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  • ivirshup · 2 ✖

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  • Repeated coordinates leads to unintuitive (broken?) indexing behaviour · 2 ✖

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  • NONE · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
580999532 https://github.com/pydata/xarray/issues/3731#issuecomment-580999532 https://api.github.com/repos/pydata/xarray/issues/3731 MDEyOklzc3VlQ29tbWVudDU4MDk5OTUzMg== ivirshup 8238804 2020-02-01T06:22:37Z 2020-02-01T06:22:37Z NONE

This has also come up over in DimensionalData.jl, which I think is going for behavior I like. What I think would happen:

da.isel(dim='ambiguous_dim')

The selection is over all dimensions of that name.

da.mean(dim='ambiguous_dim')

The command is to reduce over dimensions of that name, the reduction should be performed over all dimensions with that name.

 
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   Repeated coordinates leads to unintuitive (broken?) indexing behaviour 557257598
580534808 https://github.com/pydata/xarray/issues/3731#issuecomment-580534808 https://api.github.com/repos/pydata/xarray/issues/3731 MDEyOklzc3VlQ29tbWVudDU4MDUzNDgwOA== ivirshup 8238804 2020-01-31T01:07:28Z 2020-01-31T01:07:52Z NONE

Why not allow multiple dimensions with the same name? They can be disambiguated with positional indexing for when it matters. I think support for this would be useful for pairwise measures.

Here's a fun example/ current buggy behaviour:

```python import numpy as np import xarray as xr from string import ascii_letters

idx1 = xr.IndexVariable("dim1", [f"dim1-{i}" for i in ascii_letters[:10]]) idx2 = xr.IndexVariable("dim2", [f"dim2-{i}" for i in ascii_letters[:5]])

da1 = xr.DataArray(np.random.random_sample((10, 5)), coords=(idx1, idx2)) da2 = xr.DataArray(np.random.random_sample((5, 10)), coords=(idx2, idx1))

da1 @ da2

<xarray.DataArray ()>

array(13.06261098)

```

 
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   Repeated coordinates leads to unintuitive (broken?) indexing behaviour 557257598

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