def run(renorm, beta, S0, PI0):
params['renormalized'] = True if renorm == 1 else False
params['beta'] = beta
migdal = Migdal(params, basedir)
savedir, mu, G, D, S, GG = migdal.selfconsistency(sc_iter=200,
frac=0.4,
cont=True,
S0=S0,
PI0=PI0)
print('compute jjcorr ivn')
migdal.compute_jjcorr(savedir, G, D)
PI = params['g0']**2 * GG
migdal = RealAxisMigdal(params, basedir)
migdal.selfconsistency(sc_iter=35, frac=0.8, cont=False)
print('compute jjcorr w')
path = os.path.join(savedir, 'G.npy')
G = np.load(path)
path = os.path.join(savedir, 'GR.npy')
GR = np.load(path)
migdal.compute_jjcorr(savedir, G, GR)
return S, PI
def compute_single_particle(basedir):
lambs = [0.2, 0.4, 0.5]
S0, PI0 = None, None
fracs = [0., 0.8, 0.2]
for i, lamb in enumerate(lambs):
print('2D Renormalized Migdal')
W = 8.0
params['g0'] = lamb2g0_ilya(lamb, params['omega'], W)
print('g0 is ', params['g0'])
migdal = Migdal(params, basedir)
sc_iter = 800
savedir, mu, G, D, S, GG = migdal.selfconsistency(sc_iter,
S0=S0,
PI0=PI0,
frac=fracs[i],
cont=True)
PI = params['g0']**2 * GG
save(savedir + 'S.npy', S)
save(savedir + 'PI.npy', PI)
save(savedir + 'G.npy', G)
save(savedir + 'D.npy', D)
#sc_iter = 300
#Xsc, Xcdw = migdal.susceptibilities(sc_iter, G, D, PI, frac=0.7)
#save(savedir + 'Xsc.npy', [Xsc])
#save(savedir + 'Xcdw.npy', [Xcdw])
S0, PI0 = S, PI
def imag_axis():
interp = None
if False:
interp_folder = '/scratch/users/bln/elph/data/2d/data/data_{}renormalized_nk120_abstp0.300_dim2_g00.33665_nw128_omega0.170_dens0.800_beta16.0000_QNone'.format(
'' if renorm == 1 else 'un')
interp = Interp(interp_folder, params['nk'])
migdal = Migdal(params, basedir)
migdal.selfconsistency(sc_iter=1, frac=0.9, cont=True, interp=interp)
def compute_susceptibilities(basedir):
shutil.rmtree(basedir + 'data/')
S0, PI0 = None, None
lambs = linspace(0.01, 0.6, 20)
fracs = linspace(0.8, 0.2, len(lambs))
Xscs = []
Xcdws = []
for i, lamb in enumerate(lambs):
print('2D Renormalized Migdal')
W = 8.0
params['g0'] = lamb2g0_ilya(lamb, params['omega'], W)
print('g0 is ', params['g0'])
migdal = Migdal(params, basedir)
sc_iter = 400
savedir, mu, G, D, S, GG = migdal.selfconsistency(savedir,
sc_iter,
S0=S0,
PI0=PI0,
frac=0.2)
PI = params['g0']**2 * GG
if G is None: break
sc_iter = 300
Xsc, Xcdw = migdal.susceptibilities(savedir,
sc_iter,
G,
D,
GG,
frac=0.7)
save(savedir + 'Xsc.npy', [Xsc])
save(savedir + 'Xcdw.npy', [Xcdw])
if Xsc is None or Xcdw is None: break
Xscs.append(Xsc)
Xcdws.append(amax(Xcdw))
save(basedir + 'Xscs.npy', Xscs)
save(basedir + 'Xcdws.npy', Xcdws)
save(basedir + 'lambs.npy', lambs)
S0, PI0 = S, PI
def x_vs_t_interp():
if not params['renormalized']:
params['beta'] = 1.0
dbeta = 1.0
dbeta_min = 0.8
else:
params['beta'] = 73.0
dbeta = 9.0
dbeta_min = 2.5
S0, PI0 = None, None
while dbeta > dbeta_min and params['beta'] < 90:
#interp_folder = basedir+'data/data_renormalized_nk16_abstp0.300_dim2_g00.33665_nw256_omega0.170_dens0.800_beta{:.4f}_QNone'.format(params['beta'])
interp = None
interp_folder = basedir+'data/data_{}_nk32_abstp0.300_dim2_g00.33665_nw256_omega0.170_dens0.800_beta{:.4f}_QNone'.format('renormalized' if params['renormalized'] else 'unrenormalized', params['beta'])
if not os.path.exists(interp_folder):
print('performing interp')
interp = Interp(interp_folder, params['nk'])
else:
print('no interp')
migdal = Migdal(params, basedir)
savedir, mu, G, D, S, GG = migdal.selfconsistency(sc_iter=1000, frac=0.2, S0=S0, PI0=PI0, cont=True, interp=interp)
if G is not None:
params['beta'] += dbeta
S0 = S
PI0 = GG * params['g0']**2
Xsc, Xcdw = migdal.susceptibilities(savedir, 1000, G, D, GG, frac=1)
if Xcdw is None:
print('Xcdw blew up. Decrease beta')
dbeta /= 2
params['beta'] -= dbeta
continue
print('Xsc', Xsc)
np.save(savedir + 'Xsc.npy', [Xsc])
np.save(savedir + 'Xcdw.npy', Xcdw)
else:
print('G is None')
return
def run_imag_axis():
params = read_params(basedir, rfolder)
migdal = Migdal(params, basedir)
G = np.load(basedir + 'data/' + rfolder + 'G.npy')
D = np.load(basedir + 'data/' + rfolder + 'D.npy')
print(G.shape)
migdal.compute_jjcorr(G, D)
params = read_params(basedir, ufolder)
migdal = Migdal(params, basedir)
G = np.load(basedir + 'data/' + ufolder + 'G.npy')
D = np.load(basedir + 'data/' + ufolder + 'D.npy')
print(G.shape)
migdal.compute_jjcorr(G, D)
def x_vs_t():
if not params['renormalized']:
params['beta'] = 1.0
dbeta = 1.0
dbeta_min = 0.8
else:
params['beta'] = 1.0
dbeta = 9.0
dbeta_min = 2.5
S0, PI0 = None, None
while dbeta > dbeta_min and params['beta'] < 90:
migdal = Migdal(params, basedir)
savedir, mu, G, D, S, GG = migdal.selfconsistency(sc_iter=1000, frac=0.2, S0=S0, PI0=PI0, cont=True)
if G is not None:
params['beta'] += dbeta
S0 = S
PI0 = GG * params['g0']**2
Xsc, Xcdw = migdal.susceptibilities(savedir, 1000, G, D, GG, frac=1)
if Xcdw is None:
print('Xcdw blew up. Decrease beta')
dbeta /= 2
params['beta'] -= dbeta
continue
print('Xsc', Xsc)
np.save(savedir + 'Xsc.npy', [Xsc])
np.save(savedir + 'Xcdw.npy', Xcdw)
else:
print('G is None')
return
basedir = '/scratch/users/bln/migdal_check_vs_ilya/single_particle_unrenorm/'
if os.path.exists(basedir):
shutil.rmtree(basedir)
lamb = 0.4
frac = 0.8
S0, PI0 = None, None
print('2D unrenormalized Migdal')
params['g0'] = lamb2g0_ilya(lamb, omega, 8.0)
print('g0 = ', params['g0'])
time0 = time.time()
migdal = Migdal(params, basedir)
sc_iter = 2000
savedir, mu, G, D, S, GG = migdal.selfconsistency(sc_iter,
S0=S0,
PI0=PI0,
frac=frac)
print('savedir : ', savedir)
#save(savedir+'G.npy', G)
#save(savedir+'D.npy', D)
save(savedir + 'S.npy', S)
save(savedir + 'GG.npy', GG)
#------------------------------------------------
# reproduce the selfenergy plots
params['beta'] = 16.0
params['dim'] = 2
params['g0'] = mylamb2g0(lamb=1 / 6, omega=params['omega'], W=8.0)
params['Q'] = None
params['dw'] = 0.005
params['idelta'] = 0.010j
params['wmin'] = -5.0
params['wmax'] = 9.0
#params['wmin'] = -1.9
#params['wmax'] = 6.5
#params['wmin'] = -4.2
#params['wmax'] = +4.2
#params['idelta'] = 0.002j
migdal = Migdal(params, basedir)
def imag_axis():
interp = None
if True:
interp_folder = '/scratch/users/bln/elph/data/2d/data/data_{}renormalized_nk120_abstp0.300_dim2_g00.33665_nw128_omega0.170_dens0.800_beta16.0000_QNone'.format(
'' if renorm == 1 else 'un')
interp = Interp(interp_folder, params['nk'])
migdal.selfconsistency(sc_iter=200, frac=0.9, cont=True, interp=interp)
def real_axis():
interp = None
if True:
def test1():
migdal = Migdal(params, basedir)
savedir, mu, G, D, S, GG = migdal.selfconsistency(sc_iter=200, frac=0.6, cont=True)
Xsc, Xcdw = migdal.susceptibilities(500, G, D, GG, frac=0.7)
print('Xsc', Xsc)
def test_single_iteration():
basedir = '/scratch/users/bln/elph/debug/'
lamb = 0.6
W = 8.0
params['g0'] = sqrt(0.5 * lamb / 2.4 * params['omega'] * W)
params['nk'] = 2
params['nw'] = 512
params['beta'] = 16.0
params['dens'] = 0.8
params['omega'] = 0.5
omega = params['omega']
migdal = Migdal(params, basedir)
S0, PI0, sc_iter = None, None, 1
savedir, G, D, S, GG = migdal.selfconsistency(sc_iter, S0=S0, PI0=PI0, frac=1.0)
PI = params['g0']**2 * GG
S = fourier.t2w(S, params['beta'], 2, 'fermion')[0]
PI = fourier.t2w(PI, params['beta'], 2, 'boson')
print('S from migdal')
print(shape(S))
savedir, wn, vn, ek, mu, deriv, dndmu = migdal.setup()
print('E = ', params['band'](params['nk'], 1.0, params['tp']))
nk = params['nk']
nw = params['nw']
beta = params['beta']
wn = (2.0*arange(nw)+1.0) * pi / beta
vn = (2.0*arange(nw+1)) * pi / beta
ekmu = params['band'](nk, 1.0, params['tp']) - mu
Dv0 = -2.0*omega/(vn**2 + omega**2)
nB = 1.0/(exp(beta*omega)-1.0)
S_ = zeros((nk,nk,nw), dtype=complex)
for ik1 in range(nk):
for ik2 in range(nk):
for iq1 in range(nk):
for iq2 in range(nk):
E = ekmu[iq1,iq2]
nF = 1.0/(exp(beta*E)+1.0)
S_[ik1,ik2,:] += (nB + nF)/(1j*wn - E + omega) + (nB + 1 - nF)/(1j*wn - E - omega)
S_ *= params['g0']**2 / nk**2
print('S-S_', mean(abs(S-S_)))
figure()
plot(ravel(S).imag-ravel(S_).imag)
plot(ravel(S).real-ravel(S_).real)
title('diff Skw')
#savefig('figs/diff Skw.png')
figure()
plot(ravel(S).imag)
plot(ravel(S_).imag)
plot(ravel(S).real)
plot(ravel(S_).real)
title('Skw')
#savefig('figs/Skw.png')
show()
PI_ = zeros((nk,nk,nw+1), dtype=complex)
for ik1 in range(nk):
for ik2 in range(nk):
for iq1 in range(nk):
for iq2 in range(nk):
ip1 = ((ik1+iq1)-nk//2)%nk
ip2 = ((ik2+iq2)-nk//2)%nk
E1 = ekmu[ik1,ik2]
E2 = ekmu[ip1,ip2]
nF1 = 1.0/(exp(beta*E1)+1.0)
nF2 = 1.0/(exp(beta*E2)+1.0)
if abs(E1-E2)<1e-14:
PI_[iq1,iq2,0] += -beta * nF1 * (1-nF1)
PI_[iq1,iq2,1:] += (nF1 - nF2)/(1j*vn[1:] + E1 - E2)
else:
PI_[iq1,iq2,:] += (nF1 - nF2)/(1j*vn + E1 - E2)
PI_ *= 2.0 * params['g0']**2 / nk**2
print('PI-PI_', mean(abs(PI-PI_)))
'''
figure()
plot(ravel(PI).imag-ravel(PI_).imag)
plot(ravel(PI).real-ravel(PI_).real)
title('re diff PIkw')
#savefig('figs/diff_PIkw.png')
'''
figure()
plot(ravel(PI).imag)
plot(ravel(PI_).imag)
title('Im PIkw')
figure()
plot(ravel(PI).real)
plot(ravel(PI_).real)
title('Re PIkw')
show()