Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods

doc/api-hidden.rst

@@ -41,8 +41,6 @@
 
    core.rolling.DatasetCoarsen.all
    core.rolling.DatasetCoarsen.any
-   core.rolling.DatasetCoarsen.argmax
-   core.rolling.DatasetCoarsen.argmin
    core.rolling.DatasetCoarsen.count
    core.rolling.DatasetCoarsen.max
    core.rolling.DatasetCoarsen.mean
@@ -68,8 +66,6 @@
    core.groupby.DatasetGroupBy.where
    core.groupby.DatasetGroupBy.all
    core.groupby.DatasetGroupBy.any
-   core.groupby.DatasetGroupBy.argmax
-   core.groupby.DatasetGroupBy.argmin
    core.groupby.DatasetGroupBy.count
    core.groupby.DatasetGroupBy.max
    core.groupby.DatasetGroupBy.mean
@@ -85,8 +81,6 @@
    core.resample.DatasetResample.all
    core.resample.DatasetResample.any
    core.resample.DatasetResample.apply
-   core.resample.DatasetResample.argmax
-   core.resample.DatasetResample.argmin
    core.resample.DatasetResample.assign
    core.resample.DatasetResample.assign_coords
    core.resample.DatasetResample.bfill
@@ -110,8 +104,6 @@
    core.resample.DatasetResample.dims
    core.resample.DatasetResample.groups
 
-   core.rolling.DatasetRolling.argmax
-   core.rolling.DatasetRolling.argmin
    core.rolling.DatasetRolling.count
    core.rolling.DatasetRolling.max
    core.rolling.DatasetRolling.mean
@@ -185,8 +177,6 @@
 
    core.rolling.DataArrayCoarsen.all
    core.rolling.DataArrayCoarsen.any
-   core.rolling.DataArrayCoarsen.argmax
-   core.rolling.DataArrayCoarsen.argmin
    core.rolling.DataArrayCoarsen.count
    core.rolling.DataArrayCoarsen.max
    core.rolling.DataArrayCoarsen.mean
@@ -211,8 +201,6 @@
    core.groupby.DataArrayGroupBy.where
    core.groupby.DataArrayGroupBy.all
    core.groupby.DataArrayGroupBy.any
-   core.groupby.DataArrayGroupBy.argmax
-   core.groupby.DataArrayGroupBy.argmin
    core.groupby.DataArrayGroupBy.count
    core.groupby.DataArrayGroupBy.max
    core.groupby.DataArrayGroupBy.mean
@@ -228,8 +216,6 @@
    core.resample.DataArrayResample.all
    core.resample.DataArrayResample.any
    core.resample.DataArrayResample.apply
-   core.resample.DataArrayResample.argmax
-   core.resample.DataArrayResample.argmin
    core.resample.DataArrayResample.assign_coords
    core.resample.DataArrayResample.bfill
    core.resample.DataArrayResample.count
@@ -252,8 +238,6 @@
    core.resample.DataArrayResample.dims
    core.resample.DataArrayResample.groups
 
-   core.rolling.DataArrayRolling.argmax
-   core.rolling.DataArrayRolling.argmin
    core.rolling.DataArrayRolling.count
    core.rolling.DataArrayRolling.max
    core.rolling.DataArrayRolling.mean
@@ -423,8 +407,6 @@
 
    IndexVariable.all
    IndexVariable.any
-   IndexVariable.argmax
-   IndexVariable.argmin
    IndexVariable.argsort
    IndexVariable.astype
    IndexVariable.broadcast_equals
@@ -564,8 +546,6 @@
    CFTimeIndex.all
    CFTimeIndex.any
    CFTimeIndex.append
-   CFTimeIndex.argmax
-   CFTimeIndex.argmin
    CFTimeIndex.argsort
    CFTimeIndex.asof
    CFTimeIndex.asof_locs

doc/whats-new.rst

@@ -52,6 +52,14 @@ Enhancements
 
 New Features
 ~~~~~~~~~~~~
+- :py:meth:`DataArray.argmin` and :py:meth:`DataArray.argmax` now support
+  sequences of 'dim' arguments, and if a sequence is passed return a dict
+  (which can be passed to :py:meth:`isel` to get the value of the minimum) of
+  the indices for each dimension of the minimum or maximum of a DataArray.
+  (:pull:`3936`)
+  By `John Omotani <https://github.com/johnomotani>`_, thanks to `Keisuke Fujii
+  <https://github.com/fujiisoup>`_ for work in :pull:`1469`.
+- Added :py:meth:`DataArray.polyfit` and :py:func:`xarray.polyval` for fitting polynomials. (:issue:`3349`)
 - Added :py:meth:`xarray.infer_freq` for extending frequency inferring to CFTime indexes and data (:pull:`4033`).
   By `Pascal Bourgault <https://github.com/aulemahal>`_.
 - ``chunks='auto'`` is now supported in the ``chunks`` argument of

xarray/core/dataarray.py

@@ -3819,6 +3819,209 @@ def idxmax(
             keep_attrs=keep_attrs,
         )
 
+    def argmin(
+        self,
+        dim: Union[Hashable, Sequence[Hashable]] = None,
+        axis: int = None,
+        keep_attrs: bool = None,
+        skipna: bool = None,
+    ) -> Union["DataArray", Dict[Hashable, "DataArray"]]:
+        """Index or indices of the minimum of the DataArray over one or more dimensions.
+
+        If a sequence is passed to 'dim', then result returned as dict of DataArrays,
+        which can be passed directly to isel(). If a single str is passed to 'dim' then
+        returns a DataArray with dtype int.
+
+        If there are multiple minima, the indices of the first one found will be
+        returned.
+
+        Parameters
+        ----------
+        dim : hashable, sequence of hashable or ..., optional
+            The dimensions over which to find the minimum. By default, finds minimum over
+            all dimensions - for now returning an int for backward compatibility, but
+            this is deprecated, in future will return a dict with indices for all
+            dimensions; to return a dict with all dimensions now, pass '...'.
+        axis : int, optional
+            Axis over which to apply `argmin`. Only one of the 'dim' and 'axis' arguments
+            can be supplied.
+        keep_attrs : bool, optional
+            If True, the attributes (`attrs`) will be copied from the original
+            object to the new one.  If False (default), the new object will be
+            returned without attributes.
+        skipna : bool, optional
+            If True, skip missing values (as marked by NaN). By default, only
+            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).
+
+        Returns
+        -------
+        result : DataArray or dict of DataArray
+
+        See also
+        --------
+        Variable.argmin, DataArray.idxmin
+
+        Examples
+        --------
+        >>> array = xr.DataArray([0, 2, -1, 3], dims="x")
+        >>> array.min()
+        <xarray.DataArray ()>
+        array(-1)
+        >>> array.argmin()
+        <xarray.DataArray ()>
+        array(2)
+        >>> array.argmin(...)
+        {'x': <xarray.DataArray ()>
+        array(2)}
+        >>> array.isel(array.argmin(...))
+        array(-1)
+
+        >>> array = xr.DataArray([[[3, 2, 1], [3, 1, 2], [2, 1, 3]],
+        ...                       [[1, 3, 2], [2, -5, 1], [2, 3, 1]]],
+        ...                      dims=("x", "y", "z"))
+        >>> array.min(dim="x")
+        <xarray.DataArray (y: 3, z: 3)>
+        array([[ 1,  2,  1],
+               [ 2, -5,  1],
+               [ 2,  1,  1]])
+        Dimensions without coordinates: y, z
+        >>> array.argmin(dim="x")
+        <xarray.DataArray (y: 3, z: 3)>
+        array([[1, 0, 0],
+               [1, 1, 1],
+               [0, 0, 1]])
+        Dimensions without coordinates: y, z
+        >>> array.argmin(dim=["x"])
+        {'x': <xarray.DataArray (y: 3, z: 3)>
+        array([[1, 0, 0],
+               [1, 1, 1],
+               [0, 0, 1]])
+        Dimensions without coordinates: y, z}
+        >>> array.min(dim=("x", "z"))
+        <xarray.DataArray (y: 3)>
+        array([ 1, -5,  1])
+        Dimensions without coordinates: y
+        >>> array.argmin(dim=["x", "z"])
+        {'x': <xarray.DataArray (y: 3)>
+        array([0, 1, 0])
+        Dimensions without coordinates: y, 'z': <xarray.DataArray (y: 3)>
+        array([2, 1, 1])
+        Dimensions without coordinates: y}
+        >>> array.isel(array.argmin(dim=["x", "z"]))
+        <xarray.DataArray (y: 3)>
+        array([ 1, -5,  1])
+        Dimensions without coordinates: y
+        """
+        result = self.variable.argmin(dim, axis, keep_attrs, skipna)
+        if isinstance(result, dict):
+            return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()}
+        else:
+            return self._replace_maybe_drop_dims(result)
+
+    def argmax(
+        self,
+        dim: Union[Hashable, Sequence[Hashable]] = None,
+        axis: int = None,
+        keep_attrs: bool = None,
+        skipna: bool = None,
+    ) -> Union["DataArray", Dict[Hashable, "DataArray"]]:
+        """Index or indices of the maximum of the DataArray over one or more dimensions.
+
+        If a sequence is passed to 'dim', then result returned as dict of DataArrays,
+        which can be passed directly to isel(). If a single str is passed to 'dim' then
+        returns a DataArray with dtype int.
+
+        If there are multiple maxima, the indices of the first one found will be
+        returned.
+
+        Parameters
+        ----------
+        dim : hashable, sequence of hashable or ..., optional
+            The dimensions over which to find the maximum. By default, finds maximum over
+            all dimensions - for now returning an int for backward compatibility, but
+            this is deprecated, in future will return a dict with indices for all
+            dimensions; to return a dict with all dimensions now, pass '...'.
+        axis : int, optional
+            Axis over which to apply `argmin`. Only one of the 'dim' and 'axis' arguments
+            can be supplied.
+        keep_attrs : bool, optional
+            If True, the attributes (`attrs`) will be copied from the original
+            object to the new one.  If False (default), the new object will be
+            returned without attributes.
+        skipna : bool, optional
+            If True, skip missing values (as marked by NaN). By default, only
+            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).
+
+        Returns
+        -------
+        result : DataArray or dict of DataArray
+
+        See also
+        --------
+        Variable.argmax, DataArray.idxmax
+
+        Examples
+        --------
+        >>> array = xr.DataArray([0, 2, -1, 3], dims="x")
+        >>> array.max()
+        <xarray.DataArray ()>
+        array(3)
+        >>> array.argmax()
+        <xarray.DataArray ()>
+        array(3)
+        >>> array.argmax(...)
+        {'x': <xarray.DataArray ()>
+        array(3)}
+        >>> array.isel(array.argmax(...))
+        <xarray.DataArray ()>
+        array(3)
+
+        >>> array = xr.DataArray([[[3, 2, 1], [3, 1, 2], [2, 1, 3]],
+        ...                       [[1, 3, 2], [2, 5, 1], [2, 3, 1]]],
+        ...                      dims=("x", "y", "z"))
+        >>> array.max(dim="x")
+        <xarray.DataArray (y: 3, z: 3)>
+        array([[3, 3, 2],
+               [3, 5, 2],
+               [2, 3, 3]])
+        Dimensions without coordinates: y, z
+        >>> array.argmax(dim="x")
+        <xarray.DataArray (y: 3, z: 3)>
+        array([[0, 1, 1],
+               [0, 1, 0],
+               [0, 1, 0]])
+        Dimensions without coordinates: y, z
+        >>> array.argmax(dim=["x"])
+        {'x': <xarray.DataArray (y: 3, z: 3)>
+        array([[0, 1, 1],
+               [0, 1, 0],
+               [0, 1, 0]])
+        Dimensions without coordinates: y, z}
+        >>> array.max(dim=("x", "z"))
+        <xarray.DataArray (y: 3)>
+        array([3, 5, 3])
+        Dimensions without coordinates: y
+        >>> array.argmax(dim=["x", "z"])
+        {'x': <xarray.DataArray (y: 3)>
+        array([0, 1, 0])
+        Dimensions without coordinates: y, 'z': <xarray.DataArray (y: 3)>
+        array([0, 1, 2])
+        Dimensions without coordinates: y}
+        >>> array.isel(array.argmax(dim=["x", "z"]))
+        <xarray.DataArray (y: 3)>
+        array([3, 5, 3])
+        Dimensions without coordinates: y
+        """
+        result = self.variable.argmax(dim, axis, keep_attrs, skipna)
+        if isinstance(result, dict):
+            return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()}
+        else:
+            return self._replace_maybe_drop_dims(result)
+
     # this needs to be at the end, or mypy will confuse with `str`
     # https://mypy.readthedocs.io/en/latest/common_issues.html#dealing-with-conflicting-names
     str = utils.UncachedAccessor(StringAccessor)