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  • shoyer · 5 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
275955436 https://github.com/pydata/xarray/issues/1208#issuecomment-275955436 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3NTk1NTQzNg== shoyer 1217238 2017-01-29T23:31:29Z 2017-01-29T23:31:29Z MEMBER

@fmaussion thanks for puzzling this one out!

@ghisvail thanks for the report!

 
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   Test failures on Debian if built with bottleneck 200908727
275448697 https://github.com/pydata/xarray/issues/1208#issuecomment-275448697 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3NTQ0ODY5Nw== shoyer 1217238 2017-01-26T17:14:26Z 2017-01-26T17:14:26Z MEMBER

@ghisvail Thanks for your diligence on this.

@fmaussion If you can turn one of these into a test case for bottleneck to report upstream that would be super helpful. I would probably start with test_groupby_sum. It's likely that this only occurs for arrays with a particular strides (memory layout) and shape, which is where my blind guess that I suggested on the bottleneck tracker was inspired by.

 
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   Test failures on Debian if built with bottleneck 200908727
273569412 https://github.com/pydata/xarray/issues/1208#issuecomment-273569412 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3MzU2OTQxMg== shoyer 1217238 2017-01-18T19:06:58Z 2017-01-18T19:06:58Z MEMBER

OK, thanks for looking into this!

On Wed, Jan 18, 2017 at 10:36 AM, Ghislain Antony Vaillant notifications@github.com wrote:

We'd need to wait for numpy-1.12.1 to be absolutely sure. I don't have time to deploy a dev version of numpy to test.

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/pydata/xarray/issues/1208#issuecomment-273561115, or mute the thread https://github.com/notifications/unsubscribe-auth/ABKS1qvrlv_WzrX9rpoAD5zPY58KNJEnks5rTlu2gaJpZM4LkFPn . 

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   Test failures on Debian if built with bottleneck 200908727
273560457 https://github.com/pydata/xarray/issues/1208#issuecomment-273560457 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3MzU2MDQ1Nw== shoyer 1217238 2017-01-18T18:34:05Z 2017-01-18T18:34:05Z MEMBER

Were you able to verify that the xarray tests pass after the numpy fix?

 
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   Test failures on Debian if built with bottleneck 200908727
272763117 https://github.com/pydata/xarray/issues/1208#issuecomment-272763117 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3Mjc2MzExNw== shoyer 1217238 2017-01-16T02:58:06Z 2017-01-16T02:58:06Z MEMBER

Thanks for the report. My guess is that this is an issue with the bottleneck build -- the large float values (e.g., 1e+248) in the final tests suggest some sort of overflow and/or memory corruption. The values summed in these tests are random numbers between 0 and 1.

Unfortunately, I can't reduce this locally using the conda build of bottleneck 1.2.0 on OS X, and our build on Travis-CI (using Ubuntu and conda) is also succeeding. Do you have any more specific details that describe your test setup, other than using the pre-build bottleneck 1.2.0 package?

If my hypothesis is correct, this test on bottleneck might trigger a test failure in the ubuntu build process (but it passed in bottleneck's tests on TravisCI): https://github.com/kwgoodman/bottleneck/compare/master...shoyer:possible-reduce-bug?expand=1#diff-a0a3ffc22e0a63118ba4a18e4ab845fc

 
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   Test failures on Debian if built with bottleneck 200908727

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