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' :
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.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()
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)
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:
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 :
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)
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)) :
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]]
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)]]
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.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()
+
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')
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
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 :
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)
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'))
projects / iramuteq / commitdiff