Fix dh computation in JITStreamlineDistance and ensure float or complex in distance calculation

py_wake/site/distance.py

@@ -39,7 +39,8 @@ class StraightDistance():
             ax.legend()
 
     def setup(self, src_x_i, src_y_i, src_h_i, dst_xyh_j=None):
-        src_x_i, src_y_i, src_h_i = map(np.asarray, [src_x_i, src_y_i, src_h_i])
+        # +.0 ensures float or complex
+        src_x_i, src_y_i, src_h_i = [np.asarray(v) + .0 for v in [src_x_i, src_y_i, src_h_i]]
         self.src_x_i, self.src_y_i, self.src_h_i = src_x_i, src_y_i, src_h_i
 
         self.dx_ii = src_x_i - src_x_i[:, na]

py_wake/site/jit_streamline_distance.py

@@ -54,7 +54,7 @@ class JITStreamlineDistance(StraightDistance):
                 zip(dw_mj, dw_is, hcw_is, dist_xyz[:, :, 2], length_is)):
             dw = dw_j > 0
             hcw_mj[m, dw] += np.interp(dw_j[dw], dw_s, hcw_s)
-            dh_mj[m, dw] = np.interp(dw_j[dw], dw_s, dh_s)
+            dh_mj[m, dw] += np.interp(dw_j[dw], dw_s, dh_s)
             dw_mj[m, dw] = np.interp(dw_j[dw], dw_s, length_s)
 
         if len(np.shape(WD_il)) == 1:

py_wake/tests/test_wind_farm_models/test_enginering_wind_farm_model.py

@@ -229,7 +229,6 @@ def test_dAEP_2wt():
                                        ct_func=ct, power_func=power, power_unit='w')
     wt.set_gradient_funcs(dpower, dct)
     wfm = IEA37SimpleBastankhahGaussian(site, wt)
-    x, y = iea37_site.initial_position[np.array([0, 2, 5, 8, 14])].T
 
     # plot 2 wt case
     x, y = np.array([[0, 130 * 4], [0, 0]], dtype=float)

py_wake/tests/test_wind_farm_models/test_memory_usage.py

@@ -27,7 +27,7 @@ def test_memory_usage():
     x, y = site.initial_position.T
 
     for wfm, mem_min, mem_max in [(NOJ(site, wt), 65, 90),
-                                  (All2AllIterative(site, wt, wake_deficitModel=NOJDeficit()), 525, 540)]:
+                                  (All2AllIterative(site, wt, wake_deficitModel=NOJDeficit()), 540, 600)]:
         mem_usage, _ = memory_profiler.memory_usage(
             (wfm, (x, y), {'wd': np.arange(0, 360, 4)}), interval=0.001, max_usage=True, retval=True)