sfit_minimizer.sfit_minimize module¶
- sfit_minimizer.sfit_minimize.set_initial_step_size(options)¶
Set the step size for the
minimize()function.- Arguments:
- options: dict
To specify the step, set options[‘step’] = float. The parameter values will be iterated by this value * the calculated step size. Alternatively, options[‘step’] = ‘adaptive’ will start with a default size of 0.01. If None or ‘step’ not in options, uses a default step size of 0.1.
- Returns:
- fac: float
The initial fraction of the step that will be added in each iteration of the
minimize()routine.
- sfit_minimizer.sfit_minimize.minimize(sfit_obj, x0=None, tol=0.001, options=None, max_iter=1000, verbose=False)¶
Find the best-fit parameters for a function f using A. Gould’s sfit algorithm.
- Arguments:
- sfit_obj:
sfit_minimizer.sfit_classes.SFitFunction The function whose parameters are being fit.
- sfit_obj:
- Keywords:
- x0: list, or list-like
Initial guess for the values of the parameters
- tol: float
Chi2 tolerance; i.e., when chi2_old - chi2_new < tol, stop.
- options: dict
options for the minimizer. Currently available: ‘step’ (see
set_initial_step_size())- max_iter: int
Maximum number of iterations.
- verbose: bool
True = print results of each iteration to the screen.
- Returns: