def test_1d_fit(self):
D = {'I': ch.QuantumChannel(np.array(np.eye(4)))}
AR = SchWARMA.SchWAR([], [s_z], D)
alpha = np.sqrt(.00001)
sigma = 1.#1./2.
arg = lambda t: alpha**2*sigma*(np.sqrt(np.pi)*t*sp.erf(t/sigma)+sigma*(np.exp(-t**2/sigma**2)-1))
args = np.exp(-2*np.array([arg(t) for t in range(100)]))
args = (1.-args)/2.
AR.fit([args], 5)
self.assertEqual(5,len(AR.models[0].a))
self.assertEqual(1,len(AR.models[0].b))
AR.init_ARMAs()
AR['I']
#Note now I have brackets on the second argument
AR.fit([args], [7])
self.assertEqual(7,len(AR.models[0].a))
self.assertEqual(1,len(AR.models[0].b))
AR.init_ARMAs()
L = AR['I'].liouvillian()
#Known structure of \sigma_z arma
self.assertEqual(la.norm(L-np.diag(np.diag(L))),0.0)
self.assertEqual(L[0,0], L[3,3])
self.assertAlmostEqual(L[0,0], 1.,9)
self.assertEqual(L[1,1], np.conj(L[2,2]))
def test_3d_fit(self):
D = {'I': ch.QuantumChannel(np.array(np.eye(4,dtype=complex)))}
AR = SchWARMA.SchWAR([], [s_z], D)
alpha = np.sqrt(.00001)
sigma = 1.#1./2.
arg = lambda t: alpha**2*sigma*(np.sqrt(np.pi)*t*sp.erf(t/sigma)+sigma*(np.exp(-t**2/sigma**2)-1))
args = np.exp(-2*np.array([arg(t) for t in range(100)]))
args = (1.-args)/2.
#Note now I have brackets on the second argument
AR.fit([args, args, args], [3,5,7])
self.assertEqual(3,len(AR.models[0].a))
self.assertEqual(5,len(AR.models[1].a))
self.assertEqual(7,len(AR.models[2].a))
AR.init_ARMAs()
L = AR['I']
def test_init(self):
ARMAS = [SchWARMA.ARMA(np.random.randn(np.random.randint(5))+2) for _ in range(3)]
D = {'I': ch.QuantumChannel(np.array(np.eye(4,dtype=complex)))}
AR = SchWARMA.SchWAR(ARMAS, [s_x, s_y, s_z], D)