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  • Hoeze · 2 ✖

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  • N-dimensional boolean indexing · 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
844486483 https://github.com/pydata/xarray/issues/5179#issuecomment-844486483 https://api.github.com/repos/pydata/xarray/issues/5179 MDEyOklzc3VlQ29tbWVudDg0NDQ4NjQ4Mw== Hoeze 1200058 2021-05-19T21:27:17Z 2021-05-19T21:27:17Z NONE

fyi, I updated the boolean indexing to support additional or missing dimensions: https://gist.github.com/Hoeze/96616ef9d179180b0b7de97c97e00a27 I'm using this on a 4D-array with >300GB to flatten three of the four dimensions and it works, even on 64GB of RAM.

 
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   N-dimensional boolean indexing  860418546
821881984 https://github.com/pydata/xarray/issues/5179#issuecomment-821881984 https://api.github.com/repos/pydata/xarray/issues/5179 MDEyOklzc3VlQ29tbWVudDgyMTg4MTk4NA== Hoeze 1200058 2021-04-17T20:22:13Z 2021-04-17T20:27:25Z NONE

@max-sixty The reason is that my method is basically a special case of point-wise indexing: http://xarray.pydata.org/en/stable/indexing.html#more-advanced-indexing You can get the same result by calling: ```python core_dim_locs = {key: value for key, value in core_dim_locs_from_cond(mask, new_dim_name="newdim")}

pointwise selection

data.sel( dim_0=outliers_subset["dim_0"], dim_1=outliers_subset["dim_1"], dim_2=outliers_subset["dim_2"] ) ``` (Note that you loose chunk information by this method, that's why it is less efficient)

When you want to select random items from a N-dimensional array, you can either model the result as some sparse array or by stacking the dimensions. (OK, stacking the dimensions means also a sparse COO encoding...)

 
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   N-dimensional boolean indexing  860418546

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