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- DrTodd13 · 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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1247294419 | https://github.com/pydata/xarray/issues/6807#issuecomment-1247294419 | https://api.github.com/repos/pydata/xarray/issues/6807 | IC_kwDOAMm_X85KWDPT | DrTodd13 2584128 | 2022-09-14T20:57:35Z | 2022-09-14T20:57:35Z | NONE |
Have they improved recently to support more than 1D arrays? |
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Alternative parallel execution frameworks in xarray 1308715638 | |
972112384 | https://github.com/pydata/xarray/issues/5970#issuecomment-972112384 | https://api.github.com/repos/pydata/xarray/issues/5970 | IC_kwDOAMm_X8458UIA | DrTodd13 2584128 | 2021-11-17T21:57:28Z | 2021-11-17T22:08:03Z | NONE | @TomNicholas @keewis ```python import ramba import numpy as np import xarray ra1 = ramba.random.randn(10,20) xa1 = xarray.DataArray(ra1) np1 = xa1.data.asarray() print("np1:", np1) print("xa1.data:", xa1.data, type(xa1.data), xa1.data.dtype, type(xa1.data.dtype)) xa2 = xa1 + 10.0 xa22 = xa2 * 7.1 xa3 = np.sin(xa22) xa4 = xa3.transpose() xa5 = xa4.sum() print("xa5:", xa5, type(xa5)) ``` Using the Can anybody suggest or provide one? |
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Support Ramba distributed arrays. 1050082867 | |
972095543 | https://github.com/pydata/xarray/pull/5980#issuecomment-972095543 | https://api.github.com/repos/pydata/xarray/issues/5980 | IC_kwDOAMm_X8458QA3 | DrTodd13 2584128 | 2021-11-17T21:46:40Z | 2021-11-17T21:46:40Z | NONE | @keewis Thanks for the note. I originally tried that approach I thought but after this PR I went back and tried again and made more progress so I'm closing this PR. |
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Added ramba to duck type. 1051491070 | |
966741762 | https://github.com/pydata/xarray/issues/5970#issuecomment-966741762 | https://api.github.com/repos/pydata/xarray/issues/5970 | IC_kwDOAMm_X845n08C | DrTodd13 2584128 | 2021-11-12T01:19:52Z | 2021-11-12T01:19:52Z | NONE | @TomNicholas Please add an in-progress label for the associated PR I opened. |
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Support Ramba distributed arrays. 1050082867 |
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