From f604314207d5a698cc386cc8f383a17b16c12029 Mon Sep 17 00:00:00 2001
From: Martin Diehl <mail@martin-diehl.net>
Date: Fri, 21 Feb 2020 23:16:25 +0100
Subject: [PATCH] polishing

---
 python/damask/dadf5.py | 82 +++++++++++++++++++++++-------------------
 1 file changed, 46 insertions(+), 36 deletions(-)

diff --git a/python/damask/dadf5.py b/python/damask/dadf5.py
index c13cf527f..f1476af61 100644
--- a/python/damask/dadf5.py
+++ b/python/damask/dadf5.py
@@ -949,21 +949,20 @@ class DADF5():
 
 
     def _job(self,group,func,datasets,args,lock):
-        def _read(group,datasets,lock):
-          datasets_in = {}
-          lock.acquire()
-          with h5py.File(self.fname,'r') as f:
-            for k,v in datasets.items():
-              loc  = f[group+'/'+v]
-              datasets_in[k]={'data':loc[()],
-                              'label':v,
-                              'meta':{k2:v2.decode() for k2,v2 in loc.attrs.items()}}
-          lock.release()
-          return datasets_in
-
+        """
+        Execute job for _add_generic_pointwise
+        """
         try:
-            d = _read(group,datasets,lock)
-            r = func(**d,**args)
+            datasets_in = {}
+            lock.acquire()
+            with h5py.File(self.fname,'r') as f:
+              for arg,label in datasets.items():
+                loc  = f[group+'/'+label]
+                datasets_in[arg]={'data' :loc[()],
+                                  'label':label,
+                                  'meta': {k:v.decode() for k,v in loc.attrs.items()}}
+            lock.release()
+            r = func(**datasets_in,**args)
             return [group,r]
         except Exception as err:
             print('Error during calculation: {}.'.format(err))
@@ -971,30 +970,41 @@ class DADF5():
 
 
     def _add_generic_pointwise(self,func,datasets,args={}):
+        """
+        General function to add pointwise data.
 
-      env = Environment()
-      N_threads = int(env.options['DAMASK_NUM_THREADS'])
-      pool = multiprocessing.Pool(N_threads)
-      m = multiprocessing.Manager()
-      lock = m.Lock()
+        Parameters
+        ----------
+        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)
+        lock = multiprocessing.Manager().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)
-      for i,result in enumerate(pool.imap_unordered(default_arg,groups)):
-          util.progressBar(iteration=i,total=len(groups)-1)
-          if not result: continue
-          lock.acquire()
-          with h5py.File(self.fname, 'a') as f:
-              try:
-                  dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
-                  for l,v in result[1]['meta'].items():
-                      dataset.attrs[l]=v.encode()
-              except OSError as err:
-                  print('Could not add dataset: {}.'.format(err))
-          lock.release()
-      pool.close()
-      pool.join()
+        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))
+        for i,result in enumerate(pool.imap_unordered(default_arg,groups)):
+            util.progressBar(iteration=i+1,total=len(groups))
+            if not result: continue
+            lock.acquire()
+            with h5py.File(self.fname, 'a') as f:
+                try:
+                    dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
+                    for l,v in result[1]['meta'].items():
+                        dataset.attrs[l]=v.encode()
+                except OSError as err:
+                    print('Could not add dataset: {}.'.format(err))
+            lock.release()
+
+        pool.close()
+        pool.join()
 
 
     def to_vtk(self,labels,mode='cell'):