Introduce DNDarray.__array__() method

heat/core/dndarray.py

@@ -463,6 +463,12 @@ def __cat_halo(self) -> torch.Tensor:
             dim=self.split,
         )
 
+    def __array__(self) -> np.ndarray:
+        """
+        Returns a view of the process-local slice of the :class:`DNDarray` as a numpy ndarray, if the ``DNDarray`` resides on CPU. Otherwise, it returns a copy, on CPU, of the process-local slice of ``DNDarray`` as numpy ndarray.
+        """
+        return self.larray.cpu().__array__()
+
     def astype(self, dtype, copy=True) -> DNDarray:
         """
         Returns a casted version of this array.
@@ -472,7 +478,7 @@ def astype(self, dtype, copy=True) -> DNDarray:
         Parameters
         ----------
         dtype : datatype
-            HeAT type to which the array is cast
+            Heat type to which the array is cast
         copy : bool, optional
             By default the operation returns a copy of this array. If copy is set to ``False`` the cast is performed
             in-place and this array is returned
@@ -1111,8 +1117,9 @@ def __len__(self) -> int:
 
     def numpy(self) -> np.array:
         """
-        Convert :class:`DNDarray` to numpy array. If the ``DNDarray`` is distributed it will be merged beforehand. If the ``DNDarray``
-        resides on the GPU, it will be copied to the CPU first.
+        Returns a copy of the :class:`DNDarray` as numpy ndarray. If the ``DNDarray`` resides on the GPU, the underlying data will be copied to the CPU first.
+
+        If the ``DNDarray`` is distributed, an MPI Allgather operation will be performed before converting to np.ndarray, i.e. each MPI process will end up holding a copy of the entire array in memory.  Make sure process memory is sufficient!
 
         Examples
         --------

heat/core/tests/test_dndarray.py

@@ -231,6 +231,25 @@ def test_gethalo(self):
             self.assertTrue(data.halo_prev is prev_halo or (data.halo_prev == prev_halo).all())
             self.assertTrue(data.halo_next is next_halo or (data.halo_next == next_halo).all())
 
+    def test_array(self):
+        # undistributed case
+        x = ht.arange(6 * 7 * 8).reshape((6, 7, 8))
+        x_np = np.arange(6 * 7 * 8, dtype=np.int32).reshape((6, 7, 8))
+
+        self.assertTrue((x.__array__() == x_np).all())
+        self.assertIsInstance(x.__array__(), np.ndarray)
+        self.assertEqual(x.__array__().dtype, x_np.dtype)
+        self.assertEqual(x.__array__().shape, x.gshape)
+
+        # distributed case
+        x = ht.arange(6 * 7 * 8, dtype=ht.float64, split=0).reshape((6, 7, 8))
+        x_np = np.arange(6 * 7 * 8, dtype=np.float64).reshape((6, 7, 8))
+
+        self.assertTrue((x.__array__() == x.larray.cpu().numpy()).all())
+        self.assertIsInstance(x.__array__(), np.ndarray)
+        self.assertEqual(x.__array__().dtype, x_np.dtype)
+        self.assertEqual(x.__array__().shape, x.lshape)
+
     def test_larray(self):
         # undistributed case
         x = ht.arange(6 * 7 * 8).reshape((6, 7, 8))