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- Support for jagged array · 4 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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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
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 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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