FIX: handle fully masked data

tacaswell · tacaswell · commit 07eaa4ca538b · 2017-10-12T01:13:29.000-05:00
As min/max do not make sense for an array with no non-masked values numpy returns a singleton, `np.ma.masked`, which can not be cast to a number. In this case just treat numbers as in range (0, 1) (even though it will just be ignored due to the masking). closes #9280
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
@@ -370,8 +370,19 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
                     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 = np.ma.min(A)
+                a_max = np.ma.max(A)
+
+                # we need these try/except blocks to handle
+                # fully-masked/invalid input
+                try:
+                    a_min = a_min.astype(scaled_dtype)
+                except AttributeError:
+                    a_min = 0
+                try:
+                    a_max = a_max.astype(scaled_dtype)
+                except AttributeError:
+                    a_min = 1
                 # 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
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
@@ -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()
