seems mostly to be working

src/gui_qt/fit/fit_parameter.py

@@ -227,7 +227,7 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
             kw_p = {}
             p = []
             if global_p is None:
-                global_p = {'p': [], 'idx': [], 'var': [], 'ub': [], 'lb': []}
+                global_p = {'value': [], 'idx': [], 'var': [], 'ub': [], 'lb': []}
 
             for i, (p_i, g) in enumerate(zip(parameter, self.global_parameter)):
                 if isinstance(g, FitModelWidget):
@@ -235,7 +235,7 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
                         p.append(globs[i])
                         if is_global[i]:
                             if i not in global_p['idx']:
-                                global_p['p'].append(globs[i])
+                                global_p['value'].append(globs[i])
                                 global_p['idx'].append(i)
                                 global_p['var'].append(is_fixed[i])
                                 global_p['ub'].append(ub[i])

src/gui_qt/fit/fitwindow.py

@@ -221,7 +221,7 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
                  parameter: dict = None, add_idx: bool = False, cnt: int = 0) -> tuple[dict, int]:
         if parameter is None:
             parameter = {'parameter': {}, 'lb': (), 'ub': (), 'var': [],
-                         'glob': {'idx': [], 'p': [], 'var': [], 'lb': [], 'ub': []},
+                         'glob': {'idx': [], 'value': [], 'var': [], 'lb': [], 'ub': []},
                          'links': [], 'color': []}
 
         for i, f in enumerate(model):
@@ -269,7 +269,7 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
 
             if f['children']:
                 # recurse for children
-                child_parameter, cnt = self._prepare(f['children'], parameter=parameter, add_idx=add_idx, cnt=cnt)
+                _, cnt = self._prepare(f['children'], parameter=parameter, add_idx=add_idx, cnt=cnt)
 
         return parameter, cnt
 
@@ -288,6 +288,13 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
 
             if k in data:
                 parameter, _ = self._prepare(mod, function_use=data[k], add_idx=isinstance(func, MultiModel))
+
+                # convert positions of global parameter to corresponding names
+                global_parameter: dict = parameter['glob']
+                positions = global_parameter.pop('idx')
+                global_parameter['key'] = [pname for i, pname in enumerate(func.params) if i in positions]
+                # print(global_parameter)
+
                 if parameter is None:
                     return
 

src/gui_qt/main/management.py

@@ -467,7 +467,9 @@ class UpperManagement(QtCore.QObject):
 
             model_globs = model_p['glob']
             if model_globs:
-                m.set_global_parameter(**model_p['glob'])
+                for parameter_args in zip(*model_globs.values()):
+                    m.set_global_parameter(**{k: v for k, v in zip(model_globs.keys(), parameter_args)})
+                # m.set_global_parameter(**model_p['glob'])
 
         for links_i in links:
             self.fitter.set_link_parameter((models[links_i[0]], links_i[1]),

src/nmreval/fit/data.py

@@ -68,12 +68,19 @@ class Data(object):
     def get_model(self):
         return self.model
 
-    def set_parameter(self, parameter, var=None, ub=None, lb=None,
+    def set_parameter(self,
-                      default_bounds=False, fun_kwargs=None):
+                      values: list[float],
+                      *,
+                      var: list[bool] = None,
+                      ub: list[float] = None,
+                      lb: list[float] = None,
+                      default_bounds: bool = False,
+                      fun_kwargs: dict = None
+                      ):
         """
         Creates parameter for this data.
         If no Model is available, it falls back to the model
-        :param parameter: list of parameters
+        :param values: list of parameters
         :param var: list of boolean or boolean; False fixes parameter at given list index.
                     Single value is broadcast to all parameter
         :param ub: list of upper boundaries or float; Single value is broadcast to all parameter.
@@ -87,23 +94,37 @@ class Data(object):
         model = self.model
         if model is None:
             # Data has no unique
-            if self.minimizer is None:
+            if self.minimizer is not None:
-                model = None
-            else:
                 model = self.minimizer.fit_model
-                self.fun_kwargs.update(model.fun_kwargs)
 
         if model is None:
             raise ValueError('No model found, please set model before parameters')
 
-        if default_bounds:
+        if len(values) != len(model.params):
+            raise ValueError('Number of given parameter does not match number of model parameters')
+
+        if var is None:
+            var = [True] * len(values)
+
         if lb is None:
+            if default_bounds:
                 lb = model.lb
+            else:
+                lb = [None] * len(values)
+
         if ub is None:
+            if default_bounds:
                 ub = model.ub
+            else:
+                ub = [None] * len(values)
 
-        self.para_keys = self.parameter.add_parameter(parameter, var=var, lb=lb, ub=ub, names=model.params)
+        arg_names = ['name', 'value', 'var', 'lb', 'ub']
+        for parameter_arg in zip(model.params, values, var, lb, ub):
+            self.parameter.add(**{arg_name: arg_value for arg_name, arg_value in zip(arg_names, parameter_arg)})
 
+        self.para_keys = list(self.parameter.keys())
+
+        self.fun_kwargs.update(model.fun_kwargs)
         if fun_kwargs is not None:
             self.fun_kwargs.update(fun_kwargs)
 

src/nmreval/fit/minimizer.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import warnings
 from itertools import product
 
@@ -26,7 +28,7 @@ def _cost_scipy_glob(p: list[float], data: list[Data], varpars: list[str], used_
     # replace values
     for keys, values in zip(varpars, p):
         for data_i in data:
-            if keys in data_i.parameter:
+            if keys in data_i.parameter.keys():
                 data_i.parameter[keys].scaled_value = values
                 data_i.parameter[keys].namespace[keys] = data_i.parameter[keys].value
     r = []
@@ -53,7 +55,6 @@ class FitRoutine(object):
         self.data = []
         self.fit_model = None
         self._no_own_model = []
-        self.parameter = Parameters()
         self.result = []
         self.linked = []
         self._abort = False
@@ -107,28 +108,25 @@ class FitRoutine(object):
 
         return self.fit_model
 
-    def set_link_parameter(self, parameter: tuple, replacement: tuple):
+    def set_link_parameter(self, dismissed_param: tuple[Model | Data, str], replacement: tuple[Model, str]):
         if isinstance(replacement[0], Model):
-            if replacement[1] not in replacement[0].global_parameter:
+            if replacement[1] not in replacement[0].parameter:
-                raise KeyError(f'Parameter at pos {replacement[1]} of '
+                raise KeyError(f'Parameter {replacement[1]} of '
-                               f'model {str(replacement[0])} is not global')
+                               f'model {replacement[0]} is not global')
 
-        if isinstance(parameter[0], Model):
+        if isinstance(dismissed_param[0], Model):
-            warnings.warn(f'Replaced parameter at pos {parameter[1]} in {str(parameter[0])} '
+            warnings.warn(f'Replaced parameter {dismissed_param[1]} in {dismissed_param[0]} '
                           f'becomes global with linkage.')
 
-        self.linked.append((*parameter, *replacement))
+        self.linked.append((*dismissed_param, *replacement))
 
     def prepare_links(self):
         self._no_own_model = []
-        self.parameter = Parameters()
         _found_models = {}
         linked_sender = {}
 
         for v in self.data:
             linked_sender[v] = set()
-            for k, p_i in v.parameter.items():
-                self.parameter.add_parameter(k, p_i.copy())
 
             # set temporary model
             if v.model is None:
@@ -138,35 +136,29 @@ class FitRoutine(object):
             # register model
             if v.model not in _found_models:
                 _found_models[v.model] = []
-                for k, p in v.model.parameter.items():
+
-                    self.parameter.add_parameter(k, p)
-                # m_param = v.model.parameter.copy()
-                # self.parameter.update(m_param)
-            #
             _found_models[v.model].append(v)
 
             if v.model not in linked_sender:
                 linked_sender[v.model] = set()
 
         linked_parameter = {}
-        # for par, par_parm, repl, repl_par in self.linked:
+        for dismiss_model, dismiss_param, replace_model, replace_param in self.linked:
-        #     if isinstance(par, Data):
+            linked_sender[replace_model].add(dismiss_model)
-        #         if isinstance(repl, Data):
+            linked_sender[replace_model].add(replace_model)
-        #             linked_parameter[par.para_keys[par_parm]] = repl.para_keys[repl_par]
+
-        #         else:
+            replace_key = replace_model.parameter.get_key(replace_param)
-        #             linked_parameter[par.para_keys[par_parm]] = repl.parameter[repl_par]
+            dismiss_key = dismiss_model.parameter.get_key(dismiss_param)
-        #
+
-        #     else:
+            if isinstance(replace_model, Data):
-        #         if isinstance(repl, Data):
+                linked_parameter[dismiss_key] = replace_key
-        #             par.global_parameter[par_parm] = repl.para_keys[repl_par]
+            else:
-        #         else:
+                # print('dismiss model', dismiss_model.parameter)
-        #             par.global_parameter[par_parm] = repl.global_parameter[repl_par]
+                # print('replace model', replace_model.parameter)
-        #
+                dismiss_model.parameter.replace_parameter(dismiss_key, replace_key, replace_model.parameter[replace_key])
-        #     linked_sender[repl].add(par)
+                # print('after replacement', dismiss_model.parameter)
-        #     linked_sender[par].add(repl)
 
         for mm, m_data in _found_models.items():
-            # print('has global', mm.parameter)
             if mm.parameter:
                 for dd in m_data:
                     linked_sender[mm].add(dd)
@@ -174,14 +166,12 @@ class FitRoutine(object):
 
         coupled_data = []
         visited_data = []
-        # print('linked', linked_sender)
         for s in linked_sender.keys():
             if s in visited_data:
                 continue
             sub_graph = []
             self.find_paths(s, linked_sender, sub_graph, visited_data)
             if sub_graph:
-                # print('sub', sub_graph)
                 coupled_data.append(sub_graph)
 
         return coupled_data, linked_parameter
@@ -203,12 +193,8 @@ class FitRoutine(object):
 
     def run(self, mode='lsq'):
         self._abort = False
-        self.parameter = Parameters()
 
         fit_groups, linked_parameter = self.prepare_links()
-
-        # print(fit_groups, self.linked)
-
         for data_groups in fit_groups:
             if len(data_groups) == 1 and not self.linked:
                 data = data_groups[0]
@@ -226,6 +212,7 @@ class FitRoutine(object):
                     self._odr_single(data, p0_k, var_pars_k)
 
             else:
+                # print('INSIDE RUN')
                 data_pars, p0, lb, ub, var_pars = self._prep_global(data_groups, linked_parameter)
 
                 if mode == 'lsq':
@@ -262,18 +249,21 @@ class FitRoutine(object):
         return pp, lb, ub, var_pars
 
     def _prep_global(self, data_group, linked):
+        # print('PREP GLOBAL')
+        # print(data_group, linked)
+
         p0 = []
         lb = []
         ub = []
         var = []
         data_pars = []
 
-        # print(data_group)
-
         # loopy-loop over data that belong to one fit (linked or global)
         for data in data_group:
             # is parameter replaced by global parameter?
+            # print('SET GLOBAL')
             for k, v in data.model.parameter.items():
+                # print(k, v)
                 data.replace_parameter(k, v)
 
             actual_pars = []
@@ -312,7 +302,6 @@ class FitRoutine(object):
 
         self._no_own_model = []
 
-
     def __cost_odr(self, p: list[float], data: Data, varpars: list[str], used_pars: list[str]):
         for keys, values in zip(varpars, p):
             self.parameter[keys].scaled_value = values
@@ -361,6 +350,7 @@ class FitRoutine(object):
         with np.errstate(all='ignore'):
             res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
 
+
         err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
         for v, var_pars_k in zip(data, data_pars):
             self.make_results(v, res.x, var, var_pars_k, res.jac.shape,
@@ -457,7 +447,6 @@ class FitRoutine(object):
         if err is None:
             err = [0] * len(p)
 
-        print(p, var_pars, used_pars)
         # update parameter values
         for keys, p_value, err_value in zip(var_pars, p, err):
             if keys in data.parameter:

src/nmreval/fit/model.py

@@ -79,7 +79,14 @@ class Model(object):
             self.fun_kwargs = {k: v.default for k, v in inspect.signature(model.func).parameters.items()
                                if v.default is not inspect.Parameter.empty}
 
-    def set_global_parameter(self, key, value, var=None, lb=None, ub=None, default_bounds=False):
+    def set_global_parameter(self,
+                             key: str,
+                             value: float | str,
+                             var: bool = None,
+                             lb: float = None,
+                             ub: float = None,
+                             default_bounds: bool = False
+                             ):
         idx = [self.params.index(key)]
         if default_bounds:
             if lb is None:
@@ -87,7 +94,8 @@ class Model(object):
             if ub is None:
                 ub = [self.lb[i] for i in idx]
 
-        self.parameter.add(key, value, var=var, lb=lb, ub=ub)
+        p = self.parameter.add(key, value, var=var, lb=lb, ub=ub)
+        p.is_global = True
 
     @staticmethod
     def _prep(param_len, val):

src/nmreval/fit/parameter.py

@@ -8,6 +8,7 @@ import numpy as np
 
 class Parameters(dict):
     parameter_counter = count()
+    # is one global namespace a good idea?
     namespace: dict = {}
 
     def __init__(self):
@@ -24,7 +25,14 @@ class Parameters(dict):
             return super().__getitem__(item)
 
     def __setitem__(self, key, value):
-        super().__setitem__(key, value)
+        self.add_parameter(key, value)
+
+    def __contains__(self, item):
+        for v in self.values():
+            if item == v.name:
+                return True
+
+        return False
 
     def add(self,
             name: str,
@@ -34,7 +42,6 @@ class Parameters(dict):
             lb: float = -np.inf, ub: float = np.inf) -> Parameter:
 
         par = Parameter(name=name, value=value, var=var, lb=lb, ub=ub)
-
         key = f'p{next(Parameters.parameter_counter)}'
 
         self.add_parameter(key, par)
@@ -43,11 +50,8 @@ class Parameters(dict):
 
     def add_parameter(self, key: str, parameter: Parameter):
         self._mapping[parameter.name] = key
-        self[key] = parameter
+        super().__setitem__(key, parameter)
 
-        self._mapping[parameter.name] = key
-
-        self[key] = parameter
         parameter.eval_allowed = False
         self.namespace[key] = parameter.value
         parameter.namespace = self.namespace
@@ -63,12 +67,16 @@ class Parameters(dict):
                 p._expr = expression
 
     def replace_parameter(self, key_out: str, key_in: str, parameter: Parameter):
+        # print('replace par', key_out, key_in, parameter)
+        # print('name', parameter.name)
+
+        self.add_parameter(key_in, parameter)
         for k, v in self._mapping.items():
             if v == key_out:
                 self._mapping[k] = key_in
                 break
 
-        self.add_parameter(key_in, parameter)
+        if key_out in self.namespace:
             del self.namespace[key_out]
 
         for p in self.values():
@@ -80,6 +88,13 @@ class Parameters(dict):
                     expression = re.sub(re.escape(n), k, expression)
                 p._expr = expression
 
+    def get_key(self, name: str) -> str | None:
+        for k, v in self.items():
+            if name == v.name:
+                return k
+
+        return
+
     def get_state(self):
         return {k: v.get_state() for k, v in self.items()}
 
@@ -102,6 +117,7 @@ class Parameter:
         self.eval_allowed: bool = True
         self._expr: None | str = None
         self._expr_disp: None | str = None
+        self.is_global = False
 
         if isinstance(value, str):
             self._expr_disp = value
@@ -126,15 +142,19 @@ class Parameter:
         start = StringIO()
         if self.name:
             if self.function:
-                start.write(f"{self.name} ({self.function}):  ")
+                start.write(f"{self.name} ({self.function})")
             else:
                 start.write(self.name)
+
+        if self.is_global:
+            start.write("*")
+
         start.write(":  ")
 
         if self.var:
             start.write(f"{self.value:.4g} +/- {self.error:.4g}, init={self.init_val}")
         else:
-            start.write(f"{self.value:}")
+            start.write(f"{self.value:.4g}")
             if self._expr is None:
                 start.write(" (fixed)")
             else:
@@ -204,6 +224,9 @@ class Parameter:
         para_copy = Parameter(name=self.name, value=val, var=self.var, lb=self.lb, ub=self.ub)
         para_copy._expr = self._expr
         para_copy.namespace = self.namespace
+        para_copy.is_global = self.is_global
+        para_copy.error = self.error
+        para_copy.function = self.function
 
         return para_copy
 

src/nmreval/models/diffusion.py

@@ -125,7 +125,7 @@ class Peschier:
         q = nucleus*g*tp
 
         r1s, r1f = 1 / t1s, 1 / t1f
-        kf, ks = pf*k, (1-pf)*k
+        kf, ks = (1-pf)*k, pf*k
         a_plus = 0.5 * (d*q*q + kf + ks + r1f + r1s + np.sqrt((d*q*q + kf + r1f - ks - r1s)**2 + 4*kf*ks))
         a_minu = 0.5 * (d*q*q + kf + ks + r1f + r1s - np.sqrt((d*q*q + kf + r1f - ks - r1s)**2 + 4*kf*ks))