def calc_q_mean(S_list, i, j):
'''
'''
global dims
global Weyl
global r_list, t_list
q_mean_list = []
for w in range(nfreq):
s = S_list[i, j][3 * w:3 * w + 3]
Q = scat.calc_Q(s, dk)[0]
qlist = ut.sort_eig(Q)[0]
r = r_list[i, 3 * w:3 * w + 3, j, 0:dims[w], 0:dims[w]]
t = t_list[i, 3 * w:3 * w + 3, j, 0:dims[w], 0:dims[w]]
Q_eigstates = (ut.sort_eig(Q)[1].T)
# qmax = np.sum(
# np.absolute(np.dot(r[1], state_max))**2 * \
# ( np.angle(np.dot(r[2], state_max)) - np.angle(np.dot(r[0], state_max)) ) / (2*dk) +\
# np.absolute(np.dot(t[1], state_max))**2 * \
# ( np.angle(np.dot(t[2], state_max)) - np.angle(np.dot(t[0], state_max)) ) / (2*dk)
# )
for n, state in enumerate(Q_eigstates):
qman = np.sum(
np.absolute(np.dot(s[1], state))**2 * \
( np.angle(np.dot(s[2], state)) - np.angle(np.dot(s[0], state)) ) / (2*dk)
)
#print np.shape(t[2]), np.shape(t[0]), np.shape(state), np.shape(state[0:dims[w]]),
tran = (np.linalg.norm(np.dot(t[0], state[0:dims[w]]))**2,
np.linalg.norm(np.dot(t[1], state[0:dims[w]]))**2,
np.linalg.norm(np.dot(t[2], state[0:dims[w]]))**2)
refl = (np.linalg.norm(np.dot(r[2], state[0:dims[w]]))**2,
np.linalg.norm(np.dot(r[0], state[0:dims[w]]))**2)
if (abs((qman - qlist[n]) / qlist[n]) > 0.01):
print "TIME", i, j, w, n, qman, qlist[n]
qlist[n] = abs(qman)
if (abs((tran[1] - tran[0]) / tran[0]) > 0.001 or abs(
(tran[2] - tran[1]) / tran[1]) > 0.001):
print "TRANS", i, j, w, n, tran[1], tran[0], qman
qmax = 0.0 * qlist[-1]
#qmax = 100000.0*Weyl[0]
sdiff = np.array([
s[0] * np.exp(imag_i * qmax * dk), s[1],
s[2] * np.exp(-imag_i * qmax * dk)
])
Qdiff = scat.calc_Q(sdiff, dk)[0]
#qlist[-1] = ut.sort_eig(Qdiff)[0][-1] + qmax
#qlist = ut.sort_eig(Qdiff)[0] + qmax
q_mean = np.sum(qlist)
q_mean_list.append(q_mean)
return q_mean_list
def calc_q_mean(S_list, i, j):
'''
'''
global dims
global Weyl
global r_list, t_list
q_mean_list=[]
for w in range(nfreq):
s = S_list[i,j][3*w:3*w+3]
Q = scat.calc_Q(s,dk)[0]
qlist = ut.sort_eig(Q)[0]
r = r_list[i,3*w:3*w+3,j,0:dims[w],0:dims[w]]
t = t_list[i,3*w:3*w+3,j,0:dims[w],0:dims[w]]
Q_eigstates = (ut.sort_eig(Q)[1].T)
# qmax = np.sum(
# np.absolute(np.dot(r[1], state_max))**2 * \
# ( np.angle(np.dot(r[2], state_max)) - np.angle(np.dot(r[0], state_max)) ) / (2*dk) +\
# np.absolute(np.dot(t[1], state_max))**2 * \
# ( np.angle(np.dot(t[2], state_max)) - np.angle(np.dot(t[0], state_max)) ) / (2*dk)
# )
for n, state in enumerate(Q_eigstates):
qman = np.sum(
np.absolute(np.dot(s[1], state))**2 * \
( np.angle(np.dot(s[2], state)) - np.angle(np.dot(s[0], state)) ) / (2*dk)
)
#print np.shape(t[2]), np.shape(t[0]), np.shape(state), np.shape(state[0:dims[w]]),
tran = ( np.linalg.norm(np.dot(t[0], state[0:dims[w]]))**2,
np.linalg.norm(np.dot(t[1], state[0:dims[w]]))**2,
np.linalg.norm(np.dot(t[2], state[0:dims[w]]))**2 )
refl = ( np.linalg.norm(np.dot(r[2], state[0:dims[w]]))**2, np.linalg.norm(np.dot(r[0], state[0:dims[w]]))**2 )
if ( abs((qman-qlist[n])/qlist[n]) > 0.01 ):
print "TIME", i, j, w, n, qman, qlist[n]
qlist[n] = abs(qman)
if ( abs((tran[1]-tran[0])/tran[0]) > 0.001 or
abs((tran[2]-tran[1])/tran[1]) > 0.001):
print "TRANS", i, j, w, n, tran[1], tran[0], qman
qmax = 0.0*qlist[-1]
#qmax = 100000.0*Weyl[0]
sdiff = np.array([s[0] * np.exp(imag_i*qmax*dk), s[1], s[2] * np.exp(-imag_i*qmax*dk)])
Qdiff = scat.calc_Q(sdiff,dk)[0]
#qlist[-1] = ut.sort_eig(Qdiff)[0][-1] + qmax
#qlist = ut.sort_eig(Qdiff)[0] + qmax
q_mean = np.sum(qlist)
q_mean_list.append(q_mean)
return q_mean_list
def calc_q_eigv(S_list, i, j, w):
'''
Calculates traces of Q operators of S matrices stored in S_list.
If imaginary part of the traces are too high, their value is set
to 0 and the number of configurations at this radius and frequency
is decremented by 1.
'''
s = S_list[i,j][3*w:3*w+3]
return ut.sort_eig(scat.calc_Q(s,dk)[0])[0].real
def calc_q_eigv(S_list, i, j, w):
'''
Calculates traces of Q operators of S matrices stored in S_list.
If imaginary part of the traces are too high, their value is set
to 0 and the number of configurations at this radius and frequency
is decremented by 1.
'''
s = S_list[i,j][3*w:3*w+3]
return ut.sort_eig(scat.calc_Q(s,dk)[0])[0].real
def calc_q_mean(S_list, i, j):
'''
Calculates traces of Q operators of S matrices stored in S_list.
If imaginary part of the traces are too high, their value is set
to 0 and the number of configurations at this radius and frequency
is decremented by 1.
'''
global dims
global conf_counter
global herm_counter
q_mean_list=[]
for w in range(nfreq):
s = S_list[i,j][3*w:3*w+3]
q_mean = np.trace(scat.calc_Q(s,dk)[0]) /(2*dims[w])
q_mean_list.append(q_mean)
return q_mean_list
if (len(dims)%3 == 0):
Nk = len(dims)/3
else:
sys.exit("ABORT: # of S matrices not divisible by 3")
if (refmodes > int(min(dims)/2)):
print ("WARNING: # of modes under consideration (%i)"
"> # minimum number of modes (%i)") % (refmodes,int(min(dims)/2))
t , t_q = trans.calc_t(S, dims, refmodes)
#trans.write_teigvals(t_q, refmodes)
q = []
for i in range(Nk):
q.append(scat.calc_Q([t_q[3*i+0],t_q[3*i+1],t_q[3*i+2]], dk, inv=True)[0])
q = np.array(q)
refpos = int(0.5*(Nk-1)) # k[refpos] = kmean for odd Nk
qref = q[refpos]
tref = t_q[3*refpos+1]
tinv = np.linalg.inv(tref)
class TRC:
'''
'''
modes = 0
refmodes = 0
refpos = 0
sys.exit("ABORT: # of S matrices not divisible by 3")
if (refmodes > int(min(dims) / 2)):
print "WARNING: # of modes under consideration (%i) > # minimum number of modes (%i)" % (
refmodes, int(min(dims) / 2))
t = []
t_q = []
for i in range(3 * Nk):
m = dims[i] / 2
n = min(m, refmodes)
t.append(S[i][m:, 0:m]) # full t-matrix
t_q.append(S[i][m:m + n, 0:n]) # considered part of t-matrix
q = np.array([
scat.calc_Q([t_q[3 * i + 0], t_q[3 * i + 1], t_q[3 * i + 2]], dk,
inv=True)[0] for i in range(Nk)
])
refpos = int(0.5 * (Nk - 1)) # k[refpos] = kmean for odd Nk
### define operators at reference frequency ###
qref = q[refpos]
tref = t_q[3 * refpos + 1]
tinv = np.linalg.inv(tref)
### calculate and sort eigenvalues and left and right eigenvectors of operators ###
qeigval, qeigvec = ut.sort_eig(qref)
teigval, teigvec = ut.sort_eig(tref)
qeigvec = qeigvec.transpose()
teigvec = teigvec.transpose()
modesvec = np.identity(refmodes, dtype="complex")
def calc_q_mean(S_list, i, j):
return [np.trace( scat.calc_Q(s,dk)[0] ) for s in np.reshape(S_list[i,j], (nfreq,3))]
if (abs(q_mean.imag) > 10**(-0)):
q_mean = 0.0
conf_counter[i, w] -= 1
herm_counter += 1
#print i,w,conf_counter[i,w]
q_mean_list.append(q_mean)
return q_mean_list
if (do_pickle):
q_mean = np.array([[calc_q_mean(S, i, j) for j in range(nconf)]
for i in range(nr)])
qt_bar = np.array([[[
np.trace(scat.calc_Q(t[i, 3 * w:3 * w + 3, j], dk)[0]) / dims[w]
for j in range(nconf)
] for w in range(nfreq)] for i in range(nr)])
qr_bar = np.array([[[
np.trace(scat.calc_Q(r[i, 3 * w:3 * w + 3, j], dk)[0]) / dims[w]
for j in range(nconf)
] for w in range(nfreq)] for i in range(nr)])
qtp_bar = np.array([[[
np.trace(scat.calc_Q(tp[i, 3 * w:3 * w + 3, j], dk)[0]) / dims[w]
for j in range(nconf)
] for w in range(nfreq)] for i in range(nr)])
qrp_bar = np.array([[[
np.trace(scat.calc_Q(rp[i, 3 * w:3 * w + 3, j], dk)[0]) / dims[w]
for j in range(nconf)
] for w in range(nfreq)] for i in range(nr)])
#!/usr/bin/env python import numpy as np import scipy as sp import scipy.optimize as spopt import matplotlib.pyplot as plt import sys from math import pi as Pi from Utils import utils as ut from Scattering import scattering2D as scat filen = str(sys.argv[1]) # namestring of calculation n = int(sys.argv[2]) # number of calculation dims = int(sys.argv[3]) # number of open modes in left lead data_direc = "../../VSC2/AverageDwellTime-20130618/" + filen + "/scatterdata/" energs = scat.read_S(data_direc, filen+".%i.0"%n)[2] kvals = np.sqrt(2*energs) dk = (kvals[-1] - kvals[-2]) / 1.0 filen_i = filen+".%i.%i" % (n,0) print "reading file: "+filen_i S = scat.read_S(data_direc, filen_i)[0] q_mean = np.trace(scat.calc_Q(S,dk)[0]) /(2*dims) print q_mean.real
from Scattering import scattering2D as scat
from Utils import utils as ut
import sys
if (len(sys.argv)!=2):
sys.exit("ABORT: filen needed")
filen = str(sys.argv[1]) # namestring of calculation
S_dis, dims, energs = scat.read_S('./', filen+'.dis')
S_clean, dims, energs = scat.read_S('./', filen+'.clean')
dk = np.sqrt(2.0*energs[-1]) - np.sqrt(2.0*energs[-2])
t_dis = []
t_clean = []
for i in range(3):
nin = dims[i]/2
t_dis.append( S_dis[i][nin:,0:nin] )
t_clean.append( S_clean[i][nin:,0:nin] )
Q = scat.calc_Q(S_clean,dk)[0]
Q11 = Q[0:nin,0:nin]
Q11EigVec = ut.sort_eig(Q11)[1]
TransStates = np.dot( np.linalg.inv(t_dis[1]), np.dot( t_clean[1], Q11EigVec ))
ut.write_states(Q11EigVec.T, nin, nin, energs[-2], filen+'.clean')
ut.write_states(TransStates.T, nin, nin, energs[-2], filen+'.dis')
else:
sys.exit("ABORT: # of S matrices not divisible by 3")
if (refmodes > int(min(dims)/2)):
print "WARNING: # of modes under consideration (%i) > # minimum number of modes (%i)" % (refmodes,int(min(dims)/2))
t = []
t_q = []
for i in range(3*Nk):
m = dims[i]/2
n = min(m, refmodes)
t.append( S[i][m:,0:m] ) # full t-matrix
t_q.append( S[i][m:m+n,0:n] ) # considered part of t-matrix
q = np.array([scat.calc_Q([t_q[3*i+0],t_q[3*i+1],t_q[3*i+2]], dk, inv=True)[0] for i in range(Nk)])
refpos = int(0.5*(Nk-1)) # k[refpos] = kmean for odd Nk
### define operators at reference frequency ###
qref = q[refpos]
tref = t_q[3*refpos+1]
tinv = np.linalg.inv(tref)
### calculate and sort eigenvalues and left and right eigenvectors of operators ###
qeigval, qeigvec = ut.sort_eig(qref)
teigval, teigvec = ut.sort_eig(tref)
qeigvec = qeigvec.transpose()
teigvec = teigvec.transpose()
modesvec = np.identity(refmodes, dtype="complex")
print "Composing block %i of %i" % (block+1,Nseg),
Seg, comp = compose_block(Slist, dims, Nseg)
Seglist.append(Seg) # get new segments by composing old segments to blocks
print comp
Slist = Seglist # define blocks as new segments
Slist = np.array(Slist)
S = Slist[0]
print
print "Transmission eigenvalues at center frequency:"
print np.abs(ut.sort_eig(trans.calc_t(S, dims, 1)[0][Nw/2])[0])
print "Reflection eigenvalues at center frequency: "
print np.abs(ut.sort_eig(trans.calc_r(S, dims, 1)[0][Nw/2])[0])
Q = scat.calc_Q((S[3*(Nw/3/2)+0],S[3*(Nw/3/2)+1],S[3*(Nw/3/2)+2]), dw, inv=False)[0]
Q = scat.calc_Q((S[0],S[1],S[2]), dw, inv=False)[0]
print "Time delay eigenvalues at center frequency:"
print ut.sort_eig(Q)[0]
print "Writing output"
create_output(S, energs, filen)
#!/usr/bin/env python import numpy as np import scipy as sp import scipy.optimize as spopt import matplotlib.pyplot as plt import sys from math import pi as Pi from Utils import utils as ut from Scattering import scattering2D as scat filen = str(sys.argv[1]) # namestring of calculation n = int(sys.argv[2]) # number of calculation dims = int(sys.argv[3]) # number of open modes in left lead data_direc = "../../VSC2/AverageDwellTime-20130618/" + filen + "/scatterdata/" energs = scat.read_S(data_direc, filen + ".%i.0" % n)[2] kvals = np.sqrt(2 * energs) dk = (kvals[-1] - kvals[-2]) / 1.0 filen_i = filen + ".%i.%i" % (n, 0) print "reading file: " + filen_i S = scat.read_S(data_direc, filen_i)[0] q_mean = np.trace(scat.calc_Q(S, dk)[0]) / (2 * dims) print q_mean.real
chi = np.array(map(calc_modes, np.arange(0,nin))) def calc_yoperator(chi): '''calculate operator y-d/2 in chi-representation''' return np.dot(chi.conj() * [list(yrange),]*nin, chi.T)*dy y_op = calc_yoperator(chi) y_eigm = ut.sort_eig(y_op)[1].T #y_peaks = np.dot(y_eigm, chi) y_peaks = np.dot(y_eigm, chi) ut.write_states(y_eigm, nin, 'ypeaks') q = scat.calc_Q(t_q, dk, inv=True)[0] q_y = np.dot(y_eigm.conj(), np.dot(q, y_eigm.T)) t_y = np.dot(y_eigm.conj(), np.dot(t_q[1], y_eigm.T)) pos_range = (0, int(0.33*nin-0.5*ptc), int(0.5*nin-0.5*ptc))#, int(0.67*nin-0.5*ptc), nin-ptc) # pos_range: place states to these positions q_peaks = np.zeros((ptc,nin), dtype='complex') for i, pos in enumerate(pos_range): peak_range = range(pos,pos+ptc) q_block = q_y[np.meshgrid(peak_range,peak_range)] block_eigv = ut.sort_eig(q_block)[1].T q_peaks = np.dot(block_eigv, y_eigm[peak_range])
# take only transmitted states for q
t_ref = t_noise[refpos]
tdt_eigval, tdt_eigvec = ut.sort_eig(t_ref.T.conj().dot(t_ref))
trans_mask = np.abs(tdt_eigval) > 1.0e-3
trans_vec = tdt_eigvec[:, trans_mask]
print
print tdt_eigval
print
q_arg = np.einsum('ij,wjk,kl->wil',
trans_vec.conj().T, t_noise_full[3 * refpos:3 * refpos + 3],
trans_vec)
q = scat.calc_Q(q_arg, dw, inv=True)[0]
q = trans_vec.dot(q).dot(trans_vec.conj().T)
qeigstates = ut.sort_eig(q)[1].T
phat = qeigstates[qeigst]
phat_rand = (np.random.random(size=(refmodes)) -
0.5) - I * (np.random.random(size=(refmodes)) - 0.5)
phat_rand = phat_rand / np.linalg.norm(phat_rand)
if (qeigst < 0): phat = phat_rand
if (readst):
phat = np.load("movie." + filen + "." + "opt_rand" + ".dat.npy")[2]
if (not nloop and nloop != 0): nloop = 100000
phat = normalize(phat)
# take only transmitted states for q
t_ref = t_noise[refpos]
tdt_eigval, tdt_eigvec = ut.sort_eig(t_ref.T.conj().dot(t_ref))
trans_mask = np.abs(tdt_eigval) > 1.0e-3
trans_vec = tdt_eigvec[:,trans_mask]
print
print tdt_eigval
print
q_arg = np.einsum('ij,wjk,kl->wil',
trans_vec.conj().T, t_noise_full[3*refpos:3*refpos+3], trans_vec)
q = scat.calc_Q(q_arg, dw, inv=True)[0]
q = trans_vec.dot(q).dot(trans_vec.conj().T)
qeigstates = ut.sort_eig(q)[1].T
phat = qeigstates[qeigst]
phat_rand = (np.random.random(size=(refmodes)) - 0.5) - I * (np.random.random(size=(refmodes)) - 0.5)
phat_rand = phat_rand / np.linalg.norm(phat_rand)
if(qeigst<0): phat = phat_rand
if(readst): phat = np.load("movie."+filen+"."+"opt_rand"+".dat.npy")[2]
if (not nloop and nloop!=0): nloop = 100000
phat = normalize(phat)
phat_new = phat