The sherpa.ui module

The sherpa.ui module provides an interface to the sherpa.ui.utils.Session object, where a singleton class is used to provide the access but hidden away. This needs better explanation…

Functions

add_model(modelclass[, args, kwargs])	Create a user-defined model class.
add_user_pars(modelname, parnames[, …])	Add parameter information to a user model.
calc_chisqr([id])	Calculate the per-bin chi-squared statistic.
calc_stat([id])	Calculate the fit statistic for a data set.
calc_stat_info()	Display the statistic values for the current models.
clean()	Clear out the current Sherpa session.
conf(*args)	Estimate the confidence intervals for parameters using the confidence method.
confidence(*args)	Estimate the confidence intervals for parameters using the confidence method.
contour(*args)	Create a contour plot for an image data set.
contour_data([id])	Contour the values of an image data set.
contour_fit([id])	Contour the fit to a data set.
contour_fit_resid([id, replot, overcontour])	Contour the fit and the residuals to a data set.
contour_kernel([id])	Contour the kernel applied to the model of an image data set.
contour_model([id])	Contour the values of the model, including any PSF.
contour_psf([id])	Contour the PSF applied to the model of an image data set.
contour_ratio([id])	Contour the ratio of data to model.
contour_resid([id])	Contour the residuals of the fit.
contour_source([id])	Contour the values of the model, without any PSF.
copy_data(fromid, toid)	Copy a data set to a new identifier.
covar(*args)	Estimate the confidence intervals for parameters using the covariance method.
covariance(*args)	Estimate the confidence intervals for parameters using the covariance method.
create_model_component([typename, name])	Create a model component.
dataspace1d(start, stop[, step, numbins, …])	Create the independent axis for a 1D data set.
dataspace2d(dims[, id, dstype])	Create the independent axis for a 2D data set.
delete_data([id])	Delete a data set by identifier.
delete_model([id])	Delete the model expression for a data set.
delete_model_component(name)	Delete a model component.
delete_psf([id])	Delete the PSF model for a data set.
fake([id, method])	Simulate a data set.
fit([id])	Fit a model to one or more data sets.
freeze(*args)	Fix model parameters so they are not changed by a fit.
get_cdf_plot()	Return the data used to plot the last CDF.
get_chisqr_plot([id])	Return the data used by plot_chisqr.
get_conf()	Return the confidence-interval estimation object.
get_conf_opt([name])	Return one or all of the options for the confidence interval method.
get_conf_results()	Return the results of the last conf run.
get_confidence_results()	Return the results of the last conf run.
get_covar()	Return the covariance estimation object.
get_covar_opt([name])	Return one or all of the options for the covariance method.
get_covar_results()	Return the results of the last covar run.
get_covariance_results()	Return the results of the last covar run.
get_data([id])	Return the data set by identifier.
get_data_contour([id])	Return the data used by contour_data.
get_data_contour_prefs()	Return the preferences for contour_data.
get_data_image([id])	Return the data used by image_data.
get_data_plot([id])	Return the data used by plot_data.
get_data_plot_prefs()	Return the preferences for plot_data.
get_default_id()	Return the default data set identifier.
get_delchi_plot([id])	Return the data used by plot_delchi.
get_dep([id, filter])	Return the dependent axis of a data set.
get_dims([id, filter])	Return the dimensions of the data set.
get_draws([id, otherids, niter, covar_matrix])	Run the pyBLoCXS MCMC algorithm.
get_error([id, filter])	Return the errors on the dependent axis of a data set.
get_filter([id])	Return the filter expression for a data set.
get_fit_contour([id])	Return the data used by contour_fit.
get_fit_plot([id])	Return the data used by plot_fit.
get_fit_results()	Return the results of the last fit.
get_functions()	Return the functions provided by Sherpa.
get_indep([id])	Return the independent axes of a data set.
get_int_proj([par, id, otherids, recalc, …])	Return the interval-projection object.
get_int_unc([par, id, otherids, recalc, …])	Return the interval-uncertainty object.
get_iter_method_name()	Return the name of the iterative fitting scheme.
get_iter_method_opt([optname])	Return one or all options for the iterative-fitting scheme.
get_kernel_contour([id])	Return the data used by contour_kernel.
get_kernel_image([id])	Return the data used by image_kernel.
get_kernel_plot([id])	Return the data used by plot_kernel.
get_method([name])	Return an optimization method.
get_method_name()	Return the name of current Sherpa optimization method.
get_method_opt([optname])	Return one or all of the options for the current optimization method.
get_model([id])	Return the model expression for a data set.
get_model_autoassign_func()	Return the method used to create model component identifiers.
get_model_component(name)	Returns a model component given its name.
get_model_component_image(id[, model])	Return the data used by image_model_component.
get_model_component_plot(id[, model])	Return the data used by plot_model_component.
get_model_contour([id])	Return the data used by contour_model.
get_model_contour_prefs()	Return the preferences for contour_model.
get_model_image([id])	Return the data used by image_model.
get_model_pars(model)	Return the names of the parameters of a model.
get_model_plot([id])	Return the data used by plot_model.
get_model_plot_prefs()	Return the preferences for plot_model.
get_model_type(model)	Describe a model expression.
get_num_par([id])	Return the number of parameters in a model expression.
get_num_par_frozen([id])	Return the number of frozen parameters in a model expression.
get_num_par_thawed([id])	Return the number of thawed parameters in a model expression.
get_par(par)	Return a parameter of a model component.
get_pdf_plot()	Return the data used to plot the last PDF.
get_prior(par)	Return the prior function for a parameter.
get_proj()	Return the confidence-interval estimation object.
get_proj_opt([name])	Return one or all of the options for the confidence interval method.
get_proj_results()	Return the results of the last proj run.
get_projection_results()	Return the results of the last proj run.
get_psf([id])	Return the PSF model defined for a data set.
get_psf_contour([id])	Return the data used by contour_psf.
get_psf_image([id])	Return the data used by image_psf.
get_psf_plot([id])	Return the data used by plot_psf.
get_pvalue_plot([null_model, alt_model, …])	Return the data used by plot_pvalue.
get_pvalue_results()	Return the data calculated by the last plot_pvalue call.
get_ratio_contour([id])	Return the data used by contour_ratio.
get_ratio_image([id])	Return the data used by image_ratio.
get_ratio_plot([id])	Return the data used by plot_ratio.
get_reg_proj([par0, par1, id, otherids, …])	Return the region-projection object.
get_reg_unc([par0, par1, id, otherids, …])	Return the region-uncertainty object.
get_resid_contour([id])	Return the data used by contour_resid.
get_resid_image([id])	Return the data used by image_resid.
get_resid_plot([id])	Return the data used by plot_resid.
get_sampler()	Return the current MCMC sampler options.
get_sampler_name()	Return the name of the current MCMC sampler.
get_sampler_opt(opt)	Return an option of the current MCMC sampler.
get_scatter_plot()	Return the data used to plot the last scatter plot.
get_source([id])	Return the source model expression for a data set.
get_source_component_image(id[, model])	Return the data used by image_source_component.
get_source_component_plot(id[, model])	Return the data used by plot_source_component.
get_source_contour([id])	Return the data used by contour_source.
get_source_image([id])	Return the data used by image_source.
get_source_plot([id])	Return the data used by plot_source.
get_split_plot()	Return the plot attributes for displays with multiple plots.
get_stat([name])	Return a fit statisic.
get_stat_info()	Return the statistic values for the current models.
get_stat_name()	Return the name of the current fit statistic.
get_staterror([id, filter])	Return the statistical error on the dependent axis of a data set.
get_syserror([id, filter])	Return the systematic error on the dependent axis of a data set.
get_trace_plot()	Return the data used to plot the last trace.
guess([id, model, limits, values])	Estimate the parameter values and ranges given the loaded data.
ignore([lo, hi])	Exclude data from the fit.
ignore_id(ids[, lo, hi])	Exclude data from the fit for a data set.
image_close()	Close the image viewer.
image_data([id, newframe, tile])	Display a data set in the image viewer.
image_deleteframes()	Delete all the frames open in the image viewer.
image_fit([id, newframe, tile, deleteframes])	Display the data, model, and residuals for a data set in the image viewer.
image_getregion([coord])	Return the region defined in the image viewer.
image_kernel([id, newframe, tile])	Display the 2D kernel for a data set in the image viewer.
image_model([id, newframe, tile])	Display the model for a data set in the image viewer.
image_model_component(id[, model, newframe, …])	Display a component of the model in the image viewer.
image_open()	Start the image viewer.
image_psf([id, newframe, tile])	Display the 2D PSF model for a data set in the image viewer.
image_ratio([id, newframe, tile])	Display the ratio (data/model) for a data set in the image viewer.
image_resid([id, newframe, tile])	Display the residuals (data - model) for a data set in the image viewer.
image_setregion(reg[, coord])	Set the region to display in the image viewer.
image_source([id, newframe, tile])	Display the source expression for a data set in the image viewer.
image_source_component(id[, model, …])	Display a component of the source expression in the image viewer.
image_xpaget(arg)	Return the result of an XPA call to the image viewer.
image_xpaset(arg[, data])	Return the result of an XPA call to the image viewer.
int_proj(par[, id, otherids, replot, fast, …])	Calculate and plot the fit statistic versus fit parameter value.
int_unc(par[, id, otherids, replot, min, …])	Calculate and plot the fit statistic versus fit parameter value.
link(par, val)	Link a parameter to a value.
list_data_ids()	List the identifiers for the loaded data sets.
list_functions([outfile, clobber])	Display the functions provided by Sherpa.
list_iter_methods()	List the iterative fitting schemes.
list_methods()	List the optimization methods.
list_model_components()	List the names of all the model components.
list_model_ids()	List of all the data sets with a source expression.
list_models([show])	List the available model types.
list_priors()	Return the priors set for model parameters, if any.
list_samplers()	List the MCMC samplers.
list_stats()	List the fit statistics.
load_arrays(id, *args)	Create a data set from array values.
load_conv(modelname, filename_or_model, …)	Load a 1D convolution model.
load_data(id[, filename, ncols, colkeys, …])	Load a data set from an ASCII file.
load_filter(id[, filename, ignore, ncols])	Load the filter array from an ASCII file and add to a data set.
load_psf(modelname, filename_or_model, …)	Create a PSF model.
load_staterror(id[, filename, ncols])	Load the statistical errors from an ASCII file.
load_syserror(id[, filename, ncols])	Load the systematic errors from an ASCII file.
load_table_model(modelname, filename[, …])	Load ASCII tabular data and use it as a model component.
load_template_interpolator(name, …)	Set the template interpolation scheme.
load_template_model(modelname, templatefile)	Load a set of templates and use it as a model component.
load_user_model(func, modelname[, filename, …])	Create a user-defined model.
load_user_stat(statname, calc_stat_func[, …])	Create a user-defined statistic.
normal_sample([num, sigma, correlate, id, …])	Sample the fit statistic by taking the parameter values from a normal distribution.
notice([lo, hi])	Include data in the fit.
notice_id(ids[, lo, hi])	Include data from the fit for a data set.
paramprompt([val])	Should the user be asked for the parameter values when creating a model?
plot(*args)	Create one or more plot types.
plot_cdf(points[, name, xlabel, replot, …])	Plot the cumulative density function of an array of values.
plot_chisqr([id])	Plot the chi-squared value for each point in a data set.
plot_data([id])	Plot the data values.
plot_delchi([id])	Plot the ratio of residuals to error for a data set.
plot_fit([id])	Plot the fit results (data, model) for a data set.
plot_fit_delchi([id, replot, overplot, …])	Plot the fit results, and the residuals, for a data set.
plot_fit_resid([id, replot, overplot, …])	Plot the fit results, and the residuals, for a data set.
plot_kernel([id])	Plot the 1D kernel applied to a data set.
plot_model([id])	Plot the model for a data set.
plot_model_component(id[, model])	Plot a component of the model for a data set.
plot_pdf(points[, name, xlabel, bins, …])	Plot the probability density function of an array of values.
plot_psf([id])	Plot the 1D PSF model applied to a data set.
plot_pvalue(null_model, alt_model[, …])	Compute and plot a histogram of likelihood ratios by simulating data.
plot_ratio([id])	Plot the ratio of data to model for a data set.
plot_resid([id])	Plot the residuals (data - model) for a data set.
plot_scatter(x, y[, name, xlabel, ylabel, …])	Create a scatter plot.
plot_source([id])	Plot the source expression for a data set.
plot_source_component(id[, model])	Plot a component of the source expression for a data set.
plot_trace(points[, name, xlabel, replot, …])	Create a trace plot of row number versus value.
proj(*args)	Estimate the confidence intervals for parameters using the projection method.
projection(*args)	Estimate the confidence intervals for parameters using the projection method.
reg_proj(par0, par1[, id, otherids, replot, …])	Plot the statistic value as two parameters are varied.
reg_unc(par0, par1[, id, otherids, replot, …])	Plot the statistic value as two parameters are varied.
reset([model, id])	Reset the model parameters to their default settings.
restore([filename])	Load in a Sherpa session from a file.
save([filename, clobber])	Save the current Sherpa session to a file.
save_arrays(filename, args[, fields, …])	Write a list of arrays to an ASCII file.
save_data(id[, filename, fields, sep, …])	Save the data to a file.
save_delchi(id[, filename, clobber, sep, …])	Save the ratio of residuals (data-model) to error to a file.
save_error(id[, filename, clobber, sep, …])	Save the errors to a file.
save_filter(id[, filename, clobber, sep, …])	Save the filter array to a file.
save_model(id[, filename, clobber, sep, …])	Save the model values to a file.
save_resid(id[, filename, clobber, sep, …])	Save the residuals (data-model) to a file.
save_source(id[, filename, clobber, sep, …])	Save the model values to a file.
save_staterror(id[, filename, clobber, sep, …])	Save the statistical errors to a file.
save_syserror(id[, filename, clobber, sep, …])	Save the statistical errors to a file.
set_conf_opt(name, val)	Set an option for the confidence interval method.
set_covar_opt(name, val)	Set an option for the covariance method.
set_data(id[, data])	Set a data set.
set_default_id(id)	Set the default data set identifier.
set_dep(id[, val])	Set the dependent axis of a data set.
set_filter(id[, val, ignore])	Set the filter array of a data set.
set_full_model(id[, model])	Define the convolved model expression for a data set.
set_iter_method(meth)	Set the iterative-fitting scheme used in the fit.
set_iter_method_opt(optname, val)	Set an option for the iterative-fitting scheme.
set_method(meth)	Set the optimization method.
set_method_opt(optname, val)	Set an option for the current optimization method.
set_model(id[, model])	Set the source model expression for a data set.
set_model_autoassign_func([func])	Set the method used to create model component identifiers.
set_par(par[, val, min, max, frozen])	Set the value, limits, or behavior of a model parameter.
set_prior(par, prior)	Set the prior function to use with a parameter.
set_proj_opt(name, val)	Set an option for the projection method.
set_psf(id[, psf])	Add a PSF model to a data set.
set_sampler(sampler)	Set the MCMC sampler.
set_sampler_opt(opt, value)	Set an option for the current MCMC sampler.
set_source(id[, model])	Set the source model expression for a data set.
set_stat(stat)	Set the statistical method.
set_staterror(id[, val, fractional])	Set the statistical errors on the dependent axis of a data set.
set_syserror(id[, val, fractional])	Set the systematic errors on the dependent axis of a data set.
set_xlinear([plottype])	New plots will display a linear X axis.
set_xlog([plottype])	New plots will display a logarithmically-scaled X axis.
set_ylinear([plottype])	New plots will display a linear Y axis.
set_ylog([plottype])	New plots will display a logarithmically-scaled Y axis.
show_all([id, outfile, clobber])	Report the current state of the Sherpa session.
show_conf([outfile, clobber])	Display the results of the last conf evaluation.
show_covar([outfile, clobber])	Display the results of the last covar evaluation.
show_data([id, outfile, clobber])	Summarize the available data sets.
show_filter([id, outfile, clobber])	Show any filters applied to a data set.
show_fit([outfile, clobber])	Summarize the fit results.
show_kernel([id, outfile, clobber])	Display any kernel applied to a data set.
show_method([outfile, clobber])	Display the current optimization method and options.
show_model([id, outfile, clobber])	Display the model expression used to fit a data set.
show_proj([outfile, clobber])	Display the results of the last proj evaluation.
show_psf([id, outfile, clobber])	Display any PSF model applied to a data set.
show_source([id, outfile, clobber])	Display the source model expression for a data set.
show_stat([outfile, clobber])	Display the current fit statistic.
simulfit([id])	Fit a model to one or more data sets.
t_sample([num, dof, id, otherids, numcores])	Sample the fit statistic by taking the parameter values from a Student’s t-distribution.
thaw(*args)	Allow model parameters to be varied during a fit.
uniform_sample([num, factor, id, otherids, …])	Sample the fit statistic by taking the parameter values from an uniform distribution.
unlink(par)	Unlink a parameter value.
unpack_arrays(*args)	Create a sherpa data object from arrays of data.
unpack_data(filename[, ncols, colkeys, …])	Create a sherpa data object from an ASCII file.