#kx = np.linspace(0.004, 0.0042, steps)
omega0_bands = np.zeros((k, kx.shape[0]))
true_bands = np.zeros((k, kx.shape[0]))
###################################################
#phase diagram mu vs gam
dirS = 'bands_data'
if not os.path.exists(dirS):
os.makedirs(dirS)
try:
PLOT = str(sys.argv[1])
except:
PLOT = 'F'
if PLOT != 'P':
for i in range(kx.shape[0]):
print(omega0_bands.shape[1]-i, kx[i])
H = SNRG.Junc_eff_Ham_gen(omega=0, Wj=Wj, Lx=Lx, nodx=cutx, nody=cuty, ax=ax, ay=ay, kx=kx[i], m_eff=0.026, alp_l=alpha, alp_t=alpha, mu=mu, Vj=Vj, Gam=gam, Gam_SC_factor=0, delta=delta, phi=phi, iter=50, eta=0)
S = int(H.shape[0]/2)
H = (H[:S, :])[:, :S]
eigs, vecs = spLA.eigsh(H, k=k, sigma=0, which='LM')
idx_sort = np.argsort(eigs)
eigs = eigs[idx_sort]
#print(eigs)
#arg = np.argmin(np.absolute(eigs))
#print(arg)
omega0_bands[:, i] = eigs[:]
np.save("%s/SOCbands Lx = %.1f Wj = %.1f nodx = %.1f nody = %.1f Vj = %.1f alpha = %.1f delta = %.2f phi = %.3f mu = %.1f gam = %.1f.npy" % (dirS, Lx*.1, Junc_width, Nod_widthx, Nod_widthy, Vj, alpha, delta, phi, mu, gam), omega0_bands)
gc.collect()
#for i in range(omega0_bands.shape[0]):
# for j in range(omega0_bands.shape[1]):
def SNRG_gam_finder(ax,
ay,
mu,
gi,
gf,
Wj=0,
Lx=0,
cutxT=0,
cutyT=0,
cutxB=0,
cutyB=0,
Vj=0,
Vsc=0,
m_eff=0.026,
alpha=0,
delta=0,
phi=0,
k=20,
QX=0,
tol=1e-5,
done=False,
PLOT=False,
n=3,
plot_junction=False):
delta_gam = abs(gf - gi)
n1, n2 = step_finder(delta_gam / (2 * tol) + 1, n)
H0 = SNRG.Junc_eff_Ham_gen(omega=0,
Wj=Wj,
Lx=Lx,
cutxT=cutxT,
cutyT=cutyT,
cutxB=cutxB,
cutyB=cutyB,
ax=ax,
ay=ay,
kx=QX,
m_eff=m_eff,
alp_l=alpha,
alp_t=alpha,
mu=mu,
Vj=Vj,
Vsc=Vsc,
Gam=1e-9,
delta=delta,
phi=phi,
plot_junction=plot_junction)
eigs, vecs = spLA.eigsh(H0, k=k, sigma=0, which='LM')
vecs_hc = np.conjugate(np.transpose(vecs)) #hermitian conjugate
idx_sort = np.argsort(eigs)
eigs = eigs[idx_sort]
#print(eigs)
H_G1 = SNRG.Junc_eff_Ham_gen(
omega=0,
Wj=Wj,
Lx=Lx,
cutxT=cutxT,
cutyT=cutyT,
cutxB=cutxB,
cutyB=cutyB,
ax=ax,
ay=ay,
kx=QX,
m_eff=m_eff,
alp_l=alpha,
alp_t=alpha,
mu=mu,
Vj=Vj,
Vsc=Vsc,
Gam=1 + 1e-9,
delta=delta,
phi=phi
) #Hamiltonian with ones on Zeeman energy along x-direction sites
HG = H_G1 - H0 #the proporitonality matrix for gam-x, it is ones along the sites that have a gam value
HG0_DB = np.dot(vecs_hc, H0.dot(vecs))
HG_DB = np.dot(vecs_hc, HG.dot(vecs))
G_crit = []
delta_gam = abs(gf - gi)
steps = n1
gx = np.linspace(gi, gf, steps)
eig_arr = np.zeros((gx.shape[0]))
for i in range(gx.shape[0]):
H_DB = HG0_DB + gx[i] * HG_DB
eigs_DB, U_DB = LA.eigh(H_DB)
idx_sort = np.argsort(eigs_DB)
eigs_DB = eigs_DB[idx_sort]
eig_arr[i] = eigs_DB[int(k / 2)]
#checking edge cases
if eig_arr[0] < 1e-10:
print("eig at gx=0: ", eig_arr[0])
G_crit.append(gx[0])
if eig_arr[-1] < 1e-10:
print("eig at gx=0", gx[-1], ": ", eig_arr[0])
G_crit.append(gx[-1])
local_min_idx = np.array(argrelextrema(eig_arr, np.less)[0])
print(local_min_idx.size, "Energy local minima found at gx = ",
gx[local_min_idx])
#for i in range(local_min_idx.shape[0]):
# print(eig_arr[:, local_min_idx[i]])
#sys.exit()
if PLOT:
plt.plot(gx, eig_arr[2, :], c='r')
plt.scatter(gx[local_min_idx],
eig_arr[2, local_min_idx],
c='b',
marker='X')
plt.plot(gx, 0 * gx, c='k', lw=1)
plt.show()
for i in range(0, local_min_idx.size): #eigs_min.size
gx_c = gx[local_min_idx[i]] #first approx g_critical
gx_lower = gx[local_min_idx[i] - 1] # one step back
gx_higher = gx[local_min_idx[i] + 1] #one step forward
delta_gam = (gx_higher - gx_lower)
n_steps = n2
gx_finer = np.linspace(gx_lower, gx_higher, n_steps)
eig_arr_finer = np.zeros((gx_finer.size))
for j in range(gx_finer.shape[0]):
H_DB = HG0_DB + gx_finer[j] * HG_DB
eigs_DB, U_DB = LA.eigh(H_DB)
idx_sort = np.argsort(eigs_DB)
eigs_DB = eigs_DB[idx_sort]
eig_arr_finer[j] = eigs_DB[int(k / 2)]
min_idx_finer = np.array(argrelextrema(
eig_arr_finer, np.less)[0]) #new local minima indices
eigs_min_finer = eig_arr_finer[min_idx_finer] #isolating local minima
gx_min_finer = gx_finer[min_idx_finer]
if PLOT:
plt.plot(gx_finer, eig_arr_finer, c='b')
plt.scatter(gx_finer[min_idx_finer],
eig_arr_finer[min_idx_finer],
c='r',
marker='X')
plt.plot(gx_finer, 0 * gx_finer, c='k', lw=1)
plt.show()
for m in range(eigs_min_finer.shape[0]):
if abs(eigs_min_finer[m]) < tol:
G_crit.append(gx_min_finer[m])
print("Crossing found at Gx = {} | E = {} meV".format(
gx_min_finer[m], eigs_min_finer[m]))
if cutxT == 0 and cutxB == 0 and cutyT == 0 and cutyB == 0 and Vj == 0:
G_crit = np.array(G_crit)
return G_crit
if not done:
G_crit = G_crit + SNRG_gam_finder(ax,
ay,
mu,
gi,
gf,
Wj=Wj,
Lx=Lx,
cutxT=cutxT,
cutyT=cutyT,
cutxB=cutxB,
cutyB=cutyB,
Vj=Vj,
Vsc=Vsc,
m_eff=m_eff,
alpha=alpha,
delta=delta,
phi=phi,
k=k,
QX=np.pi / Lx,
done=True,
tol=tol,
PLOT=PLOT)
G_crit.sort()
G_crit = np.array(G_crit)
print(G_crit)
return (G_crit)
if done:
return G_crit
#gap1 = gap[0:103]
#kx_of_gap1 = kx_of_gap[0:103]
#mu2 = np.linspace(mu[103], mu[103+1], 10)
#gap2 = np.zeros(10)
#kx_of_gap2 = np.zeros(10)
#mu3 = mu[105:]
#gap3 = gap[105:]
#kx_of_gap3 = kx_of_gap[105:]
#mu = np.concatenate((mu1, mu2, mu3), axis=None)
#print(mu[0:160])
#sys.exit()
#np.save("%s/mu Wj = %.1f Lx = %.1f cutxT = %.1f cutyT = %.1f cutxB = %.1f cutyB = %.1f Vj = %.1f phi = %.2f mu_i = %.1f mu_f = %.1f gx = %.2f.npy" % (dirS, Junc_width, Lx*.1, cutxT_width, cutyT_width, cutxB_width, cutyB_width, Vj, phi, mu_i, mu_f, gx), mu)
for i in range(0, mu.shape[0]):
print(mu.shape[0]-i, "| mu =", mu[i])
GAP, KX = SNRG.gap(Wj=Wj, Lx=Lx, cutxT=cutxT, cutyT=cutyT, cutxB=cutxB, cutyB=cutyB, ax=ax, ay=ay, gam=gx, mu=mu[i], Vj=Vj, alpha=alpha, delta=delta, phi=phi, targ_steps=50000, n_avg=4, muf=mu[i], PLOT=False, tol=1e-7)
gap[i] = GAP
kx_of_gap[i] = KX
np.save("%s/gapfxmu Wj = %.1f Lx = %.1f cutxT = %.1f cutyT = %.1f cutxB = %.1f cutyB = %.1f Vj = %.1f phi = %.2f mu_i = %.1f mu_f = %.1f gx = %.2f.npy" % (dirS, Junc_width, Lx*.1, cutxT_width, cutyT_width, cutxB_width, cutyB_width, Vj, phi, mu_i, mu_f, gx), gap)
np.save("%s/kxofgapfxmu Wj = %.1f Lx = %.1f cutxT = %.1f cutyT = %.1f cutxB = %.1f cutyB = %.1f Vj = %.1f phi = %.2f mu_i = %.1f mu_f = %.1f gx = %.2f.npy" % (dirS, Junc_width, Lx*.1, cutxT_width, cutyT_width, cutxB_width, cutyB_width, Vj, phi, mu_i, mu_f, gx), kx_of_gap)
gc.collect()
#gap = np.concatenate((gap1, gap2, gap3), axis=None)
#kx_of_gap = np.concatenate((kx_of_gap1, kx_of_gap2, kx_of_gap3), axis=None)
#np.save("%s/gapfxmu Wj = %.1f Lx = %.1f cutxT = %.1f cutyT = %.1f cutxB = %.1f cutyB = %.1f Vj = %.1f phi = %.2f mu_i = %.1f mu_f = %.1f gx = %.2f.npy" % (dirS, Junc_width, Lx*.1, cutxT_width, cutyT_width, cutxB_width, cutyB_width, Vj, phi, mu_i, mu_f, gx), gap)
#np.save("%s/kxofgapfxmu Wj = %.1f Lx = %.1f cutxT = %.1f cutyT = %.1f cutxB = %.1f cutyB = %.1f Vj = %.1f phi = %.2f mu_i = %.1f mu_f = %.1f gx = %.2f.npy" % (dirS, Junc_width, Lx*.1, cutxT_width, cutyT_width, cutxB_width, cutyB_width, Vj, phi, mu_i, mu_f, gx), kx_of_gap)
#gc.collect()
sys.exit()
else:
np.save("%s/mu Wj = %.1f Lx = %.1f cutxT = %.1f cutyT = %.1f cutxB = %.1f cutyB = %.1f Vj = %.1f phi = %.2f mu_i = %.1f mu_f = %.1f gx = %.2f.npy" % (dirS, Junc_width, Lx*.1, cutxT_width, cutyT_width, cutxB_width, cutyB_width, Vj, phi, mu_i, mu_f, gx), mu)
if not os.path.exists(dirS):
os.makedirs(dirS)
try:
PLOT = str(sys.argv[1])
except:
PLOT = 'F'
if PLOT != 'P':
for i in range(mu.shape[0]):
print(steps - i, "| mu =", mu[i])
gapmu[i] = SNRG.gap(Wj=Wj,
Lx=Lx,
nodx=nodx,
nody=nody,
ax=ax,
ay=ay,
gam=gx,
mu=mu[i],
Vj=Vj,
alpha=alpha,
delta=delta,
phi=phi,
muf=10,
targ_steps=2500)[0]
np.save(
"%s/gapfxmu Wj = %.1f nodx = %.1f nody = %.1f Vj = %.1f alpha = %.1f delta = %.2f phi = %.3f mu_i = %.1f mu_f=%.1f gx=%.2f.npy"
% (dirS, Junc_width, Nod_widthx, Nod_widthy, Vj, alpha, delta, phi,
mu_i, mu_f, gx), gapmu)
np.save(
"%s/mu Wj = %.1f nodx = %.1f nody = %.1f Vj = %.1f alpha = %.1f delta = %.2f phi = %.3f mu_i = %.1f mu_f=%.1f gx=%.2f.npy"
% (dirS, Junc_width, Nod_widthx, Nod_widthy, Vj, alpha, delta, phi,
mu_i, mu_f, gx), mu)