drpangloss.plotting

Functions

posterior_predictive_summary(dra_samples, ddec_samples, flux_samples, oidata, model_class)

Compute posterior predictive means and standard deviations for visibilities and phases.

Parameters:

Name	Type	Description	Default
dra_samples	array - like	Posterior samples for Cartesian offset and flux ratio.	required
ddec_samples	array - like	Posterior samples for Cartesian offset and flux ratio.	required
flux_samples	array - like	Posterior samples for Cartesian offset and flux ratio.	required
oidata	OIData	Data object defining observable conventions and geometry.	required
model_class	type	Model class with signature model_class(dra=..., ddec=..., flux=...).	required

Returns:

Type	Description
dict	Dictionary containing vis_mean, vis_std, phi_mean, phi_std arrays.

plot_data_model_correlation(oidata, predictions_by_label, colors=None, figsize=(10, 5), phase_title='Phase correlation', square_axes=True)

Plot data-vs-model correlation panels for visibility and phase observables.

Parameters:

Name	Type	Description	Default
oidata	OIData	Observed data container.	required
predictions_by_label	dict	Mapping label -> prediction summary where each summary contains vis_mean, vis_std, phi_mean, phi_std arrays.	required
colors	list	Matplotlib color list. If omitted, cycle C0, C1, ...	None
figsize	tuple	Figure size.	(10, 5)
phase_title	str	Title for the phase panel.	'Phase correlation'
square_axes	bool	If True, enforce square panel boxes for both subplots.	True

Returns:

Type	Description
tuple	(fig, (ax1, ax2)) for visibility and phase axes.

plot_trace_panels(samples_dict, keys, title, color='C0', figsize=(10, 6))

Plot simple one-dimensional trace panels for selected sample keys.

Parameters:

Name	Type	Description	Default
samples_dict	dict	Mapping from key name to one-dimensional sample arrays.	required
keys	list	Ordered list of keys to plot.	required
title	str	Figure title.	required
color	str	Line color.	'C0'
figsize	tuple	Figure size.	(10, 6)

Returns:

Type	Description
tuple	(fig, axes).

plot_likelihood_grid(loglike_im, samples_dict, truths=None, best_point=None, truth_label='Truth', best_label='Grid max', colorbar_label='Log likelihood', cmap='inferno', figsize=(12, 6))

Plot the results of a likelihood_grid calculation.

Parameters:

Name	Type	Description	Default
loglike_im	array	The likelihood grid, output of likelihood_grid	required
samples_dict	dict	Dictionary of samples used in the grid calculation	required
truths	list	List of true values for the parameters, default None	None

plot_chainconsumer_diagnostics(chains_by_label, columns, truth, colors=None, walk_columns=None)

Plot comparison diagnostics using ChainConsumer for multiple posterior chains.

Parameters:

Name	Type	Description	Default
chains_by_label	dict	Mapping label -> pandas.DataFrame containing chain samples.	required
columns	list[str]	Columns used for contour/corner plotting.	required
truth	dict	Truth mapping for columns displayed.	required
colors	list[str]	Per-chain colors.	None
walk_columns	list[str]	Columns to show in walk plots. Defaults to columns.	None

Returns:

Type	Description
ChainConsumer	Configured ChainConsumer instance.

diagnostics_table_from_samples(samples, dra_key='dra', ddec_key='ddec', flux_key='flux', log10_flux=False)

Build a standardized diagnostics table from posterior sample arrays.

Parameters:

Name	Type	Description	Default
samples	dict - like	Mapping of sample arrays.	required
dra_key	str	Key for right-ascension offsets.	'dra'
ddec_key	str	Key for declination offsets.	'ddec'
flux_key	str	Key for flux or log10-flux samples.	'flux'
log10_flux	bool	If True, exponentiate flux_key values as base-10.	False

Returns:

Type	Description
DataFrame	Table with dra, ddec, flux, sep, and pa columns.

truth_cartesian_and_polar(truth)

Return truth mappings for shared ChainConsumer Cartesian and polar interfaces.

Parameters:

Name	Type	Description	Default
truth	dict	Mapping with dra, ddec, and flux values.	required

Returns:

Type	Description
tuple[dict, dict]	Cartesian and polar truth dictionaries.

plot_hmc_fisher_chainconsumer(hmc_table, fisher_table, truth_cartesian, colors=('#1f77b4', '#ff7f0e'))

Plot paired Cartesian and polar ChainConsumer diagnostics for HMC and Fisher-HMC.

Parameters:

Name	Type	Description	Default
hmc_table	DataFrame	Posterior table for vanilla HMC.	required
fisher_table	DataFrame	Posterior table for Fisher-reparameterized HMC.	required
truth_cartesian	dict	Truth mapping in Cartesian coordinates.	required
colors	tuple[str, str]	Colors used for HMC and Fisher-HMC chains.	('#1f77b4', '#ff7f0e')

Returns:

Type	Description
dict	Mapping containing configured ChainConsumer objects and truth mappings.

plot_recovery_residuals(params, truth, estimates_by_label, std_by_label, figsize=(8, 4))

Plot parameter recovery and normalized residuals for multiple estimators.

Parameters:

Name	Type	Description	Default
params	list[str]	Parameter names.	required
truth	array - like	Truth values in the same order as params.	required
estimates_by_label	dict	Mapping of label to posterior median arrays.	required
std_by_label	dict	Mapping of label to posterior standard-deviation arrays.	required
figsize	tuple	Base figure size.	(8, 4)

Returns:

Type	Description
tuple	((fig1, ax1), (fig2, ax2)) for recovery and residual panels.

radial_limit_summary(limit_map, dra_axis, ddec_axis, center=(0.0, 0.0), r_max=350.0, n_bins=20)

Compute radial median and percentile bands for a 2D contrast-limit map.

Parameters:

Name	Type	Description	Default
limit_map	array - like	Two-dimensional map of contrast limits.	required
dra_axis	array - like	Right-ascension axis in milliarcseconds.	required
ddec_axis	array - like	Declination axis in milliarcseconds.	required
center	tuple[float, float]	Radial center in milliarcseconds.	(0.0, 0.0)
r_max	float	Maximum radial separation to summarize.	350.0
n_bins	int	Number of radial edges (bins are n_bins - 1).	20

Returns:

Type	Description
dict	Mapping with r_centers, median, q16, and q84 arrays.

plot_contrast_limit_map(limit_map, dra_axis, ddec_axis, truth=None, unit_mode='flux_ratio', title='Contrast-limit map', cmap='inferno', figsize=(8, 6))

Plot a 2D contrast-limit map in flux-ratio or Δmag units.

Parameters:

Name	Type	Description	Default
limit_map	array - like	Two-dimensional contrast-limit map.	required
dra_axis	array - like	Right-ascension axis in milliarcseconds.	required
ddec_axis	array - like	Declination axis in milliarcseconds.	required
truth	dict or tuple	Truth location to overplot.	None
unit_mode	(flux_ratio, delta_mag)	Display units for the map.	"flux_ratio"
title	str	Axes title.	'Contrast-limit map'
cmap	str	Matplotlib colormap name.	'inferno'
figsize	tuple	Figure size.	(8, 6)

Returns:

Type	Description
tuple	(fig, ax).

plot_radial_limit_summary(radial_summary, unit_mode='flux_ratio', title='Radial limit summary', figsize=(8, 4), ax=None)

Plot radial median and percentile spread from radial_limit_summary output.

Parameters:

Name	Type	Description	Default
radial_summary	dict	Output mapping from radial_limit_summary.	required
unit_mode	(flux_ratio, delta_mag)	Display units for the y-axis.	"flux_ratio"
title	str	Plot title.	'Radial limit summary'
figsize	tuple	Figure size.	(8, 4)
ax	Axes	Existing axes to draw on. If omitted, create a new figure and axes.	None

Returns:

Type	Description
tuple	(fig, ax).

plot_optimized_and_grid(loglike_im, optimized, samples_dict)

Plot optimized contrast results alongside the brute-force grid maximum.

Parameters:

Name	Type	Description	Default
loglike_im	array	Full 3D log-likelihood cube from likelihood_grid.	required
optimized	array	2D optimized contrast map from optimized_contrast_grid.	required
samples_dict	dict	Sampling dictionary containing dra, ddec, and flux axes.	required

plot_optimized_and_sigma(contrast, sigma_grid, samples_dict, snr=False)

Plot the results of an optimized contrast grid calculation and the corresponding uncertainty grid.

Parameters:

Name	Type	Description	Default
contrast	array	The optimized contrast grid, output of optimized_contrast_grid	required
sigma_grid	array	The uncertainty grid, output of laplace_contrast_uncertainty_grid	required
samples_dict	dict	Dictionary of samples used in the grid calculation	required
snr	bool	If True, plot the SNR instead of the uncertainty, default False	False

plot_contrast_limits(contrast_limits, samples_dict, rad_width, avg_width, std_width, true_values=None, limit_label='98% Upper Limit')

Plot the contrast limits calculated with the Ruffio or Absil methods.

Parameters:

Name	Type	Description	Default
contrast_limits	array	The contrast limits calculated with the Ruffio or Absil methods.	required
samples_dict	dict	Dictionary of samples used in the grid calculation	required
rad_width	array	Radial width of the contrast limits.	required
avg_width	array	Average width of the contrast limits.	required
std_width	array	Standard deviation of the contrast limits.	required
true_values	list	List of true values for the parameters, default None	None
limit_label	str	Label used for the map title and curve legend.	'98% Upper Limit'