diff --git a/tests/test_topfarm.py b/tests/test_topfarm.py
index dde9a1f5cf89dfd004f3b793ac874d91a4f2057b..a8758a4bb8439799c10af1f5e5892a69fb256825 100644
--- a/tests/test_topfarm.py
+++ b/tests/test_topfarm.py
@@ -4,46 +4,66 @@ Created on 17. maj 2018
@author: mmpe
'''
from topfarm import TopFarm
-import unittest
import numpy as np
-from topfarm.cost_models.dummy import DummyCost
from topfarm.cost_models.cost_model_wrappers import CostModelComponent
+import pytest
-class TestTopFarm(unittest.TestCase):
- def setUp(self):
- unittest.TestCase.setUp(self)
- self.boundary = [(0, 0), (6, 0), (6, -10), (0, -10)] # turbine boundaries
- self.initial = [[6, 0], [6, -8], [1, 1], [-1, -8]] # initial turbine layouts
- self.optimal_with_constraints = np.array([[2.5, -3], [6, -7], [4.5, -3], [3, -7]]) # optimal turbine layout
- self.min_spacing = 2 # min distance between turbines
- self.optimal = np.array([[3, -3], [7, -7], [4, -3], [3, -7]]) # desired turbine layouts
+initial = [[6, 0], [6, -8], [1, 1], [-1, -8]] # initial turbine layouts
+optimal = np.array([[3, -3], [7, -7], [4, -3], [3, -7]]) # desired turbine layouts
- def cost(self, pos):
- x, y = pos.T
- opt_x, opt_y = self.optimal.T
- return np.sum((x - opt_x)**2 + (y - opt_y)**2)
- def gradients(self, pos):
- x, y = pos.T
- return (2 * x - 2 * self.optimal[:, 0]), (2 * y - 2 * self.optimal[:, 1])
+@pytest.fixture
+def topfarm_generator():
+ def _topfarm_obj(gradients, **kwargs):
+ boundary = [(0, 0), (6, 0), (6, -10), (0, -10)] # turbine boundaries
+ min_spacing = 2 # min distance between turbines
+ return TopFarm(initial, CostModelComponent(4, cost, gradients), min_spacing, boundary=boundary, **kwargs)
+ return _topfarm_obj
- def wrong_gradients(self, pos):
- x, y = pos.T
- return (2 * x - 2 * self.optimal[:, 0] + 1), (2 * y - 2 * self.optimal[:, 1])
- def testTopFarm_default_plotcomp(self):
- tf = TopFarm(self.initial, CostModelComponent(4, self.cost, self.gradients), self.min_spacing, boundary=self.boundary, plot_comp='default')
+def cost(pos):
+ x, y = pos.T
+ opt_x, opt_y = optimal.T
+ return np.sum((x - opt_x)**2 + (y - opt_y)**2)
- def testTopFarm_check_gradients(self):
- tf = TopFarm(self.initial, CostModelComponent(4, self.cost, self.gradients), self.min_spacing, boundary=self.boundary)
- tf.check(True)
- tf = TopFarm(self.initial, CostModelComponent(4, self.cost, self.wrong_gradients), self.min_spacing, boundary=self.boundary)
- self.assertRaisesRegex(Warning, "Mismatch between finite difference derivative of 'cost' wrt. 'turbineX' and derivative computed in 'cost_comp' is", tf.check)
+def gradients(pos):
+ x, y = pos.T
+ return (2 * x - 2 * optimal[:, 0]), (2 * y - 2 * optimal[:, 1])
-if __name__ == "__main__":
- #import sys;sys.argv = ['', 'Test.testName']
- unittest.main()
+def wrong_gradients(pos):
+ x, y = pos.T
+ return (2 * x - 2 * optimal[:, 0] + 1), (2 * y - 2 * optimal[:, 1])
+
+
+def testTopFarm_default_plotcomp(topfarm_generator):
+ """Check that setting plot_comp to 'default' does not fails"""
+ topfarm_generator(gradients, plot_comp='default')
+
+
+def testTopFarm_check_gradients(topfarm_generator):
+ # Check that gradients check does not raise exception for correct gradients
+ tf = topfarm_generator(gradients)
+ tf.check(True)
+
+ # Check that gradients check raises an exception for incorrect gradients
+ tf = topfarm_generator(wrong_gradients)
+ with pytest.raises(Warning, match="Mismatch between finite difference derivative of 'cost' wrt. 'turbineX' and derivative computed in 'cost_comp' is"):
+ tf.check()
+
+
+def testTopFarm_evaluate(topfarm_generator):
+ # check that evaluate function does not fail
+ tf = topfarm_generator(gradients)
+ cost, pos = tf.evaluate()
+ assert cost == 62
+ np.testing.assert_array_equal(pos, initial)
+
+
+def testTopFarm_evaluate_gradients(topfarm_generator):
+ # check taht evalueate_gradients does not fail
+ tf = topfarm_generator(gradients)
+ np.testing.assert_array_equal(tf.evaluate_gradients()['cost']['turbineX'], [[-6., -14., -8., -6.]])
diff --git a/tests/test_with_dummy.py b/tests/test_with_dummy.py
index c0e08ebaa423c524e8fba484cbbc39fb29abe224..d8687bc477670ccd8bd3d7ceb2755e8b3a4d351f 100644
--- a/tests/test_with_dummy.py
+++ b/tests/test_with_dummy.py
@@ -33,9 +33,7 @@ class Test(unittest.TestCase): # unittest version
# when
tf = TopFarm(initial, DummyCost(desired), min_spacing,
boundary=boundary)
- with warnings.catch_warnings(): # suppress OpenMDAO/SLSQP warnings
- warnings.simplefilter('ignore')
- tf.optimize()
+ tf.optimize()
tb_pos = tf.turbine_positions
# then
diff --git a/topfarm/_topfarm.py b/topfarm/_topfarm.py
index ac2a8a034602861d5072c266d04be5220e2b7d94..ed17bb88b954de5818e18ffdcfbda6d46b4639ce 100644
--- a/topfarm/_topfarm.py
+++ b/topfarm/_topfarm.py
@@ -1,13 +1,14 @@
import time
-
-from openmdao.api import Problem, ScipyOptimizeDriver, IndepVarComp
-
import numpy as np
+import warnings
+from wetb.utils.timing import print_time
+with warnings.catch_warnings():
+ warnings.simplefilter('ignore', FutureWarning)
+ from openmdao.api import Problem, ScipyOptimizeDriver, IndepVarComp
from topfarm.constraint_components.boundary_component import BoundaryComp,\
PolygonBoundaryComp
from topfarm.constraint_components.spacing_component import SpacingComp
from topfarm.plotting import PlotComp
-import warnings
class TopFarm(object):
@@ -74,12 +75,16 @@ class TopFarm(object):
def evaluate(self):
t = time.time()
- with warnings.catch_warnings(): # suppress OpenMDAO/SLSQP warnings
- warnings.filterwarnings('ignore', "Inefficient choice of derivative mode. You chose 'rev' for a problem with")
- self.problem.run_model()
+ self.problem.run_model()
print("Evaluated in\t%.3fs" % (time.time() - t))
return self.get_cost(), self.turbine_positions
+ def evaluate_gradients(self):
+ t = time.time()
+ res = self.problem.compute_totals(['cost'], wrt=['turbineX', 'turbineY'], return_format='dict')
+ print("Gradients evaluated in\t%.3fs" % (time.time() - t))
+ return res
+
def optimize(self):
t = time.time()
self.problem.run_driver()