Fix array_equal behaviour for masked arrays

docs/src/whatsnew/latest.rst

@@ -51,6 +51,11 @@ This document explains the changes made to Iris for this release
    :func:`~iris.fileformats.pp.save_pairs_from_cube` because it had no effect.
    (:pull:`5783`)
 
+#. `@stephenworsley`_ made masked arrays on Iris objects now compare as equal
+   precisely when all unmasked points are equal and when the masks are identical.
+   This is due to changes in :func:`~iris.util.array_equal` which previously
+   ignored masks entirely. (:pull:`4457`)
+
 
 🚀 Performance Enhancements
 ===========================

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

@@ -44,11 +44,28 @@ def test_nd(self):
         self.assertTrue(array_equal(array_a, array_b))
         self.assertFalse(array_equal(array_a, array_c))
 
-    def test_masked_is_ignored(self):
+    def test_masked_is_not_ignored(self):
         array_a = ma.masked_array([1, 2, 3], mask=[1, 0, 1])
         array_b = ma.masked_array([2, 2, 2], mask=[1, 0, 1])
+        self.assertTrue(array_equal(array_a, array_b))
+
+    def test_masked_is_different(self):
+        array_a = ma.masked_array([1, 2, 3], mask=[1, 0, 1])
+        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`.
@@ -406,17 +407,24 @@ def array_equal(array1, array2, withnans=False):
 
     def normalise_array(array):
         if not is_lazy_data(array):
-            array = np.asarray(array)
+            if not ma.isMaskedArray(array):
+                array = np.asanyarray(array)
         return array
 
     array1, array2 = normalise_array(array1), normalise_array(array2)
 
     eq = array1.shape == array2.shape
+    if eq:
+        array1_masked = ma.is_masked(array1)
+        eq = array1_masked == ma.is_masked(array2)
+    if eq and array1_masked:
+        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"):
             eqs = np.where(np.isnan(array1) & np.isnan(array2), True, eqs)
-        eq = bool(np.all(eqs))
+        eq = np.all(eqs)
+        eq = bool(eq) or eq is ma.masked
 
     return eq