pull_requests: 125936835

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id node_id number state locked title user body created_at updated_at closed_at merged_at merge_commit_sha assignee milestone draft head base author_association auto_merge repo url merged_by
125936835 MDExOlB1bGxSZXF1ZXN0MTI1OTM2ODM1 1457 closed 0 Feature/benchmark 2443309 - [x] Closes #1257 - [x] Tests added / passed - [x] Passes ``git diff upstream/master | flake8 --diff`` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API This is a very bare bones addition of the [asv](https://github.com/spacetelescope/asv/) benchmarking tool to xarray. I have added four very rudimentary benchmarks in the `dataset_io.py` module. Usage of `asv` is pretty straightforward but I'll outline the steps for those who want to try this out: ``` cd xarray conda install asv -c conda-forge asv run # this will install some conda environments in ./.asv/envs asv publish # this collates the results asv preview # this will launch a web server so you can visually compare the tests ``` Before I go any further, I want to get some input from @pydata/xarray on what we want to see in this PR. In previous projects, I have found designing tests after the fact can end up being fairly arbitrary and I want to avoid that if at all possible. I'm guessing that we will want to focus our efforts for now on I/O and dask related performance but how we do that is up for discussion. cc @shoyer, @rabernat, @MaximilianR, @Zac-HD 2017-06-16T00:11:52Z 2017-11-13T04:09:53Z 2017-07-26T16:17:34Z 2017-07-26T16:17:34Z 96e6e8f7ad8dd493c9d15df2951999c6dd04e8c9   2415632 0 6c058083bfa7e4e044e50ea7e048c60c35686e22 5d245b22e9500a7eb805193ba5c65bb5474a5ae1 MEMBER   13221727 https://github.com/pydata/xarray/pull/1457  

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