"""Plot ppc pit."""
from collections.abc import Mapping, Sequence
from typing import Any, Literal
import xarray as xr
from arviz_base.validate import (
validate_dict_argument,
validate_or_use_rcparam,
validate_sample_dims,
)
from arviz_plots.plots.ecdf_plot import plot_ecdf_pit
from arviz_plots.plots.utils import process_group_variables_coords
from arviz_plots.plots.utils_plot_types import warn_if_binary, warn_if_prior_predictive
from arviz_plots.plots.utils_ppc import get_ppc_pit
[docs]
def plot_ppc_pit(
dt,
*,
var_names=None,
filter_vars=None,
group="posterior_predictive",
coords=None,
sample_dims=None,
method="pot_c",
envelope_prob=None,
coverage=False,
plot_collection=None,
backend=None,
labeller=None,
aes_by_visuals: Mapping[
Literal[
"ecdf_lines",
"credible_interval",
"suspicious_points",
"p_value_text",
"xlabel",
"ylabel",
"title",
],
Sequence[str],
] = None,
visuals: Mapping[
Literal[
"ecdf_lines",
"credible_interval",
"suspicious_points",
"p_value_text",
"xlabel",
"ylabel",
"title",
"remove_axis",
],
Mapping[str, Any] | bool,
] = None,
stats: Mapping[Literal["ecdf_pit"], Mapping[str, Any] | xr.Dataset] = None,
**pc_kwargs,
):
r"""PIT Δ-ECDF values with simultaneous confidence envelope.
For a calibrated model the Probability Integral Transform (PIT) values,
$p(\tilde{y}_i \le y_i \mid y)$, should be uniformly distributed.
Where $y_i$ represents the observed data for index $i$ and $\tilde y_i$ represents
the posterior predictive sample at index $i$.
This plot shows the empirical cumulative distribution function (ECDF) of the PIT values.
To make the plot easier to interpret, we plot the Δ-ECDF, that is, the difference between
the observed ECDF and the expected CDF.
The points that contribute the most to deviations from uniformity are
computed as described in [1]_ and highlighted in the plot.
Alternatively, we can visualize the coverage of the central posterior credible intervals by
setting ``coverage=True``. This allows us to assess whether the credible intervals includes
the observed values. We can obtain the coverage of the central intervals from the PIT by
replacing the PIT with two times the absolute difference between the PIT values and 0.5.
For more details on how to interpret this plot,
see https://arviz-devs.github.io/EABM/Chapters/Prior_posterior_predictive_checks.html#pit-ecdfs.
Parameters
----------
dt : DataTree
Input data
var_names : str or list of str, optional
One or more variables to be plotted. Currently only one variable is supported.
Prefix the variables by ~ when you want to exclude them from the plot.
filter_vars : {None, “like”, “regex”}, optional, default=None
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.
group : str,
Group to be plotted. Defaults to "posterior_predictive".
It could also be "prior_predictive".
coords : dict, optional
Coordinates to plot.
sample_dims : str or sequence of hashable, optional
Dimensions to reduce unless mapped to an aesthetic.
Defaults to ``rcParams["data.sample_dims"]``
method : {"pot_c", "prit_c", "piet_c", "envelope"}, optional
Method to use for the uniformity test. Defaults to "pot_c".
Check the documentation of :func:`~arviz_plots.plot_ecdf_pit` for
more details.
envelope_prob : float, optional
Indicates the probability that should be contained within the envelope.
Defaults to ``rcParams["stats.envelope_prob"]``.
coverage : bool, optional
If True, plot the coverage of the central posterior credible intervals. Defaults to False.
plot_collection : PlotCollection, optional
backend : {"matplotlib", "bokeh", "plotly"}, optional
labeller : labeller, optional
aes_by_visuals : mapping of {str : sequence of str}, optional
Mapping of visuals to aesthetics that should use their mapping in `plot_collection`
when plotted. Valid keys are the same as for `visuals`.
visuals : mapping of {str : mapping or bool}, optional
Valid keys are:
* ecdf_lines -> passed to :func:`~arviz_plots.visuals.ecdf_line`
* credible_interval -> passed to :func:`~arviz_plots.visuals.fill_between_y`,
only when method is "envelope"
* ref_line -> passed to :func:`~arviz_plots.visuals.line_xy`
* suspicious_points -> passed to :func:`~arviz_plots.visuals.scatter_xy`
* p_value_text -> passed to :func:`~arviz_plots.visuals.annotate_xy`
only when method is not "envelope"
* xlabel -> passed to :func:`~arviz_plots.visuals.labelled_x`
* ylabel -> passed to :func:`~arviz_plots.visuals.labelled_y`
* title -> passed to :func:`~arviz_plots.visuals.labelled_title`
* remove_axis -> not passed anywhere, can only be ``False`` to skip calling this function
stats : mapping, optional
Valid keys are:
* ecdf_pit -> passed to :func:`~arviz_stats.ecdf_utils.ecdf_pit`. Default is
``{"n_simulations": 1000}``.
**pc_kwargs
Passed to :class:`arviz_plots.PlotCollection.wrap`
Returns
-------
PlotCollection
See Also
--------
plot_loo_pit : Predictive check using LOO-PIT Δ-ECDF uniformity test.
plot_ppc_dist : Predictive check using 1D marginals for predictive (and observed data).
plot_ppc_pava : Predictive check ideal for binary, ordinal or categorical data.
plot_ppc_rootogram : Predictive check ideal for discrete (count) data.
Examples
--------
Plot the ecdf-PIT for the crabs hurdle-negative-binomial dataset.
.. plot::
:context: close-figs
>>> from arviz_plots import plot_ppc_pit, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> dt = load_arviz_data('crabs_hurdle_nb')
>>> plot_ppc_pit(dt)
Plot the coverage for the crabs hurdle-negative-binomial dataset.
.. plot::
:context: close-figs
>>> plot_ppc_pit(dt, coverage=True)
.. minigallery:: plot_ppc_pit
References
----------
.. [1] Tasso et al. *LOO-PIT predictive model checking* arXiv:2603.02928 (2026).
"""
envelope_prob = validate_or_use_rcparam(envelope_prob, "stats.envelope_prob")
aes_by_visuals = validate_dict_argument(aes_by_visuals, (plot_ppc_pit, "aes_by_visuals"))
visuals = validate_dict_argument(visuals, (plot_ppc_pit, "visuals"))
stats = validate_dict_argument(stats, (plot_ppc_pit, "stats"))
predictive_dist = process_group_variables_coords(
dt, group=group, var_names=var_names, filter_vars=filter_vars, coords=coords
)
sample_dims = validate_sample_dims(sample_dims, data=predictive_dist)
observed_dist = process_group_variables_coords(
dt, group="observed_data", var_names=var_names, filter_vars=filter_vars, coords=coords
)
warn_if_binary(observed_dist, predictive_dist)
warn_if_prior_predictive(group)
if method not in {"envelope", "pot_c", "prit_c", "piet_c"}:
raise ValueError(
f"Method {method} not supported. Choose from 'envelope', 'pot_c', 'prit_c' or 'piet_c'."
)
new_dt = get_ppc_pit(predictive_dist, observed_dist, sample_dims, coverage, method)
visuals.setdefault("ylabel", {})
visuals.setdefault("remove_axis", False)
visuals.setdefault("xlabel", {"text": "ETI %" if coverage else "PIT"})
plot_collection = plot_ecdf_pit(
new_dt,
var_names=var_names,
filter_vars=filter_vars,
group="ecdf_pit",
coords=coords,
sample_dims=new_dt.ecdf_pit.dims,
method=method,
envelope_prob=envelope_prob,
coverage=coverage,
plot_collection=plot_collection,
backend=backend,
labeller=labeller,
aes_by_visuals=aes_by_visuals,
visuals=visuals,
stats=stats,
**pc_kwargs,
)
return plot_collection
