(name)
| 735 | # TODO: (benbovy - explicit indexes): check index types and/or coordinates |
| 736 | # of all datasets? |
| 737 | def get_indexes(name): |
| 738 | for ds in datasets: |
| 739 | if name in ds._indexes: |
| 740 | yield ds._indexes[name] |
| 741 | elif name == dim_name: |
| 742 | var = ds._variables[name] |
| 743 | if not var.dims: |
| 744 | data = var.set_dims(dim_name).values |
| 745 | if create_index_for_new_dim: |
| 746 | yield PandasIndex(data, dim_name, coord_dtype=var.dtype) |
| 747 | |
| 748 | # create concatenation index, needed for later reindexing |
| 749 | # use np.cumulative_sum(concat_dim_lengths, include_initial=True) when we support numpy>=2 |
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