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@ -29,6 +29,7 @@ def _cost_scipy_glob(p: list[float], data: list[Data], varpars: list[str], used_
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for keys, values in zip(varpars, p):
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for data_i in data:
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if keys in data_i.parameter.keys():
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# TODO move this to scaled_value setter
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data_i.parameter[keys].scaled_value = values
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data_i.parameter[keys].namespace[keys] = data_i.parameter[keys].value
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r = []
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@ -220,7 +221,7 @@ class FitRoutine(object):
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logger.info('Fit aborted by user')
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self._abort = True
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def run(self, mode: str=None):
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def run(self, mode: str = None):
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self._abort = False
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if mode is None:
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@ -262,6 +263,16 @@ class FitRoutine(object):
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return self.result
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def make_preview(self, x: np.ndarray) -> list[np.ndarray]:
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y_pred = []
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fit_groups, linked_parameter = self.prepare_links()
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for data_groups in fit_groups:
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data = data_groups[0]
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actual_parameters = [p.value for p in data.parameter.values()]
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y_pred.append(data.func(actual_parameters, x))
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return y_pred
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def _prep_data(self, data):
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if data.get_model() is None:
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data._model = self.fit_model
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@ -317,6 +328,7 @@ class FitRoutine(object):
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d._model = None
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self._no_own_model = []
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Parameters.reset()
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def _least_squares_single(self, data, p0, lb, ub, var):
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self.step = 0
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@ -345,7 +357,6 @@ 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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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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@ -88,6 +88,15 @@ class Parameters(dict):
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expression = re.sub(re.escape(n), k, expression)
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p._expr = expression
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def fix(self):
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for v in self.keys():
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v._value = v.value
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v.namespace = {}
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@staticmethod
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def reset():
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Parameters.namespace = {}
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def get_key(self, name: str) -> str | None:
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for k, v in self.items():
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if name == v.name:
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@ -104,6 +113,7 @@ class Parameter:
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Container for one parameter
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"""
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# TODO Parameter should know its own key
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def __init__(self, name: str, value: float | str, var: bool = True, lb: float = -np.inf, ub: float = np.inf):
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self._value: float | None = None
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self.var: bool = bool(var) if var is not None else True
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@ -181,6 +191,7 @@ class Parameter:
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@property
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def value(self) -> float:
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# TODO first _value, then _expr
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if self._expr is not None and self.eval_allowed:
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return eval(self._expr, {}, self.namespace)
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elif self._value is not None:
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