def structure(self, position, k, lmax):
position = np.asarray(position)
Nparticles = len(position)
rmax = miepy.vsh.lmax_to_rmax(lmax)
p_src = np.zeros([Nparticles, 2, rmax], dtype=complex)
factor = self.amplitude*np.exp(1j*self.phase)
for i in range(Nparticles):
dr = position[i] - self.center
if not np.any(dr):
for r,n,m in miepy.mode_indices(lmax):
if n == self.n and m == self.m:
Emn = miepy.vsh.Emn(m, n)
p_src[i,0,r] = 1/(-1j*Emn)
else:
rad, theta, phi = miepy.coordinates.cart_to_sph(*dr)
for r,n,m in miepy.mode_indices(lmax):
Emn = miepy.vsh.Emn(self.m, self.n)
Euv = miepy.vsh.Emn(m, n)
A, B = miepy.cpp.vsh_translation.vsh_translation(m, n, self.m, self.n, rad, theta, phi, k, self.mode)
p_src[i,0,r] = A/(-1j*Emn)
p_src[i,1,r] = B/(-1j*Emn)
if self.ftype == 'magnetic':
p_src = p_src[:, ::-1]
return factor*p_src
def tmatrix_core_shell(radius, thickness, wavelength, eps_core, eps_shell,
eps_m, lmax):
"""Compute the T-matrix of a core-shell, using regular Mie theory
Arguments:
radius core radius
wavelength incident wavelength
eps_core particle permittivity
eps_shell shell permittivity
eps_m medium permittivity
lmax maximum number of multipoles
"""
rmax = miepy.vsh.lmax_to_rmax(lmax)
tmatrix = np.zeros([2, rmax, 2, rmax], dtype=complex)
k_medium = 2 * np.pi * eps_m**0.5 / wavelength
particle = miepy.single_mie_core_shell(
radius,
radius + thickness,
material_in=miepy.dielectric(eps=eps_core),
material_out=miepy.dielectric(eps=eps_shell),
medium=miepy.dielectric(eps=eps_m),
lmax=lmax,
wavelength=wavelength)
particle.solve()
for i, n, m in miepy.mode_indices(lmax):
tmatrix[0, i, 0, i] = -1j * particle.an[0, n - 1]
tmatrix[1, i, 1, i] = -1j * particle.bn[0, n - 1]
return tmatrix
def _solve_interactions(self):
if self.symmetry is None:
agg_tmatrix = miepy.interactions.sphere_aggregate_tmatrix(
self.position, self.mie_scat, self.material_data.k_b)
else:
agg_tmatrix = miepy.interactions.sphere_aggregate_tmatrix_periodic(
self.position, self.mie_scat, self.material_data.k_b,
self.symmetry, self.source.k_hat)
if self.interface is not None:
r0 = self.interface.reflection_coefficients(
theta=0, wavelength=self.wavelength, medium=self.medium)[0]
z = self.interface.z
R_matrix = miepy.interactions.reflection_matrix_nia(
self.position, self.mie_scat, self.material_data.k_b, r0, z)
agg_tmatrix -= R_matrix
self.p_inc[...] = miepy.interactions.solve_linear_system(
agg_tmatrix, self.p_src, method=miepy.solver.bicgstab)
for r, n, m in miepy.mode_indices(self.lmax):
self.p_scat[...,
r] = self.p_inc[..., r] * self.mie_scat[..., n - 1]
self.p_int[...,
r] = self.p_inc[..., r] * self.mie_int[:, ::-1, n - 1]
def structure(self, position, k, lmax):
position = np.asarray(position)
Nparticles = len(position)
rmax = miepy.vsh.lmax_to_rmax(lmax)
p_src = np.empty([Nparticles, 2, rmax], dtype=complex)
for j in range(Nparticles):
phase = k * (self.k_hat[0] * position[j, 0] +
self.k_hat[1] * position[j, 1] +
self.k_hat[2] * position[j, 2]) + self.phase
for i, n, m in miepy.mode_indices(lmax):
pi_value = pi_func(n, m, self.theta)
tau_value = tau_func(n, m, self.theta)
Emn = np.abs(miepy.vsh.Emn(m, n))
factor = self.amplitude * np.exp(1j *
(phase - m * self.phi)) * Emn
p_src[j, 0,
i] = factor * (tau_value * self.polarization[0] -
1j * pi_value * self.polarization[1])
p_src[j, 1,
i] = factor * (pi_value * self.polarization[0] -
1j * tau_value * self.polarization[1])
return p_src
def _solve_without_interactions(self):
self.p_inc[...] = self.p_src
for r, n, m in miepy.mode_indices(self.lmax):
self.p_scat[...,
r] = self.p_inc[..., r] * self.mie_scat[..., n - 1]
self.p_int[...,
r] = self.p_inc[..., r] * self.mie_int[:, ::-1, n - 1]
def get_Q(p, q):
Q = np.zeros([2, rmax, 2, rmax], dtype=complex)
p_modes = ingoing if p == 1 else outgoing
q_modes = ingoing if q == 1 else outgoing
for r1, n1, m1 in miepy.mode_indices(lmax):
M1_p_k2 = p_modes.M_k2[r1]
N1_p_k2 = p_modes.N_k2[r1]
M1_p_k1 = p_modes.M_k1[r1]
N1_p_k1 = p_modes.N_k1[r1]
for r2, n2, m2 in miepy.mode_indices(lmax):
M2_q_k2 = q_modes.M_k2[r2]
N2_q_k2 = q_modes.N_k2[r2]
M2_q_k1 = q_modes.M_k1[r2]
N2_q_k1 = q_modes.N_k1[r2]
# factor = (-1)**(m2 - m1)
factor = 1
integrand = np.sum(np.cross(dS, M2_q_k2, axis=0)*N1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, N2_q_k2, axis=0)*M1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[1, r1, 1,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
integrand = np.sum(np.cross(dS, N2_q_k2, axis=0)*N1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, M2_q_k2, axis=0)*M1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[1, r1, 0,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
integrand = np.sum(np.cross(dS, M2_q_k2, axis=0)*M1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, N2_q_k2, axis=0)*N1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[0, r1, 1,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
integrand = np.sum(np.cross(dS, N2_q_k2, axis=0)*M1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, M2_q_k2, axis=0)*N1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[0, r1, 0,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
return Q
def sphere_aggregate_tmatrix_periodic(positions, mie, k, symmetry, k_hat):
"""Obtain the particle-centered aggregate T-matrix for a cluster of spheres with a given periodic symmetry
Returns T[N,2,rmax,N,2,rmax]
Arguments:
positions[N,3] particles positions
mie[N,2,lmax] mie scattering coefficients
k medium wavenumber
symmetry type of symmetry
k_hat unit k-vector
"""
Nparticles = positions.shape[0]
lmax = mie.shape[-1]
rmax = miepy.vsh.lmax_to_rmax(lmax)
# agg_tmatrix = np.zeros(shape=(Nparticles, 2, rmax, Nparticles, 2, rmax), dtype=complex)
agg_tmatrix = sphere_aggregate_tmatrix(positions, mie, k)
xpos, ypos, zpos = symmetry.generate(5000)
cells = np.array([xpos,ypos,zpos]).T
for i in range(Nparticles):
for j in range(Nparticles):
dr = positions[i] - (cells + positions[j])
rad, theta, phi = miepy.coordinates.cart_to_sph(dr[:,0], dr[:,1], dr[:,2])
for r,n,m in miepy.mode_indices(lmax):
for s,v,u in miepy.mode_indices(lmax):
phase_factor = np.exp(-1j*k*(k_hat[0]*xpos + k_hat[1]*ypos + k_hat[2]*zpos))
A_transfer, B_transfer = vsh_translation(m, n, u, v,
rad, theta, phi, k, miepy.vsh_mode.outgoing)
for a in range(2):
for b in range(2):
val = (A_transfer, B_transfer)[(a+b)%2]*phase_factor
agg_tmatrix[i,a,r,j,b,s] += np.sum(val)*mie[j,b,v-1]
return agg_tmatrix
def structure(self, position, k, lmax):
rmax = miepy.vsh.lmax_to_rmax(lmax)
p_src = np.zeros([2, rmax], dtype=complex)
phase = k * (self.k_hat[0] * position[0] + self.k_hat[1] * position[1]
+ self.k_hat[2] * position[2]) + self.phase
for i, n, m in miepy.mode_indices(lmax):
pi_value = pi_func(n, m, self.theta)
tau_value = tau_func(n, m, self.theta)
Emn = np.abs(miepy.vsh.Emn(m, n))
factor = self.amplitude * np.exp(1j * (phase - m * self.phi)) * Emn
p_src[0, i] = factor * (tau_value * self.polarization[0] -
1j * pi_value * self.polarization[1])
p_src[1, i] = factor * (pi_value * self.polarization[0] -
1j * tau_value * self.polarization[1])
return p_src
def tmatrix_sphere(radius, wavelength, eps, eps_m, lmax, conducting=False):
"""Compute the T-matrix of a sphere, using regular Mie theory
Arguments:
radius sphere radius
wavelength incident wavelength
eps particle permittivity
eps_m medium permittivity
lmax maximum number of multipoles
conducting if True, calculate for conducting sphere (default: False)
"""
rmax = miepy.vsh.lmax_to_rmax(lmax)
tmatrix = np.zeros([2, rmax, 2, rmax], dtype=complex)
k_medium = 2 * np.pi * eps_m**0.5 / wavelength
for i, n, m in miepy.mode_indices(lmax):
an, bn = miepy.mie_single.mie_sphere_scattering_coefficients(
radius, n, eps, 1, eps_m, 1, k_medium, conducting=conducting)
tmatrix[0, i, 0, i] = an
tmatrix[1, i, 1, i] = bn
return tmatrix
def cluster_cross_sections(p_cluster, p_src, k):
"""Compute the scattering, absorption, and extinction cross-sections for a cluster
Return (scat[2,lmax], abs[2,lmax], extinct[2,lmax])
Arguments:
p_cluster[2,rmax] cluster scattering coefficients
p_src[2,rmax] source scattering coefficients at origin
k wavenumber
"""
lmax = miepy.vsh.rmax_to_lmax(p_cluster.shape[1])
Cscat = np.zeros([2, lmax], dtype=float)
Cext = np.zeros([2, lmax], dtype=float)
factor = 4 * np.pi / k**2
for r, n, m in miepy.mode_indices(lmax):
Cscat[:, n - 1] += factor * np.abs(p_cluster[:, r])**2
Cext[:, n -
1] += factor * np.real(np.conj(p_src[:, r]) * p_cluster[:, r])
Cabs = Cext - Cscat
return cross_sections(Cscat, Cabs, Cext)
def rotate_tmatrix(tmatrix, quat):
"""Rotate a T-matrix
Arguments:
tmatrix tmatrix to be rotated
quat quaternion representing the rotation
Returns:
The rotated T-matrix
"""
rmax = tmatrix.shape[1]
lmax = miepy.vsh.rmax_to_lmax(rmax)
R = np.zeros([rmax, rmax], dtype=complex)
for i, n, m in miepy.mode_indices(lmax):
r = miepy.vsh.lmax_to_rmax(n)
idx = np.s_[r - (2 * n + 1):r]
R[idx, idx] = miepy.vsh.vsh_rotation_matrix(n, quat)
tmatrix_rot = np.einsum('ms,uw,asbw->ambu', R, np.conjugate(R), tmatrix)
return tmatrix_rot
def structure(self, position, k, lmax):
position = np.asarray(position)
Nparticles = len(position)
rmax = miepy.vsh.lmax_to_rmax(lmax)
p_src = np.zeros([Nparticles, 2, rmax], dtype=complex)
factor = self.amplitude * np.exp(1j * self.phase)
for i in range(Nparticles):
dr = position[i] - self.position
rad, theta, phi = miepy.coordinates.cart_to_sph(*dr)
for r, n, m in miepy.mode_indices(lmax):
for u in [-1, 0, 1]:
A, B = miepy.cpp.vsh_translation.vsh_translation(
m, n, u, 1, rad, theta, phi, k,
miepy.vsh_mode.outgoing)
p_src[i, 0, r] += -A * self.weight[u]
p_src[i, 1, r] += -B * self.weight[u]
if self.mode == 'magnetic':
p_src = p_src[:, ::-1]
return factor * p_src
def get_tmatrix(rad, dS, eps, eps_m, wavelength, lmax):
_, Ntheta, Nphi = dS.shape
theta = np.linspace(0, np.pi, Ntheta)
phi = np.linspace(0, 2 * np.pi, Nphi)
THETA, PHI = np.meshgrid(theta, phi, indexing='ij')
dS = miepy.coordinates.vec_cart_to_sph(dS, THETA, PHI)
rmax = lmax * (lmax + 2)
k1 = 2 * np.pi / wavelength
k2 = 2 * np.pi * np.sqrt(eps) / wavelength
class ingoing:
M_k2 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
N_k2 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
M_k1 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
N_k1 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
class outgoing:
M_k2 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
N_k2 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
M_k1 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
N_k1 = np.empty((rmax, 3) + THETA.shape, dtype=complex)
for r, n, m in miepy.mode_indices(lmax):
Emn = miepy.vsh.Emn(m, n)
Nfunc, Mfunc = miepy.vsh.VSH(n, m, miepy.vsh_mode.incident)
ingoing.M_k2[r] = Emn * Mfunc(rad, THETA, PHI, k2)
ingoing.N_k2[r] = Emn * Nfunc(rad, THETA, PHI, k2)
ingoing.M_k1[r] = Emn * Mfunc(rad, THETA, PHI, k1)
ingoing.N_k1[r] = Emn * Nfunc(rad, THETA, PHI, k1)
Nfunc, Mfunc = miepy.vsh.VSH(n, m, miepy.vsh_mode.outgoing)
outgoing.M_k2[r] = Emn * Mfunc(rad, THETA, PHI, k2)
outgoing.N_k2[r] = Emn * Nfunc(rad, THETA, PHI, k2)
outgoing.M_k1[r] = Emn * Mfunc(rad, THETA, PHI, k1)
outgoing.N_k1[r] = Emn * Nfunc(rad, THETA, PHI, k1)
def get_Q(p, q):
Q = np.zeros([2, rmax, 2, rmax], dtype=complex)
p_modes = ingoing if p == 1 else outgoing
q_modes = ingoing if q == 1 else outgoing
for r1, n1, m1 in miepy.mode_indices(lmax):
M1_p_k2 = p_modes.M_k2[r1]
N1_p_k2 = p_modes.N_k2[r1]
M1_p_k1 = p_modes.M_k1[r1]
N1_p_k1 = p_modes.N_k1[r1]
for r2, n2, m2 in miepy.mode_indices(lmax):
M2_q_k2 = q_modes.M_k2[r2]
N2_q_k2 = q_modes.N_k2[r2]
M2_q_k1 = q_modes.M_k1[r2]
N2_q_k1 = q_modes.N_k1[r2]
# factor = (-1)**(m2 - m1)
factor = 1
integrand = np.sum(np.cross(dS, M2_q_k2, axis=0)*N1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, N2_q_k2, axis=0)*M1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[1, r1, 1,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
integrand = np.sum(np.cross(dS, N2_q_k2, axis=0)*N1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, M2_q_k2, axis=0)*M1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[1, r1, 0,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
integrand = np.sum(np.cross(dS, M2_q_k2, axis=0)*M1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, N2_q_k2, axis=0)*N1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[0, r1, 1,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
integrand = np.sum(np.cross(dS, N2_q_k2, axis=0)*M1_p_k1, axis=0) \
+ np.sqrt(eps/eps_m)*np.sum(np.cross(dS, M2_q_k2, axis=0)*N1_p_k1, axis=0)
integrand *= 1j * k1**2 / np.pi * factor
Q[0, r1, 0,
r2] = miepy.vsh.misc.trapz_2d(theta, phi, integrand)
return Q
Q31 = -get_Q(3, 1)
Q11 = get_Q(1, 1)
T = -np.einsum('aibj,bjck->aick', Q11, np.linalg.tensorinv(Q31))
return T
def nfmds_solver(lmax,
input_kwargs,
solver=tmatrix_solvers.axisymmetric,
extended_precision=False):
"""Return the T-matrix using the Null-Field Method with discrete sources (NFM-DS)
Arguments:
lmax maximum number of multipoles
input_kwargs keyword arguments forwarded to solver.input_function
solver type of solver to use (default: axisymmetric)
extended_precision (bool) whether to use extended precision (default: False)
"""
rmax = miepy.vsh.lmax_to_rmax(lmax)
if 'conducting' in input_kwargs and input_kwargs['conducting']:
input_kwargs['index'] = 1
### create temporary directory tree
with tempfile.TemporaryDirectory() as direc:
### create 4 sub-directories
input_files_dir = '{direc}/INPUTFILES'.format(direc=direc)
os.makedirs(input_files_dir)
out_dir = '{direc}/OUTPUTFILES'.format(direc=direc)
os.makedirs(out_dir)
tmatrix_output_dir = '{direc}/TMATFILES'.format(direc=direc)
os.makedirs(tmatrix_output_dir)
sources_dir = '{direc}/TMATSOURCES'.format(direc=direc)
os.makedirs(sources_dir)
### write 3 input files
with open(
'{input_files_dir}/Input.dat'.format(
input_files_dir=input_files_dir), 'w') as f:
f.write(main_input_file())
with open(
'{input_files_dir}/InputSCT.dat'.format(
input_files_dir=input_files_dir), 'w') as f:
f.write(sct_input_file())
with open(
'{input_files_dir}/Input{name}.dat'.format(
input_files_dir=input_files_dir, name=solver.name),
'w') as f:
f.write((solver.input_function(Nrank=lmax, **input_kwargs)))
### execute program and communicate
install_path = miepy.__path__[0]
if extended_precision:
command = '{install_path}/bin/tmatrix_extended'.format(
install_path=install_path)
else:
command = '{install_path}/bin/tmatrix'.format(
install_path=install_path)
proc = subprocess.Popen([command],
cwd=sources_dir,
stdout=subprocess.PIPE,
stdin=subprocess.PIPE)
proc.communicate(str(solver.number).encode())
proc.wait()
### read T-matrix dimensions
with open(
'{tmatrix_output_dir}/Infotmatrix.dat'.format(
tmatrix_output_dir=tmatrix_output_dir), 'r') as f:
lines = f.readlines()
### last entries in the final two lines give Nrank, Mrank, respectively
m_rank_str = lines[-1].split()[-1]
n_rank_str = lines[-2].split()[-1]
### Remove the pesky comma and period from the string
m_rank = int(m_rank_str[:-1])
n_rank = int(n_rank_str[:-1])
### read T-matrix output
tmatrix_file = '{tmatrix_output_dir}/tmatrix.dat'.format(
tmatrix_output_dir=tmatrix_output_dir)
data = pandas.read_csv(
tmatrix_file, skiprows=3, delim_whitespace=True,
header=None).values.flatten() # read as flat array
data = data[~np.isnan(data)] # throw out the NaNs
data_real = data[::2] # every other element is the real part
data_imag = data[1::2]
### restructure T-matrix
T = np.zeros((2, rmax, 2, rmax), dtype=complex)
if solver == tmatrix_solvers.axisymmetric:
T_nfmds = np.reshape(data_real, (-1, 2*n_rank)) \
+ 1j*np.reshape(data_imag, (-1, 2*n_rank)) # reshape the final result to have 2*n_rank columns
for r1, n1, m1 in miepy.mode_indices(lmax,
m_start=-m_rank,
m_stop=m_rank):
for r2, n2, m2 in miepy.mode_indices(lmax,
m_start=-m_rank,
m_stop=m_rank):
if m1 != m2:
continue
n_max = n_rank - max(1, abs(m1)) + 1
l1 = n1 - max(1, abs(m1))
l2 = n2 - max(1, abs(m2))
x = 2 * n_rank * abs(m1) + l1
y = l2
factor = -1j**(n2 - n1)
T[1, r1, 1, r2] = T_nfmds[x, y] * factor
T[0, r1, 0, r2] = T_nfmds[x + n_max, y + n_max] * factor
T[0, r1, 1,
r2] = T_nfmds[x + n_max, y] * factor * np.sign(m1)
T[1, r1, 0,
r2] = T_nfmds[x, y + n_max] * factor * np.sign(m2)
else:
def rindex(n, m):
return (n - 1) * (n + 2) + m + 1
M = (data_real + 1j * data_imag).reshape([2 * rmax, 2 * rmax])
m1p, n1p = 0, 1
for i in range(rmax):
if n1p > n_rank:
if m1p >= 0:
m1p = -(m1p + 1)
else:
m1p = -m1p
n1p = abs(m1p)
m2p, n2p = 0, 1
for j in range(rmax):
if n2p > n_rank:
if m2p >= 0:
m2p = -(m2p + 1)
else:
m2p = -m2p
n2p = abs(m2p)
r1 = rindex(n1p, m1p)
r2 = rindex(n2p, m2p)
factor = -1j**(n2p - n1p)
f1 = 1
f2 = 1
if m1p % 2 == 1 and m2p % 2 == 1:
f1 *= np.sign(m1p * m2p)**(n1p + n2p + 1)
f2 *= np.sign(m1p * m2p)**(n1p + n2p)
T[0, r1, 0, r2] = M[rmax + i, rmax + j] * factor * f1
T[1, r1, 1, r2] = M[i, j] * factor * f1
T[0, r1, 1, r2] = -M[i + rmax, j] * factor * f2
T[1, r1, 0, r2] = -M[i, j + rmax] * factor * f2
n2p += 1
n1p += 1
return T
