change ALL_DIMS to equal ellipsis (pydata#3418)

* change ALL_DIMS to equal ...

* changed references & added whatsnew

* Update xarray/core/groupby.py

Co-Authored-By: Deepak Cherian <dcherian@users.noreply.github.com>

* Update xarray/core/groupby.py

Co-Authored-By: Deepak Cherian <dcherian@users.noreply.github.com>

* note in readme

Author: max-sixty

doc/examples/multidimensional-coords.rst

@@ -107,7 +107,7 @@ function to specify the output coordinates of the group.
     lat_center = np.arange(1, 90, 2)
     # group according to those bins and take the mean
     Tair_lat_mean = (ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center)
-	             .mean(xr.ALL_DIMS))
+	             .mean(...))
     # plot the result
     @savefig xarray_multidimensional_coords_14_1.png width=5in
     Tair_lat_mean.plot();

doc/groupby.rst

@@ -116,7 +116,13 @@ dimensions *other than* the provided one:
 
 .. ipython:: python
 
-    ds.groupby('x').std(xr.ALL_DIMS)
+    ds.groupby('x').std(...)
+
+.. note::
+
+    We use an ellipsis (`...`) here to indicate we want to reduce over all
+    other dimensions  
+
 
 First and last
 ~~~~~~~~~~~~~~
@@ -127,7 +133,7 @@ values for group along the grouped dimension:
 
 .. ipython:: python
 
-    ds.groupby('letters').first(xr.ALL_DIMS)
+    ds.groupby('letters').first(...)
 
 By default, they skip missing values (control this with ``skipna``).
 
@@ -142,7 +148,7 @@ coordinates. For example:
 
 .. ipython:: python
 
-    alt = arr.groupby('letters').mean(xr.ALL_DIMS)
+    alt = arr.groupby('letters').mean(...)
     alt
     ds.groupby('letters') - alt
 
@@ -195,7 +201,7 @@ __ http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_two_dimen
                 'lat': (['ny','nx'], [[10,10],[20,20]] ),},
         dims=['ny','nx'])
     da
-    da.groupby('lon').sum(xr.ALL_DIMS)
+    da.groupby('lon').sum(...)
     da.groupby('lon').apply(lambda x: x - x.mean(), shortcut=False)
 
 Because multidimensional groups have the ability to generate a very large
@@ -213,4 +219,4 @@ applying your function, and then unstacking the result:
 .. ipython:: python
 
    stacked = da.stack(gridcell=['ny', 'nx'])
-   stacked.groupby('gridcell').sum(xr.ALL_DIMS).unstack('gridcell')
+   stacked.groupby('gridcell').sum(...).unstack('gridcell')

doc/whats-new.rst

@@ -25,6 +25,11 @@ Breaking changes
 
 New Features
 ~~~~~~~~~~~~
+- Changed `xr.ALL_DIMS` to equal python's `Ellipsis` (`...`), and changed internal usages to use
+  `...` directly. As before, you can use this to instruct a `groupby` operation
+  to reduce over all dimensions. While we have no plans to remove `xr.ALL_DIMS`, we suggest
+  using `...`.
+  By `Maximilian Roos <https://github.com/max-sixty>`_
 - Added integration tests against `pint <https://pint.readthedocs.io/>`_.
   (:pull:`3238`) by `Justus Magin <https://github.com/keewis>`_.
 

xarray/core/common.py

@@ -25,10 +25,10 @@
 from .options import OPTIONS, _get_keep_attrs
 from .pycompat import dask_array_type
 from .rolling_exp import RollingExp
-from .utils import Frozen, ReprObject, either_dict_or_kwargs
+from .utils import Frozen, either_dict_or_kwargs
 
 # Used as a sentinel value to indicate a all dimensions
-ALL_DIMS = ReprObject("<all-dims>")
+ALL_DIMS = ...
 
 
 C = TypeVar("C")

xarray/core/dataset.py

@@ -49,7 +49,6 @@
 )
 from .alignment import _broadcast_helper, _get_broadcast_dims_map_common_coords, align
 from .common import (
-    ALL_DIMS,
     DataWithCoords,
     ImplementsDatasetReduce,
     _contains_datetime_like_objects,
@@ -4037,7 +4036,7 @@ def reduce(
             Dataset with this object's DataArrays replaced with new DataArrays
             of summarized data and the indicated dimension(s) removed.
         """
-        if dim is None or dim is ALL_DIMS:
+        if dim is None or dim is ...:
             dims = set(self.dims)
         elif isinstance(dim, str) or not isinstance(dim, Iterable):
             dims = {dim}
@@ -5002,7 +5001,7 @@ def quantile(
 
         if isinstance(dim, str):
             dims = {dim}
-        elif dim is None or dim is ALL_DIMS:
+        elif dim in [None, ...]:
             dims = set(self.dims)
         else:
             dims = set(dim)

xarray/core/groupby.py

@@ -7,7 +7,7 @@
 
 from . import dtypes, duck_array_ops, nputils, ops
 from .arithmetic import SupportsArithmetic
-from .common import ALL_DIMS, ImplementsArrayReduce, ImplementsDatasetReduce
+from .common import ImplementsArrayReduce, ImplementsDatasetReduce
 from .concat import concat
 from .formatting import format_array_flat
 from .options import _get_keep_attrs
@@ -712,7 +712,7 @@ def quantile(self, q, dim=None, interpolation="linear", keep_attrs=None):
         q : float in range of [0,1] (or sequence of floats)
             Quantile to compute, which must be between 0 and 1
             inclusive.
-        dim : xarray.ALL_DIMS, str or sequence of str, optional
+        dim : `...`, str or sequence of str, optional
             Dimension(s) over which to apply quantile.
             Defaults to the grouped dimension.
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
@@ -769,7 +769,7 @@ def reduce(
             Function which can be called in the form
             `func(x, axis=axis, **kwargs)` to return the result of collapsing
             an np.ndarray over an integer valued axis.
-        dim : xarray.ALL_DIMS, str or sequence of str, optional
+        dim : `...`, str or sequence of str, optional
             Dimension(s) over which to apply `func`.
         axis : int or sequence of int, optional
             Axis(es) over which to apply `func`. Only one of the 'dimension'
@@ -794,9 +794,9 @@ def reduce(
         if keep_attrs is None:
             keep_attrs = _get_keep_attrs(default=False)
 
-        if dim is not ALL_DIMS and dim not in self.dims:
+        if dim is not ... and dim not in self.dims:
             raise ValueError(
-                "cannot reduce over dimension %r. expected either xarray.ALL_DIMS to reduce over all dimensions or one or more of %r."
+                "cannot reduce over dimension %r. expected either '...' to reduce over all dimensions or one or more of %r."
                 % (dim, self.dims)
             )
 
@@ -867,7 +867,7 @@ def reduce(self, func, dim=None, keep_attrs=None, **kwargs):
             Function which can be called in the form
             `func(x, axis=axis, **kwargs)` to return the result of collapsing
             an np.ndarray over an integer valued axis.
-        dim : xarray.ALL_DIMS, str or sequence of str, optional
+        dim : `...`, str or sequence of str, optional
             Dimension(s) over which to apply `func`.
         axis : int or sequence of int, optional
             Axis(es) over which to apply `func`. Only one of the 'dimension'
@@ -895,9 +895,9 @@ def reduce(self, func, dim=None, keep_attrs=None, **kwargs):
         def reduce_dataset(ds):
             return ds.reduce(func, dim, keep_attrs, **kwargs)
 
-        if dim is not ALL_DIMS and dim not in self.dims:
+        if dim is not ... and dim not in self.dims:
             raise ValueError(
-                "cannot reduce over dimension %r. expected either xarray.ALL_DIMS to reduce over all dimensions or one or more of %r."
+                "cannot reduce over dimension %r. expected either '...' to reduce over all dimensions or one or more of %r."
                 % (dim, self.dims)
             )
 

xarray/core/variable.py

@@ -1450,7 +1450,7 @@ def reduce(
             Array with summarized data and the indicated dimension(s)
             removed.
         """
-        if dim is common.ALL_DIMS:
+        if dim == ...:
             dim = None
         if dim is not None and axis is not None:
             raise ValueError("cannot supply both 'axis' and 'dim' arguments")

xarray/tests/test_dask.py

@@ -435,8 +435,8 @@ def test_groupby(self):
         u = self.eager_array
         v = self.lazy_array
 
-        expected = u.groupby("x").mean(xr.ALL_DIMS)
-        actual = v.groupby("x").mean(xr.ALL_DIMS)
+        expected = u.groupby("x").mean(...)
+        actual = v.groupby("x").mean(...)
         self.assertLazyAndAllClose(expected, actual)
 
     def test_groupby_first(self):

xarray/tests/test_dataarray.py

@@ -13,7 +13,7 @@
 from xarray.coding.times import CFDatetimeCoder
 from xarray.convert import from_cdms2
 from xarray.core import dtypes
-from xarray.core.common import ALL_DIMS, full_like
+from xarray.core.common import full_like
 from xarray.tests import (
     LooseVersion,
     ReturnItem,
@@ -2443,8 +2443,8 @@ def test_groupby_sum(self):
                 "abc": Variable(["abc"], np.array(["a", "b", "c"])),
             }
         )["foo"]
-        assert_allclose(expected_sum_all, grouped.reduce(np.sum, dim=ALL_DIMS))
-        assert_allclose(expected_sum_all, grouped.sum(ALL_DIMS))
+        assert_allclose(expected_sum_all, grouped.reduce(np.sum, dim=...))
+        assert_allclose(expected_sum_all, grouped.sum(...))
 
         expected = DataArray(
             [
@@ -2456,7 +2456,7 @@ def test_groupby_sum(self):
         )
         actual = array["y"].groupby("abc").apply(np.sum)
         assert_allclose(expected, actual)
-        actual = array["y"].groupby("abc").sum(ALL_DIMS)
+        actual = array["y"].groupby("abc").sum(...)
         assert_allclose(expected, actual)
 
         expected_sum_axis1 = Dataset(
@@ -2590,9 +2590,9 @@ def test_groupby_math(self):
             assert_identical(expected, actual)
 
         grouped = array.groupby("abc")
-        expected_agg = (grouped.mean(ALL_DIMS) - np.arange(3)).rename(None)
+        expected_agg = (grouped.mean(...) - np.arange(3)).rename(None)
         actual = grouped - DataArray(range(3), [("abc", ["a", "b", "c"])])
-        actual_agg = actual.groupby("abc").mean(ALL_DIMS)
+        actual_agg = actual.groupby("abc").mean(...)
         assert_allclose(expected_agg, actual_agg)
 
         with raises_regex(TypeError, "only support binary ops"):
@@ -2698,7 +2698,7 @@ def test_groupby_multidim(self):
             ("lon", DataArray([5, 28, 23], coords=[("lon", [30.0, 40.0, 50.0])])),
             ("lat", DataArray([16, 40], coords=[("lat", [10.0, 20.0])])),
         ]:
-            actual_sum = array.groupby(dim).sum(ALL_DIMS)
+            actual_sum = array.groupby(dim).sum(...)
             assert_identical(expected_sum, actual_sum)
 
     def test_groupby_multidim_apply(self):

xarray/tests/test_dataset.py

@@ -11,7 +11,6 @@
 
 import xarray as xr
 from xarray import (
-    ALL_DIMS,
     DataArray,
     Dataset,
     IndexVariable,
@@ -3327,7 +3326,7 @@ def test_groupby_reduce(self):
 
         expected = data.mean("y")
         expected["yonly"] = expected["yonly"].variable.set_dims({"x": 3})
-        actual = data.groupby("x").mean(ALL_DIMS)
+        actual = data.groupby("x").mean(...)
         assert_allclose(expected, actual)
 
         actual = data.groupby("x").mean("y")
@@ -3336,12 +3335,12 @@ def test_groupby_reduce(self):
         letters = data["letters"]
         expected = Dataset(
             {
-                "xy": data["xy"].groupby(letters).mean(ALL_DIMS),
+                "xy": data["xy"].groupby(letters).mean(...),
                 "xonly": (data["xonly"].mean().variable.set_dims({"letters": 2})),
                 "yonly": data["yonly"].groupby(letters).mean(),
             }
         )
-        actual = data.groupby("letters").mean(ALL_DIMS)
+        actual = data.groupby("letters").mean(...)
         assert_allclose(expected, actual)
 
     def test_groupby_math(self):
@@ -3404,14 +3403,14 @@ def test_groupby_math_virtual(self):
             {"x": ("t", [1, 2, 3])}, {"t": pd.date_range("20100101", periods=3)}
         )
         grouped = ds.groupby("t.day")
-        actual = grouped - grouped.mean(ALL_DIMS)
+        actual = grouped - grouped.mean(...)
         expected = Dataset({"x": ("t", [0, 0, 0])}, ds[["t", "t.day"]])
         assert_identical(actual, expected)
 
     def test_groupby_nan(self):
         # nan should be excluded from groupby
         ds = Dataset({"foo": ("x", [1, 2, 3, 4])}, {"bar": ("x", [1, 1, 2, np.nan])})
-        actual = ds.groupby("bar").mean(ALL_DIMS)
+        actual = ds.groupby("bar").mean(...)
         expected = Dataset({"foo": ("bar", [1.5, 3]), "bar": [1, 2]})
         assert_identical(actual, expected)
 
@@ -3421,7 +3420,7 @@ def test_groupby_order(self):
         for vn in ["a", "b", "c"]:
             ds[vn] = DataArray(np.arange(10), dims=["t"])
         data_vars_ref = list(ds.data_vars.keys())
-        ds = ds.groupby("t").mean(ALL_DIMS)
+        ds = ds.groupby("t").mean(...)
         data_vars = list(ds.data_vars.keys())
         assert data_vars == data_vars_ref
         # coords are now at the end of the list, so the test below fails