Last modified: December 2013

URL: http://cxc.harvard.edu/sherpa/ahelp/get_sampler.html
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AHELP for CIAO 4.11 Sherpa v1

get_sampler

Context: statistics

Synopsis

Return information on the current pyBLoCXS sampler.

Syntax

get_sampler()
get_sampler_opt( [optname] )
get_sampler_name()

Description

pyBLoCXS is a sophisticated Markov chain Monte Carlo (MCMC) based algorithm designed to carry out Bayesian Low-Count X-ray Spectral (BLoCXS) analysis in the Sherpa environment. The algorithm explores parameter space at a suspected minimum - i.e. after a standard Sherpa fit.

The Sherpa get_sampler commands return information about the type of jumping rule used in MCMC.

Jumping Rules

"MH" is Metropolis-Hastings, which always jumps from the best-fit, and "MetropolisMH" is Metropolis with Metropolis-Hastings that jumps from the best-fit with probability 'p_M', else it jumps from the last accepted jump. "PragBayes" is used when effective area calibration uncertainty is to be included in the calculation. (At each nominal MCMC iteration, a new calibration product is generated, and a series of N (option in set_sampler_opt) MCMC sub-iteration steps are carried out, choosing between Metropolis and Metropolis-Hastings types of samplers with probability p_M (option in set_sampler_opt). Only the last of these sub-iterations are kept in the chain.)

The configuration options returned by get_sampler include the following:

Metropolis-Hastings Jumping Rule

Mixture of Metropolis and Metropolis-Hastings Jumping Rules

Available samplers are returned by the list_samplers command.

Refer to the pyBLoCXS documentation for additional information about the algorithm.


Examples

Example 1

sherpa> print get_sampler_name()

Return the name of the current sampler. In this example, it is the default "MetropolisMH" for Metropolis withnMetropolis-Hastings.

sherpa> load_pha("pha.fits")
sherpa> set_source(xsphabs.abs1 * powlaw1d.p1)
sherpa> set_stat("cash")
sherpa> fit()
sherpa> covar()

sherpa> print get_sampler_name()
        MetropolisMH

Example 2

sherpa> print get_sampler()

Retrieve the configuration options for the current sampler. The output for this example is:

"{'priorshape': False, 'scale': 1, 'log': False, 'defaultprior': True,
'inv': False, 'sigma_m': False, 'priors': (), 'originalscale': True,
'verbose': False}"

Example 3

sherpa> get_sampler_opt('log')

Return the current setting for the 'log' configuration option of the current sampler.

sherpa> get_sampler_opt('log')
        False

Bugs

See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.

See Also

confidence
get_conf, get_covar, get_int_proj, get_int_unc, get_proj, get_reg_proj, get_reg_unc
contrib
get_chart_spectrum, get_marx_spectrum
data
get_areascal, get_arf, get_arf_plot, get_axes, get_backscal, get_bkg, get_bkg_plot, get_bkg_scale, get_coord, get_counts, get_data, get_data_plot, get_dep, get_dims, get_error, get_exposure, get_grouping, get_indep, get_quality, get_rmf, get_specresp, get_staterror, get_syserror
filtering
get_filter
fitting
calc_stat_info, get_fit, get_stat_info
info
get_default_id, list_stats
methods
get_draws, get_iter_method_name, get_iter_method_opt, get_method
modeling
get_model, get_model_component, get_model_component_image, get_model_component_plot, get_model_plot, get_num_par, get_order_plot, get_par, get_pileup_model, get_response, get_source, get_source_component_image, get_source_component_plot, image_source
plotting
get_split_plot
psfs
get_kernel, get_psf
statistics
get_chisqr_plot, get_delchi_plot, get_prior, get_stat
utilities
get_analysis, get_rate
visualization
get_ratio, get_resid, image_getregion