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Showing content from https://python.arviz.org/en/latest/getting_started/../api/generated/arviz.InferenceData.map.html below:

arviz.InferenceData.map — ArviZ dev documentation

arviz.InferenceData.map#
InferenceData.map(fun, groups=None, filter_groups=None, inplace=False, args=None, **kwargs)[source]#

Apply a function to multiple groups.

Applies fun groupwise to the selected InferenceData groups and overwrites the group with the result of the function.

Parameters:
funcallable()

Function to be applied to each group. Assumes the function is called as fun(dataset, *args, **kwargs).

groupsstr 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

If None (default), interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names. A la pandas.filter.

inplacebool, optional

If True, modify the InferenceData object inplace, otherwise, return the modified copy.

argsarray_like, optional

Positional arguments passed to fun.

**kwargsmapping, optional

Keyword arguments passed to fun.

Returns:
InferenceData

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

Examples

Shift observed_data, prior_predictive and posterior_predictive.

import arviz as az
import numpy as np
idata = az.load_arviz_data("non_centered_eight")
idata_shifted_obs = idata.map(lambda x: x + 3, groups="observed_vars")
idata_shifted_obs

Rename and update the coordinate values in both posterior and prior groups.

idata = az.load_arviz_data("radon")
idata = idata.map(
    lambda ds: ds.rename({"g_coef": "uranium_coefs"}).assign(
        uranium_coefs=["intercept", "u_slope"]
    ),
    groups=["posterior", "prior"]
)
idata

Add extra coordinates to all groups containing observed variables

idata = az.load_arviz_data("rugby")
home_team, away_team = np.array([
    m.split() for m in idata.observed_data.match.values
]).T
idata = idata.map(
    lambda ds, **kwargs: ds.assign_coords(**kwargs),
    groups="observed_vars",
    home_team=("match", home_team),
    away_team=("match", away_team),
)
idata

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