ModelF¶
- class lsst.gauss2d.fit.ModelF¶
Bases:
ParametricModel
Attributes Summary
Methods Summary
compute_hessian
(self[, transformed, ...])compute_loglike_grad
(self[, include_prior, ...])evaluate
(self[, print, normalize_loglike])gaussians
(self, channel)offsets_parameters
(self)parameters
(self, parameters, paramfilter)setup_evaluators
(self, evaluatormode, ...)verify_jacobian
(self[, findiff_frac, ...])Attributes Documentation
- data¶
- mode¶
- outputs¶
- priors¶
- psfmodels¶
- sources¶
Methods Documentation
- compute_hessian(self: lsst.gauss2d.fit._gauss2d_fit.ModelF, transformed: bool = False, include_prior: bool = True, options: lsst.gauss2d.fit._gauss2d_fit.HessianOptions | None = None, print: bool = False) lsst.gauss2d._gauss2d.ImageF ¶
- compute_loglike_grad(self: lsst.gauss2d.fit._gauss2d_fit.ModelF, include_prior: bool = False, print: bool = False, verify: bool = False, findiff_frac: float = 0.0001, findiff_add: float = 0.0001, rtol: float = 0.001, atol: float = 0.001) list[float] ¶
- evaluate(self: lsst.gauss2d.fit._gauss2d_fit.ModelF, print: bool = False, normalize_loglike: bool = False) list[float] ¶
- gaussians(self: lsst.gauss2d.fit._gauss2d_fit.ModelF, channel: lsst.gauss2d.fit._gauss2d_fit.Channel) lsst.gauss2d._gauss2d.Gaussians ¶
- offsets_parameters(self: lsst.gauss2d.fit._gauss2d_fit.ModelF) list[tuple[lsst::modelfit::parameters::ParameterBase<double>, int]] ¶
- parameters(self: lsst.gauss2d.fit._gauss2d_fit.ModelF, parameters: list[lsst::modelfit::parameters::ParameterBase<double>] = [], paramfilter: lsst::gauss2d::fit::ParamFilter = None) list[lsst::modelfit::parameters::ParameterBase<double>] ¶
- setup_evaluators(self: lsst.gauss2d.fit._gauss2d_fit.ModelF, evaluatormode: lsst.gauss2d.fit._gauss2d_fit.EvaluatorMode = <EvaluatorMode.image: 0>, outputs: list[list[lsst.gauss2d._gauss2d.ImageF]] = [], residuals: list[lsst.gauss2d._gauss2d.ImageF] = [], outputs_prior: list[lsst.gauss2d._gauss2d.ImageF] = [], residuals_prior: lsst.gauss2d._gauss2d.ImageF = None, force: bool = False, print: bool = False) None ¶