remove dtype in any and all methods

src/delayedarray/DelayedArray.py

@@ -907,7 +907,7 @@ def var(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
                 masked=is_masked(self),
             )
 
-    def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optional[numpy.dtype] = numpy.bool_, buffer_size: int = 1e8) -> numpy.ndarray:
+    def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, buffer_size: int = 1e8) -> numpy.ndarray:
         """Test whether any array element along a given axis evaluates to True.
 
         Compute this test across the ``DelayedArray``, possibly over a
@@ -920,11 +920,6 @@ def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
                 for any. Alternatively, a tuple (multiple axes) or None (no
                 axes), see :py:func:`~numpy.any` for details.
 
-            dtype:
-                NumPy type for the output array. If None, this is automatically
-                chosen based on the type of the ``DelayedArray``, see
-                :py:func:`~numpy.any` for details.
-
             buffer_size:
                 Buffer size in bytes to use for block processing. Larger values
                 generally improve speed at the cost of memory.
@@ -939,12 +934,12 @@ def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
             return array_any(
                 self, 
                 axis=axis, 
-                dtype=dtype, 
+                dtype=numpy.bool_, 
                 reduce_over_x=lambda x, axes, op : _reduce(x, axes, op, buffer_size),
                 masked=is_masked(self),
             )
 
-    def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optional[numpy.dtype] = numpy.bool_, buffer_size: int = 1e8) -> numpy.ndarray:
+    def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, buffer_size: int = 1e8) -> numpy.ndarray:
         """Test whether all array elements along a given axis evaluate to True.
 
         Compute this test across the ``DelayedArray``, possibly over a
@@ -957,11 +952,6 @@ def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
                 for all. Alternatively, a tuple (multiple axes) or None (no
                 axes), see :py:func:`~numpy.all` for details.
 
-            dtype:
-                NumPy type for the output array. If None, this is automatically
-                chosen based on the type of the ``DelayedArray``, see
-                :py:func:`~numpy.all` for details.
-
             buffer_size:
                 Buffer size in bytes to use for block processing. Larger values
                 generally improve speed at the cost of memory.
@@ -976,7 +966,7 @@ def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
             return array_all(
                 self, 
                 axis=axis, 
-                dtype=dtype, 
+                dtype=numpy.bool_, 
                 reduce_over_x=lambda x, axes, op : _reduce(x, axes, op, buffer_size),
                 masked=is_masked(self),
             )

src/delayedarray/SparseNdarray.py

@@ -952,7 +952,7 @@ def var(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
             masked=self._is_masked,
         )
 
-    def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optional[numpy.dtype] = numpy.bool_) -> numpy.ndarray:
+    def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> numpy.ndarray:
         """Test whether any array element along a given axis evaluates to True.
 
         Compute this test across the ``SparseNdarray``, possibly over a
@@ -965,24 +965,19 @@ def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
                 for any. Alternatively, a tuple (multiple axes) or None
                 (no axes), see :py:func:`~numpy.any` for details.
 
-            dtype:
-                NumPy type for the output array. If None, this is automatically
-                chosen based on the type of the ``SparseNdarray``, see
-                :py:func:`~numpy.any` for details.
-
         Returns:
             A NumPy array containing the variances. If ``axis = None``,
             this will be a NumPy scalar instead.
         """
         return array_any(
             self, 
             axis=axis, 
-            dtype=dtype, 
+            dtype=numpy.bool_, 
             reduce_over_x=_reduce_SparseNdarray,
             masked=self._is_masked,
         )
 
-    def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optional[numpy.dtype] = numpy.bool_) -> numpy.ndarray:
+    def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> numpy.ndarray:
         """Test whether all array elements along a given axis evaluate to True.
 
         Compute this test across the ``SparseNdarray``, possibly over a
@@ -994,19 +989,14 @@ def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optiona
                 for any. Alternatively, a tuple (multiple axes) or None
                 (no axes), see :py:func:`~numpy.any` for details.
 
-            dtype:
-                NumPy type for the output array. If None, this is automatically
-                chosen based on the type of the ``SparseNdarray``, see
-                :py:func:`~numpy.any` for details.
-
         Returns:
             A NumPy array containing the variances. If ``axis = None``,
             this will be a NumPy scalar instead.
         """
         return array_all(
             self, 
             axis=axis, 
-            dtype=dtype, 
+            dtype=numpy.bool_, 
             reduce_over_x=_reduce_SparseNdarray,
             masked=self._is_masked,
         )