def getGavgFromSelfEnergy(parms, se_filename = None):
N_LAYERS = int(parms['N_LAYERS']);
FLAVORS = int(parms['FLAVORS']);
SPINS = int(parms['SPINS']);
parms['BETA'] = 10;
# prepare data
NMaxFreq = int(parms['N_MAX_FREQ']);
if se_filename is not None:
print 'load self energy from file: ', se_filename;
tmp = genfromtxt(se_filename);
if NMaxFreq > len(tmp): NMaxFreq = len(tmp);
w = tmp[:,0] + 1j*float(parms['BROADENING']);
tmp = tmp[:, 1:];
tmp = tmp[:NMaxFreq, 0::2] + 1j*tmp[:NMaxFreq, 1::2];
se = zeros((SPINS, NMaxFreq, N_LAYERS*FLAVORS), dtype = complex);
for s in range(SPINS):
for f in range(N_LAYERS*FLAVORS):
se[s, :, f] = tmp[:, SPINS*f+s];
else:
se = zeros((SPINS, NMaxFreq, N_LAYERS*FLAVORS), dtype = complex);
w = linspace(float(parms['EMIN']), float(parms['EMAX']), NMaxFreq) + 1j*float(parms['BROADENING']);
# tight binding Hamiltonian
HR, R = getHamiltonian(parms['RHAM'], 4);
parms['NORB'] = len(HR[0])
extra = { 'HR' : HR, 'R': R };
if int(val_def(parms, 'FORCE_DIAGONAL', 0)) > 0:
print 'FORCE_DIAGONAL is used';
ind = nonzero(sum(R**2, 1)==0)[0][0];
H0 = HR[ind];
else: H0 = None;
rot_mat = getRotationMatrix(N_LAYERS, FLAVORS, val_def(parms, 'ROT_MAT', None), H0);
# prepare for k-integrate
bp, wf = grule(int(parms['NUMK']));
extra.update({
'GaussianData' : [bp, wf],
'rot_mat' : rot_mat
});
delta = float(parms['DELTA']);
mu = float(parms['MU']);
# running
Gavg = averageGreen(delta, mu, w, se, parms, -1, -1, 0, extra)[1];
# swap the Gavg to the format of my code
# swap_vec = zeros((2, N_LAYERS*FLAVORS), dtype = int);
# for L in range(N_LAYERS):
# for f in range(FLAVORS): swap_vec[:,f*N_LAYERS+L] = array([f*N_LAYERS+L, L*FLAVORS+f]);
# for s in range(SPINS): Gavg[s, :, swap_vec[1]] = Gavg[s, :, swap_vec[0]];
spec = -1/pi * Gavg.imag;
if SPINS == 1: savetxt('spec.dat', c_[w.real, spec[0]]);
elif SPINS > 1:
savetxt('spec_up.dat', c_[w.real, spec[0]]);
savetxt('spec_dn.dat', c_[w.real, spec[1]]);
def getSpectraFromSelfEnergy(h5, se_filename, rham, rotmat, numk = None, setail_filename = None, it = 0):
# prepare data
w, se_refreq = ProcessSelfEnergy(se_filename, emin = -5, emax = 5, NFreq = 500);
it = h5['iter'][0] - it;
parms = load_parms(h5, it);
print 'work on iteration ', it;
if rham is not None: print 'new path for rham file is: ', rham; parms['RHAM'] = rham;
if rotmat is not None: print 'new path for rot_mat file is ', rotmat; parms['ROT_MAT'] = rotmat;
BETA = float(parms['BETA']);
N_LAYERS = int(parms['N_LAYERS']);
FLAVORS = int(parms['FLAVORS']);
SPINS = int(parms['SPINS']);
NORB = int(parms['NORB']);
dmft_id = system.getDMFTCorrIndex(parms, all = False);
dmft_id_len = len(dmft_id);
# get the se tails
tmp = h5['SolverData/selfenergy_asymp_coeffs'][:];
se_tail = tmp[tmp[:,0] == it, 1:].reshape(SPINS, 2, -1)[:, :, ::N_LAYERS];
if setail_filename is not None:
print 'use the tail from external source: ', setail_filename;
tmp = genfromtxt(setail_filename);
se_tail[:, :, dmft_id] = array([tmp[:, s::SPINS] for s in range(SPINS)]);
print se_tail;
# restore SelfEnergy
se = zeros((SPINS, len(se_refreq), N_LAYERS*FLAVORS), dtype = complex);
for s in range(SPINS):
for f in range(N_LAYERS*FLAVORS):
if f/N_LAYERS not in dmft_id: se[s,:,f] = se_tail[s, 0, f/N_LAYERS];
else:
f1 = nonzero(f/N_LAYERS == dmft_id)[0][0];
se[s, :, f] = se_refreq[:, SPINS*f1+s]*se_tail[s, 1, f/N_LAYERS] + se_tail[s, 0, f/N_LAYERS];
# tight binding Hamiltonian
if 'RHAM' in parms:
HR, R = getHamiltonian(parms['RHAM'], 4);
if parms['DTYPE'] == '3bands': FLAVORS = 3;
extra = { 'HR' : HR, 'R': R };
# rotation matrix
if int(val_def(parms, 'FORCE_DIAGONAL', 0)) > 0:
print 'FORCE_DIAGONAL is used';
ind = nonzero(sum(R**2, 1)==0)[0][0];
H0 = HR[ind];
else: H0 = None;
rot_mat = getRotationMatrix(N_LAYERS, FLAVORS, val_def(parms, 'ROT_MAT', None), H0);
# prepare for k-integrate
parms['NUMK'] = 16 if numk is None else numk;
bp, wf = grule(int(parms['NUMK']));
broadening = 0.01;
extra.update({
'GaussianData' : [bp, wf],
'rot_mat' : rot_mat
});
delta = float(parms['DELTA']);
mu = float(parms['MU']);
# running
print 'generating interacting DOS with parameters'
for k, v in parms.iteritems(): print '%s = %s'%(k, v);
Gr = averageGreen(delta, mu, w+1j*broadening, se, parms, float(parms['ND']), float(parms['DENSITY']), 0, extra)[1];
if SPINS == 1: savetxt(parms['ID']+'.idos', c_[w, -1/pi*Gr[0].imag], fmt = '%g');
elif SPINS == 2:
savetxt(parms['ID']+'_up.idos', c_[w, -1/pi*Gr[0].imag], fmt = '%g');
savetxt(parms['ID']+'_dn.idos', c_[w, -1/pi*Gr[1].imag], fmt = '%g');
# calculate original G(iwn), only consider one "LAYERS"
Giwn_orig = h5['ImpurityGreen/%d'%it][:,:,::N_LAYERS];
NMatsubara = size(Giwn_orig, 1);
wn = (2*arange(NMatsubara) + 1)*pi/BETA;
Giwn = zeros((NMatsubara, 2*FLAVORS*SPINS), dtype = float); # 2 for real and imag
for f in range(FLAVORS):
for s in range(SPINS):
Giwn[:, 2*(SPINS*f+s)] = Giwn_orig[s, :, f].real;
Giwn[:, 2*(SPINS*f+s)+1] = Giwn_orig[s, :, f].imag;
savetxt(parms['ID']+'.gmat', c_[wn, Giwn]);
# calculate G(iwn) for reference, only consider one "LAYERS"
NMatsubara = 200;
wn = (2*arange(NMatsubara) + 1)*pi/BETA;
Giwn = zeros((NMatsubara, 2*FLAVORS*SPINS), dtype = float); # 2 for real and imag
for f in range(FLAVORS):
for s in range(SPINS):
A = -1/pi * Gr[s, :, f*N_LAYERS].imag;
for n in range(NMatsubara):
tck_re = splrep(w, real(A / (1j*wn[n] - w)));
tck_im = splrep(w, imag(A / (1j*wn[n] - w)));
Giwn[n, 2*(SPINS*f+s)] = splint(w[0], w[-1], tck_re);
Giwn[n, 2*(SPINS*f+s)+1] = splint(w[0], w[-1], tck_im);
savetxt(parms['ID']+'.gmat.ref', c_[wn, Giwn]);
def HartreeRun(parms, extra):
print "Initialization using Hartree approximation\n"
N_LAYERS = int(parms['N_LAYERS']);
FLAVORS = int(parms['FLAVORS']);
SPINS = 1;
p = dict({
'MU' : float(val_def(parms, 'MU', 0)),
'N_LAYERS': N_LAYERS,
'NORB' : int(parms['NORB']),
'U' : float(parms['U']),
'J' : float(parms['J']),
'DELTA': float(val_def(parms, 'DELTA', 0)),
'ND' : N_LAYERS*float(val_def(parms, 'ND', -1)),
'DENSITY' : N_LAYERS*float(parms['DENSITY']),
'FLAVORS' : FLAVORS,
'SPINS' : 1,
'OUTPUT' : '.' + parms['DATA_FILE'] + '_HartreeInit',
'NN' : None,
'N_MAX_FREQ' : 30,
'BETA' : float(parms['BETA']),
'NUMK' : int(val_def(parms, 'INIT_NUMK', 8)),
'TUNEUP' : int(val_def(parms, 'NO_TUNEUP', 0)) == 0,
'MAX_ITER' : 15,
'ALPHA' : 0.5, # pay attention at this parm sometimes
'DTYPE' : parms['DTYPE'],
'INTEGRATE_MOD' : val_def(parms, 'INTEGRATE_MOD', 'integrate'),
'np' : parms['np']
});
for k, v in p.iteritems(): print k + ': ', v;
bp, wf = grule(p['NUMK']);
X, W = generateGaussPoints(p['N_MAX_FREQ']);
wn = 1/sqrt(X)/p['BETA'];
p.update({
'X' : X,
'W' : W,
'w' : wn
});
if p['NN'] is None and os.path.isfile(p['OUTPUT']+'.nn'): p['NN'] = p['OUTPUT'];
# running
TOL = 1e-2;
if p['NN'] is None:
nn = ones(N_LAYERS*FLAVORS, dtype = 'f8') * p['DENSITY']/p['NORB']/2; # 2 for spin
mu = p['MU'];
delta = p['DELTA'];
else:
print 'Continue from '+p['NN'];
nn = genfromtxt(p['NN']+'.nn')[2:];
mu = genfromtxt(p['NN']+'.nn')[1];
delta = genfromtxt(p['NN']+'.nn')[0];
Gavg = zeros((p['N_MAX_FREQ'], p['NORB']), dtype = 'c16');
se = zeros((SPINS, p['N_MAX_FREQ'], N_LAYERS*FLAVORS), dtype = 'c16');
stop = False;
count = 0;
ALPHA = p['ALPHA'];
corr1 = system.getDMFTCorrIndex(parms, all = False);
corr2 = array([i for i in range(FLAVORS) if i not in corr1]); # index for eg bands
old_GaussianData = extra['GaussianData'];
extra['GaussianData'] = [bp, wf];
while not stop:
count += 1;
nn_old = nn.copy();
p['MU'] = mu;
p['DELTA'] = delta;
Gavg_old = Gavg.copy();
for L in range(N_LAYERS):
se_coef = functions.get_asymp_selfenergy(p, array([nn[L:N_LAYERS*FLAVORS:N_LAYERS]]))[0, 0, :];
for s in range(SPINS):
for f in range(len(se_coef)): se[s, :, f*N_LAYERS+L] = se_coef[f];
Gavg, delta, mu, Vc = averageGreen(delta, mu, 1j*wn, se, p, p['ND'], p['DENSITY'], p['TUNEUP'], extra);
Gavg = mean(Gavg, 0);
nn = getDensity(Gavg, p);
# no spin/orbital polarization, no charge order
for L in range(N_LAYERS):
nf1 = nn[0:N_LAYERS*FLAVORS:N_LAYERS];
for id in range(FLAVORS):
if id in corr1: nf1[id] = mean(nf1[corr1]);
else: nf1[id] = mean(nf1[corr2]);
nn[L:N_LAYERS*FLAVORS:N_LAYERS] = nf1;
err = linalg.norm(r_[delta, mu, nn] - r_[p['DELTA'], p['MU'], nn_old]);
savetxt(p['OUTPUT']+'.nn', r_[delta, mu, nn]);
print 'Step %d: %.5f'%(count, err);
if (err < TOL): stop = True; print 'converged';
if count > p['MAX_ITER']: break;
mu = ALPHA*mu + (1-ALPHA)*p['MU'];
delta = ALPHA*delta + (1-ALPHA)*p['DELTA'];
nn = ALPHA*nn + (1-ALPHA)*nn_old;
# DOS
NFREQ = 500;
BROADENING = 0.03;
extra['GaussianData'] = old_GaussianData;
parms_tmp = parms.copy(); parms_tmp['DELTA'] = delta;
Eav = system.getAvgDispersion(parms_tmp, 3, extra)[0,0,:];
Ed = mean(Eav[:N_LAYERS*FLAVORS][corr1]);
Ep = mean(Eav[N_LAYERS*FLAVORS:]) if N_LAYERS*FLAVORS < p['NORB'] else Ed;
emax = min(4, p['U']);
emin = -(Ed - Ep + max(se_coef) + min(4, p['U']));
print "Energy range for HF DOS: ", emin, emax
w = linspace(emin, emax, NFREQ) + 1j*BROADENING;
se = zeros((SPINS, NFREQ, N_LAYERS*FLAVORS), dtype = 'c16');
for L in range(N_LAYERS):
se_coef = functions.get_asymp_selfenergy(p, array([nn[L:N_LAYERS*FLAVORS:N_LAYERS]]))[0, 0, :];
for s in range(SPINS):
for f in range(len(se_coef)): se[s, :, f*N_LAYERS+L] = se_coef[f];
Gr = average_green.averageGreen(delta, mu, w, se, p,p['ND'], p['DENSITY'], 0, extra)[1][0];
savetxt(parms['ID']+'.dos', c_[w.real, -1/pi*Gr.imag], fmt = '%.6f');
print ('End Hartree approx.:%d Ntot=%.2f Nd=%.2f Delta=%.4f '
'Delta_eff=%.4f')%(count, 2*sum(nn)/N_LAYERS,
2*sum(nn[:N_LAYERS*FLAVORS]/N_LAYERS),
delta, delta-mean(se_coef[corr1])), ': \n', \
nn[:N_LAYERS*FLAVORS].reshape(-1, N_LAYERS),\
'\n\n'
# os.system('rm ' + p['OUTPUT']+'.nn');
return delta, mu, array([nn for s in range(int(parms['SPINS']))]), Vc;
def initialize(h5file, parms):
if parms['ID'] in h5file.keys():
# group exists
h5 = h5file[parms['ID']];
it = h5["iter"][0];
if it < 0: del h5file[parms['ID']];
else:
parms1 = parms.copy();
# not allow to change these fixed parameters
for s in ('DELTA', 'U', 'J', 'UJRAT', 'BETA'):
if s in parms1: del parms1[s];
parms = load_parms(h5, it+1);
for k, v in parms1.iteritems():
try:
if parms[k] != parms1[k]: parms[k] = parms1[k];
except: parms[k] = parms1[k];
# NOTE: basic information for the structure
N_LAYERS = int(parms['N_LAYERS']);
SPINS = int(val_def(parms, 'SPINS', 2));
if int(val_def(parms, 'PARAMAGNET', 0)) > 0: SPINS = 1;
DENSITY = N_LAYERS*float(parms['DENSITY']);
BETA = float(parms["BETA"]);
Nd = N_LAYERS*float(val_def(parms, 'ND', -1));
if 'DTYPE' not in parms: parms['DTYPE'] = '';
if 'RHAM' in parms:
FLAVORS = 5; # 5 d-bands
HR, R = getHamiltonian(parms['RHAM'], 4); # max distance is 4
NORB = len(HR[0]);
if parms['DTYPE'] == '3bands': FLAVORS = 3;
else:
FLAVORS = int(parms['FLAVORS']);
NORB = int(parms['NORB']);
if int(val_def(parms, 'AFM', 0)) > 0:
print 'This is AFM self consistent loop!';
if SPINS == 1: exit('SPINS must be 2 for AFM calculation');
if int(val_def(parms, 'FORCE_DIAGONAL', 0)) > 0:
print 'FORCE_DIAGONAL is used';
ind = nonzero(sum(R**2, 1)==0)[0][0];
H0 = HR[ind];
else: H0 = None;
rot_mat = getRotationMatrix(N_LAYERS, FLAVORS, val_def(parms, 'ROT_MAT', None), H0);
if 'MAX_FREQ' in parms.keys(): parms['N_MAX_FREQ'] = int(round((BETA*float(parms['MAX_FREQ'])/pi - 1)/2.));
if 'N_CUTOFF' not in parms.keys():
cutoff_factor = float(val_def(parms, 'CUTOFF_FACTOR', 7));
parms['N_CUTOFF'] = int(round((BETA/pi*float(val_def(parms,'CUTOFF_FREQ', cutoff_factor*float(parms['U']))) - 1)/2.));
parms['CUTOFF_FREQ'] = (2*int(parms['N_CUTOFF'])+1)*pi/BETA;
parms['MAX_FREQ'] = (2*int(parms['N_MAX_FREQ'])+1)*pi/BETA;
parms['FLAVORS'] = FLAVORS;
# mixing
mixer = Mixing(float(val_def(parms, "MIXING", 1)), int(val_def(parms, 'MIXING_FIXED', 1)));
mixer_SE = Mixing(float(val_def(parms, "MIXING", 1)), int(val_def(parms, 'MIXING_FIXED', 1)));
wn = (2*arange(int(parms['N_MAX_FREQ']))+1)*pi/BETA;
extra = {
'correction' : zeros(SPINS),
'GaussianData' : grule(int(val_def(parms, 'NUMK', 30))),
'rot_mat' : rot_mat
};
if 'RHAM' in parms: extra.update({'HR' : HR, 'R' : R });
else: extra.update({ 'tight_binding_parms' : array([float(s) for s in parms['TB_PARMS'].split()]) });
corr_id = system.getCorrIndex(parms);
NCOR = len(corr_id);
if not parms['ID'] in h5file.keys():
it = 0;
# create main group ID and its subgroups
h5 = h5file.create_group(parms['ID']);
h5.create_dataset("iter", (1,), int, data = -1);
crt_tuple = ("ImpurityGreen", "avgGreen", "SelfEnergy", "WeissField",
"parms", "StaticCoulomb", "SolverData",
"SolverData/Gtau", "SolverData/Hybmat",
"SolverData/Hybtau", "SolverData/Observables");
for obj in crt_tuple: h5.create_group(obj);
parms['SPINS'] = SPINS;
parms['NORB'] = NORB;
parms['ND'] = Nd/float(N_LAYERS);
parms['NCOR'] = NCOR;
parms['N_TAU'] = val_def(parms, 'N_TAU', 400);
if 'UJRAT' in parms.keys(): parms['J'] = float(parms['U']) / float(parms['UJRAT']);
# generate initial conditions
if 'USE_DATAFILE' in parms.keys():
print 'Get initial data from file ' + parms['USE_DATAFILE'];
is_hdf5 = True
if os.path.abspath(parms['USE_DATAFILE']) != os.path.abspath(parms['DATA_FILE']):
try:
g5file = h5py.File(parms['USE_DATAFILE'], 'r');
g5 = g5file[val_def(parms, 'USE_DATAFILE_ID', g5file.keys()[0])];
except: is_hdf5 = False
else:
g5file = None;
g5 = h5file[val_def(parms, 'USE_DATAFILE_ID', h5file.keys()[0])];
if is_hdf5:
g5it = g5['iter'][0];
parms['MU'] = val_def(parms, 'MU0', str(g5['parms/%d/MU'%(g5it+1)][...]));
parms['DELTA'] = val_def(parms, 'DELTA', str(g5['parms/%d/DELTA'%(g5it+1)][...]));
SelfEnergy, se_coef = get_self_energy_hdf5(g5, parms, wn)
if not g5file is None: g5file.close();
del g5file, g5;
else:
parms['MU'] = val_def(parms, 'MU0', 0);
parms['DELTA'] = val_def(parms, 'DELTA', 0);
SelfEnergy, se_coef = get_self_energy_text(parms['USE_DATAFILE'], parms, wn)
else:
SelfEnergy = zeros((SPINS, int(parms["N_MAX_FREQ"]), NCOR), dtype = complex);
if int(val_def(parms, 'HARTREE_INIT', 0)) == 1:
delta, mu, nf, VCoulomb = hartree.HartreeRun(parms, extra);
nf = nf[:, corr_id];
parms['MU'] = mu; parms['DELTA'] = delta;
else:
mu = float(val_def(parms, 'MU0', 0)); # don't know which default MU is good
delta = float(val_def(parms, 'DELTA', 0));
parms['MU'] = mu; parms['DELTA'] = delta;
Gavg, Gavg0, delta, mu, VCoulomb = averageGreen(delta, mu, 1j*wn, SelfEnergy, parms, Nd, DENSITY, False, extra);
nf = getDensityFromGmat(Gavg, BETA, extra);
se_coef = zeros((SPINS, 2, NCOR), dtype = float);
for L in range(N_LAYERS): se_coef[:, :, L::N_LAYERS] = get_asymp_selfenergy(parms, nf[:, L::N_LAYERS]);
for s in range(SPINS):
for f in range(NCOR): SelfEnergy[s, :, f] = se_coef[s, 0, f];
if int(val_def(parms, 'NO_TUNEUP', 0)) == 0: parms['MU'] = mu; parms['DELTA'] = delta;
log_data(h5['SolverData'], 'selfenergy_asymp_coeffs', it, se_coef.flatten(), data_type = float);
save_data(h5, it, ['SelfEnergy'], [SelfEnergy]);
save_parms(h5, it, parms);
save_parms(h5, it+1, parms);
# get average dispersion up to nth order
NthOrder = 3;
dispersion_avg = system.getAvgDispersion(parms, NthOrder, extra);
h5.create_dataset('SolverData/AvgDispersion', dispersion_avg.shape, dtype = float, data = dispersion_avg);
h5["iter"][...] = it; # this is the mark that iteration 'it' is done
return {
'parms' : parms,
'h5' : h5,
'mixer' : mixer,
'mixer_SE': mixer_SE,
'extra' : extra,
'N_LAYERS': N_LAYERS,
'NCOR' : NCOR,
'Nd' : Nd,
'DENSITY' : DENSITY,
'wn' : wn,
'corr_id' : corr_id
}
print ' %s=%s'%('N_CUTOFF', str(parms['N_CUTOFF']))
time_spent = time.time();
print "ITERATION: %d\n"%it;
# average over k-space for Gavg then produce Weiss field
# use Gauss quadrature with C++ extension code
# mixing self energy
tmp = mixer_SE.do_task(r_[SelfEnergy_out.real.flatten(), SelfEnergy_out.imag.flatten()]).reshape(2, -1);
SelfEnergy_in = (tmp[0] + 1j*tmp[1]).reshape(SelfEnergy_out.shape);
mu_orig = mu_in; delta_orig = delta_in;
if str(it) not in h5['WeissField']:
print "Tedious part: averaging Green function";
extra.update(system.getInfoForDensityCalculation(h5, it-1));
print 'Density correction = \n', extra['correction'][:, :NCOR];
Gavg, Gavg0, delta_out, mu_out, VCoulomb = averageGreen(delta_in, mu_in, 1j*wn, SelfEnergy_in, parms, Nd, DENSITY, int(val_def(parms, 'NO_TUNEUP', 0)) == 0, extra);
delta_in, mu_in = mixer.do_task(r_[delta_out, mu_out]);
if mu_in != mu_out or delta_in != delta_out:
print 'There is mixing, average Green function once more';
extra['G_asymp_coefs'][:N_LAYERS*int(parms['FLAVORS'])] -= delta_out - delta_in;
extra['G_asymp_coefs'] -= mu_out - mu_in;
Gavg, Gavg0, delta_in, mu_in, VCoulomb = averageGreen(delta_in, mu_in, 1j*wn, SelfEnergy_in, parms, Nd, DENSITY, False, extra);
parms["DELTA"] = delta_in;
parms["MU"] = mu_in; # just want MU shown in log_density corresponding to nf
nf = getDensityFromGmat(Gavg0, float(parms['BETA']), extra);
nf = c_[nf[:,:NCOR], sum(nf[:,NCOR:], 1)];
log_density(h5, it-1, parms, nf);
parms["DELTA"] = delta_in;
parms["MU"] = mu_in;
