Make constraints a Model rather than the same as their parent class. …

…Add initial parameters to connectivity_mapping

Fixes tBuLi#282

symfit/core/fit.py

@@ -564,7 +564,7 @@ def _init_constraints(self, constraints, model):
                     )
                 else:
                     con_models.append(
-                        model.__class__.as_constraint(constraint, model)
+                        Model.as_constraint(constraint, model)
                     )
         return con_models
 

symfit/core/models.py

@@ -991,8 +991,11 @@ def __init__(self, model_dict, initial, *lsoda_args, **lsoda_kwargs):
         self.model_params = set([])
 
         # Only the ones that have a Parameter as initial parameter.
-        self.initial_params = {value for var, value in self.initial.items()
-                               if isinstance(value, Parameter)}
+        self.initial_params = set()
+        for var, value in self.initial.items():
+            if isinstance(value, Parameter):
+                self.connectivity_mapping[var].add(value)  # Make t depend on t0
+                self.initial_params.add(value)
 
         for expression in expressions:
             vars, params = seperate_symbols(expression)
@@ -1004,6 +1007,7 @@ def __init__(self, model_dict, initial, *lsoda_args, **lsoda_kwargs):
         self.model_params = sorted(self.model_params, key=sort_func)
         self.initial_params = sorted(self.initial_params, key=sort_func)
 
+
         # Make Variable object corresponding to each sigma var.
         self.sigmas = {var: Variable(name='sigma_{}'.format(var.name)) for var in self.dependent_vars}
 

tests/test_ode.py

@@ -5,7 +5,7 @@
 import pytest
 import numpy as np
 
-from symfit import parameters, variables, ODEModel, exp, Fit, D, Model, GradientModel, Parameter
+from symfit import parameters, variables, ODEModel, exp, Fit, D, Model, GradientModel, Parameter, Ge
 from symfit.core.minimizers import MINPACK
 from symfit.distributions import Gaussian
 
@@ -129,6 +129,48 @@ def test_initial_parameters(self):
         self.assertEqual([a0, c0], ode_model.initial_params)
         self.assertEqual([a0, k, l, m, p], ode_model.model_params)
 
+        print(ode_model.connectivity_mapping)
+        self.assertEqual({d: {l, c, m, d, a, a0},
+                          c: {k, a, p, c, l, m, d, c0, a0},
+                          a: {k, a, p, c, a0, d}},
+                         ode_model.connectivity_mapping)
+
+    def test_ODE_constraints(self):
+        """
+        Identical to test_polgar, but with a0 as free Parameter.
+        """
+        a, b, c, d, t = variables('a, b, c, d, t')
+        k, p, l, m = parameters('k, p, l, m')
+
+        a0 = Parameter('a0', min=0, value=10, fixed=False)
+        c0 = Parameter('c0', min=0, value=0.1)
+        b = a0 - d + a
+        model_dict = {
+            D(d, t): l * c * b - m * d,
+            D(c, t): k * a * b - p * c - l * c * b + m * d,
+            D(a, t): - k * a * b + p * c,
+        }
+
+        ode_model = ODEModel(model_dict, initial={t: 0.0, a: a0, c: c0, d: 0.0})
+
+        # Generate some data
+        tdata = np.linspace(0, 3, 1000)
+        # Eval
+        AA, AAB, BAAB = ode_model(t=tdata, k=0.1, l=0.2, m=.3, p=0.3, a0=10, c0=0)
+
+        constraints = [Ge(a0, c0)]
+
+        fit = Fit(ode_model, t=tdata, a=AA, c=AAB, d=BAAB)
+        results = fit.execute()
+        print(results)
+        self.assertGreaterEqual(results.value(a0), results.value(c0))
+        self.assertAlmostEqual(results.value(a0), 10)
+        self.assertAlmostEqual(results.value(c0), 0)
+
+        self.assertEqual([a0, c0, k, l, m, p], ode_model.params)
+        self.assertEqual([a0, c0], ode_model.initial_params)
+        self.assertEqual([a0, k, l, m, p], ode_model.model_params)
+
     def test_simple_kinetics(self):
         """
         Simple kinetics data to test fitting