numpy
diff --git a/‎numpy/core/records.py
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+10-4Lines changed: 10 additions & 4 deletions b/‎numpy/core/records.py
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+10-4Lines changed: 10 additions & 4 deletions
diff --git a/‎numpy/core/tests/test_records.py
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+11-1Lines changed: 11 additions & 1 deletion b/‎numpy/core/tests/test_records.py
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+11-1Lines changed: 11 additions & 1 deletion
diff --git a/‎numpy/doc/structured_arrays.py
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+7-27Lines changed: 7 additions & 27 deletions b/‎numpy/doc/structured_arrays.py
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+7-27Lines changed: 7 additions & 27 deletions
diff --git a/‎numpy/ma/core.py
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+5Lines changed: 5 additions & 0 deletions b/‎numpy/ma/core.py
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+5Lines changed: 5 additions & 0 deletions
@@ -423,6 +423,11 @@ def __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None,
                                       strides=strides, order=order)
         return self
 
+    def __array_finalize__(self, obj):
+        if type(obj) is not type(self) and issubclass(obj.dtype.type, nt.void):
+            recordtype = sb.dtype((record, self.dtype))
+            super(recarray, self).__setattr__('dtype', recordtype)
+
     def __getattribute__(self, attr):
         # See if ndarray has this attr, and return it if so. (note that this
         # means a field with the same name as an ndarray attr cannot be
@@ -456,6 +461,11 @@ def __getattribute__(self, attr):
     # Undo any "setting" of the attribute and do a setfield
     # Thus, you can't create attributes on-the-fly that are field names.
     def __setattr__(self, attr, val):
+
+        # Automatically convert (void) dtypes to records.
+        if attr == 'dtype' and issubclass(val.type, nt.void):
+            val = sb.dtype((record, val))
+
         newattr = attr not in self.__dict__
         try:
             ret = object.__setattr__(self, attr, val)
@@ -838,8 +848,6 @@ def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
         if copy:
             new = new.copy()
         res = new.view(recarray)
-        if issubclass(res.dtype.type, nt.void):
-            res.dtype = sb.dtype((record, res.dtype))
         return res
 
     else:
@@ -850,6 +858,4 @@ def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
         if dtype is not None and (obj.dtype != dtype):
             obj = obj.view(dtype)
         res = obj.view(recarray)
-        if issubclass(res.dtype.type, nt.void):
-            res.dtype = sb.dtype((record, res.dtype))
         return res
@@ -88,13 +88,23 @@ def test_recarray_from_repr(self):
         assert_equal(recordarr, recordarr_r)
 
         assert_equal(type(recarr_r), np.recarray)
-        assert_equal(recarr_r.dtype.type, np.void)
+        assert_equal(recarr_r.dtype.type, np.record)
         assert_equal(recarr, recarr_r)
 
         assert_equal(type(recordview_r), np.ndarray)
         assert_equal(recordview.dtype.type, np.record)
         assert_equal(recordview, recordview_r)
 
+    def test_recarray_views(self):
+        a = np.array([(1,'ABC'), (2, "DEF")],
+                     dtype=[('foo', int), ('bar', 'S4')])
+        b = np.array([1,2,3,4,5], dtype=np.int64)
+
+        assert_equal(np.rec.array(a).dtype.type, np.record)
+        assert_equal(type(np.rec.array(a)), np.recarray)
+        assert_equal(np.rec.array(b).dtype.type, np.int64)
+        assert_equal(type(np.rec.array(b)), np.recarray)
+
     def test_recarray_from_names(self):
         ra = np.rec.array([
             (1, 'abc', 3.7000002861022949, 0),
 
@@ -258,6 +258,13 @@
  >>> recordarr = arr.view(dtype=dtype((np.record, arr.dtype)), 
  ...                      type=np.recarray)
 
+For convenience, viewing an ndarray as type `np.recarray` will automatically
+convert to `np.record` datatype, so the dtype can be left out of the view: ::
+
+ >>> recordarr = arr.view(np.recarray)
+ >>> recordarr.dtype
+ dtype((numpy.record, [('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')]))
+
 Record array fields accessed by index or by attribute are returned as a record
 array if the field has a structured type but as a plain ndarray otherwise. ::
 
@@ -272,33 +279,6 @@
 attribute takes precedence. Such fields will be inaccessible by attribute but
 may still be accessed by index.
 
-Partial Attribute Access
-------------------------
-
-The differences between record arrays and plain structured arrays induce a
-small performance penalty. It is possible to apply one or the other view
-independently if desired. To allow field access by attribute only on the array
-object it is sufficient to view an array as a recarray: ::
-
- >>> recarr = arr.view(np.recarray)
-
-This type of view is commonly used, for example in np.npyio and
-np.recfunctions. Note that unlike full record arrays the individual elements of
-such a view do not have field attributes::
-
- >>> recarr[0].foo
- AttributeError: 'numpy.void' object has no attribute 'foo'
-
-To use the np.record dtype only, convert the dtype using the (base_class,
-dtype) form described in numpy.dtype.  This type of view is rarely used. ::
-
- >>> arr_records = arr.view(dtype((np.record, arr.dtype)))
-
-In documentation, the term 'structured array' will refer to objects of type
-np.ndarray with structured dtype, 'record array' will refer to structured
-arrays subclassed as np.recarray and whose dtype is of type np.record, and
-'recarray' will refer to arrays subclassed as np.recarray but whose dtype is
-not of type np.record.
 
 """
 from __future__ import division, absolute_import, print_function
@@ -3155,6 +3155,11 @@ def __setitem__(self, indx, value):
             _mask[indx] = mindx
         return
 
+    def __setattr__(self, attr, value):
+        super(MaskedArray, self).__setattr__(attr, value)
+        if attr == 'dtype':
+            mdtype = make_mask_descr(value)
+            self._mask = self._mask.view(mdtype, ndarray)
 
     def __getslice__(self, i, j):
         """x.__getslice__(i, j) <==> x[i:j]