# -*- coding: utf-8 -*-
#Author: Pierre Ratinaud
-#Copyright (c) 2008-2013 Pierre Ratinaud
+#Copyright (c) 2008-2020 Pierre Ratinaud
+#modification pour python 3 : Laurent Mérat, 6x7 - mai 2020
#License: GNU/GPL
-from chemins import ffr, simipath
-#from corpus import Corpus
+#------------------------------------
+# import des modules python
+#------------------------------------
import os
+from copy import copy
+from operator import itemgetter
+import codecs
+import logging
+
+#------------------------------------
+# import des modules wx
+#------------------------------------
+import wx
+
+#------------------------------------
+# import des fichiers du projet
+#------------------------------------
+from chemins import ffr, simipath
from analysetxt import AnalyseText
-#from ConfigParser import RawConfigParser
-#from guifunct import getPage, getCorpus
from guifunct import PrepSimi
-from functions import indices_simi, progressbar, treat_var_mod, read_list_file, print_liste
-#from tableau import Tableau
-#from tabsimi import DoSimi
-from PrintRScript import PrintSimiScript
-import wx
-from copy import copy
+from functions import indices_simi, progressbar, treat_var_mod, read_list_file, print_liste, DoConf, exec_rcode, check_Rresult
+from PrintRScript import PrintSimiScript
-import logging
log = logging.getLogger('iramuteq.textsimi')
+
class SimiTxt(AnalyseText):
+
def doanalyse(self) :
self.parametres['type'] = 'simitxt'
self.pathout.basefiles(simipath)
self.indices = indices_simi
- if self.dlg :
+ if self.dlg : # quel est le lien ???
self.makesimiparam()
#FIXME
self.actives = self.corpus.make_actives_limit(3)
self.stars = copy(self.listet)
self.parametres['stars'] = copy(self.listet)
self.parametres['sfromchi'] = False
- self.dlg.Destroy()
prep = PrepSimi(self.ira, self, self.parametres, self.pathout['selected.csv'], self.actives, indices_simi, wordlist=dictcol)
if prep.val == wx.ID_OK :
continu = True
self.parametres = prep.parametres
- self.dlg = progressbar(self.ira, 4)
+# self.dlg = progressbar(self.ira, 4)
+ else :
+ return False
else :
+ order_actives = [[i, act, self.corpus.getlemeff(act)] for i, act in enumerate(self.actives)]
+ order_actives = sorted(order_actives, key=itemgetter(2), reverse = True)
+ with open(self.pathout['selected.csv'], 'w', encoding='utf8') as f :
+ f.write('\n'.join([repr(order_actives[val][0]) for val in self.parametres['selected']]))
continu = True
if continu :
self.makefiles()
self.parametres['nbactives'] = len(self.actives)
self.parametres['fromprof'] = False
self.corpus.make_and_write_sparse_matrix_from_uces(self.actives, self.pathout['mat01.csv'], self.pathout['listeuce1.csv'])
- with open(self.pathout['actives.csv'], 'w') as f :
- f.write('\n'.join(self.actives).encode(self.ira.syscoding))
+ with open(self.pathout['actives.csv'], 'w', encoding='utf8') as f :
+ f.write('\n'.join(self.actives))
+
class SimiFromCluster(SimiTxt) :
+
def __init__(self, ira, corpus, actives, lfreq, lchi, numcluster, parametres = None, dlg = False) :
self.actives = actives
self.numcluster = numcluster
self.lfreq = lfreq
self.lchi = lchi
parametres['name'] = 'simi_classe_%i' % (numcluster + 1)
- SimiTxt.__init__(self, ira, corpus, parametres, dlg, lemdial = False)
-
+ dlg.Destroy()
+ SimiTxt.__init__(self, ira, corpus, parametres, dlg=True, lemdial = False)
+
def preferences(self) :
return self.parametres
dictcol = dict([[i, [act, self.corpus.getlemclustereff(act, self.numcluster)]] for i, act in enumerate(self.actives)])
continu = True
if self.dlg :
- #self.listet = self.corpus.make_etoiles()
- #self.listet.sort()
- self.stars = []#copy(self.listet)
- self.parametres['stars'] = 0#copy(self.listet)
+# self.dlg.Destroy()
+ self.stars = []
+ self.parametres['stars'] = 0
self.parametres['sfromchi'] = 1
prep = PrepSimi(self.ira, self, self.parametres, self.pathout['selected.csv'], self.actives, indices_simi, wordlist=dictcol)
if prep.val == wx.ID_OK :
else :
continu = False
if continu :
+ self.dlg = progressbar(self.parent, 3)
self.makefiles()
self.parametres['type'] = 'clustersimitxt'
script = PrintSimiScript(self)
else :
graph_simi = [[os.path.basename(fileout), script.txtgraph]]
print_liste(self.pathout['liste_graph'], graph_simi)
+ self.dlg.Destroy()
else :
return False
self.parametres['nbactives'] = len(self.actives)
self.parametres['fromprof'] = True
self.corpus.make_and_write_sparse_matrix_from_classe(self.actives, self.corpus.lc[self.numcluster], self.pathout['mat01.csv'])
- with open(self.pathout['actives.csv'], 'w') as f :
- f.write('\n'.join(self.actives).encode(self.ira.syscoding))
- with open(self.pathout['actives_nb.csv'], 'w') as f :
- f.write('\n'.join([`val` for val in self.lfreq]))
- with open(self.pathout['actives_chi.csv'], 'w') as f :
- f.write('\n'.join([`val` for val in self.lchi]))
-
+ with open(self.pathout['actives.csv'], 'w', encoding='utf8') as f :
+ f.write('\n'.join(self.actives))
+ with open(self.pathout['actives_nb.csv'], 'w', encoding='utf8') as f :
+ f.write('\n'.join([repr(val) for val in self.lfreq]))
+ with open(self.pathout['actives_chi.csv'], 'w', encoding='utf8') as f :
+ f.write('\n'.join([repr(val) for val in self.lchi]))