def evaluate_single(L, filling, bond_dim, correlation_type):
## Get filenames
fname = utils.find_resfile(L, filling, bond_dim)
if fname is None:
print 'No data available for L={}, n={}, M={}, {}'.format(L, filling, bond_dim, correlation_type)
return None, None
## Get correlation matrix
corr = compute(fname)
if corr is None:
print 'No data available for L={}, n={}, M={}, {}'.format(L, filling, bond_dim, correlation_type)
return None, None
if isinstance(correlation_type, utils.FixedStart):
start_site = correlation_type.start
y = corr.y[start_site, start_site+1:]
x = np.arange(1, len(y)+1, dtype=float)
d = np.column_stack([x, y])
props = deepcopy(corr.props)
elif isinstance(correlation_type, utils.Averaged):
shifts = range(-5, 6)
x,y = average_around_middle(corr.y, corr.props['L'], shifts)
d = np.column_stack([x, y])
props = deepcopy(corr.props)
props['correlation_type'] = correlation_type
return d, props
def evaluate_single(L, filling, bond_dim):
## Get filenames
fname = utils.find_resfile(L, filling, bond_dim)
if fname is None:
print 'No data available for L={}, n={}, M={}'.format(L, filling, bond_dim)
return None, None
dens = compute(fname)
d = np.column_stack([dens.x,dens.y])
props = dens.props
return d, props