MAINT: let average preserve subclass information.

This behaviour matches that for most other numpy functions (such as
np.mean).  It was initially slated for 1.12, but replaced by a
FutureWarning.  Hence, this is for 1.13.

2 files changed, 3 insertions, 18 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index df9c7f177..352512513 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -1092,19 +1092,7 @@ def average(a, axis=None, weights=None, returned=False):
     TypeError: Axis must be specified when shapes of a and weights differ.
 
     """
-    # 3/19/2016 1.12.0:
-    # replace the next few lines with "a = np.asanyarray(a)"
-    if (type(a) not in (np.ndarray, np.matrix) and
-            issubclass(type(a), np.ndarray)):
-        warnings.warn("np.average currently does not preserve subclasses, but "
-                      "will do so in the future to match the behavior of most "
-                      "other numpy functions such as np.mean. In particular, "
-                      "this means calls which returned a scalar may return a "
-                      "0-d subclass object instead.",
-                      FutureWarning, stacklevel=2)
-
-    if not isinstance(a, np.matrix):
-        a = np.asarray(a)
+    a = np.asanyarray(a)
 
     if weights is None:
         avg = a.mean(axis)
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 5bba088a8..5c2446e50 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -321,11 +321,8 @@ class TestAverage(TestCase):
         a = np.array([[1,2],[3,4]]).view(subclass)
         w = np.array([[1,2],[3,4]]).view(subclass)
 
-        with suppress_warnings() as sup:
-            # Note that the warning is spurious, because the test checks
-            # for weights while a is ignored.
-            sup.filter(FutureWarning, "np.average currently does not preserve")
-            assert_equal(type(np.average(a, weights=w)), subclass)
+        assert_equal(type(np.average(a)), subclass)
+        assert_equal(type(np.average(a, weights=w)), subclass)
 
         # also test matrices
         a = np.matrix([[1,2],[3,4]])