def compute_extrap(L, filling, bond_dim, correlation_type, at_x=None):
## Load all bond dimensions
sets = map(lambda parms: to_dataset(*utils.load_or_evaluate('densdens', evaluate, **parms)),
utils.iter_bond_dim(L=L, filling=filling, correlation_type=correlation_type))
sets = filter(lambda d: d.props is not None, sets)
## Extrapolate
return extrapolate_local.extrapolate(sets, 'Density Correlation', foreach=['L', 't\'', 'Nup_total', 'Ndown_total'], extrap_type=bond_dim.type, deg=bond_dim.deg, num_points=bond_dim.num_points, full_output_at=[at_x])
def compute_extrap(L, filling, bond_dim):
## Load all bond dimensions
sets = map(lambda parms: to_dataset(*utils.load_or_evaluate('energy', evaluate, **parms)),
utils.iter_bond_dim(L=L, filling=filling))
sets = filter(lambda d: d.props is not None, sets)
## Extrapolate
return extrapolate.extrapolate(sets, foreach=['L', 't\'', 'Nup_total', 'Ndown_total'], extrap_type=bond_dim.type, deg=bond_dim.deg, num_points=bond_dim.num_points)
def extrapolation(what, **kwargs):
what = 'extrap_'+what
fname = utils.filename(what, **kwargs)
if path.exists(fname):
return utils.load(fname)
else:
import pairfield_correlations, density_correlations, density, energy
if what == 'extrap_pairfield':
return utils.load_or_evaluate(what, pairfield_correlations.evaluate_extrap_at, **kwargs)
elif what == 'extrap_densdens':
return utils.load_or_evaluate(what, density_correlations.evaluate_extrap_at, **kwargs)
elif what == 'extrap_density':
return utils.load_or_evaluate(what, density.evaluate_extrap_at, **kwargs)
elif what == 'extrap_energy':
return utils.load_or_evaluate(what, energy.evaluate_extrap, **kwargs)
else:
raise Exception('`%s` is not a valid measurement.' % what)
def compute_fit(L, filling, bond_dim):
## Get data
data = to_dataset(*utils.load_or_evaluate('density', evaluate, L=L, filling=filling, bond_dim=bond_dim))
if data.props is None:
print 'Data not found for L={}, n={}, M={}'.format(L, filling, bond_dim)
return data
## Compute fit
d = dens_fit(data, fit_func(data.props['L'], data.props['nholes']))
return d
def result(what, **kwargs):
fname = utils.filename(what, **kwargs)
if path.exists(fname):
return utils.load(fname)
else:
try:
import pyalps_dset
import pairfield_correlations, density_correlations, density, amplitudes
if what == 'pairfield':
return utils.load_or_evaluate(what, pairfield_correlations.evaluate, **kwargs)
elif what == 'densdens':
return utils.load_or_evaluate(what, density_correlations.evaluate, **kwargs)
elif what == 'density':
return utils.load_or_evaluate(what, density.evaluate, **kwargs)
elif what == 'density_fit':
return utils.load_or_evaluate(what, density.evaluate_fit, **kwargs)
elif what == 'density_amplitudes':
return utils.load_or_evaluate(what, amplitudes.evaluate, **kwargs)
else:
raise Exception('`%s` is not a valid measurement.' % what)
except ImportError, e:
print 'To execute new evaluations you need the ALPS.Python library.'
print e
def compute_extrap(L, filling, bond_dim, correlation_type, at_x=None):
## Load all bond dimensions
sets = map(
lambda parms: to_dataset(*utils.load_or_evaluate("pairfield", evaluate, **parms)),
utils.iter_bond_dim(L=L, filling=filling, correlation_type=correlation_type),
)
sets = filter(lambda d: d.props is not None, sets)
## Extrapolate
return extrapolate_local.extrapolate(
sets,
"Pairfield Correlation",
foreach=["L", "t'", "Nup_total", "Ndown_total"],
extrap_type=bond_dim.type,
deg=bond_dim.deg,
num_points=bond_dim.num_points,
full_output_at=[at_x],
)
def compute_extrap(L, filling, bond_dim, at_x=None):
## Load all bond dimensions
sets = map(lambda parms: to_dataset(*utils.load_or_evaluate('density', evaluate, **parms)),
utils.iter_bond_dim(L=L, filling=filling))
sets = filter(lambda d: d.props is not None, sets)
# check that particle density in the middle is symmetric, otherwise exclude dataset
# for L=192 it is better to use only M > 2000
def symm_middle_filter(d):
x = int(d.props['L'] / 2)
delta = abs(d.y[x] - d.y[x-1]) if d.props['L']%2==0 else abs(d.y[x+1] - d.y[x-1])
if delta > 5e-5:
print '# Discard L={L:.0f}, n={filling}, tperp={t\'}, M={max_bond_dimension:.0f} : middle density not symmetry, delta={delta}'.format(delta=delta, **d.props)
return False
return True
sets = filter(symm_middle_filter, sets)
## Extrapolate
return extrapolate_local.extrapolate(sets, 'Rung density', foreach=['L', 't\'', 'Nup_total', 'Ndown_total'], extrap_type=bond_dim.type, deg=bond_dim.deg, num_points=bond_dim.num_points, full_output_at=[at_x])
def compute(filling, bond_dim, odd_sizes=False, amplitude_points=None):
## Load all system sizes
if odd_sizes:
densities = map(lambda parms: to_dataset(*utils.load_or_evaluate('density_fit', density.evaluate_fit, **parms)),
utils.iter_odd_system_size(bond_dim=bond_dim, filling=filling))
else:
densities = map(lambda parms: to_dataset(*utils.load_or_evaluate('density_fit', density.evaluate_fit, **parms)),
utils.iter_system_size(bond_dim=bond_dim, filling=filling))
densities = filter(lambda d: d.props is not None, densities)
if len(densities) < 3:
print 'WARNING:', 'Only got {} valid densities, you need at least 3.'.format(len(densities))
d = pyalps_dset.DataSet()
d.props = None
return d
# check that particle density in the middle is symmetric, otherwise exclude dataset
# for L=192 it is better to use only M > 2000
# def symm_middle_filter(d):
# x = int(d.props['L'] / 2)
# delta = abs(d.y[x] - d.y[x-1]) if d.props['L']%2==0 else abs(d.y[x+1] - d.y[x-1])
# if delta > 5e-5:
# print '# Discard L={L:.0f}, n={filling}, tperp={t\'}, M={max_bond_dimension:.0f} : {delta}'.format(delta=delta, **d.props)
# return False
# return True
# densities = filter(symm_middle_filter, densities)
## Only even number of pairs
densities = filter(lambda d: d.props['L']%2 != 0 or d.props['Nup_total'] % 2 == 0, densities)
amplitudes = map(compute_amplitude, densities)
for d in amplitudes:
if d.props['density_fit_error'] > abs(d.y[0]):
print 'WARNING:', 'Error in density fit is higher than amplitude magnitude.'
print 'Error per site is', d.props['density_fit_error'], 'Amplidue is', d.y[0]
print 'Dataset was:', 'L=%s filling=%s t_perp=%s M=%s' % (d.props['L'], d.props['filling'], d.props['t\''], d.props['max_bond_dimension'])
common_props = pyalps_dset.dict_intersect([d.props for d in amplitudes])
amp_vs_L = pyalps_dset.collectXY(amplitudes, 'L', 'Amplitude')
d = amp_vs_L[0]
## fit finite size analysis
sel = np.ones(len(d.x), dtype=bool)
if amplitude_points is not None and amplitude_points < len(d.x): sel[:-amplitude_points] = False
cov = np.ones((2,2))*np.nan
if sum(sel) > 2:
coeff, cov = np.polyfit(np.log(d.x[sel]), np.log(d.y[sel]), deg=1, cov=True)
else:
coeff = np.polyfit(np.log(d.x[sel]), np.log(d.y[sel]), deg=1)
r2 = rsquared(np.log(d.x), np.log(d.y), coeff)
fit_range = [d.x[0], d.x[-1]]
if amplitude_points is not None and amplitude_points < len(d.x): fit_range[0] = d.x[-amplitude_points]
## compute new Krho
slope = coeff[0]
errslope = np.sqrt(cov[0,0])
Krho = -2. * slope
errKrho = 2. * errslope
print 'M={} t_perp={} filling={:4.4f} -> Krho={:.3f} +/- {:.3f} : R^2 {:.4f}'.format(common_props['max_bond_dimension'], common_props['t\''], common_props['filling'], Krho, errKrho, r2)
d.props['fit_range'] = fit_range
d.props['fit_numpoints'] = amplitude_points
d.props['fitted_coeff'] = coeff
d.props['fitted_Krho'] = Krho
d.props['fitted_Krho_error'] = errKrho
d.props['fitted_r2'] = r2
return d
