cf_xarray.accessor.CFAccessor#

class cf_xarray.accessor.CFAccessor(obj)[source]#

Common Dataset and DataArray accessor functionality.

_assert_valid_other_comparison(other)[source]#

_drop_missing_variables(variables)[source]#

_generate_repr(rich=False)[source]#

_get_all_cell_measures()[source]#: Get all cell measures defined in the object, adding CF pre-defined measures.

_maybe_to_dataarray(obj=None)[source]#

_maybe_to_dataset(obj=None)[source]#

_process_signature(func, args, kwargs, key_mappers)[source]#

Processes a function’s signature args, kwargs.

Binds *args so that everything is a Mapping from kwarg name to values
Calls _rewrite_values to rewrite any special CF names to normal xarray names. This uses key_mappers.
Unpacks arguments if necessary before returning them.

_rewrite_values(kwargs, key_mappers, var_kws)[source]#

Rewrites the values in a Mapping from kwarg to value.

Parameters:

kwargsMapping: Mapping from kwarg name to value
key_mappersMapping: Mapping from kwarg name to a Mapper function that will convert a given CF “special” name to an xarray name.
var_kwsList[str]: List of variable kwargs that need special treatment. e.g. **indexers_kwargs in isel

Returns:

dict of kwargs with fully rewritten values.

add_canonical_attributes(override=False, skip=None, verbose=False, source=None)[source]#

Add canonical CF attributes to variables with standard names.

Attributes are parsed from the official CF standard name table [1]. This function adds an entry to the “history” attribute.

Parameters:

overridebool: Override existing attributes.
skipstr, iterable, optional: Attribute(s) to skip: {"units", "grib", "amip", "description"}.
verbosebool: Print added attributes to screen.
sourceoptional: Path of cf-standard-name-table.xml or file object containing XML data. If None, use the latest CF standard name table (requires pooch).

Returns:

DataArray or Dataset: With attributes added.

Notes

The "units" attribute is never added to datetime-like variables.

References

[1]

https://cfconventions.org/standard-names.html

property axes: dict[str, list[Hashable]]#

Mapping valid Axis standard names for .cf[] to variable names.

This is useful for checking whether a key is valid for indexing, i.e. that the attributes necessary to allow indexing by that key exist. However, it will only return the Axis names ("X", "Y", "Z", "T") present in .coords, not in .data_vars.

Returns:

dict: Dictionary with keys that can be used with __getitem__ or as .cf[key]. Keys will be the appropriate subset of (“X”, “Y”, “Z”, “T”). Values are lists of variable names that match that particular key.

property cell_measures: dict[str, list[Hashable]]#

Mapping valid cell measure standard names for .cf[] to variable names.

This is useful for checking whether a key is valid for indexing, i.e. that the attributes necessary to allow indexing by that key exist.

Returns:

dict: Dictionary of valid cell measure names that can be used with __getitem__ or .cf[key].

property cf_roles: dict[str, list[Hashable]]#

Mapping cf_role names to variable names.

Returns:

dict: Dictionary mapping cf_role names to variable names.

References

Please refer to the CF conventions document : http://cfconventions.org/Data/cf-conventions/cf-conventions-1.8/cf-conventions.html#coordinates-metadata

Examples

>>> import cf_xarray
>>> from cf_xarray.datasets import dsg
>>> dsg.cf.cf_roles
{'profile_id': ['profile'], 'trajectory_id': ['trajectory']}

property coordinates: dict[str, list[Hashable]]#

Mapping valid Coordinate standard names for .cf[] to variable names.

This is useful for checking whether a key is valid for indexing, i.e. that the attributes necessary to allow indexing by that key exist. However, it will only return the Coordinate names ("latitude", "longitude", "vertical", "time") present in .coords, not in .data_vars.

Returns:

dict: Dictionary of valid Coordinate names that can be used with __getitem__ or .cf[key]. Keys will be the appropriate subset of ("latitude", "longitude", "vertical", "time"). Values are lists of variable names that match that particular key.

curvefit(coords, func, reduce_dims=None, skipna=True, p0=None, bounds=None, param_names=None, kwargs=None)[source]#

describe()[source]#: Print a string repr to screen.

differentiate(coord, *xr_args, positive_upward=False, **xr_kwargs)[source]#

Differentiate an xarray object.

Parameters:

coordHashable: The coordinate used to compute the gradient.
positive_upwardoptional, bool: Change sign of the derivative based on the "positive" attribute of coord so that positive values indicate increasing upward. If positive=="down", then multiplied by -1.

Returns:

DataArray or Dataset: Differentiated object.