import pytest import matplotlib.pyplot as plt 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.deficit_models import VortexDipole 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 from py_wake.utils.model_utils import get_models 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, 8061.6033])), (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() 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 # 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) [(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]), (GCLDeficit(), [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]), ][1:2]) def test_deficitModel_wake_map(deficitModel, ref): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines() wf_model = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, superpositionModel=SquaredSum(), 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] 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]) plt.plot(ref, label='ref') plt.plot(mean_ref, label='Mean ref') plt.legend() plt.show() npt.assert_array_almost_equal(ref, Z[49, 100:133:2], 2) # check that ref is reasonable npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6) @pytest.mark.parametrize( '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]), (GCLDeficit(), [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]), ]) 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)), 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(), turbulenceModel=GCLTurbulence()) with pytest.raises(NotImplementedError, match="wake_radius not implemented for SelfSimilarityDeficit"): wfm(x, y) @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])), (ZongGaussianDeficit(), (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])), (NiayifarGaussianDeficit(), (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])), ]) 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]), (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]), (NiayifarGaussianDeficit(), [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] 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] 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)) @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)