From 37598ed4ee489fddb01a6696f4b0f82ee4e32e08 Mon Sep 17 00:00:00 2001
From: mmpe <mmpe@dtu.dk>
Date: Thu, 23 Sep 2021 09:13:07 +0200
Subject: [PATCH] Allow different superposition models for wake and blockage +
 add blockage to flowmap with weightedSum (was missing before)

---
 .../notebooks/EngineeringWindFarmModels.ipynb | 32 ++++++-------
 py_wake/deficit_models/deficit_model.py       | 11 ++++-
 py_wake/deficit_models/hybridinduction.py     |  4 +-
 py_wake/deficit_models/rankinehalfbody.py     |  4 +-
 py_wake/deficit_models/rathmann.py            |  4 +-
 py_wake/deficit_models/selfsimilarity.py      |  8 ++--
 py_wake/deficit_models/vortexcylinder.py      | 10 ++--
 py_wake/deficit_models/vortexdipole.py        |  4 +-
 py_wake/superposition_models.py               | 42 ++++++++---------
 .../test_deficit_models.py                    |  5 +-
 .../tests/test_ground_models/test_mirror.py   |  2 +-
 py_wake/tests/test_superposition_models.py    | 29 ++++++++++++
 .../test_enginering_wind_farm_model.py        |  3 +-
 py_wake/utils/xarray_utils.py                 |  2 +-
 .../wind_farm_models/engineering_models.py    | 46 ++++++++++++-------
 15 files changed, 128 insertions(+), 78 deletions(-)

diff --git a/docs/notebooks/EngineeringWindFarmModels.ipynb b/docs/notebooks/EngineeringWindFarmModels.ipynb
index e0c08a8f0..f221acd4b 100644
--- a/docs/notebooks/EngineeringWindFarmModels.ipynb
+++ b/docs/notebooks/EngineeringWindFarmModels.ipynb
@@ -239,7 +239,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "118 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n"
+      "119 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n"
      ]
     }
    ],
@@ -288,7 +288,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.22 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "1.14 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -731,7 +731,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24f8037d5b0>"
+       "<matplotlib.legend.Legend at 0x229f1ba8250>"
       ]
      },
      "execution_count": 19,
@@ -777,7 +777,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24f80415670>"
+       "<matplotlib.legend.Legend at 0x229f1f4c220>"
       ]
      },
      "execution_count": 20,
@@ -892,7 +892,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x24f805621f0>"
+       "<matplotlib.contour.QuadContourSet at 0x229f2096460>"
       ]
      },
      "execution_count": 22,
@@ -933,7 +933,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x24f80767400>"
+       "<matplotlib.contour.QuadContourSet at 0x229f24526d0>"
       ]
      },
      "execution_count": 23,
@@ -975,7 +975,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x24f8068f100>"
+       "<matplotlib.contour.QuadContourSet at 0x229f2583c10>"
       ]
      },
      "execution_count": 24,
@@ -1331,7 +1331,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24f811018e0>"
+       "<matplotlib.legend.Legend at 0x229f25242b0>"
       ]
      },
      "execution_count": 34,
@@ -1792,7 +1792,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24f80883700>"
+       "<matplotlib.legend.Legend at 0x22980d52b50>"
       ]
      },
      "execution_count": 45,
@@ -1838,7 +1838,7 @@
     "    n,err = get_n_err(model)\n",
     "    plt.plot(n,err,'.',ms=10, label=\"%s (%.4f)\"%(name,err))\n",
     "plt.xlabel('Number of rotor-average points')\n",
-    "plt.ylabel('Mean abs error (270$\\pm30^\\circ$) [m/s]')\n",
+    "plt.ylabel(r'Mean abs error (270$\\pm30^\\circ$) [m/s]')\n",
     "plt.legend()"
    ]
   },
@@ -1926,7 +1926,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1008x288 with 2 Axes>"
       ]
@@ -1986,7 +1986,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x24f815c1520>"
+       "<matplotlib.contour.QuadContourSet at 0x22980f59520>"
       ]
      },
      "execution_count": 49,
@@ -2255,7 +2255,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24f837d5940>"
+       "<matplotlib.legend.Legend at 0x22982eacf10>"
       ]
      },
      "execution_count": 57,
@@ -2304,7 +2304,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24f83a16c10>"
+       "<matplotlib.legend.Legend at 0x22982f3ac40>"
       ]
      },
      "execution_count": 58,
@@ -2356,7 +2356,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x24f80883b80>"
+       "<matplotlib.contour.QuadContourSet at 0x229f26edd90>"
       ]
      },
      "execution_count": 59,
@@ -2365,7 +2365,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
diff --git a/py_wake/deficit_models/deficit_model.py b/py_wake/deficit_models/deficit_model.py
index b022b161d..f1e93acbf 100644
--- a/py_wake/deficit_models/deficit_model.py
+++ b/py_wake/deficit_models/deficit_model.py
@@ -36,8 +36,17 @@ class DeficitModel(ABC):
 
 
 class BlockageDeficitModel(DeficitModel):
-    def __init__(self, upstream_only=False):
+    def __init__(self, upstream_only=False, superpositionModel=None):
+        """Parameters
+        ----------
+        upstream_only : bool, optional
+            if true, downstream deficit from this model is set to zero
+        superpositionModel : SuperpositionModel or None
+            Superposition model used to sum blockage deficit.
+            If None, the superposition model of the wind farm model is used
+        """
         self.upstream_only = upstream_only
+        self.superpositionModel = superpositionModel
 
     def calc_blockage_deficit(self, dw_ijlk, **kwargs):
         deficit_ijlk = self.calc_deficit(dw_ijlk=dw_ijlk, **kwargs)
diff --git a/py_wake/deficit_models/hybridinduction.py b/py_wake/deficit_models/hybridinduction.py
index 2cdb25cdc..4c1d71a2a 100644
--- a/py_wake/deficit_models/hybridinduction.py
+++ b/py_wake/deficit_models/hybridinduction.py
@@ -27,8 +27,8 @@ class HybridInduction(BlockageDeficitModel):
     args4deficit = ['WS_ilk', 'D_src_il', 'dw_ijlk', 'cw_ijlk', 'ct_ilk']
 
     def __init__(self, switch_radius=6.,
-                 near_rotor=SelfSimilarityDeficit2020(), far_field=VortexDipole()):
-        BlockageDeficitModel.__init__(self)
+                 near_rotor=SelfSimilarityDeficit2020(), far_field=VortexDipole(), superpositionModel=None):
+        BlockageDeficitModel.__init__(self, superpositionModel=superpositionModel)
         self.switch_radius = switch_radius
         self.near_rotor = near_rotor
         self.far_field = far_field
diff --git a/py_wake/deficit_models/rankinehalfbody.py b/py_wake/deficit_models/rankinehalfbody.py
index b48b83035..0c41a0025 100644
--- a/py_wake/deficit_models/rankinehalfbody.py
+++ b/py_wake/deficit_models/rankinehalfbody.py
@@ -15,8 +15,8 @@ class RankineHalfBody(BlockageDeficitModel):
 
     args4deficit = ['WS_ilk', 'D_src_il', 'dw_ijlk', 'cw_ijlk', 'ct_ilk']
 
-    def __init__(self, limiter=1e-10, exclude_wake=True):
-        BlockageDeficitModel.__init__(self)
+    def __init__(self, limiter=1e-10, exclude_wake=True, superpositionModel=None):
+        BlockageDeficitModel.__init__(self, superpositionModel=superpositionModel)
         # coefficients for BEM approximation by Madsen (1997)
         self.a0p = np.array([0.2460, 0.0586, 0.0883])
         # limiter to avoid singularities
diff --git a/py_wake/deficit_models/rathmann.py b/py_wake/deficit_models/rathmann.py
index 9a6c651d4..beb79c5c4 100644
--- a/py_wake/deficit_models/rathmann.py
+++ b/py_wake/deficit_models/rathmann.py
@@ -22,8 +22,8 @@ class Rathmann(BlockageDeficitModel):
 
     args4deficit = ['WS_ilk', 'D_src_il', 'dw_ijlk', 'cw_ijlk', 'ct_ilk']
 
-    def __init__(self, sct=1.0, limiter=1e-10, exclude_wake=True):
-        BlockageDeficitModel.__init__(self)
+    def __init__(self, sct=1.0, limiter=1e-10, exclude_wake=True, superpositionModel=None):
+        BlockageDeficitModel.__init__(self, superpositionModel=superpositionModel)
         # coefficients for BEM approximation by Madsen (1997)
         self.a0p = np.array([0.2460, 0.0586, 0.0883])
         # limiter to avoid singularities
diff --git a/py_wake/deficit_models/selfsimilarity.py b/py_wake/deficit_models/selfsimilarity.py
index 91c2fc47f..a60b60320 100644
--- a/py_wake/deficit_models/selfsimilarity.py
+++ b/py_wake/deficit_models/selfsimilarity.py
@@ -11,8 +11,8 @@ class SelfSimilarityDeficit(BlockageDeficitModel):
     args4deficit = ['WS_ilk', 'D_src_il', 'dw_ijlk', 'cw_ijlk', 'ct_ilk']
 
     def __init__(self, ss_gamma=1.1, ss_lambda=0.587, ss_eta=1.32,
-                 ss_alpha=8. / 9., ss_beta=np.sqrt(2), limiter=1e-10):
-        super().__init__()
+                 ss_alpha=8. / 9., ss_beta=np.sqrt(2), limiter=1e-10, superpositionModel=None):
+        super().__init__(superpositionModel=superpositionModel)
         # function constants defined in [1]
         self.ss_gamma = ss_gamma
         self.ss_lambda = ss_lambda
@@ -105,8 +105,8 @@ class SelfSimilarityDeficit2020(SelfSimilarityDeficit):
                  r12p=np.array([-0.672, 0.4897]),
                  ngp=np.array([-1.381, 2.627, -1.524, 1.336]),
                  fgp=np.array([-0.06489, 0.4911, 1.116, -0.1577]),
-                 limiter=1e-10):
-        BlockageDeficitModel.__init__(self)
+                 limiter=1e-10, superpositionModel=None):
+        BlockageDeficitModel.__init__(self, superpositionModel=superpositionModel)
         # original constants from [1]
         self.ss_alpha = ss_alpha
         self.ss_beta = ss_beta
diff --git a/py_wake/deficit_models/vortexcylinder.py b/py_wake/deficit_models/vortexcylinder.py
index 6c86c7ba4..e4ecdb708 100644
--- a/py_wake/deficit_models/vortexcylinder.py
+++ b/py_wake/deficit_models/vortexcylinder.py
@@ -20,8 +20,8 @@ class VortexCylinder(BlockageDeficitModel):
 
     args4deficit = ['WS_ilk', 'D_src_il', 'dw_ijlk', 'cw_ijlk', 'ct_ilk']
 
-    def __init__(self, limiter=1e-10, exclude_wake=True):
-        BlockageDeficitModel.__init__(self)
+    def __init__(self, limiter=1e-10, exclude_wake=True, superpositionModel=None):
+        BlockageDeficitModel.__init__(self, superpositionModel=superpositionModel)
         # coefficients for BEM approximation by Madsen (1997)
         self.a0p = np.array([0.2460, 0.0586, 0.0883])
         # limiter to avoid singularities
@@ -57,7 +57,8 @@ class VortexCylinder(BlockageDeficitModel):
         ic = (cw_ijlk / R_il[:, na, :, na] < self.limiter)
         deficit_ijlk = gammat_ilk[:, na] / 2 * (1 + dw_ijlk / np.sqrt(dw_ijlk**2 + R_il[:, na, :, na]**2)) * ic
         # singularity on rotor and close to R
-        ir = (np.abs(r_ijlk / R_il[:, na, :, na] - 1.) < self.limiter) & (np.abs(dw_ijlk / R_il[:, na, :, na]) < self.limiter)
+        ir = (np.abs(r_ijlk / R_il[:, na, :, na] - 1.) <
+              self.limiter) & (np.abs(dw_ijlk / R_il[:, na, :, na]) < self.limiter)
         deficit_ijlk = deficit_ijlk * (~ir) + gammat_ilk[:, na] / 4. * ir
         # compute deficit everywhere else
         # indices outside any of the previously computed regions
@@ -79,7 +80,8 @@ class VortexCylinder(BlockageDeficitModel):
         T1[cw_ijlk < R_il[:, na, :, na]] = 1
         div = (2 * np.pi * np.sqrt(cw_ijlk * R_il[:, na, :, na]))
         div[div == 0.] = np.inf
-        deficit_ijlk[io] = (gammat_ilk[:, na] / 2 * (T1 + dw_ijlk * k / div * (KK + (R_il[:, na, :, na] - cw_ijlk) / (R_il[:, na, :, na] + cw_ijlk) * PI)))[io]
+        deficit_ijlk[io] = (gammat_ilk[:, na] / 2 * (T1 + dw_ijlk * k / div *
+                                                     (KK + (R_il[:, na, :, na] - cw_ijlk) / (R_il[:, na, :, na] + cw_ijlk) * PI)))[io]
         if self.exclude_wake:
             # indices on rotor plane and in wake region
             iw = ((dw_ijlk / R_il[:, na, :, na] >= -self.limiter) &
diff --git a/py_wake/deficit_models/vortexdipole.py b/py_wake/deficit_models/vortexdipole.py
index 3e0595ef5..4214587b0 100644
--- a/py_wake/deficit_models/vortexdipole.py
+++ b/py_wake/deficit_models/vortexdipole.py
@@ -20,8 +20,8 @@ class VortexDipole(BlockageDeficitModel):
 
     args4deficit = ['WS_ilk', 'D_src_il', 'dw_ijlk', 'cw_ijlk', 'ct_ilk']
 
-    def __init__(self, sct=1.0, limiter=1e-10, exclude_wake=True):
-        BlockageDeficitModel.__init__(self)
+    def __init__(self, sct=1.0, limiter=1e-10, exclude_wake=True, superpositionModel=None):
+        BlockageDeficitModel.__init__(self, superpositionModel=superpositionModel)
         # coefficients for BEM approximation by Madsen (1997)
         self.a0p = np.array([0.2460, 0.0586, 0.0883])
         # limiter to avoid singularities
diff --git a/py_wake/superposition_models.py b/py_wake/superposition_models.py
index 05bf1e313..c338f9c23 100644
--- a/py_wake/superposition_models.py
+++ b/py_wake/superposition_models.py
@@ -8,28 +8,26 @@ class SuperpositionModel(ABC):
         pass
 
     @abstractmethod
-    def calc_effective_WS(self, WS_xxx, deficit_jxxx):
-        """Calculate effective wind speed
+    def __call__(self, value_jxxx):
+        """Calculate the sum of jxxx
 
         This method must be overridden by subclass
 
         Parameters
         ----------
-        WS_xxx : array_like
-            Local wind speed. xxx optionally includes destination turbine/site, wind directions, wind speeds
+
         deficit_jxxx : array_like
-            deficit caused by source turbines(j) on xxx (see above)
+            deficit caused by source turbines(j) on xxx (xxx optionally includes
+            destination turbine/site, wind directions, wind speeds
 
         Returns
         -------
-        WS_eff_xxx : array_like
-            Effective wind speed for xxx (see WS_xxx)
-
+        sum_xxx : array_like
+            sum for xxx (see above)
         """
 
 
 class AddedTurbulenceSuperpositionModel():
-    @abstractmethod
     def calc_effective_TI(self, TI_xxx, add_turb_jxxx):
         """Calculate effective turbulence intensity
 
@@ -45,27 +43,25 @@ class AddedTurbulenceSuperpositionModel():
         TI_eff_xxx : array_like
             Effective turbulence intensity xxx (see TI_xxx)
         """
+        return TI_xxx + self(add_turb_jxxx)
 
 
 class SquaredSum(SuperpositionModel):
-    def calc_effective_WS(self, WS_xxx, deficit_jxxx):
-        return WS_xxx - np.sqrt(np.sum(deficit_jxxx**2, 0))
+    def __call__(self, value_jxxx):
+        return np.sqrt(np.sum(value_jxxx**2, 0))
 
 
 class LinearSum(SuperpositionModel, AddedTurbulenceSuperpositionModel):
-    def calc_effective_WS(self, WS_xxx, deficit_jxxx):
-        return WS_xxx - np.sum(deficit_jxxx, 0)
+    def __call__(self, value_jxxx):
+        return np.sum(value_jxxx, 0)
 
     def calc_effective_TI(self, TI_xxx, add_turb_jxxx):
-        return TI_xxx + np.sum(add_turb_jxxx, 0)
+        return TI_xxx + self(add_turb_jxxx)
 
 
 class MaxSum(SuperpositionModel, AddedTurbulenceSuperpositionModel):
-    def calc_effective_WS(self, WS_xxx, deficit_jxxx):
-        return WS_xxx - np.max(deficit_jxxx, 0)
-
-    def calc_effective_TI(self, TI_xxx, add_turb_jxxx):
-        return TI_xxx + np.max(add_turb_jxxx, 0)
+    def __call__(self, value_jxxx):
+        return np.max(value_jxxx, 0)
 
 
 class SqrMaxSum(AddedTurbulenceSuperpositionModel):
@@ -89,9 +85,9 @@ class WeightedSum(SuperpositionModel):
         # maximum number of iterations used in computing weights
         self.max_iter = max_iter
 
-    def calc_effective_WS(self, WS_xxx, centerline_deficit_jxxx,
-                          convection_velocity_jxxx,
-                          sigma_sqr_jxxx, cw_jxxx, hcw_jxxx, dh_jxxx):
+    def __call__(self, WS_xxx, centerline_deficit_jxxx,
+                 convection_velocity_jxxx,
+                 sigma_sqr_jxxx, cw_jxxx, hcw_jxxx, dh_jxxx):
 
         Ws = WS_xxx + np.zeros(centerline_deficit_jxxx.shape[1:])
 
@@ -192,4 +188,4 @@ class WeightedSum(SuperpositionModel):
                 Uc_star[Ilx] = Ws[Ilx] - (sum1 + sum2)[Ilx] / Us_int[Ilx]
 
                 count += 1
-        return Ws - Us - np.sum(np.where(~Il, us, 0), axis=0)
+        return Us + np.sum(np.where(~Il, us, 0), axis=0)
diff --git a/py_wake/tests/test_deficit_models/test_deficit_models.py b/py_wake/tests/test_deficit_models/test_deficit_models.py
index 7cffc3816..def097c1a 100644
--- a/py_wake/tests/test_deficit_models/test_deficit_models.py
+++ b/py_wake/tests/test_deficit_models/test_deficit_models.py
@@ -163,7 +163,7 @@ def test_deficitModel_wake_map(deficitModel, ref):
     [(NOJDeficit(), [100., 75., 150., 100., 100.]),
      (NOJLocalDeficit(), [71., 46., 92., 71., 61.5]),
      (TurboNOJDeficit(), [99.024477, 61.553917,
-      123.107833, 92.439673, 97.034049]),
+                          123.107833, 92.439673, 97.034049]),
      (BastankhahGaussianDeficit(),
       [83.336286, 57.895893, 115.791786, 75.266662, 83.336286]),
      (IEA37SimpleBastankhahGaussianDeficit(),
@@ -323,7 +323,7 @@ def test_deficitModel_wake_map_convection(deficitModel, ref):
     'deficitModel,ref',
     # test that the result is equal to last run (no evidens that  these number are correct)
     [(ZongGaussianDeficit(),
-      [6.34, 7.05, 7.9, 8.15, 7.45, 6.19, 5.21, 5.26, 6.38, 7.32, 7.7, 7.54, 7.34, 7.18, 7.32, 7.69, 8.14])
+      [6.34, 7.05, 8.18, 8.25, 7.49, 6.2, 5.2, 5.26, 6.37, 7.33, 7.7, 7.54, 7.34, 7.18, 7.32, 7.7, 8.16])
      ])
 def test_deficitModel_wake_map_convection_all2all(deficitModel, ref):
     site = IEA37Site(16)
@@ -348,6 +348,7 @@ def test_deficitModel_wake_map_convection_all2all(deficitModel, ref):
         windTurbines.plot(x, y)
         plt.figure()
         plt.plot(Z[49, 100:133:2], label='Actual')
+        print(np.round(Z[49, 100:133:2], 2).values.tolist())
         plt.plot(ref, label='Reference')
         plt.plot(mean_ref, label='Mean ref')
         plt.legend()
diff --git a/py_wake/tests/test_ground_models/test_mirror.py b/py_wake/tests/test_ground_models/test_mirror.py
index 79f63ec3a..1cf7ce170 100644
--- a/py_wake/tests/test_ground_models/test_mirror.py
+++ b/py_wake/tests/test_ground_models/test_mirror.py
@@ -20,7 +20,7 @@ def test_Mirror_NOJ():
     site = UniformSite([1], ti=0.1)
     V80_D0 = V80()
     V80_D0._diameters = [0]
-    wt = WindTurbines.from_WindTurbines([V80(), V80_D0])
+    wt = WindTurbines.from_WindTurbine_lst([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))
diff --git a/py_wake/tests/test_superposition_models.py b/py_wake/tests/test_superposition_models.py
index edf91ee6b..b3b8dce97 100644
--- a/py_wake/tests/test_superposition_models.py
+++ b/py_wake/tests/test_superposition_models.py
@@ -9,6 +9,9 @@ from py_wake.deficit_models.noj import NOJDeficit
 from py_wake.flow_map import HorizontalGrid
 from py_wake.tests.test_deficit_models.test_noj import NibeA0
 import xarray as xr
+from py_wake.examples.data.hornsrev1 import V80
+from py_wake.deficit_models.deficit_model import BlockageDeficitModel, WakeDeficitModel
+from py_wake.deficit_models.selfsimilarity import SelfSimilarityDeficit
 
 d02 = 8.1 - 5.7
 d12 = 8.1 - 4.90473373
@@ -68,3 +71,29 @@ def test_superposition_model_indices(superpositionModel, sum_func):
         WS_eff_ilk = sim_res.flow_map(HorizontalGrid(x=[0], y=y_i, h=50)).WS_eff_xylk[:, 0]
 
         npt.assert_array_almost_equal(WS_eff_ilk, ref)
+
+
+def test_diff_wake_blockage_superposition():
+    site = UniformSite([1], 0.1)
+
+    class MyWakeDeficit(WakeDeficitModel):
+        args4deficit = ['dw_ijlk']
+
+        def calc_deficit(self, dw_ijlk, **_):
+            return (dw_ijlk > 0) * 2
+
+    class MyBlockageDeficit(BlockageDeficitModel):
+        args4deficit = ['dw_ijlk']
+
+        def __init__(self, superpositionModel=None):
+            BlockageDeficitModel.__init__(self, upstream_only=True, superpositionModel=superpositionModel)
+
+        def calc_deficit(self, dw_ijlk, **_):
+            return (dw_ijlk < 0) * .3
+
+    wfm = All2AllIterative(site, V80(), wake_deficitModel=MyWakeDeficit(), superpositionModel=SquaredSum(),
+                           blockage_deficitModel=MyBlockageDeficit(superpositionModel=LinearSum()))
+    x = np.arange(5) * 160
+    y = x * 0
+    sim_res = wfm(x, y, ws=10, wd=270)
+    npt.assert_array_almost_equal(sim_res.WS_eff.squeeze(), [10 - (4 - i) * .3 - np.sqrt(i * 2**2) for i in range(5)])
diff --git a/py_wake/tests/test_wind_farm_models/test_enginering_wind_farm_model.py b/py_wake/tests/test_wind_farm_models/test_enginering_wind_farm_model.py
index c1326f15e..547028079 100644
--- a/py_wake/tests/test_wind_farm_models/test_enginering_wind_farm_model.py
+++ b/py_wake/tests/test_wind_farm_models/test_enginering_wind_farm_model.py
@@ -27,7 +27,6 @@ from py_wake.wind_turbines import WindTurbines
 from py_wake.wind_turbines.wind_turbines_deprecated import DeprecatedOneTypeWindTurbines
 import pandas as pd
 import os
-from py_wake.deficit_models.deficit_model import BlockageDeficitModel
 from py_wake.rotor_avg_models.rotor_avg_model import CGIRotorAvg
 
 
@@ -157,7 +156,7 @@ def test_two_wt_aep():
 
 def test_aep_mixed_type():
     site = UniformSite([1], ti=0)
-    wt = WindTurbines.from_WindTurbines([IEA37_WindTurbines(), IEA37_WindTurbines()])
+    wt = WindTurbines.from_WindTurbine_lst([IEA37_WindTurbines(), IEA37_WindTurbines()])
 
     wfm = NOJ(site, wt)
 
diff --git a/py_wake/utils/xarray_utils.py b/py_wake/utils/xarray_utils.py
index e11a422e1..c7a386687 100644
--- a/py_wake/utils/xarray_utils.py
+++ b/py_wake/utils/xarray_utils.py
@@ -86,7 +86,7 @@ class interp_all():
         self.dataArray = dataArray
 
     def __call__(self, dataArray2, **kwargs):
-        interp_coords = {d: dataArray2[d] for d in self.dataArray.dims if d in dataArray2}
+        interp_coords = {d: dataArray2[d] for d in self.dataArray.dims if d in dataArray2.coords}
         return self.dataArray.interp(**interp_coords, **kwargs)
 
 
diff --git a/py_wake/wind_farm_models/engineering_models.py b/py_wake/wind_farm_models/engineering_models.py
index 6a6be023a..b9531533b 100644
--- a/py_wake/wind_farm_models/engineering_models.py
+++ b/py_wake/wind_farm_models/engineering_models.py
@@ -142,7 +142,7 @@ class EngineeringWindFarmModel(WindFarmModel):
         deficit = self.groundModel(lambda **kwargs: self.rotorAvgModel(self.wake_deficitModel.calc_deficit_downwind, **kwargs),
                                    dw_ijlk=dw_ijlk, **kwargs)
         deficit, blockage = self._add_blockage(deficit, dw_ijlk, **kwargs)
-        return deficit + blockage
+        return deficit, blockage
 
     def _calc_deficit_convection(self, dw_ijlk, **kwargs):
         """Calculate wake convection deficit (and blockage)"""
@@ -309,6 +309,7 @@ class EngineeringWindFarmModel(WindFarmModel):
             if I * J * K * 8 / 1024**2 > 10:
                 # one wt at the time to avoid memory problems
                 deficit_ijk = np.zeros((I, J, K))
+                blockage_ijk = np.zeros((I, J, K))
                 add_turb_ijk = np.zeros((I, J, K))
                 uc_ijk = np.zeros((I, J, K))
                 sigma_sqr_ijk = np.zeros((I, J, K))
@@ -322,7 +323,9 @@ class EngineeringWindFarmModel(WindFarmModel):
                         uc_ijk[i] = uc[0, :, 0]
                         sigma_sqr_ijk[i] = sigma_sqr[0, :, 0]
                     else:
-                        deficit_ijk[i] = self._calc_deficit(dw_ijlk=dw_ijlk[i][na], **args_i)[0, :, 0]
+
+                        deficit_ijk[i], blockage_ijk[i] = [v[0, :, 0]
+                                                           for v in self._calc_deficit(dw_ijlk=dw_ijlk[i][na], **args_i)]
 
                     if self.turbulenceModel:
                         add_turb_ijk[i] = self.turbulenceModel.calc_added_turbulence(
@@ -331,10 +334,12 @@ class EngineeringWindFarmModel(WindFarmModel):
                 if isinstance(self.superpositionModel, WeightedSum):
                     deficit, uc, sigma_sqr, blockage = self._calc_deficit_convection(dw_ijlk=dw_ijlk, **args)
                     deficit_ijk = deficit[:, :, 0]
+                    blockage_ijk = blockage[:, :, 0]
                     uc_ijk = uc[:, :, 0]
                     sigma_sqr_ijk = sigma_sqr[:, :, 0]
                 else:
-                    deficit_ijk = self._calc_deficit(dw_ijlk=dw_ijlk, **args)[:, :, 0]
+                    deficit_ijk, blockage_ijk = self._calc_deficit(dw_ijlk=dw_ijlk, **args)
+                    deficit_ijk, blockage_ijk = deficit_ijk[:, :, 0], blockage_ijk[:, :, 0]
                 if self.turbulenceModel:
                     add_turb_ijk = self.turbulenceModel.calc_added_turbulence(dw_ijlk=dw_ijlk, **args)[:, :, 0]
 
@@ -342,10 +347,16 @@ class EngineeringWindFarmModel(WindFarmModel):
             if isinstance(self.superpositionModel, WeightedSum):
                 cw_ijk = np.hypot(dh_ijl[..., na], hcw_ijlk)[:, :, 0]
                 hcw_ijk, dh_ijk = hcw_ijlk[:, :, 0], dh_ijl[:, :, 0, na]
-                WS_eff_jlk[:, l] = self.superpositionModel.calc_effective_WS(
+                WS_eff_jlk[:, l] = lw_j.WS_ilk[:, l_] - self.superpositionModel(
                     lw_j.WS_ilk[:, l_], deficit_ijk, uc_ijk, sigma_sqr_ijk, cw_ijk, hcw_ijk, dh_ijk)
+                if self.blockage_deficitModel:
+                    blockage_superpositionModel = self.blockage_deficitModel.superpositionModel or LinearSum()
+                    WS_eff_jlk[:, l] -= blockage_superpositionModel(blockage_ijk)
             else:
-                WS_eff_jlk[:, l] = self.superpositionModel.calc_effective_WS(lw_j.WS_ilk[:, l_], deficit_ijk)
+                WS_eff_jlk[:, l] = lw_j.WS_ilk[:, l_] - self.superpositionModel(deficit_ijk)
+                if self.blockage_deficitModel:
+                    blockage_superpositionModel = self.blockage_deficitModel.superpositionModel or self.superpositionModel
+                    WS_eff_jlk[:, l] -= blockage_superpositionModel(blockage_ijk)
 
             if self.turbulenceModel:
                 l_ = [l, 0][lw_j.TI_ilk.shape[1] == 1]
@@ -482,11 +493,11 @@ class PropagateDownwind(EngineeringWindFarmModel):
                     hcw2WT = np.array([d_nk2[i] for d_nk2, i in zip(hcw_nk, range(j)[::-1])])
                     dh2WT = np.array([d_nk2[i] for d_nk2, i in zip(dh_nk, range(j)[::-1])])
 
-                    WS_eff_lk = self.superpositionModel.calc_effective_WS(
+                    WS_eff_lk = WS_mk[m] - self.superpositionModel(
                         WS_mk[m], deficit2WT, uc2WT, sigmasqr2WT, cw2WT, hcw2WT, dh2WT)
                 else:
                     deficit2WT = np.array([d_nk2[i] for d_nk2, i in zip(deficit_nk, range(j)[::-1])])
-                    WS_eff_lk = self.superpositionModel.calc_effective_WS(WS_mk[m], deficit2WT)
+                    WS_eff_lk = WS_mk[m] - self.superpositionModel(deficit2WT)
 
                 WS_eff_mk.append(WS_eff_lk)
                 if self.turbulenceModel:
@@ -560,7 +571,7 @@ class PropagateDownwind(EngineeringWindFarmModel):
                     uc_nk.append(uc[0])
                     sigma_sqr_nk.append(sigma_sqr[0])
                 else:
-                    deficit = self._calc_deficit(dw_ijlk=dw_ijlk, **args)
+                    deficit, _ = self._calc_deficit(dw_ijlk=dw_ijlk, **args)
                 deficit_nk.append(deficit[0])
 
                 if self.turbulenceModel:
@@ -675,19 +686,22 @@ class All2AllIterative(EngineeringWindFarmModel):
             if isinstance(self.superpositionModel, WeightedSum):
                 deficit_iilk, uc_iilk, sigmasqr_iilk, blockage_iilk = self._calc_deficit_convection(**args)
             else:
-                deficit_iilk = self._calc_deficit(**args)
+                deficit_iilk, blockage_iilk = self._calc_deficit(**args)
 
             # Calculate effective wind speed
             if isinstance(self.superpositionModel, WeightedSum):
-                WS_eff_ilk = self.superpositionModel.calc_effective_WS(lw.WS_ilk, deficit_iilk,
-                                                                       uc_iilk, sigmasqr_iilk,
-                                                                       args['cw_ijlk'],
-                                                                       args['hcw_ijlk'],
-                                                                       dh_iil[..., na])
+                WS_eff_ilk = lw.WS_ilk - self.superpositionModel(lw.WS_ilk, deficit_iilk,
+                                                                 uc_iilk, sigmasqr_iilk,
+                                                                 args['cw_ijlk'],
+                                                                 args['hcw_ijlk'],
+                                                                 dh_iil[..., na])
                 # Add blockage as linear effect
-                WS_eff_ilk -= np.sum(blockage_iilk, 0)
+                if self.blockage_deficitModel:
+                    WS_eff_ilk -= (self.blockage_deficitModel.superpositionModel or LinearSum())(blockage_iilk)
             else:
-                WS_eff_ilk = self.superpositionModel.calc_effective_WS(lw.WS_ilk, deficit_iilk)
+                WS_eff_ilk = lw.WS_ilk.astype(float) - self.superpositionModel(deficit_iilk)
+                if self.blockage_deficitModel:
+                    WS_eff_ilk -= (self.blockage_deficitModel.superpositionModel or self.superpositionModel)(blockage_iilk)
 
             if self.turbulenceModel:
                 add_turb_ijlk = self.turbulenceModel.rotorAvgModel(self.turbulenceModel.calc_added_turbulence, **args)
-- 
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