Properly unpack 0D data when reading an IDS from a netCDF file

0D IDS Data is expected in the native python data types (int, float, complex, string). Before this fix, 0D numerical data would be stored as numpy.int32, numpy.float64 or numpy.complex128 when reading from a netCDF file.

Fixes #89

Author: maarten-ic

imas/backends/netcdf/nc2ids.py

@@ -157,6 +157,8 @@ def run(self, lazy: bool) -> None:
                     for index, node in indexed_tree_iter(self.ids, target_metadata):
                         value = data[index]
                         if value != getattr(var, "_FillValue", None):
+                            if isinstance(value, np.generic):
+                                value = value.item()
                             # NOTE: bypassing IDSPrimitive.value.setter logic
                             node._IDSPrimitive__value = value
 
@@ -166,10 +168,16 @@ def run(self, lazy: bool) -> None:
                 # here, we'll let IDSPrimitive.value.setter take care of it:
                 self.ids[target_metadata.path].value = data
 
-            else:
+            # We need to unpack 0D ints, floats and complex numbers. For better
+            # performance this check is done outside the for-loop:
+            elif metadata.ndim or metadata.data_type is IDSDataType.STR:
                 for index, node in indexed_tree_iter(self.ids, target_metadata):
                     # NOTE: bypassing IDSPrimitive.value.setter logic
                     node._IDSPrimitive__value = data[index]
+            else:
+                for index, node in indexed_tree_iter(self.ids, target_metadata):
+                    # NOTE: bypassing IDSPrimitive.value.setter logic
+                    node._IDSPrimitive__value = data[index].item()  # Unpack 0D value
 
     def validate_variables(self) -> None:
         """Validate that all variables in the netCDF Group exist and match the DD."""
@@ -365,7 +373,7 @@ def get_child(self, child):
                 value = var[self.index]
 
             if value is not None:
-                if isinstance(value, np.ndarray):
+                if isinstance(value, (np.ndarray, np.generic)):
                     if value.ndim == 0:  # Unpack 0D numpy arrays:
                         value = value.item()
                     else: