Fix precision drop when indexing a datetime64 arrays. (#1942)

* Fix precision drop when indexing a datetime64 arrays.

* minor style fix.

* Add a link to github issue in numpy/numpy

Author: fujiisoup

doc/whats-new.rst

@@ -41,6 +41,9 @@ Enhancements
 Bug fixes
 ~~~~~~~~~
 
+- Fix the precision drop after indexing datetime64 arrays (:issue:`1932`).
+  By `Keisuke Fujii <https://github.com/fujiisoup>`_.
+
 .. _whats-new.0.10.1:
 
 v0.10.1 (25 February 2018)

xarray/core/indexing.py

@@ -911,6 +911,10 @@ def __getitem__(self, indexer):
                 result = np.datetime64('NaT', 'ns')
             elif isinstance(result, timedelta):
                 result = np.timedelta64(getattr(result, 'value', result), 'ns')
+            elif isinstance(result, pd.Timestamp):
+                # Work around for GH: pydata/xarray#1932 and numpy/numpy#10668
+                # numpy fails to convert pd.Timestamp to np.datetime64[ns]
+                result = np.asarray(result.to_datetime64())
             elif self.dtype != object:
                 result = np.asarray(result, dtype=self.dtype)
 

xarray/tests/test_variable.py

@@ -1719,6 +1719,13 @@ def test_coordinate_alias(self):
             x = Coordinate('x', [1, 2, 3])
         assert isinstance(x, IndexVariable)
 
+    def test_datetime64(self):
+        # GH:1932  Make sure indexing keeps precision
+        t = np.array([1518418799999986560, 1518418799999996560],
+                     dtype='datetime64[ns]')
+        v = IndexVariable('t', t)
+        assert v[0].data == t[0]
+
     # These tests make use of multi-dimensional variables, which are not valid
     # IndexVariable objects:
     @pytest.mark.xfail