z sym lattices?
Former-commit-id: 3f2148ed9eaac6b4b79a97c5bafee164aeb19df4
This commit is contained in:
Marek Nečada
parent
478c93276f
commit
9e390e0d30
126
qpms/lattices.py
126
qpms/lattices.py
|
|
@ -2,6 +2,7 @@
|
||||||
Object oriented approach for the classical multiple scattering problem.
|
Object oriented approach for the classical multiple scattering problem.
|
||||||
'''
|
'''
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
nx = np.newaxis
|
||||||
import time
|
import time
|
||||||
import scipy
|
import scipy
|
||||||
import sys
|
import sys
|
||||||
|
|
@ -139,7 +140,7 @@ class Scattering(object):
|
||||||
pq_0 = np.broadcast_to(pq_0, (self.N,2,self.nelem))
|
pq_0 = np.broadcast_to(pq_0, (self.N,2,self.nelem))
|
||||||
MP_0 = np.empty((N,2,nelem),dtype=np.complex_)
|
MP_0 = np.empty((N,2,nelem),dtype=np.complex_)
|
||||||
for j in range(self.N):
|
for j in range(self.N):
|
||||||
MP_0[j] = np.tensordot(self.TMatrices[j], pq_0[j],axes=[-2,-1],[-2,-1])
|
MP_0[j] = np.tensordot(self.TMatrices[j], pq_0[j],axes=([-2,-1],[-2,-1]))
|
||||||
MP_0.shape = (N*2*self.nelem,)
|
MP_0.shape = (N*2*self.nelem,)
|
||||||
solvebtime = _time_b(verbose,step='Solving the linear equation')
|
solvebtime = _time_b(verbose,step='Solving the linear equation')
|
||||||
ab = scipy.linalg.lu_solve(self.lupiv, MP_0)
|
ab = scipy.linalg.lu_solve(self.lupiv, MP_0)
|
||||||
|
|
@ -187,4 +188,125 @@ class Scattering_2D_lattice(Scattering):
|
||||||
nelem = lMax * (lMax + 2) #!
|
nelem = lMax * (lMax + 2) #!
|
||||||
self.nelem = nelem #!
|
self.nelem = nelem #!
|
||||||
self.prepared = False
|
self.prepared = False
|
||||||
self.TMatrices = np.broadcast_to(TMatrices, (self.N,2,nelem,2,nelem)) def __init__(self):
|
self.TMatrices = np.broadcast_to(TMatrices, (self.N,2,nelem,2,nelem))
|
||||||
|
|
||||||
|
class Scattering_2D_zsym(Scattering):
|
||||||
|
def __init__(self, positions, TMatrices, k_0, lMax = None, verbose=False, J_scat=3):
|
||||||
|
Scattering.__init__(self, positions, TMatrices, k_0, lMax, verbose, J_scat)
|
||||||
|
#TODO some checks on TMatrices symmetry
|
||||||
|
self.TE_yz = np.arange(self.nelem)
|
||||||
|
self.TM_yz = self.TE_yz
|
||||||
|
self.my, self.ny = get_mn_y(self.lMax)
|
||||||
|
self.TE_NMz = (self.my + self.ny) % 2
|
||||||
|
self.TM_NMz = 1 - self.TE_NMz
|
||||||
|
# TODO možnost zadávat T-matice rovnou ve zhuštěné podobě
|
||||||
|
TMatrices_TE = TMatrices[...,TE_NMz[:,nx],TE_yz[:,nx],TE_NMZ[nx,:],TE_yz[nx,:]]
|
||||||
|
TMatrices_TM = TMatrices[...,TM_NMz[:,nx],TM_yz[:,nx],TM_NMZ[nx,:],TM_yz[nx,:]]
|
||||||
|
self.TMatrices_TE = np.broadcast_to(TMatrices_TE, (self.N, self.nelem, self.nelem))
|
||||||
|
self.TMatrices_TM = np.broadcast_to(TMatrices_TM, (self.N, self.nelem, self.nelem))
|
||||||
|
self.prepared_TE = False
|
||||||
|
self.prepared_TM = False
|
||||||
|
|
||||||
|
def prepare_partial(self, TE_or_TM, keep_interaction_matrix = False, verbose=False):
|
||||||
|
'''
|
||||||
|
TE is 0, TM is 1.
|
||||||
|
'''
|
||||||
|
btime = _time_b(verbose)
|
||||||
|
if (TE_or_TM == 0): #TE
|
||||||
|
if not self.prepared_TE:
|
||||||
|
if not self.interaction_matrix_TE:
|
||||||
|
self.build_interaction_matrix(0, verbose)
|
||||||
|
self.lupiv_TE = scipy.linalg.lu_factor(self.interaction_matrix_TE, overwrite_a = not keep_interaction_matrix)
|
||||||
|
self.prepared_TE = True
|
||||||
|
if (TE_or_TM == 1): #TM
|
||||||
|
in not self.prepared_TM:
|
||||||
|
if not self.interaction_matrix_TM:
|
||||||
|
self.build_interaction_matrix(1, verbose)
|
||||||
|
self.lupiv_TM = scipy.linalg.lu_factor(self.interaction_matrix_TM, overwrite_a = not keep_interaction_matrix)
|
||||||
|
self.prepared_TM = True
|
||||||
|
_time_e(btime, verbose)
|
||||||
|
|
||||||
|
def prepare(self, keep_interaction_matrix = False, verbose=False):
|
||||||
|
btime = _time_b(verbose)
|
||||||
|
if not self.prepared:
|
||||||
|
self.prepare_partial(0, keep_interaction_matrix, verbose)
|
||||||
|
self.prepare_partial(1, keep_interaction_matrix, verbose)
|
||||||
|
self.prepared = True
|
||||||
|
_time_e(btime, verbose)
|
||||||
|
|
||||||
|
def build_interaction_matrix(self,TE_or_TM = None, verbose = False):
|
||||||
|
#None means both
|
||||||
|
btime = _time_b(verbose)
|
||||||
|
N = self.N
|
||||||
|
my, ny = get_mn_y(self.lMax)
|
||||||
|
nelem = len(my)
|
||||||
|
idm = np.identity(nelem)
|
||||||
|
if (TE_or_TM == 0):
|
||||||
|
EoMl = (0,)
|
||||||
|
elif (TE_or_TM == 1):
|
||||||
|
EoMl = (1,)
|
||||||
|
elif (TE_or_TM is None):
|
||||||
|
EoMl = (0,1)
|
||||||
|
for EoM in EoMl:
|
||||||
|
leftmatrix = np.zeros((N,nelem,N,nelem), dtype=complex)
|
||||||
|
sbtime = _time_b(verbose, step = 'Calculating interparticle translation coefficients, T%s part' % ('M' if EoM else 'E'))
|
||||||
|
for i in range(N):
|
||||||
|
for j in range(i):
|
||||||
|
for yi in range(nelem):
|
||||||
|
for yj in range(nelem):
|
||||||
|
d_i2j = cart2sph(self.positions[j]-self.positions[i])
|
||||||
|
if ((yi - yj) % 2) == 0:
|
||||||
|
tr = Ã(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*self.k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=self.J_scat)
|
||||||
|
else:
|
||||||
|
tr = B̃(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*self.k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=self.J_scat)
|
||||||
|
leftmatrix[j,yj,i,yi] = tr
|
||||||
|
leftmatrix[i,yi,j,yj] = tr if (0 == (my[yj]+my[yi]) % 2) else -tr
|
||||||
|
_ time_e(sbtime, verbose, step = 'Calculating interparticle translation coefficients, T%s part' % ('M' if EoM else 'E'))
|
||||||
|
for j in range(N):
|
||||||
|
leftmatrix = - np.tensordot(self.TMatrices_TM[j] if EoM else self.TMatrices_TE[j],leftmatrix[j],
|
||||||
|
axes = ([-1],[0]))
|
||||||
|
leftmatrix[j,:,j,:] += idm
|
||||||
|
if EoM == 0:
|
||||||
|
self.interaction_matrix_TE = leftmatrix
|
||||||
|
if EoM == 1:
|
||||||
|
self.interaction_matrix_TM = leftmatrix
|
||||||
|
_time_e(btime, verbose)
|
||||||
|
|
||||||
|
def scatter_partial(self, TE_or_TM, pq_0, verbose = False):
|
||||||
|
'''
|
||||||
|
pq_0 is (N, nelem)-shaped array
|
||||||
|
'''
|
||||||
|
btime = _time_b(verbose)
|
||||||
|
self.prepare_partial(TE_or_TM, verbose = verbose)
|
||||||
|
pq_0 = np.broadcast_to(pq_0, (self.N, self.nelem))
|
||||||
|
MP_0 = np.empty((self.N,self.nelem),dtype=np.complex_)
|
||||||
|
for j in range(self.N):
|
||||||
|
if TE_or_TM: #TM
|
||||||
|
MP_0[j] = np.tensordot(self.TMatrices_TM[j], pq_0[j], axes=([-1],[-1]))
|
||||||
|
else: #TE
|
||||||
|
MP_0[j] = np.tensordot(self.TMatrices_TE[j], pq_0[j], axes=([-1],[-1]))
|
||||||
|
solvebtime = _time_b(verbose,step='Solving the linear equation')
|
||||||
|
ab = scipy.linalg.lu_solve(self.lupiv_TM if TE_or_EM else self.lupiv.TE, MP_0)
|
||||||
|
_time_e(solvebtime, verbose, step='Solving the linear equation')
|
||||||
|
_time_e(btime,verbose)
|
||||||
|
return ab
|
||||||
|
|
||||||
|
def scatter(self, pq_0, verbose = False):
|
||||||
|
'''
|
||||||
|
FI7ME
|
||||||
|
pq_0 is (N, nelem, 2)-shaped array
|
||||||
|
'''
|
||||||
|
btime = _time_b(verbose)
|
||||||
|
self.prepare(verbose=verbose)
|
||||||
|
pq_0 = np.broadcast_to(pq_0, (self.N,2,self.nelem))
|
||||||
|
MP_0 = np.empty((N,2,nelem),dtype=np.complex_)
|
||||||
|
for j in range(self.N):
|
||||||
|
MP_0[j] = np.tensordot(self.TMatrices[j], pq_0[j],axes=([-2,-1],[-2,-1]))
|
||||||
|
MP_0.shape = (N*2*self.nelem,)
|
||||||
|
solvebtime = _time_b(verbose,step='Solving the linear equation')
|
||||||
|
ab = scipy.linalg.lu_solve(self.lupiv, MP_0)
|
||||||
|
_time_e(solvebtime, verbose, step='Solving the linear equation')
|
||||||
|
ab.shape = (N,2,nelem)
|
||||||
|
_time_e(btime, verbose)
|
||||||
|
return ab
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue