From 3bd704a4815ad2281e61eedcee3c7935789d410b Mon Sep 17 00:00:00 2001
From: Keisuke Fujii <fUjiisoup@gmail.com>
Date: Sun, 21 Jan 2018 03:29:01 +0900
Subject: [PATCH] Add dtype support for reduce methods. (#1841)

* Add dtype support for reduce methods.

* Use bottleneck if dtype == values.dtype

* Change get -> pop

* Update comments.

* float128 -> float16

* ValueError -> TypeError

* update test
---
 doc/whats-new.rst              |  4 +++-
 xarray/core/duck_array_ops.py  | 12 +++++++-----
 xarray/tests/test_dataarray.py | 18 ++++++++++++++++++
 3 files changed, 28 insertions(+), 6 deletions(-)

diff --git a/doc/whats-new.rst b/doc/whats-new.rst
index ab3d9733856..cf0f3fc876f 100644
--- a/doc/whats-new.rst
+++ b/doc/whats-new.rst
@@ -27,9 +27,11 @@ Documentation
 
 Enhancements
 ~~~~~~~~~~~~
+- reduce methods such as :py:func:`DataArray.sum()` now accepts ``dtype``
+  arguments. (:issue:`1838`)
+  By `Keisuke Fujii <https://github.com/fujiisoup>`_.
 - Added nodatavals attribute to DataArray when using :py:func:`~xarray.open_rasterio`. (:issue:`1736`).
   By `Alan Snow <https://github.com/snowman2>`_.
-
 - :py:func:`~plot.contourf()` learned to contour 2D variables that have both a
   1D co-ordinate (e.g. time) and a 2D co-ordinate (e.g. depth as a function of
   time) (:issue:`1737`).
diff --git a/xarray/core/duck_array_ops.py b/xarray/core/duck_array_ops.py
index 2f97de4a1ba..2058ce86a99 100644
--- a/xarray/core/duck_array_ops.py
+++ b/xarray/core/duck_array_ops.py
@@ -175,11 +175,11 @@ def _create_nan_agg_method(name, numeric_only=False, np_compat=False,
                            no_bottleneck=False, coerce_strings=False,
                            keep_dims=False):
     def f(values, axis=None, skipna=None, **kwargs):
-        # ignore keyword args inserted by np.mean and other numpy aggregators
-        # automatically:
-        kwargs.pop('dtype', None)
-        kwargs.pop('out', None)
+        if kwargs.pop('out', None) is not None:
+            raise TypeError('`out` is not valid for {}'.format(name))
 
+        # If dtype is supplied, we use numpy's method.
+        dtype = kwargs.get('dtype', None)
         values = asarray(values)
 
         if coerce_strings and values.dtype.kind in 'SU':
@@ -192,7 +192,8 @@ def f(values, axis=None, skipna=None, **kwargs):
                     % (name, values.dtype))
             nanname = 'nan' + name
             if (isinstance(axis, tuple) or not values.dtype.isnative or
-                    no_bottleneck):
+                    no_bottleneck or
+                    (dtype is not None and np.dtype(dtype) != values.dtype)):
                 # bottleneck can't handle multiple axis arguments or non-native
                 # endianness
                 if np_compat:
@@ -200,6 +201,7 @@ def f(values, axis=None, skipna=None, **kwargs):
                 else:
                     eager_module = np
             else:
+                kwargs.pop('dtype', None)
                 eager_module = bn
             func = _dask_or_eager_func(nanname, eager_module)
             using_numpy_nan_func = (eager_module is np or
diff --git a/xarray/tests/test_dataarray.py b/xarray/tests/test_dataarray.py
index 5524b4c971d..39b3109c295 100644
--- a/xarray/tests/test_dataarray.py
+++ b/xarray/tests/test_dataarray.py
@@ -1784,6 +1784,24 @@ def test_reduce(self):
                              dims=['x', 'y']).mean('x')
         assert_equal(actual, expected)
 
+    def test_reduce_dtype(self):
+        coords = {'x': [-1, -2], 'y': ['ab', 'cd', 'ef'],
+                  'lat': (['x', 'y'], [[1, 2, 3], [-1, -2, -3]]),
+                  'c': -999}
+        orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=['x', 'y'])
+
+        for dtype in [np.float16, np.float32, np.float64]:
+            assert orig.astype(float).mean(dtype=dtype).dtype == dtype
+
+    def test_reduce_out(self):
+        coords = {'x': [-1, -2], 'y': ['ab', 'cd', 'ef'],
+                  'lat': (['x', 'y'], [[1, 2, 3], [-1, -2, -3]]),
+                  'c': -999}
+        orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=['x', 'y'])
+
+        with pytest.raises(TypeError):
+            orig.mean(out=np.ones(orig.shape))
+
     # skip due to bug in older versions of numpy.nanpercentile
     def test_quantile(self):
         for q in [0.25, [0.50], [0.25, 0.75]]: