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@pydata/xarray let me know any feedback on the email announcements

Success! Thanks team!

On Tue, Sep 17, 2019 at 6:33 PM Maximilian Roos notifications@github.com wrote:

Closed #3257 https://github.com/pydata/xarray/issues/3257.

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I just merged https://github.com/conda-forge/xarray-feedstock/pull/50, so the release should be available on conda-forge soon.

Great! My mistake

https://github.com/pydata/xarray/releases/tag/v0.13.0 ?

@dcherian did you add a tag? I don't see that yet

OK I've done everything but - [x] upload to PyPI - [x] update RTD - [ ] announcement email

@max-sixty Can you take over emailing the users list?

1. Issue the release announcement! For bug fix releases, I usually only email xarray@googlegroups.com. For major/feature releases, I will email a broader list (no more than once every 3-6 months): pydata@googlegroups.com, xarray@googlegroups.com, numpy-discussion@scipy.org, scipy-user@scipy.org, pyaos@lists.johnny-lin.com Google search will turn up examples of prior release announcements (look for "ANN xarray").

Thanks! Done with release on PyPI: https://pypi.org/project/xarray/0.13.0/

Time to announce!

@dcherian done! you should have permissions for both RTD and pypi now

Hahaha, I just signed up at PyPI as dcherian

EDIT: I'm dcherian on RTD also.

Maintainers permissions enable: "Can upload releases for a package. Cannot add collaborators. Cannot delete files, releases, or the project."

I'm pretty happy passing that out to anyone interested :)

@shoyer I'm at maximilianroos on PyPI but maybe we lock that down tighter than the GH privileges?

Yes, and our Twitter account! Ping back when it's on PyPI and I'll send out

@dcherian (and anyone else who's interested) could you please send me your pypi and RTD account names?

@shoyer instructions in HOW_TO_RELEASE worked perfectly :)

Great—happy to help @dcherian—thank you!

OK happy to do so.

You will need to be listed as a package owner at https://pypi.python.org/pypi/xarray for this to work.

Do you want to add the rest of the core team as owners on pypi and RTD?

OK, @max-sixty or @dcherian do one of you want to take the lead on issuing the release this time? Everything should be in the HOW_TO_RELEASE checklist, but I'm sure something will come up -- nonetheless, it's good to increase the bus factor!

Yes we should release ASAP. #3313 and #3314 are the last two items on the checklist.

Anything final remaining? A couple of nice-to-haves on https://github.com/pydata/xarray/issues/3280 but otherwise I think all done?

If so, I have some time this week to help—I can coordinate

Yes, we can merge/rebase changes into "stable" even after cutting a release.

On Tue, Sep 10, 2019 at 1:56 PM Deepak Cherian notifications@github.com wrote:

On the other hand, I feel like most doc users are just looking at the "latest" docs

I'm not so sure of this. The default is stable. and it isn't very obvious how to change it.

We can also merge that branch multiple times...

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On the other hand, I feel like most doc users are just looking at the "latest" docs

I'm not so sure of this. The default is stable. and it isn't very obvious how to change it.

We can also merge that branch multiple times...

We should also merge the scipy19-docs branch since that has some nice changes

Ideally there are a few things we should finish: - #3199 - GRIB / ECMWF example for gallery - #3131 - My own tutorial on merging / combining datasets

I agree it would be nice to get these into a release. On the other hand, I feel like most doc users are just looking at the "latest" docs, so perhaps not as urgent as the other items on here.

We should also merge the scipy19-docs branch since that has some nice changes (ping @rabernat).

It would be nice to get #3239 in since it fixes a longstanding issue with open_mfdataset.

(FYI I am on vacation — can contribute next week)

I did some searching for "FutureWarning" and "DeprecationWarning", and turned up a bunch worth cleaning up for 0.13: https://github.com/pydata/xarray/issues/3280

Otherwise I think we're ready do a release?

Can't we have a nightly build of docker images with all the xarray depencencies?

This would help on Travis yes, but not on RTD which doesn't support docker. But otherwise yes, I've also found that CI is faster and more reliable with docker. In terms of resources "in general", I wonder if it wouldn't be nice to share a common testing image base with other packages of the pydata ecosytem.

Can't we have a nightly build of docker images with all the xarray depencencies? Then the individual CI jobs would literally become something like FROM xarray-ci-py37 RUN pip install .

RTD is struggling with resources, and I can understand that. What is really annoying (and the problem is often the same on travis) is that the conda install is taking a huge part of the build process resources. See e.g. https://github.com/readthedocs/readthedocs.org/issues/6025 , where I ended up using pip for very satisfying results (!).

FYI re RTD PR feature, too many PRs! :)

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FYI: we're on the waitlist for the RTD feature

I find it very nice that I make a new release on GitHub and packages appear on PyPI and the web docs are automatically updated to the new version.

How do you do this? i.e. trigger PyPI and RTD updates with a tag on github?

It looks like there's an experimental documentation builder for pull requests with read the docs. It would be interesting to explore using it in xarray: https://blog.readthedocs.com/building-docs-for-pull-requests/

Perfect - I emailed. Anything else like this, feel free to push onto us / me

My experience has been that the manual processes (e.g., checking release notes, finishing up deprecation cycles) take up most of the effort of issuing a new release. The commands 4-9 take about 5 minutes to run. I agree that the main value of automation there would be reducing the likelihood of manual mistakes.

It looks like there's an experimental documentation builder for pull requests with read the docs. It would be interesting to explore using it in xarray: https://blog.readthedocs.com/building-docs-for-pull-requests/

Yeah, automation is probably overkill if someone does it correctly each time – it does also mean hopefully no one makes a mistake / forgets a step

4. On the master branch, commit the release in git: git commit -a -m 'Release v0.X.Y' 5. Tag the release: git tag -a v0.X.Y -m 'v0.X.Y' 6. Build source and binary wheels for pypi: git clean -xdf # this deletes all uncommited changes! python setup.py bdist_wheel sdist 7. Use twine to register and upload the release on pypi. Be careful, you can't take this back! twine upload dist/xarray-0.X.Y* You will need to be listed as a package owner at https://pypi.python.org/pypi/xarray for this to work. 8. Push your changes to master: git push upstream master git push upstream --tags 9. Update the stable branch (used by ReadTheDocs) and switch back to master: git checkout stable git rebase master

This bit seems easily scriptable

Given that's 1 hour of work about once a month... Unless there is appetite for increasing the release frequency, I would say automation is overkill

What do you think about writing a small script and deploying with an azure pipeline, triggering only on new tags? Could include the standard running tests etc. I'd need to look into how authentication re PyPI would work if done remotely. Or is this begging the xkcd cartoon?

Also it would be great if you’d like to try going through the release process. We can potentially automate and/or remove some of those steps.

There are couple of small fixes for sparse arrays with open pull requests. I would lean toward getting those in before the release.

One thing that would be especially helpful is finalizing some deprecations that have been open for a while, e.g., the removal of the inplace argument and sel_points().