get_conf
- sherpa.ui.get_conf()
Return the confidence-interval estimation object.
- Returns:
conf
- Return type:
See also
confEstimate parameter confidence intervals using the confidence method.
get_conf_optReturn one or all of the options for the confidence interval method.
set_conf_optSet an option of the conf estimation object.
Notes
The attributes of the confidence-interval object include:
epsThe precision of the calculated limits. The default is 0.01.
fastIf
Truethen the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default isFalse.max_rstatIf the reduced chi square is larger than this value, do not use (only used with chi-square statistics). The default is 3.
maxfitsThe maximum number of re-fits allowed (that is, when the
reminfilter is met). The default is 5.maxitersThe maximum number of iterations allowed when bracketing limits, before stopping for that parameter. The default is 200.
numcoresThe number of computer cores to use when evaluating results in parallel. This is only used if
parallelisTrue. The default is to use all cores.openintervalHow the
confmethod should cope with intervals that do not converge (that is, when themaxiterslimit has been reached). The default isFalse.parallelIf there is more than one free parameter then the results can be evaluated in parallel, to reduce the time required. The default is
True.reminThe 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
confis called). The default is 0.01.sigmaWhat is the error limit being calculated. The default is 1.
soft_limitsShould 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 isFalsetolThe tolerance for the fit. The default is 0.2.
verboseShould 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
reminfield to 0.05.>>> cf = get_conf() >>> cf.remin = 0.05