Merging two datasets using `xarray.combine_nested` goes wrong

[HJA24]

What is your issue?

Hi, I want to merge two xarray.Dataset-objects. I am using version 2023.5.0.

My code looks as follows:

import numpy as np
import xarray as xr

N_CHAINS = 4
N_DRAWS = 1000
N_PLAYERS = 5

player_idx = [1, 1, 2, 3, 4, 4, 0, 0, 2, 2]
opponent_idx = [0, 3, 1, 4, 1, 1, 1, 4, 3, 3]

h2h_idx = pd.MultiIndex.from_tuples(
    tuple(zip(player_idx, opponent_idx)), names=('player_id', 'opponent_id')
)

obs = xr.Dataset(
    data_vars=dict(
        n_points_won=(['h2h_id'], np.array([11, 11, 8, 9, 4, 11, 7, 11, 11, 11])),
        n_points_lost=(['h2h_id'], np.array([9, 9, 11, 11, 11, 1, 11, 2, 3, 6])),
    ),
    coords=dict(
        h2h_id=(['h2h_id'], h2h_idx),
    )
)

alpha = np.random.rand(N_CHAINS, N_DRAWS, N_PLAYERS, N_PLAYERS) * 100
beta = np.random.rand(N_CHAINS, N_DRAWS, N_PLAYERS, N_PLAYERS) * 100

pos = xr.Dataset(
    data_vars=dict(
        alpha=(['chain', 'draw', 'player_id', 'opponent_id'], alpha),
        beta=(['chain', 'draw', 'player_id', 'opponent_id'], beta),
    ),
    coords=dict(
        chain=(['chain'], list(range(N_CHAINS))),
        draw=(['draw'], list(range(N_DRAWS))),
        player_id=(['player_id'], list(range(N_PLAYERS))),
        oppponent_id=(['opponent_id'], list(range(N_PLAYERS))),
    ),
)
 

combined = xr.combine_nested([obs, pos], concat_dim=['player_id', 'opponent_id']) # this results in an error

I am getting the following error:

ValueError: concat_dims has length 2 but the datasets passed are nested in a 1-dimensional structure

How can I merge the two datasets?
The goal is to create a xarray.Dataset with coordinates h2h_id, player_id, opponent_id, chain and draw. Basically for each data variable in obs I want to concat the corresponding alpha- and beta values from pos of that particular observation (through player_id and opponent_id)

Thanks in advance

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[max-sixty]

Hi @HJA24 — this seems like a usage question rather than an issue with the library. I'll convert it to a discussion. You're more likely to get engagement if you can slim down your example into a much smaller MCVE.