def test_hABY(self):
params = self.params.copy()
wg = pymwm.create(params)
wr = 3.0 * np.pi
wi = -0.002
w = wr + wi * 1j
hs1 = np.array([wg.beta(w, alpha)
for alpha in wg.alpha_all])
As1, Bs1 = wg.coefs_numpy(hs1, w)
Y1 = wg.Ys(w, hs1, As1, Bs1)
hs2, As2, Bs2, Y2 = wg.hABY(w)
self.assertTrue(np.allclose(As1, As2))
self.assertTrue(np.allclose(Bs1, Bs2))
self.assertTrue(np.allclose(Y1, Y2))
params['clad'] = {'model': 'pec'}
wg = pymwm.create(params)
hs1 = np.array([wg.beta(w, alpha)
for alpha in wg.alpha_all])
As1, Bs1 = wg.coefs_numpy(hs1, w)
Y1 = wg.Ys(w, hs1, As1, Bs1)
hs2, As2, Bs2, Y2 = wg.hABY(w)
self.assertTrue(np.allclose(As1, As2))
self.assertTrue(np.allclose(Bs1, Bs2))
self.assertTrue(np.allclose(Y1, Y2))
def test_Yab_no_loss(self):
params = self.params.copy()
params['clad'] = {'model': 'gold_dl', 'im_factor': 0.0}
wg = pymwm.create(params)
w = 2 * np.pi / 2.0
alpha1 = ('E', 1, 1)
h1 = wg.beta(w, alpha1)
a1, b1 = wg.coef(h1, w, alpha1)
alpha2 = ('M', 1, 1)
h2 = wg.beta(w, alpha2)
a2, b2 = wg.coef(h2, w, alpha2)
s1 = 0
s2 = 1
l1 = l2 = 0
n1 = n2 = 1
m1 = m2 = 1
print(a1, b1, a2, b2)
self.assertAlmostEqual(wg.norm(w, h1, alpha1, a1, b1), 1.0)
self.assertAlmostEqual(wg.norm(w, h2, alpha2, a2, b2), 1.0)
self.assertAlmostEqual(
wg.Yab(w, h1, s1, l1, n1, m1, a1, b1,
h1, s1, l1, n1, m1, a1, b1), wg.Y(w, h1, alpha1, a1, b1))
self.assertAlmostEqual(
wg.Yab(w, h2, s2, l2, n2, m2, a2, b2,
h2, s2, l2, n2, m2, a2, b2), wg.Y(w, h2, alpha2, a2, b2))
print(wg.Yab(w, h1, s1, l1, n1, m1, a1, b1,
h2, s2, l2, n2, m2, a2, b2))
self.assertAlmostEqual(
wg.Yab(w, h1, s1, l1, n1, m1, a1, b1,
h2, s2, l2, n2, m2, a2, b2), 0.0)
def test_attributes():
wg = pymwm.create(params)
assert wg.r == 0.15
w = 2 * np.pi / 5.0
assert wg.fill(w) == 1.0
drude_lorentz = -1272.37592771801 + 351.25089220304176j
npt.assert_almost_equal(wg.clad(w), drude_lorentz)
def test_Yab_pec(self):
params = self.params.copy()
params['clad'] = {'model': 'pec'}
wg = pymwm.create(params)
w = 2 * np.pi / 5.0
alpha1 = ('E', 1, 1)
h1 = wg.beta(w, alpha1)
a1, b1 = wg.coef(h1, w, alpha1)
alpha2 = ('M', 1, 1)
h2 = wg.beta(w, alpha2)
a2, b2 = wg.coef(h2, w, alpha2)
s1 = 0
s2 = 1
l1 = l2 = 0
n1 = n2 = 1
m1 = m2 = 1
self.assertEqual(wg.norm(w, h1, alpha1, a1, b1), 1.0)
self.assertEqual(wg.norm(w, h2, alpha2, a2, b2), 1.0)
self.assertEqual(wg.Y(w, h1, alpha1, a1, b1), h1 / w)
self.assertEqual(wg.Y(w, h2, alpha2, a2, b2), w / h2)
self.assertEqual(
wg.Yab(w, h1, s1, l1, n1, m1, a1, b1,
h1, s1, l1, n1, m1, a1, b1), h1 / w)
self.assertEqual(
wg.Yab(w, h2, s2, l2, n2, m2, a2, b2,
h2, s2, l2, n2, m2, a2, b2), w / h2)
self.assertEqual(
wg.Yab(w, h1, s1, l1, n1, m1, a1, b1,
h2, s2, l2, n2, m2, a2, b2), 0.0)
def test_attributes(self):
params = self.params.copy()
wg = pymwm.create(params)
self.assertEqual(wg.r, 0.15)
w = 2 * np.pi / 5.0
self.assertEqual(wg.fill(w), 1.0)
drude_lorentz = -1272.37592771801+351.25089220304176j
self.assertAlmostEqual(wg.clad(w), drude_lorentz)
def test_norm(self):
params = self.params.copy()
wg = pymwm.create(params)
wr = 3.0 * np.pi
wi = -0.002
w = complex(wr, wi)
hs = np.array([wg.beta(w, alpha) for alpha in wg.alpha_all])
As, Bs = wg.coefs_numpy(hs, w)
for h, a, b, s, n, m in zip(hs, As, Bs, wg.s_all, wg.n_all, wg.m_all):
pol = 'E' if s == 0 else 'M'
norm = wg.norm(w, h, (pol, n, m), a, b)
self.assertAlmostEqual(norm, 1.0)
params['clad'] = {'model': 'pec'}
wg = pymwm.create(params)
hs = np.array([wg.beta(w, alpha) for alpha in wg.alpha_all])
As, Bs = wg.coefs_numpy(hs, w)
for h, a, b, s, n, m in zip(hs, As, Bs, wg.s_all, wg.n_all, wg.m_all):
pol = 'E' if s == 0 else 'M'
norm = wg.norm(w, h, (pol, n, m), a, b)
self.assertAlmostEqual(norm, 1.0)
def test_coefs(self):
params = self.params.copy()
wg = pymwm.create(params)
wr = 2.0 * np.pi
wi = -0.002
w = wr + wi * 1j
alpha_all = wg.alpha_all
hs = np.array([wg.beta(w, alpha) for alpha in alpha_all])
As1, Bs1 = wg.coefs_numpy(hs, w)
As2, Bs2 = wg.coefs(hs, w)
print(As1, As2)
self.assertTrue(np.allclose(As1, As2))
self.assertTrue(np.allclose(Bs1, Bs2))
params['clad'] = {'model': 'pec'}
wg = pymwm.create(params)
alpha_all = wg.alpha_all
hs = np.array([wg.beta(w, alpha) for alpha in alpha_all])
As1, Bs1 = wg.coefs_numpy(hs, w)
As2, Bs2 = wg.coefs(hs, w)
print(As1, As2)
self.assertTrue(np.allclose(As1, As2))
self.assertTrue(np.allclose(Bs1, Bs2))
def test_Yaa_and_Y(self):
params = self.params.copy()
wg = pymwm.create(params)
w = 2 * np.pi / 0.575
for i, alpha in enumerate(wg.alpha_all):
h = wg.beta(w, alpha)
a, b = wg.coef(h, w, alpha)
s = wg.s_all[i]
l = wg.l_all[i]
n = wg.n_all[i]
m = wg.m_all[i]
Yab = wg.Yab(w, h, s, l, n, m, a, b, h, s, l, n, m, a, b)
Y = wg.Y(w, h, alpha, a, b)
print(Yab)
print(Y)
self.assertAlmostEqual(Yab, Y)
def test_Yab_with_loss(self):
params = self.params.copy()
wg = pymwm.create(params)
w = 2 * np.pi / 2.0
alpha1 = ('E', 1, 1)
h1 = wg.beta(w, alpha1)
a1, b1 = wg.coef(h1, w, alpha1)
alpha2 = ('M', 1, 1)
h2 = wg.beta(w, alpha2)
a2, b2 = wg.coef(h2, w, alpha2)
s1 = 0
s2 = 1
l1 = l2 = 0
n1 = n2 = 1
m1 = m2 = 1
self.assertAlmostEqual(wg.norm(w, h1, alpha1, a1, b1), 1.0)
self.assertAlmostEqual(wg.norm(w, h2, alpha2, a2, b2), 1.0)
print(wg.Yab(w, h1, s1, l1, n1, m1, a1, b1, h2, s2, l2, n2, m2, a2, b2))
self.assertEqual(
wg.Yab(w, h1, s1, l1, n1, m1, a1, b1,
h2, s2, l2, n2, m2, a2, b2), 0.0)