Remove dask_array_type checks

xarray/core/arithmetic.py

@@ -16,7 +16,7 @@
 from .common import ImplementsArrayReduce, ImplementsDatasetReduce
 from .ops import IncludeCumMethods, IncludeNumpySameMethods, IncludeReduceMethods
 from .options import OPTIONS, _get_keep_attrs
-from .pycompat import dask_array_type
+from .pycompat import is_duck_array
 
 
 class SupportsArithmetic:
@@ -33,20 +33,21 @@ class SupportsArithmetic:
 
     # TODO: allow extending this with some sort of registration system
     _HANDLED_TYPES = (
-        np.ndarray,
         np.generic,
         numbers.Number,
         bytes,
         str,
-    ) + dask_array_type
+    )
 
     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
         from .computation import apply_ufunc
 
         # See the docstring example for numpy.lib.mixins.NDArrayOperatorsMixin.
         out = kwargs.get("out", ())
         for x in inputs + out:
-            if not isinstance(x, self._HANDLED_TYPES + (SupportsArithmetic,)):
+            if not is_duck_array(x) and not isinstance(
+                x, self._HANDLED_TYPES + (SupportsArithmetic,)
+            ):
                 return NotImplemented
 
         if ufunc.signature is not None:

xarray/core/duck_array_ops.py

@@ -23,9 +23,9 @@
 from numpy import take, tensordot, transpose, unravel_index  # noqa
 from numpy import where as _where
 
-from . import dask_array_compat, dask_array_ops, dtypes, npcompat, nputils
+from . import dask_array_ops, dtypes, npcompat, nputils
 from .nputils import nanfirst, nanlast
-from .pycompat import cupy_array_type, dask_array_type, is_duck_dask_array
+from .pycompat import cupy_array_type, is_duck_dask_array
 from .utils import is_duck_array
 
 try:
@@ -113,7 +113,7 @@ def isnull(data):
         return zeros_like(data, dtype=bool)
     else:
         # at this point, array should have dtype=object
-        if isinstance(data, (np.ndarray, dask_array_type)):
+        if isinstance(data, np.ndarray):
             return pandas_isnull(data)
         else:
             # Not reachable yet, but intended for use with other duck array
@@ -631,7 +631,9 @@ def sliding_window_view(array, window_shape, axis):
     The rolling dimension will be placed at the last dimension.
     """
     if is_duck_dask_array(array):
-        return dask_array_compat.sliding_window_view(array, window_shape, axis)
+        import dask.array as da
+
+        return da.lib.stride_tricks.sliding_window_view(array, window_shape, axis)
     else:
         return npcompat.sliding_window_view(array, window_shape, axis)
 

xarray/core/nanops.py

@@ -6,15 +6,6 @@
 
 from . import dtypes, nputils, utils
 from .duck_array_ops import count, fillna, isnull, where, where_method
-from .pycompat import dask_array_type
-
-try:
-    import dask.array as dask_array
-
-    from . import dask_array_compat
-except ImportError:
-    dask_array = None  # type: ignore[assignment]
-    dask_array_compat = None  # type: ignore[assignment]
 
 
 def _maybe_null_out(result, axis, mask, min_count=1):
@@ -65,34 +56,30 @@ def nanmin(a, axis=None, out=None):
     if a.dtype.kind == "O":
         return _nan_minmax_object("min", dtypes.get_pos_infinity(a.dtype), a, axis)
 
-    module = dask_array if isinstance(a, dask_array_type) else nputils
-    return module.nanmin(a, axis=axis)
+    return nputils.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)
 
-    module = dask_array if isinstance(a, dask_array_type) else nputils
-    return module.nanmax(a, axis=axis)
+    return nputils.nanmax(a, axis=axis)
 
 
 def nanargmin(a, axis=None):
     if a.dtype.kind == "O":
         fill_value = dtypes.get_pos_infinity(a.dtype)
         return _nan_argminmax_object("argmin", fill_value, a, axis=axis)
 
-    module = dask_array if isinstance(a, dask_array_type) else nputils
-    return module.nanargmin(a, axis=axis)
+    return nputils.nanargmin(a, axis=axis)
 
 
 def nanargmax(a, axis=None):
     if a.dtype.kind == "O":
         fill_value = dtypes.get_neg_infinity(a.dtype)
         return _nan_argminmax_object("argmax", fill_value, a, axis=axis)
 
-    module = dask_array if isinstance(a, dask_array_type) else nputils
-    return module.nanargmax(a, axis=axis)
+    return nputils.nanargmax(a, axis=axis)
 
 
 def nansum(a, axis=None, dtype=None, out=None, min_count=None):
@@ -128,8 +115,6 @@ def nanmean(a, axis=None, dtype=None, out=None):
         warnings.filterwarnings(
             "ignore", r"Mean of empty slice", category=RuntimeWarning
         )
-        if isinstance(a, dask_array_type):
-            return dask_array.nanmean(a, axis=axis, dtype=dtype)
 
         return np.nanmean(a, axis=axis, dtype=dtype)
 

xarray/core/variable.py

@@ -39,7 +39,6 @@
 from .pycompat import (
     DuckArrayModule,
     cupy_array_type,
-    dask_array_type,
     integer_types,
     is_duck_dask_array,
     sparse_array_type,
@@ -59,12 +58,8 @@
 )
 
 NON_NUMPY_SUPPORTED_ARRAY_TYPES = (
-    (
-        indexing.ExplicitlyIndexed,
-        pd.Index,
-    )
-    + dask_array_type
-    + cupy_array_type
+    indexing.ExplicitlyIndexed,
+    pd.Index,
 )
 # https://github.com/python/mypy/issues/224
 BASIC_INDEXING_TYPES = integer_types + (slice,)
@@ -1150,7 +1145,7 @@ def to_numpy(self) -> np.ndarray:
         data = self.data
 
         # TODO first attempt to call .to_numpy() once some libraries implement it
-        if isinstance(data, dask_array_type):
+        if hasattr(data, "chunks"):
             data = data.compute()
         if isinstance(data, cupy_array_type):
             data = data.get()