from copy import copy
from shutil import copyfile
import shelve
+import json
#from dialog import BugDialog
import logging
indices_simi = [u'cooccurrence' ,'pourcentage de cooccurrence',u'Russel',u'Jaccard', 'Kulczynski1', 'Kulczynski2', 'Mountford', 'Fager', 'simple matching', 'Hamman', 'Faith', 'Tanimoto', 'Dice', 'Phi', 'Stiles', 'Michael', 'Mozley', 'Yule', 'Yule2', 'Ochiai', 'Simpson', 'Braun-Blanquet','Chi-squared', 'Phi-squared', 'Tschuprow', 'Cramer', 'Pearson', 'binomial']
+
+def open_folder(folder):
+ if sys.platform == "win32":
+ os.startfile(folder)
+ else:
+ opener ="open" if sys.platform == "darwin" else "xdg-open"
+ #call([opener, folder])
+ call([u"%s %s &" % (opener, folder)], shell=True)
+
def normpath_win32(path) :
if not sys.platform == 'win32' :
return path
self.path = path
self.tgen = {}
self.encoding = encoding
-
+
def __getitem__(self, key):
return self.tgen[key]
-
+
def read(self, path = None):
if path is None :
path = self.path
tgen = dict([[line[0], line[1:]] for line in tgen])
self.tgen = tgen
self.path = path
-
+
def write(self, path = None):
if path is None :
path = self.path
with open(path, 'w') as f :
f.write('\n'.join(['\t'.join([val] + self.tgen[val]) for val in self.tgen]).encode(self.encoding))
-
+
def writetable(self, pathout, tgens, totocc):
etoiles = totocc.keys()
etoiles.sort()
self.matrixanalyse = dict([[mat['uuid'], mat] for mat in self.matrix])
self.ordermatrix = dict([[matrix['uuid'], i] for i, matrix in enumerate(self.matrix)])
d.close()
+ d = {}
+ d['history'] = self.history
+ d['matrix'] = self.matrix
+# with open('/home/pierre/hystory.json', 'w') as f :
+# f.write(json.dumps(d, indent=4, default=str))
def write(self) :
d = shelve.open(self.filein)
d['history'] = self.history
d['matrix'] = self.matrix
d.close()
-
+
def add(self, analyse) :
log.info('add to history %s' % analyse.get('corpus_name', 'pas un corpus'))
tosave = {'uuid' : analyse['uuid'], 'ira': analyse['ira'], 'type' : analyse['type']}
self.matrix[self.ordermatrix[tosave['matrix']]]['analyses'].append(tosave)
self.write()
self.read()
-
+
def addmultiple(self, analyses) :
log.info('add multiple')
for analyse in analyses :
self.history[self.ordercorpus[analyse['corpus']]]['analyses'].pop(todel)
elif analyse['uuid'] in self.matrixanalyse :
self.matrix = [mat for mat in self.matrix if mat['uuid'] != analyse['uuid']]
+ elif analyse.get('matrix', False) in self.matrixanalyse :
+ analyses = self.matrix[self.ordermatrix[analyse['matrix']]]['analyses']
+ topop = [i for i, val in enumerate(analyses) if analyse['uuid'] == val['uuid']][0]
+ analyses.pop(topop)
+ self.matrix[self.ordermatrix[analyse['matrix']]]['analyses'] = analyses
self.write()
self.read()
def rmtab(self, analyse) :
del self.opened[analyse['uuid']]
+ def update(self, analyse) :
+ if 'matrix_name' in analyse :
+ self.matrixanalyse[analyse['uuid']].update(analyse)
+ elif 'corpus_name' in analyse :
+ self.corpus[analyse['uuid']].update(analyse)
+ elif 'corpus' in analyse :
+ self.analyses[analyse['uuid']].update(analyse)
+ else :
+ toupdate = [an for an in self.matrixanalyse[analyse['matrix']]['analyses'] if an['uuid'] == analyse['uuid']]
+ toupdate[0].update(analyse)
+ self.write()
+ self.read()
+
def clean(self) :
corpustodel = [corpus for corpus in self.history if not os.path.exists(corpus['ira'])]
print corpustodel
for analyse in anatodel :
print 'cleaning :', analyse['name']
self.delete(analyse)
-
+
+ def dostat(self):
+ todel = {}
+ tokens = 0
+ corpusnb = {}
+ subnb = 0
+ analysenb = 0
+ hours = 0
+ minutes = 0
+ secondes = 0
+ ha = 0
+ ma = 0
+ sa = 0
+ for corpus in self.history :
+ analysenb += len(corpus.get('analyses', []))
+ analyses = corpus.get('analyses', [])
+ for analyse in analyses :
+ if os.path.exists(analyse['ira']) :
+ ana = DoConf(analyse['ira']).getoptions()
+ if 'time' in ana :
+ time = ana['time'].split()
+ ha += int(time[0].replace('h','')) * 3600
+ ma += int(time[1].replace('m','')) * 60
+ sa += int(time[2].replace('s',''))
+ if os.path.exists(corpus['ira']) :
+ param = DoConf(corpus['ira']).getoptions()
+ time = param.get('time','0h 0m 0s')
+ time = time.split()
+ hours += int(time[0].replace('h','')) * 3600
+ minutes += int(time[1].replace('m','')) * 60
+ secondes += int(time[2].replace('s',''))
+ if param.get('originalpath', False) :
+ if param['originalpath'] in corpusnb :
+ corpusnb[param['originalpath']] += 1
+ tokens += int(param['occurrences'])
+ else :
+ corpusnb[param['originalpath']] = 1
+ #print param
+ else :
+ subnb += 1
+ else :
+ if corpus['ira'] in todel :
+ todel['ira'] += 1
+ else :
+ todel['ira'] = 1
+ print u'Nbr total de corpus : %s' % len(self.history)
+ corpus_nb = len(corpusnb) + len(todel)
+ print u'Nbr de corpus différents : %s' % corpus_nb
+ lentodel = len(todel)
+ print u'Nbr de corpus à supprimer : %s' % lentodel
+ print u'Nbr de sous corpus : %s' % subnb
+ print u"Nbr total d'occurrences : %s" % tokens
+ print u'Moyenne occurrences par corpus : %f' % (tokens/corpus_nb)
+ print '---------------------'
+ print u"Nbr total d'analyses : %s" % analysenb
+ print u'Temps total indexation : %f h' % ((hours+minutes+secondes) / 3600)
+ print u'Temps total analyses : %f h' % ((ha+ma+sa) / 3600)
+
def __str__(self) :
return str(self.history)
def __init__(self, configfile=None, diff = None, parametres = None) :
self.configfile = configfile
self.conf = ConfigParser()
-
+
if configfile is not None :
configfile = normpath_win32(configfile)
self.conf.readfp(codecs.open(configfile, 'r', 'utf8'))
if 'type' not in parametres :
parametres['type'] = section
return parametres
-
+
def makeoptions(self, sections, parametres, outfile = None) :
txt = ''
for i, section in enumerate(sections) :
class BugDialog(wx.Dialog):
def __init__(self, *args, **kwds):
# begin wxGlade: MyDialog.__init__
- kwds["style"] = wx.DEFAULT_DIALOG_STYLE
+ kwds["style"] = wx.DEFAULT_DIALOG_STYLE | wx.STAY_ON_TOP
kwds["size"] = wx.Size(500, 200)
wx.Dialog.__init__(self, *args, **kwds)
self.SetTitle(kwds['title'])
# begin wxGlade: MyDialog.__set_properties
self.SetMinSize(wx.Size(500, 200))
self.text_ctrl_1.SetMinSize(wx.Size(500, 200))
-
+
# end wxGlade
def __do_layout(self):
sortedby(list,0) will return [[1, 2], [2, 3], [3, 1]]
"""
- nlist = map(lambda x, indices=indices:
+ nlist = map(lambda x, indices=indices:
map(lambda i, x=x: x[i], indices) + [x],
list)
if direct == 1:
clusters = [row[2] for row in rows if row[0] == u'**']
valclusters = [row[1:4] for row in rows if row[0] == u'****']
lp = [i for i, line in enumerate(rows) if line[0] == u'****']
- prof = [rows[lp[i] + 1:lp[i+1] - 1] for i in range(0, len(lp)-1)] + [rows[lp[-1] + 1:len(rows)]]
+ prof = [rows[lp[i] + 1:lp[i+1] - 1] for i in range(0, len(lp)-1)] + [rows[lp[-1] + 1:len(rows)]]
if Alceste :
prof = [[add_type(row, dictlem) for row in pr] for pr in prof]
- prof = [[treat_line_alceste(i,line) for i, line in enumerate(pr)] for pr in prof]
+ prof = [[treat_line_alceste(i,line) for i, line in enumerate(pr)] for pr in prof]
else :
prof = [[line + [''] for line in pr] for pr in prof]
prof = [[treat_line_alceste(i,line) for i, line in enumerate(pr)] for pr in prof]
separateurs = [[u'.', 60.0], [u'?', 60.0], [u'!', 60.0], [u'£$£', 60], [u':', 50.0], [u';', 40.0], [u',', 10.0], [u' ', 0.1]]
trouve = False # si on a trouvé un bon séparateur
iDecoupe = 0 # indice du caractere ou il faut decouper
-
+
# on découpe la chaine pour avoir au maximum 240 caractères
longueur = min(longueur, len(chaine) - 1)
chaineTravail = chaine[:longueur + 1]
nbCar = longueur
meilleur = ['', 0, 0] # type, poids et position du meilleur separateur
-
+
# on vérifie si on ne trouve pas un '$'
indice = chaineTravail.find(u'$')
if indice > -1:
# on vérifie si le caractére courant est une marque de ponctuation
for s in separateurs:
if caractere == s[0]:
- # si c'est une ponctuation
-
+ # si c'est une ponctuation
+
if s[1] / distance > float(meilleur[1]) / meilleureDistance:
# print nbCar, s[0]
meilleur[0] = s[0]
meilleur[2] = nbCar
trouve = True
iDecoupe = nbCar
-
+
# et on termine la recherche
break
# on passe au caractère précédant
nbCar = nbCar - 1
-
+
# si on a trouvé
if trouve:
fin = chaine[iDecoupe + 1:]
'EmptyText' : u"Texte vide (probablement un problème de formatage du corpus). Le problème est apparu à la ligne ",
'CorpusEncoding' : u"Problème d'encodage.",
'TextBeforeTextMark' : u"Problème de formatage : du texte avant le premier marqueur de texte (****). Le problème est survenu à la ligne ",
- 'MissingAnalyse' : u'Aucun fichier à cet emplacement :\n',
+ 'MissingAnalyse' : u'Aucun fichier à cet emplacement :\n',
}
def BugReport(parent, error = None):
for ch in parent.GetChildren():
if "<class 'wx._windows.ProgressDialog'>" == str(type(ch)):
- ch.Destroy()
+ ch.Destroy()
excName, exc, excTb = formatExceptionInfo()
if excName == 'Exception' :
print exc
dial.CenterOnParent()
dial.ShowModal()
dial.Destroy()
-
+
def PlaySound(parent):
if parent.pref.getboolean('iramuteq', 'sound') :
try:
if "gtk2" in wx.PlatformInfo:
error = Popen(['aplay','-q',os.path.join(parent.AppliPath,'son_fin.wav')])
- else :
+ else :
sound = wx.Sound(os.path.join(parent.AppliPath, 'son_fin.wav'))
sound.Play(wx.SOUND_SYNC)
except :
for val in line[1:]:
if val == u'NA' :
don = ''
- else:
+ else:
try:
don = int(val)
except:
else :
return True
+
+def launchcommand(mycommand):
+ Popen(mycommand)
+
def print_liste(filename,liste):
with open(filename,'w') as f :
for graph in liste :
- f.write(';'.join(graph)+'\n')
+ f.write(';'.join(graph).encode(sys.getdefaultencoding(), errors='replace')+'\n')
def read_list_file(filename, encoding = sys.getdefaultencoding()):
with codecs.open(filename,'rU', encoding) as f :
content=f.readlines()
ncontent=[line.replace('\n','').split(';') for line in content if line.strip() != '']
return ncontent
-
-
-
def progressbar(self, maxi) :
- if 'parent' in dir(self) :
- parent = self.parent
- else :
- parent = self
+ ira = wx.GetApp().GetTopWindow()
+ parent = ira
try :
maxi = int(maxi)
except :
maxi = 1
- return wx.ProgressDialog("Traitements",
+ prog = wx.ProgressDialog("Traitements",
"Veuillez patienter...",
maximum=maxi,
parent=parent,
style=wx.PD_APP_MODAL | wx.PD_AUTO_HIDE | wx.PD_ELAPSED_TIME | wx.PD_CAN_ABORT
)
-
+ prog.SetSize((400,150))
+ #prog.SetIcon(ira._icon)
+ return prog
def treat_var_mod(variables) :
var_mod = {}
for var in vars :
mods = ['_'.join(v) for v in varmod if v[0] == var]
var_mod[var] = mods
-
+
# for variable in variables :
# if u'_' in variable :
# forme = variable.split(u'_')
# var_mod[var].append(variable)
return var_mod
-def doconcorde(corpus, uces, mots, uci = False) :
+def doconcorde(corpus, uces, mots, uci = False, et = False) :
if not uci :
ucestxt1 = [row for row in corpus.getconcorde(uces)]
else :
ucestxt1 = dict(ucestxt1)
ucestxt = []
ucis_txt = []
- listmot = [corpus.getlems()[lem].formes for lem in mots]
- listmot = [corpus.getforme(fid).forme for lem in listmot for fid in lem]
+ if not et :
+ listmot = [corpus.getlems()[lem].formes for lem in mots]
+ listmot = [corpus.getforme(fid).forme for lem in listmot for fid in lem]
+ else :
+ listmot = mots
mothtml = ['<font color=red><b>%s</b></font>' % mot for mot in listmot]
dmots = dict(zip(listmot, mothtml))
for uce in uces :
ucetxt = ucestxt1[uce].split()
ucetxt = ' '.join([dmots.get(mot, mot) for mot in ucetxt])
if not uci :
- ucis_txt.append('<p><b>' + ' '.join(corpus.ucis[corpus.getucefromid(uce).uci].etoiles) + '</b></p>')
+ uciid = corpus.getucefromid(uce).uci
+ ucis_txt.append('<p><b>' + ' '.join(corpus.ucis[corpus.getucefromid(uce).uci].etoiles) + '<a href="%i_%i"> *%i_%i</a></b></p>' % (uciid, uce, uciid, uce))
else :
ucis_txt.append('<p><b>' + ' '.join(corpus.ucis[uce].etoiles) + '</b></p>')
ucestxt.append(ucetxt)
return ucis_txt, ucestxt
-
+
def getallstcarac(corpus, analyse) :
pathout = PathOut(analyse['ira'])
profils = ReadProfileAsDico(pathout['PROFILE_OUT'], Alceste, self.encoding)
print profils
+
+def read_chd(filein, fileout):
+ with open(filein, 'r') as f :
+ content = f.read()
+ #content = [line[3:].replace('"',"").replace(' ','') for line in content.splitlines()]
+ content = [line.split('\t') for line in content.splitlines()]
+ chd = {'name':1, 'children':[]}
+ mere={}
+ for i, line in enumerate(content) :
+ if i == 0 :
+ chd['children'] = [{'name': line[1],'size' : content[i+1][0]}, {'name':line[2], 'size': content[i+1][1]}]
+ mere[line[1]] = chd['children'][0]
+ mere[line[2]] = chd['children'][1]
+ elif not i % 2 :
+ if 'children' in mere[line[0]]:
+ mere[line[0]]['children'].append({'name': line[1],'size' : content[i+1][0]})
+ mere[line[1]] = mere[line[0]]['children'][-1]
+ mere[line[0]]['children'].append({'name': line[2],'size' : content[i+1][1]})
+ mere[line[2]] = mere[line[0]]['children'][-1]
+ else :
+ mere[line[0]]['children'] = [{'name': line[1],'size' : content[i+1][0]}, {'name':line[2], 'size': content[i+1][1]}]
+ mere[line[1]] = mere[line[0]]['children'][-2]
+ mere[line[2]] = mere[line[0]]['children'][-1]
+ with open(fileout, 'w') as f :
+ f.write(json.dumps(chd))
+
+
+translation_languages = {"Afrikaans":"af", "Albanian":"sq", "Amharic":"am", "Arabic":"ar", "Armenian":"hy", "Azeerbaijani":"az", "Basque":"eu", "Belarusian":"be", "Bengali":"bn", "Bosnian":"bs", "Bulgarian":"bg", "Catalan":"ca", "Cebuano":"ceb", "Chichewa":"ny", "Chinese (Simplified)":"zh-CN", "Chinese (Traditional)":"zh-TW", "Corsican":"co", "Croatian":"hr", "Czech":"cs", "Danish":"da", "Dutch":"nl", "English":"en", "Esperanto":"eo", "Estonian":"et", "Filipino":"tl", "Finnish":"fi", "French":"fr", "Frisian":"fy", "Galician":"gl", "Georgian":"ka", "German":"de", "Greek":"el", "Gujarati":"gu", "Haitian Creole":"ht", "Hausa":"ha", "Hawaiian":"haw", "Hebrew":"iw", "Hindi":"hi", "Hmong":"hmn ", "Hungarian":"hu", "Icelandic":"is", "Igbo":"ig", "Indonesian":"id", "Irish":"ga", "Italian":"it", "Japanese":"ja", "Javanese":"jw", "Kannada":"kn", "Kazakh":"kk", "Khmer":"km", "Korean":"ko", "Kurdish":"ku", "Kyrgyz":"ky", "Lao":"lo", "Latin":"la", "Latvian":"lv", "Lithuanian":"lt", "Luxembourgish":"lb", "Macedonian":"mk", "Malagasy":"mg", "Malay":"ms", "Malayalam":"ml", "Maltese":"mt", "Maori":"mi", "Marathi":"mr", "Mongolian":"mn", "Burmese":"my", "Nepali":"ne", "Norwegian":"no", "Pashto":"ps", "Persian":"fa", "Polish":"pl", "Portuguese":"pt", "Punjabi":"ma", "Romanian":"ro", "Russian":"ru", "Samoan":"sm", "Scots Gaelic":"gd", "Serbian":"sr", "Sesotho":"st", "Shona":"sn", "Sindhi":"sd", "Sinhala":"si", "Slovak":"sk", "Slovenian":"sl", "Somali":"so", "Spanish":"es", "Sundanese":"su", "Swahili":"sw", "Swedish":"sv", "Tajik":"tg", "Tamil":"ta", "Telugu":"te", "Thai":"th", "Turkish":"tr", "Ukrainian":"uk", "Urdu":"ur", "Uzbek":"uz", "Vietnamese":"vi", "Welsh":"cy", "Xhosa":"xh", "Yiddish":"yi", "Yoruba":"yo", "Zulu":"zu", }
+
+
+def gettranslation(words, lf, lt) :
+ import urllib2
+ import json
+ agent = {'User-Agent':
+ "Mozilla/4.0 (\
+ compatible;\
+ MSIE 6.0;\
+ Windows NT 5.1;\
+ SV1;\
+ .NET CLR 1.1.4322;\
+ .NET CLR 2.0.50727;\
+ .NET CLR 3.0.04506.30\
+ )"}
+ base_link = "https://translate.googleapis.com/translate_a/single?client=gtx&sl=%s&tl=%s&dt=t&q=%s"
+ totrans = urllib2.quote('\n'.join(words).encode('utf8'))
+ link = base_link % (lf, lt, totrans)
+ request = urllib2.Request(link, headers=agent)
+ raw_data = urllib2.urlopen(request).read()
+ data = json.loads(raw_data)
+ return [line[0].decode('utf8', errors='replace').replace(u"'", u'_').replace(u' | ', u'|').replace(u' ', u'_').replace(u'-',u'_').replace(u'\n','') for line in data[0]]
+
+def makenprof(prof, trans, deb=0) :
+ nprof=[]
+ if deb == 0 :
+ nprof.append(prof[0])
+ for i, val in enumerate(trans) :
+ line = prof[deb+i+1][:]
+ line[6] = val
+ nprof.append(line)
+ return nprof
+
+def treatempty(val) :
+ if val.strip() == '' :
+ return '_'
+ else :
+ return val
+
+def translateprofile(corpus, dictprofile, lf='it', lt='fr', maxword = 20) :
+ nprof = {}
+ lems = {}
+ for i in range(len(dictprofile)) :
+ prof = dictprofile[`i+1`]
+ try :
+ lenact = prof.index([u'*****', u'*', u'*', u'*', u'*', u'*', '', ''])
+ lensup = -1
+ except ValueError:
+ try :
+ lenact = prof.index([u'*', u'*', u'*', u'*', u'*', u'*', '', ''])
+ lensup = 0
+ except ValueError:
+ lenact = len(prof)
+ lensup = 0
+ try :
+ lensup += prof.index([u'*', u'*', u'*', u'*', u'*', u'*', '', ''])
+ lensup = lensup - lenact
+ except ValueError:
+ lensup += len(prof) - lenact
+ if lenact != 0 :
+ if lenact > maxword :
+ nlenact = maxword
+ else :
+ nlenact = lenact
+ actori = [line[6] for line in prof[1:nlenact]]
+ act = [val.replace(u'_', u' ') for val in actori]
+ act = gettranslation(act, lf, lt)
+ for j, val in enumerate(actori) :
+ if act[j] not in lems :
+ lems[act[j]] = val
+ else :
+ while act[j] in lems :
+ act[j] = act[j] + u"+"
+ lems[act[j]] = val
+ nprof[`i+1`] = makenprof(prof, act)
+
+ if lensup != 0 :
+ if lensup > maxword :
+ nlensup = maxword
+ else :
+ nlensup = lensup
+ supori = [line[6] for line in prof[(1+lenact):(lenact+nlensup+1)]]
+ sup = [val.replace(u'_', u' ') for val in supori]
+ sup = [treatempty(val) for val in sup]
+ sup = gettranslation(sup, lf, lt)
+ for j, val in enumerate(supori) :
+ if sup[j] not in lems :
+ lems[sup[j]] = val
+ else :
+ while sup[j] in lems :
+ sup[j] = sup[j] + u"+"
+ lems[sup[j]] = val
+ nprof[`i+1`].append([u'*****', u'*', u'*', u'*', u'*', u'*', '', ''])
+ nprof[`i+1`] += makenprof(prof, sup, deb=lenact)
+
+ try :
+ lenet = prof.index([u'*', u'*', u'*', u'*', u'*', u'*', '', ''])
+ nprof[`i+1`].append([u'*', u'*', u'*', u'*', u'*', u'*', '', ''])
+ nprof[`i+1`] += prof[(lenet+1):]
+ except :
+ pass
+ return nprof, lems
+
+def write_translation_profile(prof, lems, language, dictpathout) :
+ if os.path.exists(dictpathout['translations.txt']) :
+ with codecs.open(dictpathout['translations.txt'], 'r', 'utf8') as f :
+ translist = f.read()
+ translist = [line.split('\t') for line in translist.splitlines()]
+ else :
+ translist = []
+ toprint = []
+ toprint.append(['','','','','',''])
+ toprint.append([u'***', u'nb classes', `len(prof)`, u'***', '', ''])
+ for i in range(len(prof)) :
+ toprint.append([u'**', u'classe', `i+1`, u'**', '', ''])
+ toprint.append([u'****'] + prof[`i+1`][0] + [u'****'])
+ rest = [[`line[1]`, `line[2]`, `line[3]`, `line[4]`, line[6], line[7].replace('< 0,0001', '0.00009').replace('NS (','').replace(')','')] for line in prof[`i+1`][1:]]
+ for i, line in enumerate(prof[`i+1`][1:]) :
+ if line[0] == u'*' :
+ rest[i] = [u'*', u'*', u'*', u'*', u'*', u'*']
+ elif line[0] == u'*****' :
+ rest[i] = [u'*****',u'*',u'*', u'*', u'*', u'*']
+ toprint += rest
+ with open(dictpathout['translation_profile_%s.csv' % language], 'w') as f :
+ f.write('\n'.join([';'.join(line) for line in toprint]).encode('utf8'))
+ with open(dictpathout['translation_words_%s.csv' % language], 'w') as f :
+ f.write('\n'.join(['\t'.join([val, lems[val]]) for val in lems]).encode('utf8'))
+ if 'translation_profile_%s.csv' % language not in [val[0] for val in translist] :
+ translist.append(['translation_profile_%s.csv' % language, 'translation_words_%s.csv' % language])
+ with open(dictpathout['translations.txt'], 'w') as f :
+ f.write('\n'.join(['\t'.join(line) for line in translist]).encode('utf8'))
+
+def makesentidict(infile, language) :
+ #'/home/pierre/workspace/iramuteq/dev/langues/NRC/NRC-Emotion-Lexicon.csv'
+ with codecs.open(infile,'r', 'utf8') as f :
+ content = f.read()
+ content = [line.split('\t') for line in content.splitlines()]
+ titles = content.pop(0)
+ senti = ['Positive', 'Negative', 'Anger', 'Anticipation', 'Disgust', 'Fear', 'Joy', 'Sadness', 'Surprise', 'Trust']
+ sentid = {}
+ for sent in senti :
+ sentid[sent] = titles.index(sent)
+ frtitle = [val for val in titles if '(fr)' in val]
+ frid = titles.index(frtitle[0])
+ sentidict = [[line[frid].lower(), [line[sentid[sent]] for sent in senti]] for line in content]
+ pos = ['positive'] + [line[0] for line in sentidict if line[1][0] == '1']
+ neg = ['negative'] + [line[0] for line in sentidict if line[1][1] == '1']
+ anger = ['anger'] + [line[0] for line in sentidict if line[1][2] == '1']
+ anticipation = ['anticipation'] + [line[0] for line in sentidict if line[1][3] == '1']
+ disgust = ['disgust'] + [line[0] for line in sentidict if line[1][4] == '1']
+ fear = ['fear'] + [line[0] for line in sentidict if line[1][5] == '1']
+ joy = ['joy'] + [line[0] for line in sentidict if line[1][6] == '1']
+ sadness = ['sadness'] + [line[0] for line in sentidict if line[1][7] == '1']
+ surprise = ['surprise'] + [line[0] for line in sentidict if line[1][8] == '1']
+ trust = ['trust'] + [line[0] for line in sentidict if line[1][9] == '1']
+ with open('/tmp/tgenemo.csv', 'w') as f :
+ for val in [pos, neg, anger, anticipation, disgust, fear, joy, sadness, surprise, trust] :
+ f.write('\t'.join(val).encode('utf8') + '\n')
+
+def countsentfromprof(prof, encoding, sentidict) :
+ with codecs.open(prof, 'r', encoding) as f :
+ content = f.read()
+ content = [line.split(';') for line in content.splitlines()]
+ print content
+ content = [[line[0], [int(val) for val in line[1:]]] for line in content]
+ print content
+ content = dict(content)
+ print content
+
+def iratolexico(infile, outfile, encoding) :
+ with codecs.open(infile, 'r', encoding) as f :
+ for line in f :
+ if line.startswith(u'**** ') :
+ line = line.split()
+