# get_conf_results¶

sherpa.astro.ui.get_conf_results()

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.

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)