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import numpy as np
from py_wake.deficit_models.deficit_model import DeficitModel, WakeDeficitModel, BlockageDeficitModel
from py_wake.deficit_models.fuga import FugaDeficit, Fuga
from py_wake.deficit_models.gaussian import BastankhahGaussianDeficit, IEA37SimpleBastankhahGaussianDeficit,\
ZongGaussianDeficit, NiayifarGaussianDeficit, BastankhahGaussian, IEA37SimpleBastankhahGaussian, ZongGaussian,\
NiayifarGaussian
from py_wake.deficit_models.gcl import GCLDeficit, GCL, GCLLocal
from py_wake.deficit_models.noj import NOJDeficit, NOJ, NOJLocalDeficit, NOJLocal
from py_wake.deficit_models.selfsimilarity import SelfSimilarityDeficit
from py_wake.examples.data.hornsrev1 import Hornsrev1Site
from py_wake.examples.data.iea37 import iea37_path
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines, IEA37Site
from py_wake.examples.data.iea37.iea37_reader import read_iea37_windfarm
from py_wake.flow_map import HorizontalGrid, XYGrid
from py_wake.superposition_models import SquaredSum, WeightedSum
from py_wake.tests import npt
from py_wake.tests.test_files import tfp
from py_wake.turbulence_models.gcl_turb import GCLTurbulence
from py_wake.turbulence_models.stf import STF2017TurbulenceModel
from py_wake.wind_farm_models.engineering_models import PropagateDownwind, All2AllIterative
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class GCLLocalDeficit(GCLDeficit):
def __init__(self):
GCLDeficit.__init__(self, use_effective_ws=True, use_effective_ti=True)
@pytest.mark.parametrize(
'deficitModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJDeficit(), (367205.0846866496, [9833.86287, 8416.99088, 10820.37673, 13976.26422, 22169.66036,
25234.9215, 37311.64388, 42786.37028, 24781.33444, 13539.82115,
14285.22744, 31751.29488, 75140.15677, 17597.10319, 11721.21226,
7838.84383])),
(NOJLocalDeficit(), (335151.6404628441, [8355.71335, 7605.92379, 10654.172, 13047.6971, 19181.46408,
23558.34198, 36738.52415, 38663.44595, 21056.39764, 12042.79324,
13813.46269, 30999.42279, 63947.61202, 17180.40299, 11334.12323,
6972.14345])),
(BastankhahGaussianDeficit(), (355971.9717035484,
[9143.74048, 8156.71681, 11311.92915, 13955.06316, 19807.65346,
25196.64182, 39006.65223, 41463.31044, 23042.22602, 12978.30551,
14899.26913, 32320.21637, 67039.04091, 17912.40907, 12225.04134,
7513.75582])),
(IEA37SimpleBastankhahGaussianDeficit(), read_iea37_windfarm(iea37_path + 'iea37-ex16.yaml')[2]),
(FugaDeficit(LUT_path=tfp + 'fuga/2MW/Z0=0.00014617Zi=00399Zeta0=0.00E+0/'),
(398938.8941139709, [9632.92248, 9733.98766, 12462.98413, 15332.30502, 22199.91899,
27683.32851, 42975.80734, 49481.10395, 24274.96466, 15416.63681,
16681.20957, 35508.27583, 75263.59612, 19679.2854, 13687.14632,
8925.42131])),
(GCLDeficit(), (370863.6246093183,
[9385.75387, 8768.52105, 11450.13309, 14262.42186, 21178.74926,
25751.59502, 39483.21753, 44573.31533, 23652.09976, 13924.58752,
15106.11692, 32840.02909, 71830.22035, 18200.49805, 12394.7626,
(GCLLocalDeficit(), (381187.36105425097,
[9678.85358, 9003.65526, 11775.06899, 14632.42259, 21915.85495,
26419.65189, 40603.68618, 45768.58091, 24390.71103, 14567.43106,
15197.82861, 32985.67922, 75062.92788, 18281.21981, 12470.01322,
8433.77587])),
(ZongGaussianDeficit(),
(354263.1062694012, [8960.58672, 8095.48341, 11328.51572, 13924.50408, 19938.78428,
25141.4657, 39063.84731, 41152.04065, 22580.67854, 12944.55424,
14778.95385, 32294.41749, 66540.62665, 17898.1109, 12126.32111,
7494.21561])),
(NiayifarGaussianDeficit(),
(362228.4003016239, [9210.01349, 8330.06642, 11392.04851, 14082.13366, 20643.45247,
25426.07466, 39282.9259, 42344.50431, 23209.23399, 13390.08421,
14807.66824, 32535.89096, 69640.3283, 18031.93957, 12149.88163,
7752.15401])),
])
def test_IEA37_ex16(deficitModel, aep_ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
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wf_model = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel,
superpositionModel=SquaredSum(), turbulenceModel=GCLTurbulence())
aep_ilk = wf_model(x, y, wd=np.arange(0, 360, 22.5), ws=[9.8]).aep_ilk(normalize_probabilities=True)
aep_MW_l = aep_ilk.sum((0, 2)) * 1000
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# check if ref is reasonable
aep_est = 16 * 3.35 * 24 * 365 * .8 # n_wt * P_rated * hours_pr_year - 20% wake loss = 375628.8
npt.assert_allclose(aep_ref[0], aep_est, rtol=.11)
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npt.assert_allclose(aep_ref[1], [9500, 8700, 11500, 14300, 21300, 25900, 39600, 44300, 23900,
13900, 15200, 33000, 72100, 18300, 12500, 8000], rtol=.15)
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npt.assert_almost_equal(aep_MW_l.sum(), aep_ref[0], 5)
npt.assert_array_almost_equal(aep_MW_l, aep_ref[1], 5)
@pytest.mark.parametrize(
'deficitModel,ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJDeficit(),
[3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3]),
(NOJLocalDeficit(),
[3.09, 3.09, 9., 9., 5.54, 5.54, 5.54, 5.54, 5.54, 5.54, 6.73, 6.73, 6.73, 6.73, 6.73, 6.73, 6.73]),
(BastankhahGaussianDeficit(),
[0.18, 3.6, 7.27, 8.32, 7.61, 6.64, 5.96, 6.04, 6.8, 7.69, 8.08, 7.87, 7.59, 7.46, 7.55, 7.84, 8.19]),
(IEA37SimpleBastankhahGaussianDeficit(),
[3.32, 4.86, 7.0, 8.1, 7.8, 7.23, 6.86, 6.9, 7.3, 7.82, 8.11, 8.04, 7.87, 7.79, 7.85, 8.04, 8.28]),
(FugaDeficit(LUT_path=tfp + 'fuga/2MW/Z0=0.00014617Zi=00399Zeta0=0.00E+0/'),
[6.91, 7.87, 8.77, 8.88, 8.55, 7.88, 7.24, 7.32, 8.01, 8.62, 8.72, 8.42, 8.05, 7.85, 8., 8.37, 8.69]),
[2.39, 5.01, 7.74, 8.34, 7.95, 7.58, 7.29, 7.32, 7.61, 7.92, 8.11, 8.09, 7.95, 7.83, 7.92, 8.1, 8.3]),
(GCLLocalDeficit(),
[3.05, 5.24, 7.61, 8.36, 8.08, 7.81, 7.61, 7.63, 7.82, 8.01, 8.11, 8.07, 7.94, 7.83, 7.92, 8.1, 8.3]),
(ZongGaussianDeficit(),
[6.34, 7.08, 8.09, 8.36, 7.5, 6.2, 5.23, 5.34, 6.43, 7.64, 8.08, 7.75, 7.36, 7.19, 7.32, 7.69, 8.14]),
(NiayifarGaussianDeficit(),
[0.18, 3.6, 7.27, 8.32, 7.61, 6.64, 5.97, 6.04, 6.8, 7.69, 8.08, 7.87, 7.59, 7.46, 7.56, 7.84, 8.19]),
def test_deficitModel_wake_map(deficitModel, ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
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wf_model = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, superpositionModel=SquaredSum(),
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wf_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j))
X, Y = flow_map.X, flow_map.Y
Z = flow_map.WS_eff_xylk[:, :, 0, 0]
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mean_ref = [3.2, 4.9, 8., 8.2, 7.9, 7.4, 7., 7., 7.4, 7.9, 8.1, 8.1, 8., 7.8, 7.9, 8.1, 8.4]
if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
windTurbines.plot(x, y)
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plt.figure()
plt.plot(Z[49, 100:133:2])
plt.plot(ref, label='ref')
plt.plot(mean_ref, label='Mean ref')
plt.legend()
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npt.assert_array_almost_equal(ref, Z[49, 100:133:2], 2)
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# check that ref is reasonable
npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6)
@pytest.mark.parametrize(
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'deficitModel,wake_radius_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJDeficit(), [100., 75., 150., 100., 100.]),
(NOJLocalDeficit(), [71., 46., 92., 71., 61.5]),
(BastankhahGaussianDeficit(),
[83.336286, 57.895893, 115.791786, 75.266662, 83.336286]),
(IEA37SimpleBastankhahGaussianDeficit(),
[83.336286, 57.895893, 115.791786, 75.266662, 83.336286]),
(FugaDeficit(LUT_path=tfp + 'fuga/2MW/Z0=0.00014617Zi=00399Zeta0=0.00E+0/'),
[100, 50, 100, 100, 100]),
[156.949964, 97.763333, 195.526667, 113.225695, 111.340236]),
(GCLLocalDeficit(),
[156.949964, 97.763333, 195.526667, 113.225695, 111.340236]),
(ZongGaussianDeficit(), [91.15734, 66.228381, 132.456762, 94.90156, 79.198215]),
(NiayifarGaussianDeficit(), [92.880786, 67.440393, 134.880786, 84.811162, 73.880786]),
])
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def test_wake_radius(deficitModel, wake_radius_ref):
mean_ref = [105, 68, 135, 93, 123]
# check that ref is reasonable
npt.assert_allclose(wake_radius_ref, mean_ref, rtol=.5)
npt.assert_array_almost_equal(deficitModel.wake_radius(
D_src_il=np.reshape([100, 50, 100, 100, 100], (5, 1)),
dw_ijlk=np.reshape([500, 500, 1000, 500, 500], (5, 1, 1, 1)),
ct_ilk=np.reshape([.8, .8, .8, .4, .8], (5, 1, 1)),
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TI_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)),
TI_eff_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)))[:, 0, 0, 0],
wake_radius_ref)
# Check that it works when called from WindFarmModel
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, turbulenceModel=GCLTurbulence())
wfm(x=[0, 500], y=[0, 0], wd=[30], ws=[10])
if 0:
sim_res = wfm([0], [0], wd=[270], ws=10)
sim_res.flow_map(HorizontalGrid(x=np.arange(-100, 1500, 10))).WS_eff.plot()
x = np.arange(0, 1500, 10)
wr = deficitModel.wake_radius(
D_src_il=np.reshape([130], (1, 1)),
dw_ijlk=np.reshape(x, (1, len(x), 1, 1)),
ct_ilk=sim_res.CT.values,
TI_ilk=np.reshape(sim_res.TI.values, (1, 1, 1)),
TI_eff_ilk=sim_res.TI_eff.values)[0, :, 0, 0]
plt.title(deficitModel.__class__.__name__)
plt.plot(x, wr)
plt.plot(x, -wr)
plt.axis('equal')
plt.show()
def test_wake_radius_not_implemented():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=SelfSimilarityDeficit(),
with pytest.raises(NotImplementedError, match="wake_radius not implemented for SelfSimilarityDeficit"):
@pytest.mark.parametrize(
'deficitModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(BastankhahGaussianDeficit(), (346412.82243950944,
[8835.30563, 7877.90062, 11079.66832, 13565.65235, 19469.4846,
24493.53897, 38205.75284, 40045.9948, 22264.97018, 12662.90784,
14650.96535, 31289.90349, 65276.92307, 17341.39229, 12021.3049,
7331.15717])),
(342944.4057168523, [8674.44232, 7806.22033, 11114.78804, 13549.48197, 18895.50866,
24464.34244, 38326.85532, 39681.61999, 21859.59465, 12590.25899,
14530.24656, 31158.81189, 63812.14454, 17268.73912, 11922.25359,
7289.09731])),
(349044.6891842835, [8888.36909, 8025.38191, 11134.3202, 13643.08009, 19503.25093,
24633.33906, 38394.20758, 40795.69138, 22398.69011, 12972.04355,
14504.45911, 31328.69308, 66049.04782, 17362.89014, 11901.09465,
7510.13048])),
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])
def test_IEA37_ex16_convection(deficitModel, aep_ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel,
superpositionModel=WeightedSum(), turbulenceModel=GCLTurbulence())
aep_ilk = wf_model(x, y, wd=np.arange(0, 360, 22.5), ws=[9.8]).aep_ilk(normalize_probabilities=True)
aep_MW_l = aep_ilk.sum((0, 2)) * 1000
# check if ref is reasonable
aep_est = 16 * 3.35 * 24 * 365 * .8 # n_wt * P_rated * hours_pr_year - 20% wake loss = 375628.8
npt.assert_allclose(aep_ref[0], aep_est, rtol=.11)
npt.assert_allclose(aep_ref[1], [9500, 8700, 11500, 14300, 21300, 25900, 39600, 44300, 23900,
13900, 15200, 33000, 72100, 18300, 12500, 8000], rtol=.15)
npt.assert_almost_equal(aep_MW_l.sum(), aep_ref[0], 5)
npt.assert_array_almost_equal(aep_MW_l, aep_ref[1], 5)
@pytest.mark.parametrize(
'deficitModel,ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(BastankhahGaussianDeficit(),
[0.17, 3.55, 7.61, 8.12, 7.58, 6.63, 5.93, 6.04, 6.65, 7.35, 7.69, 7.65, 7.54, 7.45, 7.55, 7.84, 8.19]),
[6.34, 7.05, 7.9, 8.15, 7.45, 6.19, 5.21, 5.26, 6.38, 7.32, 7.7, 7.54, 7.34, 7.18, 7.32, 7.69, 8.14]),
[0.17, 3.55, 7.61, 8.12, 7.58, 6.63, 5.93, 6.04, 6.65, 7.35, 7.69, 7.65, 7.55, 7.45, 7.56, 7.84, 8.19]),
])
def test_deficitModel_wake_map_convection(deficitModel, ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, superpositionModel=WeightedSum(),
turbulenceModel=GCLTurbulence())
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wf_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j))
X, Y = flow_map.X, flow_map.Y
Z = flow_map.WS_eff_xylk[:, :, 0, 0]
mean_ref = [3.2, 4.9, 8., 8.2, 7.9, 7.4, 7., 7., 7.4, 7.9, 8.1, 8.1, 8., 7.8, 7.9, 8.1, 8.4]
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if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
windTurbines.plot(x, y)
plt.figure()
plt.plot(Z[49, 100:133:2], label='Actual')
plt.plot(ref, label='Reference')
plt.plot(mean_ref, label='Mean ref')
plt.legend()
plt.show()
# check that ref is reasonable
npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6)
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
@pytest.mark.parametrize(
'deficitModel,ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(ZongGaussianDeficit(),
[6.34, 7.05, 7.9, 8.15, 7.45, 6.19, 5.21, 5.26, 6.38, 7.32, 7.7, 7.54, 7.34, 7.18, 7.32, 7.69, 8.14])
])
def test_deficitModel_wake_map_convection_all2all(deficitModel, ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines, wake_deficitModel=deficitModel, superpositionModel=WeightedSum(),
blockage_deficitModel=VortexDipole(), turbulenceModel=STF2017TurbulenceModel())
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wf_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j))
X, Y = flow_map.X, flow_map.Y
Z = flow_map.WS_eff_xylk[:, :, 0, 0]
mean_ref = [3.2, 4.9, 8., 8.2, 7.9, 7.4, 7., 7., 7.4, 7.9, 8.1, 8.1, 8., 7.8, 7.9, 8.1, 8.4]
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if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
windTurbines.plot(x, y)
plt.figure()
plt.plot(Z[49, 100:133:2], label='Actual')
plt.plot(ref, label='Reference')
plt.plot(mean_ref, label='Mean ref')
plt.legend()
plt.show()
# check that ref is reasonable
npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6)
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
@pytest.mark.parametrize(
'windFarmModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJ,
(367205.0846866496, [9833.86287, 8416.99088, 10820.37673, 13976.26422, 22169.66036,
25234.9215, 37311.64388, 42786.37028, 24781.33444, 13539.82115,
14285.22744, 31751.29488, 75140.15677, 17597.10319, 11721.21226,
7838.84383])),
(NOJLocal,
(335151.6404628441, [8355.71335, 7605.92379, 10654.172, 13047.6971, 19181.46408,
23558.34198, 36738.52415, 38663.44595, 21056.39764, 12042.79324,
13813.46269, 30999.42279, 63947.61202, 17180.40299, 11334.12323,
6972.14345])),
(BastankhahGaussian, (355971.9717035484,
[9143.74048, 8156.71681, 11311.92915, 13955.06316, 19807.65346,
25196.64182, 39006.65223, 41463.31044, 23042.22602, 12978.30551,
14899.26913, 32320.21637, 67039.04091, 17912.40907, 12225.04134,
7513.75582])),
(IEA37SimpleBastankhahGaussian, read_iea37_windfarm(iea37_path + 'iea37-ex16.yaml')[2]),
(lambda *args, **kwargs: Fuga(tfp + 'fuga/2MW/Z0=0.00014617Zi=00399Zeta0=0.00E+0/', *args, **kwargs),
(398938.8941139709, [9632.92248, 9733.98766, 12462.98413, 15332.30502, 22199.91899,
27683.32851, 42975.80734, 49481.10395, 24274.96466, 15416.63681,
16681.20957, 35508.27583, 75263.59612, 19679.2854, 13687.14632,
8925.42131])),
(GCL, (370863.6246093183,
[9385.75387, 8768.52105, 11450.13309, 14262.42186, 21178.74926,
25751.59502, 39483.21753, 44573.31533, 23652.09976, 13924.58752,
15106.11692, 32840.02909, 71830.22035, 18200.49805, 12394.7626,
8061.6033])),
(GCLLocal, (381187.36105425097,
[9678.85358, 9003.65526, 11775.06899, 14632.42259, 21915.85495,
26419.65189, 40603.68618, 45768.58091, 24390.71103, 14567.43106,
15197.82861, 32985.67922, 75062.92788, 18281.21981, 12470.01322,
8433.77587])),
(ZongGaussian,
(354263.1062694012, [8960.58672, 8095.48341, 11328.51572, 13924.50408, 19938.78428,
25141.4657, 39063.84731, 41152.04065, 22580.67854, 12944.55424,
14778.95385, 32294.41749, 66540.62665, 17898.1109, 12126.32111,
7494.21561])),
(NiayifarGaussian,
(362228.4003016239, [9210.01349, 8330.06642, 11392.04851, 14082.13366, 20643.45247,
25426.07466, 39282.9259, 42344.50431, 23209.23399, 13390.08421,
14807.66824, 32535.89096, 69640.3283, 18031.93957, 12149.88163,
7752.15401])),
])
def test_IEA37_ex16_windFarmModel(windFarmModel, aep_ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = windFarmModel(site, windTurbines, turbulenceModel=GCLTurbulence())
wf_model.superpositionModel = SquaredSum()
aep_ilk = wf_model(x, y, wd=np.arange(0, 360, 22.5), ws=[9.8]).aep_ilk(normalize_probabilities=True)
aep_MW_l = aep_ilk.sum((0, 2)) * 1000
# check if ref is reasonable
aep_est = 16 * 3.35 * 24 * 365 * .8 # n_wt * P_rated * hours_pr_year - 20% wake loss = 375628.8
npt.assert_allclose(aep_ref[0], aep_est, rtol=.11)
npt.assert_allclose(aep_ref[1], [9500, 8700, 11500, 14300, 21300, 25900, 39600, 44300, 23900,
13900, 15200, 33000, 72100, 18300, 12500, 8000], rtol=.15)
npt.assert_almost_equal(aep_MW_l.sum(), aep_ref[0], 5)
npt.assert_array_almost_equal(aep_MW_l, aep_ref[1], 5)
def test_own_deficit_is_zero():
for deficitModel in get_models(DeficitModel):
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines, wake_deficitModel=deficitModel(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wf_model([0], [0])
npt.assert_array_equal(sim_res.WS_eff, sim_res.WS.broadcast_like(sim_res.WS_eff))
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@pytest.mark.parametrize('upstream_only,ref', [(False, [[9, 9, 9], # -1 upstream
[7, 7, 7]]), # - (1+2) downstream
(True, [[9, 9, 9], # -1 upstream
[8, 8, 8]])]) # -2 downstream
def test_wake_blockage_split(upstream_only, ref):
class MyWakeModel(WakeDeficitModel):
args4deficit = []
def calc_deficit(self, dw_ijlk, **kwargs):
return np.ones_like(dw_ijlk) * 2
class MyBlockageModel(BlockageDeficitModel):
args4deficit = []
def calc_deficit(self, dw_ijlk, **kwargs):
return np.ones_like(dw_ijlk)
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines, wake_deficitModel=MyWakeModel(),
blockage_deficitModel=MyBlockageModel(upstream_only=upstream_only))
sim_res = wf_model([0], [0], ws=10, wd=270)
fm = sim_res.flow_map(XYGrid(x=[-100, 100], y=[-100, 0, 100]))
if 0:
sim_res.flow_map().plot_wake_map()
print(fm.WS_eff.values.squeeze().T)
plt.show()
npt.assert_array_equal(fm.WS_eff.values.squeeze().T, ref)