fix #106, issue with autograd in combination with types

py_wake/tests/test_input_modifier_models/test_input_modifier.py

@@ -117,6 +117,5 @@ def test_floating_pitch_modifier(wfm_cls, pitch, displacement, ref):
         i = np.searchsorted(fm_wt2.x, sim_res.x.sel(ws=10, wd=270)[2]) - 1
     else:
         i = np.searchsorted(fm_wt2.x, sim_res.x[2]) - 1
-    print(fm_wt2.x.isel(x=i).item())
     npt.assert_array_almost_equal(sim_res.WS_eff.sel(ws=10, wd=270, wt=2),
                                   fm_wt2.WS_eff.isel(x=i), 3)

py_wake/tests/test_wind_farm_models/test_wind_farm_model.py

@@ -16,6 +16,7 @@ import os
 from py_wake import examples
 from py_wake.literature.iea37_case_study1 import IEA37CaseStudy1
 from py_wake.wind_farm_models.engineering_models import PropagateDownwind
+from py_wake.site._site import UniformSite
 
 
 def test_yaw_wrong_name():
@@ -190,6 +191,22 @@ def test_aep_gradients_chunks():
     npt.assert_array_almost_equal(dAEP_ref, dAEP_autograd, 8)
 
 
+def test_aep_gradients_with_types():
+    # Generating the powerCtCurves (the problem does not occur when using V80())
+    wts = WindTurbines.from_WindTurbine_lst([V80(), V80()])
+    wfm = NOJ(UniformSite(), wts)
+
+    x = [0, 500]
+    y = [0, 0]
+    t = [0, 0]
+
+    daep_autograd = wfm.aep_gradients(autograd, x=x, y=y, type=t)
+    daep_fd = wfm.aep_gradients(fd, x=x, y=y, type=t)
+    daep_cs = wfm.aep_gradients(cs, x=x, y=y, type=t)
+    npt.assert_array_almost_equal(daep_autograd, daep_cs, 10)
+    npt.assert_array_almost_equal(daep_autograd, daep_fd)
+
+
 @pytest.mark.parametrize('n_wt, shape,dims', [(16, (16,), ('wt',)),
                                               (12, (12,), ('wt',)),
                                               (12, (16,), ('wd',)),

py_wake/wind_turbines/wind_turbine_functions.py

@@ -120,18 +120,28 @@ class WindTurbineFunctionList(WindTurbineFunction):
         else:
             if isinstance(run_only, int):
                 o = 0
-                res = np.empty((1,) + np.asarray(ws).shape)
+                res = np.empty(np.asarray(ws).shape)
             else:
                 res = np.empty((len(self.output_keys),) + np.asarray(ws).shape)
                 o = run_only
 
             unique_idx = np.unique(idx)
             idx = np.zeros(ws.shape, dtype=int) + idx.reshape(idx.shape + (1,) * (len(ws.shape) - len(idx.shape)))
-            for i in unique_idx:
-                m = (idx == i)
-                res[o, m] = self.windTurbineFunction_lst[i](
-                    ws[m], run_only=run_only, **{k: self._subset(v, m) for k, v in get_kwargs(i).items()})
-            res = res[o]
+
+            if isinstance(o, int):
+                for i in unique_idx:
+                    m = (idx == i)
+                    r = self.windTurbineFunction_lst[i](ws, run_only=run_only,
+                                                        **{k: v for k, v in get_kwargs(i).items()})
+                    res = np.where(m, r, res)
+                return res
+            else:
+                for i in unique_idx:
+                    m = (idx == i)
+                    res[o, m] = self.windTurbineFunction_lst[i](
+                        ws[m], run_only=run_only, **{k: self._subset(v, m) for k, v in get_kwargs(i).items()})
+                return res[o]
+
         return res