本文整理匯總了Python中larch.Group.fit_details方法的典型用法代碼示例。如果您正苦於以下問題：Python Group.fit_details方法的具體用法？Python Group.fit_details怎麽用？Python Group.fit_details使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類larch.Group的用法示例。
在下文中一共展示了Group.fit_details方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: fit_peak
# 需要導入模塊: from larch import Group [as 別名]
# 或者: from larch.Group import fit_details [as 別名]
def fit_peak(x, y, model, dy=None, background=None, form=None, step=None,
negative=False, use_gamma=False, _larch=None):
"""fit peak to one a selection of simple 1d models
out = fit_peak(x, y, model, dy=None,
background='linear', form='linear')
arguments:
---------
x array of values at which to calculate model
y array of values for model to try to match
dy array of values for uncertainty in y data to be matched.
model name of model to use. One of (case insensitive)
'linear', 'quadratic', 'step', 'rectangle',
'gaussian', 'lorentzian', 'voigt', 'exponential'
background name of background model to use. One of (case insensitive)
None, 'constant', 'linear', or 'quadratic'
this is ignored when model is 'linear' or 'quadratic'
form name of form to use for 'step' and 'rectangle' models.
One of (case insensitive):
'linear', 'erf', or 'atan'
negative True/False for whether peak or steps are expected to go down.
use_gamma True/False for whether to use separate gamma parameter for
voigt model.
output:
-------
Group with fit parameters, and more...
"""
if form is None and step is not None:
form = step
out = Group(name='fit_peak result', x=x*1.0, y=y*1.0, dy=1.0,
model=model, background=background, form=form)
weight = None
if dy is not None:
out.dy = 1.0*dy
weight = 1.0/max(1.e-16, abs(dy))
if model.lower() not in MODELS:
_larch.writer.write('Unknown fit model: %s ' % model)
return None
kwargs = dict(negative=negative, background=background,
form=form, weight=weight, _larch=_larch)
fitclass = MODELS[model.lower()]
if fitclass == VoigtModel:
kwargs['use_gamma'] = use_gamma
mod = fitclass(**kwargs)
pars = mod.guess(out.y, out.x)
if background is not None:
bkg = MODELS[background.lower()](prefix='bkg_')
bpars = bkg.guess(out.y, x=out.x)
for p, par in bpars.items():
par.value = 0.
par.vary = True
pars += bpars
mod += bkg
out.init_params = pars
result = mod.fit(out.y, params=pars, x=out.x) # , dy=out.dy)
out.fit = mod.eval(result.params, x=out.x)
out.fit_init = mod.eval(pars, x=out.x)
out.fit_details = result
out.chi_square = result.chisqr
out.chi_reduced = result.redchi
for attr in ('aic', 'bic', 'covar', 'rfactor', 'params', 'nvarys',
'nfree', 'ndata', 'var_names', 'nfev', 'success',
'errorbars', 'message', 'lmdif_message', 'residual'):
setattr(out, attr, getattr(result, attr, None))
if background is not None:
comps = mod.eval_components(x=out.x)
out.bkg = comps['bkg_']
return out