latex": [ |
|
|
1083 | 1083 | "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$" |
|
|
1084 | 1084 | ], |
|
|
1085 | 1085 | "output_type": "pyout", |
|
|
1086 | 1086 | "prompt_number": 20, |
|
|
1087 | 1087 | "text": [ |
|
|
1088 | 1088 | "<IPython.core.display.Math at 0xa71406c>" |
|
|
1089 | 1089 | ] |
|
|
1090 | 1090 | } |
|
|
1091 | 1091 | ], |
|
|
1092 | 1092 | "prompt_number": 20 |
|
|
1093 | 1093 | }, |
|
|
1094 | 1094 | { |
|
|
1095 | 1095 | "cell_type": "markdown", |
|
|
1096 | 1096 | "metadata": { |
|
|
1097 | 1097 | "slideshow": { |
|
|
1098 | 1098 | "slide_type": "slide" |
|
|
1099 | 1099 | } |
|
|
1100 | 1100 | }, |
|
|
1101 | 1101 | "source": [ |
|
|
1102 | 1102 | "With the `Latex` class, you have to include the delimiters yourself. This allows you to use other LaTeX modes such as `eqnarray`:" |
|
|
1103 | 1103 | ] |
|
|
1104 | 1104 | }, |
|
|
1105 | 1105 | { |
|
|
1106 | 1106 | "cell_type": "code", |
|
|
1107 | 1107 | "collapsed": false, |
|
|
1108 | 1108 | "input": [ |
|
|
1109 | 1109 | "from IPython.display import Latex\n", |
|
|
1110 | 1110 | "Latex(r\"\"\"\\begin{eqnarray}\n", |
|
|
1111 | 1111 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
|
1112 | 1112 | "\\end{eqnarray}\"\"\")" |
|
|
1113 | 1113 | ], |
|
|
1114 | 1114 | "language": "python", |
|
|
1115 | 1115 | "metadata": {}, |
|
|
1116 | 1116 | "outputs": [ |
|
|
1117 | 1117 | { |
|
|
1118 | 1118 | "latex": [ |
|
|
1119 | 1119 | "\\begin{eqnarray}\n", |
|
|
1120 | 1120 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
|
1121 | 1121 | "\\end{eqnarray}" |
|
|
1122 | 1122 | ], |
|
|
1123 | 1123 | "output_type": "pyout", |
|
|
1124 | 1124 | "prompt_number": 21, |
|
|
1125 | 1125 | "text": [ |
|
|
1126 | 1126 | "<IPython.core.display.Latex at 0xa71404c>" |
|
|
1127 | 1127 | ] |
|
|
1128 | 1128 | } |
|
|
1129 | 1129 | ], |
|
|
1130 | 1130 | "prompt_number": 21 |
|
|
1131 | 1131 | }, |
|
|
1132 | 1132 | { |
|
|
1133 | 1133 | "cell_type": "markdown", |
|
|
1134 | 1134 | "metadata": { |
|
|
1135 | 1135 | "slideshow": { |
|
|
1136 | 1136 | "slide_type": "slide" |
|
|
1137 | 1137 | } |
|
|
1138 | 1138 | }, |
|
|
1139 | 1139 | "source": [ |
|
|
1140 | 1140 | "Or you can enter latex directly with the `%%latex` cell magic:" |
|
|
1141 | 1141 | ] |
|
|
1142 | 1142 | }, |
|
|
1143 | 1143 | { |
|
|
1144 | 1144 | "cell_type": "code", |
|
|
1145 | 1145 | "collapsed": false, |
|
|
1146 | 1146 | "input": [ |
|
|
1147 | 1147 | "%%latex\n", |
|
|
1148 | 1148 | "\\begin{aligned}\n", |
|
|
1149 | 1149 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
|
1150 | 1150 | "\\end{aligned}" |
|
|
1151 | 1151 | ], |
|
|
1152 | 1152 | "language": "python", |
|
|
1153 | 1153 | "metadata": {}, |
|
|
1154 | 1154 | "outputs": [ |
|
|
1155 | 1155 | { |
|
|
1156 | 1156 | "latex": [ |
|
|
1157 | 1157 | "\\begin{aligned}\n", |
|
|
1158 | 1158 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
|
1159 | 1159 | "\\end{aligned}" |
|
|
1160 | 1160 | ], |
|
|
1161 | 1161 | "output_type": "display_data", |
|
|
1162 | 1162 | "text": [ |
|
|
1163 | 1163 | "<IPython.core.display.Latex at 0xa71472c>" |
|
|
1164 | 1164 | ] |
|
|
1165 | 1165 | } |
|
|
1166 | 1166 | ], |
|
|
1167 | 1167 | "prompt_number": 22 |
|
|
1168 | 1168 | }, |
|
|
1169 | 1169 | { |
|
|
1170 | 1170 | "cell_type": "markdown", |
|
|
1171 | 1171 | "metadata": { |
|
|
1172 | 1172 | "slideshow": { |
|
|
1173 | 1173 | "slide_type": "skip" |
|
|
1174 | 1174 | } |
|
|
1175 | 1175 | }, |
|
|
1176 | 1176 | "source": [ |
|
|
1177 | 1177 | "There is also a `%%javascript` cell magic for running javascript directly,\n", |
|
|
1178 | 1178 | "and `%%svg` for manually entering SVG content." |
|
|
1179 | 1179 | ] |
|
|
1180 | 1180 | }, |
|
|
1181 | 1181 | { |
|
|
1182 | 1182 | "cell_type": "markdown", |
|
|
1183 | 1183 | "metadata": { |
|
|
1184 | 1184 | "slideshow": { |
|
|
1185 | 1185 | "slide_type": "header_slide" |
|
|
1186 | 1186 | } |
|
|
1187 | 1187 | }, |
|
|
1188 | 1188 | "source": [ |
|
|
1189 | 1189 | "# Loading external codes\n", |
|
|
1190 | 1190 | "* Drag and drop a ``.py`` in the dashboard\n", |
|
|
1191 | 1191 | "* Use ``%load`` with any local or remote url: [the Matplotlib Gallery!](http://matplotlib.sourceforge.net/gallery.html)\n", |
|
|
1192 | 1192 | "\n", |
|
|
1193 | 1193 | "In this notebook we've kept the output saved so you can see the result, but you should run the next\n", |
|
|
1194 | 1194 | "cell yourself (with an active internet connection)." |
|
|
1195 | 1195 | ] |
|
|
1196 | 1196 | }, |
|
|
1197 | 1197 | { |
|
|
1198 | 1198 | "cell_type": "markdown", |
|
|
1199 | 1199 | "metadata": { |
|
|
1200 | 1200 | "slideshow": { |
|
|
1201 | 1201 | "slide_type": "slide" |
|
|
1202 | 1202 | } |
|
|
1203 | 1203 | }, |
|
|
1204 | 1204 | "source": [ |
|
|
1205 | 1205 | "Let's make sure we have pylab again, in case we have restarted the kernel due to the crash demo above" |
|
|
1206 | 1206 | ] |
|
|
1207 | 1207 | }, |
|
|
1208 | 1208 | { |
|
|
1209 | 1209 | "cell_type": "code", |
|
|
1210 | 1210 | "collapsed": false, |
|
|
1211 | 1211 | "input": [ |
|
|
1212 | 1212 | "%pylab inline" |
|
|
1213 | 1213 | ], |
|
|
1214 | 1214 | "language": "python", |
|
|
1215 | 1215 | "metadata": {}, |
|
|
1216 | 1216 | "outputs": [ |
|
|
1217 | 1217 | { |
|
|
1218 | 1218 | "output_type": "stream", |
|
|
1219 | 1219 | "stream": "stdout", |
|
|
1220 | 1220 | "text": [ |
|
|
1221 | 1221 | "\n", |
|
|
1222 | 1222 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", |
|
|
1223 | 1223 | "For more information, type 'help(pylab)'.\n" |
|
|
1224 | 1224 | ] |
|
|
1225 | 1225 | } |
|
|
1226 | 1226 | ], |
|
|
1227 | 1227 | "prompt_number": 23 |
|
|
1228 | 1228 | }, |
|
|
1229 | 1229 | { |
|
|
1230 | 1230 | "cell_type": "code", |
|
|
1231 | 1231 | "collapsed": false, |
|
|
1232 | 1232 | "input": [ |
|
|
1233 | 1233 | "%load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py" |
|
|
1234 | 1234 | ], |
|
|
1235 | 1235 | "language": "python", |
|
|
1236 | 1236 | "metadata": {}, |
|
|
1237 | 1237 | "outputs": [], |
|
|
1238 | 1238 | "prompt_number": 24 |
|
|
1239 | 1239 | }, |
|
|
1240 | 1240 | { |
|
|
1241 | 1241 | "cell_type": "code", |
|
|
1242 | 1242 | "collapsed": false, |
|
|
1243 | 1243 | "input": [ |
|
|
1244 | 1244 | "#!/usr/bin/env python\n", |
|
|
1245 | 1245 | "\n", |
|
|
1246 | 1246 | "# implement the example graphs/integral from pyx\n", |
|
|
1247 | 1247 | "from pylab import *\n", |
|
|
1248 | 1248 | "from matplotlib.patches import Polygon\n", |
|
|
1249 | 1249 | "\n", |
|
|
1250 | 1250 | "def func(x):\n", |
|
|
1251 | 1251 | " return (x-3)*(x-5)*(x-7)+85\n", |
|
|
1252 | 1252 | "\n", |
|
|
1253 | 1253 | "ax = subplot(111)\n", |
|
|
1254 | 1254 | "\n", |
|
|
1255 | 1255 | "a, b = 2, 9 # integral area\n", |
|
|
1256 | 1256 | "x = arange(0, 10, 0.01)\n", |
|
|
1257 | 1257 | "y = func(x)\n", |
|
|
1258 | 1258 | "plot(x, y, linewidth=1)\n", |
|
|
1259 | 1259 | "\n", |
|
|
1260 | 1260 | "# make the shaded region\n", |
|
|
1261 | 1261 | "ix = arange(a, b, 0.01)\n", |
|
|
1262 | 1262 | "iy = func(ix)\n", |
|
|
1263 | 1263 | "verts = [(a,0)] + list(zip(ix,iy)) + [(b,0)]\n", |
|
|
1264 | 1264 | "poly = Polygon(verts, facecolor='0.8', edgecolor='k')\n", |
|
|
1265 | 1265 | "ax.add_patch(poly)\n", |
|
|
1266 | 1266 | "\n", |
|
|
1267 | 1267 | "text(0.5 * (a + b), 30,\n", |
|
|
1268 | 1268 | " r\"$\\int_a^b f(x)\\mathrm{d}x$\", horizontalalignment='center',\n", |
|
|
1269 | 1269 | " fontsize=20)\n", |
|
|
1270 | 1270 | "\n", |
|
|
1271 | 1271 | "axis([0,10, 0, 180])\n", |
|
|
1272 | 1272 | "figtext(0.9, 0.05, 'x')\n", |
|
|
1273 | 1273 | "figtext(0.1, 0.9, 'y')\n", |
|
|
1274 | 1274 | "ax.set_xticks((a,b))\n", |
|
|
1275 | 1275 | "ax.set_xticklabels(('a','b'))\n", |
|
|
1276 | 1276 | "ax.set_yticks([])\n", |
|
|
1277 | 1277 | "show()\n" |
|
|
1278 | 1278 | ], |
|
|
1279 | 1279 | "language": "python", |
|
|
1280 | 1280 | "metadata": { |
|
|
1281 | 1281 | "slideshow": { |
|
|
1282 | 1282 | "slide_type": "skip" |
|
|
1283 | 1283 | } |
|
|
1284 | 1284 | }, |
|
|
1285 | 1285 | "outputs": [ |
|
|
1286 | 1286 | { |
|
|
1287 | 1287 | "output_type": "display_data", |
|
|
1288 | 1288 | "png": 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|
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1289 | 1289 | "text": [ |
|
|
1290 | 1290 | "<matplotlib.figure.Figure at 0xa705fec>" |
|
|
1291 | 1291 | ] |
|
|
1292 | 1292 | } |
|
|
1293 | 1293 | ], |
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1294 | 1294 | "prompt_number": 25 |
|
|
1295 | 1295 | }, |
|
|
1296 | 1296 | { |
|
|
1297 | 1297 | "cell_type": "heading", |
|
|
1298 | 1298 | "level": 1, |
|
|
1299 | 1299 | "metadata": { |
|
|
1300 | 1300 | "slideshow": { |
|
|
1301 | 1301 | "slide_type": "header_slide" |
|
|
1302 | 1302 | } |
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|
1303 | 1303 | }, |
|
|
1304 | 1304 | "source": [ |
|
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1305 | 1305 | "IPython rocks!" |
|
|
1306 | 1306 | ] |
|
|
1307 | 1307 | }, |
|
|
1308 | 1308 | { |
|
|
1309 | 1309 | "cell_type": "markdown", |
|
|
1310 | 1310 | "metadata": {}, |
|
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1311 | 1311 | "source": [ |
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1312 | 1312 | "Just my little contribution... I have a lot of work to do but this is an exciting beginning!\n", |
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|
1313 | 1313 | "\n", |
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|
1314 | 1314 | "You can check [here](https://github.com/ipython/nbconvert/pull/69) for more information about this PR." |
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|
1315 | 1315 | ] |
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|
1316 | 1316 | }, |
|
|
1317 | 1317 | { |
|
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1318 | 1318 | "cell_type": "markdown", |
|
|
1319 | 1319 | "metadata": {}, |
|
|
1320 | 1320 | "source": [ |
|
|
1321 | 1321 | "And you can find me at:\n", |
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1322 | 1322 | "\n", |
|
|
1323 | 1323 | "* [@damian_avila](https://twitter.com/damian_avila)\n", |
|
|
1324 | 1324 | "* [OQUANTA](http://www.oquanta.info)\n", |
|
|
1325 | 1325 | "* [BLOG](http://www.damian.oquanta.info)" |
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|
1326 | 1326 | ] |
|
|
1327 | 1327 | } |
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|
1328 | 1328 | ], |
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1329 | 1329 | "metadata": {} |
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1330 | 1330 | } |
|
|
1331 | 1331 | ] |
|
|
1332 | 1332 | } No newline at end of file |
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