from py_wake.site._site import UniformWeibullSite, UniformSite
import numpy as np
from py_wake.tests import npt
import pytest
from py_wake.examples.data.ParqueFicticio import ParqueFicticio_path
from py_wake.examples.data.ParqueFicticio.parque_ficticio import ParqueFicticioSite
from py_wake.site.wasp_grid_site import WaspGridSite, WaspGridSiteBase
import os
import time
from py_wake.tests.test_files.wasp_grid_site import one_layer
from py_wake.site.distance import TerrainFollowingDistance, StraightDistance, TerrainFollowingDistance2
@pytest.fixture
def site():
return ParqueFicticioSite()
@pytest.fixture
def site2():
site = ParqueFicticioSite(distance=TerrainFollowingDistance2())
x, y = site.initial_position.T
return site, x, y
def test_WaspGridSiteDistanceClass(site):
wgs = WaspGridSite(site._ds, distance=TerrainFollowingDistance(distance_resolution=2000))
assert wgs.distance_resolution == 2000
assert wgs.distances.__func__ == TerrainFollowingDistance.distances
wgs = WaspGridSite(site._ds, distance=StraightDistance())
assert wgs.distances.__func__ == StraightDistance.distances
def test_local_wind(site):
x_i, y_i = site.initial_position.T
h_i = x_i * 0 + 70
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(3, 6)
WD_ilk, WS_ilk, TI_ilk, P_ilk = site.local_wind(x_i=x_i, y_i=y_i, h_i=h_i, wd=wdir_lst, ws=wsp_lst)
npt.assert_array_equal(WS_ilk.shape, (8, 4, 3))
WD_ilk, WS_ilk, TI_ilk, P_ilk = site.local_wind(x_i=x_i, y_i=y_i, h_i=h_i)
npt.assert_array_equal(WS_ilk.shape, (8, 360, 23))
# check probability local_wind()[-1]
npt.assert_almost_equal(site.local_wind(x_i=x_i, y_i=y_i, h_i=h_i, wd=[0], ws=[10])[-1],
site.local_wind(x_i=x_i, y_i=y_i, h_i=h_i, wd=[0], ws=[10], wd_bin_size=2)[-1] * 180, 6)
z = np.arange(30, 100)
zero = [0] * len(z)
ws = site.local_wind(x_i=x_i[:1], y_i=y_i[:1], h_i=z, wd=[0], ws=[10])[1][:, 0, 0]
ws35 = site.local_wind(x_i=x_i[:1], y_i=y_i[:1], h_i=[35], wd=[0], ws=[10])[1][:, 0, 0]
z0 = 10
if 0:
import matplotlib.pyplot as plt
plt.plot(ws, z)
plt.plot(ws35 * np.log(z / z0) / np.log(35 / z0), z)
plt.show()
# npt.assert_array_equal(10 * (z / 50)**.3, ws)
@pytest.mark.parametrize('site,dw_ref', [
(ParqueFicticioSite(distance=TerrainFollowingDistance2()),
[0., 207.7973259, 484.8129285, 727.1261764, 1039.5612311, 1263.5467003, 1490.7972623, 1841.0639107]),
(ParqueFicticioSite(distance=TerrainFollowingDistance()),
[0, 209.803579, 480.8335365, 715.6003233, 1026.9476322, 1249.5510034, 1475.1467251, 1824.1317343]),
(ParqueFicticioSite(distance=StraightDistance()),
[-0, 207, 477, 710, 1016, 1236, 1456, 1799])])
def test_distances(site, dw_ref):
x, y = site.initial_position.T
dw_ijl, cw_ijl, dh_ijl, dwo = site.distances(src_x_i=x, src_y_i=y, src_h_i=np.array([70]),
dst_x_j=x, dst_y_j=y, dst_h_j=np.array([70]),
wd_il=np.array([[0]]))
npt.assert_almost_equal(dw_ijl[0, :, 0], dw_ref)
cw_ref = [236.1, 0., -131.1, -167.8, -204.5, -131.1, -131.1, -45.4]
npt.assert_almost_equal(cw_ijl[:, 1, 0], cw_ref)
npt.assert_almost_equal(dh_ijl, np.zeros_like(dh_ijl))
def test_distances_different_points(site2):
site, x, y = site2
with pytest.raises(NotImplementedError):
site.distances(src_x_i=x, src_y_i=y, src_h_i=np.array([70]),
dst_x_j=x[1:], dst_y_j=y[1:], dst_h_j=np.array([70]),
wd_il=np.array([[0]]))
# def test_distances_wd_shape():
# site = ParqueFicticioSite(distance=TerrainFollowingDistance2())
# x, y = site.initial_position.T
# dw_ijl, cw_ijl, dh_ijl, dwo = site.distances(src_x_i=x, src_y_i=y, src_h_i=np.array([70]),
# dst_x_j=x, dst_y_j=y, dst_h_j=np.array([70]),
# wd_il=np.ones((len(x), 1)) * 180)
# npt.assert_almost_equal(dw_ijl[0, :, 0], np.array([0., -207., -477., -710., -1016., -1236., -1456., -1799.]))
# npt.assert_almost_equal(cw_ijl[:, 1, 0], np.array([-236.1, 0., 131.1, 167.8, 204.5, 131.1, 131.1, 45.4]))
# npt.assert_almost_equal(dh_ijl, np.zeros_like(dh_ijl))
def test_speed_up_using_pickle():
pkl_fn = ParqueFicticio_path + "ParqueFicticio.pkl"
if os.path.exists(pkl_fn):
os.remove(pkl_fn)
start = time.time()
site = WaspGridSiteBase.from_wasp_grd(ParqueFicticio_path, speedup_using_pickle=False)
time_wo_pkl = time.time() - start
site = WaspGridSiteBase.from_wasp_grd(ParqueFicticio_path, speedup_using_pickle=True)
assert os.path.exists(pkl_fn)
start = time.time()
site = WaspGridSiteBase.from_wasp_grd(ParqueFicticio_path, speedup_using_pickle=True)
time_w_pkl = time.time() - start
npt.assert_array_less(time_w_pkl * 10, time_wo_pkl)
def test_interp_funcs_initialization_missing_key(site):
site = ParqueFicticioSite(distance=TerrainFollowingDistance2())
site.interp_funcs_initialization(['missing'])
def test_one_layer():
site = WaspGridSiteBase.from_wasp_grd(os.path.dirname(one_layer.__file__) + "/", speedup_using_pickle=False)
def test_missing_path():
with pytest.raises(NotImplementedError):
WaspGridSiteBase.from_wasp_grd("missing_path/", speedup_using_pickle=True)
with pytest.raises(Exception, match='Path was not a directory'):
WaspGridSiteBase.from_wasp_grd("missing_path/", speedup_using_pickle=False)
def test_elevation(site):
x_i, y_i = site.initial_position.T
npt.assert_array_less(np.abs(site.elevation(x_i=x_i, y_i=y_i) -
[519.4, 567.7, 583.6, 600, 574.8, 559.9, 517.7, 474.5] # ref from wasp
), 5)
def test_plot_map(site):
import matplotlib.pyplot as plt
with pytest.raises(AttributeError, match="missing not found in dataset. Available data variables are:\nws_mean,"):
site.plot_map('missing')
with pytest.raises(AttributeError, match=r"Sector None not found. Available sectors are: \[ 1"):
site.plot_map('ws_mean')
with pytest.raises(AttributeError, match="Height missing for 'ws_mean'"):
site.plot_map('ws_mean', sector=1)
site.plot_map('elev')
plt.figure()
site.plot_map('ws_mean', 80, sector=1)
if 0:
plt.show()
def test_elevation_outside_map(site):
import matplotlib.pyplot as plt
site.plot_map('elev')
x = np.linspace(262500, 265500, 500)
y = x * 0 + 6505450
plt.plot(x, y, '--', label='Terrain line')
plt.plot(x, y + site.elevation(x, y), label='Elevation')
npt.assert_array_equal(np.round(site.elevation(x, y)[::50]),
[np.nan, np.nan, 303, 390, 491, 566, 486, 524, np.nan, np.nan])
if 0:
plt.legend()
plt.show()
def test_plot_ws_distribution(site):
x, y = site.initial_position[0]
p1 = site.plot_ws_distribution(x=x, y=y, h=70, wd=[0, 90, 180, 270])
p2 = site.plot_ws_distribution(x=x, y=y, h=70, wd=[0, 90, 180, 270], include_wd_distribution=True)
if 0:
import matplotlib.pyplot as plt
plt.show()
# print(np.round(p1[::30], 4).tolist())
npt.assert_array_almost_equal(p1[::30], [0.0001, 0.0042, 0.0072, 0.0077, 0.0063,
0.0041, 0.0021, 0.0009, 0.0003, 0.0001], 4)
# print(np.round(p2[::30], 4).tolist())
npt.assert_array_almost_equal(p2[::30], [0.0001, 0.0021, 0.0033, 0.0033, 0.0025,
0.0014, 0.0007, 0.0002, 0.0001, 0.0], 4)
def test_plot_wd_distribution(site):
x, y = site.initial_position[0]
import matplotlib.pyplot as plt
p = site.plot_wd_distribution(x=x, y=y, h=70, n_wd=12, ax=plt)
# print(np.round(p, 3).tolist())
npt.assert_array_almost_equal(p, [0.052, 0.043, 0.058, 0.085, 0.089, 0.061,
0.047, 0.083, 0.153, 0.152, 0.108, 0.068], 3)
if 0:
plt.show()
def test_plot_wd_distribution_with_ws_levels(site):
x, y = site.initial_position[0]
p = site.plot_wd_distribution(x=x, y=y, n_wd=12, ws_bins=[0, 5, 10, 15, 20, 25])
# print(np.round(p, 3).tolist())
npt.assert_array_almost_equal(p, [[0.031, 0.019, 0.003, 0.0, 0.0],
[0.023, 0.018, 0.002, 0.0, 0.0],
[0.018, 0.032, 0.007, 0.0, 0.0],
[0.018, 0.048, 0.018, 0.001, 0.0],
[0.024, 0.052, 0.014, 0.0, 0.0],
[0.029, 0.031, 0.003, 0.0, 0.0],
[0.023, 0.022, 0.002, 0.0, 0.0],
[0.022, 0.041, 0.016, 0.002, 0.0],
[0.023, 0.062, 0.048, 0.016, 0.003],
[0.027, 0.058, 0.044, 0.018, 0.004],
[0.034, 0.044, 0.023, 0.007, 0.001],
[0.036, 0.026, 0.006, 0.001, 0.0]], 3)
if 0:
import matplotlib.pyplot as plt
plt.show()