def test_spline():
wt_tab = V80()
wt_spline = V80()
wt_spline.spline_ct_power(err_tol_factor=1e-2)
ws_lst = np.arange(3, 25, .001)
# mean and max error
assert (wt_tab.power(ws_lst) - wt_spline.power(ws_lst)).mean() < 1
assert ((wt_tab.power(ws_lst) - wt_spline.power(ws_lst)).max()) < 1400
# max change of gradient 80 times lower
assert np.diff(np.diff(wt_spline.power(ws_lst))).max() * 80 < np.diff(np.diff(wt_tab.power(ws_lst))).max()
ws_pts = [6.99, 7.01]
dpdu_tab_pts = np.diag(fd(wt_tab.power)(np.array(ws_pts)))
with use_autograd_in():
dpdu_spline_pts = np.diag(autograd(wt_spline.power)(np.array(ws_pts)))
npt.assert_array_almost_equal(dpdu_spline_pts, [205555.17794162, 211859.45965873])
if 0:
plt.plot(ws_lst, wt_tab.power(ws_lst))
plt.plot(ws_lst, wt_spline.power(ws_lst))
for wt, dpdu_pts, label in [(wt_tab, dpdu_tab_pts, 'V80 tabular'),
(wt_spline, dpdu_spline_pts, 'V80 spline')]:
for ws, dpdu in zip(ws_pts, dpdu_pts):
plot_gradients(wt.power(ws), dpdu, ws, label, 1)
ax = plt.gca().twinx()
ax.plot(ws_lst, wt.power(ws_lst) - wt_spline.power(ws_lst))
plt.figure()
plt.plot(np.diff(np.diff(wt_tab.power(ws_lst))))
plt.plot(np.diff(np.diff(wt_spline.power(ws_lst))))
plt.show()
def test_set_gradients():
wt = IEA37_WindTurbines()
def dpctdu(ws, run_only):
if run_only == 0:
return np.where((ws > 4) & (ws <= 9.8),
100000 * ws, # not the right gradient, but similar to the reference
0)
else:
return np.full(ws.shape, 0)
wt.powerCtFunction.set_gradient_funcs(dpctdu)
with use_autograd_in([WindTurbines, iea37_reader, power_ct_functions, wind_turbines_deprecated]):
ws_lst = np.arange(3, 25, .1)
plt.plot(ws_lst, wt.power(ws_lst))
ws_pts = np.array([3., 6., 9., 12.])
dpdu_lst = autograd(wt.power)(ws_pts)
if 0:
for dpdu, ws in zip(dpdu_lst, ws_pts):
plot_gradients(wt.power(ws), dpdu, ws, "", 1)
plt.show()
dpdu_ref = np.where((ws_pts > 4) & (ws_pts <= 9.8),
100000 * ws_pts,
0)
npt.assert_array_almost_equal(dpdu_lst, dpdu_ref)
def test_method():
wt_linear = V80()
wt_pchip = V80(method='pchip')
wt_spline = V80(method='spline')
ws_lst = np.arange(3, 25, .001)
for wt in [wt_linear, wt_pchip, wt_spline]:
wt.enable_autograd()
ws_pts = [6.99, 7.01]
with use_autograd_in():
dpdu_linear_pts = autograd(wt_linear.power)(np.array(ws_pts))
dpdu_pchip_pts = autograd(wt_pchip.power)(np.array(ws_pts))
dpdu_spline_pts = autograd(wt_spline.power)(np.array(ws_pts))
if 0:
wt_dp_label_lst = [(wt_linear, dpdu_linear_pts, 'linear'),
(wt_pchip, dpdu_pchip_pts, 'pchip'),
(wt_spline, dpdu_spline_pts, 'spline')]
for wt, dpdu_pts, label in wt_dp_label_lst:
c = plt.plot(ws_lst, wt.power(ws_lst), label=label)[0].get_color()
gradients.color_dict[label] = c
for ws, dpdu in zip(ws_pts, dpdu_pts):
plot_gradients(wt.power(ws), dpdu, ws, label)
plt.legend()
plt.figure()
for wt, dpdu_pts, label in wt_dp_label_lst:
plt.plot(ws_lst, wt_linear.power(ws_lst) - wt.power(ws_lst), label=label)
plt.legend()
plt.ylabel('Power difference wrt. linear')
plt.figure()
for wt, dpdu_pts, label in wt_dp_label_lst:
plt.plot(np.diff(np.diff(wt.power(ws_lst))), label=label)
plt.ylabel('Change of gradient')
plt.legend()
plt.show()
# mean and max error
for wt, mean_tol, absmean_tol, max_tol in [(wt_pchip, 213, 2323, 15632),
(wt_spline, 1, 1, 1380)]:
assert np.abs((wt_linear.power(ws_lst) - wt.power(ws_lst)).mean()) < mean_tol
assert np.abs((wt_linear.power(ws_lst) - wt.power(ws_lst)).mean()) < absmean_tol
assert np.abs((wt_linear.power(ws_lst) - wt.power(ws_lst)).max()) < max_tol
for wt, diff_grad_max, dpdu_pts, ref_dpdu_pts in [(wt_linear, 64, dpdu_linear_pts, [178000.00007264, 236000.00003353]),
(wt_pchip, 0.2, dpdu_pchip_pts, [
202520.16516056, 203694.66294614]),
(wt_spline, 0.8, dpdu_spline_pts, [205555.17794162, 211859.45965873])]:
assert np.diff(np.diff(wt.power(ws_lst))).max() < diff_grad_max
npt.assert_array_almost_equal(dpdu_pts, ref_dpdu_pts)
def aep(x,
y,
h=None,
type=0,
wd=None,
ws=None,
yaw_ilk=None): # @ReservedAssignment
if gradient_method == autograd:
with use_autograd_in():
return self.aep(x, y, h, type, wd, ws, yaw_ilk)
else:
return self.aep(x, y, h, type, wd, ws, yaw_ilk)
def test_gradients():
wt = IEA37_WindTurbines()
with use_autograd_in([WindTurbines, iea37_reader]):
ws_lst = np.arange(3, 25, .1)
plt.plot(ws_lst, wt.power(ws_lst))
ws_pts = np.array([3., 6., 9., 12.])
dpdu_lst = np.diag(autograd(wt.power)(ws_pts))
if 0:
for dpdu, ws in zip(dpdu_lst, ws_pts):
plot_gradients(wt.power(ws), dpdu, ws, "", 1)
plt.show()
dpdu_ref = np.where((ws_pts > 4) & (ws_pts <= 9.8),
3 * 3350000 * (ws_pts - 4)**2 / (9.8 - 4)**3, 0)
npt.assert_array_almost_equal(dpdu_lst, dpdu_ref)
def test_set_gradients():
wt = IEA37_WindTurbines()
wt.set_gradient_funcs(lambda ws: np.where((ws > 4) & (ws <= 9.8),
100000 * ws, # not the right gradient, but similar to the reference
0), lambda ws: 0)
with use_autograd_in([WindTurbines, iea37_reader]):
ws_lst = np.arange(3, 25, .1)
plt.plot(ws_lst, wt.power(ws_lst))
ws_pts = np.array([3., 6., 9., 12.])
dpdu_lst = np.diag(autograd(wt.power)(ws_pts))
if 0:
for dpdu, ws in zip(dpdu_lst, ws_pts):
plot_gradients(wt.power(ws), dpdu, ws, "", 1)
plt.show()
dpdu_ref = np.where((ws_pts > 4) & (ws_pts <= 9.8),
100000 * ws_pts,
0)
npt.assert_array_almost_equal(dpdu_lst, dpdu_ref)
def aep(x, y, h, type, wd, ws, yaw_ilk): # @ReservedAssignment
if gradient_method == autograd:
with use_autograd_in():
return self.aep(
x,
y,
h,
type,
wd,
ws,
yaw_ilk,
normalize_probabilities=normalize_probabilities,
with_wake_loss=with_wake_loss)
else:
return self.aep(
x,
y,
h,
type,
wd,
ws,
yaw_ilk,
normalize_probabilities=normalize_probabilities,
with_wake_loss=with_wake_loss)
def test_use_autograd_in(obj):
assert wind_turbines.np == np
with use_autograd_in([obj]):
assert wind_turbines.np == anp
assert wind_turbines.np == np
ct=v80_upct[2]),
PowerCtTabular(ws=v80_upct[0],
power=v80_upct[1] * 1.1,
power_unit='w',
ct=v80_upct[2] + .1)
])), [73260., 195800., 379499.9998, 101199.9999
], [0.818, 0.001, -0.014, -0.3])])
@pytest.mark.parametrize('grad_method', [autograd, cs, fd])
def test_gradients(case, wt, dpdu_ref, dctdu_ref, grad_method):
ws_pts = np.array([3.1, 6., 9., 12.])
if isinstance(dpdu_ref, FunctionType):
dpdu_ref, dctdu_ref = dpdu_ref(ws_pts), dctdu_ref(ws_pts)
with use_autograd_in([
WindTurbines, iea37_reader, power_ct_functions,
wind_turbines_deprecated
]):
if grad_method == autograd:
wt.enable_autograd()
ws_lst = np.arange(2, 25, .1)
kwargs = {k: 1 for k in wt.function_inputs[0]}
dpdu_lst = grad_method(wt.power)(ws_pts, **kwargs)
dctdu_lst = grad_method(wt.ct)(ws_pts, **kwargs)
if 0:
gradients.color_dict = {'power': 'b', 'ct': 'r'}
plt.plot(ws_lst, wt.power(ws_lst, **kwargs), label='power')
c = plt.plot([], label='ct')[0].get_color()
for dpdu, ws in zip(dpdu_lst, ws_pts):
plot_gradients(wt.power(ws, **kwargs), dpdu, ws, "power", 1)
ax = plt.twinx()