diff --git a/py_wake/tests/test_wind_farm_models/test_wind_farm_model.py b/py_wake/tests/test_wind_farm_models/test_wind_farm_model.py
index 2ffb68ae4ea145f7ee6758eec8fb74e07c7d595e..99429942ec5fa86cd06d829b921b2245f459404a 100644
--- a/py_wake/tests/test_wind_farm_models/test_wind_farm_model.py
+++ b/py_wake/tests/test_wind_farm_models/test_wind_farm_model.py
@@ -110,10 +110,8 @@ def test_aep_chunks_input_dims(rho):
@pytest.mark.parametrize('wrt_arg', ['x', 'y', 'h',
['x', 'y'], ['x', 'h'],
- # 'wd', 'ws'
- # 'yaw'
- ])
-def test_aep_gradients_function(wrt_arg):
+ 'wd', 'ws'])
+def test_aep_gradients_function1(wrt_arg):
wfm = IEA37CaseStudy1(16, deflectionModel=JimenezWakeDeflection())
x, y = wfm.site.initial_position[np.array([0, 2, 5, 8, 14])].T
kwargs = {'x': x, 'y': y, 'h': x * 0 + wfm.windTurbines.hub_height(),
@@ -127,36 +125,43 @@ def test_aep_gradients_function(wrt_arg):
ax1, ax2 = plt.subplots(1, 2)[1]
wfm(**kwargs).flow_map(XYGrid(resolution=100)).plot_wake_map(ax=ax1)
ax2.set_title(wrt_arg)
- ax2.plot(dAEP_autograd.flatten(), '.')
+ ax2.plot(dAEP_autograd.flatten(), '.', label='autograd')
+ ax2.plot(dAEP_cs.flatten(), '.', label='cs')
+ ax2.plot(dAEP_fd.flatten(), '.', label='fd')
+ plt.legend()
plt.show()
- npt.assert_array_almost_equal(dAEP_autograd, dAEP_cs, 15)
- npt.assert_array_almost_equal(dAEP_autograd, dAEP_fd, 6)
+ npt.assert_array_almost_equal(dAEP_autograd, dAEP_cs, 14)
+ npt.assert_array_almost_equal(dAEP_autograd, dAEP_fd, 5)
npt.assert_array_equal(dAEP_autograd, wfm.aep_gradients(gradient_method=autograd, wrt_arg=wrt_arg, **kwargs))
-def test_aep_gradients_wrt_kwargs():
+@pytest.mark.parametrize('wrt_arg', ['x', 'y', 'h',
+ ['x', 'y'], ['x', 'h'],
+ 'wd', 'ws', 'yaw'])
+def test_aep_gradients_function2(wrt_arg):
wfm = IEA37CaseStudy1(16, deflectionModel=JimenezWakeDeflection())
x, y = wfm.site.initial_position[np.array([0, 2, 5, 8, 14])].T
kwargs = {'x': x, 'y': y, 'h': x * 0 + wfm.windTurbines.hub_height(),
'wd': [0], 'ws': 9.8, 'yaw': np.arange(1, 6).reshape((5, 1, 1)) * 5, 'tilt': 0}
- def aep(yaw, **kwargs):
- return wfm.aep(yaw=yaw, **kwargs)
-
- dAEP_autograd = autograd(aep)(**kwargs)
- dAEP_cs = cs(aep)(**kwargs)
- dAEP_fd = fd(aep)(**kwargs)
+ dAEP_autograd = wfm.aep_gradients(gradient_method=autograd, wrt_arg=wrt_arg, **kwargs)
+ dAEP_cs = wfm.aep_gradients(gradient_method=cs, wrt_arg=wrt_arg, **kwargs)
+ dAEP_fd = wfm.aep_gradients(gradient_method=fd, wrt_arg=wrt_arg, **kwargs)
if 0:
ax1, ax2 = plt.subplots(1, 2)[1]
wfm(**kwargs).flow_map(XYGrid(resolution=100)).plot_wake_map(ax=ax1)
-
- ax2.plot(dAEP_autograd.flatten(), '.')
+ ax2.set_title(wrt_arg)
+ ax2.plot(dAEP_autograd.flatten(), '.', label='autograd')
+ ax2.plot(dAEP_cs.flatten(), '.', label='cs')
+ ax2.plot(dAEP_fd.flatten(), '.', label='fd')
+ plt.legend()
plt.show()
- npt.assert_array_almost_equal(dAEP_autograd, dAEP_cs, 15)
- npt.assert_array_almost_equal(dAEP_autograd, dAEP_fd, 6)
+ npt.assert_array_almost_equal(dAEP_autograd, dAEP_cs, 14)
+ npt.assert_array_almost_equal(dAEP_autograd, dAEP_fd, 5)
+ npt.assert_array_equal(dAEP_autograd, wfm.aep_gradients(gradient_method=autograd, wrt_arg=wrt_arg, **kwargs))
def test_aep_gradients_parallel():
diff --git a/py_wake/wind_farm_models/engineering_models.py b/py_wake/wind_farm_models/engineering_models.py
index 056cd79ab61f002ac821b2c7072fb9008879202f..2f4ba37f8f1dfb6eb205bbedbd677244d68594c0 100644
--- a/py_wake/wind_farm_models/engineering_models.py
+++ b/py_wake/wind_farm_models/engineering_models.py
@@ -663,8 +663,9 @@ class All2AllIterative(EngineeringWindFarmModel):
WS_eff_ilk, TI_eff_ilk,
D_i, time,
I, L, K, **kwargs):
- if any([np.iscomplexobj(v) for v in [kwargs.get(k, 0)
- for k in ['x_ilk', 'y_ilk', 'h_ilk', 'D_i', 'yaw_ilk', 'tilt_ilk']]]):
+ if any([np.iscomplexobj(v) for v in ([kwargs.get(k, 0)
+ for k in ['x_ilk', 'y_ilk', 'h_ilk', 'D_i', 'yaw_ilk', 'tilt_ilk']] +
+ [ws, wd])]):
dtype = np.complex128
else:
dtype = float
diff --git a/py_wake/wind_farm_models/wind_farm_model.py b/py_wake/wind_farm_models/wind_farm_model.py
index 7ad7235e7a378d92d38623c1bc0e3459913513da..7626e784d47cd316ab3fea9c7f017e15a55aed53 100644
--- a/py_wake/wind_farm_models/wind_farm_model.py
+++ b/py_wake/wind_farm_models/wind_farm_model.py
@@ -347,8 +347,11 @@ class WindFarmModel(ABC):
compute the gradients of the aep:
gradient_function = wfm.aep_gradietns(autograd, ['x','y'])
gradients = gradient_function(x,y)
+ This behaiour only works when wrt_arg is one or more of ['x','y','h','wd', 'ws']
+
2) With additional key-word arguments, kwargs, the method returns the gradients of the aep:
gradients = wfm.aep_gradients(autograd,['x','y'],x=x,y=y)
+ This behaviour also works when wrt_arg is a keyword argument, e.g. yaw
Parameters
----------
@@ -375,12 +378,18 @@ class WindFarmModel(ABC):
gradient_method_kwargs=gradient_method_kwargs,
n_cpu=n_cpu, wd_chunks=wd_chunks, ws_chunks=ws_chunks, **kwargs)
- argnum = [['x', 'y', 'h', 'type', 'wd', 'ws', 'yaw', 'tilt'].index(a) for a in np.atleast_1d(wrt_arg)]
- f = gradient_method(self.aep, True, argnum, **gradient_method_kwargs)
-
if kwargs:
- return f(**kwargs)
+ wrt_arg = np.atleast_1d(wrt_arg)
+
+ def wrap_aep(*args, **kwargs):
+ kwargs.update({n: v for n, v in zip(wrt_arg, args)})
+ return self.aep(**kwargs)
+
+ f = gradient_method(wrap_aep, True, tuple(range(len(wrt_arg))), **gradient_method_kwargs)
+ return np.array(f(*[kwargs.pop(n) for n in wrt_arg], **kwargs))
else:
+ argnum = [['x', 'y', 'h', 'type', 'wd', 'ws'].index(a) for a in np.atleast_1d(wrt_arg)]
+ f = gradient_method(self.aep, True, argnum, **gradient_method_kwargs)
return f
def _aep_gradients_kwargs(self, kwargs):