From: pierre Date: Fri, 24 Jan 2020 11:29:03 +0000 (+0100) Subject: correction for greek X-Git-Url: http://www.iramuteq.org/git?a=commitdiff_plain;ds=sidebyside;h=b84be44daa86062735190970d5c6b855f227a7d7;hp=a5a42bbdbe6039d5d72095e50986b8f4e3390b32;p=iramuteq correction for greek --- diff --git a/functions.py b/functions.py old mode 100644 new mode 100755 index b41c483..8c0c66c --- a/functions.py +++ b/functions.py @@ -37,7 +37,8 @@ def open_folder(folder): os.startfile(folder) else: opener ="open" if sys.platform == "darwin" else "xdg-open" - call([opener, folder]) + #call([opener, folder]) + call([u"%s %s &" % (opener, folder)], shell=True) def normpath_win32(path) : if not sys.platform == 'win32' : @@ -53,10 +54,10 @@ class TGen : 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 @@ -66,13 +67,13 @@ class TGen : 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() @@ -190,6 +191,11 @@ class History : 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() @@ -223,6 +229,63 @@ class History : 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) @@ -369,7 +432,7 @@ def sortedby(list, direct, *indices): 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: @@ -415,10 +478,10 @@ def ReadProfileAsDico(File, Alceste=False, encoding = sys.getdefaultencoding()): 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] @@ -458,13 +521,13 @@ def decoupercharact(chaine, longueur, longueurOptimale, separateurs = None) : 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: @@ -481,8 +544,8 @@ def decoupercharact(chaine, longueur, longueurOptimale, separateurs = None) : # 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] @@ -490,13 +553,13 @@ def decoupercharact(chaine, longueur, longueurOptimale, separateurs = None) : 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:] @@ -510,13 +573,13 @@ exceptions = {'paragrapheOT' : u"Un problème de formatage (présence d'un marqu '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 "" == str(type(ch)): - ch.Destroy() + ch.Destroy() excName, exc, excTb = formatExceptionInfo() if excName == 'Exception' : print exc @@ -549,13 +612,13 @@ def BugReport(parent, error = None): 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 : @@ -598,7 +661,7 @@ def ReadList(filein, encoding = sys.getdefaultencoding(), sep = ';'): for val in line[1:]: if val == u'NA' : don = '' - else: + else: try: don = int(val) except: @@ -696,7 +759,7 @@ def launchcommand(mycommand): def print_liste(filename,liste): with open(filename,'w') as f : for graph in liste : - f.write(';'.join(graph).encode(sys.getdefaultencoding())+'\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 : @@ -758,7 +821,8 @@ def doconcorde(corpus, uces, mots, uci = False) : ucetxt = ucestxt1[uce].split() ucetxt = ' '.join([dmots.get(mot, mot) for mot in ucetxt]) if not uci : - ucis_txt.append('

' + ' '.join(corpus.ucis[corpus.getucefromid(uce).uci].etoiles) + '

') + uciid = corpus.getucefromid(uce).uci + ucis_txt.append('

' + ' '.join(corpus.ucis[corpus.getucefromid(uce).uci].etoiles) + ' *%i_%i

' % (uciid, uce, uciid, uce)) else : ucis_txt.append('

' + ' '.join(corpus.ucis[uce].etoiles) + '

') ucestxt.append(ucetxt) @@ -837,7 +901,7 @@ def treatempty(val) : else : return val -def translateprofile(corpus, dictprofile, lf='it', lt='fr') : +def translateprofile(corpus, dictprofile, lf='it', lt='fr', maxword = 50) : nprof = {} lems = {} for i in range(len(dictprofile)) : @@ -858,8 +922,8 @@ def translateprofile(corpus, dictprofile, lf='it', lt='fr') : except ValueError: lensup += len(prof) - lenact if lenact != 0 : - if lenact > 400 : - nlenact = 400 + if lenact > maxword : + nlenact = maxword else : nlenact = lenact actori = [line[6] for line in prof[1:nlenact]] @@ -875,8 +939,8 @@ def translateprofile(corpus, dictprofile, lf='it', lt='fr') : nprof[`i+1`] = makenprof(prof, act) if lensup != 0 : - if lensup > 400 : - nlensup = 400 + if lensup > maxword : + nlensup = maxword else : nlensup = lensup supori = [line[6] for line in prof[(1+lenact):(lenact+nlensup)]] @@ -901,7 +965,6 @@ def translateprofile(corpus, dictprofile, lf='it', lt='fr') : 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 : @@ -930,3 +993,47 @@ def write_translation_profile(prof, lems, language, dictpathout) : 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() + diff --git a/textstat.py b/textstat.py old mode 100644 new mode 100755 index c25b9dc..7fdf5bd --- a/textstat.py +++ b/textstat.py @@ -6,10 +6,12 @@ from chemins import ffr from analysetxt import AnalyseText -from functions import sortedby, progressbar, exec_rcode, check_Rresult +from functions import sortedby, progressbar, exec_rcode, check_Rresult import tempfile from time import sleep import logging +import gettext +_ = gettext.gettext logger = logging.getLogger('iramuteq.textstat') @@ -37,11 +39,15 @@ class Stat(AnalyseText) : act = [[forme, formes[forme].freq, formes[forme].gram] for forme in formes if formes[forme].act == 1] act = sortedby(act, 2, 1) act = [[i, val] for i, val in enumerate(act)] - supp = [[forme, formes[forme].freq, formes[forme].gram] for forme in formes if formes[forme].act == 2] + supp = [[forme, formes[forme].freq, formes[forme].gram] for forme in formes if formes[forme].act == 2] supp = sortedby(supp, 2, 1) supp = [[i, val] for i, val in enumerate(supp)] + ucesize = self.corpus.getucesize() + with open(self.pathout['stsize.csv'], 'w') as f : + f.write('\n'.join([`val` for val in ucesize])) + self.result = {u'total' : dict(tot), u'formes_actives' : dict(act), u'formes_supplémentaires' : dict(supp), u'hapax' : dict(hapax), u'glob' : ''} occurrences = sum([val[1][1] for val in tot]) + len(hapax) phapax = (float(len(hapax)) / float(occurrences)) * 100 @@ -51,7 +57,7 @@ class Stat(AnalyseText) : txt += ''.join([_(u'Number of texts').decode('utf8'),' : ', '%i\n' % len(self.corpus.ucis)]) txt += ''.join([_(u"Number of occurrences").decode('utf8'),' : %i\n' % occurrences]) txt += ''.join([_(u'Number of forms').decode('utf8'), ' : %i\n' % (len(formes))]) - txt += ''.join([_(u"Number of hapax").decode('utf8'),' : %i (%.2f%%' % (len(hapax),phapax), _(u'of occurrences').decode('utf8'), ' - %.2f%% ' % phapax_forme, _(u'of forms').decode('utf8'), ')\n']) + txt += ''.join([_(u"Number of hapax").decode('utf8'),' : %i (%.2f%%' % (len(hapax),phapax), _(u'of occurrences').decode('utf8'), ' - %.2f%% ' % phapax_forme, _(u'of forms').decode('utf8'), ')\n']) #print float(occurrences), float(len(self.corpus.ucis)) txt += ''.join([_(u"Mean of occurrences by text").decode('utf8'), ' : %.2f' % (float(occurrences)/float(len(self.corpus.ucis)))]) if self.dlg : @@ -73,6 +79,12 @@ class Stat(AnalyseText) : plot(tot[,1], log = 'xy', xlab='log(rangs)', ylab = 'log(frequences)', col = 'red', pch=16) dev.off() """ % (ffr(self.pathout['zipf.png'])) + txt += """ + stsize <- read.csv2("%s", header=F) + open_file_graph("%s", width = 400, height = 400) + barplot(table(stsize[,1])) + dev.off() + """ % (self.pathout['stsize.csv'], self.pathout['segments_size.png']) tmpscript = tempfile.mktemp(dir=self.parent.TEMPDIR) with open(tmpscript, 'w') as f : f.write(txt) @@ -92,4 +104,4 @@ class Stat(AnalyseText) : f.write('\n'.join([';'.join([val for val in ligne]) for ligne in toprint]).encode(self.parent.syscoding)) else : with open(self.pathout['%s.txt' % 'glob'], 'w') as f : - f.write(self.result['glob'].encode(self.parent.syscoding)) + f.write(self.result['glob'].encode(self.parent.syscoding, errors='replace'))