Fix spurious matplotlib errors

Thanks to jklymak, who both helped trace this and suggested the
solution.

properties/test_plotting.py

@@ -1,13 +1,15 @@
 """
 Property-based tests for plotting methods.
 
-Note that Hypothesis can find a 2x2 array that causes an internal error
-for *every plot method tested*.  Most are inside matplotlib.  Good luck.
+Note that we have to be careful when testing plotting functions:
+    1. Large floats can overflow, but this is not considered a MPL bug
+    2. Plotting is stateful, so we have to clean up after each call
+With those details taken care of, they all pass!
 
 """
 from __future__ import absolute_import, division, print_function
 
-import pytest
+import matplotlib.pyplot as plt
 
 from hypothesis import given, settings
 import hypothesis.strategies as st
@@ -19,35 +21,40 @@
 settings.deadline = None
 
 
-two_dimensional_array = npst.arrays(
-    dtype=st.one_of(
-        npst.unsigned_integer_dtypes(),
-        npst.integer_dtypes(),
-        npst.floating_dtypes(),
-    ),
-    shape=npst.array_shapes(min_dims=2, max_dims=2, min_side=2, max_side=4),
-)
+# This strategy is fairly complicated.  First, we pick some kwargs: either
+# an integer dtype with no restrictions on elements, or a flaoting dtype with
+# elements that won't overflow.
+two_dimensional_array = st.sampled_from([
+    dict(dtype=npst.unsigned_integer_dtypes() | npst.integer_dtypes()),
+    dict(dtype=npst.floating_dtypes(), elements=st.floats(-2.**100, 2.**100)),
+# Then, we "flatmap" this into an arrays strategy - ie create a strategy using
+# the kwargs above and a shape, then draw a value from that.
+]).flatmap(lambda kwargs: npst.arrays(
+    **kwargs, shape=st.tuples(st.integers(2, 5), st.integers(2, 5)),
+))
 
 
-@pytest.mark.xfail
 @given(two_dimensional_array)
 def test_imshow(arr):
+    # Note that we clear the plot after each call - otherwise we just get
+    # errors from polluting state with too many colormaps!
     xr.DataArray(arr).plot.imshow()
+    plt.clf()
 
 
-@pytest.mark.xfail
 @given(two_dimensional_array)
 def test_pcolormesh(arr):
     xr.DataArray(arr).plot.pcolormesh()
+    plt.clf()
 
 
-@pytest.mark.xfail
 @given(two_dimensional_array)
 def test_contour(arr):
     xr.DataArray(arr).plot.contour()
+    plt.clf()
 
 
-@pytest.mark.xfail
 @given(two_dimensional_array)
 def test_contourf(arr):
     xr.DataArray(arr).plot.contourf()
+    plt.clf()