pybroom.py 文件源码

 复制代码def _augment_lmfit_modelresult(result):
    """Tidy data values and fitted model from `lmfit.model.ModelResult`.
    """
    columns = ['x', 'data', 'best_fit', 'residual']
    d = pd.DataFrame(index=range(result.ndata), columns=columns)
    for col in columns[1:]:
        d.loc[:, col] = getattr(result, col)

    independent_vars = result.model.independent_vars
    if len(independent_vars) == 1:
        independent_var = independent_vars[0]
    else:
        msg = ('Only 1 independent variable is currently supported.\n'
               'Found independent variables: %s' % str(independent_vars))
        raise NotImplementedError(msg)

    x_array = result.userkws[independent_var]
    d.loc[:, 'x'] = x_array

    if len(result.components) > 1:
        comp_names = [c.name for c in result.components]
        for cname, comp in zip(comp_names, result.components):
            d.loc[:, cname] = comp.eval(x=d.x, **result.values)
    return d.apply(pd.to_numeric, errors='ignore')