get_conf_results
- sherpa.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.
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)