Marginal Distribution Plots¶

Visualize 2D joint distributions with covariance ellipses, marginal density curves, and optional Monte Carlo scatter overlays.

Solver API¶

plot_estimator_marginal ¶

plot_estimator_marginal(solvers, state_indices=(0, 1), sigma=None, labels=None, colors=None, state_labels=None, scatter_points=None, show_marginals=True, backend='matplotlib', backend_config=None, **kwargs) -> object

Plot 2D covariance ellipses with optional marginals from solved estimators.

Extracts the 2x2 sub-covariance and 2D sub-state from each solver using the given state_indices, then delegates to :func:plot_estimator_marginal_from_arrays.

Parameters:

Name	Type	Description	Default
`solvers`	`list`	List of solved estimator objects (BatchLeastSquares, ExtendedKalmanFilter, or UnscentedKalmanFilter).	required
`state_indices`	`tuple[int, int]`	Two state indices to extract. Default: (0, 1).	`(0, 1)`
`sigma`	`float or None`	N-sigma level for ellipse scaling. None means only mean markers are drawn. Default: None.	`None`
`labels`	`list[str] or None`	Legend label per solver. Default: "Series 0", …	`None`
`colors`	`list[str] or None`	Colour per solver. Default: colour cycle.	`None`
`state_labels`	`tuple[str, str] or None`	`(x_label, y_label)` axis labels. Default: ("State 0", "State 1").	`None`
`scatter_points`	`ndarray or None`	Shape `(N, 2)` MC sample overlay. Default: None.	`None`
`show_marginals`	`bool`	If True, add marginal density curves on top/right axes. Default: True.	`True`
`backend`	`str`	"matplotlib" or "plotly". Default: "matplotlib".	`'matplotlib'`
`backend_config`	`dict or None`	Backend-specific configuration.	`None`
`**kwargs`	`dict`	Ignored.	`{}`

Returns:

Type	Description
`object`	Generated matplotlib or plotly figure object.

Array API¶

plot_estimator_marginal_from_arrays ¶

plot_estimator_marginal_from_arrays(means, covariances, sigma=None, labels=None, colors=None, state_labels=None, scatter_points=None, show_marginals=True, backend='matplotlib', backend_config=None, **kwargs) -> object

Plot 2D covariance ellipses with optional marginal density curves from arrays.

Each entry in means / covariances represents one estimator series. Covariance ellipses are scaled using the chi-squared quantile for the requested sigma level. Optional marginal (1D Gaussian) density curves can be shown on top and right axes.