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Small edits to season grouping post #784

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Jun 17, 2025
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Small edits to season grouping post #784

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a0e2dd6
Small edits
dcherian Jun 17, 2025
e701f65
one more tweak
dcherian Jun 17, 2025
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23 changes: 19 additions & 4 deletions src/posts/season-grouping/index.md
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Expand Up @@ -70,8 +70,10 @@ Data variables:

Overlapping seasons are supported:

```
>>> ds.groupby(time=SeasonGrouper(["DJFM", "MAMJ", "JJAS", "SOND"])).count()
```python
>>> four_monthly = ds.groupby(time=SeasonGrouper(["DJFM", "MAMJ", "JJAS", "SOND"])).count()
>>> four_monthly.count()

<xarray.Dataset> Size: 43kB
Dimensions: (lat: 25, lon: 53, season: 4)
Coordinates:
Expand All @@ -83,6 +85,18 @@ Data variables:
Attributes: (5)
```

Let's make a plot using [FacetGrid](https://docs.xarray.dev/en/stable/user-guide/plotting.html#faceting)!

```
four_monthly.mean().air.plot(
col="season", cmap="magma", robust=True, col_wrap=2
)
```

<p align='center'>
<img src='/posts/season-grouping/four-month-seasons.png' width='70%' />
</p>

### SeasonResampler

```python
Expand Down Expand Up @@ -137,7 +151,7 @@ Attributes: (5)

These new Grouper objects compose well with grouping over other arrays ([see blog post](https://xarray.dev/blog/multiple-groupers/)), for example

```
```python
>>> from xarray.groupers import BinGrouper
>>>
>>> ds.groupby(lat=BinGrouper(bins=2), time=SeasonResampler(["JF", "MAM", "JJAS", "OND"])).count()
Expand All @@ -162,8 +176,9 @@ Given the user's definition of seasons, we construct the appropriate array of in

## Limitations

1. `SeasonGrouper` does not support the `drop_incomplete` option yet. This would be a great contribution.
1. `SeasonGrouper` does not support the `drop_incomplete` option yet. This would be a great contribution ([issue](https://github.com/pydata/xarray/issues/10426)).
2. `SeasonResampler` does not support overlapping seasons. This seems much harder to solve.
3. Rechunking to a seasonal frequency with `SeasonResampler`` is not supported yet. This would also be a great contribution ([issue](https://github.com/pydata/xarray/issues/10425)). Such rechunking is a remarkably useful technique in making climatology calculations much more parallel-friendly. For example: `ds.chunk(time=TimeResampler("10YS"))` rechunks so that 10 years of data is contained in a single chunk. See these two Pangeo discourse forum posts ([1](https://discourse.pangeo.io/t/xarray-memoryerror-with-groupby-workloads/4273/9), [2](https://discourse.pangeo.io/t/best-practice-advice-on-parallel-processing-a-suite-of-zarr-files-with-dask-and-xarray/5201/9)) for applications of this technique.

## Summary

Expand Down