def run(default_config: Dict) -> None:
config, output_path, run_name = setup_run(default_config)
recorder = DataRecorder.make_data_recorder(output_path)
max_evaluations = config['max_evaluations']
optimizer_config = config['optimizer_config']
site_info = SiteInfo(flatirons_site)
inner_problem = WindOptimizationProblem(site_info, config['num_turbines'])
problem = WindParametrization(inner_problem)
optimizer = ParametrizedOptimizationDriver(problem, recorder=recorder, **optimizer_config)
figure = plt.figure(1)
ax = figure.add_subplot(111)
plt.grid()
plt.tick_params(which='both', labelsize=15)
plt.xlabel('x (m)', fontsize=15)
plt.ylabel('y (m)', fontsize=15)
site_info.plot()
score, evaluation, best_solution = optimizer.central_solution()
score, evaluation = problem.objective(best_solution) if score is None else score
print(-1, ' ', score)
optimizer.problem.plot_candidate(best_solution, (1.0, 0, 0), .2)
prev = optimizer.best_solution()[1]
try:
while optimizer.num_evaluations() < max_evaluations:
print('step start')
optimizer.step()
print('step end')
proportion = min(1.0, optimizer.num_evaluations() / max_evaluations)
g = 1.0 * proportion
b = 1.0 - g
a = .5
color = (b, g, b)
score, eval, best = optimizer.best_solution()
score = problem.objective(best) if score is None else score
problem.plot_candidate(best, color, .3)
prev = best
print(optimizer.num_iterations(), ' ', optimizer.num_evaluations(), score)
except:
raise RuntimeError("Optimizer error encountered. Try modifying the config to use larger generation_size if"
" encountering singular matrix errors.")
print('best: ', optimizer.best_solution().__repr__())
optimizer.problem.plot_candidate(optimizer.best_solution()[2], (0, 0, 0), 1.0)
# Create the figure
legend_elements = [Line2D([0], [0], marker='o', color='w', markerfacecolor=(0, 0, 0), label='Optimal')]
plt.legend(handles=legend_elements)
plt.show()
optimizer.close()
def test_create_grid():
site_info = SiteInfo(flatirons_site)
bounding_shape = site_info.polygon.buffer(-200)
site_info.plot()
turbine_positions = create_grid(bounding_shape, site_info.polygon.centroid,
np.pi / 4, 200, 200, .5)
expected_positions = [[242., 497.], [383., 355.], [312.,
709.], [454., 568.],
[595., 426.], [525., 780.], [666., 638.],
[737., 850.], [878., 709.]]
for n, t in enumerate(turbine_positions):
assert (t.x == pytest.approx(expected_positions[n][0], 1e-1))
assert (t.y == pytest.approx(expected_positions[n][1], 1e-1))
def run(default_config: Dict) -> None:
config, output_path, run_name = setup_run(default_config)
recorder = DataRecorder.make_data_recorder(output_path)
max_evaluations = config['max_evaluations']
location_index = config['location']
location = locations[location_index]
site = config['site']
site_data = None
if site == 'circular':
site_data = make_circular_site(lat=location[0], lon=location[1], elev=location[2])
elif site == 'irregular':
site_data = make_irregular_site(lat=location[0], lon=location[1], elev=location[2])
else:
raise Exception("Unknown site '" + site + "'")
site_info = SiteInfo(site_data)
inner_problem = HybridOptimizationProblem(site_info, config['num_turbines'], config['solar_capacity'])
problem = HybridParametrization(inner_problem)
optimizer = ParametrizedOptimizationDriver(problem, recorder=recorder, **config['optimizer_config'])
figure = plt.figure(1)
axes = figure.add_subplot(111)
axes.set_aspect('equal')
plt.grid()
plt.tick_params(which='both', labelsize=15)
plt.xlabel('x (m)', fontsize=15)
plt.ylabel('y (m)', fontsize=15)
site_info.plot()
score, evaluation, best_solution = optimizer.central_solution()
score, evaluation = problem.objective(best_solution) if score is None else score
print(-1, ' ', score, evaluation)
print('setup 1')
num_substeps = 1
figure, axes = plt.subplots(dpi=200)
axes.set_aspect(1)
animation_writer = PillowWriter(2 * num_substeps)
animation_writer.setup(figure, os.path.join(output_path, 'trajectory.gif'), dpi=200)
print('setup 2')
_, _, central_solution = optimizer.central_solution()
print('setup 3')
bounds = problem.inner_problem.site_info.polygon.bounds
site_sw_bound = np.array([bounds[0], bounds[1]])
site_ne_bound = np.array([bounds[2], bounds[3]])
site_center = .5 * (site_sw_bound + site_ne_bound)
max_delta = max(bounds[2] - bounds[0], bounds[3] - bounds[1])
reach = (max_delta / 2) * 1.3
min_plot_bound = site_center - reach
max_plot_bound = site_center + reach
print('setup 4')
best_score, best_evaluation, best_solution = 0.0, 0.0, None
def plot_candidate(candidate):
nonlocal best_score, best_evaluation, best_solution
axes.cla()
axes.set(xlim=(min_plot_bound[0], max_plot_bound[0]), ylim=(min_plot_bound[1], max_plot_bound[1]))
wind_color = (153 / 255, 142 / 255, 195 / 255)
solar_color = (241 / 255, 163 / 255, 64 / 255)
central_color = (.5, .5, .5)
conforming_candidate, _, __ = problem.make_conforming_candidate_and_get_penalty(candidate)
problem.plot_candidate(conforming_candidate, figure, axes, central_color, central_color, alpha=.7)
if best_solution is not None:
conforming_best, _, __ = problem.make_conforming_candidate_and_get_penalty(best_solution)
problem.plot_candidate(conforming_best, figure, axes, wind_color, solar_color, alpha=1.0)
axes.set_xlabel('Best Solution AEP: {}'.format(best_evaluation))
else:
axes.set_xlabel('')
axes.legend([
Line2D([0], [0], color=wind_color, lw=8),
Line2D([0], [0], color=solar_color, lw=8),
Line2D([0], [0], color=central_color, lw=8),
],
['Wind Layout', 'Solar Layout', 'Mean Search Vector'],
loc='lower left')
animation_writer.grab_frame()
print('plot candidate')
plot_candidate(central_solution)
central_prev = central_solution
# TODO: make a smooth transition between points
# TODO: plot exclusion zones
print('begin')
try:
while optimizer.num_evaluations() < max_evaluations:
print('step start')
logger.info("Starting step, num evals {}".format(optimizer.num_evaluations()))
optimizer.step()
print('step end')
proportion = min(1.0, optimizer.num_evaluations() / max_evaluations)
g = 1.0 * proportion
b = 1.0 - g
a = .5
color = (b, g, b)
best_score, best_evaluation, best_solution = optimizer.best_solution()
central_score, central_evaluation, central_solution = optimizer.central_solution()
a1 = optimizer.converter.convert_from(central_prev)
b1 = optimizer.converter.convert_from(central_solution)
a = np.array(a1, dtype=np.float64)
b = np.array(b1, dtype=np.float64)
for i in range(num_substeps):
p = (i + 1) / num_substeps
c = (1 - p) * a + p * b
candidate = optimizer.converter.convert_to(c)
plot_candidate(candidate)
central_prev = central_solution
print(optimizer.num_iterations(), ' ', optimizer.num_evaluations(), best_score, best_evaluation)
except:
raise RuntimeError("Optimizer error encountered. Try modifying the config to use larger generation_size if"
" encountering singular matrix errors.")
animation_writer.finish()
optimizer.close()
print("Results and animation written to " + os.path.abspath(output_path))