`barracuda.plotting`¶

Matplotlib plots. Every function returns an Axes and does not show, save, or close figures.

Matplotlib plots for BARRACUDA's tidy result tables.

Matplotlib is imported only when a plotting function is called, so simulation and inference users avoid its import-time cost. Every function returns an Axes object and never calls :func:matplotlib.pyplot.show.

Attributes¶

MODEL_COLOURS `module-attribute` ¶

MODEL_COLOURS: Final[dict[str, str]] = {'homo': '#5C677D', 'z2p': '#4C78A8', 'dis2p': '#59A14F', 'hetero3': '#E45756', 'homogeneous_history_independent': '#5C677D', 'homogeneous_history_dependent': '#4C78A8', 'heterogeneous_history_independent': '#59A14F', 'heterogeneous_history_dependent': '#E45756'}

BF_THRESHOLDS_LOG10 `module-attribute` ¶

BF_THRESHOLDS_LOG10: Final[tuple[float, float, float]] = (float(np.log10(3.0)), 1.0, 2.0)

all `module-attribute` ¶

__all__ = ['BF_THRESHOLDS_LOG10', 'MODEL_COLOURS', 'plot_bayes_factor_scan', 'plot_event_count_distribution', 'plot_model_evidence', 'plot_parameter_recovery', 'plot_posterior_intervals', 'plot_posterior_pair', 'plot_rate_distribution', 'plot_trajectory_state_map']

Functions:¶

_matplotlib ¶

_matplotlib()

_axes ¶

_axes(ax: Any = None, *, figsize: tuple[float, float] = (7.2, 4.4))

_require_columns ¶

_require_columns(frame: DataFrame, columns: Sequence[str], name: str) -> None

_model_column ¶

_model_column(frame: DataFrame) -> str

_colour ¶

_colour(label: Any, index: int = 0) -> str

plot_event_count_distribution ¶

plot_event_count_distribution(frame: DataFrame, *, condition_column: str = 'condition', normalize: bool = False, ax: Any = None) -> 'Axes'

Plot an empirical count distribution, optionally split by condition.

plot_rate_distribution ¶

plot_rate_distribution(rate_distribution: str, mu_lambda: float, sigma_lambda: float, *, points: int = 320, ax: Any = None) -> 'Axes'

Plot the engaging-cell rate law used by an event-count simulation.

plot_model_evidence ¶

plot_model_evidence(evidence: DataFrame, *, condition: str | None = None, ax: Any = None, title: str | None = None) -> 'Axes'

Plot positive log10 BF(best / model) from any BARRACUDA evidence table.

_scan_value_column ¶

_scan_value_column(frame: DataFrame) -> str

plot_bayes_factor_scan ¶

plot_bayes_factor_scan(scan: DataFrame, *, scenario: str | None = None, model_keys: Sequence[str] | None = None, interval: float = 0.9, value_column: str | None = None, ax: Any = None) -> 'Axes'

Plot median Bayes-factor trajectories and replicate intervals.

Sample sizes are cumulative prefixes within each scenario/replicate, as in the package scan runners. The shaded interval therefore describes between-replicate variation, not independent samples at adjacent sizes.

plot_parameter_recovery ¶

plot_parameter_recovery(recovery: DataFrame, *, parameter: str | None = None, model_key: str | None = None, mean_column: str | None = None, ax: Any = None) -> 'Axes'

Plot posterior means and HDIs against generating truth.

plot_posterior_intervals ¶

plot_posterior_intervals(summary: DataFrame, *, parameter_column: str = 'parameter', mean_column: str = 'mean', lower_column: str = 'hdi_lower', upper_column: str = 'hdi_upper', ax: Any = None) -> 'Axes'

Plot any tidy posterior interval table as horizontal error bars.

plot_trajectory_state_map ¶

plot_trajectory_state_map(frame: DataFrame, *, condition: str | None = None, ax: Any = None) -> 'Axes'

Plot empirical lethal probability at each pre-contact history state.

plot_posterior_pair ¶

plot_posterior_pair(draws: DataFrame, x: str, y: str, *, group: str | None = 'model_key', max_points_per_group: int | None = 5000, seed: int | None = 17, ax: Any = None) -> 'Axes'

Plot paired posterior draws without breaking joint dependence.

barracuda.plotting¶