Changeset 2826 in lmdz_wrf for trunk/tools

Timestamp:

Apr 29, 2020, 3:26:57 PM (5 years ago)

Author:

lfita

Message:

Huge improvement on 'statcompare_files':

• Adding 'all' variables
• Selection of statistics
• Whether output file should be written

Location:

trunk/tools

Files:

• nc_var.py (modified) (2 diffs)
• nc_var_tools.py (modified) (4 diffs)

trunk/tools/nc_var.py

@@ -327 +327 @@
   'merge_files',                                                                     \
   'model_characteristics', 'netcdf_concatenation', 'netcdf_fold_concatenation',      \
-  'netcdf_fold_concatenation_HMT', 'netcdf_fold_slice_concatenation_HMT']
+  'netcdf_fold_concatenation_HMT', 'netcdf_fold_slice_concatenation_HMT',            \
+  'statcompare_files']
 
 ####### ###### ##### #### ### ## #
@@ -567 +568 @@
     ncvar.splitfile_dim(opts.values, opts.ncfile, opts.varname)
 elif oper == 'statcompare_files':
-    ncvar.statcompare_files(opts.values)
+    ncvar.statcompare_files(opts.values, opts.ncfile, opts.varname)
 elif oper == 'subbasin':
     ncvar.subbasin(opts.values, opts.ncfile)

trunk/tools/nc_var_tools.py

@@ -8640 +8640 @@
 #setvar_asciivalues('obstimes.inf', 'obs/re_project_accumulated-1h_PRCP.nc', 'time')
 
-def statcompare_files(values):
+def statcompare_files(values, ncfile, varname):
     """ Python script to statistically compare two different files
-      values= [file1]:[var1],[file2][var2] 
-        [file1/2]: files to compare any diferent would be [file1]-[file2]
-        [var1/2]: variable in file1/2 to compare
+      values= [kindstats],[flavour],[outfile]
+        [kindstats]: kind of statisitcs to compute
+          'diffstats': statistics among differences between variables from each file
+          'directstats': direct statistics of the fields of each file
+        [flavour]: 'temporal', 'spatial', 'both', or 'None' kind of statistics
+        [outfile]: whether an output file should be written or not
+      ncfile: [file1],[file2]
+      varname: [var1]:[var2] ',' separated sorted list of variables to use for each 
+        file ('all' for all values from both files, names must agree between files)
     """
-
     fname = 'statcompare_files'
+
     ofile='statcompare_files.nc'
 
@@ -8655 +8661 @@
         quit()
 
-    file1=values.split(',')[0].split(':')[0]
-    var1=values.split(',')[0].split(':')[1]
-    file2=values.split(',')[1].split(':')[0]
-    var2=values.split(',')[1].split(':')[1]
+    expectargs = '[kindtats],[flavour],[outfile]'
+    gen.check_arguments(fname,values,expectargs,',')
+
+    kindstats = values.split(',')[0]
+    flavour = values.split(',')[1]
+    outfile = gen.Str_Bool(values.split(',')[2])
+
+    file1 = ncfile.split(',')[0]
+    file2 = ncfile.split(',')[1]
+    if varname == 'all':
+        var1 = 'all'
+        var2 = 'all'
+    else:
+        var1 = 'values'
+        var2 = 'values'
+        varns1 = gen.str_list(varname.split(':')[0], ',')
+        varns2 = gen.str_list(varname.split(':')[1], ',')
 
     if not os.path.isfile(file1):
@@ -8667 +8686 @@
     objnc1 = NetCDFFile(file1, 'r')
 
-    if not objnc1.variables.has_key(var1):
-        print errormsg
-        print '  ' + fname + ': netCDF file "' + file1 +                            \
-          '" does not have variable "' + var1 + '" !!!!'
-        quit(-1)
+    if var1 == 'all':
+        varns1 = objnc1.variables.keys()
+        varns1.sort()
+
+    for var1 in varns1:
+        if not objnc1.variables.has_key(var1):
+            print errormsg
+            print '  ' + fname + ': netCDF file "' + file1 +                         \
+              '" does not have variable "' + var1 + '" !!!!'
+            quit(-1)
 
     if not os.path.isfile(file2):
@@ -8680 +8704 @@
     objnc2 = NetCDFFile(file2, 'r')
 
-    if not objnc2.variables.has_key(var2):
+    if var2 == 'all':
+        varns2 = objnc2.variables.keys()
+        varns2.sort()
+
+    for var2 in varns2:
+        if not objnc2.variables.has_key(var2):
+            print errormsg
+            print '  ' + fname + ': netCDF file "' + file2 +                        \
+              '" does not have variable "' + var2 + '" !!!!'
+            quit(-1)
+
+    # Comparing
+    if outfile:
+        objofile = NetCDFFile(ofile, 'w')
+
+    N1vars = len(varns1)
+    N2vars = len(varns2)
+    if N1vars != N2vars:
         print errormsg
-        print '  ' + fname + ': netCDF file "' + file2 +                            \
-          '" does not have variable "' + var2 + '" !!!!'
+        print '  ' + fname + ": provided list of variables for each file differ !!"
+        print '    amount of variables from file 1:', N1vars, 'file 2:', N2vars
+        if N1vars > N2vars:
+            print '      Vars provided: file1 file2 _______'
+            for i in range(N2vars):
+                print '        ',i, ':', varns1[i], varns2[i]
+            print '    excess of variables for file1:', varns1[N2vars:N1vars]
+        if N1vars > N2vars:
+            print '      Vars provided: file1 file2 _______'
+            for i in range(N1vars):
+                print '        ',i, ':', varns1[i], varns2[i]
+            print '    excess of variables for file2:', varns2[N1vars:N2vars]
         quit(-1)
-
-    objvar1 = objnc1.variables[var1]
-    objvar2 = objnc2.variables[var2]
-
-    Ndim1 = len(objvar1.shape)
-    Ndim2 = len(objvar2.shape)
-
-    if Ndim1 != Ndim2:
-        print errormsg
-        print '  ' + fname + ' variable: "' + var1 + '" from file "' + file1 +       \
-          '" does not have the same size:' , Ndim1, 'as variable: "' + var2 +        \
-          '" from file "' + file2 + '": ',Ndim2
-        quit(-1)
+ 
+    for iv in range(N1vars):
+        var1  = varns1[iv]
+        var2  = varns2[iv]
+
+        objvar1 = objnc1.variables[var1]
+        objvar2 = objnc2.variables[var2]
+
+        Ndim1 = len(objvar1.shape)
+        Ndim2 = len(objvar2.shape)
+
+        if Ndim1 != Ndim2:
+            print errormsg
+            print '  ' + fname + ' variable: "' + var1 + '" from file "' + file1 +   \
+              '" does not have the same size:' , Ndim1, 'as variable: "' + var2 +    \
+              '" from file "' + file2 + '": ',Ndim2
+            quit(-1)
     
-    dims1 = objvar1.shape
-    dims2 = objvar2.shape
-
-    for iid in range(Ndim1):
-        if dims1[iid] != dims2[iid]:
-            print errormsg
-            print '  ' + fname + ' shape:',iid,'is different between variables!!!'
-            print '    dim_var1:', dims1[iid], 'dim_var2:', dims2[iid]
-            print '    var1 shape:', dims1
-            print '    var2 shape:', dims2
-            quit(-1)
-
-    varvals1 = objvar1[:]
-    varvals2 = objvar2[:]
-
-    if Ndim1 == 3:
-        timecompare = stats_time2Dvars(varvals1,varvals2)
-        spacecompare = stats_space2Dvars(varvals1,varvals2)
-
-        dimt = varvals1.shape[0]
-        dimy = varvals1.shape[1]
-        dimx = varvals1.shape[2]
-
-#        print 'bias std.dev. ___________________________________'
-#        for it in range(dimt):
-#            print it,':',spacecompare.bias[it],spacecompare.stdv1Sv2[it]
-#        print 'global spatial correlation (% significance)______'
-#        for it in range(dimt):
-#            print it, ':', spacecompare.corr[it], '(',                               \
-#              (1.-spacecompare.p_value[it])*100.,')'
-#        print 'spatial means temporal correlation:',spacecompare.meancorr,'(',       \
-#          (1.-spacecompare.meanp_value)*100.,' % significance)'
-#        print 'temporal means temporal correlation:',timecompare.meancorr,'(',       \
-#          (1.-timecompare.meanp_value)*100.,' % significance)'
-
-#        print 'temporal bias map________________________________'
-#        print timecompare.meanv1Sv2
-
-#        print 'temporal correlation map_________________________'
-#        print timeecompare.corr
-
-    else:
-        print errormsg
-        print '  ' + fname + ' numbe of dimensions:' + Ndim1 + ' not ready!!!'
-        quit(-1)
-
-# Creation of results file
-##
-    objofile = NetCDFFile(ofile, 'w')
-
-# Dimensions
-    for dimn in objnc1.dimensions:
-        dimobj = objnc1.dimensions[dimn]
-        if dimobj.isunlimited():
-            print '      ' + fname + ': Unlimited dimension '
-            dimsize = None
+        dims1 = objvar1.shape
+        dims2 = objvar2.shape
+
+        for iid in range(Ndim1):
+            if dims1[iid] != dims2[iid]:
+                print errormsg
+                print '  ' + fname + ' shape:',iid,'is different between variables!!!'
+                print '    dim_var1:', dims1[iid], 'dim_var2:', dims2[iid]
+                print '    var1 shape:', dims1
+                print '    var2 shape:', dims2
+                quit(-1)
+
+        varvals1 = objvar1[:]
+        varvals2 = objvar2[:]
+
+        if flavour != 'None':
+            if Ndim1 == 3:
+                if flavour == 'temporal' or flavour == 'both':
+                    timecompare = gen.stats_time2Dvars(varvals1,varvals2)
+                if flavour == 'spatial' or flavour == 'both':
+                    spacecompare = gen.stats_space2Dvars(varvals1,varvals2)
+
+                xdimn = objvar1.dimensions[2]
+                ydimn = objvar1.dimensions[1]
+                tdimn = objvar1.dimensions[0]
+
+                dimt = varvals1.shape[0]
+                dimy = varvals1.shape[1]
+                dimx = varvals1.shape[2]
+
+            else:
+                print errormsg
+                print '  ' + fname + ' number of dimensions:', Ndim1, ' not ready !!'
+                quit(-1)
+
+        if outfile:
+            # Dimensions
+            for dimn in objnc1.dimensions:
+                dimobj = objnc1.dimensions[dimn]
+                if not gen.searchInlist(objofile.dimensions, dimn):
+                    if dimobj.isunlimited():
+                        dimsize = None
+                    else:
+                        dimsize = len(dimobj)
+    
+                    newdim = objofile.createDimension(dimn, dimsize)
+
+            if kindstats == 'diffstats':
+                # Variables
+                varattr1 = objvar1.ncattrs()
+                varattr2 = objvar2.ncattrs()
+                if gen.searchInlist(varattr1, '_fillValue') or                       \
+                  gen.searchInlist(varattr2, '_fillValue'):
+                    if gen.searchInlist(varattr1, '_fillValue'): 
+                        fillval = objvar1.getncattr('_fillValue')
+                    if gen.searchInlist(varattr2, '_fillValue'): 
+                        fillval = objvar2.getncattr('_fillValue')
+                    newvar = objofile.createVariable('diff'+var1, objvar1.dtype,     \
+                      objvar1.dimensions, fill_value=fillval)
+                else:
+                    newvar = objofile.createVariable('diff'+var1, objvar1.dtype,     \
+                      objvar1.dimensions)
+                for attrn in objvar1.ncattrs():
+                    if attrn != '_fillValue':
+                        attrval = objvar1.getncattr(attrn)
+                        newattr = newvar.setncattr(attrn, attrval)
+                newatrr = newvar.setncattr('difference', var1 + '-' + var2)
+                newvar[:] = varvals1 - varvals2
+    
+            # Statistical variables
+            typvar = objvar1.dtype
+            if typvar == type(int(1)): 
+                fillValue = gen.fillValueI
+            elif typvar == type((1.)) or typvar == type(np.float(1.)) or            \
+              typvar == type(np.float64(1.)) or typvar == type(np.float32(1.)): 
+                fillValue = gen.fillValueF
+            else:
+                print errormsg
+                print '  ' + fname + ": variable type:", typvar, "of var '" + var1 + \
+                  "' not ready !!"
+                quit(-1)
+     
+            vartype = objvar1.dtype
+            varunits = objvar2.getncattr('units')
+
+            if flavour == 'temporal' or flavour == 'both':
+                # temporal bias
+                newvar = objofile.createVariable('t_bias_'+var1+'_'+var2, vartype,  \
+                  (ydimn, xdimn), fill_value = fillValue)
+                newvar[:] = timecompare.bias
+                newattr = basicvardef(newvar, 't_bias_'+var1+'_'+var2,              \
+                  'temporal bias between ' + var1 + '&' + var2, varunits)
+     
+                # temporal Standard deviation
+                newvar = objofile.createVariable('t_stddev_'+var1+'_'+var2, vartype,\
+                  (ydimn, xdimn), fill_value = fillValue)
+                newvar[:] = timecompare.stdv1Sv2
+                newattr = basicvardef(newvar, 't_stddev_'+var1+'_'+var2,            \
+                  'temporal standard deviation between '+ var1+'&' + var2, varunits)
+     
+                # t Correlation map
+                newvar = objofile.createVariable('t_corr_'+var1+'_'+var2, vartype,  \
+                 (ydimn, xdimn), fill_value = fillValue)
+                newvar[:] = timecompare.corr
+                newattr = basicvardef(newvar, 't_corr_'+var1+'_'+var2,              \
+                  'temporal correlation between ' +  var1 + '&' + var2, '-')
+
+                # t p-value map
+                newvar= objofile.createVariable('t_p_value_'+var1+'_'+var2, vartype,\
+                  (ydimn,xdimn), fill_value = fillValue)
+                newvar[:] = timecompare.p_value
+                newattr = basicvardef(newvar, 't_p_value_'+var1+'_'+var2,           \
+                  'temporal p_value between ' + var1 + '&' + var2, '-')
+     
+                # t mean Correlation
+                newvar = objofile.createVariable('t_mean_corr_'+var1+'_'+var2, 'f4')
+                newvar[:] = timecompare.meancorr
+                newattr = basicvardef(newvar, 't_mean_corr_'+var1+'_'+var2,         \
+                  'time mean values correlation between ' +  var1 + '&' + var2, '-')
+             
+                # t mean p-value
+                newvar=objofile.createVariable('t_mean_p_value_'+var1+'_'+var2, 'f4')
+                newvar[:] = timecompare.meanp_value     
+                newattr = basicvardef(newvar, 't_mean_p_value_'+var1+'_'+var2,      \
+                  'time mean p_value between ' + var1 + '&' + var2, '-')
+
+                objofile.sync()
+     
+            if flavour == 'spatial' or flavour == 'both':
+                # space bias
+                newvar = objofile.createVariable('s_bias_'+var1+'_'+var2, vartype,  \
+                  (tdimn), fill_value = fillValue)
+                newvar[:] = spacecompare.bias
+                newattr = basicvardef(newvar, 's_bias_'+var1+'_'+var2,              \
+                  'spatial bias between ' + var1 + '&' + var2, varunits)
+
+                # space Standard deviation
+                newvar = objofile.createVariable('s_stddev_'+var1+'_'+var2, vartype,\
+                  (tdimn), fill_value = fillValue)
+                newvar[:] = spacecompare.stdv1Sv2
+                newattr = basicvardef(newvar, 's_stddev_'+var1+'_'+var2,            \
+                 'spatial standard deviation between '+ var1 + '&' + var2, varunits)
+
+                # s Correlation map
+                newvar = objofile.createVariable('s_corr_'+var1+'_'+var2, vartype,  \
+                  (tdimn), fill_value = fillValue)
+                newvar[:] = spacecompare.corr
+                newattr = basicvardef(newvar, 's_corr_'+var1+'_'+var2,              \
+                  'spatial correlation between ' +  var1 + '&' + var2, '-')
+
+                # s p-value map
+                newvar= objofile.createVariable('s_p_value_'+var1+'_'+var2, vartype,\
+                  (tdimn), fill_value = fillValue)
+                newvar[:] = spacecompare.p_value
+                newattr = basicvardef(newvar, 's_p_value_'+var1+'_'+var2,           \
+                  'spatial p_value between ' + var1 + '&' + var2, '-')
+
+                # s mean Correlation
+                newvar = objofile.createVariable('s_mean_corr_'+var1+'_'+var2, 'f4')
+                newvar[:] = spacecompare.meancorr
+                newattr = basicvardef(newvar, 's_mean_corr_'+var1+'_'+var2,         \
+                  'space mean values correlation between ' +  var1 + '&' + var2, '-')
+
+                # s mean p-value
+                newvar= objofile.createVariable('s_mean_p_value_'+var1+'_'+var2,'f4')     
+                newvar[:] = spacecompare.meanp_value
+                newattr = basicvardef(newvar, 's_mean_p_value_'+var1+'_'+var2,      \
+                  'space mean p_value between ' + var1 + '&' + var2, '-')  
+
+                objofile.sync()      
+
+            # Global attributes
+            for attrn in objnc1.ncattrs():
+                attrval = objnc1.getncattr(attrn)
+                newattr = objofile.setncattr(attrn, attrval)
+
+            add_global_PyNCplot(objofile, 'nc_var_tools', fname, '2.0')
+            newattr = objofile.setncattr('statisitcs', 'variables retrieved from ' +\
+              'files '+ file1 + ' & ' + file2)
         else:
-            dimsize = len(dimobj)
-
-        newdim = objofile.createDimension(dimn, dimsize)
-
-# Variables
-    fvars = objnc1.variables
-    for varn in fvars:
-        if varn != var1:
-            varobj = objnc1.variables[varn]
-            if varn != var1:    
-                newvar = objofile.createVariable(varn, varobj.dtype,                 \
-                  varobj.dimensions)
-                for attrn in  varobj.ncattrs():
-                    attrval = varobj.getncattr(attrn)
-                    newattr = newvar.setncattr(attrn, attrval)
-                newvar[:] = varobj[:]
-
-# Statistical variables
-    vartype = objvar1.dtype
-    varunits = objvar2.getncattr('units')
-
-    print 'vartype=',vartype
-
-# temporal bias
-    newvar = objofile.createVariable('t_bias_'+var1+'_'+var2, vartype, ('y', 'x'),   \
-      fill_value = fillValue)
-    newvar[:] = timecompare.bias
-    newattr = basicvardef(newvar, 't_bias_'+var1+'_'+var2, 'temporal bias between ' +\
-      var1 + '&' + var2, varunits)
-
-# temporal Standard deviation
-    newvar = objofile.createVariable('t_stddev_'+var1+'_'+var2, vartype, ('y', 'x'), \
-      fill_value = fillValue)
-    newvar[:] = timecompare.stdv1Sv2
-    newattr = basicvardef(newvar, 't_stddev_'+var1+'_'+var2,                         \
-      'temporal standard deviation between '+ var1 + '&' + var2, varunits)
-
-# space bias
-    newvar = objofile.createVariable('s_bias_'+var1+'_'+var2, vartype, ('time'),     \
-      fill_value = fillValue)
-    newvar[:] = spacecompare.bias
-    newattr = basicvardef(newvar, 's_bias_'+var1+'_'+var2, 'spatial bias between ' + \
-      var1 + '&' + var2, varunits)
-
-# space Standard deviation
-    newvar = objofile.createVariable('s_stddev_'+var1+'_'+var2, vartype, ('time'),   \
-      fill_value = fillValue)
-    newvar[:] = spacecompare.stdv1Sv2
-    newattr = basicvardef(newvar, 's_stddev_'+var1+'_'+var2,                         \
-      'spatial standard deviation between '+ var1 + '&' + var2, varunits)
-
-# t Correlation map
-    newvar = objofile.createVariable('t_corr_'+var1+'_'+var2, vartype, ('y','x'),    \
-      fill_value = fillValue)
-    newvar[:] = timecompare.corr
-    newattr = basicvardef(newvar, 't_corr_'+var1+'_'+var2,                           \
-      'temporal correlation between ' +  var1 + '&' + var2, '-')
-    newattr = newvar.setncattr('projection', 'lon lat')
-
-# t p-value map
-    newvar = objofile.createVariable('t_p_value_'+var1+'_'+var2, vartype, ('y','x'), \
-      fill_value = fillValue)
-    newvar[:] = timecompare.p_value
-    newattr = basicvardef(newvar, 't_p_value_'+var1+'_'+var2,                        \
-      'temporal p_value between ' + var1 + '&' + var2, '-')
-    newattr = newvar.setncattr('projection', 'lon lat')
-
-# s Correlation map
-    newvar = objofile.createVariable('s_corr_'+var1+'_'+var2, vartype, ('time'),     \
-      fill_value = fillValue)
-    newvar[:] = spacecompare.corr
-    newattr = basicvardef(newvar, 's_corr_'+var1+'_'+var2,                           \
-      'spatial correlation between ' +  var1 + '&' + var2, '-')
-
-# s p-value map
-    newvar = objofile.createVariable('s_p_value_'+var1+'_'+var2, vartype, ('time'),  \
-      fill_value = fillValue)
-    newvar[:] = spacecompare.p_value
-    newattr = basicvardef(newvar, 's_p_value_'+var1+'_'+var2,                        \
-      'spatial p_value between ' + var1 + '&' + var2, '-')
-
-#        print 'spatial means temporal correlation:',spacecompare.meancorr,'(',       \
-#          (1.-spacecompare.meanp_value)*100.,' % significance)'
-
-# t mean Correlation
-    newvar = objofile.createVariable('t_mean_corr_'+var1+'_'+var2, 'f4')
-    newvar[:] = timecompare.meancorr
-    newattr = basicvardef(newvar, 't_mean_corr_'+var1+'_'+var2,                      \
-      'time mean values correlation between ' +  var1 + '&' + var2, '-')
-
-# t mean p-value
-    newvar = objofile.createVariable('t_mean_p_value_'+var1+'_'+var2, 'f4')
-    newvar[:] = timecompare.meanp_value
-    newattr = basicvardef(newvar, 't_mean_p_value_'+var1+'_'+var2,                   \
-      'time mean p_value between ' + var1 + '&' + var2, '-')
-
-# s mean Correlation
-    newvar = objofile.createVariable('s_mean_corr_'+var1+'_'+var2, 'f4')
-    newvar[:] = spacecompare.meancorr
-    newattr = basicvardef(newvar, 's_mean_corr_'+var1+'_'+var2,                      \
-      'space mean values correlation between ' +  var1 + '&' + var2, '-')
-
-# s mean p-value
-    newvar = objofile.createVariable('s_mean_p_value_'+var1+'_'+var2, 'f4')
-    newvar[:] = spacecompare.meanp_value
-    newattr = basicvardef(newvar, 's_mean_p_value_'+var1+'_'+var2,                   \
-      'space mean p_value between ' + var1 + '&' + var2, '-')
-
-# Global attributes
-    for attrn in objnc1.ncattrs():
-        attrval = objnc1.getncattr(attrn)
-        newattr = objofile.setncattr(attrn, attrval)
-
-    newattr = objofile.setncattr('statisitcs', 'variables retrieved from files ' +   \
-      file1 + ' & ' + file2)
-    
+            if flavour == 'temporal' or flavour == 'both':
+                print 'temporal bias between ' +var1+'&'+var2,':',timecompare.bias
+                print 'temporal standard deviation ' +var1+'&'+var2,':',             \
+                  timecompare.stdv1Sv2
+                print 'temporal correlation ' +var1+'&'+var2,':',timecompare.corr
+                print 'temporal p_value ' +var1+'&'+var2,':',timecompare.p_value
+                print 'temporal mean correlation ' +var1+'&'+var2,':',               \
+                  timecompare.meancorr
+                print 'temporal mean p_value ' +var1+'&'+var2,':',                   \
+                  timecompare.meanp_value
+
+            if flavour == 'spatial' or flavour == 'both':
+                print 'spatial bias between ' +var1+'&'+var2,':',spacecompare.bias
+                print 'spatial standard deviation ' +var1+'&'+var2,':',              \
+                  spacecompare.stdv1Sv2
+                print 'spatial correlation ' +var1+'&'+var2,':',spacecompare.corr
+                print 'spatial p_value ' +var1+'&'+var2,':',spacecompare.p_value
+                print 'spatial mean correlation ' +var1+'&'+var2,':',                \
+                  spacecompare.meancorr
+                print 'spatial mean p_value ' +var1+'&'+var2,':',                    \
+                  spaceompare.meanp_value
+
     objnc1.close()
     objnc2.close()
 
-    objofile.sync()
-    objofile.close()
-
-    varsfillvalue = ['t_bias_', 't_stddev_', 's_bias_', 's_stddev_', 't_corr_',      \
-      't_p_value', 's_corr_', 's_p_value']
-
-#    for varn in varsfillvalue:
-#        print varn
-#        fill = varaddattrk('_FillValue|'+ str(fillValue) + '|R32', ofile,          \
-#          varn+var1+'_'+var2)
-
-    print '  ' + fname + ': successfull generation of file "' + ofile + '" !!!!!'
+    if outfile:
+        objofile.sync()
+        objofile.close()
+
+        print '  ' + fname + ': successfull generation of file "' + ofile + '" !!'
 
     return 
 
 #statcompare_files('control/sellonlatbox_wss_17-20.nc:wss,obs/re_project_Vu_17-20.nc:wss')
+#statcompare_files('diffstats,None,yes','/home/lluis/PY/wrfout_d01_1995-01-01_00:00:00,/home/lluis/PY/wrfout_d01_1995-01-01_00:00:00',\
+#  'T2,U10,QVAPOR:T2,U10,QVAPOR')
 
 def sellonlatlevbox(values, ncfile, varn):