def ReflQ_mag(Q, lamda, n, d, sigma, n_u, dd_u, sigma_u, n_l, dd_l, sigma_l):
'''Calculate the reflectivity from a multilayer stack with
Abeles matrix formalism. '''
#pdb.set_trace()
k_vac = 2*np.pi/lamda
kz = Q[:, np.newaxis]/2.0
n_amb2 = (n[:,-1]*n[:,-1])[:,np.newaxis]
#print n_amb2.shape
#print n.shape, n_l.shape, n_u.shape, kz.shape
k_j = np.sqrt((n*n - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
k_jl = np.sqrt((n_l*n_l - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
k_ju = np.sqrt((n_u*n_u - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
#print np.all(n_u == n), np.all(n_l == n)
#print np.all(k_ju == k_j), np.all(k_jl == k_j)
#print d.shape, n.shape, k_j.dtype
#X = ass_X(k_j)
X = im.ass_X_interfacelayer4(k_j, k_ju, k_jl, dd_u, dd_l, sigma, sigma_l, sigma_u)
P = ass_P(k_j, d)
PX = mu.dot2_Adiag(P[...,1:-1], X[...,:-1])
#print P.dtype, X.dtype, PX.dtype
M = mu.dot2(X[...,-1], reduce(mu.dot2, np.rollaxis(PX, 3)[::-1]))
r = M[1,0]/M[0,0]
return np.abs(r)**2
def ReflQ_mag(Q, lamda, n, d, sigma, n_u, dd_u, sigma_u, n_l, dd_l, sigma_l):
'''Calculate the reflectivity from a multilayer stack with
Abeles matrix formalism. '''
#pdb.set_trace()
k_vac = 2 * np.pi / lamda
kz = Q[:, np.newaxis] / 2.0
n_amb2 = (n[:, -1] * n[:, -1])[:, np.newaxis]
#print n_amb2.shape
#print n.shape, n_l.shape, n_u.shape, kz.shape
k_j = np.sqrt((n * n - n_amb2) * k_vac * k_vac + n_amb2 * kz * kz)
k_jl = np.sqrt((n_l * n_l - n_amb2) * k_vac * k_vac + n_amb2 * kz * kz)
k_ju = np.sqrt((n_u * n_u - n_amb2) * k_vac * k_vac + n_amb2 * kz * kz)
#print np.all(n_u == n), np.all(n_l == n)
#print np.all(k_ju == k_j), np.all(k_jl == k_j)
#print d.shape, n.shape, k_j.dtype
#X = ass_X(k_j)
X = im.ass_X_interfacelayer4(k_j, k_ju, k_jl, dd_u, dd_l, sigma, sigma_l,
sigma_u)
P = ass_P(k_j, d)
PX = mu.dot2_Adiag(P[..., 1:-1], X[..., :-1])
#print P.dtype, X.dtype, PX.dtype
M = mu.dot2(X[..., -1], reduce(mu.dot2, np.rollaxis(PX, 3)[::-1]))
r = M[1, 0] / M[0, 0]
return np.abs(r)**2
def ReflQ_mag(Q, lamda, n, d, sigma, n_u, dd_u, sigma_u, n_l, dd_l, sigma_l):
'''Calculate the reflectivity from a multilayer stack with
Abeles matrix formalism.
The last layer is the substrate and the first layer is the ambient
'''
#pdb.set_trace()
k_vac = 2*np.pi/(lamda*np.ones(Q.shape))[:,np.newaxis]
kz = Q[:, np.newaxis]/2.0
n_amb2 = (n[:,0]*n[:,0])[:,np.newaxis]
#print n_amb2.shape
#print n.shape, n_l.shape, n_u.shape, kz.shape
k = np.sqrt((n*n - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
k_l = np.sqrt((n_l*n_l - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
k_u = np.sqrt((n_u*n_u - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
#print 'k: ', k.shape, k_l.shape, k_u.shape
#print 'sigma_l', sigma_l.shape
#print 'dd_u ', dd_u.shape
#print np.all(n_u == n), np.all(n_l == n)
#print np.all(k_ju == k_j), np.all(k_jl == k_j)
#print d.shape, n.shape, k_j.dtype
#X = ass_X(k)
#X = im.ass_X_interfacelayer4(k, k_u, k_l, dd_u, dd_l, sigma, sigma_l, sigma_u)
X_lu = ass_X_2int(k_u[:,1:], k_l[:,:-1])
X_l = ass_X_2int(k_l[:,:-1], k[:,:-1])
X_u = ass_X_2int(k[:,1:], k_u[:,1:])
#print 'X: ', X_lu.shape, X_l.shape, X_u.shape
X = int_lay_xmean.calc_iso_Xmean(X_l, X_lu, X_u, k, k_l, k_u, dd_u, dd_l,
sigma, sigma_l, sigma_u)
P = ass_P(k, d)
PX = mu.dot2_Adiag(P[...,1:-1], X[...,1:])
#print P.dtype, X.dtype, PX.dtype
M = mu.dot2(X[...,0], reduce(mu.dot2, np.rollaxis(PX, 3)))
r = M[1,0]/M[0,0]
return np.abs(r)**2
def ReflQ(Q, lamda, n, d, sigma):
'''Calculate the reflectivity from a multilayer stack with
Abeles matrix formalism. '''
#pdb.set_trace()
k_vac = 2*np.pi/lamda
kz = Q[:, np.newaxis]/2.0
n_amb2 = n[-1]*n[-1]
k_j = np.sqrt((n*n - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
#print d.shape, n.shape, k_j.dtype
#X = ass_X(k_j)
X = im.ass_X_interfacelayer4(k_j, k_j, k_j, k_j*0+5, k_j*0+5, sigma, sigma*0, sigma*0)
P = ass_P(k_j, d-10)
PX = mu.dot2_Adiag(P[...,1:-1], X[...,:-1])
#print P.dtype, X.dtype, PX.dtype
M = mu.dot2(X[...,-1], reduce(mu.dot2, rollaxis(PX, 3)[::-1]))
r = M[1,0]/M[0,0]
return np.abs(r)**2
def ReflQ(Q, lamda, n, d, sigma):
'''Calculate the reflectivity from a multilayer stack with
Abeles matrix formalism. '''
#pdb.set_trace()
k_vac = 2*np.pi/lamda
kz = Q[:, np.newaxis]/2.0
n_amb2 = n[-1]*n[-1]
k_j = np.sqrt((n*n - n_amb2)*k_vac*k_vac + n_amb2*kz*kz)
#print d.shape, n.shape, k_j.dtype
X = ass_X(k_j)
#X = im.ass_X_interfacelayer4(k_j, k_j, k_j, k_j*0, k_j*0, sigma, sigma*0, sigma*0)
P = ass_P(k_j, d)
PX = mu.dot2_Adiag(P[...,1:-1], X[...,:-1])
#print P.dtype, X.dtype, PX.dtype
M = mu.dot2(X[...,-1], reduce(mu.dot2, rollaxis(PX, 3)[::-1]))
r = M[1,0]/M[0,0]
return np.abs(r)**2
