Source code for cf_xarray.coding

Encoders and decoders for CF conventions not implemented by Xarray.
import numpy as np
import pandas as pd
import xarray as xr

[docs] def encode_multi_index_as_compress(ds, idxnames=None): """ Encode a MultiIndexed dimension using the "compression by gathering" CF convention. Parameters ---------- ds : xarray.Dataset Dataset with at least one MultiIndexed dimension. idxnames : hashable or iterable of hashable, optional Dimensions that are MultiIndex-ed. If None, will detect all MultiIndex-ed dimensions. Returns ------- xarray.Dataset Encoded Dataset with ``name`` as a integer coordinate with a ``"compress"`` attribute. References ---------- CF conventions on `compression by gathering <>`_ """ if idxnames is None: idxnames = tuple( name for name, idx in ds.indexes.items() if isinstance(idx, pd.MultiIndex) # After the flexible indexes refactor, all MultiIndex Levels # have a MultiIndex but the name won't match. # Prior to that refactor, there is only a single MultiIndex with name=None and ( == name if is not None else True) ) elif isinstance(idxnames, str): idxnames = (idxnames,) if not idxnames: raise ValueError("No MultiIndex-ed dimensions found in Dataset.") encoded = ds.reset_index(idxnames) for idxname in idxnames: mindex = ds.indexes[idxname] coords = dict(zip(mindex.names, mindex.levels)) encoded.update(coords) for c in coords: encoded[c].attrs = ds[c].attrs encoded[c].encoding = ds[c].encoding encoded[idxname] = np.ravel_multi_index(, mindex.levshape) encoded[idxname].attrs = ds[idxname].attrs.copy() if ( "compress" in encoded[idxname].encoding or "compress" in encoded[idxname].attrs ): raise ValueError( f"Does not support the 'compress' attribute in {idxname}.encoding or {idxname}.attrs. " "This is generated automatically." ) encoded[idxname].attrs["compress"] = " ".join(mindex.names) return encoded
[docs] def decode_compress_to_multi_index(encoded, idxnames=None): """ Decode a compressed variable to a pandas MultiIndex. Parameters ---------- encoded : xarray.Dataset Encoded Dataset with variables that use "compression by gathering".capitalize. idxnames : hashable or iterable of hashable, optional Variable names that represents a compressed dimension. These variables must have the attribute ``"compress"``. If None, will detect all indexes with a ``"compress"`` attribute and decode those. Returns ------- xarray.Dataset Decoded Dataset with ``name`` as a MultiIndexed dimension. References ---------- CF conventions on `compression by gathering <>`_ """ decoded = xr.Dataset(data_vars=encoded.data_vars, attrs=encoded.attrs.copy()) if idxnames is None: idxnames = tuple( name for name in encoded.indexes if "compress" in encoded[name].attrs ) elif isinstance(idxnames, str): idxnames = (idxnames,) for idxname in idxnames: if "compress" not in encoded[idxname].attrs: raise ValueError("Attribute 'compress' not found in provided Dataset.") if not isinstance(encoded, xr.Dataset): raise ValueError( f"Must provide a Dataset. Received {type(encoded)} instead." ) names = encoded[idxname].attrs["compress"].split(" ") shape = [encoded.sizes[dim] for dim in names] indices = np.unravel_index(encoded[idxname].data, shape) try: from xarray.indexes import PandasMultiIndex variables = { dim: encoded[dim].isel({dim: xr.Variable(data=index, dims=idxname)}) for dim, index in zip(names, indices) } decoded = decoded.assign_coords(variables).set_xindex( names, PandasMultiIndex ) except ImportError: arrays = [encoded[dim].data[index] for dim, index in zip(names, indices)] mindex = pd.MultiIndex.from_arrays(arrays, names=names) decoded.coords[idxname] = mindex decoded[idxname].attrs = encoded[idxname].attrs.copy() for coord in names: variable = encoded._variables[coord] decoded[coord].attrs = variable.attrs.copy() decoded[coord].encoding = variable.encoding.copy() del decoded[idxname].attrs["compress"] return decoded