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pass on IPython Kernel example notebooks...
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@@ -4,7 +4,7 b''
4 4 "cell_type": "markdown",
5 5 "metadata": {},
6 6 "source": [
7 "# Simple animations Using clear_output"
7 "# Simple Animations Using clear_output"
8 8 ]
9 9 },
10 10 {
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@@ -36,9 +36,9 b''
36 36 "def printfunc(interval=1, reps=5):\n",
37 37 " for n in range(reps):\n",
38 38 " time.sleep(interval)\n",
39 " print 'In the background...', n\n",
39 " print('In the background... %i' % n)\n",
40 40 " sys.stdout.flush()\n",
41 " print 'All done!'\n",
41 " print('All done!')\n",
42 42 " sys.stdout.flush()"
43 43 ]
44 44 },
@@ -53,7 +53,7 b''
53 53 },
54 54 {
55 55 "cell_type": "code",
56 "execution_count": 10,
56 "execution_count": 2,
57 57 "metadata": {
58 58 "collapsed": false
59 59 },
@@ -73,7 +73,7 b''
73 73 "<BackgroundJob #3: printfunc(1,3)>"
74 74 ]
75 75 },
76 "execution_count": 10,
76 "execution_count": 2,
77 77 "metadata": {},
78 78 "output_type": "execute_result"
79 79 },
@@ -106,7 +106,7 b''
106 106 },
107 107 {
108 108 "cell_type": "code",
109 "execution_count": 11,
109 "execution_count": 3,
110 110 "metadata": {
111 111 "collapsed": false
112 112 },
@@ -116,8 +116,8 b''
116 116 "output_type": "stream",
117 117 "text": [
118 118 "Completed jobs:\n",
119 "0 : <function sleepfunc at 0x314f848>\n",
120 "2 : <function sleepfunc at 0x314f848>\n",
119 "0 : <function sleepfunc at 0x10521f2f0>\n",
120 "2 : <function sleepfunc at 0x10521f2f0>\n",
121 121 "3 : printfunc(1,3)\n",
122 122 "\n"
123 123 ]
@@ -136,7 +136,7 b''
136 136 },
137 137 {
138 138 "cell_type": "code",
139 "execution_count": 12,
139 "execution_count": 4,
140 140 "metadata": {
141 141 "collapsed": false
142 142 },
@@ -147,7 +147,7 b''
147 147 "{'args': (), 'interval': 4, 'kwargs': {}}"
148 148 ]
149 149 },
150 "execution_count": 12,
150 "execution_count": 4,
151 151 "metadata": {},
152 152 "output_type": "execute_result"
153 153 }
@@ -172,7 +172,7 b''
172 172 },
173 173 {
174 174 "cell_type": "code",
175 "execution_count": 13,
175 "execution_count": 5,
176 176 "metadata": {
177 177 "collapsed": false
178 178 },
@@ -204,7 +204,7 b''
204 204 },
205 205 {
206 206 "cell_type": "code",
207 "execution_count": 14,
207 "execution_count": 6,
208 208 "metadata": {
209 209 "collapsed": false
210 210 },
@@ -216,12 +216,12 b''
216 216 "Status of diejob1: Dead (Exception), call jobs.traceback() for details\n",
217 217 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
218 218 "\u001b[1;31mException\u001b[0m Traceback (most recent call last)\n",
219 "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
220 "\u001b[0;32m 482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
221 "\u001b[0;32m 483\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
222 "\u001b[1;32m--> 484\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
219 "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/lib/backgroundjobs.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
220 "\u001b[0;32m 489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
221 "\u001b[0;32m 490\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
222 "\u001b[1;32m--> 491\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
223 223 "\u001b[0m\n",
224 "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
224 "\u001b[1;32m<ipython-input-1-169e49434ce0>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
225 225 "\u001b[0;32m 13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
226 226 "\u001b[0;32m 14\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
227 227 "\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
@@ -233,7 +233,7 b''
233 233 }
234 234 ],
235 235 "source": [
236 "print \"Status of diejob1:\", diejob1.status\n",
236 "print(\"Status of diejob1: %s\" % diejob1.status)\n",
237 237 "diejob1.traceback() # jobs.traceback(4) would also work here, with the job number"
238 238 ]
239 239 },
@@ -246,7 +246,7 b''
246 246 },
247 247 {
248 248 "cell_type": "code",
249 "execution_count": 15,
249 "execution_count": 7,
250 250 "metadata": {
251 251 "collapsed": false
252 252 },
@@ -255,15 +255,15 b''
255 255 "name": "stdout",
256 256 "output_type": "stream",
257 257 "text": [
258 "Traceback for: <BackgroundJob #4: <function diefunc at 0x314f668>>\n",
258 "Traceback for: <BackgroundJob #4: <function diefunc at 0x10521f7b8>>\n",
259 259 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
260 260 "\u001b[1;31mException\u001b[0m Traceback (most recent call last)\n",
261 "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
262 "\u001b[0;32m 482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
263 "\u001b[0;32m 483\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
264 "\u001b[1;32m--> 484\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
261 "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/lib/backgroundjobs.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
262 "\u001b[0;32m 489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
263 "\u001b[0;32m 490\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
264 "\u001b[1;32m--> 491\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
265 265 "\u001b[0m\n",
266 "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
266 "\u001b[1;32m<ipython-input-1-169e49434ce0>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
267 267 "\u001b[0;32m 13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
268 268 "\u001b[0;32m 14\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
269 269 "\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
@@ -272,15 +272,15 b''
272 272 "\n",
273 273 "\u001b[1;31mException\u001b[0m: Dead job with interval 1\n",
274 274 "\n",
275 "Traceback for: <BackgroundJob #5: <function diefunc at 0x314f668>>\n",
275 "Traceback for: <BackgroundJob #5: <function diefunc at 0x10521f7b8>>\n",
276 276 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
277 277 "\u001b[1;31mException\u001b[0m Traceback (most recent call last)\n",
278 "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
279 "\u001b[0;32m 482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
280 "\u001b[0;32m 483\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
281 "\u001b[1;32m--> 484\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
278 "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/lib/backgroundjobs.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
279 "\u001b[0;32m 489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
280 "\u001b[0;32m 490\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
281 "\u001b[1;32m--> 491\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
282 282 "\u001b[0m\n",
283 "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
283 "\u001b[1;32m<ipython-input-1-169e49434ce0>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
284 284 "\u001b[0;32m 13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
285 285 "\u001b[0;32m 14\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
286 286 "\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
@@ -305,7 +305,7 b''
305 305 },
306 306 {
307 307 "cell_type": "code",
308 "execution_count": 16,
308 "execution_count": 8,
309 309 "metadata": {
310 310 "collapsed": false
311 311 },
@@ -332,7 +332,7 b''
332 332 },
333 333 {
334 334 "cell_type": "code",
335 "execution_count": 17,
335 "execution_count": 9,
336 336 "metadata": {
337 337 "collapsed": true
338 338 },
@@ -350,7 +350,7 b''
350 350 },
351 351 {
352 352 "cell_type": "code",
353 "execution_count": 18,
353 "execution_count": 10,
354 354 "metadata": {
355 355 "collapsed": false
356 356 },
Show file before | Show file after | Hide comments
This diff has been collapsed as it changes many lines, (1618 lines changed) Show them Hide them
@@ -55,7 +55,7 b''
55 55 }
56 56 ],
57 57 "source": [
58 "print \"Hi\""
58 "print(\"Hi\")"
59 59 ]
60 60 },
61 61 {
@@ -167,7 +167,7 b''
167 167 },
168 168 {
169 169 "cell_type": "code",
170 "execution_count": 8,
170 "execution_count": null,
171 171 "metadata": {
172 172 "collapsed": false
173 173 },
@@ -337,9 +337,11 b''
337 337 }
338 338 ],
339 339 "source": [
340 "print 'last output:', _\n",
341 "print 'next one :', __\n",
342 "print 'and next :', ___"
340 "from __future__ import print_function\n",
341 "\n",
342 "print('last output:', _)\n",
343 "print('next one :', __)\n",
344 "print('and next :', ___)"
343 345 ]
344 346 },
345 347 {
@@ -355,7 +357,7 b''
355 357 {
356 358 "data": {
357 359 "text/plain": [
358 "u'_10 == Out[10]'"
360 "'_10 == Out[10]'"
359 361 ]
360 362 },
361 363 "execution_count": 13,
@@ -377,7 +379,7 b''
377 379 {
378 380 "data": {
379 381 "text/plain": [
380 "u'In[11]'"
382 "'In[11]'"
381 383 ]
382 384 },
383 385 "execution_count": 14,
@@ -399,7 +401,7 b''
399 401 {
400 402 "data": {
401 403 "text/plain": [
402 "u'In[11]'"
404 "'In[11]'"
403 405 ]
404 406 },
405 407 "execution_count": 15,
@@ -432,9 +434,9 b''
432 434 }
433 435 ],
434 436 "source": [
435 "print 'last input:', _i\n",
436 "print 'next one :', _ii\n",
437 "print 'and next :', _iii"
437 "print('last input:', _i)\n",
438 "print('next one :', _ii)\n",
439 "print('and next :', _iii)"
438 440 ]
439 441 },
440 442 {
@@ -448,7 +450,7 b''
448 450 "name": "stdout",
449 451 "output_type": "stream",
450 452 "text": [
451 " 1: print \"Hi\"\n",
453 " 1: print(\"Hi\")\n",
452 454 " 2: ?\n",
453 455 " 3:\n",
454 456 "import collections\n",
@@ -497,7 +499,7 b''
497 499 "name": "stdout",
498 500 "output_type": "stream",
499 501 "text": [
500 "/home/fperez/ipython/tutorial/notebooks\r\n"
502 "/Users/minrk/dev/ip/mine/examples/IPython Kernel\r\n"
501 503 ]
502 504 }
503 505 ],
@@ -517,14 +519,14 b''
517 519 "output_type": "stream",
518 520 "text": [
519 521 "My current directory's files:\n",
520 "['BackgroundJobs.ipynb', 'Custom Display Logic.ipynb', 'Customizing IPython - Condensed.ipynb', 'Customizing IPython - Config.ipynb', 'Customizing IPython - Extensions.ipynb', 'Customizing IPython - Magics.ipynb', 'data', 'figs', 'flare.json', 'Index.ipynb', 'Interactive Widgets.ipynb', 'IPython - beyond plain Python.ipynb', 'kernel-embedding', 'Markdown Cells.ipynb', 'myscript.py', 'nbconvert_arch.png', 'NbConvert from command line.ipynb', 'NbConvert Python library.ipynb', 'Notebook and javascript extension.ipynb', 'Notebook Basics.ipynb', 'Overview of IPython.parallel.ipynb', 'parallel', 'Rich Display System.ipynb', 'Running a Secure Public Notebook.ipynb', 'Running Code.ipynb', 'Sample.ipynb', 'soln', 'Terminal usage.ipynb', 'text_analysis.py', 'Typesetting Math Using MathJax.ipynb']\n"
522 "['Animations Using clear_output.ipynb', 'Background Jobs.ipynb', 'Beyond Plain Python.ipynb', 'Capturing Output.ipynb', 'Cell Magics.ipynb', 'Custom Display Logic.ipynb', 'Index.ipynb', 'Old Custom Display Logic.ipynb', 'Plotting in the Notebook.ipynb', 'Raw Input in the Notebook.ipynb', 'Rich Output.ipynb', 'Script Magics.ipynb', 'SymPy.ipynb', 'Terminal Usage.ipynb', 'Third Party Rich Output.ipynb', 'Trapezoid Rule.ipynb', 'Working With External Code.ipynb', '__pycache__', 'data', 'example-demo.py', 'gui', 'ipython-completion.bash', 'ipython-get-history.py', 'ipython-qtconsole.desktop', 'ipython.desktop', 'mod.py', 'test.txt']\n"
521 523 ]
522 524 }
523 525 ],
524 526 "source": [
525 527 "files = !ls\n",
526 "print \"My current directory's files:\"\n",
527 "print files"
528 "print(\"My current directory's files:\")\n",
529 "print(files)"
528 530 ]
529 531 },
530 532 {
@@ -538,7 +540,7 b''
538 540 "name": "stdout",
539 541 "output_type": "stream",
540 542 "text": [
541 "[BackgroundJobs.ipynb, Custom Display Logic.ipynb, Customizing IPython - Condensed.ipynb, Customizing IPython - Config.ipynb, Customizing IPython - Extensions.ipynb, Customizing IPython - Magics.ipynb, data, figs, flare.json, Index.ipynb, Interactive Widgets.ipynb, IPython - beyond plain Python.ipynb, kernel-embedding, Markdown Cells.ipynb, myscript.py, nbconvert_arch.png, NbConvert from command line.ipynb, NbConvert Python library.ipynb, Notebook and javascript extension.ipynb, Notebook Basics.ipynb, Overview of IPython.parallel.ipynb, parallel, Rich Display System.ipynb, Running a Secure Public Notebook.ipynb, Running Code.ipynb, Sample.ipynb, soln, Terminal usage.ipynb, text_analysis.py, Typesetting Math Using MathJax.ipynb]\r\n"
543 "[Animations Using clear_output.ipynb, Background Jobs.ipynb, Beyond Plain Python.ipynb, Capturing Output.ipynb, Cell Magics.ipynb, Custom Display Logic.ipynb, Index.ipynb, Old Custom Display Logic.ipynb, Plotting in the Notebook.ipynb, Raw Input in the Notebook.ipynb, Rich Output.ipynb, Script Magics.ipynb, SymPy.ipynb, Terminal Usage.ipynb, Third Party Rich Output.ipynb, Trapezoid Rule.ipynb, Working With External Code.ipynb, __pycache__, data, example-demo.py, gui, ipython-completion.bash, ipython-get-history.py, ipython-qtconsole.desktop, ipython.desktop, mod.py, test.txt]\r\n"
542 544 ]
543 545 }
544 546 ],
@@ -557,7 +559,7 b''
557 559 "name": "stdout",
558 560 "output_type": "stream",
559 561 "text": [
560 "BACKGROUNDJOBS.IPYNB\r\n"
562 "ANIMATIONS USING CLEAR_OUTPUT.IPYNB\r\n"
561 563 ]
562 564 }
563 565 ],
@@ -574,7 +576,7 b''
574 576 },
575 577 {
576 578 "cell_type": "code",
577 "execution_count": 27,
579 "execution_count": 22,
578 580 "metadata": {
579 581 "collapsed": false
580 582 },
@@ -583,36 +585,33 b''
583 585 "name": "stdout",
584 586 "output_type": "stream",
585 587 "text": [
586 "00 - BackgroundJobs\r\n",
587 "01 - Custom Display Logic\r\n",
588 "02 - Customizing IPython - Condensed\r\n",
589 "03 - Customizing IPython - Config\r\n",
590 "04 - Customizing IPython - Extensions\r\n",
591 "05 - Customizing IPython - Magics\r\n",
588 "00 - Animations Using clear_output\n",
589 "01 - Background Jobs\n",
590 "02 - Beyond Plain Python\n",
591 "03 - Capturing Output\n",
592 "04 - Cell Magics\n",
593 "05 - Custom Display Logic\n",
594 "06 - Index\n",
595 "07 - Old Custom Display Logic\n",
596 "08 - Plotting in the Notebook\n",
597 "09 - Raw Input in the Notebook\n",
598 "10 - Rich Output\n",
599 "11 - Script Magics\n",
600 "12 - SymPy\n",
601 "13 - Terminal Usage\n",
602 "14 - Third Party Rich Output\n",
603 "15 - Trapezoid Rule\n",
604 "16 - Working With External Code\n",
592 605 "--\n",
593 606 "--\n",
594 607 "--\n",
595 "09 - Index\r\n",
596 "10 - Interactive Widgets\r\n",
597 "11 - IPython - beyond plain Python\r\n",
598 608 "--\n",
599 "13 - Markdown Cells\r\n",
600 609 "--\n",
601 610 "--\n",
602 "16 - NbConvert from command line\r\n",
603 "17 - NbConvert Python library\r\n",
604 "18 - Notebook and javascript extension\r\n",
605 "19 - Notebook Basics\r\n",
606 "20 - Overview of IPython.parallel\r\n",
607 611 "--\n",
608 "22 - Rich Display System\r\n",
609 "23 - Running a Secure Public Notebook\r\n",
610 "24 - Running Code\r\n",
611 "25 - Sample\r\n",
612 612 "--\n",
613 "27 - Terminal usage\r\n",
614 613 "--\n",
615 "29 - Typesetting Math Using MathJax\r\n"
614 "--\n"
616 615 ]
617 616 }
618 617 ],
@@ -622,7 +621,7 b''
622 621 " if f.endswith('ipynb'):\n",
623 622 " !echo {\"%02d\" % i} - \"{os.path.splitext(f)[0]}\"\n",
624 623 " else:\n",
625 " print '--'"
624 " print('--')"
626 625 ]
627 626 },
628 627 {
@@ -645,7 +644,7 b''
645 644 },
646 645 {
647 646 "cell_type": "code",
648 "execution_count": 28,
647 "execution_count": 23,
649 648 "metadata": {
650 649 "collapsed": false
651 650 },
@@ -663,7 +662,7 b''
663 662 },
664 663 {
665 664 "cell_type": "code",
666 "execution_count": 29,
665 "execution_count": 24,
667 666 "metadata": {
668 667 "collapsed": false
669 668 },
@@ -672,17 +671,17 b''
672 671 "name": "stdout",
673 672 "output_type": "stream",
674 673 "text": [
675 "10000000 loops, best of 3: 190 ns per loop\n"
674 "10000 loops, best of 3: 19.3 Β΅s per loop\n"
676 675 ]
677 676 }
678 677 ],
679 678 "source": [
680 "%timeit range(10)"
679 "%timeit list(range(1000))"
681 680 ]
682 681 },
683 682 {
684 683 "cell_type": "code",
685 "execution_count": 30,
684 "execution_count": 25,
686 685 "metadata": {
687 686 "collapsed": false
688 687 },
@@ -691,14 +690,14 b''
691 690 "name": "stdout",
692 691 "output_type": "stream",
693 692 "text": [
694 "1000000 loops, best of 3: 888 ns per loop\n"
693 "100000 loops, best of 3: 2.78 Β΅s per loop\n"
695 694 ]
696 695 }
697 696 ],
698 697 "source": [
699 698 "%%timeit\n",
700 "range(10)\n",
701 "range(100)"
699 "list(range(10))\n",
700 "list(range(100))"
702 701 ]
703 702 },
704 703 {
@@ -710,7 +709,7 b''
710 709 },
711 710 {
712 711 "cell_type": "code",
713 "execution_count": 31,
712 "execution_count": 26,
714 713 "metadata": {
715 714 "collapsed": false
716 715 },
@@ -719,20 +718,18 b''
719 718 "name": "stdout",
720 719 "output_type": "stream",
721 720 "text": [
722 "size: 010000000 loops, best of 3: 129 ns per loop\n",
723 " size: 1001000000 loops, best of 3: 649 ns per loop\n",
724 " size: 2001000000 loops, best of 3: 1.09 Β΅s per loop\n",
725 " size: 3001000000 loops, best of 3: 1.74 Β΅s per loop\n",
726 " size: 400100000 loops, best of 3: 2.72 Β΅s per loop\n",
727 "\n"
721 "size: 100 100000 loops, best of 3: 1.86 Β΅s per loop\n",
722 "size: 200 100000 loops, best of 3: 2.49 Β΅s per loop\n",
723 "size: 300 100000 loops, best of 3: 4.04 Β΅s per loop\n",
724 "size: 400 100000 loops, best of 3: 6.21 Β΅s per loop\n"
728 725 ]
729 726 }
730 727 ],
731 728 "source": [
732 "for i in range(5):\n",
729 "for i in range(1, 5):\n",
733 730 " size = i*100\n",
734 " print 'size:',size, \n",
735 " %timeit range(size)"
731 " print('size:', size, end=' ')\n",
732 " %timeit list(range(size))"
736 733 ]
737 734 },
738 735 {
@@ -744,7 +741,7 b''
744 741 },
745 742 {
746 743 "cell_type": "code",
747 "execution_count": 32,
744 "execution_count": 27,
748 745 "metadata": {
749 746 "collapsed": false
750 747 },
@@ -753,20 +750,21 b''
753 750 "name": "stdout",
754 751 "output_type": "stream",
755 752 "text": [
756 "My shell is: /bin/bash\n",
757 "My memory status is:\n",
758 " total used free shared buffers cached\n",
759 "Mem: 7870888 6389328 1481560 0 662860 2505172\n",
760 "-/+ buffers/cache: 3221296 4649592\n",
761 "Swap: 3905532 4852 3900680\n"
753 "My shell is: /usr/local/bin/bash\n",
754 "My disk usage is:\n",
755 "Filesystem Size Used Avail Capacity iused ifree %iused Mounted on\n",
756 "/dev/disk1 233Gi 216Gi 16Gi 94% 56788108 4190706 93% /\n",
757 "devfs 190Ki 190Ki 0Bi 100% 656 0 100% /dev\n",
758 "map -hosts 0Bi 0Bi 0Bi 100% 0 0 100% /net\n",
759 "map auto_home 0Bi 0Bi 0Bi 100% 0 0 100% /home\n"
762 760 ]
763 761 }
764 762 ],
765 763 "source": [
766 764 "%%bash\n",
767 765 "echo \"My shell is:\" $SHELL\n",
768 "echo \"My memory status is:\"\n",
769 "free"
766 "echo \"My disk usage is:\"\n",
767 "df -h"
770 768 ]
771 769 },
772 770 {
@@ -778,7 +776,7 b''
778 776 },
779 777 {
780 778 "cell_type": "code",
781 "execution_count": 33,
779 "execution_count": 28,
782 780 "metadata": {
783 781 "collapsed": false
784 782 },
@@ -787,7 +785,7 b''
787 785 "name": "stdout",
788 786 "output_type": "stream",
789 787 "text": [
790 "Writing test.txt\n"
788 "Overwriting test.txt\n"
791 789 ]
792 790 }
793 791 ],
@@ -801,7 +799,7 b''
801 799 },
802 800 {
803 801 "cell_type": "code",
804 "execution_count": 34,
802 "execution_count": 29,
805 803 "metadata": {
806 804 "collapsed": false
807 805 },
@@ -830,7 +828,7 b''
830 828 },
831 829 {
832 830 "cell_type": "code",
833 "execution_count": 35,
831 "execution_count": 30,
834 832 "metadata": {
835 833 "collapsed": false
836 834 },
@@ -874,21 +872,15 b''
874 872 "bookmark": "OSMagics",
875 873 "cat": "Other",
876 874 "cd": "OSMagics",
877 "cl": "Other",
878 875 "clear": "KernelMagics",
879 "clk": "Other",
880 876 "colors": "BasicMagics",
881 877 "config": "ConfigMagics",
882 878 "connect_info": "KernelMagics",
883 879 "cp": "Other",
884 "d": "Other",
885 "dd": "Other",
886 880 "debug": "ExecutionMagics",
887 881 "dhist": "OSMagics",
888 882 "dirs": "OSMagics",
889 "dl": "Other",
890 "doctest_mode": "KernelMagics",
891 "dx": "Other",
883 "doctest_mode": "BasicMagics",
892 884 "ed": "Other",
893 885 "edit": "KernelMagics",
894 886 "env": "OSMagics",
@@ -922,6 +914,7 b''
922 914 "mkdir": "Other",
923 915 "more": "KernelMagics",
924 916 "mv": "Other",
917 "namespace": "Other",
925 918 "notebook": "BasicMagics",
926 919 "page": "BasicMagics",
927 920 "pastebin": "CodeMagics",
@@ -956,12 +949,15 b''
956 949 "run": "ExecutionMagics",
957 950 "save": "CodeMagics",
958 951 "sc": "OSMagics",
952 "set_env": "OSMagics",
959 953 "store": "StoreMagics",
960 954 "sx": "OSMagics",
961 955 "system": "OSMagics",
962 956 "tb": "ExecutionMagics",
957 "tic": "TimerMagics",
963 958 "time": "ExecutionMagics",
964 959 "timeit": "ExecutionMagics",
960 "toc": "TimerMagics",
965 961 "unalias": "OSMagics",
966 962 "unload_ext": "ExtensionMagics",
967 963 "who": "NamespaceMagics",
@@ -973,7 +969,7 b''
973 969 },
974 970 "text/plain": [
975 971 "Available line magics:\n",
976 "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %cl %clear %clk %colors %config %connect_info %cp %d %dd %debug %dhist %dirs %dl %doctest_mode %dx %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
972 "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %namespace %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %tic %time %timeit %toc %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
977 973 "\n",
978 974 "Available cell magics:\n",
979 975 "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
@@ -981,7 +977,7 b''
981 977 "Automagic is ON, % prefix IS NOT needed for line magics."
982 978 ]
983 979 },
984 "execution_count": 35,
980 "execution_count": 30,
985 981 "metadata": {},
986 982 "output_type": "execute_result"
987 983 }
@@ -1006,7 +1002,7 b''
1006 1002 },
1007 1003 {
1008 1004 "cell_type": "code",
1009 "execution_count": 36,
1005 "execution_count": 31,
1010 1006 "metadata": {
1011 1007 "collapsed": false
1012 1008 },
@@ -1029,13 +1025,13 b''
1029 1025 "... # the sum of two elements defines the next\n",
1030 1026 "... a, b = 0, 1\n",
1031 1027 ">>> while b < 10:\n",
1032 "... print b\n",
1028 "... print(b)\n",
1033 1029 "... a, b = b, a+b"
1034 1030 ]
1035 1031 },
1036 1032 {
1037 1033 "cell_type": "code",
1038 "execution_count": 37,
1034 "execution_count": 32,
1039 1035 "metadata": {
1040 1036 "collapsed": false
1041 1037 },
@@ -1044,13 +1040,13 b''
1044 1040 "name": "stdout",
1045 1041 "output_type": "stream",
1046 1042 "text": [
1047 "0 1 2 3 4 5 6 7 8 9\n"
1043 "0 1 2 3 4 5 6 7 8 9 "
1048 1044 ]
1049 1045 }
1050 1046 ],
1051 1047 "source": [
1052 1048 "In [1]: for i in range(10):\n",
1053 " ...: print i,\n",
1049 " ...: print(i, end=' ')\n",
1054 1050 " ...: "
1055 1051 ]
1056 1052 },
@@ -1063,7 +1059,7 b''
1063 1059 },
1064 1060 {
1065 1061 "cell_type": "code",
1066 "execution_count": 38,
1062 "execution_count": 33,
1067 1063 "metadata": {
1068 1064 "collapsed": false
1069 1065 },
@@ -1072,7 +1068,7 b''
1072 1068 "name": "stdout",
1073 1069 "output_type": "stream",
1074 1070 "text": [
1075 "Writing mod.py\n"
1071 "Overwriting mod.py\n"
1076 1072 ]
1077 1073 }
1078 1074 ],
@@ -1095,7 +1091,7 b''
1095 1091 },
1096 1092 {
1097 1093 "cell_type": "code",
1098 "execution_count": 39,
1094 "execution_count": 34,
1099 1095 "metadata": {
1100 1096 "collapsed": false
1101 1097 },
@@ -1105,10 +1101,11 b''
1105 1101 "evalue": "float division by zero",
1106 1102 "output_type": "error",
1107 1103 "traceback": [
1108 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
1109 "\u001b[1;32m<ipython-input-39-a54c5799f57e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1110 "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1111 "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1104 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
1105 "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
1106 "\u001b[1;32m<ipython-input-40-a54c5799f57e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1107 "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1108 "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1112 1109 "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
1113 1110 ]
1114 1111 }
@@ -1120,7 +1117,7 b''
1120 1117 },
1121 1118 {
1122 1119 "cell_type": "code",
1123 "execution_count": 40,
1120 "execution_count": 35,
1124 1121 "metadata": {
1125 1122 "collapsed": false
1126 1123 },
@@ -1138,9 +1135,9 b''
1138 1135 "output_type": "error",
1139 1136 "traceback": [
1140 1137 "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
1141 " File \u001b[0;32m\"<ipython-input-40-5a5bcec1553f>\"\u001b[0m, line \u001b[0;32m2\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n mod.g(0)\n",
1142 " File \u001b[0;32m\"mod.py\"\u001b[0m, line \u001b[0;32m6\u001b[0m, in \u001b[0;35mg\u001b[0m\n return f(y+1)\n",
1143 "\u001b[1;36m File \u001b[1;32m\"mod.py\"\u001b[1;36m, line \u001b[1;32m3\u001b[1;36m, in \u001b[1;35mf\u001b[1;36m\u001b[0m\n\u001b[1;33m return 1.0/(x-1)\u001b[0m\n",
1138 " File \u001b[0;32m\"<ipython-input-41-8932f4bf53fa>\"\u001b[0m, line \u001b[0;32m2\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n mod.g(0)\n",
1139 " File \u001b[0;32m\"/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\"\u001b[0m, line \u001b[0;32m6\u001b[0m, in \u001b[0;35mg\u001b[0m\n return f(y+1)\n",
1140 "\u001b[1;36m File \u001b[1;32m\"/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\"\u001b[1;36m, line \u001b[1;32m3\u001b[1;36m, in \u001b[1;35mf\u001b[1;36m\u001b[0m\n\u001b[1;33m return 1.0/(x-1)\u001b[0m\n",
1144 1141 "\u001b[1;31mZeroDivisionError\u001b[0m\u001b[1;31m:\u001b[0m float division by zero\n"
1145 1142 ]
1146 1143 }
@@ -1152,7 +1149,7 b''
1152 1149 },
1153 1150 {
1154 1151 "cell_type": "code",
1155 "execution_count": 41,
1152 "execution_count": 36,
1156 1153 "metadata": {
1157 1154 "collapsed": false
1158 1155 },
@@ -1169,10 +1166,11 b''
1169 1166 "evalue": "float division by zero",
1170 1167 "output_type": "error",
1171 1168 "traceback": [
1172 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
1173 "\u001b[1;32m<ipython-input-41-81967cfaa0c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'xmode verbose'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[1;36mglobal\u001b[0m \u001b[0;36mmod.g\u001b[0m \u001b[1;34m= <function g at 0x237fc08>\u001b[0m\n",
1174 "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y=0)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[1;36mglobal\u001b[0m \u001b[0;36mf\u001b[0m \u001b[1;34m= <function f at 0x2367c08>\u001b[0m\u001b[1;34m\n \u001b[0m\u001b[0;36my\u001b[0m \u001b[1;34m= 0\u001b[0m\n",
1175 "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x=1)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[0;36mx\u001b[0m \u001b[1;34m= 1\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1169 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
1170 "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
1171 "\u001b[1;32m<ipython-input-42-539f73e80e01>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'xmode verbose'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[1;36mglobal\u001b[0m \u001b[0;36mmod.g\u001b[0m \u001b[1;34m= <function g at 0x105887d08>\u001b[0m\n",
1172 "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y=0)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[1;36mglobal\u001b[0m \u001b[0;36mf\u001b[0m \u001b[1;34m= <function f at 0x105887d90>\u001b[0m\u001b[1;34m\n \u001b[0m\u001b[0;36my\u001b[0m \u001b[1;34m= 0\u001b[0m\n",
1173 "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x=1)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[0;36mx\u001b[0m \u001b[1;34m= 1\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1176 1174 "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
1177 1175 ]
1178 1176 }
@@ -1191,7 +1189,7 b''
1191 1189 },
1192 1190 {
1193 1191 "cell_type": "code",
1194 "execution_count": 42,
1192 "execution_count": 37,
1195 1193 "metadata": {
1196 1194 "collapsed": false
1197 1195 },
@@ -1217,7 +1215,7 b''
1217 1215 },
1218 1216 {
1219 1217 "cell_type": "code",
1220 "execution_count": 43,
1218 "execution_count": 38,
1221 1219 "metadata": {
1222 1220 "collapsed": false
1223 1221 },
@@ -1238,7 +1236,7 b''
1238 1236 },
1239 1237 {
1240 1238 "cell_type": "code",
1241 "execution_count": 44,
1239 "execution_count": 39,
1242 1240 "metadata": {
1243 1241 "collapsed": false
1244 1242 },
@@ -1261,33 +1259,6 b''
1261 1259 "cell_type": "markdown",
1262 1260 "metadata": {},
1263 1261 "source": [
1264 "### Exercise"
1265 ]
1266 },
1267 {
1268 "cell_type": "markdown",
1269 "metadata": {},
1270 "source": [
1271 "Write a cell that executes in Bash and prints your current working directory as well as the date.\n",
1272 "\n",
1273 "Apologies to Windows users who may not have Bash available, not sure how to obtain the equivalent result with `cmd.exe` or Powershell."
1274 ]
1275 },
1276 {
1277 "cell_type": "code",
1278 "execution_count": null,
1279 "metadata": {
1280 "collapsed": false
1281 },
1282 "outputs": [],
1283 "source": [
1284 "%load soln/bash-script"
1285 ]
1286 },
1287 {
1288 "cell_type": "markdown",
1289 "metadata": {},
1290 "source": [
1291 1262 "## Raw Input in the notebook"
1292 1263 ]
1293 1264 },
@@ -1300,7 +1271,7 b''
1300 1271 },
1301 1272 {
1302 1273 "cell_type": "code",
1303 "execution_count": 45,
1274 "execution_count": 40,
1304 1275 "metadata": {
1305 1276 "collapsed": false
1306 1277 },
@@ -1310,10 +1281,11 b''
1310 1281 "evalue": "float division by zero",
1311 1282 "output_type": "error",
1312 1283 "traceback": [
1313 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
1314 "\u001b[1;32m<ipython-input-45-5e708f13c839>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1315 "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1316 "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1284 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
1285 "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
1286 "\u001b[1;32m<ipython-input-46-5e708f13c839>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1287 "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1288 "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1317 1289 "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
1318 1290 ]
1319 1291 }
@@ -1324,7 +1296,7 b''
1324 1296 },
1325 1297 {
1326 1298 "cell_type": "code",
1327 "execution_count": 38,
1299 "execution_count": 41,
1328 1300 "metadata": {
1329 1301 "collapsed": false
1330 1302 },
@@ -1333,24 +1305,40 b''
1333 1305 "name": "stdout",
1334 1306 "output_type": "stream",
1335 1307 "text": [
1336 "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[0;34m()\u001b[0m\n",
1337 "\u001b[0;32m 2 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1338 "\u001b[0m\u001b[0;32m----> 3 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1339 "\u001b[0m\u001b[0;32m 4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
1308 "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n",
1309 "\u001b[1;32m 2 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1310 "\u001b[0m\u001b[1;32m----> 3 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1311 "\u001b[0m\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
1340 1312 "\u001b[0m\n",
1341 "ipdb> x\n",
1342 "1\n",
1343 1313 "ipdb> up\n",
1344 "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[0;34m()\u001b[0m\n",
1345 "\u001b[0;32m 4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
1346 "\u001b[0m\u001b[0;32m 5 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1347 "\u001b[0m\u001b[0;32m----> 6 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1314 "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[1;34m()\u001b[0m\n",
1315 "\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
1316 "\u001b[0m\u001b[1;32m 5 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1317 "\u001b[0m\u001b[1;32m----> 6 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1348 1318 "\u001b[0m\n",
1349 "ipdb> y\n",
1350 "0\n",
1351 "ipdb> up\n",
1352 "> \u001b[0;32m<ipython-input-37-5e708f13c839>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
1353 "\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1319 "ipdb> down\n",
1320 "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n",
1321 "\u001b[1;32m 2 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1322 "\u001b[0m\u001b[1;32m----> 3 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1323 "\u001b[0m\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
1324 "\u001b[0m\n",
1325 "ipdb> bt\n",
1326 " \u001b[1;32m<ipython-input-46-5e708f13c839>\u001b[0m(1)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n",
1327 "\u001b[1;32m----> 1 \u001b[1;33m\u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1328 "\u001b[0m\n",
1329 " \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[1;34m()\u001b[0m\n",
1330 "\u001b[0;32m 2 \u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1331 "\u001b[0;32m 3 \u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1332 "\u001b[0;32m 4 \u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1333 "\u001b[0;32m 5 \u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1334 "\u001b[1;32m----> 6 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1335 "\u001b[0m\n",
1336 "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n",
1337 "\u001b[1;32m 1 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
1338 "\u001b[0m\u001b[1;32m 2 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1339 "\u001b[0m\u001b[1;32m----> 3 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1340 "\u001b[0m\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
1341 "\u001b[0m\u001b[1;32m 5 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
1354 1342 "\u001b[0m\n",
1355 1343 "ipdb> exit\n"
1356 1344 ]
@@ -1369,7 +1357,7 b''
1369 1357 },
1370 1358 {
1371 1359 "cell_type": "code",
1372 "execution_count": 39,
1360 "execution_count": 42,
1373 1361 "metadata": {
1374 1362 "collapsed": false
1375 1363 },
@@ -1378,14 +1366,14 b''
1378 1366 "name": "stdout",
1379 1367 "output_type": "stream",
1380 1368 "text": [
1381 "Are you enjoying this tutorial ?Yes !\n",
1382 "enjoy is : Yes !\n"
1369 "Are you enjoying this tutorial? yes\n",
1370 "enjoy is: yes\n"
1383 1371 ]
1384 1372 }
1385 1373 ],
1386 1374 "source": [
1387 "enjoy = raw_input('Are you enjoying this tutorial ?')\n",
1388 "print 'enjoy is :', enjoy"
1375 "enjoy = input('Are you enjoying this tutorial? ')\n",
1376 "print('enjoy is:', enjoy)"
1389 1377 ]
1390 1378 },
1391 1379 {
@@ -1404,7 +1392,7 b''
1404 1392 },
1405 1393 {
1406 1394 "cell_type": "code",
1407 "execution_count": 46,
1395 "execution_count": 43,
1408 1396 "metadata": {
1409 1397 "collapsed": false
1410 1398 },
@@ -1415,7 +1403,7 b''
1415 1403 },
1416 1404 {
1417 1405 "cell_type": "code",
1418 "execution_count": 47,
1406 "execution_count": 44,
1419 1407 "metadata": {
1420 1408 "collapsed": false
1421 1409 },
@@ -1427,7 +1415,7 b''
1427 1415 },
1428 1416 {
1429 1417 "cell_type": "code",
1430 "execution_count": 48,
1418 "execution_count": 45,
1431 1419 "metadata": {
1432 1420 "collapsed": false
1433 1421 },
@@ -1435,296 +1423,1008 b''
1435 1423 {
1436 1424 "data": {
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1921 "dW/kzymdG8bQOHFwbwiv8g2vUu28IryygbHBccTW4UZ4hVEQXkWsqJtbJL1T0iFJb2mr8n8LXBKA\n",
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1950 "PyLpK9qq/N2ghQEYiw+v6LxKDyODA2qr8kpRN09Jul3d3ite5+UQXnXBp5RReOU6QX24Q3hlmLs3\n",
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1952 "PUn/p6R/MZlM/tmANa+sqJuXqztN8KfV/cP+JUl/g+AKyMpfuutLg1aBIXDS4PAYHVwd4VWenVc3\n",
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1954 "P/wbk8nkPZL+WNI3S/qpBb/FD6pbwPlfTqdTf8Pw7slk8kFJPzuZTN4xnU7/dKDyl1LUzVFJ36tu\n",
1955 "NPCAujff39hW5b8JWReAIB5y15cUdbM/wZuKnHHS4PA4cXAFRd0clnRY0kXlfSORY3iV6r4rKb2F\n",
1956 "7TGPDEqMDY4llvDKn4Z4NmgVyEKIzqtvkPTbW4IrSdJ0Ov1zSW9XN2I302QyOSTpH6gbEdz+ne63\n",
1957 "qrtJ/Ppeq11BUTcHirp5jaT3S3qduuDqrZL+KsEVkKe2Ks+pGxfeJ+lFgctBvxgbHB4nDq5mo+vK\n",
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1959 "Xz+dTq9Kerekz917patxodWrJX1A0lvU7fFqJX16W5Vf21bl42PXBMAUPzrI0va0MDY4PMYGV8PI\n",
1960 "YCfH8CqHzivCKxsYGxwH4RWwzdhjg3ep+wv+/jk//xfuep82v9u61cvcdd6v/4Ckr9t1dSsq6uZu\n",
1961 "Sa9xH/Nu98PvV7dM/h1tVV4ZqxYApj2oLrhn71VaGBscHp1Xq/Hh1amFz0qfD6+OBa1iXKmeNCgR\n",
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1964 "oPMqHrGHVxuhSlE3a5GNKLPzqn+EVxjN2OGV/6Izb6Gb/0s/r837JknnFvz+W399L/+A3PLTT5H0\n",
1965 "30j6W+6/A+6nL0l6p6Q3SXpvZJ+8AYyHscE0MTY4PBa2r4axQUltVV4p6uZZde8Hb9ZmCJqylMOr\n",
1966 "mDt9Zok6vGqr8lJRN+fVnZp4SIvvzaxhbLB/hFcYzdjhlf/Oyfqcn/d/+Z+Z8/PPqTtFZ56dfv1c\n",
1967 "Rd28Qt0Xk5sl3aPuJvM+SX9V0sEtT70i6T9I+hVJ72yr8tFVPxaA7DzornRepYWxweGx82o1hFeb\n",
1968 "TqkLr24R4VXs6Lyy53l1wdVRRRJeFXWzprjGBgmvgG3GDq9Ouuu8HQS+42rem4yTkvYvGAu8RdKF\n",
1969 "XY4M/sqcH78q6c8k/Z6k35f07rYqn9jF7w8gX4wNJqaom/2Sblf3NYKvCcNhbHA1hFebclvanvJp\n",
1970 "gxudVxGOqc2SSnh1Ql0n06w9xRYdlrQm6XwkK14Ir4Btxg6vHlU3MvhJ6k4W3O6T3PXBGT8nSQ+4\n",
1971 "6ydK+pM5v/6BGT++jF9V90XkeUkPu9/nAUl/0Vblyp1cALDFI5IuSrqjqJsb26qM4Tt+WOw2dW+C\n",
1972 "n8hkn04ojA2uhvBqU27hVbKdV25M7ay6yY0jiqNrZpEUwitfu/Vl4lvFNDIosbAduM6o4dV0Or00\n",
1973 "mUx+V93C8zfMeMoXS/rz6XQ677vYf6yudfhLtC28mkwma5K+UNJv7qa2tipfsZtfBwA7aavyclE3\n",
1974 "D0m6X9K9kv5T0ILQB0YGx8HY4GoIrzYRXqXlOXXh1U0ivLLA/z+IaQ9ZTCODEp1XwHVuCPAx3yzp\n",
1975 "8yaTyd/a+oOTyeSvSPpK9/P+x26ZTCYb+7Gm0+k5ST8v6Vsmk8kdutZXqxvJectAdQPAXjA6mBZO\n",
1976 "GhzH0+pGM4+5UU0sRni1Kdfw6rmFz4qXD3pS2HuVQngVc+cV4VW//L064RUGN/obwel0+iuTyeRd\n",
1977 "kt41mUx+RNKfSnqZpO+W9EeS3ihJk8nkRnXjg09oc5xQkr5PXYfV/z2ZTH5U3SjiZ0r6dkn/fDqd\n",
1978 "vm+sPwsArIATB9PCSYMjcF2LT6vbrXJc7BfbCeHVJh9ezduzmpocOq+kuDp95kkhvIqx8yq2scFY\n",
1979 "witf39mgVSALITqvpK7D6sclvVrSu9QFT78k6Qum06lfoHdRXTD1ka2/cDqdnpL02ZJ+R9IPSnqn\n",
1980 "pL8j6Tum0+nrRqkeAFbHiYNpYWxwPIwOLo/walNunVcpL2yX0jpxMKXwKqbOK8YGh8HYIEYTpAV/\n",
1981 "Op1ekvRD7r95z7kg6a/P+bmPS3rNMNUBwCAYG0wLY4PjeVLdvjiWtu+M8GpTbuFVLp1XhFc2MDY4\n",
1982 "PMIrYJtQnVcAkBvfecXYYBoYGxyP77wivNqZD69OBa3CBsKrtBBe2cLY4PDMd7cVdbMmxgYxIsIr\n",
1983 "ABjHRueV+2KPuDE2OB7GBpdQ1M1BdTcRl5Xu0u5V+ACP8CoNLGy3xXywMgNjg/07qC5PuNhW5cXQ\n",
1984 "xSB9hFcAMI6n1d1UHBUdJClgbHA8T7or/24W84vJT7VVeTVoJTbk2nmVanDJwnZbGBscXgzhFSOD\n",
1985 "GBXhFQCMwN1MMjqYDsYGx8PY4HLYd3WtbMIr13V3SF3XXaqjO4wN2sLY4PAIr4BtCK8AYDwsbU9A\n",
1986 "UTeHJJ1Qd6P41A5Px94xNrgcwqtr+fDq2MJnpWHjpMGEu+4Ir2yJsfMqtrHBc+66XtSN1Xt2wiuM\n",
1987 "yuo/BABIkQ+v6LyK2wvc9bG2Kq8ErSQPjA0uh/DqWtl0Xin9fVcS4ZU1MXdeRRFeufcXvpPycMha\n",
1988 "FiC8wqgIrwBgPH5skM6ruLHvalyMDS6H8OpahFdpSSK8KurmgLoRzyva7KyJUYwL22MbG5Tsv86E\n",
1989 "VxgV4RUAjIexwTQQXo2LscHlEF5d67S6gGDdBQYpyyG8SuW0QR9CnI58xJOxwXFY33u17q6EVxgF\n",
1990 "4RUAjIexwTTc5a6EV+NgbHA5hFdbuGDAhzmpd1+lftKglM5pgymMDEqMDY7Fenjl60r1oAgYQ3gF\n",
1991 "AON5yF1fUtTNvpCFYE/ovBrXSXc9YXhprQWEV9c75a65hFcpd14lMTao9MKrmDqvYhwbjCW8iuk1\n",
1992 "RcR4EwgAI2mr8qykRyXtl/SiwOVg9wivRtRW5QV1N677lH4IsRc+vDq18Fl5yWXv1cZpg0GrGBbh\n",
1993 "lS2+/pg6rxgb7B/hFUZFeAUA42J0MH6EV+NjdHBndF5dL5fwis6reKQSXsXceRVTeGX9dSa8wqgI\n",
1994 "rwBgXJw4GD8fXj0atIq8cOLgzo65K+HVJh9eHVv4rPjlEF6lsrA9lfAqxoXtjA32j/AKoyK8AoBx\n",
1995 "ceJg/Oi8Gh8nDu6Mzqvr0XmVjo0xtaJu1oJWsjephFfnJF2VdDiiHZ6MDfaP8AqjIrwCgHExNhgx\n",
1996 "d9NEeDU+xgZ3Rnh1vdzCq2RPG2yr8pK6E83WZPdGfhlJhFfuNE/rI23bxTg2SHgFbEF4BQDjYmww\n",
1997 "bjdJWpf0fFuVUd98RIaxwZ0RXl0vt/Aq5c4rKY29V0mEV05so4OMDfaP8AqjIrwCgHExNhi3u9yV\n",
1998 "rqtx+c4rxgZnKOpmv7ob+qtKP8BYRS7hVQ6nDUpphVcpdMn5DibzJw66rmnGBvtHeIVREV4BwLg+\n",
1999 "JumipDuLurH6ZgTzMTIYBp1Xi/mF5KfaqrwStBJbcgmv6LyKB51XYRxWN3J6vq3Ky6GLWYH10UzC\n",
2000 "K4yK8AoARuTeNH3YPbw3YCnYHcKrMFjYvhgjg7MRXqVlY2l70Cr2JqXwKprOK8U5MijZ77xad9ez\n",
2001 "QatANgivAGB8jA7Gi/AqDBa2L0Z4NdspdyW8SgOdV7ZY7wraKsaRQcl+eEXnFUZFeAUA4/NL2zlx\n",
2002 "MD4+vHo0aBX5YWxwMcKr2XLrvEphj9IihFe2xDQ2GONJgxLhFXANwisAGB+dV/Gi8yqSsiF5AAAg\n",
2003 "AElEQVQMxgYX8+HVqYXPyk/y4ZVbRO3DHMIr+1IKrxgbHB7hFbAF4RUAjI/wKl6EV2FsdF65m3Vc\n",
2004 "i86r2ZIPr9TdlK9JOtNW5aXQxQyM8MoWxgaHZ/01JrzCqAivAGB8jA3Gi/AqgLYqz6hbCHtQdt/E\n",
2005 "h+RPGyS8ulYO4VUu+66kzcCH8MoGxgaHR+cVsAXhFQCMb6Pzii6S6NzlroRX42N0cD46r2Z7XtJl\n",
2006 "SetF3RwIXcxAcgqvfOdVDGNq86QUXjE2ODzCK2ALwisAGN9JdW/Cb5J0InAtWFJRN/sk3e4ePh6y\n",
2007 "lkxx4uB8hFcztFV5VZuhTqrdVzmGV3Re2RBT51WsY4OEV8AWhFcAMDJ3Q8XoYHxuV/d188m2Ki+G\n",
2008 "LiZDnDg4H+HVfKmPDuZy0qBEeGVNjJ1XhFc9cZMDvq6zIWtBPgivACAMlrbHx++7ejRoFflibHA+\n",
2009 "wqv5Ug+vfJBD55VxRd3coHjH12axvkx8q1hfd7PhlaTD7nq+rcrLQStBNgivACAMwqv4sKw9LMYG\n",
2010 "5yO8ms+HV8cWPitejA3GY2N0ra3KK0Er6Qdjg8OzHBCuu2tsgSAiRngFAGEwNhgfwquwGBucj/Bq\n",
2011 "vtQ7r3IKr3xYEsOY2iwpjQxKjA2OwY/jHTF4wA8jgxgd4RUAhEHnVXwIr8JibHA+wqv5CK/SEXvn\n",
2012 "VWrhVUydV1GODboOvXPu4eFFzw2AZe0YHeEVAIThwys6r+JBeBUWY4MzuFMwfTDzzKLnZorwKh2E\n",
2013 "V7bE1HkV69igZHfvFeEVRkd4BQBhPOSuL3E3n7CP8CosxgZn86HMsyzNnSmX8IrTBu1LNbyKqfOK\n",
2014 "8Ko/hFcYHeEVAATQVuUZdSHIAUl3By4Hy7nLXQmvwvCdV4wNXouRwcVSD69yOm1wY+eVwf0/y0gt\n",
2015 "vGJscByEV4BDeAUA4TA6GBc6r8Ki82o2f4oe4dVsqYdX2YwNus7Cs5LWFEdgsl1q4RVjg+Ow2uFG\n",
2016 "eIXREV4BQDj+xEGWtsfBh1ePBq0iX4RXs9F5tRjhVVr86GAMgcl2qYZXN0bQCcfYYP8IrzA6wisA\n",
2017 "CIcTByNR1M0RdTeJFySdClxOrp6TdFHdjZK1U5dCIrxajPAqLTHvvUoqvGqr8pKk8+ruJw8FLmcn\n",
2018 "jA32j/AKoyO8AoBwfOcVY4P23eGuH2+r8mrQSjLlXne6r65HeLUY4VVaCK9siWV0MOaxQcIrwCG8\n",
2019 "AoBw6LyKB/uubCC8uh7h1WK+UzL18CqH0wYlwitrYlnazthg/wivMDrCKwAIh/AqHoRXNnDi4PUI\n",
2020 "rxZLvfMqp9MGpc2whPDKhlg6r2IeG7S6sH3dXWN8TREpwisACOdhSZck3VXUzfpOT0ZQhFc20Hl1\n",
2021 "PcKrxZINr4q6OSjpsCR/Cl8OWNhui9VgZYNbJs/YYP98Pbl87oEBhFcAEIg79vvD7uG9AUvBzu5y\n",
2022 "V8KrsAivrkd4tdjz6sKd9aJuDoQupmcbXVcZ7eJjbNCWGMYGD0tak3Teve+KjfXwis4rjIbwCgDC\n",
2023 "YnQwDnRe2cDY4PUIrxZwoY4fqUut+yq3Ze0S4ZU1MYwNxjwyKBFeARsIrwAgLE4cjIMPrx4NWgXo\n",
2024 "vLqeD69OLXxW3lIdHSS8ikvK4ZXlzquYRwYlwitgA+EVAIRF51Uc6LyygfDqenRe7SzV8MoHOLmc\n",
2025 "NCgRXlnj/ywxdF4RXvWL8AqjI7wCgLAIr+JAeGUDY4PXO+auhFfzpRpe5dh5FUNYMk+K4VUMnVex\n",
2026 "jw1afY0JrzA6wisACIuxQePcSUU+vHosZC2g82qrom5u0GZ4xdjgfIRX6aDzypYYFrYzNjgMwiuM\n",
2027 "jvAKAMLa6LxyIQnsOS7pgKRn2qrkSOiwCK+udZO693Kn26q8GLoYwwiv0kF4ZUtMC9sJr/pFeIXR\n",
2028 "EV4BQFhPqXsje7M2d9fAFkYG7WBs8Frsu1oO4VU6ogyv3DenfMATa4gyi9WRtq1iHxskvAIcwisA\n",
2029 "CMgd487ooG2EV3acknRF0i1F3ewPXYwBhFfLIbxKR5ThlaRDkvZJOp9YlyRjg8OzGl6tuyvhFUZD\n",
2030 "eAUA4bG03ba73PXRoFVAbVVekXTSPTwRshYjCK+Wk2p4leNpgz4siS28SnFkUGJscAxWu9t8mMY6\n",
2031 "BYyG8AoAwiO8so3OK1v83itGBwmvlpVqeJVz55XlsGSWVMOrGDqvGBscBmODGB3hFQCEx9igbYRX\n",
2032 "trC0fRPh1XIIr9IR69hgquEVnVfDMxdeuZNu/dggnVcYDeEVAIRH55VthFe2+KXthFeEV8sivEqH\n",
2033 "D3+ORnZCb+rhVQydV4RX/TnsrufcOD8wCsIrAAjPd14RXtlEeGULY4ObfHh1KmgV9hFeJaKtysvq\n",
2034 "bubXZDsw2S7V8CqmscFYwyvf2XTEUGDLyCCCILwCgPAectd7i7rZF7IQzER4ZQtjg5vovFoO4VVa\n",
2035 "YhwdTDW8YmxwYG1VXpJ0Qd19+8HA5XiEVwiC8AoAAmur8oykxyQdkPTCwOXgeoRXtjA2uOmYuxJe\n",
2036 "LZZqeJXjaYPSltHBoFWsJvXwis6rYVl7nQmvEAThFQDYwNJ2g4q6OaBuPO2KNkMThMXY4CY6r5aT\n",
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2038 "cuu8Iryy45ykq5IOG157QHjVP8IrBEF4BQA2EF7ZxMigPYwNbiK8WkJblVeVXvfVjeqWlp9xO3Fy\n",
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2414 "kiQ1luGVJEmSJEmSGsvwSpIkSZIkSY1leCVJkiRJkqTGMrySJEmSJElSYxleSZIkSZIkqbEMryRJ\n",
2415 "kiRJktRYhleSJEmSJElqLMMrSZIkSZIkNZbhlSRJkiRJkhrL8EqSJEmSJEmNZXglSZIkSZKkxvr/\n",
2416 "gUJ07DcuELgAAAAASUVORK5CYII=\n"
1722 2417 ],
1723 2418 "text/plain": [
1724 "<matplotlib.figure.Figure at 0x3436950>"
2419 "<matplotlib.figure.Figure at 0x106c2a9b0>"
1725 2420 ]
1726 2421 },
1727 "metadata": {},
2422 "metadata": {
2423 "image/png": {
2424 "height": 392,
2425 "width": 599
2426 }
2427 },
1728 2428 "output_type": "display_data"
1729 2429 }
1730 2430 ],
@@ -1745,7 +2445,7 b''
1745 2445 },
1746 2446 {
1747 2447 "cell_type": "code",
1748 "execution_count": 43,
2448 "execution_count": 46,
1749 2449 "metadata": {
1750 2450 "collapsed": false
1751 2451 },
@@ -1755,21 +2455,21 b''
1755 2455 "output_type": "stream",
1756 2456 "text": [
1757 2457 "{\n",
1758 " \"stdin_port\": 50023, \n",
1759 " \"ip\": \"127.0.0.1\", \n",
1760 " \"control_port\": 50024, \n",
1761 " \"hb_port\": 50025, \n",
1762 " \"signature_scheme\": \"hmac-sha256\", \n",
1763 " \"key\": \"b54b8859-d64d-48bb-814a-909f9beb3316\", \n",
1764 " \"shell_port\": 50021, \n",
1765 " \"transport\": \"tcp\", \n",
1766 " \"iopub_port\": 50022\n",
2458 " \"stdin_port\": 62401,\n",
2459 " \"key\": \"64c935a7-64e8-4ab7-ab22-6e0f3ff84e02\",\n",
2460 " \"hb_port\": 62403,\n",
2461 " \"transport\": \"tcp\",\n",
2462 " \"signature_scheme\": \"hmac-sha256\",\n",
2463 " \"shell_port\": 62399,\n",
2464 " \"control_port\": 62402,\n",
2465 " \"ip\": \"127.0.0.1\",\n",
2466 " \"iopub_port\": 62400\n",
1767 2467 "}\n",
1768 2468 "\n",
1769 2469 "Paste the above JSON into a file, and connect with:\n",
1770 2470 " $> ipython <app> --existing <file>\n",
1771 2471 "or, if you are local, you can connect with just:\n",
1772 " $> ipython <app> --existing kernel-30f00f4a-230c-4e64-bea5-0e5f6a52cb40.json \n",
2472 " $> ipython <app> --existing kernel-25383540-ce7f-4529-900a-ded0e510d5d8.json \n",
1773 2473 "or even just:\n",
1774 2474 " $> ipython <app> --existing \n",
1775 2475 "if this is the most recent IPython session you have started.\n"
@@ -1789,7 +2489,7 b''
1789 2489 },
1790 2490 {
1791 2491 "cell_type": "code",
1792 "execution_count": 83,
2492 "execution_count": 47,
1793 2493 "metadata": {
1794 2494 "collapsed": false
1795 2495 },
Show file before | Show file after | Hide comments
@@ -16,7 +16,7 b''
16 16 },
17 17 {
18 18 "cell_type": "code",
19 "execution_count": 9,
19 "execution_count": 1,
20 20 "metadata": {
21 21 "collapsed": false
22 22 },
@@ -35,7 +35,7 b''
35 35 },
36 36 {
37 37 "cell_type": "code",
38 "execution_count": 10,
38 "execution_count": 2,
39 39 "metadata": {
40 40 "collapsed": false
41 41 },
@@ -55,7 +55,7 b''
55 55 },
56 56 {
57 57 "cell_type": "code",
58 "execution_count": 11,
58 "execution_count": 3,
59 59 "metadata": {
60 60 "collapsed": false
61 61 },
@@ -68,7 +68,7 b''
68 68 },
69 69 {
70 70 "cell_type": "code",
71 "execution_count": 12,
71 "execution_count": 4,
72 72 "metadata": {
73 73 "collapsed": false
74 74 },
@@ -79,7 +79,7 b''
79 79 "<IPython.utils.capture.CapturedIO at 0x1076c9310>"
80 80 ]
81 81 },
82 "execution_count": 12,
82 "execution_count": 4,
83 83 "metadata": {},
84 84 "output_type": "execute_result"
85 85 }
@@ -97,7 +97,7 b''
97 97 },
98 98 {
99 99 "cell_type": "code",
100 "execution_count": 13,
100 "execution_count": 5,
101 101 "metadata": {
102 102 "collapsed": false
103 103 },
@@ -123,7 +123,7 b''
123 123 },
124 124 {
125 125 "cell_type": "code",
126 "execution_count": 14,
126 "execution_count": 6,
127 127 "metadata": {
128 128 "collapsed": false
129 129 },
@@ -134,7 +134,7 b''
134 134 "'hi, stdout\\n'"
135 135 ]
136 136 },
137 "execution_count": 14,
137 "execution_count": 6,
138 138 "metadata": {},
139 139 "output_type": "execute_result"
140 140 }
@@ -145,7 +145,7 b''
145 145 },
146 146 {
147 147 "cell_type": "code",
148 "execution_count": 15,
148 "execution_count": 7,
149 149 "metadata": {
150 150 "collapsed": false
151 151 },
@@ -156,7 +156,7 b''
156 156 "'hi, stderr\\n'"
157 157 ]
158 158 },
159 "execution_count": 15,
159 "execution_count": 7,
160 160 "metadata": {},
161 161 "output_type": "execute_result"
162 162 }
@@ -174,7 +174,7 b''
174 174 },
175 175 {
176 176 "cell_type": "code",
177 "execution_count": 16,
177 "execution_count": 8,
178 178 "metadata": {
179 179 "collapsed": false
180 180 },
@@ -187,7 +187,7 b''
187 187 },
188 188 {
189 189 "cell_type": "code",
190 "execution_count": 17,
190 "execution_count": 9,
191 191 "metadata": {
192 192 "collapsed": false
193 193 },
@@ -206,7 +206,7 b''
206 206 },
207 207 {
208 208 "cell_type": "code",
209 "execution_count": 18,
209 "execution_count": 10,
210 210 "metadata": {
211 211 "collapsed": false
212 212 },
@@ -395,7 +395,7 b''
395 395 },
396 396 {
397 397 "cell_type": "code",
398 "execution_count": 19,
398 "execution_count": 11,
399 399 "metadata": {
400 400 "collapsed": false
401 401 },
@@ -416,7 +416,7 b''
416 416 },
417 417 {
418 418 "cell_type": "code",
419 "execution_count": 20,
419 "execution_count": 12,
420 420 "metadata": {
421 421 "collapsed": false
422 422 },
@@ -427,7 +427,7 b''
427 427 "'hi, stdout\\n'"
428 428 ]
429 429 },
430 "execution_count": 20,
430 "execution_count": 12,
431 431 "metadata": {},
432 432 "output_type": "execute_result"
433 433 }
@@ -438,7 +438,7 b''
438 438 },
439 439 {
440 440 "cell_type": "code",
441 "execution_count": 21,
441 "execution_count": 13,
442 442 "metadata": {
443 443 "collapsed": false
444 444 },
@@ -449,7 +449,7 b''
449 449 "''"
450 450 ]
451 451 },
452 "execution_count": 21,
452 "execution_count": 13,
453 453 "metadata": {},
454 454 "output_type": "execute_result"
455 455 }
@@ -460,7 +460,7 b''
460 460 },
461 461 {
462 462 "cell_type": "code",
463 "execution_count": 22,
463 "execution_count": 14,
464 464 "metadata": {
465 465 "collapsed": false
466 466 },
@@ -471,7 +471,7 b''
471 471 "[]"
472 472 ]
473 473 },
474 "execution_count": 22,
474 "execution_count": 14,
475 475 "metadata": {},
476 476 "output_type": "execute_result"
477 477 }
Show file before | Show file after | Hide comments
@@ -11,7 +11,7 b''
11 11 "cell_type": "markdown",
12 12 "metadata": {},
13 13 "source": [
14 "IPython has a system of commands we call 'magics' that provide effectively a mini command language that is orthogonal to the syntax of Python and is extensible by the user with new commands. Magics are meant to be typed interactively, so they use command-line conventions, such as using whitespace for separating arguments, dashes for options and other conventions typical of a command-line environment.\n",
14 "IPython has a system of commands we call 'magics' that provide a mini command language that is orthogonal to the syntax of Python and is extensible by the user with new commands. Magics are meant to be typed interactively, so they use command-line conventions, such as using whitespace for separating arguments, dashes for options and other conventions typical of a command-line environment.\n",
15 15 "\n",
16 16 "Magics come in two kinds:\n",
17 17 "\n",
@@ -46,6 +46,7 b''
46 46 "prun": "ExecutionMagics",
47 47 "pypy": "Other",
48 48 "python": "Other",
49 "python2": "Other",
49 50 "python3": "Other",
50 51 "ruby": "Other",
51 52 "script": "ScriptMagics",
@@ -64,19 +65,20 b''
64 65 "automagic": "AutoMagics",
65 66 "autosave": "KernelMagics",
66 67 "bookmark": "OSMagics",
68 "cat": "Other",
67 69 "cd": "OSMagics",
68 70 "clear": "KernelMagics",
69 71 "colors": "BasicMagics",
70 72 "config": "ConfigMagics",
71 73 "connect_info": "KernelMagics",
74 "cp": "Other",
72 75 "debug": "ExecutionMagics",
73 76 "dhist": "OSMagics",
74 77 "dirs": "OSMagics",
75 "doctest_mode": "KernelMagics",
78 "doctest_mode": "BasicMagics",
76 79 "ed": "Other",
77 80 "edit": "KernelMagics",
78 81 "env": "OSMagics",
79 "gist": "Other",
80 82 "gui": "BasicMagics",
81 83 "hist": "Other",
82 84 "history": "HistoryMagics",
@@ -84,7 +86,11 b''
84 86 "install_ext": "ExtensionMagics",
85 87 "install_profiles": "DeprecatedMagics",
86 88 "killbgscripts": "ScriptMagics",
89 "ldir": "Other",
87 90 "less": "KernelMagics",
91 "lf": "Other",
92 "lk": "Other",
93 "ll": "Other",
88 94 "load": "CodeMagics",
89 95 "load_ext": "ExtensionMagics",
90 96 "loadpy": "CodeMagics",
@@ -93,12 +99,16 b''
93 99 "logstart": "LoggingMagics",
94 100 "logstate": "LoggingMagics",
95 101 "logstop": "LoggingMagics",
102 "ls": "Other",
96 103 "lsmagic": "BasicMagics",
104 "lx": "Other",
97 105 "macro": "ExecutionMagics",
98 106 "magic": "BasicMagics",
99 107 "man": "KernelMagics",
100 108 "matplotlib": "PylabMagics",
109 "mkdir": "Other",
101 110 "more": "KernelMagics",
111 "mv": "Other",
102 112 "notebook": "BasicMagics",
103 113 "page": "BasicMagics",
104 114 "pastebin": "CodeMagics",
@@ -128,17 +138,18 b''
128 138 "rerun": "HistoryMagics",
129 139 "reset": "NamespaceMagics",
130 140 "reset_selective": "NamespaceMagics",
141 "rm": "Other",
142 "rmdir": "Other",
131 143 "run": "ExecutionMagics",
132 144 "save": "CodeMagics",
133 145 "sc": "OSMagics",
146 "set_env": "OSMagics",
134 147 "store": "StoreMagics",
135 148 "sx": "OSMagics",
136 149 "system": "OSMagics",
137 150 "tb": "ExecutionMagics",
138 "tic": "TimerMagics",
139 151 "time": "ExecutionMagics",
140 152 "timeit": "ExecutionMagics",
141 "toc": "TimerMagics",
142 153 "unalias": "OSMagics",
143 154 "unload_ext": "ExtensionMagics",
144 155 "who": "NamespaceMagics",
@@ -150,10 +161,10 b''
150 161 },
151 162 "text/plain": [
152 163 "Available line magics:\n",
153 "%alias %alias_magic %autocall %automagic %autosave %bookmark %cd %clear %colors %config %connect_info %debug %dhist %dirs %doctest_mode %ed %edit %env %gist %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %less %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %lsmagic %macro %magic %man %matplotlib %more %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %run %save %sc %store %sx %system %tb %tic %time %timeit %toc %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
164 "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
154 165 "\n",
155 166 "Available cell magics:\n",
156 "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
167 "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
157 168 "\n",
158 169 "Automagic is ON, % prefix IS NOT needed for line magics."
159 170 ]
@@ -214,7 +225,7 b''
214 225 "name": "stdout",
215 226 "output_type": "stream",
216 227 "text": [
217 "100 loops, best of 3: 6.05 ms per loop\n"
228 "100 loops, best of 3: 7.06 ms per loop\n"
218 229 ]
219 230 }
220 231 ],
@@ -233,7 +244,7 b''
233 244 "name": "stdout",
234 245 "output_type": "stream",
235 246 "text": [
236 "100 loops, best of 3: 6.07 ms per loop\n"
247 "100 loops, best of 3: 7.4 ms per loop\n"
237 248 ]
238 249 }
239 250 ],
@@ -394,13 +405,13 b''
394 405 "name": "stdout",
395 406 "output_type": "stream",
396 407 "text": [
397 "hello from Python 2.7.2 (default, Oct 11 2012, 20:14:37) \n",
398 "[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)]\n"
408 "hello from Python 2.7.9 (default, Jan 29 2015, 06:27:40) \n",
409 "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.56)]\n"
399 410 ]
400 411 }
401 412 ],
402 413 "source": [
403 "%%script python\n",
414 "%%script python2\n",
404 415 "import sys\n",
405 416 "print 'hello from Python %s' % sys.version"
406 417 ]
@@ -416,8 +427,8 b''
416 427 "name": "stdout",
417 428 "output_type": "stream",
418 429 "text": [
419 "hello from Python: 3.3.1 (v3.3.1:d9893d13c628, Apr 6 2013, 11:07:11) \n",
420 "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
430 "hello from Python: 3.4.2 |Continuum Analytics, Inc.| (default, Oct 21 2014, 17:42:20) \n",
431 "[GCC 4.2.1 (Apple Inc. build 5577)]\n"
421 432 ]
422 433 }
423 434 ],
@@ -447,7 +458,7 b''
447 458 "name": "stdout",
448 459 "output_type": "stream",
449 460 "text": [
450 "Hello from Ruby 1.9.3\n"
461 "Hello from Ruby 2.0.0\n"
451 462 ]
452 463 }
453 464 ],
@@ -480,45 +491,6 b''
480 491 "cell_type": "markdown",
481 492 "metadata": {},
482 493 "source": [
483 "## Exercise: write your own script that numbers input lines"
484 ]
485 },
486 {
487 "cell_type": "markdown",
488 "metadata": {},
489 "source": [
490 "Write a file, called `lnum.py`, such that the following cell works as shown (hint: don't forget about the executable bit!): "
491 ]
492 },
493 {
494 "cell_type": "code",
495 "execution_count": 29,
496 "metadata": {
497 "collapsed": false
498 },
499 "outputs": [
500 {
501 "name": "stdout",
502 "output_type": "stream",
503 "text": [
504 "0: my first line\n",
505 "1: my second\n",
506 "2: more\n",
507 "---- END ----\n"
508 ]
509 }
510 ],
511 "source": [
512 "%%script ./lnum.py\n",
513 "my first line\n",
514 "my second\n",
515 "more"
516 ]
517 },
518 {
519 "cell_type": "markdown",
520 "metadata": {},
521 "source": [
522 494 "## Capturing output"
523 495 ]
524 496 },
@@ -531,7 +503,7 b''
531 503 },
532 504 {
533 505 "cell_type": "code",
534 "execution_count": 30,
506 "execution_count": 14,
535 507 "metadata": {
536 508 "collapsed": false
537 509 },
@@ -559,7 +531,7 b''
559 531 },
560 532 {
561 533 "cell_type": "code",
562 "execution_count": 31,
534 "execution_count": 15,
563 535 "metadata": {
564 536 "collapsed": false
565 537 },
@@ -572,7 +544,7 b''
572 544 },
573 545 {
574 546 "cell_type": "code",
575 "execution_count": 32,
547 "execution_count": 16,
576 548 "metadata": {
577 549 "collapsed": false
578 550 },
@@ -612,7 +584,7 b''
612 584 },
613 585 {
614 586 "cell_type": "code",
615 "execution_count": 33,
587 "execution_count": 17,
616 588 "metadata": {
617 589 "collapsed": false
618 590 },
@@ -644,7 +616,7 b''
644 616 },
645 617 {
646 618 "cell_type": "code",
647 "execution_count": 34,
619 "execution_count": 18,
648 620 "metadata": {
649 621 "collapsed": false
650 622 },
@@ -652,10 +624,10 b''
652 624 {
653 625 "data": {
654 626 "text/plain": [
655 "<open file '<fdopen>', mode 'rb' at 0x112cd55d0>"
627 "<_io.BufferedReader name=52>"
656 628 ]
657 629 },
658 "execution_count": 34,
630 "execution_count": 18,
659 631 "metadata": {},
660 632 "output_type": "execute_result"
661 633 }
@@ -666,7 +638,7 b''
666 638 },
667 639 {
668 640 "cell_type": "code",
669 "execution_count": 35,
641 "execution_count": 19,
670 642 "metadata": {
671 643 "collapsed": false
672 644 },
@@ -689,7 +661,7 b''
689 661 }
690 662 ],
691 663 "source": [
692 "print(ruby_lines.read())"
664 "print(ruby_lines.read().decode('utf8'))"
693 665 ]
694 666 }
695 667 ],
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This diff has been collapsed as it changes many lines, (1321 lines changed) Show them Hide them
@@ -90,7 +90,7 b''
90 90 "The main idea of the first approach is that you have to implement special display methods when you define your class, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
91 91 "\n",
92 92 "* `_repr_html_`: return raw HTML as a string\n",
93 "* `_repr_json_`: return raw JSON as a string\n",
93 "* `_repr_json_`: return a JSONable dict\n",
94 94 "* `_repr_jpeg_`: return raw JPEG data\n",
95 95 "* `_repr_png_`: return raw PNG data\n",
96 96 "* `_repr_svg_`: return raw SVG data as a string\n",
@@ -164,96 +164,134 b''
164 164 {
165 165 "data": {
166 166 "image/png": [
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251 289 ],
252 290 "text/latex": [
253 291 "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
254 292 ],
255 293 "text/plain": [
256 "<__main__.Gaussian at 0x106e7ae10>"
294 "<__main__.Gaussian at 0x10665a4e0>"
257 295 ]
258 296 },
259 297 "execution_count": 4,
@@ -283,96 +321,134 b''
283 321 {
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370 446 ],
371 447 "text/latex": [
372 448 "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
373 449 ],
374 450 "text/plain": [
375 "<__main__.Gaussian at 0x106e7ae10>"
451 "<__main__.Gaussian at 0x10665a4e0>"
376 452 ]
377 453 },
378 454 "metadata": {},
@@ -400,90 +476,128 b''
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762 "AABJRU5ErkJggg==\n"
613 763 ],
614 764 "text/latex": [
615 765 "$\\mathcal{N}(\\mu=0, \\sigma=2),\\ N=2000$"
616 766 ],
617 767 "text/plain": [
618 "<__main__.Gaussian at 0x106e9ce90>"
768 "<__main__.Gaussian at 0x106ab8c18>"
619 769 ]
620 770 },
621 771 "execution_count": 7,
@@ -645,90 +795,128 b''
645 795 {
646 796 "data": {
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831 1055 },
832 1056 "metadata": {},
@@ -876,7 +1100,7 b''
876 1100 {
877 1101 "data": {
878 1102 "text/plain": [
879 "Polynomial([ 1., 2., 3.], [-10., 10.], [-1., 1.])"
1103 "Polynomial([ 1., 2., 3.], [-10., 10.], [-1, 1])"
880 1104 ]
881 1105 },
882 1106 "execution_count": 9,
@@ -1004,23 +1228,23 b''
1004 1228 "name": "stdout",
1005 1229 "output_type": "stream",
1006 1230 "text": [
1007 " text/plain : PlainTextFormatter\n",
1008 " image/jpeg : JPEGFormatter\n",
1009 " text/html : HTMLFormatter\n",
1010 " image/svg+xml : SVGFormatter\n",
1011 1231 " image/png : PNGFormatter\n",
1012 " application/javascript : JavascriptFormatter\n",
1232 " application/pdf : PDFFormatter\n",
1233 " text/html : HTMLFormatter\n",
1234 " image/jpeg : JPEGFormatter\n",
1235 " text/plain : PlainTextFormatter\n",
1013 1236 " text/markdown : MarkdownFormatter\n",
1014 " text/latex : LatexFormatter\n",
1015 1237 " application/json : JSONFormatter\n",
1016 " application/pdf : PDFFormatter\n"
1238 " application/javascript : JavascriptFormatter\n",
1239 " text/latex : LatexFormatter\n",
1240 " image/svg+xml : SVGFormatter\n"
1017 1241 ]
1018 1242 }
1019 1243 ],
1020 1244 "source": [
1021 1245 "ip = get_ipython()\n",
1022 1246 "for mime, formatter in ip.display_formatter.formatters.items():\n",
1023 " print '%24s : %s' % (mime, formatter.__class__.__name__)"
1247 " print('%24s : %s' % (mime, formatter.__class__.__name__))"
1024 1248 ]
1025 1249 },
1026 1250 {
@@ -1062,7 +1286,7 b''
1062 1286 "text": [
1063 1287 "Help on method for_type in module IPython.core.formatters:\n",
1064 1288 "\n",
1065 "for_type(self, typ, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
1289 "for_type(typ, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
1066 1290 " Add a format function for a given type.\n",
1067 1291 " \n",
1068 1292 " Parameters\n",
@@ -1106,7 +1330,7 b''
1106 1330 "text": [
1107 1331 "Help on method for_type_by_name in module IPython.core.formatters:\n",
1108 1332 "\n",
1109 "for_type_by_name(self, type_module, type_name, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
1333 "for_type_by_name(type_module, type_name, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
1110 1334 " Add a format function for a type specified by the full dotted\n",
1111 1335 " module and name of the type, rather than the type of the object.\n",
1112 1336 " \n",
@@ -1150,7 +1374,7 b''
1150 1374 },
1151 1375 {
1152 1376 "cell_type": "code",
1153 "execution_count": 18,
1377 "execution_count": 17,
1154 1378 "metadata": {
1155 1379 "collapsed": false
1156 1380 },
@@ -1169,7 +1393,7 b''
1169 1393 },
1170 1394 {
1171 1395 "cell_type": "code",
1172 "execution_count": 19,
1396 "execution_count": 18,
1173 1397 "metadata": {
1174 1398 "collapsed": false
1175 1399 },
@@ -1180,10 +1404,10 b''
1180 1404 "$P(x)=1+2 x+3 x^2$, $x \\in [-10,\\ 10]$"
1181 1405 ],
1182 1406 "text/plain": [
1183 "Polynomial([ 1., 2., 3.], [-10., 10.], [-1., 1.])"
1407 "Polynomial([ 1., 2., 3.], [-10., 10.], [-1, 1])"
1184 1408 ]
1185 1409 },
1186 "execution_count": 19,
1410 "execution_count": 18,
1187 1411 "metadata": {},
1188 1412 "output_type": "execute_result"
1189 1413 }
@@ -1194,7 +1418,7 b''
1194 1418 },
1195 1419 {
1196 1420 "cell_type": "code",
1197 "execution_count": 20,
1421 "execution_count": 19,
1198 1422 "metadata": {
1199 1423 "collapsed": false
1200 1424 },
@@ -1205,10 +1429,10 b''
1205 1429 "$P(x)=-20+71 x+-15 x^2+x^3$, $x \\in [-1,\\ 1]$"
1206 1430 ],
1207 1431 "text/plain": [
1208 "Polynomial([-20., 71., -15., 1.], [-1., 1.], [-1., 1.])"
1432 "Polynomial([-20., 71., -15., 1.], [-1, 1], [-1, 1])"
1209 1433 ]
1210 1434 },
1211 "execution_count": 20,
1435 "execution_count": 19,
1212 1436 "metadata": {},
1213 1437 "output_type": "execute_result"
1214 1438 }
@@ -1238,7 +1462,7 b''
1238 1462 },
1239 1463 {
1240 1464 "cell_type": "code",
1241 "execution_count": 21,
1465 "execution_count": 20,
1242 1466 "metadata": {
1243 1467 "collapsed": false
1244 1468 },
@@ -1255,7 +1479,7 b''
1255 1479 " self.uuid = str(uuid.uuid4())\n",
1256 1480 " \n",
1257 1481 " def _ipython_display_(self):\n",
1258 " json_data = json.dumps(zip(self.x, self.y))\n",
1482 " json_data = json.dumps(list(zip(self.x, self.y)))\n",
1259 1483 " display_html('<div id=\"{}\" style=\"height: 300px; width:80%;\"></div>'.format(self.uuid),\n",
1260 1484 " raw=True\n",
1261 1485 " )\n",
@@ -1270,7 +1494,7 b''
1270 1494 },
1271 1495 {
1272 1496 "cell_type": "code",
1273 "execution_count": 22,
1497 "execution_count": 21,
1274 1498 "metadata": {
1275 1499 "collapsed": false
1276 1500 },
@@ -1278,7 +1502,7 b''
1278 1502 {
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@@ -91,7 +91,7 b''
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444 1412 ],
445 1413 "text/plain": [
446 "<matplotlib.figure.Figure at 0x1084796d0>"
1414 "<matplotlib.figure.Figure at 0x106622080>"
447 1415 ]
448 1416 },
449 "metadata": {},
1417 "metadata": {
1418 "image/png": {
1419 "height": 392,
1420 "width": 602
1421 }
1422 },
450 1423 "output_type": "display_data"
451 1424 }
452 1425 ],
@@ -490,194 +1463,517 b''
490 1463 "metadata": {
491 1464 "collapsed": false
492 1465 },
493 "outputs": [],
494 "source": [
495 "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
496 ]
497 },
498 {
499 "cell_type": "code",
500 "execution_count": 5,
501 "metadata": {
502 "collapsed": false
503 },
504 1466 "outputs": [
505 1467 {
506 1468 "data": {
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671 1961 ],
672 1962 "text/plain": [
673 "<matplotlib.figure.Figure at 0x10848fb10>"
1963 "<matplotlib.figure.Figure at 0x106d1b198>"
674 1964 ]
675 1965 },
676 "metadata": {},
1966 "metadata": {
1967 "image/png": {
1968 "height": 403,
1969 "width": 596
1970 }
1971 },
677 1972 "output_type": "display_data"
678 1973 }
679 1974 ],
680 1975 "source": [
1976 "# %load http://matplotlib.org/mpl_examples/showcase/integral_demo.py\n",
681 1977 "\"\"\"\n",
682 1978 "Plot demonstrating the integral as the area under a curve.\n",
683 1979 "\n",
@@ -730,6 +2026,791 b''
730 2026 "\n",
731 2027 "plt.show()\n"
732 2028 ]
2029 },
2030 {
2031 "cell_type": "markdown",
2032 "metadata": {},
2033 "source": [
2034 "Matplotlib 1.4 introduces an interactive backend for use in the notebook,\n",
2035 "called 'nbagg'. You can enable this with `%matplotlib notebook`.\n",
2036 "\n",
2037 "With this backend, you will get interactive panning and zooming of matplotlib figures in your browser."
2038 ]
2039 },
2040 {
2041 "cell_type": "code",
2042 "execution_count": 5,
2043 "metadata": {
2044 "collapsed": true
2045 },
2046 "outputs": [],
2047 "source": [
2048 "%matplotlib notebook"
2049 ]
2050 },
2051 {
2052 "cell_type": "code",
2053 "execution_count": 6,
2054 "metadata": {
2055 "collapsed": false
2056 },
2057 "outputs": [
2058 {
2059 "data": {
2060 "application/javascript": [
2061 "/* Put everything inside the global mpl namespace */\n",
2062 "window.mpl = {};\n",
2063 "\n",
2064 "mpl.get_websocket_type = function() {\n",
2065 " if (typeof(WebSocket) !== 'undefined') {\n",
2066 " return WebSocket;\n",
2067 " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
2068 " return MozWebSocket;\n",
2069 " } else {\n",
2070 " alert('Your browser does not have WebSocket support.' +\n",
2071 " 'Please try Chrome, Safari or Firefox β‰₯ 6. ' +\n",
2072 " 'Firefox 4 and 5 are also supported but you ' +\n",
2073 " 'have to enable WebSockets in about:config.');\n",
2074 " };\n",
2075 "}\n",
2076 "\n",
2077 "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
2078 " this.id = figure_id;\n",
2079 "\n",
2080 " this.ws = websocket;\n",
2081 "\n",
2082 " this.supports_binary = (this.ws.binaryType != undefined);\n",
2083 "\n",
2084 " if (!this.supports_binary) {\n",
2085 " var warnings = document.getElementById(\"mpl-warnings\");\n",
2086 " if (warnings) {\n",
2087 " warnings.style.display = 'block';\n",
2088 " warnings.textContent = (\n",
2089 " \"This browser does not support binary websocket messages. \" +\n",
2090 " \"Performance may be slow.\");\n",
2091 " }\n",
2092 " }\n",
2093 "\n",
2094 " this.imageObj = new Image();\n",
2095 "\n",
2096 " this.context = undefined;\n",
2097 " this.message = undefined;\n",
2098 " this.canvas = undefined;\n",
2099 " this.rubberband_canvas = undefined;\n",
2100 " this.rubberband_context = undefined;\n",
2101 " this.format_dropdown = undefined;\n",
2102 "\n",
2103 " this.image_mode = 'full';\n",
2104 "\n",
2105 " this.root = $('<div/>');\n",
2106 " this._root_extra_style(this.root)\n",
2107 " this.root.attr('style', 'display: inline-block');\n",
2108 "\n",
2109 " $(parent_element).append(this.root);\n",
2110 "\n",
2111 " this._init_header(this);\n",
2112 " this._init_canvas(this);\n",
2113 " this._init_toolbar(this);\n",
2114 "\n",
2115 " var fig = this;\n",
2116 "\n",
2117 " this.waiting = false;\n",
2118 "\n",
2119 " this.ws.onopen = function () {\n",
2120 " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
2121 " fig.send_message(\"send_image_mode\", {});\n",
2122 " fig.send_message(\"refresh\", {});\n",
2123 " }\n",
2124 "\n",
2125 " this.imageObj.onload = function() {\n",
2126 " if (fig.image_mode == 'full') {\n",
2127 " // Full images could contain transparency (where diff images\n",
2128 " // almost always do), so we need to clear the canvas so that\n",
2129 " // there is no ghosting.\n",
2130 " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
2131 " }\n",
2132 " fig.context.drawImage(fig.imageObj, 0, 0);\n",
2133 " fig.waiting = false;\n",
2134 " };\n",
2135 "\n",
2136 " this.imageObj.onunload = function() {\n",
2137 " this.ws.close();\n",
2138 " }\n",
2139 "\n",
2140 " this.ws.onmessage = this._make_on_message_function(this);\n",
2141 "\n",
2142 " this.ondownload = ondownload;\n",
2143 "}\n",
2144 "\n",
2145 "mpl.figure.prototype._init_header = function() {\n",
2146 " var titlebar = $(\n",
2147 " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
2148 " 'ui-helper-clearfix\"/>');\n",
2149 " var titletext = $(\n",
2150 " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
2151 " 'text-align: center; padding: 3px;\"/>');\n",
2152 " titlebar.append(titletext)\n",
2153 " this.root.append(titlebar);\n",
2154 " this.header = titletext[0];\n",
2155 "}\n",
2156 "\n",
2157 "\n",
2158 "\n",
2159 "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
2160 "\n",
2161 "}\n",
2162 "\n",
2163 "\n",
2164 "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
2165 "\n",
2166 "}\n",
2167 "\n",
2168 "mpl.figure.prototype._init_canvas = function() {\n",
2169 " var fig = this;\n",
2170 "\n",
2171 " var canvas_div = $('<div/>');\n",
2172 "\n",
2173 " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
2174 "\n",
2175 " function canvas_keyboard_event(event) {\n",
2176 " return fig.key_event(event, event['data']);\n",
2177 " }\n",
2178 "\n",
2179 " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
2180 " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
2181 " this.canvas_div = canvas_div\n",
2182 " this._canvas_extra_style(canvas_div)\n",
2183 " this.root.append(canvas_div);\n",
2184 "\n",
2185 " var canvas = $('<canvas/>');\n",
2186 " canvas.addClass('mpl-canvas');\n",
2187 " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
2188 "\n",
2189 " this.canvas = canvas[0];\n",
2190 " this.context = canvas[0].getContext(\"2d\");\n",
2191 "\n",
2192 " var rubberband = $('<canvas/>');\n",
2193 " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
2194 "\n",
2195 " var pass_mouse_events = true;\n",
2196 "\n",
2197 " canvas_div.resizable({\n",
2198 " start: function(event, ui) {\n",
2199 " pass_mouse_events = false;\n",
2200 " },\n",
2201 " resize: function(event, ui) {\n",
2202 " fig.request_resize(ui.size.width, ui.size.height);\n",
2203 " },\n",
2204 " stop: function(event, ui) {\n",
2205 " pass_mouse_events = true;\n",
2206 " fig.request_resize(ui.size.width, ui.size.height);\n",
2207 " },\n",
2208 " });\n",
2209 "\n",
2210 " function mouse_event_fn(event) {\n",
2211 " if (pass_mouse_events)\n",
2212 " return fig.mouse_event(event, event['data']);\n",
2213 " }\n",
2214 "\n",
2215 " rubberband.mousedown('button_press', mouse_event_fn);\n",
2216 " rubberband.mouseup('button_release', mouse_event_fn);\n",
2217 " // Throttle sequential mouse events to 1 every 20ms.\n",
2218 " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
2219 "\n",
2220 " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
2221 " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
2222 "\n",
2223 " canvas_div.on(\"wheel\", function (event) {\n",
2224 " event = event.originalEvent;\n",
2225 " event['data'] = 'scroll'\n",
2226 " if (event.deltaY < 0) {\n",
2227 " event.step = 1;\n",
2228 " } else {\n",
2229 " event.step = -1;\n",
2230 " }\n",
2231 " mouse_event_fn(event);\n",
2232 " });\n",
2233 "\n",
2234 " canvas_div.append(canvas);\n",
2235 " canvas_div.append(rubberband);\n",
2236 "\n",
2237 " this.rubberband = rubberband;\n",
2238 " this.rubberband_canvas = rubberband[0];\n",
2239 " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
2240 " this.rubberband_context.strokeStyle = \"#000000\";\n",
2241 "\n",
2242 " this._resize_canvas = function(width, height) {\n",
2243 " // Keep the size of the canvas, canvas container, and rubber band\n",
2244 " // canvas in synch.\n",
2245 " canvas_div.css('width', width)\n",
2246 " canvas_div.css('height', height)\n",
2247 "\n",
2248 " canvas.attr('width', width);\n",
2249 " canvas.attr('height', height);\n",
2250 "\n",
2251 " rubberband.attr('width', width);\n",
2252 " rubberband.attr('height', height);\n",
2253 " }\n",
2254 "\n",
2255 " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
2256 " // upon first draw.\n",
2257 " this._resize_canvas(600, 600);\n",
2258 "\n",
2259 " // Disable right mouse context menu.\n",
2260 " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
2261 " return false;\n",
2262 " });\n",
2263 "\n",
2264 " function set_focus () {\n",
2265 " canvas.focus();\n",
2266 " canvas_div.focus();\n",
2267 " }\n",
2268 "\n",
2269 " window.setTimeout(set_focus, 100);\n",
2270 "}\n",
2271 "\n",
2272 "mpl.figure.prototype._init_toolbar = function() {\n",
2273 " var fig = this;\n",
2274 "\n",
2275 " var nav_element = $('<div/>')\n",
2276 " nav_element.attr('style', 'width: 100%');\n",
2277 " this.root.append(nav_element);\n",
2278 "\n",
2279 " // Define a callback function for later on.\n",
2280 " function toolbar_event(event) {\n",
2281 " return fig.toolbar_button_onclick(event['data']);\n",
2282 " }\n",
2283 " function toolbar_mouse_event(event) {\n",
2284 " return fig.toolbar_button_onmouseover(event['data']);\n",
2285 " }\n",
2286 "\n",
2287 " for(var toolbar_ind in mpl.toolbar_items) {\n",
2288 " var name = mpl.toolbar_items[toolbar_ind][0];\n",
2289 " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
2290 " var image = mpl.toolbar_items[toolbar_ind][2];\n",
2291 " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
2292 "\n",
2293 " if (!name) {\n",
2294 " // put a spacer in here.\n",
2295 " continue;\n",
2296 " }\n",
2297 " var button = $('<button/>');\n",
2298 " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
2299 " 'ui-button-icon-only');\n",
2300 " button.attr('role', 'button');\n",
2301 " button.attr('aria-disabled', 'false');\n",
2302 " button.click(method_name, toolbar_event);\n",
2303 " button.mouseover(tooltip, toolbar_mouse_event);\n",
2304 "\n",
2305 " var icon_img = $('<span/>');\n",
2306 " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
2307 " icon_img.addClass(image);\n",
2308 " icon_img.addClass('ui-corner-all');\n",
2309 "\n",
2310 " var tooltip_span = $('<span/>');\n",
2311 " tooltip_span.addClass('ui-button-text');\n",
2312 " tooltip_span.html(tooltip);\n",
2313 "\n",
2314 " button.append(icon_img);\n",
2315 " button.append(tooltip_span);\n",
2316 "\n",
2317 " nav_element.append(button);\n",
2318 " }\n",
2319 "\n",
2320 " var fmt_picker_span = $('<span/>');\n",
2321 "\n",
2322 " var fmt_picker = $('<select/>');\n",
2323 " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
2324 " fmt_picker_span.append(fmt_picker);\n",
2325 " nav_element.append(fmt_picker_span);\n",
2326 " this.format_dropdown = fmt_picker[0];\n",
2327 "\n",
2328 " for (var ind in mpl.extensions) {\n",
2329 " var fmt = mpl.extensions[ind];\n",
2330 " var option = $(\n",
2331 " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
2332 " fmt_picker.append(option)\n",
2333 " }\n",
2334 "\n",
2335 " // Add hover states to the ui-buttons\n",
2336 " $( \".ui-button\" ).hover(\n",
2337 " function() { $(this).addClass(\"ui-state-hover\");},\n",
2338 " function() { $(this).removeClass(\"ui-state-hover\");}\n",
2339 " );\n",
2340 "\n",
2341 " var status_bar = $('<span class=\"mpl-message\"/>');\n",
2342 " nav_element.append(status_bar);\n",
2343 " this.message = status_bar[0];\n",
2344 "}\n",
2345 "\n",
2346 "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
2347 " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
2348 " // which will in turn request a refresh of the image.\n",
2349 " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
2350 "}\n",
2351 "\n",
2352 "mpl.figure.prototype.send_message = function(type, properties) {\n",
2353 " properties['type'] = type;\n",
2354 " properties['figure_id'] = this.id;\n",
2355 " this.ws.send(JSON.stringify(properties));\n",
2356 "}\n",
2357 "\n",
2358 "mpl.figure.prototype.send_draw_message = function() {\n",
2359 " if (!this.waiting) {\n",
2360 " this.waiting = true;\n",
2361 " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
2362 " }\n",
2363 "}\n",
2364 "\n",
2365 "\n",
2366 "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
2367 " var format_dropdown = fig.format_dropdown;\n",
2368 " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
2369 " fig.ondownload(fig, format);\n",
2370 "}\n",
2371 "\n",
2372 "\n",
2373 "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
2374 " var size = msg['size'];\n",
2375 " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
2376 " fig._resize_canvas(size[0], size[1]);\n",
2377 " fig.send_message(\"refresh\", {});\n",
2378 " };\n",
2379 "}\n",
2380 "\n",
2381 "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
2382 " var x0 = msg['x0'];\n",
2383 " var y0 = fig.canvas.height - msg['y0'];\n",
2384 " var x1 = msg['x1'];\n",
2385 " var y1 = fig.canvas.height - msg['y1'];\n",
2386 " x0 = Math.floor(x0) + 0.5;\n",
2387 " y0 = Math.floor(y0) + 0.5;\n",
2388 " x1 = Math.floor(x1) + 0.5;\n",
2389 " y1 = Math.floor(y1) + 0.5;\n",
2390 " var min_x = Math.min(x0, x1);\n",
2391 " var min_y = Math.min(y0, y1);\n",
2392 " var width = Math.abs(x1 - x0);\n",
2393 " var height = Math.abs(y1 - y0);\n",
2394 "\n",
2395 " fig.rubberband_context.clearRect(\n",
2396 " 0, 0, fig.canvas.width, fig.canvas.height);\n",
2397 "\n",
2398 " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
2399 "}\n",
2400 "\n",
2401 "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
2402 " // Updates the figure title.\n",
2403 " fig.header.textContent = msg['label'];\n",
2404 "}\n",
2405 "\n",
2406 "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
2407 " var cursor = msg['cursor'];\n",
2408 " switch(cursor)\n",
2409 " {\n",
2410 " case 0:\n",
2411 " cursor = 'pointer';\n",
2412 " break;\n",
2413 " case 1:\n",
2414 " cursor = 'default';\n",
2415 " break;\n",
2416 " case 2:\n",
2417 " cursor = 'crosshair';\n",
2418 " break;\n",
2419 " case 3:\n",
2420 " cursor = 'move';\n",
2421 " break;\n",
2422 " }\n",
2423 " fig.rubberband_canvas.style.cursor = cursor;\n",
2424 "}\n",
2425 "\n",
2426 "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
2427 " fig.message.textContent = msg['message'];\n",
2428 "}\n",
2429 "\n",
2430 "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
2431 " // Request the server to send over a new figure.\n",
2432 " fig.send_draw_message();\n",
2433 "}\n",
2434 "\n",
2435 "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
2436 " fig.image_mode = msg['mode'];\n",
2437 "}\n",
2438 "\n",
2439 "mpl.figure.prototype.updated_canvas_event = function() {\n",
2440 " // Called whenever the canvas gets updated.\n",
2441 " this.send_message(\"ack\", {});\n",
2442 "}\n",
2443 "\n",
2444 "// A function to construct a web socket function for onmessage handling.\n",
2445 "// Called in the figure constructor.\n",
2446 "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
2447 " return function socket_on_message(evt) {\n",
2448 " if (evt.data instanceof Blob) {\n",
2449 " /* FIXME: We get \"Resource interpreted as Image but\n",
2450 " * transferred with MIME type text/plain:\" errors on\n",
2451 " * Chrome. But how to set the MIME type? It doesn't seem\n",
2452 " * to be part of the websocket stream */\n",
2453 " evt.data.type = \"image/png\";\n",
2454 "\n",
2455 " /* Free the memory for the previous frames */\n",
2456 " if (fig.imageObj.src) {\n",
2457 " (window.URL || window.webkitURL).revokeObjectURL(\n",
2458 " fig.imageObj.src);\n",
2459 " }\n",
2460 "\n",
2461 " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
2462 " evt.data);\n",
2463 " fig.updated_canvas_event();\n",
2464 " return;\n",
2465 " }\n",
2466 " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
2467 " fig.imageObj.src = evt.data;\n",
2468 " fig.updated_canvas_event();\n",
2469 " return;\n",
2470 " }\n",
2471 "\n",
2472 " var msg = JSON.parse(evt.data);\n",
2473 " var msg_type = msg['type'];\n",
2474 "\n",
2475 " // Call the \"handle_{type}\" callback, which takes\n",
2476 " // the figure and JSON message as its only arguments.\n",
2477 " try {\n",
2478 " var callback = fig[\"handle_\" + msg_type];\n",
2479 " } catch (e) {\n",
2480 " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
2481 " return;\n",
2482 " }\n",
2483 "\n",
2484 " if (callback) {\n",
2485 " try {\n",
2486 " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
2487 " callback(fig, msg);\n",
2488 " } catch (e) {\n",
2489 " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
2490 " }\n",
2491 " }\n",
2492 " };\n",
2493 "}\n",
2494 "\n",
2495 "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
2496 "mpl.findpos = function(e) {\n",
2497 " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
2498 " var targ;\n",
2499 " if (!e)\n",
2500 " e = window.event;\n",
2501 " if (e.target)\n",
2502 " targ = e.target;\n",
2503 " else if (e.srcElement)\n",
2504 " targ = e.srcElement;\n",
2505 " if (targ.nodeType == 3) // defeat Safari bug\n",
2506 " targ = targ.parentNode;\n",
2507 "\n",
2508 " // jQuery normalizes the pageX and pageY\n",
2509 " // pageX,Y are the mouse positions relative to the document\n",
2510 " // offset() returns the position of the element relative to the document\n",
2511 " var x = e.pageX - $(targ).offset().left;\n",
2512 " var y = e.pageY - $(targ).offset().top;\n",
2513 "\n",
2514 " return {\"x\": x, \"y\": y};\n",
2515 "};\n",
2516 "\n",
2517 "mpl.figure.prototype.mouse_event = function(event, name) {\n",
2518 " var canvas_pos = mpl.findpos(event)\n",
2519 "\n",
2520 " if (name === 'button_press')\n",
2521 " {\n",
2522 " this.canvas.focus();\n",
2523 " this.canvas_div.focus();\n",
2524 " }\n",
2525 "\n",
2526 " var x = canvas_pos.x;\n",
2527 " var y = canvas_pos.y;\n",
2528 "\n",
2529 " this.send_message(name, {x: x, y: y, button: event.button,\n",
2530 " step: event.step});\n",
2531 "\n",
2532 " /* This prevents the web browser from automatically changing to\n",
2533 " * the text insertion cursor when the button is pressed. We want\n",
2534 " * to control all of the cursor setting manually through the\n",
2535 " * 'cursor' event from matplotlib */\n",
2536 " event.preventDefault();\n",
2537 " return false;\n",
2538 "}\n",
2539 "\n",
2540 "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
2541 " // Handle any extra behaviour associated with a key event\n",
2542 "}\n",
2543 "\n",
2544 "mpl.figure.prototype.key_event = function(event, name) {\n",
2545 "\n",
2546 " // Prevent repeat events\n",
2547 " if (name == 'key_press')\n",
2548 " {\n",
2549 " if (event.which === this._key)\n",
2550 " return;\n",
2551 " else\n",
2552 " this._key = event.which;\n",
2553 " }\n",
2554 " if (name == 'key_release')\n",
2555 " this._key = null;\n",
2556 "\n",
2557 " var value = '';\n",
2558 " if (event.ctrlKey && event.which != 17)\n",
2559 " value += \"ctrl+\";\n",
2560 " if (event.altKey && event.which != 18)\n",
2561 " value += \"alt+\";\n",
2562 " if (event.shiftKey && event.which != 16)\n",
2563 " value += \"shift+\";\n",
2564 "\n",
2565 " value += 'k';\n",
2566 " value += event.which.toString();\n",
2567 "\n",
2568 " this._key_event_extra(event, name);\n",
2569 "\n",
2570 " this.send_message(name, {key: value});\n",
2571 " return false;\n",
2572 "}\n",
2573 "\n",
2574 "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
2575 " if (name == 'download') {\n",
2576 " this.handle_save(this, null);\n",
2577 " } else {\n",
2578 " this.send_message(\"toolbar_button\", {name: name});\n",
2579 " }\n",
2580 "};\n",
2581 "\n",
2582 "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
2583 " this.message.textContent = tooltip;\n",
2584 "};\n",
2585 "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
2586 "\n",
2587 "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
2588 "\n",
2589 "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
2590 " // Create a \"websocket\"-like object which calls the given IPython comm\n",
2591 " // object with the appropriate methods. Currently this is a non binary\n",
2592 " // socket, so there is still some room for performance tuning.\n",
2593 " var ws = {};\n",
2594 "\n",
2595 " ws.close = function() {\n",
2596 " comm.close()\n",
2597 " };\n",
2598 " ws.send = function(m) {\n",
2599 " //console.log('sending', m);\n",
2600 " comm.send(m);\n",
2601 " };\n",
2602 " // Register the callback with on_msg.\n",
2603 " comm.on_msg(function(msg) {\n",
2604 " //console.log('receiving', msg['content']['data'], msg);\n",
2605 " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
2606 " ws.onmessage(msg['content']['data'])\n",
2607 " });\n",
2608 " return ws;\n",
2609 "}\n",
2610 "\n",
2611 "mpl.mpl_figure_comm = function(comm, msg) {\n",
2612 " // This is the function which gets called when the mpl process\n",
2613 " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
2614 "\n",
2615 " var id = msg.content.data.id;\n",
2616 " // Get hold of the div created by the display call when the Comm\n",
2617 " // socket was opened in Python.\n",
2618 " var element = $(\"#\" + id);\n",
2619 " var ws_proxy = comm_websocket_adapter(comm)\n",
2620 "\n",
2621 " function ondownload(figure, format) {\n",
2622 " window.open(figure.imageObj.src);\n",
2623 " }\n",
2624 "\n",
2625 " var fig = new mpl.figure(id, ws_proxy,\n",
2626 " ondownload,\n",
2627 " element.get(0));\n",
2628 "\n",
2629 " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
2630 " // web socket which is closed, not our websocket->open comm proxy.\n",
2631 " ws_proxy.onopen();\n",
2632 "\n",
2633 " fig.parent_element = element.get(0);\n",
2634 " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
2635 " if (!fig.cell_info) {\n",
2636 " console.error(\"Failed to find cell for figure\", id, fig);\n",
2637 " return;\n",
2638 " }\n",
2639 "\n",
2640 " var output_index = fig.cell_info[2]\n",
2641 " var cell = fig.cell_info[0];\n",
2642 "\n",
2643 "};\n",
2644 "\n",
2645 "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
2646 " // Update the output cell to use the data from the current canvas.\n",
2647 " fig.push_to_output();\n",
2648 " var dataURL = fig.canvas.toDataURL();\n",
2649 " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
2650 " // the notebook keyboard shortcuts fail.\n",
2651 " IPython.keyboard_manager.enable()\n",
2652 " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
2653 " fig.send_message('closing', {});\n",
2654 " fig.ws.close()\n",
2655 "}\n",
2656 "\n",
2657 "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
2658 " // Turn the data on the canvas into data in the output cell.\n",
2659 " var dataURL = this.canvas.toDataURL();\n",
2660 " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
2661 "}\n",
2662 "\n",
2663 "mpl.figure.prototype.updated_canvas_event = function() {\n",
2664 " // Tell IPython that the notebook contents must change.\n",
2665 " IPython.notebook.set_dirty(true);\n",
2666 " this.send_message(\"ack\", {});\n",
2667 " var fig = this;\n",
2668 " // Wait a second, then push the new image to the DOM so\n",
2669 " // that it is saved nicely (might be nice to debounce this).\n",
2670 " setTimeout(function () { fig.push_to_output() }, 1000);\n",
2671 "}\n",
2672 "\n",
2673 "mpl.figure.prototype._init_toolbar = function() {\n",
2674 " var fig = this;\n",
2675 "\n",
2676 " var nav_element = $('<div/>')\n",
2677 " nav_element.attr('style', 'width: 100%');\n",
2678 " this.root.append(nav_element);\n",
2679 "\n",
2680 " // Define a callback function for later on.\n",
2681 " function toolbar_event(event) {\n",
2682 " return fig.toolbar_button_onclick(event['data']);\n",
2683 " }\n",
2684 " function toolbar_mouse_event(event) {\n",
2685 " return fig.toolbar_button_onmouseover(event['data']);\n",
2686 " }\n",
2687 "\n",
2688 " for(var toolbar_ind in mpl.toolbar_items){\n",
2689 " var name = mpl.toolbar_items[toolbar_ind][0];\n",
2690 " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
2691 " var image = mpl.toolbar_items[toolbar_ind][2];\n",
2692 " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
2693 "\n",
2694 " if (!name) { continue; };\n",
2695 "\n",
2696 " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
2697 " button.click(method_name, toolbar_event);\n",
2698 " button.mouseover(tooltip, toolbar_mouse_event);\n",
2699 " nav_element.append(button);\n",
2700 " }\n",
2701 "\n",
2702 " // Add the status bar.\n",
2703 " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
2704 " nav_element.append(status_bar);\n",
2705 " this.message = status_bar[0];\n",
2706 "\n",
2707 " // Add the close button to the window.\n",
2708 " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
2709 " var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
2710 " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
2711 " button.mouseover('Close figure', toolbar_mouse_event);\n",
2712 " buttongrp.append(button);\n",
2713 " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
2714 " titlebar.prepend(buttongrp);\n",
2715 "}\n",
2716 "\n",
2717 "\n",
2718 "mpl.figure.prototype._canvas_extra_style = function(el){\n",
2719 " // this is important to make the div 'focusable\n",
2720 " el.attr('tabindex', 0)\n",
2721 " // reach out to IPython and tell the keyboard manager to turn it's self\n",
2722 " // off when our div gets focus\n",
2723 "\n",
2724 " // location in version 3\n",
2725 " if (IPython.notebook.keyboard_manager) {\n",
2726 " IPython.notebook.keyboard_manager.register_events(el);\n",
2727 " }\n",
2728 " else {\n",
2729 " // location in version 2\n",
2730 " IPython.keyboard_manager.register_events(el);\n",
2731 " }\n",
2732 "\n",
2733 "}\n",
2734 "\n",
2735 "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
2736 " var manager = IPython.notebook.keyboard_manager;\n",
2737 " if (!manager)\n",
2738 " manager = IPython.keyboard_manager;\n",
2739 "\n",
2740 " // Check for shift+enter\n",
2741 " if (event.shiftKey && event.which == 13) {\n",
2742 " this.canvas_div.blur();\n",
2743 " event.shiftKey = false;\n",
2744 " // Send a \"J\" for go to next cell\n",
2745 " event.which = 74;\n",
2746 " event.keyCode = 74;\n",
2747 " manager.command_mode();\n",
2748 " manager.handle_keydown(event);\n",
2749 " }\n",
2750 "}\n",
2751 "\n",
2752 "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
2753 " fig.ondownload(fig, null);\n",
2754 "}\n",
2755 "\n",
2756 "\n",
2757 "mpl.find_output_cell = function(html_output) {\n",
2758 " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
2759 " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
2760 " // IPython event is triggered only after the cells have been serialised, which for\n",
2761 " // our purposes (turning an active figure into a static one), is too late.\n",
2762 " var cells = IPython.notebook.get_cells();\n",
2763 " var ncells = cells.length;\n",
2764 " for (var i=0; i<ncells; i++) {\n",
2765 " var cell = cells[i];\n",
2766 " if (cell.cell_type === 'code'){\n",
2767 " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
2768 " var data = cell.output_area.outputs[j];\n",
2769 " if (data.data) {\n",
2770 " // IPython >= 3 moved mimebundle to data attribute of output\n",
2771 " data = data.data;\n",
2772 " }\n",
2773 " if (data['text/html'] == html_output) {\n",
2774 " return [cell, data, j];\n",
2775 " }\n",
2776 " }\n",
2777 " }\n",
2778 " }\n",
2779 "}\n",
2780 "\n",
2781 "// Register the function which deals with the matplotlib target/channel.\n",
2782 "// The kernel may be null if the page has been refreshed.\n",
2783 "if (IPython.notebook.kernel != null) {\n",
2784 " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
2785 "}\n"
2786 ],
2787 "text/plain": [
2788 "<IPython.core.display.Javascript object>"
2789 ]
2790 },
2791 "metadata": {},
2792 "output_type": "display_data"
2793 },
2794 {
2795 "data": {
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\">"
2798 ],
2799 "text/plain": [
2800 "<IPython.core.display.HTML object>"
2801 ]
2802 },
2803 "metadata": {},
2804 "output_type": "display_data"
2805 }
2806 ],
2807 "source": [
2808 "plt.figure()\n",
2809 "x = np.linspace(0, 5 * np.pi, 1000)\n",
2810 "for n in range(1, 4):\n",
2811 " plt.plot(np.sin(n * x))\n",
2812 "plt.show()"
2813 ]
733 2814 }
734 2815 ],
735 2816 "metadata": {
Show file before | Show file after | Hide comments
@@ -59,13 +59,6 b''
59 59 ]
60 60 },
61 61 {
62 "cell_type": "markdown",
63 "metadata": {},
64 "source": [
65 "**Python 2-only**: the eval input works as well (`input` is just `eval(raw_input(prompt))`)"
66 ]
67 },
68 {
69 62 "cell_type": "code",
70 63 "execution_count": 3,
71 64 "metadata": {
@@ -73,44 +66,15 b''
73 66 },
74 67 "outputs": [
75 68 {
76 "name": "stdout",
77 "output_type": "stream",
78 "text": [
79 "How many fingers? 4\n"
80 ]
81 },
82 {
83 "data": {
84 "text/plain": [
85 "(4, int)"
86 ]
87 },
88 "execution_count": 3,
89 "metadata": {},
90 "output_type": "execute_result"
91 }
92 ],
93 "source": [
94 "fingers = input(\"How many fingers? \")\n",
95 "fingers, type(fingers)"
96 ]
97 },
98 {
99 "cell_type": "code",
100 "execution_count": 4,
101 "metadata": {
102 "collapsed": false
103 },
104 "outputs": [
105 {
106 69 "ename": "ZeroDivisionError",
107 "evalue": "integer division or modulo by zero",
70 "evalue": "division by zero",
108 71 "output_type": "error",
109 72 "traceback": [
110 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
73 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
74 "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
111 75 "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
112 76 "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36mdiv\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
113 "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
77 "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
114 78 ]
115 79 }
116 80 ],
@@ -123,7 +87,7 b''
123 87 },
124 88 {
125 89 "cell_type": "code",
126 "execution_count": 5,
90 "execution_count": 4,
127 91 "metadata": {
128 92 "collapsed": false
129 93 },
@@ -141,6 +105,19 b''
141 105 "1\n",
142 106 "ipdb> y\n",
143 107 "0\n",
108 "ipdb> bt\n",
109 " \u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m(4)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n",
110 "\u001b[0;32m 1 \u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
111 "\u001b[0;32m 2 \u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
112 "\u001b[0;32m 3 \u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
113 "\u001b[1;32m----> 4 \u001b[1;33m\u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
114 "\u001b[0m\n",
115 "> \u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m(2)\u001b[0;36mdiv\u001b[1;34m()\u001b[0m\n",
116 "\u001b[1;32m 1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
117 "\u001b[0m\u001b[1;32m----> 2 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
118 "\u001b[0m\u001b[1;32m 3 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
119 "\u001b[0m\u001b[1;32m 4 \u001b[1;33m\u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
120 "\u001b[0m\n",
144 121 "ipdb> exit\n"
145 122 ]
146 123 }
Show file before | Show file after | Hide comments
This diff has been collapsed as it changes many lines, (2773 lines changed) Show them Hide them
@@ -673,44 +673,44 b''
673 673 {
674 674 "data": {
675 675 "image/jpeg": [
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710 710 "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA//8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
711 "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAAAAANAAAACMlrWmAABAAJAAkAIAqY\n",
711 "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB0AAAAKAAAAEb/Ce2AABAAJAAkAIAqY\n",
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@@ -725,7 +725,7 b''
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@@ -733,7 +733,7 b''
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2215 "sAcfzdqzkJH/2Q==\n"
2013 2216 ],
2014 2217 "text/plain": [
2015 2218 "<IPython.core.display.Image object>"
@@ -2763,7 +2966,7 b''
2763 2966 "\n",
2764 2967 " <iframe\n",
2765 2968 " width=\"400\"\n",
2766 " height=300\"\n",
2969 " height=\"300\"\n",
2767 2970 " src=\"https://www.youtube.com/embed/sjfsUzECqK0\"\n",
2768 2971 " frameborder=\"0\"\n",
2769 2972 " allowfullscreen\n",
@@ -2771,7 +2974,7 b''
2771 2974 " "
2772 2975 ],
2773 2976 "text/plain": [
2774 "<IPython.lib.display.YouTubeVideo at 0x107c1eb50>"
2977 "<IPython.lib.display.YouTubeVideo at 0x1058e5cf8>"
2775 2978 ]
2776 2979 },
2777 2980 "execution_count": 29,
@@ -2854,15 +3057,15 b''
2854 3057 "\n",
2855 3058 " <iframe\n",
2856 3059 " width=\"100%\"\n",
2857 " height=350\"\n",
2858 " src=\"http://ipython.org\"\n",
3060 " height=\"350\"\n",
3061 " src=\"http://jupyter.org\"\n",
2859 3062 " frameborder=\"0\"\n",
2860 3063 " allowfullscreen\n",
2861 3064 " ></iframe>\n",
2862 3065 " "
2863 3066 ],
2864 3067 "text/plain": [
2865 "<IPython.lib.display.IFrame at 0x107c1ecd0>"
3068 "<IPython.lib.display.IFrame at 0x1059ae7b8>"
2866 3069 ]
2867 3070 },
2868 3071 "execution_count": 31,
@@ -2872,7 +3075,7 b''
2872 3075 ],
2873 3076 "source": [
2874 3077 "from IPython.display import IFrame\n",
2875 "IFrame('http://ipython.org', width='100%', height=350)"
3078 "IFrame('http://jupyter.org', width='100%', height=350)"
2876 3079 ]
2877 3080 },
2878 3081 {
@@ -2891,7 +3094,7 b''
2891 3094 },
2892 3095 {
2893 3096 "cell_type": "code",
2894 "execution_count": 33,
3097 "execution_count": 32,
2895 3098 "metadata": {
2896 3099 "collapsed": false
2897 3100 },
@@ -2902,10 +3105,10 b''
2902 3105 "<a href='Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>"
2903 3106 ],
2904 3107 "text/plain": [
2905 "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/Cell Magics.ipynb"
3108 "/Users/minrk/dev/ip/mine/examples/IPython Kernel/Cell Magics.ipynb"
2906 3109 ]
2907 3110 },
2908 "execution_count": 33,
3111 "execution_count": 32,
2909 3112 "metadata": {},
2910 3113 "output_type": "execute_result"
2911 3114 }
@@ -2924,7 +3127,7 b''
2924 3127 },
2925 3128 {
2926 3129 "cell_type": "code",
2927 "execution_count": 34,
3130 "execution_count": 33,
2928 3131 "metadata": {
2929 3132 "collapsed": false
2930 3133 },
@@ -2940,20 +3143,36 b''
2940 3143 "&nbsp;&nbsp;<a href='./Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>\n",
2941 3144 "&nbsp;&nbsp;<a href='./Custom Display Logic.ipynb' target='_blank'>Custom Display Logic.ipynb</a><br>\n",
2942 3145 "&nbsp;&nbsp;<a href='./example-demo.py' target='_blank'>example-demo.py</a><br>\n",
3146 "&nbsp;&nbsp;<a href='./foo.py' target='_blank'>foo.py</a><br>\n",
2943 3147 "&nbsp;&nbsp;<a href='./Index.ipynb' target='_blank'>Index.ipynb</a><br>\n",
2944 3148 "&nbsp;&nbsp;<a href='./ipython-completion.bash' target='_blank'>ipython-completion.bash</a><br>\n",
2945 3149 "&nbsp;&nbsp;<a href='./ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>\n",
2946 3150 "&nbsp;&nbsp;<a href='./ipython-qtconsole.desktop' target='_blank'>ipython-qtconsole.desktop</a><br>\n",
2947 3151 "&nbsp;&nbsp;<a href='./ipython.desktop' target='_blank'>ipython.desktop</a><br>\n",
3152 "&nbsp;&nbsp;<a href='./mod.py' target='_blank'>mod.py</a><br>\n",
2948 3153 "&nbsp;&nbsp;<a href='./Plotting in the Notebook.ipynb' target='_blank'>Plotting in the Notebook.ipynb</a><br>\n",
2949 3154 "&nbsp;&nbsp;<a href='./Raw Input in the Notebook.ipynb' target='_blank'>Raw Input in the Notebook.ipynb</a><br>\n",
2950 3155 "&nbsp;&nbsp;<a href='./Rich Output.ipynb' target='_blank'>Rich Output.ipynb</a><br>\n",
2951 3156 "&nbsp;&nbsp;<a href='./Script Magics.ipynb' target='_blank'>Script Magics.ipynb</a><br>\n",
2952 3157 "&nbsp;&nbsp;<a href='./SymPy.ipynb' target='_blank'>SymPy.ipynb</a><br>\n",
2953 3158 "&nbsp;&nbsp;<a href='./Terminal Usage.ipynb' target='_blank'>Terminal Usage.ipynb</a><br>\n",
3159 "&nbsp;&nbsp;<a href='./test.txt' target='_blank'>test.txt</a><br>\n",
2954 3160 "&nbsp;&nbsp;<a href='./Third Party Rich Output.ipynb' target='_blank'>Third Party Rich Output.ipynb</a><br>\n",
2955 3161 "&nbsp;&nbsp;<a href='./Trapezoid Rule.ipynb' target='_blank'>Trapezoid Rule.ipynb</a><br>\n",
2956 3162 "&nbsp;&nbsp;<a href='./Working With External Code.ipynb' target='_blank'>Working With External Code.ipynb</a><br>\n",
3163 "./.ipynb_checkpoints/<br>\n",
3164 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Animations Using clear_output-checkpoint.ipynb' target='_blank'>Animations Using clear_output-checkpoint.ipynb</a><br>\n",
3165 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Background Jobs-checkpoint.ipynb' target='_blank'>Background Jobs-checkpoint.ipynb</a><br>\n",
3166 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Beyond Plain Python-checkpoint.ipynb' target='_blank'>Beyond Plain Python-checkpoint.ipynb</a><br>\n",
3167 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Capturing Output-checkpoint.ipynb' target='_blank'>Capturing Output-checkpoint.ipynb</a><br>\n",
3168 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Cell Magics-checkpoint.ipynb' target='_blank'>Cell Magics-checkpoint.ipynb</a><br>\n",
3169 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Custom Display Logic-checkpoint.ipynb' target='_blank'>Custom Display Logic-checkpoint.ipynb</a><br>\n",
3170 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Index-checkpoint.ipynb' target='_blank'>Index-checkpoint.ipynb</a><br>\n",
3171 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Plotting in the Notebook-checkpoint.ipynb' target='_blank'>Plotting in the Notebook-checkpoint.ipynb</a><br>\n",
3172 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Raw Input in the Notebook-checkpoint.ipynb' target='_blank'>Raw Input in the Notebook-checkpoint.ipynb</a><br>\n",
3173 "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Rich Output-checkpoint.ipynb' target='_blank'>Rich Output-checkpoint.ipynb</a><br>\n",
3174 "./__pycache__/<br>\n",
3175 "&nbsp;&nbsp;<a href='./__pycache__/mod.cpython-34.pyc' target='_blank'>mod.cpython-34.pyc</a><br>\n",
2957 3176 "./data/<br>\n",
2958 3177 "&nbsp;&nbsp;<a href='./data/flare.json' target='_blank'>flare.json</a><br>\n",
2959 3178 "./gui/<br>\n",
@@ -2974,20 +3193,36 b''
2974 3193 " Cell Magics.ipynb\n",
2975 3194 " Custom Display Logic.ipynb\n",
2976 3195 " example-demo.py\n",
3196 " foo.py\n",
2977 3197 " Index.ipynb\n",
2978 3198 " ipython-completion.bash\n",
2979 3199 " ipython-get-history.py\n",
2980 3200 " ipython-qtconsole.desktop\n",
2981 3201 " ipython.desktop\n",
3202 " mod.py\n",
2982 3203 " Plotting in the Notebook.ipynb\n",
2983 3204 " Raw Input in the Notebook.ipynb\n",
2984 3205 " Rich Output.ipynb\n",
2985 3206 " Script Magics.ipynb\n",
2986 3207 " SymPy.ipynb\n",
2987 3208 " Terminal Usage.ipynb\n",
3209 " test.txt\n",
2988 3210 " Third Party Rich Output.ipynb\n",
2989 3211 " Trapezoid Rule.ipynb\n",
2990 3212 " Working With External Code.ipynb\n",
3213 "./.ipynb_checkpoints/\n",
3214 " Animations Using clear_output-checkpoint.ipynb\n",
3215 " Background Jobs-checkpoint.ipynb\n",
3216 " Beyond Plain Python-checkpoint.ipynb\n",
3217 " Capturing Output-checkpoint.ipynb\n",
3218 " Cell Magics-checkpoint.ipynb\n",
3219 " Custom Display Logic-checkpoint.ipynb\n",
3220 " Index-checkpoint.ipynb\n",
3221 " Plotting in the Notebook-checkpoint.ipynb\n",
3222 " Raw Input in the Notebook-checkpoint.ipynb\n",
3223 " Rich Output-checkpoint.ipynb\n",
3224 "./__pycache__/\n",
3225 " mod.cpython-34.pyc\n",
2991 3226 "./data/\n",
2992 3227 " flare.json\n",
2993 3228 "./gui/\n",
@@ -3000,7 +3235,7 b''
3000 3235 " gui-wx.py"
3001 3236 ]
3002 3237 },
3003 "execution_count": 34,
3238 "execution_count": 33,
3004 3239 "metadata": {},
3005 3240 "output_type": "execute_result"
3006 3241 }
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@@ -54,13 +54,13 b''
54 54 "name": "stdout",
55 55 "output_type": "stream",
56 56 "text": [
57 "hello from Python 2.7.1 (r271:86832, Jul 31 2011, 19:30:53) \n",
58 "[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)]\n"
57 "hello from Python 2.7.9 (default, Jan 29 2015, 06:27:40) \n",
58 "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.56)]\n"
59 59 ]
60 60 }
61 61 ],
62 62 "source": [
63 "%%script python\n",
63 "%%script python2\n",
64 64 "import sys\n",
65 65 "print 'hello from Python %s' % sys.version"
66 66 ]
@@ -76,8 +76,8 b''
76 76 "name": "stdout",
77 77 "output_type": "stream",
78 78 "text": [
79 "hello from Python: 3.2.3 (v3.2.3:3d0686d90f55, Apr 10 2012, 11:25:50) \n",
80 "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
79 "hello from Python: 3.4.2 |Continuum Analytics, Inc.| (default, Oct 21 2014, 17:42:20) \n",
80 "[GCC 4.2.1 (Apple Inc. build 5577)]\n"
81 81 ]
82 82 }
83 83 ],
@@ -107,7 +107,7 b''
107 107 "name": "stdout",
108 108 "output_type": "stream",
109 109 "text": [
110 "Hello from Ruby 1.8.7\n"
110 "Hello from Ruby 2.0.0\n"
111 111 ]
112 112 }
113 113 ],
@@ -344,7 +344,7 b''
344 344 }
345 345 ],
346 346 "source": [
347 "%%script python -Qnew\n",
347 "%%script python2 -Qnew\n",
348 348 "print 1/3"
349 349 ]
350 350 },
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@@ -303,7 +303,7 b''
303 303 {
304 304 "data": {
305 305 "text/latex": [
306 "$$\\begin{bmatrix}-2, & - 2 i, & 2 i\\end{bmatrix}$$"
306 "$$\\left [ -2, \\quad - 2 i, \\quad 2 i\\right ]$$"
307 307 ],
308 308 "text/plain": [
309 309 "[-2, -2β‹…β…ˆ, 2β‹…β…ˆ]"
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969 1811 "text/plain": [
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1812 "<matplotlib.figure.Figure at 0x1078c5e80>"
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1987 "nqmru3MisBiYBOzR19v/eOBITcvuKRS7p03JlcrPB84H3gy0b+Kly6iusPoWcMeWLjywfwrBbYPa\n",
1988 "KpVifjFwEXBRrtRy1CMHHPdP05Y8cfZ+D1w5eWjSAp7ac5hVbfcwddWnmLxoj6MX73zmpaumHvXv\n",
1989 "uVL5S8DFLjvd4eaFDqDMsnsKyf4904lUB1e/dXC1w9k9hWL39Ay5UjkCXg58EOjazMt/DXwVuKJS\n",
1990 "zK/ehh9n/5RqrrzKwMqrDcmVyjOAt0xYO/Tu/e+99uApq67l0f2XM1IbZ05cOY0VU05l+U75FUQT\n",
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2003 "KRS719xypXIeuAA4ZiMvGQS+SnWlVd2f4m7/FIJnXimI2qGBP8iVypff9Ir3n7X7Xx76zBG3/M8+\n",
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2015 "dk8h2T9eBOwE3N/X2/9A6DBZYvcUit1Lj1ypHOVK5TcD9wIf4NmDq2HgP4CDKsX8V9IwuLJ/SjsP\n",
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2019 "h4bOW2fzQgdQZtk9hZT1/nneVThZ757CsXsNLFcqd+RK5U8Av2PDg6uHgDOAU9M2uKqxf0o1V165\n",
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1316 2945 "text/plain": [
1317 "<matplotlib.figure.Figure at 0x10984b250>"
2946 "<matplotlib.figure.Figure at 0x108276278>"
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1319 2948 },
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2949 "metadata": {
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1321 2955 "output_type": "display_data"
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3933 "jKfhNwfYCKyYyItO2rTJ721JkiRJkiS1Y5BnPkmSJEmSJOn/nMUnSZIkSZIktcbikyRJkiRJklpj\n",
3934 "8UmSJEmSJEmtsfgkSZIkSZKk1lh8kiRJkiRJUmssPkmSJEmSJKk1Fp8kSZIkSZLUGotPkiRJkiRJ\n",
3935 "ao3FJ0mSJEmSJLXG4pMkSZIkSZJaY/FJkiRJkiRJrbH4JEmSJEmSpNZYfJIkSZIkSVJrLD5JkiRJ\n",
3936 "kiSpNRafJEmSJEmS1BqLT5IkSZIkSWqNxSdJkiRJkiS1xuKTJEmSJEmSWvMfdhLgUx9xCjkAAAAA\n",
3937 "SUVORK5CYII=\n"
1634 3938 ],
1635 3939 "text/plain": [
1636 "<matplotlib.figure.Figure at 0x109a2a8d0>"
3940 "<matplotlib.figure.Figure at 0x109cac9b0>"
1637 3941 ]
1638 3942 },
1639 "metadata": {},
3943 "metadata": {
3944 "image/png": {
3945 "height": 376,
3946 "width": 591
3947 }
3948 },
1640 3949 "output_type": "display_data"
1641 3950 }
1642 3951 ],
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@@ -34,7 +34,7 b''
34 34 "x = 10\n",
35 35 "y = 20\n",
36 36 "z = x+y\n",
37 "print 'z is:', z"
37 "print('z is: %s' % z)"
38 38 ]
39 39 },
40 40 {
@@ -208,7 +208,7 b''
208 208 },
209 209 {
210 210 "cell_type": "code",
211 "execution_count": 12,
211 "execution_count": 8,
212 212 "metadata": {
213 213 "collapsed": false
214 214 },
Show file before | Show file after | Hide comments
@@ -16,7 +16,7 b''
16 16 },
17 17 {
18 18 "cell_type": "code",
19 "execution_count": 7,
19 "execution_count": 1,
20 20 "metadata": {
21 21 "collapsed": false
22 22 },
@@ -41,7 +41,7 b''
41 41 },
42 42 {
43 43 "cell_type": "code",
44 "execution_count": 9,
44 "execution_count": 2,
45 45 "metadata": {
46 46 "collapsed": false
47 47 },
@@ -59,7 +59,7 b''
59 59 },
60 60 {
61 61 "cell_type": "code",
62 "execution_count": 10,
62 "execution_count": 3,
63 63 "metadata": {
64 64 "collapsed": false
65 65 },
@@ -92,7 +92,7 b''
92 92 },
93 93 {
94 94 "cell_type": "code",
95 "execution_count": 11,
95 "execution_count": 4,
96 96 "metadata": {
97 97 "collapsed": false
98 98 },
@@ -110,7 +110,7 b''
110 110 },
111 111 {
112 112 "cell_type": "code",
113 "execution_count": 12,
113 "execution_count": 5,
114 114 "metadata": {
115 115 "collapsed": false
116 116 },
@@ -195,7 +195,6 b''
195 195 " </tr>\n",
196 196 " </tbody>\n",
197 197 "</table>\n",
198 "<p>6 rows Γ— 7 columns</p>\n",
199 198 "</div>"
200 199 ],
201 200 "text/plain": [
@@ -205,12 +204,10 b''
205 204 "2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48\n",
206 205 "3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99\n",
207 206 "4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12\n",
208 "5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53\n",
209 "\n",
210 "[6 rows x 7 columns]"
207 "5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53"
211 208 ]
212 209 },
213 "execution_count": 12,
210 "execution_count": 5,
214 211 "metadata": {},
215 212 "output_type": "execute_result"
216 213 }
@@ -235,7 +232,7 b''
235 232 },
236 233 {
237 234 "cell_type": "code",
238 "execution_count": 13,
235 "execution_count": 6,
239 236 "metadata": {
240 237 "collapsed": false
241 238 },
@@ -247,7 +244,7 b''
247 244 },
248 245 {
249 246 "cell_type": "code",
250 "execution_count": 14,
247 "execution_count": 7,
251 248 "metadata": {
252 249 "collapsed": false
253 250 },
@@ -263,7 +260,7 b''
263 260 },
264 261 {
265 262 "cell_type": "code",
266 "execution_count": 15,
263 "execution_count": 8,
267 264 "metadata": {
268 265 "collapsed": false
269 266 },
@@ -281,7 +278,7 b''
281 278 " x + y"
282 279 ]
283 280 },
284 "execution_count": 15,
281 "execution_count": 8,
285 282 "metadata": {},
286 283 "output_type": "execute_result"
287 284 }
@@ -292,7 +289,7 b''
292 289 },
293 290 {
294 291 "cell_type": "code",
295 "execution_count": 16,
292 "execution_count": 9,
296 293 "metadata": {
297 294 "collapsed": false
298 295 },
@@ -308,7 +305,7 b''
308 305 " x x"
309 306 ]
310 307 },
311 "execution_count": 16,
308 "execution_count": 9,
312 309 "metadata": {},
313 310 "output_type": "execute_result"
314 311 }
@@ -320,7 +317,7 b''
320 317 },
321 318 {
322 319 "cell_type": "code",
323 "execution_count": 17,
320 "execution_count": 10,
324 321 "metadata": {
325 322 "collapsed": false
326 323 },
@@ -337,7 +334,7 b''
337 334 " 2 24 "
338 335 ]
339 336 },
340 "execution_count": 17,
337 "execution_count": 10,
341 338 "metadata": {},
342 339 "output_type": "execute_result"
343 340 }
@@ -362,7 +359,7 b''
362 359 },
363 360 {
364 361 "cell_type": "code",
365 "execution_count": 1,
362 "execution_count": 11,
366 363 "metadata": {
367 364 "collapsed": false
368 365 },
@@ -374,7 +371,7 b''
374 371 },
375 372 {
376 373 "cell_type": "code",
377 "execution_count": 5,
374 "execution_count": 12,
378 375 "metadata": {
379 376 "collapsed": false
380 377 },
@@ -393,7 +390,7 b''
393 390 },
394 391 {
395 392 "cell_type": "code",
396 "execution_count": 8,
393 "execution_count": 13,
397 394 "metadata": {
398 395 "collapsed": false
399 396 },
@@ -424,11 +421,11 b''
424 421 " );\n",
425 422 " \n",
426 423 " function load_all_libs(){\n",
427 " console.log('loading all libs')\n",
424 " console.log('Loading Vincent libs...')\n",
428 425 " vct_load_lib('http://d3js.org/d3.v3.min.js', function(){\n",
429 426 " vct_load_lib('http://d3js.org/d3.geo.projection.v0.min.js', function(){\n",
430 427 " vct_load_lib('http://wrobstory.github.io/d3-cloud/d3.layout.cloud.js', function(){\n",
431 " vct_load_lib('http://trifacta.github.com/vega/vega.js', function(){\n",
428 " vct_load_lib('http://wrobstory.github.io/vega/vega.v1.3.3.js', function(){\n",
432 429 " window.dispatchEvent(vincent_event);\n",
433 430 " });\n",
434 431 " });\n",
@@ -446,13 +443,15 b''
446 443 " }\n",
447 444 " );\n",
448 445 " require([\"d3\"], function(d3){\n",
449 " console.log('Loading from require.js...')\n",
446 " console.log('Loading Vincent from require.js...')\n",
450 447 " window.d3 = d3;\n",
451 448 " require([\"topojson\"], function(topojson){\n",
452 449 " window.topojson = topojson;\n",
453 450 " load_all_libs();\n",
454 451 " });\n",
455 452 " });\n",
453 " } else {\n",
454 " load_all_libs();\n",
456 455 " };\n",
457 456 " }else{\n",
458 457 " console.log('Require.js not found, loading manually...')\n",
@@ -475,7 +474,7 b''
475 474 },
476 475 {
477 476 "cell_type": "code",
478 "execution_count": 10,
477 "execution_count": 14,
479 478 "metadata": {
480 479 "collapsed": false
481 480 },
@@ -483,7 +482,7 b''
483 482 {
484 483 "data": {
485 484 "text/html": [
486 "<div id=\"vis47fdfca404f24684b44753131b44ed27\"></div>\n",
485 "<div id=\"vise33c59d182bd4093bad74a04ddf24501\"></div>\n",
487 486 "<script>\n",
488 487 " ( function() {\n",
489 488 " var _do_plot = function() {\n",
@@ -491,8 +490,8 b''
491 490 " window.addEventListener('vincent_libs_loaded', _do_plot)\n",
492 491 " return;\n",
493 492 " }\n",
494 " 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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494 " chart({el: \"#vise33c59d182bd4093bad74a04ddf24501\"}).update();\n",
496 495 " });\n",
497 496 " };\n",
498 497 " _do_plot();\n",
@@ -502,7 +501,7 b''
502 501 " "
503 502 ],
504 503 "text/plain": [
505 "<vincent.charts.Line at 0x10290f710>"
504 "<vincent.charts.Line at 0x107badb38>"
506 505 ]
507 506 },
508 507 "metadata": {},
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336 897 "text/plain": [
337 "<matplotlib.figure.Figure at 0x7f47175e3898>"
898 "<matplotlib.figure.Figure at 0x10664cfd0>"
338 899 ]
339 900 },
340 "metadata": {},
901 "metadata": {
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905 }
906 },
341 907 "output_type": "display_data"
342 908 }
343 909 ],
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This diff has been collapsed as it changes many lines, (676 lines changed) Show them Hide them
@@ -30,7 +30,7 b''
30 30 },
31 31 {
32 32 "cell_type": "code",
33 "execution_count": 7,
33 "execution_count": 1,
34 34 "metadata": {
35 35 "collapsed": false
36 36 },
@@ -65,7 +65,7 b''
65 65 },
66 66 {
67 67 "cell_type": "code",
68 "execution_count": 8,
68 "execution_count": 2,
69 69 "metadata": {
70 70 "collapsed": false
71 71 },
@@ -76,7 +76,7 b''
76 76 },
77 77 {
78 78 "cell_type": "code",
79 "execution_count": 9,
79 "execution_count": 3,
80 80 "metadata": {
81 81 "collapsed": false
82 82 },
@@ -87,9 +87,9 b''
87 87 },
88 88 {
89 89 "cell_type": "code",
90 "execution_count": 10,
90 "execution_count": null,
91 91 "metadata": {
92 "collapsed": false
92 "collapsed": true
93 93 },
94 94 "outputs": [],
95 95 "source": [
@@ -98,7 +98,7 b''
98 98 },
99 99 {
100 100 "cell_type": "code",
101 "execution_count": 11,
101 "execution_count": 4,
102 102 "metadata": {
103 103 "collapsed": false
104 104 },
@@ -106,179 +106,513 b''
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272 600 ],
273 601 "text/plain": [
274 "<matplotlib.figure.Figure at 0x108604e50>"
602 "<matplotlib.figure.Figure at 0x1066447f0>"
275 603 ]
276 604 },
277 "metadata": {},
605 "metadata": {
606 "image/png": {
607 "height": 403,
608 "width": 596
609 }
610 },
278 611 "output_type": "display_data"
279 612 }
280 613 ],
281 614 "source": [
615 "# %load http://matplotlib.org/mpl_examples/showcase/integral_demo.py\n",
282 616 "\"\"\"\n",
283 617 "Plot demonstrating the integral as the area under a curve.\n",
284 618 "\n",
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@@ -3,4 +3,5 b''
3 3 from IPython.display import FileLinks, display
4 4 dirs =!ls -d */
5 5 for d in dirs:
6 display(FileLinks(d)) No newline at end of file
6 if d != '__pycache__/':
7 display(FileLinks(d)) No newline at end of file
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@@ -1,1501 +0,0 b''
1 {
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "# Defining Custom Display Logic for Your Own Objects"
8 ]
9 },
10 {
11 "cell_type": "markdown",
12 "metadata": {},
13 "source": [
14 "## Overview"
15 ]
16 },
17 {
18 "cell_type": "markdown",
19 "metadata": {},
20 "source": [
21 "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",
22 "\n",
23 "* HTML\n",
24 "* JSON\n",
25 "* PNG\n",
26 "* JPEG\n",
27 "* SVG\n",
28 "* LaTeX\n",
29 "\n",
30 "This Notebook shows how you can add custom display logic to your own classes, so that they can be displayed using these rich representations. There are two ways of accomplishing this:\n",
31 "\n",
32 "1. Implementing special display methods such as `_repr_html_`.\n",
33 "2. Registering a display function for a particular type.\n",
34 "\n",
35 "In this Notebook we show how both approaches work."
36 ]
37 },
38 {
39 "cell_type": "markdown",
40 "metadata": {},
41 "source": [
42 "Before we get started, we will import the various display functions for displaying the different formats we will create."
43 ]
44 },
45 {
46 "cell_type": "code",
47 "execution_count": 1,
48 "metadata": {
49 "collapsed": false
50 },
51 "outputs": [],
52 "source": [
53 "from IPython.display import display\n",
54 "from IPython.display import (\n",
55 " display_html, display_jpeg, display_png,\n",
56 " display_javascript, display_svg, display_latex\n",
57 ")"
58 ]
59 },
60 {
61 "cell_type": "markdown",
62 "metadata": {},
63 "source": [
64 "## Implementing special display methods"
65 ]
66 },
67 {
68 "cell_type": "markdown",
69 "metadata": {},
70 "source": [
71 "The main idea of the first approach is that you have to implement special display methods, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
72 "\n",
73 "* `_repr_html_`: return raw HTML as a string\n",
74 "* `_repr_json_`: return raw JSON as a string\n",
75 "* `_repr_jpeg_`: return raw JPEG data\n",
76 "* `_repr_png_`: return raw PNG data\n",
77 "* `_repr_svg_`: return raw SVG data as a string\n",
78 "* `_repr_latex_`: return LaTeX commands in a string surrounded by \"$\"."
79 ]
80 },
81 {
82 "cell_type": "markdown",
83 "metadata": {},
84 "source": [
85 "### Model Citizen: pandas"
86 ]
87 },
88 {
89 "cell_type": "markdown",
90 "metadata": {},
91 "source": [
92 "A prominent example of a package that has IPython-aware rich representations of its objects is [pandas](http://pandas.pydata.org/).\n",
93 "\n",
94 "A pandas DataFrame has a rich HTML table representation,\n",
95 "using `_repr_html_`.\n"
96 ]
97 },
98 {
99 "cell_type": "code",
100 "execution_count": 2,
101 "metadata": {
102 "collapsed": false
103 },
104 "outputs": [],
105 "source": [
106 "import io\n",
107 "import pandas"
108 ]
109 },
110 {
111 "cell_type": "code",
112 "execution_count": 3,
113 "metadata": {
114 "collapsed": false
115 },
116 "outputs": [
117 {
118 "name": "stdout",
119 "output_type": "stream",
120 "text": [
121 "Writing data.csv\n"
122 ]
123 }
124 ],
125 "source": [
126 "%%writefile data.csv\n",
127 "Date,Open,High,Low,Close,Volume,Adj Close\n",
128 "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
129 "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
130 "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
131 "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
132 "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
133 "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53\n"
134 ]
135 },
136 {
137 "cell_type": "code",
138 "execution_count": 4,
139 "metadata": {
140 "collapsed": false
141 },
142 "outputs": [
143 {
144 "data": {
145 "text/plain": [
146 " Date Open High Low Close Volume Adj Close\n",
147 "0 2012-06-01 569.16 590.00 548.50 584.00 14077000 581.50\n",
148 "1 2012-05-01 584.90 596.76 522.18 577.73 18827900 575.26\n",
149 "2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48\n",
150 "3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99\n",
151 "4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12\n",
152 "5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53"
153 ]
154 },
155 "execution_count": 4,
156 "metadata": {},
157 "output_type": "execute_result"
158 }
159 ],
160 "source": [
161 "df = pandas.read_csv(\"data.csv\")\n",
162 "pandas.set_option('display.notebook_repr_html', False)\n",
163 "df"
164 ]
165 },
166 {
167 "cell_type": "markdown",
168 "metadata": {},
169 "source": [
170 "rich HTML can be activated via `pandas.set_option`."
171 ]
172 },
173 {
174 "cell_type": "code",
175 "execution_count": 5,
176 "metadata": {
177 "collapsed": false
178 },
179 "outputs": [
180 {
181 "data": {
182 "text/html": [
183 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
184 "<table border=\"1\" class=\"dataframe\">\n",
185 " <thead>\n",
186 " <tr style=\"text-align: right;\">\n",
187 " <th></th>\n",
188 " <th>Date</th>\n",
189 " <th>Open</th>\n",
190 " <th>High</th>\n",
191 " <th>Low</th>\n",
192 " <th>Close</th>\n",
193 " <th>Volume</th>\n",
194 " <th>Adj Close</th>\n",
195 " </tr>\n",
196 " </thead>\n",
197 " <tbody>\n",
198 " <tr>\n",
199 " <th>0</th>\n",
200 " <td> 2012-06-01</td>\n",
201 " <td> 569.16</td>\n",
202 " <td> 590.00</td>\n",
203 " <td> 548.50</td>\n",
204 " <td> 584.00</td>\n",
205 " <td> 14077000</td>\n",
206 " <td> 581.50</td>\n",
207 " </tr>\n",
208 " <tr>\n",
209 " <th>1</th>\n",
210 " <td> 2012-05-01</td>\n",
211 " <td> 584.90</td>\n",
212 " <td> 596.76</td>\n",
213 " <td> 522.18</td>\n",
214 " <td> 577.73</td>\n",
215 " <td> 18827900</td>\n",
216 " <td> 575.26</td>\n",
217 " </tr>\n",
218 " <tr>\n",
219 " <th>2</th>\n",
220 " <td> 2012-04-02</td>\n",
221 " <td> 601.83</td>\n",
222 " <td> 644.00</td>\n",
223 " <td> 555.00</td>\n",
224 " <td> 583.98</td>\n",
225 " <td> 28759100</td>\n",
226 " <td> 581.48</td>\n",
227 " </tr>\n",
228 " <tr>\n",
229 " <th>3</th>\n",
230 " <td> 2012-03-01</td>\n",
231 " <td> 548.17</td>\n",
232 " <td> 621.45</td>\n",
233 " <td> 516.22</td>\n",
234 " <td> 599.55</td>\n",
235 " <td> 26486000</td>\n",
236 " <td> 596.99</td>\n",
237 " </tr>\n",
238 " <tr>\n",
239 " <th>4</th>\n",
240 " <td> 2012-02-01</td>\n",
241 " <td> 458.41</td>\n",
242 " <td> 547.61</td>\n",
243 " <td> 453.98</td>\n",
244 " <td> 542.44</td>\n",
245 " <td> 22001000</td>\n",
246 " <td> 540.12</td>\n",
247 " </tr>\n",
248 " <tr>\n",
249 " <th>5</th>\n",
250 " <td> 2012-01-03</td>\n",
251 " <td> 409.40</td>\n",
252 " <td> 458.24</td>\n",
253 " <td> 409.00</td>\n",
254 " <td> 456.48</td>\n",
255 " <td> 12949100</td>\n",
256 " <td> 454.53</td>\n",
257 " </tr>\n",
258 " </tbody>\n",
259 "</table>\n",
260 "</div>"
261 ],
262 "text/plain": [
263 " Date Open High Low Close Volume Adj Close\n",
264 "0 2012-06-01 569.16 590.00 548.50 584.00 14077000 581.50\n",
265 "1 2012-05-01 584.90 596.76 522.18 577.73 18827900 575.26\n",
266 "2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48\n",
267 "3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99\n",
268 "4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12\n",
269 "5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53"
270 ]
271 },
272 "execution_count": 5,
273 "metadata": {},
274 "output_type": "execute_result"
275 }
276 ],
277 "source": [
278 "pandas.set_option('display.notebook_repr_html', True)\n",
279 "df"
280 ]
281 },
282 {
283 "cell_type": "code",
284 "execution_count": 6,
285 "metadata": {
286 "collapsed": false
287 },
288 "outputs": [
289 {
290 "name": "stdout",
291 "output_type": "stream",
292 "text": [
293 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
294 "<table border=\"1\" class=\"dataframe\">\n",
295 " <thead>\n",
296 " <tr style=\"text-align: right;\">\n",
297 " <th></th>\n",
298 " <th>Date</th>\n",
299 " <th>Open</th>\n",
300 " <th>High</th>\n",
301 " <th>Low</th>\n",
302 " <th>Close</th>\n",
303 " <th>Volume</th>\n",
304 " <th>Adj Close</th>\n",
305 " </tr>\n",
306 " </thead>\n",
307 " <tbody>\n",
308 " <tr>\n",
309 " <th>0</th>\n",
310 " <td> 2012-06-01</td>\n",
311 " <td> 569.16</td>\n",
312 " <td> 590.00</td>\n"
313 ]
314 }
315 ],
316 "source": [
317 "lines = df._repr_html_().splitlines()\n",
318 "print \"\\n\".join(lines[:20])"
319 ]
320 },
321 {
322 "cell_type": "markdown",
323 "metadata": {},
324 "source": [
325 "### Exercise"
326 ]
327 },
328 {
329 "cell_type": "markdown",
330 "metadata": {},
331 "source": [
332 "Write a simple `Circle` Python class. Don't even worry about properties such as radius, position, colors, etc. To help you out use the following representations (remember to wrap them in Python strings):\n",
333 "\n",
334 "For HTML:\n",
335 "\n",
336 " &#x25CB;\n",
337 "\n",
338 "For SVG:\n",
339 "\n",
340 " <svg width=\"100px\" height=\"100px\">\n",
341 " <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"white\"/>\n",
342 " </svg>\n",
343 "\n",
344 "For LaTeX (wrap with `$` and use a raw Python string):\n",
345 "\n",
346 " \\bigcirc\n",
347 "\n",
348 "After you write the class, create an instance and then use `display_html`, `display_svg` and `display_latex` to display those representations.\n",
349 "\n",
350 "Tips : you can slightly tweek the representation to know from which `_repr_*_` method it came from. \n",
351 "For example in my solution the svg representation is blue, and the HTML one show \"`HTML`\" between brackets."
352 ]
353 },
354 {
355 "cell_type": "markdown",
356 "metadata": {},
357 "source": [
358 "### Solution"
359 ]
360 },
361 {
362 "cell_type": "markdown",
363 "metadata": {},
364 "source": [
365 "Here is my simple `MyCircle` class:"
366 ]
367 },
368 {
369 "cell_type": "code",
370 "execution_count": 8,
371 "metadata": {
372 "collapsed": false
373 },
374 "outputs": [],
375 "source": [
376 "%load soln/mycircle.py"
377 ]
378 },
379 {
380 "cell_type": "markdown",
381 "metadata": {},
382 "source": [
383 "Now create an instance and use the display methods:"
384 ]
385 },
386 {
387 "cell_type": "code",
388 "execution_count": 11,
389 "metadata": {
390 "collapsed": false
391 },
392 "outputs": [],
393 "source": [
394 "c = MyCircle()"
395 ]
396 },
397 {
398 "cell_type": "code",
399 "execution_count": 12,
400 "metadata": {
401 "collapsed": false
402 },
403 "outputs": [
404 {
405 "data": {
406 "text/html": [
407 "&#x25CB; (<b>html</b>)"
408 ]
409 },
410 "metadata": {},
411 "output_type": "display_data"
412 }
413 ],
414 "source": [
415 "display_html(c)"
416 ]
417 },
418 {
419 "cell_type": "code",
420 "execution_count": 13,
421 "metadata": {
422 "collapsed": false
423 },
424 "outputs": [
425 {
426 "data": {
427 "image/svg+xml": [
428 "<svg width=\"100px\" height=\"100px\">\n",
429 " <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"blue\"/>\n",
430 " </svg>"
431 ]
432 },
433 "metadata": {},
434 "output_type": "display_data"
435 }
436 ],
437 "source": [
438 "display_svg(c)"
439 ]
440 },
441 {
442 "cell_type": "code",
443 "execution_count": 14,
444 "metadata": {
445 "collapsed": false
446 },
447 "outputs": [
448 {
449 "data": {
450 "text/latex": [
451 "$\\bigcirc \\LaTeX$"
452 ]
453 },
454 "metadata": {},
455 "output_type": "display_data"
456 }
457 ],
458 "source": [
459 "display_latex(c)"
460 ]
461 },
462 {
463 "cell_type": "code",
464 "execution_count": 15,
465 "metadata": {
466 "collapsed": false
467 },
468 "outputs": [
469 {
470 "data": {
471 "application/javascript": [
472 "alert('I am a circle!');"
473 ]
474 },
475 "metadata": {},
476 "output_type": "display_data"
477 }
478 ],
479 "source": [
480 "display_javascript(c)"
481 ]
482 },
483 {
484 "cell_type": "markdown",
485 "metadata": {},
486 "source": [
487 "## Adding IPython display support to existing objects"
488 ]
489 },
490 {
491 "cell_type": "markdown",
492 "metadata": {},
493 "source": [
494 "When you are directly writing your own classes, you can adapt them for display in IPython by following the above example. But in practice, we often need to work with existing code we can't modify. We now illustrate how to add these kinds of extended display capabilities to existing objects. To continue with our example above, we will add a PNG representation to our `Circle` class using Matplotlib."
495 ]
496 },
497 {
498 "cell_type": "markdown",
499 "metadata": {},
500 "source": [
501 "### Model citizen: sympy"
502 ]
503 },
504 {
505 "cell_type": "markdown",
506 "metadata": {},
507 "source": [
508 "[SymPy](http://sympy.org) is another model citizen that defines rich representations of its object.\n",
509 "Unlike pandas above, sympy registers display formatters via IPython's display formatter API, rather than declaring `_repr_mime_` methods."
510 ]
511 },
512 {
513 "cell_type": "code",
514 "execution_count": 16,
515 "metadata": {
516 "collapsed": false
517 },
518 "outputs": [],
519 "source": [
520 "from sympy import Rational, pi, exp, I, symbols\n",
521 "x, y, z = symbols(\"x y z\")"
522 ]
523 },
524 {
525 "cell_type": "code",
526 "execution_count": 17,
527 "metadata": {
528 "collapsed": false
529 },
530 "outputs": [
531 {
532 "data": {
533 "text/plain": [
534 "3*pi/2 + exp(I*x)/(x**2 + y)"
535 ]
536 },
537 "execution_count": 17,
538 "metadata": {},
539 "output_type": "execute_result"
540 }
541 ],
542 "source": [
543 "r = Rational(3,2)*pi + exp(I*x) / (x**2 + y)\n",
544 "r"
545 ]
546 },
547 {
548 "cell_type": "markdown",
549 "metadata": {},
550 "source": [
551 "SymPy provides an `init_printing` function that sets up advanced $\\LaTeX$\n",
552 "representations of its objects."
553 ]
554 },
555 {
556 "cell_type": "code",
557 "execution_count": 18,
558 "metadata": {
559 "collapsed": false
560 },
561 "outputs": [
562 {
563 "data": {
564 "image/png": [
565 "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAlCAYAAADV/m7fAAAABHNCSVQICAgIfAhkiAAAA9xJREFU\n",
566 "aIHt2l2IVVUUwPHfzDQ4hc1UFpaVTvoiKNoHaTCmU/lQaERR9mGUZBSkUVEQvcR9CSKIoCgoom5F\n",
567 "BX2T+RD6EBQVZI1BBoVSFA0JUkNiiX1MD+uc5szkOPfjnHvvyP3DwF4z96y1Zp+91l577Uubuuho\n",
568 "tgMNYikG8TU+wwosxs9YjTvwWy2Kj8mMV2IeZiTGythem78txwjmYitOxHs4G7vwthonbyL7cHMy\n",
569 "vga/4/g8FLcAx+FVnIVesXDeRScW1KM4uwIH8V0yHkV3PYpbjFnifzsDt2J3Il+Mg9hTq+LJcuAr\n",
570 "Ynk/VKviJtKJe3AIv2I2Hi3SWJZzcR8O4LGijBbM0yKynsCbOKkZTtyGzzGzAbaWGJ9K6mEh/hC5\n",
571 "fD1uETmvcC7AXpFkU0dGcXUDbJfRn5Oua7EjJ10VkYbwXyLnDSfyfPyJnY10Jge+FZtCSofYNAqr\n",
572 "d9PQ2YHnsBn/iEJzrditsvTiGVyBnkl0jmIVPszb2QoYEnnvLvyCY7El8akQqnkzHaLoHMKnuEpU\n",
573 "9d/jbjwldr6D+Fi8iEooo5ToOaq5CZdl5NfRlYy31KG3LL8c2HCq2f1ezIxPSJ79W1T5p+Tp1HSi\n",
574 "1vJhPT5JxgvFRE7FC+JQP5G5WCbCfyIbRTnV8oxO8TORnTg/Ga/BN3XYLqsshKfysSk/6QqsZjNJ\n",
575 "uzZfJHKvWEU9xpcQeTOVj53YJHZeeKRAX8YZrZZ7sc1Y2A6LyVudl1M1sgbviIlbjvMaYTQ7gcvF\n",
576 "IbwkJmjlJM8sxbMZeQg/qO1l5MkCXJ+M9+DMRhqfiYcz8jrRDzy9AbbL8iljZhjrX76POTnorJgl\n",
577 "ovBNm4u9Ikmua4Dtx3FqjvouxAM56quIDhHCaaJeJCbwnEY7Uid9eLDZTsBLCmxCFsgm0UnvVt2m\n",
578 "tjlPJzaKnWy63djdIC6H9olO9OIqni3l5cRaMYFEadKfl+KcGMAG0S2/EbfjLdF+q4fSJL/vElee\n",
579 "z4vTEpwsczrKlh6rxP3BVpHUL8VpdTqWJ73i2FjGB7hTtO/3i4qhCK4UTZPsYroIP6YfSMN0Pr70\n",
580 "/xZ+n5zuTHOgR1QKh0TJNWJ86VUps0X7LZuiVuCjjLxfXKj1JfJuMYEH8GQiT9c7I0QIpWfxviN9\n",
581 "sEJKR/jbdXgjI+8Sl29o/umhGi4XJ6V+sUEMiVW0oWC7c4zdG/cn8n9XHV2HeaBVGRD16SxxXbBM\n",
582 "1KmvidCqh0GRVw/HXvFNjW7xAofxcp32jjrur/Bz20St2aZCFuEnkSoG8JWxdhmmVwg3g04xefNw\n",
583 "ifjCwUhTPWrTpk2bFuJflVvSLV1580UAAAAASUVORK5CYII=\n"
584 ],
585 "text/latex": [
586 "$$\\frac{3}{2} \\pi + \\frac{e^{\\mathbf{\\imath} x}}{x^{2} + y}$$"
587 ],
588 "text/plain": [
589 " β…ˆβ‹…x \n",
590 "3β‹…Ο€ β„― \n",
591 "─── + ──────\n",
592 " 2 2 \n",
593 " x + y"
594 ]
595 },
596 "execution_count": 18,
597 "metadata": {},
598 "output_type": "execute_result"
599 }
600 ],
601 "source": [
602 "from sympy.interactive.printing import init_printing\n",
603 "init_printing()\n",
604 "r"
605 ]
606 },
607 {
608 "cell_type": "markdown",
609 "metadata": {},
610 "source": [
611 "To add a display method to an existing class, we must use IPython's display formatter API. Here we show all of the available formatters:"
612 ]
613 },
614 {
615 "cell_type": "code",
616 "execution_count": 6,
617 "metadata": {
618 "collapsed": false
619 },
620 "outputs": [
621 {
622 "name": "stdout",
623 "output_type": "stream",
624 "text": [
625 " text/html : HTMLFormatter\n",
626 " image/jpeg : JPEGFormatter\n",
627 " image/svg+xml : SVGFormatter\n",
628 " image/png : PNGFormatter\n",
629 " application/javascript : JavascriptFormatter\n",
630 " text/latex : LatexFormatter\n",
631 " application/json : JSONFormatter\n",
632 " text/plain : PlainTextFormatter\n"
633 ]
634 }
635 ],
636 "source": [
637 "ip = get_ipython()\n",
638 "for mime, formatter in ip.display_formatter.formatters.items():\n",
639 " print '%24s : %s' % (mime, formatter.__class__.__name__)\n"
640 ]
641 },
642 {
643 "cell_type": "markdown",
644 "metadata": {},
645 "source": [
646 "Let's grab the PNG formatter:"
647 ]
648 },
649 {
650 "cell_type": "code",
651 "execution_count": 20,
652 "metadata": {
653 "collapsed": false
654 },
655 "outputs": [],
656 "source": [
657 "png_f = ip.display_formatter.formatters['image/png']"
658 ]
659 },
660 {
661 "cell_type": "markdown",
662 "metadata": {},
663 "source": [
664 "We will use the `for_type` method to register our display function."
665 ]
666 },
667 {
668 "cell_type": "code",
669 "execution_count": 21,
670 "metadata": {
671 "collapsed": false
672 },
673 "outputs": [],
674 "source": [
675 "png_f.for_type?"
676 ]
677 },
678 {
679 "cell_type": "markdown",
680 "metadata": {},
681 "source": [
682 "As the docstring describes, we need to define a function the takes the object as a parameter and returns the raw PNG data."
683 ]
684 },
685 {
686 "cell_type": "code",
687 "execution_count": 22,
688 "metadata": {
689 "collapsed": false
690 },
691 "outputs": [],
692 "source": [
693 "%matplotlib inline\n",
694 "import matplotlib.pyplot as plt"
695 ]
696 },
697 {
698 "cell_type": "code",
699 "execution_count": 23,
700 "metadata": {
701 "collapsed": false
702 },
703 "outputs": [
704 {
705 "data": {
706 "text/plain": [
707 "<r Circle with r=1 at (0, 0)>"
708 ]
709 },
710 "execution_count": 23,
711 "metadata": {},
712 "output_type": "execute_result"
713 }
714 ],
715 "source": [
716 "class AnotherCircle(object):\n",
717 " def __init__(self, radius=1, center=(0,0), color='r'):\n",
718 " self.radius = radius\n",
719 " self.center = center\n",
720 " self.color = color\n",
721 " \n",
722 " def __repr__(self):\n",
723 " return \"<%s Circle with r=%s at %s>\" % (\n",
724 " self.color,\n",
725 " self.radius,\n",
726 " self.center,\n",
727 " )\n",
728 " \n",
729 "c = AnotherCircle()\n",
730 "c"
731 ]
732 },
733 {
734 "cell_type": "code",
735 "execution_count": 24,
736 "metadata": {
737 "collapsed": false
738 },
739 "outputs": [],
740 "source": [
741 "from IPython.core.pylabtools import print_figure\n",
742 "\n",
743 "def png_circle(circle):\n",
744 " \"\"\"Render AnotherCircle to png data using matplotlib\"\"\"\n",
745 " fig, ax = plt.subplots()\n",
746 " patch = plt.Circle(circle.center,\n",
747 " radius=circle.radius,\n",
748 " fc=circle.color,\n",
749 " )\n",
750 " ax.add_patch(patch)\n",
751 " plt.axis('scaled')\n",
752 " data = print_figure(fig, 'png')\n",
753 " # We MUST close the figure, otherwise IPython's display machinery\n",
754 " # will pick it up and send it as output, resulting in a double display\n",
755 " plt.close(fig)\n",
756 " return data"
757 ]
758 },
759 {
760 "cell_type": "code",
761 "execution_count": 27,
762 "metadata": {
763 "collapsed": false
764 },
765 "outputs": [
766 {
767 "name": "stdout",
768 "output_type": "stream",
769 "text": [
770 "'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00'\n"
771 ]
772 }
773 ],
774 "source": [
775 "c = AnotherCircle()\n",
776 "print repr(png_circle(c)[:10])"
777 ]
778 },
779 {
780 "cell_type": "markdown",
781 "metadata": {},
782 "source": [
783 "Now we register the display function for the type:"
784 ]
785 },
786 {
787 "cell_type": "code",
788 "execution_count": 28,
789 "metadata": {
790 "collapsed": false
791 },
792 "outputs": [],
793 "source": [
794 "png_f.for_type(AnotherCircle, png_circle)"
795 ]
796 },
797 {
798 "cell_type": "markdown",
799 "metadata": {},
800 "source": [
801 "Now all `Circle` instances have PNG representations!"
802 ]
803 },
804 {
805 "cell_type": "code",
806 "execution_count": 29,
807 "metadata": {
808 "collapsed": false
809 },
810 "outputs": [
811 {
812 "data": {
813 "image/png": [
814 "iVBORw0KGgoAAAANSUhEUgAAAY4AAAF8CAYAAADYXlxuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
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817 "mZnX8/Ho8Yjh8/m8X34iXn6u90dmtVqtICIispFcdAAiInIvLA4iIuoWFgcREXULi4OIiLqFxUFE\n",
818 "RN3C4iAiom7xdcRGFi5ciOLiYvj6+kKtVuOJJ55ATExMh+VKS0uxaNEi1NXVQa1WIz09HVFRUY6I\n",
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820 "EZELOeRU1bBhwyCXn99UbGwsTCZTh2WqqqpgsViQkpICANDr9aiurobZbHZEBCIichGHX+PYsmUL\n",
821 "9Hp9h8+NRiNUKtVlnwUHB8NoNDo6AhEROZFDi2PTpk0oKirCQw891Plg8o7Dtba2OjICERE5mUMu\n",
822 "jgPA2rVr8eOPP2LOnDkICAjo8H2tVtvhtJTZbIZOp+t0e9u2bUN7e7uj4hER0SX8/PwwYsSIHq1r\n",
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824 "OowZM0Z0DJu4U1bA/rxNrU0wNhlRe64WpiYTjE1GVDZW4qj5KIpNxThVdwoVDRUwNok/ZRrkF4QI\n",
825 "ZQR6K3sjQZuAeG08eit7Q9dLB12ADsH+wdD4a6D2V9s9ljv9HDCrc+Tm5vZ4XbuLo7q6Gps3b0Zk\n",
826 "ZCRmz5598fPp06dj06ZNAIBp06YBAAwGAxYvXoxly5ZBrVbDYDDYOzwRAKC5rRmVjZWobqxGlaUK\n",
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1065 "TkSuQmCC\n"
1066 ],
1067 "text/plain": [
1068 "<g Circle with r=2 at (1, 0)>"
1069 ]
1070 },
1071 "execution_count": 29,
1072 "metadata": {},
1073 "output_type": "execute_result"
1074 }
1075 ],
1076 "source": [
1077 "c2 = AnotherCircle(radius=2, center=(1,0), color='g')\n",
1078 "c2"
1079 ]
1080 },
1081 {
1082 "cell_type": "code",
1083 "execution_count": 30,
1084 "metadata": {
1085 "collapsed": false
1086 },
1087 "outputs": [
1088 {
1089 "data": {
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1343 ]
1344 },
1345 "metadata": {},
1346 "output_type": "display_data"
1347 }
1348 ],
1349 "source": [
1350 "display_png(c2)"
1351 ]
1352 },
1353 {
1354 "cell_type": "markdown",
1355 "metadata": {},
1356 "source": [
1357 "## return the object"
1358 ]
1359 },
1360 {
1361 "cell_type": "code",
1362 "execution_count": null,
1363 "metadata": {
1364 "collapsed": false
1365 },
1366 "outputs": [],
1367 "source": [
1368 "# for demonstration purpose, I do the same with a circle that has no _repr_javascript method\n",
1369 "class MyNoJSCircle(MyCircle):\n",
1370 " \n",
1371 " def _repr_javascript_(self):\n",
1372 " return\n",
1373 "\n",
1374 "cNoJS = MyNoJSCircle()"
1375 ]
1376 },
1377 {
1378 "cell_type": "markdown",
1379 "metadata": {},
1380 "source": [
1381 "Of course you can now still return the object, and this will use compute all the representations, store them in the notebook and show you the appropriate one."
1382 ]
1383 },
1384 {
1385 "cell_type": "code",
1386 "execution_count": null,
1387 "metadata": {
1388 "collapsed": false
1389 },
1390 "outputs": [],
1391 "source": [
1392 "cNoJS"
1393 ]
1394 },
1395 {
1396 "cell_type": "markdown",
1397 "metadata": {},
1398 "source": [
1399 "Or just use `display(object)` if you are in a middle of a loop"
1400 ]
1401 },
1402 {
1403 "cell_type": "code",
1404 "execution_count": null,
1405 "metadata": {
1406 "collapsed": false
1407 },
1408 "outputs": [],
1409 "source": [
1410 "for i in range(3):\n",
1411 " display(cNoJS)"
1412 ]
1413 },
1414 {
1415 "cell_type": "markdown",
1416 "metadata": {},
1417 "source": [
1418 "Advantage of using `display()` versus `display_*()` is that all representation will be stored in the notebook document and notebook file, they are then availlable for other frontends or post-processing tool like `nbconvert`."
1419 ]
1420 },
1421 {
1422 "cell_type": "markdown",
1423 "metadata": {},
1424 "source": [
1425 "Let's compare `display()` vs `display_html()` for our circle in the Notebook Web-app and we'll see later the difference in nbconvert."
1426 ]
1427 },
1428 {
1429 "cell_type": "code",
1430 "execution_count": null,
1431 "metadata": {
1432 "collapsed": false
1433 },
1434 "outputs": [],
1435 "source": [
1436 "print \"I should see a nice html circle in web-app, but\"\n",
1437 "print \"nothing if the format I'm viewing the notebook in\"\n",
1438 "print \"does not support html\"\n",
1439 "display_html(cNoJS)"
1440 ]
1441 },
1442 {
1443 "cell_type": "code",
1444 "execution_count": null,
1445 "metadata": {
1446 "collapsed": false
1447 },
1448 "outputs": [],
1449 "source": [
1450 "print \"Whatever the format I will see a representation\"\n",
1451 "print \"of my circle\"\n",
1452 "display(cNoJS)"
1453 ]
1454 },
1455 {
1456 "cell_type": "code",
1457 "execution_count": null,
1458 "metadata": {
1459 "collapsed": false
1460 },
1461 "outputs": [],
1462 "source": [
1463 "print \"Same if I return the object\"\n",
1464 "cNoJS"
1465 ]
1466 },
1467 {
1468 "cell_type": "code",
1469 "execution_count": null,
1470 "metadata": {
1471 "collapsed": false
1472 },
1473 "outputs": [],
1474 "source": [
1475 "print \"But not if I print it\"\n",
1476 "print cNoJS"
1477 ]
1478 }
1479 ],
1480 "metadata": {
1481 "kernelspec": {
1482 "display_name": "Python 3",
1483 "language": "python",
1484 "name": "python3"
1485 },
1486 "language_info": {
1487 "codemirror_mode": {
1488 "name": "ipython",
1489 "version": 3
1490 },
1491 "file_extension": ".py",
1492 "mimetype": "text/x-python",
1493 "name": "python",
1494 "nbconvert_exporter": "python",
1495 "pygments_lexer": "ipython3",
1496 "version": "3.4.2"
1497 }
1498 },
1499 "nbformat": 4,
1500 "nbformat_minor": 0
1501 }
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