def site():
return UniformWeibullSite(f,
A,
k,
ti,
shear=PowerShear(50,
alpha=np.zeros_like(f) + .3))
def main():
if __name__ == '__main__':
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
h_ref = 100
alpha = .1
site = UniformWeibullSite(f, A, k, ti, shear=PowerShear(h_ref=h_ref, alpha=alpha))
x_i = y_i = np.arange(5)
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(1, 20)
WD_ilk, WS_ilk, TI_ilk, P_ilk = site.local_wind(x_i=x_i, y_i=y_i, wd=wdir_lst, ws=wsp_lst)
import matplotlib.pyplot as plt
site.plot_ws_distribution(0, 0, wdir_lst)
plt.figure()
z = np.arange(1, 100)
u = [site.local_wind(x_i=[0], y_i=[0], h_i=[z_], wd=0, ws=10)[1][0][0] for z_ in z]
plt.plot(u, z)
plt.xlabel('Wind speed [m/s]')
plt.ylabel('Height [m]')
plt.show()
def test_plot_wd_distribution_with_ws_levels_xr(site):
import xarray as xr
ds = xr.Dataset(data_vars={
'Sector_frequency': ('wd', f),
'Weibull_A': ('wd', A),
'Weibull_k': ('wd', k)
},
coords={'wd': np.linspace(0, 360, len(f), endpoint=False)})
site2 = XRSite(ds,
shear=PowerShear(h_ref=100, alpha=.2),
interp_method='nearest')
p = site2.plot_wd_distribution(n_wd=12, ws_bins=[0, 5, 10, 15, 20, 25])
if 0:
plt.show()
# print(np.round(p.values, 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)
plt.close('all')
def test_local_wind(site):
x_i = y_i = np.arange(5)
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(3, 6)
lw = site.local_wind(x_i=x_i, y_i=y_i, wd=wdir_lst, ws=wsp_lst)
npt.assert_array_equal(lw.WS_ilk.shape, (5, 4, 3))
lw = site.local_wind(x_i=x_i, y_i=y_i)
npt.assert_array_equal(lw.WS_ilk.shape, (5, 360, 23))
# check probability local_wind()[-1]
npt.assert_equal(site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], wd_bin_size=1).P_ilk,
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], wd_bin_size=2).P_ilk / 2)
npt.assert_almost_equal(site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[9, 10, 11]).P_ilk.sum((1, 2)),
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], ws_bins=3).P_ilk[:, 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]).WS_ilk[:, 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]).WS_ilk[:, 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_uniform_local_wind(uniform_site):
site = uniform_site
x_i = y_i = np.arange(5)
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(3, 6)
lw = site.local_wind(x_i=x_i, y_i=y_i, h_i=100, wd=wdir_lst, ws=wsp_lst)
npt.assert_array_equal(lw.WS, 10)
npt.assert_array_equal(lw.WD, wdir_lst)
npt.assert_array_equal(lw.TI, 0.1)
npt.assert_array_equal(lw.P, np.array([0.035972, 0.070002, 0.086432, 0.147379]) * 3)
npt.assert_array_equal(site.elevation(x_i, y_i), 0)
lw = site.local_wind(x_i=x_i, y_i=y_i, h_i=100)
npt.assert_array_equal(lw.WD_ilk.shape, (1, 360, 1))
z = np.arange(1, 200)
zero = [0] * len(z)
ws100_2 = site.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10]).WS_ilk[:, 0, 0]
site.shear = PowerShear(70, alpha=.3)
ws70_3 = site.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10]).WS_ilk[:, 0, 0]
if 0:
plt.plot(ws100_2, z)
plt.plot(ws70_3, z)
plt.show()
npt.assert_array_equal(10 * (z / 100)**.2, ws100_2)
npt.assert_array_equal(10 * (z / 70)**.3, ws70_3)
def complex_ws_site():
ti = 0.1
P = weibull.cdf(np.array([3, 5, 7, 9, 11, 13]), 10, 2) - weibull.cdf(np.array([0, 3, 5, 7, 9, 11]), 10, 2)
ds = xr.Dataset(
data_vars={'Speedup': (['ws'], np.arange(.8, 1.4, .1)),
'P': (('wd', 'ws'), P[na, :] * np.array(f)[:, na]), 'TI': ti},
coords={'ws': [1.5, 4, 6, 8, 10, 12], 'wd': np.linspace(0, 360, len(f), endpoint=False)})
return XRSite(ds, shear=PowerShear(h_ref=100, alpha=.2), interp_method='linear')
def complex_grid_site():
ti = 0.1
ds = xr.Dataset(
data_vars={'Speedup': (['x', 'y'], np.arange(.8, 1.4, .1).reshape((3, 2))),
'Turning': (['x', 'y'], np.arange(-2, 4, 1).reshape((3, 2))),
'Sector_frequency': ('wd', f), 'Weibull_A': ('wd', A), 'Weibull_k': ('wd', k), 'TI': ti},
coords={'x': [0, 5, 10], 'y': [0, 5], 'wd': np.linspace(0, 360, len(f), endpoint=False)})
return XRSite(ds, shear=PowerShear(h_ref=100, alpha=.2), interp_method='linear')
def complex_fixed_pos_site():
ti = 0.1
ds = xr.Dataset(
data_vars={'Speedup': ('i', np.arange(.8, 1.3, .1)),
'Turning': ('i', np.arange(-2, 3)),
'Sector_frequency': ('wd', f), 'Weibull_A': ('wd', A), 'Weibull_k': ('wd', k), 'TI': ti},
coords={'i': np.arange(5), 'wd': np.linspace(0, 360, len(f), endpoint=False)})
x_i = np.arange(5)
return XRSite(ds, initial_position=np.array([x_i, x_i + 1]).T, shear=PowerShear(h_ref=100, alpha=.2))
def test_wrong_height():
ti = 0.1
ds = xr.Dataset(
data_vars={'Speedup': (['x', 'y', 'h'], np.arange(.8, 1.4, .1).reshape((3, 2, 1))),
'Sector_frequency': ('wd', f), 'Weibull_A': ('wd', A), 'Weibull_k': ('wd', k), 'TI': ti},
coords={'x': [0, 5, 10], 'y': [0, 5], 'h': [100], 'wd': np.linspace(0, 360, len(f), endpoint=False)})
site = XRSite(ds, shear=PowerShear(h_ref=100, alpha=.2), interp_method='linear')
y = np.arange(5)
x = y * 2
X, Y = np.meshgrid(x, y)
x_i, y_i = X.flatten(), Y.flatten()
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(3, 6)
lw = site.local_wind(x_i=x_i, y_i=y_i, h_i=100, wd=wdir_lst, ws=wsp_lst)
def test_power_shear():
shear = PowerShear(70, alpha=[.1, .2])
h_lst = np.arange(10, 100, 10)
u1, u2 = np.array([
shear(WS_ilk=[[[10]], [[10]]], WD_ilk=[[[0]], [[180]]], h_i=[h, h])
for h in h_lst
])[:, :, 0, 0].T
if 0:
import matplotlib.pyplot as plt
plt.plot(u1, h_lst, label='alpha=0.1')
plt.plot((h_lst / 70)**0.1 * 10, h_lst, ':')
plt.plot(u2, h_lst, label='alpha=0.2')
plt.plot((h_lst / 70)**0.2 * 10, h_lst, ':')
plt.show()
npt.assert_array_almost_equal(
u1, [8.23, 8.82, 9.19, 9.46, 9.67, 9.85, 10., 10.13, 10.25], 2)
npt.assert_array_almost_equal(
u2, [6.78, 7.78, 8.44, 8.94, 9.35, 9.7, 10., 10.27, 10.52], 2)
def get_site():
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 = 0.001
h_ref = 100
alpha = .1
site = UniformWeibullSite(f,
A,
k,
ti,
shear=PowerShear(h_ref=h_ref, alpha=alpha))
spacing = 2000
N = 5
theta = 76 # deg
dx = np.tan(np.radians(theta))
x = np.array([
np.linspace(0, (N - 1) * spacing, N) + i * spacing / dx
for i in range(N)
])
y = np.array(np.array([N * [i * spacing] for i in range(N)]))
initial_positions = np.column_stack((x.ravel(), y.ravel()))
eps = 2000
delta = 5
site.boundary = np.array([
(0 - delta, 0 - delta), ((N - 1) * spacing + eps, 0 - delta),
((N - 1) * spacing * (1 + 1 / dx) + eps *
(1 + np.cos(np.radians(theta))),
(N - 1) * spacing + eps * np.sin(np.radians(theta)) - delta),
((N - 1) * spacing / dx + eps * np.cos(np.radians(theta)),
(N - 1) * spacing + eps * np.sin(np.radians(theta)))
])
site.initial_position = initial_positions
return site
def test_power_shear():
shear = PowerShear(70, alpha=[.1, .2])
h_lst = np.arange(10, 100, 10)
site = UniformSite([1], .1)
wref = site.wref([0, 180], [10])
h = xr.DataArray(np.arange(10, 100, 10), [('i', np.arange(9))])
u = shear(wref.WS, wref.WD, h)
if 0:
plt.plot(u.sel(wd=0), h, label='alpha=0.1')
plt.plot((h_lst / 70)**0.1 * 10, h_lst, ':')
plt.plot(u.sel(wd=180), h, label='alpha=0.2')
plt.plot((h_lst / 70)**0.2 * 10, h_lst, ':')
plt.legend()
plt.show()
npt.assert_array_almost_equal(u.sel(
wd=0, ws=10), [8.23, 8.82, 9.19, 9.46, 9.67, 9.85, 10., 10.13, 10.25],
2)
npt.assert_array_almost_equal(u.sel(
wd=180, ws=10), [6.78, 7.78, 8.44, 8.94, 9.35, 9.7, 10., 10.27, 10.52],
2)
def uniform_weibull_site():
ti = 0.1
ds = xr.Dataset(
data_vars={'Sector_frequency': ('wd', f), 'Weibull_A': ('wd', A), 'Weibull_k': ('wd', k), 'TI': ti},
coords={'wd': np.linspace(0, 360, len(f), endpoint=False)})
return XRSite(ds, shear=PowerShear(h_ref=100, alpha=.2))
def uniform_site():
ti = 0.1
ds = xr.Dataset(
data_vars={'WS': 10, 'P': ('wd', f), 'TI': ti},
coords={'wd': np.linspace(0, 360, len(f), endpoint=False)})
return XRSite(ds, shear=PowerShear(h_ref=100, alpha=.2))
