Add 'out' keyword to argmin/argmax methods - allow numpy call signature

When np.argmin(da) is called, numpy passes an 'out' keyword argument to
argmin/argmax. Need to allow this argument to avoid errors (but an
exception is thrown if out is not None).

xarray/core/dataarray.py

@@ -3728,6 +3728,7 @@ def argmin(
         axis: Union[int, None] = None,
         keep_attrs: bool = None,
         skipna: bool = None,
+        out=None,
     ) -> Union["DataArray", Dict[Hashable, "DataArray"]]:
         """Indices of the minimum of the DataArray over one or more dimensions. Result
         returned as dict of DataArrays, which can be passed directly to isel().
@@ -3752,6 +3753,9 @@ def argmin(
             skips missing values for float dtypes; other dtypes either do not
             have a sentinel missing value (int) or skipna=True has not been
             implemented (object, datetime64 or timedelta64).
+        out : None
+            'out' should not be passed - provided for compatibility with numpy function
+            signature
 
         Returns
         -------
@@ -3812,7 +3816,7 @@ def argmin(
         array([ 1, -5,  1])
         Dimensions without coordinates: y
         """
-        result = self.variable.argmin(dim, axis, keep_attrs, skipna)
+        result = self.variable.argmin(dim, axis, keep_attrs, skipna, out)
         if isinstance(result, dict):
             return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()}
         else:
@@ -3824,6 +3828,7 @@ def argmax(
         axis: Union[int, None] = None,
         keep_attrs: bool = None,
         skipna: bool = None,
+        out=None,
     ) -> Union["DataArray", Dict[Hashable, "DataArray"]]:
         """Indices of the maximum of the DataArray over one or more dimensions. Result
         returned as dict of DataArrays, which can be passed directly to isel().
@@ -3848,6 +3853,9 @@ def argmax(
             skips missing values for float dtypes; other dtypes either do not
             have a sentinel missing value (int) or skipna=True has not been
             implemented (object, datetime64 or timedelta64).
+        out : None
+            'out' should not be passed - provided for compatibility with numpy function
+            signature
 
         Returns
         -------
@@ -3909,7 +3917,7 @@ def argmax(
         array([3, 5, 3])
         Dimensions without coordinates: y
         """
-        result = self.variable.argmax(dim, axis, keep_attrs, skipna)
+        result = self.variable.argmax(dim, axis, keep_attrs, skipna, out)
         if isinstance(result, dict):
             return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()}
         else:

xarray/core/variable.py

@@ -2086,6 +2086,7 @@ def _unravel_argminmax(
         axis: Union[int, None],
         keep_attrs: Optional[bool],
         skipna: Optional[bool],
+        out,
     ) -> Union["Variable", Dict[Hashable, "Variable"]]:
         """Apply argmin or argmax over one or more dimensions, returning the result as a
         dict of DataArray that can be passed directly to isel.
@@ -2110,7 +2111,7 @@ def _unravel_argminmax(
             # Return int index if single dimension is passed, and is not part of a
             # sequence
             return getattr(self, "_injected_" + str(argminmax))(
-                dim=dim, axis=axis, keep_attrs=keep_attrs, skipna=skipna
+                dim=dim, axis=axis, keep_attrs=keep_attrs, skipna=skipna, out=out
             )
 
         # Get a name for the new dimension that does not conflict with any existing
@@ -2127,7 +2128,7 @@ def _unravel_argminmax(
         reduce_shape = tuple(self.sizes[d] for d in dim)
 
         result_flat_indices = getattr(stacked, "_injected_" + str(argminmax))(
-            axis=-1, skipna=skipna
+            axis=-1, skipna=skipna, out=out
         )
 
         result_unravelled_indices = np.unravel_index(result_flat_indices, reduce_shape)
@@ -2151,6 +2152,7 @@ def argmin(
         axis: Union[int, None] = None,
         keep_attrs: bool = None,
         skipna: bool = None,
+        out=None,
     ) -> Union["Variable", Dict[Hashable, "Variable"]]:
         """Indices of the minimum of the DataArray over one or more dimensions. Result
         returned as dict of DataArrays, which can be passed directly to isel().
@@ -2175,6 +2177,9 @@ def argmin(
             skips missing values for float dtypes; other dtypes either do not
             have a sentinel missing value (int) or skipna=True has not been
             implemented (object, datetime64 or timedelta64).
+        out : None
+            'out' should not be passed - provided for compatibility with numpy function
+            signature
 
         Returns
         -------
@@ -2184,14 +2189,15 @@ def argmin(
         --------
         DataArray.argmin, DataArray.idxmin
         """
-        return self._unravel_argminmax("argmin", dim, axis, keep_attrs, skipna)
+        return self._unravel_argminmax("argmin", dim, axis, keep_attrs, skipna, out)
 
     def argmax(
         self,
         dim: Union[Hashable, Sequence[Hashable]] = None,
         axis: Union[int, None] = None,
         keep_attrs: bool = None,
         skipna: bool = None,
+        out=None,
     ) -> Union["Variable", Dict[Hashable, "Variable"]]:
         """Indices of the maximum of the DataArray over one or more dimensions. Result
         returned as dict of DataArrays, which can be passed directly to isel().
@@ -2216,6 +2222,9 @@ def argmax(
             skips missing values for float dtypes; other dtypes either do not
             have a sentinel missing value (int) or skipna=True has not been
             implemented (object, datetime64 or timedelta64).
+        out : None
+            'out' should not be passed - provided for compatibility with numpy function
+            signature
 
         Returns
         -------
@@ -2225,7 +2234,7 @@ def argmax(
         --------
         DataArray.argmax, DataArray.idxmax
         """
-        return self._unravel_argminmax("argmax", dim, axis, keep_attrs, skipna)
+        return self._unravel_argminmax("argmax", dim, axis, keep_attrs, skipna, out)
 
 
 ops.inject_all_ops_and_reduce_methods(Variable)