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Probabilistic Semanticizer
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SemanticizeMC.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Probablistic Semanticizer\n",
"\n",
"April 8th 2014, [Daan Odijk](http://daan.odijk.me), ILPS, UvA. Based on discussions with David Graus and Manos Tsagkias.\n",
"\n",
"## Semanticize Baseline\n",
"\n",
"Get baseline semanticizer output for finding entities."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tweet = 'Chicago Bears defensive tackle Tank Johnson was sentenced to jail on Thursday for violating probation on a 2005 weapons conviction.'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 236
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import urllib2, urllib, json\n",
"semanticized = json.loads(urllib2.urlopen(\"http://semanticize.uva.nl/api/en?text=%s&article&normalize=\" % urllib.quote(tweet)).read())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 239
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"labels = {}\n",
"for link in semanticized[\"links\"]:\n",
" labels[link[\"text\"]] = link['linkProbability']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 240
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print json.dumps(labels, indent=4)\n",
"print link[\"OutLinks\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{\n",
" \"sentenced\": 0.0033884863559352583, \n",
" \"Chicago\": 0.2375396562337232, \n",
" \"Bears\": 0.017106634248002777, \n",
" \"Thursday\": 0.014568732409911153, \n",
" \"probation\": 0.1545673076923077, \n",
" \"for\": 5.7735125266664106e-06, \n",
" \".\": 8.608402538740885e-06, \n",
" \"weapons\": 0.006045328291669584, \n",
" \"to\": 2.331022513015431e-06, \n",
" \"defensive tackle\": 0.5737014506317267, \n",
" \"was\": 5.053835005504792e-06, \n",
" \"a\": 3.069104763490784e-05, \n",
" \"violating\": 0.00016329196603527107, \n",
" \"Tank\": 0.040449685881185936, \n",
" \"Johnson\": 0.010096637247234946, \n",
" \"defensive\": 0.004593420235878337, \n",
" \"tackle\": 0.05457463884430177, \n",
" \"Chicago Bears\": 0.8807912545549194, \n",
" \"on\": 7.71670765810355e-06, \n",
" \"conviction\": 0.013607145838550798, \n",
" \"Tank Johnson\": 1.1, \n",
" \"jail\": 0.031438018320776195, \n",
" \"2005\": 0.02515431839408032\n",
"}\n",
"[{u'relatedness': 0.461, u'id': 14533, u'title': u'India'}, {u'relatedness': 0.755, u'id': 408336, u'title': u'Borstal'}, {u'relatedness': 0.736, u'id': 1126222, u'title': u'Tirunelveli'}, {u'relatedness': 0.757, u'id': 1891948, u'title': u'Pudukkottai'}]\n"
]
}
],
"prompt_number": 241
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple Model\n",
"\n",
"Create a simple model with a Bernoulli random variable to indicate whether a label is a link, a categorical variable to map labels to entities and a deterministic variable that combines the two into links."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pymc\n",
"from collections import defaultdict\n",
"\n",
"variables = {}\n",
"labels = defaultdict(list)\n",
"\n",
"#link_probability = pymc.Uniform(\"link_probability\", 0, 1)\n",
"#link = pymc.Categorical(\"link\", [link_probability, 1-link_probability], size=len(labels))\n",
"for link in semanticized[\"links\"]:\n",
" if link[\"text\"] not in labels:\n",
" label = str(\"isLink:%s\" % link[\"text\"])\n",
" variables[label] = pymc.Bernoulli(label, link['linkProbability'])\n",
" labels[link[\"text\"]].append(link)\n",
"\n",
"for label, links in labels.iteritems():\n",
" probabilities = map(lambda link: link[\"priorProbability\"], links)\n",
" variables[str(\"target:%s\" % label)] = pymc.Categorical(str(\"target:%s\" % label), probabilities)\n",
" for index, link in enumerate(links):\n",
" variable = unicode(\"link:%s\" % link[\"title\"]).encode(\"utf-8\")\n",
" parents = {\"isLink\": variables[str(\"isLink:%s\" % link[\"text\"])], \"cat\": variables[str(\"target:%s\" % label)]}\n",
" entity = pymc.Deterministic((lambda i: lambda isLink, cat: isLink and cat==i)(index), \"\", variable, parents)\n",
" variables[variable] = entity\n",
" \n",
" \n",
"#labels.values()\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 242
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieval Variable\n",
"\n",
"Add a retrieval variable that indiciates the textual likeliness of the tweet referencing an entity. First map titles to IDs."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"targets = dict((variable_name[5:], -1) for variable_name in variables.iterkeys() if variable_name.startswith(\"link:\"))\n",
"\n",
"import urllib2, urllib, json\n",
"\n",
"\n",
"for target in targets: \n",
" url = u\"http://zookst21.science.uva.nl:8007/enwiki/_search?fields=title&size=100\"\n",
" query = {\"query\": {\"query_string\": {\"query\": '\"%s\"' % target, \"default_field\": \"title\"}}}\n",
" try:\n",
" results = json.loads(urllib2.urlopen(url, json.dumps(query)).read())\n",
" except:\n",
" print url\n",
" print json.dumps(query, indent=4)\n",
" raise\n",
" for doc in results['hits']['hits']:\n",
" if doc[\"fields\"][\"title\"][0] in targets:\n",
" targets[doc[\"fields\"][\"title\"][0]] = doc[\"_id\"]\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 243
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"url = u\"http://zookst21.science.uva.nl:8007/enwiki/_search?fields=title&size=1000\"\n",
"query = {\"query\": {\"query_string\": {\"query\": tweet.replace(\" -->\", \"\")}}}\n",
"query[\"filter\"] = {\"ids\": {\"values\": list((str(v) for v in targets.values() if v > 0))}}\n",
"query = {\"query\": {\"filtered\": query}}\n",
"try:\n",
" results = json.loads(urllib2.urlopen(url, json.dumps(query)).read())\n",
"except:\n",
" print url\n",
" print json.dumps(query, indent=4)\n",
" raise"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 244
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Textual relevance is also modeled as a Bernoulli variable, with the probability based on the retrieval score."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = dict(((hit[\"fields\"][\"title\"][0], hit[\"_score\"]) for hit in results[\"hits\"][\"hits\"]))\n",
"\n",
"for target in targets.keys():\n",
" score = scores[target] if target in scores else min(scores.values())\n",
" label = str(\"text:%s\" % target)\n",
" variables[label] = pymc.Bernoulli(label, score)\n",
"\n",
" parents = {\"text\": variables[label], \"link\": variables[str(\"link:%s\" % target)]}\n",
" label = str(\"entity:%s\" % target)\n",
" variables[label] = pymc.Deterministic(lambda text, link: text and link, \"\", label, parents)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 245
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MCMC\n",
"\n",
"Run MCMC on the model and print the output."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"\n",
"model = pymc.Model(variables)\n",
"mcmc = pymc.MCMC(model)\n",
"#pymc.graph.graph(mcmc, name='graph_mine', format='png')\n",
"#import scipy.misc\n",
"#imshow(scipy.misc.imread('graph_mine.png'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 246
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mcmc.sample(5000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \r",
"[****************100%******************] 5000 of 5000 complete"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 247
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"link_probabilities = dict((variable_name[5:], float(sum(variable.trace()))/variable.stats()['n']) for variable_name, variable in variables.iteritems() if variable_name.startswith(\"link:\"))\n",
"entity_probabilities = dict((variable_name[7:], float(sum(variable.trace()))/variable.stats()['n']) for variable_name, variable in variables.iteritems() if variable_name.startswith(\"entity:\"))\n",
"\n",
"print \"entity link target\"\n",
"for link, probability in sorted(link_probabilities.iteritems(), key=lambda v: v[1], reverse=True):\n",
" print \"%.4f %.4f %s\" % (entity_probabilities[link], probability, link)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"entity link target\n",
"1.0000 1.0000 Tank Johnson\n",
"0.2330 0.8366 Chicago Bears\n",
"0.0380 0.2104 Chicago\n",
"0.0592 0.1488 Probation\n",
"0.0032 0.0268 Prison\n",
"0.0006 0.0228 Tackle (American football)\n",
"0.0028 0.0154 Tackle (football move)\n",
"0.0014 0.0142 Conviction\n",
"0.0004 0.0122 Offensive tackle\n",
"0.0030 0.0118 Tank\n",
"0.0014 0.0096 Tank (American singer)\n",
"0.0014 0.0094 Thursday (band)\n",
"0.0002 0.0066 County jail\n",
"0.0006 0.0060 Chicago Bulls\n",
"0.0010 0.0058 Weapon\n",
"0.0000 0.0056 Tank (band)\n",
"0.0010 0.0050 Chicago (band)\n",
"0.0026 0.0048 2006 Chicago Bears season\n",
"0.0022 0.0046 2007 Chicago Bears season\n",
"0.0000 0.0044 Sentence (law)\n",
"0.0008 0.0034 Chicago (2002 film)\n",
"0.0006 0.0032 Chicago (musical)\n",
"0.0008 0.0032 2010 Chicago Bears season\n",
"0.0000 0.0030 1940 Chicago Bears season\n",
"0.0000 0.0028 Tank (Taiwanese singer)\n",
"0.0004 0.0028 Thursday\n",
"0.0002 0.0026 Sydney Bears\n",
"0.0000 0.0024 Lyndon B. Johnson\n",
"0.0002 0.0024 Norm Johnson\n",
"0.0004 0.0020 1985 Chicago Bears season\n",
"0.0006 0.0020 2011 Chicago Bears season\n",
"0.0004 0.0020 Barksdale Organization\n",
"0.0002 0.0020 1938 Chicago Bears season\n",
"0.0002 0.0018 2005 in film\n",
"0.0004 0.0018 Defense (sports)\n",
"0.0004 0.0016 Tank (film)\n",
"0.0004 0.0016 2008 Chicago Bears season\n",
"0.0000 0.0016 Thursday (film)\n",
"0.0004 0.0014 2009 Chicago Bears season\n",
"0.0002 0.0014 1937 Chicago Bears season\n",
"0.0002 0.0014 Bear\n",
"0.0000 0.0014 1942 Chicago Bears season\n",
"0.0000 0.0014 1941 Chicago Bears season\n",
"0.0000 0.0014 University of Chicago\n",
"0.0004 0.0012 2005 J. League Division 1\n",
"0.0004 0.0012 2012 Chicago Bears season\n",
"0.0000 0.0012 2005\n",
"0.0002 0.0010 1988 Chicago Bears season\n",
"0.0000 0.0010 Tank (video game)\n",
"0.0000 0.0010 Johnson (composer)\n",
"0.0000 0.0010 Operating system-level virtualization\n",
"0.0000 0.0010 Tank%2C Pakistan\n",
"0.0008 0.0010 1984 Chicago Bears season\n",
"0.0000 0.0010 2005 in music\n",
"0.0000 0.0010 1943 Chicago Bears season\n",
"0.0000 0.0010 Defender (association football)\n",
"0.0000 0.0010 Tank District\n",
"0.0000 0.0010 United States Coast Guard Academy\n",
"0.0000 0.0008 Fishing tackle\n",
"0.0002 0.0008 Tank (song)\n",
"0.0000 0.0008 Military\n",
"0.0000 0.0008 1969 Chicago Bears season\n",
"0.0004 0.0008 List of Chicago Bears seasons\n",
"0.0002 0.0008 Chicago Fire Soccer Club\n",
"0.0002 0.0008 1977 Chicago Bears season\n",
"0.0000 0.0008 Union Station (Chicago)\n",
"0.0000 0.0008 Johnson\n",
"0.0000 0.0008 Irrigation tank\n",
"0.0000 0.0008 Andrew Johnson\n",
"0.0000 0.0008 Chicago Maroons football\n",
"0.0000 0.0008 Chicago Blackhawks\n",
"0.0000 0.0008 1959 Chicago Bears season\n",
"0.0004 0.0008 1989 Chicago Bears season\n",
"0.0000 0.0008 1981 Chicago Bears season\n",
"0.0002 0.0008 Defensive tackle\n",
"0.0000 0.0008 Probation (NCAA)\n",
"0.0002 0.0006 Defensive Tackle\n",
"0.0000 0.0006 1952 Chicago Bears season\n",
"0.0000 0.0006 Block and tackle\n",
"0.0000 0.0006 2005 NCAA Division I-A football season\n",
"0.0000 0.0006 2005 in literature\n",
"0.0000 0.0006 1944 Chicago Bears season\n",
"0.0000 0.0006 Baylor Bears\n",
"0.0000 0.0006 John Henry Johnson\n",
"0.0000 0.0006 O'Hare International Airport\n",
"0.0000 0.0006 1997 Chicago Bears season\n",
"0.0000 0.0006 2005 in comics\n",
"0.0002 0.0006 2004 Chicago Bears season\n",
"0.0002 0.0006 2005 NFL season\n",
"0.0000 0.0006 Samuel Johnson\n",
"0.0000 0.0006 1972 Chicago Bears season\n",
"0.0000 0.0006 1979 Chicago Bears season\n",
"0.0000 0.0006 Leap year starting on Thursday\n",
"0.0002 0.0004 Newark Bears\n",
"0.0000 0.0004 Edinburgh Festival Fringe\n",
"0.0000 0.0004 2005 Campeonato Brasileiro S\u00e9rie A\n",
"0.0002 0.0004 Gary Glitter\n",
"0.0002 0.0004 1936 Chicago Bears season\n",
"0.0002 0.0004 North Sydney Bears\n",
"0.0000 0.0004 MADtv recurring characters\n",
"0.0002 0.0004 Johnson County%2C Texas\n",
"0.0000 0.0004 1945 Chicago Bears season\n",
"0.0000 0.0004 2004\u201305 Ligue 1\n",
"0.0000 0.0004 Baylor Bears football\n",
"0.0000 0.0004 Offensive Tackle\n",
"0.0000 0.0004 1995 Chicago Bears season\n",
"0.0000 0.0004 Tank corps (Soviet Union)\n",
"0.0000 0.0004 2005 K-League\n",
"0.0000 0.0004 Eurovision Song Contest 2005\n",
"0.0000 0.0004 2005 United Soccer Leagues\n",
"0.0000 0.0004 Defensive tackle (American football)\n",
"0.0004 0.0004 1934 Chicago Bears season\n",
"0.0002 0.0004 1932 Chicago Bears season\n",
"0.0000 0.0004 2005 AFL season\n",
"0.0000 0.0004 Brown Bears football\n",
"0.0000 0.0004 1978 Chicago Bears season\n",
"0.0000 0.0004 2005 Arena Football League season\n",
"0.0000 0.0004 1925 Chicago Bears season\n",
"0.0002 0.0004 History of the Chicago Bears\n",
"0.0000 0.0004 Tank (gaming)\n",
"0.0002 0.0004 Thursday (mixtape)\n",
"0.0000 0.0004 2005 in poetry\n",
"0.0000 0.0004 2005 NCAA Men's Division I Basketball Tournament\n",
"0.0000 0.0004 1957 Chicago Bears season\n",
"0.0002 0.0004 Warwickshire County Cricket Club\n",
"0.0000 0.0004 57th Primetime Emmy Awards\n",
"0.0002 0.0004 Foreign object (professional wrestling)\n",
"0.0000 0.0004 2005 World Championships in Athletics\n",
"0.0002 0.0004 1994 Chicago Bears season\n",
"0.0000 0.0004 2005 Team Speedway Junior World Championship\n",
"0.0000 0.0004 1991 Chicago Bears season\n",
"0.0000 0.0004 1976 Chicago Bears season\n",
"0.0002 0.0004 Tank (disambiguation)\n",
"0.0002 0.0004 2005 NBA Playoffs\n",
"0.0000 0.0004 1963 Chicago Bears season\n",
"0.0000 0.0002 1971 Chicago Bears season\n",
"0.0000 0.0002 Chicago Marathon\n",
"0.0000 0.0002 Chicago (album)\n",
"0.0000 0.0002 Disciplinary probation\n",
"0.0000 0.0002 Raj Rajaratnam\n",
"0.0000 0.0002 Chicago (Prison Break)\n",
"0.0000 0.0002 Tank (album)\n",
"0.0000 0.0002 List of minor characters in the Matrix series\n",
"0.0000 0.0002 2005 AFC Futsal Championship\n",
"0.0000 0.0002 Survivor Series (2005)\n",
"0.0000 0.0002 2005 in sports\n",
"0.0000 0.0002 2005 NBA Draft\n",
"0.0002 0.0002 Sloth bear\n",
"0.0002 0.0002 Dubuque County Jail\n",
"0.0000 0.0002 2005 FIBA Asia Champions Cup\n",
"0.0000 0.0002 Taunk\n",
"0.0002 0.0002 California Golden Bears football\n",
"0.0000 0.0002 1926 Chicago Bears season\n",
"0.0000 0.0002 Chicago Avenue\n",
"0.0000 0.0002 1946 Chicago Bears season\n",
"0.0002 0.0002 2005 NASCAR Nextel Cup Series\n",
"0.0000 0.0002 Junior Johnson\n",
"0.0000 0.0002 2005 Boise State Broncos football team\n",
"0.0002 0.0002 2005 CAF Confederation Cup\n",
"0.0000 0.0002 2005 English cricket season\n",
"0.0000 0.0002 Cartier Tank\n",
"0.0000 0.0002 2005 in baseball\n",
"0.0000 0.0002 Tackle for loss\n",
"0.0000 0.0002 1990 Chicago Bears season\n",
"0.0000 0.0002 2005 Russian Premier League\n",
"0.0000 0.0002 Telus Cup \u2013 Defensive\n",
"0.0000 0.0002 Twelfth Van Cliburn International Piano Competition\n",
"0.0002 0.0002 United Center\n",
"0.0000 0.0002 Common year starting on Thursday\n",
"0.0000 0.0002 2005 World Touring Car Championship season\n",
"0.0000 0.0002 1961 Chicago Bears season\n",
"0.0002 0.0002 1986 Chicago Bears season\n",
"0.0000 0.0002 Changi Prison\n",
"0.0000 0.0002 All-Ireland Senior Hurling Championship 2005\n",
"0.0000 0.0002 2004\u201305 Greek Cup\n",
"0.0000 0.0002 Johnson County%2C Iowa\n",
"0.0000 0.0002 Tackle (Australian rules football)\n",
"0.0000 0.0002 2005 in television\n",
"0.0002 0.0002 1968 Chicago riots\n",
"0.0000 0.0002 Chicago (Sufjan Stevens song)\n",
"0.0000 0.0002 2005 AFC Cup\n",
"0.0000 0.0002 Miss Earth 2005\n",
"0.0000 0.0002 Chicago (That Toddlin' Town)\n",
"0.0000 0.0002 Chicago International Film Festival\n",
"0.0000 0.0002 Chicago Enforcers\n",
"0.0000 0.0002 2005 NECBL season\n",
"0.0000 0.0002 Johnson Outboards\n",
"0.0000 0.0002 Holly Johnson\n",
"0.0000 0.0002 Johnson County%2C Indiana\n",
"0.0000 0.0002 2005 Mediterranean Games\n",
"0.0000 0.0002 2005 WTA Tour\n",
"0.0000 0.0002 Shiv (weapon)\n",
"0.0000 0.0002 2005 NASCAR Craftsman Truck Series\n",
"0.0002 0.0002 Midfielder\n",
"0.0000 0.0002 Dan Johnson (American football)\n",
"0.0000 0.0002 List of martial arts weapons\n",
"0.0000 0.0002 Chicago Sky\n",
"0.0000 0.0002 1987 Chicago Bears season\n",
"0.0000 0.0002 Chicago Motor Speedway\n",
"0.0002 0.0002 Defensive end\n",
"0.0000 0.0002 Anti-aircraft warfare\n",
"0.0000 0.0002 2005 WNBA season\n",
"0.0000 0.0002 1970 Chicago Bears season\n",
"0.0000 0.0002 British Columbia general election%2C 2005\n",
"0.0000 0.0002 Bears FC\n",
"0.0000 0.0002 Self-defense\n",
"0.0000 0.0002 2005 British Touring Car Championship season\n",
"0.0000 0.0002 2005 Chinese Super League\n",
"0.0002 0.0002 2005 European Pairs Speedway Championship\n",
"0.0000 0.0002 Defensive jihad\n",
"0.0000 0.0002 2005 AFC U-17 Women's Championship\n",
"0.0000 0.0002 2005 UEFA Champions League Final\n",
"0.0000 0.0002 2005 in video gaming\n",
"0.0000 0.0002 October 5\n",
"0.0000 0.0002 2005 Speedway World Cup\n",
"0.0000 0.0002 Alberto Fujimori's arrest and trial\n",
"0.0000 0.0002 2005 Memorial Cup\n",
"0.0000 0.0002 Chicago Symphony Orchestra\n",
"0.0000 0.0002 Chicago metropolitan area\n",
"0.0000 0.0002 2005\u201306 in English football\n",
"0.0000 0.0002 United States federal probation and supervised release\n",
"0.0000 0.0002 Football at the 2005 Mediterranean Games\n",
"0.0000 0.0002 Episcopal Diocese of Chicago\n",
"0.0002 0.0002 National Probation Service\n",
"0.0000 0.0002 2005 Meteor Awards\n",
"0.0000 0.0002 Nuclear weapon\n",
"0.0000 0.0002 Johnson County%2C Illinois\n",
"0.0000 0.0002 2005 Grand National\n",
"0.0000 0.0002 2005 World Championships in Athletics \u2013 Men's 110 metres hurdles\n",
"0.0000 0.0002 2005 IAAF World Athletics Final\n",
"0.0000 0.0002 2005 CONCACAF Gold Cup\n",
"0.0000 0.0002 2005 World Series\n",
"0.0000 0.0002 1961\u201362 Chicago Black Hawks season\n",
"0.0000 0.0002 2005 Chicago Bears season\n",
"0.0000 0.0002 2005 International Rules Series\n",
"0.0002 0.0002 2005 New England Patriots season\n",
"0.0000 0.0002 Probation (workplace)\n",
"0.0000 0.0002 1967 Chicago Bears season\n",
"0.0000 0.0002 1958 Chicago Bears season\n",
"0.0000 0.0002 Water polo at the 2005 World Aquatics Championships \u2013 Men's tournament\n",
"0.0000 0.0002 Bears%2C Friesland\n",
"0.0000 0.0002 Johnson%2C Arkansas\n",
"0.0000 0.0002 2007 Chicago Cubs season\n",
"0.0000 0.0002 2005 Turkish Grand Prix\n",
"0.0000 0.0002 Tank (The Matrix)\n",
"0.0000 0.0002 Soldier Field\n",
"0.0000 0.0002 University of Chicago Law School\n",
"0.0000 0.0002 Hershey Bears\n",
"0.0000 0.0002 2005 New York City transit strike\n",
"0.0000 0.0002 Chicago (CTA Brown and Purple Lines station)\n",
"0.0000 0.0002 Lady Bird Johnson High School\n",
"0.0000 0.0002 2005 in NASCAR\n",
"0.0000 0.0002 1948 Chicago Bears season\n",
"0.0000 0.0002 Chindits\n",
"0.0000 0.0002 Mortar (weapon)\n",
"0.0000 0.0002 Johnson County%2C Kentucky\n",
"0.0002 0.0002 2005 New Orleans Saints season\n",
"0.0000 0.0002 2005 in British music\n",
"0.0000 0.0002 Chicago (play)\n",
"0.0000 0.0002 2005 Major League Lacrosse season\n",
"0.0000 0.0002 2005 in Canada\n",
"0.0002 0.0002 Chicago blues\n",
"0.0000 0.0002 2005 Grammy Awards\n",
"0.0000 0.0002 Lyndon B. Johnson Administration\n",
"0.0000 0.0002 Brown Bears women's ice hockey\n",
"0.0000 0.0002 2005 Canadian Professional Soccer League season\n",
"0.0000 0.0002 Ray Johnson\n",
"0.0000 0.0002 Campania regional election%2C 2005\n",
"0.0002 0.0002 2005 Green Bay Packers season\n",
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"0.0000 0.0000 2005 World Championships in Athletics \u2013 Men's 100 metres\n",
"0.0000 0.0000 2005 MTV Movie Awards\n",
"0.0000 0.0000 Buddy Johnson\n",
"0.0000 0.0000 2005 Calgary Stampeders season\n",
"0.0000 0.0000 University of Illinois at Chicago\n",
"0.0000 0.0000 National League for Democracy\n",
"0.0000 0.0000 Chicago Heights%2C Illinois\n",
"0.0000 0.0000 2005 Meistriliiga\n",
"0.0000 0.0000 Johnson%2C Minnesota\n",
"0.0000 0.0000 Central Arkansas Bears and Sugar Bears\n",
"0.0000 0.0000 2005 Bulgarian Supercup\n",
"0.0000 0.0000 History of the Chicago Cardinals\n",
"0.0000 0.0000 Johnnie Johnson (RAF officer)\n",
"0.0000 0.0000 Kanji\n",
"0.0000 0.0000 MTV Video Music Awards Latinoam\u00e9rica 2005\n",
"0.0000 0.0000 Chicago Whales\n",
"0.0000 0.0000 Chicago Race Riot of 1919\n",
"0.0000 0.0000 2005 Copa do Brasil\n",
"0.0000 0.0000 2005 Pot Black\n",
"0.0000 0.0000 VFF Cup\n",
"0.0000 0.0000 2005 Governor General's Awards\n",
"0.0000 0.0000 2005 Copa Per\u00fa\n",
"0.0000 0.0000 Reisinger\n",
"0.0000 0.0000 2005 UEFA Intertoto Cup\n",
"0.0000 0.0000 FIFA U-20 World Cup\n",
"0.0000 0.0000 2004\u201305 Football League\n",
"0.0000 0.0000 FIDE World Chess Championship 2005\n",
"0.0000 0.0000 2005 NBA All-Star Game\n",
"0.0000 0.0000 2005 BC Lions season\n",
"0.0000 0.0000 2005 FIVB Men's World Grand Champions Cup\n",
"0.0000 0.0000 Japan national football team in 2005\n",
"0.0000 0.0000 2005 Denver Broncos season\n",
"0.0000 0.0000 Chicago hip hop\n",
"0.0000 0.0000 2005 Kyrgyzstan League\n",
"0.0000 0.0000 2008 Chicago Cubs season\n",
"0.0000 0.0000 2005 Pro Bowl\n",
"0.0000 0.0000 Peru 2005 Census\n",
"0.0000 0.0000 2005 American Le Mans Series season\n",
"0.0000 0.0000 Hyde Park%2C Chicago\n",
"0.0000 0.0000 2005 in South African sport\n",
"0.0000 0.0000 Rohingya language\n",
"0.0000 0.0000 Handball at the 2005 Mediterranean Games\n",
"0.0000 0.0000 2005 FIFA Club World Championship\n",
"0.0000 0.0000 2005 Island Games\n",
"0.0000 0.0000 31st People's Choice Awards\n",
"0.0000 0.0000 Triple J Hottest 100%2C 2005\n",
"0.0000 0.0000 List of state leaders in 2005\n",
"0.0000 0.0000 2005 Football League play-offs\n",
"0.0000 0.0000 List of Sundance Film Festival award winners\n",
"0.0000 0.0000 2005 in organized crime\n",
"0.0000 0.0000 2005 Coppa Italia Final\n",
"0.0000 0.0000 Strategic defence\n",
"0.0000 0.0000 Norwegian parliamentary election%2C 2005\n",
"0.0000 0.0000 Dirty Deeds (2005 film)\n",
"0.0000 0.0000 2005 Superettan\n",
"0.0000 0.0000 2005 Super League Grand Final\n",
"0.0000 0.0000 Card-Pitt\n",
"0.0000 0.0000 World's Columbian Exposition\n",
"0.0000 0.0000 1991 Bangladesh cyclone\n",
"0.0000 0.0000 2005 Appalachian State Mountaineers football team\n",
"0.0000 0.0000 2005 Davis Cup\n",
"0.0000 0.0000 24th Hong Kong Film Awards\n",
"0.0000 0.0000 Eddie Rips Up the World Tour\n",
"0.0000 0.0000 2005 Conference USA Baseball Tournament\n",
"0.0000 0.0000 All-Ireland Senior Football Championship 2005\n",
"0.0000 0.0000 WMAQ-TV\n",
"0.0000 0.0000 David Earle Johnson\n",
"0.0000 0.0000 2002 Chicago Bears season\n",
"0.0000 0.0000 Richard Mentor Johnson\n",
"0.0000 0.0000 2005 in radio\n",
"0.0000 0.0000 2005 Yemeni League\n",
"0.0000 0.0000 Canadian Comedy Awards\n",
"0.0000 0.0000 Cook County Democratic Organization\n",
"0.0000 0.0000 Arms industry\n",
"0.0000 0.0000 2005 ICC Trophy\n",
"0.0000 0.0000 Transnistrian legislative election%2C 2005\n",
"0.0000 0.0000 2005 Canoe Sprint European Championships\n",
"0.0000 0.0000 2004\u201305 Cuban National Series\n",
"0.0000 0.0000 Chicago (CTA Blue Line station)\n",
"0.0000 0.0000 2005 AFC Champions League\n",
"0.0000 0.0000 Paarl\n",
"0.0000 0.0000 OGAE Second Chance Contest 2005\n",
"0.0000 0.0000 2004\u201305 North Carolina Tar Heels men's basketball team\n",
"0.0000 0.0000 2005 World Music Awards\n",
"0.0000 0.0000 2005 ATP Tour\n",
"0.0000 0.0000 Bloody Stupid Johnson\n",
"0.0000 0.0000 2005 Italian Grand Prix\n",
"0.0000 0.0000 List of Scottish Cup finals\n",
"0.0000 0.0000 2005 WWE Draft Lottery\n",
"0.0000 0.0000 Va people\n",
"0.0000 0.0000 2005 Carolina Challenge Cup\n"
]
}
],
"prompt_number": 248
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pymc.Matplot.plot(mcmc)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting text:2005 Tour de Romandie\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" entity:2005 Tour de Romandie\n",
"Cannot plot autocorrelation for entity:2005 Tour de Romandie"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" link:2004\u201305 Scottish Premier League\n",
"Cannot plot autocorrelation for link:2004\u201305 Scottish Premier League"
]
},
{
"ename": "ValueError",
"evalue": "matplotlib display text must have all code points < 128 or use Unicode strings",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-249-6fe105224683>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpymc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMatplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmcmc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/Matplot.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(pymc_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%s_%s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 314\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pymc/Matplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, name, format, suffix, path, common_scale, datarange, new, last, rows, num, fontmap, verbose)\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m'/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s%s%s.%s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1419\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1421\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1423\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m 2218\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2219\u001b[0m \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2220\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 2221\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 452\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 453\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0mbaseline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_math_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 326\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 327\u001b[0m w, h, d = get_text_width_height_descent(clean_line,\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mis_math_text\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m 998\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'TeX'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 999\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1000\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_math_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1001\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1002\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dodijk/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/cbook.pyc\u001b[0m in \u001b[0;36mis_math_text\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m raise ValueError(\n\u001b[0;32m-> 1927\u001b[0;31m \u001b[0;34m\"matplotlib display text must have all code points < 128 or use \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1928\u001b[0m \"Unicode strings\")\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: matplotlib display text must have all code points < 128 or use Unicode strings"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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Pl9etFxcXx5EjRwgICOCHH37g/vvvB6Bly5bExcURFRXFAw88wIIFC5zLPvvs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\n",
"text": [
"<matplotlib.figure.Figure at 0x133131f10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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txqpVq+jatSthYWHVrjV3Idedq5zmrKq5XHdTvENTluIJrUMmIi3G+++/z+uv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\nAIDEsCADAAAAkNj/ABK6nOWR0tmUAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x13624f990>"
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x13680d8d0>"
]
}
],
"prompt_number": 249
}
],
"metadata": {}
}
]
}
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