work on linked models

src/nmreval/fit/minimizer.py

@@ -153,10 +153,8 @@ class FitRoutine(object):
             if isinstance(replace_model, Data):
                 linked_parameter[dismiss_key] = replace_key
             else:
-                # print('dismiss model', dismiss_model.parameter)
-                # print('replace model', replace_model.parameter)
-                dismiss_model.parameter.replace_parameter(dismiss_key, replace_key, replace_model.parameter[replace_key])
-                # print('after replacement', dismiss_model.parameter)
+                p = dismiss_model.set_global_parameter(dismiss_param, replace_key)
+                p._expr_disp = replace_param
 
         for mm, m_data in _found_models.items():
             if mm.parameter:
@@ -212,7 +210,6 @@ 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':
@@ -249,8 +246,6 @@ 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 = []
@@ -261,14 +256,11 @@ class FitRoutine(object):
         # 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 = []
             for i, p_k in enumerate(data.para_keys):
-                # print(i, p_k)
                 p_k_used = p_k
                 v_k_used = data.parameter[p_k]
 
@@ -290,8 +282,6 @@ class FitRoutine(object):
                     ub.append(v_k_used.ub / v_k_used.scale)
                     var.append(p_k_used)
 
-                # print('aloha, ', actual_pars)
-
             data_pars.append(actual_pars)
 
         return data_pars, p0, lb, ub, var

src/nmreval/fit/model.py

@@ -6,7 +6,7 @@ from typing import Sized
 from numpy import inf
 
 from ._meta import MultiModel
-from .parameter import Parameters
+from .parameter import Parameters, Parameter
 
 
 class Model(object):
@@ -86,7 +86,7 @@ class Model(object):
                              lb: float = None,
                              ub: float = None,
                              default_bounds: bool = False
-                             ):
+                             ) -> Parameter:
         idx = [self.params.index(key)]
         if default_bounds:
             if lb is None:
@@ -97,6 +97,8 @@ class Model(object):
         p = self.parameter.add(key, value, var=var, lb=lb, ub=ub)
         p.is_global = True
 
+        return p
+
     @staticmethod
     def _prep(param_len, val):
         if isinstance(val, Sized):

src/nmreval/fit/result.py

@@ -223,6 +223,7 @@ class FitResult(Points):
         return self.nobs-self.nvar
 
     def pprint(self, statistics=True, correlations=True):
+        sstream = StringIO()
         print('Fit result:')
         print('  model :', self.name)
         print('  #data :', self.nobs)
@@ -243,7 +244,7 @@ class FitResult(Points):
     def parameter_string(self):
         ret_val = ''
 
-        for pval in self.parameter.values():
+        for pkey, pval in self.parameter.items():
             ret_val += convert(str(pval), old='tex', new='str') + '\n'
 
         if self.fun_kwargs:
@@ -255,9 +256,7 @@ class FitResult(Points):
     def _correlation_string(self):
         ret_val = ''
         for p_i, p_j, corr_ij, pcorr_ij in self.correlation_list():
-            ret_val += '  {} / {} : {:.4f} ({:.4f})\n'.format(convert(p_i, old='tex', new='str'),
-                                                              convert(p_j, old='tex', new='str'),
-                                                              corr_ij, pcorr_ij)
+            ret_val += f"  {convert(p_i, old='tex', new='str')} / {convert(p_j, old='tex', new='str')} : {corr_ij:.4f} ({pcorr_ij:.4f})\n"
         return ret_val
 
     def correlation_list(self, limit=0.1):