fit results are not complex for non-complex data
This commit is contained in:
Dominik Demuth
parent
4b6820af18
commit
7aecf962ba
@ -361,7 +361,7 @@ class FitRoutine(object):
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with np.errstate(all='ignore'):
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res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
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err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
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err, corr, partial_corr = _calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
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self.make_results(data, res.x, var, data.para_keys, res.jac.shape,
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err=err, corr=corr, partial_corr=partial_corr)
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@ -375,7 +375,7 @@ class FitRoutine(object):
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with np.errstate(all='ignore'):
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res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
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err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
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err, corr, partial_corr = _calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
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for v, var_pars_k in zip(data, data_pars):
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self.make_results(v, res.x, var, var_pars_k, res.jac.shape,
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err=err, corr=corr, partial_corr=partial_corr)
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@ -458,9 +458,18 @@ class FitRoutine(object):
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self.make_results(v, res.beta, var, var_pars_k, (sum(len(d) for d in data), len(p0)),
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err=res.sd_beta, corr=corr, partial_corr=partial_corr)
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def make_results(self, data, p, var_pars, used_pars, shape,
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err=None, corr=None, partial_corr=None):
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def make_results(
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self,
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data: Data,
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p: list[float],
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var_pars: list[str],
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used_pars: list[str],
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shape: tuple[int, int],
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err: list[float] = None,
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corr: np.ndarray = None,
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partial_corr: np.ndarray = None,
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):
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print(data.complex_type)
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if err is None:
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err = [0] * len(p)
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@ -498,22 +507,23 @@ class FitRoutine(object):
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model = data.get_model()
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self.result[idx] = FitResultCreator.make_with_model(
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model,
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data.x,
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data.y,
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actual_parameters,
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data.fun_kwargs,
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data.we_string,
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data.idx,
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*shape,
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model=model,
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x_orig=data.x,
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y_orig=data.y,
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p=actual_parameters,
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fun_kwargs=data.fun_kwargs,
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we=data.we_string,
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idx=data.idx,
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nobs=shape[0],
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nvar=shape[1],
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corr=actual_corr,
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pcorr=actual_pcorr,
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data_mode=data.complex_type,
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)
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return self.result
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@staticmethod
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def _calc_error(jac, chi, nobs, nvars):
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def _calc_error(jac, chi, nobs, nvars):
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# copy of scipy.curve_fit to calculate covariance
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# noinspection PyTupleAssignmentBalance
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try:
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@ -9,7 +9,7 @@ from ._meta import MultiModel
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from .parameter import Parameters, Parameter
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class Model(object):
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class Model:
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def __init__(self, model, *args, **kwargs):
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self.idx = kwargs.pop('idx', None)
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@ -11,6 +11,7 @@ from scipy.stats import f as fdist
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from scipy.interpolate import interp1d
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from ._meta import MultiModel
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from .model import Model
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from .parameter import Parameter
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from ..data.points import Points
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from ..data.signals import Signal
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@ -36,17 +37,30 @@ class FitResultCreator:
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else:
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resid = kwargs['y'] - y_orig
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stats = FitResultCreator.calc_statistics(resid, _y)
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stats = calc_statistics(resid, _y)
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return FitResult(kwargs['x'], kwargs['y'], x_orig, y_orig, params, dict(kwargs['choice']), resid, 0, 0,
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kwargs['name'], stats, idx)
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return FitResult(
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x=kwargs['x'],
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y=kwargs['y'],
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x_data=x_orig,
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y_data=y_orig,
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params=params,
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fun_kwargs=dict(kwargs['choice']),
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resid=resid,
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nobs=0,
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nvar=0,
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we='',
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name=kwargs['name'],
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stats=stats,
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idx=idx,
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)
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@staticmethod
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def make_with_model(
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model: 'Model',
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x_orig: np.ndarray,
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y_orig: np.ndarray,
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p: 'Parameters',
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p: list,
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fun_kwargs: dict,
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we: str,
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idx: str | None,
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@ -54,7 +68,7 @@ class FitResultCreator:
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nvar: int,
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corr: np.ndarray,
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pcorr: np.ndarray,
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data_mode: int = 1,
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data_mode: int,
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) -> FitResult:
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if np.all(x_orig > 0) and (np.max(x_orig) > 100 * np.min(x_orig)):
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islog = True
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@ -84,20 +98,12 @@ class FitResultCreator:
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actual_mode = fun_kwargs['complex_mode']
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fun_kwargs['complex_mode'] = 0
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_y = model.func(p_final, _x, **fun_kwargs)
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if not actual_mode < 0:
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if actual_mode == 1:
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_Y = _y.real
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# _y.imag = 0
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elif actual_mode == 2:
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# if data_mode ==
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_y = _y.imag
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# _y.real = 0
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_y = check_complex(model.func(p_final, _x, **fun_kwargs), actual_mode, data_mode)
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print(_y)
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fun_kwargs['complex_mode'] = actual_mode
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stats = FitResultCreator.calc_statistics(_y, resid, nobs, nvar)
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stats = calc_statistics(_y, resid, nobs, nvar)
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varied = [p.var for p in parameters.values()]
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if corr is None:
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@ -138,38 +144,9 @@ class FitResultCreator:
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pcorr=partial_correlation,
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islog=islog,
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func=model,
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data_complex=data_mode,
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)
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@staticmethod
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def calc_statistics(y, residual, nobs=None, nvar=None):
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chi = (residual**2).sum()
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try:
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r = 1 - chi/((y-np.mean(y))**2).sum()
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except RuntimeWarning:
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r = -9999
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if nobs is None:
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nobs = 1
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if nvar is None:
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nvar = 0
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dof = nobs - nvar
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loglikehood = nobs * np.log(chi / nobs)
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stats = {
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'chi^2': chi,
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'R^2': r,
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'AIC': loglikehood + 2 * nvar,
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'BIC': loglikehood + np.log(nobs) * nvar,
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'adj. R^2': 1 - (nobs-1) / (dof+1e-13) * (1-r),
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'red. chi^2': chi / (dof + 1e-13),
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}
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stats['AICc'] = stats['AIC'] + 2*(nvar+1)*nvar / (dof - 1 + 1e-13)
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return stats
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class FitResult(Points):
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@ -192,7 +169,8 @@ class FitResult(Points):
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pcorr: np.ndarray = None,
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islog: bool = False,
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func=None,
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**kwargs
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data_complex: int = 1,
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**kwargs,
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):
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self.parameter, name = self._prepare_names(params, name)
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@ -214,6 +192,7 @@ class FitResult(Points):
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self.y_data = y_data
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self._model_name = name
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self._func = func
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self._data_complex = data_complex
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@staticmethod
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def _prepare_names(parameter: dict, modelname: str):
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@ -422,20 +401,9 @@ class FitResult(Points):
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if self.func is None:
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raise ValueError('no fit function available to calculate new y values')
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actual_mode = -1
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if 'complex_mode' in self.fun_kwargs:
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actual_mode = self.fun_kwargs['complex_mode']
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self.fun_kwargs['complex_mode'] = 0
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new_fit = self.copy()
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y_values = self.func.func(self.p_final, x_values, **self.fun_kwargs)
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if not actual_mode < 0:
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if actual_mode == 1:
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y_values.imag = 0
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elif actual_mode == 2:
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y_values.real = 0
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self.fun_kwargs['complex_mode'] = actual_mode
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y_values = check_complex(y_values, self.fun_kwargs.get('complex_mode', -1), self._data_complex)
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new_fit.set_data(x_values, y_values, y_err=0.0)
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@ -446,20 +414,13 @@ class FitResult(Points):
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raise ValueError('no fit function available to calculate new y values')
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part_functions = []
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actual_mode = -1
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if 'complex_mode' in self.fun_kwargs:
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actual_mode = self.fun_kwargs['complex_mode']
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self.fun_kwargs['complex_mode'] = 0
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actual_mode = self.fun_kwargs.get('complex_mode', -1)
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for sub_name, sub_y in zip(self.func.sub_name(), self.func.sub(self.p_final, x_values, **self.fun_kwargs)):
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if not actual_mode < 0:
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if actual_mode == 1:
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sub_y.imag = 0
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elif actual_mode == 2:
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sub_y.real = 0
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sub_y = check_complex(sub_y, actual_mode, self._data_complex)
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if np.iscomplexobj(sub_y):
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part_functions.append(Signal(x_values, sub_y, name=sub_name))
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else:
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part_functions.append(Points(x_values, sub_y, name=sub_name))
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@ -467,3 +428,49 @@ class FitResult(Points):
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self.fun_kwargs['complex_mode'] = actual_mode
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return part_functions
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def check_complex(y, model_complex, data_complex):
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if not np.iscomplexobj(y):
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return y
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if model_complex == 1:
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y.imag = 0
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if data_complex == 1:
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y = y.real
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elif model_complex == 2:
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y.real = 0
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if data_complex == 1:
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y = y.imag
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return y
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def calc_statistics(y, residual, nobs=None, nvar=None):
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chi = (residual**2).sum()
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try:
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r = 1 - chi/((y-np.mean(y))**2).sum()
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except RuntimeWarning:
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r = -9999
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if nobs is None:
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nobs = 1
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if nvar is None:
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nvar = 0
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dof = nobs - nvar
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loglikehood = nobs * np.log(chi / nobs)
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stats = {
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'chi^2': chi,
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'R^2': r,
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'AIC': loglikehood + 2 * nvar,
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'BIC': loglikehood + np.log(nobs) * nvar,
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'adj. R^2': 1 - (nobs-1) / (dof+1e-13) * (1-r),
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'red. chi^2': chi / (dof + 1e-13),
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}
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stats['AICc'] = stats['AIC'] + 2*(nvar+1)*nvar / (dof - 1 + 1e-13)
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return stats
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