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Last modified: December 2013

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AHELP for CIAO 4.10 Sherpa v1


Context: plotting


Access a histogram plot of likelihood ratio test results.


get_pvalue_plot([null_model=None, alt_model=None, conv_model=None,
id=1, otherids=(), num=500, bins=25, numcores=None, recalc=False])


Access the data arrays and preferences defining the histogram plot produced by the plot_pvalue() function, a histogram of the likelihood ratios comparing fits of the null model to fits of the alternative model using faked data with Poisson noise. Data returned includes the likelihood ratio computed using the observed data, and the p-value, used to reject or accept the null model.


  • null_model - model representing the null hypothesis
  • alt_model - alternative model to compare to null
  • conv_model - convolution model (PSF or RSP) to include for fitting. default = None
  • id - Sherpa data id default = default data id
  • otherids - List of other Sherpa data ids default = ()
  • num - Number of iterations to run default = 500
  • bins - Number of bins for the histogram default = 25
  • numcores - Number of cpus to use during simulation default = number of detected cpus
  • recalc - Recalculate the likelihood ratio test simulation default = False

The get_pvalue_plot() function returns the following attributes:

  • ratios - likelihood ratios from 'num' iterations
  • lr - likelihood ratio on real, observed data
  • y - normalized frequencies of likelihood ratios
  • xlo - lower bin edges
  • xhi - upper bin edges


Example 1

sherpa> plot_pvalue(powlaw1d.p1, bpowlaw1d.bp1)
sherpa> print get_pvalue_plot()
ratios = [ 0.303   2.3149  0.5895 ..., -9.5852 -2.3156 -4.92  ]
lr = 12566.880545
xlo    = [-88.6406 -85.0598 -81.4791 ...,  -6.2833  -2.7026   0.8782]
xhi    = [-85.0598 -81.4791 -77.8983 ...,  -2.7026   0.8782   4.4589]
y      = [ 0.002  0.     0.    ...,  0.114  0.672  0.01 ]
xlabel = Likelihood Ratio
ylabel = Frequency
title  = Likelihood Ratio Distribution
histo_prefs = {'linethickness': None, 'symbolcolor': None, 'symbolfill': None, 'xlog': False, 'ylog': False, 'symbolangle': None, 'errthickness': None, 'fillcolor': None, 'linecolor': None, 'errstyle': None, 'linestyle': 1, 'symbolstyle': 0, 'errcolor': None, 'fillstyle': None, 'fillopacity': None, 'yerrorbars': False, 'symbolsize': None}

Call the get_pvalue_plot() command with no arguments to print to the screen the data arrays and preferences defining a histogram of likelihood ratios calculated using the plot_pvalue() command. View the arrays of ratios comparing fits to simulated data done with a simple (null) power-law model versus the more-complex (alternative) broken power-law model.

Example 2

sherpa> plot_pvalue(p1, p1+g1)
sherpa> my_lrt_plot = get_pvalue_plot()
sherpa> my_lrt_plot.ratios
array([ 0.303 ,  2.3149,  0.5895, ..., -9.5852, -2.3156, -4.92  ])

Store the data arrays and plot preferences returned by get_pvalue_plot() to variable "my_lrt_plot", in order to access and manipulate individual attributes within a script.


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

See Also

get_data_prof, get_data_prof_prefs, get_delchi_prof, get_delchi_prof_prefs, get_fit_prof, get_model_prof, get_model_prof_prefs, get_resid_prof, get_resid_prof_prefs, get_source_prof, get_source_prof_prefs, plot_chart_spectrum, prof_data, prof_delchi, prof_fit, prof_fit_delchi, prof_fit_resid, prof_model, prof_resid, prof_source
get_arf_plot, get_bkg_plot
list_model_ids, show_bkg_model, show_bkg_source
add_model, add_user_pars, clean, create_model_component, delete_bkg_model, delete_model, delete_model_component, get_model, get_model_autoassign_func, 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_source, get_source_component_image, get_source_component_plot, image_model, image_model_component, image_source, image_source_component, integrate, integrate1d, link, load_table_model, load_template_model, load_user_model, normal_sample, reset, save_model, save_source, set_bkg_model, set_full_model, set_model_autoassign_func, set_pileup_model, set_source, set_xsabund, set_xscosmo, set_xsxsect, set_xsxset, t_sample, uniform_sample
get_energy_flux_hist, get_lrt_plot, get_lrt_results, get_photon_flux_hist, get_pvalue_results, get_split_plot, plot, plot_arf, plot_bkg, plot_cdf, plot_chisqr, plot_data, plot_delchi, plot_energy_flux, plot_fit, plot_model, plot_model_component, plot_order, plot_pdf, plot_photon_flux, plot_pvalue, plot_ratio, plot_resid, plot_scatter, plot_source, plot_source_component, plot_trace, set_xlinear, set_xlog, set_ylinear, set_ylog
delete_psf, load_conv, plot_kernel
save_delchi, save_resid
get_chisqr_plot, get_delchi_plot
calc_chisqr, calc_energy_flux, calc_model_sum, calc_photon_flux, calc_source_sum, calc_stat, eqwidth
contour_model, contour_ratio, contour_resid, get_ratio, get_resid

Last modified: December 2013
Smithsonian Institute Smithsonian Institute

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