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+showpage

+"""Pirated example from Gensim library (a NLP specialized tool):
+https://radimrehurek.com/gensim/tut2.html
+https://radimrehurek.com/gensim/wiki.html#latent-semantic-analysis
+
+Ignacio Arroyo
+"""
+
+import gensim
+import logging
+from six import iteritems
+from gensim import corpora
+import argparse
+
+from pdb import set_trace as st # Debug the program step by step calling st()
+                                # anywhere.
+class corpus_streamer(object):
+    """ This Object streams the input raw text file row by row.
+    """
+    def __init__(self, file_name, dictionary=None, strings=None):
+        self.file_name=file_name
+        self.dictionary=dictionary
+        self.strings=strings
+
+    def __iter__(self):
+        for line in open(self.file_name):
+        # assume there's one document per line, tokens separated by whitespace
+            if self.dictionary and not self.strings:
+                yield self.dictionary.doc2bow(line.lower().split())
+            elif not self.dictionary and self.strings:
+                yield line.strip().lower()
+# Logging all our program
+logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
+                    level=logging.INFO)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--n_topics", help="Number of eigenvectors picked up.",
+                    default=2, type=int)
+parser.add_argument("--input", help="Input file to perform LSA.",
+                    required=True)
+
+args = parser.parse_args()
+
+n_topics=args.n_topics
+n_docs=0
+input_file=args.input
+#input_file='/medargsia/iarroyof/Volumen de 384 GB/data/GUs_textform_noPeriods.txt'
+#input_file='lsa_example.csv'
+#input_file='wiki_sample/wiki_75_AA.txt.cln'
+#input_file='wiki_sample/wiki_77_AA.txt'
+
+# A little stopwords list
+stoplist = set('for a of the and to in _ [ ]'.split())
+# Do not load the text corpus into memory, but stream it!
+fille=corpus_streamer(input_file, strings=True)
+dictionary=corpora.Dictionary(line.lower().split() for line in fille)#open(input_file))
+# remove stop words and words that appear only once
+stop_ids=[dictionary.token2id[stopword] for stopword in stoplist
+                                             if stopword in dictionary.token2id]
+once_ids=[tokenid for tokenid, docfreq in iteritems(dictionary.dfs)
+                                                            if docfreq == 1]
+dictionary.filter_tokens(stop_ids + once_ids)
+# remove gaps in id sequence after words that were removed
+dictionary.compactify()
+# Store the dictionary
+dictionary.save('lsa_mini.dict')
+# Reading sentences from file into a list of strings.
+# Use instead streaming objects:
+# Load stored word-id map (dictionary)
+stream_it = corpus_streamer(input_file, dictionary=dictionary)
+#for vector in stream_it:  # load one vector into memory at a time
+#    print vector
+# Convert to sparse matrix
+sparse_corpus = [text for text in stream_it]
+# Store to disk, for later use collect statistics about all tokens
+corpora.MmCorpus.serialize('lsa_mini.mm',
+                            sparse_corpus)
+## LSA zone
+# load the dictionary saved before
+id2word = dictionary.load('lsa_mini.dict')
+# Now load the sparse matrix corpus from file into a (memory friendly) streaming
+# object.
+corpus=corpora.MmCorpus('lsa_mini.mm')
+
+## IF TfidfModel
+tfidf = gensim.models.TfidfModel(corpus) # step 1 -- initialize a model
+corpus = tfidf[corpus]
+## FI TfidfModel
+# Compute the LSA vectors
+lsa=gensim.models.lsimodel.LsiModel(corpus, id2word=dictionary,
+                                                     num_topics=n_topics)
+# Print the n topics in our corpus:
+#lsa.print_topics(n_topics)
+f=open("topics_file.txt","wb")
+f.write("-------------------------------------------------\n")
+for t in lsa.show_topics():
+    f.write("%s\n" % str(t))
+    
+f.write("-------------------------------------------------\n")
+f.close()
+# create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi
+corpus_lsa = lsa[corpus]
+# Stream sentences from file into a list of strings called "sentences"
+sentences=corpus_streamer(input_file, strings=True)
+n=0
+for pertenence, sentence in zip(corpus_lsa, sentences):
+    if n_docs <= 0:
+    	#print "%s\t\t%s" % (pertenence, sentence.split("\t")[0])
+    	p=[dict(pertenence)[x] if x in dict(pertenence) else 0.0 
+                                            for x in xrange(n_topics)]
+    	print "%s %s" % ("".join(sentence.split("\t")[0].split()), 
+                            "".join(str(p)[1:].strip("]").split(",")) )
+    else:
+        if n<n_docs:
+            pertenence=[dict(pertenence)[x] if x in dict(pertenence) else 0.0 
+                                                    for x in xrange(n_topics)]
+            print "%s\t\t%s" % (pertenence, sentence)
+            n+=1
+        else:
+            break
+
+
+
+# ============================== Homework ======================================
+# Modify the program for doing this for a sample of the English Wikipedia.
+# Compute LSA for 20 topics and print the fist 10 topics.
+# Take care of avoiding loading and printing documents of a large corpus, so
+# change the number of documents to print or sample the entire set randomly and
+# print a subset.
+# ==============================================================================

+accb	0.051992807457158334 -0.027460006705936346
+acrr	0.09341452331910545 -0.038430477506537608
+ada	0.18397938683076256 -0.11782455570024868
+adiy	0.25946832985866286 -0.17087763807973277
+agar	0.068052232378298008 0.078512124508642103
+aidb	0.017620880905652076 -0.011780200952442782
+alas	0.034923943933789094 -0.024722834270162725
+allr	0.053467493826187089 0.012471885438110586
+alls	0.040874582815039973 -0.022583476107431933
+alsr	0.060446342357332788 0.051766283481064289
+appy	0.027282246326355286 -0.016278539065704145
+arac	0.037069239771292291 0.04720785771755158
+argp	0.062496705777803363 -0.035038000247626017
+argr	0.2018245727190191 -0.13481380526856895
+arsr	0.0088807916152464222 -0.002338715912557653
+ascg	0.29396291841081956 0.44452716667930525
+asnc	0.079332257125355798 -0.056143615986803612
+atoc	0.18883237944326092 -0.080586356102687426
+baer	0.06240973079061627 -0.024894814432568354
+basr	0.093294622714680378 -0.051090069871726401
+beti	0.0041893784830210647 -0.0015784298318369076
+bira	0.086534320271851692 -0.046107342793235861
+blur	0.0 0.0
+bola	0.00040297071946237878 -0.00032091800276497543
+cadc	0.085480220668489523 -0.039558779432154065
+caif	0.069621197680633989 -0.040462660331470536
+cbl	0.04178318273632816 -0.023685683415448303
+cdar	0.033429028208936742 0.022362161125991476
+chbr	0.29707253866712124 0.44810955240454214
+comr	0.0 0.0
+cpxr	0.13154067858591692 -0.034667345213563218
+cra	0.62779376954669575 -0.43789346045429117
+creb	0.059633324388609563 0.044479826906951979
+csgd	0.07535001605944816 -0.043000564435430828
+csir	0.24634649733601327 -0.16817080820179736
+cspa	0.021710589727156435 -0.0091532377269355307
+cuer	0.014431400782412269 -0.0076141044071228537
+cusr	0.018839655926104825 -0.002866935264882008
+cynr	0.087810534629474621 -0.055907646186147887
+cysb	0.047685886454475662 -0.027767722235629518
+cytr	0.034746942817366622 -0.00056359272531777689
+dan	0.033159921636034137 -0.022055956080212293
+dcur	0.067483934432422565 -0.041593526975294282
+deor	0.036856796845433248 0.00011919399350812467
+dhar	0.065470299761680534 -0.013971863072267211
+dica	0.0 0.0
+dinj-yafq	0.00058101935229529071 -0.00047325178950615072
+dnaa	0.06294426832315024 -0.033940233731381365
+dpia	0.093082198354974374 -0.059798154158116311
+dsdc	0.0063418076516338715 -0.003826826070272819
+ebgr	0.0 0.0
+envr	0.1067921955850181 -0.047759403086950307
+envy	0.040716612922993126 -0.012584282869820686
+evga	0.22405018875397456 -0.10836760042343585
+exur	0.0040774816401605157 -0.002152278539015182
+fabr	0.070686998898079195 -0.035425472142896569
+fadr	0.15174441875018924 -0.079187777853061511
+fear	0.017037932147430435 -0.010666355681252598
+fhla	0.036939403523913804 -0.018755025729404209
+flhdc	0.096705010474018499 -0.051578942231655107
+fliz	0.25861117983005488 -0.15629739890426958
+fucr	0.018913572276118739 0.012525056907558851
+fur	0.67614411673882346 -0.48179422836198893
+gade	0.15448628487072807 -0.080579918702893924
+gade-rcsb	0.10274270649984042 -0.061813575667158581
+gadw	0.28995364206488222 -0.1816126362153902
+gadx	0.31559767987587645 -0.19910160267187293
+galr	0.017144740582850482 0.011578178976343311
+gals	0.017144740578492228 0.011578179032045981
+gatr	0.30519478438200282 0.44308403647370981
+gcva	0.52205705851661466 -0.38999855269670652
+glcc	0.045325158019694813 -0.017177673950227037
+glpr	0.043216885498516711 0.0033755593866538797
+glrr	0.038441933334504352 0.022723777624042291
+gntr	0.049187963179157426 0.0011471856484028091
+gutm	0.33083342824073797 0.51007091522583192
+gutr	0.33171390265885381 0.51139431623819265
+hcar	0.069709583646935025 -0.04155679718053671
+hdfr	0.051040579750449656 -0.032208868318339814
+hipab	0.015459114494647888 -0.0098861658914747713
+hipb	0.013800384645577212 -0.0088434388795533038
+hu	0.053666888984704827 0.042116574001790204
+hyfr	0.0048796075602912563 -0.0034898109789972733
+hypt	0.036565478409314546 -0.022645216995022744
+iclr	0.066889315800593693 -0.034526538560151511
+idnr	0.026181287787880501 0.0060402748566003662
+ilvy	0.0 0.0
+iscr	0.078836365647917198 -0.052435517181787988
+kdgr	0.054706308108825825 -0.0017698723317291127
+kdpe	0.088247796425708777 0.051596992216240797
+laci	0.036707951084722912 -0.0056473426123220581
+leuo	0.40573946186734866 0.56929148241617022
+lexa	0.050201083313421116 0.016170964954657582
+lldr	0.039953280119753382 0.021191977832878173
+lrha	0.035912200776838647 -0.019312629264368469
+lsrr	0.12402489687425361 0.002245351599142454
+lysr	0.00085139622084761833 -8.4872532742886531e-05
+mali	-0.00033014016450992725 -0.00036476848145399669
+malt	0.046548807813931843 0.025289369202289125
+mara	0.18587600177786032 -0.084094787311215044
+marr	0.034579047776613039 -0.010642813598801707
+mata	0.03008983244938759 -0.016997094757538814
+maze	0.023461573191426964 -0.010781024979507095
+maze-mazf	0.023560563749667956 -0.010791894577026319
+mcbr	0.0 0.0
+melr	0.0060334472867425307 0.0022651280822319391
+metj	0.051476887235320803 -0.032087940732821771
+metr	0.1671473495785511 -0.1189895645522058
+mhpr	0.09731927194203345 -0.054258728339581903
+mlc	0.39751755056372318 0.59644361619258457
+mlra	0.07828818379029559 -0.031970220056418773
+mngr	0.28749179747325415 0.42538064115856722
+mntr	0.0019092923033075619 -0.0017053167761109087
+mode	0.052556489155517458 -0.0073139104425990623
+mpra	0.11262385618604986 -0.051665904957860033
+mqsa	0.0066753089007730287 -0.005599226877975442
+mtlr	0.10690944420537989 0.16160739334087568
+murr	0.3364112502789498 0.48065054289370157
+nac	0.14130415608179536 -0.096630742189418473
+nadr	0.066610524727059445 -0.041696005349029001
+nagc	0.38520291127118123 0.53913844341826433
+nanr	0.050986372008806659 0.040415888702188686
+narl	0.13278451848855763 -0.078259638034698958
+narp	0.087566618424971432 -0.048841811941105449
+nemr	0.0064987541881732103 -0.0040186565957900169
+nhar	0.014095853653898449 -0.0025983750920695937
+nikr	0.0043066847581446285 -0.0014768733733973333
+norr	0.0021907164993936997 -0.00053563186080693458
+nrdr	0.052215500188972294 -0.011432295137888612
+nsrr	0.65032520652512416 -0.46423735452583992
+ntrc	0.22665798179062302 -0.14153337485398579
+ompr	0.032233362127255964 -0.0099589885721252484
+oxyr	0.085887472920305852 -0.051154018062252574
+paax	0.18945099597719212 -0.10835413959498626
+pdhr	0.60403937453285261 -0.44051800188831564
+pepa	0.1253932022465423 -0.073974428665687403
+pgrr	0.0 0.0
+phob	0.12293494697407269 0.036607011249932916
+phop	0.14975872535375037 -0.010990891315520985
+prpr	0.11375741571327096 -0.061284944451031047
+pspf	0.0 0.0
+purr	0.47766580944784748 -0.35209577496659727
+puta	0.064388741759064783 -0.038218652933016621
+puur	0.21245480492228414 -0.14027558703208212
+qseb	0.035663426603148647 -0.011185741455587719
+rbsr	0.077695161521724801 -0.05349775294392882
+rcda	0.069509334285361088 -0.036257878589025454
+rcnr	0.0049015374043896824 -0.0017598311113065069
+rcsab	0.090140888075835562 -0.031354886573127627
+rcsb	0.24678625595123282 -0.15801650912922863
+rcsb-bglj	0.31220508998607344 0.47715937644079576
+relb	0.00010724253213298943 5.715352495014115e-05
+relb-rele	0.00010717270007911151 5.702978263057333e-05
+rhar	0.0041182455686604629 0.0038437052681961599
+rhas	0.02312221178967112 0.02034089309809187
+rob	0.16031218220881646 -0.0293887562018499
+rsta	0.064120472467851852 -0.031185509533898512
+rtcr	0.0040370201192737164 -0.00023726300471701135
+rutr	0.15245570658731181 -0.090731219170238186
+sdia	0.017047761493006169 -0.010385789238526138
+sgrr	0.058723050936512207 -0.038568027868210536
+slya	0.0 0.0
+soxr	0.03787535795414787 -0.019440328954682059
+soxs	0.17730694857079984 5.9808024880112463e-05
+stpa	0.31479138604166546 0.48189839213272667
+tdca	0.0055806843681201219 -0.0034227358766909717
+tdcr	0.0056255099234366907 -0.003451132070575149
+torr	0.12844238868558572 -0.075355013155888897
+trer	0.33304854847659537 0.5066387794861722
+trpr	0.064730735670671655 -0.025675249830454768
+tyrr	0.26422622645713051 -0.17882669592546416
+uhpa	0.11537978976372569 0.13410539736677768
+uidr	0.00022273781854669227 -0.0004002019024285261
+ular	0.22647155292601673 0.32742792980593627
+uxur	0.0054738201562451998 -0.0018063655401798189
+xapr	0.027156089353213658 -0.0038405714186219965
+xylr	0.026245735844159613 0.032066463953140764
+ydeo	0.088220688070894726 -0.05007875186195402
+yefm	9.9972614978804478e-05 1.5179727836036151e-05
+yefm-yoeb	0.00010341640470731619 1.5655110262773912e-05
+yeht	0.0 0.0
+yeil	0.0 0.0
+yiaj	0.062244517960345164 0.07953362698161448
+ypdb	0.0 0.0
+yqhc	0.0058963374847413666 -0.0032545759344078024
+yqji	0.051088959944720826 -0.037325537150977868
+zntr	0.00031414399292253956 1.7568785982291884e-06
+zrar	0.088247796425708777 0.051596992216240797
+zur	0.0010774017644862583 5.3505399467029303e-06

+accb	0.051689156852524315 -0.026351330751228447 0.0025182393849010761
+acrr	0.09364378694990004 -0.038190521478549032 -0.19914314943660436
+ada	0.18416796250932546 -0.1181349958714457 -0.10279593596291192
+adiy	0.25980189697238271 -0.17009145133462933 -0.47610466256798722
+agar	0.068068645700795377 0.078011448817581985 -0.00050950547789574419
+aidb	0.017639367623407617 -0.011125676706508618 -0.012100082087137358
+alas	0.035147694026535732 -0.024490860697316319 0.010491115045966053
+allr	0.053031977377400952 0.012130656338522415 0.011499163610811847
+alls	0.041252992803423288 -0.023158425613585505 -0.015469085771671954
+alsr	0.060785643302893239 0.051847934170493278 0.0011980447307381077
+appy	0.02727308908772267 -0.016009429813909917 -0.063877883509361078
+arac	0.036965934401581184 0.04711064653389483 -0.0010648596926529212
+argp	0.062693604483942178 -0.035013873841024397 -0.073782699817013755
+argr	0.20222689723261808 -0.13441133419264548 -0.22629697403178395
+arsr	0.0089403949472128801 -0.0023024158909870111 -0.011075705531114165
+ascg	0.29404341564497749 0.44398318564527522 0.033824336361496135
+asnc	0.079247370445499385 -0.05633318150488055 -0.019534982239994409
+atoc	0.18881545737494493 -0.080402940202116469 0.060879242680086071
+baer	0.062512885270917751 -0.024884446449516425 -0.12931123765891336
+basr	0.092968076904434677 -0.050878723934196984 -0.22958756714430045
+beti	0.0037827060014256164 -0.0014972257530093407 -0.0039081946029734787
+bira	0.086502753215532038 -0.046381923853117513 -0.038226092552955095
+blur	0.0 0.0 0.0
+bola	-0.00013318552927766794 -9.0594441408825866e-05 -0.0020095961416570565
+cadc	0.085298227968467755 -0.039942749641301185 -0.14108467481839237
+caif	0.069610184695421309 -0.041292696088251382 -0.023841400697120565
+cbl	0.041797318936852831 -0.023834817031183415 -0.055662636631219856
+cdar	0.033488744882232371 0.022135626119836653 0.011783911623126503
+chbr	0.29648764150323054 0.44827621300257969 0.027213072502029735
+comr	0.0 0.0 0.0
+cpxr	0.13128987509318843 -0.0345544593922908 -0.19779724725016304
+cra	0.62764178695463479 -0.43793140544537768 0.42742693337948079
+creb	0.059770599816717111 0.045177919644581711 -0.0097501894434703154
+csgd	0.075654173430521526 -0.043197612473124702 -0.16666808926127885
+csir	0.24619261168914269 -0.16801855636859175 -0.26156323710088308
+cspa	0.02151995653680637 -0.0086512045768064334 -0.032295824751918729
+cuer	0.014135134053382271 -0.0074360094418592362 -0.012375853849656195
+cusr	0.018771607505633347 -0.0031738459329844954 -0.026563667055301152
+cynr	0.087268049108309306 -0.056537148122061766 -0.055062376524264128
+cysb	0.048170694511735376 -0.027706446805000111 -0.058057392218896581
+cytr	0.034845641578552938 -0.00042334850921466223 -0.033502935661917699
+dan	0.033473698830026294 -0.022815359762882837 0.0082161197058957594
+dcur	0.067314806898536347 -0.04137545521292274 -0.04751694161940731
+deor	0.036953240796682246 0.00026253659416656199 -0.033986553916373237
+dhar	0.065571150501131992 -0.014280208187461885 -0.0058021497402977141
+dica	0.0 0.0 0.0
+dinj-yafq	0.0012472012177525394 0.00029482492887629544 0.00051935951820470489
+dnaa	0.063173909012333218 -0.033900019520539983 -0.078217237215681162
+dpia	0.093546598659448074 -0.06066173866882818 0.053479698795726546
+dsdc	0.0066275070116272213 -0.0022635124814504902 -0.0046502236492952844
+ebgr	0.0 0.0 0.0
+envr	0.10676855507978915 -0.047717279442715727 -0.23570014483714941
+envy	0.040643759063960339 -0.014304078828405911 -0.044035277835439063
+evga	0.2241622781236611 -0.10942527663636058 -0.25192419136016431
+exur	0.0035405997514942784 -0.0019600845977034822 -0.00033258196405265594
+fabr	0.071067422993727994 -0.035415003129949774 -0.088911085109096191
+fadr	0.1521567199192666 -0.079288970157513725 0.00015998951279432599
+fear	0.016921178293028326 -0.010384711367471397 -0.0090960639024517138
+fhla	0.036405312144712133 -0.019546573889441362 -0.039531857242216493
+flhdc	0.09683518824543412 -0.051433899923720959 -0.11833270626911412
+fliz	0.25858590466149906 -0.15670860898768993 -0.55869260355650019
+fucr	0.018795095261110468 0.013113191871846909 -0.0025473468695685164
+fur	0.67607193715979941 -0.48180201580773113 0.39653555453699402
+gade	0.15438903090425185 -0.080331521176462889 -0.27785175667671103
+gade-rcsb	0.10282101583092605 -0.06162538009809515 -0.19648425440648351
+gadw	0.28988412299471439 -0.18193360856505136 -0.57497431246841413
+gadx	0.31580398984840174 -0.19909150412501692 -0.55232800174960561
+galr	0.017148044921293838 0.011516363068648044 -0.0071074470930345803
+gals	0.01714804493760292 0.011516363052739247 -0.0071074470562667953
+gatr	0.30533671070807678 0.44252942938666623 0.018848887332160166
+gcva	0.52211073628927951 -0.38973107214265229 0.47294574613677837
+glcc	0.045332826371277807 -0.016899310557595866 0.011761092978086779
+glpr	0.042822049193183526 0.0021654172941484666 -0.023607011908854173
+glrr	0.038735568350909771 0.023348662567079129 -0.018662786058952496
+gntr	0.049338092723015238 0.00098645282553376143 0.0021174079128954064
+gutm	0.33059783624313371 0.50968103578330581 0.031451969704673496
+gutr	0.33148037255121765 0.5110049722132054 0.031526557788581294
+hcar	0.069740364156705661 -0.04165838542554768 -0.065251839339854137
+hdfr	0.051510512906507031 -0.031251259925220161 -0.13879960832230667
+hipab	0.015700552371106957 -0.0099129680936970842 -0.022600755926692983
+hipb	0.01405537532245648 -0.008878402484865924 -0.020420393152335233
+hu	0.053319753165141565 0.042214710989252925 -0.010236435757931477
+hyfr	0.0048510169870782174 -0.0031813704193446551 -0.0076921809089378363
+hypt	0.036188548727190656 -0.023141081015224895 -0.023571431453546558
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+zntr	-0.00034341463776330376 2.065585477909394e-05 -0.00045857236332174473 0.00013055669342981253 0.0026393391827952872 -0.0010223762422725386 -0.0010472353970503502 -0.0026590397981786969 0.0014722424089941204 0.00092289921056619103 -0.00078212314789700272 0.0055651915273671679 0.007203240326024575 -0.0041263568401883732 -0.00046986145739034307 -0.0036547244302610306 0.016427118592922001 0.0052072271653061074 0.024652735758568418 -0.0078021632732416073 -0.018569206831730824 0.0026583832290849236 0.0052170716577181232 -0.94950148654839051 -0.018201759156027078 -0.24517245041345209
+zrar	-0.088314281972883851 -0.051371334202255224 -0.041694121390547942 0.033597520461088659 0.15401935896535809 0.32705778100135152 -0.44253860880974094 0.18837847130861773 -0.61326820742419563 0.15432325149089868 0.15858362795294226 -0.054901707049796591 0.15533889032042092 -0.035780825190854612 0.011932770504746205 0.042730849295660872 -0.028082418833699486 0.053062193786128448 0.075248083112057856 -0.003314994943148387 0.0020006027436676134 0.001886984886171582 0.041794797656164624 0.0076096761988988827 0.0015696400799533935 0.0055999749369013384
+zur	-0.0011141178434467796 1.4871454180361999e-05 -0.0011373330132936571 4.4030020812024613e-05 0.0054288654389083955 -0.0023559167698856166 -0.0012484949981450227 -0.003327468539972892 0.001944346869985986 0.0012707951830031969 -0.00074746534077184141 0.0079505377311041067 0.0090233800473158553 -0.0047620835007722087 -0.00032398306165716979 -0.0037385457997938447 0.018644929468960205 0.0059071375553027168 0.026229014882134152 -0.0084119917100171464 -0.020541728943225694 0.0026611176689007835 0.0061344247690254643 -0.94992715428966579 -0.018089048603006495 -0.24260915528008506

+
+import matplotlib.patches as mpatches
+import powerlaw
+
+# Load word count file
+with open("guil/GUs_word_count.csv",'r') as f:
+    words_array =np.array([c[0] for c in list(csv.reader(f, delimiter=" "))]).astype("int")
+
+
+np.savetxt("words.txt", words_array)
+np.savetxt("words.txt", words_array, fmt='% 10d')
+# loading file of sorted frequencies
+data=np.genfromtxt("words.txt")
+
+fit = powerlaw.Fit(data)
+fit.power_law.alpha
+# \alpha = 1.8517470767126287 (GUs)
+fit.power_law.sigma
+# \sigma = 0.011286616988085252
+
+# Prueba de hipótesis Kolmogorov-Smirnof: entre la exponencial y la ley de potencias 
+# Tiene mucho más confianza la ley de potencias (R=32012.27, p-value=3.56e-275).
+fit.distribution_compare('power_law', 'exponential')
+# (32012.272128016586, 3.5658919192201475e-275)
+
+figPDF = powerlaw.plot_pdf(data, color='b')
+powerlaw.plot_pdf(data, linear_bins=True, color='r', ax=figPDF)
+
+figPDF.set_ylabel("p(BO)")
+figPDF.set_xlabel(r"BO Frequency")
+figPDF.set_title(r"Power Law in Biological Objects (BOs) of GENSOR units")
+red_patch = mpatches.Patch(color='red', label='Distribution linear bins')
+blue_patch = mpatches.Patch(color='blue', label='Distribution log bins')
+figPDF.legend(handles=[red_patch, blue_patch])