autofit.SamplesNest#

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

Bases: SamplesPDF

Contains the samples of the non-linear search, including parameter values, log likelihoods, weights and other quantites.

For example, the output class can be used to load an instance of the best-fit model, get an instance of any individual sample by the NonLinearSearch and return information on the likelihoods, errors, etc.

Parameters:

model – Maps input vectors of unit parameter values to physical values and model instances via priors.
sample_list – The list of Samples which contains the paramoeters, likelihood, weights, etc. of every sample taken by the non-linear search.
number_live_points – The number of live points used by the nested sampler.
samples_info – Contains information on the samples (e.g. total iterations, time to run the search, etc.).

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_centred_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_centred_max_lh_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_centred_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.
`samples_drawn_randomly_via_pdf_from`	Draw one or more samples randomly from the PDF, weighted by the sample weights.
`samples_within_parameter_range`	Returns a new set of Samples where all points without parameter values inside a specified range removed.
`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

`acceptance_ratio`	The ratio of accepted samples to total samples.
`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`
`model_centred`	Returns a model where every free parameter is a GaussianPrior with mean the previous result's inferred maximum log likelihood parameter values and sigma values determined from the errors on each parameter or the prior config files
`names`	A list of names of unique priors in the same order as prior ids (and therefore sample columns)
`number_live_points`
`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_accepted_samples`	The total number of accepted samples performed by the nested sampler.
`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`

__add__(other: SamplesNest) → SamplesNest[source]#

Samples can be added together, which combines their sample_list meaning that inferred parameters are computed via their joint PDF.

Parameters:: other – Another Samples class
Return type:: A class that combined the samples of the two Samples objects.

property total_accepted_samples: int#: The total number of accepted samples performed by the nested sampler.

property acceptance_ratio: float#: The ratio of accepted samples to total samples.

samples_within_parameter_range(parameter_index: int, parameter_range: [<class 'float'>, <class 'float'>]) → SamplesStored[source]#

Returns a new set of Samples where all points without parameter values inside a specified range removed.

For example, if our Samples object was for a model with 4 parameters are consistent of the following 3 sets of parameters:

[[1.0, 2.0, 3.0, 4.0]] [[100.0, 2.0, 3.0, 4.0]] [[1.0, 2.0, 3.0, 4.0]]

This function for parameter_index=0 and parameter_range=[0.0, 99.0] would remove the second sample because the value 100.0 is outside the range 0.0 -> 99.0.

Parameters:

parameter_index – The 1D index of the parameter (in the model’s vector representation) whose values lie between the parameter range if a sample is kept.
parameter_range – The minimum and maximum values of the range of parameter values this parameter must lie between for it to be kept.