diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
index 5441b0d617b5..16e13e69d53f 100644
--- a/lib/matplotlib/colors.py
+++ b/lib/matplotlib/colors.py
@@ -1302,9 +1302,8 @@ def process_value(value):
dtype = np.promote_types(dtype, np.float32)
# ensure data passed in as an ndarray subclass are interpreted as
# an ndarray. See issue #6622.
- mask = np.ma.getmask(value)
- data = np.asarray(value)
- result = np.ma.array(data, mask=mask, dtype=dtype, copy=True)
+ result = np.ma.array(np.asarray(value), mask=np.ma.getmaskarray(value),
+ dtype=dtype, copy=True)
return result, is_scalar
def __call__(self, value, clip=None):
@@ -1341,9 +1340,8 @@ def __call__(self, value, clip=None):
raise ValueError("minvalue must be less than or equal to maxvalue")
else:
if clip:
- mask = np.ma.getmask(result)
result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
- mask=mask)
+ mask=np.ma.getmaskarray(result))
# ma division is very slow; we can take a shortcut
resdat = result.data
resdat -= vmin
@@ -1459,7 +1457,7 @@ def __call__(self, value, clip=None):
result = np.ma.masked_array(
np.interp(result, [self.vmin, self.vcenter, self.vmax],
[0, 0.5, 1], left=-np.inf, right=np.inf),
- mask=np.ma.getmask(result))
+ mask=np.ma.getmaskarray(result))
if is_scalar:
result = np.atleast_1d(result)[0]
return result
@@ -1873,9 +1871,8 @@ def __call__(self, value, clip=None):
result.fill(0)
else:
if clip:
- mask = np.ma.getmask(result)
result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
- mask=mask)
+ mask=np.ma.getmaskarray(result))
resdat = result.data
resdat -= vmin
resdat[resdat < 0] = 0
diff --git a/lib/matplotlib/transforms.py b/lib/matplotlib/transforms.py
index 1471d4fe672d..62d460a9833c 100644
--- a/lib/matplotlib/transforms.py
+++ b/lib/matplotlib/transforms.py
@@ -1849,7 +1849,7 @@ def transform_affine(self, points):
mtx = self.get_matrix()
if isinstance(points, np.ma.MaskedArray):
tpoints = affine_transform(points.data, mtx)
- return np.ma.MaskedArray(tpoints, mask=np.ma.getmask(points))
+ return np.ma.MaskedArray(tpoints, mask=np.ma.getmaskarray(points))
return affine_transform(points, mtx)
if DEBUG:
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