make some more requested changes

xarray/core/computation.py

@@ -23,9 +23,10 @@
 
 import numpy as np
 
-from . import duck_array_ops, utils
+from . import dtypes, duck_array_ops, utils
 from .alignment import deep_align
 from .merge import merge_coordinates_without_align
+from .nanops import dask_array
 from .options import OPTIONS
 from .pycompat import dask_array_type
 from .utils import is_dict_like
@@ -1306,3 +1307,93 @@ def where(cond, x, y):
         dataset_join="exact",
         dask="allowed",
     )
+
+
+def _calc_idxminmax(
+    *,
+    array,
+    func: Callable,
+    dim: Optional[Hashable],
+    axis: Optional[int],
+    skipna: Optional[bool],
+    promote: Optional[bool],
+    keep_attrs: Optional[bool],
+    **kwargs: Any,
+):
+    """Apply common operations for idxmin and idxmax."""
+    # This function doesn't make sense for scalars so don't try
+    if not array.ndim:
+        ValueError("This function does not apply for scalars")
+
+    if dim is not None:
+        pass  # Use the dim if available
+    elif axis is not None:
+        dim = array.dims[axis]
+    elif array.ndim == 1:
+        # it is okay to guess the dim if there is only 1
+        dim = array.dims[0]
+    else:
+        # The dim is not specified and ambiguous.  Don't guess.
+        raise ValueError(
+            "Must supply either 'dim' or 'axis' argument " "for multidimensional arrays"
+        )
+
+    if dim in array.coords:
+        pass  # This is okay
+    elif axis is not None:
+        raise IndexError(f'Axis "{axis}" does not have coordinates')
+    else:
+        raise KeyError(f'Dimension "{dim}" does not have coordinates')
+
+    # These are dtypes with NaN values argmin and argmax can handle
+    na_dtypes = "cf0"
+
+    if skipna or (skipna is None and array.dtype.kind in na_dtypes):
+        # Need to skip NaN values since argmin and argmax can't handle them
+        allna = array.isnull().all(dim)
+        array = array.where(~allna, 0)
+        hasna = allna.any()
+    else:
+        allna = None
+        hasna = False
+
+    # If promote is None we only promote if there are NaN values.
+    if promote is None:
+        promote = hasna
+
+    if not promote and hasna and array.coords[dim].dtype.kind not in na_dtypes:
+        raise TypeError(
+            "NaN values present for NaN-incompatible dtype and Promote=False"
+        )
+
+    # This will run argmin or argmax.
+    indx = func(array, dim=dim, axis=None, keep_attrs=False, skipna=skipna, **kwargs)
+
+    # Get the coordinate we want.
+    coordarray = array[dim]
+
+    # Handle dask arrays.
+    if isinstance(array, dask_array_type):
+        res = dask_array.map_blocks(coordarray, indx, dtype=indx.dtype)
+    else:
+        res = coordarray[
+            indx,
+        ]
+
+    # Promote to a dtype that can handle NaN values if needed.
+    newdtype, fill_value = dtypes.maybe_promote(res.dtype)
+    if promote and newdtype != res.dtype:
+        res = res.astype(newdtype)
+
+    # Put the NaN values back in after removing them, if necessary.
+    if hasna and allna is not None:
+        res = res.where(~allna, fill_value)
+
+    # The dim is gone but we need to remove the corresponding coordinate.
+    del res.coords[dim]
+
+    # Put the attrs back in if needed
+    if keep_attrs:
+        res.attrs = array.attrs
+
+    return res

xarray/core/dataarray.py

@@ -55,9 +55,7 @@
 from .indexes import Indexes, default_indexes, propagate_indexes
 from .indexing import is_fancy_indexer
 from .merge import PANDAS_TYPES, _extract_indexes_from_coords
-from .nanops import dask_array
 from .options import OPTIONS
-from .pycompat import dask_array_type
 from .utils import Default, ReprObject, _check_inplace, _default, either_dict_or_kwargs
 from .variable import (
     IndexVariable,
@@ -3432,99 +3430,6 @@ def pad(
         )
         return self._from_temp_dataset(ds)
 
-    def _calc_idxminmax(
-        self,
-        *,
-        func: Callable,
-        dim: Optional[Hashable],
-        axis: Optional[int],
-        skipna: Optional[bool],
-        promote: Optional[bool],
-        keep_attrs: Optional[bool],
-        **kwargs: Any,
-    ) -> "DataArray":
-        """Apply common operations for idxmin and idxmax."""
-        # This function doesn't make sense for scalars so don't try
-        if not self.ndim:
-            ValueError("This function does not apply for scalars")
-
-        if dim is not None:
-            pass  # Use the dim if available
-        elif axis is not None:
-            dim = self.dims[axis]
-        elif self.ndim == 1:
-            # it is okay to guess the dim if there is only 1
-            dim = self.dims[0]
-        else:
-            # The dim is not specified and ambiguous.  Don't guess.
-            raise ValueError(
-                "Must supply either 'dim' or 'axis' argument "
-                "for multidimensional arrays"
-            )
-
-        if dim in self.coords:
-            pass  # This is okay
-        elif axis is not None:
-            raise IndexError(f'Axis "{axis}" does not have coordinates')
-        else:
-            raise KeyError(f'Dimension "{dim}" does not have coordinates')
-
-        # These are dtypes with NaN values argmin and argmax can handle
-        na_dtypes = "cf0"
-
-        if skipna or (skipna is None and self.dtype.kind in na_dtypes):
-            # Need to skip NaN values since argmin and argmax can't handle them
-            allna = self.isnull().all(dim)
-            array = self.where(~allna, 0)
-            hasna = allna.any()
-        else:
-            array = self
-            allna = None
-            hasna = False
-
-        # If promote is None we only promote if there are NaN values.
-        if promote is None:
-            promote = hasna
-
-        if not promote and hasna and array.coords[dim].dtype.kind not in na_dtypes:
-            raise TypeError(
-                "NaN values present for NaN-incompatible dtype and Promote=False"
-            )
-
-        # This will run argmin or argmax.
-        indx = func(
-            array, dim=dim, axis=None, keep_attrs=False, skipna=skipna, **kwargs
-        )
-
-        # Get the coordinate we want.
-        coordarray = array[dim]
-
-        # Handle dask arrays.
-        if isinstance(array, dask_array_type):
-            res = dask_array.map_blocks(coordarray, indx, dtype=indx.dtype)
-        else:
-            res = coordarray[
-                indx,
-            ]
-
-        # Promote to a dtype that can handle NaN values if needed.
-        newdtype, fill_value = dtypes.maybe_promote(res.dtype)
-        if promote and newdtype != res.dtype:
-            res = res.astype(newdtype)
-
-        # Put the NaN values back in after removing them, if necessary.
-        if hasna and allna is not None:
-            res = res.where(~allna, fill_value)
-
-        # The dim is gone but we need to remove the corresponding coordinate.
-        del res.coords[dim]
-
-        # Put the attrs back in if needed
-        if keep_attrs:
-            res.attrs = self.attrs
-
-        return res
-
     def idxmin(
         self,
         dim: Hashable = None,
@@ -3555,7 +3460,7 @@ def idxmin(
             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).
-        promote :  bool or None, default True
+        promote : bool or None, default True
             If True (default) dtypes that do not support NaN values will be
             automatically promoted to those that do.  If False a NaN in the
             results will raise a TypeError.  If None the result will only be
@@ -3616,7 +3521,8 @@ def idxmin(
         --------
         Dataset.idxmin, DataArray.idxmax, DataArray.min, DataArray.argmin
         """
-        return self._calc_idxminmax(
+        return computation._calc_idxminmax(
+            array=self,
             func=lambda x, *args, **kwargs: x.argmin(*args, **kwargs),
             dim=dim,
             axis=axis,
@@ -3656,7 +3562,7 @@ def idxmax(
             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).
-        promote :  bool or None, default True
+        promote : bool or None, default True
             If True (default) dtypes that do not support NaN values will be
             automatically promoted to those that do.  If False a NaN in the
             results will raise a TypeError.  If None the result will only be
@@ -3717,7 +3623,8 @@ def idxmax(
         --------
         Dataset.idxmax, DataArray.idxmin, DataArray.max, DataArray.argmax
         """
-        return self._calc_idxminmax(
+        return computation._calc_idxminmax(
+            array=self,
             func=lambda x, *args, **kwargs: x.argmax(*args, **kwargs),
             dim=dim,
             axis=axis,

xarray/core/dataset.py

@@ -5951,7 +5951,7 @@ def idxmin(
             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).
-        promote :  bool or None, default True
+        promote : bool or None, default True
             If True (default) dtypes that do not support NaN values will be
             automatically promoted to those that do.  If False a NaN in the
             results will raise a TypeError.  If None the result will only be
@@ -6052,7 +6052,7 @@ def idxmax(
             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).
-        promote :  bool or None, default True
+        promote : bool or None, default True
             If True (default) dtypes that do not support NaN values will be
             automatically promoted to those that do.  If False a NaN in the
             results will raise a TypeError.  If None the result will only be