#46 ensure current solution statistic remains the same with randomize…

… start values

basico/task_parameterestimation.py

@@ -1271,7 +1271,10 @@ def get_simulation_results(values_only=False, update_parameters=True, **kwargs):
             if type(solution) is dict or type(solution) is list:
                 solution = pd.DataFrame(data=solution)
         else:
-            solution = run_parameter_estimation(method='Current Solution Statistics', write_report=False)
+            settings = basico.get_task_settings(basico.T.PARAMETER_ESTIMATION, model=dm)
+            solution = run_parameter_estimation(method='Current Solution Statistics', write_report=False, randomize_start_values=False, model=dm)
+            # restore settings
+            basico.set_task_settings(basico.T.PARAMETER_ESTIMATION, settings)
 
     exp_data = []
     sim_data = []

tests/test_parameterestimation.py

@@ -13,6 +13,7 @@ def setUp(self):
         self.model = basico.load_example('LM')
         self.assertTrue(self.model.getModel().getObjectName() ==
                         'Kinetics of a  Michaelian enzyme measured spectrophotometrically')
+        basico.add_parameter_set('initial_state')
 
     def tearDown(self):
         basico.remove_datamodel(self.model)
@@ -136,6 +137,21 @@ def test_run(self):
         self.assertTrue(sol is not None)
         self.assertAlmostEqual(sol.loc['(R1).k2'].sol, 1.0, places=2)
 
+    def test_current_solution(self):
+        before = basico.get_simulation_results()
+        # ensure the results are the same, even if randomize values
+        # is activated
+        settings = basico.get_task_settings('Parameter Estimation')
+        settings['problem']['Randomize Start Values'] = True
+        basico.set_task_settings('Parameter Estimation', settings)
+
+        # now run again
+        after = basico.get_simulation_results()
+
+        # ensure that the results are the same
+        self.assertTrue(np.allclose(before[1][0].values, after[1][0].values))
+
+
     def test_remove(self):
         fit_items = basico.get_fit_parameters()
         self.assertIsNotNone(fit_items)