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]