forked from IPKM/nmreval
fit results save weight; closes #139
This commit is contained in:
Dominik Demuth
parent
f04cadf780
commit
8fb17b22fd
@ -13,7 +13,7 @@ class Data(object):
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if self.y.shape[0] != self.x.shape[0]:
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raise ValueError(f'x and y have different lengths {self.x.shape[0]} and {self.y.shape[0]}')
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self.we = self._calc_weights(we)
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self.we, self.we_string = self._calc_weights(we)
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self.idx = idx
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self.model = None
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self.minimizer = None
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@ -24,16 +24,16 @@ class Data(object):
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def __len__(self):
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return self.y.shape[0]
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def _calc_weights(self, we):
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if we is None:
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return 1.
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if isinstance(we, str):
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if we == 'y2':
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we_func = lambda yy: 1. / yy**2
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elif we.lower() == 'none':
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def _calc_weights(self, we: str | np.ndarray | None) -> tuple[np.ndarray, str]:
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if isinstance(we, str) or we is None:
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if we is None or we.lower() == 'none':
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we_string = 'None'
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we_func = lambda yy: np.ones_like(len(yy))
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elif we == 'y2':
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we_string = we
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we_func = lambda yy: 1. / yy**2
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else:
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we_string = we
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we_func = lambda yy: 1. / np.abs(yy)
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if np.iscomplexobj(self.y):
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@ -42,6 +42,7 @@ class Data(object):
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weights = we_func(self.y)
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else:
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we_string = 'yerr' # This is pure speculation that array equals error
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we = 1. / np.asarray(we)
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if np.iscomplexobj(self.y):
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if np.iscomplexobj(we):
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@ -53,7 +54,7 @@ class Data(object):
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weights[weights == np.inf] = np.finfo(float).max
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return weights
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return weights, we_string
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def set_model(self, func, *args, **kwargs):
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if isinstance(func, Model):
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@ -484,9 +484,18 @@ class FitRoutine(object):
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idx = self.data.index(data)
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model = data.get_model()
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self.result[idx] = FitResultCreator.make_with_model(model, data.x, data.y,
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actual_parameters, data.fun_kwargs, data.idx,
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*shape, corr=actual_corr, pcorr=actual_pcorr)
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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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corr=actual_corr,
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pcorr=actual_pcorr,
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)
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return self.result
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@ -42,7 +42,19 @@ class FitResultCreator:
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kwargs['name'], stats, idx)
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@staticmethod
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def make_with_model(model, x_orig, y_orig, p, fun_kwargs, idx, nobs, nvar, corr, pcorr) -> FitResult:
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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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fun_kwargs: dict,
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we: str,
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idx: str | None,
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nobs: int,
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nvar: int,
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corr: np.ndarray,
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pcorr: np.ndarray,
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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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else:
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@ -104,10 +116,25 @@ class FitResultCreator:
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correlation = corr
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partial_correlation = pcorr
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return FitResult(_x, _y, x_orig, y_orig, parameters, fun_kwargs, resid,
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nobs, nvar, model.name, stats,
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idx=idx, corr=correlation, pcorr=partial_correlation,
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islog=islog, func=model)
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return FitResult(
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x=_x,
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y=_y,
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x_data=x_orig,
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y_data=y_orig,
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params=parameters,
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fun_kwargs=fun_kwargs,
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resid=resid,
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nobs=nobs,
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nvar=nvar,
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we=we,
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name=model.name,
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stats=stats,
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idx=idx,
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corr=correlation,
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pcorr=partial_correlation,
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islog=islog,
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func=model,
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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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@ -145,12 +172,27 @@ class FitResultCreator:
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class FitResult(Points):
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def __init__(self, x: np.ndarray, y: np.ndarray,
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x_data: np.ndarray, y_data: np.ndarray,
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params: dict, fun_kwargs: dict,
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resid: np.ndarray, nobs: int, nvar: int, name: str, stats: dict,
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idx=None, corr=None, pcorr=None, islog=False, func=None,
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**kwargs):
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def __init__(
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self: FitResult,
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x: np.ndarray,
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y: np.ndarray,
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x_data: np.ndarray,
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y_data: np.ndarray,
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params: dict,
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fun_kwargs: dict,
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resid: np.ndarray,
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nobs: int,
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nvar: int,
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we: str,
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name: str,
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stats: dict,
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idx: int = None,
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corr: np.ndarray = None,
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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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):
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self.parameter, name = self._prepare_names(params, name)
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label = kwargs.get('label', name)
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@ -161,6 +203,7 @@ class FitResult(Points):
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self.statistics = stats
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self.nobs = nobs
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self.nvar = nvar
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self.we = we
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self.fun_kwargs = fun_kwargs
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self.correlation = corr
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self.partial_correlation = pcorr
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@ -229,6 +272,7 @@ class FitResult(Points):
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s.write(f' model : {self.name}\n')
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s.write(f' #data : {self.nobs}\n')
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s.write(f' #var : {self.nvar}\n')
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s.write(f' #weight: {self.we}\n')
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s.write('\nParameter\n')
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s.write(self.parameter_string())
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@ -322,10 +366,13 @@ class FitResult(Points):
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err = 0.
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f.write(f'{p.value:.8e}\t{err:.8e}\t')
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f.write('# ')
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if self.fun_kwargs:
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f.write('# ')
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for k, v in self.fun_kwargs.items():
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f.write(f"{convert(k, old='tex', new='str')}: {convert(str(v), old='tex', new='str')}\t")
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f.write(f'weight: {self.we}\t')
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f.write('\n')
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f.write(f'# line above from: {self.name} (model: {self.model_name})')
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f.write('\n')
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