issue_comments

Thanks all, but it returns the ValueError: output array is read-only. Apparently, this is caused by .expand_dims() (see https://github.com/pydata/xarray/issues/3813). The work-around seems to be python nc = nc.expand_dims(time=times_).copy() Though I have no idea why.

Thanks, it finally worked.

The values in nc.lon are technically ordered 'as they would be on a mercator projected map with origin at 0 N-0 E' considering I am dealing with data around 180 lon. Not that it would mater for pandas/xarray in that case. I suppose re-projecting it on a 0-360 would be the only way around this specific issue.

And to answer earlier comments, sub = nc_bug.sel(lon = 161.001, tolerance=.1) raised the following: KeyError: "not all values found in index 'lon'. Try setting themethodkeyword argument (example: method='nearest')." Which, when tried raised ValueError: index must be monotonic increasing or decreasing. It is indeed a problem with the order of the index.

Thanks for your help and your time.

Returned the following: NotImplementedError: cannot use ``method`` argument if any indexers are slice objects

``` Traceback (most recent call last):

Cell In [3], line 1 sub = nc_bug.sel(lon = slice(161.001, 162.001), tolerance=0.1)

File ~\Installed_Programs\Anaconda3\envs\phd\lib\site-packages\xarray\core\dataset.py:2533 in sel query_results = map_index_queries(

File ~\Installed_Programs\Anaconda3\envs\phd\lib\site-packages\xarray\core\indexing.py:183 in map_index_queries results.append(index.sel(labels, **options)) # type: ignore[call-arg]

File ~\Installed_Programs\Anaconda3\envs\phd\lib\site-packages\xarray\core\indexes.py:377 in sel indexer = _query_slice(self.index, label, coord_name, method, tolerance) ```

Wow, I should really dig deeper in the documentation sometimes. Thanks a lot, it fixed it. Although it does remove the original nan, which can be reintroduced using a mask. Thank you again for your time.

Thank you for upgrading this question. Using keewis suggestion:

We could try to work around this by wrapping argmin and argmax in a function that ravels / unravels the reduce dimensions.

I had a look into numpy documentation and came up with a work around like so: ```python import numpy as np, xarray as xr

arr = xr.DataArray(np.ones((5,6,7))) arr[1,3,2], arr[3,1,4] = 0, 2

print('Values:',arr.min(), arr.max())

min_pos = np.concatenate(np.where(arr == arr.min())) max_pos = np.concatenate(np.where(arr == arr.max())) print('Indexes on nD:', min_pos, max_pos) ``` It is not perfect I suppose but it is doing the job for now.