stat = menu.Append(wx.ID_ANY, _(u"Statistics").decode('utf8'))
spec = menu.Append(wx.ID_ANY, _(u"Specificities and CA").decode('utf8'))
classification = wx.Menu()
- reinert = classification.Append(wx.ID_ANY, _(u"Reinert method").decode('utf8'))
+ reinert = classification.Append(wx.ID_ANY, _(u"Reinert's Method").decode('utf8'))
#pam = classification.Append(wx.ID_ANY, u"Par matrice des distances")
menu.AppendMenu(-1, _(u"Clustering").decode('utf8'), classification)
- simi = menu.Append(wx.ID_ANY, _(u"Similarities analysis").decode('utf8'))
- wdc = menu.Append(wx.ID_ANY, _(u"Wordcloud").decode('utf8'))
+ simi = menu.Append(wx.ID_ANY, _(u"Similarities Analysis").decode('utf8'))
+ wdc = menu.Append(wx.ID_ANY, _(u"WordCloud").decode('utf8'))
subcorpus = wx.Menu()
subcorpusfrommeta = subcorpus.Append(wx.ID_ANY, _(u'Sub corpus from metadata').decode('utf8'))
subcorpusfromtheme = subcorpus.Append(wx.ID_ANY, _(u'Sub corpus from thematic').decode('utf8'))