def __init__(self, max_freq=30.,max_linewidth=1.,N=1000, center_freq = 0.,
tau = 0.3,r = 0.8):
Time_Delay_Network.__init__(self, max_freq,max_linewidth,N,center_freq)
self.tau = tau
self.delays = [tau]
self.r = r
self.M1=np.matrix([[r]])
self.E = lambda z: np.exp(-z*self.tau)
self.T = lambda z: np.matrix([(np.exp(-z*self.tau) - self.r)/
(1.-self.r* np.exp(-z*self.tau))])
self.T_denom = lambda z: (1.-self.r* np.exp(-z*self.tau))
self.Tp_denom = lambda z: der(self.T_denom,z)
def __init__(self, max_freq=10.,max_linewidth=10.,N=1000, center_freq = 0.,
r=0.9,tau = 1.):
Time_Delay_Network.__init__(self, max_freq,max_linewidth,N,center_freq)
self.r = r
self.delays = [tau]
e = lambda z: np.exp(-z*tau)
dim = 2
self.M1 = np.matrix([[0,r],[r,0]])
self.E = lambda z: np.matrix([[e(z),0],[0,e(z)]])
self.T_denom = lambda z: (1.-r**2* e(z)**2)
self.T = lambda z: -r*np.eye(dim) + ((1.-r**2.)/self.T_denom(z)) * \
np.matrix([[r*e(z)**2,e(z)],[e(z),r*e(z)**2]])
self.Tp_denom = lambda z: der(self.T_denom,z)
def __init__(
self,
max_freq=60.,
max_linewidth=1.,
N=5000,
center_freq=0.,
r1=0.9,
r2=0.4,
r3=0.8,
tau1=0.1,
tau2=0.23,
tau3=0.1,
tau4=0.17,
):
Time_Delay_Network.__init__(self, max_freq, max_linewidth, N,
center_freq)
self.r1 = r1
self.r2 = r2
self.r3 = r3
self.delays = [tau1, tau2, tau3, tau4]
t1 = np.sqrt(1 - r1**2)
t2 = np.sqrt(1 - r2**2)
t3 = np.sqrt(1 - r3**2)
dim = 4
M1 = np.matrix([[0, -r1, 0, 0], [-r2, 0, t2, 0], [0, 0, 0, -r3],
[t2, 0, r2, 0]])
self.M1 = M1
M2 = np.matrix([[t1, 0], [0, 0], [0, t3], [0, 0]])
M3 = np.matrix([[0, t1, 0, 0], [0, 0, 0, t3]])
M4 = np.matrix([[r1, 0], [0, r3]])
E = lambda z: np.matrix([[np.exp(-tau1 * z), 0, 0, 0],
[0, np.exp(-tau2 * z), 0, 0],
[0, 0, np.exp(-tau3 * z), 0],
[0, 0, 0, np.exp(-tau4 * z)]])
self.E = E
self.T_denom = lambda z: la.det(np.eye(dim) - M1 * E(z))
self.Tp_denom = lambda z: der(self.T_denom, z)
self.T = lambda z: M3 * E(z) * la.inv(np.eye(dim) - M1 * E(z)
) * M2 + M4
def __init__(self, max_freq=60.,max_linewidth=1.,N=5000, center_freq = 0.,
r1=0.9,r2=0.4,r3=0.8,
tau1 = 0.1, tau2 = 0.23,tau3 = 0.1,tau4 = 0.17,
):
Time_Delay_Network.__init__(self, max_freq,max_linewidth,N,center_freq)
self.r1 = r1
self.r2 = r2
self.r3 = r3
self.delays =[tau1,tau2,tau3,tau4]
t1 = np.sqrt(1-r1**2)
t2 = np.sqrt(1-r2**2)
t3 = np.sqrt(1-r3**2)
dim = 4
M1 = np.matrix([[0,-r1,0,0],
[-r2,0,t2,0],
[0,0,0,-r3],
[t2,0,r2,0]])
self.M1 = M1
M2 = np.matrix([[t1,0],
[0,0],
[0,t3],
[0,0]])
M3 = np.matrix([[0,t1,0,0],
[0,0,0,t3]])
M4 = np.matrix([[r1,0],
[0,r3]])
E = lambda z: np.matrix([[np.exp(-tau1*z),0,0,0],
[0,np.exp(-tau2*z),0,0],
[0,0,np.exp(-tau3*z),0],
[0,0,0,np.exp(-tau4*z)]])
self.E = E
self.T_denom = lambda z: la.det(np.eye(dim) - M1*E(z))
self.Tp_denom = lambda z: der(self.T_denom,z)
self.T = lambda z: M3*E(z)*la.inv(np.eye(dim) - M1*E(z))*M2+M4
def __init__(self,
max_freq=30.,
max_linewidth=1.,
N=1000,
center_freq=0.,
tau=0.3,
r=0.8):
Time_Delay_Network.__init__(self, max_freq, max_linewidth, N,
center_freq)
self.tau = tau
self.delays = [tau]
self.r = r
self.M1 = np.matrix([[r]])
self.E = lambda z: np.exp(-z * self.tau)
self.T = lambda z: np.matrix([(np.exp(-z * self.tau) - self.r) /
(1. - self.r * np.exp(-z * self.tau))])
self.T_denom = lambda z: (1. - self.r * np.exp(-z * self.tau))
self.Tp_denom = lambda z: der(self.T_denom, z)
def __init__(self,
max_freq=10.,
max_linewidth=10.,
N=1000,
center_freq=0.,
r=0.9,
tau=1.):
Time_Delay_Network.__init__(self, max_freq, max_linewidth, N,
center_freq)
self.r = r
self.delays = [tau]
e = lambda z: np.exp(-z * tau)
dim = 2
self.M1 = np.matrix([[0, r], [r, 0]])
self.E = lambda z: np.matrix([[e(z), 0], [0, e(z)]])
self.T_denom = lambda z: (1. - r**2 * e(z)**2)
self.T = lambda z: -r*np.eye(dim) + ((1.-r**2.)/self.T_denom(z)) * \
np.matrix([[r*e(z)**2,e(z)],[e(z),r*e(z)**2]])
self.Tp_denom = lambda z: der(self.T_denom, z)
def __init__(self, max_freq=100.,max_linewidth=3.,N=5000,center_freq = 0.):
Time_Delay_Network.__init__(self, max_freq,max_linewidth,N,center_freq)
tau1 = 0.1
tau2 = 0.039
tau3 = 0.11
tau4 = 0.08
self.delays = [tau1,tau2,tau3,tau4]
r = 0.9
t = np.sqrt(1-r**2)
dim = 4
M1 = np.matrix([[0,0,-r,0],
[r,0,0,0],
[0,r,0,t],
[t,0,0,0]])
self.M1 = M1
M2 = np.matrix([[t,0],
[0,t],
[0,0],
[0,-r]])
M3 = np.matrix([[0,0,t,0],
[0,t,0,-r]])
M4 = np.matrix([[r,0],
[0,0]])
E = lambda z: np.matrix([[np.exp(-tau1*z),0,0,0],
[0,np.exp(-tau2*z),0,0],
[0,0,np.exp(-tau3*z),0],
[0,0,0,np.exp(-tau4*z)]])
self.E = E
self.T_denom = lambda z: la.det(np.eye(dim) - M1*E(z))
self.Tp_denom = lambda z: der(self.T_denom,z)
self.T = lambda z: M3*E(z)*la.inv(np.eye(dim) - M1*E(z))*M2+M4
def __init__(
self,
max_freq=50.,
max_linewidth=3.,
N=1000,
center_freq=0.,
):
Time_Delay_Network.__init__(self, max_freq, max_linewidth, N,
center_freq)
tau1 = 0.1
tau2 = 0.039
tau3 = 0.11
tau4 = 0.08
self.delays = [tau1, tau2, tau3, tau4]
r = 0.9
t = np.sqrt(1 - r**2)
dim = 4
M1 = np.matrix([[0, 0, -r, 0], [r, 0, 0, 0], [0, r, 0, t],
[t, 0, 0, 0]])
self.M1 = M1
M2 = np.matrix([[t, 0], [0, t], [0, 0], [0, -r]])
M3 = np.matrix([[0, 0, t, 0], [0, t, 0, -r]])
M4 = np.matrix([[r, 0], [0, 0]])
E = lambda z: np.matrix([[np.exp(-(tau1 + tau4) * z), 0, 0, 0],
[0, np.exp(-(tau2 - tau4) * z), 0, 0],
[0, 0, np.exp(-tau3 * z), 0], [0, 0, 0, 1.]])
self.E = E
self.T_denom = lambda z: la.det(np.eye(dim) - M1 * E(z))
self.Tp_denom = lambda z: der(self.T_denom, z)
self.T = lambda z: M3 * E(z) * la.inv(np.eye(dim) - M1 * E(z)
) * M2 + M4
