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Commit ec8729f8 authored by Mads M. Pedersen's avatar Mads M. Pedersen
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Merge branch 'clean_up_dummy_test' into 'master' 

Clean up dummy test

See merge request !5
parents d04440e7 e5d4ad44
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1 merge request!5Clean up dummy test
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tests/test_with_dummy.py
+ 30
− 54
View file @ ec8729f8
"""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()
topfarm/cost_models/dummy.py
+ 8
− 65
View file @ ec8729f8
......@@ -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()
topfarm/cost_models/utils/aep_calculator.py
+ 2
− 2
View file @ ec8729f8
......@@ -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
......
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