polishing

python/damask/dadf5.py

@@ -949,21 +949,20 @@ class DADF5():
 
 
     def _job(self,group,func,datasets,args,lock):
-        def _read(group,datasets,lock):
+        """
+        Execute job for _add_generic_pointwise
+        """
+        try:
             datasets_in = {}
             lock.acquire()
             with h5py.File(self.fname,'r') as f:
-            for k,v in datasets.items():
+              for arg,label in datasets.items():
-              loc  = f[group+'/'+v]
+                loc  = f[group+'/'+label]
-              datasets_in[k]={'data':loc[()],
+                datasets_in[arg]={'data' :loc[()],
-                              'label':v,
+                                  'label':label,
-                              'meta':{k2:v2.decode() for k2,v2 in loc.attrs.items()}}
+                                  'meta': {k:v.decode() for k,v in loc.attrs.items()}}
             lock.release()
-          return datasets_in
+            r = func(**datasets_in,**args)
-
-        try:
-            d = _read(group,datasets,lock)
-            r = func(**d,**args)
             return [group,r]
         except Exception as err:
             print('Error during calculation: {}.'.format(err))
@@ -971,18 +970,28 @@ class DADF5():
 
 
     def _add_generic_pointwise(self,func,datasets,args={}):
+        """
+        General function to add pointwise data.
 
-      env = Environment()
+        Parameters
-      N_threads = int(env.options['DAMASK_NUM_THREADS'])
+        ----------
+        func : function
+          Callback function that calculates a new dataset from one or more datasets per HDF5 group.
+        datasets : dictionary
+          Details of the datasets to be used: label (in HDF5 file) and arg (argument to which the data is parsed in func).
+        args : dictionary, optional
+          Arguments parsed to func.
+        """
+        N_threads = int(Environment().options['DAMASK_NUM_THREADS'])
         pool = multiprocessing.Pool(N_threads)
-      m = multiprocessing.Manager()
+        lock = multiprocessing.Manager().Lock()
-      lock = m.Lock()
 
         groups = self.groups_with_datasets(datasets.values())
         default_arg = partial(self._job,func=func,datasets=datasets,args=args,lock=lock)
-      util.progressBar(iteration=0,total=len(groups)-1)
+
+        util.progressBar(iteration=0,total=len(groups))
         for i,result in enumerate(pool.imap_unordered(default_arg,groups)):
-          util.progressBar(iteration=i,total=len(groups)-1)
+            util.progressBar(iteration=i+1,total=len(groups))
             if not result: continue
             lock.acquire()
             with h5py.File(self.fname, 'a') as f:
@@ -993,6 +1002,7 @@ class DADF5():
                 except OSError as err:
                     print('Could not add dataset: {}.'.format(err))
             lock.release()
+
         pool.close()
         pool.join()