def compute(fname):
print "loading", fname
data = load_raw_data.loadEigenstateMeasurements(
[fname], what=["pair field 1", "pair field 2", "pair field 3", "pair field 4"]
)
flat_data = pyalps_dset.flatten(data)
flat_data = filter(lambda d: type(d) != list or (len(d) > 0 and type(d[0]) != list), flat_data)
if len(flat_data) < 4:
print "WARNING:", "Not enough datasets loaded in", fname
return None
common_props = pyalps_dset.dict_intersect([d.props for d in pyalps_dset.flatten(data)])
L = int(common_props["L"])
W = int(common_props["W"]) if "W" in common_props else 2
idx = index_map(L, W)
try:
pairfield_1 = select_to_nd(data, "pair field 1", idx, dims=4)
pairfield_2 = select_to_nd(data, "pair field 2", idx, dims=4)
pairfield_3 = select_to_nd(data, "pair field 3", idx, dims=4)
pairfield_4 = select_to_nd(data, "pair field 4", idx, dims=4)
except ObservableNotFound:
print "WARNING:", "Measurement not found in", fname
return None
ix_lower = idx[:, 0].reshape(L, 1)
ix_upper = idx[:, 1].reshape(L, 1)
jx_lower = idx[:, 0].reshape(1, L)
jx_upper = idx[:, 1].reshape(1, L)
corr = np.zeros((L, L))
corr += +pairfield_1[ix_lower, ix_upper, jx_lower, jx_upper]
corr += -pairfield_2[ix_lower, ix_upper, jx_lower, jx_upper]
corr += -pairfield_3[ix_lower, ix_upper, jx_lower, jx_upper]
corr += +pairfield_4[ix_lower, ix_upper, jx_lower, jx_upper]
d = pyalps_dset.DataSet()
d.props = deepcopy(common_props)
d.props["observable"] = "Pairfield Correlation"
d.y = corr
d.idx = idx
return d
def compute(fname, nup_name='Local density up', ndown_name='Local density down'):
data = load_raw_data.loadEigenstateMeasurements([fname], what=[nup_name, ndown_name])
d = get_density(data)
return d
def compute(fname):
data = load_raw_data.loadEigenstateMeasurements([fname],
what=[
'dens corr up-up',
'dens corr up-down',
'dens corr down-up',
'dens corr down-down',
'Local density up',
'Local density down',
])
flat_data = pyalps_dset.flatten(data)
flat_data = filter(lambda d: type(d) != list or (len(d) > 0 and type(d[0]) != list), flat_data)
if len(flat_data) < 6:
print 'WARNING:', 'Not enough datasets loaded in', fname
return None
common_props = pyalps_dset.dict_intersect([d.props for d in pyalps_dset.flatten(data)])
L = int(common_props['L'])
W = int(common_props['W']) if 'W' in common_props else 2
idx = index_map(L, W)
try:
dcor_up_up = select_to_2d(data, 'dens corr up-up' , idx)
dcor_up_down = select_to_2d(data, 'dens corr up-down' , idx)
dcor_down_up = select_to_2d(data, 'dens corr down-up' , idx)
dcor_down_down = select_to_2d(data, 'dens corr down-down', idx)
dens_up = select_to_1d(data, 'Local density up' , idx)
dens_down = select_to_1d(data, 'Local density down' , idx)
except ObservableNotFound:
print 'WARNING:', 'Measurement not found in', fname
return None
ix_lower_chain = idx[:,0]
ix_upper_chain = idx[:,1]
total_dens = dens_up + dens_down
dens_on_rungs = total_dens[ix_lower_chain] + total_dens[ix_upper_chain]
dcor = np.zeros((L,L))
## combine density correlators for <N(i)*N(j)> between rungs
dcor += dcor_up_up [np.ix_(ix_lower_chain, ix_lower_chain)]
dcor += dcor_up_down [np.ix_(ix_lower_chain, ix_lower_chain)]
dcor += dcor_down_up [np.ix_(ix_lower_chain, ix_lower_chain)]
dcor += dcor_down_down[np.ix_(ix_lower_chain, ix_lower_chain)]
dcor += dcor_up_up [np.ix_(ix_lower_chain, ix_upper_chain)]
dcor += dcor_up_down [np.ix_(ix_lower_chain, ix_upper_chain)]
dcor += dcor_down_up [np.ix_(ix_lower_chain, ix_upper_chain)]
dcor += dcor_down_down[np.ix_(ix_lower_chain, ix_upper_chain)]
dcor += dcor_up_up [np.ix_(ix_upper_chain, ix_lower_chain)]
dcor += dcor_up_down [np.ix_(ix_upper_chain, ix_lower_chain)]
dcor += dcor_down_up [np.ix_(ix_upper_chain, ix_lower_chain)]
dcor += dcor_down_down[np.ix_(ix_upper_chain, ix_lower_chain)]
dcor += dcor_up_up [np.ix_(ix_upper_chain, ix_upper_chain)]
dcor += dcor_up_down [np.ix_(ix_upper_chain, ix_upper_chain)]
dcor += dcor_down_up [np.ix_(ix_upper_chain, ix_upper_chain)]
dcor += dcor_down_down[np.ix_(ix_upper_chain, ix_upper_chain)]
## connected correlator
dcor += -np.outer(dens_on_rungs, dens_on_rungs)
d = pyalps_dset.DataSet()
d.props = deepcopy(common_props)
d.props['observable'] = 'Density Correlation'
d.y = dcor
d.idx = idx
return d
def compute(fname):
data = load_raw_data.loadEigenstateMeasurements([fname], what='Energy')
return data[0][0]
