#54 restore time course settings after run

basico/task_parameterestimation.py

@@ -1322,10 +1322,13 @@ def get_simulation_results(values_only=False, update_parameters=True, **kwargs):
             # run time course (getting only the data from the experiment)
             duration = df.iloc[-1].Time
             cols = ['Time'] + mapping[mapping.type == 'dependent']['cn'].to_list()
+            tc_settings = basico.get_task_settings(basico.T.TIME_COURSE, model=dm)
             if values_only:
                 data = basico.run_time_course_with_output(output_selection=cols, values=df.Time.to_list(), start_time=df.iloc[0].Time)
             else:
                 data = basico.run_time_course_with_output(output_selection=cols,duration=duration)
+            # restore setttings
+            basico.set_task_settings(basico.T.TIME_COURSE, tc_settings)
 
         exp_data.append(df)
         sim_data.append(data)

tests/test_parameterestimation.py

@@ -3,9 +3,9 @@
 import os
 import sys
 import numpy as np
+import pandas as pd
 import basico
 import COPASI
-import shutil
 
 class TestBasicoParamterEstimation(unittest.TestCase):
 
@@ -166,6 +166,22 @@ def test_current_solution(self):
         # ensure that the results are the same
         self.assertTrue(np.allclose(before[1][0].values, after[1][0].values))
 
+        # issue #54
+        # Run function output, looking only at times
+        function_output = basico.get_simulation_results(values_only=False)
+        df_first_false = pd.concat([df for df in function_output[1]])['Time'].unique()
+
+        # Run again, changing values_only to True
+        function_output = basico.get_simulation_results(values_only=True)
+        df_first_true = pd.concat([df for df in function_output[1]])['Time'].unique()
+
+        # Run again, changing values_only back to False
+        function_output = basico.get_simulation_results(values_only=False)
+        df_second_false = pd.concat([df for df in function_output[1]])['Time'].unique()
+
+        # Ensure that the results are the same
+        self.assertTrue(np.allclose(df_first_false, df_second_false))
+
 
     def test_remove(self):
         fit_items = basico.get_fit_parameters()