cosmetic

Author: crusaderky

array_api_compat/dask/array/_aliases.py

@@ -40,19 +40,25 @@
 isdtype = get_xp(np)(_aliases.isdtype)
 unstack = get_xp(da)(_aliases.unstack)
 
+# da.astype doesn't respect copy=True
 def astype(
     x: Array,
     dtype: Dtype,
     /,
     *,
     copy: bool = True,
-    device: Device | None = None
+    device: Optional[Device] = None
 ) -> Array:
+    """
+    Array API compatibility wrapper for astype().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
     # TODO: respect device keyword?
+
     if not copy and dtype == x.dtype:
         return x
-    # dask astype doesn't respect copy=True,
-    # so call copy manually afterwards
     x = x.astype(dtype)
     return x.copy() if copy else x
 
@@ -61,20 +67,24 @@ def astype(
 # This arange func is modified from the common one to
 # not pass stop/step as keyword arguments, which will cause
 # an error with dask
-
-# TODO: delete the xp stuff, it shouldn't be necessary
-def _dask_arange(
+def arange(
     start: Union[int, float],
     /,
     stop: Optional[Union[int, float]] = None,
     step: Union[int, float] = 1,
     *,
-    xp,
     dtype: Optional[Dtype] = None,
     device: Optional[Device] = None,
     **kwargs,
 ) -> Array:
-    _check_device(xp, device)
+    """
+    Array API compatibility wrapper for arange().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    # TODO: respect device keyword?
+
     args = [start]
     if stop is not None:
         args.append(stop)
@@ -83,13 +93,12 @@ def _dask_arange(
         # prepend the default value for start which is 0
         args.insert(0, 0)
     args.append(step)
-    return xp.arange(*args, dtype=dtype, **kwargs)
 
-arange = get_xp(da)(_dask_arange)
-eye = get_xp(da)(_aliases.eye)
+    return da.arange(*args, dtype=dtype, **kwargs)
+
 
-linspace = get_xp(da)(_aliases.linspace)
 eye = get_xp(da)(_aliases.eye)
+linspace = get_xp(da)(_aliases.linspace)
 UniqueAllResult = get_xp(da)(_aliases.UniqueAllResult)
 UniqueCountsResult = get_xp(da)(_aliases.UniqueCountsResult)
 UniqueInverseResult = get_xp(da)(_aliases.UniqueInverseResult)
@@ -112,7 +121,6 @@ def _dask_arange(
 reshape = get_xp(da)(_aliases.reshape)
 matrix_transpose = get_xp(da)(_aliases.matrix_transpose)
 vecdot = get_xp(da)(_aliases.vecdot)
-
 nonzero = get_xp(da)(_aliases.nonzero)
 ceil = get_xp(np)(_aliases.ceil)
 floor = get_xp(np)(_aliases.floor)
@@ -121,6 +129,7 @@ def _dask_arange(
 tensordot = get_xp(np)(_aliases.tensordot)
 sign = get_xp(np)(_aliases.sign)
 
+
 # asarray also adds the copy keyword, which is not present in numpy 1.0.
 def asarray(
     obj: Union[
@@ -135,7 +144,7 @@ def asarray(
     *,
     dtype: Optional[Dtype] = None,
     device: Optional[Device] = None,
-    copy: "Optional[Union[bool, np._CopyMode]]" = None,
+    copy: Optional[Union[bool, np._CopyMode]] = None,
     **kwargs,
 ) -> Array:
     """
@@ -144,6 +153,8 @@ def asarray(
     See the corresponding documentation in the array library and/or the array API
     specification for more details.
     """
+    # TODO: respect device keyword?
+
     if isinstance(obj, da.Array):
         if dtype is not None and dtype != obj.dtype:
             if copy is False:
@@ -183,15 +194,18 @@ def asarray(
 # Furthermore, the masking workaround in common._aliases.clip cannot work with
 # dask (meaning uint64 promoting to float64 is going to just be unfixed for
 # now).
-@get_xp(da)
 def clip(
     x: Array,
     /,
     min: Optional[Union[int, float, Array]] = None,
     max: Optional[Union[int, float, Array]] = None,
-    *,
-    xp,
 ) -> Array:
+    """
+    Array API compatibility wrapper for clip().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
     def _isscalar(a):
         return isinstance(a, (int, float, type(None)))
     min_shape = () if _isscalar(min) else min.shape
@@ -201,19 +215,19 @@ def _isscalar(a):
     result_shape = np.broadcast_shapes(x.shape, min_shape, max_shape)
 
     if min is not None:
-        min = xp.broadcast_to(xp.asarray(min), result_shape)
+        min = da.broadcast_to(da.asarray(min), result_shape)
     if max is not None:
-        max = xp.broadcast_to(xp.asarray(max), result_shape)
+        max = da.broadcast_to(da.asarray(max), result_shape)
 
     if min is None and max is None:
-        return xp.positive(x)
+        return da.positive(x)
 
     if min is None:
-        return astype(xp.minimum(x, max), x.dtype)
+        return astype(da.minimum(x, max), x.dtype)
     if max is None:
-        return astype(xp.maximum(x, min), x.dtype)
+        return astype(da.maximum(x, min), x.dtype)
 
-    return astype(xp.minimum(xp.maximum(x, min), max), x.dtype)
+    return astype(da.minimum(da.maximum(x, min), max), x.dtype)
 
 # exclude these from all since dask.array has no sorting functions
 _da_unsupported = ['sort', 'argsort']