(self, other, f, reflexive=False)
| 881 | return dim, positions |
| 882 | |
| 883 | def _binary_op(self, other, f, reflexive=False): |
| 884 | from xarray.core.dataarray import DataArray |
| 885 | from xarray.core.dataset import Dataset |
| 886 | |
| 887 | g = f if not reflexive else lambda x, y: f(y, x) |
| 888 | |
| 889 | self._raise_if_not_single_group() |
| 890 | (grouper,) = self.groupers |
| 891 | obj = self._original_obj |
| 892 | name = grouper.name |
| 893 | group = grouper.group |
| 894 | codes = self.encoded.codes |
| 895 | dims = group.dims |
| 896 | |
| 897 | if isinstance(group, _DummyGroup): |
| 898 | group = coord = group.to_dataarray() |
| 899 | else: |
| 900 | coord = grouper.unique_coord |
| 901 | if isinstance(coord, Variable): |
| 902 | assert coord.ndim == 1 |
| 903 | (coord_dim,) = coord.dims |
| 904 | # TODO: explicitly create Index here |
| 905 | coord = DataArray(coord, coords={coord_dim: coord.data}) |
| 906 | |
| 907 | if not isinstance(other, Dataset | DataArray): |
| 908 | raise TypeError( |
| 909 | "GroupBy objects only support binary ops " |
| 910 | "when the other argument is a Dataset or " |
| 911 | "DataArray" |
| 912 | ) |
| 913 | |
| 914 | if name not in other.dims: |
| 915 | raise ValueError( |
| 916 | "incompatible dimensions for a grouped " |
| 917 | f"binary operation: the group variable {name!r} " |
| 918 | "is not a dimension on the other argument " |
| 919 | f"with dimensions {other.dims!r}" |
| 920 | ) |
| 921 | |
| 922 | # Broadcast out scalars for backwards compatibility |
| 923 | # TODO: get rid of this when fixing GH2145 |
| 924 | for var in other.coords: |
| 925 | if other[var].ndim == 0: |
| 926 | other[var] = ( |
| 927 | other[var].drop_vars(var).expand_dims({name: other.sizes[name]}) |
| 928 | ) |
| 929 | |
| 930 | # need to handle NaNs in group or elements that don't belong to any bins |
| 931 | mask = codes == -1 |
| 932 | if mask.any(): |
| 933 | obj = obj.where(~mask, drop=True) |
| 934 | group = group.where(~mask, drop=True) |
| 935 | codes = codes.where(~mask, drop=True).astype(int) |
| 936 | |
| 937 | # if other is dask-backed, that's a hint that the |
| 938 | # "expanded" dataset is too big to hold in memory. |
| 939 | # this can be the case when `other` was read from disk |
| 940 | # and contains our lazy indexing classes |
nothing calls this directly
no test coverage detected