self.lenact = profclasse.index([u'*****', u'*', u'*', u'*', u'*', u'*', '', ''])
profclasse.pop(self.lenact)
except ValueError:
- self.lenact = len(profclasse)
+ try :
+ self.lenact = profclasse.index([u'*', u'*', u'*', u'*', u'*', u'*', '', ''])
+ profclasse.pop(self.lenact)
+ except ValueError:
+ self.lenact = len(profclasse)
try :
self.lensup = profclasse.index([u'*', u'*', u'*', u'*', u'*', u'*', '', ''])
self.lensup = self.lensup - self.lenact
profclasse.pop(self.lensup)
except ValueError:
- self.lensup = 0
+ self.lensup = len(profclasse) - self.lenact
self.lenet = len(profclasse) - (self.lenact + self.lensup)
# print self.lenact, self.lensup, self.lenet
for i, line in enumerate(classen) :
self.la = []
self.lchi = []
self.lfreq = []
+ self.tmpchi = None
#adding some art
self.il = wx.ImageList(16, 16)
def OnItemSelected(self, event):
self.currentItem = event.m_itemIndex
- #print 'OnItemSelected: "%s", "%s", "%s", "%s"\n' % (self.currentItem, self.GetItemText(self.currentItem), self.getColumnText(self.currentItem, 1), self.getColumnText(self.currentItem, 2))
- #self.log.WriteText('OnItemSelected: "%s", "%s", "%s", "%s"\n' %
- # (self.currentItem,
- # self.GetItemText(self.currentItem),
- # self.getColumnText(self.currentItem, 1),
- # self.getColumnText(self.currentItem, 2)))
def OnItemActivated(self, event):
self.currentItem = event.m_itemIndex
- #print "OnItemActivated: %s\nTopItem: %s\n" % (self.GetItemText(self.currentItem), self.GetTopItem())
- #self.log.WriteText("OnItemActivated: %s\nTopItem: %s\n" %
- # (self.GetItemText(self.currentItem), self.GetTopItem()))
def getColumnText(self, index, col):
item = self.GetItem(index, col)
def OnItemDeselected(self, evt):
pass
- #self.log.WriteText("OnItemDeselected: %s" % evt.m_itemIndex)
-
-
#---------------------------------------------------
# These methods are callbacks for implementing the
# "virtualness" of the list...
def OnGetItemAttr(self, item):
index=self.itemIndexMap[item]
- #genre=self.itemDataMap[index][2]
if index < self.lenact :
if item % 2 :
return self.attr1
else :
return None
-# if genre=="Rock":
-# return self.attr2
-# elif genre=="Jazz":
-# return self.attr1
-# elif genre=="New Age":
-# return self.attr3
-# else:
-# return None
-
#---------------------------------------------------
# Matt C, 2006/02/22
# Here's a better SortItems() method --
def quest_simi(self, evt) :
tableau = self.Source.tableau
tab = tableau.make_table_from_classe(self.cl, self.la)
- pathout = ConstructPathOut(self.Source.pathout+'/', 'simi_classe_%i' %self.cl)
+ pathout = ConstructPathOut(os.path.join(self.Source.pathout, 'simi_classe_%i' %self.cl))
self.filename = os.path.join(pathout,'mat01.csv')
tableau.printtable(self.filename, tab)
del tab
def onwordgraph(self, evt):
word = self.getColumnText(self.GetFirstSelected(), 6)
- dlg = progressbar(self, 2)
- corpus = self.Source.corpus
- uces = corpus.lc[self.cl-1]
- dlg.Update(1, u'Tableau...')
- #tab = corpus.make_table_with_classe(uces, self.la)
- pathout = ConstructPathOut(self.Source.pathout.dirout + '/' , 'simi_%s' % word)
- self.filename = os.path.join(pathout,'mat01.csv')
- dlg.Update(2, u'Ecriture...')
- #corpus.write_tab(tab, self.filename)
- #del tab
- corpus.make_and_write_sparse_matrix_from_classe(self.la, uces, self.filename)
- dlg.Destroy()
- paramsimi = {'coeff' : 0,
- 'layout' : 2,
- 'type' : 1,
- 'arbremax' : 0,
- 'coeff_tv' : 1,
- 'coeff_tv_nb' : 0,
- 'tvprop' : 0,
- 'tvmin' : 5,
- 'tvmax' : 30,
- 'coeff_te' : 1,
- 'coeff_temin' : 1,
- 'coeff_temax' : 10,
- 'label_v': 1,
- 'label_e': 0,
- 'vcex' : 1,
- 'vcexmin' : 10,
- 'vcexmax' : 25,
- 'cex' : 10,
- 'seuil_ok' : 1,
- 'seuil' : 1,
- 'cols' : (255,0,0),
- 'cola' : (200,200,200),
- 'width' : 600,
- 'height' : 600,
- 'first' : True,
- 'keep_coord' : True,
- 'alpha' : 20,
- 'film': False,
- }
- self.tableau = Tableau(self.parent, '')
- self.tableau.listactives = self.la
- self.tableau.actives = {}
- for i, val in enumerate(self.la) :
- self.tableau.actives[val] = [self.lfreq[i]]
- DoSimi(self, param = paramsimi, fromprof = ffr(self.filename), pathout = pathout, wordgraph = word)
+ if self.tmpchi is None :
+ self.tmpchi = tempfile.mktemp(dir=self.Source.parent.TEMPDIR)
+ with open(self.tmpchi, 'w') as f:
+ f.write('\n'.join([str(val) for val in self.lchi]))
+ index = self.la.index(word)
+ parametres = {'type' : 'clustersimitxt',
+ 'pathout' : self.Source.parametres['pathout'],
+ 'word' : index ,
+ 'lem' : self.Source.parametres['lem'],
+ 'tmpchi' : self.tmpchi}
+ #try :
+ self.parent.SimiFromCluster(self.parent, self.Source.corpus, self.la, self.cl - 1, parametres = parametres, dlg = progressbar(self, 4))
+ #except :
+ # print 'not acitve'
def on_graph(self, evt):
- dlg = progressbar(self, 2)
- corpus = self.Source.corpus
- uces = corpus.lc[self.cl-1]
- dlg.Update(1, u'Tableau...')
- #tab = corpus.make_table_with_classe(uces, self.la)
- pathout = ConstructPathOut(self.Source.pathout.dirout+'/', 'simi_classe_%i' %self.cl)
- self.filename = os.path.join(pathout,'mat01.csv')
- dlg.Update(2, u'Ecriture...')
- #corpus.write_tab(tab, self.filename)
- #del tab
- corpus.make_and_write_sparse_matrix_from_classe(self.la, uces, self.filename)
- dlg.Destroy()
- paramsimi = {'coeff' : 0,
- 'layout' : 2,
- 'type' : 1,
- 'arbremax' : 1,
- 'coeff_tv' : 1,
- 'coeff_tv_nb' : 0,
- 'tvprop' : 0,
- 'tvmin' : 5,
- 'tvmax' : 30,
- 'coeff_te' : 1,
- 'coeff_temin' : 1,
- 'coeff_temax' : 10,
- 'label_v': 1,
- 'label_e': 0,
- 'vcex' : 0,
- 'vcexmin' : 10,
- 'vcexmax' : 25,
- 'cex' : 10,
- 'cexfromchi' : True,
- 'sfromchi': False,
- 'seuil_ok' : 0,
- 'seuil' : 1,
- 'cols' : (255,0,0),
- 'cola' : (200,200,200),
- 'width' : 1000,
- 'height' : 1000,
- 'first' : True,
- 'keep_coord' : True,
- 'alpha' : 20,
- 'film': False,
- }
- self.tableau = Tableau(self.parent, '')
- self.tableau.listactives = self.la
- self.tableau.actives = {}
- self.tableau.lchi = self.lchi
- self.tableau.chi = {}
- self.tableau.parametre['fromprof'] = True
- for i, val in enumerate(self.la) :
- self.tableau.actives[val] = [self.lfreq[i]]
- self.tableau.chi[val] = [self.lchi[i]]
- DoSimi(self, param = paramsimi, fromprof = ffr(self.filename), pathout = pathout)
+ if self.tmpchi is None :
+ self.tmpchi = tempfile.mktemp(dir=self.Source.parent.TEMPDIR)
+ with open(self.tmpchi, 'w') as f:
+ f.write('\n'.join([str(val) for val in self.lchi]))
+ parametres = {'type' : 'clustersimitxt',
+ 'pathout' : self.Source.parametres['pathout'],
+ 'lem' : self.Source.parametres['lem'],
+ 'tmpchi' : self.tmpchi}
+
+ self.parent.SimiFromCluster(self.parent, self.Source.corpus, self.la, self.cl - 1, parametres = parametres, dlg = progressbar(self, 4))
+ #dlg = progressbar(self, 2)
+ #corpus = self.Source.corpus
+ #uces = corpus.lc[self.cl-1]
+ #dlg.Update(1, u'Tableau...')
+ ##tab = corpus.make_table_with_classe(uces, self.la)
+ #pathout = ConstructPathOut(self.Source.pathout.dirout+'/', 'simi_classe_%i' %self.cl)
+ #self.filename = os.path.join(pathout,'mat01.csv')
+ #dlg.Update(2, u'Ecriture...')
+ ##corpus.write_tab(tab, self.filename)
+ ##del tab
+ #corpus.make_and_write_sparse_matrix_from_classe(self.la, uces, self.filename)
+ #dlg.Destroy()
+ #paramsimi = {'coeff' : 0,
+ # 'layout' : 2,
+ # 'type' : 1,
+ # 'arbremax' : 1,
+ # 'coeff_tv' : 1,
+ # 'coeff_tv_nb' : 0,
+ # 'tvprop' : 0,
+ # 'tvmin' : 5,
+ # 'tvmax' : 30,
+ # 'coeff_te' : 1,
+ # 'coeff_temin' : 1,
+ # 'coeff_temax' : 10,
+ # 'label_v': 1,
+ # 'label_e': 0,
+ # 'vcex' : 0,
+ # 'vcexmin' : 10,
+ # 'vcexmax' : 25,
+ # 'cex' : 10,
+ # 'cexfromchi' : True,
+ # 'sfromchi': False,
+ # 'seuil_ok' : 0,
+ # 'seuil' : 1,
+ # 'cols' : (255,0,0),
+ # 'cola' : (200,200,200),
+ # 'width' : 1000,
+ # 'height' : 1000,
+ # 'first' : True,
+ # 'keep_coord' : True,
+ # 'alpha' : 20,
+ # 'film': False,
+ # }
+ #self.tableau = Tableau(self.parent, '')
+ #self.tableau.listactives = self.la
+ #self.tableau.actives = {}
+ #self.tableau.lchi = self.lchi
+ #self.tableau.chi = {}
+ #self.tableau.parametre['fromprof'] = True
+ #for i, val in enumerate(self.la) :
+ # self.tableau.actives[val] = [self.lfreq[i]]
+ # self.tableau.chi[val] = [self.lchi[i]]
+ #DoSimi(self, param = paramsimi, fromprof = ffr(self.filename), pathout = pathout)
def on_segments(self,evt) :
dlg = progressbar(self, 2)
projects / iramuteq / blobdiff