issue_comments

Thanks everyone. Just to be clear, I am using map_blocks in a rather complicated way (way more than this MWE) and at the moment of the map_blocks call, the code doesn't know the dimension order of the output, only the names. Moreover, in this code, the wrapped functions are too complex for map_blocks to infer the template. The idea is that all wrapped funcs reduce some dims or add some other (but with known sizes), thus I can construct a template, but the dim order is uncertain.

However, I understand that it is expected from map_blocks that the result fits the template, so my expectation was not correct. I'll leave the "workaround" in my code and close this issue. Thanks!

I expected dimension order not to matter, as it is the case in most of xarray.

Yeah... the template is special, it must exactly match what the function returns. In your case, it seems like you know the dimension order. So you can transpose the template object to that dimension order

I expected dimension order not to matter, as it is the case in most of xarray. Right now, my code wraps func in a another function that performs this transpose when needed.

Isn't it the case the order matters here because you are providing a template for map_blocks to follow? Would your code still work if you were to leave map_blocks to do the inference of the output dataset on its own as Deepak pointed out? From the code snippet you provided, it appears that map_blocks works without the template

python In [10]: dac.map_blocks(func).load() Out[10]: <xarray.DataArray <this-array> (y: 3, x: 2)> array([[0, 3], [1, 4], [2, 5]]) Dimensions without coordinates: y, x

Haha, sorry, I didn't explain this properly.

This MWE is just MWE. The use case I have of map_blocks is in a decorator. At the moment I write the map_blocks call, I do not know if func will transpose the result or not. In my case, the template is generated from a list of dimensions to reduce and new dimensions.

I realize this might be an edge case that is too narrow to merit a fix. I expected dimension order not to matter, as it is the case in most of xarray. Right now, my code wraps func in a another function that performs this tranpose when needed.

You should be providing template = dac.transpose(). Does it work then? You can also not provide template in which case it will infer the right thing.

Hi @max-sixty, just edited the top comment to include the traceback.

The MWE does work with return d.

Hi @aulemahal !

Does this work with return d? Could you add the stack trace?