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from py_wake.site._site import UniformWeibullSite, UniformSite
from py_wake.site.shear import PowerShear
f = [0.035972, 0.039487, 0.051674, 0.070002, 0.083645, 0.064348,
0.086432, 0.117705, 0.151576, 0.147379, 0.10012, 0.05166]
A = [9.176929, 9.782334, 9.531809, 9.909545, 10.04269, 9.593921,
9.584007, 10.51499, 11.39895, 11.68746, 11.63732, 10.08803]
k = [2.392578, 2.447266, 2.412109, 2.591797, 2.755859, 2.595703,
2.583984, 2.548828, 2.470703, 2.607422, 2.626953, 2.326172]
ti = .1
return UniformWeibullSite(f, A, k, ti, shear=PowerShear(50, alpha=np.zeros_like(f) + .3))
x_i = y_i = np.arange(5)
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(3, 6)
WD_ilk, WS_ilk, TI_ilk, P_lk = site.local_wind(x_i=x_i, y_i=y_i, wd=wdir_lst, ws=wsp_lst)
WD_ilk, WS_ilk, TI_ilk, P_lk = site.local_wind(x_i=x_i, y_i=y_i)
npt.assert_equal(site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], wd_bin_size=1)[-1],
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], wd_bin_size=2)[-1] / 2)
npt.assert_almost_equal(site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[9, 10, 11])[-1].sum((1, 2)),
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], ws_bins=3)[-1][:, 0, 0], 5)
z = np.arange(1, 100)
zero = [0] * len(z)
ws = site.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10])[1][:, 0, 0]
site2 = UniformWeibullSite(f, A, k, ti, shear=PowerShear(70, alpha=np.zeros_like(f) + .3))
ws70 = site2.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10])[1][:, 0, 0]
if 0:
import matplotlib.pyplot as plt
plt.plot(ws, z)
plt.plot(ws70, z)
plt.show()
npt.assert_array_equal(10 * (z / 50)**.3, ws)
npt.assert_array_equal(10 * (z / 70)**.3, ws70)
def test_elevation(site):
x_i = y_i = np.arange(5)
npt.assert_array_equal(site.elevation(x_i=x_i, y_i=y_i), np.zeros_like(x_i))
def test_site():
with pytest.raises(NotImplementedError, match="interp_method=missing_method not implemeted yet."):
site = UniformWeibullSite([1], [10], [2], .75, interp_method='missing_method')
def test_plot_ws_distribution(site):
site.plot_ws_distribution(wd=[0, 90, 180, 270])
site.plot_ws_distribution(wd=[0, 90, 180, 270], include_wd_distribution=True)
import matplotlib.pyplot as plt
plt.show()
def test_plot_wd_distribution(site):
import matplotlib.pyplot as plt
p1 = site.plot_wd_distribution(n_wd=12, ax=plt)
npt.assert_array_almost_equal(p1, f, 4)
site.plot_wd_distribution(n_wd=12, ax=plt.gca())
p2 = site.plot_wd_distribution(n_wd=360)
npt.assert_array_almost_equal(np.array(p2)[::30] * 30, f, 4)
UniformWeibullSite(f, A, k, ti, 'spline').plot_wd_distribution(n_wd=360)
UniformWeibullSite(f, A, k, ti, 'linear').plot_wd_distribution(n_wd=360)
plt.show()
def test_plot_wd_distribution_with_ws_levels(site):
p = site.plot_wd_distribution(n_wd=12, ws_bins=[0, 5, 10, 15, 20, 25])
# print(np.round(p, 4).tolist())
npt.assert_array_almost_equal(p, [[0.0075, 0.0179, 0.0091, 0.0014, 0.0001],
[0.0069, 0.0188, 0.0115, 0.0022, 0.0001],
[0.0098, 0.025, 0.0142, 0.0025, 0.0001],
[0.0109, 0.0339, 0.0214, 0.0036, 0.0001],
[0.0114, 0.0411, 0.0271, 0.004, 0.0001],
[0.0108, 0.0324, 0.0185, 0.0026, 0.0001],
[0.0147, 0.0434, 0.0247, 0.0035, 0.0001],
[0.0164, 0.0524, 0.0389, 0.0092, 0.0007],
[0.0185, 0.0595, 0.0524, 0.0184, 0.0026],
[0.0153, 0.0564, 0.054, 0.0191, 0.0024],
[0.0103, 0.0386, 0.0369, 0.0127, 0.0015],
[0.0092, 0.0231, 0.0152, 0.0038, 0.0004]], 4)
if 0:
import matplotlib.pyplot as plt
plt.show()
def test_plot_wd_distribution_with_ws_levels2(site):
p = site.plot_wd_distribution(n_wd=12, ws_bins=6)
# print(np.round(p, 3).tolist())
npt.assert_array_almost_equal(p, [[0.011, 0.02, 0.005, 0.0, 0.0],
[0.01, 0.022, 0.007, 0.0, 0.0],
[0.014, 0.028, 0.008, 0.0, 0.0],
[0.017, 0.039, 0.013, 0.001, 0.0],
[0.018, 0.049, 0.016, 0.001, 0.0],
[0.017, 0.038, 0.011, 0.0, 0.0],
[0.022, 0.049, 0.014, 0.001, 0.0],
[0.025, 0.063, 0.026, 0.002, 0.0],
[0.028, 0.074, 0.041, 0.006, 0.0],
[0.024, 0.073, 0.044, 0.007, 0.0],
[0.016, 0.051, 0.03, 0.004, 0.0],
[0.013, 0.028, 0.011, 0.001, 0.0]], 3)
if 0:
import matplotlib.pyplot as plt
plt.show()
def test_plot_ws_distribution_iea37():
from py_wake.examples.data.iea37 import IEA37Site
n_wt = 16 # must be 16, 32 or 64
site = IEA37Site(n_wt)
p = site.plot_ws_distribution(wd=[0])
npt.assert_almost_equal(p, [1 / 300] * 300)
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def test_iea37_distances():
from py_wake.examples.data.iea37 import IEA37Site
n_wt = 16 # must be 9, 16, 36, 64
site = IEA37Site(n_wt)
x, y = site.initial_position.T
WD_ilk, _, _, _ = site.local_wind(x_i=x, y_i=y,
wd=site.default_wd,
ws=site.default_ws)
dw_iil, hcw_iil, _, _ = site.wt2wt_distances(
x_i=x, y_i=y,
h_i=np.zeros_like(x),
wd_il=WD_ilk.mean(2))
# Wind direction.
wdir = np.rad2deg(np.arctan2(hcw_iil, dw_iil))
npt.assert_allclose(
wdir[:, 0, 0],
[180, -90, -18, 54, 126, -162, -90, -54, -18, 18, 54, 90, 126, 162, -162, -126],
atol=1e-4)
if 0:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.scatter(x, y)
for i, txt in enumerate(np.arange(len(x))):
ax.annotate(txt, (x[i], y[i]), fontsize='large')
def test_uniform_site_probability():
"""check that the uniform site recovers probability"""
p_wd = np.array([0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1])
wd_l = np.linspace(0, 360, p_wd.size, endpoint=False)
ws_k = np.array([12])
actual = site.probability(0, 0, 0, wd_l[na, :, na], ws_k[na, na], 360 / p_wd.size, None)
npt.assert_array_almost_equal(actual.squeeze(), p_wd)