diff --git a/wetb/prepost/hawcstab2.py b/wetb/prepost/hawcstab2.py
index 5d630e929b31eab836a357f799c2c29642c18b9c..62f812f4b2002e50b1e6e6aa65af506da77611de 100644
--- a/wetb/prepost/hawcstab2.py
+++ b/wetb/prepost/hawcstab2.py
@@ -410,9 +410,16 @@ class ReadControlTuning(object):
def __init__(self):
"""
"""
- pass
+ self._aerogains = False
def parse_line(self, line, controller):
+ """Parses the output lines with the controller tuning parameters.
+ Does not parse the aerodynamic gain lines.
+ """
+
+ if line.startswith('Aerodynamic gains'):
+ self._aerogains = True
+ return
split1 = line.split('=')
var1 = split1[0].strip()
@@ -449,8 +456,21 @@ class ReadControlTuning(object):
elif i == 10:
controller = 'aero_damp'
setattr(self, controller, dummy())
- else:
+ elif not self._aerogains:
self.parse_line(line, controller)
+ # do not break since we want to see if aero gains are included
+ # elif self._aerogains:
+ # break
+
+ self.aero_gains_units = ['[deg]', '[kNm/deg]', '[kNm/deg]',
+ '[kNm/(rad/s)]', '[kNm/(rad/s)]']
+ self.aero_gains = pd.DataFrame()
+ # in case the gains are missing from the file, don't try to read it
+ if i > 17:
+ arr = np.loadtxt(fpath, skiprows=17)
+ columns = ['theta', 'dq/dtheta', 'dq/dtheta_fit', 'dq/domega',
+ 'dq/domega_fit']
+ self.aero_gains = pd.DataFrame(arr, columns=columns)
# set some parameters to zero for the linear case, or when aerodynamic
# gain scheduling is not used
diff --git a/wetb/prepost/tests/data/controller_input_quadratic.txt b/wetb/prepost/tests/data/controller_input_quadratic.txt
index 062781f61b1750095b78c74865637282accd90ea..4b883b2a078f0bc3549bdace48445ea9a58b08c6 100644
--- a/wetb/prepost/tests/data/controller_input_quadratic.txt
+++ b/wetb/prepost/tests/data/controller_input_quadratic.txt
@@ -11,3 +11,22 @@ K1 = 7.30949 [deg], K2 = 1422.81187 [deg^2] (dq/dtheta = -176.489
Additional terms due to the Aerodynamic damping
Kp2 = 0.240394E-01 [rad/(rad/s)]
Ko1 = -1.69769 [deg], Ko2 = -15.02688 [deg^2] (dq/domega = 243.08924 kNm/(rad/s))
+********************************************
+Aerodynamic gains:
+********************************************
+ (1) theta [deg] (2) dq/dtheta [kNm/deg] (3) fit [kNm/deg] (4) dq/domega [kNm/(rad/s)] (5) fit [kNm/(rad/s)]
+ 0.00000 -1165.04860 -1182.80164 -393.03157 -950.85937
+ 4.10000 -1665.72575 -1655.44826 -6919.03943 -6544.84749
+ 6.69000 -2012.86015 -1998.12171 -13119.30826 -12659.67192
+ 8.62000 -2290.61883 -2275.67536 -18911.31597 -18515.75425
+ 10.26000 -2535.50152 -2526.42508 -24632.87239 -24364.04365
+ 11.74000 -2757.11114 -2764.46364 -30186.31522 -30329.61030
+ 13.10000 -2991.31463 -2993.03195 -36257.79933 -36386.82912
+ 14.38000 -3213.58048 -3216.75546 -42410.93450 -42591.10977
+ 15.59000 -3428.46978 -3435.91220 -48626.47812 -48904.89826
+ 16.76000 -3642.91400 -3654.91116 -55070.40445 -55424.76312
+ 17.88000 -3858.46084 -3871.07886 -61702.38984 -62048.05630
+ 18.97000 -4075.53879 -4087.58722 -68581.71761 -68852.77188
+ 20.03000 -4295.29300 -4303.93692 -75700.65394 -75809.68369
+ 21.05000 -4524.66782 -4517.52214 -83045.36607 -82820.10608
+ 22.05000 -4758.62680 -4732.06052 -90639.34883 -89993.97031
diff --git a/wetb/prepost/tests/test_hawcstab2.py b/wetb/prepost/tests/test_hawcstab2.py
index f1fb229cf213a3e0a2499b2bd5127bf02acd3fe8..20c95d440c6c572e3af55e9c2b779aede464d63c 100644
--- a/wetb/prepost/tests/test_hawcstab2.py
+++ b/wetb/prepost/tests/test_hawcstab2.py
@@ -71,6 +71,8 @@ class Tests(unittest.TestCase):
self.assertEqual(hs2.aero_damp.Ko1, -4.21472)
self.assertEqual(hs2.aero_damp.Ko2, 0.0)
+ self.assertEqual(hs2.aero_gains.shape, (0, 0))
+
def test_quadratic_file(self):
hs2 = ReadControlTuning()
@@ -91,6 +93,38 @@ class Tests(unittest.TestCase):
self.assertEqual(hs2.aero_damp.Ko1, -1.69769)
self.assertEqual(hs2.aero_damp.Ko2, -15.02688)
+ self.assertEqual(hs2.aero_gains.shape, (15, 5))
+ cols = ['theta', 'dq/dtheta', 'dq/dtheta_fit', 'dq/domega',
+ 'dq/domega_fit']
+ self.assertEqual(hs2.aero_gains.columns.tolist(), cols)
+
+ tmp = np.array([0, 4.1, 6.69, 8.62, 10.26, 11.74, 13.1, 14.38, 15.59,
+ 16.76, 17.88, 18.97, 20.03, 21.05, 22.05])
+ np.testing.assert_allclose(hs2.aero_gains['theta'].values, tmp)
+
+ tmp = [-1165.0486, -1665.72575, -2012.86015, -2290.61883,
+ -2535.50152, -2757.11114, -2991.31463, -3213.58048,
+ -3428.46978, -3642.914, -3858.46084, -4075.53879,
+ -4295.293, -4524.66782, -4758.6268]
+ np.testing.assert_allclose(hs2.aero_gains['dq/dtheta'].values, tmp)
+
+ tmp = [-1182.80164, -1655.44826, -1998.12171, -2275.67536, -2526.42508,
+ -2764.46364, -2993.03195, -3216.75546, -3435.9122, -3654.91116,
+ -3871.07886, -4087.58722, -4303.93692, -4517.52214, -4732.06052]
+ np.testing.assert_allclose(hs2.aero_gains['dq/dtheta_fit'].values, tmp)
+
+ tmp = [-393.03157, -6919.03943, -13119.30826, -18911.31597,
+ -24632.87239, -30186.31522, -36257.79933, -42410.9345,
+ -48626.47812, -55070.40445, -61702.38984, -68581.71761,
+ -75700.65394, -83045.36607, -90639.34883]
+ np.testing.assert_allclose(hs2.aero_gains['dq/domega'].values, tmp)
+
+ tmp = [-950.85937, -6544.84749, -12659.67192, -18515.75425,
+ -24364.04365, -30329.6103, -36386.82912, -42591.10977,
+ -48904.89826, -55424.76312, -62048.0563, -68852.77188,
+ -75809.68369, -82820.10608, -89993.97031]
+ np.testing.assert_allclose(hs2.aero_gains['dq/domega_fit'].values, tmp)
+
def test_ind_file(self):
fnames = ['dtu10mw_nofull_defl_u10000.ind',
'dtu10mw_nofull_fext_u10000.ind',
@@ -108,7 +142,7 @@ class Tests(unittest.TestCase):
res = results()
df_data = res.load_ind(fname)
data = np.loadtxt(fname)
- np.testing.assert_almost_equal(data, df_data.values)
+ np.testing.assert_allclose(data, df_data.values)
def test_pwr_file(self):
fnames = ['dtu10mw_nofull.pwr',
@@ -121,19 +155,7 @@ class Tests(unittest.TestCase):
df_data, units = res.load_pwr_df(fname)
data = np.loadtxt(fname)
self.assertEqual(data.shape, df_data.shape)
- print(fname)
- print(data.dtype)
- print(df_data.values.dtype)
- for i in range(data.shape[0]):
- a = data[i,:]
- b = df_data.values[i,:]
- if not np.allclose(a,b):
- print(i)
- print(a-b)
- print(a)
- print(b)
- np.testing.assert_almost_equal(a, b)
- np.testing.assert_almost_equal(data, df_data.values, decimal=6)
+ np.testing.assert_allclose(data, df_data.values)
if __name__ == "__main__":