def main():
if __name__ == '__main__':
from topfarm.cost_models.dummy import DummyCost, DummyCostPlotComp
from topfarm.constraint_components.spacing import SpacingConstraint
from topfarm.constraint_components.boundary import XYBoundaryConstraint
initial = np.array([[6, 0], [6, -8], [1,
1]]) # initial turbine layouts
optimal = np.array([[2.5, -3], [6, -7],
[4.5, -3]]) # optimal turbine layouts
boundary = np.array([(0, 0), (6, 0), (6, -10),
(0, -10)]) # turbine boundaries
desired = np.array([[3, -3], [7, -7], [4,
-3]]) # desired turbine layouts
plot_comp = DummyCostPlotComp(optimal)
tf = TopFarmProblem(
design_vars=dict(zip('xy', initial.T)),
cost_comp=DummyCost(optimal_state=desired, inputs=['x', 'y']),
constraints=[XYBoundaryConstraint(boundary),
SpacingConstraint(2)],
driver=EasyScipyOptimizeDriver(),
plot_comp=plot_comp)
tf.optimize()
plot_comp.show()
def main():
if __name__ == '__main__':
from topfarm.cost_models.dummy import DummyCost, DummyCostPlotComp
from topfarm.constraint_components.spacing import SpacingConstraint
from topfarm.constraint_components.boundary import XYBoundaryConstraint
from openmdao.api import view_model
initial = np.array([[6, 0], [6, -8], [1,
1]]) # initial turbine layouts
optimal = np.array([[2.5, -3], [6, -7],
[4.5, -3]]) # optimal turbine layouts
boundary = np.array([(0, 0), (6, 0), (6, -10),
(0, -10)]) # turbine boundaries
desired = np.array([[3, -3], [7, -7], [4,
-3]]) # desired turbine layouts
drivers = [
EasySimpleGADriver(max_gen=10,
pop_size=100,
bits={
'x': [12] * 3,
'y': [12] * 3
},
random_state=1),
EasyScipyOptimizeDriver()
]
plot_comp = DummyCostPlotComp(optimal)
tf = TopFarmProblem(
design_vars=dict(zip('xy', initial.T)),
cost_comp=DummyCost(optimal_state=desired, inputs=['x', 'y']),
constraints=[XYBoundaryConstraint(boundary),
SpacingConstraint(2)],
driver=drivers[1],
plot_comp=plot_comp)
cost, _, recorder = tf.optimize()
plot_comp.show()
def test_turbine_Type_multistart_XYZ_optimization():
plot_comp = DummyCostPlotComp(optimal, delay=.5)
plot_comp = NoPlot()
xyz = [(0, 0, 0), (1, 1, 1)]
p1 = DummyCost(optimal_state=optimal,
inputs=['x', 'y', 'z', 'type'])
p2 = TurbineXYZOptimizationProblem(
cost_comp=p1,
turbineXYZ=xyz,
min_spacing=2,
boundary_comp=get_boundary_comp(),
plot_comp=plot_comp,
driver=EasyScipyOptimizeDriver(disp=True, optimizer='COBYLA', maxiter=10))
p3 = InitialXYZOptimizationProblem(
cost_comp=p2,
turbineXYZ=xyz, min_spacing=2,
boundary_comp=get_boundary_comp(),
driver=DOEDriver(ListGenerator([[('x', [0, 4]), ('y', [2, 2]), ('z', [4, 1])]])))
tf = TurbineTypeOptimizationProblem(
cost_comp=p3,
turbineTypes=[0, 0], lower=0, upper=1,
driver=DOEDriver(FullFactorialGenerator(1)))
case_gen = tf.driver.options['generator']
cost, state, recorder = tf.optimize()
print(cost)
# print (state)
print(recorder.get('type'))
print(recorder.get('cost'))
best_index = np.argmin(recorder.get('cost'))
initial_xyz_recorder = recorder['recorder'][best_index]
xyz_recorder = initial_xyz_recorder.get('recorder')[0]
npt.assert_almost_equal(xyz_recorder['cost'][-1], cost)
def testDummyCostPlotComp():
if os.name == 'posix' and "DISPLAY" not in os.environ:
pytest.xfail("No display")
tf = xy3tb.get_tf(plot_comp=DummyCostPlotComp(xy3tb.desired))
tf.evaluate()
tf.optimize()
def test_turbineType_and_XYZ_optimization():
plot_comp = DummyCostPlotComp(optimal)
plot_comp = NoPlot()
cost_comp = DummyCost(
optimal_state=optimal,
inputs=['x', 'y', 'z', 'type'])
xyz_opt_problem = TurbineXYZOptimizationProblem(
cost_comp,
turbineXYZ=[(0, 0, 0), (1, 1, 1)],
min_spacing=2,
boundary_comp=get_boundary_comp(),
plot_comp=plot_comp,
driver=EasyScipyOptimizeDriver(disp=False))
tf = TurbineTypeOptimizationProblem(
cost_comp=xyz_opt_problem,
turbineTypes=[0, 0], lower=0, upper=1,
driver=DOEDriver(FullFactorialGenerator(2)))
cost = tf.optimize()[0]
npt.assert_almost_equal(cost, 0)
def get_tf(plot=False, **kwargs):
k = {
'cost_comp':
DummyCost(desired[:, :2], [topfarm.x_key, topfarm.y_key]),
'design_vars': {
topfarm.x_key: initial[:, 0],
topfarm.y_key: initial[:, 1]
},
'driver':
EasyScipyOptimizeDriver(disp=False, tol=1e-8),
'plot_comp':
NoPlot(),
'constraints': [
SpacingConstraint(2),
XYBoundaryConstraint(boundary),
CapacityConstraint(**capacit)
]
}
if plot:
k['plot_comp'] = DummyCostPlotComp(desired)
k.update(kwargs)
return TopFarmProblem(**k)
def main():
if __name__ == '__main__':
# ------------------------ INPUTS ------------------------
# define the conditions for the wind farm
boundary = [(0, 0), (6, 0), (6, -10), (0, -10)] # turbine boundaries
initial = np.array([[6, 0], [6, -8], [1, 1],
[-1, -8]]) # initial turbine pos
desired = np.array([[3, -3], [7, -7], [4, -3],
[3, -7]]) # desired turbine pos
optimal = np.array([[2.5, -3], [6, -7], [4.5, -3],
[3, -7]]) # optimal layout
min_spacing = 2 # min distance between turbines
try:
import matplotlib.pyplot as plt
plt.gcf()
plot_comp = DummyCostPlotComp(desired)
plot = True
except RuntimeError:
plot_comp = NoPlot()
plot = False
# ------------------------ OPTIMIZATION ------------------------
# create the wind farm and run the optimization
tf = TopFarmProblem(design_vars={
'x': initial[:, 0],
'y': initial[:, 1]
},
cost_comp=DummyCost(desired, ['x', 'y']),
constraints=[
XYBoundaryConstraint(boundary),
SpacingConstraint(min_spacing)
],
plot_comp=plot_comp,
driver=EasyScipyOptimizeDriver())
cost, state, recorder = tf.optimize()
tf.plot_comp.show()
# final position
final_x, final_y = state['x'], state['y']
# get the positions tried during optimization from the recorder
rec_x, rec_y = recorder['x'], recorder['y']
# get the final, optimal positions
optimized = tf.turbine_positions
# ------------------------ PLOT (if possible) ------------------------
if plot:
# initialize the figure and axes
fig = plt.figure(1, figsize=(7, 5))
plt.clf()
ax = plt.axes()
# plot the boundary and desired locations
ax.add_patch(
Polygon(boundary, closed=True, fill=False,
label='Boundary')) # boundary
plt.plot(desired[:, 0],
desired[:, 1],
'ok',
mfc='None',
ms=10,
label='Desired') # desired positions
# plot the history of each turbine
for i_turb in range(rec_x.shape[1]):
l, = plt.plot(rec_x[0, i_turb],
rec_y[0, i_turb],
'x',
ms=8,
label=f'Turbine {i_turb+1}') # initial
plt.plot(rec_x[:, i_turb], rec_y[:, i_turb],
c=l.get_color()) # tested values
plt.plot(rec_x[-1, i_turb],
rec_y[-1, i_turb],
'o',
ms=8,
c=l.get_color()) # final
# make a few adjustments to the plot
ax.autoscale_view() # autoscale the boundary
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=4,
mode='expand',
borderaxespad=0.) # add a legend
plt.tight_layout() # zoom the plot in
plt.axis('off') # remove the axis
# save the png
folder, file = os.path.split(__file__)
fig.savefig(folder + "/figures/" + file.replace('.py', '.png'))