Allow basic xarray commands to be applied to whole PredictionEnsemble object

I think it would be extremely useful to be able to call something like ReferenceEnsemble.sel(time=slice(1980, 2000)) and have it apply to every product (e.g., reconstruction, data, uninitialized run).

Some commands I’d like to implement:

• sel()
• isel()
• squeeze()
• groupyby()
• rolling()
• resample()

[aaronspring]

I had strong objections about introducing such things after two weeks.

[bradyrx]

@ahuang11 – thanks for the clarifications. I figured there were alleviations for all points but wanted to see what you had to say. I think accessor is the way to go, and maybe that’s the major focus for the v2 rollout that you and I can focus on. I am particularly happy to hear we can rewrite as cp = ClimPred(ds) for instance.

Yes, v2 sounds like a good target. I suppose I want to get this right ahead of time so it won’t be like a Python 2 to 3 transition if this package gets popular 😛

I agree 100%. This forced me to learn python classes anyhow so I’m not mad. Let’s put a pin in this for now and pick it up in a conference call following v1’s completion. For now, we can have the classes access our updated functions (which shouldn’t require change) and then will do a rewrite “early on” to switch to accessors.

For xr commands like .groupby(‘time.year’).mean(‘time’) or coarsen() I dont see any differences between perfect-model and hindcast type.

Agreed. My comment was more about comparisons and so on. But we can alleviate that by forcing a ds.type attribute on intiialization.

Good conversation! Excited to dig into this. Appropriately implementing this will put the package in the place I really want to see it… with smart easy-to-use objects and methods.