本文整理汇总了Python中larch.Group.fit_details方法的典型用法代码示例。如果您正苦于以下问题：Python Group.fit_details方法的具体用法？Python Group.fit_details怎么用？Python Group.fit_details使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类larch.Group的用法示例。
在下文中一共展示了Group.fit_details方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: fit_peak
# 需要导入模块: from larch import Group [as 别名]
# 或者: from larch.Group import fit_details [as 别名]
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