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Work on Rich output.
Brian E. Granger -
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1 1 {
2 2 "metadata": {
3 3 "name": "",
4 "signature": "sha256:180c055843c21d9b1ac1c9ab78517b077ff5d6526a847739908408866ac449b2"
4 "signature": "sha256:b4d7c6b90e8b3e2ab460015611518e5d598dea6903db56a26dc8a81e5d1f5722"
5 5 },
6 6 "nbformat": 3,
7 7 "nbformat_minor": 0,
8 8 "worksheets": [
9 9 {
10 10 "cells": [
11 11 {
12 12 "cell_type": "heading",
13 13 "level": 1,
14 14 "metadata": {},
15 15 "source": [
16 "IPython's Rich Display System"
16 "Rich Output"
17 17 ]
18 18 },
19 19 {
20 20 "cell_type": "markdown",
21 21 "metadata": {},
22 22 "source": [
23 "In Python, objects can declare their textual representation using the `__repr__` method. IPython expands on this idea and allows objects to declare other, richer representations including:\n",
23 "In Python, objects can declare their textual representation using the `__repr__` method. IPython expands on this idea and allows objects to declare other, rich representations including:\n",
24 24 "\n",
25 25 "* HTML\n",
26 26 "* JSON\n",
27 27 "* PNG\n",
28 28 "* JPEG\n",
29 29 "* SVG\n",
30 30 "* LaTeX\n",
31 31 "\n",
32 32 "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."
33 33 ]
34 34 },
35 35 {
36 36 "cell_type": "heading",
37 37 "level": 2,
38 38 "metadata": {},
39 39 "source": [
40 40 "Basic display imports"
41 41 ]
42 42 },
43 43 {
44 44 "cell_type": "markdown",
45 45 "metadata": {},
46 46 "source": [
47 47 "The `display` function is a general purpose tool for displaying different representations of objects. Think of it as `print` for these rich representations."
48 48 ]
49 49 },
50 50 {
51 51 "cell_type": "code",
52 52 "collapsed": false,
53 53 "input": [
54 54 "from IPython.display import display"
55 55 ],
56 56 "language": "python",
57 57 "metadata": {},
58 58 "outputs": [],
59 "prompt_number": 1
59 "prompt_number": 3
60 60 },
61 61 {
62 62 "cell_type": "markdown",
63 63 "metadata": {},
64 64 "source": [
65 65 "A few points:\n",
66 66 "\n",
67 67 "* Calling `display` on an object will send **all** possible representations to the Notebook.\n",
68 68 "* These representations are stored in the Notebook document.\n",
69 69 "* In general the Notebook will use the richest available representation.\n",
70 70 "\n",
71 71 "If you want to display a particular representation, there are specific functions for that:"
72 72 ]
73 73 },
74 74 {
75 75 "cell_type": "code",
76 76 "collapsed": false,
77 77 "input": [
78 "from IPython.display import display_pretty, display_html, display_jpeg, display_png, display_json, display_latex, display_svg"
78 "from IPython.display import (\n",
79 " display_pretty, display_html, display_jpeg,\n",
80 " display_png, display_json, display_latex, display_svg\n",
81 ")"
79 82 ],
80 83 "language": "python",
81 84 "metadata": {},
82 85 "outputs": [],
83 "prompt_number": 2
86 "prompt_number": 4
84 87 },
85 88 {
86 89 "cell_type": "heading",
87 90 "level": 2,
88 91 "metadata": {},
89 92 "source": [
90 93 "Images"
91 94 ]
92 95 },
93 96 {
94 97 "cell_type": "markdown",
95 98 "metadata": {},
96 99 "source": [
97 100 "To work with images (JPEG, PNG) use the `Image` class."
98 101 ]
99 102 },
100 103 {
101 104 "cell_type": "code",
102 105 "collapsed": false,
103 106 "input": [
104 107 "from IPython.display import Image"
105 108 ],
106 109 "language": "python",
107 110 "metadata": {},
108 111 "outputs": [],
109 "prompt_number": 3
112 "prompt_number": 5
110 113 },
111 114 {
112 115 "cell_type": "code",
113 116 "collapsed": false,
114 117 "input": [
115 118 "i = Image(filename='../images/ipython_logo.png')"
116 119 ],
117 120 "language": "python",
118 121 "metadata": {},
119 122 "outputs": [],
120 "prompt_number": 5
123 "prompt_number": 6
121 124 },
122 125 {
123 126 "cell_type": "markdown",
124 127 "metadata": {},
125 128 "source": [
126 129 "Returning an `Image` object from an expression will automatically display it:"
127 130 ]
128 131 },
129 132 {
130 133 "cell_type": "code",
131 134 "collapsed": false,
132 135 "input": [
133 136 "i"
134 137 ],
135 138 "language": "python",
136 139 "metadata": {},
137 140 "outputs": [
138 141 {
139 142 "metadata": {},
140 143 "output_type": "pyout",
141 144 "png": 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142 "prompt_number": 6,
145 "prompt_number": 7,
143 146 "text": [
144 "<IPython.core.display.Image at 0x106a91e10>"
147 "<IPython.core.display.Image object>"
145 148 ]
146 149 }
147 150 ],
148 "prompt_number": 6
151 "prompt_number": 7
149 152 },
150 153 {
151 154 "cell_type": "markdown",
152 155 "metadata": {},
153 156 "source": [
154 "Or you can pass it to `display`:"
157 "Or you can pass an object with a rich representation to `display`:"
155 158 ]
156 159 },
157 160 {
158 161 "cell_type": "code",
159 162 "collapsed": false,
160 163 "input": [
161 164 "display(i)"
162 165 ],
163 166 "language": "python",
164 167 "metadata": {},
165 168 "outputs": [
166 169 {
167 170 "metadata": {},
168 171 "output_type": "display_data",
169 172 "png": 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170 173 "text": [
171 "<IPython.core.display.Image at 0x106a91e10>"
174 "<IPython.core.display.Image object>"
172 175 ]
173 176 }
174 177 ],
175 "prompt_number": 7
178 "prompt_number": 8
176 179 },
177 180 {
178 181 "cell_type": "markdown",
179 182 "metadata": {},
180 183 "source": [
181 "An image can also be displayed from raw data or a url"
184 "An image can also be displayed from raw data or a URL."
182 185 ]
183 186 },
184 187 {
185 188 "cell_type": "code",
186 189 "collapsed": false,
187 190 "input": [
188 191 "Image(url='http://python.org/images/python-logo.gif')"
189 192 ],
190 193 "language": "python",
191 194 "metadata": {},
192 195 "outputs": [
193 196 {
194 197 "html": [
195 198 "<img src=\"http://python.org/images/python-logo.gif\"/>"
196 199 ],
197 200 "metadata": {},
198 201 "output_type": "pyout",
199 "prompt_number": 8,
202 "prompt_number": 9,
200 203 "text": [
201 "<IPython.core.display.Image at 0x107005150>"
204 "<IPython.core.display.Image object>"
202 205 ]
203 206 }
204 207 ],
205 "prompt_number": 8
208 "prompt_number": 9
206 209 },
207 210 {
208 211 "cell_type": "markdown",
209 212 "metadata": {},
210 213 "source": [
211 "SVG images are also supported out of the box (since modern browsers do a good job of rendering them):"
214 "SVG images are also supported out of the box."
212 215 ]
213 216 },
214 217 {
215 218 "cell_type": "code",
216 219 "collapsed": false,
217 220 "input": [
218 221 "from IPython.display import SVG\n",
219 "SVG(filename='images/python_logo.svg')"
222 "SVG(filename='../images/python_logo.svg')"
220 223 ],
221 224 "language": "python",
222 225 "metadata": {},
223 226 "outputs": [
224 227 {
225 228 "metadata": {},
226 229 "output_type": "pyout",
227 "prompt_number": 9,
230 "prompt_number": 10,
228 231 "svg": [
229 232 "<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",
230 233 " <metadata id=\"metadata2193\">\n",
231 234 " <rdf:RDF>\n",
232 235 " <cc:Work rdf:about=\"\">\n",
233 236 " <dc:format>image/svg+xml</dc:format>\n",
234 237 " <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
235 238 " </cc:Work>\n",
236 239 " </rdf:RDF>\n",
237 240 " </metadata>\n",
238 241 " <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"svg2\" inkscape:cx=\"243.02499\" inkscape:cy=\"71.887497\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"543\" inkscape:window-width=\"791\" inkscape:window-x=\"0\" inkscape:window-y=\"0\" inkscape:zoom=\"1.4340089\" pagecolor=\"#ffffff\"/>\n",
239 242 " <defs id=\"defs4\">\n",
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241 244 " <stop id=\"stop2797\" offset=\"0\" style=\"stop-color:#b8b8b8;stop-opacity:0.49803922\"/>\n",
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243 246 " </linearGradient>\n",
244 247 " <linearGradient id=\"linearGradient2787\">\n",
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246 249 " <stop id=\"stop2791\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
247 250 " </linearGradient>\n",
248 251 " <linearGradient id=\"linearGradient3676\">\n",
249 252 " <stop id=\"stop3678\" offset=\"0\" style=\"stop-color:#b2b2b2;stop-opacity:0.5\"/>\n",
250 253 " <stop id=\"stop3680\" offset=\"1\" style=\"stop-color:#b3b3b3;stop-opacity:0\"/>\n",
251 254 " </linearGradient>\n",
252 255 " <linearGradient id=\"linearGradient3236\">\n",
253 256 " <stop id=\"stop3244\" offset=\"0\" style=\"stop-color:#f4f4f4;stop-opacity:1\"/>\n",
254 257 " <stop id=\"stop3240\" offset=\"1\" style=\"stop-color:#ffffff;stop-opacity:1\"/>\n",
255 258 " </linearGradient>\n",
256 259 " <linearGradient id=\"linearGradient4671\">\n",
257 260 " <stop id=\"stop4673\" offset=\"0\" style=\"stop-color:#ffd43b;stop-opacity:1\"/>\n",
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259 262 " </linearGradient>\n",
260 263 " <linearGradient id=\"linearGradient4689\">\n",
261 264 " <stop id=\"stop4691\" offset=\"0\" style=\"stop-color:#5a9fd4;stop-opacity:1\"/>\n",
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263 266 " </linearGradient>\n",
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289 292 "</svg>"
290 293 ],
291 294 "text": [
292 "<IPython.core.display.SVG at 0x107005250>"
293 ]
294 }
295 ],
296 "prompt_number": 9
297 },
298 {
299 "cell_type": "heading",
300 "level": 2,
301 "metadata": {},
302 "source": [
303 "Links to local files"
304 ]
305 },
306 {
307 "cell_type": "markdown",
308 "metadata": {},
309 "source": [
310 "If we want to create a link to one of them, we can call use the `FileLink` object."
311 ]
312 },
313 {
314 "cell_type": "code",
315 "collapsed": false,
316 "input": [
317 "from IPython.display import FileLink, FileLinks\n",
318 "FileLink('Running Code.ipynb')"
319 ],
320 "language": "python",
321 "metadata": {},
322 "outputs": [
323 {
324 "html": [
325 "<a href='Running Code.ipynb' target='_blank'>Running Code.ipynb</a><br>"
326 ],
327 "metadata": {},
328 "output_type": "pyout",
329 "prompt_number": 10,
330 "text": [
331 "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Notebook/Running Code.ipynb"
295 "<IPython.core.display.SVG object>"
332 296 ]
333 297 }
334 298 ],
335 299 "prompt_number": 10
336 300 },
337 301 {
338 "cell_type": "markdown",
339 "metadata": {},
340 "source": [
341 "Alternatively, if we want to link to all of the files in a directory, we can use the `FileLinks` object, passing `'.'` to indicate that we want links generated for the current working directory. Note that if there were other directories under the current directory, `FileLinks` would work in a recursive manner creating links to files in all sub-directories as well."
342 ]
343 },
344 {
345 "cell_type": "code",
346 "collapsed": false,
347 "input": [
348 "FileLinks('.')"
349 ],
350 "language": "python",
351 "metadata": {},
352 "outputs": [
353 {
354 "html": [
355 "./<br>\n",
356 "&nbsp;&nbsp;<a href='./Animations Using clear_output.ipynb' target='_blank'>Animations Using clear_output.ipynb</a><br>\n",
357 "&nbsp;&nbsp;<a href='./Basic Output.ipynb' target='_blank'>Basic Output.ipynb</a><br>\n",
358 "&nbsp;&nbsp;<a href='./Connecting with the Qt Console.ipynb' target='_blank'>Connecting with the Qt Console.ipynb</a><br>\n",
359 "&nbsp;&nbsp;<a href='./Custom Display Logic.ipynb' target='_blank'>Custom Display Logic.ipynb</a><br>\n",
360 "&nbsp;&nbsp;<a href='./Display System.ipynb' target='_blank'>Display System.ipynb</a><br>\n",
361 "&nbsp;&nbsp;<a href='./Importing Notebooks.ipynb' target='_blank'>Importing Notebooks.ipynb</a><br>\n",
362 "&nbsp;&nbsp;<a href='./Index.ipynb' target='_blank'>Index.ipynb</a><br>\n",
363 "&nbsp;&nbsp;<a href='./Markdown Cells.ipynb' target='_blank'>Markdown Cells.ipynb</a><br>\n",
364 "&nbsp;&nbsp;<a href='./Plotting with Matplotlib.ipynb' target='_blank'>Plotting with Matplotlib.ipynb</a><br>\n",
365 "&nbsp;&nbsp;<a href='./Progress Bars.ipynb' target='_blank'>Progress Bars.ipynb</a><br>\n",
366 "&nbsp;&nbsp;<a href='./Raw Input.ipynb' target='_blank'>Raw Input.ipynb</a><br>\n",
367 "&nbsp;&nbsp;<a href='./Running Code.ipynb' target='_blank'>Running Code.ipynb</a><br>\n",
368 "&nbsp;&nbsp;<a href='./SymPy.ipynb' target='_blank'>SymPy.ipynb</a><br>\n",
369 "&nbsp;&nbsp;<a href='./Trapezoid Rule.ipynb' target='_blank'>Trapezoid Rule.ipynb</a><br>\n",
370 "&nbsp;&nbsp;<a href='./Typesetting Math Using MathJax.ipynb' target='_blank'>Typesetting Math Using MathJax.ipynb</a><br>\n",
371 "&nbsp;&nbsp;<a href='./User Interface.ipynb' target='_blank'>User Interface.ipynb</a><br>\n",
372 "./images/<br>\n",
373 "&nbsp;&nbsp;<a href='./images/animation.m4v' target='_blank'>animation.m4v</a><br>\n",
374 "&nbsp;&nbsp;<a href='./images/command_mode.png' target='_blank'>command_mode.png</a><br>\n",
375 "&nbsp;&nbsp;<a href='./images/edit_mode.png' target='_blank'>edit_mode.png</a><br>\n",
376 "&nbsp;&nbsp;<a href='./images/menubar_toolbar.png' target='_blank'>menubar_toolbar.png</a><br>\n",
377 "&nbsp;&nbsp;<a href='./images/python_logo.svg' target='_blank'>python_logo.svg</a><br>\n",
378 "./nbpackage/<br>\n",
379 "&nbsp;&nbsp;<a href='./nbpackage/__init__.py' target='_blank'>__init__.py</a><br>\n",
380 "&nbsp;&nbsp;<a href='./nbpackage/mynotebook.ipynb' target='_blank'>mynotebook.ipynb</a><br>\n",
381 "./nbpackage/nbs/<br>\n",
382 "&nbsp;&nbsp;<a href='./nbpackage/nbs/__init__.py' target='_blank'>__init__.py</a><br>\n",
383 "&nbsp;&nbsp;<a href='./nbpackage/nbs/other.ipynb' target='_blank'>other.ipynb</a><br>"
384 ],
385 "metadata": {},
386 "output_type": "pyout",
387 "prompt_number": 11,
388 "text": [
389 "./\n",
390 " Animations Using clear_output.ipynb\n",
391 " Basic Output.ipynb\n",
392 " Connecting with the Qt Console.ipynb\n",
393 " Custom Display Logic.ipynb\n",
394 " Display System.ipynb\n",
395 " Importing Notebooks.ipynb\n",
396 " Index.ipynb\n",
397 " Markdown Cells.ipynb\n",
398 " Plotting with Matplotlib.ipynb\n",
399 " Progress Bars.ipynb\n",
400 " Raw Input.ipynb\n",
401 " Running Code.ipynb\n",
402 " SymPy.ipynb\n",
403 " Trapezoid Rule.ipynb\n",
404 " Typesetting Math Using MathJax.ipynb\n",
405 " User Interface.ipynb\n",
406 "./images/\n",
407 " animation.m4v\n",
408 " command_mode.png\n",
409 " edit_mode.png\n",
410 " menubar_toolbar.png\n",
411 " python_logo.svg\n",
412 "./nbpackage/\n",
413 " __init__.py\n",
414 " mynotebook.ipynb\n",
415 "./nbpackage/nbs/\n",
416 " __init__.py\n",
417 " other.ipynb"
418 ]
419 }
420 ],
421 "prompt_number": 11
422 },
423 {
424 302 "cell_type": "heading",
425 303 "level": 3,
426 304 "metadata": {},
427 305 "source": [
428 "Embedded vs Non-embedded Images"
306 "Embedded vs non-embedded Images"
429 307 ]
430 308 },
431 309 {
432 310 "cell_type": "markdown",
433 311 "metadata": {},
434 312 "source": [
435 "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."
313 "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."
436 314 ]
437 315 },
438 316 {
439 317 "cell_type": "code",
440 318 "collapsed": false,
441 319 "input": [
442 320 "from IPython.display import Image\n",
443 321 "img_url = 'http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg'\n",
444 322 "\n",
445 323 "# by default Image data are embedded\n",
446 324 "Embed = Image(img_url)\n",
447 325 "\n",
448 326 "# if kwarg `url` is given, the embedding is assumed to be false\n",
449 327 "SoftLinked = Image(url=img_url)\n",
450 328 "\n",
451 329 "# In each case, embed can be specified explicitly with the `embed` kwarg\n",
452 330 "# ForceEmbed = Image(url=img_url, embed=True)"
453 331 ],
454 332 "language": "python",
455 333 "metadata": {},
456 334 "outputs": [],
457 335 "prompt_number": 12
458 336 },
459 337 {
460 338 "cell_type": "markdown",
461 339 "metadata": {},
462 340 "source": [
463 341 "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."
464 342 ]
465 343 },
466 344 {
467 345 "cell_type": "code",
468 346 "collapsed": false,
469 347 "input": [
470 348 "Embed"
471 349 ],
472 350 "language": "python",
473 351 "metadata": {},
474 352 "outputs": [
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ahzq+lu8VqInS3fVsHvFEGG7xU4bvFVE4bvFHpd4oqfS7xU4bv+ND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\nIAK7yOGnUDxzwzXk8M8eTWM6ZcQz7Oiuo1KvGuDqjMjbuHA13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477 355 "metadata": {},
478 356 "output_type": "pyout",
479 357 "prompt_number": 13,
480 358 "text": [
481 359 "<IPython.core.display.Image at 0x1068d0a10>"
482 360 ]
483 361 }
484 362 ],
485 363 "prompt_number": 13
486 364 },
487 365 {
488 366 "cell_type": "markdown",
489 367 "metadata": {},
490 368 "source": [
491 "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."
369 "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 smaller and always reflect the current version of the source, but the image won't display offline."
492 370 ]
493 371 },
494 372 {
495 373 "cell_type": "code",
496 374 "collapsed": false,
497 375 "input": [
498 376 "SoftLinked"
499 377 ],
500 378 "language": "python",
501 379 "metadata": {},
502 380 "outputs": [
503 381 {
504 382 "html": [
505 383 "<img src=\"http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg\"/>"
506 384 ],
507 385 "metadata": {},
508 386 "output_type": "pyout",
509 387 "prompt_number": 14,
510 388 "text": [
511 389 "<IPython.core.display.Image at 0x106ab19d0>"
512 390 ]
513 391 }
514 392 ],
515 393 "prompt_number": 14
516 394 },
517 395 {
518 396 "cell_type": "markdown",
519 397 "metadata": {},
520 398 "source": [
521 399 "Of course, if you re-run this Notebook, the two images will be the same again."
522 400 ]
523 401 },
524 402 {
525 403 "cell_type": "heading",
526 404 "level": 2,
527 405 "metadata": {},
528 406 "source": [
529 "Audio"
407 "HTML"
530 408 ]
531 409 },
532 410 {
533 411 "cell_type": "markdown",
534 412 "metadata": {},
535 413 "source": [
536 "IPython makes it easy to work with sounds interactively. The `Audio` display class allows you to create an audio control that is embedded in the Notebook. The interface is analogous to the interface of the `Image` display class. All audio formats supported by the browser can be used. Note that no single format is presently supported in all browsers."
414 "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."
537 415 ]
538 416 },
539 417 {
540 418 "cell_type": "code",
541 419 "collapsed": false,
542 420 "input": [
543 "from IPython.display import Audio\n",
544 "Audio(url=\"http://www.nch.com.au/acm/8k16bitpcm.wav\")"
421 "from IPython.display import HTML"
422 ],
423 "language": "python",
424 "metadata": {},
425 "outputs": [],
426 "prompt_number": 11
427 },
428 {
429 "cell_type": "code",
430 "collapsed": false,
431 "input": [
432 "s = \"\"\"<table>\n",
433 "<tr>\n",
434 "<th>Header 1</th>\n",
435 "<th>Header 2</th>\n",
436 "</tr>\n",
437 "<tr>\n",
438 "<td>row 1, cell 1</td>\n",
439 "<td>row 1, cell 2</td>\n",
440 "</tr>\n",
441 "<tr>\n",
442 "<td>row 2, cell 1</td>\n",
443 "<td>row 2, cell 2</td>\n",
444 "</tr>\n",
445 "</table>\"\"\""
446 ],
447 "language": "python",
448 "metadata": {},
449 "outputs": [],
450 "prompt_number": 12
451 },
452 {
453 "cell_type": "code",
454 "collapsed": false,
455 "input": [
456 "h = HTML(s)"
457 ],
458 "language": "python",
459 "metadata": {},
460 "outputs": [],
461 "prompt_number": 13
462 },
463 {
464 "cell_type": "code",
465 "collapsed": false,
466 "input": [
467 "display(h)"
545 468 ],
546 469 "language": "python",
547 470 "metadata": {},
548 471 "outputs": [
549 472 {
550 473 "html": [
551 "\n",
552 " <audio controls=\"controls\" >\n",
553 " <source src=\"http://www.nch.com.au/acm/8k16bitpcm.wav\" type=\"audio/x-wav\" />\n",
554 " Your browser does not support the audio element.\n",
555 " </audio>\n",
556 " "
474 "<table>\n",
475 "<tr>\n",
476 "<th>Header 1</th>\n",
477 "<th>Header 2</th>\n",
478 "</tr>\n",
479 "<tr>\n",
480 "<td>row 1, cell 1</td>\n",
481 "<td>row 1, cell 2</td>\n",
482 "</tr>\n",
483 "<tr>\n",
484 "<td>row 2, cell 1</td>\n",
485 "<td>row 2, cell 2</td>\n",
486 "</tr>\n",
487 "</table>"
557 488 ],
558 489 "metadata": {},
559 "output_type": "pyout",
560 "prompt_number": 15,
490 "output_type": "display_data",
561 491 "text": [
562 "<IPython.lib.display.Audio at 0x1070b2510>"
492 "<IPython.core.display.HTML object>"
563 493 ]
564 494 }
565 495 ],
566 "prompt_number": 15
496 "prompt_number": 14
567 497 },
568 498 {
569 499 "cell_type": "markdown",
570 500 "metadata": {},
571 501 "source": [
572 "A Numpy array can be auralized automatically. The Audio class normalizes and encodes the data and embed the result in the Notebook.\n",
573 "\n",
574 "For instance, when two sine waves with almost the same frequency are superimposed a phenomena known as [beats](https://en.wikipedia.org/wiki/Beat_%28acoustics%29) occur. This can be auralised as follows"
502 "You can also use the `%%html` cell magic to accomplish the same thing."
575 503 ]
576 504 },
577 505 {
578 506 "cell_type": "code",
579 507 "collapsed": false,
580 508 "input": [
581 "import numpy as np\n",
582 "max_time = 3\n",
583 "f1 = 220.0\n",
584 "f2 = 224.0\n",
585 "rate = 8000.0\n",
586 "L = 3\n",
587 "times = np.linspace(0,L,rate*L)\n",
588 "signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
589 "\n",
590 "Audio(data=signal, rate=rate)"
509 "%%html\n",
510 "<table>\n",
511 "<tr>\n",
512 "<th>Header 1</th>\n",
513 "<th>Header 2</th>\n",
514 "</tr>\n",
515 "<tr>\n",
516 "<td>row 1, cell 1</td>\n",
517 "<td>row 1, cell 2</td>\n",
518 "</tr>\n",
519 "<tr>\n",
520 "<td>row 2, cell 1</td>\n",
521 "<td>row 2, cell 2</td>\n",
522 "</tr>\n",
523 "</table>"
591 524 ],
592 525 "language": "python",
593 526 "metadata": {},
594 527 "outputs": [
595 528 {
596 529 "html": [
597 "\n",
598 " <audio controls=\"controls\" >\n",
599 " <source 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\" type=\"audio/wav\" />\n",
600 " Your browser does not support the audio element.\n",
601 " </audio>\n",
602 " "
530 "<table>\n",
531 "<tr>\n",
532 "<th>Header 1</th>\n",
533 "<th>Header 2</th>\n",
534 "</tr>\n",
535 "<tr>\n",
536 "<td>row 1, cell 1</td>\n",
537 "<td>row 1, cell 2</td>\n",
538 "</tr>\n",
539 "<tr>\n",
540 "<td>row 2, cell 1</td>\n",
541 "<td>row 2, cell 2</td>\n",
542 "</tr>\n",
543 "</table>"
603 544 ],
604 545 "metadata": {},
605 "output_type": "pyout",
606 "prompt_number": 16,
546 "output_type": "display_data",
607 547 "text": [
608 "<IPython.lib.display.Audio at 0x10828a050>"
548 "<IPython.core.display.HTML object>"
609 549 ]
610 550 }
611 551 ],
612 "prompt_number": 16
552 "prompt_number": 15
613 553 },
614 554 {
615 555 "cell_type": "heading",
616 556 "level": 2,
617 557 "metadata": {},
618 558 "source": [
619 "Video"
559 "JavaScript"
620 560 ]
621 561 },
622 562 {
623 563 "cell_type": "markdown",
624 564 "metadata": {},
625 565 "source": [
626 "More exotic objects can also be displayed, as long as their representation supports the IPython display protocol. For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other hosted content is trivial):"
566 "The Notebook also enables objects to declare a JavaScript representation. At first, this may seem odd as output is inherently visual and JavaScript is a programming language. However, this opens the door for rich output that leverages the full power of JavaScript and associated libraries such as [d3.js](http://d3js.org) for output."
627 567 ]
628 568 },
629 569 {
630 570 "cell_type": "code",
631 571 "collapsed": false,
632 572 "input": [
633 "from IPython.display import YouTubeVideo\n",
634 "YouTubeVideo('sjfsUzECqK0')"
573 "from IPython.display import Javascript"
635 574 ],
636 575 "language": "python",
637 576 "metadata": {},
638 "outputs": [
639 {
640 "html": [
641 "\n",
642 " <iframe\n",
643 " width=\"400\"\n",
644 " height=300\"\n",
645 " src=\"https://www.youtube.com/embed/sjfsUzECqK0\"\n",
646 " frameborder=\"0\"\n",
647 " allowfullscreen\n",
648 " ></iframe>\n",
649 " "
650 ],
651 "metadata": {},
652 "output_type": "pyout",
653 "prompt_number": 20,
654 "text": [
655 "<IPython.lib.display.YouTubeVideo at 0x10a0d8190>"
656 ]
657 }
658 ],
659 "prompt_number": 20
577 "outputs": [],
578 "prompt_number": 16
660 579 },
661 580 {
662 581 "cell_type": "markdown",
663 582 "metadata": {},
664 583 "source": [
665 "Using the nascent video capabilities of modern browsers, you may also be able to display local\n",
666 "videos. At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n",
667 "we will continue testing this and looking for ways to make it more robust. \n",
668 "\n",
669 "The following cell loads a local file called `animation.m4v`, encodes the raw video as base64 for http\n",
670 "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control\n",
671 "bar at the bottom with a play/pause button and a location slider."
584 "Pass a string of JavaScript source code to the `JavaScript` object and then display it."
672 585 ]
673 586 },
674 587 {
675 588 "cell_type": "code",
676 589 "collapsed": false,
677 590 "input": [
678 "from IPython.display import HTML\n",
679 "from base64 import b64encode\n",
680 "video = open(\"images/animation.m4v\", \"rb\").read()\n",
681 "video_encoded = b64encode(video).decode('ascii')\n",
682 "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
683 "HTML(data=video_tag)"
684 ],
685 "language": "python",
686 "metadata": {},
687 "outputs": [
688 {
689 "html": [
690 "<video controls alt=\"test\" 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691 ],
692 "metadata": {},
693 "output_type": "pyout",
694 "prompt_number": 18,
695 "text": [
696 "<IPython.core.display.HTML at 0x1070b3050>"
697 ]
698 }
699 ],
700 "prompt_number": 18
701 },
702 {
703 "cell_type": "heading",
704 "level": 2,
705 "metadata": {},
706 "source": [
707 "HTML"
708 ]
709 },
710 {
711 "cell_type": "markdown",
712 "metadata": {},
713 "source": [
714 "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."
715 ]
716 },
717 {
718 "cell_type": "code",
719 "collapsed": false,
720 "input": [
721 "from IPython.display import HTML"
591 "js = Javascript('alert(\"hi\")');"
722 592 ],
723 593 "language": "python",
724 594 "metadata": {},
725 595 "outputs": [],
726 "prompt_number": 19
727 },
728 {
729 "cell_type": "code",
730 "collapsed": false,
731 "input": [
732 "s = \"\"\"<table>\n",
733 "<tr>\n",
734 "<th>Header 1</th>\n",
735 "<th>Header 2</th>\n",
736 "</tr>\n",
737 "<tr>\n",
738 "<td>row 1, cell 1</td>\n",
739 "<td>row 1, cell 2</td>\n",
740 "</tr>\n",
741 "<tr>\n",
742 "<td>row 2, cell 1</td>\n",
743 "<td>row 2, cell 2</td>\n",
744 "</tr>\n",
745 "</table>\"\"\""
746 ],
747 "language": "python",
748 "metadata": {},
749 "outputs": [],
750 "prompt_number": 20
596 "prompt_number": 17
751 597 },
752 598 {
753 599 "cell_type": "code",
754 600 "collapsed": false,
755 601 "input": [
756 "h = HTML(s); h"
602 "display(js)"
757 603 ],
758 604 "language": "python",
759 605 "metadata": {},
760 606 "outputs": [
761 607 {
762 "html": [
763 "<table>\n",
764 "<tr>\n",
765 "<th>Header 1</th>\n",
766 "<th>Header 2</th>\n",
767 "</tr>\n",
768 "<tr>\n",
769 "<td>row 1, cell 1</td>\n",
770 "<td>row 1, cell 2</td>\n",
771 "</tr>\n",
772 "<tr>\n",
773 "<td>row 2, cell 1</td>\n",
774 "<td>row 2, cell 2</td>\n",
775 "</tr>\n",
776 "</table>"
608 "javascript": [
609 "alert(\"hi\")"
777 610 ],
778 611 "metadata": {},
779 "output_type": "pyout",
780 "prompt_number": 21,
612 "output_type": "display_data",
781 613 "text": [
782 "<IPython.core.display.HTML at 0x108313a90>"
614 "<IPython.core.display.Javascript object>"
783 615 ]
784 616 }
785 617 ],
786 "prompt_number": 21
618 "prompt_number": 18
787 619 },
788 620 {
789 621 "cell_type": "markdown",
790 622 "metadata": {},
791 623 "source": [
792 "If you want to write HTML or Javascript straight to the frontend,\n",
793 "you can use `%%html` or `%%javascript` cell magics. These are exactly the same as writing `display(HTML(\"\"\"cell contents\"\"\"))`, etc."
624 "The same thing can be accomplished using the `%%javascript` cell magic:"
794 625 ]
795 626 },
796 627 {
797 628 "cell_type": "code",
798 629 "collapsed": false,
799 630 "input": [
800 "%%html\n",
801 "<table>\n",
802 "<tr>\n",
803 "<th>Header 1</th>\n",
804 "<th>Header 2</th>\n",
805 "</tr>\n",
806 "<tr>\n",
807 "<td>row 1, cell 1</td>\n",
808 "<td>row 1, cell 2</td>\n",
809 "</tr>\n",
810 "<tr>\n",
811 "<td>row 2, cell 1</td>\n",
812 "<td>row 2, cell 2</td>\n",
813 "</tr>\n",
814 "</table>"
631 "%%javascript\n",
632 "\n",
633 "alert(\"hi\");"
815 634 ],
816 635 "language": "python",
817 636 "metadata": {},
818 637 "outputs": [
819 638 {
820 "html": [
821 "<table>\n",
822 "<tr>\n",
823 "<th>Header 1</th>\n",
824 "<th>Header 2</th>\n",
825 "</tr>\n",
826 "<tr>\n",
827 "<td>row 1, cell 1</td>\n",
828 "<td>row 1, cell 2</td>\n",
829 "</tr>\n",
830 "<tr>\n",
831 "<td>row 2, cell 1</td>\n",
832 "<td>row 2, cell 2</td>\n",
833 "</tr>\n",
834 "</table>"
639 "javascript": [
640 "\n",
641 "alert(\"hi\");"
835 642 ],
836 643 "metadata": {},
837 644 "output_type": "display_data",
838 645 "text": [
839 "<IPython.core.display.HTML object>"
646 "<IPython.core.display.Javascript object>"
840 647 ]
841 648 }
842 649 ],
843 "prompt_number": 1
650 "prompt_number": 20
844 651 },
845 652 {
846 653 "cell_type": "markdown",
847 654 "metadata": {},
848 655 "source": [
849 "Pandas makes use of this capability to allow `DataFrames` to be represented as HTML tables."
850 ]
851 },
852 {
853 "cell_type": "heading",
854 "level": 2,
855 "metadata": {},
856 "source": [
857 "JavaScript"
656 "Here is a more complicated example that loads `d3.js` from a CDN, uses the `%%html` magic to load CSS styles onto the page and then runs ones of the `d3.js` examples."
858 657 ]
859 658 },
860 659 {
861 660 "cell_type": "code",
862 661 "collapsed": false,
863 662 "input": [
864 "from IPython.display import Javascript"
865 ],
866 "language": "python",
867 "metadata": {},
868 "outputs": [],
869 "prompt_number": 2
870 },
871 {
872 "cell_type": "code",
873 "collapsed": false,
874 "input": [
875 663 "Javascript(\n",
876 664 " \"\"\"$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')\"\"\"\n",
877 665 ")"
878 666 ],
879 667 "language": "python",
880 668 "metadata": {},
881 669 "outputs": [
882 670 {
883 671 "javascript": [
884 672 "$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')"
885 673 ],
886 674 "metadata": {},
887 675 "output_type": "pyout",
888 "prompt_number": 8,
676 "prompt_number": 21,
889 677 "text": [
890 678 "<IPython.core.display.Javascript object>"
891 679 ]
892 680 }
893 681 ],
894 "prompt_number": 8
682 "prompt_number": 21
895 683 },
896 684 {
897 685 "cell_type": "code",
898 686 "collapsed": false,
899 687 "input": [
900 688 "%%html\n",
901 689 "<style type=\"text/css\">\n",
902 690 "\n",
903 691 "circle {\n",
904 692 " fill: rgb(31, 119, 180);\n",
905 693 " fill-opacity: .25;\n",
906 694 " stroke: rgb(31, 119, 180);\n",
907 695 " stroke-width: 1px;\n",
908 696 "}\n",
909 697 "\n",
910 698 ".leaf circle {\n",
911 699 " fill: #ff7f0e;\n",
912 700 " fill-opacity: 1;\n",
913 701 "}\n",
914 702 "\n",
915 703 "text {\n",
916 704 " font: 10px sans-serif;\n",
917 705 "}\n",
918 706 "\n",
919 707 "</style>"
920 708 ],
921 709 "language": "python",
922 710 "metadata": {},
923 711 "outputs": [
924 712 {
925 713 "html": [
926 714 "<style type=\"text/css\">\n",
927 715 "\n",
928 716 "circle {\n",
929 717 " fill: rgb(31, 119, 180);\n",
930 718 " fill-opacity: .25;\n",
931 719 " stroke: rgb(31, 119, 180);\n",
932 720 " stroke-width: 1px;\n",
933 721 "}\n",
934 722 "\n",
935 723 ".leaf circle {\n",
936 724 " fill: #ff7f0e;\n",
937 725 " fill-opacity: 1;\n",
938 726 "}\n",
939 727 "\n",
940 728 "text {\n",
941 729 " font: 10px sans-serif;\n",
942 730 "}\n",
943 731 "\n",
944 732 "</style>"
945 733 ],
946 734 "metadata": {},
947 735 "output_type": "display_data",
948 736 "text": [
949 737 "<IPython.core.display.HTML object>"
950 738 ]
951 739 }
952 740 ],
953 "prompt_number": 4
741 "prompt_number": 22
954 742 },
955 743 {
956 744 "cell_type": "code",
957 745 "collapsed": false,
958 746 "input": [
959 747 "%%javascript\n",
960 748 "\n",
961 749 "// element is the jQuery element we will append to\n",
962 750 "var e = element.get(0);\n",
963 751 " \n",
964 752 "var diameter = 600,\n",
965 753 " format = d3.format(\",d\");\n",
966 754 "\n",
967 755 "var pack = d3.layout.pack()\n",
968 756 " .size([diameter - 4, diameter - 4])\n",
969 757 " .value(function(d) { return d.size; });\n",
970 758 "\n",
971 759 "var svg = d3.select(e).append(\"svg\")\n",
972 760 " .attr(\"width\", diameter)\n",
973 761 " .attr(\"height\", diameter)\n",
974 762 " .append(\"g\")\n",
975 763 " .attr(\"transform\", \"translate(2,2)\");\n",
976 764 "\n",
977 765 "d3.json(\"data/flare.json\", function(error, root) {\n",
978 766 " var node = svg.datum(root).selectAll(\".node\")\n",
979 767 " .data(pack.nodes)\n",
980 768 " .enter().append(\"g\")\n",
981 769 " .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
982 770 " .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
983 771 "\n",
984 772 " node.append(\"title\")\n",
985 773 " .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
986 774 "\n",
987 775 " node.append(\"circle\")\n",
988 776 " .attr(\"r\", function(d) { return d.r; });\n",
989 777 "\n",
990 778 " node.filter(function(d) { return !d.children; }).append(\"text\")\n",
991 779 " .attr(\"dy\", \".3em\")\n",
992 780 " .style(\"text-anchor\", \"middle\")\n",
993 781 " .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
994 782 "});\n",
995 783 "\n",
996 784 "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
997 785 ],
998 786 "language": "python",
999 787 "metadata": {},
1000 788 "outputs": [
1001 789 {
1002 790 "javascript": [
1003 791 "\n",
1004 792 "// element is the jQuery element we will append to\n",
1005 793 "var e = element.get(0);\n",
1006 794 " \n",
1007 795 "var diameter = 600,\n",
1008 796 " format = d3.format(\",d\");\n",
1009 797 "\n",
1010 798 "var pack = d3.layout.pack()\n",
1011 799 " .size([diameter - 4, diameter - 4])\n",
1012 800 " .value(function(d) { return d.size; });\n",
1013 801 "\n",
1014 802 "var svg = d3.select(e).append(\"svg\")\n",
1015 803 " .attr(\"width\", diameter)\n",
1016 804 " .attr(\"height\", diameter)\n",
1017 805 " .append(\"g\")\n",
1018 806 " .attr(\"transform\", \"translate(2,2)\");\n",
1019 807 "\n",
1020 808 "d3.json(\"data/flare.json\", function(error, root) {\n",
1021 809 " var node = svg.datum(root).selectAll(\".node\")\n",
1022 810 " .data(pack.nodes)\n",
1023 811 " .enter().append(\"g\")\n",
1024 812 " .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
1025 813 " .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
1026 814 "\n",
1027 815 " node.append(\"title\")\n",
1028 816 " .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
1029 817 "\n",
1030 818 " node.append(\"circle\")\n",
1031 819 " .attr(\"r\", function(d) { return d.r; });\n",
1032 820 "\n",
1033 821 " node.filter(function(d) { return !d.children; }).append(\"text\")\n",
1034 822 " .attr(\"dy\", \".3em\")\n",
1035 823 " .style(\"text-anchor\", \"middle\")\n",
1036 824 " .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
1037 825 "});\n",
1038 826 "\n",
1039 827 "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
1040 828 ],
1041 829 "metadata": {},
1042 830 "output_type": "display_data",
1043 831 "text": [
1044 832 "<IPython.core.display.Javascript object>"
1045 833 ]
1046 834 }
1047 835 ],
1048 "prompt_number": 7
836 "prompt_number": 23
1049 837 },
1050 838 {
1051 839 "cell_type": "heading",
1052 840 "level": 2,
1053 841 "metadata": {},
1054 842 "source": [
1055 "Pandas"
843 "LaTeX"
1056 844 ]
1057 845 },
1058 846 {
1059 "cell_type": "code",
1060 "collapsed": false,
1061 "input": [
1062 "import pandas"
1063 ],
1064 "language": "python",
847 "cell_type": "markdown",
1065 848 "metadata": {},
1066 "outputs": [],
1067 "prompt_number": 9
849 "source": [
850 "The IPython display system also has builtin support for the display of mathematical expressions typeset in LaTeX, which is rendered in the browser using [MathJax](http://mathjax.org)."
851 ]
1068 852 },
1069 853 {
1070 854 "cell_type": "markdown",
1071 855 "metadata": {},
1072 856 "source": [
1073 "Here is a small amount of stock data for APPL:"
857 "You can pass raw LaTeX test as a string to the `Math` object:"
1074 858 ]
1075 859 },
1076 860 {
1077 861 "cell_type": "code",
1078 862 "collapsed": false,
1079 863 "input": [
1080 "%%writefile data.csv\n",
1081 "Date,Open,High,Low,Close,Volume,Adj Close\n",
1082 "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
1083 "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
1084 "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
1085 "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
1086 "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
1087 "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
864 "from IPython.display import Math\n",
865 "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
1088 866 ],
1089 867 "language": "python",
1090 868 "metadata": {},
1091 869 "outputs": [
1092 870 {
1093 "output_type": "stream",
1094 "stream": "stdout",
871 "latex": [
872 "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
873 ],
874 "metadata": {},
875 "output_type": "pyout",
876 "prompt_number": 24,
1095 877 "text": [
1096 "Writing data.csv\n"
878 "<IPython.core.display.Math object>"
1097 879 ]
1098 880 }
1099 881 ],
1100 "prompt_number": 10
882 "prompt_number": 24
1101 883 },
1102 884 {
1103 885 "cell_type": "markdown",
1104 886 "metadata": {},
1105 887 "source": [
1106 "Read this as into a `DataFrame`:"
888 "With the `Latex` class, you have to include the delimiters yourself. This allows you to use other LaTeX modes such as `eqnarray`:"
1107 889 ]
1108 890 },
1109 891 {
1110 892 "cell_type": "code",
1111 893 "collapsed": false,
1112 894 "input": [
1113 "df = pandas.read_csv('data.csv')"
895 "from IPython.display import Latex\n",
896 "Latex(r\"\"\"\\begin{eqnarray}\n",
897 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
898 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
899 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
900 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
901 "\\end{eqnarray}\"\"\")"
1114 902 ],
1115 903 "language": "python",
1116 904 "metadata": {},
1117 "outputs": [],
1118 "prompt_number": 11
905 "outputs": [
906 {
907 "latex": [
908 "\\begin{eqnarray}\n",
909 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
910 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
911 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
912 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
913 "\\end{eqnarray}"
914 ],
915 "metadata": {},
916 "output_type": "pyout",
917 "prompt_number": 25,
918 "text": [
919 "<IPython.core.display.Latex object>"
920 ]
921 }
922 ],
923 "prompt_number": 25
1119 924 },
1120 925 {
1121 926 "cell_type": "markdown",
1122 927 "metadata": {},
1123 928 "source": [
1124 "And view the HTML representation:"
929 "Or you can enter LaTeX directly with the `%%latex` cell magic:"
1125 930 ]
1126 931 },
1127 932 {
1128 933 "cell_type": "code",
1129 934 "collapsed": false,
1130 935 "input": [
1131 "df"
936 "%%latex\n",
937 "\\begin{align}\n",
938 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
939 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
940 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
941 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
942 "\\end{align}"
1132 943 ],
1133 944 "language": "python",
1134 945 "metadata": {},
1135 946 "outputs": [
1136 947 {
1137 "html": [
1138 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1139 "<table border=\"1\" class=\"dataframe\">\n",
1140 " <thead>\n",
1141 " <tr style=\"text-align: right;\">\n",
1142 " <th></th>\n",
1143 " <th>Date</th>\n",
1144 " <th>Open</th>\n",
1145 " <th>High</th>\n",
1146 " <th>Low</th>\n",
1147 " <th>Close</th>\n",
1148 " <th>Volume</th>\n",
1149 " <th>Adj Close</th>\n",
1150 " </tr>\n",
1151 " </thead>\n",
1152 " <tbody>\n",
1153 " <tr>\n",
1154 " <th>0</th>\n",
1155 " <td> 2012-06-01</td>\n",
1156 " <td> 569.16</td>\n",
1157 " <td> 590.00</td>\n",
1158 " <td> 548.50</td>\n",
1159 " <td> 584.00</td>\n",
1160 " <td> 14077000</td>\n",
1161 " <td> 581.50</td>\n",
1162 " </tr>\n",
1163 " <tr>\n",
1164 " <th>1</th>\n",
1165 " <td> 2012-05-01</td>\n",
1166 " <td> 584.90</td>\n",
1167 " <td> 596.76</td>\n",
1168 " <td> 522.18</td>\n",
1169 " <td> 577.73</td>\n",
1170 " <td> 18827900</td>\n",
1171 " <td> 575.26</td>\n",
1172 " </tr>\n",
1173 " <tr>\n",
1174 " <th>2</th>\n",
1175 " <td> 2012-04-02</td>\n",
1176 " <td> 601.83</td>\n",
1177 " <td> 644.00</td>\n",
1178 " <td> 555.00</td>\n",
1179 " <td> 583.98</td>\n",
1180 " <td> 28759100</td>\n",
1181 " <td> 581.48</td>\n",
1182 " </tr>\n",
1183 " <tr>\n",
1184 " <th>3</th>\n",
1185 " <td> 2012-03-01</td>\n",
1186 " <td> 548.17</td>\n",
1187 " <td> 621.45</td>\n",
1188 " <td> 516.22</td>\n",
1189 " <td> 599.55</td>\n",
1190 " <td> 26486000</td>\n",
1191 " <td> 596.99</td>\n",
1192 " </tr>\n",
1193 " <tr>\n",
1194 " <th>4</th>\n",
1195 " <td> 2012-02-01</td>\n",
1196 " <td> 458.41</td>\n",
1197 " <td> 547.61</td>\n",
1198 " <td> 453.98</td>\n",
1199 " <td> 542.44</td>\n",
1200 " <td> 22001000</td>\n",
1201 " <td> 540.12</td>\n",
1202 " </tr>\n",
1203 " <tr>\n",
1204 " <th>5</th>\n",
1205 " <td> 2012-01-03</td>\n",
1206 " <td> 409.40</td>\n",
1207 " <td> 458.24</td>\n",
1208 " <td> 409.00</td>\n",
1209 " <td> 456.48</td>\n",
1210 " <td> 12949100</td>\n",
1211 " <td> 454.53</td>\n",
1212 " </tr>\n",
1213 " </tbody>\n",
1214 "</table>\n",
1215 "<p>6 rows \u00d7 7 columns</p>\n",
1216 "</div>"
948 "latex": [
949 "\\begin{align}\n",
950 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
951 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
952 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
953 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
954 "\\end{align}"
1217 955 ],
1218 956 "metadata": {},
1219 "output_type": "pyout",
1220 "prompt_number": 12,
957 "output_type": "display_data",
1221 958 "text": [
1222 " Date Open High Low Close Volume Adj Close\n",
1223 "0 2012-06-01 569.16 590.00 548.50 584.00 14077000 581.50\n",
1224 "1 2012-05-01 584.90 596.76 522.18 577.73 18827900 575.26\n",
1225 "2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48\n",
1226 "3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99\n",
1227 "4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12\n",
1228 "5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53\n",
1229 "\n",
1230 "[6 rows x 7 columns]"
959 "<IPython.core.display.Latex object>"
1231 960 ]
1232 961 }
1233 962 ],
1234 "prompt_number": 12
963 "prompt_number": 26
1235 964 },
1236 965 {
1237 966 "cell_type": "heading",
1238 967 "level": 2,
1239 968 "metadata": {},
1240 969 "source": [
1241 "SymPy"
970 "Audio"
1242 971 ]
1243 972 },
1244 973 {
1245 "cell_type": "code",
1246 "collapsed": false,
1247 "input": [
1248 "from sympy.interactive.printing import init_printing\n",
1249 "init_printing(use_latex='mathjax')"
1250 ],
1251 "language": "python",
974 "cell_type": "markdown",
1252 975 "metadata": {},
1253 "outputs": [],
1254 "prompt_number": 13
976 "source": [
977 "IPython makes it easy to work with sounds interactively. The `Audio` display class allows you to create an audio control that is embedded in the Notebook. The interface is analogous to the interface of the `Image` display class. All audio formats supported by the browser can be used. Note that no single format is presently supported in all browsers."
978 ]
1255 979 },
1256 980 {
1257 981 "cell_type": "code",
1258 982 "collapsed": false,
1259 983 "input": [
1260 "from __future__ import division\n",
1261 "import sympy as sym\n",
1262 "from sympy import *\n",
1263 "x, y, z = symbols(\"x y z\")\n",
1264 "k, m, n = symbols(\"k m n\", integer=True)\n",
1265 "f, g, h = map(Function, 'fgh')"
984 "from IPython.display import Audio\n",
985 "Audio(url=\"http://www.nch.com.au/acm/8k16bitpcm.wav\")"
1266 986 ],
1267 987 "language": "python",
1268 988 "metadata": {},
1269 "outputs": [],
1270 "prompt_number": 14
989 "outputs": [
990 {
991 "html": [
992 "\n",
993 " <audio controls=\"controls\" >\n",
994 " <source src=\"http://www.nch.com.au/acm/8k16bitpcm.wav\" type=\"audio/x-wav\" />\n",
995 " Your browser does not support the audio element.\n",
996 " </audio>\n",
997 " "
998 ],
999 "metadata": {},
1000 "output_type": "pyout",
1001 "prompt_number": 28,
1002 "text": [
1003 "<IPython.lib.display.Audio object>"
1004 ]
1005 }
1006 ],
1007 "prompt_number": 28
1008 },
1009 {
1010 "cell_type": "markdown",
1011 "metadata": {},
1012 "source": [
1013 "A NumPy array can be auralized automatically. The `Audio` class normalizes and encodes the data and embeds the resulting audio in the Notebook.\n",
1014 "\n",
1015 "For instance, when two sine waves with almost the same frequency are superimposed a phenomena known as [beats](https://en.wikipedia.org/wiki/Beat_%28acoustics%29) occur. This can be auralised as follows:"
1016 ]
1271 1017 },
1272 1018 {
1273 1019 "cell_type": "code",
1274 1020 "collapsed": false,
1275 1021 "input": [
1276 "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
1022 "import numpy as np\n",
1023 "max_time = 3\n",
1024 "f1 = 220.0\n",
1025 "f2 = 224.0\n",
1026 "rate = 8000.0\n",
1027 "L = 3\n",
1028 "times = np.linspace(0,L,rate*L)\n",
1029 "signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
1030 "\n",
1031 "Audio(data=signal, rate=rate)"
1277 1032 ],
1278 1033 "language": "python",
1279 1034 "metadata": {},
1280 1035 "outputs": [
1281 1036 {
1282 "latex": [
1283 "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
1037 "html": [
1038 "\n",
1039 " <audio controls=\"controls\" >\n",
1040 " <source 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\" type=\"audio/wav\" />\n",
1041 " Your browser does not support the audio element.\n",
1042 " </audio>\n",
1043 " "
1284 1044 ],
1285 1045 "metadata": {},
1286 1046 "output_type": "pyout",
1287 "prompt_number": 15,
1047 "prompt_number": 29,
1288 1048 "text": [
1289 " \u2148\u22c5x \n",
1290 "3\u22c5\u03c0 \u212f \n",
1291 "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
1292 " 2 2 \n",
1293 " x + y"
1049 "<IPython.lib.display.Audio object>"
1294 1050 ]
1295 1051 }
1296 1052 ],
1297 "prompt_number": 15
1053 "prompt_number": 29
1054 },
1055 {
1056 "cell_type": "heading",
1057 "level": 2,
1058 "metadata": {},
1059 "source": [
1060 "Video"
1061 ]
1062 },
1063 {
1064 "cell_type": "markdown",
1065 "metadata": {},
1066 "source": [
1067 "More exotic objects can also be displayed, as long as their representation supports the IPython display protocol. For example, videos hosted externally on YouTube are easy to load:"
1068 ]
1298 1069 },
1299 1070 {
1300 1071 "cell_type": "code",
1301 1072 "collapsed": false,
1302 1073 "input": [
1303 "a = 1/x + (x*sin(x) - 1)/x\n",
1304 "a"
1074 "from IPython.display import YouTubeVideo\n",
1075 "YouTubeVideo('sjfsUzECqK0')"
1305 1076 ],
1306 1077 "language": "python",
1307 1078 "metadata": {},
1308 1079 "outputs": [
1309 1080 {
1310 "latex": [
1311 "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
1081 "html": [
1082 "\n",
1083 " <iframe\n",
1084 " width=\"400\"\n",
1085 " height=300\"\n",
1086 " src=\"https://www.youtube.com/embed/sjfsUzECqK0\"\n",
1087 " frameborder=\"0\"\n",
1088 " allowfullscreen\n",
1089 " ></iframe>\n",
1090 " "
1312 1091 ],
1313 1092 "metadata": {},
1314 1093 "output_type": "pyout",
1315 "prompt_number": 16,
1094 "prompt_number": 20,
1316 1095 "text": [
1317 "x\u22c5sin(x) - 1 1\n",
1318 "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
1319 " x x"
1096 "<IPython.lib.display.YouTubeVideo at 0x10a0d8190>"
1320 1097 ]
1321 1098 }
1322 1099 ],
1323 "prompt_number": 16
1100 "prompt_number": 20
1101 },
1102 {
1103 "cell_type": "markdown",
1104 "metadata": {},
1105 "source": [
1106 "Using the nascent video capabilities of modern browsers, you may also be able to display local\n",
1107 "videos. At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n",
1108 "we will continue testing this and looking for ways to make it more robust. \n",
1109 "\n",
1110 "The following cell loads a local file called `animation.m4v`, encodes the raw video as base64 for http\n",
1111 "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control bar at the bottom with a play/pause button and a location slider."
1112 ]
1324 1113 },
1325 1114 {
1326 1115 "cell_type": "code",
1327 1116 "collapsed": false,
1328 1117 "input": [
1329 "(1/cos(x)).series(x, 0, 6)"
1118 "from IPython.display import HTML\n",
1119 "from base64 import b64encode\n",
1120 "video = open(\"../images/animation.m4v\", \"rb\").read()\n",
1121 "video_encoded = b64encode(video).decode('ascii')\n",
1122 "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
1123 "HTML(data=video_tag)"
1330 1124 ],
1331 1125 "language": "python",
1332 1126 "metadata": {},
1333 1127 "outputs": [
1334 1128 {
1335 "latex": [
1336 "$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
1129 "html": [
1130 "<video controls alt=\"test\" 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1337 1131 ],
1338 1132 "metadata": {},
1339 1133 "output_type": "pyout",
1340 "prompt_number": 17,
1134 "prompt_number": 30,
1341 1135 "text": [
1342 " 2 4 \n",
1343 " x 5\u22c5x \u239b 6\u239e\n",
1344 "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
1345 " 2 24 "
1136 "<IPython.core.display.HTML object>"
1346 1137 ]
1347 1138 }
1348 1139 ],
1349 "prompt_number": 17
1140 "prompt_number": 30
1350 1141 },
1351 1142 {
1352 1143 "cell_type": "heading",
1353 1144 "level": 2,
1354 1145 "metadata": {},
1355 1146 "source": [
1356 1147 "External sites"
1357 1148 ]
1358 1149 },
1359 1150 {
1360 1151 "cell_type": "markdown",
1361 1152 "metadata": {},
1362 1153 "source": [
1363 1154 "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n",
1364 1155 "page for mobile users:"
1365 1156 ]
1366 1157 },
1367 1158 {
1368 1159 "cell_type": "code",
1369 1160 "collapsed": false,
1370 1161 "input": [
1371 1162 "from IPython.display import IFrame\n",
1372 1163 "IFrame('http://python.org', width='100%', height=350)"
1373 1164 ],
1374 1165 "language": "python",
1375 1166 "metadata": {},
1376 1167 "outputs": [
1377 1168 {
1378 1169 "html": [
1379 1170 "\n",
1380 1171 " <iframe\n",
1381 1172 " width=\"100%\"\n",
1382 1173 " height=350\"\n",
1383 1174 " src=\"http://python.org\"\n",
1384 1175 " frameborder=\"0\"\n",
1385 1176 " allowfullscreen\n",
1386 1177 " ></iframe>\n",
1387 1178 " "
1388 1179 ],
1389 1180 "metadata": {},
1390 1181 "output_type": "pyout",
1391 "prompt_number": 19,
1182 "prompt_number": 31,
1392 1183 "text": [
1393 "<IPython.lib.display.IFrame at 0x10a11ff10>"
1184 "<IPython.lib.display.IFrame at 0x10569f950>"
1394 1185 ]
1395 1186 }
1396 1187 ],
1397 "prompt_number": 19
1188 "prompt_number": 31
1398 1189 },
1399 1190 {
1400 1191 "cell_type": "heading",
1401 1192 "level": 2,
1402 1193 "metadata": {},
1403 1194 "source": [
1404 "LaTeX"
1195 "Links to local files"
1405 1196 ]
1406 1197 },
1407 1198 {
1408 1199 "cell_type": "markdown",
1409 1200 "metadata": {},
1410 1201 "source": [
1411 "And we also support the display of mathematical expressions typeset in LaTeX, which is rendered\n",
1412 "in the browser thanks to the [MathJax library](http://mathjax.org)."
1202 "IPython provides builtin display classes for generating links to local files. Create a link to a single file using the `FileLink` object:"
1413 1203 ]
1414 1204 },
1415 1205 {
1416 1206 "cell_type": "code",
1417 1207 "collapsed": false,
1418 1208 "input": [
1419 "from IPython.display import Math\n",
1420 "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
1209 "from IPython.display import FileLink, FileLinks\n",
1210 "FileLink('Cell Magics.ipynb')"
1421 1211 ],
1422 1212 "language": "python",
1423 1213 "metadata": {},
1424 1214 "outputs": [
1425 1215 {
1426 "latex": [
1427 "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
1216 "html": [
1217 "<a href='Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>"
1428 1218 ],
1429 1219 "metadata": {},
1430 1220 "output_type": "pyout",
1431 "prompt_number": 27,
1221 "prompt_number": 33,
1432 1222 "text": [
1433 "<IPython.core.display.Math at 0x10a82d810>"
1223 "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/Cell Magics.ipynb"
1434 1224 ]
1435 1225 }
1436 1226 ],
1437 "prompt_number": 27
1227 "prompt_number": 33
1438 1228 },
1439 1229 {
1440 1230 "cell_type": "markdown",
1441 1231 "metadata": {},
1442 1232 "source": [
1443 "With the `Latex` class, you have to include the delimiters yourself. This allows you to use other LaTeX modes such as `eqnarray`:"
1233 "Alternatively, to generate links to all of the files in a directory, use the `FileLinks` object, passing `'.'` to indicate that we want links generated for the current working directory. Note that if there were other directories under the current directory, `FileLinks` would work in a recursive manner creating links to files in all sub-directories as well."
1444 1234 ]
1445 1235 },
1446 1236 {
1447 1237 "cell_type": "code",
1448 1238 "collapsed": false,
1449 1239 "input": [
1450 "from IPython.display import Latex\n",
1451 "Latex(r\"\"\"\\begin{eqnarray}\n",
1452 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1453 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1454 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1455 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
1456 "\\end{eqnarray}\"\"\")"
1240 "FileLinks('.')"
1457 1241 ],
1458 1242 "language": "python",
1459 1243 "metadata": {},
1460 1244 "outputs": [
1461 1245 {
1462 "latex": [
1463 "\\begin{eqnarray}\n",
1464 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1465 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1466 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1467 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
1468 "\\end{eqnarray}"
1246 "html": [
1247 "./<br>\n",
1248 "&nbsp;&nbsp;<a href='./Animations Using clear_output.ipynb' target='_blank'>Animations Using clear_output.ipynb</a><br>\n",
1249 "&nbsp;&nbsp;<a href='./Background Jobs.ipynb' target='_blank'>Background Jobs.ipynb</a><br>\n",
1250 "&nbsp;&nbsp;<a href='./Beyond Plain Python.ipynb' target='_blank'>Beyond Plain Python.ipynb</a><br>\n",
1251 "&nbsp;&nbsp;<a href='./Capturing Output.ipynb' target='_blank'>Capturing Output.ipynb</a><br>\n",
1252 "&nbsp;&nbsp;<a href='./Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>\n",
1253 "&nbsp;&nbsp;<a href='./Custom Display Logic.ipynb' target='_blank'>Custom Display Logic.ipynb</a><br>\n",
1254 "&nbsp;&nbsp;<a href='./example-demo.py' target='_blank'>example-demo.py</a><br>\n",
1255 "&nbsp;&nbsp;<a href='./Index.ipynb' target='_blank'>Index.ipynb</a><br>\n",
1256 "&nbsp;&nbsp;<a href='./ipython-completion.bash' target='_blank'>ipython-completion.bash</a><br>\n",
1257 "&nbsp;&nbsp;<a href='./ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>\n",
1258 "&nbsp;&nbsp;<a href='./ipython-qtconsole.desktop' target='_blank'>ipython-qtconsole.desktop</a><br>\n",
1259 "&nbsp;&nbsp;<a href='./ipython.desktop' target='_blank'>ipython.desktop</a><br>\n",
1260 "&nbsp;&nbsp;<a href='./Plotting in the Notebook.ipynb' target='_blank'>Plotting in the Notebook.ipynb</a><br>\n",
1261 "&nbsp;&nbsp;<a href='./Raw Input in the Notebook.ipynb' target='_blank'>Raw Input in the Notebook.ipynb</a><br>\n",
1262 "&nbsp;&nbsp;<a href='./Rich Output.ipynb' target='_blank'>Rich Output.ipynb</a><br>\n",
1263 "&nbsp;&nbsp;<a href='./Script Magics.ipynb' target='_blank'>Script Magics.ipynb</a><br>\n",
1264 "&nbsp;&nbsp;<a href='./SymPy.ipynb' target='_blank'>SymPy.ipynb</a><br>\n",
1265 "&nbsp;&nbsp;<a href='./Terminal Usage.ipynb' target='_blank'>Terminal Usage.ipynb</a><br>\n",
1266 "&nbsp;&nbsp;<a href='./Third Party Rich Output.ipynb' target='_blank'>Third Party Rich Output.ipynb</a><br>\n",
1267 "&nbsp;&nbsp;<a href='./Trapezoid Rule.ipynb' target='_blank'>Trapezoid Rule.ipynb</a><br>\n",
1268 "&nbsp;&nbsp;<a href='./Working With External Code.ipynb' target='_blank'>Working With External Code.ipynb</a><br>\n",
1269 "./data/<br>\n",
1270 "&nbsp;&nbsp;<a href='./data/flare.json' target='_blank'>flare.json</a><br>\n",
1271 "./gui/<br>\n",
1272 "&nbsp;&nbsp;<a href='./gui/gui-glut.py' target='_blank'>gui-glut.py</a><br>\n",
1273 "&nbsp;&nbsp;<a href='./gui/gui-gtk.py' target='_blank'>gui-gtk.py</a><br>\n",
1274 "&nbsp;&nbsp;<a href='./gui/gui-gtk3.py' target='_blank'>gui-gtk3.py</a><br>\n",
1275 "&nbsp;&nbsp;<a href='./gui/gui-pyglet.py' target='_blank'>gui-pyglet.py</a><br>\n",
1276 "&nbsp;&nbsp;<a href='./gui/gui-qt.py' target='_blank'>gui-qt.py</a><br>\n",
1277 "&nbsp;&nbsp;<a href='./gui/gui-tk.py' target='_blank'>gui-tk.py</a><br>\n",
1278 "&nbsp;&nbsp;<a href='./gui/gui-wx.py' target='_blank'>gui-wx.py</a><br>"
1469 1279 ],
1470 1280 "metadata": {},
1471 1281 "output_type": "pyout",
1472 "prompt_number": 28,
1282 "prompt_number": 34,
1473 1283 "text": [
1474 "<IPython.core.display.Latex at 0x10a82d090>"
1284 "./\n",
1285 " Animations Using clear_output.ipynb\n",
1286 " Background Jobs.ipynb\n",
1287 " Beyond Plain Python.ipynb\n",
1288 " Capturing Output.ipynb\n",
1289 " Cell Magics.ipynb\n",
1290 " Custom Display Logic.ipynb\n",
1291 " example-demo.py\n",
1292 " Index.ipynb\n",
1293 " ipython-completion.bash\n",
1294 " ipython-get-history.py\n",
1295 " ipython-qtconsole.desktop\n",
1296 " ipython.desktop\n",
1297 " Plotting in the Notebook.ipynb\n",
1298 " Raw Input in the Notebook.ipynb\n",
1299 " Rich Output.ipynb\n",
1300 " Script Magics.ipynb\n",
1301 " SymPy.ipynb\n",
1302 " Terminal Usage.ipynb\n",
1303 " Third Party Rich Output.ipynb\n",
1304 " Trapezoid Rule.ipynb\n",
1305 " Working With External Code.ipynb\n",
1306 "./data/\n",
1307 " flare.json\n",
1308 "./gui/\n",
1309 " gui-glut.py\n",
1310 " gui-gtk.py\n",
1311 " gui-gtk3.py\n",
1312 " gui-pyglet.py\n",
1313 " gui-qt.py\n",
1314 " gui-tk.py\n",
1315 " gui-wx.py"
1475 1316 ]
1476 1317 }
1477 1318 ],
1478 "prompt_number": 28
1319 "prompt_number": 34
1320 },
1321 {
1322 "cell_type": "heading",
1323 "level": 2,
1324 "metadata": {},
1325 "source": [
1326 "Rich output and security"
1327 ]
1479 1328 },
1480 1329 {
1481 1330 "cell_type": "markdown",
1482 1331 "metadata": {},
1483 1332 "source": [
1484 "Or you can enter latex directly with the `%%latex` cell magic:"
1333 "The IPython Notebook allows arbitrary code execution in both the IPython kernel and in the browser, though HTML and JavaScript output. More importantly, because IPython has a JavaScript API for running code in the browser, HTML and JavaScript output can actually trigger code to be run in the kernel. This poses a significant security risk as it would allow IPython Notebooks to execute arbitrary code on your computers.\n",
1334 "\n",
1335 "To protect against these risks, the IPython Notebook has a security model that specifies how dangerous output is handled. Here is a short summary:\n",
1336 "\n",
1337 "* When you run code in the Notebook, all rich output is displayed.\n",
1338 "* When you open a notebook, rich output is only displayed if it doesn't contain security vulberabilities,...\n",
1339 "* ..or if you have trusted a notebook, all rich output will run upon opening it.\n",
1340 "\n",
1341 "A full description of the IPython security model can be found on [this page](http://ipython.org/ipython-doc/dev/notebook/security.html)."
1485 1342 ]
1486 1343 },
1487 1344 {
1488 "cell_type": "code",
1489 "collapsed": false,
1490 "input": [
1491 "%%latex\n",
1492 "\\begin{align}\n",
1493 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1494 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1495 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1496 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
1497 "\\end{align}"
1498 ],
1499 "language": "python",
1345 "cell_type": "heading",
1346 "level": 2,
1500 1347 "metadata": {},
1501 "outputs": [
1502 {
1503 "latex": [
1504 "\\begin{align}\n",
1505 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1506 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1507 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1508 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
1509 "\\end{align}"
1510 ],
1511 "metadata": {},
1512 "output_type": "display_data",
1513 "text": [
1514 "<IPython.core.display.Latex at 0x10a82d790>"
1515 ]
1516 }
1517 ],
1518 "prompt_number": 29
1348 "source": [
1349 "Rich output and nbviewer"
1350 ]
1351 },
1352 {
1353 "cell_type": "markdown",
1354 "metadata": {},
1355 "source": [
1356 "Much of the power of the Notebook is that it enables users to share notebooks with each other using http://nbviewer.ipython.org, without installing IPython locally. As of IPython 2.0, notebooks rendere on nbviewer will display all output, including HTML and JavaScript. Furthermore, to provide a consistent JavaScript environment on the live Notebook and nbviewer, the following JavaScript libraries are loaded onto the nbviewer page, *before* the notebook and its output is displayed:\n",
1357 "\n",
1358 "* [jQuery](http://jquery.com/)\n",
1359 "* [RequireJS](http://requirejs.org/)\n",
1360 "\n",
1361 "Libraries such as [mpld3](http://mpld3.github.io/) use these capabilities to generate interactive visualizations that work on nbviewer."
1362 ]
1519 1363 }
1520 1364 ],
1521 1365 "metadata": {}
1522 1366 }
1523 1367 ]
1524 1368 } No newline at end of file
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1 1 {
2 2 "metadata": {
3 3 "name": "",
4 "signature": "sha256:180c055843c21d9b1ac1c9ab78517b077ff5d6526a847739908408866ac449b2"
4 "signature": "sha256:123d82ef0551f78e5dca94db6e00f1e10ae07d930467cf44709ccc6a9216776a"
5 5 },
6 6 "nbformat": 3,
7 7 "nbformat_minor": 0,
8 8 "worksheets": [
9 9 {
10 10 "cells": [
11 11 {
12 12 "cell_type": "heading",
13 13 "level": 1,
14 14 "metadata": {},
15 15 "source": [
16 "IPython's Rich Display System"
16 "Third Party Libraries With Rich Output"
17 17 ]
18 18 },
19 19 {
20 20 "cell_type": "markdown",
21 21 "metadata": {},
22 22 "source": [
23 "In Python, objects can declare their textual representation using the `__repr__` method. IPython expands on this idea and allows objects to declare other, richer representations including:\n",
24 "\n",
25 "* HTML\n",
26 "* JSON\n",
27 "* PNG\n",
28 "* JPEG\n",
29 "* SVG\n",
30 "* LaTeX\n",
31 "\n",
32 "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."
23 "A number of third party libraries defined their own custom display logic. This gives their objcts rich output by default when used in the Notebook."
33 24 ]
34 25 },
35 26 {
36 "cell_type": "heading",
37 "level": 2,
38 "metadata": {},
39 "source": [
40 "Basic display imports"
41 ]
42 },
43 {
44 "cell_type": "markdown",
45 "metadata": {},
46 "source": [
47 "The `display` function is a general purpose tool for displaying different representations of objects. Think of it as `print` for these rich representations."
48 ]
49 },
50 {
51 "cell_type": "code",
52 "collapsed": false,
53 "input": [
54 "from IPython.display import display"
55 ],
56 "language": "python",
57 "metadata": {},
58 "outputs": [],
59 "prompt_number": 1
60 },
61 {
62 "cell_type": "markdown",
63 "metadata": {},
64 "source": [
65 "A few points:\n",
66 "\n",
67 "* Calling `display` on an object will send **all** possible representations to the Notebook.\n",
68 "* These representations are stored in the Notebook document.\n",
69 "* In general the Notebook will use the richest available representation.\n",
70 "\n",
71 "If you want to display a particular representation, there are specific functions for that:"
72 ]
73 },
74 {
75 "cell_type": "code",
76 "collapsed": false,
77 "input": [
78 "from IPython.display import display_pretty, display_html, display_jpeg, display_png, display_json, display_latex, display_svg"
79 ],
80 "language": "python",
81 "metadata": {},
82 "outputs": [],
83 "prompt_number": 2
84 },
85 {
86 "cell_type": "heading",
87 "level": 2,
88 "metadata": {},
89 "source": [
90 "Images"
91 ]
92 },
93 {
94 "cell_type": "markdown",
95 "metadata": {},
96 "source": [
97 "To work with images (JPEG, PNG) use the `Image` class."
98 ]
99 },
100 {
101 "cell_type": "code",
102 "collapsed": false,
103 "input": [
104 "from IPython.display import Image"
105 ],
106 "language": "python",
107 "metadata": {},
108 "outputs": [],
109 "prompt_number": 3
110 },
111 {
112 "cell_type": "code",
113 "collapsed": false,
114 "input": [
115 "i = Image(filename='../images/ipython_logo.png')"
116 ],
117 "language": "python",
118 "metadata": {},
119 "outputs": [],
120 "prompt_number": 5
121 },
122 {
123 "cell_type": "markdown",
124 "metadata": {},
125 "source": [
126 "Returning an `Image` object from an expression will automatically display it:"
127 ]
128 },
129 {
130 "cell_type": "code",
131 "collapsed": false,
132 "input": [
133 "i"
134 ],
135 "language": "python",
136 "metadata": {},
137 "outputs": [
138 {
139 "metadata": {},
140 "output_type": "pyout",
141 "png": 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142 "prompt_number": 6,
143 "text": [
144 "<IPython.core.display.Image at 0x106a91e10>"
145 ]
146 }
147 ],
148 "prompt_number": 6
149 },
150 {
151 "cell_type": "markdown",
152 "metadata": {},
153 "source": [
154 "Or you can pass it to `display`:"
155 ]
156 },
157 {
158 "cell_type": "code",
159 "collapsed": false,
160 "input": [
161 "display(i)"
162 ],
163 "language": "python",
164 "metadata": {},
165 "outputs": [
166 {
167 "metadata": {},
168 "output_type": "display_data",
169 "png": 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170 "text": [
171 "<IPython.core.display.Image at 0x106a91e10>"
172 ]
173 }
174 ],
175 "prompt_number": 7
176 },
177 {
178 "cell_type": "markdown",
179 "metadata": {},
180 "source": [
181 "An image can also be displayed from raw data or a url"
182 ]
183 },
184 {
185 "cell_type": "code",
186 "collapsed": false,
187 "input": [
188 "Image(url='http://python.org/images/python-logo.gif')"
189 ],
190 "language": "python",
191 "metadata": {},
192 "outputs": [
193 {
194 "html": [
195 "<img src=\"http://python.org/images/python-logo.gif\"/>"
196 ],
197 "metadata": {},
198 "output_type": "pyout",
199 "prompt_number": 8,
200 "text": [
201 "<IPython.core.display.Image at 0x107005150>"
202 ]
203 }
204 ],
205 "prompt_number": 8
206 },
207 {
208 "cell_type": "markdown",
209 "metadata": {},
210 "source": [
211 "SVG images are also supported out of the box (since modern browsers do a good job of rendering them):"
212 ]
213 },
214 {
215 "cell_type": "code",
216 "collapsed": false,
217 "input": [
218 "from IPython.display import SVG\n",
219 "SVG(filename='images/python_logo.svg')"
220 ],
221 "language": "python",
222 "metadata": {},
223 "outputs": [
224 {
225 "metadata": {},
226 "output_type": "pyout",
227 "prompt_number": 9,
228 "svg": [
229 "<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",
230 " <metadata id=\"metadata2193\">\n",
231 " <rdf:RDF>\n",
232 " <cc:Work rdf:about=\"\">\n",
233 " <dc:format>image/svg+xml</dc:format>\n",
234 " <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
235 " </cc:Work>\n",
236 " </rdf:RDF>\n",
237 " </metadata>\n",
238 " <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"svg2\" inkscape:cx=\"243.02499\" inkscape:cy=\"71.887497\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"543\" inkscape:window-width=\"791\" inkscape:window-x=\"0\" inkscape:window-y=\"0\" inkscape:zoom=\"1.4340089\" pagecolor=\"#ffffff\"/>\n",
239 " <defs id=\"defs4\">\n",
240 " <linearGradient id=\"linearGradient2795\">\n",
241 " <stop id=\"stop2797\" offset=\"0\" style=\"stop-color:#b8b8b8;stop-opacity:0.49803922\"/>\n",
242 " <stop id=\"stop2799\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
243 " </linearGradient>\n",
244 " <linearGradient id=\"linearGradient2787\">\n",
245 " <stop id=\"stop2789\" offset=\"0\" style=\"stop-color:#7f7f7f;stop-opacity:0.5\"/>\n",
246 " <stop id=\"stop2791\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
247 " </linearGradient>\n",
248 " <linearGradient id=\"linearGradient3676\">\n",
249 " <stop id=\"stop3678\" offset=\"0\" style=\"stop-color:#b2b2b2;stop-opacity:0.5\"/>\n",
250 " <stop id=\"stop3680\" offset=\"1\" style=\"stop-color:#b3b3b3;stop-opacity:0\"/>\n",
251 " </linearGradient>\n",
252 " <linearGradient id=\"linearGradient3236\">\n",
253 " <stop id=\"stop3244\" offset=\"0\" style=\"stop-color:#f4f4f4;stop-opacity:1\"/>\n",
254 " <stop id=\"stop3240\" offset=\"1\" style=\"stop-color:#ffffff;stop-opacity:1\"/>\n",
255 " </linearGradient>\n",
256 " <linearGradient id=\"linearGradient4671\">\n",
257 " <stop id=\"stop4673\" offset=\"0\" style=\"stop-color:#ffd43b;stop-opacity:1\"/>\n",
258 " <stop id=\"stop4675\" offset=\"1\" style=\"stop-color:#ffe873;stop-opacity:1\"/>\n",
259 " </linearGradient>\n",
260 " <linearGradient id=\"linearGradient4689\">\n",
261 " <stop id=\"stop4691\" offset=\"0\" style=\"stop-color:#5a9fd4;stop-opacity:1\"/>\n",
262 " <stop id=\"stop4693\" offset=\"1\" style=\"stop-color:#306998;stop-opacity:1\"/>\n",
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288 " </g>\n",
289 "</svg>"
290 ],
291 "text": [
292 "<IPython.core.display.SVG at 0x107005250>"
293 ]
294 }
295 ],
296 "prompt_number": 9
297 },
298 {
299 "cell_type": "heading",
300 "level": 2,
301 "metadata": {},
302 "source": [
303 "Links to local files"
304 ]
305 },
306 {
307 "cell_type": "markdown",
308 "metadata": {},
309 "source": [
310 "If we want to create a link to one of them, we can call use the `FileLink` object."
311 ]
312 },
313 {
314 "cell_type": "code",
315 "collapsed": false,
316 "input": [
317 "from IPython.display import FileLink, FileLinks\n",
318 "FileLink('Running Code.ipynb')"
319 ],
320 "language": "python",
321 "metadata": {},
322 "outputs": [
323 {
324 "html": [
325 "<a href='Running Code.ipynb' target='_blank'>Running Code.ipynb</a><br>"
326 ],
327 "metadata": {},
328 "output_type": "pyout",
329 "prompt_number": 10,
330 "text": [
331 "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Notebook/Running Code.ipynb"
332 ]
333 }
334 ],
335 "prompt_number": 10
336 },
337 {
338 "cell_type": "markdown",
339 "metadata": {},
340 "source": [
341 "Alternatively, if we want to link to all of the files in a directory, we can use the `FileLinks` object, passing `'.'` to indicate that we want links generated for the current working directory. Note that if there were other directories under the current directory, `FileLinks` would work in a recursive manner creating links to files in all sub-directories as well."
342 ]
343 },
344 {
345 "cell_type": "code",
346 "collapsed": false,
347 "input": [
348 "FileLinks('.')"
349 ],
350 "language": "python",
351 "metadata": {},
352 "outputs": [
353 {
354 "html": [
355 "./<br>\n",
356 "&nbsp;&nbsp;<a href='./Animations Using clear_output.ipynb' target='_blank'>Animations Using clear_output.ipynb</a><br>\n",
357 "&nbsp;&nbsp;<a href='./Basic Output.ipynb' target='_blank'>Basic Output.ipynb</a><br>\n",
358 "&nbsp;&nbsp;<a href='./Connecting with the Qt Console.ipynb' target='_blank'>Connecting with the Qt Console.ipynb</a><br>\n",
359 "&nbsp;&nbsp;<a href='./Custom Display Logic.ipynb' target='_blank'>Custom Display Logic.ipynb</a><br>\n",
360 "&nbsp;&nbsp;<a href='./Display System.ipynb' target='_blank'>Display System.ipynb</a><br>\n",
361 "&nbsp;&nbsp;<a href='./Importing Notebooks.ipynb' target='_blank'>Importing Notebooks.ipynb</a><br>\n",
362 "&nbsp;&nbsp;<a href='./Index.ipynb' target='_blank'>Index.ipynb</a><br>\n",
363 "&nbsp;&nbsp;<a href='./Markdown Cells.ipynb' target='_blank'>Markdown Cells.ipynb</a><br>\n",
364 "&nbsp;&nbsp;<a href='./Plotting with Matplotlib.ipynb' target='_blank'>Plotting with Matplotlib.ipynb</a><br>\n",
365 "&nbsp;&nbsp;<a href='./Progress Bars.ipynb' target='_blank'>Progress Bars.ipynb</a><br>\n",
366 "&nbsp;&nbsp;<a href='./Raw Input.ipynb' target='_blank'>Raw Input.ipynb</a><br>\n",
367 "&nbsp;&nbsp;<a href='./Running Code.ipynb' target='_blank'>Running Code.ipynb</a><br>\n",
368 "&nbsp;&nbsp;<a href='./SymPy.ipynb' target='_blank'>SymPy.ipynb</a><br>\n",
369 "&nbsp;&nbsp;<a href='./Trapezoid Rule.ipynb' target='_blank'>Trapezoid Rule.ipynb</a><br>\n",
370 "&nbsp;&nbsp;<a href='./Typesetting Math Using MathJax.ipynb' target='_blank'>Typesetting Math Using MathJax.ipynb</a><br>\n",
371 "&nbsp;&nbsp;<a href='./User Interface.ipynb' target='_blank'>User Interface.ipynb</a><br>\n",
372 "./images/<br>\n",
373 "&nbsp;&nbsp;<a href='./images/animation.m4v' target='_blank'>animation.m4v</a><br>\n",
374 "&nbsp;&nbsp;<a href='./images/command_mode.png' target='_blank'>command_mode.png</a><br>\n",
375 "&nbsp;&nbsp;<a href='./images/edit_mode.png' target='_blank'>edit_mode.png</a><br>\n",
376 "&nbsp;&nbsp;<a href='./images/menubar_toolbar.png' target='_blank'>menubar_toolbar.png</a><br>\n",
377 "&nbsp;&nbsp;<a href='./images/python_logo.svg' target='_blank'>python_logo.svg</a><br>\n",
378 "./nbpackage/<br>\n",
379 "&nbsp;&nbsp;<a href='./nbpackage/__init__.py' target='_blank'>__init__.py</a><br>\n",
380 "&nbsp;&nbsp;<a href='./nbpackage/mynotebook.ipynb' target='_blank'>mynotebook.ipynb</a><br>\n",
381 "./nbpackage/nbs/<br>\n",
382 "&nbsp;&nbsp;<a href='./nbpackage/nbs/__init__.py' target='_blank'>__init__.py</a><br>\n",
383 "&nbsp;&nbsp;<a href='./nbpackage/nbs/other.ipynb' target='_blank'>other.ipynb</a><br>"
384 ],
385 "metadata": {},
386 "output_type": "pyout",
387 "prompt_number": 11,
388 "text": [
389 "./\n",
390 " Animations Using clear_output.ipynb\n",
391 " Basic Output.ipynb\n",
392 " Connecting with the Qt Console.ipynb\n",
393 " Custom Display Logic.ipynb\n",
394 " Display System.ipynb\n",
395 " Importing Notebooks.ipynb\n",
396 " Index.ipynb\n",
397 " Markdown Cells.ipynb\n",
398 " Plotting with Matplotlib.ipynb\n",
399 " Progress Bars.ipynb\n",
400 " Raw Input.ipynb\n",
401 " Running Code.ipynb\n",
402 " SymPy.ipynb\n",
403 " Trapezoid Rule.ipynb\n",
404 " Typesetting Math Using MathJax.ipynb\n",
405 " User Interface.ipynb\n",
406 "./images/\n",
407 " animation.m4v\n",
408 " command_mode.png\n",
409 " edit_mode.png\n",
410 " menubar_toolbar.png\n",
411 " python_logo.svg\n",
412 "./nbpackage/\n",
413 " __init__.py\n",
414 " mynotebook.ipynb\n",
415 "./nbpackage/nbs/\n",
416 " __init__.py\n",
417 " other.ipynb"
418 ]
419 }
420 ],
421 "prompt_number": 11
422 },
423 {
424 "cell_type": "heading",
425 "level": 3,
426 "metadata": {},
427 "source": [
428 "Embedded vs Non-embedded Images"
429 ]
430 },
431 {
432 "cell_type": "markdown",
433 "metadata": {},
434 "source": [
435 "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."
436 ]
437 },
438 {
439 "cell_type": "code",
440 "collapsed": false,
441 "input": [
442 "from IPython.display import Image\n",
443 "img_url = 'http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg'\n",
444 "\n",
445 "# by default Image data are embedded\n",
446 "Embed = Image(img_url)\n",
447 "\n",
448 "# if kwarg `url` is given, the embedding is assumed to be false\n",
449 "SoftLinked = Image(url=img_url)\n",
450 "\n",
451 "# In each case, embed can be specified explicitly with the `embed` kwarg\n",
452 "# ForceEmbed = Image(url=img_url, embed=True)"
453 ],
454 "language": "python",
455 "metadata": {},
456 "outputs": [],
457 "prompt_number": 12
458 },
459 {
460 "cell_type": "markdown",
461 "metadata": {},
462 "source": [
463 "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."
464 ]
465 },
466 {
467 "cell_type": "code",
468 "collapsed": false,
469 "input": [
470 "Embed"
471 ],
472 "language": "python",
473 "metadata": {},
474 "outputs": [
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ahzq+lu8VqInS3fVsHvFEGG7xU4bvFVE4bvFHpd4oqfS7xU4bv+ND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\nIAK7yOGnUDxzwzXk8M8eTWM6ZcQz7Oiuo1KvGuDqjMjbuHA13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477 "metadata": {},
478 "output_type": "pyout",
479 "prompt_number": 13,
480 "text": [
481 "<IPython.core.display.Image at 0x1068d0a10>"
482 ]
483 }
484 ],
485 "prompt_number": 13
486 },
487 {
488 "cell_type": "markdown",
489 "metadata": {},
490 "source": [
491 "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."
492 ]
493 },
494 {
495 "cell_type": "code",
496 "collapsed": false,
497 "input": [
498 "SoftLinked"
499 ],
500 "language": "python",
501 "metadata": {},
502 "outputs": [
503 {
504 "html": [
505 "<img src=\"http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg\"/>"
506 ],
507 "metadata": {},
508 "output_type": "pyout",
509 "prompt_number": 14,
510 "text": [
511 "<IPython.core.display.Image at 0x106ab19d0>"
512 ]
513 }
514 ],
515 "prompt_number": 14
516 },
517 {
518 "cell_type": "markdown",
519 "metadata": {},
520 "source": [
521 "Of course, if you re-run this Notebook, the two images will be the same again."
522 ]
523 },
524 {
525 "cell_type": "heading",
526 "level": 2,
527 "metadata": {},
528 "source": [
529 "Audio"
530 ]
531 },
532 {
533 "cell_type": "markdown",
534 "metadata": {},
535 "source": [
536 "IPython makes it easy to work with sounds interactively. The `Audio` display class allows you to create an audio control that is embedded in the Notebook. The interface is analogous to the interface of the `Image` display class. All audio formats supported by the browser can be used. Note that no single format is presently supported in all browsers."
537 ]
538 },
539 {
540 "cell_type": "code",
541 "collapsed": false,
542 "input": [
543 "from IPython.display import Audio\n",
544 "Audio(url=\"http://www.nch.com.au/acm/8k16bitpcm.wav\")"
545 ],
546 "language": "python",
547 "metadata": {},
548 "outputs": [
549 {
550 "html": [
551 "\n",
552 " <audio controls=\"controls\" >\n",
553 " <source src=\"http://www.nch.com.au/acm/8k16bitpcm.wav\" type=\"audio/x-wav\" />\n",
554 " Your browser does not support the audio element.\n",
555 " </audio>\n",
556 " "
557 ],
558 "metadata": {},
559 "output_type": "pyout",
560 "prompt_number": 15,
561 "text": [
562 "<IPython.lib.display.Audio at 0x1070b2510>"
563 ]
564 }
565 ],
566 "prompt_number": 15
567 },
568 {
569 "cell_type": "markdown",
570 "metadata": {},
571 "source": [
572 "A Numpy array can be auralized automatically. The Audio class normalizes and encodes the data and embed the result in the Notebook.\n",
573 "\n",
574 "For instance, when two sine waves with almost the same frequency are superimposed a phenomena known as [beats](https://en.wikipedia.org/wiki/Beat_%28acoustics%29) occur. This can be auralised as follows"
575 ]
576 },
577 {
578 "cell_type": "code",
579 "collapsed": false,
580 "input": [
581 "import numpy as np\n",
582 "max_time = 3\n",
583 "f1 = 220.0\n",
584 "f2 = 224.0\n",
585 "rate = 8000.0\n",
586 "L = 3\n",
587 "times = np.linspace(0,L,rate*L)\n",
588 "signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
589 "\n",
590 "Audio(data=signal, rate=rate)"
591 ],
592 "language": "python",
593 "metadata": {},
594 "outputs": [
595 {
596 "html": [
597 "\n",
598 " <audio controls=\"controls\" >\n",
599 " <source 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\" type=\"audio/wav\" />\n",
600 " Your browser does not support the audio element.\n",
601 " </audio>\n",
602 " "
603 ],
604 "metadata": {},
605 "output_type": "pyout",
606 "prompt_number": 16,
607 "text": [
608 "<IPython.lib.display.Audio at 0x10828a050>"
609 ]
610 }
611 ],
612 "prompt_number": 16
613 },
614 {
615 "cell_type": "heading",
616 "level": 2,
617 "metadata": {},
618 "source": [
619 "Video"
620 ]
621 },
622 {
623 "cell_type": "markdown",
624 "metadata": {},
625 "source": [
626 "More exotic objects can also be displayed, as long as their representation supports the IPython display protocol. For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other hosted content is trivial):"
627 ]
628 },
629 {
630 "cell_type": "code",
631 "collapsed": false,
632 "input": [
633 "from IPython.display import YouTubeVideo\n",
634 "YouTubeVideo('sjfsUzECqK0')"
635 ],
636 "language": "python",
637 "metadata": {},
638 "outputs": [
639 {
640 "html": [
641 "\n",
642 " <iframe\n",
643 " width=\"400\"\n",
644 " height=300\"\n",
645 " src=\"https://www.youtube.com/embed/sjfsUzECqK0\"\n",
646 " frameborder=\"0\"\n",
647 " allowfullscreen\n",
648 " ></iframe>\n",
649 " "
650 ],
651 "metadata": {},
652 "output_type": "pyout",
653 "prompt_number": 20,
654 "text": [
655 "<IPython.lib.display.YouTubeVideo at 0x10a0d8190>"
656 ]
657 }
658 ],
659 "prompt_number": 20
660 },
661 {
662 "cell_type": "markdown",
663 "metadata": {},
664 "source": [
665 "Using the nascent video capabilities of modern browsers, you may also be able to display local\n",
666 "videos. At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n",
667 "we will continue testing this and looking for ways to make it more robust. \n",
668 "\n",
669 "The following cell loads a local file called `animation.m4v`, encodes the raw video as base64 for http\n",
670 "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control\n",
671 "bar at the bottom with a play/pause button and a location slider."
672 ]
673 },
674 {
675 "cell_type": "code",
676 "collapsed": false,
677 "input": [
678 "from IPython.display import HTML\n",
679 "from base64 import b64encode\n",
680 "video = open(\"images/animation.m4v\", \"rb\").read()\n",
681 "video_encoded = b64encode(video).decode('ascii')\n",
682 "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
683 "HTML(data=video_tag)"
684 ],
685 "language": "python",
686 "metadata": {},
687 "outputs": [
688 {
689 "html": [
690 "<video controls alt=\"test\" 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691 ],
692 "metadata": {},
693 "output_type": "pyout",
694 "prompt_number": 18,
695 "text": [
696 "<IPython.core.display.HTML at 0x1070b3050>"
697 ]
698 }
699 ],
700 "prompt_number": 18
701 },
702 {
703 "cell_type": "heading",
704 "level": 2,
705 "metadata": {},
706 "source": [
707 "HTML"
708 ]
709 },
710 {
711 "cell_type": "markdown",
712 "metadata": {},
713 "source": [
714 "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."
715 ]
716 },
717 {
718 "cell_type": "code",
719 "collapsed": false,
720 "input": [
721 "from IPython.display import HTML"
722 ],
723 "language": "python",
724 "metadata": {},
725 "outputs": [],
726 "prompt_number": 19
727 },
728 {
729 27 "cell_type": "code",
730 28 "collapsed": false,
731 29 "input": [
732 "s = \"\"\"<table>\n",
733 "<tr>\n",
734 "<th>Header 1</th>\n",
735 "<th>Header 2</th>\n",
736 "</tr>\n",
737 "<tr>\n",
738 "<td>row 1, cell 1</td>\n",
739 "<td>row 1, cell 2</td>\n",
740 "</tr>\n",
741 "<tr>\n",
742 "<td>row 2, cell 1</td>\n",
743 "<td>row 2, cell 2</td>\n",
744 "</tr>\n",
745 "</table>\"\"\""
30 "from IPython.display import display"
746 31 ],
747 32 "language": "python",
748 33 "metadata": {},
749 34 "outputs": [],
750 "prompt_number": 20
751 },
752 {
753 "cell_type": "code",
754 "collapsed": false,
755 "input": [
756 "h = HTML(s); h"
757 ],
758 "language": "python",
759 "metadata": {},
760 "outputs": [
761 {
762 "html": [
763 "<table>\n",
764 "<tr>\n",
765 "<th>Header 1</th>\n",
766 "<th>Header 2</th>\n",
767 "</tr>\n",
768 "<tr>\n",
769 "<td>row 1, cell 1</td>\n",
770 "<td>row 1, cell 2</td>\n",
771 "</tr>\n",
772 "<tr>\n",
773 "<td>row 2, cell 1</td>\n",
774 "<td>row 2, cell 2</td>\n",
775 "</tr>\n",
776 "</table>"
777 ],
778 "metadata": {},
779 "output_type": "pyout",
780 "prompt_number": 21,
781 "text": [
782 "<IPython.core.display.HTML at 0x108313a90>"
783 ]
784 }
785 ],
786 "prompt_number": 21
787 },
788 {
789 "cell_type": "markdown",
790 "metadata": {},
791 "source": [
792 "If you want to write HTML or Javascript straight to the frontend,\n",
793 "you can use `%%html` or `%%javascript` cell magics. These are exactly the same as writing `display(HTML(\"\"\"cell contents\"\"\"))`, etc."
794 ]
795 },
796 {
797 "cell_type": "code",
798 "collapsed": false,
799 "input": [
800 "%%html\n",
801 "<table>\n",
802 "<tr>\n",
803 "<th>Header 1</th>\n",
804 "<th>Header 2</th>\n",
805 "</tr>\n",
806 "<tr>\n",
807 "<td>row 1, cell 1</td>\n",
808 "<td>row 1, cell 2</td>\n",
809 "</tr>\n",
810 "<tr>\n",
811 "<td>row 2, cell 1</td>\n",
812 "<td>row 2, cell 2</td>\n",
813 "</tr>\n",
814 "</table>"
815 ],
816 "language": "python",
817 "metadata": {},
818 "outputs": [
819 {
820 "html": [
821 "<table>\n",
822 "<tr>\n",
823 "<th>Header 1</th>\n",
824 "<th>Header 2</th>\n",
825 "</tr>\n",
826 "<tr>\n",
827 "<td>row 1, cell 1</td>\n",
828 "<td>row 1, cell 2</td>\n",
829 "</tr>\n",
830 "<tr>\n",
831 "<td>row 2, cell 1</td>\n",
832 "<td>row 2, cell 2</td>\n",
833 "</tr>\n",
834 "</table>"
835 ],
836 "metadata": {},
837 "output_type": "display_data",
838 "text": [
839 "<IPython.core.display.HTML object>"
840 ]
841 }
842 ],
843 "prompt_number": 1
844 },
845 {
846 "cell_type": "markdown",
847 "metadata": {},
848 "source": [
849 "Pandas makes use of this capability to allow `DataFrames` to be represented as HTML tables."
850 ]
35 "prompt_number": 7
851 36 },
852 37 {
853 38 "cell_type": "heading",
854 39 "level": 2,
855 40 "metadata": {},
856 41 "source": [
857 "JavaScript"
42 "Pandas"
858 43 ]
859 44 },
860 45 {
861 "cell_type": "code",
862 "collapsed": false,
863 "input": [
864 "from IPython.display import Javascript"
865 ],
866 "language": "python",
867 "metadata": {},
868 "outputs": [],
869 "prompt_number": 2
870 },
871 {
872 "cell_type": "code",
873 "collapsed": false,
874 "input": [
875 "Javascript(\n",
876 " \"\"\"$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')\"\"\"\n",
877 ")"
878 ],
879 "language": "python",
880 "metadata": {},
881 "outputs": [
882 {
883 "javascript": [
884 "$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')"
885 ],
886 "metadata": {},
887 "output_type": "pyout",
888 "prompt_number": 8,
889 "text": [
890 "<IPython.core.display.Javascript object>"
891 ]
892 }
893 ],
894 "prompt_number": 8
895 },
896 {
897 "cell_type": "code",
898 "collapsed": false,
899 "input": [
900 "%%html\n",
901 "<style type=\"text/css\">\n",
902 "\n",
903 "circle {\n",
904 " fill: rgb(31, 119, 180);\n",
905 " fill-opacity: .25;\n",
906 " stroke: rgb(31, 119, 180);\n",
907 " stroke-width: 1px;\n",
908 "}\n",
909 "\n",
910 ".leaf circle {\n",
911 " fill: #ff7f0e;\n",
912 " fill-opacity: 1;\n",
913 "}\n",
914 "\n",
915 "text {\n",
916 " font: 10px sans-serif;\n",
917 "}\n",
918 "\n",
919 "</style>"
920 ],
921 "language": "python",
922 "metadata": {},
923 "outputs": [
924 {
925 "html": [
926 "<style type=\"text/css\">\n",
927 "\n",
928 "circle {\n",
929 " fill: rgb(31, 119, 180);\n",
930 " fill-opacity: .25;\n",
931 " stroke: rgb(31, 119, 180);\n",
932 " stroke-width: 1px;\n",
933 "}\n",
934 "\n",
935 ".leaf circle {\n",
936 " fill: #ff7f0e;\n",
937 " fill-opacity: 1;\n",
938 "}\n",
939 "\n",
940 "text {\n",
941 " font: 10px sans-serif;\n",
942 "}\n",
943 "\n",
944 "</style>"
945 ],
946 "metadata": {},
947 "output_type": "display_data",
948 "text": [
949 "<IPython.core.display.HTML object>"
950 ]
951 }
952 ],
953 "prompt_number": 4
954 },
955 {
956 "cell_type": "code",
957 "collapsed": false,
958 "input": [
959 "%%javascript\n",
960 "\n",
961 "// element is the jQuery element we will append to\n",
962 "var e = element.get(0);\n",
963 " \n",
964 "var diameter = 600,\n",
965 " format = d3.format(\",d\");\n",
966 "\n",
967 "var pack = d3.layout.pack()\n",
968 " .size([diameter - 4, diameter - 4])\n",
969 " .value(function(d) { return d.size; });\n",
970 "\n",
971 "var svg = d3.select(e).append(\"svg\")\n",
972 " .attr(\"width\", diameter)\n",
973 " .attr(\"height\", diameter)\n",
974 " .append(\"g\")\n",
975 " .attr(\"transform\", \"translate(2,2)\");\n",
976 "\n",
977 "d3.json(\"data/flare.json\", function(error, root) {\n",
978 " var node = svg.datum(root).selectAll(\".node\")\n",
979 " .data(pack.nodes)\n",
980 " .enter().append(\"g\")\n",
981 " .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
982 " .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
983 "\n",
984 " node.append(\"title\")\n",
985 " .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
986 "\n",
987 " node.append(\"circle\")\n",
988 " .attr(\"r\", function(d) { return d.r; });\n",
989 "\n",
990 " node.filter(function(d) { return !d.children; }).append(\"text\")\n",
991 " .attr(\"dy\", \".3em\")\n",
992 " .style(\"text-anchor\", \"middle\")\n",
993 " .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
994 "});\n",
995 "\n",
996 "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
997 ],
998 "language": "python",
999 "metadata": {},
1000 "outputs": [
1001 {
1002 "javascript": [
1003 "\n",
1004 "// element is the jQuery element we will append to\n",
1005 "var e = element.get(0);\n",
1006 " \n",
1007 "var diameter = 600,\n",
1008 " format = d3.format(\",d\");\n",
1009 "\n",
1010 "var pack = d3.layout.pack()\n",
1011 " .size([diameter - 4, diameter - 4])\n",
1012 " .value(function(d) { return d.size; });\n",
1013 "\n",
1014 "var svg = d3.select(e).append(\"svg\")\n",
1015 " .attr(\"width\", diameter)\n",
1016 " .attr(\"height\", diameter)\n",
1017 " .append(\"g\")\n",
1018 " .attr(\"transform\", \"translate(2,2)\");\n",
1019 "\n",
1020 "d3.json(\"data/flare.json\", function(error, root) {\n",
1021 " var node = svg.datum(root).selectAll(\".node\")\n",
1022 " .data(pack.nodes)\n",
1023 " .enter().append(\"g\")\n",
1024 " .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
1025 " .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
1026 "\n",
1027 " node.append(\"title\")\n",
1028 " .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
1029 "\n",
1030 " node.append(\"circle\")\n",
1031 " .attr(\"r\", function(d) { return d.r; });\n",
1032 "\n",
1033 " node.filter(function(d) { return !d.children; }).append(\"text\")\n",
1034 " .attr(\"dy\", \".3em\")\n",
1035 " .style(\"text-anchor\", \"middle\")\n",
1036 " .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
1037 "});\n",
1038 "\n",
1039 "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
1040 ],
1041 "metadata": {},
1042 "output_type": "display_data",
1043 "text": [
1044 "<IPython.core.display.Javascript object>"
1045 ]
1046 }
1047 ],
1048 "prompt_number": 7
1049 },
1050 {
1051 "cell_type": "heading",
1052 "level": 2,
46 "cell_type": "markdown",
1053 47 "metadata": {},
1054 48 "source": [
1055 "Pandas"
49 "[Pandas](http://pandas.pydata.org/) is a data analysis library for Python. Its `DataFrame` objects have an HTML table representation in the Notebook."
1056 50 ]
1057 51 },
1058 52 {
1059 53 "cell_type": "code",
1060 54 "collapsed": false,
1061 55 "input": [
1062 56 "import pandas"
1063 57 ],
1064 58 "language": "python",
1065 59 "metadata": {},
1066 60 "outputs": [],
1067 61 "prompt_number": 9
1068 62 },
1069 63 {
1070 64 "cell_type": "markdown",
1071 65 "metadata": {},
1072 66 "source": [
1073 67 "Here is a small amount of stock data for APPL:"
1074 68 ]
1075 69 },
1076 70 {
1077 71 "cell_type": "code",
1078 72 "collapsed": false,
1079 73 "input": [
1080 74 "%%writefile data.csv\n",
1081 75 "Date,Open,High,Low,Close,Volume,Adj Close\n",
1082 76 "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
1083 77 "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
1084 78 "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
1085 79 "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
1086 80 "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
1087 81 "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
1088 82 ],
1089 83 "language": "python",
1090 84 "metadata": {},
1091 85 "outputs": [
1092 86 {
1093 87 "output_type": "stream",
1094 88 "stream": "stdout",
1095 89 "text": [
1096 90 "Writing data.csv\n"
1097 91 ]
1098 92 }
1099 93 ],
1100 94 "prompt_number": 10
1101 95 },
1102 96 {
1103 97 "cell_type": "markdown",
1104 98 "metadata": {},
1105 99 "source": [
1106 100 "Read this as into a `DataFrame`:"
1107 101 ]
1108 102 },
1109 103 {
1110 104 "cell_type": "code",
1111 105 "collapsed": false,
1112 106 "input": [
1113 107 "df = pandas.read_csv('data.csv')"
1114 108 ],
1115 109 "language": "python",
1116 110 "metadata": {},
1117 111 "outputs": [],
1118 112 "prompt_number": 11
1119 113 },
1120 114 {
1121 115 "cell_type": "markdown",
1122 116 "metadata": {},
1123 117 "source": [
1124 118 "And view the HTML representation:"
1125 119 ]
1126 120 },
1127 121 {
1128 122 "cell_type": "code",
1129 123 "collapsed": false,
1130 124 "input": [
1131 125 "df"
1132 126 ],
1133 127 "language": "python",
1134 128 "metadata": {},
1135 129 "outputs": [
1136 130 {
1137 131 "html": [
1138 132 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1139 133 "<table border=\"1\" class=\"dataframe\">\n",
1140 134 " <thead>\n",
1141 135 " <tr style=\"text-align: right;\">\n",
1142 136 " <th></th>\n",
1143 137 " <th>Date</th>\n",
1144 138 " <th>Open</th>\n",
1145 139 " <th>High</th>\n",
1146 140 " <th>Low</th>\n",
1147 141 " <th>Close</th>\n",
1148 142 " <th>Volume</th>\n",
1149 143 " <th>Adj Close</th>\n",
1150 144 " </tr>\n",
1151 145 " </thead>\n",
1152 146 " <tbody>\n",
1153 147 " <tr>\n",
1154 148 " <th>0</th>\n",
1155 149 " <td> 2012-06-01</td>\n",
1156 150 " <td> 569.16</td>\n",
1157 151 " <td> 590.00</td>\n",
1158 152 " <td> 548.50</td>\n",
1159 153 " <td> 584.00</td>\n",
1160 154 " <td> 14077000</td>\n",
1161 155 " <td> 581.50</td>\n",
1162 156 " </tr>\n",
1163 157 " <tr>\n",
1164 158 " <th>1</th>\n",
1165 159 " <td> 2012-05-01</td>\n",
1166 160 " <td> 584.90</td>\n",
1167 161 " <td> 596.76</td>\n",
1168 162 " <td> 522.18</td>\n",
1169 163 " <td> 577.73</td>\n",
1170 164 " <td> 18827900</td>\n",
1171 165 " <td> 575.26</td>\n",
1172 166 " </tr>\n",
1173 167 " <tr>\n",
1174 168 " <th>2</th>\n",
1175 169 " <td> 2012-04-02</td>\n",
1176 170 " <td> 601.83</td>\n",
1177 171 " <td> 644.00</td>\n",
1178 172 " <td> 555.00</td>\n",
1179 173 " <td> 583.98</td>\n",
1180 174 " <td> 28759100</td>\n",
1181 175 " <td> 581.48</td>\n",
1182 176 " </tr>\n",
1183 177 " <tr>\n",
1184 178 " <th>3</th>\n",
1185 179 " <td> 2012-03-01</td>\n",
1186 180 " <td> 548.17</td>\n",
1187 181 " <td> 621.45</td>\n",
1188 182 " <td> 516.22</td>\n",
1189 183 " <td> 599.55</td>\n",
1190 184 " <td> 26486000</td>\n",
1191 185 " <td> 596.99</td>\n",
1192 186 " </tr>\n",
1193 187 " <tr>\n",
1194 188 " <th>4</th>\n",
1195 189 " <td> 2012-02-01</td>\n",
1196 190 " <td> 458.41</td>\n",
1197 191 " <td> 547.61</td>\n",
1198 192 " <td> 453.98</td>\n",
1199 193 " <td> 542.44</td>\n",
1200 194 " <td> 22001000</td>\n",
1201 195 " <td> 540.12</td>\n",
1202 196 " </tr>\n",
1203 197 " <tr>\n",
1204 198 " <th>5</th>\n",
1205 199 " <td> 2012-01-03</td>\n",
1206 200 " <td> 409.40</td>\n",
1207 201 " <td> 458.24</td>\n",
1208 202 " <td> 409.00</td>\n",
1209 203 " <td> 456.48</td>\n",
1210 204 " <td> 12949100</td>\n",
1211 205 " <td> 454.53</td>\n",
1212 206 " </tr>\n",
1213 207 " </tbody>\n",
1214 208 "</table>\n",
1215 209 "<p>6 rows \u00d7 7 columns</p>\n",
1216 210 "</div>"
1217 211 ],
1218 212 "metadata": {},
1219 213 "output_type": "pyout",
1220 214 "prompt_number": 12,
1221 215 "text": [
1222 216 " Date Open High Low Close Volume Adj Close\n",
1223 217 "0 2012-06-01 569.16 590.00 548.50 584.00 14077000 581.50\n",
1224 218 "1 2012-05-01 584.90 596.76 522.18 577.73 18827900 575.26\n",
1225 219 "2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48\n",
1226 220 "3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99\n",
1227 221 "4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12\n",
1228 222 "5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53\n",
1229 223 "\n",
1230 224 "[6 rows x 7 columns]"
1231 225 ]
1232 226 }
1233 227 ],
1234 228 "prompt_number": 12
1235 229 },
1236 230 {
1237 231 "cell_type": "heading",
1238 232 "level": 2,
1239 233 "metadata": {},
1240 234 "source": [
1241 235 "SymPy"
1242 236 ]
1243 237 },
1244 238 {
239 "cell_type": "markdown",
240 "metadata": {},
241 "source": [
242 "[SymPy](http://sympy.org/) is a symbolic computing library for Python. Its equation objects have LaTeX representations that are rendered in the Notebook."
243 ]
244 },
245 {
1245 246 "cell_type": "code",
1246 247 "collapsed": false,
1247 248 "input": [
1248 249 "from sympy.interactive.printing import init_printing\n",
1249 250 "init_printing(use_latex='mathjax')"
1250 251 ],
1251 252 "language": "python",
1252 253 "metadata": {},
1253 254 "outputs": [],
1254 255 "prompt_number": 13
1255 256 },
1256 257 {
1257 258 "cell_type": "code",
1258 259 "collapsed": false,
1259 260 "input": [
1260 261 "from __future__ import division\n",
1261 262 "import sympy as sym\n",
1262 263 "from sympy import *\n",
1263 264 "x, y, z = symbols(\"x y z\")\n",
1264 265 "k, m, n = symbols(\"k m n\", integer=True)\n",
1265 266 "f, g, h = map(Function, 'fgh')"
1266 267 ],
1267 268 "language": "python",
1268 269 "metadata": {},
1269 270 "outputs": [],
1270 271 "prompt_number": 14
1271 272 },
1272 273 {
1273 274 "cell_type": "code",
1274 275 "collapsed": false,
1275 276 "input": [
1276 277 "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
1277 278 ],
1278 279 "language": "python",
1279 280 "metadata": {},
1280 281 "outputs": [
1281 282 {
1282 283 "latex": [
1283 284 "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
1284 285 ],
1285 286 "metadata": {},
1286 287 "output_type": "pyout",
1287 288 "prompt_number": 15,
1288 289 "text": [
1289 290 " \u2148\u22c5x \n",
1290 291 "3\u22c5\u03c0 \u212f \n",
1291 292 "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
1292 293 " 2 2 \n",
1293 294 " x + y"
1294 295 ]
1295 296 }
1296 297 ],
1297 298 "prompt_number": 15
1298 299 },
1299 300 {
1300 301 "cell_type": "code",
1301 302 "collapsed": false,
1302 303 "input": [
1303 304 "a = 1/x + (x*sin(x) - 1)/x\n",
1304 305 "a"
1305 306 ],
1306 307 "language": "python",
1307 308 "metadata": {},
1308 309 "outputs": [
1309 310 {
1310 311 "latex": [
1311 312 "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
1312 313 ],
1313 314 "metadata": {},
1314 315 "output_type": "pyout",
1315 316 "prompt_number": 16,
1316 317 "text": [
1317 318 "x\u22c5sin(x) - 1 1\n",
1318 319 "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
1319 320 " x x"
1320 321 ]
1321 322 }
1322 323 ],
1323 324 "prompt_number": 16
1324 325 },
1325 326 {
1326 327 "cell_type": "code",
1327 328 "collapsed": false,
1328 329 "input": [
1329 330 "(1/cos(x)).series(x, 0, 6)"
1330 331 ],
1331 332 "language": "python",
1332 333 "metadata": {},
1333 334 "outputs": [
1334 335 {
1335 336 "latex": [
1336 337 "$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
1337 338 ],
1338 339 "metadata": {},
1339 340 "output_type": "pyout",
1340 341 "prompt_number": 17,
1341 342 "text": [
1342 343 " 2 4 \n",
1343 344 " x 5\u22c5x \u239b 6\u239e\n",
1344 345 "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
1345 346 " 2 24 "
1346 347 ]
1347 348 }
1348 349 ],
1349 350 "prompt_number": 17
1350 351 },
1351 352 {
1352 353 "cell_type": "heading",
1353 354 "level": 2,
1354 355 "metadata": {},
1355 356 "source": [
1356 "External sites"
357 "Vincent"
1357 358 ]
1358 359 },
1359 360 {
1360 361 "cell_type": "markdown",
1361 362 "metadata": {},
1362 363 "source": [
1363 "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n",
1364 "page for mobile users:"
364 "[Vincent](https://vincent.readthedocs.org/en/latest/) is a visualization library that uses the [Vega](http://trifacta.github.io/vega/) visualization grammar to build [d3.js](http://d3js.org/) based visualizations in the Notebook and on http://nbviewer.ipython.org. `Visualization` objects in Vincetn have rich HTML and JavaSrcript representations."
1365 365 ]
1366 366 },
1367 367 {
1368 368 "cell_type": "code",
1369 369 "collapsed": false,
1370 370 "input": [
1371 "from IPython.display import IFrame\n",
1372 "IFrame('http://python.org', width='100%', height=350)"
371 "import vincent\n",
372 "import pandas as pd"
1373 373 ],
1374 374 "language": "python",
1375 375 "metadata": {},
1376 "outputs": [
1377 {
1378 "html": [
1379 "\n",
1380 " <iframe\n",
1381 " width=\"100%\"\n",
1382 " height=350\"\n",
1383 " src=\"http://python.org\"\n",
1384 " frameborder=\"0\"\n",
1385 " allowfullscreen\n",
1386 " ></iframe>\n",
1387 " "
1388 ],
1389 "metadata": {},
1390 "output_type": "pyout",
1391 "prompt_number": 19,
1392 "text": [
1393 "<IPython.lib.display.IFrame at 0x10a11ff10>"
1394 ]
1395 }
1396 ],
1397 "prompt_number": 19
1398 },
1399 {
1400 "cell_type": "heading",
1401 "level": 2,
1402 "metadata": {},
1403 "source": [
1404 "LaTeX"
1405 ]
1406 },
1407 {
1408 "cell_type": "markdown",
1409 "metadata": {},
1410 "source": [
1411 "And we also support the display of mathematical expressions typeset in LaTeX, which is rendered\n",
1412 "in the browser thanks to the [MathJax library](http://mathjax.org)."
1413 ]
376 "outputs": [],
377 "prompt_number": 1
1414 378 },
1415 379 {
1416 380 "cell_type": "code",
1417 381 "collapsed": false,
1418 382 "input": [
1419 "from IPython.display import Math\n",
1420 "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
383 "import pandas.io.data as web\n",
384 "import datetime\n",
385 "all_data = {}\n",
386 "date_start = datetime.datetime(2010, 1, 1)\n",
387 "date_end = datetime.datetime(2014, 1, 1)\n",
388 "for ticker in ['AAPL', 'IBM', 'YHOO', 'MSFT']:\n",
389 " all_data[ticker] = web.DataReader(ticker, 'yahoo', date_start, date_end)\n",
390 "price = pd.DataFrame({tic: data['Adj Close']\n",
391 " for tic, data in all_data.items()})"
1421 392 ],
1422 393 "language": "python",
1423 394 "metadata": {},
1424 "outputs": [
1425 {
1426 "latex": [
1427 "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
1428 ],
1429 "metadata": {},
1430 "output_type": "pyout",
1431 "prompt_number": 27,
1432 "text": [
1433 "<IPython.core.display.Math at 0x10a82d810>"
1434 ]
1435 }
1436 ],
1437 "prompt_number": 27
1438 },
1439 {
1440 "cell_type": "markdown",
1441 "metadata": {},
1442 "source": [
1443 "With the `Latex` class, you have to include the delimiters yourself. This allows you to use other LaTeX modes such as `eqnarray`:"
1444 ]
395 "outputs": [],
396 "prompt_number": 5
1445 397 },
1446 398 {
1447 399 "cell_type": "code",
1448 400 "collapsed": false,
1449 401 "input": [
1450 "from IPython.display import Latex\n",
1451 "Latex(r\"\"\"\\begin{eqnarray}\n",
1452 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1453 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1454 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1455 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
1456 "\\end{eqnarray}\"\"\")"
402 "vincent.initialize_notebook()"
1457 403 ],
1458 404 "language": "python",
1459 405 "metadata": {},
1460 406 "outputs": [
1461 407 {
1462 "latex": [
1463 "\\begin{eqnarray}\n",
1464 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1465 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1466 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1467 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
1468 "\\end{eqnarray}"
408 "html": [
409 "\n",
410 " <script>\n",
411 " \n",
412 " function vct_load_lib(url, callback){\n",
413 " if(typeof d3 !== 'undefined' &&\n",
414 " url === 'http://d3js.org/d3.v3.min.js'){\n",
415 " callback()\n",
416 " }\n",
417 " var s = document.createElement('script');\n",
418 " s.src = url;\n",
419 " s.async = true;\n",
420 " s.onreadystatechange = s.onload = callback;\n",
421 " s.onerror = function(){\n",
422 " console.warn(\"failed to load library \" + url);\n",
423 " };\n",
424 " document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
425 " };\n",
426 " var vincent_event = new CustomEvent(\n",
427 " \"vincent_libs_loaded\",\n",
428 " {bubbles: true, cancelable: true}\n",
429 " );\n",
430 " \n",
431 " function load_all_libs(){\n",
432 " console.log('loading all libs')\n",
433 " vct_load_lib('http://d3js.org/d3.v3.min.js', function(){\n",
434 " vct_load_lib('http://d3js.org/d3.geo.projection.v0.min.js', function(){\n",
435 " vct_load_lib('http://wrobstory.github.io/d3-cloud/d3.layout.cloud.js', function(){\n",
436 " vct_load_lib('http://trifacta.github.com/vega/vega.js', function(){\n",
437 " window.dispatchEvent(vincent_event);\n",
438 " });\n",
439 " });\n",
440 " });\n",
441 " });\n",
442 " };\n",
443 " if(typeof define === \"function\" && define.amd){\n",
444 " if (window['d3'] === undefined ||\n",
445 " window['topojson'] === undefined){\n",
446 " require.config(\n",
447 " {paths: {\n",
448 " d3: 'http://d3js.org/d3.v3.min',\n",
449 " topojson: 'http://d3js.org/topojson.v1.min'\n",
450 " }\n",
451 " }\n",
452 " );\n",
453 " require([\"d3\"], function(d3){\n",
454 " console.log('Loading from require.js...')\n",
455 " window.d3 = d3;\n",
456 " require([\"topojson\"], function(topojson){\n",
457 " window.topojson = topojson;\n",
458 " load_all_libs();\n",
459 " });\n",
460 " });\n",
461 " };\n",
462 " }else{\n",
463 " console.log('Require.js not found, loading manually...')\n",
464 " load_all_libs();\n",
465 " };\n",
466 "\n",
467 " </script>"
1469 468 ],
1470 469 "metadata": {},
1471 "output_type": "pyout",
1472 "prompt_number": 28,
470 "output_type": "display_data",
1473 471 "text": [
1474 "<IPython.core.display.Latex at 0x10a82d090>"
472 "<IPython.core.display.HTML object>"
1475 473 ]
1476 474 }
1477 475 ],
1478 "prompt_number": 28
1479 },
1480 {
1481 "cell_type": "markdown",
1482 "metadata": {},
1483 "source": [
1484 "Or you can enter latex directly with the `%%latex` cell magic:"
1485 ]
476 "prompt_number": 8
1486 477 },
1487 478 {
1488 479 "cell_type": "code",
1489 480 "collapsed": false,
1490 481 "input": [
1491 "%%latex\n",
1492 "\\begin{align}\n",
1493 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1494 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1495 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1496 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
1497 "\\end{align}"
482 "line = vincent.Line(price[['AAPL', 'IBM', 'YHOO', 'MSFT']], width=600, height=300)\n",
483 "line.axis_titles(x='Date', y='Price')\n",
484 "line.legend(title='Ticker')\n",
485 "display(line)"
1498 486 ],
1499 487 "language": "python",
1500 488 "metadata": {},
1501 489 "outputs": [
1502 490 {
1503 "latex": [
1504 "\\begin{align}\n",
1505 "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
1506 "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
1507 "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
1508 "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
1509 "\\end{align}"
491 "html": [
492 "<div id=\"vis47fdfca404f24684b44753131b44ed27\"></div>\n",
493 "<script>\n",
494 " ( function() {\n",
495 " var _do_plot = function() {\n",
496 " if (typeof vg === 'undefined') {\n",
497 " window.addEventListener('vincent_libs_loaded', _do_plot)\n",
498 " return;\n",
499 " }\n",
500 " vg.parse.spec({\"axes\": [{\"scale\": \"x\", \"title\": \"Date\", \"type\": \"x\"}, {\"scale\": \"y\", \"title\": \"Price\", \"type\": \"y\"}], \"data\": [{\"name\": \"table\", \"values\": [{\"col\": \"AAPL\", \"idx\": 1262592000000, \"val\": 205.7}, {\"col\": \"IBM\", \"idx\": 1262592000000, \"val\": 122.62}, {\"col\": \"YHOO\", \"idx\": 1262592000000, \"val\": 17.1}, {\"col\": \"MSFT\", \"idx\": 1262592000000, \"val\": 27.67}, {\"col\": \"AAPL\", \"idx\": 1262678400000, \"val\": 206.05}, {\"col\": \"IBM\", \"idx\": 1262678400000, \"val\": 121.14}, {\"col\": \"YHOO\", \"idx\": 1262678400000, \"val\": 17.23}, {\"col\": \"MSFT\", \"idx\": 1262678400000, \"val\": 27.68}, {\"col\": \"AAPL\", \"idx\": 1262764800000, \"val\": 202.77}, {\"col\": \"IBM\", \"idx\": 1262764800000, \"val\": 120.35}, {\"col\": \"YHOO\", \"idx\": 1262764800000, \"val\": 17.17}, {\"col\": \"MSFT\", \"idx\": 1262764800000, \"val\": 27.51}, {\"col\": \"AAPL\", \"idx\": 1262851200000, \"val\": 202.4}, {\"col\": 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501 " chart({el: \"#vis47fdfca404f24684b44753131b44ed27\"}).update();\n",
502 " });\n",
503 " };\n",
504 " _do_plot();\n",
505 " })();\n",
506 "</script>\n",
507 "<style>.vega canvas {width: 100%;}</style>\n",
508 " "
1510 509 ],
1511 510 "metadata": {},
1512 511 "output_type": "display_data",
1513 512 "text": [
1514 "<IPython.core.display.Latex at 0x10a82d790>"
513 "<vincent.charts.Line at 0x10290f710>"
1515 514 ]
1516 515 }
1517 516 ],
1518 "prompt_number": 29
517 "prompt_number": 10
1519 518 }
1520 519 ],
1521 520 "metadata": {}
1522 521 }
1523 522 ]
1524 523 } No newline at end of file
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