nd-rolling

doc/computation.rst

@@ -188,9 +188,16 @@ a value when aggregating:
     r = arr.rolling(y=3, center=True, min_periods=2)
     r.mean()
 
+From version 0.17, xarray supports multidimensional rolling,
+
+.. ipython:: python
+
+    r = arr.rolling(x=2, y=3, min_periods=2)
+    r.mean()
+
 .. tip::
 
-   Note that rolling window aggregations are faster and use less memory when bottleneck_ is installed. This only applies to numpy-backed xarray objects.
+   Note that rolling window aggregations are faster and use less memory when bottleneck_ is installed. This only applies to numpy-backed xarray objects with 1d-rolling.
 
 .. _bottleneck: https://github.com/pydata/bottleneck/
 

doc/whats-new.rst

@@ -25,6 +25,9 @@ Breaking changes
 
 New Features
 ~~~~~~~~~~~~
+- :py:meth:`~xarray.DataArray.rolling` and :py:meth:`~xarray.Dataset.rolling`
+  now accept more than 1 dimension.(:pull:`4219`)
+  By `Keisuke Fujii <https://github.com/fujiisoup>`_.
 - Build :py:meth:`CFTimeIndex.__repr__` explicitly as :py:class:`pandas.Index`. Add ``calendar`` as a new
   property for :py:class:`CFTimeIndex` and show ``calendar`` and ``length`` in
   :py:meth:`CFTimeIndex.__repr__` (:issue:`2416`, :pull:`4092`)

xarray/core/common.py

@@ -802,7 +802,7 @@ def rolling(
             Minimum number of observations in window required to have a value
             (otherwise result is NA). The default, None, is equivalent to
             setting min_periods equal to the size of the window.
-        center : boolean, default False
+        center : boolean, or a mapping, default False
             Set the labels at the center of the window.
         keep_attrs : bool, optional
             If True, the object's attributes (`attrs`) will be copied from

xarray/core/dask_array_ops.py

@@ -32,6 +32,14 @@ def rolling_window(a, axis, window, center, fill_value):
     """
     import dask.array as da
 
+    # for nd-rolling.
+    # TODO It can be more efficient. Currently, the chunks at the boundaries
+    # will be copied, but it might be OK for many-chunked-arrays.
+    if hasattr(axis, "__len__"):
+        for ax, win, cen in zip(axis, window, center):
+            a = rolling_window(a, ax, win, cen, fill_value)
+        return a
+
     orig_shape = a.shape
     if axis < 0:
         axis = a.ndim + axis

xarray/core/dataset.py

@@ -5969,7 +5969,7 @@ def polyfit(
                     skipna_da = np.any(da.isnull())
 
             dims_to_stack = [dimname for dimname in da.dims if dimname != dim]
-            stacked_coords = {}
+            stacked_coords: Dict[Hashable, DataArray] = {}
             if dims_to_stack:
                 stacked_dim = utils.get_temp_dimname(dims_to_stack, "stacked")
                 rhs = da.transpose(dim, *dims_to_stack).stack(

xarray/core/nputils.py

@@ -135,14 +135,22 @@ def __setitem__(self, key, value):
 def rolling_window(a, axis, window, center, fill_value):
     """ rolling window with padding. """
     pads = [(0, 0) for s in a.shape]
-    if center:
-        start = int(window / 2)  # 10 -> 5,  9 -> 4
-        end = window - 1 - start
-        pads[axis] = (start, end)
-    else:
-        pads[axis] = (window - 1, 0)
+    if not hasattr(axis, "__len__"):
+        axis = [axis]
+        window = [window]
+        center = [center]
+
+    for ax, win, cent in zip(axis, window, center):
+        if cent:
+            start = int(win / 2)  # 10 -> 5,  9 -> 4
+            end = win - 1 - start
+            pads[ax] = (start, end)
+        else:
+            pads[ax] = (win - 1, 0)
     a = np.pad(a, pads, mode="constant", constant_values=fill_value)
-    return _rolling_window(a, window, axis)
+    for ax, win in zip(axis, window):
+        a = _rolling_window(a, win, ax)
+    return a
 
 
 def _rolling_window(a, window, axis=-1):