issues

^{Originally posted by **tlsw231** July 7, 2022} I am trying to use `map_blocks` to: ingest a multi-dimensional array as input, reduce along one dimension and add extra dimensions to the output. Is this possible? I am attaching a simple MRE below that gives me an `zip argument #2 must support iteration` error. Any pointers on what I might be doing wrong? [My real example is a 3d-dataset with `(time,lat,lon)` dimensions and I am trying to reduce along `time` while adding two new dimensions to the output. I tried so many things and got so many errors, including the one in the title, that I thought it is better to first understand how `map_blocks` works!] ``` # The goal is to feed in a 2d array, reduce along one dimension and add two new dimensions to the output. chunks={} dummy = xr.DataArray(data=np.random.random([8,100]),dims=['dim1','dim2']).chunk(chunks) def some_func(func): dims=func.dims n1 = len(func[func.dims[1]]) # This is 'dim2', we will average along 'dim1' below in the for loop newdim1 = 2; newdim2 = 5; output = xr.DataArray(np.nan*np.ones([n1,newdim1,newdim2]),dims=[dims[1],'new1','new2']) for n in range(n1): fmean = func.isel(dim2=n).mean(dims[0]).compute() for i in range(newdim1): for j in range(newdim2): output[n,i,j] = fmean return output #out = some_func(dummy) # This works template=xr.DataArray(np.nan*np.ones([len(dummy.dim2),2,5]), dims=['dim2','new1','new2']) out = xr.map_blocks(some_func,dummy,template=template).compute() # gives me the error message in the title ``` [Edit: Fixed a typo in the `n1 = len(func[func.dims[1]])` line, of course getting the same error.]