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| @@ -8,42 +8,6 b'' | |||||
| 8 | { |
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8 | { | |
| 9 | "cells": [ |
|
9 | "cells": [ | |
| 10 | { |
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10 | { | |
| 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 | "NumPy and Matplotlib examples" |
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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 | "First import NumPy and Matplotlib:" |
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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 | "%pylab inline" |
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| 30 | ], |
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| 31 | "language": "python", |
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| 32 | "metadata": {}, |
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| 33 | "outputs": [ |
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| 34 | { |
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| 35 | "output_type": "stream", |
|
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| 36 | "stream": "stdout", |
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| 37 | "text": [ |
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| 38 | "\n", |
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| 39 | "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n", |
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| 40 | "For more information, type 'help(pylab)'.\n" |
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| 41 | ] |
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| 42 | } |
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| 43 | ], |
|
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| 44 | "prompt_number": 1 |
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| 45 | }, |
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| 46 | { |
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| 47 | "cell_type": "code", |
|
11 | "cell_type": "code", | |
| 48 | "collapsed": false, |
|
12 | "collapsed": false, | |
| 49 | "input": [ |
|
13 | "input": [ | |
| @@ -55,58 +19,10 b'' | |||||
| 55 | "prompt_number": 2 |
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19 | "prompt_number": 2 | |
| 56 | }, |
|
20 | }, | |
| 57 | { |
|
21 | { | |
| 58 | "cell_type": "markdown", |
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| 59 | "metadata": {}, |
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| 60 | "source": [ |
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| 61 | "Now we show some very basic examples of how they can be used." |
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| 62 | ] |
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| 63 | }, |
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| 64 | { |
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| 65 | "cell_type": "code", |
|
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| 66 | "collapsed": false, |
|
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| 67 | "input": [ |
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| 68 | "a = np.random.uniform(size=(100,100))" |
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| 69 | ], |
|
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| 70 | "language": "python", |
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| 71 | "metadata": {}, |
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| 72 | "outputs": [], |
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| 73 | "prompt_number": 6 |
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| 74 | }, |
|
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| 75 | { |
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| 76 | "cell_type": "code", |
|
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| 77 | "collapsed": false, |
|
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| 78 | "input": [ |
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| 79 | "a.shape" |
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| 80 | ], |
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| 81 | "language": "python", |
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| 82 | "metadata": {}, |
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| 83 | "outputs": [ |
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| 84 | { |
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| 85 | "metadata": {}, |
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| 86 | "output_type": "pyout", |
|
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| 87 | "prompt_number": 7, |
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| 88 | "text": [ |
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| 89 | "(100, 100)" |
|
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| 90 | ] |
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| 91 | } |
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| 92 | ], |
|
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| 93 | "prompt_number": 7 |
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| 94 | }, |
|
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| 95 | { |
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| 96 | "cell_type": "code", |
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| 97 | "collapsed": false, |
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| 98 | "input": [ |
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| 99 | "evs = np.linalg.eigvals(a)" |
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| 100 | ], |
|
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| 101 | "language": "python", |
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| 102 | "metadata": {}, |
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| 103 | "outputs": [], |
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| 104 | "prompt_number": 8 |
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| 105 | }, |
|
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| 106 | { |
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| 107 | "cell_type": "code", |
|
22 | "cell_type": "code", | |
| 108 | "collapsed": false, |
|
23 | "collapsed": false, | |
| 109 | "input": [ |
|
24 | "input": [ | |
|
|
25 | "evs = np.zeros(100)", | |||
| 110 | "evs.shape" |
|
26 | "evs.shape" | |
| 111 | ], |
|
27 | ], | |
| 112 | "language": "python", |
|
28 | "language": "python", | |
| @@ -124,45 +40,6 b'' | |||||
| 124 | "prompt_number": 10 |
|
40 | "prompt_number": 10 | |
| 125 | }, |
|
41 | }, | |
| 126 | { |
|
42 | { | |
| 127 | "cell_type": "markdown", |
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| 128 | "metadata": {}, |
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| 129 | "source": [ |
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| 130 | "Here is a cell that has both text and PNG output:" |
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| 131 | ] |
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| 132 | }, |
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| 133 | { |
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| 134 | "cell_type": "code", |
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| 135 | "collapsed": false, |
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| 136 | "input": [ |
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| 137 | "hist(evs.real)" |
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| 138 | ], |
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| 139 | "language": "python", |
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| 140 | "metadata": {}, |
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| 141 | "outputs": [ |
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| 142 | { |
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| 143 | "metadata": {}, |
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| 144 | "output_type": "pyout", |
|
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| 145 | "prompt_number": 14, |
|
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| 146 | "text": [ |
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| 147 | "(array([95, 4, 0, 0, 0, 0, 0, 0, 0, 1]),\n", |
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| 148 | " array([ -2.93566063, 2.35937011, 7.65440086, 12.9494316 ,\n", |
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| 149 | " 18.24446235, 23.53949309, 28.83452384, 34.12955458,\n", |
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| 150 | " 39.42458533, 44.71961607, 50.01464682]),\n", |
|
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| 151 | " <a list of 10 Patch objects>)" |
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| 152 | ] |
|
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| 153 | }, |
|
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| 154 | { |
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| 155 | "metadata": {}, |
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| 156 | "output_type": "display_data", |
|
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| 157 | "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD9CAYAAAC2l2x5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", |
|
|||
| 158 | "text": [ |
|
|||
| 159 | "<matplotlib.figure.Figure at 0x108c8f1d0>" |
|
|||
| 160 | ] |
|
|||
| 161 | } |
|
|||
| 162 | ], |
|
|||
| 163 | "prompt_number": 14 |
|
|||
| 164 | }, |
|
|||
| 165 | { |
|
|||
| 166 | "cell_type": "code", |
|
43 | "cell_type": "code", | |
| 167 | "collapsed": false, |
|
44 | "collapsed": false, | |
| 168 | "input": [], |
|
45 | "input": [], | |
| @@ -70,8 +70,8 b' class TestHTMLExporter(ExportersTestsBase):' | |||||
| 70 | in_regex = r"In \[(.*)\]:" |
|
70 | in_regex = r"In \[(.*)\]:" | |
| 71 | out_regex = r"Out\[(.*)\]:" |
|
71 | out_regex = r"Out\[(.*)\]:" | |
| 72 |
|
72 | |||
| 73 |
ins = [" |
|
73 | ins = ["2", "10", " ", " ", "*", "0"] | |
| 74 |
outs = [" |
|
74 | outs = ["10"] | |
| 75 |
|
75 | |||
| 76 | assert re.findall(in_regex, output) == ins |
|
76 | assert re.findall(in_regex, output) == ins | |
| 77 | assert re.findall(out_regex, output) == outs |
|
77 | assert re.findall(out_regex, output) == outs | |
| @@ -111,8 +111,8 b' class TestLatexExporter(ExportersTestsBase):' | |||||
| 111 | in_regex = r"In \[\{\\color\{incolor\}(.*)\}\]:" |
|
111 | in_regex = r"In \[\{\\color\{incolor\}(.*)\}\]:" | |
| 112 | out_regex = r"Out\[\{\\color\{outcolor\}(.*)\}\]:" |
|
112 | out_regex = r"Out\[\{\\color\{outcolor\}(.*)\}\]:" | |
| 113 |
|
113 | |||
| 114 |
ins = [" |
|
114 | ins = ["2", "10", " ", " ", "*", "0"] | |
| 115 |
outs = [" |
|
115 | outs = ["10"] | |
| 116 |
|
116 | |||
| 117 | assert re.findall(in_regex, output) == ins |
|
117 | assert re.findall(in_regex, output) == ins | |
| 118 | assert re.findall(out_regex, output) == outs |
|
118 | assert re.findall(out_regex, output) == outs | |
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