Part 2 of update to include example 3

examples/example_3_turbine_type_optimization.py

+"""Example: optimizing a layout with constraints
+
+This example uses a dummy cost function to optimize a simple wind turbine
+layout that is subject to constraints. The optimization pushes the wind turbine
+locations to specified locations in the farm.
+"""
+import os
+
+from matplotlib.patches import Polygon
+import matplotlib.pyplot as plt
+import numpy as np
+
+from topfarm import TopFarm
+from topfarm.cost_models.dummy import DummyCost
+from topfarm._topfarm import TurbineTypeOptimizationProblem
+from openmdao.drivers.doe_generators import FullFactorialGenerator
+from topfarm.plotting import TurbineTypePlotComponent, NoPlot
+
+
+# ------------------------ INPUTS ------------------------
+
+# define the conditions for the wind farm
+positions = np.array([[0, 0], [6, 6]])  # initial turbine pos
+optimal_types = np.array([[2], [6]])  # optimal layout
+
+
+#===============================================================================
+# Setup the problem and plotting
+#===============================================================================
+
+try:
+    import matplotlib.pyplot as plt
+    plt.gcf()
+    plot_comp = TurbineTypePlotComponent(turbine_type_names=["Turbine %d" % i for i in range(10)], delay=0.1)
+    plot = True
+except RuntimeError:
+    plot_comp = NoPlot()
+    plot = False
+
+# create the wind farm
+tf = TurbineTypeOptimizationProblem(
+    cost_comp=DummyCost(optimal_types, ['turbineType']),
+    turbineTypes=[0, 0],
+    lower=0, upper=9,
+    plot_comp=plot_comp,
+    driver=FullFactorialGenerator(10))
+
+
+#===============================================================================
+# #  Run the optimization
+#===============================================================================
+state = {'turbineX': positions[:, 0], 'turbineY': positions[:, 1]}
+cost, state, recorder = tf.optimize(state)
+
+#===============================================================================
+# plot and prin the the final, optimal types
+#===============================================================================
+print(state['turbineType'])
+tf.evaluate(state)
+
+
+# save the figure
+if plot:
+    plt.savefig(os.path.basename(__file__).replace('.py', '.png'))