issue_comments

Thanks for all your work @dcherian! Did a quick test with some real-world timeseries data I've been wanting to use with max_gap and it looks good to me. I will definitely be using this in the future! 👍

Thanks so much for taking this on. Not sure how I missed the notification that you had done so.

@dcherian I checked out your branch and did some testing and found that testing this against dim="x" fails with a ValueError. ```python arr = np.array([ [np.nan, np.nan, np.nan, 1, 2, 3, 4, np.nan, 6, 7, np.nan, np.nan, np.nan, 11, np.nan, np.nan, ], [np.nan, np.nan, np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, np.nan, np.nan, np.nan, 11, np.nan, np.nan, ], [np.nan, np.nan, np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, np.nan, np.nan, np.nan, 11, np.nan, np.nan, ], [np.nan, np.nan, np.nan, 1, 2, 3, 4, np.nan, 6, 7, np.nan, np.nan, np.nan, 11, np.nan, np.nan, ], ])

da = xr.DataArray(arr, dims=["x", "y"], coords={"x": np.arange(arr.shape[0]), "y": np.arange(arr.shape[1])})

actual = da.interpolate_na("y", max_gap=2) # this works as in your test example actual = da.interpolate_na("x", max_gap=2) # this fails Error:

actual = da.interpolate_na("x", max_gap=2) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/dnowacki/Documents/xarray/xarray/core/dataarray.py", line 2026, in interpolate_na **kwargs File "/Users/dnowacki/Documents/xarray/xarray/core/missing.py", line 302, in interp_na nan_block_lengths = _get_nan_block_lengths(self, dim, index) File "/Users/dnowacki/Documents/xarray/xarray/core/missing.py", line 23, in _get_nan_block_lengths arange = ones_like(obj) * index + (index[1] - index[0]) File "/Users/dnowacki/Documents/xarray/xarray/core/dataarray.py", line 2500, in func if not reflexive File "/Users/dnowacki/Documents/xarray/xarray/core/variable.py", line 1884, in func if not reflexive ValueError: operands could not be broadcast together with shapes (4,16) (4,) ```

I fixed it by changing this line https://github.com/dcherian/xarray/blob/8899f05a13533e10c8972836afda089b5b792ca0/xarray/core/missing.py#L23 to this: python if index.shape[0] == obj.shape[0]: arange = ones_like(obj) * index[:, None] + (index[1] - index[0]) else: arange = ones_like(obj) * index + (index[1] - index[0]) Does this seem reasonable?

Thanks @stefraynaud . I'm having trouble figuring out defining the length of a gap in the irregular coordinate case.

e.g.

da4 = xr.DataArray([np.nan, np.nan, np.nan, 1, np.nan, np.nan, 4, np.nan, np.nan], dims=["y"], coords={"y": [0, 2, 5, 6, 7, 8, 10, 12, 14]})

<xarray.DataArray (y: 9)> array([nan, nan, nan, 1., nan, nan, 4., nan, nan]) Coordinates: * y (y) int64 0 2 5 6 7 8 10 12 14

What is the length of these three gaps given that xarray doesn't have any understanding of grids?

@dcherian In your example, as said @max-sixty, the middle gap has a length of 10-6=4. The length gaps at the edges cannot be computed but it doesn't matter, and the algo should work as when simply counting the nans.

I'll have a look the code, maybe for a new PR after this one.

Nice feature. How about adding the support max gaps expressed in physical units, since coordinates may be irregular?