DecompositionPlots.contributions_over_time#

DecompositionPlots.contributions_over_time(include=None, hdi_prob=0.94, original_scale=True, idata=None, dims=None, figsize=None, backend=None, return_as_pc=False, line_kwargs=None, hdi_kwargs=None, **pc_kwargs)[source]#

Plot time-series contributions for selected contribution types with HDI bands.

Creates one panel per extra-dimension combination (e.g. one per geo for geo-segmented models). Each panel overlays one mean line and HDI band per contribution type.

Parameters:

includelist of {“channels”, “baseline”, “controls”, “seasonality”}, optional: Which contribution types to plot. None means all available.
hdi_probfloat, default 0.94: Probability mass for the HDI band.
original_scalebool, default True: Whether to return contributions in original scale.
idataaz.InferenceData, optional: Override instance data for this call only.
dimsdict[str, Any], optional: Subset dimensions, e.g. {"geo": ["CA"]}.
figsizetuple[float, float], optional: Injected into figure_kwargs.
backendstr, optional: Rendering backend. Non-matplotlib requires return_as_pc=True.
return_as_pcbool, default False: If True, return the PlotCollection instead of (Figure, NDArray[Axes]).
line_kwargsdict, optional: Extra kwargs forwarded to azp.visuals.line_xy for every mean line.
hdi_kwargsdict, optional: Extra kwargs forwarded to azp.visuals.fill_between_y for every HDI band.
**pc_kwargs: Forwarded to PlotCollection.wrap(). Use col_wrap to override the default single-column layout.

Returns:

tuple[Figure, NDArray[Axes]] or PlotCollection