def solver_input_data(parms, L, hyb_data_all, AvgDispersion, VCoulomb, nf):
# prepare hybtau file for CTQMC
N_LAYERS = int(parms["N_LAYERS"])
FLAVORS = int(parms["FLAVORS"])
SPINS = int(parms["SPINS"])
corr_id = system.getCorrIndex(parms)
dmft_id = system.getDMFTCorrIndex(parms)
dmft_FLAVORS = 2 * len(dmft_id) / N_LAYERS
# 2 for SPINS
hyb_data = []
for n, d in enumerate(hyb_data_all):
data = hyb_data_all[n][:, :, dmft_id[L::N_LAYERS]]
data_out = zeros((size(data, 1), dmft_FLAVORS), dtype=data.dtype)
data_out[:, ::2] = data[0]
data_out[:, 1::2] = data[0] if SPINS == 1 else data[1]
hyb_data.append(data_out)
Eav = AvgDispersion[:, 0, dmft_id[L::N_LAYERS]]
inertHFSelfEnergy = get_inert_band_HF(parms, nf[:, L::N_LAYERS])
MU = array([float(parms["MU"]) - VCoulomb[L] - Eav[s] - inertHFSelfEnergy[s] for s in range(SPINS)])
MU_out = zeros(dmft_FLAVORS)
MU_out[::2] = MU[0]
MU_out[1::2] = MU[0] if SPINS == 1 else MU[1]
parms_copy = parms.copy()
parms_copy["FLAVORS"] = dmft_FLAVORS
print "Inert HF Self Energy: ", inertHFSelfEnergy
return {"hybtau": hyb_data[0], "hybmat": hyb_data[1], "hybtail": hyb_data[2], "MU": MU_out, "parms": parms_copy}
def show_DOS_results(h5file, filter = '', plot_what = None, L = None, f = None, Navg = 1):
print '# BETA T U Delta N_LAYERS Ndtot DOStot Nd DOS';
for s in h5file:
if filter not in s: continue;
it = h5file[s+'/iter'][0];
if str(it+1) in h5file[s+'/avgGreen'].keys(): added = 1;
else: added = 0;
try: parms = gget(h5file[s], 'parms');
except: print 'ID %s not work'%s; continue;
N_LAYERS = int(parms['N_LAYERS']); SPINS = int(parms['SPINS']);
if L is None: L = arange(N_LAYERS);
corr_id = system.getCorrIndex(parms);
nf = None;
for i in range(Navg):
if nf is None:
nf = getDensity(h5file[s], it-i);
bG = getFermiDOS(h5file[s], it+added-i);
else:
nf += getDensity(h5file[s], it-i);
bG += getFermiDOS(h5file[s], it+added-i);
nf /= Navg; bG /= Navg;
if SPINS == 1: nf = r_[nf, nf]; bG = r_[bG, bG];
nn = sum(nf[:, corr_id][:, ::N_LAYERS], 0);
bbG = sum(bG[:, corr_id][:, ::N_LAYERS], 0);
print '%s: %s %.4f %.2f %.4f %d %.4f %.4f '%(s, parms['BETA'], 1/float(parms['BETA']), float(parms['U']), float(parms['DELTA']), N_LAYERS,\
sum(nf[:,corr_id])/N_LAYERS, sum(bG)/N_LAYERS), \
(len(nn)*' %.4f' + ' ' + len(bbG)*' %.4f')%tuple(r_[nn, bbG]);
if plot_what is not None:
for s in h5file:
if filter in s: plot_data(h5file[s], plot_what, f = f, L = L);
def show_mag_results(h5file, filter = '', plot_what = None, L = 0, Navg = 1):
print '# BETA T Nd magnetization inv_susceptibility';
for s in h5file:
if filter not in s: continue;
try: parms = gget(h5file[s], 'parms');
except: print 'ID %s not work'%s; continue;
tmp = gget(h5file[s], 'log');
nd = sum(mean(tmp[1][-Navg:, :], 0));
magnetization = sum(mean(tmp[5][-Navg:, :], 0))/int(parms['N_LAYERS']);
susceptibility = magnetization / float(parms['H']);
print '%s: %s %.4f %.4f'%(s, parms['BETA'], 1/float(parms['BETA']), sum(nd)/int(parms['N_LAYERS'])), magnetization, 1./susceptibility;
if plot_what is not None:
corr_id = system.getCorrIndex(parms);
for s in h5file:
if filter in s: plot_data(h5file[s], plot_what, f = corr_id, L = L);
def run_solver(AvgDispersion, nf, w, it, parms, aWeiss, np=1, VCoulomb=None):
ID = parms["ID"]
N_LAYERS = int(parms["N_LAYERS"])
FLAVORS = int(parms["FLAVORS"])
SPINS = int(parms["SPINS"])
DATA_FILE = parms["DATA_FILE"]
TMPH5FILE = "." + DATA_FILE + ".id" + str(ID) + ".i" + str(it) + ".solver_out.h5"
if VCoulomb is None:
VCoulomb = zeros(N_LAYERS)
solver = solver_types.init_solver(parms, np)
corr_id = system.getCorrIndex(parms)
NCOR = int(parms["NCOR"])
NDMFT = 2 * len(system.getDMFTCorrIndex(parms))
# 2 for SPINS
# check save point and initialize for new iteration
try:
tmph5 = h5py.File(TMPH5FILE, "r+")
hyb_tau = tmph5["Hybtau"][:]
hyb_mat = tmph5["Hybmat"][:]
hyb_coefs = tmph5["hyb_asym_coeffs"][:].reshape(SPINS, -1, NCOR)
except:
try:
tmph5.close()
except:
pass
tmph5 = h5py.File(TMPH5FILE, "w")
tmph5.create_dataset("L", (2,), dtype=int, data=array([it, 0]))
# asymptotic coefficients, upto 3rd order for hyb
hyb_coefs = zeros((SPINS, 3, NCOR), dtype=float)
# electric chemical potential
eMU = float(parms["MU"]) - VCoulomb
for L in range(N_LAYERS):
hyb_coefs[:, :, L::N_LAYERS] = get_asymp_hybmat(
parms, nf[:, L::N_LAYERS], eMU[L], AvgDispersion[:, :, corr_id[L:NCOR:N_LAYERS]]
)
# get practical hybmat, and hybtau
Eav = AvgDispersion[:, 0, corr_id]
hyb_mat = zeros((SPINS, int(parms["N_MAX_FREQ"]), NCOR), dtype=complex)
hyb_tau = zeros((SPINS, int(parms["N_TAU"]) + 1, NCOR), dtype=float)
for s in range(SPINS):
for f in range(NCOR):
hyb_mat[s, :, f] = w + eMU[f % N_LAYERS] - Eav[s, f] - aWeiss[s, :, f]
tmp = cppext.IFT_mat2tau(
hyb_mat[s, :, f].copy(),
int(parms["N_TAU"]) + 1,
float(parms["BETA"]),
float(hyb_coefs[s, 0, f]),
float(hyb_coefs[s, 1, f]),
)
# set value >= 0 to be smaller than 0, the mean of left and right neighbors
ind = nonzero(tmp >= 0)[0]
for i in ind:
lefti = righti = i
while tmp[lefti] >= 0 and lefti > 0:
lefti -= 1
while tmp[righti] >= 0 and righti < len(tmp) - 1:
righti += 1
leftval = tmp[lefti] if tmp[lefti] < 0 else 0
rightval = tmp[righti] if tmp[righti] < 0 else 0
tmp[i] = (leftval + rightval) / 2.0
hyb_tau[s, :, f] = tmp
tmph5.create_dataset("Hybmat", hyb_mat.shape, dtype=complex, data=hyb_mat)
tmph5.create_dataset("Hybtau", hyb_tau.shape, dtype=float, data=hyb_tau)
# initialize output dataset
Gtau_shape = (int(parms["N_TAU"]) + 1, NDMFT)
tmph5.create_dataset("Gtau", Gtau_shape, dtype=float, data=zeros(Gtau_shape, dtype=float))
tmph5.create_group("Observables")
tmph5.create_dataset("hyb_asym_coeffs", hyb_coefs.flatten().shape, dtype=float, data=hyb_coefs.flatten())
# run
hyb_data = [hyb_tau, hyb_mat, hyb_coefs]
MEASURE_freq = True if "Gw" in tmph5 else False
startL = tmph5["L"][1]
sym_layers = getSymmetricLayers(tmph5, parms)
for L in range(startL, N_LAYERS):
print "Processing task ", ID, ": iteration ", it, ", layer ", L
tmph5["L"][1] = L
TMPFILE = "." + DATA_FILE + ".id" + str(ID) + ".i" + str(it) + ".L" + str(L)
if float(parms["U"]) == 0:
break
if (sym_layers is None) or (L not in sym_layers[:, 1]):
solver.prepare(TMPFILE, solver_input_data(parms, L, hyb_data, AvgDispersion, VCoulomb, nf))
tmph5.close()
ret_val = solver.run()
tmph5 = h5py.File(TMPH5FILE, "r+")
if ret_val > 0:
print "Not finish running impurity solver or problem occurs while running the solver."
os.system("rm " + TMPFILE + ".*")
tmph5.close()
return None
solver_out = solver.collect()
if solver_out is None:
tmph5.close()
return None
Gtau = solver_out[0]
obs = solver_out[1]
if len(solver_out) > 2:
MEASURE_freq = True
Giwn = solver_out[2]
Siwn = solver_out[3]
os.system("rm " + TMPFILE + ".*")
elif L in sym_layers[:, 1]: # symmetric layer, no need to calculate
sym_index = nonzero(sym_layers[:, 1] == L)[0]
sym_L = sym_layers[sym_index, 0][0]
print "L=%d is the symmetric layer of layer L=%d" % (L, sym_L)
Gtau = tmph5["Gtau"][:, sym_L::N_LAYERS]
obs = None
if tmph5["Observables"].keys() != []:
obs = dict()
for k, v in tmph5["Observables/L" + str(sym_L)].iteritems():
obs[k] = v
if MEASURE_freq:
Giwn = tmph5["Gw"][:, sym_L::N_LAYERS]
Siwn = tmph5["Sw"][:, sym_L::N_LAYERS]
# this is the only place for AFM
# only works for the 4-cell unitcell (GdFeO3 distortion)
# G-type AFM: 0-3 are the same, 0-1 and 0-2 are opposite in spin
# here I just swap values of opposite spins
if int(val_def(parms, "AFM", 0) > 0) and (L in [1, 2]):
print "AFM processing on this L"
Ntmp = NDMFT / N_LAYERS
# correlated bands per site
mapid = zeros(Ntmp, dtype=int)
mapid[0::2] = arange(1, Ntmp, 2)
mapid[1::2] = arange(0, Ntmp, 2)
Gtau = Gtau[:, mapid]
if MEASURE_freq:
Giwn = Giwn[:, mapid]
Siwn = Siwn[:, mapid]
if "nn" in obs:
tmp = obs["nn"][:]
nn = zeros((Ntmp, Ntmp), dtype=float)
pos = 0
for i in range(Ntmp):
for j in range(i + 1):
nn[i, j] = nn[j, i] = tmp[pos]
pos += 1
nn = nn[mapid]
nn = nn[:, mapid]
tmp = array([])
for i in range(Ntmp):
for j in range(i + 1):
tmp = r_[tmp, nn[i, j]]
obs["nn"] = tmp
tmph5["Gtau"][:, L::N_LAYERS] = Gtau
if MEASURE_freq:
if "Gw" not in tmph5:
matsubara_shape = (len(Giwn), NDMFT)
tmph5.create_dataset("Gw", matsubara_shape, dtype=complex, data=zeros(matsubara_shape, dtype=complex))
tmph5.create_dataset("Sw", matsubara_shape, dtype=complex, data=zeros(matsubara_shape, dtype=complex))
tmph5["Gw"][:, L::N_LAYERS] = Giwn
tmph5["Sw"][:, L::N_LAYERS] = Siwn
if obs is not None:
new_group_str = "Observables/L" + str(L)
tmph5.create_group(new_group_str)
for k, v in obs.iteritems():
tmph5.create_dataset(new_group_str + "/" + k, v.shape, dtype=v.dtype, data=v)
print "Finish iteration ", it, ", layer ", L, "\n"
print "DONE: iteration %d\n" % it
tmph5["L"][1] = N_LAYERS
tmph5.close()
return TMPH5FILE
def gget(data, st, iter = None, L = None, f = None):
if iter == None: iter = str(data['iter'][0]);
iter = str(iter);
parms = load_parms(data, int(iter));
N_LAYERS = int(parms['N_LAYERS']); FLAVORS = int(parms['FLAVORS']); SPINS = int(parms['SPINS']);
NORB = int(parms['NORB']); NCOR = int(parms['NCOR']);
corr_id = system.getCorrIndex(parms);
dmft_id = system.getDMFTCorrIndex(parms);
dmft_site_id = system.getDMFTCorrIndex(parms, all = False);
if st == 'wn':
NMaxFreq = int(parms['N_MAX_FREQ']);
BETA = float(parms['BETA']);
return (2*arange(0, NMaxFreq)+1)*pi/BETA;
if st == 'parms': return parms;
if st == 'log':
log = data['log_density'][:];
num_iter = len(log);
nraw = log[:,4:];
orbital = zeros((num_iter, FLAVORS, N_LAYERS));
orbital_abs = zeros((num_iter, FLAVORS, N_LAYERS));
spinup = zeros((num_iter, N_LAYERS));
spindn = zeros((num_iter, N_LAYERS));
density = zeros((num_iter, N_LAYERS));
magnetization = zeros((num_iter, N_LAYERS));
for n in range(num_iter):
nf = nraw[n].reshape(SPINS, -1)[:, :NCOR];
noxy = nraw[n].reshape(SPINS, -1)[:, -1]/N_LAYERS;
# if n > 0:
# nf_gtau = -data['SolverData/Gtau/'+str(n)][:, -1, :];
# nf[:, dmft_id] = nf_gtau[:, dmft_id];
nf = nf.reshape(SPINS, FLAVORS, N_LAYERS);
if SPINS == 1: nf = r_[nf, nf]; noxy = r_[noxy, noxy];
density[n] = array([sum(nf[:,:,i]) for i in range(N_LAYERS)]);
orbital[n] = (nf[0] + nf[1])/density[n];
orbital_abs[n] = (nf[0] + nf[1]);
spinup[n] = sum(nf[0], 0)/density[n] - 1./2;
spindn[n] = sum(nf[1], 0)/density[n] - 1./2;
# magnetization[n] = sum(nf[0, dmft_site_id, :] - nf[1, dmft_site_id], 0);
magnetization[n] = sum(nf[0] - nf[1], 0) + noxy[0] - noxy[1];
# magnetization[n] = sum(nf[0] - nf[1], 0);
out = (log[:,:4], density, spinup, spindn, orbital, magnetization, orbital_abs);
return out;
if L == None: L = arange(N_LAYERS);
if f == None: f = arange(FLAVORS);
L = array([L]).flatten(); f = asarray([f]).flatten();
idx = array([], dtype = 'i4');
for i in range(0,len(f)): idx = r_[idx, L + f[i]*N_LAYERS];
idx = sort(idx);
if st == 'idx': return idx;
if st == 'Gimp': return data['ImpurityGreen/' + iter][:,:,idx];
if st == 'se': return data['SelfEnergy/' + iter][:,:,idx];
if st == 'as': return data['WeissField/' + iter][:,:,idx];
if st == 'Gavg': return data['avgGreen/' + iter][:,:, corr_id][:,:,idx];
if st == 'hybmat': return data['SolverData/Hybmat/' + iter][:,:,idx];
if st == 'hybtau': return data['SolverData/Hybtau/' + iter][:,:,idx];
if st == 'Gtau': return data['SolverData/Gtau/' + iter][:,:,idx];
if st == 'G0': return 1. / data['WeissField/' + iter][:,:,idx];
if st == 'bG': return getFermiDOS(data, iter);
if st == 'Gtot': return getTotalGtau(data, iter);
if st == 'Z' : return getRenormFactor(data, npoint = 2, it = iter);
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
}
