Refactor nanops

doc/whats-new.rst

@@ -70,6 +70,15 @@ Bug fixes
 
 - Tests can be run in parallel with pytest-xdist
 
+- Follow up the renamings in dask; from dask.ghost to dask.overlap
+  By `Keisuke Fujii <https://github.com/fujiisoup>`_.
+
+  By `Tony Tung <https://github.com/ttung>`_.
+
+- Now raises a ValueError when there is a conflict between dimension names and
+  level names of MultiIndex. (:issue:`2299`)
+  By `Keisuke Fujii <https://github.com/fujiisoup>`_.
+
 - Follow up the renamings in dask; from dask.ghost to dask.overlap
   By `Keisuke Fujii <https://github.com/fujiisoup>`_.
 

xarray/core/nanops.py

@@ -33,7 +33,7 @@ def _maybe_null_out(result, axis, mask, min_count=1):
 
     if axis is not None and getattr(result, 'ndim', False):
         null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0
-        if np.any(null_mask):
+        if null_mask.any():
             dtype, fill_value = dtypes.maybe_promote(result.dtype)
             result = result.astype(dtype)
             result[null_mask] = fill_value
@@ -48,18 +48,17 @@ def _maybe_null_out(result, axis, mask, min_count=1):
 
 def _nan_argminmax_object(func, fill_value, value, axis=None, **kwargs):
     """ In house nanargmin, nanargmax for object arrays. Always return integer
-    type """
+    type
+    """
     valid_count = count(value, axis=axis)
     value = fillna(value, fill_value)
-    data = getattr(np, func)(value, axis=axis, **kwargs)
-    # dask seems return non-integer type
-    if isinstance(value, dask_array_type):
-        data = data.astype(int)
+    data = _dask_or_eager_func(func)(value, axis=axis, **kwargs)
 
+    # TODO This will evaluate dask arrays and might be costly.
     if (valid_count == 0).any():
         raise ValueError('All-NaN slice encountered')
 
-    return np.array(data, dtype=int)
+    return data
 
 
 def _nan_minmax_object(func, fill_value, value, axis=None, **kwargs):
@@ -78,19 +77,17 @@ def nanmin(a, axis=None, out=None):
         return _nan_minmax_object(
             'min', dtypes.get_pos_infinity(a.dtype), a, axis)
 
-    if isinstance(a, dask_array_type):
-        return dask_array.nanmin(a, axis=axis)
-    return nputils.nanmin(a, axis=axis)
+    module = dask_array if isinstance(a, dask_array_type) else nputils
+    return module.nanmin(a, axis=axis)
 
 
 def nanmax(a, axis=None, out=None):
     if a.dtype.kind == 'O':
         return _nan_minmax_object(
             'max', dtypes.get_neg_infinity(a.dtype), a, axis)
 
-    if isinstance(a, dask_array_type):
-        return dask_array.nanmax(a, axis=axis)
-    return nputils.nanmax(a, axis=axis)
+    module = dask_array if isinstance(a, dask_array_type) else nputils
+    return module.nanmax(a, axis=axis)
 
 
 def nanargmin(a, axis=None):
@@ -104,8 +101,8 @@ def nanargmin(a, axis=None):
         res = np.argmin(a, axis=axis)
 
     if mask is not None:
-        mask = np.all(mask, axis=axis)
-        if np.any(mask):
+        mask = mask.all(axis=axis)
+        if mask.any():
             raise ValueError("All-NaN slice encountered")
     return res
 
@@ -122,8 +119,8 @@ def nanargmax(a, axis=None):
         res = np.argmax(a, axis=axis)
 
     if mask is not None:
-        mask = np.all(mask, axis=axis)
-        if np.any(mask):
+        mask = mask.all(axis=axis)
+        if mask.any():
             raise ValueError("All-NaN slice encountered")
     return res
 

xarray/tests/test_duck_array_ops.py

@@ -316,7 +316,7 @@ def test_reduce(dim_num, dtype, dask, func, skipna, aggdim):
             # also check ddof!=0 case
             actual = getattr(da, func)(skipna=skipna, dim=aggdim, ddof=5)
             if dask:
-                isinstance(da.data, dask_array_type)
+                assert isinstance(da.data, dask_array_type)
             expected = series_reduce(da, func, skipna=skipna, dim=aggdim,
                                      ddof=5)
             assert_allclose(actual, expected, rtol=rtol)
@@ -334,7 +334,7 @@ def test_reduce(dim_num, dtype, dask, func, skipna, aggdim):
         da = construct_dataarray(dim_num, dtype, contains_nan=False, dask=dask)
         actual = getattr(da, func)(skipna=skipna)
         if dask:
-            isinstance(da.data, dask_array_type)
+            assert isinstance(da.data, dask_array_type)
         expected = getattr(np, 'nan{}'.format(func))(da.values)
         if actual.dtype == object:
             assert actual.values == np.array(expected)