issue_comments: 525154116

Feel free to refactor as you see fit, but it may still make sense to do indexing at the Variable rather than Dataset level. That potentially would let you avoid redundant operations on the entire Dataset object.

Take a look at the _localize() helper function in missing.py for an example of how to do stuff with in the underlying index. I think something like the following helper function could do the trick: python def linear_interp(var, indexes_coords): lower_indices = {} upper_indices = {} for dim, [x, new_x] in indexes_coords.items(): index = x.to_index() # ideally should precompute these, rather than calling get_indexer_nd for each # variable separately lower_indices[dim] = get_indexer_nd(index, new_x.values, method="ffill") upper_indices[dim] = get_indexer_nd(index, new_x.values, method="bfill") result = 0 for weight, indexes in ... # need to compute weights and all lower/upper combinations result += weight * var.isel(**indexes) return result