Fit

class sherpa.fit.Fit(data, model, stat=None, method=None, estmethod=None, itermethod_opts=None)[source] [edit on github]

Bases: NoNewAttributesAfterInit

Fit a model to a data set.

Changed in version 4.17.0: Changing the stat field now changes the internal IterFit object as well.

Parameters:

data (sherpa.data.Data or sherpa.data.DataSimulFit) – The data to be fit.
model (sherpa.models.model.Model or sherpa.models.model.SimulFitModel) – The model to fit to the data. It should match the data parameter (i.e. be a SimulFitModel object when data is a DataSimulFit).
stat (sherpa.stats.Stat or None, optional) – The statistic object to use. If not given then Chi2Gehrels is used.
method (sherpa.optmethods.OptMethod instance or None, optional) – The optimiser to use. If not given then sherpa.optmethods.LevMar is used.
estmethod (sherpa.estmethods.EstMethod or None, optional) – The class used to calculate errors. If not given then sherpa.estmethods.Covariance is used.
itermethod_opts (dict or None, optional) – If set, defines the iterated-fit method and options to use. It is passed through to IterFit.

Attributes Summary

stat

Return the statistic value

Methods Summary

`calc_chisqr`()	Calculate the per-bin chi-squared statistic.
`calc_stat`()	Calculate the statistic value.
`calc_stat_info`()	Calculate the statistic value and related information.
`est_errors`([methoddict, parlist])	Estimate errors.
`fit`([outfile, clobber, numcores])	Fit the model to the data.
`guess`(**kwargs)	Guess parameter values and limits.
`simulfit`(*others)	Fit multiple data sets and models simultaneously.

Attributes Documentation

stat: Return the statistic value

Methods Documentation

calc_chisqr()[source] [edit on github]

Calculate the per-bin chi-squared statistic.

Evaluate the per-bin statistic for the current model and data settings (e.g. parameter values and data filters).

Returns:: chisq – The chi-square value for each bin of the data, using the current statistic. A value of None is returned if the statistic is not a chi-square distribution.
Return type:: array or None

See also

calc_stat, calc_stat_info

calc_stat()[source] [edit on github]

Calculate the statistic value.

Evaluate the statistic for the current model and data settings (e.g. parameter values and data filters).

Returns:: stat – The current statistic value.
Return type:: number

See also

calc_chisqr, calc_stat_info

calc_stat_info()[source] [edit on github]

Calculate the statistic value and related information.

Evaluate the statistic for the current model and data settings (e.g. parameter values and data filters).

Returns:: statinfo – The current statistic value.
Return type:: StatInfoResults instance

See also

calc_chisqr, calc_stat

est_errors(methoddict=None, parlist=None)[source] [edit on github]

Estimate errors.

Calculate the low and high errors for one or more of the thawed parameters in the fit.

Parameters:

methoddict (dict or None, optional) – A dictionary mapping from lower-cased method name to the associated optimisation method instance to use. This is only used if the method is changed, as described in the Notes section below.
parlist (sequence of sherpa.models.parameter.Parameter or None, optional) – The names of the parameters for which the errors should be calculated. If set to None then all the thawed parameters are used.

Returns:

res

Return type:

ErrorEstResults

Raises:

sherpa.utils.err.EstErr – If any parameter in parlist is not valid (i.e. is not thawed or is not a member of the model expression being fit), or if the statistic is LeastSq, or if the reduced chi-square value of the current parameter values is larger than the max_rstat option (for chi-square statistics).

See also

fit

Notes

If a new minimum is found for any parameter then the calculation is automatically started for all the parameters using this new best-fit location. This can repeat until the maxfits option is reached.

Unless the Covariance estimator is being used, or the fast option is unset, then the method will be changed to NelderMead (for likelihood-based statistics) or LevMar (for chi-square based statistics) whilst calculating the errors.

fit(outfile: str | Path | WriteableTextFile | None = None, clobber: bool = False, numcores: int | None = 1) → FitResults[source] [edit on github]

Fit the model to the data.

Changed in version 4.17.0: The outfile parameter can now be sent a Path object or a file handle instead of a string.

Parameters:

outfile (str, Path, IO object, or None, optional) – If not None then information on the fit is written to this file (as defined by a filename, path, or file handle).
clobber (bool, optional) – Determines if the output file can be overwritten. This is only used when outfile is a string or Path object.
numcores (int or None, optional) – The number of cores to use in fitting simultaneous data.

Returns:

fitres

Return type:

FitResults

Raises:

sherpa.utils.err.FitErr – This is raised if clobber is False and outfile already exists or if all the bins have been masked out of the fit.

See also

est_errors, simulfit

Notes

The file created when outfile is set is a simple ASCII file with a header line containing the text “# nfev statistic” and then a list of the thawed parameters, and then one line for each iteration, with the values separated by spaces. If outfile is sent a file handle it is not closed by this routine.

Examples

Fit a very-simple model (a constant value) to a small 1D dataset:

>>> from sherpa.data import Data1D
>>> from sherpa.models.basic import Const1D
>>> from sherpa.stats import LeastSq
>>> d = Data1D("x", [-3, 5, 17, 22], [12, 3, 8, 5])
>>> m = Const1D()
>>> s = LeastSq()
>>> f = Fit(d, m, stat=s)
>>> out = f.fit()
>>> if not out.succeeded: print("Fit failed")
>>> print(out.format())
Method                = levmar
Statistic             = leastsq
Initial fit statistic = 190
Final fit statistic   = 46 at function evaluation 4
Data points           = 4
Degrees of freedom    = 3
Change in statistic   = 144
   const1d.c0     7            +/- 0.5

>>> print(m)
const1d
   Param        Type          Value          Min          Max      Units
   -----        ----          -----          ---          ---      -----
   const1d.c0   thawed            7 -3.40282e+38  3.40282e+38

Repeat the fit, after resetting the model, so we can see how the optimiser searched the parameter space:

>>> from io import StringIO
>>> m.reset()
>>> optdata = StringIO()
>>> out2 = f.fit(outfile=optdata)
>>> print(optdata.getvalue())
# nfev statistic const1d.c0
0.000000e+00 1.900000e+02 1.000000e+00
1.000000e+00 1.900000e+02 1.000000e+00
2.000000e+00 1.899834e+02 1.000345e+00
3.000000e+00 4.600000e+01 7.000000e+00
4.000000e+00 4.600002e+01 7.002417e+00
5.000000e+00 4.600000e+01 7.000000e+00

guess(**kwargs)[source] [edit on github]

Guess parameter values and limits.

The model’s sherpa.models.model.Model.guess method is called with the data values (the dependent axis of the data set) and the kwargs arguments.

simulfit(*others)[source] [edit on github]

Fit multiple data sets and models simultaneously.

The current fit object is combined with the other fit objects and a simultaneous fit is made, using the object’s statistic and optimisation method.

Parameters:: *others (sherpa.fit.Fit instances) – The data and model attributes of these arguments are used, along with those from the object.
Returns:: fitres
Return type:: FitResults

See also

fit