Functional test for Rosenbrock fit from Python

Tests/Functional/Python/PyFit/CMakeLists.txt

@@ -14,7 +14,8 @@ endforeach()
 set(tests
     "testfit_UsingBuilder.py"
     "fitsuite_api.py"
-    "fit_parameters.py"
+    "minimizer_api.py"
+    "standalone_fits.py"
 #    "testfit_GSLLMA.py"
 #    "testfit_GSLMultiMin.py"
 #    "testfit_GSLSimAn.py"

Tests/Functional/Python/PyFit/fit_parameters.py → Tests/Functional/Python/PyFit/minimizer_api.py

 """
-Testing python specific FitSuite API.
+Testing python specific API for Minimizer related classes.
 """
 import sys
 import os
@@ -20,7 +20,7 @@ class TestMinimizerHelper:
         return 42.0
 
 
-class FitParametersAPITest(unittest.TestCase):
+class MinimizerAPITest(unittest.TestCase):
 
     def test_ParametersSetIterator(self):
         """

Tests/Functional/Python/PyFit/standalone_fits.py

+"""
+Fitting scalar and residual based objective functions
+"""
+import sys
+import os
+import unittest
+
+sys.path.append("@CMAKE_LIBRARY_OUTPUT_DIRECTORY@")
+import bornagain as ba
+import numpy as np
+
+
+class Rosenbrock:
+    def __init__(self):
+        self.m_expected_minimum = 0.0
+        self.m_expected_params = [1.0, 1.0]
+        pass
+
+    def objective_function(self, params):
+        """
+        Returns the value of Rosenbrock function
+        """
+        x = params["x"].value()
+        y = params["y"].value()
+        tmp1 = y - x * x
+        tmp2 = 1 - x
+        return 100 * tmp1 * tmp1 + tmp2 * tmp2
+
+
+class StandaloneFitTest(unittest.TestCase):
+
+    def test_RosenbrockFit(self):
+        """
+        Testing fit of rosenbrock function
+        """
+        pars = ba.Parameters()
+        pars.add(ba.Parameter("x", -1.2, ba.AttLimits.limited(-5.0, 5.0), 0.01))
+        pars.add(ba.Parameter("y", 1.0, ba.AttLimits.limited(-5.0, 5.0), 0.01))
+
+        rosenbrock = Rosenbrock()
+        minimizer = ba.Minimizer()
+        result = minimizer.minimize(rosenbrock.objective_function, pars)
+
+        print(result.toString())
+
+        # check found parameter values
+        np.testing.assert_almost_equal(result.parameters().values(),
+                                       rosenbrock.m_expected_params, 3)
+
+        # check found minimum
+        np.testing.assert_almost_equal(result.minValue(),
+                                       rosenbrock.m_expected_minimum, 3)
+
+
+
+
+if __name__ == '__main__':
+    unittest.main()