multiple single fits working
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@ -136,6 +136,8 @@ class FitRoutine(object):
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linked_sender[repl].add(par)
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linked_sender[par].add(repl)
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print(_found_models)
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for mm, m_data in _found_models.items():
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if mm.global_parameter:
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for dd in m_data:
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@ -235,7 +237,7 @@ class FitRoutine(object):
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var = []
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data_pars = []
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# loopyloop over data that belong to one fit (linked or global)
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# loopy-loop over data that belong to one fit (linked or global)
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for data in data_group:
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actual_pars = []
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for i, (p_k, v_k) in enumerate(data.parameter.items()):
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@ -273,10 +275,10 @@ class FitRoutine(object):
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# COST FUNCTIONS: f(x) - y (least_square, minimize), and f(x) (ODR)
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def __cost_scipy(self, p, data, varpars, used_pars):
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for keys, values in zip(varpars, p):
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self.parameter[keys].scaled_value = values
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self.parameter[keys].namespace[keys] = self.parameter[keys].value
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data.parameter[keys].scaled_value = values
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data.parameter[keys].namespace[keys] = data.parameter[keys].value
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actual_parameters = [self.parameter[keys].value for keys in used_pars]
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actual_parameters = [data.parameter[keys].value for keys in used_pars]
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return data.cost(actual_parameters)
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def __cost_odr(self, p, data, varpars, used_pars):
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@ -438,15 +440,17 @@ class FitRoutine(object):
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# update parameter values
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for keys, p_value, err_value in zip(var_pars, p, err):
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self.parameter[keys].scaled_value = p_value
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self.parameter[keys].scaled_error = err_value
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data.parameter[keys].scaled_value = p_value
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data.parameter[keys].scaled_error = err_value
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data.parameter[keys].namespace[keys] = data.parameter[keys].value
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combinations = list(product(var_pars, var_pars))
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actual_parameters = []
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corr_idx = []
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for i, p_i in enumerate(used_pars):
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actual_parameters.append(self.parameter[p_i])
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actual_parameters.append(data.parameter[p_i])
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for j, p_j in enumerate(used_pars):
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try:
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# find the position of the parameter combinations
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@ -80,23 +80,15 @@ class Model(object):
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self.fun_kwargs = {k: v.default for k, v in inspect.signature(model.func).parameters.items()
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if v.default is not inspect.Parameter.empty}
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def set_global_parameter(self, idx, p, var=None, lb=None, ub=None, default_bounds=False):
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if idx is None:
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self.parameter = Parameters()
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self.global_parameter = {}
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return
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def set_global_parameter(self, key, value, var=None, lb=None, ub=None, default_bounds=False):
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idx = [self.params.index(key)]
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if default_bounds:
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if lb is None:
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lb = [self.lb[i] for i in idx]
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if ub is None:
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ub = [self.lb[i] for i in idx]
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gp = self.parameter.add_parameter(p, var=var, lb=lb, ub=ub)
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for k, v in zip(idx, gp):
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self.global_parameter[k] = v
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return gp
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self.parameter.add(key, value, var=var, lb=lb, ub=ub)
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@staticmethod
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def _prep(param_len, val):
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@ -60,7 +60,7 @@ class Parameters(dict):
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else:
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key = f'_p{new_idx}'
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new_keys.append(key)
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self[key] = Parameter(param[i], var=var[i], lb=lb[i], ub=ub[i])
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self[key] = Parameter(key, param[i], var=var[i], lb=lb[i], ub=ub[i])
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return new_keys
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@ -56,7 +56,6 @@ class Lorentzian:
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off (float): baseline
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"""
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# print('Lorentzian', a, mu, sigma, off)
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return (a/np.pi) * 2*sigma / (4*(x-mu)**2 + sigma**2) + off
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