arviz.InferenceData.sel#

InferenceData.sel(groups: Optional[Union[str, List[str]]] = None, filter_groups: Optional[Literal['like', 'regex']] = None, inplace: bool = False, chain_prior: Optional[bool] = None, **kwargs: Any) → Optional[arviz.data.inference_data.InferenceDataT][source]#

Perform an xarray selection on all groups.

Loops groups to perform Dataset.sel(key=item) for every kwarg if key is a dimension of the dataset. One example could be performing a burn in cut on the InferenceData object or discarding a chain. The selection is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See xarray.Dataset.sel

Parameters

groups: str or list of str, optional: Groups where the selection is to be applied. Can either be group names or metagroup names.
filter_groups: {None, “like”, “regex”}, optional, default=None: If None (default), interpret groups as the real group or metagroup names. If “like”, interpret groups as substrings of the real group or metagroup names. If “regex”, interpret groups as regular expressions on the real group or metagroup names. A la pandas.filter.
inplace: bool, optional: If True, modify the InferenceData object inplace, otherwise, return the modified copy.
chain_prior: bool, optional, deprecated: If False, do not select prior related groups using chain dim. Otherwise, use selection on chain if present. Default=False
**kwargs: mapping: It must be accepted by Dataset.sel().

Returns

InferenceData: A new InferenceData object by default. When inplace==True perform selection in-place and return None

See also

xarray.Dataset.sel: Returns a new dataset with each array indexed by tick labels along the specified dimension(s).
isel: Returns a new dataset with each array indexed along the specified dimension(s).

Examples

Use sel to discard one chain of the InferenceData object. We first check the dimensions of the original object:

In [1]: import arviz as az
   ...: idata = az.load_arviz_data("centered_eight")
   ...: del idata.prior  # prior group only has 1 chain currently
   ...: print(idata.posterior.coords)
   ...: print(idata.posterior_predictive.coords)
   ...: print(idata.observed_data.coords)
   ...: 
Coordinates:
  * chain    (chain) int64 0 1 2 3
  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
  * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
Coordinates:
  * chain    (chain) int64 0 1 2 3
  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
  * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
Coordinates:
  * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'

In order to remove the third chain:

In [2]: idata_subset = idata.sel(chain=[0, 1, 3])
   ...: print(idata_subset.posterior.coords)
   ...: print(idata_subset.posterior_predictive.coords)
   ...: print(idata_subset.observed_data.coords)
   ...: 
Coordinates:
  * chain    (chain) int64 0 1 3
  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
  * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
Coordinates:
  * chain    (chain) int64 0 1 3
  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
  * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
Coordinates:
  * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'

arviz.InferenceData.reset_index

arviz.InferenceData.set_coords