fix introduced bugs

topfarm/_topfarm.py

@@ -81,7 +81,7 @@ class TopFarmProblem(Problem):
         self.driver.add_recorder(self.recorder)
         self.run_driver()
         self.cleanup()
-        if self.driver._rec_mgr._recorders!=[]: # in openmdao<2.4 cleanup does not delete recorders
+        if self.driver._rec_mgr._recorders != []:  # in openmdao<2.4 cleanup does not delete recorders
             self.driver._rec_mgr._recorders.remove(self.recorder)
         if isinstance(self.driver, DOEDriver):
             costs = self.recorder.get('cost')
@@ -116,9 +116,9 @@ class TopFarmProblem(Problem):
         assert isinstance(self.driver, DOEDriver), 'get_DOE_list only applies to DOEDrivers, and the current driver is: %s' % type(self.driver)
         case_gen = self.driver.options['generator']
         return [c for c in case_gen(self.model.get_design_vars(recurse=True), self.model)]
-    
+
     def get_DOE_array(self):
-        return np.array([[v for k,v in c] for c in self.get_DOE_list()])
+        return np.array([[v for k, v in c] for c in self.get_DOE_list()])
 
 
 class TurbineTypeOptimizationProblem(TopFarmProblem):
@@ -329,16 +329,16 @@ def try_me():
             plot_comp=plot_comp,
             driver=EasyScipyOptimizeDriver(disp=False),
             record_id='test:latest')
-        
+
         cost, state, recorder = xyz_opt_problem.optimize()
         recorder.save()
-        
+
         tf = TurbineTypeOptimizationProblem(
             cost_comp=xyz_opt_problem,
             turbineTypes=[0, 0], lower=0, upper=1,
             driver=DOEDriver(FullFactorialGenerator(2)))
         cost, state, recorder = tf.optimize()
-        print (state)
+        print(state)
         plot_comp.show()
 
 

topfarm/tests/test_topfarm_problems/test_InitialXYZOptimizationProblem.py

@@ -37,18 +37,18 @@ def test_list_driver(get_tf):
            [('turbineX', [4, 3]), ('turbineY', [6, 5]), ('turbineZ', [2, 1])]]
 
     tf = get_tf(driver=lst)  # pure list
-    npt.assert_array_equal(tf.get_DOE_list(), xyz)
+    npt.assert_array_equal(tf.get_DOE_array(), xyz)
 
     tf = get_tf(driver=ListGenerator(lst))  # list generator
-    npt.assert_array_equal(tf.get_DOE_list(), xyz)
+    npt.assert_array_equal(tf.get_DOE_array(), xyz)
 
     tf = get_tf(driver=DOEDriver(ListGenerator(lst)))  # DOEDriver
-    npt.assert_array_equal(tf.get_DOE_list(), xyz)
+    npt.assert_array_equal(tf.get_DOE_array(), xyz)
 
 
 def test_with_uniform_generator(get_tf):
     tf = get_tf(driver=DOEDriver(UniformGenerator(10)))
-    arr = tf.get_DOE_list()
+    arr = tf.get_DOE_array()
     uta.assertGreaterEqual(arr[:, 0].min(), 10)  # x
     uta.assertLessEqual(arr[:, 0].max(), 11)  # x
     uta.assertGreaterEqual(arr[:, 1].min(), 6)  # y
@@ -63,7 +63,7 @@ def test_with_constrained_generator_convex_boundary(get_tf):
     tf = get_tf(xy_boundary=[(0, 0), (10, 0), (10, 10)], xy_boundary_type='convex_hull',
                 driver=ConstrainedXYZGenerator(UniformGenerator(10, 0)))
     print(tf.xy_boundary)
-    arr = tf.get_DOE_list()
+    arr = tf.get_DOE_array()
     print(arr.shape)
     x, y, z = [arr[:, i] for i in range(3)]
     uta.assertGreaterEqual(x.min(), 0)  # x
@@ -75,7 +75,7 @@ def test_with_constrained_generator_polygon(get_tf):
     tf = get_tf(xy_boundary=[(0, 0), (10, 0), (10, 10)],
                 xy_boundary_type='polygon',
                 driver=ConstrainedXYZGenerator(UniformGenerator(10, 0)))
-    arr = tf.get_DOE_list()
+    arr = tf.get_DOE_array()
     print(arr.shape)
     x, y = [arr[:, i] for i in range(2)]
     uta.assertGreaterEqual(x.min(), 0)  # x
@@ -91,8 +91,7 @@ def test_with_constrained_generator_spacing(get_tf):
                 xy_boundary_type='rectangle',
                 min_spacing=2,
                 driver=ConstrainedXYZGenerator(lst))
-    arr = tf.get_DOE_list()
-    print(arr)
-    #x, y, z = [arr[:, i] for i in range(3)]
+    arr = tf.get_DOE_array()
 
-    
\ No newline at end of file
+    x, y, z = [arr[:, i] for i in range(3)]
+    assert all(np.sqrt(np.diff(x)**2 + np.diff(y)**2) >= 2)

topfarm/tests/test_topfarm_problems/test_TopFarmProblem.py

@@ -23,6 +23,7 @@ optimize
 check_gradients
 as_component
 get_DOE_list
+get_DOE_array
 
 
 """
@@ -80,7 +81,7 @@ def test_optimize(turbineTypeOptimizationProblem):
     cost, state, recorder = tf.optimize()
     assert cost == 0
     np.testing.assert_array_equal(state['turbineType'], [2, 0, 1])
-    doe_list = np.squeeze(tf.get_DOE_list())
+    doe_list = np.squeeze(tf.get_DOE_array())
     np.testing.assert_array_almost_equal(recorder.get('cost'), np.sum((doe_list - [2, 0, 1])**2, 1))
 
 
@@ -139,9 +140,21 @@ def testTopFarmProblem_as_component(turbineTypeOptimizationProblem):
 
 def testTopFarmProblem_get_DOE_list(turbineTypeOptimizationProblem):
     tf = turbineTypeOptimizationProblem
-    npt.assert_array_equal(tf.get_DOE_list().shape, (27, 1, 3))
-    npt.assert_array_equal(tf.get_DOE_list()[:5], [[[0, 0, 0]],
+    npt.assert_array_equal(len(tf.get_DOE_list()), 27)
+    (k,v), = tf.get_DOE_list()[1]
+    assert k=="indeps.turbineType"
+    npt.assert_array_equal(v, [1,0,0])
+    
+    #npt.assert_array_equal(tf.get_DOE_list()[1], [[('indeps.turbineType', array([0., 0., 0.]))], [('indeps.turbineType', array([1., 0., 0.]))]])
+
+
+def testTopFarmProblem_get_DOE_array(turbineTypeOptimizationProblem):
+    tf = turbineTypeOptimizationProblem
+    npt.assert_array_equal(tf.get_DOE_array().shape, (27, 1, 3))
+    npt.assert_array_equal(tf.get_DOE_array()[:5], [[[0, 0, 0]],
                                                    [[1, 0, 0]],
                                                    [[2, 0, 0]],
                                                    [[0, 1, 0]],
                                                    [[1, 1, 0]]])
+
+