Better error message for vectorize=True in apply_ufunc with old numpy (#1963)

* Better error message for vectorize=True in apply_ufunc with old numpy

* Typo otype -> otypes

* add missing __future__ imports

* all_output_core_dims -> all_core_dims

Author: shoyer

doc/whats-new.rst

@@ -57,6 +57,9 @@ Enhancements
 Bug fixes
 ~~~~~~~~~
 
+- Raise an informative error message when using ``apply_ufunc`` with numpy
+  v1.11 (:issue:`1956`).
+  By `Stephan Hoyer <https://github.com/shoyer>`_.
 - Fix the precision drop after indexing datetime64 arrays (:issue:`1932`).
   By `Keisuke Fujii <https://github.com/fujiisoup>`_.
 - Fix kwarg `colors` clashing with auto-inferred `cmap` (:issue:`1461`)

xarray/core/computation.py

@@ -1,8 +1,8 @@
 """
 Functions for applying functions that act on arrays to xarray's labeled data.
-
-NOT PUBLIC API.
 """
+from __future__ import absolute_import, division, print_function
+from distutils.version import LooseVersion
 import functools
 import itertools
 import operator
@@ -882,10 +882,21 @@ def earth_mover_distance(first_samples,
         func = functools.partial(func, **kwargs_)
 
     if vectorize:
-        func = np.vectorize(func,
-                            otypes=output_dtypes,
-                            signature=signature.to_gufunc_string(),
-                            excluded=set(kwargs))
+        if signature.all_core_dims:
+            # we need the signature argument
+            if LooseVersion(np.__version__) < '1.12':  # pragma: no cover
+                raise NotImplementedError(
+                    'numpy 1.12 or newer required when using vectorize=True '
+                    'in xarray.apply_ufunc with non-scalar output core '
+                    'dimensions.')
+            func = np.vectorize(func,
+                                otypes=output_dtypes,
+                                signature=signature.to_gufunc_string(),
+                                excluded=set(kwargs))
+        else:
+            func = np.vectorize(func,
+                                otypes=output_dtypes,
+                                excluded=set(kwargs))
 
     variables_ufunc = functools.partial(apply_variable_ufunc, func,
                                         signature=signature,