Source code for gammapy.utils.fitting.sherpa

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from .likelihood import Likelihood

__all__ = ["optimize_sherpa", "covariance_sherpa"]


def get_sherpa_optimizer(name):
    from sherpa.optmethods import LevMar, NelderMead, MonCar, GridSearch

    return {
        "levmar": LevMar,
        "simplex": NelderMead,
        "moncar": MonCar,
        "gridsearch": GridSearch,
    }[name]()


class SherpaLikelihood(Likelihood):
    """Likelihood function interface for Sherpa."""

    def fcn(self, factors):
        self.parameters.set_parameter_factors(factors)
        return self.function(self.parameters), 0


[docs]def optimize_sherpa(parameters, function, **kwargs): """Sherpa optimization wrapper method. Parameters ---------- parameters : `~gammapy.utils.modeling.Parameters` Parameter list with starting values. function : callable Likelihood function **kwargs : dict Options passed to the optimizer instance. Returns ------- result : (factors, info, optimizer) Tuple containing the best fit factors, some info and the optimizer instance. """ method = kwargs.pop("method", "simplex") optimizer = get_sherpa_optimizer(method) optimizer.config.update(kwargs) pars = [par.factor for par in parameters.free_parameters] parmins = [par.factor_min for par in parameters.free_parameters] parmaxes = [par.factor_max for par in parameters.free_parameters] statfunc = SherpaLikelihood(function, parameters) with np.errstate(invalid="ignore"): result = optimizer.fit( statfunc=statfunc.fcn, pars=pars, parmins=parmins, parmaxes=parmaxes ) factors = result[1] info = {"success": result[0], "message": result[3], "nfev": result[4]["nfev"]} return factors, info, optimizer
[docs]def covariance_sherpa(): raise NotImplementedError