NestedResults

class dysmalpy.fitting.NestedResults(model=None, sampler_results=None, linked_posterior_names=None, blob_name=None, nPostBins=50)[source]

Bases: BayesianFitResults, FitResults

Class to hold results of nested sampling fitting to DYSMALPY models.

Notes:

The dynesty Results object (containing the results of the run) is stored in nestedResults.sampler_results.

The names of free parameters in the chain are accessed through nestedResults.chain_param_names or more generally (separate model + parameter names) through nestedResults.free_param_names

Optional Attribute:

linked_posterior_names

Indicates if best-fit parameters should be measured in multi-dimensional histogram space. It takes a list of linked parameter sets, where each set consists of len-2 tuples/lists of the component + parameter names.

Structure Explanation:

  1. To analyze component + param 1 and 2 together, and then 3 and 4 together: linked_posterior_names = [joint_param_bundle1, joint_param_bundle2] with joint_param_bundle1 = [[cmp1, par1], [cmp2, par2]] and joint_param_bundle2 = [[cmp3, par3], [cmp4, par4]], for a full array of: linked_posterior_names = [[[cmp1, par1], [cmp2, par2]],[[cmp3, par3], [cmp4, par4]]].

  2. To analyze component + param 1 and 2 together: linked_posterior_names = [joint_param_bundle1] with joint_param_bundle1 = [[cmp1, par1], [cmp2, par2]], for a full array of linked_posterior_names = [[[cmp1, par1], [cmp2, par2]]].
    Example: Look at halo: mvirial and disk+bulge: total_mass together

    linked_posterior_names = [[['halo', 'mvirial'], ['disk+bulge', 'total_mass']]]

Methods Summary

plot_run([fileout, overwrite])

Plot/replot the trace for the Bayesian fitting

reload_sampler_results([filename])

Reload the Nested sampling results saved earlier

Methods Documentation

plot_run(fileout=None, overwrite=False)[source]

Plot/replot the trace for the Bayesian fitting

reload_sampler_results(filename=None)[source]

Reload the Nested sampling results saved earlier