matplotlib
diff --git a/‎doc/_templates/autofunctions.rst
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+3-2Lines changed: 3 additions & 2 deletions b/‎doc/_templates/autofunctions.rst
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+3-2Lines changed: 3 additions & 2 deletions
diff --git a/‎lib/matplotlib/image.py
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+8-4Lines changed: 8 additions & 4 deletions b/‎lib/matplotlib/image.py
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+8-4Lines changed: 8 additions & 4 deletions
diff --git a/‎lib/matplotlib/tests/test_image.py
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+10Lines changed: 10 additions & 0 deletions b/‎lib/matplotlib/tests/test_image.py
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+10Lines changed: 10 additions & 0 deletions
@@ -2,15 +2,16 @@
 {{ fullname | escape | underline }}
 
 
-.. currentmodule:: {{ fullname }}
+.. automodule:: {{ fullname }}
+   :no-members:
 
 {% block functions %}
 {% if functions %}
 
 Functions
 ---------
 
-.. autosummary:: 
+.. autosummary::
    :template: autosummary.rst
    :toctree:
 {% for item in functions %}{% if item not in ['plotting', 'colormaps'] %}
 
@@ -368,10 +368,14 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
                     scaled_dtype = A.dtype
                 else:
                     scaled_dtype = np.float32
-                # old versions of numpy do not work with `np.nammin`
-                # and `np.nanmax` as inputs
-                a_min = np.ma.min(A).astype(scaled_dtype)
-                a_max = np.ma.max(A).astype(scaled_dtype)
+
+                a_min = A.min()
+                if a_min is np.ma.masked:
+                    a_min, a_max = 0, 1  # all masked, so values don't matter
+                else:
+                    a_min = a_min.astype(scaled_dtype)
+                    a_max = A.max().astype(scaled_dtype)
+
                 # scale the input data to [.1, .9].  The Agg
                 # interpolators clip to [0, 1] internally, use a
                 # smaller input scale to identify which of the
 
@@ -835,3 +835,13 @@ def test_imshow_deprecated_interd_warn():
         with warnings.catch_warnings(record=True) as warns:
             getattr(im, k)
         assert len(warns) == 1
+
+
+def test_full_invalid():
+    x = np.ones((10, 10))
+    x[:] = np.nan
+
+    f, ax = plt.subplots()
+    ax.imshow(x)
+
+    f.canvas.draw()