Fix resample/interpolate for non-upsampling case (#1608)

* regression test for 1605

* fix to Resample.interpolate to allow application on equal frequencies

* new assert statement

Author: Joe Hamman

xarray/core/groupby.py

@@ -236,8 +236,8 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None,
                 raise ValueError('index must be monotonic for resampling')
             s = pd.Series(np.arange(index.size), index)
             first_items = s.groupby(grouper).first()
+            full_index = first_items.index
             if first_items.isnull().any():
-                full_index = first_items.index
                 first_items = first_items.dropna()
             sbins = first_items.values.astype(np.int64)
             group_indices = ([slice(i, j)

xarray/tests/test_dataarray.py

@@ -2077,6 +2077,15 @@ def test_upsample_interpolate(self):
             # done here due to floating point arithmetic
             self.assertDataArrayAllClose(expected, actual, rtol=1e-16)
 
+    @requires_scipy
+    def test_upsample_interpolate_regression_1605(self):
+        dates = pd.date_range('2016-01-01', '2016-03-31', freq='1D')
+        expected = xr.DataArray(np.random.random((len(dates), 2, 3)),
+                                dims=('time', 'x', 'y'),
+                                coords={'time': dates})
+        actual = expected.resample(time='1D').interpolate('linear')
+        assert_allclose(actual, expected, rtol=1e-16)
+
     @requires_dask
     def test_upsample_interpolate_dask(self):
         import dask.array as da