issue_comments

Thanks for your follow up comment. I understand much better where you are coming from now! We do sincerely appreciate your feedback and contributions. Yes: we definitely want xarray to be adopted widely!

There is no doubt that these weird calendars are a continuous source of frustration. The challenge is compounded by the fast that, basically, only the climate community needs them. All of the fancy time indexing stuff in pandas is most likely there because of its value to the finance community.

Maybe a good path forward would be for @spencerkclark to outline what steps might be needed to get xarray's built in plotting to work with cftimeindex. That way, anyone who urgently needs this feature has some sort of roadmap for starting to implement it themselves.

Darn. Just when I thought the time stuff was sorted. This is (yet another) deal breaker as far as recommending mass adoption goes.

@aidanheerdegen -- support for unconventional time coordinates (via cftime) is not a trivial problem--there are many special cases and complex logic to deal with. Rather than being discouraged by this bug, I encourage you to take a longer view to see how much this support has improved over the past year. One year ago, xarray could not have decoded this time coordinate at all!

I'm curious what you are referring to with your comment about "mass adoption." To whom are you making recommendations about adoption of xarray? And what do you consider the numerous other dealbreakers to be? I hope you see that these blanket comments could be read as quite discouraging and negative to the volunteer developers working hard on xarray.

Is there an estimate when, or if, cftime indexes will be supported by xarray's .plot() method?

The work on cftimeindex is being done entirely on a volunteer basis by graduate students like @spencerkclark (👏 👏 👏). In order to make xarray development progress faster, we need more contributors to the project. In my view, the ideal place to recruit such contributors is from within the paid computational support staff from major climate modeling centers. These people are ideally poised to understand what features are most important to users, and they are actually paid to help develop tools (like xarray) to help users be more productive. We recently created a contributor guide to make it easier for new contributors to come on board. If you can think of anyone who might be interested in contributing to xarray, please let us know, either here or via a private channel.