bayes_pol

Submodules

bayes_pol.faraday_model

faraday_model.py FaradayModel definition

class bayes_pol.faraday_model.FaradayModel(*args, lam2_window_width=None, **kwargs)

Bases: BaseModel

Definition of the model

Attributes:

baseline_deterministics: Get the deterministic baseline parameter names.
baseline_freeRVs: Get the free baseline parameter names.
cloud_deterministics: Get the deterministic cloud parameter names.
cloud_freeRVs: Get the free cloud parameter names.
hyper_deterministics: Get the deterministic hyper parameter names.
hyper_freeRVs: Get the free hyper parameter names.
labeller: Get the arviz labeller.
unique_solution: Check if posterior samples suggest a unique solution.

Methods

`add_baseline_priors`([prior_baseline_coeffs])	Add baseline priors to the model.
`add_likelihood`()	Add likelihood to the model.
`add_priors`([prior_polarized_intensity, ...])	Add priors and deterministics to the model
`bic`([chain, solution])	Calculate the Bayesian information criterion at the mean point estimate.
`fit`([n, draws, rel_tolerance, ...])	Approximate posterior distribution using Variational Inference (VI).
`graph`()	Generate visualization of the model graph.
`mean_lnlike`([chain, solution])	Evaluate mean log-likelihood over posterior samples.
`null_bic`()	Evaluate the Bayesian Information Criterion for the null hypothesis (baseline only, no clouds)
`predict_baseline`([baseline_params])	Predict the un-normalized baseline model.
`reset_results`()	Reset results and convergence checks.
`sample`([init, n_init, chains, init_kwargs, ...])	Sample posterior distribution using MCMC.
`sample_posterior_predictive`([solution, thin])	Generate posterior predictive samples
`sample_prior_predictive`([samples])	Generate prior predictive samples
`sample_smc`(**kwargs)	Sample posterior distribution using Sequential Monte Carlo.
`solve`([num_gmm_samples, kl_div_threshold])	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model. SpecData key must be “Q”, “U”, and “faraday_depth_abs”. Spectral units for “Q” and “U” should be square wavelength in m2. Spectral units for “faraday_depth_abs” should be rad/m2. Order of clouds is nearest to farthest.

add_priors(prior_polarized_intensity: float = 100.0, prior_faraday_depth_mean: float = [0.0, 1000.0], prior_faraday_depth_fwhm: float = 10.0)

Add priors and deterministics to the model

Parameters:

prior_polarized_intensityfloat, optional: Prior distribution on the polarized intensity (data brightness), by default 100.0, where polarized_intensity ~ HalfNormal(sigma=prior)
prior_faraday_depth_meanIterable[float], optional: Prior distribution on the mean Faraday depth (rad/m2), by default [0.0, 1000.0], where faraday_depth_mean ~ Cauchy(alpha=prior[0], beta=prior[1])
prior_faraday_depth_fwhmfloat, optional: Prior distribution on the Faraday depth full-width at half-maximum (rad/m2), by default 10.0, where faraday_depth_fwhm ~ HalfNormal(sigma=prior)

bayes_pol.faraday_model.sinc(x: float) → float

Evaluate sin(x)/x and catch x=0.

Parameters:

xfloat: Position at which to evaluate

Returns:

float: sin(x)/x

Module contents

class bayes_pol.FaradayModel(*args, lam2_window_width=None, **kwargs)

Bases: BaseModel

Definition of the model

Attributes:

baseline_deterministics: Get the deterministic baseline parameter names.
baseline_freeRVs: Get the free baseline parameter names.
cloud_deterministics: Get the deterministic cloud parameter names.
cloud_freeRVs: Get the free cloud parameter names.
hyper_deterministics: Get the deterministic hyper parameter names.
hyper_freeRVs: Get the free hyper parameter names.
labeller: Get the arviz labeller.
unique_solution: Check if posterior samples suggest a unique solution.

Methods

`add_baseline_priors`([prior_baseline_coeffs])	Add baseline priors to the model.
`add_likelihood`()	Add likelihood to the model.
`add_priors`([prior_polarized_intensity, ...])	Add priors and deterministics to the model
`bic`([chain, solution])	Calculate the Bayesian information criterion at the mean point estimate.
`fit`([n, draws, rel_tolerance, ...])	Approximate posterior distribution using Variational Inference (VI).
`graph`()	Generate visualization of the model graph.
`mean_lnlike`([chain, solution])	Evaluate mean log-likelihood over posterior samples.
`null_bic`()	Evaluate the Bayesian Information Criterion for the null hypothesis (baseline only, no clouds)
`predict_baseline`([baseline_params])	Predict the un-normalized baseline model.
`reset_results`()	Reset results and convergence checks.
`sample`([init, n_init, chains, init_kwargs, ...])	Sample posterior distribution using MCMC.
`sample_posterior_predictive`([solution, thin])	Generate posterior predictive samples
`sample_prior_predictive`([samples])	Generate prior predictive samples
`sample_smc`(**kwargs)	Sample posterior distribution using Sequential Monte Carlo.
`solve`([num_gmm_samples, kl_div_threshold])	Identify unique solutions and break the labeling degeneracy.

add_likelihood(): Add likelihood to the model. SpecData key must be “Q”, “U”, and “faraday_depth_abs”. Spectral units for “Q” and “U” should be square wavelength in m2. Spectral units for “faraday_depth_abs” should be rad/m2. Order of clouds is nearest to farthest.

add_priors(prior_polarized_intensity: float = 100.0, prior_faraday_depth_mean: float = [0.0, 1000.0], prior_faraday_depth_fwhm: float = 10.0)

Add priors and deterministics to the model

Parameters:

prior_polarized_intensityfloat, optional: Prior distribution on the polarized intensity (data brightness), by default 100.0, where polarized_intensity ~ HalfNormal(sigma=prior)
prior_faraday_depth_meanIterable[float], optional: Prior distribution on the mean Faraday depth (rad/m2), by default [0.0, 1000.0], where faraday_depth_mean ~ Cauchy(alpha=prior[0], beta=prior[1])
prior_faraday_depth_fwhmfloat, optional: Prior distribution on the Faraday depth full-width at half-maximum (rad/m2), by default 10.0, where faraday_depth_fwhm ~ HalfNormal(sigma=prior)