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- Memory usage of `da.rolling().construct` · 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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779892262 | https://github.com/pydata/xarray/issues/3332#issuecomment-779892262 | https://api.github.com/repos/pydata/xarray/issues/3332 | MDEyOklzc3VlQ29tbWVudDc3OTg5MjI2Mg== | dcherian 2448579 | 2021-02-16T14:59:11Z | 2021-02-16T14:59:11Z | MEMBER | so it's pad then view, so a copy of the original array is made, not the strided array. |
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Memory usage of `da.rolling().construct` 496809167 | |
705098106 | https://github.com/pydata/xarray/issues/3332#issuecomment-705098106 | https://api.github.com/repos/pydata/xarray/issues/3332 | MDEyOklzc3VlQ29tbWVudDcwNTA5ODEwNg== | shoyer 1217238 | 2020-10-07T17:54:32Z | 2020-10-07T17:54:32Z | MEMBER | The loop via slicing is not a terrible option. The trick construct() uses with views only really makes sense with NumPy arrays, not with dask. There are also true streaming moving window algorithms that work very well for computing various statistics (e.g., mean and variance). These are implemented in bottleneck (e.g., |
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Memory usage of `da.rolling().construct` 496809167 | |
534709955 | https://github.com/pydata/xarray/issues/3332#issuecomment-534709955 | https://api.github.com/repos/pydata/xarray/issues/3332 | MDEyOklzc3VlQ29tbWVudDUzNDcwOTk1NQ== | shoyer 1217238 | 2019-09-24T19:21:22Z | 2019-09-24T19:21:22Z | MEMBER | It uses a view for allocating the initial result, but I think applying boundary conditions means that we end up doing a copy. |
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Memory usage of `da.rolling().construct` 496809167 | |
533908429 | https://github.com/pydata/xarray/issues/3332#issuecomment-533908429 | https://api.github.com/repos/pydata/xarray/issues/3332 | MDEyOklzc3VlQ29tbWVudDUzMzkwODQyOQ== | dcherian 2448579 | 2019-09-22T19:02:07Z | 2019-09-22T19:02:07Z | MEMBER | It should be returning a view. |
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Memory usage of `da.rolling().construct` 496809167 |
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