pint.fitter.GLSState

class pint.fitter.GLSState(fitter: Fitter, model: TimingModel, full_cov: bool = False, threshold: float | None = None)[source]

Methods

`predicted_chi2`(step, lambda_)	Predict the chi2 after taking a step based on the linear approximation
`take_step`(step[, lambda_])	Return a new state moved by lambda_*step.
`take_step_model`(step[, lambda_])	Make a new model reflecting the new parameters.

Attributes

`chi2`
`covariance_matrix`
`parameter_covariance_matrix`
`resids`
`step`

take_step(step, lambda_=1) → GLSState[source]: Return a new state moved by lambda_*step.

predicted_chi2(step, lambda_): Predict the chi2 after taking a step based on the linear approximation

take_step_model(step, lambda_=1): Make a new model reflecting the new parameters.