matplotlib
diff --git a/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+7-5Lines changed: 7 additions & 5 deletions b/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+7-5Lines changed: 7 additions & 5 deletions
diff --git a/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+19Lines changed: 19 additions & 0 deletions b/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+19Lines changed: 19 additions & 0 deletions
@@ -907,7 +907,11 @@ def process_value(value):
         if np.issubdtype(dtype, np.integer) or dtype.type is np.bool_:
             # bool_/int8/int16 -> float32; int32/int64 -> float64
             dtype = np.promote_types(dtype, np.float32)
-        result = np.ma.array(value, dtype=dtype, copy=True)
+        # ensure data passed in as an ndarray subclass are interpreted as
+        # an ndarray. See issue #6622.
+        mask = np.ma.getmask(value)
+        data = np.asarray(np.ma.getdata(value))
+        result = np.ma.array(data, mask=mask, dtype=dtype, copy=True)
         return result, is_scalar
 
     def __call__(self, value, clip=None):
@@ -937,9 +941,7 @@ def __call__(self, value, clip=None):
                 result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
                                      mask=mask)
             # ma division is very slow; we can take a shortcut
-            # use np.asarray so data passed in as an ndarray subclass are
-            # interpreted as an ndarray. See issue #6622.
-            resdat = np.asarray(result.data)
+            resdat = result.data
             resdat -= vmin
             resdat /= (vmax - vmin)
             result = np.ma.array(resdat, mask=result.mask, copy=False)
@@ -1007,7 +1009,7 @@ def __call__(self, value, clip=None):
             if clip:
                 mask = np.ma.getmask(result)
                 result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
-                                  mask=mask)
+                                     mask=mask)
             # in-place equivalent of above can be much faster
             resdat = result.data
             mask = result.mask
 
@@ -689,3 +689,22 @@ def test_tableau_order():
                   '#bcbd22', '#17becf']
 
     assert list(mcolors.TABLEAU_COLORS.values()) == dflt_cycle
+
+
+def test_ndarray_subclass_norm():
+    # Emulate an ndarray subclass that handles units
+    # which objects when adding or subtracting with other
+    # arrays. See #6622 and #8696
+    class MyArray(np.ndarray):
+        def __isub__(self, other):
+            raise RuntimeError
+
+        def __add__(self, other):
+            raise RuntimeError
+
+    data = np.arange(-10, 10, 1, dtype=float)
+
+    for norm in [mcolors.Normalize(), mcolors.LogNorm(),
+                 mcolors.SymLogNorm(3, vmax=5, linscale=1),
+                 mcolors.PowerNorm(1)]:
+        assert_array_equal(norm(data.view(MyArray)), norm(data))