Improved duck array wrapping (pydata#9798)

* lots more duck array compat, plus tests

* merge sliding_window_view

* namespaces constant

* revert dask allowed

* fix up some tests

* backwards compat sparse mask

* add as_array methods

* to_like_array helper

* only cast non-numpy

* better idxminmax approach

* fix mypy

* naming, add is_array_type

* add public doc and whats new

* update comments

* add support for chunked arrays in as_array_type

* revert array_type methods

* fix up whats new

* comment about bool_

* add jax to complete ci envs

* add pint and sparse to tests

* remove from windows

* mypy, xfail one more sparse

* add dask and a few other methods

* move whats new

ci/requirements/environment-3.13.yml

@@ -47,3 +47,5 @@ dependencies:
   - toolz
   - typing_extensions
   - zarr
+  - pip:
+      - jax # no way to get cpu-only jaxlib from conda if gpu is present

ci/requirements/environment.yml

@@ -49,3 +49,5 @@ dependencies:
   - toolz
   - typing_extensions
   - zarr
+  - pip:
+      - jax # no way to get cpu-only jaxlib from conda if gpu is present

doc/whats-new.rst

@@ -21,6 +21,9 @@ v.2024.11.1 (unreleased)
 
 New Features
 ~~~~~~~~~~~~
+- Better support wrapping additional array types (e.g. ``cupy`` or ``jax``) by calling generalized
+  duck array operations throughout more xarray methods. (:issue:`7848`, :pull:`9798`).
+  By `Sam Levang <https://github.com/slevang>`_.
 
 
 Breaking changes

xarray/core/array_api_compat.py

@@ -1,5 +1,7 @@
 import numpy as np
 
+from xarray.namedarray.pycompat import array_type
+
 
 def is_weak_scalar_type(t):
     return isinstance(t, bool | int | float | complex | str | bytes)
@@ -42,3 +44,39 @@ def result_type(*arrays_and_dtypes, xp) -> np.dtype:
         return xp.result_type(*arrays_and_dtypes)
     else:
         return _future_array_api_result_type(*arrays_and_dtypes, xp=xp)
+
+
+def get_array_namespace(*values):
+    def _get_single_namespace(x):
+        if hasattr(x, "__array_namespace__"):
+            return x.__array_namespace__()
+        elif isinstance(x, array_type("cupy")):
+            # cupy is fully compliant from xarray's perspective, but will not expose
+            # __array_namespace__ until at least v14. Special case it for now
+            import cupy as cp
+
+            return cp
+        else:
+            return np
+
+    namespaces = {_get_single_namespace(t) for t in values}
+    non_numpy = namespaces - {np}
+
+    if len(non_numpy) > 1:
+        names = [module.__name__ for module in non_numpy]
+        raise TypeError(f"Mixed array types {names} are not supported.")
+    elif non_numpy:
+        [xp] = non_numpy
+    else:
+        xp = np
+
+    return xp
+
+
+def to_like_array(array, like):
+    # Mostly for cupy compatibility, because cupy binary ops require all cupy arrays
+    xp = get_array_namespace(like)
+    if xp is not np:
+        return xp.asarray(array)
+    # avoid casting things like pint quantities to numpy arrays
+    return array

xarray/core/common.py

@@ -496,7 +496,7 @@ def clip(
             keep_attrs = _get_keep_attrs(default=True)
 
         return apply_ufunc(
-            np.clip, self, min, max, keep_attrs=keep_attrs, dask="allowed"
+            duck_array_ops.clip, self, min, max, keep_attrs=keep_attrs, dask="allowed"
         )
 
     def get_index(self, key: Hashable) -> pd.Index:
@@ -1760,7 +1760,7 @@ def _full_like_variable(
             **from_array_kwargs,
         )
     else:
-        data = np.full_like(other.data, fill_value, dtype=dtype)
+        data = duck_array_ops.full_like(other.data, fill_value, dtype=dtype)
 
     return Variable(dims=other.dims, data=data, attrs=other.attrs)
 

xarray/core/computation.py

@@ -24,6 +24,7 @@
 
 from xarray.core import dtypes, duck_array_ops, utils
 from xarray.core.alignment import align, deep_align
+from xarray.core.array_api_compat import to_like_array
 from xarray.core.common import zeros_like
 from xarray.core.duck_array_ops import datetime_to_numeric
 from xarray.core.formatting import limit_lines
@@ -1702,7 +1703,7 @@ def cross(
             )
 
     c = apply_ufunc(
-        np.cross,
+        duck_array_ops.cross,
         a,
         b,
         input_core_dims=[[dim], [dim]],
@@ -2170,13 +2171,14 @@ def _calc_idxminmax(
         chunks = dict(zip(array.dims, array.chunks, strict=True))
         dask_coord = chunkmanager.from_array(array[dim].data, chunks=chunks[dim])
         data = dask_coord[duck_array_ops.ravel(indx.data)]
-        res = indx.copy(data=duck_array_ops.reshape(data, indx.shape))
-        # we need to attach back the dim name
-        res.name = dim
     else:
-        res = array[dim][(indx,)]
-        # The dim is gone but we need to remove the corresponding coordinate.
-        del res.coords[dim]
+        arr_coord = to_like_array(array[dim].data, array.data)
+        data = arr_coord[duck_array_ops.ravel(indx.data)]
+
+    # rebuild like the argmin/max output, and rename as the dim name
+    data = duck_array_ops.reshape(data, indx.shape)
+    res = indx.copy(data=data)
+    res.name = dim
 
     if skipna or (skipna is None and array.dtype.kind in na_dtypes):
         # Put the NaN values back in after removing them

xarray/core/dataset.py

@@ -55,6 +55,7 @@
     align,
 )
 from xarray.core.arithmetic import DatasetArithmetic
+from xarray.core.array_api_compat import to_like_array
 from xarray.core.common import (
     DataWithCoords,
     _contains_datetime_like_objects,
@@ -127,7 +128,7 @@
     calculate_dimensions,
 )
 from xarray.namedarray.parallelcompat import get_chunked_array_type, guess_chunkmanager
-from xarray.namedarray.pycompat import array_type, is_chunked_array
+from xarray.namedarray.pycompat import array_type, is_chunked_array, to_numpy
 from xarray.plot.accessor import DatasetPlotAccessor
 from xarray.util.deprecation_helpers import _deprecate_positional_args, deprecate_dims
 
@@ -6620,7 +6621,7 @@ def dropna(
             array = self._variables[k]
             if dim in array.dims:
                 dims = [d for d in array.dims if d != dim]
-                count += np.asarray(array.count(dims))
+                count += to_numpy(array.count(dims).data)
                 size += math.prod([self.sizes[d] for d in dims])
 
         if thresh is not None:
@@ -8734,16 +8735,17 @@ def _integrate_one(self, coord, datetime_unit=None, cumulative=False):
                     coord_names.add(k)
             else:
                 if k in self.data_vars and dim in v.dims:
+                    coord_data = to_like_array(coord_var.data, like=v.data)
                     if _contains_datetime_like_objects(v):
                         v = datetime_to_numeric(v, datetime_unit=datetime_unit)
                     if cumulative:
                         integ = duck_array_ops.cumulative_trapezoid(
-                            v.data, coord_var.data, axis=v.get_axis_num(dim)
+                            v.data, coord_data, axis=v.get_axis_num(dim)
                         )
                         v_dims = v.dims
                     else:
                         integ = duck_array_ops.trapz(
-                            v.data, coord_var.data, axis=v.get_axis_num(dim)
+                            v.data, coord_data, axis=v.get_axis_num(dim)
                         )
                         v_dims = list(v.dims)
                         v_dims.remove(dim)