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 parameter confidence intervals using the confidence method.

confidence(*args)

Estimate parameter confidence intervals 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])

Create a contour plot of the model.

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])

Create a contour plot of the unconvolved spatial model.

copy_data(fromid, toid)

Copy a data set, creating a new identifier.

covar(*args)

Estimate parameter confidence intervals using the covariance method.

covariance(*args)

Estimate parameter confidence intervals 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 to create the fit plot.

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 to create the model-component plot.

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 to create the model plot.

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 (MCMC).

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 to create the source plot.

get_split_plot()

Return the plot attributes for displays with multiple plots.

get_stat([name])

Return the 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, replot, overplot, clearwindow])

Plot the chi-squared value for each point in a data set.

plot_data([id, replot, overplot, clearwindow])

Plot the data values.

plot_delchi([id, replot, overplot, clearwindow])

Plot the ratio of residuals to error for a data set.

plot_fit([id, replot, overplot, clearwindow])

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_ratio([id, replot, overplot, …])

Plot the fit results, and the ratio of data to model, for a data set.

plot_fit_resid([id, replot, overplot, …])

Plot the fit results, and the residuals, for a data set.

plot_kernel([id, replot, overplot, clearwindow])

Plot the 1D kernel applied to a data set.

plot_model([id, replot, overplot, clearwindow])

Plot the model for a data set.

plot_model_component(id[, model, replot, …])

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, replot, overplot, clearwindow])

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, replot, overplot, clearwindow])

Plot the ratio of data to model for a data set.

plot_resid([id, replot, overplot, clearwindow])

Plot the residuals (data - model) for a data set.

plot_scatter(x, y[, name, xlabel, ylabel, …])

Create a scatter plot.

plot_source([id, replot, overplot, clearwindow])

Plot the source expression for a data set.

plot_source_component(id[, model, replot, …])

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 parameter confidence intervals using the projection method.

projection(*args)

Estimate parameter confidence intervals 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.