The interpolate_na function is typically used to fill missing values (NAs) in a data frame or array by interpolating between existing values. It has an optional argument called max_gap which specifies the maximum number of consecutive NAs that can be filled in a single interpolation step.

However, the max_gap argument may not work as expected at the boundaries of an array, as there may not be enough data points available to fill the maximum gap. For example, if the max_gap is set to 3 and there are only two consecutive NAs at the boundary of an array, the function will not be able to fill those NAs.

One way to handle this issue is to reduce the max_gap value near the boundaries of the array. For example, you could set the max_gap to 1 for the first and last few rows or columns of the array, depending on the structure of your data. Alternatively, you could use a different interpolation method (e.g., linear interpolation) that does not require a fixed max_gap value.

It's also worth noting that the interpolate_na function may not always be the best approach for filling missing values, as it assumes that the data has a smooth, continuous structure. If your data has a more complex structure (e.g., sharp discontinuities), other methods such as regression or machine learning models may be more appropriate.

The interpolate_na method in xarray can be used to interpolate missing values in a dataset or data array. The max_gap argument is used to specify the maximum number of consecutive NaN values that can be interpolated. The max_map argument is used to specify the maximum number of interpolated values that can be used for each NaN value.

It's worth noting that the max_map argument only limits the number of interpolated values that can be used for each NaN value, but it does not limit the total number of interpolated values that can be used in the dataset. This means that if there are multiple consecutive NaN values, the max_map argument may not work as expected at the boundaries of the array.

Here's an example to illustrate this issue:

import xarray as xr import numpy as np

create a sample data array with a missing value at the beginning and end

data = np.array([np.nan, 1, 2, 3, 4, np.nan])

create a dataset with the sample data array

ds = xr.Dataset({'foo': (['x'], data)}, coords={'x': np.arange(6)})

interpolate missing values with a max_map of 2

ds_interp = ds.interpolate_na(max_gap=1, max_map=2)

In this example, we have a data array with missing values at the beginning and end, and we interpolate the missing values using a max_map of 2. However, the resulting dataset still has 4 interpolated values, which is more than the max_map of 2. This is because the max_map argument is only limiting the number of interpolated values that can be used for each NaN value, but it is not limiting the total number of interpolated values that can be used in the dataset.

To limit the total number of interpolated values in the dataset, you can use the limit argument, which specifies the maximum number of interpolated values that can be used in the entire dataset. Here's an example:

interpolate missing values with a max_map of 2 and a limit of 2

ds_interp = ds.interpolate_na(max_gap=1, max_map=2, limit=2)

In this example, we add a limit argument of 2, which limits the total number of interpolated values in the dataset to 2. This results in only 2 interpolated values in the resulting dataset, which is consistent with the limit argument.

@Ockenfuss i said you should try this three point I listed below and see if that could resolve the problem you raised.

1. Try adjusting the max_gap argument to a smaller value to see if that resolves the issue. For example, if max_gap is currently set to 10, try reducing it to 5 or even 1.

2. Consider using a different interpolation method that is better suited for the specific dataset and boundaries. For example, if linear interpolation is not working well at the array boundaries, try a cubic or spline interpolation method.

3. Check the data at the array boundaries to ensure that it is valid and not causing issues with the interpolation. For example, if there are NaN values or outliers at the boundaries, this could be affecting the interpolation.