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import numpy as np
from py_wake.site.shear import PowerShear
from py_wake.site.xrsite import XRSite
import xarray as xr
from py_wake.tests import npt
import pytest
import matplotlib.pyplot as plt
from numpy import newaxis as na
from py_wake.tests.test_files import tfp
Mads M. Pedersen
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from py_wake.examples.data.hornsrev1 import Hornsrev1Site, V80, wt9_x, wt9_y
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines, IEA37Site
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from py_wake.wind_turbines import WindTurbines
from py_wake.deficit_models.gaussian import BastankhahGaussian, BastankhahGaussianDeficit
from py_wake.wind_farm_models.engineering_models import PropagateDownwind
from py_wake.superposition_models import LinearSum
from py_wake.tests.check_speed import timeit
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from py_wake.site._site import LocalWind
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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
@pytest.fixture
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))
@pytest.fixture
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))
@pytest.fixture
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))
@pytest.fixture
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 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)
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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 test_uniform_weibull_local_wind(uniform_weibull_site):
site = uniform_weibull_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, [3, 4, 5])
npt.assert_array_equal(lw.WD, wdir_lst)
npt.assert_array_equal(lw.TI, 0.1)
# ref_site = UniformWeibullSite(p_wd=f, a=A, k=k, ti=0.1, shear=site.shear)
# lw_ref = ref_site.local_wind(x_i=x_i, y_i=y_i, h_i=100, wd=wdir_lst, ws=wsp_lst)
# print(lw_ref.P)
npt.assert_array_almost_equal(lw.P, [[0.00553222, 0.00770323, 0.00953559],
[0.0078508, 0.01177761, 0.01557493],
[0.0105829, 0.01576518, 0.02066746],
[0.01079997, 0.01656828, 0.02257487]])
def test_complex_fixed_pos_local_wind(complex_fixed_pos_site):
site = complex_fixed_pos_site
x_i, y_i = site.initial_position.T
npt.assert_array_equal(x_i, np.arange(5))
npt.assert_array_equal(y_i, np.arange(5) + 1)
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, [3, 4, 5] * np.arange(.8, 1.3, .1)[:, na])
npt.assert_array_equal(lw.WD, (wdir_lst + np.arange(-2, 3)[:, na]) % 360)
npt.assert_array_equal(lw.TI, 0.1)
npt.assert_array_almost_equal(lw.P, [[0.00553222, 0.00770323, 0.00953559],
[0.0078508, 0.01177761, 0.01557493],
[0.0105829, 0.01576518, 0.02066746],
[0.01079997, 0.01656828, 0.02257487]])
def test_complex_grid_local_wind(complex_grid_site):
site = complex_grid_site
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)
if 0:
c = plt.contourf(X, Y, lw.WS.sel(ws=5).data.reshape(X.shape))
plt.colorbar(c)
plt.figure()
c = plt.contourf(X, Y, lw.WD.sel(wd=90).data.reshape(X.shape))
plt.colorbar(c)
plt.show()
lw = site.local_wind(x_i=[2.5, 7.5], y_i=[2.5, 2.5], h_i=100, wd=wdir_lst, ws=wsp_lst)
npt.assert_array_almost_equal(lw.WS, [3, 4, 5] * np.array([0.95, 1.15])[:, na])
npt.assert_array_equal(lw.WD, (wdir_lst + np.array([-.5, 1.5])[:, na]) % 360)
npt.assert_array_equal(lw.TI, 0.1)
npt.assert_array_almost_equal(lw.P, [[0.00553222, 0.00770323, 0.00953559],
[0.0078508, 0.01177761, 0.01557493],
[0.0105829, 0.01576518, 0.02066746],
[0.01079997, 0.01656828, 0.02257487]])
def test_wd_independent_site():
ti = 0.1
ds = xr.Dataset(
data_vars={
'WS': 10, 'Sector_frequency': 1,
'Weibull_A': 4, 'Weibull_k': 2, 'TI': ti},
coords={})
site = XRSite(ds, shear=None)
npt.assert_equal(site.ds.sector_width, 360)
def test_load_save(complex_grid_site):
complex_grid_site.save(tfp + "tmp.nc")
site = XRSite.load(tfp + "tmp.nc", interp_method='linear')
test_complex_grid_local_wind(site)
def test_elevation():
ti = 0.1
ds = xr.Dataset(
data_vars={'Elevation': (['x', 'y'], np.arange(.8, 1.4, .1).reshape((3, 2))),
'P': 1, 'TI': ti},
coords={'x': [0, 5, 10], 'y': [0, 5]})
site = XRSite(ds)
npt.assert_array_almost_equal(site.elevation([2.5, 7.5], [2.5, 2.5]), [0.95, 1.15])
def test_cyclic_interpolation(uniform_site):
site = uniform_site
lw = site.local_wind([0], [0], 100, wd=np.arange(360), ws=10)
if 0:
plt.plot(np.linspace(0, 360, len(f) + 1), np.r_[f, f[0]], '.')
plt.plot(lw.wd, lw.P)
plt.show()
npt.assert_array_almost_equal(lw.P[::30], np.array(f) / 30)
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def test_from_flow_box_2wt():
site = Hornsrev1Site()
windTurbines = V80()
# simulate current and neighbour wt
wfm = BastankhahGaussian(site, windTurbines)
wd = np.arange(30)
sim_res = wfm([0, 0], [0, 500], wd=wd)
ref_aep = sim_res.aep().sel(wt=0)
wt_x, wt_y = [0], [0]
neighbour_x, neighbour_y = [0], [500]
# make site with effects of neighbour wt
sim_res = wfm(neighbour_x, neighbour_y, wd=wd)
e = 100
box = sim_res.flow_box(x=np.linspace(min(wt_x) - e, max(wt_x) + e, 21),
y=np.linspace(min(wt_y) - e, max(wt_y) + e, 21),
h=windTurbines.hub_height(windTurbines.types()))
site = XRSite.from_flow_box(box)
# Simujlate current wt and compare aep
wfm = BastankhahGaussian(site, windTurbines)
sim_res = wfm(wt_x, wt_y, wd=wd)
aep = sim_res.aep()
if 0:
site.ds.WS.sel(ws=10, wd=3).plot()
windTurbines.plot(wt_x, wt_y)
windTurbines.plot(neighbour_x, neighbour_y)
plt.show()
npt.assert_array_almost_equal(ref_aep, aep.sel(wt=0))
# import and setup site and windTurbines
site = IEA37Site(16)
# setup current, neighbour and all positions
wt_x, wt_y = site.initial_position.T
neighbour_x, neighbour_y = wt_x - 4000, wt_y
all_x, all_y = np.r_[wt_x, neighbour_x], np.r_[wt_y, neighbour_y]
windTurbines = WindTurbines.from_WindTurbines([IEA37_WindTurbines(), IEA37_WindTurbines()])
windTurbines._names = ["Current wind farm", "Neighbour wind farm"]
types = [0] * len(wt_x) + [1] * len(neighbour_x)
wf_model = PropagateDownwind(site, windTurbines,
wake_deficitModel=BastankhahGaussianDeficit(use_effective_ws=True),
superpositionModel=LinearSum())
# Consider wd=270 +/- 30 deg only
wd_lst = np.arange(240, 301)
sim_res, t = timeit(wf_model, verbose=False)(all_x, all_y, type=types, ws=9.8, wd=wd_lst)
flow_box = wf_model(neighbour_x, neighbour_y, wd=wd_lst).flow_box(
x=np.linspace(min(wt_x) - ext, max(wt_x) + ext, 53),
y=np.linspace(min(wt_y) - ext, max(wt_y) + ext, 51),
h=[100, 110, 120])
wake_site = XRSite.from_flow_box(flow_box)
wake_site.save('tmp.nc')
else:
wake_site = XRSite.load('tmp.nc')
wf_model_wake_site = PropagateDownwind(wake_site, windTurbines,
wake_deficitModel=BastankhahGaussianDeficit(use_effective_ws=True),
superpositionModel=LinearSum())
sim_res_wake_site, _ = timeit(wf_model_wake_site, verbose=False)(wt_x, wt_y, ws=9.8, wd=wd_lst)
npt.assert_allclose(sim_res.aep().sel(wt=np.arange(len(wt_x))).sum(), sim_res_wake_site.aep().sum(), rtol=0.0005)
npt.assert_array_almost_equal(sim_res.aep().sel(wt=np.arange(len(wt_x))), sim_res_wake_site.aep(), 2)
@pytest.mark.parametrize('h,wd,ws,h_i,wd_l,ws_k', [
([100, 110, 120], range(360), [9.8], 110, range(360), [9.8]),
([100, 110, 120], range(5, 25), [9.8], 110, range(10, 20), [9.8]),
([100, 110, 120], range(5, 25), [9, 10, 11], 110, range(10, 20), [10]),
([100, 110, 120], range(5, 25), [9, 10, 11], 110, range(10, 20), [10, 11]),
([100, 110, 120], range(5, 25), [8, 10, 11], 110, range(10, 20), [9.8]),
([100, 110, 120], range(5, 25), [9, 10, 11], 110, range(10, 20), [9.8]),
([100, 110, 120], range(5, 25), [9, 10, 11], 110, range(10, 20), [9.8, 10.2]),
([100, 110, 120], range(5, 25), [9, 10, 11], 110, [11.5, 12.5], [10]),
([100, 110, 120], range(5, 25), [9, 10, 11], 110, [11.5, 12.5], [10]),
([100, 110, 120], range(5, 25), [9, 10, 11], 110, [11.5, 12.5], [9.8, 10.2]),
def test_interp(h, wd, ws, h_i, wd_l, ws_k):
ds = xr.Dataset({
'TI': 1,
'P': 1,
'XYHLK': (['x', 'y', 'h', 'wd', 'ws'], np.random.rand(10, 20, len(h), len(wd), len(ws))),
'XYHL': (['x', 'y', 'h', 'wd'], np.random.rand(10, 20, len(h), len(wd))),
'XYHK': (['x', 'y', 'h', 'ws'], np.random.rand(10, 20, len(h), len(ws))),
'K': (['ws'], np.random.rand(len(ws))),
'L': (['wd'], np.random.rand(len(wd))),
'KL': (['wd', 'ws'], np.random.rand(len(wd), len(ws))),
'XY': (['x', 'y'], np.random.rand(10, 20)),
'H': (['h'], np.random.rand(len(h))),
'XYH': (['x', 'y', 'h'], np.random.rand(10, 20, len(h))),
'XYL': (['x', 'y', 'wd'], np.random.rand(10, 20, len(wd))),
'XYK': (['x', 'y', 'ws'], np.random.rand(10, 20, len(ws))),
'I': (['i'], np.random.rand(2)),
'IL': (['i', 'wd'], np.random.rand(2, len(wd))),
'IK': (['i', 'ws'], np.random.rand(2, len(ws))),
'ILK': (['i', 'wd', 'ws'], np.random.rand(2, len(wd), len(ws))),
},
coords={'x': np.linspace(0, 100, 10),
'y': np.linspace(200, 300, 20),
'h': h,
'wd': wd,
)
site = XRSite(ds)
lw = LocalWind(x_i=[25, 50], y_i=[225, 250], h_i=h_i, wd=wd_l, ws=ws_k, wd_bin_size=1)
for n in ['XYHLK', 'XYHL', 'XYHK', 'K', 'L', 'KL', 'XY', 'H', 'XYH', 'XYL', 'XYK', 'I', 'IL', 'IK', 'ILK']:
ip1 = site.interp(site.ds[n], lw.coords)
ip2 = ds[n].sel_interp_all(lw.coords)
npt.assert_array_equal(ip1.shape, ip2.shape)
npt.assert_array_almost_equal(ip1.data, ip2.data)
wd = np.arange(5)
ws = np.arange(10)
ds = xr.Dataset({
'TI': (['i', 'wd', 'ws'], np.random.rand(10, len(wd), len(ws))),
'P': 1
},
coords={'i': np.arange(10),
'wd': wd,
'ws': ws}
)
site = XRSite(ds)
with pytest.raises(ValueError, match=r"Number of points, i\(=10\), in site data variable, TI, must match "):
lw = LocalWind(x_i=[25, 50], y_i=[225, 250], h_i=110, wd=wd, ws=ws, wd_bin_size=1)
site.interp(site.ds.TI, lw.coords)
x = y = np.arange(10)
lw = LocalWind(x_i=x, y_i=y, h_i=110, wd=wd, ws=ws, wd_bin_size=1)
ip1 = site.interp(site.ds.TI, lw.coords)
ip2 = ds.TI.sel_interp_all(lw.coords)
npt.assert_array_equal(ip1.shape, ip2.shape)
npt.assert_array_almost_equal(ip1.data, ip2.data)