autofit.SamplesStored#

class autofit.SamplesStored(model: AbstractPriorModel, sample_list: List[Sample], samples_info: Optional[Dict] = None)[source]#

Bases: SamplesPDF

The Samples of a non-linear search, specifically the samples of a NonLinearSearch which maps out the posterior of parameter space and thus does provide information on parameter errors.

Parameters:: model (af.ModelMapper) – Maps input vectors of unit parameter values to physical values and model instances via priors.

Methods

`draw_randomly_via_pdf`	The parameter vector of an individual sample of the non-linear search drawn randomly from the PDF, returned as a 1D list.
`error_magnitudes_at_sigma`	The magnitude of every error after marginalization in 1D at an input sigma value of the probability density function (PDF), returned as two lists of values corresponding to the lower and upper errors.
`errors_at_lower_sigma`	The lower error of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`errors_at_sigma`	The lower and upper error of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`errors_at_upper_sigma`	The upper error of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`from_list_info_and_model`
`from_sample_index`	The parameters of an individual sample of the non-linear search, returned as a model instance.
`from_table`	Write a table of parameters, posteriors, priors and likelihoods
`info_to_json`
`max_log_likelihood`	The parameters of the maximum log likelihood sample of the NonLinearSearch returned as a model instance or list of values.
`max_log_posterior`	The parameters of the maximum log posterior sample of the NonLinearSearch returned as a model instance.
`median_pdf`	The median of the probability density function (PDF) of every parameter marginalized in 1D, returned as a model instance or list of values.
`minimise`	A copy of this object with only important samples retained
`model_absolute`	Returns a model where every free parameter is a GaussianPrior with mean the previous result's inferred maximum log likelihood parameter values and sigma the input absolute value a.
`model_bounded`	Returns a model where every free parameter is a UniformPrior with lower_limit and upper_limit the previous result's inferred maximum log likelihood parameter values minus and plus the bound `b.
`model_relative`	Returns a model where every free parameter is a GaussianPrior with mean the previous result's inferred maximum log likelihood parameter values and sigma a relative value from the result r.
`offset_values_via_input_values`	The values of an input_vector offset by the median_pdf(as_instance=False) (the PDF medians).
`path_map_for_model`
`samples_above_weight_threshold_from`	Returns a new Samples object containing only the samples with a weight above the input threshold.
`save_covariance_matrix`	Save the covariance matrix as a CSV file.
`subsamples`
`summary`
`values_at_lower_sigma`	The lower value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`values_at_sigma`	The value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as two lists of values corresponding to the lower and upper values parameter values.
`values_at_upper_sigma`	The upper value of every parameter marginalized in 1D at an input sigma value of its probability density function (PDF), returned as a list.
`values_for_path`	Returns the value for a variable with a given path for each sample in the model
`with_paths`	Create a copy of this object with only attributes specified by a list of paths.
`without_paths`	Create a copy of this object with only attributes not specified by a list of paths.
`write_table`	Write a table of parameters, posteriors, priors and likelihoods.

Attributes

`covariance_matrix`	Compute the covariance matrix of the non-linear search samples, using the method np.cov() which is described at the following link:
`instance`
`instances`	One model instance for each sample
`log_evidence`
`log_likelihood`
`log_likelihood_list`
`log_posterior_list`
`log_prior_list`
`max_log_likelihood_index`	The index of the sample with the highest log likelihood.
`max_log_likelihood_sample`	The index of the sample with the highest log likelihood.
`max_log_posterior_index`	The index of the sample with the highest log posterior.
`max_log_posterior_sample`
`names`	A list of names of unique priors in the same order as prior ids (and therefore sample columns)
`parameter_lists`
`parameters_extract`
`paths`	A list of paths to unique priors in the same order as prior ids (and therefore sample columns)
`pdf_converged`	To analyse and visualize samples the analysis must be sufficiently converged to produce smooth enough PDF for error estimate and PDF generation.
`prior_means`	The mean of every parameter used to link its inferred values and errors to priors used to sample the same (or similar) parameters in a subsequent search, where:
`time`
`total_iterations`
`total_samples`
`unconverged_sample_size`	If a set of samples are unconverged, alternative methods to compute their means, errors, etc are used.
`weight_list`