get_covar_results

sherpa.astro.ui.get_covar_results()

Return the results of the last covar run.

Returns:: results
Return type:: sherpa.fit.ErrorEstResults object
Raises:: sherpa.utils.err.SessionErr – If no covar call has been made.

See also

get_covar_opt: Return one or all of the options for the covariance method.
set_covar_opt: Set an option of the covar estimation object.

Notes

The fields of the object include:

datasets: A tuple of the data sets used in the analysis.
methodname: This will be ‘covariance’.
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.

There is also an extra_output field which is used to return the covariance matrix.

Examples

>>> res = get_covar_results()
>>> print(res)
datasets    = (1,)
methodname  = covariance
iterfitname = none
fitname     = levmar
statname    = chi2gehrels
sigma       = 1
percent     = 68.2689492137
parnames    = ('bgnd.c0',)
parvals     = (10.228675427602724,)
parmins     = (-2.4896739438296795,)
parmaxes    = (2.4896739438296795,)
nfits       = 0

In this case, of a single parameter, the covariance matrix is just the variance of the parameter:

>>> res.extra_output
array([[ 6.19847635]])