def get_LE_basis(coor, ax, ay, NN, NNb, Wj, cutx, cuty, V, mu, gz, alpha, delta, phi):
H0 = spop.HBDG(coor, ax, ay, NN, NNb=NNb, Wj=Wj, cutx=cutx, cuty=cuty, V=V, mu=mu, gammaz=gz, alpha=alpha, delta=delta, phi=phi, qx=1e-4*(np.pi/Lx), periodicX=True) #gives low energy basis
eigs_0, vecs_0 = spLA.eigsh(H0, k=k, sigma=0, which='LM')
vecs_0_hc = np.conjugate(np.transpose(vecs_0)) #hermitian conjugate
H_M0 = spop.HBDG(coor, ax, ay, NN, NNb = NNb, Wj = Wj, cutx = cutx, cuty = cuty, V = V, mu = 0, alpha = alpha, delta = delta, phi = phi, qx = 0, periodicX = True)
H_M1 = spop.HBDG(coor, ax, ay, NN, NNb = NNb, Wj = Wj, cutx = cutx, cuty = cuty, V = V, mu = 1, alpha = alpha, delta = delta, phi = phi, qx = 0, periodicX =True)
HM = H_M1 - H_M0
HM0_DB = np.dot(vecs_0_hc, H_M0.dot(vecs_0))
HM_DB = np.dot(vecs_0_hc, HM.dot(vecs_0))
return HM0_DB, HM_DB
for q in range(qx.shape[0]):
print(qx.shape[0] - q)
for i in range(mu.shape[0]):
if q == 0 or top_array[i] == 1:
if q == 0:
Q = 1e-4 * (np.pi / Lx)
else:
Q = qx[q]
H0 = spop.HBDG(coor,
ax,
ay,
NN,
NNb=NNb,
Wj=Wj,
cutx=cutx,
cuty=cuty,
V=V,
mu=mu[i],
alpha=alpha,
delta=delta,
phi=phi,
gammax=1e-4,
qx=Q,
periodicX=True) #gives low energy basis
eigs_0, vecs_0 = spLA.eigsh(H0, k=k, sigma=0, which='LM')
vecs_0_hc = np.conjugate(
np.transpose(vecs_0)) #hermitian conjugate
H_G0 = spop.HBDG(
coor,
ax,
ay,
NN,
Vj = np.linspace(v_i, v_f, v_steps) #Chemical Potential: [meV]
bands = np.zeros((v_steps, k))
cmap = cm.get_cmap('Oranges')
dirS = 'e_mu_data'
if not os.path.exists(dirS):
os.makedirs(dirS)
try:
PLOT = str(sys.argv[1])
except:
PLOT = 'F'
if PLOT != 'P':
for j in range(v_steps):
V = Vjj(coor, Wj = Wj, Vsc = Vsc, Vj = Vj[j], cutx = cutx, cuty = cuty)
print(v_steps - j)
H = spop.HBDG(coor, ax, ay, NN, NNb=NNb, Wj=Wj, cutx=cutx, cuty=cuty, V=V, mu=0, alpha=alpha, delta=delta, phi=0, qx=0, periodicX=True)
eigs, vecs = spLA.eigsh(H, k=k, sigma=0, which='LM')
idx_sort = np.argsort(eigs)
eigs = eigs[idx_sort]
bands[j, :] = eigs
np.save("%s/bands Lx = %.1f Ly = %.1f Wsc = %.1f Wj = %.1f nodx = %.1f nody = %.1f alpha = %.1f delta = %.2f v_i = %.1f v_f = %.1f.npy" % (dirS, Lx*.1, Ly*.1, SC_width, Junc_width, Nod_widthx, Nod_widthy, alpha, delta, v_i, v_f), bands)
np.save("%s/V0 Lx = %.1f Ly = %.1f Wsc = %.1f Wj = %.1f nodx = %.1f nody = %.1f alpha = %.1f delta = %.2f v_i = %.1f v_f = %.1f.npy" % (dirS, Lx*.1, Ly*.1, SC_width, Junc_width, Nod_widthx, Nod_widthy, alpha, delta, v_i, v_f), Vj)
else:
bands = np.load("%s/bands Lx = %.1f Ly = %.1f Wsc = %.1f Wj = %.1f nodx = %.1f nody = %.1f alpha = %.1f delta = %.2f v_i = %.1f v_f = %.1f.npy" % (dirS, Lx*.1, Ly*.1, SC_width, Junc_width, Nod_widthx, Nod_widthy, alpha, delta, v_i, v_f))
mu = np.load("%s/V0 Lx = %.1f Ly = %.1f Wsc = %.1f Wj = %.1f nodx = %.1f nody = %.1f alpha = %.1f delta = %.2f v_i = %.1f v_f = %.1f.npy" % (dirS, Lx*.1, Ly*.1, SC_width, Junc_width, Nod_widthx, Nod_widthy, alpha, delta, v_i, v_f))
fig = plt.figure()
for j in range(bands.shape[1]):
plt.plot(Vj, bands[:, j], c='r')
VV = sparse.bmat([[None, V], [-V, None]], format='csc', dtype='complex')
plots.junction(coor,
VV,
title='Potential Profile',
savenm='potential_profile.jpg')
k = 48
H = spop.HBDG(coor,
ax,
ay,
NN,
NNb=NNb,
Wj=Wj,
cutx=cutx,
cuty=cuty,
alpha=alpha,
delta=delta,
phi=phi,
V=V,
gammax=gx,
gammaz=gz,
mu=mu,
qx=0.00212,
periodicX=True,
periodicY=False)
eigs, vecs = spLA.eigsh(H, k=k, sigma=0, which='LM')
idx_sort = np.argsort(eigs)
eigs = eigs[idx_sort]
vecs = vecs[:, idx_sort]
print(eigs)
Ny = 50
ax = 10 #[A]
ay = 10 #[A]
coor = shps.square(Nx, Ny)
NN = nb.NN_Arr(coor)
NNb = nb.Bound_Arr(coor)
print("lattice size", coor.shape[0])
alpha = 0 #Spin-Orbit Coupling constant: [eV*A]
gammaz = 0 #Zeeman field energy contribution: [T]
delta = 0 #Superconducting Gap: [eV]
V0 = 0.0 #Amplitude of potential : [eV]
mu = 0 #Chemical Potential: [eV]
H = spop.HBDG(coor, ax, ay, NN, Wj=0)
print("H shape: ", H.shape)
num = 20 # This is the number of eigenvalues and eigenvectors you want
sigma = 0 # This is the eigenvalue we search around
which = 'LM'
eigs, vecs = spLA.eigsh(H, k=num, sigma=sigma, which=which)
plots.state_cmap(coor, eigs, vecs, n=7, title='hole n = 3 energy eigenstate')
plots.state_cmap(coor,
eigs,
vecs,
n=12,
title='particle n = 3 energy eigenstate')
plots.state_cmap(coor, eigs, vecs, n=9, title='hole n = 1 energy eigenstate')
plots.state_cmap(coor,
sys.exit()
##############################################################
#state plot
MU = 2
GX = 0.75
H = spop.HBDG(coor,
ax,
ay,
NN,
NNb=NNb,
Wj=Wj,
V=V,
mu=MU,
gammax=GX,
alpha=alpha,
delta=delta,
phi=np.pi,
qx=0,
periodicX=True,
periodicY=False)
eigs, states = spLA.eigsh(H, k=8, sigma=0, which='LM')
idx_sort = np.argsort(eigs)
print(eigs[idx_sort])
plots.state_cmap(coor, eigs, states, n=4, savenm='prob_density_nodule_n=4.png')
plots.state_cmap(coor, eigs, states, n=5, savenm='prob_density_nodule_n=5.png')
plots.state_cmap(coor, eigs, states, n=6, savenm='prob_density_nodule_n=6.png')