def test_pecdf(self):
u = self.data[-2*60*60:]
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2*60*60])
fig = se_model.cdf.plot()
fig.savefig('./output/pe_cdf.png')
plt.close('all')
def test_pecdf(self):
u = self.data[-2 * 60 * 60:]
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
fig = se_model.cdf.plot()
fig.savefig('./output/pe_cdf.png')
plt.close('all')
def test_fit_baseline_coeffs(self):
u = self.data
se_model = SteeringEntropyModel()
se_model.fit_baseline(u[:2*60*60])
r = np.array([ 1. , -0.34963801, 0.20299164, -0.20005458])
assert_array_almost_equal(se_model.b_pe, r)
def test_pedistplot(self):
u = self.data[-2*60*60:]
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2*60*60])
h, fig = se_model.get_entropy(u[-60*60:], _pedistplot=True)
fig.savefig('./output/pedist.png', dpi=300)
plt.close('all')
def test_get_entropy(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
h = se_model.get_entropy(u[-60 * 60:])
self.assertAlmostEqual(h, 2.3165502390766974)
def test_fit_baseline_coeffs(self):
u = self.data
se_model = SteeringEntropyModel()
se_model.fit_baseline(u[:2 * 60 * 60])
r = np.array([1., -0.34963801, 0.20299164, -0.20005458])
assert_array_almost_equal(se_model.b_pe, r)
def test_get_entropy(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2*60*60])
h = se_model.get_entropy(u[-60*60:])
self.assertAlmostEqual(h, 2.3165502390766974)
def test_get_entropy_empty_list(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2*60*60])
h = se_model.get_entropy([])
self.assertAlmostEqual(h, 0.)
def test_get_entropy_empty_list(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
h = se_model.get_entropy([])
self.assertAlmostEqual(h, 0.)
def test_pedistplot(self):
u = self.data[-2 * 60 * 60:]
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
h, fig = se_model.get_entropy(u[-60 * 60:], _pedistplot=True)
fig.savefig('./output/pedist.png', dpi=300)
plt.close('all')
def test_get_10pctlargeincrease(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2*60*60])
h = se_model.get_entropy(u[-60*60:])
self.assertAlmostEqual(hbas, 2.2739162914073892)
self.assertAlmostEqual(h, 2.3165502390766974)
def test_get_10pctlargeincrease(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
h = se_model.get_entropy(u[-60 * 60:])
self.assertAlmostEqual(hbas, 2.2739162914073892)
self.assertAlmostEqual(h, 2.3165502390766974)
def test0(self):
se_model = SteeringEntropyModel()
b, a = se_model._build_lpfilter(60)
rb = np.array([-0.13360409 + 0.j])
ra = np.array([
1.00000000 + 0.00000000e+00j, -2.16362300 + 0.00000000e+00j,
2.34063224 + 0.00000000e+00j, -1.56493800 + 0.00000000e+00j,
0.64665620 - 1.33112026e-16j, -0.13360409 + 2.22495512e-17j
])
assert_array_almost_equal(rb, b)
assert_array_almost_equal(ra, a)
def test0(self):
se_model = SteeringEntropyModel()
b,a = se_model._build_lpfilter(60)
rb = np.array([-0.13360409+0.j])
ra = np.array([ 1.00000000 +0.00000000e+00j,
-2.16362300 +0.00000000e+00j,
2.34063224 +0.00000000e+00j,
-1.56493800 +0.00000000e+00j,
0.64665620 -1.33112026e-16j,
-0.13360409 +2.22495512e-17j])
assert_array_almost_equal(rb, b)
assert_array_almost_equal(ra, a)
def test_repr_fit(self):
u = self.data
se_model = SteeringEntropyModel()
se_model.fit_baseline(u[:2 * 60 * 60])
se_model2 = eval(repr(se_model))
self.assertAlmostEqual(se_model.resample_fs, se_model2.resample_fs)
self.assertAlmostEqual(se_model.alpha, se_model2.alpha)
self.assertAlmostEqual(se_model.M, se_model2.M)
assert_array_almost_equal(se_model.b_pe, se_model2.b_pe)
assert_array_almost_equal(se_model.pkbas, se_model2.pkbas)
assert_array_almost_equal(se_model.bin_edges, se_model2.bin_edges)
self.assertAlmostEqual(se_model.hbas, se_model2.hbas)
self.assertEqual(repr(se_model.cdf), repr(se_model2.cdf))
def test_repr_fit(self):
u = self.data
se_model = SteeringEntropyModel()
se_model.fit_baseline(u[:2*60*60])
se_model2 = eval(repr(se_model))
self.assertAlmostEqual(se_model.resample_fs, se_model2.resample_fs)
self.assertAlmostEqual(se_model.alpha, se_model2.alpha)
self.assertAlmostEqual(se_model.M, se_model2.M)
assert_array_almost_equal(se_model.b_pe, se_model2.b_pe)
assert_array_almost_equal(se_model.pkbas, se_model2.pkbas)
assert_array_almost_equal(se_model.bin_edges,
se_model2.bin_edges)
self.assertAlmostEqual(se_model.hbas, se_model2.hbas)
self.assertEqual(repr(se_model.cdf), repr(se_model2.cdf))
def test_baseline_tsplot(self):
u = self.data[-2 * 60 * 60:]
se_model = SteeringEntropyModel()
h, fig = se_model.fit_baseline(u, _tsplot=True)
fig.savefig('./output/baseline_ts.png', dpi=300)
plt.close('all')
def test0(self):
u = self.data
se_model = SteeringEntropyModel()
x = se_model._resample(u[-60*60:], 60)
r = \
np.array([ -1.61215419e-12, -2.40321754e-04, -2.33080451e-02,
-9.04642339e-02, 1.40190084e-01, 3.74297171e-01,
3.12934111e-01, 4.95131080e-01, 1.14154168e+00,
4.42629737e+00, 7.19004268e+00, 5.76661990e+00,
1.37710045e+00, -4.04657820e+00, -4.46455455e+00,
-2.74182091e+00, -2.60887713e+00, -1.81214933e+00,
-2.29040129e+00, -2.09464324e+00, -6.74396456e-01,
3.12771720e+00, 6.63539749e+00, 6.68099218e+00,
5.23367001e+00, 1.06144188e+00, -2.78478967e+00,
-2.85377674e+00, -2.78654061e+00, -1.83510684e+00,
-1.81865681e+00, -2.16201553e+00, -1.47048413e+00,
-5.93937038e-01, -2.46527449e-01, -7.68822860e-01,
-5.15642230e-01, -6.20144084e-01, -9.12087358e-01,
-8.25760725e-01, -7.28395225e-01, -7.20917093e-01,
-7.78745685e-01, -6.08390133e-01, -4.78147581e-01,
-4.49870745e-01, -5.39254885e-01, -4.69299635e-01,
-4.69071835e-01, -4.40025322e-01, 1.40124371e-01,
2.70363641e+00, 5.00318748e+00, 4.00632747e+00,
2.51918500e+00, 9.22983233e-01, 1.43594568e+00,
2.25647645e+00, 1.80745664e+00, 1.75190705e+00,
2.34702181e-01, -1.97572019e+00, -2.37761044e+00,
-2.30117739e+00, -2.24715329e+00, -1.93689929e+00,
-1.88823604e+00, -1.50879687e+00, -7.65443800e-01,
-4.14108223e-01, -3.73872068e-01, 5.63886810e-02,
1.95715259e+00, 4.03690003e+00, 2.98087218e+00,
-6.10729709e-01, -1.55756097e+00, -2.60003697e-01,
1.62767474e+00, 3.71047087e+00, 1.09611045e+00,
9.53114039e-01, 1.72472163e+00, 6.53023245e-01,
-1.02539482e-01, -2.11323519e+00, -1.85226015e+00,
-1.74608508e+00, -1.91669067e+00, -1.04470412e+00,
-7.69943802e-01, -1.69874535e-01, -1.40893297e-01,
-2.10597725e-01, -2.52120916e-02, -1.41044777e-01,
4.56470110e-01, 2.17295580e+00, 4.19018796e+00,
3.55326774e+00, 6.20266710e-01, -1.11846410e+00,
-5.01573652e-01, -3.42985356e-01, -2.94555007e-01,
-8.08163367e-02, -3.76477302e-01, -1.50299007e+00,
-2.99860099e+00, -2.55552203e+00, -2.27317862e+00,
-2.17586942e+00, -1.97183132e+00, -2.02211316e+00,
-1.40599577e+00, -5.74288496e-01, 1.15984166e+00,
4.23182774e+00, 4.75834404e+00, 3.74200684e+00,
3.13959278e+00, 1.28464022e+00, -1.63338185e-01,
-4.59846989e-01, -3.87030930e-02, -1.47890773e-01,
-7.81543211e-02, 7.04473154e-02, -2.85587300e-01,
-4.98040519e-01, -5.05362319e-01, -2.10988255e-01,
-2.96243919e-01, -3.08167185e-01, -2.28173481e-01,
-3.39205789e-01, -2.17406734e-01, -3.07119992e-01,
-2.80856318e-01, -2.01187240e-01, -9.08931618e-02,
1.19209975e-04, -1.03781679e-01, 1.68491206e-01,
4.01036790e-01, 2.91845195e-01, 6.72996081e-02,
-3.33300925e-02, 1.26432268e-01, 8.32852023e-01,
1.88439781e+00, 1.33883460e+00, 4.80435455e-01,
-2.43751156e-01, -4.57317651e-01, -5.00984921e-01,
-7.37219937e-01, -9.84904261e-01, -1.31724659e+00,
-1.03104538e+00, -8.23229571e-01, -4.19957897e-01,
-2.62460181e-01, 1.20635374e+00, 4.33611445e+00,
4.18854222e+00, 2.14247691e+00, -2.11679116e-01,
-5.72818587e-01, 7.55383702e-01, 1.57580810e+00,
2.16247799e+00, 1.70824825e+00, 1.98917050e+00,
1.96270109e+00, 1.28671320e+00, -1.08375824e-01,
-1.05344979e+00, -1.81169560e+00, -2.51135819e+00,
-1.76963679e+00, -1.80747840e+00, -1.19208123e+00,
-1.01487994e+00, -9.33388519e-01, -3.51813158e-01,
-7.32129130e-01, -4.02376988e-01, -4.07198280e-01,
-5.57795939e-01, -9.71627735e-01, -1.77767380e+00,
-1.62379647e+00, -1.70260138e+00, -1.73850730e+00,
-1.63276843e+00, -1.74199713e+00, -1.68546087e+00,
-1.61694517e+00, -1.55739340e+00, -1.50227678e+00,
-1.58767956e+00, -1.65341566e+00, -1.56110869e+00,
-1.69875060e+00, -1.70323970e+00, -1.65476178e+00,
-1.76280986e+00, -1.55644944e+00, -1.48442341e+00,
-1.66990811e+00, -1.69235942e+00, -1.69908318e+00,
-1.62778083e+00, -1.67340936e+00, -1.64669665e+00,
-1.58731759e+00, -1.53574846e+00, -1.64873524e+00,
-2.25637968e+00, -4.03930444e+00, -5.10310300e+00,
3.74270189e-01, 4.87412168e+00, 3.09786073e+00,
3.63976627e+00, 3.53954132e+00, 3.06438935e+00,
3.55312209e+00, 3.24430576e+00, 3.68814282e+00,
3.61790133e+00, 3.66176895e+00, 3.67115383e+00,
3.51618968e+00, 3.68577762e+00, 3.69906184e+00,
4.05961352e+00, 4.07009574e+00, 3.95738239e+00])
assert_array_almost_equal(r,x)
def test_fit_baseline_hbas(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2*60*60])
self.assertAlmostEqual(hbas, 2.2739162914073892)
def test_lpfilter(self):
u = self.data[-2 * 60 * 60:]
se_model = SteeringEntropyModel()
fig = se_model._lpfilter_bode()
fig.savefig('./output/lpfilter.png')
plt.close('all')
def test_called_without_fit(self):
u = self.data
se_model = SteeringEntropyModel()
with self.assertRaises(Exception):
se_model.get_entropy()
def test_fit_baseline_m_adj(self):
u = self.data
se_model = SteeringEntropyModel(M=7)
hbas = se_model.fit_baseline(u[:2*60*60])
self.assertAlmostEqual(hbas, 2.2938478599767125)
def test_called_without_fit(self):
u = self.data
se_model = SteeringEntropyModel()
with self.assertRaises(Exception):
se_model.get_entropy()
def test_fit_baseline_resamplefs_adj(self):
u = self.data
se_model = SteeringEntropyModel(resample_fs=5.)
hbas = se_model.fit_baseline(u[:2*60*60])
self.assertAlmostEqual(hbas, 2.492228125216982)
def test_fit_baseline_resamplefs_adj(self):
u = self.data
se_model = SteeringEntropyModel(resample_fs=5.)
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
self.assertAlmostEqual(hbas, 2.492228125216982)
def test_fit_baseline_m_adj(self):
u = self.data
se_model = SteeringEntropyModel(M=7)
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
self.assertAlmostEqual(hbas, 2.2938478599767125)
def test_fit_baseline_hbas(self):
u = self.data
se_model = SteeringEntropyModel()
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
self.assertAlmostEqual(hbas, 2.2739162914073892)
def test_lpfilter(self):
u = self.data[-2*60*60:]
se_model = SteeringEntropyModel()
fig = se_model._lpfilter_bode()
fig.savefig('./output/lpfilter.png')
plt.close('all')
def test_baseline_tsplot(self):
u = self.data[-2*60*60:]
se_model = SteeringEntropyModel()
h,fig = se_model.fit_baseline(u, _tsplot=True)
fig.savefig('./output/baseline_ts.png', dpi=300)
plt.close('all')
def test_repr_nofit(self):
se_model = SteeringEntropyModel()
self.assertEqual(repr(se_model), repr(eval(repr(se_model))))
def test_fit_baseline_alpha_adj(self):
u = self.data
se_model = SteeringEntropyModel(alpha=0.05)
hbas = se_model.fit_baseline(u[:2 * 60 * 60])
self.assertAlmostEqual(hbas, 1.6410033779603466)
def test0(self):
u = self.data
se_model = SteeringEntropyModel()
x = se_model._resample(u[-60 * 60:], 60)
r = \
np.array([ -1.61215419e-12, -2.40321754e-04, -2.33080451e-02,
-9.04642339e-02, 1.40190084e-01, 3.74297171e-01,
3.12934111e-01, 4.95131080e-01, 1.14154168e+00,
4.42629737e+00, 7.19004268e+00, 5.76661990e+00,
1.37710045e+00, -4.04657820e+00, -4.46455455e+00,
-2.74182091e+00, -2.60887713e+00, -1.81214933e+00,
-2.29040129e+00, -2.09464324e+00, -6.74396456e-01,
3.12771720e+00, 6.63539749e+00, 6.68099218e+00,
5.23367001e+00, 1.06144188e+00, -2.78478967e+00,
-2.85377674e+00, -2.78654061e+00, -1.83510684e+00,
-1.81865681e+00, -2.16201553e+00, -1.47048413e+00,
-5.93937038e-01, -2.46527449e-01, -7.68822860e-01,
-5.15642230e-01, -6.20144084e-01, -9.12087358e-01,
-8.25760725e-01, -7.28395225e-01, -7.20917093e-01,
-7.78745685e-01, -6.08390133e-01, -4.78147581e-01,
-4.49870745e-01, -5.39254885e-01, -4.69299635e-01,
-4.69071835e-01, -4.40025322e-01, 1.40124371e-01,
2.70363641e+00, 5.00318748e+00, 4.00632747e+00,
2.51918500e+00, 9.22983233e-01, 1.43594568e+00,
2.25647645e+00, 1.80745664e+00, 1.75190705e+00,
2.34702181e-01, -1.97572019e+00, -2.37761044e+00,
-2.30117739e+00, -2.24715329e+00, -1.93689929e+00,
-1.88823604e+00, -1.50879687e+00, -7.65443800e-01,
-4.14108223e-01, -3.73872068e-01, 5.63886810e-02,
1.95715259e+00, 4.03690003e+00, 2.98087218e+00,
-6.10729709e-01, -1.55756097e+00, -2.60003697e-01,
1.62767474e+00, 3.71047087e+00, 1.09611045e+00,
9.53114039e-01, 1.72472163e+00, 6.53023245e-01,
-1.02539482e-01, -2.11323519e+00, -1.85226015e+00,
-1.74608508e+00, -1.91669067e+00, -1.04470412e+00,
-7.69943802e-01, -1.69874535e-01, -1.40893297e-01,
-2.10597725e-01, -2.52120916e-02, -1.41044777e-01,
4.56470110e-01, 2.17295580e+00, 4.19018796e+00,
3.55326774e+00, 6.20266710e-01, -1.11846410e+00,
-5.01573652e-01, -3.42985356e-01, -2.94555007e-01,
-8.08163367e-02, -3.76477302e-01, -1.50299007e+00,
-2.99860099e+00, -2.55552203e+00, -2.27317862e+00,
-2.17586942e+00, -1.97183132e+00, -2.02211316e+00,
-1.40599577e+00, -5.74288496e-01, 1.15984166e+00,
4.23182774e+00, 4.75834404e+00, 3.74200684e+00,
3.13959278e+00, 1.28464022e+00, -1.63338185e-01,
-4.59846989e-01, -3.87030930e-02, -1.47890773e-01,
-7.81543211e-02, 7.04473154e-02, -2.85587300e-01,
-4.98040519e-01, -5.05362319e-01, -2.10988255e-01,
-2.96243919e-01, -3.08167185e-01, -2.28173481e-01,
-3.39205789e-01, -2.17406734e-01, -3.07119992e-01,
-2.80856318e-01, -2.01187240e-01, -9.08931618e-02,
1.19209975e-04, -1.03781679e-01, 1.68491206e-01,
4.01036790e-01, 2.91845195e-01, 6.72996081e-02,
-3.33300925e-02, 1.26432268e-01, 8.32852023e-01,
1.88439781e+00, 1.33883460e+00, 4.80435455e-01,
-2.43751156e-01, -4.57317651e-01, -5.00984921e-01,
-7.37219937e-01, -9.84904261e-01, -1.31724659e+00,
-1.03104538e+00, -8.23229571e-01, -4.19957897e-01,
-2.62460181e-01, 1.20635374e+00, 4.33611445e+00,
4.18854222e+00, 2.14247691e+00, -2.11679116e-01,
-5.72818587e-01, 7.55383702e-01, 1.57580810e+00,
2.16247799e+00, 1.70824825e+00, 1.98917050e+00,
1.96270109e+00, 1.28671320e+00, -1.08375824e-01,
-1.05344979e+00, -1.81169560e+00, -2.51135819e+00,
-1.76963679e+00, -1.80747840e+00, -1.19208123e+00,
-1.01487994e+00, -9.33388519e-01, -3.51813158e-01,
-7.32129130e-01, -4.02376988e-01, -4.07198280e-01,
-5.57795939e-01, -9.71627735e-01, -1.77767380e+00,
-1.62379647e+00, -1.70260138e+00, -1.73850730e+00,
-1.63276843e+00, -1.74199713e+00, -1.68546087e+00,
-1.61694517e+00, -1.55739340e+00, -1.50227678e+00,
-1.58767956e+00, -1.65341566e+00, -1.56110869e+00,
-1.69875060e+00, -1.70323970e+00, -1.65476178e+00,
-1.76280986e+00, -1.55644944e+00, -1.48442341e+00,
-1.66990811e+00, -1.69235942e+00, -1.69908318e+00,
-1.62778083e+00, -1.67340936e+00, -1.64669665e+00,
-1.58731759e+00, -1.53574846e+00, -1.64873524e+00,
-2.25637968e+00, -4.03930444e+00, -5.10310300e+00,
3.74270189e-01, 4.87412168e+00, 3.09786073e+00,
3.63976627e+00, 3.53954132e+00, 3.06438935e+00,
3.55312209e+00, 3.24430576e+00, 3.68814282e+00,
3.61790133e+00, 3.66176895e+00, 3.67115383e+00,
3.51618968e+00, 3.68577762e+00, 3.69906184e+00,
4.05961352e+00, 4.07009574e+00, 3.95738239e+00])
assert_array_almost_equal(r, x)
def test_fit_baseline_alpha_adj(self):
u = self.data
se_model = SteeringEntropyModel(alpha=0.05)
hbas = se_model.fit_baseline(u[:2*60*60])
self.assertAlmostEqual(hbas, 1.6410033779603466)
