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Updating a few example notebooks to v3.
Brian Granger -
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1 {
1 {
2 "worksheets": [
2 "metadata": {
3 {
3 "name": "BackgroundJobs"
4 "cells": [
4 },
5 {
5 "nbformat": 3,
6 "source": "# Simple interactive bacgkround jobs with IPython\n\nWe start by loading the `backgroundjobs` library and defining a few trivial functions to illustrate things with.",
6 "nbformat_minor": 0,
7 "cell_type": "markdown"
7 "worksheets": [
8 },
8 {
9 {
9 "cells": [
10 "cell_type": "code",
10 {
11 "language": "python",
11 "cell_type": "markdown",
12 "outputs": [],
12 "metadata": {},
13 "collapsed": false,
13 "source": [
14 "prompt_number": 35,
14 "# Simple interactive bacgkround jobs with IPython\n",
15 "input": "from IPython.lib import backgroundjobs as bg\n\nimport sys\nimport time\n\ndef sleepfunc(interval=2, *a, **kw):\n args = dict(interval=interval,\n args=a,\n kwargs=kw)\n time.sleep(interval)\n return args\n\ndef diefunc(interval=2, *a, **kw):\n time.sleep(interval)\n raise Exception(\"Dead job with interval %s\" % interval)\n\ndef printfunc(interval=1, reps=5):\n for n in range(reps):\n time.sleep(interval)\n print 'In the background...', n\n sys.stdout.flush()\n print 'All done!'\n sys.stdout.flush()"
15 "\n",
16 },
16 "We start by loading the `backgroundjobs` library and defining a few trivial functions to illustrate things with."
17 {
17 ]
18 "source": "Now, we can create a job manager (called simply `jobs`) and use it to submit new jobs.\n<br>\nRun the cell below and wait a few seconds for the whole thing to finish, until you see the \"All done!\" printout.",
18 },
19 "cell_type": "markdown"
19 {
20 },
20 "cell_type": "code",
21 {
21 "collapsed": false,
22 "cell_type": "code",
22 "input": [
23 "language": "python",
23 "from IPython.lib import backgroundjobs as bg\n",
24 "outputs": [
24 "\n",
25 {
25 "import sys\n",
26 "output_type": "stream",
26 "import time\n",
27 "stream": "stdout",
27 "\n",
28 "text": "Starting job # 0 in a separate thread.\nStarting job # 2 in a separate thread.\nStarting job # 3 in a separate thread.\nStarting job # 4 in a separate thread.\nStarting job # 5 in a separate thread.\n"
28 "def sleepfunc(interval=2, *a, **kw):\n",
29 },
29 " args = dict(interval=interval,\n",
30 {
30 " args=a,\n",
31 "output_type": "stream",
31 " kwargs=kw)\n",
32 "stream": "stdout",
32 " time.sleep(interval)\n",
33 "text": "In the background... 0\n"
33 " return args\n",
34 },
34 "\n",
35 {
35 "def diefunc(interval=2, *a, **kw):\n",
36 "output_type": "stream",
36 " time.sleep(interval)\n",
37 "stream": "stdout",
37 " raise Exception(\"Dead job with interval %s\" % interval)\n",
38 "text": "In the background... 1\n"
38 "\n",
39 },
39 "def printfunc(interval=1, reps=5):\n",
40 {
40 " for n in range(reps):\n",
41 "output_type": "stream",
41 " time.sleep(interval)\n",
42 "stream": "stdout",
42 " print 'In the background...', n\n",
43 "text": "In the background... 2\n"
43 " sys.stdout.flush()\n",
44 },
44 " print 'All done!'\n",
45 {
45 " sys.stdout.flush()"
46 "output_type": "stream",
46 ],
47 "stream": "stdout",
47 "language": "python",
48 "text": "All done!\n"
48 "metadata": {},
49 }
49 "outputs": [],
50 ],
50 "prompt_number": 35
51 "collapsed": false,
51 },
52 "prompt_number": 36,
52 {
53 "input": "jobs = bg.BackgroundJobManager()\n\n# Start a few jobs, the first one will have ID # 0\njobs.new(sleepfunc, 4)\njobs.new(sleepfunc, kw={'reps':2})\njobs.new('printfunc(1,3)')\n\n# This makes a couple of jobs which will die. Let's keep a reference to\n# them for easier traceback reporting later\ndiejob1 = jobs.new(diefunc, 1)\ndiejob2 = jobs.new(diefunc, 2)"
53 "cell_type": "markdown",
54 },
54 "metadata": {},
55 {
55 "source": [
56 "source": "You can check the status of your jobs at any time:",
56 "Now, we can create a job manager (called simply `jobs`) and use it to submit new jobs.\n",
57 "cell_type": "markdown"
57 "<br>\n",
58 },
58 "Run the cell below and wait a few seconds for the whole thing to finish, until you see the \"All done!\" printout."
59 {
59 ]
60 "cell_type": "code",
60 },
61 "language": "python",
61 {
62 "outputs": [
62 "cell_type": "code",
63 {
63 "collapsed": false,
64 "output_type": "stream",
64 "input": [
65 "stream": "stdout",
65 "jobs = bg.BackgroundJobManager()\n",
66 "text": "Completed jobs:\n0 : &lt;function sleepfunc at 0x30e1578&gt;\n2 : &lt;function sleepfunc at 0x30e1578&gt;\n3 : printfunc(1,3)\n\nDead jobs:\n4 : &lt;function diefunc at 0x304d488&gt;\n5 : &lt;function diefunc at 0x304d488&gt;\n\n"
66 "\n",
67 }
67 "# Start a few jobs, the first one will have ID # 0\n",
68 ],
68 "jobs.new(sleepfunc, 4)\n",
69 "collapsed": false,
69 "jobs.new(sleepfunc, kw={'reps':2})\n",
70 "prompt_number": 37,
70 "jobs.new('printfunc(1,3)')\n",
71 "input": "jobs.status()"
71 "\n",
72 },
72 "# This makes a couple of jobs which will die. Let's keep a reference to\n",
73 {
73 "# them for easier traceback reporting later\n",
74 "source": "For any completed job, you can get its result easily:",
74 "diejob1 = jobs.new(diefunc, 1)\n",
75 "cell_type": "markdown"
75 "diejob2 = jobs.new(diefunc, 2)"
76 },
76 ],
77 {
77 "language": "python",
78 "cell_type": "code",
78 "metadata": {},
79 "language": "python",
79 "outputs": [
80 "outputs": [],
80 {
81 "collapsed": false,
81 "output_type": "stream",
82 "prompt_number": 43,
82 "stream": "stdout",
83 "input": "jobs[0].result\nj0 = jobs[0]\nj0.join?"
83 "text": [
84 },
84 "Starting job # 0 in a separate thread.\n",
85 {
85 "Starting job # 2 in a separate thread.\n",
86 "source": "You can get the traceback of any dead job. Run the line\nbelow again interactively until it prints a traceback (check the status\nof the job):\n",
86 "Starting job # 3 in a separate thread.\n",
87 "cell_type": "markdown"
87 "Starting job # 4 in a separate thread.\n",
88 },
88 "Starting job # 5 in a separate thread.\n"
89 {
89 ]
90 "cell_type": "code",
90 },
91 "language": "python",
91 {
92 "outputs": [
92 "output_type": "stream",
93 {
93 "stream": "stdout",
94 "output_type": "stream",
94 "text": [
95 "stream": "stdout",
95 "In the background... 0\n"
96 "text": "Status of diejob1: Dead (Exception), call jobs.traceback() for details\n<span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">Exception</span> Traceback (most recent call last)\n<span class=\"ansigreen\">/home/fperez/usr/lib/python2.6/site-packages/IPython/lib/backgroundjobs.py</span> in <span class=\"ansicyan\">call</span><span class=\"ansiblue\">(self)</span>\n<span class=\"ansigreen\"> 462</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 463</span> <span class=\"ansigreen\">def</span> call<span class=\"ansiyellow\">(</span>self<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">--&gt; 464</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">return</span> self<span class=\"ansiyellow\">.</span>func<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">*</span>self<span class=\"ansiyellow\">.</span>args<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>self<span class=\"ansiyellow\">.</span>kwargs<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansigreen\">/home/fperez/ipython/ipython/docs/examples/lib/&lt;ipython-input-15-54795a097787&gt;</span> in <span class=\"ansicyan\">diefunc</span><span class=\"ansiblue\">(interval, *a, **kw)</span>\n<span class=\"ansigreen\"> 14</span> <span class=\"ansigreen\">def</span> diefunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">2</span><span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">*</span>a<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>kw<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 15</span> time<span class=\"ansiyellow\">.</span>sleep<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">---&gt; 16</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">raise</span> Exception<span class=\"ansiyellow\">(</span><span class=\"ansiblue\">&quot;Dead job with interval %s&quot;</span> <span class=\"ansiyellow\">%</span> interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 17</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 18</span> <span class=\"ansigreen\">def</span> printfunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">,</span> reps<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">5</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">Exception</span>: Dead job with interval 1\n"
96 ]
97 }
97 },
98 ],
98 {
99 "collapsed": false,
99 "output_type": "stream",
100 "prompt_number": 34,
100 "stream": "stdout",
101 "input": "print \"Status of diejob1:\", diejob1.status\ndiejob1.traceback() # jobs.traceback(4) would also work here, with the job number"
101 "text": [
102 },
102 "In the background... 1\n"
103 {
103 ]
104 "source": "This will print all tracebacks for all dead jobs:",
104 },
105 "cell_type": "markdown"
105 {
106 },
106 "output_type": "stream",
107 {
107 "stream": "stdout",
108 "cell_type": "code",
108 "text": [
109 "language": "python",
109 "In the background... 2\n"
110 "outputs": [
110 ]
111 {
111 },
112 "output_type": "stream",
112 {
113 "stream": "stdout",
113 "output_type": "stream",
114 "text": "Traceback for: &lt;BackgroundJob #4: &lt;function diefunc at 0x30df758&gt;&gt;\n<span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">Exception</span> Traceback (most recent call last)\n<span class=\"ansigreen\">/home/fperez/usr/lib/python2.6/site-packages/IPython/lib/backgroundjobs.py</span> in <span class=\"ansicyan\">call</span><span class=\"ansiblue\">(self)</span>\n<span class=\"ansigreen\"> 462</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 463</span> <span class=\"ansigreen\">def</span> call<span class=\"ansiyellow\">(</span>self<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">--&gt; 464</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">return</span> self<span class=\"ansiyellow\">.</span>func<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">*</span>self<span class=\"ansiyellow\">.</span>args<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>self<span class=\"ansiyellow\">.</span>kwargs<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansigreen\">/home/fperez/ipython/ipython/docs/examples/lib/&lt;ipython-input-15-54795a097787&gt;</span> in <span class=\"ansicyan\">diefunc</span><span class=\"ansiblue\">(interval, *a, **kw)</span>\n<span class=\"ansigreen\"> 14</span> <span class=\"ansigreen\">def</span> diefunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">2</span><span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">*</span>a<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>kw<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 15</span> time<span class=\"ansiyellow\">.</span>sleep<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">---&gt; 16</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">raise</span> Exception<span class=\"ansiyellow\">(</span><span class=\"ansiblue\">&quot;Dead job with interval %s&quot;</span> <span class=\"ansiyellow\">%</span> interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 17</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 18</span> <span class=\"ansigreen\">def</span> printfunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">,</span> reps<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">5</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">Exception</span>: Dead job with interval 1\n\nTraceback for: &lt;BackgroundJob #5: &lt;function diefunc at 0x30df758&gt;&gt;\n<span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">Exception</span> Traceback (most recent call last)\n<span class=\"ansigreen\">/home/fperez/usr/lib/python2.6/site-packages/IPython/lib/backgroundjobs.py</span> in <span class=\"ansicyan\">call</span><span class=\"ansiblue\">(self)</span>\n<span class=\"ansigreen\"> 462</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 463</span> <span class=\"ansigreen\">def</span> call<span class=\"ansiyellow\">(</span>self<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">--&gt; 464</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">return</span> self<span class=\"ansiyellow\">.</span>func<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">*</span>self<span class=\"ansiyellow\">.</span>args<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>self<span class=\"ansiyellow\">.</span>kwargs<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansigreen\">/home/fperez/ipython/ipython/docs/examples/lib/&lt;ipython-input-15-54795a097787&gt;</span> in <span class=\"ansicyan\">diefunc</span><span class=\"ansiblue\">(interval, *a, **kw)</span>\n<span class=\"ansigreen\"> 14</span> <span class=\"ansigreen\">def</span> diefunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">2</span><span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">*</span>a<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>kw<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 15</span> time<span class=\"ansiyellow\">.</span>sleep<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">---&gt; 16</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">raise</span> Exception<span class=\"ansiyellow\">(</span><span class=\"ansiblue\">&quot;Dead job with interval %s&quot;</span> <span class=\"ansiyellow\">%</span> interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 17</span> <span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 18</span> <span class=\"ansigreen\">def</span> printfunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">,</span> reps<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">5</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n\n<span class=\"ansired\">Exception</span>: Dead job with interval 2\n\n"
114 "stream": "stdout",
115 }
115 "text": [
116 ],
116 "All done!\n"
117 "collapsed": false,
117 ]
118 "prompt_number": 33,
118 }
119 "input": "jobs.traceback()"
119 ],
120 },
120 "prompt_number": 36
121 {
121 },
122 "source": "The job manager can be flushed of all completed jobs at any time:",
122 {
123 "cell_type": "markdown"
123 "cell_type": "markdown",
124 },
124 "metadata": {},
125 {
125 "source": [
126 "cell_type": "code",
126 "You can check the status of your jobs at any time:"
127 "language": "python",
127 ]
128 "outputs": [
128 },
129 {
129 {
130 "output_type": "stream",
130 "cell_type": "code",
131 "stream": "stdout",
131 "collapsed": false,
132 "text": "No jobs to flush.\n"
132 "input": [
133 }
133 "jobs.status()"
134 ],
134 ],
135 "collapsed": false,
135 "language": "python",
136 "prompt_number": 25,
136 "metadata": {},
137 "input": "jobs.flush()"
137 "outputs": [
138 },
138 {
139 {
139 "output_type": "stream",
140 "source": "After that, the status is simply empty:",
140 "stream": "stdout",
141 "cell_type": "markdown"
141 "text": [
142 },
142 "Completed jobs:\n",
143 {
143 "0 : &lt;function sleepfunc at 0x30e1578&gt;\n",
144 "cell_type": "code",
144 "2 : &lt;function sleepfunc at 0x30e1578&gt;\n",
145 "language": "python",
145 "3 : printfunc(1,3)\n",
146 "outputs": [],
146 "\n",
147 "collapsed": true,
147 "Dead jobs:\n",
148 "prompt_number": 27,
148 "4 : &lt;function diefunc at 0x304d488&gt;\n",
149 "input": "jobs.status()"
149 "5 : &lt;function diefunc at 0x304d488&gt;\n",
150 },
150 "\n"
151 {
151 ]
152 "source": "It's easy to wait on a job:",
152 }
153 "cell_type": "markdown"
153 ],
154 },
154 "prompt_number": 37
155 {
155 },
156 "cell_type": "code",
156 {
157 "language": "python",
157 "cell_type": "markdown",
158 "outputs": [
158 "metadata": {},
159 {
159 "source": [
160 "output_type": "stream",
160 "For any completed job, you can get its result easily:"
161 "stream": "stdout",
161 ]
162 "text": "Starting job # 7 in a separate thread.\nWill wait for j now...\n"
162 },
163 },
163 {
164 {
164 "cell_type": "code",
165 "output_type": "stream",
165 "collapsed": false,
166 "stream": "stdout",
166 "input": [
167 "text": "Result from j:\n"
167 "jobs[0].result\n",
168 },
168 "j0 = jobs[0]\n",
169 {
169 "j0.join?"
170 "output_type": "pyout",
170 ],
171 "prompt_number": 46,
171 "language": "python",
172 "text": "{&apos;args&apos;: (), &apos;interval&apos;: 2, &apos;kwargs&apos;: {}}"
172 "metadata": {},
173 }
173 "outputs": [],
174 ],
174 "prompt_number": 43
175 "collapsed": false,
175 },
176 "prompt_number": 46,
176 {
177 "input": "j = jobs.new(sleepfunc, 2)\nprint \"Will wait for j now...\"\nsys.stdout.flush()\nj.join()\nprint \"Result from j:\"\nj.result"
177 "cell_type": "markdown",
178 },
178 "metadata": {},
179 {
179 "source": [
180 "input": "",
180 "You can get the traceback of any dead job. Run the line\n",
181 "cell_type": "code",
181 "below again interactively until it prints a traceback (check the status\n",
182 "collapsed": true,
182 "of the job):\n"
183 "language": "python",
183 ]
184 "outputs": []
184 },
185 }
185 {
186 ]
186 "cell_type": "code",
187 }
187 "collapsed": false,
188 ],
188 "input": [
189 "metadata": {
189 "print \"Status of diejob1:\", diejob1.status\n",
190 "name": "BackgroundJobs"
190 "diejob1.traceback() # jobs.traceback(4) would also work here, with the job number"
191 },
191 ],
192 "nbformat": 2
192 "language": "python",
193 "metadata": {},
194 "outputs": [
195 {
196 "output_type": "stream",
197 "stream": "stdout",
198 "text": [
199 "Status of diejob1: Dead (Exception), call jobs.traceback() for details\n",
200 "<span class=\"ansired\">---------------------------------------------------------------------------</span>\n",
201 "<span class=\"ansired\">Exception</span> Traceback (most recent call last)\n",
202 "<span class=\"ansigreen\">/home/fperez/usr/lib/python2.6/site-packages/IPython/lib/backgroundjobs.py</span> in <span class=\"ansicyan\">call</span><span class=\"ansiblue\">(self)</span>\n",
203 "<span class=\"ansigreen\"> 462</span> <span class=\"ansiyellow\"></span>\n",
204 "<span class=\"ansigreen\"> 463</span> <span class=\"ansigreen\">def</span> call<span class=\"ansiyellow\">(</span>self<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
205 "<span class=\"ansigreen\">--&gt; 464</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">return</span> self<span class=\"ansiyellow\">.</span>func<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">*</span>self<span class=\"ansiyellow\">.</span>args<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>self<span class=\"ansiyellow\">.</span>kwargs<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
206 "\n",
207 "<span class=\"ansigreen\">/home/fperez/ipython/ipython/docs/examples/lib/&lt;ipython-input-15-54795a097787&gt;</span> in <span class=\"ansicyan\">diefunc</span><span class=\"ansiblue\">(interval, *a, **kw)</span>\n",
208 "<span class=\"ansigreen\"> 14</span> <span class=\"ansigreen\">def</span> diefunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">2</span><span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">*</span>a<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>kw<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
209 "<span class=\"ansigreen\"> 15</span> time<span class=\"ansiyellow\">.</span>sleep<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
210 "<span class=\"ansigreen\">---&gt; 16</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">raise</span> Exception<span class=\"ansiyellow\">(</span><span class=\"ansiblue\">&quot;Dead job with interval %s&quot;</span> <span class=\"ansiyellow\">%</span> interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
211 "<span class=\"ansigreen\"> 17</span> <span class=\"ansiyellow\"></span>\n",
212 "<span class=\"ansigreen\"> 18</span> <span class=\"ansigreen\">def</span> printfunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">,</span> reps<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">5</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
213 "\n",
214 "<span class=\"ansired\">Exception</span>: Dead job with interval 1\n"
215 ]
216 }
217 ],
218 "prompt_number": 34
219 },
220 {
221 "cell_type": "markdown",
222 "metadata": {},
223 "source": [
224 "This will print all tracebacks for all dead jobs:"
225 ]
226 },
227 {
228 "cell_type": "code",
229 "collapsed": false,
230 "input": [
231 "jobs.traceback()"
232 ],
233 "language": "python",
234 "metadata": {},
235 "outputs": [
236 {
237 "output_type": "stream",
238 "stream": "stdout",
239 "text": [
240 "Traceback for: &lt;BackgroundJob #4: &lt;function diefunc at 0x30df758&gt;&gt;\n",
241 "<span class=\"ansired\">---------------------------------------------------------------------------</span>\n",
242 "<span class=\"ansired\">Exception</span> Traceback (most recent call last)\n",
243 "<span class=\"ansigreen\">/home/fperez/usr/lib/python2.6/site-packages/IPython/lib/backgroundjobs.py</span> in <span class=\"ansicyan\">call</span><span class=\"ansiblue\">(self)</span>\n",
244 "<span class=\"ansigreen\"> 462</span> <span class=\"ansiyellow\"></span>\n",
245 "<span class=\"ansigreen\"> 463</span> <span class=\"ansigreen\">def</span> call<span class=\"ansiyellow\">(</span>self<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
246 "<span class=\"ansigreen\">--&gt; 464</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">return</span> self<span class=\"ansiyellow\">.</span>func<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">*</span>self<span class=\"ansiyellow\">.</span>args<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>self<span class=\"ansiyellow\">.</span>kwargs<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
247 "\n",
248 "<span class=\"ansigreen\">/home/fperez/ipython/ipython/docs/examples/lib/&lt;ipython-input-15-54795a097787&gt;</span> in <span class=\"ansicyan\">diefunc</span><span class=\"ansiblue\">(interval, *a, **kw)</span>\n",
249 "<span class=\"ansigreen\"> 14</span> <span class=\"ansigreen\">def</span> diefunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">2</span><span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">*</span>a<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>kw<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
250 "<span class=\"ansigreen\"> 15</span> time<span class=\"ansiyellow\">.</span>sleep<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
251 "<span class=\"ansigreen\">---&gt; 16</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">raise</span> Exception<span class=\"ansiyellow\">(</span><span class=\"ansiblue\">&quot;Dead job with interval %s&quot;</span> <span class=\"ansiyellow\">%</span> interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
252 "<span class=\"ansigreen\"> 17</span> <span class=\"ansiyellow\"></span>\n",
253 "<span class=\"ansigreen\"> 18</span> <span class=\"ansigreen\">def</span> printfunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">,</span> reps<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">5</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
254 "\n",
255 "<span class=\"ansired\">Exception</span>: Dead job with interval 1\n",
256 "\n",
257 "Traceback for: &lt;BackgroundJob #5: &lt;function diefunc at 0x30df758&gt;&gt;\n",
258 "<span class=\"ansired\">---------------------------------------------------------------------------</span>\n",
259 "<span class=\"ansired\">Exception</span> Traceback (most recent call last)\n",
260 "<span class=\"ansigreen\">/home/fperez/usr/lib/python2.6/site-packages/IPython/lib/backgroundjobs.py</span> in <span class=\"ansicyan\">call</span><span class=\"ansiblue\">(self)</span>\n",
261 "<span class=\"ansigreen\"> 462</span> <span class=\"ansiyellow\"></span>\n",
262 "<span class=\"ansigreen\"> 463</span> <span class=\"ansigreen\">def</span> call<span class=\"ansiyellow\">(</span>self<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
263 "<span class=\"ansigreen\">--&gt; 464</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">return</span> self<span class=\"ansiyellow\">.</span>func<span class=\"ansiyellow\">(</span><span class=\"ansiyellow\">*</span>self<span class=\"ansiyellow\">.</span>args<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>self<span class=\"ansiyellow\">.</span>kwargs<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
264 "\n",
265 "<span class=\"ansigreen\">/home/fperez/ipython/ipython/docs/examples/lib/&lt;ipython-input-15-54795a097787&gt;</span> in <span class=\"ansicyan\">diefunc</span><span class=\"ansiblue\">(interval, *a, **kw)</span>\n",
266 "<span class=\"ansigreen\"> 14</span> <span class=\"ansigreen\">def</span> diefunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">2</span><span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">*</span>a<span class=\"ansiyellow\">,</span> <span class=\"ansiyellow\">**</span>kw<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
267 "<span class=\"ansigreen\"> 15</span> time<span class=\"ansiyellow\">.</span>sleep<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
268 "<span class=\"ansigreen\">---&gt; 16</span><span class=\"ansiyellow\"> </span><span class=\"ansigreen\">raise</span> Exception<span class=\"ansiyellow\">(</span><span class=\"ansiblue\">&quot;Dead job with interval %s&quot;</span> <span class=\"ansiyellow\">%</span> interval<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
269 "<span class=\"ansigreen\"> 17</span> <span class=\"ansiyellow\"></span>\n",
270 "<span class=\"ansigreen\"> 18</span> <span class=\"ansigreen\">def</span> printfunc<span class=\"ansiyellow\">(</span>interval<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">,</span> reps<span class=\"ansiyellow\">=</span><span class=\"ansicyan\">5</span><span class=\"ansiyellow\">)</span><span class=\"ansiyellow\">:</span><span class=\"ansiyellow\"></span>\n",
271 "\n",
272 "<span class=\"ansired\">Exception</span>: Dead job with interval 2\n",
273 "\n"
274 ]
275 }
276 ],
277 "prompt_number": 33
278 },
279 {
280 "cell_type": "markdown",
281 "metadata": {},
282 "source": [
283 "The job manager can be flushed of all completed jobs at any time:"
284 ]
285 },
286 {
287 "cell_type": "code",
288 "collapsed": false,
289 "input": [
290 "jobs.flush()"
291 ],
292 "language": "python",
293 "metadata": {},
294 "outputs": [
295 {
296 "output_type": "stream",
297 "stream": "stdout",
298 "text": [
299 "No jobs to flush.\n"
300 ]
301 }
302 ],
303 "prompt_number": 25
304 },
305 {
306 "cell_type": "markdown",
307 "metadata": {},
308 "source": [
309 "After that, the status is simply empty:"
310 ]
311 },
312 {
313 "cell_type": "code",
314 "collapsed": true,
315 "input": [
316 "jobs.status()"
317 ],
318 "language": "python",
319 "metadata": {},
320 "outputs": [],
321 "prompt_number": 27
322 },
323 {
324 "cell_type": "markdown",
325 "metadata": {},
326 "source": [
327 "It's easy to wait on a job:"
328 ]
329 },
330 {
331 "cell_type": "code",
332 "collapsed": false,
333 "input": [
334 "j = jobs.new(sleepfunc, 2)\n",
335 "print \"Will wait for j now...\"\n",
336 "sys.stdout.flush()\n",
337 "j.join()\n",
338 "print \"Result from j:\"\n",
339 "j.result"
340 ],
341 "language": "python",
342 "metadata": {},
343 "outputs": [
344 {
345 "output_type": "stream",
346 "stream": "stdout",
347 "text": [
348 "Starting job # 7 in a separate thread.\n",
349 "Will wait for j now...\n"
350 ]
351 },
352 {
353 "output_type": "stream",
354 "stream": "stdout",
355 "text": [
356 "Result from j:\n"
357 ]
358 },
359 {
360 "output_type": "pyout",
361 "prompt_number": 46,
362 "text": [
363 "{&apos;args&apos;: (), &apos;interval&apos;: 2, &apos;kwargs&apos;: {}}"
364 ]
365 }
366 ],
367 "prompt_number": 46
368 },
369 {
370 "cell_type": "code",
371 "collapsed": true,
372 "input": [],
373 "language": "python",
374 "metadata": {},
375 "outputs": []
376 }
377 ],
378 "metadata": {}
379 }
380 ]
193 } No newline at end of file
381 }
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@@ -1,228 +1,308 b''
1 {
1 {
2 "nbformat": 2,
2 "metadata": {
3 "metadata": {
3 "name": "rmt"
4 "name": "rmt"
4 },
5 },
5 "nbformat": 3,
6 "worksheets": [
6 "nbformat_minor": 0,
7 {
7 "worksheets": [
8 "cells": [
8 {
9 {
9 "cells": [
10 "source": "# Eigenvalue distribution of Gaussian orthogonal random matrices",
10 {
11 "cell_type": "markdown"
11 "cell_type": "markdown",
12 },
12 "metadata": {},
13 {
13 "source": [
14 "source": "The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices \nfrom the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest\nneighbor eigenvalue distribution $\\rho(s)$.",
14 "# Eigenvalue distribution of Gaussian orthogonal random matrices"
15 "cell_type": "markdown"
15 ]
16 },
16 },
17 {
17 {
18 "cell_type": "code",
18 "cell_type": "markdown",
19 "language": "python",
19 "metadata": {},
20 "outputs": [],
20 "source": [
21 "collapsed": false,
21 "The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices \n",
22 "prompt_number": 1,
22 "from the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest\n",
23 "input": "from rmtkernel import ensemble_diffs, normalize_diffs, GOE\nimport numpy as np\nfrom IPython.parallel import Client"
23 "neighbor eigenvalue distribution $\\rho(s)$."
24 },
24 ]
25 {
25 },
26 "source": "## Wigner's nearest neighbor eigenvalue distribution",
26 {
27 "cell_type": "markdown"
27 "cell_type": "code",
28 },
28 "collapsed": false,
29 {
29 "input": [
30 "source": "The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution\nfor the GOE:\n\n$$\\rho(s) = \\frac{\\pi s}{2} \\exp(-\\pi s^2/4)$$",
30 "from rmtkernel import ensemble_diffs, normalize_diffs, GOE\n",
31 "cell_type": "markdown"
31 "import numpy as np\n",
32 },
32 "from IPython.parallel import Client"
33 {
33 ],
34 "cell_type": "code",
34 "language": "python",
35 "language": "python",
35 "metadata": {},
36 "outputs": [],
36 "outputs": [],
37 "collapsed": true,
37 "prompt_number": 1
38 "prompt_number": 2,
38 },
39 "input": "def wigner_dist(s):\n \"\"\"Returns (s, rho(s)) for the Wigner GOE distribution.\"\"\"\n return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.)"
39 {
40 },
40 "cell_type": "markdown",
41 {
41 "metadata": {},
42 "cell_type": "code",
42 "source": [
43 "language": "python",
43 "## Wigner's nearest neighbor eigenvalue distribution"
44 "outputs": [],
44 ]
45 "collapsed": true,
45 },
46 "prompt_number": 3,
46 {
47 "input": "def generate_wigner_data():\n s = np.linspace(0.0,4.0,400)\n rhos = wigner_dist(s)\n return s, rhos"
47 "cell_type": "markdown",
48 },
48 "metadata": {},
49 {
49 "source": [
50 "cell_type": "code",
50 "The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution\n",
51 "language": "python",
51 "for the GOE:\n",
52 "outputs": [],
52 "\n",
53 "collapsed": false,
53 "$$\\rho(s) = \\frac{\\pi s}{2} \\exp(-\\pi s^2/4)$$"
54 "prompt_number": 4,
54 ]
55 "input": "s, rhos = generate_wigner_data()"
55 },
56 },
56 {
57 {
57 "cell_type": "code",
58 "cell_type": "code",
58 "collapsed": true,
59 "language": "python",
59 "input": [
60 "outputs": [
60 "def wigner_dist(s):\n",
61 {
61 " \"\"\"Returns (s, rho(s)) for the Wigner GOE distribution.\"\"\"\n",
62 "output_type": "pyout",
62 " return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.)"
63 "prompt_number": 17,
63 ],
64 "text": "&lt;matplotlib.text.Text at 0x3828790&gt;"
64 "language": "python",
65 },
65 "metadata": {},
66 {
66 "outputs": [],
67 "output_type": "display_data",
67 "prompt_number": 2
68 "png": 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oUeqcDCkYQljOrILx8ssvs2DBAlq1asWMGTOIc/Kps1Iwai83N5gxQ33KcIGH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\ngvad9HfeeSc33nhjpe87wr2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68 },
69 }
69 {
70 ],
70 "cell_type": "code",
71 "collapsed": false,
71 "collapsed": true,
72 "prompt_number": 17,
72 "input": [
73 "input": "plot(s, rhos)\nxlabel('Normalized level spacing s')\nylabel('Probability $\\rho(s)$')"
73 "def generate_wigner_data():\n",
74 },
74 " s = np.linspace(0.0,4.0,400)\n",
75 {
75 " rhos = wigner_dist(s)\n",
76 "source": "## Serial calculation of nearest neighbor eigenvalue distribution",
76 " return s, rhos"
77 "cell_type": "markdown"
77 ],
78 },
78 "language": "python",
79 {
79 "metadata": {},
80 "source": "In this section we numerically construct and diagonalize a large number of GOE random matrices\nand compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core.",
80 "outputs": [],
81 "cell_type": "markdown"
81 "prompt_number": 3
82 },
82 },
83 {
83 {
84 "cell_type": "code",
84 "cell_type": "code",
85 "language": "python",
85 "collapsed": false,
86 "outputs": [],
86 "input": [
87 "collapsed": true,
87 "s, rhos = generate_wigner_data()"
88 "prompt_number": 6,
88 ],
89 "input": "def serial_diffs(num, N):\n \"\"\"Compute the nearest neighbor distribution for num NxX matrices.\"\"\"\n diffs = ensemble_diffs(num, N)\n normalized_diffs = normalize_diffs(diffs)\n return normalized_diffs"
89 "language": "python",
90 },
90 "metadata": {},
91 {
91 "outputs": [],
92 "cell_type": "code",
92 "prompt_number": 4
93 "language": "python",
93 },
94 "outputs": [],
94 {
95 "collapsed": true,
95 "cell_type": "code",
96 "prompt_number": 7,
96 "collapsed": false,
97 "input": "serial_nmats = 1000\nserial_matsize = 50"
97 "input": [
98 },
98 "plot(s, rhos)\n",
99 {
99 "xlabel('Normalized level spacing s')\n",
100 "cell_type": "code",
100 "ylabel('Probability $\\rho(s)$')"
101 "language": "python",
101 ],
102 "outputs": [
102 "language": "python",
103 {
103 "metadata": {},
104 "output_type": "stream",
104 "outputs": [
105 "text": "1 loops, best of 1: 1.19 s per loop"
105 {
106 }
106 "output_type": "pyout",
107 ],
107 "prompt_number": 17,
108 "collapsed": false,
108 "text": [
109 "prompt_number": 8,
109 "&lt;matplotlib.text.Text at 0x3828790&gt;"
110 "input": "%timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize)"
110 ]
111 },
111 },
112 {
112 {
113 "cell_type": "code",
113 "output_type": "display_data",
114 "language": "python",
114 "png": 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oUeqcDCkYQljOrILx8ssvs2DBAlq1asWMGTOIc/Kps1Iwai83N5gxQ33KcIGH\nZSHsyqw+jG7dupGWloabmxu5ubkMGTKEDRs22CPfdVWnHS4/X53MlZMjiw7WVsXFEBwMr78O996r\ndRoh7M+m8zCKi4txc3MDoHnz5pw/f97iCzmKzEzo3FmKRW1Wp45aLKZOVYuHEMI8ZhWM7du307Rp\nU9PXjh07TN83a9bM1hmtats2sMFK6cLJDB4MjRvDsmVaJxHCeZg1Surq1au2zmE3mZnQp4/WKYTW\ndDp44w0YNw4eeEDt2xBCXJ9Fq9W6gsxMqOFEdeEi+vWDDh3gk0+0TiKEc6jWBkqOwtKOm/x88PCA\n3FyoX9+GwYTTyMyEQYNg715o3lzrNELYh102UHJ2O3aAn58UC/G3rl3V/ozp07VOIoTjq1UFQ5qj\nREXeeAMWL4Y9e7ROIoRjk4Ihar3WrdWJfBMnymQ+Ia5HCoYQwD//CceOQWKi1kmEcFy1ptP78mW1\nU/PsWbDyXkzCRaxdqxaOnTtlYqdwbdLpXYVdu9T9u6VYiMqEh4O/v7pvhhCivFpTMKQ5Spjjvffg\n3Xfh5EmtkwjheKRgCFHKrbfCk0/ClClaJxHC8UjBEOIaL7+s7peRnq51EiEciyYFIzU1FT8/P3x8\nfJg7d26595csWUJQUBBBQUE8/PDD7Nu3r0bXu3JFnbQXHFyj04haomlTeOstGD9eVrMVojRNCsaE\nCRNISEhg3bp1xMfHc+bMmTLve3t7k5qaym+//UZERASvvfZaja73++9wyy3qB4EQ5njkEXVBwg8/\n1DqJEI7D7gUjLy8PgD59+uDp6Ul4eDjp1zz79+zZE3d3dwCioqJqvFmTNEcJS9WpA//+N0ybBkeO\naJ1GCMdg94KRkZGBr6+v6bW/vz9paWmVHv/RRx8xePDgGl1TCoaoDl9feO45tRPceWcrCWE9Zu2H\noZV169axePFiNm/eXOkxsbGxpu8NBgMGg6HcMZmZcM89NggoXN7kybBiBXz6KYwZo3UaIaonJSWF\nlJSUGp/H7jO98/LyMBgMZGVlATB+/HgiIyOJiooqc9z27du5//77WbNmDZ06darwXObMViwuhhtv\nhMOHoUUL6/wOonbZvh3694esLLUvTAhn5zQzvUv6JlJTU8nOziY5OZmwsLAyxxw9epRhw4axZMmS\nSouFuQ4eVAuFFAtRXYGB8PTT6u580jQlajNNmqTi4uKIjo7GaDQSExODh4cHCQkJAERHRzNjxgxy\ncnIYN25XGuKtAAAVR0lEQVQcAG5ubmzZsqVa15L+C2ENL78M3brB0qXqCCohaiOXX3zwxRehWTN1\n+WohamLrVoiKUpuobrpJ6zRCVJ/TNEnZmzxhCGvp3l3t+H7mGa2TCKENly4YiiIFQ1jXtGnqqgEr\nVmidRAj7c+mCcfQoNGwozQfCeho2hE8+UZcNOXFC6zRC2JdLFwx5uhC2cMcd8I9/wMiR6jplQtQW\nUjCEqIZ//Qvq11ebqISoLaRgCFENdevCkiXw+eewZo3WaYSwD5ctGIoC27ZJwRC207q1WjQefxyO\nH9c6jRC257IF4+RJuHoV2rXTOolwZX37QkwMjBgBRqPWaYSwLZctGCXNUTqd1kmEq5syRd1rZepU\nrZMIYVsuXTC6ddM6hagN6tSBL76A//wHEhO1TiOE7bh0wZD+C2EvHh5qwRg7Vp3/I4QrkoIhhJX0\n6gXPPw9Dh8LFi1qnEcL6XHLxwdOn4bbbICdH+jCEfSmKukPfn3/CypVQz6G3KBO1lSw+WEpWFuj1\nUiyE/el0sGCBOmIqJkb2zxCuxSULhjRHCS25ucFXX8GmTTBrltZphLAeKRhC2ECzZvDDD/DBB2rx\nEMIVSMEQwkbatYPVq+Gf/4Sff9Y6jRA153Kd3rm50L69+mfduhoFE6KUpCQYNQpSU9XBGEJoTTq9\n//LrrxAUJMVCOI6ICHjrLejfH/bv1zqNENWnScFITU3Fz88PHx8f5s6dW+79vXv30rNnTxo2bMjs\n2bMtOrc0RwlHNHo0vPoq3H23FA3hvDQZJT5hwgQSEhLw9PQkIiKCkSNH4uHhYXq/ZcuWzJ07l5Ur\nV1p87sxM9V9yQjiaJ55Qh93edRf89JM0TwnnY/cnjLy8PAD69OmDp6cn4eHhpKenlzmmVatWdO/e\nHTc3N4vPL08YwpGNHQszZqhPGr//rnUaISxj94KRkZGBr6+v6bW/vz9paWlWOfelS3DkCPj5WeV0\nQtjEmDHw+utq0di7V+s0QpjP6RcuiI2NNX3v4WGgc2cD1XgwEcKuHn9cbZ7q108dRRUQoHUi4cpS\nUlJISUmp8XnsXjBCQkJ4/vnnTa937dpFZGRktc9XumDMmyfNUcJ5jBoFDRqoTxqLFkEN/jcQ4roM\nBgMGg8H0evr06dU6j92bpNzd3QF1pFR2djbJycmEhYVVeKyl44Sl/0I4mxEj4Jtv1FFU8fFapxHi\n+jSZuLdhwwbGjRuH0WgkJiaGmJgYEhISAIiOjubPP/8kJCSE8+fPU6dOHZo2bcru3bu54YYbyoa/\nZvJJcDB8/DF0727XX0eIGjt0CKKiYMAAeO89WeVW2FZ1J+65zEzvwkJo0UJd0rxhQ42DCVENubnw\n4INQvz58+aW67asQtlDrZ3rv3KmOa5diIZxV8+bqgoXt26ubMR04oHUiIcpymYIh/RfCFbi5qftp\nREdDz55qZ7jztgEIVyMFQwgHo9OpK9z++CO88w488gj8Nd9VCE1JwRDCQQUGwtatat9ccLAskS60\n5xKd3kaj2v77v/9BkyZapxLC+lavVvcKHzcOXn5Z7RgXorpqdaf3nj3g6SnFQriuwYPVveq3blWX\n709O1jqRqI1comBIc5SoDdq2VZ80Zs5UO8WHDVPXThPCXqRgCOFEdDr1aWP3brVfo1s3dfXbggKt\nk4naQAqGEE6oYUN45RXYtg22b4fOndUlRoqLtU4mXJnTd3pfuaLQvDkcO6Z2fAtRG61bBy+8AEYj\nvPQSPPSQLC8iKldrO73374ebbpJiIWq3/v3Vp41334UPP4Tbb4ePPoKiIq2TCVfi9AVDmqOEUOl0\n6hLpqanw+eewahV4e8Ps2XDhgtbphCuQgiGEC+rdG77/HhITYcsWdX2qUaPUvcSln0NUlxQMIVyY\nXg/Llqn7hwcHw7PPQseOaoe5LG4oLOX0nd7u7gr790OrVlqnEcI5/Pqr2mS1dCn4+KhPHvfco87z\nELVDrd0Po317haNHtU4ihPMxGmHNGli8WJ053r49RESo/SC9eqnbxwrXVGsLxpAhCitXap1ECOd2\n5QpkZEBSklpEdu+GPn3+LiCdOqmd6sI11NqCMX26wquvap1ECNeSk6PO7SgpIMXFal9ht25//9mu\nnRQRZ1VrC8bq1Qr33KN1EiFcl6KoE2O3bVMHmWzbpn4pilo8SgpIly7g5SUr6ToDp5q4l5qaip+f\nHz4+PsydO7fCY1566SW8vb3p1q0be/furfRczjBCKiUlResIZpGc1uMMGcG8nDoddOgAQ4fCa6+p\n28j++ae6eu4//6kWiM8/V5uumjZVj+3bF0aPVte5+uILdS+PEyeqP6TXle6nM9Nk8YAJEyaQkJCA\np6cnERERjBw5Eg8PD9P7W7ZsYePGjWzdupWkpCQmT55MYmJihedyhpEdKSkpGAwGrWNUSXJajzNk\nhOrn1OngllvUr8GD//75lSvq08jhw3DokPrnf//79/fnz6tPIe3agYcHtGxZ/s/S3zdpol7L1e+n\ns7B7wcj7a6/JPn36ABAeHk56ejpRUVGmY9LT03nggQdo0aIFI0eOZOrUqZWeT9pQhXAc9eqp8zw6\ndoS77y7//sWLkJ0Nf/wBZ8+qX2fOqPNEfv7579cl7129qhaOq1dhwwa1gDRqVPFX48aVv3ftcfXr\nQ506ULfu33+W/l4+Vypm94KRkZGBr6+v6bW/vz9paWllCsaWLVt49NFHTa9btWrFwYMHufXWW+2a\nVQhhXTfcAAEB6pc5CgrUwvH66+qCivn56s8KCsp+X1CgNpNV9l7pr/x8dUjx1avqV3Fx+T91uooL\nSenvK/pZbq46UfJ6f0+n+7sglS5M9vxZdTnkepaKopTrkNFV8ptW9nNHM336dK0jmEVyWo8zZATn\nyZmQYL+civJ3QbHU2bPOcT+rw+4FIyQkhOeff970eteuXURGRpY5JiwsjN27dxMREQHA6dOn8fb2\nLncuJx7gJYQQTsfuo6Tc3d0BdaRUdnY2ycnJhIWFlTkmLCyMr7/+mrNnz7J06VL8/PzsHVMIIcQ1\nNGmSiouLIzo6GqPRSExMDB4eHiQkJAAQHR1NaGgovXv3pnv37rRo0YLFixdrEVMIIURpioPbsGGD\n4uvrq3Tq1En54IMPKjxmypQpSseOHZWuXbsqe/bssXNCVVU5169frzRr1kwJDg5WgoODlddee83u\nGUePHq20bt1aCQgIqPQYR7iXVeV0hHupKIpy9OhRxWAwKP7+/krfvn2VJUuWVHic1vfUnJxa39OC\nggIlNDRUCQoKUsLCwpT33nuvwuO0vpfm5NT6XpZ25coVJTg4WLnnnnsqfN/S++nwBSM4OFjZsGGD\nkp2drdx+++3K6dOny7yfnp6u9OrVSzl79qyydOlSJSoqyiFzrl+/Xhk8eLAm2UqkpqYqmZmZlX4Q\nO8q9rCqnI9xLRVGUkydPKllZWYqiKMrp06eVjh07KufPny9zjCPcU3NyOsI9vXTpkqIoilJYWKh0\n7txZ2b9/f5n3HeFeKkrVOR3hXpaYPXu28vDDD1eYpzr306H3wyg9Z8PT09M0Z6O0a+ds7NmzxyFz\ngvad9HfeeSc33nhjpe87wr2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115 "outputs": [],
115 }
116 "collapsed": false,
116 ],
117 "prompt_number": 9,
117 "prompt_number": 17
118 "input": "serial_diffs = serial_diffs(serial_nmats, serial_matsize)"
118 },
119 },
119 {
120 {
120 "cell_type": "markdown",
121 "source": "The numerical computation agrees with the predictions of Wigner, but it would be nice to get more\nstatistics. For that we will do a parallel computation.",
121 "metadata": {},
122 "cell_type": "markdown"
122 "source": [
123 },
123 "## Serial calculation of nearest neighbor eigenvalue distribution"
124 {
124 ]
125 "cell_type": "code",
125 },
126 "language": "python",
126 {
127 "outputs": [
127 "cell_type": "markdown",
128 {
128 "metadata": {},
129 "output_type": "pyout",
129 "source": [
130 "prompt_number": 10,
130 "In this section we numerically construct and diagonalize a large number of GOE random matrices\n",
131 "text": "&lt;matplotlib.text.Text at 0x3475bd0&gt;"
131 "and compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core."
132 },
132 ]
133 {
133 },
134 "output_type": "display_data",
134 {
135 "png": 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CWKxqWTAyczM5EH8Af2d/raMIU0lpDXtfhSeeB12+1mmEsEjVsmDsu7SPjk06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\nSfn7+yt3d3fl5+en1q5dW2I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135 "cell_type": "code",
136 }
136 "collapsed": true,
137 ],
137 "input": [
138 "collapsed": false,
138 "def serial_diffs(num, N):\n",
139 "prompt_number": 10,
139 " \"\"\"Compute the nearest neighbor distribution for num NxX matrices.\"\"\"\n",
140 "input": "hist_data = hist(serial_diffs, bins=30, normed=True)\nplot(s, rhos)\nxlabel('Normalized level spacing s')\nylabel('Probability $P(s)$')"
140 " diffs = ensemble_diffs(num, N)\n",
141 },
141 " normalized_diffs = normalize_diffs(diffs)\n",
142 {
142 " return normalized_diffs"
143 "source": "## Parallel calculation of nearest neighbor eigenvalue distribution",
143 ],
144 "cell_type": "markdown"
144 "language": "python",
145 },
145 "metadata": {},
146 {
146 "outputs": [],
147 "source": "Here we perform a parallel computation, where each process constructs and diagonalizes a subset of\nthe overall set of random matrices.",
147 "prompt_number": 6
148 "cell_type": "markdown"
148 },
149 },
149 {
150 {
150 "cell_type": "code",
151 "cell_type": "code",
151 "collapsed": true,
152 "language": "python",
152 "input": [
153 "outputs": [],
153 "serial_nmats = 1000\n",
154 "collapsed": true,
154 "serial_matsize = 50"
155 "prompt_number": 11,
155 ],
156 "input": "def parallel_diffs(rc, num, N):\n nengines = len(rc.targets)\n num_per_engine = num/nengines\n print \"Running with\", num_per_engine, \"per engine.\"\n ar = rc.apply_async(ensemble_diffs, num_per_engine, N)\n diffs = np.array(ar.get()).flatten()\n normalized_diffs = normalize_diffs(diffs)\n return normalized_diffs"
156 "language": "python",
157 },
157 "metadata": {},
158 {
158 "outputs": [],
159 "cell_type": "code",
159 "prompt_number": 7
160 "language": "python",
160 },
161 "outputs": [],
161 {
162 "collapsed": true,
162 "cell_type": "code",
163 "prompt_number": 12,
163 "collapsed": false,
164 "input": "client = Client()\nview = client[:]\nview.run('rmtkernel.py')\nview.block = False"
164 "input": [
165 },
165 "%timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize)"
166 {
166 ],
167 "cell_type": "code",
167 "language": "python",
168 "language": "python",
168 "metadata": {},
169 "outputs": [],
169 "outputs": [
170 "collapsed": true,
170 {
171 "prompt_number": 13,
171 "output_type": "stream",
172 "input": "parallel_nmats = 40*serial_nmats\nparallel_matsize = 50"
172 "stream": "stdout",
173 },
173 "text": [
174 {
174 "1 loops, best of 1: 1.19 s per loop"
175 "cell_type": "code",
175 ]
176 "language": "python",
176 }
177 "outputs": [
177 ],
178 {
178 "prompt_number": 8
179 "output_type": "stream",
179 },
180 "text": "Running with 10000 per engine.\n1 loops, best of 1: 14 s per loop"
180 {
181 },
181 "cell_type": "code",
182 {
182 "collapsed": false,
183 "output_type": "stream"
183 "input": [
184 }
184 "serial_diffs = serial_diffs(serial_nmats, serial_matsize)"
185 ],
185 ],
186 "collapsed": false,
186 "language": "python",
187 "prompt_number": 14,
187 "metadata": {},
188 "input": "%timeit -r1 -n1 parallel_diffs(view, parallel_nmats, parallel_matsize)"
188 "outputs": [],
189 },
189 "prompt_number": 9
190 {
190 },
191 "cell_type": "code",
191 {
192 "language": "python",
192 "cell_type": "markdown",
193 "outputs": [
193 "metadata": {},
194 {
194 "source": [
195 "output_type": "stream",
195 "The numerical computation agrees with the predictions of Wigner, but it would be nice to get more\n",
196 "text": "Running with 10000 per engine."
196 "statistics. For that we will do a parallel computation."
197 }
197 ]
198 ],
198 },
199 "collapsed": false,
199 {
200 "prompt_number": 15,
200 "cell_type": "code",
201 "input": "pdiffs = parallel_diffs(view, parallel_nmats, parallel_matsize)"
201 "collapsed": false,
202 },
202 "input": [
203 {
203 "hist_data = hist(serial_diffs, bins=30, normed=True)\n",
204 "source": "Again, the agreement with the Wigner distribution is excellent, but now we have better\nstatistics.",
204 "plot(s, rhos)\n",
205 "cell_type": "markdown"
205 "xlabel('Normalized level spacing s')\n",
206 },
206 "ylabel('Probability $P(s)$')"
207 {
207 ],
208 "cell_type": "code",
208 "language": "python",
209 "language": "python",
209 "metadata": {},
210 "outputs": [
210 "outputs": [
211 {
211 {
212 "output_type": "pyout",
212 "output_type": "pyout",
213 "prompt_number": 16,
213 "prompt_number": 10,
214 "text": "&lt;matplotlib.text.Text at 0x376c950&gt;"
214 "text": [
215 },
215 "&lt;matplotlib.text.Text at 0x3475bd0&gt;"
216 {
216 ]
217 "output_type": "display_data",
217 },
218 "png": 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EVW18Ex4dCOqcsCeEU7BoD+Py5cu0bdvW+LxNmzZcvnzZ6qFszXDKIFd3O7oL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\nYgzjDehT9fgt8mPlwZWy+q2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218 {
219 }
219 "output_type": "display_data",
220 ],
220 "png": 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CWKxqWTAyczM5EH8Af2d/raMIU0lpDXtfhSeeB12+1mmEsEjVsmDsu7SPjk06\n0uCBBlpHEaZ04GWokQFdV2qdRAiLVC0LhoxfVFPKFn74AvzngcMZrdMIYXGqZcGQ8YtqLKk9RL4B\nT00omPRWCGGwalcwku4kcSb5DL4P+WodRWglegbk14DuWgcRwrJUu4KxO243vVr1oqZtTa2jCK0o\nG9j8OfSCEwkntE4jhMWodgUj8mIk/q39tY4htHbTBX6xocM/O6Cz1aHTFX/Y2ztonVIIs1LtCkbE\nxQj6tO6jdQxhDg7mQ2Z/6Pl/FNzBr+gjLe2mpvGEMDfVqmAkZyRz4eYFOjfvrHUUYS42fwrdFhfc\n2lUIcV/VqmDsvbSXbg91w87WTusowlyktoSd78Ow8WCbrXUaIcxatSoYERcj8Gvtp3UMYW6OToCU\nltDnba2TCGHWqlXBiLwYKeMXogQ62PIxdFkNrSO1DiOE2ao2BSM1K5WTCSfxedBH6yjCHN1uDj98\nDsPHQt0bWqcRwixVm4Kx//J+Hm3xKLVq1NI6ijBXZwcVHJ4a/j+gy9M6jRBmp9oUjIiLEfg5+2Fv\n71DiOff3PkQ1tnt+wWy2fm9pnUQIs6NJwYiMjMTNzY127dqxbNmyYsvXrl2Lp6cnnp6ejB07ltOn\nT1d8mxcj6dOqz91z64ufc1/0IaotZQub1kHnT+BhrcMIYV40KRgzZ84kJCSEsLAwVqxYQWJiYpHl\nLi4uREZG8uuvvxIQEMDbb1fs7JU7OXf49fqvdG8pkwcJA9xuBt+thacgPjVe6zRCmA2TF4yUlBQA\n+vTpQ+vWrRkwYADR0dFF2nTv3p0GDQruVREYGEhERESFthkVH0Wnpp2oY1enQusR1UicP8TY0HJW\ny1KnDpHpQ0R1Y/KCERsbi6urq/65u7s7UVFRpbb/+OOPGTp0aIW2WTh+IYRR9uZD1iDoG0xphy9l\n+hBRndTQOsD9hIWFsWbNGvbv319qm3nz5ul/9vf3x9/fv1ibyIuRzO4xuwoSCqumgO++gqDOcKkX\n/PGk1omEKJfw8HDCw8MrvB6dUsqko7wpKSn4+/tz5MgRAKZPn87AgQMJDAws0u63337j6aefZtu2\nbbRt27bEdel0OsqKn5WbheMHjlx5+Qr2tezvngVV1kcuq01lrMOctmNOWczwMz8YDWOHwn92wg3P\nYm1M/CckRIUZ8t1ZEpMfkiocm4iMjCQuLo6dO3fi61v0ZkaXLl1i+PDhrF27ttRiYaiYKzG4Orpi\nX8u+QusbXyY1AAAWJ0lEQVQR1dgVX/hpeUHRqH9F6zRCaEaTQ1KLFy8mKCiInJwcZsyYgaOjIyEh\nIQAEBQXx1ltvkZyczNSpUwGws7MjJiamXNuS6UBEpTg+Ehqdh7FD4PNIyK6vdSIhTM7kh6QqkyG7\nVQO+GsA0n2k80f4J/Xss57CJGR6esZrtlCeLgqEvQP2r8M3mgtu8yiEpYYEs5pCUKeXk5RAVH0Wv\nVr20jiKsgg62rgSbXBg4E7nIU1Q3Vl0wDl87TJtGbXCoLefKi0qSbwcbNxTMatt9kdZphDApsz6t\ntqJk/EJUiawGsG4rTO4BchmGqEaseg9DbpgkqkxKK/h6MwyFA5cPaJ1GCJOw2oKRl5/Hvsv76N2q\nt9ZRhLW61gW+hye/eZLo+Oiy2wth4ay2YPx24zea1WtG03pNtY4irNlZ+PzJzxn69VBirpTv1G8h\nLIXVFgwZvxCmEvhIIJ89+RlDvx5K7JVYreMIUWWstmDI+IUwpSGPDOHTJz5lyNdDpGgIq2VVF+7Z\n2zv8d/bQfwAhQGpJ77SUi8vM+SI2S99O5WW597/BLX9sYfKPk9k6ditdH+xaxnuF0IZcuAf/vZue\n0zHIcoFUuZueMK2h7YfyyROfMOTrIRy8elDrOEJUKuu8DqN1JFyU8QuhjSfaP4FSisB1gWx8ZqOM\npQmrYVV7GHrOEXBRxi+Edp50fZI1w9YwYsMI1vy2Rus4QlQKKywYClpHQJwUDKGt/g/3Z/f43by5\n+03mhc+TSQqFxbO+guFwtmAW0VvOWicRgg5NOhA1OYqfz/7MuB/GkZWbpXUkIcrNqs6S0ul00Hk1\nOIfDd6UdBrCkM3ks74why9lO5WUp60/I3t6BtMybMAyoC3wDZPx3ef36jUhNTS5jO0JUHjlLqpAM\neAszk5Z2E3IUbMyDy6/C822h8R8UnrmnPxVcCDNnhQVDxi+EmVI2EPYu7HsVJvWCDhu0TiSEUazr\ntNoGQI0sSHpE6yRClO7w83DdC4aPhbbb4GetAwlhGOvaw3Dm7uEoncZBRPVRA51Od99Hia4+CiGH\nQekgCLnIT1gE6yoYrZHxC2FiuRSfTcDA2QWy68GPn8IvMHjtYN7f9z75Kr/qIwtRTtZXMGT8Qlia\n4xA7JZYtp7cw4KsBXEm9onUiIUpkNQXjatpVqA0kdNA6ihBGa92wNbvH78avtR+eH3ny7/3/Jicv\nR+tYQhRhNQUj8mIkXKLgTBQhLFANmxq86fcm+yfvJ+x8GJ4febLr/C6tYwmhZzXfrpEXI+Gi1imE\nqLhHGj/Cz//zMwv6LmDyj5MZuXEkl1Muax1LCOspGBEXIyBO6xRCVA6dTsdTrk9x4sUTuDm54RXi\nxYI9C8jIySj7zUJUEasoGAnpCcSnxsMNrZMIUbnq2NVhvv98YqfEEns1loeXPszC/QtJz07XOpqo\nhqyiYOy5tIeeLXuCnJEorJRLIxe+H/U9P/3PT0RdicJlqQsL9iwgNavEW0oKUSWsomDI/buFZSv7\n4j97ewcAvJp5sfGZjewev5uTiSdxWeLC3PC5JGfI5IWi6llFwYi8GCl3NRMWrOyL//46QaG7kztf\nDfuKqOejuJJ6hYeXPsykzZPYd2mf3HdDVBmLn948+U4yrRa3Iml2ErVq1MKcpr22nO2YUxb5zKW1\nud+f6vXb1/nPr//h0yOfYqOzYZLXJMZ5jqNpvaZlrFdUR9V2evN9l/fh+6AvNW1rah1FCM00q9eM\n2T1nc+rFU6weupoTiSdov7w9w9YPI/R0KLn5uVpHFFbA4vcwgncEY1/Tnjf93rw70Zv5/IvQcrZj\nTlnkM5fWxtg/1bSsNNYfX8+nRz7lTNIZAtoGENgukIFtB+JQ28GodQnrUt49DIsvGD6rfXi/3/v4\nOftJwbCKLPKZS2tTkT/V+NR4fjrzE1vPbCU8LpxOTTsR2C6QwHaBeDTxKH1WXWGVqm3BqPtOXRJn\nJ/JAjQekYFhFFvnMJbOjYHC8dIbe6jUzN5PwuHC2ntnK1tNbyVN5DGw7kO4Pdcf3QV/aO7bHRmfx\nR6vFfVTbgtH7s95ETozUPzefP3BL2o45ZZHPXJE2xv45K6U4lXiKHed2EH0lmpgrMSTeSeTRFo/i\n86APPg/64PugL83rNzdqvcK8lbdgWPwd9/yc/bSOIITF0ul0uDm54ebkpn8t8U4isVdiib4SzceH\nPub5H5+ntl1tfB70wc3RjbYObWnn0I52jdvhVMdJDmdVI5rsYURGRhIUFERubi4zZsxg+vTpxdq8\n9tprrF+/nkaNGrF27VpcXV2LtdHpdOw4u4P+D/fXPzfPfxGGA/4m2E5F2+ym5JymzGItexjh/Lcv\nzXcPIzw8HH9///u2UUpx/uZ5Yq/G8kfiH5y9eZYzSWc4m3yWnPwc2jq0/W8RcWinf+5U16nSDm0Z\nktMcWEpOi9rDmDlzJiEhIbRu3ZqAgADGjBmDo6OjfnlMTAx79uzh4MGDbN++neDgYEJDQ0tcV4+W\nPUwVuwLCKf2L2JyEYxk5LUE4ltCXhnzB6XQ6HnZ4mIcdHi627GbGTc4kFxSPM0lnCLsQxqqDqzh3\n8xy3Mm/RuHZjmtRtQpO6TWhar2nBz3Xu+blwWd2m1LarXaGc5sBScpaXyQtGSkoKAH36FFyZPWDA\nAKKjowkMDNS3iY6OZsSIETg4ODBmzBjeeOONUtdXt2bdqg0shMWoYcDhITug6I2Z5s+ff9/lhqzj\nr+rXb8Sdm3dIvJPIn+l/6h830m/wZ/qfnEk+o//5z/Q/uXH7Bjqdjno161HXri51a9bV/1yvZj0u\nnLzAlS1X9M8L/3/2rDlkpt6B7LuRcimYU+6eR93a9hz77Vdq2NQo9WGrs5VDawYwecGIjY0tcnjJ\n3d2dqKioIgUjJiaG5557Tv/cycmJc+fO8fDDxf+FI4QoVDjFyP389dDWvLuP0pYbso7i0tJ02Nna\n0bx+c4MGzJVSZORmkJ6dTnpOOrezb5Oefff/c9L5z57/4NPCR78sNTuVq7evktnkDjw4EmreLnjY\nZoNNbpFHus0pHvvyMXLycsjNzy3xkafysNXZFikidrZ2pRaYwkNtOgqKjE6nQ4eOa4euseXjLfpl\nhUXor+0Kf9ayXXmY5aC3UqrY8bXSPmTx1w3pjMpoY+w65hvQpjK2U5E28yk9pymzmPIzV2WW+Qa0\nqYztVLTNX3/nlbOdyv4X+6ZVm0pZsqHM98YZcLOcvLv/yyLLuGB/cT30eoXeb85MXjC6du3KP/7x\nD/3z48ePM3DgwCJtfH19OXHiBAEBAQAkJCTg4uJSbF0WfEawEEJYHJNfndOgQQOg4EypuLg4du7c\nia+vb5E2vr6+bNq0iaSkJNatW4ebm1tJqxJCCGFCmhySWrx4MUFBQeTk5DBjxgwcHR0JCQkBICgo\nCB8fH3r16sWjjz6Kg4MDa9as0SKmEEKIeykzFxERoVxdXVXbtm3V0qVLS2wzZ84c1aZNG9W5c2d1\n8uRJEycsUFbO3bt3K3t7e+Xl5aW8vLzU22+/bfKMEydOVE2aNFEeHh6ltjGHviwrpzn0pVJKXbp0\nSfn7+yt3d3fl5+en1q5dW2I7rfvUkJxa92lGRoby8fFRnp6eytfXV3344YclttO6Lw3JqXVf3is3\nN1d5eXmpIUOGlLjc2P40+4Lh5eWlIiIiVFxcnGrfvr1KSEgosjw6Olr17NlTJSUlqXXr1qnAwECz\nzLl79241dOhQTbIVioyMVIcPHy71i9hc+rKsnObQl0opde3aNXXkyBGllFIJCQmqTZs2KjU1tUgb\nc+hTQ3KaQ5+mp6crpZTKzMxUHTp0UGfOnCmy3Bz6Uqmyc5pDXxZauHChGjt2bIl5ytOfZj3D2L3X\nbLRu3Vp/zca9/nrNxsmTJ80yJ2g/SN+7d28aNWpU6nJz6EsoOydo35cAzZo1w8vLCwBHR0c6dOjA\nwYMHi7Qxhz41JCdo36d16tQB4Pbt2+Tm5lKrVq0iy82hL6HsnKB9XwLEx8fz008/8fzzz5eYpzz9\nadYFo7RrNu4VExODu7u7/nnhNRumZEhOnU7H/v378fLy4uWXXzZ5RkOYQ18awhz78uzZsxw/fhwf\nH58ir5tbn5aW0xz6ND8/H09PT5o2bcq0adNo2bJlkeXm0pdl5TSHvgSYNWsWH3zwATY2JX/Nl6c/\nzbpgGEIZcc2Gljp37szly5eJjY3F3d2dmTNnah2pGOnL8klLS2PUqFEsWrSIunWLzjxgTn16v5zm\n0Kc2Njb8+uuvnD17lpUrV3LkyJEiy82lL8vKaQ59GRoaSpMmTfD29i51b6c8/WnWBaNr166cOnVK\n//z48eN069atSJvCazYKlXbNRlUyJGf9+vWpU6cOdnZ2TJ48mdjYWLKyKnaBUGUzh740hDn1ZU5O\nDsOHD+e5557jySefLLbcXPq0rJzm1KfOzs4MHjy42GFdc+nLQqXlNIe+3L9/Pz/++CNt2rRhzJgx\n/PLLL4wbN65Im/L0p1kXDEu5ZsOQnDdu3NBX8y1bttCpU6cSj31qyRz60hDm0pdKKSZPnoyHhwcv\nvfRSiW3MoU8Nyal1nyYmJnLr1i0AkpKS2LFjR7HCZg59aUhOrfsSYMGCBVy+fJkLFy7wzTff8Pjj\nj/Of//ynSJvy9KdZTg1yL0u5ZqOsnN9++y2rVq2iRo0adOrUiYULF5o845gxY4iIiCAxMZGWLVsy\nf/58cnJy9BnNpS/LymkOfQmwb98+1qxZQ6dOnfD29gYK/lAvXbqkz2oOfWpITq379Nq1a4wfP568\nvDyaNWtGcHAwzZs3N7u/dUNyat2XJSk81FTR/rToO+4JIYQwHbM+JCWEEMJ8SMEQQghhECkYQggh\nDCIFQwghhEGkYIhKZWNjQ3BwsP75v//977/cArTq+fv7c/jwYQACAwNJTU2t0PrCw8MZOnSowa9X\nxbaq0tWrV3nmmWdMuk1hmaRgiEpVs2ZNvv/+e5KSkgDjr8TNy8urcIZ7t7l161bs7e0rvE5r1qJF\nCzZu3Kh1DGEBpGCISmVnZ8cLL7zAokWLii27evUqM2fOxNPTk1mzZnHjxg0AJkyYwMsvv4yvry+v\nvvoqEydO5JVXXsHHx4f27dtz5MgRXnjhBTp06MC8efP06/v73/9O165d6dGjB6tXry4xj7OzM0lJ\nSXz00Ud4e3vj7e1NmzZtePzxx4GCecDGjRuHr68vc+bM0V+RGxsbS9++ffH29mb79u1lfu6MjAw+\n/PBD/Pz8CAwMJDw8HIDu3bsXuZq2cO8nMzOzxPaluXz5MoMGDcLLywtPT0/OnTtHXFwc7u7uTJ48\nGTc3N+bPn6/P//bbb+Pj40PXrl1ZsGBBkfW88soreHt706VLFy5cuEBcXBwdO3YE4IsvvmD06NEM\nHjwYDw8Pli5dqn/vtm3b6N69Oz4+Prz00ktMnz69WM6jR4/St29fvLy86Ny5M7dv3y6z74QFqcjU\nuUL8Vb169VRqaqpydnZWKSkp6t///reaN2+eUkqpWbNmqffff18ppdSCBQvU7NmzlVJKjR8/Xvn5\n+emn3J4wYYIaNGiQysrKUl988YWqV6+eCg8PV1lZWcrNzU0/dXxycrJSSqmsrCzl6+urbt++rZRS\nyt/fXx06dEgppZSzs7NKSkrS58vJyVG9e/dWoaGh+ra3bt1SSik1e/Zs9c033yillOrUqZOKjo5W\nt2/fVgMHDixxeujdu3fr7zPw+eefqyVLliillLp+/bry8fFRSim1aNEiNXfuXKWUUlevXlXt27e/\nb/t713mvuXPnqk8++UT/GTIyMtSFCxeUTqdT3333ncrMzFRPP/20+vbbb4v0TW5urho6dKg6deqU\nvq9XrFih77c7d+6oCxcu6KeS//zzz1WTJk3U1atXVWpqqnrooYdUdna2ysnJUc7OzurChQsqKSlJ\nde7cWU2fPr1YzvHjx6uwsDClVME04Lm5ucXaCMslexii0tWvX59x48YV+dcpwM8//8ykSZMAmDx5\nMlu2bAEKDiGNGDGC+vXr69uOGDGCmjVr0r17dxo2bIifnx81a9bE29tbPxPwzp07CQwMxNvbm/Pn\nz/PLL7+UmW3GjBn07duXwMBADh06xLFjx/D398fb25vQ0FAiIyO5cuUKSil8fHyoW7cuo0aNKnO6\n6k2bNrF69Wq8vb0ZOHAgN27c4MKFC4wcOZJvv/0WgA0bNujHCkpqf/78+VLX37VrVxYvXsx7771H\ncnIyDzzwAFAwLc2wYcOoVasWY8aMYdu2bQAcPHiQ4cOH06lTJw4fPsyOHTvIzs5m9+7dTJkyBSg4\nfFi7du1i2xowYADNmzenfv36uLu7c/jwYaKioujYsSPOzs44ODjwxBNPlNgn3bt3Z86cOSxfvpzc\n3FxsbW3L/J0Iy2H2U4MIy/TSSy/RuXNnJk6cWOT10r54mzdvXuR54fxcNWvWpGHDhvrXa9asSXZ2\nNmlpacyZM4c9e/bw4IMPMmzYMG7evHnfTF988QWXL19m5cqVQME01R4eHuzevbtIu/j4eMM+5D3y\n8/NZsWIFffr0KbascePG/P7772zYsEE/NUNp7Qun6/irwMBAunTpwpo1a+jZsycbN24s0i+FCsdv\npk+fzrfffouHhwezZs3i5s2b6HS6Emco/au/9ndmZiY1atQoMjZU2jqCgoLo37+/fiqS6OhomjZt\net/tCcshexiiSjRq1IiRI0fy6aef6r9oBg8ezJdffkl+fj6fffYZTzzxRLnWrZTi1q1b2NnZ0axZ\nM06fPs2uXbvu+55Dhw6xcOFCvvrqK/1rXbt25caNG/o9lvT0dM6cOcNDDz2Era0tsbGxpKens2HD\nhjIzjR07lpCQENLS0gCKTHk9atQo3nvvPVJTU/Hw8CizfUkuXLign7uob9+++nGRlJQUfvjhB7Ky\nsli/fj0DBw4kMzOTtLQ0nJ2duXLlCps3bwYKxpcee+wxVq9ejVKKrKwsMjIyyvxsOp2Obt268fvv\nvxMXF0dycjKhoaElntBw7tw5XFxc+N///V9cXV3N4l4lovJIwRCV6t4vkVdeeYXExET98+DgYC5d\nuoS3tzc3btzg5ZdfLvF9f31e0rKWLVsyfPhwPDw8mDZtWqmnohb+q3rFihXcvHmTxx57DG9vb154\n4QUAvvrqK1atWkWnTp3o0aMHf/zxBwAff/wxr732Gr169cLT07PEL0edTqd/fcSIEfj4+BAQEICH\nhwdz587VtxsxYgTr169n5MiRRV4rqf2967zXhg0b8PDwoGvXrty5c0e/LldXV3788Ue8vLzw8PAg\nMDCQBx54gDlz5uDj48OoUaMYPHiwfj3vvPMOZ8+exdPTk549e+pPPCjcZmnbt7W1Zfny5YwaNYqB\nAwfSsWNH2rRpU6zdkiVL6NixIz4+Pri6utKjR48Sfy/CMsnkg0JYqLi4OIYOHcrvv/9uku2lp6dT\nt25dUlJSGDJkCJ988gnt27c3ybaFeZAxDCEsmCnvODdv3jzCwsKws7Pj2WeflWJRDckehhBCCIPI\nGIYQQgiDSMEQQghhECkYQgghDCIFQwghhEGkYAghhDCIFAwhhBAG+X8LAxP1Be5fBAAAAABJRU5E\nrkJggg==\n"
221 "collapsed": false,
221 }
222 "prompt_number": 16,
222 ],
223 "input": "hist_data = hist(pdiffs, bins=30, normed=True)\nplot(s, rhos)\nxlabel('Normalized level spacing s')\nylabel('Probability $P(s)$')"
223 "prompt_number": 10
224 }
224 },
225 ]
225 {
226 }
226 "cell_type": "markdown",
227 ]
227 "metadata": {},
228 "source": [
229 "## Parallel calculation of nearest neighbor eigenvalue distribution"
230 ]
231 },
232 {
233 "cell_type": "markdown",
234 "metadata": {},
235 "source": [
236 "Here we perform a parallel computation, where each process constructs and diagonalizes a subset of\n",
237 "the overall set of random matrices."
238 ]
239 },
240 {
241 "cell_type": "code",
242 "collapsed": true,
243 "input": [
244 "def parallel_diffs(rc, num, N):\n",
245 " nengines = len(rc.targets)\n",
246 " num_per_engine = num/nengines\n",
247 " print \"Running with\", num_per_engine, \"per engine.\"\n",
248 " ar = rc.apply_async(ensemble_diffs, num_per_engine, N)\n",
249 " diffs = np.array(ar.get()).flatten()\n",
250 " normalized_diffs = normalize_diffs(diffs)\n",
251 " return normalized_diffs"
252 ],
253 "language": "python",
254 "metadata": {},
255 "outputs": [],
256 "prompt_number": 11
257 },
258 {
259 "cell_type": "code",
260 "collapsed": true,
261 "input": [
262 "client = Client()\n",
263 "view = client[:]\n",
264 "view.run('rmtkernel.py')\n",
265 "view.block = False"
266 ],
267 "language": "python",
268 "metadata": {},
269 "outputs": [],
270 "prompt_number": 12
271 },
272 {
273 "cell_type": "code",
274 "collapsed": true,
275 "input": [
276 "parallel_nmats = 40*serial_nmats\n",
277 "parallel_matsize = 50"
278 ],
279 "language": "python",
280 "metadata": {},
281 "outputs": [],
282 "prompt_number": 13
283 },
284 {
285 "cell_type": "code",
286 "collapsed": false,
287 "input": [
288 "%timeit -r1 -n1 parallel_diffs(view, parallel_nmats, parallel_matsize)"
289 ],
290 "language": "python",
291 "metadata": {},
292 "outputs": [
293 {
294 "output_type": "stream",
295 "stream": "stdout",
296 "text": [
297 "Running with 10000 per engine.\n",
298 "1 loops, best of 1: 14 s per loop"
299 ]
300 }
301 ],
302 "prompt_number": 14
303 }
304 ],
305 "metadata": {}
306 }
307 ]
228 } No newline at end of file
308 }
Show file before | Show file after | Hide comments
@@ -1,56 +1,61 b''
1 {
1 {
2 "metadata": {
2 "metadata": {
3 "name": "gilsleep"
3 "name": "gilsleep"
4 },
4 },
5 "nbformat": 2,
5 "nbformat": 3,
6 "nbformat_minor": 0,
6 "worksheets": [
7 "worksheets": [
7 {
8 {
8 "cells": [
9 "cells": [
9 {
10 {
10 "cell_type": "markdown",
11 "cell_type": "markdown",
12 "metadata": {},
11 "source": [
13 "source": [
12 "Holding the GIL for too long could disrupt the heartbeat due to non-copying sends.",
14 "Holding the GIL for too long could disrupt the heartbeat due to non-copying sends.\n",
13 "",
15 "\n",
14 "The following cell repeatedly calls a function that holds the GIL for five seconds.",
16 "The following cell repeatedly calls a function that holds the GIL for five seconds.\n",
15 "",
17 "\n",
16 "The heartbeat will fail after a few iterations prior to fixing Issue [#1260](https://github.com/ipython/ipython/issues/1260)."
18 "The heartbeat will fail after a few iterations prior to fixing Issue [#1260](https://github.com/ipython/ipython/issues/1260)."
17 ]
19 ]
18 },
20 },
19 {
21 {
20 "cell_type": "code",
22 "cell_type": "code",
21 "collapsed": false,
23 "collapsed": false,
22 "input": [
24 "input": [
23 "import sys",
25 "import sys\n",
24 "import time",
26 "import time\n",
25 "",
27 "\n",
26 "from cython import inline",
28 "from cython import inline\n",
27 "",
29 "\n",
28 "def gilsleep(t):",
30 "def gilsleep(t):\n",
29 " \"\"\"gil-holding sleep with cython.inline\"\"\"",
31 " \"\"\"gil-holding sleep with cython.inline\"\"\"\n",
30 " code = '\\n'.join([",
32 " code = '\\n'.join([\n",
31 " 'from posix cimport unistd',",
33 " 'from posix cimport unistd',\n",
32 " 'unistd.sleep(t)',",
34 " 'unistd.sleep(t)',\n",
33 " ])",
35 " ])\n",
34 " while True:",
36 " while True:\n",
35 " inline(code, quiet=True, t=t)",
37 " inline(code, quiet=True, t=t)\n",
36 " print time.time()",
38 " print time.time()\n",
37 " sys.stdout.flush() # this is important",
39 " sys.stdout.flush() # this is important\n",
38 "",
40 "\n",
39 "gilsleep(5)"
41 "gilsleep(5)"
40 ],
42 ],
41 "language": "python",
43 "language": "python",
42 "outputs": [],
44 "metadata": {},
45 "outputs": [],
43 "prompt_number": 1
46 "prompt_number": 1
44 },
47 },
45 {
48 {
46 "cell_type": "code",
49 "cell_type": "code",
47 "collapsed": true,
50 "collapsed": true,
48 "input": [],
51 "input": [],
49 "language": "python",
52 "language": "python",
50 "outputs": [],
53 "metadata": {},
54 "outputs": [],
51 "prompt_number": "&nbsp;"
55 "prompt_number": "&nbsp;"
52 }
56 }
53 ]
57 ],
58 "metadata": {}
54 }
59 }
55 ]
60 ]
56 } No newline at end of file
61 }
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