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  • keewis · 2 ✖

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  • Unnamed dimensions · 2 ✖

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  • MEMBER · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1435715372 https://github.com/pydata/xarray/issues/4079#issuecomment-1435715372 https://api.github.com/repos/pydata/xarray/issues/4079 IC_kwDOAMm_X85Vk0cs keewis 14808389 2023-02-18T16:51:14Z 2023-02-18T16:51:14Z MEMBER

hmm... would np.nan work?

not sure about alignment, but at least obj.sizes would break with multiple dims: while it does not compare as equal, the hash() of np.nan stays constant (same with any other object, I guess).

 
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   Unnamed dimensions 621078539
1434805981 https://github.com/pydata/xarray/issues/4079#issuecomment-1434805981 https://api.github.com/repos/pydata/xarray/issues/4079 IC_kwDOAMm_X85VhWbd keewis 14808389 2023-02-17T15:28:07Z 2023-02-17T15:28:07Z MEMBER

I'd probably use itertools.count() or the uuid module to generate globally unique dimension names... something like ```python class UnnamedDimensions: def __init__(self): self.dimension_names = (f"unnamed_dim{number}" for number in itertools.count())

def __call__(self, n):
    return list(itertools.islice(dimension_names, None, n))

unnamed_dimensions = _UnnamedDimensions() or using `uuid` (probably overkill):python def unnamed_dimensions(n): return [uuid.uuid4() for _ in range(n)] you'd use it like this:python d1 = xr.DataArray(data=[1, 2], dims=unnamed_dimensions(1)) d2 = xr.DataArray(data=[[1, 2]], dims=unnamed_dimensions(2)) ``` which would make "unnamed" a bit more explicit.

Edit: that's probably not so different from what you meant with d1_i

 
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   Unnamed dimensions 621078539

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