ModelD

class lsst.gauss2d.fit.ModelD

Bases: ParametricModel

Attributes Summary

data
mode
outputs
priors
psfmodels
sources

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.ModelD, transformed: bool = False, include_prior: bool = True, options: lsst.gauss2d.fit._gauss2d_fit.HessianOptions | None = None, print: bool = False) → lsst.gauss2d._gauss2d.ImageD

compute_loglike_grad(self: lsst.gauss2d.fit._gauss2d_fit.ModelD, 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.ModelD, print: bool = False, normalize_loglike: bool = False) → list[float]

gaussians(self: lsst.gauss2d.fit._gauss2d_fit.ModelD, channel: lsst.gauss2d.fit._gauss2d_fit.Channel) → lsst.gauss2d._gauss2d.Gaussians

offsets_parameters(self: lsst.gauss2d.fit._gauss2d_fit.ModelD) → list[tuple[lsst::modelfit::parameters::ParameterBase<double>, int]]

parameters(self: lsst.gauss2d.fit._gauss2d_fit.ModelD, 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.ModelD, evaluatormode: lsst.gauss2d.fit._gauss2d_fit.EvaluatorMode = <EvaluatorMode.image: 0>, outputs: list[list[lsst.gauss2d._gauss2d.ImageD]] = [], residuals: list[lsst.gauss2d._gauss2d.ImageD] = [], outputs_prior: list[lsst.gauss2d._gauss2d.ImageD] = [], residuals_prior: lsst.gauss2d._gauss2d.ImageD = None, force: bool = False, print: bool = False) → None

verify_jacobian(self: lsst.gauss2d.fit._gauss2d_fit.ModelD, findiff_frac: float = 0.0001, findiff_add: float = 0.0001, rtol: float = 0.001, atol: float = 0.001, max_diff_ll: float = 0) → list[str]