Return an object where each NaN element in 'obj' is replaced by the length of the gap the element is in.
(
obj: Dataset | DataArray | Variable, dim: Hashable, index: Variable
)
| 46 | |
| 47 | |
| 48 | def _get_nan_block_lengths( |
| 49 | obj: Dataset | DataArray | Variable, dim: Hashable, index: Variable |
| 50 | ): |
| 51 | """ |
| 52 | Return an object where each NaN element in 'obj' is replaced by the |
| 53 | length of the gap the element is in. |
| 54 | """ |
| 55 | |
| 56 | # make variable so that we get broadcasting for free |
| 57 | index = Variable([dim], index) |
| 58 | |
| 59 | # algorithm from https://github.com/pydata/xarray/pull/3302#discussion_r324707072 |
| 60 | arange = ones_like(obj) * index |
| 61 | valid = obj.notnull() |
| 62 | valid_arange = arange.where(valid) |
| 63 | cumulative_nans = valid_arange.ffill(dim=dim).fillna(index[0]) |
| 64 | |
| 65 | nan_block_lengths = ( |
| 66 | cumulative_nans.diff(dim=dim, label="upper") |
| 67 | .reindex({dim: obj[dim]}) |
| 68 | .where(valid) |
| 69 | .bfill(dim=dim) |
| 70 | .where(~valid, 0) |
| 71 | .fillna(index[-1] - valid_arange.max(dim=[dim])) |
| 72 | ) |
| 73 | |
| 74 | return nan_block_lengths |
| 75 | |
| 76 | |
| 77 | class BaseInterpolator: |
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