numpy
diff --git a/‎numpy/ma/core.py
Lines changed: 6 additions & 0 deletions b/‎numpy/ma/core.py
Lines changed: 6 additions & 0 deletions
diff --git a/‎numpy/ma/tests/test_subclassing.py
Lines changed: 42 additions & 0 deletions b/‎numpy/ma/tests/test_subclassing.py
Lines changed: 42 additions & 0 deletions
@@ -2837,6 +2837,12 @@ def __new__(cls, data=None, mask=nomask, dtype=None, copy=False,
             _data = ndarray.view(_data, type(data))
         else:
             _data = ndarray.view(_data, cls)
+
+        # Handle the case where data is not a subclass of ndarray, but
+        # still has the _mask attribute like MaskedArrays
         if hasattr(data, '_mask') and not isinstance(data, ndarray):
+            _data._mask = data._mask
+
         # Process mask.
         # Type of the mask
         mdtype = make_mask_descr(_data.dtype)
 
@@ -343,3 +343,45 @@ def test_pure_subclass_info_preservation(self):
         diff2 = arr1 - arr2
         assert_('info' in diff2._optinfo)
         assert_(diff2._optinfo['info'] == 'test')
+
+
+class ArrayNoInheritance:
+    """Quantity-like class that does not inherit from ndarray"""
+    def __init__(self, data, units):
+        self.magnitude = data
+        self.units = units
+
+    def __getattr__(self, attr):
+        return getattr(self.magnitude, attr)
+
+
+def test_array_no_inheritance():
+    data_masked = np.ma.array([1, 2, 3], mask=[True, False, True])
+    data_masked_units = ArrayNoInheritance(data_masked, 'meters')
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units)
+    assert_equal(data_masked.data, new_array.data)
+    assert_equal(data_masked.mask, new_array.mask)
+    # Test sharing the mask
+    data_masked.mask = [True, False, False]
+    assert_equal(data_masked.mask, new_array.mask)
+    assert_(new_array.sharedmask)
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units, copy=True)
+    assert_equal(data_masked.data, new_array.data)
+    assert_equal(data_masked.mask, new_array.mask)
+    # Test that the mask is not shared when copy=True
+    data_masked.mask = [True, False, True]
+    assert_equal([True, False, False], new_array.mask)
+    assert_(not new_array.sharedmask)
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units, keep_mask=False)
+    assert_equal(data_masked.data, new_array.data)
+    # The change did not affect the original mask
+    assert_equal(data_masked.mask, [True, False, True])
+    # Test that the mask is False and not shared when keep_mask=False
+    assert_(not new_array.mask)
+    assert_(not new_array.sharedmask)