def tests_smart_start():
xs_ref = [
1.6, 14.1, 1.6, 7.9, 14.1, 7.9, 19.9, 19.9, 7.8, 5.8, 14.2, 5.8, 1.5,
16.2, 16.2, 1.6, 3.7, 14.1, 7.9, 3.7
]
ys_ref = [
1.6, 1.6, 7.9, 1.6, 7.9, 7.9, 1.6, 7.9, 5.8, 7.8, 5.8, 1.5, 5.8, 7.8,
1.5, 3.7, 1.6, 3.7, 3.7, 7.9
]
N_WT = 20
min_space = 2.1
XX, YY, val = egg_tray_map()
xs, ys = smart_start(XX, YY, val, N_WT, min_space, seed=0)
if 0:
import matplotlib.pyplot as plt
plt.contourf(XX, YY, val, 100)
for i in range(N_WT):
circle = plt.Circle((xs[i], ys[i]),
min_space / 2,
color='b',
fill=False)
plt.gcf().gca().add_artist(circle)
plt.plot(xs[i], ys[i], 'rx')
print(np.round(xs, 1).tolist())
print(np.round(ys, 1).tolist())
plt.axis('equal')
plt.show()
npt.assert_array_almost_equal([xs, ys], [xs_ref, ys_ref])
def test_smart_start_polygon_boundary():
xs_ref = [1.6, 1.6, 3.6]
ys_ref = [1.6, 3.7, 2.3]
x = np.arange(0, 5.1, 0.1)
y = np.arange(0, 5.1, 0.1)
YY, XX = np.meshgrid(y, x)
ZZ = np.sin(XX) + np.sin(YY)
min_spacing = 2.1
tf = xy3tb.get_tf(constraints=[
SpacingConstraint(min_spacing),
XYBoundaryConstraint([(0, 0), (5, 3), (5, 5), (0, 5)], 'polygon')
])
tf.smart_start(XX, YY, ZZ)
if 0:
import matplotlib.pyplot as plt
plt.contourf(XX, YY, ZZ, 100)
plt.plot(tf.xy_boundary[:, 0], tf.xy_boundary[:, 1], 'k')
for x, y in tf.turbine_positions:
circle = plt.Circle((x, y), min_spacing / 2, color='b', fill=False)
plt.gcf().gca().add_artist(circle)
plt.plot(x, y, 'rx')
plt.axis('equal')
plt.show()
npt.assert_array_almost_equal(tf.turbine_positions,
np.array([xs_ref, ys_ref]).T)
def tests_smart_start():
xs_ref = [
1.6, 14.1, 1.6, 7.9, 14.1, 7.9, 19.9, 19.9, 7.8, 5.8, 14.2, 5.8, 1.5,
16.2, 16.2, 1.6, 3.7, 14.1, 7.9, 3.7
]
ys_ref = [
1.6, 1.6, 7.9, 1.6, 7.9, 7.9, 1.6, 7.9, 5.8, 7.8, 5.8, 1.5, 5.8, 7.8,
1.5, 3.7, 1.6, 3.7, 3.7, 7.9
]
x = np.arange(0, 20, 0.1)
y = np.arange(0, 10, 0.1)
YY, XX = np.meshgrid(y, x)
val = np.sin(XX) + np.sin(YY)
N_WT = 20
min_space = 2.1
np.random.seed(0)
xs, ys = smart_start(XX, YY, val, N_WT, min_space)
npt.assert_array_almost_equal([xs, ys], [xs_ref, ys_ref])
if 0:
import matplotlib.pyplot as plt
plt.contourf(XX, YY, val, 100)
for i in range(N_WT):
circle = plt.Circle((xs[i], ys[i]),
min_space / 2,
color='b',
fill=False)
plt.gcf().gca().add_artist(circle)
plt.plot(xs[i], ys[i], 'rx')
print(np.round(xs, 1).tolist())
print(np.round(ys, 1).tolist())
plt.axis('equal')
plt.show()
def test_smart_start():
xs_ref = [1.6, 1.6, 3.7]
ys_ref = [1.6, 3.7, 1.6]
x = np.arange(0, 5, 0.1)
y = np.arange(0, 5, 0.1)
YY, XX = np.meshgrid(y, x)
ZZ = np.sin(XX) + np.sin(YY)
min_spacing = 2.1
tf = xy3tb.get_tf(constraints=[SpacingConstraint(min_spacing)])
tf.smart_start(XX, YY, ZZ, seed=0)
try:
npt.assert_array_almost_equal(tf.turbine_positions,
np.array([xs_ref, ys_ref]).T)
except AssertionError:
# wt2 and wt3 may switch
npt.assert_array_almost_equal(tf.turbine_positions,
np.array([ys_ref, xs_ref]).T)
if 0:
import matplotlib.pyplot as plt
plt.contourf(XX, YY, ZZ, 100)
for x, y in tf.turbine_positions:
circle = plt.Circle((x, y), min_spacing / 2, color='b', fill=False)
plt.gcf().gca().add_artist(circle)
plt.plot(x, y, 'rx')
plt.axis('equal')
plt.show()
def test_TopFarmProblem():
tf = xy3tb.get_tf(design_vars={'x': [3, 7, 4], 'y': [-3, -7, -3]},
constraints=[])
cost, state, _ = tf.optimize()
npt.assert_almost_equal(cost, 0)
npt.assert_array_almost_equal(state['x'], xy3tb.desired[:, 0])
npt.assert_array_almost_equal(state['y'], xy3tb.desired[:, 1])
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 test_TopFarmProblemXYBoundaryConstraintPolygon():
tf = xy3tb.get_tf(design_vars={'x': [3, 7, 4], 'y': [-3, -7, -3]},
constraints=[XYBoundaryConstraint(xy3tb.boundary, 'polygon')])
# constraint violated
tf.evaluate({'x': xy3tb.desired[:, 0], 'y': xy3tb.desired[:, 1]})
npt.assert_equal(tf['boundaryDistances'][1], -1)
_, state, _ = tf.optimize()
npt.assert_array_almost_equal(state['x'], [3, 6, 4])
npt.assert_array_almost_equal(state['y'], xy3tb.optimal[:, 1])
def test_TopFarmProblemXYBoundaryConstraint():
tf = xy3tb.get_tf(design_vars={'x': [3, 7, 4], 'y': [-3, -7, -3]},
constraints=[XYBoundaryConstraint(xy3tb.boundary)])
tf.evaluate({'x': xy3tb.desired[:, 0], 'y': xy3tb.desired[:, 1]})
npt.assert_equal(tf['boundaryDistances'][1, 3], -1)
_, state, _ = tf.optimize()
npt.assert_array_almost_equal(state['x'], [3, 6, 4])
npt.assert_array_almost_equal(state['y'], xy3tb.optimal[:, 1])
desvars = tf.driver._designvars
for xy in 'xy':
for lu in ['lower', 'upper']:
npt.assert_equal(desvars['indeps.' + xy][lu], np.nan)
def testCostModelComponentAdditionalOutput():
def aep_cost(x, y):
opt_x, opt_y = optimal.T
return -np.sum((x - opt_x)**2 + (y - opt_y)**2), {'add_out': sum(x)}
tf = get_tf(
AEPCostModelComponent(['x', 'y'],
4,
aep_cost,
aep_gradients,
additional_output=[('add_out', 0)]))
_, state, _ = tf.optimize()
npt.assert_array_almost_equal(tf.turbine_positions[:, :2],
optimal_with_constraints, 5)
npt.assert_equal(sum(state['x']), state['add_out'])
def tests_smart_start_random():
xs_ref = [
1.7, 13.9, 1.4, 7.7, 14.4, 7.6, 19.7, 19.7, 8.7, 19.4, 15.8, 12.4, 7.7,
9.8, 14.1, 1.8, 9.7, 6.6, 13.6, 3.5
]
ys_ref = [
7.9, 1.4, 1.7, 1.3, 7.9, 8.4, 1.7, 8.7, 6.4, 6.6, 2.3, 7.1, 3.5, 1.6,
5.8, 5.8, 8.3, 6.5, 3.5, 1.4
]
N_WT = 20
min_space = 2.1
XX, YY, val = egg_tray_map()
np.random.seed(0)
with pytest.raises(expected_exception=AssertionError):
xs, ys = smart_start(XX,
YY,
val,
N_WT,
min_space,
random_pct=101,
seed=0)
xs, ys = smart_start(XX, YY, val, N_WT, min_space, random_pct=1, seed=0)
if 0:
import matplotlib.pyplot as plt
plt.contourf(XX, YY, val, 100)
plt.plot(XX, YY, ',k')
for i in range(N_WT):
circle = plt.Circle((xs[i], ys[i]),
min_space / 2,
color='b',
fill=False)
plt.gcf().gca().add_artist(circle)
plt.plot(xs[i], ys[i], 'rx')
print(np.round(xs, 1).tolist())
print(np.round(ys, 1).tolist())
plt.axis('equal')
plt.show()
npt.assert_array_almost_equal([xs, ys], [xs_ref, ys_ref])
def test_WindResource():
f = [
0.035972, 0.039487, 0.051674, 0.070002, 0.083645, 0.064348, 0.086432,
0.117705, 0.151576, 0.147379, 0.10012, 0.05166
]
A = [
9.176929, 9.782334, 9.531809, 9.909545, 10.04269, 9.593921, 9.584007,
10.51499, 11.39895, 11.68746, 11.63732, 10.08803
]
k = [
2.392578, 2.447266, 2.412109, 2.591797, 2.755859, 2.595703, 2.583984,
2.548828, 2.470703, 2.607422, 2.626953, 2.326172
]
ti = np.zeros_like(f) + .1
wr = WindResource(f, A, k, ti)
wdir, ws, ti, weight = wr([0], [0], [4, 5])
npt.assert_array_almost_equal(
weight,
np.array([[0.071381703], [0.088361194]]) * 0.035972 * (12 / 360))
def test_TopFarmProblemXYBoundaryConstraint():
tf = xy3tb.get_tf(design_vars={
'x': [3, 7, 4],
'y': [-3, -7, -3]
},
constraints=[XYBoundaryConstraint(xy3tb.boundary)])
tf.evaluate({'x': xy3tb.desired[:, 0], 'y': xy3tb.desired[:, 1]})
npt.assert_equal(tf['boundaryDistances'][1, 3], -1)
_, state, _ = tf.optimize()
npt.assert_array_almost_equal(state['x'], [3, 6, 4])
npt.assert_array_almost_equal(state['y'], xy3tb.optimal[:, 1])
desvars = tf.driver._designvars
if tuple(map(int, scipy.__version__.split("."))) < (1, 5, 0):
for xy in 'xy':
for lu in ['lower', 'upper']:
npt.assert_equal(desvars['indeps.' + xy][lu], np.nan)
else:
for i, xy in enumerate('xy'):
for lu, func in zip(['lower', 'upper'], (np.min, np.max)):
npt.assert_equal(desvars['indeps.' + xy][lu],
func(xy3tb.boundary[:, i]))
