X-Git-Url: http://iramuteq.org/git?p=iramuteq;a=blobdiff_plain;f=ProfList.py;h=db822cf1f6f2e0bbe59716a65e5a5257b7f59fe9;hp=596b4e6c70a579fbeae6df0709ccd44064d02706;hb=be2de22859c8f59abe9e1082981e4c3a62e9b7ca;hpb=d33694ef233bd4a28e69d88e9eef3c5c129442fe

diff --git a/ProfList.py b/ProfList.py
index 596b4e6..db822cf 100644
--- a/ProfList.py
+++ b/ProfList.py
@@ -504,59 +504,60 @@ class ProfListctrlPanel(wx.ListCtrl, listmix.ListCtrlAutoWidthMixin, listmix.Col
         DoSimi(self, param = paramsimi, fromprof = ffr(self.filename), pathout = pathout, wordgraph = word)
 
     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)
+        self.parent.SimiFromCluster(self.parent, self.Source.corpus, self.la, self.cl - 1, parametres = {'type' : 'clustersimitxt', 'pathout' : self.Source.parametres['pathout']}, 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)