interpret data to normalize as ndarrays

Nathan Goldbaum · Nathan Goldbaum · commit 063bcac1ec10 · 2017-05-31T17:18:54.000-05:00
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -1009,7 +1009,7 @@ def __call__(self, value, clip=None):
                 result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
                                   mask=mask)
             # in-place equivalent of above can be much faster
-            resdat = result.data
+            resdat = np.asarray(result.data)
             mask = result.mask
             if mask is np.ma.nomask:
                 mask = (resdat <= 0)
@@ -1104,7 +1104,7 @@ def __call__(self, value, clip=None):
                 result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
                                      mask=mask)
             # in-place equivalent of above can be much faster
-            resdat = self._transform(result.data)
+            resdat = self._transform(np.asarray(result.data))
             resdat -= self._lower
             resdat /= (self._upper - self._lower)
 
@@ -1198,7 +1198,7 @@ def __call__(self, value, clip=None):
                 mask = np.ma.getmask(result)
                 result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
                                      mask=mask)
-            resdat = result.data
+            resdat = np.asarray(result.data)
             resdat -= vmin
             np.power(resdat, gamma, resdat)
             resdat /= (vmax - vmin) ** gamma
