Session

class sherpa.ui.utils.Session[source] [edit on github]

Bases: NoNewAttributesAfterInit

Methods Summary

`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, *kwargs)	Create a contour plot for an image data set.
`contour_data`([id, replot, overcontour])	Contour the values of an image data set.
`contour_fit`([id, replot, overcontour])	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, replot, overcontour])	Contour the kernel applied to the model of an image data set.
`contour_model`([id, replot, overcontour])	Create a contour plot of the model.
`contour_psf`([id, replot, overcontour])	Contour the PSF applied to the model of an image data set.
`contour_ratio`([id, replot, overcontour])	Contour the ratio of data to model.
`contour_resid`([id, replot, overcontour])	Contour the residuals of the fit.
`contour_source`([id, replot, overcontour])	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, recalc])	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_contour_prefs`(contourtype[, id])	Return the preferences for the given contour type.
`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, recalc])	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, recalc])	Return the data used by plot_data.
`get_data_plot_prefs`([id])	Return the preferences for plot_data.
`get_default_id`()	Return the default data set identifier.
`get_delchi_plot`([id, recalc])	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, format, delim])	Return the filter expression for a data set.
`get_fit_contour`([id, recalc])	Return the data used by contour_fit.
`get_fit_plot`([id, recalc])	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, recalc])	Return the data used by contour_kernel.
`get_kernel_image`([id])	Return the data used by image_kernel.
`get_kernel_plot`([id, recalc])	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, recalc])	Return the data used to create the model-component plot.
`get_model_components_plot`([id])	Return the data used by plot_model_components.
`get_model_contour`([id, recalc])	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, recalc])	Return the data used to create the model plot.
`get_model_plot_prefs`([id])	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_plot_prefs`(plottype[, id])	Return the preferences for the given plot type.
`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, recalc])	Return the data used by contour_psf.
`get_psf_image`([id])	Return the data used by image_psf.
`get_psf_plot`([id, recalc])	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, recalc])	Return the data used by contour_ratio.
`get_ratio_image`([id])	Return the data used by image_ratio.
`get_ratio_plot`([id, recalc])	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, recalc])	Return the data used by contour_resid.
`get_resid_image`([id])	Return the data used by image_resid.
`get_resid_plot`([id, recalc])	Return the data used by plot_resid.
`get_rng`()	Return the RNG generator in use.
`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, recalc])	Return the data used by plot_source_component.
`get_source_components_plot`([id])	Return the data used by plot_source_components.
`get_source_contour`([id, recalc])	Return the data used by contour_source.
`get_source_image`([id])	Return the data used by image_source.
`get_source_plot`([id, recalc])	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_psf_ids`()	List of all the data sets with a PSF.
`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, *kwargs)	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_model_components`([id, overplot, ...])	Plot all the components of a model.
`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_source_components`([id, overplot, ...])	Plot all the components of a source.
`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_plot_backend`(backend)	Change the plot backend.
`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_rng`(rng)	Set the RNG generator.
`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.

Methods Documentation

add_model(modelclass, args=(), kwargs={}) → None[source] [edit on github]

Create a user-defined model class.

Create a model from a class. The name of the class can then be used to create model components - e.g. with set_model or create_model_component - as with any existing Sherpa model.

Parameters:

modelclass – A class derived from sherpa.models.model.ArithmeticModel. This class defines the functional form and the parameters of the model.
args – Arguments for the class constructor.
kwargs – Keyword arguments for the class constructor.

See also

create_model_component: Create a model component.
list_models: List the available model types.
load_table_model: Load tabular data and use it as a model component.
load_user_model: Create a user-defined model.
set_model: Set the source model expression for a data set.

Notes

The load_user_model function is designed to make it easy to add a model, but the interface is not the same as the existing models (such as having to call both load_user_model and add_user_pars for each new instance). The add_model function is used to add a model as a Python class, which is more work to set up, but then acts the same way as the existing models.

Examples

The following example creates a model type called “mygauss1d” which will behave exactly the same as the existing “gauss1d” model. Normally the class used with add_model would add new functionality.

>>> from sherpa.models import Gauss1D
>>> class MyGauss1D(Gauss1D):
...     pass
...
>>> add_model(MyGauss1D)
>>> set_source(mygauss1d.g1 + mygauss1d.g2)

add_user_pars(modelname, parnames, parvals=None, parmins=None, parmaxs=None, parunits=None, parfrozen=None) → None[source] [edit on github]

Add parameter information to a user model.

Parameters:

modelname (str) – The name of the user model (created by load_user_model).
parnames (array of str) – The names of the parameters. The order of all the parameter arrays must match that expected by the model function (the first argument to load_user_model).
parvals (array of number, optional) – The default values of the parameters. If not given each parameter is set to 0.
parmins (array of number, optional) – The minimum values of the parameters (hard limit). The default value is -3.40282e+38.
parmaxs (array of number, optional) – The maximum values of the parameters (hard limit). The default value is 3.40282e+38.
parunits (array of str, optional) – The units of the parameters. This is only used in screen output (i.e. is informational in nature).
parfrozen (array of bool, optional) – Should each parameter be frozen. The default is that all parameters are thawed.

See also

add_model: Create a user-defined model class.
load_user_model: Create a user-defined model.
set_par: Set the value, limits, or behavior of a model parameter.

Notes

The parameters must be specified in the order that the function expects. That is, if the function has two parameters, pars[0]=’slope’ and pars[1]=’y_intercept’, then the call to add_user_pars must use the order [“slope”, “y_intercept”].

Examples

Create a user model for the function profile called “myprof”, which has two parameters called “core” and “ampl”, both of which will start with a value of 0.

>>> load_user_model(profile, "myprof")
>>> add_user_pars("myprof", ["core", "ampl"])

Set the starting values, minimum values, and whether or not the parameter is frozen by default, for the “prof” model:

>>> pnames = ["core", "ampl", "intflag"]
>>> pvals = [10, 200, 1]
>>> pmins = [0.01, 0, 0]
>>> pfreeze = [False, False, True]
>>> add_user_pars("prof", pnames, pvals,
...               parmins=pmins, parfrozen=pfreeze)

calc_chisqr(id: int | str | None = None, *otherids: int | str)[source] [edit on github]

Calculate the per-bin chi-squared statistic.

Evaluate the model for one or more data sets, compare it to the data using the current statistic, and return an array of chi-squared values for each bin. No fitting is done, as the current model parameter, and any filters, are used.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
*otherids (int or str, optional) – Other data sets to use in the calculation.

Returns:

chisq – The chi-square value for each bin of the data, using the current statistic (as set by set_stat). A value of None is returned if the statistic is not a chi-square distribution.

Return type:

array or None

See also

calc_stat: Calculate the fit statistic for a data set.
calc_stat_info: Display the statistic values for the current models.
set_stat: Set the statistical method.

Notes

The output array length equals the sum of the arrays lengths of the requested data sets.

Examples

When called with no arguments, the return value is the chi-squared statistic for each bin in the data sets which have a defined model.

>>> calc_chisqr()

Supplying a specific data set ID to calc_chisqr - such as “1” or “src” - will return the chi-squared statistic array for only that data set.

>>> calc_chisqr(1)
>>> calc_chisqr("src")

Restrict the calculation to just datasets 1 and 3:

>>> calc_chisqr(1, 3)

calc_stat(id: int | str | None = None, *otherids: int | str)[source] [edit on github]

Calculate the fit statistic for a data set.

Evaluate the model for one or more data sets, compare it to the data using the current statistic, and return the value. No fitting is done, as the current model parameter, and any filters, are used.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
*otherids (int or str, optional) – Other data sets to use in the calculation.

Returns:

stat – The current statistic value.

Return type:

number

See also

calc_chisqr: Calculate the per-bin chi-squared statistic.
calc_stat_info: Display the statistic values for the current models.
set_stat: Set the statistical method.

Examples

Calculate the statistic for the model and data in the default data set:

>>> stat = calc_stat()

Find the statistic for data set 3:

>>> stat = calc_stat(3)

When fitting to multiple data sets, you can get the contribution to the total fit statistic from only one data set, or from several by listing the datasets explicitly. The following finds the contribution from the data sets labelled “core” and “jet”:

>>> stat = calc_stat("core", "jet")

Calculate the statistic value using two different statistics:

>>> set_stat('cash')
>>> s1 = calc_stat()
>>> set_stat('cstat')
>>> s2 = calc_stat()

calc_stat_info()[source] [edit on github]

Display the statistic values for the current models.

Displays the statistic value for each data set, and the combined fit, using the current set of models, parameters, and ranges. The output is printed to stdout, and so is intended for use in interactive analysis. The get_stat_info function returns the same information but as an array of Python structures.

See also

calc_stat: Calculate the fit statistic for a data set.
get_stat_info: Return the statistic values for the current models.

Notes

If a fit to a particular data set has not been made, or values - such as parameter settings, the noticed data range, or choice of statistic - have been changed since the last fit, then the results for that data set may not be meaningful and will therefore bias the results for the simultaneous results.

The information returned by calc_stat_info includes:

Dataset: The dataset identifier (or identifiers).
Statistic: The name of the statistic used to calculate the results.
Fit statistic value: The current fit statistic value.
Data points: The number of bins used in the fit.
Degrees of freedom: The number of bins minus the number of thawed parameters.

Some fields are only returned for a subset of statistics:

Probability (Q-value): A measure of the probability that one would observe the reduced statistic value, or a larger value, if the assumed model is true and the best-fit model parameters are the true parameter values.
Reduced statistic: The fit statistic value divided by the number of degrees of freedom.

Examples

>>> calc_stat_info()

clean() → None[source] [edit on github]

Clear out the current Sherpa session.

The clean function removes all data sets and model assignments, and restores the default settings for the optimisation and fit statistic.

Changed in version 4.15.0: The model names are now removed from the global symbol table.

See also

save: Save the current Sherpa session to a file.
restore: Load in a Sherpa session from a file.
sherpa.astro.ui.save_all: Save the Sherpa session as an ASCII file.

Examples

>>> clean()

After the call to clean, the line and bgnd variables will be removed, so accessing them would cause a NameError.

>>> set_source(gauss1d.line + const1d.bgnd)
>>> bgnd.c0.min = 0
>>> print(line)
>>> clean()

conf(*args)[source] [edit on github]

Estimate parameter confidence intervals using the confidence method.

The conf command computes confidence interval bounds for the specified model parameters in the dataset. A given parameter’s value is varied along a grid of values while the values of all the other thawed parameters are allowed to float to new best-fit values. The get_conf and set_conf_opt commands can be used to configure the error analysis; an example being changing the ‘sigma’ field to 1.6 (i.e. 90%) from its default value of 1. The output from the routine is displayed on screen, and the get_conf_results routine can be used to retrieve the results.

Parameters:

id (int or str, optional) – The data set, or sets, that provides the data. If not given then all data sets with an associated model are used simultaneously.
parameter (sherpa.models.parameter.Parameter, optional) – The default is to calculate the confidence limits on all thawed parameters of the model, or models, for all the data sets. The evaluation can be restricted by listing the parameters to use. Note that each parameter should be given as a separate argument, rather than as a list. For example conf(g1.ampl, g1.sigma).
model (sherpa.models.model.Model, optional) – Select all the thawed parameters in the model.

See also

covar: Estimate the confidence intervals using the covariance method.
get_conf: Return the confidence-interval estimation object.
get_conf_results: Return the results of the last conf run.
int_proj: Plot the statistic value as a single parameter is varied.
int_unc: Plot the statistic value as a single parameter is varied.
reg_proj: Plot the statistic value as two parameters are varied.
reg_unc: Plot the statistic value as two parameters are varied.
set_conf_opt: Set an option of the conf estimation object.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with multiple ids or parameters values, the order is unimportant, since any argument that is not defined as a model parameter is assumed to be a data id.

The conf function is different to covar, in that in that all other thawed parameters are allowed to float to new best-fit values, instead of being fixed to the initial best-fit values as they are in covar. While conf is more general (e.g. allowing the user to examine the parameter space away from the best-fit point), it is in the strictest sense no more accurate than covar for determining confidence intervals.

The conf function is a replacement for the proj function, which uses a different algorithm to estimate parameter confidence limits.

An estimated confidence interval is accurate if and only if:

the chi^2 or logL surface in parameter space is approximately shaped like a multi-dimensional paraboloid, and
the best-fit point is sufficiently far from parameter space boundaries.

One may determine if these conditions hold, for example, by plotting the fit statistic as a function of each parameter’s values (the curve should approximate a parabola) and by examining contour plots of the fit statistics made by varying the values of two parameters at a time (the contours should be elliptical, and parameter space boundaries should be no closer than approximately 3 sigma from the best-fit point). The int_proj and reg_proj commands may be used for this.

If either of the conditions given above does not hold, then the output from conf may be meaningless except to give an idea of the scale of the confidence intervals. To accurately determine the confidence intervals, one would have to reparameterize the model, use Monte Carlo simulations, or Bayesian methods.

As the calculation can be computer intensive, the default behavior is to use all available CPU cores to speed up the analysis. This can be changed be varying the numcores option - or setting parallel to False - either with set_conf_opt or get_conf.

As conf estimates intervals for each parameter independently, the relationship between sigma and the change in statistic value delta_S can be particularly simple: sigma = the square root of delta_S for statistics sampled from the chi-square distribution and for the Cash statistic, and is approximately equal to the square root of (2 * delta_S) for fits based on the general log-likelihood. The default setting is to calculate the one-sigma interval, which can be changed with the sigma option to set_conf_opt or get_conf.

The limit calculated by conf is basically a 1-dimensional root in the translated coordinate system (translated by the value of the statistic at the minimum plus sigma^2). The Taylor series expansion of the multi-dimensional function at the minimum is:

f(x + dx) ~ f(x) + grad( f(x) )^T dx + dx^T Hessian( f(x) ) dx + ...

where x is understood to be the n-dimensional vector representing the free parameters to be fitted and the super-script ‘T’ is the transpose of the row-vector. At or near the minimum, the gradient of the function is zero or negligible, respectively. So the leading term of the expansion is quadratic. The best root finding algorithm for a curve which is approximately parabolic is Muller’s method [1]. Muller’s method is a generalization of the secant method [2]: the secant method is an iterative root finding method that approximates the function by a straight line through two points, whereas Muller’s method is an iterative root finding method that approxmiates the function by a quadratic polynomial through three points.

Three data points are the minimum input to Muller’s root finding method. The first point to be submitted to the Muller’s root finding method is the point at the minimum. To strategically choose the other two data points, the confidence function uses the output from covariance as the second data point. To generate the third data points for the input to Muller’s root finding method, the secant root finding method is used since it only requires two data points to generate the next best approximation of the root.

However, there are cases where conf cannot locate the root even though the root is bracketed within an interval (perhaps due to the bad resolution of the data). In such cases, when the option openinterval is set to False (which is the default), the routine will print a warning message about not able to find the root within the set tolerance and the function will return the average of the open interval which brackets the root. If openinterval is set to True then conf will print the minimal open interval which brackets the root (not to be confused with the lower and upper bound of the confidence interval). The most accurate thing to do is to return an open interval where the root is localized/bracketed rather then the average of the open interval (since the average of the interval is not a root within the specified tolerance).

References

Muller, David E., “A Method for Solving Algebraic Equations Using an Automatic Computer,” MTAC, 10 (1956), 208-215.
Numerical Recipes in Fortran, 2nd edition, 1986, Press et al., p. 347

Examples

Evaluate confidence intervals for all thawed parameters in all data sets with an associated source model. The results are then stored in the variable res.

>>> conf()
>>> res = get_conf_results()

Only evaluate the parameters associated with data set 2:

>>> conf(2)

Only evaluate the intervals for the pos.xpos and pos.ypos parameters:

>>> conf(pos.xpos, pos.ypos)

Change the limits to be 1.6 sigma (90%) rather than the default 1 sigma.

>>> set_conf_opt('sigma', 1.6)
>>> conf()

Only evaluate the clus.kt parameter for the data sets with identifiers “obs1”, “obs5”, and “obs6”. This will still use the 1.6 sigma setting from the previous run.

>>> conf("obs1", "obs5", "obs6", clus.kt)

Only use two cores when evaluating the errors for the parameters used in the model for data set 3:

>>> set_conf_opt('numcores', 2)
>>> conf(3)

Estimate the errors for all the thawed parameters from the line model and the clus.kt parameter for datasets 1, 3, and 4:

>>> conf(1, 3, 4, line, clus.kt)

confidence(*args) [edit on github]

Estimate parameter confidence intervals using the confidence method.

The conf command computes confidence interval bounds for the specified model parameters in the dataset. A given parameter’s value is varied along a grid of values while the values of all the other thawed parameters are allowed to float to new best-fit values. The get_conf and set_conf_opt commands can be used to configure the error analysis; an example being changing the ‘sigma’ field to 1.6 (i.e. 90%) from its default value of 1. The output from the routine is displayed on screen, and the get_conf_results routine can be used to retrieve the results.

Parameters:

id (int or str, optional) – The data set, or sets, that provides the data. If not given then all data sets with an associated model are used simultaneously.
parameter (sherpa.models.parameter.Parameter, optional) – The default is to calculate the confidence limits on all thawed parameters of the model, or models, for all the data sets. The evaluation can be restricted by listing the parameters to use. Note that each parameter should be given as a separate argument, rather than as a list. For example conf(g1.ampl, g1.sigma).
model (sherpa.models.model.Model, optional) – Select all the thawed parameters in the model.

See also

covar: Estimate the confidence intervals using the covariance method.
get_conf: Return the confidence-interval estimation object.
get_conf_results: Return the results of the last conf run.
int_proj: Plot the statistic value as a single parameter is varied.
int_unc: Plot the statistic value as a single parameter is varied.
reg_proj: Plot the statistic value as two parameters are varied.
reg_unc: Plot the statistic value as two parameters are varied.
set_conf_opt: Set an option of the conf estimation object.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with multiple ids or parameters values, the order is unimportant, since any argument that is not defined as a model parameter is assumed to be a data id.

The conf function is different to covar, in that in that all other thawed parameters are allowed to float to new best-fit values, instead of being fixed to the initial best-fit values as they are in covar. While conf is more general (e.g. allowing the user to examine the parameter space away from the best-fit point), it is in the strictest sense no more accurate than covar for determining confidence intervals.

The conf function is a replacement for the proj function, which uses a different algorithm to estimate parameter confidence limits.

An estimated confidence interval is accurate if and only if:

the chi^2 or logL surface in parameter space is approximately shaped like a multi-dimensional paraboloid, and
the best-fit point is sufficiently far from parameter space boundaries.

One may determine if these conditions hold, for example, by plotting the fit statistic as a function of each parameter’s values (the curve should approximate a parabola) and by examining contour plots of the fit statistics made by varying the values of two parameters at a time (the contours should be elliptical, and parameter space boundaries should be no closer than approximately 3 sigma from the best-fit point). The int_proj and reg_proj commands may be used for this.

If either of the conditions given above does not hold, then the output from conf may be meaningless except to give an idea of the scale of the confidence intervals. To accurately determine the confidence intervals, one would have to reparameterize the model, use Monte Carlo simulations, or Bayesian methods.

As the calculation can be computer intensive, the default behavior is to use all available CPU cores to speed up the analysis. This can be changed be varying the numcores option - or setting parallel to False - either with set_conf_opt or get_conf.

As conf estimates intervals for each parameter independently, the relationship between sigma and the change in statistic value delta_S can be particularly simple: sigma = the square root of delta_S for statistics sampled from the chi-square distribution and for the Cash statistic, and is approximately equal to the square root of (2 * delta_S) for fits based on the general log-likelihood. The default setting is to calculate the one-sigma interval, which can be changed with the sigma option to set_conf_opt or get_conf.

The limit calculated by conf is basically a 1-dimensional root in the translated coordinate system (translated by the value of the statistic at the minimum plus sigma^2). The Taylor series expansion of the multi-dimensional function at the minimum is:

f(x + dx) ~ f(x) + grad( f(x) )^T dx + dx^T Hessian( f(x) ) dx + ...

where x is understood to be the n-dimensional vector representing the free parameters to be fitted and the super-script ‘T’ is the transpose of the row-vector. At or near the minimum, the gradient of the function is zero or negligible, respectively. So the leading term of the expansion is quadratic. The best root finding algorithm for a curve which is approximately parabolic is Muller’s method [1]. Muller’s method is a generalization of the secant method [2]: the secant method is an iterative root finding method that approximates the function by a straight line through two points, whereas Muller’s method is an iterative root finding method that approxmiates the function by a quadratic polynomial through three points.

Three data points are the minimum input to Muller’s root finding method. The first point to be submitted to the Muller’s root finding method is the point at the minimum. To strategically choose the other two data points, the confidence function uses the output from covariance as the second data point. To generate the third data points for the input to Muller’s root finding method, the secant root finding method is used since it only requires two data points to generate the next best approximation of the root.

However, there are cases where conf cannot locate the root even though the root is bracketed within an interval (perhaps due to the bad resolution of the data). In such cases, when the option openinterval is set to False (which is the default), the routine will print a warning message about not able to find the root within the set tolerance and the function will return the average of the open interval which brackets the root. If openinterval is set to True then conf will print the minimal open interval which brackets the root (not to be confused with the lower and upper bound of the confidence interval). The most accurate thing to do is to return an open interval where the root is localized/bracketed rather then the average of the open interval (since the average of the interval is not a root within the specified tolerance).

References

Muller, David E., “A Method for Solving Algebraic Equations Using an Automatic Computer,” MTAC, 10 (1956), 208-215.
Numerical Recipes in Fortran, 2nd edition, 1986, Press et al., p. 347

Examples

Evaluate confidence intervals for all thawed parameters in all data sets with an associated source model. The results are then stored in the variable res.

>>> conf()
>>> res = get_conf_results()

Only evaluate the parameters associated with data set 2:

>>> conf(2)

Only evaluate the intervals for the pos.xpos and pos.ypos parameters:

>>> conf(pos.xpos, pos.ypos)

Change the limits to be 1.6 sigma (90%) rather than the default 1 sigma.

>>> set_conf_opt('sigma', 1.6)
>>> conf()

Only evaluate the clus.kt parameter for the data sets with identifiers “obs1”, “obs5”, and “obs6”. This will still use the 1.6 sigma setting from the previous run.

>>> conf("obs1", "obs5", "obs6", clus.kt)

Only use two cores when evaluating the errors for the parameters used in the model for data set 3:

>>> set_conf_opt('numcores', 2)
>>> conf(3)

Estimate the errors for all the thawed parameters from the line model and the clus.kt parameter for datasets 1, 3, and 4:

>>> conf(1, 3, 4, line, clus.kt)

contour(*args, **kwargs) → None[source] [edit on github]

Create a contour plot for an image data set.

Create one or more contour plots, depending on the arguments it is set: a plot type, followed by an optional data set identifier, and this can be repeated. If no data set identifier is given for a plot type, the default identifier - as returned by get_default_id - is used. This is for 2D data sets.

Changed in version 4.17.0: The keyword arguments can now be set per plot by using a sequence of values. The layout can be changed with the rows and cols arguments and the automatic calculation no longer forces two rows. Handling of the overcontour flag has been improved.

Changed in version 4.12.2: Keyword arguments, such as alpha, can be sent to each plot.

Parameters:

args – The contour-plot names and identifiers.
rows – The number of rows and columns (if set).
cols – The number of rows and columns (if set).
kwargs – The plot arguments applied to each contour plot.

Raises:

sherpa.utils.err.DataErr – The data set does not support the requested plot type.

Notes

The supported plot types depend on the data set type, and include the following list. There are also individual functions, with contour_ prepended to the plot type, such as contour_data and the contour_fit_resid variant:

data: The data.
fit: Contours of the data and the source model.
fit_resid: Two plots: the first is the contours of the data and the source model and the second is the residuals.
kernel: The kernel.
model: The source model including any PSF convolution set by set_psf.
psf: The PSF.
ratio: Contours of the ratio image, formed by dividing the data by the model.
resid: Contours of the residual image, formed by subtracting the model from the data.
source: The source model (without any PSF convolution set by set_psf).

The keyword arguments are sent to each plot (so care must be taken to ensure they are valid for all plots).

Examples

>>> contour('data')

>>> contour('data', 1, 'data', 2)

>>> contour('data', 'model')

>>> contour('data', 'model', 'fit', 'resid')

>>> contour('data', 'model', alpha=0.7)

Use a single column rather than single row to display the contour plots:

>>> contour('data', 'model', cols=1)

contour_data(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Contour the values of an image data set.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_data. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_data_contour: Return the data used by contour_data.
get_data_contour_prefs: Return the preferences for contour_data.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.

Examples

Plot the data from the default data set:

>>> contour_data()

Contour the data and then overplot the data from the second data set:

>>> contour_data()
>>> contour_data(2, overcontour=True)

contour_fit(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Contour the fit to a data set.

Overplot the model - including any PSF - on the data. The preferences are the same as contour_data and contour_model.

Parameters:

id (int, str, or None, optional) – The data set that provides the data and model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_fit. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_fit_contour: Return the data used by contour_fit.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.

Examples

Plot the fit for the default data set:

>>> contour_fit()

Overplot the fit to data set ‘s2’ on that of the default data set:

>>> contour_fit()
>>> contour_fit('s2', overcontour=True)

contour_fit_resid(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Contour the fit and the residuals to a data set.

Overplot the model - including any PSF - on the data. In a separate plot contour the residuals. The preferences are the same as contour_data and contour_model.

Parameters:

id (int, str, or None, optional) – The data set that provides the data and model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_fit_resid. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_fit_contour: Return the data used by contour_fit.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
contour_fit: Contour the fit to a data set.
contour_resid: Contour the residuals of the fit.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.

Examples

Plot the fit and residuals for the default data set:

>>> contour_fit_resid()

contour_kernel(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Contour the kernel applied to the model of an image data set.

If the data set has no PSF applied to it, the model is displayed.

Parameters:

id (int, str, or None, optional) – The data set that provides the model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_kernel. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_psf_contour: Return the data used by contour_psf.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
contour_psf: Contour the PSF applied to the model of an image data set.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.
set_psf: Add a PSF model to a data set.

contour_model(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Create a contour plot of the model.

Displays a contour plot of the values of the model, evaluated on the data, including any PSF kernel convolution (if set).

Parameters:

id (int, str, or None, optional) – The data set that provides the model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_model. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_model_contour: Return the data used by contour_model.
get_model_contour_prefs: Return the preferences for contour_model.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
contour_source: Create a contour plot of the unconvolved spatial model.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.
set_psf: Add a PSF model to a data set.

Examples

Plot the model from the default data set:

>>> contour_model()

Compare the model without and with the PSF component, for the “img” data set:

>>> contour_source("img")
>>> contour_model("img", overcontour=True)

contour_psf(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Contour the PSF applied to the model of an image data set.

If the data set has no PSF applied to it, the model is displayed.

Parameters:

id (int, str, or None, optional) – The data set that provides the model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_psf. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_psf_contour: Return the data used by contour_psf.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
contour_kernel: Contour the kernel applied to the model of an image data set.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.
set_psf: Add a PSF model to a data set.

contour_ratio(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Contour the ratio of data to model.

The ratio image is formed by dividing the data by the current model, including any PSF. The preferences are the same as contour_data.

Parameters:

id (int, str, or None, optional) – The data set that provides the data and model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_ratio. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_ratio_contour: Return the data used by contour_ratio.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.

Examples

Plot the ratio from the default data set:

>>> contour_ratio()

Overplot the ratio on the residuals:

>>> contour_resid('img')
>>> contour_ratio('img', overcontour=True)

contour_resid(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Contour the residuals of the fit.

The residuals are formed by subtracting the current model - including any PSF - from the data. The preferences are the same as contour_data.

Parameters:

id (int, str, or None, optional) – The data set that provides the data and model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_resid. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_resid_contour: Return the data used by contour_resid.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.

Examples

Plot the residuals from the default data set:

>>> contour_resid()

Overplot the residuals on the model:

>>> contour_model('img')
>>> contour_resid('img', overcontour=True)

contour_source(id: int | str | None = None, replot=False, overcontour=False, **kwargs) → None[source] [edit on github]

Create a contour plot of the unconvolved spatial model.

Displays a contour plot of the values of the model, evaluated on the data, without any PSF kernel convolution applied. The preferences are the same as contour_model.

Parameters:

id (int, str, or None, optional) – The data set that provides the model. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to contour_source. The default is False.
overcontour (bool, optional) – If True then add the data to an existing plot, otherwise create a new contour plot. The default is False.

See also

get_source_contour: Return the data used by contour_source.
get_default_id: Return the default data set identifier.
contour: Create one or more plot types.
contour_model: Create a contour plot of the model.
sherpa.astro.ui.set_coord: Set the coordinate system to use for image analysis.
set_psf: Add a PSF model to a data set.

Examples

Plot the model from the default data set:

>>> contour_source()

Compare the model without and with the PSF component, for the “img” data set:

>>> contour_model("img")
>>> contour_source("img", overcontour=True)

copy_data(fromid: int | str, toid: int | str) → None[source] [edit on github]

Copy a data set, creating a new identifier.

After copying the data set, any changes made to the original data set (that is, the fromid identifier) will not be reflected in the new (the toid identifier) data set.

Parameters:

fromid (int or str) – The input data set.
toid (int or str) – The output data set.

Raises:

sherpa.utils.err.IdentifierErr – If there is no data set with a fromid identifier.

See also

delete_data, load_data, set_data

Examples

>>> copy_data(1, 2)

Rename the data set with identifier 2 to “orig”, and then delete the old data set:

>>> copy_data(2, "orig")
>>> delete_data(2)

covar(*args)[source] [edit on github]

Estimate parameter confidence intervals using the covariance method.

The covar command computes confidence interval bounds for the specified model parameters in the dataset, using the covariance matrix of the statistic. The get_covar and set_covar_opt commands can be used to configure the error analysis; an example being changing the sigma field to 1.6 (i.e. 90%) from its default value of 1. The output from the routine is displayed on screen, and the get_covar_results routine can be used to retrieve the results.

Parameters:

id (int or str, optional) – The data set, or sets, that provides the data. If not given then all data sets with an associated model are used simultaneously.
parameter (sherpa.models.parameter.Parameter, optional) – The default is to calculate the confidence limits on all thawed parameters of the model, or models, for all the data sets. The evaluation can be restricted by listing the parameters to use. Note that each parameter should be given as a separate argument, rather than as a list. For example covar(g1.ampl, g1.sigma).
model (sherpa.models.model.Model, optional) – Select all the thawed parameters in the model.

See also

covar: Estimate the confidence intervals using the confidence method.
get_covar: Return the covariance estimation object.
get_covar_results: Return the results of the last covar run.
int_proj: Plot the statistic value as a single parameter is varied.
int_unc: Plot the statistic value as a single parameter is varied.
reg_proj: Plot the statistic value as two parameters are varied.
reg_unc: Plot the statistic value as two parameters are varied.
set_covar_opt: Set an option of the covar estimation object.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with multiple ids or parameters values, the order is unimportant, since any argument that is not defined as a model parameter is assumed to be a data id.

The covar command is different to conf, in that in that all other thawed parameters are fixed, rather than being allowed to float to new best-fit values. While conf is more general (e.g. allowing the user to examine the parameter space away from the best-fit point), it is in the strictest sense no more accurate than covar for determining confidence intervals.

An estimated confidence interval is accurate if and only if:

the chi^2 or logL surface in parameter space is approximately shaped like a multi-dimensional paraboloid, and
the best-fit point is sufficiently far from parameter space boundaries.

One may determine if these conditions hold, for example, by plotting the fit statistic as a function of each parameter’s values (the curve should approximate a parabola) and by examining contour plots of the fit statistics made by varying the values of two parameters at a time (the contours should be elliptical, and parameter space boundaries should be no closer than approximately 3 sigma from the best-fit point). The int_proj and reg_proj commands may be used for this.

If either of the conditions given above does not hold, then the output from covar may be meaningless except to give an idea of the scale of the confidence intervals. To accurately determine the confidence intervals, one would have to reparameterize the model, use Monte Carlo simulations, or Bayesian methods.

As covar estimates intervals for each parameter independently, the relationship between sigma and the change in statistic value delta_S can be particularly simple: sigma = the square root of delta_S for statistics sampled from the chi-square distribution and for the Cash statistic, and is approximately equal to the square root of (2 * delta_S) for fits based on the general log-likelihood. The default setting is to calculate the one-sigma interval, which can be changed with the sigma option to set_covar_opt or get_covar.

Examples

Evaluate confidence intervals for all thawed parameters in all data sets with an associated source model. The results are then stored in the variable res.

>>> covar()
>>> res = get_covar_results()

Only evaluate the parameters associated with data set 2.

>>> covar(2)

Only evaluate the intervals for the pos.xpos and pos.ypos parameters:

>>> covar(pos.xpos, pos.ypos)

Change the limits to be 1.6 sigma (90%) rather than the default 1 sigma.

>>> set_covar_ope('sigma', 1.6)
>>> covar()

Only evaluate the clus.kt parameter for the data sets with identifiers “obs1”, “obs5”, and “obs6”. This will still use the 1.6 sigma setting from the previous run.

>>> covar("obs1", "obs5", "obs6", clus.kt)

Estimate the errors for all the thawed parameters from the line model and the clus.kt parameter for datasets 1, 3, and 4:

>>> covar(1, 3, 4, line, clus.kt)

covariance(*args) [edit on github]

Estimate parameter confidence intervals using the covariance method.

The covar command computes confidence interval bounds for the specified model parameters in the dataset, using the covariance matrix of the statistic. The get_covar and set_covar_opt commands can be used to configure the error analysis; an example being changing the sigma field to 1.6 (i.e. 90%) from its default value of 1. The output from the routine is displayed on screen, and the get_covar_results routine can be used to retrieve the results.

Parameters:

id (int or str, optional) – The data set, or sets, that provides the data. If not given then all data sets with an associated model are used simultaneously.
parameter (sherpa.models.parameter.Parameter, optional) – The default is to calculate the confidence limits on all thawed parameters of the model, or models, for all the data sets. The evaluation can be restricted by listing the parameters to use. Note that each parameter should be given as a separate argument, rather than as a list. For example covar(g1.ampl, g1.sigma).
model (sherpa.models.model.Model, optional) – Select all the thawed parameters in the model.

See also

covar: Estimate the confidence intervals using the confidence method.
get_covar: Return the covariance estimation object.
get_covar_results: Return the results of the last covar run.
int_proj: Plot the statistic value as a single parameter is varied.
int_unc: Plot the statistic value as a single parameter is varied.
reg_proj: Plot the statistic value as two parameters are varied.
reg_unc: Plot the statistic value as two parameters are varied.
set_covar_opt: Set an option of the covar estimation object.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with multiple ids or parameters values, the order is unimportant, since any argument that is not defined as a model parameter is assumed to be a data id.

The covar command is different to conf, in that in that all other thawed parameters are fixed, rather than being allowed to float to new best-fit values. While conf is more general (e.g. allowing the user to examine the parameter space away from the best-fit point), it is in the strictest sense no more accurate than covar for determining confidence intervals.

An estimated confidence interval is accurate if and only if:

the chi^2 or logL surface in parameter space is approximately shaped like a multi-dimensional paraboloid, and
the best-fit point is sufficiently far from parameter space boundaries.

One may determine if these conditions hold, for example, by plotting the fit statistic as a function of each parameter’s values (the curve should approximate a parabola) and by examining contour plots of the fit statistics made by varying the values of two parameters at a time (the contours should be elliptical, and parameter space boundaries should be no closer than approximately 3 sigma from the best-fit point). The int_proj and reg_proj commands may be used for this.

If either of the conditions given above does not hold, then the output from covar may be meaningless except to give an idea of the scale of the confidence intervals. To accurately determine the confidence intervals, one would have to reparameterize the model, use Monte Carlo simulations, or Bayesian methods.

As covar estimates intervals for each parameter independently, the relationship between sigma and the change in statistic value delta_S can be particularly simple: sigma = the square root of delta_S for statistics sampled from the chi-square distribution and for the Cash statistic, and is approximately equal to the square root of (2 * delta_S) for fits based on the general log-likelihood. The default setting is to calculate the one-sigma interval, which can be changed with the sigma option to set_covar_opt or get_covar.

Examples

Evaluate confidence intervals for all thawed parameters in all data sets with an associated source model. The results are then stored in the variable res.

>>> covar()
>>> res = get_covar_results()

Only evaluate the parameters associated with data set 2.

>>> covar(2)

Only evaluate the intervals for the pos.xpos and pos.ypos parameters:

>>> covar(pos.xpos, pos.ypos)

Change the limits to be 1.6 sigma (90%) rather than the default 1 sigma.

>>> set_covar_ope('sigma', 1.6)
>>> covar()

Only evaluate the clus.kt parameter for the data sets with identifiers “obs1”, “obs5”, and “obs6”. This will still use the 1.6 sigma setting from the previous run.

>>> covar("obs1", "obs5", "obs6", clus.kt)

Estimate the errors for all the thawed parameters from the line model and the clus.kt parameter for datasets 1, 3, and 4:

>>> covar(1, 3, 4, line, clus.kt)

create_model_component(typename=None, name=None)[source] [edit on github]

Create a model component.

Model components created by this function are set to their default values. Components can also be created directly using the syntax typename.name, such as in calls to set_model and set_source (unless you have called set_model_autoassign_func to change the default model auto-assignment setting).

Parameters:

typename (str) – The name of the model. This should match an entry from the return value of list_models, and defines the type of model.
name (str) – The name used to refer to this instance, or component, of the model. A Python variable will be created with this name that can be used to inspect and change the model parameters, as well as use it in model expressions.

Returns:

model

Return type:

the sherpa.models.Model object created

See also

delete_model_component: Delete a model component.
get_model_component: Returns a model component given its name.
list_models: List the available model types.
list_model_components: List the names of all the model components.
set_model: Set the source model expression for a data set.
set_model_autoassign_func: Set the method used to create model component identifiers.

Notes

This function can over-write an existing component. If the over-written component is part of a source expression - as set by set_model - then the model evaluation will still use the old model definition (and be able to change the fit parameters), but direct access to its parameters is not possible since the name now refers to the new component (this is true using direct access, such as mname.parname, or with set_par).

Examples

Create an instance of the powlaw1d model called pl, and then freeze its gamma parameter to 2.6.

>>> create_model_component("powlaw1d", "pl")
>>> pl.gamma = 2.6
>>> freeze(pl.gamma)

Create a blackbody model called bb, check that it is recognized as a component, and display its parameters:

>>> create_model_component("bbody", "bb")
>>> list_model_components()
>>> print(bb)
>>> print(bb.ampl)

dataspace1d(start, stop, step=1, numbins=None, id: int | str | None = None, dstype=<class 'sherpa.data.Data1DInt'>) → None[source] [edit on github]

Create the independent axis for a 1D data set.

Create an “empty” one-dimensional data set by defining the grid on which the points are defined (the independent axis). The values are set to 0.

Parameters:

start (number) – The minimum value of the axis.
stop (number) – The maximum value of the axis.
step (number, optional) – The separation between each grid point. This is not used if numbins is set.
numbins (int, optional) – The number of grid points. This overrides the step setting.
id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
dstype (data class to use, optional) – What type of data is to be used. Supported values include Data1DInt (the default) and Data1D.

See also

dataspace2d: Create the independent axis for a 2D data set.
get_dep: Return the dependent axis of a data set.
get_indep: Return the independent axes of a data set.
set_dep: Set the dependent axis of a data set.

Notes

The meaning of the stop parameter depends on whether it is a binned or unbinned data set (as set by the dstype parameter).

Examples

Create a binned data set, starting at 1 and with a bin-width of 1.

>>> dataspace1d(1, 5, 1)
>>> print(get_indep())
(array([ 1.,  2.,  3.,  4.]), array([ 2.,  3.,  4.,  5.]))

This time for an un-binned data set:

>>> dataspace1d(1, 5, 1, dstype=Data1D)
>>> print(get_indep())
(array([ 1.,  2.,  3.,  4.,  5.]),)

Specify the number of bins rather than the grid spacing:

>>> dataspace1d(1, 5, numbins=5, id=2)
>>> (xlo, xhi) = get_indep(2)
>>> xlo
array([ 1. ,  1.8,  2.6,  3.4,  4.2])
>>> xhi
array([ 1.8,  2.6,  3.4,  4.2,  5. ])

>>> dataspace1d(1, 5, numbins=5, id=3, dstype=Data1D)
>>> (x, ) = get_indep(3)
>>> x
array([ 1.,  2.,  3.,  4.,  5.])

dataspace2d(dims, id: int | str | None = None, dstype=<class 'sherpa.data.Data2D'>) → None[source] [edit on github]

Create the independent axis for a 2D data set.

Create an “empty” two-dimensional data set by defining the grid on which the points are defined (the independent axis). The values are set to 0.

Parameters:

dims (sequence of 2 number) – The dimensions of the grid in (width,height) order.
id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
dstype (data class to use, optional) – What type of data is to be used. Supported values include Data2D (the default) and Data2DInt.

See also

dataspace1d: Create the independent axis for a 1D data set.
get_dep: Return the dependent axis of a data set.
get_indep: Return the independent axes of a data set.
set_dep: Set the dependent axis of a data set.

Examples

Create a 200 pixel by 150 pixel grid (number of columns by number of rows) and display it (each pixel has a value of 0):

>>> dataspace2d([200, 150])
>>> image_data()

Create a data space called “fakeimg”:

>>> dataspace2d([nx,ny], id="fakeimg")

delete_data(id: int | str | None = None) → None[source] [edit on github]

Delete a data set by identifier.

The data set, and any associated structures - such as the ARF and RMF for PHA data sets - are removed.

Parameters:: id (int, str, or None, optional) – The data set to delete. If not given then the default identifier is used, as returned by get_default_id.

See also

clean: Clear all stored session data.
copy_data: Copy a data set to a new identifier.
delete_model: Delete the model expression from a data set.
get_default_id: Return the default data set identifier.
list_data_ids: List the identifiers for the loaded data sets.

Notes

The source expression is not removed by this function.

Examples

Delete the data from the default data set:

>>> delete_data()

Delete the data set identified as ‘src’:

>>> delete_data('src')

delete_model(id: int | str | None = None) → None[source] [edit on github]

Delete the model expression for a data set.

This removes the model expression, created by set_model, for a data set. It does not delete the components of the expression.

Parameters:: id (int, str, or None, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.

See also

clean: Clear all stored session data.
delete_data: Delete a data set by identifier.
get_default_id: Return the default data set identifier.
set_model: Set the source model expression for a data set.
show_model: Display the source model expression for a data set.

Examples

Remove the model expression for the default data set:

>>> delete_model()

Remove the model expression for the data set with the identifier called ‘src’:

>>> delete_model('src')

delete_model_component(name: str) → None[source] [edit on github]

Delete a model component.

Parameters:: name (str) – The name used to refer to this instance, or component, of the model. The corresponding Python variable will be deleted by this function.

See also

create_model_component: Create a model component.
delete_model: Delete the model expression for a data set.
list_models: List the available model types.
list_model_components: List the names of all the model components.
set_model: Set the source model expression for a data set.
set_model_autoassign_func: Set the method used to create model component identifiers.

Notes

It is an error to try to delete a component that is part of a model expression - i.e. included as part of an expression in a set_model or set_source call. In such a situation, use the delete_model function to remove the source expression before calling delete_model_component.

Examples

If a model instance called pl has been created - e.g. by create_model_component('powlaw1d', 'pl') - then the following will remove it:

>>> delete_model_component('pl')

delete_psf(id: int | str | None = None) → None[source] [edit on github]

Delete the PSF model for a data set.

Remove the PSF convolution applied to a source model.

Parameters:: id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.

See also

list_psf_ids: List of all the data sets with a PSF.
load_psf: Create a PSF model.
set_psf: Add a PSF model to a data set.
get_psf: Return the PSF model defined for a data set.

Examples

>>> delete_psf()

>>> delete_psf('core')

fake(id: int | str | None = None, method=<function poisson_noise>) → None[source] [edit on github]

Simulate a data set.

Take a data set, evaluate the model for each bin, and then use this value to create a data value from each bin. The default behavior is to use a Poisson distribution, with the model value as the expectation value of the distribution.

Parameters:

id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
method (func) – The function used to create a random realisation of a data set.

See also

dataspace1d: Create the independent axis for a 1D data set.
dataspace2d: Create the independent axis for a 2D data set.
get_dep: Return the dependent axis of a data set.
load_arrays: Create a data set from array values.
set_model: Set the source model expression for a data set.

Notes

The function for the method argument accepts a single argument, the data values, and should return an array of the same shape as the input, with the data values to use.

The function can be called on any data set, it does not need to have been created with dataspace1d or dataspace2d.

Specific data set types may have their own, specialized, version of this function.

Examples

Create a random realisation of the model - a constant plus gaussian line - for the range x=-5 to 5.

>>> dataspace1d(-5, 5, 0.5, dstype=Data1D)
>>> set_source(gauss1d.gline + const1d.bgnd)
>>> bgnd.c0 = 2
>>> gline.fwhm = 4
>>> gline.ampl = 5
>>> gline.pos = 1
>>> fake()
>>> plot_data()
>>> plot_model(overplot=True)

For a 2D data set, display the simulated data, model, and residuals:

>>> dataspace2d([150, 80], id='fakeimg')
>>> set_source('fakeimg', beta2d.src + polynom2d.bg)
>>> src.xpos, src.ypos = 75, 40
>>> src.r0, src.alpha = 15, 2.3
>>> src.ellip, src.theta = 0.4, 1.32
>>> src.ampl = 100
>>> bg.c, bg.cx1, bg.cy1 = 3, 0.4, 0.3
>>> fake('fakeimg')
>>> image_fit('fakeimg')

fit(id: int | str | None = None, *otherids: int | str, **kwargs) → None[source] [edit on github]

Fit a model to one or more data sets.

Use forward fitting to find the best-fit model to one or more data sets, given the chosen statistic and optimization method. The fit proceeds until the results converge or the number of iterations exceeds the maximum value (these values can be changed with set_method_opt). An iterative scheme can be added using set_iter_method to try and improve the fit. The final fit results are displayed to the screen and can be retrieved with get_fit_results.

Changed in version 4.17.0: The outfile parameter can now be sent a Path object or a file handle instead of a string.

Parameters:

id (int or str, optional) – The data set that provides the data. If not given then all data sets with an associated model are fit simultaneously.
*otherids (int or str, optional) – Other data sets to use in the calculation.
outfile (str, Path, IO object, or None, optional) – If set, then the fit results will be written to a file with this name. The file contains the per-iteration fit results.
clobber (bool, optional) – This flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting). This is only used if outfile is set to a string or Path object.

Raises:

sherpa.utils.err.FitErr – If filename already exists and clobber is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
contour_fit: Contour the fit to a data set.
covar: Estimate the confidence intervals using the confidence method.
freeze: Fix model parameters so they are not changed by a fit.
get_fit_results: Return the results of the last fit.
plot_fit: Plot the fit results (data, model) for a data set.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
set_stat: Set the statistical method.
set_method: Change the optimization method.
set_method_opt: Change an option of the current optimization method.
set_full_model: Define the convolved model expression for a data set.
set_iter_method: Set the iterative-fitting scheme used in the fit.
set_model: Set the model expression for a data set.
show_fit: Summarize the fit results.
thaw: Allow model parameters to be varied during a fit.

Notes

If outfile is sent a file handle then it is not closed by this routine.

Examples

Simultaneously fit all data sets with models and then store the results in the variable fres:

>>> fit()
>>> fres = get_fit_results()

Fit just the data set ‘img’:

>>> fit('img')

Simultaneously fit data sets 1, 2, and 3:

>>> fit(1, 2, 3)

Fit data set ‘jet’ and write the fit results to the text file ‘jet.fit’, over-writing it if it already exists:

>>> fit('jet', outfile='jet.fit', clobber=True)

Store the per-iteration values in a StringIO object and extract the data into the variable txt (this avoids the need to create a file):

>>> from io import StringIO
>>> out = StringIO()
>>> fit(outfile=out)
>>> txt = out.getvalue()

freeze(*args)[source] [edit on github]

Fix model parameters so they are not changed by a fit.

The arguments can be parameters or models, in which case all parameters of the model are frozen. If no arguments are given then nothing is changed.

Parameters:: args (sequence of str or Parameter or Model) – The parameters or models to freeze.

See also

fit: Fit one or more data sets.
link: Link a parameter value to an associated value.
set_par: Set the value, limits, or behavior of a model parameter.
thaw: Allow model parameters to be varied during a fit.
unlink: Unlink a parameter value.

Notes

The thaw function can be used to reverse this setting, so that parameters can be varied in a fit.

Examples

Fix the FWHM parameter of the line model (in this case a gauss1d model) so that it will not be varied in the fit.

>>> set_source(const1d.bgnd + gauss1d.line)
>>> line.fwhm = 2.1
>>> freeze(line.fwhm)
>>> fit()

Freeze all parameters of the line model and then re-fit:

>>> freeze(line)
>>> fit()

Freeze the nh parameter of the gal model and the abund parameter of the src model:

>>> freeze(gal.nh, src.abund)

get_cdf_plot()[source] [edit on github]

Return the data used to plot the last CDF.

Returns:: plot – An object containing the data used by the last call to plot_cdf. The fields will be None if the function has not been called.
Return type:: a sherpa.plot.CDFPlot instance

See also

plot_cdf: Plot the cumulative density function of an array.

get_chisqr_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by plot_chisqr.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_chisqr (or get_chisqr_plot) are returned, otherwise the data is re-generated.

Returns:

resid_data

Return type:

a sherpa.plot.ChisqrPlot instance

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_delchi_plot: Return the data used by plot_delchi.
get_ratio_plot: Return the data used by plot_ratio.
get_resid_plot: Return the data used by plot_resid.
plot_chisqr: Plot the chi-squared value for each point in a data set.

Examples

Return the residual data, measured as chi square, for the default data set:

>>> rplot = get_chisqr_plot()
>>> np.min(rplot.y)
0.0005140622701128954
>>> np.max(rplot.y)
8.379696454792295

Display the contents of the residuals plot for data set 2:

>>> print(get_chisqr_plot(2))

Overplot the residuals plot from the ‘core’ data set on the ‘jet’ data set:

>>> r1 = get_chisqr_plot('jet')
>>> r2 = get_chisqr_plot('core')
>>> r1.plot()
>>> r2.overplot()

get_conf()[source] [edit on github]

Return the confidence-interval estimation object.

Returns:: conf
Return type:: object

See also

conf: Estimate parameter confidence intervals using the confidence method.
get_conf_opt: Return one or all of the options for the confidence interval method.
set_conf_opt: Set an option of the conf estimation object.

Notes

The attributes of the confidence-interval object include:

eps: The precision of the calculated limits. The default is 0.01.
fast: If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False.
max_rstat: If the reduced chi square is larger than this value, do not use (only used with chi-square statistics). The default is 3.
maxfits: The maximum number of re-fits allowed (that is, when the remin filter is met). The default is 5.
maxiters: The maximum number of iterations allowed when bracketing limits, before stopping for that parameter. The default is 200.
numcores: The number of computer cores to use when evaluating results in parallel. This is only used if parallel is True. The default is to use all cores.
openinterval: How the conf method should cope with intervals that do not converge (that is, when the maxiters limit has been reached). The default is False.
parallel: If there is more than one free parameter then the results can be evaluated in parallel, to reduce the time required. The default is True.
remin: The minimum difference in statistic value for a new fit location to be considered better than the current best fit (which starts out as the starting location of the fit at the time conf is called). The default is 0.01.
sigma: What is the error limit being calculated. The default is 1.
soft_limits: Should the search be restricted to the soft limits of the parameters (True), or can parameter values go out all the way to the hard limits if necessary (False). The default is False
tol: The tolerance for the fit. The default is 0.2.
verbose: Should extra information be displayed during fitting? The default is False.

Examples

>>> print(get_conf())
name         = confidence
numcores     = 8
verbose      = False
openinterval = False
max_rstat    = 3
maxiters     = 200
soft_limits  = False
eps          = 0.01
fast         = False
maxfits      = 5
remin        = 0.01
tol          = 0.2
sigma        = 1
parallel     = True

Change the remin field to 0.05.

>>> cf = get_conf()
>>> cf.remin = 0.05

get_conf_opt(name=None)[source] [edit on github]

Return one or all of the options for the confidence interval method.

This is a helper function since the options can also be read directly using the object returned by get_conf.

Parameters:: name (str, optional) – If not given, a dictionary of all the options are returned. When given, the individual value is returned.
Returns:: value
Return type:: dictionary or value
Raises:: sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

conf: Estimate parameter confidence intervals using the confidence method.
get_conf: Return the confidence-interval estimation object.
set_conf_opt: Set an option of the conf estimation object.

Examples

>>> get_conf_opt('verbose')
False

>>> copts = get_conf_opt()
>>> copts['verbose']
False

get_conf_results()[source] [edit on github]

Return the results of the last conf run.

Returns:: results
Return type:: sherpa.fit.ErrorEstResults object
Raises:: sherpa.utils.err.SessionErr – If no conf call has been made.

See also

get_conf_opt: Return one or all of the options for the confidence interval method.
set_conf_opt: Set an option of the conf estimation object.

Notes

The fields of the object include:

datasets: A tuple of the data sets used in the analysis.
methodname: This will be ‘confidence’.
iterfitname: The name of the iterated-fit method used, if any.
fitname: The name of the optimization method used.
statname: The name of the fit statistic used.
sigma: The sigma value used to calculate the confidence intervals.
percent: The percentage of the signal contained within the confidence intervals (calculated from the sigma value assuming a normal distribution).
parnames: A tuple of the parameter names included in the analysis.
parvals: A tuple of the best-fit parameter values, in the same order as parnames.
parmins: A tuple of the lower error bounds, in the same order as parnames.
parmaxes: A tuple of the upper error bounds, in the same order as parnames.
nfits: The number of model evaluations.

Examples

>>> res = get_conf_results()
>>> print(res)
datasets    = (1,)
methodname  = confidence
iterfitname = none
fitname     = levmar
statname    = chi2gehrels
sigma       = 1
percent     = 68.2689492137
parnames    = ('p1.gamma', 'p1.ampl')
parvals     = (2.1585155113403327, 0.00022484014787994827)
parmins     = (-0.082785567348122591, -1.4825550342799376e-05)
parmaxes    = (0.083410634144100104, 1.4825550342799376e-05)
nfits       = 13

The following converts the above into a dictionary where the keys are the parameter names and the values are the tuple (best-fit value, lower-limit, upper-limit):

>>> pvals1 = zip(res.parvals, res.parmins, res.parmaxes)
>>> pvals2 = [(v, v+l, v+h) for (v, l, h) in pvals1]
>>> dres = dict(zip(res.parnames, pvals2))
>>> dres['p1.gamma']
(2.1585155113403327, 2.07572994399221, 2.241926145484433)

get_confidence_results() [edit on github]

Return the results of the last conf run.

Returns:: results
Return type:: sherpa.fit.ErrorEstResults object
Raises:: sherpa.utils.err.SessionErr – If no conf call has been made.

See also

get_conf_opt: Return one or all of the options for the confidence interval method.
set_conf_opt: Set an option of the conf estimation object.

Notes

The fields of the object include:

datasets: A tuple of the data sets used in the analysis.
methodname: This will be ‘confidence’.
iterfitname: The name of the iterated-fit method used, if any.
fitname: The name of the optimization method used.
statname: The name of the fit statistic used.
sigma: The sigma value used to calculate the confidence intervals.
percent: The percentage of the signal contained within the confidence intervals (calculated from the sigma value assuming a normal distribution).
parnames: A tuple of the parameter names included in the analysis.
parvals: A tuple of the best-fit parameter values, in the same order as parnames.
parmins: A tuple of the lower error bounds, in the same order as parnames.
parmaxes: A tuple of the upper error bounds, in the same order as parnames.
nfits: The number of model evaluations.

Examples

>>> res = get_conf_results()
>>> print(res)
datasets    = (1,)
methodname  = confidence
iterfitname = none
fitname     = levmar
statname    = chi2gehrels
sigma       = 1
percent     = 68.2689492137
parnames    = ('p1.gamma', 'p1.ampl')
parvals     = (2.1585155113403327, 0.00022484014787994827)
parmins     = (-0.082785567348122591, -1.4825550342799376e-05)
parmaxes    = (0.083410634144100104, 1.4825550342799376e-05)
nfits       = 13

The following converts the above into a dictionary where the keys are the parameter names and the values are the tuple (best-fit value, lower-limit, upper-limit):

>>> pvals1 = zip(res.parvals, res.parmins, res.parmaxes)
>>> pvals2 = [(v, v+l, v+h) for (v, l, h) in pvals1]
>>> dres = dict(zip(res.parnames, pvals2))
>>> dres['p1.gamma']
(2.1585155113403327, 2.07572994399221, 2.241926145484433)

get_contour_prefs(contourtype: str, id: int | str | None = None)[source] [edit on github]

Return the preferences for the given contour type.

Added in version 4.16.0.

Parameters:

contourtype (str) – The contour type, such as “data”, “model”, or “resid”. The “fit” argument is not supported.
id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.

Returns:

prefs – Changing the values of this dictionary will change any new contour plots. This dictionary will be empty if no plot backend is available.

Return type:

dict

Notes

The meaning of the fields depend on the chosen plot backend. A value of None means to use the default value for that attribute, or not to use that setting.

The “fit” argument can not be used, even though there is a get_fit_contour call. Either use the “data” or “model” arguments to access the desired plot type, or use get_fit_contour() and access the datacontour and modelcontour attributes directly.

Examples

Change the contours of the model to be drawn partly opaque (with the matplotlib backend):

>>> prefs = get_contour_prefs("data")
>>> prefs['alpha'] = 0.7
>>> contour_data()
>>> contour_model(overcontour=True)

get_covar()[source] [edit on github]

Return the covariance estimation object.

Returns:: covar
Return type:: object

See also

covar: Estimate parameter confidence intervals using the covariance method.
get_covar_opt: Return one or all of the options for the covariance method.
set_covar_opt: Set an option of the covar estimation object.

Notes

The attributes of the covariance object include:

eps: The precision of the calculated limits. The default is 0.01.
maxiters: The maximum number of iterations allowed before stopping for that parameter. The default is 200.
sigma: What is the error limit being calculated. The default is 1.
soft_limits: Should the search be restricted to the soft limits of the parameters (True), or can parameter values go out all the way to the hard limits if necessary (False). The default is False

Examples

>>> print(get_covar())
name        = covariance
sigma       = 1
maxiters    = 200
soft_limits = False
eps         = 0.01

Change the sigma field to 1.9.

>>> cv = get_covar()
>>> cv.sigma = 1.6

get_covar_opt(name=None)[source] [edit on github]

Return one or all of the options for the covariance method.

This is a helper function since the options can also be read directly using the object returned by get_covar.

Parameters:: name (str, optional) – If not given, a dictionary of all the options are returned. When given, the individual value is returned.
Returns:: value
Return type:: dictionary or value
Raises:: sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

covar: Estimate parameter confidence intervals using the covariance method.
get_covar: Return the covariance estimation object.
set_covar_opt: Set an option of the covar estimation object.

Examples

>>> get_covar_opt('sigma')
1

>>> copts = get_covar_opt()
>>> copts['sigma']
1

get_covar_results()[source] [edit on github]

Return the results of the last covar run.

Returns:: results
Return type:: sherpa.fit.ErrorEstResults object
Raises:: sherpa.utils.err.SessionErr – If no covar call has been made.

See also

get_covar_opt: Return one or all of the options for the covariance method.
set_covar_opt: Set an option of the covar estimation object.

Notes

The fields of the object include:

datasets: A tuple of the data sets used in the analysis.
methodname: This will be ‘covariance’.
iterfitname: The name of the iterated-fit method used, if any.
fitname: The name of the optimization method used.
statname: The name of the fit statistic used.
sigma: The sigma value used to calculate the confidence intervals.
percent: The percentage of the signal contained within the confidence intervals (calculated from the sigma value assuming a normal distribution).
parnames: A tuple of the parameter names included in the analysis.
parvals: A tuple of the best-fit parameter values, in the same order as parnames.
parmins: A tuple of the lower error bounds, in the same order as parnames.
parmaxes: A tuple of the upper error bounds, in the same order as parnames.
nfits: The number of model evaluations.

There is also an extra_output field which is used to return the covariance matrix.

Examples

>>> res = get_covar_results()
>>> print(res)
datasets    = (1,)
methodname  = covariance
iterfitname = none
fitname     = levmar
statname    = chi2gehrels
sigma       = 1
percent     = 68.2689492137
parnames    = ('bgnd.c0',)
parvals     = (10.228675427602724,)
parmins     = (-2.4896739438296795,)
parmaxes    = (2.4896739438296795,)
nfits       = 0

In this case, of a single parameter, the covariance matrix is just the variance of the parameter:

>>> res.extra_output
array([[ 6.19847635]])

get_covariance_results() [edit on github]

Return the results of the last covar run.

Returns:: results
Return type:: sherpa.fit.ErrorEstResults object
Raises:: sherpa.utils.err.SessionErr – If no covar call has been made.

See also

get_covar_opt: Return one or all of the options for the covariance method.
set_covar_opt: Set an option of the covar estimation object.

Notes

The fields of the object include:

datasets: A tuple of the data sets used in the analysis.
methodname: This will be ‘covariance’.
iterfitname: The name of the iterated-fit method used, if any.
fitname: The name of the optimization method used.
statname: The name of the fit statistic used.
sigma: The sigma value used to calculate the confidence intervals.
percent: The percentage of the signal contained within the confidence intervals (calculated from the sigma value assuming a normal distribution).
parnames: A tuple of the parameter names included in the analysis.
parvals: A tuple of the best-fit parameter values, in the same order as parnames.
parmins: A tuple of the lower error bounds, in the same order as parnames.
parmaxes: A tuple of the upper error bounds, in the same order as parnames.
nfits: The number of model evaluations.

There is also an extra_output field which is used to return the covariance matrix.

Examples

>>> res = get_covar_results()
>>> print(res)
datasets    = (1,)
methodname  = covariance
iterfitname = none
fitname     = levmar
statname    = chi2gehrels
sigma       = 1
percent     = 68.2689492137
parnames    = ('bgnd.c0',)
parvals     = (10.228675427602724,)
parmins     = (-2.4896739438296795,)
parmaxes    = (2.4896739438296795,)
nfits       = 0

In this case, of a single parameter, the covariance matrix is just the variance of the parameter:

>>> res.extra_output
array([[ 6.19847635]])

get_data(id: int | str | None = None) → Data[source] [edit on github]

Return the data set by identifier.

The object returned by the call can be used to query and change properties of the data set.

Parameters:: id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
Returns:: instance – The data instance.
Return type:: sherpa.data.Data
Raises:: sherpa.utils.err.IdentifierErr – No data has been loaded for this data set.

See also

copy_data: Copy a data set to a new identifier.
delete_data: Delete a data set by identifier.
load_data: Create a data set from a file.
set_data: Set a data set.

Examples

>>> d = get_data()

>>> dimg = get_data('img')

>>> load_arrays('tst', [10, 20, 30], [5.4, 2.3, 9.8])
>>> print(get_data('tst'))
name      =
x         = Int64[3]
y         = Float64[3]
staterror = None
syserror  = None

get_data_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_data.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_data (or get_data_contour) are returned, otherwise the data is re-generated.

Returns:

resid_data – The y attribute contains the residual values and the x0 and x1 arrays contain the corresponding coordinate values, as one-dimensional arrays.

Return type:

a sherpa.plot.DataContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_data_image: Return the data used by image_data.
contour_data: Contour the values of an image data set.
image_data: Display a data set in the image viewer.

Examples

Return the data for the default data set:

>>> dinfo = get_data_contour()

get_data_contour_prefs()[source] [edit on github]

Return the preferences for contour_data.

Returns:: prefs – Changing the values of this dictionary will change any new contour plots. The default is an empty dictionary.
Return type:: dict

See also

contour_data, get_contour_prefs

Notes

The meaning of the fields depend on the chosen plot backend. A value of None (or not set) means to use the default value for that attribute, unless indicated otherwise.

alpha: The transparency value used to draw the contours, where 0 is fully transparent and 1 is fully opaque.
colors: The colors to draw the contours.
linewidths: What thickness of line to draw the contours.
xlog: Should the X axis be drawn with a logarithmic scale? The default is False.
ylog: Should the Y axis be drawn with a logarithmic scale? The default is False.

Examples

Change the contours to be drawn in ‘green’:

>>> contour_data()
>>> prefs = get_data_contour_prefs()
>>> prefs['color'] = 'green'
>>> contour_data()

get_data_image(id: int | str | None = None)[source] [edit on github]

Return the data used by image_data.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.

Returns:

data_img – The y attribute contains the ratio values as a 2D NumPy array.

Return type:

a sherpa.image.DataImage instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

contour_data: Contour the values of an image data set.
image_data: Display a data set in the image viewer.

Examples

Return the image data for the default data set:

>>> dinfo = get_data_image()
>>> dinfo.y.shape
(150, 175)

get_data_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by plot_data.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_data (or get_data_plot) are returned, otherwise the data is re-generated.

Returns:

data – An object representing the data used to create the plot by plot_data. The relationship between the returned values and the values in the data set depend on the data type. For example PHA data are plotted in units controlled by sherpa.astro.ui.set_analysis, but are stored as channels and counts, and may have been grouped and the background estimate removed.

Return type:

a sherpa.plot.DataPlot instance

See also

get_data_plot_prefs: Return the preferences for plot_data.
get_default_id: Return the default data set identifier.
plot_data: Plot the data values.

get_data_plot_prefs(id: int | str | None = None)[source] [edit on github]

Return the preferences for plot_data.

The plot preferences may depend on the data set, so it is now an optional argument.

Changed in version 4.12.2: The id argument has been given.

Parameters:: id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
Returns:: prefs – Changing the values of this dictionary will change any new data plots. This dictionary will be empty if no plot backend is available.
Return type:: dict

Notes

The meaning of the fields depend on the chosen plot backend. A value of None means to use the default value for that attribute, unless indicated otherwise. These preferences are used by the following commands: plot_data, plot_bkg, plot_ratio, and the “fit” variants, such as plot_fit, plot_fit_resid, and plot_bkg_fit.

The following preferences are recognized by the matplotlib backend:

alpha: The transparency value used to draw the line or symbol, where 0 is fully transparent and 1 is fully opaque.
barsabove: The barsabove argument for the matplotlib errorbar function.
capsize: The capsize argument for the matplotlib errorbar function.
color: The color to use (will be over-ridden by more-specific options below). The default is None.
ecolor: The color to draw error bars. The default is None.
linestyle: How should the line connecting the data points be drawn. The default is ‘None’, which means no line is drawn.
marker: What style is used for the symbols. The default is '.' which indicates a point.
markerfacecolor: What color to draw the symbol representing the data points. The default is None.
markersize: What size is the symbol drawn. The default is None.
xerrorbars: Should error bars be drawn for the X axis. The default is False.
xlog: Should the X axis be drawn with a logarithmic scale? The default is False. This field can also be changed with the set_xlog and set_xlinear functions.
yerrorbars: Should error bars be drawn for the Y axis. The default is True.
ylog: Should the Y axis be drawn with a logarithmic scale? The default is False. This field can also be changed with the set_ylog and set_ylinear functions.

Examples

After these commands, any data plot will use a green symbol and not display Y error bars.

>>> prefs = get_data_plot_prefs()
>>> prefs['color'] = 'green'
>>> prefs['yerrorbars'] = False

get_default_id() → int | str[source] [edit on github]

Return the default data set identifier.

The Sherpa data id ties data, model, fit, and plotting information into a data set easily referenced by id. The default identifier, used by many commands, is returned by this command and can be changed by set_default_id.

Returns:: id – The default data set identifier used by certain Sherpa functions when an identifier is not given, or set to None.
Return type:: int or str

See also

list_data_ids: List the identifiers for the loaded data sets.
set_default_id: Set the default data set identifier.

Notes

The default Sherpa data set identifier is the integer 1.

Examples

Display the default identifier:

>>> print(get_default_id())

Store the default identifier and use it as an argument to call another Sherpa routine:

>>> defid = get_default_id()
>>> load_arrays(defid, x, y)

get_delchi_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by plot_delchi.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_delchi (or get_delchi_plot) are returned, otherwise the data is re-generated.

Returns:

resid_data

Return type:

a sherpa.plot.DelchiPlot instance

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_chisqr_plot: Return the data used by plot_chisqr.
get_ratio_plot: Return the data used by plot_ratio.
get_resid_plot: Return the data used by plot_resid.
plot_delchi: Plot the ratio of residuals to error for a data set.

Examples

Return the residual data, measured in units of the error, for the default data set:

>>> rplot = get_delchi_plot()
>>> np.min(rplot.y)
-2.85648373819671875
>>> np.max(rplot.y)
2.89477053577520982

Display the contents of the residuals plot for data set 2:

>>> print(get_delchi_plot(2))

Overplot the residuals plot from the ‘core’ data set on the ‘jet’ data set:

>>> r1 = get_delchi_plot('jet')
>>> r2 = get_delchi_plot('core')
>>> r1.plot()
>>> r2.overplot()

get_dep(id: int | str | None = None, filter=False)[source] [edit on github]

Return the dependent axis of a data set.

This function returns the data values (the dependent axis) for each point or pixel in the data set.

Parameters:

id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

axis – The dependent axis values. The model estimate is compared to these values during fitting.

Return type:

array

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_error: Return the errors on the dependent axis of a data set.
get_indep: Return the independent axis of a data set.
list_data_ids: List the identifiers for the loaded data sets.

Examples

>>> load_arrays(1, [10, 15, 19], [4, 5, 9])
>>> get_dep()
array([4, 5, 9])

>>> x0 = [10, 15, 12, 19]
>>> x1 = [12, 14, 10, 17]
>>> y = [4, 5, 9, -2]
>>> load_arrays(2, x0, x1, y, Data2D)
>>> get_dep(2)
array([ 4,  5,  9, -2])

If the filter flag is set then the return will be limited to the data that is used in the fit:

>>> load_arrays(1, [10, 15, 19], [4, 5, 9])
>>> ignore_id(1, 17, None)
>>> get_dep()
array([4, 5, 9])
>>> get_dep(filter=True)
array([4, 5])

get_dims(id: int | str | None = None, filter=False)[source] [edit on github]

Return the dimensions of the data set.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
filter (bool, optional) – If True then apply any filter to the data set before returning the dimensions. The default is False.

Returns:

dims

Return type:

a tuple of int

See also

ignore: Exclude data from the fit.
sherpa.astro.ui.ignore2d: Exclude a spatial region from an image.
notice: Include data in the fit.
sherpa.astro.ui.notice2d: Include a spatial region of an image.

Examples

Display the dimensions for the default data set:

>>> print(get_dims())

Find the number of bins in dataset ‘a2543’ without and with any filters applied to it:

>>> nall = get_dims('a2543')
>>> nfilt = get_dims('a2543', filter=True)

get_draws(id: int | str | None = None, otherids: Sequence[int | str] = (), niter=1000, covar_matrix=None)[source] [edit on github]

Run the pyBLoCXS MCMC algorithm.

The function runs a Markov Chain Monte Carlo (MCMC) algorithm designed to carry out Bayesian Low-Count X-ray Spectral (BLoCXS) analysis. It explores the model parameter space at the suspected statistic minimum (i.e. after using fit). The return values include the statistic value, parameter values, and an acceptance flag indicating whether the row represents a jump from the current location or not. For more information see the sherpa.sim module and the reference given below.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, optional) – Other data sets to use in the calculation.
niter (int, optional) – The number of draws to use. The default is 1000.
covar_matrix (2D array, optional) – The covariance matrix to use. If None then the result from get_covar_results().extra_output is used.

Returns:

The results of the MCMC chain. The stats and accept arrays contain niter+1 elements, with the first row being the starting values. The params array has (nparams, niter+1) elements, where nparams is the number of free parameters in the model expression, and the first column contains the values that the chain starts at. The accept array contains boolean values, indicating whether the jump, or step, was accepted (True), so the parameter values and statistic change, or it wasn’t, in which case there is no change to the previous row. The sherpa.utils.get_error_estimates routine can be used to calculate the credible one-sigma interval from the params array.

Return type:

stats, accept, params

Notes

The chain is run using fit information associated with the specified data set, or sets, the currently set sampler (set_sampler) and parameter priors (set_prior), for a specified number of iterations. The model should have been fit to find the best-fit parameters, and covar called, before running get_draws. The results from get_draws is used to estimate the parameter distributions.

The set_rng routine is used to control how the random numbers are generated.

References

“Analysis of Energy Spectra with Low Photon Counts via Bayesian Posterior Simulation”, van Dyk, D.A., Connors, A., Kashyap, V.L., & Siemiginowska, A. 2001, Ap.J., 548, 224

Examples

Fit a source and then run a chain to investigate the parameter distributions. The distribution of the stats values created by the chain is then displayed, using plot_trace, and the parameter distributions for the first two thawed parameters are displayed. The first one as a cumulative distribution using plot_cdf and the second one as a probability distribution using plot_pdf. Finally the acceptance fraction (number of draws where the chain moved) is displayed. Note that in a full analysis session a burn-in period would normally be removed from the chain before using the results.

>>> fit()
>>> covar()
>>> stats, accept, params = get_draws(1, niter=1e4)
>>> plot_trace(stats, name='stat')
>>> names = [p.fullname for p in get_source().pars if not p.frozen]
>>> plot_cdf(params[0,:], name=names[0], xlabel=names[0])
>>> plot_pdf(params[1,:], name=names[1], xlabel=names[1])
>>> accept[:-1].sum() * 1.0 / len(accept - 1)
0.4287

The following runs the chain on multiple data sets, with identifiers ‘core’, ‘jet1’, and ‘jet2’:

>>> stats, accept, params = get_draws('core', ['jet1', 'jet2'], niter=1e4)

get_error(id: int | str | None = None, filter=False)[source] [edit on github]

Return the errors on the dependent axis of a data set.

The function returns the total errors (a quadrature addition of the statistical and systematic errors) on the values (dependent axis) of a data set. The individual components can be retrieved with the get_staterror and get_syserror functions.

Parameters:

id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

errors – The error for each data point, formed by adding the statistical and systematic errors in quadrature. The size of this array depends on the filter argument.

Return type:

array

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_error: Return the errors on the dependent axis of a data set.
get_indep: Return the independent axis of a data set.
get_staterror: Return the statistical errors on the dependent axis of a data set.
get_syserror: Return the systematic errors on the dependent axis of a data set.
list_data_ids: List the identifiers for the loaded data sets.

Notes

The default behavior is to not apply any filter defined on the independent axes to the results, so that the return value is for all points (or bins) in the data set. Set the filter argument to True to apply this filter.

Examples

Return the error values for the default data set, ignoring any filter applied to it:

>>> err = get_error()

Ensure that the return values are for the selected (filtered) points in the default data set (the return array may be smaller than in the previous example):

>>> err = get_error(filter=True)

Find the errors for the “core” data set:

>>> err = get_error('core', filter=True)

get_filter(id: int | str | None = None, format: str | None = None, delim: str | None = None) → str[source] [edit on github]

Return the filter expression for a data set.

This returns the filter expression, created by one or more calls to ignore and notice, for the data set.

Changed in version 4.17.0: The format and delim arguments can now be set.

Changed in version 4.14.0: The filter expressions have been tweaked for Data1DInt and PHA data sets (when using energy or wavelength units) and now describe the full range of the bins, rather than the mid-points.

Parameters:

id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
format (str or None, optional) – If set, use this rather than the default format value for the dataset.
delim (str or None, optional) – If set, use this rather than the default delim value for the dataset.

Returns:

filter – The empty string or a string expression representing the filter used. For PHA data dets the units are controlled by the analysis setting for the data set.

Return type:

str

Raises:

sherpa.utils.err.ArgumentErr – If the data set does not exist.

See also

ignore: Exclude data from the fit.
load_filter: Load the filter array from a file and add to a data set.
notice: Include data in the fit.
save_filter: Save the filter array to a file.
show_filter: Show any filters applied to a data set.
set_filter: Set the filter array of a data set.

Examples

The default filter is the full dataset, given in the format lowval:hival (for a Data1D dataset like this these are inclusive limits):

>>> load_arrays(1, [10, 15, 20, 25], [5, 7, 4, 2])
>>> get_filter()
'10.0000:25.0000'

Change the formatting of the output:

>>> get_filter(format="%d", delim="-")
"10-25"

The notice call restricts the data to the range between 14 and 30. The resulting filter is the combination of this range and the data:

>>> notice(14, 30)
>>> get_filter()
'15.0000:25.0000'

Ignoring the point at x=20 means that only the points at x=15 and x=25 remain, so a comma-separated list is used:

>>> ignore(19, 22)
>>> get_filter()
'15.0000,25.0000'

The filter equivalent to the per-bin array of filter values:

>>> set_filter([1, 1, 0, 1])
>>> get_filter()
'10.0000:15.0000,25.0000'

For an integrated data set (Data1DInt and DataPHA with energy or wavelength units)

>>> load_arrays(1, [10, 15, 20, 25], [15, 20, 23, 30], [5, 7, 4, 2], Data1DInt)
>>> get_filter()
'10.0000:30.0000'

For integrated datasets the limits are now inclusive only for the lower limit, but in this the end-point ends within a bin so is is included:

>>> notice(17, 28)
>>> get_filter()
'15.0000:30.0000'

There is no data in the range 23 to 24 so the ignore doesn’t change anything:

>>> ignore(23, 24)
>>> get_filter()
'15.0000:30.0000'

However it does match the range 22 to 23 and so changes the filter:

>>> ignore(22, 23)
>>> get_filter()
'15.0000:20.0000,25:000:30.0000'

Return the filter for data set 3:

>>> get_filter(3)

get_fit_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_fit.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_fit (or get_fit_contour) are returned, otherwise the data is re-generated.

Returns:

fit_data – An object representing the data used to create the plot by contour_fit. It contains the data from get_data_contour and get_model_contour in the datacontour and modelcontour attributes.

Return type:

a sherpa.plot.FitContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_data_image: Return the data used by image_data.
get_model_image: Return the data used by image_model.
contour_data: Contour the values of an image data set.
contour_model: Contour the values of the model, including any PSF.
image_data: Display a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.

Examples

Return the contour data for the default data set:

>>> finfo = get_fit_contour()

get_fit_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used to create the fit plot.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_fit (or get_fit_plot) are returned, otherwise the data is re-generated.

Returns:

data – An object representing the data used to create the plot by plot_fit. It contains the data from get_data_plot and get_model_plot in the dataplot and modelplot attributes.

Return type:

a sherpa.plot.FitPlot instance

See also

get_data_plot_prefs: Return the preferences for plot_data.
get_model_plot_prefs: Return the preferences for plot_model.
get_default_id: Return the default data set identifier.
plot_data: Plot the data values.
plot_model: Plot the model for a data set.

Examples

Create the data needed to create the “fit plot” for the default data set and display it:

>>> fplot = get_fit_plot()
>>> print(fplot)

Return the plot data for data set 2, and then use it to create a plot:

>>> f2 = get_fit_plot(2)
>>> f2.plot()

The fit plot consists of a combination of a data plot and a model plot, which are captured in the dataplot and modelplot attributes of the return value. These can be used to display the plots individually, such as:

>>> f2.dataplot.plot()
>>> f2.modelplot.plot()

or, to combine the two:

>>> f2.dataplot.plot()
>>> f2.modelplot.overplot()

get_fit_results() → FitResults[source] [edit on github]

Return the results of the last fit.

This function returns the results from the most-recent fit. The returned value includes information on the parameter values and fit statistic.

Returns:: stats – The results of the last fit. It does not reflect any changes made to the model parameter, or settings, since the last fit.
Return type:: a sherpa.fit.FitResults instance

See also

calc_stat: Calculate the fit statistic for a data set.
calc_stat_info: Display the statistic values for the current models.
fit: Fit a model to one or more data sets.
get_stat_info: Return the statistic values for the current models.
set_iter_method: Set the iterative-fitting scheme used in the fit.

Notes

The fields of the object include:

datasets: A sequence of the data set ids included in the results.
itermethodname: What iterated-fit scheme was used, if any (as set by set_iter_method).
statname: The name of the statistic function (as used in set_stat).
succeeded: Was the fit successful (did it converge)?
parnames: A tuple of the parameter names that were varied in the fit (the thawed parameters in the model expression).
parvals: A tuple of the parameter values, in the same order as parnames.
statval: The statistic value after the fit.
istatval: The statistic value at the start of the fit.
dstatval: The change in the statistic value (istatval - statval).
numpoints: The number of bins used in the fits.
dof: The number of degrees of freedom in the fit (the number of bins minus the number of free parameters).
qval: The Q-value (probability) that one would observe the reduced statistic value, or a larger value, if the assumed model is true and the current model parameters are the true parameter values. This will be None if the value can not be calculated with the current statistic (e.g. the Cash statistic).
rstat: The reduced statistic value (the statval field divided by dof). This is not calculated for all statistics.
message: A message about the results of the fit (e.g. if the fit was unable to converge). The format and contents depend on the optimisation method.
nfev: The number of model evaluations made during the fit.

Examples

Display the fit results:

>>> print(get_fit_results())

Inspect the fit results:

>>> res = get_fit_results()
>>> res.statval
498.21750663761935
>>> res.dof
439
>>> res.parnames
('pl.gamma', 'pl.ampl', 'gline.fwhm', 'gline.pos', 'gline.ampl')
>>> res.parvals
(-0.20659543380329071, 0.00030398852609788524, 100.0, 4900.0, 0.001)

get_functions() → list[str][source] [edit on github]

Return the functions provided by Sherpa.

Returns:: functions
Return type:: list of str

See also

list_functions: Display the functions provided by Sherpa.

get_indep(id: int | str | None = None)[source] [edit on github]

Return the independent axes of a data set.

This function returns the coordinates of each point, or pixel, in the data set.

Parameters:: id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
Returns:: axis – The independent axis values. These are the values at which the model is evaluated during fitting.
Return type:: tuple of arrays
Raises:: sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_dep: Return the dependent axis of a data set.
list_data_ids: List the identifiers for the loaded data sets.

Examples

For a one-dimensional data set, the X values are returned:

>>> load_arrays(1, [10, 15, 19], [4, 5, 9])
>>> get_indep()
(array([10, 15, 19]),)

For a 2D data set the X0 and X1 values are returned:

>>> x0 = [10, 15, 12, 19]
>>> x1 = [12, 14, 10, 17]
>>> y = [4, 5, 9, -2]
>>> load_arrays(2, x0, x1, y, Data2D)
>>> get_indep(2)
(array([10, 15, 12, 19]), array([12, 14, 10, 17]))

get_int_proj(par=None, id: int | str | None = None, otherids: Sequence[int | str] | None = None, recalc=False, fast=True, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None)[source] [edit on github]

Return the interval-projection object.

This returns (and optionally calculates) the data used to display the int_proj plot. Note that if the the recalc parameter is False (the default value) then all other parameters are ignored and the results of the last int_proj call are returned.

Changed in version 4.16.1: The log parameter can now be set to True.

Parameters:

par – The parameter to plot. This argument is only used if recalc is set to True.
id (str, int, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (list of str or int, or None, optional) – Other data sets to use in the calculation.
recalc (bool, optional) – The default value (False) means that the results from the last call to int_proj (or get_int_proj) are returned, ignoring all other parameter values. Otherwise, the statistic curve is re-calculated, but not plotted.
fast (bool, optional) – If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False.
min (number, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (int, optional) – The number of steps to use. This is used when delv is set to None.
delv (number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

Returns:

iproj – The fields of this object can be used to re-create the plot created by int_proj.

Return type:

a sherpa.plot.IntervalProjection instance

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
int_proj: Calculate and plot the fit statistic versus fit parameter value.
int_unc: Calculate and plot the fit statistic versus fit parameter value.
reg_proj: Plot the statistic value as two parameters are varied.

Examples

Return the results of the int_proj run:

>>> int_proj(src.xpos)
>>> iproj = get_int_proj()
>>> min(iproj.y)
119.55942437129544

Since the recalc parameter has not been changed to True, the following will return the results for the last call to int_proj, which may not have been for the src.ypos parameter:

>>> iproj = get_int_proj(src.ypos)

Create the data without creating a plot:

>>> iproj = get_int_proj(pl.gamma, recalc=True)

Specify the range and step size for the parameter, in this case varying linearly between 12 and 14 with 51 values:

>>> iproj = get_int_proj(src.r0, id="src", min=12, max=14,
...                      nloop=51, recalc=True)

get_int_unc(par=None, id: int | str | None = None, otherids: Sequence[int | str] | None = None, recalc=False, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None)[source] [edit on github]

Return the interval-uncertainty object.

This returns (and optionally calculates) the data used to display the int_unc plot. Note that if the the recalc parameter is False (the default value) then all other parameters are ignored and the results of the last int_unc call are returned.

Changed in version 4.16.1: The log parameter can now be set to True.

Parameters:

par – The parameter to plot. This argument is only used if recalc is set to True.
id (str, int, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (list of str or int, or None, optional) – Other data sets to use in the calculation.
recalc (bool, optional) – The default value (False) means that the results from the last call to int_proj (or get_int_proj) are returned, ignoring all other parameter values. Otherwise, the statistic curve is re-calculated, but not plotted.
min (number, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (int, optional) – The number of steps to use. This is used when delv is set to None.
delv (number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

Returns:

iunc – The fields of this object can be used to re-create the plot created by int_unc.

Return type:

a sherpa.plot.IntervalUncertainty instance

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
int_proj: Calculate and plot the fit statistic versus fit parameter value.
int_unc: Calculate and plot the fit statistic versus fit parameter value.
reg_proj: Plot the statistic value as two parameters are varied.

Examples

Return the results of the int_unc run:

>>> int_unc(src.xpos)
>>> iunc = get_int_unc()
>>> min(iunc.y)
119.55942437129544

Since the recalc parameter has not been changed to True, the following will return the results for the last call to int_unc, which may not have been for the src.ypos parameter:

>>> iunc = get_int_unc(src.ypos)

Create the data without creating a plot:

>>> iunc = get_int_unc(pl.gamma, recalc=True)

Specify the range and step size for the parameter, in this case varying linearly between 12 and 14 with 51 values:

>>> iunc = get_int_unc(src.r0, id="src", min=12, max=14,
...                    nloop=51, recalc=True)

get_iter_method_name() → str[source] [edit on github]

Return the name of the iterative fitting scheme.

Returns:: name – The name of the iterative fitting scheme set by set_iter_method.
Return type:: {‘none’, ‘sigmarej’}

See also

list_iter_methods: List the iterative fitting schemes.
set_iter_method: Set the iterative-fitting scheme used in the fit.

Examples

>>> print(get_iter_method_name())

get_iter_method_opt(optname: str | None = None) → Any[source] [edit on github]

Return one or all options for the iterative-fitting scheme.

The options available for the iterative-fitting methods are described in set_iter_method_opt.

Parameters:: optname (str, optional) – If not given, a dictionary of all the options are returned. When given, the individual value is returned.
Returns:: value – The dictionary is empty when no iterative scheme is being used.
Return type:: dictionary or value
Raises:: sherpa.utils.err.ArgumentErr – If the optname argument is not recognized.

See also

get_iter_method_name: Return the name of the iterative fitting scheme.
set_iter_method_opt: Set an option for the iterative-fitting scheme.
set_iter_method: Set the iterative-fitting scheme used in the fit.

Examples

Display the settings of the current iterative-fitting method:

>>> print(get_iter_method_opt())

Switch to the sigmarej scheme and find out the current settings:

>>> set_iter_method('sigmarej')
>>> opts = get_iter_method_opt()

Return the ‘maxiters’ setting (if applicable):

>>> get_iter_method_opt('maxiters')

get_kernel_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_kernel.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_kernel (or get_kernel_contour) are returned, otherwise the data is re-generated.

Returns:

psf_data

Return type:

a sherpa.plot.PSFKernelContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_psf_contour: Return the data used by contour_psf.
contour_kernel: Contour the kernel applied to the model of an image data set.
contour_psf: Contour the PSF applied to the model of an image data set.

Examples

Return the contour data for the kernel for the default data set:

>>> kplot = get_kernel_contour()

get_kernel_image(id: int | str | None = None)[source] [edit on github]

Return the data used by image_kernel.

Parameters:: id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
Returns:: psf_data
Return type:: a sherpa.image.PSFKernelImage instance
Raises:: sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_psf_image: Return the data used by image_psf.
image_kernel: Display the 2D kernel for a data set in the image viewer.
image_psf: Display the 2D PSF model for a data set in the image viewer.

Examples

Return the image data for the kernel for the default data set:

>>> lplot = get_kernel_image()
>>> iplot.y.shape
(51, 51)

get_kernel_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by plot_kernel.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_kernel (or get_kernel_plot) are returned, otherwise the data is re-generated.

Returns:

kernel_plot

Return type:

a sherpa.plot.PSFKernelPlot instance

Raises:

sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_psf_plot: Return the data used by plot_psf.
plot_kernel: Plot the 1D kernel applied to a data set.
plot_psf: Plot the 1D PSF model applied to a data set.

Examples

Return the plot data and then create a plot with it:

>>> kplot = get_kernel_plot()
>>> kplot.plot()

get_method(name: str | None = None) → OptMethod[source] [edit on github]

Return an optimization method.

Parameters:: name (str, optional) – If not given, the current method is returned, otherwise it should be one of the names returned by the list_methods function.
Returns:: method – An object representing the optimization method.
Return type:: object
Raises:: sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

get_method_opt: Get the options for the current optimization method.
list_methods: List the supported optimization methods.
set_method: Change the optimization method.
set_method_opt: Change an option of the current optimization method.

Examples

The fields of the object returned by get_method can be used to view or change the method options.

>>> method = ui.get_method()
>>> print(method.name)
levmar
>>> print(method)
name    = levmar
ftol    = 1.19209289551e-07
xtol    = 1.19209289551e-07
gtol    = 1.19209289551e-07
maxfev  = None
epsfcn  = 1.19209289551e-07
factor  = 100.0
verbose = 0
>>> method.maxfev = 5000

get_method_name() → str[source] [edit on github]

Return the name of current Sherpa optimization method.

Returns:: name – The name of the current optimization method, in lower case. This may not match the value sent to set_method because some methods can be set by multiple names.
Return type:: str

See also

get_method: Return an optimization method.
get_method_opt: Get the options for the current optimization method.

Examples

>>> get_method_name()
'levmar'

The ‘neldermead’ method can also be referred to as ‘simplex’:

>>> set_method('simplex')
>>> get_method_name()
'neldermead'

get_method_opt(optname: str | None = None) → Any[source] [edit on github]

Return one or all of the options for the current optimization method.

This is a helper function since the optimization options can also be read directly using the object returned by get_method.

Parameters:: optname (str, optional) – If not given, a dictionary of all the options are returned. When given, the individual value is returned.
Returns:: value
Return type:: dictionary or value
Raises:: sherpa.utils.err.ArgumentErr – If the optname argument is not recognized.

See also

get_method: Return an optimization method.
set_method: Change the optimization method.
set_method_opt: Change an option of the current optimization method.

Examples

>>> get_method_opt('maxfev') is None
True

>>> mopts = get_method_opt()
>>> mopts['maxfev'] is None
True

get_model(id: int | str | None = None) → Model[source] [edit on github]

Return the model expression for a data set.

This returns the model expression for a data set, including any instrument response (e.g. PSF or ARF and RMF) whether created automatically or explicitly, with set_full_model.

Parameters:: id (int, str, or None, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.
Returns:: This can contain multiple model components and any instrument response. Changing attributes of this model changes the model used by the data set.
Return type:: instance

See also

delete_model: Delete the model expression from a data set.
get_model_pars: Return the names of the parameters of a model.
get_model_type: Describe a model expression.
get_source: Return the source model expression for a data set.
list_model_ids: List of all the data sets with a source expression.
sherpa.astro.ui.set_bkg_model: Set the background model expression for a data set.
set_model: Set the source model expression for a data set.
set_full_model: Define the convolved model expression for a data set.
show_model: Display the source model expression for a data set.

Examples

Return the model fitted to the default data set:

>>> mdl = get_model()
>>> len(mdl.pars)
5

get_model_autoassign_func() → Callable[[str, Model], None][source] [edit on github]

Return the method used to create model component identifiers.

Provides access to the function which is used by create_model_component and when creating model components directly to add an identifier in the current Python namespace.

Returns:: The model function set by set_model_autoassign_func.
Return type:: func

See also

create_model_component: Create a model component.
set_model: Set the source model expression for a data set.
set_model_autoassign_func: Set the method used to create model component identifiers.

get_model_component(name: str) → Model[source] [edit on github]

Returns a model component given its name.

Parameters:: name (str) – The name of the model component.
Returns:: component – The model component object.
Return type:: a sherpa.models.model.Model instance
Raises:: sherpa.utils.err.IdentifierErr – If there is no model component with the given name.

See also

create_model_component: Create a model component.
get_model: Return the model expression for a data set.
get_source: Return the source model expression for a data set.
list_model_components: List the names of all the model components.
set_model: Set the source model expression for a data set.

Notes

The model instances are named as modeltype.username, and it is the username component that is used here to access the instance.

Examples

When a model component is created, a variable is created that contains the model instance. The instance can also be returned with get_model_component, which can then be queried or used to change the model settings:

>>> create_model_component('gauss1d', 'gline')
>>> gmodel = get_model_component('gline')
>>> gmodel.name
'gauss1d.gline'
>>> print([p.name for p in gmodel.pars])
['fwhm', 'pos', 'ampl']
>>> gmodel.fwhm.val = 12.2
>>> gmodel.fwhm.freeze()

get_model_component_image(id, model=None)[source] [edit on github]

Return the data used by image_model_component.

Parameters:

id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string).

Returns:

cpt_img – The y attribute contains the component model values as a 2D NumPy array.

Return type:

a sherpa.image.ComponentModelImage instance

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_source_component_image: Return the data used by image_source_component.
get_model_image: Return the data used by image_model.
image_model: Display the model for a data set in the image viewer.
image_model_component: Display a component of the model in the image viewer.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Examples

Return the gsrc component values for the default data set:

>>> minfo = get_model_component_image(gsrc)

Get the bgnd model pixel values for data set 2:

>>> minfo = get_model_component_image(2, bgnd)

get_model_component_plot(id, model=None, recalc=True)[source] [edit on github]

Return the data used to create the model-component plot.

Parameters:

id (int or str, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to use (the name, if a string).
recalc (bool, optional) – If False then the results from the last call to plot_model_component (or get_model_component_plot) are returned, otherwise the data is re-generated.

Returns:

An object representing the data used to create the plot by plot_model_component. The return value depends on the data set (e.g. 1D binned or un-binned).

Return type:

instance

See also

get_model_plot: Return the data used to create the model plot.
plot_model: Plot the model for a data set.
plot_model_component: Plot a component of the model for a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Examples

Return the plot data for the pl component used in the default data set:

>>> cplot = get_model_component_plot(pl)

Return the full source model (fplot) and then for the components gal * pl and gal * gline, for the data set ‘jet’:

>>> fmodel = xsphabs.gal * (powlaw1d.pl + gauss1d.gline)
>>> set_source('jet', fmodel)
>>> fit('jet')
>>> fplot = get_model_plot('jet')
>>> plot1 = get_model_component_plot('jet', pl*gal)
>>> plot2 = get_model_component_plot('jet', gline*gal)

get_model_components_plot(id: int | str | None = None) → MultiPlot[source] [edit on github]

Return the data used by plot_model_components.

Added in version 4.16.1.

Parameters:: id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
Returns:: plot – A plot object containing the individual plot objects.
Return type:: MultiPlot

Notes

Unlike get_model_component this routine does not accept either model or recalc arguments.

Examples

Return the plot data for the individual components used in the default data set. In this case it will report plots for gal * pl and gal * gline:

>>> set_source(xsphabs.gal * (powlaw1d.pl + gauss1d.gline))
>>> cplots = get_model_components_plot()

get_model_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_model.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_model (or get_model_contour) are returned, otherwise the data is re-generated.

Returns:

model_data – The y attribute contains the model values and the x0 and x1 arrays contain the corresponding coordinate values, as one-dimensional arrays.

Return type:

a sherpa.plot.ModelContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_model_image: Return the data used by image_model.
contour_model: Contour the values of the model, including any PSF.
image_model: Display the model for a data set in the image viewer.

Examples

Return the model pixel values for the default data set:

>>> minfo = get_model_contour()

get_model_contour_prefs()[source] [edit on github]

Return the preferences for contour_model.

Returns:: prefs – Changing the values of this dictionary will change any new contour plots.
Return type:: dict

See also

contour_model, get_contour_prefs

Notes

The meaning of the fields depend on the chosen plot backend. A value of None (or not set) means to use the default value for that attribute, unless indicated otherwise.

alpha: The transparency value used to draw the contours, where 0 is fully transparent and 1 is fully opaque.
colors: The colors to draw the contours.
linewidths: What thickness of line to draw the contours.
xlog: Should the X axis be drawn with a logarithmic scale? The default is False.
ylog: Should the Y axis be drawn with a logarithmic scale? The default is False.

Examples

Change the contours for the model to be drawn in ‘orange’:

>>> prefs = get_model_contour_prefs()
>>> prefs['color'] = 'orange'
>>> contour_data()
>>> contour_model(overcontour=True)

get_model_image(id: int | str | None = None)[source] [edit on github]

Return the data used by image_model.

Evaluate the source expression for the image pixels - including any PSF convolution defined by set_psf - and return the results.

Parameters:: id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
Returns:: src_img – The y attribute contains the source model values as a 2D NumPy array.
Return type:: a sherpa.image.ModelImage instance
Raises:: sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_source_image: Return the data used by image_source.
contour_model: Contour the values of the model, including any PSF.
image_model: Display the model for a data set in the image viewer.
set_psf: Add a PSF model to a data set.

Examples

Calculate the residuals (data - model) for the default data set:

>>> minfo = get_model_image()
>>> dinfo = get_data_image()
>>> resid = dinfo.y - minfo.y

get_model_pars(model: Model | str) → list[str][source] [edit on github]

Return the names of the parameters of a model.

Added in version 4.16.1: Linked components are now included without having to include them in the model expression.

Parameters:: model (str or a sherpa.models.model.Model object)
Returns:: names – The names of the parameters in the model expression. These names do not include the name of the parent component.
Return type:: list of str

See also

create_model_component: Create a model component.
get_model: Return the model expression for a data set.
get_model_type: Describe a model expression.
get_source: Return the source model expression for a data set.

Examples

>>> mdl = gauss2d.src + const2d.bgnd
>>> get_model_pars(mdl)
['fwhm', 'xpos', 'ypos', 'ellip', 'theta', 'ampl', 'c0']

The return value is unchanged if the two models are linked together, such as setting the background amplitude to 1% of the source amplitude:

>>> bgnd.c0 = 0.01 * src.ampl
>>> get_model_pars(mdl)
['fwhm', 'xpos', 'ypos', 'ellip', 'theta', 'ampl', 'c0']

If a model expression contains linked parameters that are not part of the model expression then they will also be included (in this case both the const2d and scale1d parameters are named ‘c0’, hence the duplication):

>>> scale1d.sep
<Scale1D model instance 'scale1d.sep'>
>>> src.ypos = src.xpos + sep.c0
>>> get_model_pars(mdl)
['fwhm', 'xpos', 'ypos', 'ellip', 'theta', 'ampl', 'c0', 'c0']

get_model_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used to create the model plot.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_model (or get_model_plot) are returned, otherwise the data is re-generated.

Returns:

An object representing the data used to create the plot by plot_model. The return value depends on the data set (e.g. 1D binned or un-binned).

Return type:

instance

See also

get_model_plot_prefs: Return the preferences for plot_model.
plot_model: Plot the model for a data set.

Examples

>>> mplot = get_model_plot()
>>> print(mplot)

get_model_plot_prefs(id: int | str | None = None)[source] [edit on github]

Return the preferences for plot_model.

The plot preferences may depend on the data set, so it is now an optional argument.

Changed in version 4.12.2: The id argument has been given.

Parameters:: id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
Returns:: prefs – Changing the values of this dictionary will change any new model plots. This dictionary will be empty if no plot backend is available.
Return type:: dict

See also

get_plot_prefs, plot_model, set_xlinearm, set_ylinear, set_ylog

Notes

The meaning of the fields depend on the chosen plot backend. A value of None means to use the default value for that attribute, unless indicated otherwise. These preferences are used by the following commands: plot_model, plot_ratio, plot_bkg_model, and the “fit” variants, such as plot_fit, plot_fit_resid, and plot_bkg_fit.

The preferences recognized by the matplotlib backend are the same as for get_data_plot_prefs.

Examples

After these commands, any model plot will use a green line to display the model:

>>> prefs = get_model_plot_prefs()
>>> prefs['color'] = 'green'

get_model_type(model)[source] [edit on github]

Describe a model expression.

Parameters:: model (str or a sherpa.models.model.Model object)
Returns:: type – The name of the model expression.
Return type:: str

See also

create_model_component: Create a model component.
get_model: Return the model expression for a data set.
get_model_pars: Return the names of the parameters of a model.
get_source: Return the source model expression for a data set.

Examples

>>> create_model_component("powlaw1d", "pl")
>>> get_model_type("pl")
'powlaw1d'

For expressions containing more than one component, the result is likely to be ‘binaryopmodel’

>>> get_model_type(const1d.norm * (polynom1d.poly + gauss1d.gline))
'binaryopmodel'

For sources with some form of an instrument model - such as a PSF convolution for an image or a PHA file with response information from the ARF and RMF - the response can depend on whether the expression contains this extra information or not:

>>> get_model_type(get_source('spec'))
'binaryopmodel'
>>> get_model_type(get_model('spec'))
'rspmodelpha'

get_num_par(id: int | str | None = None) → int[source] [edit on github]

Return the number of parameters in a model expression.

The get_num_par function returns the number of parameters, both frozen and thawed, in the model assigned to a data set.

Added in version 4.16.1: Linked components are now included without having to include them in the model expression.

Parameters:: id (int, str, or None, optional) – The data set containing the model expression. If not given then the default identifier is used, as returned by get_default_id.
Returns:: npar – The number of parameters in the model expression. This sums up all the parameters of the components in the expression, and includes both frozen and thawed components.
Return type:: int
Raises:: sherpa.utils.err.IdentifierErr – If no model expression has been set for the data set (with set_model or set_source).

See also

get_num_par_frozen: Return the number of frozen parameters.
get_num_par_thawed: Return the number of thawed parameters.
set_model: Set the source model expression for a data set.

Examples

Return the total number of parameters for the default data set:

>>> print(get_num_par())

Find the number of parameters for the model associated with the data set called “jet”:

>>> njet = get_num_par('jet')

get_num_par_frozen(id: int | str | None = None) → int[source] [edit on github]

Return the number of frozen parameters in a model expression.

The get_num_par_frozen function returns the number of frozen parameters in the model assigned to a data set.

Added in version 4.16.1: Linked components are now included without having to include them in the model expression.

Parameters:: id (int, str, or None, optional) – The data set containing the model expression. If not given then the default identifier is used, as returned by get_default_id.
Returns:: npar – The number of parameters in the model expression. This sums up all the frozen parameters of the components in the expression.
Return type:: int
Raises:: sherpa.utils.err.IdentifierErr – If no model expression has been set for the data set (with set_model or set_source).

See also

get_num_par: Return the number of parameters.
get_num_par_thawed: Return the number of thawed parameters.
set_model: Set the source model expression for a data set.

Examples

Return the number of frozen parameters for the default data set:

>>> print(get_num_par_frozen())

Find the number of frozen parameters for the model associated with the data set called “jet”:

>>> njet = get_num_par_frozen('jet')

get_num_par_thawed(id: int | str | None = None) → int[source] [edit on github]

Return the number of thawed parameters in a model expression.

The get_num_par_thawed function returns the number of thawed parameters in the model assigned to a data set.

Added in version 4.16.1: Linked components are now included without having to include them in the model expression.

Parameters:: id (int, str, or None, optional) – The data set containing the model expression. If not given then the default identifier is used, as returned by get_default_id.
Returns:: npar – The number of parameters in the model expression. This sums up all the thawed parameters of the components in the expression.
Return type:: int
Raises:: sherpa.utils.err.IdentifierErr – If no model expression has been set for the data set (with set_model or set_source).

See also

get_num_par: Return the number of parameters.
get_num_par_frozen: Return the number of frozen parameters.
set_model: Set the source model expression for a data set.

Examples

Return the number of thawed parameters for the default data set:

>>> print(get_num_par_thawed())

Find the number of thawed parameters for the model associated with the data set called “jet”:

>>> njet = get_num_par_thawed('jet')

get_par(par)[source] [edit on github]

Return a parameter of a model component.

Parameters:: par (str) – The name of the parameter, using the format “componentname.parametername”.
Returns:: par – The parameter values - e.g. current value, limits, and whether it is frozen - can be changed using this object.
Return type:: a sherpa.models.parameter.Parameter instance
Raises:: sherpa.utils.err.ArgumentErr – If the par argument is invalid: the model component does not exist or the given model has no parameter with that name.

See also

set_par: Set the value, limits, or behavior of a model parameter.

Examples

Return the “c0” parameter of the “bgnd” model component and change it to be frozen:

>>> p = get_par('bgnd.c0')
>>> p.frozen = True

get_pdf_plot()[source] [edit on github]

Return the data used to plot the last PDF.

Returns:: plot – An object containing the data used by the last call to plot_pdf. The fields will be None if the function has not been called.
Return type:: a sherpa.plot.PDFPlot instance

See also

plot_pdf: Plot the probability density function of an array.

get_plot_prefs(plottype: str, id: int | str | None = None, **kwargs)[source] [edit on github]

Return the preferences for the given plot type.

Added in version 4.16.0.

Parameters:

plottype (str) – The type of plt, such as “data”, “model”, or “resid”. The “fit” argument is not supported.
id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.

Returns:

prefs – Changing the values of this dictionary will change any new data plots. This dictionary will be empty if no plot backend is available.

Return type:

dict

Notes

The meaning of the fields depend on the chosen plot backend. A value of None means to use the default value for that attribute, or not to use that setting.

The “fit” argument can not be used, even though there is a get_fit_plot call. Either use the “data” or “model” arguments to access the desired plot type, or use get_fit_plot() and access the dataplot and modelplot attributes directly.

Examples

After these commands, any data plot will use a green symbol and not display Y error bars.

>>> prefs = get_plot_prefs("data")
>>> prefs['color'] = 'green'
>>> prefs['yerrorbars'] = False

get_prior(par)[source] [edit on github]

Return the prior function for a parameter (MCMC).

The default behavior of the pyBLoCXS MCMC sampler (run by the get_draws function) is to use a flat prior for each parameter. The get_prior routine finds the current prior assigned to a parameter, and set_prior is used to change it.

Parameters:: par (a sherpa.models.parameter.Parameter instance) – A parameter of a model instance.
Returns:: The parameter prior set by a previous call to set_prior. This may be a function or model instance.
Return type:: prior
Raises:: ValueError – If a prior has not been set for the parameter.

See also

set_prior: Set the prior function to use with a parameter.

Examples

>>> prior = get_prior(bgnd.c0)
>>> print(prior)

get_proj()[source] [edit on github]

Return the confidence-interval estimation object.

Note

The conf function should be used instead of proj.

Returns:: proj
Return type:: object

See also

conf: Estimate parameter confidence intervals using the confidence method.
get_proj_opt: Return one or all of the options for the confidence interval method.
proj: Estimate confidence intervals for fit parameters.
set_proj_opt: Set an option of the proj estimation object.

Notes

The attributes of the object include:

eps: The precision of the calculated limits. The default is 0.01.
fast: If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False.
max_rstat: If the reduced chi square is larger than this value, do not use (only used with chi-square statistics). The default is 3.
maxfits: The maximum number of re-fits allowed (that is, when the remin filter is met). The default is 5.
maxiters: The maximum number of iterations allowed when bracketing limits, before stopping for that parameter. The default is 200.
numcores: The number of computer cores to use when evaluating results in parallel. This is only used if parallel is True. The default is to use all cores.
parallel: If there is more than one free parameter then the results can be evaluated in parallel, to reduce the time required. The default is True.
remin: The minimum difference in statistic value for a new fit location to be considered better than the current best fit (which starts out as the starting location of the fit at the time proj is called). The default is 0.01.
sigma: What is the error limit being calculated. The default is 1.
soft_limits: Should the search be restricted to the soft limits of the parameters (True), or can parameter values go out all the way to the hard limits if necessary (False). The default is False
tol: The tolerance for the fit. The default is 0.2.

Examples

>>> print(get_proj())
name        = projection
numcores    = 8
max_rstat   = 3
maxiters    = 200
soft_limits = False
eps         = 0.01
fast        = False
maxfits     = 5
remin       = 0.01
tol         = 0.2
sigma       = 1
parallel    = True

get_proj_opt(name=None)[source] [edit on github]

Return one or all of the options for the confidence interval method.

Note

The conf function should be used instead of proj.

This is a helper function since the options can also be read directly using the object returned by get_proj.

Parameters:: name (str, optional) – If not given, a dictionary of all the options are returned. When given, the individual value is returned.
Returns:: value
Return type:: dictionary or value
Raises:: sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

conf: Estimate parameter confidence intervals using the confidence method.
proj: Estimate confidence intervals for fit parameters.
get_proj: Return the confidence-interval estimation object.
set_proj_opt: Set an option of the proj estimation object.

Examples

>>> get_proj_opt('sigma')
1

>>> popts = get_proj_opt()
>>> popts['sigma']
1

get_proj_results()[source] [edit on github]

Return the results of the last proj run.

Note

The conf function should be used instead of proj.

Returns:: results
Return type:: sherpa.fit.ErrorEstResults object
Raises:: sherpa.utils.err.SessionErr – If no proj call has been made.

See also

conf: Estimate parameter confidence intervals using the confidence method.
proj: Estimate confidence intervals for fit parameters.
get_proj_opt: Return one or all of the options for the projection method.
set_proj_opt: Set an option of the proj estimation object.

Notes

The fields of the object include:

datasets: A tuple of the data sets used in the analysis.
methodname: This will be ‘projection’.
iterfitname: The name of the iterated-fit method used, if any.
fitname: The name of the optimization method used.
statname: The name of the fit statistic used.
sigma: The sigma value used to calculate the confidence intervals.
percent: The percentage of the signal contained within the confidence intervals (calculated from the sigma value assuming a normal distribution).
parnames: A tuple of the parameter names included in the analysis.
parvals: A tuple of the best-fit parameter values, in the same order as parnames.
parmins: A tuple of the lower error bounds, in the same order as parnames.
parmaxes: A tuple of the upper error bounds, in the same order as parnames.
nfits: The number of model evaluations.

Examples

>>> res = get_proj_results()
>>> print(res)
datasets    = ('src',)
methodname  = projection
iterfitname = none
fitname     = levmar
statname    = chi2gehrels
sigma       = 1
percent     = 68.2689492137
parnames    = ('bgnd.c0',)
parvals     = (9.1958148476800918,)
parmins     = (-2.0765029551804268,)
parmaxes    = (2.0765029551935186,)
nfits       = 0

get_projection_results() [edit on github]

Return the results of the last proj run.

Note

The conf function should be used instead of proj.

Returns:: results
Return type:: sherpa.fit.ErrorEstResults object
Raises:: sherpa.utils.err.SessionErr – If no proj call has been made.

See also

conf: Estimate parameter confidence intervals using the confidence method.
proj: Estimate confidence intervals for fit parameters.
get_proj_opt: Return one or all of the options for the projection method.
set_proj_opt: Set an option of the proj estimation object.

Notes

The fields of the object include:

datasets: A tuple of the data sets used in the analysis.
methodname: This will be ‘projection’.
iterfitname: The name of the iterated-fit method used, if any.
fitname: The name of the optimization method used.
statname: The name of the fit statistic used.
sigma: The sigma value used to calculate the confidence intervals.
percent: The percentage of the signal contained within the confidence intervals (calculated from the sigma value assuming a normal distribution).
parnames: A tuple of the parameter names included in the analysis.
parvals: A tuple of the best-fit parameter values, in the same order as parnames.
parmins: A tuple of the lower error bounds, in the same order as parnames.
parmaxes: A tuple of the upper error bounds, in the same order as parnames.
nfits: The number of model evaluations.

Examples

>>> res = get_proj_results()
>>> print(res)
datasets    = ('src',)
methodname  = projection
iterfitname = none
fitname     = levmar
statname    = chi2gehrels
sigma       = 1
percent     = 68.2689492137
parnames    = ('bgnd.c0',)
parvals     = (9.1958148476800918,)
parmins     = (-2.0765029551804268,)
parmaxes    = (2.0765029551935186,)
nfits       = 0

get_psf(id: int | str | None = None)[source] [edit on github]

Return the PSF model defined for a data set.

Return the parameter settings for the PSF model assigned to the data set.

Parameters:: id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
Returns:: psf
Return type:: a sherpa.instrument.PSFModel instance
Raises:: sherpa.utils.err.IdentifierErr – If no PSF model has been set for the data set.

See also

delete_psf: Delete the PSF model for a data set.
image_psf: Display the 2D PSF model for a data set in the image viewer.
list_psf_ids: List of all the data sets with a PSF.
load_psf: Create a PSF model.
plot_psf: Plot the 1D PSF model applied to a data set.
set_psf: Add a PSF model to a data set.

Examples

Change the size and center of the PSF for the default data set:

>>> psf = get_psf()
>>> psf.size = (21, 21)
>>> psf.center = (10, 10)

get_psf_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_psf.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_psf (or get_psf_contour) are returned, otherwise the data is re-generated.

Returns:

psf_data

Return type:

a sherpa.plot.PSFContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_kernel_contour: Return the data used by contour_kernel.
contour_kernel: Contour the kernel applied to the model of an image data set.
contour_psf: Contour the PSF applied to the model of an image data set.

Examples

Return the contour data for the PSF for the default data set:

>>> cplot = get_psf_contour()

get_psf_image(id: int | str | None = None)[source] [edit on github]

Return the data used by image_psf.

Parameters:: id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
Returns:: psf_data
Return type:: a sherpa.image.PSFImage instance
Raises:: sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_kernel_image: Return the data used by image_kernel.
image_kernel: Display the 2D kernel for a data set in the image viewer.
image_psf: Display the 2D PSF model for a data set in the image viewer.

Examples

Return the image data for the PSF for the default data set:

>>> iplot = get_psf_image()
>>> iplot.y.shape
(175, 200)

get_psf_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by plot_psf.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_psf (or get_psf_plot) are returned, otherwise the data is re-generated.

Returns:

psf_plot

Return type:

a sherpa.plot.PSFPlot instance

Raises:

sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_kernel_plot: Return the data used by plot_kernel.
plot_kernel: Plot the 1D kernel applied to a data set.
plot_psf: Plot the 1D PSF model applied to a data set.

Examples

Return the plot data and then create a plot with it:

>>> pplot = get_psf_plot()
>>> pplot.plot()

get_pvalue_plot(null_model=None, alt_model=None, conv_model=None, id: int | str = 1, otherids: Sequence[int | str] = (), num=500, bins=25, numcores=None, recalc=False)[source] [edit on github]

Return the data used by plot_pvalue.

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.

Changed in version 4.17.0: The “wstat” statistic can now be used with this routine.

Parameters:

null_model – The model expression for the null hypothesis.
alt_model – The model expression for the alternative hypothesis.
conv_model (optional) – An expression used to modify the model so that it can be compared to the data (e.g. a PSF or PHA response).
id (int or str, optional) – The data set that provides the data. The default is 1.
otherids (sequence of int or str, optional) – Other data sets to use in the calculation.
num (int, optional) – The number of simulations to run. The default is 500.
bins (int, optional) – The number of bins to use to create the histogram. The default is 25.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.
recalc (bool, optional) – The default value (False) means that the results from the last call to plot_pvalue or get_pvalue_plot are returned. If True, the values are re-calculated.

Returns:

plot

Return type:

a sherpa.plot.LRHistogram instance

Notes

The set_rng routine is used to control how the random numbers are generated.

Examples

Return the values from the last call to plot_pvalue:

>>> pvals = get_pvalue_plot()
>>> pvals.ppp
0.472

Run 500 simulations for the two models and print the results:

>>> pvals = get_pvalue_plot(mdl1, mdl2, recalc=True, num=500)
>>> print(pvals)

get_pvalue_results()[source] [edit on github]

Return the data calculated by the last plot_pvalue call.

The get_pvalue_results function returns the likelihood ratio test results computed by the plot_pvalue command, which compares fits of the null model to fits of the alternative model using faked data with Poisson noise. The likelihood ratio based on the observed data is returned, along with the p-value, used to reject or accept the null model.

Changed in version 4.17.0: The “wstat” statistic can now be used with this routine.

Changed in version 4.15.1: The parnames and parvals attributes have been added. They are intended to debug problem cases and so are not displayed by default.

Returns:: plot – If plot_pvalue or get_pvalue_plot have been called then the return value is a sherpa.sim.simulate.LikelihoodRatioResults instance, otherwise None is returned.
Return type:: None or a sherpa.sim.simulate.LikelihoodRatioResults instance

See also

plot_value: Compute and plot a histogram of likelihood ratios by simulating data.
get_pvalue_plot: Return the data used by plot_pvalue.

Notes

The fields of the returned (LikelihoodRatioResults) object are:

ratios: The calculated likelihood ratio for each iteration.
stats: The calculated fit statistics for each iteration, stored as the null model and then the alt model in a nsim by 2 array.
samples: The parameter samples array for each simulation, stored in a nsim by npar array.
lr: The likelihood ratio of the observed data for the null and alternate models.
ppp: The p value of the observed data for the null and alternate models.
null: The fit statistic of the null model on the observed data.
alt: The fit statistic of the alternate model on the observed data.
parnames: The names of the fitted parameters in the alternate model. This will be larger than the number of parameters returned in the samples field.
parvals: The best-fit parameter values to the alternate model for each simulation, stored as a nsim by len(parnames) array.

Examples

Return the results of the last pvalue analysis and display the results - first using the format method, which provides a summary of the data, and then a look at the individual fields in the returned object. The last call displays the contents of one of the fields (ppp).

>>> res = get_pvalue_results()
>>> print(res.format())
>>> print(res)
>>> print(res.ppp)

Display the ratio values to check they look sensible (such as not dropping to a long range of 0’s, although this can also suggest the alternate model is not preferred to the null model):

>>> plot_trace(res.ratios, name="ratios")

Look at the cumulative distribution of the ratios:

>>> plot_cdf(res.ratios, name="ratios")

The parvals field shows the fitted parameter values for the alternate model at each iteration:

>>> plot_trace(res.parvals[:, 0], name=res.parnames[0])
>>> plot_trace(res.parvals[:, 1], name=res.parnames[1])

get_ratio_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_ratio.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_ratio (or get_ratio_contour) are returned, otherwise the data is re-generated.

Returns:

ratio_data – The y attribute contains the ratio values and the x0 and x1 arrays contain the corresponding coordinate values, as one-dimensional arrays.

Return type:

a sherpa.plot.RatioContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_ratio_image: Return the data used by image_ratio.
get_resid_contour: Return the data used by contour_resid.
contour_ratio: Contour the ratio of data to model.
image_ratio: Display the ratio (data/model) for a data set in the image viewer.

Examples

Return the ratio data for the default data set:

>>> rinfo = get_ratio_contour()

get_ratio_image(id: int | str | None = None)[source] [edit on github]

Return the data used by image_ratio.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.

Returns:

ratio_img – The y attribute contains the ratio values as a 2D NumPy array.

Return type:

a sherpa.image.RatioImage instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_resid_image: Return the data used by image_resid.
contour_ratio: Contour the ratio of data to model.
image_ratio: Display the ratio (data/model) for a data set in the image viewer.

Examples

Return the ratio data for the default data set:

>>> rinfo = get_ratio_image()

get_ratio_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by plot_ratio.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_ratio (or get_ratio_plot) are returned, otherwise the data is re-generated.

Returns:

ratio_data

Return type:

a sherpa.plot.RatioPlot instance

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_chisqr_plot: Return the data used by plot_chisqr.
get_delchi_plot: Return the data used by plot_delchi.
get_resid_plot: Return the data used by plot_resid.
plot_ratio: Plot the ratio of data to model for a data set.

Examples

Return the ratio of the data to the model for the default data set:

>>> rplot = get_ratio_plot()
>>> np.min(rplot.y)
0.6320905073750186
>>> np.max(rplot.y)
1.5170172177000447

Display the contents of the ratio plot for data set 2:

>>> print(get_ratio_plot(2))

Overplot the ratio plot from the ‘core’ data set on the ‘jet’ data set:

>>> r1 = get_ratio_plot('jet')
>>> r2 = get_ratio_plot('core')
>>> r1.plot()
>>> r2.overplot()

get_reg_proj(par0=None, par1=None, id: int | str | None = None, otherids: Sequence[int | str] | None = None, recalc=False, fast=True, min=None, max=None, nloop=(10, 10), delv=None, fac=4, log=(False, False), sigma=(1, 2, 3), levels=None, numcores=None)[source] [edit on github]

Return the region-projection object.

This returns (and optionally calculates) the data used to display the reg_proj contour plot. Note that if the the recalc parameter is False (the default value) then all other parameters are ignored and the results of the last reg_proj call are returned.

Changed in version 4.16.1: The log parameter can now be set to True for one or both parameters.

Parameters:

par0 – The parameters to plot on the X and Y axes, respectively. These arguments are only used if recalc is set to True.
par1 – The parameters to plot on the X and Y axes, respectively. These arguments are only used if recalc is set to True.
id (str, int, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (list of str or int, or None, optional) – Other data sets to use in the calculation.
recalc (bool, optional) – The default value (False) means that the results from the last call to reg_proj (or get_reg_proj) are returned, ignoring all other parameter values. Otherwise, the statistic curve is re-calculated, but not plotted.
fast (bool, optional) – If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False.
min (pair of numbers, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (pair of number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (pair of int, optional) – The number of steps to use. This is used when delv is set to None.
delv (pair of number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (pair of bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
sigma (sequence of number, optional) – The levels at which to draw the contours. The units are the change in significance relative to the starting value, in units of sigma.
levels (sequence of number, optional) – The numeric values at which to draw the contours. This overrides the sigma parameter, if set (the default is None).
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

Returns:

rproj – The fields of this object can be used to re-create the plot created by reg_proj.

Return type:

a sherpa.plot.RegionProjection instance

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
int_proj: Calculate and plot the fit statistic versus fit parameter value.
int_unc: Calculate and plot the fit statistic versus fit parameter value.
reg_proj: Plot the statistic value as two parameters are varied.
reg_unc: Plot the statistic value as two parameters are varied.

Examples

Return the results for the reg_proj run for the xpos and ypos parameters of the src component, for the default data set:

>>> reg_proj(src.xpos, src.ypos)
>>> rproj = get_reg_proj()

Since the recalc parameter has not been changed to True, the following will return the results for the last call to reg_proj, which may not have been for the r0 and alpha parameters:

>>> rprog = get_reg_proj(src.r0, src.alpha)

Create the data without creating a plot:

>>> rproj = get_reg_proj(pl.gamma, gal.nh, recalc=True)

Specify the range and step size for both the parameters, in this case pl.gamma should vary between 0.5 and 2.5, with gal.nh between 0.01 and 1, both with 51 values and the nH range done over a log scale:

>>> rproj = get_reg_proj(pl.gamma, gal.nh, id="src",
...                      min=(0.5, 0.01), max=(2.5, 1),
...                      nloop=(51, 51), log=(False, True),
...                      recalc=True)

get_reg_unc(par0=None, par1=None, id: int | str | None = None, otherids: Sequence[int | str] | None = None, recalc=False, min=None, max=None, nloop=(10, 10), delv=None, fac=4, log=(False, False), sigma=(1, 2, 3), levels=None, numcores=None)[source] [edit on github]

Return the region-uncertainty object.

This returns (and optionally calculates) the data used to display the reg_unc contour plot. Note that if the the recalc parameter is False (the default value) then all other parameters are ignored and the results of the last reg_unc call are returned.

Changed in version 4.16.1: The log parameter can now be set to True for one or both parameters.

Parameters:

par0 – The parameters to plot on the X and Y axes, respectively. These arguments are only used if recalc is set to True.
par1 – The parameters to plot on the X and Y axes, respectively. These arguments are only used if recalc is set to True.
id (str, int, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (list of str or int, or None, optional) – Other data sets to use in the calculation.
recalc (bool, optional) – The default value (False) means that the results from the last call to reg_unc (or get_reg_unc) are returned, ignoring all other parameter values. Otherwise, the statistic curve is re-calculated, but not plotted.
fast (bool, optional) – If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False.
min (pair of numbers, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (pair of number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (pair of int, optional) – The number of steps to use. This is used when delv is set to None.
delv (pair of number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (pair of bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
sigma (sequence of number, optional) – The levels at which to draw the contours. The units are the change in significance relative to the starting value, in units of sigma.
levels (sequence of number, optional) – The numeric values at which to draw the contours. This overrides the sigma parameter, if set (the default is None).
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

Returns:

rproj – The fields of this object can be used to re-create the plot created by reg_unc.

Return type:

a sherpa.plot.RegionUncertainty instance

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
int_proj: Calculate and plot the fit statistic versus fit parameter value.
int_unc: Calculate and plot the fit statistic versus fit parameter value.
reg_proj: Plot the statistic value as two parameters are varied.
reg_unc: Plot the statistic value as two parameters are varied.

Examples

Return the results for the reg_unc run for the xpos and ypos parameters of the src component, for the default data set:

>>> reg_unc(src.xpos, src.ypos)
>>> runc = get_reg_unc()

Since the recalc parameter has not been changed to True, the following will return the results for the last call to reg_unc, which may not have been for the r0 and alpha parameters:

>>> runc = get_reg_unc(src.r0, src.alpha)

Create the data without creating a plot:

>>> runc = get_reg_unc(pl.gamma, gal.nh, recalc=True)

Specify the range and step size for both the parameters, in this case pl.gamma should vary between 0.5 and 2.5, with gal.nh between 0.01 and 1, both with 51 values and the nH range done over a log scale:

>>> runc = get_reg_unc(pl.gamma, gal.nh, id="src",
...                    min=(0.5, 0.01), max=(2.5, 1),
...                    nloop=(51, 51), log=(False, True),
...                    recalc=True)

get_resid_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_resid.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_resid (or get_resid_contour) are returned, otherwise the data is re-generated.

Returns:

resid_data – The y attribute contains the residual values and the x0 and x1 arrays contain the corresponding coordinate values, as one-dimensional arrays.

Return type:

a sherpa.plot.ResidContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_ratio_contour: Return the data used by contour_ratio.
get_resid_image: Return the data used by image_resid.
contour_resid: Contour the residuals of the fit.
image_resid: Display the residuals (data - model) for a data set in the image viewer.

Examples

Return the residual data for the default data set:

>>> rinfo = get_resid_contour()

get_resid_image(id: int | str | None = None)[source] [edit on github]

Return the data used by image_resid.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.

Returns:

resid_img – The y attribute contains the residual values as a 2D NumPy array.

Return type:

a sherpa.image.ResidImage instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_ratio_image: Return the data used by image_ratio.
contour_resid: Contour the residuals of the fit.
image_resid: Display the residuals (data - model) for a data set in the image viewer.

Examples

Return the residual data for the default data set:

>>> rinfo = get_resid_image()

get_resid_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by plot_resid.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_resid (or get_resid_plot) are returned, otherwise the data is re-generated.

Returns:

resid_data

Return type:

a sherpa.plot.ResidPlot instance

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_chisqr_plot: Return the data used by plot_chisqr.
get_delchi_plot: Return the data used by plot_delchi.
get_ratio_plot: Return the data used by plot_ratio.
plot_resid: Plot the residuals (data - model) for a data set.

Examples

Return the residual data for the default data set:

>>> rplot = get_resid_plot()
>>> np.min(rplot.y)
-2.9102595936209896
>>> np.max(rplot.y)
4.0897404063790104

Display the contents of the residuals plot for data set 2:

>>> print(get_resid_plot(2))

Overplot the residuals plot from the ‘core’ data set on the ‘jet’ data set:

>>> r1 = get_resid_plot('jet')
>>> r2 = get_resid_plot('core')
>>> r1.plot()
>>> r2.overplot()

get_rng() → Generator | RandomState | None[source] [edit on github]

Return the RNG generator in use.

The return can be None, which means that the routines in numpy.random are used, and thus are affected by calls to numpy.random.seed, otherwise the supplied generator is used to create random numbers. See https://numpy.org/doc/stable/reference/random/legacy.html for more information.

Added in version 4.16.0.

See also

set_rng

get_sampler()[source] [edit on github]

Return the current MCMC sampler options.

Returns the options for the current pyBLoCXS MCMC sampling method (jumping rules).

Returns:: options – A copy of the options for the chosen sampler. Use set_sampler_opt to change these values. The fields depend on the current sampler.
Return type:: dict

See also

get_sampler_name: Return the name of the current MCMC sampler.
get_sampler_opt: Return an option of the current MCMC sampler.
set_sampler: Set the MCMC sampler.
set_sampler_opt: Set an option for the current MCMC sampler.

Examples

>>> print(get_sampler())

get_sampler_name()[source] [edit on github]

Return the name of the current MCMC sampler.

Returns:: name
Return type:: str

See also

get_sampler: Return the current MCMC sampler options.
set_sampler: Set the MCMC sampler.

Examples

>>> get_sampler_name()
'MetropolisMH'

get_sampler_opt(opt)[source] [edit on github]

Return an option of the current MCMC sampler.

Returns:: opt – The name of the option. The fields depend on the current sampler.
Return type:: str

See also

get_sampler: Return the current MCMC sampler options.
set_sampler_opt: Set an option for the current MCMC sampler.

Examples

>>> get_sampler_opt('log')
False

get_scatter_plot()[source] [edit on github]

Return the data used to plot the last scatter plot.

Returns:: plot – An object containing the data used by the last call to plot_scatter. The fields will be None if the function has not been called.
Return type:: a sherpa.plot.ScatterPlot instance

See also

plot_scatter: Create a scatter plot.

get_source(id: int | str | None = None) → Model[source] [edit on github]

Return the source model expression for a data set.

This returns the model expression created by set_model or set_source. It does not include any instrument response.

Parameters:: id (int, str, or None, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.
Returns:: model – This can contain multiple model components. Changing attributes of this model changes the model used by the data set.
Return type:: a sherpa.models.Model object

See also

delete_model: Delete the model expression from a data set.
get_model: Return the model expression for a data set.
get_model_pars: Return the names of the parameters of a model.
get_model_type: Describe a model expression.
list_model_ids: List of all the data sets with a source expression.
sherpa.astro.ui.set_bkg_model: Set the background model expression for a data set.
set_model: Set the source model expression for a data set.
set_full_model: Define the convolved model expression for a data set.
show_model: Display the source model expression for a data set.

Examples

Return the source expression for the default data set, display it, and then find the number of parameters in it:

>>> src = get_source()
>>> print(src)
>>> len(src.pars)
5

Set the source expression for data set ‘obs2’ to be equal to the model of data set ‘obs1’ multiplied by a scalar value:

>>> set_source('obs2', const1d.norm * get_source('obs1'))

get_source_component_image(id, model=None)[source] [edit on github]

Return the data used by image_source_component.

Parameters:

id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string).

Returns:

cpt_img – The y attribute contains the component model values as a 2D NumPy array.

Return type:

a sherpa.image.ComponentSourceImage instance

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_model_component_image: Return the data used by image_model_component.
get_source_image: Return the data used by image_source.
image_source: Display the source expression for a data set in the image viewer.
image_source_component: Display a component of the source expression in the image viewer.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Examples

Return the gsrc component values for the default data set:

>>> sinfo = get_source_component_image(gsrc)

Get the ‘bgnd’ model pixel values for data set 2:

>>> sinfo = get_source_component_image(2, bgnd)

get_source_component_plot(id, model=None, recalc=True)[source] [edit on github]

Return the data used by plot_source_component.

Parameters:

id (int or str, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to use (the name, if a string).
recalc (bool, optional) – If False then the results from the last call to plot_source_component (or get_source_component_plot) are returned, otherwise the data is re-generated.

Returns:

An object representing the data used to create the plot by plot_source_component. The return value depends on the data set (e.g. 1D binned or un-binned).

Return type:

instance

See also

get_source_plot: Return the data used to create the source plot.
plot_source: Plot the source expression for a data set.
plot_source_component: Plot a component of the source expression for a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Examples

Return the plot data for the pl component used in the default data set:

>>> cplot = get_source_component_plot(pl)

Return the full source model (fplot) and then for the components gal * pl and gal * gline, for the data set ‘jet’:

>>> fmodel = xsphabs.gal * (powlaw1d.pl + gauss1d.gline)
>>> set_source('jet', fmodel)
>>> fit('jet')
>>> fplot = get_source('jet')
>>> plot1 = get_source_component_plot('jet', pl*gal)
>>> plot2 = get_source_component_plot('jet', gline*gal)

get_source_components_plot(id: IdType | None = None) → Multiplot[source] [edit on github]

Return the data used by plot_source_components.

Added in version 4.16.1.

Parameters:: id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
Returns:: plot – A plot object containing the individual plot objects.
Return type:: MultiPlot

Notes

Unlike get_source_component this routine does not accept either model or recalc arguments.

Examples

Return the plot data for the individual components used in the default data set. In this case it will report plots for gal * pl and gal * gline:

>>> set_source(xsphabs.gal * (powlaw1d.pl + gauss1d.gline))
>>> cplots = get_source_components_plot()

get_source_contour(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used by contour_source.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to contour_source (or get_source_contour) are returned, otherwise the data is re-generated.

Returns:

source_data – The y attribute contains the model values and the x0 and x1 arrays contain the corresponding coordinate values, as one-dimensional arrays.

Return type:

a sherpa.plot.SourceContour instance

Raises:

sherpa.utils.err.DataErr – If the data set is not 2D.
sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_source_image: Return the data used by image_source.
contour_source: Contour the values of the model, without any PSF.
image_source: Display the source expression for a data set in the image viewer.

Examples

Return the source model pixel values for the default data set:

>>> sinfo = get_source_contour()

get_source_image(id: int | str | None = None)[source] [edit on github]

Return the data used by image_source.

Evaluate the source expression for the image pixels - without any PSF convolution - and return the results.

Parameters:: id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
Returns:: src_img – The y attribute contains the source model values as a 2D NumPy array.
Return type:: a sherpa.image.SourceImage instance
Raises:: sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_model_image: Return the data used by image_model.
contour_source: Contour the values of the model, without any PSF.
image_source: Display the source expression for a data set in the image viewer.

Examples

Return the model data for the default data set:

>>> sinfo = get_source_image()
>>> sinfo.y.shape
(150, 175)

get_source_plot(id: int | str | None = None, recalc=True)[source] [edit on github]

Return the data used to create the source plot.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
recalc (bool, optional) – If False then the results from the last call to plot_source (or get_source_plot) are returned, otherwise the data is re-generated.

Returns:

An object representing the data used to create the plot by plot_source. The return value depends on the data set (e.g. 1D binned or un-binned).

Return type:

instance

See also

get_model_plot: Return the data used to create the model plot.
plot_model: Plot the model for a data set.
plot_source: Plot the source expression for a data set.

Examples

Retrieve the source plot information for the default data set and then display it:

>>> splot = get_source_plot()
>>> print(splot)

Return the plot data for data set 2, and then use it to create a plot:

>>> s2 = get_source_plot(2)
>>> s2.plot()

Display the two source plots for the ‘jet’ and ‘core’ datasets on the same plot:

>>> splot1 = get_source_plot(id='jet')
>>> splot2 = get_source_plot(id='core')
>>> splot1.plot()
>>> splot2.overplot()

get_split_plot()[source] [edit on github]

Return the plot attributes for displays with multiple plots.

Returns:: splot
Return type:: a sherpa.plot.SplitPlot instance

See also

contour, plot

Examples

Change the layout of the plot and contour commands to display three vertical plots:

>>> sp = get_split_plot()
>>> sp.rows = 3
>>> sp.cols = 1

get_stat(name: str | None = None) → Stat[source] [edit on github]

Return the fit statisic.

Parameters:: name (str, optional) – If not given, the current fit statistic is returned, otherwise it should be one of the names returned by the list_stats function.
Returns:: stat – An object representing the fit statistic.
Return type:: object
Raises:: sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

get_stat_name: Return the name of the current fit statistic.
list_stats: List the fit statistics.
set_stat: Change the fit statistic.

Examples

Return the currently-selected statistic, display its name, and read the help documentation for it:

>>> stat = get_stat()
>>> stat.name
'chi2gehrels'
>>> help(stat)

Read the help for the “wstat” statistic:

>>> help(get_stat('wstat'))

get_stat_info()[source] [edit on github]

Return the statistic values for the current models.

Calculate the statistic value for each data set, and the combined fit, using the current set of models, parameters, and ranges.

Returns:: stats – The values for each data set. If there are multiple model expressions then the last element will be the value for the combined data sets.
Return type:: array of sherpa.fit.StatInfoResults

See also

calc_stat: Calculate the fit statistic for a data set.
calc_stat_info: Display the statistic values for the current models.
get_fit_results: Return the results of the last fit.
list_data_ids: List the identifiers for the loaded data sets.
list_model_ids: List of all the data sets with a source expression.

Notes

If a fit to a particular data set has not been made, or values - such as parameter settings, the noticed data range, or choice of statistic - have been changed since the last fit, then the results for that data set may not be meaningful and will therefore bias the results for the simultaneous results.

The return value of get_stat_info differs to get_fit_results since it includes values for each data set, individually, rather than just the combined results.

The fields of the object include:

name: The name of the data set, or sets, as a string.
ids: A sequence of the data set ids (it may be a tuple or array) included in the results.
bkg_ids: A sequence of the background data set ids (it may be a tuple or array) included in the results, if any.
statname: The name of the statistic function (as used in set_stat).
statval: The statistic value.
numpoints: The number of bins used in the fits.
dof: The number of degrees of freedom in the fit (the number of bins minus the number of free parameters).
qval: The Q-value (probability) that one would observe the reduced statistic value, or a larger value, if the assumed model is true and the current model parameters are the true parameter values. This will be None if the value can not be calculated with the current statistic (e.g. the Cash statistic).
rstat: The reduced statistic value (the statval field divided by dof). This is not calculated for all statistics.

Examples

>>> res = get_stat_info()
>>> res[0].statval
498.21750663761935
>>> res[0].dof
439

get_stat_name() → str[source] [edit on github]

Return the name of the current fit statistic.

Returns:: name – The name of the current fit statistic method, in lower case.
Return type:: str

See also

get_stat: Return a fit statistic.
set_stat: Set the fit statistic.

Examples

>>> get_stat_name()
'chi2gehrels'

>>> set_stat('cash')
>>> get_stat_name()
'cash'

get_staterror(id: int | str | None = None, filter=False)[source] [edit on github]

Return the statistical error on the dependent axis of a data set.

The function returns the statistical errors on the values (dependenent axis) of a data set. These may have been set explicitly - either when the data set was created or with a call to set_staterror - or as defined by the chosen fit statistic (such as “chi2gehrels”).

Parameters:

id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

staterrors – The statistical error for each data point. This may be estimated from the data (e.g. with the chi2gehrels statistic) or have been set explicitly (set_staterror). The size of this array depends on the filter argument.

Return type:

array

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_error: Return the errors on the dependent axis of a data set.
get_indep: Return the independent axis of a data set.
get_syserror: Return the systematic errors on the dependent axis of a data set.
list_data_ids: List the identifiers for the loaded data sets.
set_staterror: Set the statistical errors on the dependent axis of a data set.

Notes

The default behavior is to not apply any filter defined on the independent axes to the results, so that the return value is for all points (or bins) in the data set. Set the filter argument to True to apply this filter.

Examples

If not explicitly given, the statistical errors on a data set may be calculated from the data values (the independent axis), depending on the chosen statistic:

>>> load_arrays(1, [10, 15, 19], [4, 5, 9])
>>> set_stat('chi2datavar')
>>> get_staterror()
array([ 2.        ,  2.23606798,  3.        ])
>>> set_stat('chi2gehrels')
>>> get_staterror()
array([ 3.17944947,  3.39791576,  4.122499  ])

If the statistical errors are set - either when the data set is created or with a call to set_staterror - then these values will be used, no matter the statistic:

>>> load_arrays(1, [10, 15, 19], [4, 5, 9], [2, 3, 5])
>>> set_stat('chi2datavar')
>>> get_staterror()
array([2, 3, 5])
>>> set_stat('chi2gehrels')
>>> get_staterror()
array([2, 3, 5])

get_syserror(id: int | str | None = None, filter=False)[source] [edit on github]

Return the systematic error on the dependent axis of a data set.

The function returns the systematic errors on the values (dependenent axis) of a data set. It is an error if called on a data set with no systematic errors (which are set with set_syserror).

Parameters:

id (int, str, or None, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

syserrors – The systematic error for each data point. The size of this array depends on the filter argument.

Return type:

array

Raises:

sherpa.utils.err.DataErr – If the data set has no systematic errors.
sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_error: Return the errors on the dependent axis of a data set.
get_indep: Return the independent axis of a data set.
get_staterror: Return the statistical errors on the dependent axis of a data set.
list_data_ids: List the identifiers for the loaded data sets.
set_syserror: Set the systematic errors on the dependent axis of a data set.

Notes

The default behavior is to not apply any filter defined on the independent axes to the results, so that the return value is for all points (or bins) in the data set. Set the filter argument to True to apply this filter.

Examples

Return the systematic error for the default data set:

>>> yerr = get_syserror()

Return an array that has been filtered to match the data:

>>> yerr = get_syserror(filter=True)

Return the filtered errors for data set “core”:

>>> yerr = get_syserror("core", filter=True)

get_trace_plot()[source] [edit on github]

Return the data used to plot the last trace.

Returns:: plot – An object containing the data used by the last call to plot_trace. The fields will be None if the function has not been called.
Return type:: a sherpa.plot.TracePlot instance

See also

plot_trace: Create a trace plot of row number versus value.

guess(id=None, model=None, limits=True, values=True)[source] [edit on github]

Estimate the parameter values and ranges given the loaded data.

The guess function can change the parameter values and limits to match the loaded data. This is generally limited to changing the amplitude and position parameters (sometimes just the values and sometimes just the limits). The parameters that are changed depend on the type of model.

Changed in version 4.17.0: The guess routine will now work with composite models and those which include an instrumental response, such as an ARF. It only works on individual models, so the values, and limits, guessed are only approximate.

Parameters:

id (int or str, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
model – Change the parameters of this model component. If None, then the source expression is assumed to consist of a single component, and that component is used.
limits (bool) – Should the parameter limits be changed? The default is True.
values (bool) – Should the parameter values be changed? The default is True.

See also

get_default_id: Return the default data set identifier.
reset: Reset the model parameters to their default settings.
set_par: Set the value, limits, or behavior of a model parameter.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

The guess function can reduce the time required to fit a data set by moving the parameters closer to a realistic solution. It can also be useful because it can set bounds on the parameter values based on the data: for instance, many two-dimensional models will limit their xpos and ypos values to lie within the data area. This can be done manually, but guess simplifies this, at least for those parameters that are supported. Instrument models - such as an ARF and RMF - should be set up before calling guess.

Examples

Since the source expression contains only one component, guess can be called with no arguments:

>>> set_source(polynom1d.poly)
>>> guess()

Both components - that is, gal and pl - will be passed to the guess routine, but not all models can have their parameters guessed, and there is no attempt to recognize that the models are combined (in this case by multiplication):

>>> set_source(xsphabs.gal * powlaw1d.pl)
>>> guess()

In this case, guess is called on each component separately.

>>> set_source(gauss1d.line + powlaw1d.cont)
>>> guess(line)
>>> guess(cont)

In this example, the values of the src model component are guessed from the “src” data set, whereas the bgnd component is guessed from the “bgnd” data set.

>>> set_source("src", gauss2d.src + const2d.bgnd)
>>> set_source("bgnd", bgnd)
>>> guess("src", src)
>>> guess("bgnd", bgnd)

The above could also have been written as:

>>> guess("src", src)
>>> guess("bgnd")

Set the source model for the default dataset. Guess is run to determine the values of the model component “p1” and the limits of the model component “g1”:

>>> set_source(powlaw1d.p1 + gauss1d.g1)
>>> guess(p1, limits=False)
>>> guess(g1, values=False)

ignore(lo=None, hi=None, **kwargs) → None[source] [edit on github]

Exclude data from the fit.

Select one or more ranges of data to exclude by filtering on the independent axis value. The filter is applied to all data sets.

Changed in version 4.15.0: The change in the filter is now reported for each dataset.

Changed in version 4.14.0: Integrated data sets - so Data1DInt and DataPHA when using energy or wavelengths - now ensure that the hi argument is exclusive and better handling of the lo argument when it matches a bin edge. This can result in the same filter selecting a smaller number of bins than in earlier versions of Sherpa.

Parameters:

lo (number or str, optional) – The lower bound of the filter (when a number) or a string expression listing ranges in the form a:b, with multiple ranges allowed, where the ranges are separated by a ,. The term :b means exclude everything up to b (an exclusive limit for integrated datasets), and a: means exclude everything that is higher than, or equal to, a.
hi (number, optional) – The upper bound of the filter when lo is not a string.
bkg_id (int or str, optional) – The filter will be applied to the associated background component of the data set if bkg_id is set. Only PHA data sets support this option; if not given, then the filter is applied to all background components as well as the source data.

See also

ignore_id: Exclude data from the fit for a data set.
sherpa.astro.ui.ignore2d: Exclude a spatial region from an image.
notice: Include data in the fit.
show_filter: Show any filters applied to a data set.

Notes

The order of ignore and notice calls is important, and the results are a union, rather than intersection, of the combination.

For binned data sets, the bin is excluded if the ignored range falls anywhere within the bin.

The units used depend on the analysis setting of the data set, if appropriate.

To filter a 2D data set by a shape use ignore2d.

The report of the change in the filter expression can be controlled with the SherpaVerbosity context manager, as shown in the examples below.

Examples

Ignore all data points with an X value (the independent axis) between 12 and 18. For this one-dimensional data set, this means that the second bin is ignored:

>>> load_arrays(1, [10, 15, 20, 30], [5, 10, 7, 13])
>>> ignore(12, 18)
dataset 1: 10:30 -> 10,20:30 x
>>> get_dep(filter=True)
array([ 5,  7, 13])

Filtering X values that are 25 or larger means that the last point is also ignored:

>>> ignore(lo=25)
dataset 1: 10,20:30 -> 10,20 x
>>> get_dep(filter=True)
array([ 5,  7])

The notice call removes the previous filter, and then a multi-range filter is applied to exclude values between 8 and 12 and 18 and 22:

>>> notice()
dataset 1: 10,20 -> 10:30 x
>>> ignore("8:12, 18:22")
dataset 1: 10:30 -> 15:30 x
dataset 1: 15:30 -> 15,30 x
>>> get_dep(filter=True)
array([10, 13])

The SherpaVerbosity context manager can be used to hide the screen output:

>>> from sherpa.utils.logging import SherpaVerbosity
>>> with SherpaVerbosity("WARN"):
...     ignore(hi=12)
...

ignore_id(ids: int | str | Sequence[int | str], lo=None, hi=None, **kwargs) → None[source] [edit on github]

Exclude data from the fit for a data set.

Select one or more ranges of data to exclude by filtering on the independent axis value. The filter is applied to the given data set, or sets.

Changed in version 4.15.0: The change in the filter is now reported for the dataset.

Changed in version 4.14.0: Integrated data sets - so Data1DInt and DataPHA when using energy or wavelengths - now ensure that the hi argument is exclusive and better handling of the lo argument when it matches a bin edge. This can result in the same filter selecting a smaller number of bins than in earlier versions of Sherpa.

Parameters:

ids (int or str, or array of int or str) – The data set, or sets, to use.
lo (number or str, optional) – The lower bound of the filter (when a number) or a string expression listing ranges in the form a:b, with multiple ranges allowed, where the ranges are separated by a ,. The term :b means exclude everything up to b (an exclusive limit for integrated datasets), and a: means exclude everything that is higher than, or equal to, a.
hi (number, optional) – The upper bound of the filter when lo is not a string.
bkg_id (int or str, optional) – The filter will be applied to the associated background component of the data set if bkg_id is set. Only PHA data sets support this option; if not given, then the filter is applied to all background components as well as the source data.

See also

ignore: Exclude data from the fit.
sherpa.astro.ui.ignore2d: Exclude a spatial region from an image.
notice_id: Include data from the fit for a data set.
show_filter: Show any filters applied to a data set.

Notes

The order of ignore and notice calls is important.

The units used depend on the analysis setting of the data set, if appropriate.

To filter a 2D data set by a shape use ignore2d.

Examples

Ignore all data points with an X value (the independent axis) between 12 and 18 for data set 1:

>>> ignore_id(1, 12, 18)
dataset 1: 10:30 -> 10,20:30 x

Ignore the range up to 0.5 and 7 and above, for data sets 1, 2, and 3:

>>> ignore_id([1, 2, 3], hi=0.5)
dataset 1: 0.00146:14.9504 -> 0.584:14.9504 Energy (keV)
dataset 2: 0.00146:14.9504 -> 0.6424:14.9504 Energy (keV)
dataset 3: 0.00146:14.9504 -> 0.511:14.9504 Energy (keV)
>>> ignore_id([1, 2, 3], lo=7)
dataset 1: 0.584:14.9504 -> 0.584:4.4384 Energy (keV)
dataset 2: 0.6424:14.9504 -> 0.6424:5.1392 Energy (keV)
dataset 3: 0.511:14.9504 -> 0.511:4.526 Energy (keV)

Apply the same filter as the previous example, but to data sets “core” and “jet”, and hide the screen output:

>>> from sherpa.utils.logging import SherpaVerbosity
>>> with SherpaVerbsity("WARN"):
...     ignore_id(["core", "jet"], ":0.5,7:")
...

image_close() → None[source] [edit on github]

Close the image viewer.

Close the image viewer created by a previous call to one of the image_xxx functions.

See also

image_deleteframes: Delete all the frames open in the image viewer.
image_getregion: Return the region defined in the image viewer.
image_open: Start the image viewer.
image_setregion: Set the region to display in the image viewer.
image_xpaget: Return the result of an XPA call to the image viewer.
image_xpaset: Send an XPA command to the image viewer.

Examples

>>> image_close()

image_data(id: int | str | None = None, newframe=False, tile=False) → None[source] [edit on github]

Display a data set in the image viewer.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_data_image: Return the data used by image_data.
image_close: Close the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_open: Open the image viewer.
image_source: Display the model for a data set in the image viewer.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

Display the data in default data set.

>>> image_data()

Display data set 2 in a new frame so that the data in the current frame is not destroyed. The new data will be displayed in a single frame (i.e. the only data shown by the viewer).

>>> image_data(2, newframe=True)

Display data sets ‘i1’ and ‘i2’ side by side:

>>> image_data('i1')
>>> image_data('i2', newframe=True, tile=True)

image_deleteframes() → None[source] [edit on github]

Delete all the frames open in the image viewer.

Delete all the frames - in other words, images - being displayed in the image viewer (e.g. as created by image_data or image_fit).

See also

image_close: Close the image viewer.
image_getregion: Return the region defined in the image viewer.
image_open: Create the image viewer.
image_setregion: Set the region to display in the image viewer.
image_xpaget: Return the result of an XPA call to the image viewer.
image_xpaset: Send an XPA command to the image viewer.

Examples

>>> image_deleteframes()

image_fit(id: int | str | None = None, newframe=True, tile=True, deleteframes=True) → None[source] [edit on github]

Display the data, model, and residuals for a data set in the image viewer.

This function displays the data, model (including any instrument response), and the residuals (data - model), for a data set.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.
deleteframes (bool, optional) – Should existing frames be deleted? The default is True.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

image_close: Close the image viewer.
image_data: Display a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_open: Open the image viewer.
image_resid: Display the residuals (data - model) for a data set in the image viewer.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

Display the fit results - that is, the data, model, and residuals - for the default data set.

>>> image_fit()

Do not tile the frames (the three frames are loaded, but only the last displayed, the residuals), and then change the frame being displayed to the second one (the model).

>>> image_fit('img', tile=False)
>>> image_xpaset('frame 2')

image_getregion(coord='')[source] [edit on github]

Return the region defined in the image viewer.

The regions defined in the current frame are returned.

Parameters:: coord (str, optional) – The coordinate system to use.
Returns:: region – The region, or regions, or the empty string.
Return type:: str
Raises:: sherpa.utils.err.DS9Err – Invalid coordinate system.

See also

image_setregion: Set the region to display in the image viewer.
image_xpaget: Return the result of an XPA call to the image viewer.
image_xpaset: Send an XPA command to the image viewer.

Examples

>>> image_getregion()
'circle(123,128,12.377649);-box(130,121,14,14,329.93142);'

>>> image_getregion('physical')
'circle(3920.5,4080.5,396.08476);-rotbox(4144.5,3856.5,448,448,329.93142);'

image_kernel(id: int | str | None = None, newframe=False, tile=False) → None[source] [edit on github]

Display the 2D kernel for a data set in the image viewer.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_kernel_image: Return the data used by image_kernel.
image_close: Close the image viewer.
image_data: Display a data set in the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_open: Open the image viewer.
image_source: Display the model for a data set in the image viewer.
plot_kernel: Plot the 1D kernel applied to a data set.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

>>> image_kernel()

>>> image_kernel(2)

image_model(id: int | str | None = None, newframe=False, tile=False) → None[source] [edit on github]

Display the model for a data set in the image viewer.

This function evaluates and displays the model expression for a data set, including any instrument response (e.g. PSF or ARF and RMF) whether created automatically or with set_full_model.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_model_image: Return the data used by image_model.
image_close: Close the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model_component: Display a component of the model in the image viewer.
image_open: Open the image viewer.
image_source: Display the model for a data set in the image viewer.
image_source_component: Display a component of the source expression in the image viewer.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

Display the model for the default data set.

>>> image_model()

Display the model for data set 2 in a new frame so that the data in the current frame is not destroyed. The new data will be displayed in a single frame (i.e. the only data shown by the viewer).

>>> image_model(2, newframe=True)

Display the models for data sets ‘i1’ and ‘i2’ side by side:

>>> image_model('i1')
>>> image_model('i2', newframe=True, tile=True)

image_model_component(id, model=None, newframe=False, tile=False) → None[source] [edit on github]

Display a component of the model in the image viewer.

This function evaluates and displays a component of the model expression for a data set, including any instrument response. Use image_source_component to exclude the response.

The image viewer is automatically started if it is not already open.

Parameters:

id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string).
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_model_component_image: Return the data used by image_model_component.
image_close: Close the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_open: Open the image viewer.
image_source: Display the source expression for a data set in the image viewer.
image_source_component: Display a component of the source expression in the image viewer.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Image visualization is optional, and provided by the DS9 application.

Examples

Display the full source model and then just the ‘gsrc’ component for the default data set:

>>> image_model()
>>> image_model_component(gsrc)

Display the ‘clus’ component of the model for the ‘img’ data set side by side without the with any instrument response (such as convolution with a PSF model):

>>> image_source_component('img', 'clus')
>>> image_model_component('img', 'clus', newframe=True,
...                       tile=True)

image_open() → None[source] [edit on github]

Start the image viewer.

The image viewer will be started, if found. Calling this function when the viewer has already been started will not cause a second viewer to be started. The image viewer will be started automatically by any of the commands like image_data.

See also

image_close: Close the image viewer.
image_deleteframes: Delete all the frames open in the image viewer.
image_getregion: Return the region defined in the image viewer.
image_setregion: Set the region to display in the image viewer.
image_xpaget: Return the result of an XPA call to the image viewer.
image_xpaset: Send an XPA command to the image viewer.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

>>> image_open()

image_psf(id: int | str | None = None, newframe=False, tile=False) → None[source] [edit on github]

Display the 2D PSF model for a data set in the image viewer.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist.

See also

get_psf_image: Return the data used by image_psf.
image_close: Close the image viewer.
image_data: Display a data set in the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_open: Open the image viewer.
image_source: Display the model for a data set in the image viewer.
plot_psf: Plot the 1D PSF model applied to a data set.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

>>> image_psf()

>>> image_psf(2)

image_ratio(id: int | str | None = None, newframe=False, tile=False) → None[source] [edit on github]

Display the ratio (data/model) for a data set in the image viewer.

This function displays the ratio data/model for a data set.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_ratio_image: Return the data used by image_ratio.
image_close: Close the image viewer.
image_data: Display a data set in the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_open: Open the image viewer.
image_resid: Display the residuals (data - model) for a data set in the image viewer.
image_source: Display the model for a data set in the image viewer.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

Display the ratio (data/model) for the default data set.

>>> image_ratio()

image_resid(id: int | str | None = None, newframe=False, tile=False) → None[source] [edit on github]

Display the residuals (data - model) for a data set in the image viewer.

This function displays the residuals (data - model) for a data set.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_resid_image: Return the data used by image_resid.
image_close: Close the image viewer.
image_data: Display a data set in the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_open: Open the image viewer.
image_ratio: Display the ratio (data/model) for a data set in the image viewer.
image_source: Display the model for a data set in the image viewer.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

Display the residuals for the default data set.

>>> image_resid()

Display the residuals for data set 2 in a new frame so that the data in the current frame is not destroyed. The new data will be displayed in a single frame (i.e. the only data shown by the viewer).

>>> image_resid(2, newframe=True)

Display the residuals for data sets ‘i1’ and ‘i2’ side by side:

>>> image_resid('i1')
>>> image_resid('i2', newframe=True, tile=True)

image_setregion(reg, coord='')[source] [edit on github]

Set the region to display in the image viewer.

Parameters:

reg (str) – The region to display.
coord (str, optional) – The coordinate system to use.

Raises:

sherpa.utils.err.DS9Err – Invalid coordinate system.

See also

image_getregion: Return the region defined in the image viewer.
image_xpaget: Return the result of an XPA call to the image viewer.
image_xpaset: Send an XPA command to the image viewer.

Examples

Add a circle, in the physical coordinate system, to the data from the default data set:

>>> image_data()
>>> image_setregion('circle(4234.53,3245.29,46.74)', 'physical')

Copy the region from the current frame, create a new frame displaying the residuals from data set ‘img’, and then display the region on it:

>>> r = image_getregion()
>>> image_resid('img', newframe=True)
>>> image_setregion(r)

image_source(id: int | str | None = None, newframe=False, tile=False) → None[source] [edit on github]

Display the source expression for a data set in the image viewer.

This function evaluates and displays the model expression for a data set, without any instrument response.

The image viewer is automatically started if it is not already open.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_source_image: Return the data used by image_source.
image_close: Close the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_model_component: Display a component of the model in the image viewer.
image_open: Open the image viewer.
image_source_component: Display a component of the source expression in the image viewer.

Notes

Image visualization is optional, and provided by the DS9 application.

Examples

Display the source model for the default data set.

>>> image_source()

Display the source model for data set 2 in a new frame so that the data in the current frame is not destroyed. The new data will be displayed in a single frame (i.e. the only data shown by the viewer).

>>> image_source(2, newframe=True)

Display the source models for data sets ‘i1’ and ‘i2’ side by side:

>>> image_source('i1')
>>> image_source('i2', newframe=True, tile=True)

image_source_component(id, model=None, newframe=False, tile=False) → None[source] [edit on github]

Display a component of the source expression in the image viewer.

This function evaluates and displays a component of the model expression for a data set, without any instrument response. Use image_model_component to include any response.

The image viewer is automatically started if it is not already open.

Parameters:

id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string).
newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame.
tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed.

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_source_component_image: Return the data used by image_source_component.
image_close: Close the image viewer.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
image_model: Display the model for a data set in the image viewer.
image_model_component: Display a component of the model in the image viewer.
image_open: Open the image viewer.
image_source: Display the source expression for a data set in the image viewer.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Image visualization is optional, and provided by the DS9 application.

Examples

Display the full source model and then just the ‘gsrc’ component for the default data set:

>>> image_source()
>>> image_source_component(gsrc)

Display the ‘clus’ and ‘bgnd’ components of the model for the ‘img’ data set side by side:

>>> image_source_component('img', 'clus')
>>> image_source_component('img', 'bgnd', newframe=True,
...                        tile=True)

image_xpaget(arg)[source] [edit on github]

Return the result of an XPA call to the image viewer.

Send a query to the image viewer.

Parameters:

arg (str) – A command to send to the image viewer via XPA.

Returns:

returnval

Return type:

str

Raises:

sherpa.utils.err.DS9Err – The image viewer is not running.
sherpa.utils.err.RuntimeErr – If the command is not recognized.

See also

image_close: Close the image viewer.
image_getregion: Return the region defined in the image viewer.
image_open: Create the image viewer.
image_setregion: Set the region to display in the image viewer.
image_xpaset: Send an XPA command to the image viewer.

Notes

The XPA access point of the ds9 image viewer lets commands and queries to be sent to the viewer.

Examples

Return the current zoom setting of the active frame:

>>> image_xpaget('zoom')
'1\n'

image_xpaset(arg, data=None)[source] [edit on github]

Return the result of an XPA call to the image viewer.

Send a command to the image viewer.

Parameters:

arg (str) – A command to send to the image viewer via XPA.
data (optional) – The data for the command.

Raises:

sherpa.utils.err.DS9Err – The image viewer is not running.
sherpa.utils.err.RuntimeErr – If the command is not recognized or could not be completed.

See also

image_close: Close the image viewer.
image_getregion: Return the region defined in the image viewer.
image_open: Create the image viewer.
image_setregion: Set the region to display in the image viewer.
image_xpaset: Send an XPA command to the image viewer.

Notes

The XPA access point of the ds9 image viewer lets commands and queries to be sent to the viewer.

Examples

Change the zoom setting of the active frame:

>>> image_xpaset('zoom 4')

Overlay the coordinate grid on the current frame:

>>> image_xpaset('grid yes')

Add the region file ‘src.reg’ to the display:

>>> image_xpaset('regions src.reg')

Create a png version of the image being displayed:

>>> image_xpaset('saveimage png /tmp/img.png')

int_proj(par, id: int | str | None = None, otherids: Sequence[int | str] | None = None, replot=False, fast=True, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None, overplot=False) → None[source] [edit on github]

Calculate and plot the fit statistic versus fit parameter value.

Create a confidence plot of the fit statistic as a function of parameter value. Dashed lines are added to indicate the current statistic value and the parameter value at this point. The parameter value is varied over a grid of points and the free parameters re-fit. It is expected that this is run after a successful fit, so that the parameter values identify the best-fit location.

Changed in version 4.16.1: The log parameter can now be set to True.

Parameters:

par – The parameter to plot.
id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, or None, optional) – Other data sets to use in the calculation.
replot (bool, optional) – Set to True to use the values calculated by the last call to int_proj. The default is False.
fast (bool, optional) – If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False.
min (number, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (int, optional) – The number of steps to use. This is used when delv is set to None.
delv (number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
get_int_proj: Return the interval-projection object.
int_unc: Calculate and plot the fit statistic versus fit parameter value.
reg_proj: Plot the statistic value as two parameters are varied.

Notes

The difference to int_unc is that at each step, a fit is made to the remaining thawed parameters in the source model. This makes the result a more-accurate rendering of the projected shape of the hypersurface formed by the statistic, but the run-time is longer than, the results of int_unc, which does not vary any other parameter. If there are no free parameters in the source expression, other than the parameter being plotted, then the results will be the same.

Examples

Vary the gamma parameter of the p1 model component for all data sets with a source expression.

>>> int_proj(p1.gamma)

Use only the data in data set 1:

>>> int_proj(p1.gamma, id=1)

Use two data sets (‘obs1’ and ‘obs2’):

>>> int_proj(clus.kt, id='obs1', otherids=['obs2'])

Vary the bgnd.c0 parameter between 1e-4 and 2e-4, using 41 points:

>>> int_proj(bgnd.c0, min=1e-4, max=2e-4, step=41)

This time define the step size, rather than the number of steps to use:

>>> int_proj(bgnd.c0, min=1e-4, max=2e-4, delv=2e-6)

Overplot the int_proj results for the parameter on top of the int_unc values:

>>> int_unc(mdl.xpos)
>>> int_proj(mdl.xpos, overplot=True)

int_unc(par, id: int | str | None = None, otherids: Sequence[int | str] | None = None, replot=False, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None, overplot=False) → None[source] [edit on github]

Calculate and plot the fit statistic versus fit parameter value.

Create a confidence plot of the fit statistic as a function of parameter value. Dashed lines are added to indicate the current statistic value and the parameter value at this point. The parameter value is varied over a grid of points and the statistic evaluated while holding the other parameters fixed. It is expected that this is run after a successful fit, so that the parameter values identify the best-fit location.

Changed in version 4.16.1: The log parameter can now be set to True.

Parameters:

par – The parameter to plot.
id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, or None, optional) – Other data sets to use in the calculation.
replot (bool, optional) – Set to True to use the values calculated by the last call to int_proj. The default is False.
min (number, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (int, optional) – The number of steps to use. This is used when delv is set to None.
delv (number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
get_int_unc: Return the interval-uncertainty object.
int_proj: Calculate and plot the fit statistic versus fit parameter value.
reg_unc: Plot the statistic value as two parameters are varied.

Notes

The difference to int_proj is that at each step only the single parameter value is varied while all other parameters remain at their starting value. This makes the result a less-accurate rendering of the projected shape of the hypersurface formed by the statistic, but the run-time is likely shorter than, the results of int_proj, which fits the model to the remaining thawed parameters at each step. If there are no free parameters in the source expression, other than the parameter being plotted, then the results will be the same.

Examples

Vary the gamma parameter of the p1 model component for all data sets with a source expression.

>>> int_unc(p1.gamma)

Use only the data in data set 1:

>>> int_unc(p1.gamma, id=1)

Use two data sets (‘obs1’ and ‘obs2’):

>>> int_unc(clus.kt, id='obs1', otherids=['obs2'])

Vary the bgnd.c0 parameter between 1e-4 and 2e-4, using 41 points:

>>> int_unc(bgnd.c0, min=1e-4, max=2e-4, step=41)

This time define the step size, rather than the number of steps to use:

>>> int_unc(bgnd.c0, min=1e-4, max=2e-4, delv=2e-6)

Overplot the int_unc results for the parameter on top of the int_proj values:

>>> int_proj(mdl.xpos)
>>> int_unc(mdl.xpos, overplot=True)

link(par, val)[source] [edit on github]

Link a parameter to a value.

A parameter can be linked to another parameter value, or function of that value, rather than be an independent value. As the linked-to values change, the parameter value will change.

Changed in version 4.16.1: Source models no longer have to contain the linked parameter.

Parameters:

par (str or Parameter) – The parameter to link.
val – The value - which can be a numeric value or a function of other model parameters, to set par to.

See also

freeze: Fix model parameters so they are not changed by a fit.
set_par: Set the value, limits, or behavior of a model parameter.
thaw: Allow model parameters to be varied during a fit.
unlink: Unlink a parameter value.

Notes

The link attribute of the parameter is set to match the mathematical expression used for val.

In the following, the fit will vary the pos parameter even though the src2 component is not part of the source expression (this behavior changed in the 4.16.1 release):

>>> set_source(1, gauss1d.src1)
>>> gauss1d.src2
>>> link(src1.pos, src2.pos)
>>> fit(1)

The lpars attribute of a source model will include these “extra” parameters:

>>> get_source(1).lpars
(<Parameter 'pos' of model 'src2'>,)

Examples

The fwhm parameter of the g2 model is set to be the same as the fwhm parameter of the g1 model.

>>> link(g2.fwhm, g1.fwhm)

Fix the pos parameter of g2 to be 2.3 more than the pos parameter of the g1 model.

>>> gauss1d.g1
>>> gauss1d.g2
>>> g1.pos = 12.2
>>> link(g2.pos, g1.pos + 2.3)
>>> g2.pos.val
14.5
>>> g1.pos = 12.1
>>> g2.pos.val
14.399999999999999

list_data_ids() → list[int | str][source] [edit on github]

List the identifiers for the loaded data sets.

Returns:: ids – A list of the data set identifiers that have been created by commands like load_data and load_arrays.
Return type:: list of int or str

See also

delete_data: Delete a data set by identifier.
load_arrays: Create a data set from arrays of data.
load_data: Create a data set from a file.

Examples

In this case only one data set has been loaded:

>>> list_data_ids()
[1]

Two data sets have been loaded, using string identifiers:

>>> list_data_ids()
['nucleus', 'jet']

list_functions(outfile=None, clobber=False) → None[source] [edit on github]

Display the functions provided by Sherpa.

Unlike the other list_xxx commands, this does not return an array. Instead it acts like the show_xxx family of commands.

Parameters:

outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

get_functions: Return the functions provided by Sherpa.
show_all: Report the current state of the Sherpa session.

list_iter_methods() → list[str][source] [edit on github]

List the iterative fitting schemes.

Returns:: schemes – A list of the names that can be used with set_iter_method.
Return type:: list of str

See also

get_iter_method_name: Return the name of the iterative fitting scheme.
set_iter_method: Set the iterative-fitting scheme used in the fit.

Examples

>>> list_iter_methods()
['none', 'sigmarej']

list_methods() → list[str][source] [edit on github]

List the optimization methods.

Returns:: methods – A list of the names that can be used with set_method.
Return type:: list of str

See also

get_method_name: Return the name of the current optimization method.
set_method: Set the optimization method.

Examples

>>> list_methods()
['gridsearch', 'levmar', 'moncar', 'neldermead', 'simplex']

list_model_components() → list[str][source] [edit on github]

List the names of all the model components.

Models are created either directly - by using the form mname.mid, where mname is the name of the model, such as gauss1d, and mid is the name of the component - or with the create_model_component function, which accepts mname and mid as separate arguments. This function returns all the mid values that have been created.

Returns:: ids – The identifiers for all the model components that have been created. They do not need to be associated with a source expression (i.e. they do not need to have been included in a call to set_model).
Return type:: list of str

See also

create_model_component: Create a model component.
delete_model_component: Delete a model component.
list_models: List the available model types.
list_model_ids: List of all the data sets with a source expression.
set_model: Set the source model expression for a data set.

Examples

The gal and pl model components are created - as versions of the xsphabs and powlaw1d model types - which means that the list of model components returned as mids will contain both strings.

>>> set_model(xsphabs.gal * powlaw1d.pl)
>>> mids = list_model_components()
>>> 'gal' in mids
True
>>> 'pl' in mids
True

The model component does not need to be included as part of a source expression for it to be included in the output of this function:

>>> create_model_component('gauss2d', 'gsrc')
>>> 'gsrc' in list_model_components()
True

list_model_ids() → list[int | str][source] [edit on github]

List of all the data sets with a source expression.

Returns:: ids – The identifiers for all the data sets which have a source expression set by set_model or set_source.
Return type:: list of int or str

See also

list_data_ids: List the identifiers for the loaded data sets.
list_model_components: List the names of all the model components.
list_psf_ids: List of all the data sets with a PSF.
set_model: Set the source model expression for a data set.

list_models(show: str = 'all') → list[str][source] [edit on github]

List the available model types.

Parameters:: show ({ 'all', '1d', '2d', 'xspec' }, optional) – What type of model should be returned. The default is ‘all’. An unrecognized value is treated as ‘all’.
Returns:: models
Return type:: list of str

See also

create_model_components: Create a model component.
list_model_components: List the current model components.

Examples

>>> models = list_models()
>>> models[0:5]
['absorptionedge',
 'absorptiongaussian',
 'absorptionlorentz',
 'absorptionvoigt',
 'accretiondisk']

>>> list_models('2d')
['beta2d',
 'box2d',
 'const2d',
 'delta2d',
 'devaucouleurs2d',
 'disk2d',
 'gauss2d',
 'lorentz2d',
 'normgauss2d',
 'polynom2d',
 'scale2d',
 'sersic2d',
 'shell2d',
 'sigmagauss2d']

list_priors()[source] [edit on github]

Return the priors set for model parameters, if any.

Returns:: priors – The dictionary of mappings between parameters (keys) and prior functions (values) created by set_prior.
Return type:: dict

See also

get_prior: Return the prior function for a parameter (MCMC).
set_prior: Set the prior function to use with a parameter.

Examples

In this example a prior on the PhoIndex parameter of the pl instance has been set to be a gaussian:

>>> list_priors()
{'pl.PhoIndex': <Gauss1D model instance 'gauss1d.gline'>}

list_psf_ids() → list[int | str][source] [edit on github]

List of all the data sets with a PSF.

Added in version 4.12.2.

Returns:: ids – The identifiers for all the data sets which have a PSF model set by set_psf.
Return type:: list of int or str

See also

list_data_ids: List the identifiers for the loaded data sets.
list_model_ids: List of all the data sets with a source expression.
set_psf: Add a PSF model to a data set.

list_samplers()[source] [edit on github]

List the MCMC samplers.

Returns:: samplers – A list of the names (in lower case) that can be used with set_sampler.
Return type:: list of str

See also

get_sampler_name: Return the name of the current MCMC sampler.

Examples

>>> list_samplers()
['metropolismh', 'fullbayes', 'mh', 'pragbayes']

list_stats() → list[str][source] [edit on github]

List the fit statistics.

Returns:: stat – A list of the names that can be used with set_stat.
Return type:: list of str

See also

get_stat_name: Return the name of the current statistical method.
set_stat: Set the statistical method.

Examples

>>> list_stats()
['cash',
 'chi2',
 'chi2constvar',
 'chi2datavar',
 'chi2gehrels',
 'chi2modvar',
 'chi2xspecvar',
 'cstat',
 'leastsq',
 'wstat']

load_arrays(id: int | str, *args) → None[source] [edit on github]

Create a data set from array values.

Parameters:

id (int or str) – The identifier for the data set to use.
*args – Two or more arrays, followed by the type of data set to create.

See also

copy_data: Copy a data set to a new identifier.
delete_data: Delete a data set by identifier.
get_data: Return the data set by identifier.
load_data: Create a data set from a file.
set_data: Set a data set.
unpack_arrays: Create a sherpa data object from arrays of data.

Notes

The data type identifier, which defaults to Data1D, determines the number, and order, of the required inputs.

Identifier	Required Fields	Optional Fields
Data1D	x, y	statistical error, systematic error
Data1DInt	xlo, xhi, y	statistical error, systematic error
Data2D	x0, x1, y	shape, statistical error, systematic error
Data2DInt	x0lo, x1lo, x0hi, x1hi, y	shape, statistical error, systematic error

The shape argument should be a tuple giving the size of the data (ny,nx).

Examples

Create a 1D data set with three points:

>>> load_arrays(1, [10, 12, 15], [4.2, 12.1, 8.4])

Create a 1D data set, with the identifier ‘prof’, from the arrays x (independent axis), y (dependent axis), and dy (statistical error on the dependent axis):

>>> load_arrays('prof', x, y, dy)

Explicitly define the type of the data set:

>>> load_arrays('prof', x, y, dy, Data1D)

Data set 1 is a histogram, where the bins cover the range 1-3, 3-5, and 5-7 with values 4, 5, and 9 respectively.

>>> load_arrays(1, [1, 3, 5], [3, 5, 7], [4, 5, 9], Data1DInt)

load_conv(modelname, filename_or_model, *args, **kwargs)[source] [edit on github]

Load a 1D convolution model.

The convolution model can be defined either by a data set, read from a file, or an analytic model, using a Sherpa model instance. A source model can be convolved with this model by including modelname in the set_model call, using the form:

modelname(modelexpr)

Parameters:

modelname (str) – The identifier for this PSF model.
filename_or_model (str or model instance) – This can be the name of an ASCII file or a Sherpa model component.
args – Arguments for unpack_data if filename_or_model is a file.
kwargs – Keyword arguments for unpack_data if filename_or_model is a file.

See also

delete_psf: Delete the PSF model for a data set.
load_psf: Create a PSF model.
load_table_model: Load tabular data and use it as a model component.
set_full_model: Define the convolved model expression for a data set.
set_model: Set the source model expression for a data set.
set_psf: Add a PSF model to a data set.

Examples

Create a 1D data set, assign a box model - which is flat between the xlow and xhi values and zero elsewhere - and then display the model values. Then add in a convolution component by a gaussian and overplot the resulting source model with two different widths.

>>> dataspace1d(-10, 10, 0.5, id='tst', dstype=Data1D)
>>> set_source('tst', box1d.bmdl)
>>> bmdl.xlow = -2
>>> bmdl.xhi = 3
>>> plot_source('tst')
>>> load_conv('conv', normgauss1d.gconv)
>>> gconv.fwhm = 2
>>> set_source('tst', conv(bmdl))
>>> plot_source('tst', overplot=True)
>>> gconv.fwhm = 5
>>> plot_source('tst', overplot=True)

Create a convolution component called “cmodel” which uses the data in the file “conv.dat”, which should have two columns (the X and Y values).

>>> load_conv('cmodel', 'conv.dat')

load_data(id, filename=None, ncols=2, colkeys=None, dstype=<class 'sherpa.data.Data1D'>, sep=' ', comment='#', require_floats=True) → None[source] [edit on github]

Load a data set from an ASCII file.

Parameters:

id (int or str) – The identifier for the data set to use.
filename (str) – The name of the ASCII file to read in.
ncols (int, optional) – The number of columns to read in (the first ncols columns in the file).
colkeys (array of str, optional) – An array of the column name to read in. The default is None.
dstype (data class to use, optional) – What type of data is to be used. Supported values include Data1D (the default), Data1DInt, Data2D, and Data2DInt.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
require_floats (bool, optional) – If True (the default), non-numeric data values will raise a ValueError.

Raises:

ValueError – If a column value can not be converted into a numeric value and the require_floats parameter is True.

See also

get_data: Return the data set by identifier.
load_arrays: Create a data set from array values.
unpack_arrays: Create a sherpa data object from arrays of data.
unpack_data: Create a sherpa data object from a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

See unpack_data for a description of the supported file format.

Examples

>>> load_data('tbl.dat')

>>> load_data('hist.dat', dstype=Data1DInt)

>>> cols = ['rmid', 'sur_bri', 'sur_bri_err']
>>> load_data(2, 'profile.dat', colkeys=cols)

load_filter(id, filename=None, ignore=False, ncols=2, *args, **kwargs) → None[source] [edit on github]

Load the filter array from an ASCII file and add to a data set.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the ASCII file that contains the filter information.
ignore (bool, optional) – If False (the default) then include bins with a non-zero filter value, otherwise exclude these bins.
colkeys (array of str, optional) – An array of the column name to read in. The default is None.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.

See also

get_filter: Return the filter expression for a data set.
ignore: Exclude data from the fit.
notice: Include data in the fit.
save_filter: Save the filter array to a file.
set_filter: Set the filter array of a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

See unpack_data for a description of the supported file format.

Examples

Read in the first column of the file and apply it to the default data set:

>>> load_filter('filt.dat')

Select the FILTER column of the file:

>>> load_filter(2, 'filt.dat', colkeys=['FILTER'])

load_psf(modelname, filename_or_model, *args, **kwargs)[source] [edit on github]

Create a PSF model.

Create a PSF model representing either an array of data, read from a file, or a model component (such as a gaussian). The set_psf function is used to associate this model with a data set.

Parameters:

modelname (str) – The identifier for this PSF model.
filename_or_model (str or model instance) – This can be the name of an ASCII file or a Sherpa model component.
args – Arguments for unpack_data if filename_or_model is a file.
kwargs – Keyword arguments for unpack_data if filename_or_model is a file.

See also

delete_psf: Delete the PSF model for a data set.
load_conv: Load a 1D convolution model.
load_table_model: Load tabular data and use it as a model component.
set_full_model: Define the convolved model expression for a data set.
set_model: Set the source model expression for a data set.
set_psf: Add a PSF model to a data set.

Examples

Create a PSF model using a 2D gaussian:

>>> load_psf('psf1', gauss2d.gpsf)
>>> set_psf('psf1')
>>> gpsf.fwhm = 4.2
>>> gpsf.ellip = 0.2
>>> gpsf.theta = 30 * np.pi / 180
>>> image_psf()

Create a PSF model from the data in the ASCII file ‘line_profile.dat’ and apply it to the data set called ‘bgnd’:

>>> load_psf('pmodel', 'line_profile.dat')
>>> set_psf('bgnd', 'pmodel')

load_staterror(id, filename=None, ncols=2, *args, **kwargs) → None[source] [edit on github]

Load the statistical errors from an ASCII file.

Read in a column or image from a file and use the values as the statistical errors for a data set. This over rides the errors calculated by any statistic, such as chi2gehrels or chi2datavar.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the ASCII file to read in.
ncols (int, optional) – The number of columns to read in (the first ncols columns in the file).
colkeys (array of str, optional) – An array of the column name to read in. The default is None.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.

See also

get_staterror: Return the statistical error on the dependent axis of a data set.
load_syserror: Load the systematic errors from a file.
set_staterror: Set the statistical errors on the dependent axis of a data set.
set_stat: Set the statistical method.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

See unpack_data for a description of the supported file format.

Examples

Read in the first column from ‘tbl.dat’:

>>> load_staterror('tbl.dat')

Use the column labelled ‘col3’

>>> load_staterror('tbl.dat', colkeys=['col3'])

Read in the first column from the file ‘errors.dat’ as the statistical errors for the ‘core’ data set:

>>> load_staterror('core', 'errors.dat')

load_syserror(id, filename=None, ncols=2, *args, **kwargs) → None[source] [edit on github]

Load the systematic errors from an ASCII file.

Read in a column or image from a file and use the values as the systematic errors for a data set.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the ASCII file to read in.
ncols (int, optional) – The number of columns to read in (the first ncols columns in the file).
colkeys (array of str, optional) – An array of the column name to read in. The default is None.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.

See also

get_syserror: Return the systematic error on the dependent axis of a data set.
load_staterror: Load the statistical errors from a file.
set_syserror: Set the systematic errors on the dependent axis of a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

See unpack_data for a description of the supported file format.

Examples

Read in the first column from ‘tbl.dat’:

>>> load_syserror('tbl.dat')

Use the column labelled ‘col3’

>>> load_syserror('tbl.dat', colkeys=['col3'])

Read in the first column from the file ‘errors.dat’ as the systematic errors for the ‘core’ data set:

>>> load_syserror('core', 'errors.dat')

load_table_model(modelname, filename, ncols=2, colkeys=None, dstype=<class 'sherpa.data.Data1D'>, sep=' ', comment='#', method=<function linear_interp>)[source] [edit on github]

Load ASCII tabular data and use it as a model component.

A table model is defined on a grid of points which is interpolated onto the independent axis of the data set. The model has a single parameter, ampl, which is used to scale the data, and it can be fixed or allowed to vary during a fit.

Parameters:

modelname (str) – The identifier for this table model.
filename (str) – The name of the ASCII file to read in.
ncols (int, optional) – The number of columns to read in (the first ncols columns in the file). It should be 1 or 2.
colkeys (array of str, optional) – An array of the column name to read in. The default is None, which uses the first ncols columns in the file. The default column names are col followed by the column number, so col1 for the first column.
dstype (data class to use, optional) – What type of data is to be used. Supported values include Data1D (the default) and Data1DInt.
sep (str, optional) – The separator character for columns. The default is ' '.
comment (str, optional) – Lines starting with this string are ignored. The default is '#'.
method (func) – The interpolation method to use to map the input data onto the coordinate grid of the data set. Linear, nearest-neighbor, and polynomial schemes are provided in the sherpa.utils module.

See also

load_conv: Load a 1D convolution model.
load_psf: Create a PSF model
load_template_model: Load a set of templates and use it as a model component.
set_model: Set the source model expression for a data set.
set_full_model: Define the convolved model expression for a data set.

Notes

Examples of interpolation schemes provided by sherpa.utils are: linear_interp, nearest_interp, neville, and neville2d.

See unpack_data for a description of the supported file format.

When reading in two columns, the data will be re-ordered so that the first column read in (the independent axis) is numerically increasing.

If ncols=1, only the model values (dependent axis) are read in. In this case, the data set to which the model is applied - via set_source - must have the same number of data points as the model.

When used with an integrated data set (for example, Data1DInt), then the first column of the table - the independent axis - should be the left-edge of the bin, and the second column is the integrated value for that bin.

Examples

Load in the data from filt.dat and use it to multiply the source model (a power law and a gaussian). Allow the amplitude for the table model to vary between 1 and 1e6, starting at 1e3.

>>> load_table_model('filt', 'filt.dat')
>>> set_source(filt * (powlaw1d.pl + gauss1d.gline))
>>> set_par(filt.ampl, 1e3, min=1, max=1e6)

load_template_interpolator(name, interpolator_class, **kwargs)[source] [edit on github]

Set the template interpolation scheme.

Parameters:

name (str)
interpolator_class – An interpolator class.
**kwargs – The arguments for the interpolator.

See also

load_template_model: Load a set of templates and use it as a model component.

Examples

Create an interpolator name that can be used as the template_interpolator_name argument to load_template_model.

>>> from sherpa.models import KNNInterpolator
>>> load_template_interpolator('myint', KNNInterpolator, k=4, order=3)

load_template_model(modelname, templatefile, dstype=<class 'sherpa.data.Data1D'>, sep=' ', comment='#', method=<function linear_interp>, template_interpolator_name='default')[source] [edit on github]

Load a set of templates and use it as a model component.

A template model can be considered to be an extension of the table model supported by load_table_model. In the template case, a set of models (the “templates”) are read in and then compared to the data, with the best-fit being used to return a set of parameters.

Parameters:

modelname (str) – The identifier for this table model.
templatefile (str) – The name of the file to read in. This file lists the template data files.
dstype (data class to use, optional) – What type of data is to be used. This is currently unused.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
method (func) – The interpolation method to use to map the input data onto the coordinate grid of the data set. Linear, nearest-neighbor, and polynomial schemes are provided in the sherpa.utils module.
template_interpolator_name (str) – The method used to interpolate within the set of templates. The default is default. A value of None turns off the interpolation; in this case the grid-search optimiser must be used to fit the data.

See also

load_conv: Load a 1D convolution model.
load_psf: Create a PSF model
load_table_model: Load tabular data and use it as a model component.
load_template_interpolator: Set the template interpolation scheme.
set_model: Set the source model expression for a data set.
set_full_model: Define the convolved model expression for a data set.

Notes

Examples of interpolation schemes provided by sherpa.utils are: linear_interp, nearest_interp, and neville.

The template index file is the argument to load_template_model, and is used to list the data files. It is an ASCII file with one line per template, and each line containing the model parameters (numeric values), followed by the MODELFLAG column and then the file name for the data file (its name must begin with FILE). The MODELFLAG column is used to indicate whether a file should be used or not; a value of 1 means that the file should be used, and a value of 0 that the line should be ignored. The parameter names are set by the column names.

The data file - the last column of the template index file - is read in and the first two columns used to set up the x and y values (Data1D) or xlo, xhi, and y values (Data1DInt). These files must be in ASCII format.

The method parameter determines how the template data values are interpolated onto the source data grid.

The template_interpolator_name parameter determines how the dependent axis (Y) values are interpolated when the parameter values are varied. This interpolation can be turned off by using a value of None, in which case the grid-search optimiser must be used. See load_template_interpolator for how to create a valid interpolator. The “default” interpolator uses sherpa.models.KNNInterpolator with k=2 and order=2.

Examples

Load in the templates from the file “index.tmpl” as the model component “kerr”, and set it as the source model for the default data set. The optimisation method is switched to use a grid search for the parameters of this model.

>>> load_template_model("kerr", "index.tmpl")
>>> set_source(kerr)
>>> set_method('gridsearch')
>>> set_method_opt('sequence', kerr.parvals)
>>> fit()

Fit a constant plus the templates, using the neville scheme for integrating the template onto the data grid. The Monte-Carlo based optimiser is used.

>>> load_template_model('tbl', 'table.lis',
...                     sherpa.utils.neville)
>>> set_source(tbl + const1d.bgnd)
>>> set_method('moncar')

load_user_model(func, modelname, filename=None, ncols=2, colkeys=None, dstype=<class 'sherpa.data.Data1D'>, sep=' ', comment='#')[source] [edit on github]

Create a user-defined model.

Assign a name to a function; this name can then be used as any other name of a model component, either in a source expression - such as with set_model - or to change a parameter value. The add_user_pars function should be called after load_user_model to set up the parameter names and defaults.

Parameters:

func (func) – The function that evaluates the model.
modelname (str) – The name to use to refer to the model component.
filename (str, optional) – Set this to include data from this file in the model. The file should contain two columns, and the second column is stored in the _y attribute of the model.
ncols (int, optional) – The number of columns to read in (the first ncols columns in the file).
colkeys (array of str, optional) – An array of the column name to read in. The default is None.
dstype (data class to use, optional) – What type of data is to be used. Supported values include Data1D (the default), Data1DInt, Data2D, and Data2DInt.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.

See also

add_model: Create a user-defined model class.
add_user_pars: Add parameter information to a user model.
load_table_model: Load tabular data and use it as a model component.
load_template_model: Load a set of templates and use it as a model component.
set_model: Set the source model expression for a data set.

Notes

The load_user_model function is designed to make it easy to add a model, but the interface is not the same as the existing models (such as having to call both load_user_model and add_user_pars for each new instance). The add_model function is used to add a model as a Python class, which is more work to set up, but then acts the same way as the existing models.

The function used for the model depends on the dimensions of the data. For a 1D model, the signature is:

def func1d(pars, x, xhi=None):

where, if xhi is not None, then the dataset is binned and the x argument is the low edge of each bin. The pars argument is the parameter array - the names, defaults, and limits can be set with add_user_pars - and should not be changed. The return value is an array the same size as x.

For 2D models, the signature is:

def func2d(pars, x0, x1, x0hi=None, x1hi=None):

There is no way using this interface to indicate that the model is for 1D or 2D data.

The format for the input file, and how to control what columns to read, are described in the help for the unpack_data function.

Examples

Create a two-parameter model of the form “y = mx + c”, where the intercept is the first parameter and the slope the second, set the parameter names and default values, then use it in a source expression:

>>> def func1d(pars, x, xhi=None):
...     if xhi is not None:
...         x = (x + xhi) / 2
...     return x * pars[1] + pars[0]
...
>>> load_user_model(func1d, "myfunc")
>>> add_user_pars(myfunc, ["c", "m"], [0, 1])
>>> set_source(myfunc + gauss1d.gline)

load_user_stat(statname, calc_stat_func, calc_err_func=None, priors={}) → None[source] [edit on github]

Create a user-defined statistic.

The choice of statistics - that is, the numeric value that is minimised during the fit - can be extended by providing a function to calculate a numeric value given the data. The statistic is given a name and then can be used just like any of the pre-defined statistics.

Parameters:

statname (str) – The name to use for the new statistic when calling set_stat.
calc_stat_func (func) – The function that calculates the statistic.
calc_err_func (func, optional) – How to calculate the statistical error on a data point.
priors (dict) – A dictionary of hyper-parameters for the priors.

See also

calc_stat: Calculate the fit statistic for a data set.
set_stat: Set the statistical method.

Notes

The calc_stat_func should have the following signature:

def func(data, model, staterror=None, syserrr=None, weight=None)

where data is the array of dependent values, model the array of the predicted values, staterror and syserror are arrays of statistical and systematic errors respectively (if valid), and weight an array of weights. The return value is the pair (stat_value, stat_per_bin), where stat_value is a scalar and stat_per_bin is an array the same length as data.

The calc_err_func should have the following signature:

def func(data)

and returns an array the same length as data.

Examples

Define a chi-square statistic with the label “qstat”:

>>> def qstat(d, m, staterr=None, syserr=None, w=None):
...     if staterr is None:
...         staterr = 1
...     c = ((d-m) / staterr)
...     return ((c*c).sum(), c)
...
>>> load_user_stat("qstat", qstat)
>>> set_stat("qstat")

normal_sample(num=1, sigma=1, correlate=True, id: int | str | None = None, otherids: Sequence[int | str] = (), numcores=None)[source] [edit on github]

Sample the fit statistic by taking the parameter values from a normal distribution.

For each iteration (sample), change the thawed parameters by drawing values from a uni- or multi-variate normal (Gaussian) distribution, and calculate the fit statistic.

Parameters:

num (int, optional) – The number of samples to use (default is 1).
sigma (number, optional) – The width of the normal distribution (the default is 1).
correlate (bool, optional) – Should a multi-variate normal be used, with parameters set by the covariance matrix (True) or should a uni-variate normal be used (False)?
id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, optional) – Other data sets to use in the calculation.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

Returns:

A NumPy array table with the first column representing the statistic and later columns the parameters used.

Return type:

samples

See also

fit: Fit a model to one or more data sets.
set_model: Set the source model expression for a data set.
set_stat: Set the statistical method.
t_sample: Sample from the Student’s t-distribution.
uniform_sample: Sample from a uniform distribution.

Notes

All thawed model parameters are sampled from the Gaussian distribution, where the mean is set as the best-fit parameter value and the variance is determined by the diagonal elements of the covariance matrix. The multi-variate Gaussian is assumed by default for correlated parameters, using the off-diagonal elements of the covariance matrix.

Examples

The model fit to the default data set has three free parameters. The median value of the statistic calculated by normal_sample is returned:

>>> ans = normal_sample(num=10000)
>>> ans.shape
(1000, 4)
>>> np.median(ans[:,0])
119.82959326927781

notice(lo=None, hi=None, **kwargs) → None[source] [edit on github]

Include data in the fit.

Select one or more ranges of data to include by filtering on the independent axis value. The filter is applied to all data sets.

Changed in version 4.15.0: The change in the filter is now reported for each dataset.

Changed in version 4.14.0: Integrated data sets - so Data1DInt and DataPHA when using energy or wavelengths - now ensure that the hi argument is exclusive and better handling of the lo argument when it matches a bin edge. This can result in the same filter selecting a smaller number of bins than in earlier versions of Sherpa.

Parameters:

lo (number or str, optional) – The lower bound of the filter (when a number) or a string expression listing ranges in the form a:b, with multiple ranges allowed, where the ranges are separated by a ,. The term :b means include everything up to b (an exclusive limit for integrated datasets), and a: means include everything that is higher than, or equal to, a.
hi (number, optional) – The upper bound of the filter when lo is not a string.
bkg_id (int or str, optional) – The filter will be applied to the associated background component of the data set if bkg_id is set. Only PHA data sets support this option; if not given, then the filter is applied to all background components as well as the source data.

See also

notice_id: Include data for a data set.
sherpa.astro.ui.notice2d: Include a spatial region in an image.
ignore: Exclude data from the fit.
show_filter: Show any filters applied to a data set.

Notes

The order of ignore and notice calls is important, and the results are a union, rather than intersection, of the combination.

If notice is called on an un-filtered data set, then the ranges outside the noticed range are excluded: it can be thought of as if ignore had been used to remove all data points. If notice is called after a filter has been applied then the filter is applied to the existing data.

For binned data sets, the bin is included if the noticed range falls anywhere within the bin, but excluding the hi value (except for PHA data sets when using channel units).

The units used depend on the analysis setting of the data set, if appropriate.

To filter a 2D data set by a shape use notice2d.

The report of the change in the filter expression can be controlled with the SherpaVerbosity context manager, as shown in the examples below.

Examples

Since the notice call is applied to an un-filtered data set, the filter chooses only those points that lie within the range 12 <= X <= 18.

>>> load_arrays(1, [10, 15, 20, 30], [5, 10, 7, 13])
>>> notice(12, 28)
dataset 1: 10:30 -> 15:20 x
>>> get_dep(filter=True)
array([10,  7])

As no limits are given, the whole data set is included:

>>> notice()
dataset 1: 15:20 -> 10:30 x
>>> get_dep(filter=True)
array([ 5, 10,  7, 13])

The ignore call excludes the first two points, but the notice call adds back in the second point:

>>> ignore(hi=17)
dataset 1: 10:30 -> 20:30 x
>>> notice(12, 16)
dataset 1: 20:30 -> 15:30 x
>>> get_dep(filter=True)
array([10,  7, 13])

Only include data points in the range 8<=X<=12 and 18<=X=22:

>>> ignore()
dataset 1: 15:30 x -> no data
>>> notice("8:12, 18:22")
dataset 1: no data -> 10 x
dataset 1: 10 -> 10,20 x
>>> get_dep(filter=True)
array([5, 7])

The messages from notice and ignore use the standard Sherpa logging infrastructure, and so can be ignored by using SherpaVerbosity:

>>> from sherpa.utils.logging import SherpaVerbosity
>>> with SherpaVerbosity("WARN"):
...     notice()
...

notice_id(ids: int | str | Sequence[int | str], lo=None, hi=None, **kwargs) → None[source] [edit on github]

Include data from the fit for a data set.

Select one or more ranges of data to include by filtering on the independent axis value. The filter is applied to the given data set, or data sets.

Changed in version 4.15.0: The change in the filter is now reported for the dataset.

Changed in version 4.14.0: Integrated data sets - so Data1DInt and DataPHA when using energy or wavelengths - now ensure that the hi argument is exclusive and better handling of the lo argument when it matches a bin edge. This can result in the same filter selecting a smaller number of bins than in earlier versions of Sherpa.

Parameters:

ids (int or str, or array of int or str) – The data set, or sets, to use.
lo (number or str, optional) – The lower bound of the filter (when a number) or a string expression listing ranges in the form a:b, with multiple ranges allowed, where the ranges are separated by a ,. The term :b means include everything up to b (an exclusive limit for integrated datasets), and a: means include everything that is higher than, or equal to, a.
hi (number, optional) – The upper bound of the filter when lo is not a string.
bkg_id (int or str, optional) – The filter will be applied to the associated background component of the data set if bkg_id is set. Only PHA data sets support this option; if not given, then the filter is applied to all background components as well as the source data.

See also

ignore_id: Exclude data from the fit for a data set.
sherpa.astro.ui.ignore2d: Exclude a spatial region from an image.
notice: Include data in the fit.
show_filter: Show any filters applied to a data set.

Notes

The order of ignore and notice calls is important.

The units used depend on the analysis setting of the data set, if appropriate.

To filter a 2D data set by a shape use ignore2d.

The report of the change in the filter expression can be controlled with the SherpaVerbosity context manager, as shown in the examples below.

Examples

Include all data points with an X value (the independent axis) between 12 and 18 for data set 1:

>>> notice_id(1, 12, 18)
dataset 1: 10:30 -> 15:20 x

Include the range 0.5 to 7, for data sets 1, 2, and 3 (the screen output will depend on the existing data and filters applied to them):

>>> notice_id([1, 2, 3], 0.5, 7)
dataset 1: 0.00146:14.9504 -> 0.4818:9.0374 Energy (keV)
dataset 2: 0.00146:14.9504 -> 0.4964:13.6072 Energy (keV)
dataset 3: 0.00146:14.9504 -> 0.4234:9.3878 Energy (keV)

Apply the filter 0.5 to 2 and 2.2 to 7 to the data sets “core” and “jet”, and hide the screen output:

>>> from sherpa.utils.logging import SherpaVerbosity
>>> with SherpaVerbsity("WARN"):
...     notice_id(["core", "jet"], "0.5:2, 2.2:7")
...

paramprompt(val=False) → None[source] [edit on github]

Should the user be asked for the parameter values when creating a model?

When val is True, calls to set_model will cause the user to be prompted for each parameter in the expression. The prompt includes the parameter name and default value, in []: the valid responses are

return which accepts the default
value which changes the parameter value
value, min which changes the value and the minimum value
value, min, max which changes the value, minimum, and maximum values

The value, min, and max components are optional, so “,-5” will use the default parameter value and set its minimum to -5, while “2,,10” will change the parameter value to 2 and its maximum to 10, but leave the minimum at its default. If any value is invalid then the parameter is re-prompted.

Parameters:: val (bool, optional) – If True, the user will be prompted to enter each parameter value, including support for changing the minimum and maximum values, when a model component is created. The default is False.

See also

set_model: Set the source model expression for a data set.
set_par: Set the value, limits, or behavior of a model parameter.
show_model: Display the model expression used to fit a data set.

Notes

Setting this to True only makes sense in an interactive environment. It is designed to be similar to the parameter prompting provided by XSPEC.

Examples

In the following, the default parameter settings are accepted for the pl.gamma parameter, the starting values for the pl.ref and gline.pos values are changed, the starting value and ranges of both the pl.ampl and gline.ampl parameters are set, and the gline.fwhm parameter is set to 100, with its maximum changed to 10000.

>>> paramprompt(True)
>>> set_source(powlaw1d.pl + gauss1d.gline)
pl.gamma parameter value [1]
pl.ref parameter value [1] 4500
pl.ampl parameter value [1] 1.0e-5,1.0e-8,0.01
gline.fwhm parameter value [10] 100,,10000
gline.pos parameter value [0] 4900
gline.ampl parameter value [1] 1.0e-3,1.0e-7,1

plot(*args, **kwargs) → None[source] [edit on github]

Create one or more plot types.

The plot function creates one or more plots, depending on the arguments it is sent: a plot type, followed by optional identifiers, and this can be repeated. If no data set identifier is given for a plot type, the default identifier - as returned by get_default_id - is used.

Changed in version 4.17.0: The keyword arguments can now be set per plot by using a sequence of values. The layout can be changed with the rows and cols arguments and the automatic calculation no longer forces two rows. Handling of the overplot flag has been improved.

Changed in version 4.15.0: A number of labels, such as “bkgfit”, are marked as deprecated and using them will cause a warning message to be displayed, indicating the new label to use.

Changed in version 4.12.2: Keyword arguments, such as alpha and ylog, can be sent to each plot.

Parameters:

args – The plot names and identifiers.
rows – The number of rows and columns (if set).
cols – The number of rows and columns (if set).
kwargs – The plot arguments applied to each plot.

Raises:

sherpa.utils.err.ArgumentErr – The label is invalid.

Notes

The supported plot types depend on the data set type, and include the following list. There are also individual functions, with plot_ prepended to the plot type, such as plot_data. There are also several multiple-plot commands, such as plot_fit_ratio, plot_fit_resid, and plot_fit_delchi.

arf: The ARF for the data set (only for DataPHA data sets).
bkg: The background.
bkg_chisqr: The chi-squared statistic calculated for each bin when fitting the background.
bkg_delchi: The residuals for each bin, calculated as (data-model) divided by the error, for the background.
bkg_fit: The data (as points) and the convolved model (as a line), for the background data set.
bkg_model: The convolved background model.
bkg_ratio: The residuals for each bin, calculated as data/model, for the background data set.
bkg_resid: The residuals for each bin, calculated as (data-model), for the background data set.
bkg_source: The un-convolved background model.
chisqr: The chi-squared statistic calculated for each bin.
data: The data (which may be background subtracted).
delchi: The residuals for each bin, calculated as (data-model) divided by the error.
fit: The data (as points) and the convolved model (as a line).
kernel: The PSF kernel associated with the data set.
model: The convolved model.
model_component: Part of the full model expression (convolved).
model_components: Parts of the full model expression (convolved).
order: Plot the model for a selected response
psf: The unfiltered PSF kernel associated with the data set.
ratio: The residuals for each bin, calculated as data/model.
resid: The residuals for each bin, calculated as (data-model).
source: The un-convolved model.
source_component: Part of the full model expression (un-convolved).
source_components: Parts of the full model expression (un-convolved).

The plots can be specialized for a particular data type, such as the set_analysis command controlling the units used for PHA data sets.

Given a plot name, such as “data”, the remaining arguments up to the next plot name match those from the corresponding plot_xxx call (in this case plot_data), ignoring the replot, overplot, and clearwindow arguments. So the call

>>> plot("data", "bkg", 1, "up", ylog=True)

can be thought of as combining the plots created by calling plot_data(ylog=True) and plot_bkg(1, “up”, ylog=True).

The plot capabilities depend on what plotting backend, if any, is installed. If there is none available, a warning message will be displayed when sherpa.ui or sherpa.astro.ui is imported, and the plot set of commands will not create any plots. The choice of back end is made by changing the options.plot_pkg setting in the Sherpa configuration file.

The keyword arguments are sent to each plot (so care must be taken to ensure they are valid for all plots).

Examples

Plot the data for the default data set. This is the same as plot_data.

>>> plot("data")

Plot the data for data set 2.

>>> plot("data", 2)

Plot the data and ARF for the default data set, in two seaparate plots.

>>> plot("data", "arf")

Plot the fit (data and model) for data sets 1 and 2, in two separate plots.

>>> plot("fit", 1, "fit", 2)

Plot the fit (data and model) for data sets “fit” and “jet”, in two separate plots.

>>> plot("fit", "nucleus", "fit", "jet")

Draw the data and model plots both with a log-scale for the y axis:

>>> plot("data", "model", ylog=True)

Plot the background data components “up” and “down” for dataset 1:

>>> plot("bkg", 1, "up", "bkg", 1, "down")

Draw both data and model for the default dataset in black, but with partial opacity:

>>> plot("data", "model", color="black", alpha=0.5)

Draw the two plots in black but with different opacities:

>>> plot("data", "model", color="black", alpha=[1, 0.5])

Label each plot (the output depends on the backend and the plot options):

>>> plot("data", "model", label=["data", "model"])

Draw the two plots with different y-axis scalings:

>>> plot("data", 2, "model", 2, ylog=[False, True])

Change the layout to a single column of plots:

>>> plot("data", "data", 2, cols=1)

Use a two-column by three-row display (although in this case only one of the rows or cols arguments needed to be given):

>>> plot("data", "data", 2, "model", "model", 2,
...      "resid", "resid", 2, rows=3, cols=2)

Create a display for three plots, vertically aligned, but only display plots in the first two:

>>> plot("data", "model", cols=1, rows=3)

Draw the data and residuals for the default dataset and then overplot those from dataset 2:

>>> plot("data", "resid", cols=1, color="black")
>>> plot("data", 2, "resid", 2, overplot=True, color="black", alpha=0.5)

plot_cdf(points, name='x', xlabel='x', replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the cumulative density function of an array of values.

Create and plot the cumulative density function (CDF) of the input array. Median and upper- and lower- quartiles are marked on the plot.

Parameters:

points (array) – The values used to create the cumulative density function.
name (str, optional) – The label to use as part of the plot title.
xlabel (str, optional) – The label for the X and part of the Y axes.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_cdf. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_cdf_plot: Return the data used to plot the last CDF.
get_draws: Run the pyBLoCXS MCMC algorithm.
plot_pdf: Plot the probability density function of an array.
plot_scatter: Create a scatter plot.

Examples

>>> mu, sigma, n = 100, 15, 500
>>> x = np.random.normal(loc=mu, scale=sigma, size=n)
>>> plot_cdf(x)

>>> plot_cdf(x, xlabel="x pos", name="Simulations")

plot_chisqr(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

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

This function displays the square of the residuals (data - model) divided by the error, for a data set.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_chisqr. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_chisqr_plot: Return the data used by plot_chisqr.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_delchi: Plot the ratio of residuals to error for a data set.
plot_ratio: Plot the ratio of data to model for a data set.
plot_resid: Plot the residuals (data - model) for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Examples

Plot the chi-quare values for each point in the default data set:

>>> plot_chisqr()

Overplot the values from the ‘core’ data set on those from the ‘jet’ dataset:

>>> plot_chisqr('jet')
>>> plot_chisqr('core', overplot=True)

plot_data(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the data values.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_data. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_data_plot: 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.
plot: Create one or more plot types.
sherpa.astro.ui.set_analysis: Set the units used when fitting and displaying spectral data.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The additional arguments supported by plot_data are the same as the keywords of the dictionary returned by get_data_plot_prefs.

Examples

Plot the data from the default data set:

>>> plot_data()

Plot the data from data set 1:

>>> plot_data(1)

Plot the data from data set labelled “jet” and then overplot the “core” data set. The set_xlog command is used to select a logarithmic scale for the X axis.

>>> set_xlog("data")
>>> plot_data("jet")
>>> plot_data("core", overplot=True)

The following example requires that the Matplotlib backend is selected, and uses a Matplotlib function to create a subplot (in this case one filling the bottom half of the plot area) and then calls plot_data with the clearwindow argument set to False to use this subplot. If the clearwindow argument had not been used then the plot area would have been cleared and the plot would have filled the area.

>>> plt.subplot(2, 1, 2)
>>> plot_data(clearwindow=False)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_data_plot_prefs. Examples include (for the Matplotlib backend): adding a “cap” to the error bars:

>>> plot_data(capsize=4)

changing the symbol to a square:

>>> plot_data(marker='s')

using a dotted line to connect the points:

>>> plot_data(linestyle='dotted')

and plotting multiple data sets on the same plot, using a log scale for the Y axis, setting the alpha transparency for each plot, and explicitly setting the colors of the last two datasets:

>>> plot_data(ylog=True, alpha=0.7)
>>> plot_data(2, overplot=True, alpha=0.7, color='brown')
>>> plot_data(3, overplot=True, alpha=0.7, color='purple')

Set the labels used for the X and Y axes for the data. In this example the matplotlib backend is used and so the LaTeX support is used to display an Angstrom symbol as part of the X axis label. Note that the labels will be retained for other plots, including other plot types such as plot_model() or plot_fit_resid().

>>> d = get_data()
>>> d.set_xlabel(r"x axis [$\AA$]")
>>> d.set_ylabel("y axis")
>>> plot_data()

plot_delchi(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

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

This function displays the residuals (data - model) divided by the error, for a data set.

Changed in version 4.12.0: The Y axis is now always drawn using a linear scale.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_delchi. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_delchi_plot: Return the data used by plot_delchi.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_chisqr: Plot the chi-squared value for each point in a data set.
plot_ratio: Plot the ratio of data to model for a data set.
plot_resid: Plot the residuals (data - model) for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.

Notes

The additional arguments supported by plot_delchi are the same as the keywords of the dictionary returned by get_data_plot_prefs.

The ylog setting is ignored, and the Y axis is drawn using a linear scale.

Examples

Plot the residuals for the default data set, divided by the error value for each bin:

>>> plot_delchi()

Overplot the values from the ‘core’ data set on those from the ‘jet’ dataset:

>>> plot_delchi('jet')
>>> plot_delchi('core', overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_data_plot_prefs. The following sets error bars to be orange and the marker to be a circle (larger than the default one):

>>> plot_delchi(ecolor='orange', marker='o')

plot_fit(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the fit results (data, model) for a data set.

This function creates a plot containing the data and the model (including any instrument response) for a data set.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_fit. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_fit_plot: Return the data used to create the fit plot.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_fit_delchi: Plot the fit results, and the residuals, for a data set.
plot_fit_ratio: Plot the fit results, and the ratio of data to model, for a data set.
plot_fit_resid: Plot the fit results, and the residuals, for a data set.
plot_data: Plot the data values.
plot_model: Plot the model for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The additional arguments supported by plot_fit are the same as the keywords of the dictionary returned by get_data_plot_prefs.

Examples

Plot the fit results for the default data set:

>>> plot_fit()

Overplot the ‘core’ results on those from the ‘jet’ data set, using a logarithmic scale for the X axis:

>>> set_xlog()
>>> plot_fit('jet')
>>> plot_fit('core', overplot=True)

Keyword arguments can be given to override the plot preferences; for example the following sets the y axis to a log scale, but only for this plot:

>>> plot_fit(ylog=True)

The color can be changed for both the data and model using (note that the keyword name and supported values depends on the plot backend; this example assumes that Matplotlib is being used):

>>> plot_fit(color='orange')

Draw the fits for two datasets, setting the second one partially transparent (this assumes Matplotlib is used):

>>> plot_fit(1)
>>> plot_fit(2, alpha=0.7, overplot=True)

plot_fit_delchi(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

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

This creates two plots - the first from plot_fit and the second from plot_delchi - for a data set.

Changed in version 4.12.2: The overplot option now works.

Changed in version 4.12.0: The Y axis of the delchi plot is now always drawn using a linear scale.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_fit_delchi. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_fit_plot: Return the data used to create the fit plot.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_fit: Plot the fit results for a data set.
plot_fit_ratio: Plot the fit results, and the ratio of data to model, for a data set.
plot_fit_resid: Plot the fit results, and the residuals, for a data set.
plot_data: Plot the data values.
plot_delchi: Plot the ratio of residuals to error for a data set.
plot_model: Plot the model for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The additional arguments supported by plot_fit_delchi are the same as the keywords of the dictionary returned by get_data_plot_prefs, and are applied to both plots.

For the delchi plot, the ylog setting is ignored, and the Y axis is drawn using a linear scale.

Examples

Plot the results for the default data set:

>>> plot_fit_delchi()

Overplot the ‘core’ results on those from the ‘jet’ data set, using a logarithmic scale for the X axis:

>>> set_xlog()
>>> plot_fit_delchi('jet')
>>> plot_fit_delchi('core', overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_data_plot_prefs. The following sets the error bars to be drawn in gray when using the Matplotlib backend:

>>> plot_fit_delchi(ecolor='gray')

plot_fit_ratio(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

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

This creates two plots - the first from plot_fit and the second from plot_ratio - for a data set.

Changed in version 4.12.2: The overplot option now works.

Added in version 4.12.0.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_fit_ratio. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_fit_plot: Return the data used to create the fit plot.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_fit: Plot the fit results for a data set.
plot_fit_resid: Plot the fit results, and the residuals, for a data set.
plot_fit_delchi: Plot the fit results, and the residuals, for a data set.
plot_data: Plot the data values.
plot_model: Plot the model for a data set.
plot_ratio: Plot the ratio of data to model for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The additional arguments supported by plot_fit_ratio are the same as the keywords of the dictionary returned by get_data_plot_prefs, and are applied to both plots.

For the ratio plot, the ylog setting is ignored, and the Y axis is drawn using a linear scale.

Examples

Plot the results for the default data set:

>>> plot_fit_ratio()

Overplot the ‘core’ results on those from the ‘jet’ data set, using a logarithmic scale for the X axis:

>>> set_xlog()
>>> plot_fit_ratio('jet')
>>> plot_fit_ratio('core', overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_data_plot_prefs. The following sets the plots to use square symbols (this includes the model as well as data in the top plot) and turns off any line between plots, when using the Matplotlib backend:

>>> plot_fit_ratio(marker='s', linestyle='none')

plot_fit_resid(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

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

This creates two plots - the first from plot_fit and the second from plot_resid - for a data set.

Changed in version 4.12.2: The overplot option now works.

Changed in version 4.12.0: The Y axis of the residual plot is now always drawn using a linear scale.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the previous values. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_fit_plot: Return the data used to create the fit plot.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_fit: Plot the fit results for a data set.
plot_fit_delchi: Plot the fit results, and the residuals, for a data set.
plot_fit_ratio: Plot the fit results, and the ratio of data to model, for a data set.
plot_data: Plot the data values.
plot_model: Plot the model for a data set.
plot_resid: Plot the residuals (data - model) for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The additional arguments supported by plot_fit_resid are the same as the keywords of the dictionary returned by get_data_plot_prefs, and are applied to both plots.

For the residual plot, the ylog setting is ignored, and the Y axis is drawn using a linear scale.

Examples

Plot the results for the default data set:

>>> plot_fit_resid()

Overplot the ‘core’ results on those from the ‘jet’ data set, using a logarithmic scale for the X axis:

>>> set_xlog()
>>> plot_fit_resid('jet')
>>> plot_fit_resid('core', overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_data_plot_prefs. The following sets the data in both plots to be drawn in a blue color, have caps on the error bars, but to only draw the y error bars:

>>> plot_fit_resid(capsize=4, color='skyblue', xerrorbars=False)

plot_kernel(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the 1D kernel applied to a data set.

The plot_psf function shows the full PSF, from which the kernel is derived.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_kernel. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_kernel_plot: Return the data used by plot_kernel.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_psf: Plot the 1D PSF model applied to a data set.
set_psf: Add a PSF model to a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Examples

Create a model (a step function) that is convolved by a gaussian, and display the kernel overplotted on the PSF:

>>> dataspace1d(1, 10, step=1, dstype=Data1D)
>>> set_model(steplo1d.stp)
>>> stp.xcut = 4.4
>>> load_psf('psf1', gauss1d.gline)
>>> set_psf('psf1')
>>> gline.fwhm = 1.2
>>> plot_psf()
>>> plot_kernel(overplot=True)

plot_model(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the model for a data set.

This function plots the model for a data set, which includes any instrument response (e.g. a convolution created by set_psf).

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_model. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_model_plot: Return the data used to create the model plot.
get_model_plot_prefs: Return the preferences for plot_model.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_model_component: Plot a component of the model for a data set.
plot_source: Plot the source expression for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The additional arguments supported by plot_model are the same as the keywords of the dictionary returned by get_model_plot_prefs.

For PHA data sets the model plot created by plot_model differs to the model plot created by plot_fit: the fit version uses the grouping of the data set whereas the plot_model version shows the ungrouped data (that is, it uses the instrumental grid). The filters used are the same in both cases.

Examples

Plot the convolved source model for the default data set:

>>> plot_model()

Overplot the model for data set 2 on data set 1:

>>> plot_model(1)
>>> plot_model(2, overplot=True)

Create the equivalent of plot_fit('jet'):

>>> plot_data('jet')
>>> plot_model('jet', overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_model_plot_prefs. The following plots the model using a log scale for both axes, and then overplots the model from data set 2 using a dashed line and slightly transparent:

>>> plot_model(xlog=True, ylog=True)
>>> plot_model(2, overplot=True, alpha=0.7, linestyle='dashed')

plot_model_component(id, model=None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot a component of the model for a data set.

This function evaluates and plots a component of the model expression for a data set, including any instrument response. Use plot_source_component to display without any response. For PHA data, the response model is automatically added by the routine unless the model contains a response.

Parameters:

id (int or str, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string).
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_model_component. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_model_component_plot: Return the data used to create the model-component plot.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_model_components: Plot all the components of a model.
plot_source_component: Plot a component of the source expression for a data set.
plot_model: Plot the model for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

The additional keyword arguments match the keywords of the dictionary returned by get_model_plot_prefs.

Examples

Overplot the pl component of the model expression for the default data set:

>>> plot_model()
>>> plot_model_component(pl, overplot=True)

Display the results for the ‘jet’ data set (data and model), and then overplot the pl component evaluated for the ‘jet’ and ‘core’ data sets:

>>> plot_fit('jet')
>>> plot_model_component('jet', pl, overplot=True)
>>> plot_model_component('core', pl, overplot=True)

For PHA data sets the response is automatically added, but it can also be explicitly included, which will create the same plot:

>>> plot_model_component(pl)
>>> rsp = get_response()
>>> plot_model_component(rsp(pl))

plot_model_components(id: int | str | None = None, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot all the components of a model.

Display the individual model components of a source expression.

Changed in version 4.17.0: The keyword arguments can now be set per plot by using a sequence of values.

Added in version 4.16.1.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False. This is only used for the first component.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? This is only used for the first component.

Notes

The additional keyword arguments match the keywords of the dictionary returned by get_model_plot_prefs.

Examples

Display two plots, the first for the gal * pl component and the second for gal * line:

>>> set_source(xsphabs.gal * (powlaw1d.pl + xsgaussian.line))
>>> plot_model_components(alpha=0.6)

Plot the combined model and then overplot the two components in black, partly opaque, and using dotted and dashed line styles:

>>> plot_model(label="combined")
>>> plot_model_components(overplot=True, color="black",
...                       linestyle=["dotted", "dashed"],
...                       label=["model 1", "model 2"],
...                       alpha=0.5)

plot_pdf(points, name='x', xlabel='x', bins=12, normed=True, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the probability density function of an array of values.

Create and plot the probability density function (PDF) of the input array.

Parameters:

points (array) – The values used to create the probability density function.
name (str, optional) – The label to use as part of the plot title.
xlabel (str, optional) – The label for the X axis
bins (int, optional) – The number of bins to use to create the PDF.
normed (bool, optional) – Should the PDF be normalized (the default is True).
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_pdf. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_draws: Run the pyBLoCXS MCMC algorithm.
get_pdf_plot: Return the data used to plot the last PDF.
plot_cdf: Plot the cumulative density function of an array.
plot_scatter: Create a scatter plot.

Examples

>>> mu, sigma, n = 100, 15, 500
>>> x = np.random.normal(loc=mu, scale=sigma, size=n)
>>> plot_pdf(x, bins=25)

>>> plot_pdf(x, normed=False, xlabel="mu", name="Simulations")

plot_psf(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the 1D PSF model applied to a data set.

The plot_kernel function shows the data used to convolve the model.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_psf. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_psf_plot: Return the data used by plot_psf.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_kernel: Plot the 1D kernel applied to a data set.
set_psf: Add a PSF model to a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Examples

Create a model (a step function) that is convolved by a gaussian, and display the PSF:

>>> dataspace1d(1, 10, step=1, dstype=Data1D)
>>> set_model(steplo1d.stp)
>>> stp.xcut = 4.4
>>> load_psf('psf1', gauss1d.gline)
>>> set_psf('psf1')
>>> gline.fwhm = 1.2
>>> plot_psf()

plot_pvalue(null_model, alt_model, conv_model=None, id: int | str = 1, otherids: Sequence[int | str] = (), num=500, bins=25, numcores=None, replot=False, overplot=False, clearwindow=True, **kwargs)[source] [edit on github]

Compute and plot a histogram of likelihood ratios by simulating data.

Compare the likelihood of the null model to an alternative model by running a number of simulations to calibrate the likelihood ratio test statistics. The distribution of the simulated likelihood ratios is plotted and compared to the likelihoods of the two models fit to the observed data. The fit statistic must be set to a likelihood-based method, such as “cash” or “cstat”. Screen output is created as well as the plot; these values can be retrieved with get_pvalue_results.

The algorithm is based on the description in Sec.5.2 in “Statistics, Handle with Care: Detecting Multiple Model Components with the Likelihood Ratio Test” by Protassov et al., 2002, The Astrophysical Journal, 571, 545; <doi:10.1086/339856>

Changed in version 4.17.0: The “wstat” statistic can now be used with this routine.

Parameters:

null_model – The model expression for the null hypothesis.
alt_model – The model expression for the alternative hypothesis.
conv_model (optional) – An expression used to modify the model so that it can be compared to the data (e.g. a PSF or PHA response).
id (int or str, optional) – The data set that provides the data. The default is 1.
otherids (sequence of int or str, optional) – Other data sets to use in the calculation.
num (int, optional) – The number of simulations to run. The default is 500.
bins (int, optional) – The number of bins to use to create the histogram. The default is 25.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_pvalue. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

TypeError – An invalid statistic.

See also

get_pvalue_plot: Return the data used by plot_pvalue.
get_pvalue_results: Return the data calculated by the last plot_pvalue call.

Notes

Each simulation involves creating a data set using the observed data simulated with Poisson noise.

For the likelihood ratio test to be valid, the following conditions must hold:

The null model is nested within the alternative model.

The extra parameters of the alternative model have Gaussian (normal) distributions that are not truncated by the boundaries of the parameter spaces.

Examples

Use the likelihood ratio to see if the data in data set 1 has a statistically-significant gaussian component:

>>> create_model_component('powlaw1d', 'pl')
>>> create_model_component('gauss1d', 'gline')
>>> plot_pvalue(pl, pl + gline)

Use 1000 simulations and use the data from data sets ‘core’, ‘jet1’, and ‘jet2’:

>>> mdl1 = pl
>>> mdl2 = pl + gline
>>> plot_pvalue(mdl1, mdl2, id='core', otherids=('jet1', 'jet2'),
...             num=1000)

Apply a convolution to the models before fitting:

>>> rsp = get_psf()
>>> plot_pvalue(mdl1, mdl2, conv_model=rsp)

plot_ratio(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

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

This function displays the ratio data / model for a data set.

Changed in version 4.12.0: The Y axis is now always drawn using a linear scale.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_ratio. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_ratio_plot: Return the data used by plot_ratio.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_chisqr: Plot the chi-squared value for each point in a data set.
plot_delchi: Plot the ratio of residuals to error for a data set.
plot_resid: Plot the residuals (data - model) for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.

Notes

The additional arguments supported by plot_ratio are the same as the keywords of the dictionary returned by get_data_plot_prefs.

The ylog setting is ignored, and the Y axis is drawn using a linear scale.

Examples

Plot the ratio of data to model for the default data set:

>>> plot_ratio()

Overplot the ratios from the ‘core’ data set on those from the ‘jet’ dataset:

>>> plot_ratio('jet')
>>> plot_ratio('core', overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_data_plot_prefs. The following sets the X axis to a log scale and draws a solid line between the points:

>>> plot_ratio(xlog=True, linestyle='solid')

plot_resid(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

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

This function displays the residuals (data - model) for a data set.

Changed in version 4.12.0: The Y axis is now always drawn using a linear scale.

Parameters:

id (int, str, or None, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_resid. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises:

sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

See also

get_resid_plot: Return the data used by plot_resid.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_chisqr: Plot the chi-squared value for each point in a data set.
plot_delchi: Plot the ratio of residuals to error for a data set.
plot_ratio: Plot the ratio of data to model for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.

Notes

The additional arguments supported by plot_resid are the same as the keywords of the dictionary returned by get_data_plot_prefs.

The ylog setting is ignored, and the Y axis is drawn using a linear scale.

Examples

Plot the residuals for the default data set:

>>> plot_resid()

Overplot the residuals from the ‘core’ data set on those from the ‘jet’ dataset:

>>> plot_resid('jet')
>>> plot_resid('core', overplot=True)

Add the residuals to the plot of the data, for the default data set:

>>> plot_data()
>>> plot_resid(overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_data_plot_prefs. The following sets the cap length for the ends of the error bars:

>>> plot_resid(capsize=5)

plot_scatter(x, y, name='(x,y)', xlabel='x', ylabel='y', replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Create a scatter plot.

Parameters:

x (array) – The values to plot on the X axis.
y (array) – The values to plot on the Y axis. This must match the size of the x array.
name (str, optional) – The plot title.
xlabel (str, optional) – The label for the X axis.
ylabel (str, optional) – The label for the Y axis.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_scatter. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_scatter_plot: Return the data used to plot the last scatter plot.
plot_trace: Create a trace plot of row number versus value.

Examples

Plot the X and Y points:

>>> mu, sigma, n = 100, 15, 500
>>> x = mu + sigma * np.random.randn(n)
>>> y = mu + sigma * np.random.randn(n)
>>> plot_scatter(x, y)

Change the axis labels and the plot title:

>>> plot_scatter(nh, kt, xlabel='nH', ylabel='kT', name='Simulations')

plot_source(id: int | str | None = None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot the source expression for a data set.

This function plots the source model for a data set. This does not include any instrument response (e.g. a convolution created by set_psf).

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_source. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_source_plot: Return the data used to create the source plot.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_model: Plot the model for a data set.
plot_source_component: Plot a component of the source expression for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The additional arguments supported by plot_source are the same as the keywords of the dictionary returned by get_model_plot_prefs.

Examples

Plot the unconvolved source model for the default data set:

>>> plot_source()

Overplot the source model for data set 2 on data set 1:

>>> plot_source(1)
>>> plot_source(2, overplot=True)

Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_model_plot_prefs. The following plots the source using a log scale for both axes, and then overplots the source from data set “jet” using a dashed line:

>>> plot_source(xlog=True, ylog=True)
>>> plot_source('jet', overplot=True, linestyle='dashed')

plot_source_component(id, model=None, replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot a component of the source expression for a data set.

This function evaluates and plots a component of the model expression for a data set, without any instrument response. Use plot_model_component to include any response.

Parameters:

id (int or str, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string).
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_source_component. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_source_component_plot: Return the data used by plot_source_component.
get_default_id: Return the default data set identifier.
plot: Create one or more plot types.
plot_model_component: Plot a component of the model for a data set.
plot_source_components: Plot all the components of a source.
plot_source: Plot the source expression for a data set.
set_xlinear: New plots will display a linear X axis.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

The additional keyword arguments match the keywords of the dictionary returned by get_model_plot_prefs.

Examples

Overplot the pl component of the source expression for the default data set:

>>> plot_source()
>>> plot_source_component(pl, overplot=True)

plot_source_components(id: int | str | None = None, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Plot all the components of a source.

Display the individual components of a source expression.

Changed in version 4.17.0: The keyword arguments can now be set per plot by using a sequence of values.

Added in version 4.16.1.

Parameters:

id (int, str, or None, optional) – The data set that provides the data. If not given then the default identifier is used, as returned by get_default_id.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False. This is only used for the first component.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)? This is only used for the first component.

See also

get_source_component_plots, plot_model_components, plot_source_component

Notes

The additional keyword arguments match the keywords of the dictionary returned by get_model_plot_prefs.

Examples

Display two plots, the first for the gal * pl component and the second for gal * line:

>>> set_source(xsphabs.gal * (powlaw1d.pl + xsgaussian.line))
>>> plot_source_components(alpha=0.6)

Plot the combined source and then overplot the two components in black, partly opaque, and using dotted and dashed line styles:

>>> plot_source(label="combined")
>>> plot_source_components(overplot=True, color="black",
...                        linestyle=["dotted", "dashed"],
...                        label=["model 1", "model 2"],
...                        alpha=0.5)

plot_trace(points, name='x', xlabel='x', replot=False, overplot=False, clearwindow=True, **kwargs) → None[source] [edit on github]

Create a trace plot of row number versus value.

Display a plot of the points array values (Y axis) versus row number (X axis). This can be useful to view how a value changes, such as the value of a parameter returned by get_draws.

Parameters:

points (array) – The values to plot on the Y axis.
name (str, optional) – The label to use on the Y axis and as part of the plot title.
xlabel (str, optional)
replot (bool, optional) – Set to True to use the values calculated by the last call to plot_trace. The default is False.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.
clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

See also

get_draws: Run the pyBLoCXS MCMC algorithm.
get_trace_plot: Return the data used to plot the last trace.
plot_cdf: Plot the cumulative density function of an array.
plot_pdf: Plot the probability density function of an array.
plot_scatter: Create a scatter plot.

Examples

Plot the trace of the 500 elements in the x array:

>>> mu, sigma = 100, 15
>>> x = mu + sigma * np.random.randn(500)
>>> plot_trace(x)

Use “ampl” as the Y axis label:

>>> plot_trace(ampl, name='ampl')

proj(*args)[source] [edit on github]

Estimate parameter confidence intervals using the projection method.

Note

The conf function should be used instead of proj.

The proj command computes confidence interval bounds for the specified model parameters in the dataset. A given parameter’s value is varied along a grid of values while the values of all the other thawed parameters are allowed to float to new best-fit values. The get_proj and set_proj_opt commands can be used to configure the error analysis; an example being changing the ‘sigma’ field to 1.6 (i.e. 90%) from its default value of 1. The output from the routine is displayed on screen, and the get_proj_results routine can be used to retrieve the results.

Parameters:

id (int or str, optional) – The data set, or sets, that provides the data. If not given then all data sets with an associated model are used simultaneously.
parameter (sherpa.models.parameter.Parameter, optional) – The default is to calculate the confidence limits on all thawed parameters of the model, or models, for all the data sets. The evaluation can be restricted by listing the parameters to use. Note that each parameter should be given as a separate argument, rather than as a list. For example proj(g1.ampl, g1.sigma).
model (sherpa.models.model.Model, optional) – Select all the thawed parameters in the model.

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
get_proj: Return the confidence-interval estimation object.
get_proj_results: Return the results of the last proj run.
int_proj: Plot the statistic value as a single parameter is varied.
reg_proj: Plot the statistic value as two parameters are varied.
set_proj_opt: Set an option of the proj estimation object.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with multiple ids or parameters values, the order is unimportant, since any argument that is not defined as a model parameter is assumed to be a data id.

The proj command is different to covar, in that all other thawed parameters are allowed to float to new best-fit values, instead of being fixed to the initial best-fit values. While proj is more general (e.g. allowing the user to examine the parameter space away from the best-fit point), it is in the strictest sense no more accurate than covar for determining confidence intervals.

An estimated confidence interval is accurate if and only if:

the chi^2 or logL surface in parameter space is approximately shaped like a multi-dimensional paraboloid, and
the best-fit point is sufficiently far from parameter space boundaries.

One may determine if these conditions hold, for example, by plotting the fit statistic as a function of each parameter’s values (the curve should approximate a parabola) and by examining contour plots of the fit statistics made by varying the values of two parameters at a time (the contours should be elliptical, and parameter space boundaries should be no closer than approximately 3 sigma from the best-fit point). The int_proj and reg_proj commands may be used for this.

If either of the conditions given above does not hold, then the output from proj may be meaningless except to give an idea of the scale of the confidence intervals. To accurately determine the confidence intervals, one would have to reparameterize the model, use Monte Carlo simulations, or Bayesian methods.

As the calculation can be computer intensive, the default behavior is to use all available CPU cores to speed up the analysis. This can be changed be varying the numcores option - or setting parallel to False - either with set_proj_opt or get_proj.

As proj estimates intervals for each parameter independently, the relationship between sigma and the change in statistic value delta_S can be particularly simple: sigma = the square root of delta_S for statistics sampled from the chi-square distribution and for the Cash statistic, and is approximately equal to the square root of (2 * delta_S) for fits based on the general log-likelihood. The default setting is to calculate the one-sigma interval, which can be changed with the sigma option to set_proj_opt or get_proj.

projection(*args) [edit on github]

Estimate parameter confidence intervals using the projection method.

Note

The conf function should be used instead of proj.

The proj command computes confidence interval bounds for the specified model parameters in the dataset. A given parameter’s value is varied along a grid of values while the values of all the other thawed parameters are allowed to float to new best-fit values. The get_proj and set_proj_opt commands can be used to configure the error analysis; an example being changing the ‘sigma’ field to 1.6 (i.e. 90%) from its default value of 1. The output from the routine is displayed on screen, and the get_proj_results routine can be used to retrieve the results.

Parameters:

id (int or str, optional) – The data set, or sets, that provides the data. If not given then all data sets with an associated model are used simultaneously.
parameter (sherpa.models.parameter.Parameter, optional) – The default is to calculate the confidence limits on all thawed parameters of the model, or models, for all the data sets. The evaluation can be restricted by listing the parameters to use. Note that each parameter should be given as a separate argument, rather than as a list. For example proj(g1.ampl, g1.sigma).
model (sherpa.models.model.Model, optional) – Select all the thawed parameters in the model.

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
get_proj: Return the confidence-interval estimation object.
get_proj_results: Return the results of the last proj run.
int_proj: Plot the statistic value as a single parameter is varied.
reg_proj: Plot the statistic value as two parameters are varied.
set_proj_opt: Set an option of the proj estimation object.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with multiple ids or parameters values, the order is unimportant, since any argument that is not defined as a model parameter is assumed to be a data id.

The proj command is different to covar, in that all other thawed parameters are allowed to float to new best-fit values, instead of being fixed to the initial best-fit values. While proj is more general (e.g. allowing the user to examine the parameter space away from the best-fit point), it is in the strictest sense no more accurate than covar for determining confidence intervals.

An estimated confidence interval is accurate if and only if:

the chi^2 or logL surface in parameter space is approximately shaped like a multi-dimensional paraboloid, and
the best-fit point is sufficiently far from parameter space boundaries.

One may determine if these conditions hold, for example, by plotting the fit statistic as a function of each parameter’s values (the curve should approximate a parabola) and by examining contour plots of the fit statistics made by varying the values of two parameters at a time (the contours should be elliptical, and parameter space boundaries should be no closer than approximately 3 sigma from the best-fit point). The int_proj and reg_proj commands may be used for this.

If either of the conditions given above does not hold, then the output from proj may be meaningless except to give an idea of the scale of the confidence intervals. To accurately determine the confidence intervals, one would have to reparameterize the model, use Monte Carlo simulations, or Bayesian methods.

As the calculation can be computer intensive, the default behavior is to use all available CPU cores to speed up the analysis. This can be changed be varying the numcores option - or setting parallel to False - either with set_proj_opt or get_proj.

As proj estimates intervals for each parameter independently, the relationship between sigma and the change in statistic value delta_S can be particularly simple: sigma = the square root of delta_S for statistics sampled from the chi-square distribution and for the Cash statistic, and is approximately equal to the square root of (2 * delta_S) for fits based on the general log-likelihood. The default setting is to calculate the one-sigma interval, which can be changed with the sigma option to set_proj_opt or get_proj.

reg_proj(par0, par1, id: int | str | None = None, otherids: Sequence[int | str] | None = None, replot=False, fast=True, min=None, max=None, nloop=(10, 10), delv=None, fac=4, log=(False, False), sigma=(1, 2, 3), levels=None, numcores=None, overplot=False) → None[source] [edit on github]

Plot the statistic value as two parameters are varied.

Create a confidence plot of the fit statistic as a function of parameter value. Dashed lines are added to indicate the current statistic value and the parameter value at this point. The parameter value is varied over a grid of points and the free parameters re-fit. It is expected that this is run after a successful fit, so that the parameter values are at the best-fit location.

Parameters:

par0 – The parameters to plot on the X and Y axes, respectively.
par1 – The parameters to plot on the X and Y axes, respectively.
id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, or None, optional) – Other data sets to use in the calculation.
replot (bool, optional) – Set to True to use the values calculated by the last call to int_proj. The default is False.
fast (bool, optional) – If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False.
min (pair of numbers, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (pair of number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (pair of int, optional) – The number of steps to use. This is used when delv is set to None.
delv (pair of number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (pair of bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
sigma (sequence of number, optional) – The levels at which to draw the contours. The units are the change in significance relative to the starting value, in units of sigma.
levels (sequence of number, optional) – The numeric values at which to draw the contours. This overrides the sigma parameter, if set (the default is None).
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
get_reg_proj: Return the interval-projection object.
int_proj: Calculate and plot the fit statistic versus fit parameter value.
reg_unc: Plot the statistic value as two parameters are varied.

Notes

The difference to reg_unc is that at each step, a fit is made to the remaining thawed parameters in the source model. This makes the result a more-accurate rendering of the projected shape of the hypersurface formed by the statistic, but the run-time is longer than, the results of reg_unc, which does not vary any other parameter. If there are no free parameters in the model, other than the parameters being plotted, then the results will be the same.

Examples

Vary the xpos and ypos parameters of the gsrc model component for all data sets with a source expression.

>>> reg_proj(gsrc.xpos, gsrc.ypos)

Use only the data in data set 1:

>>> reg_proj(gsrc.xpos, gsrc.ypos, id=1)

Only display the one- and three-sigma contours:

>>> reg_proj(gsrc.xpos, gsrc.ypos, sigma=(1, 3))

Display contours at values of 5, 10, and 20 more than the statistic value of the source model for data set 1:

>>> s0 = calc_stat(id=1)
>>> lvls = s0 + np.asarray([5, 10, 20])
>>> reg_proj(gsrc.xpos, gsrc.ypos, levels=lvls, id=1)

Increase the limits of the plot and the number of steps along each axis:

>>> reg_proj(gsrc.xpos, gsrc.ypos, id=1, fac=6, nloop=(41, 41))

Compare the ampl parameters of the g and b model components, for data sets ‘core’ and ‘jet’, over the given ranges:

>>> reg_proj(g.ampl, b.ampl, min=(0, 1e-4), max=(0.2, 5e-4),
...          nloop=(51, 51), id='core', otherids=['jet'])

reg_unc(par0, par1, id: int | str | None = None, otherids: Sequence[int | str] | None = None, replot=False, min=None, max=None, nloop=(10, 10), delv=None, fac=4, log=(False, False), sigma=(1, 2, 3), levels=None, numcores=None, overplot=False) → None[source] [edit on github]

Plot the statistic value as two parameters are varied.

Create a confidence plot of the fit statistic as a function of parameter value. Dashed lines are added to indicate the current statistic value and the parameter value at this point. The parameter value is varied over a grid of points and the statistic evaluated while holding the other parameters fixed. It is expected that this is run after a successful fit, so that the parameter values are at the best-fit location.

Parameters:

par0 – The parameters to plot on the X and Y axes, respectively.
par1 – The parameters to plot on the X and Y axes, respectively.
id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, or None, optional) – Other data sets to use in the calculation.
replot (bool, optional) – Set to True to use the values calculated by the last call to int_proj. The default is False.
min (pair of numbers, optional) – The minimum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
max (pair of number, optional) – The maximum parameter value for the calculation. The default value of None means that the limit is calculated from the covariance, using the fac value.
nloop (pair of int, optional) – The number of steps to use. This is used when delv is set to None.
delv (pair of number, optional) – The step size for the parameter. Setting this overrides the nloop parameter. The default is None.
fac (number, optional) – When min or max is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log (pair of bool, optional) – Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
sigma (sequence of number, optional) – The levels at which to draw the contours. The units are the change in significance relative to the starting value, in units of sigma.
levels (sequence of number, optional) – The numeric values at which to draw the contours. This overrides the sigma parameter, if set (the default is None).
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.
overplot (bool, optional) – If True then add the data to an existing plot, otherwise create a new plot. The default is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
get_reg_unc: Return the interval-uncertainty object.
int_unc: Calculate and plot the fit statistic versus fit parameter value.
reg_proj: Plot the statistic value as two parameters are varied.

Notes

The difference to reg_proj is that at each step only the pair of parameters are varied, while all the other parameters remain at their starting value. This makes the result a less-accurate rendering of the projected shape of the hypersurface formed by the statistic, but the run-time is likely shorter than, the results of reg_proj, which fits the model to the remaining thawed parameters at each step. If there are no free parameters in the model, other than the parameters being plotted, then the results will be the same.

Examples

Vary the xpos and ypos parameters of the gsrc model component for all data sets with a source expression.

>>> reg_unc(gsrc.xpos, gsrc.ypos)

Use only the data in data set 1:

>>> reg_unc(gsrc.xpos, gsrc.ypos, id=1)

Only display the one- and three-sigma contours:

>>> reg_unc(gsrc.xpos, gsrc.ypos, sigma=(1, 3))

Display contours at values of 5, 10, and 20 more than the statistic value of the source model for data set 1:

>>> s0 = calc_stat(id=1)
>>> lvls = s0 + np.asarray([5, 10, 20])
>>> reg_unc(gsrc.xpos, gsrc.ypos, levels=lvls, id=1)

Increase the limits of the plot and the number of steps along each axis:

>>> reg_unc(gsrc.xpos, gsrc.ypos, id=1, fac=6, nloop=(41, 41))

Compare the ampl parameters of the g and b model components, for data sets ‘core’ and ‘jet’, over the given ranges:

>>> reg_unc(g.ampl, b.ampl, min=(0, 1e-4), max=(0.2, 5e-4),
...         nloop=(51, 51), id='core', otherids=['jet'])

Overplot the results on the reg_proj plot:

>>> reg_proj(s1.c0, s2.xpos)
>>> reg_unc(s1.c0, s2.xpos, overplot=True)

reset(model=None, id: int | str | None = None) → None[source] [edit on github]

Reset the model parameters to their default settings.

The reset function restores the parameter values to the default value set by guess or to the user-defined default. If the user set initial model values or soft limits - e.g. either with set_par or by using parameter prompting via paramprompt - then reset will restore these values and limits even after guess or fit has been called.

Parameters:

model (optional) – The model component or expression to reset. The default is to use all source expressions.
id (int, str, or None, optional) – The data set to use. The default is to use all data sets with a source expression.

See also

fit: Fit one or more data sets.
guess: Set model parameters to values matching the data.
paramprompt: Control how parameter values are set.
set_par: Set the value, limits, or behavior of a model parameter.

Examples

The following examples assume that the source model has been set using:

>>> set_source(powlaw1d.pl * xsphabs.gal)

Fit the model and then reset the values of both components (pl and gal):

>>> fit()
>>> reset()

Reset just the parameters of the pl model component:

>>> reset(pl)

Reset all the components of the source expression for data set 2.

>>> reset(get_source(2))

restore(filename='sherpa.save') → None[source] [edit on github]

Load in a Sherpa session from a file.

Warning

Security risk: The imported functions and objects could contain arbitrary Python code and be malicious. Never use this function on untrusted input.

Parameters:: filename (str, optional) – The name of the file to read the results from. The default is ‘sherpa.save’.
Raises:: IOError – If filename does not exist.

See also

clean: Clear all stored session data.
save: Save the current Sherpa session to a file.

Notes

The input to restore must have been created with the save command. This is a binary file, which may not be portable between versions of Sherpa, but is platform independent. A warning message may be created if a file saved by an older (or newer) version of Sherpa is loaded. An example of such a message is:

WARNING: Could not determine whether the model is discrete.
This probably means that you have restored a session saved with a previous version of Sherpa.
Falling back to assuming that the model is continuous.

Examples

Load in the Sherpa session from ‘sherpa.save’.

>>> restore()

Load in the session from the given file:

>>> restore('/data/m31/setup.sherpa')

save(filename='sherpa.save', clobber=False) → None[source] [edit on github]

Save the current Sherpa session to a file.

Parameters:

filename (str, optional) – The name of the file to write the results to. The default is ‘sherpa.save’.
clobber (bool, optional) – This flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

clean: Clear all stored session data.
restore: Load in a Sherpa session from a file.
sherpa.astro.ui.save_all: Save the Sherpa session as an ASCII file.

Notes

The current Sherpa session is saved using the Python pickle module. The output is a binary file, which may not be portable between versions of Sherpa, but is platform independent, and contains all the data. This means that files created by save can be sent to collaborators to share results.

Examples

Save the current session to the file ‘sherpa.save’.

>>> save()

Save the current session to the file ‘bestfit.sherpa’, overwriting any existing version of the file.

>>> save('bestfit.sherpa', clobber=True)

save_arrays(filename, args, fields=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Write a list of arrays to an ASCII file.

Parameters:

filename (str) – The name of the file to write the array to.
args (array of arrays) – The arrays to write out.
fields (array of str) – The column names (should match the size of args).
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

save_data: Save the data to a file.

Examples

Write the x and y columns from the default data set to the file ‘src.dat’:

>>> x = get_indep()
>>> y = get_dep()
>>> save_arrays('src.dat', [x, y])

Use the column names “r” and “surbri” for the columns:

>>> save_arrays('prof.txt', [x, y], fields=["r", "surbri"],
...             clobber=True)

save_data(id, filename=None, fields=None, sep=' ', comment='#', clobber=False, linebreak='\n', format='%g') → None[source] [edit on github]

Save the data to a file.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to. The data is written out as an ASCII file.
fields (array of str, optional) – The attributes of the data set to write out. If None, write out all the columns.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IdentifierErr – If there is no matching data set.
sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

save_arrays: Write a list of arrays to a file.
save_delchi: Save the ratio of residuals (data-model) to error to a file.
save_error: Save the errors to a file.
save_filter: Save the filter array to a file.
save_resid: Save the residuals (data-model) to a file.
save_staterror: Save the statistical errors to a file.
save_syserror: Save the statistical errors to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

Examples

Write the default data set out to the ASCII file ‘src.dat’:

>>> save_data('src.dat')

Only write out the x, y, and staterror columns for data set ‘rprof’ to the file ‘prof.out’, over-writing it if it already exists:

>>> save_data('rprof', 'prof.out', clobber=True,
...           fields=['x', 'y', 'staterror'])

save_delchi(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the ratio of residuals (data-model) to error to a file.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IdentifierErr – If no model has been set for this data set.
sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

save_data: Save the data to a file.
save_resid: Save the residuals (data-model) to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and DELCHI. The residuals array respects any filter setting for the data set.

Examples

Write the residuals to the file “delchi.dat”:

>>> save_delchi('delchi.dat')

Write the residuals from the data set ‘jet’ to the file “delchi.dat”:

>>> save_resid('jet', "delchi.dat", clobber=True)

save_error(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the errors to a file.

The total errors for a data set are the quadrature combination of the statistical and systematic errors. The systematic errors can be 0. If the statistical errors have not been set explicitly, then the values calculated by the statistic - such as chi2gehrels or chi2datavar - will be used.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

get_error: Return the errors on the dependent axis of a data set.
load_staterror: Load the statistical errors from a file.
load_syserror: Load the systematic errors from a file.
save_data: Save the data to a file.
save_staterror: Save the statistical errors to a file.
save_syserror: Save the systematic errors to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and ERR.

Examples

Write out the errors from the default data set to the file ‘errs.dat’.

>>> save_error('errs.dat')

Over-write the file it it already exists, and take the data from the data set “jet”:

>>> save_error('jet', 'err.out', clobber=True)

save_filter(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the filter array to a file.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.DataErr – If the data set has not been filtered.
sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

load_filter: Load the filter array from a file and add to a data set.
save_data: Save the data to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and FILTER.

Examples

Write the filter from the default data set as an ASCII file:

>>> save_filter('filt.dat')

save_model(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the model values to a file.

The model is evaluated on the grid of the data set, including any instrument response (such as a PSF).

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IdentifierErr – If no model has been set for this data set.
sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

save_data: Save the data to a file.
save_source: Save the model values to a file.
set_model: Set the source model expression for a data set.
set_full_model: Define the convolved model expression for a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and MODEL (for 1D data). The residuals array respects any filter setting for the data set.

Examples

Write the model to the file “model.dat”:

>>> save_model('model.dat')

Write the model from the data set ‘jet’ to the file “jet.mdl”:

>>> save_model('jet', "jet.mdl", clobber=True)

save_resid(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the residuals (data-model) to a file.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IdentifierErr – If no model has been set for this data set.
sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

save_data: Save the data to a file.
save_delchi: Save the ratio of residuals (data-model) to error to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and RESID. The residuals array respects any filter setting for the data set.

Examples

Write the residuals to the file “resid.dat”:

>>> save_resid('resid.dat')

Write the residuals from the data set ‘jet’ to the file “resid.dat”:

>>> save_resid('jet', "resid.dat", clobber=True)

save_source(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the model values to a file.

The model is evaluated on the grid of the data set, but does not include any instrument response (such as a PSF).

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – This flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IdentifierErr – If no model has been set for this data set.
sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

save_data: Save the data to a file.
save_model: Save the model values to a file.
set_full_model: Define the convolved model expression for a data set.
set_model: Set the source model expression for a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and SOURCE (for 1D data). The residuals array respects any filter setting for the data set.

Examples

Write the model to the file “model.dat”:

>>> save_source('model.dat')

Write the model from the data set ‘jet’ to the file “jet.mdl”:

>>> save_source('jet', "jet.mdl", clobber=True)

save_staterror(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the statistical errors to a file.

If the statistical errors have not been set explicitly, then the values calculated by the statistic - such as chi2gehrels or chi2datavar - will be used.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

load_staterror: Load the statistical errors from a file.
save_error: Save the errors to a file.
save_syserror: Save the systematic errors to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and STAT_ERR.

Examples

Write out the statistical errors from the default data set to the file ‘errs.dat’.

>>> save_staterror('errs.dat')

Over-write the file it it already exists, and take the data from the data set “jet”:

>>> save_staterror('jet', 'err.out', clobber=True)

save_syserror(id, filename=None, clobber=False, sep=' ', comment='#', linebreak='\n', format='%g') → None[source] [edit on github]

Save the statistical errors to a file.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
filename (str) – The name of the file to write the array to.
clobber (bool, optional) – If filename is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
linebreak (str, optional) – Indicate a new line. The default is '\n'.
format (str, optional) – The format used to write out the numeric values. The default is '%g%'.

Raises:

sherpa.utils.err.IOErr – If the data set does not contain any systematic errors.
sherpa.utils.err.IOErr – If filename already exists and clobber is False.

See also

load_syserror: Load the systematic errors from a file.
save_error: Save the errors to a file.
save_staterror: Save the statistical errors to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

The output file contains the columns X and SYS_ERR.

Examples

Write out the systematic errors from the default data set to the file ‘errs.dat’.

>>> save_syserror('errs.dat')

Over-write the file it it already exists, and take the data from the data set “jet”:

>>> save_syserror('jet', 'err.out', clobber=True)

set_conf_opt(name, val)[source] [edit on github]

Set an option for the confidence interval method.

This is a helper function since the options can also be set directly using the object returned by get_conf.

Parameters:

name (str) – The name of the option to set. The get_conf routine can be used to find out valid values for this argument.
val – The new value for the option.

Raises:

sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

conf: Estimate parameter confidence intervals using the confidence method.
get_conf: Return the conf estimation object.
get_conf_opt: Return one or all options of the conf estimation object.

Examples

>>> set_conf_opt('parallel', False)

set_covar_opt(name, val)[source] [edit on github]

Set an option for the covariance method.

This is a helper function since the options can also be set directly using the object returned by get_covar.

Parameters:

name (str) – The name of the option to set. The get_covar routine can be used to find out valid values for this argument.
val – The new value for the option.

Raises:

sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

covar: Estimate parameter confidence intervals using the covariance method.
get_covar: Return the covar estimation object.
get_covar_opt: Return one or all options of the covar estimation object.

Examples

>>> set_covar_opt('sigma', 1.6)

set_data(id, data=None) → None[source] [edit on github]

Set a data set.

Parameters:

id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
data (instance of a sherpa.Data.Data-derived class) – The new contents of the data set. This can be copied from an existing data set or loaded in from a file (e.g. unpack_data).

See also

copy_data: Copy a data set to a new identifier.
delete_data: Delete a data set by identifier.
get_data: Return the data set by identifier.
load_data: Create a data set from a file.
unpack_data: Read in a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the data parameter. If given two un-named arguments, then they are interpreted as the id and data parameters, respectively.

Examples

>>> d1 = get_data(2)
>>> set_data(d1)

Copy the background data from the default data set into a new data set identified as ‘bkg’:

>>> set_data('bkg', get_bkg())

set_default_id(id: int | str) → None[source] [edit on github]

Set the default data set identifier.

The Sherpa data id ties data, model, fit, and plotting information into a data set easily referenced by id. The default identifier, used by many commands, is changed by this command. The current setting can be found by using get_default_id.

Parameters:: id (int or str) – The default data set identifier to be used by certain Sherpa functions when an identifier is not given, or set to None.

See also

get_default_id: Return the default data set identifier.
list_data_ids: List the identifiers for the loaded data sets.

Notes

The default Sherpa data set identifier is the integer 1.

Examples

After the following, many commands, such as set_source, will use ‘src’ as the default data set identifier:

>>> set_default_id('src')

Restore the default data set identifier.

>>> set_default_id(1)

set_dep(id, val=None) → None[source] [edit on github]

Set the dependent axis of a data set.

Parameters:

id (int or str, optional) – The data set to use. If not given then the default identifier is used, as returned by get_default_id.
val (array) – The array of values for the dependent axis.

See also

dataspace1d: Create the independent axis for a 1D data set.
dataspace2d: Create the independent axis for a 2D data set.
get_dep: Return the dependent axis of a data set.
load_arrays: Create a data set from array values.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the val parameter. If given two un-named arguments, then they are interpreted as the id and val parameters, respectively.

Examples

Create a 1D data set with values at (0,4), (2,10), (4,12), (6,8), (8,2), and (10,12):

>>> dataspace1d(0, 10, 2, dstype=Data1D)
>>> set_dep([4, 10, 12, 8, 2, 12])

Set the values for the data set ‘src’:

>>> set_dep('src', y1)

set_filter(id, val=None, ignore=False) → None[source] [edit on github]

Set the filter array of a data set.

Parameters:

id (int or str, optional) – The data set to use. If not given then the default identifier is used, as returned by get_default_id.
val (array) – The array of filter values (0 or 1). The size should match the array returned by get_dep.
ignore (bool, optional) – If False (the default) then include bins with a non-zero filter value, otherwise exclude these bins.

See also

get_dep: Return the dependent axis of a data set.
get_filter: Return the filter expression for a data set.
ignore: Exclude data from the fit.
load_filter: Load the filter array from a file and add to a data set.
notice: Include data in the fit.
save_filter: Save the filter array to a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the val parameter. If given two un-named arguments, then they are interpreted as the id and val parameters, respectively.

Examples

Ignore those bins with a value less 20.

>>> d = get_dep()
>>> set_filter(d >= 20)

set_full_model(id, model=None)[source] [edit on github]

Define the convolved model expression for a data set.

The model expression created by set_model can be modified by “instrumental effects”, such as a PSF set by set_psf. The set_full_model function is for when this is not sufficient, and full control is needed. An example of when this would be if different PSF models should be applied to different source components.

Parameters:

id (int or str, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.Model object) – This defines the model used to fit the data. It can be a Python expression or a string version of it.

See also

fit: Fit one or more data sets.
set_psf: Add a PSF model to a data set.
set_model: Set the source model expression for a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Some functions - such as plot_source - may not work for model expressions created by set_full_model.

Examples

Apply different PSFs to different components, as well as an unconvolved component:

>>> load_psf("psf1", "psf1.dat")
>>> load_psf("psf2", "psf2.dat")
>>> smodel = psf1(gauss2d.src1) + psf2(beta2d.src2) + const2d.bgnd
>>> set_full_model("src", smodel)

set_iter_method(meth: str) → None[source] [edit on github]

Set the iterative-fitting scheme used in the fit.

Control whether an iterative scheme should be applied to the fit.

Changed in version 4.14.1: The “primini” scheme has been removed from Sherpa.

Parameters:: meth ({ 'none', 'sigmarej' }) – The name of the scheme used during the fit; ‘none’ means no scheme is used. It is only valid to change the scheme when a chi-square statistic is in use.
Raises:: TypeError – When the meth argument is not recognized.

See also

fit: Fit a model to one or more data sets.
get_iter_method_name: Return the name of the iterative fitting scheme.
get_iter_method_opt: Return one or all options for the iterative-fitting scheme.
list_iter_methods: List the iterative fitting schemes.
set_iter_method_opt: Set an option for the iterative-fitting scheme.
set_stat: Set the statistical method.

Notes

The parameters of the schemes are described in set_iter_method_opt.

This is a chi-square statistic where the variance is computed from model amplitudes derived in the previous iteration of the fit. This ‘Iterative Weighting’ ([1]) attempts to remove biased estimates of model parameters.

The variance in bin i is estimated to be:

sigma^2_i^j = S(i, t_s^(j-1)) + (A_s/A_b)^2 B_off(i, t_b^(j-1))

where j is the number of iterations that have been carried out in the fitting process, B_off is the background model amplitude in bin i of the off-source region, and t_s^(j-1) and t_b^(j-1) are the set of source and background model parameter values derived during the iteration previous to the current one. The variances are set to an array of ones on the first iteration.

In addition to reducing parameter estimate bias, this statistic can be used even when the number of counts in each bin is small (< 5), although the user should proceed with caution.

The sigmarej scheme is based on the IRAF ``sfit` function <https://iraf.readthedocs.io/en/latest/tasks/noao/imred/specred/sfit.html>`_, where after a fit data points are excluded if the value of (data-model)/error) exceeds a threshold, and the data re-fit. This removal of data points continues until the fit has converged. The error removal can be asymmetric, since there are separate parameters for the lower and upper limits.

References

“Multiparameter linear least-squares fitting to Poisson data one count at a time”, Wheaton et al. 1995, ApJ 438, 322

Examples

Switch to the ‘sigmarej’ scheme for iterative fitting and change the low and high rejection limits to 4 and 3 respectively:

>>> set_iter_method('sigmarej')
>>> set_iter_method_opt('lrej') = 4
>>> set_iter_method_opt('hrej') = 3

Remove any iterative-fitting method:

>>> set_iter_method('none')

set_iter_method_opt(optname: str, val: Any) → None[source] [edit on github]

Set an option for the iterative-fitting scheme.

Parameters:

optname (str) – The name of the option to set. The get_iter_method_opt routine can be used to find out valid values for this argument.
val – The new value for the option.

Raises:

sherpa.utils.err.ArgumentErr – If the optname argument is not recognized.

See also

get_iter_method_name: Return the name of the iterative fitting scheme.
get_iter_method_opt: Return one or all options for the iterative-fitting scheme.
list_iter_methods: List the iterative fitting schemes.
set_iter_method: Set the iterative-fitting scheme used in the fit.

Notes

The supported fields for the sigmarej scheme are:

grow: The number of points adjacent to a rejected point that should also be removed. A value of 0 means that only the discrepant point is removed whereas a value of 1 means that the two adjacent points (one lower and one higher) will also be removed.
hrej: The rejection criterion in units of sigma, for data points above the model (it must be >= 0).
lrej: The rejection criterion in units of sigma, for data points below the model (it must be >= 0).
maxiters: The maximum number of iterations to perform. If this value is 0 then the fit will run until it has converged.

Examples

Reject any points that are more than 5 sigma away from the best fit model and re-fit.

>>> set_iter_method('sigmarej')
>>> set_iter_method_opt('lrej', 5)
>>> set_iter_method_opt('hrej', 5)
>>> fit()

set_method(meth: OptMethod | str) → None[source] [edit on github]

Set the optimization method.

The primary task of Sherpa is to fit a model M(p) to a set of observed data, where the vector p denotes the model parameters. An optimization method is one that is used to determine the vector of model parameter values, p0, for which the chosen fit statistic is minimized.

Parameters:: meth (str) – The name of the method (case is not important). The list_methods function returns the list of supported values.
Raises:: sherpa.utils.err.ArgumentErr – If the meth argument is not recognized.

See also

get_method_name: Return the name of the current optimization method.
list_methods: List the supported optimization methods.
set_stat: Set the fit statistic.

Notes

The available methods include:

levmar: The Levenberg-Marquardt method is an interface to the MINPACK subroutine lmdif to find the local minimum of nonlinear least squares functions of several variables by a modification of the Levenberg-Marquardt algorithm [1].
moncar: The implementation of the moncar method is based on [2].
neldermead: The implementation of the Nelder Mead Simplex direct search is based on [3].
simplex: This is another name for neldermead.

References

J.J. More, “The Levenberg Marquardt algorithm: implementation and theory,” in Lecture Notes in Mathematics 630: Numerical Analysis, G.A. Watson (Ed.), Springer-Verlag: Berlin, 1978, pp.105-116.
Storn, R. and Price, K. “Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces.” J. Global Optimization 11, 341-359, 1997.
Jeffrey C. Lagarias, James A. Reeds, Margaret H. Wright, Paul E. Wright “Convergence Properties of the Nelder-Mead Simplex Algorithm in Low Dimensions”, SIAM Journal on Optimization,Vol. 9, No. 1 (1998), pages 112-147.

Examples

>>> set_method('neldermead')

set_method_opt(optname: str, val: Any) → None[source] [edit on github]

Set an option for the current optimization method.

This is a helper function since the optimization options can also be set directly using the object returned by get_method.

Parameters:

optname (str) – The name of the option to set. The get_method and get_method_opt routines can be used to find out valid values for this argument.
val – The new value for the option.

Raises:

sherpa.utils.err.ArgumentErr – If the optname argument is not recognized.

See also

get_method: Return an optimization method.
get_method_opt: Return one or all options of the current optimization method.
set_method: Change the optimization method.

Examples

Change the maxfev parameter for the current optimizer to 2000.

>>> set_method_opt('maxfev', 2000)

set_model(id, model=None)[source] [edit on github]

Set the source model expression for a data set.

The function is available as both set_model and set_source. The model fit to the data can be further modified by instrument responses which can be set explicitly - e.g. by set_psf - or be defined automatically by the type of data being used (e.g. the ARF and RMF of a PHA data set). The set_full_model command can be used to explicitly include the instrument response if necessary.

Parameters:

id (int or str, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.Model object) – This defines the model used to fit the data. It can be a Python expression or a string version of it.

See also

delete_model: Delete the model expression from a data set.
fit: Fit one or more data sets.
freeze: Fix model parameters so they are not changed by a fit.
get_source: Return the source model expression for a data set.
integrate1d: Integrate 1D source expressions.
sherpa.astro.ui.set_bkg_model: Set the background model expression for a data set.
set_full_model: Define the convolved model expression for a data set.
show_model: Display the source model expression for a data set.
set_par: Set the value, limits, or behavior of a model parameter.
thaw: Allow model parameters to be varied during a fit.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

PHA data sets will automatically apply the instrumental response (ARF and RMF) to the source expression. For some cases this is not useful - for example, when different responses should be applied to different model components - in which case set_full_model should be used instead.

Model caching is available via the model cache attribute. A non-zero value for this attribute means that the results of evaluating the model will be cached if all the parameters are frozen, which may lead to a reduction in the time taken to evaluate a fit. A zero value turns off the caching. The default setting for X-Spec and 1D analytic models is that cache is 5, but 0 for the 2D analytic models.

The integrate1d model can be used to apply a numerical integration to an arbitrary model expression.

Examples

Create an instance of the powlaw1d model type, called pl, and use it as the model for the default data set.

>>> set_model(polynom1d.pl)

Create a model for the default dataset which is the xsphabs model multiplied by the sum of an xsapec and powlaw1d models (the model components are identified by the labels gal, clus, and pl).

>>> set_model(xsphabs.gal * (xsapec.clus + powlaw1d.pl))

Repeat the previous example, using a string to define the model expression:

>>> set_model('xsphabs.gal * (xsapec.clus + powlaw1d.pl)')

Use the same model component (src, a gauss2d model) for the two data sets (‘src1’ and ‘src2’).

>>> set_model('src1',  gauss2d.src + const2d.bgnd1)
>>> set_model('src2', src + const2d.bgnd2)

Share an expression - in this case three gaussian lines - between three data sets. The normalization of this line complex is allowed to vary in data sets 2 and 3 (the norm2 and norm3 components of the const1d model), and each data set has a separate polynom1d component (bgnd1, bgnd2, and bgnd3). The c1 parameters of the polynom1d model components are thawed and then linked together (to reduce the number of free parameters):

>>> lines = gauss1d.l1 + gauss1d.l2 + gauss1d.l3
>>> set_model(1, lines + polynom1d.bgnd1)
>>> set_model(2, lines * const1d.norm2 + polynom1d.bgnd2)
>>> set_model(3, lines * const1d.norm3 + polynom1d.bgnd3)
>>> thaw(bgnd1.c1, bgnd2.c1, bgnd3.c1)
>>> link(bgnd2.c2, bgnd1.c1)
>>> link(bgnd3.c3, bgnd1.c1)

For this expression, the gal component is frozen, so it is not varied in the fit. The cache attribute is set to a non-zero value to ensure that it is cached during a fit (this is actually the default value for this model so it not normally needed).

>>> set_model(xsphabs.gal * (xsapec.clus + powlaw1d.pl))
>>> gal.nh = 0.0971
>>> freeze(gal)
>>> gal.cache = 1

set_model_autoassign_func(func: Callable[[str, Model], None] | None = None) → None[source] [edit on github]

Set the method used to create model component identifiers.

When a model component is created, the default behavior is to add the component to the default Python namespace. This is controlled by a function which can be changed with this routine.

Parameters:: func (function reference) – The function to use: this should accept two arguments, a string (component name), and the model instance.

See also

create_model_component: Create a model component.
get_model_autoassign_func: Return the method used to create model component identifiers
set_model: Set the source model expression for a data set.

Notes

The default assignment function first renames a model component to include the model type and user-defined identifier. It then updates the ‘__main__’ module’s dictionary with the model identifier as the key and the model instance as the value. Similarly, it updates the ‘__builtin__’ module’s dictionary just like ‘__main__’ for compatibility with IPython.

set_par(par, val=None, min=None, max=None, frozen=None)[source] [edit on github]

Set the value, limits, or behavior of a model parameter.

Parameters:

par (str) – The name of the parameter, using the format “componentname.parametername”.
val (number, optional) – Change the current value of the parameter.
min (number, optional) – Change the minimum value of the parameter (the soft limit).
max (number, optional) – Change the maximum value of the parameter (the soft limit).
frozen (bool, optional) – Freeze (True) or thaw (False) the parameter.

Raises:

sherpa.utils.err.ArgumentErr – If the par argument is invalid: the model component does not exist or the given model has no parameter with that name.

See also

freeze: Fix model parameters so they are not changed by a fit.
get_par: Return a parameter of a model component.
link: Link a parameter value to an associated value.
thaw: Allow model parameters to be varied during a fit.
unlink: Unlink a parameter value.

Notes

The parameter object can be used to change these values directly, by setting the attribute with the same name as the argument - so that:

set_par('emis.flag', val=2, frozen=True)

is the same as:

emis.flag.val = 2
emis.flag.frozen = True

Examples

Change the parameter value to 23.

>>> set_par('bgnd.c0', 23)

Restrict the line.ampl parameter to be between 1e-4 and 10 and to have a value of 0.1.

>>> set_par('line.ampl', 0.1, min=1e-4, max=10)

set_plot_backend(backend) → None[source] [edit on github]

Change the plot backend.

This will reset any plot structures, such as that returned by get_data_plot.

Added in version 4.16.0.

Parameters:: backend (str) – The name of the plot backend.

set_prior(par, prior)[source] [edit on github]

Set the prior function to use with a parameter.

The default prior used by get_draws for each parameter is flat, varying between the soft minimum and maximum values of the parameter (as given by the min and max attributes of the parameter object). The set_prior function is used to change the form of the prior for a parameter, and get_prior returns the current prior for a parameter.

Parameters:

par (a sherpa.models.parameter.Parameter instance) – A parameter of a model instance.
prior (function or sherpa.models.model.Model instance) – The function to use for a prior. It must accept a single argument and return a value of the same size as the input.

See also

get_draws: Run the pyBLoCXS MCMC algorithm.
get_prior: Return the prior function for a parameter (MCMC).
set_sampler: Set the MCMC sampler.

Examples

Set the prior for the kT parameter of the therm component to be a gaussian, centered on 1.7 keV and with a FWHM of 0.35 keV:

>>> create_model_component('xsapec', 'therm')
>>> create_model_component('gauss1d', 'p_temp')
>>> p_temp.pos = 1.7
>>> p_temp.fwhm = 0.35
>>> set_prior(therm.kT, p_temp)

Create a function (lognorm) and use it as the prior of the nH parameter of the abs1 model component:

>>> def lognorm(x):
...     nh = 20
...     sigma = 0.5  # use a sigma of 0.5
...     # nH is in units of 10^-22 so convert
...     dx = np.log10(x) + 22 - nh
...     norm = sigma / np.sqrt(2 * np.pi)
...     return norm * np.exp(-0.5 * dx * dx / (sigma * sigma))
...
>>> create_model_component('xsphabs', 'abs1')
>>> set_prior(abs1.nH, lognorm)

set_proj_opt(name, val)[source] [edit on github]

Set an option for the projection method.

Note

The conf function should be used instead of proj.

This is a helper function since the options can also be set directly using the object returned by get_proj.

Parameters:

name (str) – The name of the option to set. The get_proj routine can be used to find out valid values for this argument.
val – The new value for the option.

Raises:

sherpa.utils.err.ArgumentErr – If the name argument is not recognized.

See also

conf: Estimate parameter confidence intervals using the confidence method.
proj: Estimate parameter confidence intervals using the projection method.
get_proj: Return the proj estimation object.
get_proj_opt: Return one or all options of the proj estimation object.

Examples

>>> set_proj_opt('parallel', False)

set_psf(id, psf=None)[source] [edit on github]

Add a PSF model to a data set.

After this call, the model that is fit to the data (as set by set_model) will be convolved by the given PSF model. The term “psf” is used in functions to refer to the data sent to this function whereas the term “kernel” refers to the data that is used in the actual convolution (this can be re-normalized and a sub-set of the PSF data).

Parameters:

id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.
psf (str or sherpa.instrument.PSFModel instance) – The PSF model created by load_psf.

See also

delete_psf: Delete the PSF model for a data set.
get_psf: Return the PSF model defined for a data set.
image_psf: Display the 2D PSF model for a data set in the image viewer.
load_psf: Create a PSF model.
plot_psf: Plot the 1D PSF model applied to a data set.
set_full_model: Define the convolved model expression for a data set.
set_model: Set the source model expression for a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the psf parameter. If given two un-named arguments, then they are interpreted as the id and psf parameters, respectively.

A PSF component should only be applied to a single data set. This is not enforced by the system, and incorrect results can occur if this condition is not true.

The point spread function (PSF) is defined by the full (unfiltered) PSF image loaded into Sherpa or the PSF model expression evaluated over the full range of the dataset; both types of PSFs are established with the load_psf command. The kernel is the subsection of the PSF image or model which is used to convolve the data. This subsection is created from the PSF when the size and center of the kernel are defined by the command set_psf. While the kernel and PSF might be congruent, defining a smaller kernel helps speed the convolution process by restricting the number of points within the PSF that must be evaluated.

In a 1-D PSF model, a radial profile or 1-D model array is used to convolve (fold) the given source model using the Fast Fourier Transform (FFT) technique. In a 2-D PSF model, an image or 2-D model array is used.

The parameters of a PSF model include:

kernel: The data used for the convolution (file name or model instance).
size: The number of pixels used in the convolution (this can be a subset of the full PSF). This is a scalar (1D) or a sequence (2D, width then height) value.
center: The center of the kernel. This is a scalar (1D) or a sequence (2D, width then height) value. The kernel centroid must always be at the center of the extracted sub-image, otherwise, systematic shifts will occur in the best-fit positions.
radial: Set to 1 to use a symmetric array. The default is 0 to reduce edge effects.
norm: Should the kernel be normalized so that it sums to 1? This summation is done over the full data set (not the subset defined by the size parameter). The default is 1 (yes).

Examples

Use the data in the ASCII file ‘line_profile.dat’ as the PSF for the default data set:

>>> load_psf('psf1', 'line_profile.dat')
>>> set_psf(psf1)

Use the same PSF for different data sets:

>>> load_psf('p1', 'psf.img')
>>> load_psf('p2', 'psf.img')
>>> set_psf(1, 'p1')
>>> set_psf(2, 'p2')

Restrict the convolution to a sub-set of the PSF data and compare the two:

>>> set_psf(psf1)
>>> psf1.size = (41,41)
>>> image_psf()
>>> image_kernel(newframe=True, tile=True)

set_rng(rng: Generator | RandomState | None) → None[source] [edit on github]

Set the RNG generator.

Added in version 4.16.0: This replaces the seed argument for certain routines and the need to explicitly call numpy.random.seed in others.

Parameters:: rng (numpy.random.Generator, numpy.random.RandomState, or None) – Determines how random numbers are created. If set to None then the routines in numpy.random are used, and so can be controlled by calling numpy.random.seed.

See also

get_rng

set_sampler(sampler)[source] [edit on github]

Set the MCMC sampler.

The sampler determines the type of jumping rule to be used when running the MCMC analysis.

Parameters:: sampler (str or sherpa.sim.Sampler instance) – When a string, the name of the sampler to use (case insensitive). The supported options are given by the list_samplers function.

See also

get_draws: Run the pyBLoCXS MCMC algorithm.
list_samplers: List the MCMC samplers.
set_sampler: Set the MCMC sampler.
set_sampler_opt: Set an option for the current MCMC sampler.

Notes

The jumping rules are:

MH: The Metropolis-Hastings rule, which jumps from the best-fit location, even if the previous iteration had moved away from it.
MetropolisMH: This is the Metropolis with Metropolis-Hastings algorithm, that jumps from the best-fit with probability p_M, otherwise it jumps from the last accepted jump. The value of p_M can be changed using set_sampler_opt.
PragBayes: This is used when the 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 (the nsubiters option) MCMC sub-iteration steps are carried out, choosing between Metropolis and Metropolis-Hastings types of samplers with probability p_M. Only the last of these sub-iterations are kept in the chain. The nsubiters and p_M values can be changed using set_sampler_opt.
FullBayes: Another sampler for use when including uncertainties due to the effective area.

Examples

>>> set_sampler('metropolismh')

set_sampler_opt(opt, value)[source] [edit on github]

Set an option for the current MCMC sampler.

Parameters:

opt (str) – The option to change. Use get_sampler to view the available options for the current sampler.
value – The value for the option.

See also

get_sampler: Return the current MCMC sampler options.
set_prior: Set the prior function to use with a parameter.
set_sampler: Set the MCMC sampler.

Notes

The options depend on the sampler. The options include:

defaultprior: Set to False when the default prior (flat, between the parameter’s soft limits) should not be used. Use set_prior to set the form of the prior for each parameter.
inv: A bool, or array of bools, to indicate which parameter is on the inverse scale.
log: A bool, or array of bools, to indicate which parameter is on the logarithm (natural log) scale.
original: A bool, or array of bools, to indicate which parameter is on the original scale.
p_M: The proportion of jumps generated by the Metropolis jumping rule.
priorshape: An array of bools indicating which parameters have a user-defined prior functions set with set_prior.
scale: Multiply the output of covar by this factor and use the result as the scale of the t-distribution.

Examples

>>> set_sampler_opt('scale', 3)

set_source(id, model=None) [edit on github]

Set the source model expression for a data set.

The function is available as both set_model and set_source. The model fit to the data can be further modified by instrument responses which can be set explicitly - e.g. by set_psf - or be defined automatically by the type of data being used (e.g. the ARF and RMF of a PHA data set). The set_full_model command can be used to explicitly include the instrument response if necessary.

Parameters:

id (int or str, optional) – The data set containing the source expression. If not given then the default identifier is used, as returned by get_default_id.
model (str or sherpa.models.Model object) – This defines the model used to fit the data. It can be a Python expression or a string version of it.

See also

delete_model: Delete the model expression from a data set.
fit: Fit one or more data sets.
freeze: Fix model parameters so they are not changed by a fit.
get_source: Return the source model expression for a data set.
integrate1d: Integrate 1D source expressions.
sherpa.astro.ui.set_bkg_model: Set the background model expression for a data set.
set_full_model: Define the convolved model expression for a data set.
show_model: Display the source model expression for a data set.
set_par: Set the value, limits, or behavior of a model parameter.
thaw: Allow model parameters to be varied during a fit.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

PHA data sets will automatically apply the instrumental response (ARF and RMF) to the source expression. For some cases this is not useful - for example, when different responses should be applied to different model components - in which case set_full_model should be used instead.

Model caching is available via the model cache attribute. A non-zero value for this attribute means that the results of evaluating the model will be cached if all the parameters are frozen, which may lead to a reduction in the time taken to evaluate a fit. A zero value turns off the caching. The default setting for X-Spec and 1D analytic models is that cache is 5, but 0 for the 2D analytic models.

The integrate1d model can be used to apply a numerical integration to an arbitrary model expression.

Examples

Create an instance of the powlaw1d model type, called pl, and use it as the model for the default data set.

>>> set_model(polynom1d.pl)

Create a model for the default dataset which is the xsphabs model multiplied by the sum of an xsapec and powlaw1d models (the model components are identified by the labels gal, clus, and pl).

>>> set_model(xsphabs.gal * (xsapec.clus + powlaw1d.pl))

Repeat the previous example, using a string to define the model expression:

>>> set_model('xsphabs.gal * (xsapec.clus + powlaw1d.pl)')

Use the same model component (src, a gauss2d model) for the two data sets (‘src1’ and ‘src2’).

>>> set_model('src1',  gauss2d.src + const2d.bgnd1)
>>> set_model('src2', src + const2d.bgnd2)

Share an expression - in this case three gaussian lines - between three data sets. The normalization of this line complex is allowed to vary in data sets 2 and 3 (the norm2 and norm3 components of the const1d model), and each data set has a separate polynom1d component (bgnd1, bgnd2, and bgnd3). The c1 parameters of the polynom1d model components are thawed and then linked together (to reduce the number of free parameters):

>>> lines = gauss1d.l1 + gauss1d.l2 + gauss1d.l3
>>> set_model(1, lines + polynom1d.bgnd1)
>>> set_model(2, lines * const1d.norm2 + polynom1d.bgnd2)
>>> set_model(3, lines * const1d.norm3 + polynom1d.bgnd3)
>>> thaw(bgnd1.c1, bgnd2.c1, bgnd3.c1)
>>> link(bgnd2.c2, bgnd1.c1)
>>> link(bgnd3.c3, bgnd1.c1)

For this expression, the gal component is frozen, so it is not varied in the fit. The cache attribute is set to a non-zero value to ensure that it is cached during a fit (this is actually the default value for this model so it not normally needed).

>>> set_model(xsphabs.gal * (xsapec.clus + powlaw1d.pl))
>>> gal.nh = 0.0971
>>> freeze(gal)
>>> gal.cache = 1

set_stat(stat: str | Stat) → None[source] [edit on github]

Set the statistical method.

Changes the method used to evaluate the fit statistic, that is the numerical measure that determines how closely the model represents the data.

Parameters:: stat (str or sherpa.stats.Stat instance) – When a string, the name of the statistic (case is not important): see list_stats() for supported values. Otherwise an instance of the statistic to use.
Raises:: sherpa.utils.err.ArgumentErr – If the stat argument is not recognized.

See also

calc_stat: Calculate the statistic value for a dataset.
get_stat_name: Return the current statistic method.
list_stats: List the supported fit statistics.
load_user_stat: Create a user-defined statistic.

Notes

The available statistics include:

cash: A maximum likelihood function [1].
chi2: Chi-squared statistic using the supplied error values.
chi2constvar: Chi-squared with constant variance computed from the counts data.
chi2datavar: Chi-squared with data variance. If the data has 0 counts then the error for that bin is 0.
chi2gehrels: Chi-squared with gehrels method [2]. This is the default method.
chi2modvar: Chi-squared with model amplitude variance.
chi2xspecvar: Chi-squared with data variance to match XSPEC. Errors from zero-count channels (source or background) are ignored if the other channel (background or source) contains counts, or replaced by a minimum value (when both source or background are empty). It should not be used when a model is fit to the background rather than the background is subtracted from the data.
cstat: A maximum likelihood function (the XSPEC implementation of the Cash function) [3]. This does not include support for including the background.
wstat: A maximum likelihood function which includes the background data as part of the fit (i.e. for when it is not being explicitly modelled) (the XSPEC implementation of the Cash function) [3].
leastsq: The least-squares statisic (the error is not used in this statistic).

References

Examples

>>> set_stat('cash')

set_staterror(id, val=None, fractional=False) → None[source] [edit on github]

Set the statistical errors on the dependent axis of a data set.

These values override the errors calculated by any statistic, such as chi2gehrels or chi2datavar.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
val (array or scalar) – The systematic error.
fractional (bool, optional) – If False (the default value), then the val parameter is the absolute value, otherwise the val parameter represents the fractional error, so the absolute value is calculated as get_dep() * val (and val must be a scalar).

See also

load_staterror: Set the statistical errors on the dependent axis of a data set.
load_syserror: Set the systematic errors on the dependent axis of a data set.
set_syserror: Set the systematic errors on the dependent axis of a data set.
get_error: Return the errors on the dependent axis of a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the val parameter. If given two un-named arguments, then they are interpreted as the id and val parameters, respectively.

Examples

Set the statistical error for the default data set to the value in dys (a scalar or an array):

>>> set_staterror(dys)

Set the statistical error on the ‘core’ data set to be 5% of the data values:

>>> set_staterror('core', 0.05, fractional=True)

set_syserror(id, val=None, fractional=False) → None[source] [edit on github]

Set the systematic errors on the dependent axis of a data set.

Parameters:

id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id.
val (array or scalar) – The systematic error.
fractional (bool, optional) – If False (the default value), then the val parameter is the absolute value, otherwise the val parameter represents the fractional error, so the absolute value is calculated as get_dep() * val (and val must be a scalar).

See also

load_staterror: Set the statistical errors on the dependent axis of a data set.
load_syserror: Set the systematic errors on the dependent axis of a data set.
set_staterror: Set the statistical errors on the dependent axis of a data set.
get_error: Return the errors on the dependent axis of a data set.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the val parameter. If given two un-named arguments, then they are interpreted as the id and val parameters, respectively.

Examples

Set the systematic error for the default data set to the value in dys (a scalar or an array):

>>> set_syserror(dys)

Set the systematic error on the ‘core’ data set to be 5% of the data values:

>>> set_syserror('core', 0.05, fractional=True)

set_xlinear(plottype: str = 'all') → None[source] [edit on github]

New plots will display a linear X axis.

This setting only affects plots created after the call to set_xlinear.

Parameters:: plottype (optional) – The type of plot that is to use a log-scaled X axis. The options are the same as accepted by plot, together with the ‘all’ option (which is the default setting).

See also

plot: Create one or more plot types.
set_xlog: New plots will display a logarithmically-scaled X axis.
set_ylinear: New plots will display a linear Y axis.

Examples

Use a linear X axis for ‘data’ plots:

>>> set_xlinear('data')
>>> plot('data', 'arf')

All plots use a linear scale for the X axis.

>>> set_xlinear()
>>> plot_fit()

set_xlog(plottype: str = 'all') → None[source] [edit on github]

New plots will display a logarithmically-scaled X axis.

This setting only affects plots created after the call to set_xlog.

Parameters:: plottype (optional) – The type of plot that is to use a log-scaled X axis. The options are the same as accepted by plot, together with the ‘all’ option (which is the default setting).

See also

plot: Create one or more plot types.
set_xlinear: New plots will display a linear X axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Examples

Use a logarithmic scale for the X axis of ‘data’ plots:

>>> set_xlog('data')
>>> plot('data', 'arf')

All plots use a logarithmic scale for the X axis.

>>> set_xlog()
>>> plot_fit()

set_ylinear(plottype: str = 'all') → None[source] [edit on github]

New plots will display a linear Y axis.

This setting only affects plots created after the call to set_ylinear.

Parameters:: plottype (optional) – The type of plot that is to use a log-scaled X axis. The options are the same as accepted by plot, together with the ‘all’ option (which is the default setting).

See also

plot: Create one or more plot types.
set_xlinear: New plots will display a linear X axis.
set_ylog: New plots will display a logarithmically-scaled Y axis.

Examples

Use a linear Y axis for ‘data’ plots:

>>> set_ylinear('data')
>>> plot('data', 'arf')

All plots use a linear scale for the Y axis.

>>> set_ylinear()
>>> plot_fit()

set_ylog(plottype: str = 'all') → None[source] [edit on github]

New plots will display a logarithmically-scaled Y axis.

This setting only affects plots created after the call to set_ylog.

Parameters:: plottype (optional) – The type of plot that is to use a log-scaled X axis. The options are the same as accepted by plot, together with the ‘all’ option (which is the default setting).

See also

plot: Create one or more plot types.
set_xlog: New plots will display a logarithmically-scaled x axis.
set_ylinear: New plots will display a linear Y axis.

Examples

Use a logarithmic scale for the Y axis of ‘data’ plots:

>>> set_ylog('data')
>>> plot('data', 'arf')

All plots use a logarithmic scale for the Y axis.

>>> set_ylog()
>>> plot_fit()

show_all(id: int | str | None = None, outfile=None, clobber=False) → None[source] [edit on github]

Report the current state of the Sherpa session.

Display information about one or all of the data sets that have been loaded into the Sherpa session. The information shown includes that provided by the other show_xxx routines, and depends on the type of data that is loaded.

Parameters:

id (int, str, or None, optional) – The data set. If not given then all data sets are displayed.
outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

clean: Clear all stored session data.
list_data_ids: List the identifiers for the loaded data sets.
save: Save the current Sherpa session to a file.
sherpa.astro.ui.save_all: Save the Sherpa session as an ASCII file.
sherpa.astro.ui.show_bkg: Show the details of the PHA background data sets.
sherpa.astro.ui.show_bkg_model: Display the background model expression for a data set.
sherpa.astro.ui.show_bkg_source: Display the background model expression for a data set.
show_conf: Display the results of the last conf evaluation.
show_covar: Display the results of the last covar evaluation.
show_data: Summarize the available data sets.
show_filter: Show any filters applied to a data set.
show_fit: Summarize the fit results.
show_kernel: Display any kernel applied to a data set.
show_method: Display the current optimization method and options.
show_model: Display the model expression used to fit a data set.
show_proj: Display the results of the last proj evaluation.
show_psf: Display any PSF model applied to a data set.
show_source: Display the source model expression for a data set.
show_stat: Display the current fit statistic.

show_conf(outfile=None, clobber=False) → None[source] [edit on github]

Display the results of the last conf evaluation.

The output includes the best-fit model parameter values, associated confidence limits, choice of statistic, and details on the best fit location.

Parameters:

outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
show_all: Report the current state of the Sherpa session.

show_covar(outfile=None, clobber=False) → None[source] [edit on github]

Display the results of the last covar evaluation.

The output includes the best-fit model parameter values, associated confidence limits, choice of statistic, and details on the best fit location.

Parameters:

outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

covar: Estimate parameter confidence intervals using the covariance method.
show_all: Report the current state of the Sherpa session.

show_data(id: int | str | None = None, outfile=None, clobber=False) → None[source] [edit on github]

Summarize the available data sets.

Display information on the data sets that have been loaded. The details depend on the type of the data set (e.g. 1D, image, PHA files).

Parameters:

id (int, str, or None, optional) – The data set. If not given then all data sets are displayed.
outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

list_data_ids: List the identifiers for the loaded data sets.
show_all: Report the current state of the Sherpa session.

show_filter(id: int | str | None = None, outfile=None, clobber=False) → None[source] [edit on github]

Show any filters applied to a data set.

Display any filters that have been applied to the independent axis or axes of the data set.

Parameters:

id (int, str, or None, optional) – The data set. If not given then all data sets are displayed.
outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

ignore: Exclude data from the fit.
sherpa.astro.ui.ignore2d: Exclude a spatial region from an image.
list_data_ids: List the identifiers for the loaded data sets.
notice: Include data in the fit.
sherpa.astro.ui.notice2d: Include a spatial region of an image.
show_all: Report the current state of the Sherpa session.

show_fit(outfile=None, clobber=False) → None[source] [edit on github]

Summarize the fit results.

Display the results of the last call to fit, including: optimization method, statistic, and details of the fit (it does not reflect any changes made after the fit, such as to the model expression or fit parameters).

Parameters:

outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

fit: Fit one or more data sets.
get_fit_results: Return the results of the last fit.
list_data_ids: List the identifiers for the loaded data sets.
list_model_ids: List of all the data sets with a source expression.
show_all: Report the current state of the Sherpa session.

show_kernel(id: int | str | None = None, outfile=None, clobber=False) → None[source] [edit on github]

Display any kernel applied to a data set.

The kernel represents the subset of the PSF model that is used to fit the data. The show_psf function shows the un-filtered version.

Parameters:

id (int, str, or None, optional) – The data set. If not given then all data sets are displayed.
outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

image_kernel: Plot the 2D kernel applied to a data set.
list_data_ids: List the identifiers for the loaded data sets.
load_psf: Create a PSF model.
plot_kernel: Plot the 1D kernel applied to a data set.
set_psf: Add a PSF model to a data set.
show_all: Report the current state of the Sherpa session.
show_psf: Display any PSF model applied to a data set.

Notes

The point spread function (PSF) is defined by the full (unfiltered) PSF image or model expression evaluated over the full range of the dataset; both types of PSFs are established with load_psf. The kernel is the subsection of the PSF image or model which is used to convolve the data: this is changed using set_psf. While the kernel and PSF might be congruent, defining a smaller kernel helps speed the convolution process by restricting the number of points within the PSF that must be evaluated.

show_method(outfile=None, clobber=False) → None[source] [edit on github]

Display the current optimization method and options.

Parameters:

outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

get_method: Return an optimization method.
get_method_opt: Return one or all options of the current optimization method.
show_all: Report the current state of the Sherpa session.

Examples

>>> set_method('levmar')
>>> show_method()
Optimization Method: LevMar
name    = levmar
ftol    = 1.19209289551e-07
xtol    = 1.19209289551e-07
gtol    = 1.19209289551e-07
maxfev  = x
epsfcn  = 1.19209289551e-07
factor  = 100.0
verbose = 0

show_model(id: int | str | None = None, outfile=None, clobber=False) → None[source] [edit on github]

Display the model expression used to fit a data set.

This displays the model used to fit the data set, that is, the expression set by set_model or set_source combined with any instrumental responses, together with the parameter values of the model. The show_source function displays just the source expression, without the instrumental components (if any).

Parameters:

id (int, str, or None, optional) – The data set. If not given then all source expressions are displayed.
outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

list_model_ids: List of all the data sets with a source expression.
set_model: Set the source model expression for a data set.
show_all: Report the current state of the Sherpa session.
show_source: Display the source model expression for a data set.

show_proj(outfile=None, clobber=False) → None[source] [edit on github]

Display the results of the last proj evaluation.

The output includes the best-fit model parameter values, associated confidence limits, choice of statistic, and details on the best fit location.

Parameters:

outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

proj: Estimate parameter confidence intervals using the projection method.
show_all: Report the current state of the Sherpa session.

show_psf(id: int | str | None = None, outfile=None, clobber=False) → None[source] [edit on github]

Display any PSF model applied to a data set.

The PSF model represents the full model or data set that is applied to the source expression. The show_kernel function shows the filtered version.

Parameters:

id (int, str, or None, optional) – The data set. If not given then all data sets are displayed.
outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

image_psf: View the 2D PSF model applied to a data set.
list_data_ids: List the identifiers for the loaded data sets.
load_psf: Create a PSF model.
plot_psf: Plot the 1D PSF model applied to a data set.
set_psf: Add a PSF model to a data set.
show_all: Report the current state of the Sherpa session.
show_kernel: Display any kernel applied to a data set.

Notes

The point spread function (PSF) is defined by the full (unfiltered) PSF image or model expression evaluated over the full range of the dataset; both types of PSFs are established with load_psf. The kernel is the subsection of the PSF image or model which is used to convolve the data: this is changed using set_psf. While the kernel and PSF might be congruent, defining a smaller kernel helps speed the convolution process by restricting the number of points within the PSF that must be evaluated.

show_source(id: int | str | None = None, outfile=None, clobber=False) → None[source] [edit on github]

Display the source model expression for a data set.

This displays the source model for a data set, that is, the expression set by set_model or set_source, as well as the parameter values for the model. The show_model function displays the model that is fit to the data; that is, it includes any instrument responses.

Parameters:

id (int, str, or None, optional) – The data set. If not given then all source expressions are displayed.
outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

list_model_ids: List of all the data sets with a source expression.
set_model: Set the source model expression for a data set.
show_all: Report the current state of the Sherpa session.
show_model: Display the model expression used to fit a data set.

show_stat(outfile=None, clobber=False) → None[source] [edit on github]

Display the current fit statistic.

Parameters:

outfile (str, optional) – If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber (bool, optional) – If outfile is not None, then this flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting).

Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

calc_stat: Calculate the fit statistic for a data set.
calc_stat_info: Display the statistic values for the current models.
get_stat: Return a fit-statistic method.
show_all: Report the current state of the Sherpa session.

Examples

>>> set_stat('cash')
>>> show_stat()
Statistic: Cash
Maximum likelihood function

simulfit(id: int | str | None = None, *otherids: int | str, **kwargs) → None [edit on github]

Fit a model to one or more data sets.

Use forward fitting to find the best-fit model to one or more data sets, given the chosen statistic and optimization method. The fit proceeds until the results converge or the number of iterations exceeds the maximum value (these values can be changed with set_method_opt). An iterative scheme can be added using set_iter_method to try and improve the fit. The final fit results are displayed to the screen and can be retrieved with get_fit_results.

Changed in version 4.17.0: The outfile parameter can now be sent a Path object or a file handle instead of a string.

Parameters:

id (int or str, optional) – The data set that provides the data. If not given then all data sets with an associated model are fit simultaneously.
*otherids (int or str, optional) – Other data sets to use in the calculation.
outfile (str, Path, IO object, or None, optional) – If set, then the fit results will be written to a file with this name. The file contains the per-iteration fit results.
clobber (bool, optional) – This flag controls whether an existing file can be overwritten (True) or if it raises an exception (False, the default setting). This is only used if outfile is set to a string or Path object.

Raises:

sherpa.utils.err.FitErr – If filename already exists and clobber is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
contour_fit: Contour the fit to a data set.
covar: Estimate the confidence intervals using the confidence method.
freeze: Fix model parameters so they are not changed by a fit.
get_fit_results: Return the results of the last fit.
plot_fit: Plot the fit results (data, model) for a data set.
image_fit: Display the data, model, and residuals for a data set in the image viewer.
set_stat: Set the statistical method.
set_method: Change the optimization method.
set_method_opt: Change an option of the current optimization method.
set_full_model: Define the convolved model expression for a data set.
set_iter_method: Set the iterative-fitting scheme used in the fit.
set_model: Set the model expression for a data set.
show_fit: Summarize the fit results.
thaw: Allow model parameters to be varied during a fit.

Notes

If outfile is sent a file handle then it is not closed by this routine.

Examples

Simultaneously fit all data sets with models and then store the results in the variable fres:

>>> fit()
>>> fres = get_fit_results()

Fit just the data set ‘img’:

>>> fit('img')

Simultaneously fit data sets 1, 2, and 3:

>>> fit(1, 2, 3)

Fit data set ‘jet’ and write the fit results to the text file ‘jet.fit’, over-writing it if it already exists:

>>> fit('jet', outfile='jet.fit', clobber=True)

Store the per-iteration values in a StringIO object and extract the data into the variable txt (this avoids the need to create a file):

>>> from io import StringIO
>>> out = StringIO()
>>> fit(outfile=out)
>>> txt = out.getvalue()

t_sample(num=1, dof=None, id: int | str | None = None, otherids: Sequence[int | str] = (), numcores=None)[source] [edit on github]

Sample the fit statistic by taking the parameter values from a Student’s t-distribution.

For each iteration (sample), change the thawed parameters by drawing values from a Student’s t-distribution, and calculate the fit statistic.

Parameters:

num (int, optional) – The number of samples to use (default is 1).
dof (optional) – The number of degrees of freedom to use (the default is to use the number from the current fit).
id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, optional) – Other data sets to use in the calculation.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

Returns:

A NumPy array table with the first column representing the statistic and later columns the parameters used.

Return type:

samples

See also

fit: Fit a model to one or more data sets.
normal_sample: Sample from the normal distribution.
set_model: Set the source model expression for a data set.
set_stat: Set the statistical method.
uniform_sample: Sample from a uniform distribution.

Examples

The model fit to the default data set has three free parameters. The median value of the statistic calculated by t_sample is returned:

>>> ans = t_sample(num=10000)
>>> ans.shape
(1000, 4)
>>> np.median(ans[:,0])
119.9764357725326

thaw(*args)[source] [edit on github]

Allow model parameters to be varied during a fit.

The arguments can be parameters or models, in which case all parameters of the model are thawed. If no arguments are given then nothing is changed.

Parameters:: args (sequence of str or Parameter or Model) – The parameters or models to thaw.

See also

fit: Fit one or more data sets.
freeze: Fix model parameters so they are not changed by a fit.
link: Link a parameter value to an associated value.
set_par: Set the value, limits, or behavior of a model parameter.
unlink: Unlink a parameter value.

Notes

The freeze function can be used to reverse this setting, so that parameters are “frozen” and so remain constant during a fit.

Certain parameters may be marked as “always frozen”, in which case using the parameter in a call to thaw will raise an error. If the model is sent to thaw then the “always frozen” parameter will be skipped.

Examples

Ensure that the FWHM parameter of the line model (in this case a gauss1d model) will be varied in any fit.

>>> set_source(const1d.bgnd + gauss1d.line)
>>> thaw(line.fwhm)
>>> fit()

Thaw all parameters of the line model and then re-fit:

>>> thaw(line)
>>> fit()

Thaw the nh parameter of the gal model and the abund parameter of the src model:

>>> thaw(gal.nh, src.abund)

uniform_sample(num=1, factor=4, id: int | str | None = None, otherids: Sequence[int | str] = (), numcores=None)[source] [edit on github]

Sample the fit statistic by taking the parameter values from an uniform distribution.

For each iteration (sample), change the thawed parameters by drawing values from a uniform distribution, and calculate the fit statistic.

Parameters:

num (int, optional) – The number of samples to use (default is 1).
factor (number, optional) – Multiplier to expand the scale parameter (default is 4).
id (int, str, or None, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids (sequence of int or str, optional) – Other data sets to use in the calculation.
numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

Returns:

A NumPy array table with the first column representing the statistic and later columns the parameters used.

Return type:

samples

See also

fit: Fit a model to one or more data sets.
normal_sample: Sample from a normal distribution.
set_model: Set the source model expression for a data set.
set_stat: Set the statistical method.
t_sample: Sample from the Student’s t-distribution.

Examples

The model fit to the default data set has three free parameters. The median value of the statistic calculated by uniform_sample is returned:

>>> ans = uniform_sample(num=10000)
>>> ans.shape
(1000, 4)
>>> np.median(ans[:,0])
284.66534775948134

unlink(par)[source] [edit on github]

Unlink a parameter value.

Remove any parameter link - created by link - for the parameter. The parameter value is reset to the value it had before link was called.

Parameters:: par (str or Parameter) – The parameter to unlink. If the parameter is not linked then nothing happens.

See also

freeze: Fix model parameters so they are not changed by a fit.
link: Link a parameter to a value.
set_par: Set the value, limits, or behavior of a model parameter.
thaw: Allow model parameters to be varied during a fit.

Examples

>>> unlink(bgnd.ampl)

unpack_arrays(*args)[source] [edit on github]

Create a sherpa data object from arrays of data.

The object returned by unpack_arrays can be used in a set_data call.

Parameters:

args (array_like) – Arrays of data. The order, and number, is determined by the dstype parameter, and listed in the load_arrays routine.
dstype – The data set type. The default is Data1D and values include: Data1D, Data1DInt, Data2D, and Data2DInt. It is expected to be derived from sherpa.data.BaseData.

Returns:

The data set object matching the requested dstype.

Return type:

instance

See also

get_data: Return the data set by identifier.
load_arrays: Create a data set from array values.
set_data: Set a data set.
unpack_data: Create a sherpa data object from a file.

Examples

Create a 1D (unbinned) data set from the values in the x and y arrays. Use the returned object to create a data set labelled “oned”:

>>> x = [1, 3, 7, 12]
>>> y = [2.3, 3.2, -5.4, 12.1]
>>> dat = unpack_arrays(x, y)
>>> set_data("oned", dat)

Include statistical errors on the data:

>>> edat = unpack_arrays(x, y, dy)

Create a “binned” 1D data set, giving the low, and high edges of the independent axis (xlo and xhi respectively) and the dependent values for this grid (y):

>>> hdat = unpack_arrays(xlo, xhi, y, Data1DInt)

unpack_data(filename, ncols=2, colkeys=None, dstype=<class 'sherpa.data.Data1D'>, sep=' ', comment='#', require_floats=True)[source] [edit on github]

Create a sherpa data object from an ASCII file.

This function is used to read in columns from an ASCII file and convert them to a Sherpa data object.

Parameters:

filename (str) – The name of the ASCII file to read in.
ncols (int, optional) – The number of columns to read in (the first ncols columns in the file).
colkeys (array of str, optional) – An array of the column name to read in. The default is None.
dstype (data class to use, optional) – What type of data is to be used. Supported values include Data1D (the default), Data1DInt, Data2D, and Data2DInt.
sep (str, optional) – The separator character. The default is ' '.
comment (str, optional) – The comment character. The default is '#'.
require_floats (bool, optional) – If True (the default), non-numeric data values will raise a ValueError.

Returns:

The data set object.

Return type:

instance

Raises:

ValueError – If a column value can not be converted into a numeric value and the require_floats parameter is True.

See also

get_data: Return the data set by identifier.
load_arrays: Create a data set from array values.
load_data: Load a data set from a file.
set_data: Set a data set.
unpack_arrays: Create a sherpa data object from arrays of data.

Notes

The file reading is performed by sherpa.io.get_ascii_data, which reads in each line from the file, strips out any unsupported characters (replacing them by the sep argument), skips empty lines, and then identifies whether it is a comment or data line.

The list of unsupported characters are: tab, new line, carriage return, comma, semi-colon, colon, space, and “|”.

The last comment line before the data is used to define the column names, splitting the line by the sep argument. If there are no comment lines then the columns are named starting at col1, col2, increasing up to the number of columns.

Data lines are separated into columns - splitting by the sep comment - and then converted to NumPy arrays. If the require_floats argument is True then the column will be converted to the sherpa.utils.SherpaFloat type, with an error raised if this fails.

An error is raised if the number of columns per row is not constant.

If the colkeys argument is used then a case-sensitive match is used to determine what columns to return.

Examples

Create a data object from the first two columns of the file “src.dat” and use it to create a Sherpa data set called “src”:

>>> dat = unpack_data('src.dat')
>>> set_data('src', dat)

Read in the first three columns - the independent axis (x), the dependent variable (y), and the error on y:

>>> dat = unpack_data('src.dat', ncols=3)

Read in the X and Y columns from the file. The last line before the data must contain the column names:

>>> dat = unpack_data('src.dat', colkeys=['X', 'Y'])

Read in a histogram:

>>> cols = ['XLO', 'XHI', 'Y']
>>> idat = unpack_data('hist.dat', colkeys=cols,
...                    dstype=ui.Data1DInt)