eval parameter

src/nmreval/fit/minimizer.py

@@ -103,7 +103,7 @@ class FitRoutine(object):
             linked_sender[v] = set()
             self.parameter.update(v.parameter.copy())
 
-            # set temporaray model
+            # set temporary model
             if v.model is None:
                 v.model = self.fit_model
                 self._no_own_model.append(v)
@@ -111,9 +111,9 @@ class FitRoutine(object):
             # register model
             if v.model not in _found_models:
                 _found_models[v.model] = []
-                m_param = v.model.parameter.copy()
-                self.parameter.update(m_param)
-
+                # m_param = v.model.parameter.copy()
+                # self.parameter.update(m_param)
+            #
             _found_models[v.model].append(v)
 
             if v.model not in linked_sender:
@@ -208,6 +208,7 @@ class FitRoutine(object):
         return self.result
 
     def _prep_data(self, data):
+
         if data.get_model() is None:
             data._model = self.fit_model
             self._no_own_model.append(data)
@@ -273,12 +274,9 @@ class FitRoutine(object):
     def __cost_scipy(self, p, data, varpars, used_pars):
         for keys, values in zip(varpars, p):
             self.parameter[keys].scaled_value = values
-            self.parameter._namespace[keys] = self.parameter[keys].scaled_value
-
-            print(id(self.parameter._namespace), id(self.parameter[keys].namespace))
+            self.parameter[keys].namespace[keys] = self.parameter[keys].value
 
         actual_parameters = [self.parameter[keys].value for keys in used_pars]
-        print(actual_parameters)
         return data.cost(actual_parameters)
 
     def __cost_odr(self, p, data, varpars, used_pars):

src/nmreval/fit/parameter.py

@@ -66,6 +66,7 @@ class Parameters(dict):
     def add(self, name: str | Parameter, value: str | float | int = None, *, var=True, lb=-np.inf, ub=np.inf):
         if isinstance(name, Parameter):
             par = name
+            name = par.name
         else:
             par = Parameter(name=name, value=value, var=var, lb=lb, ub=ub)
         self[name] = par
@@ -75,7 +76,6 @@ class Parameters(dict):
         par.eval_allowed = True
 
     def copy(self) -> Parameters:
-        print('huhuuhuh')
         new_para_dict = Parameters()
         for k, v in self.items():
             new_para = v.copy()
@@ -171,6 +171,8 @@ class Parameter:
     @property
     def value(self) -> float:
         if self._expr is not None and self.eval_allowed:
+            print(self._expr, self.namespace)
+            print(eval(self._expr, self.namespace))
             return eval(self._expr, self.namespace)
         elif self._value is not None:
             return self._value

src/nmreval/models/spectrum.py

@@ -35,8 +35,8 @@ class Gaussian:
 class Lorentzian:
     type = 'Spectrum'
     name = 'Lorentzian'
-    equation = 'A (2/\pi)w/[4*(x-\mu)^{2} + w^{2}] + A_{0}'
-    params = ['A', '\mu', 'w', 'A_{0}']
+    equation = r'A (2/\pi)w/[4*(x-\mu)^{2} + w^{2}] + A_{0}'
+    params = ['A', r'\mu', 'w', 'A_{0}']
     ext_params = None
     bounds = [(0, None), (None, None), (0, None), (None, None)]
 
@@ -56,15 +56,16 @@ class Lorentzian:
             off (float): baseline
 
         """
+        print('Lorentzian', a, mu, sigma, off)
         return (a/np.pi) * 2*sigma / (4*(x-mu)**2 + sigma**2) + off
 
 
 class PseudoVoigt:
     type = 'Spectrum'
     name = 'Pseudo Voigt'
-    equation = 'A [R*2/\pi*w/[4*(x-\mu)^{2} + w^{2}] + ' \
-               '(1-R)*sqrt(4*ln(2)/pi)/w*exp(-4*ln(2)[(x-\mu)/w]^{2})] + A_{0}'
-    params = ['A', 'R', '\mu', 'w', 'A_{0}']
+    equation = r'A [R*2/\pi*w/[4*(x-\mu)^{2} + w^{2}] + ' \
+               r'(1-R)*sqrt(4*ln(2)/pi)/w*exp(-4*ln(2)[(x-\mu)/w]^{2})] + A_{0}'
+    params = ['A', 'R', r'\mu', 'w', 'A_{0}']
     ext_params = None
     bounds = [(0, None), (0, 1), (None, None), (0, None)]