Modifier odf2csv pour que la moyenne ne soit pas calculee automatiquement

Correction d'un bogue lors du moyennage ou on traitait les QQQQ ou QFFF pour les
moyennes
Ajout du fichier setup.py servant a faire une distribution (setup.py bdist)

src/convert_all.sh

@@ -6,5 +6,5 @@ cd ../test_data
 for ficin in *.ODF
 do
   ficout=`basename ${ficin} .ODF`
-   ../src/odf2csv.py -i ${ficin} -o ${ficout}_all.csv --all
+   ../src/odf2csv.py -i ${ficin} -o ${ficout}all.csv
 done

src/convert_mean.sh

 #!/bin/sh
 
-#Convertir tous les fichiers odf en conservant
-# toutes les donnees
+#Convertir tous les fichiers odf en
+#moyennant au 10 metres en eliminitant douteux et erreurs
 cd ../test_data
 for ficin in *.ODF
 do
   ficout=`basename ${ficin} .ODF`
-   ../src/odf2csv.py -i ${ficin} -o ${ficout}_mean.csv --mean 10
+   ../src/odf2csv.py -i ${ficin} -o ${ficout}mean.csv --doubtfull --error --mean 10
 done

src/odf2csv.py

 #!/usr/bin/env python
+#! python
 
 #####################################
 #
@@ -17,18 +18,18 @@ import math
 from class_variable import variable
 import odf_fcns
 
-RemplacerDouteux = True
-RemplacerErreur = True
-CalculerMoyenne = True
+RemplacerDouteux = False
+RemplacerErreur = False
+CalculerMoyenne = False
 intervalle = 1.0
 #On recupere les parametres d'appel
 
 try:
     opts,args = getopt.getopt(sys.argv[1:], \
-                'i:o:m:deah',\
+                'i:o:m:deh',\
                     ['input=','output=',\
-                     'mean=','all','error',\
-                     'douteux','help'])
+                     'mean=','error',\
+                     'doubtfull','help'])
 except getopt.error, msg:
     print msg
     odf_fcns.usage()
@@ -42,35 +43,31 @@ for o , a in opts:
         
     elif o in ("-i","--input"):
         if a in ("-i","--input","-o","--output","-d",\
-                 "--douteux","-e","--error","-h","--help","--mean","-m",\
-                 "-a","--all"):
+                 "--doubtfull","-e","--error","-h","--help","--mean","-m"):
             odf_fcns.usage()
             sys.exit(2)
             
         fnomin = a
     elif o in ("-o","--output"):
         if a in  ("-i","--input","-o","--output","-d",\
-                 "--douteux","-e","--error","-h","--help","--mean","-m",\
-                 "-a","--all"):
+                 "--doubtfull","-e","--error","-h","--help","--mean","-m"):
             odf_fcns.usage()
             sys.exit(2)
         
         fnomout = a
     elif o in ("-m","--mean"):
         if a in  ("-i","--input","-o","--output","-d",\
-                 "--douteux","-e","--error","-h","--help","--mean","-m",\
-                 "-a","--all"):
+                 "--doubtfull","-e","--error","-h","--help","--mean","-m"):
             odf_fcns.usage()
             sys.exit(2)
         
         intervalle = float(a)
+        CalculerMoyenne = True
 
-    elif o in ("-d","--douteux"):
-        RemplacerDouteux = False
+    elif o in ("-d","--doubtfull"):
+        RemplacerDouteux = True
     elif o in ("-e","--error"):
-        RemplacerErreur = False
-    elif o in ("-a","--all"):
-        CalculerMoyenne = False
+        RemplacerErreur = True
     else:
         print "Option inconnue"
         odf_fcns.usage()
@@ -354,34 +351,40 @@ if (CalculerMoyenne):
         
 #   Ici, on traite toutes les variables sauf PRES et DEPTH
     for var in varlist:
-        if not DEPTH.search(var.getcode()) and not PRES.search(var.getcode()):
+        
+        if not DEPTH.search(var.getcode()) and not PRES.search(var.getcode()) and \
+           not  QCFF.search(var.getcode()) and not  QQQQ.search(var.getcode()):
             print "On traite:", var.getcode()
             if(var_q.has_key(var)):
                 qualite = var_q.get(var)
-                mean_dat = []
                 qualite_dat = []
 
-                for k in range(num_depth):
-                    sum_dat=0
-                    n_dat=0
-                    for i in range(var.getnbval()):
-                        if float(profondeur.getvaleur(i)) > (depth[k]-intervalle/2.0) and \
-                             float(profondeur.getvaleur(i)) <= (depth[k]+intervalle/2.0) and \
-                             float(var.getvaleur(i)) > -99 :
-                            sum_dat=sum_dat+ float(var.getvaleur(i))
-                            n_dat=n_dat+1
-
-                    if n_dat == 0 :
-                        mean_dat.append(-99.0)
-                    else:
-                        mean_dat.append(sum_dat/n_dat)
+            mean_dat = []
+
+
+            for k in range(num_depth):
+                sum_dat=0
+                n_dat=0
+                for i in range(var.getnbval()):
+                    if float(profondeur.getvaleur(i)) > (depth[k]-intervalle/2.0) and \
+                           float(profondeur.getvaleur(i)) <= (depth[k]+intervalle/2.0) and \
+                           float(var.getvaleur(i)) > -99 :
+                        sum_dat=sum_dat+ float(var.getvaleur(i))
+                        n_dat=n_dat+1
 
+                if n_dat == 0 :
+                    mean_dat.append(-99.0)
+                else:
+                    mean_dat.append(sum_dat/n_dat)
+
+                if(var_q.has_key(var)):
                     if mean_dat[k] == -99:
                         qualite_dat.append(9)
                     elif mean_dat[k] > -99:
                         qualite_dat.append(1)
 
-                var.replaceallvaleurs(mean_dat)
+            var.replaceallvaleurs(mean_dat)
+            if(var_q.has_key(var)):
                 qualite.replaceallvaleurs(qualite_dat)
 
 
@@ -391,31 +394,35 @@ if (CalculerMoyenne):
             print "On traite:", var.getcode()
             if(var_q.has_key(var)):
                 qualite = var_q.get(var)
-                mean_dat = []
                 qualite_dat = []
-                for k in range(num_depth):
-                    sum_dat=0
-                    n_dat=0
-                    for i in range(var.getnbval()):
-                        if DEPTH.search(var.getcode()):
-                            sum_dat=sum_dat+depth[k]
-                            n_dat=n_dat+1
-                        elif PRES.search(var.getcode()):
-                            sum_dat=sum_dat+odf_fcns.depth2pres(depth[k],float(init_lat))
-                            n_dat=n_dat+1
-
-
-                    if n_dat == 0 :
-                        mean_dat.append(-99.0)
-                    else:
-                        mean_dat.append(sum_dat/n_dat)
 
+            mean_dat = []
+
+            for k in range(num_depth):
+                sum_dat=0
+                n_dat=0
+                for i in range(var.getnbval()):
+                    if DEPTH.search(var.getcode()):
+                        sum_dat=sum_dat+depth[k]
+                        n_dat=n_dat+1
+                    elif PRES.search(var.getcode()):
+                        sum_dat=sum_dat+odf_fcns.depth2pres(depth[k],float(init_lat))
+                        n_dat=n_dat+1
+
+
+                if n_dat == 0 :
+                    mean_dat.append(-99.0)
+                else:
+                    mean_dat.append(sum_dat/n_dat)
+
+                if(var_q.has_key(var)):
                     if mean_dat[k] == -99:
                         qualite_dat.append(9)
                     elif mean_dat[k] > -99:
                         qualite_dat.append(1)
 
-                var.replaceallvaleurs(mean_dat)
+            var.replaceallvaleurs(mean_dat)
+            if(var_q.has_key(var)):
                 qualite.replaceallvaleurs(qualite_dat)
 
 
@@ -485,9 +492,11 @@ fout.write(chaine)
 for i in range(varlist[0].getnbval()):
     chaine =''
     for var in varlist:
+        print "var = ", var.getcode()
         if CalculerMoyenne and QCFF.search(var.getcode()):
             pass
         else:
+            print "i = ", i 
             chaine = chaine + var.getvaleur(i) + '\t'
     chaine = chaine + '\n'
     fout.write(chaine)

src/odf_fcns.py

@@ -56,10 +56,8 @@ def usage():
       print "ficout: fichier sortie (output file"
       print "interval : intervale de profondeurs pour donnees moyennees"
       print "             (depth interval for mean data)"
-      print "-a :  toutes les donnees : ne pas moyenner"
-      print "      (all data : do not average)"
-      print "-d :  conserver donnees douteuses (keep data flaged as doubtfull)"
-      print "-e :  conserver donnees erronnees (keep data flaged as erroneous)"
+      print "-d :  eliminer donnees douteuses (discard data flaged as doubtfull)"
+      print "-e :  eliminer donnees erronnees (discard data flaged as erroneous)"
 
   
 

src/setup.py

+from distutils.core import setup
+
+auteur = "James Caveen"
+nom="odf2csv"
+ver="1.0"
+
+modules = ["class_variable","odf_fcns"]
+script  = ["odf2csv.py"]
+
+setup(name=nom,author=auteur,version=ver,py_modules = modules,scripts=script)