get_int_unc

Synopsis

Return the confidence data defining an int_unc() plot

Syntax

get_int_unc( [par=None, id, otherids=None, recalc=False, min=None,
max=None, nloop=20, delv=None, fac=1, log=False,numcores] )

The get_int_unc() function returns the data defining a confidence plot of fit statistic versus model parameter value for a single thawed model parameter, produced by the int_unc() function. (The parameter value is varied on the computed grid until the fit statistic is increased by delta_S, which is a function of sigma. For example, delta_S = 1 if the statistic is chi^2 and fac = 1 . The best-fit statistic is determined at each grid point, and all other parameters are fixed to the initial best-fit values.)

The get_int_unc() function returns information on the most recent confidence plot produced with int_unc(), independent of the arguments supplied, unless the 'recalc' argument is set to True . For example, if a confidence plot is produced for the amplitude of a power law model, and then get_int_unc() is used to obtain confidence data on the gamma parameter of the power law model, the information returned by get_int_unc() will correspond to the confidence plot of the amplitude parameter, *unless* the 'recalc' argument is switched on.

The computationally intensive projection function is parallelized to make use of multi-core systems (i.e., laptops or desktops with 2 or 4 cores) to provide significant improvements in efficiency compared to previous releases of Sherpa; the 'numcores' argument may be used to specify how the cores should be used when projection is run.

par - source model parameter
id, otherids - the id(s) of the dataset(s) to use; if not given, uses the default dataset id (id=1 by default, see "ahelp get_default_id"); otherids=None
recalc - re-calculate confidence data? ; default=False
min - minimum grid boundary; default=None, which calculates the value from the covariance. This is always a linear quantity, regardless of the log setting.
max - maximum grid boundary; default=None, which calculates the value from the covariance. This is always a linear quantity, regardless of the log setting.
nloop - bin size for calculating the step size (delv); default=20
delv - step size; default=None, which calculates the value using the min, max, and nloop values
fac - the number of sigma (i.e., the change in statistic) for the plot; if not given, uses the default dataset id (id=1 by default, see "ahelp get_default_id")
log - use log space for the grid interval? default=False
numcores - number of cores to use in parallelization; default is to use all cores available (2 or 4)

Example 1

sherpa> print(get_int_unc())

When called with no arguments from within the print command, get_int_unc() returns the data defining the most recently produced int_unc() confidence plot.

Example 2

sherpa> print(get_int_unc("p1.gamma", id=2, recalc=True))

This command will calculate and return the int_unc() confidence data for the gamma parameter of power law model 'p1', which is assigned to data set 2.

sherpa> print(get_int_unc("p1.gamma", id=2, recalc=True))
min   = 1.17073187312
max   = 1.32139089886
nloop = 20
delv  = None
fac   = 1
log   = False

Bugs

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