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1 | { | |||
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2 | "metadata": { | |||
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3 | "name": "Progress Bars" | |||
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4 | }, | |||
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5 | "nbformat": 3, | |||
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6 | "nbformat_minor": 0, | |||
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7 | "worksheets": [ | |||
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8 | { | |||
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9 | "cells": [ | |||
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10 | { | |||
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11 | "cell_type": "heading", | |||
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12 | "level": 1, | |||
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13 | "metadata": {}, | |||
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14 | "source": [ | |||
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15 | "Two Examples of Progress Bars" | |||
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16 | ] | |||
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17 | }, | |||
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18 | { | |||
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19 | "cell_type": "heading", | |||
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20 | "level": 2, | |||
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21 | "metadata": {}, | |||
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22 | "source": [ | |||
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23 | "A Javascript Progress Bar" | |||
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24 | ] | |||
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25 | }, | |||
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26 | { | |||
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27 | "cell_type": "markdown", | |||
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28 | "metadata": {}, | |||
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29 | "source": [ | |||
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30 | "Here is a simple progress bar using HTML/Javascript:" | |||
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31 | ] | |||
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32 | }, | |||
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33 | { | |||
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34 | "cell_type": "code", | |||
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35 | "collapsed": false, | |||
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36 | "input": [ | |||
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37 | "import uuid\n", | |||
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38 | "import time\n", | |||
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39 | "from IPython.display import HTML, Javascript, display\n", | |||
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40 | "\n", | |||
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41 | "divid = str(uuid.uuid4())\n", | |||
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42 | "\n", | |||
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43 | "pb = HTML(\n", | |||
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44 | "\"\"\"\n", | |||
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45 | "<div style=\"border: 1px solid black; width:500px\">\n", | |||
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46 | " <div id=\"%s\" style=\"background-color:blue; width:0%%\"> </div>\n", | |||
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47 | "</div> \n", | |||
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48 | "\"\"\" % divid)\n", | |||
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49 | "display(pb)\n", | |||
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50 | "for i in range(1,101):\n", | |||
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51 | " time.sleep(0.1)\n", | |||
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52 | " \n", | |||
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53 | " display(Javascript(\"$('div#%s').width('%i%%')\" % (divid, i)))" | |||
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54 | ], | |||
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55 | "language": "python", | |||
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56 | "metadata": {}, | |||
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57 | "outputs": [], | |||
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58 | "prompt_number": 2 | |||
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59 | }, | |||
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60 | { | |||
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61 | "cell_type": "markdown", | |||
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62 | "metadata": {}, | |||
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63 | "source": [ | |||
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64 | "The above simply makes a div that is a box, and a blue div inside it with a unique ID \n", | |||
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65 | "(so that the javascript won't collide with other similar progress bars on the same page). \n", | |||
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66 | "\n", | |||
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67 | "Then, at every progress point, we run a simple jQuery call to resize the blue box to\n", | |||
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68 | "the appropriate fraction of the width of its containing box, and voil\u00e0 a nice\n", | |||
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69 | "HTML/Javascript progress bar!" | |||
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70 | ] | |||
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71 | }, | |||
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72 | { | |||
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73 | "cell_type": "heading", | |||
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74 | "level": 1, | |||
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75 | "metadata": {}, | |||
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76 | "source": [ | |||
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77 | "ProgressBar class" | |||
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78 | ] | |||
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79 | }, | |||
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80 | { | |||
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81 | "cell_type": "markdown", | |||
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82 | "metadata": {}, | |||
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83 | "source": [ | |||
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84 | "And finally, here is a progress bar *class* extracted from [PyMC](http://code.google.com/p/pymc/), which will work in regular Python as well as in the IPython Notebook" | |||
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85 | ] | |||
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86 | }, | |||
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87 | { | |||
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88 | "cell_type": "code", | |||
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89 | "collapsed": true, | |||
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90 | "input": [ | |||
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91 | "import sys, time\n", | |||
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92 | "\n", | |||
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93 | "class ProgressBar:\n", | |||
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94 | " def __init__(self, iterations):\n", | |||
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95 | " self.iterations = iterations\n", | |||
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96 | " self.prog_bar = '[]'\n", | |||
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97 | " self.fill_char = '*'\n", | |||
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98 | " self.width = 50\n", | |||
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99 | " self.__update_amount(0)\n", | |||
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100 | "\n", | |||
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101 | " def animate(self, iter):\n", | |||
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102 | " print '\\r', self,\n", | |||
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103 | " sys.stdout.flush()\n", | |||
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104 | " self.update_iteration(iter + 1)\n", | |||
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105 | "\n", | |||
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106 | " def update_iteration(self, elapsed_iter):\n", | |||
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107 | " self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)\n", | |||
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108 | " self.prog_bar += ' %d of %s complete' % (elapsed_iter, self.iterations)\n", | |||
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109 | "\n", | |||
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110 | " def __update_amount(self, new_amount):\n", | |||
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111 | " percent_done = int(round((new_amount / 100.0) * 100.0))\n", | |||
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112 | " all_full = self.width - 2\n", | |||
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113 | " num_hashes = int(round((percent_done / 100.0) * all_full))\n", | |||
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114 | " self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'\n", | |||
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115 | " pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))\n", | |||
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116 | " pct_string = '%d%%' % percent_done\n", | |||
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117 | " self.prog_bar = self.prog_bar[0:pct_place] + \\\n", | |||
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118 | " (pct_string + self.prog_bar[pct_place + len(pct_string):])\n", | |||
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119 | "\n", | |||
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120 | " def __str__(self):\n", | |||
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121 | " return str(self.prog_bar)" | |||
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122 | ], | |||
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123 | "language": "python", | |||
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124 | "metadata": {}, | |||
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125 | "outputs": [], | |||
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126 | "prompt_number": 3 | |||
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127 | }, | |||
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128 | { | |||
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129 | "cell_type": "code", | |||
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130 | "collapsed": false, | |||
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131 | "input": [ | |||
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132 | "p = ProgressBar(1000)\n", | |||
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133 | "for i in range(1001):\n", | |||
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134 | " time.sleep(0.002)\n", | |||
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135 | " p.animate(i)" | |||
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136 | ], | |||
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137 | "language": "python", | |||
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138 | "metadata": {}, | |||
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139 | "outputs": [], | |||
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140 | "prompt_number": 4 | |||
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141 | } | |||
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142 | ], | |||
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143 | "metadata": {} | |||
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144 | } | |||
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145 | ] | |||
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146 | } No newline at end of file |
@@ -1,259 +1,201 b'' | |||||
1 | { |
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1 | { | |
2 | "metadata": { |
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2 | "metadata": { | |
3 | "name": "Animations Using clear_output" |
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3 | "name": "Animations Using clear_output" | |
4 | }, |
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4 | }, | |
5 | "nbformat": 3, |
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5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
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6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
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7 | "worksheets": [ | |
8 | { |
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8 | { | |
9 | "cells": [ |
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9 | "cells": [ | |
10 | { |
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10 | { | |
11 | "cell_type": "heading", |
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11 | "cell_type": "heading", | |
12 | "level": 1, |
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12 | "level": 1, | |
13 | "metadata": {}, |
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13 | "metadata": {}, | |
14 | "source": [ |
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14 | "source": [ | |
15 |
"Simple animations |
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15 | "Simple animations Using clear_output" | |
16 | ] |
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16 | ] | |
17 | }, |
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17 | }, | |
18 | { |
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18 | { | |
19 | "cell_type": "markdown", |
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19 | "cell_type": "markdown", | |
20 | "metadata": {}, |
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20 | "metadata": {}, | |
21 | "source": [ |
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21 | "source": [ | |
22 | "Sometimes you want to print progress in-place, but don't want\n", |
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22 | "Sometimes you want to clear the output area in the middle of a calculation. This can be useful for doing simple animations. In terminals, there is the carriage-return (`'\\r'`) for overwriting a single line, but the notebook frontend does not support this behavior.\n", | |
23 | "to keep growing the output area. In terminals, there is the carriage-return\n", |
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24 | "(`'\\r'`) for overwriting a single line, but the notebook frontend does not support this\n", |
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25 | "behavior (yet).\n", |
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26 | "\n", |
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23 | "\n", | |
27 | "What the notebook *does* support is explicit `clear_output`, and you can use this to replace previous\n", |
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24 | "To clear output in the Notebook you can use the `clear_output` function." | |
28 | "output (specifying stdout/stderr or the special IPython display outputs)." |
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25 | ] | |
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26 | }, | |||
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27 | { | |||
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28 | "cell_type": "heading", | |||
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29 | "level": 2, | |||
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30 | "metadata": {}, | |||
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31 | "source": [ | |||
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32 | "Simple example" | |||
29 | ] |
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33 | ] | |
30 | }, |
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34 | }, | |
31 | { |
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35 | { | |
32 | "cell_type": "markdown", |
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36 | "cell_type": "markdown", | |
33 | "metadata": {}, |
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37 | "metadata": {}, | |
34 | "source": [ |
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38 | "source": [ | |
35 |
" |
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39 | "Here we show our progress iterating through a list:" | |
36 | ] |
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40 | ] | |
37 | }, |
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41 | }, | |
38 | { |
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42 | { | |
39 | "cell_type": "code", |
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43 | "cell_type": "code", | |
40 | "collapsed": true, |
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44 | "collapsed": true, | |
41 | "input": [ |
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45 | "input": [ | |
42 | "import sys\n", |
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46 | "import sys\n", | |
43 | "import time" |
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47 | "import time" | |
44 | ], |
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48 | ], | |
45 | "language": "python", |
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49 | "language": "python", | |
46 | "metadata": {}, |
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50 | "metadata": {}, | |
47 | "outputs": [] |
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51 | "outputs": [], | |
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52 | "prompt_number": 1 | |||
48 | }, |
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53 | }, | |
49 | { |
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54 | { | |
50 | "cell_type": "code", |
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55 | "cell_type": "code", | |
51 | "collapsed": false, |
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56 | "collapsed": false, | |
52 | "input": [ |
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57 | "input": [ | |
53 | "from IPython.display import clear_output\n", |
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58 | "from IPython.display import clear_output\n", | |
54 | "for i in range(10):\n", |
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59 | "for i in range(10):\n", | |
55 | " time.sleep(0.25)\n", |
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60 | " time.sleep(0.25)\n", | |
56 | " clear_output()\n", |
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61 | " clear_output()\n", | |
57 | " print i\n", |
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62 | " print i\n", | |
58 | " sys.stdout.flush()" |
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63 | " sys.stdout.flush()" | |
59 | ], |
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64 | ], | |
60 | "language": "python", |
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65 | "language": "python", | |
61 | "metadata": {}, |
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66 | "metadata": {}, | |
62 |
"outputs": [ |
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67 | "outputs": [ | |
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68 | { | |||
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69 | "output_type": "stream", | |||
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70 | "stream": "stdout", | |||
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71 | "text": [ | |||
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72 | "9\n" | |||
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73 | ] | |||
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74 | } | |||
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75 | ], | |||
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76 | "prompt_number": 2 | |||
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77 | }, | |||
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78 | { | |||
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79 | "cell_type": "heading", | |||
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80 | "level": 2, | |||
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81 | "metadata": {}, | |||
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82 | "source": [ | |||
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83 | "AsyncResult.wait_interactive" | |||
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84 | ] | |||
63 | }, |
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85 | }, | |
64 | { |
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86 | { | |
65 | "cell_type": "markdown", |
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87 | "cell_type": "markdown", | |
66 | "metadata": {}, |
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88 | "metadata": {}, | |
67 | "source": [ |
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89 | "source": [ | |
68 | "The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,\n", |
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90 | "The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,\n", | |
69 | "so you can watch as your parallel computation completes.\n", |
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91 | "so you can watch as your parallel computation completes.\n", | |
70 | "\n", |
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92 | "\n", | |
71 | "**This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#tab2)**" |
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93 | "**This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#tab2)**" | |
72 | ] |
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94 | ] | |
73 | }, |
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95 | }, | |
74 | { |
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96 | { | |
75 | "cell_type": "code", |
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97 | "cell_type": "code", | |
76 | "collapsed": false, |
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98 | "collapsed": false, | |
77 | "input": [ |
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99 | "input": [ | |
78 | "from IPython import parallel\n", |
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100 | "from IPython import parallel\n", | |
79 | "rc = parallel.Client()\n", |
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101 | "rc = parallel.Client()\n", | |
80 | "view = rc.load_balanced_view()\n", |
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102 | "view = rc.load_balanced_view()\n", | |
81 | "\n", |
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103 | "\n", | |
82 | "amr = view.map_async(time.sleep, [0.5]*100)\n", |
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104 | "amr = view.map_async(time.sleep, [0.5]*100)\n", | |
83 | "\n", |
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105 | "\n", | |
84 | "amr.wait_interactive()" |
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106 | "amr.wait_interactive()" | |
85 | ], |
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107 | ], | |
86 | "language": "python", |
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108 | "language": "python", | |
87 | "metadata": {}, |
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109 | "metadata": {}, | |
88 |
"outputs": [ |
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110 | "outputs": [ | |
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111 | { | |||
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112 | "output_type": "stream", | |||
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113 | "stream": "stdout", | |||
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114 | "text": [ | |||
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115 | " 100/100 tasks finished after 30 s" | |||
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116 | ] | |||
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117 | }, | |||
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118 | { | |||
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119 | "output_type": "stream", | |||
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120 | "stream": "stdout", | |||
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121 | "text": [ | |||
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122 | "\n", | |||
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123 | "done\n" | |||
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124 | ] | |||
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125 | } | |||
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126 | ], | |||
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127 | "prompt_number": 3 | |||
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128 | }, | |||
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129 | { | |||
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130 | "cell_type": "heading", | |||
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131 | "level": 2, | |||
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132 | "metadata": {}, | |||
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133 | "source": [ | |||
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134 | "Matplotlib example" | |||
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135 | ] | |||
89 | }, |
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136 | }, | |
90 | { |
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137 | { | |
91 | "cell_type": "markdown", |
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138 | "cell_type": "markdown", | |
92 | "metadata": {}, |
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139 | "metadata": {}, | |
93 | "source": [ |
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140 | "source": [ | |
94 |
"You can also use `clear_output()` to clear figures and plots. |
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141 | "You can also use `clear_output()` to clear figures and plots." | |
95 | "\n", |
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96 | "This time, we need to make sure we are using inline pylab (**requires matplotlib**)" |
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97 | ] |
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142 | ] | |
98 | }, |
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143 | }, | |
99 | { |
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144 | { | |
100 | "cell_type": "code", |
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145 | "cell_type": "code", | |
101 | "collapsed": false, |
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146 | "collapsed": false, | |
102 | "input": [ |
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147 | "input": [ | |
103 | "%pylab inline" |
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148 | "%pylab inline" | |
104 | ], |
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149 | ], | |
105 | "language": "python", |
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150 | "language": "python", | |
106 | "metadata": {}, |
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151 | "metadata": {}, | |
107 |
"outputs": [ |
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152 | "outputs": [ | |
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153 | { | |||
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154 | "output_type": "stream", | |||
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155 | "stream": "stdout", | |||
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156 | "text": [ | |||
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157 | "\n", | |||
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158 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", | |||
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159 | "For more information, type 'help(pylab)'.\n" | |||
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160 | ] | |||
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161 | } | |||
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162 | ], | |||
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163 | "prompt_number": 4 | |||
108 | }, |
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164 | }, | |
109 | { |
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165 | { | |
110 | "cell_type": "code", |
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166 | "cell_type": "code", | |
111 | "collapsed": false, |
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167 | "collapsed": false, | |
112 | "input": [ |
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168 | "input": [ | |
113 | "from scipy.special import jn\n", |
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169 | "from scipy.special import jn\n", | |
114 | "x = np.linspace(0,5)\n", |
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170 | "x = np.linspace(0,5)\n", | |
115 | "f, ax = plt.subplots()\n", |
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171 | "f, ax = plt.subplots()\n", | |
116 | "ax.set_title(\"Bessel functions\")\n", |
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172 | "ax.set_title(\"Bessel functions\")\n", | |
117 | "\n", |
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173 | "\n", | |
118 | "for n in range(1,10):\n", |
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174 | "for n in range(1,10):\n", | |
119 | " time.sleep(1)\n", |
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175 | " time.sleep(1)\n", | |
120 | " ax.plot(x, jn(x,n))\n", |
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176 | " ax.plot(x, jn(x,n))\n", | |
121 | " clear_output()\n", |
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177 | " clear_output()\n", | |
122 | " display(f)\n", |
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178 | " display(f)\n", | |
123 | "\n", |
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179 | "\n", | |
124 | "# close the figure at the end, so we don't get a duplicate\n", |
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180 | "# close the figure at the end, so we don't get a duplicate\n", | |
125 | "# of the last plot\n", |
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181 | "# of the last plot\n", | |
126 | "plt.close()" |
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182 | "plt.close()" | |
127 | ], |
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183 | ], | |
128 | "language": "python", |
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184 | "language": "python", | |
129 | "metadata": {}, |
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185 | "metadata": {}, | |
130 |
"outputs": [ |
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186 | "outputs": [ | |
131 |
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187 | { | |
132 | { |
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188 | "output_type": "display_data", | |
133 | "cell_type": "heading", |
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189 | "png": 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Ij8pHVYeqqNGqBmxb26Jm55qw62oH2za2sKgibTiXF6O7FixZsgQrV64sHHWe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\n", | |
134 |
|
|
190 | "text": [ | |
135 | "metadata": {}, |
|
191 | "<matplotlib.figure.Figure at 0x1082fcbd0>" | |
136 | "source": [ |
|
192 | ] | |
137 | "A Javascript Progress Bar" |
|
193 | } | |
138 | ] |
|
|||
139 | }, |
|
|||
140 | { |
|
|||
141 | "cell_type": "markdown", |
|
|||
142 | "metadata": {}, |
|
|||
143 | "source": [ |
|
|||
144 | "`clear_output()` is still something of a hack, and if you want to do a progress bar in the notebook\n", |
|
|||
145 | "it is better to just use Javascript/HTML if you can.\n", |
|
|||
146 | "\n", |
|
|||
147 | "Here is a simple progress bar using HTML/Javascript:" |
|
|||
148 | ] |
|
|||
149 | }, |
|
|||
150 | { |
|
|||
151 | "cell_type": "code", |
|
|||
152 | "collapsed": false, |
|
|||
153 | "input": [ |
|
|||
154 | "import uuid\n", |
|
|||
155 | "from IPython.display import HTML, Javascript, display\n", |
|
|||
156 | "\n", |
|
|||
157 | "divid = str(uuid.uuid4())\n", |
|
|||
158 | "\n", |
|
|||
159 | "pb = HTML(\n", |
|
|||
160 | "\"\"\"\n", |
|
|||
161 | "<div style=\"border: 1px solid black; width:500px\">\n", |
|
|||
162 | " <div id=\"%s\" style=\"background-color:blue; width:0%%\"> </div>\n", |
|
|||
163 | "</div> \n", |
|
|||
164 | "\"\"\" % divid)\n", |
|
|||
165 | "display(pb)\n", |
|
|||
166 | "for i in range(1,101):\n", |
|
|||
167 | " time.sleep(0.1)\n", |
|
|||
168 | " \n", |
|
|||
169 | " display(Javascript(\"$('div#%s').width('%i%%')\" % (divid, i)))" |
|
|||
170 | ], |
|
|||
171 | "language": "python", |
|
|||
172 | "metadata": {}, |
|
|||
173 | "outputs": [] |
|
|||
174 | }, |
|
|||
175 | { |
|
|||
176 | "cell_type": "markdown", |
|
|||
177 | "metadata": {}, |
|
|||
178 | "source": [ |
|
|||
179 | "The above simply makes a div that is a box, and a blue div inside it with a unique ID \n", |
|
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180 | "(so that the javascript won't collide with other similar progress bars on the same page). \n", |
|
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181 | "\n", |
|
|||
182 | "Then, at every progress point, we run a simple jQuery call to resize the blue box to\n", |
|
|||
183 | "the appropriate fraction of the width of its containing box, and voil\u00e0 a nice\n", |
|
|||
184 | "HTML/Javascript progress bar!" |
|
|||
185 | ] |
|
|||
186 | }, |
|
|||
187 | { |
|
|||
188 | "cell_type": "heading", |
|
|||
189 | "level": 2, |
|
|||
190 | "metadata": {}, |
|
|||
191 | "source": [ |
|
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192 | "ProgressBar class" |
|
|||
193 | ] |
|
|||
194 | }, |
|
|||
195 | { |
|
|||
196 | "cell_type": "markdown", |
|
|||
197 | "metadata": {}, |
|
|||
198 | "source": [ |
|
|||
199 | "And finally, here is a progress bar *class* extracted from [PyMC](http://code.google.com/p/pymc/), which will work in regular Python as well as in the IPython Notebook" |
|
|||
200 | ] |
|
|||
201 | }, |
|
|||
202 | { |
|
|||
203 | "cell_type": "code", |
|
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204 | "collapsed": true, |
|
|||
205 | "input": [ |
|
|||
206 | "import sys, time\n", |
|
|||
207 | "\n", |
|
|||
208 | "class ProgressBar:\n", |
|
|||
209 | " def __init__(self, iterations):\n", |
|
|||
210 | " self.iterations = iterations\n", |
|
|||
211 | " self.prog_bar = '[]'\n", |
|
|||
212 | " self.fill_char = '*'\n", |
|
|||
213 | " self.width = 50\n", |
|
|||
214 | " self.__update_amount(0)\n", |
|
|||
215 | "\n", |
|
|||
216 | " def animate(self, iter):\n", |
|
|||
217 | " print '\\r', self,\n", |
|
|||
218 | " sys.stdout.flush()\n", |
|
|||
219 | " self.update_iteration(iter + 1)\n", |
|
|||
220 | "\n", |
|
|||
221 | " def update_iteration(self, elapsed_iter):\n", |
|
|||
222 | " self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)\n", |
|
|||
223 | " self.prog_bar += ' %d of %s complete' % (elapsed_iter, self.iterations)\n", |
|
|||
224 | "\n", |
|
|||
225 | " def __update_amount(self, new_amount):\n", |
|
|||
226 | " percent_done = int(round((new_amount / 100.0) * 100.0))\n", |
|
|||
227 | " all_full = self.width - 2\n", |
|
|||
228 | " num_hashes = int(round((percent_done / 100.0) * all_full))\n", |
|
|||
229 | " self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'\n", |
|
|||
230 | " pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))\n", |
|
|||
231 | " pct_string = '%d%%' % percent_done\n", |
|
|||
232 | " self.prog_bar = self.prog_bar[0:pct_place] + \\\n", |
|
|||
233 | " (pct_string + self.prog_bar[pct_place + len(pct_string):])\n", |
|
|||
234 | "\n", |
|
|||
235 | " def __str__(self):\n", |
|
|||
236 | " return str(self.prog_bar)" |
|
|||
237 | ], |
|
194 | ], | |
238 | "language": "python", |
|
195 | "prompt_number": 5 | |
239 | "metadata": {}, |
|
|||
240 | "outputs": [] |
|
|||
241 | }, |
|
|||
242 | { |
|
|||
243 | "cell_type": "code", |
|
|||
244 | "collapsed": false, |
|
|||
245 | "input": [ |
|
|||
246 | "p = ProgressBar(1000)\n", |
|
|||
247 | "for i in range(1001):\n", |
|
|||
248 | " time.sleep(0.002)\n", |
|
|||
249 | " p.animate(i)" |
|
|||
250 | ], |
|
|||
251 | "language": "python", |
|
|||
252 | "metadata": {}, |
|
|||
253 | "outputs": [] |
|
|||
254 | } |
|
196 | } | |
255 | ], |
|
197 | ], | |
256 | "metadata": {} |
|
198 | "metadata": {} | |
257 | } |
|
199 | } | |
258 | ] |
|
200 | ] | |
259 | } No newline at end of file |
|
201 | } |
@@ -1,404 +1,399 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
|
2 | "metadata": { | |
3 | "name": "Custom Display Logic" |
|
3 | "name": "Custom Display Logic" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
|
10 | { | |
11 | "cell_type": "heading", |
|
11 | "cell_type": "heading", | |
12 | "level": 1, |
|
12 | "level": 1, | |
13 | "metadata": {}, |
|
13 | "metadata": {}, | |
14 | "source": [ |
|
14 | "source": [ | |
15 |
" |
|
15 | "Defining Custom Display Logic for Your Own Objects" | |
16 | ] |
|
16 | ] | |
17 | }, |
|
17 | }, | |
18 | { |
|
18 | { | |
19 | "cell_type": "markdown", |
|
19 | "cell_type": "markdown", | |
20 | "metadata": {}, |
|
20 | "metadata": {}, | |
21 | "source": [ |
|
21 | "source": [ | |
22 | "IPython extends the idea of the ``__repr__`` method in Python to support multiple representations for a given\n", |
|
22 | "IPython extends the idea of the ``__repr__`` method in Python to support multiple representations for a given\n", | |
23 | "object, which clients can use to display the object according to their capabilities. An object can return multiple\n", |
|
23 | "object, which clients can use to display the object according to their capabilities. An object can return multiple\n", | |
24 | "representations of itself by implementing special methods, and you can also define at runtime custom display \n", |
|
24 | "representations of itself by implementing special methods, and you can also define at runtime custom display \n", | |
25 |
"functions for existing objects whose methods you can't or won't modify. In this notebook, we show how both approaches work. |
|
25 | "functions for existing objects whose methods you can't or won't modify. In this notebook, we show how both approaches work." | |
26 | "\n", |
|
|||
27 | "<br/>\n", |
|
|||
28 | "**Note:** this notebook has had all output cells stripped out so we can include it in the IPython documentation with \n", |
|
|||
29 | "a minimal file size. You'll need to manually execute the cells to see the output (you can run all of them with the \n", |
|
|||
30 | "\"Run All\" button, or execute each individually)." |
|
|||
31 | ] |
|
26 | ] | |
32 | }, |
|
27 | }, | |
33 | { |
|
28 | { | |
34 | "cell_type": "markdown", |
|
29 | "cell_type": "markdown", | |
35 | "metadata": {}, |
|
30 | "metadata": {}, | |
36 | "source": [ |
|
31 | "source": [ | |
37 | "Parts of this notebook need the inline matplotlib backend:" |
|
32 | "Parts of this notebook need the inline matplotlib backend:" | |
38 | ] |
|
33 | ] | |
39 | }, |
|
34 | }, | |
40 | { |
|
35 | { | |
41 | "cell_type": "code", |
|
36 | "cell_type": "code", | |
42 | "collapsed": false, |
|
37 | "collapsed": false, | |
43 | "input": [ |
|
38 | "input": [ | |
44 | "%pylab inline" |
|
39 | "%pylab inline" | |
45 | ], |
|
40 | ], | |
46 | "language": "python", |
|
41 | "language": "python", | |
47 | "metadata": {}, |
|
42 | "metadata": {}, | |
48 | "outputs": [] |
|
43 | "outputs": [] | |
49 | }, |
|
44 | }, | |
50 | { |
|
45 | { | |
51 | "cell_type": "heading", |
|
46 | "cell_type": "heading", | |
52 | "level": 2, |
|
47 | "level": 2, | |
53 | "metadata": {}, |
|
48 | "metadata": {}, | |
54 | "source": [ |
|
49 | "source": [ | |
55 | "Custom-built classes with dedicated ``_repr_*_`` methods" |
|
50 | "Custom-built classes with dedicated ``_repr_*_`` methods" | |
56 | ] |
|
51 | ] | |
57 | }, |
|
52 | }, | |
58 | { |
|
53 | { | |
59 | "cell_type": "markdown", |
|
54 | "cell_type": "markdown", | |
60 | "metadata": {}, |
|
55 | "metadata": {}, | |
61 | "source": [ |
|
56 | "source": [ | |
62 | "In our first example, we illustrate how objects can expose directly to IPython special representations of\n", |
|
57 | "In our first example, we illustrate how objects can expose directly to IPython special representations of\n", | |
63 | "themselves, by providing methods such as ``_repr_svg_``, ``_repr_png_``, ``_repr_latex_``, etc. For a full\n", |
|
58 | "themselves, by providing methods such as ``_repr_svg_``, ``_repr_png_``, ``_repr_latex_``, etc. For a full\n", | |
64 | "list of the special ``_repr_*_`` methods supported, see the code in ``IPython.core.displaypub``.\n", |
|
59 | "list of the special ``_repr_*_`` methods supported, see the code in ``IPython.core.displaypub``.\n", | |
65 | "\n", |
|
60 | "\n", | |
66 | "As an illustration, we build a class that holds data generated by sampling a Gaussian distribution with given mean \n", |
|
61 | "As an illustration, we build a class that holds data generated by sampling a Gaussian distribution with given mean \n", | |
67 | "and variance. The class can display itself in a variety of ways: as a LaTeX expression or as an image in PNG or SVG \n", |
|
62 | "and variance. The class can display itself in a variety of ways: as a LaTeX expression or as an image in PNG or SVG \n", | |
68 | "format. Each frontend can then decide which representation it can handle.\n", |
|
63 | "format. Each frontend can then decide which representation it can handle.\n", | |
69 | "Further, we illustrate how to expose directly to the user the ability to directly access the various alternate \n", |
|
64 | "Further, we illustrate how to expose directly to the user the ability to directly access the various alternate \n", | |
70 | "representations (since by default displaying the object itself will only show one, and which is shown will depend on the \n", |
|
65 | "representations (since by default displaying the object itself will only show one, and which is shown will depend on the \n", | |
71 | "required representations that even cache necessary data in cases where it may be expensive to compute.\n", |
|
66 | "required representations that even cache necessary data in cases where it may be expensive to compute.\n", | |
72 | "\n", |
|
67 | "\n", | |
73 | "The next cell defines the Gaussian class:" |
|
68 | "The next cell defines the Gaussian class:" | |
74 | ] |
|
69 | ] | |
75 | }, |
|
70 | }, | |
76 | { |
|
71 | { | |
77 | "cell_type": "code", |
|
72 | "cell_type": "code", | |
78 | "collapsed": false, |
|
73 | "collapsed": false, | |
79 | "input": [ |
|
74 | "input": [ | |
80 | "from IPython.core.pylabtools import print_figure\n", |
|
75 | "from IPython.core.pylabtools import print_figure\n", | |
81 | "from IPython.display import Image, SVG, Math\n", |
|
76 | "from IPython.display import Image, SVG, Math\n", | |
82 | "\n", |
|
77 | "\n", | |
83 | "class Gaussian(object):\n", |
|
78 | "class Gaussian(object):\n", | |
84 | " \"\"\"A simple object holding data sampled from a Gaussian distribution.\n", |
|
79 | " \"\"\"A simple object holding data sampled from a Gaussian distribution.\n", | |
85 | " \"\"\"\n", |
|
80 | " \"\"\"\n", | |
86 | " def __init__(self, mean=0, std=1, size=1000):\n", |
|
81 | " def __init__(self, mean=0, std=1, size=1000):\n", | |
87 | " self.data = np.random.normal(mean, std, size)\n", |
|
82 | " self.data = np.random.normal(mean, std, size)\n", | |
88 | " self.mean = mean\n", |
|
83 | " self.mean = mean\n", | |
89 | " self.std = std\n", |
|
84 | " self.std = std\n", | |
90 | " self.size = size\n", |
|
85 | " self.size = size\n", | |
91 | " # For caching plots that may be expensive to compute\n", |
|
86 | " # For caching plots that may be expensive to compute\n", | |
92 | " self._png_data = None\n", |
|
87 | " self._png_data = None\n", | |
93 | " self._svg_data = None\n", |
|
88 | " self._svg_data = None\n", | |
94 | " \n", |
|
89 | " \n", | |
95 | " def _figure_data(self, format):\n", |
|
90 | " def _figure_data(self, format):\n", | |
96 | " fig, ax = plt.subplots()\n", |
|
91 | " fig, ax = plt.subplots()\n", | |
97 | " ax.plot(self.data, 'o')\n", |
|
92 | " ax.plot(self.data, 'o')\n", | |
98 | " ax.set_title(self._repr_latex_())\n", |
|
93 | " ax.set_title(self._repr_latex_())\n", | |
99 | " data = print_figure(fig, format)\n", |
|
94 | " data = print_figure(fig, format)\n", | |
100 | " # We MUST close the figure, otherwise IPython's display machinery\n", |
|
95 | " # We MUST close the figure, otherwise IPython's display machinery\n", | |
101 | " # will pick it up and send it as output, resulting in a double display\n", |
|
96 | " # will pick it up and send it as output, resulting in a double display\n", | |
102 | " plt.close(fig)\n", |
|
97 | " plt.close(fig)\n", | |
103 | " return data\n", |
|
98 | " return data\n", | |
104 | " \n", |
|
99 | " \n", | |
105 | " # Here we define the special repr methods that provide the IPython display protocol\n", |
|
100 | " # Here we define the special repr methods that provide the IPython display protocol\n", | |
106 | " # Note that for the two figures, we cache the figure data once computed.\n", |
|
101 | " # Note that for the two figures, we cache the figure data once computed.\n", | |
107 | " \n", |
|
102 | " \n", | |
108 | " def _repr_png_(self):\n", |
|
103 | " def _repr_png_(self):\n", | |
109 | " if self._png_data is None:\n", |
|
104 | " if self._png_data is None:\n", | |
110 | " self._png_data = self._figure_data('png')\n", |
|
105 | " self._png_data = self._figure_data('png')\n", | |
111 | " return self._png_data\n", |
|
106 | " return self._png_data\n", | |
112 | "\n", |
|
107 | "\n", | |
113 | "\n", |
|
108 | "\n", | |
114 | " def _repr_svg_(self):\n", |
|
109 | " def _repr_svg_(self):\n", | |
115 | " if self._svg_data is None:\n", |
|
110 | " if self._svg_data is None:\n", | |
116 | " self._svg_data = self._figure_data('svg')\n", |
|
111 | " self._svg_data = self._figure_data('svg')\n", | |
117 | " return self._svg_data\n", |
|
112 | " return self._svg_data\n", | |
118 | " \n", |
|
113 | " \n", | |
119 | " def _repr_latex_(self):\n", |
|
114 | " def _repr_latex_(self):\n", | |
120 | " return r'$\\mathcal{N}(\\mu=%.2g, \\sigma=%.2g),\\ N=%d$' % (self.mean,\n", |
|
115 | " return r'$\\mathcal{N}(\\mu=%.2g, \\sigma=%.2g),\\ N=%d$' % (self.mean,\n", | |
121 | " self.std, self.size)\n", |
|
116 | " self.std, self.size)\n", | |
122 | " \n", |
|
117 | " \n", | |
123 | " # We expose as properties some of the above reprs, so that the user can see them\n", |
|
118 | " # We expose as properties some of the above reprs, so that the user can see them\n", | |
124 | " # directly (since otherwise the client dictates which one it shows by default)\n", |
|
119 | " # directly (since otherwise the client dictates which one it shows by default)\n", | |
125 | " @property\n", |
|
120 | " @property\n", | |
126 | " def png(self):\n", |
|
121 | " def png(self):\n", | |
127 | " return Image(self._repr_png_(), embed=True)\n", |
|
122 | " return Image(self._repr_png_(), embed=True)\n", | |
128 | " \n", |
|
123 | " \n", | |
129 | " @property\n", |
|
124 | " @property\n", | |
130 | " def svg(self):\n", |
|
125 | " def svg(self):\n", | |
131 | " return SVG(self._repr_svg_())\n", |
|
126 | " return SVG(self._repr_svg_())\n", | |
132 | " \n", |
|
127 | " \n", | |
133 | " @property\n", |
|
128 | " @property\n", | |
134 | " def latex(self):\n", |
|
129 | " def latex(self):\n", | |
135 | " return Math(self._repr_svg_())\n", |
|
130 | " return Math(self._repr_svg_())\n", | |
136 | " \n", |
|
131 | " \n", | |
137 | " # An example of using a property to display rich information, in this case\n", |
|
132 | " # An example of using a property to display rich information, in this case\n", | |
138 | " # the histogram of the distribution. We've hardcoded the format to be png\n", |
|
133 | " # the histogram of the distribution. We've hardcoded the format to be png\n", | |
139 | " # in this case, but in production code it would be trivial to make it an option\n", |
|
134 | " # in this case, but in production code it would be trivial to make it an option\n", | |
140 | " @property\n", |
|
135 | " @property\n", | |
141 | " def hist(self):\n", |
|
136 | " def hist(self):\n", | |
142 | " fig, ax = plt.subplots()\n", |
|
137 | " fig, ax = plt.subplots()\n", | |
143 | " ax.hist(self.data, bins=100)\n", |
|
138 | " ax.hist(self.data, bins=100)\n", | |
144 | " ax.set_title(self._repr_latex_())\n", |
|
139 | " ax.set_title(self._repr_latex_())\n", | |
145 | " data = print_figure(fig, 'png')\n", |
|
140 | " data = print_figure(fig, 'png')\n", | |
146 | " plt.close(fig)\n", |
|
141 | " plt.close(fig)\n", | |
147 | " return Image(data, embed=True)" |
|
142 | " return Image(data, embed=True)" | |
148 | ], |
|
143 | ], | |
149 | "language": "python", |
|
144 | "language": "python", | |
150 | "metadata": {}, |
|
145 | "metadata": {}, | |
151 | "outputs": [] |
|
146 | "outputs": [] | |
152 | }, |
|
147 | }, | |
153 | { |
|
148 | { | |
154 | "cell_type": "markdown", |
|
149 | "cell_type": "markdown", | |
155 | "metadata": {}, |
|
150 | "metadata": {}, | |
156 | "source": [ |
|
151 | "source": [ | |
157 | "Now, we create an instance of the Gaussian distribution, whose default representation will be its LaTeX form:" |
|
152 | "Now, we create an instance of the Gaussian distribution, whose default representation will be its LaTeX form:" | |
158 | ] |
|
153 | ] | |
159 | }, |
|
154 | }, | |
160 | { |
|
155 | { | |
161 | "cell_type": "code", |
|
156 | "cell_type": "code", | |
162 | "collapsed": false, |
|
157 | "collapsed": false, | |
163 | "input": [ |
|
158 | "input": [ | |
164 | "x = Gaussian()\n", |
|
159 | "x = Gaussian()\n", | |
165 | "x" |
|
160 | "x" | |
166 | ], |
|
161 | ], | |
167 | "language": "python", |
|
162 | "language": "python", | |
168 | "metadata": {}, |
|
163 | "metadata": {}, | |
169 | "outputs": [] |
|
164 | "outputs": [] | |
170 | }, |
|
165 | }, | |
171 | { |
|
166 | { | |
172 | "cell_type": "markdown", |
|
167 | "cell_type": "markdown", | |
173 | "metadata": {}, |
|
168 | "metadata": {}, | |
174 | "source": [ |
|
169 | "source": [ | |
175 | "We can view the data in png or svg formats:" |
|
170 | "We can view the data in png or svg formats:" | |
176 | ] |
|
171 | ] | |
177 | }, |
|
172 | }, | |
178 | { |
|
173 | { | |
179 | "cell_type": "code", |
|
174 | "cell_type": "code", | |
180 | "collapsed": false, |
|
175 | "collapsed": false, | |
181 | "input": [ |
|
176 | "input": [ | |
182 | "x.png" |
|
177 | "x.png" | |
183 | ], |
|
178 | ], | |
184 | "language": "python", |
|
179 | "language": "python", | |
185 | "metadata": {}, |
|
180 | "metadata": {}, | |
186 | "outputs": [] |
|
181 | "outputs": [] | |
187 | }, |
|
182 | }, | |
188 | { |
|
183 | { | |
189 | "cell_type": "code", |
|
184 | "cell_type": "code", | |
190 | "collapsed": false, |
|
185 | "collapsed": false, | |
191 | "input": [ |
|
186 | "input": [ | |
192 | "x.svg" |
|
187 | "x.svg" | |
193 | ], |
|
188 | ], | |
194 | "language": "python", |
|
189 | "language": "python", | |
195 | "metadata": {}, |
|
190 | "metadata": {}, | |
196 | "outputs": [] |
|
191 | "outputs": [] | |
197 | }, |
|
192 | }, | |
198 | { |
|
193 | { | |
199 | "cell_type": "markdown", |
|
194 | "cell_type": "markdown", | |
200 | "metadata": {}, |
|
195 | "metadata": {}, | |
201 | "source": [ |
|
196 | "source": [ | |
202 | "Since IPython only displays by default as an ``Out[]`` cell the result of the last computation, we can use the\n", |
|
197 | "Since IPython only displays by default as an ``Out[]`` cell the result of the last computation, we can use the\n", | |
203 | "``display()`` function to show more than one representation in a single cell:" |
|
198 | "``display()`` function to show more than one representation in a single cell:" | |
204 | ] |
|
199 | ] | |
205 | }, |
|
200 | }, | |
206 | { |
|
201 | { | |
207 | "cell_type": "code", |
|
202 | "cell_type": "code", | |
208 | "collapsed": false, |
|
203 | "collapsed": false, | |
209 | "input": [ |
|
204 | "input": [ | |
210 | "display(x.png)\n", |
|
205 | "display(x.png)\n", | |
211 | "display(x.svg)" |
|
206 | "display(x.svg)" | |
212 | ], |
|
207 | ], | |
213 | "language": "python", |
|
208 | "language": "python", | |
214 | "metadata": {}, |
|
209 | "metadata": {}, | |
215 | "outputs": [] |
|
210 | "outputs": [] | |
216 | }, |
|
211 | }, | |
217 | { |
|
212 | { | |
218 | "cell_type": "markdown", |
|
213 | "cell_type": "markdown", | |
219 | "metadata": {}, |
|
214 | "metadata": {}, | |
220 | "source": [ |
|
215 | "source": [ | |
221 | "Now let's create a new Gaussian with different parameters" |
|
216 | "Now let's create a new Gaussian with different parameters" | |
222 | ] |
|
217 | ] | |
223 | }, |
|
218 | }, | |
224 | { |
|
219 | { | |
225 | "cell_type": "code", |
|
220 | "cell_type": "code", | |
226 | "collapsed": false, |
|
221 | "collapsed": false, | |
227 | "input": [ |
|
222 | "input": [ | |
228 | "x2 = Gaussian(0.5, 0.2, 2000)\n", |
|
223 | "x2 = Gaussian(0.5, 0.2, 2000)\n", | |
229 | "x2" |
|
224 | "x2" | |
230 | ], |
|
225 | ], | |
231 | "language": "python", |
|
226 | "language": "python", | |
232 | "metadata": {}, |
|
227 | "metadata": {}, | |
233 | "outputs": [] |
|
228 | "outputs": [] | |
234 | }, |
|
229 | }, | |
235 | { |
|
230 | { | |
236 | "cell_type": "markdown", |
|
231 | "cell_type": "markdown", | |
237 | "metadata": {}, |
|
232 | "metadata": {}, | |
238 | "source": [ |
|
233 | "source": [ | |
239 | "We can easily compare them by displaying their histograms" |
|
234 | "We can easily compare them by displaying their histograms" | |
240 | ] |
|
235 | ] | |
241 | }, |
|
236 | }, | |
242 | { |
|
237 | { | |
243 | "cell_type": "code", |
|
238 | "cell_type": "code", | |
244 | "collapsed": false, |
|
239 | "collapsed": false, | |
245 | "input": [ |
|
240 | "input": [ | |
246 | "display(x.hist)\n", |
|
241 | "display(x.hist)\n", | |
247 | "display(x2.hist)" |
|
242 | "display(x2.hist)" | |
248 | ], |
|
243 | ], | |
249 | "language": "python", |
|
244 | "language": "python", | |
250 | "metadata": {}, |
|
245 | "metadata": {}, | |
251 | "outputs": [] |
|
246 | "outputs": [] | |
252 | }, |
|
247 | }, | |
253 | { |
|
248 | { | |
254 | "cell_type": "markdown", |
|
249 | "cell_type": "markdown", | |
255 | "metadata": {}, |
|
250 | "metadata": {}, | |
256 | "source": [ |
|
251 | "source": [ | |
257 | "## Adding IPython display support to existing objects\n", |
|
252 | "## Adding IPython display support to existing objects\n", | |
258 | "\n", |
|
253 | "\n", | |
259 | "When you are directly writing your own classes, you can adapt them for display in IPython by \n", |
|
254 | "When you are directly writing your own classes, you can adapt them for display in IPython by \n", | |
260 | "following the above example. But in practice, we often need to work with existing code we\n", |
|
255 | "following the above example. But in practice, we often need to work with existing code we\n", | |
261 | "can't modify. \n", |
|
256 | "can't modify. \n", | |
262 | "\n", |
|
257 | "\n", | |
263 | "We now illustrate how to add these kinds of extended display capabilities to existing objects.\n", |
|
258 | "We now illustrate how to add these kinds of extended display capabilities to existing objects.\n", | |
264 | "We will use the numpy polynomials and change their default representation to be a formatted\n", |
|
259 | "We will use the numpy polynomials and change their default representation to be a formatted\n", | |
265 | "LaTeX expression.\n", |
|
260 | "LaTeX expression.\n", | |
266 | "\n", |
|
261 | "\n", | |
267 | "First, consider how a numpy polynomial object renders by default:" |
|
262 | "First, consider how a numpy polynomial object renders by default:" | |
268 | ] |
|
263 | ] | |
269 | }, |
|
264 | }, | |
270 | { |
|
265 | { | |
271 | "cell_type": "code", |
|
266 | "cell_type": "code", | |
272 | "collapsed": false, |
|
267 | "collapsed": false, | |
273 | "input": [ |
|
268 | "input": [ | |
274 | "p = np.polynomial.Polynomial([1,2,3], [-10, 10])\n", |
|
269 | "p = np.polynomial.Polynomial([1,2,3], [-10, 10])\n", | |
275 | "p" |
|
270 | "p" | |
276 | ], |
|
271 | ], | |
277 | "language": "python", |
|
272 | "language": "python", | |
278 | "metadata": {}, |
|
273 | "metadata": {}, | |
279 | "outputs": [] |
|
274 | "outputs": [] | |
280 | }, |
|
275 | }, | |
281 | { |
|
276 | { | |
282 | "cell_type": "markdown", |
|
277 | "cell_type": "markdown", | |
283 | "metadata": {}, |
|
278 | "metadata": {}, | |
284 | "source": [ |
|
279 | "source": [ | |
285 | "Next, we define a function that pretty-prints a polynomial as a LaTeX string:" |
|
280 | "Next, we define a function that pretty-prints a polynomial as a LaTeX string:" | |
286 | ] |
|
281 | ] | |
287 | }, |
|
282 | }, | |
288 | { |
|
283 | { | |
289 | "cell_type": "code", |
|
284 | "cell_type": "code", | |
290 | "collapsed": false, |
|
285 | "collapsed": false, | |
291 | "input": [ |
|
286 | "input": [ | |
292 | "def poly2latex(p):\n", |
|
287 | "def poly2latex(p):\n", | |
293 | " terms = ['%.2g' % p.coef[0]]\n", |
|
288 | " terms = ['%.2g' % p.coef[0]]\n", | |
294 | " if len(p) > 1:\n", |
|
289 | " if len(p) > 1:\n", | |
295 | " term = 'x'\n", |
|
290 | " term = 'x'\n", | |
296 | " c = p.coef[1]\n", |
|
291 | " c = p.coef[1]\n", | |
297 | " if c!=1:\n", |
|
292 | " if c!=1:\n", | |
298 | " term = ('%.2g ' % c) + term\n", |
|
293 | " term = ('%.2g ' % c) + term\n", | |
299 | " terms.append(term)\n", |
|
294 | " terms.append(term)\n", | |
300 | " if len(p) > 2:\n", |
|
295 | " if len(p) > 2:\n", | |
301 | " for i in range(2, len(p)):\n", |
|
296 | " for i in range(2, len(p)):\n", | |
302 | " term = 'x^%d' % i\n", |
|
297 | " term = 'x^%d' % i\n", | |
303 | " c = p.coef[i]\n", |
|
298 | " c = p.coef[i]\n", | |
304 | " if c!=1:\n", |
|
299 | " if c!=1:\n", | |
305 | " term = ('%.2g ' % c) + term\n", |
|
300 | " term = ('%.2g ' % c) + term\n", | |
306 | " terms.append(term)\n", |
|
301 | " terms.append(term)\n", | |
307 | " px = '$P(x)=%s$' % '+'.join(terms)\n", |
|
302 | " px = '$P(x)=%s$' % '+'.join(terms)\n", | |
308 | " dom = r', domain: $[%.2g,\\ %.2g]$' % tuple(p.domain)\n", |
|
303 | " dom = r', domain: $[%.2g,\\ %.2g]$' % tuple(p.domain)\n", | |
309 | " return px+dom" |
|
304 | " return px+dom" | |
310 | ], |
|
305 | ], | |
311 | "language": "python", |
|
306 | "language": "python", | |
312 | "metadata": {}, |
|
307 | "metadata": {}, | |
313 | "outputs": [] |
|
308 | "outputs": [] | |
314 | }, |
|
309 | }, | |
315 | { |
|
310 | { | |
316 | "cell_type": "markdown", |
|
311 | "cell_type": "markdown", | |
317 | "metadata": {}, |
|
312 | "metadata": {}, | |
318 | "source": [ |
|
313 | "source": [ | |
319 | "This produces, on our polynomial ``p``, the following:" |
|
314 | "This produces, on our polynomial ``p``, the following:" | |
320 | ] |
|
315 | ] | |
321 | }, |
|
316 | }, | |
322 | { |
|
317 | { | |
323 | "cell_type": "code", |
|
318 | "cell_type": "code", | |
324 | "collapsed": false, |
|
319 | "collapsed": false, | |
325 | "input": [ |
|
320 | "input": [ | |
326 | "poly2latex(p)" |
|
321 | "poly2latex(p)" | |
327 | ], |
|
322 | ], | |
328 | "language": "python", |
|
323 | "language": "python", | |
329 | "metadata": {}, |
|
324 | "metadata": {}, | |
330 | "outputs": [] |
|
325 | "outputs": [] | |
331 | }, |
|
326 | }, | |
332 | { |
|
327 | { | |
333 | "cell_type": "code", |
|
328 | "cell_type": "code", | |
334 | "collapsed": false, |
|
329 | "collapsed": false, | |
335 | "input": [ |
|
330 | "input": [ | |
336 | "from IPython.display import Latex\n", |
|
331 | "from IPython.display import Latex\n", | |
337 | "Latex(poly2latex(p))" |
|
332 | "Latex(poly2latex(p))" | |
338 | ], |
|
333 | ], | |
339 | "language": "python", |
|
334 | "language": "python", | |
340 | "metadata": {}, |
|
335 | "metadata": {}, | |
341 | "outputs": [] |
|
336 | "outputs": [] | |
342 | }, |
|
337 | }, | |
343 | { |
|
338 | { | |
344 | "cell_type": "markdown", |
|
339 | "cell_type": "markdown", | |
345 | "metadata": {}, |
|
340 | "metadata": {}, | |
346 | "source": [ |
|
341 | "source": [ | |
347 | "But we can configure IPython to do this automatically for us as follows. We hook into the\n", |
|
342 | "But we can configure IPython to do this automatically for us as follows. We hook into the\n", | |
348 | "IPython display system and instruct it to use ``poly2latex`` for the latex mimetype, when\n", |
|
343 | "IPython display system and instruct it to use ``poly2latex`` for the latex mimetype, when\n", | |
349 | "encountering objects of the ``Polynomial`` type defined in the\n", |
|
344 | "encountering objects of the ``Polynomial`` type defined in the\n", | |
350 | "``numpy.polynomial.polynomial`` module:" |
|
345 | "``numpy.polynomial.polynomial`` module:" | |
351 | ] |
|
346 | ] | |
352 | }, |
|
347 | }, | |
353 | { |
|
348 | { | |
354 | "cell_type": "code", |
|
349 | "cell_type": "code", | |
355 | "collapsed": false, |
|
350 | "collapsed": false, | |
356 | "input": [ |
|
351 | "input": [ | |
357 | "ip = get_ipython()\n", |
|
352 | "ip = get_ipython()\n", | |
358 | "latex_formatter = ip.display_formatter.formatters['text/latex']\n", |
|
353 | "latex_formatter = ip.display_formatter.formatters['text/latex']\n", | |
359 | "latex_formatter.for_type_by_name('numpy.polynomial.polynomial',\n", |
|
354 | "latex_formatter.for_type_by_name('numpy.polynomial.polynomial',\n", | |
360 | " 'Polynomial', poly2latex)" |
|
355 | " 'Polynomial', poly2latex)" | |
361 | ], |
|
356 | ], | |
362 | "language": "python", |
|
357 | "language": "python", | |
363 | "metadata": {}, |
|
358 | "metadata": {}, | |
364 | "outputs": [] |
|
359 | "outputs": [] | |
365 | }, |
|
360 | }, | |
366 | { |
|
361 | { | |
367 | "cell_type": "markdown", |
|
362 | "cell_type": "markdown", | |
368 | "metadata": {}, |
|
363 | "metadata": {}, | |
369 | "source": [ |
|
364 | "source": [ | |
370 | "For more examples on how to use the above system, and how to bundle similar print functions\n", |
|
365 | "For more examples on how to use the above system, and how to bundle similar print functions\n", | |
371 | "into a convenient IPython extension, see the ``IPython/extensions/sympyprinting.py`` file. \n", |
|
366 | "into a convenient IPython extension, see the ``IPython/extensions/sympyprinting.py`` file. \n", | |
372 | "The machinery that defines the display system is in the ``display.py`` and ``displaypub.py`` \n", |
|
367 | "The machinery that defines the display system is in the ``display.py`` and ``displaypub.py`` \n", | |
373 | "files in ``IPython/core``.\n", |
|
368 | "files in ``IPython/core``.\n", | |
374 | "\n", |
|
369 | "\n", | |
375 | "Once our special printer has been loaded, all polynomials will be represented by their \n", |
|
370 | "Once our special printer has been loaded, all polynomials will be represented by their \n", | |
376 | "mathematical form instead:" |
|
371 | "mathematical form instead:" | |
377 | ] |
|
372 | ] | |
378 | }, |
|
373 | }, | |
379 | { |
|
374 | { | |
380 | "cell_type": "code", |
|
375 | "cell_type": "code", | |
381 | "collapsed": false, |
|
376 | "collapsed": false, | |
382 | "input": [ |
|
377 | "input": [ | |
383 | "p" |
|
378 | "p" | |
384 | ], |
|
379 | ], | |
385 | "language": "python", |
|
380 | "language": "python", | |
386 | "metadata": {}, |
|
381 | "metadata": {}, | |
387 | "outputs": [] |
|
382 | "outputs": [] | |
388 | }, |
|
383 | }, | |
389 | { |
|
384 | { | |
390 | "cell_type": "code", |
|
385 | "cell_type": "code", | |
391 | "collapsed": false, |
|
386 | "collapsed": false, | |
392 | "input": [ |
|
387 | "input": [ | |
393 | "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n", |
|
388 | "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n", | |
394 | "p2" |
|
389 | "p2" | |
395 | ], |
|
390 | ], | |
396 | "language": "python", |
|
391 | "language": "python", | |
397 | "metadata": {}, |
|
392 | "metadata": {}, | |
398 | "outputs": [] |
|
393 | "outputs": [] | |
399 | } |
|
394 | } | |
400 | ], |
|
395 | ], | |
401 | "metadata": {} |
|
396 | "metadata": {} | |
402 | } |
|
397 | } | |
403 | ] |
|
398 | ] | |
404 | } No newline at end of file |
|
399 | } |
@@ -1,270 +1,278 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
|
2 | "metadata": { | |
3 | "name": "Cython Magics" |
|
3 | "name": "Cython Magics" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
|
10 | { | |
11 | "cell_type": "heading", |
|
11 | "cell_type": "heading", | |
12 | "level": 1, |
|
12 | "level": 1, | |
13 | "metadata": {}, |
|
13 | "metadata": {}, | |
14 | "source": [ |
|
14 | "source": [ | |
15 |
"Cython Magic Functions |
|
15 | "Cython Magic Functions" | |
16 | ] |
|
16 | ] | |
17 | }, |
|
17 | }, | |
18 | { |
|
18 | { | |
19 | "cell_type": "heading", |
|
19 | "cell_type": "heading", | |
20 | "level": 2, |
|
20 | "level": 2, | |
21 | "metadata": {}, |
|
21 | "metadata": {}, | |
22 | "source": [ |
|
22 | "source": [ | |
23 | "Loading the extension" |
|
23 | "Loading the extension" | |
24 | ] |
|
24 | ] | |
25 | }, |
|
25 | }, | |
26 | { |
|
26 | { | |
27 | "cell_type": "markdown", |
|
27 | "cell_type": "markdown", | |
28 | "metadata": {}, |
|
28 | "metadata": {}, | |
29 | "source": [ |
|
29 | "source": [ | |
30 | "IPtyhon has a `cythonmagic` extension that contains a number of magic functions for working with Cython code. This extension can be loaded using the `%load_ext` magic as follows:" |
|
30 | "IPtyhon has a `cythonmagic` extension that contains a number of magic functions for working with Cython code. This extension can be loaded using the `%load_ext` magic as follows:" | |
31 | ] |
|
31 | ] | |
32 | }, |
|
32 | }, | |
33 | { |
|
33 | { | |
34 | "cell_type": "code", |
|
34 | "cell_type": "code", | |
35 | "collapsed": false, |
|
35 | "collapsed": false, | |
36 | "input": [ |
|
36 | "input": [ | |
37 | "%load_ext cythonmagic" |
|
37 | "%load_ext cythonmagic" | |
38 | ], |
|
38 | ], | |
39 | "language": "python", |
|
39 | "language": "python", | |
40 | "metadata": {}, |
|
40 | "metadata": {}, | |
41 | "outputs": [], |
|
41 | "outputs": [], | |
42 | "prompt_number": 1 |
|
42 | "prompt_number": 1 | |
43 | }, |
|
43 | }, | |
44 | { |
|
44 | { | |
45 | "cell_type": "heading", |
|
45 | "cell_type": "heading", | |
46 | "level": 2, |
|
46 | "level": 2, | |
47 | "metadata": {}, |
|
47 | "metadata": {}, | |
48 | "source": [ |
|
48 | "source": [ | |
49 | "The %cython_inline magic" |
|
49 | "The %cython_inline magic" | |
50 | ] |
|
50 | ] | |
51 | }, |
|
51 | }, | |
52 | { |
|
52 | { | |
53 | "cell_type": "markdown", |
|
53 | "cell_type": "markdown", | |
54 | "metadata": {}, |
|
54 | "metadata": {}, | |
55 | "source": [ |
|
55 | "source": [ | |
56 | "The `%%cython_inline` magic uses `Cython.inline` to compile a Cython expression. This allows you to enter and run a function body with Cython code. Use a bare `return` statement to return values. " |
|
56 | "The `%%cython_inline` magic uses `Cython.inline` to compile a Cython expression. This allows you to enter and run a function body with Cython code. Use a bare `return` statement to return values. " | |
57 | ] |
|
57 | ] | |
58 | }, |
|
58 | }, | |
59 | { |
|
59 | { | |
60 | "cell_type": "code", |
|
60 | "cell_type": "code", | |
61 | "collapsed": false, |
|
61 | "collapsed": false, | |
62 | "input": [ |
|
62 | "input": [ | |
63 | "a = 10\n", |
|
63 | "a = 10\n", | |
64 | "b = 20" |
|
64 | "b = 20" | |
65 | ], |
|
65 | ], | |
66 | "language": "python", |
|
66 | "language": "python", | |
67 | "metadata": {}, |
|
67 | "metadata": {}, | |
68 | "outputs": [], |
|
68 | "outputs": [], | |
69 | "prompt_number": 2 |
|
69 | "prompt_number": 2 | |
70 | }, |
|
70 | }, | |
71 | { |
|
71 | { | |
72 | "cell_type": "code", |
|
72 | "cell_type": "code", | |
73 | "collapsed": false, |
|
73 | "collapsed": false, | |
74 | "input": [ |
|
74 | "input": [ | |
75 | "%%cython_inline\n", |
|
75 | "%%cython_inline\n", | |
76 | "return a+b" |
|
76 | "return a+b" | |
77 | ], |
|
77 | ], | |
78 | "language": "python", |
|
78 | "language": "python", | |
79 | "metadata": {}, |
|
79 | "metadata": {}, | |
80 | "outputs": [ |
|
80 | "outputs": [ | |
81 | { |
|
81 | { | |
82 | "output_type": "pyout", |
|
82 | "output_type": "pyout", | |
83 | "prompt_number": 3, |
|
83 | "prompt_number": 3, | |
84 | "text": [ |
|
84 | "text": [ | |
85 | "30" |
|
85 | "30" | |
86 | ] |
|
86 | ] | |
87 | } |
|
87 | } | |
88 | ], |
|
88 | ], | |
89 | "prompt_number": 3 |
|
89 | "prompt_number": 3 | |
90 | }, |
|
90 | }, | |
91 | { |
|
91 | { | |
92 | "cell_type": "heading", |
|
92 | "cell_type": "heading", | |
93 | "level": 2, |
|
93 | "level": 2, | |
94 | "metadata": {}, |
|
94 | "metadata": {}, | |
95 | "source": [ |
|
95 | "source": [ | |
96 | "The %cython_pyximport magic" |
|
96 | "The %cython_pyximport magic" | |
97 | ] |
|
97 | ] | |
98 | }, |
|
98 | }, | |
99 | { |
|
99 | { | |
100 | "cell_type": "markdown", |
|
100 | "cell_type": "markdown", | |
101 | "metadata": {}, |
|
101 | "metadata": {}, | |
102 | "source": [ |
|
102 | "source": [ | |
103 | "The `%%cython_pyximport` magic allows you to enter arbitrary Cython code into a cell. That Cython code is written as a `.pyx` file in the current working directory and then imported using `pyximport`. You have the specify the name of the module that the Code will appear in. All symbols from the module are imported automatically by the magic function." |
|
103 | "The `%%cython_pyximport` magic allows you to enter arbitrary Cython code into a cell. That Cython code is written as a `.pyx` file in the current working directory and then imported using `pyximport`. You have the specify the name of the module that the Code will appear in. All symbols from the module are imported automatically by the magic function." | |
104 | ] |
|
104 | ] | |
105 | }, |
|
105 | }, | |
106 | { |
|
106 | { | |
107 | "cell_type": "code", |
|
107 | "cell_type": "code", | |
108 | "collapsed": false, |
|
108 | "collapsed": false, | |
109 | "input": [ |
|
109 | "input": [ | |
110 | "%%cython_pyximport foo\n", |
|
110 | "%%cython_pyximport foo\n", | |
111 | "def f(x):\n", |
|
111 | "def f(x):\n", | |
112 | " return 4.0*x" |
|
112 | " return 4.0*x" | |
113 | ], |
|
113 | ], | |
114 | "language": "python", |
|
114 | "language": "python", | |
115 | "metadata": {}, |
|
115 | "metadata": {}, | |
116 | "outputs": [], |
|
116 | "outputs": [], | |
117 | "prompt_number": 4 |
|
117 | "prompt_number": 4 | |
118 | }, |
|
118 | }, | |
119 | { |
|
119 | { | |
120 | "cell_type": "code", |
|
120 | "cell_type": "code", | |
121 | "collapsed": false, |
|
121 | "collapsed": false, | |
122 | "input": [ |
|
122 | "input": [ | |
123 | "f(10)" |
|
123 | "f(10)" | |
124 | ], |
|
124 | ], | |
125 | "language": "python", |
|
125 | "language": "python", | |
126 | "metadata": {}, |
|
126 | "metadata": {}, | |
127 | "outputs": [ |
|
127 | "outputs": [ | |
128 | { |
|
128 | { | |
129 | "output_type": "pyout", |
|
129 | "output_type": "pyout", | |
130 | "prompt_number": 5, |
|
130 | "prompt_number": 5, | |
131 | "text": [ |
|
131 | "text": [ | |
132 | "40.0" |
|
132 | "40.0" | |
133 | ] |
|
133 | ] | |
134 | } |
|
134 | } | |
135 | ], |
|
135 | ], | |
136 | "prompt_number": 5 |
|
136 | "prompt_number": 5 | |
137 | }, |
|
137 | }, | |
138 | { |
|
138 | { | |
139 | "cell_type": "heading", |
|
139 | "cell_type": "heading", | |
140 | "level": 2, |
|
140 | "level": 2, | |
141 | "metadata": {}, |
|
141 | "metadata": {}, | |
142 | "source": [ |
|
142 | "source": [ | |
143 | "The %cython magic" |
|
143 | "The %cython magic" | |
144 | ] |
|
144 | ] | |
145 | }, |
|
145 | }, | |
146 | { |
|
146 | { | |
147 | "cell_type": "markdown", |
|
147 | "cell_type": "markdown", | |
148 | "metadata": {}, |
|
148 | "metadata": {}, | |
149 | "source": [ |
|
149 | "source": [ | |
150 | "Probably the most important magic is the `%cython` magic. This is similar to the `%%cython_pyximport` magic, but doesn't require you to specify a module name. Instead, the `%%cython` magic uses manages everything using temporary files in the `~/.cython/magic` directory. All of the symbols in the Cython module are imported automatically by the magic.\n", |
|
150 | "Probably the most important magic is the `%cython` magic. This is similar to the `%%cython_pyximport` magic, but doesn't require you to specify a module name. Instead, the `%%cython` magic uses manages everything using temporary files in the `~/.cython/magic` directory. All of the symbols in the Cython module are imported automatically by the magic.\n", | |
151 | "\n", |
|
151 | "\n", | |
152 | "Here is a simple example of a Black-Scholes options pricing algorithm written in Cython. Please note that this example might not compile on non-POSIX systems (e.g., Windows) because of a missing `erf` symbol." |
|
152 | "Here is a simple example of a Black-Scholes options pricing algorithm written in Cython. Please note that this example might not compile on non-POSIX systems (e.g., Windows) because of a missing `erf` symbol." | |
153 | ] |
|
153 | ] | |
154 | }, |
|
154 | }, | |
155 | { |
|
155 | { | |
156 | "cell_type": "code", |
|
156 | "cell_type": "code", | |
157 | "collapsed": false, |
|
157 | "collapsed": false, | |
158 | "input": [ |
|
158 | "input": [ | |
159 | "%%cython\n", |
|
159 | "%%cython\n", | |
160 | "cimport cython\n", |
|
160 | "cimport cython\n", | |
161 | "from libc.math cimport exp, sqrt, pow, log, erf\n", |
|
161 | "from libc.math cimport exp, sqrt, pow, log, erf\n", | |
162 | "\n", |
|
162 | "\n", | |
163 | "@cython.cdivision(True)\n", |
|
163 | "@cython.cdivision(True)\n", | |
164 | "cdef double std_norm_cdf(double x) nogil:\n", |
|
164 | "cdef double std_norm_cdf(double x) nogil:\n", | |
165 | " return 0.5*(1+erf(x/sqrt(2.0)))\n", |
|
165 | " return 0.5*(1+erf(x/sqrt(2.0)))\n", | |
166 | "\n", |
|
166 | "\n", | |
167 | "@cython.cdivision(True)\n", |
|
167 | "@cython.cdivision(True)\n", | |
168 | "def black_scholes(double s, double k, double t, double v,\n", |
|
168 | "def black_scholes(double s, double k, double t, double v,\n", | |
169 | " double rf, double div, double cp):\n", |
|
169 | " double rf, double div, double cp):\n", | |
170 | " \"\"\"Price an option using the Black-Scholes model.\n", |
|
170 | " \"\"\"Price an option using the Black-Scholes model.\n", | |
171 | " \n", |
|
171 | " \n", | |
172 | " s : initial stock price\n", |
|
172 | " s : initial stock price\n", | |
173 | " k : strike price\n", |
|
173 | " k : strike price\n", | |
174 | " t : expiration time\n", |
|
174 | " t : expiration time\n", | |
175 | " v : volatility\n", |
|
175 | " v : volatility\n", | |
176 | " rf : risk-free rate\n", |
|
176 | " rf : risk-free rate\n", | |
177 | " div : dividend\n", |
|
177 | " div : dividend\n", | |
178 | " cp : +1/-1 for call/put\n", |
|
178 | " cp : +1/-1 for call/put\n", | |
179 | " \"\"\"\n", |
|
179 | " \"\"\"\n", | |
180 | " cdef double d1, d2, optprice\n", |
|
180 | " cdef double d1, d2, optprice\n", | |
181 | " with nogil:\n", |
|
181 | " with nogil:\n", | |
182 | " d1 = (log(s/k)+(rf-div+0.5*pow(v,2))*t)/(v*sqrt(t))\n", |
|
182 | " d1 = (log(s/k)+(rf-div+0.5*pow(v,2))*t)/(v*sqrt(t))\n", | |
183 | " d2 = d1 - v*sqrt(t)\n", |
|
183 | " d2 = d1 - v*sqrt(t)\n", | |
184 | " optprice = cp*s*exp(-div*t)*std_norm_cdf(cp*d1) - \\\n", |
|
184 | " optprice = cp*s*exp(-div*t)*std_norm_cdf(cp*d1) - \\\n", | |
185 | " cp*k*exp(-rf*t)*std_norm_cdf(cp*d2)\n", |
|
185 | " cp*k*exp(-rf*t)*std_norm_cdf(cp*d2)\n", | |
186 | " return optprice" |
|
186 | " return optprice" | |
187 | ], |
|
187 | ], | |
188 | "language": "python", |
|
188 | "language": "python", | |
189 | "metadata": {}, |
|
189 | "metadata": {}, | |
190 | "outputs": [], |
|
190 | "outputs": [], | |
191 | "prompt_number": 6 |
|
191 | "prompt_number": 6 | |
192 | }, |
|
192 | }, | |
193 | { |
|
193 | { | |
194 | "cell_type": "code", |
|
194 | "cell_type": "code", | |
195 | "collapsed": false, |
|
195 | "collapsed": false, | |
196 | "input": [ |
|
196 | "input": [ | |
197 | "black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)" |
|
197 | "black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)" | |
198 | ], |
|
198 | ], | |
199 | "language": "python", |
|
199 | "language": "python", | |
200 | "metadata": {}, |
|
200 | "metadata": {}, | |
201 | "outputs": [ |
|
201 | "outputs": [ | |
202 | { |
|
202 | { | |
203 | "output_type": "pyout", |
|
203 | "output_type": "pyout", | |
204 | "prompt_number": 7, |
|
204 | "prompt_number": 7, | |
205 | "text": [ |
|
205 | "text": [ | |
206 | "10.327861752731728" |
|
206 | "10.327861752731728" | |
207 | ] |
|
207 | ] | |
208 | } |
|
208 | } | |
209 | ], |
|
209 | ], | |
210 | "prompt_number": 7 |
|
210 | "prompt_number": 7 | |
211 | }, |
|
211 | }, | |
212 | { |
|
212 | { | |
213 | "cell_type": "code", |
|
213 | "cell_type": "code", | |
214 | "collapsed": false, |
|
214 | "collapsed": false, | |
215 | "input": [ |
|
215 | "input": [ | |
216 | "%timeit black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)" |
|
216 | "%timeit black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)" | |
217 | ], |
|
217 | ], | |
218 | "language": "python", |
|
218 | "language": "python", | |
219 | "metadata": {}, |
|
219 | "metadata": {}, | |
220 | "outputs": [ |
|
220 | "outputs": [ | |
221 | { |
|
221 | { | |
222 | "output_type": "stream", |
|
222 | "output_type": "stream", | |
223 | "stream": "stdout", |
|
223 | "stream": "stdout", | |
224 | "text": [ |
|
224 | "text": [ | |
225 | "1000000 loops, best of 3: 821 ns per loop\n" |
|
225 | "1000000 loops, best of 3: 821 ns per loop\n" | |
226 | ] |
|
226 | ] | |
227 | } |
|
227 | } | |
228 | ], |
|
228 | ], | |
229 | "prompt_number": 8 |
|
229 | "prompt_number": 8 | |
230 | }, |
|
230 | }, | |
231 | { |
|
231 | { | |
|
232 | "cell_type": "heading", | |||
|
233 | "level": 2, | |||
|
234 | "metadata": {}, | |||
|
235 | "source": [ | |||
|
236 | "External libraries" | |||
|
237 | ] | |||
|
238 | }, | |||
|
239 | { | |||
232 | "cell_type": "markdown", |
|
240 | "cell_type": "markdown", | |
233 | "metadata": {}, |
|
241 | "metadata": {}, | |
234 | "source": [ |
|
242 | "source": [ | |
235 | "Cython allows you to specify additional libraries to be linked with your extension, you can do so with the `-l` flag (also spelled `--lib`). Note that this flag can be passed more than once to specify multiple libraries, such as `-lm -llib2 --lib lib3`. Here's a simple example of how to access the system math library:" |
|
243 | "Cython allows you to specify additional libraries to be linked with your extension, you can do so with the `-l` flag (also spelled `--lib`). Note that this flag can be passed more than once to specify multiple libraries, such as `-lm -llib2 --lib lib3`. Here's a simple example of how to access the system math library:" | |
236 | ] |
|
244 | ] | |
237 | }, |
|
245 | }, | |
238 | { |
|
246 | { | |
239 | "cell_type": "code", |
|
247 | "cell_type": "code", | |
240 | "collapsed": false, |
|
248 | "collapsed": false, | |
241 | "input": [ |
|
249 | "input": [ | |
242 | "%%cython -lm\n", |
|
250 | "%%cython -lm\n", | |
243 | "from libc.math cimport sin\n", |
|
251 | "from libc.math cimport sin\n", | |
244 | "print 'sin(1)=', sin(1)" |
|
252 | "print 'sin(1)=', sin(1)" | |
245 | ], |
|
253 | ], | |
246 | "language": "python", |
|
254 | "language": "python", | |
247 | "metadata": {}, |
|
255 | "metadata": {}, | |
248 | "outputs": [ |
|
256 | "outputs": [ | |
249 | { |
|
257 | { | |
250 | "output_type": "stream", |
|
258 | "output_type": "stream", | |
251 | "stream": "stdout", |
|
259 | "stream": "stdout", | |
252 | "text": [ |
|
260 | "text": [ | |
253 | "sin(1)= 0.841470984808\n" |
|
261 | "sin(1)= 0.841470984808\n" | |
254 | ] |
|
262 | ] | |
255 | } |
|
263 | } | |
256 | ], |
|
264 | ], | |
257 | "prompt_number": 9 |
|
265 | "prompt_number": 9 | |
258 | }, |
|
266 | }, | |
259 | { |
|
267 | { | |
260 | "cell_type": "markdown", |
|
268 | "cell_type": "markdown", | |
261 | "metadata": {}, |
|
269 | "metadata": {}, | |
262 | "source": [ |
|
270 | "source": [ | |
263 | "You can similarly use the `-I/--include` flag to add include directories to the search path, and `-c/--compile-args` to add extra flags that are passed to Cython via the `extra_compile_args` of the distutils `Extension` class. Please see [the Cython docs on C library usage](http://docs.cython.org/src/tutorial/clibraries.html) for more details on the use of these flags." |
|
271 | "You can similarly use the `-I/--include` flag to add include directories to the search path, and `-c/--compile-args` to add extra flags that are passed to Cython via the `extra_compile_args` of the distutils `Extension` class. Please see [the Cython docs on C library usage](http://docs.cython.org/src/tutorial/clibraries.html) for more details on the use of these flags." | |
264 | ] |
|
272 | ] | |
265 | } |
|
273 | } | |
266 | ], |
|
274 | ], | |
267 | "metadata": {} |
|
275 | "metadata": {} | |
268 | } |
|
276 | } | |
269 | ] |
|
277 | ] | |
270 | } No newline at end of file |
|
278 | } |
@@ -1,252 +1,252 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
|
2 | "metadata": { | |
3 | "name": "Data Publication API" |
|
3 | "name": "Data Publication API" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
|
10 | { | |
11 | "cell_type": "heading", |
|
11 | "cell_type": "heading", | |
12 | "level": 1, |
|
12 | "level": 1, | |
13 | "metadata": {}, |
|
13 | "metadata": {}, | |
14 | "source": [ |
|
14 | "source": [ | |
15 | "IPython's Data Publication API" |
|
15 | "IPython's Data Publication API" | |
16 | ] |
|
16 | ] | |
17 | }, |
|
17 | }, | |
18 | { |
|
18 | { | |
19 | "cell_type": "markdown", |
|
19 | "cell_type": "markdown", | |
20 | "metadata": {}, |
|
20 | "metadata": {}, | |
21 | "source": [ |
|
21 | "source": [ | |
22 |
"IPython has an API that allows IPython Engines to publish data back to the Client. This |
|
22 | "IPython has an API that allows IPython Engines to publish data back to the Client. This Notebook shows how this API works." | |
23 | ] |
|
23 | ] | |
24 | }, |
|
24 | }, | |
25 | { |
|
25 | { | |
26 | "cell_type": "heading", |
|
26 | "cell_type": "heading", | |
27 | "level": 2, |
|
27 | "level": 2, | |
28 | "metadata": {}, |
|
28 | "metadata": {}, | |
29 | "source": [ |
|
29 | "source": [ | |
30 | "Setup" |
|
30 | "Setup" | |
31 | ] |
|
31 | ] | |
32 | }, |
|
32 | }, | |
33 | { |
|
33 | { | |
34 | "cell_type": "markdown", |
|
34 | "cell_type": "markdown", | |
35 | "metadata": {}, |
|
35 | "metadata": {}, | |
36 | "source": [ |
|
36 | "source": [ | |
37 | "We begin by enabling pylab mode and creating a `Client` object to work with an IPython cluster." |
|
37 | "We begin by enabling pylab mode and creating a `Client` object to work with an IPython cluster." | |
38 | ] |
|
38 | ] | |
39 | }, |
|
39 | }, | |
40 | { |
|
40 | { | |
41 | "cell_type": "code", |
|
41 | "cell_type": "code", | |
42 | "collapsed": false, |
|
42 | "collapsed": false, | |
43 | "input": [ |
|
43 | "input": [ | |
44 | "%pylab inline" |
|
44 | "%pylab inline" | |
45 | ], |
|
45 | ], | |
46 | "language": "python", |
|
46 | "language": "python", | |
47 | "metadata": {}, |
|
47 | "metadata": {}, | |
48 | "outputs": [ |
|
48 | "outputs": [ | |
49 | { |
|
49 | { | |
50 | "output_type": "stream", |
|
50 | "output_type": "stream", | |
51 | "stream": "stdout", |
|
51 | "stream": "stdout", | |
52 | "text": [ |
|
52 | "text": [ | |
53 | "\n", |
|
53 | "\n", | |
54 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", |
|
54 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", | |
55 | "For more information, type 'help(pylab)'.\n" |
|
55 | "For more information, type 'help(pylab)'.\n" | |
56 | ] |
|
56 | ] | |
57 | } |
|
57 | } | |
58 | ], |
|
58 | ], | |
59 | "prompt_number": 48 |
|
59 | "prompt_number": 48 | |
60 | }, |
|
60 | }, | |
61 | { |
|
61 | { | |
62 | "cell_type": "code", |
|
62 | "cell_type": "code", | |
63 | "collapsed": false, |
|
63 | "collapsed": false, | |
64 | "input": [ |
|
64 | "input": [ | |
65 | "from IPython.parallel import Client" |
|
65 | "from IPython.parallel import Client" | |
66 | ], |
|
66 | ], | |
67 | "language": "python", |
|
67 | "language": "python", | |
68 | "metadata": {}, |
|
68 | "metadata": {}, | |
69 | "outputs": [], |
|
69 | "outputs": [], | |
70 | "prompt_number": 12 |
|
70 | "prompt_number": 12 | |
71 | }, |
|
71 | }, | |
72 | { |
|
72 | { | |
73 | "cell_type": "code", |
|
73 | "cell_type": "code", | |
74 | "collapsed": false, |
|
74 | "collapsed": false, | |
75 | "input": [ |
|
75 | "input": [ | |
76 | "c = Client()\n", |
|
76 | "c = Client()\n", | |
77 | "dv = c[:]\n", |
|
77 | "dv = c[:]\n", | |
78 | "dv.block = False" |
|
78 | "dv.block = False" | |
79 | ], |
|
79 | ], | |
80 | "language": "python", |
|
80 | "language": "python", | |
81 | "metadata": {}, |
|
81 | "metadata": {}, | |
82 | "outputs": [], |
|
82 | "outputs": [], | |
83 | "prompt_number": 13 |
|
83 | "prompt_number": 13 | |
84 | }, |
|
84 | }, | |
85 | { |
|
85 | { | |
86 | "cell_type": "heading", |
|
86 | "cell_type": "heading", | |
87 | "level": 2, |
|
87 | "level": 2, | |
88 | "metadata": {}, |
|
88 | "metadata": {}, | |
89 | "source": [ |
|
89 | "source": [ | |
90 | "Simple publication" |
|
90 | "Simple publication" | |
91 | ] |
|
91 | ] | |
92 | }, |
|
92 | }, | |
93 | { |
|
93 | { | |
94 | "cell_type": "markdown", |
|
94 | "cell_type": "markdown", | |
95 | "metadata": {}, |
|
95 | "metadata": {}, | |
96 | "source": [ |
|
96 | "source": [ | |
97 | "Here is a simple Python function we are going to run on the Engines. This function uses `publish_data` to publish a simple Python dictionary when it is run." |
|
97 | "Here is a simple Python function we are going to run on the Engines. This function uses `publish_data` to publish a simple Python dictionary when it is run." | |
98 | ] |
|
98 | ] | |
99 | }, |
|
99 | }, | |
100 | { |
|
100 | { | |
101 | "cell_type": "code", |
|
101 | "cell_type": "code", | |
102 | "collapsed": false, |
|
102 | "collapsed": false, | |
103 | "input": [ |
|
103 | "input": [ | |
104 | "def publish_it():\n", |
|
104 | "def publish_it():\n", | |
105 | " from IPython.zmq.datapub import publish_data\n", |
|
105 | " from IPython.zmq.datapub import publish_data\n", | |
106 | " publish_data(dict(a='hi'))" |
|
106 | " publish_data(dict(a='hi'))" | |
107 | ], |
|
107 | ], | |
108 | "language": "python", |
|
108 | "language": "python", | |
109 | "metadata": {}, |
|
109 | "metadata": {}, | |
110 | "outputs": [], |
|
110 | "outputs": [], | |
111 | "prompt_number": 14 |
|
111 | "prompt_number": 14 | |
112 | }, |
|
112 | }, | |
113 | { |
|
113 | { | |
114 | "cell_type": "markdown", |
|
114 | "cell_type": "markdown", | |
115 | "metadata": {}, |
|
115 | "metadata": {}, | |
116 | "source": [ |
|
116 | "source": [ | |
117 | "We run the function on the Engines using `apply_async` and save the returned `AsyncResult` object:" |
|
117 | "We run the function on the Engines using `apply_async` and save the returned `AsyncResult` object:" | |
118 | ] |
|
118 | ] | |
119 | }, |
|
119 | }, | |
120 | { |
|
120 | { | |
121 | "cell_type": "code", |
|
121 | "cell_type": "code", | |
122 | "collapsed": false, |
|
122 | "collapsed": false, | |
123 | "input": [ |
|
123 | "input": [ | |
124 | "ar = dv.apply_async(publish_it)" |
|
124 | "ar = dv.apply_async(publish_it)" | |
125 | ], |
|
125 | ], | |
126 | "language": "python", |
|
126 | "language": "python", | |
127 | "metadata": {}, |
|
127 | "metadata": {}, | |
128 | "outputs": [], |
|
128 | "outputs": [], | |
129 | "prompt_number": 15 |
|
129 | "prompt_number": 15 | |
130 | }, |
|
130 | }, | |
131 | { |
|
131 | { | |
132 | "cell_type": "markdown", |
|
132 | "cell_type": "markdown", | |
133 | "metadata": {}, |
|
133 | "metadata": {}, | |
134 | "source": [ |
|
134 | "source": [ | |
135 | "The published data from each engine is then available under the `.data` attribute of the `AsyncResult` object." |
|
135 | "The published data from each engine is then available under the `.data` attribute of the `AsyncResult` object." | |
136 | ] |
|
136 | ] | |
137 | }, |
|
137 | }, | |
138 | { |
|
138 | { | |
139 | "cell_type": "code", |
|
139 | "cell_type": "code", | |
140 | "collapsed": false, |
|
140 | "collapsed": false, | |
141 | "input": [ |
|
141 | "input": [ | |
142 | "ar.data" |
|
142 | "ar.data" | |
143 | ], |
|
143 | ], | |
144 | "language": "python", |
|
144 | "language": "python", | |
145 | "metadata": {}, |
|
145 | "metadata": {}, | |
146 | "outputs": [ |
|
146 | "outputs": [ | |
147 | { |
|
147 | { | |
148 | "output_type": "pyout", |
|
148 | "output_type": "pyout", | |
149 | "prompt_number": 16, |
|
149 | "prompt_number": 16, | |
150 | "text": [ |
|
150 | "text": [ | |
151 | "[{'a': 'hi'}, {'a': 'hi'}, {'a': 'hi'}, {'a': 'hi'}]" |
|
151 | "[{'a': 'hi'}, {'a': 'hi'}, {'a': 'hi'}, {'a': 'hi'}]" | |
152 | ] |
|
152 | ] | |
153 | } |
|
153 | } | |
154 | ], |
|
154 | ], | |
155 | "prompt_number": 16 |
|
155 | "prompt_number": 16 | |
156 | }, |
|
156 | }, | |
157 | { |
|
157 | { | |
158 | "cell_type": "markdown", |
|
158 | "cell_type": "markdown", | |
159 | "metadata": {}, |
|
159 | "metadata": {}, | |
160 | "source": [ |
|
160 | "source": [ | |
161 | "Each time `publish_data` is called, the `.data` attribute is updated with the most recently published data." |
|
161 | "Each time `publish_data` is called, the `.data` attribute is updated with the most recently published data." | |
162 | ] |
|
162 | ] | |
163 | }, |
|
163 | }, | |
164 | { |
|
164 | { | |
165 | "cell_type": "heading", |
|
165 | "cell_type": "heading", | |
166 | "level": 2, |
|
166 | "level": 2, | |
167 | "metadata": {}, |
|
167 | "metadata": {}, | |
168 | "source": [ |
|
168 | "source": [ | |
169 | "Simulation loop" |
|
169 | "Simulation loop" | |
170 | ] |
|
170 | ] | |
171 | }, |
|
171 | }, | |
172 | { |
|
172 | { | |
173 | "cell_type": "markdown", |
|
173 | "cell_type": "markdown", | |
174 | "metadata": {}, |
|
174 | "metadata": {}, | |
175 | "source": [ |
|
175 | "source": [ | |
176 | "In many cases, the Engines will be running a simulation loop and we will want to publish data at each time step of the simulation. To show how this works, we create a mock simulation function that iterates over a loop and publishes a NumPy array and loop variable at each time step. By inserting a call to `time.sleep(1)`, we ensure that new data will be published every second." |
|
176 | "In many cases, the Engines will be running a simulation loop and we will want to publish data at each time step of the simulation. To show how this works, we create a mock simulation function that iterates over a loop and publishes a NumPy array and loop variable at each time step. By inserting a call to `time.sleep(1)`, we ensure that new data will be published every second." | |
177 | ] |
|
177 | ] | |
178 | }, |
|
178 | }, | |
179 | { |
|
179 | { | |
180 | "cell_type": "code", |
|
180 | "cell_type": "code", | |
181 | "collapsed": false, |
|
181 | "collapsed": false, | |
182 | "input": [ |
|
182 | "input": [ | |
183 | "def simulation_loop():\n", |
|
183 | "def simulation_loop():\n", | |
184 | " from IPython.zmq.datapub import publish_data\n", |
|
184 | " from IPython.zmq.datapub import publish_data\n", | |
185 | " import time\n", |
|
185 | " import time\n", | |
186 | " import numpy as np\n", |
|
186 | " import numpy as np\n", | |
187 | " for i in range(10):\n", |
|
187 | " for i in range(10):\n", | |
188 | " publish_data(dict(a=np.random.rand(20), i=i))\n", |
|
188 | " publish_data(dict(a=np.random.rand(20), i=i))\n", | |
189 | " time.sleep(1)" |
|
189 | " time.sleep(1)" | |
190 | ], |
|
190 | ], | |
191 | "language": "python", |
|
191 | "language": "python", | |
192 | "metadata": {}, |
|
192 | "metadata": {}, | |
193 | "outputs": [], |
|
193 | "outputs": [], | |
194 | "prompt_number": 57 |
|
194 | "prompt_number": 57 | |
195 | }, |
|
195 | }, | |
196 | { |
|
196 | { | |
197 | "cell_type": "markdown", |
|
197 | "cell_type": "markdown", | |
198 | "metadata": {}, |
|
198 | "metadata": {}, | |
199 | "source": [ |
|
199 | "source": [ | |
200 | "Again, we run the `simulation_loop` function in parallel using `apply_async` and save the returned `AsyncResult` object." |
|
200 | "Again, we run the `simulation_loop` function in parallel using `apply_async` and save the returned `AsyncResult` object." | |
201 | ] |
|
201 | ] | |
202 | }, |
|
202 | }, | |
203 | { |
|
203 | { | |
204 | "cell_type": "code", |
|
204 | "cell_type": "code", | |
205 | "collapsed": false, |
|
205 | "collapsed": false, | |
206 | "input": [ |
|
206 | "input": [ | |
207 | "ar = dv.apply_async(simulation_loop)" |
|
207 | "ar = dv.apply_async(simulation_loop)" | |
208 | ], |
|
208 | ], | |
209 | "language": "python", |
|
209 | "language": "python", | |
210 | "metadata": {}, |
|
210 | "metadata": {}, | |
211 | "outputs": [], |
|
211 | "outputs": [], | |
212 | "prompt_number": 58 |
|
212 | "prompt_number": 58 | |
213 | }, |
|
213 | }, | |
214 | { |
|
214 | { | |
215 | "cell_type": "markdown", |
|
215 | "cell_type": "markdown", | |
216 | "metadata": {}, |
|
216 | "metadata": {}, | |
217 | "source": [ |
|
217 | "source": [ | |
218 | "New data will be published by the Engines every second. Anytime we access `ar.data`, we will get the most recently published data." |
|
218 | "New data will be published by the Engines every second. Anytime we access `ar.data`, we will get the most recently published data." | |
219 | ] |
|
219 | ] | |
220 | }, |
|
220 | }, | |
221 | { |
|
221 | { | |
222 | "cell_type": "code", |
|
222 | "cell_type": "code", | |
223 | "collapsed": false, |
|
223 | "collapsed": false, | |
224 | "input": [ |
|
224 | "input": [ | |
225 | "data = ar.data\n", |
|
225 | "data = ar.data\n", | |
226 | "for i, d in enumerate(data):\n", |
|
226 | "for i, d in enumerate(data):\n", | |
227 | " plot(d['a'], label='engine: '+str(i))\n", |
|
227 | " plot(d['a'], label='engine: '+str(i))\n", | |
228 | "title('Data published at time step: ' + str(data[0]['i']))\n", |
|
228 | "title('Data published at time step: ' + str(data[0]['i']))\n", | |
229 | "legend()" |
|
229 | "legend()" | |
230 | ], |
|
230 | ], | |
231 | "language": "python", |
|
231 | "language": "python", | |
232 | "metadata": {}, |
|
232 | "metadata": {}, | |
233 | "outputs": [ |
|
233 | "outputs": [ | |
234 | { |
|
234 | { | |
235 | "output_type": "pyout", |
|
235 | "output_type": "pyout", | |
236 | "prompt_number": 61, |
|
236 | "prompt_number": 61, | |
237 | "text": [ |
|
237 | "text": [ | |
238 | "<matplotlib.legend.Legend at 0x10a8ed8d0>" |
|
238 | "<matplotlib.legend.Legend at 0x10a8ed8d0>" | |
239 | ] |
|
239 | ] | |
240 | }, |
|
240 | }, | |
241 | { |
|
241 | { | |
242 | "output_type": "display_data", |
|
242 | "output_type": "display_data", | |
243 | "png": 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E3ib3eDzDxbMVFWRrVRUhhJBFFy6QvY7eb29TU0NIVJTHpxlCCRg2uHZ2Nhw5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QAAAf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\nEOGmzhhjIcJNnTHGQoSbOmOMhcgnmx+mZE+SBv4AAAAASUVORK5CYII=\n" 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243 | "png": 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E3ib3eDzDxbMVFWRrVRUhhJBFFy6QvY7eb29TU0NIVJTHpxlCCRg2uHZ2Nhw5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Ul1yOcQURQ2enqi/8Xvw5iIQqnBZkOTrBktI4+gbkwGxUw1xBXx7XxhYIJ651gn7my6M+6vD8xJ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\nEOGmzhhjIcJNnTHGQoSbOmOMhcgnmx+mZE+SBv4AAAAASUVORK5CYII=\n" 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244 | } |
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244 | } | |
245 | ], |
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245 | ], | |
246 | "prompt_number": 61 |
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246 | "prompt_number": 61 | |
247 | } |
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247 | } | |
248 | ], |
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248 | ], | |
249 | "metadata": {} |
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249 | "metadata": {} | |
250 | } |
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250 | } | |
251 | ] |
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251 | ] | |
252 | } No newline at end of file |
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252 | } |
@@ -1,371 +1,371 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
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2 | "metadata": { | |
3 | "name": "Octave Magic" |
|
3 | "name": "Octave Magic" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
|
10 | { | |
11 | "cell_type": "heading", |
|
11 | "cell_type": "heading", | |
12 | "level": 1, |
|
12 | "level": 1, | |
13 | "metadata": {}, |
|
13 | "metadata": {}, | |
14 | "source": [ |
|
14 | "source": [ | |
15 |
" |
|
15 | "Using Octave Inside IPython" | |
16 | ] |
|
16 | ] | |
17 | }, |
|
17 | }, | |
18 | { |
|
18 | { | |
19 | "cell_type": "heading", |
|
19 | "cell_type": "heading", | |
20 | "level": 2, |
|
20 | "level": 2, | |
21 | "metadata": {}, |
|
21 | "metadata": {}, | |
22 | "source": [ |
|
22 | "source": [ | |
23 | "Installation" |
|
23 | "Installation" | |
24 | ] |
|
24 | ] | |
25 | }, |
|
25 | }, | |
26 | { |
|
26 | { | |
27 | "cell_type": "markdown", |
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27 | "cell_type": "markdown", | |
28 | "metadata": {}, |
|
28 | "metadata": {}, | |
29 | "source": [ |
|
29 | "source": [ | |
30 | "The `octavemagic` extension provides the ability to interact with Octave. It depends on the `oct2py` package,\n", |
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30 | "The `octavemagic` extension provides the ability to interact with Octave. It depends on the `oct2py` package,\n", | |
31 | "which may be installed using `easy_install`.\n", |
|
31 | "which may be installed using `easy_install`.\n", | |
32 | "\n", |
|
32 | "\n", | |
33 | "To enable the extension, load it as follows:" |
|
33 | "To enable the extension, load it as follows:" | |
34 | ] |
|
34 | ] | |
35 | }, |
|
35 | }, | |
36 | { |
|
36 | { | |
37 | "cell_type": "code", |
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37 | "cell_type": "code", | |
38 | "collapsed": false, |
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38 | "collapsed": false, | |
39 | "input": [ |
|
39 | "input": [ | |
40 | "%load_ext octavemagic" |
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40 | "%load_ext octavemagic" | |
41 | ], |
|
41 | ], | |
42 | "language": "python", |
|
42 | "language": "python", | |
43 | "metadata": {}, |
|
43 | "metadata": {}, | |
44 | "outputs": [], |
|
44 | "outputs": [], | |
45 | "prompt_number": 18 |
|
45 | "prompt_number": 18 | |
46 | }, |
|
46 | }, | |
47 | { |
|
47 | { | |
48 | "cell_type": "heading", |
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48 | "cell_type": "heading", | |
49 | "level": 2, |
|
49 | "level": 2, | |
50 | "metadata": {}, |
|
50 | "metadata": {}, | |
51 | "source": [ |
|
51 | "source": [ | |
52 | "Overview" |
|
52 | "Overview" | |
53 | ] |
|
53 | ] | |
54 | }, |
|
54 | }, | |
55 | { |
|
55 | { | |
56 | "cell_type": "markdown", |
|
56 | "cell_type": "markdown", | |
57 | "metadata": {}, |
|
57 | "metadata": {}, | |
58 | "source": [ |
|
58 | "source": [ | |
59 | "Loading the extension enables three magic functions: `%octave`, `%octave_push`, and `%octave_pull`.\n", |
|
59 | "Loading the extension enables three magic functions: `%octave`, `%octave_push`, and `%octave_pull`.\n", | |
60 | "\n", |
|
60 | "\n", | |
61 | "The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.\n", |
|
61 | "The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.\n", | |
62 | "Here you see an example of how to execute a single line of Octave, and how to transfer the generated value back to Python:" |
|
62 | "Here you see an example of how to execute a single line of Octave, and how to transfer the generated value back to Python:" | |
63 | ] |
|
63 | ] | |
64 | }, |
|
64 | }, | |
65 | { |
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65 | { | |
66 | "cell_type": "code", |
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66 | "cell_type": "code", | |
67 | "collapsed": false, |
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67 | "collapsed": false, | |
68 | "input": [ |
|
68 | "input": [ | |
69 | "x = %octave [1 2; 3 4];\n", |
|
69 | "x = %octave [1 2; 3 4];\n", | |
70 | "x" |
|
70 | "x" | |
71 | ], |
|
71 | ], | |
72 | "language": "python", |
|
72 | "language": "python", | |
73 | "metadata": {}, |
|
73 | "metadata": {}, | |
74 | "outputs": [ |
|
74 | "outputs": [ | |
75 | { |
|
75 | { | |
76 | "output_type": "pyout", |
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76 | "output_type": "pyout", | |
77 | "prompt_number": 19, |
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77 | "prompt_number": 19, | |
78 | "text": [ |
|
78 | "text": [ | |
79 | "array([[ 1., 2.],\n", |
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79 | "array([[ 1., 2.],\n", | |
80 | " [ 3., 4.]])" |
|
80 | " [ 3., 4.]])" | |
81 | ] |
|
81 | ] | |
82 | } |
|
82 | } | |
83 | ], |
|
83 | ], | |
84 | "prompt_number": 19 |
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84 | "prompt_number": 19 | |
85 | }, |
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85 | }, | |
86 | { |
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86 | { | |
87 | "cell_type": "code", |
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87 | "cell_type": "code", | |
88 | "collapsed": false, |
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88 | "collapsed": false, | |
89 | "input": [ |
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89 | "input": [ | |
90 | "a = [1, 2, 3]\n", |
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90 | "a = [1, 2, 3]\n", | |
91 | "\n", |
|
91 | "\n", | |
92 | "%octave_push a\n", |
|
92 | "%octave_push a\n", | |
93 | "%octave a = a * 2;\n", |
|
93 | "%octave a = a * 2;\n", | |
94 | "%octave_pull a\n", |
|
94 | "%octave_pull a\n", | |
95 | "a" |
|
95 | "a" | |
96 | ], |
|
96 | ], | |
97 | "language": "python", |
|
97 | "language": "python", | |
98 | "metadata": {}, |
|
98 | "metadata": {}, | |
99 | "outputs": [ |
|
99 | "outputs": [ | |
100 | { |
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100 | { | |
101 | "output_type": "pyout", |
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101 | "output_type": "pyout", | |
102 | "prompt_number": 20, |
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102 | "prompt_number": 20, | |
103 | "text": [ |
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103 | "text": [ | |
104 | "array([[2, 4, 6]])" |
|
104 | "array([[2, 4, 6]])" | |
105 | ] |
|
105 | ] | |
106 | } |
|
106 | } | |
107 | ], |
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107 | ], | |
108 | "prompt_number": 20 |
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108 | "prompt_number": 20 | |
109 | }, |
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109 | }, | |
110 | { |
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110 | { | |
111 | "cell_type": "markdown", |
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111 | "cell_type": "markdown", | |
112 | "metadata": {}, |
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112 | "metadata": {}, | |
113 | "source": [ |
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113 | "source": [ | |
114 | "When using the cell magic, `%%octave` (note the double `%`), multiple lines of Octave can be executed together. Unlike\n", |
|
114 | "When using the cell magic, `%%octave` (note the double `%`), multiple lines of Octave can be executed together. Unlike\n", | |
115 | "with the single cell magic, no value is returned, so we use the `-i` and `-o` flags to specify input and output variables." |
|
115 | "with the single cell magic, no value is returned, so we use the `-i` and `-o` flags to specify input and output variables." | |
116 | ] |
|
116 | ] | |
117 | }, |
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117 | }, | |
118 | { |
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118 | { | |
119 | "cell_type": "code", |
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119 | "cell_type": "code", | |
120 | "collapsed": false, |
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120 | "collapsed": false, | |
121 | "input": [ |
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121 | "input": [ | |
122 | "%%octave -i x -o y\n", |
|
122 | "%%octave -i x -o y\n", | |
123 | "y = x + 3;" |
|
123 | "y = x + 3;" | |
124 | ], |
|
124 | ], | |
125 | "language": "python", |
|
125 | "language": "python", | |
126 | "metadata": {}, |
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126 | "metadata": {}, | |
127 | "outputs": [], |
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127 | "outputs": [], | |
128 | "prompt_number": 21 |
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128 | "prompt_number": 21 | |
129 | }, |
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129 | }, | |
130 | { |
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130 | { | |
131 | "cell_type": "code", |
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131 | "cell_type": "code", | |
132 | "collapsed": false, |
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132 | "collapsed": false, | |
133 | "input": [ |
|
133 | "input": [ | |
134 | "y" |
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134 | "y" | |
135 | ], |
|
135 | ], | |
136 | "language": "python", |
|
136 | "language": "python", | |
137 | "metadata": {}, |
|
137 | "metadata": {}, | |
138 | "outputs": [ |
|
138 | "outputs": [ | |
139 | { |
|
139 | { | |
140 | "output_type": "pyout", |
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140 | "output_type": "pyout", | |
141 | "prompt_number": 22, |
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141 | "prompt_number": 22, | |
142 | "text": [ |
|
142 | "text": [ | |
143 | "array([[ 4., 5.],\n", |
|
143 | "array([[ 4., 5.],\n", | |
144 | " [ 6., 7.]])" |
|
144 | " [ 6., 7.]])" | |
145 | ] |
|
145 | ] | |
146 | } |
|
146 | } | |
147 | ], |
|
147 | ], | |
148 | "prompt_number": 22 |
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148 | "prompt_number": 22 | |
149 | }, |
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149 | }, | |
150 | { |
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150 | { | |
151 | "cell_type": "heading", |
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151 | "cell_type": "heading", | |
152 | "level": 2, |
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152 | "level": 2, | |
153 | "metadata": {}, |
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153 | "metadata": {}, | |
154 | "source": [ |
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154 | "source": [ | |
155 | "Plotting" |
|
155 | "Plotting" | |
156 | ] |
|
156 | ] | |
157 | }, |
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157 | }, | |
158 | { |
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158 | { | |
159 | "cell_type": "markdown", |
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159 | "cell_type": "markdown", | |
160 | "metadata": {}, |
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160 | "metadata": {}, | |
161 | "source": [ |
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161 | "source": [ | |
162 | "Plot output is automatically captured and displayed, and using the `-f` flag you may choose its format (currently, `png` and `svg` are supported)." |
|
162 | "Plot output is automatically captured and displayed, and using the `-f` flag you may choose its format (currently, `png` and `svg` are supported)." | |
163 | ] |
|
163 | ] | |
164 | }, |
|
164 | }, | |
165 | { |
|
165 | { | |
166 | "cell_type": "code", |
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166 | "cell_type": "code", | |
167 | "collapsed": false, |
|
167 | "collapsed": false, | |
168 | "input": [ |
|
168 | "input": [ | |
169 | "%%octave -f svg\n", |
|
169 | "%%octave -f svg\n", | |
170 | "\n", |
|
170 | "\n", | |
171 | "p = [12 -2.5 -8 -0.1 8];\n", |
|
171 | "p = [12 -2.5 -8 -0.1 8];\n", | |
172 | "x = 0:0.01:1;\n", |
|
172 | "x = 0:0.01:1;\n", | |
173 | "\n", |
|
173 | "\n", | |
174 | "polyout(p, 'x')\n", |
|
174 | "polyout(p, 'x')\n", | |
175 | "plot(x, polyval(p, x));" |
|
175 | "plot(x, polyval(p, x));" | |
176 | ], |
|
176 | ], | |
177 | "language": "python", |
|
177 | "language": "python", | |
178 | "metadata": {}, |
|
178 | "metadata": {}, | |
179 | "outputs": [ |
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179 | "outputs": [ | |
180 | { |
|
180 | { | |
181 | "output_type": "display_data", |
|
181 | "output_type": "display_data", | |
182 | "text": [ |
|
182 | "text": [ | |
183 | "12*x^4 - 2.5*x^3 - 8*x^2 - 0.1*x^1 + 8" |
|
183 | "12*x^4 - 2.5*x^3 - 8*x^2 - 0.1*x^1 + 8" | |
184 | ] |
|
184 | ] | |
185 | }, |
|
185 | }, | |
186 | { |
|
186 | { | |
187 | "output_type": "display_data", |
|
187 | "output_type": "display_data", | |
188 | "svg": [ |
|
188 | "svg": [ | |
189 | "<svg height=\"240px\" viewBox=\"0 0 192 115\" width=\"400px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", |
|
189 | "<svg height=\"240px\" viewBox=\"0 0 192 115\" width=\"400px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", | |
190 | "\n", |
|
190 | "\n", | |
191 | "<desc>Produced by GNUPLOT 4.4 patchlevel 0 </desc>\n", |
|
191 | "<desc>Produced by GNUPLOT 4.4 patchlevel 0 </desc>\n", | |
192 | "\n", |
|
192 | "\n", | |
193 | "<defs>\n", |
|
193 | "<defs>\n", | |
194 | "\n", |
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194 | "\n", | |
195 | "\t<circle id=\"gpDot\" r=\"0.5\" stroke-width=\"0.5\"/>\n", |
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195 | "\t<circle id=\"gpDot\" r=\"0.5\" stroke-width=\"0.5\"/>\n", | |
196 | "\t<path d=\"M-1,0 h2 M0,-1 v2\" id=\"gpPt0\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n", |
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196 | "\t<path d=\"M-1,0 h2 M0,-1 v2\" id=\"gpPt0\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n", | |
197 | "\t<path d=\"M-1,-1 L1,1 M1,-1 L-1,1\" id=\"gpPt1\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n", |
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197 | "\t<path d=\"M-1,-1 L1,1 M1,-1 L-1,1\" id=\"gpPt1\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n", | |
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203 | "\t<path d=\"M0,-1.33 L-1.33,0.67 L1.33,0.67 z\" id=\"gpPt7\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n", | |
204 | "\t<use fill=\"currentColor\" id=\"gpPt8\" stroke=\"none\" xlink:href=\"#gpPt7\"/>\n", |
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204 | "\t<use fill=\"currentColor\" id=\"gpPt8\" stroke=\"none\" xlink:href=\"#gpPt7\"/>\n", | |
205 | "\t<use id=\"gpPt9\" stroke=\"currentColor\" transform=\"rotate(180)\" xlink:href=\"#gpPt7\"/>\n", |
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207 | "\t<use id=\"gpPt11\" stroke=\"currentColor\" transform=\"rotate(45)\" xlink:href=\"#gpPt3\"/>\n", |
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208 | "\t<use fill=\"currentColor\" id=\"gpPt12\" stroke=\"none\" xlink:href=\"#gpPt11\"/>\n", |
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208 | "\t<use fill=\"currentColor\" id=\"gpPt12\" stroke=\"none\" xlink:href=\"#gpPt11\"/>\n", | |
209 | "</defs>\n", |
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209 | "</defs>\n", | |
210 | "<g style=\"fill:none; color:white; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter\">\n", |
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210 | "<g style=\"fill:none; color:white; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter\">\n", | |
211 | "</g>\n", |
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211 | "</g>\n", | |
212 | "<g style=\"fill:none; color:white; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", |
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212 | "<g style=\"fill:none; color:white; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", | |
213 | "</g>\n", |
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213 | "</g>\n", | |
214 | "<g style=\"fill:none; color:black; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", |
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214 | "<g style=\"fill:none; color:black; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", | |
215 | "\t<path d=\"M36.4,91.2 L44.8,91.2 M177.9,91.2 L169.5,91.2 \"/>\n", |
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215 | "\t<path d=\"M36.4,91.2 L44.8,91.2 M177.9,91.2 L169.5,91.2 \"/>\n", | |
216 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,94.2)\">\n", |
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216 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,94.2)\">\n", | |
217 | "\t\t<text><tspan>6</tspan>\n", |
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217 | "\t\t<text><tspan>6</tspan>\n", | |
218 | "\t\t</text>\n", |
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218 | "\t\t</text>\n", | |
219 | "\t</g>\n", |
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219 | "\t</g>\n", | |
220 | "\t<path d=\"M36.4,79.8 L44.8,79.8 M177.9,79.8 L169.5,79.8 \"/>\n", |
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220 | "\t<path d=\"M36.4,79.8 L44.8,79.8 M177.9,79.8 L169.5,79.8 \"/>\n", | |
221 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,82.8)\">\n", |
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221 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,82.8)\">\n", | |
222 | "\t\t<text><tspan>6.5</tspan>\n", |
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222 | "\t\t<text><tspan>6.5</tspan>\n", | |
223 | "\t\t</text>\n", |
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223 | "\t\t</text>\n", | |
224 | "\t</g>\n", |
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224 | "\t</g>\n", | |
225 | "\t<path d=\"M36.4,68.4 L44.8,68.4 M177.9,68.4 L169.5,68.4 \"/>\n", |
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226 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,71.4)\">\n", | |
227 | "\t\t<text><tspan>7</tspan>\n", |
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227 | "\t\t<text><tspan>7</tspan>\n", | |
228 | "\t\t</text>\n", |
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228 | "\t\t</text>\n", | |
229 | "\t</g>\n", |
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229 | "\t</g>\n", | |
230 | "\t<path d=\"M36.4,57.0 L44.8,57.0 M177.9,57.0 L169.5,57.0 \"/>\n", |
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230 | "\t<path d=\"M36.4,57.0 L44.8,57.0 M177.9,57.0 L169.5,57.0 \"/>\n", | |
231 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,60.0)\">\n", |
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231 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,60.0)\">\n", | |
232 | "\t\t<text><tspan>7.5</tspan>\n", |
|
232 | "\t\t<text><tspan>7.5</tspan>\n", | |
233 | "\t\t</text>\n", |
|
233 | "\t\t</text>\n", | |
234 | "\t</g>\n", |
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234 | "\t</g>\n", | |
235 | "\t<path d=\"M36.4,45.5 L44.8,45.5 M177.9,45.5 L169.5,45.5 \"/>\n", |
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235 | "\t<path d=\"M36.4,45.5 L44.8,45.5 M177.9,45.5 L169.5,45.5 \"/>\n", | |
236 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,48.5)\">\n", |
|
236 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,48.5)\">\n", | |
237 | "\t\t<text><tspan>8</tspan>\n", |
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237 | "\t\t<text><tspan>8</tspan>\n", | |
238 | "\t\t</text>\n", |
|
238 | "\t\t</text>\n", | |
239 | "\t</g>\n", |
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239 | "\t</g>\n", | |
240 | "\t<path d=\"M36.4,34.1 L44.8,34.1 M177.9,34.1 L169.5,34.1 \"/>\n", |
|
240 | "\t<path d=\"M36.4,34.1 L44.8,34.1 M177.9,34.1 L169.5,34.1 \"/>\n", | |
241 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,37.1)\">\n", |
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241 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,37.1)\">\n", | |
242 | "\t\t<text><tspan>8.5</tspan>\n", |
|
242 | "\t\t<text><tspan>8.5</tspan>\n", | |
243 | "\t\t</text>\n", |
|
243 | "\t\t</text>\n", | |
244 | "\t</g>\n", |
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244 | "\t</g>\n", | |
245 | "\t<path d=\"M36.4,22.7 L44.8,22.7 M177.9,22.7 L169.5,22.7 \"/>\n", |
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245 | "\t<path d=\"M36.4,22.7 L44.8,22.7 M177.9,22.7 L169.5,22.7 \"/>\n", | |
246 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,25.7)\">\n", |
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246 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,25.7)\">\n", | |
247 | "\t\t<text><tspan>9</tspan>\n", |
|
247 | "\t\t<text><tspan>9</tspan>\n", | |
248 | "\t\t</text>\n", |
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248 | "\t\t</text>\n", | |
249 | "\t</g>\n", |
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249 | "\t</g>\n", | |
250 | "\t<path d=\"M36.4,11.3 L44.8,11.3 M177.9,11.3 L169.5,11.3 \"/>\n", |
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250 | "\t<path d=\"M36.4,11.3 L44.8,11.3 M177.9,11.3 L169.5,11.3 \"/>\n", | |
251 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,14.3)\">\n", |
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251 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,14.3)\">\n", | |
252 | "\t\t<text><tspan>9.5</tspan>\n", |
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252 | "\t\t<text><tspan>9.5</tspan>\n", | |
253 | "\t\t</text>\n", |
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253 | "\t\t</text>\n", | |
254 | "\t</g>\n", |
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254 | "\t</g>\n", | |
255 | "\t<path d=\"M36.4,91.2 L36.4,82.8 M36.4,11.3 L36.4,19.7 \"/>\n", |
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255 | "\t<path d=\"M36.4,91.2 L36.4,82.8 M36.4,11.3 L36.4,19.7 \"/>\n", | |
256 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(36.4,106.2)\">\n", |
|
256 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(36.4,106.2)\">\n", | |
257 | "\t\t<text><tspan>0</tspan>\n", |
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257 | "\t\t<text><tspan>0</tspan>\n", | |
258 | "\t\t</text>\n", |
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258 | "\t\t</text>\n", | |
259 | "\t</g>\n", |
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259 | "\t</g>\n", | |
260 | "\t<path d=\"M64.7,91.2 L64.7,82.8 M64.7,11.3 L64.7,19.7 \"/>\n", |
|
260 | "\t<path d=\"M64.7,91.2 L64.7,82.8 M64.7,11.3 L64.7,19.7 \"/>\n", | |
261 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(64.7,106.2)\">\n", |
|
261 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(64.7,106.2)\">\n", | |
262 | "\t\t<text><tspan>0.2</tspan>\n", |
|
262 | "\t\t<text><tspan>0.2</tspan>\n", | |
263 | "\t\t</text>\n", |
|
263 | "\t\t</text>\n", | |
264 | "\t</g>\n", |
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264 | "\t</g>\n", | |
265 | "\t<path d=\"M93.0,91.2 L93.0,82.8 M93.0,11.3 L93.0,19.7 \"/>\n", |
|
265 | "\t<path d=\"M93.0,91.2 L93.0,82.8 M93.0,11.3 L93.0,19.7 \"/>\n", | |
266 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(93.0,106.2)\">\n", |
|
266 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(93.0,106.2)\">\n", | |
267 | "\t\t<text><tspan>0.4</tspan>\n", |
|
267 | "\t\t<text><tspan>0.4</tspan>\n", | |
268 | "\t\t</text>\n", |
|
268 | "\t\t</text>\n", | |
269 | "\t</g>\n", |
|
269 | "\t</g>\n", | |
270 | "\t<path d=\"M121.3,91.2 L121.3,82.8 M121.3,11.3 L121.3,19.7 \"/>\n", |
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270 | "\t<path d=\"M121.3,91.2 L121.3,82.8 M121.3,11.3 L121.3,19.7 \"/>\n", | |
271 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(121.3,106.2)\">\n", |
|
271 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(121.3,106.2)\">\n", | |
272 | "\t\t<text><tspan>0.6</tspan>\n", |
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272 | "\t\t<text><tspan>0.6</tspan>\n", | |
273 | "\t\t</text>\n", |
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273 | "\t\t</text>\n", | |
274 | "\t</g>\n", |
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274 | "\t</g>\n", | |
275 | "\t<path d=\"M149.6,91.2 L149.6,82.8 M149.6,11.3 L149.6,19.7 \"/>\n", |
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275 | "\t<path d=\"M149.6,91.2 L149.6,82.8 M149.6,11.3 L149.6,19.7 \"/>\n", | |
276 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(149.6,106.2)\">\n", |
|
276 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(149.6,106.2)\">\n", | |
277 | "\t\t<text><tspan>0.8</tspan>\n", |
|
277 | "\t\t<text><tspan>0.8</tspan>\n", | |
278 | "\t\t</text>\n", |
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278 | "\t\t</text>\n", | |
279 | "\t</g>\n", |
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279 | "\t</g>\n", | |
280 | "\t<path d=\"M177.9,91.2 L177.9,82.8 M177.9,11.3 L177.9,19.7 \"/>\n", |
|
280 | "\t<path d=\"M177.9,91.2 L177.9,82.8 M177.9,11.3 L177.9,19.7 \"/>\n", | |
281 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(177.9,106.2)\">\n", |
|
281 | "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(177.9,106.2)\">\n", | |
282 | "\t\t<text><tspan>1</tspan>\n", |
|
282 | "\t\t<text><tspan>1</tspan>\n", | |
283 | "\t\t</text>\n", |
|
283 | "\t\t</text>\n", | |
284 | "\t</g>\n", |
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284 | "\t</g>\n", | |
285 | "\t<path d=\"M36.4,11.3 L36.4,91.2 L177.9,91.2 L177.9,11.3 L36.4,11.3 Z \"/>\n", |
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285 | "\t<path d=\"M36.4,11.3 L36.4,91.2 L177.9,91.2 L177.9,11.3 L36.4,11.3 Z \"/>\n", | |
286 | "</g>\n", |
|
286 | "</g>\n", | |
287 | "\t<a xlink:title=\"Plot #1\">\n", |
|
287 | "\t<a xlink:title=\"Plot #1\">\n", | |
288 | "<g style=\"fill:none; color:red; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", |
|
288 | "<g style=\"fill:none; color:red; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", | |
289 | "\t<path d=\"M36.4,45.5 L37.8,45.6 L39.2,45.7 L40.6,45.8 L42.1,45.9 L43.5,46.1 L44.9,46.3 L46.3,46.6 L47.7,46.9 L49.1,47.3 L50.6,47.6 L52.0,48.0 L53.4,48.5 L54.8,49.0 L56.2,49.5 L57.6,50.0 L59.0,50.6 L60.5,51.3 L61.9,51.9 L63.3,52.6 L64.7,53.3 L66.1,54.1 L67.5,54.9 L68.9,55.7 L70.4,56.5 L71.8,57.3 L73.2,58.2 L74.6,59.1 L76.0,60.1 L77.4,61.0 L78.9,62.0 L80.3,63.0 L81.7,64.0 L83.1,65.0 L84.5,66.0 L85.9,67.0 L87.3,68.1 L88.8,69.1 L90.2,70.2 L91.6,71.3 L93.0,72.3 L94.4,73.4 L95.8,74.4 L97.2,75.5 L98.7,76.5 L100.1,77.5 L101.5,78.5 L102.9,79.5 L104.3,80.5 L105.7,81.4 L107.2,82.4 L108.6,83.2 L110.0,84.1 L111.4,84.9 L112.8,85.7 L114.2,86.5 L115.6,87.2 L117.1,87.8 L118.5,88.4 L119.9,89.0 L121.3,89.5 L122.7,89.9 L124.1,90.3 L125.5,90.6 L127.0,90.8 L128.4,91.0 L129.8,91.0 L131.2,91.0 L132.6,90.9 L134.0,90.7 L135.5,90.4 L136.9,90.0 L138.3,89.5 L139.7,88.9 L141.1,88.2 L142.5,87.4 L143.9,86.4 L145.4,85.3 L146.8,84.1 L148.2,82.8 L149.6,81.3 L151.0,79.6 L152.4,77.8 L153.8,75.9 L155.3,73.8 L156.7,71.5 L158.1,69.0 L159.5,66.4 L160.9,63.6 L162.3,60.6 L163.8,57.4 L165.2,54.0 L166.6,50.4 L168.0,46.6 L169.4,42.6 L170.8,38.3 L172.2,33.9 L173.7,29.2 L175.1,24.2 L176.5,19.0 L177.9,13.6 \" stroke=\"rgb( 0, 0, 255)\"/>\n", |
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289 | "\t<path d=\"M36.4,45.5 L37.8,45.6 L39.2,45.7 L40.6,45.8 L42.1,45.9 L43.5,46.1 L44.9,46.3 L46.3,46.6 L47.7,46.9 L49.1,47.3 L50.6,47.6 L52.0,48.0 L53.4,48.5 L54.8,49.0 L56.2,49.5 L57.6,50.0 L59.0,50.6 L60.5,51.3 L61.9,51.9 L63.3,52.6 L64.7,53.3 L66.1,54.1 L67.5,54.9 L68.9,55.7 L70.4,56.5 L71.8,57.3 L73.2,58.2 L74.6,59.1 L76.0,60.1 L77.4,61.0 L78.9,62.0 L80.3,63.0 L81.7,64.0 L83.1,65.0 L84.5,66.0 L85.9,67.0 L87.3,68.1 L88.8,69.1 L90.2,70.2 L91.6,71.3 L93.0,72.3 L94.4,73.4 L95.8,74.4 L97.2,75.5 L98.7,76.5 L100.1,77.5 L101.5,78.5 L102.9,79.5 L104.3,80.5 L105.7,81.4 L107.2,82.4 L108.6,83.2 L110.0,84.1 L111.4,84.9 L112.8,85.7 L114.2,86.5 L115.6,87.2 L117.1,87.8 L118.5,88.4 L119.9,89.0 L121.3,89.5 L122.7,89.9 L124.1,90.3 L125.5,90.6 L127.0,90.8 L128.4,91.0 L129.8,91.0 L131.2,91.0 L132.6,90.9 L134.0,90.7 L135.5,90.4 L136.9,90.0 L138.3,89.5 L139.7,88.9 L141.1,88.2 L142.5,87.4 L143.9,86.4 L145.4,85.3 L146.8,84.1 L148.2,82.8 L149.6,81.3 L151.0,79.6 L152.4,77.8 L153.8,75.9 L155.3,73.8 L156.7,71.5 L158.1,69.0 L159.5,66.4 L160.9,63.6 L162.3,60.6 L163.8,57.4 L165.2,54.0 L166.6,50.4 L168.0,46.6 L169.4,42.6 L170.8,38.3 L172.2,33.9 L173.7,29.2 L175.1,24.2 L176.5,19.0 L177.9,13.6 \" stroke=\"rgb( 0, 0, 255)\"/>\n", | |
290 | "</g>\n", |
|
290 | "</g>\n", | |
291 | "\t</a>\n", |
|
291 | "\t</a>\n", | |
292 | "<g style=\"fill:none; color:black; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", |
|
292 | "<g style=\"fill:none; color:black; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n", | |
293 | "</g>\n", |
|
293 | "</g>\n", | |
294 | "</svg>" |
|
294 | "</svg>" | |
295 | ] |
|
295 | ] | |
296 | } |
|
296 | } | |
297 | ], |
|
297 | ], | |
298 | "prompt_number": 23 |
|
298 | "prompt_number": 23 | |
299 | }, |
|
299 | }, | |
300 | { |
|
300 | { | |
301 | "cell_type": "markdown", |
|
301 | "cell_type": "markdown", | |
302 | "metadata": {}, |
|
302 | "metadata": {}, | |
303 | "source": [ |
|
303 | "source": [ | |
304 | "The plot size is adjusted using the `-s` flag:" |
|
304 | "The plot size is adjusted using the `-s` flag:" | |
305 | ] |
|
305 | ] | |
306 | }, |
|
306 | }, | |
307 | { |
|
307 | { | |
308 | "cell_type": "code", |
|
308 | "cell_type": "code", | |
309 | "collapsed": false, |
|
309 | "collapsed": false, | |
310 | "input": [ |
|
310 | "input": [ | |
311 | "%%octave -s 500,500\n", |
|
311 | "%%octave -s 500,500\n", | |
312 | "\n", |
|
312 | "\n", | |
313 | "# butterworth filter, order 2, cutoff pi/2 radians\n", |
|
313 | "# butterworth filter, order 2, cutoff pi/2 radians\n", | |
314 | "b = [0.292893218813452 0.585786437626905 0.292893218813452];\n", |
|
314 | "b = [0.292893218813452 0.585786437626905 0.292893218813452];\n", | |
315 | "a = [1 0 0.171572875253810];\n", |
|
315 | "a = [1 0 0.171572875253810];\n", | |
316 | "freqz(b, a, 32);" |
|
316 | "freqz(b, a, 32);" | |
317 | ], |
|
317 | ], | |
318 | "language": "python", |
|
318 | "language": "python", | |
319 | "metadata": {}, |
|
319 | "metadata": {}, | |
320 | "outputs": [ |
|
320 | "outputs": [ | |
321 | { |
|
321 | { | |
322 | "output_type": "display_data", |
|
322 | "output_type": "display_data", | |
323 | "png": 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l8munSLUoVYJj5rF1lmV39bkMw4BrjlHHCoMw9SbGQ7okVbggO21BCdrszYfK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\noLWOJ5MfPqUANvwAgmKwshJ2uRwAAAAASUVORK5CYII=\n" 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323 | "png": 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2ZvTl5+jRo0WrkCu02YtuPQtKEYTRdR32HzmO02q1NE0LP0L9gjCTmZ+fX1xcjPquoJdv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\noLWOJ5MfPqUANvwAgmKwshJ2uRwAAAAASUVORK5CYII=\n" | |
324 | } |
|
324 | } | |
325 | ], |
|
325 | ], | |
326 | "prompt_number": 24 |
|
326 | "prompt_number": 24 | |
327 | }, |
|
327 | }, | |
328 | { |
|
328 | { | |
329 | "cell_type": "code", |
|
329 | "cell_type": "code", | |
330 | "collapsed": false, |
|
330 | "collapsed": false, | |
331 | "input": [ |
|
331 | "input": [ | |
332 | "%%octave -s 600,200 -f png\n", |
|
332 | "%%octave -s 600,200 -f png\n", | |
333 | "\n", |
|
333 | "\n", | |
334 | "subplot(121);\n", |
|
334 | "subplot(121);\n", | |
335 | "[x, y] = meshgrid(0:0.1:3);\n", |
|
335 | "[x, y] = meshgrid(0:0.1:3);\n", | |
336 | "r = sin(x - 0.5).^2 + cos(y - 0.5).^2;\n", |
|
336 | "r = sin(x - 0.5).^2 + cos(y - 0.5).^2;\n", | |
337 | "surf(x, y, r);\n", |
|
337 | "surf(x, y, r);\n", | |
338 | "\n", |
|
338 | "\n", | |
339 | "subplot(122);\n", |
|
339 | "subplot(122);\n", | |
340 | "sombrero()" |
|
340 | "sombrero()" | |
341 | ], |
|
341 | ], | |
342 | "language": "python", |
|
342 | "language": "python", | |
343 | "metadata": {}, |
|
343 | "metadata": {}, | |
344 | "outputs": [ |
|
344 | "outputs": [ | |
345 | { |
|
345 | { | |
346 | "output_type": "display_data", |
|
346 | "output_type": "display_data", | |
347 | "png": 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o+c0vuXcdy7LFFxupVMr3KaNMwCoUiqSkpLCwsNDQ0MDAwN27dxe/TpNTDoRQ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\n" 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347 | "png": 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o+c0vuXcdy7LFFxupVMr3KaNMwCoUiqSkpLCwsNDQ0MDAwN27dxe/TpNTDoRQ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\n5/72haEj66ampq5du4bTAEiSlH7OEZt6qIr97kcT3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| |
348 | } |
|
348 | } | |
349 | ], |
|
349 | ], | |
350 | "prompt_number": 25 |
|
350 | "prompt_number": 25 | |
351 | }, |
|
351 | }, | |
352 | { |
|
352 | { | |
353 | "cell_type": "heading", |
|
353 | "cell_type": "heading", | |
354 | "level": 2, |
|
354 | "level": 2, | |
355 | "metadata": {}, |
|
355 | "metadata": {}, | |
356 | "source": [ |
|
356 | "source": [ | |
357 | "Future work" |
|
357 | "Future work" | |
358 | ] |
|
358 | ] | |
359 | }, |
|
359 | }, | |
360 | { |
|
360 | { | |
361 | "cell_type": "markdown", |
|
361 | "cell_type": "markdown", | |
362 | "metadata": {}, |
|
362 | "metadata": {}, | |
363 | "source": [ |
|
363 | "source": [ | |
364 | "After the next release of `oct2py`, we'll add the ability to interrupt/kill the current Octave session without restarting the Python kernel." |
|
364 | "After the next release of `oct2py`, we'll add the ability to interrupt/kill the current Octave session without restarting the Python kernel." | |
365 | ] |
|
365 | ] | |
366 | } |
|
366 | } | |
367 | ], |
|
367 | ], | |
368 | "metadata": {} |
|
368 | "metadata": {} | |
369 | } |
|
369 | } | |
370 | ] |
|
370 | ] | |
371 | } No newline at end of file |
|
371 | } |
@@ -1,218 +1,271 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
|
2 | "metadata": { | |
3 | "name": "Part 4 - Markdown Cells" |
|
3 | "name": "Part 4 - Markdown Cells" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
|
10 | { | |
11 | "cell_type": "heading", |
|
11 | "cell_type": "heading", | |
12 | "level": 1, |
|
12 | "level": 1, | |
13 | "metadata": {}, |
|
13 | "metadata": {}, | |
14 | "source": [ |
|
14 | "source": [ | |
15 | "Markdown Cells" |
|
15 | "Markdown Cells" | |
16 | ] |
|
16 | ] | |
17 | }, |
|
17 | }, | |
18 | { |
|
18 | { | |
19 | "cell_type": "markdown", |
|
19 | "cell_type": "markdown", | |
20 | "metadata": {}, |
|
20 | "metadata": {}, | |
21 | "source": [ |
|
21 | "source": [ | |
22 | "Text can be added to IPython Notebooks using Markdown cells. Markdown is a popular markup language that is a superset of HTML. Its specification can be found here:\n", |
|
22 | "Text can be added to IPython Notebooks using Markdown cells. Markdown is a popular markup language that is a superset of HTML. Its specification can be found here:\n", | |
23 | "\n", |
|
23 | "\n", | |
24 | "<http://daringfireball.net/projects/markdown/>" |
|
24 | "<http://daringfireball.net/projects/markdown/>" | |
25 | ] |
|
25 | ] | |
26 | }, |
|
26 | }, | |
27 | { |
|
27 | { | |
28 | "cell_type": "heading", |
|
28 | "cell_type": "heading", | |
29 | "level": 2, |
|
29 | "level": 2, | |
30 | "metadata": {}, |
|
30 | "metadata": {}, | |
31 | "source": [ |
|
31 | "source": [ | |
32 | "Markdown basics" |
|
32 | "Markdown basics" | |
33 | ] |
|
33 | ] | |
34 | }, |
|
34 | }, | |
35 | { |
|
35 | { | |
36 | "cell_type": "markdown", |
|
36 | "cell_type": "markdown", | |
37 | "metadata": {}, |
|
37 | "metadata": {}, | |
38 | "source": [ |
|
38 | "source": [ | |
39 | "You can make text *italic* or **bold**." |
|
39 | "You can make text *italic* or **bold**." | |
40 | ] |
|
40 | ] | |
41 | }, |
|
41 | }, | |
42 | { |
|
42 | { | |
43 | "cell_type": "markdown", |
|
43 | "cell_type": "markdown", | |
44 | "metadata": {}, |
|
44 | "metadata": {}, | |
45 | "source": [ |
|
45 | "source": [ | |
46 | "You can build nested itemized or enumerated lists:\n", |
|
46 | "You can build nested itemized or enumerated lists:\n", | |
47 | "\n", |
|
47 | "\n", | |
48 | "* One\n", |
|
48 | "* One\n", | |
49 | " - Sublist\n", |
|
49 | " - Sublist\n", | |
50 | " - This\n", |
|
50 | " - This\n", | |
51 | " - Sublist\n", |
|
51 | " - Sublist\n", | |
52 | " - That\n", |
|
52 | " - That\n", | |
53 | " - The other thing\n", |
|
53 | " - The other thing\n", | |
54 | "* Two\n", |
|
54 | "* Two\n", | |
55 | " - Sublist\n", |
|
55 | " - Sublist\n", | |
56 | "* Three\n", |
|
56 | "* Three\n", | |
57 | " - Sublist\n", |
|
57 | " - Sublist\n", | |
58 | "\n", |
|
58 | "\n", | |
59 | "Now another list:\n", |
|
59 | "Now another list:\n", | |
60 | "\n", |
|
60 | "\n", | |
61 | "1. Here we go\n", |
|
61 | "1. Here we go\n", | |
62 | " 1. Sublist\n", |
|
62 | " 1. Sublist\n", | |
63 | " 2. Sublist\n", |
|
63 | " 2. Sublist\n", | |
64 | "2. There we go\n", |
|
64 | "2. There we go\n", | |
65 | "3. Now this" |
|
65 | "3. Now this" | |
66 | ] |
|
66 | ] | |
67 | }, |
|
67 | }, | |
68 | { |
|
68 | { | |
69 | "cell_type": "markdown", |
|
69 | "cell_type": "markdown", | |
70 | "metadata": {}, |
|
70 | "metadata": {}, | |
71 | "source": [ |
|
71 | "source": [ | |
72 | "You can add horizontal rules:\n", |
|
72 | "You can add horizontal rules:\n", | |
73 | "\n", |
|
73 | "\n", | |
74 | "---" |
|
74 | "---" | |
75 | ] |
|
75 | ] | |
76 | }, |
|
76 | }, | |
77 | { |
|
77 | { | |
78 | "cell_type": "markdown", |
|
78 | "cell_type": "markdown", | |
79 | "metadata": {}, |
|
79 | "metadata": {}, | |
80 | "source": [ |
|
80 | "source": [ | |
81 | "Here is a blockquote:\n", |
|
81 | "Here is a blockquote:\n", | |
82 | "\n", |
|
82 | "\n", | |
83 | "> Beautiful is better than ugly.\n", |
|
83 | "> Beautiful is better than ugly.\n", | |
84 | "> Explicit is better than implicit.\n", |
|
84 | "> Explicit is better than implicit.\n", | |
85 | "> Simple is better than complex.\n", |
|
85 | "> Simple is better than complex.\n", | |
86 | "> Complex is better than complicated.\n", |
|
86 | "> Complex is better than complicated.\n", | |
87 | "> Flat is better than nested.\n", |
|
87 | "> Flat is better than nested.\n", | |
88 | "> Sparse is better than dense.\n", |
|
88 | "> Sparse is better than dense.\n", | |
89 | "> Readability counts.\n", |
|
89 | "> Readability counts.\n", | |
90 | "> Special cases aren't special enough to break the rules.\n", |
|
90 | "> Special cases aren't special enough to break the rules.\n", | |
91 | "> Although practicality beats purity.\n", |
|
91 | "> Although practicality beats purity.\n", | |
92 | "> Errors should never pass silently.\n", |
|
92 | "> Errors should never pass silently.\n", | |
93 | "> Unless explicitly silenced.\n", |
|
93 | "> Unless explicitly silenced.\n", | |
94 | "> In the face of ambiguity, refuse the temptation to guess.\n", |
|
94 | "> In the face of ambiguity, refuse the temptation to guess.\n", | |
95 | "> There should be one-- and preferably only one --obvious way to do it.\n", |
|
95 | "> There should be one-- and preferably only one --obvious way to do it.\n", | |
96 | "> Although that way may not be obvious at first unless you're Dutch.\n", |
|
96 | "> Although that way may not be obvious at first unless you're Dutch.\n", | |
97 | "> Now is better than never.\n", |
|
97 | "> Now is better than never.\n", | |
98 | "> Although never is often better than *right* now.\n", |
|
98 | "> Although never is often better than *right* now.\n", | |
99 | "> If the implementation is hard to explain, it's a bad idea.\n", |
|
99 | "> If the implementation is hard to explain, it's a bad idea.\n", | |
100 | "> If the implementation is easy to explain, it may be a good idea.\n", |
|
100 | "> If the implementation is easy to explain, it may be a good idea.\n", | |
101 | "> Namespaces are one honking great idea -- let's do more of those!" |
|
101 | "> Namespaces are one honking great idea -- let's do more of those!" | |
102 | ] |
|
102 | ] | |
103 | }, |
|
103 | }, | |
104 | { |
|
104 | { | |
105 | "cell_type": "markdown", |
|
105 | "cell_type": "markdown", | |
106 | "metadata": {}, |
|
106 | "metadata": {}, | |
107 | "source": [ |
|
107 | "source": [ | |
108 | "And shorthand for links:\n", |
|
108 | "And shorthand for links:\n", | |
109 | "\n", |
|
109 | "\n", | |
110 | "[IPython's website](http://ipython.org)" |
|
110 | "[IPython's website](http://ipython.org)" | |
111 | ] |
|
111 | ] | |
112 | }, |
|
112 | }, | |
113 | { |
|
113 | { | |
114 | "cell_type": "heading", |
|
114 | "cell_type": "heading", | |
115 | "level": 2, |
|
115 | "level": 2, | |
116 | "metadata": {}, |
|
116 | "metadata": {}, | |
117 | "source": [ |
|
117 | "source": [ | |
118 | "Headings" |
|
118 | "Headings" | |
119 | ] |
|
119 | ] | |
120 | }, |
|
120 | }, | |
121 | { |
|
121 | { | |
122 | "cell_type": "markdown", |
|
122 | "cell_type": "markdown", | |
123 | "metadata": {}, |
|
123 | "metadata": {}, | |
124 | "source": [ |
|
124 | "source": [ | |
125 | "If you want, you can add headings using Markdown's syntax:\n", |
|
125 | "If you want, you can add headings using Markdown's syntax:\n", | |
126 | "\n", |
|
126 | "\n", | |
127 | "# Heading 1\n", |
|
127 | "# Heading 1\n", | |
128 | "# Heading 2\n", |
|
128 | "# Heading 2\n", | |
129 | "## Heading 2.1\n", |
|
129 | "## Heading 2.1\n", | |
130 | "## Heading 2.2" |
|
130 | "## Heading 2.2" | |
131 | ] |
|
131 | ] | |
132 | }, |
|
132 | }, | |
133 | { |
|
133 | { | |
134 | "cell_type": "markdown", |
|
134 | "cell_type": "markdown", | |
135 | "metadata": {}, |
|
135 | "metadata": {}, | |
136 | "source": [ |
|
136 | "source": [ | |
137 | "**BUT most of the time you should use the Notebook's Heading Cells to organize your Notebook content**, as they provide meaningful structure that can be interpreted by other tools, not just large bold fonts." |
|
137 | "**BUT most of the time you should use the Notebook's Heading Cells to organize your Notebook content**, as they provide meaningful structure that can be interpreted by other tools, not just large bold fonts." | |
138 | ] |
|
138 | ] | |
139 | }, |
|
139 | }, | |
140 | { |
|
140 | { | |
141 | "cell_type": "heading", |
|
141 | "cell_type": "heading", | |
142 | "level": 2, |
|
142 | "level": 2, | |
143 | "metadata": {}, |
|
143 | "metadata": {}, | |
144 | "source": [ |
|
144 | "source": [ | |
145 | "Embedded code" |
|
145 | "Embedded code" | |
146 | ] |
|
146 | ] | |
147 | }, |
|
147 | }, | |
148 | { |
|
148 | { | |
149 | "cell_type": "markdown", |
|
149 | "cell_type": "markdown", | |
150 | "metadata": {}, |
|
150 | "metadata": {}, | |
151 | "source": [ |
|
151 | "source": [ | |
152 | "You can embed code meant for illustration instead of execution in Python:\n", |
|
152 | "You can embed code meant for illustration instead of execution in Python:\n", | |
153 | "\n", |
|
153 | "\n", | |
154 | " def f(x):\n", |
|
154 | " def f(x):\n", | |
155 | " \"\"\"a docstring\"\"\"\n", |
|
155 | " \"\"\"a docstring\"\"\"\n", | |
156 | " return x**2\n", |
|
156 | " return x**2\n", | |
157 | "\n", |
|
157 | "\n", | |
158 | "or other languages:\n", |
|
158 | "or other languages:\n", | |
159 | "\n", |
|
159 | "\n", | |
160 | " if (i=0; i<n; i++) {\n", |
|
160 | " if (i=0; i<n; i++) {\n", | |
161 | " printf(\"hello %d\\n\", i);\n", |
|
161 | " printf(\"hello %d\\n\", i);\n", | |
162 | " x += 4;\n", |
|
162 | " x += 4;\n", | |
163 | " }" |
|
163 | " }" | |
164 | ] |
|
164 | ] | |
165 | }, |
|
165 | }, | |
166 | { |
|
166 | { | |
167 | "cell_type": "heading", |
|
167 | "cell_type": "heading", | |
168 | "level": 2, |
|
168 | "level": 2, | |
169 | "metadata": {}, |
|
169 | "metadata": {}, | |
170 | "source": [ |
|
170 | "source": [ | |
171 |
"LaTeX |
|
171 | "LaTeX equations" | |
172 | ] |
|
172 | ] | |
173 | }, |
|
173 | }, | |
174 | { |
|
174 | { | |
175 | "cell_type": "markdown", |
|
175 | "cell_type": "markdown", | |
176 | "metadata": {}, |
|
176 | "metadata": {}, | |
177 | "source": [ |
|
177 | "source": [ | |
178 | "Courtesy of MathJax, you can include mathematical expressions both inline: \n", |
|
178 | "Courtesy of MathJax, you can include mathematical expressions both inline: \n", | |
179 | "$e^{i\\pi} + 1 = 0$ and displayed:\n", |
|
179 | "$e^{i\\pi} + 1 = 0$ and displayed:\n", | |
180 | "\n", |
|
180 | "\n", | |
181 | "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$" |
|
181 | "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$" | |
182 | ] |
|
182 | ] | |
183 | }, |
|
183 | }, | |
184 | { |
|
184 | { | |
185 | "cell_type": "heading", |
|
185 | "cell_type": "heading", | |
186 | "level": 2, |
|
186 | "level": 2, | |
187 | "metadata": {}, |
|
187 | "metadata": {}, | |
188 | "source": [ |
|
188 | "source": [ | |
189 | "General HTML" |
|
189 | "General HTML" | |
190 | ] |
|
190 | ] | |
191 | }, |
|
191 | }, | |
192 | { |
|
192 | { | |
193 | "cell_type": "markdown", |
|
193 | "cell_type": "markdown", | |
194 | "metadata": {}, |
|
194 | "metadata": {}, | |
195 | "source": [ |
|
195 | "source": [ | |
196 | "Because Markdown is a superset of HTML you can even add things like HTML tables:\n", |
|
196 | "Because Markdown is a superset of HTML you can even add things like HTML tables:\n", | |
197 | "\n", |
|
197 | "\n", | |
198 | "<table>\n", |
|
198 | "<table>\n", | |
199 | "<tr>\n", |
|
199 | "<tr>\n", | |
200 | "<th>Header 1</th>\n", |
|
200 | "<th>Header 1</th>\n", | |
201 | "<th>Header 2</th>\n", |
|
201 | "<th>Header 2</th>\n", | |
202 | "</tr>\n", |
|
202 | "</tr>\n", | |
203 | "<tr>\n", |
|
203 | "<tr>\n", | |
204 | "<td>row 1, cell 1</td>\n", |
|
204 | "<td>row 1, cell 1</td>\n", | |
205 | "<td>row 1, cell 2</td>\n", |
|
205 | "<td>row 1, cell 2</td>\n", | |
206 | "</tr>\n", |
|
206 | "</tr>\n", | |
207 | "<tr>\n", |
|
207 | "<tr>\n", | |
208 | "<td>row 2, cell 1</td>\n", |
|
208 | "<td>row 2, cell 1</td>\n", | |
209 | "<td>row 2, cell 2</td>\n", |
|
209 | "<td>row 2, cell 2</td>\n", | |
210 | "</tr>\n", |
|
210 | "</tr>\n", | |
211 | "</table>" |
|
211 | "</table>" | |
212 | ] |
|
212 | ] | |
|
213 | }, | |||
|
214 | { | |||
|
215 | "cell_type": "heading", | |||
|
216 | "level": 2, | |||
|
217 | "metadata": {}, | |||
|
218 | "source": [ | |||
|
219 | "Local files" | |||
|
220 | ] | |||
|
221 | }, | |||
|
222 | { | |||
|
223 | "cell_type": "markdown", | |||
|
224 | "metadata": {}, | |||
|
225 | "source": [ | |||
|
226 | "If you have local files in your Notebook directory, you can refer to these files in Markdown cells via relative URLs that are prefixed with `files/`:\n", | |||
|
227 | "\n", | |||
|
228 | " files/[subdirectory/]<filename>\n", | |||
|
229 | "\n", | |||
|
230 | "For example, in the example Notebook folder, we have the Python logo:\n", | |||
|
231 | "\n", | |||
|
232 | " <img src=\"files/python-logo.svg\" />\n", | |||
|
233 | "\n", | |||
|
234 | "<img src=\"/files/python-logo.svg\" />\n", | |||
|
235 | "\n", | |||
|
236 | "and a video with the HTML5 video tag:\n", | |||
|
237 | "\n", | |||
|
238 | " <video controls src=\"files/animation.m4v\" />\n", | |||
|
239 | "\n", | |||
|
240 | "<video controls src=\"/files/animation.m4v\" />\n", | |||
|
241 | "\n", | |||
|
242 | "These do not embed the data into the notebook file, and require that the files exist when you are viewing the notebook." | |||
|
243 | ] | |||
|
244 | }, | |||
|
245 | { | |||
|
246 | "cell_type": "heading", | |||
|
247 | "level": 3, | |||
|
248 | "metadata": {}, | |||
|
249 | "source": [ | |||
|
250 | "Security of local files" | |||
|
251 | ] | |||
|
252 | }, | |||
|
253 | { | |||
|
254 | "cell_type": "markdown", | |||
|
255 | "metadata": {}, | |||
|
256 | "source": [ | |||
|
257 | "Note that this means that the IPython notebook server also acts as a generic file server\n", | |||
|
258 | "for files inside the same tree as your notebooks. Access is not granted outside the\n", | |||
|
259 | "notebook folder so you have strict control over what files are visible, but for this\n", | |||
|
260 | "reason it is highly recommended that you do not run the notebook server with a notebook\n", | |||
|
261 | "directory at a high level in your filesystem (e.g. your home directory).\n", | |||
|
262 | "\n", | |||
|
263 | "When you run the notebook in a password-protected manner, local file access is restricted\n", | |||
|
264 | "to authenticated users unless read-only views are active." | |||
|
265 | ] | |||
213 | } |
|
266 | } | |
214 | ], |
|
267 | ], | |
215 | "metadata": {} |
|
268 | "metadata": {} | |
216 | } |
|
269 | } | |
217 | ] |
|
270 | ] | |
218 | } No newline at end of file |
|
271 | } |
@@ -1,712 +1,857 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
|
2 | "metadata": { | |
3 | "name": "Part 5 - Rich Display System" |
|
3 | "name": "Part 5 - Rich Display System" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
|
10 | { | |
|
11 | "cell_type": "heading", | |||
|
12 | "level": 1, | |||
|
13 | "metadata": {}, | |||
|
14 | "source": [ | |||
|
15 | "IPython's Rich Display System" | |||
|
16 | ] | |||
|
17 | }, | |||
|
18 | { | |||
|
19 | "cell_type": "markdown", | |||
|
20 | "metadata": {}, | |||
|
21 | "source": [ | |||
|
22 | "In Python, objects can declare their textual representation using the `__repr__` and `__str__` methods. IPython expands on this idea and allows objects to declare other, richer representations including:\n", | |||
|
23 | "\n", | |||
|
24 | "* HTML\n", | |||
|
25 | "* JSON\n", | |||
|
26 | "* Images = PNG/JPEG\n", | |||
|
27 | "* SVG\n", | |||
|
28 | "* LaTeX\n", | |||
|
29 | "\n", | |||
|
30 | "A single object can declare some or all of these representations; all are handled by IPython's *display system*. This Notebook shows how you can use this display system to incorporate a broad range of content into your Notebooks." | |||
|
31 | ] | |||
|
32 | }, | |||
|
33 | { | |||
|
34 | "cell_type": "heading", | |||
|
35 | "level": 2, | |||
|
36 | "metadata": {}, | |||
|
37 | "source": [ | |||
|
38 | "Basic display imports" | |||
|
39 | ] | |||
|
40 | }, | |||
|
41 | { | |||
|
42 | "cell_type": "markdown", | |||
|
43 | "metadata": {}, | |||
|
44 | "source": [ | |||
|
45 | "The `display` function is a general purpose tool for displaying different representations of objects. Think of it as `print` for these rich representations." | |||
|
46 | ] | |||
|
47 | }, | |||
|
48 | { | |||
|
49 | "cell_type": "code", | |||
|
50 | "collapsed": false, | |||
|
51 | "input": [ | |||
|
52 | "from IPython.display import display" | |||
|
53 | ], | |||
|
54 | "language": "python", | |||
|
55 | "metadata": {}, | |||
|
56 | "outputs": [], | |||
|
57 | "prompt_number": 8 | |||
|
58 | }, | |||
|
59 | { | |||
11 | "cell_type": "markdown", |
|
60 | "cell_type": "markdown", | |
12 | "metadata": {}, |
|
61 | "metadata": {}, | |
13 | "source": [ |
|
62 | "source": [ | |
14 | "## Rich displays: include anyting a browser can show\n", |
|
63 | "A few points:\n", | |
15 | "\n", |
|
64 | "\n", | |
16 | "Note that we have an actual protocol for this, see the `display_protocol` notebook for further details.\n", |
|
65 | "* Calling `display` on an object will send **all** possible representations to the Notebook.\n", | |
|
66 | "* These representations are stored in the Notebook document.\n", | |||
|
67 | "* In general the Notebook will use the richest available representation.\n", | |||
17 | "\n", |
|
68 | "\n", | |
18 | "### Images" |
|
69 | "If you want to display a particular representationa, there are specific functions for that:" | |
|
70 | ] | |||
|
71 | }, | |||
|
72 | { | |||
|
73 | "cell_type": "code", | |||
|
74 | "collapsed": false, | |||
|
75 | "input": [ | |||
|
76 | "from IPython.display import display_pretty, display_html, display_jpeg, display_png, display_json, display_latex, display_svg" | |||
|
77 | ], | |||
|
78 | "language": "python", | |||
|
79 | "metadata": {}, | |||
|
80 | "outputs": [], | |||
|
81 | "prompt_number": 11 | |||
|
82 | }, | |||
|
83 | { | |||
|
84 | "cell_type": "heading", | |||
|
85 | "level": 2, | |||
|
86 | "metadata": {}, | |||
|
87 | "source": [ | |||
|
88 | "Images" | |||
|
89 | ] | |||
|
90 | }, | |||
|
91 | { | |||
|
92 | "cell_type": "markdown", | |||
|
93 | "metadata": {}, | |||
|
94 | "source": [ | |||
|
95 | "To work with images (JPEG, PNG) use the `Image` class." | |||
19 | ] |
|
96 | ] | |
20 | }, |
|
97 | }, | |
21 | { |
|
98 | { | |
22 | "cell_type": "code", |
|
99 | "cell_type": "code", | |
23 | "collapsed": false, |
|
100 | "collapsed": false, | |
24 | "input": [ |
|
101 | "input": [ | |
25 |
"from IPython.display import Image |
|
102 | "from IPython.display import Image" | |
26 | "Image(filename='../../source/_static/logo.png')" |
|
103 | ], | |
|
104 | "language": "python", | |||
|
105 | "metadata": {}, | |||
|
106 | "outputs": [], | |||
|
107 | "prompt_number": 2 | |||
|
108 | }, | |||
|
109 | { | |||
|
110 | "cell_type": "code", | |||
|
111 | "collapsed": false, | |||
|
112 | "input": [ | |||
|
113 | "i = Image(filename='../../docs/source/_static/logo.png')" | |||
|
114 | ], | |||
|
115 | "language": "python", | |||
|
116 | "metadata": {}, | |||
|
117 | "outputs": [], | |||
|
118 | "prompt_number": 5 | |||
|
119 | }, | |||
|
120 | { | |||
|
121 | "cell_type": "markdown", | |||
|
122 | "metadata": {}, | |||
|
123 | "source": [ | |||
|
124 | "Returning an `Image` object from an expression will automatically display it:" | |||
|
125 | ] | |||
|
126 | }, | |||
|
127 | { | |||
|
128 | "cell_type": "code", | |||
|
129 | "collapsed": false, | |||
|
130 | "input": [ | |||
|
131 | "i" | |||
27 | ], |
|
132 | ], | |
28 | "language": "python", |
|
133 | "language": "python", | |
29 | "metadata": {}, |
|
134 | "metadata": {}, | |
30 | "outputs": [ |
|
135 | "outputs": [ | |
31 | { |
|
136 | { | |
32 | "output_type": "pyout", |
|
137 | "output_type": "pyout", | |
33 | "png": 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138 | "png": 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| |
34 |
"prompt_number": |
|
139 | "prompt_number": 6, | |
35 | "text": [ |
|
140 | "text": [ | |
36 |
"<IPython.core.display.Image at 0x10 |
|
141 | "<IPython.core.display.Image at 0x107ea26d0>" | |
37 | ] |
|
142 | ] | |
38 | } |
|
143 | } | |
39 | ], |
|
144 | ], | |
40 |
"prompt_number": |
|
145 | "prompt_number": 6 | |
|
146 | }, | |||
|
147 | { | |||
|
148 | "cell_type": "markdown", | |||
|
149 | "metadata": {}, | |||
|
150 | "source": [ | |||
|
151 | "Or you can pass it to `display`:" | |||
|
152 | ] | |||
|
153 | }, | |||
|
154 | { | |||
|
155 | "cell_type": "code", | |||
|
156 | "collapsed": false, | |||
|
157 | "input": [ | |||
|
158 | "display(i)" | |||
|
159 | ], | |||
|
160 | "language": "python", | |||
|
161 | "metadata": {}, | |||
|
162 | "outputs": [ | |||
|
163 | { | |||
|
164 | "output_type": "display_data", | |||
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165 | "png": 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| |||
|
166 | "text": [ | |||
|
167 | "<IPython.core.display.Image at 0x107ea26d0>" | |||
|
168 | ] | |||
|
169 | } | |||
|
170 | ], | |||
|
171 | "prompt_number": 9 | |||
41 | }, |
|
172 | }, | |
42 | { |
|
173 | { | |
43 | "cell_type": "markdown", |
|
174 | "cell_type": "markdown", | |
44 | "metadata": {}, |
|
175 | "metadata": {}, | |
45 | "source": [ |
|
176 | "source": [ | |
46 | "An image can also be displayed from raw data or a url" |
|
177 | "An image can also be displayed from raw data or a url" | |
47 | ] |
|
178 | ] | |
48 | }, |
|
179 | }, | |
49 | { |
|
180 | { | |
50 | "cell_type": "code", |
|
181 | "cell_type": "code", | |
51 | "collapsed": false, |
|
182 | "collapsed": false, | |
52 | "input": [ |
|
183 | "input": [ | |
53 | "Image(url='http://python.org/images/python-logo.gif')" |
|
184 | "Image(url='http://python.org/images/python-logo.gif')" | |
54 | ], |
|
185 | ], | |
55 | "language": "python", |
|
186 | "language": "python", | |
56 | "metadata": {}, |
|
187 | "metadata": {}, | |
57 | "outputs": [ |
|
188 | "outputs": [ | |
58 | { |
|
189 | { | |
59 | "html": [ |
|
190 | "html": [ | |
60 | "<img src=\"http://python.org/images/python-logo.gif\" />" |
|
191 | "<img src=\"http://python.org/images/python-logo.gif\" />" | |
61 | ], |
|
192 | ], | |
62 | "output_type": "pyout", |
|
193 | "output_type": "pyout", | |
63 | "prompt_number": 2, |
|
194 | "prompt_number": 2, | |
64 | "text": [ |
|
195 | "text": [ | |
65 | "<IPython.core.display.Image at 0x1060e7410>" |
|
196 | "<IPython.core.display.Image at 0x1060e7410>" | |
66 | ] |
|
197 | ] | |
67 | } |
|
198 | } | |
68 | ], |
|
199 | ], | |
69 | "prompt_number": 2 |
|
200 | "prompt_number": 2 | |
70 | }, |
|
201 | }, | |
71 | { |
|
202 | { | |
72 | "cell_type": "markdown", |
|
203 | "cell_type": "markdown", | |
73 | "metadata": {}, |
|
204 | "metadata": {}, | |
74 | "source": [ |
|
205 | "source": [ | |
75 | "SVG images are also supported out of the box (since modern browsers do a good job of rendering them):" |
|
206 | "SVG images are also supported out of the box (since modern browsers do a good job of rendering them):" | |
76 | ] |
|
207 | ] | |
77 | }, |
|
208 | }, | |
78 | { |
|
209 | { | |
79 | "cell_type": "code", |
|
210 | "cell_type": "code", | |
80 | "collapsed": false, |
|
211 | "collapsed": false, | |
81 | "input": [ |
|
212 | "input": [ | |
82 | "from IPython.display import SVG\n", |
|
213 | "from IPython.display import SVG\n", | |
83 | "SVG(filename='python-logo.svg')" |
|
214 | "SVG(filename='python-logo.svg')" | |
84 | ], |
|
215 | ], | |
85 | "language": "python", |
|
216 | "language": "python", | |
86 | "metadata": {}, |
|
217 | "metadata": {}, | |
87 | "outputs": [ |
|
218 | "outputs": [ | |
88 | { |
|
219 | { | |
89 | "output_type": "pyout", |
|
220 | "output_type": "pyout", | |
90 | "prompt_number": 3, |
|
221 | "prompt_number": 3, | |
91 | "svg": [ |
|
222 | "svg": [ | |
92 | "<svg height=\"115.02pt\" id=\"svg2\" inkscape:version=\"0.43\" sodipodi:docbase=\"/home/sdeibel\" sodipodi:docname=\"logo-python-generic.svg\" sodipodi:version=\"0.32\" version=\"1.0\" width=\"388.84pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:cc=\"http://web.resource.org/cc/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:inkscape=\"http://www.inkscape.org/namespaces/inkscape\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:sodipodi=\"http://inkscape.sourceforge.net/DTD/sodipodi-0.dtd\" xmlns:svg=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", |
|
223 | "<svg height=\"115.02pt\" id=\"svg2\" inkscape:version=\"0.43\" sodipodi:docbase=\"/home/sdeibel\" sodipodi:docname=\"logo-python-generic.svg\" sodipodi:version=\"0.32\" version=\"1.0\" width=\"388.84pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:cc=\"http://web.resource.org/cc/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:inkscape=\"http://www.inkscape.org/namespaces/inkscape\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:sodipodi=\"http://inkscape.sourceforge.net/DTD/sodipodi-0.dtd\" xmlns:svg=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", | |
93 | " <metadata id=\"metadata2193\">\n", |
|
224 | " <metadata id=\"metadata2193\">\n", | |
94 | " <rdf:RDF>\n", |
|
225 | " <rdf:RDF>\n", | |
95 | " <cc:Work rdf:about=\"\">\n", |
|
226 | " <cc:Work rdf:about=\"\">\n", | |
96 | " <dc:format>image/svg+xml</dc:format>\n", |
|
227 | " <dc:format>image/svg+xml</dc:format>\n", | |
97 | " <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n", |
|
228 | " <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n", | |
98 | " </cc:Work>\n", |
|
229 | " </cc:Work>\n", | |
99 | " </rdf:RDF>\n", |
|
230 | " </rdf:RDF>\n", | |
100 | " </metadata>\n", |
|
231 | " </metadata>\n", | |
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281 | " <path d=\"M 110.46717 132.28575 A 48.948284 8.6066771 0 1 1 12.570599,132.28575 A 48.948284 8.6066771 0 1 1 110.46717 132.28575 z\" id=\"path1894\" style=\"opacity:0.44382019;fill:url(#radialGradient1480);fill-opacity:1;fill-rule:nonzero;stroke:none;stroke-width:20;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1\" transform=\"matrix(0.73406,0,0,0.809524,16.24958,27.00935)\"/>\n", | |
151 | " </g>\n", |
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282 | " </g>\n", | |
152 | "</svg>" |
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283 | "</svg>" | |
153 | ], |
|
284 | ], | |
154 | "text": [ |
|
285 | "text": [ | |
155 | "<IPython.core.display.SVG at 0x10fb998d0>" |
|
286 | "<IPython.core.display.SVG at 0x10fb998d0>" | |
156 | ] |
|
287 | ] | |
157 | } |
|
288 | } | |
158 | ], |
|
289 | ], | |
159 | "prompt_number": 3 |
|
290 | "prompt_number": 3 | |
160 | }, |
|
291 | }, | |
161 | { |
|
292 | { | |
162 |
"cell_type": " |
|
293 | "cell_type": "heading", | |
|
294 | "level": 3, | |||
163 | "metadata": {}, |
|
295 | "metadata": {}, | |
164 | "source": [ |
|
296 | "source": [ | |
165 |
" |
|
297 | "Embedded vs Non-embedded Images" | |
166 | ] |
|
298 | ] | |
167 | }, |
|
299 | }, | |
168 | { |
|
300 | { | |
169 | "cell_type": "markdown", |
|
301 | "cell_type": "markdown", | |
170 | "metadata": {}, |
|
302 | "metadata": {}, | |
171 | "source": [ |
|
303 | "source": [ | |
172 | "As of IPython 0.13, images are embedded by default for compatibility with QtConsole, and the ability to still be displayed offline.\n", |
|
304 | "By default, image data is embedded in the Notebook document so that the images can be viewed offline. However it is also possible to tell the `Image` class to only store a *link* to the image. Let's see how this works using a webcam at Berkeley." | |
173 | "\n", |
|
|||
174 | "Let's look at the differences:" |
|
|||
175 | ] |
|
305 | ] | |
176 | }, |
|
306 | }, | |
177 | { |
|
307 | { | |
178 | "cell_type": "code", |
|
308 | "cell_type": "code", | |
179 | "collapsed": false, |
|
309 | "collapsed": false, | |
180 | "input": [ |
|
310 | "input": [ | |
181 | "# by default Image data are embedded\n", |
|
311 | "# by default Image data are embedded\n", | |
182 | "Embed = Image( 'http://scienceview.berkeley.edu/view/images/newview.jpg')\n", |
|
312 | "Embed = Image( 'http://scienceview.berkeley.edu/view/images/newview.jpg')\n", | |
183 | "\n", |
|
313 | "\n", | |
184 | "# if kwarg `url` is given, the embedding is assumed to be false\n", |
|
314 | "# if kwarg `url` is given, the embedding is assumed to be false\n", | |
185 | "SoftLinked = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg')\n", |
|
315 | "SoftLinked = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg')\n", | |
186 | "\n", |
|
316 | "\n", | |
187 | "# In each case, embed can be specified explicitly with the `embed` kwarg\n", |
|
317 | "# In each case, embed can be specified explicitly with the `embed` kwarg\n", | |
188 | "# ForceEmbed = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg', embed=True)" |
|
318 | "# ForceEmbed = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg', embed=True)" | |
189 | ], |
|
319 | ], | |
190 | "language": "python", |
|
320 | "language": "python", | |
191 | "metadata": {}, |
|
321 | "metadata": {}, | |
192 | "outputs": [], |
|
322 | "outputs": [], | |
193 | "prompt_number": 4 |
|
323 | "prompt_number": 4 | |
194 | }, |
|
324 | }, | |
195 | { |
|
325 | { | |
196 | "cell_type": "markdown", |
|
326 | "cell_type": "markdown", | |
197 | "metadata": {}, |
|
327 | "metadata": {}, | |
198 | "source": [ |
|
328 | "source": [ | |
199 | "Today's image from a webcam at Berkeley, (at the time I created this notebook). This should also work in the Qtconsole.\n", |
|
329 | "Here is the embedded version. Note that this image was pulled from the webcam when this code cell was originally run and stored in the Notebook. Unless we rerun this cell, this is not todays image." | |
200 | "Drawback is that the saved notebook will be larger, but the image will still be present offline." |
|
|||
201 | ] |
|
330 | ] | |
202 | }, |
|
331 | }, | |
203 | { |
|
332 | { | |
204 | "cell_type": "code", |
|
333 | "cell_type": "code", | |
205 | "collapsed": false, |
|
334 | "collapsed": false, | |
206 | "input": [ |
|
335 | "input": [ | |
207 | "Embed" |
|
336 | "Embed" | |
208 | ], |
|
337 | ], | |
209 | "language": "python", |
|
338 | "language": "python", | |
210 | "metadata": {}, |
|
339 | "metadata": {}, | |
211 | "outputs": [ |
|
340 | "outputs": [ | |
212 | { |
|
341 | { | |
213 | "jpeg": 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+lASoKhSUc4oCA1M0AM+tDNAHPGaG6oAbsVCc1RQpNAmhAZoE0KLm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JsEjY4wSD5ZrN0s+s9D9PaXqHQ/Ul3rd9ds/i313vAgg+nw8Rs7qAQSuMHO0epxzknJKK/3Nc1D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\nfwpJIyi7nVphszkjeCo54rKUor2g7esdX/Grqbpq66YurnqC60m+s7Sxu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+lASoKhSUc4oCA1M0AM+tDNAHPGaG6oAbsVCc1RQpNAmhAZoE0KLm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JsEjY4wSD5ZrN0s+s9D9PaXqHQ/Ul3rd9ds/i313vAgg+nw8Rs7qAQSuMHO0epxzknJKK/3Nc1D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\nfwpJIyi7nVphszkjeCo54rKUor2g7esdX/Grqbpq66YurnqC60m+s7Sxu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| |
214 | "output_type": "pyout", |
|
343 | "output_type": "pyout", | |
215 | "prompt_number": 5, |
|
344 | "prompt_number": 5, | |
216 | "text": [ |
|
345 | "text": [ | |
217 | "<IPython.core.display.Image at 0x10fb99b50>" |
|
346 | "<IPython.core.display.Image at 0x10fb99b50>" | |
218 | ] |
|
347 | ] | |
219 | } |
|
348 | } | |
220 | ], |
|
349 | ], | |
221 | "prompt_number": 5 |
|
350 | "prompt_number": 5 | |
222 | }, |
|
351 | }, | |
223 | { |
|
352 | { | |
224 | "cell_type": "markdown", |
|
353 | "cell_type": "markdown", | |
225 | "metadata": {}, |
|
354 | "metadata": {}, | |
226 | "source": [ |
|
355 | "source": [ | |
227 |
" |
|
356 | "Here is today's image from same webcam at Berkeley, (refreshed every minutes, if you reload the notebook), visible only with an active internet connection, that should be different from the previous one. Notebooks 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." | |
228 | "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." |
|
|||
229 | ] |
|
357 | ] | |
230 | }, |
|
358 | }, | |
231 | { |
|
359 | { | |
232 | "cell_type": "code", |
|
360 | "cell_type": "code", | |
233 | "collapsed": false, |
|
361 | "collapsed": false, | |
234 | "input": [ |
|
362 | "input": [ | |
235 | "SoftLinked" |
|
363 | "SoftLinked" | |
236 | ], |
|
364 | ], | |
237 | "language": "python", |
|
365 | "language": "python", | |
238 | "metadata": {}, |
|
366 | "metadata": {}, | |
239 | "outputs": [ |
|
367 | "outputs": [ | |
240 | { |
|
368 | { | |
241 | "html": [ |
|
369 | "html": [ | |
242 | "<img src=\"http://scienceview.berkeley.edu/view/images/newview.jpg\" />" |
|
370 | "<img src=\"http://scienceview.berkeley.edu/view/images/newview.jpg\" />" | |
243 | ], |
|
371 | ], | |
244 | "output_type": "pyout", |
|
372 | "output_type": "pyout", | |
245 | "prompt_number": 6, |
|
373 | "prompt_number": 6, | |
246 | "text": [ |
|
374 | "text": [ | |
247 | "<IPython.core.display.Image at 0x10fb99b10>" |
|
375 | "<IPython.core.display.Image at 0x10fb99b10>" | |
248 | ] |
|
376 | ] | |
249 | } |
|
377 | } | |
250 | ], |
|
378 | ], | |
251 | "prompt_number": 6 |
|
379 | "prompt_number": 6 | |
252 | }, |
|
380 | }, | |
253 | { |
|
381 | { | |
254 | "cell_type": "markdown", |
|
382 | "cell_type": "markdown", | |
255 | "metadata": {}, |
|
383 | "metadata": {}, | |
256 | "source": [ |
|
384 | "source": [ | |
257 |
"Of course, if you re-run th |
|
385 | "Of course, if you re-run this Notebook, the two images will be the same again." | |
258 | ] |
|
386 | ] | |
259 | }, |
|
387 | }, | |
260 | { |
|
388 | { | |
261 |
"cell_type": " |
|
389 | "cell_type": "heading", | |
|
390 | "level": 2, | |||
262 | "metadata": {}, |
|
391 | "metadata": {}, | |
263 | "source": [ |
|
392 | "source": [ | |
264 |
" |
|
393 | "Video" | |
265 | ] |
|
394 | ] | |
266 | }, |
|
395 | }, | |
267 | { |
|
396 | { | |
268 | "cell_type": "markdown", |
|
397 | "cell_type": "markdown", | |
269 | "metadata": {}, |
|
398 | "metadata": {}, | |
270 | "source": [ |
|
399 | "source": [ | |
271 | "And more exotic objects can also be displayed, as long as their representation supports \n", |
|
400 | "And more exotic objects can also be displayed, as long as their representation supports \n", | |
272 | "the IPython display protocol.\n", |
|
401 | "the IPython display protocol.\n", | |
273 | "\n", |
|
402 | "\n", | |
274 | "For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other\n", |
|
403 | "For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other\n", | |
275 | "hosted content is trivial):" |
|
404 | "hosted content is trivial):" | |
276 | ] |
|
405 | ] | |
277 | }, |
|
406 | }, | |
278 | { |
|
407 | { | |
279 | "cell_type": "code", |
|
408 | "cell_type": "code", | |
280 | "collapsed": false, |
|
409 | "collapsed": false, | |
281 | "input": [ |
|
410 | "input": [ | |
282 | "from IPython.display import YouTubeVideo\n", |
|
411 | "from IPython.display import YouTubeVideo\n", | |
283 | "# a talk about IPython at Sage Days at U. Washington, Seattle.\n", |
|
412 | "# a talk about IPython at Sage Days at U. Washington, Seattle.\n", | |
284 | "# Video credit: William Stein.\n", |
|
413 | "# Video credit: William Stein.\n", | |
285 | "YouTubeVideo('1j_HxD4iLn8')" |
|
414 | "YouTubeVideo('1j_HxD4iLn8')" | |
286 | ], |
|
415 | ], | |
287 | "language": "python", |
|
416 | "language": "python", | |
288 | "metadata": {}, |
|
417 | "metadata": {}, | |
289 | "outputs": [ |
|
418 | "outputs": [ | |
290 | { |
|
419 | { | |
291 | "html": [ |
|
420 | "html": [ | |
292 | "\n", |
|
421 | "\n", | |
293 | " <iframe\n", |
|
422 | " <iframe\n", | |
294 | " width=\"400\"\n", |
|
423 | " width=\"400\"\n", | |
295 | " height=\"300\"\n", |
|
424 | " height=\"300\"\n", | |
296 | " src=\"http://www.youtube.com/embed/1j_HxD4iLn8\"\n", |
|
425 | " src=\"http://www.youtube.com/embed/1j_HxD4iLn8\"\n", | |
297 | " frameborder=\"0\"\n", |
|
426 | " frameborder=\"0\"\n", | |
298 | " allowfullscreen\n", |
|
427 | " allowfullscreen\n", | |
299 | " ></iframe>\n", |
|
428 | " ></iframe>\n", | |
300 | " " |
|
429 | " " | |
301 | ], |
|
430 | ], | |
302 | "output_type": "pyout", |
|
431 | "output_type": "pyout", | |
303 | "prompt_number": 7, |
|
432 | "prompt_number": 7, | |
304 | "text": [ |
|
433 | "text": [ | |
305 | "<IPython.lib.display.YouTubeVideo at 0x10fba2190>" |
|
434 | "<IPython.lib.display.YouTubeVideo at 0x10fba2190>" | |
306 | ] |
|
435 | ] | |
307 | } |
|
436 | } | |
308 | ], |
|
437 | ], | |
309 | "prompt_number": 7 |
|
438 | "prompt_number": 7 | |
310 | }, |
|
439 | }, | |
311 | { |
|
440 | { | |
312 | "cell_type": "markdown", |
|
441 | "cell_type": "markdown", | |
313 | "metadata": {}, |
|
442 | "metadata": {}, | |
314 | "source": [ |
|
443 | "source": [ | |
315 | "Using the nascent video capabilities of modern browsers, you may also be able to display local\n", |
|
444 | "Using the nascent video capabilities of modern browsers, you may also be able to display local\n", | |
316 | "videos. At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n", |
|
445 | "videos. At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n", | |
317 | "we will continue testing this and looking for ways to make it more robust. \n", |
|
446 | "we will continue testing this and looking for ways to make it more robust. \n", | |
318 | "\n", |
|
447 | "\n", | |
319 | "The following cell loads a local file called `animation.m4v`, encodes the raw video as base64 for http\n", |
|
448 | "The following cell loads a local file called `animation.m4v`, encodes the raw video as base64 for http\n", | |
320 | "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control\n", |
|
449 | "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control\n", | |
321 | "bar at the bottom with a play/pause button and a location slider." |
|
450 | "bar at the bottom with a play/pause button and a location slider." | |
322 | ] |
|
451 | ] | |
323 | }, |
|
452 | }, | |
324 | { |
|
453 | { | |
325 | "cell_type": "code", |
|
454 | "cell_type": "code", | |
326 | "collapsed": false, |
|
455 | "collapsed": false, | |
327 | "input": [ |
|
456 | "input": [ | |
328 | "from IPython.display import HTML\n", |
|
457 | "from IPython.display import HTML\n", | |
329 | "video = open(\"animation.m4v\", \"rb\").read()\n", |
|
458 | "video = open(\"animation.m4v\", \"rb\").read()\n", | |
330 | "video_encoded = video.encode(\"base64\")\n", |
|
459 | "video_encoded = video.encode(\"base64\")\n", | |
331 | "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n", |
|
460 | "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n", | |
332 | "HTML(data=video_tag)" |
|
461 | "HTML(data=video_tag)" | |
333 | ], |
|
462 | ], | |
334 | "language": "python", |
|
463 | "language": "python", | |
335 | "metadata": {}, |
|
464 | "metadata": {}, | |
336 | "outputs": [ |
|
465 | "outputs": [ | |
337 | { |
|
466 | { | |
338 | "html": [ |
|
467 | "html": [ | |
339 | "<video controls alt=\"test\" src=\"data:video/x-m4v;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAqiW1kYXQAAAKMBgX//4jcRem9\n", |
|
468 | "<video controls alt=\"test\" src=\"data:video/x-m4v;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAqiW1kYXQAAAKMBgX//4jcRem9\n", | |
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469 | "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTE4IC0gSC4yNjQvTVBFRy00IEFWQyBjb2RlYyAtIENv\n", | |
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666 | "AAAAAAAAACRkaW5mAAAAHGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAAAeBzdGJsAAAAtHN0c2QA\n", | |
538 | "AAAAAAAAAQAAAKRhdmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAY4BhgBIAAAASAAAAAAAAAAB\n", |
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667 | "AAAAAAAAAQAAAKRhdmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAY4BhgBIAAAASAAAAAAAAAAB\n", | |
539 | "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAAMmF2Y0MBZAAV/+EAGWdkABWs\n", |
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668 | "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAAMmF2Y0MBZAAV/+EAGWdkABWs\n", | |
540 | "2UGQz6mhAAADAAEAAAMAMg8WLZYBAAZo6+PLIsAAAAAcdXVpZGtoQPJfJE/FujmlG88DI/MAAAAA\n", |
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669 | "2UGQz6mhAAADAAEAAAMAMg8WLZYBAAZo6+PLIsAAAAAcdXVpZGtoQPJfJE/FujmlG88DI/MAAAAA\n", | |
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|
670 | "AAAAGHN0dHMAAAAAAAAAAQAAABQAAAABAAAAFHN0c3MAAAAAAAAAAQAAAAEAAAAYY3R0cwAAAAAA\n", | |
542 | "AAABAAAAFAAAAAIAAAAcc3RzYwAAAAAAAAABAAAAAQAAAAEAAAABAAAAZHN0c3oAAAAAAAAAAAAA\n", |
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671 | "AAABAAAAFAAAAAIAAAAcc3RzYwAAAAAAAAABAAAAAQAAAAEAAAABAAAAZHN0c3oAAAAAAAAAAAAA\n", | |
543 | "ABQAAA05AAACqQAAAl8AAAITAAACiwAAAh8AAAIvAAABiAAAAVsAAAE5AAABWwAAAUQAAAFmAAAA\n", |
|
672 | "ABQAAA05AAACqQAAAl8AAAITAAACiwAAAh8AAAIvAAABiAAAAVsAAAE5AAABWwAAAUQAAAFmAAAA\n", | |
544 | "/QAAAUEAAAEaAAABKQAAAQAAAADlAAAAzQAAAGBzdGNvAAAAAAAAABQAAAAsAAANZQAAEA4AABJt\n", |
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673 | "/QAAAUEAAAEaAAABKQAAAQAAAADlAAAAzQAAAGBzdGNvAAAAAAAAABQAAAAsAAANZQAAEA4AABJt\n", | |
545 | "AAAUgAAAFwsAABkqAAAbWQAAHOEAAB48AAAfdQAAINAAACIUAAAjegAAJHcAACW4AAAm0gAAJ/sA\n", |
|
674 | "AAAUgAAAFwsAABkqAAAbWQAAHOEAAB48AAAfdQAAINAAACIUAAAjegAAJHcAACW4AAAm0gAAJ/sA\n", | |
546 | "ACj7AAAp4AAAAGF1ZHRhAAAAWW1ldGEAAAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAA\n", |
|
675 | "ACj7AAAp4AAAAGF1ZHRhAAAAWW1ldGEAAAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAA\n", | |
547 | "AAAAAAAALGlsc3QAAAAkqXRvbwAAABxkYXRhAAAAAQAAAABMYXZmNTIuMTExLjA=\n", |
|
676 | "AAAAAAAALGlsc3QAAAAkqXRvbwAAABxkYXRhAAAAAQAAAABMYXZmNTIuMTExLjA=\n", | |
548 | "\">" |
|
677 | "\">" | |
549 | ], |
|
678 | ], | |
550 | "output_type": "pyout", |
|
679 | "output_type": "pyout", | |
551 | "prompt_number": 8, |
|
680 | "prompt_number": 8, | |
552 | "text": [ |
|
681 | "text": [ | |
553 | "<IPython.core.display.HTML at 0x10fba28d0>" |
|
682 | "<IPython.core.display.HTML at 0x10fba28d0>" | |
554 | ] |
|
683 | ] | |
555 | } |
|
684 | } | |
556 | ], |
|
685 | ], | |
557 | "prompt_number": 8 |
|
686 | "prompt_number": 8 | |
558 | }, |
|
687 | }, | |
559 | { |
|
688 | { | |
|
689 | "cell_type": "heading", | |||
|
690 | "level": 2, | |||
|
691 | "metadata": {}, | |||
|
692 | "source": [ | |||
|
693 | "HTML" | |||
|
694 | ] | |||
|
695 | }, | |||
|
696 | { | |||
|
697 | "cell_type": "markdown", | |||
|
698 | "metadata": {}, | |||
|
699 | "source": [ | |||
|
700 | "Python objects can declare HTML representations that will be displayed in the Notebook. If you have some HTML you want to display, simply use the `HTML` class." | |||
|
701 | ] | |||
|
702 | }, | |||
|
703 | { | |||
560 | "cell_type": "markdown", |
|
704 | "cell_type": "markdown", | |
561 | "metadata": {}, |
|
705 | "metadata": {}, | |
562 | "source": [ |
|
706 | "source": [ | |
563 | "### External sites\n", |
|
707 | "### External sites\n", | |
564 | "\n", |
|
708 | "\n", | |
565 | "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n", |
|
709 | "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n", | |
566 | "page for mobile users:" |
|
710 | "page for mobile users:" | |
567 | ] |
|
711 | ] | |
568 | }, |
|
712 | }, | |
569 | { |
|
713 | { | |
570 | "cell_type": "code", |
|
714 | "cell_type": "code", | |
571 | "collapsed": false, |
|
715 | "collapsed": false, | |
572 | "input": [ |
|
716 | "input": [ | |
573 | "HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')" |
|
717 | "HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')" | |
574 | ], |
|
718 | ], | |
575 | "language": "python", |
|
719 | "language": "python", | |
576 | "metadata": {}, |
|
720 | "metadata": {}, | |
577 | "outputs": [ |
|
721 | "outputs": [ | |
578 | { |
|
722 | { | |
579 | "html": [ |
|
723 | "html": [ | |
580 | "<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>" |
|
724 | "<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>" | |
581 | ], |
|
725 | ], | |
582 | "output_type": "pyout", |
|
726 | "output_type": "pyout", | |
583 | "prompt_number": 9, |
|
727 | "prompt_number": 9, | |
584 | "text": [ |
|
728 | "text": [ | |
585 | "<IPython.core.display.HTML at 0x1094900d0>" |
|
729 | "<IPython.core.display.HTML at 0x1094900d0>" | |
586 | ] |
|
730 | ] | |
587 | } |
|
731 | } | |
588 | ], |
|
732 | ], | |
589 | "prompt_number": 9 |
|
733 | "prompt_number": 9 | |
590 | }, |
|
734 | }, | |
591 | { |
|
735 | { | |
|
736 | "cell_type": "heading", | |||
|
737 | "level": 2, | |||
|
738 | "metadata": {}, | |||
|
739 | "source": [ | |||
|
740 | "LaTeX" | |||
|
741 | ] | |||
|
742 | }, | |||
|
743 | { | |||
592 | "cell_type": "markdown", |
|
744 | "cell_type": "markdown", | |
593 | "metadata": {}, |
|
745 | "metadata": {}, | |
594 | "source": [ |
|
746 | "source": [ | |
595 | "### Mathematics\n", |
|
|||
596 | "\n", |
|
|||
597 | "And we also support the display of mathematical expressions typeset in LaTeX, which is rendered\n", |
|
747 | "And we also support the display of mathematical expressions typeset in LaTeX, which is rendered\n", | |
598 |
"in the browser thanks to the [MathJax library](http://mathjax.org). |
|
748 | "in the browser thanks to the [MathJax library](http://mathjax.org)." | |
599 | "\n", |
|
|||
600 | "Note that this is *different* from the above examples. Above we were typing mathematical expressions\n", |
|
|||
601 | "in Markdown cells (along with normal text) and letting the browser render them; now we are displaying\n", |
|
|||
602 | "the output of a Python computation as a LaTeX expression wrapped by the `Math()` object so the browser\n", |
|
|||
603 | "renders it. The `Math` object will add the needed LaTeX delimiters (`$$`) if they are not provided:" |
|
|||
604 | ] |
|
749 | ] | |
605 | }, |
|
750 | }, | |
606 | { |
|
751 | { | |
607 | "cell_type": "code", |
|
752 | "cell_type": "code", | |
608 | "collapsed": false, |
|
753 | "collapsed": false, | |
609 | "input": [ |
|
754 | "input": [ | |
610 | "from IPython.display import Math\n", |
|
755 | "from IPython.display import Math\n", | |
611 | "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')" |
|
756 | "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')" | |
612 | ], |
|
757 | ], | |
613 | "language": "python", |
|
758 | "language": "python", | |
614 | "metadata": {}, |
|
759 | "metadata": {}, | |
615 | "outputs": [ |
|
760 | "outputs": [ | |
616 | { |
|
761 | { | |
617 | "latex": [ |
|
762 | "latex": [ | |
618 | "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$" |
|
763 | "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$" | |
619 | ], |
|
764 | ], | |
620 | "output_type": "pyout", |
|
765 | "output_type": "pyout", | |
621 | "prompt_number": 10, |
|
766 | "prompt_number": 10, | |
622 | "text": [ |
|
767 | "text": [ | |
623 | "<IPython.core.display.Math at 0x10fba26d0>" |
|
768 | "<IPython.core.display.Math at 0x10fba26d0>" | |
624 | ] |
|
769 | ] | |
625 | } |
|
770 | } | |
626 | ], |
|
771 | ], | |
627 | "prompt_number": 10 |
|
772 | "prompt_number": 10 | |
628 | }, |
|
773 | }, | |
629 | { |
|
774 | { | |
630 | "cell_type": "markdown", |
|
775 | "cell_type": "markdown", | |
631 | "metadata": {}, |
|
776 | "metadata": {}, | |
632 | "source": [ |
|
777 | "source": [ | |
633 | "With the `Latex` class, you have to include the delimiters yourself. This allows you to use other LaTeX modes such as `eqnarray`:" |
|
778 | "With the `Latex` class, you have to include the delimiters yourself. This allows you to use other LaTeX modes such as `eqnarray`:" | |
634 | ] |
|
779 | ] | |
635 | }, |
|
780 | }, | |
636 | { |
|
781 | { | |
637 | "cell_type": "code", |
|
782 | "cell_type": "code", | |
638 | "collapsed": false, |
|
783 | "collapsed": false, | |
639 | "input": [ |
|
784 | "input": [ | |
640 | "from IPython.display import Latex\n", |
|
785 | "from IPython.display import Latex\n", | |
641 | "Latex(r\"\"\"\\begin{eqnarray}\n", |
|
786 | "Latex(r\"\"\"\\begin{eqnarray}\n", | |
642 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
787 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", | |
643 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", |
|
788 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", | |
644 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", |
|
789 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", | |
645 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", |
|
790 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", | |
646 | "\\end{eqnarray}\"\"\")" |
|
791 | "\\end{eqnarray}\"\"\")" | |
647 | ], |
|
792 | ], | |
648 | "language": "python", |
|
793 | "language": "python", | |
649 | "metadata": {}, |
|
794 | "metadata": {}, | |
650 | "outputs": [ |
|
795 | "outputs": [ | |
651 | { |
|
796 | { | |
652 | "latex": [ |
|
797 | "latex": [ | |
653 | "\\begin{eqnarray}\n", |
|
798 | "\\begin{eqnarray}\n", | |
654 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
799 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", | |
655 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", |
|
800 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", | |
656 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", |
|
801 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", | |
657 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", |
|
802 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", | |
658 | "\\end{eqnarray}" |
|
803 | "\\end{eqnarray}" | |
659 | ], |
|
804 | ], | |
660 | "output_type": "pyout", |
|
805 | "output_type": "pyout", | |
661 | "prompt_number": 11, |
|
806 | "prompt_number": 11, | |
662 | "text": [ |
|
807 | "text": [ | |
663 | "<IPython.core.display.Latex at 0x10fba2c10>" |
|
808 | "<IPython.core.display.Latex at 0x10fba2c10>" | |
664 | ] |
|
809 | ] | |
665 | } |
|
810 | } | |
666 | ], |
|
811 | ], | |
667 | "prompt_number": 11 |
|
812 | "prompt_number": 11 | |
668 | }, |
|
813 | }, | |
669 | { |
|
814 | { | |
670 | "cell_type": "markdown", |
|
815 | "cell_type": "markdown", | |
671 | "metadata": {}, |
|
816 | "metadata": {}, | |
672 | "source": [ |
|
817 | "source": [ | |
673 | "Or you can enter latex directly with the `%%latex` cell magic:" |
|
818 | "Or you can enter latex directly with the `%%latex` cell magic:" | |
674 | ] |
|
819 | ] | |
675 | }, |
|
820 | }, | |
676 | { |
|
821 | { | |
677 | "cell_type": "code", |
|
822 | "cell_type": "code", | |
678 | "collapsed": false, |
|
823 | "collapsed": false, | |
679 | "input": [ |
|
824 | "input": [ | |
680 | "%%latex\n", |
|
825 | "%%latex\n", | |
681 | "\\begin{aligned}\n", |
|
826 | "\\begin{aligned}\n", | |
682 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
827 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", | |
683 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", |
|
828 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", | |
684 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", |
|
829 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", | |
685 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n", |
|
830 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n", | |
686 | "\\end{aligned}" |
|
831 | "\\end{aligned}" | |
687 | ], |
|
832 | ], | |
688 | "language": "python", |
|
833 | "language": "python", | |
689 | "metadata": {}, |
|
834 | "metadata": {}, | |
690 | "outputs": [ |
|
835 | "outputs": [ | |
691 | { |
|
836 | { | |
692 | "latex": [ |
|
837 | "latex": [ | |
693 | "\\begin{aligned}\n", |
|
838 | "\\begin{aligned}\n", | |
694 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", |
|
839 | "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", | |
695 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", |
|
840 | "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", | |
696 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", |
|
841 | "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", | |
697 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n", |
|
842 | "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n", | |
698 | "\\end{aligned}" |
|
843 | "\\end{aligned}" | |
699 | ], |
|
844 | ], | |
700 | "output_type": "display_data", |
|
845 | "output_type": "display_data", | |
701 | "text": [ |
|
846 | "text": [ | |
702 | "<IPython.core.display.Latex at 0x10a617c90>" |
|
847 | "<IPython.core.display.Latex at 0x10a617c90>" | |
703 | ] |
|
848 | ] | |
704 | } |
|
849 | } | |
705 | ], |
|
850 | ], | |
706 | "prompt_number": 12 |
|
851 | "prompt_number": 12 | |
707 | } |
|
852 | } | |
708 | ], |
|
853 | ], | |
709 | "metadata": {} |
|
854 | "metadata": {} | |
710 | } |
|
855 | } | |
711 | ] |
|
856 | ] | |
712 | } |
|
857 | } |
@@ -1,921 +1,928 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
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2 | "metadata": { | |
3 | "name": "R Magics" |
|
3 | "name": "R Magics" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
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6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
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10 | { | |
11 | "cell_type": "heading", |
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11 | "cell_type": "heading", | |
12 | "level": 1, |
|
12 | "level": 1, | |
13 | "metadata": {}, |
|
13 | "metadata": {}, | |
14 | "source": [ |
|
14 | "source": [ | |
15 | "Rmagic Functions Extension" |
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15 | "Using R Within the IPython Notebok" | |
|
16 | ] | |||
|
17 | }, | |||
|
18 | { | |||
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19 | "cell_type": "markdown", | |||
|
20 | "metadata": {}, | |||
|
21 | "source": [ | |||
|
22 | "Using the `rmagic` extension, users can run R code from within the IPython Notebook. This example Notebook demonstrates this capability. " | |||
16 | ] |
|
23 | ] | |
17 | }, |
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24 | }, | |
18 | { |
|
25 | { | |
19 | "cell_type": "code", |
|
26 | "cell_type": "code", | |
20 | "collapsed": false, |
|
27 | "collapsed": false, | |
21 | "input": [ |
|
28 | "input": [ | |
22 | "%pylab inline" |
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29 | "%pylab inline" | |
23 | ], |
|
30 | ], | |
24 | "language": "python", |
|
31 | "language": "python", | |
25 | "metadata": {}, |
|
32 | "metadata": {}, | |
26 | "outputs": [] |
|
33 | "outputs": [] | |
27 | }, |
|
34 | }, | |
28 | { |
|
35 | { | |
29 | "cell_type": "heading", |
|
36 | "cell_type": "heading", | |
30 | "level": 2, |
|
37 | "level": 2, | |
31 | "metadata": {}, |
|
38 | "metadata": {}, | |
32 | "source": [ |
|
39 | "source": [ | |
33 | "Line magics" |
|
40 | "Line magics" | |
34 | ] |
|
41 | ] | |
35 | }, |
|
42 | }, | |
36 | { |
|
43 | { | |
37 | "cell_type": "markdown", |
|
44 | "cell_type": "markdown", | |
38 | "metadata": {}, |
|
45 | "metadata": {}, | |
39 | "source": [ |
|
46 | "source": [ | |
40 | "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:" |
|
47 | "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:" | |
41 | ] |
|
48 | ] | |
42 | }, |
|
49 | }, | |
43 | { |
|
50 | { | |
44 | "cell_type": "code", |
|
51 | "cell_type": "code", | |
45 | "collapsed": true, |
|
52 | "collapsed": true, | |
46 | "input": [ |
|
53 | "input": [ | |
47 | "%load_ext rmagic " |
|
54 | "%load_ext rmagic " | |
48 | ], |
|
55 | ], | |
49 | "language": "python", |
|
56 | "language": "python", | |
50 | "metadata": {}, |
|
57 | "metadata": {}, | |
51 | "outputs": [], |
|
58 | "outputs": [], | |
52 | "prompt_number": 1 |
|
59 | "prompt_number": 1 | |
53 | }, |
|
60 | }, | |
54 | { |
|
61 | { | |
55 | "cell_type": "markdown", |
|
62 | "cell_type": "markdown", | |
56 | "metadata": {}, |
|
63 | "metadata": {}, | |
57 | "source": [ |
|
64 | "source": [ | |
58 | "A typical use case one imagines is having some numpy arrays, wanting to compute some statistics of interest on these\n", |
|
65 | "A typical use case one imagines is having some numpy arrays, wanting to compute some statistics of interest on these\n", | |
59 | " arrays and return the result back to python. Let's suppose we just want to fit a simple linear model to a scatterplot." |
|
66 | " arrays and return the result back to python. Let's suppose we just want to fit a simple linear model to a scatterplot." | |
60 | ] |
|
67 | ] | |
61 | }, |
|
68 | }, | |
62 | { |
|
69 | { | |
63 | "cell_type": "code", |
|
70 | "cell_type": "code", | |
64 | "collapsed": false, |
|
71 | "collapsed": false, | |
65 | "input": [ |
|
72 | "input": [ | |
66 | "import numpy as np\n", |
|
73 | "import numpy as np\n", | |
67 | "import pylab\n", |
|
74 | "import pylab\n", | |
68 | "X = np.array([0,1,2,3,4])\n", |
|
75 | "X = np.array([0,1,2,3,4])\n", | |
69 | "Y = np.array([3,5,4,6,7])\n", |
|
76 | "Y = np.array([3,5,4,6,7])\n", | |
70 | "pylab.scatter(X, Y)" |
|
77 | "pylab.scatter(X, Y)" | |
71 | ], |
|
78 | ], | |
72 | "language": "python", |
|
79 | "language": "python", | |
73 | "metadata": {}, |
|
80 | "metadata": {}, | |
74 | "outputs": [ |
|
81 | "outputs": [ | |
75 | { |
|
82 | { | |
76 | "output_type": "pyout", |
|
83 | "output_type": "pyout", | |
77 | "prompt_number": 2, |
|
84 | "prompt_number": 2, | |
78 | "text": [ |
|
85 | "text": [ | |
79 | "<matplotlib.collections.PathCollection at 0x10f32f610>" |
|
86 | "<matplotlib.collections.PathCollection at 0x10f32f610>" | |
80 | ] |
|
87 | ] | |
81 | } |
|
88 | } | |
82 | ], |
|
89 | ], | |
83 | "prompt_number": 2 |
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90 | "prompt_number": 2 | |
84 | }, |
|
91 | }, | |
85 | { |
|
92 | { | |
86 | "cell_type": "markdown", |
|
93 | "cell_type": "markdown", | |
87 | "metadata": {}, |
|
94 | "metadata": {}, | |
88 | "source": [ |
|
95 | "source": [ | |
89 | "We can accomplish this by first pushing variables to R, fitting a model and returning the results. The line magic %Rpush copies its arguments to variables of the same name in rpy2. The %R line magic evaluates the string in rpy2 and returns the results. In this case, the coefficients of a linear model." |
|
96 | "We can accomplish this by first pushing variables to R, fitting a model and returning the results. The line magic %Rpush copies its arguments to variables of the same name in rpy2. The %R line magic evaluates the string in rpy2 and returns the results. In this case, the coefficients of a linear model." | |
90 | ] |
|
97 | ] | |
91 | }, |
|
98 | }, | |
92 | { |
|
99 | { | |
93 | "cell_type": "code", |
|
100 | "cell_type": "code", | |
94 | "collapsed": false, |
|
101 | "collapsed": false, | |
95 | "input": [ |
|
102 | "input": [ | |
96 | "%Rpush X Y\n", |
|
103 | "%Rpush X Y\n", | |
97 | "%R lm(Y~X)$coef" |
|
104 | "%R lm(Y~X)$coef" | |
98 | ], |
|
105 | ], | |
99 | "language": "python", |
|
106 | "language": "python", | |
100 | "metadata": {}, |
|
107 | "metadata": {}, | |
101 | "outputs": [ |
|
108 | "outputs": [ | |
102 | { |
|
109 | { | |
103 | "output_type": "pyout", |
|
110 | "output_type": "pyout", | |
104 | "prompt_number": 3, |
|
111 | "prompt_number": 3, | |
105 | "text": [ |
|
112 | "text": [ | |
106 | "array([ 3.2, 0.9])" |
|
113 | "array([ 3.2, 0.9])" | |
107 | ] |
|
114 | ] | |
108 | } |
|
115 | } | |
109 | ], |
|
116 | ], | |
110 | "prompt_number": 3 |
|
117 | "prompt_number": 3 | |
111 | }, |
|
118 | }, | |
112 | { |
|
119 | { | |
113 | "cell_type": "markdown", |
|
120 | "cell_type": "markdown", | |
114 | "metadata": {}, |
|
121 | "metadata": {}, | |
115 | "source": [ |
|
122 | "source": [ | |
116 | "We can check that this is correct fairly easily:" |
|
123 | "We can check that this is correct fairly easily:" | |
117 | ] |
|
124 | ] | |
118 | }, |
|
125 | }, | |
119 | { |
|
126 | { | |
120 | "cell_type": "code", |
|
127 | "cell_type": "code", | |
121 | "collapsed": false, |
|
128 | "collapsed": false, | |
122 | "input": [ |
|
129 | "input": [ | |
123 | "Xr = X - X.mean(); Yr = Y - Y.mean()\n", |
|
130 | "Xr = X - X.mean(); Yr = Y - Y.mean()\n", | |
124 | "slope = (Xr*Yr).sum() / (Xr**2).sum()\n", |
|
131 | "slope = (Xr*Yr).sum() / (Xr**2).sum()\n", | |
125 | "intercept = Y.mean() - X.mean() * slope\n", |
|
132 | "intercept = Y.mean() - X.mean() * slope\n", | |
126 | "(intercept, slope)" |
|
133 | "(intercept, slope)" | |
127 | ], |
|
134 | ], | |
128 | "language": "python", |
|
135 | "language": "python", | |
129 | "metadata": {}, |
|
136 | "metadata": {}, | |
130 | "outputs": [ |
|
137 | "outputs": [ | |
131 | { |
|
138 | { | |
132 | "output_type": "pyout", |
|
139 | "output_type": "pyout", | |
133 | "prompt_number": 4, |
|
140 | "prompt_number": 4, | |
134 | "text": [ |
|
141 | "text": [ | |
135 | "(3.2000000000000002, 0.90000000000000002)" |
|
142 | "(3.2000000000000002, 0.90000000000000002)" | |
136 | ] |
|
143 | ] | |
137 | } |
|
144 | } | |
138 | ], |
|
145 | ], | |
139 | "prompt_number": 4 |
|
146 | "prompt_number": 4 | |
140 | }, |
|
147 | }, | |
141 | { |
|
148 | { | |
142 | "cell_type": "markdown", |
|
149 | "cell_type": "markdown", | |
143 | "metadata": {}, |
|
150 | "metadata": {}, | |
144 | "source": [ |
|
151 | "source": [ | |
145 | "It is also possible to return more than one value with %R." |
|
152 | "It is also possible to return more than one value with %R." | |
146 | ] |
|
153 | ] | |
147 | }, |
|
154 | }, | |
148 | { |
|
155 | { | |
149 | "cell_type": "code", |
|
156 | "cell_type": "code", | |
150 | "collapsed": false, |
|
157 | "collapsed": false, | |
151 | "input": [ |
|
158 | "input": [ | |
152 | "%R resid(lm(Y~X)); coef(lm(X~Y))\n" |
|
159 | "%R resid(lm(Y~X)); coef(lm(X~Y))\n" | |
153 | ], |
|
160 | ], | |
154 | "language": "python", |
|
161 | "language": "python", | |
155 | "metadata": {}, |
|
162 | "metadata": {}, | |
156 | "outputs": [ |
|
163 | "outputs": [ | |
157 | { |
|
164 | { | |
158 | "output_type": "pyout", |
|
165 | "output_type": "pyout", | |
159 | "prompt_number": 5, |
|
166 | "prompt_number": 5, | |
160 | "text": [ |
|
167 | "text": [ | |
161 | "array([-2.5, 0.9])" |
|
168 | "array([-2.5, 0.9])" | |
162 | ] |
|
169 | ] | |
163 | } |
|
170 | } | |
164 | ], |
|
171 | ], | |
165 | "prompt_number": 5 |
|
172 | "prompt_number": 5 | |
166 | }, |
|
173 | }, | |
167 | { |
|
174 | { | |
168 | "cell_type": "markdown", |
|
175 | "cell_type": "markdown", | |
169 | "metadata": {}, |
|
176 | "metadata": {}, | |
170 | "source": [ |
|
177 | "source": [ | |
171 | "One can also easily capture the results of %R into python objects. Like R, the return value of this multiline expression (multiline in the sense that it is separated by ';') is the final value, which is \n", |
|
178 | "One can also easily capture the results of %R into python objects. Like R, the return value of this multiline expression (multiline in the sense that it is separated by ';') is the final value, which is \n", | |
172 | "the *coef(lm(X~Y))*. To pull other variables from R, there is one more magic." |
|
179 | "the *coef(lm(X~Y))*. To pull other variables from R, there is one more magic." | |
173 | ] |
|
180 | ] | |
174 | }, |
|
181 | }, | |
175 | { |
|
182 | { | |
176 | "cell_type": "markdown", |
|
183 | "cell_type": "markdown", | |
177 | "metadata": {}, |
|
184 | "metadata": {}, | |
178 | "source": [ |
|
185 | "source": [ | |
179 | "There are two more line magics, %Rpull and %Rget. Both are useful after some R code has been executed and there are variables\n", |
|
186 | "There are two more line magics, %Rpull and %Rget. Both are useful after some R code has been executed and there are variables\n", | |
180 | "in the rpy2 namespace that one would like to retrieve. The main difference is that one\n", |
|
187 | "in the rpy2 namespace that one would like to retrieve. The main difference is that one\n", | |
181 | " returns the value (%Rget), while the other pulls it to self.shell.user_ns (%Rpull). Imagine we've stored the results\n", |
|
188 | " returns the value (%Rget), while the other pulls it to self.shell.user_ns (%Rpull). Imagine we've stored the results\n", | |
182 | "of some calculation in the variable \"a\" in rpy2's namespace. By using the %R magic, we can obtain these results and\n", |
|
189 | "of some calculation in the variable \"a\" in rpy2's namespace. By using the %R magic, we can obtain these results and\n", | |
183 | "store them in b. We can also pull them directly to user_ns with %Rpull. They are both views on the same data." |
|
190 | "store them in b. We can also pull them directly to user_ns with %Rpull. They are both views on the same data." | |
184 | ] |
|
191 | ] | |
185 | }, |
|
192 | }, | |
186 | { |
|
193 | { | |
187 | "cell_type": "code", |
|
194 | "cell_type": "code", | |
188 | "collapsed": false, |
|
195 | "collapsed": false, | |
189 | "input": [ |
|
196 | "input": [ | |
190 | "b = %R a=resid(lm(Y~X))\n", |
|
197 | "b = %R a=resid(lm(Y~X))\n", | |
191 | "%Rpull a\n", |
|
198 | "%Rpull a\n", | |
192 | "print a\n", |
|
199 | "print a\n", | |
193 | "assert id(b.data) == id(a.data)\n", |
|
200 | "assert id(b.data) == id(a.data)\n", | |
194 | "%R -o a" |
|
201 | "%R -o a" | |
195 | ], |
|
202 | ], | |
196 | "language": "python", |
|
203 | "language": "python", | |
197 | "metadata": {}, |
|
204 | "metadata": {}, | |
198 | "outputs": [ |
|
205 | "outputs": [ | |
199 | { |
|
206 | { | |
200 | "output_type": "stream", |
|
207 | "output_type": "stream", | |
201 | "stream": "stdout", |
|
208 | "stream": "stdout", | |
202 | "text": [ |
|
209 | "text": [ | |
203 | "[-0.2 0.9 -1. 0.1 0.2]\n" |
|
210 | "[-0.2 0.9 -1. 0.1 0.2]\n" | |
204 | ] |
|
211 | ] | |
205 | } |
|
212 | } | |
206 | ], |
|
213 | ], | |
207 | "prompt_number": 6 |
|
214 | "prompt_number": 6 | |
208 | }, |
|
215 | }, | |
209 | { |
|
216 | { | |
210 | "cell_type": "markdown", |
|
217 | "cell_type": "markdown", | |
211 | "metadata": {}, |
|
218 | "metadata": {}, | |
212 | "source": [ |
|
219 | "source": [ | |
213 | "%Rpull is equivalent to calling %R with just -o\n" |
|
220 | "%Rpull is equivalent to calling %R with just -o\n" | |
214 | ] |
|
221 | ] | |
215 | }, |
|
222 | }, | |
216 | { |
|
223 | { | |
217 | "cell_type": "code", |
|
224 | "cell_type": "code", | |
218 | "collapsed": false, |
|
225 | "collapsed": false, | |
219 | "input": [ |
|
226 | "input": [ | |
220 | "%R d=resid(lm(Y~X)); e=coef(lm(Y~X))\n", |
|
227 | "%R d=resid(lm(Y~X)); e=coef(lm(Y~X))\n", | |
221 | "%R -o d -o e\n", |
|
228 | "%R -o d -o e\n", | |
222 | "%Rpull e\n", |
|
229 | "%Rpull e\n", | |
223 | "print d\n", |
|
230 | "print d\n", | |
224 | "print e\n", |
|
231 | "print e\n", | |
225 | "import numpy as np\n", |
|
232 | "import numpy as np\n", | |
226 | "np.testing.assert_almost_equal(d, a)" |
|
233 | "np.testing.assert_almost_equal(d, a)" | |
227 | ], |
|
234 | ], | |
228 | "language": "python", |
|
235 | "language": "python", | |
229 | "metadata": {}, |
|
236 | "metadata": {}, | |
230 | "outputs": [ |
|
237 | "outputs": [ | |
231 | { |
|
238 | { | |
232 | "output_type": "stream", |
|
239 | "output_type": "stream", | |
233 | "stream": "stdout", |
|
240 | "stream": "stdout", | |
234 | "text": [ |
|
241 | "text": [ | |
235 | "[-0.2 0.9 -1. 0.1 0.2]\n", |
|
242 | "[-0.2 0.9 -1. 0.1 0.2]\n", | |
236 | "[ 3.2 0.9]\n" |
|
243 | "[ 3.2 0.9]\n" | |
237 | ] |
|
244 | ] | |
238 | } |
|
245 | } | |
239 | ], |
|
246 | ], | |
240 | "prompt_number": 7 |
|
247 | "prompt_number": 7 | |
241 | }, |
|
248 | }, | |
242 | { |
|
249 | { | |
243 | "cell_type": "markdown", |
|
250 | "cell_type": "markdown", | |
244 | "metadata": {}, |
|
251 | "metadata": {}, | |
245 | "source": [ |
|
252 | "source": [ | |
246 | "On the other hand %Rpush is equivalent to calling %R with just -i and no trailing code." |
|
253 | "On the other hand %Rpush is equivalent to calling %R with just -i and no trailing code." | |
247 | ] |
|
254 | ] | |
248 | }, |
|
255 | }, | |
249 | { |
|
256 | { | |
250 | "cell_type": "code", |
|
257 | "cell_type": "code", | |
251 | "collapsed": false, |
|
258 | "collapsed": false, | |
252 | "input": [ |
|
259 | "input": [ | |
253 | "A = np.arange(20)\n", |
|
260 | "A = np.arange(20)\n", | |
254 | "%R -i A\n", |
|
261 | "%R -i A\n", | |
255 | "%R mean(A)\n" |
|
262 | "%R mean(A)\n" | |
256 | ], |
|
263 | ], | |
257 | "language": "python", |
|
264 | "language": "python", | |
258 | "metadata": {}, |
|
265 | "metadata": {}, | |
259 | "outputs": [ |
|
266 | "outputs": [ | |
260 | { |
|
267 | { | |
261 | "output_type": "pyout", |
|
268 | "output_type": "pyout", | |
262 | "prompt_number": 8, |
|
269 | "prompt_number": 8, | |
263 | "text": [ |
|
270 | "text": [ | |
264 | "array([ 9.5])" |
|
271 | "array([ 9.5])" | |
265 | ] |
|
272 | ] | |
266 | } |
|
273 | } | |
267 | ], |
|
274 | ], | |
268 | "prompt_number": 8 |
|
275 | "prompt_number": 8 | |
269 | }, |
|
276 | }, | |
270 | { |
|
277 | { | |
271 | "cell_type": "markdown", |
|
278 | "cell_type": "markdown", | |
272 | "metadata": {}, |
|
279 | "metadata": {}, | |
273 | "source": [ |
|
280 | "source": [ | |
274 | "The magic %Rget retrieves one variable from R." |
|
281 | "The magic %Rget retrieves one variable from R." | |
275 | ] |
|
282 | ] | |
276 | }, |
|
283 | }, | |
277 | { |
|
284 | { | |
278 | "cell_type": "code", |
|
285 | "cell_type": "code", | |
279 | "collapsed": false, |
|
286 | "collapsed": false, | |
280 | "input": [ |
|
287 | "input": [ | |
281 | "%Rget A" |
|
288 | "%Rget A" | |
282 | ], |
|
289 | ], | |
283 | "language": "python", |
|
290 | "language": "python", | |
284 | "metadata": {}, |
|
291 | "metadata": {}, | |
285 | "outputs": [ |
|
292 | "outputs": [ | |
286 | { |
|
293 | { | |
287 | "output_type": "pyout", |
|
294 | "output_type": "pyout", | |
288 | "prompt_number": 9, |
|
295 | "prompt_number": 9, | |
289 | "text": [ |
|
296 | "text": [ | |
290 | "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", |
|
297 | "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", | |
291 | " 17, 18, 19], dtype=int32)" |
|
298 | " 17, 18, 19], dtype=int32)" | |
292 | ] |
|
299 | ] | |
293 | } |
|
300 | } | |
294 | ], |
|
301 | ], | |
295 | "prompt_number": 9 |
|
302 | "prompt_number": 9 | |
296 | }, |
|
303 | }, | |
297 | { |
|
304 | { | |
298 | "cell_type": "heading", |
|
305 | "cell_type": "heading", | |
299 | "level": 2, |
|
306 | "level": 2, | |
300 | "metadata": {}, |
|
307 | "metadata": {}, | |
301 | "source": [ |
|
308 | "source": [ | |
302 | "Plotting and capturing output" |
|
309 | "Plotting and capturing output" | |
303 | ] |
|
310 | ] | |
304 | }, |
|
311 | }, | |
305 | { |
|
312 | { | |
306 | "cell_type": "markdown", |
|
313 | "cell_type": "markdown", | |
307 | "metadata": {}, |
|
314 | "metadata": {}, | |
308 | "source": [ |
|
315 | "source": [ | |
309 | "R's console (i.e. its stdout() connection) is captured by ipython, as are any plots which are published as PNG files like the notebook with arguments --pylab inline. As a call to %R may produce a return value (see above) we must ask what happens to a magic like the one below. The R code specifies that something is published to the notebook. If anything is published to the notebook, that call to %R returns None." |
|
316 | "R's console (i.e. its stdout() connection) is captured by ipython, as are any plots which are published as PNG files like the notebook with arguments --pylab inline. As a call to %R may produce a return value (see above) we must ask what happens to a magic like the one below. The R code specifies that something is published to the notebook. If anything is published to the notebook, that call to %R returns None." | |
310 | ] |
|
317 | ] | |
311 | }, |
|
318 | }, | |
312 | { |
|
319 | { | |
313 | "cell_type": "code", |
|
320 | "cell_type": "code", | |
314 | "collapsed": false, |
|
321 | "collapsed": false, | |
315 | "input": [ |
|
322 | "input": [ | |
316 | "v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)\n", |
|
323 | "v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)\n", | |
317 | "print 'v1 is:', v1\n", |
|
324 | "print 'v1 is:', v1\n", | |
318 | "v2 = %R mean(X)*mean(Y)\n", |
|
325 | "v2 = %R mean(X)*mean(Y)\n", | |
319 | "print 'v2 is:', v2" |
|
326 | "print 'v2 is:', v2" | |
320 | ], |
|
327 | ], | |
321 | "language": "python", |
|
328 | "language": "python", | |
322 | "metadata": {}, |
|
329 | "metadata": {}, | |
323 | "outputs": [ |
|
330 | "outputs": [ | |
324 | { |
|
331 | { | |
325 | "output_type": "display_data", |
|
332 | "output_type": "display_data", | |
326 | "text": [ |
|
333 | "text": [ | |
327 | "\n", |
|
334 | "\n", | |
328 | "Call:\n", |
|
335 | "Call:\n", | |
329 | "lm(formula = Y ~ X)\n", |
|
336 | "lm(formula = Y ~ X)\n", | |
330 | "\n", |
|
337 | "\n", | |
331 | "Residuals:\n", |
|
338 | "Residuals:\n", | |
332 | " 1 2 3 4 5 \n", |
|
339 | " 1 2 3 4 5 \n", | |
333 | "-0.2 0.9 -1.0 0.1 0.2 \n", |
|
340 | "-0.2 0.9 -1.0 0.1 0.2 \n", | |
334 | "\n", |
|
341 | "\n", | |
335 | "Coefficients:\n", |
|
342 | "Coefficients:\n", | |
336 | " Estimate Std. Error t value Pr(>|t|) \n", |
|
343 | " Estimate Std. Error t value Pr(>|t|) \n", | |
337 | "(Intercept) 3.2000 0.6164 5.191 0.0139 *\n", |
|
344 | "(Intercept) 3.2000 0.6164 5.191 0.0139 *\n", | |
338 | "X 0.9000 0.2517 3.576 0.0374 *\n", |
|
345 | "X 0.9000 0.2517 3.576 0.0374 *\n", | |
339 | "---\n", |
|
346 | "---\n", | |
340 | "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n", |
|
347 | "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n", | |
341 | "\n", |
|
348 | "\n", | |
342 | "Residual standard error: 0.7958 on 3 degrees of freedom\n", |
|
349 | "Residual standard error: 0.7958 on 3 degrees of freedom\n", | |
343 | "Multiple R-squared: 0.81,\tAdjusted R-squared: 0.7467 \n", |
|
350 | "Multiple R-squared: 0.81,\tAdjusted R-squared: 0.7467 \n", | |
344 | "F-statistic: 12.79 on 1 and 3 DF, p-value: 0.03739 \n", |
|
351 | "F-statistic: 12.79 on 1 and 3 DF, p-value: 0.03739 \n", | |
345 | "\n" |
|
352 | "\n" | |
346 | ] |
|
353 | ] | |
347 | }, |
|
354 | }, | |
348 | { |
|
355 | { | |
349 | "output_type": "display_data", |
|
356 | "output_type": "display_data", | |
350 | "png": 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357 | "png": 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RokWLiIhYsWJFnH766bW6Rtu2baNt27b7Pde6dWuvKRegLl26xMknnxy33357TJgwIaqq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\nrkJggg==\n" | |
351 | }, |
|
358 | }, | |
352 | { |
|
359 | { | |
353 | "output_type": "stream", |
|
360 | "output_type": "stream", | |
354 | "stream": "stdout", |
|
361 | "stream": "stdout", | |
355 | "text": [ |
|
362 | "text": [ | |
356 | "v1 is: [ 10.]\n", |
|
363 | "v1 is: [ 10.]\n", | |
357 | "v2 is: [ 10.]\n" |
|
364 | "v2 is: [ 10.]\n" | |
358 | ] |
|
365 | ] | |
359 | } |
|
366 | } | |
360 | ], |
|
367 | ], | |
361 | "prompt_number": 10 |
|
368 | "prompt_number": 10 | |
362 | }, |
|
369 | }, | |
363 | { |
|
370 | { | |
364 | "cell_type": "heading", |
|
371 | "cell_type": "heading", | |
365 | "level": 2, |
|
372 | "level": 2, | |
366 | "metadata": {}, |
|
373 | "metadata": {}, | |
367 | "source": [ |
|
374 | "source": [ | |
368 | "What value is returned from %R?" |
|
375 | "What value is returned from %R?" | |
369 | ] |
|
376 | ] | |
370 | }, |
|
377 | }, | |
371 | { |
|
378 | { | |
372 | "cell_type": "markdown", |
|
379 | "cell_type": "markdown", | |
373 | "metadata": {}, |
|
380 | "metadata": {}, | |
374 | "source": [ |
|
381 | "source": [ | |
375 | "Some calls have no particularly interesting return value, the magic %R will not return anything in this case. The return value in rpy2 is actually NULL so %R returns None." |
|
382 | "Some calls have no particularly interesting return value, the magic %R will not return anything in this case. The return value in rpy2 is actually NULL so %R returns None." | |
376 | ] |
|
383 | ] | |
377 | }, |
|
384 | }, | |
378 | { |
|
385 | { | |
379 | "cell_type": "code", |
|
386 | "cell_type": "code", | |
380 | "collapsed": false, |
|
387 | "collapsed": false, | |
381 | "input": [ |
|
388 | "input": [ | |
382 | "v = %R plot(X,Y)\n", |
|
389 | "v = %R plot(X,Y)\n", | |
383 | "assert v == None" |
|
390 | "assert v == None" | |
384 | ], |
|
391 | ], | |
385 | "language": "python", |
|
392 | "language": "python", | |
386 | "metadata": {}, |
|
393 | "metadata": {}, | |
387 | "outputs": [ |
|
394 | "outputs": [ | |
388 | { |
|
395 | { | |
389 | "output_type": "display_data", |
|
396 | "output_type": "display_data", | |
390 | "png": 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397 | "png": 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RokWLiIhYsWJFnH766bW6Rtu2baNt27b7Pde6dWuvKRegLl26xMknnxy33357TJgwIaqq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\nrkJggg==\n" | |
391 | } |
|
398 | } | |
392 | ], |
|
399 | ], | |
393 | "prompt_number": 11 |
|
400 | "prompt_number": 11 | |
394 | }, |
|
401 | }, | |
395 | { |
|
402 | { | |
396 | "cell_type": "markdown", |
|
403 | "cell_type": "markdown", | |
397 | "metadata": {}, |
|
404 | "metadata": {}, | |
398 | "source": [ |
|
405 | "source": [ | |
399 | "Also, if the return value of a call to %R (in line mode) has just been printed to the console, then its value is also not returned." |
|
406 | "Also, if the return value of a call to %R (in line mode) has just been printed to the console, then its value is also not returned." | |
400 | ] |
|
407 | ] | |
401 | }, |
|
408 | }, | |
402 | { |
|
409 | { | |
403 | "cell_type": "code", |
|
410 | "cell_type": "code", | |
404 | "collapsed": false, |
|
411 | "collapsed": false, | |
405 | "input": [ |
|
412 | "input": [ | |
406 | "v = %R print(X)\n", |
|
413 | "v = %R print(X)\n", | |
407 | "assert v == None" |
|
414 | "assert v == None" | |
408 | ], |
|
415 | ], | |
409 | "language": "python", |
|
416 | "language": "python", | |
410 | "metadata": {}, |
|
417 | "metadata": {}, | |
411 | "outputs": [ |
|
418 | "outputs": [ | |
412 | { |
|
419 | { | |
413 | "output_type": "display_data", |
|
420 | "output_type": "display_data", | |
414 | "text": [ |
|
421 | "text": [ | |
415 | "[1] 0 1 2 3 4\n" |
|
422 | "[1] 0 1 2 3 4\n" | |
416 | ] |
|
423 | ] | |
417 | } |
|
424 | } | |
418 | ], |
|
425 | ], | |
419 | "prompt_number": 12 |
|
426 | "prompt_number": 12 | |
420 | }, |
|
427 | }, | |
421 | { |
|
428 | { | |
422 | "cell_type": "markdown", |
|
429 | "cell_type": "markdown", | |
423 | "metadata": {}, |
|
430 | "metadata": {}, | |
424 | "source": [ |
|
431 | "source": [ | |
425 | "But, if the last value did not print anything to console, the value is returned:\n" |
|
432 | "But, if the last value did not print anything to console, the value is returned:\n" | |
426 | ] |
|
433 | ] | |
427 | }, |
|
434 | }, | |
428 | { |
|
435 | { | |
429 | "cell_type": "code", |
|
436 | "cell_type": "code", | |
430 | "collapsed": false, |
|
437 | "collapsed": false, | |
431 | "input": [ |
|
438 | "input": [ | |
432 | "v = %R print(summary(X)); X\n", |
|
439 | "v = %R print(summary(X)); X\n", | |
433 | "print 'v:', v" |
|
440 | "print 'v:', v" | |
434 | ], |
|
441 | ], | |
435 | "language": "python", |
|
442 | "language": "python", | |
436 | "metadata": {}, |
|
443 | "metadata": {}, | |
437 | "outputs": [ |
|
444 | "outputs": [ | |
438 | { |
|
445 | { | |
439 | "output_type": "display_data", |
|
446 | "output_type": "display_data", | |
440 | "text": [ |
|
447 | "text": [ | |
441 | " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", |
|
448 | " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", | |
442 | " 0 1 2 2 3 4 \n" |
|
449 | " 0 1 2 2 3 4 \n" | |
443 | ] |
|
450 | ] | |
444 | }, |
|
451 | }, | |
445 | { |
|
452 | { | |
446 | "output_type": "stream", |
|
453 | "output_type": "stream", | |
447 | "stream": "stdout", |
|
454 | "stream": "stdout", | |
448 | "text": [ |
|
455 | "text": [ | |
449 | "v: [0 1 2 3 4]\n" |
|
456 | "v: [0 1 2 3 4]\n" | |
450 | ] |
|
457 | ] | |
451 | } |
|
458 | } | |
452 | ], |
|
459 | ], | |
453 | "prompt_number": 13 |
|
460 | "prompt_number": 13 | |
454 | }, |
|
461 | }, | |
455 | { |
|
462 | { | |
456 | "cell_type": "markdown", |
|
463 | "cell_type": "markdown", | |
457 | "metadata": {}, |
|
464 | "metadata": {}, | |
458 | "source": [ |
|
465 | "source": [ | |
459 | "The return value can be suppressed by a trailing ';' or an -n argument.\n" |
|
466 | "The return value can be suppressed by a trailing ';' or an -n argument.\n" | |
460 | ] |
|
467 | ] | |
461 | }, |
|
468 | }, | |
462 | { |
|
469 | { | |
463 | "cell_type": "code", |
|
470 | "cell_type": "code", | |
464 | "collapsed": true, |
|
471 | "collapsed": true, | |
465 | "input": [ |
|
472 | "input": [ | |
466 | "%R -n X" |
|
473 | "%R -n X" | |
467 | ], |
|
474 | ], | |
468 | "language": "python", |
|
475 | "language": "python", | |
469 | "metadata": {}, |
|
476 | "metadata": {}, | |
470 | "outputs": [], |
|
477 | "outputs": [], | |
471 | "prompt_number": 14 |
|
478 | "prompt_number": 14 | |
472 | }, |
|
479 | }, | |
473 | { |
|
480 | { | |
474 | "cell_type": "code", |
|
481 | "cell_type": "code", | |
475 | "collapsed": true, |
|
482 | "collapsed": true, | |
476 | "input": [ |
|
483 | "input": [ | |
477 | "%R X; " |
|
484 | "%R X; " | |
478 | ], |
|
485 | ], | |
479 | "language": "python", |
|
486 | "language": "python", | |
480 | "metadata": {}, |
|
487 | "metadata": {}, | |
481 | "outputs": [], |
|
488 | "outputs": [], | |
482 | "prompt_number": 15 |
|
489 | "prompt_number": 15 | |
483 | }, |
|
490 | }, | |
484 | { |
|
491 | { | |
485 | "cell_type": "heading", |
|
492 | "cell_type": "heading", | |
486 | "level": 2, |
|
493 | "level": 2, | |
487 | "metadata": {}, |
|
494 | "metadata": {}, | |
488 | "source": [ |
|
495 | "source": [ | |
489 | "Cell level magic" |
|
496 | "Cell level magic" | |
490 | ] |
|
497 | ] | |
491 | }, |
|
498 | }, | |
492 | { |
|
499 | { | |
493 | "cell_type": "markdown", |
|
500 | "cell_type": "markdown", | |
494 | "metadata": {}, |
|
501 | "metadata": {}, | |
495 | "source": [ |
|
502 | "source": [ | |
496 | "Often, we will want to do more than a simple linear regression model. There may be several lines of R code that we want to \n", |
|
503 | "Often, we will want to do more than a simple linear regression model. There may be several lines of R code that we want to \n", | |
497 | "use before returning to python. This is the cell-level magic.\n", |
|
504 | "use before returning to python. This is the cell-level magic.\n", | |
498 | "\n", |
|
505 | "\n", | |
499 | "\n", |
|
506 | "\n", | |
500 | "For the cell level magic, inputs can be passed via the -i or --inputs argument in the line. These variables are copied \n", |
|
507 | "For the cell level magic, inputs can be passed via the -i or --inputs argument in the line. These variables are copied \n", | |
501 | "from the shell namespace to R's namespace using rpy2.robjects.r.assign. It would be nice not to have to copy these into R: rnumpy ( http://bitbucket.org/njs/rnumpy/wiki/API ) has done some work to limit or at least make transparent the number of copies of an array. This seems like a natural thing to try to build on. Arrays can be output from R via the -o or --outputs argument in the line. All other arguments are sent to R's png function, which is the graphics device used to create the plots.\n", |
|
508 | "from the shell namespace to R's namespace using rpy2.robjects.r.assign. It would be nice not to have to copy these into R: rnumpy ( http://bitbucket.org/njs/rnumpy/wiki/API ) has done some work to limit or at least make transparent the number of copies of an array. This seems like a natural thing to try to build on. Arrays can be output from R via the -o or --outputs argument in the line. All other arguments are sent to R's png function, which is the graphics device used to create the plots.\n", | |
502 | "\n", |
|
509 | "\n", | |
503 | "We can redo the above calculations in one ipython cell. We might also want to add some output such as a summary\n", |
|
510 | "We can redo the above calculations in one ipython cell. We might also want to add some output such as a summary\n", | |
504 | " from R or perhaps the standard plotting diagnostics of the lm." |
|
511 | " from R or perhaps the standard plotting diagnostics of the lm." | |
505 | ] |
|
512 | ] | |
506 | }, |
|
513 | }, | |
507 | { |
|
514 | { | |
508 | "cell_type": "code", |
|
515 | "cell_type": "code", | |
509 | "collapsed": false, |
|
516 | "collapsed": false, | |
510 | "input": [ |
|
517 | "input": [ | |
511 | "%%R -i X,Y -o XYcoef\n", |
|
518 | "%%R -i X,Y -o XYcoef\n", | |
512 | "XYlm = lm(Y~X)\n", |
|
519 | "XYlm = lm(Y~X)\n", | |
513 | "XYcoef = coef(XYlm)\n", |
|
520 | "XYcoef = coef(XYlm)\n", | |
514 | "print(summary(XYlm))\n", |
|
521 | "print(summary(XYlm))\n", | |
515 | "par(mfrow=c(2,2))\n", |
|
522 | "par(mfrow=c(2,2))\n", | |
516 | "plot(XYlm)" |
|
523 | "plot(XYlm)" | |
517 | ], |
|
524 | ], | |
518 | "language": "python", |
|
525 | "language": "python", | |
519 | "metadata": {}, |
|
526 | "metadata": {}, | |
520 | "outputs": [ |
|
527 | "outputs": [ | |
521 | { |
|
528 | { | |
522 | "output_type": "display_data", |
|
529 | "output_type": "display_data", | |
523 | "text": [ |
|
530 | "text": [ | |
524 | "\n", |
|
531 | "\n", | |
525 | "Call:\n", |
|
532 | "Call:\n", | |
526 | "lm(formula = Y ~ X)\n", |
|
533 | "lm(formula = Y ~ X)\n", | |
527 | "\n", |
|
534 | "\n", | |
528 | "Residuals:\n", |
|
535 | "Residuals:\n", | |
529 | " 1 2 3 4 5 \n", |
|
536 | " 1 2 3 4 5 \n", | |
530 | "-0.2 0.9 -1.0 0.1 0.2 \n", |
|
537 | "-0.2 0.9 -1.0 0.1 0.2 \n", | |
531 | "\n", |
|
538 | "\n", | |
532 | "Coefficients:\n", |
|
539 | "Coefficients:\n", | |
533 | " Estimate Std. Error t value Pr(>|t|) \n", |
|
540 | " Estimate Std. Error t value Pr(>|t|) \n", | |
534 | "(Intercept) 3.2000 0.6164 5.191 0.0139 *\n", |
|
541 | "(Intercept) 3.2000 0.6164 5.191 0.0139 *\n", | |
535 | "X 0.9000 0.2517 3.576 0.0374 *\n", |
|
542 | "X 0.9000 0.2517 3.576 0.0374 *\n", | |
536 | "---\n", |
|
543 | "---\n", | |
537 | "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n", |
|
544 | "Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n", | |
538 | "\n", |
|
545 | "\n", | |
539 | "Residual standard error: 0.7958 on 3 degrees of freedom\n", |
|
546 | "Residual standard error: 0.7958 on 3 degrees of freedom\n", | |
540 | "Multiple R-squared: 0.81,\tAdjusted R-squared: 0.7467 \n", |
|
547 | "Multiple R-squared: 0.81,\tAdjusted R-squared: 0.7467 \n", | |
541 | "F-statistic: 12.79 on 1 and 3 DF, p-value: 0.03739 \n", |
|
548 | "F-statistic: 12.79 on 1 and 3 DF, p-value: 0.03739 \n", | |
542 | "\n" |
|
549 | "\n" | |
543 | ] |
|
550 | ] | |
544 | }, |
|
551 | }, | |
545 | { |
|
552 | { | |
546 | "output_type": "display_data", |
|
553 | "output_type": "display_data", | |
547 | "png": 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ff28o7sePHyc0NJT9+/djYWGRbzqMmpMUYBMZPXo0lStXZurUqQB069aNtWvX8uDBA1RV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\nXqXv3bsXRVE4uG8f8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NZT0D4+PsLNzU00btxYfP/99zqf5f79+8Lc3FzUqFFDODk5CScn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\n" 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ff28o7sePHyc0NJT9+/djYWGRbzqMmpMUYBMZPXo0lStXZurUqQB069aNtWvX8uDBA1RV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\n" | |
548 | } |
|
555 | } | |
549 | ], |
|
556 | ], | |
550 | "prompt_number": 16 |
|
557 | "prompt_number": 16 | |
551 | }, |
|
558 | }, | |
552 | { |
|
559 | { | |
553 | "cell_type": "heading", |
|
560 | "cell_type": "heading", | |
554 | "level": 2, |
|
561 | "level": 2, | |
555 | "metadata": {}, |
|
562 | "metadata": {}, | |
556 | "source": [ |
|
563 | "source": [ | |
557 | "Passing data back and forth" |
|
564 | "Passing data back and forth" | |
558 | ] |
|
565 | ] | |
559 | }, |
|
566 | }, | |
560 | { |
|
567 | { | |
561 | "cell_type": "markdown", |
|
568 | "cell_type": "markdown", | |
562 | "metadata": {}, |
|
569 | "metadata": {}, | |
563 | "source": [ |
|
570 | "source": [ | |
564 | "Currently, data is passed through RMagics.pyconverter when going from python to R and RMagics.Rconverter when \n", |
|
571 | "Currently, data is passed through RMagics.pyconverter when going from python to R and RMagics.Rconverter when \n", | |
565 | "going from R to python. These currently default to numpy.ndarray. Future work will involve writing better converters, most likely involving integration with http://pandas.sourceforge.net.\n", |
|
572 | "going from R to python. These currently default to numpy.ndarray. Future work will involve writing better converters, most likely involving integration with http://pandas.sourceforge.net.\n", | |
566 | "\n", |
|
573 | "\n", | |
567 | "Passing ndarrays into R seems to require a copy, though once an object is returned to python, this object is NOT copied, and it is possible to change its values.\n" |
|
574 | "Passing ndarrays into R seems to require a copy, though once an object is returned to python, this object is NOT copied, and it is possible to change its values.\n" | |
568 | ] |
|
575 | ] | |
569 | }, |
|
576 | }, | |
570 | { |
|
577 | { | |
571 | "cell_type": "code", |
|
578 | "cell_type": "code", | |
572 | "collapsed": true, |
|
579 | "collapsed": true, | |
573 | "input": [ |
|
580 | "input": [ | |
574 | "seq1 = np.arange(10)" |
|
581 | "seq1 = np.arange(10)" | |
575 | ], |
|
582 | ], | |
576 | "language": "python", |
|
583 | "language": "python", | |
577 | "metadata": {}, |
|
584 | "metadata": {}, | |
578 | "outputs": [], |
|
585 | "outputs": [], | |
579 | "prompt_number": 17 |
|
586 | "prompt_number": 17 | |
580 | }, |
|
587 | }, | |
581 | { |
|
588 | { | |
582 | "cell_type": "code", |
|
589 | "cell_type": "code", | |
583 | "collapsed": false, |
|
590 | "collapsed": false, | |
584 | "input": [ |
|
591 | "input": [ | |
585 | "%%R -i seq1 -o seq2\n", |
|
592 | "%%R -i seq1 -o seq2\n", | |
586 | "seq2 = rep(seq1, 2)\n", |
|
593 | "seq2 = rep(seq1, 2)\n", | |
587 | "print(seq2)" |
|
594 | "print(seq2)" | |
588 | ], |
|
595 | ], | |
589 | "language": "python", |
|
596 | "language": "python", | |
590 | "metadata": {}, |
|
597 | "metadata": {}, | |
591 | "outputs": [ |
|
598 | "outputs": [ | |
592 | { |
|
599 | { | |
593 | "output_type": "display_data", |
|
600 | "output_type": "display_data", | |
594 | "text": [ |
|
601 | "text": [ | |
595 | " [1] 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n" |
|
602 | " [1] 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n" | |
596 | ] |
|
603 | ] | |
597 | } |
|
604 | } | |
598 | ], |
|
605 | ], | |
599 | "prompt_number": 18 |
|
606 | "prompt_number": 18 | |
600 | }, |
|
607 | }, | |
601 | { |
|
608 | { | |
602 | "cell_type": "code", |
|
609 | "cell_type": "code", | |
603 | "collapsed": false, |
|
610 | "collapsed": false, | |
604 | "input": [ |
|
611 | "input": [ | |
605 | "seq2[::2] = 0\n", |
|
612 | "seq2[::2] = 0\n", | |
606 | "seq2" |
|
613 | "seq2" | |
607 | ], |
|
614 | ], | |
608 | "language": "python", |
|
615 | "language": "python", | |
609 | "metadata": {}, |
|
616 | "metadata": {}, | |
610 | "outputs": [ |
|
617 | "outputs": [ | |
611 | { |
|
618 | { | |
612 | "output_type": "pyout", |
|
619 | "output_type": "pyout", | |
613 | "prompt_number": 19, |
|
620 | "prompt_number": 19, | |
614 | "text": [ |
|
621 | "text": [ | |
615 | "array([0, 1, 0, 3, 0, 5, 0, 7, 0, 9, 0, 1, 0, 3, 0, 5, 0, 7, 0, 9], dtype=int32)" |
|
622 | "array([0, 1, 0, 3, 0, 5, 0, 7, 0, 9, 0, 1, 0, 3, 0, 5, 0, 7, 0, 9], dtype=int32)" | |
616 | ] |
|
623 | ] | |
617 | } |
|
624 | } | |
618 | ], |
|
625 | ], | |
619 | "prompt_number": 19 |
|
626 | "prompt_number": 19 | |
620 | }, |
|
627 | }, | |
621 | { |
|
628 | { | |
622 | "cell_type": "code", |
|
629 | "cell_type": "code", | |
623 | "collapsed": false, |
|
630 | "collapsed": false, | |
624 | "input": [ |
|
631 | "input": [ | |
625 | "%%R\n", |
|
632 | "%%R\n", | |
626 | "print(seq2)" |
|
633 | "print(seq2)" | |
627 | ], |
|
634 | ], | |
628 | "language": "python", |
|
635 | "language": "python", | |
629 | "metadata": {}, |
|
636 | "metadata": {}, | |
630 | "outputs": [ |
|
637 | "outputs": [ | |
631 | { |
|
638 | { | |
632 | "output_type": "display_data", |
|
639 | "output_type": "display_data", | |
633 | "text": [ |
|
640 | "text": [ | |
634 | " [1] 0 1 0 3 0 5 0 7 0 9 0 1 0 3 0 5 0 7 0 9\n" |
|
641 | " [1] 0 1 0 3 0 5 0 7 0 9 0 1 0 3 0 5 0 7 0 9\n" | |
635 | ] |
|
642 | ] | |
636 | } |
|
643 | } | |
637 | ], |
|
644 | ], | |
638 | "prompt_number": 20 |
|
645 | "prompt_number": 20 | |
639 | }, |
|
646 | }, | |
640 | { |
|
647 | { | |
641 | "cell_type": "markdown", |
|
648 | "cell_type": "markdown", | |
642 | "metadata": {}, |
|
649 | "metadata": {}, | |
643 | "source": [ |
|
650 | "source": [ | |
644 | "Once the array data has been passed to R, modifring its contents does not modify R's copy of the data." |
|
651 | "Once the array data has been passed to R, modifring its contents does not modify R's copy of the data." | |
645 | ] |
|
652 | ] | |
646 | }, |
|
653 | }, | |
647 | { |
|
654 | { | |
648 | "cell_type": "code", |
|
655 | "cell_type": "code", | |
649 | "collapsed": false, |
|
656 | "collapsed": false, | |
650 | "input": [ |
|
657 | "input": [ | |
651 | "seq1[0] = 200\n", |
|
658 | "seq1[0] = 200\n", | |
652 | "%R print(seq1)" |
|
659 | "%R print(seq1)" | |
653 | ], |
|
660 | ], | |
654 | "language": "python", |
|
661 | "language": "python", | |
655 | "metadata": {}, |
|
662 | "metadata": {}, | |
656 | "outputs": [ |
|
663 | "outputs": [ | |
657 | { |
|
664 | { | |
658 | "output_type": "display_data", |
|
665 | "output_type": "display_data", | |
659 | "text": [ |
|
666 | "text": [ | |
660 | " [1] 0 1 2 3 4 5 6 7 8 9\n" |
|
667 | " [1] 0 1 2 3 4 5 6 7 8 9\n" | |
661 | ] |
|
668 | ] | |
662 | } |
|
669 | } | |
663 | ], |
|
670 | ], | |
664 | "prompt_number": 21 |
|
671 | "prompt_number": 21 | |
665 | }, |
|
672 | }, | |
666 | { |
|
673 | { | |
667 | "cell_type": "markdown", |
|
674 | "cell_type": "markdown", | |
668 | "metadata": {}, |
|
675 | "metadata": {}, | |
669 | "source": [ |
|
676 | "source": [ | |
670 | "But, if we pass data as both input and output, then the value of \"data\" in user_ns will be overwritten and the\n", |
|
677 | "But, if we pass data as both input and output, then the value of \"data\" in user_ns will be overwritten and the\n", | |
671 | "new array will be a view of the data in R's copy." |
|
678 | "new array will be a view of the data in R's copy." | |
672 | ] |
|
679 | ] | |
673 | }, |
|
680 | }, | |
674 | { |
|
681 | { | |
675 | "cell_type": "code", |
|
682 | "cell_type": "code", | |
676 | "collapsed": false, |
|
683 | "collapsed": false, | |
677 | "input": [ |
|
684 | "input": [ | |
678 | "print seq1\n", |
|
685 | "print seq1\n", | |
679 | "%R -i seq1 -o seq1\n", |
|
686 | "%R -i seq1 -o seq1\n", | |
680 | "print seq1\n", |
|
687 | "print seq1\n", | |
681 | "seq1[0] = 200\n", |
|
688 | "seq1[0] = 200\n", | |
682 | "%R print(seq1)\n", |
|
689 | "%R print(seq1)\n", | |
683 | "seq1_view = %R seq1\n", |
|
690 | "seq1_view = %R seq1\n", | |
684 | "assert(id(seq1_view.data) == id(seq1.data))" |
|
691 | "assert(id(seq1_view.data) == id(seq1.data))" | |
685 | ], |
|
692 | ], | |
686 | "language": "python", |
|
693 | "language": "python", | |
687 | "metadata": {}, |
|
694 | "metadata": {}, | |
688 | "outputs": [ |
|
695 | "outputs": [ | |
689 | { |
|
696 | { | |
690 | "output_type": "stream", |
|
697 | "output_type": "stream", | |
691 | "stream": "stdout", |
|
698 | "stream": "stdout", | |
692 | "text": [ |
|
699 | "text": [ | |
693 | "[200 1 2 3 4 5 6 7 8 9]\n", |
|
700 | "[200 1 2 3 4 5 6 7 8 9]\n", | |
694 | "[200 1 2 3 4 5 6 7 8 9]\n" |
|
701 | "[200 1 2 3 4 5 6 7 8 9]\n" | |
695 | ] |
|
702 | ] | |
696 | }, |
|
703 | }, | |
697 | { |
|
704 | { | |
698 | "output_type": "display_data", |
|
705 | "output_type": "display_data", | |
699 | "text": [ |
|
706 | "text": [ | |
700 | " [1] 200 1 2 3 4 5 6 7 8 9\n" |
|
707 | " [1] 200 1 2 3 4 5 6 7 8 9\n" | |
701 | ] |
|
708 | ] | |
702 | } |
|
709 | } | |
703 | ], |
|
710 | ], | |
704 | "prompt_number": 22 |
|
711 | "prompt_number": 22 | |
705 | }, |
|
712 | }, | |
706 | { |
|
713 | { | |
707 | "cell_type": "heading", |
|
714 | "cell_type": "heading", | |
708 | "level": 2, |
|
715 | "level": 2, | |
709 | "metadata": {}, |
|
716 | "metadata": {}, | |
710 | "source": [ |
|
717 | "source": [ | |
711 | "Exception handling\n" |
|
718 | "Exception handling\n" | |
712 | ] |
|
719 | ] | |
713 | }, |
|
720 | }, | |
714 | { |
|
721 | { | |
715 | "cell_type": "markdown", |
|
722 | "cell_type": "markdown", | |
716 | "metadata": {}, |
|
723 | "metadata": {}, | |
717 | "source": [ |
|
724 | "source": [ | |
718 | "Exceptions are handled by passing back rpy2's exception and the line that triggered it." |
|
725 | "Exceptions are handled by passing back rpy2's exception and the line that triggered it." | |
719 | ] |
|
726 | ] | |
720 | }, |
|
727 | }, | |
721 | { |
|
728 | { | |
722 | "cell_type": "code", |
|
729 | "cell_type": "code", | |
723 | "collapsed": false, |
|
730 | "collapsed": false, | |
724 | "input": [ |
|
731 | "input": [ | |
725 | "try:\n", |
|
732 | "try:\n", | |
726 | " %R -n nosuchvar\n", |
|
733 | " %R -n nosuchvar\n", | |
727 | "except Exception as e:\n", |
|
734 | "except Exception as e:\n", | |
728 | " print e.message\n", |
|
735 | " print e.message\n", | |
729 | " pass" |
|
736 | " pass" | |
730 | ], |
|
737 | ], | |
731 | "language": "python", |
|
738 | "language": "python", | |
732 | "metadata": {}, |
|
739 | "metadata": {}, | |
733 | "outputs": [ |
|
740 | "outputs": [ | |
734 | { |
|
741 | { | |
735 | "output_type": "stream", |
|
742 | "output_type": "stream", | |
736 | "stream": "stdout", |
|
743 | "stream": "stdout", | |
737 | "text": [ |
|
744 | "text": [ | |
738 | "parsing and evaluating line \"nosuchvar\".\n", |
|
745 | "parsing and evaluating line \"nosuchvar\".\n", | |
739 | "R error message: \"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n", |
|
746 | "R error message: \"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n", | |
740 | "\"\n", |
|
747 | "\"\n", | |
741 | " R stdout:\"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n", |
|
748 | " R stdout:\"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n", | |
742 | "\"\n", |
|
749 | "\"\n", | |
743 | "\n" |
|
750 | "\n" | |
744 | ] |
|
751 | ] | |
745 | } |
|
752 | } | |
746 | ], |
|
753 | ], | |
747 | "prompt_number": 23 |
|
754 | "prompt_number": 23 | |
748 | }, |
|
755 | }, | |
749 | { |
|
756 | { | |
750 | "cell_type": "heading", |
|
757 | "cell_type": "heading", | |
751 | "level": 2, |
|
758 | "level": 2, | |
752 | "metadata": {}, |
|
759 | "metadata": {}, | |
753 | "source": [ |
|
760 | "source": [ | |
754 | "Structured arrays and data frames\n" |
|
761 | "Structured arrays and data frames\n" | |
755 | ] |
|
762 | ] | |
756 | }, |
|
763 | }, | |
757 | { |
|
764 | { | |
758 | "cell_type": "markdown", |
|
765 | "cell_type": "markdown", | |
759 | "metadata": {}, |
|
766 | "metadata": {}, | |
760 | "source": [ |
|
767 | "source": [ | |
761 | "In R, data frames play an important role as they allow array-like objects of mixed type with column names (and row names). In bumpy, the closest analogy is a structured array with named fields. In future work, it would be nice to use pandas to return full-fledged DataFrames from rpy2. In the mean time, structured arrays can be passed back and forth with the -d flag to %R, %Rpull, and %Rget" |
|
768 | "In R, data frames play an important role as they allow array-like objects of mixed type with column names (and row names). In bumpy, the closest analogy is a structured array with named fields. In future work, it would be nice to use pandas to return full-fledged DataFrames from rpy2. In the mean time, structured arrays can be passed back and forth with the -d flag to %R, %Rpull, and %Rget" | |
762 | ] |
|
769 | ] | |
763 | }, |
|
770 | }, | |
764 | { |
|
771 | { | |
765 | "cell_type": "code", |
|
772 | "cell_type": "code", | |
766 | "collapsed": true, |
|
773 | "collapsed": true, | |
767 | "input": [ |
|
774 | "input": [ | |
768 | "datapy= np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')],\n", |
|
775 | "datapy= np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')],\n", | |
769 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])\n" |
|
776 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])\n" | |
770 | ], |
|
777 | ], | |
771 | "language": "python", |
|
778 | "language": "python", | |
772 | "metadata": {}, |
|
779 | "metadata": {}, | |
773 | "outputs": [], |
|
780 | "outputs": [], | |
774 | "prompt_number": 24 |
|
781 | "prompt_number": 24 | |
775 | }, |
|
782 | }, | |
776 | { |
|
783 | { | |
777 | "cell_type": "code", |
|
784 | "cell_type": "code", | |
778 | "collapsed": true, |
|
785 | "collapsed": true, | |
779 | "input": [ |
|
786 | "input": [ | |
780 | "%%R -i datapy -d datar\n", |
|
787 | "%%R -i datapy -d datar\n", | |
781 | "datar = datapy" |
|
788 | "datar = datapy" | |
782 | ], |
|
789 | ], | |
783 | "language": "python", |
|
790 | "language": "python", | |
784 | "metadata": {}, |
|
791 | "metadata": {}, | |
785 | "outputs": [], |
|
792 | "outputs": [], | |
786 | "prompt_number": 25 |
|
793 | "prompt_number": 25 | |
787 | }, |
|
794 | }, | |
788 | { |
|
795 | { | |
789 | "cell_type": "code", |
|
796 | "cell_type": "code", | |
790 | "collapsed": false, |
|
797 | "collapsed": false, | |
791 | "input": [ |
|
798 | "input": [ | |
792 | "datar" |
|
799 | "datar" | |
793 | ], |
|
800 | ], | |
794 | "language": "python", |
|
801 | "language": "python", | |
795 | "metadata": {}, |
|
802 | "metadata": {}, | |
796 | "outputs": [ |
|
803 | "outputs": [ | |
797 | { |
|
804 | { | |
798 | "output_type": "pyout", |
|
805 | "output_type": "pyout", | |
799 | "prompt_number": 26, |
|
806 | "prompt_number": 26, | |
800 | "text": [ |
|
807 | "text": [ | |
801 | "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n", |
|
808 | "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n", | |
802 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])" |
|
809 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])" | |
803 | ] |
|
810 | ] | |
804 | } |
|
811 | } | |
805 | ], |
|
812 | ], | |
806 | "prompt_number": 26 |
|
813 | "prompt_number": 26 | |
807 | }, |
|
814 | }, | |
808 | { |
|
815 | { | |
809 | "cell_type": "code", |
|
816 | "cell_type": "code", | |
810 | "collapsed": false, |
|
817 | "collapsed": false, | |
811 | "input": [ |
|
818 | "input": [ | |
812 | "%R datar2 = datapy\n", |
|
819 | "%R datar2 = datapy\n", | |
813 | "%Rpull -d datar2\n", |
|
820 | "%Rpull -d datar2\n", | |
814 | "datar2" |
|
821 | "datar2" | |
815 | ], |
|
822 | ], | |
816 | "language": "python", |
|
823 | "language": "python", | |
817 | "metadata": {}, |
|
824 | "metadata": {}, | |
818 | "outputs": [ |
|
825 | "outputs": [ | |
819 | { |
|
826 | { | |
820 | "output_type": "pyout", |
|
827 | "output_type": "pyout", | |
821 | "prompt_number": 27, |
|
828 | "prompt_number": 27, | |
822 | "text": [ |
|
829 | "text": [ | |
823 | "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n", |
|
830 | "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n", | |
824 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])" |
|
831 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])" | |
825 | ] |
|
832 | ] | |
826 | } |
|
833 | } | |
827 | ], |
|
834 | ], | |
828 | "prompt_number": 27 |
|
835 | "prompt_number": 27 | |
829 | }, |
|
836 | }, | |
830 | { |
|
837 | { | |
831 | "cell_type": "code", |
|
838 | "cell_type": "code", | |
832 | "collapsed": false, |
|
839 | "collapsed": false, | |
833 | "input": [ |
|
840 | "input": [ | |
834 | "%Rget -d datar2" |
|
841 | "%Rget -d datar2" | |
835 | ], |
|
842 | ], | |
836 | "language": "python", |
|
843 | "language": "python", | |
837 | "metadata": {}, |
|
844 | "metadata": {}, | |
838 | "outputs": [ |
|
845 | "outputs": [ | |
839 | { |
|
846 | { | |
840 | "output_type": "pyout", |
|
847 | "output_type": "pyout", | |
841 | "prompt_number": 28, |
|
848 | "prompt_number": 28, | |
842 | "text": [ |
|
849 | "text": [ | |
843 | "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n", |
|
850 | "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n", | |
844 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])" |
|
851 | " dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])" | |
845 | ] |
|
852 | ] | |
846 | } |
|
853 | } | |
847 | ], |
|
854 | ], | |
848 | "prompt_number": 28 |
|
855 | "prompt_number": 28 | |
849 | }, |
|
856 | }, | |
850 | { |
|
857 | { | |
851 | "cell_type": "markdown", |
|
858 | "cell_type": "markdown", | |
852 | "metadata": {}, |
|
859 | "metadata": {}, | |
853 | "source": [ |
|
860 | "source": [ | |
854 | "For arrays without names, the -d argument has no effect because the R object has no colnames or names." |
|
861 | "For arrays without names, the -d argument has no effect because the R object has no colnames or names." | |
855 | ] |
|
862 | ] | |
856 | }, |
|
863 | }, | |
857 | { |
|
864 | { | |
858 | "cell_type": "code", |
|
865 | "cell_type": "code", | |
859 | "collapsed": false, |
|
866 | "collapsed": false, | |
860 | "input": [ |
|
867 | "input": [ | |
861 | "Z = np.arange(6)\n", |
|
868 | "Z = np.arange(6)\n", | |
862 | "%R -i Z\n", |
|
869 | "%R -i Z\n", | |
863 | "%Rget -d Z" |
|
870 | "%Rget -d Z" | |
864 | ], |
|
871 | ], | |
865 | "language": "python", |
|
872 | "language": "python", | |
866 | "metadata": {}, |
|
873 | "metadata": {}, | |
867 | "outputs": [ |
|
874 | "outputs": [ | |
868 | { |
|
875 | { | |
869 | "output_type": "pyout", |
|
876 | "output_type": "pyout", | |
870 | "prompt_number": 29, |
|
877 | "prompt_number": 29, | |
871 | "text": [ |
|
878 | "text": [ | |
872 | "array([0, 1, 2, 3, 4, 5], dtype=int32)" |
|
879 | "array([0, 1, 2, 3, 4, 5], dtype=int32)" | |
873 | ] |
|
880 | ] | |
874 | } |
|
881 | } | |
875 | ], |
|
882 | ], | |
876 | "prompt_number": 29 |
|
883 | "prompt_number": 29 | |
877 | }, |
|
884 | }, | |
878 | { |
|
885 | { | |
879 | "cell_type": "markdown", |
|
886 | "cell_type": "markdown", | |
880 | "metadata": {}, |
|
887 | "metadata": {}, | |
881 | "source": [ |
|
888 | "source": [ | |
882 | "For mixed-type data frames in R, if the -d flag is not used, then an array of a single type is returned and\n", |
|
889 | "For mixed-type data frames in R, if the -d flag is not used, then an array of a single type is returned and\n", | |
883 | "its value is transposed. This would be nice to fix, but it seems something that should be fixed at the rpy2 level (See: https://bitbucket.org/lgautier/rpy2/issue/44/numpyrecarray-as-dataframe)" |
|
890 | "its value is transposed. This would be nice to fix, but it seems something that should be fixed at the rpy2 level (See: https://bitbucket.org/lgautier/rpy2/issue/44/numpyrecarray-as-dataframe)" | |
884 | ] |
|
891 | ] | |
885 | }, |
|
892 | }, | |
886 | { |
|
893 | { | |
887 | "cell_type": "code", |
|
894 | "cell_type": "code", | |
888 | "collapsed": false, |
|
895 | "collapsed": false, | |
889 | "input": [ |
|
896 | "input": [ | |
890 | "%Rget datar2" |
|
897 | "%Rget datar2" | |
891 | ], |
|
898 | ], | |
892 | "language": "python", |
|
899 | "language": "python", | |
893 | "metadata": {}, |
|
900 | "metadata": {}, | |
894 | "outputs": [ |
|
901 | "outputs": [ | |
895 | { |
|
902 | { | |
896 | "output_type": "pyout", |
|
903 | "output_type": "pyout", | |
897 | "prompt_number": 30, |
|
904 | "prompt_number": 30, | |
898 | "text": [ |
|
905 | "text": [ | |
899 | "array([['1', '2', '3'],\n", |
|
906 | "array([['1', '2', '3'],\n", | |
900 | " ['2', '3', '2'],\n", |
|
907 | " ['2', '3', '2'],\n", | |
901 | " ['a', 'b', 'c']], \n", |
|
908 | " ['a', 'b', 'c']], \n", | |
902 | " dtype='|S1')" |
|
909 | " dtype='|S1')" | |
903 | ] |
|
910 | ] | |
904 | } |
|
911 | } | |
905 | ], |
|
912 | ], | |
906 | "prompt_number": 30 |
|
913 | "prompt_number": 30 | |
907 | }, |
|
914 | }, | |
908 | { |
|
915 | { | |
909 | "cell_type": "code", |
|
916 | "cell_type": "code", | |
910 | "collapsed": true, |
|
917 | "collapsed": true, | |
911 | "input": [], |
|
918 | "input": [], | |
912 | "language": "python", |
|
919 | "language": "python", | |
913 | "metadata": {}, |
|
920 | "metadata": {}, | |
914 | "outputs": [], |
|
921 | "outputs": [], | |
915 | "prompt_number": 30 |
|
922 | "prompt_number": 30 | |
916 | } |
|
923 | } | |
917 | ], |
|
924 | ], | |
918 | "metadata": {} |
|
925 | "metadata": {} | |
919 | } |
|
926 | } | |
920 | ] |
|
927 | ] | |
921 | } No newline at end of file |
|
928 | } |
@@ -1,474 +1,482 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
|
2 | "metadata": { | |
3 | "name": "Script Magics" |
|
3 | "name": "Script Magics" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
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7 | "worksheets": [ | |
8 | { |
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8 | { | |
9 | "cells": [ |
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9 | "cells": [ | |
10 | { |
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10 | { | |
11 | "cell_type": "heading", |
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11 | "cell_type": "heading", | |
12 | "level": 1, |
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12 | "level": 1, | |
13 | "metadata": {}, |
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13 | "metadata": {}, | |
14 | "source": [ |
|
14 | "source": [ | |
15 | "Running Scripts from IPython" |
|
15 | "Running Scripts from IPython" | |
16 | ] |
|
16 | ] | |
17 | }, |
|
17 | }, | |
18 | { |
|
18 | { | |
19 | "cell_type": "markdown", |
|
19 | "cell_type": "markdown", | |
20 | "metadata": {}, |
|
20 | "metadata": {}, | |
21 | "source": [ |
|
21 | "source": [ | |
22 | "IPython has a `%%script` cell magic, which lets you run a cell in\n", |
|
22 | "IPython has a `%%script` cell magic, which lets you run a cell in\n", | |
23 | "a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n", |
|
23 | "a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n", | |
24 | "\n", |
|
24 | "\n", | |
25 | "It can even be a script of your own, which expects input on stdin." |
|
25 | "It can even be a script of your own, which expects input on stdin." | |
26 | ] |
|
26 | ] | |
27 | }, |
|
27 | }, | |
28 | { |
|
28 | { | |
29 | "cell_type": "code", |
|
29 | "cell_type": "code", | |
30 | "collapsed": false, |
|
30 | "collapsed": false, | |
31 | "input": [ |
|
31 | "input": [ | |
32 | "import sys" |
|
32 | "import sys" | |
33 | ], |
|
33 | ], | |
34 | "language": "python", |
|
34 | "language": "python", | |
35 | "metadata": {}, |
|
35 | "metadata": {}, | |
36 | "outputs": [], |
|
36 | "outputs": [], | |
37 | "prompt_number": 1 |
|
37 | "prompt_number": 1 | |
38 | }, |
|
38 | }, | |
39 | { |
|
39 | { | |
|
40 | "cell_type": "heading", | |||
|
41 | "level": 2, | |||
|
42 | "metadata": {}, | |||
|
43 | "source": [ | |||
|
44 | "Basic usage" | |||
|
45 | ] | |||
|
46 | }, | |||
|
47 | { | |||
40 | "cell_type": "markdown", |
|
48 | "cell_type": "markdown", | |
41 | "metadata": {}, |
|
49 | "metadata": {}, | |
42 | "source": [ |
|
50 | "source": [ | |
43 | "To use it, simply pass a path or shell command to the program you want to run on the `%%script` line,\n", |
|
51 | "To use it, simply pass a path or shell command to the program you want to run on the `%%script` line,\n", | |
44 | "and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed." |
|
52 | "and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed." | |
45 | ] |
|
53 | ] | |
46 | }, |
|
54 | }, | |
47 | { |
|
55 | { | |
48 | "cell_type": "code", |
|
56 | "cell_type": "code", | |
49 | "collapsed": false, |
|
57 | "collapsed": false, | |
50 | "input": [ |
|
58 | "input": [ | |
51 | "%%script python\n", |
|
59 | "%%script python\n", | |
52 | "import sys\n", |
|
60 | "import sys\n", | |
53 | "print 'hello from Python %s' % sys.version" |
|
61 | "print 'hello from Python %s' % sys.version" | |
54 | ], |
|
62 | ], | |
55 | "language": "python", |
|
63 | "language": "python", | |
56 | "metadata": {}, |
|
64 | "metadata": {}, | |
57 | "outputs": [ |
|
65 | "outputs": [ | |
58 | { |
|
66 | { | |
59 | "output_type": "stream", |
|
67 | "output_type": "stream", | |
60 | "stream": "stdout", |
|
68 | "stream": "stdout", | |
61 | "text": [ |
|
69 | "text": [ | |
62 | "hello from Python 2.7.1 (r271:86832, Jul 31 2011, 19:30:53) \n", |
|
70 | "hello from Python 2.7.1 (r271:86832, Jul 31 2011, 19:30:53) \n", | |
63 | "[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)]\n" |
|
71 | "[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)]\n" | |
64 | ] |
|
72 | ] | |
65 | } |
|
73 | } | |
66 | ], |
|
74 | ], | |
67 | "prompt_number": 2 |
|
75 | "prompt_number": 2 | |
68 | }, |
|
76 | }, | |
69 | { |
|
77 | { | |
70 | "cell_type": "code", |
|
78 | "cell_type": "code", | |
71 | "collapsed": false, |
|
79 | "collapsed": false, | |
72 | "input": [ |
|
80 | "input": [ | |
73 | "%%script python3\n", |
|
81 | "%%script python3\n", | |
74 | "import sys\n", |
|
82 | "import sys\n", | |
75 | "print('hello from Python: %s' % sys.version)" |
|
83 | "print('hello from Python: %s' % sys.version)" | |
76 | ], |
|
84 | ], | |
77 | "language": "python", |
|
85 | "language": "python", | |
78 | "metadata": {}, |
|
86 | "metadata": {}, | |
79 | "outputs": [ |
|
87 | "outputs": [ | |
80 | { |
|
88 | { | |
81 | "output_type": "stream", |
|
89 | "output_type": "stream", | |
82 | "stream": "stdout", |
|
90 | "stream": "stdout", | |
83 | "text": [ |
|
91 | "text": [ | |
84 | "hello from Python: 3.2.3 (v3.2.3:3d0686d90f55, Apr 10 2012, 11:25:50) \n", |
|
92 | "hello from Python: 3.2.3 (v3.2.3:3d0686d90f55, Apr 10 2012, 11:25:50) \n", | |
85 | "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n" |
|
93 | "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n" | |
86 | ] |
|
94 | ] | |
87 | } |
|
95 | } | |
88 | ], |
|
96 | ], | |
89 | "prompt_number": 3 |
|
97 | "prompt_number": 3 | |
90 | }, |
|
98 | }, | |
91 | { |
|
99 | { | |
92 | "cell_type": "markdown", |
|
100 | "cell_type": "markdown", | |
93 | "metadata": {}, |
|
101 | "metadata": {}, | |
94 | "source": [ |
|
102 | "source": [ | |
95 | "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n", |
|
103 | "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n", | |
96 | "\n", |
|
104 | "\n", | |
97 | "These are all equivalent to `%%script <name>`" |
|
105 | "These are all equivalent to `%%script <name>`" | |
98 | ] |
|
106 | ] | |
99 | }, |
|
107 | }, | |
100 | { |
|
108 | { | |
101 | "cell_type": "code", |
|
109 | "cell_type": "code", | |
102 | "collapsed": false, |
|
110 | "collapsed": false, | |
103 | "input": [ |
|
111 | "input": [ | |
104 | "%%ruby\n", |
|
112 | "%%ruby\n", | |
105 | "puts \"Hello from Ruby #{RUBY_VERSION}\"" |
|
113 | "puts \"Hello from Ruby #{RUBY_VERSION}\"" | |
106 | ], |
|
114 | ], | |
107 | "language": "python", |
|
115 | "language": "python", | |
108 | "metadata": {}, |
|
116 | "metadata": {}, | |
109 | "outputs": [ |
|
117 | "outputs": [ | |
110 | { |
|
118 | { | |
111 | "output_type": "stream", |
|
119 | "output_type": "stream", | |
112 | "stream": "stdout", |
|
120 | "stream": "stdout", | |
113 | "text": [ |
|
121 | "text": [ | |
114 | "Hello from Ruby 1.8.7\n" |
|
122 | "Hello from Ruby 1.8.7\n" | |
115 | ] |
|
123 | ] | |
116 | } |
|
124 | } | |
117 | ], |
|
125 | ], | |
118 | "prompt_number": 4 |
|
126 | "prompt_number": 4 | |
119 | }, |
|
127 | }, | |
120 | { |
|
128 | { | |
121 | "cell_type": "code", |
|
129 | "cell_type": "code", | |
122 | "collapsed": false, |
|
130 | "collapsed": false, | |
123 | "input": [ |
|
131 | "input": [ | |
124 | "%%bash\n", |
|
132 | "%%bash\n", | |
125 | "echo \"hello from $BASH\"" |
|
133 | "echo \"hello from $BASH\"" | |
126 | ], |
|
134 | ], | |
127 | "language": "python", |
|
135 | "language": "python", | |
128 | "metadata": {}, |
|
136 | "metadata": {}, | |
129 | "outputs": [ |
|
137 | "outputs": [ | |
130 | { |
|
138 | { | |
131 | "output_type": "stream", |
|
139 | "output_type": "stream", | |
132 | "stream": "stdout", |
|
140 | "stream": "stdout", | |
133 | "text": [ |
|
141 | "text": [ | |
134 | "hello from /usr/local/bin/bash\n" |
|
142 | "hello from /usr/local/bin/bash\n" | |
135 | ] |
|
143 | ] | |
136 | } |
|
144 | } | |
137 | ], |
|
145 | ], | |
138 | "prompt_number": 5 |
|
146 | "prompt_number": 5 | |
139 | }, |
|
147 | }, | |
140 | { |
|
148 | { | |
141 | "cell_type": "heading", |
|
149 | "cell_type": "heading", | |
142 | "level": 2, |
|
150 | "level": 2, | |
143 | "metadata": {}, |
|
151 | "metadata": {}, | |
144 | "source": [ |
|
152 | "source": [ | |
145 | "Capturing output" |
|
153 | "Capturing output" | |
146 | ] |
|
154 | ] | |
147 | }, |
|
155 | }, | |
148 | { |
|
156 | { | |
149 | "cell_type": "markdown", |
|
157 | "cell_type": "markdown", | |
150 | "metadata": {}, |
|
158 | "metadata": {}, | |
151 | "source": [ |
|
159 | "source": [ | |
152 | "You can also capture stdout/err from these subprocesses into Python variables, instead of letting them go directly to stdout/err" |
|
160 | "You can also capture stdout/err from these subprocesses into Python variables, instead of letting them go directly to stdout/err" | |
153 | ] |
|
161 | ] | |
154 | }, |
|
162 | }, | |
155 | { |
|
163 | { | |
156 | "cell_type": "code", |
|
164 | "cell_type": "code", | |
157 | "collapsed": false, |
|
165 | "collapsed": false, | |
158 | "input": [ |
|
166 | "input": [ | |
159 | "%%bash\n", |
|
167 | "%%bash\n", | |
160 | "echo \"hi, stdout\"\n", |
|
168 | "echo \"hi, stdout\"\n", | |
161 | "echo \"hello, stderr\" >&2\n" |
|
169 | "echo \"hello, stderr\" >&2\n" | |
162 | ], |
|
170 | ], | |
163 | "language": "python", |
|
171 | "language": "python", | |
164 | "metadata": {}, |
|
172 | "metadata": {}, | |
165 | "outputs": [ |
|
173 | "outputs": [ | |
166 | { |
|
174 | { | |
167 | "output_type": "stream", |
|
175 | "output_type": "stream", | |
168 | "stream": "stdout", |
|
176 | "stream": "stdout", | |
169 | "text": [ |
|
177 | "text": [ | |
170 | "hi, stdout\n" |
|
178 | "hi, stdout\n" | |
171 | ] |
|
179 | ] | |
172 | }, |
|
180 | }, | |
173 | { |
|
181 | { | |
174 | "output_type": "stream", |
|
182 | "output_type": "stream", | |
175 | "stream": "stderr", |
|
183 | "stream": "stderr", | |
176 | "text": [ |
|
184 | "text": [ | |
177 | "hello, stderr\n" |
|
185 | "hello, stderr\n" | |
178 | ] |
|
186 | ] | |
179 | } |
|
187 | } | |
180 | ], |
|
188 | ], | |
181 | "prompt_number": 6 |
|
189 | "prompt_number": 6 | |
182 | }, |
|
190 | }, | |
183 | { |
|
191 | { | |
184 | "cell_type": "code", |
|
192 | "cell_type": "code", | |
185 | "collapsed": false, |
|
193 | "collapsed": false, | |
186 | "input": [ |
|
194 | "input": [ | |
187 | "%%bash --out output --err error\n", |
|
195 | "%%bash --out output --err error\n", | |
188 | "echo \"hi, stdout\"\n", |
|
196 | "echo \"hi, stdout\"\n", | |
189 | "echo \"hello, stderr\" >&2" |
|
197 | "echo \"hello, stderr\" >&2" | |
190 | ], |
|
198 | ], | |
191 | "language": "python", |
|
199 | "language": "python", | |
192 | "metadata": {}, |
|
200 | "metadata": {}, | |
193 | "outputs": [], |
|
201 | "outputs": [], | |
194 | "prompt_number": 7 |
|
202 | "prompt_number": 7 | |
195 | }, |
|
203 | }, | |
196 | { |
|
204 | { | |
197 | "cell_type": "code", |
|
205 | "cell_type": "code", | |
198 | "collapsed": false, |
|
206 | "collapsed": false, | |
199 | "input": [ |
|
207 | "input": [ | |
200 | "print error\n", |
|
208 | "print error\n", | |
201 | "print output" |
|
209 | "print output" | |
202 | ], |
|
210 | ], | |
203 | "language": "python", |
|
211 | "language": "python", | |
204 | "metadata": {}, |
|
212 | "metadata": {}, | |
205 | "outputs": [ |
|
213 | "outputs": [ | |
206 | { |
|
214 | { | |
207 | "output_type": "stream", |
|
215 | "output_type": "stream", | |
208 | "stream": "stdout", |
|
216 | "stream": "stdout", | |
209 | "text": [ |
|
217 | "text": [ | |
210 | "hello, stderr\n", |
|
218 | "hello, stderr\n", | |
211 | "\n", |
|
219 | "\n", | |
212 | "hi, stdout\n", |
|
220 | "hi, stdout\n", | |
213 | "\n" |
|
221 | "\n" | |
214 | ] |
|
222 | ] | |
215 | } |
|
223 | } | |
216 | ], |
|
224 | ], | |
217 | "prompt_number": 8 |
|
225 | "prompt_number": 8 | |
218 | }, |
|
226 | }, | |
219 | { |
|
227 | { | |
220 | "cell_type": "heading", |
|
228 | "cell_type": "heading", | |
221 | "level": 2, |
|
229 | "level": 2, | |
222 | "metadata": {}, |
|
230 | "metadata": {}, | |
223 | "source": [ |
|
231 | "source": [ | |
224 | "Background Scripts" |
|
232 | "Background Scripts" | |
225 | ] |
|
233 | ] | |
226 | }, |
|
234 | }, | |
227 | { |
|
235 | { | |
228 | "cell_type": "markdown", |
|
236 | "cell_type": "markdown", | |
229 | "metadata": {}, |
|
237 | "metadata": {}, | |
230 | "source": [ |
|
238 | "source": [ | |
231 | "These scripts can be run in the background, by adding the `--bg` flag.\n", |
|
239 | "These scripts can be run in the background, by adding the `--bg` flag.\n", | |
232 | "\n", |
|
240 | "\n", | |
233 | "When you do this, output is discarded unless you use the `--out/err`\n", |
|
241 | "When you do this, output is discarded unless you use the `--out/err`\n", | |
234 | "flags to store output as above." |
|
242 | "flags to store output as above." | |
235 | ] |
|
243 | ] | |
236 | }, |
|
244 | }, | |
237 | { |
|
245 | { | |
238 | "cell_type": "code", |
|
246 | "cell_type": "code", | |
239 | "collapsed": false, |
|
247 | "collapsed": false, | |
240 | "input": [ |
|
248 | "input": [ | |
241 | "%%ruby --bg --out ruby_lines\n", |
|
249 | "%%ruby --bg --out ruby_lines\n", | |
242 | "for n in 1...10\n", |
|
250 | "for n in 1...10\n", | |
243 | " sleep 1\n", |
|
251 | " sleep 1\n", | |
244 | " puts \"line #{n}\"\n", |
|
252 | " puts \"line #{n}\"\n", | |
245 | " STDOUT.flush\n", |
|
253 | " STDOUT.flush\n", | |
246 | "end" |
|
254 | "end" | |
247 | ], |
|
255 | ], | |
248 | "language": "python", |
|
256 | "language": "python", | |
249 | "metadata": {}, |
|
257 | "metadata": {}, | |
250 | "outputs": [ |
|
258 | "outputs": [ | |
251 | { |
|
259 | { | |
252 | "output_type": "stream", |
|
260 | "output_type": "stream", | |
253 | "stream": "stdout", |
|
261 | "stream": "stdout", | |
254 | "text": [ |
|
262 | "text": [ | |
255 | "Starting job # 0 in a separate thread.\n" |
|
263 | "Starting job # 0 in a separate thread.\n" | |
256 | ] |
|
264 | ] | |
257 | } |
|
265 | } | |
258 | ], |
|
266 | ], | |
259 | "prompt_number": 9 |
|
267 | "prompt_number": 9 | |
260 | }, |
|
268 | }, | |
261 | { |
|
269 | { | |
262 | "cell_type": "markdown", |
|
270 | "cell_type": "markdown", | |
263 | "metadata": {}, |
|
271 | "metadata": {}, | |
264 | "source": [ |
|
272 | "source": [ | |
265 | "When you do store output of a background thread, these are the stdout/err *pipes*,\n", |
|
273 | "When you do store output of a background thread, these are the stdout/err *pipes*,\n", | |
266 | "rather than the text of the output." |
|
274 | "rather than the text of the output." | |
267 | ] |
|
275 | ] | |
268 | }, |
|
276 | }, | |
269 | { |
|
277 | { | |
270 | "cell_type": "code", |
|
278 | "cell_type": "code", | |
271 | "collapsed": false, |
|
279 | "collapsed": false, | |
272 | "input": [ |
|
280 | "input": [ | |
273 | "ruby_lines" |
|
281 | "ruby_lines" | |
274 | ], |
|
282 | ], | |
275 | "language": "python", |
|
283 | "language": "python", | |
276 | "metadata": {}, |
|
284 | "metadata": {}, | |
277 | "outputs": [ |
|
285 | "outputs": [ | |
278 | { |
|
286 | { | |
279 | "output_type": "pyout", |
|
287 | "output_type": "pyout", | |
280 | "prompt_number": 10, |
|
288 | "prompt_number": 10, | |
281 | "text": [ |
|
289 | "text": [ | |
282 | "<open file '<fdopen>', mode 'rb' at 0x10a4be660>" |
|
290 | "<open file '<fdopen>', mode 'rb' at 0x10a4be660>" | |
283 | ] |
|
291 | ] | |
284 | } |
|
292 | } | |
285 | ], |
|
293 | ], | |
286 | "prompt_number": 10 |
|
294 | "prompt_number": 10 | |
287 | }, |
|
295 | }, | |
288 | { |
|
296 | { | |
289 | "cell_type": "code", |
|
297 | "cell_type": "code", | |
290 | "collapsed": false, |
|
298 | "collapsed": false, | |
291 | "input": [ |
|
299 | "input": [ | |
292 | "print ruby_lines.read()" |
|
300 | "print ruby_lines.read()" | |
293 | ], |
|
301 | ], | |
294 | "language": "python", |
|
302 | "language": "python", | |
295 | "metadata": {}, |
|
303 | "metadata": {}, | |
296 | "outputs": [ |
|
304 | "outputs": [ | |
297 | { |
|
305 | { | |
298 | "output_type": "stream", |
|
306 | "output_type": "stream", | |
299 | "stream": "stdout", |
|
307 | "stream": "stdout", | |
300 | "text": [ |
|
308 | "text": [ | |
301 | "line 1\n", |
|
309 | "line 1\n", | |
302 | "line 2\n", |
|
310 | "line 2\n", | |
303 | "line 3\n", |
|
311 | "line 3\n", | |
304 | "line 4\n", |
|
312 | "line 4\n", | |
305 | "line 5\n", |
|
313 | "line 5\n", | |
306 | "line 6\n", |
|
314 | "line 6\n", | |
307 | "line 7\n", |
|
315 | "line 7\n", | |
308 | "line 8\n", |
|
316 | "line 8\n", | |
309 | "line 9\n", |
|
317 | "line 9\n", | |
310 | "\n" |
|
318 | "\n" | |
311 | ] |
|
319 | ] | |
312 | } |
|
320 | } | |
313 | ], |
|
321 | ], | |
314 | "prompt_number": 11 |
|
322 | "prompt_number": 11 | |
315 | }, |
|
323 | }, | |
316 | { |
|
324 | { | |
317 | "cell_type": "heading", |
|
325 | "cell_type": "heading", | |
318 | "level": 2, |
|
326 | "level": 2, | |
319 | "metadata": {}, |
|
327 | "metadata": {}, | |
320 | "source": [ |
|
328 | "source": [ | |
321 | "Arguments to subcommand" |
|
329 | "Arguments to subcommand" | |
322 | ] |
|
330 | ] | |
323 | }, |
|
331 | }, | |
324 | { |
|
332 | { | |
325 | "cell_type": "markdown", |
|
333 | "cell_type": "markdown", | |
326 | "metadata": {}, |
|
334 | "metadata": {}, | |
327 | "source": [ |
|
335 | "source": [ | |
328 | "You can pass arguments the subcommand as well,\n", |
|
336 | "You can pass arguments the subcommand as well,\n", | |
329 | "such as this example instructing Python to use integer division from Python 3:" |
|
337 | "such as this example instructing Python to use integer division from Python 3:" | |
330 | ] |
|
338 | ] | |
331 | }, |
|
339 | }, | |
332 | { |
|
340 | { | |
333 | "cell_type": "code", |
|
341 | "cell_type": "code", | |
334 | "collapsed": false, |
|
342 | "collapsed": false, | |
335 | "input": [ |
|
343 | "input": [ | |
336 | "%%script python -Qnew\n", |
|
344 | "%%script python -Qnew\n", | |
337 | "print 1/3" |
|
345 | "print 1/3" | |
338 | ], |
|
346 | ], | |
339 | "language": "python", |
|
347 | "language": "python", | |
340 | "metadata": {}, |
|
348 | "metadata": {}, | |
341 | "outputs": [ |
|
349 | "outputs": [ | |
342 | { |
|
350 | { | |
343 | "output_type": "stream", |
|
351 | "output_type": "stream", | |
344 | "stream": "stdout", |
|
352 | "stream": "stdout", | |
345 | "text": [ |
|
353 | "text": [ | |
346 | "0.333333333333\n" |
|
354 | "0.333333333333\n" | |
347 | ] |
|
355 | ] | |
348 | } |
|
356 | } | |
349 | ], |
|
357 | ], | |
350 | "prompt_number": 12 |
|
358 | "prompt_number": 12 | |
351 | }, |
|
359 | }, | |
352 | { |
|
360 | { | |
353 | "cell_type": "markdown", |
|
361 | "cell_type": "markdown", | |
354 | "metadata": {}, |
|
362 | "metadata": {}, | |
355 | "source": [ |
|
363 | "source": [ | |
356 | "You can really specify *any* program for `%%script`,\n", |
|
364 | "You can really specify *any* program for `%%script`,\n", | |
357 | "for instance here is a 'program' that echos the lines of stdin, with delays between each line." |
|
365 | "for instance here is a 'program' that echos the lines of stdin, with delays between each line." | |
358 | ] |
|
366 | ] | |
359 | }, |
|
367 | }, | |
360 | { |
|
368 | { | |
361 | "cell_type": "code", |
|
369 | "cell_type": "code", | |
362 | "collapsed": false, |
|
370 | "collapsed": false, | |
363 | "input": [ |
|
371 | "input": [ | |
364 | "%%script --bg --out bashout bash -c \"while read line; do echo $line; sleep 1; done\"\n", |
|
372 | "%%script --bg --out bashout bash -c \"while read line; do echo $line; sleep 1; done\"\n", | |
365 | "line 1\n", |
|
373 | "line 1\n", | |
366 | "line 2\n", |
|
374 | "line 2\n", | |
367 | "line 3\n", |
|
375 | "line 3\n", | |
368 | "line 4\n", |
|
376 | "line 4\n", | |
369 | "line 5\n" |
|
377 | "line 5\n" | |
370 | ], |
|
378 | ], | |
371 | "language": "python", |
|
379 | "language": "python", | |
372 | "metadata": {}, |
|
380 | "metadata": {}, | |
373 | "outputs": [ |
|
381 | "outputs": [ | |
374 | { |
|
382 | { | |
375 | "output_type": "stream", |
|
383 | "output_type": "stream", | |
376 | "stream": "stdout", |
|
384 | "stream": "stdout", | |
377 | "text": [ |
|
385 | "text": [ | |
378 | "Starting job # 2 in a separate thread.\n" |
|
386 | "Starting job # 2 in a separate thread.\n" | |
379 | ] |
|
387 | ] | |
380 | } |
|
388 | } | |
381 | ], |
|
389 | ], | |
382 | "prompt_number": 13 |
|
390 | "prompt_number": 13 | |
383 | }, |
|
391 | }, | |
384 | { |
|
392 | { | |
385 | "cell_type": "markdown", |
|
393 | "cell_type": "markdown", | |
386 | "metadata": {}, |
|
394 | "metadata": {}, | |
387 | "source": [ |
|
395 | "source": [ | |
388 | "Remember, since the output of a background script is just the stdout pipe,\n", |
|
396 | "Remember, since the output of a background script is just the stdout pipe,\n", | |
389 | "you can read it as lines become available:" |
|
397 | "you can read it as lines become available:" | |
390 | ] |
|
398 | ] | |
391 | }, |
|
399 | }, | |
392 | { |
|
400 | { | |
393 | "cell_type": "code", |
|
401 | "cell_type": "code", | |
394 | "collapsed": false, |
|
402 | "collapsed": false, | |
395 | "input": [ |
|
403 | "input": [ | |
396 | "import time\n", |
|
404 | "import time\n", | |
397 | "tic = time.time()\n", |
|
405 | "tic = time.time()\n", | |
398 | "line = True\n", |
|
406 | "line = True\n", | |
399 | "while True:\n", |
|
407 | "while True:\n", | |
400 | " line = bashout.readline()\n", |
|
408 | " line = bashout.readline()\n", | |
401 | " if not line:\n", |
|
409 | " if not line:\n", | |
402 | " break\n", |
|
410 | " break\n", | |
403 | " sys.stdout.write(\"%.1fs: %s\" %(time.time()-tic, line))\n", |
|
411 | " sys.stdout.write(\"%.1fs: %s\" %(time.time()-tic, line))\n", | |
404 | " sys.stdout.flush()\n" |
|
412 | " sys.stdout.flush()\n" | |
405 | ], |
|
413 | ], | |
406 | "language": "python", |
|
414 | "language": "python", | |
407 | "metadata": {}, |
|
415 | "metadata": {}, | |
408 | "outputs": [ |
|
416 | "outputs": [ | |
409 | { |
|
417 | { | |
410 | "output_type": "stream", |
|
418 | "output_type": "stream", | |
411 | "stream": "stdout", |
|
419 | "stream": "stdout", | |
412 | "text": [ |
|
420 | "text": [ | |
413 | "0.0s: line 1\n" |
|
421 | "0.0s: line 1\n" | |
414 | ] |
|
422 | ] | |
415 | }, |
|
423 | }, | |
416 | { |
|
424 | { | |
417 | "output_type": "stream", |
|
425 | "output_type": "stream", | |
418 | "stream": "stdout", |
|
426 | "stream": "stdout", | |
419 | "text": [ |
|
427 | "text": [ | |
420 | "1.0s: line 2\n" |
|
428 | "1.0s: line 2\n" | |
421 | ] |
|
429 | ] | |
422 | }, |
|
430 | }, | |
423 | { |
|
431 | { | |
424 | "output_type": "stream", |
|
432 | "output_type": "stream", | |
425 | "stream": "stdout", |
|
433 | "stream": "stdout", | |
426 | "text": [ |
|
434 | "text": [ | |
427 | "2.0s: line 3\n" |
|
435 | "2.0s: line 3\n" | |
428 | ] |
|
436 | ] | |
429 | }, |
|
437 | }, | |
430 | { |
|
438 | { | |
431 | "output_type": "stream", |
|
439 | "output_type": "stream", | |
432 | "stream": "stdout", |
|
440 | "stream": "stdout", | |
433 | "text": [ |
|
441 | "text": [ | |
434 | "3.0s: line 4\n" |
|
442 | "3.0s: line 4\n" | |
435 | ] |
|
443 | ] | |
436 | }, |
|
444 | }, | |
437 | { |
|
445 | { | |
438 | "output_type": "stream", |
|
446 | "output_type": "stream", | |
439 | "stream": "stdout", |
|
447 | "stream": "stdout", | |
440 | "text": [ |
|
448 | "text": [ | |
441 | "4.0s: line 5\n" |
|
449 | "4.0s: line 5\n" | |
442 | ] |
|
450 | ] | |
443 | } |
|
451 | } | |
444 | ], |
|
452 | ], | |
445 | "prompt_number": 14 |
|
453 | "prompt_number": 14 | |
446 | }, |
|
454 | }, | |
447 | { |
|
455 | { | |
448 | "cell_type": "heading", |
|
456 | "cell_type": "heading", | |
449 | "level": 2, |
|
457 | "level": 2, | |
450 | "metadata": {}, |
|
458 | "metadata": {}, | |
451 | "source": [ |
|
459 | "source": [ | |
452 | "Configuring the default ScriptMagics" |
|
460 | "Configuring the default ScriptMagics" | |
453 | ] |
|
461 | ] | |
454 | }, |
|
462 | }, | |
455 | { |
|
463 | { | |
456 | "cell_type": "markdown", |
|
464 | "cell_type": "markdown", | |
457 | "metadata": {}, |
|
465 | "metadata": {}, | |
458 | "source": [ |
|
466 | "source": [ | |
459 | "The list of aliased script magics is configurable.\n", |
|
467 | "The list of aliased script magics is configurable.\n", | |
460 | "\n", |
|
468 | "\n", | |
461 | "The default is to pick from a few common interpreters, and use them if found, but you can specify your own in ipython_config.py:\n", |
|
469 | "The default is to pick from a few common interpreters, and use them if found, but you can specify your own in ipython_config.py:\n", | |
462 | "\n", |
|
470 | "\n", | |
463 | " c.ScriptMagics.scripts = ['R', 'pypy', 'myprogram']\n", |
|
471 | " c.ScriptMagics.scripts = ['R', 'pypy', 'myprogram']\n", | |
464 | "\n", |
|
472 | "\n", | |
465 | "And if any of these programs do not apear on your default PATH, then you would also need to specify their location with:\n", |
|
473 | "And if any of these programs do not apear on your default PATH, then you would also need to specify their location with:\n", | |
466 | "\n", |
|
474 | "\n", | |
467 | " c.ScriptMagics.script_paths = {'myprogram': '/opt/path/to/myprogram'}" |
|
475 | " c.ScriptMagics.script_paths = {'myprogram': '/opt/path/to/myprogram'}" | |
468 | ] |
|
476 | ] | |
469 | } |
|
477 | } | |
470 | ], |
|
478 | ], | |
471 | "metadata": {} |
|
479 | "metadata": {} | |
472 | } |
|
480 | } | |
473 | ] |
|
481 | ] | |
474 | } No newline at end of file |
|
482 | } |
@@ -1,150 +1,147 b'' | |||||
1 | { |
|
1 | { | |
2 | "metadata": { |
|
2 | "metadata": { | |
3 | "name": "Trapezoid Rule" |
|
3 | "name": "Trapezoid Rule" | |
4 | }, |
|
4 | }, | |
5 | "nbformat": 3, |
|
5 | "nbformat": 3, | |
6 | "nbformat_minor": 0, |
|
6 | "nbformat_minor": 0, | |
7 | "worksheets": [ |
|
7 | "worksheets": [ | |
8 | { |
|
8 | { | |
9 | "cells": [ |
|
9 | "cells": [ | |
10 | { |
|
10 | { | |
|
11 | "cell_type": "heading", | |||
|
12 | "level": 1, | |||
|
13 | "metadata": {}, | |||
|
14 | "source": [ | |||
|
15 | "Basic Numerical Integration: the Trapezoid Rule" | |||
|
16 | ] | |||
|
17 | }, | |||
|
18 | { | |||
11 | "cell_type": "markdown", |
|
19 | "cell_type": "markdown", | |
12 | "metadata": {}, |
|
20 | "metadata": {}, | |
13 | "source": [ |
|
21 | "source": [ | |
14 | "Basic numerical integration: the trapezoid rule\n", |
|
|||
15 | "===============================================\n", |
|
|||
16 | "\n", |
|
|||
17 | "A simple illustration of the trapezoid rule for definite integration:\n", |
|
22 | "A simple illustration of the trapezoid rule for definite integration:\n", | |
18 | "\n", |
|
23 | "\n", | |
19 | "$$\n", |
|
24 | "$$\n", | |
20 | "\\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right).\n", |
|
25 | "\\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right).\n", | |
21 | "$$\n", |
|
26 | "$$\n", | |
22 | "<br>\n", |
|
27 | "<br>\n", | |
23 | "First, we define a simple function and sample it between 0 and 10 at 200 points" |
|
28 | "First, we define a simple function and sample it between 0 and 10 at 200 points" | |
24 | ] |
|
29 | ] | |
25 | }, |
|
30 | }, | |
26 | { |
|
31 | { | |
27 | "cell_type": "code", |
|
32 | "cell_type": "code", | |
28 | "collapsed": false, |
|
33 | "collapsed": false, | |
29 | "input": [ |
|
34 | "input": [ | |
30 | "%pylab inline" |
|
35 | "%pylab inline" | |
31 | ], |
|
36 | ], | |
32 | "language": "python", |
|
37 | "language": "python", | |
33 | "metadata": {}, |
|
38 | "metadata": {}, | |
34 | "outputs": [ |
|
39 | "outputs": [ | |
35 | { |
|
40 | { | |
36 | "output_type": "stream", |
|
41 | "output_type": "stream", | |
37 | "stream": "stdout", |
|
42 | "stream": "stdout", | |
38 | "text": [ |
|
43 | "text": [ | |
39 | "\n", |
|
44 | "\n", | |
40 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", |
|
45 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", | |
41 | "For more information, type 'help(pylab)'.\n" |
|
46 | "For more information, type 'help(pylab)'.\n" | |
42 | ] |
|
47 | ] | |
43 | } |
|
48 | } | |
44 | ], |
|
49 | ], | |
45 | "prompt_number": 1 |
|
50 | "prompt_number": 1 | |
46 | }, |
|
51 | }, | |
47 | { |
|
52 | { | |
48 | "cell_type": "code", |
|
53 | "cell_type": "code", | |
49 | "collapsed": true, |
|
54 | "collapsed": true, | |
50 | "input": [ |
|
55 | "input": [ | |
51 | "def f(x):\n", |
|
56 | "def f(x):\n", | |
52 | " return (x-3)*(x-5)*(x-7)+85\n", |
|
57 | " return (x-3)*(x-5)*(x-7)+85\n", | |
53 | "\n", |
|
58 | "\n", | |
54 | "x = linspace(0, 10, 200)\n", |
|
59 | "x = linspace(0, 10, 200)\n", | |
55 | "y = f(x)" |
|
60 | "y = f(x)" | |
56 | ], |
|
61 | ], | |
57 | "language": "python", |
|
62 | "language": "python", | |
58 | "metadata": {}, |
|
63 | "metadata": {}, | |
59 | "outputs": [], |
|
64 | "outputs": [], | |
60 | "prompt_number": 2 |
|
65 | "prompt_number": 2 | |
61 | }, |
|
66 | }, | |
62 | { |
|
67 | { | |
63 | "cell_type": "markdown", |
|
68 | "cell_type": "markdown", | |
64 | "metadata": {}, |
|
69 | "metadata": {}, | |
65 | "source": [ |
|
70 | "source": [ | |
66 | "Choose a region to integrate over and take only a few points in that region" |
|
71 | "Choose a region to integrate over and take only a few points in that region" | |
67 | ] |
|
72 | ] | |
68 | }, |
|
73 | }, | |
69 | { |
|
74 | { | |
70 | "cell_type": "code", |
|
75 | "cell_type": "code", | |
71 | "collapsed": true, |
|
76 | "collapsed": true, | |
72 | "input": [ |
|
77 | "input": [ | |
73 | "a, b = 1, 9\n", |
|
78 | "a, b = 1, 9\n", | |
74 | "xint = x[logical_and(x>=a, x<=b)][::30]\n", |
|
79 | "xint = x[logical_and(x>=a, x<=b)][::30]\n", | |
75 | "yint = y[logical_and(x>=a, x<=b)][::30]" |
|
80 | "yint = y[logical_and(x>=a, x<=b)][::30]" | |
76 | ], |
|
81 | ], | |
77 | "language": "python", |
|
82 | "language": "python", | |
78 | "metadata": {}, |
|
83 | "metadata": {}, | |
79 | "outputs": [], |
|
84 | "outputs": [], | |
80 | "prompt_number": 3 |
|
85 | "prompt_number": 3 | |
81 | }, |
|
86 | }, | |
82 | { |
|
87 | { | |
83 | "cell_type": "markdown", |
|
88 | "cell_type": "markdown", | |
84 | "metadata": {}, |
|
89 | "metadata": {}, | |
85 | "source": [ |
|
90 | "source": [ | |
86 | "Plot both the function and the area below it in the trapezoid approximation" |
|
91 | "Plot both the function and the area below it in the trapezoid approximation" | |
87 | ] |
|
92 | ] | |
88 | }, |
|
93 | }, | |
89 | { |
|
94 | { | |
90 | "cell_type": "code", |
|
95 | "cell_type": "code", | |
91 | "collapsed": false, |
|
96 | "collapsed": false, | |
92 | "input": [ |
|
97 | "input": [ | |
93 | "plot(x, y, lw=2)\n", |
|
98 | "plot(x, y, lw=2)\n", | |
94 | "axis([0, 10, 0, 140])\n", |
|
99 | "axis([0, 10, 0, 140])\n", | |
95 | "fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)\n", |
|
100 | "fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)\n", | |
96 | "text(0.5 * (a + b), 30,r\"$\\int_a^b f(x)dx$\", horizontalalignment='center', fontsize=20);" |
|
101 | "text(0.5 * (a + b), 30,r\"$\\int_a^b f(x)dx$\", horizontalalignment='center', fontsize=20);" | |
97 | ], |
|
102 | ], | |
98 | "language": "python", |
|
103 | "language": "python", | |
99 | "metadata": {}, |
|
104 | "metadata": {}, | |
100 | "outputs": [ |
|
105 | "outputs": [ | |
101 | { |
|
106 | { | |
102 | "output_type": "display_data", |
|
107 | "output_type": "display_data", | |
103 | "png": 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Hx1NTU0O/fv3UKU4I0S7Fx8cTHx9v8us1RqPR2NIX5ebmMnbsWNLS0gD46aef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\n" 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108 | "png": 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Hx1NTU0O/fv3UKU4I0S7Fx8cTHx9v8us1RqPR2NIX5ebmMnbsWNLS0gD46aef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\n" | |
104 | } |
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109 | } | |
105 | ], |
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110 | ], | |
106 | "prompt_number": 4 |
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111 | "prompt_number": 4 | |
107 | }, |
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112 | }, | |
108 | { |
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113 | { | |
109 | "cell_type": "markdown", |
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114 | "cell_type": "markdown", | |
110 | "metadata": {}, |
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115 | "metadata": {}, | |
111 | "source": [ |
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116 | "source": [ | |
112 | "Compute the integral both at high accuracy and with the trapezoid approximation" |
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117 | "Compute the integral both at high accuracy and with the trapezoid approximation" | |
113 | ] |
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118 | ] | |
114 | }, |
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119 | }, | |
115 | { |
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120 | { | |
116 | "cell_type": "code", |
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121 | "cell_type": "code", | |
117 | "collapsed": false, |
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122 | "collapsed": false, | |
118 | "input": [ |
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123 | "input": [ | |
119 | "from scipy.integrate import quad, trapz\n", |
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124 | "from scipy.integrate import quad, trapz\n", | |
120 | "integral, error = quad(f, 1, 9)\n", |
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125 | "integral, error = quad(f, 1, 9)\n", | |
121 | "print \"The integral is:\", integral, \"+/-\", error\n", |
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126 | "print \"The integral is:\", integral, \"+/-\", error\n", | |
122 | "print \"The trapezoid approximation with\", len(xint), \"points is:\", trapz(yint, xint)" |
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127 | "print \"The trapezoid approximation with\", len(xint), \"points is:\", trapz(yint, xint)" | |
123 | ], |
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128 | ], | |
124 | "language": "python", |
|
129 | "language": "python", | |
125 | "metadata": {}, |
|
130 | "metadata": {}, | |
126 | "outputs": [ |
|
131 | "outputs": [ | |
127 | { |
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132 | { | |
128 | "output_type": "stream", |
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133 | "output_type": "stream", | |
129 | "stream": "stdout", |
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134 | "stream": "stdout", | |
130 | "text": [ |
|
135 | "text": [ | |
131 | "The integral is: 680.0 +/- 7.54951656745e-12\n", |
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136 | "The integral is: 680.0 +/- 7.54951656745e-12\n", | |
132 | "The trapezoid approximation with 6 points is: 621.286411141\n" |
|
137 | "The trapezoid approximation with 6 points is: 621.286411141\n" | |
133 | ] |
|
138 | ] | |
134 | } |
|
139 | } | |
135 | ], |
|
140 | ], | |
136 | "prompt_number": 5 |
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141 | "prompt_number": 5 | |
137 | }, |
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138 | { |
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139 | "cell_type": "code", |
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|||
140 | "collapsed": true, |
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141 | "input": [], |
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142 | "language": "python", |
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143 | "metadata": {}, |
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144 | "outputs": [] |
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145 | } |
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142 | } | |
146 | ], |
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143 | ], | |
147 | "metadata": {} |
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144 | "metadata": {} | |
148 | } |
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145 | } | |
149 | ] |
|
146 | ] | |
150 | } No newline at end of file |
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147 | } |
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@@ -1,9075 +0,0 b'' | |||||
1 | { |
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2 | "metadata": { |
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3 | "name": "JS Progress Bar" |
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4 | }, |
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5 | "nbformat": 3, |
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6 | "nbformat_minor": 0, |
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7 | "worksheets": [ |
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8 | { |
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9 | "cells": [ |
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10 | { |
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11 | "cell_type": "heading", |
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12 | "level": 1, |
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13 | "metadata": {}, |
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14 | "source": [ |
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15 | "A Javascript Progress Bar" |
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16 | ] |
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17 | }, |
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18 | { |
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19 | "cell_type": "markdown", |
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20 | "metadata": {}, |
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21 | "source": [ |
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22 | "Here is a simple progress bar using HTML/Javascript:" |
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23 | ] |
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24 | }, |
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25 | { |
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26 | "cell_type": "code", |
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27 | "collapsed": false, |
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28 | "input": [ |
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29 | "import uuid\n", |
|
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30 | "import time\n", |
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31 | "from IPython.display import HTML, Javascript, display\n", |
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32 | "\n", |
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33 | "divid = str(uuid.uuid4())\n", |
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34 | "\n", |
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35 | "pb = HTML(\n", |
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36 | "\"\"\"\n", |
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|||
37 | "<div style=\"border: 1px solid black; width:500px\">\n", |
|
|||
38 | " <div id=\"%s\" style=\"background-color:blue; width:0%%\"> </div>\n", |
|
|||
39 | "</div> \n", |
|
|||
40 | "\"\"\" % divid)\n", |
|
|||
41 | "display(pb)\n", |
|
|||
42 | "for i in range(1,101):\n", |
|
|||
43 | " time.sleep(0.1)\n", |
|
|||
44 | " \n", |
|
|||
45 | " display(Javascript(\"$('div#%s').width('%i%%')\" % (divid, i)))" |
|
|||
46 | ], |
|
|||
47 | "language": "python", |
|
|||
48 | "metadata": {}, |
|
|||
49 | "outputs": [ |
|
|||
50 | { |
|
|||
51 | "html": [ |
|
|||
52 | "\n", |
|
|||
53 | "<div style=\"border: 1px solid black; width:500px\">\n", |
|
|||
54 | " <div id=\"9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f\" style=\"background-color:blue; width:0%\"> </div>\n", |
|
|||
55 | "</div> \n" |
|
|||
56 | ], |
|
|||
57 | "output_type": "display_data", |
|
|||
58 | "text": [ |
|
|||
59 | "<IPython.core.display.HTML at 0x1087be210>" |
|
|||
60 | ] |
|
|||
61 | }, |
|
|||
62 | { |
|
|||
63 | "javascript": [ |
|
|||
64 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('1%')" |
|
|||
65 | ], |
|
|||
66 | "output_type": "display_data", |
|
|||
67 | "text": [ |
|
|||
68 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
69 | ] |
|
|||
70 | }, |
|
|||
71 | { |
|
|||
72 | "javascript": [ |
|
|||
73 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('2%')" |
|
|||
74 | ], |
|
|||
75 | "output_type": "display_data", |
|
|||
76 | "text": [ |
|
|||
77 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
78 | ] |
|
|||
79 | }, |
|
|||
80 | { |
|
|||
81 | "javascript": [ |
|
|||
82 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('3%')" |
|
|||
83 | ], |
|
|||
84 | "output_type": "display_data", |
|
|||
85 | "text": [ |
|
|||
86 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
87 | ] |
|
|||
88 | }, |
|
|||
89 | { |
|
|||
90 | "javascript": [ |
|
|||
91 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('4%')" |
|
|||
92 | ], |
|
|||
93 | "output_type": "display_data", |
|
|||
94 | "text": [ |
|
|||
95 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
96 | ] |
|
|||
97 | }, |
|
|||
98 | { |
|
|||
99 | "javascript": [ |
|
|||
100 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('5%')" |
|
|||
101 | ], |
|
|||
102 | "output_type": "display_data", |
|
|||
103 | "text": [ |
|
|||
104 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
105 | ] |
|
|||
106 | }, |
|
|||
107 | { |
|
|||
108 | "javascript": [ |
|
|||
109 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('6%')" |
|
|||
110 | ], |
|
|||
111 | "output_type": "display_data", |
|
|||
112 | "text": [ |
|
|||
113 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
114 | ] |
|
|||
115 | }, |
|
|||
116 | { |
|
|||
117 | "javascript": [ |
|
|||
118 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('7%')" |
|
|||
119 | ], |
|
|||
120 | "output_type": "display_data", |
|
|||
121 | "text": [ |
|
|||
122 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
123 | ] |
|
|||
124 | }, |
|
|||
125 | { |
|
|||
126 | "javascript": [ |
|
|||
127 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('8%')" |
|
|||
128 | ], |
|
|||
129 | "output_type": "display_data", |
|
|||
130 | "text": [ |
|
|||
131 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
132 | ] |
|
|||
133 | }, |
|
|||
134 | { |
|
|||
135 | "javascript": [ |
|
|||
136 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('9%')" |
|
|||
137 | ], |
|
|||
138 | "output_type": "display_data", |
|
|||
139 | "text": [ |
|
|||
140 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
141 | ] |
|
|||
142 | }, |
|
|||
143 | { |
|
|||
144 | "javascript": [ |
|
|||
145 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('10%')" |
|
|||
146 | ], |
|
|||
147 | "output_type": "display_data", |
|
|||
148 | "text": [ |
|
|||
149 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
150 | ] |
|
|||
151 | }, |
|
|||
152 | { |
|
|||
153 | "javascript": [ |
|
|||
154 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('11%')" |
|
|||
155 | ], |
|
|||
156 | "output_type": "display_data", |
|
|||
157 | "text": [ |
|
|||
158 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
159 | ] |
|
|||
160 | }, |
|
|||
161 | { |
|
|||
162 | "javascript": [ |
|
|||
163 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('12%')" |
|
|||
164 | ], |
|
|||
165 | "output_type": "display_data", |
|
|||
166 | "text": [ |
|
|||
167 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
168 | ] |
|
|||
169 | }, |
|
|||
170 | { |
|
|||
171 | "javascript": [ |
|
|||
172 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('13%')" |
|
|||
173 | ], |
|
|||
174 | "output_type": "display_data", |
|
|||
175 | "text": [ |
|
|||
176 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
177 | ] |
|
|||
178 | }, |
|
|||
179 | { |
|
|||
180 | "javascript": [ |
|
|||
181 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('14%')" |
|
|||
182 | ], |
|
|||
183 | "output_type": "display_data", |
|
|||
184 | "text": [ |
|
|||
185 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
186 | ] |
|
|||
187 | }, |
|
|||
188 | { |
|
|||
189 | "javascript": [ |
|
|||
190 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('15%')" |
|
|||
191 | ], |
|
|||
192 | "output_type": "display_data", |
|
|||
193 | "text": [ |
|
|||
194 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
195 | ] |
|
|||
196 | }, |
|
|||
197 | { |
|
|||
198 | "javascript": [ |
|
|||
199 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('16%')" |
|
|||
200 | ], |
|
|||
201 | "output_type": "display_data", |
|
|||
202 | "text": [ |
|
|||
203 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
204 | ] |
|
|||
205 | }, |
|
|||
206 | { |
|
|||
207 | "javascript": [ |
|
|||
208 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('17%')" |
|
|||
209 | ], |
|
|||
210 | "output_type": "display_data", |
|
|||
211 | "text": [ |
|
|||
212 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
213 | ] |
|
|||
214 | }, |
|
|||
215 | { |
|
|||
216 | "javascript": [ |
|
|||
217 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('18%')" |
|
|||
218 | ], |
|
|||
219 | "output_type": "display_data", |
|
|||
220 | "text": [ |
|
|||
221 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
222 | ] |
|
|||
223 | }, |
|
|||
224 | { |
|
|||
225 | "javascript": [ |
|
|||
226 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('19%')" |
|
|||
227 | ], |
|
|||
228 | "output_type": "display_data", |
|
|||
229 | "text": [ |
|
|||
230 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
231 | ] |
|
|||
232 | }, |
|
|||
233 | { |
|
|||
234 | "javascript": [ |
|
|||
235 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('20%')" |
|
|||
236 | ], |
|
|||
237 | "output_type": "display_data", |
|
|||
238 | "text": [ |
|
|||
239 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
240 | ] |
|
|||
241 | }, |
|
|||
242 | { |
|
|||
243 | "javascript": [ |
|
|||
244 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('21%')" |
|
|||
245 | ], |
|
|||
246 | "output_type": "display_data", |
|
|||
247 | "text": [ |
|
|||
248 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
249 | ] |
|
|||
250 | }, |
|
|||
251 | { |
|
|||
252 | "javascript": [ |
|
|||
253 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('22%')" |
|
|||
254 | ], |
|
|||
255 | "output_type": "display_data", |
|
|||
256 | "text": [ |
|
|||
257 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
258 | ] |
|
|||
259 | }, |
|
|||
260 | { |
|
|||
261 | "javascript": [ |
|
|||
262 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('23%')" |
|
|||
263 | ], |
|
|||
264 | "output_type": "display_data", |
|
|||
265 | "text": [ |
|
|||
266 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
267 | ] |
|
|||
268 | }, |
|
|||
269 | { |
|
|||
270 | "javascript": [ |
|
|||
271 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('24%')" |
|
|||
272 | ], |
|
|||
273 | "output_type": "display_data", |
|
|||
274 | "text": [ |
|
|||
275 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
276 | ] |
|
|||
277 | }, |
|
|||
278 | { |
|
|||
279 | "javascript": [ |
|
|||
280 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('25%')" |
|
|||
281 | ], |
|
|||
282 | "output_type": "display_data", |
|
|||
283 | "text": [ |
|
|||
284 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
285 | ] |
|
|||
286 | }, |
|
|||
287 | { |
|
|||
288 | "javascript": [ |
|
|||
289 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('26%')" |
|
|||
290 | ], |
|
|||
291 | "output_type": "display_data", |
|
|||
292 | "text": [ |
|
|||
293 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
294 | ] |
|
|||
295 | }, |
|
|||
296 | { |
|
|||
297 | "javascript": [ |
|
|||
298 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('27%')" |
|
|||
299 | ], |
|
|||
300 | "output_type": "display_data", |
|
|||
301 | "text": [ |
|
|||
302 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
303 | ] |
|
|||
304 | }, |
|
|||
305 | { |
|
|||
306 | "javascript": [ |
|
|||
307 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('28%')" |
|
|||
308 | ], |
|
|||
309 | "output_type": "display_data", |
|
|||
310 | "text": [ |
|
|||
311 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
312 | ] |
|
|||
313 | }, |
|
|||
314 | { |
|
|||
315 | "javascript": [ |
|
|||
316 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('29%')" |
|
|||
317 | ], |
|
|||
318 | "output_type": "display_data", |
|
|||
319 | "text": [ |
|
|||
320 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
321 | ] |
|
|||
322 | }, |
|
|||
323 | { |
|
|||
324 | "javascript": [ |
|
|||
325 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('30%')" |
|
|||
326 | ], |
|
|||
327 | "output_type": "display_data", |
|
|||
328 | "text": [ |
|
|||
329 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
330 | ] |
|
|||
331 | }, |
|
|||
332 | { |
|
|||
333 | "javascript": [ |
|
|||
334 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('31%')" |
|
|||
335 | ], |
|
|||
336 | "output_type": "display_data", |
|
|||
337 | "text": [ |
|
|||
338 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
339 | ] |
|
|||
340 | }, |
|
|||
341 | { |
|
|||
342 | "javascript": [ |
|
|||
343 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('32%')" |
|
|||
344 | ], |
|
|||
345 | "output_type": "display_data", |
|
|||
346 | "text": [ |
|
|||
347 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
348 | ] |
|
|||
349 | }, |
|
|||
350 | { |
|
|||
351 | "javascript": [ |
|
|||
352 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('33%')" |
|
|||
353 | ], |
|
|||
354 | "output_type": "display_data", |
|
|||
355 | "text": [ |
|
|||
356 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
357 | ] |
|
|||
358 | }, |
|
|||
359 | { |
|
|||
360 | "javascript": [ |
|
|||
361 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('34%')" |
|
|||
362 | ], |
|
|||
363 | "output_type": "display_data", |
|
|||
364 | "text": [ |
|
|||
365 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
366 | ] |
|
|||
367 | }, |
|
|||
368 | { |
|
|||
369 | "javascript": [ |
|
|||
370 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('35%')" |
|
|||
371 | ], |
|
|||
372 | "output_type": "display_data", |
|
|||
373 | "text": [ |
|
|||
374 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
375 | ] |
|
|||
376 | }, |
|
|||
377 | { |
|
|||
378 | "javascript": [ |
|
|||
379 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('36%')" |
|
|||
380 | ], |
|
|||
381 | "output_type": "display_data", |
|
|||
382 | "text": [ |
|
|||
383 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
384 | ] |
|
|||
385 | }, |
|
|||
386 | { |
|
|||
387 | "javascript": [ |
|
|||
388 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('37%')" |
|
|||
389 | ], |
|
|||
390 | "output_type": "display_data", |
|
|||
391 | "text": [ |
|
|||
392 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
393 | ] |
|
|||
394 | }, |
|
|||
395 | { |
|
|||
396 | "javascript": [ |
|
|||
397 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('38%')" |
|
|||
398 | ], |
|
|||
399 | "output_type": "display_data", |
|
|||
400 | "text": [ |
|
|||
401 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
402 | ] |
|
|||
403 | }, |
|
|||
404 | { |
|
|||
405 | "javascript": [ |
|
|||
406 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('39%')" |
|
|||
407 | ], |
|
|||
408 | "output_type": "display_data", |
|
|||
409 | "text": [ |
|
|||
410 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
411 | ] |
|
|||
412 | }, |
|
|||
413 | { |
|
|||
414 | "javascript": [ |
|
|||
415 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('40%')" |
|
|||
416 | ], |
|
|||
417 | "output_type": "display_data", |
|
|||
418 | "text": [ |
|
|||
419 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
420 | ] |
|
|||
421 | }, |
|
|||
422 | { |
|
|||
423 | "javascript": [ |
|
|||
424 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('41%')" |
|
|||
425 | ], |
|
|||
426 | "output_type": "display_data", |
|
|||
427 | "text": [ |
|
|||
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|||
820 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('85%')" |
|
|||
821 | ], |
|
|||
822 | "output_type": "display_data", |
|
|||
823 | "text": [ |
|
|||
824 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
825 | ] |
|
|||
826 | }, |
|
|||
827 | { |
|
|||
828 | "javascript": [ |
|
|||
829 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('86%')" |
|
|||
830 | ], |
|
|||
831 | "output_type": "display_data", |
|
|||
832 | "text": [ |
|
|||
833 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
834 | ] |
|
|||
835 | }, |
|
|||
836 | { |
|
|||
837 | "javascript": [ |
|
|||
838 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('87%')" |
|
|||
839 | ], |
|
|||
840 | "output_type": "display_data", |
|
|||
841 | "text": [ |
|
|||
842 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
843 | ] |
|
|||
844 | }, |
|
|||
845 | { |
|
|||
846 | "javascript": [ |
|
|||
847 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('88%')" |
|
|||
848 | ], |
|
|||
849 | "output_type": "display_data", |
|
|||
850 | "text": [ |
|
|||
851 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
852 | ] |
|
|||
853 | }, |
|
|||
854 | { |
|
|||
855 | "javascript": [ |
|
|||
856 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('89%')" |
|
|||
857 | ], |
|
|||
858 | "output_type": "display_data", |
|
|||
859 | "text": [ |
|
|||
860 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
861 | ] |
|
|||
862 | }, |
|
|||
863 | { |
|
|||
864 | "javascript": [ |
|
|||
865 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('90%')" |
|
|||
866 | ], |
|
|||
867 | "output_type": "display_data", |
|
|||
868 | "text": [ |
|
|||
869 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
870 | ] |
|
|||
871 | }, |
|
|||
872 | { |
|
|||
873 | "javascript": [ |
|
|||
874 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('91%')" |
|
|||
875 | ], |
|
|||
876 | "output_type": "display_data", |
|
|||
877 | "text": [ |
|
|||
878 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
879 | ] |
|
|||
880 | }, |
|
|||
881 | { |
|
|||
882 | "javascript": [ |
|
|||
883 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('92%')" |
|
|||
884 | ], |
|
|||
885 | "output_type": "display_data", |
|
|||
886 | "text": [ |
|
|||
887 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
888 | ] |
|
|||
889 | }, |
|
|||
890 | { |
|
|||
891 | "javascript": [ |
|
|||
892 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('93%')" |
|
|||
893 | ], |
|
|||
894 | "output_type": "display_data", |
|
|||
895 | "text": [ |
|
|||
896 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
897 | ] |
|
|||
898 | }, |
|
|||
899 | { |
|
|||
900 | "javascript": [ |
|
|||
901 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('94%')" |
|
|||
902 | ], |
|
|||
903 | "output_type": "display_data", |
|
|||
904 | "text": [ |
|
|||
905 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
906 | ] |
|
|||
907 | }, |
|
|||
908 | { |
|
|||
909 | "javascript": [ |
|
|||
910 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('95%')" |
|
|||
911 | ], |
|
|||
912 | "output_type": "display_data", |
|
|||
913 | "text": [ |
|
|||
914 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
915 | ] |
|
|||
916 | }, |
|
|||
917 | { |
|
|||
918 | "javascript": [ |
|
|||
919 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('96%')" |
|
|||
920 | ], |
|
|||
921 | "output_type": "display_data", |
|
|||
922 | "text": [ |
|
|||
923 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
924 | ] |
|
|||
925 | }, |
|
|||
926 | { |
|
|||
927 | "javascript": [ |
|
|||
928 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('97%')" |
|
|||
929 | ], |
|
|||
930 | "output_type": "display_data", |
|
|||
931 | "text": [ |
|
|||
932 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
933 | ] |
|
|||
934 | }, |
|
|||
935 | { |
|
|||
936 | "javascript": [ |
|
|||
937 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('98%')" |
|
|||
938 | ], |
|
|||
939 | "output_type": "display_data", |
|
|||
940 | "text": [ |
|
|||
941 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
942 | ] |
|
|||
943 | }, |
|
|||
944 | { |
|
|||
945 | "javascript": [ |
|
|||
946 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('99%')" |
|
|||
947 | ], |
|
|||
948 | "output_type": "display_data", |
|
|||
949 | "text": [ |
|
|||
950 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
951 | ] |
|
|||
952 | }, |
|
|||
953 | { |
|
|||
954 | "javascript": [ |
|
|||
955 | "$('div#9f6aa1a1-e30d-43d4-ae49-5ffe0e86e95f').width('100%')" |
|
|||
956 | ], |
|
|||
957 | "output_type": "display_data", |
|
|||
958 | "text": [ |
|
|||
959 | "<IPython.core.display.Javascript at 0x1087be250>" |
|
|||
960 | ] |
|
|||
961 | } |
|
|||
962 | ], |
|
|||
963 | "prompt_number": 2 |
|
|||
964 | }, |
|
|||
965 | { |
|
|||
966 | "cell_type": "markdown", |
|
|||
967 | "metadata": {}, |
|
|||
968 | "source": [ |
|
|||
969 | "The above simply makes a div that is a box, and a blue div inside it with a unique ID \n", |
|
|||
970 | "(so that the javascript won't collide with other similar progress bars on the same page). \n", |
|
|||
971 | "\n", |
|
|||
972 | "Then, at every progress point, we run a simple jQuery call to resize the blue box to\n", |
|
|||
973 | "the appropriate fraction of the width of its containing box, and voil\u00e0 a nice\n", |
|
|||
974 | "HTML/Javascript progress bar!" |
|
|||
975 | ] |
|
|||
976 | }, |
|
|||
977 | { |
|
|||
978 | "cell_type": "heading", |
|
|||
979 | "level": 1, |
|
|||
980 | "metadata": {}, |
|
|||
981 | "source": [ |
|
|||
982 | "ProgressBar class" |
|
|||
983 | ] |
|
|||
984 | }, |
|
|||
985 | { |
|
|||
986 | "cell_type": "markdown", |
|
|||
987 | "metadata": {}, |
|
|||
988 | "source": [ |
|
|||
989 | "And finally, here is a progress bar *class* extracted from [PyMC](http://code.google.com/p/pymc/), which will work in regular Python as well as in the IPython Notebook" |
|
|||
990 | ] |
|
|||
991 | }, |
|
|||
992 | { |
|
|||
993 | "cell_type": "code", |
|
|||
994 | "collapsed": true, |
|
|||
995 | "input": [ |
|
|||
996 | "import sys, time\n", |
|
|||
997 | "\n", |
|
|||
998 | "class ProgressBar:\n", |
|
|||
999 | " def __init__(self, iterations):\n", |
|
|||
1000 | " self.iterations = iterations\n", |
|
|||
1001 | " self.prog_bar = '[]'\n", |
|
|||
1002 | " self.fill_char = '*'\n", |
|
|||
1003 | " self.width = 50\n", |
|
|||
1004 | " self.__update_amount(0)\n", |
|
|||
1005 | "\n", |
|
|||
1006 | " def animate(self, iter):\n", |
|
|||
1007 | " print '\\r', self,\n", |
|
|||
1008 | " sys.stdout.flush()\n", |
|
|||
1009 | " self.update_iteration(iter + 1)\n", |
|
|||
1010 | "\n", |
|
|||
1011 | " def update_iteration(self, elapsed_iter):\n", |
|
|||
1012 | " self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)\n", |
|
|||
1013 | " self.prog_bar += ' %d of %s complete' % (elapsed_iter, self.iterations)\n", |
|
|||
1014 | "\n", |
|
|||
1015 | " def __update_amount(self, new_amount):\n", |
|
|||
1016 | " percent_done = int(round((new_amount / 100.0) * 100.0))\n", |
|
|||
1017 | " all_full = self.width - 2\n", |
|
|||
1018 | " num_hashes = int(round((percent_done / 100.0) * all_full))\n", |
|
|||
1019 | " self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'\n", |
|
|||
1020 | " pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))\n", |
|
|||
1021 | " pct_string = '%d%%' % percent_done\n", |
|
|||
1022 | " self.prog_bar = self.prog_bar[0:pct_place] + \\\n", |
|
|||
1023 | " (pct_string + self.prog_bar[pct_place + len(pct_string):])\n", |
|
|||
1024 | "\n", |
|
|||
1025 | " def __str__(self):\n", |
|
|||
1026 | " return str(self.prog_bar)" |
|
|||
1027 | ], |
|
|||
1028 | "language": "python", |
|
|||
1029 | "metadata": {}, |
|
|||
1030 | "outputs": [], |
|
|||
1031 | "prompt_number": 3 |
|
|||
1032 | }, |
|
|||
1033 | { |
|
|||
1034 | "cell_type": "code", |
|
|||
1035 | "collapsed": false, |
|
|||
1036 | "input": [ |
|
|||
1037 | "p = ProgressBar(1000)\n", |
|
|||
1038 | "for i in range(1001):\n", |
|
|||
1039 | " time.sleep(0.002)\n", |
|
|||
1040 | " p.animate(i)" |
|
|||
1041 | ], |
|
|||
1042 | "language": "python", |
|
|||
1043 | "metadata": {}, |
|
|||
1044 | "outputs": [ |
|
|||
1045 | { |
|
|||
1046 | "output_type": "stream", |
|
|||
1047 | "stream": "stdout", |
|
|||
1048 | "text": [ |
|
|||
1049 | "\r", |
|
|||
1050 | "[ 0% ]" |
|
|||
1051 | ] |
|
|||
1052 | }, |
|
|||
1053 | { |
|
|||
1054 | "output_type": "stream", |
|
|||
1055 | "stream": "stdout", |
|
|||
1056 | "text": [ |
|
|||
1057 | " \r", |
|
|||
1058 | "[ 0% ] 1 of 1000 complete" |
|
|||
1059 | ] |
|
|||
1060 | }, |
|
|||
1061 | { |
|
|||
1062 | "output_type": "stream", |
|
|||
1063 | "stream": "stdout", |
|
|||
1064 | "text": [ |
|
|||
1065 | " \r", |
|
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1066 | "[ 0% ] 2 of 1000 complete" |
|
|||
1067 | ] |
|
|||
1068 | }, |
|
|||
1069 | { |
|
|||
1070 | "output_type": "stream", |
|
|||
1071 | "stream": "stdout", |
|
|||
1072 | "text": [ |
|
|||
1073 | " \r", |
|
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1074 | "[ 0% ] 3 of 1000 complete" |
|
|||
1075 | ] |
|
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1076 | }, |
|
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1077 | { |
|
|||
1078 | "output_type": "stream", |
|
|||
1079 | "stream": "stdout", |
|
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1080 | "text": [ |
|
|||
1081 | " \r", |
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1082 | "[ 0% ] 4 of 1000 complete" |
|
|||
1083 | ] |
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1084 | }, |
|
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1085 | { |
|
|||
1086 | "output_type": "stream", |
|
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1087 | "stream": "stdout", |
|
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1088 | "text": [ |
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1089 | " \r", |
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1090 | "[ 1% ] 5 of 1000 complete" |
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1091 | ] |
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1092 | }, |
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1093 | { |
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1094 | "output_type": "stream", |
|
|||
1095 | "stream": "stdout", |
|
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1096 | "text": [ |
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1097 | " \r", |
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1098 | "[ 1% ] 6 of 1000 complete" |
|
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1099 | ] |
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1100 | }, |
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1101 | { |
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1102 | "output_type": "stream", |
|
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1103 | "stream": "stdout", |
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1104 | "text": [ |
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1105 | " \r", |
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1106 | "[ 1% ] 7 of 1000 complete" |
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1107 | ] |
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1108 | }, |
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1109 | { |
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1110 | "output_type": "stream", |
|
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1111 | "stream": "stdout", |
|
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1112 | "text": [ |
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1113 | " \r", |
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1114 | "[ 1% ] 8 of 1000 complete" |
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1115 | ] |
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1116 | }, |
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1117 | { |
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1118 | "output_type": "stream", |
|
|||
1119 | "stream": "stdout", |
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1120 | "text": [ |
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1121 | " \r", |
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1123 | ] |
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1124 | }, |
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1125 | { |
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1126 | "output_type": "stream", |
|
|||
1127 | "stream": "stdout", |
|
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1128 | "text": [ |
|
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1129 | " \r", |
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1130 | "[ 1% ] 10 of 1000 complete" |
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1131 | ] |
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1132 | }, |
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1133 | { |
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1134 | "output_type": "stream", |
|
|||
1135 | "stream": "stdout", |
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1136 | "text": [ |
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1137 | " \r", |
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1138 | "[ 1% ] 11 of 1000 complete" |
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1139 | ] |
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1140 | }, |
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1141 | { |
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1142 | "output_type": "stream", |
|
|||
1143 | "stream": "stdout", |
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1144 | "text": [ |
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1145 | " \r", |
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1146 | "[ 1% ] 12 of 1000 complete" |
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1147 | ] |
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1148 | }, |
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1149 | { |
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1150 | "output_type": "stream", |
|
|||
1151 | "stream": "stdout", |
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1152 | "text": [ |
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1153 | " \r", |
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1154 | "[ 1% ] 13 of 1000 complete" |
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1155 | ] |
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1156 | }, |
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1157 | { |
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1158 | "output_type": "stream", |
|
|||
1159 | "stream": "stdout", |
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1160 | "text": [ |
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1161 | " \r", |
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1162 | "[ 1% ] 14 of 1000 complete" |
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1163 | ] |
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1164 | }, |
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1165 | { |
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1166 | "output_type": "stream", |
|
|||
1167 | "stream": "stdout", |
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1168 | "text": [ |
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1169 | " \r", |
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1170 | "[* 2% ] 15 of 1000 complete" |
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1171 | ] |
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1172 | }, |
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1173 | { |
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1174 | "output_type": "stream", |
|
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1175 | "stream": "stdout", |
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1176 | "text": [ |
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1177 | " \r", |
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1178 | "[* 2% ] 16 of 1000 complete" |
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1179 | ] |
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1180 | }, |
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1181 | { |
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1182 | "output_type": "stream", |
|
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1183 | "stream": "stdout", |
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1184 | "text": [ |
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1187 | ] |
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1188 | }, |
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1189 | { |
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1190 | "output_type": "stream", |
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1191 | "stream": "stdout", |
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1192 | "text": [ |
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1194 | "[* 2% ] 18 of 1000 complete" |
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1195 | ] |
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1196 | }, |
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1197 | { |
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1198 | "output_type": "stream", |
|
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1199 | "stream": "stdout", |
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1200 | "text": [ |
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1201 | " \r", |
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1202 | "[* 2% ] 19 of 1000 complete" |
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1203 | ] |
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1204 | }, |
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1205 | { |
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1206 | "output_type": "stream", |
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1207 | "stream": "stdout", |
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1208 | "text": [ |
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1209 | " \r", |
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1211 | ] |
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1212 | }, |
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1213 | { |
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1214 | "output_type": "stream", |
|
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1215 | "stream": "stdout", |
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1216 | "text": [ |
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1220 | }, |
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1221 | { |
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1222 | "output_type": "stream", |
|
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1223 | "stream": "stdout", |
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1224 | "text": [ |
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1225 | " \r", |
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1227 | ] |
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1228 | }, |
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1229 | { |
|
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1230 | "output_type": "stream", |
|
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1231 | "stream": "stdout", |
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1232 | "text": [ |
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1234 | "[* 2% ] 23 of 1000 complete" |
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1235 | ] |
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1236 | }, |
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1237 | { |
|
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1238 | "output_type": "stream", |
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1351 | "stream": "stdout", |
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1359 | "stream": "stdout", |
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1367 | "stream": "stdout", |
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1375 | "stream": "stdout", |
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1383 | "stream": "stdout", |
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1391 | "stream": "stdout", |
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1399 | "stream": "stdout", |
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1407 | "stream": "stdout", |
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1415 | "stream": "stdout", |
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1423 | "stream": "stdout", |
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1431 | "stream": "stdout", |
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1436 | }, |
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|
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1439 | "stream": "stdout", |
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|
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1447 | "stream": "stdout", |
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|
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1455 | "stream": "stdout", |
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1460 | }, |
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|
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1463 | "stream": "stdout", |
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1469 | { |
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1470 | "output_type": "stream", |
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1471 | "stream": "stdout", |
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1479 | "stream": "stdout", |
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1487 | "stream": "stdout", |
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1495 | "stream": "stdout", |
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1500 | }, |
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1503 | "stream": "stdout", |
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1511 | "stream": "stdout", |
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1519 | "stream": "stdout", |
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1524 | }, |
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1527 | "stream": "stdout", |
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1535 | "stream": "stdout", |
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1543 | "stream": "stdout", |
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1551 | "stream": "stdout", |
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1559 | "stream": "stdout", |
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1567 | "stream": "stdout", |
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1575 | "stream": "stdout", |
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1580 | }, |
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1581 | { |
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1582 | "output_type": "stream", |
|
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1583 | "stream": "stdout", |
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1588 | }, |
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1589 | { |
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1590 | "output_type": "stream", |
|
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1591 | "stream": "stdout", |
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1597 | { |
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|
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1599 | "stream": "stdout", |
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1600 | "text": [ |
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|
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1607 | "stream": "stdout", |
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1612 | }, |
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|
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1615 | "stream": "stdout", |
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|
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1623 | "stream": "stdout", |
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|
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1631 | "stream": "stdout", |
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|
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1639 | "stream": "stdout", |
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|
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1647 | "stream": "stdout", |
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|
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1655 | "stream": "stdout", |
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1660 | }, |
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1662 | "output_type": "stream", |
|
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1663 | "stream": "stdout", |
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1670 | "output_type": "stream", |
|
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1671 | "stream": "stdout", |
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|
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1679 | "stream": "stdout", |
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1686 | "output_type": "stream", |
|
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1687 | "stream": "stdout", |
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1694 | "output_type": "stream", |
|
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1695 | "stream": "stdout", |
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1700 | }, |
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|
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1703 | "stream": "stdout", |
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|||
1708 | }, |
|
|||
1709 | { |
|
|||
1710 | "output_type": "stream", |
|
|||
1711 | "stream": "stdout", |
|
|||
1712 | "text": [ |
|
|||
1713 | " \r", |
|
|||
1714 | "[**** 8% ] 83 of 1000 complete" |
|
|||
1715 | ] |
|
|||
1716 | }, |
|
|||
1717 | { |
|
|||
1718 | "output_type": "stream", |
|
|||
1719 | "stream": "stdout", |
|
|||
1720 | "text": [ |
|
|||
1721 | " \r", |
|
|||
1722 | "[**** 8% ] 84 of 1000 complete" |
|
|||
1723 | ] |
|
|||
1724 | }, |
|
|||
1725 | { |
|
|||
1726 | "output_type": "stream", |
|
|||
1727 | "stream": "stdout", |
|
|||
1728 | "text": [ |
|
|||
1729 | " \r", |
|
|||
1730 | "[**** 9% ] 85 of 1000 complete" |
|
|||
1731 | ] |
|
|||
1732 | }, |
|
|||
1733 | { |
|
|||
1734 | "output_type": "stream", |
|
|||
1735 | "stream": "stdout", |
|
|||
1736 | "text": [ |
|
|||
1737 | " \r", |
|
|||
1738 | "[**** 9% ] 86 of 1000 complete" |
|
|||
1739 | ] |
|
|||
1740 | }, |
|
|||
1741 | { |
|
|||
1742 | "output_type": "stream", |
|
|||
1743 | "stream": "stdout", |
|
|||
1744 | "text": [ |
|
|||
1745 | " \r", |
|
|||
1746 | "[**** 9% ] 87 of 1000 complete" |
|
|||
1747 | ] |
|
|||
1748 | }, |
|
|||
1749 | { |
|
|||
1750 | "output_type": "stream", |
|
|||
1751 | "stream": "stdout", |
|
|||
1752 | "text": [ |
|
|||
1753 | " \r", |
|
|||
1754 | "[**** 9% ] 88 of 1000 complete" |
|
|||
1755 | ] |
|
|||
1756 | }, |
|
|||
1757 | { |
|
|||
1758 | "output_type": "stream", |
|
|||
1759 | "stream": "stdout", |
|
|||
1760 | "text": [ |
|
|||
1761 | " \r", |
|
|||
1762 | "[**** 9% ] 89 of 1000 complete" |
|
|||
1763 | ] |
|
|||
1764 | }, |
|
|||
1765 | { |
|
|||
1766 | "output_type": "stream", |
|
|||
1767 | "stream": "stdout", |
|
|||
1768 | "text": [ |
|
|||
1769 | " \r", |
|
|||
1770 | "[**** 9% ] 90 of 1000 complete" |
|
|||
1771 | ] |
|
|||
1772 | }, |
|
|||
1773 | { |
|
|||
1774 | "output_type": "stream", |
|
|||
1775 | "stream": "stdout", |
|
|||
1776 | "text": [ |
|
|||
1777 | " \r", |
|
|||
1778 | "[**** 9% ] 91 of 1000 complete" |
|
|||
1779 | ] |
|
|||
1780 | }, |
|
|||
1781 | { |
|
|||
1782 | "output_type": "stream", |
|
|||
1783 | "stream": "stdout", |
|
|||
1784 | "text": [ |
|
|||
1785 | " \r", |
|
|||
1786 | "[**** 9% ] 92 of 1000 complete" |
|
|||
1787 | ] |
|
|||
1788 | }, |
|
|||
1789 | { |
|
|||
1790 | "output_type": "stream", |
|
|||
1791 | "stream": "stdout", |
|
|||
1792 | "text": [ |
|
|||
1793 | " \r", |
|
|||
1794 | "[**** 9% ] 93 of 1000 complete" |
|
|||
1795 | ] |
|
|||
1796 | }, |
|
|||
1797 | { |
|
|||
1798 | "output_type": "stream", |
|
|||
1799 | "stream": "stdout", |
|
|||
1800 | "text": [ |
|
|||
1801 | " \r", |
|
|||
1802 | "[**** 9% ] 94 of 1000 complete" |
|
|||
1803 | ] |
|
|||
1804 | }, |
|
|||
1805 | { |
|
|||
1806 | "output_type": "stream", |
|
|||
1807 | "stream": "stdout", |
|
|||
1808 | "text": [ |
|
|||
1809 | " \r", |
|
|||
1810 | "[***** 10% ] 95 of 1000 complete" |
|
|||
1811 | ] |
|
|||
1812 | }, |
|
|||
1813 | { |
|
|||
1814 | "output_type": "stream", |
|
|||
1815 | "stream": "stdout", |
|
|||
1816 | "text": [ |
|
|||
1817 | " \r", |
|
|||
1818 | "[***** 10% ] 96 of 1000 complete" |
|
|||
1819 | ] |
|
|||
1820 | }, |
|
|||
1821 | { |
|
|||
1822 | "output_type": "stream", |
|
|||
1823 | "stream": "stdout", |
|
|||
1824 | "text": [ |
|
|||
1825 | " \r", |
|
|||
1826 | "[***** 10% ] 97 of 1000 complete" |
|
|||
1827 | ] |
|
|||
1828 | }, |
|
|||
1829 | { |
|
|||
1830 | "output_type": "stream", |
|
|||
1831 | "stream": "stdout", |
|
|||
1832 | "text": [ |
|
|||
1833 | " \r", |
|
|||
1834 | "[***** 10% ] 98 of 1000 complete" |
|
|||
1835 | ] |
|
|||
1836 | }, |
|
|||
1837 | { |
|
|||
1838 | "output_type": "stream", |
|
|||
1839 | "stream": "stdout", |
|
|||
1840 | "text": [ |
|
|||
1841 | " \r", |
|
|||
1842 | "[***** 10% ] 99 of 1000 complete" |
|
|||
1843 | ] |
|
|||
1844 | }, |
|
|||
1845 | { |
|
|||
1846 | "output_type": "stream", |
|
|||
1847 | "stream": "stdout", |
|
|||
1848 | "text": [ |
|
|||
1849 | " \r", |
|
|||
1850 | "[***** 10% ] 100 of 1000 complete" |
|
|||
1851 | ] |
|
|||
1852 | }, |
|
|||
1853 | { |
|
|||
1854 | "output_type": "stream", |
|
|||
1855 | "stream": "stdout", |
|
|||
1856 | "text": [ |
|
|||
1857 | " \r", |
|
|||
1858 | "[***** 10% ] 101 of 1000 complete" |
|
|||
1859 | ] |
|
|||
1860 | }, |
|
|||
1861 | { |
|
|||
1862 | "output_type": "stream", |
|
|||
1863 | "stream": "stdout", |
|
|||
1864 | "text": [ |
|
|||
1865 | " \r", |
|
|||
1866 | "[***** 10% ] 102 of 1000 complete" |
|
|||
1867 | ] |
|
|||
1868 | }, |
|
|||
1869 | { |
|
|||
1870 | "output_type": "stream", |
|
|||
1871 | "stream": "stdout", |
|
|||
1872 | "text": [ |
|
|||
1873 | " \r", |
|
|||
1874 | "[***** 10% ] 103 of 1000 complete" |
|
|||
1875 | ] |
|
|||
1876 | }, |
|
|||
1877 | { |
|
|||
1878 | "output_type": "stream", |
|
|||
1879 | "stream": "stdout", |
|
|||
1880 | "text": [ |
|
|||
1881 | " \r", |
|
|||
1882 | "[***** 10% ] 104 of 1000 complete" |
|
|||
1883 | ] |
|
|||
1884 | }, |
|
|||
1885 | { |
|
|||
1886 | "output_type": "stream", |
|
|||
1887 | "stream": "stdout", |
|
|||
1888 | "text": [ |
|
|||
1889 | " \r", |
|
|||
1890 | "[***** 11% ] 105 of 1000 complete" |
|
|||
1891 | ] |
|
|||
1892 | }, |
|
|||
1893 | { |
|
|||
1894 | "output_type": "stream", |
|
|||
1895 | "stream": "stdout", |
|
|||
1896 | "text": [ |
|
|||
1897 | " \r", |
|
|||
1898 | "[***** 11% ] 106 of 1000 complete" |
|
|||
1899 | ] |
|
|||
1900 | }, |
|
|||
1901 | { |
|
|||
1902 | "output_type": "stream", |
|
|||
1903 | "stream": "stdout", |
|
|||
1904 | "text": [ |
|
|||
1905 | " \r", |
|
|||
1906 | "[***** 11% ] 107 of 1000 complete" |
|
|||
1907 | ] |
|
|||
1908 | }, |
|
|||
1909 | { |
|
|||
1910 | "output_type": "stream", |
|
|||
1911 | "stream": "stdout", |
|
|||
1912 | "text": [ |
|
|||
1913 | " \r", |
|
|||
1914 | "[***** 11% ] 108 of 1000 complete" |
|
|||
1915 | ] |
|
|||
1916 | }, |
|
|||
1917 | { |
|
|||
1918 | "output_type": "stream", |
|
|||
1919 | "stream": "stdout", |
|
|||
1920 | "text": [ |
|
|||
1921 | " \r", |
|
|||
1922 | "[***** 11% ] 109 of 1000 complete" |
|
|||
1923 | ] |
|
|||
1924 | }, |
|
|||
1925 | { |
|
|||
1926 | "output_type": "stream", |
|
|||
1927 | "stream": "stdout", |
|
|||
1928 | "text": [ |
|
|||
1929 | " \r", |
|
|||
1930 | "[***** 11% ] 110 of 1000 complete" |
|
|||
1931 | ] |
|
|||
1932 | }, |
|
|||
1933 | { |
|
|||
1934 | "output_type": "stream", |
|
|||
1935 | "stream": "stdout", |
|
|||
1936 | "text": [ |
|
|||
1937 | " \r", |
|
|||
1938 | "[***** 11% ] 111 of 1000 complete" |
|
|||
1939 | ] |
|
|||
1940 | }, |
|
|||
1941 | { |
|
|||
1942 | "output_type": "stream", |
|
|||
1943 | "stream": "stdout", |
|
|||
1944 | "text": [ |
|
|||
1945 | " \r", |
|
|||
1946 | "[***** 11% ] 112 of 1000 complete" |
|
|||
1947 | ] |
|
|||
1948 | }, |
|
|||
1949 | { |
|
|||
1950 | "output_type": "stream", |
|
|||
1951 | "stream": "stdout", |
|
|||
1952 | "text": [ |
|
|||
1953 | " \r", |
|
|||
1954 | "[***** 11% ] 113 of 1000 complete" |
|
|||
1955 | ] |
|
|||
1956 | }, |
|
|||
1957 | { |
|
|||
1958 | "output_type": "stream", |
|
|||
1959 | "stream": "stdout", |
|
|||
1960 | "text": [ |
|
|||
1961 | " \r", |
|
|||
1962 | "[***** 11% ] 114 of 1000 complete" |
|
|||
1963 | ] |
|
|||
1964 | }, |
|
|||
1965 | { |
|
|||
1966 | "output_type": "stream", |
|
|||
1967 | "stream": "stdout", |
|
|||
1968 | "text": [ |
|
|||
1969 | " \r", |
|
|||
1970 | "[****** 12% ] 115 of 1000 complete" |
|
|||
1971 | ] |
|
|||
1972 | }, |
|
|||
1973 | { |
|
|||
1974 | "output_type": "stream", |
|
|||
1975 | "stream": "stdout", |
|
|||
1976 | "text": [ |
|
|||
1977 | " \r", |
|
|||
1978 | "[****** 12% ] 116 of 1000 complete" |
|
|||
1979 | ] |
|
|||
1980 | }, |
|
|||
1981 | { |
|
|||
1982 | "output_type": "stream", |
|
|||
1983 | "stream": "stdout", |
|
|||
1984 | "text": [ |
|
|||
1985 | " \r", |
|
|||
1986 | "[****** 12% ] 117 of 1000 complete" |
|
|||
1987 | ] |
|
|||
1988 | }, |
|
|||
1989 | { |
|
|||
1990 | "output_type": "stream", |
|
|||
1991 | "stream": "stdout", |
|
|||
1992 | "text": [ |
|
|||
1993 | " \r", |
|
|||
1994 | "[****** 12% ] 118 of 1000 complete" |
|
|||
1995 | ] |
|
|||
1996 | }, |
|
|||
1997 | { |
|
|||
1998 | "output_type": "stream", |
|
|||
1999 | "stream": "stdout", |
|
|||
2000 | "text": [ |
|
|||
2001 | " \r", |
|
|||
2002 | "[****** 12% ] 119 of 1000 complete" |
|
|||
2003 | ] |
|
|||
2004 | }, |
|
|||
2005 | { |
|
|||
2006 | "output_type": "stream", |
|
|||
2007 | "stream": "stdout", |
|
|||
2008 | "text": [ |
|
|||
2009 | " \r", |
|
|||
2010 | "[****** 12% ] 120 of 1000 complete" |
|
|||
2011 | ] |
|
|||
2012 | }, |
|
|||
2013 | { |
|
|||
2014 | "output_type": "stream", |
|
|||
2015 | "stream": "stdout", |
|
|||
2016 | "text": [ |
|
|||
2017 | " \r", |
|
|||
2018 | "[****** 12% ] 121 of 1000 complete" |
|
|||
2019 | ] |
|
|||
2020 | }, |
|
|||
2021 | { |
|
|||
2022 | "output_type": "stream", |
|
|||
2023 | "stream": "stdout", |
|
|||
2024 | "text": [ |
|
|||
2025 | " \r", |
|
|||
2026 | "[****** 12% ] 122 of 1000 complete" |
|
|||
2027 | ] |
|
|||
2028 | }, |
|
|||
2029 | { |
|
|||
2030 | "output_type": "stream", |
|
|||
2031 | "stream": "stdout", |
|
|||
2032 | "text": [ |
|
|||
2033 | " \r", |
|
|||
2034 | "[****** 12% ] 123 of 1000 complete" |
|
|||
2035 | ] |
|
|||
2036 | }, |
|
|||
2037 | { |
|
|||
2038 | "output_type": "stream", |
|
|||
2039 | "stream": "stdout", |
|
|||
2040 | "text": [ |
|
|||
2041 | " \r", |
|
|||
2042 | "[****** 12% ] 124 of 1000 complete" |
|
|||
2043 | ] |
|
|||
2044 | }, |
|
|||
2045 | { |
|
|||
2046 | "output_type": "stream", |
|
|||
2047 | "stream": "stdout", |
|
|||
2048 | "text": [ |
|
|||
2049 | " \r", |
|
|||
2050 | "[****** 13% ] 125 of 1000 complete" |
|
|||
2051 | ] |
|
|||
2052 | }, |
|
|||
2053 | { |
|
|||
2054 | "output_type": "stream", |
|
|||
2055 | "stream": "stdout", |
|
|||
2056 | "text": [ |
|
|||
2057 | " \r", |
|
|||
2058 | "[****** 13% ] 126 of 1000 complete" |
|
|||
2059 | ] |
|
|||
2060 | }, |
|
|||
2061 | { |
|
|||
2062 | "output_type": "stream", |
|
|||
2063 | "stream": "stdout", |
|
|||
2064 | "text": [ |
|
|||
2065 | " \r", |
|
|||
2066 | "[****** 13% ] 127 of 1000 complete" |
|
|||
2067 | ] |
|
|||
2068 | }, |
|
|||
2069 | { |
|
|||
2070 | "output_type": "stream", |
|
|||
2071 | "stream": "stdout", |
|
|||
2072 | "text": [ |
|
|||
2073 | " \r", |
|
|||
2074 | "[****** 13% ] 128 of 1000 complete" |
|
|||
2075 | ] |
|
|||
2076 | }, |
|
|||
2077 | { |
|
|||
2078 | "output_type": "stream", |
|
|||
2079 | "stream": "stdout", |
|
|||
2080 | "text": [ |
|
|||
2081 | " \r", |
|
|||
2082 | "[****** 13% ] 129 of 1000 complete" |
|
|||
2083 | ] |
|
|||
2084 | }, |
|
|||
2085 | { |
|
|||
2086 | "output_type": "stream", |
|
|||
2087 | "stream": "stdout", |
|
|||
2088 | "text": [ |
|
|||
2089 | " \r", |
|
|||
2090 | "[****** 13% ] 130 of 1000 complete" |
|
|||
2091 | ] |
|
|||
2092 | }, |
|
|||
2093 | { |
|
|||
2094 | "output_type": "stream", |
|
|||
2095 | "stream": "stdout", |
|
|||
2096 | "text": [ |
|
|||
2097 | " \r", |
|
|||
2098 | "[****** 13% ] 131 of 1000 complete" |
|
|||
2099 | ] |
|
|||
2100 | }, |
|
|||
2101 | { |
|
|||
2102 | "output_type": "stream", |
|
|||
2103 | "stream": "stdout", |
|
|||
2104 | "text": [ |
|
|||
2105 | " \r", |
|
|||
2106 | "[****** 13% ] 132 of 1000 complete" |
|
|||
2107 | ] |
|
|||
2108 | }, |
|
|||
2109 | { |
|
|||
2110 | "output_type": "stream", |
|
|||
2111 | "stream": "stdout", |
|
|||
2112 | "text": [ |
|
|||
2113 | " \r", |
|
|||
2114 | "[****** 13% ] 133 of 1000 complete" |
|
|||
2115 | ] |
|
|||
2116 | }, |
|
|||
2117 | { |
|
|||
2118 | "output_type": "stream", |
|
|||
2119 | "stream": "stdout", |
|
|||
2120 | "text": [ |
|
|||
2121 | " \r", |
|
|||
2122 | "[****** 13% ] 134 of 1000 complete" |
|
|||
2123 | ] |
|
|||
2124 | }, |
|
|||
2125 | { |
|
|||
2126 | "output_type": "stream", |
|
|||
2127 | "stream": "stdout", |
|
|||
2128 | "text": [ |
|
|||
2129 | " \r", |
|
|||
2130 | "[******* 14% ] 135 of 1000 complete" |
|
|||
2131 | ] |
|
|||
2132 | }, |
|
|||
2133 | { |
|
|||
2134 | "output_type": "stream", |
|
|||
2135 | "stream": "stdout", |
|
|||
2136 | "text": [ |
|
|||
2137 | " \r", |
|
|||
2138 | "[******* 14% ] 136 of 1000 complete" |
|
|||
2139 | ] |
|
|||
2140 | }, |
|
|||
2141 | { |
|
|||
2142 | "output_type": "stream", |
|
|||
2143 | "stream": "stdout", |
|
|||
2144 | "text": [ |
|
|||
2145 | " \r", |
|
|||
2146 | "[******* 14% ] 137 of 1000 complete" |
|
|||
2147 | ] |
|
|||
2148 | }, |
|
|||
2149 | { |
|
|||
2150 | "output_type": "stream", |
|
|||
2151 | "stream": "stdout", |
|
|||
2152 | "text": [ |
|
|||
2153 | " \r", |
|
|||
2154 | "[******* 14% ] 138 of 1000 complete" |
|
|||
2155 | ] |
|
|||
2156 | }, |
|
|||
2157 | { |
|
|||
2158 | "output_type": "stream", |
|
|||
2159 | "stream": "stdout", |
|
|||
2160 | "text": [ |
|
|||
2161 | " \r", |
|
|||
2162 | "[******* 14% ] 139 of 1000 complete" |
|
|||
2163 | ] |
|
|||
2164 | }, |
|
|||
2165 | { |
|
|||
2166 | "output_type": "stream", |
|
|||
2167 | "stream": "stdout", |
|
|||
2168 | "text": [ |
|
|||
2169 | " \r", |
|
|||
2170 | "[******* 14% ] 140 of 1000 complete" |
|
|||
2171 | ] |
|
|||
2172 | }, |
|
|||
2173 | { |
|
|||
2174 | "output_type": "stream", |
|
|||
2175 | "stream": "stdout", |
|
|||
2176 | "text": [ |
|
|||
2177 | " \r", |
|
|||
2178 | "[******* 14% ] 141 of 1000 complete" |
|
|||
2179 | ] |
|
|||
2180 | }, |
|
|||
2181 | { |
|
|||
2182 | "output_type": "stream", |
|
|||
2183 | "stream": "stdout", |
|
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2191 | "stream": "stdout", |
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2199 | "stream": "stdout", |
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2223 | "stream": "stdout", |
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2279 | "stream": "stdout", |
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2287 | "stream": "stdout", |
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2295 | "stream": "stdout", |
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2303 | "stream": "stdout", |
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|
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2311 | "stream": "stdout", |
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2316 | }, |
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|
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2319 | "stream": "stdout", |
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2324 | }, |
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2325 | { |
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2327 | "stream": "stdout", |
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2332 | }, |
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|
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2335 | "stream": "stdout", |
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2340 | }, |
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2341 | { |
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|
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2343 | "stream": "stdout", |
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2348 | }, |
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|
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2351 | "stream": "stdout", |
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2356 | }, |
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|
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2359 | "stream": "stdout", |
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2364 | }, |
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|
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2367 | "stream": "stdout", |
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|
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2372 | }, |
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2373 | { |
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|
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2375 | "stream": "stdout", |
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|
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|
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2380 | }, |
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2381 | { |
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|
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2383 | "stream": "stdout", |
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|
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2388 | }, |
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2389 | { |
|
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|
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2391 | "stream": "stdout", |
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|
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2396 | }, |
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|
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2399 | "stream": "stdout", |
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|
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2404 | }, |
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2405 | { |
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|
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2407 | "stream": "stdout", |
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|
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|
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2412 | }, |
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2413 | { |
|
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|
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2415 | "stream": "stdout", |
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|
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|
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2420 | }, |
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2421 | { |
|
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|
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2423 | "stream": "stdout", |
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|
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|
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2427 | ] |
|
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2428 | }, |
|
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2429 | { |
|
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|
|||
2431 | "stream": "stdout", |
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|
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2436 | }, |
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2437 | { |
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|
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2439 | "stream": "stdout", |
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|
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2445 | { |
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|
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2447 | "stream": "stdout", |
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2453 | { |
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|
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2455 | "stream": "stdout", |
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|
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2460 | }, |
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2461 | { |
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|
|||
2463 | "stream": "stdout", |
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|
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|
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2468 | }, |
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2469 | { |
|
|||
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|
|||
2471 | "stream": "stdout", |
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|
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|
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2476 | }, |
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2477 | { |
|
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|
|||
2479 | "stream": "stdout", |
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|
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|
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|
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2484 | }, |
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2485 | { |
|
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|
|||
2487 | "stream": "stdout", |
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|
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2492 | }, |
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2493 | { |
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|
|||
2495 | "stream": "stdout", |
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|
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2500 | }, |
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2501 | { |
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|
|||
2503 | "stream": "stdout", |
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|
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2508 | }, |
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2509 | { |
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|
|||
2511 | "stream": "stdout", |
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|
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2516 | }, |
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2517 | { |
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|||
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|
|||
2519 | "stream": "stdout", |
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|
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|
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2523 | ] |
|
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2524 | }, |
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2525 | { |
|
|||
2526 | "output_type": "stream", |
|
|||
2527 | "stream": "stdout", |
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2528 | "text": [ |
|
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|
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2531 | ] |
|
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2532 | }, |
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2533 | { |
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2534 | "output_type": "stream", |
|
|||
2535 | "stream": "stdout", |
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|
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2540 | }, |
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2541 | { |
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|
|||
2543 | "stream": "stdout", |
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|
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2548 | }, |
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2549 | { |
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|
|||
2551 | "stream": "stdout", |
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|
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|
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|
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2556 | }, |
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2557 | { |
|
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|
|||
2559 | "stream": "stdout", |
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2560 | "text": [ |
|
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|
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|
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2564 | }, |
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2565 | { |
|
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|
|||
2567 | "stream": "stdout", |
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|
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|
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2572 | }, |
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2573 | { |
|
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|
|||
2575 | "stream": "stdout", |
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2576 | "text": [ |
|
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|
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2579 | ] |
|
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2580 | }, |
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2581 | { |
|
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|
|||
2583 | "stream": "stdout", |
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|
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|
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2588 | }, |
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2589 | { |
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2590 | "output_type": "stream", |
|
|||
2591 | "stream": "stdout", |
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|
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|
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2596 | }, |
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2597 | { |
|
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|
|||
2599 | "stream": "stdout", |
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|
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|
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2603 | ] |
|
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2604 | }, |
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2605 | { |
|
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|
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2607 | "stream": "stdout", |
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|
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|
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|
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2612 | }, |
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2613 | { |
|
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|
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2615 | "stream": "stdout", |
|
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|
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|
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|
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2620 | }, |
|
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2621 | { |
|
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2622 | "output_type": "stream", |
|
|||
2623 | "stream": "stdout", |
|
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2624 | "text": [ |
|
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|
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|
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2628 | }, |
|
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2629 | { |
|
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2630 | "output_type": "stream", |
|
|||
2631 | "stream": "stdout", |
|
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|
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|
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|
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2636 | }, |
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2637 | { |
|
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|
|||
2639 | "stream": "stdout", |
|
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2640 | "text": [ |
|
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|
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|
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2643 | ] |
|
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2644 | }, |
|
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2645 | { |
|
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|
|||
2647 | "stream": "stdout", |
|
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2648 | "text": [ |
|
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|
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|
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|
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2652 | }, |
|
|||
2653 | { |
|
|||
2654 | "output_type": "stream", |
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2655 | "stream": "stdout", |
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2659 | ] |
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2660 | }, |
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|
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2663 | "stream": "stdout", |
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2664 | "text": [ |
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2665 | " \r", |
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2667 | ] |
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2668 | }, |
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2669 | { |
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2671 | "stream": "stdout", |
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2672 | "text": [ |
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2673 | " \r", |
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2675 | ] |
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2676 | }, |
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2677 | { |
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|
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2679 | "stream": "stdout", |
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2680 | "text": [ |
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2683 | ] |
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2684 | }, |
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2685 | { |
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2686 | "output_type": "stream", |
|
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2687 | "stream": "stdout", |
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2692 | }, |
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2693 | { |
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|
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2695 | "stream": "stdout", |
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2700 | }, |
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2701 | { |
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2703 | "stream": "stdout", |
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2707 | ] |
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2708 | }, |
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2709 | { |
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2711 | "stream": "stdout", |
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2716 | }, |
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2717 | { |
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|
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2719 | "stream": "stdout", |
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2723 | ] |
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2724 | }, |
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2725 | { |
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|
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2727 | "stream": "stdout", |
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2728 | "text": [ |
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2729 | " \r", |
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2731 | ] |
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2732 | }, |
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2733 | { |
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2735 | "stream": "stdout", |
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2740 | }, |
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2741 | { |
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2743 | "stream": "stdout", |
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2748 | }, |
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2749 | { |
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2751 | "stream": "stdout", |
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2756 | }, |
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|
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2759 | "stream": "stdout", |
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2764 | }, |
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2765 | { |
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|
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2767 | "stream": "stdout", |
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2772 | }, |
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2773 | { |
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|
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2775 | "stream": "stdout", |
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2780 | }, |
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2781 | { |
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|
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2783 | "stream": "stdout", |
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2788 | }, |
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2789 | { |
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|
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2791 | "stream": "stdout", |
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2796 | }, |
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2797 | { |
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|
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2799 | "stream": "stdout", |
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2800 | "text": [ |
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|
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|
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2804 | }, |
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2805 | { |
|
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|
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2807 | "stream": "stdout", |
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|
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|
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2812 | }, |
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2813 | { |
|
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|
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2815 | "stream": "stdout", |
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|
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|
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2820 | }, |
|
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2821 | { |
|
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|
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2823 | "stream": "stdout", |
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|
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2827 | ] |
|
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2828 | }, |
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2829 | { |
|
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|
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2831 | "stream": "stdout", |
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|
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|
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2836 | }, |
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|
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2839 | "stream": "stdout", |
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|
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|
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2845 | { |
|
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|
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2847 | "stream": "stdout", |
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|
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|
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|
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|
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2855 | "stream": "stdout", |
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|
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2860 | }, |
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|
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2863 | "stream": "stdout", |
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|
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|
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2868 | }, |
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2869 | { |
|
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|
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2871 | "stream": "stdout", |
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|
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|
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2876 | }, |
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2877 | { |
|
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|
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2879 | "stream": "stdout", |
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|
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|
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2885 | { |
|
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|
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2887 | "stream": "stdout", |
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|
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|
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2892 | }, |
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|
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|
|||
2895 | "stream": "stdout", |
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|
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|
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2900 | }, |
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2901 | { |
|
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|
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2903 | "stream": "stdout", |
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|
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|
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|
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2909 | { |
|
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|
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2911 | "stream": "stdout", |
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|
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|
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2916 | }, |
|
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2917 | { |
|
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|
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2919 | "stream": "stdout", |
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|
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|
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2924 | }, |
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2925 | { |
|
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|
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2927 | "stream": "stdout", |
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|
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|
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2932 | }, |
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2933 | { |
|
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|
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2935 | "stream": "stdout", |
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|
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|
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2941 | { |
|
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|
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2943 | "stream": "stdout", |
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|
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|
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2948 | }, |
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2949 | { |
|
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|
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2951 | "stream": "stdout", |
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|
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|
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2956 | }, |
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2957 | { |
|
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|
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2959 | "stream": "stdout", |
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|
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|
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|
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2964 | }, |
|
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2965 | { |
|
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2966 | "output_type": "stream", |
|
|||
2967 | "stream": "stdout", |
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|
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|
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2972 | }, |
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2973 | { |
|
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|
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2975 | "stream": "stdout", |
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|
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2979 | ] |
|
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2980 | }, |
|
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2981 | { |
|
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2982 | "output_type": "stream", |
|
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2983 | "stream": "stdout", |
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|
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|
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2988 | }, |
|
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2989 | { |
|
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2990 | "output_type": "stream", |
|
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2991 | "stream": "stdout", |
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2992 | "text": [ |
|
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|
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2995 | ] |
|
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2996 | }, |
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2997 | { |
|
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2998 | "output_type": "stream", |
|
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2999 | "stream": "stdout", |
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3000 | "text": [ |
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|
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|
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3004 | }, |
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3005 | { |
|
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3006 | "output_type": "stream", |
|
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3007 | "stream": "stdout", |
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|
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|
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3011 | ] |
|
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3012 | }, |
|
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3013 | { |
|
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3014 | "output_type": "stream", |
|
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3015 | "stream": "stdout", |
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|
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|
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|
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3020 | }, |
|
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3021 | { |
|
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|
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3023 | "stream": "stdout", |
|
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|
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|
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|
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3028 | }, |
|
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3029 | { |
|
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3030 | "output_type": "stream", |
|
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3031 | "stream": "stdout", |
|
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|
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|
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|
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3036 | }, |
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3037 | { |
|
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|
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3039 | "stream": "stdout", |
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|
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|
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|
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3044 | }, |
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3045 | { |
|
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|
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3047 | "stream": "stdout", |
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|
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|
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|
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|
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|
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3055 | "stream": "stdout", |
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|
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|
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3060 | }, |
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3061 | { |
|
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|
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3063 | "stream": "stdout", |
|
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|
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|
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|
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3068 | }, |
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3069 | { |
|
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3070 | "output_type": "stream", |
|
|||
3071 | "stream": "stdout", |
|
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|
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|
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|
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3076 | }, |
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|
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|
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3079 | "stream": "stdout", |
|
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|
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|
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|
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|
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3085 | { |
|
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|
|||
3087 | "stream": "stdout", |
|
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|
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|
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|
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3092 | }, |
|
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|
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|
|||
3095 | "stream": "stdout", |
|
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|
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|
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|
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3100 | }, |
|
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3101 | { |
|
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|
|||
3103 | "stream": "stdout", |
|
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|
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|
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|
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3108 | }, |
|
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|
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|
|||
3111 | "stream": "stdout", |
|
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3112 | "text": [ |
|
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|
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3115 | ] |
|
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3116 | }, |
|
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3117 | { |
|
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3118 | "output_type": "stream", |
|
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3119 | "stream": "stdout", |
|
|||
3120 | "text": [ |
|
|||
3121 | " \r", |
|
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|
|||
3123 | ] |
|
|||
3124 | }, |
|
|||
3125 | { |
|
|||
3126 | "output_type": "stream", |
|
|||
3127 | "stream": "stdout", |
|
|||
3128 | "text": [ |
|
|||
3129 | " \r", |
|
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3130 | "[************ 26% ] 260 of 1000 complete" |
|
|||
3131 | ] |
|
|||
3132 | }, |
|
|||
3133 | { |
|
|||
3134 | "output_type": "stream", |
|
|||
3135 | "stream": "stdout", |
|
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3136 | "text": [ |
|
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3137 | " \r", |
|
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3138 | "[************ 26% ] 261 of 1000 complete" |
|
|||
3139 | ] |
|
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3140 | }, |
|
|||
3141 | { |
|
|||
3142 | "output_type": "stream", |
|
|||
3143 | "stream": "stdout", |
|
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3144 | "text": [ |
|
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3145 | " \r", |
|
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3146 | "[************ 26% ] 262 of 1000 complete" |
|
|||
3147 | ] |
|
|||
3148 | }, |
|
|||
3149 | { |
|
|||
3150 | "output_type": "stream", |
|
|||
3151 | "stream": "stdout", |
|
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3152 | "text": [ |
|
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3153 | " \r", |
|
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3154 | "[************ 26% ] 263 of 1000 complete" |
|
|||
3155 | ] |
|
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3156 | }, |
|
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3157 | { |
|
|||
3158 | "output_type": "stream", |
|
|||
3159 | "stream": "stdout", |
|
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3160 | "text": [ |
|
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3161 | " \r", |
|
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3162 | "[************ 26% ] 264 of 1000 complete" |
|
|||
3163 | ] |
|
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3164 | }, |
|
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3165 | { |
|
|||
3166 | "output_type": "stream", |
|
|||
3167 | "stream": "stdout", |
|
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3168 | "text": [ |
|
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3169 | " \r", |
|
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|
|||
3171 | ] |
|
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3172 | }, |
|
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3173 | { |
|
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3174 | "output_type": "stream", |
|
|||
3175 | "stream": "stdout", |
|
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3176 | "text": [ |
|
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3177 | " \r", |
|
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|
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3179 | ] |
|
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3180 | }, |
|
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3181 | { |
|
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3182 | "output_type": "stream", |
|
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3183 | "stream": "stdout", |
|
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3184 | "text": [ |
|
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3185 | " \r", |
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|
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3187 | ] |
|
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3188 | }, |
|
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3189 | { |
|
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3190 | "output_type": "stream", |
|
|||
3191 | "stream": "stdout", |
|
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3192 | "text": [ |
|
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3193 | " \r", |
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|
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3195 | ] |
|
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3196 | }, |
|
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3197 | { |
|
|||
3198 | "output_type": "stream", |
|
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3199 | "stream": "stdout", |
|
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3200 | "text": [ |
|
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3201 | " \r", |
|
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|
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3203 | ] |
|
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3204 | }, |
|
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3205 | { |
|
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3206 | "output_type": "stream", |
|
|||
3207 | "stream": "stdout", |
|
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3208 | "text": [ |
|
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3209 | " \r", |
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|
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3211 | ] |
|
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3212 | }, |
|
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3213 | { |
|
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3214 | "output_type": "stream", |
|
|||
3215 | "stream": "stdout", |
|
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3216 | "text": [ |
|
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|
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3219 | ] |
|
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3220 | }, |
|
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3221 | { |
|
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3222 | "output_type": "stream", |
|
|||
3223 | "stream": "stdout", |
|
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3224 | "text": [ |
|
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|
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3227 | ] |
|
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3228 | }, |
|
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3229 | { |
|
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3230 | "output_type": "stream", |
|
|||
3231 | "stream": "stdout", |
|
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3232 | "text": [ |
|
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|
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3235 | ] |
|
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3236 | }, |
|
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3237 | { |
|
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3238 | "output_type": "stream", |
|
|||
3239 | "stream": "stdout", |
|
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3240 | "text": [ |
|
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|
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|
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3243 | ] |
|
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3244 | }, |
|
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3245 | { |
|
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3246 | "output_type": "stream", |
|
|||
3247 | "stream": "stdout", |
|
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3248 | "text": [ |
|
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|
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|
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3251 | ] |
|
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3252 | }, |
|
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3253 | { |
|
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3254 | "output_type": "stream", |
|
|||
3255 | "stream": "stdout", |
|
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3256 | "text": [ |
|
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|
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|
|||
3259 | ] |
|
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3260 | }, |
|
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3261 | { |
|
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3262 | "output_type": "stream", |
|
|||
3263 | "stream": "stdout", |
|
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3264 | "text": [ |
|
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|
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|
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3267 | ] |
|
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3268 | }, |
|
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3269 | { |
|
|||
3270 | "output_type": "stream", |
|
|||
3271 | "stream": "stdout", |
|
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3272 | "text": [ |
|
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3273 | " \r", |
|
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|
|||
3275 | ] |
|
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3276 | }, |
|
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3277 | { |
|
|||
3278 | "output_type": "stream", |
|
|||
3279 | "stream": "stdout", |
|
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3280 | "text": [ |
|
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3281 | " \r", |
|
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|
|||
3283 | ] |
|
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3284 | }, |
|
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3285 | { |
|
|||
3286 | "output_type": "stream", |
|
|||
3287 | "stream": "stdout", |
|
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3288 | "text": [ |
|
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3289 | " \r", |
|
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3290 | "[************* 28% ] 280 of 1000 complete" |
|
|||
3291 | ] |
|
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3292 | }, |
|
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3293 | { |
|
|||
3294 | "output_type": "stream", |
|
|||
3295 | "stream": "stdout", |
|
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3296 | "text": [ |
|
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3297 | " \r", |
|
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|
|||
3299 | ] |
|
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3300 | }, |
|
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3301 | { |
|
|||
3302 | "output_type": "stream", |
|
|||
3303 | "stream": "stdout", |
|
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3304 | "text": [ |
|
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|
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|
|||
3307 | ] |
|
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3308 | }, |
|
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3309 | { |
|
|||
3310 | "output_type": "stream", |
|
|||
3311 | "stream": "stdout", |
|
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3312 | "text": [ |
|
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3313 | " \r", |
|
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3314 | "[************* 28% ] 283 of 1000 complete" |
|
|||
3315 | ] |
|
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3316 | }, |
|
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3317 | { |
|
|||
3318 | "output_type": "stream", |
|
|||
3319 | "stream": "stdout", |
|
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3320 | "text": [ |
|
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3321 | " \r", |
|
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3322 | "[************* 28% ] 284 of 1000 complete" |
|
|||
3323 | ] |
|
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3324 | }, |
|
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3325 | { |
|
|||
3326 | "output_type": "stream", |
|
|||
3327 | "stream": "stdout", |
|
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3328 | "text": [ |
|
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3329 | " \r", |
|
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3330 | "[************* 28% ] 285 of 1000 complete" |
|
|||
3331 | ] |
|
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3332 | }, |
|
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3333 | { |
|
|||
3334 | "output_type": "stream", |
|
|||
3335 | "stream": "stdout", |
|
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3336 | "text": [ |
|
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3337 | " \r", |
|
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|
|||
3339 | ] |
|
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3340 | }, |
|
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3341 | { |
|
|||
3342 | "output_type": "stream", |
|
|||
3343 | "stream": "stdout", |
|
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3344 | "text": [ |
|
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|
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3346 | "[************** 29% ] 287 of 1000 complete" |
|
|||
3347 | ] |
|
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3348 | }, |
|
|||
3349 | { |
|
|||
3350 | "output_type": "stream", |
|
|||
3351 | "stream": "stdout", |
|
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3352 | "text": [ |
|
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3353 | " \r", |
|
|||
3354 | "[************** 29% ] 288 of 1000 complete" |
|
|||
3355 | ] |
|
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3356 | }, |
|
|||
3357 | { |
|
|||
3358 | "output_type": "stream", |
|
|||
3359 | "stream": "stdout", |
|
|||
3360 | "text": [ |
|
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3361 | " \r", |
|
|||
3362 | "[************** 29% ] 289 of 1000 complete" |
|
|||
3363 | ] |
|
|||
3364 | }, |
|
|||
3365 | { |
|
|||
3366 | "output_type": "stream", |
|
|||
3367 | "stream": "stdout", |
|
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3368 | "text": [ |
|
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3369 | " \r", |
|
|||
3370 | "[************** 29% ] 290 of 1000 complete" |
|
|||
3371 | ] |
|
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3372 | }, |
|
|||
3373 | { |
|
|||
3374 | "output_type": "stream", |
|
|||
3375 | "stream": "stdout", |
|
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3376 | "text": [ |
|
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3377 | " \r", |
|
|||
3378 | "[************** 29% ] 291 of 1000 complete" |
|
|||
3379 | ] |
|
|||
3380 | }, |
|
|||
3381 | { |
|
|||
3382 | "output_type": "stream", |
|
|||
3383 | "stream": "stdout", |
|
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3384 | "text": [ |
|
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3385 | " \r", |
|
|||
3386 | "[************** 29% ] 292 of 1000 complete" |
|
|||
3387 | ] |
|
|||
3388 | }, |
|
|||
3389 | { |
|
|||
3390 | "output_type": "stream", |
|
|||
3391 | "stream": "stdout", |
|
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3392 | "text": [ |
|
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3393 | " \r", |
|
|||
3394 | "[************** 29% ] 293 of 1000 complete" |
|
|||
3395 | ] |
|
|||
3396 | }, |
|
|||
3397 | { |
|
|||
3398 | "output_type": "stream", |
|
|||
3399 | "stream": "stdout", |
|
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3400 | "text": [ |
|
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3401 | " \r", |
|
|||
3402 | "[************** 29% ] 294 of 1000 complete" |
|
|||
3403 | ] |
|
|||
3404 | }, |
|
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3405 | { |
|
|||
3406 | "output_type": "stream", |
|
|||
3407 | "stream": "stdout", |
|
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3408 | "text": [ |
|
|||
3409 | " \r", |
|
|||
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|
|||
3411 | ] |
|
|||
3412 | }, |
|
|||
3413 | { |
|
|||
3414 | "output_type": "stream", |
|
|||
3415 | "stream": "stdout", |
|
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3416 | "text": [ |
|
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|
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|
|||
3419 | ] |
|
|||
3420 | }, |
|
|||
3421 | { |
|
|||
3422 | "output_type": "stream", |
|
|||
3423 | "stream": "stdout", |
|
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3424 | "text": [ |
|
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3425 | " \r", |
|
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3426 | "[************** 30% ] 297 of 1000 complete" |
|
|||
3427 | ] |
|
|||
3428 | }, |
|
|||
3429 | { |
|
|||
3430 | "output_type": "stream", |
|
|||
3431 | "stream": "stdout", |
|
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3432 | "text": [ |
|
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3433 | " \r", |
|
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|
|||
3435 | ] |
|
|||
3436 | }, |
|
|||
3437 | { |
|
|||
3438 | "output_type": "stream", |
|
|||
3439 | "stream": "stdout", |
|
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3440 | "text": [ |
|
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3441 | " \r", |
|
|||
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|
|||
3443 | ] |
|
|||
3444 | }, |
|
|||
3445 | { |
|
|||
3446 | "output_type": "stream", |
|
|||
3447 | "stream": "stdout", |
|
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3448 | "text": [ |
|
|||
3449 | " \r", |
|
|||
3450 | "[************** 30% ] 300 of 1000 complete" |
|
|||
3451 | ] |
|
|||
3452 | }, |
|
|||
3453 | { |
|
|||
3454 | "output_type": "stream", |
|
|||
3455 | "stream": "stdout", |
|
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3456 | "text": [ |
|
|||
3457 | " \r", |
|
|||
3458 | "[************** 30% ] 301 of 1000 complete" |
|
|||
3459 | ] |
|
|||
3460 | }, |
|
|||
3461 | { |
|
|||
3462 | "output_type": "stream", |
|
|||
3463 | "stream": "stdout", |
|
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3464 | "text": [ |
|
|||
3465 | " \r", |
|
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|
|||
3467 | ] |
|
|||
3468 | }, |
|
|||
3469 | { |
|
|||
3470 | "output_type": "stream", |
|
|||
3471 | "stream": "stdout", |
|
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3472 | "text": [ |
|
|||
3473 | " \r", |
|
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3474 | "[************** 30% ] 303 of 1000 complete" |
|
|||
3475 | ] |
|
|||
3476 | }, |
|
|||
3477 | { |
|
|||
3478 | "output_type": "stream", |
|
|||
3479 | "stream": "stdout", |
|
|||
3480 | "text": [ |
|
|||
3481 | " \r", |
|
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3482 | "[************** 30% ] 304 of 1000 complete" |
|
|||
3483 | ] |
|
|||
3484 | }, |
|
|||
3485 | { |
|
|||
3486 | "output_type": "stream", |
|
|||
3487 | "stream": "stdout", |
|
|||
3488 | "text": [ |
|
|||
3489 | " \r", |
|
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3490 | "[*************** 31% ] 305 of 1000 complete" |
|
|||
3491 | ] |
|
|||
3492 | }, |
|
|||
3493 | { |
|
|||
3494 | "output_type": "stream", |
|
|||
3495 | "stream": "stdout", |
|
|||
3496 | "text": [ |
|
|||
3497 | " \r", |
|
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|
|||
3499 | ] |
|
|||
3500 | }, |
|
|||
3501 | { |
|
|||
3502 | "output_type": "stream", |
|
|||
3503 | "stream": "stdout", |
|
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3504 | "text": [ |
|
|||
3505 | " \r", |
|
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|
|||
3507 | ] |
|
|||
3508 | }, |
|
|||
3509 | { |
|
|||
3510 | "output_type": "stream", |
|
|||
3511 | "stream": "stdout", |
|
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3512 | "text": [ |
|
|||
3513 | " \r", |
|
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|
|||
3515 | ] |
|
|||
3516 | }, |
|
|||
3517 | { |
|
|||
3518 | "output_type": "stream", |
|
|||
3519 | "stream": "stdout", |
|
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3520 | "text": [ |
|
|||
3521 | " \r", |
|
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|
|||
3523 | ] |
|
|||
3524 | }, |
|
|||
3525 | { |
|
|||
3526 | "output_type": "stream", |
|
|||
3527 | "stream": "stdout", |
|
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3528 | "text": [ |
|
|||
3529 | " \r", |
|
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|
|||
3531 | ] |
|
|||
3532 | }, |
|
|||
3533 | { |
|
|||
3534 | "output_type": "stream", |
|
|||
3535 | "stream": "stdout", |
|
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3536 | "text": [ |
|
|||
3537 | " \r", |
|
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|
|||
3539 | ] |
|
|||
3540 | }, |
|
|||
3541 | { |
|
|||
3542 | "output_type": "stream", |
|
|||
3543 | "stream": "stdout", |
|
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3544 | "text": [ |
|
|||
3545 | " \r", |
|
|||
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|
|||
3547 | ] |
|
|||
3548 | }, |
|
|||
3549 | { |
|
|||
3550 | "output_type": "stream", |
|
|||
3551 | "stream": "stdout", |
|
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3552 | "text": [ |
|
|||
3553 | " \r", |
|
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|
|||
3555 | ] |
|
|||
3556 | }, |
|
|||
3557 | { |
|
|||
3558 | "output_type": "stream", |
|
|||
3559 | "stream": "stdout", |
|
|||
3560 | "text": [ |
|
|||
3561 | " \r", |
|
|||
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|
|||
3563 | ] |
|
|||
3564 | }, |
|
|||
3565 | { |
|
|||
3566 | "output_type": "stream", |
|
|||
3567 | "stream": "stdout", |
|
|||
3568 | "text": [ |
|
|||
3569 | " \r", |
|
|||
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|
|||
3571 | ] |
|
|||
3572 | }, |
|
|||
3573 | { |
|
|||
3574 | "output_type": "stream", |
|
|||
3575 | "stream": "stdout", |
|
|||
3576 | "text": [ |
|
|||
3577 | " \r", |
|
|||
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|
|||
3579 | ] |
|
|||
3580 | }, |
|
|||
3581 | { |
|
|||
3582 | "output_type": "stream", |
|
|||
3583 | "stream": "stdout", |
|
|||
3584 | "text": [ |
|
|||
3585 | " \r", |
|
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3586 | "[*************** 32% ] 317 of 1000 complete" |
|
|||
3587 | ] |
|
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3588 | }, |
|
|||
3589 | { |
|
|||
3590 | "output_type": "stream", |
|
|||
3591 | "stream": "stdout", |
|
|||
3592 | "text": [ |
|
|||
3593 | " \r", |
|
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3594 | "[*************** 32% ] 318 of 1000 complete" |
|
|||
3595 | ] |
|
|||
3596 | }, |
|
|||
3597 | { |
|
|||
3598 | "output_type": "stream", |
|
|||
3599 | "stream": "stdout", |
|
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3600 | "text": [ |
|
|||
3601 | " \r", |
|
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3602 | "[*************** 32% ] 319 of 1000 complete" |
|
|||
3603 | ] |
|
|||
3604 | }, |
|
|||
3605 | { |
|
|||
3606 | "output_type": "stream", |
|
|||
3607 | "stream": "stdout", |
|
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3608 | "text": [ |
|
|||
3609 | " \r", |
|
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3610 | "[*************** 32% ] 320 of 1000 complete" |
|
|||
3611 | ] |
|
|||
3612 | }, |
|
|||
3613 | { |
|
|||
3614 | "output_type": "stream", |
|
|||
3615 | "stream": "stdout", |
|
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3616 | "text": [ |
|
|||
3617 | " \r", |
|
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3618 | "[*************** 32% ] 321 of 1000 complete" |
|
|||
3619 | ] |
|
|||
3620 | }, |
|
|||
3621 | { |
|
|||
3622 | "output_type": "stream", |
|
|||
3623 | "stream": "stdout", |
|
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3624 | "text": [ |
|
|||
3625 | " \r", |
|
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3626 | "[*************** 32% ] 322 of 1000 complete" |
|
|||
3627 | ] |
|
|||
3628 | }, |
|
|||
3629 | { |
|
|||
3630 | "output_type": "stream", |
|
|||
3631 | "stream": "stdout", |
|
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3632 | "text": [ |
|
|||
3633 | " \r", |
|
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3634 | "[*************** 32% ] 323 of 1000 complete" |
|
|||
3635 | ] |
|
|||
3636 | }, |
|
|||
3637 | { |
|
|||
3638 | "output_type": "stream", |
|
|||
3639 | "stream": "stdout", |
|
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3640 | "text": [ |
|
|||
3641 | " \r", |
|
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3642 | "[*************** 32% ] 324 of 1000 complete" |
|
|||
3643 | ] |
|
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3644 | }, |
|
|||
3645 | { |
|
|||
3646 | "output_type": "stream", |
|
|||
3647 | "stream": "stdout", |
|
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3648 | "text": [ |
|
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3649 | " \r", |
|
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3650 | "[**************** 33% ] 325 of 1000 complete" |
|
|||
3651 | ] |
|
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3652 | }, |
|
|||
3653 | { |
|
|||
3654 | "output_type": "stream", |
|
|||
3655 | "stream": "stdout", |
|
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3656 | "text": [ |
|
|||
3657 | " \r", |
|
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3658 | "[**************** 33% ] 326 of 1000 complete" |
|
|||
3659 | ] |
|
|||
3660 | }, |
|
|||
3661 | { |
|
|||
3662 | "output_type": "stream", |
|
|||
3663 | "stream": "stdout", |
|
|||
3664 | "text": [ |
|
|||
3665 | " \r", |
|
|||
3666 | "[**************** 33% ] 327 of 1000 complete" |
|
|||
3667 | ] |
|
|||
3668 | }, |
|
|||
3669 | { |
|
|||
3670 | "output_type": "stream", |
|
|||
3671 | "stream": "stdout", |
|
|||
3672 | "text": [ |
|
|||
3673 | " \r", |
|
|||
3674 | "[**************** 33% ] 328 of 1000 complete" |
|
|||
3675 | ] |
|
|||
3676 | }, |
|
|||
3677 | { |
|
|||
3678 | "output_type": "stream", |
|
|||
3679 | "stream": "stdout", |
|
|||
3680 | "text": [ |
|
|||
3681 | " \r", |
|
|||
3682 | "[**************** 33% ] 329 of 1000 complete" |
|
|||
3683 | ] |
|
|||
3684 | }, |
|
|||
3685 | { |
|
|||
3686 | "output_type": "stream", |
|
|||
3687 | "stream": "stdout", |
|
|||
3688 | "text": [ |
|
|||
3689 | " \r", |
|
|||
3690 | "[**************** 33% ] 330 of 1000 complete" |
|
|||
3691 | ] |
|
|||
3692 | }, |
|
|||
3693 | { |
|
|||
3694 | "output_type": "stream", |
|
|||
3695 | "stream": "stdout", |
|
|||
3696 | "text": [ |
|
|||
3697 | " \r", |
|
|||
3698 | "[**************** 33% ] 331 of 1000 complete" |
|
|||
3699 | ] |
|
|||
3700 | }, |
|
|||
3701 | { |
|
|||
3702 | "output_type": "stream", |
|
|||
3703 | "stream": "stdout", |
|
|||
3704 | "text": [ |
|
|||
3705 | " \r", |
|
|||
3706 | "[**************** 33% ] 332 of 1000 complete" |
|
|||
3707 | ] |
|
|||
3708 | }, |
|
|||
3709 | { |
|
|||
3710 | "output_type": "stream", |
|
|||
3711 | "stream": "stdout", |
|
|||
3712 | "text": [ |
|
|||
3713 | " \r", |
|
|||
3714 | "[**************** 33% ] 333 of 1000 complete" |
|
|||
3715 | ] |
|
|||
3716 | }, |
|
|||
3717 | { |
|
|||
3718 | "output_type": "stream", |
|
|||
3719 | "stream": "stdout", |
|
|||
3720 | "text": [ |
|
|||
3721 | " \r", |
|
|||
3722 | "[**************** 33% ] 334 of 1000 complete" |
|
|||
3723 | ] |
|
|||
3724 | }, |
|
|||
3725 | { |
|
|||
3726 | "output_type": "stream", |
|
|||
3727 | "stream": "stdout", |
|
|||
3728 | "text": [ |
|
|||
3729 | " \r", |
|
|||
3730 | "[**************** 34% ] 335 of 1000 complete" |
|
|||
3731 | ] |
|
|||
3732 | }, |
|
|||
3733 | { |
|
|||
3734 | "output_type": "stream", |
|
|||
3735 | "stream": "stdout", |
|
|||
3736 | "text": [ |
|
|||
3737 | " \r", |
|
|||
3738 | "[**************** 34% ] 336 of 1000 complete" |
|
|||
3739 | ] |
|
|||
3740 | }, |
|
|||
3741 | { |
|
|||
3742 | "output_type": "stream", |
|
|||
3743 | "stream": "stdout", |
|
|||
3744 | "text": [ |
|
|||
3745 | " \r", |
|
|||
3746 | "[**************** 34% ] 337 of 1000 complete" |
|
|||
3747 | ] |
|
|||
3748 | }, |
|
|||
3749 | { |
|
|||
3750 | "output_type": "stream", |
|
|||
3751 | "stream": "stdout", |
|
|||
3752 | "text": [ |
|
|||
3753 | " \r", |
|
|||
3754 | "[**************** 34% ] 338 of 1000 complete" |
|
|||
3755 | ] |
|
|||
3756 | }, |
|
|||
3757 | { |
|
|||
3758 | "output_type": "stream", |
|
|||
3759 | "stream": "stdout", |
|
|||
3760 | "text": [ |
|
|||
3761 | " \r", |
|
|||
3762 | "[**************** 34% ] 339 of 1000 complete" |
|
|||
3763 | ] |
|
|||
3764 | }, |
|
|||
3765 | { |
|
|||
3766 | "output_type": "stream", |
|
|||
3767 | "stream": "stdout", |
|
|||
3768 | "text": [ |
|
|||
3769 | " \r", |
|
|||
3770 | "[**************** 34% ] 340 of 1000 complete" |
|
|||
3771 | ] |
|
|||
3772 | }, |
|
|||
3773 | { |
|
|||
3774 | "output_type": "stream", |
|
|||
3775 | "stream": "stdout", |
|
|||
3776 | "text": [ |
|
|||
3777 | " \r", |
|
|||
3778 | "[**************** 34% ] 341 of 1000 complete" |
|
|||
3779 | ] |
|
|||
3780 | }, |
|
|||
3781 | { |
|
|||
3782 | "output_type": "stream", |
|
|||
3783 | "stream": "stdout", |
|
|||
3784 | "text": [ |
|
|||
3785 | " \r", |
|
|||
3786 | "[**************** 34% ] 342 of 1000 complete" |
|
|||
3787 | ] |
|
|||
3788 | }, |
|
|||
3789 | { |
|
|||
3790 | "output_type": "stream", |
|
|||
3791 | "stream": "stdout", |
|
|||
3792 | "text": [ |
|
|||
3793 | " \r", |
|
|||
3794 | "[**************** 34% ] 343 of 1000 complete" |
|
|||
3795 | ] |
|
|||
3796 | }, |
|
|||
3797 | { |
|
|||
3798 | "output_type": "stream", |
|
|||
3799 | "stream": "stdout", |
|
|||
3800 | "text": [ |
|
|||
3801 | " \r", |
|
|||
3802 | "[**************** 34% ] 344 of 1000 complete" |
|
|||
3803 | ] |
|
|||
3804 | }, |
|
|||
3805 | { |
|
|||
3806 | "output_type": "stream", |
|
|||
3807 | "stream": "stdout", |
|
|||
3808 | "text": [ |
|
|||
3809 | " \r", |
|
|||
3810 | "[***************** 35% ] 345 of 1000 complete" |
|
|||
3811 | ] |
|
|||
3812 | }, |
|
|||
3813 | { |
|
|||
3814 | "output_type": "stream", |
|
|||
3815 | "stream": "stdout", |
|
|||
3816 | "text": [ |
|
|||
3817 | " \r", |
|
|||
3818 | "[***************** 35% ] 346 of 1000 complete" |
|
|||
3819 | ] |
|
|||
3820 | }, |
|
|||
3821 | { |
|
|||
3822 | "output_type": "stream", |
|
|||
3823 | "stream": "stdout", |
|
|||
3824 | "text": [ |
|
|||
3825 | " \r", |
|
|||
3826 | "[***************** 35% ] 347 of 1000 complete" |
|
|||
3827 | ] |
|
|||
3828 | }, |
|
|||
3829 | { |
|
|||
3830 | "output_type": "stream", |
|
|||
3831 | "stream": "stdout", |
|
|||
3832 | "text": [ |
|
|||
3833 | " \r", |
|
|||
3834 | "[***************** 35% ] 348 of 1000 complete" |
|
|||
3835 | ] |
|
|||
3836 | }, |
|
|||
3837 | { |
|
|||
3838 | "output_type": "stream", |
|
|||
3839 | "stream": "stdout", |
|
|||
3840 | "text": [ |
|
|||
3841 | " \r", |
|
|||
3842 | "[***************** 35% ] 349 of 1000 complete" |
|
|||
3843 | ] |
|
|||
3844 | }, |
|
|||
3845 | { |
|
|||
3846 | "output_type": "stream", |
|
|||
3847 | "stream": "stdout", |
|
|||
3848 | "text": [ |
|
|||
3849 | " \r", |
|
|||
3850 | "[***************** 35% ] 350 of 1000 complete" |
|
|||
3851 | ] |
|
|||
3852 | }, |
|
|||
3853 | { |
|
|||
3854 | "output_type": "stream", |
|
|||
3855 | "stream": "stdout", |
|
|||
3856 | "text": [ |
|
|||
3857 | " \r", |
|
|||
3858 | "[***************** 35% ] 351 of 1000 complete" |
|
|||
3859 | ] |
|
|||
3860 | }, |
|
|||
3861 | { |
|
|||
3862 | "output_type": "stream", |
|
|||
3863 | "stream": "stdout", |
|
|||
3864 | "text": [ |
|
|||
3865 | " \r", |
|
|||
3866 | "[***************** 35% ] 352 of 1000 complete" |
|
|||
3867 | ] |
|
|||
3868 | }, |
|
|||
3869 | { |
|
|||
3870 | "output_type": "stream", |
|
|||
3871 | "stream": "stdout", |
|
|||
3872 | "text": [ |
|
|||
3873 | " \r", |
|
|||
3874 | "[***************** 35% ] 353 of 1000 complete" |
|
|||
3875 | ] |
|
|||
3876 | }, |
|
|||
3877 | { |
|
|||
3878 | "output_type": "stream", |
|
|||
3879 | "stream": "stdout", |
|
|||
3880 | "text": [ |
|
|||
3881 | " \r", |
|
|||
3882 | "[***************** 35% ] 354 of 1000 complete" |
|
|||
3883 | ] |
|
|||
3884 | }, |
|
|||
3885 | { |
|
|||
3886 | "output_type": "stream", |
|
|||
3887 | "stream": "stdout", |
|
|||
3888 | "text": [ |
|
|||
3889 | " \r", |
|
|||
3890 | "[***************** 36% ] 355 of 1000 complete" |
|
|||
3891 | ] |
|
|||
3892 | }, |
|
|||
3893 | { |
|
|||
3894 | "output_type": "stream", |
|
|||
3895 | "stream": "stdout", |
|
|||
3896 | "text": [ |
|
|||
3897 | " \r", |
|
|||
3898 | "[***************** 36% ] 356 of 1000 complete" |
|
|||
3899 | ] |
|
|||
3900 | }, |
|
|||
3901 | { |
|
|||
3902 | "output_type": "stream", |
|
|||
3903 | "stream": "stdout", |
|
|||
3904 | "text": [ |
|
|||
3905 | " \r", |
|
|||
3906 | "[***************** 36% ] 357 of 1000 complete" |
|
|||
3907 | ] |
|
|||
3908 | }, |
|
|||
3909 | { |
|
|||
3910 | "output_type": "stream", |
|
|||
3911 | "stream": "stdout", |
|
|||
3912 | "text": [ |
|
|||
3913 | " \r", |
|
|||
3914 | "[***************** 36% ] 358 of 1000 complete" |
|
|||
3915 | ] |
|
|||
3916 | }, |
|
|||
3917 | { |
|
|||
3918 | "output_type": "stream", |
|
|||
3919 | "stream": "stdout", |
|
|||
3920 | "text": [ |
|
|||
3921 | " \r", |
|
|||
3922 | "[***************** 36% ] 359 of 1000 complete" |
|
|||
3923 | ] |
|
|||
3924 | }, |
|
|||
3925 | { |
|
|||
3926 | "output_type": "stream", |
|
|||
3927 | "stream": "stdout", |
|
|||
3928 | "text": [ |
|
|||
3929 | " \r", |
|
|||
3930 | "[***************** 36% ] 360 of 1000 complete" |
|
|||
3931 | ] |
|
|||
3932 | }, |
|
|||
3933 | { |
|
|||
3934 | "output_type": "stream", |
|
|||
3935 | "stream": "stdout", |
|
|||
3936 | "text": [ |
|
|||
3937 | " \r", |
|
|||
3938 | "[***************** 36% ] 361 of 1000 complete" |
|
|||
3939 | ] |
|
|||
3940 | }, |
|
|||
3941 | { |
|
|||
3942 | "output_type": "stream", |
|
|||
3943 | "stream": "stdout", |
|
|||
3944 | "text": [ |
|
|||
3945 | " \r", |
|
|||
3946 | "[***************** 36% ] 362 of 1000 complete" |
|
|||
3947 | ] |
|
|||
3948 | }, |
|
|||
3949 | { |
|
|||
3950 | "output_type": "stream", |
|
|||
3951 | "stream": "stdout", |
|
|||
3952 | "text": [ |
|
|||
3953 | " \r", |
|
|||
3954 | "[***************** 36% ] 363 of 1000 complete" |
|
|||
3955 | ] |
|
|||
3956 | }, |
|
|||
3957 | { |
|
|||
3958 | "output_type": "stream", |
|
|||
3959 | "stream": "stdout", |
|
|||
3960 | "text": [ |
|
|||
3961 | " \r", |
|
|||
3962 | "[***************** 36% ] 364 of 1000 complete" |
|
|||
3963 | ] |
|
|||
3964 | }, |
|
|||
3965 | { |
|
|||
3966 | "output_type": "stream", |
|
|||
3967 | "stream": "stdout", |
|
|||
3968 | "text": [ |
|
|||
3969 | " \r", |
|
|||
3970 | "[****************** 37% ] 365 of 1000 complete" |
|
|||
3971 | ] |
|
|||
3972 | }, |
|
|||
3973 | { |
|
|||
3974 | "output_type": "stream", |
|
|||
3975 | "stream": "stdout", |
|
|||
3976 | "text": [ |
|
|||
3977 | " \r", |
|
|||
3978 | "[****************** 37% ] 366 of 1000 complete" |
|
|||
3979 | ] |
|
|||
3980 | }, |
|
|||
3981 | { |
|
|||
3982 | "output_type": "stream", |
|
|||
3983 | "stream": "stdout", |
|
|||
3984 | "text": [ |
|
|||
3985 | " \r", |
|
|||
3986 | "[****************** 37% ] 367 of 1000 complete" |
|
|||
3987 | ] |
|
|||
3988 | }, |
|
|||
3989 | { |
|
|||
3990 | "output_type": "stream", |
|
|||
3991 | "stream": "stdout", |
|
|||
3992 | "text": [ |
|
|||
3993 | " \r", |
|
|||
3994 | "[****************** 37% ] 368 of 1000 complete" |
|
|||
3995 | ] |
|
|||
3996 | }, |
|
|||
3997 | { |
|
|||
3998 | "output_type": "stream", |
|
|||
3999 | "stream": "stdout", |
|
|||
4000 | "text": [ |
|
|||
4001 | " \r", |
|
|||
4002 | "[****************** 37% ] 369 of 1000 complete" |
|
|||
4003 | ] |
|
|||
4004 | }, |
|
|||
4005 | { |
|
|||
4006 | "output_type": "stream", |
|
|||
4007 | "stream": "stdout", |
|
|||
4008 | "text": [ |
|
|||
4009 | " \r", |
|
|||
4010 | "[****************** 37% ] 370 of 1000 complete" |
|
|||
4011 | ] |
|
|||
4012 | }, |
|
|||
4013 | { |
|
|||
4014 | "output_type": "stream", |
|
|||
4015 | "stream": "stdout", |
|
|||
4016 | "text": [ |
|
|||
4017 | " \r", |
|
|||
4018 | "[****************** 37% ] 371 of 1000 complete" |
|
|||
4019 | ] |
|
|||
4020 | }, |
|
|||
4021 | { |
|
|||
4022 | "output_type": "stream", |
|
|||
4023 | "stream": "stdout", |
|
|||
4024 | "text": [ |
|
|||
4025 | " \r", |
|
|||
4026 | "[****************** 37% ] 372 of 1000 complete" |
|
|||
4027 | ] |
|
|||
4028 | }, |
|
|||
4029 | { |
|
|||
4030 | "output_type": "stream", |
|
|||
4031 | "stream": "stdout", |
|
|||
4032 | "text": [ |
|
|||
4033 | " \r", |
|
|||
4034 | "[****************** 37% ] 373 of 1000 complete" |
|
|||
4035 | ] |
|
|||
4036 | }, |
|
|||
4037 | { |
|
|||
4038 | "output_type": "stream", |
|
|||
4039 | "stream": "stdout", |
|
|||
4040 | "text": [ |
|
|||
4041 | " \r", |
|
|||
4042 | "[****************** 37% ] 374 of 1000 complete" |
|
|||
4043 | ] |
|
|||
4044 | }, |
|
|||
4045 | { |
|
|||
4046 | "output_type": "stream", |
|
|||
4047 | "stream": "stdout", |
|
|||
4048 | "text": [ |
|
|||
4049 | " \r", |
|
|||
4050 | "[****************** 38% ] 375 of 1000 complete" |
|
|||
4051 | ] |
|
|||
4052 | }, |
|
|||
4053 | { |
|
|||
4054 | "output_type": "stream", |
|
|||
4055 | "stream": "stdout", |
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4056 | "text": [ |
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4057 | " \r", |
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4058 | "[****************** 38% ] 376 of 1000 complete" |
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4059 | ] |
|
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4060 | }, |
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4061 | { |
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4062 | "output_type": "stream", |
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4063 | "stream": "stdout", |
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4064 | "text": [ |
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4065 | " \r", |
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4066 | "[****************** 38% ] 377 of 1000 complete" |
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4067 | ] |
|
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4068 | }, |
|
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4069 | { |
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4070 | "output_type": "stream", |
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4071 | "stream": "stdout", |
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4072 | "text": [ |
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4073 | " \r", |
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4074 | "[****************** 38% ] 378 of 1000 complete" |
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4075 | ] |
|
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4076 | }, |
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4077 | { |
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4078 | "output_type": "stream", |
|
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4079 | "stream": "stdout", |
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4080 | "text": [ |
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4081 | " \r", |
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4082 | "[****************** 38% ] 379 of 1000 complete" |
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4083 | ] |
|
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4084 | }, |
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4085 | { |
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4086 | "output_type": "stream", |
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4087 | "stream": "stdout", |
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4088 | "text": [ |
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4089 | " \r", |
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4090 | "[****************** 38% ] 380 of 1000 complete" |
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4091 | ] |
|
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4092 | }, |
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4093 | { |
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4094 | "output_type": "stream", |
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4095 | "stream": "stdout", |
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4096 | "text": [ |
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4097 | " \r", |
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4098 | "[****************** 38% ] 381 of 1000 complete" |
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4099 | ] |
|
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4100 | }, |
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4101 | { |
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4102 | "output_type": "stream", |
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4103 | "stream": "stdout", |
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4104 | "text": [ |
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4105 | " \r", |
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4106 | "[****************** 38% ] 382 of 1000 complete" |
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4107 | ] |
|
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4108 | }, |
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4109 | { |
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4110 | "output_type": "stream", |
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4111 | "stream": "stdout", |
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4112 | "text": [ |
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4113 | " \r", |
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4115 | ] |
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4116 | }, |
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4117 | { |
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4118 | "output_type": "stream", |
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4119 | "stream": "stdout", |
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4120 | "text": [ |
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4121 | " \r", |
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4123 | ] |
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4124 | }, |
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4125 | { |
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4126 | "output_type": "stream", |
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4127 | "stream": "stdout", |
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4128 | "text": [ |
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4129 | " \r", |
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4131 | ] |
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4132 | }, |
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4133 | { |
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4134 | "output_type": "stream", |
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4135 | "stream": "stdout", |
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4136 | "text": [ |
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4139 | ] |
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4140 | }, |
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4141 | { |
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4142 | "output_type": "stream", |
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4143 | "stream": "stdout", |
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4144 | "text": [ |
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4145 | " \r", |
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4147 | ] |
|
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4148 | }, |
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4149 | { |
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4150 | "output_type": "stream", |
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4151 | "stream": "stdout", |
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4152 | "text": [ |
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4153 | " \r", |
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4155 | ] |
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4156 | }, |
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4157 | { |
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4158 | "output_type": "stream", |
|
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4159 | "stream": "stdout", |
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4160 | "text": [ |
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4161 | " \r", |
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4163 | ] |
|
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4164 | }, |
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4165 | { |
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4166 | "output_type": "stream", |
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4167 | "stream": "stdout", |
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4168 | "text": [ |
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4169 | " \r", |
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4171 | ] |
|
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4172 | }, |
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4173 | { |
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4175 | "stream": "stdout", |
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4176 | "text": [ |
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4179 | ] |
|
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4180 | }, |
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4181 | { |
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|
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4183 | "stream": "stdout", |
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|
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4188 | }, |
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4189 | { |
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4190 | "output_type": "stream", |
|
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4191 | "stream": "stdout", |
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4192 | "text": [ |
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4195 | ] |
|
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4196 | }, |
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4197 | { |
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4198 | "output_type": "stream", |
|
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4199 | "stream": "stdout", |
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4203 | ] |
|
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4204 | }, |
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4205 | { |
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4206 | "output_type": "stream", |
|
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4207 | "stream": "stdout", |
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4208 | "text": [ |
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4211 | ] |
|
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4212 | }, |
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4213 | { |
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4214 | "output_type": "stream", |
|
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4215 | "stream": "stdout", |
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4216 | "text": [ |
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4219 | ] |
|
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4220 | }, |
|
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4221 | { |
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4222 | "output_type": "stream", |
|
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4223 | "stream": "stdout", |
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4224 | "text": [ |
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|
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4228 | }, |
|
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4229 | { |
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4230 | "output_type": "stream", |
|
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4231 | "stream": "stdout", |
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4232 | "text": [ |
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|
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4235 | ] |
|
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4236 | }, |
|
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4237 | { |
|
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|
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4239 | "stream": "stdout", |
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4240 | "text": [ |
|
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|
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4243 | ] |
|
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4244 | }, |
|
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4245 | { |
|
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|
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4247 | "stream": "stdout", |
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4248 | "text": [ |
|
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|
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4251 | ] |
|
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4252 | }, |
|
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4253 | { |
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|
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4255 | "stream": "stdout", |
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|
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|
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4259 | ] |
|
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4260 | }, |
|
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4261 | { |
|
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4262 | "output_type": "stream", |
|
|||
4263 | "stream": "stdout", |
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4264 | "text": [ |
|
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|
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4267 | ] |
|
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4268 | }, |
|
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4269 | { |
|
|||
4270 | "output_type": "stream", |
|
|||
4271 | "stream": "stdout", |
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4272 | "text": [ |
|
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|
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4275 | ] |
|
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4276 | }, |
|
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4277 | { |
|
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4278 | "output_type": "stream", |
|
|||
4279 | "stream": "stdout", |
|
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4280 | "text": [ |
|
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|
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4283 | ] |
|
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4284 | }, |
|
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4285 | { |
|
|||
4286 | "output_type": "stream", |
|
|||
4287 | "stream": "stdout", |
|
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4288 | "text": [ |
|
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|
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4291 | ] |
|
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4292 | }, |
|
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4293 | { |
|
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|
|||
4295 | "stream": "stdout", |
|
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4296 | "text": [ |
|
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|
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4299 | ] |
|
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4300 | }, |
|
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4301 | { |
|
|||
4302 | "output_type": "stream", |
|
|||
4303 | "stream": "stdout", |
|
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4304 | "text": [ |
|
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|
|||
4307 | ] |
|
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4308 | }, |
|
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4309 | { |
|
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4310 | "output_type": "stream", |
|
|||
4311 | "stream": "stdout", |
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4312 | "text": [ |
|
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4313 | " \r", |
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|
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4315 | ] |
|
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4316 | }, |
|
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4317 | { |
|
|||
4318 | "output_type": "stream", |
|
|||
4319 | "stream": "stdout", |
|
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4320 | "text": [ |
|
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4321 | " \r", |
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4322 | "[******************** 41% ] 409 of 1000 complete" |
|
|||
4323 | ] |
|
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4324 | }, |
|
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4325 | { |
|
|||
4326 | "output_type": "stream", |
|
|||
4327 | "stream": "stdout", |
|
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4328 | "text": [ |
|
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4329 | " \r", |
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|
|||
4331 | ] |
|
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4332 | }, |
|
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4333 | { |
|
|||
4334 | "output_type": "stream", |
|
|||
4335 | "stream": "stdout", |
|
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4336 | "text": [ |
|
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|
|||
4339 | ] |
|
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4340 | }, |
|
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4341 | { |
|
|||
4342 | "output_type": "stream", |
|
|||
4343 | "stream": "stdout", |
|
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4344 | "text": [ |
|
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|
|||
4347 | ] |
|
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4348 | }, |
|
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4349 | { |
|
|||
4350 | "output_type": "stream", |
|
|||
4351 | "stream": "stdout", |
|
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4352 | "text": [ |
|
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4354 | "[******************** 41% ] 413 of 1000 complete" |
|
|||
4355 | ] |
|
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4356 | }, |
|
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4357 | { |
|
|||
4358 | "output_type": "stream", |
|
|||
4359 | "stream": "stdout", |
|
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4360 | "text": [ |
|
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|
|||
4363 | ] |
|
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4364 | }, |
|
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4365 | { |
|
|||
4366 | "output_type": "stream", |
|
|||
4367 | "stream": "stdout", |
|
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4368 | "text": [ |
|
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|
|||
4371 | ] |
|
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4372 | }, |
|
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4373 | { |
|
|||
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|
|||
4375 | "stream": "stdout", |
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4376 | "text": [ |
|
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|
|||
4379 | ] |
|
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4380 | }, |
|
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4381 | { |
|
|||
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|
|||
4383 | "stream": "stdout", |
|
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4384 | "text": [ |
|
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|
|||
4387 | ] |
|
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4388 | }, |
|
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4389 | { |
|
|||
4390 | "output_type": "stream", |
|
|||
4391 | "stream": "stdout", |
|
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4392 | "text": [ |
|
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4394 | "[******************** 42% ] 418 of 1000 complete" |
|
|||
4395 | ] |
|
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4396 | }, |
|
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4397 | { |
|
|||
4398 | "output_type": "stream", |
|
|||
4399 | "stream": "stdout", |
|
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4400 | "text": [ |
|
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4401 | " \r", |
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|
|||
4403 | ] |
|
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4404 | }, |
|
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4405 | { |
|
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|
|||
4407 | "stream": "stdout", |
|
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4408 | "text": [ |
|
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4409 | " \r", |
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|
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4411 | ] |
|
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4412 | }, |
|
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4413 | { |
|
|||
4414 | "output_type": "stream", |
|
|||
4415 | "stream": "stdout", |
|
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4416 | "text": [ |
|
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|
|||
4419 | ] |
|
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4420 | }, |
|
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4421 | { |
|
|||
4422 | "output_type": "stream", |
|
|||
4423 | "stream": "stdout", |
|
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4424 | "text": [ |
|
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|
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4427 | ] |
|
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4428 | }, |
|
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4429 | { |
|
|||
4430 | "output_type": "stream", |
|
|||
4431 | "stream": "stdout", |
|
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4432 | "text": [ |
|
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4433 | " \r", |
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|
|||
4435 | ] |
|
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4436 | }, |
|
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4437 | { |
|
|||
4438 | "output_type": "stream", |
|
|||
4439 | "stream": "stdout", |
|
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4440 | "text": [ |
|
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4441 | " \r", |
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|
|||
4443 | ] |
|
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4444 | }, |
|
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4445 | { |
|
|||
4446 | "output_type": "stream", |
|
|||
4447 | "stream": "stdout", |
|
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4448 | "text": [ |
|
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4449 | " \r", |
|
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|
|||
4451 | ] |
|
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4452 | }, |
|
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4453 | { |
|
|||
4454 | "output_type": "stream", |
|
|||
4455 | "stream": "stdout", |
|
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4456 | "text": [ |
|
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|
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|
|||
4459 | ] |
|
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4460 | }, |
|
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4461 | { |
|
|||
4462 | "output_type": "stream", |
|
|||
4463 | "stream": "stdout", |
|
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4464 | "text": [ |
|
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4465 | " \r", |
|
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|
|||
4467 | ] |
|
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4468 | }, |
|
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4469 | { |
|
|||
4470 | "output_type": "stream", |
|
|||
4471 | "stream": "stdout", |
|
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4472 | "text": [ |
|
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4473 | " \r", |
|
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|
|||
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|
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4476 | }, |
|
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4477 | { |
|
|||
4478 | "output_type": "stream", |
|
|||
4479 | "stream": "stdout", |
|
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4480 | "text": [ |
|
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4481 | " \r", |
|
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|
|||
4483 | ] |
|
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4484 | }, |
|
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4485 | { |
|
|||
4486 | "output_type": "stream", |
|
|||
4487 | "stream": "stdout", |
|
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4488 | "text": [ |
|
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4489 | " \r", |
|
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|
|||
4491 | ] |
|
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4492 | }, |
|
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4493 | { |
|
|||
4494 | "output_type": "stream", |
|
|||
4495 | "stream": "stdout", |
|
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4496 | "text": [ |
|
|||
4497 | " \r", |
|
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|
|||
4499 | ] |
|
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4500 | }, |
|
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4501 | { |
|
|||
4502 | "output_type": "stream", |
|
|||
4503 | "stream": "stdout", |
|
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4504 | "text": [ |
|
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4505 | " \r", |
|
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|
|||
4507 | ] |
|
|||
4508 | }, |
|
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4509 | { |
|
|||
4510 | "output_type": "stream", |
|
|||
4511 | "stream": "stdout", |
|
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4512 | "text": [ |
|
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4513 | " \r", |
|
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|
|||
4515 | ] |
|
|||
4516 | }, |
|
|||
4517 | { |
|
|||
4518 | "output_type": "stream", |
|
|||
4519 | "stream": "stdout", |
|
|||
4520 | "text": [ |
|
|||
4521 | " \r", |
|
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4522 | "[********************* 43% ] 434 of 1000 complete" |
|
|||
4523 | ] |
|
|||
4524 | }, |
|
|||
4525 | { |
|
|||
4526 | "output_type": "stream", |
|
|||
4527 | "stream": "stdout", |
|
|||
4528 | "text": [ |
|
|||
4529 | " \r", |
|
|||
4530 | "[********************* 44% ] 435 of 1000 complete" |
|
|||
4531 | ] |
|
|||
4532 | }, |
|
|||
4533 | { |
|
|||
4534 | "output_type": "stream", |
|
|||
4535 | "stream": "stdout", |
|
|||
4536 | "text": [ |
|
|||
4537 | " \r", |
|
|||
4538 | "[********************* 44% ] 436 of 1000 complete" |
|
|||
4539 | ] |
|
|||
4540 | }, |
|
|||
4541 | { |
|
|||
4542 | "output_type": "stream", |
|
|||
4543 | "stream": "stdout", |
|
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4544 | "text": [ |
|
|||
4545 | " \r", |
|
|||
4546 | "[********************* 44% ] 437 of 1000 complete" |
|
|||
4547 | ] |
|
|||
4548 | }, |
|
|||
4549 | { |
|
|||
4550 | "output_type": "stream", |
|
|||
4551 | "stream": "stdout", |
|
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4552 | "text": [ |
|
|||
4553 | " \r", |
|
|||
4554 | "[********************* 44% ] 438 of 1000 complete" |
|
|||
4555 | ] |
|
|||
4556 | }, |
|
|||
4557 | { |
|
|||
4558 | "output_type": "stream", |
|
|||
4559 | "stream": "stdout", |
|
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4560 | "text": [ |
|
|||
4561 | " \r", |
|
|||
4562 | "[********************* 44% ] 439 of 1000 complete" |
|
|||
4563 | ] |
|
|||
4564 | }, |
|
|||
4565 | { |
|
|||
4566 | "output_type": "stream", |
|
|||
4567 | "stream": "stdout", |
|
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4568 | "text": [ |
|
|||
4569 | " \r", |
|
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4570 | "[********************* 44% ] 440 of 1000 complete" |
|
|||
4571 | ] |
|
|||
4572 | }, |
|
|||
4573 | { |
|
|||
4574 | "output_type": "stream", |
|
|||
4575 | "stream": "stdout", |
|
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4576 | "text": [ |
|
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4577 | " \r", |
|
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4578 | "[********************* 44% ] 441 of 1000 complete" |
|
|||
4579 | ] |
|
|||
4580 | }, |
|
|||
4581 | { |
|
|||
4582 | "output_type": "stream", |
|
|||
4583 | "stream": "stdout", |
|
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4584 | "text": [ |
|
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4585 | " \r", |
|
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4586 | "[********************* 44% ] 442 of 1000 complete" |
|
|||
4587 | ] |
|
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4588 | }, |
|
|||
4589 | { |
|
|||
4590 | "output_type": "stream", |
|
|||
4591 | "stream": "stdout", |
|
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4592 | "text": [ |
|
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4593 | " \r", |
|
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4594 | "[********************* 44% ] 443 of 1000 complete" |
|
|||
4595 | ] |
|
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4596 | }, |
|
|||
4597 | { |
|
|||
4598 | "output_type": "stream", |
|
|||
4599 | "stream": "stdout", |
|
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4600 | "text": [ |
|
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4601 | " \r", |
|
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4602 | "[********************* 44% ] 444 of 1000 complete" |
|
|||
4603 | ] |
|
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4604 | }, |
|
|||
4605 | { |
|
|||
4606 | "output_type": "stream", |
|
|||
4607 | "stream": "stdout", |
|
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4608 | "text": [ |
|
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4609 | " \r", |
|
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4610 | "[**********************45% ] 445 of 1000 complete" |
|
|||
4611 | ] |
|
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4612 | }, |
|
|||
4613 | { |
|
|||
4614 | "output_type": "stream", |
|
|||
4615 | "stream": "stdout", |
|
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4616 | "text": [ |
|
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4617 | " \r", |
|
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4618 | "[**********************45% ] 446 of 1000 complete" |
|
|||
4619 | ] |
|
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4620 | }, |
|
|||
4621 | { |
|
|||
4622 | "output_type": "stream", |
|
|||
4623 | "stream": "stdout", |
|
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4624 | "text": [ |
|
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4625 | " \r", |
|
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4626 | "[**********************45% ] 447 of 1000 complete" |
|
|||
4627 | ] |
|
|||
4628 | }, |
|
|||
4629 | { |
|
|||
4630 | "output_type": "stream", |
|
|||
4631 | "stream": "stdout", |
|
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4632 | "text": [ |
|
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4633 | " \r", |
|
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4634 | "[**********************45% ] 448 of 1000 complete" |
|
|||
4635 | ] |
|
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4636 | }, |
|
|||
4637 | { |
|
|||
4638 | "output_type": "stream", |
|
|||
4639 | "stream": "stdout", |
|
|||
4640 | "text": [ |
|
|||
4641 | " \r", |
|
|||
4642 | "[**********************45% ] 449 of 1000 complete" |
|
|||
4643 | ] |
|
|||
4644 | }, |
|
|||
4645 | { |
|
|||
4646 | "output_type": "stream", |
|
|||
4647 | "stream": "stdout", |
|
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4648 | "text": [ |
|
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4649 | " \r", |
|
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4650 | "[**********************45% ] 450 of 1000 complete" |
|
|||
4651 | ] |
|
|||
4652 | }, |
|
|||
4653 | { |
|
|||
4654 | "output_type": "stream", |
|
|||
4655 | "stream": "stdout", |
|
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4656 | "text": [ |
|
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4657 | " \r", |
|
|||
4658 | "[**********************45% ] 451 of 1000 complete" |
|
|||
4659 | ] |
|
|||
4660 | }, |
|
|||
4661 | { |
|
|||
4662 | "output_type": "stream", |
|
|||
4663 | "stream": "stdout", |
|
|||
4664 | "text": [ |
|
|||
4665 | " \r", |
|
|||
4666 | "[**********************45% ] 452 of 1000 complete" |
|
|||
4667 | ] |
|
|||
4668 | }, |
|
|||
4669 | { |
|
|||
4670 | "output_type": "stream", |
|
|||
4671 | "stream": "stdout", |
|
|||
4672 | "text": [ |
|
|||
4673 | " \r", |
|
|||
4674 | "[**********************45% ] 453 of 1000 complete" |
|
|||
4675 | ] |
|
|||
4676 | }, |
|
|||
4677 | { |
|
|||
4678 | "output_type": "stream", |
|
|||
4679 | "stream": "stdout", |
|
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4680 | "text": [ |
|
|||
4681 | " \r", |
|
|||
4682 | "[**********************45% ] 454 of 1000 complete" |
|
|||
4683 | ] |
|
|||
4684 | }, |
|
|||
4685 | { |
|
|||
4686 | "output_type": "stream", |
|
|||
4687 | "stream": "stdout", |
|
|||
4688 | "text": [ |
|
|||
4689 | " \r", |
|
|||
4690 | "[**********************46% ] 455 of 1000 complete" |
|
|||
4691 | ] |
|
|||
4692 | }, |
|
|||
4693 | { |
|
|||
4694 | "output_type": "stream", |
|
|||
4695 | "stream": "stdout", |
|
|||
4696 | "text": [ |
|
|||
4697 | " \r", |
|
|||
4698 | "[**********************46% ] 456 of 1000 complete" |
|
|||
4699 | ] |
|
|||
4700 | }, |
|
|||
4701 | { |
|
|||
4702 | "output_type": "stream", |
|
|||
4703 | "stream": "stdout", |
|
|||
4704 | "text": [ |
|
|||
4705 | " \r", |
|
|||
4706 | "[**********************46% ] 457 of 1000 complete" |
|
|||
4707 | ] |
|
|||
4708 | }, |
|
|||
4709 | { |
|
|||
4710 | "output_type": "stream", |
|
|||
4711 | "stream": "stdout", |
|
|||
4712 | "text": [ |
|
|||
4713 | " \r", |
|
|||
4714 | "[**********************46% ] 458 of 1000 complete" |
|
|||
4715 | ] |
|
|||
4716 | }, |
|
|||
4717 | { |
|
|||
4718 | "output_type": "stream", |
|
|||
4719 | "stream": "stdout", |
|
|||
4720 | "text": [ |
|
|||
4721 | " \r", |
|
|||
4722 | "[**********************46% ] 459 of 1000 complete" |
|
|||
4723 | ] |
|
|||
4724 | }, |
|
|||
4725 | { |
|
|||
4726 | "output_type": "stream", |
|
|||
4727 | "stream": "stdout", |
|
|||
4728 | "text": [ |
|
|||
4729 | " \r", |
|
|||
4730 | "[**********************46% ] 460 of 1000 complete" |
|
|||
4731 | ] |
|
|||
4732 | }, |
|
|||
4733 | { |
|
|||
4734 | "output_type": "stream", |
|
|||
4735 | "stream": "stdout", |
|
|||
4736 | "text": [ |
|
|||
4737 | " \r", |
|
|||
4738 | "[**********************46% ] 461 of 1000 complete" |
|
|||
4739 | ] |
|
|||
4740 | }, |
|
|||
4741 | { |
|
|||
4742 | "output_type": "stream", |
|
|||
4743 | "stream": "stdout", |
|
|||
4744 | "text": [ |
|
|||
4745 | " \r", |
|
|||
4746 | "[**********************46% ] 462 of 1000 complete" |
|
|||
4747 | ] |
|
|||
4748 | }, |
|
|||
4749 | { |
|
|||
4750 | "output_type": "stream", |
|
|||
4751 | "stream": "stdout", |
|
|||
4752 | "text": [ |
|
|||
4753 | " \r", |
|
|||
4754 | "[**********************46% ] 463 of 1000 complete" |
|
|||
4755 | ] |
|
|||
4756 | }, |
|
|||
4757 | { |
|
|||
4758 | "output_type": "stream", |
|
|||
4759 | "stream": "stdout", |
|
|||
4760 | "text": [ |
|
|||
4761 | " \r", |
|
|||
4762 | "[**********************46% ] 464 of 1000 complete" |
|
|||
4763 | ] |
|
|||
4764 | }, |
|
|||
4765 | { |
|
|||
4766 | "output_type": "stream", |
|
|||
4767 | "stream": "stdout", |
|
|||
4768 | "text": [ |
|
|||
4769 | " \r", |
|
|||
4770 | "[**********************47% ] 465 of 1000 complete" |
|
|||
4771 | ] |
|
|||
4772 | }, |
|
|||
4773 | { |
|
|||
4774 | "output_type": "stream", |
|
|||
4775 | "stream": "stdout", |
|
|||
4776 | "text": [ |
|
|||
4777 | " \r", |
|
|||
4778 | "[**********************47% ] 466 of 1000 complete" |
|
|||
4779 | ] |
|
|||
4780 | }, |
|
|||
4781 | { |
|
|||
4782 | "output_type": "stream", |
|
|||
4783 | "stream": "stdout", |
|
|||
4784 | "text": [ |
|
|||
4785 | " \r", |
|
|||
4786 | "[**********************47% ] 467 of 1000 complete" |
|
|||
4787 | ] |
|
|||
4788 | }, |
|
|||
4789 | { |
|
|||
4790 | "output_type": "stream", |
|
|||
4791 | "stream": "stdout", |
|
|||
4792 | "text": [ |
|
|||
4793 | " \r", |
|
|||
4794 | "[**********************47% ] 468 of 1000 complete" |
|
|||
4795 | ] |
|
|||
4796 | }, |
|
|||
4797 | { |
|
|||
4798 | "output_type": "stream", |
|
|||
4799 | "stream": "stdout", |
|
|||
4800 | "text": [ |
|
|||
4801 | " \r", |
|
|||
4802 | "[**********************47% ] 469 of 1000 complete" |
|
|||
4803 | ] |
|
|||
4804 | }, |
|
|||
4805 | { |
|
|||
4806 | "output_type": "stream", |
|
|||
4807 | "stream": "stdout", |
|
|||
4808 | "text": [ |
|
|||
4809 | " \r", |
|
|||
4810 | "[**********************47% ] 470 of 1000 complete" |
|
|||
4811 | ] |
|
|||
4812 | }, |
|
|||
4813 | { |
|
|||
4814 | "output_type": "stream", |
|
|||
4815 | "stream": "stdout", |
|
|||
4816 | "text": [ |
|
|||
4817 | " \r", |
|
|||
4818 | "[**********************47% ] 471 of 1000 complete" |
|
|||
4819 | ] |
|
|||
4820 | }, |
|
|||
4821 | { |
|
|||
4822 | "output_type": "stream", |
|
|||
4823 | "stream": "stdout", |
|
|||
4824 | "text": [ |
|
|||
4825 | " \r", |
|
|||
4826 | "[**********************47% ] 472 of 1000 complete" |
|
|||
4827 | ] |
|
|||
4828 | }, |
|
|||
4829 | { |
|
|||
4830 | "output_type": "stream", |
|
|||
4831 | "stream": "stdout", |
|
|||
4832 | "text": [ |
|
|||
4833 | " \r", |
|
|||
4834 | "[**********************47% ] 473 of 1000 complete" |
|
|||
4835 | ] |
|
|||
4836 | }, |
|
|||
4837 | { |
|
|||
4838 | "output_type": "stream", |
|
|||
4839 | "stream": "stdout", |
|
|||
4840 | "text": [ |
|
|||
4841 | " \r", |
|
|||
4842 | "[**********************47% ] 474 of 1000 complete" |
|
|||
4843 | ] |
|
|||
4844 | }, |
|
|||
4845 | { |
|
|||
4846 | "output_type": "stream", |
|
|||
4847 | "stream": "stdout", |
|
|||
4848 | "text": [ |
|
|||
4849 | " \r", |
|
|||
4850 | "[**********************48% ] 475 of 1000 complete" |
|
|||
4851 | ] |
|
|||
4852 | }, |
|
|||
4853 | { |
|
|||
4854 | "output_type": "stream", |
|
|||
4855 | "stream": "stdout", |
|
|||
4856 | "text": [ |
|
|||
4857 | " \r", |
|
|||
4858 | "[**********************48% ] 476 of 1000 complete" |
|
|||
4859 | ] |
|
|||
4860 | }, |
|
|||
4861 | { |
|
|||
4862 | "output_type": "stream", |
|
|||
4863 | "stream": "stdout", |
|
|||
4864 | "text": [ |
|
|||
4865 | " \r", |
|
|||
4866 | "[**********************48% ] 477 of 1000 complete" |
|
|||
4867 | ] |
|
|||
4868 | }, |
|
|||
4869 | { |
|
|||
4870 | "output_type": "stream", |
|
|||
4871 | "stream": "stdout", |
|
|||
4872 | "text": [ |
|
|||
4873 | " \r", |
|
|||
4874 | "[**********************48% ] 478 of 1000 complete" |
|
|||
4875 | ] |
|
|||
4876 | }, |
|
|||
4877 | { |
|
|||
4878 | "output_type": "stream", |
|
|||
4879 | "stream": "stdout", |
|
|||
4880 | "text": [ |
|
|||
4881 | " \r", |
|
|||
4882 | "[**********************48% ] 479 of 1000 complete" |
|
|||
4883 | ] |
|
|||
4884 | }, |
|
|||
4885 | { |
|
|||
4886 | "output_type": "stream", |
|
|||
4887 | "stream": "stdout", |
|
|||
4888 | "text": [ |
|
|||
4889 | " \r", |
|
|||
4890 | "[**********************48% ] 480 of 1000 complete" |
|
|||
4891 | ] |
|
|||
4892 | }, |
|
|||
4893 | { |
|
|||
4894 | "output_type": "stream", |
|
|||
4895 | "stream": "stdout", |
|
|||
4896 | "text": [ |
|
|||
4897 | " \r", |
|
|||
4898 | "[**********************48% ] 481 of 1000 complete" |
|
|||
4899 | ] |
|
|||
4900 | }, |
|
|||
4901 | { |
|
|||
4902 | "output_type": "stream", |
|
|||
4903 | "stream": "stdout", |
|
|||
4904 | "text": [ |
|
|||
4905 | " \r", |
|
|||
4906 | "[**********************48% ] 482 of 1000 complete" |
|
|||
4907 | ] |
|
|||
4908 | }, |
|
|||
4909 | { |
|
|||
4910 | "output_type": "stream", |
|
|||
4911 | "stream": "stdout", |
|
|||
4912 | "text": [ |
|
|||
4913 | " \r", |
|
|||
4914 | "[**********************48% ] 483 of 1000 complete" |
|
|||
4915 | ] |
|
|||
4916 | }, |
|
|||
4917 | { |
|
|||
4918 | "output_type": "stream", |
|
|||
4919 | "stream": "stdout", |
|
|||
4920 | "text": [ |
|
|||
4921 | " \r", |
|
|||
4922 | "[**********************48% ] 484 of 1000 complete" |
|
|||
4923 | ] |
|
|||
4924 | }, |
|
|||
4925 | { |
|
|||
4926 | "output_type": "stream", |
|
|||
4927 | "stream": "stdout", |
|
|||
4928 | "text": [ |
|
|||
4929 | " \r", |
|
|||
4930 | "[**********************49% ] 485 of 1000 complete" |
|
|||
4931 | ] |
|
|||
4932 | }, |
|
|||
4933 | { |
|
|||
4934 | "output_type": "stream", |
|
|||
4935 | "stream": "stdout", |
|
|||
4936 | "text": [ |
|
|||
4937 | " \r", |
|
|||
4938 | "[**********************49% ] 486 of 1000 complete" |
|
|||
4939 | ] |
|
|||
4940 | }, |
|
|||
4941 | { |
|
|||
4942 | "output_type": "stream", |
|
|||
4943 | "stream": "stdout", |
|
|||
4944 | "text": [ |
|
|||
4945 | " \r", |
|
|||
4946 | "[**********************49% ] 487 of 1000 complete" |
|
|||
4947 | ] |
|
|||
4948 | }, |
|
|||
4949 | { |
|
|||
4950 | "output_type": "stream", |
|
|||
4951 | "stream": "stdout", |
|
|||
4952 | "text": [ |
|
|||
4953 | " \r", |
|
|||
4954 | "[**********************49% ] 488 of 1000 complete" |
|
|||
4955 | ] |
|
|||
4956 | }, |
|
|||
4957 | { |
|
|||
4958 | "output_type": "stream", |
|
|||
4959 | "stream": "stdout", |
|
|||
4960 | "text": [ |
|
|||
4961 | " \r", |
|
|||
4962 | "[**********************49% ] 489 of 1000 complete" |
|
|||
4963 | ] |
|
|||
4964 | }, |
|
|||
4965 | { |
|
|||
4966 | "output_type": "stream", |
|
|||
4967 | "stream": "stdout", |
|
|||
4968 | "text": [ |
|
|||
4969 | " \r", |
|
|||
4970 | "[**********************49% ] 490 of 1000 complete" |
|
|||
4971 | ] |
|
|||
4972 | }, |
|
|||
4973 | { |
|
|||
4974 | "output_type": "stream", |
|
|||
4975 | "stream": "stdout", |
|
|||
4976 | "text": [ |
|
|||
4977 | " \r", |
|
|||
4978 | "[**********************49% ] 491 of 1000 complete" |
|
|||
4979 | ] |
|
|||
4980 | }, |
|
|||
4981 | { |
|
|||
4982 | "output_type": "stream", |
|
|||
4983 | "stream": "stdout", |
|
|||
4984 | "text": [ |
|
|||
4985 | " \r", |
|
|||
4986 | "[**********************49% ] 492 of 1000 complete" |
|
|||
4987 | ] |
|
|||
4988 | }, |
|
|||
4989 | { |
|
|||
4990 | "output_type": "stream", |
|
|||
4991 | "stream": "stdout", |
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|||
4992 | "text": [ |
|
|||
4993 | " \r", |
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4994 | "[**********************49% ] 493 of 1000 complete" |
|
|||
4995 | ] |
|
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4996 | }, |
|
|||
4997 | { |
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|||
4998 | "output_type": "stream", |
|
|||
4999 | "stream": "stdout", |
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5000 | "text": [ |
|
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5001 | " \r", |
|
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5002 | "[**********************49% ] 494 of 1000 complete" |
|
|||
5003 | ] |
|
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5004 | }, |
|
|||
5005 | { |
|
|||
5006 | "output_type": "stream", |
|
|||
5007 | "stream": "stdout", |
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5008 | "text": [ |
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5009 | " \r", |
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5010 | "[**********************50% ] 495 of 1000 complete" |
|
|||
5011 | ] |
|
|||
5012 | }, |
|
|||
5013 | { |
|
|||
5014 | "output_type": "stream", |
|
|||
5015 | "stream": "stdout", |
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5016 | "text": [ |
|
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5017 | " \r", |
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5018 | "[**********************50% ] 496 of 1000 complete" |
|
|||
5019 | ] |
|
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5020 | }, |
|
|||
5021 | { |
|
|||
5022 | "output_type": "stream", |
|
|||
5023 | "stream": "stdout", |
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5024 | "text": [ |
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5025 | " \r", |
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5026 | "[**********************50% ] 497 of 1000 complete" |
|
|||
5027 | ] |
|
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5028 | }, |
|
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5029 | { |
|
|||
5030 | "output_type": "stream", |
|
|||
5031 | "stream": "stdout", |
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5032 | "text": [ |
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5033 | " \r", |
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5034 | "[**********************50% ] 498 of 1000 complete" |
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|||
5035 | ] |
|
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5036 | }, |
|
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5037 | { |
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5038 | "output_type": "stream", |
|
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5039 | "stream": "stdout", |
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5040 | "text": [ |
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5041 | " \r", |
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5042 | "[**********************50% ] 499 of 1000 complete" |
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5043 | ] |
|
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5044 | }, |
|
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5045 | { |
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5046 | "output_type": "stream", |
|
|||
5047 | "stream": "stdout", |
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5048 | "text": [ |
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5049 | " \r", |
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5050 | "[**********************50% ] 500 of 1000 complete" |
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5051 | ] |
|
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5052 | }, |
|
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5053 | { |
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5054 | "output_type": "stream", |
|
|||
5055 | "stream": "stdout", |
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5056 | "text": [ |
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5057 | " \r", |
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5058 | "[**********************50% ] 501 of 1000 complete" |
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5059 | ] |
|
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5060 | }, |
|
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5061 | { |
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5062 | "output_type": "stream", |
|
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5063 | "stream": "stdout", |
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5064 | "text": [ |
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5065 | " \r", |
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5066 | "[**********************50% ] 502 of 1000 complete" |
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5067 | ] |
|
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5068 | }, |
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5069 | { |
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5070 | "output_type": "stream", |
|
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5071 | "stream": "stdout", |
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5072 | "text": [ |
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5073 | " \r", |
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5074 | "[**********************50% ] 503 of 1000 complete" |
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5075 | ] |
|
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5076 | }, |
|
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5077 | { |
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5078 | "output_type": "stream", |
|
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5079 | "stream": "stdout", |
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5080 | "text": [ |
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5081 | " \r", |
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5082 | "[**********************50% ] 504 of 1000 complete" |
|
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5083 | ] |
|
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5084 | }, |
|
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5085 | { |
|
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5086 | "output_type": "stream", |
|
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5087 | "stream": "stdout", |
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5088 | "text": [ |
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5089 | " \r", |
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5090 | "[**********************51% ] 505 of 1000 complete" |
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5091 | ] |
|
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5092 | }, |
|
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5093 | { |
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5094 | "output_type": "stream", |
|
|||
5095 | "stream": "stdout", |
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5096 | "text": [ |
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5097 | " \r", |
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5098 | "[**********************51% ] 506 of 1000 complete" |
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5099 | ] |
|
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5100 | }, |
|
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5101 | { |
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5102 | "output_type": "stream", |
|
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5103 | "stream": "stdout", |
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5104 | "text": [ |
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5105 | " \r", |
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5106 | "[**********************51% ] 507 of 1000 complete" |
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5107 | ] |
|
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5108 | }, |
|
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5109 | { |
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5110 | "output_type": "stream", |
|
|||
5111 | "stream": "stdout", |
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5112 | "text": [ |
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5113 | " \r", |
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5114 | "[**********************51% ] 508 of 1000 complete" |
|
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5115 | ] |
|
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5116 | }, |
|
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5117 | { |
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5118 | "output_type": "stream", |
|
|||
5119 | "stream": "stdout", |
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5120 | "text": [ |
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5121 | " \r", |
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5122 | "[**********************51% ] 509 of 1000 complete" |
|
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5123 | ] |
|
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5124 | }, |
|
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5125 | { |
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5126 | "output_type": "stream", |
|
|||
5127 | "stream": "stdout", |
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5128 | "text": [ |
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5129 | " \r", |
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5130 | "[**********************51% ] 510 of 1000 complete" |
|
|||
5131 | ] |
|
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5132 | }, |
|
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5133 | { |
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5134 | "output_type": "stream", |
|
|||
5135 | "stream": "stdout", |
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5136 | "text": [ |
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5137 | " \r", |
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5138 | "[**********************51% ] 511 of 1000 complete" |
|
|||
5139 | ] |
|
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5140 | }, |
|
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5141 | { |
|
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5142 | "output_type": "stream", |
|
|||
5143 | "stream": "stdout", |
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5144 | "text": [ |
|
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5145 | " \r", |
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5146 | "[**********************51% ] 512 of 1000 complete" |
|
|||
5147 | ] |
|
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5148 | }, |
|
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5149 | { |
|
|||
5150 | "output_type": "stream", |
|
|||
5151 | "stream": "stdout", |
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5152 | "text": [ |
|
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5153 | " \r", |
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5154 | "[**********************51% ] 513 of 1000 complete" |
|
|||
5155 | ] |
|
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5156 | }, |
|
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5157 | { |
|
|||
5158 | "output_type": "stream", |
|
|||
5159 | "stream": "stdout", |
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5160 | "text": [ |
|
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5161 | " \r", |
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5162 | "[**********************51% ] 514 of 1000 complete" |
|
|||
5163 | ] |
|
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5164 | }, |
|
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5165 | { |
|
|||
5166 | "output_type": "stream", |
|
|||
5167 | "stream": "stdout", |
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5168 | "text": [ |
|
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5169 | " \r", |
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5170 | "[**********************52% ] 515 of 1000 complete" |
|
|||
5171 | ] |
|
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5172 | }, |
|
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5173 | { |
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|||
5174 | "output_type": "stream", |
|
|||
5175 | "stream": "stdout", |
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5176 | "text": [ |
|
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5177 | " \r", |
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5178 | "[**********************52% ] 516 of 1000 complete" |
|
|||
5179 | ] |
|
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5180 | }, |
|
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5181 | { |
|
|||
5182 | "output_type": "stream", |
|
|||
5183 | "stream": "stdout", |
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5184 | "text": [ |
|
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5185 | " \r", |
|
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5186 | "[**********************52% ] 517 of 1000 complete" |
|
|||
5187 | ] |
|
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5188 | }, |
|
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5189 | { |
|
|||
5190 | "output_type": "stream", |
|
|||
5191 | "stream": "stdout", |
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5192 | "text": [ |
|
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5193 | " \r", |
|
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5194 | "[**********************52% ] 518 of 1000 complete" |
|
|||
5195 | ] |
|
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5196 | }, |
|
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5197 | { |
|
|||
5198 | "output_type": "stream", |
|
|||
5199 | "stream": "stdout", |
|
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5200 | "text": [ |
|
|||
5201 | " \r", |
|
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5202 | "[**********************52% ] 519 of 1000 complete" |
|
|||
5203 | ] |
|
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5204 | }, |
|
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5205 | { |
|
|||
5206 | "output_type": "stream", |
|
|||
5207 | "stream": "stdout", |
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5208 | "text": [ |
|
|||
5209 | " \r", |
|
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5210 | "[**********************52% ] 520 of 1000 complete" |
|
|||
5211 | ] |
|
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5212 | }, |
|
|||
5213 | { |
|
|||
5214 | "output_type": "stream", |
|
|||
5215 | "stream": "stdout", |
|
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5216 | "text": [ |
|
|||
5217 | " \r", |
|
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5218 | "[**********************52% ] 521 of 1000 complete" |
|
|||
5219 | ] |
|
|||
5220 | }, |
|
|||
5221 | { |
|
|||
5222 | "output_type": "stream", |
|
|||
5223 | "stream": "stdout", |
|
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5224 | "text": [ |
|
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5225 | " \r", |
|
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5226 | "[**********************52% ] 522 of 1000 complete" |
|
|||
5227 | ] |
|
|||
5228 | }, |
|
|||
5229 | { |
|
|||
5230 | "output_type": "stream", |
|
|||
5231 | "stream": "stdout", |
|
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5232 | "text": [ |
|
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5233 | " \r", |
|
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5234 | "[**********************52% ] 523 of 1000 complete" |
|
|||
5235 | ] |
|
|||
5236 | }, |
|
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5237 | { |
|
|||
5238 | "output_type": "stream", |
|
|||
5239 | "stream": "stdout", |
|
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5240 | "text": [ |
|
|||
5241 | " \r", |
|
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5242 | "[**********************52% ] 524 of 1000 complete" |
|
|||
5243 | ] |
|
|||
5244 | }, |
|
|||
5245 | { |
|
|||
5246 | "output_type": "stream", |
|
|||
5247 | "stream": "stdout", |
|
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5248 | "text": [ |
|
|||
5249 | " \r", |
|
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5250 | "[**********************53% ] 525 of 1000 complete" |
|
|||
5251 | ] |
|
|||
5252 | }, |
|
|||
5253 | { |
|
|||
5254 | "output_type": "stream", |
|
|||
5255 | "stream": "stdout", |
|
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5256 | "text": [ |
|
|||
5257 | " \r", |
|
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5258 | "[**********************53% ] 526 of 1000 complete" |
|
|||
5259 | ] |
|
|||
5260 | }, |
|
|||
5261 | { |
|
|||
5262 | "output_type": "stream", |
|
|||
5263 | "stream": "stdout", |
|
|||
5264 | "text": [ |
|
|||
5265 | " \r", |
|
|||
5266 | "[**********************53% ] 527 of 1000 complete" |
|
|||
5267 | ] |
|
|||
5268 | }, |
|
|||
5269 | { |
|
|||
5270 | "output_type": "stream", |
|
|||
5271 | "stream": "stdout", |
|
|||
5272 | "text": [ |
|
|||
5273 | " \r", |
|
|||
5274 | "[**********************53% ] 528 of 1000 complete" |
|
|||
5275 | ] |
|
|||
5276 | }, |
|
|||
5277 | { |
|
|||
5278 | "output_type": "stream", |
|
|||
5279 | "stream": "stdout", |
|
|||
5280 | "text": [ |
|
|||
5281 | " \r", |
|
|||
5282 | "[**********************53% ] 529 of 1000 complete" |
|
|||
5283 | ] |
|
|||
5284 | }, |
|
|||
5285 | { |
|
|||
5286 | "output_type": "stream", |
|
|||
5287 | "stream": "stdout", |
|
|||
5288 | "text": [ |
|
|||
5289 | " \r", |
|
|||
5290 | "[**********************53% ] 530 of 1000 complete" |
|
|||
5291 | ] |
|
|||
5292 | }, |
|
|||
5293 | { |
|
|||
5294 | "output_type": "stream", |
|
|||
5295 | "stream": "stdout", |
|
|||
5296 | "text": [ |
|
|||
5297 | " \r", |
|
|||
5298 | "[**********************53% ] 531 of 1000 complete" |
|
|||
5299 | ] |
|
|||
5300 | }, |
|
|||
5301 | { |
|
|||
5302 | "output_type": "stream", |
|
|||
5303 | "stream": "stdout", |
|
|||
5304 | "text": [ |
|
|||
5305 | " \r", |
|
|||
5306 | "[**********************53% ] 532 of 1000 complete" |
|
|||
5307 | ] |
|
|||
5308 | }, |
|
|||
5309 | { |
|
|||
5310 | "output_type": "stream", |
|
|||
5311 | "stream": "stdout", |
|
|||
5312 | "text": [ |
|
|||
5313 | " \r", |
|
|||
5314 | "[**********************53% ] 533 of 1000 complete" |
|
|||
5315 | ] |
|
|||
5316 | }, |
|
|||
5317 | { |
|
|||
5318 | "output_type": "stream", |
|
|||
5319 | "stream": "stdout", |
|
|||
5320 | "text": [ |
|
|||
5321 | " \r", |
|
|||
5322 | "[**********************53% ] 534 of 1000 complete" |
|
|||
5323 | ] |
|
|||
5324 | }, |
|
|||
5325 | { |
|
|||
5326 | "output_type": "stream", |
|
|||
5327 | "stream": "stdout", |
|
|||
5328 | "text": [ |
|
|||
5329 | " \r", |
|
|||
5330 | "[**********************54%* ] 535 of 1000 complete" |
|
|||
5331 | ] |
|
|||
5332 | }, |
|
|||
5333 | { |
|
|||
5334 | "output_type": "stream", |
|
|||
5335 | "stream": "stdout", |
|
|||
5336 | "text": [ |
|
|||
5337 | " \r", |
|
|||
5338 | "[**********************54%* ] 536 of 1000 complete" |
|
|||
5339 | ] |
|
|||
5340 | }, |
|
|||
5341 | { |
|
|||
5342 | "output_type": "stream", |
|
|||
5343 | "stream": "stdout", |
|
|||
5344 | "text": [ |
|
|||
5345 | " \r", |
|
|||
5346 | "[**********************54%* ] 537 of 1000 complete" |
|
|||
5347 | ] |
|
|||
5348 | }, |
|
|||
5349 | { |
|
|||
5350 | "output_type": "stream", |
|
|||
5351 | "stream": "stdout", |
|
|||
5352 | "text": [ |
|
|||
5353 | " \r", |
|
|||
5354 | "[**********************54%* ] 538 of 1000 complete" |
|
|||
5355 | ] |
|
|||
5356 | }, |
|
|||
5357 | { |
|
|||
5358 | "output_type": "stream", |
|
|||
5359 | "stream": "stdout", |
|
|||
5360 | "text": [ |
|
|||
5361 | " \r", |
|
|||
5362 | "[**********************54%* ] 539 of 1000 complete" |
|
|||
5363 | ] |
|
|||
5364 | }, |
|
|||
5365 | { |
|
|||
5366 | "output_type": "stream", |
|
|||
5367 | "stream": "stdout", |
|
|||
5368 | "text": [ |
|
|||
5369 | " \r", |
|
|||
5370 | "[**********************54%* ] 540 of 1000 complete" |
|
|||
5371 | ] |
|
|||
5372 | }, |
|
|||
5373 | { |
|
|||
5374 | "output_type": "stream", |
|
|||
5375 | "stream": "stdout", |
|
|||
5376 | "text": [ |
|
|||
5377 | " \r", |
|
|||
5378 | "[**********************54%* ] 541 of 1000 complete" |
|
|||
5379 | ] |
|
|||
5380 | }, |
|
|||
5381 | { |
|
|||
5382 | "output_type": "stream", |
|
|||
5383 | "stream": "stdout", |
|
|||
5384 | "text": [ |
|
|||
5385 | " \r", |
|
|||
5386 | "[**********************54%* ] 542 of 1000 complete" |
|
|||
5387 | ] |
|
|||
5388 | }, |
|
|||
5389 | { |
|
|||
5390 | "output_type": "stream", |
|
|||
5391 | "stream": "stdout", |
|
|||
5392 | "text": [ |
|
|||
5393 | " \r", |
|
|||
5394 | "[**********************54%* ] 543 of 1000 complete" |
|
|||
5395 | ] |
|
|||
5396 | }, |
|
|||
5397 | { |
|
|||
5398 | "output_type": "stream", |
|
|||
5399 | "stream": "stdout", |
|
|||
5400 | "text": [ |
|
|||
5401 | " \r", |
|
|||
5402 | "[**********************54%* ] 544 of 1000 complete" |
|
|||
5403 | ] |
|
|||
5404 | }, |
|
|||
5405 | { |
|
|||
5406 | "output_type": "stream", |
|
|||
5407 | "stream": "stdout", |
|
|||
5408 | "text": [ |
|
|||
5409 | " \r", |
|
|||
5410 | "[**********************55%* ] 545 of 1000 complete" |
|
|||
5411 | ] |
|
|||
5412 | }, |
|
|||
5413 | { |
|
|||
5414 | "output_type": "stream", |
|
|||
5415 | "stream": "stdout", |
|
|||
5416 | "text": [ |
|
|||
5417 | " \r", |
|
|||
5418 | "[**********************55%* ] 546 of 1000 complete" |
|
|||
5419 | ] |
|
|||
5420 | }, |
|
|||
5421 | { |
|
|||
5422 | "output_type": "stream", |
|
|||
5423 | "stream": "stdout", |
|
|||
5424 | "text": [ |
|
|||
5425 | " \r", |
|
|||
5426 | "[**********************55%* ] 547 of 1000 complete" |
|
|||
5427 | ] |
|
|||
5428 | }, |
|
|||
5429 | { |
|
|||
5430 | "output_type": "stream", |
|
|||
5431 | "stream": "stdout", |
|
|||
5432 | "text": [ |
|
|||
5433 | " \r", |
|
|||
5434 | "[**********************55%* ] 548 of 1000 complete" |
|
|||
5435 | ] |
|
|||
5436 | }, |
|
|||
5437 | { |
|
|||
5438 | "output_type": "stream", |
|
|||
5439 | "stream": "stdout", |
|
|||
5440 | "text": [ |
|
|||
5441 | " \r", |
|
|||
5442 | "[**********************55%* ] 549 of 1000 complete" |
|
|||
5443 | ] |
|
|||
5444 | }, |
|
|||
5445 | { |
|
|||
5446 | "output_type": "stream", |
|
|||
5447 | "stream": "stdout", |
|
|||
5448 | "text": [ |
|
|||
5449 | " \r", |
|
|||
5450 | "[**********************55%* ] 550 of 1000 complete" |
|
|||
5451 | ] |
|
|||
5452 | }, |
|
|||
5453 | { |
|
|||
5454 | "output_type": "stream", |
|
|||
5455 | "stream": "stdout", |
|
|||
5456 | "text": [ |
|
|||
5457 | " \r", |
|
|||
5458 | "[**********************55%* ] 551 of 1000 complete" |
|
|||
5459 | ] |
|
|||
5460 | }, |
|
|||
5461 | { |
|
|||
5462 | "output_type": "stream", |
|
|||
5463 | "stream": "stdout", |
|
|||
5464 | "text": [ |
|
|||
5465 | " \r", |
|
|||
5466 | "[**********************55%* ] 552 of 1000 complete" |
|
|||
5467 | ] |
|
|||
5468 | }, |
|
|||
5469 | { |
|
|||
5470 | "output_type": "stream", |
|
|||
5471 | "stream": "stdout", |
|
|||
5472 | "text": [ |
|
|||
5473 | " \r", |
|
|||
5474 | "[**********************55%* ] 553 of 1000 complete" |
|
|||
5475 | ] |
|
|||
5476 | }, |
|
|||
5477 | { |
|
|||
5478 | "output_type": "stream", |
|
|||
5479 | "stream": "stdout", |
|
|||
5480 | "text": [ |
|
|||
5481 | " \r", |
|
|||
5482 | "[**********************55%* ] 554 of 1000 complete" |
|
|||
5483 | ] |
|
|||
5484 | }, |
|
|||
5485 | { |
|
|||
5486 | "output_type": "stream", |
|
|||
5487 | "stream": "stdout", |
|
|||
5488 | "text": [ |
|
|||
5489 | " \r", |
|
|||
5490 | "[**********************56%** ] 555 of 1000 complete" |
|
|||
5491 | ] |
|
|||
5492 | }, |
|
|||
5493 | { |
|
|||
5494 | "output_type": "stream", |
|
|||
5495 | "stream": "stdout", |
|
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5496 | "text": [ |
|
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5497 | " \r", |
|
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5498 | "[**********************56%** ] 556 of 1000 complete" |
|
|||
5499 | ] |
|
|||
5500 | }, |
|
|||
5501 | { |
|
|||
5502 | "output_type": "stream", |
|
|||
5503 | "stream": "stdout", |
|
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5504 | "text": [ |
|
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5505 | " \r", |
|
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5506 | "[**********************56%** ] 557 of 1000 complete" |
|
|||
5507 | ] |
|
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5508 | }, |
|
|||
5509 | { |
|
|||
5510 | "output_type": "stream", |
|
|||
5511 | "stream": "stdout", |
|
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5512 | "text": [ |
|
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5513 | " \r", |
|
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5514 | "[**********************56%** ] 558 of 1000 complete" |
|
|||
5515 | ] |
|
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5516 | }, |
|
|||
5517 | { |
|
|||
5518 | "output_type": "stream", |
|
|||
5519 | "stream": "stdout", |
|
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5520 | "text": [ |
|
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5521 | " \r", |
|
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5522 | "[**********************56%** ] 559 of 1000 complete" |
|
|||
5523 | ] |
|
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5524 | }, |
|
|||
5525 | { |
|
|||
5526 | "output_type": "stream", |
|
|||
5527 | "stream": "stdout", |
|
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5528 | "text": [ |
|
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5529 | " \r", |
|
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5530 | "[**********************56%** ] 560 of 1000 complete" |
|
|||
5531 | ] |
|
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5532 | }, |
|
|||
5533 | { |
|
|||
5534 | "output_type": "stream", |
|
|||
5535 | "stream": "stdout", |
|
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5536 | "text": [ |
|
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5537 | " \r", |
|
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5538 | "[**********************56%** ] 561 of 1000 complete" |
|
|||
5539 | ] |
|
|||
5540 | }, |
|
|||
5541 | { |
|
|||
5542 | "output_type": "stream", |
|
|||
5543 | "stream": "stdout", |
|
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5544 | "text": [ |
|
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5545 | " \r", |
|
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5546 | "[**********************56%** ] 562 of 1000 complete" |
|
|||
5547 | ] |
|
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5548 | }, |
|
|||
5549 | { |
|
|||
5550 | "output_type": "stream", |
|
|||
5551 | "stream": "stdout", |
|
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5552 | "text": [ |
|
|||
5553 | " \r", |
|
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5554 | "[**********************56%** ] 563 of 1000 complete" |
|
|||
5555 | ] |
|
|||
5556 | }, |
|
|||
5557 | { |
|
|||
5558 | "output_type": "stream", |
|
|||
5559 | "stream": "stdout", |
|
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5560 | "text": [ |
|
|||
5561 | " \r", |
|
|||
5562 | "[**********************56%** ] 564 of 1000 complete" |
|
|||
5563 | ] |
|
|||
5564 | }, |
|
|||
5565 | { |
|
|||
5566 | "output_type": "stream", |
|
|||
5567 | "stream": "stdout", |
|
|||
5568 | "text": [ |
|
|||
5569 | " \r", |
|
|||
5570 | "[**********************56%** ] 565 of 1000 complete" |
|
|||
5571 | ] |
|
|||
5572 | }, |
|
|||
5573 | { |
|
|||
5574 | "output_type": "stream", |
|
|||
5575 | "stream": "stdout", |
|
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5576 | "text": [ |
|
|||
5577 | " \r", |
|
|||
5578 | "[**********************57%** ] 566 of 1000 complete" |
|
|||
5579 | ] |
|
|||
5580 | }, |
|
|||
5581 | { |
|
|||
5582 | "output_type": "stream", |
|
|||
5583 | "stream": "stdout", |
|
|||
5584 | "text": [ |
|
|||
5585 | " \r", |
|
|||
5586 | "[**********************57%** ] 567 of 1000 complete" |
|
|||
5587 | ] |
|
|||
5588 | }, |
|
|||
5589 | { |
|
|||
5590 | "output_type": "stream", |
|
|||
5591 | "stream": "stdout", |
|
|||
5592 | "text": [ |
|
|||
5593 | " \r", |
|
|||
5594 | "[**********************57%** ] 568 of 1000 complete" |
|
|||
5595 | ] |
|
|||
5596 | }, |
|
|||
5597 | { |
|
|||
5598 | "output_type": "stream", |
|
|||
5599 | "stream": "stdout", |
|
|||
5600 | "text": [ |
|
|||
5601 | " \r", |
|
|||
5602 | "[**********************57%** ] 569 of 1000 complete" |
|
|||
5603 | ] |
|
|||
5604 | }, |
|
|||
5605 | { |
|
|||
5606 | "output_type": "stream", |
|
|||
5607 | "stream": "stdout", |
|
|||
5608 | "text": [ |
|
|||
5609 | " \r", |
|
|||
5610 | "[**********************57%** ] 570 of 1000 complete" |
|
|||
5611 | ] |
|
|||
5612 | }, |
|
|||
5613 | { |
|
|||
5614 | "output_type": "stream", |
|
|||
5615 | "stream": "stdout", |
|
|||
5616 | "text": [ |
|
|||
5617 | " \r", |
|
|||
5618 | "[**********************57%** ] 571 of 1000 complete" |
|
|||
5619 | ] |
|
|||
5620 | }, |
|
|||
5621 | { |
|
|||
5622 | "output_type": "stream", |
|
|||
5623 | "stream": "stdout", |
|
|||
5624 | "text": [ |
|
|||
5625 | " \r", |
|
|||
5626 | "[**********************57%** ] 572 of 1000 complete" |
|
|||
5627 | ] |
|
|||
5628 | }, |
|
|||
5629 | { |
|
|||
5630 | "output_type": "stream", |
|
|||
5631 | "stream": "stdout", |
|
|||
5632 | "text": [ |
|
|||
5633 | " \r", |
|
|||
5634 | "[**********************57%** ] 573 of 1000 complete" |
|
|||
5635 | ] |
|
|||
5636 | }, |
|
|||
5637 | { |
|
|||
5638 | "output_type": "stream", |
|
|||
5639 | "stream": "stdout", |
|
|||
5640 | "text": [ |
|
|||
5641 | " \r", |
|
|||
5642 | "[**********************57%** ] 574 of 1000 complete" |
|
|||
5643 | ] |
|
|||
5644 | }, |
|
|||
5645 | { |
|
|||
5646 | "output_type": "stream", |
|
|||
5647 | "stream": "stdout", |
|
|||
5648 | "text": [ |
|
|||
5649 | " \r", |
|
|||
5650 | "[**********************57%** ] 575 of 1000 complete" |
|
|||
5651 | ] |
|
|||
5652 | }, |
|
|||
5653 | { |
|
|||
5654 | "output_type": "stream", |
|
|||
5655 | "stream": "stdout", |
|
|||
5656 | "text": [ |
|
|||
5657 | " \r", |
|
|||
5658 | "[**********************58%*** ] 576 of 1000 complete" |
|
|||
5659 | ] |
|
|||
5660 | }, |
|
|||
5661 | { |
|
|||
5662 | "output_type": "stream", |
|
|||
5663 | "stream": "stdout", |
|
|||
5664 | "text": [ |
|
|||
5665 | " \r", |
|
|||
5666 | "[**********************58%*** ] 577 of 1000 complete" |
|
|||
5667 | ] |
|
|||
5668 | }, |
|
|||
5669 | { |
|
|||
5670 | "output_type": "stream", |
|
|||
5671 | "stream": "stdout", |
|
|||
5672 | "text": [ |
|
|||
5673 | " \r", |
|
|||
5674 | "[**********************58%*** ] 578 of 1000 complete" |
|
|||
5675 | ] |
|
|||
5676 | }, |
|
|||
5677 | { |
|
|||
5678 | "output_type": "stream", |
|
|||
5679 | "stream": "stdout", |
|
|||
5680 | "text": [ |
|
|||
5681 | " \r", |
|
|||
5682 | "[**********************58%*** ] 579 of 1000 complete" |
|
|||
5683 | ] |
|
|||
5684 | }, |
|
|||
5685 | { |
|
|||
5686 | "output_type": "stream", |
|
|||
5687 | "stream": "stdout", |
|
|||
5688 | "text": [ |
|
|||
5689 | " \r", |
|
|||
5690 | "[**********************58%*** ] 580 of 1000 complete" |
|
|||
5691 | ] |
|
|||
5692 | }, |
|
|||
5693 | { |
|
|||
5694 | "output_type": "stream", |
|
|||
5695 | "stream": "stdout", |
|
|||
5696 | "text": [ |
|
|||
5697 | " \r", |
|
|||
5698 | "[**********************58%*** ] 581 of 1000 complete" |
|
|||
5699 | ] |
|
|||
5700 | }, |
|
|||
5701 | { |
|
|||
5702 | "output_type": "stream", |
|
|||
5703 | "stream": "stdout", |
|
|||
5704 | "text": [ |
|
|||
5705 | " \r", |
|
|||
5706 | "[**********************58%*** ] 582 of 1000 complete" |
|
|||
5707 | ] |
|
|||
5708 | }, |
|
|||
5709 | { |
|
|||
5710 | "output_type": "stream", |
|
|||
5711 | "stream": "stdout", |
|
|||
5712 | "text": [ |
|
|||
5713 | " \r", |
|
|||
5714 | "[**********************58%*** ] 583 of 1000 complete" |
|
|||
5715 | ] |
|
|||
5716 | }, |
|
|||
5717 | { |
|
|||
5718 | "output_type": "stream", |
|
|||
5719 | "stream": "stdout", |
|
|||
5720 | "text": [ |
|
|||
5721 | " \r", |
|
|||
5722 | "[**********************58%*** ] 584 of 1000 complete" |
|
|||
5723 | ] |
|
|||
5724 | }, |
|
|||
5725 | { |
|
|||
5726 | "output_type": "stream", |
|
|||
5727 | "stream": "stdout", |
|
|||
5728 | "text": [ |
|
|||
5729 | " \r", |
|
|||
5730 | "[**********************59%*** ] 585 of 1000 complete" |
|
|||
5731 | ] |
|
|||
5732 | }, |
|
|||
5733 | { |
|
|||
5734 | "output_type": "stream", |
|
|||
5735 | "stream": "stdout", |
|
|||
5736 | "text": [ |
|
|||
5737 | " \r", |
|
|||
5738 | "[**********************59%*** ] 586 of 1000 complete" |
|
|||
5739 | ] |
|
|||
5740 | }, |
|
|||
5741 | { |
|
|||
5742 | "output_type": "stream", |
|
|||
5743 | "stream": "stdout", |
|
|||
5744 | "text": [ |
|
|||
5745 | " \r", |
|
|||
5746 | "[**********************59%*** ] 587 of 1000 complete" |
|
|||
5747 | ] |
|
|||
5748 | }, |
|
|||
5749 | { |
|
|||
5750 | "output_type": "stream", |
|
|||
5751 | "stream": "stdout", |
|
|||
5752 | "text": [ |
|
|||
5753 | " \r", |
|
|||
5754 | "[**********************59%*** ] 588 of 1000 complete" |
|
|||
5755 | ] |
|
|||
5756 | }, |
|
|||
5757 | { |
|
|||
5758 | "output_type": "stream", |
|
|||
5759 | "stream": "stdout", |
|
|||
5760 | "text": [ |
|
|||
5761 | " \r", |
|
|||
5762 | "[**********************59%*** ] 589 of 1000 complete" |
|
|||
5763 | ] |
|
|||
5764 | }, |
|
|||
5765 | { |
|
|||
5766 | "output_type": "stream", |
|
|||
5767 | "stream": "stdout", |
|
|||
5768 | "text": [ |
|
|||
5769 | " \r", |
|
|||
5770 | "[**********************59%*** ] 590 of 1000 complete" |
|
|||
5771 | ] |
|
|||
5772 | }, |
|
|||
5773 | { |
|
|||
5774 | "output_type": "stream", |
|
|||
5775 | "stream": "stdout", |
|
|||
5776 | "text": [ |
|
|||
5777 | " \r", |
|
|||
5778 | "[**********************59%*** ] 591 of 1000 complete" |
|
|||
5779 | ] |
|
|||
5780 | }, |
|
|||
5781 | { |
|
|||
5782 | "output_type": "stream", |
|
|||
5783 | "stream": "stdout", |
|
|||
5784 | "text": [ |
|
|||
5785 | " \r", |
|
|||
5786 | "[**********************59%*** ] 592 of 1000 complete" |
|
|||
5787 | ] |
|
|||
5788 | }, |
|
|||
5789 | { |
|
|||
5790 | "output_type": "stream", |
|
|||
5791 | "stream": "stdout", |
|
|||
5792 | "text": [ |
|
|||
5793 | " \r", |
|
|||
5794 | "[**********************59%*** ] 593 of 1000 complete" |
|
|||
5795 | ] |
|
|||
5796 | }, |
|
|||
5797 | { |
|
|||
5798 | "output_type": "stream", |
|
|||
5799 | "stream": "stdout", |
|
|||
5800 | "text": [ |
|
|||
5801 | " \r", |
|
|||
5802 | "[**********************59%*** ] 594 of 1000 complete" |
|
|||
5803 | ] |
|
|||
5804 | }, |
|
|||
5805 | { |
|
|||
5806 | "output_type": "stream", |
|
|||
5807 | "stream": "stdout", |
|
|||
5808 | "text": [ |
|
|||
5809 | " \r", |
|
|||
5810 | "[**********************60%**** ] 595 of 1000 complete" |
|
|||
5811 | ] |
|
|||
5812 | }, |
|
|||
5813 | { |
|
|||
5814 | "output_type": "stream", |
|
|||
5815 | "stream": "stdout", |
|
|||
5816 | "text": [ |
|
|||
5817 | " \r", |
|
|||
5818 | "[**********************60%**** ] 596 of 1000 complete" |
|
|||
5819 | ] |
|
|||
5820 | }, |
|
|||
5821 | { |
|
|||
5822 | "output_type": "stream", |
|
|||
5823 | "stream": "stdout", |
|
|||
5824 | "text": [ |
|
|||
5825 | " \r", |
|
|||
5826 | "[**********************60%**** ] 597 of 1000 complete" |
|
|||
5827 | ] |
|
|||
5828 | }, |
|
|||
5829 | { |
|
|||
5830 | "output_type": "stream", |
|
|||
5831 | "stream": "stdout", |
|
|||
5832 | "text": [ |
|
|||
5833 | " \r", |
|
|||
5834 | "[**********************60%**** ] 598 of 1000 complete" |
|
|||
5835 | ] |
|
|||
5836 | }, |
|
|||
5837 | { |
|
|||
5838 | "output_type": "stream", |
|
|||
5839 | "stream": "stdout", |
|
|||
5840 | "text": [ |
|
|||
5841 | " \r", |
|
|||
5842 | "[**********************60%**** ] 599 of 1000 complete" |
|
|||
5843 | ] |
|
|||
5844 | }, |
|
|||
5845 | { |
|
|||
5846 | "output_type": "stream", |
|
|||
5847 | "stream": "stdout", |
|
|||
5848 | "text": [ |
|
|||
5849 | " \r", |
|
|||
5850 | "[**********************60%**** ] 600 of 1000 complete" |
|
|||
5851 | ] |
|
|||
5852 | }, |
|
|||
5853 | { |
|
|||
5854 | "output_type": "stream", |
|
|||
5855 | "stream": "stdout", |
|
|||
5856 | "text": [ |
|
|||
5857 | " \r", |
|
|||
5858 | "[**********************60%**** ] 601 of 1000 complete" |
|
|||
5859 | ] |
|
|||
5860 | }, |
|
|||
5861 | { |
|
|||
5862 | "output_type": "stream", |
|
|||
5863 | "stream": "stdout", |
|
|||
5864 | "text": [ |
|
|||
5865 | " \r", |
|
|||
5866 | "[**********************60%**** ] 602 of 1000 complete" |
|
|||
5867 | ] |
|
|||
5868 | }, |
|
|||
5869 | { |
|
|||
5870 | "output_type": "stream", |
|
|||
5871 | "stream": "stdout", |
|
|||
5872 | "text": [ |
|
|||
5873 | " \r", |
|
|||
5874 | "[**********************60%**** ] 603 of 1000 complete" |
|
|||
5875 | ] |
|
|||
5876 | }, |
|
|||
5877 | { |
|
|||
5878 | "output_type": "stream", |
|
|||
5879 | "stream": "stdout", |
|
|||
5880 | "text": [ |
|
|||
5881 | " \r", |
|
|||
5882 | "[**********************60%**** ] 604 of 1000 complete" |
|
|||
5883 | ] |
|
|||
5884 | }, |
|
|||
5885 | { |
|
|||
5886 | "output_type": "stream", |
|
|||
5887 | "stream": "stdout", |
|
|||
5888 | "text": [ |
|
|||
5889 | " \r", |
|
|||
5890 | "[**********************61%**** ] 605 of 1000 complete" |
|
|||
5891 | ] |
|
|||
5892 | }, |
|
|||
5893 | { |
|
|||
5894 | "output_type": "stream", |
|
|||
5895 | "stream": "stdout", |
|
|||
5896 | "text": [ |
|
|||
5897 | " \r", |
|
|||
5898 | "[**********************61%**** ] 606 of 1000 complete" |
|
|||
5899 | ] |
|
|||
5900 | }, |
|
|||
5901 | { |
|
|||
5902 | "output_type": "stream", |
|
|||
5903 | "stream": "stdout", |
|
|||
5904 | "text": [ |
|
|||
5905 | " \r", |
|
|||
5906 | "[**********************61%**** ] 607 of 1000 complete" |
|
|||
5907 | ] |
|
|||
5908 | }, |
|
|||
5909 | { |
|
|||
5910 | "output_type": "stream", |
|
|||
5911 | "stream": "stdout", |
|
|||
5912 | "text": [ |
|
|||
5913 | " \r", |
|
|||
5914 | "[**********************61%**** ] 608 of 1000 complete" |
|
|||
5915 | ] |
|
|||
5916 | }, |
|
|||
5917 | { |
|
|||
5918 | "output_type": "stream", |
|
|||
5919 | "stream": "stdout", |
|
|||
5920 | "text": [ |
|
|||
5921 | " \r", |
|
|||
5922 | "[**********************61%**** ] 609 of 1000 complete" |
|
|||
5923 | ] |
|
|||
5924 | }, |
|
|||
5925 | { |
|
|||
5926 | "output_type": "stream", |
|
|||
5927 | "stream": "stdout", |
|
|||
5928 | "text": [ |
|
|||
5929 | " \r", |
|
|||
5930 | "[**********************61%**** ] 610 of 1000 complete" |
|
|||
5931 | ] |
|
|||
5932 | }, |
|
|||
5933 | { |
|
|||
5934 | "output_type": "stream", |
|
|||
5935 | "stream": "stdout", |
|
|||
5936 | "text": [ |
|
|||
5937 | " \r", |
|
|||
5938 | "[**********************61%**** ] 611 of 1000 complete" |
|
|||
5939 | ] |
|
|||
5940 | }, |
|
|||
5941 | { |
|
|||
5942 | "output_type": "stream", |
|
|||
5943 | "stream": "stdout", |
|
|||
5944 | "text": [ |
|
|||
5945 | " \r", |
|
|||
5946 | "[**********************61%**** ] 612 of 1000 complete" |
|
|||
5947 | ] |
|
|||
5948 | }, |
|
|||
5949 | { |
|
|||
5950 | "output_type": "stream", |
|
|||
5951 | "stream": "stdout", |
|
|||
5952 | "text": [ |
|
|||
5953 | " \r", |
|
|||
5954 | "[**********************61%**** ] 613 of 1000 complete" |
|
|||
5955 | ] |
|
|||
5956 | }, |
|
|||
5957 | { |
|
|||
5958 | "output_type": "stream", |
|
|||
5959 | "stream": "stdout", |
|
|||
5960 | "text": [ |
|
|||
5961 | " \r", |
|
|||
5962 | "[**********************61%**** ] 614 of 1000 complete" |
|
|||
5963 | ] |
|
|||
5964 | }, |
|
|||
5965 | { |
|
|||
5966 | "output_type": "stream", |
|
|||
5967 | "stream": "stdout", |
|
|||
5968 | "text": [ |
|
|||
5969 | " \r", |
|
|||
5970 | "[**********************62%***** ] 615 of 1000 complete" |
|
|||
5971 | ] |
|
|||
5972 | }, |
|
|||
5973 | { |
|
|||
5974 | "output_type": "stream", |
|
|||
5975 | "stream": "stdout", |
|
|||
5976 | "text": [ |
|
|||
5977 | " \r", |
|
|||
5978 | "[**********************62%***** ] 616 of 1000 complete" |
|
|||
5979 | ] |
|
|||
5980 | }, |
|
|||
5981 | { |
|
|||
5982 | "output_type": "stream", |
|
|||
5983 | "stream": "stdout", |
|
|||
5984 | "text": [ |
|
|||
5985 | " \r", |
|
|||
5986 | "[**********************62%***** ] 617 of 1000 complete" |
|
|||
5987 | ] |
|
|||
5988 | }, |
|
|||
5989 | { |
|
|||
5990 | "output_type": "stream", |
|
|||
5991 | "stream": "stdout", |
|
|||
5992 | "text": [ |
|
|||
5993 | " \r", |
|
|||
5994 | "[**********************62%***** ] 618 of 1000 complete" |
|
|||
5995 | ] |
|
|||
5996 | }, |
|
|||
5997 | { |
|
|||
5998 | "output_type": "stream", |
|
|||
5999 | "stream": "stdout", |
|
|||
6000 | "text": [ |
|
|||
6001 | " \r", |
|
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6002 | "[**********************62%***** ] 619 of 1000 complete" |
|
|||
6003 | ] |
|
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6004 | }, |
|
|||
6005 | { |
|
|||
6006 | "output_type": "stream", |
|
|||
6007 | "stream": "stdout", |
|
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6008 | "text": [ |
|
|||
6009 | " \r", |
|
|||
6010 | "[**********************62%***** ] 620 of 1000 complete" |
|
|||
6011 | ] |
|
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6012 | }, |
|
|||
6013 | { |
|
|||
6014 | "output_type": "stream", |
|
|||
6015 | "stream": "stdout", |
|
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6016 | "text": [ |
|
|||
6017 | " \r", |
|
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6018 | "[**********************62%***** ] 621 of 1000 complete" |
|
|||
6019 | ] |
|
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6020 | }, |
|
|||
6021 | { |
|
|||
6022 | "output_type": "stream", |
|
|||
6023 | "stream": "stdout", |
|
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6024 | "text": [ |
|
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6025 | " \r", |
|
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6026 | "[**********************62%***** ] 622 of 1000 complete" |
|
|||
6027 | ] |
|
|||
6028 | }, |
|
|||
6029 | { |
|
|||
6030 | "output_type": "stream", |
|
|||
6031 | "stream": "stdout", |
|
|||
6032 | "text": [ |
|
|||
6033 | " \r", |
|
|||
6034 | "[**********************62%***** ] 623 of 1000 complete" |
|
|||
6035 | ] |
|
|||
6036 | }, |
|
|||
6037 | { |
|
|||
6038 | "output_type": "stream", |
|
|||
6039 | "stream": "stdout", |
|
|||
6040 | "text": [ |
|
|||
6041 | " \r", |
|
|||
6042 | "[**********************62%***** ] 624 of 1000 complete" |
|
|||
6043 | ] |
|
|||
6044 | }, |
|
|||
6045 | { |
|
|||
6046 | "output_type": "stream", |
|
|||
6047 | "stream": "stdout", |
|
|||
6048 | "text": [ |
|
|||
6049 | " \r", |
|
|||
6050 | "[**********************63%***** ] 625 of 1000 complete" |
|
|||
6051 | ] |
|
|||
6052 | }, |
|
|||
6053 | { |
|
|||
6054 | "output_type": "stream", |
|
|||
6055 | "stream": "stdout", |
|
|||
6056 | "text": [ |
|
|||
6057 | " \r", |
|
|||
6058 | "[**********************63%***** ] 626 of 1000 complete" |
|
|||
6059 | ] |
|
|||
6060 | }, |
|
|||
6061 | { |
|
|||
6062 | "output_type": "stream", |
|
|||
6063 | "stream": "stdout", |
|
|||
6064 | "text": [ |
|
|||
6065 | " \r", |
|
|||
6066 | "[**********************63%***** ] 627 of 1000 complete" |
|
|||
6067 | ] |
|
|||
6068 | }, |
|
|||
6069 | { |
|
|||
6070 | "output_type": "stream", |
|
|||
6071 | "stream": "stdout", |
|
|||
6072 | "text": [ |
|
|||
6073 | " \r", |
|
|||
6074 | "[**********************63%***** ] 628 of 1000 complete" |
|
|||
6075 | ] |
|
|||
6076 | }, |
|
|||
6077 | { |
|
|||
6078 | "output_type": "stream", |
|
|||
6079 | "stream": "stdout", |
|
|||
6080 | "text": [ |
|
|||
6081 | " \r", |
|
|||
6082 | "[**********************63%***** ] 629 of 1000 complete" |
|
|||
6083 | ] |
|
|||
6084 | }, |
|
|||
6085 | { |
|
|||
6086 | "output_type": "stream", |
|
|||
6087 | "stream": "stdout", |
|
|||
6088 | "text": [ |
|
|||
6089 | " \r", |
|
|||
6090 | "[**********************63%***** ] 630 of 1000 complete" |
|
|||
6091 | ] |
|
|||
6092 | }, |
|
|||
6093 | { |
|
|||
6094 | "output_type": "stream", |
|
|||
6095 | "stream": "stdout", |
|
|||
6096 | "text": [ |
|
|||
6097 | " \r", |
|
|||
6098 | "[**********************63%***** ] 631 of 1000 complete" |
|
|||
6099 | ] |
|
|||
6100 | }, |
|
|||
6101 | { |
|
|||
6102 | "output_type": "stream", |
|
|||
6103 | "stream": "stdout", |
|
|||
6104 | "text": [ |
|
|||
6105 | " \r", |
|
|||
6106 | "[**********************63%***** ] 632 of 1000 complete" |
|
|||
6107 | ] |
|
|||
6108 | }, |
|
|||
6109 | { |
|
|||
6110 | "output_type": "stream", |
|
|||
6111 | "stream": "stdout", |
|
|||
6112 | "text": [ |
|
|||
6113 | " \r", |
|
|||
6114 | "[**********************63%***** ] 633 of 1000 complete" |
|
|||
6115 | ] |
|
|||
6116 | }, |
|
|||
6117 | { |
|
|||
6118 | "output_type": "stream", |
|
|||
6119 | "stream": "stdout", |
|
|||
6120 | "text": [ |
|
|||
6121 | " \r", |
|
|||
6122 | "[**********************63%***** ] 634 of 1000 complete" |
|
|||
6123 | ] |
|
|||
6124 | }, |
|
|||
6125 | { |
|
|||
6126 | "output_type": "stream", |
|
|||
6127 | "stream": "stdout", |
|
|||
6128 | "text": [ |
|
|||
6129 | " \r", |
|
|||
6130 | "[**********************64%****** ] 635 of 1000 complete" |
|
|||
6131 | ] |
|
|||
6132 | }, |
|
|||
6133 | { |
|
|||
6134 | "output_type": "stream", |
|
|||
6135 | "stream": "stdout", |
|
|||
6136 | "text": [ |
|
|||
6137 | " \r", |
|
|||
6138 | "[**********************64%****** ] 636 of 1000 complete" |
|
|||
6139 | ] |
|
|||
6140 | }, |
|
|||
6141 | { |
|
|||
6142 | "output_type": "stream", |
|
|||
6143 | "stream": "stdout", |
|
|||
6144 | "text": [ |
|
|||
6145 | " \r", |
|
|||
6146 | "[**********************64%****** ] 637 of 1000 complete" |
|
|||
6147 | ] |
|
|||
6148 | }, |
|
|||
6149 | { |
|
|||
6150 | "output_type": "stream", |
|
|||
6151 | "stream": "stdout", |
|
|||
6152 | "text": [ |
|
|||
6153 | " \r", |
|
|||
6154 | "[**********************64%****** ] 638 of 1000 complete" |
|
|||
6155 | ] |
|
|||
6156 | }, |
|
|||
6157 | { |
|
|||
6158 | "output_type": "stream", |
|
|||
6159 | "stream": "stdout", |
|
|||
6160 | "text": [ |
|
|||
6161 | " \r", |
|
|||
6162 | "[**********************64%****** ] 639 of 1000 complete" |
|
|||
6163 | ] |
|
|||
6164 | }, |
|
|||
6165 | { |
|
|||
6166 | "output_type": "stream", |
|
|||
6167 | "stream": "stdout", |
|
|||
6168 | "text": [ |
|
|||
6169 | " \r", |
|
|||
6170 | "[**********************64%****** ] 640 of 1000 complete" |
|
|||
6171 | ] |
|
|||
6172 | }, |
|
|||
6173 | { |
|
|||
6174 | "output_type": "stream", |
|
|||
6175 | "stream": "stdout", |
|
|||
6176 | "text": [ |
|
|||
6177 | " \r", |
|
|||
6178 | "[**********************64%****** ] 641 of 1000 complete" |
|
|||
6179 | ] |
|
|||
6180 | }, |
|
|||
6181 | { |
|
|||
6182 | "output_type": "stream", |
|
|||
6183 | "stream": "stdout", |
|
|||
6184 | "text": [ |
|
|||
6185 | " \r", |
|
|||
6186 | "[**********************64%****** ] 642 of 1000 complete" |
|
|||
6187 | ] |
|
|||
6188 | }, |
|
|||
6189 | { |
|
|||
6190 | "output_type": "stream", |
|
|||
6191 | "stream": "stdout", |
|
|||
6192 | "text": [ |
|
|||
6193 | " \r", |
|
|||
6194 | "[**********************64%****** ] 643 of 1000 complete" |
|
|||
6195 | ] |
|
|||
6196 | }, |
|
|||
6197 | { |
|
|||
6198 | "output_type": "stream", |
|
|||
6199 | "stream": "stdout", |
|
|||
6200 | "text": [ |
|
|||
6201 | " \r", |
|
|||
6202 | "[**********************64%****** ] 644 of 1000 complete" |
|
|||
6203 | ] |
|
|||
6204 | }, |
|
|||
6205 | { |
|
|||
6206 | "output_type": "stream", |
|
|||
6207 | "stream": "stdout", |
|
|||
6208 | "text": [ |
|
|||
6209 | " \r", |
|
|||
6210 | "[**********************65%****** ] 645 of 1000 complete" |
|
|||
6211 | ] |
|
|||
6212 | }, |
|
|||
6213 | { |
|
|||
6214 | "output_type": "stream", |
|
|||
6215 | "stream": "stdout", |
|
|||
6216 | "text": [ |
|
|||
6217 | " \r", |
|
|||
6218 | "[**********************65%****** ] 646 of 1000 complete" |
|
|||
6219 | ] |
|
|||
6220 | }, |
|
|||
6221 | { |
|
|||
6222 | "output_type": "stream", |
|
|||
6223 | "stream": "stdout", |
|
|||
6224 | "text": [ |
|
|||
6225 | " \r", |
|
|||
6226 | "[**********************65%****** ] 647 of 1000 complete" |
|
|||
6227 | ] |
|
|||
6228 | }, |
|
|||
6229 | { |
|
|||
6230 | "output_type": "stream", |
|
|||
6231 | "stream": "stdout", |
|
|||
6232 | "text": [ |
|
|||
6233 | " \r", |
|
|||
6234 | "[**********************65%****** ] 648 of 1000 complete" |
|
|||
6235 | ] |
|
|||
6236 | }, |
|
|||
6237 | { |
|
|||
6238 | "output_type": "stream", |
|
|||
6239 | "stream": "stdout", |
|
|||
6240 | "text": [ |
|
|||
6241 | " \r", |
|
|||
6242 | "[**********************65%****** ] 649 of 1000 complete" |
|
|||
6243 | ] |
|
|||
6244 | }, |
|
|||
6245 | { |
|
|||
6246 | "output_type": "stream", |
|
|||
6247 | "stream": "stdout", |
|
|||
6248 | "text": [ |
|
|||
6249 | " \r", |
|
|||
6250 | "[**********************65%****** ] 650 of 1000 complete" |
|
|||
6251 | ] |
|
|||
6252 | }, |
|
|||
6253 | { |
|
|||
6254 | "output_type": "stream", |
|
|||
6255 | "stream": "stdout", |
|
|||
6256 | "text": [ |
|
|||
6257 | " \r", |
|
|||
6258 | "[**********************65%****** ] 651 of 1000 complete" |
|
|||
6259 | ] |
|
|||
6260 | }, |
|
|||
6261 | { |
|
|||
6262 | "output_type": "stream", |
|
|||
6263 | "stream": "stdout", |
|
|||
6264 | "text": [ |
|
|||
6265 | " \r", |
|
|||
6266 | "[**********************65%****** ] 652 of 1000 complete" |
|
|||
6267 | ] |
|
|||
6268 | }, |
|
|||
6269 | { |
|
|||
6270 | "output_type": "stream", |
|
|||
6271 | "stream": "stdout", |
|
|||
6272 | "text": [ |
|
|||
6273 | " \r", |
|
|||
6274 | "[**********************65%****** ] 653 of 1000 complete" |
|
|||
6275 | ] |
|
|||
6276 | }, |
|
|||
6277 | { |
|
|||
6278 | "output_type": "stream", |
|
|||
6279 | "stream": "stdout", |
|
|||
6280 | "text": [ |
|
|||
6281 | " \r", |
|
|||
6282 | "[**********************65%****** ] 654 of 1000 complete" |
|
|||
6283 | ] |
|
|||
6284 | }, |
|
|||
6285 | { |
|
|||
6286 | "output_type": "stream", |
|
|||
6287 | "stream": "stdout", |
|
|||
6288 | "text": [ |
|
|||
6289 | " \r", |
|
|||
6290 | "[**********************66%******* ] 655 of 1000 complete" |
|
|||
6291 | ] |
|
|||
6292 | }, |
|
|||
6293 | { |
|
|||
6294 | "output_type": "stream", |
|
|||
6295 | "stream": "stdout", |
|
|||
6296 | "text": [ |
|
|||
6297 | " \r", |
|
|||
6298 | "[**********************66%******* ] 656 of 1000 complete" |
|
|||
6299 | ] |
|
|||
6300 | }, |
|
|||
6301 | { |
|
|||
6302 | "output_type": "stream", |
|
|||
6303 | "stream": "stdout", |
|
|||
6304 | "text": [ |
|
|||
6305 | " \r", |
|
|||
6306 | "[**********************66%******* ] 657 of 1000 complete" |
|
|||
6307 | ] |
|
|||
6308 | }, |
|
|||
6309 | { |
|
|||
6310 | "output_type": "stream", |
|
|||
6311 | "stream": "stdout", |
|
|||
6312 | "text": [ |
|
|||
6313 | " \r", |
|
|||
6314 | "[**********************66%******* ] 658 of 1000 complete" |
|
|||
6315 | ] |
|
|||
6316 | }, |
|
|||
6317 | { |
|
|||
6318 | "output_type": "stream", |
|
|||
6319 | "stream": "stdout", |
|
|||
6320 | "text": [ |
|
|||
6321 | " \r", |
|
|||
6322 | "[**********************66%******* ] 659 of 1000 complete" |
|
|||
6323 | ] |
|
|||
6324 | }, |
|
|||
6325 | { |
|
|||
6326 | "output_type": "stream", |
|
|||
6327 | "stream": "stdout", |
|
|||
6328 | "text": [ |
|
|||
6329 | " \r", |
|
|||
6330 | "[**********************66%******* ] 660 of 1000 complete" |
|
|||
6331 | ] |
|
|||
6332 | }, |
|
|||
6333 | { |
|
|||
6334 | "output_type": "stream", |
|
|||
6335 | "stream": "stdout", |
|
|||
6336 | "text": [ |
|
|||
6337 | " \r", |
|
|||
6338 | "[**********************66%******* ] 661 of 1000 complete" |
|
|||
6339 | ] |
|
|||
6340 | }, |
|
|||
6341 | { |
|
|||
6342 | "output_type": "stream", |
|
|||
6343 | "stream": "stdout", |
|
|||
6344 | "text": [ |
|
|||
6345 | " \r", |
|
|||
6346 | "[**********************66%******* ] 662 of 1000 complete" |
|
|||
6347 | ] |
|
|||
6348 | }, |
|
|||
6349 | { |
|
|||
6350 | "output_type": "stream", |
|
|||
6351 | "stream": "stdout", |
|
|||
6352 | "text": [ |
|
|||
6353 | " \r", |
|
|||
6354 | "[**********************66%******* ] 663 of 1000 complete" |
|
|||
6355 | ] |
|
|||
6356 | }, |
|
|||
6357 | { |
|
|||
6358 | "output_type": "stream", |
|
|||
6359 | "stream": "stdout", |
|
|||
6360 | "text": [ |
|
|||
6361 | " \r", |
|
|||
6362 | "[**********************66%******* ] 664 of 1000 complete" |
|
|||
6363 | ] |
|
|||
6364 | }, |
|
|||
6365 | { |
|
|||
6366 | "output_type": "stream", |
|
|||
6367 | "stream": "stdout", |
|
|||
6368 | "text": [ |
|
|||
6369 | " \r", |
|
|||
6370 | "[**********************67%******* ] 665 of 1000 complete" |
|
|||
6371 | ] |
|
|||
6372 | }, |
|
|||
6373 | { |
|
|||
6374 | "output_type": "stream", |
|
|||
6375 | "stream": "stdout", |
|
|||
6376 | "text": [ |
|
|||
6377 | " \r", |
|
|||
6378 | "[**********************67%******* ] 666 of 1000 complete" |
|
|||
6379 | ] |
|
|||
6380 | }, |
|
|||
6381 | { |
|
|||
6382 | "output_type": "stream", |
|
|||
6383 | "stream": "stdout", |
|
|||
6384 | "text": [ |
|
|||
6385 | " \r", |
|
|||
6386 | "[**********************67%******* ] 667 of 1000 complete" |
|
|||
6387 | ] |
|
|||
6388 | }, |
|
|||
6389 | { |
|
|||
6390 | "output_type": "stream", |
|
|||
6391 | "stream": "stdout", |
|
|||
6392 | "text": [ |
|
|||
6393 | " \r", |
|
|||
6394 | "[**********************67%******* ] 668 of 1000 complete" |
|
|||
6395 | ] |
|
|||
6396 | }, |
|
|||
6397 | { |
|
|||
6398 | "output_type": "stream", |
|
|||
6399 | "stream": "stdout", |
|
|||
6400 | "text": [ |
|
|||
6401 | " \r", |
|
|||
6402 | "[**********************67%******* ] 669 of 1000 complete" |
|
|||
6403 | ] |
|
|||
6404 | }, |
|
|||
6405 | { |
|
|||
6406 | "output_type": "stream", |
|
|||
6407 | "stream": "stdout", |
|
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6408 | "text": [ |
|
|||
6409 | " \r", |
|
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6410 | "[**********************67%******* ] 670 of 1000 complete" |
|
|||
6411 | ] |
|
|||
6412 | }, |
|
|||
6413 | { |
|
|||
6414 | "output_type": "stream", |
|
|||
6415 | "stream": "stdout", |
|
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6416 | "text": [ |
|
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6417 | " \r", |
|
|||
6418 | "[**********************67%******* ] 671 of 1000 complete" |
|
|||
6419 | ] |
|
|||
6420 | }, |
|
|||
6421 | { |
|
|||
6422 | "output_type": "stream", |
|
|||
6423 | "stream": "stdout", |
|
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6424 | "text": [ |
|
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6425 | " \r", |
|
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6426 | "[**********************67%******* ] 672 of 1000 complete" |
|
|||
6427 | ] |
|
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6428 | }, |
|
|||
6429 | { |
|
|||
6430 | "output_type": "stream", |
|
|||
6431 | "stream": "stdout", |
|
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6432 | "text": [ |
|
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6433 | " \r", |
|
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6434 | "[**********************67%******* ] 673 of 1000 complete" |
|
|||
6435 | ] |
|
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6436 | }, |
|
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6437 | { |
|
|||
6438 | "output_type": "stream", |
|
|||
6439 | "stream": "stdout", |
|
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6440 | "text": [ |
|
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6441 | " \r", |
|
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6442 | "[**********************67%******* ] 674 of 1000 complete" |
|
|||
6443 | ] |
|
|||
6444 | }, |
|
|||
6445 | { |
|
|||
6446 | "output_type": "stream", |
|
|||
6447 | "stream": "stdout", |
|
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6448 | "text": [ |
|
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6449 | " \r", |
|
|||
6450 | "[**********************68%******** ] 675 of 1000 complete" |
|
|||
6451 | ] |
|
|||
6452 | }, |
|
|||
6453 | { |
|
|||
6454 | "output_type": "stream", |
|
|||
6455 | "stream": "stdout", |
|
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6456 | "text": [ |
|
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6457 | " \r", |
|
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6458 | "[**********************68%******** ] 676 of 1000 complete" |
|
|||
6459 | ] |
|
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6460 | }, |
|
|||
6461 | { |
|
|||
6462 | "output_type": "stream", |
|
|||
6463 | "stream": "stdout", |
|
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6464 | "text": [ |
|
|||
6465 | " \r", |
|
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6466 | "[**********************68%******** ] 677 of 1000 complete" |
|
|||
6467 | ] |
|
|||
6468 | }, |
|
|||
6469 | { |
|
|||
6470 | "output_type": "stream", |
|
|||
6471 | "stream": "stdout", |
|
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6472 | "text": [ |
|
|||
6473 | " \r", |
|
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6474 | "[**********************68%******** ] 678 of 1000 complete" |
|
|||
6475 | ] |
|
|||
6476 | }, |
|
|||
6477 | { |
|
|||
6478 | "output_type": "stream", |
|
|||
6479 | "stream": "stdout", |
|
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6480 | "text": [ |
|
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6481 | " \r", |
|
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6482 | "[**********************68%******** ] 679 of 1000 complete" |
|
|||
6483 | ] |
|
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6484 | }, |
|
|||
6485 | { |
|
|||
6486 | "output_type": "stream", |
|
|||
6487 | "stream": "stdout", |
|
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6488 | "text": [ |
|
|||
6489 | " \r", |
|
|||
6490 | "[**********************68%******** ] 680 of 1000 complete" |
|
|||
6491 | ] |
|
|||
6492 | }, |
|
|||
6493 | { |
|
|||
6494 | "output_type": "stream", |
|
|||
6495 | "stream": "stdout", |
|
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6496 | "text": [ |
|
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6497 | " \r", |
|
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6498 | "[**********************68%******** ] 681 of 1000 complete" |
|
|||
6499 | ] |
|
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6500 | }, |
|
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6501 | { |
|
|||
6502 | "output_type": "stream", |
|
|||
6503 | "stream": "stdout", |
|
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6504 | "text": [ |
|
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6505 | " \r", |
|
|||
6506 | "[**********************68%******** ] 682 of 1000 complete" |
|
|||
6507 | ] |
|
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6508 | }, |
|
|||
6509 | { |
|
|||
6510 | "output_type": "stream", |
|
|||
6511 | "stream": "stdout", |
|
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6512 | "text": [ |
|
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6513 | " \r", |
|
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6514 | "[**********************68%******** ] 683 of 1000 complete" |
|
|||
6515 | ] |
|
|||
6516 | }, |
|
|||
6517 | { |
|
|||
6518 | "output_type": "stream", |
|
|||
6519 | "stream": "stdout", |
|
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6520 | "text": [ |
|
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6521 | " \r", |
|
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6522 | "[**********************68%******** ] 684 of 1000 complete" |
|
|||
6523 | ] |
|
|||
6524 | }, |
|
|||
6525 | { |
|
|||
6526 | "output_type": "stream", |
|
|||
6527 | "stream": "stdout", |
|
|||
6528 | "text": [ |
|
|||
6529 | " \r", |
|
|||
6530 | "[**********************69%******** ] 685 of 1000 complete" |
|
|||
6531 | ] |
|
|||
6532 | }, |
|
|||
6533 | { |
|
|||
6534 | "output_type": "stream", |
|
|||
6535 | "stream": "stdout", |
|
|||
6536 | "text": [ |
|
|||
6537 | " \r", |
|
|||
6538 | "[**********************69%******** ] 686 of 1000 complete" |
|
|||
6539 | ] |
|
|||
6540 | }, |
|
|||
6541 | { |
|
|||
6542 | "output_type": "stream", |
|
|||
6543 | "stream": "stdout", |
|
|||
6544 | "text": [ |
|
|||
6545 | " \r", |
|
|||
6546 | "[**********************69%******** ] 687 of 1000 complete" |
|
|||
6547 | ] |
|
|||
6548 | }, |
|
|||
6549 | { |
|
|||
6550 | "output_type": "stream", |
|
|||
6551 | "stream": "stdout", |
|
|||
6552 | "text": [ |
|
|||
6553 | " \r", |
|
|||
6554 | "[**********************69%******** ] 688 of 1000 complete" |
|
|||
6555 | ] |
|
|||
6556 | }, |
|
|||
6557 | { |
|
|||
6558 | "output_type": "stream", |
|
|||
6559 | "stream": "stdout", |
|
|||
6560 | "text": [ |
|
|||
6561 | " \r", |
|
|||
6562 | "[**********************69%******** ] 689 of 1000 complete" |
|
|||
6563 | ] |
|
|||
6564 | }, |
|
|||
6565 | { |
|
|||
6566 | "output_type": "stream", |
|
|||
6567 | "stream": "stdout", |
|
|||
6568 | "text": [ |
|
|||
6569 | " \r", |
|
|||
6570 | "[**********************69%******** ] 690 of 1000 complete" |
|
|||
6571 | ] |
|
|||
6572 | }, |
|
|||
6573 | { |
|
|||
6574 | "output_type": "stream", |
|
|||
6575 | "stream": "stdout", |
|
|||
6576 | "text": [ |
|
|||
6577 | " \r", |
|
|||
6578 | "[**********************69%******** ] 691 of 1000 complete" |
|
|||
6579 | ] |
|
|||
6580 | }, |
|
|||
6581 | { |
|
|||
6582 | "output_type": "stream", |
|
|||
6583 | "stream": "stdout", |
|
|||
6584 | "text": [ |
|
|||
6585 | " \r", |
|
|||
6586 | "[**********************69%******** ] 692 of 1000 complete" |
|
|||
6587 | ] |
|
|||
6588 | }, |
|
|||
6589 | { |
|
|||
6590 | "output_type": "stream", |
|
|||
6591 | "stream": "stdout", |
|
|||
6592 | "text": [ |
|
|||
6593 | " \r", |
|
|||
6594 | "[**********************69%******** ] 693 of 1000 complete" |
|
|||
6595 | ] |
|
|||
6596 | }, |
|
|||
6597 | { |
|
|||
6598 | "output_type": "stream", |
|
|||
6599 | "stream": "stdout", |
|
|||
6600 | "text": [ |
|
|||
6601 | " \r", |
|
|||
6602 | "[**********************69%******** ] 694 of 1000 complete" |
|
|||
6603 | ] |
|
|||
6604 | }, |
|
|||
6605 | { |
|
|||
6606 | "output_type": "stream", |
|
|||
6607 | "stream": "stdout", |
|
|||
6608 | "text": [ |
|
|||
6609 | " \r", |
|
|||
6610 | "[**********************70%********* ] 695 of 1000 complete" |
|
|||
6611 | ] |
|
|||
6612 | }, |
|
|||
6613 | { |
|
|||
6614 | "output_type": "stream", |
|
|||
6615 | "stream": "stdout", |
|
|||
6616 | "text": [ |
|
|||
6617 | " \r", |
|
|||
6618 | "[**********************70%********* ] 696 of 1000 complete" |
|
|||
6619 | ] |
|
|||
6620 | }, |
|
|||
6621 | { |
|
|||
6622 | "output_type": "stream", |
|
|||
6623 | "stream": "stdout", |
|
|||
6624 | "text": [ |
|
|||
6625 | " \r", |
|
|||
6626 | "[**********************70%********* ] 697 of 1000 complete" |
|
|||
6627 | ] |
|
|||
6628 | }, |
|
|||
6629 | { |
|
|||
6630 | "output_type": "stream", |
|
|||
6631 | "stream": "stdout", |
|
|||
6632 | "text": [ |
|
|||
6633 | " \r", |
|
|||
6634 | "[**********************70%********* ] 698 of 1000 complete" |
|
|||
6635 | ] |
|
|||
6636 | }, |
|
|||
6637 | { |
|
|||
6638 | "output_type": "stream", |
|
|||
6639 | "stream": "stdout", |
|
|||
6640 | "text": [ |
|
|||
6641 | " \r", |
|
|||
6642 | "[**********************70%********* ] 699 of 1000 complete" |
|
|||
6643 | ] |
|
|||
6644 | }, |
|
|||
6645 | { |
|
|||
6646 | "output_type": "stream", |
|
|||
6647 | "stream": "stdout", |
|
|||
6648 | "text": [ |
|
|||
6649 | " \r", |
|
|||
6650 | "[**********************70%********* ] 700 of 1000 complete" |
|
|||
6651 | ] |
|
|||
6652 | }, |
|
|||
6653 | { |
|
|||
6654 | "output_type": "stream", |
|
|||
6655 | "stream": "stdout", |
|
|||
6656 | "text": [ |
|
|||
6657 | " \r", |
|
|||
6658 | "[**********************70%********* ] 701 of 1000 complete" |
|
|||
6659 | ] |
|
|||
6660 | }, |
|
|||
6661 | { |
|
|||
6662 | "output_type": "stream", |
|
|||
6663 | "stream": "stdout", |
|
|||
6664 | "text": [ |
|
|||
6665 | " \r", |
|
|||
6666 | "[**********************70%********* ] 702 of 1000 complete" |
|
|||
6667 | ] |
|
|||
6668 | }, |
|
|||
6669 | { |
|
|||
6670 | "output_type": "stream", |
|
|||
6671 | "stream": "stdout", |
|
|||
6672 | "text": [ |
|
|||
6673 | " \r", |
|
|||
6674 | "[**********************70%********* ] 703 of 1000 complete" |
|
|||
6675 | ] |
|
|||
6676 | }, |
|
|||
6677 | { |
|
|||
6678 | "output_type": "stream", |
|
|||
6679 | "stream": "stdout", |
|
|||
6680 | "text": [ |
|
|||
6681 | " \r", |
|
|||
6682 | "[**********************70%********* ] 704 of 1000 complete" |
|
|||
6683 | ] |
|
|||
6684 | }, |
|
|||
6685 | { |
|
|||
6686 | "output_type": "stream", |
|
|||
6687 | "stream": "stdout", |
|
|||
6688 | "text": [ |
|
|||
6689 | " \r", |
|
|||
6690 | "[**********************71%********* ] 705 of 1000 complete" |
|
|||
6691 | ] |
|
|||
6692 | }, |
|
|||
6693 | { |
|
|||
6694 | "output_type": "stream", |
|
|||
6695 | "stream": "stdout", |
|
|||
6696 | "text": [ |
|
|||
6697 | " \r", |
|
|||
6698 | "[**********************71%********* ] 706 of 1000 complete" |
|
|||
6699 | ] |
|
|||
6700 | }, |
|
|||
6701 | { |
|
|||
6702 | "output_type": "stream", |
|
|||
6703 | "stream": "stdout", |
|
|||
6704 | "text": [ |
|
|||
6705 | " \r", |
|
|||
6706 | "[**********************71%********* ] 707 of 1000 complete" |
|
|||
6707 | ] |
|
|||
6708 | }, |
|
|||
6709 | { |
|
|||
6710 | "output_type": "stream", |
|
|||
6711 | "stream": "stdout", |
|
|||
6712 | "text": [ |
|
|||
6713 | " \r", |
|
|||
6714 | "[**********************71%********* ] 708 of 1000 complete" |
|
|||
6715 | ] |
|
|||
6716 | }, |
|
|||
6717 | { |
|
|||
6718 | "output_type": "stream", |
|
|||
6719 | "stream": "stdout", |
|
|||
6720 | "text": [ |
|
|||
6721 | " \r", |
|
|||
6722 | "[**********************71%********* ] 709 of 1000 complete" |
|
|||
6723 | ] |
|
|||
6724 | }, |
|
|||
6725 | { |
|
|||
6726 | "output_type": "stream", |
|
|||
6727 | "stream": "stdout", |
|
|||
6728 | "text": [ |
|
|||
6729 | " \r", |
|
|||
6730 | "[**********************71%********* ] 710 of 1000 complete" |
|
|||
6731 | ] |
|
|||
6732 | }, |
|
|||
6733 | { |
|
|||
6734 | "output_type": "stream", |
|
|||
6735 | "stream": "stdout", |
|
|||
6736 | "text": [ |
|
|||
6737 | " \r", |
|
|||
6738 | "[**********************71%********* ] 711 of 1000 complete" |
|
|||
6739 | ] |
|
|||
6740 | }, |
|
|||
6741 | { |
|
|||
6742 | "output_type": "stream", |
|
|||
6743 | "stream": "stdout", |
|
|||
6744 | "text": [ |
|
|||
6745 | " \r", |
|
|||
6746 | "[**********************71%********* ] 712 of 1000 complete" |
|
|||
6747 | ] |
|
|||
6748 | }, |
|
|||
6749 | { |
|
|||
6750 | "output_type": "stream", |
|
|||
6751 | "stream": "stdout", |
|
|||
6752 | "text": [ |
|
|||
6753 | " \r", |
|
|||
6754 | "[**********************71%********* ] 713 of 1000 complete" |
|
|||
6755 | ] |
|
|||
6756 | }, |
|
|||
6757 | { |
|
|||
6758 | "output_type": "stream", |
|
|||
6759 | "stream": "stdout", |
|
|||
6760 | "text": [ |
|
|||
6761 | " \r", |
|
|||
6762 | "[**********************71%********* ] 714 of 1000 complete" |
|
|||
6763 | ] |
|
|||
6764 | }, |
|
|||
6765 | { |
|
|||
6766 | "output_type": "stream", |
|
|||
6767 | "stream": "stdout", |
|
|||
6768 | "text": [ |
|
|||
6769 | " \r", |
|
|||
6770 | "[**********************72%********** ] 715 of 1000 complete" |
|
|||
6771 | ] |
|
|||
6772 | }, |
|
|||
6773 | { |
|
|||
6774 | "output_type": "stream", |
|
|||
6775 | "stream": "stdout", |
|
|||
6776 | "text": [ |
|
|||
6777 | " \r", |
|
|||
6778 | "[**********************72%********** ] 716 of 1000 complete" |
|
|||
6779 | ] |
|
|||
6780 | }, |
|
|||
6781 | { |
|
|||
6782 | "output_type": "stream", |
|
|||
6783 | "stream": "stdout", |
|
|||
6784 | "text": [ |
|
|||
6785 | " \r", |
|
|||
6786 | "[**********************72%********** ] 717 of 1000 complete" |
|
|||
6787 | ] |
|
|||
6788 | }, |
|
|||
6789 | { |
|
|||
6790 | "output_type": "stream", |
|
|||
6791 | "stream": "stdout", |
|
|||
6792 | "text": [ |
|
|||
6793 | " \r", |
|
|||
6794 | "[**********************72%********** ] 718 of 1000 complete" |
|
|||
6795 | ] |
|
|||
6796 | }, |
|
|||
6797 | { |
|
|||
6798 | "output_type": "stream", |
|
|||
6799 | "stream": "stdout", |
|
|||
6800 | "text": [ |
|
|||
6801 | " \r", |
|
|||
6802 | "[**********************72%********** ] 719 of 1000 complete" |
|
|||
6803 | ] |
|
|||
6804 | }, |
|
|||
6805 | { |
|
|||
6806 | "output_type": "stream", |
|
|||
6807 | "stream": "stdout", |
|
|||
6808 | "text": [ |
|
|||
6809 | " \r", |
|
|||
6810 | "[**********************72%********** ] 720 of 1000 complete" |
|
|||
6811 | ] |
|
|||
6812 | }, |
|
|||
6813 | { |
|
|||
6814 | "output_type": "stream", |
|
|||
6815 | "stream": "stdout", |
|
|||
6816 | "text": [ |
|
|||
6817 | " \r", |
|
|||
6818 | "[**********************72%********** ] 721 of 1000 complete" |
|
|||
6819 | ] |
|
|||
6820 | }, |
|
|||
6821 | { |
|
|||
6822 | "output_type": "stream", |
|
|||
6823 | "stream": "stdout", |
|
|||
6824 | "text": [ |
|
|||
6825 | " \r", |
|
|||
6826 | "[**********************72%********** ] 722 of 1000 complete" |
|
|||
6827 | ] |
|
|||
6828 | }, |
|
|||
6829 | { |
|
|||
6830 | "output_type": "stream", |
|
|||
6831 | "stream": "stdout", |
|
|||
6832 | "text": [ |
|
|||
6833 | " \r", |
|
|||
6834 | "[**********************72%********** ] 723 of 1000 complete" |
|
|||
6835 | ] |
|
|||
6836 | }, |
|
|||
6837 | { |
|
|||
6838 | "output_type": "stream", |
|
|||
6839 | "stream": "stdout", |
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6840 | "text": [ |
|
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6841 | " \r", |
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6842 | "[**********************72%********** ] 724 of 1000 complete" |
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6843 | ] |
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6844 | }, |
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6845 | { |
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6846 | "output_type": "stream", |
|
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6847 | "stream": "stdout", |
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6848 | "text": [ |
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6849 | " \r", |
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6850 | "[**********************73%********** ] 725 of 1000 complete" |
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6851 | ] |
|
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6852 | }, |
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6853 | { |
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6854 | "output_type": "stream", |
|
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6855 | "stream": "stdout", |
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6856 | "text": [ |
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6857 | " \r", |
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6858 | "[**********************73%********** ] 726 of 1000 complete" |
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6859 | ] |
|
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6860 | }, |
|
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6861 | { |
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6862 | "output_type": "stream", |
|
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6863 | "stream": "stdout", |
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6864 | "text": [ |
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6865 | " \r", |
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6866 | "[**********************73%********** ] 727 of 1000 complete" |
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6867 | ] |
|
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6868 | }, |
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6869 | { |
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6870 | "output_type": "stream", |
|
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6871 | "stream": "stdout", |
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6872 | "text": [ |
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6873 | " \r", |
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6874 | "[**********************73%********** ] 728 of 1000 complete" |
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6875 | ] |
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6876 | }, |
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6877 | { |
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6878 | "output_type": "stream", |
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6879 | "stream": "stdout", |
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6880 | "text": [ |
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6881 | " \r", |
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6882 | "[**********************73%********** ] 729 of 1000 complete" |
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6883 | ] |
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6884 | }, |
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6885 | { |
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6886 | "output_type": "stream", |
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6887 | "stream": "stdout", |
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6888 | "text": [ |
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6889 | " \r", |
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6890 | "[**********************73%********** ] 730 of 1000 complete" |
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6891 | ] |
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6892 | }, |
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6893 | { |
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6894 | "output_type": "stream", |
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6895 | "stream": "stdout", |
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6896 | "text": [ |
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6897 | " \r", |
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6898 | "[**********************73%********** ] 731 of 1000 complete" |
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6899 | ] |
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6900 | }, |
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6901 | { |
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6902 | "output_type": "stream", |
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6903 | "stream": "stdout", |
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6904 | "text": [ |
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6905 | " \r", |
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6906 | "[**********************73%********** ] 732 of 1000 complete" |
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6907 | ] |
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6908 | }, |
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6909 | { |
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6910 | "output_type": "stream", |
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6911 | "stream": "stdout", |
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6912 | "text": [ |
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6913 | " \r", |
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6914 | "[**********************73%********** ] 733 of 1000 complete" |
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6915 | ] |
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6916 | }, |
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6917 | { |
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6918 | "output_type": "stream", |
|
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6919 | "stream": "stdout", |
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6920 | "text": [ |
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6921 | " \r", |
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6922 | "[**********************73%********** ] 734 of 1000 complete" |
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6923 | ] |
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6924 | }, |
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6925 | { |
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6926 | "output_type": "stream", |
|
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6927 | "stream": "stdout", |
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6928 | "text": [ |
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6929 | " \r", |
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6930 | "[**********************74%*********** ] 735 of 1000 complete" |
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6931 | ] |
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6932 | }, |
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6933 | { |
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6934 | "output_type": "stream", |
|
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6935 | "stream": "stdout", |
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6936 | "text": [ |
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6937 | " \r", |
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6938 | "[**********************74%*********** ] 736 of 1000 complete" |
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6939 | ] |
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6940 | }, |
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6941 | { |
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6942 | "output_type": "stream", |
|
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6943 | "stream": "stdout", |
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6944 | "text": [ |
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6945 | " \r", |
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6946 | "[**********************74%*********** ] 737 of 1000 complete" |
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6947 | ] |
|
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6948 | }, |
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6949 | { |
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6950 | "output_type": "stream", |
|
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6951 | "stream": "stdout", |
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6952 | "text": [ |
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6953 | " \r", |
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6954 | "[**********************74%*********** ] 738 of 1000 complete" |
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6955 | ] |
|
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6956 | }, |
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6957 | { |
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6958 | "output_type": "stream", |
|
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6959 | "stream": "stdout", |
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6960 | "text": [ |
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6961 | " \r", |
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6962 | "[**********************74%*********** ] 739 of 1000 complete" |
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6963 | ] |
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6964 | }, |
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6965 | { |
|
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6966 | "output_type": "stream", |
|
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6967 | "stream": "stdout", |
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6968 | "text": [ |
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6969 | " \r", |
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6970 | "[**********************74%*********** ] 740 of 1000 complete" |
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6971 | ] |
|
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6972 | }, |
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6973 | { |
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6974 | "output_type": "stream", |
|
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6975 | "stream": "stdout", |
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6976 | "text": [ |
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6977 | " \r", |
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6978 | "[**********************74%*********** ] 741 of 1000 complete" |
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6979 | ] |
|
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6980 | }, |
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6981 | { |
|
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6982 | "output_type": "stream", |
|
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6983 | "stream": "stdout", |
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6984 | "text": [ |
|
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6985 | " \r", |
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6986 | "[**********************74%*********** ] 742 of 1000 complete" |
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6987 | ] |
|
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6988 | }, |
|
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6989 | { |
|
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6990 | "output_type": "stream", |
|
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6991 | "stream": "stdout", |
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6992 | "text": [ |
|
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6993 | " \r", |
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6994 | "[**********************74%*********** ] 743 of 1000 complete" |
|
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6995 | ] |
|
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6996 | }, |
|
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6997 | { |
|
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6998 | "output_type": "stream", |
|
|||
6999 | "stream": "stdout", |
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7000 | "text": [ |
|
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7001 | " \r", |
|
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7002 | "[**********************74%*********** ] 744 of 1000 complete" |
|
|||
7003 | ] |
|
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7004 | }, |
|
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7005 | { |
|
|||
7006 | "output_type": "stream", |
|
|||
7007 | "stream": "stdout", |
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7008 | "text": [ |
|
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7009 | " \r", |
|
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7010 | "[**********************75%*********** ] 745 of 1000 complete" |
|
|||
7011 | ] |
|
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7012 | }, |
|
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7013 | { |
|
|||
7014 | "output_type": "stream", |
|
|||
7015 | "stream": "stdout", |
|
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7016 | "text": [ |
|
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7017 | " \r", |
|
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7018 | "[**********************75%*********** ] 746 of 1000 complete" |
|
|||
7019 | ] |
|
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7020 | }, |
|
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7021 | { |
|
|||
7022 | "output_type": "stream", |
|
|||
7023 | "stream": "stdout", |
|
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7024 | "text": [ |
|
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7025 | " \r", |
|
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7026 | "[**********************75%*********** ] 747 of 1000 complete" |
|
|||
7027 | ] |
|
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7028 | }, |
|
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7029 | { |
|
|||
7030 | "output_type": "stream", |
|
|||
7031 | "stream": "stdout", |
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7032 | "text": [ |
|
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7033 | " \r", |
|
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7034 | "[**********************75%*********** ] 748 of 1000 complete" |
|
|||
7035 | ] |
|
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7036 | }, |
|
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7037 | { |
|
|||
7038 | "output_type": "stream", |
|
|||
7039 | "stream": "stdout", |
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7040 | "text": [ |
|
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7041 | " \r", |
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7042 | "[**********************75%*********** ] 749 of 1000 complete" |
|
|||
7043 | ] |
|
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7044 | }, |
|
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7045 | { |
|
|||
7046 | "output_type": "stream", |
|
|||
7047 | "stream": "stdout", |
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7048 | "text": [ |
|
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7049 | " \r", |
|
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7050 | "[**********************75%*********** ] 750 of 1000 complete" |
|
|||
7051 | ] |
|
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7052 | }, |
|
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7053 | { |
|
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7054 | "output_type": "stream", |
|
|||
7055 | "stream": "stdout", |
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7056 | "text": [ |
|
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7057 | " \r", |
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7058 | "[**********************75%*********** ] 751 of 1000 complete" |
|
|||
7059 | ] |
|
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7060 | }, |
|
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7061 | { |
|
|||
7062 | "output_type": "stream", |
|
|||
7063 | "stream": "stdout", |
|
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7064 | "text": [ |
|
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7065 | " \r", |
|
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7066 | "[**********************75%*********** ] 752 of 1000 complete" |
|
|||
7067 | ] |
|
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7068 | }, |
|
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7069 | { |
|
|||
7070 | "output_type": "stream", |
|
|||
7071 | "stream": "stdout", |
|
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7072 | "text": [ |
|
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7073 | " \r", |
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7074 | "[**********************75%*********** ] 753 of 1000 complete" |
|
|||
7075 | ] |
|
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7076 | }, |
|
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7077 | { |
|
|||
7078 | "output_type": "stream", |
|
|||
7079 | "stream": "stdout", |
|
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7080 | "text": [ |
|
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7081 | " \r", |
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7082 | "[**********************75%*********** ] 754 of 1000 complete" |
|
|||
7083 | ] |
|
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7084 | }, |
|
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7085 | { |
|
|||
7086 | "output_type": "stream", |
|
|||
7087 | "stream": "stdout", |
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7088 | "text": [ |
|
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7089 | " \r", |
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7090 | "[**********************76%*********** ] 755 of 1000 complete" |
|
|||
7091 | ] |
|
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7092 | }, |
|
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7093 | { |
|
|||
7094 | "output_type": "stream", |
|
|||
7095 | "stream": "stdout", |
|
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7096 | "text": [ |
|
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7097 | " \r", |
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7098 | "[**********************76%*********** ] 756 of 1000 complete" |
|
|||
7099 | ] |
|
|||
7100 | }, |
|
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7101 | { |
|
|||
7102 | "output_type": "stream", |
|
|||
7103 | "stream": "stdout", |
|
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7104 | "text": [ |
|
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7105 | " \r", |
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7106 | "[**********************76%*********** ] 757 of 1000 complete" |
|
|||
7107 | ] |
|
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7108 | }, |
|
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7109 | { |
|
|||
7110 | "output_type": "stream", |
|
|||
7111 | "stream": "stdout", |
|
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7112 | "text": [ |
|
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7113 | " \r", |
|
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7114 | "[**********************76%*********** ] 758 of 1000 complete" |
|
|||
7115 | ] |
|
|||
7116 | }, |
|
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7117 | { |
|
|||
7118 | "output_type": "stream", |
|
|||
7119 | "stream": "stdout", |
|
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7120 | "text": [ |
|
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7121 | " \r", |
|
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7122 | "[**********************76%*********** ] 759 of 1000 complete" |
|
|||
7123 | ] |
|
|||
7124 | }, |
|
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7125 | { |
|
|||
7126 | "output_type": "stream", |
|
|||
7127 | "stream": "stdout", |
|
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7128 | "text": [ |
|
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7129 | " \r", |
|
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7130 | "[**********************76%*********** ] 760 of 1000 complete" |
|
|||
7131 | ] |
|
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7132 | }, |
|
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7133 | { |
|
|||
7134 | "output_type": "stream", |
|
|||
7135 | "stream": "stdout", |
|
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7136 | "text": [ |
|
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7137 | " \r", |
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7138 | "[**********************76%*********** ] 761 of 1000 complete" |
|
|||
7139 | ] |
|
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7140 | }, |
|
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7141 | { |
|
|||
7142 | "output_type": "stream", |
|
|||
7143 | "stream": "stdout", |
|
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7144 | "text": [ |
|
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7145 | " \r", |
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7146 | "[**********************76%*********** ] 762 of 1000 complete" |
|
|||
7147 | ] |
|
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7148 | }, |
|
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7149 | { |
|
|||
7150 | "output_type": "stream", |
|
|||
7151 | "stream": "stdout", |
|
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7152 | "text": [ |
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7153 | " \r", |
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7154 | "[**********************76%*********** ] 763 of 1000 complete" |
|
|||
7155 | ] |
|
|||
7156 | }, |
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7157 | { |
|
|||
7158 | "output_type": "stream", |
|
|||
7159 | "stream": "stdout", |
|
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7160 | "text": [ |
|
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7161 | " \r", |
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7162 | "[**********************76%*********** ] 764 of 1000 complete" |
|
|||
7163 | ] |
|
|||
7164 | }, |
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7165 | { |
|
|||
7166 | "output_type": "stream", |
|
|||
7167 | "stream": "stdout", |
|
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7168 | "text": [ |
|
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7169 | " \r", |
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7170 | "[**********************77%************ ] 765 of 1000 complete" |
|
|||
7171 | ] |
|
|||
7172 | }, |
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7173 | { |
|
|||
7174 | "output_type": "stream", |
|
|||
7175 | "stream": "stdout", |
|
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7176 | "text": [ |
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7177 | " \r", |
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7178 | "[**********************77%************ ] 766 of 1000 complete" |
|
|||
7179 | ] |
|
|||
7180 | }, |
|
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7181 | { |
|
|||
7182 | "output_type": "stream", |
|
|||
7183 | "stream": "stdout", |
|
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7184 | "text": [ |
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7185 | " \r", |
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7186 | "[**********************77%************ ] 767 of 1000 complete" |
|
|||
7187 | ] |
|
|||
7188 | }, |
|
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7189 | { |
|
|||
7190 | "output_type": "stream", |
|
|||
7191 | "stream": "stdout", |
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7192 | "text": [ |
|
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7193 | " \r", |
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7194 | "[**********************77%************ ] 768 of 1000 complete" |
|
|||
7195 | ] |
|
|||
7196 | }, |
|
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7197 | { |
|
|||
7198 | "output_type": "stream", |
|
|||
7199 | "stream": "stdout", |
|
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7200 | "text": [ |
|
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7201 | " \r", |
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7202 | "[**********************77%************ ] 769 of 1000 complete" |
|
|||
7203 | ] |
|
|||
7204 | }, |
|
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7205 | { |
|
|||
7206 | "output_type": "stream", |
|
|||
7207 | "stream": "stdout", |
|
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7208 | "text": [ |
|
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7209 | " \r", |
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7210 | "[**********************77%************ ] 770 of 1000 complete" |
|
|||
7211 | ] |
|
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7212 | }, |
|
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7213 | { |
|
|||
7214 | "output_type": "stream", |
|
|||
7215 | "stream": "stdout", |
|
|||
7216 | "text": [ |
|
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7217 | " \r", |
|
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7218 | "[**********************77%************ ] 771 of 1000 complete" |
|
|||
7219 | ] |
|
|||
7220 | }, |
|
|||
7221 | { |
|
|||
7222 | "output_type": "stream", |
|
|||
7223 | "stream": "stdout", |
|
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7224 | "text": [ |
|
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7225 | " \r", |
|
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7226 | "[**********************77%************ ] 772 of 1000 complete" |
|
|||
7227 | ] |
|
|||
7228 | }, |
|
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7229 | { |
|
|||
7230 | "output_type": "stream", |
|
|||
7231 | "stream": "stdout", |
|
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7232 | "text": [ |
|
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7233 | " \r", |
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7234 | "[**********************77%************ ] 773 of 1000 complete" |
|
|||
7235 | ] |
|
|||
7236 | }, |
|
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7237 | { |
|
|||
7238 | "output_type": "stream", |
|
|||
7239 | "stream": "stdout", |
|
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7240 | "text": [ |
|
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7241 | " \r", |
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7242 | "[**********************77%************ ] 774 of 1000 complete" |
|
|||
7243 | ] |
|
|||
7244 | }, |
|
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7245 | { |
|
|||
7246 | "output_type": "stream", |
|
|||
7247 | "stream": "stdout", |
|
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7248 | "text": [ |
|
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7249 | " \r", |
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7250 | "[**********************78%************ ] 775 of 1000 complete" |
|
|||
7251 | ] |
|
|||
7252 | }, |
|
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7253 | { |
|
|||
7254 | "output_type": "stream", |
|
|||
7255 | "stream": "stdout", |
|
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7256 | "text": [ |
|
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7257 | " \r", |
|
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7258 | "[**********************78%************ ] 776 of 1000 complete" |
|
|||
7259 | ] |
|
|||
7260 | }, |
|
|||
7261 | { |
|
|||
7262 | "output_type": "stream", |
|
|||
7263 | "stream": "stdout", |
|
|||
7264 | "text": [ |
|
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7265 | " \r", |
|
|||
7266 | "[**********************78%************ ] 777 of 1000 complete" |
|
|||
7267 | ] |
|
|||
7268 | }, |
|
|||
7269 | { |
|
|||
7270 | "output_type": "stream", |
|
|||
7271 | "stream": "stdout", |
|
|||
7272 | "text": [ |
|
|||
7273 | " \r", |
|
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7274 | "[**********************78%************ ] 778 of 1000 complete" |
|
|||
7275 | ] |
|
|||
7276 | }, |
|
|||
7277 | { |
|
|||
7278 | "output_type": "stream", |
|
|||
7279 | "stream": "stdout", |
|
|||
7280 | "text": [ |
|
|||
7281 | " \r", |
|
|||
7282 | "[**********************78%************ ] 779 of 1000 complete" |
|
|||
7283 | ] |
|
|||
7284 | }, |
|
|||
7285 | { |
|
|||
7286 | "output_type": "stream", |
|
|||
7287 | "stream": "stdout", |
|
|||
7288 | "text": [ |
|
|||
7289 | " \r", |
|
|||
7290 | "[**********************78%************ ] 780 of 1000 complete" |
|
|||
7291 | ] |
|
|||
7292 | }, |
|
|||
7293 | { |
|
|||
7294 | "output_type": "stream", |
|
|||
7295 | "stream": "stdout", |
|
|||
7296 | "text": [ |
|
|||
7297 | " \r", |
|
|||
7298 | "[**********************78%************ ] 781 of 1000 complete" |
|
|||
7299 | ] |
|
|||
7300 | }, |
|
|||
7301 | { |
|
|||
7302 | "output_type": "stream", |
|
|||
7303 | "stream": "stdout", |
|
|||
7304 | "text": [ |
|
|||
7305 | " \r", |
|
|||
7306 | "[**********************78%************ ] 782 of 1000 complete" |
|
|||
7307 | ] |
|
|||
7308 | }, |
|
|||
7309 | { |
|
|||
7310 | "output_type": "stream", |
|
|||
7311 | "stream": "stdout", |
|
|||
7312 | "text": [ |
|
|||
7313 | " \r", |
|
|||
7314 | "[**********************78%************ ] 783 of 1000 complete" |
|
|||
7315 | ] |
|
|||
7316 | }, |
|
|||
7317 | { |
|
|||
7318 | "output_type": "stream", |
|
|||
7319 | "stream": "stdout", |
|
|||
7320 | "text": [ |
|
|||
7321 | " \r", |
|
|||
7322 | "[**********************78%************ ] 784 of 1000 complete" |
|
|||
7323 | ] |
|
|||
7324 | }, |
|
|||
7325 | { |
|
|||
7326 | "output_type": "stream", |
|
|||
7327 | "stream": "stdout", |
|
|||
7328 | "text": [ |
|
|||
7329 | " \r", |
|
|||
7330 | "[**********************79%************* ] 785 of 1000 complete" |
|
|||
7331 | ] |
|
|||
7332 | }, |
|
|||
7333 | { |
|
|||
7334 | "output_type": "stream", |
|
|||
7335 | "stream": "stdout", |
|
|||
7336 | "text": [ |
|
|||
7337 | " \r", |
|
|||
7338 | "[**********************79%************* ] 786 of 1000 complete" |
|
|||
7339 | ] |
|
|||
7340 | }, |
|
|||
7341 | { |
|
|||
7342 | "output_type": "stream", |
|
|||
7343 | "stream": "stdout", |
|
|||
7344 | "text": [ |
|
|||
7345 | " \r", |
|
|||
7346 | "[**********************79%************* ] 787 of 1000 complete" |
|
|||
7347 | ] |
|
|||
7348 | }, |
|
|||
7349 | { |
|
|||
7350 | "output_type": "stream", |
|
|||
7351 | "stream": "stdout", |
|
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7352 | "text": [ |
|
|||
7353 | " \r", |
|
|||
7354 | "[**********************79%************* ] 788 of 1000 complete" |
|
|||
7355 | ] |
|
|||
7356 | }, |
|
|||
7357 | { |
|
|||
7358 | "output_type": "stream", |
|
|||
7359 | "stream": "stdout", |
|
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7360 | "text": [ |
|
|||
7361 | " \r", |
|
|||
7362 | "[**********************79%************* ] 789 of 1000 complete" |
|
|||
7363 | ] |
|
|||
7364 | }, |
|
|||
7365 | { |
|
|||
7366 | "output_type": "stream", |
|
|||
7367 | "stream": "stdout", |
|
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7368 | "text": [ |
|
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7369 | " \r", |
|
|||
7370 | "[**********************79%************* ] 790 of 1000 complete" |
|
|||
7371 | ] |
|
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7372 | }, |
|
|||
7373 | { |
|
|||
7374 | "output_type": "stream", |
|
|||
7375 | "stream": "stdout", |
|
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7376 | "text": [ |
|
|||
7377 | " \r", |
|
|||
7378 | "[**********************79%************* ] 791 of 1000 complete" |
|
|||
7379 | ] |
|
|||
7380 | }, |
|
|||
7381 | { |
|
|||
7382 | "output_type": "stream", |
|
|||
7383 | "stream": "stdout", |
|
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7384 | "text": [ |
|
|||
7385 | " \r", |
|
|||
7386 | "[**********************79%************* ] 792 of 1000 complete" |
|
|||
7387 | ] |
|
|||
7388 | }, |
|
|||
7389 | { |
|
|||
7390 | "output_type": "stream", |
|
|||
7391 | "stream": "stdout", |
|
|||
7392 | "text": [ |
|
|||
7393 | " \r", |
|
|||
7394 | "[**********************79%************* ] 793 of 1000 complete" |
|
|||
7395 | ] |
|
|||
7396 | }, |
|
|||
7397 | { |
|
|||
7398 | "output_type": "stream", |
|
|||
7399 | "stream": "stdout", |
|
|||
7400 | "text": [ |
|
|||
7401 | " \r", |
|
|||
7402 | "[**********************79%************* ] 794 of 1000 complete" |
|
|||
7403 | ] |
|
|||
7404 | }, |
|
|||
7405 | { |
|
|||
7406 | "output_type": "stream", |
|
|||
7407 | "stream": "stdout", |
|
|||
7408 | "text": [ |
|
|||
7409 | " \r", |
|
|||
7410 | "[**********************80%************* ] 795 of 1000 complete" |
|
|||
7411 | ] |
|
|||
7412 | }, |
|
|||
7413 | { |
|
|||
7414 | "output_type": "stream", |
|
|||
7415 | "stream": "stdout", |
|
|||
7416 | "text": [ |
|
|||
7417 | " \r", |
|
|||
7418 | "[**********************80%************* ] 796 of 1000 complete" |
|
|||
7419 | ] |
|
|||
7420 | }, |
|
|||
7421 | { |
|
|||
7422 | "output_type": "stream", |
|
|||
7423 | "stream": "stdout", |
|
|||
7424 | "text": [ |
|
|||
7425 | " \r", |
|
|||
7426 | "[**********************80%************* ] 797 of 1000 complete" |
|
|||
7427 | ] |
|
|||
7428 | }, |
|
|||
7429 | { |
|
|||
7430 | "output_type": "stream", |
|
|||
7431 | "stream": "stdout", |
|
|||
7432 | "text": [ |
|
|||
7433 | " \r", |
|
|||
7434 | "[**********************80%************* ] 798 of 1000 complete" |
|
|||
7435 | ] |
|
|||
7436 | }, |
|
|||
7437 | { |
|
|||
7438 | "output_type": "stream", |
|
|||
7439 | "stream": "stdout", |
|
|||
7440 | "text": [ |
|
|||
7441 | " \r", |
|
|||
7442 | "[**********************80%************* ] 799 of 1000 complete" |
|
|||
7443 | ] |
|
|||
7444 | }, |
|
|||
7445 | { |
|
|||
7446 | "output_type": "stream", |
|
|||
7447 | "stream": "stdout", |
|
|||
7448 | "text": [ |
|
|||
7449 | " \r", |
|
|||
7450 | "[**********************80%************* ] 800 of 1000 complete" |
|
|||
7451 | ] |
|
|||
7452 | }, |
|
|||
7453 | { |
|
|||
7454 | "output_type": "stream", |
|
|||
7455 | "stream": "stdout", |
|
|||
7456 | "text": [ |
|
|||
7457 | " \r", |
|
|||
7458 | "[**********************80%************* ] 801 of 1000 complete" |
|
|||
7459 | ] |
|
|||
7460 | }, |
|
|||
7461 | { |
|
|||
7462 | "output_type": "stream", |
|
|||
7463 | "stream": "stdout", |
|
|||
7464 | "text": [ |
|
|||
7465 | " \r", |
|
|||
7466 | "[**********************80%************* ] 802 of 1000 complete" |
|
|||
7467 | ] |
|
|||
7468 | }, |
|
|||
7469 | { |
|
|||
7470 | "output_type": "stream", |
|
|||
7471 | "stream": "stdout", |
|
|||
7472 | "text": [ |
|
|||
7473 | " \r", |
|
|||
7474 | "[**********************80%************* ] 803 of 1000 complete" |
|
|||
7475 | ] |
|
|||
7476 | }, |
|
|||
7477 | { |
|
|||
7478 | "output_type": "stream", |
|
|||
7479 | "stream": "stdout", |
|
|||
7480 | "text": [ |
|
|||
7481 | " \r", |
|
|||
7482 | "[**********************80%************* ] 804 of 1000 complete" |
|
|||
7483 | ] |
|
|||
7484 | }, |
|
|||
7485 | { |
|
|||
7486 | "output_type": "stream", |
|
|||
7487 | "stream": "stdout", |
|
|||
7488 | "text": [ |
|
|||
7489 | " \r", |
|
|||
7490 | "[**********************81%************** ] 805 of 1000 complete" |
|
|||
7491 | ] |
|
|||
7492 | }, |
|
|||
7493 | { |
|
|||
7494 | "output_type": "stream", |
|
|||
7495 | "stream": "stdout", |
|
|||
7496 | "text": [ |
|
|||
7497 | " \r", |
|
|||
7498 | "[**********************81%************** ] 806 of 1000 complete" |
|
|||
7499 | ] |
|
|||
7500 | }, |
|
|||
7501 | { |
|
|||
7502 | "output_type": "stream", |
|
|||
7503 | "stream": "stdout", |
|
|||
7504 | "text": [ |
|
|||
7505 | " \r", |
|
|||
7506 | "[**********************81%************** ] 807 of 1000 complete" |
|
|||
7507 | ] |
|
|||
7508 | }, |
|
|||
7509 | { |
|
|||
7510 | "output_type": "stream", |
|
|||
7511 | "stream": "stdout", |
|
|||
7512 | "text": [ |
|
|||
7513 | " \r", |
|
|||
7514 | "[**********************81%************** ] 808 of 1000 complete" |
|
|||
7515 | ] |
|
|||
7516 | }, |
|
|||
7517 | { |
|
|||
7518 | "output_type": "stream", |
|
|||
7519 | "stream": "stdout", |
|
|||
7520 | "text": [ |
|
|||
7521 | " \r", |
|
|||
7522 | "[**********************81%************** ] 809 of 1000 complete" |
|
|||
7523 | ] |
|
|||
7524 | }, |
|
|||
7525 | { |
|
|||
7526 | "output_type": "stream", |
|
|||
7527 | "stream": "stdout", |
|
|||
7528 | "text": [ |
|
|||
7529 | " \r", |
|
|||
7530 | "[**********************81%************** ] 810 of 1000 complete" |
|
|||
7531 | ] |
|
|||
7532 | }, |
|
|||
7533 | { |
|
|||
7534 | "output_type": "stream", |
|
|||
7535 | "stream": "stdout", |
|
|||
7536 | "text": [ |
|
|||
7537 | " \r", |
|
|||
7538 | "[**********************81%************** ] 811 of 1000 complete" |
|
|||
7539 | ] |
|
|||
7540 | }, |
|
|||
7541 | { |
|
|||
7542 | "output_type": "stream", |
|
|||
7543 | "stream": "stdout", |
|
|||
7544 | "text": [ |
|
|||
7545 | " \r", |
|
|||
7546 | "[**********************81%************** ] 812 of 1000 complete" |
|
|||
7547 | ] |
|
|||
7548 | }, |
|
|||
7549 | { |
|
|||
7550 | "output_type": "stream", |
|
|||
7551 | "stream": "stdout", |
|
|||
7552 | "text": [ |
|
|||
7553 | " \r", |
|
|||
7554 | "[**********************81%************** ] 813 of 1000 complete" |
|
|||
7555 | ] |
|
|||
7556 | }, |
|
|||
7557 | { |
|
|||
7558 | "output_type": "stream", |
|
|||
7559 | "stream": "stdout", |
|
|||
7560 | "text": [ |
|
|||
7561 | " \r", |
|
|||
7562 | "[**********************81%************** ] 814 of 1000 complete" |
|
|||
7563 | ] |
|
|||
7564 | }, |
|
|||
7565 | { |
|
|||
7566 | "output_type": "stream", |
|
|||
7567 | "stream": "stdout", |
|
|||
7568 | "text": [ |
|
|||
7569 | " \r", |
|
|||
7570 | "[**********************82%************** ] 815 of 1000 complete" |
|
|||
7571 | ] |
|
|||
7572 | }, |
|
|||
7573 | { |
|
|||
7574 | "output_type": "stream", |
|
|||
7575 | "stream": "stdout", |
|
|||
7576 | "text": [ |
|
|||
7577 | " \r", |
|
|||
7578 | "[**********************82%************** ] 816 of 1000 complete" |
|
|||
7579 | ] |
|
|||
7580 | }, |
|
|||
7581 | { |
|
|||
7582 | "output_type": "stream", |
|
|||
7583 | "stream": "stdout", |
|
|||
7584 | "text": [ |
|
|||
7585 | " \r", |
|
|||
7586 | "[**********************82%************** ] 817 of 1000 complete" |
|
|||
7587 | ] |
|
|||
7588 | }, |
|
|||
7589 | { |
|
|||
7590 | "output_type": "stream", |
|
|||
7591 | "stream": "stdout", |
|
|||
7592 | "text": [ |
|
|||
7593 | " \r", |
|
|||
7594 | "[**********************82%************** ] 818 of 1000 complete" |
|
|||
7595 | ] |
|
|||
7596 | }, |
|
|||
7597 | { |
|
|||
7598 | "output_type": "stream", |
|
|||
7599 | "stream": "stdout", |
|
|||
7600 | "text": [ |
|
|||
7601 | " \r", |
|
|||
7602 | "[**********************82%************** ] 819 of 1000 complete" |
|
|||
7603 | ] |
|
|||
7604 | }, |
|
|||
7605 | { |
|
|||
7606 | "output_type": "stream", |
|
|||
7607 | "stream": "stdout", |
|
|||
7608 | "text": [ |
|
|||
7609 | " \r", |
|
|||
7610 | "[**********************82%************** ] 820 of 1000 complete" |
|
|||
7611 | ] |
|
|||
7612 | }, |
|
|||
7613 | { |
|
|||
7614 | "output_type": "stream", |
|
|||
7615 | "stream": "stdout", |
|
|||
7616 | "text": [ |
|
|||
7617 | " \r", |
|
|||
7618 | "[**********************82%************** ] 821 of 1000 complete" |
|
|||
7619 | ] |
|
|||
7620 | }, |
|
|||
7621 | { |
|
|||
7622 | "output_type": "stream", |
|
|||
7623 | "stream": "stdout", |
|
|||
7624 | "text": [ |
|
|||
7625 | " \r", |
|
|||
7626 | "[**********************82%************** ] 822 of 1000 complete" |
|
|||
7627 | ] |
|
|||
7628 | }, |
|
|||
7629 | { |
|
|||
7630 | "output_type": "stream", |
|
|||
7631 | "stream": "stdout", |
|
|||
7632 | "text": [ |
|
|||
7633 | " \r", |
|
|||
7634 | "[**********************82%************** ] 823 of 1000 complete" |
|
|||
7635 | ] |
|
|||
7636 | }, |
|
|||
7637 | { |
|
|||
7638 | "output_type": "stream", |
|
|||
7639 | "stream": "stdout", |
|
|||
7640 | "text": [ |
|
|||
7641 | " \r", |
|
|||
7642 | "[**********************82%************** ] 824 of 1000 complete" |
|
|||
7643 | ] |
|
|||
7644 | }, |
|
|||
7645 | { |
|
|||
7646 | "output_type": "stream", |
|
|||
7647 | "stream": "stdout", |
|
|||
7648 | "text": [ |
|
|||
7649 | " \r", |
|
|||
7650 | "[**********************83%*************** ] 825 of 1000 complete" |
|
|||
7651 | ] |
|
|||
7652 | }, |
|
|||
7653 | { |
|
|||
7654 | "output_type": "stream", |
|
|||
7655 | "stream": "stdout", |
|
|||
7656 | "text": [ |
|
|||
7657 | " \r", |
|
|||
7658 | "[**********************83%*************** ] 826 of 1000 complete" |
|
|||
7659 | ] |
|
|||
7660 | }, |
|
|||
7661 | { |
|
|||
7662 | "output_type": "stream", |
|
|||
7663 | "stream": "stdout", |
|
|||
7664 | "text": [ |
|
|||
7665 | " \r", |
|
|||
7666 | "[**********************83%*************** ] 827 of 1000 complete" |
|
|||
7667 | ] |
|
|||
7668 | }, |
|
|||
7669 | { |
|
|||
7670 | "output_type": "stream", |
|
|||
7671 | "stream": "stdout", |
|
|||
7672 | "text": [ |
|
|||
7673 | " \r", |
|
|||
7674 | "[**********************83%*************** ] 828 of 1000 complete" |
|
|||
7675 | ] |
|
|||
7676 | }, |
|
|||
7677 | { |
|
|||
7678 | "output_type": "stream", |
|
|||
7679 | "stream": "stdout", |
|
|||
7680 | "text": [ |
|
|||
7681 | " \r", |
|
|||
7682 | "[**********************83%*************** ] 829 of 1000 complete" |
|
|||
7683 | ] |
|
|||
7684 | }, |
|
|||
7685 | { |
|
|||
7686 | "output_type": "stream", |
|
|||
7687 | "stream": "stdout", |
|
|||
7688 | "text": [ |
|
|||
7689 | " \r", |
|
|||
7690 | "[**********************83%*************** ] 830 of 1000 complete" |
|
|||
7691 | ] |
|
|||
7692 | }, |
|
|||
7693 | { |
|
|||
7694 | "output_type": "stream", |
|
|||
7695 | "stream": "stdout", |
|
|||
7696 | "text": [ |
|
|||
7697 | " \r", |
|
|||
7698 | "[**********************83%*************** ] 831 of 1000 complete" |
|
|||
7699 | ] |
|
|||
7700 | }, |
|
|||
7701 | { |
|
|||
7702 | "output_type": "stream", |
|
|||
7703 | "stream": "stdout", |
|
|||
7704 | "text": [ |
|
|||
7705 | " \r", |
|
|||
7706 | "[**********************83%*************** ] 832 of 1000 complete" |
|
|||
7707 | ] |
|
|||
7708 | }, |
|
|||
7709 | { |
|
|||
7710 | "output_type": "stream", |
|
|||
7711 | "stream": "stdout", |
|
|||
7712 | "text": [ |
|
|||
7713 | " \r", |
|
|||
7714 | "[**********************83%*************** ] 833 of 1000 complete" |
|
|||
7715 | ] |
|
|||
7716 | }, |
|
|||
7717 | { |
|
|||
7718 | "output_type": "stream", |
|
|||
7719 | "stream": "stdout", |
|
|||
7720 | "text": [ |
|
|||
7721 | " \r", |
|
|||
7722 | "[**********************83%*************** ] 834 of 1000 complete" |
|
|||
7723 | ] |
|
|||
7724 | }, |
|
|||
7725 | { |
|
|||
7726 | "output_type": "stream", |
|
|||
7727 | "stream": "stdout", |
|
|||
7728 | "text": [ |
|
|||
7729 | " \r", |
|
|||
7730 | "[**********************84%*************** ] 835 of 1000 complete" |
|
|||
7731 | ] |
|
|||
7732 | }, |
|
|||
7733 | { |
|
|||
7734 | "output_type": "stream", |
|
|||
7735 | "stream": "stdout", |
|
|||
7736 | "text": [ |
|
|||
7737 | " \r", |
|
|||
7738 | "[**********************84%*************** ] 836 of 1000 complete" |
|
|||
7739 | ] |
|
|||
7740 | }, |
|
|||
7741 | { |
|
|||
7742 | "output_type": "stream", |
|
|||
7743 | "stream": "stdout", |
|
|||
7744 | "text": [ |
|
|||
7745 | " \r", |
|
|||
7746 | "[**********************84%*************** ] 837 of 1000 complete" |
|
|||
7747 | ] |
|
|||
7748 | }, |
|
|||
7749 | { |
|
|||
7750 | "output_type": "stream", |
|
|||
7751 | "stream": "stdout", |
|
|||
7752 | "text": [ |
|
|||
7753 | " \r", |
|
|||
7754 | "[**********************84%*************** ] 838 of 1000 complete" |
|
|||
7755 | ] |
|
|||
7756 | }, |
|
|||
7757 | { |
|
|||
7758 | "output_type": "stream", |
|
|||
7759 | "stream": "stdout", |
|
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7760 | "text": [ |
|
|||
7761 | " \r", |
|
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7762 | "[**********************84%*************** ] 839 of 1000 complete" |
|
|||
7763 | ] |
|
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7764 | }, |
|
|||
7765 | { |
|
|||
7766 | "output_type": "stream", |
|
|||
7767 | "stream": "stdout", |
|
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7768 | "text": [ |
|
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7769 | " \r", |
|
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7770 | "[**********************84%*************** ] 840 of 1000 complete" |
|
|||
7771 | ] |
|
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7772 | }, |
|
|||
7773 | { |
|
|||
7774 | "output_type": "stream", |
|
|||
7775 | "stream": "stdout", |
|
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7776 | "text": [ |
|
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7777 | " \r", |
|
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7778 | "[**********************84%*************** ] 841 of 1000 complete" |
|
|||
7779 | ] |
|
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7780 | }, |
|
|||
7781 | { |
|
|||
7782 | "output_type": "stream", |
|
|||
7783 | "stream": "stdout", |
|
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7784 | "text": [ |
|
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7785 | " \r", |
|
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7786 | "[**********************84%*************** ] 842 of 1000 complete" |
|
|||
7787 | ] |
|
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7788 | }, |
|
|||
7789 | { |
|
|||
7790 | "output_type": "stream", |
|
|||
7791 | "stream": "stdout", |
|
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7792 | "text": [ |
|
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7793 | " \r", |
|
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7794 | "[**********************84%*************** ] 843 of 1000 complete" |
|
|||
7795 | ] |
|
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7796 | }, |
|
|||
7797 | { |
|
|||
7798 | "output_type": "stream", |
|
|||
7799 | "stream": "stdout", |
|
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7800 | "text": [ |
|
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7801 | " \r", |
|
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7802 | "[**********************84%*************** ] 844 of 1000 complete" |
|
|||
7803 | ] |
|
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7804 | }, |
|
|||
7805 | { |
|
|||
7806 | "output_type": "stream", |
|
|||
7807 | "stream": "stdout", |
|
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7808 | "text": [ |
|
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7809 | " \r", |
|
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7810 | "[**********************85%**************** ] 845 of 1000 complete" |
|
|||
7811 | ] |
|
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7812 | }, |
|
|||
7813 | { |
|
|||
7814 | "output_type": "stream", |
|
|||
7815 | "stream": "stdout", |
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7816 | "text": [ |
|
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7817 | " \r", |
|
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7818 | "[**********************85%**************** ] 846 of 1000 complete" |
|
|||
7819 | ] |
|
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7820 | }, |
|
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7821 | { |
|
|||
7822 | "output_type": "stream", |
|
|||
7823 | "stream": "stdout", |
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7824 | "text": [ |
|
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7825 | " \r", |
|
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7826 | "[**********************85%**************** ] 847 of 1000 complete" |
|
|||
7827 | ] |
|
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7828 | }, |
|
|||
7829 | { |
|
|||
7830 | "output_type": "stream", |
|
|||
7831 | "stream": "stdout", |
|
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7832 | "text": [ |
|
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7833 | " \r", |
|
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7834 | "[**********************85%**************** ] 848 of 1000 complete" |
|
|||
7835 | ] |
|
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7836 | }, |
|
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7837 | { |
|
|||
7838 | "output_type": "stream", |
|
|||
7839 | "stream": "stdout", |
|
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7840 | "text": [ |
|
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7841 | " \r", |
|
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7842 | "[**********************85%**************** ] 849 of 1000 complete" |
|
|||
7843 | ] |
|
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7844 | }, |
|
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7845 | { |
|
|||
7846 | "output_type": "stream", |
|
|||
7847 | "stream": "stdout", |
|
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7848 | "text": [ |
|
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7849 | " \r", |
|
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7850 | "[**********************85%**************** ] 850 of 1000 complete" |
|
|||
7851 | ] |
|
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7852 | }, |
|
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7853 | { |
|
|||
7854 | "output_type": "stream", |
|
|||
7855 | "stream": "stdout", |
|
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7856 | "text": [ |
|
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7857 | " \r", |
|
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7858 | "[**********************85%**************** ] 851 of 1000 complete" |
|
|||
7859 | ] |
|
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7860 | }, |
|
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7861 | { |
|
|||
7862 | "output_type": "stream", |
|
|||
7863 | "stream": "stdout", |
|
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7864 | "text": [ |
|
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7865 | " \r", |
|
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7866 | "[**********************85%**************** ] 852 of 1000 complete" |
|
|||
7867 | ] |
|
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7868 | }, |
|
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7869 | { |
|
|||
7870 | "output_type": "stream", |
|
|||
7871 | "stream": "stdout", |
|
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7872 | "text": [ |
|
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7873 | " \r", |
|
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7874 | "[**********************85%**************** ] 853 of 1000 complete" |
|
|||
7875 | ] |
|
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7876 | }, |
|
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7877 | { |
|
|||
7878 | "output_type": "stream", |
|
|||
7879 | "stream": "stdout", |
|
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7880 | "text": [ |
|
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7881 | " \r", |
|
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7882 | "[**********************85%**************** ] 854 of 1000 complete" |
|
|||
7883 | ] |
|
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7884 | }, |
|
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7885 | { |
|
|||
7886 | "output_type": "stream", |
|
|||
7887 | "stream": "stdout", |
|
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7888 | "text": [ |
|
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7889 | " \r", |
|
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7890 | "[**********************86%**************** ] 855 of 1000 complete" |
|
|||
7891 | ] |
|
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7892 | }, |
|
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7893 | { |
|
|||
7894 | "output_type": "stream", |
|
|||
7895 | "stream": "stdout", |
|
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7896 | "text": [ |
|
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7897 | " \r", |
|
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7898 | "[**********************86%**************** ] 856 of 1000 complete" |
|
|||
7899 | ] |
|
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7900 | }, |
|
|||
7901 | { |
|
|||
7902 | "output_type": "stream", |
|
|||
7903 | "stream": "stdout", |
|
|||
7904 | "text": [ |
|
|||
7905 | " \r", |
|
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7906 | "[**********************86%**************** ] 857 of 1000 complete" |
|
|||
7907 | ] |
|
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7908 | }, |
|
|||
7909 | { |
|
|||
7910 | "output_type": "stream", |
|
|||
7911 | "stream": "stdout", |
|
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7912 | "text": [ |
|
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7913 | " \r", |
|
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7914 | "[**********************86%**************** ] 858 of 1000 complete" |
|
|||
7915 | ] |
|
|||
7916 | }, |
|
|||
7917 | { |
|
|||
7918 | "output_type": "stream", |
|
|||
7919 | "stream": "stdout", |
|
|||
7920 | "text": [ |
|
|||
7921 | " \r", |
|
|||
7922 | "[**********************86%**************** ] 859 of 1000 complete" |
|
|||
7923 | ] |
|
|||
7924 | }, |
|
|||
7925 | { |
|
|||
7926 | "output_type": "stream", |
|
|||
7927 | "stream": "stdout", |
|
|||
7928 | "text": [ |
|
|||
7929 | " \r", |
|
|||
7930 | "[**********************86%**************** ] 860 of 1000 complete" |
|
|||
7931 | ] |
|
|||
7932 | }, |
|
|||
7933 | { |
|
|||
7934 | "output_type": "stream", |
|
|||
7935 | "stream": "stdout", |
|
|||
7936 | "text": [ |
|
|||
7937 | " \r", |
|
|||
7938 | "[**********************86%**************** ] 861 of 1000 complete" |
|
|||
7939 | ] |
|
|||
7940 | }, |
|
|||
7941 | { |
|
|||
7942 | "output_type": "stream", |
|
|||
7943 | "stream": "stdout", |
|
|||
7944 | "text": [ |
|
|||
7945 | " \r", |
|
|||
7946 | "[**********************86%**************** ] 862 of 1000 complete" |
|
|||
7947 | ] |
|
|||
7948 | }, |
|
|||
7949 | { |
|
|||
7950 | "output_type": "stream", |
|
|||
7951 | "stream": "stdout", |
|
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7952 | "text": [ |
|
|||
7953 | " \r", |
|
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7954 | "[**********************86%**************** ] 863 of 1000 complete" |
|
|||
7955 | ] |
|
|||
7956 | }, |
|
|||
7957 | { |
|
|||
7958 | "output_type": "stream", |
|
|||
7959 | "stream": "stdout", |
|
|||
7960 | "text": [ |
|
|||
7961 | " \r", |
|
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7962 | "[**********************86%**************** ] 864 of 1000 complete" |
|
|||
7963 | ] |
|
|||
7964 | }, |
|
|||
7965 | { |
|
|||
7966 | "output_type": "stream", |
|
|||
7967 | "stream": "stdout", |
|
|||
7968 | "text": [ |
|
|||
7969 | " \r", |
|
|||
7970 | "[**********************87%***************** ] 865 of 1000 complete" |
|
|||
7971 | ] |
|
|||
7972 | }, |
|
|||
7973 | { |
|
|||
7974 | "output_type": "stream", |
|
|||
7975 | "stream": "stdout", |
|
|||
7976 | "text": [ |
|
|||
7977 | " \r", |
|
|||
7978 | "[**********************87%***************** ] 866 of 1000 complete" |
|
|||
7979 | ] |
|
|||
7980 | }, |
|
|||
7981 | { |
|
|||
7982 | "output_type": "stream", |
|
|||
7983 | "stream": "stdout", |
|
|||
7984 | "text": [ |
|
|||
7985 | " \r", |
|
|||
7986 | "[**********************87%***************** ] 867 of 1000 complete" |
|
|||
7987 | ] |
|
|||
7988 | }, |
|
|||
7989 | { |
|
|||
7990 | "output_type": "stream", |
|
|||
7991 | "stream": "stdout", |
|
|||
7992 | "text": [ |
|
|||
7993 | " \r", |
|
|||
7994 | "[**********************87%***************** ] 868 of 1000 complete" |
|
|||
7995 | ] |
|
|||
7996 | }, |
|
|||
7997 | { |
|
|||
7998 | "output_type": "stream", |
|
|||
7999 | "stream": "stdout", |
|
|||
8000 | "text": [ |
|
|||
8001 | " \r", |
|
|||
8002 | "[**********************87%***************** ] 869 of 1000 complete" |
|
|||
8003 | ] |
|
|||
8004 | }, |
|
|||
8005 | { |
|
|||
8006 | "output_type": "stream", |
|
|||
8007 | "stream": "stdout", |
|
|||
8008 | "text": [ |
|
|||
8009 | " \r", |
|
|||
8010 | "[**********************87%***************** ] 870 of 1000 complete" |
|
|||
8011 | ] |
|
|||
8012 | }, |
|
|||
8013 | { |
|
|||
8014 | "output_type": "stream", |
|
|||
8015 | "stream": "stdout", |
|
|||
8016 | "text": [ |
|
|||
8017 | " \r", |
|
|||
8018 | "[**********************87%***************** ] 871 of 1000 complete" |
|
|||
8019 | ] |
|
|||
8020 | }, |
|
|||
8021 | { |
|
|||
8022 | "output_type": "stream", |
|
|||
8023 | "stream": "stdout", |
|
|||
8024 | "text": [ |
|
|||
8025 | " \r", |
|
|||
8026 | "[**********************87%***************** ] 872 of 1000 complete" |
|
|||
8027 | ] |
|
|||
8028 | }, |
|
|||
8029 | { |
|
|||
8030 | "output_type": "stream", |
|
|||
8031 | "stream": "stdout", |
|
|||
8032 | "text": [ |
|
|||
8033 | " \r", |
|
|||
8034 | "[**********************87%***************** ] 873 of 1000 complete" |
|
|||
8035 | ] |
|
|||
8036 | }, |
|
|||
8037 | { |
|
|||
8038 | "output_type": "stream", |
|
|||
8039 | "stream": "stdout", |
|
|||
8040 | "text": [ |
|
|||
8041 | " \r", |
|
|||
8042 | "[**********************87%***************** ] 874 of 1000 complete" |
|
|||
8043 | ] |
|
|||
8044 | }, |
|
|||
8045 | { |
|
|||
8046 | "output_type": "stream", |
|
|||
8047 | "stream": "stdout", |
|
|||
8048 | "text": [ |
|
|||
8049 | " \r", |
|
|||
8050 | "[**********************88%***************** ] 875 of 1000 complete" |
|
|||
8051 | ] |
|
|||
8052 | }, |
|
|||
8053 | { |
|
|||
8054 | "output_type": "stream", |
|
|||
8055 | "stream": "stdout", |
|
|||
8056 | "text": [ |
|
|||
8057 | " \r", |
|
|||
8058 | "[**********************88%***************** ] 876 of 1000 complete" |
|
|||
8059 | ] |
|
|||
8060 | }, |
|
|||
8061 | { |
|
|||
8062 | "output_type": "stream", |
|
|||
8063 | "stream": "stdout", |
|
|||
8064 | "text": [ |
|
|||
8065 | " \r", |
|
|||
8066 | "[**********************88%***************** ] 877 of 1000 complete" |
|
|||
8067 | ] |
|
|||
8068 | }, |
|
|||
8069 | { |
|
|||
8070 | "output_type": "stream", |
|
|||
8071 | "stream": "stdout", |
|
|||
8072 | "text": [ |
|
|||
8073 | " \r", |
|
|||
8074 | "[**********************88%***************** ] 878 of 1000 complete" |
|
|||
8075 | ] |
|
|||
8076 | }, |
|
|||
8077 | { |
|
|||
8078 | "output_type": "stream", |
|
|||
8079 | "stream": "stdout", |
|
|||
8080 | "text": [ |
|
|||
8081 | " \r", |
|
|||
8082 | "[**********************88%***************** ] 879 of 1000 complete" |
|
|||
8083 | ] |
|
|||
8084 | }, |
|
|||
8085 | { |
|
|||
8086 | "output_type": "stream", |
|
|||
8087 | "stream": "stdout", |
|
|||
8088 | "text": [ |
|
|||
8089 | " \r", |
|
|||
8090 | "[**********************88%***************** ] 880 of 1000 complete" |
|
|||
8091 | ] |
|
|||
8092 | }, |
|
|||
8093 | { |
|
|||
8094 | "output_type": "stream", |
|
|||
8095 | "stream": "stdout", |
|
|||
8096 | "text": [ |
|
|||
8097 | " \r", |
|
|||
8098 | "[**********************88%***************** ] 881 of 1000 complete" |
|
|||
8099 | ] |
|
|||
8100 | }, |
|
|||
8101 | { |
|
|||
8102 | "output_type": "stream", |
|
|||
8103 | "stream": "stdout", |
|
|||
8104 | "text": [ |
|
|||
8105 | " \r", |
|
|||
8106 | "[**********************88%***************** ] 882 of 1000 complete" |
|
|||
8107 | ] |
|
|||
8108 | }, |
|
|||
8109 | { |
|
|||
8110 | "output_type": "stream", |
|
|||
8111 | "stream": "stdout", |
|
|||
8112 | "text": [ |
|
|||
8113 | " \r", |
|
|||
8114 | "[**********************88%***************** ] 883 of 1000 complete" |
|
|||
8115 | ] |
|
|||
8116 | }, |
|
|||
8117 | { |
|
|||
8118 | "output_type": "stream", |
|
|||
8119 | "stream": "stdout", |
|
|||
8120 | "text": [ |
|
|||
8121 | " \r", |
|
|||
8122 | "[**********************88%***************** ] 884 of 1000 complete" |
|
|||
8123 | ] |
|
|||
8124 | }, |
|
|||
8125 | { |
|
|||
8126 | "output_type": "stream", |
|
|||
8127 | "stream": "stdout", |
|
|||
8128 | "text": [ |
|
|||
8129 | " \r", |
|
|||
8130 | "[**********************89%****************** ] 885 of 1000 complete" |
|
|||
8131 | ] |
|
|||
8132 | }, |
|
|||
8133 | { |
|
|||
8134 | "output_type": "stream", |
|
|||
8135 | "stream": "stdout", |
|
|||
8136 | "text": [ |
|
|||
8137 | " \r", |
|
|||
8138 | "[**********************89%****************** ] 886 of 1000 complete" |
|
|||
8139 | ] |
|
|||
8140 | }, |
|
|||
8141 | { |
|
|||
8142 | "output_type": "stream", |
|
|||
8143 | "stream": "stdout", |
|
|||
8144 | "text": [ |
|
|||
8145 | " \r", |
|
|||
8146 | "[**********************89%****************** ] 887 of 1000 complete" |
|
|||
8147 | ] |
|
|||
8148 | }, |
|
|||
8149 | { |
|
|||
8150 | "output_type": "stream", |
|
|||
8151 | "stream": "stdout", |
|
|||
8152 | "text": [ |
|
|||
8153 | " \r", |
|
|||
8154 | "[**********************89%****************** ] 888 of 1000 complete" |
|
|||
8155 | ] |
|
|||
8156 | }, |
|
|||
8157 | { |
|
|||
8158 | "output_type": "stream", |
|
|||
8159 | "stream": "stdout", |
|
|||
8160 | "text": [ |
|
|||
8161 | " \r", |
|
|||
8162 | "[**********************89%****************** ] 889 of 1000 complete" |
|
|||
8163 | ] |
|
|||
8164 | }, |
|
|||
8165 | { |
|
|||
8166 | "output_type": "stream", |
|
|||
8167 | "stream": "stdout", |
|
|||
8168 | "text": [ |
|
|||
8169 | " \r", |
|
|||
8170 | "[**********************89%****************** ] 890 of 1000 complete" |
|
|||
8171 | ] |
|
|||
8172 | }, |
|
|||
8173 | { |
|
|||
8174 | "output_type": "stream", |
|
|||
8175 | "stream": "stdout", |
|
|||
8176 | "text": [ |
|
|||
8177 | " \r", |
|
|||
8178 | "[**********************89%****************** ] 891 of 1000 complete" |
|
|||
8179 | ] |
|
|||
8180 | }, |
|
|||
8181 | { |
|
|||
8182 | "output_type": "stream", |
|
|||
8183 | "stream": "stdout", |
|
|||
8184 | "text": [ |
|
|||
8185 | " \r", |
|
|||
8186 | "[**********************89%****************** ] 892 of 1000 complete" |
|
|||
8187 | ] |
|
|||
8188 | }, |
|
|||
8189 | { |
|
|||
8190 | "output_type": "stream", |
|
|||
8191 | "stream": "stdout", |
|
|||
8192 | "text": [ |
|
|||
8193 | " \r", |
|
|||
8194 | "[**********************89%****************** ] 893 of 1000 complete" |
|
|||
8195 | ] |
|
|||
8196 | }, |
|
|||
8197 | { |
|
|||
8198 | "output_type": "stream", |
|
|||
8199 | "stream": "stdout", |
|
|||
8200 | "text": [ |
|
|||
8201 | " \r", |
|
|||
8202 | "[**********************89%****************** ] 894 of 1000 complete" |
|
|||
8203 | ] |
|
|||
8204 | }, |
|
|||
8205 | { |
|
|||
8206 | "output_type": "stream", |
|
|||
8207 | "stream": "stdout", |
|
|||
8208 | "text": [ |
|
|||
8209 | " \r", |
|
|||
8210 | "[**********************90%****************** ] 895 of 1000 complete" |
|
|||
8211 | ] |
|
|||
8212 | }, |
|
|||
8213 | { |
|
|||
8214 | "output_type": "stream", |
|
|||
8215 | "stream": "stdout", |
|
|||
8216 | "text": [ |
|
|||
8217 | " \r", |
|
|||
8218 | "[**********************90%****************** ] 896 of 1000 complete" |
|
|||
8219 | ] |
|
|||
8220 | }, |
|
|||
8221 | { |
|
|||
8222 | "output_type": "stream", |
|
|||
8223 | "stream": "stdout", |
|
|||
8224 | "text": [ |
|
|||
8225 | " \r", |
|
|||
8226 | "[**********************90%****************** ] 897 of 1000 complete" |
|
|||
8227 | ] |
|
|||
8228 | }, |
|
|||
8229 | { |
|
|||
8230 | "output_type": "stream", |
|
|||
8231 | "stream": "stdout", |
|
|||
8232 | "text": [ |
|
|||
8233 | " \r", |
|
|||
8234 | "[**********************90%****************** ] 898 of 1000 complete" |
|
|||
8235 | ] |
|
|||
8236 | }, |
|
|||
8237 | { |
|
|||
8238 | "output_type": "stream", |
|
|||
8239 | "stream": "stdout", |
|
|||
8240 | "text": [ |
|
|||
8241 | " \r", |
|
|||
8242 | "[**********************90%****************** ] 899 of 1000 complete" |
|
|||
8243 | ] |
|
|||
8244 | }, |
|
|||
8245 | { |
|
|||
8246 | "output_type": "stream", |
|
|||
8247 | "stream": "stdout", |
|
|||
8248 | "text": [ |
|
|||
8249 | " \r", |
|
|||
8250 | "[**********************90%****************** ] 900 of 1000 complete" |
|
|||
8251 | ] |
|
|||
8252 | }, |
|
|||
8253 | { |
|
|||
8254 | "output_type": "stream", |
|
|||
8255 | "stream": "stdout", |
|
|||
8256 | "text": [ |
|
|||
8257 | " \r", |
|
|||
8258 | "[**********************90%****************** ] 901 of 1000 complete" |
|
|||
8259 | ] |
|
|||
8260 | }, |
|
|||
8261 | { |
|
|||
8262 | "output_type": "stream", |
|
|||
8263 | "stream": "stdout", |
|
|||
8264 | "text": [ |
|
|||
8265 | " \r", |
|
|||
8266 | "[**********************90%****************** ] 902 of 1000 complete" |
|
|||
8267 | ] |
|
|||
8268 | }, |
|
|||
8269 | { |
|
|||
8270 | "output_type": "stream", |
|
|||
8271 | "stream": "stdout", |
|
|||
8272 | "text": [ |
|
|||
8273 | " \r", |
|
|||
8274 | "[**********************90%****************** ] 903 of 1000 complete" |
|
|||
8275 | ] |
|
|||
8276 | }, |
|
|||
8277 | { |
|
|||
8278 | "output_type": "stream", |
|
|||
8279 | "stream": "stdout", |
|
|||
8280 | "text": [ |
|
|||
8281 | " \r", |
|
|||
8282 | "[**********************90%****************** ] 904 of 1000 complete" |
|
|||
8283 | ] |
|
|||
8284 | }, |
|
|||
8285 | { |
|
|||
8286 | "output_type": "stream", |
|
|||
8287 | "stream": "stdout", |
|
|||
8288 | "text": [ |
|
|||
8289 | " \r", |
|
|||
8290 | "[**********************91%******************* ] 905 of 1000 complete" |
|
|||
8291 | ] |
|
|||
8292 | }, |
|
|||
8293 | { |
|
|||
8294 | "output_type": "stream", |
|
|||
8295 | "stream": "stdout", |
|
|||
8296 | "text": [ |
|
|||
8297 | " \r", |
|
|||
8298 | "[**********************91%******************* ] 906 of 1000 complete" |
|
|||
8299 | ] |
|
|||
8300 | }, |
|
|||
8301 | { |
|
|||
8302 | "output_type": "stream", |
|
|||
8303 | "stream": "stdout", |
|
|||
8304 | "text": [ |
|
|||
8305 | " \r", |
|
|||
8306 | "[**********************91%******************* ] 907 of 1000 complete" |
|
|||
8307 | ] |
|
|||
8308 | }, |
|
|||
8309 | { |
|
|||
8310 | "output_type": "stream", |
|
|||
8311 | "stream": "stdout", |
|
|||
8312 | "text": [ |
|
|||
8313 | " \r", |
|
|||
8314 | "[**********************91%******************* ] 908 of 1000 complete" |
|
|||
8315 | ] |
|
|||
8316 | }, |
|
|||
8317 | { |
|
|||
8318 | "output_type": "stream", |
|
|||
8319 | "stream": "stdout", |
|
|||
8320 | "text": [ |
|
|||
8321 | " \r", |
|
|||
8322 | "[**********************91%******************* ] 909 of 1000 complete" |
|
|||
8323 | ] |
|
|||
8324 | }, |
|
|||
8325 | { |
|
|||
8326 | "output_type": "stream", |
|
|||
8327 | "stream": "stdout", |
|
|||
8328 | "text": [ |
|
|||
8329 | " \r", |
|
|||
8330 | "[**********************91%******************* ] 910 of 1000 complete" |
|
|||
8331 | ] |
|
|||
8332 | }, |
|
|||
8333 | { |
|
|||
8334 | "output_type": "stream", |
|
|||
8335 | "stream": "stdout", |
|
|||
8336 | "text": [ |
|
|||
8337 | " \r", |
|
|||
8338 | "[**********************91%******************* ] 911 of 1000 complete" |
|
|||
8339 | ] |
|
|||
8340 | }, |
|
|||
8341 | { |
|
|||
8342 | "output_type": "stream", |
|
|||
8343 | "stream": "stdout", |
|
|||
8344 | "text": [ |
|
|||
8345 | " \r", |
|
|||
8346 | "[**********************91%******************* ] 912 of 1000 complete" |
|
|||
8347 | ] |
|
|||
8348 | }, |
|
|||
8349 | { |
|
|||
8350 | "output_type": "stream", |
|
|||
8351 | "stream": "stdout", |
|
|||
8352 | "text": [ |
|
|||
8353 | " \r", |
|
|||
8354 | "[**********************91%******************* ] 913 of 1000 complete" |
|
|||
8355 | ] |
|
|||
8356 | }, |
|
|||
8357 | { |
|
|||
8358 | "output_type": "stream", |
|
|||
8359 | "stream": "stdout", |
|
|||
8360 | "text": [ |
|
|||
8361 | " \r", |
|
|||
8362 | "[**********************91%******************* ] 914 of 1000 complete" |
|
|||
8363 | ] |
|
|||
8364 | }, |
|
|||
8365 | { |
|
|||
8366 | "output_type": "stream", |
|
|||
8367 | "stream": "stdout", |
|
|||
8368 | "text": [ |
|
|||
8369 | " \r", |
|
|||
8370 | "[**********************92%******************* ] 915 of 1000 complete" |
|
|||
8371 | ] |
|
|||
8372 | }, |
|
|||
8373 | { |
|
|||
8374 | "output_type": "stream", |
|
|||
8375 | "stream": "stdout", |
|
|||
8376 | "text": [ |
|
|||
8377 | " \r", |
|
|||
8378 | "[**********************92%******************* ] 916 of 1000 complete" |
|
|||
8379 | ] |
|
|||
8380 | }, |
|
|||
8381 | { |
|
|||
8382 | "output_type": "stream", |
|
|||
8383 | "stream": "stdout", |
|
|||
8384 | "text": [ |
|
|||
8385 | " \r", |
|
|||
8386 | "[**********************92%******************* ] 917 of 1000 complete" |
|
|||
8387 | ] |
|
|||
8388 | }, |
|
|||
8389 | { |
|
|||
8390 | "output_type": "stream", |
|
|||
8391 | "stream": "stdout", |
|
|||
8392 | "text": [ |
|
|||
8393 | " \r", |
|
|||
8394 | "[**********************92%******************* ] 918 of 1000 complete" |
|
|||
8395 | ] |
|
|||
8396 | }, |
|
|||
8397 | { |
|
|||
8398 | "output_type": "stream", |
|
|||
8399 | "stream": "stdout", |
|
|||
8400 | "text": [ |
|
|||
8401 | " \r", |
|
|||
8402 | "[**********************92%******************* ] 919 of 1000 complete" |
|
|||
8403 | ] |
|
|||
8404 | }, |
|
|||
8405 | { |
|
|||
8406 | "output_type": "stream", |
|
|||
8407 | "stream": "stdout", |
|
|||
8408 | "text": [ |
|
|||
8409 | " \r", |
|
|||
8410 | "[**********************92%******************* ] 920 of 1000 complete" |
|
|||
8411 | ] |
|
|||
8412 | }, |
|
|||
8413 | { |
|
|||
8414 | "output_type": "stream", |
|
|||
8415 | "stream": "stdout", |
|
|||
8416 | "text": [ |
|
|||
8417 | " \r", |
|
|||
8418 | "[**********************92%******************* ] 921 of 1000 complete" |
|
|||
8419 | ] |
|
|||
8420 | }, |
|
|||
8421 | { |
|
|||
8422 | "output_type": "stream", |
|
|||
8423 | "stream": "stdout", |
|
|||
8424 | "text": [ |
|
|||
8425 | " \r", |
|
|||
8426 | "[**********************92%******************* ] 922 of 1000 complete" |
|
|||
8427 | ] |
|
|||
8428 | }, |
|
|||
8429 | { |
|
|||
8430 | "output_type": "stream", |
|
|||
8431 | "stream": "stdout", |
|
|||
8432 | "text": [ |
|
|||
8433 | " \r", |
|
|||
8434 | "[**********************92%******************* ] 923 of 1000 complete" |
|
|||
8435 | ] |
|
|||
8436 | }, |
|
|||
8437 | { |
|
|||
8438 | "output_type": "stream", |
|
|||
8439 | "stream": "stdout", |
|
|||
8440 | "text": [ |
|
|||
8441 | " \r", |
|
|||
8442 | "[**********************92%******************* ] 924 of 1000 complete" |
|
|||
8443 | ] |
|
|||
8444 | }, |
|
|||
8445 | { |
|
|||
8446 | "output_type": "stream", |
|
|||
8447 | "stream": "stdout", |
|
|||
8448 | "text": [ |
|
|||
8449 | " \r", |
|
|||
8450 | "[**********************93%******************** ] 925 of 1000 complete" |
|
|||
8451 | ] |
|
|||
8452 | }, |
|
|||
8453 | { |
|
|||
8454 | "output_type": "stream", |
|
|||
8455 | "stream": "stdout", |
|
|||
8456 | "text": [ |
|
|||
8457 | " \r", |
|
|||
8458 | "[**********************93%******************** ] 926 of 1000 complete" |
|
|||
8459 | ] |
|
|||
8460 | }, |
|
|||
8461 | { |
|
|||
8462 | "output_type": "stream", |
|
|||
8463 | "stream": "stdout", |
|
|||
8464 | "text": [ |
|
|||
8465 | " \r", |
|
|||
8466 | "[**********************93%******************** ] 927 of 1000 complete" |
|
|||
8467 | ] |
|
|||
8468 | }, |
|
|||
8469 | { |
|
|||
8470 | "output_type": "stream", |
|
|||
8471 | "stream": "stdout", |
|
|||
8472 | "text": [ |
|
|||
8473 | " \r", |
|
|||
8474 | "[**********************93%******************** ] 928 of 1000 complete" |
|
|||
8475 | ] |
|
|||
8476 | }, |
|
|||
8477 | { |
|
|||
8478 | "output_type": "stream", |
|
|||
8479 | "stream": "stdout", |
|
|||
8480 | "text": [ |
|
|||
8481 | " \r", |
|
|||
8482 | "[**********************93%******************** ] 929 of 1000 complete" |
|
|||
8483 | ] |
|
|||
8484 | }, |
|
|||
8485 | { |
|
|||
8486 | "output_type": "stream", |
|
|||
8487 | "stream": "stdout", |
|
|||
8488 | "text": [ |
|
|||
8489 | " \r", |
|
|||
8490 | "[**********************93%******************** ] 930 of 1000 complete" |
|
|||
8491 | ] |
|
|||
8492 | }, |
|
|||
8493 | { |
|
|||
8494 | "output_type": "stream", |
|
|||
8495 | "stream": "stdout", |
|
|||
8496 | "text": [ |
|
|||
8497 | " \r", |
|
|||
8498 | "[**********************93%******************** ] 931 of 1000 complete" |
|
|||
8499 | ] |
|
|||
8500 | }, |
|
|||
8501 | { |
|
|||
8502 | "output_type": "stream", |
|
|||
8503 | "stream": "stdout", |
|
|||
8504 | "text": [ |
|
|||
8505 | " \r", |
|
|||
8506 | "[**********************93%******************** ] 932 of 1000 complete" |
|
|||
8507 | ] |
|
|||
8508 | }, |
|
|||
8509 | { |
|
|||
8510 | "output_type": "stream", |
|
|||
8511 | "stream": "stdout", |
|
|||
8512 | "text": [ |
|
|||
8513 | " \r", |
|
|||
8514 | "[**********************93%******************** ] 933 of 1000 complete" |
|
|||
8515 | ] |
|
|||
8516 | }, |
|
|||
8517 | { |
|
|||
8518 | "output_type": "stream", |
|
|||
8519 | "stream": "stdout", |
|
|||
8520 | "text": [ |
|
|||
8521 | " \r", |
|
|||
8522 | "[**********************93%******************** ] 934 of 1000 complete" |
|
|||
8523 | ] |
|
|||
8524 | }, |
|
|||
8525 | { |
|
|||
8526 | "output_type": "stream", |
|
|||
8527 | "stream": "stdout", |
|
|||
8528 | "text": [ |
|
|||
8529 | " \r", |
|
|||
8530 | "[**********************94%******************** ] 935 of 1000 complete" |
|
|||
8531 | ] |
|
|||
8532 | }, |
|
|||
8533 | { |
|
|||
8534 | "output_type": "stream", |
|
|||
8535 | "stream": "stdout", |
|
|||
8536 | "text": [ |
|
|||
8537 | " \r", |
|
|||
8538 | "[**********************94%******************** ] 936 of 1000 complete" |
|
|||
8539 | ] |
|
|||
8540 | }, |
|
|||
8541 | { |
|
|||
8542 | "output_type": "stream", |
|
|||
8543 | "stream": "stdout", |
|
|||
8544 | "text": [ |
|
|||
8545 | " \r", |
|
|||
8546 | "[**********************94%******************** ] 937 of 1000 complete" |
|
|||
8547 | ] |
|
|||
8548 | }, |
|
|||
8549 | { |
|
|||
8550 | "output_type": "stream", |
|
|||
8551 | "stream": "stdout", |
|
|||
8552 | "text": [ |
|
|||
8553 | " \r", |
|
|||
8554 | "[**********************94%******************** ] 938 of 1000 complete" |
|
|||
8555 | ] |
|
|||
8556 | }, |
|
|||
8557 | { |
|
|||
8558 | "output_type": "stream", |
|
|||
8559 | "stream": "stdout", |
|
|||
8560 | "text": [ |
|
|||
8561 | " \r", |
|
|||
8562 | "[**********************94%******************** ] 939 of 1000 complete" |
|
|||
8563 | ] |
|
|||
8564 | }, |
|
|||
8565 | { |
|
|||
8566 | "output_type": "stream", |
|
|||
8567 | "stream": "stdout", |
|
|||
8568 | "text": [ |
|
|||
8569 | " \r", |
|
|||
8570 | "[**********************94%******************** ] 940 of 1000 complete" |
|
|||
8571 | ] |
|
|||
8572 | }, |
|
|||
8573 | { |
|
|||
8574 | "output_type": "stream", |
|
|||
8575 | "stream": "stdout", |
|
|||
8576 | "text": [ |
|
|||
8577 | " \r", |
|
|||
8578 | "[**********************94%******************** ] 941 of 1000 complete" |
|
|||
8579 | ] |
|
|||
8580 | }, |
|
|||
8581 | { |
|
|||
8582 | "output_type": "stream", |
|
|||
8583 | "stream": "stdout", |
|
|||
8584 | "text": [ |
|
|||
8585 | " \r", |
|
|||
8586 | "[**********************94%******************** ] 942 of 1000 complete" |
|
|||
8587 | ] |
|
|||
8588 | }, |
|
|||
8589 | { |
|
|||
8590 | "output_type": "stream", |
|
|||
8591 | "stream": "stdout", |
|
|||
8592 | "text": [ |
|
|||
8593 | " \r", |
|
|||
8594 | "[**********************94%******************** ] 943 of 1000 complete" |
|
|||
8595 | ] |
|
|||
8596 | }, |
|
|||
8597 | { |
|
|||
8598 | "output_type": "stream", |
|
|||
8599 | "stream": "stdout", |
|
|||
8600 | "text": [ |
|
|||
8601 | " \r", |
|
|||
8602 | "[**********************94%******************** ] 944 of 1000 complete" |
|
|||
8603 | ] |
|
|||
8604 | }, |
|
|||
8605 | { |
|
|||
8606 | "output_type": "stream", |
|
|||
8607 | "stream": "stdout", |
|
|||
8608 | "text": [ |
|
|||
8609 | " \r", |
|
|||
8610 | "[**********************95%********************* ] 945 of 1000 complete" |
|
|||
8611 | ] |
|
|||
8612 | }, |
|
|||
8613 | { |
|
|||
8614 | "output_type": "stream", |
|
|||
8615 | "stream": "stdout", |
|
|||
8616 | "text": [ |
|
|||
8617 | " \r", |
|
|||
8618 | "[**********************95%********************* ] 946 of 1000 complete" |
|
|||
8619 | ] |
|
|||
8620 | }, |
|
|||
8621 | { |
|
|||
8622 | "output_type": "stream", |
|
|||
8623 | "stream": "stdout", |
|
|||
8624 | "text": [ |
|
|||
8625 | " \r", |
|
|||
8626 | "[**********************95%********************* ] 947 of 1000 complete" |
|
|||
8627 | ] |
|
|||
8628 | }, |
|
|||
8629 | { |
|
|||
8630 | "output_type": "stream", |
|
|||
8631 | "stream": "stdout", |
|
|||
8632 | "text": [ |
|
|||
8633 | " \r", |
|
|||
8634 | "[**********************95%********************* ] 948 of 1000 complete" |
|
|||
8635 | ] |
|
|||
8636 | }, |
|
|||
8637 | { |
|
|||
8638 | "output_type": "stream", |
|
|||
8639 | "stream": "stdout", |
|
|||
8640 | "text": [ |
|
|||
8641 | " \r", |
|
|||
8642 | "[**********************95%********************* ] 949 of 1000 complete" |
|
|||
8643 | ] |
|
|||
8644 | }, |
|
|||
8645 | { |
|
|||
8646 | "output_type": "stream", |
|
|||
8647 | "stream": "stdout", |
|
|||
8648 | "text": [ |
|
|||
8649 | " \r", |
|
|||
8650 | "[**********************95%********************* ] 950 of 1000 complete" |
|
|||
8651 | ] |
|
|||
8652 | }, |
|
|||
8653 | { |
|
|||
8654 | "output_type": "stream", |
|
|||
8655 | "stream": "stdout", |
|
|||
8656 | "text": [ |
|
|||
8657 | " \r", |
|
|||
8658 | "[**********************95%********************* ] 951 of 1000 complete" |
|
|||
8659 | ] |
|
|||
8660 | }, |
|
|||
8661 | { |
|
|||
8662 | "output_type": "stream", |
|
|||
8663 | "stream": "stdout", |
|
|||
8664 | "text": [ |
|
|||
8665 | " \r", |
|
|||
8666 | "[**********************95%********************* ] 952 of 1000 complete" |
|
|||
8667 | ] |
|
|||
8668 | }, |
|
|||
8669 | { |
|
|||
8670 | "output_type": "stream", |
|
|||
8671 | "stream": "stdout", |
|
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8672 | "text": [ |
|
|||
8673 | " \r", |
|
|||
8674 | "[**********************95%********************* ] 953 of 1000 complete" |
|
|||
8675 | ] |
|
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8676 | }, |
|
|||
8677 | { |
|
|||
8678 | "output_type": "stream", |
|
|||
8679 | "stream": "stdout", |
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8680 | "text": [ |
|
|||
8681 | " \r", |
|
|||
8682 | "[**********************95%********************* ] 954 of 1000 complete" |
|
|||
8683 | ] |
|
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8684 | }, |
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8685 | { |
|
|||
8686 | "output_type": "stream", |
|
|||
8687 | "stream": "stdout", |
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8688 | "text": [ |
|
|||
8689 | " \r", |
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8690 | "[**********************96%********************* ] 955 of 1000 complete" |
|
|||
8691 | ] |
|
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8692 | }, |
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8693 | { |
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|||
8694 | "output_type": "stream", |
|
|||
8695 | "stream": "stdout", |
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8696 | "text": [ |
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|||
8697 | " \r", |
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8698 | "[**********************96%********************* ] 956 of 1000 complete" |
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8699 | ] |
|
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8700 | }, |
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8701 | { |
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|||
8702 | "output_type": "stream", |
|
|||
8703 | "stream": "stdout", |
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8704 | "text": [ |
|
|||
8705 | " \r", |
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8706 | "[**********************96%********************* ] 957 of 1000 complete" |
|
|||
8707 | ] |
|
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8708 | }, |
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8709 | { |
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|||
8710 | "output_type": "stream", |
|
|||
8711 | "stream": "stdout", |
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8712 | "text": [ |
|
|||
8713 | " \r", |
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8714 | "[**********************96%********************* ] 958 of 1000 complete" |
|
|||
8715 | ] |
|
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8716 | }, |
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8717 | { |
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|||
8718 | "output_type": "stream", |
|
|||
8719 | "stream": "stdout", |
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8720 | "text": [ |
|
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8721 | " \r", |
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8722 | "[**********************96%********************* ] 959 of 1000 complete" |
|
|||
8723 | ] |
|
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8724 | }, |
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8725 | { |
|
|||
8726 | "output_type": "stream", |
|
|||
8727 | "stream": "stdout", |
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8728 | "text": [ |
|
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8729 | " \r", |
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8730 | "[**********************96%********************* ] 960 of 1000 complete" |
|
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8731 | ] |
|
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8732 | }, |
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8733 | { |
|
|||
8734 | "output_type": "stream", |
|
|||
8735 | "stream": "stdout", |
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8736 | "text": [ |
|
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8737 | " \r", |
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8738 | "[**********************96%********************* ] 961 of 1000 complete" |
|
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8739 | ] |
|
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8740 | }, |
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8741 | { |
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|||
8742 | "output_type": "stream", |
|
|||
8743 | "stream": "stdout", |
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8744 | "text": [ |
|
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8745 | " \r", |
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8746 | "[**********************96%********************* ] 962 of 1000 complete" |
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8747 | ] |
|
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8748 | }, |
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8749 | { |
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8750 | "output_type": "stream", |
|
|||
8751 | "stream": "stdout", |
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8752 | "text": [ |
|
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8753 | " \r", |
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8754 | "[**********************96%********************* ] 963 of 1000 complete" |
|
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8755 | ] |
|
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8756 | }, |
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8757 | { |
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8758 | "output_type": "stream", |
|
|||
8759 | "stream": "stdout", |
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8760 | "text": [ |
|
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8761 | " \r", |
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8762 | "[**********************96%********************* ] 964 of 1000 complete" |
|
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8763 | ] |
|
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8764 | }, |
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8765 | { |
|
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8766 | "output_type": "stream", |
|
|||
8767 | "stream": "stdout", |
|
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8768 | "text": [ |
|
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8769 | " \r", |
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8770 | "[**********************97%********************** ] 965 of 1000 complete" |
|
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8771 | ] |
|
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8772 | }, |
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8773 | { |
|
|||
8774 | "output_type": "stream", |
|
|||
8775 | "stream": "stdout", |
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8776 | "text": [ |
|
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8777 | " \r", |
|
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8778 | "[**********************97%********************** ] 966 of 1000 complete" |
|
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8779 | ] |
|
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8780 | }, |
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8781 | { |
|
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8782 | "output_type": "stream", |
|
|||
8783 | "stream": "stdout", |
|
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8784 | "text": [ |
|
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8785 | " \r", |
|
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8786 | "[**********************97%********************** ] 967 of 1000 complete" |
|
|||
8787 | ] |
|
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8788 | }, |
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8789 | { |
|
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8790 | "output_type": "stream", |
|
|||
8791 | "stream": "stdout", |
|
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8792 | "text": [ |
|
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8793 | " \r", |
|
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8794 | "[**********************97%********************** ] 968 of 1000 complete" |
|
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8795 | ] |
|
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8796 | }, |
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8797 | { |
|
|||
8798 | "output_type": "stream", |
|
|||
8799 | "stream": "stdout", |
|
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8800 | "text": [ |
|
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8801 | " \r", |
|
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8802 | "[**********************97%********************** ] 969 of 1000 complete" |
|
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8803 | ] |
|
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8804 | }, |
|
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8805 | { |
|
|||
8806 | "output_type": "stream", |
|
|||
8807 | "stream": "stdout", |
|
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8808 | "text": [ |
|
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8809 | " \r", |
|
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8810 | "[**********************97%********************** ] 970 of 1000 complete" |
|
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8811 | ] |
|
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8812 | }, |
|
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8813 | { |
|
|||
8814 | "output_type": "stream", |
|
|||
8815 | "stream": "stdout", |
|
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8816 | "text": [ |
|
|||
8817 | " \r", |
|
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8818 | "[**********************97%********************** ] 971 of 1000 complete" |
|
|||
8819 | ] |
|
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8820 | }, |
|
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8821 | { |
|
|||
8822 | "output_type": "stream", |
|
|||
8823 | "stream": "stdout", |
|
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8824 | "text": [ |
|
|||
8825 | " \r", |
|
|||
8826 | "[**********************97%********************** ] 972 of 1000 complete" |
|
|||
8827 | ] |
|
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8828 | }, |
|
|||
8829 | { |
|
|||
8830 | "output_type": "stream", |
|
|||
8831 | "stream": "stdout", |
|
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8832 | "text": [ |
|
|||
8833 | " \r", |
|
|||
8834 | "[**********************97%********************** ] 973 of 1000 complete" |
|
|||
8835 | ] |
|
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8836 | }, |
|
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8837 | { |
|
|||
8838 | "output_type": "stream", |
|
|||
8839 | "stream": "stdout", |
|
|||
8840 | "text": [ |
|
|||
8841 | " \r", |
|
|||
8842 | "[**********************97%********************** ] 974 of 1000 complete" |
|
|||
8843 | ] |
|
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8844 | }, |
|
|||
8845 | { |
|
|||
8846 | "output_type": "stream", |
|
|||
8847 | "stream": "stdout", |
|
|||
8848 | "text": [ |
|
|||
8849 | " \r", |
|
|||
8850 | "[**********************98%********************** ] 975 of 1000 complete" |
|
|||
8851 | ] |
|
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8852 | }, |
|
|||
8853 | { |
|
|||
8854 | "output_type": "stream", |
|
|||
8855 | "stream": "stdout", |
|
|||
8856 | "text": [ |
|
|||
8857 | " \r", |
|
|||
8858 | "[**********************98%********************** ] 976 of 1000 complete" |
|
|||
8859 | ] |
|
|||
8860 | }, |
|
|||
8861 | { |
|
|||
8862 | "output_type": "stream", |
|
|||
8863 | "stream": "stdout", |
|
|||
8864 | "text": [ |
|
|||
8865 | " \r", |
|
|||
8866 | "[**********************98%********************** ] 977 of 1000 complete" |
|
|||
8867 | ] |
|
|||
8868 | }, |
|
|||
8869 | { |
|
|||
8870 | "output_type": "stream", |
|
|||
8871 | "stream": "stdout", |
|
|||
8872 | "text": [ |
|
|||
8873 | " \r", |
|
|||
8874 | "[**********************98%********************** ] 978 of 1000 complete" |
|
|||
8875 | ] |
|
|||
8876 | }, |
|
|||
8877 | { |
|
|||
8878 | "output_type": "stream", |
|
|||
8879 | "stream": "stdout", |
|
|||
8880 | "text": [ |
|
|||
8881 | " \r", |
|
|||
8882 | "[**********************98%********************** ] 979 of 1000 complete" |
|
|||
8883 | ] |
|
|||
8884 | }, |
|
|||
8885 | { |
|
|||
8886 | "output_type": "stream", |
|
|||
8887 | "stream": "stdout", |
|
|||
8888 | "text": [ |
|
|||
8889 | " \r", |
|
|||
8890 | "[**********************98%********************** ] 980 of 1000 complete" |
|
|||
8891 | ] |
|
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8892 | }, |
|
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8893 | { |
|
|||
8894 | "output_type": "stream", |
|
|||
8895 | "stream": "stdout", |
|
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8896 | "text": [ |
|
|||
8897 | " \r", |
|
|||
8898 | "[**********************98%********************** ] 981 of 1000 complete" |
|
|||
8899 | ] |
|
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8900 | }, |
|
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8901 | { |
|
|||
8902 | "output_type": "stream", |
|
|||
8903 | "stream": "stdout", |
|
|||
8904 | "text": [ |
|
|||
8905 | " \r", |
|
|||
8906 | "[**********************98%********************** ] 982 of 1000 complete" |
|
|||
8907 | ] |
|
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8908 | }, |
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8909 | { |
|
|||
8910 | "output_type": "stream", |
|
|||
8911 | "stream": "stdout", |
|
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8912 | "text": [ |
|
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8913 | " \r", |
|
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8914 | "[**********************98%********************** ] 983 of 1000 complete" |
|
|||
8915 | ] |
|
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8916 | }, |
|
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8917 | { |
|
|||
8918 | "output_type": "stream", |
|
|||
8919 | "stream": "stdout", |
|
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8920 | "text": [ |
|
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8921 | " \r", |
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8922 | "[**********************98%********************** ] 984 of 1000 complete" |
|
|||
8923 | ] |
|
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8924 | }, |
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8925 | { |
|
|||
8926 | "output_type": "stream", |
|
|||
8927 | "stream": "stdout", |
|
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8928 | "text": [ |
|
|||
8929 | " \r", |
|
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8930 | "[**********************99%***********************] 985 of 1000 complete" |
|
|||
8931 | ] |
|
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8932 | }, |
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8933 | { |
|
|||
8934 | "output_type": "stream", |
|
|||
8935 | "stream": "stdout", |
|
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8936 | "text": [ |
|
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8937 | " \r", |
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8938 | "[**********************99%***********************] 986 of 1000 complete" |
|
|||
8939 | ] |
|
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8940 | }, |
|
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8941 | { |
|
|||
8942 | "output_type": "stream", |
|
|||
8943 | "stream": "stdout", |
|
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8944 | "text": [ |
|
|||
8945 | " \r", |
|
|||
8946 | "[**********************99%***********************] 987 of 1000 complete" |
|
|||
8947 | ] |
|
|||
8948 | }, |
|
|||
8949 | { |
|
|||
8950 | "output_type": "stream", |
|
|||
8951 | "stream": "stdout", |
|
|||
8952 | "text": [ |
|
|||
8953 | " \r", |
|
|||
8954 | "[**********************99%***********************] 988 of 1000 complete" |
|
|||
8955 | ] |
|
|||
8956 | }, |
|
|||
8957 | { |
|
|||
8958 | "output_type": "stream", |
|
|||
8959 | "stream": "stdout", |
|
|||
8960 | "text": [ |
|
|||
8961 | " \r", |
|
|||
8962 | "[**********************99%***********************] 989 of 1000 complete" |
|
|||
8963 | ] |
|
|||
8964 | }, |
|
|||
8965 | { |
|
|||
8966 | "output_type": "stream", |
|
|||
8967 | "stream": "stdout", |
|
|||
8968 | "text": [ |
|
|||
8969 | " \r", |
|
|||
8970 | "[**********************99%***********************] 990 of 1000 complete" |
|
|||
8971 | ] |
|
|||
8972 | }, |
|
|||
8973 | { |
|
|||
8974 | "output_type": "stream", |
|
|||
8975 | "stream": "stdout", |
|
|||
8976 | "text": [ |
|
|||
8977 | " \r", |
|
|||
8978 | "[**********************99%***********************] 991 of 1000 complete" |
|
|||
8979 | ] |
|
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8980 | }, |
|
|||
8981 | { |
|
|||
8982 | "output_type": "stream", |
|
|||
8983 | "stream": "stdout", |
|
|||
8984 | "text": [ |
|
|||
8985 | " \r", |
|
|||
8986 | "[**********************99%***********************] 992 of 1000 complete" |
|
|||
8987 | ] |
|
|||
8988 | }, |
|
|||
8989 | { |
|
|||
8990 | "output_type": "stream", |
|
|||
8991 | "stream": "stdout", |
|
|||
8992 | "text": [ |
|
|||
8993 | " \r", |
|
|||
8994 | "[**********************99%***********************] 993 of 1000 complete" |
|
|||
8995 | ] |
|
|||
8996 | }, |
|
|||
8997 | { |
|
|||
8998 | "output_type": "stream", |
|
|||
8999 | "stream": "stdout", |
|
|||
9000 | "text": [ |
|
|||
9001 | " \r", |
|
|||
9002 | "[**********************99%***********************] 994 of 1000 complete" |
|
|||
9003 | ] |
|
|||
9004 | }, |
|
|||
9005 | { |
|
|||
9006 | "output_type": "stream", |
|
|||
9007 | "stream": "stdout", |
|
|||
9008 | "text": [ |
|
|||
9009 | " \r", |
|
|||
9010 | "[*********************100%***********************] 995 of 1000 complete" |
|
|||
9011 | ] |
|
|||
9012 | }, |
|
|||
9013 | { |
|
|||
9014 | "output_type": "stream", |
|
|||
9015 | "stream": "stdout", |
|
|||
9016 | "text": [ |
|
|||
9017 | " \r", |
|
|||
9018 | "[*********************100%***********************] 996 of 1000 complete" |
|
|||
9019 | ] |
|
|||
9020 | }, |
|
|||
9021 | { |
|
|||
9022 | "output_type": "stream", |
|
|||
9023 | "stream": "stdout", |
|
|||
9024 | "text": [ |
|
|||
9025 | " \r", |
|
|||
9026 | "[*********************100%***********************] 997 of 1000 complete" |
|
|||
9027 | ] |
|
|||
9028 | }, |
|
|||
9029 | { |
|
|||
9030 | "output_type": "stream", |
|
|||
9031 | "stream": "stdout", |
|
|||
9032 | "text": [ |
|
|||
9033 | " \r", |
|
|||
9034 | "[*********************100%***********************] 998 of 1000 complete" |
|
|||
9035 | ] |
|
|||
9036 | }, |
|
|||
9037 | { |
|
|||
9038 | "output_type": "stream", |
|
|||
9039 | "stream": "stdout", |
|
|||
9040 | "text": [ |
|
|||
9041 | " \r", |
|
|||
9042 | "[*********************100%***********************] 999 of 1000 complete" |
|
|||
9043 | ] |
|
|||
9044 | }, |
|
|||
9045 | { |
|
|||
9046 | "output_type": "stream", |
|
|||
9047 | "stream": "stdout", |
|
|||
9048 | "text": [ |
|
|||
9049 | " \r", |
|
|||
9050 | "[*********************100%***********************] 1000 of 1000 complete" |
|
|||
9051 | ] |
|
|||
9052 | }, |
|
|||
9053 | { |
|
|||
9054 | "output_type": "stream", |
|
|||
9055 | "stream": "stdout", |
|
|||
9056 | "text": [ |
|
|||
9057 | "\n" |
|
|||
9058 | ] |
|
|||
9059 | } |
|
|||
9060 | ], |
|
|||
9061 | "prompt_number": 4 |
|
|||
9062 | }, |
|
|||
9063 | { |
|
|||
9064 | "cell_type": "code", |
|
|||
9065 | "collapsed": false, |
|
|||
9066 | "input": [], |
|
|||
9067 | "language": "python", |
|
|||
9068 | "metadata": {}, |
|
|||
9069 | "outputs": [] |
|
|||
9070 | } |
|
|||
9071 | ], |
|
|||
9072 | "metadata": {} |
|
|||
9073 | } |
|
|||
9074 | ] |
|
|||
9075 | } No newline at end of file |
|
@@ -1,54 +0,0 b'' | |||||
1 | { |
|
|||
2 | "metadata": { |
|
|||
3 | "name": "Local Files" |
|
|||
4 | }, |
|
|||
5 | "nbformat": 3, |
|
|||
6 | "nbformat_minor": 0, |
|
|||
7 | "worksheets": [ |
|
|||
8 | { |
|
|||
9 | "cells": [ |
|
|||
10 | { |
|
|||
11 | "cell_type": "markdown", |
|
|||
12 | "metadata": {}, |
|
|||
13 | "source": [ |
|
|||
14 | "## Local Files\n", |
|
|||
15 | "\n", |
|
|||
16 | "The above examples embed images and video from the notebook filesystem in the output\n", |
|
|||
17 | "areas of code cells. It is also possible to request these files directly in markdown cells\n", |
|
|||
18 | "if they reside in the notebook directory via relative urls prefixed with `files/`:\n", |
|
|||
19 | "\n", |
|
|||
20 | " files/[subdirectory/]<filename>\n", |
|
|||
21 | "\n", |
|
|||
22 | "\n", |
|
|||
23 | "For example, in the example notebook folder, we have the Python logo, addressed as:\n", |
|
|||
24 | "\n", |
|
|||
25 | " <img src=\"files/python-logo.svg\" />\n", |
|
|||
26 | "\n", |
|
|||
27 | "<img src=\"/files/python-logo.svg\" />\n", |
|
|||
28 | "\n", |
|
|||
29 | "and a video with the HTML5 video tag:\n", |
|
|||
30 | "\n", |
|
|||
31 | " <video controls src=\"files/animation.m4v\" />\n", |
|
|||
32 | "\n", |
|
|||
33 | "<video controls src=\"/files/animation.m4v\" />\n", |
|
|||
34 | "\n", |
|
|||
35 | "These do not embed the data into the notebook file,\n", |
|
|||
36 | "and require that the files exist when you are viewing the notebook.\n", |
|
|||
37 | "\n", |
|
|||
38 | "### Security of local files\n", |
|
|||
39 | "\n", |
|
|||
40 | "Note that this means that the IPython notebook server also acts as a generic file server\n", |
|
|||
41 | "for files inside the same tree as your notebooks. Access is not granted outside the\n", |
|
|||
42 | "notebook folder so you have strict control over what files are visible, but for this\n", |
|
|||
43 | "reason it is highly recommended that you do not run the notebook server with a notebook\n", |
|
|||
44 | "directory at a high level in your filesystem (e.g. your home directory).\n", |
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45 | "\n", |
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46 | "When you run the notebook in a password-protected manner, local file access is restricted\n", |
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47 | "to authenticated users unless read-only views are active." |
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48 | ] |
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49 | } |
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50 | ], |
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51 | "metadata": {} |
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52 | } |
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53 | ] |
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54 | } No newline at end of file |
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