def test_state_array(turbineTypeOptimizationProblem):
tf = turbineTypeOptimizationProblem
arr = tf.state_array(['type', 'type'])
npt.assert_equal(arr.shape, [3, 2])
npt.assert_array_equal(arr, [[0, 0],
[0, 0],
[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]]])
def test_turbineType_optimization():
optimal = np.array([[1], [0]])
tf = TopFarmProblem(design_vars={'type': (optimal[:, 0], 0, 1)},
cost_comp=DummyCost(optimal_state=optimal,
inputs=['type']),
driver=DOEDriver(FullFactorialGenerator(2)))
cost, state, _ = tf.optimize()
assert cost == 0
npt.assert_array_equal(state['type'], [1, 0])
def test_TopFarmProblemSpacingConstraint():
tf = xy3tb.get_tf(design_vars={'x': [3, 7, 4], 'y': [-3, -7, -3]},
constraints=[SpacingConstraint(2)])
tf.evaluate({'x': xy3tb.desired[:, 0], 'y': xy3tb.desired[:, 1]})
npt.assert_array_equal(tf['wtSeparationSquared'], [32, 1, 25])
_, state, _ = tf.optimize()
npt.assert_array_almost_equal(state['x'], [2.5, 7, 4.5])
npt.assert_array_almost_equal(state['y'], xy3tb.optimal[:, 1])
def testTopFarm():
tf = TopFarm(xy3tb.initial,
DummyCost(xy3tb.desired, ['x', 'y']),
min_spacing=2,
boundary=xy3tb.boundary)
cost, state = tf.evaluate()
assert cost == 45
npt.assert_array_equal(state['x'], xy3tb.initial[:, 0])
npt.assert_array_equal(state['y'], xy3tb.initial[:, 1])
def test_update_state(turbineTypeOptimizationProblem, types, cost):
tf = turbineTypeOptimizationProblem
c, state = tf.evaluate({'type': types})
npt.assert_equal(c, cost)
npt.assert_array_equal(state['type'], types)
# wrong shape
c, state = tf.evaluate({'type': [types]})
npt.assert_equal(c, cost)
npt.assert_array_equal(state['type'], types)
# missing key
c, state = tf.evaluate({'missing': types})
def test_maxiter_CostModelComponent():
tf = get_tf(
AEPCostModelComponent(['x', 'y'],
4,
aep_cost,
aep_gradients,
max_eval=10))
cost, state, recorder = tf.optimize()
assert 10 <= tf.cost_comp.counter <= 11, tf.cost_comp.counter
npt.assert_array_equal(recorder['AEP'][tf.cost_comp.n_func_eval],
recorder['AEP'][tf.cost_comp.n_func_eval:])
def test_turbineType_optimization():
cost_comp = DummyCost(optimal_state=optimal, inputs=['type'])
tf = TurbineTypeOptimizationProblem(cost_comp=cost_comp,
turbineTypes=[0, 0],
lower=0,
upper=1,
driver=DOEDriver(
FullFactorialGenerator(2)))
cost, state, _ = tf.optimize()
assert cost == 0
npt.assert_array_equal(state['type'], [1, 0])
def test_TopFarmProblemXYBoundaryPenaltyAndLimits():
tf = xy3tb.get_tf(design_vars={'x': ([3, 7, 4], -1, 5), 'y': ([-3, -7, -3], -9, -1)},
driver=EasyRandomSearchDriver(RandomizeTurbinePosition(1), 10),
constraints=[XYBoundaryConstraint(xy3tb.boundary)])
tf.evaluate({'x': xy3tb.desired[:, 0], 'y': xy3tb.desired[:, 1]})
npt.assert_equal(tf['boundaryDistances'][1, 3], -1)
desvars = tf.driver._designvars
npt.assert_equal(desvars['indeps.x']['lower'], 0)
npt.assert_equal(desvars['indeps.x']['upper'], 5)
npt.assert_array_equal(desvars['indeps.y']['lower'], -9)
npt.assert_array_equal(desvars['indeps.y']['upper'], -1)
def test_TopFarmProblemLimits():
tf = xy3tb.get_tf(design_vars={'x': (xy3tb.initial[:, 0], -3, 3),
'y': (xy3tb.initial[:, 1], [-4, -3, -2], [2, 3, 4])},
driver=EasyRandomSearchDriver(RandomizeTurbinePosition(1), max_iter=100),
constraints=[])
tf.evaluate()
desvars = tf.driver._designvars
npt.assert_equal(desvars['indeps.x']['lower'], -3)
npt.assert_equal(desvars['indeps.x']['upper'], 3)
npt.assert_array_equal(desvars['indeps.y']['lower'], [-4, -3, -2])
npt.assert_array_equal(desvars['indeps.y']['upper'], [2, 3, 4])
def test_list_driver(get_tf):
xyz = [[[1, 2], [3, 4], [5, 6]], [[4, 3], [6, 5], [2, 1]]]
lst = [[('x', [1, 2]), ('y', [3, 4]), ('z', [5, 6])],
[('x', [4, 3]), ('y', [6, 5]), ('z', [2, 1])]]
tf = get_tf(driver=lst) # pure list
npt.assert_array_equal(tf.get_DOE_array(), xyz)
tf = get_tf(driver=ListGenerator(lst)) # list generator
npt.assert_array_equal(tf.get_DOE_array(), xyz)
tf = get_tf(driver=DOEDriver(ListGenerator(lst))) # DOEDriver
npt.assert_array_equal(tf.get_DOE_array(), xyz)
def testTopFarmProblem_get_DOE_list(turbineTypeOptimizationProblem):
tf = turbineTypeOptimizationProblem
npt.assert_array_equal(len(tf.get_DOE_list()), 27)
(k, v), = tf.get_DOE_list()[1]
assert k == "indeps.type"
npt.assert_array_equal(v, [1, 0, 0])
