|AHELP for CIAO 4.9 Sherpa v1||
Perform a fit using current optimization method and fit statistic.
fit(id=None, otherids=None, bkg_only=False, outfile="fit.out", clobber=True) fit_bkg(id=None, otherids=None, outfile="fit.out", clobber=True)
The fit() function fits the defined model(s) to the data set(s) by data ID. It may be called with no arguments, in which case a fit is done simultaneously on all data sets ready for fitting, i.e., all data sets which have been assigned source models. If the 'bkg_only' argument is changed from the default False to True, only the background data sets ready for fitting will be fit; source data sets ready for fitting will be ignored in this case.
If a background file and background responses are defined for the data set which is being fit, the background is automatically fit as well.
The fit_bkg() function fits the defined model(s) to the background data set(s) by ID. It may be called with no arguments, in which case a fit is done simultaneously on all data sets for which the user has defined a model to be fit.
- id, otherids - the id(s) of the data set(s) to use; default is to fit all datasets for which a model has been defined
- bkg_only - a Boolean switch to indicate if only background data sets should be fit; default=False
- outfile - name of the file in which to store all parameter values and fit statistic obtained during the fitting process (e.g., "fit.out")
- clobber - a Boolean switch which indicates whether or not the 'outfile' should be overwritten, if the file already exists; default=True
The fit is done with the specified optimization method and statistic; the default values are levmar and chi2gehrels, respectively. The fit proceeds until it converges to within the set tolerance or hits the maximum number of iterations. The resulting best fit parameters and final statistic value are displayed to the user. The best-fit parameters are accessible with get_fit_results().
If one of the available iterative fitting methods in Sherpa is set for fitting (see "ahelp set_iter_method"), the chosen fit optimization method will be called several times when the fit command is run, until the fit can no longer be improved. If the iterative fitting method is set to "none" (default), then no iterative fitting is done when the fit command is called; the optimization method is called once, and Sherpa otherwise operates as expected.
A single data set can be fit using its data set ID number or string.
The fit function called with no arguments will simultaneously fit all data sets to which models have been assigned.
The fit function called with the 'bkg_only' argument set to True and no data set ids specified will simultaneously fit all background data sets to which models have been assigned; no source models ready for fitting will be included in the fit.
sherpa> fit(2, bkg_only=True)
In this case, the fit function fits only the background data set(s) associated with data set 2.
sherpa> load_data("src.pha") sherpa> load_bkg("bkg.pha") sherpa> load_bkg_rmf("rmf.fits") sherpa> load_bkg_arf("arf.fits") sherpa> set_bkg_model(powlaw1d.p1) sherpa> fit_bkg()
Load source and background spectra. Define response files and a model for the background data, then fit it.
sherpa> fit(3, outfile="fit_info.out", clobber=True)
In this example, data set 3 will be fit, and all the parameter values and the fit statistic used by the optimization method will be stored in the file "fit_info.out". This file will be overwritten if it already exists because clobber=True.
When multiple data sets are indicated, a simultaneous fit is performed and the statistics value is summed over all the data sets. For example, one may wish to simultaneously fit a source and background data set, multiple spectral orders of PHA grating data, etc. In this example, the LEG +1 and -1 spectral orders of an HRC-S/LETG grating data set are fit simultaneously by data ID:
sherpa> fit("leg_m1","leg_p1") Datasets = 'leg_m1', 'leg_p1' Method = levmar Statistic = chi2gehrels Initial fit statistic = 48024.6 Final fit statistic = 26310.8 at function evaluation 62 Data points = 32768 Degrees of freedom = 32765 Probability [Q-value] = 1 Reduced statistic = 0.803016 Change in statistic = 21713.8 bpow.ampl 0.636989 bpow.index1 0.52646 bpow.index2 2.16595 sherpa> print(get_fit_results()) datasets = ('leg_m1', 'leg_p1') itermethodname = none methodname = levmar statname = chi2gehrels succeeded = True parnames = ('bpow.ampl', 'bpow.index1', 'bpow.index2') parvals = (0.63698867502154433, 0.52646030609918448, 2.1659465149789296) statval = 26310.806281 istatval = 48024.6491653 dstatval = 21713.8428843 numpoints = 32768 dof = 32765 qval = 1.0 rstat = 0.803015604488 message = successful termination nfev = 62
See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.