Fix bool weights

doc/whats-new.rst

@@ -119,6 +119,8 @@ Bug fixes
 - Fix bug in time parsing failing to fall back to cftime. This was causing time
   variables with a time unit of `'msecs'` to fail to parse. (:pull:`3998`)
   By `Ryan May <https://github.com/dopplershift>`_.
+- Fix weighted mean when passing boolean weights (:issue:`4074`).
+  By `Mathias Hauser <https://github.com/mathause>`_.
 - Fix html repr in untrusted notebooks: fallback to plain text repr. (:pull:`4053`)
   By `Benoit Bovy <https://github.com/benbovy>`_.
 
@@ -186,7 +188,7 @@ New Features
 
 - Weighted array reductions are now supported via the new :py:meth:`DataArray.weighted`
   and :py:meth:`Dataset.weighted` methods. See :ref:`comput.weighted`. (:issue:`422`, :pull:`2922`).
-  By `Mathias Hauser <https://github.com/mathause>`_
+  By `Mathias Hauser <https://github.com/mathause>`_.
 - The new jupyter notebook repr (``Dataset._repr_html_`` and
   ``DataArray._repr_html_``) (introduced in 0.14.1) is now on by default. To
   disable, use ``xarray.set_options(display_style="text")``.

xarray/core/weighted.py

@@ -142,7 +142,14 @@ def _sum_of_weights(
         # we need to mask data values that are nan; else the weights are wrong
         mask = da.notnull()
 
-        sum_of_weights = self._reduce(mask, self.weights, dim=dim, skipna=False)
+        # bool -> int, because ``xr.dot([True, True], [True, True])`` -> True
+        # (and not 2); GH4074
+        if self.weights.dtype == bool:
+            sum_of_weights = self._reduce(
+                mask, self.weights.astype(int), dim=dim, skipna=False
+            )
+        else:
+            sum_of_weights = self._reduce(mask, self.weights, dim=dim, skipna=False)
 
         # 0-weights are not valid
         valid_weights = sum_of_weights != 0.0

xarray/tests/test_weighted.py

@@ -59,6 +59,18 @@ def test_weighted_sum_of_weights_nan(weights, expected):
     assert_equal(expected, result)
 
 
+def test_weighted_sum_of_weights_bool():
+    # https://github.com/pydata/xarray/issues/4074
+
+    da = DataArray([1, 2])
+    weights = DataArray([True, True])
+    result = da.weighted(weights).sum_of_weights()
+
+    expected = DataArray(2)
+
+    assert_equal(expected, result)
+
+
 @pytest.mark.parametrize("da", ([1.0, 2], [1, np.nan], [np.nan, np.nan]))
 @pytest.mark.parametrize("factor", [0, 1, 3.14])
 @pytest.mark.parametrize("skipna", (True, False))
@@ -158,6 +170,17 @@ def test_weighted_mean_nan(weights, expected, skipna):
     assert_equal(expected, result)
 
 
+def test_weighted_mean_bool():
+    # https://github.com/pydata/xarray/issues/4074
+    da = DataArray([1, 1])
+    weights = DataArray([True, True])
+    expected = DataArray(1)
+
+    result = da.weighted(weights).mean()
+
+    assert_equal(expected, result)
+
+
 def expected_weighted(da, weights, dim, skipna, operation):
     """
     Generate expected result using ``*`` and ``sum``. This is checked against