superstats.diagnostics.plots#

Plotting functions for priors, posteriors, and recovery diagnostics.

superstats.diagnostics.plots.plot_calibration(estimates, targets, variable_keys=None, variable_names=None, color='#356673', title_fontsize=22, label_fontsize=18, metric_fontsize=18, tick_fontsize=16, **kwargs)[source]#

Plot time-invariant calibration (ECDF).

Thin wrapper around bf.diagnostics.plots.calibration_ecdf that accepts dict-or-array input via prepare_plot_data, consistent with plot_time_varying_verification.

Parameters:

estimatesMapping[str, np.ndarray] or np.ndarray: Posterior estimates. If a dict, per-key arrays sharing the same leading shape, keyed by variable. If an array, shape (num_sims, num_samples, num_params) directly.
targetsMapping[str, np.ndarray] or np.ndarray: Ground-truth values, matching the input type of estimates. If a dict, per-key arrays. If an array, shape (num_sims, num_params) directly.
variable_keyssequence of str or None, optional, default: None: Which keys to select and plot, and in what order, when estimates/targets are dicts. By default, all keys, in dict insertion order. Ignored for array input.
variable_namessequence of str or None, optional, default: None: Display names for the plotted columns. Defaults to variable_keys (dict input) or param_0, param_1, … (array input).
colorstr, optional, default: “#822621”: Base color for the calibration ECDF lines.
title_fontsizeint, optional, default: 22: The font size of the panel titles.
label_fontsizeint, optional, default: 18: The font size of the axis label texts.
metric_fontsizeint, optional, default: 18: The font size of the displayed calibration metric text.
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
**kwargs: Forwarded to bf.diagnostics.plots.calibration_ecdf (e.g. figsize, num_row, num_col).

Returns:

figplt.Figure - the calibration diagnostic figure

Parameters:

estimates (Mapping[str, ndarray] | ndarray)
targets (Mapping[str, ndarray] | ndarray)
variable_keys (Sequence[str] | None)
variable_names (Sequence[str] | None)
color (str)
title_fontsize (int)
label_fontsize (int)
metric_fontsize (int)
tick_fontsize (int)

superstats.diagnostics.plots.plot_joint_prior(local_params, hyper_params, shared_params, param_bounds=None, mixture_names=None, hyper_param_groups=None, marginal=True, color='#356673', title_fontsize=22, tick_fontsize=16, alpha=0.5, figsize=None)[source]#

Plot joint prior diagnostics combining hyperparameter distributions, shared parameter histograms, and time-varying trajectories.

Parameters:

local_paramsdict of np.ndarray, each of shape (num_trajectories, num_steps): Mapping from parameter name to an array of trajectories. Every StochasticTransition parameter is unconditionally added here by JointPrior.sample, so together with shared_params this is the authoritative set of row names.
hyper_paramsdict of np.ndarray: Mapping from hyperparameter name to an array of samples. Keys are f”{param_name}_{hyper_key}”.
shared_paramsdict of np.ndarray: Mapping from parameter name to an array of shared parameter samples.
param_boundsdict or None, optional, default: None: Mapping from parameter name to (lower, upper) y-axis limits.
mixture_namesdict or None, optional, default: None: Mapping from parameter name to a list of component names for mixture weight parameters.
hyper_param_groupsdict or None, optional, default: None: Mapping from each parameter name to the exact list of hyper_params keys it owns. Required to correctly separate rows when one parameter name is a prefix of another at an underscore boundary (e.g. “v_1” and “v_1_2”), since str.startswith cannot disambiguate that case from key strings alone. When provided (e.g. by JointPrior.plot_joint_prior), this is used instead of prefix matching. If omitted, falls back to prefix matching, which can misassign hyperparameters in the presence of such name collisions.
marginalbool, optional, default: True: Whether to draw a marginal KDE panel beside each trajectory panel.
colorstr, optional, default: BASE_COLOR: Base plotting color for KDEs and trajectories.
title_fontsizeint, optional, default: 22: The font size of the panel titles (parameter names).
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
alphafloat in [0, 1], optional, default: 0.5: The opacity of individual trajectories.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If no plottable parameters are found across local_params, hyper_params, and shared_params.

Parameters:

local_params (Mapping[str, ndarray])
hyper_params (Mapping[str, ndarray])
shared_params (Mapping[str, ndarray])
param_bounds (Mapping[str, tuple[float, float]] | None)
mixture_names (Mapping[str, Sequence[str]] | None)
hyper_param_groups (Mapping[str, Sequence[str]] | None)
marginal (bool)
color (str)
title_fontsize (int)
tick_fontsize (int)
alpha (float)
figsize (tuple[float, float] | None)

Return type:

Figure

superstats.diagnostics.plots.plot_posterior_resimulation(pred_data, real_data, data_dim=0, kind='trajectory', aggregation=None, aggregate_strategy='full_uncertainty', uncertainty_fun='95hdi', smoothing=None, smoothing_window=5, marginal=True, spaghetti=False, num_cols=3, color='#356673', real_color='black', alpha=0.4, label_fontsize=14, tick_fontsize=12, figsize=None, max_discrete_values=30)[source]#

Plot posterior predictive resimulations against the observed data.

Parameters:

pred_datamapping of np.ndarray: Posterior resimulated data, mapping observation names to arrays of shape (num_datasets, num_resims, num_steps).
real_datamapping of np.ndarray: Observed data, mapping observation names to arrays of shape (num_datasets, num_steps).
data_dimint or str, optional, default: 0: Which observation variable to plot. Strings select by key and integers index the predictive mapping’s key order.
kind{“trajectory”, “dist”}, optional, default: “trajectory”: “trajectory”: band/center over steps. “dist”: distribution across steps.
aggregationcallable() or None, optional, default: None: None: one panel per dataset. callable: a single panel aggregated across datasets. Called as aggregation(x, axis=…) (e.g. np.mean, np.median). Also used (instead of a hardcoded median) to collapse resims into a per-dataset representative when aggregate_strategy=”no_epistemic”.
aggregate_strategy{“full_uncertainty”, “no_epistemic”}, optional, default: “full_uncertainty”: Only used when aggregation is not None. “full_uncertainty”: flatten datasets and posterior resims together, then summarize. Captures both epistemic and aleatoric uncertainty. “no_epistemic”: collapse resims to one representative trajectory per dataset first (via aggregation), then aggregate across datasets. Removes epistemic uncertainty.
uncertainty_fun{“std”, “95ci”, “mad”, “95hdi”} or callable() or None, optional, default: “95hdi”: “trajectory” mode only. Function to draw a band around the resimulated center line.
smoothing{“sma”, “ema”} or None, optional, default: None: “trajectory” mode only. Causal (past-only) smoothing applied to the real trajectories and, for resimulated data, to the trajectories that result after aggregate_strategy has pooled resims - i.e. pooling happens on raw data, smoothing is applied afterward, and the center/uncertainty band are computed on the smoothed result.
smoothing_windowint, optional, default: 5: Window size for sma, or span parameter for ema.
marginalbool, optional, default: True: “trajectory” mode only. Attach a marginal KDE panel of the resimulated draws to the right of each trajectory axis.
spaghettibool, optional, default: False: “trajectory” mode only. Per-dataset panels: overlay individual resim draws behind the band. Aggregated panel: overlay each dataset’s own representative trajectory (via aggregation) behind the aggregate band.
num_colsint, optional, default: 3: Number of columns when aggregation is None (per-dataset grid).
colorstr, optional, default: BASE_COLOR: Color for bands / centers / histograms.
real_colorstr, optional, default: “black”: Color for the observed data.
alphafloat in [0, 1], optional, default: 0.4: Alpha for spaghetti lines.
label_fontsizeint, optional, default: 14: The font size of the axis label texts.
tick_fontsizeint, optional, default: 12: The font size of the axis tick labels.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.
max_discrete_valuesint, optional, default: 30: “dist” mode, per-dataset panels only. Maximum number of discrete categories to treat the data as discrete.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If kind is not “trajectory” or “dist”, if pred_data or real_data don’t have the expected shape, if their (num_datasets, num_steps) don’t match, or if aggregate_strategy is not “full_uncertainty” or “no_epistemic”.

Parameters:

pred_data (Mapping[str, ndarray])
real_data (Mapping[str, ndarray])
data_dim (int | str)
kind (Literal['trajectory', 'dist'])
aggregation (Callable | None)
aggregate_strategy (Literal['full_uncertainty', 'no_epistemic'])
uncertainty_fun (Literal['std', '95ci', 'mad', '95hdi'] | ~collections.abc.Callable | None)
smoothing (Literal['sma', 'ema'] | None)
smoothing_window (int)
marginal (bool)
spaghetti (bool)
num_cols (int)
color (str)
real_color (str)
alpha (float)
label_fontsize (int)
tick_fontsize (int)
figsize (tuple[float, float] | None)
max_discrete_values (int)

Return type:

Figure

superstats.diagnostics.plots.plot_push_forward(data, data_dim=0, kind='dist', aggregation=None, uncertainty_fun='95ci', marginal=True, spaghetti=False, alpha=0.5, num_cols=3, color='#356673', title_fontsize=22, label_fontsize=18, tick_fontsize=16, figsize=None, max_discrete_values=30)[source]#

Plot prior push-forward for a single data dimension.

Parameters:

datamapping of np.ndarray: Simulation data from the generative model, mapping observation names to arrays of shape (batch_size, steps).
data_dimint or str, optional, default: 0: Which observation variable to plot. Strings select by key and integers index the mapping’s key order.
kind{“dist”, “trajectory”}, optional, default: “dist”: Plot type: distribution of summary statistics or time-series trajectories.
aggregationcallable() or None, optional, default: None: Aggregation function over the dataset dimension, called as aggregation(x, axis=…) (e.g. np.mean, np.median). If None, individual datasets are shown in separate panels. If specified, all datasets are aggregated into a single panel.
uncertainty_fun{“std”, “95ci”, “mad”, “95hdi”} or callable() or None, optional, default: “95ci”: Uncertainty function. Only used when aggregation is not None and kind is “trajectory”. Ignored (with a warning) otherwise.
marginalbool, optional, default: True: Whether to draw marginal distributions beside trajectory plots.
spaghettibool, optional, default: False: Whether to draw individual trajectories behind the aggregate line.
num_colsint, optional, default: 3: Number of columns when rendering individual panels.
alphafloat in [0, 1], optional, default: 0.5: Alpha value for individual dataset traces.
colorstr, optional, default: “#822621”: Base color for plotted lines and fills.
title_fontsizeint, optional, default: 22: The font size of the panel titles.
label_fontsizeint, optional, default: 18: The font size of the axis label texts.
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.
max_discrete_valuesint, optional, default: 30: Maximum number of discrete categories to treat the data as discrete.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If kind is not “dist” or “trajectory”, or if data has an unsupported shape.

Parameters:

data (Mapping[str, ndarray])
data_dim (int | str)
kind (Literal['trajectory', 'dist'])
aggregation (Callable | None)
uncertainty_fun (Literal['std', '95ci', 'mad', '95hdi'] | ~collections.abc.Callable | None)
marginal (bool)
spaghetti (bool)
alpha (float)
num_cols (int)
color (str)
title_fontsize (int)
label_fontsize (int)
tick_fontsize (int)
figsize (tuple[float, float] | None)
max_discrete_values (int)

superstats.diagnostics.plots.plot_recovery(estimates, targets, variable_keys=None, variable_names=None, color='#356673', title_fontsize=22, label_fontsize=18, metric_fontsize=18, tick_fontsize=16, **kwargs)[source]#

Plot time-invariant parameter recovery.

Thin wrapper around bf.diagnostics.plots.recovery that accepts dict-or-array input via prepare_plot_data, consistent with plot_time_varying_verification.

Parameters:

estimatesMapping[str, np.ndarray] or np.ndarray: Posterior estimates. If a dict, per-key arrays sharing the same leading shape, keyed by variable. If an array, shape (num_sims, num_samples, num_params) directly.
targetsMapping[str, np.ndarray] or np.ndarray: Ground-truth values, matching the input type of estimates. If a dict, per-key arrays. If an array, shape (num_sims, num_params) directly.
variable_keyssequence of str or None, optional, default: None: Which keys to select and plot, and in what order, when estimates/targets are dicts. By default, all keys, in dict insertion order. Ignored for array input.
variable_namessequence of str or None, optional, default: None: Display names for the plotted columns. Defaults to variable_keys (dict input) or param_0, param_1, … (array input).
colorstr, optional, default: “#822621”: Base color for plotted lines and fills.
title_fontsizeint, optional, default: 22: The font size of the panel titles.
label_fontsizeint, optional, default: 18: The font size of the axis label texts.
metric_fontsizeint, optional, default: 18: The font size of the displayed recovery metric text.
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
**kwargs: Forwarded to bf.diagnostics.plots.recovery (e.g. figsize, num_row, num_col).

Returns:

figplt.Figure - the recovery diagnostic figure

Parameters:

estimates (Mapping[str, ndarray] | ndarray)
targets (Mapping[str, ndarray] | ndarray)
variable_keys (Sequence[str] | None)
variable_names (Sequence[str] | None)
color (str)
title_fontsize (int)
label_fontsize (int)
metric_fontsize (int)
tick_fontsize (int)

superstats.diagnostics.plots.plot_time_invariant_posterior(estimates, targets=None, variable_keys=None, variable_names=None, aggregation=None, mixture_names=None, num_cols=2, color='#356673', title_fontsize=22, label_fontsize=18, tick_fontsize=16, figsize=None)[source]#

Plot time-invariant parameter posteriors.

Parameters:

estimatesMapping[str, np.ndarray] or np.ndarray: Posterior samples. If a dict, values of shape (num_datasets, num_post_samples, num_steps, num_components), keyed by variable. If an array, shape (num_datasets, num_post_samples, num_steps, num_params) directly - treated as single-component parameters; mixture grouping is not inferable from array input.
targetsMapping[str, np.ndarray], np.ndarray, or None, optional, default: None: Ground-truth values, matching the input type of estimates. If a dict, values of shape (num_datasets, num_components). If an array, shape (num_datasets, num_params) directly. If given, drawn as black dashed vertical lines. When aggregation is None, one solid line per panel marks that panel’s specific dataset’s true value. When aggregation is given, the per-dataset true values are collapsed with aggregation and a single solid line is drawn per panel.
variable_keyssequence of str or None, optional, default: None: Which variables to select and plot, and in what order, when estimates is a dict. By default, all keys, in dict insertion order. Ignored for array input.
variable_namessequence of str or None, optional, default: None: Display names (used for panel labels/titles), in the same order as variable_keys (or the array’s last axis). Defaults to variable_keys for dict input, or param_0, param_1, … for array input.
aggregationcallable() or None, optional, default: None: Controls both the posterior layout and the target summary. If None: one panel per (dataset, parameter) pair; rows=params, cols=datasets, param name as row label, dataset index as column title; targets (if given) are shown per dataset. If a callable (e.g. np.mean, np.median): posterior samples are pooled across datasets into one panel per parameter, arranged in a num_cols-column grid; targets (if given) are collapsed across datasets with aggregation into a single reference value per panel.
mixture_namesdict or None, optional, default: None: Mapping from base parameter name (e.g. “a”, without any “_mixture_weights” suffix) to a list of component names. Defaults to “component 0”, “component 1”, … when not supplied. Only applies to dict input with multi-component values.
num_colsint, optional, default: 2: Number of subplot columns when aggregation is not None.
colorstr, optional, default: BASE_COLOR: Base color for non-mixture parameters.
title_fontsizeint, optional, default: 22: The font size of the panel titles.
label_fontsizeint, optional, default: 18: The font size of the row labels (param names, non-pooled layout only).
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If no variables are found to plot (empty variable_keys, whether resolved by default or passed explicitly), or if variable_names doesn’t match the number of variables for array input.

Parameters:

estimates (Mapping[str, ndarray] | ndarray)
targets (Mapping[str, ndarray] | ndarray | None)
variable_keys (Sequence[str] | None)
variable_names (Sequence[str] | None)
aggregation (Callable | None)
mixture_names (dict | None)
num_cols (int)
color (str)
title_fontsize (int)
label_fontsize (int)
tick_fontsize (int)
figsize (tuple[float, float] | None)

Return type:

Figure

superstats.diagnostics.plots.plot_time_invariant_prior(hyper_params, shared_params, mixture_names=None, color='#356673', num_cols=2, title_fontsize=22, label_fontsize=18, tick_fontsize=16, figsize=None)[source]#

Plot time-invariant parameter distributions.

Parameters:

hyper_paramsdict of np.ndarray: Mapping from parameter name to an array of hyperparameter samples.
shared_paramsdict of np.ndarray: Mapping from parameter name to an array of shared parameter samples.
mixture_namesdict or None, optional, default: None: Mapping from parameter name to a list of component names for mixture weight parameters.
colorstr, optional, default: BASE_COLOR: Base color for non-mixture histograms.
num_colsint, optional, default: 2: Number of subplot columns.
title_fontsizeint, optional, default: 22: The font size of the panel titles.
label_fontsizeint, optional, default: 18: The font size of the axis labels.
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If both hyper_params and shared_params are empty.

Parameters:

hyper_params (Mapping[str, ndarray])
shared_params (Mapping[str, ndarray])
mixture_names (Mapping[str, Sequence[str]] | None)
color (str)
num_cols (int)
title_fontsize (int)
label_fontsize (int)
tick_fontsize (int)
figsize (tuple[float, float] | None)

Return type:

Figure

superstats.diagnostics.plots.plot_time_varying_posterior(estimates, targets=None, variable_keys=None, variable_names=None, aggregation=None, aggregate_strategy='full_uncertainty', uncertainty_fun='95ci', smoothing=None, smoothing_window=5, marginal=True, num_cols=2, alpha=0.5, color='#356673', title_fontsize=22, label_fontsize=18, tick_fontsize=16, figsize=None)[source]#

Plot time-varying parameter posteriors.

Parameters:

estimatesMapping[str, np.ndarray] or np.ndarray: Posterior samples. If a dict, values of shape (num_datasets, num_post_samples, num_steps, 1), keyed by variable. If an array, shape (num_datasets, num_post_samples, num_steps, num_params) directly.
targetsMapping[str, np.ndarray], np.ndarray, or None, optional, default: None: Ground-truth trajectories, matching the input type of estimates. If a dict, values of shape (num_datasets, num_steps, 1). If an array, shape (num_datasets, num_steps, num_params) directly. If given, drawn as a black dashed line on top of each panel: the raw per-dataset trajectory when aggregation is None, or aggregated across datasets (using aggregation) when aggregation is not None. Smoothed with the same smoothing settings as the posterior trajectories, for a fair visual comparison.
variable_keyssequence of str or None, optional, default: None: Which variables to select and plot, and in what order, when estimates/targets are dicts. By default, all keys, in dict insertion order. Ignored for array input.
variable_namessequence of str or None, optional, default: None: Display names (used for panel labels/titles), in the same order as variable_keys (or the array’s last axis). Defaults to variable_keys for dict input, or param_0, param_1, … for array input.
aggregationcallable() or None, optional, default: None: None: one panel per (param, dataset). callable: one panel per param, aggregated across datasets. Called as aggregation(trajectories, axis=0) and must return a (T,) center. The same function aggregates targets across datasets when both targets and aggregation are given.
aggregate_strategy{“full_uncertainty”, “no_epistemic”}, optional, default: “full_uncertainty”: Only used when aggregation is not None. “full_uncertainty”: flatten datasets and posterior samples, then summarize. “no_epistemic”: median across posterior samples per dataset first, then aggregate.
uncertainty_fun{“std”, “95ci”, “mad”, “95hdi”} or callable() or None, optional, default: “95ci”: Band drawn around the center line. A callable receives (N, T) trajectories and must return (lo, hi), each of shape (T,).
smoothing{“sma”, “ema”} or None, optional, default: None: Applied to each trajectory (and to targets, if given) before computing the center, uncertainty, and marginal.
smoothing_windowint, optional, default: 5: Window size for sma, or span parameter for ema.
marginalbool, optional, default: True: Attach a marginal KDE panel to the right of each trajectory axis. The KDE is computed on the same array used for the uncertainty band.
num_colsint, optional, default: 2: Number of subplot columns when aggregation is not None.
colorstr, optional, default: BASE_COLOR: Line and band color.
alphafloat in [0, 1], optional, default: 0.5: Alpha for the uncertainty band.
title_fontsizeint, optional, default: 22: The font size of the panel titles.
label_fontsizeint, optional, default: 18: The font size of the axis label texts.
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If estimates/targets are inconsistent (mismatched dict keys, or a variable_names length mismatch for array input), or if aggregate_strategy, or uncertainty_fun when given as a string, is not one of the recognized values.

Parameters:

estimates (Mapping[str, ndarray] | ndarray)
targets (Mapping[str, ndarray] | ndarray | None)
variable_keys (Sequence[str] | None)
variable_names (Sequence[str] | None)
aggregation (Callable | None)
aggregate_strategy (Literal['full_uncertainty', 'no_epistemic'])
uncertainty_fun (Literal['std', '95ci', 'mad', '95hdi'] | ~collections.abc.Callable | None)
smoothing (Literal['sma', 'ema'] | None)
smoothing_window (int)
marginal (bool)
num_cols (int)
alpha (float)
color (str)
title_fontsize (int)
label_fontsize (int)
tick_fontsize (int)
figsize (tuple[float, float] | None)

Return type:

Figure

superstats.diagnostics.plots.plot_time_varying_prior(local_params, param_bounds=None, num_cols=2, marginal=True, alpha=0.5, color='#356673', title_fontsize=22, label_fontsize=18, tick_fontsize=16, figsize=None)[source]#

Plot time-varying parameter trajectories with marginal KDE.

Parameters:

local_paramsdict of np.ndarray, each of shape (num_trajectories, num_steps): Mapping from parameter name to an array of trajectories.
param_boundsdict or None, optional, default: None: Mapping from parameter name to (lower, upper) y-axis limits.
num_colsint, optional, default: 2: Number of subplot columns.
marginalbool, optional, default: True: Whether to draw a marginal KDE panel beside each trajectory panel.
alphafloat in [0, 1], optional, default: 0.5: The opacity of individual trajectories.
colorstr, optional, default: BASE_COLOR: Line color for individual trajectories and marginal KDE.
title_fontsizeint, optional, default: 22: The font size of the panel titles.
label_fontsizeint, optional, default: 18: The font size of the axis labels.
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If local_params is empty.

Parameters:

local_params (Mapping[str, ndarray])
param_bounds (Mapping[str, tuple[float, float]] | None)
num_cols (int)
marginal (bool)
alpha (float)
color (str)
title_fontsize (int)
label_fontsize (int)
tick_fontsize (int)
figsize (tuple[float, float] | None)

Return type:

Figure

superstats.diagnostics.plots.plot_time_varying_verification(estimates, targets, variable_keys=None, variable_names=None, aggregation=<function median>, colors=['#356673', '#AE534C', '#6B4A6E', '#566B54'], title_fontsize=22, label_fontsize=18, tick_fontsize=16, figsize=None)[source]#

Plot recovery diagnostics over steps for time-varying parameters.

Parameters:

estimatesMapping[str, np.ndarray] or np.ndarray: Posterior samples. If a dict, values of shape (num_sim, num_samples, num_steps), keyed by variable. If an array, shape (num_sim, num_samples, num_steps, num_params) directly.
targetsMapping[str, np.ndarray] or np.ndarray: Ground-truth parameter trajectories, matching the input type of estimates. If a dict, values of shape (num_sim, num_steps). If an array, shape (num_sim, num_steps, num_params) directly.
variable_keyssequence of str or None, optional, default: None: Which variables to select and plot, and in what order, when estimates/targets are dicts. By default, all keys, in dict insertion order. Ignored for array input.
variable_namessequence of str or None, optional, default: None: Display names for the plotted columns, in the same order as variable_keys (or the array’s last axis). Defaults to variable_keys for dict input, or param_0, param_1, … for array input.
aggregationcallable(), optional, default: np.median: Aggregation function passed through to each metric (nrmse, contraction, calibration) when collapsing across simulations. Typically np.mean or np.median.
colorsstr or sequence of str, optional, default: METRIC_COLORS: Row colors, one per metric in the fixed order: correlation, nrmse, contraction, calibration. A single str is applied to all four rows.
title_fontsizeint, optional, default: 22: The font size of the column titles (parameter names).
label_fontsizeint, optional, default: 18: The font size of the axis label texts and row labels.
tick_fontsizeint, optional, default: 16: The font size of the axis tick labels.
figsizetuple of two floats or None, optional, default: None: Explicit figure size in inches. If None, the default layout size is used.

Returns:

figplt.Figure - the figure instance for optional saving

Raises:

ValueError: If estimates/targets are inconsistent (see _prepare_plot_data), or if colors is a sequence whose length doesn’t match the number of metrics (4).

Parameters:

estimates (Mapping[str, ndarray] | ndarray)
targets (Mapping[str, ndarray] | ndarray)
variable_keys (Sequence[str] | None)
variable_names (Sequence[str] | None)
aggregation (Callable)
colors (str | Sequence[str])
title_fontsize (int)
label_fontsize (int)
tick_fontsize (int)
figsize (tuple[float, float] | None)

Modules

`posterior_resimulation`	Posterior predictive resimulation plots.
`posterior_samples`	Posterior sample visualization helpers.
`prior_push_forward`	Prior push-forward plotting helpers.
`prior_samples`	Prior sample visualization helpers.
`time_invariant_verification`	Time-invariant posterior recovery and calibration plots.
`time_varying_verification`	Time-varying posterior recovery diagnostics.