Fix array_equal behaviour for masked arrays

lib/iris/tests/unit/util/test_array_equal.py

@@ -54,6 +54,18 @@ def test_masked_is_different(self):
         array_b = ma.masked_array([1, 2, 2], mask=[0, 0, 1])
         self.assertFalse(array_equal(array_a, array_b))
 
+    def test_masked_isnt_unmasked(self):
+        array_a = np.array([1, 2, 2])
+        array_b = ma.masked_array([1, 2, 2], mask=[0, 0, 1])
+        self.assertFalse(array_equal(array_a, array_b))
+
+    def test_masked_unmasked_equivelance(self):
+        array_a = np.array([1, 2, 2])
+        array_b = ma.masked_array([1, 2, 2])
+        array_c = ma.masked_array([1, 2, 2], mask=[0, 0, 0])
+        self.assertTrue(array_equal(array_a, array_b))
+        self.assertTrue(array_equal(array_a, array_c))
+
     def test_fully_masked_arrays(self):
         array_a = ma.masked_array(np.arange(24).reshape(2, 3, 4), mask=True)
         array_b = ma.masked_array(np.arange(24).reshape(2, 3, 4), mask=True)

lib/iris/util.py

@@ -395,8 +395,9 @@ def array_equal(array1, array2, withnans=False):
 
     Notes
     -----
-    This provides much the same functionality as :func:`numpy.array_equal`, but
-    with additional support for arrays of strings and NaN-tolerant operation.
+    This provides similar functionality to :func:`numpy.array_equal`, but
+    will compare arrays as unequal when their masks differ and has
+    additional support for arrays of strings and NaN-tolerant operation.
 
     This function maintains laziness when called; it does not realise data.
     See more at :doc:`/userguide/real_and_lazy_data`.
@@ -417,7 +418,7 @@ def normalise_array(array):
         array1_masked = ma.is_masked(array1)
         eq = array1_masked == ma.is_masked(array2)
     if eq and array1_masked:
-        eq = np.array_equal(ma.getmask(array1), ma.getmask(array2))
+        eq = np.array_equal(ma.getmaskarray(array1), ma.getmaskarray(array2))
     if eq:
         eqs = array1 == array2
         if withnans and (array1.dtype.kind == "f" or array2.dtype.kind == "f"):