def prepare(self, prefix, in_data):
print 'Prepare running solver for ' + prefix;
self.prefix = prefix;
parms = in_data['parms'];
BETA = float(parms['BETA']);
NCOR = int(parms['FLAVORS']) / 2;
self.beta = BETA;
self.Ntau = int(parms['N_TAU']) + 1;
self.Ncor = NCOR;
self.measure = int(parms['MEASURE'])
hyb_mat = in_data['hybmat'];
hyb_tail = in_data['hybtail'];
wn = (2*arange(size(hyb_mat, 0))+1)*pi/BETA;
savetxt(prefix+'.hybmat.real', c_[wn, hyb_mat.real]);
savetxt(prefix+'.hybmat.imag', c_[wn, hyb_mat.imag]);
savetxt(prefix+'.hybmat.tail', hyb_tail);
savetxt(prefix+'.MUvector', in_data['MU']);
Umatrix = generate_Umatrix(float(parms['U']), float(parms['J']),
NCOR, val_def(parms, 'INTERACTION_TYPE', 'SlaterKanamori'));
savetxt(prefix+'.Umatrix', Umatrix);
# prepare parms file for CTQMC
QMC_parms = {
'SWEEPS_EACH_NODE' : int(val_def(parms, 'SWEEPS', 500000))/self.args['np'],
'THERMALIZATION' : val_def(parms, 'THERMALIZATION', 50000),
'N_MEAS' : val_def(parms, 'N_MEAS', 100),
'BETA' : parms['BETA'],
'U_MATRIX' : prefix+'.Umatrix',
'MU_VECTOR' : prefix + '.MUvector',
'HYB_MAT' : prefix + '.hybmat',
'NCOR' : NCOR,
'HDF5_OUTPUT' : prefix + '.solution.h5',
'N_LEGENDRE' : val_def(parms, 'TRIQS_N_LEGENDRE', 50),
'ACCUMULATION' : val_def(parms, 'TRIQS_ACCUMULATION', 'legendre'),
'SPINFLIP' : val_def(parms, 'TRIQS_SPINFLIP', 1),
'MEASURE' : self.measure,
};
solver_parms_file = open(prefix + '.parms', 'w');
for k, v in QMC_parms.iteritems(): solver_parms_file.write(k + ' = ' + str(v) + ';\n');
def prepare(self, prefix, in_data):
# prepare hybtau file for CTQMC
print 'Prepare running solver for ' + prefix;
self.prefix = prefix;
self.list_obs = None;
self.parms = in_data['parms'];
self.MEASURE_freq = int(val_def(in_data['parms'], 'MEASURE_freq', 1));
parms = in_data['parms'];
FLAVORS = int(parms['FLAVORS']);
# prepare parms file for CTQMC
QMC_parms = {
'SEED' : random.random_integers(10000),
'SWEEPS' : int(val_def(parms, 'SWEEPS', 500000)),
'THERMALIZATION' : int(val_def(parms, 'THERMALIZATION', 300)),
'N_TAU' : int(parms['N_TAU']),
'N_HISTOGRAM_ORDERS' : int(val_def(parms, 'N_ORDER', 50)),
'N_MEAS' : int(val_def(parms, 'N_MEAS', 100)),
'N_CYCLES' : int(val_def(parms, 'N_CYCLES', 30)),
'BETA' : float(parms['BETA']),
'U_MATRIX' : self.prefix+'.Umatrix',
'MU_VECTOR' : self.prefix+'.MUvector',
'BASENAME' : prefix,
'DELTA' : prefix + '.hybtau',
'N_ORBITALS' : FLAVORS,
'MEASURE_freq' : self.MEASURE_freq,
'N_MATSUBARA' : int(parms['N_CUTOFF']),
'MAX_TIME' : val_def(parms, 'MAX_TIME', 80000),
};
self.Norder = QMC_parms['N_HISTOGRAM_ORDERS'];
solver_parms_file = open(prefix + '.parms', 'w');
for k, v in QMC_parms.iteritems(): solver_parms_file.write(k + ' = ' + str(v) + ';\n');
# Umatrix: either Slater-Kanamori form or using Slater integrals
Umatrix = generate_Umatrix(float(parms['U']), float(parms['J']),
FLAVORS/2, val_def(parms, 'INTERACTION_TYPE', 'SlaterKanamori'));
hyb_tau = in_data['hybtau'];
hyb_tau = c_[linspace(0, float(parms['BETA']), int(parms['N_TAU']) + 1), hyb_tau];
savetxt(prefix+'.hybtau', hyb_tau);
savetxt(self.prefix+'.Umatrix', Umatrix);
savetxt(self.prefix+'.MUvector', in_data['MU']);
