Synopsis
Return the results of the last conf run.
Syntax
get_confidence_results() Alias: get_conf_results
Examples
Example 1
>>> 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 = 13Example 2
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)
Notes
The fields of the object include:
| Item | Definition |
|---|---|
| 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. |
Bugs
See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.