Add var and std to weighted computations (#5870)

Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com>

doc/api.rst

@@ -779,12 +779,18 @@ Weighted objects
 
    core.weighted.DataArrayWeighted
    core.weighted.DataArrayWeighted.mean
+   core.weighted.DataArrayWeighted.std
    core.weighted.DataArrayWeighted.sum
+   core.weighted.DataArrayWeighted.sum_of_squares
    core.weighted.DataArrayWeighted.sum_of_weights
+   core.weighted.DataArrayWeighted.var
    core.weighted.DatasetWeighted
    core.weighted.DatasetWeighted.mean
+   core.weighted.DatasetWeighted.std
    core.weighted.DatasetWeighted.sum
+   core.weighted.DatasetWeighted.sum_of_squares
    core.weighted.DatasetWeighted.sum_of_weights
+   core.weighted.DatasetWeighted.var
 
 
 Coarsen objects

doc/user-guide/computation.rst

@@ -263,7 +263,7 @@ Weighted array reductions
 
 :py:class:`DataArray` and :py:class:`Dataset` objects include :py:meth:`DataArray.weighted`
 and :py:meth:`Dataset.weighted` array reduction methods. They currently
-support weighted ``sum`` and weighted ``mean``.
+support weighted ``sum``, ``mean``, ``std`` and ``var``.
 
 .. ipython:: python
 
@@ -298,13 +298,27 @@ The weighted sum corresponds to:
     weighted_sum = (prec * weights).sum()
     weighted_sum
 
-and the weighted mean to:
+the weighted mean to:
 
 .. ipython:: python
 
     weighted_mean = weighted_sum / weights.sum()
     weighted_mean
 
+the weighted variance to:
+
+.. ipython:: python
+
+    weighted_var = weighted_prec.sum_of_squares() / weights.sum()
+    weighted_var
+
+and the weighted standard deviation to:
+
+.. ipython:: python
+
+    weighted_std = np.sqrt(weighted_var)
+    weighted_std
+
 However, the functions also take missing values in the data into account:
 
 .. ipython:: python
@@ -327,7 +341,7 @@ If the weights add up to to 0, ``sum`` returns 0:
 
     data.weighted(weights).sum()
 
-and ``mean`` returns ``NaN``:
+and ``mean``, ``std`` and ``var`` return ``NaN``:
 
 .. ipython:: python
 

doc/whats-new.rst

@@ -23,6 +23,8 @@ v0.19.1 (unreleased)
 
 New Features
 ~~~~~~~~~~~~
+- Add :py:meth:`var`, :py:meth:`std` and :py:meth:`sum_of_squares` to :py:meth:`Dataset.weighted` and :py:meth:`DataArray.weighted`.
+  By `Christian Jauvin <https://github.com/cjauvin>`_.
 - Added a :py:func:`get_options` method to xarray's root namespace (:issue:`5698`, :pull:`5716`)
   By `Pushkar Kopparla <https://github.com/pkopparla>`_.
 - Xarray now does a better job rendering variable names that are long LaTeX sequences when plotting (:issue:`5681`, :pull:`5682`).

xarray/core/weighted.py

@@ -1,4 +1,6 @@
-from typing import TYPE_CHECKING, Generic, Hashable, Iterable, Optional, Union
+from typing import TYPE_CHECKING, Generic, Hashable, Iterable, Optional, Union, cast
+
+import numpy as np
 
 from . import duck_array_ops
 from .computation import dot
@@ -35,7 +37,7 @@
     """
 
 _SUM_OF_WEIGHTS_DOCSTRING = """
-    Calculate the sum of weights, accounting for missing values in the data
+    Calculate the sum of weights, accounting for missing values in the data.
 
     Parameters
     ----------
@@ -177,13 +179,25 @@ def _sum_of_weights(
 
         return sum_of_weights.where(valid_weights)
 
+    def _sum_of_squares(
+        self,
+        da: "DataArray",
+        dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
+        skipna: Optional[bool] = None,
+    ) -> "DataArray":
+        """Reduce a DataArray by a weighted ``sum_of_squares`` along some dimension(s)."""
+
+        demeaned = da - da.weighted(self.weights).mean(dim=dim)
+
+        return self._reduce((demeaned ** 2), self.weights, dim=dim, skipna=skipna)
+
     def _weighted_sum(
         self,
         da: "DataArray",
         dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
         skipna: Optional[bool] = None,
     ) -> "DataArray":
-        """Reduce a DataArray by a by a weighted ``sum`` along some dimension(s)."""
+        """Reduce a DataArray by a weighted ``sum`` along some dimension(s)."""
 
         return self._reduce(da, self.weights, dim=dim, skipna=skipna)
 
@@ -201,6 +215,30 @@ def _weighted_mean(
 
         return weighted_sum / sum_of_weights
 
+    def _weighted_var(
+        self,
+        da: "DataArray",
+        dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
+        skipna: Optional[bool] = None,
+    ) -> "DataArray":
+        """Reduce a DataArray by a weighted ``var`` along some dimension(s)."""
+
+        sum_of_squares = self._sum_of_squares(da, dim=dim, skipna=skipna)
+
+        sum_of_weights = self._sum_of_weights(da, dim=dim)
+
+        return sum_of_squares / sum_of_weights
+
+    def _weighted_std(
+        self,
+        da: "DataArray",
+        dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
+        skipna: Optional[bool] = None,
+    ) -> "DataArray":
+        """Reduce a DataArray by a weighted ``std`` along some dimension(s)."""
+
+        return cast("DataArray", np.sqrt(self._weighted_var(da, dim, skipna)))
+
     def _implementation(self, func, dim, **kwargs):
 
         raise NotImplementedError("Use `Dataset.weighted` or `DataArray.weighted`")
@@ -215,6 +253,17 @@ def sum_of_weights(
             self._sum_of_weights, dim=dim, keep_attrs=keep_attrs
         )
 
+    def sum_of_squares(
+        self,
+        dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
+        skipna: Optional[bool] = None,
+        keep_attrs: Optional[bool] = None,
+    ) -> T_Xarray:
+
+        return self._implementation(
+            self._sum_of_squares, dim=dim, skipna=skipna, keep_attrs=keep_attrs
+        )
+
     def sum(
         self,
         dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
@@ -237,6 +286,28 @@ def mean(
             self._weighted_mean, dim=dim, skipna=skipna, keep_attrs=keep_attrs
         )
 
+    def var(
+        self,
+        dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
+        skipna: Optional[bool] = None,
+        keep_attrs: Optional[bool] = None,
+    ) -> T_Xarray:
+
+        return self._implementation(
+            self._weighted_var, dim=dim, skipna=skipna, keep_attrs=keep_attrs
+        )
+
+    def std(
+        self,
+        dim: Optional[Union[Hashable, Iterable[Hashable]]] = None,
+        skipna: Optional[bool] = None,
+        keep_attrs: Optional[bool] = None,
+    ) -> T_Xarray:
+
+        return self._implementation(
+            self._weighted_std, dim=dim, skipna=skipna, keep_attrs=keep_attrs
+        )
+
     def __repr__(self):
         """provide a nice str repr of our Weighted object"""
 
@@ -275,6 +346,18 @@ def _inject_docstring(cls, cls_name):
         cls=cls_name, fcn="mean", on_zero="NaN"
     )
 
+    cls.sum_of_squares.__doc__ = _WEIGHTED_REDUCE_DOCSTRING_TEMPLATE.format(
+        cls=cls_name, fcn="sum_of_squares", on_zero="0"
+    )
+
+    cls.var.__doc__ = _WEIGHTED_REDUCE_DOCSTRING_TEMPLATE.format(
+        cls=cls_name, fcn="var", on_zero="NaN"
+    )
+
+    cls.std.__doc__ = _WEIGHTED_REDUCE_DOCSTRING_TEMPLATE.format(
+        cls=cls_name, fcn="std", on_zero="NaN"
+    )
+
 
 _inject_docstring(DataArrayWeighted, "DataArray")
 _inject_docstring(DatasetWeighted, "Dataset")