diff --git a/tests/test_with_dummy.py b/tests/test_with_dummy.py
index 398852100c68baa56a2cfbfbb37a2fbb49741ccc..e0026c3f78797ccf3ee80584e7b421596bbd0214 100644
--- a/tests/test_with_dummy.py
+++ b/tests/test_with_dummy.py
@@ -1,69 +1,45 @@
"""Tests for TOPFARM
"""
import warnings
-
import numpy as np
from topfarm.topfarm import TopFarm
+from topfarm.cost_models.dummy import DummyCost
+import unittest
-from topfarm.cost_models.dummy import DummyCost_v2
-
-
-def test_optimize_4tb():
- """Optimize 4-turbine layout and check final positions
-
- The farm boundaries and min spacing are chosen such that the desired
- turbine positions are not within the boundaries or constraints.
- """
-
- # test options
- dec_prec = 4 # decimal precision for comparison
-
- # given
- boundary = [(0, 0), (6, 0), (6, -10), (0, -10)] # turbine boundaries
- initial = [[6, 0], [6, -8], [1, 1], [-1, -8]] # initial turbine layouts
- desired = [[3, -3], [7, -7], [4, -3], [3, -7]] # desired turbine layouts
- optimal = np.array([[2.5, -3], [6, -7],
- [4.5, -3], [3, -7]]) # optimal turbine layout
- min_spacing = 2 # min distance between turbines
- # when
- tf = TopFarm(initial, DummyCost_v2(desired), min_spacing,
- boundary=boundary)
- tf.evaluate()
- with warnings.catch_warnings(): # suppress OpenMDAO/SLSQP warnings
- warnings.simplefilter('ignore')
- tf.optimize()
- tb_pos = tf.turbine_positions
+class Test(unittest.TestCase): # unittest version
- # # then
- np.testing.assert_array_almost_equal(tb_pos, optimal, dec_prec)
+ def test_optimize_4tb(self):
+ """Optimize 4-turbine layout and check final positions
+ The farm boundaries and min spacing are chosen such that the desired
+ turbine positions are not within the boundaries or constraints.
+ """
-# class Test(unittest.TestCase): # unittest version
+ # test options
+ dec_prec = 4 # decimal precision for comparison
- # def test_topfarm_with_dummy(self):
- # from topfarm.cost_models.dummy import DummyCost, DummyCostPlotComp
+ # given
+ boundary = [(0, 0), (6, 0), (6, -10), (0, -10)] # turbine boundaries
+ initial = [[6, 0], [6, -8], [1, 1], [-1, -8]] # initial turbine layouts
+ desired = [[3, -3], [7, -7], [4, -3], [3, -7]] # desired turbine layouts
+ optimal = np.array([[2.5, -3], [6, -7], [4.5, -3], [3, -7]]) # optimal turbine layout
+ min_spacing = 2 # min distance between turbines
- # eps = 1e-6
- # optimal = [(3, -3), (7, -7), (4, -3), (3, -7)]
- # initial = [[6, 0], [6, -8], [1, 1], [-1, -8]]
- # boundary = [(0, 0), (6, 0), (6, -10), (0, -10)]
- # plot = True
- # plot_comp = None
- # if plot:
- # plot_comp = DummyCostPlotComp(optimal)
- # tf = TopFarm(initial, DummyCost(optimal), 2, boundary=boundary, plot_comp=plot_comp)
- # tf.evaluate()
- # tf.optimize()
+ # when
+ tf = TopFarm(initial, DummyCost(desired), min_spacing,
+ boundary=boundary)
+ with warnings.catch_warnings(): # suppress OpenMDAO/SLSQP warnings
+ warnings.simplefilter('ignore')
+ tf.optimize()
+ tb_pos = tf.turbine_positions
- # tb_pos = tf.turbine_positions
- # self.assertGreater(sum((tb_pos[2] - tb_pos[0])**2), 2**2 - eps)
- # np.testing.assert_array_almost_equal(tb_pos[3], optimal[3], 3)
- # self.assertLess(tb_pos[1][0], 6 + eps)
- # if plot:
- # plot_comp.show()
+ # then
+ tol = 1e-6
+ self.assertGreater(sum((tb_pos[2] - tb_pos[0])**2), 2**2 - tol) # check min spacing
+ self.assertLess(tb_pos[1][0], 6 + tol) # check within border
+ np.testing.assert_array_almost_equal(tb_pos, optimal, dec_prec)
-# if __name__ == "__main__":
- # #import sys;sys.argv = ['', 'Test.test_topfarm']
- # unittest.main()
+if __name__ == "__main__":
+ unittest.main()
diff --git a/topfarm/cost_models/dummy.py b/topfarm/cost_models/dummy.py
index 0088698abc658fd2e4d2ca80896950101db22676..21dc7e45b2c09188f7f3b22779d929962f36503c 100644
--- a/topfarm/cost_models/dummy.py
+++ b/topfarm/cost_models/dummy.py
@@ -7,55 +7,6 @@ from topfarm.topfarm import TopFarm
class DummyCost(ExplicitComponent):
- """Evaluates the equation f(x,y) = (x-optimal_x)^2 + (y+optimal_y)^2 - 3.
- """
-
- def __init__(self, optimal_positions):
- ExplicitComponent.__init__(self)
- self.optimal = np.array(optimal_positions)
- self.N = self.optimal.shape[0]
- self.history = []
-
- def cost(self, x, y, optimal=None):
- if optimal is not None:
- opt_x, opt_y = optimal
- else:
- opt_x, opt_y = np.array(self.optimal).T
- #return (x - opt_x)**2 + (x - opt_x) * (y - opt_y) + (y - opt_y)**2 - 3.0
- return (x - opt_x)**2 + (y - opt_y)**2 - 3.0
-
- def setup(self):
- self.add_input('turbineX', val=np.zeros(self.N), units='m')
- self.add_input('turbineY', val=np.zeros(self.N), units='m')
-
- self.add_output('cost', val=0.0)
-
- # Finite difference all partials.
- self.declare_partials('cost', '*')
- #self.declare_partials('*', '*', method='fd')
-
- def compute(self, inputs, outputs):
- """
- f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
-
- Optimal solution (minimum): x = 6.6667; y = -7.3333
- """
- x = inputs['turbineX']
- y = inputs['turbineY']
-
- #print(x, y, sum(self.cost(x, y)))
- outputs['cost'] = np.sum(self.cost(x, y))
-
- def compute_partials(self, inputs, J):
- x = inputs['turbineX']
- y = inputs['turbineY']
- #J['aep', 'turbineX'] = -(2 * x - 2 * np.array(self.optimal)[:, 0] + y - np.array(self.optimal)[:, 1])
- #J['aep', 'turbineY'] = -(2 * y - 2 * np.array(self.optimal)[:, 1] + x - np.array(self.optimal)[:, 0])
- J['cost', 'turbineX'] = (2 * x - 2 * np.array(self.optimal)[:, 0])
- J['cost', 'turbineY'] = (2 * y - 2 * np.array(self.optimal)[:, 1])
-
-
-class DummyCost_v2(ExplicitComponent):
"""Sum of squared error between current positions and optimal positions
Evaluates the equation
@@ -67,31 +18,24 @@ class DummyCost_v2(ExplicitComponent):
ExplicitComponent.__init__(self)
self.optimal = np.array(optimal_positions)
self.N = self.optimal.shape[0]
- self.history = []
def cost(self, x, y):
"""Evaluate cost function"""
- opt_x, opt_y = self.optimal.T
- return np.sum((x - opt_x)**2 + (y - opt_y)**2)
-
+
def setup(self):
self.add_input('turbineX', val=np.zeros(self.N), units='m')
self.add_input('turbineY', val=np.zeros(self.N), units='m')
-
self.add_output('cost', val=0.0)
-
- # Finite difference all partials.
self.declare_partials('cost', '*')
- # self.declare_partials('*', '*', method='fd')
-
+
def compute(self, inputs, outputs):
"""
f(x,y) = SUM(x_i - optx_i)^2 + SUM(y_i + opty_i)^2
"""
x = inputs['turbineX']
y = inputs['turbineY']
-
- outputs['cost'] = self.cost(x, y)
+ opt_x, opt_y = self.optimal.T
+ outputs['cost'] = np.sum((x - opt_x)**2 + (y - opt_y)**2)
def compute_partials(self, inputs, J):
x = inputs['turbineX']
@@ -99,18 +43,17 @@ class DummyCost_v2(ExplicitComponent):
J['cost', 'turbineX'] = (2 * x - 2 * np.array(self.optimal)[:, 0])
J['cost', 'turbineY'] = (2 * y - 2 * np.array(self.optimal)[:, 1])
+
class DummyCostPlotComp(PlotComp):
def __init__(self, optimal, memory=10):
super().__init__(memory)
self.optimal = optimal
-
+
def init_plot(self, boundary):
PlotComp.init_plot(self, boundary)
for c, (optx, opty) in zip(self.colors, self.optimal):
plt.plot(optx, opty, 'ko', ms=10)
- plt.plot(optx, opty, 'o',color=c, ms=8)
-
-
+ plt.plot(optx, opty, 'o', color=c, ms=8)
if __name__ == '__main__':
@@ -127,4 +70,4 @@ if __name__ == '__main__':
tf = TopFarm(turbines, DummyCost(optimal), minSpacing * rotorDiameter, boundary=boundary, plot_comp=plot_comp)
# tf.check()
tf.optimize()
- plot_comp.show()
\ No newline at end of file
+ plot_comp.show()
diff --git a/topfarm/cost_models/utils/aep_calculator.py b/topfarm/cost_models/utils/aep_calculator.py
index c956d2fcee71e31c9542c0a865fb1c1334d1756a..fbbc28cf4c261b4b3a379d30669060a8dd8eb695 100644
--- a/topfarm/cost_models/utils/aep_calculator.py
+++ b/topfarm/cost_models/utils/aep_calculator.py
@@ -16,8 +16,8 @@ class AEPCalculator(object):
def __init__(self, wind_resource, wake_model, wdir=np.arange(360), wsp=np.arange(3, 25)):
"""
- wind_resource: f(turbine_positions, wdir, wsp) -> WS[nWT,nWdir,nWsp], TI[nWT,nWdir,nWsp), Weight[nWdir,nWsp]
- wake_model: f(turbine_positions, WS[nWT,nWdir,nWsp], TI[nWT,nWdir,nWsp) -> power[nWdir,nWsp]
+ wind_resource: f(turbine_positions, wdir, wsp) -> WD[nWT,nWdir,nWsp], WS[nWT,nWdir,nWsp], TI[nWT,nWdir,nWsp), Weight[nWdir,nWsp]
+ wake_model: f(turbine_positions, WD[nWT,nWdir,nWsp], WS[nWT,nWdir,nWsp], TI[nWT,nWdir,nWsp) -> power[nWdir,nWsp] (W)
"""
self.wind_resource = wind_resource
self.wake_model = wake_model