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Python 3
python3
 
.. currentmodule:: cf_xarray

---
tags: [remove-cell]
---
import cf_xarray as cfxr
import numpy as np
import pandas as pd
import xarray as xr
xr.set_options(display_expand_data=False)

Encoding and decoding

cf_xarray aims to support encoding and decoding variables using CF conventions not yet implemented by Xarray.

Geometries

See for more.

Compression by gathering

The "compression by gathering" convention could be used for either {py:class}pandas.MultiIndex objects or pydata/sparse arrays.

MultiIndex

cf_xarray provides {py:func}encode_multi_index_as_compress and {py:func}decode_compress_to_multi_index to encode MultiIndex-ed dimensions using "compression by gethering".

Here's a test dataset

ds = xr.Dataset(
    {"landsoilt": ("landpoint", np.random.randn(4), {"foo": "bar"})},
    {
        "landpoint": pd.MultiIndex.from_product(
            [["a", "b"], [1, 2]], names=("lat", "lon")
        )
    },
)
ds

First encode (note the "compress" attribute on the landpoint variable)

encoded = cfxr.encode_multi_index_as_compress(ds, "landpoint")
encoded

At this point, we can write encoded to a CF-compliant dataset using {py:func}xarray.Dataset.to_netcdf for example. After reading that file, decode using

decoded = cfxr.decode_compress_to_multi_index(encoded, "landpoint")
decoded

We roundtrip perfectly

ds.identical(decoded)

Sparse arrays

This is unsupported currently but a pull request is welcome!