def write_parameters(self):
"""Write parameters as `testname.parms/.model`."""
pyalps.writeParameterFile(self.testname+'.parms', self.inputs['parms'])
pyalps.writeParameterFile(self.testname+'.model', self.inputs['model'])
'SYMMETRIZATION' : 1,
'SWEEPS' : 10000,
'BETA' : 30,
'THERMALIZATION' : 500,
'U' : u,
'J' : j,
't0' : 0.5,
't1' : 1
}
)
# For more precise calculations we propose to enhance the SWEEPS
#write the input file and run the simulation
for p in parms:
input_file = pyalps.writeParameterFile('parm_u_'+str(p['U'])+'_j_'+str(p['J']),p)
res = pyalps.runDMFT(input_file)
listobs = ['0', '2'] # flavor 0 is SYMMETRIZED with 1, flavor 2 is SYMMETRIZED with 3
data = pyalps.loadMeasurements(pyalps.getResultFiles(pattern='parm_u_*h5'), respath='/simulation/results/G_tau', what=listobs, verbose=True)
for d in pyalps.flatten(data):
d.x = d.x*d.props["BETA"]/float(d.props["N"])
d.y = -d.y
d.props['label'] = r'$U=$'+str(d.props['U'])+'; flavor='+str(d.props['observable'][len(d.props['observable'])-1])
plt.figure()
plt.yscale('log')
plt.xlabel(r'$\tau$')
plt.ylabel(r'$G_{flavor}(\tau)$')
plt.title('DMFT-05: Orbitally Selective Mott Transition on the Bethe lattice')
pyalps.plot.plot(data)
'CONVERGED': 0.0025,
'FLAVORS': 2,
'H': 0,
'H_INIT': 0.0,
'MAX_IT': 12,
'MAX_TIME': 600,
'MU': 0,
'N': 1000,
'NMATSUBARA': 1000,
'N_MEAS': 10000,
'N_ORDER': 50,
'OMEGA_LOOP': 1,
'SEED': 0,
'SITES': 1,
'SOLVER': 'hybridization',
'SC_WRITE_DELTA': 1,
'SYMMETRIZATION': 1,
'U': 3,
't': 0.707106781186547,
'SWEEPS': 2500,
'THERMALIZATION': 500,
'BETA': 32
})
# For more precise calculations we propose to you to:
# enhance the MAX_TIME, MAX_IT and lower CONVERGED
#write the input file and run the simulation
input_file = pyalps.writeParameterFile('parm_hyb', parms[0])
res = pyalps.runDMFT(input_file)
'OMEGA_LOOP': 1,
'SEED': 0,
'SITES': 1,
'SOLVER': 'hybridization',
'SC_WRITE_DELTA': 1,
'SYMMETRIZATION': 0,
'U': 3,
't': 0.707106781186547,
'SWEEPS': int(10000 * b / 16.),
'THERMALIZATION': 1000,
'BETA': b
})
#write the input file and run the simulation
for p in parms:
input_file = pyalps.writeParameterFile('parm_beta_' + str(p['BETA']), p)
res = pyalps.runDMFT(input_file)
listobs = ['0', '1']
data = pyalps.loadMeasurements(pyalps.getResultFiles(pattern='parm_beta_*h5'),
respath='/simulation/results/G_tau',
what=listobs)
for d in pyalps.flatten(data):
d.x = d.x * d.props["BETA"] / float(d.props["N"])
d.props['label'] = r'$\beta=$' + str(d.props['BETA']) + '; flavor=' + str(
d.props['observable'][len(d.props['observable']) - 1])
plt.figure()
plt.xlabel(r'$\tau$')
plt.ylabel(r'$G_{flavor}(\tau)$')
'N_ORDER' : 50, # histogram size
'TWODBS' : 1, # the Hilbert transformation integral runs in k-space, sets square lattice
't' : 1, # the nearest-neighbor hopping
'tprime' : 0, # the second nearest-neighbor hopping
'L' : 64, # discretization in k-space in the Hilbert transformation
'GENERAL_FOURIER_TRANSFORMER' : 1, # Fourier transformer for a general bandstructure
'EPS_0' : 0, # potential shift for the flavor 0
'EPS_1' : 0, # potential shift for the flavor 1
'EPSSQ_0' : 4, # the second moment of the bandstructure for the flavor 0
'EPSSQ_1' : 4, # the second moment of the bandstructure for the flavor 1
}
)
#write the input file and run the simulation
for p in parms:
input_file = pyalps.writeParameterFile('hybrid_TWODBS_beta_'+str(p['BETA'])+'_U_'+str(p['U']),p)
res = pyalps.runDMFT(input_file)
listobs=['0'] # we look only at flavor=0
data = pyalps.loadMeasurements(pyalps.getResultFiles(pattern='hybrid_TWODBS*h5'), respath='/simulation/results/G_tau', what=listobs, verbose=True)
for d in pyalps.flatten(data):
d.x = d.x*d.props["BETA"]/float(d.props["N"])
d.props['label'] = r'$\beta=$'+str(d.props['BETA'])
plt.figure()
plt.xlabel(r'$\tau$')
plt.ylabel(r'$G_{flavor=0}(\tau)$')
plt.title('DMFT-08, TWODBS option: Hubbard model on the square lattice')
pyalps.plot.plot(data)
plt.legend()