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Last modified: December 2007

URL: http://cxc.harvard.edu/sherpabeta/int_unc.py.html
AHELP for Sherpa 4.0 Beta int_unc Context: py.sherpa

Synopsis

Create a confidence plot of fit statistic vs. parameter value.

Syntax

int_unc(par,
[id=1,otherids=None,replot=False,min=None,max=None,nloop=20,delv=None,fa
c=1,log=False,overplot=False,clearwindow=True])

Description

At each step calculate the statistic with the other parameter(s) frozen at best fit values.

Example

sherpa> int_unc(p1.gamma)
sherpa> int_unc(p1.gamma, 1)
sherpa> int_unc(p1.gamma, 1, [2,3])
sherpa> int_unc(p1.gamma, min=2.0, max=2.5)
sherpa> int_unc(p1.gamma, log=True)
sherpa> int_unc(p1.gamma, replot=True, overplot=True)
sherpa> int_unc(p1.gamma, id="bkg")

The int_unc function can be called with just one parameter argument, in which case all the default arguments will be used. Simultaneous fits can be used given the first id and a list of otherids. Data set IDs can be integers or strings; in the latter case a string can denote useful information about the data (e.g., whether it is your source or background data set). The limits of the plot can be specified using the min and max keyword arguments. To quickly replot the cached arrays from a previous calculation, in the case of overplotting multiple int_unc plots in one window, the replot and overplot keyword arguments can be set.

Bugs

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

Last modified: December 2007



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