Parallel data handling now also supports checkpointing

- update waLBerla package in order to make use of this function!

datahandling/datahandling_interface.py

@@ -300,6 +300,17 @@ class DataHandling(ABC):
 
         return self.reduce_float_sequence([result], 'max', all_reduce=True)[0] if reduce else result
 
+    def save_all(self, file):
+        """Saves all field data to disk into a file"""
+
+    def load_all(self, file):
+        """Loads all field data from disk into a file
+
+        Works only if save_all was called with exactly the same field sizes, layouts etc.
+        When run in parallel save and load has to be called with the same number of processes.
+        Use for check pointing only - to store results use VTK output
+        """
+
     def __str__(self):
         result = ""
 

datahandling/parallel_datahandling.py

+import os
 import numpy as np
 import warnings
 from pystencils import Field
@@ -369,3 +370,17 @@ class ParallelDataHandling(DataHandling):
     @property
     def world_rank(self):
         return wlb.mpi.worldRank()
+
+    def save_all(self, directory):
+        if not os.path.exists(directory):
+            os.mkdir(directory)
+        if os.path.isfile(directory):
+            raise RuntimeError("Trying to save to {}, but file exists already".format(directory))
+
+        for field_name, data_name in self._field_name_to_cpu_data_name.items():
+            self.blocks.writeBlockData(data_name, os.path.join(directory, field_name + ".dat"))
+
+    def load_all(self, directory):
+        for field_name, data_name in self._field_name_to_cpu_data_name.items():
+            self.blocks.readBlockData(data_name, os.path.join(directory, field_name + ".dat"))
+

datahandling/serial_datahandling.py

@@ -392,7 +392,7 @@ class SerialDataHandling(DataHandling):
                 continue
             if file_contents[arr_name].shape != arr_contents.shape:
                 print("Skipping read data {} because shapes don't match. "
-                      "Read array shape {}, exising array shape {}".format(arr_name, file_contents[arr_name].shape,
-                                                                           arr_contents.shape))
+                      "Read array shape {}, existing array shape {}".format(arr_name, file_contents[arr_name].shape,
+                                                                            arr_contents.shape))
                 continue
             np.copyto(arr_contents, file_contents[arr_name])