def test_from_WindTurbines():
vestas_v80_wtg = WindTurbines.from_WAsP_wtg(
os.path.join(wtg_path, 'Vestas-V80.wtg'))
NEG_2750_wtg = WindTurbines.from_WAsP_wtg(
os.path.join(wtg_path, 'NEG-Micon-2750.wtg'))
_test_wts_wtg(
WindTurbines.from_WindTurbines([vestas_v80_wtg, NEG_2750_wtg]))
def test_twotype_windturbines():
v80 = V80()
def power(ws, types):
power = v80.power(ws)
# add 10% type 1 turbines
power[types == 1] *= 1.1
return power
wts = WindTurbines(names=['V80', 'V88'],
diameters=[80, 88],
hub_heights=[70, 77],
ct_funcs=[v80.ct_funcs[0], v80.ct_funcs[0]],
power_funcs=[v80.power, lambda ws: v80.power(ws) * 1.1],
power_unit='w')
import matplotlib.pyplot as plt
types0 = [0] * 9
types1 = [0, 0, 0, 1, 1, 1, 0, 0, 0]
types2 = [1] * 9
wts.plot(wt9_x, wt9_y, types1)
wfm = NOJ(Hornsrev1Site(), wts)
npt.assert_almost_equal(
wfm(wt9_x, wt9_y, type=types0).aep(), 81.2066072392765)
npt.assert_almost_equal(
wfm(wt9_x, wt9_y, type=types1).aep(), 83.72420504573488)
npt.assert_almost_equal(
wfm(wt9_x, wt9_y, type=types2).aep(), 88.87227386796884)
if 0:
plt.show()
def test_deprecated_from_WindTurbines():
v80 = V80Deprecated()
v88 = WindTurbine('V88',
88,
77,
powerCtFunction=PowerCtTabular(
hornsrev1.power_curve[:, 0],
hornsrev1.power_curve[:, 1] * 1.1, 'w',
hornsrev1.ct_curve[:, 1]))
with pytest.raises(
AssertionError,
match='from_WindTurbines no longer supports DeprecatedWindTurbines'
):
WindTurbines.from_WindTurbines([v80, v88])
def test_from_WAsP_wtg():
import os
from py_wake.examples.data import wtg_path
from py_wake.wind_turbines import WindTurbines
vestas_v80_wtg = os.path.join(wtg_path, 'Vestas-V80.wtg')
NEG_2750_wtg = os.path.join(wtg_path, 'NEG-Micon-2750.wtg')
wts_wtg = WindTurbines.from_WAsP_wtg([vestas_v80_wtg, NEG_2750_wtg])
assert (wts_wtg.name(types=0) == 'Vestas V80 (2MW, Offshore)')
assert (wts_wtg.diameter(types=0) == 80)
assert (wts_wtg.hub_height(types=0) == 67)
npt.assert_array_equal(
wts_wtg.power(np.array([0, 3, 5, 9, 18, 26]), type_i=0),
np.array([0, 0, 154000, 996000, 2000000, 0]))
npt.assert_array_equal(
wts_wtg.ct(np.array([1, 4, 7, 9, 17, 27]), type_i=0),
np.array([0, 0.818, 0.805, 0.807, 0.167, 0]))
assert (wts_wtg.name(types=1) == 'NEG-Micon 2750/92 (2750 kW)')
assert (wts_wtg.diameter(types=1) == 92)
assert (wts_wtg.hub_height(types=1) == 70)
npt.assert_array_equal(
wts_wtg.power(np.array([0, 3, 5, 9, 18, 26]), type_i=1),
np.array([0, 0, 185000, 1326000, 2748000, 0]))
npt.assert_array_equal(
wts_wtg.ct(np.array([1, 4, 7, 9, 17, 27]), type_i=1),
np.array([0, 0.871, 0.841, 0.797, 0.175, 0]))
def test_Mirror_NOJ():
# Compare points in flow map with ws of WT at same position
site = UniformSite([1], ti=0.1)
V80_D0 = V80()
V80_D0._diameters = [0]
wt = WindTurbines.from_WindTurbines([V80(), V80_D0])
wfm = NOJ(site, wt, k=.5, groundModel=Mirror())
sim_res = wfm([0], [0], h=[50], wd=0)
fm_ref = sim_res.flow_map(YZGrid(x=0, y=np.arange(-70, 0, 20), z=10))
ref = fm_ref.WS_eff_xylk[:, 0, 0, 0].values
res = np.array([
wfm([0, 0], [0, y], [50, 10], type=[0, 1],
wd=0).WS_eff.sel(wt=1).item() for y in fm_ref.X[0]
])
if 0:
fm_res = sim_res.flow_map(YZGrid(x=0, y=np.arange(-100, 10, 1)))
fm_res.plot_wake_map()
plt.plot(fm_ref.X[0], fm_ref.Y[0], '.')
plt.plot(fm_ref.X[0], ref * 10, label='ref, WS*10')
plt.plot(fm_ref.X[0], res * 10, label='Res, WS*10')
plt.legend()
plt.show()
plt.close()
npt.assert_array_equal(res, ref)
def test_aep_mixed_type():
site = UniformSite([1], ti=0)
wt = WindTurbines.from_WindTurbines(
[IEA37_WindTurbines(), IEA37_WindTurbines()])
wfm = NOJ(site, wt)
sim_res = wfm([0, 500], [0, 0], type=[0, 1], wd=270)
npt.assert_almost_equal(
sim_res.aep(with_wake_loss=False).sum(),
2 * wfm([0], [0], wd=270).aep(with_wake_loss=False).sum())
def get_wt(power_ct_func):
if isinstance(power_ct_func, list):
N = len(power_ct_func)
return WindTurbines(names=['Test'] * N,
diameters=[80] * N,
hub_heights=[70] * N,
power_ct_funcs=power_ct_func)
else:
return WindTurbine(name='Test',
diameter=80,
hub_height=70,
powerCtFunction=power_ct_func)
def test_DeprecatedWindTurbines():
v80 = V80Deprecated()
for wts in [v80,
WindTurbines(names=['V80'], diameters=[80], hub_heights=[70],
ct_funcs=v80._ct_funcs, power_funcs=v80._power_funcs, power_unit='w')]:
types0 = [0] * 9
for wfm in get_wfms(wts):
# wfm = NOJ(Hornsrev1Site(), wts)
npt.assert_array_equal(wts.types(), [0])
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types0), 81.2066072392765)
def test_WindTurbines():
u_p, p = np.asarray(hornsrev1.power_curve).T.copy()
u_ct, ct = hornsrev1.ct_curve.T
npt.assert_array_equal(u_p, u_ct)
curve = PowerCtTabular(ws=u_p, power=p, power_unit='w', ct=ct)
for wts in [WindTurbine(name='V80', diameter=80, hub_height=70, powerCtFunction=curve),
WindTurbines(names=['V80'], diameters=[80], hub_heights=[70], powerCtFunctions=[curve])]:
types0 = [0] * 9
for wfm in get_wfms(wts):
npt.assert_array_equal(wts.types(), [0])
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types0), 81.2066072392765)
def test_plot_yz2_types():
wt = WindTurbines.from_WindTurbines([IEA37_WindTurbines(), V80()])
yaw_lst = np.array([-30, 0, 30])
for wd in 0, 45, 90:
plt.figure()
wt.plot_yz(yaw_lst * 20, types=[0, 1, 0], wd=wd, yaw=yaw_lst)
for i, yaw in enumerate(yaw_lst):
plt.plot([], 'gray', label="WT %d yaw: %d deg" % (i, yaw))
plt.legend()
plt.title("WD: %s" % wd)
if 0:
plt.show()
plt.close()
def test_twotype_windturbines():
v80 = V80()
v88 = WindTurbine('V88', 88, 77,
powerCtFunction=PowerCtTabular(
hornsrev1.power_curve[:, 0], hornsrev1.power_curve[:, 1] * 1.1, 'w',
hornsrev1.ct_curve[:, 1]))
wts = WindTurbines.from_WindTurbines([v80, v88])
types0 = [0] * 9
types1 = [0, 0, 0, 1, 1, 1, 0, 0, 0]
types2 = [1] * 9
for wfm in get_wfms(wts):
npt.assert_array_equal(wts.types(), [0, 1])
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types0), 81.2066072392765)
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types1), 83.72420504573488)
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types2), 88.87227386796884)
def test_neighbour_farm_speed():
# 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)
if 1:
ext = 100
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)
def test_DeprecatedWindTurbines():
for wts in [
V80Deprecated(),
WindTurbines(
names=['V80'],
diameters=[80],
hub_heights=[70],
ct_funcs=[
lambda ws: np.interp(ws, hornsrev1.ct_curve[:, 0],
hornsrev1.ct_curve[:, 1])
],
power_funcs=[
lambda ws: np.interp(ws, hornsrev1.power_curve[:, 0],
hornsrev1.power_curve[:, 1])
],
power_unit='w')
]:
types0 = [0] * 9
wfm = NOJ(Hornsrev1Site(), wts)
npt.assert_array_equal(wts.types(), [0])
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types0),
81.2066072392765)
aep_ls = sim_res_ls.aep()
aep_ms = sim_res_ms.aep()
print(aep_ss, aep_ls, aep_ms)
# plot wake map
for sim_res, aep in [['sim_res_ss', 'aep_ss'], ['sim_res_ls', 'aep_ls'], ['sim_res_ms', 'aep_ms']]:
flow_map = locals()[sim_res].flow_map(wd=30, ws=9.8)
flow_map.plot_wake_map()
flow_map.plot_windturbines()
plt.title('%s AEP: %.2f GWh' % (aep, locals()[aep]))
plt.show()
from py_wake.site import WaspGridSite
from py_wake.wind_turbines import WindTurbines
import pandas as pd
layout = pd.read_table('/Users/luqi/开发/PyWake2_LQ/py_wake/examples/CGN_RD/site/cgn_layout.txt')
site_cgn = WaspGridSite.from_wasp_grd('/Users/luqi/开发/PyWake2_LQ/py_wake/examples/CGN_RD/grd', speedup_using_pickle=False)
wtg_g4 = WindTurbines.from_WAsP_wtg('/Users/luqi/开发/PyWake2_LQ/py_wake/examples/CGN_RD/power/SWT-4.0-130-CGN 1.223.wtg')
type_i, h_i, D_i = wtg_g4.get_defaults(len(layout['X']))
lw = site_cgn.local_wind(np.array(layout['X']), np.array(layout['Y']), h_i) # localwind
cgn_model_ss = NOJ(site_cgn, wtg_g4, k=0.05, superpositionModel=SquaredSum())
cgn_sim_res_ss = cgn_model_ss(np.array(layout['X']), np.array(layout['Y']))
cgn_aep_grs = cgn_sim_res_ss.aep(with_wake_loss=False)
cgn_aep_net = cgn_sim_res_ss.aep()
print(cgn_aep_grs, cgn_aep_net, '%.2f ' % ((1-cgn_aep_net/cgn_aep_grs)*100) + '%')
cgn_flow_map = cgn_sim_res_ss.flow_map(wd=30, ws=10)
cgn_flow_map.plot_wake_map()
cgn_flow_map.plot_windturbines()
plt.title('%s AEP: %.2f GWh' % ('CGN NET', cgn_aep_net))
plt.show()
import multiprocessing
from py_wake.examples.data.hornsrev1 import Hornsrev1Site, wt_x, wt_y
from py_wake import IEA37SimpleBastankhahGaussian
from py_wake.tests.check_speed import timeit
import numpy as np
from py_wake.tests import npt
from py_wake.wind_turbines import WindTurbines
from py_wake.examples.data import wtg_path
wt = WindTurbines.from_WAsP_wtg(wtg_path + "Vestas-V80.wtg")
site = Hornsrev1Site()
wf_model = IEA37SimpleBastankhahGaussian(site, wt)
wd_lst = np.arange(0, 360, 10)
def aep_wd(args):
x, y, wd = args
return wf_model(x, y, wd=wd, ws=None).aep().sum()
def aep_all_multiprocessing(pool, x, y):
return np.sum(pool.map(aep_wd, [(x, y, i) for i in wd_lst]))
def aep_wfm_xy(args):
wfm, x, y = args
return wfm(x, y, wd=wd_lst).aep().sum()
def aep_xy(args):
def get_wfm(grad=True):
wt = WindTurbines.from_WAsP_wtg(wtg_path + "Vestas-V80.wtg", )
site = Hornsrev1Site()
return IEA37SimpleBastankhahGaussian(site, wt)
def main(obj=False, max_con_on=True):
if __name__ == '__main__':
start = time.time()
try:
import matplotlib.pyplot as plt
plt.gcf()
plot = True
except RuntimeError:
plot = False
# ------ DEFINE WIND TURBINE TYPES, LOCATIONS & STORE METADATA -------
windTurbines = WindTurbines(
names=['Ghost_T1', 'T2'],
diameters=[40, 84],
hub_heights=[70, hornsrev1.HornsrevV80().hub_height()],
ct_funcs=[dummy_thrust(ct_rated=0),
hornsrev1.HornsrevV80().ct],
power_funcs=[
cube_power(power_rated=0),
cube_power(power_rated=3000)
],
# hornsrev1.HornsrevV80()._power],
power_unit='kW')
Drotor_vector = windTurbines._diameters
power_rated_vec = np.array(
[pcurv(25) / 1000 for pcurv in windTurbines._power_funcs])
hub_height_vector = windTurbines._hub_heights
x, y = np.meshgrid(range(-840, 840, 420), range(-840, 840, 420))
n_wt = len(x.flatten())
# initial turbine positions and other independent variables
ext_vars = {'x': x.flatten(), 'y': y.flatten(), 'obj': obj * 1}
capconst = []
if max_con_on:
capconst = [
CapacityConstraint(max_capacity=30.01,
rated_power_array=power_rated_vec)
]
# ---------------- DEFINE SITE & SELECT WAKE MODEL -------------------
# site = UniformWeibullSite(p_wd=[50, 50], a=[9, 9], k=[2.3, 2.3], ti=.1, alpha=0, h_ref=100)
site = UniformWeibullSite(p_wd=[100], a=[9], k=[2.3], ti=.1)
site.default_ws = [9] # reduce the number of calculations
site.default_wd = [0] # reduce the number of calculations
wake_model = NOJ(site, windTurbines)
AEPCalc = AEPCalculator(wake_model)
# ------------- OUTPUTS AEP PER TURBINE & FARM IRR -------------------
def aep_func(x, y, type, obj, **kwargs
): # TODO fix type as input change to topfarm turbinetype
out = AEPCalc.calculate_AEP(x_i=x, y_i=y,
type_i=type.astype(int)).sum((1, 2))
if obj: # if objective is AEP; output the total Farm_AEP
out = np.sum(out)
return out * 10**6
def irr_func(aep, type, **kwargs):
idx = type.astype(int)
return economic_evaluation(Drotor_vector[idx],
power_rated_vec[idx],
hub_height_vector[idx],
aep).calculate_irr()
# ----- WRAP AEP AND IRR INTO TOPFARM COMPONENTS AND THEN GROUP -----
aep_comp = CostModelComponent(input_keys=[
topfarm.x_key, topfarm.y_key, topfarm.type_key, ('obj', obj)
],
n_wt=n_wt,
cost_function=aep_func,
output_key="aep",
output_unit="GWh",
objective=obj,
output_val=np.zeros(n_wt),
income_model=True)
comps = [aep_comp] # AEP component is always in the group
if not obj: # if objective is IRR initiate/add irr_comp
irr_comp = CostModelComponent(input_keys=[topfarm.type_key, 'aep'],
n_wt=n_wt,
cost_function=irr_func,
output_key="irr",
output_unit="%",
objective=True)
comps.append(irr_comp)
group = TopFarmGroup(comps)
# - INITIATE THE PROBLEM WITH ONLY TURBINE TYPE AS DESIGN VARIABLES -
tf = TopFarmProblem(
design_vars={
topfarm.type_key: ([0] * n_wt, 0, len(windTurbines._names) - 1)
},
cost_comp=group,
driver=EasyRandomSearchDriver(randomize_func=RandomizeAllUniform(
[topfarm.type_key]),
max_iter=1),
# driver=EasySimpleGADriver(max_gen=2, random_state=1),
constraints=capconst,
# plot_comp=TurbineTypePlotComponent(windTurbines._names),
plot_comp=NoPlot(),
ext_vars=ext_vars)
cost, state, rec = tf.optimize()
# view_model(problem, outfile='ex5_n2.html', show_browser=False)
end = time.time()
print(end - start)
# %%
# ------------------- OPTIONAL VISUALIZATION OF WAKES ----------------
post_visual, save = False, False
if post_visual:
# import matplotlib.pyplot as plt
for cou, (i, j, k, co, ae) in enumerate(
zip(rec['x'], rec['y'], rec['type'], rec['cost'],
rec['aep'])):
AEPCalc.calculate_AEP(x_i=i, y_i=j, type_i=k)
AEPCalc.plot_wake_map(wt_x=i,
wt_y=j,
wt_type=k,
wd=site.default_wd[0],
ws=site.default_ws[0],
levels=np.arange(2.5, 12, .1))
windTurbines.plot(i, j, types=k)
title = f'IRR: {-np.round(co,2)} %, AEP : {round(np.sum(ae))} GWh, '
if "totalcapacity" in rec.keys():
title += f'Total Capacity: {rec["totalcapacity"][cou]} MW'
plt.title(title)
if save:
plt.savefig(
r'..\..\..\ima2\obj_AEP_{}_MaxConstraint_{}_{}.png'.
format(obj, max_con_on, cou))
plt.show()
from py_wake.site._site import UniformSite
from py_wake.tests import npt
from py_wake.flow_map import HorizontalGrid
from py_wake.wind_turbines import WindTurbines
from py_wake.wind_farm_models.engineering_models import All2AllIterative
from py_wake.deficit_models.noj import NOJDeficit
from py_wake.superposition_models import LinearSum, WeightedSum
from py_wake.turbulence_models.stf import STF2017TurbulenceModel
from py_wake.wind_turbines.power_ct_functions import PowerCtFunction
# Two turbines, 0: Nibe-A, 1:Ct=0
NibeA0 = WindTurbines(
names=['Nibe-A'] * 2,
diameters=[40] * 2,
hub_heights=[50] * 2,
# only define for ct
powerCtFunctions=[
PowerCtFunction(['ws'], lambda ws, run_only: ws * 0 + 8 / 9, 'w'),
PowerCtFunction(['ws'], lambda ws, run_only: ws * 0, 'w')
])
def test_NOJ_Nibe_result():
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
site = UniformSite([1], 0.1)
x_i = [0, 0, 0]
y_i = [0, -40, -100]
h_i = [50, 50, 50]
wfm = All2AllIterative(site,
NibeA0,
import pytest
import numpy as np
from py_wake import NOJ
from py_wake.site._site import UniformSite
from py_wake.superposition_models import LinearSum, SquaredSum, MaxSum
from py_wake.tests import npt
from py_wake.wind_turbines import WindTurbines
# Two turbines, 0: Nibe-A, 1:Ct=0
NibeA0 = WindTurbines(names=['Nibe-A'] * 2,
diameters=[40] * 2,
hub_heights=[50] * 2,
ct_funcs=[lambda _: 8 / 9, lambda _: 0],
power_funcs=[lambda _: 0] * 2,
power_unit='w')
d02 = 8.1 - 5.7
d12 = 8.1 - 4.90473373
@pytest.mark.parametrize('superpositionModel,res',
[(LinearSum(), 8.1 - (d02 + d12)),
(SquaredSum(), 8.1 - np.hypot(d02, d12)),
(MaxSum(), 8.1 - d12)])
def test_superposition_models(superpositionModel, res):
site = UniformSite([1], 0.1)
wake_model = NOJ(site, NibeA0, superpositionModel=superpositionModel)
x_i = [0, 0, 0]
y_i = [0, -40, -100]
h_i = [50, 50, 50]
WS_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [0, 0, 1], 0.0,
types0 = [0] * 9
types1 = [0, 0, 0, 1, 1, 1, 0, 0, 0]
types2 = [1] * 9
for wfm in get_wfms(wts):
npt.assert_array_equal(wts.types(), [0, 1])
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types0), 81.2066072392765)
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types1), 83.72420504573488)
npt.assert_almost_equal(wfm.aep(wt9_x, wt9_y, type=types2), 88.87227386796884)
@pytest.mark.parametrize('wts_wtg', [
lambda: WindTurbine.from_WAsP_wtg([os.path.join(wtg_path, 'Vestas-V80.wtg'),
os.path.join(wtg_path, 'NEG-Micon-2750.wtg')]),
lambda:WindTurbines.from_WindTurbine_lst([WindTurbine.from_WAsP_wtg(os.path.join(wtg_path, 'Vestas-V80.wtg')),
WindTurbine.from_WAsP_wtg(os.path.join(wtg_path, 'NEG-Micon-2750.wtg'))])])
def test_wasp_wtg(wts_wtg):
wts_wtg = wts_wtg()
assert(wts_wtg.name(type=0) == 'Vestas V80 (2MW, Offshore)')
assert(wts_wtg.diameter(type=0) == 80)
assert(wts_wtg.hub_height(type=0) == 67)
npt.assert_array_equal(wts_wtg.power(np.array([0, 3, 3.99, 4, 5, 9, 18, 25, 25.01, 100]),
type=0), np.array([0, 0, 0, 66600, 154000, 996000, 2e6, 2e6, 0, 0]))
npt.assert_array_equal(wts_wtg.ct(np.array([1, 3.99, 4, 7, 9, 17, 25, 25.01, 100]), type=0),
np.array([0.052, 0.052, 0.818, 0.805, 0.807, 0.167, 0.052, 0.052, 0.052]))
assert(wts_wtg.name(type=1) == 'NEG-Micon 2750/92 (2750 kW)')
assert(wts_wtg.diameter(type=1) == 92)
assert(wts_wtg.hub_height(type=1) == 70)
npt.assert_array_equal(wts_wtg.power(np.array([0, 3, 3.99, 4, 5, 9, 18, 25, 25.01, 100]), type=1),
np.array([0, 0, 0, 55000, 185000, 1326000, 2748000, 2750000, 0, 0]))
