lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-05-14 19:43:41 +02:00
Code Issues Projects Releases Packages Wiki Activity

refactor: Classification of fit method in fits.least_squares simplified,

Browse source 
precision of imiunit solver adjusted, prefitting for alternative methods
removed.
This commit is contained in:
Fabian Joswig 2022-02-02 10:05:48 +00:00
parent e92b1d9e9b
commit d602bea5b7
1 changed files with 9 additions and 14 deletions
Download patch file Download diff file Expand all files Collapse all files

23
pyerrors/fits.py
View file

@ -96,7 +96,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
initial_guess : list initial_guess : list
can provide an initial guess for the input parameters. Relevant for can provide an initial guess for the input parameters. Relevant for
non-linear fits with many parameters. non-linear fits with many parameters.
method : str method : str, optional
can be used to choose an alternative method for the minimization of chisquare. can be used to choose an alternative method for the minimization of chisquare.
The possible methods are the ones which can be used for scipy.optimize.minimize and The possible methods are the ones which can be used for scipy.optimize.minimize and
migrad of iminuit. If no method is specified, Levenberg-Marquard is used. migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
@ -487,26 +487,21 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
chisq = anp.sum(((y_f - model) / dy_f) ** 2) chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq return chisq
if 'method' in kwargs and not (kwargs.get('method', 'Levenberg-Marquardt') == 'Levenberg-Marquardt'): output.method = kwargs.get('method', 'Levenberg-Marquardt')
output.method = kwargs.get('method') if not silent:
if not silent: print('Method:', output.method)
print('Method:', kwargs.get('method'))
if kwargs.get('method') == 'migrad': if output.method != 'Levenberg-Marquardt':
fit_result = iminuit.minimize(chisqfunc, x0) if output.method == 'migrad':
fit_result = iminuit.minimize(chisqfunc, fit_result.x) fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4) # Stopping crieterion 0.002 * tol * errordef
output.iterations = fit_result.nfev output.iterations = fit_result.nfev
else: else:
fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method')) fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12)
fit_result = scipy.optimize.minimize(chisqfunc, fit_result.x, method=kwargs.get('method'), tol=1e-12)
output.iterations = fit_result.nit output.iterations = fit_result.nit
chisquare = fit_result.fun chisquare = fit_result.fun
else: else:
output.method = 'Levenberg-Marquardt'
if not silent:
print('Method: Levenberg-Marquardt')
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals(p): def chisqfunc_residuals(p):
model = func(p, x) model = func(p, x)