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  • Support for jagged array · 4 ✖

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  • MEMBER · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1458213875 https://github.com/pydata/xarray/issues/1482#issuecomment-1458213875 https://api.github.com/repos/pydata/xarray/issues/1482 IC_kwDOAMm_X85W6pPz dcherian 2448579 2023-03-07T13:54:23Z 2023-03-07T13:54:23Z MEMBER

@Material-Scientist We have decent support for pydata/sparse arrays. It seems like these would work for you

We do not support the pandas extension arrays at the moment.

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  Support for jagged array 243964948
320997555 https://github.com/pydata/xarray/issues/1482#issuecomment-320997555 https://api.github.com/repos/pydata/xarray/issues/1482 MDEyOklzc3VlQ29tbWVudDMyMDk5NzU1NQ== shoyer 1217238 2017-08-08T15:46:55Z 2017-08-08T15:46:55Z MEMBER

I understand why this could be useful, but I don't see how we could possibly make it work.

The notion of "fixed dimension size" is fundamental to both NumPy arrays (upon which xarray is based) and the xarray Dataset/DataArray. There are lots of various workarounds (e.g., using padding or a MultiIndex) but first class support for jagged arrays would break our existing data model too severely.

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  Support for jagged array 243964948
316328685 https://github.com/pydata/xarray/issues/1482#issuecomment-316328685 https://api.github.com/repos/pydata/xarray/issues/1482 MDEyOklzc3VlQ29tbWVudDMxNjMyODY4NQ== fujiisoup 6815844 2017-07-19T09:33:41Z 2017-07-19T09:42:23Z MEMBER

I have a similar use case and I often use MultiIndex, which (partly) enables to handle hierarchical data structure.

For example, ```python In [1]: import xarray as xr ...: import numpy as np ...: ...: # image 0, size [3, 4] ...: data0 = xr.DataArray(np.arange(12).reshape(3, 4), dims=['x', 'y'], ...: coords={'x': np.linspace(0, 1, 3), ...: 'y': np.linspace(0, 1, 4), ...: 'image_index': 0}) ...: # image 1, size [4, 5] ...: data1 = xr.DataArray(np.arange(20).reshape(4, 5), dims=['x', 'y'], ...: coords={'x': np.linspace(0, 1, 4), ...: 'y': np.linspace(0, 1, 5), ...: 'image_index': 1}) ...: ...: data = xr.concat([data0.expand_dims('image_index').stack(xy=['x', 'y', 'image_index']), ...: data1.expand_dims('image_index').stack(xy=['x', 'y', 'image_index'])], ...: dim='xy')

In [2]: data Out[2]: <xarray.DataArray (xy: 32)> array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]) Coordinates: * xy (xy) MultiIndex - x (xy) float64 0.0 0.0 0.0 0.0 0.5 0.5 0.5 0.5 1.0 1.0 1.0 ... - y (xy) float64 0.0 0.3333 0.6667 1.0 0.0 0.3333 0.6667 1.0 ... - image_index (xy) int64 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 ...

In [3]: data.sel(image_index=0) # gives data0 Out[3]: <xarray.DataArray (xy: 12)> array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) Coordinates: * xy (xy) MultiIndex - x (xy) float64 0.0 0.0 0.0 0.0 0.5 0.5 0.5 0.5 1.0 1.0 1.0 1.0 - y (xy) float64 0.0 0.3333 0.6667 1.0 0.0 0.3333 0.6667 1.0 0.0 ...

In [4]: data.sel(x=0.0) # x==0.0 for both images Out[4]: <xarray.DataArray (xy: 9)> array([0, 1, 2, 3, 0, 1, 2, 3, 4]) Coordinates: * xy (xy) MultiIndex - y (xy) float64 0.0 0.3333 0.6667 1.0 0.0 0.25 0.5 0.75 1.0 - image_index (xy) int64 0 0 0 0 1 1 1 1 1 ```

<s>I think the above solution is essentially equivalent with

all images of the same size into its own dimension</s>

EDIT: I didn't understand the comment correctly. The above corresponds to that all the images are flattened out and combined along one large dimension.

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  Support for jagged array 243964948
316317971 https://github.com/pydata/xarray/issues/1482#issuecomment-316317971 https://api.github.com/repos/pydata/xarray/issues/1482 MDEyOklzc3VlQ29tbWVudDMxNjMxNzk3MQ== fmaussion 10050469 2017-07-19T08:52:20Z 2017-07-19T08:52:20Z MEMBER

"Supported", yes, in the sense that you can create a DataArray for each of your differently sized arrays without any problem. If you want to store them all in a Dataset, you'll have to give a different dimension name for each new dimension, which can be clumsy.

However, it is true that xarray shines at handling more structured data and that most examples in the docs are those of dataset variables sharing similar dimensions. What kind of "support" exactly were you thinking of?

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  Support for jagged array 243964948

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