Python Group.dy方法代码示例

# 需要导入模块: from larch import Group [as 别名]
# 或者: from larch.Group import dy [as 别名]
def fit_peak(x, y, model, dy=None, background=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', step='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'
    step        name of step model 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...
    """
    out = Group(x=x*1.0, y=y*1.0, dy=1.0, model=model,
                background=background, step=step)
    if dy is not None:
        out.dy = 1.0*dy
    if model.lower() not in MODELS:
        _larch.writer.write('Unknown fit model: %s ' % model)
        return None

    kwargs = dict(negative=negative, background=background,
                  step=step, _larch=_larch)

    fitclass = MODELS[model.lower()]
    if fitclass == VoigtModel:
        kwargs['use_gamma'] = use_gamma

    mod = fitclass(**kwargs)
    mod.guess_starting_values(out.y, out.x)

    out.fit_init = mod.model(x=out.x)
    if background is not None:
        out.bkg_init = mod.calc_background(out.x)
        out.fit_init += out.bkg_init

    mod.fit(out.y, x=out.x, dy=out.dy, _larch=_larch)

    out.fit = mod.model(x=out.x)
    if background is not None:
        out.bkg = mod.calc_background(out.x)
        out.fit += out.bkg
    out.params = mod.params
    return out

# 需要导入模块: from larch import Group [as 别名]
# 或者: from larch.Group import dy [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